From 43fba18e743cd450b5b0c38864793d5a761aa6ed Mon Sep 17 00:00:00 2001
From: Kai Aeberli <kai.aeberli@gmail.com>
Date: Sun, 3 Dec 2017 15:01:01 +0000
Subject: [PATCH] first commit

---
 .gitignore                                    |     3 +
 README.md                                     |     1 +
 boston_housing/boston_housing.html            | 14167 +++++++++++
 boston_housing/boston_housing.ipynb           |   741 +
 ... slice by integer example-checkpoint.ipynb |   187 +
 ... spot prediction notebook-checkpoint.ipynb |  2140 ++
 .../example 3d surface plot-checkpoint.ipynb  |  1247 +
 .../kaggle_bm-checkpoint.ipynb                |  1864 ++
 ..._spot_prediction_notebook-checkpoint.ipynb |  3605 +++
 .../500_epoch_lookback_40.weights.best.hdf5   |   Bin 0 -> 141008 bytes
 .../500_epochs_40_lookback.weights.best.hdf5  |   Bin 0 -> 141008 bytes
 ...0_lookback_better_errors.weights.best.hdf5 |   Bin 0 -> 140152 bytes
 ...0_epochs_40_lookback_pca.weights.best.hdf5 |   Bin 0 -> 141008 bytes
 ..._lookback_pca_unshuffled.weights.best.hdf5 |   Bin 0 -> 140976 bytes
 ...ck_pca_unshuffled_binary.weights.best.hdf5 |   Bin 0 -> 254592 bytes
 ...Learning Capstone Project Proposal V1.docx |   Bin 0 -> 32134 bytes
 ...Learning Capstone Project Proposal V2.docx |   Bin 0 -> 26568 bytes
 ...hine Learning Capstone Project Report.docx |   Bin 0 -> 21028 bytes
 ...aws_v1 - fx spot prediction notebook.ipynb |  2140 ++
 capstone_project/bm_kaggle.weights.best.hdf5  |   Bin 0 -> 137168 bytes
 capstone_project/chromedriver.exe             |   Bin 0 -> 8510976 bytes
 .../add_row_number_to_existing_query.sql      |    12 +
 .../convert types to datetime.sql             |   Bin 0 -> 1162 bytes
 .../data preparation/feature creation.sql     |    68 +
 .../data preparation/get_data.sql             |    89 +
 .../data preparation/get_data_1y.sql          |    54 +
 .../import csv to sql server.sql              |    71 +
 .../data preparation/screenscraper_fx_spot.py |   192 +
 capstone_project/kaggle_bm.ipynb              |  1864 ++
 capstone_project/log_results.xlsx             |   Bin 0 -> 7487 bytes
 .../main_fx_spot_prediction_notebook.ipynb    |  3605 +++
 .../mine_initial.weights.best.hdf5            |   Bin 0 -> 141112 bytes
 capstone_project/proposal.pdf                 |   Bin 0 -> 542836 bytes
 capstone_project/sim_log.xlsx                 |   Bin 0 -> 5586 bytes
 customer_segments/customer_segments.html      | 20007 ++++++++++++++++
 customer_segments/customer_segments.ipynb     |  1821 ++
 deep_learning_examples/__init__.py            |     0
 deep_learning_examples/cross_entropy.py       |    26 +
 .../XOR_neural_network.py                     |    50 +
 .../example_keras_neural_networks/__init__.py |     0
 .../basic_examples.py                         |    45 +
 .../logistic_regression_algo/__init__.py      |     0
 .../logistic_regression_algo/data.csv         |   100 +
 .../logistic_regression_algo.py               |   105 +
 .../perceptron_algorithm/__init__.py          |     0
 .../perceptron_algorithm/data.csv             |   100 +
 .../perceptron_classification.py              |    70 +
 deep_learning_examples/softmax.py             |    16 +
 examples/testdb_kai.sql                       |   Bin 0 -> 7162 bytes
 finding_donors/finding_donors.html            | 15124 ++++++++++++
 finding_donors/finding_donors.ipynb           |  1382 ++
 git commit and push with dummy message.bat    |     2 +
 git pull.bat                                  |     2 +
 git rebase fetch.bat                          |     3 +
 smartcab/agent.py                             |   406 +
 smartcab/logs/sim_default-learning.csv        |    31 +
 smartcab/logs/sim_default-learning.txt        |   256 +
 smartcab/logs/sim_improved-learning.csv       |  1462 ++
 smartcab/logs/sim_improved-learning.txt       |   292 +
 smartcab/logs/sim_improved-learning_ref.csv   |  4617 ++++
 smartcab/logs/sim_improved-learning_ref.txt   |   292 +
 smartcab/logs/sim_no-learning.csv             |    31 +
 smartcab/report.html                          | 19184 +++++++++++++++
 smartcab/smartcab.ipynb                       |   690 +
 titanic_survival_exploration/titanic_data.csv |   892 +
 .../titanic_survival_exploration.html         | 13379 +++++++++++
 .../titanic_survival_exploration.ipynb        |   865 +
 67 files changed, 113300 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 README.md
 create mode 100644 boston_housing/boston_housing.html
 create mode 100644 boston_housing/boston_housing.ipynb
 create mode 100644 capstone_project/.ipynb_checkpoints/Pandas multilevel column slice by integer example-checkpoint.ipynb
 create mode 100644 capstone_project/.ipynb_checkpoints/aws_v1 - fx spot prediction notebook-checkpoint.ipynb
 create mode 100644 capstone_project/.ipynb_checkpoints/example 3d surface plot-checkpoint.ipynb
 create mode 100644 capstone_project/.ipynb_checkpoints/kaggle_bm-checkpoint.ipynb
 create mode 100644 capstone_project/.ipynb_checkpoints/main_fx_spot_prediction_notebook-checkpoint.ipynb
 create mode 100644 capstone_project/500_epoch_lookback_40.weights.best.hdf5
 create mode 100644 capstone_project/500_epochs_40_lookback.weights.best.hdf5
 create mode 100644 capstone_project/500_epochs_40_lookback_better_errors.weights.best.hdf5
 create mode 100644 capstone_project/500_epochs_40_lookback_pca.weights.best.hdf5
 create mode 100644 capstone_project/500_epochs_40_lookback_pca_unshuffled.weights.best.hdf5
 create mode 100644 capstone_project/500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5
 create mode 100644 capstone_project/Machine Learning Capstone Project Proposal V1.docx
 create mode 100644 capstone_project/Machine Learning Capstone Project Proposal V2.docx
 create mode 100644 capstone_project/Machine Learning Capstone Project Report.docx
 create mode 100644 capstone_project/aws_v1 - fx spot prediction notebook.ipynb
 create mode 100644 capstone_project/bm_kaggle.weights.best.hdf5
 create mode 100644 capstone_project/chromedriver.exe
 create mode 100644 capstone_project/data preparation/add_row_number_to_existing_query.sql
 create mode 100644 capstone_project/data preparation/convert types to datetime.sql
 create mode 100644 capstone_project/data preparation/feature creation.sql
 create mode 100644 capstone_project/data preparation/get_data.sql
 create mode 100644 capstone_project/data preparation/get_data_1y.sql
 create mode 100644 capstone_project/data preparation/import csv to sql server.sql
 create mode 100644 capstone_project/data preparation/screenscraper_fx_spot.py
 create mode 100644 capstone_project/kaggle_bm.ipynb
 create mode 100644 capstone_project/log_results.xlsx
 create mode 100644 capstone_project/main_fx_spot_prediction_notebook.ipynb
 create mode 100644 capstone_project/mine_initial.weights.best.hdf5
 create mode 100644 capstone_project/proposal.pdf
 create mode 100644 capstone_project/sim_log.xlsx
 create mode 100644 customer_segments/customer_segments.html
 create mode 100644 customer_segments/customer_segments.ipynb
 create mode 100644 deep_learning_examples/__init__.py
 create mode 100644 deep_learning_examples/cross_entropy.py
 create mode 100644 deep_learning_examples/example_keras_neural_networks/XOR_neural_network.py
 create mode 100644 deep_learning_examples/example_keras_neural_networks/__init__.py
 create mode 100644 deep_learning_examples/example_keras_neural_networks/basic_examples.py
 create mode 100644 deep_learning_examples/logistic_regression_algo/__init__.py
 create mode 100644 deep_learning_examples/logistic_regression_algo/data.csv
 create mode 100644 deep_learning_examples/logistic_regression_algo/logistic_regression_algo.py
 create mode 100644 deep_learning_examples/perceptron_algorithm/__init__.py
 create mode 100644 deep_learning_examples/perceptron_algorithm/data.csv
 create mode 100644 deep_learning_examples/perceptron_algorithm/perceptron_classification.py
 create mode 100644 deep_learning_examples/softmax.py
 create mode 100644 examples/testdb_kai.sql
 create mode 100644 finding_donors/finding_donors.html
 create mode 100644 finding_donors/finding_donors.ipynb
 create mode 100644 git commit and push with dummy message.bat
 create mode 100644 git pull.bat
 create mode 100644 git rebase fetch.bat
 create mode 100644 smartcab/agent.py
 create mode 100644 smartcab/logs/sim_default-learning.csv
 create mode 100644 smartcab/logs/sim_default-learning.txt
 create mode 100644 smartcab/logs/sim_improved-learning.csv
 create mode 100644 smartcab/logs/sim_improved-learning.txt
 create mode 100644 smartcab/logs/sim_improved-learning_ref.csv
 create mode 100644 smartcab/logs/sim_improved-learning_ref.txt
 create mode 100644 smartcab/logs/sim_no-learning.csv
 create mode 100644 smartcab/report.html
 create mode 100644 smartcab/smartcab.ipynb
 create mode 100644 titanic_survival_exploration/titanic_data.csv
 create mode 100644 titanic_survival_exploration/titanic_survival_exploration.html
 create mode 100644 titanic_survival_exploration/titanic_survival_exploration.ipynb

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..5175b92
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
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+capstone_project/eurusd_features.csv
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..5b10aa1
--- /dev/null
+++ b/README.md
@@ -0,0 +1 @@
+# kai_code
\ No newline at end of file
diff --git a/boston_housing/boston_housing.html b/boston_housing/boston_housing.html
new file mode 100644
index 0000000..1aaf064
--- /dev/null
+++ b/boston_housing/boston_housing.html
@@ -0,0 +1,14167 @@
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+}
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+  content: "\e103";
+}
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+}
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+}
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+}
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+  content: "\e107";
+}
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+  content: "\e108";
+}
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+}
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+  content: "\e110";
+}
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+  content: "\e111";
+}
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+}
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+}
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+}
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+}
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+}
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+  content: "\e117";
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  content: "\e153";
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+  content: "\e155";
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  content: "\e175";
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  content: "\e195";
+}
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+  content: "\e197";
+}
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+  content: "\e198";
+}
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+  content: "\e199";
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  content: "\e206";
+}
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+  content: "\e209";
+}
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+}
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+}
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+}
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+}
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+}
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+  content: "\e215";
+}
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+  content: "\e216";
+}
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+  content: "\26fa";
+}
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+  content: "\e218";
+}
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+  content: "\e219";
+}
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+  content: "\f8ff";
+}
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+  content: "\e221";
+}
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+  content: "\231b";
+}
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+  content: "\e223";
+}
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+}
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+  content: "\e225";
+}
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+  content: "\e226";
+}
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+  content: "\e227";
+}
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+  content: "\e227";
+}
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+  content: "\e227";
+}
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+  content: "\00a5";
+}
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+  content: "\00a5";
+}
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+}
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+  content: "\20bd";
+}
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+  content: "\e230";
+}
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+  content: "\e231";
+}
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+  content: "\e232";
+}
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+  content: "\e233";
+}
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+  content: "\e234";
+}
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+  content: "\e235";
+}
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+  content: "\e236";
+}
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+  content: "\e237";
+}
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+  content: "\e238";
+}
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+  content: "\e239";
+}
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+  content: "\e240";
+}
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+  content: "\e241";
+}
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+  content: "\e242";
+}
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+}
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+  content: "\e244";
+}
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+  content: "\e245";
+}
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+  content: "\e246";
+}
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+  content: "\e247";
+}
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+  content: "\e248";
+}
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+  content: "\e249";
+}
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+  content: "\e250";
+}
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+}
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+}
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+}
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+}
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+  content: "\e255";
+}
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+  content: "\e256";
+}
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+  content: "\e257";
+}
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+  content: "\e258";
+}
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+  content: "\e259";
+}
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+  content: "\e260";
+}
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+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
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+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
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+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
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+h5,
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+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
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+  line-height: 1;
+  color: #777777;
+}
+h1,
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+  margin-bottom: 9px;
+}
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+}
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+}
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+}
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+}
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+}
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+  font-size: 12px;
+}
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+  margin: 0 0 9px;
+}
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+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
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+  font-size: 92%;
+}
+mark,
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+  background-color: #fcf8e3;
+  padding: .2em;
+}
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+  text-align: left;
+}
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+  text-align: right;
+}
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+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
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+}
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+  text-transform: lowercase;
+}
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+}
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+  text-transform: capitalize;
+}
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+  color: #777777;
+}
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+  color: #337ab7;
+}
+a.text-primary:hover,
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+  color: #286090;
+}
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+}
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+  color: #31708f;
+}
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+}
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+  color: #8a6d3b;
+}
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+}
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+  color: #a94442;
+}
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+}
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+  color: #fff;
+  background-color: #337ab7;
+}
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+}
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+}
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+}
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+}
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+}
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+  background-color: #fcf8e3;
+}
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+}
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+}
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+}
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+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
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+  margin-bottom: 9px;
+}
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+}
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+}
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+  margin-left: -5px;
+}
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+  padding-left: 5px;
+  padding-right: 5px;
+}
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+  margin-bottom: 18px;
+}
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+}
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+  font-weight: bold;
+}
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+  margin-left: 0;
+}
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+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
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+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
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+  text-transform: uppercase;
+}
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+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
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+blockquote ol:last-child {
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+}
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+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
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+}
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+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
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+}
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+}
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+}
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+  font-style: normal;
+  line-height: 1.42857143;
+}
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+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
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+  color: #888;
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+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
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+  font-size: 100%;
+  font-weight: bold;
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+}
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+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
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+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
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+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
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+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
+}
+.fa-calendar:before {
+  content: "\f073";
+}
+.fa-random:before {
+  content: "\f074";
+}
+.fa-comment:before {
+  content: "\f075";
+}
+.fa-magnet:before {
+  content: "\f076";
+}
+.fa-chevron-up:before {
+  content: "\f077";
+}
+.fa-chevron-down:before {
+  content: "\f078";
+}
+.fa-retweet:before {
+  content: "\f079";
+}
+.fa-shopping-cart:before {
+  content: "\f07a";
+}
+.fa-folder:before {
+  content: "\f07b";
+}
+.fa-folder-open:before {
+  content: "\f07c";
+}
+.fa-arrows-v:before {
+  content: "\f07d";
+}
+.fa-arrows-h:before {
+  content: "\f07e";
+}
+.fa-bar-chart-o:before,
+.fa-bar-chart:before {
+  content: "\f080";
+}
+.fa-twitter-square:before {
+  content: "\f081";
+}
+.fa-facebook-square:before {
+  content: "\f082";
+}
+.fa-camera-retro:before {
+  content: "\f083";
+}
+.fa-key:before {
+  content: "\f084";
+}
+.fa-gears:before,
+.fa-cogs:before {
+  content: "\f085";
+}
+.fa-comments:before {
+  content: "\f086";
+}
+.fa-thumbs-o-up:before {
+  content: "\f087";
+}
+.fa-thumbs-o-down:before {
+  content: "\f088";
+}
+.fa-star-half:before {
+  content: "\f089";
+}
+.fa-heart-o:before {
+  content: "\f08a";
+}
+.fa-sign-out:before {
+  content: "\f08b";
+}
+.fa-linkedin-square:before {
+  content: "\f08c";
+}
+.fa-thumb-tack:before {
+  content: "\f08d";
+}
+.fa-external-link:before {
+  content: "\f08e";
+}
+.fa-sign-in:before {
+  content: "\f090";
+}
+.fa-trophy:before {
+  content: "\f091";
+}
+.fa-github-square:before {
+  content: "\f092";
+}
+.fa-upload:before {
+  content: "\f093";
+}
+.fa-lemon-o:before {
+  content: "\f094";
+}
+.fa-phone:before {
+  content: "\f095";
+}
+.fa-square-o:before {
+  content: "\f096";
+}
+.fa-bookmark-o:before {
+  content: "\f097";
+}
+.fa-phone-square:before {
+  content: "\f098";
+}
+.fa-twitter:before {
+  content: "\f099";
+}
+.fa-facebook:before {
+  content: "\f09a";
+}
+.fa-github:before {
+  content: "\f09b";
+}
+.fa-unlock:before {
+  content: "\f09c";
+}
+.fa-credit-card:before {
+  content: "\f09d";
+}
+.fa-rss:before {
+  content: "\f09e";
+}
+.fa-hdd-o:before {
+  content: "\f0a0";
+}
+.fa-bullhorn:before {
+  content: "\f0a1";
+}
+.fa-bell:before {
+  content: "\f0f3";
+}
+.fa-certificate:before {
+  content: "\f0a3";
+}
+.fa-hand-o-right:before {
+  content: "\f0a4";
+}
+.fa-hand-o-left:before {
+  content: "\f0a5";
+}
+.fa-hand-o-up:before {
+  content: "\f0a6";
+}
+.fa-hand-o-down:before {
+  content: "\f0a7";
+}
+.fa-arrow-circle-left:before {
+  content: "\f0a8";
+}
+.fa-arrow-circle-right:before {
+  content: "\f0a9";
+}
+.fa-arrow-circle-up:before {
+  content: "\f0aa";
+}
+.fa-arrow-circle-down:before {
+  content: "\f0ab";
+}
+.fa-globe:before {
+  content: "\f0ac";
+}
+.fa-wrench:before {
+  content: "\f0ad";
+}
+.fa-tasks:before {
+  content: "\f0ae";
+}
+.fa-filter:before {
+  content: "\f0b0";
+}
+.fa-briefcase:before {
+  content: "\f0b1";
+}
+.fa-arrows-alt:before {
+  content: "\f0b2";
+}
+.fa-group:before,
+.fa-users:before {
+  content: "\f0c0";
+}
+.fa-chain:before,
+.fa-link:before {
+  content: "\f0c1";
+}
+.fa-cloud:before {
+  content: "\f0c2";
+}
+.fa-flask:before {
+  content: "\f0c3";
+}
+.fa-cut:before,
+.fa-scissors:before {
+  content: "\f0c4";
+}
+.fa-copy:before,
+.fa-files-o:before {
+  content: "\f0c5";
+}
+.fa-paperclip:before {
+  content: "\f0c6";
+}
+.fa-save:before,
+.fa-floppy-o:before {
+  content: "\f0c7";
+}
+.fa-square:before {
+  content: "\f0c8";
+}
+.fa-navicon:before,
+.fa-reorder:before,
+.fa-bars:before {
+  content: "\f0c9";
+}
+.fa-list-ul:before {
+  content: "\f0ca";
+}
+.fa-list-ol:before {
+  content: "\f0cb";
+}
+.fa-strikethrough:before {
+  content: "\f0cc";
+}
+.fa-underline:before {
+  content: "\f0cd";
+}
+.fa-table:before {
+  content: "\f0ce";
+}
+.fa-magic:before {
+  content: "\f0d0";
+}
+.fa-truck:before {
+  content: "\f0d1";
+}
+.fa-pinterest:before {
+  content: "\f0d2";
+}
+.fa-pinterest-square:before {
+  content: "\f0d3";
+}
+.fa-google-plus-square:before {
+  content: "\f0d4";
+}
+.fa-google-plus:before {
+  content: "\f0d5";
+}
+.fa-money:before {
+  content: "\f0d6";
+}
+.fa-caret-down:before {
+  content: "\f0d7";
+}
+.fa-caret-up:before {
+  content: "\f0d8";
+}
+.fa-caret-left:before {
+  content: "\f0d9";
+}
+.fa-caret-right:before {
+  content: "\f0da";
+}
+.fa-columns:before {
+  content: "\f0db";
+}
+.fa-unsorted:before,
+.fa-sort:before {
+  content: "\f0dc";
+}
+.fa-sort-down:before,
+.fa-sort-desc:before {
+  content: "\f0dd";
+}
+.fa-sort-up:before,
+.fa-sort-asc:before {
+  content: "\f0de";
+}
+.fa-envelope:before {
+  content: "\f0e0";
+}
+.fa-linkedin:before {
+  content: "\f0e1";
+}
+.fa-rotate-left:before,
+.fa-undo:before {
+  content: "\f0e2";
+}
+.fa-legal:before,
+.fa-gavel:before {
+  content: "\f0e3";
+}
+.fa-dashboard:before,
+.fa-tachometer:before {
+  content: "\f0e4";
+}
+.fa-comment-o:before {
+  content: "\f0e5";
+}
+.fa-comments-o:before {
+  content: "\f0e6";
+}
+.fa-flash:before,
+.fa-bolt:before {
+  content: "\f0e7";
+}
+.fa-sitemap:before {
+  content: "\f0e8";
+}
+.fa-umbrella:before {
+  content: "\f0e9";
+}
+.fa-paste:before,
+.fa-clipboard:before {
+  content: "\f0ea";
+}
+.fa-lightbulb-o:before {
+  content: "\f0eb";
+}
+.fa-exchange:before {
+  content: "\f0ec";
+}
+.fa-cloud-download:before {
+  content: "\f0ed";
+}
+.fa-cloud-upload:before {
+  content: "\f0ee";
+}
+.fa-user-md:before {
+  content: "\f0f0";
+}
+.fa-stethoscope:before {
+  content: "\f0f1";
+}
+.fa-suitcase:before {
+  content: "\f0f2";
+}
+.fa-bell-o:before {
+  content: "\f0a2";
+}
+.fa-coffee:before {
+  content: "\f0f4";
+}
+.fa-cutlery:before {
+  content: "\f0f5";
+}
+.fa-file-text-o:before {
+  content: "\f0f6";
+}
+.fa-building-o:before {
+  content: "\f0f7";
+}
+.fa-hospital-o:before {
+  content: "\f0f8";
+}
+.fa-ambulance:before {
+  content: "\f0f9";
+}
+.fa-medkit:before {
+  content: "\f0fa";
+}
+.fa-fighter-jet:before {
+  content: "\f0fb";
+}
+.fa-beer:before {
+  content: "\f0fc";
+}
+.fa-h-square:before {
+  content: "\f0fd";
+}
+.fa-plus-square:before {
+  content: "\f0fe";
+}
+.fa-angle-double-left:before {
+  content: "\f100";
+}
+.fa-angle-double-right:before {
+  content: "\f101";
+}
+.fa-angle-double-up:before {
+  content: "\f102";
+}
+.fa-angle-double-down:before {
+  content: "\f103";
+}
+.fa-angle-left:before {
+  content: "\f104";
+}
+.fa-angle-right:before {
+  content: "\f105";
+}
+.fa-angle-up:before {
+  content: "\f106";
+}
+.fa-angle-down:before {
+  content: "\f107";
+}
+.fa-desktop:before {
+  content: "\f108";
+}
+.fa-laptop:before {
+  content: "\f109";
+}
+.fa-tablet:before {
+  content: "\f10a";
+}
+.fa-mobile-phone:before,
+.fa-mobile:before {
+  content: "\f10b";
+}
+.fa-circle-o:before {
+  content: "\f10c";
+}
+.fa-quote-left:before {
+  content: "\f10d";
+}
+.fa-quote-right:before {
+  content: "\f10e";
+}
+.fa-spinner:before {
+  content: "\f110";
+}
+.fa-circle:before {
+  content: "\f111";
+}
+.fa-mail-reply:before,
+.fa-reply:before {
+  content: "\f112";
+}
+.fa-github-alt:before {
+  content: "\f113";
+}
+.fa-folder-o:before {
+  content: "\f114";
+}
+.fa-folder-open-o:before {
+  content: "\f115";
+}
+.fa-smile-o:before {
+  content: "\f118";
+}
+.fa-frown-o:before {
+  content: "\f119";
+}
+.fa-meh-o:before {
+  content: "\f11a";
+}
+.fa-gamepad:before {
+  content: "\f11b";
+}
+.fa-keyboard-o:before {
+  content: "\f11c";
+}
+.fa-flag-o:before {
+  content: "\f11d";
+}
+.fa-flag-checkered:before {
+  content: "\f11e";
+}
+.fa-terminal:before {
+  content: "\f120";
+}
+.fa-code:before {
+  content: "\f121";
+}
+.fa-mail-reply-all:before,
+.fa-reply-all:before {
+  content: "\f122";
+}
+.fa-star-half-empty:before,
+.fa-star-half-full:before,
+.fa-star-half-o:before {
+  content: "\f123";
+}
+.fa-location-arrow:before {
+  content: "\f124";
+}
+.fa-crop:before {
+  content: "\f125";
+}
+.fa-code-fork:before {
+  content: "\f126";
+}
+.fa-unlink:before,
+.fa-chain-broken:before {
+  content: "\f127";
+}
+.fa-question:before {
+  content: "\f128";
+}
+.fa-info:before {
+  content: "\f129";
+}
+.fa-exclamation:before {
+  content: "\f12a";
+}
+.fa-superscript:before {
+  content: "\f12b";
+}
+.fa-subscript:before {
+  content: "\f12c";
+}
+.fa-eraser:before {
+  content: "\f12d";
+}
+.fa-puzzle-piece:before {
+  content: "\f12e";
+}
+.fa-microphone:before {
+  content: "\f130";
+}
+.fa-microphone-slash:before {
+  content: "\f131";
+}
+.fa-shield:before {
+  content: "\f132";
+}
+.fa-calendar-o:before {
+  content: "\f133";
+}
+.fa-fire-extinguisher:before {
+  content: "\f134";
+}
+.fa-rocket:before {
+  content: "\f135";
+}
+.fa-maxcdn:before {
+  content: "\f136";
+}
+.fa-chevron-circle-left:before {
+  content: "\f137";
+}
+.fa-chevron-circle-right:before {
+  content: "\f138";
+}
+.fa-chevron-circle-up:before {
+  content: "\f139";
+}
+.fa-chevron-circle-down:before {
+  content: "\f13a";
+}
+.fa-html5:before {
+  content: "\f13b";
+}
+.fa-css3:before {
+  content: "\f13c";
+}
+.fa-anchor:before {
+  content: "\f13d";
+}
+.fa-unlock-alt:before {
+  content: "\f13e";
+}
+.fa-bullseye:before {
+  content: "\f140";
+}
+.fa-ellipsis-h:before {
+  content: "\f141";
+}
+.fa-ellipsis-v:before {
+  content: "\f142";
+}
+.fa-rss-square:before {
+  content: "\f143";
+}
+.fa-play-circle:before {
+  content: "\f144";
+}
+.fa-ticket:before {
+  content: "\f145";
+}
+.fa-minus-square:before {
+  content: "\f146";
+}
+.fa-minus-square-o:before {
+  content: "\f147";
+}
+.fa-level-up:before {
+  content: "\f148";
+}
+.fa-level-down:before {
+  content: "\f149";
+}
+.fa-check-square:before {
+  content: "\f14a";
+}
+.fa-pencil-square:before {
+  content: "\f14b";
+}
+.fa-external-link-square:before {
+  content: "\f14c";
+}
+.fa-share-square:before {
+  content: "\f14d";
+}
+.fa-compass:before {
+  content: "\f14e";
+}
+.fa-toggle-down:before,
+.fa-caret-square-o-down:before {
+  content: "\f150";
+}
+.fa-toggle-up:before,
+.fa-caret-square-o-up:before {
+  content: "\f151";
+}
+.fa-toggle-right:before,
+.fa-caret-square-o-right:before {
+  content: "\f152";
+}
+.fa-euro:before,
+.fa-eur:before {
+  content: "\f153";
+}
+.fa-gbp:before {
+  content: "\f154";
+}
+.fa-dollar:before,
+.fa-usd:before {
+  content: "\f155";
+}
+.fa-rupee:before,
+.fa-inr:before {
+  content: "\f156";
+}
+.fa-cny:before,
+.fa-rmb:before,
+.fa-yen:before,
+.fa-jpy:before {
+  content: "\f157";
+}
+.fa-ruble:before,
+.fa-rouble:before,
+.fa-rub:before {
+  content: "\f158";
+}
+.fa-won:before,
+.fa-krw:before {
+  content: "\f159";
+}
+.fa-bitcoin:before,
+.fa-btc:before {
+  content: "\f15a";
+}
+.fa-file:before {
+  content: "\f15b";
+}
+.fa-file-text:before {
+  content: "\f15c";
+}
+.fa-sort-alpha-asc:before {
+  content: "\f15d";
+}
+.fa-sort-alpha-desc:before {
+  content: "\f15e";
+}
+.fa-sort-amount-asc:before {
+  content: "\f160";
+}
+.fa-sort-amount-desc:before {
+  content: "\f161";
+}
+.fa-sort-numeric-asc:before {
+  content: "\f162";
+}
+.fa-sort-numeric-desc:before {
+  content: "\f163";
+}
+.fa-thumbs-up:before {
+  content: "\f164";
+}
+.fa-thumbs-down:before {
+  content: "\f165";
+}
+.fa-youtube-square:before {
+  content: "\f166";
+}
+.fa-youtube:before {
+  content: "\f167";
+}
+.fa-xing:before {
+  content: "\f168";
+}
+.fa-xing-square:before {
+  content: "\f169";
+}
+.fa-youtube-play:before {
+  content: "\f16a";
+}
+.fa-dropbox:before {
+  content: "\f16b";
+}
+.fa-stack-overflow:before {
+  content: "\f16c";
+}
+.fa-instagram:before {
+  content: "\f16d";
+}
+.fa-flickr:before {
+  content: "\f16e";
+}
+.fa-adn:before {
+  content: "\f170";
+}
+.fa-bitbucket:before {
+  content: "\f171";
+}
+.fa-bitbucket-square:before {
+  content: "\f172";
+}
+.fa-tumblr:before {
+  content: "\f173";
+}
+.fa-tumblr-square:before {
+  content: "\f174";
+}
+.fa-long-arrow-down:before {
+  content: "\f175";
+}
+.fa-long-arrow-up:before {
+  content: "\f176";
+}
+.fa-long-arrow-left:before {
+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
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+<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Model-Evaluation-&amp;-Validation">Model Evaluation &amp; Validation<a class="anchor-link" href="#Model-Evaluation-&amp;-Validation">&#182;</a></h2><h2 id="Project:-Predicting-Boston-Housing-Prices">Project: Predicting Boston Housing Prices<a class="anchor-link" href="#Project:-Predicting-Boston-Housing-Prices">&#182;</a></h2><p>Welcome to the first project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with <strong>'Implementation'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!</p>
+<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
+<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#182;</a></h2><p>In this project, you will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a <em>good fit</em> could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis.</p>
+<p>The dataset for this project originates from the <a href="https://archive.ics.uci.edu/ml/datasets/Housing">UCI Machine Learning Repository</a>. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. For the purposes of this project, the following preprocessing steps have been made to the dataset:</p>
+<ul>
+<li>16 data points have an <code>'MEDV'</code> value of 50.0. These data points likely contain <strong>missing or censored values</strong> and have been removed.</li>
+<li>1 data point has an <code>'RM'</code> value of 8.78. This data point can be considered an <strong>outlier</strong> and has been removed.</li>
+<li>The features <code>'RM'</code>, <code>'LSTAT'</code>, <code>'PTRATIO'</code>, and <code>'MEDV'</code> are essential. The remaining <strong>non-relevant features</strong> have been excluded.</li>
+<li>The feature <code>'MEDV'</code> has been <strong>multiplicatively scaled</strong> to account for 35 years of market inflation.</li>
+</ul>
+<p>Run the code cell below to load the Boston housing dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported.</p>
+
+</div>
+</div>
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+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import libraries necessary for this project</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">ShuffleSplit</span>
+
+<span class="c1"># Import supplementary visualizations code visuals.py</span>
+<span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>
+
+<span class="c1"># Pretty display for notebooks</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="c1"># Load the Boston housing dataset</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;housing.csv&#39;</span><span class="p">)</span>
+<span class="n">prices</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;MEDV&#39;</span><span class="p">]</span>
+<span class="n">features</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;MEDV&#39;</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+    
+<span class="c1"># Success</span>
+
+<span class="k">print</span> <span class="s2">&quot;Boston housing dataset has {} data points with {} variables each.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Boston housing dataset has 489 data points with 4 variables each.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Data-Exploration">Data Exploration<a class="anchor-link" href="#Data-Exploration">&#182;</a></h2><p>In this first section of this project, you will make a cursory investigation about the Boston housing data and provide your observations. Familiarizing yourself with the data through an explorative process is a fundamental practice to help you better understand and justify your results.</p>
+<p>Since the main goal of this project is to construct a working model which has the capability of predicting the value of houses, we will need to separate the dataset into <strong>features</strong> and the <strong>target variable</strong>. The <strong>features</strong>, <code>'RM'</code>, <code>'LSTAT'</code>, and <code>'PTRATIO'</code>, give us quantitative information about each data point. The <strong>target variable</strong>, <code>'MEDV'</code>, will be the variable we seek to predict. These are stored in <code>features</code> and <code>prices</code>, respectively.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Calculate-Statistics">Implementation: Calculate Statistics<a class="anchor-link" href="#Implementation:-Calculate-Statistics">&#182;</a></h3><p>For your very first coding implementation, you will calculate descriptive statistics about the Boston housing prices. Since <code>numpy</code> has already been imported for you, use this library to perform the necessary calculations. These statistics will be extremely important later on to analyze various prediction results from the constructed model.</p>
+<p>In the code cell below, you will need to implement the following:</p>
+<ul>
+<li>Calculate the minimum, maximum, mean, median, and standard deviation of <code>'MEDV'</code>, which is stored in <code>prices</code>.<ul>
+<li>Store each calculation in their respective variable.</li>
+</ul>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Minimum price of the data</span>
+<span class="n">minimum_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>
+
+<span class="c1"># TODO: Maximum price of the data</span>
+<span class="n">maximum_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>
+
+<span class="c1"># TODO: Mean price of the data</span>
+<span class="n">mean_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>
+
+<span class="c1"># TODO: Median price of the data</span>
+<span class="n">median_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>
+
+<span class="c1"># TODO: Standard deviation of prices of the data</span>
+<span class="n">std_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>
+
+<span class="c1"># Show the calculated statistics</span>
+<span class="k">print</span> <span class="s2">&quot;Statistics for Boston housing dataset:</span><span class="se">\n</span><span class="s2">&quot;</span>
+<span class="k">print</span> <span class="s2">&quot;Minimum price: ${:,.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">minimum_price</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;Maximum price: ${:,.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">maximum_price</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;Mean price: ${:,.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mean_price</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;Median price ${:,.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">median_price</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;Standard deviation of prices: ${:,.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">std_price</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Statistics for Boston housing dataset:
+
+Minimum price: $105,000.00
+Maximum price: $1,024,800.00
+Mean price: $454,342.94
+Median price $438,900.00
+Standard deviation of prices: $165,171.13
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-1---Feature-Observation">Question 1 - Feature Observation<a class="anchor-link" href="#Question-1---Feature-Observation">&#182;</a></h3><p>As a reminder, we are using three features from the Boston housing dataset: <code>'RM'</code>, <code>'LSTAT'</code>, and <code>'PTRATIO'</code>. For each data point (neighborhood):</p>
+<ul>
+<li><code>'RM'</code> is the average number of rooms among homes in the neighborhood.</li>
+<li><code>'LSTAT'</code> is the percentage of homeowners in the neighborhood considered "lower class" (working poor).</li>
+<li><code>'PTRATIO'</code> is the ratio of students to teachers in primary and secondary schools in the neighborhood.</li>
+</ul>
+<p><em>Using your intuition, for each of the three features above, do you think that an increase in the value of that feature would lead to an <strong>increase</strong> in the value of <code>'MEDV'</code> or a <strong>decrease</strong> in the value of <code>'MEDV'</code>? Justify your answer for each.</em><br>
+<strong>Hint:</strong> Would you expect a home that has an <code>'RM'</code> value of 6 be worth more or less than a home that has an <code>'RM'</code> value of 7?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> higher RM should lead to an increase in MEDV, as more rooms means higher price. Higher LSTAT should lead to a decrease in MEDV, as more lower class people means less purchasing power so lower home prices. Higher PTRATIO should lead to a decrease in home prices, as richer people may go to neighbourhoods with more advantageous, lower student to teacher ratios.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Developing-a-Model">Developing a Model<a class="anchor-link" href="#Developing-a-Model">&#182;</a></h2><p>In this second section of the project, you will develop the tools and techniques necessary for a model to make a prediction. Being able to make accurate evaluations of each model's performance through the use of these tools and techniques helps to greatly reinforce the confidence in your predictions.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Define-a-Performance-Metric">Implementation: Define a Performance Metric<a class="anchor-link" href="#Implementation:-Define-a-Performance-Metric">&#182;</a></h3><p>It is difficult to measure the quality of a given model without quantifying its performance over training and testing. This is typically done using some type of performance metric, whether it is through calculating some type of error, the goodness of fit, or some other useful measurement. For this project, you will be calculating the <a href="http://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination"><em>coefficient of determination</em></a>, R<sup>2</sup>, to quantify your model's performance. The coefficient of determination for a model is a useful statistic in regression analysis, as it often describes how "good" that model is at making predictions.</p>
+<p>The values for R<sup>2</sup> range from 0 to 1, which captures the percentage of squared correlation between the predicted and actual values of the <strong>target variable</strong>. A model with an R<sup>2</sup> of 0 is no better than a model that always predicts the <em>mean</em> of the target variable, whereas a model with an R<sup>2</sup> of 1 perfectly predicts the target variable. Any value between 0 and 1 indicates what percentage of the target variable, using this model, can be explained by the <strong>features</strong>. <em>A model can be given a negative R<sup>2</sup> as well, which indicates that the model is <strong>arbitrarily worse</strong> than one that always predicts the mean of the target variable.</em></p>
+<p>For the <code>performance_metric</code> function in the code cell below, you will need to implement the following:</p>
+<ul>
+<li>Use <code>r2_score</code> from <code>sklearn.metrics</code> to perform a performance calculation between <code>y_true</code> and <code>y_predict</code>.</li>
+<li>Assign the performance score to the <code>score</code> variable.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import &#39;r2_score&#39;</span>
+
+<span class="k">def</span> <span class="nf">performance_metric</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Calculates and returns the performance score between </span>
+<span class="sd">        true and predicted values based on the metric chosen. &quot;&quot;&quot;</span>
+    
+    <span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">r2_score</span>
+    <span class="c1"># TODO: Calculate the performance score between &#39;y_true&#39; and &#39;y_predict&#39;</span>
+    <span class="n">score</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
+    
+    <span class="c1"># Return the score</span>
+    <span class="k">return</span> <span class="n">score</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-2---Goodness-of-Fit">Question 2 - Goodness of Fit<a class="anchor-link" href="#Question-2---Goodness-of-Fit">&#182;</a></h3><p>Assume that a dataset contains five data points and a model made the following predictions for the target variable:</p>
+<table>
+<thead><tr>
+<th style="text-align:center">True Value</th>
+<th style="text-align:center">Prediction</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:center">3.0</td>
+<td style="text-align:center">2.5</td>
+</tr>
+<tr>
+<td style="text-align:center">-0.5</td>
+<td style="text-align:center">0.0</td>
+</tr>
+<tr>
+<td style="text-align:center">2.0</td>
+<td style="text-align:center">2.1</td>
+</tr>
+<tr>
+<td style="text-align:center">7.0</td>
+<td style="text-align:center">7.8</td>
+</tr>
+<tr>
+<td style="text-align:center">4.2</td>
+<td style="text-align:center">5.3</td>
+</tr>
+</tbody>
+</table>
+<p><em>Would you consider this model to have successfully captured the variation of the target variable? Why or why not?</em></p>
+<p>Run the code cell below to use the <code>performance_metric</code> function and calculate this model's coefficient of determination.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Calculate the performance of this model</span>
+<span class="n">score</span> <span class="o">=</span> <span class="n">performance_metric</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mf">4.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">,</span> <span class="mf">7.8</span><span class="p">,</span> <span class="mf">5.3</span><span class="p">])</span>
+<span class="k">print</span> <span class="s2">&quot;Model has a coefficient of determination, R^2, of {:.3f}.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">score</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Model has a coefficient of determination, R^2, of 0.923.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer</strong>:yes, because r squared is high and thus 92.3 % of the dependent variable is explained by the independent variable.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Shuffle-and-Split-Data">Implementation: Shuffle and Split Data<a class="anchor-link" href="#Implementation:-Shuffle-and-Split-Data">&#182;</a></h3><p>Your next implementation requires that you take the Boston housing dataset and split the data into training and testing subsets. Typically, the data is also shuffled into a random order when creating the training and testing subsets to remove any bias in the ordering of the dataset.</p>
+<p>For the code cell below, you will need to implement the following:</p>
+<ul>
+<li>Use <code>train_test_split</code> from <code>sklearn.cross_validation</code> to shuffle and split the <code>features</code> and <code>prices</code> data into training and testing sets.<ul>
+<li>Split the data into 80% training and 20% testing.</li>
+<li>Set the <code>random_state</code> for <code>train_test_split</code> to a value of your choice. This ensures results are consistent.</li>
+</ul>
+</li>
+<li>Assign the train and testing splits to <code>X_train</code>, <code>X_test</code>, <code>y_train</code>, and <code>y_test</code>.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import &#39;train_test_split&#39;</span>
+<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="c1"># TODO: Shuffle and split the data into training and testing subsets</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">prices</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+
+<span class="c1"># Success</span>
+<span class="k">print</span> <span class="s2">&quot;Training and testing split was successful.&quot;</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Training and testing split was successful.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-3---Training-and-Testing">Question 3 - Training and Testing<a class="anchor-link" href="#Question-3---Training-and-Testing">&#182;</a></h3><p><em>What is the benefit to splitting a dataset into some ratio of training and testing subsets for a learning algorithm?</em><br>
+<strong>Hint:</strong> What could go wrong with not having a way to test your model?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> It helps to prevent overfitting ensuring the trained model generalizes well to out of sample data.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Analyzing-Model-Performance">Analyzing Model Performance<a class="anchor-link" href="#Analyzing-Model-Performance">&#182;</a></h2><p>In this third section of the project, you'll take a look at several models' learning and testing performances on various subsets of training data. Additionally, you'll investigate one particular algorithm with an increasing <code>'max_depth'</code> parameter on the full training set to observe how model complexity affects performance. Graphing your model's performance based on varying criteria can be beneficial in the analysis process, such as visualizing behavior that may not have been apparent from the results alone.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Learning-Curves">Learning Curves<a class="anchor-link" href="#Learning-Curves">&#182;</a></h3><p>The following code cell produces four graphs for a decision tree model with different maximum depths. Each graph visualizes the learning curves of the model for both training and testing as the size of the training set is increased. Note that the shaded region of a learning curve denotes the uncertainty of that curve (measured as the standard deviation). The model is scored on both the training and testing sets using R<sup>2</sup>, the coefficient of determination.</p>
+<p>Run the code cell below and use these graphs to answer the following question.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Produce learning curves for varying training set sizes and maximum depths</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">ModelLearning</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">prices</span><span class="p">)</span>
+</pre></div>
+
+</div>
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+>
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+</div>
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+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-4---Learning-the-Data">Question 4 - Learning the Data<a class="anchor-link" href="#Question-4---Learning-the-Data">&#182;</a></h3><p><em>Choose one of the graphs above and state the maximum depth for the model. What happens to the score of the training curve as more training points are added? What about the testing curve? Would having more training points benefit the model?</em><br>
+<strong>Hint:</strong> Are the learning curves converging to particular scores?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> The max depth is 3, after that overfitting occurs. As more examples are added, the training score will decline a bit as it gets harder to fit all examples. But testing also gets better, until the two almost converge. Adding more training point would benefit the model a bit, as training and testing curves would converge even more. However, additional points are not always helpful, as at some point no new information becomes available, so the model predictions on the test set will not improve in accuracy further, so the testing score doesnt improve more. The reason why the training score is high initially is that it is easy to fit the data as there are few examples. As more examples becomes available, it gets more difficult to fit so testing score drops.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Complexity-Curves">Complexity Curves<a class="anchor-link" href="#Complexity-Curves">&#182;</a></h3><p>The following code cell produces a graph for a decision tree model that has been trained and validated on the training data using different maximum depths. The graph produces two complexity curves — one for training and one for validation. Similar to the <strong>learning curves</strong>, the shaded regions of both the complexity curves denote the uncertainty in those curves, and the model is scored on both the training and validation sets using the <code>performance_metric</code> function.</p>
+<p>Run the code cell below and use this graph to answer the following two questions.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="n">vs</span><span class="o">.</span><span class="n">ModelComplexity</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+</pre></div>
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-5---Bias-Variance-Tradeoff">Question 5 - Bias-Variance Tradeoff<a class="anchor-link" href="#Question-5---Bias-Variance-Tradeoff">&#182;</a></h3><p><em>When the model is trained with a maximum depth of 1, does the model suffer from high bias or from high variance? How about when the model is trained with a maximum depth of 10? What visual cues in the graph justify your conclusions?</em><br>
+<strong>Hint:</strong> How do you know when a model is suffering from high bias or high variance?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> At max depth 1, it suffers from high bias - r^2 is generally low for both training and validation, so it has low predictive power. At max depth 10, it suffers from high variance, as r^2 for training goes to 1, but validation remains low, meaning model is overfitting and does not generalise well. These conclusions are based on the fact that the training and validation scores diverge at higher max depth, with training improving, whereas validation is dropping off and even declining.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-6---Best-Guess-Optimal-Model">Question 6 - Best-Guess Optimal Model<a class="anchor-link" href="#Question-6---Best-Guess-Optimal-Model">&#182;</a></h3><p><em>Which maximum depth do you think results in a model that best generalizes to unseen data? What intuition lead you to this answer?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> It seems to be max depth of 3, as afterwards validation declines whereas training continues to rise, showing the model cannot generalise well anymore, so there is no benefit to increasing max depth.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Evaluating-Model-Performance">Evaluating Model Performance<a class="anchor-link" href="#Evaluating-Model-Performance">&#182;</a></h2><p>In this final section of the project, you will construct a model and make a prediction on the client's feature set using an optimized model from <code>fit_model</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-7---Grid-Search">Question 7 - Grid Search<a class="anchor-link" href="#Question-7---Grid-Search">&#182;</a></h3><p><em>What is the grid search technique and how it can be applied to optimize a learning algorithm?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> The grid search space is a space occupied by parameters to a model. The grid search technique will try all possible points on this space (combination of parameters) The output of grid search are many models, each using different parameters. This is a selective search, as only points on the parameter grid will be used. The search is guided by chosing a range of values for each parameter dimension picking a step size large enough to expect the output to vary in some interesting way. In case of enum parameters, we could earch across several different classifiers for example, to get an idea which works best.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-8---Cross-Validation">Question 8 - Cross-Validation<a class="anchor-link" href="#Question-8---Cross-Validation">&#182;</a></h3><p><em>What is the k-fold cross-validation training technique? What benefit does this technique provide for grid search when optimizing a model?</em><br>
+<strong>Hint:</strong> Much like the reasoning behind having a testing set, what could go wrong with using grid search without a cross-validated set?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> The K Fold cross validation technique splits the dataset into training and cross validation sets. More specifically, it splits the data into K subsets, using one of the subsets as validation and the k-1 others combined as the training set. It then trains and evaluates the ML algorithm. In the next iteration it will pick a different training and cross validation set and repeat the previous step. Once all K combinations have been explored, an average performance estimate is computed across the outputs of each of the k steps run. The benefit of this technique to grid search is that you get a more reliable, stable estimate for each point on the parameter grid, as it will be run using several splits of training / testing data, eliminating potential bias in the dataset and given stable values no matter how the dataset is divided into training and testing. This K fold validation is useful for grid search as each point on the grid will get a more reliable estimate of model performance than not using K folder validation. If we limit grid search to a single dataset (no K fold validation) overfitting would be an issue, as the specific choice of model function might lead to lack of generalisation to out of sample data. Applying this generalisation inside the dataset, many times over, make the estimate more stable.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Fitting-a-Model">Implementation: Fitting a Model<a class="anchor-link" href="#Implementation:-Fitting-a-Model">&#182;</a></h3><p>Your final implementation requires that you bring everything together and train a model using the <strong>decision tree algorithm</strong>. To ensure that you are producing an optimized model, you will train the model using the grid search technique to optimize the <code>'max_depth'</code> parameter for the decision tree. The <code>'max_depth'</code> parameter can be thought of as how many questions the decision tree algorithm is allowed to ask about the data before making a prediction. Decision trees are part of a class of algorithms called <em>supervised learning algorithms</em>.</p>
+<p>In addition, you will find your implementation is using <code>ShuffleSplit()</code> for an alternative form of cross-validation (see the <code>'cv_sets'</code> variable). While it is not the K-Fold cross-validation technique you describe in <strong>Question 8</strong>, this type of cross-validation technique is just as useful!. The <code>ShuffleSplit()</code> implementation below will create 10 (<code>'n_splits'</code>) shuffled sets, and for each shuffle, 20% (<code>'test_size'</code>) of the data will be used as the <em>validation set</em>. While you're working on your implementation, think about the contrasts and similarities it has to the K-fold cross-validation technique.</p>
+<p>Please note that ShuffleSplit has different parameters in scikit-learn versions 0.17 and 0.18.
+For the <code>fit_model</code> function in the code cell below, you will need to implement the following:</p>
+<ul>
+<li>Use <a href="http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html"><code>DecisionTreeRegressor</code></a> from <code>sklearn.tree</code> to create a decision tree regressor object.<ul>
+<li>Assign this object to the <code>'regressor'</code> variable.</li>
+</ul>
+</li>
+<li>Create a dictionary for <code>'max_depth'</code> with the values from 1 to 10, and assign this to the <code>'params'</code> variable.</li>
+<li>Use <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html"><code>make_scorer</code></a> from <code>sklearn.metrics</code> to create a scoring function object.<ul>
+<li>Pass the <code>performance_metric</code> function as a parameter to the object.</li>
+<li>Assign this scoring function to the <code>'scoring_fnc'</code> variable.</li>
+</ul>
+</li>
+<li>Use <a href="http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html"><code>GridSearchCV</code></a> from <code>sklearn.grid_search</code> to create a grid search object.<ul>
+<li>Pass the variables <code>'regressor'</code>, <code>'params'</code>, <code>'scoring_fnc'</code>, and <code>'cv_sets'</code> as parameters to the object. </li>
+<li>Assign the <code>GridSearchCV</code> object to the <code>'grid'</code> variable.</li>
+</ul>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[13]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import &#39;make_scorer&#39;, &#39;DecisionTreeRegressor&#39;, and &#39;GridSearchCV&#39;</span>
+
+<span class="k">def</span> <span class="nf">fit_model</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Performs grid search over the &#39;max_depth&#39; parameter for a </span>
+<span class="sd">        decision tree regressor trained on the input data [X, y]. &quot;&quot;&quot;</span>
+    
+    <span class="c1"># Create cross-validation sets from the training data</span>
+    <span class="c1"># sklearn version 0.18: ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)</span>
+    <span class="c1"># sklearn versiin 0.17: ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None)</span>
+    <span class="n">cv_sets</span> <span class="o">=</span> <span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">n_iter</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.20</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Create a decision tree regressor object</span>
+    <span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
+    <span class="n">regressor</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">random_state</span> <span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Create a dictionary for the parameter &#39;max_depth&#39; with a range from 1 to 10</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+    <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;max_depth&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">11</span><span class="p">)}</span>
+    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;params&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Transform &#39;performance_metric&#39; into a scoring function using &#39;make_scorer&#39; </span>
+    <span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span>
+    <span class="n">scoring_fnc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">performance_metric</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Create the grid search object</span>
+    <span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
+    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">regressor</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">params</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring_fnc</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv_sets</span><span class="p">)</span>
+
+    <span class="c1"># Fit the grid search object to the data to compute the optimal model</span>
+    <span class="n">grid</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+    <span class="c1"># Return the optimal model after fitting the data</span>
+    <span class="k">return</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_estimator_</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Making-Predictions">Making Predictions<a class="anchor-link" href="#Making-Predictions">&#182;</a></h3><p>Once a model has been trained on a given set of data, it can now be used to make predictions on new sets of input data. In the case of a <em>decision tree regressor</em>, the model has learned <em>what the best questions to ask about the input data are</em>, and can respond with a prediction for the <strong>target variable</strong>. You can use these predictions to gain information about data where the value of the target variable is unknown — such as data the model was not trained on.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-9---Optimal-Model">Question 9 - Optimal Model<a class="anchor-link" href="#Question-9---Optimal-Model">&#182;</a></h3><p><em>What maximum depth does the optimal model have? How does this result compare to your guess in <strong>Question 6</strong>?</em></p>
+<p>Run the code block below to fit the decision tree regressor to the training data and produce an optimal model.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[14]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Fit the training data to the model using grid search</span>
+<span class="n">reg</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+<span class="c1"># Produce the value for &#39;max_depth&#39;</span>
+<span class="k">print</span> <span class="s2">&quot;Parameter &#39;max_depth&#39; is {} for the optimal model.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">get_params</span><span class="p">()[</span><span class="s1">&#39;max_depth&#39;</span><span class="p">])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>C:\Anaconda3\envs\udacity\lib\site-packages\sklearn\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
+  DeprecationWarning)
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Parameter &#39;max_depth&#39; is 4 for the optimal model.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> The max depth of the optimal model is 4. My guess would have been 3, as it seems with 4, training accuracy goes up, but much more so than test accuracy, which almost flatlines.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-10---Predicting-Selling-Prices">Question 10 - Predicting Selling Prices<a class="anchor-link" href="#Question-10---Predicting-Selling-Prices">&#182;</a></h3><p>Imagine that you were a real estate agent in the Boston area looking to use this model to help price homes owned by your clients that they wish to sell. You have collected the following information from three of your clients:</p>
+<table>
+<thead><tr>
+<th style="text-align:center">Feature</th>
+<th style="text-align:center">Client 1</th>
+<th style="text-align:center">Client 2</th>
+<th style="text-align:center">Client 3</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:center">Total number of rooms in home</td>
+<td style="text-align:center">5 rooms</td>
+<td style="text-align:center">4 rooms</td>
+<td style="text-align:center">8 rooms</td>
+</tr>
+<tr>
+<td style="text-align:center">Neighborhood poverty level (as %)</td>
+<td style="text-align:center">17%</td>
+<td style="text-align:center">32%</td>
+<td style="text-align:center">3%</td>
+</tr>
+<tr>
+<td style="text-align:center">Student-teacher ratio of nearby schools</td>
+<td style="text-align:center">15-to-1</td>
+<td style="text-align:center">22-to-1</td>
+<td style="text-align:center">12-to-1</td>
+</tr>
+</tbody>
+</table>
+<p><em>What price would you recommend each client sell his/her home at? Do these prices seem reasonable given the values for the respective features?</em><br>
+<strong>Hint:</strong> Use the statistics you calculated in the <strong>Data Exploration</strong> section to help justify your response.</p>
+<p>Run the code block below to have your optimized model make predictions for each client's home.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[16]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Produce a matrix for client data</span>
+<span class="n">client_data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">15</span><span class="p">],</span> <span class="c1"># Client 1</span>
+               <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">22</span><span class="p">],</span> <span class="c1"># Client 2</span>
+               <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">12</span><span class="p">]]</span>  <span class="c1"># Client 3</span>
+
+
+
+<span class="c1"># Show predictions</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">price</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">client_data</span><span class="p">)):</span>
+    <span class="k">print</span> <span class="s2">&quot;Predicted selling price for Client {}&#39;s home: ${:,.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">price</span><span class="p">)</span>
+    
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Predicted selling price for Client 1&#39;s home: $411,931.58
+Predicted selling price for Client 2&#39;s home: $235,620.00
+Predicted selling price for Client 3&#39;s home: $922,740.00
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> Client 1's home: around \$410k, Client 2's home: around\$235k, Client 3's home: around \$920k. Prices seem reasonable, as Client 3's home has low neigbourhood poverty, low student teacher ratio and a lot of bedrooms. C2 home should be the lowest, as least rooms, worst student teacher ratio, and highest neighbourhood poverty. C1 works too and is in between C3 and C2, as better student teacher ratio, half the poverty level, and 1 extra room compared to C2. More generally, prices are in the range of the dataset and a large house in a good neighborhood is close to the maximum prices which is expected</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Sensitivity">Sensitivity<a class="anchor-link" href="#Sensitivity">&#182;</a></h3><p>An optimal model is not necessarily a robust model. Sometimes, a model is either too complex or too simple to sufficiently generalize to new data. Sometimes, a model could use a learning algorithm that is not appropriate for the structure of the data given. Other times, the data itself could be too noisy or contain too few samples to allow a model to adequately capture the target variable — i.e., the model is underfitted. Run the code cell below to run the <code>fit_model</code> function ten times with different training and testing sets to see how the prediction for a specific client changes with the data it's trained on.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="n">vs</span><span class="o">.</span><span class="n">PredictTrials</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">prices</span><span class="p">,</span> <span class="n">fit_model</span><span class="p">,</span> <span class="n">client_data</span><span class="p">)</span> <span class="c1"># predicts client 1</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 1: $391,183.33
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 2: $419,700.00
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 3: $415,800.00
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 4: $420,622.22
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 5: $413,334.78
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 6: $411,931.58
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 7: $399,663.16
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 8: $407,232.00
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 9: $351,577.61
+(&#39;params&#39;, {&#39;max_depth&#39;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})
+Trial 10: $413,700.00
+
+Range in prices: $69,044.61
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-11---Applicability">Question 11 - Applicability<a class="anchor-link" href="#Question-11---Applicability">&#182;</a></h3><p><em>In a few sentences, discuss whether the constructed model should or should not be used in a real-world setting.</em><br>
+<strong>Hint:</strong> Some questions to answering:</p>
+<ul>
+<li><em>How relevant today is data that was collected from 1978?</em></li>
+<li><em>Are the features present in the data sufficient to describe a home?</em></li>
+<li><em>Is the model robust enough to make consistent predictions?</em></li>
+<li><em>Would data collected in an urban city like Boston be applicable in a rural city?</em></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> Data from 1978 may not be that relevant, as underlying factors may have changed, such as the distribution of wealth across neighbourhoods, or the number of teachers and students in schools. However, if there were no such trends, the dataset could be a good proxy, as price was corrected for inflation. The number of features are probably not sufficient to describe a home - condition, size of land, age and other features may have additional or more important predictive power. The model does not seem very robust - a range of around 70k in predictions on just 10 iterations would be unacceptable to a prospective seller. Finally, urban data may apply to rural settings as there are less neigbourhoods, prices are generally lower, and properties illiquid so not many transactions so price discovery may not follow the same pattern as in a city. Thus the model's use should be restricted to an urban setting, additional features should be added to control the variance of predictions, and ideally updated data would give a better price estimate that may come closer to a real transaction as of today.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
+<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+
+ 
+
+
+</html>
diff --git a/boston_housing/boston_housing.ipynb b/boston_housing/boston_housing.ipynb
new file mode 100644
index 0000000..8ff5536
--- /dev/null
+++ b/boston_housing/boston_housing.ipynb
@@ -0,0 +1,741 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning Engineer Nanodegree\n",
+    "## Model Evaluation & Validation\n",
+    "## Project: Predicting Boston Housing Prices\n",
+    "\n",
+    "Welcome to the first project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!\n",
+    "\n",
+    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
+    "\n",
+    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting Started\n",
+    "In this project, you will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a *good fit* could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis.\n",
+    "\n",
+    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Housing). The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. For the purposes of this project, the following preprocessing steps have been made to the dataset:\n",
+    "- 16 data points have an `'MEDV'` value of 50.0. These data points likely contain **missing or censored values** and have been removed.\n",
+    "- 1 data point has an `'RM'` value of 8.78. This data point can be considered an **outlier** and has been removed.\n",
+    "- The features `'RM'`, `'LSTAT'`, `'PTRATIO'`, and `'MEDV'` are essential. The remaining **non-relevant features** have been excluded.\n",
+    "- The feature `'MEDV'` has been **multiplicatively scaled** to account for 35 years of market inflation.\n",
+    "\n",
+    "Run the code cell below to load the Boston housing dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Boston housing dataset has 489 data points with 4 variables each.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import libraries necessary for this project\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.cross_validation import ShuffleSplit\n",
+    "\n",
+    "# Import supplementary visualizations code visuals.py\n",
+    "import visuals as vs\n",
+    "\n",
+    "# Pretty display for notebooks\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Load the Boston housing dataset\n",
+    "data = pd.read_csv('housing.csv')\n",
+    "prices = data['MEDV']\n",
+    "features = data.drop('MEDV', axis = 1)\n",
+    "    \n",
+    "# Success\n",
+    "\n",
+    "print \"Boston housing dataset has {} data points with {} variables each.\".format(*data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Exploration\n",
+    "In this first section of this project, you will make a cursory investigation about the Boston housing data and provide your observations. Familiarizing yourself with the data through an explorative process is a fundamental practice to help you better understand and justify your results.\n",
+    "\n",
+    "Since the main goal of this project is to construct a working model which has the capability of predicting the value of houses, we will need to separate the dataset into **features** and the **target variable**. The **features**, `'RM'`, `'LSTAT'`, and `'PTRATIO'`, give us quantitative information about each data point. The **target variable**, `'MEDV'`, will be the variable we seek to predict. These are stored in `features` and `prices`, respectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Calculate Statistics\n",
+    "For your very first coding implementation, you will calculate descriptive statistics about the Boston housing prices. Since `numpy` has already been imported for you, use this library to perform the necessary calculations. These statistics will be extremely important later on to analyze various prediction results from the constructed model.\n",
+    "\n",
+    "In the code cell below, you will need to implement the following:\n",
+    "- Calculate the minimum, maximum, mean, median, and standard deviation of `'MEDV'`, which is stored in `prices`.\n",
+    "  - Store each calculation in their respective variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statistics for Boston housing dataset:\n",
+      "\n",
+      "Minimum price: $105,000.00\n",
+      "Maximum price: $1,024,800.00\n",
+      "Mean price: $454,342.94\n",
+      "Median price $438,900.00\n",
+      "Standard deviation of prices: $165,171.13\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Minimum price of the data\n",
+    "minimum_price = np.min(prices)\n",
+    "\n",
+    "# TODO: Maximum price of the data\n",
+    "maximum_price = np.max(prices)\n",
+    "\n",
+    "# TODO: Mean price of the data\n",
+    "mean_price = np.mean(prices)\n",
+    "\n",
+    "# TODO: Median price of the data\n",
+    "median_price = np.median(prices)\n",
+    "\n",
+    "# TODO: Standard deviation of prices of the data\n",
+    "std_price = np.std(prices)\n",
+    "\n",
+    "# Show the calculated statistics\n",
+    "print \"Statistics for Boston housing dataset:\\n\"\n",
+    "print \"Minimum price: ${:,.2f}\".format(minimum_price)\n",
+    "print \"Maximum price: ${:,.2f}\".format(maximum_price)\n",
+    "print \"Mean price: ${:,.2f}\".format(mean_price)\n",
+    "print \"Median price ${:,.2f}\".format(median_price)\n",
+    "print \"Standard deviation of prices: ${:,.2f}\".format(std_price)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1 - Feature Observation\n",
+    "As a reminder, we are using three features from the Boston housing dataset: `'RM'`, `'LSTAT'`, and `'PTRATIO'`. For each data point (neighborhood):\n",
+    "- `'RM'` is the average number of rooms among homes in the neighborhood.\n",
+    "- `'LSTAT'` is the percentage of homeowners in the neighborhood considered \"lower class\" (working poor).\n",
+    "- `'PTRATIO'` is the ratio of students to teachers in primary and secondary schools in the neighborhood.\n",
+    "\n",
+    "_Using your intuition, for each of the three features above, do you think that an increase in the value of that feature would lead to an **increase** in the value of `'MEDV'` or a **decrease** in the value of `'MEDV'`? Justify your answer for each._  \n",
+    "**Hint:** Would you expect a home that has an `'RM'` value of 6 be worth more or less than a home that has an `'RM'` value of 7?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** higher RM should lead to an increase in MEDV, as more rooms means higher price. Higher LSTAT should lead to a decrease in MEDV, as more lower class people means less purchasing power so lower home prices. Higher PTRATIO should lead to a decrease in home prices, as richer people may go to neighbourhoods with more advantageous, lower student to teacher ratios."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "\n",
+    "## Developing a Model\n",
+    "In this second section of the project, you will develop the tools and techniques necessary for a model to make a prediction. Being able to make accurate evaluations of each model's performance through the use of these tools and techniques helps to greatly reinforce the confidence in your predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Define a Performance Metric\n",
+    "It is difficult to measure the quality of a given model without quantifying its performance over training and testing. This is typically done using some type of performance metric, whether it is through calculating some type of error, the goodness of fit, or some other useful measurement. For this project, you will be calculating the [*coefficient of determination*](http://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination), R<sup>2</sup>, to quantify your model's performance. The coefficient of determination for a model is a useful statistic in regression analysis, as it often describes how \"good\" that model is at making predictions. \n",
+    "\n",
+    "The values for R<sup>2</sup> range from 0 to 1, which captures the percentage of squared correlation between the predicted and actual values of the **target variable**. A model with an R<sup>2</sup> of 0 is no better than a model that always predicts the *mean* of the target variable, whereas a model with an R<sup>2</sup> of 1 perfectly predicts the target variable. Any value between 0 and 1 indicates what percentage of the target variable, using this model, can be explained by the **features**. _A model can be given a negative R<sup>2</sup> as well, which indicates that the model is **arbitrarily worse** than one that always predicts the mean of the target variable._\n",
+    "\n",
+    "For the `performance_metric` function in the code cell below, you will need to implement the following:\n",
+    "- Use `r2_score` from `sklearn.metrics` to perform a performance calculation between `y_true` and `y_predict`.\n",
+    "- Assign the performance score to the `score` variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# TODO: Import 'r2_score'\n",
+    "\n",
+    "def performance_metric(y_true, y_predict):\n",
+    "    \"\"\" Calculates and returns the performance score between \n",
+    "        true and predicted values based on the metric chosen. \"\"\"\n",
+    "    \n",
+    "    from sklearn.metrics import r2_score\n",
+    "    # TODO: Calculate the performance score between 'y_true' and 'y_predict'\n",
+    "    score = r2_score(y_true, y_predict)\n",
+    "    \n",
+    "    # Return the score\n",
+    "    return score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2 - Goodness of Fit\n",
+    "Assume that a dataset contains five data points and a model made the following predictions for the target variable:\n",
+    "\n",
+    "| True Value | Prediction |\n",
+    "| :-------------: | :--------: |\n",
+    "| 3.0 | 2.5 |\n",
+    "| -0.5 | 0.0 |\n",
+    "| 2.0 | 2.1 |\n",
+    "| 7.0 | 7.8 |\n",
+    "| 4.2 | 5.3 |\n",
+    "*Would you consider this model to have successfully captured the variation of the target variable? Why or why not?* \n",
+    "\n",
+    "Run the code cell below to use the `performance_metric` function and calculate this model's coefficient of determination."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model has a coefficient of determination, R^2, of 0.923.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate the performance of this model\n",
+    "score = performance_metric([3, -0.5, 2, 7, 4.2], [2.5, 0.0, 2.1, 7.8, 5.3])\n",
+    "print \"Model has a coefficient of determination, R^2, of {:.3f}.\".format(score)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer**:yes, because r squared is high and thus 92.3 % of the dependent variable is explained by the independent variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Shuffle and Split Data\n",
+    "Your next implementation requires that you take the Boston housing dataset and split the data into training and testing subsets. Typically, the data is also shuffled into a random order when creating the training and testing subsets to remove any bias in the ordering of the dataset.\n",
+    "\n",
+    "For the code cell below, you will need to implement the following:\n",
+    "- Use `train_test_split` from `sklearn.cross_validation` to shuffle and split the `features` and `prices` data into training and testing sets.\n",
+    "  - Split the data into 80% training and 20% testing.\n",
+    "  - Set the `random_state` for `train_test_split` to a value of your choice. This ensures results are consistent.\n",
+    "- Assign the train and testing splits to `X_train`, `X_test`, `y_train`, and `y_test`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training and testing split was successful.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Import 'train_test_split'\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "\n",
+    "# TODO: Shuffle and split the data into training and testing subsets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size=0.2, random_state=5)\n",
+    "\n",
+    "# Success\n",
+    "print \"Training and testing split was successful.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3 - Training and Testing\n",
+    "*What is the benefit to splitting a dataset into some ratio of training and testing subsets for a learning algorithm?*  \n",
+    "**Hint:** What could go wrong with not having a way to test your model?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** It helps to prevent overfitting ensuring the trained model generalizes well to out of sample data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "\n",
+    "## Analyzing Model Performance\n",
+    "In this third section of the project, you'll take a look at several models' learning and testing performances on various subsets of training data. Additionally, you'll investigate one particular algorithm with an increasing `'max_depth'` parameter on the full training set to observe how model complexity affects performance. Graphing your model's performance based on varying criteria can be beneficial in the analysis process, such as visualizing behavior that may not have been apparent from the results alone."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning Curves\n",
+    "The following code cell produces four graphs for a decision tree model with different maximum depths. Each graph visualizes the learning curves of the model for both training and testing as the size of the training set is increased. Note that the shaded region of a learning curve denotes the uncertainty of that curve (measured as the standard deviation). The model is scored on both the training and testing sets using R<sup>2</sup>, the coefficient of determination.  \n",
+    "\n",
+    "Run the code cell below and use these graphs to answer the following question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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u6HvQQQcdMGjQoFGvvfZaCkBRUZHv+OOPHzJkyJADp0yZMnjUqFEHvP/++0ne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\n4wsfeeSRrOLi4s+8ddi0aVN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CKBCAXr1sRLgHH7R9gRYssIN8bdwIw4e3dw0VRVGUDkrE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LdeuC28X7iEn72Ft6O3jd7+7W+XP5evfGXlYHP0eqngFsYGr4XbCm/Dx23cWL5NK1dWfr/lRgBr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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x485a0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Produce learning curves for varying training set sizes and maximum depths\n",
+    "vs.ModelLearning(features, prices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4 - Learning the Data\n",
+    "*Choose one of the graphs above and state the maximum depth for the model. What happens to the score of the training curve as more training points are added? What about the testing curve? Would having more training points benefit the model?*  \n",
+    "**Hint:** Are the learning curves converging to particular scores?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** The max depth is 3, after that overfitting occurs. As more examples are added, the training score will decline a bit as it gets harder to fit all examples. But testing also gets better, until the two almost converge. Adding more training point would benefit the model a bit, as training and testing curves would converge even more. However, additional points are not always helpful, as at some point no new information becomes available, so the model predictions on the test set will not improve in accuracy further, so the testing score doesnt improve more. The reason why the training score is high initially is that it is easy to fit the data as there are few examples. As more examples becomes available, it gets more difficult to fit so testing score drops. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Complexity Curves\n",
+    "The following code cell produces a graph for a decision tree model that has been trained and validated on the training data using different maximum depths. The graph produces two complexity curves — one for training and one for validation. Similar to the **learning curves**, the shaded regions of both the complexity curves denote the uncertainty in those curves, and the model is scored on both the training and validation sets using the `performance_metric` function.  \n",
+    "\n",
+    "Run the code cell below and use this graph to answer the following two questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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u7YvmsuPa75Pafz+K//4vKm65G9/OBhKVFTR852u0ff6TtjOKCt24ZE8B/ymT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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb7153c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vs.ModelComplexity(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 5 - Bias-Variance Tradeoff\n",
+    "*When the model is trained with a maximum depth of 1, does the model suffer from high bias or from high variance? How about when the model is trained with a maximum depth of 10? What visual cues in the graph justify your conclusions?*  \n",
+    "**Hint:** How do you know when a model is suffering from high bias or high variance?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** At max depth 1, it suffers from high bias - r^2 is generally low for both training and validation, so it has low predictive power. At max depth 10, it suffers from high variance, as r^2 for training goes to 1, but validation remains low, meaning model is overfitting and does not generalise well. These conclusions are based on the fact that the training and validation scores diverge at higher max depth, with training improving, whereas validation is dropping off and even declining. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 6 - Best-Guess Optimal Model\n",
+    "*Which maximum depth do you think results in a model that best generalizes to unseen data? What intuition lead you to this answer?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** It seems to be max depth of 3, as afterwards validation declines whereas training continues to rise, showing the model cannot generalise well anymore, so there is no benefit to increasing max depth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----\n",
+    "\n",
+    "## Evaluating Model Performance\n",
+    "In this final section of the project, you will construct a model and make a prediction on the client's feature set using an optimized model from `fit_model`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 7 - Grid Search\n",
+    "*What is the grid search technique and how it can be applied to optimize a learning algorithm?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** The grid search space is a space occupied by parameters to a model. The grid search technique will try all possible points on this space (combination of parameters) The output of grid search are many models, each using different parameters. This is a selective search, as only points on the parameter grid will be used. The search is guided by chosing a range of values for each parameter dimension picking a step size large enough to expect the output to vary in some interesting way. In case of enum parameters, we could earch across several different classifiers for example, to get an idea which works best."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 8 - Cross-Validation\n",
+    "*What is the k-fold cross-validation training technique? What benefit does this technique provide for grid search when optimizing a model?*  \n",
+    "**Hint:** Much like the reasoning behind having a testing set, what could go wrong with using grid search without a cross-validated set?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** The K Fold cross validation technique splits the dataset into training and cross validation sets. More specifically, it splits the data into K subsets, using one of the subsets as validation and the k-1 others combined as the training set. It then trains and evaluates the ML algorithm. In the next iteration it will pick a different training and cross validation set and repeat the previous step. Once all K combinations have been explored, an average performance estimate is computed across the outputs of each of the k steps run. The benefit of this technique to grid search is that you get a more reliable, stable estimate for each point on the parameter grid, as it will be run using several splits of training / testing data, eliminating potential bias in the dataset and given stable values no matter how the dataset is divided into training and testing. This K fold validation is useful for grid search as each point on the grid will get a more reliable estimate of model performance than not using K folder validation. If we limit grid search to a single dataset (no K fold validation) overfitting would be an issue, as the specific choice of model function might lead to lack of generalisation to out of sample data. Applying this generalisation inside the dataset, many times over, make the estimate more stable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Fitting a Model\n",
+    "Your final implementation requires that you bring everything together and train a model using the **decision tree algorithm**. To ensure that you are producing an optimized model, you will train the model using the grid search technique to optimize the `'max_depth'` parameter for the decision tree. The `'max_depth'` parameter can be thought of as how many questions the decision tree algorithm is allowed to ask about the data before making a prediction. Decision trees are part of a class of algorithms called *supervised learning algorithms*.\n",
+    "\n",
+    "In addition, you will find your implementation is using `ShuffleSplit()` for an alternative form of cross-validation (see the `'cv_sets'` variable). While it is not the K-Fold cross-validation technique you describe in **Question 8**, this type of cross-validation technique is just as useful!. The `ShuffleSplit()` implementation below will create 10 (`'n_splits'`) shuffled sets, and for each shuffle, 20% (`'test_size'`) of the data will be used as the *validation set*. While you're working on your implementation, think about the contrasts and similarities it has to the K-fold cross-validation technique.\n",
+    "\n",
+    "Please note that ShuffleSplit has different parameters in scikit-learn versions 0.17 and 0.18.\n",
+    "For the `fit_model` function in the code cell below, you will need to implement the following:\n",
+    "- Use [`DecisionTreeRegressor`](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) from `sklearn.tree` to create a decision tree regressor object.\n",
+    "  - Assign this object to the `'regressor'` variable.\n",
+    "- Create a dictionary for `'max_depth'` with the values from 1 to 10, and assign this to the `'params'` variable.\n",
+    "- Use [`make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html) from `sklearn.metrics` to create a scoring function object.\n",
+    "  - Pass the `performance_metric` function as a parameter to the object.\n",
+    "  - Assign this scoring function to the `'scoring_fnc'` variable.\n",
+    "- Use [`GridSearchCV`](http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html) from `sklearn.grid_search` to create a grid search object.\n",
+    "  - Pass the variables `'regressor'`, `'params'`, `'scoring_fnc'`, and `'cv_sets'` as parameters to the object. \n",
+    "  - Assign the `GridSearchCV` object to the `'grid'` variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# TODO: Import 'make_scorer', 'DecisionTreeRegressor', and 'GridSearchCV'\n",
+    "\n",
+    "def fit_model(X, y):\n",
+    "    \"\"\" Performs grid search over the 'max_depth' parameter for a \n",
+    "        decision tree regressor trained on the input data [X, y]. \"\"\"\n",
+    "    \n",
+    "    # Create cross-validation sets from the training data\n",
+    "    # sklearn version 0.18: ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)\n",
+    "    # sklearn versiin 0.17: ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None)\n",
+    "    cv_sets = ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.20, random_state = 0)\n",
+    "\n",
+    "    # TODO: Create a decision tree regressor object\n",
+    "    from sklearn.tree import DecisionTreeRegressor\n",
+    "    regressor = DecisionTreeRegressor(random_state =1)\n",
+    "\n",
+    "    # TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10\n",
+    "    import numpy as np\n",
+    "    params = {\"max_depth\": np.arange(1,11)}\n",
+    "    print(\"params\", params)\n",
+    "\n",
+    "    # TODO: Transform 'performance_metric' into a scoring function using 'make_scorer' \n",
+    "    from sklearn.metrics import make_scorer\n",
+    "    scoring_fnc = make_scorer(performance_metric)\n",
+    "\n",
+    "    # TODO: Create the grid search object\n",
+    "    from sklearn.grid_search import GridSearchCV\n",
+    "    grid = GridSearchCV(estimator=regressor, param_grid=params, scoring=scoring_fnc, cv=cv_sets)\n",
+    "\n",
+    "    # Fit the grid search object to the data to compute the optimal model\n",
+    "    grid = grid.fit(X, y)\n",
+    "\n",
+    "    # Return the optimal model after fitting the data\n",
+    "    return grid.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making Predictions\n",
+    "Once a model has been trained on a given set of data, it can now be used to make predictions on new sets of input data. In the case of a *decision tree regressor*, the model has learned *what the best questions to ask about the input data are*, and can respond with a prediction for the **target variable**. You can use these predictions to gain information about data where the value of the target variable is unknown — such as data the model was not trained on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 9 - Optimal Model\n",
+    "_What maximum depth does the optimal model have? How does this result compare to your guess in **Question 6**?_  \n",
+    "\n",
+    "Run the code block below to fit the decision tree regressor to the training data and produce an optimal model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\envs\\udacity\\lib\\site-packages\\sklearn\\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
+      "  DeprecationWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter 'max_depth' is 4 for the optimal model.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fit the training data to the model using grid search\n",
+    "reg = fit_model(X_train, y_train)\n",
+    "\n",
+    "# Produce the value for 'max_depth'\n",
+    "print \"Parameter 'max_depth' is {} for the optimal model.\".format(reg.get_params()['max_depth'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** The max depth of the optimal model is 4. My guess would have been 3, as it seems with 4, training accuracy goes up, but much more so than test accuracy, which almost flatlines."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 10 - Predicting Selling Prices\n",
+    "Imagine that you were a real estate agent in the Boston area looking to use this model to help price homes owned by your clients that they wish to sell. You have collected the following information from three of your clients:\n",
+    "\n",
+    "| Feature | Client 1 | Client 2 | Client 3 |\n",
+    "| :---: | :---: | :---: | :---: |\n",
+    "| Total number of rooms in home | 5 rooms | 4 rooms | 8 rooms |\n",
+    "| Neighborhood poverty level (as %) | 17% | 32% | 3% |\n",
+    "| Student-teacher ratio of nearby schools | 15-to-1 | 22-to-1 | 12-to-1 |\n",
+    "*What price would you recommend each client sell his/her home at? Do these prices seem reasonable given the values for the respective features?*  \n",
+    "**Hint:** Use the statistics you calculated in the **Data Exploration** section to help justify your response.  \n",
+    "\n",
+    "Run the code block below to have your optimized model make predictions for each client's home."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted selling price for Client 1's home: $411,931.58\n",
+      "Predicted selling price for Client 2's home: $235,620.00\n",
+      "Predicted selling price for Client 3's home: $922,740.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Produce a matrix for client data\n",
+    "client_data = [[5, 17, 15], # Client 1\n",
+    "               [4, 32, 22], # Client 2\n",
+    "               [8, 3, 12]]  # Client 3\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Show predictions\n",
+    "for i, price in enumerate(reg.predict(client_data)):\n",
+    "    print \"Predicted selling price for Client {}'s home: ${:,.2f}\".format(i+1, price)\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** Client 1's home: around \\$410k, Client 2's home: around\\$235k, Client 3's home: around \\$920k. Prices seem reasonable, as Client 3's home has low neigbourhood poverty, low student teacher ratio and a lot of bedrooms. C2 home should be the lowest, as least rooms, worst student teacher ratio, and highest neighbourhood poverty. C1 works too and is in between C3 and C2, as better student teacher ratio, half the poverty level, and 1 extra room compared to C2. More generally, prices are in the range of the dataset and a large house in a good neighborhood is close to the maximum prices which is expected"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sensitivity\n",
+    "An optimal model is not necessarily a robust model. Sometimes, a model is either too complex or too simple to sufficiently generalize to new data. Sometimes, a model could use a learning algorithm that is not appropriate for the structure of the data given. Other times, the data itself could be too noisy or contain too few samples to allow a model to adequately capture the target variable — i.e., the model is underfitted. Run the code cell below to run the `fit_model` function ten times with different training and testing sets to see how the prediction for a specific client changes with the data it's trained on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 1: $391,183.33\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 2: $419,700.00\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 3: $415,800.00\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 4: $420,622.22\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 5: $413,334.78\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 6: $411,931.58\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 7: $399,663.16\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 8: $407,232.00\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 9: $351,577.61\n",
+      "('params', {'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})\n",
+      "Trial 10: $413,700.00\n",
+      "\n",
+      "Range in prices: $69,044.61\n"
+     ]
+    }
+   ],
+   "source": [
+    "vs.PredictTrials(features, prices, fit_model, client_data) # predicts client 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 11 - Applicability\n",
+    "*In a few sentences, discuss whether the constructed model should or should not be used in a real-world setting.*  \n",
+    "**Hint:** Some questions to answering:\n",
+    "- *How relevant today is data that was collected from 1978?*\n",
+    "- *Are the features present in the data sufficient to describe a home?*\n",
+    "- *Is the model robust enough to make consistent predictions?*\n",
+    "- *Would data collected in an urban city like Boston be applicable in a rural city?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** Data from 1978 may not be that relevant, as underlying factors may have changed, such as the distribution of wealth across neighbourhoods, or the number of teachers and students in schools. However, if there were no such trends, the dataset could be a good proxy, as price was corrected for inflation. The number of features are probably not sufficient to describe a home - condition, size of land, age and other features may have additional or more important predictive power. The model does not seem very robust - a range of around 70k in predictions on just 10 iterations would be unacceptable to a prospective seller. Finally, urban data may apply to rural settings as there are less neigbourhoods, prices are generally lower, and properties illiquid so not many transactions so price discovery may not follow the same pattern as in a city. Thus the model's use should be restricted to an urban setting, additional features should be added to control the variance of predictions, and ideally updated data would give a better price estimate that may come closer to a real transaction as of today."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
+    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/capstone_project/.ipynb_checkpoints/Pandas multilevel column slice by integer example-checkpoint.ipynb b/capstone_project/.ipynb_checkpoints/Pandas multilevel column slice by integer example-checkpoint.ipynb
new file mode 100644
index 0000000..d16910a
--- /dev/null
+++ b/capstone_project/.ipynb_checkpoints/Pandas multilevel column slice by integer example-checkpoint.ipynb	
@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd, numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],\n",
+    "          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]\n",
+    "tuples = list(zip(*arrays))\n",
+    "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\n",
+    "df = pd.DataFrame(np.random.randn(6, 6), index=index[:6], columns=index[:6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first              bar                 baz                 foo          \n",
+      "second             one       two       one       two       one       two\n",
+      "first second                                                            \n",
+      "bar   one    -0.318200  0.178610  0.028051  1.686810 -0.894416  0.288034\n",
+      "      two     2.550545 -0.149372 -0.219050 -0.655504  2.841000 -0.284396\n",
+      "baz   one     0.011945  1.588152 -0.024538 -0.488825  0.014111  1.452298\n",
+      "      two    -0.756507  0.798701 -0.057006  1.253656 -0.674178 -0.207080\n",
+      "foo   one    -0.768974 -1.127536  0.358538 -1.231684 -0.149561 -1.597307\n",
+      "      two     0.088282  1.052544  0.518868 -1.152717  0.211445 -0.269728\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first              bar\n",
+      "second             one\n",
+      "first second          \n",
+      "bar   one    -0.318200\n",
+      "      two     2.550545\n",
+      "baz   one     0.011945\n",
+      "      two    -0.756507\n",
+      "foo   one    -0.768974\n",
+      "      two     0.088282\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>first</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">bar</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>second</th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>first</th>\n",
+       "      <th>second</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
+       "      <th>one</th>\n",
+       "      <td>-0.318200</td>\n",
+       "      <td>0.178610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>two</th>\n",
+       "      <td>2.550545</td>\n",
+       "      <td>-0.149372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
+       "      <th>one</th>\n",
+       "      <td>0.011945</td>\n",
+       "      <td>1.588152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>two</th>\n",
+       "      <td>-0.756507</td>\n",
+       "      <td>0.798701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
+       "      <th>one</th>\n",
+       "      <td>-0.768974</td>\n",
+       "      <td>-1.127536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>two</th>\n",
+       "      <td>0.088282</td>\n",
+       "      <td>1.052544</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "first              bar          \n",
+       "second             one       two\n",
+       "first second                    \n",
+       "bar   one    -0.318200  0.178610\n",
+       "      two     2.550545 -0.149372\n",
+       "baz   one     0.011945  1.588152\n",
+       "      two    -0.756507  0.798701\n",
+       "foo   one    -0.768974 -1.127536\n",
+       "      two     0.088282  1.052544"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(df.loc[:,(slice(\"bar\"), slice('one'))])\n",
+    "\n",
+    "df.loc[:,slice(df.columns.levels[0][0])]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/capstone_project/.ipynb_checkpoints/aws_v1 - fx spot prediction notebook-checkpoint.ipynb b/capstone_project/.ipynb_checkpoints/aws_v1 - fx spot prediction notebook-checkpoint.ipynb
new file mode 100644
index 0000000..b0f4034
--- /dev/null
+++ b/capstone_project/.ipynb_checkpoints/aws_v1 - fx spot prediction notebook-checkpoint.ipynb	
@@ -0,0 +1,2140 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Machine learning capstone project - fx spot prediction\n",
+    "\n",
+    "The goal is to create features that can help predict the bid price, using a lookback period of a few minutes.\n",
+    "\n",
+    "Try to include the bid offer spread - from the benchmark model it seems volume is not an important feature so it is not a problem that i dont have this data point.\n",
+    "\n",
+    "I took inspiration from : https://www.kaggle.com/kimy07/eurusd-15-minute-interval-price-prediction/notebook\n",
+    "\n",
+    "Introduction\n",
+    "This notebook trains a LSTM model that predicts the bid price of EURUSD 15 minutes in the future by looking at last five hours of data. While there is no requirement for the input to be contiguous, it's been empirically observed that having the contiguous input does improve the accuracy of the model. I suspect that having day of the week and hour of the day as the features mitigates some of the seasonality and contiguousness problems.\n",
+    "\n",
+    "Disclaimer: This exercise has been carried out using a small sample data which only contains 14880 samples (2015-12-29 00:00:00 to 2016-05-31 23:45:00) and lacks ASK prices. Which restricts the ability for the model to approach a better accuracy.\n",
+    "\n",
+    "Improvements\n",
+    "\n",
+    "To tune the model further, I would recommend having at least 5 years worth of data, have ASK price (so that you can compute the spread), and increasing the epoch to 3000.\n",
+    "Adding more cross-axial features. Such as spread.\n",
+    "If you are looking into classification approach (PASS, BUY, SELL), consider adding some technical indicators that is more sensitive to more recent data.\n",
+    "Consider adding non-numerical data, e.g. news, Tweets. The catch is that you have to get the data under one minute for trading, otherwise the news will be reflected before you even make a trade. If anybody knows how to get the news streamed really fast, please let me know.\n",
+    "\n",
+    "Credits : Dave Y. Kim, Mahmoud Elsaftawy,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run on EC2:\n",
+    "Enter the repo directory: cd aind2-cnn\n",
+    "Activate the new environment: source activate aind2\n",
+    "Start Jupyter: jupyter notebook --ip=0.0.0.0 --no-browser\n",
+    "Find this line in output and copy url to browser: \n",
+    "Copy/paste this URL into your browser when you connect for the first time to login with a token: \n",
+    "http://0.0.0.0:8888/?token=3156e...\n",
+    "\n",
+    "change the 0.0.0.0 with EC2 IP.\n",
+    "\n",
+    "you should see the checked out repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:90% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "from subprocess import check_output\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "  \n",
+       "    $('.ctb_hideshow').toggle();\n",
+       "    \n",
+       "    </script>\n",
+       " "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pypyodbc\n",
+    "display(HTML(\"\"\"<script>\n",
+    "  \n",
+    "    $('.ctb_hideshow').toggle();\n",
+    "    \n",
+    "    </script>\n",
+    " \"\"\"))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get data from mongodb\n",
+    "import io\n",
+    "import pymongo\n",
+    "from pymongo import MongoClient\n",
+    "import datetime\n",
+    "\n",
+    "#client = MongoClient(connect=False) #Makes it \"good enough\" for our multi-threaded use case. \n",
+    "\n",
+    "# mng_client = pymongo.MongoClient('localhost', 27017)\n",
+    "# mng_db = mng_client['fx_prediction'] # Replace mongo db name\n",
+    "# collection_name = 'fx_tick_data_typed' # Replace mongo db table name\n",
+    "# db = mng_db[collection_name]\n",
+    "\n",
+    "#print(db.count())\n",
+    "#min_date = datetime.datetime(2016, 1, 1, 0)\n",
+    "#max_date = datetime.datetime(2016, 12, 1, 0)\n",
+    "min_date = \"1Jan16\"\n",
+    "max_date = \"1Feb16\"\n",
+    "\n",
+    "#https://bitbucket.org/djcbeach/monary/wiki/Home use to speed up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simname = \"mine\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1Jan16\n",
+      "1Feb16\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(min_date)\n",
+    "print(max_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# # each of these is a stage in the pipeline - match, project, group, project.\n",
+    "\n",
+    "# cursor_group = db.aggregate(\n",
+    "#    [\n",
+    "      \n",
+    "#        {\"$match\":{\n",
+    "#            \"date\": {\n",
+    "#                \"$gte\": min_date\n",
+    "#                , \"$lte\": max_date\n",
+    "#            }\n",
+    "#        }           \n",
+    "#        },\n",
+    "       \n",
+    "#        {\n",
+    "#            \"$project\": {\n",
+    "#                \"_id\" : 0\n",
+    "#                , \"bo_spread\": {\"$subtract\": [\"$ask\", \"$bid\"]}\n",
+    "#                , \"bid\": 1\n",
+    "#                , \"ask\": 1\n",
+    "#                , \"date\": 1\n",
+    "               \n",
+    "#            }\n",
+    "#        },\n",
+    "       \n",
+    "       \n",
+    "#        {\n",
+    "#         \"$group\" : {\n",
+    "#            #\"_id\" : \"null\",\n",
+    "#            \"_id\": {\n",
+    "#                \"dateAgg\": { \"$dateToString\": { \"format\": \"%G/%m/%d %H:%M\", \"date\": \"$date\" } }\n",
+    "#                },\n",
+    "#            #\"high\": { \"$sum\": { \"$multiply\": [ \"$price\", \"$quantity\" ] } },\n",
+    "#             \"dateSample\": {\"$first\": \"$date\"},\n",
+    "#             \"high\": { \"$max\": \"$bid\"},\n",
+    "#             \"low\": { \"$min\": \"$bid\"},\n",
+    "#             \"open\": { \"$first\": \"$bid\"},\n",
+    "#             \"close\": { \"$last\": \"$bid\"},\n",
+    "#            \"avg_bo_spread\": { \"$avg\": \"$bo_spread\" },\n",
+    "#            \"max_bo_spread\": { \"$max\": \"$bo_spread\" },\n",
+    "#            \"min_bo_spread\": { \"$min\": \"$bo_spread\" },\n",
+    "#            \"count\": { \"$sum\": 1 }\n",
+    "#         }\n",
+    "#       },\n",
+    "       \n",
+    "#        {\n",
+    "#            \"$project\": {\n",
+    "#                \"_id\" : 0\n",
+    "#                , \"date\": \"$dateSample\"\n",
+    "#                , \"high\": 1\n",
+    "#                , \"low\": 1\n",
+    "#                , \"open\": 1\n",
+    "#                , \"close\": 1\n",
+    "#                , \"avg_bo_spread\": 1\n",
+    "#                , \"max_bo_spread\": 1\n",
+    "#                , \"min_bo_spread\": 1\n",
+    "#                , \"count\": 1\n",
+    "#            }\n",
+    "#        }\n",
+    "       \n",
+    "#    ], allowDiskUse=True\n",
+    "# )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# cursor_group = db.aggregate(\n",
+    "#    [\n",
+    "#       {\"$match\":{\n",
+    "#            \"date\": {\n",
+    "#                \"$gte\": min_date\n",
+    "#                , \"$lte\": max_date\n",
+    "#            }\n",
+    "#        }           \n",
+    "#        },\n",
+    "       \n",
+    "             \n",
+    "#        {\n",
+    "#         \"$group\" : {\n",
+    "#            #\"_id\" : \"null\",\n",
+    "#            \"_id\": {\n",
+    "#                #\"month\": {\"$month\": \"$date\"}, \n",
+    "#                #\"day\"  : {\"$dayOfMonth\": \"$date\"}, \n",
+    "#                #\"year\" : {\"$year\": \"$date\"},\n",
+    "#                \"time\": { \"$dateToString\": { \"format\": \"%G/%m/%d %H:%M\", \"date\": \"$date\" } }\n",
+    "#                #\"date\": { \"$dateFromParts\": {\"year\": \"$date\", \"month\": \"$date\", \"day\": \"$date\", \"hour\": \"$date\", \"minute\": \"$date\"}}\n",
+    "#                },\n",
+    "#            #\"high\": { \"$sum\": { \"$multiply\": [ \"$price\", \"$quantity\" ] } },\n",
+    "#             \"high\": { \"$max\": \"$date\"},\n",
+    "#             \"low\": { \"$min\": \"$date\"},                                \n",
+    "#            \"count\": { \"$sum\": 1 }\n",
+    "#         }\n",
+    "#       }\n",
+    "#    ], allowDiskUse=True\n",
+    "# )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "strConnDef = \"DRIVER={ODBC Driver 13 for SQL Server};SERVER=localhost,1433;DATABASE=kai_dw;uid=kai_ta;pwd=tenpen12\"\n",
+    "def getQueryRaw(strQuery, params=None, strConn=strConnDef, commitOn=None):\n",
+    "\n",
+    "    if commitOn is None:\n",
+    "        commitOn = False\n",
+    "\n",
+    "    if params is None:\n",
+    "        params = []\n",
+    "\n",
+    "    pypyodbc.lowercase = False\n",
+    "    conn = pypyodbc.connect(strConn)\n",
+    "    cursor = conn.cursor()\n",
+    "    cursor.execute(strQuery, params)\n",
+    "\n",
+    "    if commitOn:\n",
+    "        conn.commit()\n",
+    "        return \"sql insert was successful.\", \"sql insert was successful.\"\n",
+    "    try:\n",
+    "        rows = cursor.fetchall()\n",
+    "        #print(\"rows\", rows)\n",
+    "        # print(\"PARAMS:\", params)\n",
+    "        description = cursor.description\n",
+    "        conn.close()\n",
+    "        return rows, description\n",
+    "    except:\n",
+    "        # print(\"THE QUERY: \" + strQuery) TODO: add query\n",
+    "        conn.close()\n",
+    "        raise ValueError(\"There was an error fetching a sql query. Make sure the index exists for your selected dates. THE PARAMS: \", params)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def getQueryDataframe(strQuery, params=None, strConn=strConnDef, columnMustAlwaysExist=None, commitOn=None):\n",
+    "\n",
+    "    rows, cursorDescription = getQueryRaw(strQuery, params, strConn, commitOn)\n",
+    "    if commitOn:\n",
+    "        return \"sql insert was successful.\"\n",
+    "\n",
+    "    if len(rows) == 0:\n",
+    "        print(\"No rows were returned.\")\n",
+    "        print(\"THE PARAMS: \", params)\n",
+    "        print(\"THE QUERY: \" + strQuery)\n",
+    "        print(\"Rows length is zero. No records returned\")\n",
+    "\n",
+    "        if columnMustAlwaysExist is None:\n",
+    "            columnMustAlwaysExist = \"Empty\"\n",
+    "\n",
+    "        columns = [\"Information\", columnMustAlwaysExist]\n",
+    "        rows = [\n",
+    "            [\"No results were returned.\", \"There is no data.\"]\n",
+    "            , [\"No results were returned.\", \"There is no data.\"]\n",
+    "        ]\n",
+    "\n",
+    "    else:\n",
+    "        # bytes conversion needed because of the linux pypyodbc bug\n",
+    "        columns = [column[0].decode(\"cp1252\") if type(column[0]) == bytes else column[0] for column in\n",
+    "                   cursorDescription]\n",
+    "\n",
+    "    results = pd.DataFrame(data=rows, columns=columns)\n",
+    "\n",
+    "\n",
+    "    return results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>bid_price</th>\n",
+       "      <th>ask_price</th>\n",
+       "      <th>bo_spread</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>count</th>\n",
+       "      <th>open</th>\n",
+       "      <th>close</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:15.493</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08751</td>\n",
+       "      <td>0.00050</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:38.993</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08749</td>\n",
+       "      <td>0.00046</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:41.493</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08749</td>\n",
+       "      <td>0.00036</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:41.993</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08745</td>\n",
+       "      <td>0.00032</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:44.743</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08745</td>\n",
+       "      <td>0.00042</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08703</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  month  day  hour  weekday                       date  bid_price  \\\n",
+       "0  2016      1    3    17        1  0 2016-01-03 17:00:15.493    1.08701   \n",
+       "1  2016      1    3    17        1  0 2016-01-03 17:00:38.993    1.08703   \n",
+       "2  2016      1    3    17        1  0 2016-01-03 17:00:41.493    1.08713   \n",
+       "3  2016      1    3    17        1  0 2016-01-03 17:00:41.993    1.08713   \n",
+       "4  2016      1    3    17        1  0 2016-01-03 17:00:44.743    1.08703   \n",
+       "\n",
+       "   ask_price  bo_spread     high      low  avg_bo_spread  count     open  \\\n",
+       "0    1.08751    0.00050  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "1    1.08749    0.00046  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "2    1.08749    0.00036  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "3    1.08745    0.00032  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "4    1.08745    0.00042  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "\n",
+       "     close  \n",
+       "0  1.08701  \n",
+       "1  1.08703  \n",
+       "2  1.08713  \n",
+       "3  1.08713  \n",
+       "4  1.08703  "
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str_query = \"\"\"\n",
+    "\n",
+    "\n",
+    "select\n",
+    "    --distinct\n",
+    "    const.year, const.month, const.day, const.hour, const.weekday, round(const.minute/15,0) * 15\n",
+    "    , const.snaptime 'date'\n",
+    "    , const.bid_price\n",
+    "    , const.ask_price\n",
+    "    , const.ask_price - const.bid_price 'bo_spread'\n",
+    "\t, max(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0))'high'\n",
+    "\t, min(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'low'\n",
+    "    , avg(const.ask_price - const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'avg_bo_spread'\n",
+    "\t--, min(const.snaptime) 'open_datetime'\n",
+    "\t--, max(const.snaptime) 'close_datetime'\n",
+    "\t, count(*) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'count'\n",
+    "    , first_value(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0) order by const.snaptime) 'open'\n",
+    "    , last_value(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0) order by const.snaptime) 'close'\n",
+    "from dbo.fx_spot_data_features const\n",
+    "where\n",
+    "    const.snaptime >= '\"\"\"+min_date+\"\"\"'\n",
+    "    and const.snaptime <= '\"\"\"+max_date+\"\"\"'\n",
+    "    \n",
+    "--group by const.year, const.month, const.day, const.hour, round(const.minute/15,0)\n",
+    "--order by const.year, const.month, const.day, const.hour, round(const.minute/15,0)\n",
+    "order by const.snaptime\n",
+    "\n",
+    "\"\"\"\n",
+    "res = getQueryDataframe(str_query)\n",
+    "print(res.count())\n",
+    "res.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'date'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2441\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2442\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'date'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-141-6cacc01fd7ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#cursor = list(cursor_group)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf_res\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m#df_res.rename(columns={\"_id\": \"date\"}, inplace=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/data/eurusd_features.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   2828\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2829\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2830\u001b[0;31m                 \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2831\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2832\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1962\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1964\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1966\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1969\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1970\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1973\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1643\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1645\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1646\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3590\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3591\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2442\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2444\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2446\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'date'"
+     ]
+    }
+   ],
+   "source": [
+    "#cursor = list(cursor_group)\n",
+    "df_res =  pd.DataFrame(res)\n",
+    "df_res.set_index('date', inplace=True)\n",
+    "#df_res.rename(columns={\"_id\": \"date\"}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_res.to_csv(\"eurusd_features.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_res = pd.read_csv(\"data/eurusd_features.csv\")\n",
+    "df_res.set_index('date', inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# load kaggle reference dataset for comparison\n",
+    "#df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_sample.csv')\n",
+    "df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_01.01.2010-31.12.2016.csv')\n",
+    "\n",
+    "# Rename bid OHLC columns\n",
+    "df_kaggle.rename(columns={'Time' : 'date', 'Open' : 'open', 'Close' : 'close', \n",
+    "                   'High' : 'high', 'Low' : 'low', 'Close' : 'close', 'Volume' : 'volume'}, inplace=True)\n",
+    "df_kaggle['date'] = pd.to_datetime(df_kaggle['date'], infer_datetime_format=True)\n",
+    "df_kaggle.set_index('date', inplace=True)\n",
+    "df_kaggle = df_kaggle.astype(float)\n",
+    "\n",
+    "simname = \"bm_kaggle\"\n",
+    "\n",
+    "df_res = df_kaggle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5f75faee80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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U52TgncDGqro9ySO7qVaSJC1FH4+InA3MVtW+qroHuArYNNLnRcDVVXU7QFV9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\nkiSpM/8/Q2Ep4tIP3CQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5f6b3bfeb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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rtu+G59fZxrx8+KSmsbppXUwIIdOduBbrYddG3T+pbXqU9XmrvlGP9OjUvt22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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5f6987be80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#df_res.date = pd.to_datetime(df_res.date.dateAgg, format='%Y%m%d %H:%M')\n",
+    "df_res.head()\n",
+    "sns.pairplot(df_res.loc[:,[\"bo_spread\", \"hour\", \"close\"]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "year             2081682\n",
+      "month            2081682\n",
+      "day              2081682\n",
+      "hour             2081682\n",
+      "weekday          2081682\n",
+      "Unnamed: 6       2081682\n",
+      "bid_price        2081682\n",
+      "ask_price        2081682\n",
+      "bo_spread        2081682\n",
+      "high             2081682\n",
+      "low              2081682\n",
+      "avg_bo_spread    2081682\n",
+      "count            2081682\n",
+      "open             2081682\n",
+      "close            2081682\n",
+      "avg_price        2081682\n",
+      "range            2081682\n",
+      "ohlc_price       2081682\n",
+      "oc_diff          2081682\n",
+      "period_return    2081682\n",
+      "pca              2081682\n",
+      "dtype: int64\n",
+      "2016-01-03 17:00:15.493 2016-01-31 23:59:41.170\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>Unnamed: 6</th>\n",
+       "      <th>bid_price</th>\n",
+       "      <th>ask_price</th>\n",
+       "      <th>bo_spread</th>\n",
+       "      <th>high</th>\n",
+       "      <th>...</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>count</th>\n",
+       "      <th>open</th>\n",
+       "      <th>close</th>\n",
+       "      <th>avg_price</th>\n",
+       "      <th>range</th>\n",
+       "      <th>ohlc_price</th>\n",
+       "      <th>oc_diff</th>\n",
+       "      <th>period_return</th>\n",
+       "      <th>pca</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:15.493</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08751</td>\n",
+       "      <td>0.00050</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086965</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1322.240112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:38.993</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08749</td>\n",
+       "      <td>0.00046</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086970</td>\n",
+       "      <td>-0.00002</td>\n",
+       "      <td>1.000018</td>\n",
+       "      <td>-1322.160522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:41.493</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08749</td>\n",
+       "      <td>0.00036</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086995</td>\n",
+       "      <td>-0.00012</td>\n",
+       "      <td>1.000110</td>\n",
+       "      <td>-1322.161377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:41.993</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08745</td>\n",
+       "      <td>0.00032</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086995</td>\n",
+       "      <td>-0.00012</td>\n",
+       "      <td>1.000110</td>\n",
+       "      <td>-1322.161865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:44.743</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08745</td>\n",
+       "      <td>0.00042</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086970</td>\n",
+       "      <td>-0.00002</td>\n",
+       "      <td>1.000018</td>\n",
+       "      <td>-1322.161011</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         year  month  day  hour  weekday  Unnamed: 6  \\\n",
+       "date                                                                   \n",
+       "2016-01-03 17:00:15.493  2016      1    3    17        1           0   \n",
+       "2016-01-03 17:00:38.993  2016      1    3    17        1           0   \n",
+       "2016-01-03 17:00:41.493  2016      1    3    17        1           0   \n",
+       "2016-01-03 17:00:41.993  2016      1    3    17        1           0   \n",
+       "2016-01-03 17:00:44.743  2016      1    3    17        1           0   \n",
+       "\n",
+       "                         bid_price  ask_price  bo_spread     high  \\\n",
+       "date                                                                \n",
+       "2016-01-03 17:00:15.493    1.08701    1.08751    0.00050  1.08723   \n",
+       "2016-01-03 17:00:38.993    1.08703    1.08749    0.00046  1.08723   \n",
+       "2016-01-03 17:00:41.493    1.08713    1.08749    0.00036  1.08723   \n",
+       "2016-01-03 17:00:41.993    1.08713    1.08745    0.00032  1.08723   \n",
+       "2016-01-03 17:00:44.743    1.08703    1.08745    0.00042  1.08723   \n",
+       "\n",
+       "                            ...       avg_bo_spread  count     open    close  \\\n",
+       "date                        ...                                                \n",
+       "2016-01-03 17:00:15.493     ...            0.000165    142  1.08701  1.08701   \n",
+       "2016-01-03 17:00:38.993     ...            0.000165    142  1.08701  1.08703   \n",
+       "2016-01-03 17:00:41.493     ...            0.000165    142  1.08701  1.08713   \n",
+       "2016-01-03 17:00:41.993     ...            0.000165    142  1.08701  1.08713   \n",
+       "2016-01-03 17:00:44.743     ...            0.000165    142  1.08701  1.08703   \n",
+       "\n",
+       "                         avg_price    range  ohlc_price  oc_diff  \\\n",
+       "date                                                               \n",
+       "2016-01-03 17:00:15.493    1.08692  0.00062    1.086965  0.00000   \n",
+       "2016-01-03 17:00:38.993    1.08692  0.00062    1.086970 -0.00002   \n",
+       "2016-01-03 17:00:41.493    1.08692  0.00062    1.086995 -0.00012   \n",
+       "2016-01-03 17:00:41.993    1.08692  0.00062    1.086995 -0.00012   \n",
+       "2016-01-03 17:00:44.743    1.08692  0.00062    1.086970 -0.00002   \n",
+       "\n",
+       "                         period_return          pca  \n",
+       "date                                                 \n",
+       "2016-01-03 17:00:15.493       1.000000 -1322.240112  \n",
+       "2016-01-03 17:00:38.993       1.000018 -1322.160522  \n",
+       "2016-01-03 17:00:41.493       1.000110 -1322.161377  \n",
+       "2016-01-03 17:00:41.993       1.000110 -1322.161865  \n",
+       "2016-01-03 17:00:44.743       1.000018 -1322.161011  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#plt.savefig(\"test.png\")\n",
+    "print(df_res.count())\n",
+    "print(df_res.index.min(), df_res.index.max())\n",
+    "df_res.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['year', 'month', 'day', 'hour', 'weekday', 'Unnamed: 6', 'bid_price',\n",
+       "       'ask_price', 'bo_spread', 'high', 'low', 'avg_bo_spread', 'count',\n",
+       "       'open', 'close', 'avg_price', 'range', 'ohlc_price', 'oc_diff',\n",
+       "       'period_return', 'pca'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#df.drop([\"vol\"], axis=1, inplace=True)\n",
+    "df_res.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df_res\n",
+    "# all these should refer to the price prediction period, so for tick it doesnt exist\n",
+    "#df['low'] = df.bid.min()\n",
+    "#df['high'] = df.bid.max()\n",
+    "#df['open'] = df.bid.iat[1]\n",
+    "#df['close'] = df.bid.iat[-1]\n",
+    "\n",
+    "# to include seasonality as a feature\n",
+    "#df['hour'] = df.index.hour\n",
+    "#df['day']  = df.index.weekday\n",
+    "#df['week'] = df.index.week\n",
+    "#df['month'] = df.index.month\n",
+    "\n",
+    "#df['momentum']  = df['volume'] * (df['open'] - df['close'])\n",
+    "df['avg_price'] = (df['low'] + df['high'])/2\n",
+    "df['range']     = df['high'] - df['low']\n",
+    "df['ohlc_price'] = (df['low'] + df['high'] + df['open'] + df['close'])/4\n",
+    "df['oc_diff']    = df['open'] - df['close']\n",
+    "#df['bo_spread'] = df.ask - df.bid\n",
+    "df['period_return'] = df.close / df.open"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2081682\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Z/uECZy1pn/bx5RHD6gNIBo0EFokpd+fwSIG27PS/57VkUmTTprEACaEEIBJTI4Ui+WJp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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8dd194c358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create ohlc prices\n",
+    "df_res.head()\n",
+    "print(df.high.count())\n",
+    "sns.distplot(df.period_return)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#df.drop([\"_id\"], axis=1, inplace=True)\n",
+    "df.head()\n",
+    "#df.to_excel(\"df_res.xlsx\")\n",
+    "import dill as pickle\n",
+    "with open(simname+'_fx_features.pkl', 'wb') as file:\n",
+    "    pickle.dump(df, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add PCA as a feature instead of for reducing the dimensionality. This improves the accuracy a bit.\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "dataset = df.copy().values.astype('float32')\n",
+    "pca_features = df.columns.tolist()\n",
+    "\n",
+    "pca = PCA(n_components=1)\n",
+    "df['pca'] = pca.fit_transform(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jTVjoAozKxfopdVr65o+/efWr2UDgWLGAMsd+8YIw99WeFCwY2W8b0/5ewZPf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8dec67bcf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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GW+tc5tgd2MeemHTfP3n9uTMef/eTzmf3k9Yd2brjheetnf4L4J7Dj+eew8eS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\n6CRJkmru/wNh7xHC3s7IPwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8dec722748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ZvEmSJDURkzdJkqQm4pw3SZUw4/9+0OgQJKkp2PMmSZLUREzeJEmS\nmojJmyRJUhMxeZMkSWoiJm+SJElNxORNkiSpiZi8SZIkNZF+k7eIuDgino2I+3opj4iYFhGzI+Ke\niHhruf6NEXF3zeuliDimLFsjIm6IiIfLn6vXt1mSJEkj00B63i4BJvZRPgnYpHwdBpwHkJkPZuZW\nmbkVsA3wMvDTcp8TgJmZuQkws1yWJElSP/pN3jLzVuD5PjaZAlyWhduA1SJi3W7b7AY8kpmP1+xz\nafn+UmCvwYUtSZK0bKrHnLf1gSdqlp8s19XaF7i8ZnmdzJxbvn8aWKe3yiPisIiYFRGz5s2bV4dw\nJUmSmteQX7AQEWOA9wI/6ak8MxPI3vbPzAsyc3xmjh87duwQRSlJktQc6pG8zQE2rFneoFzXaRJw\nZ2Y+U7Pumc6h1fLns3WIQ5IkacSrR/J2DbB/edXpdsCLNUOiAPvRdci0c58DyvcHAD+vQxySJEkj\nXmt/G0TE5cDOwFoR8SRwCjAaIDPPB64DJgOzKa4oPahm35WAPYCPdav2dODHEXEw8DjwgaVtiCRJ\n0rKg3+QtM/frpzyBI3op+xewZg/rn6O4AlWSJEmD4BMWJEmSmojJmyRJUhMxeZMkSWoiJm+SJElN\nxORNkiSpiZi8SZIkNRGTN0mSpCZi8iZJktRETN4kSZKaiMmbJElSE4ni6VbNISLmUTwLdaRaC/hb\no4NoANu9bFlW2w3Lbttt97LFdi/y2swcW+8DNVXyNtJFxKzMHN/oOIab7V62LKvthmW37bZ72WK7\nh57DppIkSU3E5E2SJKmJmLxVywWNDqBBbPeyZVltNyy7bbfdyxbbPcSc8yZJktRE7HmTJElqIiZv\ndRIRa0TEDRHxcPlz9V62mxgRD0bE7Ig4YSD7R8SJ5fYPRsSEct0qEXF3zetvEXFOWXZgRMyrKTtk\nJLW9XH9zua6zjWuX65eLiCvKfX4fEeNGSrsjYsWIuDYi/hwR90fE6TXbD+l33lsbasojIqaV5fdE\nxFvr3f5y/TYRcW9ZNi0iop7tbGS7I2KPiLijbN8dEbFrzT49/r6PkHaPi4h/17Tt/Jp9RvL3/eHo\neg7viIityrJh/b6HsO3vj+Jc1RER47vVN5L/jffY7rr+G89MX3V4AWcCJ5TvTwDO6GGbUcAjwMbA\nGOCPwOZ97Q9sXm63HLBRuf+oHuq+A9ipfH8gcO5IbjtwMzC+h+N8Aji/fL8vcMVIaTewIrBLuc0Y\n4NfApKH+zvtqQ802k4HpQADbAb8fou/9D2X9UR5v0hB+v8Pd7q2B9cr3WwJzao7T4+/7CGn3OOC+\nXmIZsd93t3rfBDzSiO97iNu+GfDG7u1h5P8b763ddfs3bs9b/UwBLi3fXwrs1cM22wKzM/PRzHwF\n+FG5X1/7TwF+lJnzM/MvwOyynoUi4g3A2hT/mTdCw9reTyxXArsN4V9uw9ruzHw5M28CKOu6E9ig\nzm3qSV9t6DQFuCwLtwGrRcS6/ew7qPaX9b0qM2/L4mx3GT1/5vUyrO3OzLsy86ly/f3AChGx3FA1\nrg/D/X33aKR/393sV+7TKEPS9sx8IDMf7OF4I/rfeG/true/cZO3+lknM+eW758G1ulhm/WBJ2qW\nnyzX9bV/X/t06uxhqr36ZJ+ya/bKiNhwcE0ZtEa1/dKye/nkmgRt4T6Z2Qa8CKw5+CYNSMO+84hY\nDdgTmFmzeqi+84H8Dva2TT3bv375vq846mm4211rH+DOzJxfs66n3/eh0Ih2b1S27ZaI2LHmGMvK\n9/1B4PJu64br+4aha/tgjzdSvvOBWKp/462DONAyLyJuBF7dQ9FJtQuZmRGxxJfxLsH++wIfrVn+\nX+DyzJwfER+j+Gtv1x73HKAKtv3DmTknIlYBrqJo/2VLetzeVLDdREQrxYl+WmY+Wq6u+3c+nJb2\n82tWPbU7IrYAzgDeVbN6WH7fh0u3ds8FXpOZz0XENsDPys9gxOnl+3478HJm3lezekR93+qqHv/G\nTd4GITN3760sIp6JiHUzc27ZpfpsD5vNAWp7RDYo1wH0tn9f+xARbwFaM/OOmjifq9n+fyjmXCyV\nqrU9Mzt//iMifkjRhX1ZzT5PlknOqkDt5zEoVWt36QLg4cw8pybOun/nNfqLp69tRvex72DbP4eu\nw8Q9xVFPw91uImID4KfA/pn5SOf6Pn7fh8KwtrvseZhfvr8jIh4B3sAy8H2X9qVbr9swf98wdG0f\n7PFGynfeq7r9G89hmhA50l/A1+k6KfXMHrZpBR6lmKDZOcFxi772B7ag68TOR6m5YAE4Hfhit+Os\nW/N+b+C2kdT2sq61ym1GU8xtO7xcPoKuFyz8eKS0uyw7jeKvspbh+s77akPNNu+m66TePwxR+7tP\nZp48hN/vcLd7tXK79/UQR4+/7yOk3WNrvt+NKf4DXGOkf9/lckvZ3o0b9X0PZdtr9r2ZrhP3R/S/\n8T7aXbd/40P2y7CsvSjmVc0EHgZurDn5rAdcV7PdZOAhiqtUTupv/7LspHL7B+l25U35y7Npt3Vf\no5gM+Ufgpu7lzd52YCWKq2vvKdv5zZp/+MsDP6GYAPsHak6KI6DdGwAJPADcXb4OGY7vvKc2AIez\nKGkO4Ftl+b10PWHV7XceGA/cV5adS3mj8SH8joet3cDngX/VfLd3U1yI1Ovv+whp9z5lu+6muAhn\nz2Xh+y7LdqbbH1qN+L6HsO17U8wFmw88A8yoKRvJ/8Z7bDd1/DfuExYkSZKaiFebSpIkNRGTN0mS\npCZi8iZJktRETN4kSZKaiMmbJElSEzF5kyRJaiImb5IkSU3E5E2SJKmJ/H+R2oE1UwVndAAAAABJ\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e22e71cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ZgKmTR/VZaGZmpebEzswqS0szOxub9it+29tOZFtaXAHwpW/9nJqaGr77lQv7Mjozs5Jy\nYmdmFefFp/ZfJLF12y62bdvd/nr985v7MiQzs7Lgc+zMrLIEjJpyyH7F13z913z9K79of71nj9jT\n7GugmFn/4hk7M6ssEnt2738o9rFlzxG5tRJ/dcpU5LzOzPoZJ3ZmVjlS5vby+pb9qgYPgR17106w\nYNEqnNmZWX/jxM7MKl5zcwu7du5btnXH/smfmVm18zl2ZlbRAnjwvhW0tpY6EjOz0nNiZ2YVr35Q\nHeMmDGfYsAGlDsXMrKR8KNbMKsKu7Z3fB/beu5fy3NqX+zAaM7Py5Bk7M6sI5068vNO6Y48f34eR\nmJmVr24TO0nXS3pB0tJO6iXpm5JWSnpU0utydWdKWpHqrsyVf1XS46n97ZJGpPLJknZJWpIe1/bG\nIM2ssv12/h+hFSIK3/v1tz9/uI8jMjMrT8XM2P0AOLOL+rOAaelxGfAdAEm1wDWpfjpwvqTpaZvf\nACdExInAE8Cnc/2tiogZ6TGvB2Mxsyr19Y99P7vUSaEVEgMH8Owz+9871sysP+o2sYuI+4Gu7s0z\nB7gxMg8CIySNBWYBKyNidUQ0AfNTWyLi7ohoTts/CEw4kEGYWXVrabuUifb9yjr21WM5//1vYPYZ\nx/d9UGZmZag3zrEbD6zJvV6byjor7+gfgF/lXk9Jh2HvkzS7F+Izs2oR+87YvfOiN3PMiRM4ZHB9\niQIyMysvJV0VK+mzQDPwo1S0HpgUEZsknQT8VNLxEbHfcjdJl5Ed+mXSpEl9FbKZlVLrvufYrX36\nRYYeOpBNL3hFrJkZ9E5itw6YmHs9IZUN6KQcAEkXAecAZ0Q6IzoiGoHG9HyxpFXAMcCijjuNiOuA\n6wBmzpxZ+IxqM6tqt3z/94yZNJL167fiqzeZmfXOodg7gAvT6thTgK0RsR5YCEyTNEVSPTA3tUXS\nmcCngL+LiPYbAUlqSIsukDSVbEHG6l6I0cyqQYdbvzbubOGZp7bQtNv/tjMzgyL+iSvpZuA0YLSk\ntcDnyGbjiIhrgTuBs4GVwE7g4lTXLOkK4C6gFrg+Ipalbr8FDAR+o+wm3Q+mFbCnAl+QtAdoBeZF\nRFcLN8ysPymQv9XXQ1MT2WHaGu3fwMysH+k2sYuI87upD6DglUMj4k6yxK9j+dGdtL8NuK27mMzM\n2jQ1NePDsGZmGX8bmlllSRcpvujsq6Ale17g6nZmZv2SbylmZhVpw6pt0NKalluZmRk4sTOzCtHZ\n7cTAX2RmZm38fWhmZS8K3Uosp3ZAHwViZlbmnNiZWflrn60rPGvX0tJ3oZiZlTMndmZWOQrldQPh\n8LFD+zwUM7Ny5MTOzMpfa4GMbk8zAMOH1tPU1NTHAZmZlScndmZW0YYNG8QR49OMna9PbGb9nBM7\nM6to27fu4s2nz8xe+M5iZtbPObEzs8qifaflRo47jN/f/RffTszMDN95wswqSYekTsCu7U2seWYL\n1PrrzMzMM3ZmVrECaGzcwZRpR5Q6FDOzsuB/4ppZWdu9e3eX9S89v4dtO9ZDzYFfpXjxmnUAnDRx\n/AH3ZWZWCp6xM7Oy9h8f+07XDQQ16p1/o55/w62cf8OtvdKXmVkpOLEzs7J2/38vJAreWiLaf7Q0\nNxfV168eXE5zc+Hbk33ytl+8wgjNzMqHEzszK297um9S7C3FbrhzAXv2FO7w8fUv9CAoM7Py5MTO\nzMrWC2tfJJr3dH3h4dy32IABXV/yZOW6Tfz5sacL1l38V7N6HqCZWZlxYmdmZetz7/5y1w0iyL7G\nAiJ4w5umddvnR7/9y4LlJ0+e0OV2v3tqBX96ZlW3/ZuZlZITOzMrWysferr7Rq2t0AJI3P/7J2ht\nDZqaOj/nrvAZdvDJ237ZXn/Ml7++X/0l9/6M991zW/fxmJmVkC93YmYVL38nsUcffZYHF+6dWdu+\no4nGliZGDR/aXtbc0kpd7b7/rt3euDcZbPWtycysQnnGzswqUnvu1TYFlxKzBxY8yT33Lm9v99ZL\n/5NzPvjdfbb9/I137dff4cMP2a9sT7GrMszMyoQTOzOrePkJtsFDBrDxxe1dtv/Fwsfbn3/wv27j\nXV//AX9cvS47pJt6XLd5Myd/91tsa+r6AslmZuXEh2LNrPLlFsP+n+/9CeprCzYrdIT1j6ueJYBB\nddA0EJrSt+L1CxewZU8zb7nxe7zj2OOyjbtedGtmVnKesTOz8tfZOW8dEq29CyOCoYP2/Xpb98KW\nzvsBdgc0t9A+a/fb5U8AsGFPI/+1dAkBRGQdfOqBnwHwznuu4csPH5wLG7c2PkBEZ0s9zMwK6zax\nk3S9pBckLe2kXpK+KWmlpEclvS5Xd6akFanuylz5SEm/kfRk+nlYru7Tqf0KSW8/0AGaWZWrAYh9\nEq8I2L5736To3E9dT7RmuV0Af1n9XJfdrsldyDh/pl1rayu3PbuUo265iie2v8AP1yyg5WAkYI0L\niFaf42dmPVPMjN0PgDO7qD8LmJYelwHfAZBUC1yT6qcD50uanra5EvhdREwDfpdek+rnAsenfX47\n9WNm/dCxbziq+0bRyfOmKNhMAK3w99+8hbsffqw9a6sB6PLbJuvhG3/6bXrdSktr9vP+55d3ttEr\nN+QCamoH9H6/ZlbVuk3sIuJ+YHMXTeYAN0bmQWCEpLHALGBlRKyOiCZgfmrbts0N6fkNwDty5fMj\nojEingJWpn7MrB9a8WAnFwRW7hhsPn/reGi2FWjucAQ21+aTN9zV3kUrZElefvIt8q+zDX/41MP7\nx9N6EE6+2/g3tG5/qff7NbOq1hvn2I0H1uRer01lnZUDjImI9en588CYbvoys/6o03Pien6huejw\nkw7XMBbs940Ybf/JJXvfPP1d+7QZUAefeOSmomJobGzkW4+fXlRb2Ak+x87MeqjkiyciOymmx9/S\nki6TtEjSoo0bNx6EyMysYvTkG0TFz66p/T97nTBqHJMHj2DywMH7V3bj50/9vAetW6C+vkf9m5n1\nRmK3DpiYez0hlXVWDrAhHa4l/Xyhm772ExHXRcTMiJjZ0NBwwIMws/IzYEiHKzJ1zKOKSOjGjhq0\nz+b5vK7TpQkSFDi9TYhD6uu56YwL+MRJXZ16XNiRHNmD1k3w0q96vA8z6996I7G7A7gwrY49Bdia\nDrMuBKZJmiKpnmxRxB25bd6fnr8f+FmufK6kgZKmkC3IWNALMZpZBbpz282otmfrp7JZtr3Z24ZN\nu6Em+7IrdLi1M6eOHL1f2/818RgARg4awimHT+lRXAALWdiD1jNg5Dk93oeZ9W/FXO7kZuAB4FhJ\nayVdImmepHmpyZ3AarKFDt8DPgQQEc3AFcBdwHLg1ohYlra5GnirpCeBt6TXpPpbgceAXwOXR4TX\n+5v1d69gpq7NP//j25h8xDAA3nva8YX7S2X/Z+47GD8kO/w5bswRWXlN9njX1Onc++IzrNyyke17\nGlmxZQP1QD01HNL1ctp2rfy4+MAPvQRqBxbf3syMIu48ERHnd1MfwOWd1N1Jlvh1LN8EnNHJNlcB\nV3UXl5n1E+rBgYUO588JOOv0V3PW6a/muz/+A+e/7XXc/NBj7fW17F1DMVhw8lFHsn5XE9TBaRPH\nMX/FUkYPPIRzph7Fy2ph3a7d3P/MSt553Ay27WmkCfjlaR9FRZ5rd+G4n3Ljc+/oviFA8x4Y6Ftd\nmFnP+JZiZlb+ipihE0BdlgjVAK11+yZFH3zX7C633xlQV1NDa/pWrE8LF3583vmMHjyUhzc+x7+9\n4S0MqR9IawRvHJMdil3y4jPUSowbPLLbGIcPH84Vw+/pfjAAOz4OO+rgiJ6fy2dm/VfJV8WamR0o\n1RQ/s/Wrz3+g/ULEl5x+YvttZf/xlOymOZ95Y3bpzOEpsZs8YiRD6+uZPX4yQ+qzQ6M1EkMHZM+/\nveK3XPtEkclajzV338TMLMczdmZWWaLD9F1n/zztJNcbc/hQvnP5uTyw4hne8YYTef2xR7Ps+Q1c\n+uYsobvn6WcAGFEzoNsz504YdgQvN75EjXp+Xb3i/BbwAgozK55n7Mys8kgFrzHXMZfrbB7vDcdN\n5pjxoxg8oJ6xI4dyylF7r7L0vbnv5otvO4MFGzd0fjmU5NYz5lFXW0Nt7cH6Kh1xkPo1s2rlxM7M\nKkdrFzNjtQWyvC6O0P7Lj37Dt3/1ByY3jOKECWPbyw8ZMIC5rz2RV40eXVRIW1saeal5d1Fte6zu\nQwenXzOrWj4Ua2YVaP8ETx1ejBjW9V0bWoElTz7Taf0jz28oKpIdNBXVrseGfhoGHXZw+jazquXE\nzswqW7rEScdUb8rE0dDNoopVm3d2Wjd+xPCidj+qbjB1PbkkS5Fqhl7c632aWfVzYmdmla2Te782\nNbd0m9h15a+POoZfzO1+xuxfT3wnNQewHzOz3uTEzswqW1pIETX7zppFQHeLVRu6OFpbW1PDhEO7\nX7zw7RW/oZYa3nD4MUUEa2Z2cHnxhJlVroDT/vZ4kBg5akhWlu7CVVer/dZTdNTdVeKGD+z+ll4r\ndjzPih3PdR+rmVkf8IydmVWeNBMnoC798/SWn/8TZ5z27+1Nnl67udtu/ur4ow84lP98/fupPQjn\n2JmZvRJO7MyssnQ4p+6+e5d3aJBdVvikGZOp6eJ6J0eMGMpxk4444HCmHzr+gPswM+stTuzMrKKN\nHFXLhh0t6flgxkw4lJWrNvKF/2dOl9vd9In3ceghgw54/4fWDz7gPszMeosTOzOrHPusPs2eq6YW\n0j0iduzYTfOewbQWuM5dR6OGOSEzs+rjxM7MKlsuh2vc3cqTq16itXTRmJmVlBM7M6toNXX7zs69\n6ujhvHrGsSWKxsystLyUy8wq2nOr9rQ/f+Mbp1E3aAi3/XRhCSMyMysdJ3ZmVjmiw7lzdbXU5k6V\n+7d/fzdPPb2R5u4uUGdmVqWc2JlZ5Shw+7Dvz/8Y4ycd2v66ts639zKz/suJnZmVtelvyl1EuOOM\nHTB23Aiu/8k/tb8eNHBAX4RlZlaWnNiZWVn7+u8+DzU10FrcWteLLngzr5sx6SBHZWZWnrwq1szK\nmiTUfghWBWft8o6ZdgS7djUd/MDMzMqQZ+zMrPIUONeuzcrVL/DAQ6v6MBgzs/LhxM7MKosEAzo/\nj+5/HnyChx95uu/iMTMrI0UldpLOlLRC0kpJVxaoP0zS7ZIelbRA0gm5uo9KWippmaSP5cpvkbQk\nPZ6WtCSVT5a0K1d3bW8M1Mz6h8NHDefQ4b5dmJn1T92eYyepFrgGeCuwFlgo6Y6IeCzX7DPAkog4\nV9KrUvszUoL3AWAW0AT8WtIvImJlRLw3t4+vAVtz/a2KiBkHOjgzqyI1NR3uFZt58YWXWfn4ek45\nNbvbxNQpDTz1zIt9HZ2ZWVkoZsZuFrAyIlZHRBMwH5jToc104B6AiHgcmCxpDHAc8FBE7IyIZuA+\n4Lz8hsrOin4PcPMBjcTMqppqCn9djWoYxoxZU9tfjxlzGEceObqvwjIzKyvFJHbjgTW512tTWd4j\npIRN0izgSGACsBSYLWmUpMHA2cDEDtvOBjZExJO5sinpMOx9kmYXPRoz63f2NDbz0qbt7a9XP/U8\nS5etK2FEZmal01uLJ64GRqTz5D4MPAy0RMRy4MvA3cCvgSVAS4dtz2ff2br1wKR0KPbjwE2Shnfc\noaTLJC2StGjjxo29NAwzK1sDsjNHLvrndMBgUD0AL23ZwYpla9ubDR9+CMOGDurz8MzMykExid06\n9p1lm5DK2kXEyxFxcUrGLgQagNWp7vsRcVJEnAq8BDzRtp2kOrKZvltyfTVGxKb0fDGwCjimY1AR\ncV1EzIyImQ0NDUUN1swq19BxA5BqeGnjlqygObtW3aBBAxg5amh7u3v/uIKFi58uQYRmZqVXTGK3\nEJgmaYqkemAucEe+gaQRqQ7gUuD+iHg51R2efk4iS+Juym36FuDxiFib66shLdhA0lRgGilJNLP+\n66/ePhOAn333vqygOfvRtHsPW7fsam+38sn1fR2amVnZ6DaxS4sergDuApYDt0bEMknzJM1LzY4D\nlkpaAZwFfDTXxW2SHgN+DlweEVtydXPZf9HEqcCj6bDuj4F5EbH5FYzNzKrEvK9dwFvfPRtqa3jt\nX09rLx8U1upTAAAU8UlEQVQ4qI5hIwZz5FF7Z+23b99ZihDNzMpCUbcUi4g7gTs7lF2be/4ABQ6X\nprpOFz9ExEUFym4DbismLjPrH8790NtY8NtHAdjywrb28po60dLcyp6m5vayHc7rzKwf850nzKwi\nvO604wF4fu2m9rJd2/cgiQEDfNtrMzNwYmdmFaKurhZqYNzUMe1ltQPEgPpahg7buwp2UH2hrc3M\n+gcndmZWOVph3ZPPt788/ZzXsn7tZu67e2l72e6mUgRmZlYenNiZWcU4ZNhAXn3K0e2vNzy3hfpD\n6hky3NetMzODIhdPmJmVgy/88HLGTBrFj/7zbo6ePo6/fsdM7v7Fw/zi1kW87ZzXljo8M7OSc2Jn\nZhXjhJOz2bqP/ft728te87qpbH1pd6lCMjMrKz4Ua2YVrbZWDB48oNRhmJmVBSd2ZlbRtm9v5Pl1\nL5U6DDOzsuDEzswq2pSjD+fv3ntKqcMwMysLPsfOzCra4CGDmHyUV8WamYFn7MzMzMyqhhM7MzMz\nsyrhxM7MKtqunY2sefrFUodhZlYWnNiZWUUbdEg9Y8Ye2v66YXRtCaMxMystJ3ZmVtEkUT9w73Xs\nLnjfGZz46okljMjMrHS8KtbMqsrfnv1a/ubMGaUOw8ysJDxjZ2ZVp6ZGpQ7BzKwknNiZWUXbvauJ\n9Ws3lzoMM7Oy4MTOzCragPo6Dj1sSKnDMDMrC07szKyi1dbWMHjIwFKHYWZWFpzYmVlF27plB8se\nebbUYZiZlQUndmZW0Xbv3sP6tS+VOgwzs7LgxM7MKtpzT2/i4QWrSh2GmVlZcGJnZhVt245dvPD8\nllKHYWZWFopK7CSdKWmFpJWSrixQf5ik2yU9KmmBpBNydR+VtFTSMkkfy5X/q6R1kpakx9m5uk+n\nfa2Q9PYDHaSZVa8VS9ey8on1pQ7DzKwsdJvYSaoFrgHOAqYD50ua3qHZZ4AlEXEicCHwjbTtCcAH\ngFnAa4BzJB2d2+7rETEjPe5M20wH5gLHA2cC304xmJntp66ullr54IOZGRQ3YzcLWBkRqyOiCZgP\nzOnQZjpwD0BEPA5MljQGOA54KCJ2RkQzcB9wXjf7mwPMj4jGiHgKWJliMDPbz3EnTuL41/jesGZm\nUFxiNx5Yk3u9NpXlPUJK2CTNAo4EJgBLgdmSRkkaDJwN5L+BP5wO314v6bAe7M/MDIBhIw5h1JhD\nSx2GmVlZ6K3jF1cDIyQtAT4MPAy0RMRy4MvA3cCvgSVAS9rmO8BUYAawHvhaT3Yo6TJJiyQt2rhx\nY++MwswqzgP3Lef+u5eWOgwzs7JQTGK3jn1n2SaksnYR8XJEXBwRM8jOsWsAVqe670fESRFxKvAS\n8EQq3xARLRHRCnyPvYdbu91f2v66iJgZETMbGhqKGIaZVaPmpj3sbmwsdRhmZmWhmMRuITBN0hRJ\n9WQLG+7IN5A0ItUBXArcHxEvp7rD089JZIdrb0qvx+a6OJfssC2p77mSBkqaAkwDFrySwZlZ9bvr\njsXscV5nZgZAXXcNIqJZ0hXAXUAtcH1ELJM0L9VfS7ZI4gZJASwDLsl1cZukUcAe4PKIaLvg1Fck\nzQACeBr4YOpvmaRbgceA5rRNC2ZmBRx19Dj+smS/SX0zs36p28QOIF2K5M4OZdfmnj8AHNPJtrM7\nKb+gi/1dBVxVTGxm1r+NGnsoOLEzMwN85wkzq3AP3PdYqUMwMysbTuzMrKJFlDoCM7Py4cTOzCpa\nw+hhpQ7BzKxsOLEzs4r2mpOPpa6os4XNzKqfEzszq2hSK6pRqcMwMysLTuzMrKLt2rmLlhafaGdm\nBk7szKzCTT16HA2H+16xZmbgxM7MKlz94AEMHFzffUMzs37AiZ2ZVbRnVz7P+mdfLHUYZmZlwYmd\nmVW0Deu3sGePz7EzMwMndmZW4Y6aNpYhw30o1swMnNiZWYVb/fgGdrzcVOowzMzKghM7M6toGuRr\n2JmZtXFiZ2YVbcvmnaUOwcysbDixM7OKtmvbrlKHYGZWNpzYmVlFe3mLZ+zMzNo4sTOzirZ7z55S\nh2BmVjac2JlZRfM17MzM9nJiZ2YVrWV3qSMwMysfTuzMzMzMqoQTOzMzM7Mq4cTOzMzMrEo4sTOz\niqbaUkdgZlY+nNiZWUWLllJHYGZWPopK7CSdKWmFpJWSrixQf5ik2yU9KmmBpBNydR+VtFTSMkkf\ny5V/VdLjaZvbJY1I5ZMl7ZK0JD2u7Y2BmpmZmVW7bhM7SbXANcBZwHTgfEnTOzT7DLAkIk4ELgS+\nkbY9AfgAMAt4DXCOpKPTNr8BTkjbPAF8OtffqoiYkR7zXvHozMzMzPqRYmbsZgErI2J1RDQB84E5\nHdpMB+4BiIjHgcmSxgDHAQ9FxM6IaAbuA85L7e5OZQAPAhMOeDRmZmZm/Vgxid14YE3u9dpUlvcI\nKWGTNAs4kixRWwrMljRK0mDgbGBigX38A/Cr3Osp6TDsfZJmFzUSMzMzs36urpf6uRr4hqQlwF+A\nh4GWiFgu6cvA3cAOYAmwz6nOkj4LNAM/SkXrgUkRsUnSScBPJR0fES932O4y4DKASZMm9dIwzMzM\nzCpXMTN269h3lm1CKmsXES9HxMURMYPsHLsGYHWq+35EnBQRpwIvkZ1PB4Cki4BzgPdFRKT2jRGx\nKT1fDKwCjukYVERcFxEzI2JmQ0NDseM1MzMzq1rFJHYLgWmSpkiqB+YCd+QbSBqR6gAuBe5vm2GT\ndHj6OYnscO1N6fWZwKeAv4uInbm+GtKCDSRNBaaRkkQzMzMz61y3h2IjolnSFcBdQC1wfUQskzQv\n1V9LtkjiBkkBLAMuyXVxm6RRwB7g8ojYksq/BQwEfiMJ4MG0AvZU4AuS9gCtwLyI2NwLYzUzMzOr\nakWdYxcRdwJ3dii7Nvf8AQocLk11BRc/RMTRnZTfBtxWTFxmZmZmtpfvPGFmZmZWJZzYmZmZmVUJ\nJ3ZmZmZmVcKJnZmZmVmVcGJnZmZmViWc2JmZmZlVCSd2ZmZmZlXCiZ2ZmZlZlXBiZ2ZmZlYlnNiZ\nmZmZVQkndmZW0WoHlDoCM7Py4cTOzCra8OEDSx2CmVnZcGJnZhXt9bOPpbau1FGYmZUHJ3ZmVuFE\nTY2/yszMwImdmVW4upoaWltaSx2GmVlZcGJnZhXtyeVraGkpdRRmZuXBiZ2ZVbQNz28udQhmZmXD\niZ2ZVbRRDcNKHYKZWdlwYmdmFW3m7BMYdqgveWJmBuCLBJhZRTv5lGns2Lyr1GGYmZUFz9iZWUUb\nOmwgY8aNKHUYZmZlwYmdmVW0uro66uprSx2GmVlZcGJnZhVt3ZqX+Muip0sdhplZWXBiZ2YVbcDA\nGgbU+3RhMzMoMrGTdKakFZJWSrqyQP1hkm6X9KikBZJOyNV9VNJSScskfSxXPlLSbyQ9mX4elqv7\ndNrXCklvP9BBmln1GjJ4EKPH+JInZmZQRGInqRa4BjgLmA6cL2l6h2afAZZExInAhcA30rYnAB8A\nZgGvAc6RdHTa5krgdxExDfhdek3qey5wPHAm8O0Ug5nZflQDtTX+ijAzg+Jm7GYBKyNidUQ0AfOB\nOR3aTAfuAYiIx4HJksYAxwEPRcTOiGgG7gPOS9vMAW5Iz28A3pErnx8RjRHxFLAyxWBmth9RQ02t\nzyoxM4PiErvxwJrc67WpLO8RUsImaRZwJDABWArMljRK0mDgbGBi2mZMRKxPz58HxvRgf2ZmABwx\nYQRvOPXYUodhZlYWeuuM46uBb0haAvwFeBhoiYjlkr4M3A3sAJYA+92uOyJCUvRkh5IuAy4DmDRp\n0gGGb2aVavjwwUx/jb8DzMyguBm7deydZYNsJm5dvkFEvBwRF0fEDLJz7BqA1anu+xFxUkScCrwE\nPJE22yBpLED6+UKx+0v9XhcRMyNiZkNDQxHDMDMzM6tuxSR2C4FpkqZIqidb2HBHvoGkEakO4FLg\n/oh4OdUdnn5OIjtce1Nqdwfw/vT8/cDPcuVzJQ2UNAWYBix4JYMzMzMz60+6PRQbEc2SrgDuAmqB\n6yNimaR5qf5askUSN6TDqcuAS3Jd3CZpFLAHuDwitqTyq4FbJV0CPAO8J/W3TNKtwGNAc9pmv8O3\nZmZmZrYvRfTo1LayNHPmzFi0aFGpwzAzMzPrlqTFETHzYPTtawSYmZmZVQkndmZmZmZVwomdmZmZ\nWZVwYmdmZmZWJZzYmZmZmVUJJ3ZmZmZmVcKJnZmZmVmVcGJnZmZmViWc2JmZmZlVCSd2ZmZmZlXC\niZ2ZmZlZlXBiZ2ZmZlYlnNiZmZmZVQkndmZmZmZVwomdmZmZWZVwYmdmZmZWJZzYmZmZmVUJJ3Zm\nZmZmVcKJnZmZmVmVcGJnZmZmViWc2JmZmZlVCSd2ZmZmZlXCiZ2ZmZlZlXBiZ2ZmZlYlikrsJJ0p\naYWklZKuLFB/mKTbJT0qaYGkE3J1/yRpmaSlkm6WNCiV3yJpSXo8LWlJKp8saVeu7treGqyZmZlZ\nNavrroGkWuAa4K3AWmChpDsi4rFcs88ASyLiXEmvSu3PkDQe+AgwPSJ2SboVmAv8ICLem9vH14Ct\nuf5WRcSMAx2cmZmZWX9SzIzdLGBlRKyOiCZgPjCnQ5vpwD0AEfE4MFnSmFRXBxwiqQ4YDDyX31CS\ngPcAN7/iUZiZmZlZUYndeGBN7vXaVJb3CHAegKRZwJHAhIhYB/wH8CywHtgaEXd32HY2sCEinsyV\nTUmHYe+TNLvo0ZiZmZn1Y721eOJqYEQ6T+7DwMNAi6TDyGb3pgDjgCGS/r7Dtuez72zdemBSOhT7\nceAmScM77lDSZZIWSVq0cePGXhqGmZmZWeUqJrFbB0zMvZ6QytpFxMsRcXFKxi4EGoDVwFuApyJi\nY0TsAX4CvLFtu3R49jzgllxfjRGxKT1fDKwCjukYVERcFxEzI2JmQ0NDUYM1MzMzq2bFJHYLgWmS\npkiqJ1v8cEe+gaQRqQ7gUuD+iHiZ7BDsKZIGp3PpzgCW5zZ9C/B4RKzN9dWQFmwgaSowjSxJNDMz\nM7MudLsqNiKaJV0B3AXUAtdHxDJJ81L9tcBxwA2SAlgGXJLqHpL0Y+DPQDPZIdrrct3PZf9FE6cC\nX5C0B2gF5kXE5q5iXLx48YuSnul2tJVtNPBiqYMoAY+7f/G4+xePu3/xuPc68mDtTBFxsPq2XiRp\nUUTMLHUcfc3j7l887v7F4+5fPO6+4TtPmJmZmVUJJ3ZmZmZmVcKJXeW4rvsmVcnj7l887v7F4+5f\nPO4+4HPszMzMzKqEZ+zMzMzMqoQTuz4gaaSk30h6Mv08rJN2Z0paIWmlpCuL2V7Sp1P7FZLensqG\npVuytT1elPS/U91Fkjbm6i6tlnGn8ntTWdv4Dk/lAyXdkrZ5SNLkahl3uk7kLyU9LmmZpKtz7Q/6\n593ZOHL1kvTNVP+opNf19nuQyk+S9JdU901J6u2xlmrckt4qaXEa32JJp+e2Kfg7XyXjnixpV25s\n1+a2qebP+33a9zu8VdKMVFcNn/e7lX1XtUqa2aG/av77LjjuXv/7jgg/DvID+ApwZXp+JfDlAm1q\nye6yMRWoJ7v/7vSutgemp3YDyW7btgqoLdD3YuDU9Pwi4FvVOm7gXmBmgf18CLg2PZ8L3FIt4wYG\nA3+d2tQDfwDO6ovPu6tx5NqcDfwKEHAK8NBB+uwXpP6V9ndWFY37tcC49PwEYF1uPwV/56tk3JOB\npZ3EUrWfd4d+Xw2sqrLP+zjg2I5jofr/vjsbd6/+fXvGrm/MAW5Iz28A3lGgzSxgZUSsjogmYH7a\nrqvt5wDzI7sN21PAytRPO0nHAIeT/c++r5Vs3N3E8mPgjIP4L74+HXdE7IyI3wOkvv5Mduu/vtDV\nONrMAW6MzINk95Ue2822PXoPUn/DI+LByL4Nb6Tw+95b+nTcEfFwRDyXypcBh0gaeLAG14W+/rwL\nqvbPu4Pz0zalcFDGHRHLI2JFgf1V9d93Z+Pu7b9vJ3Z9Y0xErE/PnwfGFGgzHliTe702lXW1fVfb\ntGmbncqvknlnmvL9saSJHDylGvcNacr6X3LJW/s2EdEMbAVG9XxIRSnZ5y1pBPC3wO9yxQfz8y7m\nd7CzNr35HoxPz7uKozf19bjz3gn8OSIac2WFfucPhlKMe0oa232SZuf20V8+7/ey/x2aKv3z7un+\nquXzLsYB/313e0sxK46k3wJHFKj6bP5FRISyW6+9Iq9g+7nABbnXPwdujohGSR8k+1fi6QW3LEIZ\njvt9EbFO0jDgNrKx3/hK99uZMhw3kurI/gfwzYhou79yr37epXCg72GlKjRuSccDXwbelivuk9/5\nvtJh3OuBSRGxSdJJwE/Te1B1Ovm8TwZ2RsTSXHFVfd62V2/9fTux6yUR8ZbO6iRtkDQ2ItanqdoX\nCjRbB+RnUyakMoDOtu9qGyS9BqiLiMW5ODfl2v8X2Tker1i5jTsi2n5uk3QT2bT4jblt1qYE6FAg\n/170SLmNO7kOeDIi/ncuzl79vAvoLqau2gzoYtuevgfr2Pfwc6E4elNfjxtJE4DbgQsjYlVbeRe/\n8wdDn447zVo0pueLJa0CjqEffN7JfvdTr5LPu6f7q5bPu1O9+vcdfXACZn9/AF9l3xNkv1KgTR2w\nmuyE0bYTLo/vanvgePY90XQ1ucUTwNXA5zvsZ2zu+bnAg9Uy7tTX6NRmANm5dPPS68vZd/HErdUy\n7lT3RbJ/zdX05efd1Thybf6GfU8yXnCQ3oOOJ1effRA/474e94jU7rwCcRT8na+ScTfkPt+pZP+D\nHFntn3d6XZPGO7XaPu/ctvey7yKCqv777mLcvfr3fVDeFD/2+yUZRXbO05PAb3NfTOOAO3Ptzgae\nIFtR89nutk91n03tV9BhlVD65XpVh7IvkZ2c+Qjw+471lTxuYAjZCuBH0xi/kftSGAT8X7KTcReQ\n+7KsgnFPAAJYDixJj0v76vMuNA5gHnuTagHXpPq/sO8XWq/9zgMzgaWp7lukC7AfxM+5z8YN/DOw\nI/f5LiFbFNXp73yVjPudaVxLyBYF/W1/+LxT3Wl0+IdYFX3e55Kde9YIbADuytVV8993wXHTy3/f\nvvOEmZmZWZXwqlgzMzOzKuHEzszMzKxKOLEzMzMzqxJO7MzMzMyqhBM7MzMzsyrhxM7MzMysSjix\nMzMzM6sSTuzMzMzMqsT/DwTnGDSgc8FCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e25268208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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mtzY3sKWpvleGt5eaTx6Yfs2sYvlQrJmVob0Pfardi5HDsnPkqqo6PibbAjyz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\nswrx/wBqQ1b+/IecaAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e25007e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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m9X0fqdP6qmvLN5Cb3o5sT9O/1tbGmABl1T+SnTEq4THxl8cyjuDNOu53vX9r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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e22f4d780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.colors as colors\n",
+    "import matplotlib.cm as cm\n",
+    "import pylab\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "norm = colors.Normalize(df['ohlc_price'].values.min(), df['ohlc_price'].values.max())\n",
+    "color = cm.viridis(norm(df['ohlc_price'].values))\n",
+    "plt.scatter(df['ohlc_price'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "plt.title('ohlc_price vs pca')\n",
+    "plt.show()\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs pca')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['ohlc_price'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs ohlc_price')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['period_return'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs period_return')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['period_return'].shift().values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs period_return shift')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['bo_spread'].values.min(), df['bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['bo_spread'].values))\n",
+    "    plt.scatter(df['bo_spread'].values, df['period_return'].shift().values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('bo_spread vs period_return shift')\n",
+    "    plt.show()\n",
+    "\n",
+    "#plt.figure(figsize=(10,5))\n",
+    "#norm = colors.Normalize(df['volume'].values.min(), df['volume'].values.max())\n",
+    "#color = cm.viridis(norm(df['volume'].values))\n",
+    "#plt.scatter(df['volume'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "#plt.title('volume vs pca')\n",
+    "#plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "norm = colors.Normalize(df['ohlc_price'].values.min(), df['ohlc_price'].values.max())\n",
+    "color = cm.viridis(norm(df['ohlc_price'].values))\n",
+    "plt.scatter(df['ohlc_price'].shift().values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "plt.title('ohlc_price - 15min future vs pca')\n",
+    "plt.show()\n",
+    "\n",
+    "#plt.figure(figsize=(10,5))\n",
+    "#norm = colors.Normalize(df['volume'].values.min(), df['volume'].values.max())\n",
+    "#color = cm.viridis(norm(df['volume'].values))\n",
+    "#plt.scatter(df['volume'].shift().values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "#plt.title('volume - 15min future vs pca')\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this creates a training dataset for the model\n",
+    "def create_dataset(dataset, look_back=20):\n",
+    "    dataX, dataY = [], []\n",
+    "    for i in range(len(dataset)-look_back-1):\n",
+    "        a = dataset[i:(i+look_back)]\n",
+    "        dataX.append(a)\n",
+    "        dataY.append(dataset[i + look_back])\n",
+    "    return np.array(dataX), np.array(dataY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iIiIiIhICFLyJiIiIiIiEAA2bFBERERGRxqVHBTSIet5ERERERERCgII3ERERERGREKBh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eJLNxAaWvt+y9rsv/jycMaNXYbDrnDnXV1ISNDx1ZfOB5R77u3O6VN6Zjz/AwJBfIKW2a/f6tKf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d61dd20b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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TLzyCs//6b35b9ftacVuln2PWceydcOJRAHQsOPYWMWH8lHXu+8rwt+jcOYyZ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8dd8659c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check feature correlation, to see what correlates with the close price\n",
+    "colormap = plt.cm.inferno\n",
+    "plt.figure(figsize=(15,15))\n",
+    "plt.title('Pearson correlation of features', y=1.05, size=15)\n",
+    "sns.heatmap(df.corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(15,5))\n",
+    "corr = df.corr()\n",
+    "sns.heatmap(corr[corr.index == 'close'], linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create random forest regressor - random decision trees, like weak learner, ada boost\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Scale and create datasets\n",
+    "target_index = df.columns.tolist().index('close')\n",
+    "dataset = df.values.astype('float32')\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "dataset = scaler.fit_transform(dataset)\n",
+    "\n",
+    "# Set look_back to 20 which is 5 hours (15min*20)\n",
+    "X, y = create_dataset(dataset, look_back=1)\n",
+    "y = y[:,target_index]\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[2]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-113-c9eeae1a0f99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# fit model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mforest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mforest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mforest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                     verbose=self.verbose, class_weight=self.class_weight)\n\u001b[0;32m--> 327\u001b[0;31m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m    328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m             \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    777\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    623\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    330\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight)\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0mcurr_sample_weight\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mcompute_sample_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'balanced'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1122\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1125\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    360\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 362\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# fit model\n",
+    "forest = RandomForestRegressor(n_estimators = 100)\n",
+    "forest = forest.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature ranking:\n",
+      "0. pca 19 (0.453368)\n",
+      "1. ask_price 7 (0.217941)\n",
+      "2. ohlc_price 16 (0.075821)\n",
+      "3. close 13 (0.069535)\n",
+      "4. period_return 18 (0.064586)\n",
+      "5. bid_price 6 (0.060840)\n",
+      "6. oc_diff 17 (0.057908)\n",
+      "7. month 1 (0.000000)\n",
+      "8. day 2 (0.000000)\n",
+      "9. hour 3 (0.000000)\n",
+      "10. weekday 4 (0.000000)\n",
+      "11.  5 (0.000000)\n",
+      "12. low 9 (0.000000)\n",
+      "13. high 8 (0.000000)\n",
+      "14. avg_bo_spread 10 (0.000000)\n",
+      "15. count 11 (0.000000)\n",
+      "16. open 12 (0.000000)\n",
+      "17. avg_price 14 (0.000000)\n",
+      "18. range 15 (0.000000)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3zeuTPK+7/890/U+SPKu7d+33WOdltgdRHvjABz7yxhtvXORaYF127NiRJHnz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e03d9b940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find feature with best explanatory power to predict close price\n",
+    "importances = forest.feature_importances_\n",
+    "std = np.std([forest.feature_importances_ for forest in forest.estimators_], axis=0)\n",
+    "indices = np.argsort(importances)[::-1]\n",
+    "\n",
+    "column_list = df.columns.tolist()\n",
+    "print(\"Feature ranking:\")\n",
+    "for f in range(X.shape[1]-1):\n",
+    "    print(\"%d. %s %d (%f)\" % (f, column_list[indices[f]], indices[f], importances[indices[f]]))\n",
+    "\n",
+    "# Plot the feature importances coming from the forest of decision trees\n",
+    "plt.figure(figsize=(20,10))\n",
+    "plt.title(\"Feature importances\")\n",
+    "plt.bar(range(X.shape[1]), importances[indices],\n",
+    "       color=\"salmon\", yerr=std[indices], align=\"center\")\n",
+    "plt.xticks(range(X.shape[1]), indices)\n",
+    "plt.xlim([-1, X.shape[1]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-116-93770cf0dfc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'low'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'high'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# plot first 200 entries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    251\u001b[0m     \"\"\"\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    300\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    169\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    330\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2198\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2199\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2200\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2201\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2202\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    465\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1144\u001b[0;31m                 renderer, self, dsu, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1146\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2424\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2426\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2428\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/legend.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    481\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegendPatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 483\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legend_box\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'legend'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    280\u001b[0m                                                         renderer)\n\u001b[1;32m    281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 282\u001b[0;31m         \u001b[0mpx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_offset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mget_offset\u001b[0;34m(self, width, height, xdescent, ydescent, renderer)\u001b[0m\n\u001b[1;32m    219\u001b[0m         \"\"\"\n\u001b[1;32m    220\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/legend.py\u001b[0m in \u001b[0;36m_findoffset_best\u001b[0;34m(self, width, height, xdescent, ydescent, renderer)\u001b[0m\n\u001b[1;32m    432\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_findoffset_best\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0;34m\"Helper function to locate the legend at its best position\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m         \u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_find_best_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mox\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mxdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mydescent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/legend.py\u001b[0m in \u001b[0;36m_find_best_position\u001b[0;34m(self, width, height, renderer, consider)\u001b[0m\n\u001b[1;32m    948\u001b[0m             \u001b[0;31m# take their into account when checking vertex overlaps in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m             \u001b[0;31m# the next line.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m             \u001b[0mbadness\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlegendBox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_contains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m             \u001b[0mbadness\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlegendBox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_contains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m             \u001b[0mbadness\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlegendBox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_overlaps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbboxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mcount_contains\u001b[0;34m(self, vertices)\u001b[0m\n\u001b[1;32m    658\u001b[0m             \u001b[0mdy0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    659\u001b[0m             \u001b[0mdx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 660\u001b[0;31m             \u001b[0mdy1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    661\u001b[0m         \u001b[0minside\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdx0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdx1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdy0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdy1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minside\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# plot close price, compare to low and high price\n",
+    "ax = df.plot(x=df.index, y='close', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in df.index]\n",
+    "plt.fill_between(x=index, y1='low',y2='high', data=df, alpha=0.4)\n",
+    "plt.show()\n",
+    "\n",
+    "# plot first 200 entries \n",
+    "p = df[:200].copy()\n",
+    "ax = p.plot(x=p.index, y='close', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in p.index]\n",
+    "plt.fill_between(x=index, y1='low', y2='high', data=p, alpha=0.4)\n",
+    "plt.title('zoomed, first 200')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale and create datasets\n",
+    "target_index = df.columns.tolist().index('close')\n",
+    "high_index = df.columns.tolist().index('high')\n",
+    "low_index = df.columns.tolist().index('low')\n",
+    "dataset = df.values.astype('float32')\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "dataset = scaler.fit_transform(dataset)\n",
+    "\n",
+    "# Create y_scaler to inverse it later\n",
+    "y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "t_y = df['close'].values.astype('float32')\n",
+    "t_y = np.reshape(t_y, (-1, 1))\n",
+    "y_scaler = y_scaler.fit(t_y)\n",
+    "    \n",
+    "# Set look_back to 20 which is 5 hours (15min*20)\n",
+    "X, y = create_dataset(dataset, look_back=1)\n",
+    "y = y[:,target_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set training data size\n",
+    "# We have a large enough dataset. So divid into 98% training / 1%  development / 1% test sets\n",
+    "train_size = int(len(X) * 0.99)\n",
+    "trainX = X[:train_size]\n",
+    "trainY = y[:train_size]\n",
+    "testX = X[train_size:]\n",
+    "testY = y[train_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "lstm_9 (LSTM)                (None, 1, 20)             3360      \n",
+      "_________________________________________________________________\n",
+      "lstm_10 (LSTM)               (None, 1, 20)             3280      \n",
+      "_________________________________________________________________\n",
+      "lstm_11 (LSTM)               (None, 1, 10)             1240      \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 1, 10)             0         \n",
+      "_________________________________________________________________\n",
+      "lstm_12 (LSTM)               (None, 4)                 240       \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_6 (Dense)              (None, 1)                 5         \n",
+      "=================================================================\n",
+      "Total params: 8,145\n",
+      "Trainable params: 8,145\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Input, LSTM, Dense\n",
+    "\n",
+    "# create a small LSTM network\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(20, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))\n",
+    "model.add(LSTM(20, return_sequences=True))\n",
+    "model.add(LSTM(10, return_sequences=True))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(LSTM(4, return_sequences=False))\n",
+    "model.add(Dense(4, kernel_initializer='uniform', activation='relu'))\n",
+    "model.add(Dense(1, kernel_initializer='uniform', activation='relu'))\n",
+    "\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mae', 'mse'])\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error improved from inf to 0.02140, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00001: val_mean_squared_error improved from 0.02140 to 0.00056, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00002: val_mean_squared_error improved from 0.00056 to 0.00035, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00003: val_mean_squared_error improved from 0.00035 to 0.00024, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00004: val_mean_squared_error improved from 0.00024 to 0.00011, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00005: val_mean_squared_error improved from 0.00011 to 0.00006, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00006: val_mean_squared_error improved from 0.00006 to 0.00005, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00007: val_mean_squared_error improved from 0.00005 to 0.00003, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00008: val_mean_squared_error did not improve\n",
+      "Epoch 00009: val_mean_squared_error did not improve\n",
+      "Epoch 00010: val_mean_squared_error did not improve\n",
+      "Epoch 00011: val_mean_squared_error did not improve\n",
+      "Epoch 00012: val_mean_squared_error did not improve\n",
+      "Epoch 00013: val_mean_squared_error did not improve\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error improved from 0.00003 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00016: val_mean_squared_error did not improve\n",
+      "Epoch 00017: val_mean_squared_error improved from 0.00002 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00018: val_mean_squared_error improved from 0.00002 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00019: val_mean_squared_error improved from 0.00002 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error did not improve\n",
+      "Epoch 00022: val_mean_squared_error improved from 0.00002 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00023: val_mean_squared_error did not improve\n",
+      "Epoch 00024: val_mean_squared_error improved from 0.00002 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "Epoch 00026: val_mean_squared_error did not improve\n",
+      "Epoch 00027: val_mean_squared_error did not improve\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error did not improve\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error did not improve\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n",
+      "Epoch 00033: val_mean_squared_error did not improve\n",
+      "Epoch 00034: val_mean_squared_error did not improve\n",
+      "Epoch 00035: val_mean_squared_error did not improve\n",
+      "Epoch 00036: val_mean_squared_error did not improve\n",
+      "Epoch 00037: val_mean_squared_error did not improve\n",
+      "Epoch 00038: val_mean_squared_error did not improve\n",
+      "Epoch 00039: val_mean_squared_error did not improve\n",
+      "Epoch 00040: val_mean_squared_error did not improve\n",
+      "Epoch 00041: val_mean_squared_error did not improve\n",
+      "Epoch 00042: val_mean_squared_error did not improve\n",
+      "Epoch 00043: val_mean_squared_error did not improve\n",
+      "Epoch 00044: val_mean_squared_error did not improve\n",
+      "Epoch 00045: val_mean_squared_error did not improve\n",
+      "Epoch 00046: val_mean_squared_error did not improve\n",
+      "Epoch 00047: val_mean_squared_error did not improve\n",
+      "Epoch 00048: val_mean_squared_error did not improve\n",
+      "Epoch 00049: val_mean_squared_error did not improve\n",
+      "Epoch 00050: val_mean_squared_error did not improve\n",
+      "Epoch 00051: val_mean_squared_error did not improve\n",
+      "Epoch 00052: val_mean_squared_error did not improve\n",
+      "Epoch 00053: val_mean_squared_error did not improve\n",
+      "Epoch 00054: val_mean_squared_error did not improve\n",
+      "Epoch 00055: val_mean_squared_error did not improve\n",
+      "Epoch 00056: val_mean_squared_error did not improve\n",
+      "Epoch 00057: val_mean_squared_error did not improve\n",
+      "Epoch 00058: val_mean_squared_error did not improve\n",
+      "Epoch 00059: val_mean_squared_error did not improve\n",
+      "Epoch 00060: val_mean_squared_error did not improve\n",
+      "Epoch 00061: val_mean_squared_error did not improve\n",
+      "Epoch 00062: val_mean_squared_error did not improve\n",
+      "Epoch 00063: val_mean_squared_error did not improve\n",
+      "Epoch 00064: val_mean_squared_error did not improve\n",
+      "Epoch 00065: val_mean_squared_error did not improve\n",
+      "Epoch 00066: val_mean_squared_error did not improve\n",
+      "Epoch 00067: val_mean_squared_error did not improve\n",
+      "Epoch 00068: val_mean_squared_error did not improve\n",
+      "Epoch 00069: val_mean_squared_error did not improve\n",
+      "Epoch 00070: val_mean_squared_error did not improve\n",
+      "Epoch 00071: val_mean_squared_error did not improve\n",
+      "Epoch 00072: val_mean_squared_error did not improve\n",
+      "Epoch 00073: val_mean_squared_error did not improve\n",
+      "Epoch 00074: val_mean_squared_error did not improve\n",
+      "Epoch 00075: val_mean_squared_error did not improve\n",
+      "Epoch 00076: val_mean_squared_error did not improve\n",
+      "Epoch 00077: val_mean_squared_error did not improve\n",
+      "Epoch 00078: val_mean_squared_error did not improve\n",
+      "Epoch 00079: val_mean_squared_error did not improve\n",
+      "Epoch 00080: val_mean_squared_error did not improve\n",
+      "Epoch 00081: val_mean_squared_error did not improve\n",
+      "Epoch 00082: val_mean_squared_error did not improve\n",
+      "Epoch 00083: val_mean_squared_error did not improve\n",
+      "Epoch 00084: val_mean_squared_error did not improve\n",
+      "Epoch 00085: val_mean_squared_error did not improve\n",
+      "Epoch 00086: val_mean_squared_error did not improve\n",
+      "Epoch 00087: val_mean_squared_error did not improve\n",
+      "Epoch 00088: val_mean_squared_error did not improve\n",
+      "Epoch 00089: val_mean_squared_error did not improve\n",
+      "Epoch 00090: val_mean_squared_error did not improve\n",
+      "Epoch 00091: val_mean_squared_error did not improve\n",
+      "Epoch 00092: val_mean_squared_error did not improve\n",
+      "Epoch 00093: val_mean_squared_error did not improve\n",
+      "Epoch 00094: val_mean_squared_error did not improve\n",
+      "Epoch 00095: val_mean_squared_error did not improve\n",
+      "Epoch 00096: val_mean_squared_error did not improve\n",
+      "Epoch 00097: val_mean_squared_error did not improve\n",
+      "Epoch 00098: val_mean_squared_error did not improve\n",
+      "Epoch 00099: val_mean_squared_error did not improve\n",
+      "CPU times: user 16min 23s, sys: 1min 23s, total: 17min 46s\n",
+      "Wall time: 15min 1s\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Save the best weight during training.\n",
+    "simname = \"bm_kaggle_4\"\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_mean_squared_error', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "# Fit\n",
+    "callbacks_list = [checkpoint]\n",
+    "%time history = model.fit(trainX, trainY, epochs=100, batch_size=10000, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 100\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d9849c5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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OGbPTyT1/k3zjI8m3/zo5MpWsvSC5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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8daa0cde48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d98422cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epoch = len(history.history['loss'])\n",
+    "print(\"epoch\", epoch)\n",
+    "for k in list(history.history.keys()):\n",
+    "    if 'val' not in k:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(history.history[k])\n",
+    "        plt.plot(history.history['val_' + k])\n",
+    "        plt.title(k)\n",
+    "        plt.ylabel(k)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left')\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0022660818189899888"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(history.history['val_mean_absolute_error'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As seen from the above, the model seems to have converged nicely, but the mean absolute error on the development data remains at ~0.003X which means the model is unusable in practice. Ideally, we want to get ~0.0005. Let's go back to the best weight, and decay the learning rate while retraining the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00001: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0009000000427477062\n",
+      "Epoch 00002: val_mean_squared_error did not improve\n",
+      "Epoch 00003: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0008100000384729356\n",
+      "Epoch 00004: val_mean_squared_error did not improve\n",
+      "Epoch 00005: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0007290000503417104\n",
+      "Epoch 00006: val_mean_squared_error did not improve\n",
+      "Epoch 00007: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0006561000715009868\n",
+      "Epoch 00008: val_mean_squared_error did not improve\n",
+      "Epoch 00009: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0005904900433961303\n",
+      "Epoch 00010: val_mean_squared_error did not improve\n",
+      "Epoch 00011: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0005314410547725857\n",
+      "Epoch 00012: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00013: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00047829695977270604\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0004304672533180565\n",
+      "Epoch 00016: val_mean_squared_error did not improve\n",
+      "Epoch 00017: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00038742052274756136\n",
+      "Epoch 00018: val_mean_squared_error did not improve\n",
+      "Epoch 00019: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0003486784757114947\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00031381062290165574\n",
+      "Epoch 00022: val_mean_squared_error did not improve\n",
+      "Epoch 00023: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0002824295632308349\n",
+      "Epoch 00024: val_mean_squared_error did not improve\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00025418660952709616\n",
+      "Epoch 00026: val_mean_squared_error did not improve\n",
+      "Epoch 00027: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00022876793809700757\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00020589114428730683\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00018530203378759326\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n"
+     ]
+    }
+   ],
+   "source": [
+    "# tune model by starting from best weights and rerunning with decaying learning rate\n",
+    "# Load the weight that worked the best\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "#epoch=60\n",
+    "\n",
+    "# Train again with decaying learning rate\n",
+    "from keras.callbacks import LearningRateScheduler\n",
+    "import keras.backend as K\n",
+    "\n",
+    "def scheduler(epoch):\n",
+    "    if epoch%2==0 and epoch!=0:\n",
+    "        lr = K.get_value(model.optimizer.lr)\n",
+    "        K.set_value(model.optimizer.lr, lr*.9)\n",
+    "        print(\"lr changed to {}\".format(lr*.9))\n",
+    "    return K.get_value(model.optimizer.lr)\n",
+    "lr_decay = LearningRateScheduler(scheduler)\n",
+    "\n",
+    "callbacks_list = [checkpoint, lr_decay]\n",
+    "history = model.fit(trainX, trainY, epochs=int(epoch/3), batch_size=10000, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d983efef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d9836e2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d982bcf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epoch = len(history.history['loss'])\n",
+    "for k in list(history.history.keys()):\n",
+    "    if 'val' not in k:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(history.history[k])\n",
+    "        plt.plot(history.history['val_' + k])\n",
+    "        plt.title(k)\n",
+    "        plt.ylabel(k)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left')\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0021724022948290786"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(history.history['val_mean_absolute_error'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The variance should have improved slightly. However, unless the mean absolute error is not small enough. The model is still not an usable model in practice. This is mainly due to only using the sample data for training and limiting epoch to a few hundreds.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3N9ne78BjU9q+nnB77Dj/oLNjx/1NZfYHFmX67Q2Ax+Ntpaet2vJSVV3N+tLa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\nV21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zvtHaM9RkNIiLP4uIZ/O1/0ZEzM/lp0XE26V24+7SPnWvf6MYU2drEBdNu29ExPKI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\n8lwR3hyQgDYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d982b3ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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XyVsvd1be98rv6cIfWz8lb2puTt57yJ9f4nF/Tt4U8pf8x/49SSE/mL9Z0r/730f1a+/k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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8ddc98c2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE :  8.5686068097e-06\n",
+      "MAE :  0.00260148191793\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    20817.000000\n",
+       "mean         0.002528\n",
+       "std          0.001476\n",
+       "min         -0.010324\n",
+       "25%          0.001668\n",
+       "50%          0.002496\n",
+       "75%          0.003467\n",
+       "max          0.010337\n",
+       "Name: diff, dtype: float64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+    "\n",
+    "# Benchmark\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "\n",
+    "pred = model.predict(testX)\n",
+    "\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(np.reshape(pred, (pred.shape[0])))\n",
+    "predictions['actual'] = testY\n",
+    "predictions = predictions.astype(float)\n",
+    "\n",
+    "predictions.plot(figsize=(20,10))\n",
+    "plt.show()\n",
+    "\n",
+    "predictions['diff'] = predictions['predicted'] - predictions['actual']\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.distplot(predictions['diff']);\n",
+    "plt.title('Distribution of differences between actual and prediction')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"MSE : \", mean_squared_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "print(\"MAE : \", mean_absolute_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "predictions['diff'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GJUy+5iLev+6qdNsm3WZwxYCzAZj99PyM8evpK1/jDAlJkiRJkiRJkiRJkqSV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1nu50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i6Zxo3p6+n+R0D2uWRNa+gqBFsWiWPjSf//yQKqppDkO70iIjx5ILwM+/ZIasM33GUoY+eBOr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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8ddca30240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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HsikNZZI6zspMoGMIZgAQA4VyTdlUQn1mHX3cy3cM6NCp+Y4+JtDLCGYAEAPF\nSue2Y2r2mH0juvdoXvW66/hjA72IYAYAMVCq1Dq6IrPhcQdGNb9U1ffOLHb8sYFeRDADgBgolDu3\nT2azx+0flSTdOzHb8ccGehHBDABioFgOp8fs4M5B9acTuudovuOPDfQighkAxECxEk6PWaLPdM3e\nEd1DjxnQEQQzAIg451wwxyyEYCZJj90/ovuPz6nCnplA2xHMACDiFpaqqrvObsfU7HEHRrVUrevb\nk5TNANqNYAYAETdbqEjq7HZMzc4uAGCeGdBuBDMAiLh8MQhmYdQxk6QD4zmN9adYmQl0AMEMACJu\nzgezbEjBzMz0mP2j+vpRghnQbgQzAIi42WK4Q5mS9Lj9Izp0amF5M3UA7UEwA4CIOzuUmQytDY/d\nP6pa3em+48wzA9qJYAYAERf25H8p6DGTpHtYAAC0FcEMACIuX6woYaZUwkJrw87hrPaMZFkAALQZ\nwQwAIi5frCibTsgsvGAmBYVm72EBANBWBDMAiLjZQjm0UhnNHrt/VA+dKSjvh1YBtB7BDAAibnqx\nrIEIBLPlQrPH6DUD2oVgBgARN1uohLois+ExfgEAOwAA7UMwA4CIm4nIUOZILqVHbB9gnhnQRgQz\nAIgw55wPZuH3mEl+AQArM4G2IZgBQIQtlmuq1JwGMuH3mEnBAoCTc0s6OVcKuylAVyKYAUCEzSyW\nJYW3gfn5HnfAF5plOBNoC4IZAETYTKERzKIxlHn1nhEl+owFAECbEMwAIMKmI9Zjlksn9MhdQ/r3\nw6flnAu7OUDXIZgBQIQ19skciEiPmSS96EmX6J6js/r0fSfDbgrQdQhmABBhUesxk6SfufaArto1\nqD/85AMqV+thNwfoKgQzAIiw2UJZZlI2QsEsmejT637saj18pqAPfPmhsJsDdBWCGQBE2HShrNFc\nSn0hb2B+vqddtUNPu2qH3vXZQ8srRwFsXXQmLQAALjBTqGisPx3KY990x5GL3v74A6P64uHTeudn\nD+mNP/HoDrUK6G70mAFAhM0sljU2EE4wW8uu4ax+9roD+uDtD+vwqYWwmwN0BYIZAERY0GOWCrsZ\nq/r1H7lK/amE3vrJB8JuCtAVGMoEgAibLZR1zd7hsJuxqs/cd1JPPbhdn7pvUm/6p/t1cOfgiue9\n8PpLOtwyIJ7oMQOACJuO8FBmw5Ov2Kax/pQ+c/9k2E0BYo9gBgARVSzXtFSthzb5f71SiT5df/k2\nTcwUlS9Wwm4OEGsEMwCIqGm/T2aU55g1XLV7SJL0nZPzIbcEiDeCGQBEVKM+WNSHMiVp11BGI7kU\nwQzYIoIZAETUzHKPWfSDmZmiystFAAAgAElEQVTpql2DOnxqQbU6m5sDm0UwA4CImvEbmMdhKFOS\nrto1pKVqXQ9PL4bdFCC2CGYAEFFxGsqUpCt2DKrPpO9MUmwW2CyCGQBEVGMoczQXjx6zbCqhS7cN\nMM8M2AKCGQBE1MxiWcPZpJKJ+LxVP3LXkCbnSpTNADYpPr/tANBjZgqV2AxjNly1KyibcYheM2BT\nCGYAEFEzhXIsVmQ22zWc0XA2qW8TzIBNIZgBQEQFwSwe88sagrIZQ5TNADZpS8HMzH7DzO4zs2+a\n2YfNLGtml5vZHWZ22Mz+1szS/tyM//qwv/2ypuu81h//tpk9c2tPCQC6w8xi/IYypbNlM45MF8Ju\nChA7mw5mZrZP0q9KutY5d42khKQXSHqbpHc45w5KmpH0Mn+Xl0ma8cff4c+TmV3t7/doSc+S9Kdm\nlthsuwCgW8RxKFOSDu70ZTMYzgQ2bKtDmUlJOTNLSuqXdELSD0v6qL/9/ZKe5z+/wX8tf/vTzcz8\n8Y8455acc9+TdFjSdVtsFwDEWqlSU6Fc03gMe8womwFs3qaDmXPumKT/LemIgkCWl3S3pFnnXNWf\nNiFpn/98n6Sj/r5Vf/625uMr3OccZvZyM7vLzO6amprabNMBIPJmfdX/0ZjNMWu4ateQTuRLmqNs\nBrAhWxnKHFPQ23W5pL2SBhQMRbaNc+7dzrlrnXPX7tixo50PBQChahSXHY/hUKYkXbVrUJJ06BS9\nZsBGbGUo80ckfc85N+Wcq0j6mKSnShr1Q5uStF/SMf/5MUkHJMnfPiLpTPPxFe4DAD2psR3TaEyD\n2e7hrC+bwfZMwEZsJZgdkfQkM+v3c8WeLul+Sf8m6af8OTdK+rj//Bb/tfzttznnnD/+Ar9q83JJ\nV0r6yhbaBQCxt7yB+UA8hzLPls2YV/BWD2A9tjLH7A4Fk/i/Kukb/lrvlvRqSa80s8MK5pC9x9/l\nPZK2+eOvlPQaf537JN2sINR9StIrnHO1zbYLALrBdMyHMiVp90hWpUpdhTJv6cB6Jdc+ZXXOuTdI\nesN5hx/UCqsqnXMlST+9ynXeIuktW2kLAHST2ZgPZUrScDbo7WPfTGD9qPwPABE0XShrMJNUOhnf\nt+mRXBDMWJkJrF98f+MBoIvNFiqxLZXRMOyDWb5EMAPWi2AGABE0vViOZXHZZoOZpEz0mAEbQTAD\ngAiaLZRjPb9MkhJ9pqFsUvlide2TAUgimAFAJE0XyhqP+VCmFMwzm2MoE1g3ghkARNDsYiX2PWZS\nMM+MVZnA+hHMACBiKrW65peqsZ9jJgXBjDlmwPoRzAAgYhr7ZI51w1BmNqWlal3zDGcC60IwA4CI\nmfXbMXXLUKYknZwrhdwSIB4IZgAQMdO+6n83DGU2isyeyBPMgPUgmAFAxMwWGtsxdcFQpg9mkwQz\nYF0IZgAQMdOLwVBmN/SYDWWDLZkJZsD6EMwAIGLOTv6PfzBLJfrUn07oBHPMgHUhmAFAxMwslpVL\nJZRNJcJuSkuM5FI6SY8ZsC4EMwCImJlCpStKZTQMZ1NM/gfWiWAGABEzUyhrrAvmlzWM5FKaZCgT\nWBeCGQBEzEyh3BXzyxqGc0lNL5ZVqtTCbgoQeQQzAIiYmcXu6zGTpFNzSyG3BIg+ghkAREzXzTFr\n1DJjOBNYE8EMACKkWqsrX6x011BmtlH9vxhyS4DoI5gBQITki0Fx2W7qMaP6P7B+BDMAiJAZv4F5\nN80xy6YSGswkGcoE1oFgBgAR0k1V/5vtGs7QYwasA8EMACJkZrE7g9mekRxFZoF1IJgBQIQs95gN\ndM8cM0naPZLVSYYygTURzAAgQpbnmHVZj9nu4axOzS+pWquH3RQg0ghmABAhM4tlpZN96k93xwbm\nDbtHsqrVnU4vlMNuChBpBDMAiJBgO6aUzCzsprTU7uGsJIrMAmshmAFAhEwvdldx2YbdIz6YUWQW\nuCiCGQBEyEyhrPEuqmHWsMcHM1ZmAhdHMAOACJmaX9KOoUzYzWi58YG00ok+hjKBNRDMACAinHNB\nMBvsvmBmZto1QpFZYC0EMwCIiMVyTcVKrSt7zCRpzzBFZoG1EMwAICKm5pckqWuD2S6KzAJrIpgB\nQER0ezDbM5LViXxJzrmwmwJEVjLsBgBAL7vpjiPLn3/jWF6SdOf3ZnR0uvvKSuwezqpcrWumUOnK\nladAK9BjBgARMV8KtmMazHbn38xna5kxnAmshmAGABGxsFRVn6nrtmNqWA5mc93XGwi0CsEMACJi\noVTVQCapvi7bjqlheVum/FLILQGii2AGABExX6pqKNOdw5hSsKihz9iWCbgYghkARMTCUrVr55dJ\nUirRpx1DGWqZARdBMAOAiJgvVTSUSYXdjLbaPZxlWybgIghmABABdee6vsdMChYAsCoTWB3BDAAi\noFiuqe6koS4PZntGcgQz4CIIZgAQAfNLVUnSYBdP/pekXcNZzS9VteCfL4BzEcwAIAIWSkFQGcp2\n9xyzXcPBdlOnmGcGrIhgBgAR0Kj6383lMiRpzG/FNFOohNwSIJoIZgAQAY2hvW6f/D/e74PZYjnk\nlgDRRDADgAiYL1WVSpgyye5+W25sXj5dIJgBK+nudwAAiImFpaoGM0lZl27H1LAczOgxA1ZEMAOA\nCFgoVbt+RaYUbNCeTvYxlAmsgmAGABEwv1Tp+hWZkmRmGu9P02MGrIJgBgARMF/q/qr/DWMDac0w\nxwxYEcEMAEJWqzsVyrWuL5XRMD6QoscMWAXBDABC1iulMhrG+tPUMQNWQTADgJAtV/3PdP8cMylY\nmUmPGbAyghkAhGx+yVf975Ees/GBtPLFiqq1ethNASKHYAYAIWv0mPXKUOY42zIBqyKYAUDI5htz\nzHpk8v9YY1smVmYCF+iNdwEAiLD5UlXZVJ9Sie79W/mmO44sf3741IIk6ea7juoR2wfPOe+F11/S\n0XYBUdO97wIAEBMLS9WemfgvSQOZhCSpsFQLuSVA9BDMACBkC6VKz8wvk6T+dPBcF8vVkFsCRA/B\nDABCNt8j+2Q29Kd9j1mZHjPgfAQzAAjZwlK1Z0plSFIq0ad0sk+FJXrMgPMRzAAgROVqXUvVes9s\nx9QwkE5okR4z4AIEMwAI0dntmHpn8r8kDWSSKjDHDLgAwQwAQjRf6q2q/w396YQWWZUJXIBgBgAh\nmi/1VnHZhoF0klWZwAoIZgAQosZQZi/2mFHHDLgQwQwAQjRfqsoUzLnqJQOZpMq1uipsZA6cg2AG\nACFaWKpoIJNUn1nYTemoRpFZapkB5yKYAUCI5ku9VcOsoVFkdpFaZsA5CGYAEKKFpd6q+t/QGLql\nxww4F8EMAEK00KM9ZgONHjNWZgLnIJgBQEicc5rv0R6zfv+cGcoEzkUwA4CQzBWrqtVdz1X9l6Rc\nKiETQ5nA+QhmABCSqYWSJPXcPpmSlOgzZVMJesyA8xDMACAkp+aXJEmDPTjHTJIGMgl6zIDzEMwA\nICRTPpj1Yo+ZFNQyY/I/cC6CGQCEZDmY9eAcMylYmcm2TMC5CGYAEJKphSU/16o334r7M0kV6DED\nztGb7wYAEAFT80sayiRlPbYdU8NAOqnFck3OubCbAkTGloKZmY2a2UfN7Ftm9oCZPdnMxs3sVjM7\n5P8d8+eamb3LzA6b2b1m9sSm69zozz9kZjdu9UkBQBxMzS/17MR/KZj8X6s7latsZA40bLXH7J2S\nPuWc+z5Jj5P0gKTXSPqsc+5KSZ/1X0vSsyVd6T9eLunPJMnMxiW9QdL1kq6T9IZGmAOAbnZ6odyz\nE/+lsxuZL7IyE1i26WBmZiOSflDSeyTJOVd2zs1KukHS+/1p75f0PP/5DZI+4AK3Sxo1sz2Sninp\nVufctHNuRtKtkp612XYBQFycmiv1ZHHZhgE2MgcusJUes8slTUn6azP7mpn9lZkNSNrlnDvhz5mU\ntMt/vk/S0ab7T/hjqx2/gJm93MzuMrO7pqamttB0AAjXUrWmM4tljeR6uMdseSNzghnQsJVglpT0\nREl/5px7gqRFnR22lCS5YEZny2Z1Oufe7Zy71jl37Y4dO1p1WQDouFNzQamMkRw9ZgxlAmdtJZhN\nSJpwzt3hv/6ogqB20g9Ryv97yt9+TNKBpvvv98dWOw4AXetEPtiOabiHhzIbc8wKDGUCyzYdzJxz\nk5KOmtkj/aGnS7pf0i2SGisrb5T0cf/5LZJ+3q/OfJKkvB/y/LSkZ5jZmJ/0/wx/DAC61uScD2Y9\n3GOWTfWpz+gxA5ptdXLDr0j6kJmlJT0o6aUKwt7NZvYySQ9Ler4/918kPUfSYUkFf66cc9Nm9mZJ\nd/rz3uScm95iuwAg0ibzRUm9PZRpZhpIU2QWaLalYOac+7qka1e46ekrnOskvWKV67xX0nu30hYA\niJPJ/JIG0gllkr1d57s/k9Ai2zIBy3r7HQEAQjI5V9SukWzPVv1vYCNz4FwEMwAIwWS+pD0j2bCb\nETo2MgfORTADgBBM5kvaNUww68/QYwY0I5gBQIfV6k4n55foMVPQY1Ys11RnI3NAEsEMADruzMKS\nanWn3fSYqT+dlJNUomQGIIlgBgAd1yguu3skF3JLwjeQofo/0IxgBgAd1iguS4+ZNJBmv0ygGcEM\nADpscrnHjGDW2MicWmZAgGAGAB02OVdSKmHaNpAOuymha2xkTo8ZECCYAUCHTeZL2jmUVV9fbxeX\nlc5uZL7IRuaAJIIZAHTcZL7EMKaXTvYplTAm/wMewQwAOmxyjmDWrJ+NzIFlBDMA6CDnXNBjxorM\nZQNpNjIHGghmANBBc8WqipUaVf+b9GfoMQMaCGYA0EHLNcwIZssG0gnmmAEewQwAOuhEviiJ4rLN\n6DEDziKYAUAHnaTH7AID6YRKlbpqdTYyBwhmANBBjX0ydw4RzBqWa5nRawYQzACgk07OlbR9MKN0\nkrffhgG/LVOBlZkAwQwAOulEvqTdI5mwmxEp/X5bJnrMAIIZAHRUUMMsF3YzImXQ95gtsC0TQDAD\ngE4Kqv7TY9ZsOZiVCGYAwQwAOqRUqWm2UNGeEXrMmuXSCfUZG5kDEsEMADpm0q/I3EUNs3P0mWkg\nk2QoE5CUDLsBANCtbrrjyDlfPzi1IEm6//icbqoeWekuPWuQYAZIoscMADpmrlSRJA3n+Jv4fAQz\nIEAwA4AOyReD4DGSTYXckuhhKBMIEMwAoEPmihVlkn3KpBJhNyVyBjNJLS5V5RzbMqG3EcwAoEPy\nxYpGcvSWrWQwk1Sl5rRYpvo/ehvBDAA6ZK5U0TDBbEWNWman55dCbgkQLoIZAHTIXLHC/LJVDGaD\nYHZmkWCG3kYwA4AOqNWd5ktVVmSuotFjNjVfDrklQLgIZgDQAQtLVTmJocxVDDSGMhfoMUNvI5gB\nQAfMFYMaZgxlrmwgE6xUPbNAjxl6G8EMADogX2wUlyWYrSTZ16dcKkGPGXoewQwAOqBR9Z9yGasb\nzCQJZuh5BDMA6IB8saJkn6k/TXHZ1QxmkwxloucRzACgA/LFoIaZmYXdlMgaoMcMIJgBQCfMFasa\nzlIq42IYygQIZgDQEVT9X9tgJqm5UlVLVbZlQu8imAFAmznnqPq/Do0is8wzQy8jmAFAmxXKNVXr\njh6zNRDMAIIZALQdpTLWp7FfJvPM0MsIZgDQZhSXXZ/l/TIJZuhhBDMAaLNGMKPH7OIYygQIZgDQ\ndnPFikxngwdWlk6yLRNAMAOANpsrVjWUTSrRR3HZtWwfSusMwQw9jGAGAG2Wp4bZum0fzOg0Q5no\nYQQzAGizfLHC/LJ12jaQYSgTPY1gBgBtNlekx2y9dgyl6TFDTyOYAUAblSo1LVXrVP1fp+2DGU0v\nLqlWd2E3BQgFwQwA2miOGmYbsm0grbqTZgr0mqE3EcwAoI3yVP3fkO1DGUnUMkPvIpgBQBvNUVx2\nQ7YPBsGMBQDoVQQzAGijfLEqSRrKUlx2PbYPpiURzNC7CGYA0EZzxYr60wmlErzdrsfZHjOGMtGb\neKcAgDaihtnGDGdTSvYZPWboWQQzAGijuRLBbCP6+kzbBtmWCb2LYAYAbZSnuOyGsS0TehnBDADa\npFKrq1CuaZjishuybZBtmdC7CGYA0CaUytic7YNp6pihZxHMAKBN5kpBqQyC2cbsGMxoamFJzrEt\nE3oPwQwA2iS/vB0TNcw2YttgWuVqXfNL1bCbAnQcwQwA2mR5KJM5ZhvSqGXGcCZ6EcEMANokX6wo\nk+xTJpUIuymxwrZM6GUEMwBoE2qYbc42vy0TtczQiwhmANAmVP3fnB2+x2yKoUz0IIIZALTJXLFC\nDbNNGBvwG5nP02OG3kMwA4A2qNbqmi9Vqfq/CalEn8b6UzqzSDBD7yGYAUAbTC0syYkaZpu1fTCj\n0/MMZaL3EMwAoA1O5EuSpBFqmG3KtsE0qzLRkwhmANAGkz6YMZS5OdsHMzqzSI8Zeg/BDADaYLnH\njMn/mxIMZdJjht5DMAOANjg5V1Kyz5RLU1x2M7YPpjW/VFWpUgu7KUBHEcwAoA1O5EsayaVkZmE3\nJZaWt2ViOBM9hmAGAG0wmS8yv2wLlrdlYjgTPYZgBgBtMDlXolTGFixvy0QtM/QYghkAtFi97nQy\nv0TV/y3YMRT0mJ2cI5ihtxDMAKDFpgtllWt1aphtwe7hrBJ9puOzxbCbAnQUwQwAWowaZluXTPRp\n93BWEzMEM/QWghkAtNjkctV/gtlW7BvN6RjBDD1my8HMzBJm9jUz+4T/+nIzu8PMDpvZ35pZ2h/P\n+K8P+9sva7rGa/3xb5vZM7faJgAI04k5esxaYf9YTscYykSPaUWP2a9JeqDp67dJeodz7qCkGUkv\n88dfJmnGH3+HP09mdrWkF0h6tKRnSfpTM6MiI4DYmswXlewzDWaYY7YV+8ZyOpEvqlKrh90UoGO2\nFMzMbL+kH5P0V/5rk/TDkj7qT3m/pOf5z2/wX8vf/nR//g2SPuKcW3LOfU/SYUnXbaVdABCmE/mS\ndg5l1Edx2S3ZN5pT3Z0dGgZ6wVZ7zP5Y0m9Lavw5s03SrHOu6r+ekLTPf75P0lFJ8rfn/fnLx1e4\nzznM7OVmdpeZ3TU1NbXFpgNAe5ycK2n3SDbsZsTe/rF+SWI4Ez1l08HMzJ4r6ZRz7u4WtueinHPv\nds5d65y7dseOHZ16WADYkBP5kvaM5MJuRuztGwteQ1ZmopdspcfsqZJ+wswekvQRBUOY75Q0amaN\niRX7JR3znx+TdECS/O0jks40H1/hPgAQK845TebpMWuFPf41ZGUmesmmZ6Y6514r6bWSZGY/JOk3\nnXMvMrO/k/RTCsLajZI+7u9yi//6y/7225xzzsxukXSTmb1d0l5JV0r6ymbbBQBhmitVVSjXtHuY\nYLYZN91x5Jyvh7JJfeHQ1PJOAJL0wusv6XSzgI5pRx2zV0t6pZkdVjCH7D3++HskbfPHXynpNZLk\nnLtP0s2S7pf0KUmvcM7V2tAuAGi7RqX6PaMEs1YYzaU0UyiH3QygY1qylts59zlJn/OfP6gVVlU6\n50qSfnqV+79F0lta0RYACFNjPtSBsX7dV5wLuTXxN9qfZvI/egqV/wGghY5OFyQFxVGxdWP9aeUL\nFdWdC7spQEcQzACghSZmiupPJzQ+kA67KV1htD+lmnOaL1XXPhnoAgQzAGihiZmC9o/lZBSXbYmx\n/mBbq1nmmaFHEMwAoIWOzhR1wBdGxdaN9gc9j7OFSsgtATqDYAYALdToMUNrjPlgxspM9AqCGQC0\nSL5Y0XypuryVELYunexTfzpBjxl6BsEMAFqksSLzwDg9Zq001p/WbJEeM/QGghkAtEijhhk9Zq01\n2p/SzCI9ZugNBDMAaJGJGWqYtcNoLqXZYlmOWmboAQQzAGiRiZmihjJJjeRSYTelq4wNpFWpOS2W\n2a0P3Y9gBgAtMjFT0D5qmLXcaK5RMoN5Zuh+BDMAaJGj00UdGGd+WauN+iKzM6zMRA8gmAFACzjn\nqGHWJmP99JihdxDMAKAFZgsVLZZrrMhsg1w6oUyyjx4z9ASCGQC0wFG/IvMAPWZtMdafpscMPYFg\nBgAtQA2z9hrtT1H9Hz2BYAYALdCo+r+fqv9tMdqfZr9M9ASCGQC0wMRMUSO5lIaz1DBrh7H+lJaq\ndRWpZYYuRzADgBZgRWZ7jTZWZrJnJrocwQwAWuDoTFEHmF/WNmONWmbsmYkuRzADgC2ihln70WOG\nXkEwA4AtOrNYVqlSJ5i10UA6oVTCWJmJrkcwA4AtaqzIZDum9jEzjeZYmYnuRzADgC2ihllnjA1Q\nywzdj2AGAFvUqPrPUGZ70WOGXkAwA4AtmpgpanwgrYFMMuymdLXR/pQK5ZoK5WrYTQHahmAGAFs0\nMVOkt6wDxvzKzGN+6BjoRgQzANiiiekCNcw6YNTXMpuYJZihexHMAGAL6nWniVl6zDphlB4z9ACC\nGQBswdTCkspVaph1wlA2qWSf6eEzi2E3BWgbghkAbMFEY0UmNczars9MO4YyOnxqIeymAG1DMAOA\nLWjUMDtAj1lH7BjK6BDBDF2MYAYAW9Co+r9vlB6zTtg5lNHETJGSGehaFN0BgA266Y4jy59/7ttT\nGswk9Q9fOxZii3rHzqGsJOnBqUVds28k5NYArUePGQBswWyhojFfxgHtt3MoI0k6dGo+5JYA7UEw\nA4AtmC6Ul8s4oP22DWaU7DMdOsk8M3QnghkAbFLdOeULFY0PEMw6JdFnumz7ACsz0bUIZgCwSXPF\nimrOLVekR2dcuXOQYIauRTADgE2aXixLksYZyuyogzsH9dCZRS1Va2E3BWg5ghkAbNIxv2fjnlFq\nmHXSwZ2DqjvpodOFsJsCtBzBDAA26dhsUaO5lAYzVB7qpIM7ByWxMhPdiWAGAJs0MVPUPir+d9wV\nOwZlJlZmoisRzABgEwrlqqYXy9rPMGbHZVMJXTLer8NTBDN0H4IZAGzCMb9H5r4xtmIKw8EdgzpM\njxm6EMEMADZhwk/830ePWSgO7hrUg6cXVK3Vw24K0FIEMwDYhGMzRW0bSCuXToTdlJ505c4hVWpO\nR6ZZmYnuQjADgE2YmCloPxP/Q3N2ZSbDmeguBDMA2KC5UkVzpar2M78sNI1gxg4A6DYEMwDYoMbE\nf3rMwjOYSWrPSJZghq5DMAOADZqYKcok7RkhmIXp4M5Bisyi6xDMAGCDjs0WtGs4q3SSt9AwXblz\nSIdPLahed2E3BWgZ3lUAYAOcc1T8j4iDOwdVqtSX9ywFugHBDAA2YGKmqEK5xvyyCLhyFwsA0H0I\nZgCwAfdO5CVRWDYKDu5gM3N0H4IZAGzAvROzSvSZdo9kw25KzxsbSGv7YJoeM3QVghkAbMA9E7Pa\nM5JVso+3zygIVmYSzNA9eGcBgHWq152+eWyOYcwIObgz2MzcOVZmojsQzABgnR48vaiFpSoT/yPk\nyp1Dml+q6tT8UthNAVqCYAYA63TvxKwkaR9bMUXGlY09M08ynInuQDADgHW6dyKv/nRCO4cyYTcF\n3tnNzFmZie5AMAOAdbpnYlbX7B1Rn1nYTYG3Yyij4WySBQDoGgQzAFiHSq2u+4/P6TH7R8JuCpqY\nmR61Z1j3H58LuylASxDMAGAdvnNyXkvVuh5LMIucR+8d0QMn5lSt1cNuCrBlybAbAABxcMeD05Kk\nJxwY0xcPnw65Nb3tpjuOnPP1fKmipWpd/+e2w9o1fLbw7wuvv6TTTQO2jB4zAFiHT9x7XN+3e0iX\nbGNFZtTs8XXljrOZOboAwQwA1nB0uqCvHpnVTzx+b9hNwQp2DGaU7DOCGboCwQwA1vDP3zghSfrx\nxxLMoqixd+nxfCnspgBbRjADgDX80z3H9fgDozowzjBmVO0dzen4bFF1tmZCzBHMAOAivju1oPuO\nz+nHH0dvWZTtHclpqVrXzGI57KYAW8KqTABocv6Kv88+cFImqVytX3AbomPvaLAa83i+pG2D7MyA\n+KLHDABW4ZzTvRN5XbZ9QCO5VNjNwUXsGs6qz1iZifgjmAHAKibnSppaWKKobAykEn3aOZTViTzB\nDPFGMAOAVdw7kVefSdfsJZjFwd7RrI7NluRYAIAYI5gBwAqCYcxZHdw5qIEM03HjYO9oTotLVc2X\nqmE3Bdg0ghkArODoTFEzhYoeu2807KZgnfaMsAMA4o9gBgAruHdiVsk+09V7h8NuCtZp70hjZSbB\nDPFFMAOA89Sd0zeO5XXVriFlU4mwm4N1yqQS2jaQ1vFZdgBAfBHMAOA8D51e1HypymrMGNo7mqPH\nDLFGMAOA83zjWF6phOn7djOMGTd7R3OaLVRUWGIBAOKJYAYATZxzeuDEnK7cOaR0krfIuGneAQCI\nI951AKDJsdmi5kpVJv3H1F5WZiLmNh3MzOyAmf2bmd1vZveZ2a/54+NmdquZHfL/jvnjZmbvMrPD\nZnavmT2x6Vo3+vMPmdmNW39aALA595+YU59J37drKOymYBMGMkmN5FLMM0NsbaXHrCrpVc65qyU9\nSdIrzOxqSa+R9Fnn3JWSPuu/lqRnS7rSf7xc0p9JQZCT9AZJ10u6TtIbGmEOADrt/uNzunTbgPop\nKhtbe0eyrMxEbG06mDnnTjjnvuo/n5f0gKR9km6Q9H5/2vslPc9/foOkD7jA7ZJGzWyPpGdKutU5\nN+2cm5F0q6RnbbZdALBZD51e1Kn5JV29h2HMONs7mtOZhSUtsgAAMdSSOWZmdpmkJ0i6Q9Iu59wJ\nf9OkpF3+832SjjbdbcIfW+34So/zcjO7y8zumpqaakXTAWDZrfeflCSCWcztHc3JSXrgxFzYTQE2\nbMvBzMwGJf29pF93zp3zW+CCnWRbtpusc+7dzrlrnXPX7tixo1WXBQBJ0mfun9SekazGBtJhNwVb\nsHc0WABw33GCGeJnS8HMzFIKQtmHnHMf84dP+iFK+X9P+ePHJB1ouvt+f2y14wDQMacXlnTXwzN6\nFL1lsTecTWogndB9x2n9kCoAABjDSURBVPNhNwXYsK2syjRJ75H0gHPu7U033SKpsbLyRkkfbzr+\n83515pMk5f2Q56clPcPMxvyk/2f4YwDQMbc9cErOMYzZDcxM+8ZyuvvhmbCbAmzYVpYdPVXSiyV9\nw8y+7o/9jqS3SrrZzF4m6WFJz/e3/Yuk50g6LKkg6aWS5JybNrM3S7rTn/cm59z0FtoFABv2mfsn\ntW80pz1+I2zE2yO2D+pT903q1FxJO4f5niI+Nh3MnHNflGSr3Pz0Fc53kl6xyrXeK+m9m20LAGxF\noVzVvx86rRdef4mCwQDE3RU7B6X7pC9994ye94QV15MBkUTlfwA97wvfOa2lal0/evWutU9GLOwZ\nyWokl9KXvns67KYAG0IwA9DzPnP/pEZyKV132XjYTUGL9JnpSY8Y15e+eybspgAbQjAD0NOqtbpu\n+9YpPf1RO5VM8JbYTZ5yxXZNzBR1dLoQdlOAdeNdCEBPu/OhGc0WKnrG1bvDbgpa7ClXbJMkhjMR\nKwQzAD3t1vtPKpPs0w9etT3spqDFDu4c1I6hDMOZiBWCGYCe9h+HT+u6y8fVn2bT8m5jZnrKFdv0\npe+eUVAYAIg+ghmAnnVmYUnfPjmvJ/shL3Sfp1yxTVPzSzp8aiHspgDrQjAD0LNufzCoZf3kRxDM\nutVTrgiGqBnORFwQzAD0rC9997QGM0k9Zt9I2E1BmxwY79f+sRwLABAbBDMAPevLD57RdZePUyaj\nyz3lim26/cFp1erMM0P08W4EoCednCvpwalFhjF7wFOu2K58saIHTsyF3RRgTQQzAD3py37OERP/\nux/1zBAnrA8H0BNuuuPIOV9/7KsTyqX+X3t3Hh1Xfd5//P3MjPZ9s6zFi+QF49gyYMcmDksgCQmJ\nCSlNgZA0QJpD0zb0dEmTtPSXLr/8DrQ07Una01LqhCRNISQQmpilBFIWBxsvGFtmkxfZWJJlS7YW\na19mvr8/5soWHAlMGM29mvm8jufozp17r577zPjOo/u99/sNs7ulh8bWXp+ikmSYU5jN4jn5bDl4\nklsuWeR3OCJvSWfMRCQtHezsp648j5CZ36FIEqxfVMb2Q12MRWN+hyLyllSYiUja6R4cpXtwjPqK\nPL9DkSRZv6iMwdEoe1p6/A5F5C2pKVNE0k5z5wAA9RX5PkciM2ly8/Xg6DgG3PXMQS4/fqaz2RvW\nzfchMpHp6YyZiKSd5s5+8jLDVBZk+R2KJEluZoSq4mwOekW5SFCpMBORtOKco/nEAHUV+ZiuL0sr\nS+YU8PrJAQZHxv0ORWRaKsxEJK2cHBild2iMRbq+LO2srCki5uBl9WcmAabCTETSyunry8p1fVm6\nqSrKpjQvk5fa1D2KBJcKMxFJK80n+inIjlCen+l3KJJkZsbKmiIOdvYzoOZMCSgVZiKSNpxzNHcO\nUF+ep+vL0tREc+Yras6UgFJhJiJpo6NvhP6RcRapm4y0NdGcuVfNmRJQKsxEJG00d8b7r1L/Zelr\nojmzWc2ZElAqzEQkbRzsHKA4J4OS3Ay/QxEfnW7OPKrmTAkeFWYikhaGRqPsO97HsqpCXV+W5qqK\nsinLy2TvUTVnSvCoMBORtPDS0V7GY47z5xX7HYr4zMxY4TVndg2M+h2OyBuoMBORtPDikR7K87Oo\nLcnxOxQJgInmzMdfPuZ3KCJvoMJMRFJeS9cgh08OcP78YjVjCnCmOfORxna/QxF5AxVmIpLyfra7\nDYDzatWMKXETzZlbm09ysn/E73BETlNhJiIpzTnHT3e1sbAsj5I89fYvZ6ysKSIaczz+8nG/QxE5\nTYWZiKS0Pa29NJ+IN2OKTFZVlM3Cslwe3avmTAkOFWYiktIe2tVKZiTEypoiv0ORgDEzPt5Qxdbm\nk5xQc6YEhAozEUlZY9EYmxrb+fDySrIzwn6HIwG0oaGaaMzx2Eu6O1OCQYWZiKSsZ5o66RoY5Zrz\na/wORQJq2dwCFs/JZ9Oeo36HIgKoMBORFPbQi22U5WVyydIKv0ORgDIzrmqoZsfhLtp7h/wOR0SF\nmYikpt6hMZ549ThXraomI6xDnUxvw6oqnEN9mkkg6GglIinp0b3tjI7HuOYCNWPKW1tUkc97qgvZ\npMJMAkCFmYiknKHRKD98/nUWVeTpbkw5K1etqmZPSw9HTg76HYqkORVmIpJS+obHuPG723m1/RR/\n/OGlGoJJzsrHV1YB8PBe3QQg/lJhJiIpo3tglM9s3MauI918+9Pns6Gh2u+QZJaYV5rLBfOL2bRH\nzZniLxVmIpISOk4Nc93dW3ntWB93f261ijJ5x65aVc2r7ac40NHndyiSxlSYicis19o9yLX/vpXW\n7iG+d9N7uXxZpd8hySz08ZVVmKGzZuIrFWYiMqs1Hevj2ru20jUwyg+/sI71i8v9DklmqTmF2VxY\nV8amxqM45/wOR9JUxO8ARER+Xc8dOMEX//MFcjLD/OiW97G8utDvkGSWuXfbkTc8ryzMZmvzSb75\ni31UF+ecnn/DuvnJDk3SlAozEZmVHnyhla8+2Eh9RR6fPK+G3S097G7p8TssmeXeU13Iz/e00dja\n+4bCTCRZ1JQpIrOKc45vPbmfP/3JHtbVl/LA762nODfT77AkReRlRVg8J5/Gth41Z4ovVJiJyKwR\nizm++mAj//TkPn7zglruuWkthdkZfoclKea8ecX0DI6x73i/36FIGlJhJiKzxn9sbubHO1u59fLF\n/MNvNZAZ0SFMEm9FTRFFORk83dShs2aSdDqqicissLe1lzsfb+LKFXP5E/XoLzMoEgpxyZJyXu8a\n5NDJAb/DkTSji/9FJLAm7pgbHY/xL0/tJy8rwuoFJdy3vcXnyCTVrVlYylNNnTzd1El9eb7f4Uga\n0RkzEQm8hxuPcrJ/lN9aXUtupv6elJmXEQ5x0eJyDnT009qtgc0leVSYiUigvdTWy87Xu7lkaQX1\nFTpzIcmztq6U7IwQTzd1+h2KpBEVZiISWD2Dozz0Yhs1xTl88Nw5focjaSY7I8z6ReW80n6KpmMa\nP1OSQ4WZiARSNOb4yQutRGOO6947j0hIhytJvvX1ZWSGQ/zr0wf8DkXShI50IhI4zjn+8r/3cujE\nAFetqqI8P8vvkCRN5WZFWFdXyqY9Rzl8QndoysxTYSYigeKc4xuPvMp921v4wNIKVi8o9TskSXPv\nX1JOJBzirmcO+h2KpAEVZiISKP/05H6+86tD3LR+IR9eXul3OCIUZmdw3Zp5PLirVeOxyoxTYSYi\ngXHXMwf59i/3c+2aWr6+Ybk6kZXA+IPLFjOnIJtr79rKvduOaEQAmTEqzEQkEP5z62HueOw1NjRU\ncfs1DYRCKsokOOYWZbPp1otYV1/KXzy0l6880MjwWNTvsCQFqadGEUm6iR79J+xt6+W+7UdYNreA\ndXVl3L9DPftL8JTmZfK9m9fyrSf38e3/PcAr7ae467OrmVea63dokkJ0xkxEfNXRN8yDu1qZV5LD\np9fOJ6wzZRJg4ZDxJ1ecw3duXENL1yAb/vlXbNzcTP/IuN+hSYpQYSYivhkZj3LvtiNEQsYN6xaQ\nEdYhSWaHD55bycO3Xsy5VQV845FXed/tv+SOx17j+Klhv0OTWU5NmSLiC+ccD73YRmffCDe/v46i\nnAy/QxKZ1pub3yd8YlUN588rYfOBE9z97EG+86tmPnleDX9w2WIWluclOUpJBSrMRMQXzx/qorG1\nlyuWV7J4jsbAlNlrXmkuN6ydz/sXl/HdXx3i/p0t/PfuNj574QL+8PIllORl+h2izCJqNxCRpDvS\nNcijje0sm1vAJUsr/A5HJCEWlOXxN1ev4NmvXManVs/j+1sOc+mdT/EfzzYzMq47OOXs2Gzti2XN\nmjVu586dfochIu/Qyf4RLv/mM4QMvnTZEnIyw36HJDIjjp0a5n9eamff8X5KcjO4ZGkFDTXF/M7F\ndX6HJj4wsxecc2vebjk1ZYpI0sRijj+6fzcDI+N88dJFKsokpc0tzOam9XXs7+jj8ZeO8bPdR3mk\nsZ1dLd186oJaLvaGehKZTIWZiCTNvz1zkM37T3D1edVUF+f4HY5IUiyZU8Diy/Jp6xli15Eethw4\nwSON7ZTnZ3Htmlpufn8dFQVZfocpAaGmTBFJih2Hu7j+7ue5csVc3ldfpuGWJG2Nx2LsO9bHC0d6\neK39FOGQsXpBCZcsqXjDjQI3rJvvY5SSaGrKFJHA6BoY5dZ7X2ReSQ63X7OSTXva/Q5JxDeRUIjl\n1UUsry7iRN8Iz+7vZOfhbnYc7qKhtphLl1ZQWZjtd5jiExVmIjKjYjHHn/54N10Do/z099dTkK3+\nykQmlBdkcc0FtXzw3EqeO3CC7Ye62NPSw9q6Uj66Yi6l6moj7eiqQxGZUXdvbuappk7+z4ZzWVFT\n5Hc4IoFUlJPBx1ZW8ZWPnMOF9WXsONzFB+58inueO8RYNOZ3eJJEKsxEZMZsOXiCOx9v4mMr5/LZ\nCxf4HY5I4OVmRbhqVTW3Xr6EVfOK+ZtNr3DltzbzVFMHs/WacHlnVJiJSMINj0W547HX+OzGbcwv\nzeWO32zQxf4i70BlYTY/+PxaNn5uDePRGDffs4Nr/m2LCrQ0oGvMRCShdh3p5s9+soeDnQNct2Ye\nt204l0JdVybyjt23vQWAz19Uxwuvd/NMUyc337OD2pIcLl82h3MqC/iMzkSnHBVmIpIQw2NRvvmL\nJjZuPkRhTgY3rV/I0soCHtYdmCLvSiQUYl1dGasXlPDi6z08va+DH2x9naqibPpGxrlieSX1FRpv\nNlWoHzMReVc6+oa5b1sLP9z2Op19I6xdGL+bLDtDvfqLzIRozPHikW6eP3SSoz3DACyZk88V76nk\nQ+dWsqKmiAyNKBA4Z9uPmQozEfm17G7p4ftbDvNw41HGoo4PnFPBFy9dRHPngN+hiaSNS8+p4ImX\nj/GLV46z7VAX0ZgjMxLi3KpCVtYU0lBTzIqaIhbPySczomLNTyrMRCSh7t12hL7hMRpbe9nd0kNb\nzxBZkRAXLCjhfXVllGtIGRFfDY6Oc6Cjn7buIVp7hjjaM8TIeLyrjUjIqK/IY2llAcvmFnDO3EIa\naovUkW0Sqed/EUmIgZFxnnjlON/bcogDHf3EHFQXZbOhoYoL5peoyVIkIHIzIzTUFtNQWwxAzDm6\n+kdp6x3ieO8wx04N89yBEzzceOa6z+KcDOaV5jK/NJcvXFzH8upCsiL6P+2nwBRmZvZR4FtAGNjo\nnLvD55BEUpJzjr6RcTr7RhgYGad/ZJzBkSgDo/Hp9p5hWroHae0eorV7kOOnRgAozs3g4iUVnDev\nWH9li8wCITPKC7LiZ7Nrz8wfHoty/NQwrd1DHOkapKVrkL1tvTyyt51IyFg8J5/l1YUsryrkPdXx\nZtDy/Ex1eZMkgWjKNLMwsA/4MNAK7AA+7Zx7Zbp1/GzKdM4xHnNEY97PqCPmJh7x1x0QDhkZoRDh\nsBEJxR/hkOnDPYMmvzcT708sFn9vos4Ri0HI4u9NJBQiEo6/J+GQETYjFHr79yYac4yOxxgZjzI6\nHmN4LMbg2DgDI+MMjEQZHB1ncDSKWfzAOLHtcMjIzgiTkxkmJyN8etq8bcbjjjEec4yMxRgeizI0\nFmXYmx6Nxrx9gahz8c+ZAzMwM0IGhhFzjsHReCwDI+MMjEbpGx6jo2+EjlPDHD81wtBYdNr9C1m8\nF/KS3ExKcjMpzsugvjyfBWW5hPTZFUlJp4bGONI1yNGeIY72DtHeO0zf8Pjp1zPCRnFOJiV5Gbx3\nYSlzC7MpzsukOCeD4twMinMyycsKEzI7feyDM8enySbqjsnlh3PgcN7PM9+jzvtejbn4Md25M+vF\nl4gLmZEZCZERDpERNjLDISLedEY4FIjv39nWlLkWOOCcawYwsx8BVwPTFmYz7Uv37uKXr3YQ8z4c\neB+amIt/ib5bZwqB+JfphInPjL7+4qbK9Jv/A0+8N1GvaHm3Jt4bs0m/35uIegeH2SIzHCIzEiIr\nEqIgO4PCnAxqS3IpyI5QkB0hOxImMxI6/ciKhMnPihA+iwJVRFJHYU4GK2qK3jBsWt/wGO29w5zo\nH6FncIzuwVF6Bsd4dG873YNjPkb764uELF40xv/FC0eMzV+9jPL8YFwnG5TCrAZomfS8FVj35oXM\n7BbgFu9pv5k1zXBc5cCJGf4d6UT5TDzlNPGU08RSPhNPOU2wim8kJadn1RtwUAqzs+Kcuxu4O1m/\nz8x2ns1pRzk7ymfiKaeJp5wmlvKZeMpp4gUpp0Hp1KQNmDfpea03T0RERCRtBKUw2wEsMbM6M8sE\nrgd+7nNMIiIiIkkViKZM59y4mX0JeJx4dxnfdc697HNYkMRm0zShfCaecpp4ymliKZ+Jp5wmXmBy\nGojuMkREREQkOE2ZIiIiImlPhZmIiIhIQKRFYWZmpWb2hJnt936WTLPcjd4y+83sxknzV5vZXjM7\nYGbfNq/r4Om2a2ZXm1mjme02s51mdlFy9jR5fMjpZ7yc7jWzLWa2Kjl7mhw+5HOZmW01sxEz+3Jy\n9jI5zOyjZtbk5eJrU7yeZWb3e69vM7OFk177c29+k5l95O226d2wtM2bf79381LKSXJOv+TNc2ZW\nPtP75ock5/O/vPkvmdl3zSxjpvfPD0nO6XfMbI/3nfSAmeUndGfc6aFdUvcB/D3wNW/6a8DfTbFM\nKdDs/Szxpku817YDFxLvKPgx4Mq32i6Qz5nr9xqA1/zOQQrkdP2kda8Etvmdg1mezznAe4H/B3zZ\n7/1PYB7DwEGgHsgE9gDL37TM7wN3edPXA/d708u95bOAOm874bfaJvBj4Hpv+i7g9/zOQQrk9Hxg\nIXAYKPd7/1Mgnx/zjgsG3KfPaEJyWjhpu/+Id4xN1CMtzpgRH97p+97094FPTrHMR4AnnHNdzrlu\n4Ango2ZWRfxNeN7F34UfTFp/yu065/q9ZQHymHpkodku2Tnd4m0D4HneMCRvSkh2PjucczuA2Tmu\nyvROD+/mnBsFJoZ3m2xyTh4APuidYbwa+JFzbsQ5dwg44G1vym1661zubQOmf99mu6TlFMA596Jz\n7vBM75SPkp3PR52H+B9wqXbshOTn9BSAt34OCf6OT5fCrNI51+5NHwMqp1hmqmGharxH6xTz33K7\nZvYbZvYa8Ajw+Xe9B8GT9JxO8jvEzwqlEj/zmUqmy9GUyzjnxoFeoOwt1p1ufhnQ421jut+VCpKZ\n03TgSz69JszfBv7nXe9B8CQ9p2Z2D/Fj6jLgnxOxExMC0Y9ZIpjZk8DcKV66bfIT55wzs4SfwXrz\ndp1zDwEPmdklwP8FPpTo3znTgpZTL6bLiBdms+66vSDmU0TSxr8CzzrnNvsdSCpwzt1sZmHiRdl1\nwD2J2nbKFGbOuWkLHzM7bmZVzrl2r9mnY4rF2oAPTHpeCzztza990/yJ4aLedrvOuWfNrN7Myp1z\ns2rQ2aDl1MwagI3Er586+Wvskq+Cls8UdTbDu00s02pmEaAIOPk26041/yRQbGYR7y/wVB1KLpk5\nTQdJz6eZ/RVQAfxuAuIPIl8+o865qJn9CPgKCSzM0qUp8+fAxB1sNwI/m2KZx4ErzKzE4neuXQE8\n7jUDnTKzC7325M9NWn/K7ZrZYm9ZzOwC4hcVzrpC4m0kO6fzgZ8Cv+2c2zcTO+SzpOYzhZ3N8G6T\nc/Ip4H+9629+Dlzv3b1VBywhfk3OlNv01nnK2wakbn6TltMk7EsQJDWfZvYF4tenfto5F5vhffNL\n0nJqcYvh9DVmnwBeS+jeJOIOgqA/iLcj/xLYDzwJlHrz1wAbJy33eeIX/h0Abp40fw3wEvE7NP6F\nM3dcTrfdrwIvA7uBrcBFfucgBXK6Eej2crob2Ol3DmZ5PucSv2biFNDjTRcmY1+TkMuPAfu8XNzm\nzftb4BPedDbwEy+H24H6Seve5q3XhHdn63Tb9ObXe9s44G0zy+/9T4Gc/qH3eRwHjk7+/KfKI8n5\nHPfmTRw7v+73/s/mnBI/ofUcsJf4Mfe/En3s1JBMIiIiIgGRLk2ZIiIiIoGnwkxEREQkIFSYiYiI\niASECjMRERGRgFBhJiIiIhIQKsxEJK2Z2V+b2ZfN7G/N7EPevIvN7GUz221mOWZ2p/f8Tr/jFZHU\nljI9/4uIvBvOua9PevoZ4Hbn3A8BzOwW4n3ARX0JTkTShvoxE5G0Y2a3Ee8FvIP4QMUvACuAh4Fi\n4O+JD3K8BSgAPk68Q8nbnXP3+xGziKQHnTETkbRiZquJD69yHvFj4C7ihRkAzrmNZnYR8LBz7gFv\nnX7n3Hl+xCsi6UWFmYikm4uBh5xzgwBmli5jNIrILKCL/0VEREQCQoWZiKSbZ4FPendbFgBX+R2Q\niMgENWWKSFpxzu0ys/uBPcQv/t/hc0giIqfprkwRERGRgFBTpoiIiEhAqDATERERCQgVZiIiIiIB\nocJMREREJCBUmImIiIgEhAozERERkYBQYSYiIiISEP8f4TVwJfqINXMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8ddcacffd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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86euF77v3dyxcuJCEhASOOmoMp512OikpKSx97z2OO/ZozIxf/PJ/6N27N2vX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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d8377b400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE :  6.42847482193e-09\n",
+      "MAE :  7.12554587774e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    20817.000000\n",
+       "mean         0.000069\n",
+       "std          0.000040\n",
+       "min         -0.000283\n",
+       "25%          0.000046\n",
+       "50%          0.000068\n",
+       "75%          0.000095\n",
+       "max          0.000283\n",
+       "Name: diff, dtype: float64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred = model.predict(testX)\n",
+    "pred = y_scaler.inverse_transform(pred)\n",
+    "close = y_scaler.inverse_transform(np.reshape(testY, (testY.shape[0], 1)))\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(np.reshape(pred, (pred.shape[0])))\n",
+    "predictions['close'] = pd.Series(np.reshape(close, (close.shape[0])))\n",
+    "\n",
+    "p = df[-pred.shape[0]:].copy()\n",
+    "predictions.index = p.index\n",
+    "predictions = predictions.astype(float)\n",
+    "predictions = predictions.merge(p[['low', 'high']], right_index=True, left_index=True)\n",
+    "\n",
+    "ax = predictions.plot(x=predictions.index, y='close', c='red', figsize=(40,10))\n",
+    "ax = predictions.plot(x=predictions.index, y='predicted', c='blue', figsize=(40,10), ax=ax)\n",
+    "index = [str(item) for item in predictions.index]\n",
+    "plt.fill_between(x=index, y1='low', y2='high', data=p, alpha=0.4)\n",
+    "plt.title('Prediction vs Actual (low and high as blue region)')\n",
+    "plt.show()\n",
+    "\n",
+    "predictions['diff'] = predictions['predicted'] - predictions['close']\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.distplot(predictions['diff']);\n",
+    "plt.title('Distribution of differences between actual and prediction ')\n",
+    "plt.show()\n",
+    "\n",
+    "g = sns.jointplot(\"diff\", \"predicted\", data=predictions, kind=\"kde\", space=0)\n",
+    "plt.title('Distributtion of error and price')\n",
+    "plt.show()\n",
+    "\n",
+    "# predictions['correct'] = (predictions['predicted'] <= predictions['high']) & (predictions['predicted'] >= predictions['low'])\n",
+    "# sns.factorplot(data=predictions, x='correct', kind='count')\n",
+    "\n",
+    "print(\"MSE : \", mean_squared_error(predictions['predicted'].values, predictions['close'].values))\n",
+    "print(\"MAE : \", mean_absolute_error(predictions['predicted'].values, predictions['close'].values))\n",
+    "predictions['diff'].describe()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "celltoolbar": "Hide code",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/capstone_project/.ipynb_checkpoints/example 3d surface plot-checkpoint.ipynb b/capstone_project/.ipynb_checkpoints/example 3d surface plot-checkpoint.ipynb
new file mode 100644
index 0000000..e75394a
--- /dev/null
+++ b/capstone_project/.ipynb_checkpoints/example 3d surface plot-checkpoint.ipynb	
@@ -0,0 +1,1247 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:70% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pypyodbc\n",
+    "import sys\n",
+    "from IPython.core import display as ICD\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from IPython.core.display import display, HTML\n",
+    "\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_iboxx = pd.read_excel(\"out.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mem_date</th>\n",
+       "      <th>date</th>\n",
+       "      <th>indexname</th>\n",
+       "      <th>isd_bond</th>\n",
+       "      <th>mv_weight_roll</th>\n",
+       "      <th>spread_weight_roll</th>\n",
+       "      <th>date_lm</th>\n",
+       "      <th>mv_weight_eom_lm</th>\n",
+       "      <th>spread_weight_eom_lm</th>\n",
+       "      <th>isNew</th>\n",
+       "      <th>mv_weight_change_on_roll</th>\n",
+       "      <th>spread_weight_change_on_roll</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2012-01-31</td>\n",
+       "      <td>2012-02-29</td>\n",
+       "      <td>ref loans + hy daily price &gt; 60</td>\n",
+       "      <td>432729</td>\n",
+       "      <td>0.014356</td>\n",
+       "      <td>0.095617</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0.014356</td>\n",
+       "      <td>0.095617</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.014356</td>\n",
+       "      <td>0.095617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2012-01-31</td>\n",
+       "      <td>2012-02-29</td>\n",
+       "      <td>ref loans + hy daily price &gt; 60</td>\n",
+       "      <td>435019</td>\n",
+       "      <td>0.012384</td>\n",
+       "      <td>0.058010</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0.012384</td>\n",
+       "      <td>0.058010</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.012384</td>\n",
+       "      <td>0.058010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2012-01-31</td>\n",
+       "      <td>2012-02-29</td>\n",
+       "      <td>ref loans + hy daily price &gt; 60</td>\n",
+       "      <td>435491</td>\n",
+       "      <td>0.033652</td>\n",
+       "      <td>0.074094</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0.033652</td>\n",
+       "      <td>0.074094</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.033652</td>\n",
+       "      <td>0.074094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2012-01-31</td>\n",
+       "      <td>2012-02-29</td>\n",
+       "      <td>ref loans + hy daily price &gt; 60</td>\n",
+       "      <td>437262</td>\n",
+       "      <td>0.073359</td>\n",
+       "      <td>0.025315</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0.073359</td>\n",
+       "      <td>0.025315</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.073359</td>\n",
+       "      <td>0.025315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012-01-31</td>\n",
+       "      <td>2012-02-29</td>\n",
+       "      <td>ref loans + hy daily price &gt; 60</td>\n",
+       "      <td>437444</td>\n",
+       "      <td>0.023363</td>\n",
+       "      <td>0.079587</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0.023363</td>\n",
+       "      <td>0.079587</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.023363</td>\n",
+       "      <td>0.079587</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mem_date       date                        indexname isd_bond  \\\n",
+       "0 2012-01-31 2012-02-29  ref loans + hy daily price > 60   432729   \n",
+       "1 2012-01-31 2012-02-29  ref loans + hy daily price > 60   435019   \n",
+       "2 2012-01-31 2012-02-29  ref loans + hy daily price > 60   435491   \n",
+       "3 2012-01-31 2012-02-29  ref loans + hy daily price > 60   437262   \n",
+       "4 2012-01-31 2012-02-29  ref loans + hy daily price > 60   437444   \n",
+       "\n",
+       "   mv_weight_roll  spread_weight_roll date_lm  mv_weight_eom_lm  \\\n",
+       "0        0.014356            0.095617     NaT          0.014356   \n",
+       "1        0.012384            0.058010     NaT          0.012384   \n",
+       "2        0.033652            0.074094     NaT          0.033652   \n",
+       "3        0.073359            0.025315     NaT          0.073359   \n",
+       "4        0.023363            0.079587     NaT          0.023363   \n",
+       "\n",
+       "   spread_weight_eom_lm  isNew  mv_weight_change_on_roll  \\\n",
+       "0              0.095617      1                  0.014356   \n",
+       "1              0.058010      1                  0.012384   \n",
+       "2              0.074094      1                  0.033652   \n",
+       "3              0.025315      1                  0.073359   \n",
+       "4              0.079587      1                  0.023363   \n",
+       "\n",
+       "   spread_weight_change_on_roll  \n",
+       "0                      0.095617  \n",
+       "1                      0.058010  \n",
+       "2                      0.074094  \n",
+       "3                      0.025315  \n",
+       "4                      0.079587  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_iboxx.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_piv = pd.pivot_table(df_iboxx.loc[(df_iboxx.mem_date <= '2015-06-30')&(df_iboxx.indexname == 'ref loans + hy daily price > 60 stable spreads 10 day')]\n",
+    "                        , values=[\n",
+    "                                  'mv_weight_eom_lm', 'spread_weight_eom_lm'\n",
+    "                                  , 'mv_weight_change_on_roll', 'spread_weight_change_on_roll'\n",
+    "                                 ]\n",
+    "                        , index=['mem_date']\n",
+    "                        , columns=['indexname', 'isd_bond']\n",
+    "                        , aggfunc=np.sum)\n",
+    "\n",
+    "df_piv.fillna(0,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "         "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
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a=0,o=0;o<i.length;o++)e=(0|n.words[o])-(0|i.words[o])+a,a=e>>26,this.words[o]=67108863&e;for(;0!==a&&o<n.length;o++)e=(0|n.words[o])+a,a=e>>26,this.words[o]=67108863&e;if(0===a&&o<n.length&&n!==this)for(;o<n.length;o++)this.words[o]=n.words[o];return this.length=Math.max(this.length,o),n!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var k=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0|o[0],h=8191&c,f=c>>>13,d=0|o[1],p=8191&d,m=d>>>13,v=0|o[2],g=8191&v,y=v>>>13,b=0|o[3],x=8191&b,_=b>>>13,w=0|o[4],M=8191&w,k=w>>>13,A=0|o[5],T=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,I=0|o[7],z=8191&I,D=I>>>13,P=0|o[8],O=8191&P,R=P>>>13,F=0|o[9],j=8191&F,N=F>>>13,B=0|s[0],U=8191&B,V=B>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ht=8191&ct,ft=ct>>>13,dt=0|s[9],pt=8191&dt,mt=dt>>>13;r.negative=t.negative^e.negative,r.length=19,n=Math.imul(h,U),i=Math.imul(h,V),i=i+Math.imul(f,U)|0,a=Math.imul(f,V);var vt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(p,U),i=Math.imul(p,V),i=i+Math.imul(m,U)|0,a=Math.imul(m,V),n=n+Math.imul(h,q)|0,i=i+Math.imul(h,G)|0,i=i+Math.imul(f,q)|0,a=a+Math.imul(f,G)|0;var gt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(g,U),i=Math.imul(g,V),i=i+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(p,q)|0,i=i+Math.imul(p,G)|0,i=i+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(h,W)|0,i=i+Math.imul(h,X)|0,i=i+Math.imul(f,W)|0,a=a+Math.imul(f,X)|0;var yt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(x,U),i=Math.imul(x,V),i=i+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(g,q)|0,i=i+Math.imul(g,G)|0,i=i+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(p,W)|0,i=i+Math.imul(p,X)|0,i=i+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0,n=n+Math.imul(h,J)|0,i=i+Math.imul(h,K)|0,i=i+Math.imul(f,J)|0,a=a+Math.imul(f,K)|0;var bt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=Math.imul(M,V),i=i+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(x,q)|0,i=i+Math.imul(x,G)|0,i=i+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(g,W)|0,i=i+Math.imul(g,X)|0,i=i+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(p,J)|0,i=i+Math.imul(p,K)|0,i=i+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0,n=n+Math.imul(h,$)|0,i=i+Math.imul(h,tt)|0,i=i+Math.imul(f,$)|0,a=a+Math.imul(f,tt)|0;var xt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=Math.imul(T,V),i=i+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(M,q)|0,i=i+Math.imul(M,G)|0,i=i+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(x,W)|0,i=i+Math.imul(x,X)|0,i=i+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(g,J)|0,i=i+Math.imul(g,K)|0,i=i+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(p,$)|0,i=i+Math.imul(p,tt)|0,i=i+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(h,rt)|0,i=i+Math.imul(h,nt)|0,i=i+Math.imul(f,rt)|0,a=a+Math.imul(f,nt)|0;var _t=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=Math.imul(L,V),i=i+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(T,q)|0,i=i+Math.imul(T,G)|0,i=i+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(M,W)|0,i=i+Math.imul(M,X)|0,i=i+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(x,J)|0,i=i+Math.imul(x,K)|0,i=i+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(g,$)|0,i=i+Math.imul(g,tt)|0,i=i+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(p,rt)|0,i=i+Math.imul(p,nt)|0,i=i+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(h,at)|0,i=i+Math.imul(h,ot)|0,i=i+Math.imul(f,at)|0,a=a+Math.imul(f,ot)|0;var wt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(z,U),i=Math.imul(z,V),i=i+Math.imul(D,U)|0,a=Math.imul(D,V),n=n+Math.imul(L,q)|0,i=i+Math.imul(L,G)|0,i=i+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(T,W)|0,i=i+Math.imul(T,X)|0,i=i+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(M,J)|0,i=i+Math.imul(M,K)|0,i=i+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(x,$)|0,i=i+Math.imul(x,tt)|0,i=i+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(g,rt)|0,i=i+Math.imul(g,nt)|0,i=i+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(p,at)|0,i=i+Math.imul(p,ot)|0,i=i+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(h,lt)|0,i=i+Math.imul(h,ut)|0,i=i+Math.imul(f,lt)|0,a=a+Math.imul(f,ut)|0;var Mt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(O,U),i=Math.imul(O,V),i=i+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(z,q)|0,i=i+Math.imul(z,G)|0,i=i+Math.imul(D,q)|0,a=a+Math.imul(D,G)|0,n=n+Math.imul(L,W)|0,i=i+Math.imul(L,X)|0,i=i+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(T,J)|0,i=i+Math.imul(T,K)|0,i=i+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(M,$)|0,i=i+Math.imul(M,tt)|0,i=i+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(x,rt)|0,i=i+Math.imul(x,nt)|0,i=i+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(g,at)|0,i=i+Math.imul(g,ot)|0,i=i+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(p,lt)|0,i=i+Math.imul(p,ut)|0,i=i+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(h,ht)|0,i=i+Math.imul(h,ft)|0,i=i+Math.imul(f,ht)|0,a=a+Math.imul(f,ft)|0;var kt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(j,U),i=Math.imul(j,V),i=i+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(O,q)|0,i=i+Math.imul(O,G)|0,i=i+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(z,W)|0,i=i+Math.imul(z,X)|0,i=i+Math.imul(D,W)|0,a=a+Math.imul(D,X)|0,n=n+Math.imul(L,J)|0,i=i+Math.imul(L,K)|0,i=i+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(T,$)|0,i=i+Math.imul(T,tt)|0,i=i+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(M,rt)|0,i=i+Math.imul(M,nt)|0,i=i+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(x,at)|0,i=i+Math.imul(x,ot)|0,i=i+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(g,lt)|0,i=i+Math.imul(g,ut)|0,i=i+Math.imul(y,lt)|0,a=a+Math.imul(y,ut)|0,n=n+Math.imul(p,ht)|0,i=i+Math.imul(p,ft)|0,i=i+Math.imul(m,ht)|0,a=a+Math.imul(m,ft)|0,n=n+Math.imul(h,pt)|0,i=i+Math.imul(h,mt)|0,i=i+Math.imul(f,pt)|0,a=a+Math.imul(f,mt)|0;var At=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(j,q),i=Math.imul(j,G),i=i+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(O,W)|0,i=i+Math.imul(O,X)|0,i=i+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(z,J)|0,i=i+Math.imul(z,K)|0,i=i+Math.imul(D,J)|0,a=a+Math.imul(D,K)|0,n=n+Math.imul(L,$)|0,i=i+Math.imul(L,tt)|0,i=i+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(T,rt)|0,i=i+Math.imul(T,nt)|0,i=i+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(M,at)|0,i=i+Math.imul(M,ot)|0,i=i+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(x,lt)|0,i=i+Math.imul(x,ut)|0,i=i+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(g,ht)|0,i=i+Math.imul(g,ft)|0,i=i+Math.imul(y,ht)|0,a=a+Math.imul(y,ft)|0,n=n+Math.imul(p,pt)|0,i=i+Math.imul(p,mt)|0,i=i+Math.imul(m,pt)|0,a=a+Math.imul(m,mt)|0;var Tt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(j,W),i=Math.imul(j,X),i=i+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(O,J)|0,i=i+Math.imul(O,K)|0,i=i+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(z,$)|0,i=i+Math.imul(z,tt)|0,i=i+Math.imul(D,$)|0,a=a+Math.imul(D,tt)|0,n=n+Math.imul(L,rt)|0,i=i+Math.imul(L,nt)|0,i=i+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(T,at)|0,i=i+Math.imul(T,ot)|0,i=i+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(M,lt)|0,i=i+Math.imul(M,ut)|0,i=i+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(x,ht)|0,i=i+Math.imul(x,ft)|0,i=i+Math.imul(_,ht)|0,a=a+Math.imul(_,ft)|0,n=n+Math.imul(g,pt)|0,i=i+Math.imul(g,mt)|0,i=i+Math.imul(y,pt)|0,a=a+Math.imul(y,mt)|0;var St=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(j,J),i=Math.imul(j,K),i=i+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(O,$)|0,i=i+Math.imul(O,tt)|0,i=i+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(z,rt)|0,i=i+Math.imul(z,nt)|0,i=i+Math.imul(D,rt)|0,a=a+Math.imul(D,nt)|0,n=n+Math.imul(L,at)|0,i=i+Math.imul(L,ot)|0,i=i+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(T,lt)|0,i=i+Math.imul(T,ut)|0,i=i+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(M,ht)|0,i=i+Math.imul(M,ft)|0,i=i+Math.imul(k,ht)|0,a=a+Math.imul(k,ft)|0,n=n+Math.imul(x,pt)|0,i=i+Math.imul(x,mt)|0,i=i+Math.imul(_,pt)|0,a=a+Math.imul(_,mt)|0;var Et=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(j,$),i=Math.imul(j,tt),i=i+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(O,rt)|0,i=i+Math.imul(O,nt)|0,i=i+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(z,at)|0,i=i+Math.imul(z,ot)|0,i=i+Math.imul(D,at)|0,a=a+Math.imul(D,ot)|0,n=n+Math.imul(L,lt)|0,i=i+Math.imul(L,ut)|0,i=i+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(T,ht)|0,i=i+Math.imul(T,ft)|0,i=i+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0,n=n+Math.imul(M,pt)|0,i=i+Math.imul(M,mt)|0,i=i+Math.imul(k,pt)|0,a=a+Math.imul(k,mt)|0;var Lt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(j,rt),i=Math.imul(j,nt),i=i+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(O,at)|0,i=i+Math.imul(O,ot)|0,i=i+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(z,lt)|0,i=i+Math.imul(z,ut)|0,i=i+Math.imul(D,lt)|0,a=a+Math.imul(D,ut)|0,n=n+Math.imul(L,ht)|0,\n",
+       "i=i+Math.imul(L,ft)|0,i=i+Math.imul(C,ht)|0,a=a+Math.imul(C,ft)|0,n=n+Math.imul(T,pt)|0,i=i+Math.imul(T,mt)|0,i=i+Math.imul(S,pt)|0,a=a+Math.imul(S,mt)|0;var Ct=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(j,at),i=Math.imul(j,ot),i=i+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(O,lt)|0,i=i+Math.imul(O,ut)|0,i=i+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(z,ht)|0,i=i+Math.imul(z,ft)|0,i=i+Math.imul(D,ht)|0,a=a+Math.imul(D,ft)|0,n=n+Math.imul(L,pt)|0,i=i+Math.imul(L,mt)|0,i=i+Math.imul(C,pt)|0,a=a+Math.imul(C,mt)|0;var It=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(j,lt),i=Math.imul(j,ut),i=i+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(O,ht)|0,i=i+Math.imul(O,ft)|0,i=i+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0,n=n+Math.imul(z,pt)|0,i=i+Math.imul(z,mt)|0,i=i+Math.imul(D,pt)|0,a=a+Math.imul(D,mt)|0;var zt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,n=Math.imul(j,ht),i=Math.imul(j,ft),i=i+Math.imul(N,ht)|0,a=Math.imul(N,ft),n=n+Math.imul(O,pt)|0,i=i+Math.imul(O,mt)|0,i=i+Math.imul(R,pt)|0,a=a+Math.imul(R,mt)|0;var Dt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Dt>>>26)|0,Dt&=67108863,n=Math.imul(j,pt),i=Math.imul(j,mt),i=i+Math.imul(N,pt)|0,a=Math.imul(N,mt);var Pt=(u+n|0)+((8191&i)<<13)|0;return u=(a+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,l[0]=vt,l[1]=gt,l[2]=yt,l[3]=bt,l[4]=xt,l[5]=_t,l[6]=wt,l[7]=Mt,l[8]=kt,l[9]=At,l[10]=Tt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=It,l[16]=zt,l[17]=Dt,l[18]=Pt,0!==u&&(l[19]=u,r.length++),r};Math.imul||(k=u),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?k(this,t,e):r<63?u(this,t,e):r<1024?c(this,t,e):h(this,t,e)},f.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},f.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},f.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},f.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var h=l,f=u,d=0;d<o;d++){var p=r[c+d],m=n[c+d],v=r[c+d+o],g=n[c+d+o],y=h*v-f*g;g=h*g+f*v,v=y,r[c+d]=p+v,n[c+d]=m+g,r[c+d+o]=p-v,n[c+d+o]=m-g,d!==s&&(y=l*h-u*f,f=l*f+u*h,h=y)}},f.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},f.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},f.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},f.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},f.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},f.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,h,n,i);for(var d=0;d<n;d++){var p=s[d]*c[d]-l[d]*h[d];l[d]=s[d]*h[d]+l[d]*c[d],s[d]=p}return this.conjugate(s,l,n),this.transform(s,l,f,a,n,i),this.conjugate(f,a,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),h(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=l(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){n(\"number\"==typeof t&&t>=0);var i;i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var h=0|this.words[u];this.words[u]=c<<26-a|h>>>a,c=h&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a=t.length+r;this._expand(a);var o,s=0;for(i=0;i<t.length;i++){o=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;o-=67108863&l,s=(o>>26)-(l/67108864|0),this.words[i+r]=67108863&o}for(;i<this.length-r;i++)o=(0|this.words[i+r])+s,s=o>>26,this.words[i+r]=67108863&o;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)o=-(0|this.words[i])+s,s=o>>26,this.words[i]=67108863&o;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=this.length-t.length,n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){s=new a(null),s.length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864*(0|n.words[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){if(n(!t.isZero()),this.isZero())return{div:new a(0),mod:new a(0)};var i,o,s;return 0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e)},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),h=e.clone();!e.isZero();){for(var f=0,d=1;0==(e.words[0]&d)&&f<26;++f,d<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var p=0,m=1;0==(r.words[0]&m)&&p<26;++p,m<<=1);if(p>0)for(r.iushrn(p);p-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var l=0,u=1;0==(e.words[0]&u)&&l<26;++l,u<<=1);if(l>0)for(e.iushrn(l);l-- >0;)i.isOdd()&&i.iadd(s),i.iushrn(1);for(var c=0,h=1;0==(r.words[0]&h)&&c<26;++c,h<<=1);if(c>0)for(r.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(s),o.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(o)):(r.isub(e),o.isub(i))}var f;return f=0===e.cmpn(1)?i:o,f.cmpn(0)<0&&f.iadd(t),f},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];s+=a,a=s>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e=t<0;if(0!==this.negative&&!e)return-1;if(0===this.negative&&e)return 1;this.strip();var r;if(this.length>1)r=1;else{e&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];r=i===t?0:i<t?-1:1}return 0!==this.negative?0|-r:r},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 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e=t.clone();return e.red=null,e},a.mont=function(t){return new b(t)},i(b,y),b.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},b.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},b.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},b.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},b.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(void 0===e||e,this)},{}],69:[function(t,e,r){\"use 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r=\"abcdef\".split(\"\").concat(e),n=[];return t.indexOf(\"lo\")>=0&&n.push(\"lo=e[k+n]\"),t.indexOf(\"hi\")>=0&&n.push(\"hi=e[k+o]\"),r.push(i.replace(\"_\",n.join()).replace(\"$\",t)),Function.apply(void 0,r)}e.exports=n;var i=\"for(var j=2*a,k=j*c,l=k,m=c,n=b,o=a+b,p=c;d>p;++p,k+=j){var _;if($)if(m===p)m+=1,l+=j;else{for(var s=0;j>s;++s){var t=e[k+s];e[k+s]=e[l],e[l++]=t}var u=f[p];f[p]=f[m],f[m++]=u}}return m\"},{}],75:[function(t,e,r){\"use strict\";function n(t,e){e<=4*f?i(0,e-1,t):h(0,e-1,t)}function i(t,e,r){for(var n=2*(t+1),i=t+1;i<=e;++i){for(var a=r[n++],o=r[n++],s=i,l=n-2;s-- >t;){var u=r[l-2],c=r[l-1];if(u<a)break;if(u===a&&c<o)break;r[l]=u,r[l+1]=c,l-=2}r[l]=a,r[l+1]=o}}function a(t,e,r){t*=2,e*=2;var n=r[t],i=r[t+1];r[t]=r[e],r[t+1]=r[e+1],r[e]=n,r[e+1]=i}function o(t,e,r){t*=2,e*=2,r[t]=r[e],r[t+1]=r[e+1]}function s(t,e,r,n){t*=2,e*=2,r*=2;var i=n[t],a=n[t+1];n[t]=n[e],n[t+1]=n[e+1],n[e]=n[r],n[e+1]=n[r+1],n[r]=i,n[r+1]=a}function 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c=t(\"./convert\"),h=t(\"./transform\"),f=t(\"./clip\"),d=t(\"./wrap\"),p=t(\"./tile\");i.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,solidChildren:!1,tolerance:3,extent:4096,buffer:64,debug:0},i.prototype.splitTile=function(t,e,r,n,i,l,c){for(var h=[t,e,r,n],d=this.options,m=d.debug,v=null;h.length;){n=h.pop(),r=h.pop(),e=h.pop(),t=h.pop();var g=1<<e,y=a(e,r,n),b=this.tiles[y],x=e===d.maxZoom?0:d.tolerance/(g*d.extent);if(!b&&(m>1&&console.time(\"creation\"),b=this.tiles[y]=p(t,g,r,n,x,e===d.maxZoom),this.tileCoords.push({z:e,x:r,y:n}),m)){m>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,b.numFeatures,b.numPoints,b.numSimplified),console.timeEnd(\"creation\"));var _=\"z\"+e;this.stats[_]=(this.stats[_]||0)+1,this.total++}if(b.source=t,i){if(e===d.maxZoom||e===i)continue;var w=1<<i-e;if(r!==Math.floor(l/w)||n!==Math.floor(c/w))continue}else 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s={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z2:e,transformed:!1,min:[2,1],max:[-1,0]},l=0;l<t.length;l++){s.numFeatures++,i(s,t[l],a,o);var u=t[l].min,c=t[l].max;u[0]<s.min[0]&&(s.min[0]=u[0]),u[1]<s.min[1]&&(s.min[1]=u[1]),c[0]>s.max[0]&&(s.max[0]=c[0]),c[1]>s.max[1]&&(s.max[1]=c[1])}return s}function i(t,e,r,n){var i,o,s,l,u=e.geometry,c=e.type,h=[],f=r*r;if(1===c)for(i=0;i<u.length;i++)h.push(u[i]),t.numPoints++,t.numSimplified++;else for(i=0;i<u.length;i++)if(s=u[i],n||!(2===c&&s.dist<r||3===c&&s.area<f)){var d=[];for(o=0;o<s.length;o++)l=s[o],(n||l[2]>f)&&(d.push(l),t.numSimplified++),t.numPoints++;3===c&&a(d,s.outer),h.push(d)}else t.numPoints+=s.length;if(h.length){var p={geometry:h,type:c,tags:e.tags||null};null!==e.id&&(p.id=e.id),t.features.push(p)}}function a(t,e){o(t)<0===e&&t.reverse()}function o(t){for(var e,r,n=0,i=0,a=t.length,o=a-1;i<a;o=i++)e=t[i],r=t[o],n+=(r[0]-e[0])*(e[1]+r[1]);return n}e.exports=n},{}],145:[function(t,e,r){\"use 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k=0;k<3;++k){this.lineEnable[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].primalOffset,this.lineColor[k],this.lineWidth[k]*this.pixelRatio),this.lineMirror[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].mirrorOffset,this.lineColor[k],this.lineWidth[k]*this.pixelRatio);for(var I=n(y,A[k].primalMinor),z=n(b,A[k].mirrorMinor),D=this.lineTickLength,E=0;E<3;++E){var P=M/r[5*E];I[E]*=D[E]*P,z[E]*=D[E]*P}this.lineTickEnable[k]&&this._lines.drawAxisTicks(k,A[k].primalOffset,I,this.lineTickColor[k],this.lineTickWidth[k]*this.pixelRatio),this.lineTickMirror[k]&&this._lines.drawAxisTicks(k,A[k].mirrorOffset,z,this.lineTickColor[k],this.lineTickWidth[k]*this.pixelRatio)}this._text.bind(r,i,a,this.pixelRatio);for(var k=0;k<3;++k){for(var O=A[k].primalMinor,R=n(x,A[k].primalOffset),E=0;E<3;++E)this.lineTickEnable[k]&&(R[E]+=M*O[E]*Math.max(this.lineTickLength[E],0)/r[5*E]);if(this.tickEnable[k]){for(var E=0;E<3;++E)R[E]+=M*O[E]*this.tickPad[E]/r[5*E];this._text.drawTicks(k,this.tickSize[k],this.tickAngle[k],R,this.tickColor[k])}if(this.labelEnable[k]){for(var E=0;E<3;++E)R[E]+=M*O[E]*this.labelPad[E]/r[5*E];R[k]+=.5*(s[0][k]+s[1][k]),this._text.drawLabel(k,this.labelSize[k],this.labelAngle[k],R,this.labelColor[k])}}},p.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":149,\"./lib/cube.js\":150,\"./lib/lines.js\":151,\"./lib/text.js\":153,\"./lib/ticks.js\":154}],149:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}function i(t){for(var e=[],r=[],i=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,h=[0,0,0],f=[0,0,0],d=-1;d<=1;d+=2){r.push(i,i+2,i+1,i+1,i+2,i+3),h[l]=d,f[l]=d;for(var p=-1;p<=1;p+=2){h[u]=p;for(var m=-1;m<=1;m+=2)h[c]=m,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),i+=1}var v=u;u=c,c=v}var g=a(t,new 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o=n(c);o[t]=1,this.shader.uniforms.majorAxis=o,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=i,this.shader.uniforms.lineWidth=a;var s=n(f);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},m.drawGrid=function(t,e,r,a,o,s){if(this.gridCount[t]){var l=n(h);l[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=l;var u=i(d,a);u[e]+=r[0][e],this.shader.uniforms.offset=u;var p=n(c);p[t]=1,this.shader.uniforms.majorAxis=p;var m=n(f);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=s,this.shader.uniforms.color=o,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},m.drawZero=function(t,e,r,a,o,s){var l=n(h);this.shader.uniforms.majorAxis=l,l[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=l;var u=i(d,a);u[t]+=r[0][t],this.shader.uniforms.offset=u;var 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- pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\",\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\",null,[{name:\"position\",type:\"vec3\"}])};r.text=function(t){return n(t,\"#define GLSLIFY 1\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvoid main() {  \\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n  mat2 planeXform = scale * mat2(cos(angle), sin(angle),\\n                                -sin(angle), cos(angle));\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n  vec4 worldPosition = model * vec4(dataPosition, 1);\\n  \\n  //Compute clip position\\n  vec4 viewPosition = view * worldPosition;\\n  vec4 clipPosition = projection * viewPosition;\\n  clipPosition /= clipPosition.w;\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec4(viewOffset, 0, 0);\\n\\n  //Done\\n  gl_Position = clipPosition;\\n}\",\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\",null,[{name:\"position\",type:\"vec3\"}])};r.bg=function(t){return n(t,\"#define GLSLIFY 1\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  if(dot(normal, enable) > 0.0) {\\n    vec3 nPosition = mix(bounds[0], bounds[1], 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n  colorChannel = abs(normal);\\n}\",\"precision mediump float;\\n#define 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p=[0,0,0];f.drawTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=p;a[0]=a[1]=a[2]=0,a[t]=1,this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}};var m=[0,0,0];f.drawLabel=function(t,e,r,n,i){this.labelCount[t]&&(this.shader.uniforms.axis=m,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},f.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this,t(\"_process\"))},{\"./shaders\":152,_process:487,\"gl-buffer\":156,\"gl-vao\":271,\"vectorize-text\":554}],154:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,u=o%a;o<0?(l=0|-Math.ceil(l),u=0|-u):(l=0|Math.floor(l),u|=0);var c=\"\"+l;if(o<0&&(c=\"-\"+c),i){for(var h=\"\"+u;h.length<i;)h=\"0\"+h;return c+\".\"+h}return c}function i(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(var o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r}function a(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}r.create=i,r.equal=a},{}],155:[function(t,e,r){\"use strict\";function n(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}function i(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=p,s=m,l=0;l<3;++l)s[l]=o[l]=r[l];s[3]=o[3]=1,s[a]+=1,h(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,h(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,c=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+c*c)}return t}function a(t,e,r,n,a){var h=e.model||f,p=e.view||f,m=e.projection||f,y=t.bounds,a=a||l(h,p,m,y),b=a.axis;a.edges;u(d,p,h),u(d,m,d);for(var x=v,_=0;_<3;++_)x[_].lo=1/0,x[_].hi=-1/0,x[_].pixelsPerDataUnit=1/0;var w=o(c(d,d));c(d,d);for(var M=0;M<3;++M){var k=(M+1)%3,A=(M+2)%3,T=g;t:for(var _=0;_<2;++_){var S=[];if(b[M]<0!=!!_){T[M]=y[_][M];for(var E=0;E<2;++E){T[k]=y[E^_][k];for(var L=0;L<2;++L)T[A]=y[L^E^_][A],S.push(T.slice())}for(var E=0;E<w.length;++E){if(0===S.length)continue t;S=s.positive(S,w[E])}for(var E=0;E<S.length;++E)for(var A=S[E],C=i(g,d,A,r,n),L=0;L<3;++L)x[L].lo=Math.min(x[L].lo,A[L]),x[L].hi=Math.max(x[L].hi,A[L]),L!==M&&(x[L].pixelsPerDataUnit=Math.min(x[L].pixelsPerDataUnit,Math.abs(C[L])))}}}return x}e.exports=a;var o=t(\"extract-frustum-planes\"),s=t(\"split-polygon\"),l=t(\"./lib/cube.js\"),u=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/transpose\"),h=t(\"gl-vec4/transformMat4\"),f=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),d=new Float32Array(16),p=[0,0,0,1],m=[0,0,0,1],v=[new n(1/0,-1/0,1/0),new n(1/0,-1/0,1/0),new n(1/0,-1/0,1/0)],g=[0,0,0]},{\"./lib/cube.js\":150,\"extract-frustum-planes\":130,\"gl-mat4/multiply\":183,\"gl-mat4/transpose\":191,\"gl-vec4/transformMat4\":277,\"split-polygon\":526}],156:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}function i(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function a(t,e){for(var r=l.malloc(t.length,e),n=t.length,i=0;i<n;++i)r[i]=t[i];return r}function o(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}function s(t,e,r,i){if(r=r||t.ARRAY_BUFFER,i=i||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(i!==t.DYNAMIC_DRAW&&i!==t.STATIC_DRAW&&i!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var a=t.createBuffer(),o=new n(t,r,a,0,i);return o.update(e),o}var l=t(\"typedarray-pool\"),u=t(\"ndarray-ops\"),c=t(\"ndarray\"),h=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"],f=n.prototype;f.bind=function(){this.gl.bindBuffer(this.type,this.handle)},f.unbind=function(){this.gl.bindBuffer(this.type,null)},f.dispose=function(){this.gl.deleteBuffer(this.handle)},f.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 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n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":157}],159:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.plot=t,this.shader=e,this.bufferHi=r,this.bufferLo=n,this.bounds=[1/0,1/0,-1/0,-1/0],this.numPoints=0,this.color=[0,0,0,1]}function i(t,e){var r=a(t.gl,l.vertex,l.fragment),i=o(t.gl),s=o(t.gl),u=new n(t,r,i,s);return u.update(e),t.addObject(u),u}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"typedarray-pool\"),l=t(\"./lib/shaders\");e.exports=i;var u=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]],c=n.prototype;c.draw=function(){var t=new Float32Array([0,0]),e=new Float32Array([0,0]),r=new Float32Array([0,0]),n=new Float32Array([0,0]),i=[1,1];return function(){var a=this.plot,o=this.shader,s=this.bounds,l=this.numPoints;if(l){var c=a.gl,h=a.dataBox,f=a.viewBox,d=a.pixelRatio,p=s[2]-s[0],m=s[3]-s[1],v=h[2]-h[0],g=h[3]-h[1],y=2*p/v,b=2*m/g,x=(s[0]-h[0]-.5*v)/p,_=(s[1]-h[1]-.5*g)/m;t[0]=y,t[1]=b,e[0]=y-t[0],e[1]=b-t[1],r[0]=x,r[1]=_,n[0]=x-r[0],n[1]=_-r[1];var w=f[2]-f[0],M=f[3]-f[1];i[0]=2*d/w,i[1]=2*d/M,o.bind(),o.uniforms.scaleHi=t,o.uniforms.scaleLo=e,o.uniforms.translateHi=r,o.uniforms.translateLo=n,o.uniforms.pixelScale=i,o.uniforms.color=this.color,this.bufferLo.bind(),o.attributes.positionLo.pointer(c.FLOAT,!1,16,0),this.bufferHi.bind(),o.attributes.positionHi.pointer(c.FLOAT,!1,16,0),o.attributes.pixelOffset.pointer(c.FLOAT,!1,16,8),c.drawArrays(c.TRIANGLES,0,l*u.length)}}}(),c.drawPick=function(t){return t},c.pick=function(){return null},c.update=function(t){t=t||{};var e,r,n,i=t.positions||[],a=t.errors||[],o=1;\"lineWidth\"in t&&(o=+t.lineWidth);var l=5;\"capSize\"in t&&(l=+t.capSize),this.color=(t.color||[0,0,0,1]).slice();var c=this.bounds=[1/0,1/0,-1/0,-1/0],h=this.numPoints=i.length>>1;for(e=0;e<h;++e)r=i[2*e],n=i[2*e+1],c[0]=Math.min(r,c[0]),c[1]=Math.min(n,c[1]),c[2]=Math.max(r,c[2]),c[3]=Math.max(n,c[3]);c[2]===c[0]&&(c[2]+=1),c[3]===c[1]&&(c[3]+=1);var f=1/(c[2]-c[0]),d=1/(c[3]-c[1]),p=c[0],m=c[1],v=s.mallocFloat64(h*u.length*4),g=s.mallocFloat32(h*u.length*4),y=s.mallocFloat32(h*u.length*4),b=0;for(e=0;e<h;++e){r=i[2*e],n=i[2*e+1];for(var x=a[4*e],_=a[4*e+1],w=a[4*e+2],M=a[4*e+3],k=0;k<u.length;++k){var A=u[k],T=A[0],S=A[1];T<0?T*=x:T>0&&(T*=_),S<0?S*=w:S>0&&(S*=M),v[b++]=f*(r-p+T),v[b++]=d*(n-m+S),v[b++]=o*A[2]+(l+o)*A[4],v[b++]=o*A[3]+(l+o)*A[5]}}for(e=0;e<v.length;e++)g[e]=v[e],y[e]=v[e]-g[e];this.bufferHi.update(g),this.bufferLo.update(y),s.free(v)},c.dispose=function(){this.plot.removeObject(this),this.shader.dispose(),this.bufferHi.dispose(),this.bufferLo.dispose()}},{\"./lib/shaders\":160,\"gl-buffer\":156,\"gl-shader\":255,\"typedarray-pool\":541}],160:[function(t,e,r){e.exports={\n",
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n(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1}function i(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}function a(t,e,r,n){for(var i=f[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}function o(t){var e=t.gl,r=s(e),i=l(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),a=u(e);a.attributes.position.location=0,a.attributes.color.location=1,a.attributes.offset.location=2;var o=new n(e,r,i,a);return o.update(t),o}e.exports=o;var s=t(\"gl-buffer\"),l=t(\"gl-vao\"),u=t(\"./shaders/index\"),c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],h=n.prototype;h.isOpaque=function(){return this.opacity>=1},h.isTransparent=function(){return this.opacity<1},h.drawTransparent=h.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||c,i=r.projection=t.projection||c;r.model=t.model||c,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],o=n[13],s=n[14],l=n[15],u=this.pixelRatio*(i[3]*a+i[7]*o+i[11]*s+i[15]*l)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]),r.capSize=this.capSize[h]*u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var f=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=(n+e)%3,o=[0,0,0];o[a]=i,r.push(o)}t[e]=r}return t}();h.update=function(t){t=t||{},\"lineWidth\"in t&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),\"opacity\"in t&&(this.opacity=t.opacity);var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var o=[],s=r.length,l=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var u=0;u<3;++u){this.lineOffset[u]=l;t:for(var c=0;c<s;++c){for(var h=r[c],f=0;f<3;++f)if(isNaN(h[f])||!isFinite(h[f]))continue t;var d=n[c],p=e[u];if(Array.isArray(p[0])&&(p=e[c]),3===p.length&&(p=[p[0],p[1],p[2],1]),!isNaN(d[0][u])&&!isNaN(d[1][u])){if(d[0][u]<0){var m=h.slice();m[u]+=d[0][u],o.push(h[0],h[1],h[2],p[0],p[1],p[2],p[3],0,0,0,m[0],m[1],m[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,m),l+=2+a(o,m,p,u)}if(d[1][u]>0){var m=h.slice();m[u]+=d[1][u],o.push(h[0],h[1],h[2],p[0],p[1],p[2],p[3],0,0,0,m[0],m[1],m[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,m),l+=2+a(o,m,p,u)}}}this.lineCount[u]=l-this.lineOffset[u]}this.buffer.update(o)}},h.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":163,\"gl-buffer\":156,\"gl-vao\":271}],162:[function(t,e,r){e.exports=function(t){\"string\"==typeof t&&(t=[t]);for(var e=[].slice.call(arguments,1),r=[],n=0;n<t.length-1;n++)r.push(t[n],e[n]||\"\");return r.push(t[n]),r.join(\"\")}},{}],163:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n  vec4 worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * view * worldPosition;\\n  fragColor           = color;\\n  fragPosition        = position;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if(any(lessThan(fragPosition, clipBounds[0])) || any(greaterThan(fragPosition, clipBounds[1]))) {\\n    discard;\\n  }\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":255,glslify:162}],164:[function(t,e,r){\"use strict\";function n(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function i(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function a(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);y=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;y[n]=i}}function o(t){switch(t){case p:throw new Error(\"gl-fbo: Framebuffer unsupported\");case m:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case v:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case g:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function s(t,e,r,n,i,a){if(!n)return null;var o=d(t,e,r,i,n);return o.magFilter=t.NEAREST,o.minFilter=t.NEAREST,o.mipSamples=1,o.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,a,t.TEXTURE_2D,o.handle,0),o}function l(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function u(t){var e=n(t.gl),r=t.gl,a=t.handle=r.createFramebuffer(),u=t._shape[0],c=t._shape[1],h=t.color.length,f=t._ext,d=t._useStencil,p=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,a);for(var v=0;v<h;++v)t.color[v]=s(r,u,c,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===h?(t._color_rb=l(r,u,c,r.RGBA4,r.COLOR_ATTACHMENT0),f&&f.drawBuffersWEBGL(y[0])):h>1&&f.drawBuffersWEBGL(y[h]);var g=r.getExtension(\"WEBGL_depth_texture\");g?d?t.depth=s(r,u,c,g.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p&&(t.depth=s(r,u,c,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):p&&d?t._depth_rb=l(r,u,c,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p?t._depth_rb=l(r,u,c,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=l(r,u,c,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(var v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),i(r,e),o(b)}i(r,e)}function c(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var l=0;l<i;++l)this.color[l]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var c=this,h=[0|e,0|r];Object.defineProperties(h,{0:{get:function(){return c._shape[0]},set:function(t){return c.width=t}},1:{get:function(){return c._shape[1]},set:function(t){return c.height=t}}}),this._shapeVector=h,u(this)}function h(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var a=t.gl,s=a.getParameter(a.MAX_RENDERBUFFER_SIZE);if(e<0||e>s||r<0||r>s)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var l=n(a),u=0;u<t.color.length;++u)t.color[u].shape=t._shape;t._color_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._color_rb),a.renderbufferStorage(a.RENDERBUFFER,a.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&a.renderbufferStorage(a.RENDERBUFFER,a.STENCIL_INDEX,t._shape[0],t._shape[1])),a.bindFramebuffer(a.FRAMEBUFFER,t.handle);var c=a.checkFramebufferStatus(a.FRAMEBUFFER);c!==a.FRAMEBUFFER_COMPLETE&&(t.dispose(),i(a,l),o(c)),i(a,l)}}function f(t,e,r,n){p||(p=t.FRAMEBUFFER_UNSUPPORTED,m=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,v=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,g=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var i=t.getExtension(\"WEBGL_draw_buffers\");if(!y&&i&&a(t,i),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var o=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>o||r<0||r>o)throw new Error(\"gl-fbo: Parameters are too large for FBO\");n=n||{};var s=1;if(\"color\"in n){if((s=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(s>1){if(!i)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(s>t.getParameter(i.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+s+\" draw buffers\")}}var l=t.UNSIGNED_BYTE,u=t.getExtension(\"OES_texture_float\");if(n.float&&s>0){if(!u)throw new Error(\"gl-fbo: Context does not support floating point textures\");l=t.FLOAT}else n.preferFloat&&s>0&&u&&(l=t.FLOAT);var h=!0;\"depth\"in n&&(h=!!n.depth);var f=!1;return\"stencil\"in n&&(f=!!n.stencil),new c(t,e,r,l,s,h,f,i)}var d=t(\"gl-texture2d\");e.exports=f;var p,m,v,g,y=null,b=c.prototype;Object.defineProperties(b,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return h(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return t|=0,h(this,t,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,h(this,this._shape[0],t),t},enumerable:!1}}),b.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},b.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":267}],165:[function(t,e,r){function n(t,e,r){\"use strict\";var n=o(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===a.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var u=i(\"Error compiling %s shader %s:\\n\",l,n),c=i(\"%s%s\",u,t),h=t.split(\"\\n\"),f={},d=0;d<h.length;d++){var p=h[d];if(\"\"!==p){var m=parseInt(p.split(\":\")[2]);if(isNaN(m))throw new Error(i(\"Could not parse error: %s\",p));f[m]=p}}for(var v=s(e).split(\"\\n\"),d=0;d<v.length;d++)if(f[d+3]||f[d+2]||f[d+1]){var g=v[d];if(u+=g+\"\\n\",f[d+1]){var y=f[d+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),u+=i(\"^^^ %s\\n\\n\",y)}}return{long:u.trim(),short:c.trim()}}var i=t(\"sprintf-js\").sprintf,a=t(\"gl-constants/lookup\"),o=t(\"glsl-shader-name\"),s=t(\"add-line-numbers\");e.exports=n},{\"add-line-numbers\":40,\"gl-constants/lookup\":158,\"glsl-shader-name\":279,\"sprintf-js\":527}],166:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}function i(t,e){var r=t.gl,i=l(r,c.vertex,c.fragment),a=l(r,c.pickVertex,c.pickFragment),o=u(r),s=u(r),h=u(r),f=u(r),d=new n(t,i,a,o,s,h,f);return d.update(e),t.addObject(d),d}e.exports=i;var a=t(\"binary-search-bounds\"),o=t(\"iota-array\"),s=t(\"typedarray-pool\"),l=t(\"gl-shader\"),u=t(\"gl-buffer\"),c=t(\"./lib/shaders\"),h=n.prototype,f=[0,0,1,0,0,1,1,0,1,1,0,1];h.draw=function(){var t=[1,0,0,0,1,0,0,0,1];return function(){var e=this.plot,r=this.shader,n=this.bounds,i=this.numVertices;if(!(i<=0)){var a=e.gl,o=e.dataBox,s=n[2]-n[0],l=n[3]-n[1],u=o[2]-o[0],c=o[3]-o[1];t[0]=2*s/u,t[4]=2*l/c,t[6]=2*(n[0]-o[0])/u-1,t[7]=2*(n[1]-o[1])/c-1,r.bind();var h=r.uniforms;h.viewTransform=t,h.shape=this.shape;var f=r.attributes;this.positionBuffer.bind(),f.position.pointer(),this.weightBuffer.bind(),f.weight.pointer(a.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(a.UNSIGNED_BYTE,!0),a.drawArrays(a.TRIANGLES,0,i)}}}(),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2*u/h,t[4]=2*c/f,t[6]=2*(a[0]-l[0])/h-1,t[7]=2*(a[1]-l[1])/f-1;for(var d=0;d<4;++d)e[d]=r>>8*d&255;this.pickOffset=r,i.bind();var p=i.uniforms;p.viewTransform=t,p.pickOffset=e,p.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},h.update=function(t){t=t||{};var e=t.shape||[0,0],r=t.x||o(e[0]),n=t.y||o(e[1]),i=t.z||new Float32Array(e[0]*e[1]);this.xData=r,this.yData=n;var l=t.colorLevels||[0],u=t.colorValues||[0,0,0,1],c=l.length,h=this.bounds,d=h[0]=r[0],p=h[1]=n[0],m=h[2]=r[r.length-1],v=h[3]=n[n.length-1],g=1/(m-d),y=1/(v-p),b=e[0],x=e[1];this.shape=[b,x];var _=(b-1)*(x-1)*(f.length>>>1);this.numVertices=_;for(var w=s.mallocUint8(4*_),M=s.mallocFloat32(2*_),k=s.mallocUint8(2*_),A=s.mallocUint32(_),T=0,S=0;S<x-1;++S)for(var E=y*(n[S]-p),L=y*(n[S+1]-p),C=0;C<b-1;++C)for(var I=g*(r[C]-d),z=g*(r[C+1]-d),D=0;D<f.length;D+=2){var P,O,R,F,j=f[D],N=f[D+1],B=(S+N)*b+(C+j),U=i[B],V=a.le(l,U);if(V<0)P=u[0],O=u[1],R=u[2],F=u[3];else if(V===c-1)P=u[4*c-4],O=u[4*c-3],R=u[4*c-2],F=u[4*c-1];else{var H=(U-l[V])/(l[V+1]-l[V]),q=1-H,G=4*V,Y=4*(V+1);P=q*u[G]+H*u[Y],O=q*u[G+1]+H*u[Y+1],R=q*u[G+2]+H*u[Y+2],F=q*u[G+3]+H*u[Y+3]}w[4*T]=255*P,w[4*T+1]=255*O,w[4*T+2]=255*R,w[4*T+3]=255*F,M[2*T]=.5*I+.5*z,M[2*T+1]=.5*E+.5*L,k[2*T]=j,k[2*T+1]=N,A[T]=S*b+C,T+=1}this.positionBuffer.update(M),this.weightBuffer.update(k),this.colorBuffer.update(w),this.idBuffer.update(A),s.free(M),s.free(w),s.free(k),s.free(A)},h.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":167,\"binary-search-bounds\":168,\"gl-buffer\":156,\"gl-shader\":255,\"iota-array\":293,\"typedarray-pool\":541}],167:[function(t,e,r){\"use strict\";e.exports={fragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\",vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\",pickFragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\",pickVertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"}},{}],168:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],169:[function(t,e,r){r.lineVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi, dLo;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, screenShape;\\nuniform float width;\\n\\nvarying vec2 direction;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n  return (posHi + trHi) * scHi\\n       + (posLo + trLo) * scHi\\n       + (posHi + trHi) * scLo\\n       + (posLo + trLo) * scLo;\\n}\\n\\n\\nvec2 project_2_1(vec2 scHi, vec2 scLo, vec2 posHi, vec2 posLo) {\\n  return scHi * posHi\\n       + scLo * posHi\\n       + scHi * posLo\\n       + scLo * posLo;\\n}\\n\\nvoid main() {\\n  vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n  vec2 dir = project_2_1(scaleHi, scaleLo, dHi, dLo);\\n  vec2 n = 0.5 * width * normalize(screenShape.yx * vec2(dir.y, -dir.x)) / screenShape.xy;\\n  vec2 tangent = normalize(screenShape.xy * dir);\\n  if(dir.x < 0.0 || (dir.x == 0.0 && dir.y < 0.0)) {\\n    direction = -tangent;\\n  } else {\\n    direction = tangent;\\n  }\\n  gl_Position = vec4(p + n, 0.0, 1.0);\\n}\",r.lineFragment=\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nuniform vec2 screenShape;\\nuniform sampler2D dashPattern;\\nuniform float dashLength;\\n\\nvarying vec2 direction;\\n\\nvoid main() {\\n  float t = fract(dot(direction, gl_FragCoord.xy) / dashLength);\\n  vec4 pcolor = color * texture2D(dashPattern, vec2(t, 0.0)).r;\\n  gl_FragColor = vec4(pcolor.rgb * pcolor.a, pcolor.a);\\n}\",r.mitreVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo;\\nuniform float radius;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n  return (posHi + trHi) * scHi\\n       + (posLo + trLo) * scHi\\n       + (posHi + trHi) * scLo\\n       + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n  vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n  gl_Position = vec4(p, 0.0, 1.0);\\n  gl_PointSize = radius;\\n}\",r.mitreFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  if(length(gl_PointCoord.xy - 0.5) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = vec4(color.rgb, color.a);\\n}\",r.pickVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi;\\nattribute vec4 pick0, pick1;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, screenShape;\\nuniform float width;\\n\\nvarying vec4 pickA, pickB;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n  return (posHi + trHi) * scHi\\n       + (posLo + trLo) * scHi\\n       + (posHi + trHi) * scLo\\n       + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n  vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n  vec2 n = width * normalize(screenShape.yx * vec2(dHi.y, -dHi.x)) / screenShape.xy;\\n  gl_Position = vec4(p + n, 0, 1);\\n  pickA = pick0;\\n  pickB = pick1;\\n}\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 pickOffset;\\n\\nvarying vec4 pickA, pickB;\\n\\nvoid main() {\\n  vec4 fragId = vec4(pickA.xyz, 0.0);\\n  if(pickB.w > pickA.w) {\\n    fragId.xyz = pickB.xyz;\\n  }\\n\\n  fragId += pickOffset;\\n\\n  fragId.y += floor(fragId.x / 256.0);\\n  fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n  fragId.z += floor(fragId.y / 256.0);\\n  fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n  fragId.w += floor(fragId.z / 256.0);\\n  fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n  gl_FragColor = fragId / 255.0;\\n}\",r.fillVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, projectAxis;\\nuniform float projectValue, depth;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n  return (posHi + trHi) * scHi\\n       + (posLo + trLo) * scHi\\n       + (posHi + trHi) * scLo\\n       + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n  vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n  if(dHi.y < 0.0 || (dHi.y == 0.0 && dHi.x < 0.0)) {\\n    if(dot(p, projectAxis) < projectValue) {\\n      p = p * (1.0 - abs(projectAxis)) + projectAxis * projectValue;\\n    }\\n  }\\n  gl_Position = vec4(p, depth, 1);\\n}\",r.fillFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = vec4(color.rgb * color.a, color.a);\\n}\"},{}],170:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l){this.plot=t,this.dashPattern=e,this.lineBufferHi=r,this.lineBufferLo=n,this.pickBuffer=i,this.lineShader=a,this.mitreShader=o,this.fillShader=s,this.pickShader=l,this.usingDashes=!1,this.bounds=[1/0,1/0,-1/0,-1/0],this.width=1,this.color=[0,0,1,1],this.fill=[!1,!1,!1,!1],this.fillColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.data=null,this.numPoints=0,this.vertCount=0,this.pickOffset=0}function i(t){return t.map(function(t){return t.slice()})}function a(t,e){var r=t.gl,i=s(r),a=s(r),u=s(r),c=l(r,[1,1]),f=o(r,h.lineVertex,h.lineFragment),d=o(r,h.mitreVertex,h.mitreFragment),p=o(r,h.fillVertex,h.fillFragment),m=o(r,h.pickVertex,h.pickFragment),v=new n(t,c,i,a,u,f,d,p,m);return t.addObject(v),v.update(e),v}e.exports=a;var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"gl-texture2d\"),u=t(\"ndarray\"),c=t(\"typedarray-pool\"),h=t(\"./lib/shaders\"),f=n.prototype;f.setProjectionModel=function(){var t={scaleHi:new Float32Array([0,0]),scaleLo:new Float32Array([0,0]),translateHi:new Float32Array([0,0]),translateLo:new Float32Array([0,0]),screenShape:[0,0]};return function(){var e=this.bounds,r=this.plot.viewBox,n=this.plot.dataBox,i=e[2]-e[0],a=e[3]-e[1],o=n[2]-n[0],s=n[3]-n[1],l=r[2]-r[0],u=r[3]-r[1],c=2*i/o,h=2*a/s,f=(e[0]-n[0]-.5*o)/i,d=(e[1]-n[1]-.5*s)/a;return t.scaleHi[0]=c,t.scaleHi[1]=h,t.scaleLo[0]=c-t.scaleHi[0],t.scaleLo[1]=h-t.scaleHi[1],t.translateHi[0]=f,t.translateHi[1]=d,t.translateLo[0]=f-t.translateHi[0],t.translateLo[1]=d-t.translateHi[1],t.screenShape[0]=l,t.screenShape[1]=u,t}}(),f.setProjectionUniforms=function(t,e){t.scaleHi=e.scaleHi,t.scaleLo=e.scaleLo,t.translateHi=e.translateHi,t.translateLo=e.translateLo,t.screenShape=e.screenShape},f.draw=function(){var t=[1,0],e=[-1,0],r=[0,1],n=[0,-1];return function(){var i=this.vertCount;if(i){var a=this.setProjectionModel(),o=this.plot,s=this.width,l=o.gl,u=o.pixelRatio,c=this.color,h=this.fillShader.attributes;this.lineBufferLo.bind(),h.aLo.pointer(l.FLOAT,!1,16,0),this.lineBufferHi.bind();var f=this.fill;if(f[0]||f[1]||f[2]||f[3]){var d=this.fillShader;d.bind();var p=d.uniforms;this.setProjectionUniforms(p,a),p.depth=o.nextDepthValue(),h.aHi.pointer(l.FLOAT,!1,16,0),h.dHi.pointer(l.FLOAT,!1,16,8),l.depthMask(!0),l.enable(l.DEPTH_TEST);var m=this.fillColor;f[0]&&(p.color=m[0],p.projectAxis=e,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[1]&&(p.color=m[1],p.projectAxis=n,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[2]&&(p.color=m[2],p.projectAxis=t,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[3]&&(p.color=m[3],p.projectAxis=r,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),l.depthMask(!1),l.disable(l.DEPTH_TEST)}var v=this.lineShader;v.bind(),this.lineBufferLo.bind(),v.attributes.aLo.pointer(l.FLOAT,!1,16,0),v.attributes.dLo.pointer(l.FLOAT,!1,16,8),this.lineBufferHi.bind();var g=v.uniforms;this.setProjectionUniforms(g,a),g.color=c,g.width=s*u,g.dashPattern=this.dashPattern.bind(),g.dashLength=this.dashLength*u;var y=v.attributes;if(y.aHi.pointer(l.FLOAT,!1,16,0),y.dHi.pointer(l.FLOAT,!1,16,8),l.drawArrays(l.TRIANGLES,0,i),s>2&&!this.usingDashes){var b=this.mitreShader;this.lineBufferLo.bind(),b.attributes.aLo.pointer(l.FLOAT,!1,48,0),this.lineBufferHi.bind(),b.bind();var x=b.uniforms;this.setProjectionUniforms(x,a),x.color=c,x.radius=s*u,b.attributes.aHi.pointer(l.FLOAT,!1,48,0),l.drawArrays(l.POINTS,0,i/3|0)}}}}(),f.drawPick=function(){var t=[0,0,0,0];return function(e){var r=this.vertCount,n=this.numPoints;if(this.pickOffset=e,!r)return e+n;var i=this.setProjectionModel(),a=this.plot,o=this.width,s=a.gl,l=a.pickPixelRatio,u=this.pickShader,c=this.pickBuffer;t[0]=255&e,t[1]=e>>>8&255,t[2]=e>>>16&255,t[3]=e>>>24,u.bind();var h=u.uniforms;this.setProjectionUniforms(h,i),h.width=o*l,h.pickOffset=t;var f=u.attributes;return this.lineBufferHi.bind(),f.aHi.pointer(s.FLOAT,!1,16,0),f.dHi.pointer(s.FLOAT,!1,16,8),this.lineBufferLo.bind(),f.aLo.pointer(s.FLOAT,!1,16,0),c.bind(),f.pick0.pointer(s.UNSIGNED_BYTE,!1,8,0),f.pick1.pointer(s.UNSIGNED_BYTE,!1,8,4),s.drawArrays(s.TRIANGLES,0,r),e+n}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(r<n||r>=n+i)return null;var a=r-n,o=this.data;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}},f.update=function(t){t=t||{};var e,r,n,a,o,s=this.plot.gl;this.color=(t.color||[0,0,1,1]).slice(),this.width=+(t.width||1),this.fill=(t.fill||[!1,!1,!1,!1]).slice(),this.fillColor=i(t.fillColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var h=t.dashes||[1],f=0;for(e=0;e<h.length;++e)f+=h[e];var d=c.mallocUint8(f);n=0;var p=255;for(e=0;e<h.length;++e){for(r=0;r<h[e];++r)d[n++]=p;p^=255}this.dashPattern.dispose(),this.usingDashes=h.length>1,this.dashPattern=l(s,u(d,[f,1,4],[1,0,0])),this.dashPattern.minFilter=s.NEAREST,this.dashPattern.magFilter=s.NEAREST,this.dashLength=f,c.free(d);var m=t.positions;this.data=m;var v=this.bounds;v[0]=v[1]=1/0,v[2]=v[3]=-1/0;var g=this.numPoints=m.length>>>1;if(0!==g){for(e=0;e<g;++e)a=m[2*e],o=m[2*e+1],isNaN(a)||isNaN(o)||(v[0]=Math.min(v[0],a),v[1]=Math.min(v[1],o),v[2]=Math.max(v[2],a),v[3]=Math.max(v[3],o));v[0]===v[2]&&(v[2]+=1),v[3]===v[1]&&(v[3]+=1);var y=c.mallocFloat64(24*(g-1)),b=c.mallocFloat32(24*(g-1)),x=c.mallocFloat32(24*(g-1)),_=c.mallocUint32(12*(g-1)),w=b.length,M=_.length;n=g;for(var k=0;n>1;){var A=--n;a=m[2*n],o=m[2*n+1];var T=A-1,S=m[2*T],E=m[2*T+1];if(!(isNaN(a)||isNaN(o)||isNaN(S)||isNaN(E))){k+=1,a=(a-v[0])/(v[2]-v[0]),o=(o-v[1])/(v[3]-v[1]),S=(S-v[0])/(v[2]-v[0]),E=(E-v[1])/(v[3]-v[1]);var L=S-a,C=E-o,I=A|1<<24,z=A-1,D=A,P=A-1|1<<24;y[--w]=-C,y[--w]=-L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z,y[--w]=C,y[--w]=L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=-C,y[--w]=-L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=C,y[--w]=L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=-C,y[--w]=-L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z,y[--w]=C,y[--w]=L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z}}for(e=0;e<y.length;e++)b[e]=y[e],x[e]=y[e]-b[e];this.vertCount=6*k,this.lineBufferHi.update(b.subarray(w)),this.lineBufferLo.update(x.subarray(w)),this.pickBuffer.update(_.subarray(M)),c.free(y),c.free(b),c.free(x),c.free(_)}},f.dispose=function(){this.plot.removeObject(this),this.lineBufferLo.dispose(),this.lineBufferHi.dispose(),this.pickBuffer.dispose(),this.lineShader.dispose(),this.mitreShader.dispose(),this.fillShader.dispose(),this.pickShader.dispose(),this.dashPattern.dispose()}},{\"./lib/shaders\":169,\"gl-buffer\":156,\"gl-shader\":255,\"gl-texture2d\":267,ndarray:467,\"typedarray-pool\":541}],171:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvoid main() {\\n  vec4 projected = projection * view * model * vec4(position, 1.0);\\n  vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\\n  vec2 tangent = normalize(screenShape * tangentClip.xy);\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\\n\\n  gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\",a=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return n(t,i,\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\\n    discard;\\n  }\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\",null,a)},r.createPickShader=function(t){return n(t,i,\"precision mediump float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\nlowp vec4 encode_float_1_0(highp float v) {\\n  highp float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  highp vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  highp float e = floor(log2(av));\\n  highp float m = av * pow(2.0, -e) - 1.0;\\n  \\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n  \\n  //Unpack exponent\\n  highp float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0; \\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\n\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\\n    discard;\\n  }\\n  gl_FragColor = vec4(pickId/255.0, encode_float_1_0(pixelArcLength).xyz);\\n}\",null,a)}},{\"gl-shader\":255}],172:[function(t,e,r){\"use strict\";function n(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function i(t){\n",
+       "for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function a(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function o(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}function s(t){var e=t.gl||t.scene&&t.scene.gl,r=m(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var n=v(e);n.attributes.position.location=0,n.attributes.nextPosition.location=1,n.attributes.arcLength.location=2,n.attributes.lineWidth.location=3,n.attributes.color.location=4;for(var i=l(e),a=u(e,[{buffer:i,size:3,offset:0,stride:48},{buffer:i,size:3,offset:12,stride:48},{buffer:i,size:1,offset:24,stride:48},{buffer:i,size:1,offset:28,stride:48},{buffer:i,size:4,offset:32,stride:48}]),s=d(new Array(1024),[256,1,4]),h=0;h<1024;++h)s.data[h]=255;var f=c(e,s);f.wrap=e.REPEAT;var p=new o(e,r,n,i,a,f);return p.update(t),p}e.exports=s;var l=t(\"gl-buffer\"),u=t(\"gl-vao\"),c=t(\"gl-texture2d\"),h=t(\"glsl-read-float\"),f=t(\"binary-search-bounds\"),d=t(\"ndarray\"),p=t(\"./lib/shaders\"),m=p.createShader,v=p.createPickShader,g=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],y=o.prototype;y.isTransparent=function(){return this.opacity<1},y.isOpaque=function(){return this.opacity>=1},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||g,view:t.view||g,projection:t.projection||g,clipBounds:i(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||g,view:t.view||g,projection:t.projection||g,pickId:this.pickId,clipBounds:i(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.update=function(t){var e,r;this.dirty=!0;var i=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),\"opacity\"in t&&(this.opacity=+t.opacity);var a=t.position||t.positions;if(a){var o=t.color||t.colors||[0,0,0,1],s=t.lineWidth||1,l=[],u=[],c=[],h=0,p=0,m=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],v=!1;t:for(e=1;e<a.length;++e){var g=a[e-1],y=a[e];for(u.push(h),c.push(g.slice()),r=0;r<3;++r){if(isNaN(g[r])||isNaN(y[r])||!isFinite(g[r])||!isFinite(y[r])){if(!i&&l.length>0){for(var b=0;b<24;++b)l.push(l[l.length-12]);p+=2,v=!0}continue t}m[0][r]=Math.min(m[0][r],g[r],y[r]),m[1][r]=Math.max(m[1][r],g[r],y[r])}var x,_;Array.isArray(o[0])?(x=o[e-1],_=o[e]):x=_=o,3===x.length&&(x=[x[0],x[1],x[2],1]),3===_.length&&(_=[_[0],_[1],_[2],1]);var w;w=Array.isArray(s)?s[e-1]:s;var 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null;if(r===this.arcLength.length-1)return new a(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],o=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),s=1-o,l=[0,0,0],u=0;u<3;++u)l[u]=s*n[u]+o*i[u];var c=Math.min(o<.5?r:r+1,this.points.length-1);return new a(e,l,c,this.points[c])}},{\"./lib/shaders\":171,\"binary-search-bounds\":66,\"gl-buffer\":156,\"gl-texture2d\":267,\"gl-vao\":271,\"glsl-read-float\":278,ndarray:467}],173:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r*a-i*n;return o?(o=1/o,t[0]=a*o,t[1]=-n*o,t[2]=-i*o,t[3]=r*o,t):null}e.exports=n},{}],174:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=c*o-s*u,f=-c*a+s*l,d=u*a-o*l,p=r*h+n*f+i*d;return p?(p=1/p,t[0]=h*p,t[1]=(-c*n+i*u)*p,t[2]=(s*n-i*o)*p,t[3]=f*p,t[4]=(c*r-i*l)*p,t[5]=(-s*r+i*a)*p,t[6]=d*p,t[7]=(-u*r+n*l)*p,t[8]=(o*r-n*a)*p,t):null}e.exports=n},{}],175:[function(t,e,r){function n(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}e.exports=n},{}],176:[function(t,e,r){function n(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],177:[function(t,e,r){function n(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],h=t[10],f=t[11],d=t[12],p=t[13],m=t[14],v=t[15];return(e*o-r*a)*(h*v-f*m)-(e*s-n*a)*(c*v-f*p)+(e*l-i*a)*(c*m-h*p)+(r*s-n*o)*(u*v-f*d)-(r*l-i*o)*(u*m-h*d)+(n*l-i*s)*(u*p-c*d)}e.exports=n},{}],178:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,h=n*s,f=i*o,d=i*s,p=i*l,m=a*o,v=a*s,g=a*l;return t[0]=1-h-p,t[1]=c+g,t[2]=f-v,t[3]=0,t[4]=c-g,t[5]=1-u-p,t[6]=d+m,t[7]=0,t[8]=f+v,t[9]=d-m,t[10]=1-u-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],179:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,h=n*l,f=n*u,d=i*l,p=i*u,m=a*u,v=o*s,g=o*l,y=o*u;return t[0]=1-(d+m),t[1]=h+y,t[2]=f-g,t[3]=0,t[4]=h-y,t[5]=1-(c+m),t[6]=p+v,t[7]=0,t[8]=f+g,t[9]=p-v,t[10]=1-(c+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}e.exports=n},{}],180:[function(t,e,r){function n(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],181:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15],y=r*s-n*o,b=r*l-i*o,x=r*u-a*o,_=n*l-i*s,w=n*u-a*s,M=i*u-a*l,k=c*m-h*p,A=c*v-f*p,T=c*g-d*p,S=h*v-f*m,E=h*g-d*m,L=f*g-d*v,C=y*L-b*E+x*S+_*T-w*A+M*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*M-v*w+g*_)*C,t[3]=(f*w-h*M-d*_)*C,t[4]=(l*T-o*L-u*A)*C,t[5]=(r*L-i*T+a*A)*C,t[6]=(v*x-p*M-g*b)*C,t[7]=(c*M-f*x+d*b)*C,t[8]=(o*E-s*T+u*k)*C,t[9]=(n*T-r*E-a*k)*C,t[10]=(p*w-m*x+g*y)*C,t[11]=(h*x-c*w-d*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*b-p*_-v*y)*C,t[15]=(c*_-h*b+f*y)*C,t):null}e.exports=n},{}],182:[function(t,e,r){function n(t,e,r,n){var a,o,s,l,u,c,h,f,d,p,m=e[0],v=e[1],g=e[2],y=n[0],b=n[1],x=n[2],_=r[0],w=r[1],M=r[2];return Math.abs(m-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(g-M)<1e-6?i(t):(h=m-_,f=v-w,d=g-M,p=1/Math.sqrt(h*h+f*f+d*d),h*=p,f*=p,d*=p,a=b*d-x*f,o=x*h-y*d,s=y*f-b*h,p=Math.sqrt(a*a+o*o+s*s),p?(p=1/p,a*=p,o*=p,s*=p):(a=0,o=0,s=0),l=f*s-d*o,u=d*a-h*s,c=h*o-f*a,p=Math.sqrt(l*l+u*u+c*c),p?(p=1/p,l*=p,u*=p,c*=p):(l=0,u=0,c=0),t[0]=a,t[1]=l,t[2]=h,t[3]=0,t[4]=o,t[5]=u,t[6]=f,t[7]=0,t[8]=s,t[9]=c,t[10]=d,t[11]=0,t[12]=-(a*m+o*v+s*g),t[13]=-(l*m+u*v+c*g),t[14]=-(h*m+f*v+d*g),t[15]=1,t)}var i=t(\"./identity\");e.exports=n},{\"./identity\":180}],183:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],m=e[12],v=e[13],g=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*h+w*m,t[1]=b*i+x*l+_*f+w*v,t[2]=b*a+x*u+_*d+w*g,t[3]=b*o+x*c+_*p+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*h+w*m,t[5]=b*i+x*l+_*f+w*v,t[6]=b*a+x*u+_*d+w*g,t[7]=b*o+x*c+_*p+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*h+w*m,t[9]=b*i+x*l+_*f+w*v,t[10]=b*a+x*u+_*d+w*g,t[11]=b*o+x*c+_*p+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*h+w*m,t[13]=b*i+x*l+_*f+w*v,t[14]=b*a+x*u+_*d+w*g,t[15]=b*o+x*c+_*p+w*y,t}e.exports=n},{}],184:[function(t,e,r){function n(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}e.exports=n},{}],185:[function(t,e,r){function n(t,e,r,n){var i,a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T,S,E=n[0],L=n[1],C=n[2],I=Math.sqrt(E*E+L*L+C*C);return Math.abs(I)<1e-6?null:(I=1/I,E*=I,L*=I,C*=I,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],u=e[2],c=e[3],h=e[4],f=e[5],d=e[6],p=e[7],m=e[8],v=e[9],g=e[10],y=e[11],b=E*E*o+a,x=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,M=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,T=L*C*o-E*i,S=C*C*o+a,t[0]=s*b+h*x+m*_,t[1]=l*b+f*x+v*_,t[2]=u*b+d*x+g*_,t[3]=c*b+p*x+y*_,t[4]=s*w+h*M+m*k,t[5]=l*w+f*M+v*k,t[6]=u*w+d*M+g*k,t[7]=c*w+p*M+y*k,t[8]=s*A+h*T+m*S,t[9]=l*A+f*T+v*S,t[10]=u*A+d*T+g*S,t[11]=c*A+p*T+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)}e.exports=n},{}],186:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=a*i+u*n,t[5]=o*i+c*n,t[6]=s*i+h*n,t[7]=l*i+f*n,t[8]=u*i-a*n,t[9]=c*i-o*n,t[10]=h*i-s*n,t[11]=f*i-l*n,t}e.exports=n},{}],187:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-h*n,t[3]=l*i-f*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+h*i,t[11]=l*n+f*i,t}e.exports=n},{}],188:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],h=e[6],f=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+h*n,t[3]=l*i+f*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=h*i-s*n,t[7]=f*i-l*n,t}e.exports=n},{}],189:[function(t,e,r){function n(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}e.exports=n},{}],190:[function(t,e,r){function n(t,e,r){var n,i,a,o,s,l,u,c,h,f,d,p,m=r[0],v=r[1],g=r[2];return e===t?(t[12]=e[0]*m+e[4]*v+e[8]*g+e[12],t[13]=e[1]*m+e[5]*v+e[9]*g+e[13],t[14]=e[2]*m+e[6]*v+e[10]*g+e[14],t[15]=e[3]*m+e[7]*v+e[11]*g+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=h,t[9]=f,t[10]=d,t[11]=p,t[12]=n*m+s*v+h*g+e[12],t[13]=i*m+l*v+f*g+e[13],t[14]=a*m+u*v+d*g+e[14],t[15]=o*m+c*v+p*g+e[15]),t}e.exports=n},{}],191:[function(t,e,r){function n(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}e.exports=n},{}],192:[function(t,e,r){\"use strict\";function n(t,e){switch(e.length){case 0:break;case 1:t[0]=1/e[0];break;case 4:i(t,e);break;case 9:a(t,e);break;case 16:o(t,e);break;default:throw new Error(\"currently supports matrices up to 4x4\")}return t}e.exports=n;var i=t(\"gl-mat2/invert\"),a=t(\"gl-mat3/invert\"),o=t(\"gl-mat4/invert\")},{\"gl-mat2/invert\":173,\"gl-mat3/invert\":174,\"gl-mat4/invert\":181}],193:[function(t,e,r){r.glMatrix=t(\"./gl-matrix/common.js\"),r.mat2=t(\"./gl-matrix/mat2.js\"),r.mat2d=t(\"./gl-matrix/mat2d.js\"),r.mat3=t(\"./gl-matrix/mat3.js\"),r.mat4=t(\"./gl-matrix/mat4.js\"),r.quat=t(\"./gl-matrix/quat.js\"),r.vec2=t(\"./gl-matrix/vec2.js\"),r.vec3=t(\"./gl-matrix/vec3.js\"),r.vec4=t(\"./gl-matrix/vec4.js\")},{\"./gl-matrix/common.js\":194,\"./gl-matrix/mat2.js\":195,\"./gl-matrix/mat2d.js\":196,\"./gl-matrix/mat3.js\":197,\"./gl-matrix/mat4.js\":198,\"./gl-matrix/quat.js\":199,\"./gl-matrix/vec2.js\":200,\"./gl-matrix/vec3.js\":201,\"./gl-matrix/vec4.js\":202}],194:[function(t,e,r){var n={};n.EPSILON=1e-6,n.ARRAY_TYPE=\"undefined\"!=typeof 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t[0]=d*n+f*o,t[1]=d*i+f*s,t[2]=d*a+f*l,t[3]=d*o-f*n,t[4]=d*s-f*i,t[5]=d*l-f*a,t[6]=u,t[7]=c,t[8]=h,t},i.scale=function(t,e,r){var n=r[0],i=r[1];return t[0]=n*e[0],t[1]=n*e[1],t[2]=n*e[2],t[3]=i*e[3],t[4]=i*e[4],t[5]=i*e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t},i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=1,t[5]=0,t[6]=e[0],t[7]=e[1],t[8]=1,t},i.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=e[1],t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},i.fromMat2d=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=0,t[3]=e[2],t[4]=e[3],t[5]=0,t[6]=e[4],t[7]=e[5],t[8]=1,t},i.fromQuat=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,h=n*s,f=i*o,d=i*s,p=i*l,m=a*o,v=a*s,g=a*l;return t[0]=1-h-p,t[3]=c-g,t[6]=f+v,t[1]=c+g,t[4]=1-u-p,t[7]=d-m,t[2]=f-v,t[5]=d+m,t[8]=1-u-h,t},i.normalFromMat4=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15],y=r*s-n*o,b=r*l-i*o,x=r*u-a*o,_=n*l-i*s,w=n*u-a*s,M=i*u-a*l,k=c*m-h*p,A=c*v-f*p,T=c*g-d*p,S=h*v-f*m,E=h*g-d*m,L=f*g-d*v,C=y*L-b*E+x*S+_*T-w*A+M*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(l*T-o*L-u*A)*C,t[2]=(o*E-s*T+u*k)*C,t[3]=(i*E-n*L-a*S)*C,t[4]=(r*L-i*T+a*A)*C,t[5]=(n*T-r*E-a*k)*C,t[6]=(m*M-v*w+g*_)*C,t[7]=(v*x-p*M-g*b)*C,t[8]=(p*w-m*x+g*y)*C,t):null},i.str=function(t){return\"mat3(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\")\"},i.frob=function(t){return Math.sqrt(Math.pow(t[0],2)+Math.pow(t[1],2)+Math.pow(t[2],2)+Math.pow(t[3],2)+Math.pow(t[4],2)+Math.pow(t[5],2)+Math.pow(t[6],2)+Math.pow(t[7],2)+Math.pow(t[8],2))},i.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t[3]=e[3]+r[3],t[4]=e[4]+r[4],t[5]=e[5]+r[5],t[6]=e[6]+r[6],t[7]=e[7]+r[7],t[8]=e[8]+r[8],t},i.subtract=function(t,e,r){return t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t[3]=e[3]-r[3],t[4]=e[4]-r[4],t[5]=e[5]-r[5],t[6]=e[6]-r[6],t[7]=e[7]-r[7],t[8]=e[8]-r[8],t},i.sub=i.subtract,i.multiplyScalar=function(t,e,r){return t[0]=e[0]*r,t[1]=e[1]*r,t[2]=e[2]*r,t[3]=e[3]*r,t[4]=e[4]*r,t[5]=e[5]*r,t[6]=e[6]*r,t[7]=e[7]*r,t[8]=e[8]*r,t},i.multiplyScalarAndAdd=function(t,e,r,n){return t[0]=e[0]+r[0]*n,t[1]=e[1]+r[1]*n,t[2]=e[2]+r[2]*n,t[3]=e[3]+r[3]*n,t[4]=e[4]+r[4]*n,t[5]=e[5]+r[5]*n,t[6]=e[6]+r[6]*n,t[7]=e[7]+r[7]*n,t[8]=e[8]+r[8]*n,t},i.exactEquals=function(t,e){return t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]&&t[4]===e[4]&&t[5]===e[5]&&t[6]===e[6]&&t[7]===e[7]&&t[8]===e[8]},i.equals=function(t,e){var r=t[0],i=t[1],a=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],h=t[8],f=e[0],d=e[1],p=e[2],m=e[3],v=e[4],g=e[5],y=t[6],b=e[7],x=e[8];return Math.abs(r-f)<=n.EPSILON*Math.max(1,Math.abs(r),Math.abs(f))&&Math.abs(i-d)<=n.EPSILON*Math.max(1,Math.abs(i),Math.abs(d))&&Math.abs(a-p)<=n.EPSILON*Math.max(1,Math.abs(a),Math.abs(p))&&Math.abs(o-m)<=n.EPSILON*Math.max(1,Math.abs(o),Math.abs(m))&&Math.abs(s-v)<=n.EPSILON*Math.max(1,Math.abs(s),Math.abs(v))&&Math.abs(l-g)<=n.EPSILON*Math.max(1,Math.abs(l),Math.abs(g))&&Math.abs(u-y)<=n.EPSILON*Math.max(1,Math.abs(u),Math.abs(y))&&Math.abs(c-b)<=n.EPSILON*Math.max(1,Math.abs(c),Math.abs(b))&&Math.abs(h-x)<=n.EPSILON*Math.max(1,Math.abs(h),Math.abs(x))},e.exports=i},{\"./common.js\":194}],198:[function(t,e,r){var n=t(\"./common.js\"),i={scalar:{},SIMD:{}};i.create=function(){var t=new n.ARRAY_TYPE(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.clone=function(t){var e=new n.ARRAY_TYPE(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},i.copy=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.fromValues=function(t,e,r,i,a,o,s,l,u,c,h,f,d,p,m,v){var g=new n.ARRAY_TYPE(16);return g[0]=t,g[1]=e,g[2]=r,g[3]=i,g[4]=a,g[5]=o,g[6]=s,g[7]=l,g[8]=u,g[9]=c,g[10]=h,g[11]=f,g[12]=d,g[13]=p,g[14]=m,g[15]=v,g},i.set=function(t,e,r,n,i,a,o,s,l,u,c,h,f,d,p,m,v){return t[0]=e,t[1]=r,t[2]=n,t[3]=i,t[4]=a,t[5]=o,t[6]=s,t[7]=l,t[8]=u,t[9]=c,t[10]=h,t[11]=f,t[12]=d,t[13]=p,t[14]=m,t[15]=v,t},i.identity=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.scalar.transpose=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t},i.SIMD.transpose=function(t,e){var r,n,i,a,o,s,l,u,c,h;return r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12),o=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,a,0,1,4,5),l=SIMD.Float32x4.shuffle(o,s,0,2,4,6),u=SIMD.Float32x4.shuffle(o,s,1,3,5,7),SIMD.Float32x4.store(t,0,l),SIMD.Float32x4.store(t,4,u),o=SIMD.Float32x4.shuffle(r,n,2,3,6,7),s=SIMD.Float32x4.shuffle(i,a,2,3,6,7),c=SIMD.Float32x4.shuffle(o,s,0,2,4,6),h=SIMD.Float32x4.shuffle(o,s,1,3,5,7),SIMD.Float32x4.store(t,8,c),SIMD.Float32x4.store(t,12,h),t},i.transpose=n.USE_SIMD?i.SIMD.transpose:i.scalar.transpose,i.scalar.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15],y=r*s-n*o,b=r*l-i*o,x=r*u-a*o,_=n*l-i*s,w=n*u-a*s,M=i*u-a*l,k=c*m-h*p,A=c*v-f*p,T=c*g-d*p,S=h*v-f*m,E=h*g-d*m,L=f*g-d*v,C=y*L-b*E+x*S+_*T-w*A+M*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*M-v*w+g*_)*C,t[3]=(f*w-h*M-d*_)*C,t[4]=(l*T-o*L-u*A)*C,t[5]=(r*L-i*T+a*A)*C,t[6]=(v*x-p*M-g*b)*C,t[7]=(c*M-f*x+d*b)*C,t[8]=(o*E-s*T+u*k)*C,t[9]=(n*T-r*E-a*k)*C,t[10]=(p*w-m*x+g*y)*C,t[11]=(h*x-c*w-d*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*b-p*_-v*y)*C,t[15]=(c*_-h*b+f*y)*C,t):null},i.SIMD.invert=function(t,e){var r,n,i,a,o,s,l,u,c,h,f=SIMD.Float32x4.load(e,0),d=SIMD.Float32x4.load(e,4),p=SIMD.Float32x4.load(e,8),m=SIMD.Float32x4.load(e,12);return o=SIMD.Float32x4.shuffle(f,d,0,1,4,5),n=SIMD.Float32x4.shuffle(p,m,0,1,4,5),r=SIMD.Float32x4.shuffle(o,n,0,2,4,6),n=SIMD.Float32x4.shuffle(n,o,1,3,5,7),o=SIMD.Float32x4.shuffle(f,d,2,3,6,7),a=SIMD.Float32x4.shuffle(p,m,2,3,6,7),i=SIMD.Float32x4.shuffle(o,a,0,2,4,6),a=SIMD.Float32x4.shuffle(a,o,1,3,5,7),o=SIMD.Float32x4.mul(i,a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),s=SIMD.Float32x4.mul(n,o),l=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(SIMD.Float32x4.mul(n,o),s),l=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),l),l=SIMD.Float32x4.swizzle(l,2,3,0,1),o=SIMD.Float32x4.mul(n,i),o=SIMD.Float32x4.swizzle(o,1,0,3,2),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),s),c=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(a,o)),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),o=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(n,2,3,0,1),a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),i=SIMD.Float32x4.swizzle(i,2,3,0,1),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,o),s),u=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(i,o)),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),u),u=SIMD.Float32x4.swizzle(u,2,3,0,1),o=SIMD.Float32x4.mul(r,n),o=SIMD.Float32x4.swizzle(o,1,0,3,2),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),u),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(i,o),c),o=SIMD.Float32x4.swizzle(o,2,3,0,1),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,o),u),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(i,o)),o=SIMD.Float32x4.mul(r,a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(i,o)),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,o),u),o=SIMD.Float32x4.swizzle(o,2,3,0,1),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,o),l),u=SIMD.Float32x4.sub(u,SIMD.Float32x4.mul(n,o)),o=SIMD.Float32x4.mul(r,i),o=SIMD.Float32x4.swizzle(o,1,0,3,2),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),l),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(n,o)),o=SIMD.Float32x4.swizzle(o,2,3,0,1),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(a,o)),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,o),c),h=SIMD.Float32x4.mul(r,s),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,2,3,0,1),h),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,1,0,3,2),h),o=SIMD.Float32x4.reciprocalApproximation(h),h=SIMD.Float32x4.sub(SIMD.Float32x4.add(o,o),SIMD.Float32x4.mul(h,SIMD.Float32x4.mul(o,o))),(h=SIMD.Float32x4.swizzle(h,0,0,0,0))?(SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(h,s)),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(h,l)),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(h,u)),SIMD.Float32x4.store(t,12,SIMD.Float32x4.mul(h,c)),t):null},i.invert=n.USE_SIMD?i.SIMD.invert:i.scalar.invert,i.scalar.adjoint=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15];return t[0]=s*(f*g-d*v)-h*(l*g-u*v)+m*(l*d-u*f),t[1]=-(n*(f*g-d*v)-h*(i*g-a*v)+m*(i*d-a*f)),t[2]=n*(l*g-u*v)-s*(i*g-a*v)+m*(i*u-a*l),t[3]=-(n*(l*d-u*f)-s*(i*d-a*f)+h*(i*u-a*l)),t[4]=-(o*(f*g-d*v)-c*(l*g-u*v)+p*(l*d-u*f)),t[5]=r*(f*g-d*v)-c*(i*g-a*v)+p*(i*d-a*f),t[6]=-(r*(l*g-u*v)-o*(i*g-a*v)+p*(i*u-a*l)),t[7]=r*(l*d-u*f)-o*(i*d-a*f)+c*(i*u-a*l),t[8]=o*(h*g-d*m)-c*(s*g-u*m)+p*(s*d-u*h),t[9]=-(r*(h*g-d*m)-c*(n*g-a*m)+p*(n*d-a*h)),t[10]=r*(s*g-u*m)-o*(n*g-a*m)+p*(n*u-a*s),t[11]=-(r*(s*d-u*h)-o*(n*d-a*h)+c*(n*u-a*s)),t[12]=-(o*(h*v-f*m)-c*(s*v-l*m)+p*(s*f-l*h)),t[13]=r*(h*v-f*m)-c*(n*v-i*m)+p*(n*f-i*h),t[14]=-(r*(s*v-l*m)-o*(n*v-i*m)+p*(n*l-i*s)),t[15]=r*(s*f-l*h)-o*(n*f-i*h)+c*(n*l-i*s),t},i.SIMD.adjoint=function(t,e){var r,n,i,a,o,s,l,u,c,h,f,d,p,r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12)\n",
+       ";return c=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,a,0,1,4,5),o=SIMD.Float32x4.shuffle(c,s,0,2,4,6),s=SIMD.Float32x4.shuffle(s,c,1,3,5,7),c=SIMD.Float32x4.shuffle(r,n,2,3,6,7),u=SIMD.Float32x4.shuffle(i,a,2,3,6,7),l=SIMD.Float32x4.shuffle(c,u,0,2,4,6),u=SIMD.Float32x4.shuffle(u,c,1,3,5,7),c=SIMD.Float32x4.mul(l,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.mul(s,c),f=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(SIMD.Float32x4.mul(s,c),h),f=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),f),f=SIMD.Float32x4.swizzle(f,2,3,0,1),c=SIMD.Float32x4.mul(s,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),h),p=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(u,c)),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),p),p=SIMD.Float32x4.swizzle(p,2,3,0,1),c=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,3,0,1),u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),l=SIMD.Float32x4.swizzle(l,2,3,0,1),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),h),d=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(l,c)),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),d),d=SIMD.Float32x4.swizzle(d,2,3,0,1),c=SIMD.Float32x4.mul(o,s),c=SIMD.Float32x4.swizzle(c,1,0,3,2),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),d),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(l,c),p),c=SIMD.Float32x4.swizzle(c,2,3,0,1),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(u,c),d),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(l,c)),c=SIMD.Float32x4.mul(o,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(l,c)),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),d),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),f),d=SIMD.Float32x4.sub(d,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.mul(o,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),f),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(u,c)),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),p),SIMD.Float32x4.store(t,0,h),SIMD.Float32x4.store(t,4,f),SIMD.Float32x4.store(t,8,d),SIMD.Float32x4.store(t,12,p),t},i.adjoint=n.USE_SIMD?i.SIMD.adjoint:i.scalar.adjoint,i.determinant=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],h=t[10],f=t[11],d=t[12],p=t[13],m=t[14],v=t[15];return(e*o-r*a)*(h*v-f*m)-(e*s-n*a)*(c*v-f*p)+(e*l-i*a)*(c*m-h*p)+(r*s-n*o)*(u*v-f*d)-(r*l-i*o)*(u*m-h*d)+(n*l-i*s)*(u*p-c*d)},i.SIMD.multiply=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4.load(r,0),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,3,3,3,3),o))));SIMD.Float32x4.store(t,0,l);var u=SIMD.Float32x4.load(r,4),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,3,3,3,3),o))));SIMD.Float32x4.store(t,4,c);var h=SIMD.Float32x4.load(r,8),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,3,3,3,3),o))));SIMD.Float32x4.store(t,8,f);var d=SIMD.Float32x4.load(r,12),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,3,3,3,3),o))));return SIMD.Float32x4.store(t,12,p),t},i.scalar.multiply=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],m=e[12],v=e[13],g=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*h+w*m,t[1]=b*i+x*l+_*f+w*v,t[2]=b*a+x*u+_*d+w*g,t[3]=b*o+x*c+_*p+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*h+w*m,t[5]=b*i+x*l+_*f+w*v,t[6]=b*a+x*u+_*d+w*g,t[7]=b*o+x*c+_*p+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*h+w*m,t[9]=b*i+x*l+_*f+w*v,t[10]=b*a+x*u+_*d+w*g,t[11]=b*o+x*c+_*p+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*h+w*m,t[13]=b*i+x*l+_*f+w*v,t[14]=b*a+x*u+_*d+w*g,t[15]=b*o+x*c+_*p+w*y,t},i.multiply=n.USE_SIMD?i.SIMD.multiply:i.scalar.multiply,i.mul=i.multiply,i.scalar.translate=function(t,e,r){var n,i,a,o,s,l,u,c,h,f,d,p,m=r[0],v=r[1],g=r[2];return e===t?(t[12]=e[0]*m+e[4]*v+e[8]*g+e[12],t[13]=e[1]*m+e[5]*v+e[9]*g+e[13],t[14]=e[2]*m+e[6]*v+e[10]*g+e[14],t[15]=e[3]*m+e[7]*v+e[11]*g+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=h,t[9]=f,t[10]=d,t[11]=p,t[12]=n*m+s*v+h*g+e[12],t[13]=i*m+l*v+f*g+e[13],t[14]=a*m+u*v+d*g+e[14],t[15]=o*m+c*v+p*g+e[15]),t},i.SIMD.translate=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4(r[0],r[1],r[2],0);e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11]),n=SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(s,0,0,0,0)),i=SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(s,1,1,1,1)),a=SIMD.Float32x4.mul(a,SIMD.Float32x4.swizzle(s,2,2,2,2));var l=SIMD.Float32x4.add(n,SIMD.Float32x4.add(i,SIMD.Float32x4.add(a,o)));return SIMD.Float32x4.store(t,12,l),t},i.translate=n.USE_SIMD?i.SIMD.translate:i.scalar.translate,i.scalar.scale=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.SIMD.scale=function(t,e,r){var n,i,a,o=SIMD.Float32x4(r[0],r[1],r[2],0);return n=SIMD.Float32x4.load(e,0),SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(o,0,0,0,0))),i=SIMD.Float32x4.load(e,4),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(o,1,1,1,1))),a=SIMD.Float32x4.load(e,8),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(a,SIMD.Float32x4.swizzle(o,2,2,2,2))),t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.scale=n.USE_SIMD?i.SIMD.scale:i.scalar.scale,i.rotate=function(t,e,r,i){var a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T,S,E,L=i[0],C=i[1],I=i[2],z=Math.sqrt(L*L+C*C+I*I);return Math.abs(z)<n.EPSILON?null:(z=1/z,L*=z,C*=z,I*=z,a=Math.sin(r),o=Math.cos(r),s=1-o,l=e[0],u=e[1],c=e[2],h=e[3],f=e[4],d=e[5],p=e[6],m=e[7],v=e[8],g=e[9],y=e[10],b=e[11],x=L*L*s+o,_=C*L*s+I*a,w=I*L*s-C*a,M=L*C*s-I*a,k=C*C*s+o,A=I*C*s+L*a,T=L*I*s+C*a,S=C*I*s-L*a,E=I*I*s+o,t[0]=l*x+f*_+v*w,t[1]=u*x+d*_+g*w,t[2]=c*x+p*_+y*w,t[3]=h*x+m*_+b*w,t[4]=l*M+f*k+v*A,t[5]=u*M+d*k+g*A,t[6]=c*M+p*k+y*A,t[7]=h*M+m*k+b*A,t[8]=l*T+f*S+v*E,t[9]=u*T+d*S+g*E,t[10]=c*T+p*S+y*E,t[11]=h*T+m*S+b*E,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)},i.scalar.rotateX=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=a*i+u*n,t[5]=o*i+c*n,t[6]=s*i+h*n,t[7]=l*i+f*n,t[8]=u*i-a*n,t[9]=c*i-o*n,t[10]=h*i-s*n,t[11]=f*i-l*n,t},i.SIMD.rotateX=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,4),o=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,4,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),t},i.rotateX=n.USE_SIMD?i.SIMD.rotateX:i.scalar.rotateX,i.scalar.rotateY=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-h*n,t[3]=l*i-f*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+h*i,t[11]=l*n+f*i,t},i.SIMD.rotateY=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,0),o=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,n),SIMD.Float32x4.mul(o,i))),t},i.rotateY=n.USE_SIMD?i.SIMD.rotateY:i.scalar.rotateY,i.scalar.rotateZ=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],h=e[6],f=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+h*n,t[3]=l*i+f*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=h*i-s*n,t[7]=f*i-l*n,t},i.SIMD.rotateZ=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,0),o=SIMD.Float32x4.load(e,4);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,4,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),t},i.rotateZ=n.USE_SIMD?i.SIMD.rotateZ:i.scalar.rotateZ,i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotation=function(t,e,r){var i,a,o,s=r[0],l=r[1],u=r[2],c=Math.sqrt(s*s+l*l+u*u);return Math.abs(c)<n.EPSILON?null:(c=1/c,s*=c,l*=c,u*=c,i=Math.sin(e),a=Math.cos(e),o=1-a,t[0]=s*s*o+a,t[1]=l*s*o+u*i,t[2]=u*s*o-l*i,t[3]=0,t[4]=s*l*o-u*i,t[5]=l*l*o+a,t[6]=u*l*o+s*i,t[7]=0,t[8]=s*u*o+l*i,t[9]=l*u*o-s*i,t[10]=u*u*o+a,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t)},i.fromXRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromYRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromZRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotationTranslation=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,h=n*l,f=n*u,d=i*l,p=i*u,m=a*u,v=o*s,g=o*l,y=o*u;return t[0]=1-(d+m),t[1]=h+y,t[2]=f-g,t[3]=0,t[4]=h-y,t[5]=1-(c+m),t[6]=p+v,t[7]=0,t[8]=f+g,t[9]=p-v,t[10]=1-(c+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.getTranslation=function(t,e){return t[0]=e[12],t[1]=e[13],t[2]=e[14],t},i.getRotation=function(t,e){var r=e[0]+e[5]+e[10],n=0;return r>0?(n=2*Math.sqrt(r+1),t[3]=.25*n,t[0]=(e[6]-e[9])/n,t[1]=(e[8]-e[2])/n,t[2]=(e[1]-e[4])/n):e[0]>e[5]&e[0]>e[10]?(n=2*Math.sqrt(1+e[0]-e[5]-e[10]),t[3]=(e[6]-e[9])/n,t[0]=.25*n,t[1]=(e[1]+e[4])/n,t[2]=(e[8]+e[2])/n):e[5]>e[10]?(n=2*Math.sqrt(1+e[5]-e[0]-e[10]),t[3]=(e[8]-e[2])/n,t[0]=(e[1]+e[4])/n,t[1]=.25*n,t[2]=(e[6]+e[9])/n):(n=2*Math.sqrt(1+e[10]-e[0]-e[5]),t[3]=(e[1]-e[4])/n,t[0]=(e[8]+e[2])/n,t[1]=(e[6]+e[9])/n,t[2]=.25*n),t},i.fromRotationTranslationScale=function(t,e,r,n){var i=e[0],a=e[1],o=e[2],s=e[3],l=i+i,u=a+a,c=o+o,h=i*l,f=i*u,d=i*c,p=a*u,m=a*c,v=o*c,g=s*l,y=s*u,b=s*c,x=n[0],_=n[1],w=n[2];return t[0]=(1-(p+v))*x,t[1]=(f+b)*x,t[2]=(d-y)*x,t[3]=0,t[4]=(f-b)*_,t[5]=(1-(h+v))*_,t[6]=(m+g)*_,t[7]=0,t[8]=(d+y)*w,t[9]=(m-g)*w,t[10]=(1-(h+p))*w,t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.fromRotationTranslationScaleOrigin=function(t,e,r,n,i){var a=e[0],o=e[1],s=e[2],l=e[3],u=a+a,c=o+o,h=s+s,f=a*u,d=a*c,p=a*h,m=o*c,v=o*h,g=s*h,y=l*u,b=l*c,x=l*h,_=n[0],w=n[1],M=n[2],k=i[0],A=i[1],T=i[2];return t[0]=(1-(m+g))*_,t[1]=(d+x)*_,t[2]=(p-b)*_,t[3]=0,t[4]=(d-x)*w,t[5]=(1-(f+g))*w,t[6]=(v+y)*w,t[7]=0,t[8]=(p+b)*M,t[9]=(v-y)*M,t[10]=(1-(f+m))*M,t[11]=0,t[12]=r[0]+k-(t[0]*k+t[4]*A+t[8]*T),t[13]=r[1]+A-(t[1]*k+t[5]*A+t[9]*T),t[14]=r[2]+T-(t[2]*k+t[6]*A+t[10]*T),t[15]=1,t},i.fromQuat=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,h=n*s,f=i*o,d=i*s,p=i*l,m=a*o,v=a*s,g=a*l;return t[0]=1-h-p,t[1]=c+g,t[2]=f-v,t[3]=0,t[4]=c-g,t[5]=1-u-p,t[6]=d+m,t[7]=0,t[8]=f+v,t[9]=d-m,t[10]=1-u-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.frustum=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),u=1/(a-o);return t[0]=2*a*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*a*l,t[6]=0,t[7]=0,t[8]=(r+e)*s,t[9]=(i+n)*l,t[10]=(o+a)*u,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*a*2*u,t[15]=0,t},i.perspective=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t},i.perspectiveFromFieldOfView=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),u=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=u,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*u*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t},i.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t},i.lookAt=function(t,e,r,a){var o,s,l,u,c,h,f,d,p,m,v=e[0],g=e[1],y=e[2],b=a[0],x=a[1],_=a[2],w=r[0],M=r[1],k=r[2];return Math.abs(v-w)<n.EPSILON&&Math.abs(g-M)<n.EPSILON&&Math.abs(y-k)<n.EPSILON?i.identity(t):(f=v-w,d=g-M,p=y-k,m=1/Math.sqrt(f*f+d*d+p*p),f*=m,d*=m,p*=m,o=x*p-_*d,s=_*f-b*p,l=b*d-x*f,m=Math.sqrt(o*o+s*s+l*l),m?(m=1/m,o*=m,s*=m,l*=m):(o=0,s=0,l=0),u=d*l-p*s,c=p*o-f*l,h=f*s-d*o,m=Math.sqrt(u*u+c*c+h*h),m?(m=1/m,u*=m,c*=m,h*=m):(u=0,c=0,h=0),t[0]=o,t[1]=u,t[2]=f,t[3]=0,t[4]=s,t[5]=c,t[6]=d,t[7]=0,t[8]=l,t[9]=h,t[10]=p,t[11]=0,t[12]=-(o*v+s*g+l*y),t[13]=-(u*v+c*g+h*y),t[14]=-(f*v+d*g+p*y),t[15]=1,t)},i.str=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"},i.frob=function(t){return Math.sqrt(Math.pow(t[0],2)+Math.pow(t[1],2)+Math.pow(t[2],2)+Math.pow(t[3],2)+Math.pow(t[4],2)+Math.pow(t[5],2)+Math.pow(t[6],2)+Math.pow(t[7],2)+Math.pow(t[8],2)+Math.pow(t[9],2)+Math.pow(t[10],2)+Math.pow(t[11],2)+Math.pow(t[12],2)+Math.pow(t[13],2)+Math.pow(t[14],2)+Math.pow(t[15],2))},i.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t[3]=e[3]+r[3],t[4]=e[4]+r[4],t[5]=e[5]+r[5],t[6]=e[6]+r[6],t[7]=e[7]+r[7],t[8]=e[8]+r[8],t[9]=e[9]+r[9],t[10]=e[10]+r[10],t[11]=e[11]+r[11],t[12]=e[12]+r[12],t[13]=e[13]+r[13],t[14]=e[14]+r[14],t[15]=e[15]+r[15],t},i.subtract=function(t,e,r){return t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t[3]=e[3]-r[3],t[4]=e[4]-r[4],t[5]=e[5]-r[5],t[6]=e[6]-r[6],t[7]=e[7]-r[7],t[8]=e[8]-r[8],t[9]=e[9]-r[9],t[10]=e[10]-r[10],t[11]=e[11]-r[11],t[12]=e[12]-r[12],t[13]=e[13]-r[13],t[14]=e[14]-r[14],t[15]=e[15]-r[15],t},i.sub=i.subtract,i.multiplyScalar=function(t,e,r){return t[0]=e[0]*r,t[1]=e[1]*r,t[2]=e[2]*r,t[3]=e[3]*r,t[4]=e[4]*r,t[5]=e[5]*r,t[6]=e[6]*r,t[7]=e[7]*r,t[8]=e[8]*r,t[9]=e[9]*r,t[10]=e[10]*r,t[11]=e[11]*r,t[12]=e[12]*r,t[13]=e[13]*r,t[14]=e[14]*r,t[15]=e[15]*r,t},i.multiplyScalarAndAdd=function(t,e,r,n){return t[0]=e[0]+r[0]*n,t[1]=e[1]+r[1]*n,t[2]=e[2]+r[2]*n,t[3]=e[3]+r[3]*n,t[4]=e[4]+r[4]*n,t[5]=e[5]+r[5]*n,t[6]=e[6]+r[6]*n,t[7]=e[7]+r[7]*n,t[8]=e[8]+r[8]*n,t[9]=e[9]+r[9]*n,t[10]=e[10]+r[10]*n,t[11]=e[11]+r[11]*n,t[12]=e[12]+r[12]*n,t[13]=e[13]+r[13]*n,t[14]=e[14]+r[14]*n,t[15]=e[15]+r[15]*n,t},i.exactEquals=function(t,e){return t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]&&t[4]===e[4]&&t[5]===e[5]&&t[6]===e[6]&&t[7]===e[7]&&t[8]===e[8]&&t[9]===e[9]&&t[10]===e[10]&&t[11]===e[11]&&t[12]===e[12]&&t[13]===e[13]&&t[14]===e[14]&&t[15]===e[15]},i.equals=function(t,e){var r=t[0],i=t[1],a=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],h=t[8],f=t[9],d=t[10],p=t[11],m=t[12],v=t[13],g=t[14],y=t[15],b=e[0],x=e[1],_=e[2],w=e[3],M=e[4],k=e[5],A=e[6],T=e[7],S=e[8],E=e[9],L=e[10],C=e[11],I=e[12],z=e[13],D=e[14],P=e[15];return 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b=m(t),x=m(t),w=m(t),M=m(t),k=m(t),A=v(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:x,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2},{buffer:M,type:t.FLOAT,size:3}]),T=m(t),S=m(t),E=m(t),L=m(t),C=v(t,[{buffer:T,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:S,type:t.FLOAT,size:4},{buffer:E,type:t.FLOAT,size:2}]),I=m(t),z=m(t),D=m(t),P=m(t),O=m(t),R=v(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:O,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:D,type:t.FLOAT,size:2},{buffer:P,type:t.FLOAT,size:1}]),F=m(t),j=v(t,[{buffer:F,type:t.FLOAT,size:3}]),N=new n(t,y,r,i,a,o,d,p,b,k,x,w,M,A,T,L,S,E,C,I,O,z,D,P,R,F,j);return N.update(e),N}var p=t(\"gl-shader\"),m=t(\"gl-buffer\"),v=t(\"gl-vao\"),g=t(\"gl-texture2d\"),y=t(\"normals\"),b=t(\"gl-mat4/multiply\"),x=t(\"gl-mat4/invert\"),_=t(\"ndarray\"),w=t(\"colormap\"),M=t(\"simplicial-complex-contour\"),k=t(\"typedarray-pool\"),A=t(\"./lib/shaders\"),T=t(\"./lib/closest-point\"),S=A.meshShader,E=A.wireShader,L=A.pointShader,C=A.pickShader,I=A.pointPickShader,z=A.contourShader,D=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=n.prototype;P.isOpaque=function(){return this.opacity>=1},P.isTransparent=function(){return this.opacity<1},P.pickSlots=1,P.setPickBase=function(t){this.pickId=t},P.highlight=function(t){if(!t||!this.contourEnable)return void(this.contourCount=0);for(var e=M(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=k.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var h=u[0];2===u.length&&(h=u[c]);for(var f=n[h][0],d=n[h][1],p=i[h],m=1-p,v=this.positions[f],g=this.positions[d],y=0;y<3;++y)o[s++]=p*v[y]+m*g[y]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),k.free(o)},P.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=g(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(i(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var s=[],l=[],u=[],c=[],h=[],f=[],d=[],p=[],m=[],v=[],b=[],x=[],_=[],w=[];this.cells=r,this.positions=n;var M=t.vertexNormals,k=t.cellNormals,A=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,T=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=y.faceNormals(r,n,T)),k||M||(M=y.vertexNormals(r,n,A));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,I=t.vertexIntensity,z=t.cellUVs,D=t.cellIntensity,P=1/0,O=-1/0;if(!C&&!z)if(I)if(t.vertexIntensityBounds)P=+t.vertexIntensityBounds[0],O=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];P=Math.min(P,F),O=Math.max(O,F)}else if(D)for(var R=0;R<D.length;++R){var F=D[R];P=Math.min(P,F),O=Math.max(O,F)}else for(var R=0;R<n.length;++R){var F=n[R][2];P=Math.min(P,F),O=Math.max(O,F)}this.intensity=I||(D?a(r,n.length,D):o(n));var j=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var R=0;R<n.length;++R)for(var B=n[R],U=0;U<3;++U)!isNaN(B[U])&&isFinite(B[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],B[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],B[U]));var V=0,H=0,q=0;t:for(var R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(var Y=G[0],B=n[Y],U=0;U<3;++U)if(isNaN(B[U])||!isFinite(B[U]))continue t;v.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?b.push(W[0],W[1],W[2],1):b.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],x.push(X[0],X[1]),j?_.push(j[Y]):_.push(N),w.push(R),q+=1;break;case 2:for(var U=0;U<2;++U)for(var Y=G[U],B=n[Y],Z=0;Z<3;++Z)if(isNaN(B[Z])||!isFinite(B[Z]))continue t;for(var U=0;U<2;++U){var Y=G[U],B=n[Y];f.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?d.push(W[0],W[1],W[2],1):d.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],p.push(X[0],X[1]),m.push(R)}H+=1;break;case 3:for(var U=0;U<3;++U)for(var Y=G[U],B=n[Y],Z=0;Z<3;++Z)if(isNaN(B[Z])||!isFinite(B[Z]))continue t;for(var U=0;U<3;++U){var Y=G[U],B=n[Y];s.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?l.push(W[0],W[1],W[2],1):l.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],c.push(X[0],X[1]);var J;J=M?M[Y]:k[R],u.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(b),this.pointUVs.update(x),this.pointSizes.update(_),this.pointIds.update(new Uint32Array(w)),this.edgePositions.update(f),this.edgeColors.update(d),this.edgeUVs.update(p),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(s),this.triangleColors.update(l),this.triangleUVs.update(c),this.triangleNormals.update(u),this.triangleIds.update(new Uint32Array(h))}},P.drawTransparent=P.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||D,n=t.view||D,i=t.projection||D,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var l=new Array(16);b(l,s.view,s.model),b(l,s.projection,l),x(l,l);for(var o=0;o<3;++o)s.eyePosition[o]=l[12+o]/l[15];for(var u=l[15],o=0;o<3;++o)u+=this.lightPosition[o]*l[4*o+3];for(var o=0;o<3;++o){for(var c=l[12+o],h=0;h<3;++h)c+=l[4*h+o]*this.lightPosition[h];s.lightPosition[o]=c/u}if(this.triangleCount>0){var f=this.triShader;f.bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var f=this.lineShader;f.bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()}if(this.pointCount>0){var f=this.pointShader;f.bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var f=this.contourShader;f.bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind()}},P.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||D,n=t.view||D,i=t.projection||D,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255},l=this.pickShader;if(l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0){var l=this.pointPickShader;l.bind(),l.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}},P.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=T(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;for(var s=o[2],l=0,a=0;a<r.length;++a)l+=s[a]*this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},P.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=d},{\"./lib/closest-point\":203,\"./lib/shaders\":204,colormap:99,\"gl-buffer\":156,\"gl-mat4/invert\":181,\"gl-mat4/multiply\":183,\"gl-shader\":255,\"gl-texture2d\":267,\"gl-vao\":271,ndarray:467,normals:469,\"simplicial-complex-contour\":517,\"typedarray-pool\":541}],206:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r}function i(t){var e=t.gl;return new n(t,a(e,[0,0,0,1,1,0,1,1]),o(e,s.boxVert,s.lineFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"./shaders\"),l=n.prototype;l.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},l.drawBox=function(){var t=[0,0],e=[0,0];return function(r,n,i,a,o){var s=this.plot,l=this.shader,u=s.gl;t[0]=r,t[1]=n,e[0]=i,e[1]=a,l.uniforms.lo=t,l.uniforms.hi=e,l.uniforms.color=o,u.drawArrays(u.TRIANGLE_STRIP,0,4)}}(),l.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":209,\"gl-buffer\":156,\"gl-shader\":212}],207:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function i(t,e){return t-e}function a(t){var e=t.gl;return new n(t,o(e),s(e,u.gridVert,u.gridFrag),s(e,u.tickVert,u.gridFrag))}e.exports=a;var o=t(\"gl-buffer\"),s=t(\"gl-shader\"),l=t(\"binary-search-bounds\"),u=t(\"./shaders\"),c=n.prototype;c.draw=function(){var t=[0,0],e=[0,0],r=[0,0];return function(){for(var n=this.plot,i=this.vbo,a=this.shader,o=this.ticks,s=n.gl,l=n._tickBounds,u=n.dataBox,c=n.viewBox,h=n.gridLineWidth,f=n.gridLineColor,d=n.gridLineEnable,p=n.pixelRatio,m=0;m<2;++m){var v=l[m],g=l[m+2],y=g-v,b=.5*(u[m+2]+u[m]),x=u[m+2]-u[m];e[m]=2*y/x,t[m]=2*(v-b)/x}a.bind(),i.bind(),a.attributes.dataCoord.pointer(),a.uniforms.dataShift=t,a.uniforms.dataScale=e;for(var _=0,m=0;m<2;++m){r[0]=r[1]=0,r[m]=1,a.uniforms.dataAxis=r,a.uniforms.lineWidth=h[m]/(c[m+2]-c[m])*p,a.uniforms.color=f[m];var w=6*o[m].length;d[m]&&w&&s.drawArrays(s.TRIANGLES,_,w),_+=w}}}(),c.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],a=[0,0],o=[0,0];return function(){for(var s=this.plot,u=this.vbo,c=this.tickShader,h=this.ticks,f=s.gl,d=s._tickBounds,p=s.dataBox,m=s.viewBox,v=s.pixelRatio,g=s.screenBox,y=g[2]-g[0],b=g[3]-g[1],x=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var M=d[w],k=d[w+2],A=k-M,T=.5*(p[w+2]+p[w]),S=p[w+2]-p[w];e[w]=2*A/S,t[w]=2*(M-T)/S}e[0]*=x/y,t[0]*=x/y,e[1]*=_/b,t[1]*=_/b,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var E=c.uniforms;E.dataShift=t,E.dataScale=e;var L=s.tickMarkLength,C=s.tickMarkWidth,I=s.tickMarkColor,z=6*h[0].length,D=Math.min(l.ge(h[0],(p[0]-d[0])/(d[2]-d[0]),i),h[0].length),P=Math.min(l.gt(h[0],(p[2]-d[0])/(d[2]-d[0]),i),h[0].length),O=0+6*D,R=6*Math.max(0,P-D),F=Math.min(l.ge(h[1],(p[1]-d[1])/(d[3]-d[1]),i),h[1].length),j=Math.min(l.gt(h[1],(p[3]-d[1])/(d[3]-d[1]),i),h[1].length),N=z+6*F,B=6*Math.max(0,j-F);a[0]=2*(m[0]-L[1])/y-1,a[1]=(m[3]+m[1])/b-1,o[0]=L[1]*v/y,o[1]=C[1]*v/b,B&&(E.color=I[1],E.tickScale=o,E.dataAxis=n,E.screenOffset=a,f.drawArrays(f.TRIANGLES,N,B)),a[0]=(m[2]+m[0])/y-1,a[1]=2*(m[1]-L[0])/b-1,o[0]=C[0]*v/y,o[1]=L[0]*v/b,R&&(E.color=I[0],E.tickScale=o,E.dataAxis=r,E.screenOffset=a,f.drawArrays(f.TRIANGLES,O,R)),a[0]=2*(m[2]+L[3])/y-1,a[1]=(m[3]+m[1])/b-1,o[0]=L[3]*v/y,o[1]=C[3]*v/b,B&&(E.color=I[3],E.tickScale=o,E.dataAxis=n,E.screenOffset=a,f.drawArrays(f.TRIANGLES,N,B)),a[0]=(m[2]+m[0])/y-1,a[1]=2*(m[3]+L[2])/b-1,o[0]=C[2]*v/y,o[1]=L[2]*v/b,R&&(E.color=I[2],E.tickScale=o,E.dataAxis=r,E.screenOffset=a,f.drawArrays(f.TRIANGLES,O,R))}}(),c.update=function(){var t=[1,1,-1,-1,1,-1],e=[1,-1,1,1,-1,-1];return function(r){for(var n=r.ticks,i=r.bounds,a=new Float32Array(18*(n[0].length+n[1].length)),o=(this.plot.zeroLineEnable,0),s=[[],[]],l=0;l<2;++l)for(var u=s[l],c=n[l],h=i[l],f=i[l+2],d=0;d<c.length;++d){var p=(c[d].x-h)/(f-h);u.push(p);for(var m=0;m<6;++m)a[o++]=p,a[o++]=t[m],a[o++]=e[m]}this.ticks=s,this.vbo.update(a)}}(),c.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":209,\"binary-search-bounds\":211,\"gl-buffer\":156,\"gl-shader\":212}],208:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r}function i(t){var e=t.gl;return new n(t,a(e,[-1,-1,-1,1,1,-1,1,1]),o(e,s.lineVert,s.lineFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"./shaders\"),l=n.prototype;l.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},l.drawLine=function(){var t=[0,0],e=[0,0];return function(r,n,i,a,o,s){var l=this.plot,u=this.shader,c=l.gl;t[0]=r,t[1]=n,e[0]=i,e[1]=a,u.uniforms.start=t,u.uniforms.end=e,u.uniforms.width=o*l.pixelRatio,u.uniforms.color=s,c.drawArrays(c.TRIANGLE_STRIP,0,4)}}(),l.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":209,\"gl-buffer\":156,\"gl-shader\":212}],209:[function(t,e,r){\"use strict\";var n=\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\";e.exports={lineVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\",lineFrag:n,textVert:\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\",textFrag:n,gridVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * 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strict\";function n(t,e){if(!s){var r=Object.keys(o);s={};for(var n=0;n<r.length;++n){var i=r[n];s[t[i]]=o[i]}}return s[e]}function i(t,e){for(var r=t.getProgramParameter(e,t.ACTIVE_UNIFORMS),i=[],a=0;a<r;++a){var o=t.getActiveUniform(e,a);if(o){var s=n(t,o.type);if(o.size>1)for(var l=0;l<o.size;++l)i.push({name:o.name.replace(\"[0]\",\"[\"+l+\"]\"),type:s});else i.push({name:o.name,type:s})}}return i}function a(t,e){for(var r=t.getProgramParameter(e,t.ACTIVE_ATTRIBUTES),i=[],a=0;a<r;++a){var o=t.getActiveAttrib(e,a);o&&i.push({name:o.name,type:n(t,o.type)})}return i}r.uniforms=i,r.attributes=a;var o={FLOAT:\"float\",FLOAT_VEC2:\"vec2\",FLOAT_VEC3:\"vec3\",FLOAT_VEC4:\"vec4\",INT:\"int\",INT_VEC2:\"ivec2\",INT_VEC3:\"ivec3\",INT_VEC4:\"ivec4\",BOOL:\"bool\",BOOL_VEC2:\"bvec2\",BOOL_VEC3:\"bvec3\",BOOL_VEC4:\"bvec4\",FLOAT_MAT2:\"mat2\",FLOAT_MAT3:\"mat3\",FLOAT_MAT4:\"mat4\",SAMPLER_2D:\"sampler2D\",SAMPLER_CUBE:\"samplerCube\"},s=null},{}],218:[function(t,e,r){\"use strict\";function 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c=t(\"./GLError\"),h=t(\"gl-format-compiler-error\"),f=\"undefined\"==typeof WeakMap?t(\"weakmap-shim\"):WeakMap,d=new f,p=0;n.prototype.dispose=function(){if(0==--this.count){for(var t=this.cache,e=t.gl,r=this.programs,n=0,i=r.length;n<i;++n){var a=t.programs[r[n]];a&&(delete t.programs[n],e.deleteProgram(a))}e.deleteShader(this.shader),delete t.shaders[this.type===e.FRAGMENT_SHADER|0][this.src]}};var m=i.prototype;m.getShaderReference=function(t,e){var r=this.gl,i=this.shaders[t===r.FRAGMENT_SHADER|0],o=i[e];if(o&&r.isShader(o.shader))o.count+=1;else{var s=a(r,t,e);o=i[e]=new n(p++,e,t,s,[],1,this)}return o},m.getProgram=function(t,e,r,n){var i=[t.id,e.id,r.join(\":\"),n.join(\":\")].join(\"@\"),a=this.programs[i];return a&&this.gl.isProgram(a)||(this.programs[i]=a=o(this.gl,t.shader,e.shader,r,n),t.programs.push(i),e.programs.push(i)),a}},{\"./GLError\":213,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],219:[function(t,e,r){\"use strict\";function n(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}function i(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function a(t,e){return t.x-e.x}function o(t){var e=t.gl,r=s(e,[e.drawingBufferWidth,e.drawingBufferHeight]),i=new n(e,r);return i.grid=l(i),i.text=u(i),i.line=c(i),i.box=h(i),i.update(t),i}e.exports=o;var s=t(\"gl-select-static\"),l=t(\"./lib/grid\"),u=t(\"./lib/text\"),c=t(\"./lib/line\"),h=t(\"./lib/box\"),f=n.prototype;f.setDirty=function(){this.dirty=this.pickDirty=!0},f.setOverlayDirty=function(){this.dirty=!0},f.nextDepthValue=function(){return this._depthCounter++/65536},f.draw=function(){return function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT),t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,d=this.zeroLineWidth;if(h[0]||h[1]){o.bind();for(var p=0;p<2;++p)if(h[p]&&n[p]<=0&&n[p+2]>=0){var m=e[p]-n[p]*(e[p+2]-e[p])/(n[p+2]-n[p]);0===p?o.drawLine(m,e[1],m,e[3],d[p],f[p]):o.drawLine(e[0],m,e[2],m,d[p],f[p])}}for(var p=0;p<l.length;++p)l[p].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var v=this.borderLineEnable,g=this.borderLineWidth,y=this.borderLineColor;v[1]&&o.drawLine(r[0],r[1]-.5*g[1]*i,r[0],r[3]+.5*g[3]*i,g[1],y[1]),v[0]&&o.drawLine(r[0]-.5*g[0]*i,r[1],r[2]+.5*g[2]*i,r[1],g[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5*g[1]*i,r[2],r[3]+.5*g[3]*i,g[3],y[3]),v[2]&&o.drawLine(r[0]-.5*g[0]*i,r[3],r[2]+.5*g[2]*i,r[3],g[2],y[2]),s.bind();for(var p=0;p<2;++p)s.drawTicks(p);this.titleEnable&&s.drawTitle();for(var b=this.overlays,p=0;p<b.length;++p)b[p].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}}}(),f.drawPick=function(){return function(){if(!this.static){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}}}(),f.pick=function(){return function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var h=u[c].pick(a,o,l);if(h)return h}return null}}}(),f.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},f.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},f.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},f.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,o=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/o,10,10/o]),this.borderColor=(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=i(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=i(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=i(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t&&!t.titleEnable),this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=i(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=i(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=i(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var s=t.ticks||[[],[]],l=this._tickBounds;l[0]=l[1]=1/0,l[2]=l[3]=-1/0;for(var u=0;u<2;++u){var c=s[u].slice(0);0!==c.length&&(c.sort(a),l[u]=Math.min(l[u],c[0].x),l[u+2]=Math.max(l[u+2],c[c.length-1].x))}this.grid.update({bounds:l,ticks:s}),this.text.update({bounds:l,ticks:s,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},f.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(var t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},f.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},f.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},f.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},f.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":206,\"./lib/grid\":207,\"./lib/line\":208,\"./lib/text\":210,\"gl-select-static\":254}],220:[function(t,e,r){var n=t(\"gl-shader\");e.exports=function(t){return n(t,\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\",\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\",null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":255}],221:[function(t,e,r){\"use strict\";function n(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function i(t,e){var r=null;try{r=t.getContext(\"webgl\",e),r||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}function a(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){var r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function o(t){return\"boolean\"!=typeof t||t}function s(t){function e(){if(!w&&G.autoResize){var t=M.parentNode,e=1,r=1;t&&t!==document.body?(e=t.clientWidth,r=t.clientHeight):(e=window.innerWidth,r=window.innerHeight);var n=0|Math.ceil(e*G.pixelRatio),i=0|Math.ceil(r*G.pixelRatio);if(n!==M.width||i!==M.height){M.width=n,M.height=i;var a=M.style;a.position=a.position||\"absolute\",a.left=\"0px\",a.top=\"0px\",a.width=e+\"px\",a.height=r+\"px\",N=!0}}}function r(){for(var t=O.length,e=j.length,r=0;r<e;++r)F[r]=0;t:for(var r=0;r<t;++r){var n=O[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(F[a]+i<255){R[r]=a,n.setPickBase(F[a]+1),F[a]+=i;continue t}var o=f(A,q);R[r]=e,j.push(o),F.push(i),n.setPickBase(1),e+=1}else R[r]=-1}for(;e>0&&0===F[e-1];)F.pop(),j.pop().dispose()}function s(){if(G.contextLost)return!0;A.isContextLost()&&(G.contextLost=!0,G.mouseListener.enabled=!1,G.selection.object=null,G.oncontextloss&&G.oncontextloss())}function b(){if(!s()){A.colorMask(!0,!0,!0,!0),A.depthMask(!0),A.disable(A.BLEND),A.enable(A.DEPTH_TEST);for(var t=O.length,e=j.length,r=0;r<e;++r){var n=j[r];n.shape=Y,n.begin();for(var i=0;i<t;++i)if(R[i]===r){var a=O[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(H))}n.end()}}}function x(){if(!s()){e();var t=G.camera.tick();H.view=G.camera.matrix,N=N||t,B=B||t,z.pixelRatio=G.pixelRatio,P.pixelRatio=G.pixelRatio;var r=O.length,n=Z[0],i=Z[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<r;++o){var l=O[o];l.pixelRatio=G.pixelRatio,l.axes=G.axes,N=N||!!l.dirty,B=B||!!l.dirty;var u=l.bounds;if(u)for(var h=u[0],f=u[1],d=0;d<3;++d)n[d]=Math.min(n[d],h[d]),i[d]=Math.max(i[d],f[d])}var m=G.bounds;if(G.autoBounds)for(var d=0;d<3;++d){if(i[d]<n[d])n[d]=-1,i[d]=1;else{n[d]===i[d]&&(n[d]-=1,i[d]+=1);var g=.05*(i[d]-n[d]);n[d]=n[d]-g,i[d]=i[d]+g}m[0][d]=n[d],m[1][d]=i[d]}for(var y=!1,d=0;d<3;++d)y=y||J[0][d]!==m[0][d]||J[1][d]!==m[1][d],J[0][d]=m[0][d],J[1][d]=m[1][d];if(B=B||y,N=N||y){if(y){for(var x=[0,0,0],o=0;o<3;++o)x[o]=a((m[1][o]-m[0][o])/10);z.autoTicks?z.update({bounds:m,tickSpacing:x}):z.update({bounds:m})}var _=A.drawingBufferWidth,w=A.drawingBufferHeight;q[0]=_,q[1]=w,Y[0]=0|Math.max(_/G.pixelRatio,1),Y[1]=0|Math.max(w/G.pixelRatio,1),v(U,G.fovy,_/w,G.zNear,G.zFar);for(var o=0;o<16;++o)V[o]=0;V[15]=1;for(var M=0,o=0;o<3;++o)M=Math.max(M,m[1][o]-m[0][o]);for(var o=0;o<3;++o)G.autoScale?V[5*o]=G.aspect[o]/(m[1][o]-m[0][o]):V[5*o]=1/M,G.autoCenter&&(V[12+o]=.5*-V[5*o]*(m[0][o]+m[1][o]));for(var o=0;o<r;++o){var l=O[o];l.axesBounds=m,G.clipToBounds&&(l.clipBounds=m)}S.object&&(G.snapToData?P.position=S.dataCoordinate:P.position=S.dataPosition,P.bounds=m),B&&(B=!1,b()),G.axesPixels=c(G.axes,H,_,w),G.onrender&&G.onrender(),A.bindFramebuffer(A.FRAMEBUFFER,null),A.viewport(0,0,_,w);var k=G.clearColor;A.clearColor(k[0],k[1],k[2],k[3]),A.clear(A.COLOR_BUFFER_BIT|A.DEPTH_BUFFER_BIT),A.depthMask(!0),A.colorMask(!0,!0,!0,!0),A.enable(A.DEPTH_TEST),A.depthFunc(A.LEQUAL),A.disable(A.BLEND),A.disable(A.CULL_FACE);var T=!1;z.enable&&(T=T||z.isTransparent(),z.draw(H)),P.axes=z,S.object&&P.draw(H),A.disable(A.CULL_FACE);for(var o=0;o<r;++o){var l=O[o];l.axes=z,l.pixelRatio=G.pixelRatio,l.isOpaque&&l.isOpaque()&&l.draw(H),l.isTransparent&&l.isTransparent()&&(T=!0)}if(T){E.shape=q,E.bind(),A.clear(A.DEPTH_BUFFER_BIT),A.colorMask(!1,!1,!1,!1),A.depthMask(!0),A.depthFunc(A.LESS),z.enable&&z.isTransparent()&&z.drawTransparent(H);for(var o=0;o<r;++o){var l=O[o];l.isOpaque&&l.isOpaque()&&l.draw(H)}A.enable(A.BLEND),A.blendEquation(A.FUNC_ADD),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.colorMask(!0,!0,!0,!0),A.depthMask(!1),A.clearColor(0,0,0,0),A.clear(A.COLOR_BUFFER_BIT),z.isTransparent()&&z.drawTransparent(H);for(var o=0;o<r;++o){var l=O[o];l.isTransparent&&l.isTransparent()&&l.drawTransparent(H)}A.bindFramebuffer(A.FRAMEBUFFER,null),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.disable(A.DEPTH_TEST),L.bind(),E.color[0].bind(0),L.uniforms.accumBuffer=0,p(A),A.disable(A.BLEND)}N=!1;for(var o=0;o<r;++o)O[o].dirty=!1}}}function _(){w||G.contextLost||(requestAnimationFrame(_),x())}t=t||{};var w=!1,M=(t.pixelRatio||parseFloat(window.devicePixelRatio),t.canvas);if(!M)if(M=document.createElement(\"canvas\"),t.container){var k=t.container;k.appendChild(M)}else document.body.appendChild(M);var A=t.gl;if(A||(A=i(M,t.glOptions||{premultipliedAlpha:!0,antialias:!0})),!A)throw new Error(\"webgl not supported\");var T=t.bounds||[[-10,-10,-10],[10,10,10]],S=new n,E=d(A,[A.drawingBufferWidth,A.drawingBufferHeight],{preferFloat:!y}),L=g(A),C=t.camera||{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:\"turntable\"},I=t.axes||{},z=u(A,I);z.enable=!I.disable;var D=t.spikes||{},P=h(A,D),O=[],R=[],F=[],j=[],N=!0,B=!0,U=new Array(16),V=new Array(16),H={view:null,projection:U,model:V},B=!0,q=[A.drawingBufferWidth,A.drawingBufferHeight],G={gl:A,contextLost:!1,pixelRatio:t.pixelRatio||parseFloat(window.devicePixelRatio),canvas:M,selection:S,camera:l(M,C),axes:z,axesPixels:null,spikes:P,bounds:T,objects:O,shape:q,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:o(t.autoResize),autoBounds:o(t.autoBounds),autoScale:!!t.autoScale,autoCenter:o(t.autoCenter),clipToBounds:o(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:H,oncontextloss:null,mouseListener:null},Y=[A.drawingBufferWidth/G.pixelRatio|0,A.drawingBufferHeight/G.pixelRatio|0];G.autoResize&&e(),window.addEventListener(\"resize\",e),G.update=function(t){w||(t=t||{},N=!0,B=!0)},G.add=function(t){w||(t.axes=z,O.push(t),R.push(-1),N=!0,B=!0,r())},G.remove=function(t){if(!w){var e=O.indexOf(t);e<0||(O.splice(e,1),R.pop(),N=!0,B=!0,r())}},G.dispose=function(){if(!w&&(w=!0,window.removeEventListener(\"resize\",e),M.removeEventListener(\"webglcontextlost\",s),G.mouseListener.enabled=!1,!G.contextLost)){z.dispose(),P.dispose();for(var t=0;t<O.length;++t)O[t].dispose();E.dispose();for(var t=0;t<j.length;++t)j[t].dispose();L.dispose(),A=null,z=null,P=null,O=[]}};var W=!1,X=0;G.mouseListener=m(M,function(t,e,r){if(!w){var n=j.length,i=O.length,a=S.object;S.distance=1/0,S.mouse[0]=e,S.mouse[1]=r,S.object=null,S.screen=null,S.dataCoordinate=S.dataPosition=null;var o=!1;if(t&&X)W=!0;else{W&&(B=!0),W=!1;for(var s=0;s<n;++s){var l=j[s].query(e,Y[1]-r-1,G.pickRadius);if(l){if(l.distance>S.distance)continue;for(var u=0;u<i;++u){var c=O[u];if(R[u]===s){var h=c.pick(l);h&&(S.buttons=t,S.screen=l.coord,S.distance=l.distance,S.object=c,S.index=h.distance,S.dataPosition=h.position,S.dataCoordinate=h.dataCoordinate,S.data=h,o=!0)}}}}}a&&a!==S.object&&(a.highlight&&a.highlight(null),N=!0),S.object&&(S.object.highlight&&S.object.highlight(S.data),N=!0),o=o||S.object!==a,o&&G.onselect&&G.onselect(S),1&t&&!(1&X)&&G.onclick&&G.onclick(S),X=t}}),M.addEventListener(\"webglcontextlost\",s);var Z=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],J=[Z[0].slice(),Z[1].slice()];return _(),G.redraw=function(){w||(N=!0,x())},G}e.exports=s;var l=t(\"3d-view-controls\"),u=t(\"gl-axes3d\"),c=t(\"gl-axes3d/properties\"),h=t(\"gl-spikes3d\"),f=t(\"gl-select-static\"),d=t(\"gl-fbo\"),p=t(\"a-big-triangle\"),m=t(\"mouse-change\"),v=t(\"gl-mat4/perspective\"),g=t(\"./lib/shader\"),y=t(\"is-mobile\")()},{\"./lib/shader\":220,\"3d-view-controls\":36,\"a-big-triangle\":39,\"gl-axes3d\":148,\"gl-axes3d/properties\":155,\"gl-fbo\":164,\"gl-mat4/perspective\":184,\"gl-select-static\":254,\"gl-spikes3d\":264,\"is-mobile\":296,\"mouse-change\":452}],222:[function(t,e,r){r.pointVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\",r.pickVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"},{}],223:[function(t,e,r){arguments[4][212][0].apply(r,arguments)},{\"./lib/GLError\":224,\"./lib/create-attributes\":225,\"./lib/create-uniforms\":226,\"./lib/reflect\":227,\"./lib/runtime-reflect\":228,\"./lib/shader-cache\":229,dup:212}],224:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],225:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\n",
+       "\"./GLError\":224,dup:214}],226:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":224,\"./reflect\":227,dup:215}],227:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],228:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],229:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":224,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],230:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}function i(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}function a(t,e){var r=t.gl,i=s(r),a=s(r),l=o(r,u.pointVertex,u.pointFragment),c=o(r,u.pickVertex,u.pickFragment),h=new n(t,i,a,l,c);return h.update(e),t.addObject(h),h}var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"typedarray-pool\"),u=t(\"./lib/shader\");e.exports=a;var c=n.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){function e(e,r){return e in t?t[e]:r}var r;t=t||{},this.sizeMin=e(\"sizeMin\",.5),this.sizeMax=e(\"sizeMax\",20),this.color=e(\"color\",[1,0,0,1]).slice(),this.areaRatio=e(\"areaRatio\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.blend=e(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,a=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,o=t.positions,s=i?o:l.mallocFloat32(o.length),u=a?t.idToIndex:l.mallocInt32(n);if(i||s.set(o),!a)for(s.set(o),r=0;r<n;r++)u[r]=r;this.points=o,this.offsetBuffer.update(s),this.pickBuffer.update(u),i||l.free(s),a||l.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=void 0!==r,a=n?this.pickShader:this.shader,o=this.plot.gl,s=this.plot.dataBox;if(0===this.pointCount)return r;var l=s[2]-s[0],u=s[3]-s[1],c=i(this.points,s),h=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(c,.33333)));t[0]=2/l,t[4]=2/u,t[6]=-2*s[0]/l-1,t[7]=-2*s[1]/u-1,this.offsetBuffer.bind(),a.bind(),a.attributes.position.pointer(),a.uniforms.matrix=t,a.uniforms.color=this.color,a.uniforms.borderColor=this.borderColor,a.uniforms.pointCloud=h<5,a.uniforms.pointSize=h,a.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),n&&(e[0]=255&r,e[1]=r>>8&255,e[2]=r>>16&255,e[3]=r>>24&255,this.pickBuffer.bind(),a.attributes.pickId.pointer(o.UNSIGNED_BYTE),a.uniforms.pickOffset=e,this.pickOffset=r);var f=o.getParameter(o.BLEND),d=o.getParameter(o.DITHER);return f&&!this.blend&&o.disable(o.BLEND),d&&o.disable(o.DITHER),o.drawArrays(o.POINTS,0,this.pointCount),f&&!this.blend&&o.enable(o.BLEND),d&&o.enable(o.DITHER),r+this.pointCount}}(),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":222,\"gl-buffer\":156,\"gl-shader\":223,\"typedarray-pool\":541}],231:[function(t,e,r){function n(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],h=e[2],f=e[3],d=r[0],p=r[1],m=r[2],v=r[3];return a=u*d+c*p+h*m+f*v,a<0&&(a=-a,d=-d,p=-p,m=-m,v=-v),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*d,t[1]=s*c+l*p,t[2]=s*h+l*m,t[3]=s*f+l*v,t}e.exports=n},{}],232:[function(t,e,r){\"use strict\";e.exports={vertex:\"precision highp float;\\n#define GLSLIFY 1\\n\\n\\nvec4 computePosition_1_0(vec2 posHi, vec2 posLo, vec2 scHi, vec2 scLo, vec2 trHi, vec2 trLo) {\\n  return vec4((posHi + trHi) * scHi\\n  \\t\\t\\t//FIXME: this thingy does not give noticeable precision gain, need test\\n            + (posLo + trLo) * scHi\\n            + (posHi + trHi) * scLo\\n            + (posLo + trLo) * scLo\\n            , 0, 1);\\n}\\n\\n\\nattribute vec2 positionHi, positionLo;\\nattribute float size, border;\\nattribute vec2 char, color;\\n\\n//this is 64-bit form of scale and translate\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pixelRatio;\\nuniform vec4 viewBox;\\nuniform sampler2D palette;\\n\\nvarying vec4 charColor, borderColor;\\nvarying vec2 charId;\\nvarying vec2 pointCoord;\\nvarying float pointSize;\\nvarying float borderWidth;\\n\\n\\nvoid main() {\\n  charColor = texture2D(palette, vec2(color.x / 255., 0));\\n  borderColor = texture2D(palette, vec2(color.y / 255., 0));\\n\\n  gl_PointSize = size * pixelRatio;\\n  pointSize = size * pixelRatio;\\n\\n  charId = char;\\n  borderWidth = border;\\n\\n  gl_Position = computePosition_1_0(\\n    positionHi, positionLo,\\n    scaleHi, scaleLo,\\n    translateHi, translateLo);\\n\\n  pointCoord = viewBox.xy + (viewBox.zw - viewBox.xy) * (gl_Position.xy * .5 + .5);\\n}\\n\",fragment:\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D chars;\\nuniform vec2 charsShape;\\nuniform float charsStep, pixelRatio, charOffset;\\n\\nvarying vec4 borderColor;\\nvarying vec4 charColor;\\nvarying vec2 charId;\\nvarying vec2 pointCoord;\\nvarying float pointSize;\\nvarying float borderWidth;\\n\\nvoid main() {\\n\\tvec2 pointUV = (pointCoord - gl_FragCoord.xy + pointSize * .5) / pointSize;\\n\\tpointUV.x = 1. - pointUV.x;\\n\\tvec2 texCoord = ((charId + pointUV) * charsStep) / charsShape;\\n\\tfloat dist = texture2D(chars, texCoord).r;\\n\\n\\t//max-distance alpha\\n\\tif (dist < 1e-2)\\n\\t\\tdiscard;\\n\\n\\tfloat gamma = .0045 * charsStep / pointSize;\\n\\n    //null-border case\\n \\tif (borderWidth * borderColor.a == 0.) {\\n\\t\\tfloat charAmt = smoothstep(.748 - gamma, .748 + gamma, dist);\\n\\t\\tgl_FragColor = vec4(charColor.rgb, charAmt*charColor.a);\\n\\t\\treturn;\\n\\t}\\n\\n\\tfloat dif = 5. * pixelRatio * borderWidth / pointSize;\\n\\tfloat borderLevel = .748 - dif * .5;\\n\\tfloat charLevel = .748 + dif * .5;\\n\\n\\tfloat borderAmt = smoothstep(borderLevel - gamma, borderLevel + gamma, dist);\\n\\tfloat charAmt = smoothstep(charLevel - gamma, charLevel + gamma, dist);\\n\\n\\tvec4 color = borderColor;\\n\\tcolor.a *= borderAmt;\\n\\n\\tgl_FragColor = mix(color, charColor, charAmt);\\n}\\n\",pickVertex:\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 positionHi, positionLo;\\nattribute vec4 id;\\nattribute float size;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform vec4 pickOffset;\\nuniform float pixelRatio;\\n\\nvarying vec4 fragColor;\\n\\n\\nvec4 computePosition_1_0(vec2 posHi, vec2 posLo, vec2 scHi, vec2 scLo, vec2 trHi, vec2 trLo) {\\n  return vec4((posHi + trHi) * scHi\\n  \\t\\t\\t//FIXME: this thingy does not give noticeable precision gain, need test\\n            + (posLo + trLo) * scHi\\n            + (posHi + trHi) * scLo\\n            + (posLo + trLo) * scLo\\n            , 0, 1);\\n}\\n\\n\\nvoid main() {\\n  vec4 fragId = id + pickOffset;\\n\\n  fragId.y += floor(fragId.x / 256.0);\\n  fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n  fragId.z += floor(fragId.y / 256.0);\\n  fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n  fragId.w += floor(fragId.z / 256.0);\\n  fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n  fragColor = fragId / 255.0;\\n\\n  gl_PointSize = size * .25 * pixelRatio;\\n\\n  gl_Position = computePosition_1_0(\\n    positionHi, positionLo,\\n    scaleHi, scaleLo,\\n    translateHi, translateLo);\\n}\\n\",pickFragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\\n\"}},{}],233:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],234:[function(t,e,r){arguments[4][212][0].apply(r,arguments)},{\"./lib/GLError\":235,\"./lib/create-attributes\":236,\"./lib/create-uniforms\":237,\"./lib/reflect\":238,\"./lib/runtime-reflect\":239,\"./lib/shader-cache\":240,dup:212}],235:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],236:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\"./GLError\":235,dup:214}],237:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":235,\"./reflect\":238,dup:215}],238:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],239:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],240:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":235,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],241:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){a<=4*f?i(0,a-1,t,e,r,n):h(0,a-1,t,e,r,n)}function i(t,e,r,n,i,a){for(var o=t+1;o<=e;++o){for(var s=r[o],l=n[2*o],u=n[2*o+1],c=i[o],h=a[o],f=o;f>t;){var d=r[f-1],p=n[2*(f-1)];if((d-s||l-p)>=0)break;r[f]=d,n[2*f]=p,n[2*f+1]=n[2*f-1],i[f]=i[f-1],a[f]=a[f-1],f-=1}r[f]=s,n[2*f]=l,n[2*f+1]=u,i[f]=c,a[f]=h}}function a(t,e,r,n,i,a){var o=r[t],s=n[2*t],l=n[2*t+1],u=i[t],c=a[t];r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],a[t]=a[e],r[e]=o,n[2*e]=s,n[2*e+1]=l,i[e]=u,a[e]=c}function o(t,e,r,n,i,a){r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],a[t]=a[e]}function s(t,e,r,n,i,a,o){var s=n[t],l=i[2*t],u=i[2*t+1],c=a[t],h=o[t];n[t]=n[e],i[2*t]=i[2*e],i[2*t+1]=i[2*e+1],a[t]=a[e],o[t]=o[e],n[e]=n[r],i[2*e]=i[2*r],i[2*e+1]=i[2*r+1],a[e]=a[r],o[e]=o[r],n[r]=s,i[2*r]=l,i[2*r+1]=u,a[r]=c,o[r]=h}function l(t,e,r,n,i,a,o,s,l,u,c){s[t]=s[e],l[2*t]=l[2*e],l[2*t+1]=l[2*e+1],u[t]=u[e],c[t]=c[e],s[e]=r,l[2*e]=n,l[2*e+1]=i,u[e]=a,c[e]=o}function 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if(!c(it,P,O,R,F,r,n,d))for(;;){if(c(S,P,O,R,F,r,n,d)){c(S,L,C,I,z,r,n,d)?(s(it,T,S,r,n,d,p),++T,--S):(a(it,S,r,n,d,p),--S);break}if(--S<it)break}l(t,T-1,L,C,I,z,D,r,n,d,p),l(e,S+1,P,O,R,F,j,r,n,d,p),T-2-t<=f?i(t,T-2,r,n,d,p):h(t,T-2,r,n,d,p),e-(S+2)<=f?i(S+2,e,r,n,d,p):h(S+2,e,r,n,d,p),S-T<=f?i(T,S,r,n,d,p):h(T,S,r,n,d,p)}e.exports=n;var f=32},{}],242:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){for(var l=r,u=r;u<n;++u){var c=t[2*u],h=t[2*u+1],f=e[u];i<=c&&c<=o&&a<=h&&h<=s&&(u===l?l+=1:(t[2*u]=t[2*l],t[2*u+1]=t[2*l+1],e[u]=e[l],t[2*l]=c,t[2*l+1]=h,e[l]=f,l+=1))}return l}function i(t,e,r){this.pixelSize=t,this.offset=e,this.count=r}function a(t,e,r,a){function l(i,a,o,s,u,c){var h=.5*o,f=s+1,d=u-s;r[_]=d,x[_++]=c;for(var p=0;p<2;++p)for(var m=0;m<2;++m){var v=i+p*h,g=a+m*h,y=n(t,e,f,u,v,g,v+h,g+h);if(y!==f){if(y-f>=Math.max(.9*d,32)){var b=u+s>>>1;l(v,g,h,f,b,c+1),f=b}l(v,g,h,f,y,c+1),f=y}}}var u=t.length>>>1;if(u<1)return[];for(var c=1/0,h=1/0,f=-1/0,d=-1/0,p=0;p<u;++p){var m=t[2*p],v=t[2*p+1];c=Math.min(c,m),f=Math.max(f,m),h=Math.min(h,v),d=Math.max(d,v),e[p]=p}c===f&&(f+=1+Math.abs(f)),h===d&&(d+=1+Math.abs(f));var g=1/(f-c),y=1/(d-h),b=Math.max(f-c,d-h);a=a||[0,0,0,0],a[0]=c,a[1]=h,a[2]=f,a[3]=d;var x=o.mallocInt32(u),_=0;l(c,h,b,0,u,0),s(x,t,e,r,u);for(var w=[],M=0,k=u,_=u-1;_>=0;--_){t[2*_]=(t[2*_]-c)*g,t[2*_+1]=(t[2*_+1]-h)*y;var A=x[_];A!==M&&(w.push(new i(b*Math.pow(.5,A),_+1,k-(_+1))),k=_+1,M=A)}return w.push(new i(b*Math.pow(.5,A+1),0,k)),o.free(x),w}var o=t(\"typedarray-pool\"),s=t(\"./lib/sort\");e.exports=a},{\"./lib/sort\":241,\"typedarray-pool\":541}],243:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.sizeBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.charBuffer=s,this.pointCount=0,this.pickOffset=0,this.points=null,this.scales=[],this.xCoords=[],this.charCanvas=document.createElement(\"canvas\"),this.charTexture=m(this.plot.gl,this.charCanvas),this.charStep=400,this.charFit=.255,this.snapThreshold=1e4,this.paletteTexture=m(this.plot.gl,[256,1])}function i(){var t=this.plot,e=t.viewBox,r=t.dataBox,n=t.pixelRatio,i=r[2]-r[0],a=r[3]-r[1],u=2/i,c=2/a,h=-r[0]-.5*i,f=-r[1]-.5*a;_[0]=u,w[0]=u-_[0],_[1]=c,w[1]=c-_[1],M[0]=h,k[0]=h-M[0],M[1]=f,k[1]=f-M[1];var d=e[2]-e[0],p=e[3]-e[1];o=Math.min(i/d,a/p),A[0]=2*n/d,A[1]=2*n/p,s=r[0],l=r[2]}function a(t,e){var r=t.gl,i=u(r,f.vertex,f.fragment),a=u(r,f.pickVertex,f.pickFragment),o=c(r),s=c(r),l=c(r),h=c(r),d=c(r),p=new n(t,i,a,o,s,l,h,d);return p.update(e),t.addObject(p),p}e.exports=a;var o,s,l,u=t(\"gl-shader\"),c=t(\"gl-buffer\"),h=t(\"typedarray-pool\"),f=t(\"./lib/shaders\"),d=t(\"snap-points-2d\"),p=t(\"font-atlas-sdf\"),m=t(\"gl-texture2d\"),v=t(\"color-id\"),g=t(\"ndarray\"),y=t(\"clamp\"),b=t(\"binary-search-bounds\"),x=n.prototype,_=new Float32Array([0,0]),w=new Float32Array([0,0]),M=new Float32Array([0,0]),k=new Float32Array([0,0]),A=[0,0],T=[0,0,0,0];x.drawPick=function(t){var e=void 0!==t,r=this.plot,n=this.pointCount,a=n>this.snapThreshold;if(!n)return t;i.call(this);var u=r.gl,c=e?this.pickShader:this.shader,h=u.isEnabled(u.BLEND);if(c.bind(),e){this.pickOffset=t;for(var f=0;f<4;++f)T[f]=t>>8*f&255;c.uniforms.pickOffset=T,this.idBuffer.bind(),c.attributes.id.pointer(u.UNSIGNED_BYTE,!1)}else u.blendFuncSeparate(u.SRC_ALPHA,u.ONE_MINUS_SRC_ALPHA,u.ONE,u.ONE_MINUS_SRC_ALPHA),u.blendColor(0,0,0,1),h||u.enable(u.BLEND),this.colorBuffer.bind(),c.attributes.color.pointer(u.UNSIGNED_BYTE,!1),this.charBuffer.bind(),c.attributes.char.pointer(u.UNSIGNED_BYTE,!1),c.uniforms.chars=this.charTexture.bind(0),c.uniforms.charsShape=[this.charCanvas.width,this.charCanvas.height],c.uniforms.charsStep=this.charStep,c.uniforms.palette=this.paletteTexture.bind(1);this.sizeBuffer.bind(),c.attributes.size.pointer(u.FLOAT,!1,8,0),e||c.attributes.border.pointer(u.FLOAT,!1,8,4),this.positionBuffer.bind(),c.attributes.positionHi.pointer(u.FLOAT,!1,16,0),c.attributes.positionLo.pointer(u.FLOAT,!1,16,8),c.uniforms.pixelRatio=r.pixelRatio,c.uniforms.scaleHi=_,c.uniforms.scaleLo=w,c.uniforms.translateHi=M,c.uniforms.translateLo=k,c.uniforms.viewBox=r.viewBox;var d=this.scales;if(a)for(var p=d.length-1;p>=0;p--){var m=d[p];if(!(m.pixelSize&&m.pixelSize<1.25*o&&p>1)){var v=m.offset,g=m.count+v,y=b.ge(this.xCoords,s,v,g-1),x=b.lt(this.xCoords,l,y,g-1)+1;x>y&&u.drawArrays(u.POINTS,y,x-y)}}else u.drawArrays(u.POINTS,0,n);if(e)return t+n;h?u.blendFunc(u.ONE,u.ONE_MINUS_SRC_ALPHA):u.disable(u.BLEND)},x.draw=x.drawPick,x.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}},x.update=function(t){t=t||{};var e=t.positions||[],r=t.colors||[],n=t.glyphs||[],i=t.sizes||[],a=t.borderWidths||[],o=t.borderColors||[],s=this.plot.gl,l=this.pointCount,u=l>this.snapThreshold;if(null!=t.positions){this.points=e,l=this.points.length/2,u=l>this.snapThreshold;var c=h.mallocFloat32(2*l),f=h.mallocFloat64(2*l),m=h.mallocUint32(l),b=h.mallocFloat32(4*l);f.set(this.points),u&&(this.i2idx&&h.free(this.i2idx),this.i2idx=h.mallocInt32(l),this.scales=d(f,this.i2idx,c)),this.pointCount=l;for(var x=0;x<l;++x){var _=u?this.i2idx[x]:x;m[x]=_;var w=e[2*_],M=e[2*_+1];b[4*x]=w,b[4*x+1]=M,b[4*x+2]=w-b[4*x],b[4*x+3]=M-b[4*x+1],this.xCoords[x]=w}this.idBuffer.update(m),this.positionBuffer.update(b),h.free(b),h.free(m),h.free(f),h.free(c)}for(var k=h.mallocFloat32(2*l),A=h.mallocUint8(2*l),T=h.mallocUint8(2*l),S={},E=[],L=[],C=[],x=0,I=l,z=0;x<I;++x){var D=[255*r[4*x],255*r[4*x+1],255*r[4*x+2],255*r[4*x+3]],P=v(D,!1);null==S[P]&&(S[P]=z++,L.push(D[0]),L.push(D[1]),L.push(D[2]),L.push(D[3])),E.push(P),o&&o.length&&(D=[255*o[4*x],255*o[4*x+1],255*o[4*x+2],255*o[4*x+3]],P=v(D,!1),null==S[P]&&(S[P]=z++,L.push(D[0]),L.push(D[1]),L.push(D[2]),L.push(D[3])),C.push(P))}for(var O={},x=0,I=l,z=0;x<I;x++){var R=n[x];null==O[R]&&(O[R]=z++)}for(var F=0,x=0,I=i.length;x<I;++x)i[x]>F&&(F=i[x]);var j=this.charStep;this.charStep=y(Math.ceil(4*F),128,768);var N=Object.keys(O),B=this.charStep,U=Math.floor(B/2),V=s.getParameter(s.MAX_TEXTURE_SIZE),H=V/B*(V/B),q=Math.min(V,B*N.length),G=Math.min(V,B*Math.ceil(B*N.length/V)),Y=Math.floor(q/B);N.length>H&&console.warn(\"gl-scatter2d-fancy: number of characters is more than maximum texture size. Try reducing it.\"),this.chars&&this.chars+\"\"==N+\"\"&&this.charStep==j||(this.charCanvas=p({canvas:this.charCanvas,family:\"sans-serif\",size:U,shape:[q,G],step:[B,B],chars:N,align:!0,fit:this.charFit}),this.chars=N);for(var x=0;x<l;++x){var _=u?this.i2idx[x]:x,W=i[_],X=a[_];k[2*x]=2*W,k[2*x+1]=X;var P=E[_],Z=S[P];A[2*x]=Z;var J=C[_],K=S[J];A[2*x+1]=K;var R=n[_],Q=O[R];T[2*x+1]=Math.floor(Q/Y),T[2*x]=Q%Y}this.sizeBuffer.update(k),this.colorBuffer.update(A),this.charBuffer.update(T),this.charTexture.shape=[this.charCanvas.width,this.charCanvas.height],this.charCanvas&&this.charCanvas.width&&this.charTexture.setPixels(this.charCanvas),this.paletteTexture.setPixels(g(L.slice(0,1024),[256,1,4])),h.free(k),h.free(A),h.free(T)},x.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.sizeBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.charBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":232,\"binary-search-bounds\":233,clamp:88,\"color-id\":92,\"font-atlas-sdf\":134,\"gl-buffer\":156,\"gl-shader\":234,\"gl-texture2d\":267,ndarray:467,\"snap-points-2d\":242,\"typedarray-pool\":541}],244:[function(t,e,r){r.pointVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 positionHi, positionLo;\\nattribute float weight;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pointSize, useWeight;\\n\\nvarying float fragWeight;\\n\\n\\nvec4 pfx_1_0(vec2 scaleHi, vec2 scaleLo, vec2 translateHi, vec2 translateLo, vec2 positionHi, vec2 positionLo) {\\n  return vec4((positionHi + translateHi) * scaleHi\\n            + (positionLo + translateLo) * scaleHi\\n            + (positionHi + translateHi) * scaleLo\\n            + (positionLo + translateLo) * scaleLo, 0.0, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position = pfx_1_0(scaleHi, scaleLo, translateHi, translateLo, positionHi, positionLo);\\n  gl_PointSize = pointSize;\\n  fragWeight = mix(1.0, weight, useWeight);\\n}\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\n\\nvarying float fragWeight;\\n\\nfloat smoothStep(float x, float y) {\\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\\n}\\n\\nvoid main() {\\n  float radius = length(2.0*gl_PointCoord.xy-1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  vec4 baseColor = mix(borderColor, color, smoothStep(radius, centerFraction));\\n  float alpha = 1.0 - pow(1.0 - baseColor.a, fragWeight);\\n  gl_FragColor = vec4(baseColor.rgb * alpha, alpha);\\n}\\n\",r.pickVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nvec4 pfx_1_0(vec2 scaleHi, vec2 scaleLo, vec2 translateHi, vec2 translateLo, vec2 positionHi, vec2 positionLo) {\\n  return vec4((positionHi + translateHi) * scaleHi\\n            + (positionLo + translateLo) * scaleHi\\n            + (positionHi + translateHi) * scaleLo\\n            + (positionLo + translateLo) * scaleLo, 0.0, 1.0);\\n}\\n\\nattribute vec2 positionHi, positionLo;\\nattribute vec4 pickId;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_Position = pfx_1_0(scaleHi, scaleLo, translateHi, translateLo, positionHi, positionLo);\\n  gl_PointSize = pointSize;\\n  fragId = id;\\n}\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\"},{}],245:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],246:[function(t,e,r){arguments[4][241][0].apply(r,arguments)},{dup:241}],247:[function(t,e,r){arguments[4][242][0].apply(r,arguments)},{\"./lib/sort\":246,dup:242,\"typedarray-pool\":541}],248:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o){this.plot=t,this.positionBufferHi=e,this.positionBufferLo=r,this.pickBuffer=n,this.weightBuffer=i,this.shader=a,this.pickShader=o,this.scales=[],this.size=12,this.borderSize=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0,this.points=null,this.xCoords=null,this.snapPoints=!0}function i(t,e){var r=t.gl,i=o(r),s=o(r),l=o(r),u=o(r),h=a(r,c.pointVertex,c.pointFragment),f=a(r,c.pickVertex,c.pickFragment),d=new n(t,i,s,l,u,h,f);return d.update(e),t.addObject(d),d}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"binary-search-bounds\"),l=t(\"snap-points-2d\"),u=t(\"typedarray-pool\"),c=t(\"./lib/shader\"),h=t(\"array-normalize\"),f=t(\"array-bounds\");e.exports=i;var d=n.prototype,p=new Float32Array(2),m=new Float32Array(2),v=new Float32Array(2),g=new Float32Array(2),y=[0,0,0,0];d.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBufferHi.dispose(),this.positionBufferLo.dispose(),this.pickBuffer.dispose(),this.xCoords&&u.free(this.xCoords),this.plot.removeObject(this)},d.update=function(t){function e(e,r){return e in t?t[e]:r}if(t=t||{},this.size=e(\"size\",12),this.color=e(\"color\",[1,0,0,1]).slice(),this.borderSize=e(\"borderSize\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.snapPoints=e(\"snapPoints\",!0),null!=t.positions){this.xCoords&&u.free(this.xCoords),this.points=t.positions;var r=this.points.length>>>1,n=u.mallocInt32(r),i=u.mallocFloat32(r),a=u.mallocFloat64(2*r);if(a.set(this.points),this.snapPoints)this.scales=l(a,n,i,this.bounds);else{this.bounds=f(a,2),h(a,2,this.bounds);for(var o=0;o<r;o++)n[o]=o,i[o]=1}var s=u.mallocFloat64(r),c=u.mallocFloat32(2*r),d=u.mallocFloat32(2*r);c.set(a);for(var o=0,p=0;o<r;o++,p+=2)d[p]=a[p]-c[p],d[p+1]=a[p+1]-c[p+1],s[o]=a[p];this.positionBufferHi.update(c),this.positionBufferLo.update(d),this.pickBuffer.update(n),this.weightBuffer.update(i),u.free(c),u.free(d),u.free(i),u.free(a),u.free(n),this.xCoords=s,this.pointCount=r,this.pickOffset=0}},d.draw=function(t){var e=void 0!==t,r=this.plot,n=e?this.pickShader:this.shader,i=this.scales,a=this.positionBufferHi,o=this.positionBufferLo,s=this.pickBuffer,l=this.bounds,u=this.size,c=this.borderSize,h=r.gl,f=e?r.pickPixelRatio:r.pixelRatio,d=r.viewBox,b=r.dataBox;if(0===this.pointCount)return t;var x=l[2]-l[0],_=l[3]-l[1],w=b[2]-b[0],M=b[3]-b[1],k=(d[2]-d[0])*f/r.pixelRatio,A=(d[3]-d[1])*f/r.pixelRatio,T=this.pixelSize=Math.min(w/k,M/A),S=2*x/w,E=2*_/M;p[0]=S,p[1]=E,m[0]=S-p[0],m[1]=E-p[1];var L=(l[0]-b[0]-.5*w)/x,C=(l[1]-b[1]-.5*M)/_;v[0]=L,v[1]=C,g[0]=L-v[0],g[1]=C-v[1],n.bind(),n.uniforms.scaleHi=p,n.uniforms.scaleLo=m,n.uniforms.translateHi=v,n.uniforms.translateLo=g,n.uniforms.color=this.color,n.uniforms.borderColor=this.borderColor,n.uniforms.pointSize=f*(u+c),n.uniforms.centerFraction=0===this.borderSize?2:u/(u+c+1.25),a.bind(),n.attributes.positionHi.pointer(),o.bind(),n.attributes.positionLo.pointer(),e?(this.pickOffset=t,y[0]=255&t,y[1]=t>>8&255,y[2]=t>>16&255,y[3]=t>>24&255,n.uniforms.pickOffset=y,s.bind(),n.attributes.pickId.pointer(h.UNSIGNED_BYTE)):(n.uniforms.useWeight=1,this.weightBuffer.bind(),n.attributes.weight.pointer());var I=!0;if(this.snapPoints)for(var z=i.length-1;z>=0;z--){var D=i[z];if(!(D.pixelSize<T&&z>1)){var P=this.getVisibleRange(D),O=P[0],R=P[1];R>O&&h.drawArrays(h.POINTS,O,R-O),!e&&I&&(I=!1,n.uniforms.useWeight=0)}}else h.drawArrays(h.POINTS,0,this.pointCount);return t+this.pointCount},d.getVisibleRange=function(t){var e=this.plot.dataBox,r=this.bounds,n=this.pixelSize,i=this.size,a=this.plot.pixelRatio,o=r[2]-r[0];r[3],r[1];if(!t)for(var t,l=this.scales.length-1;l>=0&&(t=this.scales[l],t.pixelSize<n&&l>1);l--);var u=this.xCoords,c=(e[0]-r[0]-n*i*a)/o,h=(e[2]-r[0]+n*i*a)/o,f=t.offset,d=t.count+f,p=s.ge(u,c,f,d-1);return[p,s.lt(u,h,p,d-1)+1]},d.drawPick=d.draw,d.pick=function(t,e,r){var n=r-this.pickOffset;return n<0||n>=this.pointCount?null:{object:this,pointId:n,dataCoord:[this.points[2*n],this.points[2*n+1]]}}},{\"./lib/shader\":244,\"array-bounds\":44,\"array-normalize\":45,\"binary-search-bounds\":245,\"gl-buffer\":156,\"gl-shader\":255,\"snap-points-2d\":247,\"typedarray-pool\":541}],249:[function(t,e,r){\"use strict\";function n(t,e){var r=a[e];if(r||(r=a[e]={}),t in r)return r[t];for(var n=i(t,{textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),o=i(t,{triangles:!0,textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<n.positions.length;++l)for(var u=n.positions[l],c=0;c<2;++c)s[0][c]=Math.min(s[0][c],u[c]),s[1][c]=Math.max(s[1][c],u[c]);return r[t]=[o,n,s]}var i=t(\"vectorize-text\");e.exports=n;var a={}},{\"vectorize-text\":554}],250:[function(t,e,r){function n(t,e){var r=i(t,e),n=r.attributes;return n.position.location=0,n.color.location=1,n.glyph.location=2,n.id.location=3,r}var i=t(\"gl-shader\"),a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(position, clipBounds[0]))   || \\n     any(greaterThan(position, clipBounds[1])) ) {\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n    \\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(position, clipBounds[0])) || any(greaterThan(position, clipBounds[1]))) {\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n    \\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(position, clipBounds[0]))   ||\\n     any(greaterThan(position, clipBounds[1])) ) {\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\",l=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   ||\\n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n    discard;\\n  } else {\\n    gl_FragColor = interpColor * opacity;\\n  }\\n}\\n\",u=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   || \\n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n    discard;\\n  } else {\\n    gl_FragColor = vec4(pickGroup, pickId.bgr);\\n  }\\n}\",c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],h={vertex:a,fragment:l,attributes:c},f={vertex:o,fragment:l,attributes:c},d={vertex:s,fragment:l,attributes:c},p={vertex:a,fragment:u,attributes:c},m={vertex:o,fragment:u,attributes:c},v={vertex:s,fragment:u,attributes:c};r.createPerspective=function(t){return n(t,h)},r.createOrtho=function(t){return n(t,f)},r.createProject=function(t){return n(t,d)},r.createPickPerspective=function(t){return n(t,p)},r.createPickOrtho=function(t){return n(t,m)},r.createPickProject=function(t){return n(t,v)}},{\"gl-shader\":255}],251:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function i(t,e,r,i){return n(i,i,r),n(i,i,e),n(i,i,t)}function a(t,e){this.index=t,this.dataCoordinate=this.position=e}function o(t,e,r,n,i,o,s,l,u,c,h,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=o,this.glyphBuffer=s,this.idBuffer=l,this.vao=u,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=h,this.pickProjectShader=f,this.points=[],this._selectResult=new a(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}function s(t){return t[0]=t[1]=t[2]=0,t}function l(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function u(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function c(t){for(var e=L,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}function h(t,e,r,n,a){var o,h=e.axesProject,f=e.gl,d=t.uniforms,p=r.model||x,m=r.view||x,v=r.projection||x,y=e.axesBounds,b=c(e.clipBounds);o=e.axes?e.axes.lastCubeProps.axis:[1,1,1],w[0]=2/f.drawingBufferWidth,w[1]=2/f.drawingBufferHeight,t.bind(),d.view=m,d.projection=v,d.screenSize=w,d.highlightId=e.highlightId,d.highlightScale=e.highlightScale,d.clipBounds=b,\n",
+       "d.pickGroup=e.pickId/255,d.pixelRatio=e.pixelRatio;for(var _=0;_<3;++_)if(h[_]&&e.projectOpacity[_]<1===n){d.scale=e.projectScale[_],d.opacity=e.projectOpacity[_];for(var L=S,C=0;C<16;++C)L[C]=0;for(var C=0;C<4;++C)L[5*C]=1;L[5*_]=0,o[_]<0?L[12+_]=y[0][_]:L[12+_]=y[1][_],g(L,p,L),d.model=L;var I=(_+1)%3,z=(_+2)%3,D=s(M),P=s(k);D[I]=1,P[z]=1;var O=i(v,m,p,l(A,D)),R=i(v,m,p,l(T,P));if(Math.abs(O[1])>Math.abs(R[1])){var F=O;O=R,R=F,F=D,D=P,P=F;var j=I;I=z,z=j}O[0]<0&&(D[I]=-1),R[1]>0&&(P[z]=-1);for(var N=0,B=0,C=0;C<4;++C)N+=Math.pow(p[4*I+C],2),B+=Math.pow(p[4*z+C],2);D[I]/=Math.sqrt(N),P[z]/=Math.sqrt(B),d.axes[0]=D,d.axes[1]=P,d.fragClipBounds[0]=u(E,b[0],_,-1e8),d.fragClipBounds[1]=u(E,b[1],_,1e8),e.vao.draw(f.TRIANGLES,e.vertexCount),e.lineWidth>0&&(f.lineWidth(e.lineWidth),e.vao.draw(f.LINES,e.lineVertexCount,e.vertexCount))}}function f(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1||a){t.bind();var s=t.uniforms;s.model=n.model||x,s.view=n.view||x,s.projection=n.projection||x,w[0]=2/o.drawingBufferWidth,w[1]=2/o.drawingBufferHeight,s.screenSize=w,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=z,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}h(e,r,n,i,a),r.vao.unbind()}function d(t){var e=t.gl,r=y.createPerspective(e),n=y.createOrtho(e),i=y.createProject(e),a=y.createPickPerspective(e),s=y.createPickOrtho(e),l=y.createPickProject(e),u=p(e),c=p(e),h=p(e),f=p(e),d=m(e,[{buffer:u,size:3,type:e.FLOAT},{buffer:c,size:4,type:e.FLOAT},{buffer:h,size:2,type:e.FLOAT},{buffer:f,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new o(e,r,n,i,u,c,h,f,d,a,s,l);return v.update(t),v}var p=t(\"gl-buffer\"),m=t(\"gl-vao\"),v=t(\"typedarray-pool\"),g=t(\"gl-mat4/multiply\"),y=t(\"./lib/shaders\"),b=t(\"./lib/glyphs\"),x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];e.exports=d;var _=o.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},_.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var w=[0,0],M=[0,0,0],k=[0,0,0],A=[0,0,0,1],T=[0,0,0,1],S=x.slice(),E=[0,0,0],L=[[0,0,0],[0,0,0]],C=[-1e8,-1e8,-1e8],I=[1e8,1e8,1e8],z=[C,I];_.draw=function(t){f(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},_.drawTransparent=function(t){f(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},_.drawPick=function(t){f(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},_.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(t=t||{},\"perspective\"in t&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(\"projectOpacity\"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{var r=+t.projectOpacity;this.projectOpacity=[r,r,r]}\"opacity\"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font||\"normal\",a=t.alignment||[0,0],o=[1/0,1/0,1/0],s=[-1/0,-1/0,-1/0],l=t.glyph,u=t.color,c=t.size,h=t.angle,f=t.lineColor,d=0,p=0,m=0,g=n.length;t:for(var y=0;y<g;++y){for(var x=n[y],_=0;_<3;++_)if(isNaN(x[_])||!isFinite(x[_]))continue t;var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var M=w[0],k=w[1],A=w[2];p+=3*M.cells.length,m+=2*k.edges.length}var T=p+m,S=v.mallocFloat(3*T),E=v.mallocFloat(4*T),L=v.mallocFloat(2*T),C=v.mallocUint32(T),I=[0,a[1]],z=0,D=p,P=[0,0,0,1],O=[0,0,0,1],R=Array.isArray(u)&&Array.isArray(u[0]),F=Array.isArray(f)&&Array.isArray(f[0]);t:for(var y=0;y<g;++y){for(var x=n[y],_=0;_<3;++_){if(isNaN(x[_])||!isFinite(x[_])){d+=1;continue t}s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_])}var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var M=w[0],k=w[1],A=w[2];if(Array.isArray(u)){var j;if(j=R?u[y]:u,3===j.length){for(var _=0;_<3;++_)P[_]=j[_];P[3]=1}else if(4===j.length)for(var _=0;_<4;++_)P[_]=j[_]}else P[0]=P[1]=P[2]=0,P[3]=1;if(Array.isArray(f)){var j;if(j=F?f[y]:f,3===j.length){for(var _=0;_<3;++_)O[_]=j[_];O[_]=1}else if(4===j.length)for(var _=0;_<4;++_)O[_]=j[_]}else O[0]=O[1]=O[2]=0,O[3]=1;var N=.5;Array.isArray(c)?N=+c[y]:c?N=+c:this.useOrtho&&(N=12);var B=0;Array.isArray(h)?B=+h[y]:h&&(B=+h);for(var U=Math.cos(B),V=Math.sin(B),x=n[y],_=0;_<3;++_)s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_]);a[0]<0?I[0]=a[0]*(1+A[1][0]):a[0]>0&&(I[0]=-a[0]*(1+A[0][0]));for(var H=M.cells,q=M.positions,_=0;_<H.length;++_)for(var G=H[_],Y=0;Y<3;++Y){for(var W=0;W<3;++W)S[3*z+W]=x[W];for(var W=0;W<4;++W)E[4*z+W]=P[W];C[z]=d;var X=q[G[Y]];L[2*z]=N*(U*X[0]-V*X[1]+I[0]),L[2*z+1]=N*(V*X[0]+U*X[1]+I[1]),z+=1}for(var H=k.edges,q=k.positions,_=0;_<H.length;++_)for(var G=H[_],Y=0;Y<2;++Y){for(var W=0;W<3;++W)S[3*D+W]=x[W];for(var W=0;W<4;++W)E[4*D+W]=O[W];C[D]=d;var X=q[G[Y]];L[2*D]=N*(U*X[0]-V*X[1]+I[0]),L[2*D+1]=N*(V*X[0]+U*X[1]+I[1]),D+=1}d+=1}this.vertexCount=p,this.lineVertexCount=m,this.pointBuffer.update(S),this.colorBuffer.update(E),this.glyphBuffer.update(L),this.idBuffer.update(new 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n(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}function i(t,e){var r=t.gl,i=o(r,[0,0,0,1,1,0,1,1]),l=a(r,s.boxVertex,s.boxFragment),u=new n(t,i,l);return u.update(e),t.addOverlay(u),u}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"./lib/shaders\");e.exports=i;var l=n.prototype;l.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,h=(e[0]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],f=(e[1]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1],d=(e[2]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],p=(e[3]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1];if(h=Math.max(h,u[0]),f=Math.max(f,u[1]),d=Math.min(d,u[2]),p=Math.min(p,u[3]),!(d<h||p<f)){o.bind();var 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n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}function a(t,e){return new i(t,o(t,e),s.mallocUint8(e[0]*e[1]*4))}e.exports=a;var o=t(\"gl-fbo\"),s=t(\"typedarray-pool\"),l=t(\"ndarray\"),u=t(\"bit-twiddle\").nextPow2,c=t(\"cwise/lib/wrapper\")({args:[\"array\",{offset:[0,0,1],array:0},{offset:[0,0,2],array:0},{offset:[0,0,3],array:0},\"scalar\",\"scalar\",\"index\"],pre:{body:\"{this_closestD2=1e8,this_closestX=-1,this_closestY=-1}\",args:[],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[]},body:{body:\"{if(_inline_46_arg0_<255||_inline_46_arg1_<255||_inline_46_arg2_<255||_inline_46_arg3_<255){var 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this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){s.free(this.buffer);for(var n=this.buffer=s.mallocUint8(u(r*e*4)),i=0;i<r*e*4;++i)n[i]=255}return t}}}),h.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},h.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},h.query=function(t,e,r){if(!this.gl)return null;var i=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var a=0|Math.min(Math.max(t-r,0),i[0]),o=0|Math.min(Math.max(t+r,0),i[0]),s=0|Math.min(Math.max(e-r,0),i[1]),u=0|Math.min(Math.max(e+r,0),i[1]);if(o<=a||u<=s)return null;var h=[o-a,u-s],f=l(this.buffer,[h[0],h[1],4],[4,4*i[0],1],4*(a+i[0]*s)),d=c(f.hi(h[0],h[1],1),r,r),p=d[0],m=d[1];return 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o=t(\"./lib/create-uniforms\"),s=t(\"./lib/create-attributes\"),l=t(\"./lib/reflect\"),u=t(\"./lib/shader-cache\"),c=t(\"./lib/runtime-reflect\"),h=t(\"./lib/GLError\"),f=n.prototype;f.bind=function(){this.program||this._relink(),this.gl.useProgram(this.program)},f.dispose=function(){this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},f.update=function(t,e,r,n){function a(){d.program=u.program(p,d._vref,d._fref,x,_);for(var t=0;t<r.length;++t)E[t]=p.getUniformLocation(d.program,r[t].name)}if(!e||1===arguments.length){var f=t;t=f.vertex,e=f.fragment,r=f.uniforms,n=f.attributes}var d=this,p=d.gl,m=d._vref;d._vref=u.shader(p,p.VERTEX_SHADER,t),m&&m.dispose(),d.vertShader=d._vref.shader;var v=this._fref;if(d._fref=u.shader(p,p.FRAGMENT_SHADER,e),v&&v.dispose(),d.fragShader=d._fref.shader,!r||!n){var g=p.createProgram();if(p.attachShader(g,d.fragShader),p.attachShader(g,d.vertShader),p.linkProgram(g),!p.getProgramParameter(g,p.LINK_STATUS)){var y=p.getProgramInfoLog(g);throw new h(y,\"Error linking program:\"+y)}r=r||c.uniforms(p,g),n=n||c.attributes(p,g),p.deleteProgram(g)}n=n.slice(),n.sort(i);for(var b=[],x=[],_=[],w=0;w<n.length;++w){var M=n[w];if(M.type.indexOf(\"mat\")>=0){for(var k=0|M.type.charAt(M.type.length-1),A=new Array(k),T=0;T<k;++T)A[T]=_.length,x.push(M.name+\"[\"+T+\"]\"),\"number\"==typeof M.location?_.push(M.location+T):Array.isArray(M.location)&&M.location.length===k&&\"number\"==typeof M.location[T]?_.push(0|M.location[T]):_.push(-1);b.push({name:M.name,type:M.type,locations:A})}else b.push({name:M.name,type:M.type,locations:[_.length]}),x.push(M.name),\"number\"==typeof M.location?_.push(0|M.location):_.push(-1)}for(var S=0,w=0;w<_.length;++w)if(_[w]<0){for(;_.indexOf(S)>=0;)S+=1;_[w]=S}var E=new 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n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}function i(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r}e.exports=i;var a=n.prototype;a.update=function(t){t=t||{},this.enable=(t.enable||[!0,!0,!1,!1]).slice(),this.width=(t.width||[1,1,1,1]).slice(),this.color=(t.color||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map(function(t){return t.slice()}),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},a.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),u=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,u,s[0],u,e[0],r[0]),t[1]&&a.drawLine(l,u,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,u,s[2],u,e[2],r[2]),t[3]&&a.drawLine(l,u,l,s[3],e[3],r[3])}},a.dispose=function(){this.plot.removeOverlay(this)}},{}],263:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\");e.exports=function(t){return n(t,\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta 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s=[t,e,r,0,0,0,1];s[n+3]=1,s[n]=a,i.push.apply(i,s),s[6]=-1,i.push.apply(i,s),s[n]=o,i.push.apply(i,s),i.push.apply(i,s),s[6]=1,i.push.apply(i,s),s[n]=a,i.push.apply(i,s)}var i=[];r(0,0,0,0,0,1),r(0,0,0,1,0,1),r(0,0,0,2,0,1),r(1,0,0,1,-1,1),r(1,0,0,2,-1,1),r(0,1,0,0,-1,1),r(0,1,0,2,-1,1),r(0,0,1,0,-1,1),r(0,0,1,1,-1,1);var l=a(t,i),u=o(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),c=s(t);c.attributes.position.location=0,c.attributes.color.location=1,c.attributes.weight.location=2;var h=new n(t,l,u,c);return h.update(e),h}var a=t(\"gl-buffer\"),o=t(\"gl-vao\"),s=t(\"./shaders/index\");e.exports=i;var l=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],u=n.prototype,c=[0,0,0],h=[0,0,0],f=[0,0];u.isTransparent=function(){return!1},u.drawTransparent=function(t){},u.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||l,o=t.view||l,s=t.projection||l;this.axes&&(i=this.axes.lastCubeProps.axis);for(var u=c,d=h,p=0;p<3;++p)i&&i[p]<0?(u[p]=this.bounds[0][p],d[p]=this.bounds[1][p]):(u[p]=this.bounds[1][p],d[p]=this.bounds[0][p]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=o,n.uniforms.projection=s,n.uniforms.coordinates=[this.position,u,d],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(var p=0;p<3;++p)n.uniforms.lineWidth=this.lineWidth[p]*this.pixelRatio,this.enabled[p]&&(r.draw(e.TRIANGLES,6,6*p),this.drawSides[p]&&r.draw(e.TRIANGLES,12,18+12*p));r.unbind()},u.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},u.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":263,\"gl-buffer\":156,\"gl-vao\":271}],265:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  worldCoordinate = vec3(uv.zw, f.x);\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution_2_0(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\n\\n\\nfloat beckmannSpecular_1_1(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution_2_0(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\n\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (kill > 0.0 ||\\n    any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n    discard;\\n  }\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular_1_1(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor = step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) + step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z = clipPosition.z + zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f;\\n  kill = -1.0;\\n  worldCoordinate = dataCoordinate;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if(kill > 0.0 ||\\n    any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n    discard;\\n  }\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\";r.createShader=function(t){var e=n(t,i,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,i,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,o,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,o,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":255}],266:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}function i(t){var e=x([y({colormap:t,nshades:R,format:\"rgba\"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return b.divseq(e,255),e}function a(t,e,r,i,a,o,s,l,u,c,h,f,d,p){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=i,this._pickShader=a,this._coordinateBuffer=o,this._vao=s,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=f,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new n([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=p,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[_(g.mallocFloat(1024),[0,0]),_(g.mallocFloat(1024),[0,0]),_(g.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}function o(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||j,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=N.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],M(l,t.model,l);var u=N.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return N.showSurface=o,N.showContour=s,N}function s(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=B;n.model=t.model||D,n.view=t.view||D,n.projection=t.projection||D,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=k(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],s=0;s<3;++s)a[s]=Math.min(Math.max(this.clipBounds[i][s],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=V,n.vertexColor=this.vertexColor;var l=U;for(M(l,n.view,n.model),M(l,n.projection,l),k(l,l),i=0;i<3;++i)n.eyePosition[i]=l[12+i]/l[15];var u=l[15];for(i=0;i<3;++i)u+=this.lightPosition[i]*l[4*i+3];for(i=0;i<3;++i){var c=l[12+i];for(s=0;s<3;++s)c+=l[4*s+i]*this.lightPosition[s];n.lightPosition[i]=c/u}var h=o(n,this);if(h.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=h.projections[i],this._shader.uniforms.clipBounds=h.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(h.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var d=this._contourVAO;for(d.bind(),i=0;i<3;++i)for(f.uniforms.permutation=O[i],r.lineWidth(this.contourWidth[i]),s=0;s<this.contourLevels[i].length;++s)this._contourCounts[i][s]&&(s===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==s&&s-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),f.uniforms.height=this.contourLevels[i][s],d.draw(r.LINES,this._contourCounts[i][s],this._contourOffsets[i][s]));for(i=0;i<3;++i)for(f.uniforms.model=h.projections[i],f.uniforms.clipBounds=h.clipBounds[i],s=0;s<3;++s)if(this.contourProject[i][s]){f.uniforms.permutation=O[s],r.lineWidth(this.contourWidth[s]);for(var p=0;p<this.contourLevels[s].length;++p)p===this.highlightLevel[s]?(f.uniforms.contourColor=this.highlightColor[s],f.uniforms.contourTint=this.highlightTint[s]):0!==p&&p-1!==this.highlightLevel[s]||(f.uniforms.contourColor=this.contourColor[s],f.uniforms.contourTint=this.contourTint[s]),f.uniforms.height=this.contourLevels[s][p],d.draw(r.LINES,this._contourCounts[s][p],this._contourOffsets[s][p])}for(d=this._dynamicVAO,d.bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=O[i],r.lineWidth(this.dynamicWidth[i]),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),s=0;s<3;++s)this.contourProject[s][i]&&(f.uniforms.model=h.projections[s],f.uniforms.clipBounds=h.clipBounds[s],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));d.unbind()}}function l(t,e){var r=e.shape.slice(),n=t.shape.slice();b.assign(t.lo(1,1).hi(r[0],r[1]),e),b.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),b.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),b.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),b.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function u(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function c(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function h(t){if(Array.isArray(t)){if(Array.isArray(t))return[c(t[0]),c(t[1]),c(t[2])];var e=c(t);return[e.slice(),e.slice(),e.slice()]}}function f(t){var e=t.gl,r=E(e),n=C(e),i=L(e),o=I(e),s=p(e),l=m(e,[{buffer:s,size:4,stride:z,offset:0},{buffer:s,size:3,stride:z,offset:16},{buffer:s,size:3,stride:z,offset:28}]),u=p(e),c=m(e,[{buffer:u,size:4,stride:20,offset:0},{buffer:u,size:1,stride:20,offset:16}]),h=p(e),f=m(e,[{buffer:h,size:2,type:e.FLOAT}]),d=v(e,1,R,e.RGBA,e.UNSIGNED_BYTE);d.minFilter=e.LINEAR,d.magFilter=e.LINEAR;var g=new a(e,[0,0],[[0,0,0],[0,0,0]],r,n,s,l,d,i,o,u,c,h,f),y={levels:[[],[],[]]};for(var b in t)y[b]=t[b];return y.colormap=y.colormap||\"jet\",g.update(y),g}e.exports=f\n",
+       ";var d=t(\"bit-twiddle\"),p=t(\"gl-buffer\"),m=t(\"gl-vao\"),v=t(\"gl-texture2d\"),g=t(\"typedarray-pool\"),y=t(\"colormap\"),b=t(\"ndarray-ops\"),x=t(\"ndarray-pack\"),_=t(\"ndarray\"),w=t(\"surface-nets\"),M=t(\"gl-mat4/multiply\"),k=t(\"gl-mat4/invert\"),A=t(\"binary-search-bounds\"),T=t(\"ndarray-gradient\"),S=t(\"./lib/shaders\"),E=S.createShader,L=S.createContourShader,C=S.createPickShader,I=S.createPickContourShader,z=40,D=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],O=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];!function(){for(var t=0;t<3;++t){var e=O[t],r=(t+1)%3,n=(t+2)%3;e[r+0]=1,e[n+3]=1,e[t+6]=1}}();var R=256,F=a.prototype;F.isTransparent=function(){return this.opacity<1},F.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},F.pickSlots=1,F.setPickBase=function(t){this.pickId=t};var j=[0,0,0],N={showSurface:!1,showContour:!1,projections:[D.slice(),D.slice(),D.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]},B={model:D,view:D,projection:D,inverseModel:D.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},U=D.slice(),V=[1,0,0,0,1,0,0,0,1];F.draw=function(t){return s.call(this,t,!1)},F.drawTransparent=function(t){return s.call(this,t,!0)};var H={model:D,view:D,projection:D,inverseModel:D,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};F.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=H;r.model=t.model||D,r.view=t.view||D,r.projection=t.projection||D,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=V;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var s=o(r,this);if(s.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=s.projections[n],this._pickShader.uniforms.clipBounds=s.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(s.showContour){var l=this._contourPickShader;l.bind(),l.uniforms=r;var u=this._contourVAO;for(u.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),l.uniforms.permutation=O[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(l.uniforms.height=this.contourLevels[a][n],u.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(l.uniforms.model=s.projections[n],l.uniforms.clipBounds=s.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){l.uniforms.permutation=O[a],e.lineWidth(this.contourWidth[a]);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(l.uniforms.height=this.contourLevels[a][c],u.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}u.unbind()}},F.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var h=c?a:1-a,f=0;f<2;++f)for(var d=f?l:1-l,p=i+c,m=s+f,v=h*d,g=0;g<3;++g)u[g]+=this._field[g].get(p,m)*v;for(var y=this._pickResult.level,b=0;b<3;++b)if(y[b]=A.le(this.contourLevels[b],u[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]<this.contourLevels[b].length-1){var x=this.contourLevels[b][y[b]],_=this.contourLevels[b][y[b]+1];Math.abs(x-u[b])>Math.abs(_-u[b])&&(y[b]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],g=0;g<3;++g)r.dataCoordinate[g]=this._field[g].get(r.index[0],r.index[1]);return r},F.update=function(t){t=t||{},this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=u(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=u(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=u(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=h(t.contourColor)),\"contourProject\"in t&&(this.contourProject=u(t.contourProject,function(t){return u(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=h(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=u(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=u(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var n=(e.shape[0]+2)*(e.shape[1]+2);n>this._field[2].data.length&&(g.freeFloat(this._field[2].data),this._field[2].data=g.mallocFloat(d.nextPow2(n))),this._field[2]=_(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),l(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(g.freeFloat(this._field[o].data),this._field[o].data=g.mallocFloat(this._field[2].size)),this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var s=t.coords;if(!Array.isArray(s)||3!==s.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=s[o];for(y=0;y<2;++y)if(c.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");l(this._field[o],c)}}else if(t.ticks){var f=t.ticks;if(!Array.isArray(f)||2!==f.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=f[o];if((Array.isArray(p)||p.length)&&(p=_(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var m=_(p.data,a);m.stride[o]=p.stride[0],m.stride[1^o]=0,l(this._field[o],m)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var b=this._field,x=_(g.mallocFloat(3*b[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)T(x.pick(o),b[o],\"mirror\");var M=_(g.mallocFloat(3*b[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var k=x.get(0,o,y,0),A=x.get(0,o,y,1),S=x.get(1,o,y,0),E=x.get(1,o,y,1),L=x.get(2,o,y,0),C=x.get(2,o,y,1),I=S*C-E*L,z=L*A-C*k,D=k*E-A*S,O=Math.sqrt(I*I+z*z+D*D);O<1e-8?(O=Math.max(Math.abs(I),Math.abs(z),Math.abs(D)),O<1e-8?(D=1,z=I=0,O=1):O=1/O):O=1/Math.sqrt(O),M.set(o,y,0,I*O),M.set(o,y,1,z*O),M.set(o,y,2,D*O)}g.free(x.data);var R=[1/0,1/0,1/0],F=[-1/0,-1/0,-1/0],j=1/0,N=-1/0,B=(a[0]-1)*(a[1]-1)*6,U=g.mallocFloat(d.nextPow2(10*B)),V=0,H=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var q=0;q<2;++q)for(var G=0;G<2;++G)for(var Y=0;Y<3;++Y){var W=this._field[Y].get(1+o+q,1+y+G);if(isNaN(W)||!isFinite(W))continue t}for(Y=0;Y<6;++Y){var X=o+P[Y][0],Z=y+P[Y][1],J=this._field[0].get(X+1,Z+1),K=this._field[1].get(X+1,Z+1);W=this._field[2].get(X+1,Z+1);var Q=W;I=M.get(X+1,Z+1,0),z=M.get(X+1,Z+1,1),D=M.get(X+1,Z+1,2),t.intensity&&(Q=t.intensity.get(X,Z)),U[V++]=X,U[V++]=Z,U[V++]=J,U[V++]=K,U[V++]=W,U[V++]=0,U[V++]=Q,U[V++]=I,U[V++]=z,U[V++]=D,R[0]=Math.min(R[0],J),R[1]=Math.min(R[1],K),R[2]=Math.min(R[2],W),j=Math.min(j,Q),F[0]=Math.max(F[0],J),F[1]=Math.max(F[1],K),F[2]=Math.max(F[2],W),N=Math.max(N,Q),H+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],N=+t.intensityBounds[1]),o=6;o<V;o+=10)U[o]=(U[o]-j)/(N-j);this._vertexCount=H,this._coordinateBuffer.update(U.subarray(0,V)),g.freeFloat(U),g.free(M.data),this.bounds=[R,F],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===N||(r=!0),this.intensityBounds=[j,N]}if(\"levels\"in t){var $=t.levels;for($=Array.isArray($[0])?$.slice():[[],[],$],o=0;o<3;++o)$[o]=$[o].slice(),$.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if($[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<$[o].length;++y)if($[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=$}if(r){b=this._field,a=this.shape;for(var tt=[],et=0;et<3;++et){$=this.contourLevels[et];var rt=[],nt=[],it=[0,0,0];for(o=0;o<$.length;++o){var at=w(this._field[et],$[o]);rt.push(tt.length/5|0),H=0;t:for(y=0;y<at.cells.length;++y){var ot=at.cells[y];for(Y=0;Y<2;++Y){var st=at.positions[ot[Y]],lt=st[0],ut=0|Math.floor(lt),ct=lt-ut,ht=st[1],ft=0|Math.floor(ht),dt=ht-ft,pt=!1;e:for(var mt=0;mt<3;++mt){it[mt]=0;var vt=(et+mt+1)%3;for(q=0;q<2;++q){var gt=q?ct:1-ct;for(X=0|Math.min(Math.max(ut+q,0),a[0]),G=0;G<2;++G){var yt=G?dt:1-dt;if(Z=0|Math.min(Math.max(ft+G,0),a[1]),W=mt<2?this._field[vt].get(X,Z):(this.intensity.get(X,Z)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(W)||isNaN(W)){pt=!0;break e}var bt=gt*yt;it[mt]+=bt*W}}}if(pt){if(Y>0){for(var xt=0;xt<5;++xt)tt.pop();H-=1}continue t}tt.push(it[0],it[1],st[0],st[1],it[2]),H+=1}}nt.push(H)}this._contourOffsets[et]=rt,this._contourCounts[et]=nt}var _t=g.mallocFloat(tt.length);for(o=0;o<tt.length;++o)_t[o]=tt[o];this._contourBuffer.update(_t),g.freeFloat(_t)}t.colormap&&this._colorMap.setPixels(i(t.colormap))},F.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)g.freeFloat(this._field[t].data)},F.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=g.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var s=(o+1)%3,l=(o+2)%3,u=this._field[o],c=this._field[s],h=this._field[l],f=(this.intensity,w(u,r[o])),d=f.cells,p=f.positions;for(this._dynamicOffsets[o]=n,e=0;e<d.length;++e)for(var m=d[e],v=0;v<2;++v){var y=p[m[v]],b=+y[0],x=0|b,_=0|Math.min(x+1,i[0]),M=b-x,k=1-M,A=+y[1],T=0|A,S=0|Math.min(T+1,i[1]),E=A-T,L=1-E,C=k*L,I=k*E,z=M*L,D=M*E,P=C*c.get(x,T)+I*c.get(x,S)+z*c.get(_,T)+D*c.get(_,S),O=C*h.get(x,T)+I*h.get(x,S)+z*h.get(_,T)+D*h.get(_,S);if(isNaN(P)||isNaN(O)){v&&(n-=1);break}a[2*n+0]=P,a[2*n+1]=O,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),g.freeFloat(a)}}},{\"./lib/shaders\":265,\"binary-search-bounds\":66,\"bit-twiddle\":67,colormap:99,\"gl-buffer\":156,\"gl-mat4/invert\":181,\"gl-mat4/multiply\":183,\"gl-texture2d\":267,\"gl-vao\":271,ndarray:467,\"ndarray-gradient\":458,\"ndarray-ops\":461,\"ndarray-pack\":462,\"surface-nets\":531,\"typedarray-pool\":541}],267:[function(t,e,r){\"use strict\";function n(t){g=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],y=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],b=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function i(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}function a(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function o(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}function s(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function l(t,e,r,n,i,a,o,l){var u=l.dtype,c=l.shape.slice();if(c.length<2||c.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var h=0,f=0,d=s(c,l.stride.slice());\"float32\"===u?h=t.FLOAT:\"float64\"===u?(h=t.FLOAT,d=!1,u=\"float32\"):\"uint8\"===u?h=t.UNSIGNED_BYTE:(h=t.UNSIGNED_BYTE,d=!1,u=\"uint8\");if(2===c.length)f=t.LUMINANCE,c=[c[0],c[1],1],l=p(l.data,c,[l.stride[0],l.stride[1],1],l.offset);else{if(3!==c.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===c[2])f=t.ALPHA;else if(2===c[2])f=t.LUMINANCE_ALPHA;else if(3===c[2])f=t.RGB;else{if(4!==c[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");f=t.RGBA}c[2]}if(f!==t.LUMINANCE&&f!==t.ALPHA||i!==t.LUMINANCE&&i!==t.ALPHA||(f=i),f!==i)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var g=l.size,y=o.indexOf(n)<0;if(y&&o.push(n),h===a&&d)0===l.offset&&l.data.length===g?y?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,a,l.data):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,a,l.data):y?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,a,l.data.subarray(l.offset,l.offset+g)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,a,l.data.subarray(l.offset,l.offset+g));else{var b;b=a===t.FLOAT?v.mallocFloat32(g):v.mallocUint8(g);var _=p(b,c,[c[2],c[2]*c[0],1]);h===t.FLOAT&&a===t.UNSIGNED_BYTE?x(_,l):m.assign(_,l),y?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,a,b.subarray(0,g)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,a,b.subarray(0,g)),a===t.FLOAT?v.freeFloat32(b):v.freeUint8(b)}}function u(t){var e=t.createTexture();return 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a=s(n,e.stride.slice()),l=0;\"float32\"===r?l=t.FLOAT:\"float64\"===r?(l=t.FLOAT,a=!1,r=\"float32\"):\"uint8\"===r?l=t.UNSIGNED_BYTE:(l=t.UNSIGNED_BYTE,a=!1,r=\"uint8\");var c=0;if(2===n.length)c=t.LUMINANCE,n=[n[0],n[1],1],e=p(e.data,n,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==n.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===n[2])c=t.ALPHA;else if(2===n[2])c=t.LUMINANCE_ALPHA;else if(3===n[2])c=t.RGB;else{if(4!==n[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");c=t.RGBA}}l!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(l=t.UNSIGNED_BYTE,a=!1);var h,f,d=e.size;if(a)h=0===e.offset&&e.data.length===d?e.data:e.data.subarray(e.offset,e.offset+d);else{var g=[n[2],n[2]*n[0],1];f=v.malloc(d,r);var y=p(f,n,g,0);\"float32\"!==r&&\"float64\"!==r||l!==t.UNSIGNED_BYTE?m.assign(y,e):x(y,e),h=f.subarray(0,d)}var b=u(t);return t.texImage2D(t.TEXTURE_2D,0,c,n[0],n[1],0,c,l,h),a||v.free(f),new o(t,b,n[0],n[1],c,l)}function 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Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},n.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=i+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var a=new Int32Array(e+r+this.keys.length+this.bboxes.length);a[0]=this.extent,a[1]=this.n,a[2]=this.padding;for(var o=e,s=0;s<t.length;s++){var l=t[s];a[i+s]=o,a.set(l,o),o+=l.length}return a[i+t.length]=o,a.set(this.keys,o),o+=this.keys.length,a[i+t.length+1]=o,a.set(this.bboxes,o),o+=this.bboxes.length,a.buffer}},{}],288:[function(t,e,r){(function(r){\"use strict\";var n,i=t(\"is-browser\");n=\"function\"==typeof r.matchMedia?!r.matchMedia(\"(hover: none)\").matches:i,e.exports=n}).call(this,\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{\"is-browser\":294}],289:[function(t,e,r){r.read=function(t,e,r,n,i){var 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f=c[h];i[h]=f<0?e:a[f]}var d=this.orient();if(d>0)return u;u.lastVisited=-n,0===d&&o.push(u)}}}return null},f.walk=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,a=this.tuple,o=e?this.interior.length*Math.random()|0:this.interior.length-1,s=this.interior[o];t:for(;!s.boundary;){for(var l=s.vertices,u=s.adjacent,c=0;c<=n;++c)a[c]=i[l[c]];s.lastVisited=r;for(var c=0;c<=n;++c){var h=u[c];if(!(h.lastVisited>=r)){var f=a[c];a[c]=t;var d=this.orient();if(a[c]=f,d<0){s=h;continue t}h.boundary?h.lastVisited=-r:h.lastVisited=r}}return}return s},f.addPeaks=function(t,e){var r=this.vertices.length-1,o=this.dimension,s=this.vertices,l=this.tuple,u=this.interior,c=this.simplices,h=[e];e.lastVisited=r,e.vertices[e.vertices.indexOf(-1)]=r,e.boundary=!1,u.push(e);for(var f=[];h.length>0;){var e=h.pop(),d=e.vertices,p=e.adjacent,m=d.indexOf(r);if(!(m<0))for(var v=0;v<=o;++v)if(v!==m){var g=p[v];if(g.boundary&&!(g.lastVisited>=r)){var 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 gl_Position.xy += extrude * gl_Position.w;\\n    }\\n\\n    // This is a minimum blur distance that serves as a faux-antialiasing for\\n    // the circle. since blur is a ratio of the circle's size and the intent is\\n    // to keep the blur at roughly 1px, the two are inversely related.\\n    v_antialiasblur = 1.0 / u_devicepixelratio / radius;\\n}\\n\"},line:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\nuniform float u_blur;\\n\\nvarying vec2 v_linewidth;\\nvarying vec2 v_normal;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    // Calculate the distance of the pixel from the line in pixels.\\n    float dist = length(v_normal) * v_linewidth.s;\\n\\n    // Calculate the antialiasing fade factor. This is either when fading in\\n    // the line in case of an offset line (v_linewidth.t) or when fading out\\n    // (v_linewidth.s)\\n    float blur = u_blur * v_gamma_scale;\\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n    gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform mediump float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\nuniform mediump float u_blur;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    vec2 a_extrude = a_data.xy - 128.0;\\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\\n\\n    // We store the texture normals in the most insignificant bit\\n    // transform y so that 0 => -1 and 1 => 1\\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n    // y is 1 if the normal points up, and -1 if it points down\\n    mediump vec2 normal = mod(a_pos, 2.0);\\n    normal.y = sign(normal.y - 0.5);\\n    v_normal = normal;\\n\\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n    // Scale the extrusion vector down to a normal and then up by the line width\\n    // of this vertex.\\n    mediump vec2 dist = outset * a_extrude * scale;\\n\\n    // Calculate the offset when drawing a line that is to the side of the actual line.\\n    // We do this by creating a vector that points towards the extrude, but rotate\\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\\n    // extrude vector points in another direction.\\n    mediump float u = 0.5 * a_direction;\\n    mediump float t = 1.0 - abs(u);\\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n    // Remove the texture normal bit of the position before scaling it with the\\n    // model/view matrix.\\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n\\n    // position of y on the screen\\n    float y = gl_Position.y / gl_Position.w;\\n\\n    // how much features are squished in the y direction by the tilt\\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n    // how much features are squished in all directions by the perspectiveness\\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n    v_linewidth = vec2(outset, inset);\\n    v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},linepattern:{\n",
+       "fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_blur;\\n\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_fade;\\nuniform float u_opacity;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    // Calculate the distance of the pixel from the line in pixels.\\n    float dist = length(v_normal) * v_linewidth.s;\\n\\n    // Calculate the antialiasing fade factor. This is either when fading in\\n    // the line in case of an offset line (v_linewidth.t) or when fading out\\n    // (v_linewidth.s)\\n    float blur = u_blur * v_gamma_scale;\\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n    float x_a = mod(v_linesofar / u_pattern_size_a.x, 1.0);\\n    float x_b = mod(v_linesofar / u_pattern_size_b.x, 1.0);\\n    float y_a = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_a.y);\\n    float y_b = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_b.y);\\n    vec2 pos_a = mix(u_pattern_tl_a, u_pattern_br_a, vec2(x_a, y_a));\\n    vec2 pos_b = mix(u_pattern_tl_b, u_pattern_br_b, vec2(x_b, y_b));\\n\\n    vec4 color = mix(texture2D(u_image, pos_a), texture2D(u_image, pos_b), u_fade);\\n\\n    alpha *= u_opacity;\\n\\n    gl_FragColor = color * alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform mediump float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    vec2 a_extrude = a_data.xy - 128.0;\\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\\n    float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n    // We store the texture normals in the most insignificant bit\\n    // transform y so that 0 => -1 and 1 => 1\\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n    // y is 1 if the normal points up, and -1 if it points down\\n    mediump vec2 normal = mod(a_pos, 2.0);\\n    normal.y = sign(normal.y - 0.5);\\n    v_normal = normal;\\n\\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n    // Scale the extrusion vector down to a normal and then up by the line width\\n    // of this vertex.\\n    mediump vec2 dist = outset * a_extrude * scale;\\n\\n    // Calculate the offset when drawing a line that is to the side of the actual line.\\n    // We do this by creating a vector that points towards the extrude, but rotate\\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\\n    // extrude vector points in another direction.\\n    mediump float u = 0.5 * a_direction;\\n    mediump float t = 1.0 - abs(u);\\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n    // Remove the texture normal bit of the position before scaling it with the\\n    // model/view matrix.\\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n    v_linesofar = a_linesofar;\\n\\n    // position of y on the screen\\n    float y = gl_Position.y / gl_Position.w;\\n\\n    // how much features are squished in the y direction by the tilt\\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n    // how much features are squished in all directions by the perspectiveness\\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n    v_linewidth = vec2(outset, inset);\\n    v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},linesdfpattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\n\\nuniform float u_blur;\\nuniform sampler2D u_image;\\nuniform float u_sdfgamma;\\nuniform float u_mix;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    // Calculate the distance of the pixel from the line in pixels.\\n    float dist = length(v_normal) * v_linewidth.s;\\n\\n    // Calculate the antialiasing fade factor. This is either when fading in\\n    // the line in case of an offset line (v_linewidth.t) or when fading out\\n    // (v_linewidth.s)\\n    float blur = u_blur * v_gamma_scale;\\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n    float sdfdist_a = texture2D(u_image, v_tex_a).a;\\n    float sdfdist_b = texture2D(u_image, v_tex_b).a;\\n    float sdfdist = mix(sdfdist_a, sdfdist_b, u_mix);\\n    alpha *= smoothstep(0.5 - u_sdfgamma, 0.5 + u_sdfgamma, sdfdist);\\n\\n    gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform vec2 u_patternscale_a;\\nuniform float u_tex_y_a;\\nuniform vec2 u_patternscale_b;\\nuniform float u_tex_y_b;\\nuniform float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    vec2 a_extrude = a_data.xy - 128.0;\\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\\n    float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n    // We store the texture normals in the most insignificant bit\\n    // transform y so that 0 => -1 and 1 => 1\\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n    // y is 1 if the normal points up, and -1 if it points down\\n    mediump vec2 normal = mod(a_pos, 2.0);\\n    normal.y = sign(normal.y - 0.5);\\n    v_normal = normal;\\n\\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n    // Scale the extrusion vector down to a normal and then up by the line width\\n    // of this vertex.\\n    mediump vec2 dist = outset * a_extrude * scale;\\n\\n    // Calculate the offset when drawing a line that is to the side of the actual line.\\n    // We do this by creating a vector that points towards the extrude, but rotate\\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\\n    // extrude vector points in another direction.\\n    mediump float u = 0.5 * a_direction;\\n    mediump float t = 1.0 - abs(u);\\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n    // Remove the texture normal bit of the position before scaling it with the\\n    // model/view matrix.\\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n\\n    v_tex_a = vec2(a_linesofar * u_patternscale_a.x, normal.y * u_patternscale_a.y + u_tex_y_a);\\n    v_tex_b = vec2(a_linesofar * u_patternscale_b.x, normal.y * u_patternscale_b.y + u_tex_y_b);\\n\\n    // position of y on the screen\\n    float y = gl_Position.y / gl_Position.w;\\n\\n    // how much features are squished in the y direction by the tilt\\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n    // how much features are squished in all directions by the perspectiveness\\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n    v_linewidth = vec2(outset, inset);\\n    v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},outline:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n#pragma mapbox: define lowp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n    #pragma mapbox: initialize lowp vec4 outline_color\\n    #pragma mapbox: initialize lowp float opacity\\n\\n    float dist = length(v_pos - gl_FragCoord.xy);\\n    float alpha = smoothstep(1.0, 0.0, dist);\\n    gl_FragColor = outline_color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define lowp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n    #pragma mapbox: initialize lowp vec4 outline_color\\n    #pragma mapbox: initialize lowp float opacity\\n\\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n    v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},outlinepattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n    vec2 imagecoord = mod(v_pos_a, 1.0);\\n    vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\\n    vec4 color1 = texture2D(u_image, pos);\\n\\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n    vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\\n    vec4 color2 = texture2D(u_image, pos2);\\n\\n    // find distance to outline for alpha interpolation\\n\\n    float dist = length(v_pos - gl_FragCoord.xy);\\n    float alpha = smoothstep(1.0, 0.0, dist);\\n    \\n\\n    gl_FragColor = mix(color1, color2, u_mix) * alpha * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n    vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\\n    vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\\n\\n    // the correct offset needs to be calculated.\\n    //\\n    // The offset depends on how many pixels are between the world origin and\\n    // the edge of the tile:\\n    // vec2 offset = mod(pixel_coord, size)\\n    //\\n    // At high zoom levels there are a ton of pixels between the world origin\\n    // and the edge of the tile. The glsl spec only guarantees 16 bits of\\n    // precision for highp floats. We need more than that.\\n    //\\n    // The pixel_coord is passed in as two 16 bit values:\\n    // pixel_coord_upper = floor(pixel_coord / 2^16)\\n    // pixel_coord_lower = mod(pixel_coord, 2^16)\\n    //\\n    // The offset is calculated in a series of steps that should preserve this precision:\\n    vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\\n    vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\\n\\n    v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\\n    v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\\n\\n    v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},pattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n\\n    vec2 imagecoord = mod(v_pos_a, 1.0);\\n    vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\\n    vec4 color1 = texture2D(u_image, pos);\\n\\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n    vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\\n    vec4 color2 = texture2D(u_image, pos2);\\n\\n    gl_FragColor = mix(color1, color2, u_mix) * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n    vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\\n    vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\\n\\n    // the correct offset needs to be calculated.\\n    //\\n    // The offset depends on how many pixels are between the world origin and\\n    // the edge of the tile:\\n    // vec2 offset = mod(pixel_coord, size)\\n    //\\n    // At high zoom levels there are a ton of pixels between the world origin\\n    // and the edge of the tile. The glsl spec only guarantees 16 bits of\\n    // precision for highp floats. We need more than that.\\n    //\\n    // The pixel_coord is passed in as two 16 bit values:\\n    // pixel_coord_upper = floor(pixel_coord / 2^16)\\n    // pixel_coord_lower = mod(pixel_coord, 2^16)\\n    //\\n    // The offset is calculated in a series of steps that should preserve this precision:\\n    vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\\n    vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\\n\\n    v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\\n    v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\\n}\\n\"},raster:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity0;\\nuniform float u_opacity1;\\nuniform sampler2D u_image0;\\nuniform sampler2D u_image1;\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nuniform float u_brightness_low;\\nuniform float u_brightness_high;\\n\\nuniform float u_saturation_factor;\\nuniform float u_contrast_factor;\\nuniform vec3 u_spin_weights;\\n\\nvoid main() {\\n\\n    // read and cross-fade colors from the main and parent tiles\\n    vec4 color0 = texture2D(u_image0, v_pos0);\\n    vec4 color1 = texture2D(u_image1, v_pos1);\\n    vec4 color = color0 * u_opacity0 + color1 * u_opacity1;\\n    vec3 rgb = color.rgb;\\n\\n    // spin\\n    rgb = vec3(\\n        dot(rgb, u_spin_weights.xyz),\\n        dot(rgb, u_spin_weights.zxy),\\n        dot(rgb, u_spin_weights.yzx));\\n\\n    // saturation\\n    float average = (color.r + color.g + color.b) / 3.0;\\n    rgb += (average - rgb) * u_saturation_factor;\\n\\n    // contrast\\n    rgb = (rgb - 0.5) * u_contrast_factor + 0.5;\\n\\n    // brightness\\n    vec3 u_high_vec = vec3(u_brightness_low, u_brightness_low, u_brightness_low);\\n    vec3 u_low_vec = vec3(u_brightness_high, u_brightness_high, u_brightness_high);\\n\\n    gl_FragColor = vec4(mix(u_high_vec, u_low_vec, rgb), color.a);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_tl_parent;\\nuniform float u_scale_parent;\\nuniform float u_buffer_scale;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nvoid main() {\\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n    v_pos0 = (((a_texture_pos / 32767.0) - 0.5) / u_buffer_scale ) + 0.5;\\n    v_pos1 = (v_pos0 * u_scale_parent) + u_tl_parent;\\n}\\n\"},icon:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform sampler2D u_texture;\\nuniform sampler2D u_fadetexture;\\nuniform lowp float u_opacity;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\n\\nvoid main() {\\n    lowp float alpha = texture2D(u_fadetexture, v_fade_tex).a * u_opacity;\\n    gl_FragColor = texture2D(u_texture, v_tex) * alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_offset;\\nattribute vec2 a_texture_pos;\\nattribute vec4 a_data;\\n\\n\\n// matrix is for the vertex position.\\nuniform mat4 u_matrix;\\n\\nuniform mediump float u_zoom;\\nuniform bool u_rotate_with_map;\\nuniform vec2 u_extrude_scale;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\n\\nvoid main() {\\n    vec2 a_tex = a_texture_pos.xy;\\n    mediump float a_labelminzoom = a_data[0];\\n    mediump vec2 a_zoom = a_data.pq;\\n    mediump float a_minzoom = a_zoom[0];\\n    mediump float a_maxzoom = a_zoom[1];\\n\\n    // u_zoom is the current zoom level adjusted for the change in font size\\n    mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\\n\\n    vec2 extrude = u_extrude_scale * (a_offset / 64.0);\\n    if (u_rotate_with_map) {\\n        gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\\n        gl_Position.z += z * gl_Position.w;\\n    } else {\\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n    }\\n\\n    v_tex = a_tex / u_texsize;\\n    v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\\n}\\n\"},sdf:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform sampler2D u_texture;\\nuniform sampler2D u_fadetexture;\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\nuniform lowp float u_buffer;\\nuniform lowp float u_gamma;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    lowp float dist = texture2D(u_texture, v_tex).a;\\n    lowp float fade_alpha = texture2D(u_fadetexture, v_fade_tex).a;\\n    lowp float gamma = u_gamma * v_gamma_scale;\\n    lowp float alpha = smoothstep(u_buffer - gamma, u_buffer + gamma, dist) * fade_alpha;\\n\\n    gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nconst float PI = 3.141592653589793;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_offset;\\nattribute vec2 a_texture_pos;\\nattribute vec4 a_data;\\n\\n\\n// matrix is for the vertex position.\\nuniform mat4 u_matrix;\\n\\nuniform mediump float u_zoom;\\nuniform bool u_rotate_with_map;\\nuniform bool u_pitch_with_map;\\nuniform mediump float u_pitch;\\nuniform mediump float u_bearing;\\nuniform mediump float u_aspect_ratio;\\nuniform vec2 u_extrude_scale;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    vec2 a_tex = a_texture_pos.xy;\\n    mediump float a_labelminzoom = a_data[0];\\n    mediump vec2 a_zoom = a_data.pq;\\n    mediump float a_minzoom = a_zoom[0];\\n    mediump float a_maxzoom = a_zoom[1];\\n\\n    // u_zoom is the current zoom level adjusted for the change in font size\\n    mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\\n\\n    // pitch-alignment: map\\n    // rotation-alignment: map | viewport\\n    if (u_pitch_with_map) {\\n        lowp float angle = u_rotate_with_map ? (a_data[1] / 256.0 * 2.0 * PI) : u_bearing;\\n        lowp float asin = sin(angle);\\n        lowp float acos = cos(angle);\\n        mat2 RotationMatrix = mat2(acos, asin, -1.0 * asin, acos);\\n        vec2 offset = RotationMatrix * a_offset;\\n        vec2 extrude = u_extrude_scale * (offset / 64.0);\\n        gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\\n        gl_Position.z += z * gl_Position.w;\\n    // pitch-alignment: viewport\\n    // rotation-alignment: map\\n    } else if (u_rotate_with_map) {\\n        // foreshortening factor to apply on pitched maps\\n        // as a label goes from horizontal <=> vertical in angle\\n        // it goes from 0% foreshortening to up to around 70% foreshortening\\n        lowp float pitchfactor = 1.0 - cos(u_pitch * sin(u_pitch * 0.75));\\n\\n        lowp float lineangle = a_data[1] / 256.0 * 2.0 * PI;\\n\\n        // use the lineangle to position points a,b along the line\\n        // project the points and calculate the label angle in projected space\\n        // this calculation allows labels to be rendered unskewed on pitched maps\\n        vec4 a = u_matrix * vec4(a_pos, 0, 1);\\n        vec4 b = u_matrix * vec4(a_pos + vec2(cos(lineangle),sin(lineangle)), 0, 1);\\n        lowp float angle = atan((b[1]/b[3] - a[1]/a[3])/u_aspect_ratio, b[0]/b[3] - a[0]/a[3]);\\n        lowp float asin = sin(angle);\\n        lowp float acos = cos(angle);\\n        mat2 RotationMatrix = mat2(acos, -1.0 * asin, asin, acos);\\n\\n        vec2 offset = RotationMatrix * (vec2((1.0-pitchfactor)+(pitchfactor*cos(angle*2.0)), 1.0) * a_offset);\\n        vec2 extrude = u_extrude_scale * (offset / 64.0);\\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n        gl_Position.z += z * gl_Position.w;\\n    // pitch-alignment: viewport\\n    // rotation-alignment: viewport\\n    } else {\\n        vec2 extrude = u_extrude_scale * (a_offset / 64.0);\\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n    }\\n\\n    v_gamma_scale = (gl_Position.w - 0.5);\\n\\n    v_tex = a_tex / u_texsize;\\n    v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\\n}\\n\"},collisionbox:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_zoom;\\nuniform float u_maxzoom;\\n\\nvarying float v_max_zoom;\\nvarying float v_placement_zoom;\\n\\nvoid main() {\\n\\n    float alpha = 0.5;\\n\\n    gl_FragColor = vec4(0.0, 1.0, 0.0, 1.0) * alpha;\\n\\n    if (v_placement_zoom > u_zoom) {\\n        gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\\n    }\\n\\n    if (u_zoom >= v_max_zoom) {\\n        gl_FragColor = vec4(0.0, 0.0, 0.0, 1.0) * alpha * 0.25;\\n    }\\n\\n    if (v_placement_zoom >= u_maxzoom) {\\n        gl_FragColor = vec4(0.0, 0.0, 1.0, 1.0) * alpha * 0.2;\\n    }\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_extrude;\\nattribute vec2 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform float u_scale;\\n\\nvarying float v_max_zoom;\\nvarying float v_placement_zoom;\\n\\nvoid main() {\\n    gl_Position = u_matrix * vec4(a_pos + a_extrude / u_scale, 0.0, 1.0);\\n\\n    v_max_zoom = a_data.x;\\n    v_placement_zoom = a_data.y;\\n}\\n\"}},e.exports.util=\"float evaluate_zoom_function_1(const vec4 values, const float t) {\\n    if (t < 1.0) {\\n        return mix(values[0], values[1], t);\\n    } else if (t < 2.0) {\\n        return mix(values[1], values[2], t - 1.0);\\n    } else {\\n        return mix(values[2], values[3], t - 2.0);\\n    }\\n}\\nvec4 evaluate_zoom_function_4(const vec4 value0, const vec4 value1, const vec4 value2, const vec4 value3, const float t) {\\n    if (t < 1.0) {\\n        return mix(value0, value1, t);\\n    } else if (t < 2.0) {\\n        return mix(value1, value2, t - 1.0);\\n    } else {\\n        return mix(value2, value3, t - 2.0);\\n    }\\n}\\n\"},{path:476}],304:[function(t,e,r){\"use strict\";function n(t,e){this.message=(t?t+\": \":\"\")+i.apply(i,Array.prototype.slice.call(arguments,2)),null!==e&&void 0!==e&&e.__line__&&(this.line=e.__line__)}var i=t(\"util\").format;e.exports=n},{util:549}],305:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=1;e<arguments.length;e++){var r=arguments[e];for(var n in r)t[n]=r[n]}return t}},{}],306:[function(t,e,r){\"use strict\";e.exports=function(t){return t instanceof Number?\"number\":t instanceof String?\"string\":t instanceof Boolean?\"boolean\":Array.isArray(t)?\"array\":null===t?\"null\":typeof t}},{}],307:[function(t,e,r){\"use strict\";e.exports=function(t){return t instanceof Number||t instanceof String||t instanceof Boolean?t.valueOf():t}},{}],308:[function(t,e,r){\"use strict\";var n=t(\"../error/validation_error\"),i=t(\"../util/get_type\"),a=t(\"../util/extend\");e.exports=function(e){var 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h=2;h<s.length;h++)r=a(s[h]),\"$type\"==s[1]?c=c.concat(i({key:l+\"[\"+h+\"]\",value:s[h],valueSpec:u.geometry_type,style:e.style,styleSpec:e.styleSpec})):\"string\"===r&&\"@\"===s[h][0]?c.push(new n(l+\"[\"+h+\"]\",s[h],\"filter value cannot be a constant\")):\"string\"!==r&&\"number\"!==r&&\"boolean\"!==r&&c.push(new n(l+\"[\"+h+\"]\",s[h],\"string, number, or boolean expected, %s found\",r));break;case\"any\":case\"all\":case\"none\":for(h=1;h<s.length;h++)c=c.concat(t({key:l+\"[\"+h+\"]\",value:s[h],style:e.style,styleSpec:e.styleSpec}));break;case\"has\":case\"!has\":r=a(s[1]),2!==s.length?c.push(new n(l,s,'filter array for \"%s\" operator must have 2 elements',s[0])):\"string\"!==r?c.push(new n(l+\"[1]\",s[1],\"string expected, %s found\",r)):\"@\"===s[1][0]&&c.push(new n(l+\"[1]\",s[1],\"filter key cannot be a constant\"))}return c}},{\"../error/validation_error\":304,\"../util/get_type\":306,\n",
+       "\"../util/unbundle_jsonlint\":307,\"./validate_enum\":313}],315:[function(t,e,r){\"use strict\";var n=t(\"../error/validation_error\"),i=t(\"../util/get_type\"),a=t(\"./validate\"),o=t(\"./validate_object\"),s=t(\"./validate_array\"),l=t(\"./validate_number\");e.exports=function(t){function e(t){var e=[],a=t.value;return e=e.concat(s({key:t.key,value:a,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:r})),\"array\"===i(a)&&0===a.length&&e.push(new n(t.key,a,\"array must have at least one stop\")),e}function r(t){var e=[],r=t.value,s=t.key;if(\"array\"!==i(r))return[new n(s,r,\"array expected, %s found\",i(r))];if(2!==r.length)return[new n(s,r,\"array length %d expected, length %d found\",2,r.length)];var f=i(r[0]);if(c||(c=f),f!==c)return[new n(s,r,\"%s stop key type must match previous stop key type %s\",f,c)];if(\"object\"===f){if(void 0===r[0].zoom)return[new n(s,r,\"object stop key must have zoom\")];if(void 0===r[0].value)return[new 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e=this.arrayGroups[t],r=0;r<e.length;r++)if(!e[r].isEmpty())return!1;return!0},n.prototype.getTransferables=function(t){for(var e in this.arrayGroups)for(var r=this.arrayGroups[e],n=0;n<r.length;n++)r[n].getTransferables(t)},n.prototype.setUniforms=function(t,e,r,n,i){for(var a=this.paintAttributes[e][n.id].uniforms,o=0;o<a.length;o++){var s=a[o],l=r[s.name];t[\"uniform\"+s.components+\"fv\"](l,s.getValue(n,i))}},n.prototype.serialize=function(){return{layerId:this.layer.id,zoom:this.zoom,arrays:c.mapObject(this.arrayGroups,function(t){return t.map(function(t){return t.serialize()})}),paintVertexArrayTypes:c.mapObject(this.paintVertexArrayTypes,function(t){return c.mapObject(t,function(t){return t.serialize()})}),childLayerIds:this.childLayers.map(function(t){return t.id})}},n.prototype.createFilter=function(){this.filter||(this.filter=s(this.layer.filter))};var d={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};n.prototype.recalculateStyleLayers=function(){for(var t=0;t<this.childLayers.length;t++)this.childLayers[t].recalculate(this.zoom,d)},n.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a=0;a<this.childLayers.length;a++)for(var o=this.childLayers[a],s=this.arrayGroups[t],l=n.index;l<s.length;l++){var u=s[l],c=u.layoutVertexArray.length,h=u.paintVertexArrays[o.id];h.resize(c);for(var f=this.paintAttributes[t][o.id].attributes,d=0;d<f.length;d++)for(var p=f[d],m=p.getValue(o,e,r),v=p.multiplier||1,g=p.components||1,y=l===n.index?i:0,b=y;b<c;b++)for(var x=h.get(b),_=0;_<g;_++){var w=g>1?p.name+_:p.name;x[w]=m[_]*v}}},n.VertexArrayType=function(t){return new h({members:t,alignment:4})},n.ElementArrayType=function(t){return new h({members:[{type:\"Uint16\",name:\"vertices\",components:t||3}]})}},{\"../util/struct_array\":440,\"../util/util\":442,\"./array_group\":328,\"./bucket/circle_bucket\":330,\"./bucket/fill_bucket\":331,\"./bucket/line_bucket\":332,\"./bucket/symbol_bucket\":333,\"./buffer_group\":335,assert:47,\"feature-filter\":132}],330:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=i.EXTENT;e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.addCircleVertex=function(t,e,r,n,i){return t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)},n.prototype.programInterfaces={circle:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"}]),elementArrayType:new i.ElementArrayType,paintAttributes:[{name:\"a_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"circle-color\",e,r)},multiplier:255,paintProperty:\"circle-color\"},{name:\"a_radius\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-radius\",e,r)]},multiplier:10,paintProperty:\"circle-radius\"},{name:\"a_blur\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-blur\",e,r)]},multiplier:10,paintProperty:\"circle-blur\"},{name:\"a_opacity\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-opacity\",e,r)]},multiplier:255,paintProperty:\"circle-opacity\"}]}},n.prototype.addFeature=function(t){for(var e={zoom:this.zoom},r=o(t),n=this.prepareArrayGroup(\"circle\",0),i=n.layoutVertexArray.length,a=0;a<r.length;a++)for(var l=0;l<r[a].length;l++){var u=r[a][l].x,c=r[a][l].y;if(!(u<0||u>=s||c<0||c>=s)){var h=this.prepareArrayGroup(\"circle\",4),f=h.layoutVertexArray,d=this.addCircleVertex(f,u,c,-1,-1);this.addCircleVertex(f,u,c,1,-1),this.addCircleVertex(f,u,c,1,1),this.addCircleVertex(f,u,c,-1,1),h.elementArray.emplaceBack(d,d+1,d+2),h.elementArray.emplaceBack(d,d+3,d+2)}}this.populatePaintArrays(\"circle\",e,t.properties,n,i)}},{\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337}],331:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=t(\"earcut\"),l=t(\"../../util/classify_rings\");e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.programInterfaces={fill:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"}]),elementArrayType:new i.ElementArrayType(1),elementArrayType2:new i.ElementArrayType(2),paintAttributes:[{name:\"a_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"fill-color\",e,r)},multiplier:255,paintProperty:\"fill-color\"},{name:\"a_outline_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"fill-outline-color\",e,r)},multiplier:255,paintProperty:\"fill-outline-color\"},{name:\"a_opacity\",components:1,type:\"Uint8\",getValue:function(t,e,r){return[t.getPaintValue(\"fill-opacity\",e,r)]},multiplier:255,paintProperty:\"fill-opacity\"}]}},n.prototype.addFeature=function(t){for(var e=o(t),r=l(e,500),n=this.prepareArrayGroup(\"fill\",0),i=n.layoutVertexArray.length,a=0;a<r.length;a++)this.addPolygon(r[a]);this.populatePaintArrays(\"fill\",{zoom:this.zoom},t.properties,n,i)},n.prototype.addPolygon=function(t){for(var e=0,r=0;r<t.length;r++)e+=t[r].length;for(var n=this.prepareArrayGroup(\"fill\",e),i=[],a=[],o=n.layoutVertexArray.length,l=0;l<t.length;l++){var u=t[l];l>0&&a.push(i.length/2);for(var c=0;c<u.length;c++){var h=u[c],f=n.layoutVertexArray.emplaceBack(h.x,h.y);c>=1&&n.elementArray2.emplaceBack(f-1,f),i.push(h.x),i.push(h.y)}}for(var d=s(i,a),p=0;p<d.length;p++)n.elementArray.emplaceBack(d[p]+o)}},{\"../../util/classify_rings\":430,\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337,earcut:126}],332:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=i.EXTENT,l=Math.cos(Math.PI/180*37.5),u=Math.pow(2,14)/.5;e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.addLineVertex=function(t,e,r,n,i,a,o){return t.emplaceBack(e.x<<1|n,e.y<<1|i,Math.round(63*r.x)+128,Math.round(63*r.y)+128,1+(0===a?0:a<0?-1:1)|(.5*o&63)<<2,.5*o>>6)},n.prototype.programInterfaces={line:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}]),elementArrayType:new i.ElementArrayType}},n.prototype.addFeature=function(t){for(var e=o(t,15),r=0;r<e.length;r++)this.addLine(e[r],this.layer.layout[\"line-join\"],this.layer.layout[\"line-cap\"],this.layer.layout[\"line-miter-limit\"],this.layer.layout[\"line-round-limit\"])},n.prototype.addLine=function(t,e,r,n,i){for(var a=t.length;a>2&&t[a-1].equals(t[a-2]);)a--;if(!(t.length<2)){\"bevel\"===e&&(n=1.05);var o=s/(512*this.overscaling)*15,u=t[0],c=t[a-1],h=u.equals(c);if(this.prepareArrayGroup(\"line\",10*a),2!==a||!h){this.distance=0;var f,d,p,m,v,g,y,b=r,x=h?\"butt\":r,_=!0;this.e1=this.e2=this.e3=-1,h&&(f=t[a-2],v=u.sub(f)._unit()._perp());for(var w=0;w<a;w++)if(!(p=h&&w===a-1?t[1]:t[w+1])||!t[w].equals(p)){v&&(m=v),f&&(d=f),f=t[w],v=p?p.sub(f)._unit()._perp():m,m=m||v;var M=m.add(v)._unit(),k=M.x*v.x+M.y*v.y,A=1/k,T=k<l&&d&&p;if(T&&w>0){var S=f.dist(d);if(S>2*o){var E=f.sub(f.sub(d)._mult(o/S)._round());this.distance+=E.dist(d),this.addCurrentVertex(E,this.distance,m.mult(1),0,0,!1),d=E}}var L=d&&p,C=L?e:p?b:x;if(L&&\"round\"===C&&(A<i?C=\"miter\":A<=2&&(C=\"fakeround\")),\"miter\"===C&&A>n&&(C=\"bevel\"),\"bevel\"===C&&(A>2&&(C=\"flipbevel\"),A<n&&(C=\"miter\")),d&&(this.distance+=f.dist(d)),\"miter\"===C)M._mult(A),this.addCurrentVertex(f,this.distance,M,0,0,!1);else if(\"flipbevel\"===C){if(A>100)M=v.clone();else{var I=m.x*v.y-m.y*v.x>0?-1:1,z=A*m.add(v).mag()/m.sub(v).mag();M._perp()._mult(z*I)}this.addCurrentVertex(f,this.distance,M,0,0,!1),this.addCurrentVertex(f,this.distance,M.mult(-1),0,0,!1)}else if(\"bevel\"===C||\"fakeround\"===C){var D=m.x*v.y-m.y*v.x>0,P=-Math.sqrt(A*A-1);if(D?(y=0,g=P):(g=0,y=P),_||this.addCurrentVertex(f,this.distance,m,g,y,!1),\"fakeround\"===C){for(var O,R=Math.floor(8*(.5-(k-.5))),F=0;F<R;F++)O=v.mult((F+1)/(R+1))._add(m)._unit(),this.addPieSliceVertex(f,this.distance,O,D);this.addPieSliceVertex(f,this.distance,M,D);for(var j=R-1;j>=0;j--)O=m.mult((j+1)/(R+1))._add(v)._unit(),this.addPieSliceVertex(f,this.distance,O,D)}p&&this.addCurrentVertex(f,this.distance,v,-g,-y,!1)}else\"butt\"===C?(_||this.addCurrentVertex(f,this.distance,m,0,0,!1),p&&this.addCurrentVertex(f,this.distance,v,0,0,!1)):\"square\"===C?(_||(this.addCurrentVertex(f,this.distance,m,1,1,!1),this.e1=this.e2=-1),p&&this.addCurrentVertex(f,this.distance,v,-1,-1,!1)):\"round\"===C&&(_||(this.addCurrentVertex(f,this.distance,m,0,0,!1),this.addCurrentVertex(f,this.distance,m,1,1,!0),this.e1=this.e2=-1),p&&(this.addCurrentVertex(f,this.distance,v,-1,-1,!0),this.addCurrentVertex(f,this.distance,v,0,0,!1)));if(T&&w<a-1){var N=f.dist(p);if(N>2*o){var B=f.add(p.sub(f)._mult(o/N)._round());this.distance+=B.dist(f),this.addCurrentVertex(B,this.distance,v.mult(1),0,0,!1),f=B}}_=!1}}}},n.prototype.addCurrentVertex=function(t,e,r,n,i,a){var o,s=a?1:0,l=this.arrayGroups.line[this.arrayGroups.line.length-1],c=l.layoutVertexArray,h=l.elementArray;o=r.clone(),n&&o._sub(r.perp()._mult(n)),this.e3=this.addLineVertex(c,t,o,s,0,n,e),this.e1>=0&&this.e2>=0&&h.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,o=r.mult(-1),i&&o._sub(r.perp()._mult(i)),this.e3=this.addLineVertex(c,t,o,s,1,-i,e),this.e1>=0&&this.e2>=0&&h.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,e>u/2&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,a))},n.prototype.addPieSliceVertex=function(t,e,r,n){var i=n?1:0;r=r.mult(n?-1:1);var a=this.arrayGroups.line[this.arrayGroups.line.length-1],o=a.layoutVertexArray,s=a.elementArray;this.e3=this.addLineVertex(o,t,r,0,i,0,e),this.e1>=0&&this.e2>=0&&s.emplaceBack(this.e1,this.e2,this.e3),n?this.e2=this.e3:this.e1=this.e3}},{\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337}],333:[function(t,e,r){\"use strict\";function n(t){o.apply(this,arguments),this.showCollisionBoxes=t.showCollisionBoxes,this.overscaling=t.overscaling,this.collisionBoxArray=t.collisionBoxArray,this.symbolQuadsArray=t.symbolQuadsArray,this.symbolInstancesArray=t.symbolInstancesArray,this.sdfIcons=t.sdfIcons,this.iconsNeedLinear=t.iconsNeedLinear,this.adjustedTextSize=t.adjustedTextSize,this.adjustedIconSize=t.adjustedIconSize,this.fontstack=t.fontstack}function i(t,e,r,n,i,a,o,s,l,u,c){return t.emplaceBack(e,r,Math.round(64*n),Math.round(64*i),a/4,o/4,10*(u||0),c,10*(s||0),10*Math.min(l||25,25))}var a=t(\"point-geometry\"),o=t(\"../bucket\"),s=t(\"../../symbol/anchor\"),l=t(\"../../symbol/get_anchors\"),u=t(\"../../util/token\"),c=t(\"../../symbol/quads\"),h=t(\"../../symbol/shaping\"),f=t(\"../../symbol/resolve_text\"),d=t(\"../../symbol/mergelines\"),p=t(\"../../symbol/clip_line\"),m=t(\"../../util/util\"),v=t(\"../load_geometry\"),g=t(\"../../symbol/collision_feature\"),y=h.shapeText,b=h.shapeIcon,x=c.getGlyphQuads,_=c.getIconQuads,w=o.EXTENT;e.exports=n,n.MAX_QUADS=65535,n.prototype=m.inherit(o,{}),n.prototype.serialize=function(){var t=o.prototype.serialize.apply(this);return t.sdfIcons=this.sdfIcons,t.iconsNeedLinear=this.iconsNeedLinear,t.adjustedTextSize=this.adjustedTextSize,t.adjustedIconSize=this.adjustedIconSize,t.fontstack=this.fontstack,t};var M=new o.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_offset\",components:2,type:\"Int16\"},{name:\"a_texture_pos\",components:2,type:\"Uint16\"},{name:\"a_data\",components:4,type:\"Uint8\"}]),k=new o.ElementArrayType;n.prototype.addCollisionBoxVertex=function(t,e,r,n,i){return t.emplaceBack(e.x,e.y,Math.round(r.x),Math.round(r.y),10*n,10*i)},n.prototype.programInterfaces={glyph:{layoutVertexArrayType:M,elementArrayType:k},icon:{layoutVertexArrayType:M,elementArrayType:k},collisionBox:{layoutVertexArrayType:new o.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"},{name:\"a_data\",components:2,type:\"Uint8\"}])}},n.prototype.populateArrays=function(t,e,r){var n={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};this.adjustedTextMaxSize=this.layer.getLayoutValue(\"text-size\",{zoom:18,zoomHistory:n}),this.adjustedTextSize=this.layer.getLayoutValue(\"text-size\",{zoom:this.zoom+1,zoomHistory:n}),this.adjustedIconMaxSize=this.layer.getLayoutValue(\"icon-size\",{zoom:18,zoomHistory:n}),this.adjustedIconSize=this.layer.getLayoutValue(\"icon-size\",{zoom:this.zoom+1,zoomHistory:n});var i=512*this.overscaling;this.tilePixelRatio=w/i,this.compareText={},this.iconsNeedLinear=!1,this.symbolInstancesStartIndex=this.symbolInstancesArray.length;var a=this.layer.layout,o=this.features,s=this.textFeatures,l=.5,c=.5;switch(a[\"text-anchor\"]){case\"right\":case\"top-right\":case\"bottom-right\":l=1;break;case\"left\":case\"top-left\":case\"bottom-left\":l=0}switch(a[\"text-anchor\"]){case\"bottom\":case\"bottom-right\":case\"bottom-left\":c=1;break;case\"top\":case\"top-right\":case\"top-left\":c=0}for(var h=\"right\"===a[\"text-justify\"]?1:\"left\"===a[\"text-justify\"]?0:.5,f=24*a[\"text-line-height\"],p=\"line\"!==a[\"symbol-placement\"]?24*a[\"text-max-width\"]:0,g=24*a[\"text-letter-spacing\"],x=[24*a[\"text-offset\"][0],24*a[\"text-offset\"][1]],_=this.fontstack=a[\"text-font\"].join(\",\"),M=[],k=0;k<o.length;k++)M.push(v(o[k]));if(\"line\"===a[\"symbol-placement\"]){var A=d(o,s,M);M=A.geometries,o=A.features,s=A.textFeatures}for(var T,S,E=0;E<o.length;E++)if(M[E]){if(T=s[E]?y(s[E],e[_],p,f,l,c,h,g,x):null,a[\"icon-image\"]){var L=u(o[E].properties,a[\"icon-image\"]),C=r[L];S=b(C,a),C&&(void 0===this.sdfIcons?this.sdfIcons=C.sdf:this.sdfIcons!==C.sdf&&m.warnOnce(\"Style sheet warning: Cannot mix SDF and non-SDF icons in one buffer\"),1!==C.pixelRatio?this.iconsNeedLinear=!0:0===a[\"icon-rotate\"]&&this.layer.isLayoutValueFeatureConstant(\"icon-rotate\")||(this.iconsNeedLinear=!0))}else S=null;(T||S)&&this.addFeature(M[E],T,S,o[E])}this.symbolInstancesEndIndex=this.symbolInstancesArray.length,this.placeFeatures(t,this.showCollisionBoxes),this.trimArrays()},n.prototype.addFeature=function(t,e,r,n){var i=this.layer.layout,a=this.adjustedTextSize/24,o=void 0!==this.adjustedTextMaxSize?this.adjustedTextMaxSize:this.adjustedTextSize,u=this.tilePixelRatio*a,c=this.tilePixelRatio*o/24,h=this.tilePixelRatio*this.adjustedIconSize,f=this.tilePixelRatio*i[\"symbol-spacing\"],d=i[\"symbol-avoid-edges\"],m=i[\"text-padding\"]*this.tilePixelRatio,v=i[\"icon-padding\"]*this.tilePixelRatio,g=i[\"text-max-angle\"]/180*Math.PI,y=\"map\"===i[\"text-rotation-alignment\"]&&\"line\"===i[\"symbol-placement\"],b=\"map\"===i[\"icon-rotation-alignment\"]&&\"line\"===i[\"symbol-placement\"],x=i[\"text-allow-overlap\"]||i[\"icon-allow-overlap\"]||i[\"text-ignore-placement\"]||i[\"icon-ignore-placement\"],_=\"line\"===i[\"symbol-placement\"],M=f/2;_&&(t=p(t,0,0,w,w));for(var k=0;k<t.length;k++){var A,T=t[k];A=_?l(T,f,g,e,r,24,c,this.overscaling,w):[new s(T[0].x,T[0].y,0)];for(var S=0,E=A.length;S<E;S++){var L=A[S];if(!(e&&_&&this.anchorIsTooClose(e.text,M,L))){var C=!(L.x<0||L.x>w||L.y<0||L.y>w);if(!d||C){var I=C||x;this.addSymbolInstance(L,T,e,r,this.layer,I,this.symbolInstancesArray.length,this.collisionBoxArray,n.index,this.sourceLayerIndex,this.index,u,m,y,h,v,b,{zoom:this.zoom},n.properties)}}}}},n.prototype.anchorIsTooClose=function(t,e,r){var n=this.compareText;if(t in n){for(var i=n[t],a=i.length-1;a>=0;a--)if(r.dist(i[a])<e)return!0}else n[t]=[];return n[t].push(r),!1},n.prototype.placeFeatures=function(t,e){this.recalculateStyleLayers(),this.createArrays();var r=this.layer.layout,n=t.maxScale,i=\"map\"===r[\"text-rotation-alignment\"]&&\"line\"===r[\"symbol-placement\"],a=\"map\"===r[\"icon-rotation-alignment\"]&&\"line\"===r[\"symbol-placement\"];if(r[\"text-allow-overlap\"]||r[\"icon-allow-overlap\"]||r[\"text-ignore-placement\"]||r[\"icon-ignore-placement\"]){var o=this.symbolInstancesArray.toArray(this.symbolInstancesStartIndex,this.symbolInstancesEndIndex),s=t.angle,l=Math.sin(s),u=Math.cos(s);this.sortedSymbolInstances=o.sort(function(t,e){return(l*t.anchorPointX+u*t.anchorPointY|0)-(l*e.anchorPointX+u*e.anchorPointY|0)||e.index-t.index})}for(var c=this.symbolInstancesStartIndex;c<this.symbolInstancesEndIndex;c++){var h=this.sortedSymbolInstances?this.sortedSymbolInstances[c-this.symbolInstancesStartIndex]:this.symbolInstancesArray.get(c),f={boxStartIndex:h.textBoxStartIndex,boxEndIndex:h.textBoxEndIndex},d={boxStartIndex:h.iconBoxStartIndex,boxEndIndex:h.iconBoxEndIndex},p=!(h.textBoxStartIndex===h.textBoxEndIndex),m=!(h.iconBoxStartIndex===h.iconBoxEndIndex),v=r[\"text-optional\"]||!p,g=r[\"icon-optional\"]||!m,y=p?t.placeCollisionFeature(f,r[\"text-allow-overlap\"],r[\"symbol-avoid-edges\"]):t.minScale,b=m?t.placeCollisionFeature(d,r[\"icon-allow-overlap\"],r[\"symbol-avoid-edges\"]):t.minScale;v||g?!g&&y?y=Math.max(b,y):!v&&b&&(b=Math.max(b,y)):b=y=Math.max(b,y),p&&(t.insertCollisionFeature(f,y,r[\"text-ignore-placement\"]),y<=n&&this.addSymbols(\"glyph\",h.glyphQuadStartIndex,h.glyphQuadEndIndex,y,r[\"text-keep-upright\"],i,t.angle)),m&&(t.insertCollisionFeature(d,b,r[\"icon-ignore-placement\"]),b<=n&&this.addSymbols(\"icon\",h.iconQuadStartIndex,h.iconQuadEndIndex,b,r[\"icon-keep-upright\"],a,t.angle))}e&&this.addToDebugBuffers(t)},n.prototype.addSymbols=function(t,e,r,n,a,o,s){for(var l=this.prepareArrayGroup(t,4*(r-e)),u=l.elementArray,c=l.layoutVertexArray,h=this.zoom,f=Math.max(Math.log(n)/Math.LN2+h,0),d=e;d<r;d++){var p=this.symbolQuadsArray.get(d).SymbolQuad,m=(p.anchorAngle+s+Math.PI)%(2*Math.PI);if(!(a&&o&&(m<=Math.PI/2||m>3*Math.PI/2))){var v=p.tl,g=p.tr,y=p.bl,b=p.br,x=p.tex,_=p.anchorPoint,w=Math.max(h+Math.log(p.minScale)/Math.LN2,f),M=Math.min(h+Math.log(p.maxScale)/Math.LN2,25);if(!(M<=w)){w===f&&(w=0);var k=Math.round(p.glyphAngle/(2*Math.PI)*256),A=i(c,_.x,_.y,v.x,v.y,x.x,x.y,w,M,f,k);i(c,_.x,_.y,g.x,g.y,x.x+x.w,x.y,w,M,f,k),i(c,_.x,_.y,y.x,y.y,x.x,x.y+x.h,w,M,f,k),i(c,_.x,_.y,b.x,b.y,x.x+x.w,x.y+x.h,w,M,f,k),u.emplaceBack(A,A+1,A+2),u.emplaceBack(A+1,A+2,A+3)}}}},n.prototype.updateIcons=function(t){this.recalculateStyleLayers();var e=this.layer.layout[\"icon-image\"];if(e)for(var r=0;r<this.features.length;r++){var n=u(this.features[r].properties,e);n&&(t[n]=!0)}},n.prototype.updateFont=function(t){this.recalculateStyleLayers();var e=this.layer.layout[\"text-font\"],r=t[e]=t[e]||{};this.textFeatures=f(this.features,this.layer.layout,r)},n.prototype.addToDebugBuffers=function(t){for(var e=this.prepareArrayGroup(\"collisionBox\",0),r=e.layoutVertexArray,n=-t.angle,i=t.yStretch,o=this.symbolInstancesStartIndex;o<this.symbolInstancesEndIndex;o++){var s=this.symbolInstancesArray.get(o);s.textCollisionFeature={boxStartIndex:s.textBoxStartIndex,boxEndIndex:s.textBoxEndIndex},s.iconCollisionFeature={boxStartIndex:s.iconBoxStartIndex,boxEndIndex:s.iconBoxEndIndex};for(var l=0;l<2;l++){var u=s[0===l?\"textCollisionFeature\":\"iconCollisionFeature\"];if(u)for(var c=u.boxStartIndex;c<u.boxEndIndex;c++){var h=this.collisionBoxArray.get(c),f=h.anchorPoint,d=new a(h.x1,h.y1*i)._rotate(n),p=new a(h.x2,h.y1*i)._rotate(n),m=new a(h.x1,h.y2*i)._rotate(n),v=new a(h.x2,h.y2*i)._rotate(n),g=Math.max(0,Math.min(25,this.zoom+Math.log(h.maxScale)/Math.LN2)),y=Math.max(0,Math.min(25,this.zoom+Math.log(h.placementScale)/Math.LN2));this.addCollisionBoxVertex(r,f,d,g,y),this.addCollisionBoxVertex(r,f,p,g,y),this.addCollisionBoxVertex(r,f,p,g,y),this.addCollisionBoxVertex(r,f,v,g,y),this.addCollisionBoxVertex(r,f,v,g,y),this.addCollisionBoxVertex(r,f,m,g,y),this.addCollisionBoxVertex(r,f,m,g,y),this.addCollisionBoxVertex(r,f,d,g,y)}}}},n.prototype.addSymbolInstance=function(t,e,r,i,a,o,s,l,u,c,h,f,d,p,v,y,b,w,M){var k,A,T,S,E,L,C,I;if(r&&(C=o?x(t,r,f,e,a,p):[],E=new g(l,e,t,u,c,h,r,f,d,p,!1)),k=this.symbolQuadsArray.length,C&&C.length)for(var z=0;z<C.length;z++)this.addSymbolQuad(C[z]);A=this.symbolQuadsArray.length;var D=E?E.boxStartIndex:this.collisionBoxArray.length,P=E?E.boxEndIndex:this.collisionBoxArray.length;i&&(I=o?_(t,i,v,e,a,b,r,w,M):[],L=new g(l,e,t,u,c,h,i,v,y,b,!0)),T=this.symbolQuadsArray.length,I&&1===I.length&&this.addSymbolQuad(I[0]),S=this.symbolQuadsArray.length;var O=L?L.boxStartIndex:this.collisionBoxArray.length,R=L?L.boxEndIndex:this.collisionBoxArray.length;return S>n.MAX_QUADS&&m.warnOnce(\"Too many symbols being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),A>n.MAX_QUADS&&m.warnOnce(\"Too many glyphs being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),this.symbolInstancesArray.emplaceBack(D,P,O,R,k,A,T,S,t.x,t.y,s)},n.prototype.addSymbolQuad=function(t){return this.symbolQuadsArray.emplaceBack(t.anchorPoint.x,t.anchorPoint.y,t.tl.x,t.tl.y,t.tr.x,t.tr.y,t.bl.x,t.bl.y,t.br.x,t.br.y,t.tex.h,t.tex.w,t.tex.x,t.tex.y,t.anchorAngle,t.glyphAngle,t.maxScale,t.minScale)}},{\"../../symbol/anchor\":391,\"../../symbol/clip_line\":393,\"../../symbol/collision_feature\":395,\"../../symbol/get_anchors\":397,\"../../symbol/mergelines\":400,\"../../symbol/quads\":401,\"../../symbol/resolve_text\":402,\"../../symbol/shaping\":403,\"../../util/token\":441,\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337,\"point-geometry\":484}],334:[function(t,e,r){\"use strict\";function n(t,e,r){this.arrayBuffer=t.arrayBuffer,this.length=t.length,this.attributes=e.members,this.itemSize=e.bytesPerElement,this.type=r,this.arrayType=e}e.exports=n,n.prototype.bind=function(t){var e=t[this.type];this.buffer?t.bindBuffer(e,this.buffer):(this.buffer=t.createBuffer(),t.bindBuffer(e,this.buffer),t.bufferData(e,this.arrayBuffer,t.STATIC_DRAW),this.arrayBuffer=null)};var i={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\"};n.prototype.setVertexAttribPointers=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],a=e[n.name];void 0!==a&&t.vertexAttribPointer(a,n.components,t[i[n.type]],!1,this.arrayType.bytesPerElement,n.offset)}},n.prototype.destroy=function(t){this.buffer&&t.deleteBuffer(this.buffer)},n.BufferType={VERTEX:\"ARRAY_BUFFER\",ELEMENT:\"ELEMENT_ARRAY_BUFFER\"}},{}],335:[function(t,e,r){\"use strict\";function n(t,e){this.layoutVertexBuffer=new a(t.layoutVertexArray,e.layoutVertexArrayType,a.BufferType.VERTEX),t.elementArray&&(this.elementBuffer=new a(t.elementArray,e.elementArrayType,a.BufferType.ELEMENT));var r,n=this.vaos={};t.elementArray2&&(this.elementBuffer2=new a(t.elementArray2,e.elementArrayType2,a.BufferType.ELEMENT),r=this.secondVaos={}),this.paintVertexBuffers=i.mapObject(t.paintVertexArrays,function(i,s){return n[s]=new o,t.elementArray2&&(r[s]=new o),new a(i,e.paintVertexArrayTypes[s],a.BufferType.VERTEX)})}var i=t(\"../util/util\"),a=t(\"./buffer\"),o=t(\"../render/vertex_array_object\");e.exports=n,n.prototype.destroy=function(t){this.layoutVertexBuffer.destroy(t),this.elementBuffer&&this.elementBuffer.destroy(t),this.elementBuffer2&&this.elementBuffer2.destroy(t);for(var e in this.paintVertexBuffers)this.paintVertexBuffers[e].destroy(t);for(var r in this.vaos)this.vaos[r].destroy(t);for(var n in this.secondVaos)this.secondVaos[n].destroy(t)}},{\"../render/vertex_array_object\":357,\"../util/util\":442,\"./buffer\":334}],336:[function(t,e,r){\"use strict\";function n(t,e,r){if(t.grid){var n=t,i=e;t=n.coord,e=n.overscaling,this.grid=new p(n.grid),this.featureIndexArray=new k(n.featureIndexArray),this.rawTileData=i,this.bucketLayerIDs=n.bucketLayerIDs}else this.grid=new p(h,16,0),this.featureIndexArray=new k;this.coord=t,this.overscaling=e,this.x=t.x,this.y=t.y,this.z=t.z-Math.log(e)/Math.LN2,this.setCollisionTile(r)}function i(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function a(t,e){return e-t}function o(t){return t[\"line-gap-width\"]>0?t[\"line-gap-width\"]+2*t[\"line-width\"]:t[\"line-width\"]}function s(t,e,r,n,i){if(!e[0]&&!e[1])return t;e=u.convert(e),\"viewport\"===r&&e._rotate(-n);for(var a=[],o=0;o<t.length;o++){for(var s=t[o],l=[],c=0;c<s.length;c++)l.push(s[c].sub(e._mult(i)));a.push(l)}return a}function l(t,e){for(var r=[],n=new u(0,0),i=0;i<t.length;i++){for(var a=t[i],o=[],s=0;s<a.length;s++){var l=a[s-1],c=a[s],h=a[s+1],f=0===s?n:c.sub(l)._unit()._perp(),d=s===a.length-1?n:h.sub(c)._unit()._perp(),p=f._add(d)._unit(),m=p.x*d.x+p.y*d.y;p._mult(1/m),o.push(p._mult(e)._add(c))}r.push(o)}return r}var u=t(\"point-geometry\"),c=t(\"./load_geometry\"),h=t(\"./bucket\").EXTENT,f=t(\"feature-filter\"),d=t(\"../util/struct_array\"),p=t(\"grid-index\"),m=t(\"../util/dictionary_coder\"),v=t(\"vector-tile\"),g=t(\"pbf\"),y=t(\"../util/vectortile_to_geojson\"),b=t(\"../util/util\").arraysIntersect,x=t(\"../util/intersection_tests\"),_=x.multiPolygonIntersectsBufferedMultiPoint,w=x.multiPolygonIntersectsMultiPolygon,M=x.multiPolygonIntersectsBufferedMultiLine,k=new d({members:[{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]});e.exports=n,n.prototype.insert=function(t,e,r,n){var i=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(e,r,n);for(var a=c(t),o=0;o<a.length;o++){for(var s=a[o],l=[1/0,1/0,-1/0,-1/0],u=0;u<s.length;u++){var h=s[u];l[0]=Math.min(l[0],h.x),l[1]=Math.min(l[1],h.y),l[2]=Math.max(l[2],h.x),l[3]=Math.max(l[3],h.y)}this.grid.insert(i,l[0],l[1],l[2],l[3])}},n.prototype.setCollisionTile=function(t){this.collisionTile=t},n.prototype.serialize=function(){var t={coord:this.coord,overscaling:this.overscaling,grid:this.grid.toArrayBuffer(),featureIndexArray:this.featureIndexArray.serialize(),bucketLayerIDs:this.bucketLayerIDs};return{data:t,transferables:[t.grid,t.featureIndexArray.arrayBuffer]}},n.prototype.query=function(t,e){this.vtLayers||(this.vtLayers=new v.VectorTile(new g(new Uint8Array(this.rawTileData))).layers,this.sourceLayerCoder=new m(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"]));var r={},n=t.params||{},s=h/t.tileSize/t.scale,l=f(n.filter),c=0;for(var d in e){var p=e[d],y=p.paint,b=0;\"line\"===p.type?b=o(y)/2+Math.abs(y[\"line-offset\"])+i(y[\"line-translate\"]):\"fill\"===p.type?b=i(y[\"fill-translate\"]):\"circle\"===p.type&&(b=y[\"circle-radius\"]+i(y[\"circle-translate\"])),c=Math.max(c,b*s)}for(var x=t.queryGeometry.map(function(t){return t.map(function(t){return new u(t.x,t.y)})}),_=1/0,w=1/0,M=-1/0,k=-1/0,A=0;A<x.length;A++)for(var T=x[A],S=0;S<T.length;S++){var E=T[S];_=Math.min(_,E.x),w=Math.min(w,E.y),M=Math.max(M,E.x),k=Math.max(k,E.y)}var L=this.grid.query(_-c,w-c,M+c,k+c);L.sort(a),this.filterMatching(r,L,this.featureIndexArray,x,l,n.layers,e,t.bearing,s);var C=this.collisionTile.queryRenderedSymbols(_,w,M,k,t.scale);return C.sort(),this.filterMatching(r,C,this.collisionTile.collisionBoxArray,x,l,n.layers,e,t.bearing,s),r},n.prototype.filterMatching=function(t,e,r,n,i,a,u,h,f){for(var d,p=0;p<e.length;p++){var m=e[p];if(m!==d){d=m;var v=r.get(m),g=this.bucketLayerIDs[v.bucketIndex];if(!a||b(a,g)){var x=this.sourceLayerCoder.decode(v.sourceLayerIndex),k=this.vtLayers[x],A=k.feature(v.featureIndex);if(i(A))for(var T=null,S=0;S<g.length;S++){var E=g[S];if(!(a&&a.indexOf(E)<0)){var L=u[E];if(L){var C;if(\"symbol\"!==L.type){T||(T=c(A));var I=L.paint;if(\"line\"===L.type){C=s(n,I[\"line-translate\"],I[\"line-translate-anchor\"],h,f);var z=o(I)/2*f;if(I[\"line-offset\"]&&(T=l(T,I[\"line-offset\"]*f)),!M(C,T,z))continue}else if(\"fill\"===L.type){if(C=s(n,I[\"fill-translate\"],I[\"fill-translate-anchor\"],h,f),!w(C,T))continue}else if(\"circle\"===L.type){C=s(n,I[\"circle-translate\"],I[\"circle-translate-anchor\"],h,f);var D=I[\"circle-radius\"]*f;if(!_(C,T,D))continue}}var P=new y(A,this.z,this.x,this.y);P.layer=L.serialize({includeRefProperties:!0});var O=t[E];void 0===O&&(O=t[E]=[]),O.push(P)}}}}}}}},{\"../util/dictionary_coder\":432,\"../util/intersection_tests\":437,\"../util/struct_array\":440,\"../util/util\":442,\"../util/vectortile_to_geojson\":443,\"./bucket\":329,\"./load_geometry\":337,\"feature-filter\":132,\"grid-index\":287,pbf:478,\"point-geometry\":484,\"vector-tile\":550}],337:[function(t,e,r){\"use strict\";function n(t){return{min:-1*Math.pow(2,t-1),max:Math.pow(2,t-1)-1}}var i=t(\"../util/util\"),a=t(\"./bucket\").EXTENT,o=t(\"assert\"),s={15:n(15),16:n(16)};e.exports=function(t,e){var r=s[e||16];o(r);for(var n=a/t.extent,l=t.loadGeometry(),u=0;u<l.length;u++)for(var c=l[u],h=0;h<c.length;h++){var f=c[h];f.x=Math.round(f.x*n),f.y=Math.round(f.y*n),(f.x<r.min||f.x>r.max||f.y<r.min||f.y>r.max)&&i.warnOnce(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\")}return l}},{\"../util/util\":442,\"./bucket\":329,assert:47}],338:[function(t,e,r){\"use strict\";function n(t,e,r){this.column=t,this.row=e,this.zoom=r}e.exports=n,n.prototype={clone:function(){return new n(this.column,this.row,this.zoom)},zoomTo:function(t){return this.clone()._zoomTo(t)},sub:function(t){return this.clone()._sub(t)},_zoomTo:function(t){var e=Math.pow(2,t-this.zoom);return this.column*=e,this.row*=e,this.zoom=t,this},_sub:function(t){return t=t.zoomTo(this.zoom),this.column-=t.column,this.row-=t.row,this}}},{}],339:[function(t,e,r){\"use strict\";function n(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}e.exports=n\n",
+       ";var i=t(\"../util/util\").wrap;n.prototype.wrap=function(){return new n(i(this.lng,-180,180),this.lat)},n.prototype.toArray=function(){return[this.lng,this.lat]},n.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},n.convert=function(t){return t instanceof n?t:Array.isArray(t)?new n(t[0],t[1]):t}},{\"../util/util\":442}],340:[function(t,e,r){\"use strict\";function n(t,e){t&&(e?this.extend(t).extend(e):4===t.length?this.extend([t[0],t[1]]).extend([t[2],t[3]]):this.extend(t[0]).extend(t[1]))}e.exports=n;var i=t(\"./lng_lat\");n.prototype={extend:function(t){var e,r,a=this._sw,o=this._ne;if(t instanceof i)e=t,r=t;else{if(!(t instanceof n))return t?this.extend(i.convert(t)||n.convert(t)):this;if(e=t._sw,r=t._ne,!e||!r)return this}return a||o?(a.lng=Math.min(e.lng,a.lng),a.lat=Math.min(e.lat,a.lat),o.lng=Math.max(r.lng,o.lng),o.lat=Math.max(r.lat,o.lat)):(this._sw=new i(e.lng,e.lat),this._ne=new i(r.lng,r.lat)),this},getCenter:function(){return new i((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},getSouthWest:function(){return this._sw},getNorthEast:function(){return this._ne},getNorthWest:function(){return new i(this.getWest(),this.getNorth())},getSouthEast:function(){return new i(this.getEast(),this.getSouth())},getWest:function(){return this._sw.lng},getSouth:function(){return this._sw.lat},getEast:function(){return this._ne.lng},getNorth:function(){return this._ne.lat},toArray:function(){return[this._sw.toArray(),this._ne.toArray()]},toString:function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"}},n.convert=function(t){return!t||t instanceof n?t:new n(t)}},{\"./lng_lat\":339}],341:[function(t,e,r){\"use strict\";function n(t,e){this.tileSize=512,this._minZoom=t||0,this._maxZoom=e||22,this.latRange=[-85.05113,85.05113],this.width=0,this.height=0,this._center=new i(0,0),this.zoom=0,this.angle=0,this._altitude=1.5,this._pitch=0,this._unmodified=!0}var i=t(\"./lng_lat\"),a=t(\"point-geometry\"),o=t(\"./coordinate\"),s=t(\"../util/util\").wrap,l=t(\"../util/interpolate\"),u=t(\"../source/tile_coord\"),c=t(\"../data/bucket\").EXTENT,h=t(\"gl-matrix\"),f=h.vec4,d=h.mat4,p=h.mat2;e.exports=n,n.prototype={get minZoom(){return this._minZoom},set minZoom(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},get maxZoom(){return this._maxZoom},set maxZoom(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},get worldSize(){return this.tileSize*this.scale},get centerPoint(){return this.size._div(2)},get size(){return new a(this.width,this.height)},get bearing(){return-this.angle/Math.PI*180},set bearing(t){var e=-s(t,-180,180)*Math.PI/180;this.angle!==e&&(this._unmodified=!1,this.angle=e,this._calcMatrices(),this.rotationMatrix=p.create(),p.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},get pitch(){return this._pitch/Math.PI*180},set pitch(t){var e=Math.min(60,t)/180*Math.PI;this._pitch!==e&&(this._unmodified=!1,this._pitch=e,this._calcMatrices())},get altitude(){return this._altitude},set altitude(t){var e=Math.max(.75,t);this._altitude!==e&&(this._unmodified=!1,this._altitude=e,this._calcMatrices())},get zoom(){return this._zoom},set zoom(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._calcMatrices(),this._constrain())},get center(){return this._center},set center(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._calcMatrices(),this._constrain())},coveringZoomLevel:function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},coveringTiles:function(t){var e=this.coveringZoomLevel(t),r=e;if(e<t.minzoom)return[];e>t.maxzoom&&(e=t.maxzoom);var n=this,i=n.locationCoordinate(n.center)._zoomTo(e),o=new a(i.column-.5,i.row-.5);return u.cover(e,[n.pointCoordinate(new a(0,0))._zoomTo(e),n.pointCoordinate(new a(n.width,0))._zoomTo(e),n.pointCoordinate(new a(n.width,n.height))._zoomTo(e),n.pointCoordinate(new a(0,n.height))._zoomTo(e)],t.reparseOverscaled?r:e).sort(function(t,e){return o.dist(t)-o.dist(e)})},resize:function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._calcMatrices(),this._constrain()},get unmodified(){return this._unmodified},zoomScale:function(t){return Math.pow(2,t)},scaleZoom:function(t){return Math.log(t)/Math.LN2},project:function(t,e){return new a(this.lngX(t.lng,e),this.latY(t.lat,e))},unproject:function(t,e){return new i(this.xLng(t.x,e),this.yLat(t.y,e))},get x(){return this.lngX(this.center.lng)},get y(){return this.latY(this.center.lat)},get point(){return new a(this.x,this.y)},lngX:function(t,e){return(180+t)*(e||this.worldSize)/360},latY:function(t,e){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))*(e||this.worldSize)/360},xLng:function(t,e){return 360*t/(e||this.worldSize)-180},yLat:function(t,e){var r=180-360*t/(e||this.worldSize);return 360/Math.PI*Math.atan(Math.exp(r*Math.PI/180))-90},panBy:function(t){var e=this.centerPoint._add(t);this.center=this.pointLocation(e)},setLocationAtPoint:function(t,e){var r=this.locationCoordinate(t),n=this.pointCoordinate(e),i=this.pointCoordinate(this.centerPoint),a=n._sub(r);this._unmodified=!1,this.center=this.coordinateLocation(i._sub(a))},locationPoint:function(t){return this.coordinatePoint(this.locationCoordinate(t))},pointLocation:function(t){return this.coordinateLocation(this.pointCoordinate(t))},locationCoordinate:function(t){var e=this.zoomScale(this.tileZoom)/this.worldSize,r=i.convert(t);return new o(this.lngX(r.lng)*e,this.latY(r.lat)*e,this.tileZoom)},coordinateLocation:function(t){var e=this.zoomScale(t.zoom);return new i(this.xLng(t.column,e),this.yLat(t.row,e))},pointCoordinate:function(t){var e=[t.x,t.y,0,1],r=[t.x,t.y,1,1];f.transformMat4(e,e,this.pixelMatrixInverse),f.transformMat4(r,r,this.pixelMatrixInverse);var n=e[3],i=r[3],a=e[0]/n,s=r[0]/i,u=e[1]/n,c=r[1]/i,h=e[2]/n,d=r[2]/i,p=h===d?0:(0-h)/(d-h),m=this.worldSize/this.zoomScale(this.tileZoom);return new o(l(a,s,p)/m,l(u,c,p)/m,this.tileZoom)},coordinatePoint:function(t){var e=this.worldSize/this.zoomScale(t.zoom),r=[t.column*e,t.row*e,0,1];return f.transformMat4(r,r,this.pixelMatrix),new a(r[0]/r[3],r[1]/r[3])},calculatePosMatrix:function(t,e){void 0===e&&(e=1/0),t instanceof u&&(t=t.toCoordinate(e));var r=Math.min(t.zoom,e),n=this.worldSize/Math.pow(2,r),i=new Float64Array(16);return d.identity(i),d.translate(i,i,[t.column*n,t.row*n,0]),d.scale(i,i,[n/c,n/c,1]),d.multiply(i,this.projMatrix,i),new Float32Array(i)},_constrain:function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var t,e,r,n,i,o,s,l,u=this.size,c=this._unmodified;this.latRange&&(t=this.latY(this.latRange[1]),e=this.latY(this.latRange[0]),i=e-t<u.y?u.y/(e-t):0),this.lngRange&&(r=this.lngX(this.lngRange[0]),n=this.lngX(this.lngRange[1]),o=n-r<u.x?u.x/(n-r):0);var h=Math.max(o||0,i||0);if(h)return this.center=this.unproject(new a(o?(n+r)/2:this.x,i?(e+t)/2:this.y)),this.zoom+=this.scaleZoom(h),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var f=this.y,d=u.y/2;f-d<t&&(l=t+d),f+d>e&&(l=e-d)}if(this.lngRange){var p=this.x,m=u.x/2;p-m<r&&(s=r+m),p+m>n&&(s=n-m)}void 0===s&&void 0===l||(this.center=this.unproject(new a(void 0!==s?s:this.x,void 0!==l?l:this.y))),this._unmodified=c,this._constraining=!1}},_calcMatrices:function(){if(this.height){var t=Math.atan(.5/this.altitude),e=Math.sin(t)*this.altitude/Math.sin(Math.PI/2-this._pitch-t),r=Math.cos(Math.PI/2-this._pitch)*e+this.altitude,n=new Float64Array(16);if(d.perspective(n,2*Math.atan(this.height/2/this.altitude),this.width/this.height,.1,r),d.translate(n,n,[0,0,-this.altitude]),d.scale(n,n,[1,-1,1/this.height]),d.rotateX(n,n,this._pitch),d.rotateZ(n,n,this.angle),d.translate(n,n,[-this.x,-this.y,0]),this.projMatrix=n,n=d.create(),d.scale(n,n,[this.width/2,-this.height/2,1]),d.translate(n,n,[1,-1,0]),this.pixelMatrix=d.multiply(new Float64Array(16),n,this.projMatrix),!(n=d.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=n}}}},{\"../data/bucket\":329,\"../source/tile_coord\":369,\"../util/interpolate\":436,\"../util/util\":442,\"./coordinate\":338,\"./lng_lat\":339,\"gl-matrix\":193,\"point-geometry\":484}],342:[function(t,e,r){\"use strict\";var n={\" \":[16,[]],\"!\":[10,[5,21,5,7,-1,-1,5,2,4,1,5,0,6,1,5,2]],'\"':[16,[4,21,4,14,-1,-1,12,21,12,14]],\"#\":[21,[11,25,4,-7,-1,-1,17,25,10,-7,-1,-1,4,12,18,12,-1,-1,3,6,17,6]],$:[20,[8,25,8,-4,-1,-1,12,25,12,-4,-1,-1,17,18,15,20,12,21,8,21,5,20,3,18,3,16,4,14,5,13,7,12,13,10,15,9,16,8,17,6,17,3,15,1,12,0,8,0,5,1,3,3]],\"%\":[24,[21,21,3,0,-1,-1,8,21,10,19,10,17,9,15,7,14,5,14,3,16,3,18,4,20,6,21,8,21,10,20,13,19,16,19,19,20,21,21,-1,-1,17,7,15,6,14,4,14,2,16,0,18,0,20,1,21,3,21,5,19,7,17,7]],\"&\":[26,[23,12,23,13,22,14,21,14,20,13,19,11,17,6,15,3,13,1,11,0,7,0,5,1,4,2,3,4,3,6,4,8,5,9,12,13,13,14,14,16,14,18,13,20,11,21,9,20,8,18,8,16,9,13,11,10,16,3,18,1,20,0,22,0,23,1,23,2]],\"'\":[10,[5,19,4,20,5,21,6,20,6,18,5,16,4,15]],\"(\":[14,[11,25,9,23,7,20,5,16,4,11,4,7,5,2,7,-2,9,-5,11,-7]],\")\":[14,[3,25,5,23,7,20,9,16,10,11,10,7,9,2,7,-2,5,-5,3,-7]],\"*\":[16,[8,21,8,9,-1,-1,3,18,13,12,-1,-1,13,18,3,12]],\"+\":[26,[13,18,13,0,-1,-1,4,9,22,9]],\",\":[10,[6,1,5,0,4,1,5,2,6,1,6,-1,5,-3,4,-4]],\"-\":[26,[4,9,22,9]],\".\":[10,[5,2,4,1,5,0,6,1,5,2]],\"/\":[22,[20,25,2,-7]],0:[20,[9,21,6,20,4,17,3,12,3,9,4,4,6,1,9,0,11,0,14,1,16,4,17,9,17,12,16,17,14,20,11,21,9,21]],1:[20,[6,17,8,18,11,21,11,0]],2:[20,[4,16,4,17,5,19,6,20,8,21,12,21,14,20,15,19,16,17,16,15,15,13,13,10,3,0,17,0]],3:[20,[5,21,16,21,10,13,13,13,15,12,16,11,17,8,17,6,16,3,14,1,11,0,8,0,5,1,4,2,3,4]],4:[20,[13,21,3,7,18,7,-1,-1,13,21,13,0]],5:[20,[15,21,5,21,4,12,5,13,8,14,11,14,14,13,16,11,17,8,17,6,16,3,14,1,11,0,8,0,5,1,4,2,3,4]],6:[20,[16,18,15,20,12,21,10,21,7,20,5,17,4,12,4,7,5,3,7,1,10,0,11,0,14,1,16,3,17,6,17,7,16,10,14,12,11,13,10,13,7,12,5,10,4,7]],7:[20,[17,21,7,0,-1,-1,3,21,17,21]],8:[20,[8,21,5,20,4,18,4,16,5,14,7,13,11,12,14,11,16,9,17,7,17,4,16,2,15,1,12,0,8,0,5,1,4,2,3,4,3,7,4,9,6,11,9,12,13,13,15,14,16,16,16,18,15,20,12,21,8,21]],9:[20,[16,14,15,11,13,9,10,8,9,8,6,9,4,11,3,14,3,15,4,18,6,20,9,21,10,21,13,20,15,18,16,14,16,9,15,4,13,1,10,0,8,0,5,1,4,3]],\":\":[10,[5,14,4,13,5,12,6,13,5,14,-1,-1,5,2,4,1,5,0,6,1,5,2]],\";\":[10,[5,14,4,13,5,12,6,13,5,14,-1,-1,6,1,5,0,4,1,5,2,6,1,6,-1,5,-3,4,-4]],\"<\":[24,[20,18,4,9,20,0]],\"=\":[26,[4,12,22,12,-1,-1,4,6,22,6]],\">\":[24,[4,18,20,9,4,0]],\"?\":[18,[3,16,3,17,4,19,5,20,7,21,11,21,13,20,14,19,15,17,15,15,14,13,13,12,9,10,9,7,-1,-1,9,2,8,1,9,0,10,1,9,2]],\"@\":[27,[18,13,17,15,15,16,12,16,10,15,9,14,8,11,8,8,9,6,11,5,14,5,16,6,17,8,-1,-1,12,16,10,14,9,11,9,8,10,6,11,5,-1,-1,18,16,17,8,17,6,19,5,21,5,23,7,24,10,24,12,23,15,22,17,20,19,18,20,15,21,12,21,9,20,7,19,5,17,4,15,3,12,3,9,4,6,5,4,7,2,9,1,12,0,15,0,18,1,20,2,21,3,-1,-1,19,16,18,8,18,6,19,5]],A:[18,[9,21,1,0,-1,-1,9,21,17,0,-1,-1,4,7,14,7]],B:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,15,17,13,16,12,13,11,-1,-1,4,11,13,11,16,10,17,9,18,7,18,4,17,2,16,1,13,0,4,0]],C:[21,[18,16,17,18,15,20,13,21,9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5]],D:[21,[4,21,4,0,-1,-1,4,21,11,21,14,20,16,18,17,16,18,13,18,8,17,5,16,3,14,1,11,0,4,0]],E:[19,[4,21,4,0,-1,-1,4,21,17,21,-1,-1,4,11,12,11,-1,-1,4,0,17,0]],F:[18,[4,21,4,0,-1,-1,4,21,17,21,-1,-1,4,11,12,11]],G:[21,[18,16,17,18,15,20,13,21,9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,18,8,-1,-1,13,8,18,8]],H:[22,[4,21,4,0,-1,-1,18,21,18,0,-1,-1,4,11,18,11]],I:[8,[4,21,4,0]],J:[16,[12,21,12,5,11,2,10,1,8,0,6,0,4,1,3,2,2,5,2,7]],K:[21,[4,21,4,0,-1,-1,18,21,4,7,-1,-1,9,12,18,0]],L:[17,[4,21,4,0,-1,-1,4,0,16,0]],M:[24,[4,21,4,0,-1,-1,4,21,12,0,-1,-1,20,21,12,0,-1,-1,20,21,20,0]],N:[22,[4,21,4,0,-1,-1,4,21,18,0,-1,-1,18,21,18,0]],O:[22,[9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,19,8,19,13,18,16,17,18,15,20,13,21,9,21]],P:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,14,17,12,16,11,13,10,4,10]],Q:[22,[9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,19,8,19,13,18,16,17,18,15,20,13,21,9,21,-1,-1,12,4,18,-2]],R:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,15,17,13,16,12,13,11,4,11,-1,-1,11,11,18,0]],S:[20,[17,18,15,20,12,21,8,21,5,20,3,18,3,16,4,14,5,13,7,12,13,10,15,9,16,8,17,6,17,3,15,1,12,0,8,0,5,1,3,3]],T:[16,[8,21,8,0,-1,-1,1,21,15,21]],U:[22,[4,21,4,6,5,3,7,1,10,0,12,0,15,1,17,3,18,6,18,21]],V:[18,[1,21,9,0,-1,-1,17,21,9,0]],W:[24,[2,21,7,0,-1,-1,12,21,7,0,-1,-1,12,21,17,0,-1,-1,22,21,17,0]],X:[20,[3,21,17,0,-1,-1,17,21,3,0]],Y:[18,[1,21,9,11,9,0,-1,-1,17,21,9,11]],Z:[20,[17,21,3,0,-1,-1,3,21,17,21,-1,-1,3,0,17,0]],\"[\":[14,[4,25,4,-7,-1,-1,5,25,5,-7,-1,-1,4,25,11,25,-1,-1,4,-7,11,-7]],\"\\\\\":[14,[0,21,14,-3]],\"]\":[14,[9,25,9,-7,-1,-1,10,25,10,-7,-1,-1,3,25,10,25,-1,-1,3,-7,10,-7]],\"^\":[16,[6,15,8,18,10,15,-1,-1,3,12,8,17,13,12,-1,-1,8,17,8,0]],_:[16,[0,-2,16,-2]],\"`\":[10,[6,21,5,20,4,18,4,16,5,15,6,16,5,17]],a:[19,[15,14,15,0,-1,-1,15,11,13,13,11,14,8,14,6,13,4,11,3,8,3,6,4,3,6,1,8,0,11,0,13,1,15,3]],b:[19,[4,21,4,0,-1,-1,4,11,6,13,8,14,11,14,13,13,15,11,16,8,16,6,15,3,13,1,11,0,8,0,6,1,4,3]],c:[18,[15,11,13,13,11,14,8,14,6,13,4,11,3,8,3,6,4,3,6,1,8,0,11,0,13,1,15,3]],d:[19,[15,21,15,0,-1,-1,15,11,13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a,o,s,l,u,c,h,f,d=[];for(a=0,o=t.length;a<o;a++)if(u=n[t[a]]){for(f=null,s=0,l=u[1].length;s<l;s+=2)-1===u[1][s]&&-1===u[1][s+1]?f=null:(c=e+u[1][s]*i,h=r-u[1][s+1]*i,f&&d.push(f.x,f.y,c,h),f={x:c,y:h});e+=u[0]*i}return d}},{}],343:[function(t,e,r){\"use strict\";var n=e.exports={};n.version=t(\"../package.json\").version,n.Map=t(\"./ui/map\"),n.Control=t(\"./ui/control/control\"),n.Navigation=t(\"./ui/control/navigation\"),n.Geolocate=t(\"./ui/control/geolocate\"),n.Attribution=t(\"./ui/control/attribution\"),n.Popup=t(\"./ui/popup\"),n.Marker=t(\"./ui/marker\"),n.Style=t(\"./style/style\"),n.LngLat=t(\"./geo/lng_lat\"),n.LngLatBounds=t(\"./geo/lng_lat_bounds\"),n.Point=t(\"point-geometry\"),n.Evented=t(\"./util/evented\"),n.util=t(\"./util/util\"),n.supported=t(\"./util/browser\").supported;var i=t(\"./util/ajax\");n.util.getJSON=i.getJSON,n.util.getArrayBuffer=i.getArrayBuffer;var a=t(\"./util/config\");n.config=a,Object.defineProperty(n,\"accessToken\",{get:function(){return a.ACCESS_TOKEN},set:function(t){a.ACCESS_TOKEN=t}})},{\"../package.json\":444,\"./geo/lng_lat\":339,\"./geo/lng_lat_bounds\":340,\"./style/style\":378,\"./ui/control/attribution\":409,\"./ui/control/control\":410,\"./ui/control/geolocate\":411,\"./ui/control/navigation\":412,\"./ui/map\":421,\"./ui/marker\":422,\"./ui/popup\":423,\"./util/ajax\":425,\"./util/browser\":426,\"./util/config\":431,\"./util/evented\":434,\"./util/util\":442,\"point-geometry\":484}],344:[function(t,e,r){\"use strict\";var n=t(\"assert\");e.exports=function(t){for(var e={define:{},initialize:{}},r=0;r<t.length;r++){var i=t[r];n(\"u_\"===i.name.slice(0,2));var a=\"{precision} \"+(1===i.components?\"float\":\"vec\"+i.components);e.define[i.name.slice(2)]=\"uniform \"+a+\" \"+i.name+\";\\n\",e.initialize[i.name.slice(2)]=a+\" \"+i.name.slice(2)+\" = \"+i.name+\";\\n\"}return e}},{assert:47}],345:[function(t,e,r){\"use strict\";function n(t,e,r){var n,s=t.gl,l=t.transform,u=r.paint[\"background-color\"],c=r.paint[\"background-pattern\"],h=r.paint[\"background-opacity\"],f=c?t.spriteAtlas.getPosition(c.from,!0):null,d=c?t.spriteAtlas.getPosition(c.to,!0):null;if(t.setDepthSublayer(0),f&&d){if(t.isOpaquePass)return;n=t.useProgram(\"pattern\"),s.uniform1i(n.u_image,0),s.uniform2fv(n.u_pattern_tl_a,f.tl),s.uniform2fv(n.u_pattern_br_a,f.br),s.uniform2fv(n.u_pattern_tl_b,d.tl),s.uniform2fv(n.u_pattern_br_b,d.br),s.uniform1f(n.u_opacity,h),s.uniform1f(n.u_mix,c.t),s.uniform2fv(n.u_pattern_size_a,f.size),s.uniform2fv(n.u_pattern_size_b,d.size),s.uniform1f(n.u_scale_a,c.fromScale),s.uniform1f(n.u_scale_b,c.toScale),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),t.tileExtentPatternVAO.bind(s,n,t.tileExtentBuffer)}else{if(t.isOpaquePass!==(1===u[3]))return;var p=a([{name:\"u_color\",components:4},{name:\"u_opacity\",components:1}]);n=t.useProgram(\"fill\",[],p,p),s.uniform4fv(n.u_color,u),s.uniform1f(n.u_opacity,h),t.tileExtentVAO.bind(s,n,t.tileExtentBuffer)}s.disable(s.STENCIL_TEST);for(var m=l.coveringTiles({tileSize:o}),v=0;v<m.length;v++){var g=m[v];if(f&&d){var y={coord:g,tileSize:o};s.uniform1f(n.u_tile_units_to_pixels,1/i(y,1,t.transform.tileZoom));var b=y.tileSize*Math.pow(2,t.transform.tileZoom-y.coord.z),x=b*(y.coord.x+g.w*Math.pow(2,y.coord.z)),_=b*y.coord.y;s.uniform2f(n.u_pixel_coord_upper,x>>16,_>>16),s.uniform2f(n.u_pixel_coord_lower,65535&x,65535&_)}s.uniformMatrix4fv(n.u_matrix,!1,t.transform.calculatePosMatrix(g)),s.drawArrays(s.TRIANGLE_STRIP,0,t.tileExtentBuffer.length)}s.stencilMask(0),s.stencilFunc(s.EQUAL,128,128)}var i=t(\"../source/pixels_to_tile_units\"),a=t(\"./create_uniform_pragmas\"),o=512;e.exports=n},{\"../source/pixels_to_tile_units\":363,\"./create_uniform_pragmas\":344}],346:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=t.gl;t.setDepthSublayer(0),t.depthMask(!1),a.disable(a.STENCIL_TEST);for(var o=0;o<n.length;o++){var s=n[o],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.circle;if(c){var h=u.paintAttributes.circle[r.id],f=t.useProgram(\"circle\",h.defines,h.vertexPragmas,h.fragmentPragmas);\"map\"===r.paint[\"circle-pitch-scale\"]?(a.uniform1i(f.u_scale_with_map,!0),a.uniform2f(f.u_extrude_scale,t.transform.pixelsToGLUnits[0]*t.transform.altitude,t.transform.pixelsToGLUnits[1]*t.transform.altitude)):(a.uniform1i(f.u_scale_with_map,!1),a.uniform2fv(f.u_extrude_scale,t.transform.pixelsToGLUnits)),a.uniform1f(f.u_devicepixelratio,i.devicePixelRatio),a.uniformMatrix4fv(f.u_matrix,!1,t.translatePosMatrix(s.posMatrix,l,r.paint[\"circle-translate\"],r.paint[\"circle-translate-anchor\"])),u.setUniforms(a,\"circle\",f,r,{zoom:t.transform.zoom});for(var d=0;d<c.length;d++){var p=c[d];p.vaos[r.id].bind(a,f,p.layoutVertexBuffer,p.elementBuffer,p.paintVertexBuffers[r.id]),a.drawElements(a.TRIANGLES,3*p.elementBuffer.length,a.UNSIGNED_SHORT,0)}}}}}}var i=t(\"../util/browser\");e.exports=n},{\"../util/browser\":426}],347:[function(t,e,r){\"use strict\";function n(t,e,r,n){var i=t.gl;i.enable(i.STENCIL_TEST);for(var a=t.useProgram(\"collisionbox\"),o=0;o<n.length;o++){var s=n[o],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.collisionBox;if(c&&c.length){var h=c[0];0!==h.layoutVertexBuffer.length&&(i.uniformMatrix4fv(a.u_matrix,!1,s.posMatrix),t.enableTileClippingMask(s),t.lineWidth(1),i.uniform1f(a.u_scale,Math.pow(2,t.transform.zoom-l.coord.z)),i.uniform1f(a.u_zoom,10*t.transform.zoom),i.uniform1f(a.u_maxzoom,10*(l.coord.z+1)),h.vaos[r.id].bind(i,a,h.layoutVertexBuffer),i.drawArrays(i.LINES,0,h.layoutVertexBuffer.length))}}}}e.exports=n},{}],348:[function(t,e,r){\"use strict\";function n(t,e,r){if(!t.isOpaquePass&&t.options.debug)for(var n=0;n<r.length;n++)i(t,e,r[n])}function i(t,e,r){var n=t.gl;n.disable(n.STENCIL_TEST),t.lineWidth(1*o.devicePixelRatio);var i=r.posMatrix,h=t.useProgram(\"debug\");n.uniformMatrix4fv(h.u_matrix,!1,i),n.uniform4f(h.u_color,1,0,0,1),t.debugVAO.bind(n,h,t.debugBuffer),n.drawArrays(n.LINE_STRIP,0,t.debugBuffer.length);for(var f=a(r.toString(),50,200,5),d=new t.PosArray,p=0;p<f.length;p+=2)d.emplaceBack(f[p],f[p+1]);var m=new u(d.serialize(),t.PosArray.serialize(),u.BufferType.VERTEX);(new c).bind(n,h,m),n.uniform4f(h.u_color,1,1,1,1);for(var v=e.getTile(r).tileSize,g=l/(Math.pow(2,t.transform.zoom-r.z)*v),y=[[-1,-1],[-1,1],[1,-1],[1,1]],b=0;b<y.length;b++){var x=y[b];n.uniformMatrix4fv(h.u_matrix,!1,s.translate([],i,[g*x[0],g*x[1],0])),n.drawArrays(n.LINES,0,m.length)}n.uniform4f(h.u_color,0,0,0,1),n.uniformMatrix4fv(h.u_matrix,!1,i),n.drawArrays(n.LINES,0,m.length)}var a=t(\"../lib/debugtext\"),o=t(\"../util/browser\"),s=t(\"gl-matrix\").mat4,l=t(\"../data/bucket\").EXTENT,u=t(\"../data/buffer\"),c=t(\"./vertex_array_object\");e.exports=n},{\"../data/bucket\":329,\"../data/buffer\":334,\"../lib/debugtext\":342,\"../util/browser\":426,\"./vertex_array_object\":357,\"gl-matrix\":193}],349:[function(t,e,r){\"use strict\";function n(t,e,r,n){var o=t.gl;o.enable(o.STENCIL_TEST);var s;if(s=!r.paint[\"fill-pattern\"]&&(r.isPaintValueFeatureConstant(\"fill-color\")&&r.isPaintValueFeatureConstant(\"fill-opacity\")&&1===r.paint[\"fill-color\"][3]&&1===r.paint[\"fill-opacity\"]),t.isOpaquePass===s){t.setDepthSublayer(1);for(var l=0;l<n.length;l++)i(t,e,r,n[l])}if(!t.isOpaquePass&&r.paint[\"fill-antialias\"]){t.lineWidth(2),t.depthMask(!1);var u=r.getPaintProperty(\"fill-outline-color\");(u||!r.paint[\"fill-pattern\"])&&u?t.setDepthSublayer(2):t.setDepthSublayer(0);for(var c=0;c<n.length;c++)a(t,e,r,n[c])}}function i(t,e,r,n){var i=e.getTile(n),a=i.getBucket(r);if(a){var s=a.bufferGroups.fill;if(s){var l,u=t.gl,c=r.paint[\"fill-pattern\"];if(c)l=t.useProgram(\"pattern\"),o(c,r.paint[\"fill-opacity\"],i,n,t,l),u.activeTexture(u.TEXTURE0),t.spriteAtlas.bind(u,!0);else{var h=a.paintAttributes.fill[r.id];l=t.useProgram(\"fill\",h.defines,h.vertexPragmas,h.fragmentPragmas),a.setUniforms(u,\"fill\",l,r,{zoom:t.transform.zoom})}u.uniformMatrix4fv(l.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint[\"fill-translate\"],r.paint[\"fill-translate-anchor\"])),t.enableTileClippingMask(n);for(var f=0;f<s.length;f++){var d=s[f];d.vaos[r.id].bind(u,l,d.layoutVertexBuffer,d.elementBuffer,d.paintVertexBuffers[r.id]),u.drawElements(u.TRIANGLES,d.elementBuffer.length,u.UNSIGNED_SHORT,0)}}}}function a(t,e,r,n){var i=e.getTile(n),a=i.getBucket(r);if(a){var s,l=t.gl,u=a.bufferGroups.fill,c=r.paint[\"fill-pattern\"],h=r.paint[\"fill-opacity\"],f=r.getPaintProperty(\"fill-outline-color\");if(c&&!f)s=t.useProgram(\"outlinepattern\"),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight);else{var d=a.paintAttributes.fill[r.id];s=t.useProgram(\"outline\",d.defines,d.vertexPragmas,d.fragmentPragmas),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight),l.uniform1f(s.u_opacity,h),a.setUniforms(l,\"fill\",s,r,{zoom:t.transform.zoom})}l.uniformMatrix4fv(s.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint[\"fill-translate\"],r.paint[\"fill-translate-anchor\"])),c&&o(c,h,i,n,t,s),t.enableTileClippingMask(n);for(var p=0;p<u.length;p++){var m=u[p];m.secondVaos[r.id].bind(l,s,m.layoutVertexBuffer,m.elementBuffer2,m.paintVertexBuffers[r.id]),l.drawElements(l.LINES,2*m.elementBuffer2.length,l.UNSIGNED_SHORT,0)}}}function o(t,e,r,n,i,a){var o=i.gl,l=i.spriteAtlas.getPosition(t.from,!0),u=i.spriteAtlas.getPosition(t.to,!0);if(l&&u){o.uniform1i(a.u_image,0),o.uniform2fv(a.u_pattern_tl_a,l.tl),o.uniform2fv(a.u_pattern_br_a,l.br),o.uniform2fv(a.u_pattern_tl_b,u.tl),o.uniform2fv(a.u_pattern_br_b,u.br),o.uniform1f(a.u_opacity,e),o.uniform1f(a.u_mix,t.t),o.uniform1f(a.u_tile_units_to_pixels,1/s(r,1,i.transform.tileZoom)),o.uniform2fv(a.u_pattern_size_a,l.size),o.uniform2fv(a.u_pattern_size_b,u.size),o.uniform1f(a.u_scale_a,t.fromScale),o.uniform1f(a.u_scale_b,t.toScale);var c=r.tileSize*Math.pow(2,i.transform.tileZoom-r.coord.z),h=c*(r.coord.x+n.w*Math.pow(2,r.coord.z)),f=c*r.coord.y;o.uniform2f(a.u_pixel_coord_upper,h>>16,f>>16),o.uniform2f(a.u_pixel_coord_lower,65535&h,65535&f),o.activeTexture(o.TEXTURE0),i.spriteAtlas.bind(o,!0)}}var s=t(\"../source/pixels_to_tile_units\");e.exports=n},{\"../source/pixels_to_tile_units\":363}],350:[function(t,e,r){\"use strict\";var n=t(\"../util/browser\"),i=t(\"gl-matrix\").mat2,a=t(\"../source/pixels_to_tile_units\");e.exports=function(t,e,r,o){if(!t.isOpaquePass){t.setDepthSublayer(0),t.depthMask(!1);var s=t.gl;if(s.enable(s.STENCIL_TEST),!(r.paint[\"line-width\"]<=0)){var l=1/n.devicePixelRatio,u=r.paint[\"line-blur\"]+l,c=r.paint[\"line-color\"],h=t.transform,f=i.create();i.scale(f,f,[1,Math.cos(h._pitch)]),i.rotate(f,f,t.transform.angle);var d,p,m,v,g,y=Math.sqrt(h.height*h.height/4*(1+h.altitude*h.altitude)),b=h.height/2*Math.tan(h._pitch),x=(y+b)/y-1,_=r.paint[\"line-dasharray\"],w=r.paint[\"line-pattern\"];if(_)d=t.useProgram(\"linesdfpattern\"),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform4fv(d.u_color,c),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]),p=t.lineAtlas.getDash(_.from,\"round\"===r.layout[\"line-cap\"]),m=t.lineAtlas.getDash(_.to,\"round\"===r.layout[\"line-cap\"]),s.uniform1i(d.u_image,0),s.activeTexture(s.TEXTURE0),t.lineAtlas.bind(s),s.uniform1f(d.u_tex_y_a,p.y),s.uniform1f(d.u_tex_y_b,m.y),s.uniform1f(d.u_mix,_.t),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f);else if(w){if(v=t.spriteAtlas.getPosition(w.from,!0),g=t.spriteAtlas.getPosition(w.to,!0),!v||!g)return;d=t.useProgram(\"linepattern\"),s.uniform1i(d.u_image,0),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform2fv(d.u_pattern_tl_a,v.tl),s.uniform2fv(d.u_pattern_br_a,v.br),s.uniform2fv(d.u_pattern_tl_b,g.tl),s.uniform2fv(d.u_pattern_br_b,g.br),s.uniform1f(d.u_fade,w.t),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f)}else d=t.useProgram(\"line\"),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f),s.uniform4fv(d.u_color,c),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]);for(var M=0;M<o.length;M++){var k=o[M],A=e.getTile(k),T=A.getBucket(r);if(T){var S=T.bufferGroups.line;if(S){t.enableTileClippingMask(k);var E=t.translatePosMatrix(k.posMatrix,A,r.paint[\"line-translate\"],r.paint[\"line-translate-anchor\"]);s.uniformMatrix4fv(d.u_matrix,!1,E);var L=1/a(A,1,t.transform.zoom);if(_){var C=p.width*_.fromScale,I=m.width*_.toScale,z=[1/a(A,C,t.transform.tileZoom),-p.height/2],D=[1/a(A,I,t.transform.tileZoom),-m.height/2],P=t.lineAtlas.width/(256*Math.min(C,I)*n.devicePixelRatio)/2;s.uniform1f(d.u_ratio,L),s.uniform2fv(d.u_patternscale_a,z),s.uniform2fv(d.u_patternscale_b,D),s.uniform1f(d.u_sdfgamma,P)}else w?(s.uniform1f(d.u_ratio,L),s.uniform2fv(d.u_pattern_size_a,[a(A,v.size[0]*w.fromScale,t.transform.tileZoom),g.size[1]]),s.uniform2fv(d.u_pattern_size_b,[a(A,g.size[0]*w.toScale,t.transform.tileZoom),g.size[1]])):s.uniform1f(d.u_ratio,L);for(var O=0;O<S.length;O++){var R=S[O];R.vaos[r.id].bind(s,d,R.layoutVertexBuffer,R.elementBuffer),s.drawElements(s.TRIANGLES,3*R.elementBuffer.length,s.UNSIGNED_SHORT,0)}}}}}}}},{\"../source/pixels_to_tile_units\":363,\"../util/browser\":426,\"gl-matrix\":193}],351:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=t.gl;a.enable(a.DEPTH_TEST),t.depthMask(!0),a.depthFunc(a.LESS);for(var o=n.length&&n[0].z,s=0;s<n.length;s++){var l=n[s];t.setDepthSublayer(l.z-o),i(t,e,r,l)}a.depthFunc(a.LEQUAL)}}function i(t,e,r,n){var i=t.gl;i.disable(i.STENCIL_TEST);var u=e.getTile(n),c=t.transform.calculatePosMatrix(n,e.maxzoom),h=t.useProgram(\"raster\");i.uniformMatrix4fv(h.u_matrix,!1,c),i.uniform1f(h.u_brightness_low,r.paint[\"raster-brightness-min\"]),i.uniform1f(h.u_brightness_high,r.paint[\"raster-brightness-max\"]),i.uniform1f(h.u_saturation_factor,s(r.paint[\"raster-saturation\"])),i.uniform1f(h.u_contrast_factor,o(r.paint[\"raster-contrast\"])),i.uniform3fv(h.u_spin_weights,a(r.paint[\"raster-hue-rotate\"]));var f,d,p=u.source&&u.source.findLoadedParent(n,0,{}),m=l(u,p,r,t.transform);i.activeTexture(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,u.texture),i.activeTexture(i.TEXTURE1),p?(i.bindTexture(i.TEXTURE_2D,p.texture),f=Math.pow(2,p.coord.z-u.coord.z),d=[u.coord.x*f%1,u.coord.y*f%1]):(i.bindTexture(i.TEXTURE_2D,u.texture),m[1]=0),i.uniform2fv(h.u_tl_parent,d||[0,0]),i.uniform1f(h.u_scale_parent,f||1),i.uniform1f(h.u_buffer_scale,1),i.uniform1f(h.u_opacity0,m[0]),i.uniform1f(h.u_opacity1,m[1]),i.uniform1i(h.u_image0,0),i.uniform1i(h.u_image1,1);var v=u.boundsBuffer||t.rasterBoundsBuffer;(u.boundsVAO||t.rasterBoundsVAO).bind(i,h,v),i.drawArrays(i.TRIANGLE_STRIP,0,v.length)}function a(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}function o(t){return t>0?1/(1-t):1+t}function s(t){return t>0?1-1/(1.001-t):-t}function l(t,e,r,n){var i=[1,0],a=r.paint[\"raster-fade-duration\"];if(t.source&&a>0){var o=(new Date).getTime(),s=(o-t.timeAdded)/a,l=e?(o-e.timeAdded)/a:-1,c=n.coveringZoomLevel(t.source),h=!!e&&Math.abs(e.coord.z-c)>Math.abs(t.coord.z-c);!e||h?(i[0]=u.clamp(s,0,1),i[1]=1-i[0]):(i[0]=u.clamp(1-l,0,1),i[1]=1-i[0])}var f=r.paint[\"raster-opacity\"];return i[0]*=f,i[1]*=f,i}var u=t(\"../util/util\"),c=t(\"../util/struct_array\");e.exports=n,n.RasterBoundsArray=new c({members:[{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]})},{\"../util/struct_array\":440,\"../util/util\":442}],352:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=!(r.layout[\"text-allow-overlap\"]||r.layout[\"icon-allow-overlap\"]||r.layout[\"text-ignore-placement\"]||r.layout[\"icon-ignore-placement\"]),o=t.gl;a?o.disable(o.STENCIL_TEST):o.enable(o.STENCIL_TEST),t.setDepthSublayer(0),t.depthMask(!1),o.disable(o.DEPTH_TEST),i(t,e,r,n,!1,r.paint[\"icon-translate\"],r.paint[\"icon-translate-anchor\"],r.layout[\"icon-rotation-alignment\"],r.layout[\"icon-rotation-alignment\"],r.layout[\"icon-size\"],r.paint[\"icon-halo-width\"],r.paint[\"icon-halo-color\"],r.paint[\"icon-halo-blur\"],r.paint[\"icon-opacity\"],r.paint[\"icon-color\"]),\n",
+       "i(t,e,r,n,!0,r.paint[\"text-translate\"],r.paint[\"text-translate-anchor\"],r.layout[\"text-rotation-alignment\"],r.layout[\"text-pitch-alignment\"],r.layout[\"text-size\"],r.paint[\"text-halo-width\"],r.paint[\"text-halo-color\"],r.paint[\"text-halo-blur\"],r.paint[\"text-opacity\"],r.paint[\"text-color\"]),o.enable(o.DEPTH_TEST),e.map.showCollisionBoxes&&s(t,e,r,n)}}function i(t,e,r,n,i,o,s,l,u,c,h,f,d,p,m){for(var v=0;v<n.length;v++){var g=e.getTile(n[v]),y=g.getBucket(r);if(y){var b=y.bufferGroups,x=i?b.glyph:b.icon;x.length&&(t.enableTileClippingMask(n[v]),a(t,r,n[v].posMatrix,g,y,x,i,i||y.sdfIcons,!i&&y.iconsNeedLinear,i?y.adjustedTextSize:y.adjustedIconSize,y.fontstack,o,s,l,u,c,h,f,d,p,m))}}}function a(t,e,r,n,i,a,s,u,c,h,f,d,p,m,v,g,y,b,x,_,w){var M,k,A,T=t.gl,S=t.transform,E=\"map\"===m,L=\"map\"===v,C=s?24:1,I=g/C;if(L?(k=l(n,1,t.transform.zoom)*I,A=1/Math.cos(S._pitch),M=[k,k]):(k=t.transform.altitude*I,A=1,M=[S.pixelsToGLUnits[0]*k,S.pixelsToGLUnits[1]*k]),s||t.style.sprite.loaded()){var z=t.useProgram(u?\"sdf\":\"icon\");if(T.uniformMatrix4fv(z.u_matrix,!1,t.translatePosMatrix(r,n,d,p)),T.uniform1i(z.u_rotate_with_map,E),T.uniform1i(z.u_pitch_with_map,L),T.uniform2fv(z.u_extrude_scale,M),T.activeTexture(T.TEXTURE0),T.uniform1i(z.u_texture,0),s){var D=f&&t.glyphSource.getGlyphAtlas(f);if(!D)return;D.updateTexture(T),T.uniform2f(z.u_texsize,D.width/4,D.height/4)}else{var P=t.options.rotating||t.options.zooming,O=1!==I||o.devicePixelRatio!==t.spriteAtlas.pixelRatio||c,R=L||t.transform.pitch;t.spriteAtlas.bind(T,u||P||O||R),T.uniform2f(z.u_texsize,t.spriteAtlas.width/4,t.spriteAtlas.height/4)}var F=Math.log(g/h)/Math.LN2||0;T.uniform1f(z.u_zoom,10*(t.transform.zoom-F)),T.activeTexture(T.TEXTURE1),t.frameHistory.bind(T),T.uniform1i(z.u_fadetexture,1);var j;if(u){var N=.105*C/g/o.devicePixelRatio;if(y){T.uniform1f(z.u_gamma,(1.19*x/I/8+N)*A),T.uniform4fv(z.u_color,b),T.uniform1f(z.u_opacity,_),T.uniform1f(z.u_buffer,(6-y/I)/8);for(var B=0;B<a.length;B++)j=a[B],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3*j.elementBuffer.length,T.UNSIGNED_SHORT,0)}T.uniform1f(z.u_gamma,N*A),T.uniform4fv(z.u_color,w),T.uniform1f(z.u_opacity,_),T.uniform1f(z.u_buffer,.75),T.uniform1f(z.u_pitch,S.pitch/360*2*Math.PI),T.uniform1f(z.u_bearing,S.bearing/360*2*Math.PI),T.uniform1f(z.u_aspect_ratio,S.width/S.height);for(var U=0;U<a.length;U++)j=a[U],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3*j.elementBuffer.length,T.UNSIGNED_SHORT,0)}else{T.uniform1f(z.u_opacity,_);for(var V=0;V<a.length;V++)j=a[V],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3*j.elementBuffer.length,T.UNSIGNED_SHORT,0)}}}var o=t(\"../util/browser\"),s=t(\"./draw_collision_debug\"),l=t(\"../source/pixels_to_tile_units\");e.exports=n},{\"../source/pixels_to_tile_units\":363,\"../util/browser\":426,\"./draw_collision_debug\":347}],353:[function(t,e,r){\"use strict\";function n(){this.changeTimes=new Float64Array(256),this.changeOpacities=new Uint8Array(256),this.opacities=new Uint8ClampedArray(256),this.array=new Uint8Array(this.opacities.buffer),this.fadeDuration=300,this.previousZoom=0,this.firstFrame=!0}e.exports=n,n.prototype.record=function(t){var e=Date.now();this.firstFrame&&(e=0,this.firstFrame=!1),t=Math.floor(10*t);var r;if(t<this.previousZoom)for(r=t+1;r<=this.previousZoom;r++)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];else for(r=t;r>this.previousZoom;r--)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];for(r=0;r<256;r++){var n=e-this.changeTimes[r],i=n/this.fadeDuration*255;this.opacities[r]=r<=t?this.changeOpacities[r]+i:this.changeOpacities[r]-i}this.changed=!0,this.previousZoom=t},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.changed&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,256,1,t.ALPHA,t.UNSIGNED_BYTE,this.array),this.changed=!1)):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,256,1,0,t.ALPHA,t.UNSIGNED_BYTE,this.array))}},{}],354:[function(t,e,r){\"use strict\";function n(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.width*this.height*this.bytes),this.positions={}}var i=t(\"../util/util\");e.exports=n,n.prototype.setSprite=function(t){this.sprite=t},n.prototype.getDash=function(t,e){var r=t.join(\",\")+e;return this.positions[r]||(this.positions[r]=this.addDash(t,e)),this.positions[r]},n.prototype.addDash=function(t,e){var r=e?7:0,n=2*r+1;if(this.nextRow+n>this.height)return i.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<t.length;o++)a+=t[o];for(var s=this.width/a,l=s/2,u=t.length%2==1,c=-r;c<=r;c++)for(var h=this.nextRow+r+c,f=this.width*h,d=u?-t[t.length-1]:0,p=t[0],m=1,v=0;v<this.width;v++){for(;p<v/s;)d=p,p+=t[m],u&&m===t.length-1&&(p+=t[0]),m++;var g,y=Math.abs(v-d*s),b=Math.abs(v-p*s),x=Math.min(y,b),_=m%2==1;if(e){var w=r?c/r*(l+1):0;if(_){var M=l-Math.abs(w);g=Math.sqrt(x*x+M*M)}else g=l-Math.sqrt(x*x+w*w)}else g=(_?1:-1)*x;this.data[3+4*(f+v)]=Math.max(0,Math.min(255,g+128))}var k={y:(this.nextRow+r+.5)/this.height,height:2*r/this.height,width:a};return this.nextRow+=n,this.dirty=!0,k},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.RGBA,t.UNSIGNED_BYTE,this.data))):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width,this.height,0,t.RGBA,t.UNSIGNED_BYTE,this.data))}},{\"../util/util\":442}],355:[function(t,e,r){\"use strict\";function n(t,e){this.gl=t,this.transform=e,this.reusableTextures={},this.preFbos={},this.frameHistory=new o,this.setup(),this.numSublayers=s.maxUnderzooming+s.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.lineWidthRange=t.getParameter(t.ALIASED_LINE_WIDTH_RANGE)}var i=t(\"../util/browser\"),a=t(\"gl-matrix\").mat4,o=t(\"./frame_history\"),s=t(\"../source/source_cache\"),l=t(\"../data/bucket\").EXTENT,u=t(\"../source/pixels_to_tile_units\"),c=t(\"../util/util\"),h=t(\"../util/struct_array\"),f=t(\"../data/buffer\"),d=t(\"./vertex_array_object\"),p=t(\"./draw_raster\").RasterBoundsArray,m=t(\"./create_uniform_pragmas\");e.exports=n,c.extend(n.prototype,t(\"./painter/use_program\")),n.prototype.resize=function(t,e){var r=this.gl;this.width=t*i.devicePixelRatio,this.height=e*i.devicePixelRatio,r.viewport(0,0,this.width,this.height)},n.prototype.setup=function(){var t=this.gl;t.verbose=!0,t.enable(t.BLEND),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.enable(t.STENCIL_TEST),t.enable(t.DEPTH_TEST),t.depthFunc(t.LEQUAL),this._depthMask=!1,t.depthMask(!1);var e=this.PosArray=new h({members:[{name:\"a_pos\",type:\"Int16\",components:2}]}),r=new e;r.emplaceBack(0,0),r.emplaceBack(l,0),r.emplaceBack(0,l),r.emplaceBack(l,l),this.tileExtentBuffer=new f(r.serialize(),e.serialize(),f.BufferType.VERTEX),this.tileExtentVAO=new d,this.tileExtentPatternVAO=new d;var n=new e;n.emplaceBack(0,0),n.emplaceBack(l,0),n.emplaceBack(l,l),n.emplaceBack(0,l),n.emplaceBack(0,0),this.debugBuffer=new f(n.serialize(),e.serialize(),f.BufferType.VERTEX),this.debugVAO=new d;var i=new p;i.emplaceBack(0,0,0,0),i.emplaceBack(l,0,32767,0),i.emplaceBack(0,l,0,32767),i.emplaceBack(l,l,32767,32767),this.rasterBoundsBuffer=new f(i.serialize(),p.serialize(),f.BufferType.VERTEX),this.rasterBoundsVAO=new d},n.prototype.clearColor=function(){var t=this.gl;t.clearColor(0,0,0,0),t.clear(t.COLOR_BUFFER_BIT)},n.prototype.clearStencil=function(){var t=this.gl;t.clearStencil(0),t.stencilMask(255),t.clear(t.STENCIL_BUFFER_BIT)},n.prototype.clearDepth=function(){var t=this.gl;t.clearDepth(1),this.depthMask(!0),t.clear(t.DEPTH_BUFFER_BIT)},n.prototype._renderTileClippingMasks=function(t){var e=this.gl;e.colorMask(!1,!1,!1,!1),this.depthMask(!1),e.disable(e.DEPTH_TEST),e.enable(e.STENCIL_TEST),e.stencilMask(248),e.stencilOp(e.KEEP,e.KEEP,e.REPLACE);var r=1;this._tileClippingMaskIDs={};for(var n=0;n<t.length;n++){var i=t[n],a=this._tileClippingMaskIDs[i.id]=r++<<3;e.stencilFunc(e.ALWAYS,a,248);var o=m([{name:\"u_color\",components:4},{name:\"u_opacity\",components:1}]),s=this.useProgram(\"fill\",[],o,o);e.uniformMatrix4fv(s.u_matrix,!1,i.posMatrix),this.tileExtentVAO.bind(e,s,this.tileExtentBuffer),e.drawArrays(e.TRIANGLE_STRIP,0,this.tileExtentBuffer.length)}e.stencilMask(0),e.colorMask(!0,!0,!0,!0),this.depthMask(!0),e.enable(e.DEPTH_TEST)},n.prototype.enableTileClippingMask=function(t){var e=this.gl;e.stencilFunc(e.EQUAL,this._tileClippingMaskIDs[t.id],248)},n.prototype.prepareBuffers=function(){},n.prototype.bindDefaultFramebuffer=function(){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,null)};var v={symbol:t(\"./draw_symbol\"),circle:t(\"./draw_circle\"),line:t(\"./draw_line\"),fill:t(\"./draw_fill\"),raster:t(\"./draw_raster\"),background:t(\"./draw_background\"),debug:t(\"./draw_debug\")};n.prototype.render=function(t,e){this.style=t,this.options=e,this.lineAtlas=t.lineAtlas,this.spriteAtlas=t.spriteAtlas,this.spriteAtlas.setSprite(t.sprite),this.glyphSource=t.glyphSource,this.frameHistory.record(this.transform.zoom),this.prepareBuffers(),this.clearColor(),this.clearDepth(),this.showOverdrawInspector(e.showOverdrawInspector),this.depthRange=(t._order.length+2)*this.numSublayers*this.depthEpsilon,this.renderPass({isOpaquePass:!0}),this.renderPass({isOpaquePass:!1})},n.prototype.renderPass=function(t){var e=this.style._groups,r=t.isOpaquePass;this.currentLayer=r?this.style._order.length:-1;for(var n=0;n<e.length;n++){var i,a=e[r?e.length-1-n:n],o=this.style.sources[a.source],s=[];if(o){for(s=o.getVisibleCoordinates(),i=0;i<s.length;i++)s[i].posMatrix=this.transform.calculatePosMatrix(s[i],o.maxzoom);this.clearStencil(),o.prepare&&o.prepare(),o.isTileClipped&&this._renderTileClippingMasks(s)}for(r?(this._showOverdrawInspector||this.gl.disable(this.gl.BLEND),this.isOpaquePass=!0):(this.gl.enable(this.gl.BLEND),this.isOpaquePass=!1,s.reverse()),i=0;i<a.length;i++){var l=a[r?a.length-1-i:i];this.currentLayer+=r?-1:1,this.renderLayer(this,o,l,s)}o&&v.debug(this,o,s)}},n.prototype.depthMask=function(t){t!==this._depthMask&&(this._depthMask=t,this.gl.depthMask(t))},n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||n.length)&&(this.id=r.id,v[r.type](t,e,r,n))},n.prototype.setDepthSublayer=function(t){var e=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon,r=e-1+this.depthRange;this.gl.depthRange(r,e)},n.prototype.translatePosMatrix=function(t,e,r,n){if(!r[0]&&!r[1])return t;if(\"viewport\"===n){var i=Math.sin(-this.transform.angle),o=Math.cos(-this.transform.angle);r=[r[0]*o-r[1]*i,r[0]*i+r[1]*o]}var s=[u(e,r[0],this.transform.zoom),u(e,r[1],this.transform.zoom),0],l=new Float32Array(16);return a.translate(l,t,s),l},n.prototype.saveTexture=function(t){var e=this.reusableTextures[t.size];e?e.push(t):this.reusableTextures[t.size]=[t]},n.prototype.getTexture=function(t){var e=this.reusableTextures[t];return e&&e.length>0?e.pop():null},n.prototype.lineWidth=function(t){this.gl.lineWidth(c.clamp(t,this.lineWidthRange[0],this.lineWidthRange[1]))},n.prototype.showOverdrawInspector=function(t){if(t||this._showOverdrawInspector){this._showOverdrawInspector=t;var e=this.gl;if(t){e.blendFunc(e.CONSTANT_COLOR,e.ONE);e.blendColor(1/8,1/8,1/8,0),e.clearColor(0,0,0,1),e.clear(e.COLOR_BUFFER_BIT)}else e.blendFunc(e.ONE,e.ONE_MINUS_SRC_ALPHA)}}},{\"../data/bucket\":329,\"../data/buffer\":334,\"../source/pixels_to_tile_units\":363,\"../source/source_cache\":367,\"../util/browser\":426,\"../util/struct_array\":440,\"../util/util\":442,\"./create_uniform_pragmas\":344,\"./draw_background\":345,\"./draw_circle\":346,\"./draw_debug\":348,\"./draw_fill\":349,\"./draw_line\":350,\"./draw_raster\":351,\"./draw_symbol\":352,\"./frame_history\":353,\"./painter/use_program\":356,\"./vertex_array_object\":357,\"gl-matrix\":193}],356:[function(t,e,r){\"use strict\";function n(t,e){return t.replace(/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,function(t,r,n,i,a){return e[r][a].replace(/{type}/g,i).replace(/{precision}/g,n)})}var i=t(\"assert\"),a=t(\"../../util/util\"),o=t(\"mapbox-gl-shaders\"),s=o.util;e.exports._createProgram=function(t,e,r,l){for(var u=this.gl,c=u.createProgram(),h=o[t],f=\"#define MAPBOX_GL_JS;\\n\",d=0;d<e.length;d++)f+=\"#define \"+e[d]+\";\\n\";var p=u.createShader(u.FRAGMENT_SHADER);u.shaderSource(p,n(f+h.fragmentSource,l)),u.compileShader(p),i(u.getShaderParameter(p,u.COMPILE_STATUS),u.getShaderInfoLog(p)),u.attachShader(c,p);var m=u.createShader(u.VERTEX_SHADER);u.shaderSource(m,n(f+s+h.vertexSource,r)),u.compileShader(m),i(u.getShaderParameter(m,u.COMPILE_STATUS),u.getShaderInfoLog(m)),u.attachShader(c,m),u.linkProgram(c),i(u.getProgramParameter(c,u.LINK_STATUS),u.getProgramInfoLog(c));for(var v={},g=u.getProgramParameter(c,u.ACTIVE_ATTRIBUTES),y=0;y<g;y++){var b=u.getActiveAttrib(c,y);v[b.name]=u.getAttribLocation(c,b.name)}for(var x={},_=u.getProgramParameter(c,u.ACTIVE_UNIFORMS),w=0;w<_;w++){var M=u.getActiveUniform(c,w);x[M.name]=u.getUniformLocation(c,M.name)}return a.extend({program:c,definition:h,attributes:v,numAttributes:g},v,x)},e.exports._createProgramCached=function(t,e,r,n){this.cache=this.cache||{};var i=JSON.stringify({name:t,defines:e,vertexPragmas:r,fragmentPragmas:n});return this.cache[i]||(this.cache[i]=this._createProgram(t,e,r,n)),this.cache[i]},e.exports.useProgram=function(t,e,r,n){var i=this.gl;e=e||[],this._showOverdrawInspector&&(e=e.concat(\"OVERDRAW_INSPECTOR\"));var a=this._createProgramCached(t,e,r,n);return this.currentProgram!==a&&(i.useProgram(a.program),this.currentProgram=a),a}},{\"../../util/util\":442,assert:47,\"mapbox-gl-shaders\":303}],357:[function(t,e,r){\"use strict\";function n(){this.boundProgram=null,this.boundVertexBuffer=null,this.boundVertexBuffer2=null,this.boundElementBuffer=null,this.vao=null}var i=t(\"assert\");e.exports=n,n.prototype.bind=function(t,e,r,n,i){void 0===t.extVertexArrayObject&&(t.extVertexArrayObject=t.getExtension(\"OES_vertex_array_object\"));var a=!this.vao||this.boundProgram!==e||this.boundVertexBuffer!==r||this.boundVertexBuffer2!==i||this.boundElementBuffer!==n;!t.extVertexArrayObject||a?this.freshBind(t,e,r,n,i):t.extVertexArrayObject.bindVertexArrayOES(this.vao)},n.prototype.freshBind=function(t,e,r,n,a){var o,s=e.numAttributes;if(t.extVertexArrayObject)this.vao&&this.destroy(t),this.vao=t.extVertexArrayObject.createVertexArrayOES(),t.extVertexArrayObject.bindVertexArrayOES(this.vao),o=0,this.boundProgram=e,this.boundVertexBuffer=r,this.boundVertexBuffer2=a,this.boundElementBuffer=n;else{o=t.currentNumAttributes||0;for(var l=s;l<o;l++)i(0!==l),t.disableVertexAttribArray(l)}for(var u=o;u<s;u++)t.enableVertexAttribArray(u);r.bind(t),r.setVertexAttribPointers(t,e),a&&(a.bind(t),a.setVertexAttribPointers(t,e)),n&&n.bind(t),t.currentNumAttributes=s},n.prototype.unbind=function(t){var e=t.extVertexArrayObject;e&&e.bindVertexArrayOES(null)},n.prototype.destroy=function(t){var e=t.extVertexArrayObject;e&&this.vao&&(e.deleteVertexArrayOES(this.vao),this.vao=null)}},{assert:47}],358:[function(t,e,r){\"use strict\";function n(t,e,r){e=e||{},this.id=t,this.dispatcher=r,this._data=e.data,void 0!==e.maxzoom&&(this.maxzoom=e.maxzoom),e.type&&(this.type=e.type);var n=s/this.tileSize;this.workerOptions=a.extend({source:this.id,cluster:e.cluster||!1,geojsonVtOptions:{buffer:(void 0!==e.buffer?e.buffer:128)*n,tolerance:(void 0!==e.tolerance?e.tolerance:.375)*n,extent:s,maxZoom:this.maxzoom},superclusterOptions:{maxZoom:Math.min(e.clusterMaxZoom,this.maxzoom-1)||this.maxzoom-1,extent:s,radius:(e.clusterRadius||50)*n,log:!1}},e.workerOptions),this._updateWorkerData(function(t){if(t)return void this.fire(\"error\",{error:t});this.fire(\"load\")}.bind(this))}var i=t(\"../util/evented\"),a=t(\"../util/util\"),o=t(\"resolve-url\"),s=t(\"../data/bucket\").EXTENT;e.exports=n,n.prototype=a.inherit(i,{type:\"geojson\",minzoom:0,maxzoom:18,tileSize:512,isTileClipped:!0,reparseOverscaled:!0,onAdd:function(t){this.map=t},setData:function(t){return this._data=t,this._updateWorkerData(function(t){if(t)return this.fire(\"error\",{error:t});this.fire(\"change\")}.bind(this)),this},_updateWorkerData:function(t){var e=a.extend({},this.workerOptions),r=this._data;\"string\"==typeof r?e.url=\"undefined\"!=typeof window?o(window.location.href,r):r:e.data=JSON.stringify(r),this.workerID=this.dispatcher.send(this.type+\".loadData\",e,function(e){this._loaded=!0,t(e)}.bind(this))},loadTile:function(t,e){var r=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,n={type:this.type,uid:t.uid,coord:t.coord,zoom:t.coord.z,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,overscaling:r,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID=this.dispatcher.send(\"load tile\",n,function(r,n){if(t.unloadVectorData(this.map.painter),!t.aborted)return r?e(r):(t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null))}.bind(this),this.workerID)},abortTile:function(t){t.aborted=!0},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send(\"remove tile\",{uid:t.uid,source:this.id},function(){},t.workerID)},serialize:function(){return{type:this.type,data:this._data}}})},{\"../data/bucket\":329,\"../util/evented\":434,\"../util/util\":442,\"resolve-url\":501}],359:[function(t,e,r){\"use strict\";function n(t,e,r){r&&(this.loadGeoJSON=r),h.call(this,t,e)}var i=t(\"../util/util\"),a=t(\"../util/ajax\"),o=t(\"geojson-rewind\"),s=t(\"./geojson_wrapper\"),l=t(\"vt-pbf\"),u=t(\"supercluster\"),c=t(\"geojson-vt\"),h=t(\"./vector_tile_worker_source\");e.exports=n,n.prototype=i.inherit(h,{_geoJSONIndexes:{},loadVectorData:function(t,e){var r=t.source,n=t.coord;if(!this._geoJSONIndexes[r])return e(null,null);var i=this._geoJSONIndexes[r].getTile(Math.min(n.z,t.maxZoom),n.x,n.y);if(!i)return e(null,null);var a=new s(i.features);a.name=\"_geojsonTileLayer\";var o=l({layers:{_geojsonTileLayer:a}});0===o.byteOffset&&o.byteLength===o.buffer.byteLength||(o=new Uint8Array(o)),e(null,{tile:a,rawTileData:o.buffer})},loadData:function(t,e){var r=function(r,n){return r?e(r):\"object\"!=typeof n?e(new Error(\"Input data is not a valid GeoJSON object.\")):(o(n,!0),void this._indexData(n,t,function(r,n){if(r)return e(r);this._geoJSONIndexes[t.source]=n,e(null)}.bind(this)))}.bind(this);this.loadGeoJSON(t,r)},loadGeoJSON:function(t,e){if(t.url)a.getJSON(t.url,e);else{if(\"string\"!=typeof t.data)return e(new Error(\"Input data is not a valid GeoJSON object.\"));try{return e(null,JSON.parse(t.data))}catch(t){return e(new Error(\"Input data is not a valid GeoJSON object.\"))}}},_indexData:function(t,e,r){try{e.cluster?r(null,u(e.superclusterOptions).load(t.features)):r(null,c(t,e.geojsonVtOptions))}catch(t){return r(t)}}})},{\"../util/ajax\":425,\"../util/util\":442,\"./geojson_wrapper\":360,\"./vector_tile_worker_source\":371,\"geojson-rewind\":138,\"geojson-vt\":142,supercluster:529,\"vt-pbf\":556}],360:[function(t,e,r){\"use strict\";function n(t){this.features=t,this.length=t.length,this.extent=s}function i(t){if(this.type=t.type,1===t.type){this.rawGeometry=[];for(var e=0;e<t.geometry.length;e++)this.rawGeometry.push([t.geometry[e]])}else this.rawGeometry=t.geometry;this.properties=t.tags,this.extent=s}var a=t(\"point-geometry\"),o=t(\"vector-tile\").VectorTileFeature,s=t(\"../data/bucket\").EXTENT;e.exports=n,n.prototype.feature=function(t){return new i(this.features[t])},i.prototype.loadGeometry=function(){var t=this.rawGeometry;this.geometry=[];for(var e=0;e<t.length;e++){for(var r=t[e],n=[],i=0;i<r.length;i++)n.push(new a(r[i][0],r[i][1]));this.geometry.push(n)}return this.geometry},i.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var t=this.geometry,e=1/0,r=-1/0,n=1/0,i=-1/0,a=0;a<t.length;a++)for(var o=t[a],s=0;s<o.length;s++){var l=o[s];e=Math.min(e,l.x),r=Math.max(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.y)}return[e,n,r,i]},i.prototype.toGeoJSON=o.prototype.toGeoJSON},{\"../data/bucket\":329,\"point-geometry\":484,\"vector-tile\":550}],361:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r,this.url=e.url,this.coordinates=e.coordinates,u.getImage(e.url,function(t,r){if(t)return this.fire(\"error\",{error:t});this.image=r,this.image.addEventListener(\"load\",function(){this.map._rerender()}.bind(this)),this._loaded=!0,this.fire(\"load\"),this.map&&this.setCoordinates(e.coordinates)}.bind(this))}var i=t(\"../util/util\"),a=t(\"./tile_coord\"),o=t(\"../geo/lng_lat\"),s=t(\"point-geometry\"),l=t(\"../util/evented\"),u=t(\"../util/ajax\"),c=t(\"../data/bucket\").EXTENT,h=t(\"../render/draw_raster\").RasterBoundsArray,f=t(\"../data/buffer\"),d=t(\"../render/vertex_array_object\");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,onAdd:function(t){this.map=t,this.image&&this.setCoordinates(this.coordinates)},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(o.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new a(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)*c),Math.round((e.row-n.row)*c))}),this.fire(\"change\"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=new h;e.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),e.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,32767,0),e.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,32767),e.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,32767,32767),this.tile.buckets={},this.tile.boundsBuffer=new f(e.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new d,this.tile.state=\"loaded\"},prepare:function(){if(this._loaded&&this.image&&this.image.complete&&this.tile){var t=this.map.painter,e=t.gl;this._prepared?(e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,this.image)):(this.tile.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,this.image))}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state=\"errored\",e(null))},serialize:function(){return{type:\"image\",urls:this.url,coordinates:this.coordinates}}})},{\"../data/bucket\":329,\"../data/buffer\":334,\"../geo/lng_lat\":339,\"../render/draw_raster\":351,\"../render/vertex_array_object\":357,\"../util/ajax\":425,\"../util/evented\":434,\"../util/util\":442,\"./tile_coord\":369,\"point-geometry\":484}],362:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i=t(\"../util/ajax\"),a=t(\"../util/browser\"),o=t(\"../util/mapbox\").normalizeSourceURL;e.exports=function(t,e){var r=function(t,r){if(t)return e(t);var i=n.pick(r,[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\"]);r.vector_layers&&(i.vectorLayers=r.vector_layers,i.vectorLayerIds=i.vectorLayers.map(function(t){return t.id})),e(null,i)};t.url?i.getJSON(o(t.url),r):a.frame(r.bind(null,null,t))}},{\"../util/ajax\":425,\"../util/browser\":426,\"../util/mapbox\":439,\"../util/util\":442}],363:[function(t,e,r){\"use strict\";var n=t(\"../data/bucket\");e.exports=function(t,e,r){return e*(n.EXTENT/(t.tileSize*Math.pow(2,r-t.coord.z)))}},{\"../data/bucket\":329}],364:[function(t,e,r){\"use strict\";function n(t,e){var r=t.coord,n=e.coord;return r.z-n.z||r.y-n.y||r.w-n.w||r.x-n.x}function i(t){for(var e=t[0]||{},r=1;r<t.length;r++){var n=t[r];for(var i in n){var a=n[i],o=e[i];if(void 0===o)o=e[i]=a;else for(var s=0;s<a.length;s++)o.push(a[s])}}return e}var a=t(\"./tile_coord\");r.rendered=function(t,e,r,a,o,s){var l=t.tilesIn(r);l.sort(n);for(var u=[],c=0;c<l.length;c++){var h=l[c];h.tile.featureIndex&&u.push(h.tile.featureIndex.query({queryGeometry:h.queryGeometry,scale:h.scale,tileSize:h.tile.tileSize,bearing:s,params:a},e))}return i(u)},r.source=function(t,e){for(var r=t.getRenderableIds().map(function(e){return t.getTileByID(e)}),n=[],i={},o=0;o<r.length;o++){var s=r[o],l=new a(Math.min(s.sourceMaxZoom,s.coord.z),s.coord.x,s.coord.y,0).id;i[l]||(i[l]=!0,s.querySourceFeatures(n,e))}return n}},{\"./tile_coord\":369}],365:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r,i.extend(this,i.pick(e,[\"url\",\"scheme\",\"tileSize\"])),s(e,function(t,e){if(t)return this.fire(\"error\",t);i.extend(this,e),this.fire(\"load\")}.bind(this))}var i=t(\"../util/util\"),a=t(\"../util/ajax\"),o=t(\"../util/evented\"),s=t(\"./load_tilejson\"),l=t(\"../util/mapbox\").normalizeTileURL;e.exports=n,n.prototype=i.inherit(o,{minzoom:0,maxzoom:22,roundZoom:!0,scheme:\"xyz\",tileSize:512,_loaded:!1,onAdd:function(t){this.map=t},serialize:function(){return{type:\"raster\",url:this.url,tileSize:this.tileSize}},loadTile:function(t,e){function r(r,n){if(delete t.request,!t.aborted){if(r)return e(r);var i=this.map.painter.gl;t.texture=this.map.painter.getTexture(n.width),t.texture?(i.bindTexture(i.TEXTURE_2D,t.texture),i.texSubImage2D(i.TEXTURE_2D,0,0,0,i.RGBA,i.UNSIGNED_BYTE,n)):(t.texture=i.createTexture(),i.bindTexture(i.TEXTURE_2D,t.texture),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR_MIPMAP_NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,n),t.texture.size=n.width),i.generateMipmap(i.TEXTURE_2D),this.map.animationLoop.set(this.map.style.rasterFadeDuration),t.state=\"loaded\",e(null)}}var n=l(t.coord.url(this.tiles,null,this.scheme),this.url,this.tileSize);t.request=a.getImage(n,r.bind(this))},abortTile:function(t){t.request&&(t.request.abort(),delete t.request)},unloadTile:function(t){t.texture&&this.map.painter.saveTexture(t.texture)}})},{\"../util/ajax\":425,\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./load_tilejson\":362}],366:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i={vector:t(\"../source/vector_tile_source\"),raster:t(\"../source/raster_tile_source\"),geojson:t(\"../source/geojson_source\"),video:t(\"../source/video_source\"),image:t(\"../source/image_source\")};r.create=function(t,e,r){if(e=new i[e.type](t,e,r),e.id!==t)throw new Error(\"Expected Source id to be \"+t+\" instead of \"+e.id);return n.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],e),e},r.getType=function(t){return i[t]},r.setType=function(t,e){i[t]=e}},{\"../source/geojson_source\":358,\"../source/image_source\":361,\"../source/raster_tile_source\":365,\"../source/vector_tile_source\":370,\"../source/video_source\":372,\"../util/util\":442}],367:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r;var n=this._source=o.create(t,e,r).on(\"load\",function(){this.map&&this._source.onAdd&&this._source.onAdd(this.map),this._sourceLoaded=!0,this.tileSize=n.tileSize,this.minzoom=n.minzoom,this.maxzoom=n.maxzoom,this.roundZoom=n.roundZoom,this.reparseOverscaled=n.reparseOverscaled,this.isTileClipped=n.isTileClipped,this.attribution=n.attribution,this.vectorLayerIds=n.vectorLayerIds,this.fire(\"load\")}.bind(this)).on(\"error\",function(t){this._sourceErrored=!0,this.fire(\"error\",t)}.bind(this)).on(\"change\",function(){this.reload(),this.transform&&this.update(this.transform,this.map&&this.map.style.rasterFadeDuration),this.fire(\"change\")}.bind(this));this._tiles={},this._cache=new c(0,this.unloadTile.bind(this)),this._isIdRenderable=this._isIdRenderable.bind(this)}function i(t,e,r){var n=r.zoomTo(Math.min(t.z,e));return{x:(n.column-(t.x+t.w*Math.pow(2,t.z)))*d,y:(n.row-t.y)*d}}function a(t,e){return t%32-e%32}var o=t(\"./source\"),s=t(\"./tile\"),l=t(\"../util/evented\"),u=t(\"./tile_coord\"),c=t(\"../util/lru_cache\"),h=t(\"../geo/coordinate\"),f=t(\"../util/util\"),d=t(\"../data/bucket\").EXTENT;e.exports=n,n.maxOverzooming=10,n.maxUnderzooming=3,n.prototype=f.inherit(l,{onAdd:function(t){this.map=t,this._source&&this._source.onAdd&&this._source.onAdd(t)},loaded:function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},getSource:function(){return this._source},loadTile:function(t,e){return this._source.loadTile(t,e)},unloadTile:function(t){if(this._source.unloadTile)return this._source.unloadTile(t)},abortTile:function(t){if(this._source.abortTile)return this._source.abortTile(t)},serialize:function(){return this._source.serialize()},prepare:function(){if(this._sourceLoaded&&this._source.prepare)return this._source.prepare()},getIds:function(){return Object.keys(this._tiles).map(Number).sort(a)},getRenderableIds:function(){return this.getIds().filter(this._isIdRenderable)},_isIdRenderable:function(t){return this._tiles[t].isRenderable()&&!this._coveredTiles[t]},reload:function(){this._cache.reset();for(var t in this._tiles){var e=this._tiles[t];\"loading\"!==e.state&&(e.state=\"reloading\"),this.loadTile(this._tiles[t],this._tileLoaded.bind(this,this._tiles[t]))}},_tileLoaded:function(t,e){if(e)return t.state=\"errored\",this.fire(\"tile.error\",{tile:t,error:e}),void this._source.fire(\"tile.error\",{tile:t,error:e});t.source=this,t.timeAdded=(new Date).getTime(),this.fire(\"tile.load\",{tile:t}),this._source.fire(\"tile.load\",{tile:t})},getTile:function(t){return this.getTileByID(t.id)},getTileByID:function(t){return this._tiles[t]},getZoom:function(t){return t.zoom+t.scaleZoom(t.tileSize/this.tileSize)},findLoadedChildren:function(t,e,r){var n=!1;for(var i in this._tiles){var a=this._tiles[i];if(!(r[i]||!a.isRenderable()||a.coord.z<=t.z||a.coord.z>e)){var o=Math.pow(2,Math.min(a.coord.z,this.maxzoom)-Math.min(t.z,this.maxzoom));if(Math.floor(a.coord.x/o)===t.x&&Math.floor(a.coord.y/o)===t.y)for(r[i]=!0,n=!0;a&&a.coord.z-1>t.z;){var s=a.coord.parent(this.maxzoom).id;a=this._tiles[s],a&&a.isRenderable()&&(delete r[i],r[s]=!0)}}}return n},findLoadedParent:function(t,e,r){for(var n=t.z-1;n>=e;n--){t=t.parent(this.maxzoom);var i=this._tiles[t.id];if(i&&i.isRenderable())return r[t.id]=!0,i;if(this._cache.has(t.id))return this.addTile(t),r[t.id]=!0,this._tiles[t.id]}},updateCacheSize:function(t){var e=Math.ceil(t.width/t.tileSize)+1,r=Math.ceil(t.height/t.tileSize)+1,n=e*r;this._cache.setMaxSize(Math.floor(5*n))},update:function(t,e){if(this._sourceLoaded){var r,i,a;this.updateCacheSize(t);var o=(this.roundZoom?Math.round:Math.floor)(this.getZoom(t)),s=Math.max(o-n.maxOverzooming,this.minzoom),l=Math.max(o+n.maxUnderzooming,this.minzoom),c={},h=(new Date).getTime();this._coveredTiles={};var d=this.used?t.coveringTiles(this._source):[];for(r=0;r<d.length;r++)i=d[r],a=this.addTile(i),c[i.id]=!0,a.isRenderable()||this.findLoadedChildren(i,l,c)||this.findLoadedParent(i,s,c);for(var p={},m=Object.keys(c),v=0;v<m.length;v++){var g=m[v];i=u.fromID(g),a=this._tiles[g],a&&a.timeAdded>h-(e||0)&&(this.findLoadedChildren(i,l,c)&&(c[g]=!0),this.findLoadedParent(i,s,p))}var y;for(y in p)c[y]||(this._coveredTiles[y]=!0);for(y in p)c[y]=!0;var b=f.keysDifference(this._tiles,c);for(r=0;r<b.length;r++)this.removeTile(+b[r]);this.transform=t}},addTile:function(t){var e=this._tiles[t.id];if(e)return e;var r=t.wrapped()\n",
+       ";if(e=this._tiles[r.id],e||(e=this._cache.get(r.id))&&this._redoPlacement&&this._redoPlacement(e),!e){var n=t.z,i=n>this.maxzoom?Math.pow(2,n-this.maxzoom):1;e=new s(r,this.tileSize*i,this.maxzoom),this.loadTile(e,this._tileLoaded.bind(this,e))}return e.uses++,this._tiles[t.id]=e,this.fire(\"tile.add\",{tile:e}),this._source.fire(\"tile.add\",{tile:e}),e},removeTile:function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this.fire(\"tile.remove\",{tile:e}),this._source.fire(\"tile.remove\",{tile:e}),e.uses>0||(e.isRenderable()?this._cache.add(e.coord.wrapped().id,e):(e.aborted=!0,this.abortTile(e),this.unloadTile(e))))},clearTiles:function(){for(var t in this._tiles)this.removeTile(t);this._cache.reset()},tilesIn:function(t){for(var e={},r=this.getIds(),n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0].zoom,c=0;c<t.length;c++){var f=t[c];n=Math.min(n,f.column),a=Math.min(a,f.row),o=Math.max(o,f.column),s=Math.max(s,f.row)}for(var p=0;p<r.length;p++){var m=this._tiles[r[p]],v=u.fromID(r[p]),g=[i(v,m.sourceMaxZoom,new h(n,a,l)),i(v,m.sourceMaxZoom,new h(o,s,l))];if(g[0].x<d&&g[0].y<d&&g[1].x>=0&&g[1].y>=0){for(var y=[],b=0;b<t.length;b++)y.push(i(v,m.sourceMaxZoom,t[b]));var x=e[m.coord.id];void 0===x&&(x=e[m.coord.id]={tile:m,coord:v,queryGeometry:[],scale:Math.pow(2,this.transform.zoom-m.coord.z)}),x.queryGeometry.push(y)}}var _=[];for(var w in e)_.push(e[w]);return _},redoPlacement:function(){for(var t=this.getIds(),e=0;e<t.length;e++){this.getTileByID(t[e]).redoPlacement(this)}},getVisibleCoordinates:function(){return this.getRenderableIds().map(u.fromID)}})},{\"../data/bucket\":329,\"../geo/coordinate\":338,\"../util/evented\":434,\"../util/lru_cache\":438,\"../util/util\":442,\"./source\":366,\"./tile\":368,\"./tile_coord\":369}],368:[function(t,e,r){\"use strict\";function n(t,e,r){this.coord=t,this.uid=a.uniqueId(),this.uses=0,this.tileSize=e,this.sourceMaxZoom=r,this.buckets={},this.state=\"loading\"}function i(t,e){if(e){for(var r={},n=0;n<t.length;n++){var i=e.getLayer(t[n].layerId);if(i){var s=o.create(a.extend({layer:i,childLayers:t[n].childLayerIds.map(e.getLayer.bind(e)).filter(function(t){return t})},t[n]));r[s.id]=s}}return r}}var a=t(\"../util/util\"),o=t(\"../data/bucket\"),s=t(\"../data/feature_index\"),l=t(\"vector-tile\"),u=t(\"pbf\"),c=t(\"../util/vectortile_to_geojson\"),h=t(\"feature-filter\"),f=t(\"../symbol/collision_tile\"),d=t(\"../symbol/collision_box\"),p=t(\"../symbol/symbol_instances\"),m=t(\"../symbol/symbol_quads\");e.exports=n,n.prototype={loadVectorData:function(t,e){this.state=\"loaded\",t&&(this.collisionBoxArray=new d(t.collisionBoxArray),this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.symbolInstancesArray=new p(t.symbolInstancesArray),this.symbolQuadsArray=new m(t.symbolQuadsArray),this.featureIndex=new s(t.featureIndex,t.rawTileData,this.collisionTile),this.rawTileData=t.rawTileData,this.buckets=i(t.buckets,e))},reloadSymbolData:function(t,e,r){if(\"unloaded\"!==this.state){this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.featureIndex.setCollisionTile(this.collisionTile);for(var n in this.buckets){var o=this.buckets[n];\"symbol\"===o.type&&(o.destroy(e.gl),delete this.buckets[n])}a.extend(this.buckets,i(t.buckets,r))}},unloadVectorData:function(t){for(var e in this.buckets){this.buckets[e].destroy(t.gl)}this.collisionBoxArray=null,this.symbolQuadsArray=null,this.symbolInstancesArray=null,this.collisionTile=null,this.featureIndex=null,this.rawTileData=null,this.buckets=null,this.state=\"unloaded\"},redoPlacement:function(t){function e(e,r){this.reloadSymbolData(r,t.map.painter,t.map.style),t.fire(\"tile.load\",{tile:this}),this.state=\"loaded\",this.redoWhenDone&&(this.redoPlacement(t),this.redoWhenDone=!1)}if(\"loaded\"!==this.state||\"reloading\"===this.state)return void(this.redoWhenDone=!0);this.state=\"reloading\",t.dispatcher.send(\"redo placement\",{uid:this.uid,source:t.id,angle:t.map.transform.angle,pitch:t.map.transform.pitch,showCollisionBoxes:t.map.showCollisionBoxes},e.bind(this),this.workerID)},getBucket:function(t){return this.buckets&&this.buckets[t.ref||t.id]},querySourceFeatures:function(t,e){if(this.rawTileData){this.vtLayers||(this.vtLayers=new l.VectorTile(new u(new Uint8Array(this.rawTileData))).layers);var r=this.vtLayers._geojsonTileLayer||this.vtLayers[e.sourceLayer];if(r)for(var n=h(e.filter),i={z:this.coord.z,x:this.coord.x,y:this.coord.y},a=0;a<r.length;a++){var o=r.feature(a);if(n(o)){var s=new c(o,this.coord.z,this.coord.x,this.coord.y);s.tile=i,t.push(s)}}}},isRenderable:function(){return\"loaded\"===this.state||\"reloading\"===this.state}}},{\"../data/bucket\":329,\"../data/feature_index\":336,\"../symbol/collision_box\":394,\"../symbol/collision_tile\":396,\"../symbol/symbol_instances\":405,\"../symbol/symbol_quads\":406,\"../util/util\":442,\"../util/vectortile_to_geojson\":443,\"feature-filter\":132,pbf:478,\"vector-tile\":550}],369:[function(t,e,r){\"use strict\";function n(t,e,r,n){l(!isNaN(t)&&t>=0&&t%1==0),l(!isNaN(e)&&e>=0&&e%1==0),l(!isNaN(r)&&r>=0&&r%1==0),isNaN(n)&&(n=0),this.z=+t,this.x=+e,this.y=+r,this.w=+n,(n*=2)<0&&(n=-1*n-1);var i=1<<this.z;this.id=32*(i*i*n+i*this.y+this.x)+this.z,this.posMatrix=null}function i(t,e,r){for(var n,i=\"\",a=t;a>0;a--)n=1<<a-1,i+=(e&n?1:0)+(r&n?2:0);return i}function a(t,e){if(t.row>e.row){var r=t;t=e,e=r}return{x0:t.column,y0:t.row,x1:e.column,y1:e.row,dx:e.column-t.column,dy:e.row-t.row}}function o(t,e,r,n,i){var a=Math.max(r,Math.floor(e.y0)),o=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dy*t.dx<e.x1:t.x1-e.dy/t.dy*t.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,u=e.dx/e.dy,c=t.dx>0,h=e.dx<0,f=a;f<o;f++){var d=l*Math.max(0,Math.min(t.dy,f+c-t.y0))+t.x0,p=u*Math.max(0,Math.min(e.dy,f+h-e.y0))+e.x0;i(Math.floor(p),Math.ceil(d),f)}}function s(t,e,r,n,i,s){var l,u=a(t,e),c=a(e,r),h=a(r,t);u.dy>c.dy&&(l=u,u=c,c=l),u.dy>h.dy&&(l=u,u=h,h=l),c.dy>h.dy&&(l=c,c=h,h=l),u.dy&&o(h,u,n,i,s),c.dy&&o(h,c,n,i,s)}var l=t(\"assert\"),u=t(\"whoots-js\"),c=t(\"../geo/coordinate\");e.exports=n,n.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y},n.prototype.toCoordinate=function(t){var e=Math.min(this.z,t),r=Math.pow(2,e),n=this.y,i=this.x+r*this.w;return new c(i,n,e)},n.fromID=function(t){var e=t%32,r=1<<e,i=(t-e)/32,a=i%r,o=(i-a)/r%r,s=Math.floor(i/(r*r));return s%2!=0&&(s=-1*s-1),s/=2,new n(e,a,o,s)},n.prototype.url=function(t,e,r){var n=u.getTileBBox(this.x,this.y,this.z),a=i(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",Math.min(this.z,e||this.z)).replace(\"{x}\",this.x).replace(\"{y}\",\"tms\"===r?Math.pow(2,this.z)-this.y-1:this.y).replace(\"{quadkey}\",a).replace(\"{bbox-epsg-3857}\",n)},n.prototype.parent=function(t){return 0===this.z?null:this.z>t?new n(this.z-1,this.x,this.y,this.w):new n(this.z-1,Math.floor(this.x/2),Math.floor(this.y/2),this.w)},n.prototype.wrapped=function(){return new n(this.z,this.x,this.y,0)},n.prototype.children=function(t){if(this.z>=t)return[new n(this.z+1,this.x,this.y,this.w)];var e=this.z+1,r=2*this.x,i=2*this.y;return[new n(e,r,i,this.w),new n(e,r+1,i,this.w),new n(e,r,i+1,this.w),new n(e,r+1,i+1,this.w)]},n.cover=function(t,e,r){function i(t,e,i){var s,l,u;if(i>=0&&i<=a)for(s=t;s<e;s++)l=(s%a+a)%a,u=new n(r,l,i,Math.floor(s/a)),o[u.id]=u}var a=1<<t,o={};return s(e[0],e[1],e[2],0,a,i),s(e[2],e[3],e[0],0,a,i),Object.keys(o).map(function(t){return o[t]})}},{\"../geo/coordinate\":338,assert:47,\"whoots-js\":566}],370:[function(t,e,r){\"use strict\";function n(t,e,r){if(this.id=t,this.dispatcher=r,a.extend(this,a.pick(e,[\"url\",\"scheme\",\"tileSize\"])),this._options=a.extend({type:\"vector\"},e),512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");o(e,function(t,e){if(t)return void this.fire(\"error\",t);a.extend(this,e),this.fire(\"load\")}.bind(this))}var i=t(\"../util/evented\"),a=t(\"../util/util\"),o=t(\"./load_tilejson\"),s=t(\"../util/mapbox\").normalizeTileURL;e.exports=n,n.prototype=a.inherit(i,{minzoom:0,maxzoom:22,scheme:\"xyz\",tileSize:512,reparseOverscaled:!0,isTileClipped:!0,onAdd:function(t){this.map=t},serialize:function(){return a.extend({},this._options)},loadTile:function(t,e){function r(r,n){if(!t.aborted){if(r)return e(r);t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null),t.reloadCallback&&(this.loadTile(t,t.reloadCallback),t.reloadCallback=null)}}var n=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,i={url:s(t.coord.url(this.tiles,this.maxzoom,this.scheme),this.url),uid:t.uid,coord:t.coord,zoom:t.coord.z,tileSize:this.tileSize*n,source:this.id,overscaling:n,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID?\"loading\"===t.state?t.reloadCallback=e:(i.rawTileData=t.rawTileData,this.dispatcher.send(\"reload tile\",i,r.bind(this),t.workerID)):t.workerID=this.dispatcher.send(\"load tile\",i,r.bind(this))},abortTile:function(t){this.dispatcher.send(\"abort tile\",{uid:t.uid,source:this.id},null,t.workerID)},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send(\"remove tile\",{uid:t.uid,source:this.id},null,t.workerID)}})},{\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./load_tilejson\":362}],371:[function(t,e,r){\"use strict\";function n(t,e,r){this.actor=t,this.styleLayers=e,r&&(this.loadVectorData=r),this.loading={},this.loaded={}}var i=t(\"../util/ajax\"),a=t(\"vector-tile\"),o=t(\"pbf\"),s=t(\"./worker_tile\");e.exports=n,n.prototype={loadTile:function(t,e){function r(t,r){return delete this.loading[n][i],t?e(t):r?(a.data=r.tile,a.parse(a.data,this.styleLayers.getLayerFamilies(),this.actor,r.rawTileData,e),this.loaded[n]=this.loaded[n]||{},void(this.loaded[n][i]=a)):e(null,null)}var n=t.source,i=t.uid;this.loading[n]||(this.loading[n]={});var a=this.loading[n][i]=new s(t);a.abort=this.loadVectorData(t,r.bind(this))},reloadTile:function(t,e){var r=this.loaded[t.source],n=t.uid;if(r&&r[n]){var i=r[n];i.parse(i.data,this.styleLayers.getLayerFamilies(),this.actor,t.rawTileData,e)}},abortTile:function(t){var e=this.loading[t.source],r=t.uid;e&&e[r]&&e[r].abort&&(e[r].abort(),delete e[r])},removeTile:function(t){var e=this.loaded[t.source],r=t.uid;e&&e[r]&&delete e[r]},loadVectorData:function(t,e){function r(t,r){if(t)return e(t);var n=new a.VectorTile(new o(new Uint8Array(r)));e(t,{tile:n,rawTileData:r})}var n=i.getArrayBuffer(t.url,r.bind(this));return function(){n.abort()}},redoPlacement:function(t,e){var r=this.loaded[t.source],n=this.loading[t.source],i=t.uid;if(r&&r[i]){var a=r[i],o=a.redoPlacement(t.angle,t.pitch,t.showCollisionBoxes);o.result&&e(null,o.result,o.transferables)}else n&&n[i]&&(n[i].angle=t.angle)}}},{\"../util/ajax\":425,\"./worker_tile\":374,pbf:478,\"vector-tile\":550}],372:[function(t,e,r){\"use strict\";function n(t,e){this.id=t,this.urls=e.urls,this.coordinates=e.coordinates,u.getVideo(e.urls,function(t,r){if(t)return this.fire(\"error\",{error:t});this.video=r,this.video.loop=!0;var n;this.video.addEventListener(\"playing\",function(){n=this.map.style.animationLoop.set(1/0),this.map._rerender()}.bind(this)),this.video.addEventListener(\"pause\",function(){this.map.style.animationLoop.cancel(n)}.bind(this)),this.map&&(this.video.play(),this.setCoordinates(e.coordinates)),this.fire(\"load\")}.bind(this))}var i=t(\"../util/util\"),a=t(\"./tile_coord\"),o=t(\"../geo/lng_lat\"),s=t(\"point-geometry\"),l=t(\"../util/evented\"),u=t(\"../util/ajax\"),c=t(\"../data/bucket\").EXTENT,h=t(\"../render/draw_raster\").RasterBoundsArray,f=t(\"../data/buffer\"),d=t(\"../render/vertex_array_object\");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,roundZoom:!0,getVideo:function(){return this.video},onAdd:function(t){this.map||(this.map=t,this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(o.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new a(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)*c),Math.round((e.row-n.row)*c))}),this.fire(\"change\"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=new h;e.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),e.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,32767,0),e.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,32767),e.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,32767,32767),this.tile.buckets={},this.tile.boundsBuffer=new f(e.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new d,this.tile.state=\"loaded\"},prepare:function(){if(!(this.video.readyState<2)&&this.tile){var t=this.map.painter.gl;this._prepared?(t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texSubImage2D(t.TEXTURE_2D,0,0,0,t.RGBA,t.UNSIGNED_BYTE,this.video)):(this._prepared=!0,this.tile.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,this.video)),this._currentTime=this.video.currentTime}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state=\"errored\",e(null))},serialize:function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}})},{\"../data/bucket\":329,\"../data/buffer\":334,\"../geo/lng_lat\":339,\"../render/draw_raster\":351,\"../render/vertex_array_object\":357,\"../util/ajax\":425,\"../util/evented\":434,\"../util/util\":442,\"./tile_coord\":369,\"point-geometry\":484}],373:[function(t,e,r){\"use strict\";function n(t){this.self=t,this.actor=new a(t,this);var e={getLayers:function(){return this.layers}.bind(this),getLayerFamilies:function(){return this.layerFamilies}.bind(this)};this.workerSources={vector:new l(this.actor,e),geojson:new u(this.actor,e)},this.self.registerWorkerSource=function(t,r){if(this.workerSources[t])throw new Error('Worker source with name \"'+t+'\" already registered.');this.workerSources[t]=new r(this.actor,e)}.bind(this)}function i(t){var e={};for(var r in t){var n=t[r],i=n.ref||n.id,a=t[i];a.layout&&\"none\"===a.layout.visibility||(e[i]=e[i]||[],r===i?e[i].unshift(n):e[i].push(n))}return e}var a=t(\"../util/actor\"),o=t(\"../style/style_layer\"),s=t(\"../util/util\"),l=t(\"./vector_tile_worker_source\"),u=t(\"./geojson_worker_source\");e.exports=function(t){return new n(t)},s.extend(n.prototype,{\"set layers\":function(t){function e(t){var e=o.create(t,t.ref&&r.layers[t.ref]);e.updatePaintTransitions({},{transition:!1}),r.layers[e.id]=e}this.layers={};for(var r=this,n=[],a=0;a<t.length;a++){var s=t[a];\"fill\"!==s.type&&\"line\"!==s.type&&\"circle\"!==s.type&&\"symbol\"!==s.type||(s.ref?n.push(a):e(s))}for(var l=0;l<n.length;l++)e(t[n[l]]);this.layerFamilies=i(this.layers)},\"update layers\":function(t){function e(t){var e=a.layers[t.ref];a.layers[t.id]?a.layers[t.id].set(t,e):a.layers[t.id]=o.create(t,e),a.layers[t.id].updatePaintTransitions({},{transition:!1})}var r,n,a=this;for(r in t)n=t[r],n.ref&&e(n);for(r in t)n=t[r],n.ref||e(n);this.layerFamilies=i(this.layers)},\"load tile\":function(t,e){var r=t.type||\"vector\";this.workerSources[r].loadTile(t,e)},\"reload tile\":function(t,e){var r=t.type||\"vector\";this.workerSources[r].reloadTile(t,e)},\"abort tile\":function(t){var e=t.type||\"vector\";this.workerSources[e].abortTile(t)},\"remove tile\":function(t){var e=t.type||\"vector\";this.workerSources[e].removeTile(t)},\"redo placement\":function(t,e){var r=t.type||\"vector\";this.workerSources[r].redoPlacement(t,e)},\"load worker source\":function(t,e){try{this.self.importScripts(t.url),e()}catch(t){e(t)}}})},{\"../style/style_layer\":381,\"../util/actor\":424,\"../util/util\":442,\"./geojson_worker_source\":359,\"./vector_tile_worker_source\":371}],374:[function(t,e,r){\"use strict\";function n(t){this.coord=t.coord,this.uid=t.uid,this.zoom=t.zoom,this.tileSize=t.tileSize,this.source=t.source,this.overscaling=t.overscaling,this.angle=t.angle,this.pitch=t.pitch,this.showCollisionBoxes=t.showCollisionBoxes}function i(t){return!t.isEmpty()}function a(t){return t.serialize()}function o(t){var e=[];for(var r in t)t[r].getTransferables(e);return e}function s(t){return t.id}var l=t(\"../data/feature_index\"),u=t(\"../symbol/collision_tile\"),c=t(\"../data/bucket\"),h=t(\"../symbol/collision_box\"),f=t(\"../util/dictionary_coder\"),d=t(\"../util/util\"),p=t(\"../symbol/symbol_instances\"),m=t(\"../symbol/symbol_quads\");e.exports=n,n.prototype.parse=function(t,e,r,n,v){function g(t,e){for(var r=0;r<t.length;r++){var n=t.feature(r);n.index=r;for(var i in e)e[i].filter(n)&&e[i].features.push(n)}}function y(t){if(t)return v(t);if(2===++N){for(var e=P.length-1;e>=0;e--)b(E,P[e]);x()}}function b(t,e){if(e.populateArrays(A,j,F),\"symbol\"!==e.type)for(var r=0;r<e.features.length;r++){var n=e.features[r];T.insert(n,n.index,e.sourceLayerIndex,e.index)}e.features=null}function x(){E.status=\"done\",E.redoPlacementAfterDone&&(E.redoPlacement(E.angle,E.pitch,null),E.redoPlacementAfterDone=!1);var t=T.serialize(),e=A.serialize(),r=E.collisionBoxArray.serialize(),s=E.symbolInstancesArray.serialize(),l=E.symbolQuadsArray.serialize(),u=[n].concat(t.transferables).concat(e.transferables),c=D.filter(i);v(null,{buckets:c.map(a),featureIndex:t.data,collisionTile:e.data,collisionBoxArray:r,symbolInstancesArray:s,symbolQuadsArray:l,rawTileData:n},o(c).concat(u))}this.status=\"parsing\",this.data=t,this.collisionBoxArray=new h,this.symbolInstancesArray=new p,this.symbolQuadsArray=new m;var _,w,M,k,A=new u(this.angle,this.pitch,this.collisionBoxArray),T=new l(this.coord,this.overscaling,A,t.layers),S=new f(t.layers?Object.keys(t.layers).sort():[\"_geojsonTileLayer\"]),E=this,L={},C={},I=0;for(var z in e)w=e[z][0],w.source===this.source&&(w.ref||w.minzoom&&this.zoom<w.minzoom||w.maxzoom&&this.zoom>=w.maxzoom||w.layout&&\"none\"===w.layout.visibility||t.layers&&!t.layers[w.sourceLayer]||(k=c.create({layer:w,index:I++,childLayers:e[z],zoom:this.zoom,overscaling:this.overscaling,showCollisionBoxes:this.showCollisionBoxes,collisionBoxArray:this.collisionBoxArray,symbolQuadsArray:this.symbolQuadsArray,symbolInstancesArray:this.symbolInstancesArray,sourceLayerIndex:S.encode(w.sourceLayer||\"_geojsonTileLayer\")}),k.createFilter(),L[w.id]=k,t.layers&&(M=w.sourceLayer,C[M]=C[M]||{},C[M][w.id]=k)));if(t.layers)for(M in C)1===w.version&&d.warnOnce('Vector tile source \"'+this.source+'\" layer \"'+M+'\" does not use vector tile spec v2 and therefore may have some rendering errors.'),(w=t.layers[M])&&g(w,C[M]);else g(t,L);var D=[],P=this.symbolBuckets=[],O=[];T.bucketLayerIDs={};for(var R in L)k=L[R],0!==k.features.length&&(T.bucketLayerIDs[k.index]=k.childLayers.map(s),D.push(k),\"symbol\"===k.type?P.push(k):O.push(k));var F={},j={},N=0;if(P.length>0){for(_=P.length-1;_>=0;_--)P[_].updateIcons(F),P[_].updateFont(j);for(var B in j)j[B]=Object.keys(j[B]).map(Number);F=Object.keys(F),r.send(\"get glyphs\",{uid:this.uid,stacks:j},function(t,e){j=e,y(t)}),F.length?r.send(\"get icons\",{icons:F},function(t,e){F=e,y(t)}):y()}for(_=O.length-1;_>=0;_--)b(this,O[_]);if(0===P.length)return x()},n.prototype.redoPlacement=function(t,e,r){if(\"done\"!==this.status)return this.redoPlacementAfterDone=!0,this.angle=t,{};for(var n=new u(t,e,this.collisionBoxArray),s=this.symbolBuckets,l=s.length-1;l>=0;l--)s[l].placeFeatures(n,r);var c=n.serialize(),h=s.filter(i);return{result:{buckets:h.map(a),collisionTile:c.data},transferables:o(h).concat(c.transferables)}}},{\"../data/bucket\":329,\"../data/feature_index\":336,\"../symbol/collision_box\":394,\"../symbol/collision_tile\":396,\"../symbol/symbol_instances\":405,\"../symbol/symbol_quads\":406,\"../util/dictionary_coder\":432,\"../util/util\":442}],375:[function(t,e,r){\"use strict\";function n(){this.n=0,this.times=[]}e.exports=n,n.prototype.stopped=function(){return this.times=this.times.filter(function(t){return t.time>=(new Date).getTime()}),!this.times.length},n.prototype.set=function(t){return this.times.push({id:this.n,time:t+(new Date).getTime()}),this.n++},n.prototype.cancel=function(t){this.times=this.times.filter(function(e){return e.id!==t})}},{}],376:[function(t,e,r){\"use strict\";function n(t){this.base=t,this.retina=s.devicePixelRatio>1;var e=this.retina?\"@2x\":\"\";o.getJSON(l(t,e,\".json\"),function(t,e){if(t)return void this.fire(\"error\",{error:t});this.data=e,this.img&&this.fire(\"load\")}.bind(this)),o.getImage(l(t,e,\".png\"),function(t,e){if(t)return void this.fire(\"error\",{error:t});for(var r=e.getData(),n=e.data=new Uint8Array(r.length),i=0;i<r.length;i+=4){var a=r[i+3]/255;n[i+0]=r[i+0]*a,n[i+1]=r[i+1]*a,n[i+2]=r[i+2]*a,n[i+3]=r[i+3]}this.img=e,this.data&&this.fire(\"load\")}.bind(this))}function i(){}var a=t(\"../util/evented\"),o=t(\"../util/ajax\"),s=t(\"../util/browser\"),l=t(\"../util/mapbox\").normalizeSpriteURL;e.exports=n,n.prototype=Object.create(a),n.prototype.toJSON=function(){return this.base},n.prototype.loaded=function(){return!(!this.data||!this.img)},n.prototype.resize=function(){if(s.devicePixelRatio>1!==this.retina){var t=new n(this.base);t.on(\"load\",function(){this.img=t.img,this.data=t.data,this.retina=t.retina}.bind(this))}},i.prototype={x:0,y:0,width:0,height:0,pixelRatio:1,sdf:!1},n.prototype.getSpritePosition=function(t){if(!this.loaded())return new i;var e=this.data&&this.data[t];return e&&this.img?e:new i}},{\"../util/ajax\":425,\"../util/browser\":426,\"../util/evented\":434,\"../util/mapbox\":439}],377:[function(t,e,r){\"use strict\";var n=t(\"csscolorparser\").parseCSSColor,i=t(\"../util/util\"),a=t(\"./style_function\"),o={};e.exports=function t(e){if(a.isFunctionDefinition(e))return i.extend({},e,{stops:e.stops.map(function(e){return[e[0],t(e[1])]})});if(\"string\"==typeof e){if(!o[e]){var r=n(e);if(!r)throw new Error(\"Invalid color \"+e);o[e]=[r[0]/255*r[3],r[1]/255*r[3],r[2]/255*r[3],r[3]]}return o[e]}throw new Error(\"Invalid color \"+e)}},{\"../util/util\":442,\"./style_function\":380,csscolorparser:108}],378:[function(t,e,r){\"use strict\";function n(t,e,r){this.animationLoop=e||new m,this.dispatcher=new p(r||1,this),this.spriteAtlas=new l(1024,1024),this.lineAtlas=new u(256,512),this._layers={},this._order=[],this._groups=[],this.sources={},this.zoomHistory={},c.bindAll([\"_forwardSourceEvent\",\"_forwardTileEvent\",\"_forwardLayerEvent\",\"_redoPlacement\"],this),this._resetUpdates();var n=function(t,e){if(t)return void this.fire(\"error\",{error:t});if(!v.emitErrors(this,v(e))){this._loaded=!0,this.stylesheet=e,this.updateClasses();var r=e.sources;for(var n in r)this.addSource(n,r[n]);e.sprite&&(this.sprite=new o(e.sprite),this.sprite.on(\"load\",this.fire.bind(this,\"change\"))),this.glyphSource=new s(e.glyphs),this._resolve(),this.fire(\"load\")}}.bind(this);\"string\"==typeof t?h.getJSON(f(t),n):d.frame(n.bind(this,null,t)),this.on(\"source.load\",function(t){var e=t.source;if(e&&e.vectorLayerIds)for(var r in this._layers){var n=this._layers[r];n.source===e.id&&this._validateLayer(n)}})}var i=t(\"../util/evented\"),a=t(\"./style_layer\"),o=t(\"./image_sprite\"),s=t(\"../symbol/glyph_source\"),l=t(\"../symbol/sprite_atlas\"),u=t(\"../render/line_atlas\"),c=t(\"../util/util\"),h=t(\"../util/ajax\"),f=t(\"../util/mapbox\").normalizeStyleURL,d=t(\"../util/browser\"),p=t(\"../util/dispatcher\"),m=t(\"./animation_loop\"),v=t(\"./validate_style\"),g=t(\"../source/source\"),y=t(\"../source/query_features\"),b=t(\"../source/source_cache\"),x=t(\"./style_spec\"),_=t(\"./style_function\");e.exports=n,n.prototype=c.inherit(i,{_loaded:!1,_validateLayer:function(t){var e=this.sources[t.source];t.sourceLayer&&e&&e.vectorLayerIds&&-1===e.vectorLayerIds.indexOf(t.sourceLayer)&&this.fire(\"error\",{error:new Error('Source layer \"'+t.sourceLayer+'\" does not exist on source \"'+e.id+'\" as specified by style layer \"'+t.id+'\"')})},loaded:function(){if(!this._loaded)return!1;if(Object.keys(this._updates.sources).length)return!1;for(var t in this.sources)if(!this.sources[t].loaded())return!1;return!(this.sprite&&!this.sprite.loaded())},_resolve:function(){var t,e;this._layers={},this._order=this.stylesheet.layers.map(function(t){return t.id});for(var r=0;r<this.stylesheet.layers.length;r++)e=this.stylesheet.layers[r],e.ref||(t=a.create(e),this._layers[t.id]=t,t.on(\"error\",this._forwardLayerEvent));for(var n=0;n<this.stylesheet.layers.length;n++)if(e=this.stylesheet.layers[n],e.ref){var i=this.getLayer(e.ref);t=a.create(e,i),this._layers[t.id]=t,t.on(\"error\",this._forwardLayerEvent)}this._groupLayers(),this._updateWorkerLayers()},_groupLayers:function(){var t;this._groups=[];for(var e=0;e<this._order.length;++e){var r=this._layers[this._order[e]];t&&r.source===t.source||(t=[],t.source=r.source,this._groups.push(t)),t.push(r)}},_updateWorkerLayers:function(t){this.dispatcher.broadcast(t?\"update layers\":\"set layers\",this._serializeLayers(t))},_serializeLayers:function(t){t=t||this._order;for(var e=[],r={includeRefProperties:!0},n=0;n<t.length;n++)e.push(this._layers[t[n]].serialize(r));return e},_applyClasses:function(t,e){if(this._loaded){t=t||[],e=e||{transition:!0};var r=this.stylesheet.transition||{},n=this._updates.allPaintProps?this._layers:this._updates.paintProps;for(var i in n){var a=this._layers[i],o=this._updates.paintProps[i];if(this._updates.allPaintProps||o.all)a.updatePaintTransitions(t,e,r,this.animationLoop);else for(var s in o)this._layers[i].updatePaintTransition(s,t,e,r,this.animationLoop)}}},_recalculate:function(t){for(var e in this.sources)this.sources[e].used=!1;this._updateZoomHistory(t),this.rasterFadeDuration=300;for(var r in this._layers){var n=this._layers[r];n.recalculate(t,this.zoomHistory),!n.isHidden(t)&&n.source&&(this.sources[n.source].used=!0)}Math.floor(this.z)!==Math.floor(t)&&this.animationLoop.set(300),this.z=t,this.fire(\"zoom\")},_updateZoomHistory:function(t){var e=this.zoomHistory;void 0===e.lastIntegerZoom&&(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=0,e.lastZoom=t),Math.floor(e.lastZoom)<Math.floor(t)?(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=Date.now()):Math.floor(e.lastZoom)>Math.floor(t)&&(e.lastIntegerZoom=Math.floor(t+1),e.lastIntegerZoomTime=Date.now()),e.lastZoom=t},_checkLoaded:function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},update:function(t,e){if(!this._updates.changed)return this;if(this._updates.allLayers)this._groupLayers(),this._updateWorkerLayers();else{var r=Object.keys(this._updates.layers);r.length&&this._updateWorkerLayers(r)}var n,i=Object.keys(this._updates.sources);for(n=0;n<i.length;n++)this._reloadSource(i[n]);for(n=0;n<this._updates.events.length;n++){var a=this._updates.events[n];this.fire(a[0],a[1])}return this._applyClasses(t,e),this._updates.changed&&this.fire(\"change\"),this._resetUpdates(),this},_resetUpdates:function(){this._updates={events:[],layers:{},sources:{},paintProps:{}}},addSource:function(t,e){if(this._checkLoaded(),void 0!==this.sources[t])throw new Error(\"There is already a source with this ID\");if(!e.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(e)+\".\");return[\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(e.type)>=0&&this._handleErrors(v.source,\"sources.\"+t,e)?this:(e=new b(t,e,this.dispatcher),this.sources[t]=e,e.style=this,e.on(\"load\",this._forwardSourceEvent).on(\"error\",this._forwardSourceEvent).on(\"change\",this._forwardSourceEvent).on(\"tile.add\",this._forwardTileEvent).on(\"tile.load\",this._forwardTileEvent).on(\"tile.error\",this._forwardTileEvent).on(\"tile.remove\",this._forwardTileEvent).on(\"tile.stats\",this._forwardTileEvent),this._updates.events.push([\"source.add\",{source:e}]),this._updates.changed=!0,this)},removeSource:function(t){if(this._checkLoaded(),void 0===this.sources[t])throw new Error(\"There is no source with this ID\");var e=this.sources[t];return delete this.sources[t],delete this._updates.sources[t],e.off(\"load\",this._forwardSourceEvent).off(\"error\",this._forwardSourceEvent).off(\"change\",this._forwardSourceEvent).off(\"tile.add\",this._forwardTileEvent).off(\"tile.load\",this._forwardTileEvent).off(\"tile.error\",this._forwardTileEvent).off(\"tile.remove\",this._forwardTileEvent).off(\"tile.stats\",this._forwardTileEvent),this._updates.events.push([\"source.remove\",{source:e}]),this._updates.changed=!0,this},getSource:function(t){return this.sources[t]&&this.sources[t].getSource()},addLayer:function(t,e){if(this._checkLoaded(),!(t instanceof a)){if(this._handleErrors(v.layer,\"layers.\"+t.id,t,!1,{arrayIndex:-1}))return this;var r=t.ref&&this.getLayer(t.ref);t=a.create(t,r)}return this._validateLayer(t),t.on(\"error\",this._forwardLayerEvent),this._layers[t.id]=t,this._order.splice(e?this._order.indexOf(e):1/0,0,t.id),this._updates.allLayers=!0,t.source&&(this._updates.sources[t.source]=!0),this._updates.events.push([\"layer.add\",{layer:t}]),this.updateClasses(t.id)},removeLayer:function(t){this._checkLoaded();var e=this._layers[t];if(void 0===e)throw new Error(\"There is no layer with this ID\");for(var r in this._layers)this._layers[r].ref===t&&this.removeLayer(r);return e.off(\"error\",this._forwardLayerEvent),delete this._layers[t],delete this._updates.layers[t],delete this._updates.paintProps[t],this._order.splice(this._order.indexOf(t),1),this._updates.allLayers=!0,this._updates.events.push([\"layer.remove\",{layer:e}]),this._updates.changed=!0,this},getLayer:function(t){return this._layers[t]},getReferentLayer:function(t){var e=this.getLayer(t);return e.ref&&(e=this.getLayer(e.ref)),e},setLayerZoomRange:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return n.minzoom===e&&n.maxzoom===r?this:(null!=e&&(n.minzoom=e),null!=r&&(n.maxzoom=r),this._updateLayer(n))},setFilter:function(t,e){this._checkLoaded();var r=this.getReferentLayer(t);return null!==e&&this._handleErrors(v.filter,\"layers.\"+r.id+\".filter\",e)?this:c.deepEqual(r.filter,e)?this:(r.filter=c.clone(e),this._updateLayer(r))},getFilter:function(t){return this.getReferentLayer(t).filter},setLayoutProperty:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return c.deepEqual(n.getLayoutProperty(e),r)?this:(n.setLayoutProperty(e,r),this._updateLayer(n))},getLayoutProperty:function(t,e){return this.getReferentLayer(t).getLayoutProperty(e)},setPaintProperty:function(t,e,r,n){this._checkLoaded();var i=this.getLayer(t);if(c.deepEqual(i.getPaintProperty(e,n),r))return this;var a=i.isPaintValueFeatureConstant(e);return i.setPaintProperty(e,r,n),!(r&&_.isFunctionDefinition(r)&&\"$zoom\"!==r.property&&void 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a=t(\"./style_function\"),o=t(\"./parse_color\"),s=t(\"../util/util\");e.exports=n},{\"../util/util\":442,\"./parse_color\":377,\"./style_function\":380}],380:[function(t,e,r){\"use strict\";var n=t(\"mapbox-gl-function\");r.interpolated=function(t){var e=n.interpolated(t),r=function(t,r){return e(t&&t.zoom,r||{})};return r.isFeatureConstant=e.isFeatureConstant,r.isZoomConstant=e.isZoomConstant,r},r[\"piecewise-constant\"]=function(t){var e=n[\"piecewise-constant\"](t),r=function(t,r){return e(t&&t.zoom,r||{})};return r.isFeatureConstant=e.isFeatureConstant,r.isZoomConstant=e.isZoomConstant,r},r.isFunctionDefinition=n.isFunctionDefinition},{\"mapbox-gl-function\":302}],381:[function(t,e,r){\"use strict\";function n(t,e){this.set(t,e)}function i(t){return t.value}var a=t(\"../util/util\"),o=t(\"./style_transition\"),s=t(\"./style_declaration\"),l=t(\"./style_spec\"),u=t(\"./validate_style\"),c=t(\"./parse_color\"),h=t(\"../util/evented\");e.exports=n;n.create=function(e,r){return new({background:t(\"./style_layer/background_style_layer\"),circle:t(\"./style_layer/circle_style_layer\"),fill:t(\"./style_layer/fill_style_layer\"),line:t(\"./style_layer/line_style_layer\"),raster:t(\"./style_layer/raster_style_layer\"),symbol:t(\"./style_layer/symbol_style_layer\")}[(r||e).type])(e,r)},n.prototype=a.inherit(h,{set:function(t,e){this.id=t.id,this.ref=t.ref,this.metadata=t.metadata,this.type=(e||t).type,this.source=(e||t).source,this.sourceLayer=(e||t)[\"source-layer\"],this.minzoom=(e||t).minzoom,this.maxzoom=(e||t).maxzoom,this.filter=(e||t).filter,this.paint={},this.layout={},this._paintSpecifications=l[\"paint_\"+this.type],this._layoutSpecifications=l[\"layout_\"+this.type],this._paintTransitions={},this._paintTransitionOptions={},this._paintDeclarations={},this._layoutDeclarations={},this._layoutFunctions={};var r,n;for(var i in t){var a=i.match(/^paint(?:\\.(.*))?$/);if(a){var o=a[1]||\"\";for(r in t[i])this.setPaintProperty(r,t[i][r],o)}}if(this.ref)this._layoutDeclarations=e._layoutDeclarations;else for(n in t.layout)this.setLayoutProperty(n,t.layout[n]);for(r in this._paintSpecifications)this.paint[r]=this.getPaintValue(r);for(n in this._layoutSpecifications)this._updateLayoutValue(n)},setLayoutProperty:function(t,e){if(null==e)delete this._layoutDeclarations[t];else{var r=\"layers.\"+this.id+\".layout.\"+t;if(this._handleErrors(u.layoutProperty,r,t,e))return;this._layoutDeclarations[t]=new s(this._layoutSpecifications[t],e)}this._updateLayoutValue(t)},getLayoutProperty:function(t){return this._layoutDeclarations[t]&&this._layoutDeclarations[t].value},getLayoutValue:function(t,e,r){var n=this._layoutSpecifications[t],i=this._layoutDeclarations[t];return i?i.calculate(e,r):n.default},setPaintProperty:function(t,e,r){var n=\"layers.\"+this.id+(r?'[\"paint.'+r+'\"].':\".paint.\")+t;if(a.endsWith(t,\"-transition\"))if(this._paintTransitionOptions[r||\"\"]||(this._paintTransitionOptions[r||\"\"]={}),null===e||void 0===e)delete this._paintTransitionOptions[r||\"\"][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintTransitionOptions[r||\"\"][t]=e}else if(this._paintDeclarations[r||\"\"]||(this._paintDeclarations[r||\"\"]={}),null===e||void 0===e)delete this._paintDeclarations[r||\"\"][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintDeclarations[r||\"\"][t]=new s(this._paintSpecifications[t],e)}},getPaintProperty:function(t,e){return e=e||\"\",a.endsWith(t,\"-transition\")?this._paintTransitionOptions[e]&&this._paintTransitionOptions[e][t]:this._paintDeclarations[e]&&this._paintDeclarations[e][t]&&this._paintDeclarations[e][t].value},getPaintValue:function(t,e,r){var n=this._paintSpecifications[t],i=this._paintTransitions[t];return 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this._paintDeclarations){e[\"\"===r?\"paint\":\"paint.\"+r]=a.mapObject(this._paintDeclarations[r],i)}return(!this.ref||t&&t.includeRefProperties)&&a.extend(e,{type:this.type,source:this.source,\"source-layer\":this.sourceLayer,filter:this.filter,layout:a.mapObject(this._layoutDeclarations,i)}),a.filterObject(e,function(t,e){return void 0!==t&&!(\"layout\"===e&&!Object.keys(t).length)})},_applyPaintDeclaration:function(t,e,r,n,i){var l=r.transition?this._paintTransitions[t]:void 0,u=this._paintSpecifications[t];if(null!==e&&void 0!==e||(e=new s(u,u.default)),!l||l.declaration.json!==e.json){var c=a.extend({duration:300,delay:0},n,this.getPaintProperty(t+\"-transition\")),h=this._paintTransitions[t]=new o(u,e,l,c);h.instant()||(h.loopID=i.set(h.endTime-Date.now())),l&&i.cancel(l.loopID)}},_updateLayoutValue:function(t){var e=this._layoutDeclarations[t];e&&e.isFunction?this._layoutFunctions[t]=!0:(delete 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i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],384:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");n.prototype=i.inherit(a,{getPaintValue:function(t,e,r){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintValue.call(this,\"fill-color\",e,r):a.prototype.getPaintValue.call(this,t,e,r)},getPaintValueStopZoomLevels:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintValueStopZoomLevels.call(this,\"fill-color\"):a.prototype.getPaintValueStopZoomLevels.call(this,arguments)},getPaintInterpolationT:function(t,e){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintInterpolationT.call(this,\"fill-color\",e):a.prototype.getPaintInterpolationT.call(this,t,e)},isPaintValueFeatureConstant:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.isPaintValueFeatureConstant.call(this,\"fill-color\"):a.prototype.isPaintValueFeatureConstant.call(this,t)},isPaintValueZoomConstant:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.isPaintValueZoomConstant.call(this,\"fill-color\"):a.prototype.isPaintValueZoomConstant.call(this,t)}}),e.exports=n},{\"../../util/util\":442,\"../style_layer\":381}],385:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{getPaintValue:function(t,e,r){var n=a.prototype.getPaintValue.apply(this,arguments);if(n&&\"line-dasharray\"===t){var i=Math.floor(e.zoom);this._flooredZoom!==i&&(this._flooredZoom=i,this._flooredLineWidth=this.getPaintValue(\"line-width\",e,r)),n.fromScale*=this._flooredLineWidth,n.toScale*=this._flooredLineWidth}return n}})},{\"../../util/util\":442,\"../style_layer\":381}],386:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],387:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{isHidden:function(){if(a.prototype.isHidden.apply(this,arguments))return!0;var t=0===this.paint[\"text-opacity\"]||!this.layout[\"text-field\"],e=0===this.paint[\"icon-opacity\"]||!this.layout[\"icon-image\"];return!(!t||!e)},getLayoutValue:function(t,e,r){return(\"text-rotation-alignment\"!==t||\"line\"!==this.getLayoutValue(\"symbol-placement\",e,r)||this.getLayoutProperty(\"text-rotation-alignment\"))&&(\"icon-rotation-alignment\"!==t||\"line\"!==this.getLayoutValue(\"symbol-placement\",e,r)||this.getLayoutProperty(\"icon-rotation-alignment\"))?\"text-pitch-alignment\"!==t||this.getLayoutProperty(\"text-pitch-alignment\")?a.prototype.getLayoutValue.apply(this,arguments):this.getLayoutValue(\"text-rotation-alignment\"):\"map\"}})},{\"../../util/util\":442,\"../style_layer\":381}],388:[function(t,e,r){\"use strict\";e.exports=t(\"mapbox-gl-style-spec/reference/latest.min\")},{\"mapbox-gl-style-spec/reference/latest.min\":325}],389:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.declaration=e,this.startTime=this.endTime=(new 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r}},{\"../util/interpolate\":436,\"../util/util\":442}],390:[function(t,e,r){\"use strict\";e.exports=t(\"mapbox-gl-style-spec/lib/validate_style.min\"),e.exports.emitErrors=function(t,e){if(e&&e.length){for(var r=0;r<e.length;r++)t.fire(\"error\",{error:new Error(e[r].message)});return!0}return!1},e.exports.throwErrors=function(t,e){if(e)for(var r=0;r<e.length;r++)throw new Error(e[r].message)}},{\"mapbox-gl-style-spec/lib/validate_style.min\":324}],391:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.x=t,this.y=e,this.angle=r,void 0!==n&&(this.segment=n)}var i=t(\"point-geometry\");e.exports=n,n.prototype=Object.create(i.prototype),n.prototype.clone=function(){return new n(this.x,this.y,this.angle,this.segment)}},{\"point-geometry\":484}],392:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],u=0;s<r/2;){var c=t[o-1],h=t[o],f=t[o+1];if(!f)return!1;var d=c.angleTo(h)-h.angleTo(f);for(d=Math.abs((d+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:d}),u+=d;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;o++,s+=h.dist(f)}return!0}e.exports=n},{}],393:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){for(var o=[],s=0;s<t.length;s++)for(var l,u=t[s],c=0;c<u.length-1;c++){var h=u[c],f=u[c+1];h.x<e&&f.x<e||(h.x<e?h=new i(e,h.y+(f.y-h.y)*((e-h.x)/(f.x-h.x)))._round():f.x<e&&(f=new i(e,h.y+(f.y-h.y)*((e-h.x)/(f.x-h.x)))._round()),h.y<r&&f.y<r||(h.y<r?h=new i(h.x+(f.x-h.x)*((r-h.y)/(f.y-h.y)),r)._round():f.y<r&&(f=new i(h.x+(f.x-h.x)*((r-h.y)/(f.y-h.y)),r)._round()),h.x>=n&&f.x>=n||(h.x>=n?h=new i(n,h.y+(f.y-h.y)*((n-h.x)/(f.x-h.x)))._round():f.x>=n&&(f=new i(n,h.y+(f.y-h.y)*((n-h.x)/(f.x-h.x)))._round()),h.y>=a&&f.y>=a||(h.y>=a?h=new i(h.x+(f.x-h.x)*((a-h.y)/(f.y-h.y)),a)._round():f.y>=a&&(f=new i(h.x+(f.x-h.x)*((a-h.y)/(f.y-h.y)),a)._round()),l&&h.equals(l[l.length-1])||(l=[h],o.push(l)),l.push(f)))))}return o}var i=t(\"point-geometry\");e.exports=n},{\"point-geometry\":484}],394:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=e.exports=new n({members:[{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Float32\",name:\"maxScale\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"},{type:\"Int16\",name:\"bbox0\"},{type:\"Int16\",name:\"bbox1\"},{type:\"Int16\",name:\"bbox2\"},{type:\"Int16\",name:\"bbox3\"},{type:\"Float32\",name:\"placementScale\"}]});i.extendAll(o.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"point-geometry\":484}],395:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u,c){var h=o.top*s-l,f=o.bottom*s+l,d=o.left*s-l,p=o.right*s+l;if(this.boxStartIndex=t.length,u){var m=f-h,v=p-d;if(m>0)if(m=Math.max(10*s,m),c){var g=e[r.segment+1].sub(e[r.segment])._unit()._mult(v),y=[r.sub(g),r.add(g)];this._addLineCollisionBoxes(t,y,r,0,v,m,n,i,a)}else this._addLineCollisionBoxes(t,e,r,r.segment,v,m,n,i,a)}else t.emplaceBack(r.x,r.y,d,h,p,f,1/0,n,i,a,0,0,0,0,0);this.boxEndIndex=t.length}e.exports=n,n.prototype._addLineCollisionBoxes=function(t,e,r,n,i,a,o,s,l){var u=a/2,c=Math.floor(i/u),h=-a/2,f=this.boxes,d=r,p=n+1,m=h;do{if(--p<0)return f;m-=e[p].dist(d),d=e[p]}while(m>-i/2);for(var v=e[p].dist(e[p+1]),g=0;g<c;g++){for(var y=-i/2+g*u;m+v<y;){if(m+=v,++p+1>=e.length)return f;v=e[p].dist(e[p+1])}var b=y-m,x=e[p],_=e[p+1],w=_.sub(x)._unit()._mult(b)._add(x)._round(),M=Math.max(Math.abs(y-h)-u/2,0),k=i/2/M;t.emplaceBack(w.x,w.y,-a/2,-a/2,a/2,a/2,k,o,s,l,0,0,0,0,0)}return f}},{}],396:[function(t,e,r){\"use strict\";function n(t,e,r){if(\"object\"==typeof t){var n=t;r=e,t=n.angle,e=n.pitch,this.grid=new o(n.grid),this.ignoredGrid=new o(n.ignoredGrid)}else this.grid=new o(a,12,6),this.ignoredGrid=new o(a,12,0);this.angle=t,this.pitch=e;var i=Math.sin(t),s=Math.cos(t);if(this.rotationMatrix=[s,-i,i,s],this.reverseRotationMatrix=[s,i,-i,s],this.yStretch=1/Math.cos(e/180*Math.PI),this.yStretch=Math.pow(this.yStretch,1.3),this.collisionBoxArray=r,0===r.length){r.emplaceBack();r.emplaceBack(0,0,0,-32767,0,32767,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(a,0,0,-32767,0,32767,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,0,-32767,0,32767,0,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,a,-32767,0,32767,0,32767,0,0,0,0,0,0,0,0,0)}this.tempCollisionBox=r.get(0),this.edges=[r.get(1),r.get(2),r.get(3),r.get(4)]}var i=t(\"point-geometry\"),a=t(\"../data/bucket\").EXTENT,o=t(\"grid-index\");e.exports=n,n.prototype.serialize=function(){var t={angle:this.angle,pitch:this.pitch,grid:this.grid.toArrayBuffer(),ignoredGrid:this.ignoredGrid.toArrayBuffer()};return{data:t,transferables:[t.grid,t.ignoredGrid]}},n.prototype.minScale=.25,n.prototype.maxScale=2,n.prototype.placeCollisionFeature=function(t,e,r){for(var n=this.collisionBoxArray,a=this.minScale,o=this.rotationMatrix,s=this.yStretch,l=t.boxStartIndex;l<t.boxEndIndex;l++){var u=n.get(l),c=u.anchorPoint._matMult(o),h=c.x,f=c.y,d=h+u.x1,p=f+u.y1*s,m=h+u.x2,v=f+u.y2*s;if(u.bbox0=d,u.bbox1=p,u.bbox2=m,u.bbox3=v,!e)for(var g=this.grid.query(d,p,m,v),y=0;y<g.length;y++){var b=n.get(g[y]),x=b.anchorPoint._matMult(o);if((a=this.getPlacementScale(a,c,u,x,b))>=this.maxScale)return a}if(r){var _;if(this.angle){var w=this.reverseRotationMatrix,M=new i(u.x1,u.y1).matMult(w),k=new i(u.x2,u.y1).matMult(w),A=new i(u.x1,u.y2).matMult(w),T=new i(u.x2,u.y2).matMult(w);_=this.tempCollisionBox,_.anchorPointX=u.anchorPoint.x,_.anchorPointY=u.anchorPoint.y,_.x1=Math.min(M.x,k.x,A.x,T.x),_.y1=Math.min(M.y,k.x,A.x,T.x),_.x2=Math.max(M.x,k.x,A.x,T.x),_.y2=Math.max(M.y,k.x,A.x,T.x),_.maxScale=u.maxScale}else _=u;for(var S=0;S<this.edges.length;S++){var E=this.edges[S];if((a=this.getPlacementScale(a,u.anchorPoint,_,E.anchorPoint,E))>=this.maxScale)return a}}}return a},n.prototype.queryRenderedSymbols=function(t,e,r,n,a){var o={},s=[],l=this.collisionBoxArray,u=this.rotationMatrix,c=new i(t,e)._matMult(u),h=this.tempCollisionBox;h.anchorX=c.x,h.anchorY=c.y,h.x1=0,h.y1=0,h.x2=r-t,h.y2=n-e,h.maxScale=a,a=h.maxScale;for(var f=[c.x+h.x1/a,c.y+h.y1/a*this.yStretch,c.x+h.x2/a,c.y+h.y2/a*this.yStretch],d=this.grid.query(f[0],f[1],f[2],f[3]),p=this.ignoredGrid.query(f[0],f[1],f[2],f[3]),m=0;m<p.length;m++)d.push(p[m]);for(var v=0;v<d.length;v++){var g=l.get(d[v]),y=g.sourceLayerIndex,b=g.featureIndex;if(void 0===o[y]&&(o[y]={}),!o[y][b]){var x=g.anchorPoint.matMult(u);this.getPlacementScale(this.minScale,c,h,x,g)>=a&&(o[y][b]=!0,s.push(d[v]))}}return s},n.prototype.getPlacementScale=function(t,e,r,n,i){var a=e.x-n.x,o=e.y-n.y,s=(i.x1-r.x2)/a,l=(i.x2-r.x1)/a,u=(i.y1-r.y2)*this.yStretch/o,c=(i.y2-r.y1)*this.yStretch/o;(isNaN(s)||isNaN(l))&&(s=l=1),(isNaN(u)||isNaN(c))&&(u=c=1);var h=Math.min(Math.max(s,l),Math.max(u,c)),f=i.maxScale,d=r.maxScale;return h>f&&(h=f),h>d&&(h=d),h>t&&h>=i.placementScale&&(t=h),t},n.prototype.insertCollisionFeature=function(t,e,r){for(var n=r?this.ignoredGrid:this.grid,i=this.collisionBoxArray,a=t.boxStartIndex;a<t.boxEndIndex;a++){var o=i.get(a);o.placementScale=e,e<this.maxScale&&n.insert(a,o.bbox0,o.bbox1,o.bbox2,o.bbox3)}}},{\"../data/bucket\":329,\"grid-index\":287,\"point-geometry\":484}],397:[function(t,e,r){\"use strict\";function n(t,e,r,n,a,o,s,l,u){var c=n?.6*o*s:0,h=Math.max(n?n.right-n.left:0,a?a.right-a.left:0),f=0===t[0].x||t[0].x===u||0===t[0].y||t[0].y===u;e-h*s<e/4&&(e=h*s+e/4);var d=2*o;return i(t,f?e/2*l%e:(h/2+d)*s*l%e,e,c,r,h*s,f,!1,u)}function i(t,e,r,n,l,u,c,h,f){for(var d=u/2,p=0,m=0;m<t.length-1;m++)p+=t[m].dist(t[m+1]);for(var v=0,g=e-r,y=[],b=0;b<t.length-1;b++){for(var x=t[b],_=t[b+1],w=x.dist(_),M=_.angleTo(x);g+r<v+w;){g+=r;var k=(g-v)/w,A=a(x.x,_.x,k),T=a(x.y,_.y,k);if(A>=0&&A<f&&T>=0&&T<f&&g-d>=0&&g+d<=p){var S=new o(A,T,M,b)._round();n&&!s(t,S,u,n,l)||y.push(S)}}v+=w}return h||y.length||c||(y=i(t,v/2,r,n,l,u,c,!0,f)),y}var a=t(\"../util/interpolate\"),o=t(\"../symbol/anchor\"),s=t(\"./check_max_angle\");e.exports=n},{\"../symbol/anchor\":391,\"../util/interpolate\":436,\"./check_max_angle\":392}],398:[function(t,e,r){\"use strict\";function n(){this.width=o,this.height=o,this.bin=new i(this.width,this.height),this.index={},this.ids={},this.data=new Uint8Array(this.width*this.height)}var i=t(\"shelf-pack\"),a=t(\"../util/util\"),o=128;e.exports=n,n.prototype.getGlyphs=function(){var t,e,r,n={};for(var i in this.ids)t=i.split(\"#\"),e=t[0],r=t[1],n[e]||(n[e]=[]),n[e].push(r);return n},n.prototype.getRects=function(){var t,e,r,n={};for(var i in this.ids)t=i.split(\"#\"),e=t[0],r=t[1],n[e]||(n[e]={}),n[e][r]=this.index[i];return n},n.prototype.addGlyph=function(t,e,r,n){if(!r)return null;var i=e+\"#\"+r.id;if(this.index[i])return this.ids[i].indexOf(t)<0&&this.ids[i].push(t),this.index[i];if(!r.bitmap)return null;var o=r.width+2*n,s=r.height+2*n,l=o+2,u=s+2;l+=4-l%4,u+=4-u%4;var c=this.bin.packOne(l,u);if(c||(this.resize(),c=this.bin.packOne(l,u)),!c)return a.warnOnce(\"glyph bitmap overflow\"),null;this.index[i]=c,this.ids[i]=[t];for(var h=this.data,f=r.bitmap,d=0;d<s;d++)for(var p=this.width*(c.y+d+1)+c.x+1,m=o*d,v=0;v<o;v++)h[p+v]=f[m+v];return this.dirty=!0,c},n.prototype.resize=function(){var t=this.width,e=this.height;if(!(t>=2048||e>=2048)){this.texture&&(this.gl&&this.gl.deleteTexture(this.texture),this.texture=null),this.width*=4,this.height*=4,this.bin.resize(this.width,this.height);for(var r=new ArrayBuffer(this.width*this.height),n=0;n<e;n++){var i=new Uint8Array(this.data.buffer,e*n,t);new Uint8Array(r,e*n*4,t).set(i)}this.data=new Uint8Array(r)}},n.prototype.bind=function(t){this.gl=t,this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,this.width,this.height,0,t.ALPHA,t.UNSIGNED_BYTE,null))},n.prototype.updateTexture=function(t){this.bind(t),this.dirty&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.ALPHA,t.UNSIGNED_BYTE,this.data),this.dirty=!1)}},{\"../util/util\":442,\"shelf-pack\":514}],399:[function(t,e,r){\"use strict\";function n(t){this.url=t&&o(t),this.atlases={},this.stacks={},this.loading={}}function i(t,e,r){this.advance=t.advance,this.left=t.left-r-1,this.top=t.top+r+1,this.rect=e}function a(t,e,r,n){return n=n||\"abc\",r.replace(\"{s}\",n[t.length%n.length]).replace(\"{fontstack}\",t).replace(\"{range}\",e)}var o=t(\"../util/mapbox\").normalizeGlyphsURL,s=t(\"../util/ajax\").getArrayBuffer,l=t(\"../util/glyphs\"),u=t(\"../symbol/glyph_atlas\"),c=t(\"pbf\");e.exports=n,n.prototype.getSimpleGlyphs=function(t,e,r,n){void 0===this.stacks[t]&&(this.stacks[t]={}),void 0===this.atlases[t]&&(this.atlases[t]=new u);for(var a,o={},s=this.stacks[t],l=this.atlases[t],c={},h=0,f=0;f<e.length;f++){var d=e[f];if(a=Math.floor(d/256),s[a]){var p=s[a].glyphs[d],m=l.addGlyph(r,t,p,3);p&&(o[d]=new i(p,m,3))}else void 0===c[a]&&(c[a]=[],h++),c[a].push(d)}h||n(void 0,o,t);var v=function(e,a,s){if(!e)for(var u=this.stacks[t][a]=s.stacks[0],f=0;f<c[a].length;f++){var d=c[a][f],p=u.glyphs[d],m=l.addGlyph(r,t,p,3);p&&(o[d]=new i(p,m,3))}--h||n(void 0,o,t)}.bind(this);for(var g in c)this.loadRange(t,g,v)},n.prototype.loadRange=function(t,e,r){if(256*e>65535)return r(\"glyphs > 65535 not supported\");void 0===this.loading[t]&&(this.loading[t]={});var n=this.loading[t];if(n[e])n[e].push(r);else{n[e]=[r];var i=256*e+\"-\"+(256*e+255),o=a(t,i,this.url);s(o,function(t,r){for(var i=!t&&new l(new c(new Uint8Array(r))),a=0;a<n[e].length;a++)n[e][a](t,e,i);delete n[e]})}},n.prototype.getGlyphAtlas=function(t){return this.atlases[t]}},{\"../symbol/glyph_atlas\":398,\"../util/ajax\":425,\"../util/glyphs\":435,\"../util/mapbox\":439,pbf:478}],400:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){function n(n){c.push(t[n]),h.push(r[n]),f.push(e[n]),d++}function i(t,e,r){var n=u[t];return delete u[t],u[e]=n,h[n][0].pop(),h[n][0]=h[n][0].concat(r[0]),n}function a(t,e,r){var n=l[e];return delete l[e],l[t]=n,h[n][0].shift(),h[n][0]=r[0].concat(h[n][0]),n}function o(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}var s,l={},u={},c=[],h=[],f=[],d=0;for(s=0;s<t.length;s++){var p=r[s],m=e[s];if(m){var v=o(m,p),g=o(m,p,!0);if(v in u&&g in l&&u[v]!==l[g]){var y=a(v,g,p),b=i(v,g,h[y]);delete l[v],delete u[g],u[o(m,h[b],!0)]=b,h[y]=null}else v in u?i(v,g,p):g in l?a(v,g,p):(n(s),l[v]=d-1,u[g]=d-1)}else n(s)}return{features:c,textFeatures:f,geometries:h}}},{}],401:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u){this.anchorPoint=t,this.tl=e,this.tr=r,this.bl=n,this.br=i,this.tex=a,this.anchorAngle=o,this.glyphAngle=s,this.minScale=l,this.maxScale=u}function i(t,e,r,i,a,o,u,c,h){var f,d,p,m,v=e.image.rect,g=a.layout,y=e.left-1,b=y+v.w/e.image.pixelRatio,x=e.top-1,_=x+v.h/e.image.pixelRatio;if(\"none\"!==g[\"icon-text-fit\"]&&u){var w=b-y,M=_-x,k=g[\"text-size\"]/24,A=u.left*k,T=u.right*k,S=u.top*k,E=u.bottom*k,L=T-A,C=E-S,I=g[\"icon-text-fit-padding\"][0],z=g[\"icon-text-fit-padding\"][1],D=g[\"icon-text-fit-padding\"][2],P=g[\"icon-text-fit-padding\"][3],O=\"width\"===g[\"icon-text-fit\"]?.5*(C-M):0,R=\"height\"===g[\"icon-text-fit\"]?.5*(L-w):0,F=\"width\"===g[\"icon-text-fit\"]||\"both\"===g[\"icon-text-fit\"]?L:w,j=\"height\"===g[\"icon-text-fit\"]||\"both\"===g[\"icon-text-fit\"]?C:M;f=new s(A+R-P,S+O-I),d=new s(A+R+z+F,S+O-I),p=new s(A+R+z+F,S+O+D+j),m=new s(A+R-P,S+O+D+j)}else f=new s(y,x),d=new s(b,x),p=new s(b,_),m=new s(y,_);var N=a.getLayoutValue(\"icon-rotate\",c,h)*Math.PI/180;if(o){var B=i[t.segment];if(t.y===B.y&&t.x===B.x&&t.segment+1<i.length){var U=i[t.segment+1];N+=Math.atan2(t.y-U.y,t.x-U.x)+Math.PI}else N+=Math.atan2(t.y-B.y,t.x-B.x)}if(N){var V=Math.sin(N),H=Math.cos(N),q=[H,-V,V,H];f=f.matMult(q),d=d.matMult(q),m=m.matMult(q),p=p.matMult(q)}return[new n(new s(t.x,t.y),f,d,m,p,e.image.rect,0,0,l,1/0)]}function a(t,e,r,i,a,u){for(var c=a.layout[\"text-rotate\"]*Math.PI/180,h=a.layout[\"text-keep-upright\"],f=e.positionedGlyphs,d=[],p=0;p<f.length;p++){var m=f[p],v=m.glyph,g=v.rect;if(g){var y,b=(m.x+v.advance/2)*r,x=l;u?(y=[],x=o(y,t,b,i,t.segment,!0),h&&(x=Math.min(x,o(y,t,b,i,t.segment,!1)))):y=[{anchorPoint:new s(t.x,t.y),offset:0,angle:0,maxScale:1/0,minScale:l}];for(var _=m.x+v.left,w=m.y-v.top,M=_+g.w,k=w+g.h,A=new s(_,w),T=new s(M,w),S=new s(_,k),E=new s(M,k),L=0;L<y.length;L++){var C=y[L],I=A,z=T,D=S,P=E;if(c){var O=Math.sin(c),R=Math.cos(c),F=[R,-O,O,R];I=I.matMult(F),z=z.matMult(F),D=D.matMult(F),P=P.matMult(F)}var j=Math.max(C.minScale,x),N=(t.angle+C.offset+2*Math.PI)%(2*Math.PI),B=(C.angle+C.offset+2*Math.PI)%(2*Math.PI);d.push(new n(C.anchorPoint,I,z,D,P,g,N,B,j,C.maxScale))}}}return d}function o(t,e,r,n,i,a){var o=!a;r<0&&(a=!a),a&&i++;var u=new s(e.x,e.y),c=n[i],h=1/0;r=Math.abs(r);for(var f=l;;){var d=u.dist(c),p=r/d,m=Math.atan2(c.y-u.y,c.x-u.x);if(a||(m+=Math.PI),t.push({anchorPoint:u,offset:o?Math.PI:0,minScale:p,maxScale:h,angle:(m+2*Math.PI)%(2*Math.PI)}),p<=f)break;for(u=c;u.equals(c);)if(i+=a?1:-1,!(c=n[i]))return p;var v=c.sub(u)._unit();u=u.sub(v._mult(d)),h=p}return f}var s=t(\"point-geometry\");e.exports={getIconQuads:i,getGlyphQuads:a,SymbolQuad:n};var l=.5},{\"point-geometry\":484}],402:[function(t,e,r){\"use strict\";function n(t,e,r){for(var n=[],a=0,o=t.length;a<o;a++){var s=i(t[a].properties,e[\"text-field\"]);if(s){s=s.toString();var l=e[\"text-transform\"];\"uppercase\"===l?s=s.toLocaleUpperCase():\"lowercase\"===l&&(s=s.toLocaleLowerCase());for(var u=0;u<s.length;u++)r[s.charCodeAt(u)]=!0;n[a]=s}else n[a]=null}return n}var i=t(\"../util/token\");e.exports=n},{\"../util/token\":441}],403:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.codePoint=t,this.x=e,this.y=r,this.glyph=n}function i(t,e,r,n,i,a){this.positionedGlyphs=t,this.text=e,this.top=r,this.bottom=n,this.left=i,this.right=a}function a(t,e,r,a,s,l,u,c,h){for(var f=[],d=new i(f,t,h[1],h[1],h[0],h[0]),p=0,m=0;m<t.length;m++){var v=t.charCodeAt(m),g=e[v];g&&(f.push(new n(v,p,-17,g)),p+=g.advance+c)}return!!f.length&&(o(d,e,a,r,s,l,u,h),d)}function o(t,e,r,n,i,a,o,u){var c=null,d=0,p=0,m=0,v=0,g=t.positionedGlyphs;if(n)for(var y=0;y<g.length;y++){var b=g[y];if(b.x-=d,b.y+=r*m,b.x>n&&null!==c){var x=g[c+1].x;v=Math.max(x,v);for(var _=c+1;_<=y;_++)g[_].y+=r,g[_].x-=x;if(o){var w=c;h[g[c].codePoint]&&w--,s(g,e,p,w,o)}p=c+1,c=null,d+=x,m++}f[b.codePoint]&&(c=y)}var M=g[g.length-1],k=M.x+e[M.codePoint].advance;v=Math.max(v,k);var A=(m+1)*r;s(g,e,p,g.length-1,o),l(g,o,i,a,v,r,m,u),t.top+=-a*A,t.bottom=t.top+A,t.left+=-i*v,t.right=t.left+v}function s(t,e,r,n,i){for(var a=e[t[n].codePoint].advance,o=(t[n].x+a)*i,s=r;s<=n;s++)t[s].x-=o}\n",
+       "function l(t,e,r,n,i,a,o,s){for(var l=(e-r)*i+s[0],u=(-n*(o+1)+.5)*a+s[1],c=0;c<t.length;c++)t[c].x+=l,t[c].y+=u}function u(t,e){if(!t||!t.rect)return null;var r=e[\"icon-offset\"][0],n=e[\"icon-offset\"][1],i=r-t.width/2,a=i+t.width,o=n-t.height/2;return new c(t,o,o+t.height,i,a)}function c(t,e,r,n,i){this.image=t,this.top=e,this.bottom=r,this.left=n,this.right=i}e.exports={shapeText:a,shapeIcon:u};var h={32:!0,8203:!0},f={32:!0,38:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0}},{}],404:[function(t,e,r){\"use strict\";function n(t,e){this.width=t,this.height=e,this.bin=new o(t,e),this.images={},this.data=!1,this.texture=0,this.filter=0,this.pixelRatio=1,this.dirty=!0}function i(t,e,r,n,i,a,o,s,l,u,c){var h,f,d=n*e+r,p=s*a+o;if(c)for(p-=a,f=-1;f<=u;f++,d=((f+u)%u+n)*e+r,p+=a)for(h=-1;h<=l;h++)i[p+h]=t[d+(h+l)%l];else for(f=0;f<u;f++,d+=e,p+=a)for(h=0;h<l;h++)i[p+h]=t[d+h]}function a(t,e,r,n,i){this.rect=t,this.width=e,this.height=r,this.sdf=n,this.pixelRatio=i}var o=t(\"shelf-pack\"),s=t(\"../util/browser\"),l=t(\"../util/util\");e.exports=n,n.prototype.allocateImage=function(t,e){t/=this.pixelRatio,e/=this.pixelRatio;var r=t+2+(4-(t+2)%4),n=e+2+(4-(e+2)%4),i=this.bin.packOne(r,n);return i||(l.warnOnce(\"SpriteAtlas out of space.\"),null)},n.prototype.getImage=function(t,e){if(this.images[t])return this.images[t];if(!this.sprite)return null;var r=this.sprite.getSpritePosition(t);if(!r.width||!r.height)return null;var n=this.allocateImage(r.width,r.height);if(!n)return null;var i=new a(n,r.width/r.pixelRatio,r.height/r.pixelRatio,r.sdf,r.pixelRatio/this.pixelRatio);return this.images[t]=i,this.copy(n,r,e),i},n.prototype.getPosition=function(t,e){var r=this.getImage(t,e),n=r&&r.rect;if(!n)return null;var i=r.width*r.pixelRatio,a=r.height*r.pixelRatio;return{size:[r.width,r.height],tl:[(n.x+1)/this.width,(n.y+1)/this.height],br:[(n.x+1+i)/this.width,(n.y+1+a)/this.height]}},n.prototype.allocate=function(){if(!this.data){var t=Math.floor(this.width*this.pixelRatio),e=Math.floor(this.height*this.pixelRatio);this.data=new Uint32Array(t*e);for(var r=0;r<this.data.length;r++)this.data[r]=0}},n.prototype.copy=function(t,e,r){if(this.sprite.img.data){var n=new Uint32Array(this.sprite.img.data.buffer);this.allocate();var a=this.data;i(n,this.sprite.img.width,e.x,e.y,a,this.width*this.pixelRatio,(t.x+1)*this.pixelRatio,(t.y+1)*this.pixelRatio,e.width,e.height,r),this.dirty=!0}},n.prototype.setSprite=function(t){t&&(this.pixelRatio=s.devicePixelRatio>1?2:1,this.canvas&&(this.canvas.width=this.width*this.pixelRatio,this.canvas.height=this.height*this.pixelRatio)),this.sprite=t},n.prototype.addIcons=function(t,e){for(var r=0;r<t.length;r++)this.getImage(t[r]);e(null,this.images)},n.prototype.bind=function(t,e){var r=!1;this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),r=!0);var n=e?t.LINEAR:t.NEAREST;n!==this.filter&&(t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,n),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,n),this.filter=n),this.dirty&&(this.allocate(),r?t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width*this.pixelRatio,this.height*this.pixelRatio,0,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)):t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width*this.pixelRatio,this.height*this.pixelRatio,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)),this.dirty=!1)}},{\"../util/browser\":426,\"../util/util\":442,\"shelf-pack\":514}],405:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=e.exports=new n({members:[{type:\"Uint16\",name:\"textBoxStartIndex\"},{type:\"Uint16\",name:\"textBoxEndIndex\"},{type:\"Uint16\",name:\"iconBoxStartIndex\"},{type:\"Uint16\",name:\"iconBoxEndIndex\"},{type:\"Uint16\",name:\"glyphQuadStartIndex\"},{type:\"Uint16\",name:\"glyphQuadEndIndex\"},{type:\"Uint16\",name:\"iconQuadStartIndex\"},{type:\"Uint16\",name:\"iconQuadEndIndex\"},{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int8\",name:\"index\"}]});i.extendAll(o.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"point-geometry\":484}],406:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=t(\"./quads\").SymbolQuad,s=e.exports=new n({members:[{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Float32\",name:\"tlX\"},{type:\"Float32\",name:\"tlY\"},{type:\"Float32\",name:\"trX\"},{type:\"Float32\",name:\"trY\"},{type:\"Float32\",name:\"blX\"},{type:\"Float32\",name:\"blY\"},{type:\"Float32\",name:\"brX\"},{type:\"Float32\",name:\"brY\"},{type:\"Int16\",name:\"texH\"},{type:\"Int16\",name:\"texW\"},{type:\"Int16\",name:\"texX\"},{type:\"Int16\",name:\"texY\"},{type:\"Float32\",name:\"anchorAngle\"},{type:\"Float32\",name:\"glyphAngle\"},{type:\"Float32\",name:\"maxScale\"},{type:\"Float32\",name:\"minScale\"}]});i.extendAll(s.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)},get SymbolQuad(){return new o(this.anchorPoint,new a(this.tlX,this.tlY),new a(this.trX,this.trY),new a(this.blX,this.blY),new a(this.brX,this.brY),{x:this.texX,y:this.texY,h:this.texH,w:this.texW,height:this.texH,width:this.texW},this.anchorAngle,this.glyphAngle,this.minScale,this.maxScale)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"./quads\":401,\"point-geometry\":484}],407:[function(t,e,r){\"use strict\";var n=t(\"../util/dom\"),i=t(\"point-geometry\"),a={scrollZoom:t(\"./handler/scroll_zoom\"),boxZoom:t(\"./handler/box_zoom\"),dragRotate:t(\"./handler/drag_rotate\"),dragPan:t(\"./handler/drag_pan\"),keyboard:t(\"./handler/keyboard\"),doubleClickZoom:t(\"./handler/dblclick_zoom\"),touchZoomRotate:t(\"./handler/touch_zoom_rotate\")};e.exports=function(t,e){function r(t){g(\"mouseout\",t)}function o(e){t.stop(),_=n.mousePos(b,e),g(\"mousedown\",e)}function s(e){var r=t.dragRotate&&t.dragRotate.isActive();x&&!r&&g(\"contextmenu\",x),x=null,g(\"mouseup\",e)}function l(e){if(!(t.dragPan&&t.dragPan.isActive()||t.dragRotate&&t.dragRotate.isActive())){for(var r=e.toElement||e.target;r&&r!==b;)r=r.parentNode;r===b&&g(\"mousemove\",e)}}function u(e){t.stop(),y(\"touchstart\",e),!e.touches||e.touches.length>1||(w?(clearTimeout(w),w=null,g(\"dblclick\",e)):w=setTimeout(d,300))}function c(t){y(\"touchmove\",t)}function h(t){y(\"touchend\",t)}function f(t){y(\"touchcancel\",t)}function d(){w=null}function p(t){n.mousePos(b,t).equals(_)&&g(\"click\",t)}function m(t){g(\"dblclick\",t),t.preventDefault()}function v(t){x=t,t.preventDefault()}function g(e,r){var i=n.mousePos(b,r);return t.fire(e,{lngLat:t.unproject(i),point:i,originalEvent:r})}function y(e,r){var a=n.touchPos(b,r),o=a.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new i(0,0));return t.fire(e,{lngLat:t.unproject(o),point:o,lngLats:a.map(function(e){return t.unproject(e)},this),points:a,originalEvent:r})}var b=t.getCanvasContainer(),x=null,_=null,w=null;for(var M in a)t[M]=new a[M](t,e),e.interactive&&e[M]&&t[M].enable();b.addEventListener(\"mouseout\",r,!1),b.addEventListener(\"mousedown\",o,!1),b.addEventListener(\"mouseup\",s,!1),b.addEventListener(\"mousemove\",l,!1),b.addEventListener(\"touchstart\",u,!1),b.addEventListener(\"touchend\",h,!1),b.addEventListener(\"touchmove\",c,!1),b.addEventListener(\"touchcancel\",f,!1),b.addEventListener(\"click\",p,!1),b.addEventListener(\"dblclick\",m,!1),b.addEventListener(\"contextmenu\",v,!1)}},{\"../util/dom\":428,\"./handler/box_zoom\":413,\"./handler/dblclick_zoom\":414,\"./handler/drag_pan\":415,\"./handler/drag_rotate\":416,\"./handler/keyboard\":417,\"./handler/scroll_zoom\":418,\"./handler/touch_zoom_rotate\":419,\"point-geometry\":484}],408:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i=t(\"../util/interpolate\"),a=t(\"../util/browser\"),o=t(\"../geo/lng_lat\"),s=t(\"../geo/lng_lat_bounds\"),l=t(\"point-geometry\"),u=e.exports=function(){};n.extend(u.prototype,{getCenter:function(){return this.transform.center},setCenter:function(t,e){return this.jumpTo({center:t},e),this},panBy:function(t,e,r){return this.panTo(this.transform.center,n.extend({offset:l.convert(t).mult(-1)},e),r),this},panTo:function(t,e,r){return this.easeTo(n.extend({center:t},e),r)},getZoom:function(){return this.transform.zoom},setZoom:function(t,e){return this.jumpTo({zoom:t},e),this},zoomTo:function(t,e,r){return this.easeTo(n.extend({zoom:t},e),r)},zoomIn:function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},zoomOut:function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},getBearing:function(){return this.transform.bearing},setBearing:function(t,e){return this.jumpTo({bearing:t},e),this},rotateTo:function(t,e,r){return this.easeTo(n.extend({bearing:t},e),r)},resetNorth:function(t,e){return this.rotateTo(0,n.extend({duration:1e3},t),e),this},snapToNorth:function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},getPitch:function(){return this.transform.pitch},setPitch:function(t,e){return this.jumpTo({pitch:t},e),this},fitBounds:function(t,e,r){e=n.extend({padding:0,offset:[0,0],maxZoom:1/0},e),t=s.convert(t);var i=l.convert(e.offset),a=this.transform,o=a.project(t.getNorthWest()),u=a.project(t.getSouthEast()),c=u.sub(o),h=(a.width-2*e.padding-2*Math.abs(i.x))/c.x,f=(a.height-2*e.padding-2*Math.abs(i.y))/c.y;return e.center=a.unproject(o.add(u).div(2)),e.zoom=Math.min(a.scaleZoom(a.scale*Math.min(h,f)),e.maxZoom),e.bearing=0,e.linear?this.easeTo(e,r):this.flyTo(e,r)},jumpTo:function(t,e){this.stop();var r=this.transform,n=!1,i=!1,a=!1;return\"zoom\"in t&&r.zoom!==+t.zoom&&(n=!0,r.zoom=+t.zoom),\"center\"in t&&(r.center=o.convert(t.center)),\"bearing\"in t&&r.bearing!==+t.bearing&&(i=!0,r.bearing=+t.bearing),\"pitch\"in t&&r.pitch!==+t.pitch&&(a=!0,r.pitch=+t.pitch),this.fire(\"movestart\",e).fire(\"move\",e),n&&this.fire(\"zoomstart\",e).fire(\"zoom\",e).fire(\"zoomend\",e),i&&this.fire(\"rotate\",e),a&&this.fire(\"pitch\",e),this.fire(\"moveend\",e)},easeTo:function(t,e){this.stop(),t=n.extend({offset:[0,0],duration:500,easing:n.ease},t);var r,a,s=this.transform,u=l.convert(t.offset),c=this.getZoom(),h=this.getBearing(),f=this.getPitch(),d=\"zoom\"in t?+t.zoom:c,p=\"bearing\"in t?this._normalizeBearing(t.bearing,h):h,m=\"pitch\"in t?+t.pitch:f;\"center\"in t?(r=o.convert(t.center),a=s.centerPoint.add(u)):\"around\"in t?(r=o.convert(t.around),a=s.locationPoint(r)):(a=s.centerPoint.add(u),r=s.pointLocation(a));var v=s.locationPoint(r);return!1===t.animate&&(t.duration=0),this.zooming=d!==c,this.rotating=h!==p,this.pitching=m!==f,t.noMoveStart||this.fire(\"movestart\",e),this.zooming&&this.fire(\"zoomstart\",e),clearTimeout(this._onEaseEnd),this._ease(function(t){this.zooming&&(s.zoom=i(c,d,t)),this.rotating&&(s.bearing=i(h,p,t)),this.pitching&&(s.pitch=i(f,m,t)),s.setLocationAtPoint(r,v.add(a.sub(v)._mult(t))),this.fire(\"move\",e),this.zooming&&this.fire(\"zoom\",e),this.rotating&&this.fire(\"rotate\",e),this.pitching&&this.fire(\"pitch\",e)},function(){t.delayEndEvents?this._onEaseEnd=setTimeout(this._easeToEnd.bind(this,e),t.delayEndEvents):this._easeToEnd(e)}.bind(this),t),this},_easeToEnd:function(t){var e=this.zooming;this.zooming=!1,this.rotating=!1,this.pitching=!1,e&&this.fire(\"zoomend\",t),this.fire(\"moveend\",t)},flyTo:function(t,e){function r(t){var e=(A*A-k*k+(t?-1:1)*L*L*T*T)/(2*(t?A:k)*L*T);return Math.log(Math.sqrt(e*e+1)-e)}function a(t){return(Math.exp(t)-Math.exp(-t))/2}function s(t){return(Math.exp(t)+Math.exp(-t))/2}function u(t){return a(t)/s(t)}this.stop(),t=n.extend({offset:[0,0],speed:1.2,curve:1.42,easing:n.ease},t);var c=this.transform,h=l.convert(t.offset),f=this.getZoom(),d=this.getBearing(),p=this.getPitch(),m=\"center\"in t?o.convert(t.center):this.getCenter(),v=\"zoom\"in t?+t.zoom:f,g=\"bearing\"in t?this._normalizeBearing(t.bearing,d):d,y=\"pitch\"in t?+t.pitch:p;Math.abs(c.center.lng)+Math.abs(m.lng)>180&&(c.center.lng>0&&m.lng<0?m.lng+=360:c.center.lng<0&&m.lng>0&&(m.lng-=360));var b=c.zoomScale(v-f),x=c.point,_=\"center\"in t?c.project(m).sub(h.div(b)):x,w=c.worldSize,M=t.curve,k=Math.max(c.width,c.height),A=k/b,T=_.sub(x).mag();if(\"minZoom\"in t){var S=n.clamp(Math.min(t.minZoom,f,v),c.minZoom,c.maxZoom),E=k/c.zoomScale(S-f);M=Math.sqrt(E/T*2)}var L=M*M,C=r(0),I=function(t){return s(C)/s(C+M*t)},z=function(t){return k*((s(C)*u(C+M*t)-a(C))/L)/T},D=(r(1)-C)/M;if(Math.abs(T)<1e-6){if(Math.abs(k-A)<1e-6)return this.easeTo(t);var P=A<k?-1:1;D=Math.abs(Math.log(A/k))/M,z=function(){return 0},I=function(t){return Math.exp(P*M*t)}}if(\"duration\"in t)t.duration=+t.duration;else{var O=\"screenSpeed\"in t?+t.screenSpeed/M:+t.speed;t.duration=1e3*D/O}return this.zooming=!0,d!==g&&(this.rotating=!0),p!==y&&(this.pitching=!0),this.fire(\"movestart\",e),this.fire(\"zoomstart\",e),this._ease(function(t){var r=t*D,n=z(r);c.zoom=f+c.scaleZoom(1/I(r)),c.center=c.unproject(x.add(_.sub(x).mult(n)),w),this.rotating&&(c.bearing=i(d,g,t)),this.pitching&&(c.pitch=i(p,y,t)),this.fire(\"move\",e),this.fire(\"zoom\",e),this.rotating&&this.fire(\"rotate\",e),this.pitching&&this.fire(\"pitch\",e)},function(){this.zooming=!1,this.rotating=!1,this.pitching=!1,this.fire(\"zoomend\",e),this.fire(\"moveend\",e)},t),this},isEasing:function(){return!!this._abortFn},stop:function(){return this._abortFn&&(this._abortFn(),this._finishEase()),this},_ease:function(t,e,r){this._finishFn=e,this._abortFn=a.timed(function(e){t.call(this,r.easing(e)),1===e&&this._finishEase()},!1===r.animate?0:r.duration,this)},_finishEase:function(){delete this._abortFn;var t=this._finishFn;delete this._finishFn,t.call(this)},_normalizeBearing:function(t,e){t=n.wrap(t,-180,180);var r=Math.abs(t-e);return Math.abs(t-360-e)<r&&(t-=360),Math.abs(t+360-e)<r&&(t+=360),t},_updateEasing:function(t,e,r){var i;if(this.ease){var a=this.ease,o=(Date.now()-a.start)/a.duration,s=a.easing(o+.01)-a.easing(o),l=.27/Math.sqrt(s*s+1e-4)*.01,u=Math.sqrt(.0729-l*l);i=n.bezier(l,u,.25,1)}else i=r?n.bezier.apply(n,r):n.ease;return this.ease={start:(new Date).getTime(),to:Math.pow(2,e),duration:t,easing:i},i}})},{\"../geo/lng_lat\":339,\"../geo/lng_lat_bounds\":340,\"../util/browser\":426,\"../util/interpolate\":436,\"../util/util\":442,\"point-geometry\":484}],409:[function(t,e,r){\"use strict\";function n(t){o.setOptions(this,t)}var i=t(\"./control\"),a=t(\"../../util/dom\"),o=t(\"../../util/util\");e.exports=n,n.createAttributionString=function(t){var e=[];for(var r in t){var n=t[r];n.attribution&&e.indexOf(n.attribution)<0&&e.push(n.attribution)}return e.sort(function(t,e){return t.length-e.length}),e=e.filter(function(t,r){for(var n=r+1;n<e.length;n++)if(e[n].indexOf(t)>=0)return!1;return!0}),e.join(\" | \")},n.prototype=o.inherit(i,{options:{position:\"bottom-right\"},onAdd:function(t){var e=this._container=a.create(\"div\",\"mapboxgl-ctrl-attrib\",t.getContainer());return this._update(),t.on(\"source.load\",this._update.bind(this)),t.on(\"source.change\",this._update.bind(this)),t.on(\"source.remove\",this._update.bind(this)),t.on(\"moveend\",this._updateEditLink.bind(this)),e},_update:function(){this._map.style&&(this._container.innerHTML=n.createAttributionString(this._map.style.sources)),this._editLink=this._container.getElementsByClassName(\"mapbox-improve-map\")[0],this._updateEditLink()},_updateEditLink:function(){if(this._editLink){var t=this._map.getCenter();this._editLink.href=\"https://www.mapbox.com/map-feedback/#/\"+t.lng+\"/\"+t.lat+\"/\"+Math.round(this._map.getZoom()+1)}}})},{\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],410:[function(t,e,r){\"use strict\";function n(){}var i=t(\"../../util/util\"),a=t(\"../../util/evented\");e.exports=n,n.prototype={addTo:function(t){this._map=t;var e=this._container=this.onAdd(t);if(this.options&&this.options.position){var r=this.options.position,n=t._controlCorners[r];e.className+=\" mapboxgl-ctrl\",-1!==r.indexOf(\"bottom\")?n.insertBefore(e,n.firstChild):n.appendChild(e)}return this},remove:function(){return this._container.parentNode.removeChild(this._container),this.onRemove&&this.onRemove(this._map),this._map=null,this}},i.extend(n.prototype,a)},{\"../../util/evented\":434,\"../../util/util\":442}],411:[function(t,e,r){\"use strict\";function n(t){s.setOptions(this,t)}var i=t(\"./control\"),a=t(\"../../util/browser\"),o=t(\"../../util/dom\"),s=t(\"../../util/util\");e.exports=n;var l={enableHighAccuracy:!1,timeout:6e3};n.prototype=s.inherit(i,{options:{position:\"top-right\"},onAdd:function(t){var e=this._container=o.create(\"div\",\"mapboxgl-ctrl-group\",t.getContainer());return a.supportsGeolocation?(this._container.addEventListener(\"contextmenu\",this._onContextMenu.bind(this)),this._geolocateButton=o.create(\"button\",\"mapboxgl-ctrl-icon mapboxgl-ctrl-geolocate\",this._container),this._geolocateButton.type=\"button\",this._geolocateButton.addEventListener(\"click\",this._onClickGeolocate.bind(this)),e):e},_onContextMenu:function(t){t.preventDefault()},_onClickGeolocate:function(){navigator.geolocation.getCurrentPosition(this._success.bind(this),this._error.bind(this),l),this._timeoutId=setTimeout(this._finish.bind(this),1e4)},_success:function(t){this._map.jumpTo({center:[t.coords.longitude,t.coords.latitude],zoom:17,bearing:0,pitch:0}),this.fire(\"geolocate\",t),this._finish()},_error:function(t){this.fire(\"error\",t),this._finish()},_finish:function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0}})},{\"../../util/browser\":426,\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],412:[function(t,e,r){\"use strict\";function n(t){s.setOptions(this,t)}function i(t){return new MouseEvent(t.type,{button:2,buttons:2,bubbles:!0,cancelable:!0,detail:t.detail,view:t.view,screenX:t.screenX,screenY:t.screenY,clientX:t.clientX,clientY:t.clientY,movementX:t.movementX,movementY:t.movementY,ctrlKey:t.ctrlKey,shiftKey:t.shiftKey,altKey:t.altKey,metaKey:t.metaKey})}var a=t(\"./control\"),o=t(\"../../util/dom\"),s=t(\"../../util/util\");e.exports=n,n.prototype=s.inherit(a,{options:{position:\"top-right\"},onAdd:function(t){var e=\"mapboxgl-ctrl\",r=this._container=o.create(\"div\",e+\"-group\",t.getContainer());return this._container.addEventListener(\"contextmenu\",this._onContextMenu.bind(this)),this._zoomInButton=this._createButton(e+\"-icon \"+e+\"-zoom-in\",t.zoomIn.bind(t)),this._zoomOutButton=this._createButton(e+\"-icon \"+e+\"-zoom-out\",t.zoomOut.bind(t)),this._compass=this._createButton(e+\"-icon \"+e+\"-compass\",t.resetNorth.bind(t)),this._compassArrow=o.create(\"div\",\"arrow\",this._compass),this._compass.addEventListener(\"mousedown\",this._onCompassDown.bind(this)),this._onCompassMove=this._onCompassMove.bind(this),this._onCompassUp=this._onCompassUp.bind(this),t.on(\"rotate\",this._rotateCompassArrow.bind(this)),this._rotateCompassArrow(),this._el=t.getCanvasContainer(),r},_onContextMenu:function(t){t.preventDefault()},_onCompassDown:function(t){0===t.button&&(o.disableDrag(),document.addEventListener(\"mousemove\",this._onCompassMove),document.addEventListener(\"mouseup\",this._onCompassUp),this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassMove:function(t){0===t.button&&(this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassUp:function(t){0===t.button&&(document.removeEventListener(\"mousemove\",this._onCompassMove),document.removeEventListener(\"mouseup\",this._onCompassUp),o.enableDrag(),this._el.dispatchEvent(i(t)),t.stopPropagation())},_createButton:function(t,e){var r=o.create(\"button\",t,this._container);return r.type=\"button\",r.addEventListener(\"click\",function(){e()}),r},_rotateCompassArrow:function(){var t=\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassArrow.style.transform=t}})},{\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],413:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../geo/lng_lat_bounds\"),o=t(\"../../util/util\");e.exports=n,n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener(\"mousedown\",this._onMouseDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onMouseDown),this._enabled=!1)},_onMouseDown:function(t){t.shiftKey&&0===t.button&&(document.addEventListener(\"mousemove\",this._onMouseMove,!1),document.addEventListener(\"keydown\",this._onKeyDown,!1),document.addEventListener(\"mouseup\",this._onMouseUp,!1),i.disableDrag(),this._startPos=i.mousePos(this._el,t),this._active=!0)},_onMouseMove:function(t){var e=this._startPos,r=i.mousePos(this._el,t);this._box||(this._box=i.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var n=Math.min(e.x,r.x),a=Math.max(e.x,r.x),o=Math.min(e.y,r.y),s=Math.max(e.y,r.y);i.setTransform(this._box,\"translate(\"+n+\"px,\"+o+\"px)\"),this._box.style.width=a-n+\"px\",this._box.style.height=s-o+\"px\"},_onMouseUp:function(t){if(0===t.button){var e=this._startPos,r=i.mousePos(this._el,t),n=new a(this._map.unproject(e),this._map.unproject(r));this._finish(),e.x===r.x&&e.y===r.y?this._fireEvent(\"boxzoomcancel\",t):this._map.fitBounds(n,{linear:!0}).fire(\"boxzoomend\",{originalEvent:t,boxZoomBounds:n})}},_onKeyDown:function(t){27===t.keyCode&&(this._finish(),this._fireEvent(\"boxzoomcancel\",t))},_finish:function(){this._active=!1,document.removeEventListener(\"mousemove\",this._onMouseMove,!1),document.removeEventListener(\"keydown\",this._onKeyDown,!1),document.removeEventListener(\"mouseup\",this._onMouseUp,!1),this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(this._box.parentNode.removeChild(this._box),this._box=null),i.enableDrag()},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})}}},{\"../../geo/lng_lat_bounds\":340,\"../../util/dom\":428,\"../../util/util\":442}],414:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._onDblClick=this._onDblClick.bind(this)}e.exports=n,n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._map.on(\"dblclick\",this._onDblClick),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._map.off(\"dblclick\",this._onDblClick),this._enabled=!1)},_onDblClick:function(t){this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)}}},{}],415:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/util\");e.exports=n;var o=a.bezier(0,0,.3,1);n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener(\"mousedown\",this._onDown),this._el.addEventListener(\"touchstart\",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onDown),this._el.removeEventListener(\"touchstart\",this._onDown),this._enabled=!1)},_onDown:function(t){this._ignoreEvent(t)||this.isActive()||(t.touches?(document.addEventListener(\"touchmove\",this._onMove),document.addEventListener(\"touchend\",this._onTouchEnd)):(document.addEventListener(\"mousemove\",this._onMove),document.addEventListener(\"mouseup\",this._onMouseUp)),this._active=!1,this._startPos=this._pos=i.mousePos(this._el,t),this._inertia=[[Date.now(),this._pos]])},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()||(this._active=!0,this._fireEvent(\"dragstart\",t),this._fireEvent(\"movestart\",t));var e=i.mousePos(this._el,t),r=this._map;r.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),e]),r.transform.setLocationAtPoint(r.transform.pointLocation(this._pos),e),this._fireEvent(\"drag\",t),this._fireEvent(\"move\",t),this._pos=e,t.preventDefault()}},_onUp:function(t){if(this.isActive()){this._active=!1,this._fireEvent(\"dragend\",t),this._drainInertiaBuffer();var e=function(){this._fireEvent(\"moveend\",t)}.bind(this),r=this._inertia;if(r.length<2)return void e();var n=r[r.length-1],i=r[0],a=n[1].sub(i[1]),s=(n[0]-i[0])/1e3;if(0===s||n[1].equals(i[1]))return void e();var l=a.mult(.3/s),u=l.mag();u>1400&&(u=1400,l._unit()._mult(u));var c=u/750,h=l.mult(-c/2);this._map.panBy(h,{duration:1e3*c,easing:o,noMoveStart:!0},{originalEvent:t})}},_onMouseUp:function(t){this._ignoreEvent(t)||(this._onUp(t),document.removeEventListener(\"mousemove\",this._onMove),document.removeEventListener(\"mouseup\",this._onMouseUp))},_onTouchEnd:function(t){this._ignoreEvent(t)||(this._onUp(t),document.removeEventListener(\"touchmove\",this._onMove),document.removeEventListener(\"touchend\",this._onTouchEnd))},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragRotate&&e.dragRotate.isActive())return!0;if(t.touches)return t.touches.length>1;if(t.ctrlKey)return!0;return\"mousemove\"===t.type?!1&t.buttons:0!==t.button},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>0&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442}],416:[function(t,e,r){\"use strict\";function n(t,e){this._map=t,this._el=t.getCanvasContainer(),this._bearingSnap=e.bearingSnap,o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"point-geometry\"),o=t(\"../../util/util\");e.exports=n;var s=o.bezier(0,0,.25,1);n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener(\"mousedown\",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onDown),this._enabled=!1)},_onDown:function(t){if(!this._ignoreEvent(t)&&!this.isActive()){document.addEventListener(\"mousemove\",this._onMove),document.addEventListener(\"mouseup\",this._onUp),this._active=!1,this._inertia=[[Date.now(),this._map.getBearing()]],this._startPos=this._pos=i.mousePos(this._el,t),this._center=this._map.transform.centerPoint;var e=this._startPos.sub(this._center);e.mag()<200&&(this._center=this._startPos.add(new a(-200,0)._rotate(e.angle()))),t.preventDefault()}},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()||(this._active=!0,this._fireEvent(\"rotatestart\",t),this._fireEvent(\"movestart\",t));var e=this._map;e.stop();var r=this._pos,n=i.mousePos(this._el,t),a=this._center,o=r.sub(a).angleWith(n.sub(a))/Math.PI*180,s=e.getBearing()-o,l=this._inertia,u=l[l.length-1];this._drainInertiaBuffer(),l.push([Date.now(),e._normalizeBearing(s,u[1])]),e.transform.bearing=s,this._fireEvent(\"rotate\",t),this._fireEvent(\"move\",t),this._pos=n}},_onUp:function(t){if(!this._ignoreEvent(t)&&(document.removeEventListener(\"mousemove\",this._onMove),document.removeEventListener(\"mouseup\",this._onUp),this.isActive())){this._active=!1,this._fireEvent(\"rotateend\",t),this._drainInertiaBuffer();var e=this._map,r=e.getBearing(),n=this._inertia,i=function(){Math.abs(r)<this._bearingSnap?e.resetNorth({noMoveStart:!0},{originalEvent:t}):this._fireEvent(\"moveend\",t)}.bind(this);if(n.length<2)return void i();var a=n[0],o=n[n.length-1],l=n[n.length-2],u=e._normalizeBearing(r,l[1]),c=o[1]-a[1],h=c<0?-1:1,f=(o[0]-a[0])/1e3;if(0===c||0===f)return void i();var d=Math.abs(c*(.25/f));d>180&&(d=180);var p=d/180;u+=h*d*(p/2),Math.abs(e._normalizeBearing(u,0))<this._bearingSnap&&(u=e._normalizeBearing(0,u)),e.rotateTo(u,{duration:1e3*p,easing:s,noMoveStart:!0},{originalEvent:t})}},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragPan&&e.dragPan.isActive())return!0;if(t.touches)return t.touches.length>1;var r=t.ctrlKey?1:2,n=t.ctrlKey?0:2;return\"mousemove\"===t.type?t.buttons&0===r:t.button!==n},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>0&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442,\"point-geometry\":484}],417:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._onKeyDown=this._onKeyDown.bind(this)}e.exports=n;n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener(\"keydown\",this._onKeyDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"keydown\",this._onKeyDown),this._enabled=!1)},_onKeyDown:function(t){if(!(t.altKey||t.ctrlKey||t.metaKey)){var e=this._map,r={originalEvent:t};if(!e.isEasing())switch(t.keyCode){case 61:case 107:case 171:case 187:e.zoomTo(Math.round(e.getZoom())+(t.shiftKey?2:1),r);break;case 189:case 109:case 173:e.zoomTo(Math.round(e.getZoom())-(t.shiftKey?2:1),r);break;case 37:t.shiftKey?e.easeTo({bearing:e.getBearing()-2},r):(t.preventDefault(),e.panBy([-80,0],r));break;case 39:t.shiftKey?e.easeTo({bearing:e.getBearing()+2},r):(t.preventDefault(),e.panBy([80,0],r));break;case 38:t.shiftKey?e.easeTo({pitch:e.getPitch()+5},r):(t.preventDefault(),e.panBy([0,-80],r));break;case 40:t.shiftKey?e.easeTo({pitch:Math.max(e.getPitch()-5,0)},r):(t.preventDefault(),e.panBy([0,80],r))}}}}},{}],418:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/browser\"),o=t(\"../../util/util\");e.exports=n;var s=\"undefined\"!=typeof navigator?navigator.userAgent.toLowerCase():\"\",l=-1!==s.indexOf(\"firefox\"),u=-1!==s.indexOf(\"safari\")&&-1===s.indexOf(\"chrom\");n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener(\"wheel\",this._onWheel,!1),this._el.addEventListener(\"mousewheel\",this._onWheel,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"wheel\",this._onWheel),this._el.removeEventListener(\"mousewheel\",this._onWheel),this._enabled=!1)},_onWheel:function(t){var e;\"wheel\"===t.type?(e=t.deltaY,l&&t.deltaMode===window.WheelEvent.DOM_DELTA_PIXEL&&(e/=a.devicePixelRatio),t.deltaMode===window.WheelEvent.DOM_DELTA_LINE&&(e*=40)):\"mousewheel\"===t.type&&(e=-t.wheelDeltaY,u&&(e/=3));var r=a.now(),n=r-(this._time||0);this._pos=i.mousePos(this._el,t),this._time=r,0!==e&&e%4.000244140625==0?(this._type=\"wheel\",e=Math.floor(e/4)):0!==e&&Math.abs(e)<4?this._type=\"trackpad\":n>400?(this._type=null,this._lastValue=e,this._timeout=setTimeout(this._onTimeout,40)):this._type||(this._type=Math.abs(n*e)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,e+=this._lastValue)),t.shiftKey&&e&&(e/=4),this._type&&this._zoom(-e,t),t.preventDefault()},_onTimeout:function(){this._type=\"wheel\",this._zoom(-this._lastValue)},_zoom:function(t,e){if(0!==t){var r=this._map,n=2/(1+Math.exp(-Math.abs(t/100)));t<0&&0!==n&&(n=1/n);var i=r.ease?r.ease.to:r.transform.scale,a=r.transform.scaleZoom(i*n);r.zoomTo(a,{duration:0,around:r.unproject(this._pos),delayEndEvents:200},{originalEvent:e})}}}},{\"../../util/browser\":426,\"../../util/dom\":428,\"../../util/util\":442}],419:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/util\");e.exports=n;var o=a.bezier(0,0,.15,1);n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener(\"touchstart\",this._onStart,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"touchstart\",this._onStart),this._enabled=!1)},disableRotation:function(){this._rotationDisabled=!0},enableRotation:function(){this._rotationDisabled=!1},_onStart:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]);this._startVec=e.sub(r),this._startScale=this._map.transform.scale,this._startBearing=this._map.transform.bearing,this._gestureIntent=void 0,this._inertia=[],\n",
+       "document.addEventListener(\"touchmove\",this._onMove,!1),document.addEventListener(\"touchend\",this._onEnd,!1)}},_onMove:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]),n=e.add(r).div(2),a=e.sub(r),o=a.mag()/this._startVec.mag(),s=this._rotationDisabled?0:180*a.angleWith(this._startVec)/Math.PI,l=this._map;if(this._gestureIntent){var u={duration:0,around:l.unproject(n)};\"rotate\"===this._gestureIntent&&(u.bearing=this._startBearing+s),\"zoom\"!==this._gestureIntent&&\"rotate\"!==this._gestureIntent||(u.zoom=l.transform.scaleZoom(this._startScale*o)),l.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),o,n]),l.easeTo(u,{originalEvent:t})}else{var c=Math.abs(1-o)>.15;Math.abs(s)>4?this._gestureIntent=\"rotate\":c&&(this._gestureIntent=\"zoom\"),this._gestureIntent&&(this._startVec=a,this._startScale=l.transform.scale,this._startBearing=l.transform.bearing)}t.preventDefault()}},_onEnd:function(t){document.removeEventListener(\"touchmove\",this._onMove),document.removeEventListener(\"touchend\",this._onEnd),this._drainInertiaBuffer();var e=this._inertia,r=this._map;if(e.length<2)return void r.snapToNorth({},{originalEvent:t});var n=e[e.length-1],i=e[0],a=r.transform.scaleZoom(this._startScale*n[1]),s=r.transform.scaleZoom(this._startScale*i[1]),l=a-s,u=(n[0]-i[0])/1e3,c=n[2];if(0===u||a===s)return void r.snapToNorth({},{originalEvent:t});var h=.15*l/u;Math.abs(h)>2.5&&(h=h>0?2.5:-2.5);var f=1e3*Math.abs(h/(12*.15)),d=a+h*f/2e3;d<0&&(d=0),r.easeTo({zoom:d,duration:f,easing:o,around:r.unproject(c)},{originalEvent:t})},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>2&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442}],420:[function(t,e,r){\"use strict\";function n(){i.bindAll([\"_onHashChange\",\"_updateHash\"],this)}e.exports=n;var i=t(\"../util/util\");n.prototype={addTo:function(t){return this._map=t,window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},remove:function(){return window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),delete this._map,this},_onHashChange:function(){var t=location.hash.replace(\"#\",\"\").split(\"/\");return t.length>=3&&(this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:+(t[3]||0)}),!0)},_updateHash:function(){var t=this._map.getCenter(),e=this._map.getZoom(),r=this._map.getBearing(),n=Math.max(0,Math.ceil(Math.log(e)/Math.LN2)),i=\"#\"+Math.round(100*e)/100+\"/\"+t.lat.toFixed(n)+\"/\"+t.lng.toFixed(n)+(r?\"/\"+Math.round(10*r)/10:\"\");window.history.replaceState(\"\",\"\",i)}}},{\"../util/util\":442}],421:[function(t,e,r){\"use strict\";function n(t){t.parentNode&&t.parentNode.removeChild(t)}var i=t(\"../util/canvas\"),a=t(\"../util/util\"),o=t(\"../util/browser\"),s=t(\"../util/browser\").window,l=t(\"../util/evented\"),u=t(\"../util/dom\"),c=t(\"../style/style\"),h=t(\"../style/animation_loop\"),f=t(\"../render/painter\"),d=t(\"../geo/transform\"),p=t(\"./hash\"),m=t(\"./bind_handlers\"),v=t(\"./camera\"),g=t(\"../geo/lng_lat\"),y=t(\"../geo/lng_lat_bounds\"),b=t(\"point-geometry\"),x=t(\"./control/attribution\"),_={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:0,maxZoom:20,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,workerCount:Math.max(o.hardwareConcurrency-1,1)},w=e.exports=function(t){if(t=a.extend({},_,t),t.workerCount<1)throw new Error(\"workerCount must an integer greater than or equal to 1.\");this._interactive=t.interactive,this._failIfMajorPerformanceCaveat=t.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=t.preserveDrawingBuffer,this._trackResize=t.trackResize,this._workerCount=t.workerCount,this._bearingSnap=t.bearingSnap,\"string\"==typeof t.container?this._container=document.getElementById(t.container):this._container=t.container,this.animationLoop=new h,this.transform=new d(t.minZoom,t.maxZoom),t.maxBounds&&this.setMaxBounds(t.maxBounds),a.bindAll([\"_forwardStyleEvent\",\"_forwardSourceEvent\",\"_forwardLayerEvent\",\"_forwardTileEvent\",\"_onStyleLoad\",\"_onStyleChange\",\"_onSourceAdd\",\"_onSourceRemove\",\"_onSourceUpdate\",\"_onWindowOnline\",\"_onWindowResize\",\"_update\",\"_render\"],this),this._setupContainer(),this._setupPainter(),this.on(\"move\",this._update.bind(this,!1)),this.on(\"zoom\",this._update.bind(this,!0)),this.on(\"moveend\",function(){this.animationLoop.set(300),this._rerender()}.bind(this)),void 0!==s&&(s.addEventListener(\"online\",this._onWindowOnline,!1),s.addEventListener(\"resize\",this._onWindowResize,!1)),m(this,t),this._hash=t.hash&&(new p).addTo(this),this._hash&&this._hash._onHashChange()||this.jumpTo({center:t.center,zoom:t.zoom,bearing:t.bearing,pitch:t.pitch}),this.stacks={},this._classes=[],this.resize(),t.classes&&this.setClasses(t.classes),t.style&&this.setStyle(t.style),t.attributionControl&&this.addControl(new x(t.attributionControl));var e=this.fire.bind(this,\"error\");this.on(\"style.error\",e),this.on(\"source.error\",e),this.on(\"tile.error\",e),this.on(\"layer.error\",e)};a.extend(w.prototype,l),a.extend(w.prototype,v.prototype),a.extend(w.prototype,{addControl:function(t){return t.addTo(this),this},addClass:function(t,e){return this._classes.indexOf(t)>=0||\"\"===t?this:(this._classes.push(t),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},removeClass:function(t,e){var r=this._classes.indexOf(t);return r<0||\"\"===t?this:(this._classes.splice(r,1),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},setClasses:function(t,e){for(var r={},n=0;n<t.length;n++)\"\"!==t[n]&&(r[t[n]]=!0);return this._classes=Object.keys(r),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0)},hasClass:function(t){return this._classes.indexOf(t)>=0},getClasses:function(){return this._classes},resize:function(){var t=0,e=0;return this._container&&(t=this._container.offsetWidth||400,e=this._container.offsetHeight||300),this._canvas.resize(t,e),this.transform.resize(t,e),this.painter.resize(t,e),this.fire(\"movestart\").fire(\"move\").fire(\"resize\").fire(\"moveend\")},getBounds:function(){var t=new y(this.transform.pointLocation(new b(0,0)),this.transform.pointLocation(this.transform.size));return(this.transform.angle||this.transform.pitch)&&(t.extend(this.transform.pointLocation(new b(this.transform.size.x,0))),t.extend(this.transform.pointLocation(new b(0,this.transform.size.y)))),t},setMaxBounds:function(t){if(t){var e=y.convert(t);this.transform.lngRange=[e.getWest(),e.getEast()],this.transform.latRange=[e.getSouth(),e.getNorth()],this.transform._constrain(),this._update()}else null!==t&&void 0!==t||(this.transform.lngRange=[],this.transform.latRange=[],this._update());return this},setMinZoom:function(t){if((t=null===t||void 0===t?0:t)>=0&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between 0 and the current maxZoom, inclusive\")},setMaxZoom:function(t){if((t=null===t||void 0===t?20:t)>=this.transform.minZoom&&t<=20)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be between the current minZoom and 20, inclusive\")},project:function(t){return this.transform.locationPoint(g.convert(t))},unproject:function(t){return this.transform.pointLocation(b.convert(t))},queryRenderedFeatures:function(){var t,e={};return 2===arguments.length?(t=arguments[0],e=arguments[1]):1===arguments.length&&function(t){return t instanceof b||Array.isArray(t)}(arguments[0])?t=arguments[0]:1===arguments.length&&(e=arguments[0]),this.style.queryRenderedFeatures(this._makeQueryGeometry(t),e,this.transform.zoom,this.transform.angle)},_makeQueryGeometry:function(t){void 0===t&&(t=[b.convert([0,0]),b.convert([this.transform.width,this.transform.height])]);var e;if(t instanceof b||\"number\"==typeof t[0])e=[b.convert(t)];else{var r=[b.convert(t[0]),b.convert(t[1])];e=[r[0],new b(r[1].x,r[0].y),r[1],new b(r[0].x,r[1].y),r[0]]}return e=e.map(function(t){return this.transform.pointCoordinate(t)}.bind(this))},querySourceFeatures:function(t,e){return this.style.querySourceFeatures(t,e)},setStyle:function(t){return this.style&&(this.style.off(\"load\",this._onStyleLoad).off(\"error\",this._forwardStyleEvent).off(\"change\",this._onStyleChange).off(\"source.add\",this._onSourceAdd).off(\"source.remove\",this._onSourceRemove).off(\"source.load\",this._onSourceUpdate).off(\"source.error\",this._forwardSourceEvent).off(\"source.change\",this._onSourceUpdate).off(\"layer.add\",this._forwardLayerEvent).off(\"layer.remove\",this._forwardLayerEvent).off(\"layer.error\",this._forwardLayerEvent).off(\"tile.add\",this._forwardTileEvent).off(\"tile.remove\",this._forwardTileEvent).off(\"tile.load\",this._update).off(\"tile.error\",this._forwardTileEvent).off(\"tile.stats\",this._forwardTileEvent)._remove(),this.off(\"rotate\",this.style._redoPlacement),this.off(\"pitch\",this.style._redoPlacement)),t?(this.style=t instanceof c?t:new c(t,this.animationLoop,this._workerCount),this.style.on(\"load\",this._onStyleLoad).on(\"error\",this._forwardStyleEvent).on(\"change\",this._onStyleChange).on(\"source.add\",this._onSourceAdd).on(\"source.remove\",this._onSourceRemove).on(\"source.load\",this._onSourceUpdate).on(\"source.error\",this._forwardSourceEvent).on(\"source.change\",this._onSourceUpdate).on(\"layer.add\",this._forwardLayerEvent).on(\"layer.remove\",this._forwardLayerEvent).on(\"layer.error\",this._forwardLayerEvent).on(\"tile.add\",this._forwardTileEvent).on(\"tile.remove\",this._forwardTileEvent).on(\"tile.load\",this._update).on(\"tile.error\",this._forwardTileEvent).on(\"tile.stats\",this._forwardTileEvent),this.on(\"rotate\",this.style._redoPlacement),this.on(\"pitch\",this.style._redoPlacement),this):(this.style=null,this)},getStyle:function(){if(this.style)return this.style.serialize()},addSource:function(t,e){return this.style.addSource(t,e),this._update(!0),this},addSourceType:function(t,e,r){return this.style.addSourceType(t,e,r)},removeSource:function(t){return this.style.removeSource(t),this._update(!0),this},getSource:function(t){return this.style.getSource(t)},addLayer:function(t,e){return this.style.addLayer(t,e),this._update(!0),this},removeLayer:function(t){return this.style.removeLayer(t),this._update(!0),this},getLayer:function(t){return this.style.getLayer(t)},setFilter:function(t,e){return this.style.setFilter(t,e),this._update(!0),this},setLayerZoomRange:function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0),this},getFilter:function(t){return this.style.getFilter(t)},setPaintProperty:function(t,e,r,n){return this.style.setPaintProperty(t,e,r,n),this._update(!0),this},getPaintProperty:function(t,e,r){return this.style.getPaintProperty(t,e,r)},setLayoutProperty:function(t,e,r){return this.style.setLayoutProperty(t,e,r),this._update(!0),this},getLayoutProperty:function(t,e){return this.style.getLayoutProperty(t,e)},getContainer:function(){return this._container},getCanvasContainer:function(){return this._canvasContainer},getCanvas:function(){return this._canvas.getElement()},_setupContainer:function(){var t=this._container;t.classList.add(\"mapboxgl-map\");var e=this._canvasContainer=u.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=new i(this,e);var r=this._controlContainer=u.create(\"div\",\"mapboxgl-control-container\",t),n=this._controlCorners={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(t){n[t]=u.create(\"div\",\"mapboxgl-ctrl-\"+t,r)})},_setupPainter:function(){var t=this._canvas.getWebGLContext({failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer});if(!t)return void this.fire(\"error\",{error:new Error(\"Failed to initialize WebGL\")});this.painter=new f(t,this.transform)},_contextLost:function(t){t.preventDefault(),this._frameId&&o.cancelFrame(this._frameId),this.fire(\"webglcontextlost\",{originalEvent:t})},_contextRestored:function(t){this._setupPainter(),this.resize(),this._update(),this.fire(\"webglcontextrestored\",{originalEvent:t})},loaded:function(){return!this._styleDirty&&!this._sourcesDirty&&!(!this.style||!this.style.loaded())},_update:function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this._rerender(),this):this},_render:function(){try{this.style&&this._styleDirty&&(this._styleDirty=!1,this.style.update(this._classes,this._classOptions),this._classOptions=null,this.style._recalculate(this.transform.zoom)),this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.painter.render(this.style,{debug:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,vertices:this.vertices,rotating:this.rotating,zooming:this.zooming}),this.fire(\"render\"),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(\"load\")),this._frameId=null,this.animationLoop.stopped()||(this._styleDirty=!0),(this._sourcesDirty||this._repaint||this._styleDirty)&&this._rerender()}catch(t){this.fire(\"error\",{error:t})}return this},remove:function(){this._hash&&this._hash.remove(),o.cancelFrame(this._frameId),this.setStyle(null),void 0!==s&&s.removeEventListener(\"resize\",this._onWindowResize,!1);var t=this.painter.gl.getExtension(\"WEBGL_lose_context\");t&&t.loseContext(),n(this._canvasContainer),n(this._controlContainer),this._container.classList.remove(\"mapboxgl-map\")},_rerender:function(){this.style&&!this._frameId&&(this._frameId=o.frame(this._render))},_forwardStyleEvent:function(t){this.fire(\"style.\"+t.type,a.extend({style:t.target},t))},_forwardSourceEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_forwardLayerEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_forwardTileEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_onStyleLoad:function(t){this.transform.unmodified&&this.jumpTo(this.style.stylesheet),this.style.update(this._classes,{transition:!1}),this._forwardStyleEvent(t)},_onStyleChange:function(t){this._update(!0),this._forwardStyleEvent(t)},_onSourceAdd:function(t){var e=t.source;e.onAdd&&e.onAdd(this),this._forwardSourceEvent(t)},_onSourceRemove:function(t){var e=t.source;e.onRemove&&e.onRemove(this),this._forwardSourceEvent(t)},_onSourceUpdate:function(t){this._update(),this._forwardSourceEvent(t)},_onWindowOnline:function(){this._update()},_onWindowResize:function(){this._trackResize&&this.stop().resize()._update()}}),a.extendAll(w.prototype,{_showTileBoundaries:!1,get showTileBoundaries(){return this._showTileBoundaries},set showTileBoundaries(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},_showCollisionBoxes:!1,get showCollisionBoxes(){return this._showCollisionBoxes},set showCollisionBoxes(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,this.style._redoPlacement())},_showOverdrawInspector:!1,get showOverdrawInspector(){return this._showOverdrawInspector},set showOverdrawInspector(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},_repaint:!1,get repaint(){return this._repaint},set repaint(t){this._repaint=t,this._update()},_vertices:!1,get vertices(){return this._vertices},set vertices(t){this._vertices=t,this._update()}})},{\"../geo/lng_lat\":339,\"../geo/lng_lat_bounds\":340,\"../geo/transform\":341,\"../render/painter\":355,\"../style/animation_loop\":375,\"../style/style\":378,\"../util/browser\":426,\"../util/canvas\":427,\"../util/dom\":428,\"../util/evented\":434,\"../util/util\":442,\"./bind_handlers\":407,\"./camera\":408,\"./control/attribution\":409,\"./hash\":420,\"point-geometry\":484}],422:[function(t,e,r){\"use strict\";function n(t,e){t||(t=i.create(\"div\")),t.classList.add(\"mapboxgl-marker\"),this._el=t,this._offset=o.convert(e&&e.offset||[0,0]),this._update=this._update.bind(this)}e.exports=n;var i=t(\"../util/dom\"),a=t(\"../geo/lng_lat\"),o=t(\"point-geometry\");n.prototype={addTo:function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._el),t.on(\"move\",this._update),this._update(),this},remove:function(){this._map&&(this._map.off(\"move\",this._update),this._map=null);var t=this._el.parentNode;return t&&t.removeChild(this._el),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=a.convert(t),this._update(),this},getElement:function(){return this._el},_update:function(){if(this._map){var t=this._map.project(this._lngLat)._add(this._offset);i.setTransform(this._el,\"translate(\"+t.x+\"px,\"+t.y+\"px)\")}}}},{\"../geo/lng_lat\":339,\"../util/dom\":428,\"point-geometry\":484}],423:[function(t,e,r){\"use strict\";function n(t){i.setOptions(this,t),i.bindAll([\"_update\",\"_onClickClose\"],this)}e.exports=n;var i=t(\"../util/util\"),a=t(\"../util/evented\"),o=t(\"../util/dom\"),s=t(\"../geo/lng_lat\");n.prototype=i.inherit(a,{options:{closeButton:!0,closeOnClick:!0},addTo:function(t){return this._map=t,this._map.on(\"move\",this._update),this.options.closeOnClick&&this._map.on(\"click\",this._onClickClose),this._update(),this},remove:function(){return this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._container&&(this._container.parentNode.removeChild(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"click\",this._onClickClose),delete this._map),this.fire(\"close\"),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=s.convert(t),this._update(),this},setText:function(t){return this.setDOMContent(document.createTextNode(t))},setHTML:function(t){var e,r=document.createDocumentFragment(),n=document.createElement(\"body\");for(n.innerHTML=t;;){if(!(e=n.firstChild))break;r.appendChild(e)}return this.setDOMContent(r)},setDOMContent:function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},_createContent:function(){this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._content=o.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=o.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClickClose))},_update:function(){if(this._map&&this._lngLat&&this._content){this._container||(this._container=o.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=o.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content));var t=this._map.project(this._lngLat).round(),e=this.options.anchor;if(!e){var 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s=0,l=0;l<3;++l)s+=Math.pow(r[l]-e[l],2);this.radius.set(t,.5*Math.log(Math.max(s,1e-6))),this.center.set(t,r[0],r[1],r[2])},d.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},d.setMatrix=function(t,e){var r=this.computedRotation;f(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),a(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;h(n,e);var i=n[15];if(Math.abs(i)>1e-6){var o=n[12]/i,s=n[13]/i,l=n[14]/i;this.recalcMatrix(t);var u=Math.exp(this.computedRadius[0]);this.center.set(t,o-n[2]*u,s-n[6]*u,l-n[10]*u),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},d.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},d.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},d.getDistanceLimits=function(t){var e=this.radius.bounds;return 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a=i.viewportWidth,o=i.viewportHeight;t.width=t.width||a-t.xOffset,t.height=t.height||o-t.yOffset,t.needsCopy=!0,de(t.xOffset>=0&&t.xOffset<a&&t.yOffset>=0&&t.yOffset<o&&t.width>0&&t.width<=a&&t.height>0&&t.height<=o,\"copy texture read out of bounds\")}else if(n){if(Qt(n))t.channels=t.channels||4,t.data=n,\"type\"in e||t.type!==Or||(t.type=At(n));else if(gt(n))t.channels=t.channels||4,Tt(t,n),t.alignment=1,t.needsFree=!0;else if(Z(n)){var s=n.data;Array.isArray(s)||t.type!==Or||(t.type=At(s));var l,c,h,f,d,p,m=n.shape,v=n.stride;3===m.length?(h=m[2],p=v[2]):(de(2===m.length,\"invalid ndarray pixel data, must be 2 or 3D\"),h=1,p=1),l=m[0],c=m[1],f=v[0],d=v[1],t.alignment=1,t.width=l,t.height=c,t.channels=h,t.format=t.internalformat=hn[h],t.needsFree=!0,Lt(t,s,f,d,p,n.offset)}else if(xt(n)||_t(n))xt(n)?t.element=n:t.element=n.canvas,t.width=t.element.width,t.height=t.element.height,t.channels=4;else if(wt(n))t.element=n,t.width=n.naturalWidth,t.height=n.naturalHeight,t.channels=4;else if(Mt(n))t.element=n,t.width=n.videoWidth,t.height=n.videoHeight,t.channels=4;else if(yt(n)){var g=t.width||n[0].length,y=t.height||n.length,b=t.channels;b=mt(n[0][0])?b||n[0][0].length:b||1;for(var x=Ce.shape(n),_=1,w=0;w<x.length;++w)_*=x[w];var M=St(t,_);Ce.flatten(n,x,\"\",M),Et(t,M),t.alignment=1,t.width=g,t.height=y,t.channels=b,t.format=t.internalformat=hn[b],t.needsFree=!0}}else t.width=t.width||1,t.height=t.height||1,t.channels=t.channels||4;t.type===jr?de(r.extensions.indexOf(\"oes_texture_float\")>=0,\"oes_texture_float extension not enabled\"):t.type===wr&&de(r.extensions.indexOf(\"oes_texture_half_float\")>=0,\"oes_texture_half_float extension not enabled\")}function d(e,r,i){var a=e.element,o=e.data,s=e.internalformat,l=e.format,u=e.type,h=e.width,f=e.height;c(e),a?t.texImage2D(r,i,l,l,u,a):e.compressed?t.compressedTexImage2D(r,i,s,h,f,0,o):e.needsCopy?(n(),t.copyTexImage2D(r,i,l,e.xOffset,e.yOffset,h,f,0)):t.texImage2D(r,i,l,h,f,0,l,u,o)}function p(e,r,i,a,o){var s=e.element,l=e.data,u=e.internalformat,h=e.format,f=e.type,d=e.width,p=e.height;c(e),s?t.texSubImage2D(r,o,i,a,h,f,s):e.compressed?t.compressedTexSubImage2D(r,o,i,a,u,d,p,l):e.needsCopy?(n(),t.copyTexSubImage2D(r,o,i,a,e.xOffset,e.yOffset,d,p)):t.texSubImage2D(r,o,i,a,d,p,h,f,l)}function m(){return K.pop()||new h}function v(t){t.needsFree&&Le.freeType(t.data),h.call(t),K.push(t)}function g(){s.call(this),this.genMipmaps=!1,this.mipmapHint=$r,this.mipmask=0,this.images=Array(16)}function y(t,e,r){var n=t.images[0]=m();t.mipmask=1,n.width=t.width=e,n.height=t.height=r,n.channels=t.channels=4}function b(t,e){var r=null;if(kt(e))r=t.images[0]=m(),l(r,t),f(r,e),t.mipmask=1;else if(u(t,e),Array.isArray(e.mipmap))for(var n=e.mipmap,i=0;i<n.length;++i)r=t.images[i]=m(),l(r,t),r.width>>=i,r.height>>=i,f(r,n[i]),t.mipmask|=1<<i;else 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r=0;r<32;++r)if(0!=(e.mipmask&1<<r))if(e.target===nr)t.texImage2D(nr,r,e.internalformat,e.width>>r,e.height>>r,0,e.internalformat,e.type,null);else for(var n=0;n<6;++n)t.texImage2D(ar+n,r,e.internalformat,e.width>>r,e.height>>r,0,e.internalformat,e.type,null);A(e.texInfo,e.target)})}var P={\"don't care\":$r,\"dont care\":$r,nice:en,fast:tn},O={repeat:Ur,clamp:Vr,mirror:Hr},R={nearest:Yr,linear:Wr},F=$t({mipmap:Kr,\"nearest mipmap nearest\":Xr,\"linear mipmap nearest\":Zr,\"nearest mipmap linear\":Jr,\"linear mipmap linear\":Kr},R),j={none:0,browser:ln},N={uint8:Or,rgba4:pr,rgb565:vr,\"rgb5 a1\":mr},B={alpha:sr,luminance:ur,\"luminance alpha\":cr,rgb:lr,rgba:or,rgba4:hr,\"rgb5 a1\":fr,rgb565:dr},U={};e.ext_srgb&&(B.srgb=xr,B.srgba=_r),e.oes_texture_float&&(N.float32=N.float=jr),e.oes_texture_half_float&&(N.float16=N[\"half float\"]=wr),e.webgl_depth_texture&&($t(B,{depth:yr,\"depth stencil\":br}),$t(N,{uint16:Rr,uint32:Fr,\"depth 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i=this.computedMatrix;m[0]=i[2],m[1]=i[6],m[2]=i[10];for(var a=this.computedUp,o=this.computedRight,s=this.computedToward,l=0;l<3;++l)i[4*l]=a[l],i[4*l+1]=o[l],i[4*l+2]=s[l];c(i,i,n,m);for(var l=0;l<3;++l)a[l]=i[4*l],o[l]=i[4*l+1];this.up.set(t,a[0],a[1],a[2]),this.right.set(t,o[0],o[1],o[2])}},p.pan=function(t,e,r,i){e=e||0,r=r||0,i=i||0,this.recalcMatrix(t);var a=this.computedMatrix,o=(Math.exp(this.computedRadius[0]),a[1]),s=a[5],l=a[9],u=n(o,s,l);o/=u,s/=u,l/=u;var c=a[0],h=a[4],f=a[8],d=c*o+h*s+f*l;c-=o*d,h-=s*d,f-=l*d;var p=n(c,h,f);c/=p,h/=p,f/=p;var m=c*e+o*r,v=h*e+s*r,g=f*e+l*r;this.center.move(t,m,v,g);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+i),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,a){var o=1;\"number\"==typeof r&&(o=0|r),(o<0||o>3)&&(o=1);var s=(o+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var l=e[o],c=e[o+4],h=e[o+8];if(a){var 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this._validate(t,this.minMonth,this.minDay,i.local.invalidYear||i.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate);return t=n.year(),t<0&&t++,n.day()+30*(n.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),i.calendars.ethiopian=n},{\"../main\":581,\"object-assign\":470}],571:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function i(t,e){return t-e*Math.floor(t/e)}var a=t(\"../main\"),o=t(\"object-assign\");n.prototype=new a.baseCalendar,o(n.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom 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t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,a.local.invalidMonth),12===e&&this.leapYear(t)?30:8===e&&5===i(this.daysInYear(t),10)?30:9===e&&3===i(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var n=this._validate(t,e,r,a.local.invalidDate);return{yearType:(this.leapYear(n)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(n)%10-3]}},toJD:function(t,e,r){var n=this._validate(t,e,r,a.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var i=t<=0?t+1:t,o=this.jdEpoch+this._delay1(i)+this._delay2(i)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(var s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(var s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return i(3*(n+1),7)<3&&n++,n},_delay2:function(t){var 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this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,o.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if(t=t.split(\".\"),t.length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,o.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate),!0},extraInfo:function(t,e,r){var n=this._validate(t,e,r,o.local.invalidDate),i=n.toJD(),a=this._toHaab(i),s=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[s[0]-1],tzolkinDay:s[0],tzolkinTrecena:s[1]}},_toHaab:function(t){t-=this.jdEpoch;var e=i(t+8+340,365);return[Math.floor(e/20)+1,i(e,20)]},_toTzolkin:function(t){return t-=this.jdEpoch,[a(t+20,20),a(t+4,13)]},toJD:function(t,e,r){var n=this._validate(t,e,r,o.local.invalidDate);return n.day()+20*n.month()+360*n.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),o.calendars.mayan=n},{\"../main\":581,\"object-assign\":470}],575:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\");n.prototype=new i.baseCalendar;var o=i.instance(\"gregorian\");a(n.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var 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i.baseCalendar,a(n.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,i.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var a=i.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,\n",
+       "s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=i.instance(),r=e.fromJD(t),n=r.year(),a=r.dayOfYear(),o=n+56;this._createMissingCalendarData(o);for(var s=9,l=this.NEPALI_CALENDAR_DATA[o][0],u=this.NEPALI_CALENDAR_DATA[o][s]-l+1;a>u;)s++,s>12&&(s=1,o++),u+=this.NEPALI_CALENDAR_DATA[o][s];var c=this.NEPALI_CALENDAR_DATA[o][s]-(u-a);return this.newDate(o,s,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29,29,30,30],2077:[17,31,32,31,32,31,30,30,30,29,30,29,31],2078:[17,31,31,31,32,31,31,30,29,30,29,30,30],2079:[17,31,31,32,31,31,31,30,29,30,29,30,30],2080:[16,31,32,31,32,31,30,30,30,29,29,30,30],2081:[17,31,31,32,32,31,30,30,30,29,30,30,30],2082:[17,31,32,31,32,31,30,30,30,29,30,30,30],2083:[17,31,31,32,31,31,30,30,30,29,30,30,30],2084:[17,31,31,32,31,31,30,30,30,29,30,30,30],2085:[17,31,32,31,32,31,31,30,30,29,30,30,30],2086:[17,31,32,31,32,31,30,30,30,29,30,30,30],2087:[16,31,31,32,31,31,31,30,30,29,30,30,30],2088:[16,30,31,32,32,30,31,30,30,29,30,30,30],2089:[17,31,32,31,32,31,30,30,30,29,30,30,30],2090:[17,31,32,31,32,31,30,30,30,29,30,30,30],2091:[16,31,31,32,31,31,31,30,30,29,30,30,30],2092:[16,31,31,32,32,31,30,30,30,29,30,30,30],2093:[17,31,32,31,32,31,30,30,30,29,30,30,30],2094:[17,31,31,32,31,31,30,30,30,29,30,30,30],2095:[17,31,31,32,31,31,31,30,29,30,30,30,30],2096:[17,30,31,32,32,31,30,30,29,30,29,30,30],2097:[17,31,32,31,32,31,30,30,30,29,30,30,30],2098:[17,31,31,32,31,31,31,29,30,29,30,30,31],2099:[17,31,31,32,31,31,31,30,29,29,30,30,30],2100:[17,31,32,31,32,30,31,30,29,30,29,30,30]}}),i.calendars.nepali=n},{\"../main\":581,\"object-assign\":470}],577:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function i(t,e){return t-e*Math.floor(t/e)}var a=t(\"../main\"),o=t(\"object-assign\");n.prototype=new a.baseCalendar,o(n.prototype,{name:\"Persian\",jdEpoch:1948320.5,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Persian\",epochs:[\"BP\",\"AP\"],monthNames:[\"Farvardin\",\"Ordibehesht\",\"Khordad\",\"Tir\",\"Mordad\",\"Shahrivar\",\"Mehr\",\"Aban\",\"Azar\",\"Day\",\"Bahman\",\"Esfand\"],monthNamesShort:[\"Far\",\"Ord\",\"Kho\",\"Tir\",\"Mor\",\"Sha\",\"Meh\",\"Aba\",\"Aza\",\"Day\",\"Bah\",\"Esf\"],dayNames:[\"Yekshambe\",\"Doshambe\",\"Seshambe\",\"Ch\\xe6harshambe\",\"Panjshambe\",\"Jom'e\",\"Shambe\"],dayNamesShort:[\"Yek\",\"Do\",\"Se\",\"Ch\\xe6\",\"Panj\",\"Jom\",\"Sha\"],dayNamesMin:[\"Ye\",\"Do\",\"Se\",\"Ch\",\"Pa\",\"Jo\",\"Sh\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,a.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,a.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var n=this._validate(t,e,r,a.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var o=t-(t>=0?474:473),s=474+i(o,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(o/2820)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=t-this.toJD(475,1,1),r=Math.floor(e/1029983),n=i(e,1029983),a=2820;if(1029982!==n){var o=Math.floor(n/366),s=i(n,366);a=Math.floor((2134*o+2816*s+2815)/1028522)+o+1}var l=a+2820*r+474;l=l<=0?l-1:l;var u=t-this.toJD(l,1,1)+1,c=u<=186?Math.ceil(u/31):Math.ceil((u-6)/30),h=t-this.toJD(l,c,1)+1;return this.newDate(l,c,h)}}),a.calendars.persian=n,a.calendars.jalali=n},{\"../main\":581,\"object-assign\":470}],578:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\"),o=i.instance();n.prototype=new i.baseCalendar,a(n.prototype,{name:\"Taiwan\",jdEpoch:2419402.5,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(e.year());return o.leapYear(t)},weekOfYear:function(t,e,r){var n=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(n.year());return o.weekOfYear(t,n.month(),n.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate),t=this._t2gYear(n.year());return o.toJD(t,n.month(),n.day())},fromJD:function(t){var e=o.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),i.calendars.taiwan=n},{\"../main\":581,\"object-assign\":470}],579:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\"),o=i.instance();n.prototype=new i.baseCalendar,a(n.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(e.year());return o.leapYear(t)},weekOfYear:function(t,e,r){var n=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(n.year());return o.weekOfYear(t,n.month(),n.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate),t=this._t2gYear(n.year());return o.toJD(t,n.month(),n.day())},fromJD:function(t){var e=o.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),i.calendars.thai=n},{\"../main\":581,\"object-assign\":470}],580:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\");n.prototype=new i.baseCalendar,a(n.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear);return 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n(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function i(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function a(t,e){return t=\"\"+t,\"000000\".substring(0,e-t.length)+t}function o(){this.shortYearCutoff=\"+10\"}function s(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}var l=t(\"object-assign\");l(n.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance(),n.newDate(t,e,r)},substituteDigits:function(t){\n",
+       "return function(e){return(e+\"\").replace(/[0-9]/g,function(e){return t[e]})}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),l(i.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+a(Math.abs(this.year()),4)+\"-\"+a(this.month(),2)+\"-\"+a(this.day(),2)}}),l(o.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new i(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+a(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return 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o=i[e];o.anchor&&\"free\"!==o.anchor&&(o.anchor=f.cleanId(o.anchor)),o.overlaying&&(o.overlaying=f.cleanId(o.overlaying)),o.type||(o.isdate?o.type=\"date\":o.islog?o.type=\"log\":!1===o.isdate&&!1===o.islog&&(o.type=\"linear\")),\"withzero\"!==o.autorange&&\"tozero\"!==o.autorange||(o.autorange=!0,o.rangemode=\"tozero\"),delete o.islog,delete o.isdate,delete o.categories,a(o,\"domain\")&&delete o.domain,void 0!==o.autotick&&(void 0===o.tickmode&&(o.tickmode=o.autotick?\"auto\":\"linear\"),delete o.autotick)}var s=Array.isArray(t.annotations)?t.annotations.length:0;for(e=0;e<s;e++){var u=t.annotations[e];c.isPlainObject(u)&&(u.ref&&(\"paper\"===u.ref?(u.xref=\"paper\",u.yref=\"paper\"):\"data\"===u.ref&&(u.xref=\"x\",u.yref=\"y\"),delete u.ref),n(u,\"xref\"),n(u,\"yref\"))}var p=Array.isArray(t.shapes)?t.shapes.length:0;for(e=0;e<p;e++){var m=t.shapes[e];c.isPlainObject(m)&&(n(m,\"xref\"),n(m,\"yref\"))}var v=t.legend;v&&(v.x>3?(v.x=1.02,v.xanchor=\"left\"):v.x<-2&&(v.x=-.02,v.xanchor=\"right\"),v.y>3?(v.y=1.02,v.yanchor=\"bottom\"):v.y<-2&&(v.y=-.02,v.yanchor=\"top\")),\"rotate\"===t.dragmode&&(t.dragmode=\"orbit\"),t.scene1&&(t.scene||(t.scene=t.scene1),delete t.scene1);var g=h.getSubplotIds(t,\"gl3d\");for(e=0;e<g.length;e++){var y=t[g[e]],b=y.cameraposition;if(Array.isArray(b)&&4===b[0].length){var x=b[0],_=b[1],w=b[2],M=l([],x),k=[];for(r=0;r<3;++r)k[r]=_[e]+w*M[2+4*r];y.camera={eye:{x:k[0],y:k[1],z:k[2]},center:{x:_[0],y:_[1],z:_[2]},up:{x:M[1],y:M[5],z:M[9]}},delete y.cameraposition}}return d.clean(t),t},r.cleanData=function(t,e){for(var n=[],o=(t.concat(Array.isArray(e)?e:[]).filter(function(t){return\"uid\"in t}).map(function(t){return t.uid})),s=0;s<t.length;s++){var l,p=t[s];if(!(\"uid\"in p)||-1!==n.indexOf(p.uid)){var m;for(l=0;l<100&&(m=c.randstr(o),-1!==n.indexOf(m));l++);p.uid=c.randstr(o),o.push(p.uid)}if(n.push(p.uid),\"histogramy\"===p.type&&\"xbins\"in p&&!(\"ybins\"in p)&&(p.ybins=p.xbins,delete p.xbins),p.error_y&&\"opacity\"in p.error_y){var v=d.defaults,g=p.error_y.color||(u.traceIs(p,\"bar\")?d.defaultLine:v[s%v.length]);p.error_y.color=d.addOpacity(d.rgb(g),d.opacity(g)*p.error_y.opacity),delete p.error_y.opacity}if(\"bardir\"in p&&(\"h\"!==p.bardir||!u.traceIs(p,\"bar\")&&\"histogram\"!==p.type.substr(0,9)||(p.orientation=\"h\",r.swapXYData(p)),delete p.bardir),\"histogramy\"===p.type&&r.swapXYData(p),\"histogramx\"!==p.type&&\"histogramy\"!==p.type||(p.type=\"histogram\"),\"scl\"in p&&(p.colorscale=p.scl,delete p.scl),\"reversescl\"in p&&(p.reversescale=p.reversescl,delete p.reversescl),p.xaxis&&(p.xaxis=f.cleanId(p.xaxis,\"x\")),p.yaxis&&(p.yaxis=f.cleanId(p.yaxis,\"y\")),u.traceIs(p,\"gl3d\")&&p.scene&&(p.scene=h.subplotsRegistry.gl3d.cleanId(p.scene)),u.traceIs(p,\"pie\")||u.traceIs(p,\"bar\")||(Array.isArray(p.textposition)?p.textposition=p.textposition.map(i):p.textposition&&(p.textposition=i(p.textposition))),u.traceIs(p,\"2dMap\")&&(\"YIGnBu\"===p.colorscale&&(p.colorscale=\"YlGnBu\"),\"YIOrRd\"===p.colorscale&&(p.colorscale=\"YlOrRd\")),u.traceIs(p,\"markerColorscale\")&&p.marker){var y=p.marker;\"YIGnBu\"===y.colorscale&&(y.colorscale=\"YlGnBu\"),\"YIOrRd\"===y.colorscale&&(y.colorscale=\"YlOrRd\")}if(\"surface\"===p.type&&c.isPlainObject(p.contours)){var b=[\"x\",\"y\",\"z\"];for(l=0;l<b.length;l++){var x=p.contours[b[l]];c.isPlainObject(x)&&(x.highlightColor&&(x.highlightcolor=x.highlightColor,delete x.highlightColor),x.highlightWidth&&(x.highlightwidth=x.highlightWidth,delete x.highlightWidth))}}if(Array.isArray(p.transforms)){var _=p.transforms;for(l=0;l<_.length;l++){var w=_[l];if(c.isPlainObject(w))switch(w.type){case\"filter\":w.filtersrc&&(w.target=w.filtersrc,delete w.filtersrc),w.calendar&&(w.valuecalendar||(w.valuecalendar=w.calendar),delete w.calendar);break;case\"groupby\":if(w.styles=w.styles||w.style,w.styles&&!Array.isArray(w.styles)){var M=w.styles,k=Object.keys(M);w.styles=[];for(var A=0;A<k.length;A++)w.styles.push({target:k[A],value:M[k[A]]})}}}}a(p,\"line\")&&delete p.line,\"marker\"in p&&(a(p.marker,\"line\")&&delete p.marker.line,a(p,\"marker\")&&delete p.marker),d.clean(p)}},r.swapXYData=function(t){var e;if(c.swapAttrs(t,[\"?\",\"?0\",\"d?\",\"?bins\",\"nbins?\",\"autobin?\",\"?src\",\"error_?\"]),Array.isArray(t.z)&&Array.isArray(t.z[0])&&(t.transpose?delete t.transpose:t.transpose=!0),t.error_x&&t.error_y){var r=t.error_y,n=\"copy_ystyle\"in r?r.copy_ystyle:!(r.color||r.thickness||r.width);c.swapAttrs(t,[\"error_?.copy_ystyle\"]),n&&c.swapAttrs(t,[\"error_?.color\",\"error_?.thickness\",\"error_?.width\"])}if(\"string\"==typeof t.hoverinfo){var i=t.hoverinfo.split(\"+\");for(e=0;e<i.length;e++)\"x\"===i[e]?i[e]=\"y\":\"y\"===i[e]&&(i[e]=\"x\");t.hoverinfo=i.join(\"+\")}},r.coerceTraceIndices=function(t,e){return s(e)?[e]:Array.isArray(e)&&e.length?e:t.data.map(function(t,e){return e})},r.manageArrayContainers=function(t,e,r){var n=t.obj,i=t.parts,a=i.length,o=i[a-1],l=s(o);if(l&&null===e){var u=i.slice(0,a-1).join(\".\");c.nestedProperty(n,u).get().splice(o,1)}else l&&void 0===t.get()?(void 0===t.get()&&(r[t.astr]=null),t.set(e)):t.set(e)};var p=/(\\.[^\\[\\]\\.]+|\\[[^\\[\\]\\.]+\\])$/;r.hasParent=function(t,e){for(var r=o(e);r;){if(r in t)return!0;r=o(r)}return!1};var m=[\"x\",\"y\",\"z\"];r.clearAxisTypes=function(t,e,r){for(var n=0;n<e.length;n++)for(var i=t._fullData[n],a=0;a<3;a++){var o=f.getFromTrace(t,i,m[a]);if(o&&\"log\"!==o.type){var s=o._name,l=o._id.substr(1);if(\"scene\"===l.substr(0,5)){if(void 0!==r[l])continue;s=l+\".\"+s}var u=s+\".type\";void 0===r[s]&&void 0===r[u]&&c.nestedProperty(t.layout,u).set(null)}}}},{\"../components/color\":604,\"../lib\":728,\"../plots/cartesian/axes\":772,\"../plots/plots\":831,\"../registry\":846,\"fast-isnumeric\":131,\"gl-mat4/fromQuat\":178}],758:[function(t,e,r){\"use strict\";var n=t(\"../lib/nested_property\"),i=t(\"../lib/is_plain_object\"),a=t(\"../lib/noop\"),o=t(\"../lib/loggers\"),s=t(\"../lib/search\").sorterAsc,l=t(\"../registry\");r.containerArrayMatch=t(\"./container_array_match\");var u=r.isAddVal=function(t){return\"add\"===t||i(t)},c=r.isRemoveVal=function(t){return null===t||\"remove\"===t};r.applyContainerArrayChanges=function(t,e,r,i){var h=e.astr,f=l.getComponentMethod(h,\"supplyLayoutDefaults\"),d=l.getComponentMethod(h,\"draw\"),p=l.getComponentMethod(h,\"drawOne\"),m=i.replot||i.recalc||f===a||d===a,v=t.layout,g=t._fullLayout;if(r[\"\"]){Object.keys(r).length>1&&o.warn(\"Full array edits are incompatible with other edits\",h);var y=r[\"\"][\"\"];if(c(y))e.set(null);else{if(!Array.isArray(y))return o.warn(\"Unrecognized full array edit value\",h,y),!0;e.set(y)}return!m&&(f(v,g),d(t),!0)}var b,x,_,w,M,k,A,T=Object.keys(r).map(Number).sort(s),S=e.get(),E=S||[],L=n(g,h).get(),C=[],I=-1,z=E.length;for(b=0;b<T.length;b++)if(_=T[b],w=r[_],M=Object.keys(w),k=w[\"\"],A=u(k),_<0||_>E.length-(A?0:1))o.warn(\"index out of range\",h,_);else if(void 0!==k)M.length>1&&o.warn(\"Insertion & removal are incompatible with edits to the same index.\",h,_),c(k)?C.push(_):A?(\"add\"===k&&(k={}),E.splice(_,0,k),L&&L.splice(_,0,{})):o.warn(\"Unrecognized full object edit value\",h,_,k),-1===I&&(I=_);else for(x=0;x<M.length;x++)n(E[_],M[x]).set(w[M[x]]);for(b=C.length-1;b>=0;b--)E.splice(C[b],1),L&&L.splice(C[b],1);if(E.length?S||e.set(E):e.set(null),m)return!1;if(f(v,g),p!==a){var D;if(-1===I)D=T;else{for(z=Math.max(E.length,z),D=[],b=0;b<T.length&&!((_=T[b])>=I);b++)D.push(_);for(b=I;b<z;b++)D.push(b)}for(b=0;b<D.length;b++)p(t,D[b])}else d(t);return!0}},{\"../lib/is_plain_object\":730,\"../lib/loggers\":732,\"../lib/nested_property\":735,\"../lib/noop\":736,\"../lib/search\":745,\"../registry\":846,\"./container_array_match\":755}],759:[function(t,e,r){\"use strict\";function n(t,e){try{t._fullLayout._paper.style(\"background\",e)}catch(t){w.error(t)}}function i(t,e){n(t,I.combine(e,\"white\"))}function a(t,e){t._context||(t._context=w.extendDeep({},_.defaultConfig));var r,a,o,s=t._context;if(e){for(a=Object.keys(e),r=0;r<a.length;r++)\"editable\"!==(o=a[r])&&\"edits\"!==o&&o in s&&(\"setBackground\"===o&&\"opaque\"===e[o]?s[o]=i:s[o]=e[o]);e.plot3dPixelRatio&&!s.plotGlPixelRatio&&(s.plotGlPixelRatio=s.plot3dPixelRatio);var l=e.editable;if(void 0!==l)for(s.editable=l,a=Object.keys(s.edits),r=0;r<a.length;r++)s.edits[a[r]]=l;if(e.edits)for(a=Object.keys(e.edits),r=0;r<a.length;r++)(o=a[r])in s.edits&&(s.edits[o]=e.edits[o])}s.staticPlot&&(s.editable=!1,s.edits={},s.autosizable=!1,s.scrollZoom=!1,s.doubleClick=!1,s.showTips=!1,s.showLink=!1,s.displayModeBar=!1),\"hover\"!==s.displayModeBar||x||(s.displayModeBar=!0),\"transparent\"!==s.setBackground&&\"function\"==typeof s.setBackground||(s.setBackground=n)}function o(t,e,r){var n=y.select(t).selectAll(\".plot-container\").data([0]);n.enter().insert(\"div\",\":first-child\").classed(\"plot-container plotly\",!0);var i=n.selectAll(\".svg-container\").data([0]);i.enter().append(\"div\").classed(\"svg-container\",!0).style(\"position\",\"relative\"),i.html(\"\"),e&&(t.data=e),r&&(t.layout=r),E.manager.fillLayout(t),i.style({width:t._fullLayout.width+\"px\",height:t._fullLayout.height+\"px\"}),t.framework=E.manager.framework(t),t.framework({data:t.data,layout:t.layout},i.node()),t.framework.setUndoPoint();var a=t.framework.svg(),o=1,s=t._fullLayout.title;\"\"!==s&&s||(o=0);var l=function(){this.call(P.convertToTspans,t)},u=a.select(\".title-group text\").call(l);if(t._context.edits.titleText){s&&\"Click to enter title\"!==s||(o=.2,u.attr({\"data-unformatted\":\"Click to enter title\"}).text(\"Click to enter title\").style({opacity:o}).on(\"mouseover.opacity\",function(){y.select(this).transition().duration(100).style(\"opacity\",1)}).on(\"mouseout.opacity\",function(){y.select(this).transition().duration(1e3).style(\"opacity\",0)}));var c=function(){this.call(P.makeEditable,{gd:t}).on(\"edit\",function(e){t.framework({layout:{title:e}}),this.text(e).call(l),this.call(c)}).on(\"cancel\",function(){var t=this.attr(\"data-unformatted\");this.text(t).call(l)})};u.call(c)}return t._context.setBackground(t,t._fullLayout.paper_bgcolor),S.addLinks(t),Promise.resolve()}function s(t,e){var r,n,i=e+1,a=[];for(r=0;r<t.length;r++)n=t[r],n<0?a.push(i+n):a.push(n);return a}function l(t,e,r){var n,i;for(n=0;n<e.length;n++){if((i=e[n])!==parseInt(i,10))throw new Error(\"all values in \"+r+\" must be integers\");if(i>=t.data.length||i<-t.data.length)throw new Error(r+\" must be valid indices for gd.data.\");if(e.indexOf(i,n+1)>-1||i>=0&&e.indexOf(-t.data.length+i)>-1||i<0&&e.indexOf(t.data.length+i)>-1)throw new Error(\"each index in \"+r+\" must be unique.\")}}function u(t,e,r){if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array.\");if(void 0===e)throw new Error(\"currentIndices is a required argument.\");if(Array.isArray(e)||(e=[e]),l(t,e,\"currentIndices\"),void 0===r||Array.isArray(r)||(r=[r]),void 0!==r&&l(t,r,\"newIndices\"),void 0!==r&&e.length!==r.length)throw new Error(\"current and new indices must be of equal length.\")}function c(t,e,r){var n,i;if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array.\");if(void 0===e)throw new Error(\"traces must be defined.\");for(Array.isArray(e)||(e=[e]),n=0;n<e.length;n++)if(\"object\"!=typeof(i=e[n])||Array.isArray(i)||null===i)throw new Error(\"all values in traces array must be non-array objects\");if(void 0===r||Array.isArray(r)||(r=[r]),void 0!==r&&r.length!==e.length)throw new Error(\"if indices is specified, traces.length must equal indices.length\")}function h(t,e,r,n){var i=w.isPlainObject(n);if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array\");if(!w.isPlainObject(e))throw new Error(\"update must be a key:value object\");if(void 0===r)throw new Error(\"indices must be an integer or array of integers\");l(t,r,\"indices\");for(var a in e){if(!Array.isArray(e[a])||e[a].length!==r.length)throw new Error(\"attribute \"+a+\" must be an array of length equal to indices array length\");if(i&&(!(a in n)||!Array.isArray(n[a])||n[a].length!==e[a].length))throw new Error(\"when maxPoints is set as a key:value object it must contain a 1:1 corrispondence with the keys and number of traces in the update object\")}}function f(t,e,r,n){var i,a,o,l,u,c=w.isPlainObject(n),h=[];Array.isArray(r)||(r=[r]),r=s(r,t.data.length-1);for(var f in e)for(var d=0;d<r.length;d++){if(i=t.data[r[d]],o=w.nestedProperty(i,f),a=o.get(),l=e[f][d],!Array.isArray(l))throw new Error(\"attribute: \"+f+\" index: \"+d+\" must be an array\");if(!Array.isArray(a))throw new Error(\"cannot extend missing or non-array attribute: \"+f);u=c?n[f][d]:n,b(u)||(u=-1),h.push({prop:o,target:a,insert:l,maxp:Math.floor(u)})}return h}function d(t,e,r,n,i,a){h(t,e,r,n);for(var o,s,l,u=f(t,e,r,n),c=[],d={},p={},m=0;m<u.length;m++)s=u[m].prop,l=u[m].maxp,o=i(u[m].target,u[m].insert),l>=0&&l<o.length&&(c=a(o,l)),l=u[m].target.length,s.set(o),Array.isArray(d[s.astr])||(d[s.astr]=[]),Array.isArray(p[s.astr])||(p[s.astr]=[]),d[s.astr].push(c),p[s.astr].push(l);return{update:d,maxPoints:p}}function p(t){return void 0===t?null:t}function m(t,e,r){function n(){return r.map(function(){})}function i(t){var e=_.Axes.id2name(t);-1===u.indexOf(e)&&u.push(e)}function a(t){return\"LAYOUT\"+t+\".autorange\"}function o(t){return\"LAYOUT\"+t+\".range\"}function s(i,a,o){if(Array.isArray(i))return void i.forEach(function(t){s(t,a,o)});if(!(i in e||R.hasParent(e,i))){var l;l=\"LAYOUT\"===i.substr(0,6)?w.nestedProperty(t.layout,i.replace(\"LAYOUT\",\"\")):w.nestedProperty(f[r[o]],i),i in v||(v[i]=n()),void 0===v[i][o]&&(v[i][o]=p(l.get())),void 0!==a&&l.set(a)}}var l,u,c=t._fullLayout,h=t._fullData,f=t.data,d=j.traceFlags(),m={},v={},g={};for(var y in e){if(R.hasParent(e,y))throw 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n,i,a,o,s=V.getFromId(t,e),l=w.select(t).selectAll(\"g.\"+e+\"tick\"),u={selection:l,side:s.side},h=e.charAt(0),f=t._fullLayout._size,d=s.titlefont.size;if(l.size()){var p=L.getTranslate(l.node().parentNode);u.offsetLeft=p.x,u.offsetTop=p.y}var 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if(\"select\"===u||\"lasso\"===u){o.on(\".zoom\",null);var c;\"select\"===u?c=function(t,e){(t.range={})[i.id]=[n([e.xmin,e.ymin]),n([e.xmax,e.ymax])]}:\"lasso\"===u&&(c=function(t,e,r){(t.lassoPoints={})[i.id]=r.filtered.map(n)});var h={element:i.bgRect.node(),gd:a,plotinfo:{xaxis:i.xaxis,yaxis:i.yaxis,fillRangeItems:c},xaxes:[i.xaxis],yaxes:[i.yaxis],subplot:i.id};h.prepFn=function(t,e,r){v(t,e,r,h,u)},h.doneFn=function(e,r){2===r&&t._zoomlayer.selectAll(\".select-outline\").remove()},m.init(h)}o.on(\"mousemove\",function(){var t=i.projection.invert(s.mouse(this));if(!t||isNaN(t[0])||isNaN(t[1]))return m.unhover(a,s.event);i.xaxis.p2c=function(){return t[0]},i.yaxis.p2c=function(){return t[1]},f.hover(a,s.event,i.id)}),o.on(\"mouseout\",function(){m.unhover(a,s.event)}),o.on(\"click\",function(){f.click(a,s.event)})}},_.makeFramework=function(){var 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h=e._isScoped=\"world\"!==o,f=e._isConic=-1!==u.indexOf(\"conic\");e._isClipped=!!a.lonaxisSpan[u];for(var d=0;d<s.length;d++){var p,m=s[d],v=[30,10][d];if(h)p=l[m+\"Range\"];else{var g=a[m+\"Span\"],y=(g[u]||g[\"*\"])/2,b=r(\"projection.rotation.\"+m.substr(0,3),l.projRotate[d]);p=[b-y,b+y]}r(m+\".tick0\",r(m+\".range\",p)[0]),r(m+\".dtick\",v),n=r(m+\".showgrid\"),n&&(r(m+\".gridcolor\"),r(m+\".gridwidth\"))}var x=e.lonaxis.range,_=e.lataxis.range,w=x[0],M=x[1];w>0&&M<0&&(M+=360);var k,A=(w+M)/2;if(!c){var T=h?l.projRotate:[A,0,0];k=r(\"projection.rotation.lon\",T[0]),r(\"projection.rotation.lat\",T[1]),r(\"projection.rotation.roll\",T[2]),n=r(\"showcoastlines\",!h),n&&(r(\"coastlinecolor\"),r(\"coastlinewidth\")),n=r(\"showocean\"),n&&r(\"oceancolor\")}var 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r=t[0],n=t[1],i=t[2],a=t[3],o=e[0],s=e[1],l=e[2],u=e[3];return[r*o-n*s-i*l-a*u,r*s+n*o+i*u-a*l,r*l-n*u+i*o+a*s,r*u+n*l-i*s+a*o]}function f(t,e){if(t&&e){var r=x(t,e),n=Math.sqrt(b(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,b(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}function d(t,e,r){var n=v(e,2,t[0]);n=v(n,1,t[1]),n=v(n,0,t[2]-r[2]);var i,a,o=e[0],s=e[1],l=e[2],u=n[0],c=n[1],h=n[2],f=Math.atan2(s,o)*A,d=Math.sqrt(o*o+s*s);Math.abs(c)>d?(a=(c>0?90:-90)-f,i=0):(a=Math.asin(c/d)*A-f,i=Math.sqrt(d*d-c*c));var m=180-a-2*f,g=(Math.atan2(h,u)-Math.atan2(l,i))*A,y=(Math.atan2(h,u)-Math.atan2(l,-i))*A;return p(r[0],r[1],a,g)<=p(r[0],r[1],m,y)?[a,g,r[2]]:[m,y,r[2]]}function p(t,e,r,n){var i=m(r-t),a=m(n-e);return Math.sqrt(i*i+a*a)}function m(t){return(t%360+540)%360-180}function v(t,e,r){var n=r*k,i=t.slice(),a=0===e?1:0,o=2===e?1:2,s=Math.cos(n),l=Math.sin(n);return i[a]=t[a]*s-t[o]*l,i[o]=t[o]*s+t[a]*l,i}function g(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*A,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*A,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*A]}function y(t){var e=t[0]*k,r=t[1]*k,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}function b(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}function x(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}function _(t){for(var e=0,r=arguments.length,n=[];++e<r;)n.push(arguments[e]);var i=w.dispatch.apply(null,n);return i.of=function(e,r){return function(n){var a;try{a=n.sourceEvent=w.event,n.target=t,w.event=n,i[n.type].apply(e,r)}finally{w.event=a}}},i}var w=t(\"d3\"),M=t(\"../../lib\"),k=Math.PI/180,A=180/Math.PI,T={cursor:\"pointer\"},S={cursor:\"auto\"};e.exports=n},{\"../../lib\":728,d3:122}],806:[function(t,e,r){\"use strict\";function n(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}function i(t){function e(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function r(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}function i(n,i,a){function o(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(f[e]=i,f[e+2]=a,h.dataBox=f,t.setRanges(f)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}var s,u,f=t.calcDataBox(),d=c.viewBox,p=h.lastPos[0],m=h.lastPos[1],v=l.MINDRAG*c.pixelRatio,g=l.MINZOOM*c.pixelRatio;switch(i*=c.pixelRatio,a*=c.pixelRatio,a=d[3]-d[1]-a,t.fullLayout.dragmode){case\"zoom\":if(n){var y=i/(d[2]-d[0])*(f[2]-f[0])+f[0],b=a/(d[3]-d[1])*(f[3]-f[1])+f[1];h.boxInited||(h.boxStart[0]=y,h.boxStart[1]=b,h.dragStart[0]=i,h.dragStart[1]=a),h.boxEnd[0]=y,h.boxEnd[1]=b,h.boxInited=!0,h.boxEnabled||h.boxStart[0]===h.boxEnd[0]&&h.boxStart[1]===h.boxEnd[1]||(h.boxEnabled=!0);var x=Math.abs(h.dragStart[0]-i)<g,_=Math.abs(h.dragStart[1]-a)<g;if(!r()||x&&_)x&&(h.boxEnd[0]=h.boxStart[0]),_&&(h.boxEnd[1]=h.boxStart[1]);else{s=h.boxEnd[0]-h.boxStart[0],u=h.boxEnd[1]-h.boxStart[1];var w=(f[3]-f[1])/(f[2]-f[0]);Math.abs(s*w)>Math.abs(u)?(h.boxEnd[1]=h.boxStart[1]+Math.abs(s)*w*(u>=0?1:-1),h.boxEnd[1]<f[1]?(h.boxEnd[1]=f[1],h.boxEnd[0]=h.boxStart[0]+(f[1]-h.boxStart[1])/Math.abs(w)):h.boxEnd[1]>f[3]&&(h.boxEnd[1]=f[3],h.boxEnd[0]=h.boxStart[0]+(f[3]-h.boxStart[1])/Math.abs(w))):(h.boxEnd[0]=h.boxStart[0]+Math.abs(u)/w*(s>=0?1:-1),h.boxEnd[0]<f[0]?(h.boxEnd[0]=f[0],h.boxEnd[1]=h.boxStart[1]+(f[0]-h.boxStart[0])*Math.abs(w)):h.boxEnd[0]>f[2]&&(h.boxEnd[0]=f[2],h.boxEnd[1]=h.boxStart[1]+(f[2]-h.boxStart[0])*Math.abs(w)))}}else h.boxEnabled?(s=h.boxStart[0]!==h.boxEnd[0],u=h.boxStart[1]!==h.boxEnd[1],s||u?(s&&(o(0,h.boxStart[0],h.boxEnd[0]),t.xaxis.autorange=!1),u&&(o(1,h.boxStart[1],h.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),h.boxEnabled=!1,h.boxInited=!1):h.boxInited&&(h.boxInited=!1);break;case\"pan\":h.boxEnabled=!1,h.boxInited=!1,n?(h.panning||(h.dragStart[0]=i,h.dragStart[1]=a),Math.abs(h.dragStart[0]-i)<v&&(i=h.dragStart[0]),Math.abs(h.dragStart[1]-a)<v&&(a=h.dragStart[1]),s=(p-i)*(f[2]-f[0])/(c.viewBox[2]-c.viewBox[0]),u=(m-a)*(f[3]-f[1])/(c.viewBox[3]-c.viewBox[1]),f[0]+=s,f[2]+=s,f[1]+=u,f[3]+=u,t.setRanges(f),h.panning=!0,h.lastInputTime=Date.now(),e(),t.cameraChanged(),t.handleAnnotations()):h.panning&&(h.panning=!1,t.relayoutCallback())}h.lastPos[0]=i,h.lastPos[1]=a}var u=t.mouseContainer,c=t.glplot,h=new n(u,c);return h.mouseListener=a(u,i),u.addEventListener(\"touchstart\",function(t){var e=s(t.changedTouches[0],u);i(0,e[0],e[1]),i(1,e[0],e[1])}),u.addEventListener(\"touchmove\",function(t){t.preventDefault();var e=s(t.changedTouches[0],u);i(1,e[0],e[1])}),u.addEventListener(\"touchend\",function(){i(0,h.lastPos[0],h.lastPos[1])}),h.wheelListener=o(u,function(r,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=c.viewBox,o=h.lastPos[0],s=h.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),u=o/(a[2]-a[0])*(i[2]-i[0])+i[0],f=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-u)*l+u,i[2]=(i[2]-u)*l+u,i[1]=(i[1]-f)*l+f,i[3]=(i[3]-f)*l+f,t.setRanges(i),h.lastInputTime=Date.now(),e(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),h}var a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"../cartesian/constants\");e.exports=i},{\"../cartesian/constants\":777,\"mouse-change\":452,\"mouse-event-offset\":453,\"mouse-wheel\":455}],807:[function(t,e,r){\"use strict\";function n(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}function i(t){return new n(t)}var a=t(\"../plots\"),o=t(\"../cartesian/axes\"),s=t(\"../../lib/html2unicode\"),l=t(\"../../lib/str2rgbarray\"),u=n.prototype,c=[\"xaxis\",\"yaxis\"];u.merge=function(t){this.titleEnable=!1,this.backgroundColor=l(t.plot_bgcolor);var e,r,n,i,a,o,u,h,f,d,p;for(d=0;d<2;++d){for(e=c[d],r=t[this.scene[e]._name],n=/Click to enter .+ title/.test(r.title)?\"\":r.title,p=0;p<=2;p+=2)this.labelEnable[d+p]=!1,this.labels[d+p]=s(n),this.labelColor[d+p]=l(r.titlefont.color),this.labelFont[d+p]=r.titlefont.family,this.labelSize[d+p]=r.titlefont.size,this.labelPad[d+p]=this.getLabelPad(e,r),this.tickEnable[d+p]=!1,this.tickColor[d+p]=l((r.tickfont||{}).color),this.tickAngle[d+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[d+p]=this.getTickPad(r),this.tickMarkLength[d+p]=0,this.tickMarkWidth[d+p]=r.tickwidth||0,this.tickMarkColor[d+p]=l(r.tickcolor),this.borderLineEnable[d+p]=!1,this.borderLineColor[d+p]=l(r.linecolor),this.borderLineWidth[d+p]=r.linewidth||0;u=this.hasSharedAxis(r),a=this.hasAxisInDfltPos(e,r)&&!u,o=this.hasAxisInAltrPos(e,r)&&!u,i=r.mirror||!1,h=u?-1!==String(i).indexOf(\"all\"):!!i,f=u?\"allticks\"===i:-1!==String(i).indexOf(\"ticks\"),a?this.labelEnable[d]=!0:o&&(this.labelEnable[d+2]=!0),a?this.tickEnable[d]=r.showticklabels:o&&(this.tickEnable[d+2]=r.showticklabels),(a||h)&&(this.borderLineEnable[d]=r.showline),(o||h)&&(this.borderLineEnable[d+2]=r.showline),(a||f)&&(this.tickMarkLength[d]=this.getTickMarkLength(r)),(o||f)&&(this.tickMarkLength[d+2]=this.getTickMarkLength(r)),this.gridLineEnable[d]=r.showgrid,this.gridLineColor[d]=l(r.gridcolor),this.gridLineWidth[d]=r.gridwidth,this.zeroLineEnable[d]=r.zeroline,this.zeroLineColor[d]=l(r.zerolinecolor),this.zeroLineWidth[d]=r.zerolinewidth}},u.hasSharedAxis=function(t){var e=this.scene,r=a.getSubplotIds(e.fullLayout,\"gl2d\");return 0!==o.findSubplotsWithAxis(r,t).indexOf(e.id)},u.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},u.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},u.getLabelPad=function(t,e){var r=e.titlefont.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},u.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},u.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=i},{\n",
+       "\"../../lib/html2unicode\":726,\"../../lib/str2rgbarray\":749,\"../cartesian/axes\":772,\"../plots\":831}],808:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../plots\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),u=t(\"../../components/fx/layout_attributes\");r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.layoutAttributes.plot_bgcolor,hoverlabel:u.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=a.getSubplotIds(e,\"gl2d\"),o=0;o<n.length;o++){var s=n[o],l=e._plots[s],u=a.getSubplotData(r,\"gl2d\",s),c=l._scene2d;void 0===c&&(c=new i({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),l._scene2d=c),c.plot(u,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=a.getSubplotIds(n,\"gl2d\"),o=0;o<i.length;o++){var s=i[o],u=n._plots[s];if(u._scene2d){0===a.getSubplotData(t,\"gl2d\",s).length&&(u._scene2d.destroy(),delete n._plots[s])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=a.getSubplotIds(e,\"gl2d\"),n=0;n<r.length;n++){var i=e._plots[r[n]],s=i._scene2d,l=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":l,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),s.destroy()}},r.updateFx=function(t){for(var e=a.getSubplotIds(t,\"gl2d\"),r=0;r<e.length;r++){t._plots[e[r]]._scene2d.updateFx(t.dragmode)}}},{\"../../components/fx/layout_attributes\":646,\"../../constants/xmlns_namespaces\":709,\"../../plot_api/edit_types\":756,\"../cartesian\":782,\"../cartesian/attributes\":771,\"../cartesian/constants\":777,\"../plots\":831,\"./scene2d\":809}],809:[function(t,e,r){\"use strict\";function n(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context.scrollZoom,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.glplotOptions=p(this),this.glplotOptions.merge(e),this.glplot=c(this.glplotOptions),this.camera=m(this),this.traces={},this.spikes=h(this.glplot),this.selectBox=f(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.bounds=[1/0,1/0,-1/0,-1/0],this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw()}function i(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1}var a,o,s=t(\"../../registry\"),l=t(\"../../plots/cartesian/axes\"),u=t(\"../../components/fx\"),c=t(\"gl-plot2d\"),h=t(\"gl-spikes2d\"),f=t(\"gl-select-box\"),d=t(\"webgl-context\"),p=t(\"./convert\"),m=t(\"./camera\"),v=t(\"../../lib/html2unicode\"),g=t(\"../../lib/show_no_webgl_msg\"),y=t(\"../../plots/cartesian/constraints\"),b=y.enforce,x=y.clean,_=[\"xaxis\",\"yaxis\"];e.exports=n;var w=n.prototype;w.makeFramework=function(){if(this.staticPlot){if(!(o||(a=document.createElement(\"canvas\"),o=d({canvas:a,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=a,this.gl=o}else{var t=document.createElement(\"canvas\"),e=d({canvas:t,premultipliedAlpha:!0});e||g(this),this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r),r.className+=\"user-select-none\";var n=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");n.style.position=\"absolute\",n.style.top=n.style.left=\"0px\",n.style.width=n.style.height=\"100%\",n.style[\"z-index\"]=20,n.style[\"pointer-events\"]=\"none\";var i=this.mouseContainer=document.createElement(\"div\");i.style.position=\"absolute\",i.style[\"pointer-events\"]=\"auto\";var s=this.container;s.appendChild(r),s.appendChild(n),s.appendChild(i);var l=this;i.addEventListener(\"mouseout\",function(){l.isMouseOver=!1,l.unhover()}),i.addEventListener(\"mouseover\",function(){l.isMouseOver=!0})},w.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(a),this.updateSize(this.canvas),this.glplot.setDirty(),this.glplot.draw();var e=this.glplot.gl,r=e.drawingBufferWidth,n=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var i=new Uint8Array(r*n*4);e.readPixels(0,0,r,n,e.RGBA,e.UNSIGNED_BYTE,i);for(var o=0,s=n-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=i[4*(r*o+l)+u];i[4*(r*o+l)+u]=i[4*(r*s+l)+u],i[4*(r*s+l)+u]=c}var h=document.createElement(\"canvas\");h.width=r,h.height=n;var f=h.getContext(\"2d\"),d=f.createImageData(r,n);d.data.set(i),f.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=h.toDataURL(\"image/jpeg\");break;case\"webp\":p=h.toDataURL(\"image/webp\");break;default:p=h.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(a),p},w.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),this.redraw&&this.redraw(),t},w.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[l.calcTicks(this.xaxis),l.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=v(t[e][r].text+\"\");return t},w.updateRefs=function(t){this.fullLayout=t;var e=l.subplotMatch,r=\"xaxis\"+this.id.match(e)[1],n=\"yaxis\"+this.id.match(e)[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},w.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout;n.xaxis.autorange=e.autorange,n.xaxis.range=e.range.slice(0),n.yaxis.autorange=r.autorange,n.yaxis.range=r.range.slice(0);var i={lastInputTime:this.camera.lastInputTime};i[e._name]=e.range.slice(0),i[r._name]=r.range.slice(0),t.emit(\"plotly_relayout\",i)},w.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();i(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},w.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&s.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},w.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map(function(e){t[e].dispose(),delete t[e]}),this.glplot.dispose(),this.staticPlot||this.container.removeChild(this.canvas),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},w.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};x(s,this.xaxis),x(s,this.yaxis);var u=r._size,c=this.xaxis.domain,h=this.yaxis.domain;o.viewBox=[u.l+c[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-c[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(c[1]-c[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+c[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\";var f=this.bounds;f[0]=f[1]=1/0,f[2]=f[3]=-1/0;var d,p,m=Object.keys(this.traces);for(p=0;p<m.length;++p)for(var v=this.traces[m[p]],g=0;g<2;++g)f[g]=Math.min(f[g],v.bounds[g]),f[g+2]=Math.max(f[g+2],v.bounds[g+2]);for(p=0;p<2;++p)f[p]>f[p+2]&&(f[p]=-1,f[p+2]=1),d=this[_[p]],d._length=o.viewBox[p+2]-o.viewBox[p],l.doAutoRange(d),d.setScale();b(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},w.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},w.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},w.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if(i=t[n],i.uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort(function(t,e){return t._trace.index-e._trace.index})},w.updateFx=function(t){this.mouseContainer.style[\"pointer-events\"]=\"lasso\"===t||\"select\"===t?\"none\":\"auto\",this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},w.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};u.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},w.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,s=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var l=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(l[c]=t.dataBox[c],l[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var h=i._size,f=this.xaxis.domain,d=this.yaxis.domain;a=t.pick(o/t.pixelRatio+h.l+f[0]*h.w,s/t.pixelRatio-(h.t+(1-d[1])*h.h));var p=a&&a.object._trace.handlePick(a);if(p&&n&&this.emitPointAction(p,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&p&&(!this.lastPickResult||this.lastPickResult.traceUid!==p.trace.uid||this.lastPickResult.dataCoord[0]!==p.dataCoord[0]||this.lastPickResult.dataCoord[1]!==p.dataCoord[1])){var m=p;this.lastPickResult={traceUid:p.trace?p.trace.uid:null,dataCoord:p.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(p,\"plotly_hover\");var v=this.fullData[m.trace.index]||{},g=m.pointIndex,y=u.castHoverinfo(v,i,g);if(y&&\"all\"!==y){var b=y.split(\"+\");-1===b.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===b.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===b.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===b.indexOf(\"text\")&&(m.textLabel=void 0),-1===b.indexOf(\"name\")&&(m.name=void 0)}u.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:u.castHoverOption(v,g,\"bgcolor\")||m.color,borderColor:u.castHoverOption(v,g,\"bordercolor\"),fontFamily:u.castHoverOption(v,g,\"font.family\"),fontSize:u.castHoverOption(v,g,\"font.size\"),fontColor:u.castHoverOption(v,g,\"font.color\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},w.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),u.loneUnhover(this.svgContainer))},w.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return l.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/fx\":645,\"../../lib/html2unicode\":726,\"../../lib/show_no_webgl_msg\":747,\"../../plots/cartesian/axes\":772,\"../../plots/cartesian/constraints\":779,\"../../registry\":846,\"./camera\":806,\"./convert\":807,\"gl-plot2d\":219,\"gl-select-box\":253,\"gl-spikes2d\":262,\"webgl-context\":563}],810:[function(t,e,r){\"use strict\";function n(t,e){function r(e,r,n,a){var o=p.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,c=\"zoom\"===o,f=!!a.control,d=!!a.alt,y=!!a.shift,b=!!(1&e),x=!!(2&e),_=!!(4&e),w=1/t.clientHeight,M=w*(r-m),k=w*(n-v),A=p.flipX?1:-1,T=p.flipY?1:-1,S=i(),E=Math.PI*p.rotateSpeed;if((s&&b&&!f&&!d&&!y||b&&!f&&!d&&y)&&u.rotate(S,A*E*M,-T*E*k,0),(l&&b&&!f&&!d&&!y||x||b&&f&&!d&&!y)&&u.pan(S,-p.translateSpeed*M*h,p.translateSpeed*k*h,0),c&&b&&!f&&!d&&!y||_||b&&!f&&d&&!y){var L=-p.zoomSpeed*k/window.innerHeight*(S-u.lastT())*100;u.pan(S,0,0,h*(Math.exp(L)-1))}return m=r,v=n,g=a,!0}}t=t||document.body,e=e||{};var n=[.01,1/0];\"distanceLimits\"in e&&(n[0]=e.distanceLimits[0],n[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(n[0]=e.zoomMin),\"zoomMax\"in e&&(n[1]=e.zoomMax);var u=a({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:n}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],h=0,f=t.clientWidth,d=t.clientHeight,p={keyBindingMode:\"rotate\",view:u,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,tick:function(){var e=i(),r=this.delay,n=e-2*r;u.idle(e-r),u.recalcMatrix(n),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===f&&t.clientHeight===d;return f=t.clientWidth,d=t.clientHeight,a?!l:(h=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};Object.defineProperties(p,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),n=u.computedCenter.slice();if(u.setMode(t),\"turntable\"===t){var a=i();u._active.lookAt(a,r,n,e),u._active.lookAt(a+500,r,n,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return h},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(n)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",function(t){return t.preventDefault(),!1});var m=0,v=0,g={shift:!1,control:!1,alt:!1,meta:!1};return p.mouseListener=o(t,r),t.addEventListener(\"touchstart\",function(e){var n=l(e.changedTouches[0],t);r(0,n[0],n[1],g),r(1,n[0],n[1],g)}),t.addEventListener(\"touchmove\",function(e){var n=l(e.changedTouches[0],t);r(1,n[0],n[1],g)}),t.addEventListener(\"touchend\",function(){r(0,m,v,g)}),p.wheelListener=s(t,function(t,e){if(!1!==p.keyBindingMode){var r=p.flipX?1:-1,n=p.flipY?1:-1,a=i();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*p.rotateSpeed/window.innerWidth);else{var o=-p.zoomSpeed*n*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,h*(Math.exp(o)-1))}}},!0),p}e.exports=n;var i=t(\"right-now\"),a=t(\"3d-view\"),o=t(\"mouse-change\"),s=t(\"mouse-wheel\"),l=t(\"mouse-event-offset\")},{\"3d-view\":37,\"mouse-change\":452,\"mouse-event-offset\":453,\"mouse-wheel\":455,\"right-now\":502}],811:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../plots\"),s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=o.getSubplotIds(e,\"gl3d\"),i=0;i<n.length;i++){var l=n[i],u=o.getSubplotData(r,\"gl3d\",l),c=e[l],h=c._scene;h||(h=new a({id:l,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),c._scene=h),h.cameraInitial||(h.cameraInitial=s.extendDeep({},c.camera)),h.plot(u,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=o.getSubplotIds(n,\"gl3d\"),a=0;a<i.length;a++){var s=i[a];!e[s]&&n[s]._scene&&(n[s]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+s).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=o.getSubplotIds(e,\"gl3d\"),n=e._size,i=0;i<r.length;i++){var a=e[r[i]],s=a.domain,u=a._scene,c=u.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*s.x[0],y:n.t+n.h*(1-s.y[1]),width:n.w*(s.x[1]-s.x[0]),height:n.h*(s.y[1]-s.y[0]),preserveAspectRatio:\"none\"}),u.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=o.getSubplotIds(t,\"gl3d\"),r=0;r<e.length;r++){t[e[r]]._scene.updateFx(t.dragmode,t.hovermode)}}},{\"../../components/fx/layout_attributes\":646,\"../../constants/xmlns_namespaces\":709,\"../../lib\":728,\"../../plot_api/edit_types\":756,\"../plots\":831,\"./layout/attributes\":812,\"./layout/defaults\":816,\"./layout/layout_attributes\":817,\"./scene\":821}],812:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],813:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:i.title,titlefont:i.titlefont,type:i.type,autorange:i.autorange,rangemode:i.rangemode,range:i.range,tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,separatethousands:i.separatethousands,tickformat:i.tickformat,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth},\"plot\",\"from-root\")},{\"../../../components/color\":604,\"../../../lib/extend\":717,\"../../../plot_api/edit_types\":756,\"../../cartesian/layout_attributes\":783}],814:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"./axis_attributes\"),o=t(\"../../cartesian/type_defaults\"),s=t(\"../../cartesian/axis_defaults\"),l=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){function u(t,e){return i.coerce(c,h,a,t,e)}for(var c,h,f=0;f<l.length;f++){var 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a=t(\"../../../lib/html2unicode\"),o=t(\"../../../lib/str2rgbarray\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"];n.prototype.merge=function(t){for(var e=this,r=0;r<3;++r){var n=t[s[r]];n.visible?(e.labels[r]=a(n.title),\"titlefont\"in n&&(n.titlefont.color&&(e.labelColor[r]=o(n.titlefont.color)),n.titlefont.family&&(e.labelFont[r]=n.titlefont.family),n.titlefont.size&&(e.labelSize[r]=n.titlefont.size)),\"showline\"in n&&(e.lineEnable[r]=n.showline),\"linecolor\"in n&&(e.lineColor[r]=o(n.linecolor)),\"linewidth\"in n&&(e.lineWidth[r]=n.linewidth),\"showgrid\"in n&&(e.gridEnable[r]=n.showgrid),\"gridcolor\"in n&&(e.gridColor[r]=o(n.gridcolor)),\"gridwidth\"in n&&(e.gridWidth[r]=n.gridwidth),\"log\"===n.type?e.zeroEnable[r]=!1:\"zeroline\"in n&&(e.zeroEnable[r]=n.zeroline),\"zerolinecolor\"in n&&(e.zeroLineColor[r]=o(n.zerolinecolor)),\"zerolinewidth\"in n&&(e.zeroLineWidth[r]=n.zerolinewidth),\"ticks\"in n&&n.ticks?e.lineTickEnable[r]=!0:e.lineTickEnable[r]=!1,\"ticklen\"in n&&(e.lineTickLength[r]=e._defaultLineTickLength[r]=n.ticklen),\"tickcolor\"in n&&(e.lineTickColor[r]=o(n.tickcolor)),\"tickwidth\"in n&&(e.lineTickWidth[r]=n.tickwidth),\"tickangle\"in n&&(e.tickAngle[r]=\"auto\"===n.tickangle?0:Math.PI*-n.tickangle/180),\"showticklabels\"in n&&(e.tickEnable[r]=n.showticklabels),\"tickfont\"in n&&(n.tickfont.color&&(e.tickColor[r]=o(n.tickfont.color)),n.tickfont.family&&(e.tickFont[r]=n.tickfont.family),n.tickfont.size&&(e.tickSize[r]=n.tickfont.size)),\"mirror\"in n?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(n.mirror)?(e.lineTickMirror[r]=!0,e.lineMirror[r]=!0):!0===n.mirror?(e.lineTickMirror[r]=!1,e.lineMirror[r]=!0):(e.lineTickMirror[r]=!1,e.lineMirror[r]=!1):e.lineMirror[r]=!1,\"showbackground\"in n&&!1!==n.showbackground?(e.backgroundEnable[r]=!0,e.backgroundColor[r]=o(n.backgroundcolor)):e.backgroundEnable[r]=!1):(e.tickEnable[r]=!1,e.labelEnable[r]=!1,e.lineEnable[r]=!1,e.lineTickEnable[r]=!1,e.gridEnable[r]=!1,e.zeroEnable[r]=!1,e.backgroundEnable[r]=!1)}},e.exports=i},{\"../../../lib/html2unicode\":726,\"../../../lib/str2rgbarray\":749}],816:[function(t,e,r){\"use strict\";function n(t,e,r,n){for(var i=r(\"bgcolor\"),s=a.combine(i,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],c=0;c<u.length;c++)r(\"camera.\"+u[c]+\".x\"),r(\"camera.\"+u[c]+\".y\"),r(\"camera.\"+u[c]+\".z\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),f=h?\"manual\":\"auto\",d=r(\"aspectmode\",f);h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode),l(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:s,calendar:n.calendar}),o.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n),r(\"dragmode\",n.getDfltFromLayout(\"dragmode\")),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}var i=t(\"../../../lib\"),a=t(\"../../../components/color\"),o=t(\"../../../registry\"),s=t(\"../../subplot_defaults\"),l=t(\"./axis_defaults\"),u=t(\"./layout_attributes\");e.exports=function(t,e,r){function a(e){if(!o){return i.validate(t[e],u[e])?t[e]:void 0}}var o=e._basePlotModules.length>1;s(t,e,r,{type:\"gl3d\",attributes:u,handleDefaults:n,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:a,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":604,\"../../../lib\":728,\"../../../registry\":846,\"../../subplot_defaults\":838,\"./axis_defaults\":814,\"./layout_attributes\":817}],817:[function(t,e,r){\"use strict\";function n(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}var i=t(\"./axis_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(n(0,0,1),{}),center:a(n(0,0,0),{}),eye:a(n(1.25,1.25,1.25),{}),editType:\"camera\"},domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1,editType:\"plot\"},{valType:\"number\",min:0,max:1,editType:\"plot\"}],dflt:[0,1],editType:\"plot\"},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1,editType:\"plot\"},{valType:\"number\",min:0,max:1,editType:\"plot\"}],dflt:[0,1],editType:\"plot\"},editType:\"plot\"},aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:i,yaxis:i,zaxis:i,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],dflt:\"turntable\",editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":728,\"../../../lib/extend\":717,\"./axis_attributes\":813}],818:[function(t,e,r){\"use strict\";function n(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}function i(t){var e=new n;return e.merge(t),e}var a=t(\"../../../lib/str2rgbarray\"),o=[\"xaxis\",\"yaxis\",\"zaxis\"];n.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[o[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=a(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=i},{\"../../../lib/str2rgbarray\":749}],819:[function(t,e,r){\"use strict\";function n(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}function i(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,i=t.fullSceneLayout,c=[[],[],[]],h=0;h<3;++h){var f=i[l[h]];if(f._length=(r[h].hi-r[h].lo)*r[h].pixelsPerDataUnit/t.dataScale[h],Math.abs(f._length)===1/0)c[h]=[];else{f.range[0]=r[h].lo/t.dataScale[h],f.range[1]=r[h].hi/t.dataScale[h],f._m=1/(t.dataScale[h]*r[h].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var d=f.tickmode;if(\"auto\"===f.tickmode){f.tickmode=\"linear\";var p=f.nticks||o.constrain(f._length/40,4,9);a.autoTicks(f,Math.abs(f.range[1]-f.range[0])/p)}for(var m=a.calcTicks(f),v=0;v<m.length;++v)m[v].x=m[v].x*t.dataScale[h],m[v].text=s(m[v].text);c[h]=m,f.tickmode=d}}e.ticks=c;for(var h=0;h<3;++h){u[h]=.5*(t.glplot.bounds[0][h]+t.glplot.bounds[1][h]);for(var v=0;v<2;++v)e.bounds[v][h]=t.glplot.bounds[v][h]}t.contourLevels=n(c)}e.exports=i;var a=t(\"../../cartesian/axes\"),o=t(\"../../../lib\"),s=t(\"../../../lib/html2unicode\"),l=[\"xaxis\",\"yaxis\",\"zaxis\"],u=[0,0,0]},{\"../../../lib\":728,\"../../../lib/html2unicode\":726,\"../../cartesian/axes\":772}],820:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}function i(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}e.exports=i},{}],821:[function(t,e,r){\"use strict\";function n(t){function e(e,r){var n=t.fullSceneLayout[e];return v.tickText(n,n.d2l(r),\"hover\").text}var r,n=t.svgContainer,i=t.container.getBoundingClientRect(),a=i.width,o=i.height;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+a+\" \"+o),n.setAttributeNS(null,\"width\",a),n.setAttributeNS(null,\"height\",o),k(t),t.glplot.axes.update(t.axesOptions);for(var s=Object.keys(t.traces),l=null,u=t.glplot.selection,c=0;c<s.length;++c)r=t.traces[s[c]],\"skip\"!==r.data.hoverinfo&&r.handlePick(u)&&(l=r),r.setContourLevels&&r.setContourLevels();var h;if(null!==l){var f=_(t.glplot.cameraParams,u.dataCoordinate);r=l.data;var d=u.index,p=g.castHoverinfo(r,t.fullLayout,d),m=e(\"xaxis\",u.traceCoordinate[0]),y=e(\"yaxis\",u.traceCoordinate[1]),b=e(\"zaxis\",u.traceCoordinate[2]);if(\"all\"!==p){var x=p.split(\"+\");-1===x.indexOf(\"x\")&&(m=void 0),-1===x.indexOf(\"y\")&&(y=void 0),-1===x.indexOf(\"z\")&&(b=void 0),-1===x.indexOf(\"text\")&&(u.textLabel=void 0),-1===x.indexOf(\"name\")&&(l.name=void 0)}t.fullSceneLayout.hovermode&&g.loneHover({x:(.5+.5*f[0]/f[3])*a,y:(.5-.5*f[1]/f[3])*o,xLabel:m,yLabel:y,zLabel:b,text:u.textLabel,name:l.name,color:g.castHoverOption(r,d,\"bgcolor\")||l.color,borderColor:g.castHoverOption(r,d,\"bordercolor\"),fontFamily:g.castHoverOption(r,d,\"font.family\"),fontSize:g.castHoverOption(r,d,\"font.size\"),fontColor:g.castHoverOption(r,d,\"font.color\")},{container:n,gd:t.graphDiv});var w={x:u.traceCoordinate[0],y:u.traceCoordinate[1],z:u.traceCoordinate[2],data:r._input,fullData:r,curveNumber:r.index,pointNumber:d};g.appendArrayPointValue(w,r,d);var M={points:[w]};u.buttons&&u.distance<5?t.graphDiv.emit(\"plotly_click\",M):t.graphDiv.emit(\"plotly_hover\",M),h=M}else g.loneUnhover(n),t.graphDiv.emit(\"plotly_unhover\",h);t.drawAnnotations(t)}function i(t,e,r,i){var a={canvas:r,gl:i,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!(h||(c=document.createElement(\"canvas\"),h=d({canvas:c,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");a.pixelRatio=t.pixelRatio,a.gl=h,a.canvas=c}try{t.glplot=f(a)}catch(e){b(t)}var o=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+\".camera\"]=u(t.camera),t.saveCamera(t.graphDiv.layout),t.graphDiv.emit(\"plotly_relayout\",e)}};if(t.glplot.canvas.addEventListener(\"mouseup\",o.bind(null,t)),t.glplot.canvas.addEventListener(\"wheel\",o.bind(null,t)),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",function(t){m.warn(\"Lost WebGL context.\"),t.preventDefault()}),!t.camera){var s=t.fullSceneLayout.camera;t.camera=x(t.container,{center:[s.center.x,s.center.y,s.center.z],\n",
+       "eye:[s.eye.x,s.eye.y,s.eye.z],up:[s.up.x,s.up.y,s.up.z],zoomMin:.1,zoomMax:100,mode:\"orbit\"})}return t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=n.bind(null,t),t.traces={},!0}function a(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\",r.appendChild(a),this.svgContainer=a,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=w(e[this.id]),this.spikeOptions=M(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=p.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=p.getComponentMethod(\"annotations3d\",\"draw\"),i(this,e)}function o(t,e,r,n,i){for(var a,o=0;o<e.length;++o)if(Array.isArray(e[o]))for(var s=0;s<e[o].length;++s)a=t.d2l(e[o][s],0,i),!isNaN(a)&&isFinite(a)&&(n[0][r]=Math.min(n[0][r],a),n[1][r]=Math.max(n[1][r],a));else a=t.d2l(e[o],0,i),!isNaN(a)&&isFinite(a)&&(n[0][r]=Math.min(n[0][r],a),n[1][r]=Math.max(n[1][r],a))}function s(t,e,r){var n=t.fullSceneLayout;o(n.xaxis,e.x,0,r,e.xcalendar),o(n.yaxis,e.y,1,r,e.ycalendar),o(n.zaxis,e.z,2,r,e.zcalendar)}function l(t){return[[t.eye.x,t.eye.y,t.eye.z],[t.center.x,t.center.y,t.center.z],[t.up.x,t.up.y,t.up.z]]}function u(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}var c,h,f=t(\"gl-plot3d\"),d=t(\"webgl-context\"),p=t(\"../../registry\"),m=t(\"../../lib\"),v=t(\"../../plots/cartesian/axes\"),g=t(\"../../components/fx\"),y=t(\"../../lib/str2rgbarray\"),b=t(\"../../lib/show_no_webgl_msg\"),x=t(\"./camera\"),_=t(\"./project\"),w=t(\"./layout/convert\"),M=t(\"./layout/spikes\"),k=t(\"./layout/tick_marks\"),A=a.prototype;A.recoverContext=function(){function t(){return r.isContextLost()?void requestAnimationFrame(t):i(e,e.fullLayout,n,r)?void e.plot.apply(e,e.plotArgs):void m.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")}var e=this,r=this.glplot.gl,n=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(t)};var T=[\"xaxis\",\"yaxis\",\"zaxis\"];A.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,l,u,c=e[this.id],h=r[this.id];c.bgcolor?this.glplot.clearColor=y(c.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=c,this.glplotLayout=c,this.axesOptions.merge(c),this.spikeOptions.merge(c),this.setCamera(c.camera),this.updateFx(c.dragmode,c.hovermode),this.glplot.update({}),this.setConvert(l),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)n=t[a],!0===n.visible&&s(this,n,f);var d=[1,1,1];for(o=0;o<3;++o)f[0][o]>f[1][o]?d[o]=1:f[1][o]===f[0][o]?d[o]=1:d[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=d,this.convertAnnotations(this),a=0;a<t.length;++a)n=t[a],!0===n.visible&&(i=this.traces[n.uid],i?i.update(n):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible)continue t;i=this.traces[p[a]],i.dispose(),delete this.traces[p[a]]}this.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var m=[[0,0,0],[0,0,0]],v=[],g={};for(a=0;a<3;++a){if(l=c[T[a]],u=l.type,u in g?(g[u].acc*=d[a],g[u].count+=1):g[u]={acc:d[a],count:1},l.autorange){m[0][a]=1/0,m[1][a]=-1/0;var b=this.glplot.objects,x=this.fullSceneLayout.annotations||[],_=l._name.charAt(0);for(o=0;o<b.length;o++){var w=b[o].bounds;m[0][a]=Math.min(m[0][a],w[0][a]/d[a]),m[1][a]=Math.max(m[1][a],w[1][a]/d[a])}for(o=0;o<x.length;o++){var M=x[o];if(M.visible){var k=l.r2l(M[_]);m[0][a]=Math.min(m[0][a],k),m[1][a]=Math.max(m[1][a],k)}}if(\"rangemode\"in l&&\"tozero\"===l.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var A=m[1][a]-m[0][a];m[0][a]-=A/32,m[1][a]+=A/32}}else{var S=l.range;m[0][a]=l.r2l(S[0]),m[1][a]=l.r2l(S[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.bounds[0][a]=m[0][a]*d[a],this.glplot.bounds[1][a]=m[1][a]*d[a]}var E=[1,1,1];for(a=0;a<3;++a){l=c[T[a]],u=l.type;var L=g[u];E[a]=Math.pow(L.acc,1/L.count)/d[a]}var C;if(\"auto\"===c.aspectmode)C=Math.max.apply(null,E)/Math.min.apply(null,E)<=4?E:[1,1,1];else if(\"cube\"===c.aspectmode)C=[1,1,1];else if(\"data\"===c.aspectmode)C=E;else{if(\"manual\"!==c.aspectmode)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var 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f=h.getContext(\"2d\"),d=f.createImageData(r,n);d.data.set(i),f.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=h.toDataURL(\"image/jpeg\");break;case\"webp\":p=h.toDataURL(\"image/webp\");break;default:p=h.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(c),p},A.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[T[t]];v.setConvert(e,this.fullLayout),e.setScale=m.noop}},e.exports=a},{\"../../components/fx\":645,\"../../lib\":728,\"../../lib/show_no_webgl_msg\":747,\"../../lib/str2rgbarray\":749,\"../../plots/cartesian/axes\":772,\"../../registry\":846,\"./camera\":810,\"./layout/convert\":815,\"./layout/spikes\":818,\"./layout/tick_marks\":819,\"./project\":820,\"gl-plot3d\":221,\"webgl-context\":563}],822:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"../components/color/attributes\"),a=n({editType:\"calc\"});a.family.dflt='\"Open Sans\", verdana, arial, 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strict\";e.exports={styleUrlPrefix:\"mapbox://styles/mapbox/\",styleUrlSuffix:\"v9\",controlContainerClassName:\"mapboxgl-control-container\",noAccessTokenErrorMsg:[\"Missing Mapbox access token.\",\"Mapbox trace type require a Mapbox access token to be registered.\",\"For example:\",\"  Plotly.plot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });\",\"More info here: https://www.mapbox.com/help/define-access-token/\"].join(\"\\n\"),mapOnErrorMsg:\"Mapbox error.\"}},{}],824:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=t.split(\" \"),i=r[0],a=r[1],o=Array.isArray(e)?n.mean(e):e,s=.5+o/100,l=1.5+o/100,u=[\"\",\"\"],c=[0,0];switch(i){case\"top\":u[0]=\"top\",c[1]=-l;break;case\"bottom\":u[0]=\"bottom\",c[1]=l}switch(a){case\"left\":u[1]=\"right\",c[0]=-s;break;case\"right\":u[1]=\"left\",c[0]=s}var h;return h=u[0]&&u[1]?u.join(\"-\"):u[0]?u[0]:u[1]?u[1]:\"center\",{anchor:h,offset:c}}},{\"../../lib\":728}],825:[function(t,e,r){\"use 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h=s[c],f=o.getSubplotCalcData(r,\"mapbox\",h),d=e[h],p=d._subplot;d.accesstoken=u,p||(p=l({gd:t,container:e._glcontainer.node(),id:h,fullLayout:e,staticPlot:t._context.staticPlot}),e[h]._subplot=p),p.viewInitial||(p.viewInitial={center:a.extendFlat({},d.center),zoom:d.zoom,bearing:d.bearing,pitch:d.pitch}),p.plot(f,e,t._promises)}},r.clean=function(t,e,r,n){for(var i=o.getSubplotIds(n,\"mapbox\"),a=0;a<i.length;a++){var s=i[a];!e[s]&&n[s]._subplot&&n[s]._subplot.destroy()}},r.toSVG=function(t){for(var e=t._fullLayout,r=o.getSubplotIds(e,\"mapbox\"),n=e._size,i=0;i<r.length;i++){var a=e[r[i]],l=a.domain,u=a._subplot,c=u.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:s.svg,\"xlink:href\":c,x:n.l+n.w*l.x[0],y:n.t+n.h*(1-l.y[1]),width:n.w*(l.x[1]-l.x[0]),height:n.h*(l.y[1]-l.y[0]),preserveAspectRatio:\"none\"}),u.destroy()}},r.updateFx=function(t){for(var e=o.getSubplotIds(t,\"mapbox\"),r=0;r<e.length;r++){t[e[r]]._subplot.updateFx(t)}}},{\"../../constants/xmlns_namespaces\":709,\"../../lib\":728,\"../plots\":831,\"./constants\":823,\"./layout_attributes\":827,\"./layout_defaults\":828,\"./mapbox\":829,\"mapbox-gl\":343}],826:[function(t,e,r){\"use strict\";function n(t,e){this.mapbox=t,this.map=t.map,this.uid=t.uid+\"-layer\"+e,this.idSource=this.uid+\"-source\",this.idLayer=this.uid+\"-layer\",this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}function i(t){var e=t.source;return s.isPlainObject(e)||\"string\"==typeof e&&e.length>0}function a(t){var e={},r={};switch(t.type){case\"circle\":s.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":s.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity});break;case\"fill\":s.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var n=t.symbol,i=l(n.textposition,n.iconsize);s.extendFlat(e,{\"icon-image\":n.icon+\"-15\",\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":i.anchor,\"text-offset\":i.offset}),s.extendFlat(r,{\"icon-color\":t.color,\"text-color\":n.textfont.color,\"text-opacity\":t.opacity})}return{layout:e,paint:r}}function o(t){var e,r=t.sourcetype,n=t.source,i={type:r},a=\"string\"==typeof n;return\"geojson\"===r?e=\"data\":\"vector\"===r&&(e=a?\"url\":\"tiles\"),i[e]=n,i}var s=t(\"../../lib\"),l=t(\"./convert_text_opts\"),u=n.prototype;u.update=function(t){this.visible?this.needsNewSource(t)?(this.updateLayer(t),this.updateSource(t)):this.needsNewLayer(t)&&this.updateLayer(t):(this.updateSource(t),this.updateLayer(t)),this.updateStyle(t),this.visible=i(t)},u.needsNewSource=function(t){return this.sourceType!==t.sourcetype||this.source!==t.source||this.layerType!==t.type},u.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==t.below},u.updateSource=function(t){var e=this.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,i(t)){var r=o(t);e.addSource(this.idSource,r)}},u.updateLayer=function(t){var e=this.map;if(e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),this.layerType=t.type,i(t)){e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer||\"\",type:t.type},t.below);var r={visibility:\"visible\"};this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",r)}},u.updateStyle=function(t){var e=a(t);i(t)&&(this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.mapbox.setOptions(this.idLayer,\"setPaintProperty\",e.paint))},u.dispose=function(){var t=this.map;t.removeLayer(this.idLayer),t.removeSource(this.idSource)},e.exports=function(t,e,r){var i=new n(t,e);return i.update(r),i}},{\"../../lib\":728,\"./convert_text_opts\":824}],827:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../font_attributes\"),o=t(\"../../traces/scatter/attributes\").textposition,s=t(\"../../plot_api/edit_types\").overrideAll,l=a({});l.family.dflt=\"Open Sans Regular, Arial Unicode MS 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a=t(\"../../lib\"),o=t(\"../subplot_defaults\"),s=t(\"./layout_attributes\");e.exports=function(t,e,r){o(t,e,r,{type:\"mapbox\",attributes:s,handleDefaults:n,partition:\"y\"})}},{\"../../lib\":728,\"../subplot_defaults\":838,\"./layout_attributes\":827}],829:[function(t,e,r){\"use strict\";function n(t){this.id=t.id,this.gd=t.gd,this.container=t.container,this.isStatic=t.staticPlot;var e=t.fullLayout;this.uid=e._uid+\"-\"+this.id,this.opts=e[this.id],this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(e),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[]}function i(t){var e=d.style.values,r=d.style.dflt,n={};return u.isPlainObject(t)?(n.id=t.id,n.style=t):\"string\"==typeof t?(n.id=t,n.style=-1!==e.indexOf(t)?a(t):t):(n.id=r,n.style=a(r)),n}function a(t){return f.styleUrlPrefix+t+\"-\"+f.styleUrlSuffix}function o(t){return[t.lon,t.lat]}var 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n=t(\"../../../components/color/attributes\"),i=t(\"./axis_attributes\"),a=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=a({domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:i,baxis:i,caxis:i},\"plot\",\"from-root\")},{\"../../../components/color/attributes\":603,\"../../../plot_api/edit_types\":756,\"./axis_attributes\":841}],845:[function(t,e,r){\"use strict\";function n(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e)}function i(t){a.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}var 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r,n,i,a,o,s,l=this,d=t.domain,p=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],g=d.y[1]-d.y[0],y=v*e.w,b=g*e.h,x=t.sum,w=t.aaxis.min,M=t.baxis.min,k=t.caxis.min;y>_*b?(a=b,i=a*_):(i=y,a=i/_),o=v*i/y,s=g*a/b,r=e.l+e.w*p-i/2,n=e.t+e.h*(1-m)-a/2,l.x0=r,l.y0=n,l.w=i,l.h=a,l.sum=x,l.xaxis={type:\"linear\",range:[w+2*k-x,x-w-2*M],domain:[p-o/2,p+o/2],_id:\"x\"},h(l.xaxis,l.graphDiv._fullLayout),l.xaxis.setScale(),l.xaxis.isPtWithinRange=function(t){return t.a>=l.aaxis.range[0]&&t.a<=l.aaxis.range[1]&&t.b>=l.baxis.range[1]&&t.b<=l.baxis.range[0]&&t.c>=l.caxis.range[1]&&t.c<=l.caxis.range[0]},l.yaxis={type:\"linear\",range:[w,x-M-k],domain:[m-s/2,m+s/2],_id:\"y\"},h(l.yaxis,l.graphDiv._fullLayout),l.yaxis.setScale(),l.yaxis.isPtWithinRange=function(){return!0};var 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w=b.MINZOOM/2+.87,M=\"m-0.87,.5h\"+w+\"v3h-\"+(w+5.2)+\"l\"+(w/2+2.6)+\",-\"+(.87*w+4.5)+\"l2.6,1.5l-\"+w/2+\",\"+.87*w+\"Z\",k=\"m0.87,.5h-\"+w+\"v3h\"+(w+5.2)+\"l-\"+(w/2+2.6)+\",-\"+(.87*w+4.5)+\"l-2.6,1.5l\"+w/2+\",\"+.87*w+\"Z\",A=\"m0,1l\"+w/2+\",\"+.87*w+\"l2.6,-1.5l-\"+(w/2+2.6)+\",-\"+(.87*w+4.5)+\"l-\"+(w/2+2.6)+\",\"+(.87*w+4.5)+\"l2.6,1.5l\"+w/2+\",-\"+.87*w+\"Z\",T=!0;x.initInteractions=function(){function t(t,e,r){var n=F.getBoundingClientRect();w=e-n.left,S=r-n.top,E={a:R.aaxis.range[0],b:R.baxis.range[1],c:R.caxis.range[1]},C=E,L=R.aaxis.range[1]-E.a,I=o(R.graphDiv._fullLayout[R.id].bgcolor).getLuminance(),z=\"M0,\"+R.h+\"L\"+R.w/2+\", 0L\"+R.w+\",\"+R.h+\"Z\",D=!1,P=N.append(\"path\").attr(\"class\",\"zoombox\").attr(\"transform\",\"translate(\"+R.x0+\", \"+R.y0+\")\").style({fill:I>.2?\"rgba(0,0,0,0)\":\"rgba(255,255,255,0)\",\"stroke-width\":0}).attr(\"d\",z),O=N.append(\"path\").attr(\"class\",\"zoombox-corners\").attr(\"transform\",\"translate(\"+R.x0+\", 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i=n.traces[e];i&&d(r.modules[e]._module.attributes,i)}}function a(t,e){var n=r.componentsRegistry[t].schema;if(n&&n.transforms){var i=n.transforms[e];i&&d(r.transformsRegistry[e].attributes,i)}}function o(t,e){var n=r.componentsRegistry[t].schema;if(n&&n.subplots){var i=r.subplotsRegistry[e],a=i.layoutAttributes,o=\"subplot\"===i.attr?i.name:i.attr;Array.isArray(o)&&(o=o[0]);var s=n.subplots[o];a&&s&&d(a,s)}}function s(t){return\"object\"==typeof t&&(t=t.type),t}var l=t(\"./lib/loggers\"),u=t(\"./lib/noop\"),c=t(\"./lib/push_unique\"),h=t(\"./lib/extend\"),f=h.extendFlat,d=h.extendDeepAll,p=t(\"./plots/attributes\"),m=t(\"./plots/layout_attributes\");r.modules={},r.allCategories={},r.allTypes=[],r.subplotsRegistry={},r.transformsRegistry={},r.componentsRegistry={},r.layoutArrayContainers=[],r.layoutArrayRegexes=[],r.traceLayoutAttributes={},r.register=function(t,e,n,a){if(r.modules[e])return void l.log(\"Type \"+e+\" already registered\");for(var 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v._infolayer.selectAll(\".cb\"+d).remove();\"heatmap\"===f.coloring?(s.zauto&&!1===s.autocontour&&(s._input.zmin=s.zmin=f.start-f.size/2,s._input.zmax=s.zmax=s.zmin+_.length*f.size),y(t,e,[n])):(v._paper.selectAll(\".hm\"+d).remove(),v._infolayer.selectAll(\"g.rangeslider-container\").selectAll(\".hm\"+d).remove()),b(_),x(_);var w=p.c2p(u[0],!0),M=p.c2p(u[u.length-1],!0),k=m.c2p(h[0],!0),A=m.c2p(h[h.length-1],!0),T=[[w,A],[M,A],[M,k],[w,k]],S=r.makeContourGroup(e,n,g);a(S,T,f),o(S,_,T,f),l(S,_,t,n[0],f,T),c(S,e,v._clips,n[0],T)}function i(t,e,r){for(var n=t.size,i=[],a=_(t),o=t.start;o<a;o+=n)if(i.push({level:o,crossings:{},starts:[],edgepaths:[],paths:[],xaxis:e.xaxis,yaxis:e.yaxis,x:r.x,y:r.y,z:r.z,smoothing:r.trace.line.smoothing}),i.length>1e3){d.warn(\"Too many contours, clipping at 1000\",t);break}return i}function a(t,e,r){var n=t.selectAll(\"g.contourbg\").data([0]);n.enter().append(\"g\").classed(\"contourbg\",!0);var 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val(pB, dim1B) + val(pC, dim1C) + val(pD, dim1D));\\n\\n    vec2 dimensionToggle = vec2(x, 1.0 - x);\\n\\n    vec2 scatterToggle = vec2(scatter, 1.0 - scatter);\\n\\n    float y = dot(yy, dimensionToggle);\\n    mat2 xy = mat2(viewBoxSize * yy + dimensionToggle, viewBoxSize * vec2(x, y));\\n\\n    vec2 viewBoxXY = viewBoxPosition + xy * scatterToggle;\\n\\n    float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n    gl_Position = vec4(\\n        xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n        depthOrHide,\\n        1.0\\n    );\\n\\n    // pick coloring\\n    fragColor = vec4(pf.rgb, 1.0);\\n}\\n\":\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 p0, p1, p2, p3,\\n               p4, p5, p6, p7,\\n               p8, p9, pa, pb,\\n               pc, pd, pe;\\n\\nattribute vec4 pf;\\n\\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution,\\n             viewBoxPosition,\\n             viewBoxSize;\\n\\nuniform sampler2D palette;\\n\\nuniform vec2 colorClamp;\\n\\nuniform float scatter;\\n\\nvarying vec4 fragColor;\\n\\nvec4 zero = vec4(0, 0, 0, 0);\\nvec4 unit = vec4(1, 1, 1, 1);\\nvec2 xyProjection = vec2(1, 1);\\n\\nmat4 mclamp(mat4 m, mat4 lo, mat4 hi) {\\n    return mat4(clamp(m[0], lo[0], hi[0]),\\n                clamp(m[1], lo[1], hi[1]),\\n                clamp(m[2], lo[2], hi[2]),\\n                clamp(m[3], lo[3], hi[3]));\\n}\\n\\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\\n    return mclamp(p, lo, hi) == p;\\n}\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * unit, unit);\\n}\\n\\nvoid main() {\\n\\n    float x = 0.5 * sign(pf[3]) + 0.5;\\n    float prominence = abs(pf[3]);\\n    float depth = 1.0 - prominence;\\n\\n    mat4 pA = mat4(p0, p1, p2, p3);\\n    mat4 pB = mat4(p4, p5, p6, p7);\\n    mat4 pC = mat4(p8, p9, pa, pb);\\n    mat4 pD = mat4(pc, pd, pe, abs(pf));\\n\\n    float show = float(mshow(pA, loA, hiA) &&\\n                       mshow(pB, loB, hiB) &&\\n                       mshow(pC, loC, hiC) &&\\n                       mshow(pD, loD, hiD));\\n\\n    vec2 yy = show * vec2(val(pA, dim2A) + val(pB, dim2B) + val(pC, dim2C) + val(pD, dim2D),\\n                          val(pA, dim1A) + val(pB, dim1B) + val(pC, dim1C) + val(pD, dim1D));\\n\\n    vec2 dimensionToggle = vec2(x, 1.0 - x);\\n\\n    vec2 scatterToggle = vec2(scatter, 1.0 - scatter);\\n\\n    float y = dot(yy, dimensionToggle);\\n    mat2 xy = mat2(viewBoxSize * yy + dimensionToggle, viewBoxSize * vec2(x, y));\\n\\n    vec2 viewBoxXY = viewBoxPosition + xy * scatterToggle;\\n\\n    float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n    gl_Position = vec4(\\n        xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n        depthOrHide,\\n        1.0\\n    );\\n\\n    // visible coloring\\n    float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\\n    fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\\n}\\n\",frag:\"precision lowp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\",primitive:\"lines\",lineWidth:1,attributes:P,uniforms:{resolution:O.prop(\"resolution\"),viewBoxPosition:O.prop(\"viewBoxPosition\"),viewBoxSize:O.prop(\"viewBoxSize\"),dim1A:O.prop(\"dim1A\"),dim2A:O.prop(\"dim2A\"),dim1B:O.prop(\"dim1B\"),dim2B:O.prop(\"dim2B\"),dim1C:O.prop(\"dim1C\"),dim2C:O.prop(\"dim2C\"),dim1D:O.prop(\"dim1D\"),dim2D:O.prop(\"dim2D\"),loA:O.prop(\"loA\"),hiA:O.prop(\"hiA\"),loB:O.prop(\"loB\"),hiB:O.prop(\"hiB\"),loC:O.prop(\"loC\"),hiC:O.prop(\"hiC\"),loD:O.prop(\"loD\"),hiD:O.prop(\"hiD\"),palette:R,colorClamp:O.prop(\"colorClamp\"),scatter:O.prop(\"scatter\")},offset:O.prop(\"offset\"),count:O.prop(\"count\")}),j=[0,1],N=[];return{setColorDomain:y,render:x,readPixel:w,readPixels:M,destroy:O.destroy}}},{\"./constants\":1001,regl:499}],1005:[function(t,e,r){\"use strict\";function n(t){return t.key}function i(t){return[t]}function a(t){return!(\"visible\"in t)||t.visible}function o(t){var e=t.range?t.range[0]:w.min(t.values),r=t.range?t.range[1]:w.max(t.values);return!isNaN(e)&&isFinite(e)||(e=0),!isNaN(r)&&isFinite(r)||(r=0),e===r&&(void 0===e?(e=0,r=1):0===e?(e-=1,r+=1):(e*=.9,r*=1.1)),[e,r]}function s(t,e){var r,n,i,a,o;for(r=0,n=t.range(),i=1/0,a=n[0],o;r<n.length;r++){if((o=Math.abs(n[r]-e))>i)return a;i=o,a=n[r]}return n[n.length-1]}function l(t,e){return function(r,n){if(e){var i=e[n];return null===i||void 0===i?t(r):i}return t(r)}}function u(t,e,r){var n=o(r),i=r.ticktext;return r.tickvals?w.scale.ordinal().domain(r.tickvals.map(l(w.format(r.tickformat),i))).range(r.tickvals.map(function(t){return(t-n[0])/(n[1]-n[0])}).map(function(r){return t-e+r*(e-(t-e))})):w.scale.linear().domain(n).range([t-e,e])}function c(t,e){return w.scale.linear().range([t-e,e])}function h(t){return w.scale.linear().domain(o(t))}function f(t){var e=o(t);return t.tickvals&&w.scale.ordinal().domain(t.tickvals).range(t.tickvals.map(function(t){return(t-e[0])/(e[1]-e[0])}))}function d(t){var e=t.map(function(t){return t[0]}),r=t.map(function(t){return t[1]}),n=r.map(function(t){return w.rgb(t)}),i=function(t){return function(e){return e[t]}},a=\"rgb\".split(\"\").map(function(t){return w.scale.linear().clamp(!0).domain(e).range(n.map(i(t)))});return function(t){return a.map(function(e){return e(t)})}}function p(t){return t[0]}function m(t,e,r){var n=p(e),i=n.trace,o=n.lineColor,s=n.cscale,l=i.line,u=i.domain,c=i.dimensions,f=t.width,m=i.labelfont,v=i.tickfont,g=i.rangefont,y=_.extendDeep({},l,{color:o.map(h({values:o,range:[l.cmin,l.cmax]})),blockLineCount:x.blockLineCount,canvasOverdrag:x.overdrag*x.canvasPixelRatio}),b=Math.floor(f*(u.x[1]-u.x[0])),w=Math.floor(t.height*(u.y[1]-u.y[0])),M=t.margin||{l:80,r:80,t:100,b:80},k=b,A=w;return{key:r,colCount:c.filter(a).length,dimensions:c,tickDistance:x.tickDistance,unitToColor:d(s),lines:y,labelFont:m,tickFont:v,rangeFont:g,translateX:u.x[0]*f,translateY:t.height-u.y[1]*t.height,pad:M,canvasWidth:k*x.canvasPixelRatio+2*y.canvasOverdrag,canvasHeight:A*x.canvasPixelRatio,width:k,height:A,canvasPixelRatio:x.canvasPixelRatio}}function v(t){var e=t.width,r=t.height,n=t.dimensions,i=t.canvasPixelRatio,o=function(r){return e*r/Math.max(1,t.colCount-1)},s=x.verticalPadding/(r*i),l=1-2*s,d=function(t){return s+l*t},p={key:t.key,xScale:o,model:t},m={};return p.dimensions=n.filter(a).map(function(e,n){var a=h(e),s=m[e.label];return m[e.label]=(s||0)+1,{key:e.label+(s?\"__\"+s:\"\"),label:e.label,tickFormat:e.tickformat,tickvals:e.tickvals,ticktext:e.ticktext,ordinal:!!e.tickvals,scatter:x.scatter||e.scatter,xIndex:n,crossfilterDimensionIndex:n,visibleIndex:e._index,height:r,values:e.values,paddedUnitValues:e.values.map(a).map(d),xScale:o,x:o(n),canvasX:o(n)*i,unitScale:c(r,x.verticalPadding),domainScale:u(r,x.verticalPadding,e),ordinalScale:f(e),domainToUnitScale:a,filter:e.constraintrange?e.constraintrange.map(a):[0,1],parent:p,model:t}}),p}function g(t){return x.layers.map(function(e){return{key:e,context:\"contextLineLayer\"===e,pick:\"pickLineLayer\"===e,viewModel:t,model:t.model}})}function y(t){t.classed(\"axisExtentText\",!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"default\").style(\"user-select\",\"none\")}var b=t(\"./lines\"),x=t(\"./constants\"),_=t(\"../../lib\"),w=t(\"d3\"),M=t(\"../../components/drawing\");e.exports=function(t,e,r,a,o){function l(t){var e=t.selectAll(\"defs\").data(i,n);e.enter().append(\"defs\");var r=e.selectAll(\"#filterBarPattern\").data(i,n);r.enter().append(\"pattern\").attr(\"id\",\"filterBarPattern\").attr(\"patternUnits\",\"userSpaceOnUse\"),r.attr(\"x\",-x.bar.width).attr(\"width\",x.bar.capturewidth).attr(\"height\",function(t){return t.model.height});var a=r.selectAll(\"rect\").data(i,n);a.enter().append(\"rect\").attr(\"shape-rendering\",\"crispEdges\"),a.attr(\"height\",function(t){return t.model.height}).attr(\"width\",x.bar.width).attr(\"x\",x.bar.width/2).attr(\"fill\",x.bar.fillcolor).attr(\"fill-opacity\",x.bar.fillopacity).attr(\"stroke\",x.bar.strokecolor).attr(\"stroke-opacity\",x.bar.strokeopacity).attr(\"stroke-width\",x.bar.strokewidth)}function u(t){return t.dimensions.some(function(t){return 0!==t.filter[0]||1!==t.filter[1]})}function c(t,e){for(var r=e.panels||(e.panels=[]),n=t.each(function(t){return t})[e.key].map(function(t){return t.__data__}),i=n.length-1,a=0;a<1;a++)for(var o=0;o<i;o++){var s=r[o+a*i]||(r[o+a*i]={}),l=n[o],u=n[o+1];s.dim1=l,s.dim2=u,s.canvasX=l.canvasX,s.panelSizeX=u.canvasX-l.canvasX,s.panelSizeY=e.model.canvasHeight/1,s.y=a*s.panelSizeY,s.canvasY=e.model.canvasHeight-s.y-s.panelSizeY}}function h(t,e){for(var r=e.panels||(e.panels=[]),n=t.each(function(t){return t})[e.key].map(function(t){return t.__data__}),i=n.length-1,a=i,o=0;o<i;o++)for(var s=0;s<i;s++){var l=r[s+o*i]||(r[s+o*i]={}),u=n[s],c=n[s+1];l.dim1=n[o+1],l.dim2=c,l.canvasX=u.canvasX,l.panelSizeX=c.canvasX-u.canvasX,l.panelSizeY=e.model.canvasHeight/a,l.y=o*l.panelSizeY,l.canvasY=e.model.canvasHeight-l.y-l.panelSizeY}}function f(t,e){return(x.scatter?h:c)(t,e)}function d(t){return t.ordinal?function(){return\"\"}:w.format(t.tickFormat)}function _(){W=!0,T=!0}function k(t){S=!1;var e=t.parent,r=t.brush.extent(),n=e.dimensions,i=n[t.xIndex].filter,a=W&&r[0]===r[1];a&&(t.brush.clear(),w.select(this).select(\"rect.extent\").attr(\"y\",-100));var o=a?[0,1]:r.slice();if(o[0]!==i[0]||o[1]!==i[1]){n[t.xIndex].filter=o,e.focusLineLayer&&e.focusLineLayer.render(e.panels,!0);var s=u(e);!X&&s?(e.contextLineLayer&&e.contextLineLayer.render(e.panels,!0),X=!0):X&&!s&&(e.contextLineLayer&&e.contextLineLayer.render(e.panels,!0,!0),X=!1)}W=!1}function A(t){var e=t.parent,r=t.brush.extent(),n=r[0]===r[1],i=e.dimensions,a=i[t.xIndex].filter;if(!n&&t.ordinal&&(a[0]=s(t.ordinalScale,a[0]),a[1]=s(t.ordinalScale,a[1]),a[0]===a[1]&&(a[0]=Math.max(0,a[0]-.05),a[1]=Math.min(1,a[1]+.05)),w.select(this).transition().duration(150).call(t.brush.extent(a)),e.focusLineLayer.render(e.panels,!0)),e.pickLineLayer&&e.pickLineLayer.render(e.panels,!0),S=!0,T=\"ending\",o&&o.filterChanged){var l=t.domainToUnitScale.invert,u=a.map(l);o.filterChanged(e.key,t.visibleIndex,u)}}var T=!1,S=!0,E=r.filter(function(t){return p(t).trace.visible}).map(m.bind(0,a)).map(v);t.selectAll(\".parcoords-line-layers\").remove();var L=t.selectAll(\".parcoords-line-layers\").data(E,n);L.enter().insert(\"div\",\".\"+e.attr(\"class\").split(\" \").join(\" .\")).classed(\"parcoords-line-layers\",!0).style(\"box-sizing\",\"content-box\"),L.style(\"transform\",function(t){return\"translate(\"+(t.model.translateX-x.overdrag)+\"px,\"+t.model.translateY+\"px)\"});var C=L.selectAll(\".parcoords-lines\").data(g,n),I={renderers:[],dimensions:[]},z=null;C.enter().append(\"canvas\").attr(\"class\",function(t){return\"parcoords-lines \"+(t.context?\"context\":t.pick?\"pick\":\"focus\")}).style(\"box-sizing\",\"content-box\").style(\"float\",\"left\").style(\"clear\",\"both\").style(\"left\",0).style(\"overflow\",\"visible\").style(\"position\",function(t,e){return\"absolute\"}).filter(function(t){return t.pick}).on(\"mousemove\",function(t){if(S&&t.lineLayer&&o&&o.hover){var e=w.event,r=this.width,n=this.height,i=w.mouse(this),a=i[0],s=i[1];if(a<0||s<0||a>=r||s>=n)return;var l=t.lineLayer.readPixel(a,n-1-s),u=0!==l[3],c=u?l[2]+256*(l[1]+256*l[0]):null,h={x:a,y:s,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:c};c!==z&&(u?o.hover(h):o.unhover&&o.unhover(h),z=c)}}),C.style(\"margin\",function(t){var e=t.model.pad;return e.t+\"px \"+e.r+\"px \"+e.b+\"px \"+e.l+\"px\"}).attr(\"width\",function(t){return t.model.canvasWidth}).attr(\"height\",function(t){return t.model.canvasHeight}).style(\"width\",function(t){return t.model.width+2*x.overdrag+\"px\"}).style(\"height\",function(t){return t.model.height+\"px\"}).style(\"opacity\",function(t){return t.pick?.01:1}),e.style(\"background\",\"rgba(255, 255, 255, 0)\");var D=e.selectAll(\".parcoords\").data(E,n);D.exit().remove(),D.enter().append(\"g\").classed(\"parcoords\",!0).attr(\"overflow\",\"visible\").style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"overflow\",\"visible\").style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\").call(l),D.attr(\"width\",function(t){return t.model.width+t.model.pad.l+t.model.pad.r}).attr(\"height\",function(t){return t.model.height+t.model.pad.t+t.model.pad.b\n",
+       "}).attr(\"transform\",function(t){return\"translate(\"+t.model.translateX+\",\"+t.model.translateY+\")\"});var P=D.selectAll(\".parcoordsControlView\").data(i,n);P.enter().append(\"g\").classed(\"parcoordsControlView\",!0).style(\"box-sizing\",\"content-box\"),P.attr(\"transform\",function(t){return\"translate(\"+t.model.pad.l+\",\"+t.model.pad.t+\")\"});var O=P.selectAll(\".yAxis\").data(function(t){return t.dimensions},n);O.enter().append(\"g\").classed(\"yAxis\",!0).each(function(t){I.dimensions.push(t)}),P.each(function(t){f(O,t)}),C.each(function(t){t.lineLayer=b(this,t.model.lines,t.model.canvasWidth,t.model.canvasHeight,t.viewModel.dimensions,t.viewModel.panels,t.model.unitToColor,t.context,t.pick,x.scatter),t.viewModel[t.key]=t.lineLayer,I.renderers.push(function(){t.lineLayer.render(t.viewModel.panels,!0)}),t.lineLayer.render(t.viewModel.panels,!t.context)}),O.attr(\"transform\",function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"}),O.call(w.behavior.drag().origin(function(t){return t}).on(\"drag\",function(t){var e=t.parent;S=!1,T||(t.x=Math.max(-x.overdrag,Math.min(t.model.width+x.overdrag,w.event.x)),t.canvasX=t.x*t.model.canvasPixelRatio,O.sort(function(t,e){return t.x-e.x}).each(function(e,r){e.xIndex=r,e.x=t===e?e.x:e.xScale(e.xIndex),e.canvasX=e.x*e.model.canvasPixelRatio}),f(O,e),O.filter(function(e){return 0!==Math.abs(t.xIndex-e.xIndex)}).attr(\"transform\",function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"}),w.select(this).attr(\"transform\",\"translate(\"+t.x+\", 0)\"),O.each(function(r,n,i){i===t.parent.key&&(e.dimensions[n]=r)}),e.contextLineLayer&&e.contextLineLayer.render(e.panels,!1,!u(e)),e.focusLineLayer.render&&e.focusLineLayer.render(e.panels))}).on(\"dragend\",function(t){var e=t.parent;if(T)return void(\"ending\"===T&&(T=!1));t.x=t.xScale(t.xIndex),t.canvasX=t.x*t.model.canvasPixelRatio,f(O,e),w.select(this).attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),e.contextLineLayer&&e.contextLineLayer.render(e.panels,!1,!u(e)),e.focusLineLayer&&e.focusLineLayer.render(e.panels),e.pickLineLayer&&e.pickLineLayer.render(e.panels,!0),S=!0,o&&o.axesMoved&&o.axesMoved(e.key,e.dimensions.map(function(t){return t.crossfilterDimensionIndex}))})),O.exit().remove();var R=O.selectAll(\".axisOverlays\").data(i,n);R.enter().append(\"g\").classed(\"axisOverlays\",!0),R.selectAll(\".axis\").remove();var F=R.selectAll(\".axis\").data(i,n);F.enter().append(\"g\").classed(\"axis\",!0),F.each(function(t){var e=t.model.height/t.model.tickDistance,r=t.domainScale,n=r.domain();w.select(this).call(w.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(e,t.tickFormat).tickValues(t.ordinal?n:null).tickFormat(t.ordinal?function(t){return t}:null).scale(r)),M.font(F.selectAll(\"text\"),t.model.tickFont)}),F.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),F.selectAll(\"text\").style(\"text-shadow\",\"1px 1px 1px #fff, -1px -1px 1px #fff, 1px -1px 1px #fff, -1px 1px 1px #fff\").style(\"cursor\",\"default\").style(\"user-select\",\"none\");var j=R.selectAll(\".axisHeading\").data(i,n);j.enter().append(\"g\").classed(\"axisHeading\",!0);var N=j.selectAll(\".axisTitle\").data(i,n);N.enter().append(\"text\").classed(\"axisTitle\",!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"ew-resize\").style(\"user-select\",\"none\").style(\"pointer-events\",\"auto\"),N.attr(\"transform\",\"translate(0,\"+-x.axisTitleOffset+\")\").text(function(t){return t.label}).each(function(t){M.font(N,t.model.labelFont)});var B=R.selectAll(\".axisExtent\").data(i,n);B.enter().append(\"g\").classed(\"axisExtent\",!0);var U=B.selectAll(\".axisExtentTop\").data(i,n);U.enter().append(\"g\").classed(\"axisExtentTop\",!0),U.attr(\"transform\",\"translate(0,\"+-x.axisExtentOffset+\")\");var V=U.selectAll(\".axisExtentTopText\").data(i,n);V.enter().append(\"text\").classed(\"axisExtentTopText\",!0).attr(\"alignment-baseline\",\"after-edge\").call(y),V.text(function(t){return d(t)(t.domainScale.domain().slice(-1)[0])}).each(function(t){M.font(V,t.model.rangeFont)});var H=B.selectAll(\".axisExtentBottom\").data(i,n);H.enter().append(\"g\").classed(\"axisExtentBottom\",!0),H.attr(\"transform\",function(t){return\"translate(0,\"+(t.model.height+x.axisExtentOffset)+\")\"});var q=H.selectAll(\".axisExtentBottomText\").data(i,n);q.enter().append(\"text\").classed(\"axisExtentBottomText\",!0).attr(\"alignment-baseline\",\"before-edge\").call(y),q.text(function(t){return d(t)(t.domainScale.domain()[0])}).each(function(t){M.font(q,t.model.rangeFont)});var 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n=t(\"../scattergl/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"}}},{\"../scattergl/attributes\":1077}],1019:[function(t,e,r){\"use strict\";function n(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=a(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}function i(t,e){var r=new n(t,e.uid);return r.update(e),r}var a=t(\"gl-pointcloud2d\"),o=t(\"../../lib/str2rgbarray\"),s=t(\"../scatter/get_trace_color\"),l=[\"xaxis\",\"yaxis\"],u=n.prototype;u.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},u.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=s(t,{})},u.updateFast=function(t){var e,r,n,i,a,s,l=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,c=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,f=t.indices,d=this.bounds;if(c){if(n=c,e=c.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(s=0;s<e;s++)i=n[2*s],a=n[2*s+1],i<d[0]&&(d[0]=i),i>d[2]&&(d[2]=i),a<d[1]&&(d[1]=a),a>d[3]&&(d[3]=a);if(f)r=f;else for(r=new Int32Array(e),s=0;s<e;s++)r[s]=s}else for(e=l.length,n=new Float32Array(2*e),r=new 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r,n,i,a=e||.5,o=0;o<2;o++)r=this.scene[l[o]],n=r._min,n||(n=[]),n.push({val:t[o],pad:a}),i=r._max,i||(i=[]),i.push({val:t[o+2],pad:a})},u.dispose=function(){this.pointcloud.dispose()},e.exports=i},{\"../../lib/str2rgbarray\":749,\"../scatter/get_trace_color\":1040,\"gl-pointcloud2d\":230}],1020:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\")}},{\"../../lib\":728,\"./attributes\":1018}],1021:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"../scatter3d/calc\"),n.plot=t(\"./convert\"),n.moduleType=\"trace\",n.name=\"pointcloud\",n.basePlotModule=t(\"../../plots/gl2d\"),n.categories=[\"gl2d\",\"showLegend\"],n.meta={},e.exports=n},{\"../../plots/gl2d\":808,\"../scatter3d/calc\":1056,\"./attributes\":1018,\"./convert\":1019,\"./defaults\":1020}],1022:[function(t,e,r){\"use strict\";var n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;e.exports=l({hoverinfo:s({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hoverlabel:o.hoverlabel,domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),node:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:.5,arrayOk:!0}},pad:{valType:\"number\",arrayOk:!1,min:0,dflt:20},thickness:{valType:\"number\",arrayOk:!1,min:1,dflt:20}},link:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0}},source:{valType:\"data_array\",dflt:[]},target:{valType:\"data_array\",dflt:[]},value:{valType:\"data_array\",dflt:[]}}},\"calc\",\"nested\")},{\"../../components/color/attributes\":603,\"../../components/fx/attributes\":637,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../../plots/font_attributes\":796}],1023:[function(t,e,r){\"use 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n(e.node.label,e.link.source,e.link.target)&&(a.error(\"Circularity is present in the Sankey data. Removing all nodes and links.\"),e.link.label=[],e.link.source=[],e.link.target=[],e.link.value=[],e.link.color=[],e.node.label=[],e.node.color=[]),[{link:e.link,node:e.node}]}},{\"../../lib\":728,\"strongly-connected-components\":528}],1025:[function(t,e,r){\"use strict\";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:\"cubic-in-out\"}},{}],1026:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../components/color/attributes\").defaults,o=t(\"../../components/color\"),s=t(\"tinycolor2\");e.exports=function(t,e,r,l){function u(r,a){return n.coerce(t,e,i,r,a)}u(\"node.label\"),u(\"node.pad\"),u(\"node.thickness\"),u(\"node.line.color\"),u(\"node.line.width\");var c=function(t){return a[t%a.length]};u(\"node.color\",e.node.label.map(function(t,e){return o.addOpacity(c(e),.8)})),u(\"link.label\"),u(\"link.source\"),u(\"link.target\"),u(\"link.value\"),u(\"link.line.color\"),u(\"link.line.width\"),u(\"link.color\",e.link.value.map(function(){return s(l.paper_bgcolor).getLuminance()<.333?\"rgba(255, 255, 255, 0.6)\":\"rgba(0, 0, 0, 0.2)\"})),u(\"domain.x\"),u(\"domain.y\"),u(\"orientation\"),u(\"valueformat\"),u(\"valuesuffix\"),u(\"arrangement\"),n.coerceFont(u,\"textfont\",n.extendFlat({},l.font));var h=function(t,r){return-1===e.link.source.indexOf(r)&&-1===e.link.target.indexOf(r)};e.node.label.some(h)&&n.warn(\"Some of the nodes are neither sources nor targets, they will not be displayed.\")}},{\"../../components/color\":604,\"../../components/color/attributes\":603,\"../../lib\":728,\"./attributes\":1022,tinycolor2:534}],1027:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.moduleType=\"trace\",n.name=\"sankey\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"noOpacity\"],n.meta={},e.exports=n},{\"./attributes\":1022,\"./base_plot\":1023,\"./calc\":1024,\"./defaults\":1026,\"./plot\":1028}],1028:[function(t,e,r){\"use strict\";function n(t){return\"\"!==t}function i(t,e){return t.filter(function(t){return t.key===e.traceId})}function a(t,e){p.select(t).select(\"path\").style(\"fill-opacity\",e),p.select(t).select(\"rect\").style(\"fill-opacity\",e)}function o(t){p.select(t).select(\"text.name\").style(\"fill\",\"black\")}function s(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)||-1!==t.node.targetLinks.indexOf(e.link)}}function l(t){return function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function u(t,e,r){\n",
+       "e&&r&&i(r,e).selectAll(\".sankeyLink\").filter(s(e)).call(h.bind(0,e,r,!1))}function c(t,e,r){e&&r&&i(r,e).selectAll(\".sankeyLink\").filter(s(e)).call(f.bind(0,e,r,!1))}function h(t,e,r,n){var a=n.datum().link.label;n.style(\"fill-opacity\",.4),a&&i(e,t).selectAll(\".sankeyLink\").filter(function(t){return t.link.label===a}).style(\"fill-opacity\",.4),r&&i(e,t).selectAll(\".sankeyNode\").filter(l(t)).call(u)}function f(t,e,r,n){var a=n.datum().link.label;n.style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),a&&i(e,t).selectAll(\".sankeyLink\").filter(function(t){return t.link.label===a}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),r&&i(e,t).selectAll(\".sankeyNode\").filter(l(t)).call(c)}function d(t,e){var r=t.hoverlabel||{},n=y.nestedProperty(r,e).get();return!Array.isArray(n)&&n}var p=t(\"d3\"),m=t(\"./render\"),v=t(\"../../components/fx\"),g=t(\"../../components/color\"),y=t(\"../../lib\");e.exports=function(t,e){var r=t._fullLayout,i=r._paper,s=r._size,l=function(e,r){var n=r.link;n.originalEvent=p.event,t._hoverdata=[n],v.click(t,{target:!0})},y=function(e,r,n){var i=r.link;i.originalEvent=p.event,p.select(e).call(h.bind(0,r,n,!0)),v.hover(t,i,\"sankey\")},b=function(e,i){var s=i.link.trace,l=t._fullLayout._paperdiv.node().getBoundingClientRect(),u=e.getBoundingClientRect(),c=u.left+u.width/2,h=u.top+u.height/2,f=v.loneHover({x:c-l.left,y:h-l.top,name:p.format(i.valueFormat)(i.link.value)+i.valueSuffix,text:[i.link.label||\"\",[\"Source:\",i.link.source.label].join(\" \"),[\"Target:\",i.link.target.label].join(\" \")].filter(n).join(\"<br>\"),color:d(s,\"bgcolor\")||g.addOpacity(i.tinyColorHue,1),borderColor:d(s,\"bordercolor\"),fontFamily:d(s,\"font.family\"),fontSize:d(s,\"font.size\"),fontColor:d(s,\"font.color\"),idealAlign:p.event.x<c?\"right\":\"left\"},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});a(f,.65),o(f)},x=function(e,n,i){p.select(e).call(f.bind(0,n,i,!0)),t.emit(\"plotly_unhover\",{event:p.event,points:[n.link]}),v.loneUnhover(r._hoverlayer.node())},_=function(e,r,n){var i=r.node;i.originalEvent=p.event,t._hoverdata=[i],p.select(e).call(c,r,n),v.click(t,{target:!0})},w=function(e,r,n){var i=r.node;i.originalEvent=p.event,p.select(e).call(u,r,n),v.hover(t,i,\"sankey\")},M=function(e,i){var s=i.node.trace,l=p.select(e).select(\".nodeRect\"),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),c=l.node().getBoundingClientRect(),h=c.left-2-u.left,f=c.right+2-u.left,m=c.top+c.height/4-u.top,g=v.loneHover({x0:h,x1:f,y:m,name:p.format(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,[\"Incoming flow count:\",i.node.targetLinks.length].join(\" \"),[\"Outgoing flow count:\",i.node.sourceLinks.length].join(\" 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u(M),{key:r,trace:i,guid:Math.floor(1e12*(1+Math.random())),horizontal:h,width:_,height:w,nodePad:f,nodeLineColor:p,nodeLineWidth:m,linkLineColor:v,linkLineWidth:g,valueFormat:y,valueSuffix:b,textFont:x,translateX:o.x[0]*_+t.margin.l,translateY:t.height-o.y[1]*t.height+t.margin.t,dragParallel:h?w:_,dragPerpendicular:h?_:w,nodes:M,links:k,arrangement:c,sankey:A,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function f(t,e,r){var n=P(r.color),i=r.source.label+\"|\"+r.target.label,a=t[i];t[i]=(a||0)+1;var o=i+\"__\"+t[i];return r.trace=e.trace,r.curveNumber=e.trace.index,{key:o,traceId:e.key,link:r,tinyColorHue:O.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkLineColor:e.linkLineColor,linkLineWidth:e.linkLineWidth,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,interactionState:e.interactionState}}function d(t,e,r){var n=P(r.color),i=z.nodePadAcross,a=e.nodePad/2,o=r.dx,s=Math.max(.5,r.dy),l=r.label,u=t[l];t[l]=(u||0)+1;var c=l+\"__\"+t[l];return 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a(t).trace.visible}).map(h.bind(null,r)),n);l.exit().remove(),l.enter().append(\"g\").classed(\"sankey\",!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").style(\"box-sizing\",\"content-box\").attr(\"transform\",x),l.transition().ease(z.ease).duration(z.duration).attr(\"transform\",x);var u=l.selectAll(\".sankeyLinks\").data(i,n);u.enter().append(\"g\").classed(\"sankeyLinks\",!0).style(\"fill\",\"none\");var c=u.selectAll(\".sankeyLink\").data(function(t){var e={};return t.sankey.links().filter(function(t){return t.value}).map(f.bind(null,e,t))},n);c.enter().append(\"path\").classed(\"sankeyLink\",!0).attr(\"d\",m).call(S,l,s.linkEvents),c.style(\"stroke\",function(t){return b(t)?O.tinyRGB(P(t.linkLineColor)):t.tinyColorHue}).style(\"stroke-opacity\",function(t){return 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n=t(\"../../components/colorscale/color_attributes\"),i=t(\"../../components/errorbars/attributes\"),a=t(\"../../components/colorbar/attributes\"),o=t(\"../../plots/font_attributes\"),s=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),u=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},x0:{valType:\"any\",dflt:0,editType:\"calc+clearAxisTypes\"},dx:{valType:\"number\",dflt:1,editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y0:{valType:\"any\",dflt:0,editType:\"calc+clearAxisTypes\"},dy:{valType:\"number\",dflt:1,editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"],editType:\"calc\"},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"],editType:\"style\"},line:{color:{valType:\"color\",editType:\"style\"},width:{valType:\"number\",min:0,dflt:2,editType:\"style\"},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\",editType:\"plot\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1,editType:\"plot\"},dash:u({},s,{editType:\"style\"}),simplify:{valType:\"boolean\",dflt:!0,editType:\"plot\"},editType:\"plot\"},connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},cliponaxis:{valType:\"boolean\",dflt:!0,editType:\"plot\"},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\",editType:\"calc\"},fillcolor:{valType:\"color\",editType:\"style\"},marker:u({symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0,editType:\"style\"},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0,editType:\"style\"},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0,editType:\"calcIfAutorange\"},maxdisplayed:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},sizeref:{valType:\"number\",dflt:1,editType:\"calc\"},sizemin:{valType:\"number\",min:0,dflt:0,editType:\"calc\"},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\",editType:\"calc\"},showscale:{valType:\"boolean\",dflt:!1,editType:\"calc\"},colorbar:a,line:u({width:{valType:\"number\",min:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\",editType:\"calc\"},color:{valType:\"color\",arrayOk:!0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top 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r,u,c,h=i.getFromId(t,e.xaxis||\"x\"),f=i.getFromId(t,e.yaxis||\"y\"),d=h.makeCalcdata(e,\"x\"),p=f.makeCalcdata(e,\"y\"),m=Math.min(d.length,p.length);h._minDtick=0,f._minDtick=0,d.length>m&&d.splice(m,d.length-m),p.length>m&&p.splice(m,p.length-m);var v={padded:!0},g={padded:!0};if(o.hasMarkers(e)){if(r=e.marker,u=r.size,Array.isArray(u)){var y={type:\"linear\"};i.setConvert(y),u=y.makeCalcdata(e.marker,\"size\"),u.length>m&&u.splice(m,u.length-m)}var b,x=1.6*(e.marker.sizeref||1);b=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/x),3)}:function(t){return 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i=t(\"../scatter/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/errorbars/attributes\"),s=t(\"../../constants/gl3d_dashes\"),l=t(\"../../constants/gl3d_markers\"),u=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,h=i.line,f=i.marker,d=f.line,p=e.exports=c({x:i.x,y:i.y,z:{valType:\"data_array\"},text:u({},i.text,{}),hovertext:u({},i.hovertext,{}),mode:u({},i.mode,{dflt:\"lines+markers\"}),surfaceaxis:{valType:\"enumerated\",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:\"color\"},projection:{x:n(\"x\"),y:n(\"y\"),z:n(\"z\")},connectgaps:i.connectgaps,line:u({width:h.width,dash:{valType:\"enumerated\",values:Object.keys(s),dflt:\"solid\"},showscale:{valType:\"boolean\",dflt:!1}},a(\"line\")),marker:u({symbol:{valType:\"enumerated\",values:Object.keys(l),dflt:\"circle\",arrayOk:!0},size:u({},f.size,{dflt:8}),sizeref:f.sizeref,sizemin:f.sizemin,sizemode:f.sizemode,opacity:u({},f.opacity,{arrayOk:!1}),showscale:f.showscale,colorbar:f.colorbar,line:u({width:u({},d.width,{arrayOk:!1})},a(\"marker.line\"))},a(\"marker\")),textposition:u({},i.textposition,{dflt:\"top 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u=t.opacity;if(t.marker&&t.marker.opacity&&(u*=t.marker.opacity),r={gl:o,position:l.position,color:l.scatterColor,size:l.scatterSize,glyph:l.scatterMarker,opacity:u,orthographic:!0,lineWidth:l.scatterLineWidth,lineColor:l.scatterLineColor,project:l.project,projectScale:l.projectScale,projectOpacity:l.projectOpacity},-1!==this.mode.indexOf(\"markers\")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=p(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),a={gl:o,position:l.position,glyph:l.text,color:l.textColor,size:l.textSize,angle:l.textAngle,alignment:l.textOffset,font:l.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf(\"text\")?this.textMarkers?this.textMarkers.update(a):(this.textMarkers=p(a),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),n={gl:o,position:l.position,color:l.errorColor,error:l.errorBounds,lineWidth:l.errorLineWidth,capSize:l.errorCapSize,opacity:t.opacity},this.errorBars?l.errorBounds?this.errorBars.update(n):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):l.errorBounds&&(this.errorBars=m(n),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),l.delaunayAxis>=0){var 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a=0,o=r(\"x\"),s=r(\"y\"),l=r(\"z\");return i.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],n),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),a<o.length&&(e.x=o.slice(0,a)),a<s.length&&(e.y=s.slice(0,a)),a<l.length&&(e.z=l.slice(0,a))),a}var i=t(\"../../registry\"),a=t(\"../../lib\"),o=t(\"../scatter/subtypes\"),s=t(\"../scatter/marker_defaults\"),l=t(\"../scatter/line_defaults\"),u=t(\"../scatter/text_defaults\"),c=t(\"../../components/errorbars/defaults\"),h=t(\"./attributes\");e.exports=function(t,e,r,i){function f(r,n){return a.coerce(t,e,h,r,n)}if(!n(t,e,f,i))return void(e.visible=!1);f(\"text\"),f(\"hovertext\"),f(\"mode\"),o.hasLines(e)&&(f(\"connectgaps\"),l(t,e,r,i,f)),o.hasMarkers(e)&&s(t,e,r,i,f),o.hasText(e)&&u(t,e,i,f);var d=(e.line||{}).color,p=(e.marker||{}).color;f(\"surfaceaxis\")>=0&&f(\"surfacecolor\",d||p);for(var m=[\"x\",\"y\",\"z\"],v=0;v<3;++v){var g=\"projection.\"+m[v];f(g+\".show\")&&(f(g+\".opacity\"),f(g+\".scale\"))}c(t,e,r,{axis:\"z\"}),c(t,e,r,{axis:\"y\",inherit:\"z\"}),c(t,e,r,{axis:\"x\",inherit:\"z\"})}},{\"../../components/errorbars/defaults\":633,\"../../lib\":728,\"../../registry\":846,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1055}],1060:[function(t,e,r){\"use strict\";var n={};n.plot=t(\"./convert\"),n.attributes=t(\"./attributes\"),n.markerSymbols=t(\"../../constants/gl3d_markers\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.moduleType=\"trace\",n.name=\"scatter3d\",n.basePlotModule=t(\"../../plots/gl3d\"),n.categories=[\"gl3d\",\"symbols\",\"markerColorscale\",\"showLegend\"],n.meta={},e.exports=n},{\"../../constants/gl3d_markers\":705,\"../../plots/gl3d\":811,\"../scatter/colorbar\":1034,\"./attributes\":1055,\"./calc\":1056,\"./convert\":1058,\"./defaults\":1059}],1061:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=n.marker,u=n.line,c=l.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:s({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:s({symbol:l.symbol,opacity:l.opacity,maxdisplayed:l.maxdisplayed,size:l.size,sizeref:l.sizeref,sizemin:l.sizemin,sizemode:l.sizemode,line:s({width:c.width,editType:\"calc\"},a(\"marker\".line)),gradient:l.gradient,editType:\"calc\"},a(\"marker\"),{showscale:l.showscale,colorbar:o}),textfont:n.textfont,textposition:n.textposition,hoverinfo:s({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":605,\"../../components/colorscale/color_attributes\":611,\"../../lib/extend\":717,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1062:[function(t,e,r){\"use 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o(e),s(d,e),d}}},{\"../../plots/cartesian/axes\":772,\"../carpet/lookup_carpetid\":903,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035,\"../scatter/subtypes\":1052,\"fast-isnumeric\":131}],1063:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),u=t(\"../scatter/text_defaults\"),c=t(\"../scatter/fillcolor_defaults\"),h=t(\"./attributes\");e.exports=function(t,e,r,f){function d(r,i){return n.coerce(t,e,h,r,i)}d(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var p,m=d(\"a\"),v=d(\"b\");if(!(p=Math.min(m.length,v.length)))return void(e.visible=!1);m&&p<m.length&&(e.a=m.slice(0,p)),v&&p<v.length&&(e.b=v.slice(0,p)),d(\"text\"),d(\"mode\",p<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,f,d),l(t,e,d),d(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,f,d,{gradient:!0}),a.hasText(e)&&u(t,e,f,d);var g=[];(a.hasMarkers(e)||a.hasText(e))&&(d(\"marker.maxdisplayed\"),g.push(\"points\")),d(\"fill\"),\"none\"!==e.fill&&(c(t,e,r,d),a.hasLines(e)||l(t,e,d)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||g.push(\"fills\"),d(\"hoveron\",g.join(\"+\")||\"points\")}},{\"../../lib\":728,\"../scatter/constants\":1036,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/line_shape_defaults\":1045,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1061}],1064:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\");e.exports=function(t,e,r,i){function a(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}var o=n(t,e,r,i);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,u=t.xa._length,c=u*l/2,h=u-c;return s.x0=Math.max(Math.min(s.x0,h),c),s.x1=Math.max(Math.min(s.x1,h),c),o}var f=s.cd[s.index];s.a=f.a,s.b=f.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var d=s.trace,p=d._carpet,m=f.hi||d.hoverinfo,v=m.split(\"+\"),g=[];-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\"]),-1!==v.indexOf(\"a\")&&a(p.aaxis,f.a),-1!==v.indexOf(\"b\")&&a(p.baxis,f.b);var y=p.ab2ij([f.a,f.b]),b=Math.floor(y[0]),x=y[0]-b,_=Math.floor(y[1]),w=y[1]-_,M=p.evalxy([],b,_,x,w);return g.push(\"y: \"+M[1].toFixed(3)),s.extraText=g.join(\"<br>\"),o}}},{\"../scatter/hover\":1041}],1065:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattercarpet\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"carpet\",\"symbols\",\"markerColorscale\",\"showLegend\",\"carpetDependent\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":782,\"../scatter/colorbar\":1034,\"./attributes\":1061,\"./calc\":1062,\"./defaults\":1063,\"./hover\":1064,\"./plot\":1066,\"./select\":1067,\"./style\":1068}],1066:[function(t,e,r){\"use strict\";var n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r){var o,s,l,u=r[0][0].carpet,c={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,c,r),o=0;o<r.length;o++)s=r[o][0].trace,l=c.plot.selectAll(\"g.trace\"+s.uid+\" .js-line\"),a.setClipUrl(l,u._clipPathId)}},{\"../../components/drawing\":628,\"../../plots/cartesian/axes\":772,\"../scatter/plot\":1049}],1067:[function(t,e,r){\"use strict\";var n=t(\"../scatter/select\");e.exports=function(t,e){var r=n(t,e);if(r){var i,a,o,s=t.cd;for(o=0;o<r.length;o++)i=r[o],a=s[i.pointNumber],i.a=a.a,i.b=a.b,i.c=a.c,delete i.x,delete i.y;return r}}},{\"../scatter/select\":1050}],1068:[function(t,e,r){\"use strict\";var n=t(\"../scatter/style\");e.exports=function(t){for(var e=t._fullLayout._modules,r=0;r<e.length;r++)if(\"scatter\"===e[r].name)return;n(t)}},{\"../scatter/style\":1051}],1069:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,u=n.marker,c=n.line,h=u.line;e.exports=l({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country names\"],dflt:\"ISO-3\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),hovertext:s({},n.hovertext,{}),textfont:n.textfont,textposition:n.textposition,line:{color:c.color,width:c.width,dash:o},connectgaps:n.connectgaps,marker:s({symbol:u.symbol,opacity:u.opacity,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,showscale:u.showscale,colorbar:u.colorbar,line:s({width:h.width},a(\"marker.line\")),gradient:u.gradient},a(\"marker\")),fill:{valType:\"enumerated\",values:[\"none\",\"toself\"],dflt:\"none\"},fillcolor:n.fillcolor,hoverinfo:s({},i.hoverinfo,{flags:[\"lon\",\"lat\",\"location\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorscale/color_attributes\":611,\"../../components/drawing/attributes\":627,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1070:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/colorscale_calc\"),o=t(\"../scatter/arrays_to_calcdata\");e.exports=function(t,e){for(var r=Array.isArray(e.locations),s=r?e.locations.length:e.lon.length,l=new Array(s),u=0;u<s;u++){var c=l[u]={};if(r){var h=e.locations[u];c.loc=\"string\"==typeof h?h:null}else{var f=e.lon[u],d=e.lat[u];n(f)&&n(d)?c.lonlat=[+f,+d]:c.lonlat=[i,i]}}return o(l,e),a(e),l}},{\"../../constants/numerical\":707,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035,\"fast-isnumeric\":131}],1071:[function(t,e,r){\"use strict\";function n(t,e,r){var n,i,a=0,o=r(\"locations\");return o?(r(\"locationmode\"),a=o.length):(n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length),a<n.length&&(e.lon=n.slice(0,a)),a<i.length&&(e.lat=i.slice(0,a)),a)}var i=t(\"../../lib\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,h){function f(r,n){return i.coerce(t,e,c,r,n)}if(!n(t,e,f))return void(e.visible=!1);f(\"text\"),f(\"hovertext\"),f(\"mode\"),a.hasLines(e)&&(s(t,e,r,h,f),f(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,f,{gradient:!0}),a.hasText(e)&&l(t,e,h,f),f(\"fill\"),\"none\"!==e.fill&&u(t,e,r,f)}},{\"../../lib\":728,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1069}],1072:[function(t,e,r){\"use strict\";e.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null,t}},{}],1073:[function(t,e,r){\"use strict\";function n(t,e,r){function n(t){return a.tickText(r,r.c2l(t),\"hover\").text+\"\\xb0\"}var i=e.hi||t.hoverinfo,o=\"all\"===i?u.hoverinfo.flags:i.split(\"+\"),s=-1!==o.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==o.indexOf(\"lon\"),h=-1!==o.indexOf(\"lat\"),f=-1!==o.indexOf(\"text\"),d=[];return s?d.push(e.loc):c&&h?d.push(\"(\"+n(e.lonlat[0])+\", \"+n(e.lonlat[1])+\")\"):c?d.push(\"lon: \"+n(e.lonlat[0])):h&&d.push(\"lat: \"+n(e.lonlat[1])),f&&l(e,t,d),d.join(\"<br>\")}var i=t(\"../../components/fx\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,s=t(\"../scatter/get_trace_color\"),l=t(\"../scatter/fill_hover_text\"),u=t(\"./attributes\");e.exports=function(t,e,r){function a(t){var n=t.lonlat;if(n[0]===o)return 1/0;if(d(n))return 1/0;var i=p(n),a=p([e,r]),s=Math.abs(i[0]-a[0]),l=Math.abs(i[1]-a[1]),u=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-u,1-3/u)}var l=t.cd,u=l[0].trace,c=t.xa,h=t.ya,f=t.subplot,d=f.projection.isLonLatOverEdges,p=f.project;if(i.getClosest(l,a,t),!1!==t.index){var m=l[t.index],v=m.lonlat,g=[c.c2p(v),h.c2p(v)],y=m.mrc||1;return t.x0=g[0]-y,t.x1=g[0]+y,t.y0=g[1]-y,t.y1=g[1]+y,t.loc=m.loc,t.lon=v[0],t.lat=v[1],t.color=s(u,m),t.extraText=n(u,m,f.mockAxis),[t]}}},{\"../../components/fx\":645,\"../../constants/numerical\":707,\"../../plots/cartesian/axes\":772,\"../scatter/fill_hover_text\":1038,\"../scatter/get_trace_color\":1040,\"./attributes\":1069}],1074:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.hoverPoints=t(\"./hover\"),n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergeo\",n.basePlotModule=t(\"../../plots/geo\"),n.categories=[\"geo\",\"symbols\",\"markerColorscale\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/geo\":800,\"../scatter/colorbar\":1034,\"./attributes\":1069,\"./calc\":1070,\"./defaults\":1071,\"./event_data\":1072,\"./hover\":1073,\"./plot\":1075,\"./select\":1076}],1075:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0].trace;if(Array.isArray(r.locations))for(var n=c(r,e),i=r.locationmode,a=0;a<t.length;a++){var o=t[a],s=h(i,o.loc,n);o.lonlat=s?s.properties.ct:[u,u]}}function i(t){var e=t.layers.frontplot.selectAll(\".trace.scattergeo\");e.style(\"opacity\",function(t){return t[0].trace.opacity}),e.each(function(e){var r=e[0].trace,n=a.select(this);n.selectAll(\"path.point\").call(o.pointStyle,r,t.graphDiv),n.selectAll(\"text\").call(o.textPointStyle,r,t.graphDiv)}),e.selectAll(\"path.js-line\").style(\"fill\",\"none\").each(function(t){var e=a.select(this),r=t.trace,n=r.line||{};e.call(s.stroke,n.color).call(o.dashLine,n.dash||\"\",n.width||0),\"none\"!==r.fill&&e.call(s.fill,r.fillcolor)})}var a=t(\"d3\"),o=t(\"../../components/drawing\"),s=t(\"../../components/color\"),l=t(\"../../lib\"),u=t(\"../../constants/numerical\").BADNUM,c=t(\"../../lib/topojson_utils\").getTopojsonFeatures,h=t(\"../../lib/geo_location_utils\").locationToFeature,f=t(\"../../lib/geojson_utils\"),d=t(\"../scatter/subtypes\")\n",
+       ";e.exports=function(t,e){function r(t){return t[0].trace.uid}function o(t,e){t.lonlat[0]===u&&a.select(e).remove()}for(var s=0;s<e.length;s++)n(e[s],t.topojson);var c=t.layers.frontplot.select(\".scatterlayer\").selectAll(\"g.trace.scattergeo\").data(e,r);c.enter().append(\"g\").attr(\"class\",\"trace scattergeo\"),c.exit().remove(),c.selectAll(\"*\").remove(),c.each(function(t){var e=t[0].node3=a.select(this),r=t[0].trace;if(d.hasLines(r)||\"none\"!==r.fill){var n=f.calcTraceToLineCoords(t),i=\"none\"!==r.fill?f.makePolygon(n):f.makeLine(n);e.selectAll(\"path.js-line\").data([{geojson:i,trace:r}]).enter().append(\"path\").classed(\"js-line\",!0).style(\"stroke-miterlimit\",2)}d.hasMarkers(r)&&e.selectAll(\"path.point\").data(l.identity).enter().append(\"path\").classed(\"point\",!0).each(function(t){o(t,this)}),d.hasText(r)&&e.selectAll(\"g\").data(l.identity).enter().append(\"g\").append(\"text\").each(function(t){o(t,this)})}),i(t)}},{\"../../components/color\":604,\"../../components/drawing\":628,\"../../constants/numerical\":707,\"../../lib\":728,\"../../lib/geo_location_utils\":720,\"../../lib/geojson_utils\":721,\"../../lib/topojson_utils\":753,\"../scatter/subtypes\":1052,d3:122}],1076:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"../../constants/interactions\").DESELECTDIM;e.exports=function(t,e){var r,a,o,s,l,u=t.cd,c=t.xaxis,h=t.yaxis,f=[],d=u[0].trace,p=u[0].node3,m=!n.hasMarkers(d)&&!n.hasText(d);if(!0===d.visible&&!m){var v=d.marker,g=Array.isArray(v.opacity)?1:v.opacity;if(!1===e)for(l=0;l<u.length;l++)u[l].dim=0;else for(l=0;l<u.length;l++)r=u[l],a=r.lonlat,o=c.c2p(a),s=h.c2p(a),e.contains([o,s])?(f.push({pointNumber:l,lon:a[0],lat:a[1]}),r.dim=0):r.dim=1;return p.selectAll(\"path.point\").style(\"opacity\",function(t){return((t.mo+1||g+1)-1)*(t.dim?i:1)}),p.selectAll(\"text\").style(\"opacity\",function(t){return t.dim?i:1}),f}}},{\"../../constants/interactions\":706,\"../scatter/subtypes\":1052}],1077:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../constants/gl2d_dashes\"),o=t(\"../../constants/gl2d_markers\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,u=n.line,c=n.marker,h=c.line,f=e.exports=l({x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,text:s({},n.text,{}),mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\"],extras:[\"none\"]},line:{color:u.color,width:u.width,dash:{valType:\"enumerated\",values:Object.keys(a),dflt:\"solid\"}},marker:s({},i(\"marker\"),{symbol:{valType:\"enumerated\",values:Object.keys(o),dflt:\"circle\",arrayOk:!0},size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,opacity:c.opacity,showscale:c.showscale,colorbar:c.colorbar,line:s({},i(\"marker.line\"),{width:h.width})}),connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"tozeroy\",\"tozerox\"]}),fillcolor:n.fillcolor,error_y:n.error_y,error_x:n.error_x},\"calc\",\"nested\");f.x.editType=f.y.editType=f.x0.editType=f.y0.editType=\"calc+clearAxisTypes\"},{\"../../components/colorscale/color_attributes\":611,\"../../constants/gl2d_dashes\":702,\"../../constants/gl2d_markers\":703,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../scatter/attributes\":1031}],1078:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\"),i=t(\"../scatter/arrays_to_calcdata\"),a=t(\"../scatter/colorscale_calc\");e.exports=function(t,e){var r,o=t._fullLayout.dragmode;if(\"lasso\"===o||\"select\"===o){var s,l=n.getFromId(t,e.xaxis||\"x\"),u=n.getFromId(t,e.yaxis||\"y\"),c=l.makeCalcdata(e,\"x\"),h=u.makeCalcdata(e,\"y\"),f=Math.min(c.length,h.length);for(r=new Array(f),s=0;s<f;s++)r[s]={x:c[s],y:h[s]}}else r=[{x:!1,y:!1,trace:e,t:{}}],i(r,e);return a(e),r}},{\"../../plots/cartesian/axes\":772,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035}],1079:[function(t,e,r){\"use strict\";function n(t,e){this.scene=t,this.uid=e,this.type=\"scattergl\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.connectgaps=!0,this.index=null,this.idToIndex=[],this.bounds=[0,0,0,0],this.isVisible=!1,this.hasLines=!1,this.hasErrorX=!1,this.hasErrorY=!1,this.hasMarkers=!1,this.line=this.initObject(m,{positions:new Float64Array(0),color:[0,0,0,1],width:1,fill:[!1,!1,!1,!1],fillColor:[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],dashes:[1]},0),this.errorX=this.initObject(v,{positions:new Float64Array(0),errors:new Float64Array(0),lineWidth:1,capSize:0,color:[0,0,0,1]},1),this.errorY=this.initObject(v,{positions:new Float64Array(0),errors:new Float64Array(0),lineWidth:1,capSize:0,color:[0,0,0,1]},2);var r={positions:new Float64Array(0),sizes:[],colors:[],glyphs:[],borderWidths:[],borderColors:[],size:12,color:[0,0,0,1],borderSize:1,borderColor:[0,0,0,1],snapPoints:!0},n=y.extendFlat({},r,{snapPoints:!1});this.scatter=this.initObject(d,r,3),this.fancyScatter=this.initObject(p,r,4),this.selectScatter=this.initObject(d,n,5)}function i(t,e,r){return Array.isArray(e)||(e=[e]),a(t,e,r)}function a(t,e,r){for(var n=new Array(r),i=e[0],a=0;a<r;++a)n[a]=t(a>=e.length?i:e[a]);return n}function o(t,e,r){return l(O(t,r),P(e,r),r)}function s(t,e,r,n){var i=k(t,e,n);return i=Array.isArray(i[0])?i:a(y.identity,[i],n),l(i,P(r,n),n)}function l(t,e,r){for(var n=new Array(4*r),i=0;i<r;++i){for(var a=0;a<3;++a)n[4*i+a]=t[i][a];n[4*i+3]=t[i][3]*e[i]}return n}function u(t){return\"\"===t.split(\"-open\")[1]}function c(t,e,r,n,i){var a,o=i?C:1;for(a=0;a<3;a++)t[4*r+a]=e[4*n+a];t[4*r+a]=o*e[4*n+a]}function h(t){for(var e,r=t.length,n=Math.max(1,(r-1)/Math.min(Math.max(r,1),1e3)),i=0;i<r;i+=n)if(e=t[Math.floor(i)],!(g(e)||e instanceof Date))return!1;return!0}function f(t,e,r){var i=new n(t,e.uid);return i.update(e,r),i}var d=t(\"gl-scatter2d\"),p=t(\"gl-scatter2d-sdf\"),m=t(\"gl-line2d\"),v=t(\"gl-error2d\"),g=t(\"fast-isnumeric\"),y=t(\"../../lib\"),b=t(\"../../plots/cartesian/axes\"),x=t(\"../../plots/cartesian/axis_autotype\"),_=t(\"../../components/errorbars\"),w=t(\"../../lib/str2rgbarray\"),M=t(\"../../lib/typed_array_truncate\"),k=t(\"../../lib/gl_format_color\"),A=t(\"../scatter/subtypes\"),T=t(\"../scatter/make_bubble_size_func\"),S=t(\"../scatter/get_trace_color\"),E=t(\"../../constants/gl2d_markers\"),L=t(\"../../constants/gl2d_dashes\"),C=t(\"../../constants/interactions\").DESELECTDIM,I=[\"xaxis\",\"yaxis\"],z=[0,0,0,0],D=n.prototype;D.initObject=function(t,e,r){function n(){u||(u=t(s,e),u._trace=o,u._index=r),u.update(e)}function i(){u&&u.update(l)}function a(){u&&u.dispose()}var o=this,s=o.scene.glplot,l=y.extendFlat({},e),u=null;return{options:e,update:n,clear:i,dispose:a}},D.handlePick=function(t){var e=t.pointId;(t.object!==this.line||this.connectgaps)&&(e=this.idToIndex[t.pointId]);var r=this.pickXData[e];return{trace:this,dataCoord:t.dataCoord,traceCoord:[g(r)||!y.isDateTime(r)?r:y.dateTime2ms(r),this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:Array.isArray(this.color)?this.color[e]:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},D.isFancy=function(t){if(\"linear\"!==this.scene.xaxis.type&&\"date\"!==this.scene.xaxis.type)return!0;if(\"linear\"!==this.scene.yaxis.type)return!0;if(!t.x||!t.y)return!0;if(this.hasMarkers){var e=t.marker||{};if(Array.isArray(e.symbol)||\"circle\"!==e.symbol||Array.isArray(e.size)||Array.isArray(e.color)||Array.isArray(e.line.width)||Array.isArray(e.line.color)||Array.isArray(e.opacity))return!0}return!(!this.hasLines||this.connectgaps)||(!!this.hasErrorX||!!this.hasErrorY)};var P=i.bind(null,function(t){return+t}),O=i.bind(null,w),R=i.bind(null,function(t){return 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Float64Array(2*l),f=this.bounds,d=0,p=0,m=t.selection,v=t.xcalendar,b=h(o),_=!b&&\"date\"===x(o,v);if(b||_)for(e=0;e<l;++e)i=o[e],a=s[e],g(a)&&(b||(i=y.dateTime2ms(i,v)),c[p++]=i,c[p++]=a,u[d++]=e,f[0]=Math.min(f[0],i),f[1]=Math.min(f[1],a),f[2]=Math.max(f[2],i),f[3]=Math.max(f[3],a));if(c=M(c,p),this.idToIndex=u,m&&m.length)for(r=new Float64Array(2*m.length),e=0,n=m.length;e<n;e++)r[2*e+0]=m[e].x,r[2*e+1]=m[e].y;this.updateLines(t,c),this.updateError(\"X\",t),this.updateError(\"Y\",t);var k;if(this.hasMarkers){var A,T,S;r?(this.scatter.options.positions=null,A=w(t.marker.color),T=w(t.marker.line.color),S=t.opacity*t.marker.opacity*C,A[3]*=S,this.scatter.options.color=A,T[3]*=S,this.scatter.options.borderColor=T,k=t.marker.size,this.scatter.options.size=k,this.scatter.options.borderSize=t.marker.line.width,this.scatter.update(),this.scatter.options.positions=c,this.selectScatter.options.positions=r,A=w(t.marker.color),T=w(t.marker.line.color),S=t.opacity*t.marker.opacity,A[3]*=S,this.selectScatter.options.color=A,T[3]*=S,this.selectScatter.options.borderColor=T,k=t.marker.size,this.selectScatter.options.size=k,this.selectScatter.options.borderSize=t.marker.line.width,this.selectScatter.update()):(this.scatter.options.positions=c,A=w(t.marker.color),T=w(t.marker.line.color),S=t.opacity*t.marker.opacity,A[3]*=S,this.scatter.options.color=A,T[3]*=S,this.scatter.options.borderColor=T,k=t.marker.size,this.scatter.options.size=k,this.scatter.options.borderSize=t.marker.line.width,this.scatter.update())}else this.scatter.clear();this.fancyScatter.clear(),this.expandAxesFast(f,k)},D.updateFancy=function(t){var e=this.scene,r=e.xaxis,n=e.yaxis,a=this.bounds,o=t.selection,l=this.pickXData=r.makeCalcdata(t,\"x\").slice(),h=this.pickYData=n.makeCalcdata(t,\"y\").slice();this.xData=l.slice(),this.yData=h.slice();var f,d,p,m,v,g,y,b=_.calcFromTrace(t,e.fullLayout),x=l.length,w=new Array(x),k=new Float64Array(2*x),A=new Float64Array(4*x),S=new Float64Array(4*x),L=0,C=0,I=0,D=0,O=\"log\"===r.type?r.d2l:function(t){return t},F=\"log\"===n.type?n.d2l:function(t){return t};for(f=0;f<x;++f)this.xData[f]=d=O(l[f]),this.yData[f]=p=F(h[f]),isNaN(d)||isNaN(p)||(w[L++]=f,k[C++]=d,k[C++]=p,m=A[I++]=d-b[f].xs||0,v=A[I++]=b[f].xh-d||0,A[I++]=0,A[I++]=0,S[D++]=0,S[D++]=0,g=S[D++]=p-b[f].ys||0,y=S[D++]=b[f].yh-p||0,a[0]=Math.min(a[0],d-m),a[1]=Math.min(a[1],p-g),a[2]=Math.max(a[2],d+v),a[3]=Math.max(a[3],p+y));k=M(k,C),this.idToIndex=w,this.updateLines(t,k),this.updateError(\"X\",t,k,A),this.updateError(\"Y\",t,k,S);var j,N;if(o&&o.length)for(N={},f=0;f<o.length;f++)N[o[f].pointNumber]=!0;if(this.hasMarkers){this.scatter.options.positions=k,this.scatter.options.sizes=new Array(L),this.scatter.options.glyphs=new Array(L),this.scatter.options.borderWidths=new Array(L),this.scatter.options.colors=new Array(4*L),this.scatter.options.borderColors=new Array(4*L);var B,U,V,H,q,G,Y,W,X,Z,J=T(t),K=t.marker,Q=K.opacity,$=t.opacity,tt=R(K.symbol,x),et=s(K,Q,$,x),rt=P(K.line.width,x),nt=s(K.line,Q,$,x);for(j=i(J,K.size,x),f=0;f<L;++f)B=w[f],V=tt[B],H=E[V],q=u(V),G=N&&!N[B],Y=H.noBorder&&!q?nt:et,W=q?et:nt,U=j[B],X=rt[B],Z=H.noBorder||H.noFill?.1*U:0,this.scatter.options.sizes[f]=4*U,this.scatter.options.glyphs[f]=H.unicode,this.scatter.options.borderWidths[f]=.5*(X>Z?X-Z:0),!q||H.noBorder||H.noFill?c(this.scatter.options.colors,Y,f,B,G):c(this.scatter.options.colors,z,f,0),c(this.scatter.options.borderColors,W,f,B,G);N?(this.scatter.options.positions=null,this.fancyScatter.update(),this.scatter.options.positions=k):this.fancyScatter.update()}else this.fancyScatter.clear();this.scatter.clear(),this.expandAxesFancy(l,h,j)},D.updateLines=function(t,e){var r;if(this.hasLines){var n=e;if(!t.connectgaps){var i=0,a=this.xData,s=this.yData;for(n=new Float64Array(2*a.length),r=0;r<a.length;++r)n[i++]=a[r],n[i++]=s[r]}this.line.options.positions=n;var l=o(t.line.color,t.opacity,1),u=Math.round(.5*this.line.options.width),c=(L[t.line.dash]||[1]).slice();for(r=0;r<c.length;++r)c[r]*=u;switch(t.fill){case\"tozeroy\":this.line.options.fill=[!1,!0,!1,!1];break;case\"tozerox\":this.line.options.fill=[!0,!1,!1,!1];break;default:this.line.options.fill=[!1,!1,!1,!1]}var h=w(t.fillcolor);this.line.options.color=l,this.line.options.width=2*t.line.width,this.line.options.dashes=c,this.line.options.fillColor=[h,h,h,h],this.line.update()}else this.line.clear()},D.updateError=function(t,e,r,n){var i=this[\"error\"+t],a=e[\"error_\"+t.toLowerCase()];\"x\"===t.toLowerCase()&&a.copy_ystyle&&(a=e.error_y),this[\"hasError\"+t]?(i.options.positions=r,i.options.errors=n,i.options.capSize=a.width,i.options.lineWidth=a.thickness/2,i.options.color=o(a.color,1,1),i.update()):i.clear()},D.expandAxesFast=function(t,e){for(var r,n,i,a=e||10,o=0;o<2;o++)r=this.scene[I[o]],n=r._min,n||(n=[]),n.push({val:t[o],pad:a}),i=r._max,i||(i=[]),i.push({val:t[o+2],pad:a})},D.expandAxesFancy=function(t,e,r){var n=this.scene,i={padded:!0,ppad:r};b.expand(n.xaxis,t,i),b.expand(n.yaxis,e,i)},D.dispose=function(){this.line.dispose(),this.errorX.dispose(),this.errorY.dispose(),this.scatter.dispose(),this.fancyScatter.dispose()},e.exports=f},{\"../../components/errorbars\":634,\"../../constants/gl2d_dashes\":702,\"../../constants/gl2d_markers\":703,\"../../constants/interactions\":706,\"../../lib\":728,\"../../lib/gl_format_color\":724,\"../../lib/str2rgbarray\":749,\"../../lib/typed_array_truncate\":754,\"../../plots/cartesian/axes\":772,\"../../plots/cartesian/axis_autotype\":773,\"../scatter/get_trace_color\":1040,\"../scatter/make_bubble_size_func\":1047,\"../scatter/subtypes\":1052,\"fast-isnumeric\":131,\"gl-error2d\":159,\"gl-line2d\":170,\"gl-scatter2d\":248,\"gl-scatter2d-sdf\":243}],1080:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/xy_defaults\"),s=t(\"../scatter/marker_defaults\"),l=t(\"../scatter/line_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),c=t(\"../../components/errorbars/defaults\"),h=t(\"./attributes\");e.exports=function(t,e,r,f){function d(r,i){return n.coerce(t,e,h,r,i)}var p=o(t,e,f,d);if(!p)return void(e.visible=!1);d(\"text\"),d(\"mode\",p<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(d(\"connectgaps\"),l(t,e,r,f,d)),a.hasMarkers(e)&&s(t,e,r,f,d),d(\"fill\"),\"none\"!==e.fill&&u(t,e,r,d),c(t,e,r,{axis:\"y\"}),c(t,e,r,{axis:\"x\",inherit:\"y\"})}},{\"../../components/errorbars/defaults\":633,\"../../lib\":728,\"../scatter/constants\":1036,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/xy_defaults\":1054,\"./attributes\":1077}],1081:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.hoverPoints=t(\"../scatter/hover\"),n.calc=t(\"./calc\"),n.plot=t(\"./convert\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergl\",n.basePlotModule=t(\"../../plots/gl2d\"),n.categories=[\"gl2d\",\"symbols\",\"errorBarsOK\",\"markerColorscale\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/gl2d\":808,\"../scatter/colorbar\":1034,\"../scatter/hover\":1041,\"./attributes\":1077,\"./calc\":1078,\"./convert\":1079,\"./defaults\":1080,\"./select\":1082}],1082:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\");e.exports=function(t,e){var r,i,a,o,s=t.cd,l=t.xaxis,u=t.yaxis,c=[],h=s[0].trace,f=s[0]._glTrace,d=f.scene,p=!n.hasMarkers(h)&&!n.hasText(h);if(!0===h.visible&&!p){if(!1===e)for(r=0;r<s.length;r++)s[r].dim=0;else 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n=t(\"../scattergeo/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/mapbox/layout_attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorbar/attributes\"),l=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,c=n.line,h=n.marker;e.exports=u({lon:n.lon,lat:n.lat,mode:l({},i.mode,{dflt:\"markers\"}),text:l({},i.text,{}),hovertext:l({},i.hovertext,{}),line:{color:c.color,width:c.width},connectgaps:i.connectgaps,marker:{symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},opacity:h.opacity,size:h.size,sizeref:h.sizeref,sizemin:h.sizemin,sizemode:h.sizemode,color:h.color,colorscale:h.colorscale,cauto:h.cauto,cmax:h.cmax,cmin:h.cmin,autocolorscale:h.autocolorscale,reversescale:h.reversescale,showscale:h.showscale,colorbar:s},fill:n.fill,fillcolor:i.fillcolor,textfont:a.layers.symbol.textfont,textposition:a.layers.symbol.textposition,hoverinfo:l({},o.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorbar/attributes\":605,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../../plots/mapbox/layout_attributes\":827,\"../scatter/attributes\":1031,\"../scattergeo/attributes\":1069}],1084:[function(t,e,r){\"use 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../components/drawing/attributes\").dash,l=t(\"../../lib/extend\").extendFlat,u=n.marker,c=n.line,h=u.line;e.exports={a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},c:{valType:\"data_array\",editType:\"calc\"},sum:{valType:\"number\",dflt:0,min:0,editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),hovertext:l({},n.hovertext,{}),line:{color:c.color,width:c.width,dash:s,shape:l({},c.shape,{values:[\"linear\",\"spline\"]}),smoothing:c.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,cliponaxis:n.cliponaxis,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:l({symbol:u.symbol,opacity:u.opacity,maxdisplayed:u.maxdisplayed,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,line:l({width:h.width,editType:\"calc\"},a(\"marker.line\")),gradient:u.gradient,editType:\"calc\"},a(\"marker\"),{showscale:u.showscale,colorbar:o}),textfont:n.textfont,textposition:n.textposition,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"c\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":605,\"../../components/colorscale/color_attributes\":611,\"../../components/drawing/attributes\":627,\"../../lib/extend\":717,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1092:[function(t,e,r){\"use 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n=t(\"../scatter/hover\"),i=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r,a){function o(t,e){y.push(t._hovertitle+\": \"+i.tickText(t,e,\"hover\").text)}var s=n(t,e,r,a);if(s&&!1!==s[0].index){var l=s[0];if(void 0===l.index){var u=1-l.y0/t.ya._length,c=t.xa._length,h=c*u/2,f=c-h;return l.x0=Math.max(Math.min(l.x0,f),h),l.x1=Math.max(Math.min(l.x1,f),h),s}var d=l.cd[l.index];l.a=d.a,l.b=d.b,l.c=d.c,l.xLabelVal=void 0,l.yLabelVal=void 0;var p=l.trace,m=p._ternary,v=d.hi||p.hoverinfo,g=v.split(\"+\"),y=[];return-1!==g.indexOf(\"all\")&&(g=[\"a\",\"b\",\"c\"]),-1!==g.indexOf(\"a\")&&o(m.aaxis,d.a),-1!==g.indexOf(\"b\")&&o(m.baxis,d.b),-1!==g.indexOf(\"c\")&&o(m.caxis,d.c),l.extraText=y.join(\"<br>\"),s}}},{\"../../plots/cartesian/axes\":772,\"../scatter/hover\":1041}],1095:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scatterternary\",n.basePlotModule=t(\"../../plots/ternary\"),\n",
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a=0,o=0;o<i.length;o++)e=(0|n.words[o])-(0|i.words[o])+a,a=e>>26,this.words[o]=67108863&e;for(;0!==a&&o<n.length;o++)e=(0|n.words[o])+a,a=e>>26,this.words[o]=67108863&e;if(0===a&&o<n.length&&n!==this)for(;o<n.length;o++)this.words[o]=n.words[o];return this.length=Math.max(this.length,o),n!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var k=function(t,e,r){var 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gt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(g,U),i=Math.imul(g,V),i=i+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(p,q)|0,i=i+Math.imul(p,G)|0,i=i+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(h,W)|0,i=i+Math.imul(h,X)|0,i=i+Math.imul(f,W)|0,a=a+Math.imul(f,X)|0;var yt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(x,U),i=Math.imul(x,V),i=i+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(g,q)|0,i=i+Math.imul(g,G)|0,i=i+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(p,W)|0,i=i+Math.imul(p,X)|0,i=i+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0,n=n+Math.imul(h,J)|0,i=i+Math.imul(h,K)|0,i=i+Math.imul(f,J)|0,a=a+Math.imul(f,K)|0;var bt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=Math.imul(M,V),i=i+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(x,q)|0,i=i+Math.imul(x,G)|0,i=i+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(g,W)|0,i=i+Math.imul(g,X)|0,i=i+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(p,J)|0,i=i+Math.imul(p,K)|0,i=i+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0,n=n+Math.imul(h,$)|0,i=i+Math.imul(h,tt)|0,i=i+Math.imul(f,$)|0,a=a+Math.imul(f,tt)|0;var xt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=Math.imul(T,V),i=i+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(M,q)|0,i=i+Math.imul(M,G)|0,i=i+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(x,W)|0,i=i+Math.imul(x,X)|0,i=i+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(g,J)|0,i=i+Math.imul(g,K)|0,i=i+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(p,$)|0,i=i+Math.imul(p,tt)|0,i=i+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(h,rt)|0,i=i+Math.imul(h,nt)|0,i=i+Math.imul(f,rt)|0,a=a+Math.imul(f,nt)|0;var _t=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=Math.imul(L,V),i=i+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(T,q)|0,i=i+Math.imul(T,G)|0,i=i+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(M,W)|0,i=i+Math.imul(M,X)|0,i=i+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(x,J)|0,i=i+Math.imul(x,K)|0,i=i+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(g,$)|0,i=i+Math.imul(g,tt)|0,i=i+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(p,rt)|0,i=i+Math.imul(p,nt)|0,i=i+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(h,at)|0,i=i+Math.imul(h,ot)|0,i=i+Math.imul(f,at)|0,a=a+Math.imul(f,ot)|0;var wt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(z,U),i=Math.imul(z,V),i=i+Math.imul(D,U)|0,a=Math.imul(D,V),n=n+Math.imul(L,q)|0,i=i+Math.imul(L,G)|0,i=i+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(T,W)|0,i=i+Math.imul(T,X)|0,i=i+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(M,J)|0,i=i+Math.imul(M,K)|0,i=i+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(x,$)|0,i=i+Math.imul(x,tt)|0,i=i+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(g,rt)|0,i=i+Math.imul(g,nt)|0,i=i+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(p,at)|0,i=i+Math.imul(p,ot)|0,i=i+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(h,lt)|0,i=i+Math.imul(h,ut)|0,i=i+Math.imul(f,lt)|0,a=a+Math.imul(f,ut)|0;var 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kt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(j,U),i=Math.imul(j,V),i=i+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(O,q)|0,i=i+Math.imul(O,G)|0,i=i+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(z,W)|0,i=i+Math.imul(z,X)|0,i=i+Math.imul(D,W)|0,a=a+Math.imul(D,X)|0,n=n+Math.imul(L,J)|0,i=i+Math.imul(L,K)|0,i=i+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(T,$)|0,i=i+Math.imul(T,tt)|0,i=i+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(M,rt)|0,i=i+Math.imul(M,nt)|0,i=i+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(x,at)|0,i=i+Math.imul(x,ot)|0,i=i+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(g,lt)|0,i=i+Math.imul(g,ut)|0,i=i+Math.imul(y,lt)|0,a=a+Math.imul(y,ut)|0,n=n+Math.imul(p,ht)|0,i=i+Math.imul(p,ft)|0,i=i+Math.imul(m,ht)|0,a=a+Math.imul(m,ft)|0,n=n+Math.imul(h,pt)|0,i=i+Math.imul(h,mt)|0,i=i+Math.imul(f,pt)|0,a=a+Math.imul(f,mt)|0;var At=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(j,q),i=Math.imul(j,G),i=i+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(O,W)|0,i=i+Math.imul(O,X)|0,i=i+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(z,J)|0,i=i+Math.imul(z,K)|0,i=i+Math.imul(D,J)|0,a=a+Math.imul(D,K)|0,n=n+Math.imul(L,$)|0,i=i+Math.imul(L,tt)|0,i=i+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(T,rt)|0,i=i+Math.imul(T,nt)|0,i=i+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(M,at)|0,i=i+Math.imul(M,ot)|0,i=i+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(x,lt)|0,i=i+Math.imul(x,ut)|0,i=i+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(g,ht)|0,i=i+Math.imul(g,ft)|0,i=i+Math.imul(y,ht)|0,a=a+Math.imul(y,ft)|0,n=n+Math.imul(p,pt)|0,i=i+Math.imul(p,mt)|0,i=i+Math.imul(m,pt)|0,a=a+Math.imul(m,mt)|0;var Tt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(j,W),i=Math.imul(j,X),i=i+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(O,J)|0,i=i+Math.imul(O,K)|0,i=i+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(z,$)|0,i=i+Math.imul(z,tt)|0,i=i+Math.imul(D,$)|0,a=a+Math.imul(D,tt)|0,n=n+Math.imul(L,rt)|0,i=i+Math.imul(L,nt)|0,i=i+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(T,at)|0,i=i+Math.imul(T,ot)|0,i=i+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(M,lt)|0,i=i+Math.imul(M,ut)|0,i=i+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(x,ht)|0,i=i+Math.imul(x,ft)|0,i=i+Math.imul(_,ht)|0,a=a+Math.imul(_,ft)|0,n=n+Math.imul(g,pt)|0,i=i+Math.imul(g,mt)|0,i=i+Math.imul(y,pt)|0,a=a+Math.imul(y,mt)|0;var St=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(j,J),i=Math.imul(j,K),i=i+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(O,$)|0,i=i+Math.imul(O,tt)|0,i=i+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(z,rt)|0,i=i+Math.imul(z,nt)|0,i=i+Math.imul(D,rt)|0,a=a+Math.imul(D,nt)|0,n=n+Math.imul(L,at)|0,i=i+Math.imul(L,ot)|0,i=i+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(T,lt)|0,i=i+Math.imul(T,ut)|0,i=i+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(M,ht)|0,i=i+Math.imul(M,ft)|0,i=i+Math.imul(k,ht)|0,a=a+Math.imul(k,ft)|0,n=n+Math.imul(x,pt)|0,i=i+Math.imul(x,mt)|0,i=i+Math.imul(_,pt)|0,a=a+Math.imul(_,mt)|0;var Et=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(j,$),i=Math.imul(j,tt),i=i+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(O,rt)|0,i=i+Math.imul(O,nt)|0,i=i+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(z,at)|0,i=i+Math.imul(z,ot)|0,i=i+Math.imul(D,at)|0,a=a+Math.imul(D,ot)|0,n=n+Math.imul(L,lt)|0,i=i+Math.imul(L,ut)|0,i=i+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(T,ht)|0,i=i+Math.imul(T,ft)|0,i=i+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0,n=n+Math.imul(M,pt)|0,i=i+Math.imul(M,mt)|0,i=i+Math.imul(k,pt)|0,a=a+Math.imul(k,mt)|0;var Lt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(j,rt),i=Math.imul(j,nt),i=i+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(O,at)|0,i=i+Math.imul(O,ot)|0,i=i+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(z,lt)|0,i=i+Math.imul(z,ut)|0,i=i+Math.imul(D,lt)|0,a=a+Math.imul(D,ut)|0,n=n+Math.imul(L,ht)|0,\n",
+       "i=i+Math.imul(L,ft)|0,i=i+Math.imul(C,ht)|0,a=a+Math.imul(C,ft)|0,n=n+Math.imul(T,pt)|0,i=i+Math.imul(T,mt)|0,i=i+Math.imul(S,pt)|0,a=a+Math.imul(S,mt)|0;var Ct=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(j,at),i=Math.imul(j,ot),i=i+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(O,lt)|0,i=i+Math.imul(O,ut)|0,i=i+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(z,ht)|0,i=i+Math.imul(z,ft)|0,i=i+Math.imul(D,ht)|0,a=a+Math.imul(D,ft)|0,n=n+Math.imul(L,pt)|0,i=i+Math.imul(L,mt)|0,i=i+Math.imul(C,pt)|0,a=a+Math.imul(C,mt)|0;var It=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(j,lt),i=Math.imul(j,ut),i=i+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(O,ht)|0,i=i+Math.imul(O,ft)|0,i=i+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0,n=n+Math.imul(z,pt)|0,i=i+Math.imul(z,mt)|0,i=i+Math.imul(D,pt)|0,a=a+Math.imul(D,mt)|0;var zt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,n=Math.imul(j,ht),i=Math.imul(j,ft),i=i+Math.imul(N,ht)|0,a=Math.imul(N,ft),n=n+Math.imul(O,pt)|0,i=i+Math.imul(O,mt)|0,i=i+Math.imul(R,pt)|0,a=a+Math.imul(R,mt)|0;var Dt=(u+n|0)+((8191&i)<<13)|0;u=(a+(i>>>13)|0)+(Dt>>>26)|0,Dt&=67108863,n=Math.imul(j,pt),i=Math.imul(j,mt),i=i+Math.imul(N,pt)|0,a=Math.imul(N,mt);var Pt=(u+n|0)+((8191&i)<<13)|0;return u=(a+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,l[0]=vt,l[1]=gt,l[2]=yt,l[3]=bt,l[4]=xt,l[5]=_t,l[6]=wt,l[7]=Mt,l[8]=kt,l[9]=At,l[10]=Tt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=It,l[16]=zt,l[17]=Dt,l[18]=Pt,0!==u&&(l[19]=u,r.length++),r};Math.imul||(k=u),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?k(this,t,e):r<63?u(this,t,e):r<1024?c(this,t,e):h(this,t,e)},f.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},f.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},f.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},f.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var h=l,f=u,d=0;d<o;d++){var p=r[c+d],m=n[c+d],v=r[c+d+o],g=n[c+d+o],y=h*v-f*g;g=h*g+f*v,v=y,r[c+d]=p+v,n[c+d]=m+g,r[c+d+o]=p-v,n[c+d+o]=m-g,d!==s&&(y=l*h-u*f,f=l*f+u*h,h=y)}},f.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},f.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},f.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},f.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},f.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},f.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,h,n,i);for(var d=0;d<n;d++){var p=s[d]*c[d]-l[d]*h[d];l[d]=s[d]*h[d]+l[d]*c[d],s[d]=p}return this.conjugate(s,l,n),this.transform(s,l,f,a,n,i),this.conjugate(f,a,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),h(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=l(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){n(\"number\"==typeof t&&t>=0);var i;i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var h=0|this.words[u];this.words[u]=c<<26-a|h>>>a,c=h&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a=t.length+r;this._expand(a);var o,s=0;for(i=0;i<t.length;i++){o=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;o-=67108863&l,s=(o>>26)-(l/67108864|0),this.words[i+r]=67108863&o}for(;i<this.length-r;i++)o=(0|this.words[i+r])+s,s=o>>26,this.words[i+r]=67108863&o;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)o=-(0|this.words[i])+s,s=o>>26,this.words[i]=67108863&o;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=this.length-t.length,n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){s=new a(null),s.length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864*(0|n.words[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){if(n(!t.isZero()),this.isZero())return{div:new a(0),mod:new a(0)};var i,o,s;return 0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e)},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),h=e.clone();!e.isZero();){for(var f=0,d=1;0==(e.words[0]&d)&&f<26;++f,d<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var p=0,m=1;0==(r.words[0]&m)&&p<26;++p,m<<=1);if(p>0)for(r.iushrn(p);p-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var l=0,u=1;0==(e.words[0]&u)&&l<26;++l,u<<=1);if(l>0)for(e.iushrn(l);l-- >0;)i.isOdd()&&i.iadd(s),i.iushrn(1);for(var c=0,h=1;0==(r.words[0]&h)&&c<26;++c,h<<=1);if(c>0)for(r.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(s),o.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(o)):(r.isub(e),o.isub(i))}var f;return f=0===e.cmpn(1)?i:o,f.cmpn(0)<0&&f.iadd(t),f},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];s+=a,a=s>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e=t<0;if(0!==this.negative&&!e)return-1;if(0===this.negative&&e)return 1;this.strip();var r;if(this.length>1)r=1;else{e&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];r=i===t?0:i<t?-1:1}return 0!==this.negative?0|-r:r},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new y(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var A={k256:null,p224:null,p192:null,p25519:null};d.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},d.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),r=this.imulK(r),r=r.iadd(this.tmp),e=r.bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},d.prototype.split=function(t,e){t.iushrn(this.n,0,e)},d.prototype.imulK=function(t){return t.imul(this.k)},i(p,d),p.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},p.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(m,d),i(v,d),i(g,d),g.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(A[t])return A[t];var e;if(\"k256\"===t)e=new p;else if(\"p224\"===t)e=new m;else if(\"p192\"===t)e=new v;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new g}return A[t]=e,e},y.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},y.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},y.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},y.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},y.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},y.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return 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c=t(\"./convert\"),h=t(\"./transform\"),f=t(\"./clip\"),d=t(\"./wrap\"),p=t(\"./tile\");i.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,solidChildren:!1,tolerance:3,extent:4096,buffer:64,debug:0},i.prototype.splitTile=function(t,e,r,n,i,l,c){for(var h=[t,e,r,n],d=this.options,m=d.debug,v=null;h.length;){n=h.pop(),r=h.pop(),e=h.pop(),t=h.pop();var g=1<<e,y=a(e,r,n),b=this.tiles[y],x=e===d.maxZoom?0:d.tolerance/(g*d.extent);if(!b&&(m>1&&console.time(\"creation\"),b=this.tiles[y]=p(t,g,r,n,x,e===d.maxZoom),this.tileCoords.push({z:e,x:r,y:n}),m)){m>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,b.numFeatures,b.numPoints,b.numSimplified),console.timeEnd(\"creation\"));var _=\"z\"+e;this.stats[_]=(this.stats[_]||0)+1,this.total++}if(b.source=t,i){if(e===d.maxZoom||e===i)continue;var w=1<<i-e;if(r!==Math.floor(l/w)||n!==Math.floor(c/w))continue}else if(e===d.indexMaxZoom||b.numPoints<=d.indexMaxPoints)continue;if(d.solidChildren||!u(b,d.extent,d.buffer)){b.source=null,m>1&&console.time(\"clipping\");var M,k,A,T,S,E,L=.5*d.buffer/d.extent,C=.5-L,I=.5+L,z=1+L;M=k=A=T=null,S=f(t,g,r-L,r+I,0,o,b.min[0],b.max[0]),E=f(t,g,r+C,r+z,0,o,b.min[0],b.max[0]),S&&(M=f(S,g,n-L,n+I,1,s,b.min[1],b.max[1]),k=f(S,g,n+C,n+z,1,s,b.min[1],b.max[1])),E&&(A=f(E,g,n-L,n+I,1,s,b.min[1],b.max[1]),T=f(E,g,n+C,n+z,1,s,b.min[1],b.max[1])),m>1&&console.timeEnd(\"clipping\"),t.length&&(h.push(M||[],e+1,2*r,2*n),h.push(k||[],e+1,2*r,2*n+1),h.push(A||[],e+1,2*r+1,2*n),h.push(T||[],e+1,2*r+1,2*n+1))}else i&&(v=e)}return v},i.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,o=n.debug,s=1<<t;e=(e%s+s)%s;var l=a(t,e,r);if(this.tiles[l])return h.tile(this.tiles[l],i);o>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var c,f=t,d=e,p=r;!c&&f>0;)f--,d=Math.floor(d/2),p=Math.floor(p/2),c=this.tiles[a(f,d,p)];if(!c||!c.source)return null;if(o>1&&console.log(\"found parent tile z%d-%d-%d\",f,d,p),u(c,i,n.buffer))return h.tile(c,i);o>1&&console.time(\"drilling down\");var m=this.splitTile(c.source,f,d,p,t,e,r);if(o>1&&console.timeEnd(\"drilling down\"),null!==m){var v=1<<t-m;l=a(m,Math.floor(e/v),Math.floor(r/v))}return this.tiles[l]?h.tile(this.tiles[l],i):null}},{\"./clip\":139,\"./convert\":140,\"./tile\":144,\"./transform\":145,\"./wrap\":146}],143:[function(t,e,r){\"use strict\";function n(t,e){var r,n,a,o,s=e*e,l=t.length,u=0,c=l-1,h=[];for(t[u][2]=1,t[c][2]=1;c;){for(n=0,r=u+1;r<c;r++)(a=i(t[r],t[u],t[c]))>n&&(o=r,n=a);n>s?(t[o][2]=n,h.push(u),h.push(o),u=o):(c=h.pop(),u=h.pop())}}function i(t,e,r){var n=e[0],i=e[1],a=r[0],o=r[1],s=t[0],l=t[1],u=a-n,c=o-i;if(0!==u||0!==c){var h=((s-n)*u+(l-i)*c)/(u*u+c*c);h>1?(n=a,i=o):h>0&&(n+=u*h,i+=c*h)}return u=s-n,c=l-i,u*u+c*c}e.exports=n},{}],144:[function(t,e,r){\"use strict\";function n(t,e,r,n,a,o){for(var s={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z2:e,transformed:!1,min:[2,1],max:[-1,0]},l=0;l<t.length;l++){s.numFeatures++,i(s,t[l],a,o);var u=t[l].min,c=t[l].max;u[0]<s.min[0]&&(s.min[0]=u[0]),u[1]<s.min[1]&&(s.min[1]=u[1]),c[0]>s.max[0]&&(s.max[0]=c[0]),c[1]>s.max[1]&&(s.max[1]=c[1])}return s}function i(t,e,r,n){var i,o,s,l,u=e.geometry,c=e.type,h=[],f=r*r;if(1===c)for(i=0;i<u.length;i++)h.push(u[i]),t.numPoints++,t.numSimplified++;else for(i=0;i<u.length;i++)if(s=u[i],n||!(2===c&&s.dist<r||3===c&&s.area<f)){var d=[];for(o=0;o<s.length;o++)l=s[o],(n||l[2]>f)&&(d.push(l),t.numSimplified++),t.numPoints++;3===c&&a(d,s.outer),h.push(d)}else t.numPoints+=s.length;if(h.length){var p={geometry:h,type:c,tags:e.tags||null};null!==e.id&&(p.id=e.id),t.features.push(p)}}function a(t,e){o(t)<0===e&&t.reverse()}function o(t){for(var e,r,n=0,i=0,a=t.length,o=a-1;i<a;o=i++)e=t[i],r=t[o],n+=(r[0]-e[0])*(e[1]+r[1]);return n}e.exports=n},{}],145:[function(t,e,r){\"use 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o=t(\"./clip\"),s=t(\"./feature\");e.exports=n},{\"./clip\":139,\"./feature\":141}],147:[function(t,e,r){function n(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e||{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width),\"number\"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf(\"webgl\")&&a.push(\"experimental-\"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}e.exports=n},{}],148:[function(t,e,r){\"use strict\";function n(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function 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a(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}function o(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var h=a,f=s,d=o,p=l\n",
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p=0;p<3;++p)h[d][p]!==this.bounds[d][p]&&(c=!0),this.bounds[d][p]=h[d][p];if(\"ticks\"in t){r=t.ticks,s=!0,this.autoTicks=!1;for(var d=0;d<3;++d)this.tickSpacing[d]=0}else n(\"tickSpacing\")&&(this.autoTicks=!0,c=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),c=!0,s=!0,this._firstInit=!1),c&&this.autoTicks&&(r=f.create(this.bounds,this.tickSpacing),s=!0),s){for(var d=0;d<3;++d)r[d].sort(function(t,e){return t.x-e.x});f.equal(r,this.ticks)?s=!1:this.ticks=r}i(\"tickEnable\"),a(\"tickFont\")&&(s=!0),n(\"tickSize\"),n(\"tickAngle\"),n(\"tickPad\"),o(\"tickColor\");var m=a(\"labels\");a(\"labelFont\")&&(m=!0),i(\"labelEnable\"),n(\"labelSize\"),n(\"labelPad\"),o(\"labelColor\"),i(\"lineEnable\"),i(\"lineMirror\"),n(\"lineWidth\"),o(\"lineColor\"),i(\"lineTickEnable\"),i(\"lineTickMirror\"),n(\"lineTickLength\"),n(\"lineTickWidth\"),o(\"lineTickColor\"),i(\"gridEnable\"),n(\"gridWidth\"),o(\"gridColor\"),i(\"zeroEnable\"),o(\"zeroLineColor\"),n(\"zeroLineWidth\"),i(\"backgroundEnable\"),o(\"backgroundColor\"),this._text?this._text&&(m||s)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=l(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&s&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=u(this.gl,this.bounds,this.ticks))};var m=[new a,new a,new a],v=[0,0,0],g={model:d,view:d,projection:d};p.isOpaque=function(){return!0},p.isTransparent=function(){return!1},p.drawTransparent=function(t){};var y=[0,0,0],b=[0,0,0],x=[0,0,0];p.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||d,i=t.view||d,a=t.projection||d,s=this.bounds,l=h(r,i,a,s),u=l.cubeEdges,c=l.axis,f=i[12],p=i[13],_=i[14],w=i[15],M=this.pixelRatio*(a[3]*f+a[7]*p+a[11]*_+a[15]*w)/e.drawingBufferHeight,k=0;k<3;++k)this.lastCubeProps.cubeEdges[k]=u[k],this.lastCubeProps.axis[k]=c[k];for(var A=m,k=0;k<3;++k)o(m[k],k,this.bounds,u,c);for(var e=this.gl,T=v,k=0;k<3;++k)this.backgroundEnable[k]?T[k]=c[k]:T[k]=0;this._background.draw(r,i,a,s,T,this.backgroundColor),this._lines.bind(r,i,a,this);for(var k=0;k<3;++k){var S=[0,0,0];c[k]>0?S[k]=s[1][k]:S[k]=s[0][k];for(var E=0;E<2;++E){var L=(k+1+E)%3,C=(k+1+(1^E))%3;this.gridEnable[L]&&this._lines.drawGrid(L,C,this.bounds,S,this.gridColor[L],this.gridWidth[L]*this.pixelRatio)}for(var E=0;E<2;++E){var L=(k+1+E)%3,C=(k+1+(1^E))%3;this.zeroEnable[C]&&s[0][C]<=0&&s[1][C]>=0&&this._lines.drawZero(L,C,this.bounds,S,this.zeroLineColor[C],this.zeroLineWidth[C]*this.pixelRatio)}}for(var k=0;k<3;++k){this.lineEnable[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].primalOffset,this.lineColor[k],this.lineWidth[k]*this.pixelRatio),this.lineMirror[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].mirrorOffset,this.lineColor[k],this.lineWidth[k]*this.pixelRatio);for(var I=n(y,A[k].primalMinor),z=n(b,A[k].mirrorMinor),D=this.lineTickLength,E=0;E<3;++E){var P=M/r[5*E];I[E]*=D[E]*P,z[E]*=D[E]*P}this.lineTickEnable[k]&&this._lines.drawAxisTicks(k,A[k].primalOffset,I,this.lineTickColor[k],this.lineTickWidth[k]*this.pixelRatio),this.lineTickMirror[k]&&this._lines.drawAxisTicks(k,A[k].mirrorOffset,z,this.lineTickColor[k],this.lineTickWidth[k]*this.pixelRatio)}this._text.bind(r,i,a,this.pixelRatio);for(var k=0;k<3;++k){for(var O=A[k].primalMinor,R=n(x,A[k].primalOffset),E=0;E<3;++E)this.lineTickEnable[k]&&(R[E]+=M*O[E]*Math.max(this.lineTickLength[E],0)/r[5*E]);if(this.tickEnable[k]){for(var E=0;E<3;++E)R[E]+=M*O[E]*this.tickPad[E]/r[5*E];this._text.drawTicks(k,this.tickSize[k],this.tickAngle[k],R,this.tickColor[k])}if(this.labelEnable[k]){for(var E=0;E<3;++E)R[E]+=M*O[E]*this.labelPad[E]/r[5*E];R[k]+=.5*(s[0][k]+s[1][k]),this._text.drawLabel(k,this.labelSize[k],this.labelAngle[k],R,this.labelColor[k])}}},p.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":149,\"./lib/cube.js\":150,\"./lib/lines.js\":151,\"./lib/text.js\":153,\"./lib/ticks.js\":154}],149:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}function i(t){for(var e=[],r=[],i=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,h=[0,0,0],f=[0,0,0],d=-1;d<=1;d+=2){r.push(i,i+2,i+1,i+1,i+2,i+3),h[l]=d,f[l]=d;for(var p=-1;p<=1;p+=2){h[u]=p;for(var m=-1;m<=1;m+=2)h[c]=m,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),i+=1}var v=u;u=c,c=v}var g=a(t,new 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s=t(\"gl-buffer\"),l=t(\"gl-vao\"),u=t(\"./shaders\").line,c=[0,0,0],h=[0,0,0],f=[0,0,0],d=[0,0,0],p=[1,1],m=a.prototype;m.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,p[0]=this.gl.drawingBufferWidth,p[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=p,this.vao.bind()},m.drawAxisLine=function(t,e,r,a,o){var s=n(h);this.shader.uniforms.majorAxis=h,s[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=s;var l=i(d,r);l[t]+=e[0][t],this.shader.uniforms.offset=l,this.shader.uniforms.lineWidth=o,this.shader.uniforms.color=a;var u=n(f);u[(t+2)%3]=1,this.shader.uniforms.screenAxis=u,this.vao.draw(this.gl.TRIANGLES,6);var u=n(f);u[(t+1)%3]=1,this.shader.uniforms.screenAxis=u,this.vao.draw(this.gl.TRIANGLES,6)},m.drawAxisTicks=function(t,e,r,i,a){if(this.tickCount[t]){var o=n(c);o[t]=1,this.shader.uniforms.majorAxis=o,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=i,this.shader.uniforms.lineWidth=a;var s=n(f);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},m.drawGrid=function(t,e,r,a,o,s){if(this.gridCount[t]){var l=n(h);l[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=l;var u=i(d,a);u[e]+=r[0][e],this.shader.uniforms.offset=u;var p=n(c);p[t]=1,this.shader.uniforms.majorAxis=p;var m=n(f);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=s,this.shader.uniforms.color=o,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},m.drawZero=function(t,e,r,a,o,s){var l=n(h);this.shader.uniforms.majorAxis=l,l[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=l;var u=i(d,a);u[t]+=r[0][t],this.shader.uniforms.offset=u;var 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- pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\",\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\",null,[{name:\"position\",type:\"vec3\"}])};r.text=function(t){return n(t,\"#define GLSLIFY 1\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvoid main() {  \\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n  mat2 planeXform = scale * mat2(cos(angle), sin(angle),\\n                                -sin(angle), cos(angle));\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n  vec4 worldPosition = model * vec4(dataPosition, 1);\\n  \\n  //Compute clip 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n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":157}],159:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.plot=t,this.shader=e,this.bufferHi=r,this.bufferLo=n,this.bounds=[1/0,1/0,-1/0,-1/0],this.numPoints=0,this.color=[0,0,0,1]}function i(t,e){var r=a(t.gl,l.vertex,l.fragment),i=o(t.gl),s=o(t.gl),u=new n(t,r,i,s);return u.update(e),t.addObject(u),u}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"typedarray-pool\"),l=t(\"./lib/shaders\");e.exports=i;var 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n(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1}function i(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}function a(t,e,r,n){for(var i=f[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}function o(t){var e=t.gl,r=s(e),i=l(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),a=u(e);a.attributes.position.location=0,a.attributes.color.location=1,a.attributes.offset.location=2;var o=new n(e,r,i,a);return o.update(t),o}e.exports=o;var s=t(\"gl-buffer\"),l=t(\"gl-vao\"),u=t(\"./shaders/index\"),c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],h=n.prototype;h.isOpaque=function(){return this.opacity>=1},h.isTransparent=function(){return this.opacity<1},h.drawTransparent=h.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||c,i=r.projection=t.projection||c;r.model=t.model||c,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],o=n[13],s=n[14],l=n[15],u=this.pixelRatio*(i[3]*a+i[7]*o+i[11]*s+i[15]*l)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]),r.capSize=this.capSize[h]*u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var f=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=(n+e)%3,o=[0,0,0];o[a]=i,r.push(o)}t[e]=r}return t}();h.update=function(t){t=t||{},\"lineWidth\"in t&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),\"opacity\"in t&&(this.opacity=t.opacity);var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var o=[],s=r.length,l=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var u=0;u<3;++u){this.lineOffset[u]=l;t:for(var c=0;c<s;++c){for(var h=r[c],f=0;f<3;++f)if(isNaN(h[f])||!isFinite(h[f]))continue t;var d=n[c],p=e[u];if(Array.isArray(p[0])&&(p=e[c]),3===p.length&&(p=[p[0],p[1],p[2],1]),!isNaN(d[0][u])&&!isNaN(d[1][u])){if(d[0][u]<0){var m=h.slice();m[u]+=d[0][u],o.push(h[0],h[1],h[2],p[0],p[1],p[2],p[3],0,0,0,m[0],m[1],m[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,m),l+=2+a(o,m,p,u)}if(d[1][u]>0){var m=h.slice();m[u]+=d[1][u],o.push(h[0],h[1],h[2],p[0],p[1],p[2],p[3],0,0,0,m[0],m[1],m[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,m),l+=2+a(o,m,p,u)}}}this.lineCount[u]=l-this.lineOffset[u]}this.buffer.update(o)}},h.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":163,\"gl-buffer\":156,\"gl-vao\":271}],162:[function(t,e,r){e.exports=function(t){\"string\"==typeof t&&(t=[t]);for(var e=[].slice.call(arguments,1),r=[],n=0;n<t.length-1;n++)r.push(t[n],e[n]||\"\");return r.push(t[n]),r.join(\"\")}},{}],163:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n  vec4 worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * view * worldPosition;\\n  fragColor           = color;\\n  fragPosition        = position;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if(any(lessThan(fragPosition, clipBounds[0])) || any(greaterThan(fragPosition, clipBounds[1]))) {\\n    discard;\\n  }\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":255,glslify:162}],164:[function(t,e,r){\"use strict\";function n(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function i(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function a(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);y=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;y[n]=i}}function o(t){switch(t){case p:throw new Error(\"gl-fbo: Framebuffer unsupported\");case m:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case v:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case g:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function s(t,e,r,n,i,a){if(!n)return null;var o=d(t,e,r,i,n);return o.magFilter=t.NEAREST,o.minFilter=t.NEAREST,o.mipSamples=1,o.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,a,t.TEXTURE_2D,o.handle,0),o}function l(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function u(t){var e=n(t.gl),r=t.gl,a=t.handle=r.createFramebuffer(),u=t._shape[0],c=t._shape[1],h=t.color.length,f=t._ext,d=t._useStencil,p=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,a);for(var v=0;v<h;++v)t.color[v]=s(r,u,c,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===h?(t._color_rb=l(r,u,c,r.RGBA4,r.COLOR_ATTACHMENT0),f&&f.drawBuffersWEBGL(y[0])):h>1&&f.drawBuffersWEBGL(y[h]);var g=r.getExtension(\"WEBGL_depth_texture\");g?d?t.depth=s(r,u,c,g.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p&&(t.depth=s(r,u,c,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):p&&d?t._depth_rb=l(r,u,c,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p?t._depth_rb=l(r,u,c,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=l(r,u,c,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(var v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),i(r,e),o(b)}i(r,e)}function c(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var l=0;l<i;++l)this.color[l]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var c=this,h=[0|e,0|r];Object.defineProperties(h,{0:{get:function(){return c._shape[0]},set:function(t){return c.width=t}},1:{get:function(){return c._shape[1]},set:function(t){return c.height=t}}}),this._shapeVector=h,u(this)}function h(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var a=t.gl,s=a.getParameter(a.MAX_RENDERBUFFER_SIZE);if(e<0||e>s||r<0||r>s)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var l=n(a),u=0;u<t.color.length;++u)t.color[u].shape=t._shape;t._color_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._color_rb),a.renderbufferStorage(a.RENDERBUFFER,a.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&a.renderbufferStorage(a.RENDERBUFFER,a.STENCIL_INDEX,t._shape[0],t._shape[1])),a.bindFramebuffer(a.FRAMEBUFFER,t.handle);var c=a.checkFramebufferStatus(a.FRAMEBUFFER);c!==a.FRAMEBUFFER_COMPLETE&&(t.dispose(),i(a,l),o(c)),i(a,l)}}function f(t,e,r,n){p||(p=t.FRAMEBUFFER_UNSUPPORTED,m=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,v=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,g=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var i=t.getExtension(\"WEBGL_draw_buffers\");if(!y&&i&&a(t,i),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var o=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>o||r<0||r>o)throw new Error(\"gl-fbo: Parameters are too large for FBO\");n=n||{};var s=1;if(\"color\"in n){if((s=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(s>1){if(!i)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(s>t.getParameter(i.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+s+\" draw buffers\")}}var l=t.UNSIGNED_BYTE,u=t.getExtension(\"OES_texture_float\");if(n.float&&s>0){if(!u)throw new Error(\"gl-fbo: Context does not support floating point textures\");l=t.FLOAT}else n.preferFloat&&s>0&&u&&(l=t.FLOAT);var h=!0;\"depth\"in n&&(h=!!n.depth);var f=!1;return\"stencil\"in n&&(f=!!n.stencil),new c(t,e,r,l,s,h,f,i)}var d=t(\"gl-texture2d\");e.exports=f;var p,m,v,g,y=null,b=c.prototype;Object.defineProperties(b,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return h(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return t|=0,h(this,t,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,h(this,this._shape[0],t),t},enumerable:!1}}),b.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},b.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":267}],165:[function(t,e,r){function n(t,e,r){\"use strict\";var n=o(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===a.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var u=i(\"Error compiling %s shader %s:\\n\",l,n),c=i(\"%s%s\",u,t),h=t.split(\"\\n\"),f={},d=0;d<h.length;d++){var p=h[d];if(\"\"!==p){var m=parseInt(p.split(\":\")[2]);if(isNaN(m))throw new Error(i(\"Could not parse error: %s\",p));f[m]=p}}for(var v=s(e).split(\"\\n\"),d=0;d<v.length;d++)if(f[d+3]||f[d+2]||f[d+1]){var g=v[d];if(u+=g+\"\\n\",f[d+1]){var y=f[d+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),u+=i(\"^^^ %s\\n\\n\",y)}}return{long:u.trim(),short:c.trim()}}var i=t(\"sprintf-js\").sprintf,a=t(\"gl-constants/lookup\"),o=t(\"glsl-shader-name\"),s=t(\"add-line-numbers\");e.exports=n},{\"add-line-numbers\":40,\"gl-constants/lookup\":158,\"glsl-shader-name\":279,\"sprintf-js\":527}],166:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}function i(t,e){var r=t.gl,i=l(r,c.vertex,c.fragment),a=l(r,c.pickVertex,c.pickFragment),o=u(r),s=u(r),h=u(r),f=u(r),d=new n(t,i,a,o,s,h,f);return d.update(e),t.addObject(d),d}e.exports=i;var a=t(\"binary-search-bounds\"),o=t(\"iota-array\"),s=t(\"typedarray-pool\"),l=t(\"gl-shader\"),u=t(\"gl-buffer\"),c=t(\"./lib/shaders\"),h=n.prototype,f=[0,0,1,0,0,1,1,0,1,1,0,1];h.draw=function(){var t=[1,0,0,0,1,0,0,0,1];return function(){var e=this.plot,r=this.shader,n=this.bounds,i=this.numVertices;if(!(i<=0)){var a=e.gl,o=e.dataBox,s=n[2]-n[0],l=n[3]-n[1],u=o[2]-o[0],c=o[3]-o[1];t[0]=2*s/u,t[4]=2*l/c,t[6]=2*(n[0]-o[0])/u-1,t[7]=2*(n[1]-o[1])/c-1,r.bind();var h=r.uniforms;h.viewTransform=t,h.shape=this.shape;var f=r.attributes;this.positionBuffer.bind(),f.position.pointer(),this.weightBuffer.bind(),f.weight.pointer(a.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(a.UNSIGNED_BYTE,!0),a.drawArrays(a.TRIANGLES,0,i)}}}(),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2*u/h,t[4]=2*c/f,t[6]=2*(a[0]-l[0])/h-1,t[7]=2*(a[1]-l[1])/f-1;for(var d=0;d<4;++d)e[d]=r>>8*d&255;this.pickOffset=r,i.bind();var p=i.uniforms;p.viewTransform=t,p.pickOffset=e,p.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},h.update=function(t){t=t||{};var e=t.shape||[0,0],r=t.x||o(e[0]),n=t.y||o(e[1]),i=t.z||new Float32Array(e[0]*e[1]);this.xData=r,this.yData=n;var l=t.colorLevels||[0],u=t.colorValues||[0,0,0,1],c=l.length,h=this.bounds,d=h[0]=r[0],p=h[1]=n[0],m=h[2]=r[r.length-1],v=h[3]=n[n.length-1],g=1/(m-d),y=1/(v-p),b=e[0],x=e[1];this.shape=[b,x];var _=(b-1)*(x-1)*(f.length>>>1);this.numVertices=_;for(var w=s.mallocUint8(4*_),M=s.mallocFloat32(2*_),k=s.mallocUint8(2*_),A=s.mallocUint32(_),T=0,S=0;S<x-1;++S)for(var E=y*(n[S]-p),L=y*(n[S+1]-p),C=0;C<b-1;++C)for(var I=g*(r[C]-d),z=g*(r[C+1]-d),D=0;D<f.length;D+=2){var P,O,R,F,j=f[D],N=f[D+1],B=(S+N)*b+(C+j),U=i[B],V=a.le(l,U);if(V<0)P=u[0],O=u[1],R=u[2],F=u[3];else if(V===c-1)P=u[4*c-4],O=u[4*c-3],R=u[4*c-2],F=u[4*c-1];else{var H=(U-l[V])/(l[V+1]-l[V]),q=1-H,G=4*V,Y=4*(V+1);P=q*u[G]+H*u[Y],O=q*u[G+1]+H*u[Y+1],R=q*u[G+2]+H*u[Y+2],F=q*u[G+3]+H*u[Y+3]}w[4*T]=255*P,w[4*T+1]=255*O,w[4*T+2]=255*R,w[4*T+3]=255*F,M[2*T]=.5*I+.5*z,M[2*T+1]=.5*E+.5*L,k[2*T]=j,k[2*T+1]=N,A[T]=S*b+C,T+=1}this.positionBuffer.update(M),this.weightBuffer.update(k),this.colorBuffer.update(w),this.idBuffer.update(A),s.free(M),s.free(w),s.free(k),s.free(A)},h.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":167,\"binary-search-bounds\":168,\"gl-buffer\":156,\"gl-shader\":255,\"iota-array\":293,\"typedarray-pool\":541}],167:[function(t,e,r){\"use strict\";e.exports={fragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\",vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\",pickFragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\",pickVertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"}},{}],168:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],169:[function(t,e,r){r.lineVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi, dLo;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, screenShape;\\nuniform float width;\\n\\nvarying vec2 direction;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n  return (posHi + trHi) * scHi\\n       + (posLo + trLo) * scHi\\n       + (posHi + trHi) * scLo\\n       + (posLo + trLo) * scLo;\\n}\\n\\n\\nvec2 project_2_1(vec2 scHi, vec2 scLo, vec2 posHi, vec2 posLo) {\\n  return scHi * posHi\\n       + scLo * posHi\\n       + scHi * posLo\\n       + scLo * posLo;\\n}\\n\\nvoid main() {\\n  vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n  vec2 dir = project_2_1(scaleHi, scaleLo, dHi, dLo);\\n  vec2 n = 0.5 * width * normalize(screenShape.yx * vec2(dir.y, -dir.x)) / screenShape.xy;\\n  vec2 tangent = normalize(screenShape.xy * dir);\\n  if(dir.x < 0.0 || (dir.x == 0.0 && dir.y < 0.0)) {\\n    direction = -tangent;\\n  } else {\\n    direction = tangent;\\n  }\\n  gl_Position = vec4(p + n, 0.0, 1.0);\\n}\",r.lineFragment=\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nuniform vec2 screenShape;\\nuniform sampler2D dashPattern;\\nuniform float dashLength;\\n\\nvarying vec2 direction;\\n\\nvoid main() {\\n  float t = fract(dot(direction, gl_FragCoord.xy) / dashLength);\\n  vec4 pcolor = color * texture2D(dashPattern, vec2(t, 0.0)).r;\\n  gl_FragColor = vec4(pcolor.rgb * pcolor.a, pcolor.a);\\n}\",r.mitreVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo;\\nuniform float radius;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n  return (posHi + trHi) * scHi\\n       + (posLo + trLo) * scHi\\n       + (posHi + trHi) * scLo\\n       + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n  vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n  gl_Position = vec4(p, 0.0, 1.0);\\n  gl_PointSize = radius;\\n}\",r.mitreFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  if(length(gl_PointCoord.xy - 0.5) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = vec4(color.rgb, color.a);\\n}\",r.pickVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi;\\nattribute vec4 pick0, pick1;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, screenShape;\\nuniform float width;\\n\\nvarying vec4 pickA, pickB;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n  return (posHi + trHi) * scHi\\n       + (posLo + trLo) * scHi\\n       + (posHi + trHi) * scLo\\n       + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n  vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n  vec2 n = width * normalize(screenShape.yx * vec2(dHi.y, -dHi.x)) / screenShape.xy;\\n  gl_Position = vec4(p + n, 0, 1);\\n  pickA = pick0;\\n  pickB = pick1;\\n}\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 pickOffset;\\n\\nvarying vec4 pickA, pickB;\\n\\nvoid main() {\\n  vec4 fragId = vec4(pickA.xyz, 0.0);\\n  if(pickB.w > pickA.w) {\\n    fragId.xyz = pickB.xyz;\\n  }\\n\\n  fragId += pickOffset;\\n\\n  fragId.y += floor(fragId.x / 256.0);\\n  fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n  fragId.z += floor(fragId.y / 256.0);\\n  fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n  fragId.w += floor(fragId.z / 256.0);\\n  fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n  gl_FragColor = fragId / 255.0;\\n}\",r.fillVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, projectAxis;\\nuniform float projectValue, depth;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n  return (posHi + trHi) * scHi\\n       + (posLo + trLo) * scHi\\n       + (posHi + trHi) * scLo\\n       + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n  vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n  if(dHi.y < 0.0 || (dHi.y == 0.0 && dHi.x < 0.0)) {\\n    if(dot(p, projectAxis) < projectValue) {\\n      p = p * (1.0 - abs(projectAxis)) + projectAxis * projectValue;\\n    }\\n  }\\n  gl_Position = vec4(p, depth, 1);\\n}\",r.fillFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = vec4(color.rgb * color.a, color.a);\\n}\"},{}],170:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l){this.plot=t,this.dashPattern=e,this.lineBufferHi=r,this.lineBufferLo=n,this.pickBuffer=i,this.lineShader=a,this.mitreShader=o,this.fillShader=s,this.pickShader=l,this.usingDashes=!1,this.bounds=[1/0,1/0,-1/0,-1/0],this.width=1,this.color=[0,0,1,1],this.fill=[!1,!1,!1,!1],this.fillColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.data=null,this.numPoints=0,this.vertCount=0,this.pickOffset=0}function i(t){return t.map(function(t){return t.slice()})}function a(t,e){var r=t.gl,i=s(r),a=s(r),u=s(r),c=l(r,[1,1]),f=o(r,h.lineVertex,h.lineFragment),d=o(r,h.mitreVertex,h.mitreFragment),p=o(r,h.fillVertex,h.fillFragment),m=o(r,h.pickVertex,h.pickFragment),v=new n(t,c,i,a,u,f,d,p,m);return t.addObject(v),v.update(e),v}e.exports=a;var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"gl-texture2d\"),u=t(\"ndarray\"),c=t(\"typedarray-pool\"),h=t(\"./lib/shaders\"),f=n.prototype;f.setProjectionModel=function(){var t={scaleHi:new Float32Array([0,0]),scaleLo:new Float32Array([0,0]),translateHi:new Float32Array([0,0]),translateLo:new Float32Array([0,0]),screenShape:[0,0]};return function(){var e=this.bounds,r=this.plot.viewBox,n=this.plot.dataBox,i=e[2]-e[0],a=e[3]-e[1],o=n[2]-n[0],s=n[3]-n[1],l=r[2]-r[0],u=r[3]-r[1],c=2*i/o,h=2*a/s,f=(e[0]-n[0]-.5*o)/i,d=(e[1]-n[1]-.5*s)/a;return t.scaleHi[0]=c,t.scaleHi[1]=h,t.scaleLo[0]=c-t.scaleHi[0],t.scaleLo[1]=h-t.scaleHi[1],t.translateHi[0]=f,t.translateHi[1]=d,t.translateLo[0]=f-t.translateHi[0],t.translateLo[1]=d-t.translateHi[1],t.screenShape[0]=l,t.screenShape[1]=u,t}}(),f.setProjectionUniforms=function(t,e){t.scaleHi=e.scaleHi,t.scaleLo=e.scaleLo,t.translateHi=e.translateHi,t.translateLo=e.translateLo,t.screenShape=e.screenShape},f.draw=function(){var t=[1,0],e=[-1,0],r=[0,1],n=[0,-1];return function(){var i=this.vertCount;if(i){var a=this.setProjectionModel(),o=this.plot,s=this.width,l=o.gl,u=o.pixelRatio,c=this.color,h=this.fillShader.attributes;this.lineBufferLo.bind(),h.aLo.pointer(l.FLOAT,!1,16,0),this.lineBufferHi.bind();var f=this.fill;if(f[0]||f[1]||f[2]||f[3]){var d=this.fillShader;d.bind();var p=d.uniforms;this.setProjectionUniforms(p,a),p.depth=o.nextDepthValue(),h.aHi.pointer(l.FLOAT,!1,16,0),h.dHi.pointer(l.FLOAT,!1,16,8),l.depthMask(!0),l.enable(l.DEPTH_TEST);var m=this.fillColor;f[0]&&(p.color=m[0],p.projectAxis=e,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[1]&&(p.color=m[1],p.projectAxis=n,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[2]&&(p.color=m[2],p.projectAxis=t,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[3]&&(p.color=m[3],p.projectAxis=r,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),l.depthMask(!1),l.disable(l.DEPTH_TEST)}var v=this.lineShader;v.bind(),this.lineBufferLo.bind(),v.attributes.aLo.pointer(l.FLOAT,!1,16,0),v.attributes.dLo.pointer(l.FLOAT,!1,16,8),this.lineBufferHi.bind();var g=v.uniforms;this.setProjectionUniforms(g,a),g.color=c,g.width=s*u,g.dashPattern=this.dashPattern.bind(),g.dashLength=this.dashLength*u;var y=v.attributes;if(y.aHi.pointer(l.FLOAT,!1,16,0),y.dHi.pointer(l.FLOAT,!1,16,8),l.drawArrays(l.TRIANGLES,0,i),s>2&&!this.usingDashes){var b=this.mitreShader;this.lineBufferLo.bind(),b.attributes.aLo.pointer(l.FLOAT,!1,48,0),this.lineBufferHi.bind(),b.bind();var x=b.uniforms;this.setProjectionUniforms(x,a),x.color=c,x.radius=s*u,b.attributes.aHi.pointer(l.FLOAT,!1,48,0),l.drawArrays(l.POINTS,0,i/3|0)}}}}(),f.drawPick=function(){var t=[0,0,0,0];return function(e){var r=this.vertCount,n=this.numPoints;if(this.pickOffset=e,!r)return e+n;var i=this.setProjectionModel(),a=this.plot,o=this.width,s=a.gl,l=a.pickPixelRatio,u=this.pickShader,c=this.pickBuffer;t[0]=255&e,t[1]=e>>>8&255,t[2]=e>>>16&255,t[3]=e>>>24,u.bind();var h=u.uniforms;this.setProjectionUniforms(h,i),h.width=o*l,h.pickOffset=t;var f=u.attributes;return this.lineBufferHi.bind(),f.aHi.pointer(s.FLOAT,!1,16,0),f.dHi.pointer(s.FLOAT,!1,16,8),this.lineBufferLo.bind(),f.aLo.pointer(s.FLOAT,!1,16,0),c.bind(),f.pick0.pointer(s.UNSIGNED_BYTE,!1,8,0),f.pick1.pointer(s.UNSIGNED_BYTE,!1,8,4),s.drawArrays(s.TRIANGLES,0,r),e+n}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(r<n||r>=n+i)return null;var a=r-n,o=this.data;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}},f.update=function(t){t=t||{};var e,r,n,a,o,s=this.plot.gl;this.color=(t.color||[0,0,1,1]).slice(),this.width=+(t.width||1),this.fill=(t.fill||[!1,!1,!1,!1]).slice(),this.fillColor=i(t.fillColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var h=t.dashes||[1],f=0;for(e=0;e<h.length;++e)f+=h[e];var d=c.mallocUint8(f);n=0;var p=255;for(e=0;e<h.length;++e){for(r=0;r<h[e];++r)d[n++]=p;p^=255}this.dashPattern.dispose(),this.usingDashes=h.length>1,this.dashPattern=l(s,u(d,[f,1,4],[1,0,0])),this.dashPattern.minFilter=s.NEAREST,this.dashPattern.magFilter=s.NEAREST,this.dashLength=f,c.free(d);var m=t.positions;this.data=m;var v=this.bounds;v[0]=v[1]=1/0,v[2]=v[3]=-1/0;var g=this.numPoints=m.length>>>1;if(0!==g){for(e=0;e<g;++e)a=m[2*e],o=m[2*e+1],isNaN(a)||isNaN(o)||(v[0]=Math.min(v[0],a),v[1]=Math.min(v[1],o),v[2]=Math.max(v[2],a),v[3]=Math.max(v[3],o));v[0]===v[2]&&(v[2]+=1),v[3]===v[1]&&(v[3]+=1);var y=c.mallocFloat64(24*(g-1)),b=c.mallocFloat32(24*(g-1)),x=c.mallocFloat32(24*(g-1)),_=c.mallocUint32(12*(g-1)),w=b.length,M=_.length;n=g;for(var k=0;n>1;){var A=--n;a=m[2*n],o=m[2*n+1];var T=A-1,S=m[2*T],E=m[2*T+1];if(!(isNaN(a)||isNaN(o)||isNaN(S)||isNaN(E))){k+=1,a=(a-v[0])/(v[2]-v[0]),o=(o-v[1])/(v[3]-v[1]),S=(S-v[0])/(v[2]-v[0]),E=(E-v[1])/(v[3]-v[1]);var L=S-a,C=E-o,I=A|1<<24,z=A-1,D=A,P=A-1|1<<24;y[--w]=-C,y[--w]=-L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z,y[--w]=C,y[--w]=L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=-C,y[--w]=-L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=C,y[--w]=L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=-C,y[--w]=-L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z,y[--w]=C,y[--w]=L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z}}for(e=0;e<y.length;e++)b[e]=y[e],x[e]=y[e]-b[e];this.vertCount=6*k,this.lineBufferHi.update(b.subarray(w)),this.lineBufferLo.update(x.subarray(w)),this.pickBuffer.update(_.subarray(M)),c.free(y),c.free(b),c.free(x),c.free(_)}},f.dispose=function(){this.plot.removeObject(this),this.lineBufferLo.dispose(),this.lineBufferHi.dispose(),this.pickBuffer.dispose(),this.lineShader.dispose(),this.mitreShader.dispose(),this.fillShader.dispose(),this.pickShader.dispose(),this.dashPattern.dispose()}},{\"./lib/shaders\":169,\"gl-buffer\":156,\"gl-shader\":255,\"gl-texture2d\":267,ndarray:467,\"typedarray-pool\":541}],171:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvoid main() {\\n  vec4 projected = projection * view * model * vec4(position, 1.0);\\n  vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\\n  vec2 tangent = normalize(screenShape * tangentClip.xy);\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\\n\\n  gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\",a=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return n(t,i,\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\\n    discard;\\n  }\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\",null,a)},r.createPickShader=function(t){return n(t,i,\"precision mediump float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\nlowp vec4 encode_float_1_0(highp float v) {\\n  highp float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  highp vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  highp float e = floor(log2(av));\\n  highp float m = av * pow(2.0, -e) - 1.0;\\n  \\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n  \\n  //Unpack exponent\\n  highp float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0; \\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\n\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\\n    discard;\\n  }\\n  gl_FragColor = vec4(pickId/255.0, encode_float_1_0(pixelArcLength).xyz);\\n}\",null,a)}},{\"gl-shader\":255}],172:[function(t,e,r){\"use strict\";function n(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function i(t){\n",
+       "for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function a(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function o(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}function s(t){var e=t.gl||t.scene&&t.scene.gl,r=m(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var n=v(e);n.attributes.position.location=0,n.attributes.nextPosition.location=1,n.attributes.arcLength.location=2,n.attributes.lineWidth.location=3,n.attributes.color.location=4;for(var i=l(e),a=u(e,[{buffer:i,size:3,offset:0,stride:48},{buffer:i,size:3,offset:12,stride:48},{buffer:i,size:1,offset:24,stride:48},{buffer:i,size:1,offset:28,stride:48},{buffer:i,size:4,offset:32,stride:48}]),s=d(new Array(1024),[256,1,4]),h=0;h<1024;++h)s.data[h]=255;var f=c(e,s);f.wrap=e.REPEAT;var p=new o(e,r,n,i,a,f);return p.update(t),p}e.exports=s;var l=t(\"gl-buffer\"),u=t(\"gl-vao\"),c=t(\"gl-texture2d\"),h=t(\"glsl-read-float\"),f=t(\"binary-search-bounds\"),d=t(\"ndarray\"),p=t(\"./lib/shaders\"),m=p.createShader,v=p.createPickShader,g=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],y=o.prototype;y.isTransparent=function(){return this.opacity<1},y.isOpaque=function(){return this.opacity>=1},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||g,view:t.view||g,projection:t.projection||g,clipBounds:i(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||g,view:t.view||g,projection:t.projection||g,pickId:this.pickId,clipBounds:i(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.update=function(t){var e,r;this.dirty=!0;var i=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),\"opacity\"in t&&(this.opacity=+t.opacity);var a=t.position||t.positions;if(a){var o=t.color||t.colors||[0,0,0,1],s=t.lineWidth||1,l=[],u=[],c=[],h=0,p=0,m=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],v=!1;t:for(e=1;e<a.length;++e){var g=a[e-1],y=a[e];for(u.push(h),c.push(g.slice()),r=0;r<3;++r){if(isNaN(g[r])||isNaN(y[r])||!isFinite(g[r])||!isFinite(y[r])){if(!i&&l.length>0){for(var b=0;b<24;++b)l.push(l[l.length-12]);p+=2,v=!0}continue t}m[0][r]=Math.min(m[0][r],g[r],y[r]),m[1][r]=Math.max(m[1][r],g[r],y[r])}var x,_;Array.isArray(o[0])?(x=o[e-1],_=o[e]):x=_=o,3===x.length&&(x=[x[0],x[1],x[2],1]),3===_.length&&(_=[_[0],_[1],_[2],1]);var w;w=Array.isArray(s)?s[e-1]:s;var M=h;if(h+=n(g,y),v){for(r=0;r<2;++r)l.push(g[0],g[1],g[2],y[0],y[1],y[2],M,w,x[0],x[1],x[2],x[3]);p+=2,v=!1}l.push(g[0],g[1],g[2],y[0],y[1],y[2],M,w,x[0],x[1],x[2],x[3],g[0],g[1],g[2],y[0],y[1],y[2],M,-w,x[0],x[1],x[2],x[3],y[0],y[1],y[2],g[0],g[1],g[2],h,-w,_[0],_[1],_[2],_[3],y[0],y[1],y[2],g[0],g[1],g[2],h,w,_[0],_[1],_[2],_[3]),p+=4}if(this.buffer.update(l),u.push(h),c.push(a[a.length-1].slice()),this.bounds=m,this.vertexCount=p,this.points=c,this.arcLength=u,\"dashes\"in t){var k=t.dashes,A=k.slice();for(A.unshift(0),e=1;e<A.length;++e)A[e]=A[e-1]+A[e];var T=d(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)T.set(e,0,r,0);1&f.le(A,A[A.length-1]*e/255)?T.set(e,0,0,0):T.set(e,0,0,255)}this.texture.setPixels(T)}}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=h(t.value[0],t.value[1],t.value[2],0),r=f.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new a(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],o=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),s=1-o,l=[0,0,0],u=0;u<3;++u)l[u]=s*n[u]+o*i[u];var c=Math.min(o<.5?r:r+1,this.points.length-1);return new a(e,l,c,this.points[c])}},{\"./lib/shaders\":171,\"binary-search-bounds\":66,\"gl-buffer\":156,\"gl-texture2d\":267,\"gl-vao\":271,\"glsl-read-float\":278,ndarray:467}],173:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r*a-i*n;return o?(o=1/o,t[0]=a*o,t[1]=-n*o,t[2]=-i*o,t[3]=r*o,t):null}e.exports=n},{}],174:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=c*o-s*u,f=-c*a+s*l,d=u*a-o*l,p=r*h+n*f+i*d;return p?(p=1/p,t[0]=h*p,t[1]=(-c*n+i*u)*p,t[2]=(s*n-i*o)*p,t[3]=f*p,t[4]=(c*r-i*l)*p,t[5]=(-s*r+i*a)*p,t[6]=d*p,t[7]=(-u*r+n*l)*p,t[8]=(o*r-n*a)*p,t):null}e.exports=n},{}],175:[function(t,e,r){function n(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}e.exports=n},{}],176:[function(t,e,r){function n(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],177:[function(t,e,r){function n(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],h=t[10],f=t[11],d=t[12],p=t[13],m=t[14],v=t[15];return(e*o-r*a)*(h*v-f*m)-(e*s-n*a)*(c*v-f*p)+(e*l-i*a)*(c*m-h*p)+(r*s-n*o)*(u*v-f*d)-(r*l-i*o)*(u*m-h*d)+(n*l-i*s)*(u*p-c*d)}e.exports=n},{}],178:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,h=n*s,f=i*o,d=i*s,p=i*l,m=a*o,v=a*s,g=a*l;return t[0]=1-h-p,t[1]=c+g,t[2]=f-v,t[3]=0,t[4]=c-g,t[5]=1-u-p,t[6]=d+m,t[7]=0,t[8]=f+v,t[9]=d-m,t[10]=1-u-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],179:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,h=n*l,f=n*u,d=i*l,p=i*u,m=a*u,v=o*s,g=o*l,y=o*u;return t[0]=1-(d+m),t[1]=h+y,t[2]=f-g,t[3]=0,t[4]=h-y,t[5]=1-(c+m),t[6]=p+v,t[7]=0,t[8]=f+g,t[9]=p-v,t[10]=1-(c+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}e.exports=n},{}],180:[function(t,e,r){function n(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],181:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15],y=r*s-n*o,b=r*l-i*o,x=r*u-a*o,_=n*l-i*s,w=n*u-a*s,M=i*u-a*l,k=c*m-h*p,A=c*v-f*p,T=c*g-d*p,S=h*v-f*m,E=h*g-d*m,L=f*g-d*v,C=y*L-b*E+x*S+_*T-w*A+M*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*M-v*w+g*_)*C,t[3]=(f*w-h*M-d*_)*C,t[4]=(l*T-o*L-u*A)*C,t[5]=(r*L-i*T+a*A)*C,t[6]=(v*x-p*M-g*b)*C,t[7]=(c*M-f*x+d*b)*C,t[8]=(o*E-s*T+u*k)*C,t[9]=(n*T-r*E-a*k)*C,t[10]=(p*w-m*x+g*y)*C,t[11]=(h*x-c*w-d*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*b-p*_-v*y)*C,t[15]=(c*_-h*b+f*y)*C,t):null}e.exports=n},{}],182:[function(t,e,r){function n(t,e,r,n){var a,o,s,l,u,c,h,f,d,p,m=e[0],v=e[1],g=e[2],y=n[0],b=n[1],x=n[2],_=r[0],w=r[1],M=r[2];return Math.abs(m-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(g-M)<1e-6?i(t):(h=m-_,f=v-w,d=g-M,p=1/Math.sqrt(h*h+f*f+d*d),h*=p,f*=p,d*=p,a=b*d-x*f,o=x*h-y*d,s=y*f-b*h,p=Math.sqrt(a*a+o*o+s*s),p?(p=1/p,a*=p,o*=p,s*=p):(a=0,o=0,s=0),l=f*s-d*o,u=d*a-h*s,c=h*o-f*a,p=Math.sqrt(l*l+u*u+c*c),p?(p=1/p,l*=p,u*=p,c*=p):(l=0,u=0,c=0),t[0]=a,t[1]=l,t[2]=h,t[3]=0,t[4]=o,t[5]=u,t[6]=f,t[7]=0,t[8]=s,t[9]=c,t[10]=d,t[11]=0,t[12]=-(a*m+o*v+s*g),t[13]=-(l*m+u*v+c*g),t[14]=-(h*m+f*v+d*g),t[15]=1,t)}var i=t(\"./identity\");e.exports=n},{\"./identity\":180}],183:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],m=e[12],v=e[13],g=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*h+w*m,t[1]=b*i+x*l+_*f+w*v,t[2]=b*a+x*u+_*d+w*g,t[3]=b*o+x*c+_*p+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*h+w*m,t[5]=b*i+x*l+_*f+w*v,t[6]=b*a+x*u+_*d+w*g,t[7]=b*o+x*c+_*p+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*h+w*m,t[9]=b*i+x*l+_*f+w*v,t[10]=b*a+x*u+_*d+w*g,t[11]=b*o+x*c+_*p+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*h+w*m,t[13]=b*i+x*l+_*f+w*v,t[14]=b*a+x*u+_*d+w*g,t[15]=b*o+x*c+_*p+w*y,t}e.exports=n},{}],184:[function(t,e,r){function n(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}e.exports=n},{}],185:[function(t,e,r){function n(t,e,r,n){var i,a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T,S,E=n[0],L=n[1],C=n[2],I=Math.sqrt(E*E+L*L+C*C);return Math.abs(I)<1e-6?null:(I=1/I,E*=I,L*=I,C*=I,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],u=e[2],c=e[3],h=e[4],f=e[5],d=e[6],p=e[7],m=e[8],v=e[9],g=e[10],y=e[11],b=E*E*o+a,x=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,M=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,T=L*C*o-E*i,S=C*C*o+a,t[0]=s*b+h*x+m*_,t[1]=l*b+f*x+v*_,t[2]=u*b+d*x+g*_,t[3]=c*b+p*x+y*_,t[4]=s*w+h*M+m*k,t[5]=l*w+f*M+v*k,t[6]=u*w+d*M+g*k,t[7]=c*w+p*M+y*k,t[8]=s*A+h*T+m*S,t[9]=l*A+f*T+v*S,t[10]=u*A+d*T+g*S,t[11]=c*A+p*T+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)}e.exports=n},{}],186:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=a*i+u*n,t[5]=o*i+c*n,t[6]=s*i+h*n,t[7]=l*i+f*n,t[8]=u*i-a*n,t[9]=c*i-o*n,t[10]=h*i-s*n,t[11]=f*i-l*n,t}e.exports=n},{}],187:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-h*n,t[3]=l*i-f*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+h*i,t[11]=l*n+f*i,t}e.exports=n},{}],188:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],h=e[6],f=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+h*n,t[3]=l*i+f*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=h*i-s*n,t[7]=f*i-l*n,t}e.exports=n},{}],189:[function(t,e,r){function n(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}e.exports=n},{}],190:[function(t,e,r){function n(t,e,r){var n,i,a,o,s,l,u,c,h,f,d,p,m=r[0],v=r[1],g=r[2];return e===t?(t[12]=e[0]*m+e[4]*v+e[8]*g+e[12],t[13]=e[1]*m+e[5]*v+e[9]*g+e[13],t[14]=e[2]*m+e[6]*v+e[10]*g+e[14],t[15]=e[3]*m+e[7]*v+e[11]*g+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=h,t[9]=f,t[10]=d,t[11]=p,t[12]=n*m+s*v+h*g+e[12],t[13]=i*m+l*v+f*g+e[13],t[14]=a*m+u*v+d*g+e[14],t[15]=o*m+c*v+p*g+e[15]),t}e.exports=n},{}],191:[function(t,e,r){function n(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}e.exports=n},{}],192:[function(t,e,r){\"use strict\";function n(t,e){switch(e.length){case 0:break;case 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r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8];return t[0]=o*c-s*u,t[1]=i*u-n*c,t[2]=n*s-i*o,t[3]=s*l-a*c,t[4]=r*c-i*l,t[5]=i*a-r*s,t[6]=a*u-o*l,t[7]=n*l-r*u,t[8]=r*o-n*a,t},i.determinant=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8];return e*(u*a-o*l)+r*(-u*i+o*s)+n*(l*i-a*s)},i.multiply=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=r[0],d=r[1],p=r[2],m=r[3],v=r[4],g=r[5],y=r[6],b=r[7],x=r[8];return t[0]=f*n+d*o+p*u,t[1]=f*i+d*s+p*c,t[2]=f*a+d*l+p*h,t[3]=m*n+v*o+g*u,t[4]=m*i+v*s+g*c,t[5]=m*a+v*l+g*h,t[6]=y*n+b*o+x*u,t[7]=y*i+b*s+x*c,t[8]=y*a+b*l+x*h,t},i.mul=i.multiply,i.translate=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=r[0],d=r[1];return t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=f*n+d*o+u,t[7]=f*i+d*s+c,t[8]=f*a+d*l+h,t},i.rotate=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=Math.sin(r),d=Math.cos(r);return t[0]=d*n+f*o,t[1]=d*i+f*s,t[2]=d*a+f*l,t[3]=d*o-f*n,t[4]=d*s-f*i,t[5]=d*l-f*a,t[6]=u,t[7]=c,t[8]=h,t},i.scale=function(t,e,r){var n=r[0],i=r[1];return t[0]=n*e[0],t[1]=n*e[1],t[2]=n*e[2],t[3]=i*e[3],t[4]=i*e[4],t[5]=i*e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t},i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=1,t[5]=0,t[6]=e[0],t[7]=e[1],t[8]=1,t},i.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=e[1],t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},i.fromMat2d=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=0,t[3]=e[2],t[4]=e[3],t[5]=0,t[6]=e[4],t[7]=e[5],t[8]=1,t},i.fromQuat=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,h=n*s,f=i*o,d=i*s,p=i*l,m=a*o,v=a*s,g=a*l;return t[0]=1-h-p,t[3]=c-g,t[6]=f+v,t[1]=c+g,t[4]=1-u-p,t[7]=d-m,t[2]=f-v,t[5]=d+m,t[8]=1-u-h,t},i.normalFromMat4=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15],y=r*s-n*o,b=r*l-i*o,x=r*u-a*o,_=n*l-i*s,w=n*u-a*s,M=i*u-a*l,k=c*m-h*p,A=c*v-f*p,T=c*g-d*p,S=h*v-f*m,E=h*g-d*m,L=f*g-d*v,C=y*L-b*E+x*S+_*T-w*A+M*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(l*T-o*L-u*A)*C,t[2]=(o*E-s*T+u*k)*C,t[3]=(i*E-n*L-a*S)*C,t[4]=(r*L-i*T+a*A)*C,t[5]=(n*T-r*E-a*k)*C,t[6]=(m*M-v*w+g*_)*C,t[7]=(v*x-p*M-g*b)*C,t[8]=(p*w-m*x+g*y)*C,t):null},i.str=function(t){return\"mat3(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\")\"},i.frob=function(t){return Math.sqrt(Math.pow(t[0],2)+Math.pow(t[1],2)+Math.pow(t[2],2)+Math.pow(t[3],2)+Math.pow(t[4],2)+Math.pow(t[5],2)+Math.pow(t[6],2)+Math.pow(t[7],2)+Math.pow(t[8],2))},i.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t[3]=e[3]+r[3],t[4]=e[4]+r[4],t[5]=e[5]+r[5],t[6]=e[6]+r[6],t[7]=e[7]+r[7],t[8]=e[8]+r[8],t},i.subtract=function(t,e,r){return t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t[3]=e[3]-r[3],t[4]=e[4]-r[4],t[5]=e[5]-r[5],t[6]=e[6]-r[6],t[7]=e[7]-r[7],t[8]=e[8]-r[8],t},i.sub=i.subtract,i.multiplyScalar=function(t,e,r){return t[0]=e[0]*r,t[1]=e[1]*r,t[2]=e[2]*r,t[3]=e[3]*r,t[4]=e[4]*r,t[5]=e[5]*r,t[6]=e[6]*r,t[7]=e[7]*r,t[8]=e[8]*r,t},i.multiplyScalarAndAdd=function(t,e,r,n){return t[0]=e[0]+r[0]*n,t[1]=e[1]+r[1]*n,t[2]=e[2]+r[2]*n,t[3]=e[3]+r[3]*n,t[4]=e[4]+r[4]*n,t[5]=e[5]+r[5]*n,t[6]=e[6]+r[6]*n,t[7]=e[7]+r[7]*n,t[8]=e[8]+r[8]*n,t},i.exactEquals=function(t,e){return t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]&&t[4]===e[4]&&t[5]===e[5]&&t[6]===e[6]&&t[7]===e[7]&&t[8]===e[8]},i.equals=function(t,e){var r=t[0],i=t[1],a=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],h=t[8],f=e[0],d=e[1],p=e[2],m=e[3],v=e[4],g=e[5],y=t[6],b=e[7],x=e[8];return 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e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},i.copy=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.fromValues=function(t,e,r,i,a,o,s,l,u,c,h,f,d,p,m,v){var g=new n.ARRAY_TYPE(16);return g[0]=t,g[1]=e,g[2]=r,g[3]=i,g[4]=a,g[5]=o,g[6]=s,g[7]=l,g[8]=u,g[9]=c,g[10]=h,g[11]=f,g[12]=d,g[13]=p,g[14]=m,g[15]=v,g},i.set=function(t,e,r,n,i,a,o,s,l,u,c,h,f,d,p,m,v){return t[0]=e,t[1]=r,t[2]=n,t[3]=i,t[4]=a,t[5]=o,t[6]=s,t[7]=l,t[8]=u,t[9]=c,t[10]=h,t[11]=f,t[12]=d,t[13]=p,t[14]=m,t[15]=v,t},i.identity=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.scalar.transpose=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t},i.SIMD.transpose=function(t,e){var r,n,i,a,o,s,l,u,c,h;return r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12),o=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,a,0,1,4,5),l=SIMD.Float32x4.shuffle(o,s,0,2,4,6),u=SIMD.Float32x4.shuffle(o,s,1,3,5,7),SIMD.Float32x4.store(t,0,l),SIMD.Float32x4.store(t,4,u),o=SIMD.Float32x4.shuffle(r,n,2,3,6,7),s=SIMD.Float32x4.shuffle(i,a,2,3,6,7),c=SIMD.Float32x4.shuffle(o,s,0,2,4,6),h=SIMD.Float32x4.shuffle(o,s,1,3,5,7),SIMD.Float32x4.store(t,8,c),SIMD.Float32x4.store(t,12,h),t},i.transpose=n.USE_SIMD?i.SIMD.transpose:i.scalar.transpose,i.scalar.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15],y=r*s-n*o,b=r*l-i*o,x=r*u-a*o,_=n*l-i*s,w=n*u-a*s,M=i*u-a*l,k=c*m-h*p,A=c*v-f*p,T=c*g-d*p,S=h*v-f*m,E=h*g-d*m,L=f*g-d*v,C=y*L-b*E+x*S+_*T-w*A+M*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*M-v*w+g*_)*C,t[3]=(f*w-h*M-d*_)*C,t[4]=(l*T-o*L-u*A)*C,t[5]=(r*L-i*T+a*A)*C,t[6]=(v*x-p*M-g*b)*C,t[7]=(c*M-f*x+d*b)*C,t[8]=(o*E-s*T+u*k)*C,t[9]=(n*T-r*E-a*k)*C,t[10]=(p*w-m*x+g*y)*C,t[11]=(h*x-c*w-d*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*b-p*_-v*y)*C,t[15]=(c*_-h*b+f*y)*C,t):null},i.SIMD.invert=function(t,e){var r,n,i,a,o,s,l,u,c,h,f=SIMD.Float32x4.load(e,0),d=SIMD.Float32x4.load(e,4),p=SIMD.Float32x4.load(e,8),m=SIMD.Float32x4.load(e,12);return o=SIMD.Float32x4.shuffle(f,d,0,1,4,5),n=SIMD.Float32x4.shuffle(p,m,0,1,4,5),r=SIMD.Float32x4.shuffle(o,n,0,2,4,6),n=SIMD.Float32x4.shuffle(n,o,1,3,5,7),o=SIMD.Float32x4.shuffle(f,d,2,3,6,7),a=SIMD.Float32x4.shuffle(p,m,2,3,6,7),i=SIMD.Float32x4.shuffle(o,a,0,2,4,6),a=SIMD.Float32x4.shuffle(a,o,1,3,5,7),o=SIMD.Float32x4.mul(i,a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),s=SIMD.Float32x4.mul(n,o),l=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(SIMD.Float32x4.mul(n,o),s),l=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),l),l=SIMD.Float32x4.swizzle(l,2,3,0,1),o=SIMD.Float32x4.mul(n,i),o=SIMD.Float32x4.swizzle(o,1,0,3,2),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),s),c=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(a,o)),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),o=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(n,2,3,0,1),a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),i=SIMD.Float32x4.swizzle(i,2,3,0,1),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,o),s),u=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(i,o)),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),u),u=SIMD.Float32x4.swizzle(u,2,3,0,1),o=SIMD.Float32x4.mul(r,n),o=SIMD.Float32x4.swizzle(o,1,0,3,2),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),u),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(i,o),c),o=SIMD.Float32x4.swizzle(o,2,3,0,1),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,o),u),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(i,o)),o=SIMD.Float32x4.mul(r,a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(i,o)),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,o),u),o=SIMD.Float32x4.swizzle(o,2,3,0,1),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,o),l),u=SIMD.Float32x4.sub(u,SIMD.Float32x4.mul(n,o)),o=SIMD.Float32x4.mul(r,i),o=SIMD.Float32x4.swizzle(o,1,0,3,2),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),l),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(n,o)),o=SIMD.Float32x4.swizzle(o,2,3,0,1),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(a,o)),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,o),c),h=SIMD.Float32x4.mul(r,s),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,2,3,0,1),h),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,1,0,3,2),h),o=SIMD.Float32x4.reciprocalApproximation(h),h=SIMD.Float32x4.sub(SIMD.Float32x4.add(o,o),SIMD.Float32x4.mul(h,SIMD.Float32x4.mul(o,o))),(h=SIMD.Float32x4.swizzle(h,0,0,0,0))?(SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(h,s)),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(h,l)),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(h,u)),SIMD.Float32x4.store(t,12,SIMD.Float32x4.mul(h,c)),t):null},i.invert=n.USE_SIMD?i.SIMD.invert:i.scalar.invert,i.scalar.adjoint=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15];return t[0]=s*(f*g-d*v)-h*(l*g-u*v)+m*(l*d-u*f),t[1]=-(n*(f*g-d*v)-h*(i*g-a*v)+m*(i*d-a*f)),t[2]=n*(l*g-u*v)-s*(i*g-a*v)+m*(i*u-a*l),t[3]=-(n*(l*d-u*f)-s*(i*d-a*f)+h*(i*u-a*l)),t[4]=-(o*(f*g-d*v)-c*(l*g-u*v)+p*(l*d-u*f)),t[5]=r*(f*g-d*v)-c*(i*g-a*v)+p*(i*d-a*f),t[6]=-(r*(l*g-u*v)-o*(i*g-a*v)+p*(i*u-a*l)),t[7]=r*(l*d-u*f)-o*(i*d-a*f)+c*(i*u-a*l),t[8]=o*(h*g-d*m)-c*(s*g-u*m)+p*(s*d-u*h),t[9]=-(r*(h*g-d*m)-c*(n*g-a*m)+p*(n*d-a*h)),t[10]=r*(s*g-u*m)-o*(n*g-a*m)+p*(n*u-a*s),t[11]=-(r*(s*d-u*h)-o*(n*d-a*h)+c*(n*u-a*s)),t[12]=-(o*(h*v-f*m)-c*(s*v-l*m)+p*(s*f-l*h)),t[13]=r*(h*v-f*m)-c*(n*v-i*m)+p*(n*f-i*h),t[14]=-(r*(s*v-l*m)-o*(n*v-i*m)+p*(n*l-i*s)),t[15]=r*(s*f-l*h)-o*(n*f-i*h)+c*(n*l-i*s),t},i.SIMD.adjoint=function(t,e){var r,n,i,a,o,s,l,u,c,h,f,d,p,r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12)\n",
+       ";return c=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,a,0,1,4,5),o=SIMD.Float32x4.shuffle(c,s,0,2,4,6),s=SIMD.Float32x4.shuffle(s,c,1,3,5,7),c=SIMD.Float32x4.shuffle(r,n,2,3,6,7),u=SIMD.Float32x4.shuffle(i,a,2,3,6,7),l=SIMD.Float32x4.shuffle(c,u,0,2,4,6),u=SIMD.Float32x4.shuffle(u,c,1,3,5,7),c=SIMD.Float32x4.mul(l,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.mul(s,c),f=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(SIMD.Float32x4.mul(s,c),h),f=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),f),f=SIMD.Float32x4.swizzle(f,2,3,0,1),c=SIMD.Float32x4.mul(s,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),h),p=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(u,c)),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),p),p=SIMD.Float32x4.swizzle(p,2,3,0,1),c=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,3,0,1),u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),l=SIMD.Float32x4.swizzle(l,2,3,0,1),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),h),d=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(l,c)),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),d),d=SIMD.Float32x4.swizzle(d,2,3,0,1),c=SIMD.Float32x4.mul(o,s),c=SIMD.Float32x4.swizzle(c,1,0,3,2),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),d),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(l,c),p),c=SIMD.Float32x4.swizzle(c,2,3,0,1),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(u,c),d),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(l,c)),c=SIMD.Float32x4.mul(o,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(l,c)),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),d),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),f),d=SIMD.Float32x4.sub(d,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.mul(o,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),f),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(u,c)),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),p),SIMD.Float32x4.store(t,0,h),SIMD.Float32x4.store(t,4,f),SIMD.Float32x4.store(t,8,d),SIMD.Float32x4.store(t,12,p),t},i.adjoint=n.USE_SIMD?i.SIMD.adjoint:i.scalar.adjoint,i.determinant=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],h=t[10],f=t[11],d=t[12],p=t[13],m=t[14],v=t[15];return(e*o-r*a)*(h*v-f*m)-(e*s-n*a)*(c*v-f*p)+(e*l-i*a)*(c*m-h*p)+(r*s-n*o)*(u*v-f*d)-(r*l-i*o)*(u*m-h*d)+(n*l-i*s)*(u*p-c*d)},i.SIMD.multiply=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4.load(r,0),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,3,3,3,3),o))));SIMD.Float32x4.store(t,0,l);var u=SIMD.Float32x4.load(r,4),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,3,3,3,3),o))));SIMD.Float32x4.store(t,4,c);var h=SIMD.Float32x4.load(r,8),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,3,3,3,3),o))));SIMD.Float32x4.store(t,8,f);var d=SIMD.Float32x4.load(r,12),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,3,3,3,3),o))));return SIMD.Float32x4.store(t,12,p),t},i.scalar.multiply=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],m=e[12],v=e[13],g=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*h+w*m,t[1]=b*i+x*l+_*f+w*v,t[2]=b*a+x*u+_*d+w*g,t[3]=b*o+x*c+_*p+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*h+w*m,t[5]=b*i+x*l+_*f+w*v,t[6]=b*a+x*u+_*d+w*g,t[7]=b*o+x*c+_*p+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*h+w*m,t[9]=b*i+x*l+_*f+w*v,t[10]=b*a+x*u+_*d+w*g,t[11]=b*o+x*c+_*p+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*h+w*m,t[13]=b*i+x*l+_*f+w*v,t[14]=b*a+x*u+_*d+w*g,t[15]=b*o+x*c+_*p+w*y,t},i.multiply=n.USE_SIMD?i.SIMD.multiply:i.scalar.multiply,i.mul=i.multiply,i.scalar.translate=function(t,e,r){var n,i,a,o,s,l,u,c,h,f,d,p,m=r[0],v=r[1],g=r[2];return e===t?(t[12]=e[0]*m+e[4]*v+e[8]*g+e[12],t[13]=e[1]*m+e[5]*v+e[9]*g+e[13],t[14]=e[2]*m+e[6]*v+e[10]*g+e[14],t[15]=e[3]*m+e[7]*v+e[11]*g+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=h,t[9]=f,t[10]=d,t[11]=p,t[12]=n*m+s*v+h*g+e[12],t[13]=i*m+l*v+f*g+e[13],t[14]=a*m+u*v+d*g+e[14],t[15]=o*m+c*v+p*g+e[15]),t},i.SIMD.translate=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4(r[0],r[1],r[2],0);e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11]),n=SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(s,0,0,0,0)),i=SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(s,1,1,1,1)),a=SIMD.Float32x4.mul(a,SIMD.Float32x4.swizzle(s,2,2,2,2));var l=SIMD.Float32x4.add(n,SIMD.Float32x4.add(i,SIMD.Float32x4.add(a,o)));return SIMD.Float32x4.store(t,12,l),t},i.translate=n.USE_SIMD?i.SIMD.translate:i.scalar.translate,i.scalar.scale=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.SIMD.scale=function(t,e,r){var n,i,a,o=SIMD.Float32x4(r[0],r[1],r[2],0);return n=SIMD.Float32x4.load(e,0),SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(o,0,0,0,0))),i=SIMD.Float32x4.load(e,4),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(o,1,1,1,1))),a=SIMD.Float32x4.load(e,8),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(a,SIMD.Float32x4.swizzle(o,2,2,2,2))),t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.scale=n.USE_SIMD?i.SIMD.scale:i.scalar.scale,i.rotate=function(t,e,r,i){var a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T,S,E,L=i[0],C=i[1],I=i[2],z=Math.sqrt(L*L+C*C+I*I);return Math.abs(z)<n.EPSILON?null:(z=1/z,L*=z,C*=z,I*=z,a=Math.sin(r),o=Math.cos(r),s=1-o,l=e[0],u=e[1],c=e[2],h=e[3],f=e[4],d=e[5],p=e[6],m=e[7],v=e[8],g=e[9],y=e[10],b=e[11],x=L*L*s+o,_=C*L*s+I*a,w=I*L*s-C*a,M=L*C*s-I*a,k=C*C*s+o,A=I*C*s+L*a,T=L*I*s+C*a,S=C*I*s-L*a,E=I*I*s+o,t[0]=l*x+f*_+v*w,t[1]=u*x+d*_+g*w,t[2]=c*x+p*_+y*w,t[3]=h*x+m*_+b*w,t[4]=l*M+f*k+v*A,t[5]=u*M+d*k+g*A,t[6]=c*M+p*k+y*A,t[7]=h*M+m*k+b*A,t[8]=l*T+f*S+v*E,t[9]=u*T+d*S+g*E,t[10]=c*T+p*S+y*E,t[11]=h*T+m*S+b*E,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)},i.scalar.rotateX=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=a*i+u*n,t[5]=o*i+c*n,t[6]=s*i+h*n,t[7]=l*i+f*n,t[8]=u*i-a*n,t[9]=c*i-o*n,t[10]=h*i-s*n,t[11]=f*i-l*n,t},i.SIMD.rotateX=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,4),o=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,4,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),t},i.rotateX=n.USE_SIMD?i.SIMD.rotateX:i.scalar.rotateX,i.scalar.rotateY=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-h*n,t[3]=l*i-f*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+h*i,t[11]=l*n+f*i,t},i.SIMD.rotateY=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,0),o=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,n),SIMD.Float32x4.mul(o,i))),t},i.rotateY=n.USE_SIMD?i.SIMD.rotateY:i.scalar.rotateY,i.scalar.rotateZ=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],h=e[6],f=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+h*n,t[3]=l*i+f*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=h*i-s*n,t[7]=f*i-l*n,t},i.SIMD.rotateZ=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,0),o=SIMD.Float32x4.load(e,4);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,4,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),t},i.rotateZ=n.USE_SIMD?i.SIMD.rotateZ:i.scalar.rotateZ,i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotation=function(t,e,r){var i,a,o,s=r[0],l=r[1],u=r[2],c=Math.sqrt(s*s+l*l+u*u);return Math.abs(c)<n.EPSILON?null:(c=1/c,s*=c,l*=c,u*=c,i=Math.sin(e),a=Math.cos(e),o=1-a,t[0]=s*s*o+a,t[1]=l*s*o+u*i,t[2]=u*s*o-l*i,t[3]=0,t[4]=s*l*o-u*i,t[5]=l*l*o+a,t[6]=u*l*o+s*i,t[7]=0,t[8]=s*u*o+l*i,t[9]=l*u*o-s*i,t[10]=u*u*o+a,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t)},i.fromXRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromYRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromZRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotationTranslation=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,h=n*l,f=n*u,d=i*l,p=i*u,m=a*u,v=o*s,g=o*l,y=o*u;return t[0]=1-(d+m),t[1]=h+y,t[2]=f-g,t[3]=0,t[4]=h-y,t[5]=1-(c+m),t[6]=p+v,t[7]=0,t[8]=f+g,t[9]=p-v,t[10]=1-(c+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.getTranslation=function(t,e){return t[0]=e[12],t[1]=e[13],t[2]=e[14],t},i.getRotation=function(t,e){var r=e[0]+e[5]+e[10],n=0;return r>0?(n=2*Math.sqrt(r+1),t[3]=.25*n,t[0]=(e[6]-e[9])/n,t[1]=(e[8]-e[2])/n,t[2]=(e[1]-e[4])/n):e[0]>e[5]&e[0]>e[10]?(n=2*Math.sqrt(1+e[0]-e[5]-e[10]),t[3]=(e[6]-e[9])/n,t[0]=.25*n,t[1]=(e[1]+e[4])/n,t[2]=(e[8]+e[2])/n):e[5]>e[10]?(n=2*Math.sqrt(1+e[5]-e[0]-e[10]),t[3]=(e[8]-e[2])/n,t[0]=(e[1]+e[4])/n,t[1]=.25*n,t[2]=(e[6]+e[9])/n):(n=2*Math.sqrt(1+e[10]-e[0]-e[5]),t[3]=(e[1]-e[4])/n,t[0]=(e[8]+e[2])/n,t[1]=(e[6]+e[9])/n,t[2]=.25*n),t},i.fromRotationTranslationScale=function(t,e,r,n){var i=e[0],a=e[1],o=e[2],s=e[3],l=i+i,u=a+a,c=o+o,h=i*l,f=i*u,d=i*c,p=a*u,m=a*c,v=o*c,g=s*l,y=s*u,b=s*c,x=n[0],_=n[1],w=n[2];return t[0]=(1-(p+v))*x,t[1]=(f+b)*x,t[2]=(d-y)*x,t[3]=0,t[4]=(f-b)*_,t[5]=(1-(h+v))*_,t[6]=(m+g)*_,t[7]=0,t[8]=(d+y)*w,t[9]=(m-g)*w,t[10]=(1-(h+p))*w,t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.fromRotationTranslationScaleOrigin=function(t,e,r,n,i){var a=e[0],o=e[1],s=e[2],l=e[3],u=a+a,c=o+o,h=s+s,f=a*u,d=a*c,p=a*h,m=o*c,v=o*h,g=s*h,y=l*u,b=l*c,x=l*h,_=n[0],w=n[1],M=n[2],k=i[0],A=i[1],T=i[2];return t[0]=(1-(m+g))*_,t[1]=(d+x)*_,t[2]=(p-b)*_,t[3]=0,t[4]=(d-x)*w,t[5]=(1-(f+g))*w,t[6]=(v+y)*w,t[7]=0,t[8]=(p+b)*M,t[9]=(v-y)*M,t[10]=(1-(f+m))*M,t[11]=0,t[12]=r[0]+k-(t[0]*k+t[4]*A+t[8]*T),t[13]=r[1]+A-(t[1]*k+t[5]*A+t[9]*T),t[14]=r[2]+T-(t[2]*k+t[6]*A+t[10]*T),t[15]=1,t},i.fromQuat=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,h=n*s,f=i*o,d=i*s,p=i*l,m=a*o,v=a*s,g=a*l;return t[0]=1-h-p,t[1]=c+g,t[2]=f-v,t[3]=0,t[4]=c-g,t[5]=1-u-p,t[6]=d+m,t[7]=0,t[8]=f+v,t[9]=d-m,t[10]=1-u-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.frustum=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),u=1/(a-o);return t[0]=2*a*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*a*l,t[6]=0,t[7]=0,t[8]=(r+e)*s,t[9]=(i+n)*l,t[10]=(o+a)*u,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*a*2*u,t[15]=0,t},i.perspective=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t},i.perspectiveFromFieldOfView=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),u=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=u,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*u*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t},i.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t},i.lookAt=function(t,e,r,a){var o,s,l,u,c,h,f,d,p,m,v=e[0],g=e[1],y=e[2],b=a[0],x=a[1],_=a[2],w=r[0],M=r[1],k=r[2];return Math.abs(v-w)<n.EPSILON&&Math.abs(g-M)<n.EPSILON&&Math.abs(y-k)<n.EPSILON?i.identity(t):(f=v-w,d=g-M,p=y-k,m=1/Math.sqrt(f*f+d*d+p*p),f*=m,d*=m,p*=m,o=x*p-_*d,s=_*f-b*p,l=b*d-x*f,m=Math.sqrt(o*o+s*s+l*l),m?(m=1/m,o*=m,s*=m,l*=m):(o=0,s=0,l=0),u=d*l-p*s,c=p*o-f*l,h=f*s-d*o,m=Math.sqrt(u*u+c*c+h*h),m?(m=1/m,u*=m,c*=m,h*=m):(u=0,c=0,h=0),t[0]=o,t[1]=u,t[2]=f,t[3]=0,t[4]=s,t[5]=c,t[6]=d,t[7]=0,t[8]=l,t[9]=h,t[10]=p,t[11]=0,t[12]=-(o*v+s*g+l*y),t[13]=-(u*v+c*g+h*y),t[14]=-(f*v+d*g+p*y),t[15]=1,t)},i.str=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"},i.frob=function(t){return Math.sqrt(Math.pow(t[0],2)+Math.pow(t[1],2)+Math.pow(t[2],2)+Math.pow(t[3],2)+Math.pow(t[4],2)+Math.pow(t[5],2)+Math.pow(t[6],2)+Math.pow(t[7],2)+Math.pow(t[8],2)+Math.pow(t[9],2)+Math.pow(t[10],2)+Math.pow(t[11],2)+Math.pow(t[12],2)+Math.pow(t[13],2)+Math.pow(t[14],2)+Math.pow(t[15],2))},i.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t[3]=e[3]+r[3],t[4]=e[4]+r[4],t[5]=e[5]+r[5],t[6]=e[6]+r[6],t[7]=e[7]+r[7],t[8]=e[8]+r[8],t[9]=e[9]+r[9],t[10]=e[10]+r[10],t[11]=e[11]+r[11],t[12]=e[12]+r[12],t[13]=e[13]+r[13],t[14]=e[14]+r[14],t[15]=e[15]+r[15],t},i.subtract=function(t,e,r){return t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t[3]=e[3]-r[3],t[4]=e[4]-r[4],t[5]=e[5]-r[5],t[6]=e[6]-r[6],t[7]=e[7]-r[7],t[8]=e[8]-r[8],t[9]=e[9]-r[9],t[10]=e[10]-r[10],t[11]=e[11]-r[11],t[12]=e[12]-r[12],t[13]=e[13]-r[13],t[14]=e[14]-r[14],t[15]=e[15]-r[15],t},i.sub=i.subtract,i.multiplyScalar=function(t,e,r){return t[0]=e[0]*r,t[1]=e[1]*r,t[2]=e[2]*r,t[3]=e[3]*r,t[4]=e[4]*r,t[5]=e[5]*r,t[6]=e[6]*r,t[7]=e[7]*r,t[8]=e[8]*r,t[9]=e[9]*r,t[10]=e[10]*r,t[11]=e[11]*r,t[12]=e[12]*r,t[13]=e[13]*r,t[14]=e[14]*r,t[15]=e[15]*r,t},i.multiplyScalarAndAdd=function(t,e,r,n){return t[0]=e[0]+r[0]*n,t[1]=e[1]+r[1]*n,t[2]=e[2]+r[2]*n,t[3]=e[3]+r[3]*n,t[4]=e[4]+r[4]*n,t[5]=e[5]+r[5]*n,t[6]=e[6]+r[6]*n,t[7]=e[7]+r[7]*n,t[8]=e[8]+r[8]*n,t[9]=e[9]+r[9]*n,t[10]=e[10]+r[10]*n,t[11]=e[11]+r[11]*n,t[12]=e[12]+r[12]*n,t[13]=e[13]+r[13]*n,t[14]=e[14]+r[14]*n,t[15]=e[15]+r[15]*n,t},i.exactEquals=function(t,e){return t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]&&t[4]===e[4]&&t[5]===e[5]&&t[6]===e[6]&&t[7]===e[7]&&t[8]===e[8]&&t[9]===e[9]&&t[10]===e[10]&&t[11]===e[11]&&t[12]===e[12]&&t[13]===e[13]&&t[14]===e[14]&&t[15]===e[15]},i.equals=function(t,e){var r=t[0],i=t[1],a=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],h=t[8],f=t[9],d=t[10],p=t[11],m=t[12],v=t[13],g=t[14],y=t[15],b=e[0],x=e[1],_=e[2],w=e[3],M=e[4],k=e[5],A=e[6],T=e[7],S=e[8],E=e[9],L=e[10],C=e[11],I=e[12],z=e[13],D=e[14],P=e[15];return 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vec4 m_position  = model * vec4(position, 1.0);\\n  vec4 t_position  = view * m_position;\\n  gl_Position      = projection * t_position;\\n  f_color          = color;\\n  f_normal         = normal;\\n  f_data           = position;\\n  f_eyeDirection   = eyePosition   - position;\\n  f_lightDirection = lightPosition - position;\\n  f_uv             = uv;\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution_2_0(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\n\\n\\nfloat cookTorranceSpecular_1_1(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution_2_0(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular\\n            , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n     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vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if(any(lessThan(f_data, clipBounds[0])) || \\n     any(greaterThan(f_data, clipBounds[1]))) {\\n    discard;\\n  }\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute 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opacity;\\n}\",attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\",fragment:n,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if(any(lessThan(position, clipBounds[0])) || \\n     any(greaterThan(position, clipBounds[1]))) {\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\",fragment:n,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor,1);\\n}\\n\",attributes:[{name:\"position\",type:\"vec3\"}]}},{}],205:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=p,this.edgeColors=v,this.edgeUVs=g,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=b,this.pointColors=_,this.pointUVs=w,this.pointSizes=M,this.pointIds=x,this.pointVAO=k,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=T,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=D,this._view=D,this._projection=D,this._resolution=[1,1]}function i(t){for(var e=w({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return _(r,[256,256,4],[4,0,1])}function a(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;for(var a=t.length,i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}function o(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}function s(t){var e=p(t,S.vertex,S.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function l(t){var e=p(t,E.vertex,E.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function u(t){var e=p(t,L.vertex,L.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function c(t){var e=p(t,C.vertex,C.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function h(t){var e=p(t,I.vertex,I.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function f(t){var e=p(t,z.vertex,z.fragment);return e.attributes.position.location=0,e}function d(t,e){1===arguments.length&&(e=t,t=e.gl);var r=s(t),i=l(t),a=u(t),o=c(t),d=h(t),p=f(t),y=g(t,_(new Uint8Array([255,255,255,255]),[1,1,4]));y.generateMipmap(),y.minFilter=t.LINEAR_MIPMAP_LINEAR,y.magFilter=t.LINEAR;var b=m(t),x=m(t),w=m(t),M=m(t),k=m(t),A=v(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:x,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2},{buffer:M,type:t.FLOAT,size:3}]),T=m(t),S=m(t),E=m(t),L=m(t),C=v(t,[{buffer:T,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:S,type:t.FLOAT,size:4},{buffer:E,type:t.FLOAT,size:2}]),I=m(t),z=m(t),D=m(t),P=m(t),O=m(t),R=v(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:O,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:D,type:t.FLOAT,size:2},{buffer:P,type:t.FLOAT,size:1}]),F=m(t),j=v(t,[{buffer:F,type:t.FLOAT,size:3}]),N=new n(t,y,r,i,a,o,d,p,b,k,x,w,M,A,T,L,S,E,C,I,O,z,D,P,R,F,j);return N.update(e),N}var p=t(\"gl-shader\"),m=t(\"gl-buffer\"),v=t(\"gl-vao\"),g=t(\"gl-texture2d\"),y=t(\"normals\"),b=t(\"gl-mat4/multiply\"),x=t(\"gl-mat4/invert\"),_=t(\"ndarray\"),w=t(\"colormap\"),M=t(\"simplicial-complex-contour\"),k=t(\"typedarray-pool\"),A=t(\"./lib/shaders\"),T=t(\"./lib/closest-point\"),S=A.meshShader,E=A.wireShader,L=A.pointShader,C=A.pickShader,I=A.pointPickShader,z=A.contourShader,D=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=n.prototype;P.isOpaque=function(){return this.opacity>=1},P.isTransparent=function(){return this.opacity<1},P.pickSlots=1,P.setPickBase=function(t){this.pickId=t},P.highlight=function(t){if(!t||!this.contourEnable)return void(this.contourCount=0);for(var e=M(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=k.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var h=u[0];2===u.length&&(h=u[c]);for(var f=n[h][0],d=n[h][1],p=i[h],m=1-p,v=this.positions[f],g=this.positions[d],y=0;y<3;++y)o[s++]=p*v[y]+m*g[y]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),k.free(o)},P.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=g(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(i(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var s=[],l=[],u=[],c=[],h=[],f=[],d=[],p=[],m=[],v=[],b=[],x=[],_=[],w=[];this.cells=r,this.positions=n;var M=t.vertexNormals,k=t.cellNormals,A=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,T=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=y.faceNormals(r,n,T)),k||M||(M=y.vertexNormals(r,n,A));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,I=t.vertexIntensity,z=t.cellUVs,D=t.cellIntensity,P=1/0,O=-1/0;if(!C&&!z)if(I)if(t.vertexIntensityBounds)P=+t.vertexIntensityBounds[0],O=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];P=Math.min(P,F),O=Math.max(O,F)}else if(D)for(var R=0;R<D.length;++R){var F=D[R];P=Math.min(P,F),O=Math.max(O,F)}else for(var R=0;R<n.length;++R){var F=n[R][2];P=Math.min(P,F),O=Math.max(O,F)}this.intensity=I||(D?a(r,n.length,D):o(n));var j=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var R=0;R<n.length;++R)for(var B=n[R],U=0;U<3;++U)!isNaN(B[U])&&isFinite(B[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],B[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],B[U]));var V=0,H=0,q=0;t:for(var R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(var Y=G[0],B=n[Y],U=0;U<3;++U)if(isNaN(B[U])||!isFinite(B[U]))continue t;v.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?b.push(W[0],W[1],W[2],1):b.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],x.push(X[0],X[1]),j?_.push(j[Y]):_.push(N),w.push(R),q+=1;break;case 2:for(var U=0;U<2;++U)for(var Y=G[U],B=n[Y],Z=0;Z<3;++Z)if(isNaN(B[Z])||!isFinite(B[Z]))continue t;for(var U=0;U<2;++U){var Y=G[U],B=n[Y];f.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?d.push(W[0],W[1],W[2],1):d.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],p.push(X[0],X[1]),m.push(R)}H+=1;break;case 3:for(var U=0;U<3;++U)for(var Y=G[U],B=n[Y],Z=0;Z<3;++Z)if(isNaN(B[Z])||!isFinite(B[Z]))continue t;for(var U=0;U<3;++U){var Y=G[U],B=n[Y];s.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?l.push(W[0],W[1],W[2],1):l.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],c.push(X[0],X[1]);var J;J=M?M[Y]:k[R],u.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(b),this.pointUVs.update(x),this.pointSizes.update(_),this.pointIds.update(new Uint32Array(w)),this.edgePositions.update(f),this.edgeColors.update(d),this.edgeUVs.update(p),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(s),this.triangleColors.update(l),this.triangleUVs.update(c),this.triangleNormals.update(u),this.triangleIds.update(new Uint32Array(h))}},P.drawTransparent=P.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||D,n=t.view||D,i=t.projection||D,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var l=new Array(16);b(l,s.view,s.model),b(l,s.projection,l),x(l,l);for(var o=0;o<3;++o)s.eyePosition[o]=l[12+o]/l[15];for(var u=l[15],o=0;o<3;++o)u+=this.lightPosition[o]*l[4*o+3];for(var o=0;o<3;++o){for(var c=l[12+o],h=0;h<3;++h)c+=l[4*h+o]*this.lightPosition[h];s.lightPosition[o]=c/u}if(this.triangleCount>0){var f=this.triShader;f.bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var f=this.lineShader;f.bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()}if(this.pointCount>0){var f=this.pointShader;f.bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var f=this.contourShader;f.bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind()}},P.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||D,n=t.view||D,i=t.projection||D,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255},l=this.pickShader;if(l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0){var l=this.pointPickShader;l.bind(),l.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}},P.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=T(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;for(var s=o[2],l=0,a=0;a<r.length;++a)l+=s[a]*this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},P.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=d},{\"./lib/closest-point\":203,\"./lib/shaders\":204,colormap:99,\"gl-buffer\":156,\"gl-mat4/invert\":181,\"gl-mat4/multiply\":183,\"gl-shader\":255,\"gl-texture2d\":267,\"gl-vao\":271,ndarray:467,normals:469,\"simplicial-complex-contour\":517,\"typedarray-pool\":541}],206:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r}function i(t){var e=t.gl;return new n(t,a(e,[0,0,0,1,1,0,1,1]),o(e,s.boxVert,s.lineFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"./shaders\"),l=n.prototype;l.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},l.drawBox=function(){var t=[0,0],e=[0,0];return function(r,n,i,a,o){var s=this.plot,l=this.shader,u=s.gl;t[0]=r,t[1]=n,e[0]=i,e[1]=a,l.uniforms.lo=t,l.uniforms.hi=e,l.uniforms.color=o,u.drawArrays(u.TRIANGLE_STRIP,0,4)}}(),l.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":209,\"gl-buffer\":156,\"gl-shader\":212}],207:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function i(t,e){return t-e}function a(t){var e=t.gl;return new n(t,o(e),s(e,u.gridVert,u.gridFrag),s(e,u.tickVert,u.gridFrag))}e.exports=a;var o=t(\"gl-buffer\"),s=t(\"gl-shader\"),l=t(\"binary-search-bounds\"),u=t(\"./shaders\"),c=n.prototype;c.draw=function(){var t=[0,0],e=[0,0],r=[0,0];return function(){for(var n=this.plot,i=this.vbo,a=this.shader,o=this.ticks,s=n.gl,l=n._tickBounds,u=n.dataBox,c=n.viewBox,h=n.gridLineWidth,f=n.gridLineColor,d=n.gridLineEnable,p=n.pixelRatio,m=0;m<2;++m){var v=l[m],g=l[m+2],y=g-v,b=.5*(u[m+2]+u[m]),x=u[m+2]-u[m];e[m]=2*y/x,t[m]=2*(v-b)/x}a.bind(),i.bind(),a.attributes.dataCoord.pointer(),a.uniforms.dataShift=t,a.uniforms.dataScale=e;for(var _=0,m=0;m<2;++m){r[0]=r[1]=0,r[m]=1,a.uniforms.dataAxis=r,a.uniforms.lineWidth=h[m]/(c[m+2]-c[m])*p,a.uniforms.color=f[m];var w=6*o[m].length;d[m]&&w&&s.drawArrays(s.TRIANGLES,_,w),_+=w}}}(),c.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],a=[0,0],o=[0,0];return function(){for(var s=this.plot,u=this.vbo,c=this.tickShader,h=this.ticks,f=s.gl,d=s._tickBounds,p=s.dataBox,m=s.viewBox,v=s.pixelRatio,g=s.screenBox,y=g[2]-g[0],b=g[3]-g[1],x=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var M=d[w],k=d[w+2],A=k-M,T=.5*(p[w+2]+p[w]),S=p[w+2]-p[w];e[w]=2*A/S,t[w]=2*(M-T)/S}e[0]*=x/y,t[0]*=x/y,e[1]*=_/b,t[1]*=_/b,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var E=c.uniforms;E.dataShift=t,E.dataScale=e;var L=s.tickMarkLength,C=s.tickMarkWidth,I=s.tickMarkColor,z=6*h[0].length,D=Math.min(l.ge(h[0],(p[0]-d[0])/(d[2]-d[0]),i),h[0].length),P=Math.min(l.gt(h[0],(p[2]-d[0])/(d[2]-d[0]),i),h[0].length),O=0+6*D,R=6*Math.max(0,P-D),F=Math.min(l.ge(h[1],(p[1]-d[1])/(d[3]-d[1]),i),h[1].length),j=Math.min(l.gt(h[1],(p[3]-d[1])/(d[3]-d[1]),i),h[1].length),N=z+6*F,B=6*Math.max(0,j-F);a[0]=2*(m[0]-L[1])/y-1,a[1]=(m[3]+m[1])/b-1,o[0]=L[1]*v/y,o[1]=C[1]*v/b,B&&(E.color=I[1],E.tickScale=o,E.dataAxis=n,E.screenOffset=a,f.drawArrays(f.TRIANGLES,N,B)),a[0]=(m[2]+m[0])/y-1,a[1]=2*(m[1]-L[0])/b-1,o[0]=C[0]*v/y,o[1]=L[0]*v/b,R&&(E.color=I[0],E.tickScale=o,E.dataAxis=r,E.screenOffset=a,f.drawArrays(f.TRIANGLES,O,R)),a[0]=2*(m[2]+L[3])/y-1,a[1]=(m[3]+m[1])/b-1,o[0]=L[3]*v/y,o[1]=C[3]*v/b,B&&(E.color=I[3],E.tickScale=o,E.dataAxis=n,E.screenOffset=a,f.drawArrays(f.TRIANGLES,N,B)),a[0]=(m[2]+m[0])/y-1,a[1]=2*(m[3]+L[2])/b-1,o[0]=C[2]*v/y,o[1]=L[2]*v/b,R&&(E.color=I[2],E.tickScale=o,E.dataAxis=r,E.screenOffset=a,f.drawArrays(f.TRIANGLES,O,R))}}(),c.update=function(){var t=[1,1,-1,-1,1,-1],e=[1,-1,1,1,-1,-1];return function(r){for(var n=r.ticks,i=r.bounds,a=new Float32Array(18*(n[0].length+n[1].length)),o=(this.plot.zeroLineEnable,0),s=[[],[]],l=0;l<2;++l)for(var u=s[l],c=n[l],h=i[l],f=i[l+2],d=0;d<c.length;++d){var p=(c[d].x-h)/(f-h);u.push(p);for(var m=0;m<6;++m)a[o++]=p,a[o++]=t[m],a[o++]=e[m]}this.ticks=s,this.vbo.update(a)}}(),c.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":209,\"binary-search-bounds\":211,\"gl-buffer\":156,\"gl-shader\":212}],208:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r}function i(t){var e=t.gl;return new n(t,a(e,[-1,-1,-1,1,1,-1,1,1]),o(e,s.lineVert,s.lineFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"./shaders\"),l=n.prototype;l.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},l.drawLine=function(){var t=[0,0],e=[0,0];return function(r,n,i,a,o,s){var l=this.plot,u=this.shader,c=l.gl;t[0]=r,t[1]=n,e[0]=i,e[1]=a,u.uniforms.start=t,u.uniforms.end=e,u.uniforms.width=o*l.pixelRatio,u.uniforms.color=s,c.drawArrays(c.TRIANGLE_STRIP,0,4)}}(),l.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":209,\"gl-buffer\":156,\"gl-shader\":212}],209:[function(t,e,r){\"use strict\";var n=\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\";e.exports={lineVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\",lineFrag:n,textVert:\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\",textFrag:n,gridVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\",gridFrag:n,boxVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\",tickVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"}},{}],210:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}function i(t){var e=t.gl;return new n(t,a(e),o(e,u.textVert,u.textFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"text-cache\"),l=t(\"binary-search-bounds\"),u=t(\"./shaders\"),c=n.prototype;c.drawTicks=function(){var t=[0,0],e=[0,0],r=[0,0];return function(n){var i=this.plot,a=this.shader,o=this.tickX[n],s=this.tickOffset[n],u=i.gl,c=i.viewBox,h=i.dataBox,f=i.screenBox,d=i.pixelRatio,p=i.tickEnable,m=i.tickPad,v=i.tickColor,g=i.tickAngle,y=i.labelEnable,b=i.labelPad,x=i.labelColor,_=i.labelAngle,w=this.labelOffset[n],M=this.labelCount[n],k=l.lt(o,h[n]),A=l.le(o,h[n+2]);t[0]=t[1]=0,t[n]=1,e[n]=(c[2+n]+c[n])/(f[2+n]-f[n])-1;var T=2/f[2+(1^n)]-f[1^n];e[1^n]=T*c[1^n]-1,p[n]&&(e[1^n]-=T*d*m[n],k<A&&s[A]>s[k]&&(a.uniforms.dataAxis=t,a.uniforms.screenOffset=e,a.uniforms.color=v[n],a.uniforms.angle=g[n],u.drawArrays(u.TRIANGLES,s[k],s[A]-s[k]))),y[n]&&M&&(e[1^n]-=T*d*b[n],a.uniforms.dataAxis=r,a.uniforms.screenOffset=e,a.uniforms.color=x[n],a.uniforms.angle=_[n],u.drawArrays(u.TRIANGLES,w,M)),e[1^n]=T*c[2+(1^n)]-1,p[n+2]&&(e[1^n]+=T*d*m[n+2],k<A&&s[A]>s[k]&&(a.uniforms.dataAxis=t,a.uniforms.screenOffset=e,a.uniforms.color=v[n+2],a.uniforms.angle=g[n+2],u.drawArrays(u.TRIANGLES,s[k],s[A]-s[k]))),y[n+2]&&M&&(e[1^n]+=T*d*b[n+2],a.uniforms.dataAxis=r,a.uniforms.screenOffset=e,a.uniforms.color=x[n+2],a.uniforms.angle=_[n+2],u.drawArrays(u.TRIANGLES,w,M))}}(),c.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),c.bind=function(){var t=[0,0],e=[0,0],r=[0,0];return function(){var n=this.plot,i=this.shader,a=n._tickBounds,o=n.dataBox,s=n.screenBox,l=n.viewBox;i.bind();for(var u=0;u<2;++u){var c=a[u],h=a[u+2],f=h-c,d=.5*(o[u+2]+o[u]),p=o[u+2]-o[u],m=l[u],v=l[u+2],g=v-m,y=s[u],b=s[u+2],x=b-y;e[u]=2*f/p*g/x,t[u]=2*(c-d)/p*g/x}r[1]=2*n.pixelRatio/(s[3]-s[1]),r[0]=r[1]*(s[3]-s[1])/(s[2]-s[0]),i.uniforms.dataScale=e,i.uniforms.dataShift=t,i.uniforms.textScale=r,this.vbo.bind(),i.attributes.textCoordinate.pointer()}}(),c.update=function(t){var e,r,n,i,a,o=[],l=t.ticks,u=t.bounds;for(a=0;a<2;++a){var c=[Math.floor(o.length/3)],h=[-1/0],f=l[a];for(e=0;e<f.length;++e){var d=f[e],p=d.x,m=d.text,v=d.font||\"sans-serif\";i=d.fontSize||12;for(var g=1/(u[a+2]-u[a]),y=u[a],b=m.split(\"\\n\"),x=0;x<b.length;x++)for(n=s(v,b[x]).data,r=0;r<n.length;r+=2)o.push(n[r]*i,-n[r+1]*i-x*i*1.2,(p-y)*g);c.push(Math.floor(o.length/3)),h.push(p)}this.tickOffset[a]=c,this.tickX[a]=h}for(a=0;a<2;++a){for(this.labelOffset[a]=Math.floor(o.length/3),n=s(t.labelFont[a],t.labels[a],{textAlign:\"center\"}).data,\n",
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o=t(\"./lib/create-uniforms\"),s=t(\"./lib/create-attributes\"),l=t(\"./lib/reflect\"),u=t(\"./lib/shader-cache\"),c=t(\"./lib/runtime-reflect\"),h=t(\"./lib/GLError\"),f=n.prototype;f.bind=function(){this.program||this._relink();var t,e=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),r=this.gl.lastAttribCount;if(e>r)for(t=r;t<e;t++)this.gl.enableVertexAttribArray(t);else if(r>e)for(t=e;t<r;t++)this.gl.disableVertexAttribArray(t);this.gl.lastAttribCount=e,this.gl.useProgram(this.program)},f.dispose=function(){for(var t=this.gl.lastAttribCount,e=0;e<t;e++)this.gl.disableVertexAttribArray(e);this.gl.lastAttribCount=0,this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},f.update=function(t,e,r,n){function a(){d.program=u.program(p,d._vref,d._fref,_,w);for(var t=0;t<r.length;++t)E[t]=p.getUniformLocation(d.program,r[t].name)}if(!e||1===arguments.length){var f=t;t=f.vertex,e=f.fragment,r=f.uniforms,n=f.attributes}var d=this,p=d.gl,m=d._vref;d._vref=u.shader(p,p.VERTEX_SHADER,t),m&&m.dispose(),d.vertShader=d._vref.shader;var v=this._fref;if(d._fref=u.shader(p,p.FRAGMENT_SHADER,e),v&&v.dispose(),d.fragShader=d._fref.shader,!r||!n){var g=p.createProgram();if(p.attachShader(g,d.fragShader),p.attachShader(g,d.vertShader),p.linkProgram(g),!p.getProgramParameter(g,p.LINK_STATUS)){var y=p.getProgramInfoLog(g);throw new h(y,\"Error linking program:\"+y)}r=r||c.uniforms(p,g),n=n||c.attributes(p,g),p.deleteProgram(g)}n=n.slice(),n.sort(i);var b,x=[],_=[],w=[];for(b=0;b<n.length;++b){var M=n[b];if(M.type.indexOf(\"mat\")>=0){for(var k=0|M.type.charAt(M.type.length-1),A=new Array(k),T=0;T<k;++T)A[T]=w.length,_.push(M.name+\"[\"+T+\"]\"),\"number\"==typeof M.location?w.push(M.location+T):Array.isArray(M.location)&&M.location.length===k&&\"number\"==typeof M.location[T]?w.push(0|M.location[T]):w.push(-1);x.push({name:M.name,type:M.type,locations:A})}else x.push({name:M.name,type:M.type,locations:[w.length]}),_.push(M.name),\"number\"==typeof M.location?w.push(0|M.location):w.push(-1)}var S=0;for(b=0;b<w.length;++b)if(w[b]<0){for(;w.indexOf(S)>=0;)S+=1;w[b]=S}var E=new Array(r.length);a(),d._relink=a,d.types={uniforms:l(r),attributes:l(n)},d.attributes=s(p,d,x,w),Object.defineProperty(d,\"uniforms\",o(p,d,r,E))},e.exports=a},{\"./lib/GLError\":213,\"./lib/create-attributes\":214,\"./lib/create-uniforms\":215,\"./lib/reflect\":216,\"./lib/runtime-reflect\":217,\"./lib/shader-cache\":218}],213:[function(t,e,r){function n(t,e,r){this.shortMessage=e||\"\",this.longMessage=r||\"\",this.rawError=t||\"\",this.message=\"gl-shader: \"+(e||t||\"\")+(r?\"\\n\"+r:\"\"),this.stack=(new Error).stack}n.prototype=new Error,n.prototype.name=\"GLError\",n.prototype.constructor=n,e.exports=n},{}],214:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a){this._gl=t,this._wrapper=e,this._index=r,this._locations=n,this._dimension=i,this._constFunc=a}function i(t,e,r,i,a,o,s){for(var l=[\"gl\",\"v\"],u=[],c=0;c<a;++c)l.push(\"x\"+c),u.push(\"x\"+c);l.push(\"if(x0.length===void 0){return gl.vertexAttrib\"+a+\"f(v,\"+u.join()+\")}else{return gl.vertexAttrib\"+a+\"fv(v,x0)}\");var h=Function.apply(null,l),f=new n(t,e,r,i,a,h);Object.defineProperty(o,s,{set:function(e){return t.disableVertexAttribArray(i[r]),h(t,i[r],e),e},get:function(){return f},enumerable:!0})}function a(t,e,r,n,a,o,s){for(var l=new Array(a),u=new Array(a),c=0;c<a;++c)i(t,e,r[c],n,a,l,c),u[c]=l[c];Object.defineProperty(l,\"location\",{set:function(t){if(Array.isArray(t))for(var e=0;e<a;++e)u[e].location=t[e];else for(var e=0;e<a;++e)u[e].location=t+e;return t},get:function(){for(var t=new Array(a),e=0;e<a;++e)t[e]=n[r[e]];return t},enumerable:!0}),l.pointer=function(e,i,o,s){e=e||t.FLOAT,i=!!i,o=o||a*a,s=s||0;for(var l=0;l<a;++l){var 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t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT),t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,d=this.zeroLineWidth;if(h[0]||h[1]){o.bind();for(var p=0;p<2;++p)if(h[p]&&n[p]<=0&&n[p+2]>=0){var m=e[p]-n[p]*(e[p+2]-e[p])/(n[p+2]-n[p]);0===p?o.drawLine(m,e[1],m,e[3],d[p],f[p]):o.drawLine(e[0],m,e[2],m,d[p],f[p])}}for(var p=0;p<l.length;++p)l[p].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var v=this.borderLineEnable,g=this.borderLineWidth,y=this.borderLineColor;v[1]&&o.drawLine(r[0],r[1]-.5*g[1]*i,r[0],r[3]+.5*g[3]*i,g[1],y[1]),v[0]&&o.drawLine(r[0]-.5*g[0]*i,r[1],r[2]+.5*g[2]*i,r[1],g[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5*g[1]*i,r[2],r[3]+.5*g[3]*i,g[3],y[3]),v[2]&&o.drawLine(r[0]-.5*g[0]*i,r[3],r[2]+.5*g[2]*i,r[3],g[2],y[2]),s.bind();for(var p=0;p<2;++p)s.drawTicks(p);this.titleEnable&&s.drawTitle();for(var b=this.overlays,p=0;p<b.length;++p)b[p].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}}}(),f.drawPick=function(){return function(){if(!this.static){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}}}(),f.pick=function(){return function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var h=u[c].pick(a,o,l);if(h)return h}return null}}}(),f.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},f.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},f.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},f.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,o=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/o,10,10/o]),this.borderColor=(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=i(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=i(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=i(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t&&!t.titleEnable),this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=i(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=i(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=i(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var s=t.ticks||[[],[]],l=this._tickBounds;l[0]=l[1]=1/0,l[2]=l[3]=-1/0;for(var u=0;u<2;++u){var c=s[u].slice(0);0!==c.length&&(c.sort(a),l[u]=Math.min(l[u],c[0].x),l[u+2]=Math.max(l[u+2],c[c.length-1].x))}this.grid.update({bounds:l,ticks:s}),this.text.update({bounds:l,ticks:s,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},f.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(var t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},f.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},f.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},f.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},f.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":206,\"./lib/grid\":207,\"./lib/line\":208,\"./lib/text\":210,\"gl-select-static\":254}],220:[function(t,e,r){var n=t(\"gl-shader\");e.exports=function(t){return n(t,\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\",\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\",null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":255}],221:[function(t,e,r){\"use 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t=O.length,e=j.length,r=0;r<e;++r)F[r]=0;t:for(var r=0;r<t;++r){var n=O[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(F[a]+i<255){R[r]=a,n.setPickBase(F[a]+1),F[a]+=i;continue t}var o=f(A,q);R[r]=e,j.push(o),F.push(i),n.setPickBase(1),e+=1}else R[r]=-1}for(;e>0&&0===F[e-1];)F.pop(),j.pop().dispose()}function s(){if(G.contextLost)return!0;A.isContextLost()&&(G.contextLost=!0,G.mouseListener.enabled=!1,G.selection.object=null,G.oncontextloss&&G.oncontextloss())}function b(){if(!s()){A.colorMask(!0,!0,!0,!0),A.depthMask(!0),A.disable(A.BLEND),A.enable(A.DEPTH_TEST);for(var t=O.length,e=j.length,r=0;r<e;++r){var n=j[r];n.shape=Y,n.begin();for(var i=0;i<t;++i)if(R[i]===r){var a=O[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(H))}n.end()}}}function x(){if(!s()){e();var t=G.camera.tick();H.view=G.camera.matrix,N=N||t,B=B||t,z.pixelRatio=G.pixelRatio,P.pixelRatio=G.pixelRatio;var r=O.length,n=Z[0],i=Z[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<r;++o){var l=O[o];l.pixelRatio=G.pixelRatio,l.axes=G.axes,N=N||!!l.dirty,B=B||!!l.dirty;var u=l.bounds;if(u)for(var h=u[0],f=u[1],d=0;d<3;++d)n[d]=Math.min(n[d],h[d]),i[d]=Math.max(i[d],f[d])}var m=G.bounds;if(G.autoBounds)for(var d=0;d<3;++d){if(i[d]<n[d])n[d]=-1,i[d]=1;else{n[d]===i[d]&&(n[d]-=1,i[d]+=1);var g=.05*(i[d]-n[d]);n[d]=n[d]-g,i[d]=i[d]+g}m[0][d]=n[d],m[1][d]=i[d]}for(var y=!1,d=0;d<3;++d)y=y||J[0][d]!==m[0][d]||J[1][d]!==m[1][d],J[0][d]=m[0][d],J[1][d]=m[1][d];if(B=B||y,N=N||y){if(y){for(var x=[0,0,0],o=0;o<3;++o)x[o]=a((m[1][o]-m[0][o])/10);z.autoTicks?z.update({bounds:m,tickSpacing:x}):z.update({bounds:m})}var _=A.drawingBufferWidth,w=A.drawingBufferHeight;q[0]=_,q[1]=w,Y[0]=0|Math.max(_/G.pixelRatio,1),Y[1]=0|Math.max(w/G.pixelRatio,1),v(U,G.fovy,_/w,G.zNear,G.zFar);for(var o=0;o<16;++o)V[o]=0;V[15]=1;for(var M=0,o=0;o<3;++o)M=Math.max(M,m[1][o]-m[0][o]);for(var o=0;o<3;++o)G.autoScale?V[5*o]=G.aspect[o]/(m[1][o]-m[0][o]):V[5*o]=1/M,G.autoCenter&&(V[12+o]=.5*-V[5*o]*(m[0][o]+m[1][o]));for(var o=0;o<r;++o){var l=O[o];l.axesBounds=m,G.clipToBounds&&(l.clipBounds=m)}S.object&&(G.snapToData?P.position=S.dataCoordinate:P.position=S.dataPosition,P.bounds=m),B&&(B=!1,b()),G.axesPixels=c(G.axes,H,_,w),G.onrender&&G.onrender(),A.bindFramebuffer(A.FRAMEBUFFER,null),A.viewport(0,0,_,w);var k=G.clearColor;A.clearColor(k[0],k[1],k[2],k[3]),A.clear(A.COLOR_BUFFER_BIT|A.DEPTH_BUFFER_BIT),A.depthMask(!0),A.colorMask(!0,!0,!0,!0),A.enable(A.DEPTH_TEST),A.depthFunc(A.LEQUAL),A.disable(A.BLEND),A.disable(A.CULL_FACE);var T=!1;z.enable&&(T=T||z.isTransparent(),z.draw(H)),P.axes=z,S.object&&P.draw(H),A.disable(A.CULL_FACE);for(var o=0;o<r;++o){var l=O[o];l.axes=z,l.pixelRatio=G.pixelRatio,l.isOpaque&&l.isOpaque()&&l.draw(H),l.isTransparent&&l.isTransparent()&&(T=!0)}if(T){E.shape=q,E.bind(),A.clear(A.DEPTH_BUFFER_BIT),A.colorMask(!1,!1,!1,!1),A.depthMask(!0),A.depthFunc(A.LESS),z.enable&&z.isTransparent()&&z.drawTransparent(H);for(var o=0;o<r;++o){var l=O[o];l.isOpaque&&l.isOpaque()&&l.draw(H)}A.enable(A.BLEND),A.blendEquation(A.FUNC_ADD),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.colorMask(!0,!0,!0,!0),A.depthMask(!1),A.clearColor(0,0,0,0),A.clear(A.COLOR_BUFFER_BIT),z.isTransparent()&&z.drawTransparent(H);for(var o=0;o<r;++o){var l=O[o];l.isTransparent&&l.isTransparent()&&l.drawTransparent(H)}A.bindFramebuffer(A.FRAMEBUFFER,null),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.disable(A.DEPTH_TEST),L.bind(),E.color[0].bind(0),L.uniforms.accumBuffer=0,p(A),A.disable(A.BLEND)}N=!1;for(var o=0;o<r;++o)O[o].dirty=!1}}}function _(){w||G.contextLost||(requestAnimationFrame(_),x())}t=t||{};var w=!1,M=(t.pixelRatio||parseFloat(window.devicePixelRatio),t.canvas);if(!M)if(M=document.createElement(\"canvas\"),t.container){var k=t.container;k.appendChild(M)}else document.body.appendChild(M);var A=t.gl;if(A||(A=i(M,t.glOptions||{premultipliedAlpha:!0,antialias:!0})),!A)throw new Error(\"webgl not supported\");var T=t.bounds||[[-10,-10,-10],[10,10,10]],S=new n,E=d(A,[A.drawingBufferWidth,A.drawingBufferHeight],{preferFloat:!y}),L=g(A),C=t.camera||{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:\"turntable\"},I=t.axes||{},z=u(A,I);z.enable=!I.disable;var D=t.spikes||{},P=h(A,D),O=[],R=[],F=[],j=[],N=!0,B=!0,U=new Array(16),V=new Array(16),H={view:null,projection:U,model:V},B=!0,q=[A.drawingBufferWidth,A.drawingBufferHeight],G={gl:A,contextLost:!1,pixelRatio:t.pixelRatio||parseFloat(window.devicePixelRatio),canvas:M,selection:S,camera:l(M,C),axes:z,axesPixels:null,spikes:P,bounds:T,objects:O,shape:q,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:o(t.autoResize),autoBounds:o(t.autoBounds),autoScale:!!t.autoScale,autoCenter:o(t.autoCenter),clipToBounds:o(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:H,oncontextloss:null,mouseListener:null},Y=[A.drawingBufferWidth/G.pixelRatio|0,A.drawingBufferHeight/G.pixelRatio|0];G.autoResize&&e(),window.addEventListener(\"resize\",e),G.update=function(t){w||(t=t||{},N=!0,B=!0)},G.add=function(t){w||(t.axes=z,O.push(t),R.push(-1),N=!0,B=!0,r())},G.remove=function(t){if(!w){var e=O.indexOf(t);e<0||(O.splice(e,1),R.pop(),N=!0,B=!0,r())}},G.dispose=function(){if(!w&&(w=!0,window.removeEventListener(\"resize\",e),M.removeEventListener(\"webglcontextlost\",s),G.mouseListener.enabled=!1,!G.contextLost)){z.dispose(),P.dispose();for(var t=0;t<O.length;++t)O[t].dispose();E.dispose();for(var t=0;t<j.length;++t)j[t].dispose();L.dispose(),A=null,z=null,P=null,O=[]}};var W=!1,X=0;G.mouseListener=m(M,function(t,e,r){if(!w){var n=j.length,i=O.length,a=S.object;S.distance=1/0,S.mouse[0]=e,S.mouse[1]=r,S.object=null,S.screen=null,S.dataCoordinate=S.dataPosition=null;var o=!1;if(t&&X)W=!0;else{W&&(B=!0),W=!1;for(var s=0;s<n;++s){var l=j[s].query(e,Y[1]-r-1,G.pickRadius);if(l){if(l.distance>S.distance)continue;for(var u=0;u<i;++u){var c=O[u];if(R[u]===s){var h=c.pick(l);h&&(S.buttons=t,S.screen=l.coord,S.distance=l.distance,S.object=c,S.index=h.distance,S.dataPosition=h.position,S.dataCoordinate=h.dataCoordinate,S.data=h,o=!0)}}}}}a&&a!==S.object&&(a.highlight&&a.highlight(null),N=!0),S.object&&(S.object.highlight&&S.object.highlight(S.data),N=!0),o=o||S.object!==a,o&&G.onselect&&G.onselect(S),1&t&&!(1&X)&&G.onclick&&G.onclick(S),X=t}}),M.addEventListener(\"webglcontextlost\",s);var Z=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],J=[Z[0].slice(),Z[1].slice()];return _(),G.redraw=function(){w||(N=!0,x())},G}e.exports=s;var l=t(\"3d-view-controls\"),u=t(\"gl-axes3d\"),c=t(\"gl-axes3d/properties\"),h=t(\"gl-spikes3d\"),f=t(\"gl-select-static\"),d=t(\"gl-fbo\"),p=t(\"a-big-triangle\"),m=t(\"mouse-change\"),v=t(\"gl-mat4/perspective\"),g=t(\"./lib/shader\"),y=t(\"is-mobile\")()},{\"./lib/shader\":220,\"3d-view-controls\":36,\"a-big-triangle\":39,\"gl-axes3d\":148,\"gl-axes3d/properties\":155,\"gl-fbo\":164,\"gl-mat4/perspective\":184,\"gl-select-static\":254,\"gl-spikes3d\":264,\"is-mobile\":296,\"mouse-change\":452}],222:[function(t,e,r){r.pointVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\",r.pickVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"},{}],223:[function(t,e,r){arguments[4][212][0].apply(r,arguments)},{\"./lib/GLError\":224,\"./lib/create-attributes\":225,\"./lib/create-uniforms\":226,\"./lib/reflect\":227,\"./lib/runtime-reflect\":228,\"./lib/shader-cache\":229,dup:212}],224:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],225:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\n",
+       "\"./GLError\":224,dup:214}],226:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":224,\"./reflect\":227,dup:215}],227:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],228:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],229:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":224,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],230:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}function i(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}function a(t,e){var r=t.gl,i=s(r),a=s(r),l=o(r,u.pointVertex,u.pointFragment),c=o(r,u.pickVertex,u.pickFragment),h=new n(t,i,a,l,c);return h.update(e),t.addObject(h),h}var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"typedarray-pool\"),u=t(\"./lib/shader\");e.exports=a;var c=n.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){function e(e,r){return e in t?t[e]:r}var r;t=t||{},this.sizeMin=e(\"sizeMin\",.5),this.sizeMax=e(\"sizeMax\",20),this.color=e(\"color\",[1,0,0,1]).slice(),this.areaRatio=e(\"areaRatio\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.blend=e(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,a=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,o=t.positions,s=i?o:l.mallocFloat32(o.length),u=a?t.idToIndex:l.mallocInt32(n);if(i||s.set(o),!a)for(s.set(o),r=0;r<n;r++)u[r]=r;this.points=o,this.offsetBuffer.update(s),this.pickBuffer.update(u),i||l.free(s),a||l.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=void 0!==r,a=n?this.pickShader:this.shader,o=this.plot.gl,s=this.plot.dataBox;if(0===this.pointCount)return r;var l=s[2]-s[0],u=s[3]-s[1],c=i(this.points,s),h=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(c,.33333)));t[0]=2/l,t[4]=2/u,t[6]=-2*s[0]/l-1,t[7]=-2*s[1]/u-1,this.offsetBuffer.bind(),a.bind(),a.attributes.position.pointer(),a.uniforms.matrix=t,a.uniforms.color=this.color,a.uniforms.borderColor=this.borderColor,a.uniforms.pointCloud=h<5,a.uniforms.pointSize=h,a.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),n&&(e[0]=255&r,e[1]=r>>8&255,e[2]=r>>16&255,e[3]=r>>24&255,this.pickBuffer.bind(),a.attributes.pickId.pointer(o.UNSIGNED_BYTE),a.uniforms.pickOffset=e,this.pickOffset=r);var f=o.getParameter(o.BLEND),d=o.getParameter(o.DITHER);return f&&!this.blend&&o.disable(o.BLEND),d&&o.disable(o.DITHER),o.drawArrays(o.POINTS,0,this.pointCount),f&&!this.blend&&o.enable(o.BLEND),d&&o.enable(o.DITHER),r+this.pointCount}}(),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":222,\"gl-buffer\":156,\"gl-shader\":223,\"typedarray-pool\":541}],231:[function(t,e,r){function n(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],h=e[2],f=e[3],d=r[0],p=r[1],m=r[2],v=r[3];return a=u*d+c*p+h*m+f*v,a<0&&(a=-a,d=-d,p=-p,m=-m,v=-v),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*d,t[1]=s*c+l*p,t[2]=s*h+l*m,t[3]=s*f+l*v,t}e.exports=n},{}],232:[function(t,e,r){\"use strict\";e.exports={vertex:\"precision highp float;\\n#define GLSLIFY 1\\n\\n\\nvec4 computePosition_1_0(vec2 posHi, vec2 posLo, vec2 scHi, vec2 scLo, vec2 trHi, vec2 trLo) {\\n  return vec4((posHi + trHi) * scHi\\n  \\t\\t\\t//FIXME: this thingy does not give noticeable precision gain, need test\\n            + (posLo + trLo) * scHi\\n            + (posHi + trHi) * scLo\\n            + (posLo + trLo) * scLo\\n            , 0, 1);\\n}\\n\\n\\nattribute vec2 positionHi, positionLo;\\nattribute float size, border;\\nattribute vec2 char, color;\\n\\n//this is 64-bit form of scale and translate\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pixelRatio;\\nuniform vec4 viewBox;\\nuniform sampler2D palette;\\n\\nvarying vec4 charColor, borderColor;\\nvarying vec2 charId;\\nvarying vec2 pointCoord;\\nvarying float pointSize;\\nvarying float borderWidth;\\n\\n\\nvoid main() {\\n  charColor = texture2D(palette, vec2(color.x / 255., 0));\\n  borderColor = texture2D(palette, vec2(color.y / 255., 0));\\n\\n  gl_PointSize = size * pixelRatio;\\n  pointSize = size * pixelRatio;\\n\\n  charId = char;\\n  borderWidth = border;\\n\\n  gl_Position = computePosition_1_0(\\n    positionHi, positionLo,\\n    scaleHi, scaleLo,\\n    translateHi, translateLo);\\n\\n  pointCoord = viewBox.xy + (viewBox.zw - viewBox.xy) * (gl_Position.xy * .5 + .5);\\n}\\n\",fragment:\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D chars;\\nuniform vec2 charsShape;\\nuniform float charsStep, pixelRatio, charOffset;\\n\\nvarying vec4 borderColor;\\nvarying vec4 charColor;\\nvarying vec2 charId;\\nvarying vec2 pointCoord;\\nvarying float pointSize;\\nvarying float borderWidth;\\n\\nvoid main() {\\n\\tvec2 pointUV = (pointCoord - gl_FragCoord.xy + pointSize * .5) / pointSize;\\n\\tpointUV.x = 1. - pointUV.x;\\n\\tvec2 texCoord = ((charId + pointUV) * charsStep) / charsShape;\\n\\tfloat dist = texture2D(chars, texCoord).r;\\n\\n\\t//max-distance alpha\\n\\tif (dist < 1e-2)\\n\\t\\tdiscard;\\n\\n\\tfloat gamma = .0045 * charsStep / pointSize;\\n\\n    //null-border case\\n \\tif (borderWidth * borderColor.a == 0.) {\\n\\t\\tfloat charAmt = smoothstep(.748 - gamma, .748 + gamma, dist);\\n\\t\\tgl_FragColor = vec4(charColor.rgb, charAmt*charColor.a);\\n\\t\\treturn;\\n\\t}\\n\\n\\tfloat dif = 5. * pixelRatio * borderWidth / pointSize;\\n\\tfloat borderLevel = .748 - dif * .5;\\n\\tfloat charLevel = .748 + dif * .5;\\n\\n\\tfloat borderAmt = smoothstep(borderLevel - gamma, borderLevel + gamma, dist);\\n\\tfloat charAmt = smoothstep(charLevel - gamma, charLevel + gamma, dist);\\n\\n\\tvec4 color = borderColor;\\n\\tcolor.a *= borderAmt;\\n\\n\\tgl_FragColor = mix(color, charColor, charAmt);\\n}\\n\",pickVertex:\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 positionHi, positionLo;\\nattribute vec4 id;\\nattribute float size;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform vec4 pickOffset;\\nuniform float pixelRatio;\\n\\nvarying vec4 fragColor;\\n\\n\\nvec4 computePosition_1_0(vec2 posHi, vec2 posLo, vec2 scHi, vec2 scLo, vec2 trHi, vec2 trLo) {\\n  return vec4((posHi + trHi) * scHi\\n  \\t\\t\\t//FIXME: this thingy does not give noticeable precision gain, need test\\n            + (posLo + trLo) * scHi\\n            + (posHi + trHi) * scLo\\n            + (posLo + trLo) * scLo\\n            , 0, 1);\\n}\\n\\n\\nvoid main() {\\n  vec4 fragId = id + pickOffset;\\n\\n  fragId.y += floor(fragId.x / 256.0);\\n  fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n  fragId.z += floor(fragId.y / 256.0);\\n  fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n  fragId.w += floor(fragId.z / 256.0);\\n  fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n  fragColor = fragId / 255.0;\\n\\n  gl_PointSize = size * .25 * pixelRatio;\\n\\n  gl_Position = computePosition_1_0(\\n    positionHi, positionLo,\\n    scaleHi, scaleLo,\\n    translateHi, translateLo);\\n}\\n\",pickFragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\\n\"}},{}],233:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],234:[function(t,e,r){arguments[4][212][0].apply(r,arguments)},{\"./lib/GLError\":235,\"./lib/create-attributes\":236,\"./lib/create-uniforms\":237,\"./lib/reflect\":238,\"./lib/runtime-reflect\":239,\"./lib/shader-cache\":240,dup:212}],235:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],236:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\"./GLError\":235,dup:214}],237:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":235,\"./reflect\":238,dup:215}],238:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],239:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],240:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":235,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],241:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){a<=4*f?i(0,a-1,t,e,r,n):h(0,a-1,t,e,r,n)}function i(t,e,r,n,i,a){for(var o=t+1;o<=e;++o){for(var s=r[o],l=n[2*o],u=n[2*o+1],c=i[o],h=a[o],f=o;f>t;){var d=r[f-1],p=n[2*(f-1)];if((d-s||l-p)>=0)break;r[f]=d,n[2*f]=p,n[2*f+1]=n[2*f-1],i[f]=i[f-1],a[f]=a[f-1],f-=1}r[f]=s,n[2*f]=l,n[2*f+1]=u,i[f]=c,a[f]=h}}function a(t,e,r,n,i,a){var o=r[t],s=n[2*t],l=n[2*t+1],u=i[t],c=a[t];r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],a[t]=a[e],r[e]=o,n[2*e]=s,n[2*e+1]=l,i[e]=u,a[e]=c}function o(t,e,r,n,i,a){r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],a[t]=a[e]}function s(t,e,r,n,i,a,o){var s=n[t],l=i[2*t],u=i[2*t+1],c=a[t],h=o[t];n[t]=n[e],i[2*t]=i[2*e],i[2*t+1]=i[2*e+1],a[t]=a[e],o[t]=o[e],n[e]=n[r],i[2*e]=i[2*r],i[2*e+1]=i[2*r+1],a[e]=a[r],o[e]=o[r],n[r]=s,i[2*r]=l,i[2*r+1]=u,a[r]=c,o[r]=h}function l(t,e,r,n,i,a,o,s,l,u,c){s[t]=s[e],l[2*t]=l[2*e],l[2*t+1]=l[2*e+1],u[t]=u[e],c[t]=c[e],s[e]=r,l[2*e]=n,l[2*e+1]=i,u[e]=a,c[e]=o}function u(t,e,r,n,i){return(r[t]-r[e]||n[2*e]-n[2*t]||i[t]-i[e])<0}function c(t,e,r,n,i,a,o,s){return(e-a[t]||o[2*t]-r||i-s[t])<0}function h(t,e,r,n,d,p){var m=(e-t+1)/6|0,v=t+m,g=e-m,y=t+e>>1,b=y-m,x=y+m,_=v,w=b,M=y,k=x,A=g,T=t+1,S=e-1,E=0;u(_,w,r,n,d,p)&&(E=_,_=w,w=E),u(k,A,r,n,d,p)&&(E=k,k=A,A=E),u(_,M,r,n,d,p)&&(E=_,_=M,M=E),u(w,M,r,n,d,p)&&(E=w,w=M,M=E),u(_,k,r,n,d,p)&&(E=_,_=k,k=E),u(M,k,r,n,d,p)&&(E=M,M=k,k=E),u(w,A,r,n,d,p)&&(E=w,w=A,A=E),u(w,M,r,n,d,p)&&(E=w,w=M,M=E),u(k,A,r,n,d,p)&&(E=k,k=A,A=E);var L=r[w],C=n[2*w],I=n[2*w+1],z=d[w],D=p[w],P=r[k],O=n[2*k],R=n[2*k+1],F=d[k],j=p[k],N=_,B=M,U=A,V=v,H=y,q=g,G=r[N],Y=r[B],W=r[U];r[V]=G,r[H]=Y,r[q]=W;for(var X=0;X<2;++X){var Z=n[2*N+X],J=n[2*B+X],K=n[2*U+X];n[2*V+X]=Z,n[2*H+X]=J,n[2*q+X]=K}var Q=d[N],$=d[B],tt=d[U];d[V]=Q,d[H]=$,d[q]=tt;var et=p[N],rt=p[B],nt=p[U];p[V]=et,p[H]=rt,p[q]=nt,o(b,t,r,n,d,p),o(x,e,r,n,d,p);for(var it=T;it<=S;++it)if(c(it,L,C,I,z,r,n,d))it!==T&&a(it,T,r,n,d,p),++T;else if(!c(it,P,O,R,F,r,n,d))for(;;){if(c(S,P,O,R,F,r,n,d)){c(S,L,C,I,z,r,n,d)?(s(it,T,S,r,n,d,p),++T,--S):(a(it,S,r,n,d,p),--S);break}if(--S<it)break}l(t,T-1,L,C,I,z,D,r,n,d,p),l(e,S+1,P,O,R,F,j,r,n,d,p),T-2-t<=f?i(t,T-2,r,n,d,p):h(t,T-2,r,n,d,p),e-(S+2)<=f?i(S+2,e,r,n,d,p):h(S+2,e,r,n,d,p),S-T<=f?i(T,S,r,n,d,p):h(T,S,r,n,d,p)}e.exports=n;var f=32},{}],242:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){for(var l=r,u=r;u<n;++u){var c=t[2*u],h=t[2*u+1],f=e[u];i<=c&&c<=o&&a<=h&&h<=s&&(u===l?l+=1:(t[2*u]=t[2*l],t[2*u+1]=t[2*l+1],e[u]=e[l],t[2*l]=c,t[2*l+1]=h,e[l]=f,l+=1))}return l}function i(t,e,r){this.pixelSize=t,this.offset=e,this.count=r}function a(t,e,r,a){function l(i,a,o,s,u,c){var h=.5*o,f=s+1,d=u-s;r[_]=d,x[_++]=c;for(var p=0;p<2;++p)for(var m=0;m<2;++m){var v=i+p*h,g=a+m*h,y=n(t,e,f,u,v,g,v+h,g+h);if(y!==f){if(y-f>=Math.max(.9*d,32)){var b=u+s>>>1;l(v,g,h,f,b,c+1),f=b}l(v,g,h,f,y,c+1),f=y}}}var u=t.length>>>1;if(u<1)return[];for(var c=1/0,h=1/0,f=-1/0,d=-1/0,p=0;p<u;++p){var m=t[2*p],v=t[2*p+1];c=Math.min(c,m),f=Math.max(f,m),h=Math.min(h,v),d=Math.max(d,v),e[p]=p}c===f&&(f+=1+Math.abs(f)),h===d&&(d+=1+Math.abs(f));var g=1/(f-c),y=1/(d-h),b=Math.max(f-c,d-h);a=a||[0,0,0,0],a[0]=c,a[1]=h,a[2]=f,a[3]=d;var x=o.mallocInt32(u),_=0;l(c,h,b,0,u,0),s(x,t,e,r,u);for(var w=[],M=0,k=u,_=u-1;_>=0;--_){t[2*_]=(t[2*_]-c)*g,t[2*_+1]=(t[2*_+1]-h)*y;var A=x[_];A!==M&&(w.push(new i(b*Math.pow(.5,A),_+1,k-(_+1))),k=_+1,M=A)}return w.push(new i(b*Math.pow(.5,A+1),0,k)),o.free(x),w}var o=t(\"typedarray-pool\"),s=t(\"./lib/sort\");e.exports=a},{\"./lib/sort\":241,\"typedarray-pool\":541}],243:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.sizeBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.charBuffer=s,this.pointCount=0,this.pickOffset=0,this.points=null,this.scales=[],this.xCoords=[],this.charCanvas=document.createElement(\"canvas\"),this.charTexture=m(this.plot.gl,this.charCanvas),this.charStep=400,this.charFit=.255,this.snapThreshold=1e4,this.paletteTexture=m(this.plot.gl,[256,1])}function i(){var t=this.plot,e=t.viewBox,r=t.dataBox,n=t.pixelRatio,i=r[2]-r[0],a=r[3]-r[1],u=2/i,c=2/a,h=-r[0]-.5*i,f=-r[1]-.5*a;_[0]=u,w[0]=u-_[0],_[1]=c,w[1]=c-_[1],M[0]=h,k[0]=h-M[0],M[1]=f,k[1]=f-M[1];var d=e[2]-e[0],p=e[3]-e[1];o=Math.min(i/d,a/p),A[0]=2*n/d,A[1]=2*n/p,s=r[0],l=r[2]}function a(t,e){var r=t.gl,i=u(r,f.vertex,f.fragment),a=u(r,f.pickVertex,f.pickFragment),o=c(r),s=c(r),l=c(r),h=c(r),d=c(r),p=new n(t,i,a,o,s,l,h,d);return p.update(e),t.addObject(p),p}e.exports=a;var o,s,l,u=t(\"gl-shader\"),c=t(\"gl-buffer\"),h=t(\"typedarray-pool\"),f=t(\"./lib/shaders\"),d=t(\"snap-points-2d\"),p=t(\"font-atlas-sdf\"),m=t(\"gl-texture2d\"),v=t(\"color-id\"),g=t(\"ndarray\"),y=t(\"clamp\"),b=t(\"binary-search-bounds\"),x=n.prototype,_=new Float32Array([0,0]),w=new Float32Array([0,0]),M=new Float32Array([0,0]),k=new Float32Array([0,0]),A=[0,0],T=[0,0,0,0];x.drawPick=function(t){var e=void 0!==t,r=this.plot,n=this.pointCount,a=n>this.snapThreshold;if(!n)return t;i.call(this);var u=r.gl,c=e?this.pickShader:this.shader,h=u.isEnabled(u.BLEND);if(c.bind(),e){this.pickOffset=t;for(var f=0;f<4;++f)T[f]=t>>8*f&255;c.uniforms.pickOffset=T,this.idBuffer.bind(),c.attributes.id.pointer(u.UNSIGNED_BYTE,!1)}else u.blendFuncSeparate(u.SRC_ALPHA,u.ONE_MINUS_SRC_ALPHA,u.ONE,u.ONE_MINUS_SRC_ALPHA),u.blendColor(0,0,0,1),h||u.enable(u.BLEND),this.colorBuffer.bind(),c.attributes.color.pointer(u.UNSIGNED_BYTE,!1),this.charBuffer.bind(),c.attributes.char.pointer(u.UNSIGNED_BYTE,!1),c.uniforms.chars=this.charTexture.bind(0),c.uniforms.charsShape=[this.charCanvas.width,this.charCanvas.height],c.uniforms.charsStep=this.charStep,c.uniforms.palette=this.paletteTexture.bind(1);this.sizeBuffer.bind(),c.attributes.size.pointer(u.FLOAT,!1,8,0),e||c.attributes.border.pointer(u.FLOAT,!1,8,4),this.positionBuffer.bind(),c.attributes.positionHi.pointer(u.FLOAT,!1,16,0),c.attributes.positionLo.pointer(u.FLOAT,!1,16,8),c.uniforms.pixelRatio=r.pixelRatio,c.uniforms.scaleHi=_,c.uniforms.scaleLo=w,c.uniforms.translateHi=M,c.uniforms.translateLo=k,c.uniforms.viewBox=r.viewBox;var d=this.scales;if(a)for(var p=d.length-1;p>=0;p--){var m=d[p];if(!(m.pixelSize&&m.pixelSize<1.25*o&&p>1)){var v=m.offset,g=m.count+v,y=b.ge(this.xCoords,s,v,g-1),x=b.lt(this.xCoords,l,y,g-1)+1;x>y&&u.drawArrays(u.POINTS,y,x-y)}}else u.drawArrays(u.POINTS,0,n);if(e)return t+n;h?u.blendFunc(u.ONE,u.ONE_MINUS_SRC_ALPHA):u.disable(u.BLEND)},x.draw=x.drawPick,x.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}},x.update=function(t){t=t||{};var e=t.positions||[],r=t.colors||[],n=t.glyphs||[],i=t.sizes||[],a=t.borderWidths||[],o=t.borderColors||[],s=this.plot.gl,l=this.pointCount,u=l>this.snapThreshold;if(null!=t.positions){this.points=e,l=this.points.length/2,u=l>this.snapThreshold;var c=h.mallocFloat32(2*l),f=h.mallocFloat64(2*l),m=h.mallocUint32(l),b=h.mallocFloat32(4*l);f.set(this.points),u&&(this.i2idx&&h.free(this.i2idx),this.i2idx=h.mallocInt32(l),this.scales=d(f,this.i2idx,c)),this.pointCount=l;for(var x=0;x<l;++x){var _=u?this.i2idx[x]:x;m[x]=_;var w=e[2*_],M=e[2*_+1];b[4*x]=w,b[4*x+1]=M,b[4*x+2]=w-b[4*x],b[4*x+3]=M-b[4*x+1],this.xCoords[x]=w}this.idBuffer.update(m),this.positionBuffer.update(b),h.free(b),h.free(m),h.free(f),h.free(c)}for(var k=h.mallocFloat32(2*l),A=h.mallocUint8(2*l),T=h.mallocUint8(2*l),S={},E=[],L=[],C=[],x=0,I=l,z=0;x<I;++x){var D=[255*r[4*x],255*r[4*x+1],255*r[4*x+2],255*r[4*x+3]],P=v(D,!1);null==S[P]&&(S[P]=z++,L.push(D[0]),L.push(D[1]),L.push(D[2]),L.push(D[3])),E.push(P),o&&o.length&&(D=[255*o[4*x],255*o[4*x+1],255*o[4*x+2],255*o[4*x+3]],P=v(D,!1),null==S[P]&&(S[P]=z++,L.push(D[0]),L.push(D[1]),L.push(D[2]),L.push(D[3])),C.push(P))}for(var O={},x=0,I=l,z=0;x<I;x++){var R=n[x];null==O[R]&&(O[R]=z++)}for(var F=0,x=0,I=i.length;x<I;++x)i[x]>F&&(F=i[x]);var j=this.charStep;this.charStep=y(Math.ceil(4*F),128,768);var N=Object.keys(O),B=this.charStep,U=Math.floor(B/2),V=s.getParameter(s.MAX_TEXTURE_SIZE),H=V/B*(V/B),q=Math.min(V,B*N.length),G=Math.min(V,B*Math.ceil(B*N.length/V)),Y=Math.floor(q/B);N.length>H&&console.warn(\"gl-scatter2d-fancy: number of characters is more than maximum texture size. Try reducing it.\"),this.chars&&this.chars+\"\"==N+\"\"&&this.charStep==j||(this.charCanvas=p({canvas:this.charCanvas,family:\"sans-serif\",size:U,shape:[q,G],step:[B,B],chars:N,align:!0,fit:this.charFit}),this.chars=N);for(var x=0;x<l;++x){var _=u?this.i2idx[x]:x,W=i[_],X=a[_];k[2*x]=2*W,k[2*x+1]=X;var P=E[_],Z=S[P];A[2*x]=Z;var J=C[_],K=S[J];A[2*x+1]=K;var R=n[_],Q=O[R];T[2*x+1]=Math.floor(Q/Y),T[2*x]=Q%Y}this.sizeBuffer.update(k),this.colorBuffer.update(A),this.charBuffer.update(T),this.charTexture.shape=[this.charCanvas.width,this.charCanvas.height],this.charCanvas&&this.charCanvas.width&&this.charTexture.setPixels(this.charCanvas),this.paletteTexture.setPixels(g(L.slice(0,1024),[256,1,4])),h.free(k),h.free(A),h.free(T)},x.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.sizeBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.charBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":232,\"binary-search-bounds\":233,clamp:88,\"color-id\":92,\"font-atlas-sdf\":134,\"gl-buffer\":156,\"gl-shader\":234,\"gl-texture2d\":267,ndarray:467,\"snap-points-2d\":242,\"typedarray-pool\":541}],244:[function(t,e,r){r.pointVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 positionHi, positionLo;\\nattribute float weight;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pointSize, useWeight;\\n\\nvarying float fragWeight;\\n\\n\\nvec4 pfx_1_0(vec2 scaleHi, vec2 scaleLo, vec2 translateHi, vec2 translateLo, vec2 positionHi, vec2 positionLo) {\\n  return vec4((positionHi + translateHi) * scaleHi\\n            + (positionLo + translateLo) * scaleHi\\n            + (positionHi + translateHi) * scaleLo\\n            + (positionLo + translateLo) * scaleLo, 0.0, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position = pfx_1_0(scaleHi, scaleLo, translateHi, translateLo, positionHi, positionLo);\\n  gl_PointSize = pointSize;\\n  fragWeight = mix(1.0, weight, useWeight);\\n}\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\n\\nvarying float fragWeight;\\n\\nfloat smoothStep(float x, float y) {\\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\\n}\\n\\nvoid main() {\\n  float radius = length(2.0*gl_PointCoord.xy-1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  vec4 baseColor = mix(borderColor, color, smoothStep(radius, centerFraction));\\n  float alpha = 1.0 - pow(1.0 - baseColor.a, fragWeight);\\n  gl_FragColor = vec4(baseColor.rgb * alpha, alpha);\\n}\\n\",r.pickVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nvec4 pfx_1_0(vec2 scaleHi, vec2 scaleLo, vec2 translateHi, vec2 translateLo, vec2 positionHi, vec2 positionLo) {\\n  return vec4((positionHi + translateHi) * scaleHi\\n            + (positionLo + translateLo) * scaleHi\\n            + (positionHi + translateHi) * scaleLo\\n            + (positionLo + translateLo) * scaleLo, 0.0, 1.0);\\n}\\n\\nattribute vec2 positionHi, positionLo;\\nattribute vec4 pickId;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_Position = pfx_1_0(scaleHi, scaleLo, translateHi, translateLo, positionHi, positionLo);\\n  gl_PointSize = pointSize;\\n  fragId = id;\\n}\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\"},{}],245:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],246:[function(t,e,r){arguments[4][241][0].apply(r,arguments)},{dup:241}],247:[function(t,e,r){arguments[4][242][0].apply(r,arguments)},{\"./lib/sort\":246,dup:242,\"typedarray-pool\":541}],248:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o){this.plot=t,this.positionBufferHi=e,this.positionBufferLo=r,this.pickBuffer=n,this.weightBuffer=i,this.shader=a,this.pickShader=o,this.scales=[],this.size=12,this.borderSize=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0,this.points=null,this.xCoords=null,this.snapPoints=!0}function i(t,e){var r=t.gl,i=o(r),s=o(r),l=o(r),u=o(r),h=a(r,c.pointVertex,c.pointFragment),f=a(r,c.pickVertex,c.pickFragment),d=new n(t,i,s,l,u,h,f);return d.update(e),t.addObject(d),d}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"binary-search-bounds\"),l=t(\"snap-points-2d\"),u=t(\"typedarray-pool\"),c=t(\"./lib/shader\"),h=t(\"array-normalize\"),f=t(\"array-bounds\");e.exports=i;var d=n.prototype,p=new Float32Array(2),m=new Float32Array(2),v=new Float32Array(2),g=new Float32Array(2),y=[0,0,0,0];d.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBufferHi.dispose(),this.positionBufferLo.dispose(),this.pickBuffer.dispose(),this.xCoords&&u.free(this.xCoords),this.plot.removeObject(this)},d.update=function(t){function e(e,r){return e in t?t[e]:r}if(t=t||{},this.size=e(\"size\",12),this.color=e(\"color\",[1,0,0,1]).slice(),this.borderSize=e(\"borderSize\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.snapPoints=e(\"snapPoints\",!0),null!=t.positions){this.xCoords&&u.free(this.xCoords),this.points=t.positions;var r=this.points.length>>>1,n=u.mallocInt32(r),i=u.mallocFloat32(r),a=u.mallocFloat64(2*r);if(a.set(this.points),this.snapPoints)this.scales=l(a,n,i,this.bounds);else{this.bounds=f(a,2),h(a,2,this.bounds);for(var o=0;o<r;o++)n[o]=o,i[o]=1}var s=u.mallocFloat64(r),c=u.mallocFloat32(2*r),d=u.mallocFloat32(2*r);c.set(a);for(var o=0,p=0;o<r;o++,p+=2)d[p]=a[p]-c[p],d[p+1]=a[p+1]-c[p+1],s[o]=a[p];this.positionBufferHi.update(c),this.positionBufferLo.update(d),this.pickBuffer.update(n),this.weightBuffer.update(i),u.free(c),u.free(d),u.free(i),u.free(a),u.free(n),this.xCoords=s,this.pointCount=r,this.pickOffset=0}},d.draw=function(t){var e=void 0!==t,r=this.plot,n=e?this.pickShader:this.shader,i=this.scales,a=this.positionBufferHi,o=this.positionBufferLo,s=this.pickBuffer,l=this.bounds,u=this.size,c=this.borderSize,h=r.gl,f=e?r.pickPixelRatio:r.pixelRatio,d=r.viewBox,b=r.dataBox;if(0===this.pointCount)return t;var x=l[2]-l[0],_=l[3]-l[1],w=b[2]-b[0],M=b[3]-b[1],k=(d[2]-d[0])*f/r.pixelRatio,A=(d[3]-d[1])*f/r.pixelRatio,T=this.pixelSize=Math.min(w/k,M/A),S=2*x/w,E=2*_/M;p[0]=S,p[1]=E,m[0]=S-p[0],m[1]=E-p[1];var L=(l[0]-b[0]-.5*w)/x,C=(l[1]-b[1]-.5*M)/_;v[0]=L,v[1]=C,g[0]=L-v[0],g[1]=C-v[1],n.bind(),n.uniforms.scaleHi=p,n.uniforms.scaleLo=m,n.uniforms.translateHi=v,n.uniforms.translateLo=g,n.uniforms.color=this.color,n.uniforms.borderColor=this.borderColor,n.uniforms.pointSize=f*(u+c),n.uniforms.centerFraction=0===this.borderSize?2:u/(u+c+1.25),a.bind(),n.attributes.positionHi.pointer(),o.bind(),n.attributes.positionLo.pointer(),e?(this.pickOffset=t,y[0]=255&t,y[1]=t>>8&255,y[2]=t>>16&255,y[3]=t>>24&255,n.uniforms.pickOffset=y,s.bind(),n.attributes.pickId.pointer(h.UNSIGNED_BYTE)):(n.uniforms.useWeight=1,this.weightBuffer.bind(),n.attributes.weight.pointer());var I=!0;if(this.snapPoints)for(var z=i.length-1;z>=0;z--){var D=i[z];if(!(D.pixelSize<T&&z>1)){var P=this.getVisibleRange(D),O=P[0],R=P[1];R>O&&h.drawArrays(h.POINTS,O,R-O),!e&&I&&(I=!1,n.uniforms.useWeight=0)}}else h.drawArrays(h.POINTS,0,this.pointCount);return t+this.pointCount},d.getVisibleRange=function(t){var e=this.plot.dataBox,r=this.bounds,n=this.pixelSize,i=this.size,a=this.plot.pixelRatio,o=r[2]-r[0];r[3],r[1];if(!t)for(var t,l=this.scales.length-1;l>=0&&(t=this.scales[l],t.pixelSize<n&&l>1);l--);var u=this.xCoords,c=(e[0]-r[0]-n*i*a)/o,h=(e[2]-r[0]+n*i*a)/o,f=t.offset,d=t.count+f,p=s.ge(u,c,f,d-1);return[p,s.lt(u,h,p,d-1)+1]},d.drawPick=d.draw,d.pick=function(t,e,r){var n=r-this.pickOffset;return n<0||n>=this.pointCount?null:{object:this,pointId:n,dataCoord:[this.points[2*n],this.points[2*n+1]]}}},{\"./lib/shader\":244,\"array-bounds\":44,\"array-normalize\":45,\"binary-search-bounds\":245,\"gl-buffer\":156,\"gl-shader\":255,\"snap-points-2d\":247,\"typedarray-pool\":541}],249:[function(t,e,r){\"use strict\";function n(t,e){var r=a[e];if(r||(r=a[e]={}),t in r)return r[t];for(var n=i(t,{textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),o=i(t,{triangles:!0,textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<n.positions.length;++l)for(var u=n.positions[l],c=0;c<2;++c)s[0][c]=Math.min(s[0][c],u[c]),s[1][c]=Math.max(s[1][c],u[c]);return r[t]=[o,n,s]}var i=t(\"vectorize-text\");e.exports=n;var a={}},{\"vectorize-text\":554}],250:[function(t,e,r){function n(t,e){var r=i(t,e),n=r.attributes;return n.position.location=0,n.color.location=1,n.glyph.location=2,n.id.location=3,r}var i=t(\"gl-shader\"),a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(position, clipBounds[0]))   || \\n     any(greaterThan(position, clipBounds[1])) ) {\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n    \\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(position, clipBounds[0])) || any(greaterThan(position, clipBounds[1]))) {\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n    \\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(position, clipBounds[0]))   ||\\n     any(greaterThan(position, clipBounds[1])) ) {\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\",l=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   ||\\n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n    discard;\\n  } else {\\n    gl_FragColor = interpColor * opacity;\\n  }\\n}\\n\",u=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   || \\n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n    discard;\\n  } else {\\n    gl_FragColor = vec4(pickGroup, pickId.bgr);\\n  }\\n}\",c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],h={vertex:a,fragment:l,attributes:c},f={vertex:o,fragment:l,attributes:c},d={vertex:s,fragment:l,attributes:c},p={vertex:a,fragment:u,attributes:c},m={vertex:o,fragment:u,attributes:c},v={vertex:s,fragment:u,attributes:c};r.createPerspective=function(t){return n(t,h)},r.createOrtho=function(t){return n(t,f)},r.createProject=function(t){return n(t,d)},r.createPickPerspective=function(t){return n(t,p)},r.createPickOrtho=function(t){return n(t,m)},r.createPickProject=function(t){return n(t,v)}},{\"gl-shader\":255}],251:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function i(t,e,r,i){return n(i,i,r),n(i,i,e),n(i,i,t)}function a(t,e){this.index=t,this.dataCoordinate=this.position=e}function o(t,e,r,n,i,o,s,l,u,c,h,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=o,this.glyphBuffer=s,this.idBuffer=l,this.vao=u,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=h,this.pickProjectShader=f,this.points=[],this._selectResult=new a(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}function s(t){return t[0]=t[1]=t[2]=0,t}function l(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function u(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function c(t){for(var e=L,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}function h(t,e,r,n,a){var o,h=e.axesProject,f=e.gl,d=t.uniforms,p=r.model||x,m=r.view||x,v=r.projection||x,y=e.axesBounds,b=c(e.clipBounds);o=e.axes?e.axes.lastCubeProps.axis:[1,1,1],w[0]=2/f.drawingBufferWidth,w[1]=2/f.drawingBufferHeight,t.bind(),d.view=m,d.projection=v,d.screenSize=w,d.highlightId=e.highlightId,d.highlightScale=e.highlightScale,d.clipBounds=b,\n",
+       "d.pickGroup=e.pickId/255,d.pixelRatio=e.pixelRatio;for(var _=0;_<3;++_)if(h[_]&&e.projectOpacity[_]<1===n){d.scale=e.projectScale[_],d.opacity=e.projectOpacity[_];for(var L=S,C=0;C<16;++C)L[C]=0;for(var C=0;C<4;++C)L[5*C]=1;L[5*_]=0,o[_]<0?L[12+_]=y[0][_]:L[12+_]=y[1][_],g(L,p,L),d.model=L;var I=(_+1)%3,z=(_+2)%3,D=s(M),P=s(k);D[I]=1,P[z]=1;var O=i(v,m,p,l(A,D)),R=i(v,m,p,l(T,P));if(Math.abs(O[1])>Math.abs(R[1])){var F=O;O=R,R=F,F=D,D=P,P=F;var j=I;I=z,z=j}O[0]<0&&(D[I]=-1),R[1]>0&&(P[z]=-1);for(var N=0,B=0,C=0;C<4;++C)N+=Math.pow(p[4*I+C],2),B+=Math.pow(p[4*z+C],2);D[I]/=Math.sqrt(N),P[z]/=Math.sqrt(B),d.axes[0]=D,d.axes[1]=P,d.fragClipBounds[0]=u(E,b[0],_,-1e8),d.fragClipBounds[1]=u(E,b[1],_,1e8),e.vao.draw(f.TRIANGLES,e.vertexCount),e.lineWidth>0&&(f.lineWidth(e.lineWidth),e.vao.draw(f.LINES,e.lineVertexCount,e.vertexCount))}}function f(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1||a){t.bind();var s=t.uniforms;s.model=n.model||x,s.view=n.view||x,s.projection=n.projection||x,w[0]=2/o.drawingBufferWidth,w[1]=2/o.drawingBufferHeight,s.screenSize=w,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=z,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}h(e,r,n,i,a),r.vao.unbind()}function d(t){var e=t.gl,r=y.createPerspective(e),n=y.createOrtho(e),i=y.createProject(e),a=y.createPickPerspective(e),s=y.createPickOrtho(e),l=y.createPickProject(e),u=p(e),c=p(e),h=p(e),f=p(e),d=m(e,[{buffer:u,size:3,type:e.FLOAT},{buffer:c,size:4,type:e.FLOAT},{buffer:h,size:2,type:e.FLOAT},{buffer:f,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new o(e,r,n,i,u,c,h,f,d,a,s,l);return v.update(t),v}var p=t(\"gl-buffer\"),m=t(\"gl-vao\"),v=t(\"typedarray-pool\"),g=t(\"gl-mat4/multiply\"),y=t(\"./lib/shaders\"),b=t(\"./lib/glyphs\"),x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];e.exports=d;var _=o.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},_.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var w=[0,0],M=[0,0,0],k=[0,0,0],A=[0,0,0,1],T=[0,0,0,1],S=x.slice(),E=[0,0,0],L=[[0,0,0],[0,0,0]],C=[-1e8,-1e8,-1e8],I=[1e8,1e8,1e8],z=[C,I];_.draw=function(t){f(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},_.drawTransparent=function(t){f(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},_.drawPick=function(t){f(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},_.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(t=t||{},\"perspective\"in t&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(\"projectOpacity\"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{var r=+t.projectOpacity;this.projectOpacity=[r,r,r]}\"opacity\"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font||\"normal\",a=t.alignment||[0,0],o=[1/0,1/0,1/0],s=[-1/0,-1/0,-1/0],l=t.glyph,u=t.color,c=t.size,h=t.angle,f=t.lineColor,d=0,p=0,m=0,g=n.length;t:for(var y=0;y<g;++y){for(var x=n[y],_=0;_<3;++_)if(isNaN(x[_])||!isFinite(x[_]))continue t;var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var M=w[0],k=w[1],A=w[2];p+=3*M.cells.length,m+=2*k.edges.length}var T=p+m,S=v.mallocFloat(3*T),E=v.mallocFloat(4*T),L=v.mallocFloat(2*T),C=v.mallocUint32(T),I=[0,a[1]],z=0,D=p,P=[0,0,0,1],O=[0,0,0,1],R=Array.isArray(u)&&Array.isArray(u[0]),F=Array.isArray(f)&&Array.isArray(f[0]);t:for(var y=0;y<g;++y){for(var x=n[y],_=0;_<3;++_){if(isNaN(x[_])||!isFinite(x[_])){d+=1;continue t}s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_])}var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var M=w[0],k=w[1],A=w[2];if(Array.isArray(u)){var j;if(j=R?u[y]:u,3===j.length){for(var _=0;_<3;++_)P[_]=j[_];P[3]=1}else if(4===j.length)for(var _=0;_<4;++_)P[_]=j[_]}else P[0]=P[1]=P[2]=0,P[3]=1;if(Array.isArray(f)){var j;if(j=F?f[y]:f,3===j.length){for(var _=0;_<3;++_)O[_]=j[_];O[_]=1}else if(4===j.length)for(var _=0;_<4;++_)O[_]=j[_]}else O[0]=O[1]=O[2]=0,O[3]=1;var N=.5;Array.isArray(c)?N=+c[y]:c?N=+c:this.useOrtho&&(N=12);var B=0;Array.isArray(h)?B=+h[y]:h&&(B=+h);for(var U=Math.cos(B),V=Math.sin(B),x=n[y],_=0;_<3;++_)s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_]);a[0]<0?I[0]=a[0]*(1+A[1][0]):a[0]>0&&(I[0]=-a[0]*(1+A[0][0]));for(var H=M.cells,q=M.positions,_=0;_<H.length;++_)for(var G=H[_],Y=0;Y<3;++Y){for(var W=0;W<3;++W)S[3*z+W]=x[W];for(var W=0;W<4;++W)E[4*z+W]=P[W];C[z]=d;var X=q[G[Y]];L[2*z]=N*(U*X[0]-V*X[1]+I[0]),L[2*z+1]=N*(V*X[0]+U*X[1]+I[1]),z+=1}for(var H=k.edges,q=k.positions,_=0;_<H.length;++_)for(var G=H[_],Y=0;Y<2;++Y){for(var W=0;W<3;++W)S[3*D+W]=x[W];for(var W=0;W<4;++W)E[4*D+W]=O[W];C[D]=d;var X=q[G[Y]];L[2*D]=N*(U*X[0]-V*X[1]+I[0]),L[2*D+1]=N*(V*X[0]+U*X[1]+I[1]),D+=1}d+=1}this.vertexCount=p,this.lineVertexCount=m,this.pointBuffer.update(S),this.colorBuffer.update(E),this.glyphBuffer.update(L),this.idBuffer.update(new Uint32Array(C)),v.free(S),v.free(E),v.free(L),v.free(C),this.bounds=[o,s],this.points=n,this.pointCount=n.length}},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/glyphs\":249,\"./lib/shaders\":250,\"gl-buffer\":156,\"gl-mat4/multiply\":183,\"gl-vao\":271,\"typedarray-pool\":541}],252:[function(t,e,r){\"use strict\";r.boxVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\",r.boxFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"},{}],253:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}function i(t,e){var r=t.gl,i=o(r,[0,0,0,1,1,0,1,1]),l=a(r,s.boxVertex,s.boxFragment),u=new n(t,i,l);return u.update(e),t.addOverlay(u),u}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"./lib/shaders\");e.exports=i;var l=n.prototype;l.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,h=(e[0]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],f=(e[1]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1],d=(e[2]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],p=(e[3]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1];if(h=Math.max(h,u[0]),f=Math.max(f,u[1]),d=Math.min(d,u[2]),p=Math.min(p,u[3]),!(d<h||p<f)){o.bind();var m=s[2]-s[0],v=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,f,i),o.drawBox(0,f,h,p,i),o.drawBox(0,p,m,v,i),o.drawBox(d,f,m,p,i)),this.innerFill&&o.drawBox(h,f,d,p,n),r>0){var g=r*c;o.drawBox(h-g,f-g,d+g,f+g,a),o.drawBox(h-g,p-g,d+g,p+g,a),o.drawBox(h-g,f-g,h+g,p+g,a),o.drawBox(d-g,f-g,d+g,p+g,a)}}}},l.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},l.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":252,\"gl-buffer\":156,\"gl-shader\":255}],254:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function i(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}function a(t,e){return new i(t,o(t,e),s.mallocUint8(e[0]*e[1]*4))}e.exports=a;var o=t(\"gl-fbo\"),s=t(\"typedarray-pool\"),l=t(\"ndarray\"),u=t(\"bit-twiddle\").nextPow2,c=t(\"cwise/lib/wrapper\")({args:[\"array\",{offset:[0,0,1],array:0},{offset:[0,0,2],array:0},{offset:[0,0,3],array:0},\"scalar\",\"scalar\",\"index\"],pre:{body:\"{this_closestD2=1e8,this_closestX=-1,this_closestY=-1}\",args:[],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[]},body:{body:\"{if(_inline_46_arg0_<255||_inline_46_arg1_<255||_inline_46_arg2_<255||_inline_46_arg3_<255){var _inline_46_l=_inline_46_arg4_-_inline_46_arg6_[0],_inline_46_a=_inline_46_arg5_-_inline_46_arg6_[1],_inline_46_f=_inline_46_l*_inline_46_l+_inline_46_a*_inline_46_a;_inline_46_f<this_closestD2&&(this_closestD2=_inline_46_f,this_closestX=_inline_46_arg6_[0],this_closestY=_inline_46_arg6_[1])}}\",args:[{name:\"_inline_46_arg0_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_46_arg1_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_46_arg2_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_46_arg3_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_46_arg4_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_46_arg5_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_46_arg6_\",lvalue:!1,rvalue:!0,count:4}],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[\"_inline_46_a\",\"_inline_46_f\",\"_inline_46_l\"]},post:{body:\"{return[this_closestX,this_closestY,this_closestD2]}\",args:[],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[]},debug:!1,funcName:\"cwise\",blockSize:64}),h=i.prototype;Object.defineProperty(h,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){s.free(this.buffer);for(var n=this.buffer=s.mallocUint8(u(r*e*4)),i=0;i<r*e*4;++i)n[i]=255}return t}}}),h.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},h.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},h.query=function(t,e,r){if(!this.gl)return null;var i=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var a=0|Math.min(Math.max(t-r,0),i[0]),o=0|Math.min(Math.max(t+r,0),i[0]),s=0|Math.min(Math.max(e-r,0),i[1]),u=0|Math.min(Math.max(e+r,0),i[1]);if(o<=a||u<=s)return null;var h=[o-a,u-s],f=l(this.buffer,[h[0],h[1],4],[4,4*i[0],1],4*(a+i[0]*s)),d=c(f.hi(h[0],h[1],1),r,r),p=d[0],m=d[1];return p<0||Math.pow(this.radius,2)<d[2]?null:new n(p+a|0,m+s|0,f.get(p,m,0),[f.get(p,m,1),f.get(p,m,2),f.get(p,m,3)],Math.sqrt(d[2]))},h.dispose=function(){this.gl&&(this.fbo.dispose(),s.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":67,\"cwise/lib/wrapper\":113,\"gl-fbo\":164,ndarray:467,\"typedarray-pool\":541}],255:[function(t,e,r){\"use strict\";function n(t){this.gl=t,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}function i(t,e){return t.name<e.name?-1:1}function a(t,e,r,i,a){var o=new n(t);return o.update(e,r,i,a),o}var o=t(\"./lib/create-uniforms\"),s=t(\"./lib/create-attributes\"),l=t(\"./lib/reflect\"),u=t(\"./lib/shader-cache\"),c=t(\"./lib/runtime-reflect\"),h=t(\"./lib/GLError\"),f=n.prototype;f.bind=function(){this.program||this._relink(),this.gl.useProgram(this.program)},f.dispose=function(){this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},f.update=function(t,e,r,n){function a(){d.program=u.program(p,d._vref,d._fref,x,_);for(var t=0;t<r.length;++t)E[t]=p.getUniformLocation(d.program,r[t].name)}if(!e||1===arguments.length){var f=t;t=f.vertex,e=f.fragment,r=f.uniforms,n=f.attributes}var d=this,p=d.gl,m=d._vref;d._vref=u.shader(p,p.VERTEX_SHADER,t),m&&m.dispose(),d.vertShader=d._vref.shader;var v=this._fref;if(d._fref=u.shader(p,p.FRAGMENT_SHADER,e),v&&v.dispose(),d.fragShader=d._fref.shader,!r||!n){var g=p.createProgram();if(p.attachShader(g,d.fragShader),p.attachShader(g,d.vertShader),p.linkProgram(g),!p.getProgramParameter(g,p.LINK_STATUS)){var y=p.getProgramInfoLog(g);throw new h(y,\"Error linking program:\"+y)}r=r||c.uniforms(p,g),n=n||c.attributes(p,g),p.deleteProgram(g)}n=n.slice(),n.sort(i);for(var b=[],x=[],_=[],w=0;w<n.length;++w){var M=n[w];if(M.type.indexOf(\"mat\")>=0){for(var k=0|M.type.charAt(M.type.length-1),A=new Array(k),T=0;T<k;++T)A[T]=_.length,x.push(M.name+\"[\"+T+\"]\"),\"number\"==typeof M.location?_.push(M.location+T):Array.isArray(M.location)&&M.location.length===k&&\"number\"==typeof M.location[T]?_.push(0|M.location[T]):_.push(-1);b.push({name:M.name,type:M.type,locations:A})}else b.push({name:M.name,type:M.type,locations:[_.length]}),x.push(M.name),\"number\"==typeof M.location?_.push(0|M.location):_.push(-1)}for(var S=0,w=0;w<_.length;++w)if(_[w]<0){for(;_.indexOf(S)>=0;)S+=1;_[w]=S}var E=new Array(r.length);a(),d._relink=a,d.types={uniforms:l(r),attributes:l(n)},d.attributes=s(p,d,b,_),Object.defineProperty(d,\"uniforms\",o(p,d,r,E))},e.exports=a},{\"./lib/GLError\":256,\"./lib/create-attributes\":257,\"./lib/create-uniforms\":258,\"./lib/reflect\":259,\"./lib/runtime-reflect\":260,\"./lib/shader-cache\":261}],256:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],257:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\"./GLError\":256,dup:214}],258:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":256,\"./reflect\":259,dup:215}],259:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],260:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],261:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":256,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],262:[function(t,e,r){\"use strict\";function n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}function i(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r}e.exports=i;var a=n.prototype;a.update=function(t){t=t||{},this.enable=(t.enable||[!0,!0,!1,!1]).slice(),this.width=(t.width||[1,1,1,1]).slice(),this.color=(t.color||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map(function(t){return t.slice()}),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},a.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),u=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,u,s[0],u,e[0],r[0]),t[1]&&a.drawLine(l,u,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,u,s[2],u,e[2],r[2]),t[3]&&a.drawLine(l,u,l,s[3],e[3],r[3])}},a.dispose=function(){this.plot.removeOverlay(this)}},{}],263:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\");e.exports=function(t){return n(t,\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta = weight * clipOffset * screenShape;\\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\",\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\",null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":255}],264:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}function i(t,e){function r(t,e,r,n,a,o){var s=[t,e,r,0,0,0,1];s[n+3]=1,s[n]=a,i.push.apply(i,s),s[6]=-1,i.push.apply(i,s),s[n]=o,i.push.apply(i,s),i.push.apply(i,s),s[6]=1,i.push.apply(i,s),s[n]=a,i.push.apply(i,s)}var i=[];r(0,0,0,0,0,1),r(0,0,0,1,0,1),r(0,0,0,2,0,1),r(1,0,0,1,-1,1),r(1,0,0,2,-1,1),r(0,1,0,0,-1,1),r(0,1,0,2,-1,1),r(0,0,1,0,-1,1),r(0,0,1,1,-1,1);var l=a(t,i),u=o(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),c=s(t);c.attributes.position.location=0,c.attributes.color.location=1,c.attributes.weight.location=2;var h=new n(t,l,u,c);return h.update(e),h}var a=t(\"gl-buffer\"),o=t(\"gl-vao\"),s=t(\"./shaders/index\");e.exports=i;var l=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],u=n.prototype,c=[0,0,0],h=[0,0,0],f=[0,0];u.isTransparent=function(){return!1},u.drawTransparent=function(t){},u.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||l,o=t.view||l,s=t.projection||l;this.axes&&(i=this.axes.lastCubeProps.axis);for(var u=c,d=h,p=0;p<3;++p)i&&i[p]<0?(u[p]=this.bounds[0][p],d[p]=this.bounds[1][p]):(u[p]=this.bounds[1][p],d[p]=this.bounds[0][p]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=o,n.uniforms.projection=s,n.uniforms.coordinates=[this.position,u,d],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(var p=0;p<3;++p)n.uniforms.lineWidth=this.lineWidth[p]*this.pixelRatio,this.enabled[p]&&(r.draw(e.TRIANGLES,6,6*p),this.drawSides[p]&&r.draw(e.TRIANGLES,12,18+12*p));r.unbind()},u.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},u.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":263,\"gl-buffer\":156,\"gl-vao\":271}],265:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  worldCoordinate = vec3(uv.zw, f.x);\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution_2_0(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\n\\n\\nfloat beckmannSpecular_1_1(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution_2_0(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\n\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (kill > 0.0 ||\\n    any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n    discard;\\n  }\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular_1_1(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor = step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) + step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z = clipPosition.z + zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f;\\n  kill = -1.0;\\n  worldCoordinate = dataCoordinate;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if(kill > 0.0 ||\\n    any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n    discard;\\n  }\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\";r.createShader=function(t){var e=n(t,i,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,i,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,o,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,o,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":255}],266:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}function i(t){var e=x([y({colormap:t,nshades:R,format:\"rgba\"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return b.divseq(e,255),e}function a(t,e,r,i,a,o,s,l,u,c,h,f,d,p){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=i,this._pickShader=a,this._coordinateBuffer=o,this._vao=s,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=f,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new n([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=p,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[_(g.mallocFloat(1024),[0,0]),_(g.mallocFloat(1024),[0,0]),_(g.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}function o(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||j,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=N.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],M(l,t.model,l);var u=N.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return N.showSurface=o,N.showContour=s,N}function s(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=B;n.model=t.model||D,n.view=t.view||D,n.projection=t.projection||D,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=k(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],s=0;s<3;++s)a[s]=Math.min(Math.max(this.clipBounds[i][s],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=V,n.vertexColor=this.vertexColor;var l=U;for(M(l,n.view,n.model),M(l,n.projection,l),k(l,l),i=0;i<3;++i)n.eyePosition[i]=l[12+i]/l[15];var u=l[15];for(i=0;i<3;++i)u+=this.lightPosition[i]*l[4*i+3];for(i=0;i<3;++i){var c=l[12+i];for(s=0;s<3;++s)c+=l[4*s+i]*this.lightPosition[s];n.lightPosition[i]=c/u}var h=o(n,this);if(h.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=h.projections[i],this._shader.uniforms.clipBounds=h.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(h.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var d=this._contourVAO;for(d.bind(),i=0;i<3;++i)for(f.uniforms.permutation=O[i],r.lineWidth(this.contourWidth[i]),s=0;s<this.contourLevels[i].length;++s)this._contourCounts[i][s]&&(s===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==s&&s-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),f.uniforms.height=this.contourLevels[i][s],d.draw(r.LINES,this._contourCounts[i][s],this._contourOffsets[i][s]));for(i=0;i<3;++i)for(f.uniforms.model=h.projections[i],f.uniforms.clipBounds=h.clipBounds[i],s=0;s<3;++s)if(this.contourProject[i][s]){f.uniforms.permutation=O[s],r.lineWidth(this.contourWidth[s]);for(var p=0;p<this.contourLevels[s].length;++p)p===this.highlightLevel[s]?(f.uniforms.contourColor=this.highlightColor[s],f.uniforms.contourTint=this.highlightTint[s]):0!==p&&p-1!==this.highlightLevel[s]||(f.uniforms.contourColor=this.contourColor[s],f.uniforms.contourTint=this.contourTint[s]),f.uniforms.height=this.contourLevels[s][p],d.draw(r.LINES,this._contourCounts[s][p],this._contourOffsets[s][p])}for(d=this._dynamicVAO,d.bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=O[i],r.lineWidth(this.dynamicWidth[i]),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),s=0;s<3;++s)this.contourProject[s][i]&&(f.uniforms.model=h.projections[s],f.uniforms.clipBounds=h.clipBounds[s],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));d.unbind()}}function l(t,e){var r=e.shape.slice(),n=t.shape.slice();b.assign(t.lo(1,1).hi(r[0],r[1]),e),b.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),b.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),b.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),b.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function u(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function c(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function h(t){if(Array.isArray(t)){if(Array.isArray(t))return[c(t[0]),c(t[1]),c(t[2])];var e=c(t);return[e.slice(),e.slice(),e.slice()]}}function f(t){var e=t.gl,r=E(e),n=C(e),i=L(e),o=I(e),s=p(e),l=m(e,[{buffer:s,size:4,stride:z,offset:0},{buffer:s,size:3,stride:z,offset:16},{buffer:s,size:3,stride:z,offset:28}]),u=p(e),c=m(e,[{buffer:u,size:4,stride:20,offset:0},{buffer:u,size:1,stride:20,offset:16}]),h=p(e),f=m(e,[{buffer:h,size:2,type:e.FLOAT}]),d=v(e,1,R,e.RGBA,e.UNSIGNED_BYTE);d.minFilter=e.LINEAR,d.magFilter=e.LINEAR;var g=new a(e,[0,0],[[0,0,0],[0,0,0]],r,n,s,l,d,i,o,u,c,h,f),y={levels:[[],[],[]]};for(var b in t)y[b]=t[b];return y.colormap=y.colormap||\"jet\",g.update(y),g}e.exports=f\n",
+       ";var d=t(\"bit-twiddle\"),p=t(\"gl-buffer\"),m=t(\"gl-vao\"),v=t(\"gl-texture2d\"),g=t(\"typedarray-pool\"),y=t(\"colormap\"),b=t(\"ndarray-ops\"),x=t(\"ndarray-pack\"),_=t(\"ndarray\"),w=t(\"surface-nets\"),M=t(\"gl-mat4/multiply\"),k=t(\"gl-mat4/invert\"),A=t(\"binary-search-bounds\"),T=t(\"ndarray-gradient\"),S=t(\"./lib/shaders\"),E=S.createShader,L=S.createContourShader,C=S.createPickShader,I=S.createPickContourShader,z=40,D=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],O=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];!function(){for(var t=0;t<3;++t){var e=O[t],r=(t+1)%3,n=(t+2)%3;e[r+0]=1,e[n+3]=1,e[t+6]=1}}();var R=256,F=a.prototype;F.isTransparent=function(){return this.opacity<1},F.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},F.pickSlots=1,F.setPickBase=function(t){this.pickId=t};var j=[0,0,0],N={showSurface:!1,showContour:!1,projections:[D.slice(),D.slice(),D.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]},B={model:D,view:D,projection:D,inverseModel:D.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},U=D.slice(),V=[1,0,0,0,1,0,0,0,1];F.draw=function(t){return s.call(this,t,!1)},F.drawTransparent=function(t){return s.call(this,t,!0)};var H={model:D,view:D,projection:D,inverseModel:D,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};F.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=H;r.model=t.model||D,r.view=t.view||D,r.projection=t.projection||D,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=V;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var s=o(r,this);if(s.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=s.projections[n],this._pickShader.uniforms.clipBounds=s.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(s.showContour){var l=this._contourPickShader;l.bind(),l.uniforms=r;var u=this._contourVAO;for(u.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),l.uniforms.permutation=O[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(l.uniforms.height=this.contourLevels[a][n],u.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(l.uniforms.model=s.projections[n],l.uniforms.clipBounds=s.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){l.uniforms.permutation=O[a],e.lineWidth(this.contourWidth[a]);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(l.uniforms.height=this.contourLevels[a][c],u.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}u.unbind()}},F.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var h=c?a:1-a,f=0;f<2;++f)for(var d=f?l:1-l,p=i+c,m=s+f,v=h*d,g=0;g<3;++g)u[g]+=this._field[g].get(p,m)*v;for(var y=this._pickResult.level,b=0;b<3;++b)if(y[b]=A.le(this.contourLevels[b],u[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]<this.contourLevels[b].length-1){var x=this.contourLevels[b][y[b]],_=this.contourLevels[b][y[b]+1];Math.abs(x-u[b])>Math.abs(_-u[b])&&(y[b]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],g=0;g<3;++g)r.dataCoordinate[g]=this._field[g].get(r.index[0],r.index[1]);return r},F.update=function(t){t=t||{},this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=u(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=u(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=u(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=h(t.contourColor)),\"contourProject\"in t&&(this.contourProject=u(t.contourProject,function(t){return u(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=h(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=u(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=u(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var n=(e.shape[0]+2)*(e.shape[1]+2);n>this._field[2].data.length&&(g.freeFloat(this._field[2].data),this._field[2].data=g.mallocFloat(d.nextPow2(n))),this._field[2]=_(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),l(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(g.freeFloat(this._field[o].data),this._field[o].data=g.mallocFloat(this._field[2].size)),this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var s=t.coords;if(!Array.isArray(s)||3!==s.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=s[o];for(y=0;y<2;++y)if(c.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");l(this._field[o],c)}}else if(t.ticks){var f=t.ticks;if(!Array.isArray(f)||2!==f.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=f[o];if((Array.isArray(p)||p.length)&&(p=_(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var m=_(p.data,a);m.stride[o]=p.stride[0],m.stride[1^o]=0,l(this._field[o],m)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var b=this._field,x=_(g.mallocFloat(3*b[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)T(x.pick(o),b[o],\"mirror\");var M=_(g.mallocFloat(3*b[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var k=x.get(0,o,y,0),A=x.get(0,o,y,1),S=x.get(1,o,y,0),E=x.get(1,o,y,1),L=x.get(2,o,y,0),C=x.get(2,o,y,1),I=S*C-E*L,z=L*A-C*k,D=k*E-A*S,O=Math.sqrt(I*I+z*z+D*D);O<1e-8?(O=Math.max(Math.abs(I),Math.abs(z),Math.abs(D)),O<1e-8?(D=1,z=I=0,O=1):O=1/O):O=1/Math.sqrt(O),M.set(o,y,0,I*O),M.set(o,y,1,z*O),M.set(o,y,2,D*O)}g.free(x.data);var R=[1/0,1/0,1/0],F=[-1/0,-1/0,-1/0],j=1/0,N=-1/0,B=(a[0]-1)*(a[1]-1)*6,U=g.mallocFloat(d.nextPow2(10*B)),V=0,H=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var q=0;q<2;++q)for(var G=0;G<2;++G)for(var Y=0;Y<3;++Y){var W=this._field[Y].get(1+o+q,1+y+G);if(isNaN(W)||!isFinite(W))continue t}for(Y=0;Y<6;++Y){var X=o+P[Y][0],Z=y+P[Y][1],J=this._field[0].get(X+1,Z+1),K=this._field[1].get(X+1,Z+1);W=this._field[2].get(X+1,Z+1);var Q=W;I=M.get(X+1,Z+1,0),z=M.get(X+1,Z+1,1),D=M.get(X+1,Z+1,2),t.intensity&&(Q=t.intensity.get(X,Z)),U[V++]=X,U[V++]=Z,U[V++]=J,U[V++]=K,U[V++]=W,U[V++]=0,U[V++]=Q,U[V++]=I,U[V++]=z,U[V++]=D,R[0]=Math.min(R[0],J),R[1]=Math.min(R[1],K),R[2]=Math.min(R[2],W),j=Math.min(j,Q),F[0]=Math.max(F[0],J),F[1]=Math.max(F[1],K),F[2]=Math.max(F[2],W),N=Math.max(N,Q),H+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],N=+t.intensityBounds[1]),o=6;o<V;o+=10)U[o]=(U[o]-j)/(N-j);this._vertexCount=H,this._coordinateBuffer.update(U.subarray(0,V)),g.freeFloat(U),g.free(M.data),this.bounds=[R,F],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===N||(r=!0),this.intensityBounds=[j,N]}if(\"levels\"in t){var $=t.levels;for($=Array.isArray($[0])?$.slice():[[],[],$],o=0;o<3;++o)$[o]=$[o].slice(),$.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if($[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<$[o].length;++y)if($[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=$}if(r){b=this._field,a=this.shape;for(var tt=[],et=0;et<3;++et){$=this.contourLevels[et];var rt=[],nt=[],it=[0,0,0];for(o=0;o<$.length;++o){var at=w(this._field[et],$[o]);rt.push(tt.length/5|0),H=0;t:for(y=0;y<at.cells.length;++y){var ot=at.cells[y];for(Y=0;Y<2;++Y){var st=at.positions[ot[Y]],lt=st[0],ut=0|Math.floor(lt),ct=lt-ut,ht=st[1],ft=0|Math.floor(ht),dt=ht-ft,pt=!1;e:for(var mt=0;mt<3;++mt){it[mt]=0;var vt=(et+mt+1)%3;for(q=0;q<2;++q){var gt=q?ct:1-ct;for(X=0|Math.min(Math.max(ut+q,0),a[0]),G=0;G<2;++G){var yt=G?dt:1-dt;if(Z=0|Math.min(Math.max(ft+G,0),a[1]),W=mt<2?this._field[vt].get(X,Z):(this.intensity.get(X,Z)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(W)||isNaN(W)){pt=!0;break e}var bt=gt*yt;it[mt]+=bt*W}}}if(pt){if(Y>0){for(var xt=0;xt<5;++xt)tt.pop();H-=1}continue t}tt.push(it[0],it[1],st[0],st[1],it[2]),H+=1}}nt.push(H)}this._contourOffsets[et]=rt,this._contourCounts[et]=nt}var _t=g.mallocFloat(tt.length);for(o=0;o<tt.length;++o)_t[o]=tt[o];this._contourBuffer.update(_t),g.freeFloat(_t)}t.colormap&&this._colorMap.setPixels(i(t.colormap))},F.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)g.freeFloat(this._field[t].data)},F.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=g.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var s=(o+1)%3,l=(o+2)%3,u=this._field[o],c=this._field[s],h=this._field[l],f=(this.intensity,w(u,r[o])),d=f.cells,p=f.positions;for(this._dynamicOffsets[o]=n,e=0;e<d.length;++e)for(var m=d[e],v=0;v<2;++v){var y=p[m[v]],b=+y[0],x=0|b,_=0|Math.min(x+1,i[0]),M=b-x,k=1-M,A=+y[1],T=0|A,S=0|Math.min(T+1,i[1]),E=A-T,L=1-E,C=k*L,I=k*E,z=M*L,D=M*E,P=C*c.get(x,T)+I*c.get(x,S)+z*c.get(_,T)+D*c.get(_,S),O=C*h.get(x,T)+I*h.get(x,S)+z*h.get(_,T)+D*h.get(_,S);if(isNaN(P)||isNaN(O)){v&&(n-=1);break}a[2*n+0]=P,a[2*n+1]=O,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),g.freeFloat(a)}}},{\"./lib/shaders\":265,\"binary-search-bounds\":66,\"bit-twiddle\":67,colormap:99,\"gl-buffer\":156,\"gl-mat4/invert\":181,\"gl-mat4/multiply\":183,\"gl-texture2d\":267,\"gl-vao\":271,ndarray:467,\"ndarray-gradient\":458,\"ndarray-ops\":461,\"ndarray-pack\":462,\"surface-nets\":531,\"typedarray-pool\":541}],267:[function(t,e,r){\"use strict\";function n(t){g=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],y=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],b=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function i(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}function a(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function o(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}function s(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function l(t,e,r,n,i,a,o,l){var u=l.dtype,c=l.shape.slice();if(c.length<2||c.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var h=0,f=0,d=s(c,l.stride.slice());\"float32\"===u?h=t.FLOAT:\"float64\"===u?(h=t.FLOAT,d=!1,u=\"float32\"):\"uint8\"===u?h=t.UNSIGNED_BYTE:(h=t.UNSIGNED_BYTE,d=!1,u=\"uint8\");if(2===c.length)f=t.LUMINANCE,c=[c[0],c[1],1],l=p(l.data,c,[l.stride[0],l.stride[1],1],l.offset);else{if(3!==c.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===c[2])f=t.ALPHA;else if(2===c[2])f=t.LUMINANCE_ALPHA;else if(3===c[2])f=t.RGB;else{if(4!==c[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");f=t.RGBA}c[2]}if(f!==t.LUMINANCE&&f!==t.ALPHA||i!==t.LUMINANCE&&i!==t.ALPHA||(f=i),f!==i)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var g=l.size,y=o.indexOf(n)<0;if(y&&o.push(n),h===a&&d)0===l.offset&&l.data.length===g?y?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,a,l.data):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,a,l.data):y?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,a,l.data.subarray(l.offset,l.offset+g)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,a,l.data.subarray(l.offset,l.offset+g));else{var b;b=a===t.FLOAT?v.mallocFloat32(g):v.mallocUint8(g);var _=p(b,c,[c[2],c[2]*c[0],1]);h===t.FLOAT&&a===t.UNSIGNED_BYTE?x(_,l):m.assign(_,l),y?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,a,b.subarray(0,g)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,a,b.subarray(0,g)),a===t.FLOAT?v.freeFloat32(b):v.freeUint8(b)}}function u(t){var e=t.createTexture();return 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n=t(\"./literals\");e.exports=n.slice().concat([\"layout\",\"centroid\",\"smooth\",\"case\",\"mat2x2\",\"mat2x3\",\"mat2x4\",\"mat3x2\",\"mat3x3\",\"mat3x4\",\"mat4x2\",\"mat4x3\",\"mat4x4\",\"uint\",\"uvec2\",\"uvec3\",\"uvec4\",\"samplerCubeShadow\",\"sampler2DArray\",\"sampler2DArrayShadow\",\"isampler2D\",\"isampler3D\",\"isamplerCube\",\"isampler2DArray\",\"usampler2D\",\"usampler3D\",\"usamplerCube\",\"usampler2DArray\",\"coherent\",\"restrict\",\"readonly\",\"writeonly\",\"resource\",\"atomic_uint\",\"noperspective\",\"patch\",\"sample\",\"subroutine\",\"common\",\"partition\",\"active\",\"filter\",\"image1D\",\"image2D\",\"image3D\",\"imageCube\",\"iimage1D\",\"iimage2D\",\"iimage3D\",\"iimageCube\",\"uimage1D\",\"uimage2D\",\"uimage3D\",\"uimageCube\",\"image1DArray\",\"image2DArray\",\"iimage1DArray\",\"iimage2DArray\",\"uimage1DArray\",\"uimage2DArray\",\"image1DShadow\",\"image2DShadow\",\"image1DArrayShadow\",\"image2DArrayShadow\",\"imageBuffer\",\"iimageBuffer\",\"uimageBuffer\",\"sampler1DArray\",\"sampler1DArrayShadow\",\"isampler1D\",\"isampler1DArray\",\"usampler1D\",\"usampler1DArray\",\"isampler2DRect\",\"usampler2DRect\",\"samplerBuffer\",\"isamplerBuffer\",\"usamplerBuffer\",\"sampler2DMS\",\"isampler2DMS\",\"usampler2DMS\",\"sampler2DMSArray\",\"isampler2DMSArray\",\"usampler2DMSArray\"])},{\"./literals\":284}],284:[function(t,e,r){e.exports=[\"precision\",\"highp\",\"mediump\",\"lowp\",\"attribute\",\"const\",\"uniform\",\"varying\",\"break\",\"continue\",\"do\",\"for\",\"while\",\"if\",\"else\",\"in\",\"out\",\"inout\",\"float\",\"int\",\"void\",\"bool\",\"true\",\"false\",\"discard\",\"return\",\"mat2\",\"mat3\",\"mat4\",\"vec2\",\"vec3\",\"vec4\",\"ivec2\",\"ivec3\",\"ivec4\",\"bvec2\",\"bvec3\",\"bvec4\",\"sampler1D\",\"sampler2D\",\"sampler3D\",\"samplerCube\",\"sampler1DShadow\",\"sampler2DShadow\",\"struct\",\"asm\",\"class\",\"union\",\"enum\",\"typedef\",\"template\",\"this\",\"packed\",\"goto\",\"switch\",\"default\",\"inline\",\"noinline\",\"volatile\",\"public\",\"static\",\"extern\",\"external\",\"interface\",\"long\",\"short\",\"double\",\"half\",\"fixed\",\"unsigned\",\"input\",\"output\",\"hvec2\",\"hvec3\",\"hvec4\",\"dvec2\",\"dvec3\",\"dvec4\",\"fvec2\",\"fvec3\",\"fvec4\",\"sampler2DRect\",\"sampler3DRect\",\"sampler2DRectShadow\",\"sizeof\",\"cast\",\"namespace\",\"using\"]},{}],285:[function(t,e,r){e.exports=[\"<<=\",\">>=\",\"++\",\"--\",\"<<\",\">>\",\"<=\",\">=\",\"==\",\"!=\",\"&&\",\"||\",\"+=\",\"-=\",\"*=\",\"/=\",\"%=\",\"&=\",\"^^\",\"^=\",\"|=\",\"(\",\")\",\"[\",\"]\",\".\",\"!\",\"~\",\"*\",\"/\",\"%\",\"+\",\"-\",\"<\",\">\",\"&\",\"^\",\"|\",\"?\",\":\",\"=\",\",\",\";\",\"{\",\"}\"]},{}],286:[function(t,e,r){function 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vec2 u_extrude_scale;\\nuniform float u_devicepixelratio;\\n\\nattribute vec2 a_pos;\\n\\n#pragma mapbox: define lowp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_extrude;\\nvarying lowp float v_antialiasblur;\\n\\nvoid main(void) {\\n    #pragma mapbox: initialize lowp vec4 color\\n    #pragma mapbox: initialize mediump float radius\\n    #pragma mapbox: initialize lowp float blur\\n    #pragma mapbox: initialize lowp float opacity\\n\\n    // unencode the extrusion vector that we snuck into the a_pos vector\\n    v_extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\\n\\n    vec2 extrude = v_extrude * radius * u_extrude_scale;\\n    // multiply a_pos by 0.5, since we had it * 2 in order to sneak\\n    // in extrusion data\\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5), 0, 1);\\n\\n    if (u_scale_with_map) {\\n        gl_Position.xy += extrude;\\n    } else {\\n        gl_Position.xy += extrude * gl_Position.w;\\n    }\\n\\n    // This is a minimum blur distance that serves as a faux-antialiasing for\\n    // the circle. since blur is a ratio of the circle's size and the intent is\\n    // to keep the blur at roughly 1px, the two are inversely related.\\n    v_antialiasblur = 1.0 / u_devicepixelratio / radius;\\n}\\n\"},line:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\nuniform float u_blur;\\n\\nvarying vec2 v_linewidth;\\nvarying vec2 v_normal;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    // Calculate the distance of the pixel from the line in pixels.\\n    float dist = length(v_normal) * v_linewidth.s;\\n\\n    // Calculate the antialiasing fade factor. This is either when fading in\\n    // the line in case of an offset line (v_linewidth.t) or when fading out\\n    // (v_linewidth.s)\\n    float blur = u_blur * v_gamma_scale;\\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n    gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform mediump float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\nuniform mediump float u_blur;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    vec2 a_extrude = a_data.xy - 128.0;\\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\\n\\n    // We store the texture normals in the most insignificant bit\\n    // transform y so that 0 => -1 and 1 => 1\\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n    // y is 1 if the normal points up, and -1 if it points down\\n    mediump vec2 normal = mod(a_pos, 2.0);\\n    normal.y = sign(normal.y - 0.5);\\n    v_normal = normal;\\n\\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n    // Scale the extrusion vector down to a normal and then up by the line width\\n    // of this vertex.\\n    mediump vec2 dist = outset * a_extrude * scale;\\n\\n    // Calculate the offset when drawing a line that is to the side of the actual line.\\n    // We do this by creating a vector that points towards the extrude, but rotate\\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\\n    // extrude vector points in another direction.\\n    mediump float u = 0.5 * a_direction;\\n    mediump float t = 1.0 - abs(u);\\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n    // Remove the texture normal bit of the position before scaling it with the\\n    // model/view matrix.\\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n\\n    // position of y on the screen\\n    float y = gl_Position.y / gl_Position.w;\\n\\n    // how much features are squished in the y direction by the tilt\\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n    // how much features are squished in all directions by the perspectiveness\\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n    v_linewidth = vec2(outset, inset);\\n    v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},linepattern:{\n",
+       "fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_blur;\\n\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_fade;\\nuniform float u_opacity;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    // Calculate the distance of the pixel from the line in pixels.\\n    float dist = length(v_normal) * v_linewidth.s;\\n\\n    // Calculate the antialiasing fade factor. This is either when fading in\\n    // the line in case of an offset line (v_linewidth.t) or when fading out\\n    // (v_linewidth.s)\\n    float blur = u_blur * v_gamma_scale;\\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n    float x_a = mod(v_linesofar / u_pattern_size_a.x, 1.0);\\n    float x_b = mod(v_linesofar / u_pattern_size_b.x, 1.0);\\n    float y_a = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_a.y);\\n    float y_b = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_b.y);\\n    vec2 pos_a = mix(u_pattern_tl_a, u_pattern_br_a, vec2(x_a, y_a));\\n    vec2 pos_b = mix(u_pattern_tl_b, u_pattern_br_b, vec2(x_b, y_b));\\n\\n    vec4 color = mix(texture2D(u_image, pos_a), texture2D(u_image, pos_b), u_fade);\\n\\n    alpha *= u_opacity;\\n\\n    gl_FragColor = color * alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform mediump float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    vec2 a_extrude = a_data.xy - 128.0;\\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\\n    float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n    // We store the texture normals in the most insignificant bit\\n    // transform y so that 0 => -1 and 1 => 1\\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n    // y is 1 if the normal points up, and -1 if it points down\\n    mediump vec2 normal = mod(a_pos, 2.0);\\n    normal.y = sign(normal.y - 0.5);\\n    v_normal = normal;\\n\\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n    // Scale the extrusion vector down to a normal and then up by the line width\\n    // of this vertex.\\n    mediump vec2 dist = outset * a_extrude * scale;\\n\\n    // Calculate the offset when drawing a line that is to the side of the actual line.\\n    // We do this by creating a vector that points towards the extrude, but rotate\\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\\n    // extrude vector points in another direction.\\n    mediump float u = 0.5 * a_direction;\\n    mediump float t = 1.0 - abs(u);\\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n    // Remove the texture normal bit of the position before scaling it with the\\n    // model/view matrix.\\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n    v_linesofar = a_linesofar;\\n\\n    // position of y on the screen\\n    float y = gl_Position.y / gl_Position.w;\\n\\n    // how much features are squished in the y direction by the tilt\\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n    // how much features are squished in all directions by the perspectiveness\\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n    v_linewidth = vec2(outset, inset);\\n    v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},linesdfpattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\n\\nuniform float u_blur;\\nuniform sampler2D u_image;\\nuniform float u_sdfgamma;\\nuniform float u_mix;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    // Calculate the distance of the pixel from the line in pixels.\\n    float dist = length(v_normal) * v_linewidth.s;\\n\\n    // Calculate the antialiasing fade factor. This is either when fading in\\n    // the line in case of an offset line (v_linewidth.t) or when fading out\\n    // (v_linewidth.s)\\n    float blur = u_blur * v_gamma_scale;\\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n    float sdfdist_a = texture2D(u_image, v_tex_a).a;\\n    float sdfdist_b = texture2D(u_image, v_tex_b).a;\\n    float sdfdist = mix(sdfdist_a, sdfdist_b, u_mix);\\n    alpha *= smoothstep(0.5 - u_sdfgamma, 0.5 + u_sdfgamma, sdfdist);\\n\\n    gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform vec2 u_patternscale_a;\\nuniform float u_tex_y_a;\\nuniform vec2 u_patternscale_b;\\nuniform float u_tex_y_b;\\nuniform float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    vec2 a_extrude = a_data.xy - 128.0;\\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\\n    float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n    // We store the texture normals in the most insignificant bit\\n    // transform y so that 0 => -1 and 1 => 1\\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n    // y is 1 if the normal points up, and -1 if it points down\\n    mediump vec2 normal = mod(a_pos, 2.0);\\n    normal.y = sign(normal.y - 0.5);\\n    v_normal = normal;\\n\\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n    // Scale the extrusion vector down to a normal and then up by the line width\\n    // of this vertex.\\n    mediump vec2 dist = outset * a_extrude * scale;\\n\\n    // Calculate the offset when drawing a line that is to the side of the actual line.\\n    // We do this by creating a vector that points towards the extrude, but rotate\\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\\n    // extrude vector points in another direction.\\n    mediump float u = 0.5 * a_direction;\\n    mediump float t = 1.0 - abs(u);\\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n    // Remove the texture normal bit of the position before scaling it with the\\n    // model/view matrix.\\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n\\n    v_tex_a = vec2(a_linesofar * u_patternscale_a.x, normal.y * u_patternscale_a.y + u_tex_y_a);\\n    v_tex_b = vec2(a_linesofar * u_patternscale_b.x, normal.y * u_patternscale_b.y + u_tex_y_b);\\n\\n    // position of y on the screen\\n    float y = gl_Position.y / gl_Position.w;\\n\\n    // how much features are squished in the y direction by the tilt\\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n    // how much features are squished in all directions by the perspectiveness\\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n    v_linewidth = vec2(outset, inset);\\n    v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},outline:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n#pragma mapbox: define lowp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n    #pragma mapbox: initialize lowp vec4 outline_color\\n    #pragma mapbox: initialize lowp float opacity\\n\\n    float dist = length(v_pos - gl_FragCoord.xy);\\n    float alpha = smoothstep(1.0, 0.0, dist);\\n    gl_FragColor = outline_color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define lowp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n    #pragma mapbox: initialize lowp vec4 outline_color\\n    #pragma mapbox: initialize lowp float opacity\\n\\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n    v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},outlinepattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n    vec2 imagecoord = mod(v_pos_a, 1.0);\\n    vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\\n    vec4 color1 = texture2D(u_image, pos);\\n\\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n    vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\\n    vec4 color2 = texture2D(u_image, pos2);\\n\\n    // find distance to outline for alpha interpolation\\n\\n    float dist = length(v_pos - gl_FragCoord.xy);\\n    float alpha = smoothstep(1.0, 0.0, dist);\\n    \\n\\n    gl_FragColor = mix(color1, color2, u_mix) * alpha * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n    vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\\n    vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\\n\\n    // the correct offset needs to be calculated.\\n    //\\n    // The offset depends on how many pixels are between the world origin and\\n    // the edge of the tile:\\n    // vec2 offset = mod(pixel_coord, size)\\n    //\\n    // At high zoom levels there are a ton of pixels between the world origin\\n    // and the edge of the tile. The glsl spec only guarantees 16 bits of\\n    // precision for highp floats. We need more than that.\\n    //\\n    // The pixel_coord is passed in as two 16 bit values:\\n    // pixel_coord_upper = floor(pixel_coord / 2^16)\\n    // pixel_coord_lower = mod(pixel_coord, 2^16)\\n    //\\n    // The offset is calculated in a series of steps that should preserve this precision:\\n    vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\\n    vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\\n\\n    v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\\n    v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\\n\\n    v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},pattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n\\n    vec2 imagecoord = mod(v_pos_a, 1.0);\\n    vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\\n    vec4 color1 = texture2D(u_image, pos);\\n\\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n    vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\\n    vec4 color2 = texture2D(u_image, pos2);\\n\\n    gl_FragColor = mix(color1, color2, u_mix) * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n    vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\\n    vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\\n\\n    // the correct offset needs to be calculated.\\n    //\\n    // The offset depends on how many pixels are between the world origin and\\n    // the edge of the tile:\\n    // vec2 offset = mod(pixel_coord, size)\\n    //\\n    // At high zoom levels there are a ton of pixels between the world origin\\n    // and the edge of the tile. The glsl spec only guarantees 16 bits of\\n    // precision for highp floats. We need more than that.\\n    //\\n    // The pixel_coord is passed in as two 16 bit values:\\n    // pixel_coord_upper = floor(pixel_coord / 2^16)\\n    // pixel_coord_lower = mod(pixel_coord, 2^16)\\n    //\\n    // The offset is calculated in a series of steps that should preserve this precision:\\n    vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\\n    vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\\n\\n    v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\\n    v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\\n}\\n\"},raster:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity0;\\nuniform float u_opacity1;\\nuniform sampler2D u_image0;\\nuniform sampler2D u_image1;\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nuniform float u_brightness_low;\\nuniform float u_brightness_high;\\n\\nuniform float u_saturation_factor;\\nuniform float u_contrast_factor;\\nuniform vec3 u_spin_weights;\\n\\nvoid main() {\\n\\n    // read and cross-fade colors from the main and parent tiles\\n    vec4 color0 = texture2D(u_image0, v_pos0);\\n    vec4 color1 = texture2D(u_image1, v_pos1);\\n    vec4 color = color0 * u_opacity0 + color1 * u_opacity1;\\n    vec3 rgb = color.rgb;\\n\\n    // spin\\n    rgb = vec3(\\n        dot(rgb, u_spin_weights.xyz),\\n        dot(rgb, u_spin_weights.zxy),\\n        dot(rgb, u_spin_weights.yzx));\\n\\n    // saturation\\n    float average = (color.r + color.g + color.b) / 3.0;\\n    rgb += (average - rgb) * u_saturation_factor;\\n\\n    // contrast\\n    rgb = (rgb - 0.5) * u_contrast_factor + 0.5;\\n\\n    // brightness\\n    vec3 u_high_vec = vec3(u_brightness_low, u_brightness_low, u_brightness_low);\\n    vec3 u_low_vec = vec3(u_brightness_high, u_brightness_high, u_brightness_high);\\n\\n    gl_FragColor = vec4(mix(u_high_vec, u_low_vec, rgb), color.a);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_tl_parent;\\nuniform float u_scale_parent;\\nuniform float u_buffer_scale;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nvoid main() {\\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n    v_pos0 = (((a_texture_pos / 32767.0) - 0.5) / u_buffer_scale ) + 0.5;\\n    v_pos1 = (v_pos0 * u_scale_parent) + u_tl_parent;\\n}\\n\"},icon:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform sampler2D u_texture;\\nuniform sampler2D u_fadetexture;\\nuniform lowp float u_opacity;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\n\\nvoid main() {\\n    lowp float alpha = texture2D(u_fadetexture, v_fade_tex).a * u_opacity;\\n    gl_FragColor = texture2D(u_texture, v_tex) * alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_offset;\\nattribute vec2 a_texture_pos;\\nattribute vec4 a_data;\\n\\n\\n// matrix is for the vertex position.\\nuniform mat4 u_matrix;\\n\\nuniform mediump float u_zoom;\\nuniform bool u_rotate_with_map;\\nuniform vec2 u_extrude_scale;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\n\\nvoid main() {\\n    vec2 a_tex = a_texture_pos.xy;\\n    mediump float a_labelminzoom = a_data[0];\\n    mediump vec2 a_zoom = a_data.pq;\\n    mediump float a_minzoom = a_zoom[0];\\n    mediump float a_maxzoom = a_zoom[1];\\n\\n    // u_zoom is the current zoom level adjusted for the change in font size\\n    mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\\n\\n    vec2 extrude = u_extrude_scale * (a_offset / 64.0);\\n    if (u_rotate_with_map) {\\n        gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\\n        gl_Position.z += z * gl_Position.w;\\n    } else {\\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n    }\\n\\n    v_tex = a_tex / u_texsize;\\n    v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\\n}\\n\"},sdf:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform sampler2D u_texture;\\nuniform sampler2D u_fadetexture;\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\nuniform lowp float u_buffer;\\nuniform lowp float u_gamma;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    lowp float dist = texture2D(u_texture, v_tex).a;\\n    lowp float fade_alpha = texture2D(u_fadetexture, v_fade_tex).a;\\n    lowp float gamma = u_gamma * v_gamma_scale;\\n    lowp float alpha = smoothstep(u_buffer - gamma, u_buffer + gamma, dist) * fade_alpha;\\n\\n    gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n    gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nconst float PI = 3.141592653589793;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_offset;\\nattribute vec2 a_texture_pos;\\nattribute vec4 a_data;\\n\\n\\n// matrix is for the vertex position.\\nuniform mat4 u_matrix;\\n\\nuniform mediump float u_zoom;\\nuniform bool u_rotate_with_map;\\nuniform bool u_pitch_with_map;\\nuniform mediump float u_pitch;\\nuniform mediump float u_bearing;\\nuniform mediump float u_aspect_ratio;\\nuniform vec2 u_extrude_scale;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n    vec2 a_tex = a_texture_pos.xy;\\n    mediump float a_labelminzoom = a_data[0];\\n    mediump vec2 a_zoom = a_data.pq;\\n    mediump float a_minzoom = a_zoom[0];\\n    mediump float a_maxzoom = a_zoom[1];\\n\\n    // u_zoom is the current zoom level adjusted for the change in font size\\n    mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\\n\\n    // pitch-alignment: map\\n    // rotation-alignment: map | viewport\\n    if (u_pitch_with_map) {\\n        lowp float angle = u_rotate_with_map ? (a_data[1] / 256.0 * 2.0 * PI) : u_bearing;\\n        lowp float asin = sin(angle);\\n        lowp float acos = cos(angle);\\n        mat2 RotationMatrix = mat2(acos, asin, -1.0 * asin, acos);\\n        vec2 offset = RotationMatrix * a_offset;\\n        vec2 extrude = u_extrude_scale * (offset / 64.0);\\n        gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\\n        gl_Position.z += z * gl_Position.w;\\n    // pitch-alignment: viewport\\n    // rotation-alignment: map\\n    } else if (u_rotate_with_map) {\\n        // foreshortening factor to apply on pitched maps\\n        // as a label goes from horizontal <=> vertical in angle\\n        // it goes from 0% foreshortening to up to around 70% foreshortening\\n        lowp float pitchfactor = 1.0 - cos(u_pitch * sin(u_pitch * 0.75));\\n\\n        lowp float lineangle = a_data[1] / 256.0 * 2.0 * PI;\\n\\n        // use the lineangle to position points a,b along the line\\n        // project the points and calculate the label angle in projected space\\n        // this calculation allows labels to be rendered unskewed on pitched maps\\n        vec4 a = u_matrix * vec4(a_pos, 0, 1);\\n        vec4 b = u_matrix * vec4(a_pos + vec2(cos(lineangle),sin(lineangle)), 0, 1);\\n        lowp float angle = atan((b[1]/b[3] - a[1]/a[3])/u_aspect_ratio, b[0]/b[3] - a[0]/a[3]);\\n        lowp float asin = sin(angle);\\n        lowp float acos = cos(angle);\\n        mat2 RotationMatrix = mat2(acos, -1.0 * asin, asin, acos);\\n\\n        vec2 offset = RotationMatrix * (vec2((1.0-pitchfactor)+(pitchfactor*cos(angle*2.0)), 1.0) * a_offset);\\n        vec2 extrude = u_extrude_scale * (offset / 64.0);\\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n        gl_Position.z += z * gl_Position.w;\\n    // pitch-alignment: viewport\\n    // rotation-alignment: viewport\\n    } else {\\n        vec2 extrude = u_extrude_scale * (a_offset / 64.0);\\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n    }\\n\\n    v_gamma_scale = (gl_Position.w - 0.5);\\n\\n    v_tex = a_tex / u_texsize;\\n    v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\\n}\\n\"},collisionbox:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_zoom;\\nuniform float u_maxzoom;\\n\\nvarying float v_max_zoom;\\nvarying float v_placement_zoom;\\n\\nvoid main() {\\n\\n    float alpha = 0.5;\\n\\n    gl_FragColor = vec4(0.0, 1.0, 0.0, 1.0) * alpha;\\n\\n    if (v_placement_zoom > u_zoom) {\\n        gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\\n    }\\n\\n    if (u_zoom >= v_max_zoom) {\\n        gl_FragColor = vec4(0.0, 0.0, 0.0, 1.0) * alpha * 0.25;\\n    }\\n\\n    if (v_placement_zoom >= u_maxzoom) {\\n        gl_FragColor = vec4(0.0, 0.0, 1.0, 1.0) * alpha * 0.2;\\n    }\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_extrude;\\nattribute vec2 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform float u_scale;\\n\\nvarying float v_max_zoom;\\nvarying float v_placement_zoom;\\n\\nvoid main() {\\n    gl_Position = u_matrix * vec4(a_pos + a_extrude / u_scale, 0.0, 1.0);\\n\\n    v_max_zoom = a_data.x;\\n    v_placement_zoom = a_data.y;\\n}\\n\"}},e.exports.util=\"float evaluate_zoom_function_1(const vec4 values, const float t) {\\n    if (t < 1.0) {\\n        return mix(values[0], values[1], t);\\n    } else if (t < 2.0) {\\n        return mix(values[1], values[2], t - 1.0);\\n    } else {\\n        return mix(values[2], values[3], t - 2.0);\\n    }\\n}\\nvec4 evaluate_zoom_function_4(const vec4 value0, const vec4 value1, const vec4 value2, const vec4 value3, const float t) {\\n    if (t < 1.0) {\\n        return mix(value0, value1, t);\\n    } else if (t < 2.0) {\\n        return mix(value1, value2, t - 1.0);\\n    } else {\\n        return 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s=t(\"feature-filter\"),l=t(\"./array_group\"),u=t(\"./buffer_group\"),c=t(\"../util/util\"),h=t(\"../util/struct_array\"),f=t(\"assert\");e.exports=n,n.create=function(e){return new({fill:t(\"./bucket/fill_bucket\"),line:t(\"./bucket/line_bucket\"),circle:t(\"./bucket/circle_bucket\"),symbol:t(\"./bucket/symbol_bucket\")}[e.layer.type])(e)},n.EXTENT=8192,n.prototype.populateArrays=function(){this.createArrays(),this.recalculateStyleLayers();for(var t=0;t<this.features.length;t++)this.addFeature(this.features[t]);this.trimArrays()},n.prototype.prepareArrayGroup=function(t,e){var r=this.arrayGroups[t],n=r.length&&r[r.length-1];return n&&n.hasCapacityFor(e)||(n=new l({layoutVertexArrayType:this.programInterfaces[t].layoutVertexArrayType,elementArrayType:this.programInterfaces[t].elementArrayType,elementArrayType2:this.programInterfaces[t].elementArrayType2,paintVertexArrayTypes:this.paintVertexArrayTypes[t]}),n.index=r.length,r.push(n)),n},n.prototype.createArrays=function(){this.arrayGroups={},this.paintVertexArrayTypes={};for(var t in this.programInterfaces){this.arrayGroups[t]=[];var e=this.paintVertexArrayTypes[t]={},r=this.paintAttributes[t];for(var i in r)e[i]=new n.VertexArrayType(r[i].attributes)}},n.prototype.destroy=function(t){for(var e in this.bufferGroups)for(var r=this.bufferGroups[e],n=0;n<r.length;n++)r[n].destroy(t)},n.prototype.trimArrays=function(){for(var t in this.arrayGroups)for(var e=this.arrayGroups[t],r=0;r<e.length;r++)e[r].trim()},n.prototype.isEmpty=function(){for(var t in this.arrayGroups)for(var e=this.arrayGroups[t],r=0;r<e.length;r++)if(!e[r].isEmpty())return!1;return!0},n.prototype.getTransferables=function(t){for(var e in this.arrayGroups)for(var r=this.arrayGroups[e],n=0;n<r.length;n++)r[n].getTransferables(t)},n.prototype.setUniforms=function(t,e,r,n,i){for(var a=this.paintAttributes[e][n.id].uniforms,o=0;o<a.length;o++){var s=a[o],l=r[s.name];t[\"uniform\"+s.components+\"fv\"](l,s.getValue(n,i))}},n.prototype.serialize=function(){return{layerId:this.layer.id,zoom:this.zoom,arrays:c.mapObject(this.arrayGroups,function(t){return t.map(function(t){return t.serialize()})}),paintVertexArrayTypes:c.mapObject(this.paintVertexArrayTypes,function(t){return c.mapObject(t,function(t){return t.serialize()})}),childLayerIds:this.childLayers.map(function(t){return t.id})}},n.prototype.createFilter=function(){this.filter||(this.filter=s(this.layer.filter))};var d={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};n.prototype.recalculateStyleLayers=function(){for(var t=0;t<this.childLayers.length;t++)this.childLayers[t].recalculate(this.zoom,d)},n.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a=0;a<this.childLayers.length;a++)for(var o=this.childLayers[a],s=this.arrayGroups[t],l=n.index;l<s.length;l++){var u=s[l],c=u.layoutVertexArray.length,h=u.paintVertexArrays[o.id];h.resize(c);for(var f=this.paintAttributes[t][o.id].attributes,d=0;d<f.length;d++)for(var p=f[d],m=p.getValue(o,e,r),v=p.multiplier||1,g=p.components||1,y=l===n.index?i:0,b=y;b<c;b++)for(var x=h.get(b),_=0;_<g;_++){var w=g>1?p.name+_:p.name;x[w]=m[_]*v}}},n.VertexArrayType=function(t){return new h({members:t,alignment:4})},n.ElementArrayType=function(t){return new h({members:[{type:\"Uint16\",name:\"vertices\",components:t||3}]})}},{\"../util/struct_array\":440,\"../util/util\":442,\"./array_group\":328,\"./bucket/circle_bucket\":330,\"./bucket/fill_bucket\":331,\"./bucket/line_bucket\":332,\"./bucket/symbol_bucket\":333,\"./buffer_group\":335,assert:47,\"feature-filter\":132}],330:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=i.EXTENT;e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.addCircleVertex=function(t,e,r,n,i){return t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)},n.prototype.programInterfaces={circle:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"}]),elementArrayType:new i.ElementArrayType,paintAttributes:[{name:\"a_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"circle-color\",e,r)},multiplier:255,paintProperty:\"circle-color\"},{name:\"a_radius\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-radius\",e,r)]},multiplier:10,paintProperty:\"circle-radius\"},{name:\"a_blur\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-blur\",e,r)]},multiplier:10,paintProperty:\"circle-blur\"},{name:\"a_opacity\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-opacity\",e,r)]},multiplier:255,paintProperty:\"circle-opacity\"}]}},n.prototype.addFeature=function(t){for(var e={zoom:this.zoom},r=o(t),n=this.prepareArrayGroup(\"circle\",0),i=n.layoutVertexArray.length,a=0;a<r.length;a++)for(var l=0;l<r[a].length;l++){var u=r[a][l].x,c=r[a][l].y;if(!(u<0||u>=s||c<0||c>=s)){var h=this.prepareArrayGroup(\"circle\",4),f=h.layoutVertexArray,d=this.addCircleVertex(f,u,c,-1,-1);this.addCircleVertex(f,u,c,1,-1),this.addCircleVertex(f,u,c,1,1),this.addCircleVertex(f,u,c,-1,1),h.elementArray.emplaceBack(d,d+1,d+2),h.elementArray.emplaceBack(d,d+3,d+2)}}this.populatePaintArrays(\"circle\",e,t.properties,n,i)}},{\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337}],331:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=t(\"earcut\"),l=t(\"../../util/classify_rings\");e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.programInterfaces={fill:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"}]),elementArrayType:new i.ElementArrayType(1),elementArrayType2:new i.ElementArrayType(2),paintAttributes:[{name:\"a_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"fill-color\",e,r)},multiplier:255,paintProperty:\"fill-color\"},{name:\"a_outline_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"fill-outline-color\",e,r)},multiplier:255,paintProperty:\"fill-outline-color\"},{name:\"a_opacity\",components:1,type:\"Uint8\",getValue:function(t,e,r){return[t.getPaintValue(\"fill-opacity\",e,r)]},multiplier:255,paintProperty:\"fill-opacity\"}]}},n.prototype.addFeature=function(t){for(var e=o(t),r=l(e,500),n=this.prepareArrayGroup(\"fill\",0),i=n.layoutVertexArray.length,a=0;a<r.length;a++)this.addPolygon(r[a]);this.populatePaintArrays(\"fill\",{zoom:this.zoom},t.properties,n,i)},n.prototype.addPolygon=function(t){for(var e=0,r=0;r<t.length;r++)e+=t[r].length;for(var n=this.prepareArrayGroup(\"fill\",e),i=[],a=[],o=n.layoutVertexArray.length,l=0;l<t.length;l++){var u=t[l];l>0&&a.push(i.length/2);for(var c=0;c<u.length;c++){var h=u[c],f=n.layoutVertexArray.emplaceBack(h.x,h.y);c>=1&&n.elementArray2.emplaceBack(f-1,f),i.push(h.x),i.push(h.y)}}for(var d=s(i,a),p=0;p<d.length;p++)n.elementArray.emplaceBack(d[p]+o)}},{\"../../util/classify_rings\":430,\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337,earcut:126}],332:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=i.EXTENT,l=Math.cos(Math.PI/180*37.5),u=Math.pow(2,14)/.5;e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.addLineVertex=function(t,e,r,n,i,a,o){return t.emplaceBack(e.x<<1|n,e.y<<1|i,Math.round(63*r.x)+128,Math.round(63*r.y)+128,1+(0===a?0:a<0?-1:1)|(.5*o&63)<<2,.5*o>>6)},n.prototype.programInterfaces={line:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}]),elementArrayType:new i.ElementArrayType}},n.prototype.addFeature=function(t){for(var e=o(t,15),r=0;r<e.length;r++)this.addLine(e[r],this.layer.layout[\"line-join\"],this.layer.layout[\"line-cap\"],this.layer.layout[\"line-miter-limit\"],this.layer.layout[\"line-round-limit\"])},n.prototype.addLine=function(t,e,r,n,i){for(var a=t.length;a>2&&t[a-1].equals(t[a-2]);)a--;if(!(t.length<2)){\"bevel\"===e&&(n=1.05);var o=s/(512*this.overscaling)*15,u=t[0],c=t[a-1],h=u.equals(c);if(this.prepareArrayGroup(\"line\",10*a),2!==a||!h){this.distance=0;var f,d,p,m,v,g,y,b=r,x=h?\"butt\":r,_=!0;this.e1=this.e2=this.e3=-1,h&&(f=t[a-2],v=u.sub(f)._unit()._perp());for(var w=0;w<a;w++)if(!(p=h&&w===a-1?t[1]:t[w+1])||!t[w].equals(p)){v&&(m=v),f&&(d=f),f=t[w],v=p?p.sub(f)._unit()._perp():m,m=m||v;var M=m.add(v)._unit(),k=M.x*v.x+M.y*v.y,A=1/k,T=k<l&&d&&p;if(T&&w>0){var S=f.dist(d);if(S>2*o){var E=f.sub(f.sub(d)._mult(o/S)._round());this.distance+=E.dist(d),this.addCurrentVertex(E,this.distance,m.mult(1),0,0,!1),d=E}}var L=d&&p,C=L?e:p?b:x;if(L&&\"round\"===C&&(A<i?C=\"miter\":A<=2&&(C=\"fakeround\")),\"miter\"===C&&A>n&&(C=\"bevel\"),\"bevel\"===C&&(A>2&&(C=\"flipbevel\"),A<n&&(C=\"miter\")),d&&(this.distance+=f.dist(d)),\"miter\"===C)M._mult(A),this.addCurrentVertex(f,this.distance,M,0,0,!1);else if(\"flipbevel\"===C){if(A>100)M=v.clone();else{var I=m.x*v.y-m.y*v.x>0?-1:1,z=A*m.add(v).mag()/m.sub(v).mag();M._perp()._mult(z*I)}this.addCurrentVertex(f,this.distance,M,0,0,!1),this.addCurrentVertex(f,this.distance,M.mult(-1),0,0,!1)}else if(\"bevel\"===C||\"fakeround\"===C){var D=m.x*v.y-m.y*v.x>0,P=-Math.sqrt(A*A-1);if(D?(y=0,g=P):(g=0,y=P),_||this.addCurrentVertex(f,this.distance,m,g,y,!1),\"fakeround\"===C){for(var O,R=Math.floor(8*(.5-(k-.5))),F=0;F<R;F++)O=v.mult((F+1)/(R+1))._add(m)._unit(),this.addPieSliceVertex(f,this.distance,O,D);this.addPieSliceVertex(f,this.distance,M,D);for(var j=R-1;j>=0;j--)O=m.mult((j+1)/(R+1))._add(v)._unit(),this.addPieSliceVertex(f,this.distance,O,D)}p&&this.addCurrentVertex(f,this.distance,v,-g,-y,!1)}else\"butt\"===C?(_||this.addCurrentVertex(f,this.distance,m,0,0,!1),p&&this.addCurrentVertex(f,this.distance,v,0,0,!1)):\"square\"===C?(_||(this.addCurrentVertex(f,this.distance,m,1,1,!1),this.e1=this.e2=-1),p&&this.addCurrentVertex(f,this.distance,v,-1,-1,!1)):\"round\"===C&&(_||(this.addCurrentVertex(f,this.distance,m,0,0,!1),this.addCurrentVertex(f,this.distance,m,1,1,!0),this.e1=this.e2=-1),p&&(this.addCurrentVertex(f,this.distance,v,-1,-1,!0),this.addCurrentVertex(f,this.distance,v,0,0,!1)));if(T&&w<a-1){var N=f.dist(p);if(N>2*o){var B=f.add(p.sub(f)._mult(o/N)._round());this.distance+=B.dist(f),this.addCurrentVertex(B,this.distance,v.mult(1),0,0,!1),f=B}}_=!1}}}},n.prototype.addCurrentVertex=function(t,e,r,n,i,a){var o,s=a?1:0,l=this.arrayGroups.line[this.arrayGroups.line.length-1],c=l.layoutVertexArray,h=l.elementArray;o=r.clone(),n&&o._sub(r.perp()._mult(n)),this.e3=this.addLineVertex(c,t,o,s,0,n,e),this.e1>=0&&this.e2>=0&&h.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,o=r.mult(-1),i&&o._sub(r.perp()._mult(i)),this.e3=this.addLineVertex(c,t,o,s,1,-i,e),this.e1>=0&&this.e2>=0&&h.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,e>u/2&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,a))},n.prototype.addPieSliceVertex=function(t,e,r,n){var i=n?1:0;r=r.mult(n?-1:1);var a=this.arrayGroups.line[this.arrayGroups.line.length-1],o=a.layoutVertexArray,s=a.elementArray;this.e3=this.addLineVertex(o,t,r,0,i,0,e),this.e1>=0&&this.e2>=0&&s.emplaceBack(this.e1,this.e2,this.e3),n?this.e2=this.e3:this.e1=this.e3}},{\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337}],333:[function(t,e,r){\"use strict\";function n(t){o.apply(this,arguments),this.showCollisionBoxes=t.showCollisionBoxes,this.overscaling=t.overscaling,this.collisionBoxArray=t.collisionBoxArray,this.symbolQuadsArray=t.symbolQuadsArray,this.symbolInstancesArray=t.symbolInstancesArray,this.sdfIcons=t.sdfIcons,this.iconsNeedLinear=t.iconsNeedLinear,this.adjustedTextSize=t.adjustedTextSize,this.adjustedIconSize=t.adjustedIconSize,this.fontstack=t.fontstack}function i(t,e,r,n,i,a,o,s,l,u,c){return t.emplaceBack(e,r,Math.round(64*n),Math.round(64*i),a/4,o/4,10*(u||0),c,10*(s||0),10*Math.min(l||25,25))}var a=t(\"point-geometry\"),o=t(\"../bucket\"),s=t(\"../../symbol/anchor\"),l=t(\"../../symbol/get_anchors\"),u=t(\"../../util/token\"),c=t(\"../../symbol/quads\"),h=t(\"../../symbol/shaping\"),f=t(\"../../symbol/resolve_text\"),d=t(\"../../symbol/mergelines\"),p=t(\"../../symbol/clip_line\"),m=t(\"../../util/util\"),v=t(\"../load_geometry\"),g=t(\"../../symbol/collision_feature\"),y=h.shapeText,b=h.shapeIcon,x=c.getGlyphQuads,_=c.getIconQuads,w=o.EXTENT;e.exports=n,n.MAX_QUADS=65535,n.prototype=m.inherit(o,{}),n.prototype.serialize=function(){var t=o.prototype.serialize.apply(this);return t.sdfIcons=this.sdfIcons,t.iconsNeedLinear=this.iconsNeedLinear,t.adjustedTextSize=this.adjustedTextSize,t.adjustedIconSize=this.adjustedIconSize,t.fontstack=this.fontstack,t};var M=new o.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_offset\",components:2,type:\"Int16\"},{name:\"a_texture_pos\",components:2,type:\"Uint16\"},{name:\"a_data\",components:4,type:\"Uint8\"}]),k=new o.ElementArrayType;n.prototype.addCollisionBoxVertex=function(t,e,r,n,i){return t.emplaceBack(e.x,e.y,Math.round(r.x),Math.round(r.y),10*n,10*i)},n.prototype.programInterfaces={glyph:{layoutVertexArrayType:M,elementArrayType:k},icon:{layoutVertexArrayType:M,elementArrayType:k},collisionBox:{layoutVertexArrayType:new o.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"},{name:\"a_data\",components:2,type:\"Uint8\"}])}},n.prototype.populateArrays=function(t,e,r){var n={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};this.adjustedTextMaxSize=this.layer.getLayoutValue(\"text-size\",{zoom:18,zoomHistory:n}),this.adjustedTextSize=this.layer.getLayoutValue(\"text-size\",{zoom:this.zoom+1,zoomHistory:n}),this.adjustedIconMaxSize=this.layer.getLayoutValue(\"icon-size\",{zoom:18,zoomHistory:n}),this.adjustedIconSize=this.layer.getLayoutValue(\"icon-size\",{zoom:this.zoom+1,zoomHistory:n});var i=512*this.overscaling;this.tilePixelRatio=w/i,this.compareText={},this.iconsNeedLinear=!1,this.symbolInstancesStartIndex=this.symbolInstancesArray.length;var a=this.layer.layout,o=this.features,s=this.textFeatures,l=.5,c=.5;switch(a[\"text-anchor\"]){case\"right\":case\"top-right\":case\"bottom-right\":l=1;break;case\"left\":case\"top-left\":case\"bottom-left\":l=0}switch(a[\"text-anchor\"]){case\"bottom\":case\"bottom-right\":case\"bottom-left\":c=1;break;case\"top\":case\"top-right\":case\"top-left\":c=0}for(var h=\"right\"===a[\"text-justify\"]?1:\"left\"===a[\"text-justify\"]?0:.5,f=24*a[\"text-line-height\"],p=\"line\"!==a[\"symbol-placement\"]?24*a[\"text-max-width\"]:0,g=24*a[\"text-letter-spacing\"],x=[24*a[\"text-offset\"][0],24*a[\"text-offset\"][1]],_=this.fontstack=a[\"text-font\"].join(\",\"),M=[],k=0;k<o.length;k++)M.push(v(o[k]));if(\"line\"===a[\"symbol-placement\"]){var A=d(o,s,M);M=A.geometries,o=A.features,s=A.textFeatures}for(var T,S,E=0;E<o.length;E++)if(M[E]){if(T=s[E]?y(s[E],e[_],p,f,l,c,h,g,x):null,a[\"icon-image\"]){var L=u(o[E].properties,a[\"icon-image\"]),C=r[L];S=b(C,a),C&&(void 0===this.sdfIcons?this.sdfIcons=C.sdf:this.sdfIcons!==C.sdf&&m.warnOnce(\"Style sheet warning: Cannot mix SDF and non-SDF icons in one buffer\"),1!==C.pixelRatio?this.iconsNeedLinear=!0:0===a[\"icon-rotate\"]&&this.layer.isLayoutValueFeatureConstant(\"icon-rotate\")||(this.iconsNeedLinear=!0))}else S=null;(T||S)&&this.addFeature(M[E],T,S,o[E])}this.symbolInstancesEndIndex=this.symbolInstancesArray.length,this.placeFeatures(t,this.showCollisionBoxes),this.trimArrays()},n.prototype.addFeature=function(t,e,r,n){var i=this.layer.layout,a=this.adjustedTextSize/24,o=void 0!==this.adjustedTextMaxSize?this.adjustedTextMaxSize:this.adjustedTextSize,u=this.tilePixelRatio*a,c=this.tilePixelRatio*o/24,h=this.tilePixelRatio*this.adjustedIconSize,f=this.tilePixelRatio*i[\"symbol-spacing\"],d=i[\"symbol-avoid-edges\"],m=i[\"text-padding\"]*this.tilePixelRatio,v=i[\"icon-padding\"]*this.tilePixelRatio,g=i[\"text-max-angle\"]/180*Math.PI,y=\"map\"===i[\"text-rotation-alignment\"]&&\"line\"===i[\"symbol-placement\"],b=\"map\"===i[\"icon-rotation-alignment\"]&&\"line\"===i[\"symbol-placement\"],x=i[\"text-allow-overlap\"]||i[\"icon-allow-overlap\"]||i[\"text-ignore-placement\"]||i[\"icon-ignore-placement\"],_=\"line\"===i[\"symbol-placement\"],M=f/2;_&&(t=p(t,0,0,w,w));for(var k=0;k<t.length;k++){var A,T=t[k];A=_?l(T,f,g,e,r,24,c,this.overscaling,w):[new s(T[0].x,T[0].y,0)];for(var S=0,E=A.length;S<E;S++){var L=A[S];if(!(e&&_&&this.anchorIsTooClose(e.text,M,L))){var C=!(L.x<0||L.x>w||L.y<0||L.y>w);if(!d||C){var I=C||x;this.addSymbolInstance(L,T,e,r,this.layer,I,this.symbolInstancesArray.length,this.collisionBoxArray,n.index,this.sourceLayerIndex,this.index,u,m,y,h,v,b,{zoom:this.zoom},n.properties)}}}}},n.prototype.anchorIsTooClose=function(t,e,r){var n=this.compareText;if(t in n){for(var i=n[t],a=i.length-1;a>=0;a--)if(r.dist(i[a])<e)return!0}else n[t]=[];return n[t].push(r),!1},n.prototype.placeFeatures=function(t,e){this.recalculateStyleLayers(),this.createArrays();var r=this.layer.layout,n=t.maxScale,i=\"map\"===r[\"text-rotation-alignment\"]&&\"line\"===r[\"symbol-placement\"],a=\"map\"===r[\"icon-rotation-alignment\"]&&\"line\"===r[\"symbol-placement\"];if(r[\"text-allow-overlap\"]||r[\"icon-allow-overlap\"]||r[\"text-ignore-placement\"]||r[\"icon-ignore-placement\"]){var o=this.symbolInstancesArray.toArray(this.symbolInstancesStartIndex,this.symbolInstancesEndIndex),s=t.angle,l=Math.sin(s),u=Math.cos(s);this.sortedSymbolInstances=o.sort(function(t,e){return(l*t.anchorPointX+u*t.anchorPointY|0)-(l*e.anchorPointX+u*e.anchorPointY|0)||e.index-t.index})}for(var c=this.symbolInstancesStartIndex;c<this.symbolInstancesEndIndex;c++){var h=this.sortedSymbolInstances?this.sortedSymbolInstances[c-this.symbolInstancesStartIndex]:this.symbolInstancesArray.get(c),f={boxStartIndex:h.textBoxStartIndex,boxEndIndex:h.textBoxEndIndex},d={boxStartIndex:h.iconBoxStartIndex,boxEndIndex:h.iconBoxEndIndex},p=!(h.textBoxStartIndex===h.textBoxEndIndex),m=!(h.iconBoxStartIndex===h.iconBoxEndIndex),v=r[\"text-optional\"]||!p,g=r[\"icon-optional\"]||!m,y=p?t.placeCollisionFeature(f,r[\"text-allow-overlap\"],r[\"symbol-avoid-edges\"]):t.minScale,b=m?t.placeCollisionFeature(d,r[\"icon-allow-overlap\"],r[\"symbol-avoid-edges\"]):t.minScale;v||g?!g&&y?y=Math.max(b,y):!v&&b&&(b=Math.max(b,y)):b=y=Math.max(b,y),p&&(t.insertCollisionFeature(f,y,r[\"text-ignore-placement\"]),y<=n&&this.addSymbols(\"glyph\",h.glyphQuadStartIndex,h.glyphQuadEndIndex,y,r[\"text-keep-upright\"],i,t.angle)),m&&(t.insertCollisionFeature(d,b,r[\"icon-ignore-placement\"]),b<=n&&this.addSymbols(\"icon\",h.iconQuadStartIndex,h.iconQuadEndIndex,b,r[\"icon-keep-upright\"],a,t.angle))}e&&this.addToDebugBuffers(t)},n.prototype.addSymbols=function(t,e,r,n,a,o,s){for(var l=this.prepareArrayGroup(t,4*(r-e)),u=l.elementArray,c=l.layoutVertexArray,h=this.zoom,f=Math.max(Math.log(n)/Math.LN2+h,0),d=e;d<r;d++){var p=this.symbolQuadsArray.get(d).SymbolQuad,m=(p.anchorAngle+s+Math.PI)%(2*Math.PI);if(!(a&&o&&(m<=Math.PI/2||m>3*Math.PI/2))){var v=p.tl,g=p.tr,y=p.bl,b=p.br,x=p.tex,_=p.anchorPoint,w=Math.max(h+Math.log(p.minScale)/Math.LN2,f),M=Math.min(h+Math.log(p.maxScale)/Math.LN2,25);if(!(M<=w)){w===f&&(w=0);var k=Math.round(p.glyphAngle/(2*Math.PI)*256),A=i(c,_.x,_.y,v.x,v.y,x.x,x.y,w,M,f,k);i(c,_.x,_.y,g.x,g.y,x.x+x.w,x.y,w,M,f,k),i(c,_.x,_.y,y.x,y.y,x.x,x.y+x.h,w,M,f,k),i(c,_.x,_.y,b.x,b.y,x.x+x.w,x.y+x.h,w,M,f,k),u.emplaceBack(A,A+1,A+2),u.emplaceBack(A+1,A+2,A+3)}}}},n.prototype.updateIcons=function(t){this.recalculateStyleLayers();var e=this.layer.layout[\"icon-image\"];if(e)for(var r=0;r<this.features.length;r++){var n=u(this.features[r].properties,e);n&&(t[n]=!0)}},n.prototype.updateFont=function(t){this.recalculateStyleLayers();var e=this.layer.layout[\"text-font\"],r=t[e]=t[e]||{};this.textFeatures=f(this.features,this.layer.layout,r)},n.prototype.addToDebugBuffers=function(t){for(var e=this.prepareArrayGroup(\"collisionBox\",0),r=e.layoutVertexArray,n=-t.angle,i=t.yStretch,o=this.symbolInstancesStartIndex;o<this.symbolInstancesEndIndex;o++){var s=this.symbolInstancesArray.get(o);s.textCollisionFeature={boxStartIndex:s.textBoxStartIndex,boxEndIndex:s.textBoxEndIndex},s.iconCollisionFeature={boxStartIndex:s.iconBoxStartIndex,boxEndIndex:s.iconBoxEndIndex};for(var l=0;l<2;l++){var u=s[0===l?\"textCollisionFeature\":\"iconCollisionFeature\"];if(u)for(var c=u.boxStartIndex;c<u.boxEndIndex;c++){var h=this.collisionBoxArray.get(c),f=h.anchorPoint,d=new a(h.x1,h.y1*i)._rotate(n),p=new a(h.x2,h.y1*i)._rotate(n),m=new a(h.x1,h.y2*i)._rotate(n),v=new a(h.x2,h.y2*i)._rotate(n),g=Math.max(0,Math.min(25,this.zoom+Math.log(h.maxScale)/Math.LN2)),y=Math.max(0,Math.min(25,this.zoom+Math.log(h.placementScale)/Math.LN2));this.addCollisionBoxVertex(r,f,d,g,y),this.addCollisionBoxVertex(r,f,p,g,y),this.addCollisionBoxVertex(r,f,p,g,y),this.addCollisionBoxVertex(r,f,v,g,y),this.addCollisionBoxVertex(r,f,v,g,y),this.addCollisionBoxVertex(r,f,m,g,y),this.addCollisionBoxVertex(r,f,m,g,y),this.addCollisionBoxVertex(r,f,d,g,y)}}}},n.prototype.addSymbolInstance=function(t,e,r,i,a,o,s,l,u,c,h,f,d,p,v,y,b,w,M){var k,A,T,S,E,L,C,I;if(r&&(C=o?x(t,r,f,e,a,p):[],E=new g(l,e,t,u,c,h,r,f,d,p,!1)),k=this.symbolQuadsArray.length,C&&C.length)for(var z=0;z<C.length;z++)this.addSymbolQuad(C[z]);A=this.symbolQuadsArray.length;var D=E?E.boxStartIndex:this.collisionBoxArray.length,P=E?E.boxEndIndex:this.collisionBoxArray.length;i&&(I=o?_(t,i,v,e,a,b,r,w,M):[],L=new g(l,e,t,u,c,h,i,v,y,b,!0)),T=this.symbolQuadsArray.length,I&&1===I.length&&this.addSymbolQuad(I[0]),S=this.symbolQuadsArray.length;var O=L?L.boxStartIndex:this.collisionBoxArray.length,R=L?L.boxEndIndex:this.collisionBoxArray.length;return S>n.MAX_QUADS&&m.warnOnce(\"Too many symbols being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),A>n.MAX_QUADS&&m.warnOnce(\"Too many glyphs being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),this.symbolInstancesArray.emplaceBack(D,P,O,R,k,A,T,S,t.x,t.y,s)},n.prototype.addSymbolQuad=function(t){return this.symbolQuadsArray.emplaceBack(t.anchorPoint.x,t.anchorPoint.y,t.tl.x,t.tl.y,t.tr.x,t.tr.y,t.bl.x,t.bl.y,t.br.x,t.br.y,t.tex.h,t.tex.w,t.tex.x,t.tex.y,t.anchorAngle,t.glyphAngle,t.maxScale,t.minScale)}},{\"../../symbol/anchor\":391,\"../../symbol/clip_line\":393,\"../../symbol/collision_feature\":395,\"../../symbol/get_anchors\":397,\"../../symbol/mergelines\":400,\"../../symbol/quads\":401,\"../../symbol/resolve_text\":402,\"../../symbol/shaping\":403,\"../../util/token\":441,\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337,\"point-geometry\":484}],334:[function(t,e,r){\"use strict\";function n(t,e,r){this.arrayBuffer=t.arrayBuffer,this.length=t.length,this.attributes=e.members,this.itemSize=e.bytesPerElement,this.type=r,this.arrayType=e}e.exports=n,n.prototype.bind=function(t){var e=t[this.type];this.buffer?t.bindBuffer(e,this.buffer):(this.buffer=t.createBuffer(),t.bindBuffer(e,this.buffer),t.bufferData(e,this.arrayBuffer,t.STATIC_DRAW),this.arrayBuffer=null)};var i={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\"};n.prototype.setVertexAttribPointers=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],a=e[n.name];void 0!==a&&t.vertexAttribPointer(a,n.components,t[i[n.type]],!1,this.arrayType.bytesPerElement,n.offset)}},n.prototype.destroy=function(t){this.buffer&&t.deleteBuffer(this.buffer)},n.BufferType={VERTEX:\"ARRAY_BUFFER\",ELEMENT:\"ELEMENT_ARRAY_BUFFER\"}},{}],335:[function(t,e,r){\"use strict\";function n(t,e){this.layoutVertexBuffer=new a(t.layoutVertexArray,e.layoutVertexArrayType,a.BufferType.VERTEX),t.elementArray&&(this.elementBuffer=new a(t.elementArray,e.elementArrayType,a.BufferType.ELEMENT));var r,n=this.vaos={};t.elementArray2&&(this.elementBuffer2=new a(t.elementArray2,e.elementArrayType2,a.BufferType.ELEMENT),r=this.secondVaos={}),this.paintVertexBuffers=i.mapObject(t.paintVertexArrays,function(i,s){return n[s]=new o,t.elementArray2&&(r[s]=new o),new a(i,e.paintVertexArrayTypes[s],a.BufferType.VERTEX)})}var i=t(\"../util/util\"),a=t(\"./buffer\"),o=t(\"../render/vertex_array_object\");e.exports=n,n.prototype.destroy=function(t){this.layoutVertexBuffer.destroy(t),this.elementBuffer&&this.elementBuffer.destroy(t),this.elementBuffer2&&this.elementBuffer2.destroy(t);for(var e in this.paintVertexBuffers)this.paintVertexBuffers[e].destroy(t);for(var r in this.vaos)this.vaos[r].destroy(t);for(var n in this.secondVaos)this.secondVaos[n].destroy(t)}},{\"../render/vertex_array_object\":357,\"../util/util\":442,\"./buffer\":334}],336:[function(t,e,r){\"use strict\";function n(t,e,r){if(t.grid){var n=t,i=e;t=n.coord,e=n.overscaling,this.grid=new p(n.grid),this.featureIndexArray=new k(n.featureIndexArray),this.rawTileData=i,this.bucketLayerIDs=n.bucketLayerIDs}else this.grid=new p(h,16,0),this.featureIndexArray=new k;this.coord=t,this.overscaling=e,this.x=t.x,this.y=t.y,this.z=t.z-Math.log(e)/Math.LN2,this.setCollisionTile(r)}function i(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function a(t,e){return e-t}function o(t){return t[\"line-gap-width\"]>0?t[\"line-gap-width\"]+2*t[\"line-width\"]:t[\"line-width\"]}function s(t,e,r,n,i){if(!e[0]&&!e[1])return t;e=u.convert(e),\"viewport\"===r&&e._rotate(-n);for(var a=[],o=0;o<t.length;o++){for(var s=t[o],l=[],c=0;c<s.length;c++)l.push(s[c].sub(e._mult(i)));a.push(l)}return a}function l(t,e){for(var r=[],n=new u(0,0),i=0;i<t.length;i++){for(var a=t[i],o=[],s=0;s<a.length;s++){var l=a[s-1],c=a[s],h=a[s+1],f=0===s?n:c.sub(l)._unit()._perp(),d=s===a.length-1?n:h.sub(c)._unit()._perp(),p=f._add(d)._unit(),m=p.x*d.x+p.y*d.y;p._mult(1/m),o.push(p._mult(e)._add(c))}r.push(o)}return r}var u=t(\"point-geometry\"),c=t(\"./load_geometry\"),h=t(\"./bucket\").EXTENT,f=t(\"feature-filter\"),d=t(\"../util/struct_array\"),p=t(\"grid-index\"),m=t(\"../util/dictionary_coder\"),v=t(\"vector-tile\"),g=t(\"pbf\"),y=t(\"../util/vectortile_to_geojson\"),b=t(\"../util/util\").arraysIntersect,x=t(\"../util/intersection_tests\"),_=x.multiPolygonIntersectsBufferedMultiPoint,w=x.multiPolygonIntersectsMultiPolygon,M=x.multiPolygonIntersectsBufferedMultiLine,k=new d({members:[{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]});e.exports=n,n.prototype.insert=function(t,e,r,n){var i=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(e,r,n);for(var a=c(t),o=0;o<a.length;o++){for(var s=a[o],l=[1/0,1/0,-1/0,-1/0],u=0;u<s.length;u++){var h=s[u];l[0]=Math.min(l[0],h.x),l[1]=Math.min(l[1],h.y),l[2]=Math.max(l[2],h.x),l[3]=Math.max(l[3],h.y)}this.grid.insert(i,l[0],l[1],l[2],l[3])}},n.prototype.setCollisionTile=function(t){this.collisionTile=t},n.prototype.serialize=function(){var t={coord:this.coord,overscaling:this.overscaling,grid:this.grid.toArrayBuffer(),featureIndexArray:this.featureIndexArray.serialize(),bucketLayerIDs:this.bucketLayerIDs};return{data:t,transferables:[t.grid,t.featureIndexArray.arrayBuffer]}},n.prototype.query=function(t,e){this.vtLayers||(this.vtLayers=new v.VectorTile(new g(new Uint8Array(this.rawTileData))).layers,this.sourceLayerCoder=new m(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"]));var r={},n=t.params||{},s=h/t.tileSize/t.scale,l=f(n.filter),c=0;for(var d in e){var p=e[d],y=p.paint,b=0;\"line\"===p.type?b=o(y)/2+Math.abs(y[\"line-offset\"])+i(y[\"line-translate\"]):\"fill\"===p.type?b=i(y[\"fill-translate\"]):\"circle\"===p.type&&(b=y[\"circle-radius\"]+i(y[\"circle-translate\"])),c=Math.max(c,b*s)}for(var x=t.queryGeometry.map(function(t){return t.map(function(t){return new u(t.x,t.y)})}),_=1/0,w=1/0,M=-1/0,k=-1/0,A=0;A<x.length;A++)for(var T=x[A],S=0;S<T.length;S++){var E=T[S];_=Math.min(_,E.x),w=Math.min(w,E.y),M=Math.max(M,E.x),k=Math.max(k,E.y)}var L=this.grid.query(_-c,w-c,M+c,k+c);L.sort(a),this.filterMatching(r,L,this.featureIndexArray,x,l,n.layers,e,t.bearing,s);var C=this.collisionTile.queryRenderedSymbols(_,w,M,k,t.scale);return C.sort(),this.filterMatching(r,C,this.collisionTile.collisionBoxArray,x,l,n.layers,e,t.bearing,s),r},n.prototype.filterMatching=function(t,e,r,n,i,a,u,h,f){for(var d,p=0;p<e.length;p++){var m=e[p];if(m!==d){d=m;var v=r.get(m),g=this.bucketLayerIDs[v.bucketIndex];if(!a||b(a,g)){var x=this.sourceLayerCoder.decode(v.sourceLayerIndex),k=this.vtLayers[x],A=k.feature(v.featureIndex);if(i(A))for(var T=null,S=0;S<g.length;S++){var E=g[S];if(!(a&&a.indexOf(E)<0)){var L=u[E];if(L){var C;if(\"symbol\"!==L.type){T||(T=c(A));var I=L.paint;if(\"line\"===L.type){C=s(n,I[\"line-translate\"],I[\"line-translate-anchor\"],h,f);var z=o(I)/2*f;if(I[\"line-offset\"]&&(T=l(T,I[\"line-offset\"]*f)),!M(C,T,z))continue}else if(\"fill\"===L.type){if(C=s(n,I[\"fill-translate\"],I[\"fill-translate-anchor\"],h,f),!w(C,T))continue}else if(\"circle\"===L.type){C=s(n,I[\"circle-translate\"],I[\"circle-translate-anchor\"],h,f);var D=I[\"circle-radius\"]*f;if(!_(C,T,D))continue}}var P=new y(A,this.z,this.x,this.y);P.layer=L.serialize({includeRefProperties:!0});var O=t[E];void 0===O&&(O=t[E]=[]),O.push(P)}}}}}}}},{\"../util/dictionary_coder\":432,\"../util/intersection_tests\":437,\"../util/struct_array\":440,\"../util/util\":442,\"../util/vectortile_to_geojson\":443,\"./bucket\":329,\"./load_geometry\":337,\"feature-filter\":132,\"grid-index\":287,pbf:478,\"point-geometry\":484,\"vector-tile\":550}],337:[function(t,e,r){\"use strict\";function n(t){return{min:-1*Math.pow(2,t-1),max:Math.pow(2,t-1)-1}}var i=t(\"../util/util\"),a=t(\"./bucket\").EXTENT,o=t(\"assert\"),s={15:n(15),16:n(16)};e.exports=function(t,e){var r=s[e||16];o(r);for(var n=a/t.extent,l=t.loadGeometry(),u=0;u<l.length;u++)for(var c=l[u],h=0;h<c.length;h++){var f=c[h];f.x=Math.round(f.x*n),f.y=Math.round(f.y*n),(f.x<r.min||f.x>r.max||f.y<r.min||f.y>r.max)&&i.warnOnce(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\")}return l}},{\"../util/util\":442,\"./bucket\":329,assert:47}],338:[function(t,e,r){\"use strict\";function n(t,e,r){this.column=t,this.row=e,this.zoom=r}e.exports=n,n.prototype={clone:function(){return new n(this.column,this.row,this.zoom)},zoomTo:function(t){return this.clone()._zoomTo(t)},sub:function(t){return this.clone()._sub(t)},_zoomTo:function(t){var e=Math.pow(2,t-this.zoom);return this.column*=e,this.row*=e,this.zoom=t,this},_sub:function(t){return t=t.zoomTo(this.zoom),this.column-=t.column,this.row-=t.row,this}}},{}],339:[function(t,e,r){\"use strict\";function n(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}e.exports=n\n",
+       ";var i=t(\"../util/util\").wrap;n.prototype.wrap=function(){return new n(i(this.lng,-180,180),this.lat)},n.prototype.toArray=function(){return[this.lng,this.lat]},n.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},n.convert=function(t){return t instanceof n?t:Array.isArray(t)?new n(t[0],t[1]):t}},{\"../util/util\":442}],340:[function(t,e,r){\"use strict\";function n(t,e){t&&(e?this.extend(t).extend(e):4===t.length?this.extend([t[0],t[1]]).extend([t[2],t[3]]):this.extend(t[0]).extend(t[1]))}e.exports=n;var i=t(\"./lng_lat\");n.prototype={extend:function(t){var e,r,a=this._sw,o=this._ne;if(t instanceof i)e=t,r=t;else{if(!(t instanceof n))return t?this.extend(i.convert(t)||n.convert(t)):this;if(e=t._sw,r=t._ne,!e||!r)return this}return a||o?(a.lng=Math.min(e.lng,a.lng),a.lat=Math.min(e.lat,a.lat),o.lng=Math.max(r.lng,o.lng),o.lat=Math.max(r.lat,o.lat)):(this._sw=new i(e.lng,e.lat),this._ne=new i(r.lng,r.lat)),this},getCenter:function(){return new i((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},getSouthWest:function(){return this._sw},getNorthEast:function(){return this._ne},getNorthWest:function(){return new i(this.getWest(),this.getNorth())},getSouthEast:function(){return new i(this.getEast(),this.getSouth())},getWest:function(){return this._sw.lng},getSouth:function(){return this._sw.lat},getEast:function(){return this._ne.lng},getNorth:function(){return this._ne.lat},toArray:function(){return[this._sw.toArray(),this._ne.toArray()]},toString:function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"}},n.convert=function(t){return!t||t instanceof n?t:new n(t)}},{\"./lng_lat\":339}],341:[function(t,e,r){\"use strict\";function n(t,e){this.tileSize=512,this._minZoom=t||0,this._maxZoom=e||22,this.latRange=[-85.05113,85.05113],this.width=0,this.height=0,this._center=new i(0,0),this.zoom=0,this.angle=0,this._altitude=1.5,this._pitch=0,this._unmodified=!0}var i=t(\"./lng_lat\"),a=t(\"point-geometry\"),o=t(\"./coordinate\"),s=t(\"../util/util\").wrap,l=t(\"../util/interpolate\"),u=t(\"../source/tile_coord\"),c=t(\"../data/bucket\").EXTENT,h=t(\"gl-matrix\"),f=h.vec4,d=h.mat4,p=h.mat2;e.exports=n,n.prototype={get minZoom(){return this._minZoom},set minZoom(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},get maxZoom(){return this._maxZoom},set maxZoom(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},get worldSize(){return this.tileSize*this.scale},get centerPoint(){return this.size._div(2)},get size(){return new a(this.width,this.height)},get bearing(){return-this.angle/Math.PI*180},set bearing(t){var e=-s(t,-180,180)*Math.PI/180;this.angle!==e&&(this._unmodified=!1,this.angle=e,this._calcMatrices(),this.rotationMatrix=p.create(),p.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},get pitch(){return this._pitch/Math.PI*180},set pitch(t){var e=Math.min(60,t)/180*Math.PI;this._pitch!==e&&(this._unmodified=!1,this._pitch=e,this._calcMatrices())},get altitude(){return this._altitude},set altitude(t){var e=Math.max(.75,t);this._altitude!==e&&(this._unmodified=!1,this._altitude=e,this._calcMatrices())},get zoom(){return this._zoom},set zoom(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._calcMatrices(),this._constrain())},get center(){return this._center},set center(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._calcMatrices(),this._constrain())},coveringZoomLevel:function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},coveringTiles:function(t){var e=this.coveringZoomLevel(t),r=e;if(e<t.minzoom)return[];e>t.maxzoom&&(e=t.maxzoom);var n=this,i=n.locationCoordinate(n.center)._zoomTo(e),o=new a(i.column-.5,i.row-.5);return u.cover(e,[n.pointCoordinate(new a(0,0))._zoomTo(e),n.pointCoordinate(new a(n.width,0))._zoomTo(e),n.pointCoordinate(new a(n.width,n.height))._zoomTo(e),n.pointCoordinate(new a(0,n.height))._zoomTo(e)],t.reparseOverscaled?r:e).sort(function(t,e){return o.dist(t)-o.dist(e)})},resize:function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._calcMatrices(),this._constrain()},get unmodified(){return this._unmodified},zoomScale:function(t){return Math.pow(2,t)},scaleZoom:function(t){return Math.log(t)/Math.LN2},project:function(t,e){return new a(this.lngX(t.lng,e),this.latY(t.lat,e))},unproject:function(t,e){return new i(this.xLng(t.x,e),this.yLat(t.y,e))},get x(){return this.lngX(this.center.lng)},get y(){return this.latY(this.center.lat)},get point(){return new a(this.x,this.y)},lngX:function(t,e){return(180+t)*(e||this.worldSize)/360},latY:function(t,e){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))*(e||this.worldSize)/360},xLng:function(t,e){return 360*t/(e||this.worldSize)-180},yLat:function(t,e){var r=180-360*t/(e||this.worldSize);return 360/Math.PI*Math.atan(Math.exp(r*Math.PI/180))-90},panBy:function(t){var e=this.centerPoint._add(t);this.center=this.pointLocation(e)},setLocationAtPoint:function(t,e){var r=this.locationCoordinate(t),n=this.pointCoordinate(e),i=this.pointCoordinate(this.centerPoint),a=n._sub(r);this._unmodified=!1,this.center=this.coordinateLocation(i._sub(a))},locationPoint:function(t){return this.coordinatePoint(this.locationCoordinate(t))},pointLocation:function(t){return this.coordinateLocation(this.pointCoordinate(t))},locationCoordinate:function(t){var e=this.zoomScale(this.tileZoom)/this.worldSize,r=i.convert(t);return new o(this.lngX(r.lng)*e,this.latY(r.lat)*e,this.tileZoom)},coordinateLocation:function(t){var e=this.zoomScale(t.zoom);return new i(this.xLng(t.column,e),this.yLat(t.row,e))},pointCoordinate:function(t){var e=[t.x,t.y,0,1],r=[t.x,t.y,1,1];f.transformMat4(e,e,this.pixelMatrixInverse),f.transformMat4(r,r,this.pixelMatrixInverse);var n=e[3],i=r[3],a=e[0]/n,s=r[0]/i,u=e[1]/n,c=r[1]/i,h=e[2]/n,d=r[2]/i,p=h===d?0:(0-h)/(d-h),m=this.worldSize/this.zoomScale(this.tileZoom);return new o(l(a,s,p)/m,l(u,c,p)/m,this.tileZoom)},coordinatePoint:function(t){var e=this.worldSize/this.zoomScale(t.zoom),r=[t.column*e,t.row*e,0,1];return f.transformMat4(r,r,this.pixelMatrix),new a(r[0]/r[3],r[1]/r[3])},calculatePosMatrix:function(t,e){void 0===e&&(e=1/0),t instanceof u&&(t=t.toCoordinate(e));var r=Math.min(t.zoom,e),n=this.worldSize/Math.pow(2,r),i=new Float64Array(16);return d.identity(i),d.translate(i,i,[t.column*n,t.row*n,0]),d.scale(i,i,[n/c,n/c,1]),d.multiply(i,this.projMatrix,i),new Float32Array(i)},_constrain:function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var t,e,r,n,i,o,s,l,u=this.size,c=this._unmodified;this.latRange&&(t=this.latY(this.latRange[1]),e=this.latY(this.latRange[0]),i=e-t<u.y?u.y/(e-t):0),this.lngRange&&(r=this.lngX(this.lngRange[0]),n=this.lngX(this.lngRange[1]),o=n-r<u.x?u.x/(n-r):0);var h=Math.max(o||0,i||0);if(h)return this.center=this.unproject(new a(o?(n+r)/2:this.x,i?(e+t)/2:this.y)),this.zoom+=this.scaleZoom(h),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var f=this.y,d=u.y/2;f-d<t&&(l=t+d),f+d>e&&(l=e-d)}if(this.lngRange){var p=this.x,m=u.x/2;p-m<r&&(s=r+m),p+m>n&&(s=n-m)}void 0===s&&void 0===l||(this.center=this.unproject(new a(void 0!==s?s:this.x,void 0!==l?l:this.y))),this._unmodified=c,this._constraining=!1}},_calcMatrices:function(){if(this.height){var t=Math.atan(.5/this.altitude),e=Math.sin(t)*this.altitude/Math.sin(Math.PI/2-this._pitch-t),r=Math.cos(Math.PI/2-this._pitch)*e+this.altitude,n=new Float64Array(16);if(d.perspective(n,2*Math.atan(this.height/2/this.altitude),this.width/this.height,.1,r),d.translate(n,n,[0,0,-this.altitude]),d.scale(n,n,[1,-1,1/this.height]),d.rotateX(n,n,this._pitch),d.rotateZ(n,n,this.angle),d.translate(n,n,[-this.x,-this.y,0]),this.projMatrix=n,n=d.create(),d.scale(n,n,[this.width/2,-this.height/2,1]),d.translate(n,n,[1,-1,0]),this.pixelMatrix=d.multiply(new Float64Array(16),n,this.projMatrix),!(n=d.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=n}}}},{\"../data/bucket\":329,\"../source/tile_coord\":369,\"../util/interpolate\":436,\"../util/util\":442,\"./coordinate\":338,\"./lng_lat\":339,\"gl-matrix\":193,\"point-geometry\":484}],342:[function(t,e,r){\"use strict\";var n={\" \":[16,[]],\"!\":[10,[5,21,5,7,-1,-1,5,2,4,1,5,0,6,1,5,2]],'\"':[16,[4,21,4,14,-1,-1,12,21,12,14]],\"#\":[21,[11,25,4,-7,-1,-1,17,25,10,-7,-1,-1,4,12,18,12,-1,-1,3,6,17,6]],$:[20,[8,25,8,-4,-1,-1,12,25,12,-4,-1,-1,17,18,15,20,12,21,8,21,5,20,3,18,3,16,4,14,5,13,7,12,13,10,15,9,16,8,17,6,17,3,15,1,12,0,8,0,5,1,3,3]],\"%\":[24,[21,21,3,0,-1,-1,8,21,10,19,10,17,9,15,7,14,5,14,3,16,3,18,4,20,6,21,8,21,10,20,13,19,16,19,19,20,21,21,-1,-1,17,7,15,6,14,4,14,2,16,0,18,0,20,1,21,3,21,5,19,7,17,7]],\"&\":[26,[23,12,23,13,22,14,21,14,20,13,19,11,17,6,15,3,13,1,11,0,7,0,5,1,4,2,3,4,3,6,4,8,5,9,12,13,13,14,14,16,14,18,13,20,11,21,9,20,8,18,8,16,9,13,11,10,16,3,18,1,20,0,22,0,23,1,23,2]],\"'\":[10,[5,19,4,20,5,21,6,20,6,18,5,16,4,15]],\"(\":[14,[11,25,9,23,7,20,5,16,4,11,4,7,5,2,7,-2,9,-5,11,-7]],\")\":[14,[3,25,5,23,7,20,9,16,10,11,10,7,9,2,7,-2,5,-5,3,-7]],\"*\":[16,[8,21,8,9,-1,-1,3,18,13,12,-1,-1,13,18,3,12]],\"+\":[26,[13,18,13,0,-1,-1,4,9,22,9]],\",\":[10,[6,1,5,0,4,1,5,2,6,1,6,-1,5,-3,4,-4]],\"-\":[26,[4,9,22,9]],\".\":[10,[5,2,4,1,5,0,6,1,5,2]],\"/\":[22,[20,25,2,-7]],0:[20,[9,21,6,20,4,17,3,12,3,9,4,4,6,1,9,0,11,0,14,1,16,4,17,9,17,12,16,17,14,20,11,21,9,21]],1:[20,[6,17,8,18,11,21,11,0]],2:[20,[4,16,4,17,5,19,6,20,8,21,12,21,14,20,15,19,16,17,16,15,15,13,13,10,3,0,17,0]],3:[20,[5,21,16,21,10,13,13,13,15,12,16,11,17,8,17,6,16,3,14,1,11,0,8,0,5,1,4,2,3,4]],4:[20,[13,21,3,7,18,7,-1,-1,13,21,13,0]],5:[20,[15,21,5,21,4,12,5,13,8,14,11,14,14,13,16,11,17,8,17,6,16,3,14,1,11,0,8,0,5,1,4,2,3,4]],6:[20,[16,18,15,20,12,21,10,21,7,20,5,17,4,12,4,7,5,3,7,1,10,0,11,0,14,1,16,3,17,6,17,7,16,10,14,12,11,13,10,13,7,12,5,10,4,7]],7:[20,[17,21,7,0,-1,-1,3,21,17,21]],8:[20,[8,21,5,20,4,18,4,16,5,14,7,13,11,12,14,11,16,9,17,7,17,4,16,2,15,1,12,0,8,0,5,1,4,2,3,4,3,7,4,9,6,11,9,12,13,13,15,14,16,16,16,18,15,20,12,21,8,21]],9:[20,[16,14,15,11,13,9,10,8,9,8,6,9,4,11,3,14,3,15,4,18,6,20,9,21,10,21,13,20,15,18,16,14,16,9,15,4,13,1,10,0,8,0,5,1,4,3]],\":\":[10,[5,14,4,13,5,12,6,13,5,14,-1,-1,5,2,4,1,5,0,6,1,5,2]],\";\":[10,[5,14,4,13,5,12,6,13,5,14,-1,-1,6,1,5,0,4,1,5,2,6,1,6,-1,5,-3,4,-4]],\"<\":[24,[20,18,4,9,20,0]],\"=\":[26,[4,12,22,12,-1,-1,4,6,22,6]],\">\":[24,[4,18,20,9,4,0]],\"?\":[18,[3,16,3,17,4,19,5,20,7,21,11,21,13,20,14,19,15,17,15,15,14,13,13,12,9,10,9,7,-1,-1,9,2,8,1,9,0,10,1,9,2]],\"@\":[27,[18,13,17,15,15,16,12,16,10,15,9,14,8,11,8,8,9,6,11,5,14,5,16,6,17,8,-1,-1,12,16,10,14,9,11,9,8,10,6,11,5,-1,-1,18,16,17,8,17,6,19,5,21,5,23,7,24,10,24,12,23,15,22,17,20,19,18,20,15,21,12,21,9,20,7,19,5,17,4,15,3,12,3,9,4,6,5,4,7,2,9,1,12,0,15,0,18,1,20,2,21,3,-1,-1,19,16,18,8,18,6,19,5]],A:[18,[9,21,1,0,-1,-1,9,21,17,0,-1,-1,4,7,14,7]],B:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,15,17,13,16,12,13,11,-1,-1,4,11,13,11,16,10,17,9,18,7,18,4,17,2,16,1,13,0,4,0]],C:[21,[18,16,17,18,15,20,13,21,9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5]],D:[21,[4,21,4,0,-1,-1,4,21,11,21,14,20,16,18,17,16,18,13,18,8,17,5,16,3,14,1,11,0,4,0]],E:[19,[4,21,4,0,-1,-1,4,21,17,21,-1,-1,4,11,12,11,-1,-1,4,0,17,0]],F:[18,[4,21,4,0,-1,-1,4,21,17,21,-1,-1,4,11,12,11]],G:[21,[18,16,17,18,15,20,13,21,9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,18,8,-1,-1,13,8,18,8]],H:[22,[4,21,4,0,-1,-1,18,21,18,0,-1,-1,4,11,18,11]],I:[8,[4,21,4,0]],J:[16,[12,21,12,5,11,2,10,1,8,0,6,0,4,1,3,2,2,5,2,7]],K:[21,[4,21,4,0,-1,-1,18,21,4,7,-1,-1,9,12,18,0]],L:[17,[4,21,4,0,-1,-1,4,0,16,0]],M:[24,[4,21,4,0,-1,-1,4,21,12,0,-1,-1,20,21,12,0,-1,-1,20,21,20,0]],N:[22,[4,21,4,0,-1,-1,4,21,18,0,-1,-1,18,21,18,0]],O:[22,[9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,19,8,19,13,18,16,17,18,15,20,13,21,9,21]],P:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,14,17,12,16,11,13,10,4,10]],Q:[22,[9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,19,8,19,13,18,16,17,18,15,20,13,21,9,21,-1,-1,12,4,18,-2]],R:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,15,17,13,16,12,13,11,4,11,-1,-1,11,11,18,0]],S:[20,[17,18,15,20,12,21,8,21,5,20,3,18,3,16,4,14,5,13,7,12,13,10,15,9,16,8,17,6,17,3,15,1,12,0,8,0,5,1,3,3]],T:[16,[8,21,8,0,-1,-1,1,21,15,21]],U:[22,[4,21,4,6,5,3,7,1,10,0,12,0,15,1,17,3,18,6,18,21]],V:[18,[1,21,9,0,-1,-1,17,21,9,0]],W:[24,[2,21,7,0,-1,-1,12,21,7,0,-1,-1,12,21,17,0,-1,-1,22,21,17,0]],X:[20,[3,21,17,0,-1,-1,17,21,3,0]],Y:[18,[1,21,9,11,9,0,-1,-1,17,21,9,11]],Z:[20,[17,21,3,0,-1,-1,3,21,17,21,-1,-1,3,0,17,0]],\"[\":[14,[4,25,4,-7,-1,-1,5,25,5,-7,-1,-1,4,25,11,25,-1,-1,4,-7,11,-7]],\"\\\\\":[14,[0,21,14,-3]],\"]\":[14,[9,25,9,-7,-1,-1,10,25,10,-7,-1,-1,3,25,10,25,-1,-1,3,-7,10,-7]],\"^\":[16,[6,15,8,18,10,15,-1,-1,3,12,8,17,13,12,-1,-1,8,17,8,0]],_:[16,[0,-2,16,-2]],\"`\":[10,[6,21,5,20,4,18,4,16,5,15,6,16,5,17]],a:[19,[15,14,15,0,-1,-1,15,11,13,13,11,14,8,14,6,13,4,11,3,8,3,6,4,3,6,1,8,0,11,0,13,1,15,3]],b:[19,[4,21,4,0,-1,-1,4,11,6,13,8,14,11,14,13,13,15,11,16,8,16,6,15,3,13,1,11,0,8,0,6,1,4,3]],c:[18,[15,11,13,13,11,14,8,14,6,13,4,11,3,8,3,6,4,3,6,1,8,0,11,0,13,1,15,3]],d:[19,[15,21,15,0,-1,-1,15,11,13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a,o,s,l,u,c,h,f,d=[];for(a=0,o=t.length;a<o;a++)if(u=n[t[a]]){for(f=null,s=0,l=u[1].length;s<l;s+=2)-1===u[1][s]&&-1===u[1][s+1]?f=null:(c=e+u[1][s]*i,h=r-u[1][s+1]*i,f&&d.push(f.x,f.y,c,h),f={x:c,y:h});e+=u[0]*i}return d}},{}],343:[function(t,e,r){\"use strict\";var n=e.exports={};n.version=t(\"../package.json\").version,n.Map=t(\"./ui/map\"),n.Control=t(\"./ui/control/control\"),n.Navigation=t(\"./ui/control/navigation\"),n.Geolocate=t(\"./ui/control/geolocate\"),n.Attribution=t(\"./ui/control/attribution\"),n.Popup=t(\"./ui/popup\"),n.Marker=t(\"./ui/marker\"),n.Style=t(\"./style/style\"),n.LngLat=t(\"./geo/lng_lat\"),n.LngLatBounds=t(\"./geo/lng_lat_bounds\"),n.Point=t(\"point-geometry\"),n.Evented=t(\"./util/evented\"),n.util=t(\"./util/util\"),n.supported=t(\"./util/browser\").supported;var i=t(\"./util/ajax\");n.util.getJSON=i.getJSON,n.util.getArrayBuffer=i.getArrayBuffer;var a=t(\"./util/config\");n.config=a,Object.defineProperty(n,\"accessToken\",{get:function(){return a.ACCESS_TOKEN},set:function(t){a.ACCESS_TOKEN=t}})},{\"../package.json\":444,\"./geo/lng_lat\":339,\"./geo/lng_lat_bounds\":340,\"./style/style\":378,\"./ui/control/attribution\":409,\"./ui/control/control\":410,\"./ui/control/geolocate\":411,\"./ui/control/navigation\":412,\"./ui/map\":421,\"./ui/marker\":422,\"./ui/popup\":423,\"./util/ajax\":425,\"./util/browser\":426,\"./util/config\":431,\"./util/evented\":434,\"./util/util\":442,\"point-geometry\":484}],344:[function(t,e,r){\"use strict\";var n=t(\"assert\");e.exports=function(t){for(var e={define:{},initialize:{}},r=0;r<t.length;r++){var i=t[r];n(\"u_\"===i.name.slice(0,2));var a=\"{precision} \"+(1===i.components?\"float\":\"vec\"+i.components);e.define[i.name.slice(2)]=\"uniform \"+a+\" \"+i.name+\";\\n\",e.initialize[i.name.slice(2)]=a+\" \"+i.name.slice(2)+\" = \"+i.name+\";\\n\"}return e}},{assert:47}],345:[function(t,e,r){\"use strict\";function n(t,e,r){var n,s=t.gl,l=t.transform,u=r.paint[\"background-color\"],c=r.paint[\"background-pattern\"],h=r.paint[\"background-opacity\"],f=c?t.spriteAtlas.getPosition(c.from,!0):null,d=c?t.spriteAtlas.getPosition(c.to,!0):null;if(t.setDepthSublayer(0),f&&d){if(t.isOpaquePass)return;n=t.useProgram(\"pattern\"),s.uniform1i(n.u_image,0),s.uniform2fv(n.u_pattern_tl_a,f.tl),s.uniform2fv(n.u_pattern_br_a,f.br),s.uniform2fv(n.u_pattern_tl_b,d.tl),s.uniform2fv(n.u_pattern_br_b,d.br),s.uniform1f(n.u_opacity,h),s.uniform1f(n.u_mix,c.t),s.uniform2fv(n.u_pattern_size_a,f.size),s.uniform2fv(n.u_pattern_size_b,d.size),s.uniform1f(n.u_scale_a,c.fromScale),s.uniform1f(n.u_scale_b,c.toScale),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),t.tileExtentPatternVAO.bind(s,n,t.tileExtentBuffer)}else{if(t.isOpaquePass!==(1===u[3]))return;var p=a([{name:\"u_color\",components:4},{name:\"u_opacity\",components:1}]);n=t.useProgram(\"fill\",[],p,p),s.uniform4fv(n.u_color,u),s.uniform1f(n.u_opacity,h),t.tileExtentVAO.bind(s,n,t.tileExtentBuffer)}s.disable(s.STENCIL_TEST);for(var m=l.coveringTiles({tileSize:o}),v=0;v<m.length;v++){var g=m[v];if(f&&d){var y={coord:g,tileSize:o};s.uniform1f(n.u_tile_units_to_pixels,1/i(y,1,t.transform.tileZoom));var b=y.tileSize*Math.pow(2,t.transform.tileZoom-y.coord.z),x=b*(y.coord.x+g.w*Math.pow(2,y.coord.z)),_=b*y.coord.y;s.uniform2f(n.u_pixel_coord_upper,x>>16,_>>16),s.uniform2f(n.u_pixel_coord_lower,65535&x,65535&_)}s.uniformMatrix4fv(n.u_matrix,!1,t.transform.calculatePosMatrix(g)),s.drawArrays(s.TRIANGLE_STRIP,0,t.tileExtentBuffer.length)}s.stencilMask(0),s.stencilFunc(s.EQUAL,128,128)}var i=t(\"../source/pixels_to_tile_units\"),a=t(\"./create_uniform_pragmas\"),o=512;e.exports=n},{\"../source/pixels_to_tile_units\":363,\"./create_uniform_pragmas\":344}],346:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=t.gl;t.setDepthSublayer(0),t.depthMask(!1),a.disable(a.STENCIL_TEST);for(var o=0;o<n.length;o++){var s=n[o],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.circle;if(c){var h=u.paintAttributes.circle[r.id],f=t.useProgram(\"circle\",h.defines,h.vertexPragmas,h.fragmentPragmas);\"map\"===r.paint[\"circle-pitch-scale\"]?(a.uniform1i(f.u_scale_with_map,!0),a.uniform2f(f.u_extrude_scale,t.transform.pixelsToGLUnits[0]*t.transform.altitude,t.transform.pixelsToGLUnits[1]*t.transform.altitude)):(a.uniform1i(f.u_scale_with_map,!1),a.uniform2fv(f.u_extrude_scale,t.transform.pixelsToGLUnits)),a.uniform1f(f.u_devicepixelratio,i.devicePixelRatio),a.uniformMatrix4fv(f.u_matrix,!1,t.translatePosMatrix(s.posMatrix,l,r.paint[\"circle-translate\"],r.paint[\"circle-translate-anchor\"])),u.setUniforms(a,\"circle\",f,r,{zoom:t.transform.zoom});for(var d=0;d<c.length;d++){var p=c[d];p.vaos[r.id].bind(a,f,p.layoutVertexBuffer,p.elementBuffer,p.paintVertexBuffers[r.id]),a.drawElements(a.TRIANGLES,3*p.elementBuffer.length,a.UNSIGNED_SHORT,0)}}}}}}var i=t(\"../util/browser\");e.exports=n},{\"../util/browser\":426}],347:[function(t,e,r){\"use strict\";function n(t,e,r,n){var i=t.gl;i.enable(i.STENCIL_TEST);for(var a=t.useProgram(\"collisionbox\"),o=0;o<n.length;o++){var s=n[o],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.collisionBox;if(c&&c.length){var h=c[0];0!==h.layoutVertexBuffer.length&&(i.uniformMatrix4fv(a.u_matrix,!1,s.posMatrix),t.enableTileClippingMask(s),t.lineWidth(1),i.uniform1f(a.u_scale,Math.pow(2,t.transform.zoom-l.coord.z)),i.uniform1f(a.u_zoom,10*t.transform.zoom),i.uniform1f(a.u_maxzoom,10*(l.coord.z+1)),h.vaos[r.id].bind(i,a,h.layoutVertexBuffer),i.drawArrays(i.LINES,0,h.layoutVertexBuffer.length))}}}}e.exports=n},{}],348:[function(t,e,r){\"use strict\";function n(t,e,r){if(!t.isOpaquePass&&t.options.debug)for(var n=0;n<r.length;n++)i(t,e,r[n])}function i(t,e,r){var n=t.gl;n.disable(n.STENCIL_TEST),t.lineWidth(1*o.devicePixelRatio);var i=r.posMatrix,h=t.useProgram(\"debug\");n.uniformMatrix4fv(h.u_matrix,!1,i),n.uniform4f(h.u_color,1,0,0,1),t.debugVAO.bind(n,h,t.debugBuffer),n.drawArrays(n.LINE_STRIP,0,t.debugBuffer.length);for(var f=a(r.toString(),50,200,5),d=new t.PosArray,p=0;p<f.length;p+=2)d.emplaceBack(f[p],f[p+1]);var m=new u(d.serialize(),t.PosArray.serialize(),u.BufferType.VERTEX);(new c).bind(n,h,m),n.uniform4f(h.u_color,1,1,1,1);for(var v=e.getTile(r).tileSize,g=l/(Math.pow(2,t.transform.zoom-r.z)*v),y=[[-1,-1],[-1,1],[1,-1],[1,1]],b=0;b<y.length;b++){var x=y[b];n.uniformMatrix4fv(h.u_matrix,!1,s.translate([],i,[g*x[0],g*x[1],0])),n.drawArrays(n.LINES,0,m.length)}n.uniform4f(h.u_color,0,0,0,1),n.uniformMatrix4fv(h.u_matrix,!1,i),n.drawArrays(n.LINES,0,m.length)}var a=t(\"../lib/debugtext\"),o=t(\"../util/browser\"),s=t(\"gl-matrix\").mat4,l=t(\"../data/bucket\").EXTENT,u=t(\"../data/buffer\"),c=t(\"./vertex_array_object\");e.exports=n},{\"../data/bucket\":329,\"../data/buffer\":334,\"../lib/debugtext\":342,\"../util/browser\":426,\"./vertex_array_object\":357,\"gl-matrix\":193}],349:[function(t,e,r){\"use strict\";function n(t,e,r,n){var o=t.gl;o.enable(o.STENCIL_TEST);var s;if(s=!r.paint[\"fill-pattern\"]&&(r.isPaintValueFeatureConstant(\"fill-color\")&&r.isPaintValueFeatureConstant(\"fill-opacity\")&&1===r.paint[\"fill-color\"][3]&&1===r.paint[\"fill-opacity\"]),t.isOpaquePass===s){t.setDepthSublayer(1);for(var l=0;l<n.length;l++)i(t,e,r,n[l])}if(!t.isOpaquePass&&r.paint[\"fill-antialias\"]){t.lineWidth(2),t.depthMask(!1);var u=r.getPaintProperty(\"fill-outline-color\");(u||!r.paint[\"fill-pattern\"])&&u?t.setDepthSublayer(2):t.setDepthSublayer(0);for(var c=0;c<n.length;c++)a(t,e,r,n[c])}}function i(t,e,r,n){var i=e.getTile(n),a=i.getBucket(r);if(a){var s=a.bufferGroups.fill;if(s){var l,u=t.gl,c=r.paint[\"fill-pattern\"];if(c)l=t.useProgram(\"pattern\"),o(c,r.paint[\"fill-opacity\"],i,n,t,l),u.activeTexture(u.TEXTURE0),t.spriteAtlas.bind(u,!0);else{var h=a.paintAttributes.fill[r.id];l=t.useProgram(\"fill\",h.defines,h.vertexPragmas,h.fragmentPragmas),a.setUniforms(u,\"fill\",l,r,{zoom:t.transform.zoom})}u.uniformMatrix4fv(l.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint[\"fill-translate\"],r.paint[\"fill-translate-anchor\"])),t.enableTileClippingMask(n);for(var f=0;f<s.length;f++){var d=s[f];d.vaos[r.id].bind(u,l,d.layoutVertexBuffer,d.elementBuffer,d.paintVertexBuffers[r.id]),u.drawElements(u.TRIANGLES,d.elementBuffer.length,u.UNSIGNED_SHORT,0)}}}}function a(t,e,r,n){var i=e.getTile(n),a=i.getBucket(r);if(a){var s,l=t.gl,u=a.bufferGroups.fill,c=r.paint[\"fill-pattern\"],h=r.paint[\"fill-opacity\"],f=r.getPaintProperty(\"fill-outline-color\");if(c&&!f)s=t.useProgram(\"outlinepattern\"),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight);else{var d=a.paintAttributes.fill[r.id];s=t.useProgram(\"outline\",d.defines,d.vertexPragmas,d.fragmentPragmas),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight),l.uniform1f(s.u_opacity,h),a.setUniforms(l,\"fill\",s,r,{zoom:t.transform.zoom})}l.uniformMatrix4fv(s.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint[\"fill-translate\"],r.paint[\"fill-translate-anchor\"])),c&&o(c,h,i,n,t,s),t.enableTileClippingMask(n);for(var p=0;p<u.length;p++){var m=u[p];m.secondVaos[r.id].bind(l,s,m.layoutVertexBuffer,m.elementBuffer2,m.paintVertexBuffers[r.id]),l.drawElements(l.LINES,2*m.elementBuffer2.length,l.UNSIGNED_SHORT,0)}}}function o(t,e,r,n,i,a){var o=i.gl,l=i.spriteAtlas.getPosition(t.from,!0),u=i.spriteAtlas.getPosition(t.to,!0);if(l&&u){o.uniform1i(a.u_image,0),o.uniform2fv(a.u_pattern_tl_a,l.tl),o.uniform2fv(a.u_pattern_br_a,l.br),o.uniform2fv(a.u_pattern_tl_b,u.tl),o.uniform2fv(a.u_pattern_br_b,u.br),o.uniform1f(a.u_opacity,e),o.uniform1f(a.u_mix,t.t),o.uniform1f(a.u_tile_units_to_pixels,1/s(r,1,i.transform.tileZoom)),o.uniform2fv(a.u_pattern_size_a,l.size),o.uniform2fv(a.u_pattern_size_b,u.size),o.uniform1f(a.u_scale_a,t.fromScale),o.uniform1f(a.u_scale_b,t.toScale);var c=r.tileSize*Math.pow(2,i.transform.tileZoom-r.coord.z),h=c*(r.coord.x+n.w*Math.pow(2,r.coord.z)),f=c*r.coord.y;o.uniform2f(a.u_pixel_coord_upper,h>>16,f>>16),o.uniform2f(a.u_pixel_coord_lower,65535&h,65535&f),o.activeTexture(o.TEXTURE0),i.spriteAtlas.bind(o,!0)}}var s=t(\"../source/pixels_to_tile_units\");e.exports=n},{\"../source/pixels_to_tile_units\":363}],350:[function(t,e,r){\"use strict\";var n=t(\"../util/browser\"),i=t(\"gl-matrix\").mat2,a=t(\"../source/pixels_to_tile_units\");e.exports=function(t,e,r,o){if(!t.isOpaquePass){t.setDepthSublayer(0),t.depthMask(!1);var s=t.gl;if(s.enable(s.STENCIL_TEST),!(r.paint[\"line-width\"]<=0)){var l=1/n.devicePixelRatio,u=r.paint[\"line-blur\"]+l,c=r.paint[\"line-color\"],h=t.transform,f=i.create();i.scale(f,f,[1,Math.cos(h._pitch)]),i.rotate(f,f,t.transform.angle);var d,p,m,v,g,y=Math.sqrt(h.height*h.height/4*(1+h.altitude*h.altitude)),b=h.height/2*Math.tan(h._pitch),x=(y+b)/y-1,_=r.paint[\"line-dasharray\"],w=r.paint[\"line-pattern\"];if(_)d=t.useProgram(\"linesdfpattern\"),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform4fv(d.u_color,c),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]),p=t.lineAtlas.getDash(_.from,\"round\"===r.layout[\"line-cap\"]),m=t.lineAtlas.getDash(_.to,\"round\"===r.layout[\"line-cap\"]),s.uniform1i(d.u_image,0),s.activeTexture(s.TEXTURE0),t.lineAtlas.bind(s),s.uniform1f(d.u_tex_y_a,p.y),s.uniform1f(d.u_tex_y_b,m.y),s.uniform1f(d.u_mix,_.t),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f);else if(w){if(v=t.spriteAtlas.getPosition(w.from,!0),g=t.spriteAtlas.getPosition(w.to,!0),!v||!g)return;d=t.useProgram(\"linepattern\"),s.uniform1i(d.u_image,0),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform2fv(d.u_pattern_tl_a,v.tl),s.uniform2fv(d.u_pattern_br_a,v.br),s.uniform2fv(d.u_pattern_tl_b,g.tl),s.uniform2fv(d.u_pattern_br_b,g.br),s.uniform1f(d.u_fade,w.t),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f)}else d=t.useProgram(\"line\"),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f),s.uniform4fv(d.u_color,c),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]);for(var M=0;M<o.length;M++){var k=o[M],A=e.getTile(k),T=A.getBucket(r);if(T){var S=T.bufferGroups.line;if(S){t.enableTileClippingMask(k);var E=t.translatePosMatrix(k.posMatrix,A,r.paint[\"line-translate\"],r.paint[\"line-translate-anchor\"]);s.uniformMatrix4fv(d.u_matrix,!1,E);var L=1/a(A,1,t.transform.zoom);if(_){var C=p.width*_.fromScale,I=m.width*_.toScale,z=[1/a(A,C,t.transform.tileZoom),-p.height/2],D=[1/a(A,I,t.transform.tileZoom),-m.height/2],P=t.lineAtlas.width/(256*Math.min(C,I)*n.devicePixelRatio)/2;s.uniform1f(d.u_ratio,L),s.uniform2fv(d.u_patternscale_a,z),s.uniform2fv(d.u_patternscale_b,D),s.uniform1f(d.u_sdfgamma,P)}else w?(s.uniform1f(d.u_ratio,L),s.uniform2fv(d.u_pattern_size_a,[a(A,v.size[0]*w.fromScale,t.transform.tileZoom),g.size[1]]),s.uniform2fv(d.u_pattern_size_b,[a(A,g.size[0]*w.toScale,t.transform.tileZoom),g.size[1]])):s.uniform1f(d.u_ratio,L);for(var O=0;O<S.length;O++){var R=S[O];R.vaos[r.id].bind(s,d,R.layoutVertexBuffer,R.elementBuffer),s.drawElements(s.TRIANGLES,3*R.elementBuffer.length,s.UNSIGNED_SHORT,0)}}}}}}}},{\"../source/pixels_to_tile_units\":363,\"../util/browser\":426,\"gl-matrix\":193}],351:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=t.gl;a.enable(a.DEPTH_TEST),t.depthMask(!0),a.depthFunc(a.LESS);for(var o=n.length&&n[0].z,s=0;s<n.length;s++){var l=n[s];t.setDepthSublayer(l.z-o),i(t,e,r,l)}a.depthFunc(a.LEQUAL)}}function i(t,e,r,n){var i=t.gl;i.disable(i.STENCIL_TEST);var u=e.getTile(n),c=t.transform.calculatePosMatrix(n,e.maxzoom),h=t.useProgram(\"raster\");i.uniformMatrix4fv(h.u_matrix,!1,c),i.uniform1f(h.u_brightness_low,r.paint[\"raster-brightness-min\"]),i.uniform1f(h.u_brightness_high,r.paint[\"raster-brightness-max\"]),i.uniform1f(h.u_saturation_factor,s(r.paint[\"raster-saturation\"])),i.uniform1f(h.u_contrast_factor,o(r.paint[\"raster-contrast\"])),i.uniform3fv(h.u_spin_weights,a(r.paint[\"raster-hue-rotate\"]));var f,d,p=u.source&&u.source.findLoadedParent(n,0,{}),m=l(u,p,r,t.transform);i.activeTexture(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,u.texture),i.activeTexture(i.TEXTURE1),p?(i.bindTexture(i.TEXTURE_2D,p.texture),f=Math.pow(2,p.coord.z-u.coord.z),d=[u.coord.x*f%1,u.coord.y*f%1]):(i.bindTexture(i.TEXTURE_2D,u.texture),m[1]=0),i.uniform2fv(h.u_tl_parent,d||[0,0]),i.uniform1f(h.u_scale_parent,f||1),i.uniform1f(h.u_buffer_scale,1),i.uniform1f(h.u_opacity0,m[0]),i.uniform1f(h.u_opacity1,m[1]),i.uniform1i(h.u_image0,0),i.uniform1i(h.u_image1,1);var v=u.boundsBuffer||t.rasterBoundsBuffer;(u.boundsVAO||t.rasterBoundsVAO).bind(i,h,v),i.drawArrays(i.TRIANGLE_STRIP,0,v.length)}function a(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}function o(t){return t>0?1/(1-t):1+t}function s(t){return t>0?1-1/(1.001-t):-t}function l(t,e,r,n){var i=[1,0],a=r.paint[\"raster-fade-duration\"];if(t.source&&a>0){var o=(new Date).getTime(),s=(o-t.timeAdded)/a,l=e?(o-e.timeAdded)/a:-1,c=n.coveringZoomLevel(t.source),h=!!e&&Math.abs(e.coord.z-c)>Math.abs(t.coord.z-c);!e||h?(i[0]=u.clamp(s,0,1),i[1]=1-i[0]):(i[0]=u.clamp(1-l,0,1),i[1]=1-i[0])}var f=r.paint[\"raster-opacity\"];return i[0]*=f,i[1]*=f,i}var u=t(\"../util/util\"),c=t(\"../util/struct_array\");e.exports=n,n.RasterBoundsArray=new c({members:[{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]})},{\"../util/struct_array\":440,\"../util/util\":442}],352:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=!(r.layout[\"text-allow-overlap\"]||r.layout[\"icon-allow-overlap\"]||r.layout[\"text-ignore-placement\"]||r.layout[\"icon-ignore-placement\"]),o=t.gl;a?o.disable(o.STENCIL_TEST):o.enable(o.STENCIL_TEST),t.setDepthSublayer(0),t.depthMask(!1),o.disable(o.DEPTH_TEST),i(t,e,r,n,!1,r.paint[\"icon-translate\"],r.paint[\"icon-translate-anchor\"],r.layout[\"icon-rotation-alignment\"],r.layout[\"icon-rotation-alignment\"],r.layout[\"icon-size\"],r.paint[\"icon-halo-width\"],r.paint[\"icon-halo-color\"],r.paint[\"icon-halo-blur\"],r.paint[\"icon-opacity\"],r.paint[\"icon-color\"]),\n",
+       "i(t,e,r,n,!0,r.paint[\"text-translate\"],r.paint[\"text-translate-anchor\"],r.layout[\"text-rotation-alignment\"],r.layout[\"text-pitch-alignment\"],r.layout[\"text-size\"],r.paint[\"text-halo-width\"],r.paint[\"text-halo-color\"],r.paint[\"text-halo-blur\"],r.paint[\"text-opacity\"],r.paint[\"text-color\"]),o.enable(o.DEPTH_TEST),e.map.showCollisionBoxes&&s(t,e,r,n)}}function i(t,e,r,n,i,o,s,l,u,c,h,f,d,p,m){for(var v=0;v<n.length;v++){var g=e.getTile(n[v]),y=g.getBucket(r);if(y){var b=y.bufferGroups,x=i?b.glyph:b.icon;x.length&&(t.enableTileClippingMask(n[v]),a(t,r,n[v].posMatrix,g,y,x,i,i||y.sdfIcons,!i&&y.iconsNeedLinear,i?y.adjustedTextSize:y.adjustedIconSize,y.fontstack,o,s,l,u,c,h,f,d,p,m))}}}function a(t,e,r,n,i,a,s,u,c,h,f,d,p,m,v,g,y,b,x,_,w){var M,k,A,T=t.gl,S=t.transform,E=\"map\"===m,L=\"map\"===v,C=s?24:1,I=g/C;if(L?(k=l(n,1,t.transform.zoom)*I,A=1/Math.cos(S._pitch),M=[k,k]):(k=t.transform.altitude*I,A=1,M=[S.pixelsToGLUnits[0]*k,S.pixelsToGLUnits[1]*k]),s||t.style.sprite.loaded()){var z=t.useProgram(u?\"sdf\":\"icon\");if(T.uniformMatrix4fv(z.u_matrix,!1,t.translatePosMatrix(r,n,d,p)),T.uniform1i(z.u_rotate_with_map,E),T.uniform1i(z.u_pitch_with_map,L),T.uniform2fv(z.u_extrude_scale,M),T.activeTexture(T.TEXTURE0),T.uniform1i(z.u_texture,0),s){var D=f&&t.glyphSource.getGlyphAtlas(f);if(!D)return;D.updateTexture(T),T.uniform2f(z.u_texsize,D.width/4,D.height/4)}else{var P=t.options.rotating||t.options.zooming,O=1!==I||o.devicePixelRatio!==t.spriteAtlas.pixelRatio||c,R=L||t.transform.pitch;t.spriteAtlas.bind(T,u||P||O||R),T.uniform2f(z.u_texsize,t.spriteAtlas.width/4,t.spriteAtlas.height/4)}var F=Math.log(g/h)/Math.LN2||0;T.uniform1f(z.u_zoom,10*(t.transform.zoom-F)),T.activeTexture(T.TEXTURE1),t.frameHistory.bind(T),T.uniform1i(z.u_fadetexture,1);var j;if(u){var N=.105*C/g/o.devicePixelRatio;if(y){T.uniform1f(z.u_gamma,(1.19*x/I/8+N)*A),T.uniform4fv(z.u_color,b),T.uniform1f(z.u_opacity,_),T.uniform1f(z.u_buffer,(6-y/I)/8);for(var B=0;B<a.length;B++)j=a[B],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3*j.elementBuffer.length,T.UNSIGNED_SHORT,0)}T.uniform1f(z.u_gamma,N*A),T.uniform4fv(z.u_color,w),T.uniform1f(z.u_opacity,_),T.uniform1f(z.u_buffer,.75),T.uniform1f(z.u_pitch,S.pitch/360*2*Math.PI),T.uniform1f(z.u_bearing,S.bearing/360*2*Math.PI),T.uniform1f(z.u_aspect_ratio,S.width/S.height);for(var U=0;U<a.length;U++)j=a[U],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3*j.elementBuffer.length,T.UNSIGNED_SHORT,0)}else{T.uniform1f(z.u_opacity,_);for(var V=0;V<a.length;V++)j=a[V],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3*j.elementBuffer.length,T.UNSIGNED_SHORT,0)}}}var o=t(\"../util/browser\"),s=t(\"./draw_collision_debug\"),l=t(\"../source/pixels_to_tile_units\");e.exports=n},{\"../source/pixels_to_tile_units\":363,\"../util/browser\":426,\"./draw_collision_debug\":347}],353:[function(t,e,r){\"use strict\";function n(){this.changeTimes=new Float64Array(256),this.changeOpacities=new Uint8Array(256),this.opacities=new Uint8ClampedArray(256),this.array=new Uint8Array(this.opacities.buffer),this.fadeDuration=300,this.previousZoom=0,this.firstFrame=!0}e.exports=n,n.prototype.record=function(t){var e=Date.now();this.firstFrame&&(e=0,this.firstFrame=!1),t=Math.floor(10*t);var r;if(t<this.previousZoom)for(r=t+1;r<=this.previousZoom;r++)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];else for(r=t;r>this.previousZoom;r--)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];for(r=0;r<256;r++){var n=e-this.changeTimes[r],i=n/this.fadeDuration*255;this.opacities[r]=r<=t?this.changeOpacities[r]+i:this.changeOpacities[r]-i}this.changed=!0,this.previousZoom=t},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.changed&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,256,1,t.ALPHA,t.UNSIGNED_BYTE,this.array),this.changed=!1)):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,256,1,0,t.ALPHA,t.UNSIGNED_BYTE,this.array))}},{}],354:[function(t,e,r){\"use strict\";function n(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.width*this.height*this.bytes),this.positions={}}var i=t(\"../util/util\");e.exports=n,n.prototype.setSprite=function(t){this.sprite=t},n.prototype.getDash=function(t,e){var r=t.join(\",\")+e;return this.positions[r]||(this.positions[r]=this.addDash(t,e)),this.positions[r]},n.prototype.addDash=function(t,e){var r=e?7:0,n=2*r+1;if(this.nextRow+n>this.height)return i.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<t.length;o++)a+=t[o];for(var s=this.width/a,l=s/2,u=t.length%2==1,c=-r;c<=r;c++)for(var h=this.nextRow+r+c,f=this.width*h,d=u?-t[t.length-1]:0,p=t[0],m=1,v=0;v<this.width;v++){for(;p<v/s;)d=p,p+=t[m],u&&m===t.length-1&&(p+=t[0]),m++;var g,y=Math.abs(v-d*s),b=Math.abs(v-p*s),x=Math.min(y,b),_=m%2==1;if(e){var w=r?c/r*(l+1):0;if(_){var M=l-Math.abs(w);g=Math.sqrt(x*x+M*M)}else g=l-Math.sqrt(x*x+w*w)}else g=(_?1:-1)*x;this.data[3+4*(f+v)]=Math.max(0,Math.min(255,g+128))}var k={y:(this.nextRow+r+.5)/this.height,height:2*r/this.height,width:a};return this.nextRow+=n,this.dirty=!0,k},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.RGBA,t.UNSIGNED_BYTE,this.data))):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width,this.height,0,t.RGBA,t.UNSIGNED_BYTE,this.data))}},{\"../util/util\":442}],355:[function(t,e,r){\"use strict\";function n(t,e){this.gl=t,this.transform=e,this.reusableTextures={},this.preFbos={},this.frameHistory=new o,this.setup(),this.numSublayers=s.maxUnderzooming+s.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.lineWidthRange=t.getParameter(t.ALIASED_LINE_WIDTH_RANGE)}var i=t(\"../util/browser\"),a=t(\"gl-matrix\").mat4,o=t(\"./frame_history\"),s=t(\"../source/source_cache\"),l=t(\"../data/bucket\").EXTENT,u=t(\"../source/pixels_to_tile_units\"),c=t(\"../util/util\"),h=t(\"../util/struct_array\"),f=t(\"../data/buffer\"),d=t(\"./vertex_array_object\"),p=t(\"./draw_raster\").RasterBoundsArray,m=t(\"./create_uniform_pragmas\");e.exports=n,c.extend(n.prototype,t(\"./painter/use_program\")),n.prototype.resize=function(t,e){var r=this.gl;this.width=t*i.devicePixelRatio,this.height=e*i.devicePixelRatio,r.viewport(0,0,this.width,this.height)},n.prototype.setup=function(){var t=this.gl;t.verbose=!0,t.enable(t.BLEND),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.enable(t.STENCIL_TEST),t.enable(t.DEPTH_TEST),t.depthFunc(t.LEQUAL),this._depthMask=!1,t.depthMask(!1);var e=this.PosArray=new h({members:[{name:\"a_pos\",type:\"Int16\",components:2}]}),r=new e;r.emplaceBack(0,0),r.emplaceBack(l,0),r.emplaceBack(0,l),r.emplaceBack(l,l),this.tileExtentBuffer=new f(r.serialize(),e.serialize(),f.BufferType.VERTEX),this.tileExtentVAO=new d,this.tileExtentPatternVAO=new d;var n=new e;n.emplaceBack(0,0),n.emplaceBack(l,0),n.emplaceBack(l,l),n.emplaceBack(0,l),n.emplaceBack(0,0),this.debugBuffer=new f(n.serialize(),e.serialize(),f.BufferType.VERTEX),this.debugVAO=new d;var i=new p;i.emplaceBack(0,0,0,0),i.emplaceBack(l,0,32767,0),i.emplaceBack(0,l,0,32767),i.emplaceBack(l,l,32767,32767),this.rasterBoundsBuffer=new f(i.serialize(),p.serialize(),f.BufferType.VERTEX),this.rasterBoundsVAO=new d},n.prototype.clearColor=function(){var t=this.gl;t.clearColor(0,0,0,0),t.clear(t.COLOR_BUFFER_BIT)},n.prototype.clearStencil=function(){var t=this.gl;t.clearStencil(0),t.stencilMask(255),t.clear(t.STENCIL_BUFFER_BIT)},n.prototype.clearDepth=function(){var t=this.gl;t.clearDepth(1),this.depthMask(!0),t.clear(t.DEPTH_BUFFER_BIT)},n.prototype._renderTileClippingMasks=function(t){var e=this.gl;e.colorMask(!1,!1,!1,!1),this.depthMask(!1),e.disable(e.DEPTH_TEST),e.enable(e.STENCIL_TEST),e.stencilMask(248),e.stencilOp(e.KEEP,e.KEEP,e.REPLACE);var r=1;this._tileClippingMaskIDs={};for(var n=0;n<t.length;n++){var i=t[n],a=this._tileClippingMaskIDs[i.id]=r++<<3;e.stencilFunc(e.ALWAYS,a,248);var o=m([{name:\"u_color\",components:4},{name:\"u_opacity\",components:1}]),s=this.useProgram(\"fill\",[],o,o);e.uniformMatrix4fv(s.u_matrix,!1,i.posMatrix),this.tileExtentVAO.bind(e,s,this.tileExtentBuffer),e.drawArrays(e.TRIANGLE_STRIP,0,this.tileExtentBuffer.length)}e.stencilMask(0),e.colorMask(!0,!0,!0,!0),this.depthMask(!0),e.enable(e.DEPTH_TEST)},n.prototype.enableTileClippingMask=function(t){var e=this.gl;e.stencilFunc(e.EQUAL,this._tileClippingMaskIDs[t.id],248)},n.prototype.prepareBuffers=function(){},n.prototype.bindDefaultFramebuffer=function(){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,null)};var v={symbol:t(\"./draw_symbol\"),circle:t(\"./draw_circle\"),line:t(\"./draw_line\"),fill:t(\"./draw_fill\"),raster:t(\"./draw_raster\"),background:t(\"./draw_background\"),debug:t(\"./draw_debug\")};n.prototype.render=function(t,e){this.style=t,this.options=e,this.lineAtlas=t.lineAtlas,this.spriteAtlas=t.spriteAtlas,this.spriteAtlas.setSprite(t.sprite),this.glyphSource=t.glyphSource,this.frameHistory.record(this.transform.zoom),this.prepareBuffers(),this.clearColor(),this.clearDepth(),this.showOverdrawInspector(e.showOverdrawInspector),this.depthRange=(t._order.length+2)*this.numSublayers*this.depthEpsilon,this.renderPass({isOpaquePass:!0}),this.renderPass({isOpaquePass:!1})},n.prototype.renderPass=function(t){var e=this.style._groups,r=t.isOpaquePass;this.currentLayer=r?this.style._order.length:-1;for(var n=0;n<e.length;n++){var i,a=e[r?e.length-1-n:n],o=this.style.sources[a.source],s=[];if(o){for(s=o.getVisibleCoordinates(),i=0;i<s.length;i++)s[i].posMatrix=this.transform.calculatePosMatrix(s[i],o.maxzoom);this.clearStencil(),o.prepare&&o.prepare(),o.isTileClipped&&this._renderTileClippingMasks(s)}for(r?(this._showOverdrawInspector||this.gl.disable(this.gl.BLEND),this.isOpaquePass=!0):(this.gl.enable(this.gl.BLEND),this.isOpaquePass=!1,s.reverse()),i=0;i<a.length;i++){var l=a[r?a.length-1-i:i];this.currentLayer+=r?-1:1,this.renderLayer(this,o,l,s)}o&&v.debug(this,o,s)}},n.prototype.depthMask=function(t){t!==this._depthMask&&(this._depthMask=t,this.gl.depthMask(t))},n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||n.length)&&(this.id=r.id,v[r.type](t,e,r,n))},n.prototype.setDepthSublayer=function(t){var e=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon,r=e-1+this.depthRange;this.gl.depthRange(r,e)},n.prototype.translatePosMatrix=function(t,e,r,n){if(!r[0]&&!r[1])return t;if(\"viewport\"===n){var i=Math.sin(-this.transform.angle),o=Math.cos(-this.transform.angle);r=[r[0]*o-r[1]*i,r[0]*i+r[1]*o]}var s=[u(e,r[0],this.transform.zoom),u(e,r[1],this.transform.zoom),0],l=new Float32Array(16);return a.translate(l,t,s),l},n.prototype.saveTexture=function(t){var e=this.reusableTextures[t.size];e?e.push(t):this.reusableTextures[t.size]=[t]},n.prototype.getTexture=function(t){var e=this.reusableTextures[t];return e&&e.length>0?e.pop():null},n.prototype.lineWidth=function(t){this.gl.lineWidth(c.clamp(t,this.lineWidthRange[0],this.lineWidthRange[1]))},n.prototype.showOverdrawInspector=function(t){if(t||this._showOverdrawInspector){this._showOverdrawInspector=t;var e=this.gl;if(t){e.blendFunc(e.CONSTANT_COLOR,e.ONE);e.blendColor(1/8,1/8,1/8,0),e.clearColor(0,0,0,1),e.clear(e.COLOR_BUFFER_BIT)}else e.blendFunc(e.ONE,e.ONE_MINUS_SRC_ALPHA)}}},{\"../data/bucket\":329,\"../data/buffer\":334,\"../source/pixels_to_tile_units\":363,\"../source/source_cache\":367,\"../util/browser\":426,\"../util/struct_array\":440,\"../util/util\":442,\"./create_uniform_pragmas\":344,\"./draw_background\":345,\"./draw_circle\":346,\"./draw_debug\":348,\"./draw_fill\":349,\"./draw_line\":350,\"./draw_raster\":351,\"./draw_symbol\":352,\"./frame_history\":353,\"./painter/use_program\":356,\"./vertex_array_object\":357,\"gl-matrix\":193}],356:[function(t,e,r){\"use strict\";function n(t,e){return t.replace(/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,function(t,r,n,i,a){return e[r][a].replace(/{type}/g,i).replace(/{precision}/g,n)})}var i=t(\"assert\"),a=t(\"../../util/util\"),o=t(\"mapbox-gl-shaders\"),s=o.util;e.exports._createProgram=function(t,e,r,l){for(var u=this.gl,c=u.createProgram(),h=o[t],f=\"#define MAPBOX_GL_JS;\\n\",d=0;d<e.length;d++)f+=\"#define \"+e[d]+\";\\n\";var p=u.createShader(u.FRAGMENT_SHADER);u.shaderSource(p,n(f+h.fragmentSource,l)),u.compileShader(p),i(u.getShaderParameter(p,u.COMPILE_STATUS),u.getShaderInfoLog(p)),u.attachShader(c,p);var m=u.createShader(u.VERTEX_SHADER);u.shaderSource(m,n(f+s+h.vertexSource,r)),u.compileShader(m),i(u.getShaderParameter(m,u.COMPILE_STATUS),u.getShaderInfoLog(m)),u.attachShader(c,m),u.linkProgram(c),i(u.getProgramParameter(c,u.LINK_STATUS),u.getProgramInfoLog(c));for(var v={},g=u.getProgramParameter(c,u.ACTIVE_ATTRIBUTES),y=0;y<g;y++){var b=u.getActiveAttrib(c,y);v[b.name]=u.getAttribLocation(c,b.name)}for(var x={},_=u.getProgramParameter(c,u.ACTIVE_UNIFORMS),w=0;w<_;w++){var M=u.getActiveUniform(c,w);x[M.name]=u.getUniformLocation(c,M.name)}return a.extend({program:c,definition:h,attributes:v,numAttributes:g},v,x)},e.exports._createProgramCached=function(t,e,r,n){this.cache=this.cache||{};var i=JSON.stringify({name:t,defines:e,vertexPragmas:r,fragmentPragmas:n});return this.cache[i]||(this.cache[i]=this._createProgram(t,e,r,n)),this.cache[i]},e.exports.useProgram=function(t,e,r,n){var i=this.gl;e=e||[],this._showOverdrawInspector&&(e=e.concat(\"OVERDRAW_INSPECTOR\"));var a=this._createProgramCached(t,e,r,n);return this.currentProgram!==a&&(i.useProgram(a.program),this.currentProgram=a),a}},{\"../../util/util\":442,assert:47,\"mapbox-gl-shaders\":303}],357:[function(t,e,r){\"use strict\";function n(){this.boundProgram=null,this.boundVertexBuffer=null,this.boundVertexBuffer2=null,this.boundElementBuffer=null,this.vao=null}var i=t(\"assert\");e.exports=n,n.prototype.bind=function(t,e,r,n,i){void 0===t.extVertexArrayObject&&(t.extVertexArrayObject=t.getExtension(\"OES_vertex_array_object\"));var a=!this.vao||this.boundProgram!==e||this.boundVertexBuffer!==r||this.boundVertexBuffer2!==i||this.boundElementBuffer!==n;!t.extVertexArrayObject||a?this.freshBind(t,e,r,n,i):t.extVertexArrayObject.bindVertexArrayOES(this.vao)},n.prototype.freshBind=function(t,e,r,n,a){var o,s=e.numAttributes;if(t.extVertexArrayObject)this.vao&&this.destroy(t),this.vao=t.extVertexArrayObject.createVertexArrayOES(),t.extVertexArrayObject.bindVertexArrayOES(this.vao),o=0,this.boundProgram=e,this.boundVertexBuffer=r,this.boundVertexBuffer2=a,this.boundElementBuffer=n;else{o=t.currentNumAttributes||0;for(var l=s;l<o;l++)i(0!==l),t.disableVertexAttribArray(l)}for(var u=o;u<s;u++)t.enableVertexAttribArray(u);r.bind(t),r.setVertexAttribPointers(t,e),a&&(a.bind(t),a.setVertexAttribPointers(t,e)),n&&n.bind(t),t.currentNumAttributes=s},n.prototype.unbind=function(t){var e=t.extVertexArrayObject;e&&e.bindVertexArrayOES(null)},n.prototype.destroy=function(t){var e=t.extVertexArrayObject;e&&this.vao&&(e.deleteVertexArrayOES(this.vao),this.vao=null)}},{assert:47}],358:[function(t,e,r){\"use strict\";function n(t,e,r){e=e||{},this.id=t,this.dispatcher=r,this._data=e.data,void 0!==e.maxzoom&&(this.maxzoom=e.maxzoom),e.type&&(this.type=e.type);var n=s/this.tileSize;this.workerOptions=a.extend({source:this.id,cluster:e.cluster||!1,geojsonVtOptions:{buffer:(void 0!==e.buffer?e.buffer:128)*n,tolerance:(void 0!==e.tolerance?e.tolerance:.375)*n,extent:s,maxZoom:this.maxzoom},superclusterOptions:{maxZoom:Math.min(e.clusterMaxZoom,this.maxzoom-1)||this.maxzoom-1,extent:s,radius:(e.clusterRadius||50)*n,log:!1}},e.workerOptions),this._updateWorkerData(function(t){if(t)return void this.fire(\"error\",{error:t});this.fire(\"load\")}.bind(this))}var i=t(\"../util/evented\"),a=t(\"../util/util\"),o=t(\"resolve-url\"),s=t(\"../data/bucket\").EXTENT;e.exports=n,n.prototype=a.inherit(i,{type:\"geojson\",minzoom:0,maxzoom:18,tileSize:512,isTileClipped:!0,reparseOverscaled:!0,onAdd:function(t){this.map=t},setData:function(t){return this._data=t,this._updateWorkerData(function(t){if(t)return this.fire(\"error\",{error:t});this.fire(\"change\")}.bind(this)),this},_updateWorkerData:function(t){var e=a.extend({},this.workerOptions),r=this._data;\"string\"==typeof r?e.url=\"undefined\"!=typeof window?o(window.location.href,r):r:e.data=JSON.stringify(r),this.workerID=this.dispatcher.send(this.type+\".loadData\",e,function(e){this._loaded=!0,t(e)}.bind(this))},loadTile:function(t,e){var r=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,n={type:this.type,uid:t.uid,coord:t.coord,zoom:t.coord.z,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,overscaling:r,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID=this.dispatcher.send(\"load tile\",n,function(r,n){if(t.unloadVectorData(this.map.painter),!t.aborted)return r?e(r):(t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null))}.bind(this),this.workerID)},abortTile:function(t){t.aborted=!0},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send(\"remove tile\",{uid:t.uid,source:this.id},function(){},t.workerID)},serialize:function(){return{type:this.type,data:this._data}}})},{\"../data/bucket\":329,\"../util/evented\":434,\"../util/util\":442,\"resolve-url\":501}],359:[function(t,e,r){\"use strict\";function n(t,e,r){r&&(this.loadGeoJSON=r),h.call(this,t,e)}var i=t(\"../util/util\"),a=t(\"../util/ajax\"),o=t(\"geojson-rewind\"),s=t(\"./geojson_wrapper\"),l=t(\"vt-pbf\"),u=t(\"supercluster\"),c=t(\"geojson-vt\"),h=t(\"./vector_tile_worker_source\");e.exports=n,n.prototype=i.inherit(h,{_geoJSONIndexes:{},loadVectorData:function(t,e){var r=t.source,n=t.coord;if(!this._geoJSONIndexes[r])return e(null,null);var i=this._geoJSONIndexes[r].getTile(Math.min(n.z,t.maxZoom),n.x,n.y);if(!i)return e(null,null);var a=new s(i.features);a.name=\"_geojsonTileLayer\";var o=l({layers:{_geojsonTileLayer:a}});0===o.byteOffset&&o.byteLength===o.buffer.byteLength||(o=new Uint8Array(o)),e(null,{tile:a,rawTileData:o.buffer})},loadData:function(t,e){var r=function(r,n){return r?e(r):\"object\"!=typeof n?e(new Error(\"Input data is not a valid GeoJSON object.\")):(o(n,!0),void this._indexData(n,t,function(r,n){if(r)return e(r);this._geoJSONIndexes[t.source]=n,e(null)}.bind(this)))}.bind(this);this.loadGeoJSON(t,r)},loadGeoJSON:function(t,e){if(t.url)a.getJSON(t.url,e);else{if(\"string\"!=typeof t.data)return e(new Error(\"Input data is not a valid GeoJSON object.\"));try{return e(null,JSON.parse(t.data))}catch(t){return e(new Error(\"Input data is not a valid GeoJSON object.\"))}}},_indexData:function(t,e,r){try{e.cluster?r(null,u(e.superclusterOptions).load(t.features)):r(null,c(t,e.geojsonVtOptions))}catch(t){return r(t)}}})},{\"../util/ajax\":425,\"../util/util\":442,\"./geojson_wrapper\":360,\"./vector_tile_worker_source\":371,\"geojson-rewind\":138,\"geojson-vt\":142,supercluster:529,\"vt-pbf\":556}],360:[function(t,e,r){\"use strict\";function n(t){this.features=t,this.length=t.length,this.extent=s}function i(t){if(this.type=t.type,1===t.type){this.rawGeometry=[];for(var e=0;e<t.geometry.length;e++)this.rawGeometry.push([t.geometry[e]])}else this.rawGeometry=t.geometry;this.properties=t.tags,this.extent=s}var a=t(\"point-geometry\"),o=t(\"vector-tile\").VectorTileFeature,s=t(\"../data/bucket\").EXTENT;e.exports=n,n.prototype.feature=function(t){return new i(this.features[t])},i.prototype.loadGeometry=function(){var t=this.rawGeometry;this.geometry=[];for(var e=0;e<t.length;e++){for(var r=t[e],n=[],i=0;i<r.length;i++)n.push(new a(r[i][0],r[i][1]));this.geometry.push(n)}return this.geometry},i.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var t=this.geometry,e=1/0,r=-1/0,n=1/0,i=-1/0,a=0;a<t.length;a++)for(var o=t[a],s=0;s<o.length;s++){var l=o[s];e=Math.min(e,l.x),r=Math.max(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.y)}return[e,n,r,i]},i.prototype.toGeoJSON=o.prototype.toGeoJSON},{\"../data/bucket\":329,\"point-geometry\":484,\"vector-tile\":550}],361:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r,this.url=e.url,this.coordinates=e.coordinates,u.getImage(e.url,function(t,r){if(t)return this.fire(\"error\",{error:t});this.image=r,this.image.addEventListener(\"load\",function(){this.map._rerender()}.bind(this)),this._loaded=!0,this.fire(\"load\"),this.map&&this.setCoordinates(e.coordinates)}.bind(this))}var i=t(\"../util/util\"),a=t(\"./tile_coord\"),o=t(\"../geo/lng_lat\"),s=t(\"point-geometry\"),l=t(\"../util/evented\"),u=t(\"../util/ajax\"),c=t(\"../data/bucket\").EXTENT,h=t(\"../render/draw_raster\").RasterBoundsArray,f=t(\"../data/buffer\"),d=t(\"../render/vertex_array_object\");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,onAdd:function(t){this.map=t,this.image&&this.setCoordinates(this.coordinates)},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(o.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new a(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)*c),Math.round((e.row-n.row)*c))}),this.fire(\"change\"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=new h;e.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),e.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,32767,0),e.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,32767),e.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,32767,32767),this.tile.buckets={},this.tile.boundsBuffer=new f(e.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new d,this.tile.state=\"loaded\"},prepare:function(){if(this._loaded&&this.image&&this.image.complete&&this.tile){var t=this.map.painter,e=t.gl;this._prepared?(e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,this.image)):(this.tile.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,this.image))}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state=\"errored\",e(null))},serialize:function(){return{type:\"image\",urls:this.url,coordinates:this.coordinates}}})},{\"../data/bucket\":329,\"../data/buffer\":334,\"../geo/lng_lat\":339,\"../render/draw_raster\":351,\"../render/vertex_array_object\":357,\"../util/ajax\":425,\"../util/evented\":434,\"../util/util\":442,\"./tile_coord\":369,\"point-geometry\":484}],362:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i=t(\"../util/ajax\"),a=t(\"../util/browser\"),o=t(\"../util/mapbox\").normalizeSourceURL;e.exports=function(t,e){var r=function(t,r){if(t)return e(t);var i=n.pick(r,[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\"]);r.vector_layers&&(i.vectorLayers=r.vector_layers,i.vectorLayerIds=i.vectorLayers.map(function(t){return t.id})),e(null,i)};t.url?i.getJSON(o(t.url),r):a.frame(r.bind(null,null,t))}},{\"../util/ajax\":425,\"../util/browser\":426,\"../util/mapbox\":439,\"../util/util\":442}],363:[function(t,e,r){\"use strict\";var n=t(\"../data/bucket\");e.exports=function(t,e,r){return e*(n.EXTENT/(t.tileSize*Math.pow(2,r-t.coord.z)))}},{\"../data/bucket\":329}],364:[function(t,e,r){\"use strict\";function n(t,e){var r=t.coord,n=e.coord;return r.z-n.z||r.y-n.y||r.w-n.w||r.x-n.x}function i(t){for(var e=t[0]||{},r=1;r<t.length;r++){var n=t[r];for(var i in n){var a=n[i],o=e[i];if(void 0===o)o=e[i]=a;else for(var s=0;s<a.length;s++)o.push(a[s])}}return e}var a=t(\"./tile_coord\");r.rendered=function(t,e,r,a,o,s){var l=t.tilesIn(r);l.sort(n);for(var u=[],c=0;c<l.length;c++){var h=l[c];h.tile.featureIndex&&u.push(h.tile.featureIndex.query({queryGeometry:h.queryGeometry,scale:h.scale,tileSize:h.tile.tileSize,bearing:s,params:a},e))}return i(u)},r.source=function(t,e){for(var r=t.getRenderableIds().map(function(e){return t.getTileByID(e)}),n=[],i={},o=0;o<r.length;o++){var s=r[o],l=new a(Math.min(s.sourceMaxZoom,s.coord.z),s.coord.x,s.coord.y,0).id;i[l]||(i[l]=!0,s.querySourceFeatures(n,e))}return n}},{\"./tile_coord\":369}],365:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r,i.extend(this,i.pick(e,[\"url\",\"scheme\",\"tileSize\"])),s(e,function(t,e){if(t)return this.fire(\"error\",t);i.extend(this,e),this.fire(\"load\")}.bind(this))}var i=t(\"../util/util\"),a=t(\"../util/ajax\"),o=t(\"../util/evented\"),s=t(\"./load_tilejson\"),l=t(\"../util/mapbox\").normalizeTileURL;e.exports=n,n.prototype=i.inherit(o,{minzoom:0,maxzoom:22,roundZoom:!0,scheme:\"xyz\",tileSize:512,_loaded:!1,onAdd:function(t){this.map=t},serialize:function(){return{type:\"raster\",url:this.url,tileSize:this.tileSize}},loadTile:function(t,e){function r(r,n){if(delete t.request,!t.aborted){if(r)return e(r);var i=this.map.painter.gl;t.texture=this.map.painter.getTexture(n.width),t.texture?(i.bindTexture(i.TEXTURE_2D,t.texture),i.texSubImage2D(i.TEXTURE_2D,0,0,0,i.RGBA,i.UNSIGNED_BYTE,n)):(t.texture=i.createTexture(),i.bindTexture(i.TEXTURE_2D,t.texture),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR_MIPMAP_NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,n),t.texture.size=n.width),i.generateMipmap(i.TEXTURE_2D),this.map.animationLoop.set(this.map.style.rasterFadeDuration),t.state=\"loaded\",e(null)}}var n=l(t.coord.url(this.tiles,null,this.scheme),this.url,this.tileSize);t.request=a.getImage(n,r.bind(this))},abortTile:function(t){t.request&&(t.request.abort(),delete t.request)},unloadTile:function(t){t.texture&&this.map.painter.saveTexture(t.texture)}})},{\"../util/ajax\":425,\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./load_tilejson\":362}],366:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i={vector:t(\"../source/vector_tile_source\"),raster:t(\"../source/raster_tile_source\"),geojson:t(\"../source/geojson_source\"),video:t(\"../source/video_source\"),image:t(\"../source/image_source\")};r.create=function(t,e,r){if(e=new i[e.type](t,e,r),e.id!==t)throw new Error(\"Expected Source id to be \"+t+\" instead of \"+e.id);return n.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],e),e},r.getType=function(t){return i[t]},r.setType=function(t,e){i[t]=e}},{\"../source/geojson_source\":358,\"../source/image_source\":361,\"../source/raster_tile_source\":365,\"../source/vector_tile_source\":370,\"../source/video_source\":372,\"../util/util\":442}],367:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r;var n=this._source=o.create(t,e,r).on(\"load\",function(){this.map&&this._source.onAdd&&this._source.onAdd(this.map),this._sourceLoaded=!0,this.tileSize=n.tileSize,this.minzoom=n.minzoom,this.maxzoom=n.maxzoom,this.roundZoom=n.roundZoom,this.reparseOverscaled=n.reparseOverscaled,this.isTileClipped=n.isTileClipped,this.attribution=n.attribution,this.vectorLayerIds=n.vectorLayerIds,this.fire(\"load\")}.bind(this)).on(\"error\",function(t){this._sourceErrored=!0,this.fire(\"error\",t)}.bind(this)).on(\"change\",function(){this.reload(),this.transform&&this.update(this.transform,this.map&&this.map.style.rasterFadeDuration),this.fire(\"change\")}.bind(this));this._tiles={},this._cache=new c(0,this.unloadTile.bind(this)),this._isIdRenderable=this._isIdRenderable.bind(this)}function i(t,e,r){var n=r.zoomTo(Math.min(t.z,e));return{x:(n.column-(t.x+t.w*Math.pow(2,t.z)))*d,y:(n.row-t.y)*d}}function a(t,e){return t%32-e%32}var o=t(\"./source\"),s=t(\"./tile\"),l=t(\"../util/evented\"),u=t(\"./tile_coord\"),c=t(\"../util/lru_cache\"),h=t(\"../geo/coordinate\"),f=t(\"../util/util\"),d=t(\"../data/bucket\").EXTENT;e.exports=n,n.maxOverzooming=10,n.maxUnderzooming=3,n.prototype=f.inherit(l,{onAdd:function(t){this.map=t,this._source&&this._source.onAdd&&this._source.onAdd(t)},loaded:function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},getSource:function(){return this._source},loadTile:function(t,e){return this._source.loadTile(t,e)},unloadTile:function(t){if(this._source.unloadTile)return this._source.unloadTile(t)},abortTile:function(t){if(this._source.abortTile)return this._source.abortTile(t)},serialize:function(){return this._source.serialize()},prepare:function(){if(this._sourceLoaded&&this._source.prepare)return this._source.prepare()},getIds:function(){return Object.keys(this._tiles).map(Number).sort(a)},getRenderableIds:function(){return this.getIds().filter(this._isIdRenderable)},_isIdRenderable:function(t){return this._tiles[t].isRenderable()&&!this._coveredTiles[t]},reload:function(){this._cache.reset();for(var t in this._tiles){var e=this._tiles[t];\"loading\"!==e.state&&(e.state=\"reloading\"),this.loadTile(this._tiles[t],this._tileLoaded.bind(this,this._tiles[t]))}},_tileLoaded:function(t,e){if(e)return t.state=\"errored\",this.fire(\"tile.error\",{tile:t,error:e}),void this._source.fire(\"tile.error\",{tile:t,error:e});t.source=this,t.timeAdded=(new Date).getTime(),this.fire(\"tile.load\",{tile:t}),this._source.fire(\"tile.load\",{tile:t})},getTile:function(t){return this.getTileByID(t.id)},getTileByID:function(t){return this._tiles[t]},getZoom:function(t){return t.zoom+t.scaleZoom(t.tileSize/this.tileSize)},findLoadedChildren:function(t,e,r){var n=!1;for(var i in this._tiles){var a=this._tiles[i];if(!(r[i]||!a.isRenderable()||a.coord.z<=t.z||a.coord.z>e)){var o=Math.pow(2,Math.min(a.coord.z,this.maxzoom)-Math.min(t.z,this.maxzoom));if(Math.floor(a.coord.x/o)===t.x&&Math.floor(a.coord.y/o)===t.y)for(r[i]=!0,n=!0;a&&a.coord.z-1>t.z;){var s=a.coord.parent(this.maxzoom).id;a=this._tiles[s],a&&a.isRenderable()&&(delete r[i],r[s]=!0)}}}return n},findLoadedParent:function(t,e,r){for(var n=t.z-1;n>=e;n--){t=t.parent(this.maxzoom);var i=this._tiles[t.id];if(i&&i.isRenderable())return r[t.id]=!0,i;if(this._cache.has(t.id))return this.addTile(t),r[t.id]=!0,this._tiles[t.id]}},updateCacheSize:function(t){var e=Math.ceil(t.width/t.tileSize)+1,r=Math.ceil(t.height/t.tileSize)+1,n=e*r;this._cache.setMaxSize(Math.floor(5*n))},update:function(t,e){if(this._sourceLoaded){var r,i,a;this.updateCacheSize(t);var o=(this.roundZoom?Math.round:Math.floor)(this.getZoom(t)),s=Math.max(o-n.maxOverzooming,this.minzoom),l=Math.max(o+n.maxUnderzooming,this.minzoom),c={},h=(new Date).getTime();this._coveredTiles={};var d=this.used?t.coveringTiles(this._source):[];for(r=0;r<d.length;r++)i=d[r],a=this.addTile(i),c[i.id]=!0,a.isRenderable()||this.findLoadedChildren(i,l,c)||this.findLoadedParent(i,s,c);for(var p={},m=Object.keys(c),v=0;v<m.length;v++){var g=m[v];i=u.fromID(g),a=this._tiles[g],a&&a.timeAdded>h-(e||0)&&(this.findLoadedChildren(i,l,c)&&(c[g]=!0),this.findLoadedParent(i,s,p))}var y;for(y in p)c[y]||(this._coveredTiles[y]=!0);for(y in p)c[y]=!0;var b=f.keysDifference(this._tiles,c);for(r=0;r<b.length;r++)this.removeTile(+b[r]);this.transform=t}},addTile:function(t){var e=this._tiles[t.id];if(e)return e;var r=t.wrapped()\n",
+       ";if(e=this._tiles[r.id],e||(e=this._cache.get(r.id))&&this._redoPlacement&&this._redoPlacement(e),!e){var n=t.z,i=n>this.maxzoom?Math.pow(2,n-this.maxzoom):1;e=new s(r,this.tileSize*i,this.maxzoom),this.loadTile(e,this._tileLoaded.bind(this,e))}return e.uses++,this._tiles[t.id]=e,this.fire(\"tile.add\",{tile:e}),this._source.fire(\"tile.add\",{tile:e}),e},removeTile:function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this.fire(\"tile.remove\",{tile:e}),this._source.fire(\"tile.remove\",{tile:e}),e.uses>0||(e.isRenderable()?this._cache.add(e.coord.wrapped().id,e):(e.aborted=!0,this.abortTile(e),this.unloadTile(e))))},clearTiles:function(){for(var t in this._tiles)this.removeTile(t);this._cache.reset()},tilesIn:function(t){for(var e={},r=this.getIds(),n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0].zoom,c=0;c<t.length;c++){var f=t[c];n=Math.min(n,f.column),a=Math.min(a,f.row),o=Math.max(o,f.column),s=Math.max(s,f.row)}for(var p=0;p<r.length;p++){var m=this._tiles[r[p]],v=u.fromID(r[p]),g=[i(v,m.sourceMaxZoom,new h(n,a,l)),i(v,m.sourceMaxZoom,new h(o,s,l))];if(g[0].x<d&&g[0].y<d&&g[1].x>=0&&g[1].y>=0){for(var y=[],b=0;b<t.length;b++)y.push(i(v,m.sourceMaxZoom,t[b]));var x=e[m.coord.id];void 0===x&&(x=e[m.coord.id]={tile:m,coord:v,queryGeometry:[],scale:Math.pow(2,this.transform.zoom-m.coord.z)}),x.queryGeometry.push(y)}}var _=[];for(var w in e)_.push(e[w]);return _},redoPlacement:function(){for(var t=this.getIds(),e=0;e<t.length;e++){this.getTileByID(t[e]).redoPlacement(this)}},getVisibleCoordinates:function(){return this.getRenderableIds().map(u.fromID)}})},{\"../data/bucket\":329,\"../geo/coordinate\":338,\"../util/evented\":434,\"../util/lru_cache\":438,\"../util/util\":442,\"./source\":366,\"./tile\":368,\"./tile_coord\":369}],368:[function(t,e,r){\"use strict\";function n(t,e,r){this.coord=t,this.uid=a.uniqueId(),this.uses=0,this.tileSize=e,this.sourceMaxZoom=r,this.buckets={},this.state=\"loading\"}function i(t,e){if(e){for(var r={},n=0;n<t.length;n++){var i=e.getLayer(t[n].layerId);if(i){var s=o.create(a.extend({layer:i,childLayers:t[n].childLayerIds.map(e.getLayer.bind(e)).filter(function(t){return t})},t[n]));r[s.id]=s}}return r}}var a=t(\"../util/util\"),o=t(\"../data/bucket\"),s=t(\"../data/feature_index\"),l=t(\"vector-tile\"),u=t(\"pbf\"),c=t(\"../util/vectortile_to_geojson\"),h=t(\"feature-filter\"),f=t(\"../symbol/collision_tile\"),d=t(\"../symbol/collision_box\"),p=t(\"../symbol/symbol_instances\"),m=t(\"../symbol/symbol_quads\");e.exports=n,n.prototype={loadVectorData:function(t,e){this.state=\"loaded\",t&&(this.collisionBoxArray=new d(t.collisionBoxArray),this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.symbolInstancesArray=new p(t.symbolInstancesArray),this.symbolQuadsArray=new m(t.symbolQuadsArray),this.featureIndex=new s(t.featureIndex,t.rawTileData,this.collisionTile),this.rawTileData=t.rawTileData,this.buckets=i(t.buckets,e))},reloadSymbolData:function(t,e,r){if(\"unloaded\"!==this.state){this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.featureIndex.setCollisionTile(this.collisionTile);for(var n in this.buckets){var o=this.buckets[n];\"symbol\"===o.type&&(o.destroy(e.gl),delete this.buckets[n])}a.extend(this.buckets,i(t.buckets,r))}},unloadVectorData:function(t){for(var e in this.buckets){this.buckets[e].destroy(t.gl)}this.collisionBoxArray=null,this.symbolQuadsArray=null,this.symbolInstancesArray=null,this.collisionTile=null,this.featureIndex=null,this.rawTileData=null,this.buckets=null,this.state=\"unloaded\"},redoPlacement:function(t){function e(e,r){this.reloadSymbolData(r,t.map.painter,t.map.style),t.fire(\"tile.load\",{tile:this}),this.state=\"loaded\",this.redoWhenDone&&(this.redoPlacement(t),this.redoWhenDone=!1)}if(\"loaded\"!==this.state||\"reloading\"===this.state)return void(this.redoWhenDone=!0);this.state=\"reloading\",t.dispatcher.send(\"redo placement\",{uid:this.uid,source:t.id,angle:t.map.transform.angle,pitch:t.map.transform.pitch,showCollisionBoxes:t.map.showCollisionBoxes},e.bind(this),this.workerID)},getBucket:function(t){return this.buckets&&this.buckets[t.ref||t.id]},querySourceFeatures:function(t,e){if(this.rawTileData){this.vtLayers||(this.vtLayers=new l.VectorTile(new u(new Uint8Array(this.rawTileData))).layers);var r=this.vtLayers._geojsonTileLayer||this.vtLayers[e.sourceLayer];if(r)for(var n=h(e.filter),i={z:this.coord.z,x:this.coord.x,y:this.coord.y},a=0;a<r.length;a++){var o=r.feature(a);if(n(o)){var s=new c(o,this.coord.z,this.coord.x,this.coord.y);s.tile=i,t.push(s)}}}},isRenderable:function(){return\"loaded\"===this.state||\"reloading\"===this.state}}},{\"../data/bucket\":329,\"../data/feature_index\":336,\"../symbol/collision_box\":394,\"../symbol/collision_tile\":396,\"../symbol/symbol_instances\":405,\"../symbol/symbol_quads\":406,\"../util/util\":442,\"../util/vectortile_to_geojson\":443,\"feature-filter\":132,pbf:478,\"vector-tile\":550}],369:[function(t,e,r){\"use strict\";function n(t,e,r,n){l(!isNaN(t)&&t>=0&&t%1==0),l(!isNaN(e)&&e>=0&&e%1==0),l(!isNaN(r)&&r>=0&&r%1==0),isNaN(n)&&(n=0),this.z=+t,this.x=+e,this.y=+r,this.w=+n,(n*=2)<0&&(n=-1*n-1);var i=1<<this.z;this.id=32*(i*i*n+i*this.y+this.x)+this.z,this.posMatrix=null}function i(t,e,r){for(var n,i=\"\",a=t;a>0;a--)n=1<<a-1,i+=(e&n?1:0)+(r&n?2:0);return i}function a(t,e){if(t.row>e.row){var r=t;t=e,e=r}return{x0:t.column,y0:t.row,x1:e.column,y1:e.row,dx:e.column-t.column,dy:e.row-t.row}}function o(t,e,r,n,i){var a=Math.max(r,Math.floor(e.y0)),o=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dy*t.dx<e.x1:t.x1-e.dy/t.dy*t.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,u=e.dx/e.dy,c=t.dx>0,h=e.dx<0,f=a;f<o;f++){var d=l*Math.max(0,Math.min(t.dy,f+c-t.y0))+t.x0,p=u*Math.max(0,Math.min(e.dy,f+h-e.y0))+e.x0;i(Math.floor(p),Math.ceil(d),f)}}function s(t,e,r,n,i,s){var l,u=a(t,e),c=a(e,r),h=a(r,t);u.dy>c.dy&&(l=u,u=c,c=l),u.dy>h.dy&&(l=u,u=h,h=l),c.dy>h.dy&&(l=c,c=h,h=l),u.dy&&o(h,u,n,i,s),c.dy&&o(h,c,n,i,s)}var l=t(\"assert\"),u=t(\"whoots-js\"),c=t(\"../geo/coordinate\");e.exports=n,n.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y},n.prototype.toCoordinate=function(t){var e=Math.min(this.z,t),r=Math.pow(2,e),n=this.y,i=this.x+r*this.w;return new c(i,n,e)},n.fromID=function(t){var e=t%32,r=1<<e,i=(t-e)/32,a=i%r,o=(i-a)/r%r,s=Math.floor(i/(r*r));return s%2!=0&&(s=-1*s-1),s/=2,new n(e,a,o,s)},n.prototype.url=function(t,e,r){var n=u.getTileBBox(this.x,this.y,this.z),a=i(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",Math.min(this.z,e||this.z)).replace(\"{x}\",this.x).replace(\"{y}\",\"tms\"===r?Math.pow(2,this.z)-this.y-1:this.y).replace(\"{quadkey}\",a).replace(\"{bbox-epsg-3857}\",n)},n.prototype.parent=function(t){return 0===this.z?null:this.z>t?new n(this.z-1,this.x,this.y,this.w):new n(this.z-1,Math.floor(this.x/2),Math.floor(this.y/2),this.w)},n.prototype.wrapped=function(){return new n(this.z,this.x,this.y,0)},n.prototype.children=function(t){if(this.z>=t)return[new n(this.z+1,this.x,this.y,this.w)];var e=this.z+1,r=2*this.x,i=2*this.y;return[new n(e,r,i,this.w),new n(e,r+1,i,this.w),new n(e,r,i+1,this.w),new n(e,r+1,i+1,this.w)]},n.cover=function(t,e,r){function i(t,e,i){var s,l,u;if(i>=0&&i<=a)for(s=t;s<e;s++)l=(s%a+a)%a,u=new n(r,l,i,Math.floor(s/a)),o[u.id]=u}var a=1<<t,o={};return s(e[0],e[1],e[2],0,a,i),s(e[2],e[3],e[0],0,a,i),Object.keys(o).map(function(t){return o[t]})}},{\"../geo/coordinate\":338,assert:47,\"whoots-js\":566}],370:[function(t,e,r){\"use strict\";function n(t,e,r){if(this.id=t,this.dispatcher=r,a.extend(this,a.pick(e,[\"url\",\"scheme\",\"tileSize\"])),this._options=a.extend({type:\"vector\"},e),512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");o(e,function(t,e){if(t)return void this.fire(\"error\",t);a.extend(this,e),this.fire(\"load\")}.bind(this))}var i=t(\"../util/evented\"),a=t(\"../util/util\"),o=t(\"./load_tilejson\"),s=t(\"../util/mapbox\").normalizeTileURL;e.exports=n,n.prototype=a.inherit(i,{minzoom:0,maxzoom:22,scheme:\"xyz\",tileSize:512,reparseOverscaled:!0,isTileClipped:!0,onAdd:function(t){this.map=t},serialize:function(){return a.extend({},this._options)},loadTile:function(t,e){function r(r,n){if(!t.aborted){if(r)return e(r);t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null),t.reloadCallback&&(this.loadTile(t,t.reloadCallback),t.reloadCallback=null)}}var n=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,i={url:s(t.coord.url(this.tiles,this.maxzoom,this.scheme),this.url),uid:t.uid,coord:t.coord,zoom:t.coord.z,tileSize:this.tileSize*n,source:this.id,overscaling:n,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID?\"loading\"===t.state?t.reloadCallback=e:(i.rawTileData=t.rawTileData,this.dispatcher.send(\"reload tile\",i,r.bind(this),t.workerID)):t.workerID=this.dispatcher.send(\"load tile\",i,r.bind(this))},abortTile:function(t){this.dispatcher.send(\"abort tile\",{uid:t.uid,source:this.id},null,t.workerID)},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send(\"remove tile\",{uid:t.uid,source:this.id},null,t.workerID)}})},{\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./load_tilejson\":362}],371:[function(t,e,r){\"use strict\";function n(t,e,r){this.actor=t,this.styleLayers=e,r&&(this.loadVectorData=r),this.loading={},this.loaded={}}var i=t(\"../util/ajax\"),a=t(\"vector-tile\"),o=t(\"pbf\"),s=t(\"./worker_tile\");e.exports=n,n.prototype={loadTile:function(t,e){function r(t,r){return delete this.loading[n][i],t?e(t):r?(a.data=r.tile,a.parse(a.data,this.styleLayers.getLayerFamilies(),this.actor,r.rawTileData,e),this.loaded[n]=this.loaded[n]||{},void(this.loaded[n][i]=a)):e(null,null)}var n=t.source,i=t.uid;this.loading[n]||(this.loading[n]={});var a=this.loading[n][i]=new s(t);a.abort=this.loadVectorData(t,r.bind(this))},reloadTile:function(t,e){var r=this.loaded[t.source],n=t.uid;if(r&&r[n]){var i=r[n];i.parse(i.data,this.styleLayers.getLayerFamilies(),this.actor,t.rawTileData,e)}},abortTile:function(t){var e=this.loading[t.source],r=t.uid;e&&e[r]&&e[r].abort&&(e[r].abort(),delete e[r])},removeTile:function(t){var e=this.loaded[t.source],r=t.uid;e&&e[r]&&delete e[r]},loadVectorData:function(t,e){function r(t,r){if(t)return e(t);var n=new a.VectorTile(new o(new Uint8Array(r)));e(t,{tile:n,rawTileData:r})}var n=i.getArrayBuffer(t.url,r.bind(this));return function(){n.abort()}},redoPlacement:function(t,e){var r=this.loaded[t.source],n=this.loading[t.source],i=t.uid;if(r&&r[i]){var a=r[i],o=a.redoPlacement(t.angle,t.pitch,t.showCollisionBoxes);o.result&&e(null,o.result,o.transferables)}else n&&n[i]&&(n[i].angle=t.angle)}}},{\"../util/ajax\":425,\"./worker_tile\":374,pbf:478,\"vector-tile\":550}],372:[function(t,e,r){\"use strict\";function n(t,e){this.id=t,this.urls=e.urls,this.coordinates=e.coordinates,u.getVideo(e.urls,function(t,r){if(t)return this.fire(\"error\",{error:t});this.video=r,this.video.loop=!0;var n;this.video.addEventListener(\"playing\",function(){n=this.map.style.animationLoop.set(1/0),this.map._rerender()}.bind(this)),this.video.addEventListener(\"pause\",function(){this.map.style.animationLoop.cancel(n)}.bind(this)),this.map&&(this.video.play(),this.setCoordinates(e.coordinates)),this.fire(\"load\")}.bind(this))}var i=t(\"../util/util\"),a=t(\"./tile_coord\"),o=t(\"../geo/lng_lat\"),s=t(\"point-geometry\"),l=t(\"../util/evented\"),u=t(\"../util/ajax\"),c=t(\"../data/bucket\").EXTENT,h=t(\"../render/draw_raster\").RasterBoundsArray,f=t(\"../data/buffer\"),d=t(\"../render/vertex_array_object\");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,roundZoom:!0,getVideo:function(){return this.video},onAdd:function(t){this.map||(this.map=t,this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(o.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new a(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)*c),Math.round((e.row-n.row)*c))}),this.fire(\"change\"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=new h;e.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),e.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,32767,0),e.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,32767),e.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,32767,32767),this.tile.buckets={},this.tile.boundsBuffer=new f(e.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new d,this.tile.state=\"loaded\"},prepare:function(){if(!(this.video.readyState<2)&&this.tile){var t=this.map.painter.gl;this._prepared?(t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texSubImage2D(t.TEXTURE_2D,0,0,0,t.RGBA,t.UNSIGNED_BYTE,this.video)):(this._prepared=!0,this.tile.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,this.video)),this._currentTime=this.video.currentTime}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state=\"errored\",e(null))},serialize:function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}})},{\"../data/bucket\":329,\"../data/buffer\":334,\"../geo/lng_lat\":339,\"../render/draw_raster\":351,\"../render/vertex_array_object\":357,\"../util/ajax\":425,\"../util/evented\":434,\"../util/util\":442,\"./tile_coord\":369,\"point-geometry\":484}],373:[function(t,e,r){\"use strict\";function n(t){this.self=t,this.actor=new a(t,this);var e={getLayers:function(){return this.layers}.bind(this),getLayerFamilies:function(){return this.layerFamilies}.bind(this)};this.workerSources={vector:new l(this.actor,e),geojson:new u(this.actor,e)},this.self.registerWorkerSource=function(t,r){if(this.workerSources[t])throw new Error('Worker source with name \"'+t+'\" already registered.');this.workerSources[t]=new r(this.actor,e)}.bind(this)}function i(t){var e={};for(var r in t){var n=t[r],i=n.ref||n.id,a=t[i];a.layout&&\"none\"===a.layout.visibility||(e[i]=e[i]||[],r===i?e[i].unshift(n):e[i].push(n))}return e}var a=t(\"../util/actor\"),o=t(\"../style/style_layer\"),s=t(\"../util/util\"),l=t(\"./vector_tile_worker_source\"),u=t(\"./geojson_worker_source\");e.exports=function(t){return new n(t)},s.extend(n.prototype,{\"set layers\":function(t){function e(t){var e=o.create(t,t.ref&&r.layers[t.ref]);e.updatePaintTransitions({},{transition:!1}),r.layers[e.id]=e}this.layers={};for(var r=this,n=[],a=0;a<t.length;a++){var s=t[a];\"fill\"!==s.type&&\"line\"!==s.type&&\"circle\"!==s.type&&\"symbol\"!==s.type||(s.ref?n.push(a):e(s))}for(var l=0;l<n.length;l++)e(t[n[l]]);this.layerFamilies=i(this.layers)},\"update layers\":function(t){function e(t){var e=a.layers[t.ref];a.layers[t.id]?a.layers[t.id].set(t,e):a.layers[t.id]=o.create(t,e),a.layers[t.id].updatePaintTransitions({},{transition:!1})}var r,n,a=this;for(r in t)n=t[r],n.ref&&e(n);for(r in t)n=t[r],n.ref||e(n);this.layerFamilies=i(this.layers)},\"load tile\":function(t,e){var r=t.type||\"vector\";this.workerSources[r].loadTile(t,e)},\"reload tile\":function(t,e){var r=t.type||\"vector\";this.workerSources[r].reloadTile(t,e)},\"abort tile\":function(t){var e=t.type||\"vector\";this.workerSources[e].abortTile(t)},\"remove tile\":function(t){var e=t.type||\"vector\";this.workerSources[e].removeTile(t)},\"redo placement\":function(t,e){var r=t.type||\"vector\";this.workerSources[r].redoPlacement(t,e)},\"load worker source\":function(t,e){try{this.self.importScripts(t.url),e()}catch(t){e(t)}}})},{\"../style/style_layer\":381,\"../util/actor\":424,\"../util/util\":442,\"./geojson_worker_source\":359,\"./vector_tile_worker_source\":371}],374:[function(t,e,r){\"use strict\";function n(t){this.coord=t.coord,this.uid=t.uid,this.zoom=t.zoom,this.tileSize=t.tileSize,this.source=t.source,this.overscaling=t.overscaling,this.angle=t.angle,this.pitch=t.pitch,this.showCollisionBoxes=t.showCollisionBoxes}function i(t){return!t.isEmpty()}function a(t){return t.serialize()}function o(t){var e=[];for(var r in t)t[r].getTransferables(e);return e}function s(t){return t.id}var l=t(\"../data/feature_index\"),u=t(\"../symbol/collision_tile\"),c=t(\"../data/bucket\"),h=t(\"../symbol/collision_box\"),f=t(\"../util/dictionary_coder\"),d=t(\"../util/util\"),p=t(\"../symbol/symbol_instances\"),m=t(\"../symbol/symbol_quads\");e.exports=n,n.prototype.parse=function(t,e,r,n,v){function g(t,e){for(var r=0;r<t.length;r++){var n=t.feature(r);n.index=r;for(var i in e)e[i].filter(n)&&e[i].features.push(n)}}function y(t){if(t)return v(t);if(2===++N){for(var e=P.length-1;e>=0;e--)b(E,P[e]);x()}}function b(t,e){if(e.populateArrays(A,j,F),\"symbol\"!==e.type)for(var r=0;r<e.features.length;r++){var n=e.features[r];T.insert(n,n.index,e.sourceLayerIndex,e.index)}e.features=null}function x(){E.status=\"done\",E.redoPlacementAfterDone&&(E.redoPlacement(E.angle,E.pitch,null),E.redoPlacementAfterDone=!1);var t=T.serialize(),e=A.serialize(),r=E.collisionBoxArray.serialize(),s=E.symbolInstancesArray.serialize(),l=E.symbolQuadsArray.serialize(),u=[n].concat(t.transferables).concat(e.transferables),c=D.filter(i);v(null,{buckets:c.map(a),featureIndex:t.data,collisionTile:e.data,collisionBoxArray:r,symbolInstancesArray:s,symbolQuadsArray:l,rawTileData:n},o(c).concat(u))}this.status=\"parsing\",this.data=t,this.collisionBoxArray=new h,this.symbolInstancesArray=new p,this.symbolQuadsArray=new m;var _,w,M,k,A=new u(this.angle,this.pitch,this.collisionBoxArray),T=new l(this.coord,this.overscaling,A,t.layers),S=new f(t.layers?Object.keys(t.layers).sort():[\"_geojsonTileLayer\"]),E=this,L={},C={},I=0;for(var z in e)w=e[z][0],w.source===this.source&&(w.ref||w.minzoom&&this.zoom<w.minzoom||w.maxzoom&&this.zoom>=w.maxzoom||w.layout&&\"none\"===w.layout.visibility||t.layers&&!t.layers[w.sourceLayer]||(k=c.create({layer:w,index:I++,childLayers:e[z],zoom:this.zoom,overscaling:this.overscaling,showCollisionBoxes:this.showCollisionBoxes,collisionBoxArray:this.collisionBoxArray,symbolQuadsArray:this.symbolQuadsArray,symbolInstancesArray:this.symbolInstancesArray,sourceLayerIndex:S.encode(w.sourceLayer||\"_geojsonTileLayer\")}),k.createFilter(),L[w.id]=k,t.layers&&(M=w.sourceLayer,C[M]=C[M]||{},C[M][w.id]=k)));if(t.layers)for(M in C)1===w.version&&d.warnOnce('Vector tile source \"'+this.source+'\" layer \"'+M+'\" does not use vector tile spec v2 and therefore may have some rendering errors.'),(w=t.layers[M])&&g(w,C[M]);else g(t,L);var D=[],P=this.symbolBuckets=[],O=[];T.bucketLayerIDs={};for(var R in L)k=L[R],0!==k.features.length&&(T.bucketLayerIDs[k.index]=k.childLayers.map(s),D.push(k),\"symbol\"===k.type?P.push(k):O.push(k));var F={},j={},N=0;if(P.length>0){for(_=P.length-1;_>=0;_--)P[_].updateIcons(F),P[_].updateFont(j);for(var B in j)j[B]=Object.keys(j[B]).map(Number);F=Object.keys(F),r.send(\"get glyphs\",{uid:this.uid,stacks:j},function(t,e){j=e,y(t)}),F.length?r.send(\"get icons\",{icons:F},function(t,e){F=e,y(t)}):y()}for(_=O.length-1;_>=0;_--)b(this,O[_]);if(0===P.length)return x()},n.prototype.redoPlacement=function(t,e,r){if(\"done\"!==this.status)return this.redoPlacementAfterDone=!0,this.angle=t,{};for(var n=new u(t,e,this.collisionBoxArray),s=this.symbolBuckets,l=s.length-1;l>=0;l--)s[l].placeFeatures(n,r);var c=n.serialize(),h=s.filter(i);return{result:{buckets:h.map(a),collisionTile:c.data},transferables:o(h).concat(c.transferables)}}},{\"../data/bucket\":329,\"../data/feature_index\":336,\"../symbol/collision_box\":394,\"../symbol/collision_tile\":396,\"../symbol/symbol_instances\":405,\"../symbol/symbol_quads\":406,\"../util/dictionary_coder\":432,\"../util/util\":442}],375:[function(t,e,r){\"use strict\";function n(){this.n=0,this.times=[]}e.exports=n,n.prototype.stopped=function(){return this.times=this.times.filter(function(t){return t.time>=(new Date).getTime()}),!this.times.length},n.prototype.set=function(t){return this.times.push({id:this.n,time:t+(new Date).getTime()}),this.n++},n.prototype.cancel=function(t){this.times=this.times.filter(function(e){return e.id!==t})}},{}],376:[function(t,e,r){\"use strict\";function n(t){this.base=t,this.retina=s.devicePixelRatio>1;var e=this.retina?\"@2x\":\"\";o.getJSON(l(t,e,\".json\"),function(t,e){if(t)return void this.fire(\"error\",{error:t});this.data=e,this.img&&this.fire(\"load\")}.bind(this)),o.getImage(l(t,e,\".png\"),function(t,e){if(t)return void this.fire(\"error\",{error:t});for(var r=e.getData(),n=e.data=new Uint8Array(r.length),i=0;i<r.length;i+=4){var a=r[i+3]/255;n[i+0]=r[i+0]*a,n[i+1]=r[i+1]*a,n[i+2]=r[i+2]*a,n[i+3]=r[i+3]}this.img=e,this.data&&this.fire(\"load\")}.bind(this))}function i(){}var a=t(\"../util/evented\"),o=t(\"../util/ajax\"),s=t(\"../util/browser\"),l=t(\"../util/mapbox\").normalizeSpriteURL;e.exports=n,n.prototype=Object.create(a),n.prototype.toJSON=function(){return this.base},n.prototype.loaded=function(){return!(!this.data||!this.img)},n.prototype.resize=function(){if(s.devicePixelRatio>1!==this.retina){var t=new n(this.base);t.on(\"load\",function(){this.img=t.img,this.data=t.data,this.retina=t.retina}.bind(this))}},i.prototype={x:0,y:0,width:0,height:0,pixelRatio:1,sdf:!1},n.prototype.getSpritePosition=function(t){if(!this.loaded())return new i;var e=this.data&&this.data[t];return e&&this.img?e:new i}},{\"../util/ajax\":425,\"../util/browser\":426,\"../util/evented\":434,\"../util/mapbox\":439}],377:[function(t,e,r){\"use strict\";var n=t(\"csscolorparser\").parseCSSColor,i=t(\"../util/util\"),a=t(\"./style_function\"),o={};e.exports=function t(e){if(a.isFunctionDefinition(e))return i.extend({},e,{stops:e.stops.map(function(e){return[e[0],t(e[1])]})});if(\"string\"==typeof e){if(!o[e]){var r=n(e);if(!r)throw new Error(\"Invalid color \"+e);o[e]=[r[0]/255*r[3],r[1]/255*r[3],r[2]/255*r[3],r[3]]}return o[e]}throw new Error(\"Invalid color \"+e)}},{\"../util/util\":442,\"./style_function\":380,csscolorparser:108}],378:[function(t,e,r){\"use strict\";function n(t,e,r){this.animationLoop=e||new m,this.dispatcher=new p(r||1,this),this.spriteAtlas=new l(1024,1024),this.lineAtlas=new u(256,512),this._layers={},this._order=[],this._groups=[],this.sources={},this.zoomHistory={},c.bindAll([\"_forwardSourceEvent\",\"_forwardTileEvent\",\"_forwardLayerEvent\",\"_redoPlacement\"],this),this._resetUpdates();var n=function(t,e){if(t)return void this.fire(\"error\",{error:t});if(!v.emitErrors(this,v(e))){this._loaded=!0,this.stylesheet=e,this.updateClasses();var r=e.sources;for(var n in r)this.addSource(n,r[n]);e.sprite&&(this.sprite=new o(e.sprite),this.sprite.on(\"load\",this.fire.bind(this,\"change\"))),this.glyphSource=new s(e.glyphs),this._resolve(),this.fire(\"load\")}}.bind(this);\"string\"==typeof t?h.getJSON(f(t),n):d.frame(n.bind(this,null,t)),this.on(\"source.load\",function(t){var e=t.source;if(e&&e.vectorLayerIds)for(var r in this._layers){var n=this._layers[r];n.source===e.id&&this._validateLayer(n)}})}var i=t(\"../util/evented\"),a=t(\"./style_layer\"),o=t(\"./image_sprite\"),s=t(\"../symbol/glyph_source\"),l=t(\"../symbol/sprite_atlas\"),u=t(\"../render/line_atlas\"),c=t(\"../util/util\"),h=t(\"../util/ajax\"),f=t(\"../util/mapbox\").normalizeStyleURL,d=t(\"../util/browser\"),p=t(\"../util/dispatcher\"),m=t(\"./animation_loop\"),v=t(\"./validate_style\"),g=t(\"../source/source\"),y=t(\"../source/query_features\"),b=t(\"../source/source_cache\"),x=t(\"./style_spec\"),_=t(\"./style_function\");e.exports=n,n.prototype=c.inherit(i,{_loaded:!1,_validateLayer:function(t){var e=this.sources[t.source];t.sourceLayer&&e&&e.vectorLayerIds&&-1===e.vectorLayerIds.indexOf(t.sourceLayer)&&this.fire(\"error\",{error:new Error('Source layer \"'+t.sourceLayer+'\" does not exist on source \"'+e.id+'\" as specified by style layer \"'+t.id+'\"')})},loaded:function(){if(!this._loaded)return!1;if(Object.keys(this._updates.sources).length)return!1;for(var t in this.sources)if(!this.sources[t].loaded())return!1;return!(this.sprite&&!this.sprite.loaded())},_resolve:function(){var t,e;this._layers={},this._order=this.stylesheet.layers.map(function(t){return t.id});for(var r=0;r<this.stylesheet.layers.length;r++)e=this.stylesheet.layers[r],e.ref||(t=a.create(e),this._layers[t.id]=t,t.on(\"error\",this._forwardLayerEvent));for(var n=0;n<this.stylesheet.layers.length;n++)if(e=this.stylesheet.layers[n],e.ref){var i=this.getLayer(e.ref);t=a.create(e,i),this._layers[t.id]=t,t.on(\"error\",this._forwardLayerEvent)}this._groupLayers(),this._updateWorkerLayers()},_groupLayers:function(){var t;this._groups=[];for(var e=0;e<this._order.length;++e){var r=this._layers[this._order[e]];t&&r.source===t.source||(t=[],t.source=r.source,this._groups.push(t)),t.push(r)}},_updateWorkerLayers:function(t){this.dispatcher.broadcast(t?\"update layers\":\"set layers\",this._serializeLayers(t))},_serializeLayers:function(t){t=t||this._order;for(var e=[],r={includeRefProperties:!0},n=0;n<t.length;n++)e.push(this._layers[t[n]].serialize(r));return e},_applyClasses:function(t,e){if(this._loaded){t=t||[],e=e||{transition:!0};var r=this.stylesheet.transition||{},n=this._updates.allPaintProps?this._layers:this._updates.paintProps;for(var i in n){var a=this._layers[i],o=this._updates.paintProps[i];if(this._updates.allPaintProps||o.all)a.updatePaintTransitions(t,e,r,this.animationLoop);else for(var s in o)this._layers[i].updatePaintTransition(s,t,e,r,this.animationLoop)}}},_recalculate:function(t){for(var e in this.sources)this.sources[e].used=!1;this._updateZoomHistory(t),this.rasterFadeDuration=300;for(var r in this._layers){var n=this._layers[r];n.recalculate(t,this.zoomHistory),!n.isHidden(t)&&n.source&&(this.sources[n.source].used=!0)}Math.floor(this.z)!==Math.floor(t)&&this.animationLoop.set(300),this.z=t,this.fire(\"zoom\")},_updateZoomHistory:function(t){var e=this.zoomHistory;void 0===e.lastIntegerZoom&&(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=0,e.lastZoom=t),Math.floor(e.lastZoom)<Math.floor(t)?(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=Date.now()):Math.floor(e.lastZoom)>Math.floor(t)&&(e.lastIntegerZoom=Math.floor(t+1),e.lastIntegerZoomTime=Date.now()),e.lastZoom=t},_checkLoaded:function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},update:function(t,e){if(!this._updates.changed)return this;if(this._updates.allLayers)this._groupLayers(),this._updateWorkerLayers();else{var r=Object.keys(this._updates.layers);r.length&&this._updateWorkerLayers(r)}var n,i=Object.keys(this._updates.sources);for(n=0;n<i.length;n++)this._reloadSource(i[n]);for(n=0;n<this._updates.events.length;n++){var a=this._updates.events[n];this.fire(a[0],a[1])}return this._applyClasses(t,e),this._updates.changed&&this.fire(\"change\"),this._resetUpdates(),this},_resetUpdates:function(){this._updates={events:[],layers:{},sources:{},paintProps:{}}},addSource:function(t,e){if(this._checkLoaded(),void 0!==this.sources[t])throw new Error(\"There is already a source with this ID\");if(!e.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(e)+\".\");return[\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(e.type)>=0&&this._handleErrors(v.source,\"sources.\"+t,e)?this:(e=new b(t,e,this.dispatcher),this.sources[t]=e,e.style=this,e.on(\"load\",this._forwardSourceEvent).on(\"error\",this._forwardSourceEvent).on(\"change\",this._forwardSourceEvent).on(\"tile.add\",this._forwardTileEvent).on(\"tile.load\",this._forwardTileEvent).on(\"tile.error\",this._forwardTileEvent).on(\"tile.remove\",this._forwardTileEvent).on(\"tile.stats\",this._forwardTileEvent),this._updates.events.push([\"source.add\",{source:e}]),this._updates.changed=!0,this)},removeSource:function(t){if(this._checkLoaded(),void 0===this.sources[t])throw new Error(\"There is no source with this ID\");var e=this.sources[t];return delete this.sources[t],delete this._updates.sources[t],e.off(\"load\",this._forwardSourceEvent).off(\"error\",this._forwardSourceEvent).off(\"change\",this._forwardSourceEvent).off(\"tile.add\",this._forwardTileEvent).off(\"tile.load\",this._forwardTileEvent).off(\"tile.error\",this._forwardTileEvent).off(\"tile.remove\",this._forwardTileEvent).off(\"tile.stats\",this._forwardTileEvent),this._updates.events.push([\"source.remove\",{source:e}]),this._updates.changed=!0,this},getSource:function(t){return this.sources[t]&&this.sources[t].getSource()},addLayer:function(t,e){if(this._checkLoaded(),!(t instanceof a)){if(this._handleErrors(v.layer,\"layers.\"+t.id,t,!1,{arrayIndex:-1}))return this;var r=t.ref&&this.getLayer(t.ref);t=a.create(t,r)}return this._validateLayer(t),t.on(\"error\",this._forwardLayerEvent),this._layers[t.id]=t,this._order.splice(e?this._order.indexOf(e):1/0,0,t.id),this._updates.allLayers=!0,t.source&&(this._updates.sources[t.source]=!0),this._updates.events.push([\"layer.add\",{layer:t}]),this.updateClasses(t.id)},removeLayer:function(t){this._checkLoaded();var e=this._layers[t];if(void 0===e)throw new Error(\"There is no layer with this ID\");for(var r in this._layers)this._layers[r].ref===t&&this.removeLayer(r);return e.off(\"error\",this._forwardLayerEvent),delete this._layers[t],delete this._updates.layers[t],delete this._updates.paintProps[t],this._order.splice(this._order.indexOf(t),1),this._updates.allLayers=!0,this._updates.events.push([\"layer.remove\",{layer:e}]),this._updates.changed=!0,this},getLayer:function(t){return this._layers[t]},getReferentLayer:function(t){var e=this.getLayer(t);return e.ref&&(e=this.getLayer(e.ref)),e},setLayerZoomRange:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return n.minzoom===e&&n.maxzoom===r?this:(null!=e&&(n.minzoom=e),null!=r&&(n.maxzoom=r),this._updateLayer(n))},setFilter:function(t,e){this._checkLoaded();var r=this.getReferentLayer(t);return null!==e&&this._handleErrors(v.filter,\"layers.\"+r.id+\".filter\",e)?this:c.deepEqual(r.filter,e)?this:(r.filter=c.clone(e),this._updateLayer(r))},getFilter:function(t){return this.getReferentLayer(t).filter},setLayoutProperty:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return c.deepEqual(n.getLayoutProperty(e),r)?this:(n.setLayoutProperty(e,r),this._updateLayer(n))},getLayoutProperty:function(t,e){return this.getReferentLayer(t).getLayoutProperty(e)},setPaintProperty:function(t,e,r,n){this._checkLoaded();var i=this.getLayer(t);if(c.deepEqual(i.getPaintProperty(e,n),r))return this;var a=i.isPaintValueFeatureConstant(e);return i.setPaintProperty(e,r,n),!(r&&_.isFunctionDefinition(r)&&\"$zoom\"!==r.property&&void 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a=t(\"./style_function\"),o=t(\"./parse_color\"),s=t(\"../util/util\");e.exports=n},{\"../util/util\":442,\"./parse_color\":377,\"./style_function\":380}],380:[function(t,e,r){\"use strict\";var n=t(\"mapbox-gl-function\");r.interpolated=function(t){var e=n.interpolated(t),r=function(t,r){return e(t&&t.zoom,r||{})};return r.isFeatureConstant=e.isFeatureConstant,r.isZoomConstant=e.isZoomConstant,r},r[\"piecewise-constant\"]=function(t){var e=n[\"piecewise-constant\"](t),r=function(t,r){return e(t&&t.zoom,r||{})};return r.isFeatureConstant=e.isFeatureConstant,r.isZoomConstant=e.isZoomConstant,r},r.isFunctionDefinition=n.isFunctionDefinition},{\"mapbox-gl-function\":302}],381:[function(t,e,r){\"use strict\";function n(t,e){this.set(t,e)}function i(t){return t.value}var a=t(\"../util/util\"),o=t(\"./style_transition\"),s=t(\"./style_declaration\"),l=t(\"./style_spec\"),u=t(\"./validate_style\"),c=t(\"./parse_color\"),h=t(\"../util/evented\");e.exports=n;n.create=function(e,r){return new({background:t(\"./style_layer/background_style_layer\"),circle:t(\"./style_layer/circle_style_layer\"),fill:t(\"./style_layer/fill_style_layer\"),line:t(\"./style_layer/line_style_layer\"),raster:t(\"./style_layer/raster_style_layer\"),symbol:t(\"./style_layer/symbol_style_layer\")}[(r||e).type])(e,r)},n.prototype=a.inherit(h,{set:function(t,e){this.id=t.id,this.ref=t.ref,this.metadata=t.metadata,this.type=(e||t).type,this.source=(e||t).source,this.sourceLayer=(e||t)[\"source-layer\"],this.minzoom=(e||t).minzoom,this.maxzoom=(e||t).maxzoom,this.filter=(e||t).filter,this.paint={},this.layout={},this._paintSpecifications=l[\"paint_\"+this.type],this._layoutSpecifications=l[\"layout_\"+this.type],this._paintTransitions={},this._paintTransitionOptions={},this._paintDeclarations={},this._layoutDeclarations={},this._layoutFunctions={};var r,n;for(var i in t){var a=i.match(/^paint(?:\\.(.*))?$/);if(a){var o=a[1]||\"\";for(r in t[i])this.setPaintProperty(r,t[i][r],o)}}if(this.ref)this._layoutDeclarations=e._layoutDeclarations;else for(n in t.layout)this.setLayoutProperty(n,t.layout[n]);for(r in this._paintSpecifications)this.paint[r]=this.getPaintValue(r);for(n in this._layoutSpecifications)this._updateLayoutValue(n)},setLayoutProperty:function(t,e){if(null==e)delete this._layoutDeclarations[t];else{var r=\"layers.\"+this.id+\".layout.\"+t;if(this._handleErrors(u.layoutProperty,r,t,e))return;this._layoutDeclarations[t]=new s(this._layoutSpecifications[t],e)}this._updateLayoutValue(t)},getLayoutProperty:function(t){return this._layoutDeclarations[t]&&this._layoutDeclarations[t].value},getLayoutValue:function(t,e,r){var n=this._layoutSpecifications[t],i=this._layoutDeclarations[t];return i?i.calculate(e,r):n.default},setPaintProperty:function(t,e,r){var n=\"layers.\"+this.id+(r?'[\"paint.'+r+'\"].':\".paint.\")+t;if(a.endsWith(t,\"-transition\"))if(this._paintTransitionOptions[r||\"\"]||(this._paintTransitionOptions[r||\"\"]={}),null===e||void 0===e)delete this._paintTransitionOptions[r||\"\"][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintTransitionOptions[r||\"\"][t]=e}else if(this._paintDeclarations[r||\"\"]||(this._paintDeclarations[r||\"\"]={}),null===e||void 0===e)delete this._paintDeclarations[r||\"\"][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintDeclarations[r||\"\"][t]=new s(this._paintSpecifications[t],e)}},getPaintProperty:function(t,e){return e=e||\"\",a.endsWith(t,\"-transition\")?this._paintTransitionOptions[e]&&this._paintTransitionOptions[e][t]:this._paintDeclarations[e]&&this._paintDeclarations[e][t]&&this._paintDeclarations[e][t].value},getPaintValue:function(t,e,r){var n=this._paintSpecifications[t],i=this._paintTransitions[t];return 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this._paintDeclarations){e[\"\"===r?\"paint\":\"paint.\"+r]=a.mapObject(this._paintDeclarations[r],i)}return(!this.ref||t&&t.includeRefProperties)&&a.extend(e,{type:this.type,source:this.source,\"source-layer\":this.sourceLayer,filter:this.filter,layout:a.mapObject(this._layoutDeclarations,i)}),a.filterObject(e,function(t,e){return void 0!==t&&!(\"layout\"===e&&!Object.keys(t).length)})},_applyPaintDeclaration:function(t,e,r,n,i){var l=r.transition?this._paintTransitions[t]:void 0,u=this._paintSpecifications[t];if(null!==e&&void 0!==e||(e=new s(u,u.default)),!l||l.declaration.json!==e.json){var c=a.extend({duration:300,delay:0},n,this.getPaintProperty(t+\"-transition\")),h=this._paintTransitions[t]=new o(u,e,l,c);h.instant()||(h.loopID=i.set(h.endTime-Date.now())),l&&i.cancel(l.loopID)}},_updateLayoutValue:function(t){var e=this._layoutDeclarations[t];e&&e.isFunction?this._layoutFunctions[t]=!0:(delete 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i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],384:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");n.prototype=i.inherit(a,{getPaintValue:function(t,e,r){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintValue.call(this,\"fill-color\",e,r):a.prototype.getPaintValue.call(this,t,e,r)},getPaintValueStopZoomLevels:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintValueStopZoomLevels.call(this,\"fill-color\"):a.prototype.getPaintValueStopZoomLevels.call(this,arguments)},getPaintInterpolationT:function(t,e){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintInterpolationT.call(this,\"fill-color\",e):a.prototype.getPaintInterpolationT.call(this,t,e)},isPaintValueFeatureConstant:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.isPaintValueFeatureConstant.call(this,\"fill-color\"):a.prototype.isPaintValueFeatureConstant.call(this,t)},isPaintValueZoomConstant:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.isPaintValueZoomConstant.call(this,\"fill-color\"):a.prototype.isPaintValueZoomConstant.call(this,t)}}),e.exports=n},{\"../../util/util\":442,\"../style_layer\":381}],385:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{getPaintValue:function(t,e,r){var n=a.prototype.getPaintValue.apply(this,arguments);if(n&&\"line-dasharray\"===t){var i=Math.floor(e.zoom);this._flooredZoom!==i&&(this._flooredZoom=i,this._flooredLineWidth=this.getPaintValue(\"line-width\",e,r)),n.fromScale*=this._flooredLineWidth,n.toScale*=this._flooredLineWidth}return n}})},{\"../../util/util\":442,\"../style_layer\":381}],386:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],387:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{isHidden:function(){if(a.prototype.isHidden.apply(this,arguments))return!0;var t=0===this.paint[\"text-opacity\"]||!this.layout[\"text-field\"],e=0===this.paint[\"icon-opacity\"]||!this.layout[\"icon-image\"];return!(!t||!e)},getLayoutValue:function(t,e,r){return(\"text-rotation-alignment\"!==t||\"line\"!==this.getLayoutValue(\"symbol-placement\",e,r)||this.getLayoutProperty(\"text-rotation-alignment\"))&&(\"icon-rotation-alignment\"!==t||\"line\"!==this.getLayoutValue(\"symbol-placement\",e,r)||this.getLayoutProperty(\"icon-rotation-alignment\"))?\"text-pitch-alignment\"!==t||this.getLayoutProperty(\"text-pitch-alignment\")?a.prototype.getLayoutValue.apply(this,arguments):this.getLayoutValue(\"text-rotation-alignment\"):\"map\"}})},{\"../../util/util\":442,\"../style_layer\":381}],388:[function(t,e,r){\"use strict\";e.exports=t(\"mapbox-gl-style-spec/reference/latest.min\")},{\"mapbox-gl-style-spec/reference/latest.min\":325}],389:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.declaration=e,this.startTime=this.endTime=(new 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r}},{\"../util/interpolate\":436,\"../util/util\":442}],390:[function(t,e,r){\"use strict\";e.exports=t(\"mapbox-gl-style-spec/lib/validate_style.min\"),e.exports.emitErrors=function(t,e){if(e&&e.length){for(var r=0;r<e.length;r++)t.fire(\"error\",{error:new Error(e[r].message)});return!0}return!1},e.exports.throwErrors=function(t,e){if(e)for(var r=0;r<e.length;r++)throw new Error(e[r].message)}},{\"mapbox-gl-style-spec/lib/validate_style.min\":324}],391:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.x=t,this.y=e,this.angle=r,void 0!==n&&(this.segment=n)}var i=t(\"point-geometry\");e.exports=n,n.prototype=Object.create(i.prototype),n.prototype.clone=function(){return new n(this.x,this.y,this.angle,this.segment)}},{\"point-geometry\":484}],392:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],u=0;s<r/2;){var c=t[o-1],h=t[o],f=t[o+1];if(!f)return!1;var d=c.angleTo(h)-h.angleTo(f);for(d=Math.abs((d+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:d}),u+=d;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;o++,s+=h.dist(f)}return!0}e.exports=n},{}],393:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){for(var o=[],s=0;s<t.length;s++)for(var l,u=t[s],c=0;c<u.length-1;c++){var h=u[c],f=u[c+1];h.x<e&&f.x<e||(h.x<e?h=new i(e,h.y+(f.y-h.y)*((e-h.x)/(f.x-h.x)))._round():f.x<e&&(f=new i(e,h.y+(f.y-h.y)*((e-h.x)/(f.x-h.x)))._round()),h.y<r&&f.y<r||(h.y<r?h=new i(h.x+(f.x-h.x)*((r-h.y)/(f.y-h.y)),r)._round():f.y<r&&(f=new i(h.x+(f.x-h.x)*((r-h.y)/(f.y-h.y)),r)._round()),h.x>=n&&f.x>=n||(h.x>=n?h=new i(n,h.y+(f.y-h.y)*((n-h.x)/(f.x-h.x)))._round():f.x>=n&&(f=new i(n,h.y+(f.y-h.y)*((n-h.x)/(f.x-h.x)))._round()),h.y>=a&&f.y>=a||(h.y>=a?h=new i(h.x+(f.x-h.x)*((a-h.y)/(f.y-h.y)),a)._round():f.y>=a&&(f=new i(h.x+(f.x-h.x)*((a-h.y)/(f.y-h.y)),a)._round()),l&&h.equals(l[l.length-1])||(l=[h],o.push(l)),l.push(f)))))}return o}var i=t(\"point-geometry\");e.exports=n},{\"point-geometry\":484}],394:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=e.exports=new n({members:[{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Float32\",name:\"maxScale\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"},{type:\"Int16\",name:\"bbox0\"},{type:\"Int16\",name:\"bbox1\"},{type:\"Int16\",name:\"bbox2\"},{type:\"Int16\",name:\"bbox3\"},{type:\"Float32\",name:\"placementScale\"}]});i.extendAll(o.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"point-geometry\":484}],395:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u,c){var h=o.top*s-l,f=o.bottom*s+l,d=o.left*s-l,p=o.right*s+l;if(this.boxStartIndex=t.length,u){var m=f-h,v=p-d;if(m>0)if(m=Math.max(10*s,m),c){var g=e[r.segment+1].sub(e[r.segment])._unit()._mult(v),y=[r.sub(g),r.add(g)];this._addLineCollisionBoxes(t,y,r,0,v,m,n,i,a)}else this._addLineCollisionBoxes(t,e,r,r.segment,v,m,n,i,a)}else t.emplaceBack(r.x,r.y,d,h,p,f,1/0,n,i,a,0,0,0,0,0);this.boxEndIndex=t.length}e.exports=n,n.prototype._addLineCollisionBoxes=function(t,e,r,n,i,a,o,s,l){var u=a/2,c=Math.floor(i/u),h=-a/2,f=this.boxes,d=r,p=n+1,m=h;do{if(--p<0)return f;m-=e[p].dist(d),d=e[p]}while(m>-i/2);for(var v=e[p].dist(e[p+1]),g=0;g<c;g++){for(var y=-i/2+g*u;m+v<y;){if(m+=v,++p+1>=e.length)return f;v=e[p].dist(e[p+1])}var b=y-m,x=e[p],_=e[p+1],w=_.sub(x)._unit()._mult(b)._add(x)._round(),M=Math.max(Math.abs(y-h)-u/2,0),k=i/2/M;t.emplaceBack(w.x,w.y,-a/2,-a/2,a/2,a/2,k,o,s,l,0,0,0,0,0)}return f}},{}],396:[function(t,e,r){\"use strict\";function n(t,e,r){if(\"object\"==typeof t){var n=t;r=e,t=n.angle,e=n.pitch,this.grid=new o(n.grid),this.ignoredGrid=new o(n.ignoredGrid)}else this.grid=new o(a,12,6),this.ignoredGrid=new o(a,12,0);this.angle=t,this.pitch=e;var i=Math.sin(t),s=Math.cos(t);if(this.rotationMatrix=[s,-i,i,s],this.reverseRotationMatrix=[s,i,-i,s],this.yStretch=1/Math.cos(e/180*Math.PI),this.yStretch=Math.pow(this.yStretch,1.3),this.collisionBoxArray=r,0===r.length){r.emplaceBack();r.emplaceBack(0,0,0,-32767,0,32767,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(a,0,0,-32767,0,32767,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,0,-32767,0,32767,0,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,a,-32767,0,32767,0,32767,0,0,0,0,0,0,0,0,0)}this.tempCollisionBox=r.get(0),this.edges=[r.get(1),r.get(2),r.get(3),r.get(4)]}var i=t(\"point-geometry\"),a=t(\"../data/bucket\").EXTENT,o=t(\"grid-index\");e.exports=n,n.prototype.serialize=function(){var t={angle:this.angle,pitch:this.pitch,grid:this.grid.toArrayBuffer(),ignoredGrid:this.ignoredGrid.toArrayBuffer()};return{data:t,transferables:[t.grid,t.ignoredGrid]}},n.prototype.minScale=.25,n.prototype.maxScale=2,n.prototype.placeCollisionFeature=function(t,e,r){for(var n=this.collisionBoxArray,a=this.minScale,o=this.rotationMatrix,s=this.yStretch,l=t.boxStartIndex;l<t.boxEndIndex;l++){var u=n.get(l),c=u.anchorPoint._matMult(o),h=c.x,f=c.y,d=h+u.x1,p=f+u.y1*s,m=h+u.x2,v=f+u.y2*s;if(u.bbox0=d,u.bbox1=p,u.bbox2=m,u.bbox3=v,!e)for(var g=this.grid.query(d,p,m,v),y=0;y<g.length;y++){var b=n.get(g[y]),x=b.anchorPoint._matMult(o);if((a=this.getPlacementScale(a,c,u,x,b))>=this.maxScale)return a}if(r){var _;if(this.angle){var w=this.reverseRotationMatrix,M=new i(u.x1,u.y1).matMult(w),k=new i(u.x2,u.y1).matMult(w),A=new i(u.x1,u.y2).matMult(w),T=new i(u.x2,u.y2).matMult(w);_=this.tempCollisionBox,_.anchorPointX=u.anchorPoint.x,_.anchorPointY=u.anchorPoint.y,_.x1=Math.min(M.x,k.x,A.x,T.x),_.y1=Math.min(M.y,k.x,A.x,T.x),_.x2=Math.max(M.x,k.x,A.x,T.x),_.y2=Math.max(M.y,k.x,A.x,T.x),_.maxScale=u.maxScale}else _=u;for(var S=0;S<this.edges.length;S++){var E=this.edges[S];if((a=this.getPlacementScale(a,u.anchorPoint,_,E.anchorPoint,E))>=this.maxScale)return a}}}return a},n.prototype.queryRenderedSymbols=function(t,e,r,n,a){var o={},s=[],l=this.collisionBoxArray,u=this.rotationMatrix,c=new i(t,e)._matMult(u),h=this.tempCollisionBox;h.anchorX=c.x,h.anchorY=c.y,h.x1=0,h.y1=0,h.x2=r-t,h.y2=n-e,h.maxScale=a,a=h.maxScale;for(var f=[c.x+h.x1/a,c.y+h.y1/a*this.yStretch,c.x+h.x2/a,c.y+h.y2/a*this.yStretch],d=this.grid.query(f[0],f[1],f[2],f[3]),p=this.ignoredGrid.query(f[0],f[1],f[2],f[3]),m=0;m<p.length;m++)d.push(p[m]);for(var v=0;v<d.length;v++){var g=l.get(d[v]),y=g.sourceLayerIndex,b=g.featureIndex;if(void 0===o[y]&&(o[y]={}),!o[y][b]){var x=g.anchorPoint.matMult(u);this.getPlacementScale(this.minScale,c,h,x,g)>=a&&(o[y][b]=!0,s.push(d[v]))}}return s},n.prototype.getPlacementScale=function(t,e,r,n,i){var a=e.x-n.x,o=e.y-n.y,s=(i.x1-r.x2)/a,l=(i.x2-r.x1)/a,u=(i.y1-r.y2)*this.yStretch/o,c=(i.y2-r.y1)*this.yStretch/o;(isNaN(s)||isNaN(l))&&(s=l=1),(isNaN(u)||isNaN(c))&&(u=c=1);var h=Math.min(Math.max(s,l),Math.max(u,c)),f=i.maxScale,d=r.maxScale;return h>f&&(h=f),h>d&&(h=d),h>t&&h>=i.placementScale&&(t=h),t},n.prototype.insertCollisionFeature=function(t,e,r){for(var n=r?this.ignoredGrid:this.grid,i=this.collisionBoxArray,a=t.boxStartIndex;a<t.boxEndIndex;a++){var o=i.get(a);o.placementScale=e,e<this.maxScale&&n.insert(a,o.bbox0,o.bbox1,o.bbox2,o.bbox3)}}},{\"../data/bucket\":329,\"grid-index\":287,\"point-geometry\":484}],397:[function(t,e,r){\"use strict\";function n(t,e,r,n,a,o,s,l,u){var c=n?.6*o*s:0,h=Math.max(n?n.right-n.left:0,a?a.right-a.left:0),f=0===t[0].x||t[0].x===u||0===t[0].y||t[0].y===u;e-h*s<e/4&&(e=h*s+e/4);var d=2*o;return i(t,f?e/2*l%e:(h/2+d)*s*l%e,e,c,r,h*s,f,!1,u)}function i(t,e,r,n,l,u,c,h,f){for(var d=u/2,p=0,m=0;m<t.length-1;m++)p+=t[m].dist(t[m+1]);for(var v=0,g=e-r,y=[],b=0;b<t.length-1;b++){for(var x=t[b],_=t[b+1],w=x.dist(_),M=_.angleTo(x);g+r<v+w;){g+=r;var k=(g-v)/w,A=a(x.x,_.x,k),T=a(x.y,_.y,k);if(A>=0&&A<f&&T>=0&&T<f&&g-d>=0&&g+d<=p){var S=new o(A,T,M,b)._round();n&&!s(t,S,u,n,l)||y.push(S)}}v+=w}return h||y.length||c||(y=i(t,v/2,r,n,l,u,c,!0,f)),y}var a=t(\"../util/interpolate\"),o=t(\"../symbol/anchor\"),s=t(\"./check_max_angle\");e.exports=n},{\"../symbol/anchor\":391,\"../util/interpolate\":436,\"./check_max_angle\":392}],398:[function(t,e,r){\"use strict\";function n(){this.width=o,this.height=o,this.bin=new i(this.width,this.height),this.index={},this.ids={},this.data=new Uint8Array(this.width*this.height)}var i=t(\"shelf-pack\"),a=t(\"../util/util\"),o=128;e.exports=n,n.prototype.getGlyphs=function(){var t,e,r,n={};for(var i in this.ids)t=i.split(\"#\"),e=t[0],r=t[1],n[e]||(n[e]=[]),n[e].push(r);return n},n.prototype.getRects=function(){var t,e,r,n={};for(var i in this.ids)t=i.split(\"#\"),e=t[0],r=t[1],n[e]||(n[e]={}),n[e][r]=this.index[i];return n},n.prototype.addGlyph=function(t,e,r,n){if(!r)return null;var i=e+\"#\"+r.id;if(this.index[i])return this.ids[i].indexOf(t)<0&&this.ids[i].push(t),this.index[i];if(!r.bitmap)return null;var o=r.width+2*n,s=r.height+2*n,l=o+2,u=s+2;l+=4-l%4,u+=4-u%4;var c=this.bin.packOne(l,u);if(c||(this.resize(),c=this.bin.packOne(l,u)),!c)return a.warnOnce(\"glyph bitmap overflow\"),null;this.index[i]=c,this.ids[i]=[t];for(var h=this.data,f=r.bitmap,d=0;d<s;d++)for(var p=this.width*(c.y+d+1)+c.x+1,m=o*d,v=0;v<o;v++)h[p+v]=f[m+v];return this.dirty=!0,c},n.prototype.resize=function(){var t=this.width,e=this.height;if(!(t>=2048||e>=2048)){this.texture&&(this.gl&&this.gl.deleteTexture(this.texture),this.texture=null),this.width*=4,this.height*=4,this.bin.resize(this.width,this.height);for(var r=new ArrayBuffer(this.width*this.height),n=0;n<e;n++){var i=new Uint8Array(this.data.buffer,e*n,t);new Uint8Array(r,e*n*4,t).set(i)}this.data=new Uint8Array(r)}},n.prototype.bind=function(t){this.gl=t,this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,this.width,this.height,0,t.ALPHA,t.UNSIGNED_BYTE,null))},n.prototype.updateTexture=function(t){this.bind(t),this.dirty&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.ALPHA,t.UNSIGNED_BYTE,this.data),this.dirty=!1)}},{\"../util/util\":442,\"shelf-pack\":514}],399:[function(t,e,r){\"use strict\";function n(t){this.url=t&&o(t),this.atlases={},this.stacks={},this.loading={}}function i(t,e,r){this.advance=t.advance,this.left=t.left-r-1,this.top=t.top+r+1,this.rect=e}function a(t,e,r,n){return n=n||\"abc\",r.replace(\"{s}\",n[t.length%n.length]).replace(\"{fontstack}\",t).replace(\"{range}\",e)}var o=t(\"../util/mapbox\").normalizeGlyphsURL,s=t(\"../util/ajax\").getArrayBuffer,l=t(\"../util/glyphs\"),u=t(\"../symbol/glyph_atlas\"),c=t(\"pbf\");e.exports=n,n.prototype.getSimpleGlyphs=function(t,e,r,n){void 0===this.stacks[t]&&(this.stacks[t]={}),void 0===this.atlases[t]&&(this.atlases[t]=new u);for(var a,o={},s=this.stacks[t],l=this.atlases[t],c={},h=0,f=0;f<e.length;f++){var d=e[f];if(a=Math.floor(d/256),s[a]){var p=s[a].glyphs[d],m=l.addGlyph(r,t,p,3);p&&(o[d]=new i(p,m,3))}else void 0===c[a]&&(c[a]=[],h++),c[a].push(d)}h||n(void 0,o,t);var v=function(e,a,s){if(!e)for(var u=this.stacks[t][a]=s.stacks[0],f=0;f<c[a].length;f++){var d=c[a][f],p=u.glyphs[d],m=l.addGlyph(r,t,p,3);p&&(o[d]=new i(p,m,3))}--h||n(void 0,o,t)}.bind(this);for(var g in c)this.loadRange(t,g,v)},n.prototype.loadRange=function(t,e,r){if(256*e>65535)return r(\"glyphs > 65535 not supported\");void 0===this.loading[t]&&(this.loading[t]={});var n=this.loading[t];if(n[e])n[e].push(r);else{n[e]=[r];var i=256*e+\"-\"+(256*e+255),o=a(t,i,this.url);s(o,function(t,r){for(var i=!t&&new l(new c(new Uint8Array(r))),a=0;a<n[e].length;a++)n[e][a](t,e,i);delete n[e]})}},n.prototype.getGlyphAtlas=function(t){return this.atlases[t]}},{\"../symbol/glyph_atlas\":398,\"../util/ajax\":425,\"../util/glyphs\":435,\"../util/mapbox\":439,pbf:478}],400:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){function n(n){c.push(t[n]),h.push(r[n]),f.push(e[n]),d++}function i(t,e,r){var n=u[t];return delete u[t],u[e]=n,h[n][0].pop(),h[n][0]=h[n][0].concat(r[0]),n}function a(t,e,r){var n=l[e];return delete l[e],l[t]=n,h[n][0].shift(),h[n][0]=r[0].concat(h[n][0]),n}function o(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}var s,l={},u={},c=[],h=[],f=[],d=0;for(s=0;s<t.length;s++){var p=r[s],m=e[s];if(m){var v=o(m,p),g=o(m,p,!0);if(v in u&&g in l&&u[v]!==l[g]){var y=a(v,g,p),b=i(v,g,h[y]);delete l[v],delete u[g],u[o(m,h[b],!0)]=b,h[y]=null}else v in u?i(v,g,p):g in l?a(v,g,p):(n(s),l[v]=d-1,u[g]=d-1)}else n(s)}return{features:c,textFeatures:f,geometries:h}}},{}],401:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u){this.anchorPoint=t,this.tl=e,this.tr=r,this.bl=n,this.br=i,this.tex=a,this.anchorAngle=o,this.glyphAngle=s,this.minScale=l,this.maxScale=u}function i(t,e,r,i,a,o,u,c,h){var f,d,p,m,v=e.image.rect,g=a.layout,y=e.left-1,b=y+v.w/e.image.pixelRatio,x=e.top-1,_=x+v.h/e.image.pixelRatio;if(\"none\"!==g[\"icon-text-fit\"]&&u){var w=b-y,M=_-x,k=g[\"text-size\"]/24,A=u.left*k,T=u.right*k,S=u.top*k,E=u.bottom*k,L=T-A,C=E-S,I=g[\"icon-text-fit-padding\"][0],z=g[\"icon-text-fit-padding\"][1],D=g[\"icon-text-fit-padding\"][2],P=g[\"icon-text-fit-padding\"][3],O=\"width\"===g[\"icon-text-fit\"]?.5*(C-M):0,R=\"height\"===g[\"icon-text-fit\"]?.5*(L-w):0,F=\"width\"===g[\"icon-text-fit\"]||\"both\"===g[\"icon-text-fit\"]?L:w,j=\"height\"===g[\"icon-text-fit\"]||\"both\"===g[\"icon-text-fit\"]?C:M;f=new s(A+R-P,S+O-I),d=new s(A+R+z+F,S+O-I),p=new s(A+R+z+F,S+O+D+j),m=new s(A+R-P,S+O+D+j)}else f=new s(y,x),d=new s(b,x),p=new s(b,_),m=new s(y,_);var N=a.getLayoutValue(\"icon-rotate\",c,h)*Math.PI/180;if(o){var B=i[t.segment];if(t.y===B.y&&t.x===B.x&&t.segment+1<i.length){var U=i[t.segment+1];N+=Math.atan2(t.y-U.y,t.x-U.x)+Math.PI}else N+=Math.atan2(t.y-B.y,t.x-B.x)}if(N){var V=Math.sin(N),H=Math.cos(N),q=[H,-V,V,H];f=f.matMult(q),d=d.matMult(q),m=m.matMult(q),p=p.matMult(q)}return[new n(new s(t.x,t.y),f,d,m,p,e.image.rect,0,0,l,1/0)]}function a(t,e,r,i,a,u){for(var c=a.layout[\"text-rotate\"]*Math.PI/180,h=a.layout[\"text-keep-upright\"],f=e.positionedGlyphs,d=[],p=0;p<f.length;p++){var m=f[p],v=m.glyph,g=v.rect;if(g){var y,b=(m.x+v.advance/2)*r,x=l;u?(y=[],x=o(y,t,b,i,t.segment,!0),h&&(x=Math.min(x,o(y,t,b,i,t.segment,!1)))):y=[{anchorPoint:new s(t.x,t.y),offset:0,angle:0,maxScale:1/0,minScale:l}];for(var _=m.x+v.left,w=m.y-v.top,M=_+g.w,k=w+g.h,A=new s(_,w),T=new s(M,w),S=new s(_,k),E=new s(M,k),L=0;L<y.length;L++){var C=y[L],I=A,z=T,D=S,P=E;if(c){var O=Math.sin(c),R=Math.cos(c),F=[R,-O,O,R];I=I.matMult(F),z=z.matMult(F),D=D.matMult(F),P=P.matMult(F)}var j=Math.max(C.minScale,x),N=(t.angle+C.offset+2*Math.PI)%(2*Math.PI),B=(C.angle+C.offset+2*Math.PI)%(2*Math.PI);d.push(new n(C.anchorPoint,I,z,D,P,g,N,B,j,C.maxScale))}}}return d}function o(t,e,r,n,i,a){var o=!a;r<0&&(a=!a),a&&i++;var u=new s(e.x,e.y),c=n[i],h=1/0;r=Math.abs(r);for(var f=l;;){var d=u.dist(c),p=r/d,m=Math.atan2(c.y-u.y,c.x-u.x);if(a||(m+=Math.PI),t.push({anchorPoint:u,offset:o?Math.PI:0,minScale:p,maxScale:h,angle:(m+2*Math.PI)%(2*Math.PI)}),p<=f)break;for(u=c;u.equals(c);)if(i+=a?1:-1,!(c=n[i]))return p;var v=c.sub(u)._unit();u=u.sub(v._mult(d)),h=p}return f}var s=t(\"point-geometry\");e.exports={getIconQuads:i,getGlyphQuads:a,SymbolQuad:n};var l=.5},{\"point-geometry\":484}],402:[function(t,e,r){\"use strict\";function n(t,e,r){for(var n=[],a=0,o=t.length;a<o;a++){var s=i(t[a].properties,e[\"text-field\"]);if(s){s=s.toString();var l=e[\"text-transform\"];\"uppercase\"===l?s=s.toLocaleUpperCase():\"lowercase\"===l&&(s=s.toLocaleLowerCase());for(var u=0;u<s.length;u++)r[s.charCodeAt(u)]=!0;n[a]=s}else n[a]=null}return n}var i=t(\"../util/token\");e.exports=n},{\"../util/token\":441}],403:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.codePoint=t,this.x=e,this.y=r,this.glyph=n}function i(t,e,r,n,i,a){this.positionedGlyphs=t,this.text=e,this.top=r,this.bottom=n,this.left=i,this.right=a}function a(t,e,r,a,s,l,u,c,h){for(var f=[],d=new i(f,t,h[1],h[1],h[0],h[0]),p=0,m=0;m<t.length;m++){var v=t.charCodeAt(m),g=e[v];g&&(f.push(new n(v,p,-17,g)),p+=g.advance+c)}return!!f.length&&(o(d,e,a,r,s,l,u,h),d)}function o(t,e,r,n,i,a,o,u){var c=null,d=0,p=0,m=0,v=0,g=t.positionedGlyphs;if(n)for(var y=0;y<g.length;y++){var b=g[y];if(b.x-=d,b.y+=r*m,b.x>n&&null!==c){var x=g[c+1].x;v=Math.max(x,v);for(var _=c+1;_<=y;_++)g[_].y+=r,g[_].x-=x;if(o){var w=c;h[g[c].codePoint]&&w--,s(g,e,p,w,o)}p=c+1,c=null,d+=x,m++}f[b.codePoint]&&(c=y)}var M=g[g.length-1],k=M.x+e[M.codePoint].advance;v=Math.max(v,k);var A=(m+1)*r;s(g,e,p,g.length-1,o),l(g,o,i,a,v,r,m,u),t.top+=-a*A,t.bottom=t.top+A,t.left+=-i*v,t.right=t.left+v}function s(t,e,r,n,i){for(var a=e[t[n].codePoint].advance,o=(t[n].x+a)*i,s=r;s<=n;s++)t[s].x-=o}\n",
+       "function l(t,e,r,n,i,a,o,s){for(var l=(e-r)*i+s[0],u=(-n*(o+1)+.5)*a+s[1],c=0;c<t.length;c++)t[c].x+=l,t[c].y+=u}function u(t,e){if(!t||!t.rect)return null;var r=e[\"icon-offset\"][0],n=e[\"icon-offset\"][1],i=r-t.width/2,a=i+t.width,o=n-t.height/2;return new c(t,o,o+t.height,i,a)}function c(t,e,r,n,i){this.image=t,this.top=e,this.bottom=r,this.left=n,this.right=i}e.exports={shapeText:a,shapeIcon:u};var h={32:!0,8203:!0},f={32:!0,38:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0}},{}],404:[function(t,e,r){\"use strict\";function n(t,e){this.width=t,this.height=e,this.bin=new o(t,e),this.images={},this.data=!1,this.texture=0,this.filter=0,this.pixelRatio=1,this.dirty=!0}function i(t,e,r,n,i,a,o,s,l,u,c){var h,f,d=n*e+r,p=s*a+o;if(c)for(p-=a,f=-1;f<=u;f++,d=((f+u)%u+n)*e+r,p+=a)for(h=-1;h<=l;h++)i[p+h]=t[d+(h+l)%l];else for(f=0;f<u;f++,d+=e,p+=a)for(h=0;h<l;h++)i[p+h]=t[d+h]}function a(t,e,r,n,i){this.rect=t,this.width=e,this.height=r,this.sdf=n,this.pixelRatio=i}var o=t(\"shelf-pack\"),s=t(\"../util/browser\"),l=t(\"../util/util\");e.exports=n,n.prototype.allocateImage=function(t,e){t/=this.pixelRatio,e/=this.pixelRatio;var r=t+2+(4-(t+2)%4),n=e+2+(4-(e+2)%4),i=this.bin.packOne(r,n);return i||(l.warnOnce(\"SpriteAtlas out of space.\"),null)},n.prototype.getImage=function(t,e){if(this.images[t])return this.images[t];if(!this.sprite)return null;var r=this.sprite.getSpritePosition(t);if(!r.width||!r.height)return null;var n=this.allocateImage(r.width,r.height);if(!n)return null;var i=new a(n,r.width/r.pixelRatio,r.height/r.pixelRatio,r.sdf,r.pixelRatio/this.pixelRatio);return this.images[t]=i,this.copy(n,r,e),i},n.prototype.getPosition=function(t,e){var r=this.getImage(t,e),n=r&&r.rect;if(!n)return null;var i=r.width*r.pixelRatio,a=r.height*r.pixelRatio;return{size:[r.width,r.height],tl:[(n.x+1)/this.width,(n.y+1)/this.height],br:[(n.x+1+i)/this.width,(n.y+1+a)/this.height]}},n.prototype.allocate=function(){if(!this.data){var t=Math.floor(this.width*this.pixelRatio),e=Math.floor(this.height*this.pixelRatio);this.data=new Uint32Array(t*e);for(var r=0;r<this.data.length;r++)this.data[r]=0}},n.prototype.copy=function(t,e,r){if(this.sprite.img.data){var n=new Uint32Array(this.sprite.img.data.buffer);this.allocate();var a=this.data;i(n,this.sprite.img.width,e.x,e.y,a,this.width*this.pixelRatio,(t.x+1)*this.pixelRatio,(t.y+1)*this.pixelRatio,e.width,e.height,r),this.dirty=!0}},n.prototype.setSprite=function(t){t&&(this.pixelRatio=s.devicePixelRatio>1?2:1,this.canvas&&(this.canvas.width=this.width*this.pixelRatio,this.canvas.height=this.height*this.pixelRatio)),this.sprite=t},n.prototype.addIcons=function(t,e){for(var r=0;r<t.length;r++)this.getImage(t[r]);e(null,this.images)},n.prototype.bind=function(t,e){var r=!1;this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),r=!0);var n=e?t.LINEAR:t.NEAREST;n!==this.filter&&(t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,n),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,n),this.filter=n),this.dirty&&(this.allocate(),r?t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width*this.pixelRatio,this.height*this.pixelRatio,0,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)):t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width*this.pixelRatio,this.height*this.pixelRatio,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)),this.dirty=!1)}},{\"../util/browser\":426,\"../util/util\":442,\"shelf-pack\":514}],405:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=e.exports=new n({members:[{type:\"Uint16\",name:\"textBoxStartIndex\"},{type:\"Uint16\",name:\"textBoxEndIndex\"},{type:\"Uint16\",name:\"iconBoxStartIndex\"},{type:\"Uint16\",name:\"iconBoxEndIndex\"},{type:\"Uint16\",name:\"glyphQuadStartIndex\"},{type:\"Uint16\",name:\"glyphQuadEndIndex\"},{type:\"Uint16\",name:\"iconQuadStartIndex\"},{type:\"Uint16\",name:\"iconQuadEndIndex\"},{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int8\",name:\"index\"}]});i.extendAll(o.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"point-geometry\":484}],406:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=t(\"./quads\").SymbolQuad,s=e.exports=new n({members:[{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Float32\",name:\"tlX\"},{type:\"Float32\",name:\"tlY\"},{type:\"Float32\",name:\"trX\"},{type:\"Float32\",name:\"trY\"},{type:\"Float32\",name:\"blX\"},{type:\"Float32\",name:\"blY\"},{type:\"Float32\",name:\"brX\"},{type:\"Float32\",name:\"brY\"},{type:\"Int16\",name:\"texH\"},{type:\"Int16\",name:\"texW\"},{type:\"Int16\",name:\"texX\"},{type:\"Int16\",name:\"texY\"},{type:\"Float32\",name:\"anchorAngle\"},{type:\"Float32\",name:\"glyphAngle\"},{type:\"Float32\",name:\"maxScale\"},{type:\"Float32\",name:\"minScale\"}]});i.extendAll(s.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)},get SymbolQuad(){return new o(this.anchorPoint,new a(this.tlX,this.tlY),new a(this.trX,this.trY),new a(this.blX,this.blY),new a(this.brX,this.brY),{x:this.texX,y:this.texY,h:this.texH,w:this.texW,height:this.texH,width:this.texW},this.anchorAngle,this.glyphAngle,this.minScale,this.maxScale)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"./quads\":401,\"point-geometry\":484}],407:[function(t,e,r){\"use strict\";var n=t(\"../util/dom\"),i=t(\"point-geometry\"),a={scrollZoom:t(\"./handler/scroll_zoom\"),boxZoom:t(\"./handler/box_zoom\"),dragRotate:t(\"./handler/drag_rotate\"),dragPan:t(\"./handler/drag_pan\"),keyboard:t(\"./handler/keyboard\"),doubleClickZoom:t(\"./handler/dblclick_zoom\"),touchZoomRotate:t(\"./handler/touch_zoom_rotate\")};e.exports=function(t,e){function r(t){g(\"mouseout\",t)}function o(e){t.stop(),_=n.mousePos(b,e),g(\"mousedown\",e)}function s(e){var r=t.dragRotate&&t.dragRotate.isActive();x&&!r&&g(\"contextmenu\",x),x=null,g(\"mouseup\",e)}function l(e){if(!(t.dragPan&&t.dragPan.isActive()||t.dragRotate&&t.dragRotate.isActive())){for(var r=e.toElement||e.target;r&&r!==b;)r=r.parentNode;r===b&&g(\"mousemove\",e)}}function u(e){t.stop(),y(\"touchstart\",e),!e.touches||e.touches.length>1||(w?(clearTimeout(w),w=null,g(\"dblclick\",e)):w=setTimeout(d,300))}function c(t){y(\"touchmove\",t)}function h(t){y(\"touchend\",t)}function f(t){y(\"touchcancel\",t)}function d(){w=null}function p(t){n.mousePos(b,t).equals(_)&&g(\"click\",t)}function m(t){g(\"dblclick\",t),t.preventDefault()}function v(t){x=t,t.preventDefault()}function g(e,r){var i=n.mousePos(b,r);return t.fire(e,{lngLat:t.unproject(i),point:i,originalEvent:r})}function y(e,r){var a=n.touchPos(b,r),o=a.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new i(0,0));return t.fire(e,{lngLat:t.unproject(o),point:o,lngLats:a.map(function(e){return t.unproject(e)},this),points:a,originalEvent:r})}var b=t.getCanvasContainer(),x=null,_=null,w=null;for(var M in a)t[M]=new a[M](t,e),e.interactive&&e[M]&&t[M].enable();b.addEventListener(\"mouseout\",r,!1),b.addEventListener(\"mousedown\",o,!1),b.addEventListener(\"mouseup\",s,!1),b.addEventListener(\"mousemove\",l,!1),b.addEventListener(\"touchstart\",u,!1),b.addEventListener(\"touchend\",h,!1),b.addEventListener(\"touchmove\",c,!1),b.addEventListener(\"touchcancel\",f,!1),b.addEventListener(\"click\",p,!1),b.addEventListener(\"dblclick\",m,!1),b.addEventListener(\"contextmenu\",v,!1)}},{\"../util/dom\":428,\"./handler/box_zoom\":413,\"./handler/dblclick_zoom\":414,\"./handler/drag_pan\":415,\"./handler/drag_rotate\":416,\"./handler/keyboard\":417,\"./handler/scroll_zoom\":418,\"./handler/touch_zoom_rotate\":419,\"point-geometry\":484}],408:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i=t(\"../util/interpolate\"),a=t(\"../util/browser\"),o=t(\"../geo/lng_lat\"),s=t(\"../geo/lng_lat_bounds\"),l=t(\"point-geometry\"),u=e.exports=function(){};n.extend(u.prototype,{getCenter:function(){return this.transform.center},setCenter:function(t,e){return this.jumpTo({center:t},e),this},panBy:function(t,e,r){return this.panTo(this.transform.center,n.extend({offset:l.convert(t).mult(-1)},e),r),this},panTo:function(t,e,r){return this.easeTo(n.extend({center:t},e),r)},getZoom:function(){return this.transform.zoom},setZoom:function(t,e){return this.jumpTo({zoom:t},e),this},zoomTo:function(t,e,r){return this.easeTo(n.extend({zoom:t},e),r)},zoomIn:function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},zoomOut:function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},getBearing:function(){return this.transform.bearing},setBearing:function(t,e){return this.jumpTo({bearing:t},e),this},rotateTo:function(t,e,r){return this.easeTo(n.extend({bearing:t},e),r)},resetNorth:function(t,e){return this.rotateTo(0,n.extend({duration:1e3},t),e),this},snapToNorth:function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},getPitch:function(){return this.transform.pitch},setPitch:function(t,e){return this.jumpTo({pitch:t},e),this},fitBounds:function(t,e,r){e=n.extend({padding:0,offset:[0,0],maxZoom:1/0},e),t=s.convert(t);var i=l.convert(e.offset),a=this.transform,o=a.project(t.getNorthWest()),u=a.project(t.getSouthEast()),c=u.sub(o),h=(a.width-2*e.padding-2*Math.abs(i.x))/c.x,f=(a.height-2*e.padding-2*Math.abs(i.y))/c.y;return e.center=a.unproject(o.add(u).div(2)),e.zoom=Math.min(a.scaleZoom(a.scale*Math.min(h,f)),e.maxZoom),e.bearing=0,e.linear?this.easeTo(e,r):this.flyTo(e,r)},jumpTo:function(t,e){this.stop();var r=this.transform,n=!1,i=!1,a=!1;return\"zoom\"in t&&r.zoom!==+t.zoom&&(n=!0,r.zoom=+t.zoom),\"center\"in t&&(r.center=o.convert(t.center)),\"bearing\"in t&&r.bearing!==+t.bearing&&(i=!0,r.bearing=+t.bearing),\"pitch\"in t&&r.pitch!==+t.pitch&&(a=!0,r.pitch=+t.pitch),this.fire(\"movestart\",e).fire(\"move\",e),n&&this.fire(\"zoomstart\",e).fire(\"zoom\",e).fire(\"zoomend\",e),i&&this.fire(\"rotate\",e),a&&this.fire(\"pitch\",e),this.fire(\"moveend\",e)},easeTo:function(t,e){this.stop(),t=n.extend({offset:[0,0],duration:500,easing:n.ease},t);var r,a,s=this.transform,u=l.convert(t.offset),c=this.getZoom(),h=this.getBearing(),f=this.getPitch(),d=\"zoom\"in t?+t.zoom:c,p=\"bearing\"in t?this._normalizeBearing(t.bearing,h):h,m=\"pitch\"in t?+t.pitch:f;\"center\"in t?(r=o.convert(t.center),a=s.centerPoint.add(u)):\"around\"in t?(r=o.convert(t.around),a=s.locationPoint(r)):(a=s.centerPoint.add(u),r=s.pointLocation(a));var v=s.locationPoint(r);return!1===t.animate&&(t.duration=0),this.zooming=d!==c,this.rotating=h!==p,this.pitching=m!==f,t.noMoveStart||this.fire(\"movestart\",e),this.zooming&&this.fire(\"zoomstart\",e),clearTimeout(this._onEaseEnd),this._ease(function(t){this.zooming&&(s.zoom=i(c,d,t)),this.rotating&&(s.bearing=i(h,p,t)),this.pitching&&(s.pitch=i(f,m,t)),s.setLocationAtPoint(r,v.add(a.sub(v)._mult(t))),this.fire(\"move\",e),this.zooming&&this.fire(\"zoom\",e),this.rotating&&this.fire(\"rotate\",e),this.pitching&&this.fire(\"pitch\",e)},function(){t.delayEndEvents?this._onEaseEnd=setTimeout(this._easeToEnd.bind(this,e),t.delayEndEvents):this._easeToEnd(e)}.bind(this),t),this},_easeToEnd:function(t){var e=this.zooming;this.zooming=!1,this.rotating=!1,this.pitching=!1,e&&this.fire(\"zoomend\",t),this.fire(\"moveend\",t)},flyTo:function(t,e){function r(t){var e=(A*A-k*k+(t?-1:1)*L*L*T*T)/(2*(t?A:k)*L*T);return Math.log(Math.sqrt(e*e+1)-e)}function a(t){return(Math.exp(t)-Math.exp(-t))/2}function s(t){return(Math.exp(t)+Math.exp(-t))/2}function u(t){return a(t)/s(t)}this.stop(),t=n.extend({offset:[0,0],speed:1.2,curve:1.42,easing:n.ease},t);var c=this.transform,h=l.convert(t.offset),f=this.getZoom(),d=this.getBearing(),p=this.getPitch(),m=\"center\"in t?o.convert(t.center):this.getCenter(),v=\"zoom\"in t?+t.zoom:f,g=\"bearing\"in t?this._normalizeBearing(t.bearing,d):d,y=\"pitch\"in t?+t.pitch:p;Math.abs(c.center.lng)+Math.abs(m.lng)>180&&(c.center.lng>0&&m.lng<0?m.lng+=360:c.center.lng<0&&m.lng>0&&(m.lng-=360));var b=c.zoomScale(v-f),x=c.point,_=\"center\"in t?c.project(m).sub(h.div(b)):x,w=c.worldSize,M=t.curve,k=Math.max(c.width,c.height),A=k/b,T=_.sub(x).mag();if(\"minZoom\"in t){var S=n.clamp(Math.min(t.minZoom,f,v),c.minZoom,c.maxZoom),E=k/c.zoomScale(S-f);M=Math.sqrt(E/T*2)}var L=M*M,C=r(0),I=function(t){return s(C)/s(C+M*t)},z=function(t){return k*((s(C)*u(C+M*t)-a(C))/L)/T},D=(r(1)-C)/M;if(Math.abs(T)<1e-6){if(Math.abs(k-A)<1e-6)return this.easeTo(t);var P=A<k?-1:1;D=Math.abs(Math.log(A/k))/M,z=function(){return 0},I=function(t){return Math.exp(P*M*t)}}if(\"duration\"in t)t.duration=+t.duration;else{var O=\"screenSpeed\"in t?+t.screenSpeed/M:+t.speed;t.duration=1e3*D/O}return this.zooming=!0,d!==g&&(this.rotating=!0),p!==y&&(this.pitching=!0),this.fire(\"movestart\",e),this.fire(\"zoomstart\",e),this._ease(function(t){var r=t*D,n=z(r);c.zoom=f+c.scaleZoom(1/I(r)),c.center=c.unproject(x.add(_.sub(x).mult(n)),w),this.rotating&&(c.bearing=i(d,g,t)),this.pitching&&(c.pitch=i(p,y,t)),this.fire(\"move\",e),this.fire(\"zoom\",e),this.rotating&&this.fire(\"rotate\",e),this.pitching&&this.fire(\"pitch\",e)},function(){this.zooming=!1,this.rotating=!1,this.pitching=!1,this.fire(\"zoomend\",e),this.fire(\"moveend\",e)},t),this},isEasing:function(){return!!this._abortFn},stop:function(){return this._abortFn&&(this._abortFn(),this._finishEase()),this},_ease:function(t,e,r){this._finishFn=e,this._abortFn=a.timed(function(e){t.call(this,r.easing(e)),1===e&&this._finishEase()},!1===r.animate?0:r.duration,this)},_finishEase:function(){delete this._abortFn;var t=this._finishFn;delete this._finishFn,t.call(this)},_normalizeBearing:function(t,e){t=n.wrap(t,-180,180);var r=Math.abs(t-e);return Math.abs(t-360-e)<r&&(t-=360),Math.abs(t+360-e)<r&&(t+=360),t},_updateEasing:function(t,e,r){var i;if(this.ease){var a=this.ease,o=(Date.now()-a.start)/a.duration,s=a.easing(o+.01)-a.easing(o),l=.27/Math.sqrt(s*s+1e-4)*.01,u=Math.sqrt(.0729-l*l);i=n.bezier(l,u,.25,1)}else i=r?n.bezier.apply(n,r):n.ease;return this.ease={start:(new Date).getTime(),to:Math.pow(2,e),duration:t,easing:i},i}})},{\"../geo/lng_lat\":339,\"../geo/lng_lat_bounds\":340,\"../util/browser\":426,\"../util/interpolate\":436,\"../util/util\":442,\"point-geometry\":484}],409:[function(t,e,r){\"use strict\";function n(t){o.setOptions(this,t)}var i=t(\"./control\"),a=t(\"../../util/dom\"),o=t(\"../../util/util\");e.exports=n,n.createAttributionString=function(t){var e=[];for(var r in t){var n=t[r];n.attribution&&e.indexOf(n.attribution)<0&&e.push(n.attribution)}return e.sort(function(t,e){return t.length-e.length}),e=e.filter(function(t,r){for(var n=r+1;n<e.length;n++)if(e[n].indexOf(t)>=0)return!1;return!0}),e.join(\" | \")},n.prototype=o.inherit(i,{options:{position:\"bottom-right\"},onAdd:function(t){var e=this._container=a.create(\"div\",\"mapboxgl-ctrl-attrib\",t.getContainer());return this._update(),t.on(\"source.load\",this._update.bind(this)),t.on(\"source.change\",this._update.bind(this)),t.on(\"source.remove\",this._update.bind(this)),t.on(\"moveend\",this._updateEditLink.bind(this)),e},_update:function(){this._map.style&&(this._container.innerHTML=n.createAttributionString(this._map.style.sources)),this._editLink=this._container.getElementsByClassName(\"mapbox-improve-map\")[0],this._updateEditLink()},_updateEditLink:function(){if(this._editLink){var t=this._map.getCenter();this._editLink.href=\"https://www.mapbox.com/map-feedback/#/\"+t.lng+\"/\"+t.lat+\"/\"+Math.round(this._map.getZoom()+1)}}})},{\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],410:[function(t,e,r){\"use strict\";function n(){}var i=t(\"../../util/util\"),a=t(\"../../util/evented\");e.exports=n,n.prototype={addTo:function(t){this._map=t;var e=this._container=this.onAdd(t);if(this.options&&this.options.position){var r=this.options.position,n=t._controlCorners[r];e.className+=\" mapboxgl-ctrl\",-1!==r.indexOf(\"bottom\")?n.insertBefore(e,n.firstChild):n.appendChild(e)}return this},remove:function(){return this._container.parentNode.removeChild(this._container),this.onRemove&&this.onRemove(this._map),this._map=null,this}},i.extend(n.prototype,a)},{\"../../util/evented\":434,\"../../util/util\":442}],411:[function(t,e,r){\"use strict\";function n(t){s.setOptions(this,t)}var i=t(\"./control\"),a=t(\"../../util/browser\"),o=t(\"../../util/dom\"),s=t(\"../../util/util\");e.exports=n;var l={enableHighAccuracy:!1,timeout:6e3};n.prototype=s.inherit(i,{options:{position:\"top-right\"},onAdd:function(t){var e=this._container=o.create(\"div\",\"mapboxgl-ctrl-group\",t.getContainer());return a.supportsGeolocation?(this._container.addEventListener(\"contextmenu\",this._onContextMenu.bind(this)),this._geolocateButton=o.create(\"button\",\"mapboxgl-ctrl-icon mapboxgl-ctrl-geolocate\",this._container),this._geolocateButton.type=\"button\",this._geolocateButton.addEventListener(\"click\",this._onClickGeolocate.bind(this)),e):e},_onContextMenu:function(t){t.preventDefault()},_onClickGeolocate:function(){navigator.geolocation.getCurrentPosition(this._success.bind(this),this._error.bind(this),l),this._timeoutId=setTimeout(this._finish.bind(this),1e4)},_success:function(t){this._map.jumpTo({center:[t.coords.longitude,t.coords.latitude],zoom:17,bearing:0,pitch:0}),this.fire(\"geolocate\",t),this._finish()},_error:function(t){this.fire(\"error\",t),this._finish()},_finish:function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0}})},{\"../../util/browser\":426,\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],412:[function(t,e,r){\"use strict\";function n(t){s.setOptions(this,t)}function i(t){return new MouseEvent(t.type,{button:2,buttons:2,bubbles:!0,cancelable:!0,detail:t.detail,view:t.view,screenX:t.screenX,screenY:t.screenY,clientX:t.clientX,clientY:t.clientY,movementX:t.movementX,movementY:t.movementY,ctrlKey:t.ctrlKey,shiftKey:t.shiftKey,altKey:t.altKey,metaKey:t.metaKey})}var a=t(\"./control\"),o=t(\"../../util/dom\"),s=t(\"../../util/util\");e.exports=n,n.prototype=s.inherit(a,{options:{position:\"top-right\"},onAdd:function(t){var e=\"mapboxgl-ctrl\",r=this._container=o.create(\"div\",e+\"-group\",t.getContainer());return this._container.addEventListener(\"contextmenu\",this._onContextMenu.bind(this)),this._zoomInButton=this._createButton(e+\"-icon \"+e+\"-zoom-in\",t.zoomIn.bind(t)),this._zoomOutButton=this._createButton(e+\"-icon \"+e+\"-zoom-out\",t.zoomOut.bind(t)),this._compass=this._createButton(e+\"-icon \"+e+\"-compass\",t.resetNorth.bind(t)),this._compassArrow=o.create(\"div\",\"arrow\",this._compass),this._compass.addEventListener(\"mousedown\",this._onCompassDown.bind(this)),this._onCompassMove=this._onCompassMove.bind(this),this._onCompassUp=this._onCompassUp.bind(this),t.on(\"rotate\",this._rotateCompassArrow.bind(this)),this._rotateCompassArrow(),this._el=t.getCanvasContainer(),r},_onContextMenu:function(t){t.preventDefault()},_onCompassDown:function(t){0===t.button&&(o.disableDrag(),document.addEventListener(\"mousemove\",this._onCompassMove),document.addEventListener(\"mouseup\",this._onCompassUp),this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassMove:function(t){0===t.button&&(this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassUp:function(t){0===t.button&&(document.removeEventListener(\"mousemove\",this._onCompassMove),document.removeEventListener(\"mouseup\",this._onCompassUp),o.enableDrag(),this._el.dispatchEvent(i(t)),t.stopPropagation())},_createButton:function(t,e){var r=o.create(\"button\",t,this._container);return r.type=\"button\",r.addEventListener(\"click\",function(){e()}),r},_rotateCompassArrow:function(){var t=\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassArrow.style.transform=t}})},{\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],413:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../geo/lng_lat_bounds\"),o=t(\"../../util/util\");e.exports=n,n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener(\"mousedown\",this._onMouseDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onMouseDown),this._enabled=!1)},_onMouseDown:function(t){t.shiftKey&&0===t.button&&(document.addEventListener(\"mousemove\",this._onMouseMove,!1),document.addEventListener(\"keydown\",this._onKeyDown,!1),document.addEventListener(\"mouseup\",this._onMouseUp,!1),i.disableDrag(),this._startPos=i.mousePos(this._el,t),this._active=!0)},_onMouseMove:function(t){var e=this._startPos,r=i.mousePos(this._el,t);this._box||(this._box=i.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var n=Math.min(e.x,r.x),a=Math.max(e.x,r.x),o=Math.min(e.y,r.y),s=Math.max(e.y,r.y);i.setTransform(this._box,\"translate(\"+n+\"px,\"+o+\"px)\"),this._box.style.width=a-n+\"px\",this._box.style.height=s-o+\"px\"},_onMouseUp:function(t){if(0===t.button){var e=this._startPos,r=i.mousePos(this._el,t),n=new a(this._map.unproject(e),this._map.unproject(r));this._finish(),e.x===r.x&&e.y===r.y?this._fireEvent(\"boxzoomcancel\",t):this._map.fitBounds(n,{linear:!0}).fire(\"boxzoomend\",{originalEvent:t,boxZoomBounds:n})}},_onKeyDown:function(t){27===t.keyCode&&(this._finish(),this._fireEvent(\"boxzoomcancel\",t))},_finish:function(){this._active=!1,document.removeEventListener(\"mousemove\",this._onMouseMove,!1),document.removeEventListener(\"keydown\",this._onKeyDown,!1),document.removeEventListener(\"mouseup\",this._onMouseUp,!1),this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(this._box.parentNode.removeChild(this._box),this._box=null),i.enableDrag()},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})}}},{\"../../geo/lng_lat_bounds\":340,\"../../util/dom\":428,\"../../util/util\":442}],414:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._onDblClick=this._onDblClick.bind(this)}e.exports=n,n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._map.on(\"dblclick\",this._onDblClick),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._map.off(\"dblclick\",this._onDblClick),this._enabled=!1)},_onDblClick:function(t){this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)}}},{}],415:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/util\");e.exports=n;var o=a.bezier(0,0,.3,1);n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener(\"mousedown\",this._onDown),this._el.addEventListener(\"touchstart\",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onDown),this._el.removeEventListener(\"touchstart\",this._onDown),this._enabled=!1)},_onDown:function(t){this._ignoreEvent(t)||this.isActive()||(t.touches?(document.addEventListener(\"touchmove\",this._onMove),document.addEventListener(\"touchend\",this._onTouchEnd)):(document.addEventListener(\"mousemove\",this._onMove),document.addEventListener(\"mouseup\",this._onMouseUp)),this._active=!1,this._startPos=this._pos=i.mousePos(this._el,t),this._inertia=[[Date.now(),this._pos]])},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()||(this._active=!0,this._fireEvent(\"dragstart\",t),this._fireEvent(\"movestart\",t));var e=i.mousePos(this._el,t),r=this._map;r.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),e]),r.transform.setLocationAtPoint(r.transform.pointLocation(this._pos),e),this._fireEvent(\"drag\",t),this._fireEvent(\"move\",t),this._pos=e,t.preventDefault()}},_onUp:function(t){if(this.isActive()){this._active=!1,this._fireEvent(\"dragend\",t),this._drainInertiaBuffer();var e=function(){this._fireEvent(\"moveend\",t)}.bind(this),r=this._inertia;if(r.length<2)return void e();var n=r[r.length-1],i=r[0],a=n[1].sub(i[1]),s=(n[0]-i[0])/1e3;if(0===s||n[1].equals(i[1]))return void e();var l=a.mult(.3/s),u=l.mag();u>1400&&(u=1400,l._unit()._mult(u));var c=u/750,h=l.mult(-c/2);this._map.panBy(h,{duration:1e3*c,easing:o,noMoveStart:!0},{originalEvent:t})}},_onMouseUp:function(t){this._ignoreEvent(t)||(this._onUp(t),document.removeEventListener(\"mousemove\",this._onMove),document.removeEventListener(\"mouseup\",this._onMouseUp))},_onTouchEnd:function(t){this._ignoreEvent(t)||(this._onUp(t),document.removeEventListener(\"touchmove\",this._onMove),document.removeEventListener(\"touchend\",this._onTouchEnd))},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragRotate&&e.dragRotate.isActive())return!0;if(t.touches)return t.touches.length>1;if(t.ctrlKey)return!0;return\"mousemove\"===t.type?!1&t.buttons:0!==t.button},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>0&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442}],416:[function(t,e,r){\"use strict\";function n(t,e){this._map=t,this._el=t.getCanvasContainer(),this._bearingSnap=e.bearingSnap,o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"point-geometry\"),o=t(\"../../util/util\");e.exports=n;var s=o.bezier(0,0,.25,1);n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener(\"mousedown\",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onDown),this._enabled=!1)},_onDown:function(t){if(!this._ignoreEvent(t)&&!this.isActive()){document.addEventListener(\"mousemove\",this._onMove),document.addEventListener(\"mouseup\",this._onUp),this._active=!1,this._inertia=[[Date.now(),this._map.getBearing()]],this._startPos=this._pos=i.mousePos(this._el,t),this._center=this._map.transform.centerPoint;var e=this._startPos.sub(this._center);e.mag()<200&&(this._center=this._startPos.add(new a(-200,0)._rotate(e.angle()))),t.preventDefault()}},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()||(this._active=!0,this._fireEvent(\"rotatestart\",t),this._fireEvent(\"movestart\",t));var e=this._map;e.stop();var r=this._pos,n=i.mousePos(this._el,t),a=this._center,o=r.sub(a).angleWith(n.sub(a))/Math.PI*180,s=e.getBearing()-o,l=this._inertia,u=l[l.length-1];this._drainInertiaBuffer(),l.push([Date.now(),e._normalizeBearing(s,u[1])]),e.transform.bearing=s,this._fireEvent(\"rotate\",t),this._fireEvent(\"move\",t),this._pos=n}},_onUp:function(t){if(!this._ignoreEvent(t)&&(document.removeEventListener(\"mousemove\",this._onMove),document.removeEventListener(\"mouseup\",this._onUp),this.isActive())){this._active=!1,this._fireEvent(\"rotateend\",t),this._drainInertiaBuffer();var e=this._map,r=e.getBearing(),n=this._inertia,i=function(){Math.abs(r)<this._bearingSnap?e.resetNorth({noMoveStart:!0},{originalEvent:t}):this._fireEvent(\"moveend\",t)}.bind(this);if(n.length<2)return void i();var a=n[0],o=n[n.length-1],l=n[n.length-2],u=e._normalizeBearing(r,l[1]),c=o[1]-a[1],h=c<0?-1:1,f=(o[0]-a[0])/1e3;if(0===c||0===f)return void i();var d=Math.abs(c*(.25/f));d>180&&(d=180);var p=d/180;u+=h*d*(p/2),Math.abs(e._normalizeBearing(u,0))<this._bearingSnap&&(u=e._normalizeBearing(0,u)),e.rotateTo(u,{duration:1e3*p,easing:s,noMoveStart:!0},{originalEvent:t})}},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragPan&&e.dragPan.isActive())return!0;if(t.touches)return t.touches.length>1;var r=t.ctrlKey?1:2,n=t.ctrlKey?0:2;return\"mousemove\"===t.type?t.buttons&0===r:t.button!==n},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>0&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442,\"point-geometry\":484}],417:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._onKeyDown=this._onKeyDown.bind(this)}e.exports=n;n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener(\"keydown\",this._onKeyDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"keydown\",this._onKeyDown),this._enabled=!1)},_onKeyDown:function(t){if(!(t.altKey||t.ctrlKey||t.metaKey)){var e=this._map,r={originalEvent:t};if(!e.isEasing())switch(t.keyCode){case 61:case 107:case 171:case 187:e.zoomTo(Math.round(e.getZoom())+(t.shiftKey?2:1),r);break;case 189:case 109:case 173:e.zoomTo(Math.round(e.getZoom())-(t.shiftKey?2:1),r);break;case 37:t.shiftKey?e.easeTo({bearing:e.getBearing()-2},r):(t.preventDefault(),e.panBy([-80,0],r));break;case 39:t.shiftKey?e.easeTo({bearing:e.getBearing()+2},r):(t.preventDefault(),e.panBy([80,0],r));break;case 38:t.shiftKey?e.easeTo({pitch:e.getPitch()+5},r):(t.preventDefault(),e.panBy([0,-80],r));break;case 40:t.shiftKey?e.easeTo({pitch:Math.max(e.getPitch()-5,0)},r):(t.preventDefault(),e.panBy([0,80],r))}}}}},{}],418:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/browser\"),o=t(\"../../util/util\");e.exports=n;var s=\"undefined\"!=typeof navigator?navigator.userAgent.toLowerCase():\"\",l=-1!==s.indexOf(\"firefox\"),u=-1!==s.indexOf(\"safari\")&&-1===s.indexOf(\"chrom\");n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener(\"wheel\",this._onWheel,!1),this._el.addEventListener(\"mousewheel\",this._onWheel,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"wheel\",this._onWheel),this._el.removeEventListener(\"mousewheel\",this._onWheel),this._enabled=!1)},_onWheel:function(t){var e;\"wheel\"===t.type?(e=t.deltaY,l&&t.deltaMode===window.WheelEvent.DOM_DELTA_PIXEL&&(e/=a.devicePixelRatio),t.deltaMode===window.WheelEvent.DOM_DELTA_LINE&&(e*=40)):\"mousewheel\"===t.type&&(e=-t.wheelDeltaY,u&&(e/=3));var r=a.now(),n=r-(this._time||0);this._pos=i.mousePos(this._el,t),this._time=r,0!==e&&e%4.000244140625==0?(this._type=\"wheel\",e=Math.floor(e/4)):0!==e&&Math.abs(e)<4?this._type=\"trackpad\":n>400?(this._type=null,this._lastValue=e,this._timeout=setTimeout(this._onTimeout,40)):this._type||(this._type=Math.abs(n*e)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,e+=this._lastValue)),t.shiftKey&&e&&(e/=4),this._type&&this._zoom(-e,t),t.preventDefault()},_onTimeout:function(){this._type=\"wheel\",this._zoom(-this._lastValue)},_zoom:function(t,e){if(0!==t){var r=this._map,n=2/(1+Math.exp(-Math.abs(t/100)));t<0&&0!==n&&(n=1/n);var i=r.ease?r.ease.to:r.transform.scale,a=r.transform.scaleZoom(i*n);r.zoomTo(a,{duration:0,around:r.unproject(this._pos),delayEndEvents:200},{originalEvent:e})}}}},{\"../../util/browser\":426,\"../../util/dom\":428,\"../../util/util\":442}],419:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/util\");e.exports=n;var o=a.bezier(0,0,.15,1);n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener(\"touchstart\",this._onStart,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"touchstart\",this._onStart),this._enabled=!1)},disableRotation:function(){this._rotationDisabled=!0},enableRotation:function(){this._rotationDisabled=!1},_onStart:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]);this._startVec=e.sub(r),this._startScale=this._map.transform.scale,this._startBearing=this._map.transform.bearing,this._gestureIntent=void 0,this._inertia=[],\n",
+       "document.addEventListener(\"touchmove\",this._onMove,!1),document.addEventListener(\"touchend\",this._onEnd,!1)}},_onMove:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]),n=e.add(r).div(2),a=e.sub(r),o=a.mag()/this._startVec.mag(),s=this._rotationDisabled?0:180*a.angleWith(this._startVec)/Math.PI,l=this._map;if(this._gestureIntent){var u={duration:0,around:l.unproject(n)};\"rotate\"===this._gestureIntent&&(u.bearing=this._startBearing+s),\"zoom\"!==this._gestureIntent&&\"rotate\"!==this._gestureIntent||(u.zoom=l.transform.scaleZoom(this._startScale*o)),l.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),o,n]),l.easeTo(u,{originalEvent:t})}else{var c=Math.abs(1-o)>.15;Math.abs(s)>4?this._gestureIntent=\"rotate\":c&&(this._gestureIntent=\"zoom\"),this._gestureIntent&&(this._startVec=a,this._startScale=l.transform.scale,this._startBearing=l.transform.bearing)}t.preventDefault()}},_onEnd:function(t){document.removeEventListener(\"touchmove\",this._onMove),document.removeEventListener(\"touchend\",this._onEnd),this._drainInertiaBuffer();var e=this._inertia,r=this._map;if(e.length<2)return void r.snapToNorth({},{originalEvent:t});var n=e[e.length-1],i=e[0],a=r.transform.scaleZoom(this._startScale*n[1]),s=r.transform.scaleZoom(this._startScale*i[1]),l=a-s,u=(n[0]-i[0])/1e3,c=n[2];if(0===u||a===s)return void r.snapToNorth({},{originalEvent:t});var h=.15*l/u;Math.abs(h)>2.5&&(h=h>0?2.5:-2.5);var f=1e3*Math.abs(h/(12*.15)),d=a+h*f/2e3;d<0&&(d=0),r.easeTo({zoom:d,duration:f,easing:o,around:r.unproject(c)},{originalEvent:t})},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>2&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442}],420:[function(t,e,r){\"use strict\";function n(){i.bindAll([\"_onHashChange\",\"_updateHash\"],this)}e.exports=n;var i=t(\"../util/util\");n.prototype={addTo:function(t){return this._map=t,window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},remove:function(){return window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),delete this._map,this},_onHashChange:function(){var t=location.hash.replace(\"#\",\"\").split(\"/\");return t.length>=3&&(this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:+(t[3]||0)}),!0)},_updateHash:function(){var t=this._map.getCenter(),e=this._map.getZoom(),r=this._map.getBearing(),n=Math.max(0,Math.ceil(Math.log(e)/Math.LN2)),i=\"#\"+Math.round(100*e)/100+\"/\"+t.lat.toFixed(n)+\"/\"+t.lng.toFixed(n)+(r?\"/\"+Math.round(10*r)/10:\"\");window.history.replaceState(\"\",\"\",i)}}},{\"../util/util\":442}],421:[function(t,e,r){\"use strict\";function n(t){t.parentNode&&t.parentNode.removeChild(t)}var i=t(\"../util/canvas\"),a=t(\"../util/util\"),o=t(\"../util/browser\"),s=t(\"../util/browser\").window,l=t(\"../util/evented\"),u=t(\"../util/dom\"),c=t(\"../style/style\"),h=t(\"../style/animation_loop\"),f=t(\"../render/painter\"),d=t(\"../geo/transform\"),p=t(\"./hash\"),m=t(\"./bind_handlers\"),v=t(\"./camera\"),g=t(\"../geo/lng_lat\"),y=t(\"../geo/lng_lat_bounds\"),b=t(\"point-geometry\"),x=t(\"./control/attribution\"),_={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:0,maxZoom:20,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,workerCount:Math.max(o.hardwareConcurrency-1,1)},w=e.exports=function(t){if(t=a.extend({},_,t),t.workerCount<1)throw new Error(\"workerCount must an integer greater than or equal to 1.\");this._interactive=t.interactive,this._failIfMajorPerformanceCaveat=t.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=t.preserveDrawingBuffer,this._trackResize=t.trackResize,this._workerCount=t.workerCount,this._bearingSnap=t.bearingSnap,\"string\"==typeof t.container?this._container=document.getElementById(t.container):this._container=t.container,this.animationLoop=new h,this.transform=new d(t.minZoom,t.maxZoom),t.maxBounds&&this.setMaxBounds(t.maxBounds),a.bindAll([\"_forwardStyleEvent\",\"_forwardSourceEvent\",\"_forwardLayerEvent\",\"_forwardTileEvent\",\"_onStyleLoad\",\"_onStyleChange\",\"_onSourceAdd\",\"_onSourceRemove\",\"_onSourceUpdate\",\"_onWindowOnline\",\"_onWindowResize\",\"_update\",\"_render\"],this),this._setupContainer(),this._setupPainter(),this.on(\"move\",this._update.bind(this,!1)),this.on(\"zoom\",this._update.bind(this,!0)),this.on(\"moveend\",function(){this.animationLoop.set(300),this._rerender()}.bind(this)),void 0!==s&&(s.addEventListener(\"online\",this._onWindowOnline,!1),s.addEventListener(\"resize\",this._onWindowResize,!1)),m(this,t),this._hash=t.hash&&(new p).addTo(this),this._hash&&this._hash._onHashChange()||this.jumpTo({center:t.center,zoom:t.zoom,bearing:t.bearing,pitch:t.pitch}),this.stacks={},this._classes=[],this.resize(),t.classes&&this.setClasses(t.classes),t.style&&this.setStyle(t.style),t.attributionControl&&this.addControl(new x(t.attributionControl));var e=this.fire.bind(this,\"error\");this.on(\"style.error\",e),this.on(\"source.error\",e),this.on(\"tile.error\",e),this.on(\"layer.error\",e)};a.extend(w.prototype,l),a.extend(w.prototype,v.prototype),a.extend(w.prototype,{addControl:function(t){return t.addTo(this),this},addClass:function(t,e){return this._classes.indexOf(t)>=0||\"\"===t?this:(this._classes.push(t),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},removeClass:function(t,e){var r=this._classes.indexOf(t);return r<0||\"\"===t?this:(this._classes.splice(r,1),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},setClasses:function(t,e){for(var r={},n=0;n<t.length;n++)\"\"!==t[n]&&(r[t[n]]=!0);return this._classes=Object.keys(r),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0)},hasClass:function(t){return this._classes.indexOf(t)>=0},getClasses:function(){return this._classes},resize:function(){var t=0,e=0;return this._container&&(t=this._container.offsetWidth||400,e=this._container.offsetHeight||300),this._canvas.resize(t,e),this.transform.resize(t,e),this.painter.resize(t,e),this.fire(\"movestart\").fire(\"move\").fire(\"resize\").fire(\"moveend\")},getBounds:function(){var t=new y(this.transform.pointLocation(new b(0,0)),this.transform.pointLocation(this.transform.size));return(this.transform.angle||this.transform.pitch)&&(t.extend(this.transform.pointLocation(new b(this.transform.size.x,0))),t.extend(this.transform.pointLocation(new b(0,this.transform.size.y)))),t},setMaxBounds:function(t){if(t){var e=y.convert(t);this.transform.lngRange=[e.getWest(),e.getEast()],this.transform.latRange=[e.getSouth(),e.getNorth()],this.transform._constrain(),this._update()}else null!==t&&void 0!==t||(this.transform.lngRange=[],this.transform.latRange=[],this._update());return this},setMinZoom:function(t){if((t=null===t||void 0===t?0:t)>=0&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between 0 and the current maxZoom, inclusive\")},setMaxZoom:function(t){if((t=null===t||void 0===t?20:t)>=this.transform.minZoom&&t<=20)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be between the current minZoom and 20, inclusive\")},project:function(t){return this.transform.locationPoint(g.convert(t))},unproject:function(t){return this.transform.pointLocation(b.convert(t))},queryRenderedFeatures:function(){var t,e={};return 2===arguments.length?(t=arguments[0],e=arguments[1]):1===arguments.length&&function(t){return t instanceof b||Array.isArray(t)}(arguments[0])?t=arguments[0]:1===arguments.length&&(e=arguments[0]),this.style.queryRenderedFeatures(this._makeQueryGeometry(t),e,this.transform.zoom,this.transform.angle)},_makeQueryGeometry:function(t){void 0===t&&(t=[b.convert([0,0]),b.convert([this.transform.width,this.transform.height])]);var e;if(t instanceof b||\"number\"==typeof t[0])e=[b.convert(t)];else{var r=[b.convert(t[0]),b.convert(t[1])];e=[r[0],new b(r[1].x,r[0].y),r[1],new b(r[0].x,r[1].y),r[0]]}return e=e.map(function(t){return this.transform.pointCoordinate(t)}.bind(this))},querySourceFeatures:function(t,e){return this.style.querySourceFeatures(t,e)},setStyle:function(t){return this.style&&(this.style.off(\"load\",this._onStyleLoad).off(\"error\",this._forwardStyleEvent).off(\"change\",this._onStyleChange).off(\"source.add\",this._onSourceAdd).off(\"source.remove\",this._onSourceRemove).off(\"source.load\",this._onSourceUpdate).off(\"source.error\",this._forwardSourceEvent).off(\"source.change\",this._onSourceUpdate).off(\"layer.add\",this._forwardLayerEvent).off(\"layer.remove\",this._forwardLayerEvent).off(\"layer.error\",this._forwardLayerEvent).off(\"tile.add\",this._forwardTileEvent).off(\"tile.remove\",this._forwardTileEvent).off(\"tile.load\",this._update).off(\"tile.error\",this._forwardTileEvent).off(\"tile.stats\",this._forwardTileEvent)._remove(),this.off(\"rotate\",this.style._redoPlacement),this.off(\"pitch\",this.style._redoPlacement)),t?(this.style=t instanceof c?t:new c(t,this.animationLoop,this._workerCount),this.style.on(\"load\",this._onStyleLoad).on(\"error\",this._forwardStyleEvent).on(\"change\",this._onStyleChange).on(\"source.add\",this._onSourceAdd).on(\"source.remove\",this._onSourceRemove).on(\"source.load\",this._onSourceUpdate).on(\"source.error\",this._forwardSourceEvent).on(\"source.change\",this._onSourceUpdate).on(\"layer.add\",this._forwardLayerEvent).on(\"layer.remove\",this._forwardLayerEvent).on(\"layer.error\",this._forwardLayerEvent).on(\"tile.add\",this._forwardTileEvent).on(\"tile.remove\",this._forwardTileEvent).on(\"tile.load\",this._update).on(\"tile.error\",this._forwardTileEvent).on(\"tile.stats\",this._forwardTileEvent),this.on(\"rotate\",this.style._redoPlacement),this.on(\"pitch\",this.style._redoPlacement),this):(this.style=null,this)},getStyle:function(){if(this.style)return this.style.serialize()},addSource:function(t,e){return this.style.addSource(t,e),this._update(!0),this},addSourceType:function(t,e,r){return this.style.addSourceType(t,e,r)},removeSource:function(t){return this.style.removeSource(t),this._update(!0),this},getSource:function(t){return this.style.getSource(t)},addLayer:function(t,e){return this.style.addLayer(t,e),this._update(!0),this},removeLayer:function(t){return this.style.removeLayer(t),this._update(!0),this},getLayer:function(t){return this.style.getLayer(t)},setFilter:function(t,e){return this.style.setFilter(t,e),this._update(!0),this},setLayerZoomRange:function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0),this},getFilter:function(t){return this.style.getFilter(t)},setPaintProperty:function(t,e,r,n){return this.style.setPaintProperty(t,e,r,n),this._update(!0),this},getPaintProperty:function(t,e,r){return this.style.getPaintProperty(t,e,r)},setLayoutProperty:function(t,e,r){return this.style.setLayoutProperty(t,e,r),this._update(!0),this},getLayoutProperty:function(t,e){return this.style.getLayoutProperty(t,e)},getContainer:function(){return this._container},getCanvasContainer:function(){return this._canvasContainer},getCanvas:function(){return this._canvas.getElement()},_setupContainer:function(){var t=this._container;t.classList.add(\"mapboxgl-map\");var e=this._canvasContainer=u.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=new i(this,e);var r=this._controlContainer=u.create(\"div\",\"mapboxgl-control-container\",t),n=this._controlCorners={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(t){n[t]=u.create(\"div\",\"mapboxgl-ctrl-\"+t,r)})},_setupPainter:function(){var t=this._canvas.getWebGLContext({failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer});if(!t)return void this.fire(\"error\",{error:new Error(\"Failed to initialize WebGL\")});this.painter=new f(t,this.transform)},_contextLost:function(t){t.preventDefault(),this._frameId&&o.cancelFrame(this._frameId),this.fire(\"webglcontextlost\",{originalEvent:t})},_contextRestored:function(t){this._setupPainter(),this.resize(),this._update(),this.fire(\"webglcontextrestored\",{originalEvent:t})},loaded:function(){return!this._styleDirty&&!this._sourcesDirty&&!(!this.style||!this.style.loaded())},_update:function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this._rerender(),this):this},_render:function(){try{this.style&&this._styleDirty&&(this._styleDirty=!1,this.style.update(this._classes,this._classOptions),this._classOptions=null,this.style._recalculate(this.transform.zoom)),this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.painter.render(this.style,{debug:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,vertices:this.vertices,rotating:this.rotating,zooming:this.zooming}),this.fire(\"render\"),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(\"load\")),this._frameId=null,this.animationLoop.stopped()||(this._styleDirty=!0),(this._sourcesDirty||this._repaint||this._styleDirty)&&this._rerender()}catch(t){this.fire(\"error\",{error:t})}return this},remove:function(){this._hash&&this._hash.remove(),o.cancelFrame(this._frameId),this.setStyle(null),void 0!==s&&s.removeEventListener(\"resize\",this._onWindowResize,!1);var t=this.painter.gl.getExtension(\"WEBGL_lose_context\");t&&t.loseContext(),n(this._canvasContainer),n(this._controlContainer),this._container.classList.remove(\"mapboxgl-map\")},_rerender:function(){this.style&&!this._frameId&&(this._frameId=o.frame(this._render))},_forwardStyleEvent:function(t){this.fire(\"style.\"+t.type,a.extend({style:t.target},t))},_forwardSourceEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_forwardLayerEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_forwardTileEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_onStyleLoad:function(t){this.transform.unmodified&&this.jumpTo(this.style.stylesheet),this.style.update(this._classes,{transition:!1}),this._forwardStyleEvent(t)},_onStyleChange:function(t){this._update(!0),this._forwardStyleEvent(t)},_onSourceAdd:function(t){var e=t.source;e.onAdd&&e.onAdd(this),this._forwardSourceEvent(t)},_onSourceRemove:function(t){var e=t.source;e.onRemove&&e.onRemove(this),this._forwardSourceEvent(t)},_onSourceUpdate:function(t){this._update(),this._forwardSourceEvent(t)},_onWindowOnline:function(){this._update()},_onWindowResize:function(){this._trackResize&&this.stop().resize()._update()}}),a.extendAll(w.prototype,{_showTileBoundaries:!1,get showTileBoundaries(){return this._showTileBoundaries},set showTileBoundaries(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},_showCollisionBoxes:!1,get showCollisionBoxes(){return this._showCollisionBoxes},set showCollisionBoxes(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,this.style._redoPlacement())},_showOverdrawInspector:!1,get showOverdrawInspector(){return this._showOverdrawInspector},set showOverdrawInspector(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},_repaint:!1,get repaint(){return this._repaint},set repaint(t){this._repaint=t,this._update()},_vertices:!1,get vertices(){return this._vertices},set vertices(t){this._vertices=t,this._update()}})},{\"../geo/lng_lat\":339,\"../geo/lng_lat_bounds\":340,\"../geo/transform\":341,\"../render/painter\":355,\"../style/animation_loop\":375,\"../style/style\":378,\"../util/browser\":426,\"../util/canvas\":427,\"../util/dom\":428,\"../util/evented\":434,\"../util/util\":442,\"./bind_handlers\":407,\"./camera\":408,\"./control/attribution\":409,\"./hash\":420,\"point-geometry\":484}],422:[function(t,e,r){\"use strict\";function n(t,e){t||(t=i.create(\"div\")),t.classList.add(\"mapboxgl-marker\"),this._el=t,this._offset=o.convert(e&&e.offset||[0,0]),this._update=this._update.bind(this)}e.exports=n;var i=t(\"../util/dom\"),a=t(\"../geo/lng_lat\"),o=t(\"point-geometry\");n.prototype={addTo:function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._el),t.on(\"move\",this._update),this._update(),this},remove:function(){this._map&&(this._map.off(\"move\",this._update),this._map=null);var t=this._el.parentNode;return t&&t.removeChild(this._el),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=a.convert(t),this._update(),this},getElement:function(){return this._el},_update:function(){if(this._map){var t=this._map.project(this._lngLat)._add(this._offset);i.setTransform(this._el,\"translate(\"+t.x+\"px,\"+t.y+\"px)\")}}}},{\"../geo/lng_lat\":339,\"../util/dom\":428,\"point-geometry\":484}],423:[function(t,e,r){\"use strict\";function n(t){i.setOptions(this,t),i.bindAll([\"_update\",\"_onClickClose\"],this)}e.exports=n;var i=t(\"../util/util\"),a=t(\"../util/evented\"),o=t(\"../util/dom\"),s=t(\"../geo/lng_lat\");n.prototype=i.inherit(a,{options:{closeButton:!0,closeOnClick:!0},addTo:function(t){return this._map=t,this._map.on(\"move\",this._update),this.options.closeOnClick&&this._map.on(\"click\",this._onClickClose),this._update(),this},remove:function(){return this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._container&&(this._container.parentNode.removeChild(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"click\",this._onClickClose),delete this._map),this.fire(\"close\"),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=s.convert(t),this._update(),this},setText:function(t){return this.setDOMContent(document.createTextNode(t))},setHTML:function(t){var e,r=document.createDocumentFragment(),n=document.createElement(\"body\");for(n.innerHTML=t;;){if(!(e=n.firstChild))break;r.appendChild(e)}return this.setDOMContent(r)},setDOMContent:function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},_createContent:function(){this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._content=o.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=o.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClickClose))},_update:function(){if(this._map&&this._lngLat&&this._content){this._container||(this._container=o.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=o.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content));var t=this._map.project(this._lngLat).round(),e=this.options.anchor;if(!e){var 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s=0,l=0;l<3;++l)s+=Math.pow(r[l]-e[l],2);this.radius.set(t,.5*Math.log(Math.max(s,1e-6))),this.center.set(t,r[0],r[1],r[2])},d.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},d.setMatrix=function(t,e){var r=this.computedRotation;f(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),a(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;h(n,e);var i=n[15];if(Math.abs(i)>1e-6){var o=n[12]/i,s=n[13]/i,l=n[14]/i;this.recalcMatrix(t);var u=Math.exp(this.computedRadius[0]);this.center.set(t,o-n[2]*u,s-n[6]*u,l-n[10]*u),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},d.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},d.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},d.getDistanceLimits=function(t){var e=this.radius.bounds;return 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a=i.viewportWidth,o=i.viewportHeight;t.width=t.width||a-t.xOffset,t.height=t.height||o-t.yOffset,t.needsCopy=!0,de(t.xOffset>=0&&t.xOffset<a&&t.yOffset>=0&&t.yOffset<o&&t.width>0&&t.width<=a&&t.height>0&&t.height<=o,\"copy texture read out of bounds\")}else if(n){if(Qt(n))t.channels=t.channels||4,t.data=n,\"type\"in e||t.type!==Or||(t.type=At(n));else if(gt(n))t.channels=t.channels||4,Tt(t,n),t.alignment=1,t.needsFree=!0;else if(Z(n)){var s=n.data;Array.isArray(s)||t.type!==Or||(t.type=At(s));var l,c,h,f,d,p,m=n.shape,v=n.stride;3===m.length?(h=m[2],p=v[2]):(de(2===m.length,\"invalid ndarray pixel data, must be 2 or 3D\"),h=1,p=1),l=m[0],c=m[1],f=v[0],d=v[1],t.alignment=1,t.width=l,t.height=c,t.channels=h,t.format=t.internalformat=hn[h],t.needsFree=!0,Lt(t,s,f,d,p,n.offset)}else if(xt(n)||_t(n))xt(n)?t.element=n:t.element=n.canvas,t.width=t.element.width,t.height=t.element.height,t.channels=4;else if(wt(n))t.element=n,t.width=n.naturalWidth,t.height=n.naturalHeight,t.channels=4;else if(Mt(n))t.element=n,t.width=n.videoWidth,t.height=n.videoHeight,t.channels=4;else if(yt(n)){var g=t.width||n[0].length,y=t.height||n.length,b=t.channels;b=mt(n[0][0])?b||n[0][0].length:b||1;for(var x=Ce.shape(n),_=1,w=0;w<x.length;++w)_*=x[w];var M=St(t,_);Ce.flatten(n,x,\"\",M),Et(t,M),t.alignment=1,t.width=g,t.height=y,t.channels=b,t.format=t.internalformat=hn[b],t.needsFree=!0}}else t.width=t.width||1,t.height=t.height||1,t.channels=t.channels||4;t.type===jr?de(r.extensions.indexOf(\"oes_texture_float\")>=0,\"oes_texture_float extension not enabled\"):t.type===wr&&de(r.extensions.indexOf(\"oes_texture_half_float\")>=0,\"oes_texture_half_float extension not enabled\")}function d(e,r,i){var a=e.element,o=e.data,s=e.internalformat,l=e.format,u=e.type,h=e.width,f=e.height;c(e),a?t.texImage2D(r,i,l,l,u,a):e.compressed?t.compressedTexImage2D(r,i,s,h,f,0,o):e.needsCopy?(n(),t.copyTexImage2D(r,i,l,e.xOffset,e.yOffset,h,f,0)):t.texImage2D(r,i,l,h,f,0,l,u,o)}function p(e,r,i,a,o){var s=e.element,l=e.data,u=e.internalformat,h=e.format,f=e.type,d=e.width,p=e.height;c(e),s?t.texSubImage2D(r,o,i,a,h,f,s):e.compressed?t.compressedTexSubImage2D(r,o,i,a,u,d,p,l):e.needsCopy?(n(),t.copyTexSubImage2D(r,o,i,a,e.xOffset,e.yOffset,d,p)):t.texSubImage2D(r,o,i,a,d,p,h,f,l)}function m(){return K.pop()||new h}function v(t){t.needsFree&&Le.freeType(t.data),h.call(t),K.push(t)}function g(){s.call(this),this.genMipmaps=!1,this.mipmapHint=$r,this.mipmask=0,this.images=Array(16)}function y(t,e,r){var n=t.images[0]=m();t.mipmask=1,n.width=t.width=e,n.height=t.height=r,n.channels=t.channels=4}function b(t,e){var r=null;if(kt(e))r=t.images[0]=m(),l(r,t),f(r,e),t.mipmask=1;else if(u(t,e),Array.isArray(e.mipmap))for(var n=e.mipmap,i=0;i<n.length;++i)r=t.images[i]=m(),l(r,t),r.width>>=i,r.height>>=i,f(r,n[i]),t.mipmask|=1<<i;else 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r=0;r<32;++r)if(0!=(e.mipmask&1<<r))if(e.target===nr)t.texImage2D(nr,r,e.internalformat,e.width>>r,e.height>>r,0,e.internalformat,e.type,null);else for(var n=0;n<6;++n)t.texImage2D(ar+n,r,e.internalformat,e.width>>r,e.height>>r,0,e.internalformat,e.type,null);A(e.texInfo,e.target)})}var P={\"don't care\":$r,\"dont care\":$r,nice:en,fast:tn},O={repeat:Ur,clamp:Vr,mirror:Hr},R={nearest:Yr,linear:Wr},F=$t({mipmap:Kr,\"nearest mipmap nearest\":Xr,\"linear mipmap nearest\":Zr,\"nearest mipmap linear\":Jr,\"linear mipmap linear\":Kr},R),j={none:0,browser:ln},N={uint8:Or,rgba4:pr,rgb565:vr,\"rgb5 a1\":mr},B={alpha:sr,luminance:ur,\"luminance alpha\":cr,rgb:lr,rgba:or,rgba4:hr,\"rgb5 a1\":fr,rgb565:dr},U={};e.ext_srgb&&(B.srgb=xr,B.srgba=_r),e.oes_texture_float&&(N.float32=N.float=jr),e.oes_texture_half_float&&(N.float16=N[\"half float\"]=wr),e.webgl_depth_texture&&($t(B,{depth:yr,\"depth stencil\":br}),$t(N,{uint16:Rr,uint32:Fr,\"depth 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i=this.computedMatrix;m[0]=i[2],m[1]=i[6],m[2]=i[10];for(var a=this.computedUp,o=this.computedRight,s=this.computedToward,l=0;l<3;++l)i[4*l]=a[l],i[4*l+1]=o[l],i[4*l+2]=s[l];c(i,i,n,m);for(var l=0;l<3;++l)a[l]=i[4*l],o[l]=i[4*l+1];this.up.set(t,a[0],a[1],a[2]),this.right.set(t,o[0],o[1],o[2])}},p.pan=function(t,e,r,i){e=e||0,r=r||0,i=i||0,this.recalcMatrix(t);var a=this.computedMatrix,o=(Math.exp(this.computedRadius[0]),a[1]),s=a[5],l=a[9],u=n(o,s,l);o/=u,s/=u,l/=u;var c=a[0],h=a[4],f=a[8],d=c*o+h*s+f*l;c-=o*d,h-=s*d,f-=l*d;var p=n(c,h,f);c/=p,h/=p,f/=p;var m=c*e+o*r,v=h*e+s*r,g=f*e+l*r;this.center.move(t,m,v,g);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+i),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,a){var o=1;\"number\"==typeof r&&(o=0|r),(o<0||o>3)&&(o=1);var s=(o+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var l=e[o],c=e[o+4],h=e[o+8];if(a){var 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this._validate(t,this.minMonth,this.minDay,i.local.invalidYear||i.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate);return t=n.year(),t<0&&t++,n.day()+30*(n.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),i.calendars.ethiopian=n},{\"../main\":581,\"object-assign\":470}],571:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function i(t,e){return t-e*Math.floor(t/e)}var a=t(\"../main\"),o=t(\"object-assign\");n.prototype=new a.baseCalendar,o(n.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom 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t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,a.local.invalidMonth),12===e&&this.leapYear(t)?30:8===e&&5===i(this.daysInYear(t),10)?30:9===e&&3===i(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var n=this._validate(t,e,r,a.local.invalidDate);return{yearType:(this.leapYear(n)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(n)%10-3]}},toJD:function(t,e,r){var n=this._validate(t,e,r,a.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var i=t<=0?t+1:t,o=this.jdEpoch+this._delay1(i)+this._delay2(i)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(var s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(var s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return i(3*(n+1),7)<3&&n++,n},_delay2:function(t){var 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this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,o.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if(t=t.split(\".\"),t.length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,o.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate),!0},extraInfo:function(t,e,r){var n=this._validate(t,e,r,o.local.invalidDate),i=n.toJD(),a=this._toHaab(i),s=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[s[0]-1],tzolkinDay:s[0],tzolkinTrecena:s[1]}},_toHaab:function(t){t-=this.jdEpoch;var e=i(t+8+340,365);return[Math.floor(e/20)+1,i(e,20)]},_toTzolkin:function(t){return t-=this.jdEpoch,[a(t+20,20),a(t+4,13)]},toJD:function(t,e,r){var n=this._validate(t,e,r,o.local.invalidDate);return n.day()+20*n.month()+360*n.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),o.calendars.mayan=n},{\"../main\":581,\"object-assign\":470}],575:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\");n.prototype=new i.baseCalendar;var o=i.instance(\"gregorian\");a(n.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var 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i.baseCalendar,a(n.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,i.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var a=i.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,\n",
+       "s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=i.instance(),r=e.fromJD(t),n=r.year(),a=r.dayOfYear(),o=n+56;this._createMissingCalendarData(o);for(var s=9,l=this.NEPALI_CALENDAR_DATA[o][0],u=this.NEPALI_CALENDAR_DATA[o][s]-l+1;a>u;)s++,s>12&&(s=1,o++),u+=this.NEPALI_CALENDAR_DATA[o][s];var c=this.NEPALI_CALENDAR_DATA[o][s]-(u-a);return this.newDate(o,s,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29,29,30,30],2077:[17,31,32,31,32,31,30,30,30,29,30,29,31],2078:[17,31,31,31,32,31,31,30,29,30,29,30,30],2079:[17,31,31,32,31,31,31,30,29,30,29,30,30],2080:[16,31,32,31,32,31,30,30,30,29,29,30,30],2081:[17,31,31,32,32,31,30,30,30,29,30,30,30],2082:[17,31,32,31,32,31,30,30,30,29,30,30,30],2083:[17,31,31,32,31,31,30,30,30,29,30,30,30],2084:[17,31,31,32,31,31,30,30,30,29,30,30,30],2085:[17,31,32,31,32,31,31,30,30,29,30,30,30],2086:[17,31,32,31,32,31,30,30,30,29,30,30,30],2087:[16,31,31,32,31,31,31,30,30,29,30,30,30],2088:[16,30,31,32,32,30,31,30,30,29,30,30,30],2089:[17,31,32,31,32,31,30,30,30,29,30,30,30],2090:[17,31,32,31,32,31,30,30,30,29,30,30,30],2091:[16,31,31,32,31,31,31,30,30,29,30,30,30],2092:[16,31,31,32,32,31,30,30,30,29,30,30,30],2093:[17,31,32,31,32,31,30,30,30,29,30,30,30],2094:[17,31,31,32,31,31,30,30,30,29,30,30,30],2095:[17,31,31,32,31,31,31,30,29,30,30,30,30],2096:[17,30,31,32,32,31,30,30,29,30,29,30,30],2097:[17,31,32,31,32,31,30,30,30,29,30,30,30],2098:[17,31,31,32,31,31,31,29,30,29,30,30,31],2099:[17,31,31,32,31,31,31,30,29,29,30,30,30],2100:[17,31,32,31,32,30,31,30,29,30,29,30,30]}}),i.calendars.nepali=n},{\"../main\":581,\"object-assign\":470}],577:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function i(t,e){return t-e*Math.floor(t/e)}var a=t(\"../main\"),o=t(\"object-assign\");n.prototype=new a.baseCalendar,o(n.prototype,{name:\"Persian\",jdEpoch:1948320.5,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Persian\",epochs:[\"BP\",\"AP\"],monthNames:[\"Farvardin\",\"Ordibehesht\",\"Khordad\",\"Tir\",\"Mordad\",\"Shahrivar\",\"Mehr\",\"Aban\",\"Azar\",\"Day\",\"Bahman\",\"Esfand\"],monthNamesShort:[\"Far\",\"Ord\",\"Kho\",\"Tir\",\"Mor\",\"Sha\",\"Meh\",\"Aba\",\"Aza\",\"Day\",\"Bah\",\"Esf\"],dayNames:[\"Yekshambe\",\"Doshambe\",\"Seshambe\",\"Ch\\xe6harshambe\",\"Panjshambe\",\"Jom'e\",\"Shambe\"],dayNamesShort:[\"Yek\",\"Do\",\"Se\",\"Ch\\xe6\",\"Panj\",\"Jom\",\"Sha\"],dayNamesMin:[\"Ye\",\"Do\",\"Se\",\"Ch\",\"Pa\",\"Jo\",\"Sh\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,a.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,a.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var n=this._validate(t,e,r,a.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var o=t-(t>=0?474:473),s=474+i(o,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(o/2820)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=t-this.toJD(475,1,1),r=Math.floor(e/1029983),n=i(e,1029983),a=2820;if(1029982!==n){var o=Math.floor(n/366),s=i(n,366);a=Math.floor((2134*o+2816*s+2815)/1028522)+o+1}var l=a+2820*r+474;l=l<=0?l-1:l;var u=t-this.toJD(l,1,1)+1,c=u<=186?Math.ceil(u/31):Math.ceil((u-6)/30),h=t-this.toJD(l,c,1)+1;return this.newDate(l,c,h)}}),a.calendars.persian=n,a.calendars.jalali=n},{\"../main\":581,\"object-assign\":470}],578:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\"),o=i.instance();n.prototype=new i.baseCalendar,a(n.prototype,{name:\"Taiwan\",jdEpoch:2419402.5,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(e.year());return o.leapYear(t)},weekOfYear:function(t,e,r){var n=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(n.year());return o.weekOfYear(t,n.month(),n.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate),t=this._t2gYear(n.year());return o.toJD(t,n.month(),n.day())},fromJD:function(t){var e=o.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),i.calendars.taiwan=n},{\"../main\":581,\"object-assign\":470}],579:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\"),o=i.instance();n.prototype=new i.baseCalendar,a(n.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(e.year());return o.leapYear(t)},weekOfYear:function(t,e,r){var n=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(n.year());return o.weekOfYear(t,n.month(),n.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate),t=this._t2gYear(n.year());return o.toJD(t,n.month(),n.day())},fromJD:function(t){var e=o.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),i.calendars.thai=n},{\"../main\":581,\"object-assign\":470}],580:[function(t,e,r){function n(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\");n.prototype=new i.baseCalendar,a(n.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear);return 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n(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function i(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function a(t,e){return t=\"\"+t,\"000000\".substring(0,e-t.length)+t}function o(){this.shortYearCutoff=\"+10\"}function s(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}var l=t(\"object-assign\");l(n.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance(),n.newDate(t,e,r)},substituteDigits:function(t){\n",
+       "return function(e){return(e+\"\").replace(/[0-9]/g,function(e){return t[e]})}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),l(i.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+a(Math.abs(this.year()),4)+\"-\"+a(this.month(),2)+\"-\"+a(this.day(),2)}}),l(o.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new i(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+a(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return 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o=i[e];o.anchor&&\"free\"!==o.anchor&&(o.anchor=f.cleanId(o.anchor)),o.overlaying&&(o.overlaying=f.cleanId(o.overlaying)),o.type||(o.isdate?o.type=\"date\":o.islog?o.type=\"log\":!1===o.isdate&&!1===o.islog&&(o.type=\"linear\")),\"withzero\"!==o.autorange&&\"tozero\"!==o.autorange||(o.autorange=!0,o.rangemode=\"tozero\"),delete o.islog,delete o.isdate,delete o.categories,a(o,\"domain\")&&delete o.domain,void 0!==o.autotick&&(void 0===o.tickmode&&(o.tickmode=o.autotick?\"auto\":\"linear\"),delete o.autotick)}var s=Array.isArray(t.annotations)?t.annotations.length:0;for(e=0;e<s;e++){var u=t.annotations[e];c.isPlainObject(u)&&(u.ref&&(\"paper\"===u.ref?(u.xref=\"paper\",u.yref=\"paper\"):\"data\"===u.ref&&(u.xref=\"x\",u.yref=\"y\"),delete u.ref),n(u,\"xref\"),n(u,\"yref\"))}var p=Array.isArray(t.shapes)?t.shapes.length:0;for(e=0;e<p;e++){var m=t.shapes[e];c.isPlainObject(m)&&(n(m,\"xref\"),n(m,\"yref\"))}var v=t.legend;v&&(v.x>3?(v.x=1.02,v.xanchor=\"left\"):v.x<-2&&(v.x=-.02,v.xanchor=\"right\"),v.y>3?(v.y=1.02,v.yanchor=\"bottom\"):v.y<-2&&(v.y=-.02,v.yanchor=\"top\")),\"rotate\"===t.dragmode&&(t.dragmode=\"orbit\"),t.scene1&&(t.scene||(t.scene=t.scene1),delete t.scene1);var g=h.getSubplotIds(t,\"gl3d\");for(e=0;e<g.length;e++){var y=t[g[e]],b=y.cameraposition;if(Array.isArray(b)&&4===b[0].length){var x=b[0],_=b[1],w=b[2],M=l([],x),k=[];for(r=0;r<3;++r)k[r]=_[e]+w*M[2+4*r];y.camera={eye:{x:k[0],y:k[1],z:k[2]},center:{x:_[0],y:_[1],z:_[2]},up:{x:M[1],y:M[5],z:M[9]}},delete y.cameraposition}}return d.clean(t),t},r.cleanData=function(t,e){for(var n=[],o=(t.concat(Array.isArray(e)?e:[]).filter(function(t){return\"uid\"in t}).map(function(t){return t.uid})),s=0;s<t.length;s++){var l,p=t[s];if(!(\"uid\"in p)||-1!==n.indexOf(p.uid)){var m;for(l=0;l<100&&(m=c.randstr(o),-1!==n.indexOf(m));l++);p.uid=c.randstr(o),o.push(p.uid)}if(n.push(p.uid),\"histogramy\"===p.type&&\"xbins\"in p&&!(\"ybins\"in p)&&(p.ybins=p.xbins,delete p.xbins),p.error_y&&\"opacity\"in p.error_y){var v=d.defaults,g=p.error_y.color||(u.traceIs(p,\"bar\")?d.defaultLine:v[s%v.length]);p.error_y.color=d.addOpacity(d.rgb(g),d.opacity(g)*p.error_y.opacity),delete p.error_y.opacity}if(\"bardir\"in p&&(\"h\"!==p.bardir||!u.traceIs(p,\"bar\")&&\"histogram\"!==p.type.substr(0,9)||(p.orientation=\"h\",r.swapXYData(p)),delete p.bardir),\"histogramy\"===p.type&&r.swapXYData(p),\"histogramx\"!==p.type&&\"histogramy\"!==p.type||(p.type=\"histogram\"),\"scl\"in p&&(p.colorscale=p.scl,delete p.scl),\"reversescl\"in p&&(p.reversescale=p.reversescl,delete p.reversescl),p.xaxis&&(p.xaxis=f.cleanId(p.xaxis,\"x\")),p.yaxis&&(p.yaxis=f.cleanId(p.yaxis,\"y\")),u.traceIs(p,\"gl3d\")&&p.scene&&(p.scene=h.subplotsRegistry.gl3d.cleanId(p.scene)),u.traceIs(p,\"pie\")||u.traceIs(p,\"bar\")||(Array.isArray(p.textposition)?p.textposition=p.textposition.map(i):p.textposition&&(p.textposition=i(p.textposition))),u.traceIs(p,\"2dMap\")&&(\"YIGnBu\"===p.colorscale&&(p.colorscale=\"YlGnBu\"),\"YIOrRd\"===p.colorscale&&(p.colorscale=\"YlOrRd\")),u.traceIs(p,\"markerColorscale\")&&p.marker){var y=p.marker;\"YIGnBu\"===y.colorscale&&(y.colorscale=\"YlGnBu\"),\"YIOrRd\"===y.colorscale&&(y.colorscale=\"YlOrRd\")}if(\"surface\"===p.type&&c.isPlainObject(p.contours)){var b=[\"x\",\"y\",\"z\"];for(l=0;l<b.length;l++){var x=p.contours[b[l]];c.isPlainObject(x)&&(x.highlightColor&&(x.highlightcolor=x.highlightColor,delete x.highlightColor),x.highlightWidth&&(x.highlightwidth=x.highlightWidth,delete x.highlightWidth))}}if(Array.isArray(p.transforms)){var _=p.transforms;for(l=0;l<_.length;l++){var w=_[l];if(c.isPlainObject(w))switch(w.type){case\"filter\":w.filtersrc&&(w.target=w.filtersrc,delete w.filtersrc),w.calendar&&(w.valuecalendar||(w.valuecalendar=w.calendar),delete w.calendar);break;case\"groupby\":if(w.styles=w.styles||w.style,w.styles&&!Array.isArray(w.styles)){var M=w.styles,k=Object.keys(M);w.styles=[];for(var A=0;A<k.length;A++)w.styles.push({target:k[A],value:M[k[A]]})}}}}a(p,\"line\")&&delete p.line,\"marker\"in p&&(a(p.marker,\"line\")&&delete p.marker.line,a(p,\"marker\")&&delete p.marker),d.clean(p)}},r.swapXYData=function(t){var e;if(c.swapAttrs(t,[\"?\",\"?0\",\"d?\",\"?bins\",\"nbins?\",\"autobin?\",\"?src\",\"error_?\"]),Array.isArray(t.z)&&Array.isArray(t.z[0])&&(t.transpose?delete t.transpose:t.transpose=!0),t.error_x&&t.error_y){var r=t.error_y,n=\"copy_ystyle\"in r?r.copy_ystyle:!(r.color||r.thickness||r.width);c.swapAttrs(t,[\"error_?.copy_ystyle\"]),n&&c.swapAttrs(t,[\"error_?.color\",\"error_?.thickness\",\"error_?.width\"])}if(\"string\"==typeof t.hoverinfo){var i=t.hoverinfo.split(\"+\");for(e=0;e<i.length;e++)\"x\"===i[e]?i[e]=\"y\":\"y\"===i[e]&&(i[e]=\"x\");t.hoverinfo=i.join(\"+\")}},r.coerceTraceIndices=function(t,e){return s(e)?[e]:Array.isArray(e)&&e.length?e:t.data.map(function(t,e){return e})},r.manageArrayContainers=function(t,e,r){var n=t.obj,i=t.parts,a=i.length,o=i[a-1],l=s(o);if(l&&null===e){var u=i.slice(0,a-1).join(\".\");c.nestedProperty(n,u).get().splice(o,1)}else l&&void 0===t.get()?(void 0===t.get()&&(r[t.astr]=null),t.set(e)):t.set(e)};var p=/(\\.[^\\[\\]\\.]+|\\[[^\\[\\]\\.]+\\])$/;r.hasParent=function(t,e){for(var r=o(e);r;){if(r in t)return!0;r=o(r)}return!1};var m=[\"x\",\"y\",\"z\"];r.clearAxisTypes=function(t,e,r){for(var n=0;n<e.length;n++)for(var i=t._fullData[n],a=0;a<3;a++){var o=f.getFromTrace(t,i,m[a]);if(o&&\"log\"!==o.type){var s=o._name,l=o._id.substr(1);if(\"scene\"===l.substr(0,5)){if(void 0!==r[l])continue;s=l+\".\"+s}var u=s+\".type\";void 0===r[s]&&void 0===r[u]&&c.nestedProperty(t.layout,u).set(null)}}}},{\"../components/color\":604,\"../lib\":728,\"../plots/cartesian/axes\":772,\"../plots/plots\":831,\"../registry\":846,\"fast-isnumeric\":131,\"gl-mat4/fromQuat\":178}],758:[function(t,e,r){\"use strict\";var n=t(\"../lib/nested_property\"),i=t(\"../lib/is_plain_object\"),a=t(\"../lib/noop\"),o=t(\"../lib/loggers\"),s=t(\"../lib/search\").sorterAsc,l=t(\"../registry\");r.containerArrayMatch=t(\"./container_array_match\");var u=r.isAddVal=function(t){return\"add\"===t||i(t)},c=r.isRemoveVal=function(t){return null===t||\"remove\"===t};r.applyContainerArrayChanges=function(t,e,r,i){var h=e.astr,f=l.getComponentMethod(h,\"supplyLayoutDefaults\"),d=l.getComponentMethod(h,\"draw\"),p=l.getComponentMethod(h,\"drawOne\"),m=i.replot||i.recalc||f===a||d===a,v=t.layout,g=t._fullLayout;if(r[\"\"]){Object.keys(r).length>1&&o.warn(\"Full array edits are incompatible with other edits\",h);var y=r[\"\"][\"\"];if(c(y))e.set(null);else{if(!Array.isArray(y))return o.warn(\"Unrecognized full array edit value\",h,y),!0;e.set(y)}return!m&&(f(v,g),d(t),!0)}var b,x,_,w,M,k,A,T=Object.keys(r).map(Number).sort(s),S=e.get(),E=S||[],L=n(g,h).get(),C=[],I=-1,z=E.length;for(b=0;b<T.length;b++)if(_=T[b],w=r[_],M=Object.keys(w),k=w[\"\"],A=u(k),_<0||_>E.length-(A?0:1))o.warn(\"index out of range\",h,_);else if(void 0!==k)M.length>1&&o.warn(\"Insertion & removal are incompatible with edits to the same index.\",h,_),c(k)?C.push(_):A?(\"add\"===k&&(k={}),E.splice(_,0,k),L&&L.splice(_,0,{})):o.warn(\"Unrecognized full object edit value\",h,_,k),-1===I&&(I=_);else for(x=0;x<M.length;x++)n(E[_],M[x]).set(w[M[x]]);for(b=C.length-1;b>=0;b--)E.splice(C[b],1),L&&L.splice(C[b],1);if(E.length?S||e.set(E):e.set(null),m)return!1;if(f(v,g),p!==a){var D;if(-1===I)D=T;else{for(z=Math.max(E.length,z),D=[],b=0;b<T.length&&!((_=T[b])>=I);b++)D.push(_);for(b=I;b<z;b++)D.push(b)}for(b=0;b<D.length;b++)p(t,D[b])}else d(t);return!0}},{\"../lib/is_plain_object\":730,\"../lib/loggers\":732,\"../lib/nested_property\":735,\"../lib/noop\":736,\"../lib/search\":745,\"../registry\":846,\"./container_array_match\":755}],759:[function(t,e,r){\"use strict\";function n(t,e){try{t._fullLayout._paper.style(\"background\",e)}catch(t){w.error(t)}}function i(t,e){n(t,I.combine(e,\"white\"))}function a(t,e){t._context||(t._context=w.extendDeep({},_.defaultConfig));var r,a,o,s=t._context;if(e){for(a=Object.keys(e),r=0;r<a.length;r++)\"editable\"!==(o=a[r])&&\"edits\"!==o&&o in s&&(\"setBackground\"===o&&\"opaque\"===e[o]?s[o]=i:s[o]=e[o]);e.plot3dPixelRatio&&!s.plotGlPixelRatio&&(s.plotGlPixelRatio=s.plot3dPixelRatio);var l=e.editable;if(void 0!==l)for(s.editable=l,a=Object.keys(s.edits),r=0;r<a.length;r++)s.edits[a[r]]=l;if(e.edits)for(a=Object.keys(e.edits),r=0;r<a.length;r++)(o=a[r])in s.edits&&(s.edits[o]=e.edits[o])}s.staticPlot&&(s.editable=!1,s.edits={},s.autosizable=!1,s.scrollZoom=!1,s.doubleClick=!1,s.showTips=!1,s.showLink=!1,s.displayModeBar=!1),\"hover\"!==s.displayModeBar||x||(s.displayModeBar=!0),\"transparent\"!==s.setBackground&&\"function\"==typeof s.setBackground||(s.setBackground=n)}function o(t,e,r){var n=y.select(t).selectAll(\".plot-container\").data([0]);n.enter().insert(\"div\",\":first-child\").classed(\"plot-container plotly\",!0);var i=n.selectAll(\".svg-container\").data([0]);i.enter().append(\"div\").classed(\"svg-container\",!0).style(\"position\",\"relative\"),i.html(\"\"),e&&(t.data=e),r&&(t.layout=r),E.manager.fillLayout(t),i.style({width:t._fullLayout.width+\"px\",height:t._fullLayout.height+\"px\"}),t.framework=E.manager.framework(t),t.framework({data:t.data,layout:t.layout},i.node()),t.framework.setUndoPoint();var a=t.framework.svg(),o=1,s=t._fullLayout.title;\"\"!==s&&s||(o=0);var l=function(){this.call(P.convertToTspans,t)},u=a.select(\".title-group text\").call(l);if(t._context.edits.titleText){s&&\"Click to enter title\"!==s||(o=.2,u.attr({\"data-unformatted\":\"Click to enter title\"}).text(\"Click to enter title\").style({opacity:o}).on(\"mouseover.opacity\",function(){y.select(this).transition().duration(100).style(\"opacity\",1)}).on(\"mouseout.opacity\",function(){y.select(this).transition().duration(1e3).style(\"opacity\",0)}));var c=function(){this.call(P.makeEditable,{gd:t}).on(\"edit\",function(e){t.framework({layout:{title:e}}),this.text(e).call(l),this.call(c)}).on(\"cancel\",function(){var t=this.attr(\"data-unformatted\");this.text(t).call(l)})};u.call(c)}return t._context.setBackground(t,t._fullLayout.paper_bgcolor),S.addLinks(t),Promise.resolve()}function s(t,e){var r,n,i=e+1,a=[];for(r=0;r<t.length;r++)n=t[r],n<0?a.push(i+n):a.push(n);return a}function l(t,e,r){var n,i;for(n=0;n<e.length;n++){if((i=e[n])!==parseInt(i,10))throw new Error(\"all values in \"+r+\" must be integers\");if(i>=t.data.length||i<-t.data.length)throw new Error(r+\" must be valid indices for gd.data.\");if(e.indexOf(i,n+1)>-1||i>=0&&e.indexOf(-t.data.length+i)>-1||i<0&&e.indexOf(t.data.length+i)>-1)throw new Error(\"each index in \"+r+\" must be unique.\")}}function u(t,e,r){if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array.\");if(void 0===e)throw new Error(\"currentIndices is a required argument.\");if(Array.isArray(e)||(e=[e]),l(t,e,\"currentIndices\"),void 0===r||Array.isArray(r)||(r=[r]),void 0!==r&&l(t,r,\"newIndices\"),void 0!==r&&e.length!==r.length)throw new Error(\"current and new indices must be of equal length.\")}function c(t,e,r){var n,i;if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array.\");if(void 0===e)throw new Error(\"traces must be defined.\");for(Array.isArray(e)||(e=[e]),n=0;n<e.length;n++)if(\"object\"!=typeof(i=e[n])||Array.isArray(i)||null===i)throw new Error(\"all values in traces array must be non-array objects\");if(void 0===r||Array.isArray(r)||(r=[r]),void 0!==r&&r.length!==e.length)throw new Error(\"if indices is specified, traces.length must equal indices.length\")}function h(t,e,r,n){var i=w.isPlainObject(n);if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array\");if(!w.isPlainObject(e))throw new Error(\"update must be a key:value object\");if(void 0===r)throw new Error(\"indices must be an integer or array of integers\");l(t,r,\"indices\");for(var a in e){if(!Array.isArray(e[a])||e[a].length!==r.length)throw new Error(\"attribute \"+a+\" must be an array of length equal to indices array length\");if(i&&(!(a in n)||!Array.isArray(n[a])||n[a].length!==e[a].length))throw new Error(\"when maxPoints is set as a key:value object it must contain a 1:1 corrispondence with the keys and number of traces in the update object\")}}function f(t,e,r,n){var i,a,o,l,u,c=w.isPlainObject(n),h=[];Array.isArray(r)||(r=[r]),r=s(r,t.data.length-1);for(var f in e)for(var d=0;d<r.length;d++){if(i=t.data[r[d]],o=w.nestedProperty(i,f),a=o.get(),l=e[f][d],!Array.isArray(l))throw new Error(\"attribute: \"+f+\" index: \"+d+\" must be an array\");if(!Array.isArray(a))throw new Error(\"cannot extend missing or non-array attribute: \"+f);u=c?n[f][d]:n,b(u)||(u=-1),h.push({prop:o,target:a,insert:l,maxp:Math.floor(u)})}return h}function d(t,e,r,n,i,a){h(t,e,r,n);for(var o,s,l,u=f(t,e,r,n),c=[],d={},p={},m=0;m<u.length;m++)s=u[m].prop,l=u[m].maxp,o=i(u[m].target,u[m].insert),l>=0&&l<o.length&&(c=a(o,l)),l=u[m].target.length,s.set(o),Array.isArray(d[s.astr])||(d[s.astr]=[]),Array.isArray(p[s.astr])||(p[s.astr]=[]),d[s.astr].push(c),p[s.astr].push(l);return{update:d,maxPoints:p}}function p(t){return void 0===t?null:t}function m(t,e,r){function n(){return r.map(function(){})}function i(t){var e=_.Axes.id2name(t);-1===u.indexOf(e)&&u.push(e)}function a(t){return\"LAYOUT\"+t+\".autorange\"}function o(t){return\"LAYOUT\"+t+\".range\"}function s(i,a,o){if(Array.isArray(i))return void i.forEach(function(t){s(t,a,o)});if(!(i in e||R.hasParent(e,i))){var l;l=\"LAYOUT\"===i.substr(0,6)?w.nestedProperty(t.layout,i.replace(\"LAYOUT\",\"\")):w.nestedProperty(f[r[o]],i),i in v||(v[i]=n()),void 0===v[i][o]&&(v[i][o]=p(l.get())),void 0!==a&&l.set(a)}}var l,u,c=t._fullLayout,h=t._fullData,f=t.data,d=j.traceFlags(),m={},v={},g={};for(var y in e){if(R.hasParent(e,y))throw 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n,i,a,o,s=V.getFromId(t,e),l=w.select(t).selectAll(\"g.\"+e+\"tick\"),u={selection:l,side:s.side},h=e.charAt(0),f=t._fullLayout._size,d=s.titlefont.size;if(l.size()){var p=L.getTranslate(l.node().parentNode);u.offsetLeft=p.x,u.offsetTop=p.y}var 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if(\"select\"===u||\"lasso\"===u){o.on(\".zoom\",null);var c;\"select\"===u?c=function(t,e){(t.range={})[i.id]=[n([e.xmin,e.ymin]),n([e.xmax,e.ymax])]}:\"lasso\"===u&&(c=function(t,e,r){(t.lassoPoints={})[i.id]=r.filtered.map(n)});var h={element:i.bgRect.node(),gd:a,plotinfo:{xaxis:i.xaxis,yaxis:i.yaxis,fillRangeItems:c},xaxes:[i.xaxis],yaxes:[i.yaxis],subplot:i.id};h.prepFn=function(t,e,r){v(t,e,r,h,u)},h.doneFn=function(e,r){2===r&&t._zoomlayer.selectAll(\".select-outline\").remove()},m.init(h)}o.on(\"mousemove\",function(){var t=i.projection.invert(s.mouse(this));if(!t||isNaN(t[0])||isNaN(t[1]))return m.unhover(a,s.event);i.xaxis.p2c=function(){return t[0]},i.yaxis.p2c=function(){return t[1]},f.hover(a,s.event,i.id)}),o.on(\"mouseout\",function(){m.unhover(a,s.event)}),o.on(\"click\",function(){f.click(a,s.event)})}},_.makeFramework=function(){var 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h=e._isScoped=\"world\"!==o,f=e._isConic=-1!==u.indexOf(\"conic\");e._isClipped=!!a.lonaxisSpan[u];for(var d=0;d<s.length;d++){var p,m=s[d],v=[30,10][d];if(h)p=l[m+\"Range\"];else{var g=a[m+\"Span\"],y=(g[u]||g[\"*\"])/2,b=r(\"projection.rotation.\"+m.substr(0,3),l.projRotate[d]);p=[b-y,b+y]}r(m+\".tick0\",r(m+\".range\",p)[0]),r(m+\".dtick\",v),n=r(m+\".showgrid\"),n&&(r(m+\".gridcolor\"),r(m+\".gridwidth\"))}var x=e.lonaxis.range,_=e.lataxis.range,w=x[0],M=x[1];w>0&&M<0&&(M+=360);var k,A=(w+M)/2;if(!c){var T=h?l.projRotate:[A,0,0];k=r(\"projection.rotation.lon\",T[0]),r(\"projection.rotation.lat\",T[1]),r(\"projection.rotation.roll\",T[2]),n=r(\"showcoastlines\",!h),n&&(r(\"coastlinecolor\"),r(\"coastlinewidth\")),n=r(\"showocean\"),n&&r(\"oceancolor\")}var 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r=t[0],n=t[1],i=t[2],a=t[3],o=e[0],s=e[1],l=e[2],u=e[3];return[r*o-n*s-i*l-a*u,r*s+n*o+i*u-a*l,r*l-n*u+i*o+a*s,r*u+n*l-i*s+a*o]}function f(t,e){if(t&&e){var r=x(t,e),n=Math.sqrt(b(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,b(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}function d(t,e,r){var n=v(e,2,t[0]);n=v(n,1,t[1]),n=v(n,0,t[2]-r[2]);var i,a,o=e[0],s=e[1],l=e[2],u=n[0],c=n[1],h=n[2],f=Math.atan2(s,o)*A,d=Math.sqrt(o*o+s*s);Math.abs(c)>d?(a=(c>0?90:-90)-f,i=0):(a=Math.asin(c/d)*A-f,i=Math.sqrt(d*d-c*c));var m=180-a-2*f,g=(Math.atan2(h,u)-Math.atan2(l,i))*A,y=(Math.atan2(h,u)-Math.atan2(l,-i))*A;return p(r[0],r[1],a,g)<=p(r[0],r[1],m,y)?[a,g,r[2]]:[m,y,r[2]]}function p(t,e,r,n){var i=m(r-t),a=m(n-e);return Math.sqrt(i*i+a*a)}function m(t){return(t%360+540)%360-180}function v(t,e,r){var n=r*k,i=t.slice(),a=0===e?1:0,o=2===e?1:2,s=Math.cos(n),l=Math.sin(n);return i[a]=t[a]*s-t[o]*l,i[o]=t[o]*s+t[a]*l,i}function g(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*A,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*A,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*A]}function y(t){var e=t[0]*k,r=t[1]*k,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}function b(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}function x(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}function _(t){for(var e=0,r=arguments.length,n=[];++e<r;)n.push(arguments[e]);var i=w.dispatch.apply(null,n);return i.of=function(e,r){return function(n){var a;try{a=n.sourceEvent=w.event,n.target=t,w.event=n,i[n.type].apply(e,r)}finally{w.event=a}}},i}var w=t(\"d3\"),M=t(\"../../lib\"),k=Math.PI/180,A=180/Math.PI,T={cursor:\"pointer\"},S={cursor:\"auto\"};e.exports=n},{\"../../lib\":728,d3:122}],806:[function(t,e,r){\"use strict\";function n(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}function i(t){function e(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function r(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}function i(n,i,a){function o(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(f[e]=i,f[e+2]=a,h.dataBox=f,t.setRanges(f)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}var s,u,f=t.calcDataBox(),d=c.viewBox,p=h.lastPos[0],m=h.lastPos[1],v=l.MINDRAG*c.pixelRatio,g=l.MINZOOM*c.pixelRatio;switch(i*=c.pixelRatio,a*=c.pixelRatio,a=d[3]-d[1]-a,t.fullLayout.dragmode){case\"zoom\":if(n){var y=i/(d[2]-d[0])*(f[2]-f[0])+f[0],b=a/(d[3]-d[1])*(f[3]-f[1])+f[1];h.boxInited||(h.boxStart[0]=y,h.boxStart[1]=b,h.dragStart[0]=i,h.dragStart[1]=a),h.boxEnd[0]=y,h.boxEnd[1]=b,h.boxInited=!0,h.boxEnabled||h.boxStart[0]===h.boxEnd[0]&&h.boxStart[1]===h.boxEnd[1]||(h.boxEnabled=!0);var x=Math.abs(h.dragStart[0]-i)<g,_=Math.abs(h.dragStart[1]-a)<g;if(!r()||x&&_)x&&(h.boxEnd[0]=h.boxStart[0]),_&&(h.boxEnd[1]=h.boxStart[1]);else{s=h.boxEnd[0]-h.boxStart[0],u=h.boxEnd[1]-h.boxStart[1];var w=(f[3]-f[1])/(f[2]-f[0]);Math.abs(s*w)>Math.abs(u)?(h.boxEnd[1]=h.boxStart[1]+Math.abs(s)*w*(u>=0?1:-1),h.boxEnd[1]<f[1]?(h.boxEnd[1]=f[1],h.boxEnd[0]=h.boxStart[0]+(f[1]-h.boxStart[1])/Math.abs(w)):h.boxEnd[1]>f[3]&&(h.boxEnd[1]=f[3],h.boxEnd[0]=h.boxStart[0]+(f[3]-h.boxStart[1])/Math.abs(w))):(h.boxEnd[0]=h.boxStart[0]+Math.abs(u)/w*(s>=0?1:-1),h.boxEnd[0]<f[0]?(h.boxEnd[0]=f[0],h.boxEnd[1]=h.boxStart[1]+(f[0]-h.boxStart[0])*Math.abs(w)):h.boxEnd[0]>f[2]&&(h.boxEnd[0]=f[2],h.boxEnd[1]=h.boxStart[1]+(f[2]-h.boxStart[0])*Math.abs(w)))}}else h.boxEnabled?(s=h.boxStart[0]!==h.boxEnd[0],u=h.boxStart[1]!==h.boxEnd[1],s||u?(s&&(o(0,h.boxStart[0],h.boxEnd[0]),t.xaxis.autorange=!1),u&&(o(1,h.boxStart[1],h.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),h.boxEnabled=!1,h.boxInited=!1):h.boxInited&&(h.boxInited=!1);break;case\"pan\":h.boxEnabled=!1,h.boxInited=!1,n?(h.panning||(h.dragStart[0]=i,h.dragStart[1]=a),Math.abs(h.dragStart[0]-i)<v&&(i=h.dragStart[0]),Math.abs(h.dragStart[1]-a)<v&&(a=h.dragStart[1]),s=(p-i)*(f[2]-f[0])/(c.viewBox[2]-c.viewBox[0]),u=(m-a)*(f[3]-f[1])/(c.viewBox[3]-c.viewBox[1]),f[0]+=s,f[2]+=s,f[1]+=u,f[3]+=u,t.setRanges(f),h.panning=!0,h.lastInputTime=Date.now(),e(),t.cameraChanged(),t.handleAnnotations()):h.panning&&(h.panning=!1,t.relayoutCallback())}h.lastPos[0]=i,h.lastPos[1]=a}var u=t.mouseContainer,c=t.glplot,h=new n(u,c);return h.mouseListener=a(u,i),u.addEventListener(\"touchstart\",function(t){var e=s(t.changedTouches[0],u);i(0,e[0],e[1]),i(1,e[0],e[1])}),u.addEventListener(\"touchmove\",function(t){t.preventDefault();var e=s(t.changedTouches[0],u);i(1,e[0],e[1])}),u.addEventListener(\"touchend\",function(){i(0,h.lastPos[0],h.lastPos[1])}),h.wheelListener=o(u,function(r,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=c.viewBox,o=h.lastPos[0],s=h.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),u=o/(a[2]-a[0])*(i[2]-i[0])+i[0],f=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-u)*l+u,i[2]=(i[2]-u)*l+u,i[1]=(i[1]-f)*l+f,i[3]=(i[3]-f)*l+f,t.setRanges(i),h.lastInputTime=Date.now(),e(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),h}var a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"../cartesian/constants\");e.exports=i},{\"../cartesian/constants\":777,\"mouse-change\":452,\"mouse-event-offset\":453,\"mouse-wheel\":455}],807:[function(t,e,r){\"use strict\";function n(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}function i(t){return new n(t)}var a=t(\"../plots\"),o=t(\"../cartesian/axes\"),s=t(\"../../lib/html2unicode\"),l=t(\"../../lib/str2rgbarray\"),u=n.prototype,c=[\"xaxis\",\"yaxis\"];u.merge=function(t){this.titleEnable=!1,this.backgroundColor=l(t.plot_bgcolor);var e,r,n,i,a,o,u,h,f,d,p;for(d=0;d<2;++d){for(e=c[d],r=t[this.scene[e]._name],n=/Click to enter .+ title/.test(r.title)?\"\":r.title,p=0;p<=2;p+=2)this.labelEnable[d+p]=!1,this.labels[d+p]=s(n),this.labelColor[d+p]=l(r.titlefont.color),this.labelFont[d+p]=r.titlefont.family,this.labelSize[d+p]=r.titlefont.size,this.labelPad[d+p]=this.getLabelPad(e,r),this.tickEnable[d+p]=!1,this.tickColor[d+p]=l((r.tickfont||{}).color),this.tickAngle[d+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[d+p]=this.getTickPad(r),this.tickMarkLength[d+p]=0,this.tickMarkWidth[d+p]=r.tickwidth||0,this.tickMarkColor[d+p]=l(r.tickcolor),this.borderLineEnable[d+p]=!1,this.borderLineColor[d+p]=l(r.linecolor),this.borderLineWidth[d+p]=r.linewidth||0;u=this.hasSharedAxis(r),a=this.hasAxisInDfltPos(e,r)&&!u,o=this.hasAxisInAltrPos(e,r)&&!u,i=r.mirror||!1,h=u?-1!==String(i).indexOf(\"all\"):!!i,f=u?\"allticks\"===i:-1!==String(i).indexOf(\"ticks\"),a?this.labelEnable[d]=!0:o&&(this.labelEnable[d+2]=!0),a?this.tickEnable[d]=r.showticklabels:o&&(this.tickEnable[d+2]=r.showticklabels),(a||h)&&(this.borderLineEnable[d]=r.showline),(o||h)&&(this.borderLineEnable[d+2]=r.showline),(a||f)&&(this.tickMarkLength[d]=this.getTickMarkLength(r)),(o||f)&&(this.tickMarkLength[d+2]=this.getTickMarkLength(r)),this.gridLineEnable[d]=r.showgrid,this.gridLineColor[d]=l(r.gridcolor),this.gridLineWidth[d]=r.gridwidth,this.zeroLineEnable[d]=r.zeroline,this.zeroLineColor[d]=l(r.zerolinecolor),this.zeroLineWidth[d]=r.zerolinewidth}},u.hasSharedAxis=function(t){var e=this.scene,r=a.getSubplotIds(e.fullLayout,\"gl2d\");return 0!==o.findSubplotsWithAxis(r,t).indexOf(e.id)},u.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},u.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},u.getLabelPad=function(t,e){var r=e.titlefont.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},u.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},u.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=i},{\n",
+       "\"../../lib/html2unicode\":726,\"../../lib/str2rgbarray\":749,\"../cartesian/axes\":772,\"../plots\":831}],808:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../plots\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),u=t(\"../../components/fx/layout_attributes\");r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.layoutAttributes.plot_bgcolor,hoverlabel:u.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=a.getSubplotIds(e,\"gl2d\"),o=0;o<n.length;o++){var s=n[o],l=e._plots[s],u=a.getSubplotData(r,\"gl2d\",s),c=l._scene2d;void 0===c&&(c=new i({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),l._scene2d=c),c.plot(u,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=a.getSubplotIds(n,\"gl2d\"),o=0;o<i.length;o++){var s=i[o],u=n._plots[s];if(u._scene2d){0===a.getSubplotData(t,\"gl2d\",s).length&&(u._scene2d.destroy(),delete n._plots[s])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=a.getSubplotIds(e,\"gl2d\"),n=0;n<r.length;n++){var i=e._plots[r[n]],s=i._scene2d,l=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":l,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),s.destroy()}},r.updateFx=function(t){for(var e=a.getSubplotIds(t,\"gl2d\"),r=0;r<e.length;r++){t._plots[e[r]]._scene2d.updateFx(t.dragmode)}}},{\"../../components/fx/layout_attributes\":646,\"../../constants/xmlns_namespaces\":709,\"../../plot_api/edit_types\":756,\"../cartesian\":782,\"../cartesian/attributes\":771,\"../cartesian/constants\":777,\"../plots\":831,\"./scene2d\":809}],809:[function(t,e,r){\"use strict\";function n(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context.scrollZoom,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.glplotOptions=p(this),this.glplotOptions.merge(e),this.glplot=c(this.glplotOptions),this.camera=m(this),this.traces={},this.spikes=h(this.glplot),this.selectBox=f(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.bounds=[1/0,1/0,-1/0,-1/0],this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw()}function i(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1}var a,o,s=t(\"../../registry\"),l=t(\"../../plots/cartesian/axes\"),u=t(\"../../components/fx\"),c=t(\"gl-plot2d\"),h=t(\"gl-spikes2d\"),f=t(\"gl-select-box\"),d=t(\"webgl-context\"),p=t(\"./convert\"),m=t(\"./camera\"),v=t(\"../../lib/html2unicode\"),g=t(\"../../lib/show_no_webgl_msg\"),y=t(\"../../plots/cartesian/constraints\"),b=y.enforce,x=y.clean,_=[\"xaxis\",\"yaxis\"];e.exports=n;var w=n.prototype;w.makeFramework=function(){if(this.staticPlot){if(!(o||(a=document.createElement(\"canvas\"),o=d({canvas:a,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=a,this.gl=o}else{var t=document.createElement(\"canvas\"),e=d({canvas:t,premultipliedAlpha:!0});e||g(this),this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r),r.className+=\"user-select-none\";var n=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");n.style.position=\"absolute\",n.style.top=n.style.left=\"0px\",n.style.width=n.style.height=\"100%\",n.style[\"z-index\"]=20,n.style[\"pointer-events\"]=\"none\";var i=this.mouseContainer=document.createElement(\"div\");i.style.position=\"absolute\",i.style[\"pointer-events\"]=\"auto\";var s=this.container;s.appendChild(r),s.appendChild(n),s.appendChild(i);var l=this;i.addEventListener(\"mouseout\",function(){l.isMouseOver=!1,l.unhover()}),i.addEventListener(\"mouseover\",function(){l.isMouseOver=!0})},w.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(a),this.updateSize(this.canvas),this.glplot.setDirty(),this.glplot.draw();var e=this.glplot.gl,r=e.drawingBufferWidth,n=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var i=new Uint8Array(r*n*4);e.readPixels(0,0,r,n,e.RGBA,e.UNSIGNED_BYTE,i);for(var o=0,s=n-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=i[4*(r*o+l)+u];i[4*(r*o+l)+u]=i[4*(r*s+l)+u],i[4*(r*s+l)+u]=c}var h=document.createElement(\"canvas\");h.width=r,h.height=n;var f=h.getContext(\"2d\"),d=f.createImageData(r,n);d.data.set(i),f.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=h.toDataURL(\"image/jpeg\");break;case\"webp\":p=h.toDataURL(\"image/webp\");break;default:p=h.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(a),p},w.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),this.redraw&&this.redraw(),t},w.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[l.calcTicks(this.xaxis),l.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=v(t[e][r].text+\"\");return t},w.updateRefs=function(t){this.fullLayout=t;var e=l.subplotMatch,r=\"xaxis\"+this.id.match(e)[1],n=\"yaxis\"+this.id.match(e)[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},w.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout;n.xaxis.autorange=e.autorange,n.xaxis.range=e.range.slice(0),n.yaxis.autorange=r.autorange,n.yaxis.range=r.range.slice(0);var i={lastInputTime:this.camera.lastInputTime};i[e._name]=e.range.slice(0),i[r._name]=r.range.slice(0),t.emit(\"plotly_relayout\",i)},w.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();i(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},w.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&s.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},w.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map(function(e){t[e].dispose(),delete t[e]}),this.glplot.dispose(),this.staticPlot||this.container.removeChild(this.canvas),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},w.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};x(s,this.xaxis),x(s,this.yaxis);var u=r._size,c=this.xaxis.domain,h=this.yaxis.domain;o.viewBox=[u.l+c[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-c[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(c[1]-c[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+c[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\";var f=this.bounds;f[0]=f[1]=1/0,f[2]=f[3]=-1/0;var d,p,m=Object.keys(this.traces);for(p=0;p<m.length;++p)for(var v=this.traces[m[p]],g=0;g<2;++g)f[g]=Math.min(f[g],v.bounds[g]),f[g+2]=Math.max(f[g+2],v.bounds[g+2]);for(p=0;p<2;++p)f[p]>f[p+2]&&(f[p]=-1,f[p+2]=1),d=this[_[p]],d._length=o.viewBox[p+2]-o.viewBox[p],l.doAutoRange(d),d.setScale();b(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},w.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},w.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},w.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if(i=t[n],i.uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort(function(t,e){return t._trace.index-e._trace.index})},w.updateFx=function(t){this.mouseContainer.style[\"pointer-events\"]=\"lasso\"===t||\"select\"===t?\"none\":\"auto\",this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},w.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};u.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},w.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,s=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var l=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(l[c]=t.dataBox[c],l[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var h=i._size,f=this.xaxis.domain,d=this.yaxis.domain;a=t.pick(o/t.pixelRatio+h.l+f[0]*h.w,s/t.pixelRatio-(h.t+(1-d[1])*h.h));var p=a&&a.object._trace.handlePick(a);if(p&&n&&this.emitPointAction(p,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&p&&(!this.lastPickResult||this.lastPickResult.traceUid!==p.trace.uid||this.lastPickResult.dataCoord[0]!==p.dataCoord[0]||this.lastPickResult.dataCoord[1]!==p.dataCoord[1])){var m=p;this.lastPickResult={traceUid:p.trace?p.trace.uid:null,dataCoord:p.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(p,\"plotly_hover\");var v=this.fullData[m.trace.index]||{},g=m.pointIndex,y=u.castHoverinfo(v,i,g);if(y&&\"all\"!==y){var b=y.split(\"+\");-1===b.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===b.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===b.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===b.indexOf(\"text\")&&(m.textLabel=void 0),-1===b.indexOf(\"name\")&&(m.name=void 0)}u.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:u.castHoverOption(v,g,\"bgcolor\")||m.color,borderColor:u.castHoverOption(v,g,\"bordercolor\"),fontFamily:u.castHoverOption(v,g,\"font.family\"),fontSize:u.castHoverOption(v,g,\"font.size\"),fontColor:u.castHoverOption(v,g,\"font.color\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},w.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),u.loneUnhover(this.svgContainer))},w.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return l.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/fx\":645,\"../../lib/html2unicode\":726,\"../../lib/show_no_webgl_msg\":747,\"../../plots/cartesian/axes\":772,\"../../plots/cartesian/constraints\":779,\"../../registry\":846,\"./camera\":806,\"./convert\":807,\"gl-plot2d\":219,\"gl-select-box\":253,\"gl-spikes2d\":262,\"webgl-context\":563}],810:[function(t,e,r){\"use strict\";function n(t,e){function r(e,r,n,a){var o=p.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,c=\"zoom\"===o,f=!!a.control,d=!!a.alt,y=!!a.shift,b=!!(1&e),x=!!(2&e),_=!!(4&e),w=1/t.clientHeight,M=w*(r-m),k=w*(n-v),A=p.flipX?1:-1,T=p.flipY?1:-1,S=i(),E=Math.PI*p.rotateSpeed;if((s&&b&&!f&&!d&&!y||b&&!f&&!d&&y)&&u.rotate(S,A*E*M,-T*E*k,0),(l&&b&&!f&&!d&&!y||x||b&&f&&!d&&!y)&&u.pan(S,-p.translateSpeed*M*h,p.translateSpeed*k*h,0),c&&b&&!f&&!d&&!y||_||b&&!f&&d&&!y){var L=-p.zoomSpeed*k/window.innerHeight*(S-u.lastT())*100;u.pan(S,0,0,h*(Math.exp(L)-1))}return m=r,v=n,g=a,!0}}t=t||document.body,e=e||{};var n=[.01,1/0];\"distanceLimits\"in e&&(n[0]=e.distanceLimits[0],n[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(n[0]=e.zoomMin),\"zoomMax\"in e&&(n[1]=e.zoomMax);var u=a({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:n}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],h=0,f=t.clientWidth,d=t.clientHeight,p={keyBindingMode:\"rotate\",view:u,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,tick:function(){var e=i(),r=this.delay,n=e-2*r;u.idle(e-r),u.recalcMatrix(n),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===f&&t.clientHeight===d;return f=t.clientWidth,d=t.clientHeight,a?!l:(h=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};Object.defineProperties(p,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),n=u.computedCenter.slice();if(u.setMode(t),\"turntable\"===t){var a=i();u._active.lookAt(a,r,n,e),u._active.lookAt(a+500,r,n,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return h},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(n)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",function(t){return t.preventDefault(),!1});var m=0,v=0,g={shift:!1,control:!1,alt:!1,meta:!1};return p.mouseListener=o(t,r),t.addEventListener(\"touchstart\",function(e){var n=l(e.changedTouches[0],t);r(0,n[0],n[1],g),r(1,n[0],n[1],g)}),t.addEventListener(\"touchmove\",function(e){var n=l(e.changedTouches[0],t);r(1,n[0],n[1],g)}),t.addEventListener(\"touchend\",function(){r(0,m,v,g)}),p.wheelListener=s(t,function(t,e){if(!1!==p.keyBindingMode){var r=p.flipX?1:-1,n=p.flipY?1:-1,a=i();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*p.rotateSpeed/window.innerWidth);else{var o=-p.zoomSpeed*n*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,h*(Math.exp(o)-1))}}},!0),p}e.exports=n;var i=t(\"right-now\"),a=t(\"3d-view\"),o=t(\"mouse-change\"),s=t(\"mouse-wheel\"),l=t(\"mouse-event-offset\")},{\"3d-view\":37,\"mouse-change\":452,\"mouse-event-offset\":453,\"mouse-wheel\":455,\"right-now\":502}],811:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../plots\"),s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=o.getSubplotIds(e,\"gl3d\"),i=0;i<n.length;i++){var l=n[i],u=o.getSubplotData(r,\"gl3d\",l),c=e[l],h=c._scene;h||(h=new a({id:l,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),c._scene=h),h.cameraInitial||(h.cameraInitial=s.extendDeep({},c.camera)),h.plot(u,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=o.getSubplotIds(n,\"gl3d\"),a=0;a<i.length;a++){var s=i[a];!e[s]&&n[s]._scene&&(n[s]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+s).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=o.getSubplotIds(e,\"gl3d\"),n=e._size,i=0;i<r.length;i++){var a=e[r[i]],s=a.domain,u=a._scene,c=u.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*s.x[0],y:n.t+n.h*(1-s.y[1]),width:n.w*(s.x[1]-s.x[0]),height:n.h*(s.y[1]-s.y[0]),preserveAspectRatio:\"none\"}),u.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=o.getSubplotIds(t,\"gl3d\"),r=0;r<e.length;r++){t[e[r]]._scene.updateFx(t.dragmode,t.hovermode)}}},{\"../../components/fx/layout_attributes\":646,\"../../constants/xmlns_namespaces\":709,\"../../lib\":728,\"../../plot_api/edit_types\":756,\"../plots\":831,\"./layout/attributes\":812,\"./layout/defaults\":816,\"./layout/layout_attributes\":817,\"./scene\":821}],812:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],813:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:i.title,titlefont:i.titlefont,type:i.type,autorange:i.autorange,rangemode:i.rangemode,range:i.range,tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,separatethousands:i.separatethousands,tickformat:i.tickformat,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth},\"plot\",\"from-root\")},{\"../../../components/color\":604,\"../../../lib/extend\":717,\"../../../plot_api/edit_types\":756,\"../../cartesian/layout_attributes\":783}],814:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"./axis_attributes\"),o=t(\"../../cartesian/type_defaults\"),s=t(\"../../cartesian/axis_defaults\"),l=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){function u(t,e){return i.coerce(c,h,a,t,e)}for(var c,h,f=0;f<l.length;f++){var 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a=t(\"../../../lib/html2unicode\"),o=t(\"../../../lib/str2rgbarray\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"];n.prototype.merge=function(t){for(var e=this,r=0;r<3;++r){var n=t[s[r]];n.visible?(e.labels[r]=a(n.title),\"titlefont\"in n&&(n.titlefont.color&&(e.labelColor[r]=o(n.titlefont.color)),n.titlefont.family&&(e.labelFont[r]=n.titlefont.family),n.titlefont.size&&(e.labelSize[r]=n.titlefont.size)),\"showline\"in n&&(e.lineEnable[r]=n.showline),\"linecolor\"in n&&(e.lineColor[r]=o(n.linecolor)),\"linewidth\"in n&&(e.lineWidth[r]=n.linewidth),\"showgrid\"in n&&(e.gridEnable[r]=n.showgrid),\"gridcolor\"in n&&(e.gridColor[r]=o(n.gridcolor)),\"gridwidth\"in n&&(e.gridWidth[r]=n.gridwidth),\"log\"===n.type?e.zeroEnable[r]=!1:\"zeroline\"in n&&(e.zeroEnable[r]=n.zeroline),\"zerolinecolor\"in n&&(e.zeroLineColor[r]=o(n.zerolinecolor)),\"zerolinewidth\"in n&&(e.zeroLineWidth[r]=n.zerolinewidth),\"ticks\"in n&&n.ticks?e.lineTickEnable[r]=!0:e.lineTickEnable[r]=!1,\"ticklen\"in n&&(e.lineTickLength[r]=e._defaultLineTickLength[r]=n.ticklen),\"tickcolor\"in n&&(e.lineTickColor[r]=o(n.tickcolor)),\"tickwidth\"in n&&(e.lineTickWidth[r]=n.tickwidth),\"tickangle\"in n&&(e.tickAngle[r]=\"auto\"===n.tickangle?0:Math.PI*-n.tickangle/180),\"showticklabels\"in n&&(e.tickEnable[r]=n.showticklabels),\"tickfont\"in n&&(n.tickfont.color&&(e.tickColor[r]=o(n.tickfont.color)),n.tickfont.family&&(e.tickFont[r]=n.tickfont.family),n.tickfont.size&&(e.tickSize[r]=n.tickfont.size)),\"mirror\"in n?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(n.mirror)?(e.lineTickMirror[r]=!0,e.lineMirror[r]=!0):!0===n.mirror?(e.lineTickMirror[r]=!1,e.lineMirror[r]=!0):(e.lineTickMirror[r]=!1,e.lineMirror[r]=!1):e.lineMirror[r]=!1,\"showbackground\"in n&&!1!==n.showbackground?(e.backgroundEnable[r]=!0,e.backgroundColor[r]=o(n.backgroundcolor)):e.backgroundEnable[r]=!1):(e.tickEnable[r]=!1,e.labelEnable[r]=!1,e.lineEnable[r]=!1,e.lineTickEnable[r]=!1,e.gridEnable[r]=!1,e.zeroEnable[r]=!1,e.backgroundEnable[r]=!1)}},e.exports=i},{\"../../../lib/html2unicode\":726,\"../../../lib/str2rgbarray\":749}],816:[function(t,e,r){\"use strict\";function n(t,e,r,n){for(var i=r(\"bgcolor\"),s=a.combine(i,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],c=0;c<u.length;c++)r(\"camera.\"+u[c]+\".x\"),r(\"camera.\"+u[c]+\".y\"),r(\"camera.\"+u[c]+\".z\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),f=h?\"manual\":\"auto\",d=r(\"aspectmode\",f);h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode),l(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:s,calendar:n.calendar}),o.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n),r(\"dragmode\",n.getDfltFromLayout(\"dragmode\")),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}var i=t(\"../../../lib\"),a=t(\"../../../components/color\"),o=t(\"../../../registry\"),s=t(\"../../subplot_defaults\"),l=t(\"./axis_defaults\"),u=t(\"./layout_attributes\");e.exports=function(t,e,r){function a(e){if(!o){return i.validate(t[e],u[e])?t[e]:void 0}}var o=e._basePlotModules.length>1;s(t,e,r,{type:\"gl3d\",attributes:u,handleDefaults:n,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:a,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":604,\"../../../lib\":728,\"../../../registry\":846,\"../../subplot_defaults\":838,\"./axis_defaults\":814,\"./layout_attributes\":817}],817:[function(t,e,r){\"use strict\";function n(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}var i=t(\"./axis_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(n(0,0,1),{}),center:a(n(0,0,0),{}),eye:a(n(1.25,1.25,1.25),{}),editType:\"camera\"},domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1,editType:\"plot\"},{valType:\"number\",min:0,max:1,editType:\"plot\"}],dflt:[0,1],editType:\"plot\"},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1,editType:\"plot\"},{valType:\"number\",min:0,max:1,editType:\"plot\"}],dflt:[0,1],editType:\"plot\"},editType:\"plot\"},aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:i,yaxis:i,zaxis:i,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],dflt:\"turntable\",editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":728,\"../../../lib/extend\":717,\"./axis_attributes\":813}],818:[function(t,e,r){\"use strict\";function n(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}function i(t){var e=new n;return e.merge(t),e}var a=t(\"../../../lib/str2rgbarray\"),o=[\"xaxis\",\"yaxis\",\"zaxis\"];n.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[o[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=a(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=i},{\"../../../lib/str2rgbarray\":749}],819:[function(t,e,r){\"use strict\";function n(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}function i(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,i=t.fullSceneLayout,c=[[],[],[]],h=0;h<3;++h){var f=i[l[h]];if(f._length=(r[h].hi-r[h].lo)*r[h].pixelsPerDataUnit/t.dataScale[h],Math.abs(f._length)===1/0)c[h]=[];else{f.range[0]=r[h].lo/t.dataScale[h],f.range[1]=r[h].hi/t.dataScale[h],f._m=1/(t.dataScale[h]*r[h].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var d=f.tickmode;if(\"auto\"===f.tickmode){f.tickmode=\"linear\";var p=f.nticks||o.constrain(f._length/40,4,9);a.autoTicks(f,Math.abs(f.range[1]-f.range[0])/p)}for(var m=a.calcTicks(f),v=0;v<m.length;++v)m[v].x=m[v].x*t.dataScale[h],m[v].text=s(m[v].text);c[h]=m,f.tickmode=d}}e.ticks=c;for(var h=0;h<3;++h){u[h]=.5*(t.glplot.bounds[0][h]+t.glplot.bounds[1][h]);for(var v=0;v<2;++v)e.bounds[v][h]=t.glplot.bounds[v][h]}t.contourLevels=n(c)}e.exports=i;var a=t(\"../../cartesian/axes\"),o=t(\"../../../lib\"),s=t(\"../../../lib/html2unicode\"),l=[\"xaxis\",\"yaxis\",\"zaxis\"],u=[0,0,0]},{\"../../../lib\":728,\"../../../lib/html2unicode\":726,\"../../cartesian/axes\":772}],820:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}function i(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}e.exports=i},{}],821:[function(t,e,r){\"use strict\";function n(t){function e(e,r){var n=t.fullSceneLayout[e];return v.tickText(n,n.d2l(r),\"hover\").text}var r,n=t.svgContainer,i=t.container.getBoundingClientRect(),a=i.width,o=i.height;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+a+\" \"+o),n.setAttributeNS(null,\"width\",a),n.setAttributeNS(null,\"height\",o),k(t),t.glplot.axes.update(t.axesOptions);for(var s=Object.keys(t.traces),l=null,u=t.glplot.selection,c=0;c<s.length;++c)r=t.traces[s[c]],\"skip\"!==r.data.hoverinfo&&r.handlePick(u)&&(l=r),r.setContourLevels&&r.setContourLevels();var h;if(null!==l){var f=_(t.glplot.cameraParams,u.dataCoordinate);r=l.data;var d=u.index,p=g.castHoverinfo(r,t.fullLayout,d),m=e(\"xaxis\",u.traceCoordinate[0]),y=e(\"yaxis\",u.traceCoordinate[1]),b=e(\"zaxis\",u.traceCoordinate[2]);if(\"all\"!==p){var x=p.split(\"+\");-1===x.indexOf(\"x\")&&(m=void 0),-1===x.indexOf(\"y\")&&(y=void 0),-1===x.indexOf(\"z\")&&(b=void 0),-1===x.indexOf(\"text\")&&(u.textLabel=void 0),-1===x.indexOf(\"name\")&&(l.name=void 0)}t.fullSceneLayout.hovermode&&g.loneHover({x:(.5+.5*f[0]/f[3])*a,y:(.5-.5*f[1]/f[3])*o,xLabel:m,yLabel:y,zLabel:b,text:u.textLabel,name:l.name,color:g.castHoverOption(r,d,\"bgcolor\")||l.color,borderColor:g.castHoverOption(r,d,\"bordercolor\"),fontFamily:g.castHoverOption(r,d,\"font.family\"),fontSize:g.castHoverOption(r,d,\"font.size\"),fontColor:g.castHoverOption(r,d,\"font.color\")},{container:n,gd:t.graphDiv});var w={x:u.traceCoordinate[0],y:u.traceCoordinate[1],z:u.traceCoordinate[2],data:r._input,fullData:r,curveNumber:r.index,pointNumber:d};g.appendArrayPointValue(w,r,d);var M={points:[w]};u.buttons&&u.distance<5?t.graphDiv.emit(\"plotly_click\",M):t.graphDiv.emit(\"plotly_hover\",M),h=M}else g.loneUnhover(n),t.graphDiv.emit(\"plotly_unhover\",h);t.drawAnnotations(t)}function i(t,e,r,i){var a={canvas:r,gl:i,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!(h||(c=document.createElement(\"canvas\"),h=d({canvas:c,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");a.pixelRatio=t.pixelRatio,a.gl=h,a.canvas=c}try{t.glplot=f(a)}catch(e){b(t)}var o=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+\".camera\"]=u(t.camera),t.saveCamera(t.graphDiv.layout),t.graphDiv.emit(\"plotly_relayout\",e)}};if(t.glplot.canvas.addEventListener(\"mouseup\",o.bind(null,t)),t.glplot.canvas.addEventListener(\"wheel\",o.bind(null,t)),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",function(t){m.warn(\"Lost WebGL context.\"),t.preventDefault()}),!t.camera){var s=t.fullSceneLayout.camera;t.camera=x(t.container,{center:[s.center.x,s.center.y,s.center.z],\n",
+       "eye:[s.eye.x,s.eye.y,s.eye.z],up:[s.up.x,s.up.y,s.up.z],zoomMin:.1,zoomMax:100,mode:\"orbit\"})}return t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=n.bind(null,t),t.traces={},!0}function a(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\",r.appendChild(a),this.svgContainer=a,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=w(e[this.id]),this.spikeOptions=M(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=p.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=p.getComponentMethod(\"annotations3d\",\"draw\"),i(this,e)}function o(t,e,r,n,i){for(var a,o=0;o<e.length;++o)if(Array.isArray(e[o]))for(var s=0;s<e[o].length;++s)a=t.d2l(e[o][s],0,i),!isNaN(a)&&isFinite(a)&&(n[0][r]=Math.min(n[0][r],a),n[1][r]=Math.max(n[1][r],a));else a=t.d2l(e[o],0,i),!isNaN(a)&&isFinite(a)&&(n[0][r]=Math.min(n[0][r],a),n[1][r]=Math.max(n[1][r],a))}function s(t,e,r){var n=t.fullSceneLayout;o(n.xaxis,e.x,0,r,e.xcalendar),o(n.yaxis,e.y,1,r,e.ycalendar),o(n.zaxis,e.z,2,r,e.zcalendar)}function l(t){return[[t.eye.x,t.eye.y,t.eye.z],[t.center.x,t.center.y,t.center.z],[t.up.x,t.up.y,t.up.z]]}function u(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}var c,h,f=t(\"gl-plot3d\"),d=t(\"webgl-context\"),p=t(\"../../registry\"),m=t(\"../../lib\"),v=t(\"../../plots/cartesian/axes\"),g=t(\"../../components/fx\"),y=t(\"../../lib/str2rgbarray\"),b=t(\"../../lib/show_no_webgl_msg\"),x=t(\"./camera\"),_=t(\"./project\"),w=t(\"./layout/convert\"),M=t(\"./layout/spikes\"),k=t(\"./layout/tick_marks\"),A=a.prototype;A.recoverContext=function(){function t(){return r.isContextLost()?void requestAnimationFrame(t):i(e,e.fullLayout,n,r)?void e.plot.apply(e,e.plotArgs):void m.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")}var e=this,r=this.glplot.gl,n=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(t)};var T=[\"xaxis\",\"yaxis\",\"zaxis\"];A.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,l,u,c=e[this.id],h=r[this.id];c.bgcolor?this.glplot.clearColor=y(c.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=c,this.glplotLayout=c,this.axesOptions.merge(c),this.spikeOptions.merge(c),this.setCamera(c.camera),this.updateFx(c.dragmode,c.hovermode),this.glplot.update({}),this.setConvert(l),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)n=t[a],!0===n.visible&&s(this,n,f);var d=[1,1,1];for(o=0;o<3;++o)f[0][o]>f[1][o]?d[o]=1:f[1][o]===f[0][o]?d[o]=1:d[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=d,this.convertAnnotations(this),a=0;a<t.length;++a)n=t[a],!0===n.visible&&(i=this.traces[n.uid],i?i.update(n):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible)continue t;i=this.traces[p[a]],i.dispose(),delete this.traces[p[a]]}this.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var m=[[0,0,0],[0,0,0]],v=[],g={};for(a=0;a<3;++a){if(l=c[T[a]],u=l.type,u in g?(g[u].acc*=d[a],g[u].count+=1):g[u]={acc:d[a],count:1},l.autorange){m[0][a]=1/0,m[1][a]=-1/0;var b=this.glplot.objects,x=this.fullSceneLayout.annotations||[],_=l._name.charAt(0);for(o=0;o<b.length;o++){var w=b[o].bounds;m[0][a]=Math.min(m[0][a],w[0][a]/d[a]),m[1][a]=Math.max(m[1][a],w[1][a]/d[a])}for(o=0;o<x.length;o++){var M=x[o];if(M.visible){var k=l.r2l(M[_]);m[0][a]=Math.min(m[0][a],k),m[1][a]=Math.max(m[1][a],k)}}if(\"rangemode\"in l&&\"tozero\"===l.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var A=m[1][a]-m[0][a];m[0][a]-=A/32,m[1][a]+=A/32}}else{var S=l.range;m[0][a]=l.r2l(S[0]),m[1][a]=l.r2l(S[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.bounds[0][a]=m[0][a]*d[a],this.glplot.bounds[1][a]=m[1][a]*d[a]}var E=[1,1,1];for(a=0;a<3;++a){l=c[T[a]],u=l.type;var L=g[u];E[a]=Math.pow(L.acc,1/L.count)/d[a]}var C;if(\"auto\"===c.aspectmode)C=Math.max.apply(null,E)/Math.min.apply(null,E)<=4?E:[1,1,1];else if(\"cube\"===c.aspectmode)C=[1,1,1];else if(\"data\"===c.aspectmode)C=E;else{if(\"manual\"!==c.aspectmode)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var 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f=h.getContext(\"2d\"),d=f.createImageData(r,n);d.data.set(i),f.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=h.toDataURL(\"image/jpeg\");break;case\"webp\":p=h.toDataURL(\"image/webp\");break;default:p=h.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(c),p},A.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[T[t]];v.setConvert(e,this.fullLayout),e.setScale=m.noop}},e.exports=a},{\"../../components/fx\":645,\"../../lib\":728,\"../../lib/show_no_webgl_msg\":747,\"../../lib/str2rgbarray\":749,\"../../plots/cartesian/axes\":772,\"../../registry\":846,\"./camera\":810,\"./layout/convert\":815,\"./layout/spikes\":818,\"./layout/tick_marks\":819,\"./project\":820,\"gl-plot3d\":221,\"webgl-context\":563}],822:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"../components/color/attributes\"),a=n({editType:\"calc\"});a.family.dflt='\"Open Sans\", verdana, arial, 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strict\";e.exports={styleUrlPrefix:\"mapbox://styles/mapbox/\",styleUrlSuffix:\"v9\",controlContainerClassName:\"mapboxgl-control-container\",noAccessTokenErrorMsg:[\"Missing Mapbox access token.\",\"Mapbox trace type require a Mapbox access token to be registered.\",\"For example:\",\"  Plotly.plot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });\",\"More info here: https://www.mapbox.com/help/define-access-token/\"].join(\"\\n\"),mapOnErrorMsg:\"Mapbox error.\"}},{}],824:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=t.split(\" \"),i=r[0],a=r[1],o=Array.isArray(e)?n.mean(e):e,s=.5+o/100,l=1.5+o/100,u=[\"\",\"\"],c=[0,0];switch(i){case\"top\":u[0]=\"top\",c[1]=-l;break;case\"bottom\":u[0]=\"bottom\",c[1]=l}switch(a){case\"left\":u[1]=\"right\",c[0]=-s;break;case\"right\":u[1]=\"left\",c[0]=s}var h;return h=u[0]&&u[1]?u.join(\"-\"):u[0]?u[0]:u[1]?u[1]:\"center\",{anchor:h,offset:c}}},{\"../../lib\":728}],825:[function(t,e,r){\"use 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h=s[c],f=o.getSubplotCalcData(r,\"mapbox\",h),d=e[h],p=d._subplot;d.accesstoken=u,p||(p=l({gd:t,container:e._glcontainer.node(),id:h,fullLayout:e,staticPlot:t._context.staticPlot}),e[h]._subplot=p),p.viewInitial||(p.viewInitial={center:a.extendFlat({},d.center),zoom:d.zoom,bearing:d.bearing,pitch:d.pitch}),p.plot(f,e,t._promises)}},r.clean=function(t,e,r,n){for(var i=o.getSubplotIds(n,\"mapbox\"),a=0;a<i.length;a++){var s=i[a];!e[s]&&n[s]._subplot&&n[s]._subplot.destroy()}},r.toSVG=function(t){for(var e=t._fullLayout,r=o.getSubplotIds(e,\"mapbox\"),n=e._size,i=0;i<r.length;i++){var a=e[r[i]],l=a.domain,u=a._subplot,c=u.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:s.svg,\"xlink:href\":c,x:n.l+n.w*l.x[0],y:n.t+n.h*(1-l.y[1]),width:n.w*(l.x[1]-l.x[0]),height:n.h*(l.y[1]-l.y[0]),preserveAspectRatio:\"none\"}),u.destroy()}},r.updateFx=function(t){for(var e=o.getSubplotIds(t,\"mapbox\"),r=0;r<e.length;r++){t[e[r]]._subplot.updateFx(t)}}},{\"../../constants/xmlns_namespaces\":709,\"../../lib\":728,\"../plots\":831,\"./constants\":823,\"./layout_attributes\":827,\"./layout_defaults\":828,\"./mapbox\":829,\"mapbox-gl\":343}],826:[function(t,e,r){\"use strict\";function n(t,e){this.mapbox=t,this.map=t.map,this.uid=t.uid+\"-layer\"+e,this.idSource=this.uid+\"-source\",this.idLayer=this.uid+\"-layer\",this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}function i(t){var e=t.source;return s.isPlainObject(e)||\"string\"==typeof e&&e.length>0}function a(t){var e={},r={};switch(t.type){case\"circle\":s.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":s.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity});break;case\"fill\":s.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var n=t.symbol,i=l(n.textposition,n.iconsize);s.extendFlat(e,{\"icon-image\":n.icon+\"-15\",\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":i.anchor,\"text-offset\":i.offset}),s.extendFlat(r,{\"icon-color\":t.color,\"text-color\":n.textfont.color,\"text-opacity\":t.opacity})}return{layout:e,paint:r}}function o(t){var e,r=t.sourcetype,n=t.source,i={type:r},a=\"string\"==typeof n;return\"geojson\"===r?e=\"data\":\"vector\"===r&&(e=a?\"url\":\"tiles\"),i[e]=n,i}var s=t(\"../../lib\"),l=t(\"./convert_text_opts\"),u=n.prototype;u.update=function(t){this.visible?this.needsNewSource(t)?(this.updateLayer(t),this.updateSource(t)):this.needsNewLayer(t)&&this.updateLayer(t):(this.updateSource(t),this.updateLayer(t)),this.updateStyle(t),this.visible=i(t)},u.needsNewSource=function(t){return this.sourceType!==t.sourcetype||this.source!==t.source||this.layerType!==t.type},u.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==t.below},u.updateSource=function(t){var e=this.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,i(t)){var r=o(t);e.addSource(this.idSource,r)}},u.updateLayer=function(t){var e=this.map;if(e.getLayer(this.idLayer)&&e.removeLayer(this.idLayer),this.layerType=t.type,i(t)){e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer||\"\",type:t.type},t.below);var r={visibility:\"visible\"};this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",r)}},u.updateStyle=function(t){var e=a(t);i(t)&&(this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.mapbox.setOptions(this.idLayer,\"setPaintProperty\",e.paint))},u.dispose=function(){var t=this.map;t.removeLayer(this.idLayer),t.removeSource(this.idSource)},e.exports=function(t,e,r){var i=new n(t,e);return i.update(r),i}},{\"../../lib\":728,\"./convert_text_opts\":824}],827:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../font_attributes\"),o=t(\"../../traces/scatter/attributes\").textposition,s=t(\"../../plot_api/edit_types\").overrideAll,l=a({});l.family.dflt=\"Open Sans Regular, Arial Unicode MS 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a=t(\"../../lib\"),o=t(\"../subplot_defaults\"),s=t(\"./layout_attributes\");e.exports=function(t,e,r){o(t,e,r,{type:\"mapbox\",attributes:s,handleDefaults:n,partition:\"y\"})}},{\"../../lib\":728,\"../subplot_defaults\":838,\"./layout_attributes\":827}],829:[function(t,e,r){\"use strict\";function n(t){this.id=t.id,this.gd=t.gd,this.container=t.container,this.isStatic=t.staticPlot;var e=t.fullLayout;this.uid=e._uid+\"-\"+this.id,this.opts=e[this.id],this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(e),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[]}function i(t){var e=d.style.values,r=d.style.dflt,n={};return u.isPlainObject(t)?(n.id=t.id,n.style=t):\"string\"==typeof t?(n.id=t,n.style=-1!==e.indexOf(t)?a(t):t):(n.id=r,n.style=a(r)),n}function a(t){return f.styleUrlPrefix+t+\"-\"+f.styleUrlSuffix}function o(t){return[t.lon,t.lat]}var 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n=t(\"../../../components/color/attributes\"),i=t(\"./axis_attributes\"),a=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=a({domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:i,baxis:i,caxis:i},\"plot\",\"from-root\")},{\"../../../components/color/attributes\":603,\"../../../plot_api/edit_types\":756,\"./axis_attributes\":841}],845:[function(t,e,r){\"use strict\";function n(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e)}function i(t){a.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}var 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r,n,i,a,o,s,l=this,d=t.domain,p=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],g=d.y[1]-d.y[0],y=v*e.w,b=g*e.h,x=t.sum,w=t.aaxis.min,M=t.baxis.min,k=t.caxis.min;y>_*b?(a=b,i=a*_):(i=y,a=i/_),o=v*i/y,s=g*a/b,r=e.l+e.w*p-i/2,n=e.t+e.h*(1-m)-a/2,l.x0=r,l.y0=n,l.w=i,l.h=a,l.sum=x,l.xaxis={type:\"linear\",range:[w+2*k-x,x-w-2*M],domain:[p-o/2,p+o/2],_id:\"x\"},h(l.xaxis,l.graphDiv._fullLayout),l.xaxis.setScale(),l.xaxis.isPtWithinRange=function(t){return t.a>=l.aaxis.range[0]&&t.a<=l.aaxis.range[1]&&t.b>=l.baxis.range[1]&&t.b<=l.baxis.range[0]&&t.c>=l.caxis.range[1]&&t.c<=l.caxis.range[0]},l.yaxis={type:\"linear\",range:[w,x-M-k],domain:[m-s/2,m+s/2],_id:\"y\"},h(l.yaxis,l.graphDiv._fullLayout),l.yaxis.setScale(),l.yaxis.isPtWithinRange=function(){return!0};var 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w=b.MINZOOM/2+.87,M=\"m-0.87,.5h\"+w+\"v3h-\"+(w+5.2)+\"l\"+(w/2+2.6)+\",-\"+(.87*w+4.5)+\"l2.6,1.5l-\"+w/2+\",\"+.87*w+\"Z\",k=\"m0.87,.5h-\"+w+\"v3h\"+(w+5.2)+\"l-\"+(w/2+2.6)+\",-\"+(.87*w+4.5)+\"l-2.6,1.5l\"+w/2+\",\"+.87*w+\"Z\",A=\"m0,1l\"+w/2+\",\"+.87*w+\"l2.6,-1.5l-\"+(w/2+2.6)+\",-\"+(.87*w+4.5)+\"l-\"+(w/2+2.6)+\",\"+(.87*w+4.5)+\"l2.6,1.5l\"+w/2+\",-\"+.87*w+\"Z\",T=!0;x.initInteractions=function(){function t(t,e,r){var n=F.getBoundingClientRect();w=e-n.left,S=r-n.top,E={a:R.aaxis.range[0],b:R.baxis.range[1],c:R.caxis.range[1]},C=E,L=R.aaxis.range[1]-E.a,I=o(R.graphDiv._fullLayout[R.id].bgcolor).getLuminance(),z=\"M0,\"+R.h+\"L\"+R.w/2+\", 0L\"+R.w+\",\"+R.h+\"Z\",D=!1,P=N.append(\"path\").attr(\"class\",\"zoombox\").attr(\"transform\",\"translate(\"+R.x0+\", \"+R.y0+\")\").style({fill:I>.2?\"rgba(0,0,0,0)\":\"rgba(255,255,255,0)\",\"stroke-width\":0}).attr(\"d\",z),O=N.append(\"path\").attr(\"class\",\"zoombox-corners\").attr(\"transform\",\"translate(\"+R.x0+\", 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i=n.traces[e];i&&d(r.modules[e]._module.attributes,i)}}function a(t,e){var n=r.componentsRegistry[t].schema;if(n&&n.transforms){var i=n.transforms[e];i&&d(r.transformsRegistry[e].attributes,i)}}function o(t,e){var n=r.componentsRegistry[t].schema;if(n&&n.subplots){var i=r.subplotsRegistry[e],a=i.layoutAttributes,o=\"subplot\"===i.attr?i.name:i.attr;Array.isArray(o)&&(o=o[0]);var s=n.subplots[o];a&&s&&d(a,s)}}function s(t){return\"object\"==typeof t&&(t=t.type),t}var l=t(\"./lib/loggers\"),u=t(\"./lib/noop\"),c=t(\"./lib/push_unique\"),h=t(\"./lib/extend\"),f=h.extendFlat,d=h.extendDeepAll,p=t(\"./plots/attributes\"),m=t(\"./plots/layout_attributes\");r.modules={},r.allCategories={},r.allTypes=[],r.subplotsRegistry={},r.transformsRegistry={},r.componentsRegistry={},r.layoutArrayContainers=[],r.layoutArrayRegexes=[],r.traceLayoutAttributes={},r.register=function(t,e,n,a){if(r.modules[e])return void l.log(\"Type \"+e+\" already registered\");for(var 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v._infolayer.selectAll(\".cb\"+d).remove();\"heatmap\"===f.coloring?(s.zauto&&!1===s.autocontour&&(s._input.zmin=s.zmin=f.start-f.size/2,s._input.zmax=s.zmax=s.zmin+_.length*f.size),y(t,e,[n])):(v._paper.selectAll(\".hm\"+d).remove(),v._infolayer.selectAll(\"g.rangeslider-container\").selectAll(\".hm\"+d).remove()),b(_),x(_);var w=p.c2p(u[0],!0),M=p.c2p(u[u.length-1],!0),k=m.c2p(h[0],!0),A=m.c2p(h[h.length-1],!0),T=[[w,A],[M,A],[M,k],[w,k]],S=r.makeContourGroup(e,n,g);a(S,T,f),o(S,_,T,f),l(S,_,t,n[0],f,T),c(S,e,v._clips,n[0],T)}function i(t,e,r){for(var n=t.size,i=[],a=_(t),o=t.start;o<a;o+=n)if(i.push({level:o,crossings:{},starts:[],edgepaths:[],paths:[],xaxis:e.xaxis,yaxis:e.yaxis,x:r.x,y:r.y,z:r.z,smoothing:r.trace.line.smoothing}),i.length>1e3){d.warn(\"Too many contours, clipping at 1000\",t);break}return i}function a(t,e,r){var n=t.selectAll(\"g.contourbg\").data([0]);n.enter().append(\"g\").classed(\"contourbg\",!0);var 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val(pB, dim1B) + val(pC, dim1C) + val(pD, dim1D));\\n\\n    vec2 dimensionToggle = vec2(x, 1.0 - x);\\n\\n    vec2 scatterToggle = vec2(scatter, 1.0 - scatter);\\n\\n    float y = dot(yy, dimensionToggle);\\n    mat2 xy = mat2(viewBoxSize * yy + dimensionToggle, viewBoxSize * vec2(x, y));\\n\\n    vec2 viewBoxXY = viewBoxPosition + xy * scatterToggle;\\n\\n    float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n    gl_Position = vec4(\\n        xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n        depthOrHide,\\n        1.0\\n    );\\n\\n    // pick coloring\\n    fragColor = vec4(pf.rgb, 1.0);\\n}\\n\":\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 p0, p1, p2, p3,\\n               p4, p5, p6, p7,\\n               p8, p9, pa, pb,\\n               pc, pd, pe;\\n\\nattribute vec4 pf;\\n\\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution,\\n             viewBoxPosition,\\n             viewBoxSize;\\n\\nuniform sampler2D palette;\\n\\nuniform vec2 colorClamp;\\n\\nuniform float scatter;\\n\\nvarying vec4 fragColor;\\n\\nvec4 zero = vec4(0, 0, 0, 0);\\nvec4 unit = vec4(1, 1, 1, 1);\\nvec2 xyProjection = vec2(1, 1);\\n\\nmat4 mclamp(mat4 m, mat4 lo, mat4 hi) {\\n    return mat4(clamp(m[0], lo[0], hi[0]),\\n                clamp(m[1], lo[1], hi[1]),\\n                clamp(m[2], lo[2], hi[2]),\\n                clamp(m[3], lo[3], hi[3]));\\n}\\n\\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\\n    return mclamp(p, lo, hi) == p;\\n}\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * unit, unit);\\n}\\n\\nvoid main() {\\n\\n    float x = 0.5 * sign(pf[3]) + 0.5;\\n    float prominence = abs(pf[3]);\\n    float depth = 1.0 - prominence;\\n\\n    mat4 pA = mat4(p0, p1, p2, p3);\\n    mat4 pB = mat4(p4, p5, p6, p7);\\n    mat4 pC = mat4(p8, p9, pa, pb);\\n    mat4 pD = mat4(pc, pd, pe, abs(pf));\\n\\n    float show = float(mshow(pA, loA, hiA) &&\\n                       mshow(pB, loB, hiB) &&\\n                       mshow(pC, loC, hiC) &&\\n                       mshow(pD, loD, hiD));\\n\\n    vec2 yy = show * vec2(val(pA, dim2A) + val(pB, dim2B) + val(pC, dim2C) + val(pD, dim2D),\\n                          val(pA, dim1A) + val(pB, dim1B) + val(pC, dim1C) + val(pD, dim1D));\\n\\n    vec2 dimensionToggle = vec2(x, 1.0 - x);\\n\\n    vec2 scatterToggle = vec2(scatter, 1.0 - scatter);\\n\\n    float y = dot(yy, dimensionToggle);\\n    mat2 xy = mat2(viewBoxSize * yy + dimensionToggle, viewBoxSize * vec2(x, y));\\n\\n    vec2 viewBoxXY = viewBoxPosition + xy * scatterToggle;\\n\\n    float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n    gl_Position = vec4(\\n        xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n        depthOrHide,\\n        1.0\\n    );\\n\\n    // visible coloring\\n    float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\\n    fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\\n}\\n\",frag:\"precision lowp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\",primitive:\"lines\",lineWidth:1,attributes:P,uniforms:{resolution:O.prop(\"resolution\"),viewBoxPosition:O.prop(\"viewBoxPosition\"),viewBoxSize:O.prop(\"viewBoxSize\"),dim1A:O.prop(\"dim1A\"),dim2A:O.prop(\"dim2A\"),dim1B:O.prop(\"dim1B\"),dim2B:O.prop(\"dim2B\"),dim1C:O.prop(\"dim1C\"),dim2C:O.prop(\"dim2C\"),dim1D:O.prop(\"dim1D\"),dim2D:O.prop(\"dim2D\"),loA:O.prop(\"loA\"),hiA:O.prop(\"hiA\"),loB:O.prop(\"loB\"),hiB:O.prop(\"hiB\"),loC:O.prop(\"loC\"),hiC:O.prop(\"hiC\"),loD:O.prop(\"loD\"),hiD:O.prop(\"hiD\"),palette:R,colorClamp:O.prop(\"colorClamp\"),scatter:O.prop(\"scatter\")},offset:O.prop(\"offset\"),count:O.prop(\"count\")}),j=[0,1],N=[];return{setColorDomain:y,render:x,readPixel:w,readPixels:M,destroy:O.destroy}}},{\"./constants\":1001,regl:499}],1005:[function(t,e,r){\"use strict\";function n(t){return t.key}function i(t){return[t]}function a(t){return!(\"visible\"in t)||t.visible}function o(t){var e=t.range?t.range[0]:w.min(t.values),r=t.range?t.range[1]:w.max(t.values);return!isNaN(e)&&isFinite(e)||(e=0),!isNaN(r)&&isFinite(r)||(r=0),e===r&&(void 0===e?(e=0,r=1):0===e?(e-=1,r+=1):(e*=.9,r*=1.1)),[e,r]}function s(t,e){var r,n,i,a,o;for(r=0,n=t.range(),i=1/0,a=n[0],o;r<n.length;r++){if((o=Math.abs(n[r]-e))>i)return a;i=o,a=n[r]}return n[n.length-1]}function l(t,e){return function(r,n){if(e){var i=e[n];return null===i||void 0===i?t(r):i}return t(r)}}function u(t,e,r){var n=o(r),i=r.ticktext;return r.tickvals?w.scale.ordinal().domain(r.tickvals.map(l(w.format(r.tickformat),i))).range(r.tickvals.map(function(t){return(t-n[0])/(n[1]-n[0])}).map(function(r){return t-e+r*(e-(t-e))})):w.scale.linear().domain(n).range([t-e,e])}function c(t,e){return w.scale.linear().range([t-e,e])}function h(t){return w.scale.linear().domain(o(t))}function f(t){var e=o(t);return t.tickvals&&w.scale.ordinal().domain(t.tickvals).range(t.tickvals.map(function(t){return(t-e[0])/(e[1]-e[0])}))}function d(t){var e=t.map(function(t){return t[0]}),r=t.map(function(t){return t[1]}),n=r.map(function(t){return w.rgb(t)}),i=function(t){return function(e){return e[t]}},a=\"rgb\".split(\"\").map(function(t){return w.scale.linear().clamp(!0).domain(e).range(n.map(i(t)))});return function(t){return a.map(function(e){return e(t)})}}function p(t){return t[0]}function m(t,e,r){var n=p(e),i=n.trace,o=n.lineColor,s=n.cscale,l=i.line,u=i.domain,c=i.dimensions,f=t.width,m=i.labelfont,v=i.tickfont,g=i.rangefont,y=_.extendDeep({},l,{color:o.map(h({values:o,range:[l.cmin,l.cmax]})),blockLineCount:x.blockLineCount,canvasOverdrag:x.overdrag*x.canvasPixelRatio}),b=Math.floor(f*(u.x[1]-u.x[0])),w=Math.floor(t.height*(u.y[1]-u.y[0])),M=t.margin||{l:80,r:80,t:100,b:80},k=b,A=w;return{key:r,colCount:c.filter(a).length,dimensions:c,tickDistance:x.tickDistance,unitToColor:d(s),lines:y,labelFont:m,tickFont:v,rangeFont:g,translateX:u.x[0]*f,translateY:t.height-u.y[1]*t.height,pad:M,canvasWidth:k*x.canvasPixelRatio+2*y.canvasOverdrag,canvasHeight:A*x.canvasPixelRatio,width:k,height:A,canvasPixelRatio:x.canvasPixelRatio}}function v(t){var e=t.width,r=t.height,n=t.dimensions,i=t.canvasPixelRatio,o=function(r){return e*r/Math.max(1,t.colCount-1)},s=x.verticalPadding/(r*i),l=1-2*s,d=function(t){return s+l*t},p={key:t.key,xScale:o,model:t},m={};return p.dimensions=n.filter(a).map(function(e,n){var a=h(e),s=m[e.label];return m[e.label]=(s||0)+1,{key:e.label+(s?\"__\"+s:\"\"),label:e.label,tickFormat:e.tickformat,tickvals:e.tickvals,ticktext:e.ticktext,ordinal:!!e.tickvals,scatter:x.scatter||e.scatter,xIndex:n,crossfilterDimensionIndex:n,visibleIndex:e._index,height:r,values:e.values,paddedUnitValues:e.values.map(a).map(d),xScale:o,x:o(n),canvasX:o(n)*i,unitScale:c(r,x.verticalPadding),domainScale:u(r,x.verticalPadding,e),ordinalScale:f(e),domainToUnitScale:a,filter:e.constraintrange?e.constraintrange.map(a):[0,1],parent:p,model:t}}),p}function g(t){return x.layers.map(function(e){return{key:e,context:\"contextLineLayer\"===e,pick:\"pickLineLayer\"===e,viewModel:t,model:t.model}})}function y(t){t.classed(\"axisExtentText\",!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"default\").style(\"user-select\",\"none\")}var b=t(\"./lines\"),x=t(\"./constants\"),_=t(\"../../lib\"),w=t(\"d3\"),M=t(\"../../components/drawing\");e.exports=function(t,e,r,a,o){function l(t){var e=t.selectAll(\"defs\").data(i,n);e.enter().append(\"defs\");var r=e.selectAll(\"#filterBarPattern\").data(i,n);r.enter().append(\"pattern\").attr(\"id\",\"filterBarPattern\").attr(\"patternUnits\",\"userSpaceOnUse\"),r.attr(\"x\",-x.bar.width).attr(\"width\",x.bar.capturewidth).attr(\"height\",function(t){return t.model.height});var a=r.selectAll(\"rect\").data(i,n);a.enter().append(\"rect\").attr(\"shape-rendering\",\"crispEdges\"),a.attr(\"height\",function(t){return t.model.height}).attr(\"width\",x.bar.width).attr(\"x\",x.bar.width/2).attr(\"fill\",x.bar.fillcolor).attr(\"fill-opacity\",x.bar.fillopacity).attr(\"stroke\",x.bar.strokecolor).attr(\"stroke-opacity\",x.bar.strokeopacity).attr(\"stroke-width\",x.bar.strokewidth)}function u(t){return t.dimensions.some(function(t){return 0!==t.filter[0]||1!==t.filter[1]})}function c(t,e){for(var r=e.panels||(e.panels=[]),n=t.each(function(t){return t})[e.key].map(function(t){return t.__data__}),i=n.length-1,a=0;a<1;a++)for(var o=0;o<i;o++){var s=r[o+a*i]||(r[o+a*i]={}),l=n[o],u=n[o+1];s.dim1=l,s.dim2=u,s.canvasX=l.canvasX,s.panelSizeX=u.canvasX-l.canvasX,s.panelSizeY=e.model.canvasHeight/1,s.y=a*s.panelSizeY,s.canvasY=e.model.canvasHeight-s.y-s.panelSizeY}}function h(t,e){for(var r=e.panels||(e.panels=[]),n=t.each(function(t){return t})[e.key].map(function(t){return t.__data__}),i=n.length-1,a=i,o=0;o<i;o++)for(var s=0;s<i;s++){var l=r[s+o*i]||(r[s+o*i]={}),u=n[s],c=n[s+1];l.dim1=n[o+1],l.dim2=c,l.canvasX=u.canvasX,l.panelSizeX=c.canvasX-u.canvasX,l.panelSizeY=e.model.canvasHeight/a,l.y=o*l.panelSizeY,l.canvasY=e.model.canvasHeight-l.y-l.panelSizeY}}function f(t,e){return(x.scatter?h:c)(t,e)}function d(t){return t.ordinal?function(){return\"\"}:w.format(t.tickFormat)}function _(){W=!0,T=!0}function k(t){S=!1;var e=t.parent,r=t.brush.extent(),n=e.dimensions,i=n[t.xIndex].filter,a=W&&r[0]===r[1];a&&(t.brush.clear(),w.select(this).select(\"rect.extent\").attr(\"y\",-100));var o=a?[0,1]:r.slice();if(o[0]!==i[0]||o[1]!==i[1]){n[t.xIndex].filter=o,e.focusLineLayer&&e.focusLineLayer.render(e.panels,!0);var s=u(e);!X&&s?(e.contextLineLayer&&e.contextLineLayer.render(e.panels,!0),X=!0):X&&!s&&(e.contextLineLayer&&e.contextLineLayer.render(e.panels,!0,!0),X=!1)}W=!1}function A(t){var e=t.parent,r=t.brush.extent(),n=r[0]===r[1],i=e.dimensions,a=i[t.xIndex].filter;if(!n&&t.ordinal&&(a[0]=s(t.ordinalScale,a[0]),a[1]=s(t.ordinalScale,a[1]),a[0]===a[1]&&(a[0]=Math.max(0,a[0]-.05),a[1]=Math.min(1,a[1]+.05)),w.select(this).transition().duration(150).call(t.brush.extent(a)),e.focusLineLayer.render(e.panels,!0)),e.pickLineLayer&&e.pickLineLayer.render(e.panels,!0),S=!0,T=\"ending\",o&&o.filterChanged){var l=t.domainToUnitScale.invert,u=a.map(l);o.filterChanged(e.key,t.visibleIndex,u)}}var T=!1,S=!0,E=r.filter(function(t){return p(t).trace.visible}).map(m.bind(0,a)).map(v);t.selectAll(\".parcoords-line-layers\").remove();var L=t.selectAll(\".parcoords-line-layers\").data(E,n);L.enter().insert(\"div\",\".\"+e.attr(\"class\").split(\" \").join(\" .\")).classed(\"parcoords-line-layers\",!0).style(\"box-sizing\",\"content-box\"),L.style(\"transform\",function(t){return\"translate(\"+(t.model.translateX-x.overdrag)+\"px,\"+t.model.translateY+\"px)\"});var C=L.selectAll(\".parcoords-lines\").data(g,n),I={renderers:[],dimensions:[]},z=null;C.enter().append(\"canvas\").attr(\"class\",function(t){return\"parcoords-lines \"+(t.context?\"context\":t.pick?\"pick\":\"focus\")}).style(\"box-sizing\",\"content-box\").style(\"float\",\"left\").style(\"clear\",\"both\").style(\"left\",0).style(\"overflow\",\"visible\").style(\"position\",function(t,e){return\"absolute\"}).filter(function(t){return t.pick}).on(\"mousemove\",function(t){if(S&&t.lineLayer&&o&&o.hover){var e=w.event,r=this.width,n=this.height,i=w.mouse(this),a=i[0],s=i[1];if(a<0||s<0||a>=r||s>=n)return;var l=t.lineLayer.readPixel(a,n-1-s),u=0!==l[3],c=u?l[2]+256*(l[1]+256*l[0]):null,h={x:a,y:s,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:c};c!==z&&(u?o.hover(h):o.unhover&&o.unhover(h),z=c)}}),C.style(\"margin\",function(t){var e=t.model.pad;return e.t+\"px \"+e.r+\"px \"+e.b+\"px \"+e.l+\"px\"}).attr(\"width\",function(t){return t.model.canvasWidth}).attr(\"height\",function(t){return t.model.canvasHeight}).style(\"width\",function(t){return t.model.width+2*x.overdrag+\"px\"}).style(\"height\",function(t){return t.model.height+\"px\"}).style(\"opacity\",function(t){return t.pick?.01:1}),e.style(\"background\",\"rgba(255, 255, 255, 0)\");var D=e.selectAll(\".parcoords\").data(E,n);D.exit().remove(),D.enter().append(\"g\").classed(\"parcoords\",!0).attr(\"overflow\",\"visible\").style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"overflow\",\"visible\").style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\").call(l),D.attr(\"width\",function(t){return t.model.width+t.model.pad.l+t.model.pad.r}).attr(\"height\",function(t){return t.model.height+t.model.pad.t+t.model.pad.b\n",
+       "}).attr(\"transform\",function(t){return\"translate(\"+t.model.translateX+\",\"+t.model.translateY+\")\"});var P=D.selectAll(\".parcoordsControlView\").data(i,n);P.enter().append(\"g\").classed(\"parcoordsControlView\",!0).style(\"box-sizing\",\"content-box\"),P.attr(\"transform\",function(t){return\"translate(\"+t.model.pad.l+\",\"+t.model.pad.t+\")\"});var O=P.selectAll(\".yAxis\").data(function(t){return t.dimensions},n);O.enter().append(\"g\").classed(\"yAxis\",!0).each(function(t){I.dimensions.push(t)}),P.each(function(t){f(O,t)}),C.each(function(t){t.lineLayer=b(this,t.model.lines,t.model.canvasWidth,t.model.canvasHeight,t.viewModel.dimensions,t.viewModel.panels,t.model.unitToColor,t.context,t.pick,x.scatter),t.viewModel[t.key]=t.lineLayer,I.renderers.push(function(){t.lineLayer.render(t.viewModel.panels,!0)}),t.lineLayer.render(t.viewModel.panels,!t.context)}),O.attr(\"transform\",function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"}),O.call(w.behavior.drag().origin(function(t){return t}).on(\"drag\",function(t){var e=t.parent;S=!1,T||(t.x=Math.max(-x.overdrag,Math.min(t.model.width+x.overdrag,w.event.x)),t.canvasX=t.x*t.model.canvasPixelRatio,O.sort(function(t,e){return t.x-e.x}).each(function(e,r){e.xIndex=r,e.x=t===e?e.x:e.xScale(e.xIndex),e.canvasX=e.x*e.model.canvasPixelRatio}),f(O,e),O.filter(function(e){return 0!==Math.abs(t.xIndex-e.xIndex)}).attr(\"transform\",function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"}),w.select(this).attr(\"transform\",\"translate(\"+t.x+\", 0)\"),O.each(function(r,n,i){i===t.parent.key&&(e.dimensions[n]=r)}),e.contextLineLayer&&e.contextLineLayer.render(e.panels,!1,!u(e)),e.focusLineLayer.render&&e.focusLineLayer.render(e.panels))}).on(\"dragend\",function(t){var e=t.parent;if(T)return void(\"ending\"===T&&(T=!1));t.x=t.xScale(t.xIndex),t.canvasX=t.x*t.model.canvasPixelRatio,f(O,e),w.select(this).attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),e.contextLineLayer&&e.contextLineLayer.render(e.panels,!1,!u(e)),e.focusLineLayer&&e.focusLineLayer.render(e.panels),e.pickLineLayer&&e.pickLineLayer.render(e.panels,!0),S=!0,o&&o.axesMoved&&o.axesMoved(e.key,e.dimensions.map(function(t){return t.crossfilterDimensionIndex}))})),O.exit().remove();var R=O.selectAll(\".axisOverlays\").data(i,n);R.enter().append(\"g\").classed(\"axisOverlays\",!0),R.selectAll(\".axis\").remove();var F=R.selectAll(\".axis\").data(i,n);F.enter().append(\"g\").classed(\"axis\",!0),F.each(function(t){var e=t.model.height/t.model.tickDistance,r=t.domainScale,n=r.domain();w.select(this).call(w.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(e,t.tickFormat).tickValues(t.ordinal?n:null).tickFormat(t.ordinal?function(t){return t}:null).scale(r)),M.font(F.selectAll(\"text\"),t.model.tickFont)}),F.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),F.selectAll(\"text\").style(\"text-shadow\",\"1px 1px 1px #fff, -1px -1px 1px #fff, 1px -1px 1px #fff, -1px 1px 1px #fff\").style(\"cursor\",\"default\").style(\"user-select\",\"none\");var j=R.selectAll(\".axisHeading\").data(i,n);j.enter().append(\"g\").classed(\"axisHeading\",!0);var N=j.selectAll(\".axisTitle\").data(i,n);N.enter().append(\"text\").classed(\"axisTitle\",!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"ew-resize\").style(\"user-select\",\"none\").style(\"pointer-events\",\"auto\"),N.attr(\"transform\",\"translate(0,\"+-x.axisTitleOffset+\")\").text(function(t){return t.label}).each(function(t){M.font(N,t.model.labelFont)});var B=R.selectAll(\".axisExtent\").data(i,n);B.enter().append(\"g\").classed(\"axisExtent\",!0);var U=B.selectAll(\".axisExtentTop\").data(i,n);U.enter().append(\"g\").classed(\"axisExtentTop\",!0),U.attr(\"transform\",\"translate(0,\"+-x.axisExtentOffset+\")\");var V=U.selectAll(\".axisExtentTopText\").data(i,n);V.enter().append(\"text\").classed(\"axisExtentTopText\",!0).attr(\"alignment-baseline\",\"after-edge\").call(y),V.text(function(t){return d(t)(t.domainScale.domain().slice(-1)[0])}).each(function(t){M.font(V,t.model.rangeFont)});var H=B.selectAll(\".axisExtentBottom\").data(i,n);H.enter().append(\"g\").classed(\"axisExtentBottom\",!0),H.attr(\"transform\",function(t){return\"translate(0,\"+(t.model.height+x.axisExtentOffset)+\")\"});var q=H.selectAll(\".axisExtentBottomText\").data(i,n);q.enter().append(\"text\").classed(\"axisExtentBottomText\",!0).attr(\"alignment-baseline\",\"before-edge\").call(y),q.text(function(t){return d(t)(t.domainScale.domain()[0])}).each(function(t){M.font(q,t.model.rangeFont)});var 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n=t(\"../scattergl/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"}}},{\"../scattergl/attributes\":1077}],1019:[function(t,e,r){\"use strict\";function n(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=a(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}function i(t,e){var r=new n(t,e.uid);return r.update(e),r}var a=t(\"gl-pointcloud2d\"),o=t(\"../../lib/str2rgbarray\"),s=t(\"../scatter/get_trace_color\"),l=[\"xaxis\",\"yaxis\"],u=n.prototype;u.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},u.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=s(t,{})},u.updateFast=function(t){var e,r,n,i,a,s,l=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,c=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,f=t.indices,d=this.bounds;if(c){if(n=c,e=c.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(s=0;s<e;s++)i=n[2*s],a=n[2*s+1],i<d[0]&&(d[0]=i),i>d[2]&&(d[2]=i),a<d[1]&&(d[1]=a),a>d[3]&&(d[3]=a);if(f)r=f;else for(r=new Int32Array(e),s=0;s<e;s++)r[s]=s}else for(e=l.length,n=new Float32Array(2*e),r=new 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r,n,i,a=e||.5,o=0;o<2;o++)r=this.scene[l[o]],n=r._min,n||(n=[]),n.push({val:t[o],pad:a}),i=r._max,i||(i=[]),i.push({val:t[o+2],pad:a})},u.dispose=function(){this.pointcloud.dispose()},e.exports=i},{\"../../lib/str2rgbarray\":749,\"../scatter/get_trace_color\":1040,\"gl-pointcloud2d\":230}],1020:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\")}},{\"../../lib\":728,\"./attributes\":1018}],1021:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"../scatter3d/calc\"),n.plot=t(\"./convert\"),n.moduleType=\"trace\",n.name=\"pointcloud\",n.basePlotModule=t(\"../../plots/gl2d\"),n.categories=[\"gl2d\",\"showLegend\"],n.meta={},e.exports=n},{\"../../plots/gl2d\":808,\"../scatter3d/calc\":1056,\"./attributes\":1018,\"./convert\":1019,\"./defaults\":1020}],1022:[function(t,e,r){\"use strict\";var n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;e.exports=l({hoverinfo:s({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hoverlabel:o.hoverlabel,domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),node:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:.5,arrayOk:!0}},pad:{valType:\"number\",arrayOk:!1,min:0,dflt:20},thickness:{valType:\"number\",arrayOk:!1,min:1,dflt:20}},link:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0}},source:{valType:\"data_array\",dflt:[]},target:{valType:\"data_array\",dflt:[]},value:{valType:\"data_array\",dflt:[]}}},\"calc\",\"nested\")},{\"../../components/color/attributes\":603,\"../../components/fx/attributes\":637,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../../plots/font_attributes\":796}],1023:[function(t,e,r){\"use 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n(e.node.label,e.link.source,e.link.target)&&(a.error(\"Circularity is present in the Sankey data. Removing all nodes and links.\"),e.link.label=[],e.link.source=[],e.link.target=[],e.link.value=[],e.link.color=[],e.node.label=[],e.node.color=[]),[{link:e.link,node:e.node}]}},{\"../../lib\":728,\"strongly-connected-components\":528}],1025:[function(t,e,r){\"use strict\";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:\"cubic-in-out\"}},{}],1026:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../components/color/attributes\").defaults,o=t(\"../../components/color\"),s=t(\"tinycolor2\");e.exports=function(t,e,r,l){function u(r,a){return n.coerce(t,e,i,r,a)}u(\"node.label\"),u(\"node.pad\"),u(\"node.thickness\"),u(\"node.line.color\"),u(\"node.line.width\");var c=function(t){return a[t%a.length]};u(\"node.color\",e.node.label.map(function(t,e){return o.addOpacity(c(e),.8)})),u(\"link.label\"),u(\"link.source\"),u(\"link.target\"),u(\"link.value\"),u(\"link.line.color\"),u(\"link.line.width\"),u(\"link.color\",e.link.value.map(function(){return s(l.paper_bgcolor).getLuminance()<.333?\"rgba(255, 255, 255, 0.6)\":\"rgba(0, 0, 0, 0.2)\"})),u(\"domain.x\"),u(\"domain.y\"),u(\"orientation\"),u(\"valueformat\"),u(\"valuesuffix\"),u(\"arrangement\"),n.coerceFont(u,\"textfont\",n.extendFlat({},l.font));var h=function(t,r){return-1===e.link.source.indexOf(r)&&-1===e.link.target.indexOf(r)};e.node.label.some(h)&&n.warn(\"Some of the nodes are neither sources nor targets, they will not be displayed.\")}},{\"../../components/color\":604,\"../../components/color/attributes\":603,\"../../lib\":728,\"./attributes\":1022,tinycolor2:534}],1027:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.moduleType=\"trace\",n.name=\"sankey\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"noOpacity\"],n.meta={},e.exports=n},{\"./attributes\":1022,\"./base_plot\":1023,\"./calc\":1024,\"./defaults\":1026,\"./plot\":1028}],1028:[function(t,e,r){\"use strict\";function n(t){return\"\"!==t}function i(t,e){return t.filter(function(t){return t.key===e.traceId})}function a(t,e){p.select(t).select(\"path\").style(\"fill-opacity\",e),p.select(t).select(\"rect\").style(\"fill-opacity\",e)}function o(t){p.select(t).select(\"text.name\").style(\"fill\",\"black\")}function s(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)||-1!==t.node.targetLinks.indexOf(e.link)}}function l(t){return function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function u(t,e,r){\n",
+       "e&&r&&i(r,e).selectAll(\".sankeyLink\").filter(s(e)).call(h.bind(0,e,r,!1))}function c(t,e,r){e&&r&&i(r,e).selectAll(\".sankeyLink\").filter(s(e)).call(f.bind(0,e,r,!1))}function h(t,e,r,n){var a=n.datum().link.label;n.style(\"fill-opacity\",.4),a&&i(e,t).selectAll(\".sankeyLink\").filter(function(t){return t.link.label===a}).style(\"fill-opacity\",.4),r&&i(e,t).selectAll(\".sankeyNode\").filter(l(t)).call(u)}function f(t,e,r,n){var a=n.datum().link.label;n.style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),a&&i(e,t).selectAll(\".sankeyLink\").filter(function(t){return t.link.label===a}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),r&&i(e,t).selectAll(\".sankeyNode\").filter(l(t)).call(c)}function d(t,e){var r=t.hoverlabel||{},n=y.nestedProperty(r,e).get();return!Array.isArray(n)&&n}var p=t(\"d3\"),m=t(\"./render\"),v=t(\"../../components/fx\"),g=t(\"../../components/color\"),y=t(\"../../lib\");e.exports=function(t,e){var r=t._fullLayout,i=r._paper,s=r._size,l=function(e,r){var n=r.link;n.originalEvent=p.event,t._hoverdata=[n],v.click(t,{target:!0})},y=function(e,r,n){var i=r.link;i.originalEvent=p.event,p.select(e).call(h.bind(0,r,n,!0)),v.hover(t,i,\"sankey\")},b=function(e,i){var s=i.link.trace,l=t._fullLayout._paperdiv.node().getBoundingClientRect(),u=e.getBoundingClientRect(),c=u.left+u.width/2,h=u.top+u.height/2,f=v.loneHover({x:c-l.left,y:h-l.top,name:p.format(i.valueFormat)(i.link.value)+i.valueSuffix,text:[i.link.label||\"\",[\"Source:\",i.link.source.label].join(\" \"),[\"Target:\",i.link.target.label].join(\" \")].filter(n).join(\"<br>\"),color:d(s,\"bgcolor\")||g.addOpacity(i.tinyColorHue,1),borderColor:d(s,\"bordercolor\"),fontFamily:d(s,\"font.family\"),fontSize:d(s,\"font.size\"),fontColor:d(s,\"font.color\"),idealAlign:p.event.x<c?\"right\":\"left\"},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});a(f,.65),o(f)},x=function(e,n,i){p.select(e).call(f.bind(0,n,i,!0)),t.emit(\"plotly_unhover\",{event:p.event,points:[n.link]}),v.loneUnhover(r._hoverlayer.node())},_=function(e,r,n){var i=r.node;i.originalEvent=p.event,t._hoverdata=[i],p.select(e).call(c,r,n),v.click(t,{target:!0})},w=function(e,r,n){var i=r.node;i.originalEvent=p.event,p.select(e).call(u,r,n),v.hover(t,i,\"sankey\")},M=function(e,i){var s=i.node.trace,l=p.select(e).select(\".nodeRect\"),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),c=l.node().getBoundingClientRect(),h=c.left-2-u.left,f=c.right+2-u.left,m=c.top+c.height/4-u.top,g=v.loneHover({x0:h,x1:f,y:m,name:p.format(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,[\"Incoming flow count:\",i.node.targetLinks.length].join(\" \"),[\"Outgoing flow count:\",i.node.sourceLinks.length].join(\" 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u(M),{key:r,trace:i,guid:Math.floor(1e12*(1+Math.random())),horizontal:h,width:_,height:w,nodePad:f,nodeLineColor:p,nodeLineWidth:m,linkLineColor:v,linkLineWidth:g,valueFormat:y,valueSuffix:b,textFont:x,translateX:o.x[0]*_+t.margin.l,translateY:t.height-o.y[1]*t.height+t.margin.t,dragParallel:h?w:_,dragPerpendicular:h?_:w,nodes:M,links:k,arrangement:c,sankey:A,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function f(t,e,r){var n=P(r.color),i=r.source.label+\"|\"+r.target.label,a=t[i];t[i]=(a||0)+1;var o=i+\"__\"+t[i];return r.trace=e.trace,r.curveNumber=e.trace.index,{key:o,traceId:e.key,link:r,tinyColorHue:O.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkLineColor:e.linkLineColor,linkLineWidth:e.linkLineWidth,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,interactionState:e.interactionState}}function d(t,e,r){var n=P(r.color),i=z.nodePadAcross,a=e.nodePad/2,o=r.dx,s=Math.max(.5,r.dy),l=r.label,u=t[l];t[l]=(u||0)+1;var c=l+\"__\"+t[l];return 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a(t).trace.visible}).map(h.bind(null,r)),n);l.exit().remove(),l.enter().append(\"g\").classed(\"sankey\",!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").style(\"box-sizing\",\"content-box\").attr(\"transform\",x),l.transition().ease(z.ease).duration(z.duration).attr(\"transform\",x);var u=l.selectAll(\".sankeyLinks\").data(i,n);u.enter().append(\"g\").classed(\"sankeyLinks\",!0).style(\"fill\",\"none\");var c=u.selectAll(\".sankeyLink\").data(function(t){var e={};return t.sankey.links().filter(function(t){return t.value}).map(f.bind(null,e,t))},n);c.enter().append(\"path\").classed(\"sankeyLink\",!0).attr(\"d\",m).call(S,l,s.linkEvents),c.style(\"stroke\",function(t){return b(t)?O.tinyRGB(P(t.linkLineColor)):t.tinyColorHue}).style(\"stroke-opacity\",function(t){return 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n=t(\"../../components/colorscale/color_attributes\"),i=t(\"../../components/errorbars/attributes\"),a=t(\"../../components/colorbar/attributes\"),o=t(\"../../plots/font_attributes\"),s=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),u=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},x0:{valType:\"any\",dflt:0,editType:\"calc+clearAxisTypes\"},dx:{valType:\"number\",dflt:1,editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y0:{valType:\"any\",dflt:0,editType:\"calc+clearAxisTypes\"},dy:{valType:\"number\",dflt:1,editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"],editType:\"calc\"},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"],editType:\"style\"},line:{color:{valType:\"color\",editType:\"style\"},width:{valType:\"number\",min:0,dflt:2,editType:\"style\"},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\",editType:\"plot\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1,editType:\"plot\"},dash:u({},s,{editType:\"style\"}),simplify:{valType:\"boolean\",dflt:!0,editType:\"plot\"},editType:\"plot\"},connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},cliponaxis:{valType:\"boolean\",dflt:!0,editType:\"plot\"},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\",editType:\"calc\"},fillcolor:{valType:\"color\",editType:\"style\"},marker:u({symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0,editType:\"style\"},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0,editType:\"style\"},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0,editType:\"calcIfAutorange\"},maxdisplayed:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},sizeref:{valType:\"number\",dflt:1,editType:\"calc\"},sizemin:{valType:\"number\",min:0,dflt:0,editType:\"calc\"},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\",editType:\"calc\"},showscale:{valType:\"boolean\",dflt:!1,editType:\"calc\"},colorbar:a,line:u({width:{valType:\"number\",min:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\",editType:\"calc\"},color:{valType:\"color\",arrayOk:!0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top 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r,u,c,h=i.getFromId(t,e.xaxis||\"x\"),f=i.getFromId(t,e.yaxis||\"y\"),d=h.makeCalcdata(e,\"x\"),p=f.makeCalcdata(e,\"y\"),m=Math.min(d.length,p.length);h._minDtick=0,f._minDtick=0,d.length>m&&d.splice(m,d.length-m),p.length>m&&p.splice(m,p.length-m);var v={padded:!0},g={padded:!0};if(o.hasMarkers(e)){if(r=e.marker,u=r.size,Array.isArray(u)){var y={type:\"linear\"};i.setConvert(y),u=y.makeCalcdata(e.marker,\"size\"),u.length>m&&u.splice(m,u.length-m)}var b,x=1.6*(e.marker.sizeref||1);b=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/x),3)}:function(t){return 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i=t(\"../scatter/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/errorbars/attributes\"),s=t(\"../../constants/gl3d_dashes\"),l=t(\"../../constants/gl3d_markers\"),u=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,h=i.line,f=i.marker,d=f.line,p=e.exports=c({x:i.x,y:i.y,z:{valType:\"data_array\"},text:u({},i.text,{}),hovertext:u({},i.hovertext,{}),mode:u({},i.mode,{dflt:\"lines+markers\"}),surfaceaxis:{valType:\"enumerated\",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:\"color\"},projection:{x:n(\"x\"),y:n(\"y\"),z:n(\"z\")},connectgaps:i.connectgaps,line:u({width:h.width,dash:{valType:\"enumerated\",values:Object.keys(s),dflt:\"solid\"},showscale:{valType:\"boolean\",dflt:!1}},a(\"line\")),marker:u({symbol:{valType:\"enumerated\",values:Object.keys(l),dflt:\"circle\",arrayOk:!0},size:u({},f.size,{dflt:8}),sizeref:f.sizeref,sizemin:f.sizemin,sizemode:f.sizemode,opacity:u({},f.opacity,{arrayOk:!1}),showscale:f.showscale,colorbar:f.colorbar,line:u({width:u({},d.width,{arrayOk:!1})},a(\"marker.line\"))},a(\"marker\")),textposition:u({},i.textposition,{dflt:\"top 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u=t.opacity;if(t.marker&&t.marker.opacity&&(u*=t.marker.opacity),r={gl:o,position:l.position,color:l.scatterColor,size:l.scatterSize,glyph:l.scatterMarker,opacity:u,orthographic:!0,lineWidth:l.scatterLineWidth,lineColor:l.scatterLineColor,project:l.project,projectScale:l.projectScale,projectOpacity:l.projectOpacity},-1!==this.mode.indexOf(\"markers\")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=p(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),a={gl:o,position:l.position,glyph:l.text,color:l.textColor,size:l.textSize,angle:l.textAngle,alignment:l.textOffset,font:l.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf(\"text\")?this.textMarkers?this.textMarkers.update(a):(this.textMarkers=p(a),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),n={gl:o,position:l.position,color:l.errorColor,error:l.errorBounds,lineWidth:l.errorLineWidth,capSize:l.errorCapSize,opacity:t.opacity},this.errorBars?l.errorBounds?this.errorBars.update(n):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):l.errorBounds&&(this.errorBars=m(n),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),l.delaunayAxis>=0){var 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a=0,o=r(\"x\"),s=r(\"y\"),l=r(\"z\");return i.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],n),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),a<o.length&&(e.x=o.slice(0,a)),a<s.length&&(e.y=s.slice(0,a)),a<l.length&&(e.z=l.slice(0,a))),a}var i=t(\"../../registry\"),a=t(\"../../lib\"),o=t(\"../scatter/subtypes\"),s=t(\"../scatter/marker_defaults\"),l=t(\"../scatter/line_defaults\"),u=t(\"../scatter/text_defaults\"),c=t(\"../../components/errorbars/defaults\"),h=t(\"./attributes\");e.exports=function(t,e,r,i){function f(r,n){return a.coerce(t,e,h,r,n)}if(!n(t,e,f,i))return void(e.visible=!1);f(\"text\"),f(\"hovertext\"),f(\"mode\"),o.hasLines(e)&&(f(\"connectgaps\"),l(t,e,r,i,f)),o.hasMarkers(e)&&s(t,e,r,i,f),o.hasText(e)&&u(t,e,i,f);var d=(e.line||{}).color,p=(e.marker||{}).color;f(\"surfaceaxis\")>=0&&f(\"surfacecolor\",d||p);for(var m=[\"x\",\"y\",\"z\"],v=0;v<3;++v){var g=\"projection.\"+m[v];f(g+\".show\")&&(f(g+\".opacity\"),f(g+\".scale\"))}c(t,e,r,{axis:\"z\"}),c(t,e,r,{axis:\"y\",inherit:\"z\"}),c(t,e,r,{axis:\"x\",inherit:\"z\"})}},{\"../../components/errorbars/defaults\":633,\"../../lib\":728,\"../../registry\":846,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1055}],1060:[function(t,e,r){\"use strict\";var n={};n.plot=t(\"./convert\"),n.attributes=t(\"./attributes\"),n.markerSymbols=t(\"../../constants/gl3d_markers\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.moduleType=\"trace\",n.name=\"scatter3d\",n.basePlotModule=t(\"../../plots/gl3d\"),n.categories=[\"gl3d\",\"symbols\",\"markerColorscale\",\"showLegend\"],n.meta={},e.exports=n},{\"../../constants/gl3d_markers\":705,\"../../plots/gl3d\":811,\"../scatter/colorbar\":1034,\"./attributes\":1055,\"./calc\":1056,\"./convert\":1058,\"./defaults\":1059}],1061:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=n.marker,u=n.line,c=l.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:s({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:s({symbol:l.symbol,opacity:l.opacity,maxdisplayed:l.maxdisplayed,size:l.size,sizeref:l.sizeref,sizemin:l.sizemin,sizemode:l.sizemode,line:s({width:c.width,editType:\"calc\"},a(\"marker\".line)),gradient:l.gradient,editType:\"calc\"},a(\"marker\"),{showscale:l.showscale,colorbar:o}),textfont:n.textfont,textposition:n.textposition,hoverinfo:s({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":605,\"../../components/colorscale/color_attributes\":611,\"../../lib/extend\":717,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1062:[function(t,e,r){\"use 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o(e),s(d,e),d}}},{\"../../plots/cartesian/axes\":772,\"../carpet/lookup_carpetid\":903,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035,\"../scatter/subtypes\":1052,\"fast-isnumeric\":131}],1063:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),u=t(\"../scatter/text_defaults\"),c=t(\"../scatter/fillcolor_defaults\"),h=t(\"./attributes\");e.exports=function(t,e,r,f){function d(r,i){return n.coerce(t,e,h,r,i)}d(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var p,m=d(\"a\"),v=d(\"b\");if(!(p=Math.min(m.length,v.length)))return void(e.visible=!1);m&&p<m.length&&(e.a=m.slice(0,p)),v&&p<v.length&&(e.b=v.slice(0,p)),d(\"text\"),d(\"mode\",p<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,f,d),l(t,e,d),d(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,f,d,{gradient:!0}),a.hasText(e)&&u(t,e,f,d);var g=[];(a.hasMarkers(e)||a.hasText(e))&&(d(\"marker.maxdisplayed\"),g.push(\"points\")),d(\"fill\"),\"none\"!==e.fill&&(c(t,e,r,d),a.hasLines(e)||l(t,e,d)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||g.push(\"fills\"),d(\"hoveron\",g.join(\"+\")||\"points\")}},{\"../../lib\":728,\"../scatter/constants\":1036,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/line_shape_defaults\":1045,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1061}],1064:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\");e.exports=function(t,e,r,i){function a(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}var o=n(t,e,r,i);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,u=t.xa._length,c=u*l/2,h=u-c;return s.x0=Math.max(Math.min(s.x0,h),c),s.x1=Math.max(Math.min(s.x1,h),c),o}var f=s.cd[s.index];s.a=f.a,s.b=f.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var d=s.trace,p=d._carpet,m=f.hi||d.hoverinfo,v=m.split(\"+\"),g=[];-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\"]),-1!==v.indexOf(\"a\")&&a(p.aaxis,f.a),-1!==v.indexOf(\"b\")&&a(p.baxis,f.b);var y=p.ab2ij([f.a,f.b]),b=Math.floor(y[0]),x=y[0]-b,_=Math.floor(y[1]),w=y[1]-_,M=p.evalxy([],b,_,x,w);return g.push(\"y: \"+M[1].toFixed(3)),s.extraText=g.join(\"<br>\"),o}}},{\"../scatter/hover\":1041}],1065:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattercarpet\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"carpet\",\"symbols\",\"markerColorscale\",\"showLegend\",\"carpetDependent\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":782,\"../scatter/colorbar\":1034,\"./attributes\":1061,\"./calc\":1062,\"./defaults\":1063,\"./hover\":1064,\"./plot\":1066,\"./select\":1067,\"./style\":1068}],1066:[function(t,e,r){\"use strict\";var n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r){var o,s,l,u=r[0][0].carpet,c={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,c,r),o=0;o<r.length;o++)s=r[o][0].trace,l=c.plot.selectAll(\"g.trace\"+s.uid+\" .js-line\"),a.setClipUrl(l,u._clipPathId)}},{\"../../components/drawing\":628,\"../../plots/cartesian/axes\":772,\"../scatter/plot\":1049}],1067:[function(t,e,r){\"use strict\";var n=t(\"../scatter/select\");e.exports=function(t,e){var r=n(t,e);if(r){var i,a,o,s=t.cd;for(o=0;o<r.length;o++)i=r[o],a=s[i.pointNumber],i.a=a.a,i.b=a.b,i.c=a.c,delete i.x,delete i.y;return r}}},{\"../scatter/select\":1050}],1068:[function(t,e,r){\"use strict\";var n=t(\"../scatter/style\");e.exports=function(t){for(var e=t._fullLayout._modules,r=0;r<e.length;r++)if(\"scatter\"===e[r].name)return;n(t)}},{\"../scatter/style\":1051}],1069:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,u=n.marker,c=n.line,h=u.line;e.exports=l({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country names\"],dflt:\"ISO-3\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),hovertext:s({},n.hovertext,{}),textfont:n.textfont,textposition:n.textposition,line:{color:c.color,width:c.width,dash:o},connectgaps:n.connectgaps,marker:s({symbol:u.symbol,opacity:u.opacity,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,showscale:u.showscale,colorbar:u.colorbar,line:s({width:h.width},a(\"marker.line\")),gradient:u.gradient},a(\"marker\")),fill:{valType:\"enumerated\",values:[\"none\",\"toself\"],dflt:\"none\"},fillcolor:n.fillcolor,hoverinfo:s({},i.hoverinfo,{flags:[\"lon\",\"lat\",\"location\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorscale/color_attributes\":611,\"../../components/drawing/attributes\":627,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1070:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/colorscale_calc\"),o=t(\"../scatter/arrays_to_calcdata\");e.exports=function(t,e){for(var r=Array.isArray(e.locations),s=r?e.locations.length:e.lon.length,l=new Array(s),u=0;u<s;u++){var c=l[u]={};if(r){var h=e.locations[u];c.loc=\"string\"==typeof h?h:null}else{var f=e.lon[u],d=e.lat[u];n(f)&&n(d)?c.lonlat=[+f,+d]:c.lonlat=[i,i]}}return o(l,e),a(e),l}},{\"../../constants/numerical\":707,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035,\"fast-isnumeric\":131}],1071:[function(t,e,r){\"use strict\";function n(t,e,r){var n,i,a=0,o=r(\"locations\");return o?(r(\"locationmode\"),a=o.length):(n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length),a<n.length&&(e.lon=n.slice(0,a)),a<i.length&&(e.lat=i.slice(0,a)),a)}var i=t(\"../../lib\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,h){function f(r,n){return i.coerce(t,e,c,r,n)}if(!n(t,e,f))return void(e.visible=!1);f(\"text\"),f(\"hovertext\"),f(\"mode\"),a.hasLines(e)&&(s(t,e,r,h,f),f(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,f,{gradient:!0}),a.hasText(e)&&l(t,e,h,f),f(\"fill\"),\"none\"!==e.fill&&u(t,e,r,f)}},{\"../../lib\":728,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1069}],1072:[function(t,e,r){\"use strict\";e.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null,t}},{}],1073:[function(t,e,r){\"use strict\";function n(t,e,r){function n(t){return a.tickText(r,r.c2l(t),\"hover\").text+\"\\xb0\"}var i=e.hi||t.hoverinfo,o=\"all\"===i?u.hoverinfo.flags:i.split(\"+\"),s=-1!==o.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==o.indexOf(\"lon\"),h=-1!==o.indexOf(\"lat\"),f=-1!==o.indexOf(\"text\"),d=[];return s?d.push(e.loc):c&&h?d.push(\"(\"+n(e.lonlat[0])+\", \"+n(e.lonlat[1])+\")\"):c?d.push(\"lon: \"+n(e.lonlat[0])):h&&d.push(\"lat: \"+n(e.lonlat[1])),f&&l(e,t,d),d.join(\"<br>\")}var i=t(\"../../components/fx\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,s=t(\"../scatter/get_trace_color\"),l=t(\"../scatter/fill_hover_text\"),u=t(\"./attributes\");e.exports=function(t,e,r){function a(t){var n=t.lonlat;if(n[0]===o)return 1/0;if(d(n))return 1/0;var i=p(n),a=p([e,r]),s=Math.abs(i[0]-a[0]),l=Math.abs(i[1]-a[1]),u=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-u,1-3/u)}var l=t.cd,u=l[0].trace,c=t.xa,h=t.ya,f=t.subplot,d=f.projection.isLonLatOverEdges,p=f.project;if(i.getClosest(l,a,t),!1!==t.index){var m=l[t.index],v=m.lonlat,g=[c.c2p(v),h.c2p(v)],y=m.mrc||1;return t.x0=g[0]-y,t.x1=g[0]+y,t.y0=g[1]-y,t.y1=g[1]+y,t.loc=m.loc,t.lon=v[0],t.lat=v[1],t.color=s(u,m),t.extraText=n(u,m,f.mockAxis),[t]}}},{\"../../components/fx\":645,\"../../constants/numerical\":707,\"../../plots/cartesian/axes\":772,\"../scatter/fill_hover_text\":1038,\"../scatter/get_trace_color\":1040,\"./attributes\":1069}],1074:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.hoverPoints=t(\"./hover\"),n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergeo\",n.basePlotModule=t(\"../../plots/geo\"),n.categories=[\"geo\",\"symbols\",\"markerColorscale\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/geo\":800,\"../scatter/colorbar\":1034,\"./attributes\":1069,\"./calc\":1070,\"./defaults\":1071,\"./event_data\":1072,\"./hover\":1073,\"./plot\":1075,\"./select\":1076}],1075:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0].trace;if(Array.isArray(r.locations))for(var n=c(r,e),i=r.locationmode,a=0;a<t.length;a++){var o=t[a],s=h(i,o.loc,n);o.lonlat=s?s.properties.ct:[u,u]}}function i(t){var e=t.layers.frontplot.selectAll(\".trace.scattergeo\");e.style(\"opacity\",function(t){return t[0].trace.opacity}),e.each(function(e){var r=e[0].trace,n=a.select(this);n.selectAll(\"path.point\").call(o.pointStyle,r,t.graphDiv),n.selectAll(\"text\").call(o.textPointStyle,r,t.graphDiv)}),e.selectAll(\"path.js-line\").style(\"fill\",\"none\").each(function(t){var e=a.select(this),r=t.trace,n=r.line||{};e.call(s.stroke,n.color).call(o.dashLine,n.dash||\"\",n.width||0),\"none\"!==r.fill&&e.call(s.fill,r.fillcolor)})}var a=t(\"d3\"),o=t(\"../../components/drawing\"),s=t(\"../../components/color\"),l=t(\"../../lib\"),u=t(\"../../constants/numerical\").BADNUM,c=t(\"../../lib/topojson_utils\").getTopojsonFeatures,h=t(\"../../lib/geo_location_utils\").locationToFeature,f=t(\"../../lib/geojson_utils\"),d=t(\"../scatter/subtypes\")\n",
+       ";e.exports=function(t,e){function r(t){return t[0].trace.uid}function o(t,e){t.lonlat[0]===u&&a.select(e).remove()}for(var s=0;s<e.length;s++)n(e[s],t.topojson);var c=t.layers.frontplot.select(\".scatterlayer\").selectAll(\"g.trace.scattergeo\").data(e,r);c.enter().append(\"g\").attr(\"class\",\"trace scattergeo\"),c.exit().remove(),c.selectAll(\"*\").remove(),c.each(function(t){var e=t[0].node3=a.select(this),r=t[0].trace;if(d.hasLines(r)||\"none\"!==r.fill){var n=f.calcTraceToLineCoords(t),i=\"none\"!==r.fill?f.makePolygon(n):f.makeLine(n);e.selectAll(\"path.js-line\").data([{geojson:i,trace:r}]).enter().append(\"path\").classed(\"js-line\",!0).style(\"stroke-miterlimit\",2)}d.hasMarkers(r)&&e.selectAll(\"path.point\").data(l.identity).enter().append(\"path\").classed(\"point\",!0).each(function(t){o(t,this)}),d.hasText(r)&&e.selectAll(\"g\").data(l.identity).enter().append(\"g\").append(\"text\").each(function(t){o(t,this)})}),i(t)}},{\"../../components/color\":604,\"../../components/drawing\":628,\"../../constants/numerical\":707,\"../../lib\":728,\"../../lib/geo_location_utils\":720,\"../../lib/geojson_utils\":721,\"../../lib/topojson_utils\":753,\"../scatter/subtypes\":1052,d3:122}],1076:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"../../constants/interactions\").DESELECTDIM;e.exports=function(t,e){var r,a,o,s,l,u=t.cd,c=t.xaxis,h=t.yaxis,f=[],d=u[0].trace,p=u[0].node3,m=!n.hasMarkers(d)&&!n.hasText(d);if(!0===d.visible&&!m){var v=d.marker,g=Array.isArray(v.opacity)?1:v.opacity;if(!1===e)for(l=0;l<u.length;l++)u[l].dim=0;else for(l=0;l<u.length;l++)r=u[l],a=r.lonlat,o=c.c2p(a),s=h.c2p(a),e.contains([o,s])?(f.push({pointNumber:l,lon:a[0],lat:a[1]}),r.dim=0):r.dim=1;return p.selectAll(\"path.point\").style(\"opacity\",function(t){return((t.mo+1||g+1)-1)*(t.dim?i:1)}),p.selectAll(\"text\").style(\"opacity\",function(t){return t.dim?i:1}),f}}},{\"../../constants/interactions\":706,\"../scatter/subtypes\":1052}],1077:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../constants/gl2d_dashes\"),o=t(\"../../constants/gl2d_markers\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,u=n.line,c=n.marker,h=c.line,f=e.exports=l({x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,text:s({},n.text,{}),mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\"],extras:[\"none\"]},line:{color:u.color,width:u.width,dash:{valType:\"enumerated\",values:Object.keys(a),dflt:\"solid\"}},marker:s({},i(\"marker\"),{symbol:{valType:\"enumerated\",values:Object.keys(o),dflt:\"circle\",arrayOk:!0},size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,opacity:c.opacity,showscale:c.showscale,colorbar:c.colorbar,line:s({},i(\"marker.line\"),{width:h.width})}),connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"tozeroy\",\"tozerox\"]}),fillcolor:n.fillcolor,error_y:n.error_y,error_x:n.error_x},\"calc\",\"nested\");f.x.editType=f.y.editType=f.x0.editType=f.y0.editType=\"calc+clearAxisTypes\"},{\"../../components/colorscale/color_attributes\":611,\"../../constants/gl2d_dashes\":702,\"../../constants/gl2d_markers\":703,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../scatter/attributes\":1031}],1078:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\"),i=t(\"../scatter/arrays_to_calcdata\"),a=t(\"../scatter/colorscale_calc\");e.exports=function(t,e){var r,o=t._fullLayout.dragmode;if(\"lasso\"===o||\"select\"===o){var s,l=n.getFromId(t,e.xaxis||\"x\"),u=n.getFromId(t,e.yaxis||\"y\"),c=l.makeCalcdata(e,\"x\"),h=u.makeCalcdata(e,\"y\"),f=Math.min(c.length,h.length);for(r=new Array(f),s=0;s<f;s++)r[s]={x:c[s],y:h[s]}}else r=[{x:!1,y:!1,trace:e,t:{}}],i(r,e);return a(e),r}},{\"../../plots/cartesian/axes\":772,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035}],1079:[function(t,e,r){\"use strict\";function n(t,e){this.scene=t,this.uid=e,this.type=\"scattergl\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.connectgaps=!0,this.index=null,this.idToIndex=[],this.bounds=[0,0,0,0],this.isVisible=!1,this.hasLines=!1,this.hasErrorX=!1,this.hasErrorY=!1,this.hasMarkers=!1,this.line=this.initObject(m,{positions:new Float64Array(0),color:[0,0,0,1],width:1,fill:[!1,!1,!1,!1],fillColor:[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],dashes:[1]},0),this.errorX=this.initObject(v,{positions:new Float64Array(0),errors:new Float64Array(0),lineWidth:1,capSize:0,color:[0,0,0,1]},1),this.errorY=this.initObject(v,{positions:new Float64Array(0),errors:new Float64Array(0),lineWidth:1,capSize:0,color:[0,0,0,1]},2);var r={positions:new Float64Array(0),sizes:[],colors:[],glyphs:[],borderWidths:[],borderColors:[],size:12,color:[0,0,0,1],borderSize:1,borderColor:[0,0,0,1],snapPoints:!0},n=y.extendFlat({},r,{snapPoints:!1});this.scatter=this.initObject(d,r,3),this.fancyScatter=this.initObject(p,r,4),this.selectScatter=this.initObject(d,n,5)}function i(t,e,r){return Array.isArray(e)||(e=[e]),a(t,e,r)}function a(t,e,r){for(var n=new Array(r),i=e[0],a=0;a<r;++a)n[a]=t(a>=e.length?i:e[a]);return n}function o(t,e,r){return l(O(t,r),P(e,r),r)}function s(t,e,r,n){var i=k(t,e,n);return i=Array.isArray(i[0])?i:a(y.identity,[i],n),l(i,P(r,n),n)}function l(t,e,r){for(var n=new Array(4*r),i=0;i<r;++i){for(var a=0;a<3;++a)n[4*i+a]=t[i][a];n[4*i+3]=t[i][3]*e[i]}return n}function u(t){return\"\"===t.split(\"-open\")[1]}function c(t,e,r,n,i){var a,o=i?C:1;for(a=0;a<3;a++)t[4*r+a]=e[4*n+a];t[4*r+a]=o*e[4*n+a]}function h(t){for(var e,r=t.length,n=Math.max(1,(r-1)/Math.min(Math.max(r,1),1e3)),i=0;i<r;i+=n)if(e=t[Math.floor(i)],!(g(e)||e instanceof Date))return!1;return!0}function f(t,e,r){var i=new n(t,e.uid);return i.update(e,r),i}var d=t(\"gl-scatter2d\"),p=t(\"gl-scatter2d-sdf\"),m=t(\"gl-line2d\"),v=t(\"gl-error2d\"),g=t(\"fast-isnumeric\"),y=t(\"../../lib\"),b=t(\"../../plots/cartesian/axes\"),x=t(\"../../plots/cartesian/axis_autotype\"),_=t(\"../../components/errorbars\"),w=t(\"../../lib/str2rgbarray\"),M=t(\"../../lib/typed_array_truncate\"),k=t(\"../../lib/gl_format_color\"),A=t(\"../scatter/subtypes\"),T=t(\"../scatter/make_bubble_size_func\"),S=t(\"../scatter/get_trace_color\"),E=t(\"../../constants/gl2d_markers\"),L=t(\"../../constants/gl2d_dashes\"),C=t(\"../../constants/interactions\").DESELECTDIM,I=[\"xaxis\",\"yaxis\"],z=[0,0,0,0],D=n.prototype;D.initObject=function(t,e,r){function n(){u||(u=t(s,e),u._trace=o,u._index=r),u.update(e)}function i(){u&&u.update(l)}function a(){u&&u.dispose()}var o=this,s=o.scene.glplot,l=y.extendFlat({},e),u=null;return{options:e,update:n,clear:i,dispose:a}},D.handlePick=function(t){var e=t.pointId;(t.object!==this.line||this.connectgaps)&&(e=this.idToIndex[t.pointId]);var r=this.pickXData[e];return{trace:this,dataCoord:t.dataCoord,traceCoord:[g(r)||!y.isDateTime(r)?r:y.dateTime2ms(r),this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:Array.isArray(this.color)?this.color[e]:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},D.isFancy=function(t){if(\"linear\"!==this.scene.xaxis.type&&\"date\"!==this.scene.xaxis.type)return!0;if(\"linear\"!==this.scene.yaxis.type)return!0;if(!t.x||!t.y)return!0;if(this.hasMarkers){var e=t.marker||{};if(Array.isArray(e.symbol)||\"circle\"!==e.symbol||Array.isArray(e.size)||Array.isArray(e.color)||Array.isArray(e.line.width)||Array.isArray(e.line.color)||Array.isArray(e.opacity))return!0}return!(!this.hasLines||this.connectgaps)||(!!this.hasErrorX||!!this.hasErrorY)};var P=i.bind(null,function(t){return+t}),O=i.bind(null,w),R=i.bind(null,function(t){return 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Float64Array(2*l),f=this.bounds,d=0,p=0,m=t.selection,v=t.xcalendar,b=h(o),_=!b&&\"date\"===x(o,v);if(b||_)for(e=0;e<l;++e)i=o[e],a=s[e],g(a)&&(b||(i=y.dateTime2ms(i,v)),c[p++]=i,c[p++]=a,u[d++]=e,f[0]=Math.min(f[0],i),f[1]=Math.min(f[1],a),f[2]=Math.max(f[2],i),f[3]=Math.max(f[3],a));if(c=M(c,p),this.idToIndex=u,m&&m.length)for(r=new Float64Array(2*m.length),e=0,n=m.length;e<n;e++)r[2*e+0]=m[e].x,r[2*e+1]=m[e].y;this.updateLines(t,c),this.updateError(\"X\",t),this.updateError(\"Y\",t);var k;if(this.hasMarkers){var A,T,S;r?(this.scatter.options.positions=null,A=w(t.marker.color),T=w(t.marker.line.color),S=t.opacity*t.marker.opacity*C,A[3]*=S,this.scatter.options.color=A,T[3]*=S,this.scatter.options.borderColor=T,k=t.marker.size,this.scatter.options.size=k,this.scatter.options.borderSize=t.marker.line.width,this.scatter.update(),this.scatter.options.positions=c,this.selectScatter.options.positions=r,A=w(t.marker.color),T=w(t.marker.line.color),S=t.opacity*t.marker.opacity,A[3]*=S,this.selectScatter.options.color=A,T[3]*=S,this.selectScatter.options.borderColor=T,k=t.marker.size,this.selectScatter.options.size=k,this.selectScatter.options.borderSize=t.marker.line.width,this.selectScatter.update()):(this.scatter.options.positions=c,A=w(t.marker.color),T=w(t.marker.line.color),S=t.opacity*t.marker.opacity,A[3]*=S,this.scatter.options.color=A,T[3]*=S,this.scatter.options.borderColor=T,k=t.marker.size,this.scatter.options.size=k,this.scatter.options.borderSize=t.marker.line.width,this.scatter.update())}else this.scatter.clear();this.fancyScatter.clear(),this.expandAxesFast(f,k)},D.updateFancy=function(t){var e=this.scene,r=e.xaxis,n=e.yaxis,a=this.bounds,o=t.selection,l=this.pickXData=r.makeCalcdata(t,\"x\").slice(),h=this.pickYData=n.makeCalcdata(t,\"y\").slice();this.xData=l.slice(),this.yData=h.slice();var f,d,p,m,v,g,y,b=_.calcFromTrace(t,e.fullLayout),x=l.length,w=new Array(x),k=new Float64Array(2*x),A=new Float64Array(4*x),S=new Float64Array(4*x),L=0,C=0,I=0,D=0,O=\"log\"===r.type?r.d2l:function(t){return t},F=\"log\"===n.type?n.d2l:function(t){return t};for(f=0;f<x;++f)this.xData[f]=d=O(l[f]),this.yData[f]=p=F(h[f]),isNaN(d)||isNaN(p)||(w[L++]=f,k[C++]=d,k[C++]=p,m=A[I++]=d-b[f].xs||0,v=A[I++]=b[f].xh-d||0,A[I++]=0,A[I++]=0,S[D++]=0,S[D++]=0,g=S[D++]=p-b[f].ys||0,y=S[D++]=b[f].yh-p||0,a[0]=Math.min(a[0],d-m),a[1]=Math.min(a[1],p-g),a[2]=Math.max(a[2],d+v),a[3]=Math.max(a[3],p+y));k=M(k,C),this.idToIndex=w,this.updateLines(t,k),this.updateError(\"X\",t,k,A),this.updateError(\"Y\",t,k,S);var j,N;if(o&&o.length)for(N={},f=0;f<o.length;f++)N[o[f].pointNumber]=!0;if(this.hasMarkers){this.scatter.options.positions=k,this.scatter.options.sizes=new Array(L),this.scatter.options.glyphs=new Array(L),this.scatter.options.borderWidths=new Array(L),this.scatter.options.colors=new Array(4*L),this.scatter.options.borderColors=new Array(4*L);var B,U,V,H,q,G,Y,W,X,Z,J=T(t),K=t.marker,Q=K.opacity,$=t.opacity,tt=R(K.symbol,x),et=s(K,Q,$,x),rt=P(K.line.width,x),nt=s(K.line,Q,$,x);for(j=i(J,K.size,x),f=0;f<L;++f)B=w[f],V=tt[B],H=E[V],q=u(V),G=N&&!N[B],Y=H.noBorder&&!q?nt:et,W=q?et:nt,U=j[B],X=rt[B],Z=H.noBorder||H.noFill?.1*U:0,this.scatter.options.sizes[f]=4*U,this.scatter.options.glyphs[f]=H.unicode,this.scatter.options.borderWidths[f]=.5*(X>Z?X-Z:0),!q||H.noBorder||H.noFill?c(this.scatter.options.colors,Y,f,B,G):c(this.scatter.options.colors,z,f,0),c(this.scatter.options.borderColors,W,f,B,G);N?(this.scatter.options.positions=null,this.fancyScatter.update(),this.scatter.options.positions=k):this.fancyScatter.update()}else this.fancyScatter.clear();this.scatter.clear(),this.expandAxesFancy(l,h,j)},D.updateLines=function(t,e){var r;if(this.hasLines){var n=e;if(!t.connectgaps){var i=0,a=this.xData,s=this.yData;for(n=new Float64Array(2*a.length),r=0;r<a.length;++r)n[i++]=a[r],n[i++]=s[r]}this.line.options.positions=n;var l=o(t.line.color,t.opacity,1),u=Math.round(.5*this.line.options.width),c=(L[t.line.dash]||[1]).slice();for(r=0;r<c.length;++r)c[r]*=u;switch(t.fill){case\"tozeroy\":this.line.options.fill=[!1,!0,!1,!1];break;case\"tozerox\":this.line.options.fill=[!0,!1,!1,!1];break;default:this.line.options.fill=[!1,!1,!1,!1]}var h=w(t.fillcolor);this.line.options.color=l,this.line.options.width=2*t.line.width,this.line.options.dashes=c,this.line.options.fillColor=[h,h,h,h],this.line.update()}else this.line.clear()},D.updateError=function(t,e,r,n){var i=this[\"error\"+t],a=e[\"error_\"+t.toLowerCase()];\"x\"===t.toLowerCase()&&a.copy_ystyle&&(a=e.error_y),this[\"hasError\"+t]?(i.options.positions=r,i.options.errors=n,i.options.capSize=a.width,i.options.lineWidth=a.thickness/2,i.options.color=o(a.color,1,1),i.update()):i.clear()},D.expandAxesFast=function(t,e){for(var r,n,i,a=e||10,o=0;o<2;o++)r=this.scene[I[o]],n=r._min,n||(n=[]),n.push({val:t[o],pad:a}),i=r._max,i||(i=[]),i.push({val:t[o+2],pad:a})},D.expandAxesFancy=function(t,e,r){var n=this.scene,i={padded:!0,ppad:r};b.expand(n.xaxis,t,i),b.expand(n.yaxis,e,i)},D.dispose=function(){this.line.dispose(),this.errorX.dispose(),this.errorY.dispose(),this.scatter.dispose(),this.fancyScatter.dispose()},e.exports=f},{\"../../components/errorbars\":634,\"../../constants/gl2d_dashes\":702,\"../../constants/gl2d_markers\":703,\"../../constants/interactions\":706,\"../../lib\":728,\"../../lib/gl_format_color\":724,\"../../lib/str2rgbarray\":749,\"../../lib/typed_array_truncate\":754,\"../../plots/cartesian/axes\":772,\"../../plots/cartesian/axis_autotype\":773,\"../scatter/get_trace_color\":1040,\"../scatter/make_bubble_size_func\":1047,\"../scatter/subtypes\":1052,\"fast-isnumeric\":131,\"gl-error2d\":159,\"gl-line2d\":170,\"gl-scatter2d\":248,\"gl-scatter2d-sdf\":243}],1080:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/xy_defaults\"),s=t(\"../scatter/marker_defaults\"),l=t(\"../scatter/line_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),c=t(\"../../components/errorbars/defaults\"),h=t(\"./attributes\");e.exports=function(t,e,r,f){function d(r,i){return n.coerce(t,e,h,r,i)}var p=o(t,e,f,d);if(!p)return void(e.visible=!1);d(\"text\"),d(\"mode\",p<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(d(\"connectgaps\"),l(t,e,r,f,d)),a.hasMarkers(e)&&s(t,e,r,f,d),d(\"fill\"),\"none\"!==e.fill&&u(t,e,r,d),c(t,e,r,{axis:\"y\"}),c(t,e,r,{axis:\"x\",inherit:\"y\"})}},{\"../../components/errorbars/defaults\":633,\"../../lib\":728,\"../scatter/constants\":1036,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/xy_defaults\":1054,\"./attributes\":1077}],1081:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.hoverPoints=t(\"../scatter/hover\"),n.calc=t(\"./calc\"),n.plot=t(\"./convert\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergl\",n.basePlotModule=t(\"../../plots/gl2d\"),n.categories=[\"gl2d\",\"symbols\",\"errorBarsOK\",\"markerColorscale\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/gl2d\":808,\"../scatter/colorbar\":1034,\"../scatter/hover\":1041,\"./attributes\":1077,\"./calc\":1078,\"./convert\":1079,\"./defaults\":1080,\"./select\":1082}],1082:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\");e.exports=function(t,e){var r,i,a,o,s=t.cd,l=t.xaxis,u=t.yaxis,c=[],h=s[0].trace,f=s[0]._glTrace,d=f.scene,p=!n.hasMarkers(h)&&!n.hasText(h);if(!0===h.visible&&!p){if(!1===e)for(r=0;r<s.length;r++)s[r].dim=0;else 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n=t(\"../scattergeo/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/mapbox/layout_attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorbar/attributes\"),l=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,c=n.line,h=n.marker;e.exports=u({lon:n.lon,lat:n.lat,mode:l({},i.mode,{dflt:\"markers\"}),text:l({},i.text,{}),hovertext:l({},i.hovertext,{}),line:{color:c.color,width:c.width},connectgaps:i.connectgaps,marker:{symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},opacity:h.opacity,size:h.size,sizeref:h.sizeref,sizemin:h.sizemin,sizemode:h.sizemode,color:h.color,colorscale:h.colorscale,cauto:h.cauto,cmax:h.cmax,cmin:h.cmin,autocolorscale:h.autocolorscale,reversescale:h.reversescale,showscale:h.showscale,colorbar:s},fill:n.fill,fillcolor:i.fillcolor,textfont:a.layers.symbol.textfont,textposition:a.layers.symbol.textposition,hoverinfo:l({},o.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorbar/attributes\":605,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../../plots/mapbox/layout_attributes\":827,\"../scatter/attributes\":1031,\"../scattergeo/attributes\":1069}],1084:[function(t,e,r){\"use 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../components/drawing/attributes\").dash,l=t(\"../../lib/extend\").extendFlat,u=n.marker,c=n.line,h=u.line;e.exports={a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},c:{valType:\"data_array\",editType:\"calc\"},sum:{valType:\"number\",dflt:0,min:0,editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),hovertext:l({},n.hovertext,{}),line:{color:c.color,width:c.width,dash:s,shape:l({},c.shape,{values:[\"linear\",\"spline\"]}),smoothing:c.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,cliponaxis:n.cliponaxis,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:l({symbol:u.symbol,opacity:u.opacity,maxdisplayed:u.maxdisplayed,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,line:l({width:h.width,editType:\"calc\"},a(\"marker.line\")),gradient:u.gradient,editType:\"calc\"},a(\"marker\"),{showscale:u.showscale,colorbar:o}),textfont:n.textfont,textposition:n.textposition,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"c\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":605,\"../../components/colorscale/color_attributes\":611,\"../../components/drawing/attributes\":627,\"../../lib/extend\":717,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1092:[function(t,e,r){\"use 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n=t(\"../scatter/hover\"),i=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r,a){function o(t,e){y.push(t._hovertitle+\": \"+i.tickText(t,e,\"hover\").text)}var s=n(t,e,r,a);if(s&&!1!==s[0].index){var l=s[0];if(void 0===l.index){var u=1-l.y0/t.ya._length,c=t.xa._length,h=c*u/2,f=c-h;return l.x0=Math.max(Math.min(l.x0,f),h),l.x1=Math.max(Math.min(l.x1,f),h),s}var d=l.cd[l.index];l.a=d.a,l.b=d.b,l.c=d.c,l.xLabelVal=void 0,l.yLabelVal=void 0;var p=l.trace,m=p._ternary,v=d.hi||p.hoverinfo,g=v.split(\"+\"),y=[];return-1!==g.indexOf(\"all\")&&(g=[\"a\",\"b\",\"c\"]),-1!==g.indexOf(\"a\")&&o(m.aaxis,d.a),-1!==g.indexOf(\"b\")&&o(m.baxis,d.b),-1!==g.indexOf(\"c\")&&o(m.caxis,d.c),l.extraText=y.join(\"<br>\"),s}}},{\"../../plots/cartesian/axes\":772,\"../scatter/hover\":1041}],1095:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scatterternary\",n.basePlotModule=t(\"../../plots/ternary\"),\n",
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+       "});require(['plotly'], function(Plotly) {window.Plotly = Plotly;});}</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.plotly as py\n",
+    "import plotly.graph_objs as go\n",
+    "import plotly\n",
+    "\n",
+    "plotly.offline.init_notebook_mode()\n",
+    "\n",
+    "\n",
+    "def plot_3dsurface_with_slider(df, colFilter, nbDates, nbSecurities, title):\n",
+    "    #nbSecurities = 30\n",
+    "    #nbDates = 40\n",
+    "    maxWeight = 1.0\n",
+    "\n",
+    "\n",
+    "    dfn = df.loc[:,[colFilter]]\n",
+    "    \n",
+    "\n",
+    "    ellist = list(dfn.index)[:nbDates]\n",
+    "    data = []\n",
+    "    for el in ellist:   \n",
+    "\n",
+    "        znow= dfn.sort_values(by=[el], axis=1, ascending=0) # this needs to have access to every column\n",
+    "        znow = znow.iloc[:,range(nbSecurities)].head(nbDates)   \n",
+    "        #znowup = [[zij+0.5 for zij in zi] for zi in znow.as_matrix()]\n",
+    "        znowup = znow +0.5\n",
+    "        #display(znow)\n",
+    "        #display(znowup)\n",
+    "        data.append(go.Surface(\n",
+    "            z=znow.as_matrix()\n",
+    "\n",
+    "        ))\n",
+    "\n",
+    "\n",
+    "    steps = list()\n",
+    "\n",
+    "    # make an entry in the slider for each day\n",
+    "    for i in range(len(ellist)):\n",
+    "\n",
+    "        step = dict(\n",
+    "            method='restyle',\n",
+    "            args=['visible', [False]*len(ellist)], # set rest to visible false\n",
+    "        ) # this styles the slider?\n",
+    "        step['args'][1][i] = True # set only these ones to visible\n",
+    "        #step['args'][1][i*2+1] = True # set only these ones to visible\n",
+    "        steps.append(step)\n",
+    "\n",
+    "\n",
+    "    sliders = [dict(\n",
+    "            active=0,\n",
+    "            steps=steps\n",
+    "        )]\n",
+    "\n",
+    "    layout = go.Layout(\n",
+    "        title=title,\n",
+    "        autosize=False,\n",
+    "        width=1200,\n",
+    "        height=900,\n",
+    "        sliders = sliders,\n",
+    "        margin=dict(\n",
+    "            l=0,\n",
+    "            r=0,\n",
+    "            b=0,\n",
+    "            t=50\n",
+    "        )\n",
+    "        , scene = dict( aspectratio = dict(x = 1, y = 1.5, z = 1),\n",
+    "                        xaxis = dict(\n",
+    "                            title=\"enum\", nticks=4, range = [0,nbSecurities],),\n",
+    "                        yaxis = dict(\n",
+    "                            title=\"date\", nticks=4, range = [0,nbDates],),\n",
+    "                        zaxis = dict(\n",
+    "                            title=\"weight\",nticks=4, range = [0,maxWeight],),\n",
+    "        ),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    fig = go.Figure(data=data, layout=layout)\n",
+    "    return fig\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~kaiaeberli/5.embed\" height=\"900px\" width=\"1200px\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plot_3dsurface_with_slider(df_piv, colFilter=\"mv_weight_change_on_roll\", nbDates=5, nbSecurities=30, title=\"mv weight\")\n",
+    "py.iplot(fig, filename='elevations-3d-surface')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The draw time for this plot will be slow for all clients.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\anaconda3\\lib\\site-packages\\plotly\\api\\v1\\clientresp.py:40: UserWarning:\n",
+      "\n",
+      "Estimated Draw Time Too Long\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~kaiaeberli/5.embed\" height=\"900px\" width=\"1200px\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plot_3dsurface_with_slider(df_piv, colFilter=\"spread_weight_change_on_roll\", nbDates=40, nbSecurities=30, title=\"s weight\")\n",
+    "py.iplot(fig, filename='elevations-3d-surface')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"21\" halign=\"left\">mv_weight_eom_lm</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>indexname</th>\n",
+       "      <th colspan=\"21\" halign=\"left\">ref loans + hy daily price &gt; 60 stable spreads 10 day</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>isd_bond</th>\n",
+       "      <th>459957</th>\n",
+       "      <th>471207</th>\n",
+       "      <th>B1284457361237</th>\n",
+       "      <th>473223</th>\n",
+       "      <th>449374</th>\n",
+       "      <th>473486</th>\n",
+       "      <th>B1286271269032</th>\n",
+       "      <th>472032</th>\n",
+       "      <th>471621</th>\n",
+       "      <th>B1285111736308</th>\n",
+       "      <th>...</th>\n",
+       "      <th>495671</th>\n",
+       "      <th>495672</th>\n",
+       "      <th>495673</th>\n",
+       "      <th>495707</th>\n",
+       "      <th>495709</th>\n",
+       "      <th>495710</th>\n",
+       "      <th>495716</th>\n",
+       "      <th>495719</th>\n",
+       "      <th>495731</th>\n",
+       "      <th>495760</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mem_date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-31</th>\n",
+       "      <td>0.792310</td>\n",
+       "      <td>0.600671</td>\n",
+       "      <td>0.472331</td>\n",
+       "      <td>0.422999</td>\n",
+       "      <td>0.402223</td>\n",
+       "      <td>0.395578</td>\n",
+       "      <td>0.372544</td>\n",
+       "      <td>0.366074</td>\n",
+       "      <td>0.349387</td>\n",
+       "      <td>0.371342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-02-29</th>\n",
+       "      <td>0.815682</td>\n",
+       "      <td>0.596392</td>\n",
+       "      <td>0.474737</td>\n",
+       "      <td>0.434209</td>\n",
+       "      <td>0.405999</td>\n",
+       "      <td>0.390178</td>\n",
+       "      <td>0.387912</td>\n",
+       "      <td>0.367455</td>\n",
+       "      <td>0.351022</td>\n",
+       "      <td>0.376884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-03-31</th>\n",
+       "      <td>0.819189</td>\n",
+       "      <td>0.605105</td>\n",
+       "      <td>0.485756</td>\n",
+       "      <td>0.448240</td>\n",
+       "      <td>0.416783</td>\n",
+       "      <td>0.399525</td>\n",
+       "      <td>0.390824</td>\n",
+       "      <td>0.378158</td>\n",
+       "      <td>0.367131</td>\n",
+       "      <td>0.365057</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-04-30</th>\n",
+       "      <td>0.794913</td>\n",
+       "      <td>0.604817</td>\n",
+       "      <td>0.475874</td>\n",
+       "      <td>0.444469</td>\n",
+       "      <td>0.434348</td>\n",
+       "      <td>0.408541</td>\n",
+       "      <td>0.391641</td>\n",
+       "      <td>0.376043</td>\n",
+       "      <td>0.366198</td>\n",
+       "      <td>0.370582</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-05-31</th>\n",
+       "      <td>0.685136</td>\n",
+       "      <td>0.530009</td>\n",
+       "      <td>0.425826</td>\n",
+       "      <td>0.393537</td>\n",
+       "      <td>0.363084</td>\n",
+       "      <td>0.355187</td>\n",
+       "      <td>0.345912</td>\n",
+       "      <td>0.327617</td>\n",
+       "      <td>0.310424</td>\n",
+       "      <td>0.327583</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 4998 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                mv_weight_eom_lm            \\\n",
+       "indexname  ref loans + hy daily price > 60 stable spreads 10 day             \n",
+       "isd_bond                                                  459957    471207   \n",
+       "mem_date                                                                     \n",
+       "2012-01-31                                           0.792310     0.600671   \n",
+       "2012-02-29                                           0.815682     0.596392   \n",
+       "2012-03-31                                           0.819189     0.605105   \n",
+       "2012-04-30                                           0.794913     0.604817   \n",
+       "2012-05-31                                           0.685136     0.530009   \n",
+       "\n",
+       "                                                                        \\\n",
+       "indexname                                                                \n",
+       "isd_bond   B1284457361237    473223    449374    473486 B1286271269032   \n",
+       "mem_date                                                                 \n",
+       "2012-01-31       0.472331  0.422999  0.402223  0.395578       0.372544   \n",
+       "2012-02-29       0.474737  0.434209  0.405999  0.390178       0.387912   \n",
+       "2012-03-31       0.485756  0.448240  0.416783  0.399525       0.390824   \n",
+       "2012-04-30       0.475874  0.444469  0.434348  0.408541       0.391641   \n",
+       "2012-05-31       0.425826  0.393537  0.363084  0.355187       0.345912   \n",
+       "\n",
+       "                                               ...                         \\\n",
+       "indexname                                      ...                          \n",
+       "isd_bond      472032    471621 B1285111736308  ...   495671 495672 495673   \n",
+       "mem_date                                       ...                          \n",
+       "2012-01-31  0.366074  0.349387       0.371342  ...      0.0    0.0    0.0   \n",
+       "2012-02-29  0.367455  0.351022       0.376884  ...      0.0    0.0    0.0   \n",
+       "2012-03-31  0.378158  0.367131       0.365057  ...      0.0    0.0    0.0   \n",
+       "2012-04-30  0.376043  0.366198       0.370582  ...      0.0    0.0    0.0   \n",
+       "2012-05-31  0.327617  0.310424       0.327583  ...      0.0    0.0    0.0   \n",
+       "\n",
+       "                                                             \n",
+       "indexname                                                    \n",
+       "isd_bond   495707 495709 495710 495716 495719 495731 495760  \n",
+       "mem_date                                                     \n",
+       "2012-01-31    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "2012-02-29    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "2012-03-31    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "2012-04-30    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "2012-05-31    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "\n",
+       "[5 rows x 4998 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"10\" halign=\"left\">mv_weight_change_on_roll</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">mv_weight_eom_lm</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>indexname</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">ref loans + hy daily price &gt; 60 stable spreads 10 day</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">ref loans + hy daily price &gt; 60 stable spreads 10 day</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>isd_bond</th>\n",
+       "      <th>459957</th>\n",
+       "      <th>471207</th>\n",
+       "      <th>B1284457361237</th>\n",
+       "      <th>473223</th>\n",
+       "      <th>449374</th>\n",
+       "      <th>473486</th>\n",
+       "      <th>B1286271269032</th>\n",
+       "      <th>472032</th>\n",
+       "      <th>471621</th>\n",
+       "      <th>B1285111736308</th>\n",
+       "      <th>...</th>\n",
+       "      <th>495671</th>\n",
+       "      <th>495672</th>\n",
+       "      <th>495673</th>\n",
+       "      <th>495707</th>\n",
+       "      <th>495709</th>\n",
+       "      <th>495710</th>\n",
+       "      <th>495716</th>\n",
+       "      <th>495719</th>\n",
+       "      <th>495731</th>\n",
+       "      <th>495760</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mem_date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-31</th>\n",
+       "      <td>0.792310</td>\n",
+       "      <td>0.600671</td>\n",
+       "      <td>0.472331</td>\n",
+       "      <td>0.422999</td>\n",
+       "      <td>0.402223</td>\n",
+       "      <td>0.395578</td>\n",
+       "      <td>0.372544</td>\n",
+       "      <td>0.366074</td>\n",
+       "      <td>0.349387</td>\n",
+       "      <td>0.371342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-02-29</th>\n",
+       "      <td>0.010089</td>\n",
+       "      <td>0.007484</td>\n",
+       "      <td>0.007842</td>\n",
+       "      <td>0.005370</td>\n",
+       "      <td>0.006707</td>\n",
+       "      <td>0.004400</td>\n",
+       "      <td>0.006408</td>\n",
+       "      <td>0.006070</td>\n",
+       "      <td>0.004496</td>\n",
+       "      <td>-0.005877</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-03-31</th>\n",
+       "      <td>-0.025517</td>\n",
+       "      <td>-0.007446</td>\n",
+       "      <td>-0.012267</td>\n",
+       "      <td>-0.005929</td>\n",
+       "      <td>-0.003466</td>\n",
+       "      <td>-0.005351</td>\n",
+       "      <td>-0.003250</td>\n",
+       "      <td>-0.003145</td>\n",
+       "      <td>-0.004246</td>\n",
+       "      <td>-0.003036</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-04-30</th>\n",
+       "      <td>-0.087991</td>\n",
+       "      <td>-0.066940</td>\n",
+       "      <td>-0.050917</td>\n",
+       "      <td>-0.049038</td>\n",
+       "      <td>-0.046474</td>\n",
+       "      <td>-0.045524</td>\n",
+       "      <td>-0.041904</td>\n",
+       "      <td>-0.045430</td>\n",
+       "      <td>-0.040317</td>\n",
+       "      <td>-0.039651</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-05-31</th>\n",
+       "      <td>-0.027947</td>\n",
+       "      <td>-0.021575</td>\n",
+       "      <td>-0.065440</td>\n",
+       "      <td>-0.015903</td>\n",
+       "      <td>-0.013311</td>\n",
+       "      <td>-0.014823</td>\n",
+       "      <td>-0.026388</td>\n",
+       "      <td>-0.012011</td>\n",
+       "      <td>-0.012439</td>\n",
+       "      <td>-0.012010</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 9996 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        mv_weight_change_on_roll            \\\n",
+       "indexname  ref loans + hy daily price > 60 stable spreads 10 day             \n",
+       "isd_bond                                                  459957    471207   \n",
+       "mem_date                                                                     \n",
+       "2012-01-31                                           0.792310     0.600671   \n",
+       "2012-02-29                                           0.010089     0.007484   \n",
+       "2012-03-31                                          -0.025517    -0.007446   \n",
+       "2012-04-30                                          -0.087991    -0.066940   \n",
+       "2012-05-31                                          -0.027947    -0.021575   \n",
+       "\n",
+       "                                                                        \\\n",
+       "indexname                                                                \n",
+       "isd_bond   B1284457361237    473223    449374    473486 B1286271269032   \n",
+       "mem_date                                                                 \n",
+       "2012-01-31       0.472331  0.422999  0.402223  0.395578       0.372544   \n",
+       "2012-02-29       0.007842  0.005370  0.006707  0.004400       0.006408   \n",
+       "2012-03-31      -0.012267 -0.005929 -0.003466 -0.005351      -0.003250   \n",
+       "2012-04-30      -0.050917 -0.049038 -0.046474 -0.045524      -0.041904   \n",
+       "2012-05-31      -0.065440 -0.015903 -0.013311 -0.014823      -0.026388   \n",
+       "\n",
+       "                                               ...    \\\n",
+       "indexname                                      ...     \n",
+       "isd_bond      472032    471621 B1285111736308  ...     \n",
+       "mem_date                                       ...     \n",
+       "2012-01-31  0.366074  0.349387       0.371342  ...     \n",
+       "2012-02-29  0.006070  0.004496      -0.005877  ...     \n",
+       "2012-03-31 -0.003145 -0.004246      -0.003036  ...     \n",
+       "2012-04-30 -0.045430 -0.040317      -0.039651  ...     \n",
+       "2012-05-31 -0.012011 -0.012439      -0.012010  ...     \n",
+       "\n",
+       "                                                mv_weight_eom_lm         \\\n",
+       "indexname  ref loans + hy daily price > 60 stable spreads 10 day          \n",
+       "isd_bond                                                  495671 495672   \n",
+       "mem_date                                                                  \n",
+       "2012-01-31                                                0.0       0.0   \n",
+       "2012-02-29                                                0.0       0.0   \n",
+       "2012-03-31                                                0.0       0.0   \n",
+       "2012-04-30                                                0.0       0.0   \n",
+       "2012-05-31                                                0.0       0.0   \n",
+       "\n",
+       "                                                                    \n",
+       "indexname                                                           \n",
+       "isd_bond   495673 495707 495709 495710 495716 495719 495731 495760  \n",
+       "mem_date                                                            \n",
+       "2012-01-31    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "2012-02-29    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "2012-03-31    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "2012-04-30    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "2012-05-31    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
+       "\n",
+       "[5 rows x 9996 columns]"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = df_piv.loc[:,[\"mv_weight_eom_lm\", \"mv_weight_change_on_roll\"]]\n",
+    "asort = df_piv.loc[:,[\"mv_weight_eom_lm\"]].sort_values(by=[\"2012-03-31\"], axis='columns', ascending=0)\n",
+    "display(asort.head())\n",
+    "colsort = asort.columns.get_level_values(2) # to get sorted columns after a sort\n",
+    "#colsort = asort.columns\n",
+    "w = list(colsort)\n",
+    "#display(w)\n",
+    "b = a.loc[:,(slice(None), slice(None), w)]\n",
+    "\n",
+    "c = b.reindex(w, axis='columns', level=2)#471207\n",
+    "c.head()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The draw time for this plot will be slow for all clients.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\anaconda3\\lib\\site-packages\\plotly\\api\\v1\\clientresp.py:40: UserWarning:\n",
+      "\n",
+      "Estimated Draw Time Too Long\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~kaiaeberli/5.embed\" height=\"900px\" width=\"1200px\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col = \"mv_weight_change_on_roll\"\n",
+    "#col = \"mv_weight_eom_lm\"\n",
+    "fig = plot_3dsurface_with_slider(c, colFilter=col, nbDates=40, nbSecurities=30, title=\"order by mv weight, show change\")\n",
+    "py.iplot(fig, filename='elevations-3d-surface')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~kaiaeberli/7.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import plotly.plotly as py\n",
+    "from plotly.graph_objs import Surface\n",
+    "df = c\n",
+    "\n",
+    "\n",
+    "\n",
+    "#dfn = df.loc[:,[\"mv_weight_eom_lm\"]]\n",
+    "    \n",
+    "znow= df.loc[:,[\"mv_weight_eom_lm\"]] # this needs to have access to every column\n",
+    "#znow = znow.iloc[:,range(10)].head(10)   \n",
+    "\n",
+    "znowup = df.loc[:,[\"mv_weight_change_on_roll\"]] # this needs to have access to every column\n",
+    "znowup = znowup +2\n",
+    "#display(znow)\n",
+    "#display(znowup)\n",
+    "\n",
+    "#z1 = znow.as_matrix()\n",
+    "\n",
+    "\n",
+    "l = go.Layout(\n",
+    "    title=\"double\",\n",
+    "    autosize=False,\n",
+    "    width=1200,\n",
+    "    height=900,\n",
+    "    #sliders = sliders,\n",
+    "    margin=dict(\n",
+    "        l=0,\n",
+    "        r=0,\n",
+    "        b=0,\n",
+    "        t=50\n",
+    "    )\n",
+    "    , scene = dict( aspectratio = dict(x = 1, y = 1.5, z = 1),\n",
+    "                    xaxis = dict(\n",
+    "                        title=\"enum\", nticks=4, range = [0,10],),\n",
+    "                    yaxis = dict(\n",
+    "                        title=\"date\", nticks=4, range = [0,40],),\n",
+    "                    zaxis = dict(\n",
+    "                        title=\"weight\",nticks=4, range = [0,7],),\n",
+    "    ),\n",
+    ")\n",
+    "#f = go.Figure(data =dict(z=znow.iloc[:,range(10)].as_matrix() ,visible=True, showscale=False, opacity=0.9, type='surface'), layout=l)\n",
+    "#f2 = go.Figure(data= dict(z=znowup.iloc[:,range(10)].as_matrix() ,visible=True, showscale=False, opacity=0.9, type='surface'), layout=l)\n",
+    "py.iplot([\n",
+    "    dict(z=znow.iloc[:,range(10)].as_matrix() ,visible=True, showscale=False, opacity=0.9, type='surface'), \n",
+    "     dict(z=znowup.iloc[:,range(10)].as_matrix() ,visible=True, showscale=False, opacity=0.9, type='surface')\n",
+    "    \n",
+    "], filename=\"sdfs\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/capstone_project/.ipynb_checkpoints/kaggle_bm-checkpoint.ipynb b/capstone_project/.ipynb_checkpoints/kaggle_bm-checkpoint.ipynb
new file mode 100644
index 0000000..55f379d
--- /dev/null
+++ b/capstone_project/.ipynb_checkpoints/kaggle_bm-checkpoint.ipynb
@@ -0,0 +1,1864 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Machine learning capstone project - fx spot prediction\n",
+    "\n",
+    "The goal is to create features that can help predict the bid price, using a lookback period of a few minutes.\n",
+    "\n",
+    "Try to include the bid offer spread - from the benchmark model it seems volume is not an important feature so it is not a problem that i dont have this data point.\n",
+    "\n",
+    "I took inspiration from : https://www.kaggle.com/kimy07/eurusd-15-minute-interval-price-prediction/notebook\n",
+    "\n",
+    "Introduction\n",
+    "This notebook trains a LSTM model that predicts the bid price of EURUSD 15 minutes in the future by looking at last five hours of data. While there is no requirement for the input to be contiguous, it's been empirically observed that having the contiguous input does improve the accuracy of the model. I suspect that having day of the week and hour of the day as the features mitigates some of the seasonality and contiguousness problems.\n",
+    "\n",
+    "Disclaimer: This exercise has been carried out using a small sample data which only contains 14880 samples (2015-12-29 00:00:00 to 2016-05-31 23:45:00) and lacks ASK prices. Which restricts the ability for the model to approach a better accuracy.\n",
+    "\n",
+    "I will use 1 year of data, from 1Jan16 to 1Jan17, also in 15 minute intervals, but with tick data features.\n",
+    "\n",
+    "Improvements\n",
+    "\n",
+    "To tune the model further, I would recommend having at least 5 years worth of data, have ASK price (so that you can compute the spread), and increasing the epoch to 3000.\n",
+    "Adding more cross-axial features. Such as spread.\n",
+    "If you are looking into classification approach (PASS, BUY, SELL), consider adding some technical indicators that is more sensitive to more recent data.\n",
+    "Consider adding non-numerical data, e.g. news, Tweets. The catch is that you have to get the data under one minute for trading, otherwise the news will be reflected before you even make a trade. If anybody knows how to get the news streamed really fast, please let me know.\n",
+    "\n",
+    "Credits : Dave Y. Kim, Mahmoud Elsaftawy,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run on EC2:\n",
+    "Enter the repo directory: cd aind2-cnn\n",
+    "Activate the new environment: source activate aind2\n",
+    "Start Jupyter: jupyter notebook --ip=0.0.0.0 --no-browser\n",
+    "Find this line in output and copy url to browser: \n",
+    "Copy/paste this URL into your browser when you connect for the first time to login with a token: \n",
+    "http://0.0.0.0:8888/?token=3156e...\n",
+    "\n",
+    "change the 0.0.0.0 with EC2 IP.\n",
+    "\n",
+    "you should see the checked out repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "hideCode": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:70% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd, numpy as np\n",
+    "import pypyodbc\n",
+    "import io, datetime\n",
+    "import matplotlib.colors as colors, matplotlib.cm as cm, pylab, matplotlib.pyplot as plt\n",
+    "\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "from subprocess import check_output\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "  \n",
+       "    $('.ctb_hideshow').toggle();\n",
+       "    \n",
+       "    </script>\n",
+       " "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pypyodbc\n",
+    "display(HTML(\"\"\"<script>\n",
+    "  \n",
+    "    $('.ctb_hideshow').toggle();\n",
+    "    \n",
+    "    </script>\n",
+    " \"\"\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "hideCode": false
+   },
+   "outputs": [],
+   "source": [
+    "#kaggle dates: 2015-12-29 00:00:00 to 2016-05-31 23:45:00\n",
+    "min_date = \"29Dec15\"\n",
+    "max_date = \"31May16\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simname = \"mine\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29Dec15\n",
+      "31May16\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(min_date)\n",
+    "print(max_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "strConnDef = \"DRIVER={ODBC Driver 13 for SQL Server};SERVER=localhost,1433;DATABASE=kai_dw;uid=kai_ta;pwd=tenpen12\"\n",
+    "def getQueryRaw(strQuery, params=None, strConn=strConnDef, commitOn=None):\n",
+    "\n",
+    "    if commitOn is None:\n",
+    "        commitOn = False\n",
+    "\n",
+    "    if params is None:\n",
+    "        params = []\n",
+    "\n",
+    "    pypyodbc.lowercase = False\n",
+    "    conn = pypyodbc.connect(strConn)\n",
+    "    cursor = conn.cursor()\n",
+    "    cursor.execute(strQuery, params)\n",
+    "\n",
+    "    if commitOn:\n",
+    "        conn.commit()\n",
+    "        return \"sql insert was successful.\", \"sql insert was successful.\"\n",
+    "    try:\n",
+    "        rows = cursor.fetchall()\n",
+    "        #print(\"rows\", rows)\n",
+    "        # print(\"PARAMS:\", params)\n",
+    "        description = cursor.description\n",
+    "        conn.close()\n",
+    "        return rows, description\n",
+    "    except:\n",
+    "        # print(\"THE QUERY: \" + strQuery) TODO: add query\n",
+    "        conn.close()\n",
+    "        raise ValueError(\"There was an error fetching a sql query. Make sure the index exists for your selected dates. THE PARAMS: \", params)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def getQueryDataframe(strQuery, params=None, strConn=strConnDef, columnMustAlwaysExist=None, commitOn=None):\n",
+    "\n",
+    "    rows, cursorDescription = getQueryRaw(strQuery, params, strConn, commitOn)\n",
+    "    if commitOn:\n",
+    "        return \"sql insert was successful.\"\n",
+    "\n",
+    "    if len(rows) == 0:\n",
+    "        print(\"No rows were returned.\")\n",
+    "        print(\"THE PARAMS: \", params)\n",
+    "        print(\"THE QUERY: \" + strQuery)\n",
+    "        print(\"Rows length is zero. No records returned\")\n",
+    "\n",
+    "        if columnMustAlwaysExist is None:\n",
+    "            columnMustAlwaysExist = \"Empty\"\n",
+    "\n",
+    "        columns = [\"Information\", columnMustAlwaysExist]\n",
+    "        rows = [\n",
+    "            [\"No results were returned.\", \"There is no data.\"]\n",
+    "            , [\"No results were returned.\", \"There is no data.\"]\n",
+    "        ]\n",
+    "\n",
+    "    else:\n",
+    "        # bytes conversion needed because of the linux pypyodbc bug\n",
+    "        columns = [column[0].decode(\"cp1252\") if type(column[0]) == bytes else column[0] for column in\n",
+    "                   cursorDescription]\n",
+    "\n",
+    "    results = pd.DataFrame(data=rows, columns=columns)\n",
+    "\n",
+    "\n",
+    "    return results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create 15 minute data - this fills the 15 minutes table\n",
+    "str_query = open(\"get_data.sql\", \"r\").read() # returns prepared data\n",
+    "str_query = str_query.replace(\"/*\", \"\").replace(\"*/\", \"\")\n",
+    "\n",
+    "df = getQueryDataframe(str_query, [min_date, max_date])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "hideCode": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>15_min</th>\n",
+       "      <th>datestamp</th>\n",
+       "      <th>high_bid</th>\n",
+       "      <th>low_bid</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>nb_ticks</th>\n",
+       "      <th>open_bid</th>\n",
+       "      <th>close_bid</th>\n",
+       "      <th>last_10_tick_avg_bid_return</th>\n",
+       "      <th>last_10_tick_avg_bo_spread</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:00</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08664</td>\n",
+       "      <td>-2.760660e-06</td>\n",
+       "      <td>0.000139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>2016-01-03 17:15:00</td>\n",
+       "      <td>1.08714</td>\n",
+       "      <td>1.08662</td>\n",
+       "      <td>0.000149</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1.08666</td>\n",
+       "      <td>1.08674</td>\n",
+       "      <td>1.104220e-05</td>\n",
+       "      <td>0.000154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2016-01-03 17:30:00</td>\n",
+       "      <td>1.08699</td>\n",
+       "      <td>1.08671</td>\n",
+       "      <td>0.000141</td>\n",
+       "      <td>109</td>\n",
+       "      <td>1.08674</td>\n",
+       "      <td>1.08674</td>\n",
+       "      <td>-9.201579e-07</td>\n",
+       "      <td>0.000153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2016-01-03 17:45:00</td>\n",
+       "      <td>1.08687</td>\n",
+       "      <td>1.08655</td>\n",
+       "      <td>0.000102</td>\n",
+       "      <td>93</td>\n",
+       "      <td>1.08674</td>\n",
+       "      <td>1.08662</td>\n",
+       "      <td>7.362178e-06</td>\n",
+       "      <td>0.000094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 18:00:00</td>\n",
+       "      <td>1.08665</td>\n",
+       "      <td>1.08523</td>\n",
+       "      <td>0.000113</td>\n",
+       "      <td>459</td>\n",
+       "      <td>1.08662</td>\n",
+       "      <td>1.08574</td>\n",
+       "      <td>1.013142e-05</td>\n",
+       "      <td>0.000093</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  month  day  hour  weekday  15_min           datestamp  high_bid  \\\n",
+       "0  2016      1    3    17        1       0 2016-01-03 17:00:00   1.08723   \n",
+       "1  2016      1    3    17        1      15 2016-01-03 17:15:00   1.08714   \n",
+       "2  2016      1    3    17        1      30 2016-01-03 17:30:00   1.08699   \n",
+       "3  2016      1    3    17        1      45 2016-01-03 17:45:00   1.08687   \n",
+       "4  2016      1    3    18        1       0 2016-01-03 18:00:00   1.08665   \n",
+       "\n",
+       "   low_bid  avg_bo_spread  nb_ticks  open_bid  close_bid  \\\n",
+       "0  1.08661       0.000165       142   1.08701    1.08664   \n",
+       "1  1.08662       0.000149        80   1.08666    1.08674   \n",
+       "2  1.08671       0.000141       109   1.08674    1.08674   \n",
+       "3  1.08655       0.000102        93   1.08674    1.08662   \n",
+       "4  1.08523       0.000113       459   1.08662    1.08574   \n",
+       "\n",
+       "   last_10_tick_avg_bid_return  last_10_tick_avg_bo_spread  \n",
+       "0                -2.760660e-06                    0.000139  \n",
+       "1                 1.104220e-05                    0.000154  \n",
+       "2                -9.201579e-07                    0.000153  \n",
+       "3                 7.362178e-06                    0.000094  \n",
+       "4                 1.013142e-05                    0.000093  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# dates only have an effect if a subset of dates is needed.\n",
+    "str_query = open(\"get_data_1y.sql\", \"r\").read() # returns prepared data\n",
+    "str_query = str_query.replace(\"/*\", \"\").replace(\"*/\", \"\")\n",
+    "#print(str_query)\n",
+    "df = getQueryDataframe(str_query, [min_date, max_date])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min date 2016-01-03 17:00:00\n",
+      "max date 2016-05-30 23:45:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.set_index('datestamp', inplace=True)\n",
+    "print(\"min date\", min(df.index))\n",
+    "print(\"max date\", max(df.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"eurusd_features.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df_res = pd.read_csv(\"data/eurusd_features.csv\")\n",
+    "df_res.set_index('date', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min date 2015-12-29 00:00:00\n",
+      "max date 2016-05-31 23:45:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load kaggle reference dataset for comparison\n",
+    "df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_sample.csv')\n",
+    "#df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_01.01.2010-31.12.2016.csv')\n",
+    "\n",
+    "# Rename bid OHLC columns\n",
+    "df_kaggle.rename(columns={'Time' : 'date', 'Open' : 'open_bid', 'Close' : 'close_bid', \n",
+    "                   'High' : 'high_bid', 'Low' : 'low_bid', 'Volume' : 'volume'}, inplace=True)\n",
+    "df_kaggle['date'] = pd.to_datetime(df_kaggle['date'], infer_datetime_format=True)\n",
+    "df_kaggle.set_index('date', inplace=True)\n",
+    "df_kaggle = df_kaggle.astype(float)\n",
+    "\n",
+    "simname = \"bm_kaggle\"\n",
+    "\n",
+    "df = df_kaggle\n",
+    "print(\"min date\", min(df.index))\n",
+    "print(\"max date\", max(df.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to include seasonality as a feature\n",
+    "if simname == \"bm_kaggle\":\n",
+    "    df['hour'] = df.index.hour\n",
+    "    df['day']  = df.index.weekday\n",
+    "    df['week'] = df.index.week\n",
+    "    df['month'] = df.index.month\n",
+    "\n",
+    "    df['momentum']  = df['volume'] * (df['open_bid'] - df['close_bid'])\n",
+    "df['avg_price'] = (df['low_bid'] + df['high_bid'])/2\n",
+    "df['range']     = df['high_bid'] - df['low_bid']\n",
+    "df['ohlc_price'] = (df['low_bid'] + df['high_bid'] + df['open_bid'] + df['close_bid'])/4\n",
+    "df['oc_diff']    = df['open_bid'] - df['close_bid']\n",
+    "#df['bo_spread'] = df.ask - df.bid\n",
+    "df['period_return'] = df.close_bid / df.open_bid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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KVgq4GjZs8SoUBF7UpyOPPDKLFi3KCSeckGKxmCuuuCL33XdfVq1a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\nXocz114PPPBAFi1aNHx7vByrKhnJ8SuTQ815662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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22a03a09a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb Rows:  14880\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create ohlc prices, analyse distribution, think about feature transformation and de-trending\n",
+    "\n",
+    "fig, axarr = plt.subplots(2, 5, figsize=(30,10)) #1 row, 2 cols, x, y\n",
+    "#plt.figure(figsize=(20, 4))\n",
+    "i_row, i_col = 0,0\n",
+    "fig.suptitle(\"frequency distributions\")\n",
+    "\n",
+    "\n",
+    "sns.distplot(df.period_return-1, ax=axarr[i_row, i_col])\n",
+    "#axarr[0, 0].set_title('Axis [0,0] Subtitle')\n",
+    "\n",
+    "# i_col += 1\n",
+    "# sns.distplot(df.bo_spread, ax=axarr[i_row, i_col])\n",
+    "\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "    i_col += 1\n",
+    "    sns.distplot(df.avg_bo_spread, ax=axarr[i_row, i_col])\n",
+    "\n",
+    "    x_axis_col = \"ohlc_price\"\n",
+    "    y_axis_col = \"avg_bo_spread\"\n",
+    "    i_col += 1\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    x_axis_col = \"period_return\"\n",
+    "    y_axis_col = \"avg_bo_spread\"\n",
+    "    i_col += 1\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "\n",
+    "    i_row, i_col = 1, 0 # move down one row\n",
+    "\n",
+    "    x_axis_col = \"hour\"\n",
+    "    y_axis_col = \"avg_bo_spread\"\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "\n",
+    "    x_axis_col = \"hour\"\n",
+    "    y_axis_col = \"nb_ticks\"\n",
+    "    i_col += 1\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "    x_axis_col = \"day\"\n",
+    "    y_axis_col = \"nb_ticks\"\n",
+    "    i_col += 1\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "\n",
+    "#plt.tight_layout() # reduce overlap\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Nb Rows: \", df.high_bid.count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-95-d9adaf9b6402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# all at once\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"bo_spread\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, size, aspect, dropna, plot_kws, diag_kws, grid_kws)\u001b[0m\n\u001b[1;32m   2071\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"scatter\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2072\u001b[0m         \u001b[0mplot_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"edgecolor\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"white\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2073\u001b[0;31m         \u001b[0mplotter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2074\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"reg\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2075\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mregression\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mregplot\u001b[0m  \u001b[0;31m# Avoid circular import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_offdiag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m   1491\u001b[0m         \"\"\"\n\u001b[1;32m   1492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1493\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_lower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1494\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_upper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_lower\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m   1423\u001b[0m                 \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkw_color\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mkw_color\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1424\u001b[0m                 func(data_k[x_var], data_k[y_var], label=label_k,\n\u001b[0;32m-> 1425\u001b[0;31m                      color=color, **kwargs)\n\u001b[0m\u001b[1;32m   1426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1427\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3432\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3433\u001b[0m                          \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3434\u001b[0;31m                          edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[1;32m   3435\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3436\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4061\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ymargin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4062\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4063\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4064\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_collection\u001b[0;34m(self, collection, autolim)\u001b[0m\n\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mautolim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1762\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_datalim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_datalim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remove_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mget_datalim\u001b[0;34m(self, transData)\u001b[0m\n\u001b[1;32m    227\u001b[0m             result = mpath.get_path_collection_extents(\n\u001b[1;32m    228\u001b[0m                 \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m                 offsets, transOffset.frozen())\n\u001b[0m\u001b[1;32m    230\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transformed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36mget_path_collection_extents\u001b[0;34m(master_transform, paths, transforms, offsets, offset_transform)\u001b[0m\n\u001b[1;32m   1008\u001b[0m     return Bbox.from_extents(*_path.get_path_collection_extents(\n\u001b[1;32m   1009\u001b[0m         \u001b[0mmaster_transform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1010\u001b[0;31m         offsets, offset_transform))\n\u001b[0m\u001b[1;32m   1011\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36mvertices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_nonfinite\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vertices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvertices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \"\"\"\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# all at once\n",
+    "#sns.pairplot(df, hue=\"bo_spread\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import dill as pickle\n",
+    "with open(simname+'_eurusd_features.pkl', 'wb') as file:\n",
+    "    pickle.dump(df, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add PCA as a feature instead of for reducing the dimensionality. This improves the accuracy a bit.\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "dataset = df.copy().values.astype('float32')\n",
+    "pca_features = df.columns.tolist()\n",
+    "\n",
+    "pca = PCA(n_components=1)\n",
+    "df['pca'] = pca.fit_transform(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "hideCode": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3A2fLmjWshOEVVy4eDGYiVqU8hF/yYVxt7Xg1ShFVDCmjFbeLggQB/Tf7VQ/n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\nOedQg97MAwukkMFGheHGNF44/zh9UtVRF6mztRJH8ttmtfC5XD1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Wq9VIp9OtrxVFwfd9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\nCIIgCBtMBFyCIAiCIAgb7P8D7tjtUAeyfHoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21314e6bf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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h9I4HcSOFlFn/womQEUGGLc1zTEgrUcC1gkTDyaJX5IZh4tTRAEJTsZaPYdPI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\nJEmSJEnb7P8DWHwd8XsUmLQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21314fe7f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "norm = colors.Normalize(df['ohlc_price'].values.min(), df['ohlc_price'].values.max())\n",
+    "color = cm.viridis(norm(df['ohlc_price'].values))\n",
+    "plt.scatter(df['ohlc_price'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "plt.title('ohlc_price vs pca')\n",
+    "plt.show()\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs pca')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['ohlc_price'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs ohlc_price')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['period_return'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs period_return')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['period_return'].shift().values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs period_return shift')\n",
+    "    plt.show()\n",
+    "    \n",
+    "  \n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "norm = colors.Normalize(df['ohlc_price'].values.min(), df['ohlc_price'].values.max())\n",
+    "color = cm.viridis(norm(df['ohlc_price'].values))\n",
+    "plt.scatter(df['ohlc_price'].shift().values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "plt.title('ohlc_price - 15min future vs pca')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this creates a training dataset for the model\n",
+    "def create_dataset(dataset, look_back_rows=20):\n",
+    "    dataX, dataY = [], [] # for training\n",
+    "    # it creates for each row a 20 row lookback dataset\n",
+    "    # this expands the data by 20!\n",
+    "    for i in range(len(dataset)-look_back_rows-1): # \n",
+    "        a = dataset[i:(i+look_back_rows)] # from example 1 to 21\n",
+    "        dataX.append(a)\n",
+    "        dataY.append(dataset[i + look_back_rows]) #get example 1+20, so the next point that is to be forecasted\n",
+    "    return np.array(dataX), np.array(dataY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ubi6PPvooq1aton79+hw5cgSr1corr7yCv78/CxYsYOfOnVgsFh555BHuuOMO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JU4A/AHaNG2BTvxZaF0cATi9cQ/z+/0rNZkyfjKl5UL0skvs+e5sTl8ip2avJuBpe5Llzk9MI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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21314b4ffd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Vu3fvPJeXmzx7HC5evKhKlSopLi5Offr0kc1mk8lk0vXr1/OdOAAAAADIUBST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\nAQAAAIAhEgcAAAAAhv4/nFu8rOoxCoYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2133d2ce518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check feature correlation, to see what correlates with the close price\n",
+    "colormap = plt.cm.inferno\n",
+    "plt.figure(figsize=(15,15))\n",
+    "plt.title('Pearson correlation of features', y=1.05, size=15)\n",
+    "sns.heatmap(df.corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(15,5))\n",
+    "corr = df.corr()\n",
+    "sns.heatmap(corr[corr.index == 'close_bid'], linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create random forest regressor - random decision trees, like weak learner, ada boost\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Scale and create datasets\n",
+    "target_index = df.columns.tolist().index('close_bid') # predict this, should it be return?\n",
+    "dataset = df.values.astype('float32') # so regressor can use it\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "dataset = scaler.fit_transform(dataset) # scale features to between 0 and 1 for faster convergence\n",
+    "\n",
+    "# Set look_back to 100 which is 100 ticks\n",
+    "# look back is 1 period, to check which features predict best a 1 period return\n",
+    "look_back_rows = 1 # to work with more than one, use alternative reshape\n",
+    "X, y = create_dataset(dataset, look_back_rows=look_back_rows) # look back only 1 row\n",
+    "y = y[:,target_index]\n",
+    "#TODO:X = np.reshape(X, (X.shape[0], X.shape[2]* look_back_rows)) # to get back rows and columns\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[2])) # to get back rows and columns\n",
+    "# extend extra rows into columns, as all the prices during lookback periodd should be used as features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(14878, 11)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape\n",
+    "#y.shape\n",
+    "#X[0].shape\n",
+    "#X.shape[2]\n",
+    "#np.reshape(X, (X.shape[0]*10, X.shape[2]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fit model\n",
+    "forest = RandomForestRegressor(n_estimators = 100)\n",
+    "forest = forest.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature ranking:\n",
+      "0. close_bid 3 (0.816391)\n",
+      "1. ohlc_price 7 (0.067108)\n",
+      "2. high_bid 1 (0.064573)\n",
+      "3. avg_price 5 (0.050164)\n",
+      "4. low_bid 2 (0.001339)\n",
+      "5. open_bid 0 (0.000077)\n",
+      "6. range 6 (0.000056)\n",
+      "7. momentum 15 (0.000053)\n",
+      "8. volume 4 (0.000046)\n",
+      "9. pca 10 (0.000045)\n",
+      "10. hour 11 (0.000038)\n",
+      "11. oc_diff 8 (0.000028)\n",
+      "12. period_return 9 (0.000028)\n",
+      "13. week 13 (0.000023)\n",
+      "14. day 12 (0.000023)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21314b3e320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find feature with best explanatory power to predict close price\n",
+    "importances = forest.feature_importances_\n",
+    "std = np.std([forest.feature_importances_ for forest in forest.estimators_], axis=0)\n",
+    "indices = np.argsort(importances)[::-1] # get indices for importances\n",
+    "#print(indices)\n",
+    "\n",
+    "column_list = df.columns.tolist()\n",
+    "#print(column_list)\n",
+    "print(\"Feature ranking:\")\n",
+    "for f in range(X.shape[1]-1):\n",
+    "    print(\"%d. %s %d (%f)\" % (f, column_list[indices[f]], indices[f], importances[indices[f]]))\n",
+    "\n",
+    "    \n",
+    "# Plot the feature importances coming from the forest of decision trees and their standard deviation\n",
+    "plt.figure(figsize=(20,10))\n",
+    "plt.title(\"Feature importances\")\n",
+    "plt.bar(range(X.shape[1]), importances[indices],\n",
+    "       color=\"salmon\", yerr=std[indices], align=\"center\")\n",
+    "plt.xticks(range(X.shape[1]), indices)\n",
+    "plt.xlim([-1, X.shape[1]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IiIiIiEKCQSgmM2BNnBThH7mjttx68x2QY1od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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22a04aa9ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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AtQ3vNKqWy2Vr/P39ZTabnR+bzWYFBARowIABioyM1IgR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\neO4pAMDm087P6LoGrRolueaMrrm/c2UZUFWctdvHGg8/HgBQ8uk7Ka2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CNJXrdL4QtHu4S27N5heZ9YIiuzRK/HgLlmmKbCsdKMpd7TE1LfULyo9yul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TNplxTJ+Do3IujhlzaSyr5MTf/IKIwUj1l74p+1zTizKO+DPG0cjwGlXv4lVMe20jeTs2yxbm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\n8Whu745r/6b2bvY2dMR9HgOJ1BwhHg2t3fQEPk9vynv3JVI3rWb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jygCw7tx6XBbmdHr9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\nAIDholTKltFj0/DSiyls6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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22a04aa67b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot close price, compare to low and high price\n",
+    "ax = df.plot(x=df.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in df.index]\n",
+    "plt.fill_between(x=index, y1='low_bid',y2='high_bid', data=df, alpha=0.4)\n",
+    "plt.title(\"entire history\", fontsize=30)\n",
+    "plt.show()\n",
+    "\n",
+    "# plot first 200 entries \n",
+    "p = df[:200].copy()\n",
+    "ax = p.plot(x=p.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in p.index]\n",
+    "plt.fill_between(x=index, y1='low_bid', y2='high_bid', data=p, alpha=0.4)\n",
+    "plt.title('zoomed, first 200', fontsize=30)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale and create datasets\n",
+    "target_index = df.columns.tolist().index('close_bid')\n",
+    "high_index = df.columns.tolist().index('high_bid')\n",
+    "low_index = df.columns.tolist().index('low_bid')\n",
+    "dataset = df.values.astype('float32')\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "dataset = scaler.fit_transform(dataset)\n",
+    "\n",
+    "# Create y_scaler to inverse it later\n",
+    "y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "t_y = df['close_bid'].values.astype('float32')\n",
+    "t_y = np.reshape(t_y, (-1, 1))\n",
+    "y_scaler = y_scaler.fit(t_y)\n",
+    "    \n",
+    "# Set look_back to 20 which is 5 hours (15min*20)\n",
+    "X, y = create_dataset(dataset, look_back_rows=20)\n",
+    "y = y[:,target_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set training data size\n",
+    "# We have a large enough dataset. So divid into 98% training / 1%  development / 1% test sets\n",
+    "train_size = int(len(X) * 0.99)\n",
+    "trainX = X[:train_size]\n",
+    "trainY = y[:train_size]\n",
+    "testX = X[train_size:]\n",
+    "testY = y[train_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "lstm_9 (LSTM)                (None, 20, 20)            2960      \n",
+      "_________________________________________________________________\n",
+      "lstm_10 (LSTM)               (None, 20, 20)            3280      \n",
+      "_________________________________________________________________\n",
+      "lstm_11 (LSTM)               (None, 20, 10)            1240      \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 20, 10)            0         \n",
+      "_________________________________________________________________\n",
+      "lstm_12 (LSTM)               (None, 4)                 240       \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_6 (Dense)              (None, 1)                 5         \n",
+      "=================================================================\n",
+      "Total params: 7,745\n",
+      "Trainable params: 7,745\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Input, LSTM, Dense\n",
+    "\n",
+    "# create a small LSTM network\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(20, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))\n",
+    "model.add(LSTM(20, return_sequences=True))\n",
+    "model.add(LSTM(10, return_sequences=True))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(LSTM(4, return_sequences=False))\n",
+    "model.add(Dense(4, kernel_initializer='uniform', activation='relu'))\n",
+    "model.add(Dense(1, kernel_initializer='uniform', activation='relu'))\n",
+    "\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mae', 'mse'])\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error improved from inf to 0.26399, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00001: val_mean_squared_error improved from 0.26399 to 0.17870, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00002: val_mean_squared_error improved from 0.17870 to 0.07720, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00003: val_mean_squared_error improved from 0.07720 to 0.02238, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00004: val_mean_squared_error improved from 0.02238 to 0.01324, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00005: val_mean_squared_error did not improve\n",
+      "Epoch 00006: val_mean_squared_error did not improve\n",
+      "Epoch 00007: val_mean_squared_error improved from 0.01324 to 0.01135, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00008: val_mean_squared_error improved from 0.01135 to 0.00217, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00009: val_mean_squared_error improved from 0.00217 to 0.00062, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00010: val_mean_squared_error improved from 0.00062 to 0.00048, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00011: val_mean_squared_error improved from 0.00048 to 0.00037, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00012: val_mean_squared_error improved from 0.00037 to 0.00028, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00013: val_mean_squared_error improved from 0.00028 to 0.00020, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error did not improve\n",
+      "Epoch 00016: val_mean_squared_error improved from 0.00020 to 0.00020, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00017: val_mean_squared_error improved from 0.00020 to 0.00018, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00018: val_mean_squared_error did not improve\n",
+      "Epoch 00019: val_mean_squared_error did not improve\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error improved from 0.00018 to 0.00016, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00022: val_mean_squared_error did not improve\n",
+      "Epoch 00023: val_mean_squared_error did not improve\n",
+      "Epoch 00024: val_mean_squared_error did not improve\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "Epoch 00026: val_mean_squared_error improved from 0.00016 to 0.00016, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00027: val_mean_squared_error did not improve\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error improved from 0.00016 to 0.00015, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error improved from 0.00015 to 0.00014, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n",
+      "Epoch 00033: val_mean_squared_error did not improve\n",
+      "Epoch 00034: val_mean_squared_error improved from 0.00014 to 0.00013, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00035: val_mean_squared_error improved from 0.00013 to 0.00013, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00036: val_mean_squared_error did not improve\n",
+      "Epoch 00037: val_mean_squared_error did not improve\n",
+      "Epoch 00038: val_mean_squared_error did not improve\n",
+      "Epoch 00039: val_mean_squared_error improved from 0.00013 to 0.00011, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00040: val_mean_squared_error did not improve\n",
+      "Epoch 00041: val_mean_squared_error did not improve\n",
+      "Epoch 00042: val_mean_squared_error did not improve\n",
+      "Epoch 00043: val_mean_squared_error did not improve\n",
+      "Epoch 00044: val_mean_squared_error did not improve\n",
+      "Epoch 00045: val_mean_squared_error did not improve\n",
+      "Epoch 00046: val_mean_squared_error did not improve\n",
+      "Epoch 00047: val_mean_squared_error did not improve\n",
+      "Epoch 00048: val_mean_squared_error did not improve\n",
+      "Epoch 00049: val_mean_squared_error improved from 0.00011 to 0.00011, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00050: val_mean_squared_error did not improve\n",
+      "Epoch 00051: val_mean_squared_error did not improve\n",
+      "Epoch 00052: val_mean_squared_error improved from 0.00011 to 0.00010, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00053: val_mean_squared_error did not improve\n",
+      "Epoch 00054: val_mean_squared_error improved from 0.00010 to 0.00009, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00055: val_mean_squared_error did not improve\n",
+      "Epoch 00056: val_mean_squared_error did not improve\n",
+      "Epoch 00057: val_mean_squared_error did not improve\n",
+      "Epoch 00058: val_mean_squared_error did not improve\n",
+      "Epoch 00059: val_mean_squared_error did not improve\n",
+      "Epoch 00060: val_mean_squared_error did not improve\n",
+      "Epoch 00061: val_mean_squared_error improved from 0.00009 to 0.00009, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00062: val_mean_squared_error did not improve\n",
+      "Epoch 00063: val_mean_squared_error did not improve\n",
+      "Epoch 00064: val_mean_squared_error did not improve\n",
+      "Epoch 00065: val_mean_squared_error did not improve\n",
+      "Epoch 00066: val_mean_squared_error did not improve\n",
+      "Epoch 00067: val_mean_squared_error improved from 0.00009 to 0.00008, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00068: val_mean_squared_error did not improve\n",
+      "Epoch 00069: val_mean_squared_error did not improve\n",
+      "Epoch 00070: val_mean_squared_error did not improve\n",
+      "Epoch 00071: val_mean_squared_error did not improve\n",
+      "Epoch 00072: val_mean_squared_error did not improve\n",
+      "Epoch 00073: val_mean_squared_error did not improve\n",
+      "Epoch 00074: val_mean_squared_error did not improve\n",
+      "Epoch 00075: val_mean_squared_error improved from 0.00008 to 0.00008, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00076: val_mean_squared_error did not improve\n",
+      "Epoch 00077: val_mean_squared_error did not improve\n",
+      "Epoch 00078: val_mean_squared_error did not improve\n",
+      "Epoch 00079: val_mean_squared_error did not improve\n",
+      "Epoch 00080: val_mean_squared_error did not improve\n",
+      "Epoch 00081: val_mean_squared_error did not improve\n",
+      "Epoch 00082: val_mean_squared_error improved from 0.00008 to 0.00007, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00083: val_mean_squared_error did not improve\n",
+      "Epoch 00084: val_mean_squared_error did not improve\n",
+      "Epoch 00085: val_mean_squared_error did not improve\n",
+      "Epoch 00086: val_mean_squared_error did not improve\n",
+      "Epoch 00087: val_mean_squared_error did not improve\n",
+      "Epoch 00088: val_mean_squared_error did not improve\n",
+      "Epoch 00089: val_mean_squared_error did not improve\n",
+      "Epoch 00090: val_mean_squared_error improved from 0.00007 to 0.00006, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00091: val_mean_squared_error did not improve\n",
+      "Epoch 00092: val_mean_squared_error did not improve\n",
+      "Epoch 00093: val_mean_squared_error did not improve\n",
+      "Epoch 00094: val_mean_squared_error did not improve\n",
+      "Epoch 00095: val_mean_squared_error did not improve\n",
+      "Epoch 00096: val_mean_squared_error did not improve\n",
+      "Epoch 00097: val_mean_squared_error did not improve\n",
+      "Epoch 00098: val_mean_squared_error did not improve\n",
+      "Epoch 00099: val_mean_squared_error improved from 0.00006 to 0.00006, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00100: val_mean_squared_error did not improve\n",
+      "Epoch 00101: val_mean_squared_error improved from 0.00006 to 0.00006, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00102: val_mean_squared_error did not improve\n",
+      "Epoch 00103: val_mean_squared_error did not improve\n",
+      "Epoch 00104: val_mean_squared_error did not improve\n",
+      "Epoch 00105: val_mean_squared_error did not improve\n",
+      "Epoch 00106: val_mean_squared_error did not improve\n",
+      "Epoch 00107: val_mean_squared_error did not improve\n",
+      "Epoch 00108: val_mean_squared_error improved from 0.00006 to 0.00005, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00109: val_mean_squared_error did not improve\n",
+      "Epoch 00110: val_mean_squared_error did not improve\n",
+      "Epoch 00111: val_mean_squared_error did not improve\n",
+      "Epoch 00112: val_mean_squared_error did not improve\n",
+      "Epoch 00113: val_mean_squared_error did not improve\n",
+      "Epoch 00114: val_mean_squared_error did not improve\n",
+      "Epoch 00115: val_mean_squared_error did not improve\n",
+      "Epoch 00116: val_mean_squared_error did not improve\n",
+      "Epoch 00117: val_mean_squared_error improved from 0.00005 to 0.00005, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00118: val_mean_squared_error did not improve\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00119: val_mean_squared_error did not improve\n",
+      "Epoch 00120: val_mean_squared_error did not improve\n",
+      "Epoch 00121: val_mean_squared_error did not improve\n",
+      "Epoch 00122: val_mean_squared_error did not improve\n",
+      "Epoch 00123: val_mean_squared_error did not improve\n",
+      "Epoch 00124: val_mean_squared_error did not improve\n",
+      "Epoch 00125: val_mean_squared_error improved from 0.00005 to 0.00005, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00126: val_mean_squared_error improved from 0.00005 to 0.00005, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00127: val_mean_squared_error did not improve\n",
+      "Epoch 00128: val_mean_squared_error improved from 0.00005 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00129: val_mean_squared_error did not improve\n",
+      "Epoch 00130: val_mean_squared_error did not improve\n",
+      "Epoch 00131: val_mean_squared_error did not improve\n",
+      "Epoch 00132: val_mean_squared_error did not improve\n",
+      "Epoch 00133: val_mean_squared_error did not improve\n",
+      "Epoch 00134: val_mean_squared_error did not improve\n",
+      "Epoch 00135: val_mean_squared_error did not improve\n",
+      "Epoch 00136: val_mean_squared_error did not improve\n",
+      "Epoch 00137: val_mean_squared_error did not improve\n",
+      "Epoch 00138: val_mean_squared_error did not improve\n",
+      "Epoch 00139: val_mean_squared_error did not improve\n",
+      "Epoch 00140: val_mean_squared_error did not improve\n",
+      "Epoch 00141: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00142: val_mean_squared_error did not improve\n",
+      "Epoch 00143: val_mean_squared_error did not improve\n",
+      "Epoch 00144: val_mean_squared_error did not improve\n",
+      "Epoch 00145: val_mean_squared_error did not improve\n",
+      "Epoch 00146: val_mean_squared_error did not improve\n",
+      "Epoch 00147: val_mean_squared_error did not improve\n",
+      "Epoch 00148: val_mean_squared_error did not improve\n",
+      "Epoch 00149: val_mean_squared_error did not improve\n",
+      "Epoch 00150: val_mean_squared_error did not improve\n",
+      "Epoch 00151: val_mean_squared_error did not improve\n",
+      "Epoch 00152: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00153: val_mean_squared_error did not improve\n",
+      "Epoch 00154: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00155: val_mean_squared_error did not improve\n",
+      "Epoch 00156: val_mean_squared_error did not improve\n",
+      "Epoch 00157: val_mean_squared_error did not improve\n",
+      "Epoch 00158: val_mean_squared_error did not improve\n",
+      "Epoch 00159: val_mean_squared_error did not improve\n",
+      "Epoch 00160: val_mean_squared_error did not improve\n",
+      "Epoch 00161: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00162: val_mean_squared_error did not improve\n",
+      "Epoch 00163: val_mean_squared_error did not improve\n",
+      "Epoch 00164: val_mean_squared_error did not improve\n",
+      "Epoch 00165: val_mean_squared_error did not improve\n",
+      "Epoch 00166: val_mean_squared_error did not improve\n",
+      "Epoch 00167: val_mean_squared_error did not improve\n",
+      "Epoch 00168: val_mean_squared_error did not improve\n",
+      "Epoch 00169: val_mean_squared_error did not improve\n",
+      "Epoch 00170: val_mean_squared_error did not improve\n",
+      "Epoch 00171: val_mean_squared_error did not improve\n",
+      "Epoch 00172: val_mean_squared_error did not improve\n",
+      "Epoch 00173: val_mean_squared_error did not improve\n",
+      "Epoch 00174: val_mean_squared_error improved from 0.00004 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00175: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00176: val_mean_squared_error did not improve\n",
+      "Epoch 00177: val_mean_squared_error did not improve\n",
+      "Epoch 00178: val_mean_squared_error did not improve\n",
+      "Epoch 00179: val_mean_squared_error did not improve\n",
+      "Epoch 00180: val_mean_squared_error did not improve\n",
+      "Epoch 00181: val_mean_squared_error did not improve\n",
+      "Epoch 00182: val_mean_squared_error did not improve\n",
+      "Epoch 00183: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00184: val_mean_squared_error did not improve\n",
+      "Epoch 00185: val_mean_squared_error did not improve\n",
+      "Epoch 00186: val_mean_squared_error did not improve\n",
+      "Epoch 00187: val_mean_squared_error did not improve\n",
+      "Epoch 00188: val_mean_squared_error did not improve\n",
+      "Epoch 00189: val_mean_squared_error did not improve\n",
+      "Epoch 00190: val_mean_squared_error did not improve\n",
+      "Epoch 00191: val_mean_squared_error did not improve\n",
+      "Epoch 00192: val_mean_squared_error did not improve\n",
+      "Epoch 00193: val_mean_squared_error did not improve\n",
+      "Epoch 00194: val_mean_squared_error did not improve\n",
+      "Epoch 00195: val_mean_squared_error did not improve\n",
+      "Epoch 00196: val_mean_squared_error did not improve\n",
+      "Epoch 00197: val_mean_squared_error did not improve\n",
+      "Epoch 00198: val_mean_squared_error did not improve\n",
+      "Epoch 00199: val_mean_squared_error did not improve\n",
+      "Wall time: 8min 11s\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Save the best weight during training.\n",
+    "#simname = \"15_min_replication_1\"\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_mean_squared_error', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "# Fit\n",
+    "\"\"\"\n",
+    "it seems batch size controls convergence speed a lot! Batch size tells how many examples are propagated through the network.\n",
+    "Weights are adjusted based on results with these examples. This is useful if the full dataset takes too much memory\n",
+    "It also speeds up training, as you will converge quicker (dont have to wait for a full iteration of each example to adjust weights).\n",
+    "\"\"\"\n",
+    "callbacks_list = [checkpoint]\n",
+    "%time history = model.fit(trainX, trainY, epochs=200, batch_size=500, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 200\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131c22ef28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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68GJ2dP6+uHv6OjqT611mzQ+zY9rBubdl4Vt+O1sX362qFvac7Jak/TTt3depu+deT7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\nIiIiIiIiIiIiKrL/B8QZwLn1LW59AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21313c366d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131381b828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epoch = len(history.history['loss'])\n",
+    "print(\"epoch\", epoch)\n",
+    "for k in list(history.history.keys()):\n",
+    "    if 'val' not in k:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(history.history[k])\n",
+    "        plt.plot(history.history['val_' + k])\n",
+    "        plt.title(k, fontsize=30)\n",
+    "        plt.ylabel(k)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left', fontsize=30)\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0034859505006226755"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(history.history['val_mean_absolute_error'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As seen from the above, the model seems to have converged nicely, but the mean absolute error on the development data remains at ~0.003X which means the model is unusable in practice. Ideally, we want to get ~0.0005. Let's go back to the best weight, and decay the learning rate while retraining the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error did not improve\n",
+      "Epoch 00001: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0005904900433961303\n",
+      "Epoch 00002: val_mean_squared_error did not improve\n",
+      "Epoch 00003: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0005314410547725857\n",
+      "Epoch 00004: val_mean_squared_error did not improve\n",
+      "Epoch 00005: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00047829695977270604\n",
+      "Epoch 00006: val_mean_squared_error did not improve\n",
+      "Epoch 00007: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0004304672533180565\n",
+      "Epoch 00008: val_mean_squared_error did not improve\n",
+      "Epoch 00009: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00038742052274756136\n",
+      "Epoch 00010: val_mean_squared_error did not improve\n",
+      "Epoch 00011: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0003486784757114947\n",
+      "Epoch 00012: val_mean_squared_error did not improve\n",
+      "Epoch 00013: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00031381062290165574\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0002824295632308349\n",
+      "Epoch 00016: val_mean_squared_error did not improve\n",
+      "Epoch 00017: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00025418660952709616\n",
+      "Epoch 00018: val_mean_squared_error did not improve\n",
+      "Epoch 00019: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00022876793809700757\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00020589114428730683\n",
+      "Epoch 00022: val_mean_squared_error did not improve\n",
+      "Epoch 00023: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00018530203378759326\n",
+      "Epoch 00024: val_mean_squared_error did not improve\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00016677183302817866\n",
+      "Epoch 00026: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00027: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "lr changed to 0.00015009464841568844\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0001350851875031367\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00012157666351413355\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n",
+      "Epoch 00033: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00010941899454337544\n",
+      "Epoch 00034: val_mean_squared_error did not improve\n",
+      "Epoch 00035: val_mean_squared_error did not improve\n",
+      "lr changed to 9.847709443420172e-05\n",
+      "Epoch 00036: val_mean_squared_error did not improve\n",
+      "Epoch 00037: val_mean_squared_error did not improve\n",
+      "lr changed to 8.862938630045391e-05\n",
+      "Epoch 00038: val_mean_squared_error did not improve\n",
+      "Epoch 00039: val_mean_squared_error did not improve\n",
+      "lr changed to 7.976644701557234e-05\n",
+      "Epoch 00040: val_mean_squared_error did not improve\n",
+      "Epoch 00041: val_mean_squared_error did not improve\n",
+      "lr changed to 7.178980231401511e-05\n",
+      "Epoch 00042: val_mean_squared_error did not improve\n",
+      "Epoch 00043: val_mean_squared_error did not improve\n",
+      "lr changed to 6.461082011810504e-05\n",
+      "Epoch 00044: val_mean_squared_error did not improve\n",
+      "Epoch 00045: val_mean_squared_error did not improve\n",
+      "lr changed to 5.8149741380475466e-05\n",
+      "Epoch 00046: val_mean_squared_error did not improve\n",
+      "Epoch 00047: val_mean_squared_error did not improve\n",
+      "lr changed to 5.233476658759173e-05\n",
+      "Epoch 00048: val_mean_squared_error did not improve\n",
+      "Epoch 00049: val_mean_squared_error did not improve\n",
+      "lr changed to 4.7101289601414466e-05\n",
+      "Epoch 00050: val_mean_squared_error did not improve\n",
+      "Epoch 00051: val_mean_squared_error did not improve\n",
+      "lr changed to 4.239116096869111e-05\n",
+      "Epoch 00052: val_mean_squared_error did not improve\n",
+      "Epoch 00053: val_mean_squared_error did not improve\n",
+      "lr changed to 3.815204618149437e-05\n",
+      "Epoch 00054: val_mean_squared_error did not improve\n",
+      "Epoch 00055: val_mean_squared_error did not improve\n",
+      "lr changed to 3.4336842873017304e-05\n",
+      "Epoch 00056: val_mean_squared_error did not improve\n",
+      "Epoch 00057: val_mean_squared_error did not improve\n",
+      "lr changed to 3.0903160222806036e-05\n",
+      "Epoch 00058: val_mean_squared_error did not improve\n",
+      "Epoch 00059: val_mean_squared_error did not improve\n",
+      "lr changed to 2.7812844200525434e-05\n",
+      "Epoch 00060: val_mean_squared_error did not improve\n",
+      "Epoch 00061: val_mean_squared_error did not improve\n",
+      "lr changed to 2.5031560107890984e-05\n",
+      "Epoch 00062: val_mean_squared_error did not improve\n",
+      "Epoch 00063: val_mean_squared_error did not improve\n",
+      "lr changed to 2.2528404588229024e-05\n",
+      "Epoch 00064: val_mean_squared_error did not improve\n",
+      "Epoch 00065: val_mean_squared_error did not improve\n"
+     ]
+    }
+   ],
+   "source": [
+    "# tune model by starting from best weights and rerunning with decaying learning rate\n",
+    "# Load the weight that worked the best\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "#epoch=60\n",
+    "\n",
+    "# Train again with decaying learning rate\n",
+    "from keras.callbacks import LearningRateScheduler\n",
+    "import keras.backend as K\n",
+    "\n",
+    "def scheduler(epoch):\n",
+    "    if epoch%2==0 and epoch!=0:\n",
+    "        lr = K.get_value(model.optimizer.lr)\n",
+    "        K.set_value(model.optimizer.lr, lr*.9)\n",
+    "        print(\"lr changed to {}\".format(lr*.9))\n",
+    "    return K.get_value(model.optimizer.lr)\n",
+    "lr_decay = LearningRateScheduler(scheduler) # do sth to learning rate\n",
+    "\n",
+    "callbacks_list = [checkpoint, lr_decay] # checkin with these once in a while\n",
+    "history = model.fit(trainX, trainY, epochs=int(epoch/3), batch_size=500, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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W1DcAZvUw6lvP8Ky+99oVTc1GVFtRrG89v0Ub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\n6urquu06hw8f1smTJ2WxWPTGG2/onXfe0R//8R/rnXfe0datW/Xiiy/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2DL1T8B8u2KhWIe7LsvUwvTq+tjqT/Xqvyq2NHem4HuPXnl0rGMz01s1GhrNjyx/IO3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n5bUdw2wbZE7ITOQyMfR1VzKZjBtSribKEMH+xkz2NBw86XMtdiuvFb6NAwfrU9e6ffUTV6VR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\nDwBgRGW7x8skk4pbSvR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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131978a320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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Uuh9KhRxPHd2CA60ufPuVXrzdP4PfGzqPn3m4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WBMWzPc2ILRlOwq3YMaeoK+gFZiZ7RdO69evye/zz+p8UY2qUDP3sotAj0AAJx05fYg\n0LMYSlzx9WU79O80YAIAAACT5rRFjtLGLu21XhdbJQI9AABgLo0U6Lntttv00EMP6e6771YksteC\n8ZrXvMa1wQBca6O6LUnHDvQ44YG8xwM9BbOkvtUf+Q2eYRhaja/om8XvqNltKhqIujwh3FIaXlm+\nf6BHktYT57RV29GFxiWdO8aaOrcUzMHKrYzbK7cI9AAAAJvT0HP1Bx6RQESJUJyGHgAAAExNvllQ\nLLCgheBo523T9rnBolnWzeR5AADAHBop0FMqlfT444/r8ccfH37NMAx9+tOfdm0wANfatAM964nV\nYx0nEggrEYx7fuWW82HD8oiBHklaTQwCPVvV83pp+iVujQaXOSu3bhToWUuck85/RRvV7RkN9Eym\noScaiMhn+Fi5BQAAVG5VZchQIhi/5rblaFYvVDZmtt0QAAAA86tv9ZVvFnQuvjLyY1KXNfQAAADM\no5ECPW9+85v1kz/5k27PAuAGNqpbMmRoNTH6m5r9ZKJL2qxuq2/15TN8Y5hu8pwK1uVoduTHrNlv\nCLdqOwR6PGyvoefgS2/W7PDbZnVb/93Z73N9rsMqmEUZMm4YTDouwzAUD8ZUp6EHAIATr9KuKBGK\nXzewsxzN6LnyCyqYJS0vjB6aBwAAAI6r1Cqra/WUtVfBjiIdtgM9JoEeAAAwn0b6FP+zn/2s23MA\nuIG+1ddWdUenF5YV9oeOfbxMJK2e1RtW7nvRsKHnEB82OGGoreqOKzNhMsp2Q8+NdmOfi5+Vz/Bp\ns7o1ibEOrWAO9nsHfCPla48lHoyxcgsAgBPOsiyVWxUlQ4nr3u4E5Vm7BQAAgEnL2RdvZg/Rxp6O\n2Cu37HOFAAAA82akTxDPnDmjd77znbr77rsVDoeHX3/3u9/t2mAArrTbzMvstYaNI8eVsa90yDXz\nStvVpF6z2zh8Q8+phWUFfUFt1rbdGgsTUGqVFQ/GbhiECfmDOrNwSlvVnZlro+r1eyq1yrp5cW0i\nzxcLLminfoEVGgAAnGBmr6V2v7NvKNoJyjtNmAAAAMCk5JoFSYcL9CyGEvIZPpVo6AEAAHNqpE82\nX/WqV+m1r33tFWEeAJO1WRk0jKwvnhvL8Zzq0pxZHMvxpmG3mddCIKpYcGHkx2QpyZoAACAASURB\nVPgMn87Fz+pC/ZK6/a6L08EtlmWp1CqPvKZqLXFO7X5Hlxq7Lk92OOV2RX2rr6VIeiLPFw/FJUn1\nbmMizwcAAGaP0865GNon0BN1Aj009AAAAGCynFD58iFWbvkMn9LhJA09AABgbo3U0PPud79bjUZD\nGxsbuv3222WaphYWRv8AHcDxbVQHjTLr42roiQzeGOXtKx+8pm/1lW/mdS6+cujHrsbP6oXKhs7X\nL2otMZ6AFCan2TXV7ncOFeh5/MK/aKO6rTOx0y5PN7qCfeXQxAI9wZgkqdaua3GfNRsAAGC+VdqD\nQM++DT1OoKdBQw8AAAAm6ygrtyQpFU7pufILtFIDAIC5NFJDz5e//GW95S1v0S/8wi8ol8vpnnvu\n0T/+4z+6PRuAy2xUt2TI0Gr87FiO5zT05E1vBnqKZlldqzdcC3AYq3aIZ6u6M+6xMAEl+4qb1D4f\nRF3NCcFtVmdrzVrBbsdamtDKu7jdZFXr1CfyfAAAYPaUWk6g5/rh3oXggmLBBVZuAQAAYOJyzbwC\nvsC+4fP9pCNJWbJUtsPrAAAA82SkQM9HPvIRfe5zn9Pi4qJOnTqlP/uzP9Pv/u7vuj0bAFvf6muz\nuqNTC1lFApGxHDMdTsmQMdxN7DXOGoDlQ16xIUmrdqvPZo1Ajxc5qyJGbeg5Fz8rQ8YMB3om1dAz\nWLlFoAcAgJOr0q5KkpL7rNySpOVoVvlmXn2rP6mxAAAAAOWaBWUiS/IZI31sNZQODy6WK5qs3QIA\nAPNnpFdG/X5fy8vLw/++7bbbXBsIwLVyzYLMnjnW9VB+n1/pSMqzK7f2dipnD/3Yc/EzMmTQ0ONR\nTkNPcsRATyQQ1qmFZW1Wt2fqg6nJB3oGDT11Aj0AAJxY5dbBK7ekQWC+a/X4QAQAAAAT0+g01Og2\ntWy3yh9GOuIEeorjHgsAAGDqRgr0nDlzRo899pgMw1ClUtHv//7va2Vlxe3ZANg2q1uSNNZAjyRl\nI0sqtyvq9DpjPe4kODuVj7JyK+QP6fTCsrZrOzMV8MBoDrtyS5LWE+dk9lrDvzezoGCWJE0u0BML\nxSRJtTaBHgAATion0LMYuv7KLWmvAdNpxAQAAADc5ly8mTlCG3vavuiv2CKQDgAA5s9IgZ4HHnhA\njzzyiM6fP683vvGNevbZZ/XAAw+4PRsA24a9Kmg9sTrW42bsKx4KHrx64TgNPZK0mliR2Wsp3/Te\n//tJtxfoGa2hR9oLw23M0NqtgllULLigsD80kedj5RYAACi3KzJkHBzoWRi8vt6doSA0AAAA5tvw\n4s2jBHqchp5WaawzAQAAzIKRAj2ZTEYf+chH9M///M96/PHH9fGPf1ynTp2SJP3Wb/2WqwMCkDbt\nEMJaYrzNWJnIINCT82Kgp5FTxB9WPBg70uNX44Pfy60aa7e8pmRfWX6YQM+6HejZnJFAj2VZKpil\nibXzSHsrtwj0AABwclVaVcWDMfl9/n3vQ0MPAAAAJi3XLEiSskdZuRV2Vm7R0AMAAObPSIGegzz1\n1FPjmAPAPizL0mZ1W6eiWUUD0bEeOxMdhAnyHrv61rIs7TbzWo5mZBjGkY7hNLZszUjAA6Mrt8oK\n+YKKBiIjP2bVDsPNSqCn1qmr0+9MNNATC7JyCwCAk67Urih5g7WlTgNmruGt9wgAAADwruM09MSC\nCwr6AjT0AACAuXTsQA8Ad+XNghrd5jCAMk7OFQ85szD2Y7up3K6o0+8oe4Q3eA4aeryr1KooFU4e\nKswVDUS1HM1os7oty7JcnG40zpq7JbsSeBJC/qBC/pDqNPQAAHAimV1T7V5bi+H9121Jgw9EooEI\nK7cAAAAwMc5rz6XI4Rt6DMNQOpxS0STQAwAA5g+BHmDGbQzXbY0/0JOJDAIx+aa3Vm7t2lcLLy9k\nj3yMeCimVDipzSqBHi/p9Luqdmo3vLL8etYTq2p0m8rPwIq5/DDQM7mGHklKBGOqdRoTfU4AADAb\nys7a0tDBr6MMw9ByNKPdZl59qz+J0QAAAHDC5ZoFpcJJhfzBIz0+FUkNGrF7nTFPBgAAMF0EeoAZ\n56wIWk+sjv3Yi6G4gr6g8h5r6Nk9RgXr5VbjKyq3K6q2a+MYCxNQcT6ICicP/VgnFLdR3RrrTEdR\nmFKgJxaMqdapzURLEQAAmKxyuypJWhwhGL0czarT76hiPwYAAABwS6ffValVHrbJH8VSeNCCXWyV\nxzUWAADATDh2oIcPBQF3bVQG4QM3GnoMw1Amklau6bVAT07SGAI9CXvtFi09nlEaQ6DHCclNk1MB\nPMmVW5IUD8bU6XfV7nO1EgAAJ43T0JO8QUOPtPc6e7eRc3UmAAAAoNAsyJKlbOTo53rTkcG5wlKL\ntVsAAGC+HDvQ84M/+IPjmAPAdViWpc3qtrKRJS0Eo648Rya6pGa3qUan6crx3TBs6DnGyi1JWovb\ngZ4agR6vKNlX2Xg90FNwAj3hyTb0xEMxSVKtXZ/o8wIAgOkrt+1ATzhxw/tm7dfZzutuAAAAwC3O\na87sMS7eTDsNPSYNPQAAYL6MFOjZ3t7Wu971Lr3pTW/SpUuX9M53vlNbW4PWkF/7tV9zdUDgJCuY\nJdW7Da0tjn/dlsOpMvXS2q1cI6egL6jF0I0/jDiI09AzCwEPjKY8DPTc+Mryq8WCC8pE0tqsbk+9\nXa5gFhXyBRULLkz0eeNBO9DTYc0cAAAnzbChZ6SVW3ZDD4EeAAAAuCxnn5dePsbKrVTEWblFQw8A\nAJgvIwV63ve+9+n+++9XLBbT8vKyfuRHfkTvec973J4NOPE2q4Pg3LoL67YcmYgd6PHI2i3LsrTb\nzGs5mpHPOF7JWCaypIg/oq3a+TFNB7eVhh9EHb6hR5LWEquqderDpp9pKZhFLUXSMgxjos8bGwZ6\nGhN9XgAAMH2VdlXSqCu37IYeVm4BAADAZTmnoWfhOA09g3OFTis2AADAvBjp0/BisajXve51sixL\nhmHoJ37iJ1SrcXU/4LYNuzlmPeFeQ0/GvvIh55GGnlqnLrPXGl41fByGYWg1cVaXGrtq9dpjmA5u\nKx2joUfaW7u1YYflpsHsmmp0m1qKTHbdliQl7EBPvcPKLQAAThqnoWeUlsvFUFwhf4iGHgAAALhu\nGOiJHCPQQ0MPAACYUyMFeiKRiC5cuDBsEvjqV7+qUCjk6mAA9kIHzmooN3itoWe3ObhK+DhXbFxu\nLX5Olizt0NLjCaVWWYaMI69bc9quprlmzblSaMk+0TBJsZDT0EOgBwCAk6bcrigejMnv89/wvoZh\naDma0W4zN/VVpQAAAJhvu82CIv7IsVbTRwMRRfwRlczptnIDAACMW2CUO733ve/Vz/3cz2ljY0Nv\nectbVC6X9bGPfczt2YATzbIsbVa3lYmkFbdbNdyQjQ5aQrzS0LPbGFyx4awBOK5zdlhqs7qjW5I3\njeWYcE+pVdFiKDHSB1HXszYTgZ6iJE2locf5WVJrE+gBAOCkqbSqw3bOUSxHs9qunVe1UztymBoA\nAAA4iGVZyjfzOrNw6tir6dORJA09AABg7owU6Lnpppv0V3/1V3rhhRfU6/X0kpe8RLu7u27PBpxo\npVZZtU5dt6Ve4urzRANRxQILyjeLrj7PuDi1/+NYuSVJa/FBoGertjOW48E9lmWp3K7oXOzskY+R\nCMWVCieH6+ymYSYCPTT0AABwopjdlsxeS8nQ6GtLndfbu408gR4AAAC4otyuqNPvKjuGc73pcErn\n6xdldk1FApExTAcAADB9B67cOn/+vHZ2dnTfffcpl8spFotpcXFRFy9e1P333z+pGYETyVm35awI\nclMmmlbBLHiiTt9ZuTWuhp4zsVPyG/65CPQ8X97Q+//5d3Whfmnao7ii3mmo2+8qFR79g6jrWU+s\nqtKuqtyqjGmyw9lbuTW9QE+dQA8AACdKpT143ZM8xOuoYaDHfv0NAAAAjFuuOWiNH0ugJ5KUJBVb\nrN0CAADz48CGno9//ON6/PHHdenSJd133317DwoE9IY3vMHt2YATzWkQWZtEoCeypI3qtirt6qFO\n8k/DbjOvgOEfvkE7roAvoJXYae3UzqvX7x15ldMs+P+2v6xLjZy+WfiOzsROTXucsXPejCfDx/uz\nX0us6Mnc09qobunO8CvHMdqh7DX0pCb+3AvBqAwZqrJyCwCAE8UJMicP0bSzvOAEevKuzAQAAAA4\nrzWzh1gNu590eHCurWiWdDZ2+tjHAwAAmAUHBno+8IEPSJL+8A//UD/7sz87kYEADGzagZ71xKrr\nz5Wx3zDlmoWZD/TkGnllohn5jAMLxg7lXGJFm7UdXWrmPPtmr9fv6an8s5KknDmfH7qU7UDPOBp6\npMH32J3Z6QR6fIZvKt9rPsOnWHCBhh4AAE6Ycrsq6bANPYNGzN0GDT0AAABwR34Y6BlHQ48d6GmV\njn0sAACAWXFgoMfRbrf10EMPXfP1d7/73WMfCIBkWZY2qltKh1OKh2KuP18mMgj05M2CbtXNrj/f\nUdU7DdW7Dd2SXB/rcVfjK5IGAQ+vBnqer2yo3mlI2quqnTelYaDnuA09g9arzep01qwVzKLS4dRY\nQ2mHEQvGVCPQAwDAieI09CweItCTDC8q4AvQ0AMAAADXOK81l8cR6Bk29LByCwAAzI9Df5rY6XT0\n6KOPKp/npB7glnK7omq7pvVF99t5pL1K0/yMB0Fywzd42bEe1wl4bNWmE/AYhyd3nx7+etb/HI+q\nZH8QddxATzK8qMVQQhvVrXGMdSidflfldnUq67Yc8eCC6p2G+lZ/ajMAAIDJKrcPv3LLZ/iUjWa0\n28zJsiy3RgMAAMAJlmsW5DN8xz7fJ0npyOAYNPQAAIB5MlJDz9VNPL/4i7+on/7pn3ZlIAB767bW\n4ucm8nzDlVvmbAdBnLr/7MLxr9i43Ln4WUnS1pQaW47Lsiw9mXtaIX9I6XBKObMgy7JkGMa0Rxur\nca3ckqT1xDk9lf+mqu2aEqH4sY83qqI5OKGwFElP7DmvFg/FZclSo9tUPOh+AxgAAJg+p6HnsCs/\nl6MZXahfVL3b4HUDAAAAxi7XzCsTScvv8x/7WCm7oadEQw8AAJgjR9r3Ua/XtbPjzQ++AS/YqAya\nQ9YXJxPoWYqkZciY+WaXXZcaeqKBiLLRjLZqO568+vhiY1e7zbxeuXS7zsZOqd1rz+VKpdLwg6jj\nX7HjtDJt2OG5SSmYRUlTDvQEFyRJ9fb8/R0BAADXV2lVJUmLh2jokfZWH+w2aOiFt/X6vWG4HgAA\nzIZm11StU1d2DOu2JCnkDyoejKnQKo7leAAAALNgpIaee+65Z9j0YFmWKpUKDT2Ai5yQgRM6cFvQ\nF1AyvKicZwI9423okaTV+Iq+vvsNlVplpae4DukonswN1m3dlb1D27XzkgZXt0yyeWYSSq2yIv6I\nIoHwsY+1lhiss9usbuuOzMuOfbxRFWagoSdmX11f7dR1empTAACASSq3K4oHYwr4RjoFMOQE6Xeb\nOd2SXHdjNGAi/svGP+g/P//3+vXX/LJW4memPQ4AAJCG56LHea43HU7qQmN3LtvLAQDAyTTS2bzP\nfOYzw18bhqHFxUXF4/P1QTEwSzarW0qFk4e+gvY4MpElPVd+Qb1+bywVp27YbebkM3zKuBCGWEsM\nAj1btR3PBXq+kXtGhgzdkXm5Wr2WpMEb4luSN015svEqtcpKRY7fziMNVm5Je+vtJmWvoWd6f8cS\ndqCnPoctTgAA4PrKreqRXn8s26tunWA94FX/WnpOfauvJ3NPE+gBAGBG5OzXmJno0tiOmYqktFnb\nYWUsAACYGwcGeh5++OEDH/zWt751rMMAkMqtisrtqu7MvnKiz5uNLum75edVMEvDE/ezZreR19KY\ndipfbTW+ImkQ8Jj07/1xVNpVPV/e0K2pmxUPxZSxr2iZ9balw2r3Omp0m1q3m3WOKxVOKh6MabO6\nNZbjjWoWVm45DT3zuJYNcEun35Xf8MlnHGlbLQBMVavXltkzlQwvHvqxw4YeVm7B43ZqFyRJT+e/\nqX938w9NeRoAACDtBXrG29AzCLEXzTKBHgAAMBcODPQ8/vjjBz6YQA8wfk5jyPqE1m05nNabvFmY\nyUBPs2uq2qlpNbHiyvGd427ZK6u84qncN2XJGoaQsvYVLTlzvj50KbXKkgZBnHEwDENriXN6tvBt\n1TsNxYILYznujTiBnmm2QMVDdqCnTaAHGEWv39Nvf/n/0ErsjH7+7ncR6gHgOeVWRZKUDB0+0JMO\nJ+U3/Mo1c+MeC5iYeqehcnvwffB8eUO1Tp0P+AAAmAFOoCc7zkCP3e5dapW05tJ5ZAAAgEk6MNDz\ngQ98YPjrTqej559/Xr1eTy996UsVCIy0rQvAIW3YjSHjaiIZlVNtOngj9dKJPvco9q7YyLpy/GRo\nUfFgTFvVHVeO75Ync09Lku6yAz1LkbQMGcrPWUNPeRjoOfwHUftxAj2b1W29fGkyf+cLZknJUEJB\n3/T+DY3T0AMcyoXGJZVaZZVaZX3hxX/Qm27+76c9EgAcyjDQc4TXUX6fX5lompVb8LQd+6KNsD+k\nVq+tb+a/re878z1TngoAADgN45nI+FZu7TX0lMZ2TAAAgGka6RLjp556Sj/8wz+s9773vfr1X/91\nveENb9ATTzzh9mzAibRhN/SsTbyhZ/DGKW83iMwa50MEt9qDnMaWvFlQo9N05TnGrd1r65uF7+jM\nwimdWliWJAV9AaXCyblbuVW0Az3JMTX0SHuhOacVy219q69SqzzVdVvSXqCn3mlMdQ7AK7Yva257\n5Pn/Vy9UNqY4DQAcXsVuJlkMJ470+OVoVrVO3TOvkYGr7dQvSpJet/IDkqSn8t+a5jgAAMC228wr\nEYorEgiP7ZhOK7ZzLhEAAMDrRgr0/M7v/I4++tGP6m/+5m/08MMP66GHHtKDDz7o9mzAibRZ3VYy\nlDjSFbTH4axqmtVml1xj/DuVr7YaH9Swbte80dLzzcJ31Ol3dNfyHVd8PRNNq9Qqq9vvTmmy8XOu\nLHdqc8fBCc1NKtBTaVfVs3pTXbclSTE70FPt1KY6B+AVTqDnf7j538qyLH3y6f8os2tOeSoAGJ3z\nOip1hJVb0t7r7xwtPfAop6HnNWe+V8lQQs8WvqW+1Z/yVAAAnGy9fk/FVmns53pp6AEAAPNmpEBP\no9HQ3XffPfzvV73qVWq1Wq4NBZxU1XZNpVZZaxNetyUNKvgDhl85czYDPbvNnCT3Vm5J0mr8rCRp\n0yOBnm/knpEk3Wmv23JkIxlZslSY0baloygNG3rGF3TLRNJaCESHa+7c5vx5TLuhJ+wPKeALqN6m\noQcYhRPouWftdXrjTW9QrpnX57/98JSnAoDRldtVSdLiEV9HOa+/ndfjgNfs1C/IZ/h0JnZKd2Re\nrlqnrhcrk3kPAAAArq9gltS3+sqOOdCTCi/KkKFii0APAACYDyMFepLJpL7whS8M//sLX/iCUqnp\nNgwA88gJFqxPeN2WJPkMn5Yi6Zlt6Nlt5mXIUCY6vp3KV1u1f9+3qrMf6OlbfX0j96wSwbhuXly7\n4janbWme1m6VnCvLx7hyy1mzttvMq9l1f4VEoTkbgR7DMBQPxlTr1Kc6B+AV27XzSodTWggu6Edu\neZNuSqzpv134mv7bha9NezQAGEm5NQj0JI/a0GOvvN2loQceZFmWdmoXdWphWUFfQHdkXyFJejr/\nzSlPBgDAyea0P2Yj4z3X6/f5tRhKqGiycgsAAMyHkQI9Dz74oD7xiU/o+7//+/Xa175Wf/AHf6D3\nv//9bs8GnDjO6p+1KQR6JCkTXVKtU5fZnb0Grt1mXulISkFfwLXnOLWQVcgX1JYHGnpeqGyq2qnp\nzuwr5DOu/FGemcNAT7lVlt/wK26vixqXvbVb7v+ZF+yq36Upr9ySZAd6WLkF3Ei1XVOlXdU5u8HN\n7/PrXXf8zwr7Q/r8t/4T62cAeEK5PQhGL4YTR3q8swZht8HPPHhPwSzJ7JlaiZ2WJL0sfZv8hp9A\nDwAAU+aExcfd0CNJ6UhKpVaZFZsAAGAujBToufnmm/WXf/mXeuyxx/Too4/qU5/6lF7ykpe4PRtw\n4mzYgZ71xcmv3JIGK4gkKT9ja7favbZKrbIrb/Au5zN8Ohc/q/P1i+r0u64+13E9ufu0JOmu5Tuu\nuc35fZq1P8fjKLUqWgwlrgkvHdf6MNCzPdbjXk++NRsNPdIg0NPqtdXpdaY9CjDTnHVbzkpGadBU\nce/t/15mr6VPPv0f1ev3pjUeAIyk0qooFlw4cjB+KZKWz/CxcguetFMf/Fu+Ehv8Wx4NRHRr6hZt\nVLdUsdfRAQCAycuZg0CP0wY5TulwUj2rp2qbi9kAAID3jfTJ6GOPPaYPfehDsixLP/7jP64f+qEf\n0mc/+1m3ZwNOnI3KlhKh+JHr8I9rGASZsWYXp2lm2eVAjySdS6yob/V1vn7B9ec6jm/knlHQF9TL\n0rddc9u8rdzqW32V25WxrttyrE0w0FMwZyjQExo0HdW7jSlPAsw2J9CzclmgR5K+/+yr9ZrT36MX\nKhv6z8//l2mMBgAjK7crx3p/EfAFtBROsXILnnS+dlGStBI/M/zaHZmXSZKeyX9rKjMBAIC985Zu\nNfRIUrFVGvuxAQAAJm2kQM9DDz2kH/uxH9Pf/u3f6q677tKjjz6qv/7rv3Z7NuBEqbXrKrZKWk+s\nyjCMqczgrGrK28GDWeFcDTyJQM9afEWStFU97/pzHdWlxq4uNC7pFUu3K+QPXXN7IhhXyBdUfk4+\ndKm2a+pbfaUi4w/0ZKMZRfzhYTuWmwpmSdFAVNFAxPXnupGYvbqs2q5PeRJgtl2vocdx78v+vTKR\nJf39i4/p28V/nfRoADCSdq+tZtdUMny8CwaWF7KqtKszuZoXOMi23dBz7rJAz7/JvFySWLsFAMAU\n5Zp5hfwhJYLxsR87bV8UWDTLYz82AADApI28u+TWW2/Vl770Jd1zzz2KxWLqdFjTAYyT0xDiNIZM\ng7NyKzdjQRDnauDlhazrz7WasAM9NfcDHkf1ZO4ZSdKd2Vde93bDMJSJLmm3WZBlWZMczRWl1uDN\nd+qYH0Rdj8/waS1xTpcau65+QGVZlgpmUUv2FULTFg8uSJLqHQI9wEG2a+cV9AWv++9PNBDRu+74\nSRmGoT995vOq8f0EYAaVW4OVQouhxLGO4wTrZ+19AnAjO7ULCvlDV7Rknl44pUwkrWcL32Z1JuYS\nf68BzDrLsrTbzCsbWXLlwtYUDT0AAGCOjBToyWazevDBB/XUU0/p9a9/vT74wQ9qZWXF7dmAE2Wj\nuiVJWp9moGfY0DNbq5p2G5Nr6FmJnZUhQ1vVHdef66ie3H1GhgzdmX3FvvfJRpdk9kw1us0JTuaO\nUqsiSa6s3JIGITpLlrZq7v2Z17sNtXvtmVi3JUlx++onAgjA/nr9ni7UL2oldkY+4/ovmW9J3qT/\n8ZY3qdQq63PP/tVchCgBzJdye/A66tgNPfbrcNZuwUt6/Z4uNnav+bfcMAzdkXm5ml1Tz5VfnOKE\nwPg9lXtW/+t/fZ/+6wuPT3sUANhXrVNXu9d27VxvOmwHekwCPQAAwPtGCvR8+MMf1p133qnPfOYz\nWlhY0Nramj784Q+7PRtwojgrf9YTq1ObIRZYUMQfUb45ayu3Bh8cuLFT+Wohf1CnY6e0VdtR3+q7\n/nyHVWvX9Vz5Bd2SXFcitH8lbTYyP1dRl52GntD4G3qkvVasTRfXbhXsNXYzE+gJDVZuEegB9nex\nsauu1btiRcf1vOmmN+ilqZfoidzT+sedf57QdAAwmrIdjE4e83WU01TmrMIFvOBiY1c9q6eV2Olr\nbruDtVuYQ9u18/rk059Tu9/R188/Pe1xAGBfbp/rTUfslVstVm4BAADvGynQE4/Htbi4qM997nP6\nvd/7Pd1yyy2Kx8e/2xQ4yTarW4oHY661kIxisKoprZw5W6uadpt5JUOLCvtDE3m+tfiKWr22cs3Z\naiqSpKfyz8qSpbuydxx4v722pdkKZx2F8+Y76dL3xvpEAj2DK4JmbeVWrU2gB9iP09p1Ln5wK6XP\n8Ol/eeXbFQss6K+/84h2ahcmMR4AjKTSHqzcGltDT8P7YXGcHDv1wb/JK/Gz19x2e/pWBXwBAj2Y\nG5V2VX/w5Kdk9lryGT69WNqa9kgAsK/cMNCz5MrxF0MJ+QyfSjT0AACAOTByQ88f//Ef69y5czp1\n6pQ+9rGP6ROf+ITbswEnRr3TUN4sai1xzpW9wYeRjSyp3WvPTHNHp99V0SxpecH9dh7HamLw4a2b\nK5iO6hu5ZyRJd2ZfeeD9nDfE89HQM7iy3Lm6ZtxOLSwr5A+drIYee+VWfUa+z4FZ5ARzbtTQI0np\nSEr3veJ/Uqff1Sef/pw6vY7b4wHASIYNPeHEsY6TiSzJkEFDDzzF+bd8JXbtv+Uhf0i3p27VTv0C\n6zjgeZ1eR3/45KdVMIv6kVvepJsX17VdvchrUgAzy+2GHp/hUyqcpKEHAADMhZECPV/60pf0p3/6\np3rHO96hd77znfr0pz+tRx55xO3ZgBNjcwbWbTkywyDIbLTT5JsFWbK0HM1O7DlX7TaGrepsBXo6\nvY6eKXxbpxayOhM7deB9M5HZ+nM8jpLT0OPSyi2f4dNqfEXn6xfV7rVdeQ4n0JOZkUBPLMjKLeBG\n9hp6rr2q/3ruXv43et25H9BO/YL+03f/1s3RAGBk5fYg0LN4zNdRQX9QqXBy+OEL4AU79fOSpJV9\nwrms3cI8sCxLf/bNv9TzlRf1fadfpX938w9pNX5Wfauv8/WL0x4PAK4rb5+vdCvQI0np/5+9Ow+P\n467TRf9W9b7vWluyvEq2bCckIQkECI4dEhKWkBBCFmfhDNvcYQbOnBlgzsC9c4aBuZdz+APuXBiY\nQ0JMErKQyQJJWBJCSFjihNiSJUuWF60tyb3ve9X9o7vadmJJLalW6ft5DMcXtAAAIABJREFUHj8E\nW131syW1uqve3/s1uZEsplDlqpKdgxBCCCFEDk0FelwuF7LZMzf9yuUyjdwiREST6VoVsjD6R0lC\nECRaUEcQRGiYCUj4Bu/NGoEelTX0jMaPo1QtLdnOA5xp6ImuiUBPCjaDFQadQbJzdDk6wYPHTGZW\nkuOfGbmljkAPjdwiZGkzmVl4TG5Y698vzbhpywfQZmvFb6dfaTSqEUKIkhoNPcbVNfQAQMDqR6KY\nRIkaH4hGhDJzcBjtcBjPf/1qh68XADAUHZVzWYSI6rnx5/Ha/CFsdG7AHX03g2EY1V7TIIQQQTgf\nBQNG0tH0HrMLPPhGwJ0QQgghRKsWDfR8+ctfxpe//GVwHIcPf/jD+Kd/+id87Wtfw4033gi3W7oX\nW4SIieM51dcMT9YberpU0NCjtiCIsAs4YJWvocdutMFtcmFawhFMKzFQvzm829+/5McadUY4jY41\nMnIrCbdJmnFbAiFMJ9XYrVghDgNrgL3ejKM0HauDRW+hhh5CFpAuZZAqpZtu5xEYdUZ8ov826Fk9\nfnz00caNdEIIUUqylIZVbxElGC0E7NfC60uy9hUqBUQLcXTaFv5Z3mL1o8Xqx0h8DGWuIuPqCBHH\n6/OH8bNTv4TX7MGndt/ZeK7vdNS+7qcl2rBCCCGrFclH4TW7oWf1kp3DY6rdv4rRaE1CCCGEaNyi\nr5guvfTSc/5X0N+/9M1kQtTioZHHMRgZxpcu/RvJQwErNZWegU1vlXRXQrOEkVtqaegJ5yMA5G3o\nAYAuRwcGI0eRKqXhFGFH82pxPIcjkWHYDTZscm1o6jF+ixfjqSlUuSp0rE7iFUojXymgUC3CZZJm\n3JagS4ZAj9fsBsMwkhx/JewGK7IU6CHkvIS2ruUGeoTHfGTz9Xh07EncP/ww/o8L/wtYpqlSTEII\nEV2ymIJbpNdRwuvxcD664AgjQtQiVB811G5vXfTj+n19+M3UyziROIU+71Y5lkaIKMZTkzhw9GGY\ndEZ8Zvfd51y36LC1gWEY1Y0RJ4QQAChVS0iV0uj1bJH0PJ76dfYEBXoIIYQQonGL3l34yEc+0vi1\nZ88eXH755bjssstwySWXoK2NLuAR9StzFbx++hDS5QweHn0CPM8rvaS3yJXziOSj6HJ0quJmv68+\nEiiajyu8kppwrrYDWMqZyufTqKhWyQWwyfQ0kqU0dvq2N31j2Gf2geM5xItJiVcnnWR97W6jtGG8\nNmsLDKy+0ZYlpmK1hGw5p5pxWwK7wYZMOafK50VClLaaQA8AXBl8J3b6tmMkPobnJ18Sc2mEENK0\nUrWMfCUPl1GkQE+9MVMI3BOiZrOZOQBAxyINPUAt0AMAQ9ERyddEiFjihQT+feBHqHBVfKL/9re8\nZjXqjOiwt2ImM0vv9wghqhOpt8JLfa3XU9/Yq+XrooQQQgghwBKBHsG3vvUt7N27F9deey1uu+02\nvO9978O3vvUtqddGyKodj59EsVoCAwYDkSG8ER5UeklvITSCdDuVH7cF1C78OIx21VTph/MROAx2\nWPRmWc8brDe2qGXm/GC4Nm5rV2BH049R2/i0lUjUx9W4zdIGenSsDp32DoSyc6LX7ccKtXCcGhq4\nzmY32lDlqyhUC0ovhRDVEQI9wRUGehiGwR3bb4bL6MBTJ5/DRGpKzOURQkhTUqXa6yixmg7Pbugh\nRO1msrVAT+cSbVJb3JtgZA0U6CGaUagU8d2Be5EqpXHT1g9ip3/7eT9ugyeIQrU2eo4QQtREeC0p\nXLeUitDQEy9SQw8hhBBCtK2pQM/PfvYz/Pa3v8V1112H+++/H/feey+8XmlfcBEihoFILQRxa++N\nMLB6PDL6BLLlnMKrOtdUphboEUb+qIHf7EWsmADHc4quo8pVES3EZW/nAdTX0DMQGYae1WO7d1vT\njxHGp0UK2r3pkhAaeiQeuQXUvgc5nkOofiNfLGcCPepq6LEZbACATEldz4mEqMFMZhYG1tBoo1gJ\nh9GOO3d8HBzP4d6hB1GoUHiOECKvZDENAKKNjxVek0dy2n1tSdaPUGYWDBi02xYfuWVg9ej1bsV8\nLtxohyVErTiew33DD2EmM4t3dVyG9wavWPBje9y1TWMzKtmkRAghgkhenjZ2j6ke6ClQQw8hhBBC\ntK2pQE9LSwvsdju2bt2KkZERXH755YhEqGabqBvP8xiMDMOit+Dy9ktw3carkS5n8PjYz5Re2jkm\nU9MAgG4VBXp8Fm9tVJPCb3hihVqoKGCVP9DjM3tg0ZtV0dATyUcRys6hz7MFJp2x6cf5zfVAz1po\n6DFJ29ADnPkenBJ57FasPqtbbYEeuxDoKWcUXgkh6lLlqpjLzqPD1tb0iMOF9Hm3Yl/3lQjno3jk\n2JMirZAQQpqTFLmhx6QzwmV00sgtono8zyOUnYPf4oWxifdPjbFbMWrpIer25IlnMRgZRq9nCz62\n7YZFx7YLgR61bFIihBCBcJ0yIHGgx2awwsDqqaGHEEIIIZrX1F0Ku92OJ554Av39/Xj66adx6NAh\npFIpqddGyKrMZGYRLybQ7+uFjtVhb9d70GXvwB/nXsPR2DGll9cwlZ6BVW+Bz6ye1ishCBItKBsE\nEW4WSP0G73wYhkHQ3oHTuQgKlaLs5z+b0DS129+/rMethZFbyUZDj/SBni7JAj21hh5hZ5BanAn0\nZBVeCSHqMp8Lo8JXlxzR0awPbroG3Y4g/jT3Ol6be0OUYxJCSDOSRXEDPQAQsPoQKyREH1FKiJhS\npTSy5Rw6bM39LO/39QIAjd0iqvb70Kv49eRv0WL14y923gEdq1v04zc0GnrEbaAlhJDVisg0coth\nGHhMbsQLFOghhBBCiLY1Fej5l3/5F8RiMVx22WXo7OzEV7/6VXz+85+Xem2ErMpgPQSxy78DAKBj\ndbh9+81gGRYPjfwUxWpJyeUBAPKVPE7nI+hydC66s0pujVFNCgdBhJnKAcvKR56sRtDRAR613Z1K\nGgzXvpZ3+rcv63EukxN6Rqf453E14vVAj5g3ohbSbmuFntFhUqJAj3obemjkFiFnE5rZOuujF1dL\nz+pxT/+tMOqMeGj0PzX9nEwI0ZZUqTZyy2UUMdBj8YMHjxg9lxEVC2Vq7986mgznes0edNjaMBY/\ngZIKrhMQ8mbH4ifw0OjjsOmt+Ozue2A1WJd8jNvshMNgV0XrMCGEnC2Sj8JmsMKit0h+LrfZjUw5\ni3K1LPm5CCGEEEKk0lSgp7W1FZ/4xCcAAF/60pfw1FNP4frrrwcAfPrTn5ZudYSswmDkKFiGxQ5v\nb+P3uhyd2Nv1HkQLcfzs5C8UXF2NUH3cpaJxWwAabUGqaehRYOQWAATrN3OnRQ54LEe2nMPx5Cn0\nOLuXHWphGRY+ixeRQlSi1UkvWUzCwOph0y99wXK19KweHfY2hDKzqHJV0Y4bKyTAMizcMoSSlsNu\nrAd6SjRyi5CzCTcBxWroAYAWawC3bLsBhWoB9w09JOpzDCGELORMQ49DtGMKzZlC8J4QNRI2ZHTY\n25t+TL+vD2WugmPxE1Iti5AVOZ2L4D8GD4ABg0/u2o8Wa6CpxzEMg057O6KFOPKVvMSrJISQ5nA8\nh2ghDr9MbeyeeuO3sGGQEEIIIUSLmgr0LGZ+fl6MdRAiqkQxiYn0FLa4N8FqODftf93Gq9Fi8eM3\nUy9jPDWp0AprhCaQbrUFelQyqimcU7ihRwj0KLijbSg6Ao7nGk1Ty+WzeJEt55CvFERemTwSxRRc\nJpdsDVZdjk5U+CpCWfF+tsUKcbiMziUr0eVmqzf0ZKmhh5BznGnoaf4mYDMua7sYF7dcgFOpCTwz\n/mtRj00IIecjBHqcYjb0WGuvyynQQ9Ss0dDT5Mgt4OyxW6OSrImQlciVc/jewL3IVnL4eO+N2OrZ\nvKzHBx21axozGWVbhwkhRBAvJFHlq42QuNS8ZjcAIFGksVuEEEII0a5VB3rUNCaIEMFQZAQAsPs8\nIQijzoDb+m4CDx4PHH0MFa4i9/IaJtPTAIAuR1CxNZyPx+QCy7AqaOiJwqq3wNZEnbQU2mwt0DM6\nTKeVmzk/UB8dd76v5Wb4zbU3yEqHs1aiylWRLmVkbbYRvhenRGplqnJVJIsp1Y3bAs4euZVVeCWE\nqEsoMwuPyd3UKIPlYBgGt/bdCJ/Zg1+Mv4AxagAghEgsWUrBorfAqDOIdswzDT0R0Y5JiNhC2Vno\nWf2ybhZucvXAojdjKHoUPM9LuDpCmlPlqviPIz/GfC6Mfd1X4p0db1/2MYSAutAOTQghSovUQ+Hy\nNfTUAj3xAjX0EEIIIUS7Vh3oIUSNhBDELv/28/75Vs9mXNFxGULZOfxq4kUZV3auqfQMLHqzbLsS\nmqVjdfCY3IqGQDieQzQfVaydB6iNYGq3tSKUFXcEU7PKXAVHo6Pwm71ot7Wu6Bg+Sy1IElE4nLUS\nqVIaPHi46/W4chDassQK9MSLSfDgVR7ooZFbhAjSpQySpbTo7TwCi96Cu/tvA8MwuG/4J9SQRQiR\nVKqYXvbI1qUIN1+EJk1C1IbjOcxm59FubVlWQ6aO1aHPuw3RQhzzubCEKyRkaTzP45FjT2A0fhy7\n/Dvw4c3vX9FxhNbhGQVbhwkh5GyNQI/ZK8v53PWGnliBGnoIIYQQol0U6CFrTqlawmh8DO221kXT\n/h/Zch1cRieeG38ecyKO12lWoVLA6VwEQXuHKpuufBYvkqU0StWyIudPFJOo8FUErMqGnYKOTpS5\niiIXdcfiJ1CoFrErsGPFXyPC90BEg2MRhPnWYt+IWkyHrQ0sw2Kq3p61WrFCHADgq19AUBOL3gyW\nYZEpUaCAEMFMptbIJlWgBwA2uTbgup6rkSgm8eDIY9QCQAiRRLlaRraSg8voEPW4Fr0ZDoOdGnqI\naoXzUZS5CtrtzY/bEvT7+gDUxh4ToqQXp1/By6E/odPejrt33AqWWdnl21ZrAHpWr+gYcUIIOVtY\n9oae2ibBOI3cIoQQQoiGrTrQQzchiNqMxMZQ5irYtcSIIoveglt6P4IKX8UDI4+B4zmZVlgznZkF\nDx7dKhu3JfDXG0ViCjW7CLt+lW4vEna0KXEBbLAxbqt/xccQdrxoceRWoh7okbOhx6AzoN3WiumM\nOK1MQqBHjQ09DMPAbrAhSyO3CGmQI9ADANf07MEW90YcCh/BK6E/SXouQsj6lCqlAUgTjA5YfYgW\n4oo0WBKylNnMHIBaUH+5dnh7AVCghyjrSOQofjr2NJxGBz67+x6Y9aYVH0vH6uqtw/P0nE0IUQWh\nQVyuDZye+gY7CvQQQgghRMtWHei54YYbxFgHIaIZjBwFgCUDPQBwQaAfb2vZjZPJCbw08wepl3aO\nyXoDiDDiR218jWYXhQI99V2/So7cAoCgox7okXnmPM/zGIgMw6q3YLOrZ8XH8VlqgR6lPo+rkSym\nAMgb6AGALkcnylxZlFYmNQd6gNrYrTQFeghpkCvQwzIs7t5xK6x6Cx4bexqzCjQFEkLWtmSp9jrK\nZZQg0GPxg+M5Gl1AVGkmWw/0rOBnucvkQLejE8cTp1CoFMReGiFLmsnM4t6hB6FndfjM7rsbN6JX\nI2jvQEWh1mFCCHmzSD4KA6uHU+QWyYVY9GaYdWYkCklZzkcIIYQQIoWmAj2/+93vcOONN2Lfvn3Y\nu3cvrrrqKuzduxcAcPfdd0u5PkKWheM5DEaHYTfY0OPsauoxH9v2YVj1Fjx14tnGzXc5TKVnAABd\nTnU39ERl/Dc5m1DBqvTILeGm7pTMDT1TmRkkikn0+7ZDx+pWfByL3gy7wYaoQk1Lq6FEQw+ARmuW\n8D26GsKNLq8KR24BgM1gRb6Sp92ahNTNZGZhYA1osUofJvWY3bi976Moc2XcO/QgygqNuCSErE3J\nooQNPfXgP43dImoUqjf0dK5g5BZQG7tV5asYjR8Xc1mELCldyuB7A/ehUC1i//ZbsKHJa1pLEa5p\nCMF1QghRCs/ziOSj8Jm9Kx4luBIes4saegghhBCiaU29cvra176Gz33uc7jvvvtw//3348CBA7j/\n/vulXhshyzaZnka6lMFO//am3xg4jQ7cuPWDKFZLeGj0cdnGyE2mZ2DWmRQfKbUQodlFqVFNjUCP\nwg09Fr0ZAYsPM+mQrCMGB8L1cVuBpZumluKzeBHNx2QfK7daZwI94t+IWkxXvTVLnEBPLRDnUWtD\nj9EOAMhWcgqvhBDlVbkq5rLz6LC1yXZx8cKWXbii4zLMZGbx5IlnZTknIWR9EJoOpdj9fCbQExX9\n2ISsVig7C6vesuJ2qn5fHwAau0XkVa6W8e8DP0KsEMcHNr4PF7deINqxlRwjTgghZ8tV8shXCvDL\nfC3cY3IjXylQ+x4hhBBCNKupuxUejwd79uxBMBhEZ2dn4xchajMohCCaGLd1tsvbLkafZyuGo6M4\nOP+GFEs7R7Fawnz2NIKODll3JCxHY1STQs0u4VwEZp0JdoNNkfOfLWjvQLaSk3U3x2BkGHpGhx3e\nbas+lt/sRYWvNm7saEWymAIDRpJREYsJ2tvBgGmMxVuNWCEOh8EOo84gwsrEJ3x/ZUo0douQ+VwY\nFb664h39K3XT1g+i1dqC30y/jCP1saGEELJajZFbUjT01FvMqKGHqE2pWkY4F0W7rQ0Mw6zoGBuc\nXbAZrBiKjsq6oYOsXzzP48cjj+JUagKXtF6Ia3v2inp8aughhKhFpLF5U+ZAj7nW/B0v0tgtQggh\nhGhTU0mCiy++GN/4xjfw8ssv4+DBg41fhKjNYPQo9KwefcsMQTAMg1v7boKRNeCxsaeQLmUkWmHN\ndDoEHnxjtI8aOQx2GFmDIg09PM8jnI/Cb/Gt+EKsmIL1xpbptDw72qL5OKYzIWz1bIZZb1718Rrh\nLIXallYqXkzCbrStauTYShh1RrTZWjCVCa2q1YjjOcQLCXhV2s4DAHaDFQCQKVOghxBh13JnfRez\nXEw6Iz7Rfxv0jA4Hjj6iufAlIUSdhOcSKYLRjYaeHDX0EHWZy86DB7+qcC7LsNjh7UWimEQoOyfi\n6gg5v+fGX8Br84ew0bkBd/TdLPo1EKvBAp/ZI9v1DEIIWYjQ7ihcp5SLx+QGAMQLNHaLEEIIIdrU\nVKBnYGAAw8PD+Pd//3d8+9vfxre//W185zvfkXpthCxLNB/DTGYWvZ4tMOmMy3683+LFBzddg2w5\nh8fGnpJghWcIo3yE0T5qxDBMbVSTAg09yVIKZa6smnFkwfqOtimZKqoHo0LTVL8ox/Mr3La0EjzP\nI1lMwmNyKXL+LkcnStUSTudWvvM8XcqgwlfhNbtFXJm47IbayC0K9BAChDK1m3ZyN/QAQNDRgRu2\nXI9MOYsDRx/R3IhEQoj6pEppAIDLJP7ILavBCpveSiO3iOoIAZyOVf4sb4zditDYLSKt1+cP42en\nfgGv2YNP774LBomaXTvtHUiXM0gW05IcnxBCmqFUQ4+7fl1OzuZ1QgghhBAx6Zv5oAMHDki9DkJW\nbbA+pmKXf/uKj/HernfhtdOH8dr8Iby99W3YuYpjLUYY5dOt4kAPAPjMXsxm55Er52CtN3nIQdjt\nK9T5K00IXs3ItKNNGB23mq/ls/nNtTfKUQ3ddMlV8ihzFbgUCvR0O4J4de7PmErPoM3WsqJjxApx\nANBEQ0+WAj2EnNXQ067I+d8bvALDsVEMR0fxwtTvsK/7SkXWQQhZG5LFFCx6M4wr2OjQDL/Vh5l0\nrc1QrSOEyfojhHM7bKv7Wb7dtw0MGByJHsX7evaIsTRC3mIiNYUDRx+GWWfCZ3ffA4fRLtm5gvZ2\nDESGMJ0JwWXqlew8hBCyGKE53C9zoMfbaOihkVuEEEII0aamrry99tpr+OxnP4u77roLd955J+64\n4w5cddVVUq+NkGUZjNRCEDt9Kw9BsAyL2/s+CpZh8ZPR/0ShUhBreeeYSs/AqDOixRqQ5PhiUWpU\nU1ihHRsLcRodcBjssjT05Ct5HEucQLejEx6Rml38Ghy5lajPtXYr2NADnGnTWgktBHpsRhsAIFOi\nQA8hocwsPCa3rAHWszEMgzu33wKH0Y6nTjyHydS0IusghKwNyVIKTgnGbQkCFh8qfJVujBBVOdPQ\n07qq49gNNvQ4u3EyOYFcOSfG0gg5R7yQwPcG7kOFq+Ke/ttW3Sq1lE5HbaTsjEytw4QQcj6RfBQM\nGPhkvk7mMdeuLVJDDyGEEEK0qqlAzz/+4z9i3759qFaruP3227Fhwwbs27dP6rUR0rR8pYCxxEl0\niRCC6LS345oNexAvJvDkiedEWuEZpWoJs9l5dNk7VL+b1V9/gyX3qKZwvjbmSC2BHoZhEHR0IFaI\nS35Bdyg6Co7nsMu/Q7Rjuk0usAyryPi0lUoUUwAAt0m6G1GLCdrbwYBptGmtRKw+m5tGbhGifulS\nBslSWrF2HoHDaMdd2z+OKl/FvUMPolApKroeQog2lbkKsuUcXBK+jgpYak2awut2QtRACOda9JZV\nH6vf1wcePI7GjomwMkLOKFSK+O7AvUiV0rhp6wcla4Y+W9BeC/RMy9Q6TAgh5xPOR+EyOSUbL7gQ\nd6OhhwI9hBBCCNGmptIEZrMZN910Ey699FI4nU587Wtfw8GDB6VeGyFNOxo7hipfFS0EcU3PXrRZ\nW/C7mT/gRGJclGMKZjKz4MGj2xEU9bhSEBp6oko19Khk5BZw1gWwzKyk5xGapnb7+0U7po7VwWty\na6yhp/YmW6mRW2a9GS1WP6bqoyRWQgsNPcLILQr0kPVupv7crnSgB6iN+djb9R6czkfw2NhTSi+H\nEKJBqWIaAOAyOiQ7hxC8D2topCtZ2zLlLJKltGhNJ/3+2liioeioKMcjBAA4nsN9ww9hJjOLd3Ve\njvcGr5DlvD6zB2adufGalxBC5FaulpEsphTZvGnUGWA32KihhxBCCCGa1VSgx2QyIZFIYOPGjTh8\n+DAYhkEuR7XDRD0GwrUQxC6RdjYZWD1u3/5RAMADI4+hXC2LclwAmKyP8BFG+qiZMNM4Wg8myCWS\ni8DAGuCU8CbEcgUdwo62lY9gWkqVq2IoOgKv2SP6TWW/xYdUKY1StSTqcaWidEMPUPseLVQLiOZX\n9vWvjUAPjdwiBFBXoAcAPrT5WnQ5OvGH2YN4ff6w0sshhGhMslR7HSVpQ4+VGnqIuoQy9XFbNnEC\nPUF7B5xGB4aiIysO+BPyZk+eeBaDkWH0erbgY1s/DIZhZDkvwzDotLdjPhdGScTrW4QQ0qxoIQ4e\nfONas9w8JhfihSR4nlfk/IQQQgghq9FUoOfuu+/GF77wBezZswdPPPEErr/+euzcuVPqtRHSlCpX\nxXB0BG6TC1128UIym1w9eE/wHZjPncZzEy+IdlxhhI8WAj3CTGM5G3p4nkc4H0XA4lPVSLIuGRp6\nxhInka8UsMu/Q/QLe0LbklZaepLFJIDaG26lCN+jKx27FSskYNaZYTWsvvJfKgadASadEVlq6CHr\nnNoCPXpWj3t23Aoja8BDoz+VvSmPEKJtqXowWo6GnkiOGnqIOoSy9UCPSA09LMNih68XmXIWUxJu\n6iDrx+9DB/Hryd+i1RrAX+y8AzpWJ+v5g4528OAxW/9eIYQQOUXqrY5KBXrcZjfKXBnZCm1SJ4QQ\nQoj2NHW3/P3vfz9++MMfwm634/HHH8c3v/lNfPOb35R6bYQ05WRyAtlKDjv920UPQXxo07XwmNz4\n5cRvRKsmnkrPwMAa0GZrEeV4UjLrzbAZrIgU5LtQnylnUagWFalgXUzA6oeRNUh6MXegMW5LnNFx\nZ/ML49MK2rgpLDT0KDVyCwC664GelX7OY4UEvGa3mEuShN1gQ6ZMFzTI+jaTmYWB1aNFRaMeW20t\nuHnbDchXCrh36CFUuIrSSyKEaESyVB+5JWFDj91gg1lnppFbRDWEhh4xw7n9vj4AwJHoiGjHJOvT\nWPwEHhr9KWx6Kz6z+x5Y66OP5dQYI54OyX5uQggJNwI9XkXO7zHVrs/FC0lFzk8IIYQQshpNBXqS\nySS+8pWv4M4770SxWMSBAweQTqelXhshTRmMSheCMOvNuLXvRnA8hweOPrbqqu1ytYzZ7DyC9g5V\ntc8sxmf2IpaPy1Yz3niDZ1VXoIdlWHTaOzCXOy3qCDYBz/MYjAzDojdjq3uT6McXdsBopaEnUUzC\npDPCojcrtoagfeWBnlw5j0K1oIlAj81gQ6acodphsm5VuSrmsvNot7Wp7mfzO9ovwcUtF+BUagKP\nH/+50sshhGhEsh6MdhqlC/QwDIOA1YdwPkrjiIgqhDJzYBkWrdaAaMfc7t0KlmExRIEesgqncxH8\nYPAAGDD45K79igXIhbCblK3DhBCyEKGhR6kNnB5zbcNgophQ5PyEEEIIIavR1F2Lr3zlK9i1axcS\niQRsNhtaWlrwd3/3d1KvjZCmDEaGYdQZsc29WZLj9/v68PbWt2EiPYXfTL28qmPNZGfB8Ry6neof\ntyXwW7yo8FWkSvKE+MK5CAAgYFFPS4Ig6OgAx3OYzc6LfuxQdg6xQhw7vL2SVG/7zfWGHo0EepLF\nFNwKtvMAgNVggd/iw1R6Ztlhl1ghDgDw1sfWqZndaEOZq6DEiR9UI0QL5nNhVPgqgioZt3U2hmFw\nW99H0WZrxW+nX8Grc39WekmEEA1IloSmQ+kCPUDthkyZK8v2PoGQhfB8bYxQizUAPasX7bgWvQWb\nXT2YTE0jXcqIdlyyfuTKOXxv4F5kKznc2nsjtnqkuW7VDCG8Pp2hhh5CiPyEDYZKjdw609BDgR5C\nCCGEaE9TgZ7p6WnccsstYFkWRqMRX/jCFzA3RzOXifLms6dxOhfBdu82GHQGyc7z0a0fgt1gw9Mn\nf9HYUbASk6la00eXIyjW0iTnqwdB5Gp2CSu8Y2MxXUJFtQQXwAbCQwCA3YF+0Y8NAL56pa2c49NW\nqlwtI1POKjpuS9Dl6ES2kkNsmW/4NRXoMdgAAJlSVuGVEKIMYaTesuwxAAAgAElEQVRmhwoDPQBg\n1pvwqV13wqwz48GRn2KKxiQQQpaQLMoV6KkF8IVAPiFKiRXiKFSL6LS1iX7sfl8fePAYjo6Kfmyy\ntlW5Kv7jyI8xnwtjX/eVeEfH2xVdj1FnQIs1gFBmlprVCCGyi+SjsOgtsCkwchAAPPUG7XiRRm4R\nQgghRHuaCvTodDqk02kwDAMAGB8fB8uqayQBWZ8Go0cBALt82yU9j91ow0e3fghlroyHRh5f8Wga\nYXRPt0M7DT1CEESuZpdwXt0NPQAkuZk6EBkGy7DY4e0V/dgAYNVbYNGbNTFyS9hV7lFBoEf4Xp1K\nTy/rcUIASAsjtxqBnjLtOibrkxDoUWNDj6DVGsBdO25BmSvjB4P3I1vOKb0kQoiKpUppmHVmmHRG\nSc8jBPDDq9jwQIgYQtnahrMOuzSBHgA0dossC8/zeOTYExiNH8dufz8+vPn9Si8JQO31bqFaRDQf\nV3ophJB1hOM5RAox+OvXmJUgXGOkhh5CCCGEaFFTqZzPfe5z2L9/P0KhEP7yL/8St912Gz7/+c9L\nvTZCljQYGQYDBjv90gZ6AOCS1gvR7+vDSHwMf5x9bUXHmEpPw8Dq0WZtEXl10hFGNUUK8jX06Bld\nY7axmkhVUZ0oJjGZnsY292ZYDRZRjy1gGAZ+sxeRfGzFgTS5JGTaVd6MrkagZ2ZZj9NSQ4+tEeih\ngABZn4Tn9E4VB3qAWoPbtT17ES3EcN/QQ7SzmhCyoGQxBZfJIfl5AtZ6Qw8FeojCQpl6oEeChp52\nWys8JjeOxo6hylVFPz5Zm16d+zNeDv0JnfZ23LXj42AZdWyKDNZbh2do7BYhREbJYgoVrqLYuC0A\ncJtcYMAgXqRADyGEEEK0p6l3lDt37sS+ffsQDAYxOzuLq6++GkeOHJF6bYQsKlPO4kRiHD3ObjiM\ndsnPxzAMPt77EZh0Rvz0+M+QLKaX9fgyV0EoO49Oewd0rE6iVYrPZ6kFEuRq6InkovBZvKq54HU2\no86AVmsAM5mQqDdSByPDAIBd/h2iHfN8fBYfylwZqZK6m1gS9fpbtwoaeoRAz+QaDvQ46oGebJlG\nbpH1KZSZhcfkhlWh6u/luH7j1djh7cVwbBTPnPqV0sshhKhQhavURpcapQ9GNxp6aOQWUdiZhh7x\nw7kMw6Df34dcJY/x1JToxydrT5Wr4uenfgU9q8end90Ns96k9JIaghKOESeEkIUIbeEBBQM9OlYH\np9GBeIFGbhFCCCFEe5q6Y/7JT34SoVAIe/bswd69exEIBKReFyFLGo6OggeP3RKHIM7mNXvw4c3X\nIV/J49FjTyzrsaHMLKp8VVPjtoDa35kBg6gMDT3Zcg7ZSk7RN3hLCdo7UKyWEBFxJ/JAWJ5Aj1Bt\nGy2oexf1mUCP8g09doMNXrMHk+npZTUbxQoJ6BmdLGHD1bIZ6w09Kg96ESKFdCmDZCmt+nYeAcuw\nuLv/VvjMXjw7/jwGwkNKL4kQojKpUm3TgVOGhh6n0QEja6CGHqK4UGYOJp1RsnG3O2nsFlmGg/Nv\nIFqI4Z3tlzY2SKlFp6P2mne6PnKWEELkIFxDFVrgleIxu5EoJqntlhBCCCGa03QFxte//nX81V/9\n1Tm/CFHSgNBqEpAv0AMA7+68HJtcPXgjPIhD4eabqoSRPV2OoFRLk4Se1cNtcskyY114gxew+CU/\n10oFHbUdbVNpcXa0FSoFHIsfR6e9XfKLfUKgJyJT29JKqamhB6i19GTKWSRLqaYfEyvE4TG7Vdk0\n9WZ2GrlF1rGZ+s0MrQR6AMBmsOJTu+6EgTXgR8MPYz4XVnpJhBAVEVpE5RhdyjAMAlY/wvmI6ke6\nkrWrwlUwlzvdGI8shW2eLdAzOgr0kCVxPIdfjL8AHaPD+za8V+nlvIXT6IDT6Gi8BiaEEDk0Aj0K\nb+D0mFyo8lWkaUMbIYQQQjSmqasd+/btw6OPPoqpqSmEQqHGL0KUUuEqOBodhd/sRZu1RdZzswyL\n2/s+Cj2jwyOj/4lcOd/U4yYbgR5tNfQAtbFbiWISZa4i6XmEun6/Vd0NPYB4FdXDsWOo8FXs9veL\ncrzF+Oo7YeQan7ZSiWItOCPHjahmCK1ak6nppj6+VC0jXc5oYtwWcHagh0ZukfVHi4EeoBYuva3v\nJhSqBXx/8H4UKkWll0QIUQkhgCzHyC2gNjqhWC0hXaYbI0QZ87kwOJ5Dh61NsnOYdEZs9WzGdCbU\n2HxAyPm8Pn8Yp/MRXN5+CTwSNUatVqe9HbFCHDna0EEIkUmk3vqueKCn/rwcLyYUXQchhBBCyHI1\nFehJp9P4+te/jrvuugt33HEH7rjjDuzfv1/qtRGyoLHESRSqRewK7ADDMLKfv83Wgmt79iFZSuM/\nj/+8qcdMpaehZ/XosLVKvDrx+cxe8OARL0jb0hPWUEOPWIGewXrTlByj47TS0JMsJsEyLJxG6UdF\nNEMI4QktW0sRvk8o0EOI+mk10AMAl7ZdhCuDV2AuO48HRh6ldgxCCAAgJXMwWnjdHs7R2C2ijNnM\nHACgwy5doAcA+utjt4ajo5Keh2gXx3N4bvx5sAyL923Yo/RyFiRsUqKWHkKIXML5KHSMDh6zsk3c\nnnoTeLxA4VxCCCGEaEtTgZ5f/vKX+MMf/oAXXnih8ev555+Xem2ELEgIQezyyTtu62xXb7gSHbY2\n/H72VRyLH1/0YytcBaHMHDpt7dCxOplWKB6fRWh2kSvQo96GHrvBBo/JjWkRRm5VuSqGIiNwm1yy\nNDd5zR4wYBApqPuGS6KYgtPoUM24KuFzM9lkoCdWqO308ap0R+abWQ0WMGCQKVGghywumo/j0WNP\nolApKL0U0cxkZmFg9WixqjdIupgbt1yPza4e/Pn0AJ6feknp5RBCVCApBHpkCkYLr9vD+Ygs5yPk\nzWaytUBPp+SBnl4AoLFbZEGHwkcwlzuNS1svamymUaNgPcg+TYEeQohMIvkofGaP4tf53NTQQwgh\nhBCNaupVVFdXF5JJSi4TdeB5HoORo7Dozdji3qjYOvSsHndsvxkMGDww8lOUqqUFP3Y2O48KX0WX\nU3vjtgDAXx/VJFSkSiWcj4BlWPhU3mwSdHQgVUojWUyv6jgnk+PIVnLY5ZenaUrP6uE2uVTd0MPx\nHJLFFNwmZXftnM1pdMBtcjXd0BPTWEMPy7CwGazIUkMPWcJvZ17Bi9Ov4OD8IaWXIooqV8Vcdh7t\ntjbFLyyulJ7V47/svAMuowNPHH8Go7HFA8aEkLUvWaq9PnXK1dBjFQI96g6Mk7UrJDT02KRt22ux\nBhCw+DASG0NF4lHURHuEdh4GDK7pUW87DyB+6zAhhCwmX8kjW84pPm4LADymeqCnQIEeQgghhGhL\nU3cvGIbB9ddfj1tvvRV33nln4xchSghl5xArxLHD26t4280GZxf2dL0LkXwUPz/1qwU/bjI9DQDo\ntmsz0HOmoUfqQE8UXrNH8c/rUoSK6tVeABuQcdyWwG/xIllMoVwty3bO5ciWc6jyVbhlugnVrC5H\nB5KlVFMhLq0FegDAZrDRyC2ypPHkFABgJDam8ErEMZ8Lo8JXG7uUtcplcuIvdu0HwzD44dADdHGS\nkHVO/oYeYeQWNfQQZYSyc3AaHbAbbZKfq9/Xh0K1iJPJccnPRbRlMDKMmcwsLmm9EC3WgNLLWVTA\n4oeB1WNGhNZhQghZirCpUBWBnvrIr3iRNq4TQgghRFv0zXzQZz7zGanXQUjTBhUIQSzmA5uuweHw\nEJ6ffAkXt1yAbmfwLR8jjOrRbEOPRfqGnkKlgHQpg6C3Q7JziKWrvqNtJh1qVK8vF8/zGIgMw6wz\nYatns5jLW5Tf4sNY4iRihThabS2ynbdZQu2tS0UNPQDQ5QhiMHIUU+lpuEzbF/3YaGPklnYCPXaD\nFadzYXA8p9mmEiKtKlfFVD2ceix+fE18rczUxwx0aDzQAwCbXD346NYP4ZFjT+AHgwfwhYs+A4PO\noPSyCCEKSJZSMOmMMOvNspzPZXJCz+qpoYcoIl8pIFaIo8+zVZbz9fv68OL0KzgSHcE2zxZZzknU\nj+d5PFtv57m25yqll7MkHatDh60dM5kQqlxV9RuqCCHaJrxGDKhgFKHT6ADLsEjQJhhCCCGEaExT\nd2IuvfTS8/4iRAkDkWGwDIsdKwxSiM2kM+K2vpvAg8ePRx5Flau+5WOm0jPQMzp02NoUWOHqOY0O\n6Fm9pA094fqxhV2+aiY09ExlmhvBdD6z2XlE8lFs9/XCwDaVrRTFmXBWXLZzLoewq9yjskBPt6MW\nxptqYhdjrBAHA0Z1f4fF2I128OCRq+SVXgpRqVB2HiWu1uyVq+SbHkGnZkKgR+sNPYL3dL4Dl7Vd\njIn0FB4de1Lp5RBCFJIspuCSsemQZVj4LT6E8xHwPC/beQkBgNlsfdyWXZ732Vvdm2BgDRiKjspy\nPqINQ9ERTKVn8LaWXWiztSq9nKZ02ttR4auYy51WeimEkDUuUg/0+FTQ0MMyLNwmFzX0EEIIIURz\ntL21mqw7yWIaE6kpbHFthNVgVXo5DX3erbi8/RLMZGbx68nfnvNnVa6KmcwsOuxt0MsY3BATy7Dw\nmT2IStjQE87XavoDVuXf4C3Fa/bAoresauSWUk1TPrMwPk2du6gT9TfVct6IakZXI9AzveTHxgpx\nuExOTe10tNefT7MlGrtFzm88NQEAjR3wo7HjSi5HFEKgp3ONBHoYhsHHe29E0N6BV0Kv4pWZPym9\nJEKIzKpcFZlyFi6jvK+jAhYf8pUCspWcrOclJJSpB3pk2jhj0BnQ69mCuey85OOoiTYI7TwAcG3P\nXoVX07yg0Dpcfz1MCCFSiTQaetRxvddjciNZTJ13Qy4hhBBCiFpRoIdoypFoLQSxy7/4yBsl3LTl\nA3AY7Xhm/NeYz57Z5TSbnUeFqzQCAVrlM3uRLedQqBQkOX4kp643eIthGAZBezvCuSgKleKKjiE0\nTfX7+kRe3eIaDT0qvQCdqDf0uFXWbuMyOuEw2hvj8xZS5apIllKaGrcFADaDDQCQLlOgh5zfeHIK\nAHBNzx4AwEh8TMnliGImE4LH5FZVQHi1jDoDPrnrTlj1Fjxy7AmMpyaVXhIhREapUhqA/MFo4fV7\nOKfOwDhZu0IyN/QAaLx/o5YeAtReE4+nJnFBYKemQuLCWqebaKAlhJDVEK4/+lUwcgsAPGYXePBI\nllJKL4UQQgghpGkU6CGaMhg5CgDY5e9XeCVvZTVY8bFtN6DCVfDAyE/B8RwANAIAXY6gkstbNV/9\njVdUolFNjYYeDYzcAmo72njwCGWXv6MtWUxjPDWJza4e2GS+keyv33CJSNi2tBpCQ49bZQ09DMOg\ny9GJeDGBzCItNoliChzPwWt2y7i61XPUAz1ZCvSQBYynJmHWmbDFvQmd9nacSI6jVC0rvawVS5cy\nSJbSmrrx0iy/xYt7+m9Dlefwg8EDSJcySi+JECIT4caE0+iQ9bzC63fh9Twhcgll5sCAQbuMY476\n66O/h6Ijsp1zNapcFU8cfwZPnXgOh8NDjfdbZPV4nsezp34NALi25yqFV7M8wmtgaughhEgtko/C\nZXTAqDMqvRQAtYYeAIgVEgqvhBBCCCGkedqc/0PWpVK1jJHYGNqsLaody/S2wC5c4O/H4cgQXgn9\nCe/ufAem6oGebs039NQaRyL5qCQ3QMP5KBgwjeCQ2nXZhRFMIWxy9SzrsUcUGrcFAHaDDUadsVF5\nqzaJghDoUVdDDwB0O4IYjo5iKj2D7b5t5/2YeLF2QUCrDT0ZCvSQ88hX8pjPhbHVsxksw6LPsxUz\nmVmcTI6jz7tV6eWtyFobt/VmO3y9+MCma/D0yefwwyMP4K8u/AtNjQFUg2K1BCNrAMMwSi+FkKYl\niwo19NTfm4VV+vqSrE08zyOUmYPf4pX1JqHP4kWbrRWj8eMoV8sw6AyynXslfnbql/jV5Ivn/J7L\n6ESPswvdzi5scAaxwRFcU42FchlLnMSJ5Dh2+vrQrbENXBa9GX6zF9OZEHiep9c7hBBJVLgKYoUE\nNro2KL2UBk99A16CAj2EEEII0RAK9BDNGI2PocyVsUuBEESzGIbBx3pvwLHECTxx/Bns9G3HVHoa\nLMOiQ+M3DRsNPRKNagrno3CbXDCw2nhaEmbOr6SieiAijI6Tv2mKYRj4zV5E8zFVXrhLlFKw6C2q\n2blzNmFs3mKBnli9wUprgR67sR7oWaR9iKxfE6lp8ODR4+wCAPR5t+L5qZcwEhujQI+KvW/DezGZ\nmsLhyBCePPksbtzyAaWXpBnxQgL/44/fxNtadmP/9o+p7mclIQtJ1keXuoxyj9yqN/TQyC0io2Qp\nhWwlhy2eTbKfu9/Xi+cnX8KxxMlGY48ajcaO41cTL8Jv9uLmbR/GdCaEidQ0JlKTOBwZwuHIUONj\nWyx+dDuD6HF2Y4MziKC9E0aVh5WU9uz48wCAa3v2KbySlel0dOBw+AiSpZQqN9QQQrQvVoiDB98Y\nz6oGnvrzXZwa6wghhBCiIdq4c04IgMFGCEK9gR6g1izykS3X48GRn+Ino49jOjOLDlubZoIqCxFm\nHUckGLlVqpaQKCaxzbNF9GNLpc3aAj2jw3RmZlmPK1ZLGI2Pod3WqljTlN/iQyg7h2wlB3u9mUUt\nksVko/5WbYRWpsn09IIfcybQo86/w0Ls1NBDFnEqOQkA6HF2AwA2uzdCz+gwEh9Tclmrsh4CPSzD\nYv+OWzD72rfx/ORL2ODowsWtFyi9LE04FD6CElfGn+ZeR4vVj2t79iq9JEKakqqP3JK7ocdjckHH\n6BChkVtERqHMHACgw9Ym+7l3+vrw/ORLGIqOqDbQkyln8aPhn4BhGNyz8zb0OLux07+98eeJYhIT\nqSmMp6YwmZrGRHoKr80fwmvzhwDUXkd02NrqDT5d2ODsQrutlRr/6k4kxnEsfhzbvduw0dWt9HJW\nJGhvx+HwEUynQxToIYRIIlLfFOpXURu70NAjNGwTQgghhGiBthMGZN3geA6DkaOwG2yauFjyzvZL\n8drcIRyJjgDQ/rgtAPCbpWvoEd7gqWnHxlJ0rA7t9jaEsvOoctWmL2yOxI6hzFWwW4F2HoHPUmuP\nieZjqgr0FKsl5CsF9DjlvQnVLK/ZDZvB2hijdz6abeipfx1kyzmFV0LUaDx1bqDHpDNio2sDjidO\nIVPKNhqetGQmMwsDq0eL1a/0UiRl0ZvxqV134f957Tv48cijaLe1osMu/41PrRFC5C6jE0+f/AXa\nrC24sGWXwqsiZGlnGnocsp5Xx+rgs3ho5BaRVShbD/Qo8HNtk6sHZp0JQ5Gj4Ld+SHVNbjzP48Gj\njyFZSuFDm65tvIY7m9vkgjvgwgWBnQBq11zC+Sgm6gGf8dQUpjMzmM6E8ApeBQAYWAO6HB3Y4Oxq\nhHwCFp/q/v5yeHb81wCg6dBvp73WOjyTmT0n7EWI2pW5CgqVAvKVAgrVAgIWPyx6s9LLIucRqb82\n9Kvoeq+wiTBeoIYeQgghhGgHBXqIJkylZ5AqpXFZ28VgGVbp5SyJYRjc2ncTvv7qt1DmKujS2Dz1\n87EarLDozYgUxA/0hOu7ebUU6AGALnsHptIzmM+Fm76QPBBWvmnKb679O0fyUWyoj9BRg0S97lat\nuwMZhkGXvRMj8THkyjlYDda3fEysPoNba4EeWz3Qky5nFF4JURue5zGemoTX7IHLdOYGcZ93K8YS\nJ3EscQIXtexWcIXLV+WqmMvOo8PeronXFKvVbmvF/u0fw/8+8mP8YPB+/P3bPweL3qL0slQrV85j\nLHES3Y4gbu/7KP7Xn/8//Gj4J/BZvI3Ri4SoVbKUBiB/Qw9QG7s1lBtBrpyH1UDPMUR6QkNPpwIN\nPXpWjz7vVhwKH8HpfASt1oDsa1jMy6E/4nBkCNvcm3H1hvc29RiWYdFqDaDVGsClbRcBqL1mCmXn\nMJGaqo3qStcafU4mJxqPs+ot6HYE0ePsQrezCxucQdW+nxPLeGoSR2PHsM29GVvcG5VezooF64Ge\n6czyx4gTshJVrop8tYBCpXhOICdfqf/eef+79itfLTb+u8JXzznuVvcmfP6izyj0tyKLCasw0GMz\nWGFg9dTQQwghhBBNoUAP0YSB+k7p3Soft3W2FqsfH9nyATxx4hn0ebUzSmoxPrMXp3Nh8Dwv6i48\n4Q1eQGNNCZ2ODmC2FjhrJtDD8RyORI/CaXRgg1O5kFdjfJoEbUurkWwEetTZ0AMA3c4gRuJjmEqH\n0Hue7+tYIQ6bwQqTzqjA6lbOpDPCwOqRLVFDDzlXtBBHppzFRZ7N5/x+r2crnsYvMBIb01ygZz4X\nRoWvIriGx2292UUtuzHZ/V78avJF/Gj4YXxq153rIsy0EsPREXA8h93+fgQdHbh7x8fx/cH78b2B\n+/D3l/z1OcE2QtQmWUzBqDPCrMAudSGYH8lH0W3Q/mYGon6h7Bz0rF6x95D9vj4cCh/BUHREVYGe\n2ew8fjr2NGx6K+7cccuqft7rWB26HJ3ocnTiXfVMa6lawlQ6hInUJCbS05hITWEkPnbOKFaX0XlO\nwGezayOMOsNq/2qq8dz48wCA92/UbjsPUGugtegtjVG0hKxEopjEQHgYyVLqrJDO+QM7Za68onOY\ndEaYdWbYDTb4LT6YdSZY9GaY9WYci5/A8cSpBTddEWVFVdjIzjAMPCY34gUK9BBCCCFEOyjQQzRh\nMDIMPaNDn3eb0ktZliuD78S7Oy9fMzfNfBYvpjMhpMsZOEWs8g/ntNrQU7uqOZ0J4TJcvOTHn0xO\nIFPO4oqOSxX9mlBroEeou3WpeEen0M4wlZl5S6CH53nECgm02VqUWNqqMAwDm8GGTDmr9FKIyrx5\n3Jag29EJi96M0djY+R6masJNi451FOgBgA9uugYT6WkMRobxi/HfaP4mlFQaIfJALUR+QWAnPrTp\nWjx18jl8f/BH+PzbPg3DGropSdaWZCkFt1GZYHTAUgtVhPMRdCsYXCfrA8dzmMvOo93aotj7qh2+\nXgDAUGQEV3W9W5E1vFm5Wsa9Qw+izFVwT/9t8Jjdop/DqDNis7sHm909jd/LlnOYPKvBZyI1hcOR\nIRyODAEAOmxt+G+X/JXmNj2cz1R6BoORo9jk6sFW9+alH6BiDMMgaG/H8cQpFKulNfH5IfJIlzJ4\n4/QgXj99CCcS4+DBn/fjDKweZr0ZFp0ZHpO7/t8mmOthHIvefE44p/bfFpj19d/TmWHWmxZ9nn/m\n1K/w81O/wmj8BN5GI3JVJ5yPwqQzNsa8q4Xb7MbpeATlapne2xFCCCFEEyjQQ1Qvmo9jJjOLHd5e\nmPUmpZezbGslzAMAfnMtCBLNx8QN9KiwgrUZnfY2MGAwnW6uonqw0TTVL+WyluStfx6lGJ+2Gsli\nCgDgUXOgpx7imkxNv+XPMuUsylxZc+O2BHaDrTH+jhDBePL8gR4dq8M292Ycjgwhko9q6vlbCPSs\np4YeoPY5+0T/bfi/D34bPz/1S3Q7g+iv34wkNRWugqHoKHxmDzrOGuHyvg17MJs9jYPzf8YDI4/h\nrh0fF7WpkBAxVLkqMqUsWt3KNIUErLWfA8LrekKkFM5FUOYqioZz3SYXgvYOHE+cRKFSVMW1iidO\nPIOZzCze1Xk5LgjslO28NoMV233bsN1X24DF8zwSxSQm0tM4OPdnHAofweNjT+PWvptkW5NUhHae\n63r2rYnXAp32dowlTiKUmcNGV/fSDyDrVq6cx+HIEF6fP4TR+HFwPAcA2OLeiItbLkC7rRVmvQUW\nvakRztGz0t926PNuw89P/Qoj8TEK9KgMz/OIFGIIWHyqe74UrjvGi0m0aKwtnhBCCCHrEwV6iOod\niR4FAOzS0LittcpnORPo2ejaINpxI/koXEan5naEmfVmBCw+TGdCTY0hG4gMwcgasM2j7Ag2o84A\nl9HZqL5Vi0RJ/Q09fosXFr0ZU5mZt/xZrBAHUKsu1yK7wYbpTIh2KJFzjKcmwTJso53qbH3erTgc\nGcJo7Dj8ndoL9HSus0APADiMdnxy135868/fxX1DD+KLb/9rTYWxpDaWOIlCtYB3tF9yzs90hmFw\ne99NiOQjODj/Btptrbim5yoFV0rIW6XLGfDg4VKsoace6MlRoIdIL5SdB4Cmxh5Lqd/Xh+lMCMfi\nx7E7oOymjSORo3hx+hW0WVtw05YPKLoWhmHgMbvhMbvR7+3FN1//f/Fy6E/Y4evDBQr/O61GKDOH\nQ+Ej2ODsQp93q9LLEUXQ3gGg1jpMgR7yZsVqCYORYbw+fxjD0RFU+CoAYIOjCxe3XoCLWnZL0gS2\nHBscQZh1ZoxosDl2rUuVMihVS6p8vylct0sUExToIYQQQogmSFYdwnEcvvrVr+KWW27B/v37MTEx\ncc6fv/DCC7jppptwyy234JFHHln0McPDw3j3u9+N/fv3Y//+/XjmmWekWjZRIaHVZJd/u8IrIb56\n84iYzS5lroJYIdHY1as1nY4O5Cp5xJaYvTyfPY3TuQi2+3phVEFYwmfxIl5MoMpVlV5KQ6Le0OM2\nKXMjqhkMw6DL3onTuQjylcI5fxZtBHo02tBjrFUgZys5hVdC1KLCVTCVCSFobz/v81Zv/UbG0bi2\nLp7OZELwmNywGqxKL0URG5xduGXbR5Cr5PH9wftRqpaUXpJqnHnN+dYQuUFnwKd23wWPyY2nTj6H\nw+Ejci+PkEUJTYcuhV5Hec0esAxLbX9EFiFhfKZN2UDPTn8fAGAoOqLoOpLFNA4cfQR6Rod7+m+D\nUUUbZQw6A+7ecSsMrB4PjDzaeK7SIqGd5/09e1XXNrFSnY5awF0IvBNS5io4HB7CD488gC/97p9w\n79CDGIgMocUawAc3XYv/6/Iv4u/f/jns7X6P4mEeoN4c69mMSD6qurHy6120ILSxexVeyVt5TLWv\n3XghqfBKCCGEEEKaI1mg59e//jVKpRIefvhh/O3f/i3+9dqgyDwAACAASURBVF//tfFn5XIZ3/jG\nN/DDH/4QBw4cwMMPP4xIJLLgY4aGhnDPPffgwIEDOHDgAK677jqplk1UJl8p4Fj8BLrsHap4o7je\n+c9q6BFLLB8DDx4BizZ3RJy9o20xA4vcJFSC3+IFx3OIFxcPIskpUUxCz+hUN1v7zbqctaaSN49a\ni2k80GOr/7unS1mFV0LUYiYziwpXecu4LUGLxQ+PyY1jsTOV62qXLmWQLKXRqfCOfqW9s+PtuKLj\nMsxkZvHQ6OPgeV7pJSmO53kMhIdh0Vuwxb3xvB/jNDrw6d13w8gacN/wT5oeuUmIHJQO9OhZPbwm\nN43cIrIIZecAKN/Q0+Pshk1vxVB0VLGfpRzP4cDRh5EpZ3HDlusRdHQoso7FdNjbcMOW65Et53Dg\n6COaed14trnsafz59AC67B3Y6Vs7m83ara1gGZZe06xzVa6K4egoDgw/gi+//D/w/cEf4fXTh+Ey\nOXFtz17890v/K/77Zf8V1/ZcpcrNeEJj1ii19KiK0NoYUGFDj7t+j2GpzZmEEEIIIWohWaDn9ddf\nx7vf/W4AwIUXXogjR87sYj1x4gS6u7vhcrlgNBpx8cUX4+DBgws+5siRI3jxxRdx++234x/+4R+Q\nyWSkWjZRmaOxY6jyVexUSQhivfOaa4GeSD24IAbhor8a3+A1o6t+wXQ6/dYRTGcbiAyDAYOdvj45\nlrUkv/C5VNEOpkQhCZfJqfrdjt32WqDnzWO3hAsB2h25VWsryZYp0ENqTiUnAWDBQA/DMOj1bkG2\nklsy1KgWZ8Ztqe9ml9xu3vZhbHB24dW5P+O3M79XejmKm86EEC8m0O/rhY7VLfhxXY4O3NV/K0rV\nEr43cB9SpbSMqyRkYclSLdDjNDoUW0PA6keqlEahUlRsDWR9CGXmYNVbFBsxJ2AZFtt92xAvJjBb\nHwMmt99MvYyjsWPY4evFe4NXKLKGZlzZ+U70+/pwNHYML06/ovRylu0XEy+AB49rN+5T/fvV5TDo\nDGiztmAmO6vJoBVZOY7nMBY/iZ+M/if+4ZWv4d8O/2/8ce41mHQm7O1+D754yV/j/7z87/HBTdco\nHp5cSl99rL3WmmPXukj9eq/frL7rvR6TCwBUtcmREEIIIWQxeqkOnMlkYLfbG/9fp9OhUqlAr9cj\nk8nA4ThzodFmsyGTySz4mN27d+Pmm2/Gzp078d3vfhf/9m//hi9+8YsLntvjsUKvX/hCPFFOILC8\nC8xjJ2tvxq7cegkCXuUuTpMzPGYXEqX4sj+XC8nHawG9zW1B0Y4ppwvs24DDwOlyeMH1JwspnEpO\noNe/CZs622Ve4fltzHQC40BRn1XFv3uVqyJVTmObb5Mq1rOYC0zbgGHgdGn+nLVmudpN3W2dXXCY\n7As9XLXakz7gFMBaqqr/HBB5zJ6ohV8u6ulDwHn+r4lLc7vwx9nXMF2cwsWb1L9b+U+xWiB1e4f6\nn2vk8KUrP4sv/vLreHzsaewKbkFfYMt5P249/Fu9OH8cAPCuTZcs+fe9OvAOpJHATwafwr1HH8BX\n93xeFeM0yfpWma+FaDa0tCr2PdvlbattyDAXEPBoo31zPTy/rTXFSgnhfBR9gS1oaVF+VO/lPRfi\ntflDGC+cwgUbt8p67lPxKTx58lm4zE584V2fgMus/L/HYv7mXffgvz33z3jyxLO4fNNubHAHlV5S\nU+bSp3Fw/g10uTqwd/tlYBnJ9kaKrpnnuM3+boQm5sBZimh1tMiwKqIUnudxIjaB30++ht9PvY5Y\nvhZmcJkcuGbLlbii+xJs82/S1Nc4APj9dvgGPRhLnIDPZwPLamv9a1X6RC1svi3YjYBd/Ndbq3kN\nZ3PrgVeBHJ+h14KEENWh5yVCyPlIFuix2+3IZs/s8uc4Dnq9/rx/ls1m4XA4FnzM1VdfDaezdmHi\n6quvxj//8z8veu54PCfmX4WIJBBwIBxufhczx3N4fWYQLqMT9opnWY8l0vGY3BhPTWFuPrHoDvZm\nnQrXWk5MZZtGP8csHEY7TkQmFlz/H0KvgQeP7e4+1fwdjZVaG8up0yGEncqvKVFMgud52Fm7av6N\nFqLjLTDpjBg763MeCDgwmwrDqDMin+RQYNT9dzgfvlj7GR2KRBG2aG/9RHyjp0/AqrdAV7AgXDz/\n10S7vnYj5vWpI3in/x1yLm9FRudOAQAcnFv1zzXy0OOeHbfjO4d+gP/58vfxpbf/zVvG9Sz39ZtW\n/XHiDegYHYKG7qb+vu/yX4ETrVM4OP8Gvv3yfbhz+y1rasc+0Z5QLAIA4PMGxb5nHag9fxwLTcJW\ncSmyhuVYL89va81Eaqo2stkYUMXnL2joBgMGr04elvW1ULFawrcO/gBVroo7em9GKc0gnFb+32Nx\nDG7vvRnfHbgX33r5P/D3l/y1JgKxDx39GXiex9XBPYhGtNNm2uxznN8QAAAMTI5B32KRellEAaHM\nHF6bP4TX5w8hUqi1NFv0Fryj/e24uPUCbHNvblzf09LX+Nm2ubbgD7MH8capUXQ7tREWXOtmEvO1\ncFjWgHBe3J9PYryGM+vMmE9FVfFaghBCBPQelZD1bbFAn2SR9YsuuggvvfQSAODQoUPYtm1b4882\nb96MiYmJ/5+9+w6P4z7vRf+d2d4reicIohHsVu8mLUuy3GSruVC2EiuJc05uTsrNc3Nz006Se3MS\n5zlJHDsukVzULMmSLduSrWJ1URKbAKISJEB0YHexve/O3D92ZylZLCi7+5vZfT9/6RGJ3VciCe7M\nvL/vF4FAAKlUCocPH8bu3bvP+zX33nsvBgcHAQBvvvkm+vv7SzU2kZHTwTOIpmMYcPfSAxIZcemd\nEEQB/mSwKK8ndSq7FVq5BQDN5kb4kwFE0+deJhz0jgAABmRUHec2SPVp8qjcCuR/P/3mg2Q54jke\nzeYmLEdXkMymCv9+NRGAU+9Q7PcrqXIrQpVbBLnfB564D23Wlgue0LRqLWg01eNUcArpbLqME27M\nQmQRGl6NWqMykiPKYZujE5/svBmhVBjfOfFDZIQM65HKzp8IYDaygG2OThjU+jV9Dcdx+FzPZ9Bu\nbcXbS0fx3MxLpR2SkIsI5Su3bDq2lVsA4Il7mc1AKt9CvtpKLhUwFq0ZbdYWnApOI56Jl+19H5/4\nKZZjHtzQcjX6XN1le9/N2u7uxTVNV2AxuoynTv2c9TgX5Yuv4q2lI6gz1mJ37QDrcUqiOV9FOx9W\nRoUuWZuVmBfPTL2A//nWP+Pv3v4afnnmRYRSYeyr24Xf2XEP/uGqv8Dnez+LXue2ohzWY02q3Rpb\npdotufDEfXDq7LL9/eXQ26hyixBCCCGKUbKEngMHDuD111/HnXfeCVEU8fd///d4+umnEYvFcMcd\nd+DP/uzPcO+990IURdx2222oq6s759cAwF/91V/hb//2b6HRaOB2uy+a0EMqw5AMlyAI4Movgvji\nq4WlkM3wxL0wa0xrfoAmRy2WJoyuTmAuvIBu5/vrSlLZNEZXJ1BnrEWdsYbRhB9k1Vqg5tXwxeWy\n0JN7CGXXyf80NwC0WptwKjiF+cgCttjaEUvHEc/E0WFrZT3ahpk1uZqwKC30EOROvwNAu/Xiv6d7\nnF1YmF3C6eCZD3wPlJOskMVidBmN5gbFxciX2g0tV+NMaBZHVt7Fjyd/htu3fZL1SGW10cVbjUqD\nrwwcxD8e/lf89NSzqDfWYkcNHTwgbASTIWh5DfQqdp+pa/IL+tLCPiGlsBDJVYI2yWShBwD6Xd2Y\nDs1gdPUk9tTuKPn7HV0ZxBuLb6PF3IiPd95U8vcrtk9tvQUTgVN4ee4N9Dm7sd0t39rWX535NQRR\nwEfbb6jYz49N5lwt+Fz+zxZRLn8igCMr7+LI8nHMhHNp2GpejZ0127G3die2u3uhU2kZT1ka3c5c\n5eGo/yQ+0n4942lIIpNEOBVBk6O8VZTr4dDZsRhdRiKTgF7B96QJIYQQUh1KttDD8zz+5m/+5n3/\nrrOzs/DPN9xwA2644YaLfg0A9Pf345FHHinNoES2hryj0PIabHPI9+FgNXLr8ws9RUh2yQpZ+BJ+\ntFlaNv1aLDUXboB9cKFn3H8SaSGNHTJbTOM5Hi69Uz4LPYlcQo9dAQk9ANBibgIAzITnscXWDm80\n9//RqXewHGtTTBoTACBMCz0EwFRwBgDQbr349+ceZxdenH0VY/6Tsl7oWY55kBGzhe/Z5CyO4/C5\n3s9iMbqMl+feQJulBZc27GU9VtlIS+Qb+bvaprPgd3bcg68d+Q/cP/Iw/njvVwsPxggpp2AqDKvO\nyjQp0KV3ggNHCT2kpBYiSwCABpOcFnp68POp5zDsGyv5Qs9qwo+Hxp6AltfgS/13Q8OX7LZeyWhV\nGnyp7y78r8P/hh+OPob/69I/hFXLLl3sfPyJAN5cPIwagwt7a3eyHqdkLFozbFor5mmhR7GCyRDu\nH34IJwOnAeTu9/Q5u7G3bid21vTDoK78KjWL1oxmcyNOB6aQyqagrdDFJaWQ7hkX4yBoqTj0uQOF\n/mQQDbTQQwghhBCZq8zjJUTxlmMeLMdW0OPcpohO9Wry3oSezVpNBCCIAmqMyq3bAoBmS265Yy7y\nwYjqQU/+IWGNvBZ6gNyFdTQTQyxdvmj48zlbuaWMhJ6W/K/5bCh36s0Ty/15cOmUu9AjVW5FU7TQ\nQ4DpkLTQc/GEnk5bB1ScSvbx5tJDikZatjgnnUqL3x74AgxqPR4efwKz+VO9lS6eiWPCfwotliY4\n9PYNvUaLpQkH++5EKpvCN969H+FUpMhTEnJhWSGLcCoCm5btYrRGpYFdZ4MnTgk9pHQWoktw6Oyy\nSnhtsTTBojFjxDcOQRRK9j6CKOCB4UcQz8TxmW0fR52ptmTvVWrNlkZ8ovMmhNMR/HD0MYiiyHqk\nD3hu5iVkxSxubLtBtpUxxdJkabhgjTiRtxdmXsHJwGlssbXjzu5P4x+u/At8dde9uKxhX1Us8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7VmJeTIXOsB5n06Ql8oEyV2O2WVvwhd7bkcym8M3B+xGmekNSBMFUfjFaRg9Nzib0eBlPQipF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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131aa8a5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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iIBoNPLFzFc/ur8awyJ1h7lUoEuNy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yHX/MPTUERERJvCaC52dDWTB+XoLxejv4iIiIiowFy+YQBY8xT8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7qqnqriFaH0lZZyWR+nB0ah+5owlnITUgCUqgoK2ImWFn31HDbrdT0VmFv5cfRi/DmTcQXMbw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\n5yafqbOTVqqotVjrazerqLVEB7u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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131ab49a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epoch = len(history.history['loss']) # get epoch length from any of the columns\n",
+    "for k in list(history.history.keys()):\n",
+    "    if 'val' not in k:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(history.history[k]) # this is for train\n",
+    "        plt.plot(history.history['val_' + k]) # this is for test\n",
+    "        plt.title(k, fontsize=30)\n",
+    "        plt.ylabel(k)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left', fontsize=30)\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0035122722055874436"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(history.history['val_mean_absolute_error'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The variance should have improved slightly. However, unless the mean absolute error is not small enough. The model is still not an usable model in practice. This is mainly due to only using the sample data for training and limiting epoch to a few hundreds.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visually compare the delta between the prediction and actual (scaled values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ViYiIiIiIiIi0TKFQJ/j22685diylu8sQERERERERkT5kU9Y2Ht/6VyrqKjt0v46kb0Fl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\niIiIiIiILEAKhUREREREREREFqD/CyM3fMbHfs4CAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21319777f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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mFxUASCJQAXmlrjnNI1TdgaqVQAUgzxGogDxS272GKl0jVEV+j1z0ogIAAhWQ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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131aa08470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE :  1.47740581385e-05\n",
+      "MAE :  0.00256412901334\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    149.000000\n",
+       "mean       0.000289\n",
+       "std        0.003846\n",
+       "min       -0.009856\n",
+       "25%       -0.001409\n",
+       "50%        0.000065\n",
+       "75%        0.001438\n",
+       "max        0.015438\n",
+       "Name: diff, dtype: float64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+    "\n",
+    "# Benchmark\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "\n",
+    "pred = model.predict(testX) # predict on testset\n",
+    "\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(np.reshape(pred, (pred.shape[0])))\n",
+    "predictions['actual'] = testY\n",
+    "predictions = predictions.astype(float)\n",
+    "\n",
+    "predictions.plot(figsize=(20,10))\n",
+    "plt.title(\"Predicted close vs actual\")\n",
+    "plt.show()\n",
+    "\n",
+    "predictions['diff'] = predictions['actual'] - predictions['predicted']\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.distplot(predictions['diff']);\n",
+    "plt.title('Distribution of differences: actual minus predicted')\n",
+    "plt.show()\n",
+    "# if predicted minus actual is positive, this is \n",
+    "\n",
+    "print(\"MSE : \", mean_squared_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "print(\"MAE : \", mean_absolute_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "predictions['diff'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare the unscaled values and see if the prediction falls within the Low and High\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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teZ0TMBp0dM+Kw2r3UlXvorbBQ7AdAi9+n4Zn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px8so+HH3bTr98/41zln0wEcwRBEARBEARBEJohgjmCIAjCP011g5PtJQ14fAf/gjgjwUinjGO/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\nkNz5s9jUsz9xnz+tWgm3hynX3E3S7sBTsZGNJ5yGd+89LVhlw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ECYQi2laRuA/u03ds1cj771TR8s8lSabLLc/pZyl8wUWKnHCibE6nmhvIVF1taM4c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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x213137f9828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Cy+TJk7Vq1ao2X3/06NH64x//GHv9Rx55RK+//rouvPBCvfrqqwoEApJa7vp86KGH1NTU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\nRERESfD/AXZaow4oj9opAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131a8cbac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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161ZMnjy5wXMtXLgQ/v7+SExMhF6vx2OPPYbPPvsM8+bNw+nTpzFt2jTcfPPNDbZJS0vD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\ns/8HkrAgsxv62WMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131a8d43c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE :  1.16343369599e-07\n",
+      "MAE :  0.000227541731508\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    149.000000\n",
+       "mean      -0.000026\n",
+       "std        0.000341\n",
+       "min       -0.001370\n",
+       "25%       -0.000128\n",
+       "50%       -0.000006\n",
+       "75%        0.000125\n",
+       "max        0.000875\n",
+       "Name: diff, dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred = model.predict(testX)\n",
+    "pred = y_scaler.inverse_transform(pred)\n",
+    "close = y_scaler.inverse_transform(np.reshape(testY, (testY.shape[0], 1)))\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(np.reshape(pred, (pred.shape[0])))\n",
+    "predictions['close_bid'] = pd.Series(np.reshape(close, (close.shape[0])))\n",
+    "\n",
+    "p = df[-pred.shape[0]:].copy()\n",
+    "predictions.index = p.index\n",
+    "predictions = predictions.astype(float)\n",
+    "predictions = predictions.merge(p[['low_bid', 'high_bid']], right_index=True, left_index=True)\n",
+    "\n",
+    "ax = predictions.plot(x=predictions.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "ax = predictions.plot(x=predictions.index, y='predicted', c='blue', figsize=(40,10), ax=ax)\n",
+    "index = [str(item) for item in predictions.index]\n",
+    "plt.fill_between(x=index, y1='low_bid', y2='high_bid', data=p, alpha=0.4)\n",
+    "plt.title('Prediction vs Actual (low and high as blue region)')\n",
+    "plt.show()\n",
+    "\n",
+    "predictions['diff'] = predictions['predicted'] - predictions['close_bid']\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.distplot(predictions['diff']);\n",
+    "plt.title('Distribution of differences between actual and prediction ')\n",
+    "plt.show()\n",
+    "\n",
+    "g = sns.jointplot(\"diff\", \"predicted\", data=predictions, kind=\"kde\", space=0)\n",
+    "plt.title('Distributtion of error and price')\n",
+    "plt.show()\n",
+    "\n",
+    "# predictions['correct'] = (predictions['predicted'] <= predictions['high']) & (predictions['predicted'] >= predictions['low'])\n",
+    "# sns.factorplot(data=predictions, x='correct', kind='count')\n",
+    "\n",
+    "print(\"MSE : \", mean_squared_error(predictions['predicted'].values, predictions['close_bid'].values))\n",
+    "print(\"MAE : \", mean_absolute_error(predictions['predicted'].values, predictions['close_bid'].values))\n",
+    "predictions['diff'].describe()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "so it looks i improved on the previous results, by using lookback of 1 tick, 100 iterations, and my additional features. However, can i predict fast enough to make trading decisions?\n",
+    "\n",
+    "Sim results:\n",
+    "\n",
+    "- it got a lot worse when i changed lookback to 20 ticks"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "celltoolbar": "Hide code",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/capstone_project/.ipynb_checkpoints/main_fx_spot_prediction_notebook-checkpoint.ipynb b/capstone_project/.ipynb_checkpoints/main_fx_spot_prediction_notebook-checkpoint.ipynb
new file mode 100644
index 0000000..ad1113a
--- /dev/null
+++ b/capstone_project/.ipynb_checkpoints/main_fx_spot_prediction_notebook-checkpoint.ipynb
@@ -0,0 +1,3605 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "# Machine learning capstone project - fx spot prediction\n",
+    "\n",
+    "The goal is to create features that can help predict the bid price, using a lookback period of a few minutes.\n",
+    "\n",
+    "Try to include the bid offer spread - from the benchmark model it seems volume is not an important feature so it is not a problem that i dont have this data point.\n",
+    "\n",
+    "I took inspiration from : https://www.kaggle.com/kimy07/eurusd-15-minute-interval-price-prediction/notebook\n",
+    "\n",
+    "Introduction\n",
+    "This notebook trains a LSTM model that predicts the bid price of EURUSD 15 minutes in the future by looking at last five hours of data. While there is no requirement for the input to be contiguous, it's been empirically observed that having the contiguous input does improve the accuracy of the model. I suspect that having day of the week and hour of the day as the features mitigates some of the seasonality and contiguousness problems.\n",
+    "\n",
+    "Disclaimer: This exercise has been carried out using a small sample data which only contains 14880 samples (2015-12-29 00:00:00 to 2016-05-31 23:45:00) and lacks ASK prices. Which restricts the ability for the model to approach a better accuracy.\n",
+    "\n",
+    "I will use 1 year of data, from 1Jan16 to 1Jan17, also in 15 minute intervals, but with tick data features.\n",
+    "\n",
+    "Improvements\n",
+    "\n",
+    "To tune the model further, I would recommend having at least 5 years worth of data, have ASK price (so that you can compute the spread), and increasing the epoch to 3000.\n",
+    "Adding more cross-axial features. Such as spread.\n",
+    "If you are looking into classification approach (PASS, BUY, SELL), consider adding some technical indicators that is more sensitive to more recent data.\n",
+    "Consider adding non-numerical data, e.g. news, Tweets. The catch is that you have to get the data under one minute for trading, otherwise the news will be reflected before you even make a trade. If anybody knows how to get the news streamed really fast, please let me know.\n",
+    "\n",
+    "Credits : Dave Y. Kim, Mahmoud Elsaftawy,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "To run on EC2:\n",
+    "- Enter the repo directory: cd aind2-cnn\n",
+    "- Activate the new environment: source activate aind2\n",
+    "- Start Jupyter: jupyter notebook --ip=0.0.0.0 --no-browser\n",
+    "- Find this line in output and copy url to browser: \n",
+    "- Copy/paste this URL into your browser when you connect for the first time to login with a token: http://0.0.0.0:8888/?token=3156e...\n",
+    "- change the 0.0.0.0 with EC2 IP.\n",
+    "- you should see the checked out repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:70% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd, numpy as np\n",
+    "import pypyodbc\n",
+    "import io, datetime, os\n",
+    "import matplotlib.colors as colors, matplotlib.cm as cm, pylab, matplotlib.pyplot as plt\n",
+    "from collections import OrderedDict\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "from subprocess import check_output\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "  \n",
+       "    $('.ctb_hideshow').css({'display': 'block'});\n",
+       "    \n",
+       "    </script>\n",
+       " "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pypyodbc\n",
+    "display(HTML(\"\"\"<script>\n",
+    "  \n",
+    "    $('.ctb_hideshow').css({'display': 'block'});\n",
+    "    \n",
+    "    </script>\n",
+    " \"\"\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "initval = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "#kaggle dates: 2015-12-29 00:00:00 to 2016-05-31 23:45:00\n",
+    "min_date = \"29Dec15\"\n",
+    "max_date = \"31May16\"\n",
+    "\n",
+    "if initval:\n",
+    "    rerunSQL = False\n",
+    "    log = False\n",
+    "    useKaggle = False\n",
+    "    runLSTMBinary = False\n",
+    "    simname = \"500_epochs\"\n",
+    "    sim_desc = \"\"\"\n",
+    "    kaggle params but with 500 epochs to account for more features\n",
+    "    \"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if log:\n",
+    "    #log = {\"simname\": [\"mine_initial\", simname], \"sim_desc\": [\"kaggle params\", sim_desc]}\n",
+    "    #df_log = pd.DataFrame(log)\n",
+    "    if os.path.isfile(\"sim_log.xlsx\"):\n",
+    "        df_log = pd.read_excel(\"sim_log.xlsx\")\n",
+    "        df_log.loc[len(df_log)]= [simname, sim_desc] \n",
+    "        df_log.to_excel(\"sim_log.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>simname</th>\n",
+       "      <td>500_epochs</td>\n",
+       "      <td>500_epoch_lookback_40</td>\n",
+       "      <td>linear regression</td>\n",
+       "      <td>linear regression</td>\n",
+       "      <td>500_epochs_40_lookback_pca_unshuffled</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sim_desc</th>\n",
+       "      <td>\\nkaggle params but with 500 epochs to account...</td>\n",
+       "      <td>500 iterations, lookback 40</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "      <td>added directional errors checking and pca as f...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSE</th>\n",
+       "      <td>1.59188e-07</td>\n",
+       "      <td>1.97246e-07</td>\n",
+       "      <td>6.79626e-07</td>\n",
+       "      <td>6.55241e-08</td>\n",
+       "      <td>4.82937e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MAE</th>\n",
+       "      <td>0.000285781</td>\n",
+       "      <td>0.000340846</td>\n",
+       "      <td>0.000536307</td>\n",
+       "      <td>0.000202905</td>\n",
+       "      <td>0.000594482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>102</td>\n",
+       "      <td>102</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-9.29131e-07</td>\n",
+       "      <td>-3.83515e-05</td>\n",
+       "      <td>-5.08408e-05</td>\n",
+       "      <td>-2.90581e-05</td>\n",
+       "      <td>0.000506372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.000400953</td>\n",
+       "      <td>0.00044465</td>\n",
+       "      <td>0.000826849</td>\n",
+       "      <td>0.000255566</td>\n",
+       "      <td>0.000478296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-0.00135148</td>\n",
+       "      <td>-0.00127888</td>\n",
+       "      <td>-0.00317997</td>\n",
+       "      <td>-0.000772953</td>\n",
+       "      <td>-0.00072515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-0.000158489</td>\n",
+       "      <td>-0.000304043</td>\n",
+       "      <td>-0.000402606</td>\n",
+       "      <td>-0.000200629</td>\n",
+       "      <td>0.000192821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>6.07371e-05</td>\n",
+       "      <td>-5.84126e-06</td>\n",
+       "      <td>-3.07747e-05</td>\n",
+       "      <td>-2.43187e-05</td>\n",
+       "      <td>0.000540495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.000239968</td>\n",
+       "      <td>0.00017789</td>\n",
+       "      <td>0.00031692</td>\n",
+       "      <td>0.000116229</td>\n",
+       "      <td>0.000830978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.000755668</td>\n",
+       "      <td>0.00128782</td>\n",
+       "      <td>0.00327372</td>\n",
+       "      <td>0.0005337</td>\n",
+       "      <td>0.00159335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mse train all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3917e-07</td>\n",
+       "      <td>4.45245e-07</td>\n",
+       "      <td>5.16241e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mse test all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.79626e-07</td>\n",
+       "      <td>6.55241e-08</td>\n",
+       "      <td>4.82937e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mae train all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000423565</td>\n",
+       "      <td>0.000426773</td>\n",
+       "      <td>0.000505385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mae test all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000536307</td>\n",
+       "      <td>0.000202905</td>\n",
+       "      <td>0.000594482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean avg bo spread:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>how often sign of price change is same:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.446602</td>\n",
+       "      <td>0.533981</td>\n",
+       "      <td>0.882353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than 0.1 percent in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.655556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than predicted in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.978261</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.366667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than predicted by more than 0.001 USD per EUR in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if not same sign, how often is actual worse than -0.1 percent return from predicted in both directions</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if not same sign, how often is actual worse than -0.1 percent return in both directions</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                    0  \\\n",
+       "simname                                                                                    500_epochs   \n",
+       "sim_desc                                            \\nkaggle params but with 500 epochs to account...   \n",
+       "MSE                                                                                       1.59188e-07   \n",
+       "MAE                                                                                       0.000285781   \n",
+       "count                                                                                             102   \n",
+       "mean                                                                                     -9.29131e-07   \n",
+       "std                                                                                       0.000400953   \n",
+       "min                                                                                       -0.00135148   \n",
+       "25%                                                                                      -0.000158489   \n",
+       "50%                                                                                       6.07371e-05   \n",
+       "75%                                                                                       0.000239968   \n",
+       "max                                                                                       0.000755668   \n",
+       "mse train all feature:                                                                              0   \n",
+       "mse test all feature:                                                                               0   \n",
+       "mae train all feature:                                                                              0   \n",
+       "mae test all feature:                                                                               0   \n",
+       "mean avg bo spread:                                                                                 0   \n",
+       "how often sign of price change is same:                                                             0   \n",
+       "if same sign, how often is actual better than 0...                                                  0   \n",
+       "if same sign, how often is actual better than p...                                                  0   \n",
+       "if same sign, how often is actual better than p...                                                  0   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "\n",
+       "                                                                              1  \\\n",
+       "simname                                                   500_epoch_lookback_40   \n",
+       "sim_desc                                            500 iterations, lookback 40   \n",
+       "MSE                                                                 1.97246e-07   \n",
+       "MAE                                                                 0.000340846   \n",
+       "count                                                                       102   \n",
+       "mean                                                               -3.83515e-05   \n",
+       "std                                                                  0.00044465   \n",
+       "min                                                                 -0.00127888   \n",
+       "25%                                                                -0.000304043   \n",
+       "50%                                                                -5.84126e-06   \n",
+       "75%                                                                  0.00017789   \n",
+       "max                                                                  0.00128782   \n",
+       "mse train all feature:                                                        0   \n",
+       "mse test all feature:                                                         0   \n",
+       "mae train all feature:                                                        0   \n",
+       "mae test all feature:                                                         0   \n",
+       "mean avg bo spread:                                                           0   \n",
+       "how often sign of price change is same:                                       0   \n",
+       "if same sign, how often is actual better than 0...                            0   \n",
+       "if same sign, how often is actual better than p...                            0   \n",
+       "if same sign, how often is actual better than p...                            0   \n",
+       "if not same sign, how often is actual worse tha...                            0   \n",
+       "if not same sign, how often is actual worse tha...                            0   \n",
+       "\n",
+       "                                                                    2  \\\n",
+       "simname                                             linear regression   \n",
+       "sim_desc                                               1 row lookback   \n",
+       "MSE                                                       6.79626e-07   \n",
+       "MAE                                                       0.000536307   \n",
+       "count                                                             103   \n",
+       "mean                                                     -5.08408e-05   \n",
+       "std                                                       0.000826849   \n",
+       "min                                                       -0.00317997   \n",
+       "25%                                                      -0.000402606   \n",
+       "50%                                                      -3.07747e-05   \n",
+       "75%                                                        0.00031692   \n",
+       "max                                                        0.00327372   \n",
+       "mse train all feature:                                     4.3917e-07   \n",
+       "mse test all feature:                                     6.79626e-07   \n",
+       "mae train all feature:                                    0.000423565   \n",
+       "mae test all feature:                                     0.000536307   \n",
+       "mean avg bo spread:                                       3.98687e-05   \n",
+       "how often sign of price change is same:                      0.446602   \n",
+       "if same sign, how often is actual better than 0...           0.152174   \n",
+       "if same sign, how often is actual better than p...           0.978261   \n",
+       "if same sign, how often is actual better than p...           0.152174   \n",
+       "if not same sign, how often is actual worse tha...           0.105263   \n",
+       "if not same sign, how often is actual worse tha...           0.105263   \n",
+       "\n",
+       "                                                                    3  \\\n",
+       "simname                                             linear regression   \n",
+       "sim_desc                                               1 row lookback   \n",
+       "MSE                                                       6.55241e-08   \n",
+       "MAE                                                       0.000202905   \n",
+       "count                                                             103   \n",
+       "mean                                                     -2.90581e-05   \n",
+       "std                                                       0.000255566   \n",
+       "min                                                      -0.000772953   \n",
+       "25%                                                      -0.000200629   \n",
+       "50%                                                      -2.43187e-05   \n",
+       "75%                                                       0.000116229   \n",
+       "max                                                         0.0005337   \n",
+       "mse train all feature:                                    4.45245e-07   \n",
+       "mse test all feature:                                     6.55241e-08   \n",
+       "mae train all feature:                                    0.000426773   \n",
+       "mae test all feature:                                     0.000202905   \n",
+       "mean avg bo spread:                                       3.98687e-05   \n",
+       "how often sign of price change is same:                      0.533981   \n",
+       "if same sign, how often is actual better than 0...                  0   \n",
+       "if same sign, how often is actual better than p...                  1   \n",
+       "if same sign, how often is actual better than p...                  0   \n",
+       "if not same sign, how often is actual worse tha...                  0   \n",
+       "if not same sign, how often is actual worse tha...                  0   \n",
+       "\n",
+       "                                                                                                    4  \n",
+       "simname                                                         500_epochs_40_lookback_pca_unshuffled  \n",
+       "sim_desc                                            added directional errors checking and pca as f...  \n",
+       "MSE                                                                                       4.82937e-07  \n",
+       "MAE                                                                                       0.000594482  \n",
+       "count                                                                                             102  \n",
+       "mean                                                                                      0.000506372  \n",
+       "std                                                                                       0.000478296  \n",
+       "min                                                                                       -0.00072515  \n",
+       "25%                                                                                       0.000192821  \n",
+       "50%                                                                                       0.000540495  \n",
+       "75%                                                                                       0.000830978  \n",
+       "max                                                                                        0.00159335  \n",
+       "mse train all feature:                                                                    5.16241e-07  \n",
+       "mse test all feature:                                                                     4.82937e-07  \n",
+       "mae train all feature:                                                                    0.000505385  \n",
+       "mae test all feature:                                                                     0.000594482  \n",
+       "mean avg bo spread:                                                                       3.98687e-05  \n",
+       "how often sign of price change is same:                                                      0.882353  \n",
+       "if same sign, how often is actual better than 0...                                           0.655556  \n",
+       "if same sign, how often is actual better than p...                                           0.366667  \n",
+       "if same sign, how often is actual better than p...                                          0.0666667  \n",
+       "if not same sign, how often is actual worse tha...                                           0.333333  \n",
+       "if not same sign, how often is actual worse tha...                                                  0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_log = pd.read_excel(\"sim_log.xlsx\")\n",
+    "display(pd.read_excel(\"log_results.xlsx\").T)\n",
+    "#display(df_log)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# create 15 minute data - this fills the 15 minutes table\n",
+    "if rerunSQL:\n",
+    "    str_query = open(\"get_data.sql\", \"r\").read() # returns prepared data\n",
+    "    str_query = str_query.replace(\"/*\", \"\").replace(\"*/\", \"\")\n",
+    "\n",
+    "    df = getQueryDataframe(str_query, [min_date, max_date])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# dates only have an effect if a subset of dates is needed.\n",
+    "if rerunSQL:\n",
+    "    str_query = open(\"get_data_1y.sql\", \"r\").read() # returns prepared data\n",
+    "    str_query = str_query.replace(\"/*\", \"\").replace(\"*/\", \"\")\n",
+    "    #print(str_query)\n",
+    "    df = getQueryDataframe(str_query, [min_date, max_date])\n",
+    "    df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if rerunSQL:\n",
+    "    df.set_index('datestamp', inplace=True)\n",
+    "    df.index = pd.to_datetime(df.index) # else fill betweeen doesnt work\n",
+    "    print(\"min date\", min(df.index))\n",
+    "    print(\"max date\", max(df.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if rerunSQL:\n",
+    "    df.to_csv(\"data/eurusd_features.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"data/eurusd_features.csv\")\n",
+    "df.set_index('datestamp', inplace=True)\n",
+    "df.index = pd.to_datetime(df.index) # else fill betweeen doesnt work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if useKaggle:\n",
+    "    # load kaggle reference dataset for comparison\n",
+    "    df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_sample.csv')\n",
+    "    #df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_01.01.2010-31.12.2016.csv')\n",
+    "\n",
+    "    # Rename bid OHLC columns\n",
+    "    df_kaggle.rename(columns={'Time' : 'date', 'Open' : 'open_bid', 'Close' : 'close_bid', \n",
+    "                       'High' : 'high_bid', 'Low' : 'low_bid', 'Volume' : 'volume'}, inplace=True)\n",
+    "    df_kaggle['date'] = pd.to_datetime(df_kaggle['date'], infer_datetime_format=True)\n",
+    "    df_kaggle.set_index('date', inplace=True)\n",
+    "    df_kaggle = df_kaggle.astype(float)\n",
+    "\n",
+    "    simname = \"bm_kaggle\"\n",
+    "\n",
+    "    df = df_kaggle\n",
+    "    print(\"min date\", min(df.index))\n",
+    "    print(\"max date\", max(df.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# to include seasonality as a feature\n",
+    "if simname == \"bm_kaggle\":\n",
+    "    df['hour'] = df.index.hour\n",
+    "    df['day']  = df.index.weekday\n",
+    "    df['week'] = df.index.week\n",
+    "    df['month'] = df.index.month\n",
+    "    df['momentum']  = df['volume'] * (df['open_bid'] - df['close_bid'])\n",
+    "    \n",
+    "df['avg_price'] = (df['low_bid'] + df['high_bid'])/2\n",
+    "df['range']     = df['high_bid'] - df['low_bid']\n",
+    "df['ohlc_price'] = (df['low_bid'] + df['high_bid'] + df['open_bid'] + df['close_bid'])/4\n",
+    "df['oc_diff']    = df['open_bid'] - df['close_bid']\n",
+    "df['period_return'] = df.close_bid / df.open_bid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Explore dataset - show some graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
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RERERERENSEqVnvGl7ebbUrZVHXYAhOhkjfzl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\nvFj6C1G9P8ndO4ic8VFp00bFzvzYmE5JCAcAgKkqv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cdif0jSeagwK84rkAIu8//Wk8fL/YSgZX5otwe7KGuAowcmtruuklS/3icwCA7bNPoY2bgKqr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\nALW1GQBgjG4EACg/fJf2uJJmVgy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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3c366ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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Yz75Icp2rKeZ/7yBmmvv8cxPpedOZPKD0J0MtSVlfmGzPc6ERHpn2Qqy+J1zWQP0im4JZXi7EvP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Mqx4ZVl3M6yKioc0XaA/jNLXFF8bpjk6tQKJRjWSzJr62V5IE5aMPwfjg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\nLj8Jx4xJRXGuBSlmLeSyI3crXdIb4Hr6eQDAzFsux9H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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3c476d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot close price, compare to low and high price\n",
+    "ax = df.plot(x=df.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in df.index]\n",
+    "plt.fill_between(x=index, y1='low_bid',y2='high_bid', data=df, alpha=0.4)\n",
+    "plt.title(\"entire history\", fontsize=30)\n",
+    "plt.show()\n",
+    "\n",
+    "# plot first 200 entries \n",
+    "p = df[:200].copy()\n",
+    "ax = p.plot(x=p.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in p.index]\n",
+    "plt.fill_between(x=index, y1='low_bid', y2='high_bid', data=p, alpha=0.4)\n",
+    "plt.title('zoomed, first 200', fontsize=30)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- there are periods where the price doesnt move, probably weekends. Maybe dont consider these for training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pltGraph(xname, yname, irow, icol, df, xval=None, yval=None, title=None, norm_axis=None):\n",
+    "    x_axis_col = xname\n",
+    "    y_axis_col = yname\n",
+    "    if xval is None:\n",
+    "        xval = df[x_axis_col]\n",
+    "    if yval is None:\n",
+    "        yval = df[y_axis_col]\n",
+    "    if title is None:\n",
+    "        title = x_axis_col + \" vs \" + y_axis_col\n",
+    "    if norm_axis is None:\n",
+    "        norm_axis = \"x\"\n",
+    "  \n",
+    "    axarr[irow, icol].scatter(xval.values, yval.values, color=\"green\", lw=0, cmap=pylab.cm.cool, alpha=0.8, s=2)\n",
+    "    axarr[irow, icol].set_xlim(xval.values.min(), xval.values.max())\n",
+    "    axarr[irow, icol].set_ylim(yval.values.min(), yval.values.max())\n",
+    "    axarr[irow, icol].set_xlabel(x_axis_col)\n",
+    "    axarr[irow, icol].set_ylabel(yname)\n",
+    "    axarr[irow, icol].set_title(title)\n",
+    "    axarr[irow, icol].grid(False)\n",
+    "    return icol + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AlBzaj6DQmAMBubPyPTjd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RAAAg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SpA1h/rf/ZM0vqxje6RnSd4xm03GXk+K9/svQqLSkbh3J8I1/Z+LusQxq8wgPtx9Mq5AbWXDv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Z4GtHqw0tt7wj5UVrYtSwkbYg1fksxoRueg6vA3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0dYZZI5h4nZV48jtPs/Z7I1LBGcGrO17RWQ440lAMFlbwNYyat+QZlGIrN4u/XjM3EpMl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\nS1jtpXit7I/MZTN5hUy89xL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sZPz2Oxpq4PKy2/mqIcPhg7f+fDC66liSRXVdFcZuHNvZ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4lV01CNOu8nok/m5w8tQFKfZNFFKZfHiecdzympXDPvPdl2aADQ7msXjaeC1WwTT/pUscAN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\n7JOoGSWo2Pa59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8weuJvrRJPfXB4n5r33PfFrA8nWaqqo+j7w0kerwv7a3WJM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\nuHABq1atwsaNG/HGG29g586dcDqdKC8vR0JCA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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3c79bac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb Rows:  10204\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create ohlc prices, analyse distribution, think about feature transformation and de-trending\n",
+    "\n",
+    "fig, axarr = plt.subplots(4, 4, figsize=(40,20)) #1 row, 2 cols, x, y\n",
+    "#plt.figure(figsize=(20, 4))\n",
+    "irow, icol = 0,0\n",
+    "fig.suptitle(\"frequency distributions\")\n",
+    "\n",
+    "\n",
+    "sns.distplot(df.period_return-1, ax=axarr[irow, icol])\n",
+    "axarr[irow, icol].set_title(\"dist period_return\")\n",
+    "#axarr[0, 0].set_title('Axis [0,0] Subtitle')\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "    icol+=1\n",
+    "    sns.distplot(df.avg_bo_spread, ax=axarr[irow, icol])\n",
+    "    axarr[irow, icol].set_title(\"dist avg_bo_spread\")\n",
+    "    icol+=1\n",
+    "    icol = pltGraph(\"ohlc_price\", \"avg_bo_spread\", irow, icol, df)\n",
+    "    qlow, qhigh = 0.01, 0.99\n",
+    "    df_mask = df.loc[(df.period_return < df.period_return.quantile(qhigh) ) & (df.period_return > df.period_return.quantile(qlow)) & (df.avg_bo_spread < df.avg_bo_spread.quantile(qhigh) ) & (df.avg_bo_spread > df.avg_bo_spread.quantile(qlow)),:]\n",
+    "    #df_mask = df.loc[(df.period_return < df.period_return.quantile(qhigh) ) & (df.period_return > df.period_return.quantile(qlow)) & (df.avg_bo_spread < 0.0001 ) & (df.avg_bo_spread > -0.0001),:]\n",
+    "    icol = pltGraph(\"period_return\", \"avg_bo_spread\", irow, icol, df_mask)\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    irow, icol = 1, 0 # move down one row\n",
+    "    \n",
+    "    icol = pltGraph(\"ohlc_price\", \"avg_bo_spread\", irow, icol, df)\n",
+    "    icol = pltGraph(\"hour\", \"avg_bo_spread\", irow, icol, df)\n",
+    "    icol = pltGraph(\"hour\", \"nb_ticks\", irow, icol, df)\n",
+    "    icol = pltGraph(\"day\", \"nb_ticks\", irow, icol, df)\n",
+    "    \n",
+    "    irow, icol = 2, 0\n",
+    "    df_mask = df.loc[(df.period_return < df.period_return.quantile(qhigh) ) & (df.period_return > df.period_return.quantile(qlow)),:]\n",
+    "    icol = pltGraph(\"hour\", \"period_return\", irow, icol, df_mask)\n",
+    "    icol = pltGraph(\"hour\", \"ohlc_price\", irow, icol, df)\n",
+    "    icol = pltGraph(\"weekday\", \"period_return\", irow, icol, df)\n",
+    "    icol = pltGraph(\"weekday\", \"nb_ticks\", irow, icol, df)\n",
+    "    \n",
+    "    irow, icol = 3, 0\n",
+    "    res = df.loc[:,[\"weekday\", \"avg_bo_spread\"]].groupby(\"weekday\").std()\n",
+    "    res.plot(kind=\"bar\", ax=axarr[irow, icol])\n",
+    "    #display(res)\n",
+    "    \n",
+    "    icol = pltGraph(\"weekday\", \"avg_bo_spread\", irow, icol+1, df)\n",
+    "            \n",
+    "\n",
+    "#plt.tight_layout() # reduce overlap\n",
+    "    plt.show()\n",
+    "\n",
+    "print(\"Nb Rows: \", df.high_bid.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- it seems the hours with the least number of ticks have the highest bo spread. This is expected, as during low activity traders might set spreads wide to avoid surprises.\n",
+    "- period return is between two closing bids, 15 minutes apart. It might be a spurious measure.\n",
+    "- do i have to stick to 15 minute intervals to complete this?\n",
+    "- weekday 1 seems to have higher stdev of avg bo spread"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Add PCA as a feature and show graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# Add PCA as a feature instead of for reducing the dimensionality. This improves the accuracy a bit.\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "df_np = df.copy().values.astype('float32')\n",
+    "pca_features = df.columns.tolist()\n",
+    "\n",
+    "pca = PCA(n_components=1)\n",
+    "df['pca'] = pca.fit_transform(df_np)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kWG3MerVXRJuyrZCm1sY+Nar9NrNig93RwKFT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gghhBACkI4zIYQQQgghhBBCCCGEEEIIIQDpOBNCCCGEEEIIIYQQQgghhBACkI4zIYQQQggh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\n19a0adPslvXw4cOaP3++d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5rZcY7GtH3G8p6zKfsmakLb53VbeCvF\nJEQpryDPMqRjWlZqsZbUVPQAAPAXXx1uyNdiJ7OktESN2zVaGbkZOpP9h4wyyk9+mt7xJUnS7IMz\nFBpQR3Vr1lVaZqr+r8vruqbWNZp/aK4WdV2s0Z89rtTM37Ug8lWt+uGfRX78JKUlavzucVrb9/0K\nD7MCAICv89XYqSQmk0nJycm6/vrr3V2UInx9qEbr4c4kOXXos6S0xHIP1e1s3hBfeloZPa089lS0\njN6wb6Wp6vK7cnvmRFbs9uEVGta/vNtyxn54+/ljy9f2pzJxk0OJs9dee03PPfdchTdS1RwJYMwn\nwfZfEjRz/zTlFeQrNet3GVVQZD4/+ale7fp64ranlJzxmz45/bFW3rdK43ePU74xTzPufEnzD83V\n2r7vKy0rVeN2jVaAXw3Le9PMQYYvnXCovnzt5gnA/Xy18sdXY6eYhChl52WrwJSvc9nndNmYLUmK\nqN1ABcYC5RnzdCH3vK4KvFqXci/KT34KD66nQL8g/aPnWxq7c7SevmOcVhxdrqkdpqt3075Fni0V\nHV4FQMUQ2wHex1djJ7PVq1frtdde0+XLly3TGjZsqM8++8yNpSrO1xNnkorFaCU9L1zxLimeTygv\n828V20Z4jizHO7EcV9LxcvSadfRddElpiZqyb6LHfy++dv742v5IVfCOs88//1wO5Nc8mr0unikZ\nybotvJVm3z1P2XnZkorvo1FGpWWnavbBGXr7hxVKzvxN23/5WFM7TJf1ITH3LlvcfbnlvWnmoRvN\nXT+T0hLpAgqvRTdmAHCcL8RO9izqulhz7pknk0mKbvk/lukXcy4q/coZZeZmSpIu5J6XUUblK19n\nsv5QgSlf23/5WDX8a+iaWtcoOy9b8w/NtQydbf1siUmIUkxClLb/kiCp7KGBeC4BFUNsB8ATvfPO\nO9qyZYv69OmjTz/9VC+//LJatWrl7mL5hPLe760rTUtLmpXnWeLIcGue/nzy1HJ5G085jt74/uSK\nHDtnHW97x8vRa9ac3DTPZ17Oelh/s7YR7Z36vbjqfPPG86c0vrY/leVQj7OHH35Yf/zxh2655RYF\nBQVZps+fP9+lhaso25Y/9rKl5os1ryBPF3MuKP3KmTLX62fwV7B/sLILshVRu4Fq+AdIkmbfPU/z\nD81VVPNh2vLLJsXePFIrjhYm0NpGtLcM6zhoS181Cmms9/tv9poTsDq38qnO+14SjgkAZ/PVVtPe\nHjvZsu5tllNwRWnZqQ6v209+Cg2so4u5F/TMHRP1yemPlZF7SfM6vWLpcWY9vPX2XxJ07vI5Tfty\nkubd83/FhnS05q6WiBV5HvIMhSfivAS8j6/GTmZRUVF6//33tWLFCjVr1kzdu3fX4MGDtWnTJncX\nrQhv63Hmyl4ErniWeMJwjvZU9ji667lb3u26upyuOh+rQ1xTkWNXFb2IHDn29noFVsXvOV/sRQXH\nVSZu8n/xxRdfdGTGdu3aqUmTJmrYsKHlv5tuuqnCG3al7OzcIn/XCaqjexv3LDYM0D0NI9U+ooO2\nnPxQucbcYuuxZZJJQf6BulJwRTX9ayqqebR2/fapOl3XWZ+d/kQJ/43XsBb/owWJczS0xXC99f2b\nlu1eF9JQnRt1VZ+mD+jGazzzuKVkJKtOUJ0if8duH657G/dUnaA6xT53Z9mqYnvW++7sdbvrOFaW\nt5YbgOcKDg4qeyYv5c2xk606QXV0+7WttOXkJmXkZijXmOPwugP9AhXkX1MBhhp6496luqbmtYr/\n5UMlph1S36b9LI2L6tWqrzxjrv7no4f045/HNbXDDK364Z9aEBlnN3ZKyUjWmF1PakFknEICQ5WR\ne6lKnlMViRFcGVc4yhx/eHMcAufjXAC8jy/HTpK0bds2NWjQQFdddZV27dql2267TWvXrtXDDz/s\n7qIVUVbs5Gls68WcvW5nMsd47oybSuLocbQXb7krHjRv95ZrbtV1IQ0dnt+V5XTV+ehp54sr2Kvj\nLmu/yzrezvh9YF6+tHXVCaqj+5r0LlKO60IauuzeZL1dV28DnqsycZNDPc5KM2jQIH344YeVWYXT\nldbyx5zdvpyfrbyCfBlVoNSs38u9jeCAYOUU5CjflK9rg+rJYJCCAoI0r9MrGrnjYfn5+WlBp1fV\ntXH3Yr3crDPrntIaoqwxam0/r8pyu6tlgCv2kVYOAFCUr7eatsfbYiep8Pm1+/QuTdzzjAps3gdb\nHg81j9E36YfV54b+2vLLRi3v8ZYighvogU29dO5Kuj4ckGCZNyK4gaTShwYyi0mIkqRS32lQ0nO9\nqnqPuTPmM8cfCyLjvOJdAQCAkvl67PTTTz9p48aNmjx5sp555hkdOHBAY8aMUWxsrLuLVoQrepx5\nSv2QVPH3FVVmXZVdpjzrdMb6S4stS6r3cdd3XN5ePZ50LrqKM89xZ2+zPOurbB2jM+spvanOszqc\n4yjk8neclcbb3t/RMLSRpnaYLklKzUpRVm5WudfhL39l5Wcp35Sv0IA6Op/7p9KvnNF9je9X76Z9\n9Urn17Sy5yqtOLpcUfEDSxxHNSUjWQ9tLfzc3WP7ljSGqflv68+rerxpd42v6ortMVbsX9x9zgOA\nu3hb7JSSkayo+IGa8sUEtQ5vW+H11FCgNpxYq5MXftbSI6/rv5d+1eM7YiVJb/Vapfmd4hQR3EAR\nwQ00dudoDdrSt9QyxW4fLqnw2bq27/tlJs3sxS4VjWkq8hx357PfHH84+10BlUEcAACwp0WLFhow\nYID8/Pw0b948LVmyxOOSZq7gSe/1si1LSWWy967astblyLYl58dN1uVwxrEubR2l1fu4KwYrbwzo\nCbGiK5XnnVzOui5dcY07o47RmfWU5V1XWfcYe/M6g7O/V1/gK/vhbJVOnBkMBmeUw+WsL8Y5X81S\nh/p3ySijLuVdLPe6rFtaZxdkqVZALUnS2z+s0JhPn9TkfeP18/mftajrYtXwr6G0rML3gJgrdszS\nslJ1+tIpHU0/4lBQUtZnlVXWjc06ibYgMq5KH6T2WunYU97jU97gzRl8PQBxhCf9KACAquYtsZO1\nJfcul4wGHU4/VOF15KlwSCOTTDKYCkPQ37NStPv0Lj356eOavG+8Hto6UGlZqbqcf1m/ZxZ+Zv2s\nSEpLVFJaYpEfZea/S3u+2v6Is66UWdV7TYX3ycxZcYmr1iEVjePcsX3bdRIHoLI4fwDfFBcXp7i4\nOEnS5cuXtWzZMi1ZssTNpXI9T2pk60jD6ZSMZE3ZN7HMuqHy7Jcr4wPrclRFssETvkdbnlgmd3H0\nHChtvoo0vHPFNe6shJezONIDVfor8e6KBLwjZSypR2h5+MpvGl/ZD1eodOLMG1ifAGlZqTp16b/a\ncGKtU9ZdYCpQZt5fXfQ3nFirXGOuZh+coePn/qMnbhutKfsmKiUjWdt/KRx+yFyWiOAGahBynW4L\nb2W5YLf/klBiEs28H0lpiRUqqyOVOo5m+c375Mj6nc3eBZ2SkezQzbas9ZQ2X0WPO4rzpB8FnoCH\nEwBPlZJROMz09l8+Vo7pilPWaZLJkkSrG1hXCw69LINBqle7vp5qNU4RwQ1Uwz9AVwddo8n7xmvg\n5r6W5/yAzfdr4JY+lpgqKS1Rg+MfsPxt735qnSQrqZWxvbjCUbYtmO1Nt1ceR9ZbVqzi6sScq35E\nEQd4F0+MU7zlB76nlw/wRLt379bKlSslSfXq1dM777yjTz75xM2lqhqe9Fy0N/qQ7efm3uyOrsuR\n+VwZH1iv11XJBnfd9335eePsfStvr8bShuN0pD7R+t/OHPLRlVy1fuv6VXPi3ZHekK64N9hLmpU3\ntvSV3zS+sh+uUC0SZ9YnQNuI9oq8rqvLthVoCJLf/39Y5x58UZP3jdeApoO1+/QuPbI9RrtP79KC\nyDjL/LUCauto+hFL0ux/P3tME9o+b7dlj7mnl72klTV7CZ7SKm/K213dXuvspLRExSREVUlyybbH\nm7lSb/zucWW2drLet/K0MDEfd5JnzsMNuZC3VPwAqL7yjXm6oe4NCjAEyCDn9pa7kHtBf1xOVfdG\nPZVvzNeUfRO0+/QuXcm/oikdXlCtgNoqMOVb5m8ceoMWdHpV8w/NtbzX7M0eb+vVpFeKND6yboAU\nkxBVpIFNWlZqkee/vbjCXiLM/Jkt8/KSisVt9t4dW9I9317STVKRFt+285v3raLKegZV9EeUI2Ui\nDvAOnhqneMMPfE89doCny8/P15UrfzXWycvLc2NpIKlIjGRvurM4M7HgDq6+75fWkMqTnzeujFXd\ntT5H4hBH61vLk3yTHBsitTKc8RujJLbDx5sT745c966+N1Q0tvTme5Y1X9kPZ6s27zgzVzisOfZv\n7Ux2XWulXFOOjDJKki5cOa+6gVfp5YMv6sUD03V1UJgWJr6scbtGKyq+cCiiJ24brSc+fVQv75+t\n+Yfm6s0ebyu8dj3L+mJvHinprxuldSbetleauVJocPwDSkpLLDFJVNJ024ok23XbTrN+AIzfPU6X\n87M1dudohyqDSvq8tPms57FNHi7qulhr+75famsnezf/kgJA2+XaRrTXgsg4jd89zqkPbEc+d2Xw\n444M4XbrAAAgAElEQVTAylODOXfxhoofAM7lLbGTVDisdEZuhpZ+u1hX1bhKJjm77IXre/uHFTqb\nna5aNWpr9oFZSstO1cz903Qx94JSMpM1/6u5GrtztGJajtDwWx5WVPNheuK20Rq/e5zCa9fThLbP\n69WkVzSh7fNKy0q1NOZJy0pVXkGe0rJSNWXfRA1oOlhT9k0sUgLbuMK6gZB1rGP7g9c8zdxiUlKR\n+7m58sde3GX+3Ho95vfemuczJ/PsNaayXqd5SHBH2T6HHWnhWZ5nd2lldbaq2IYreUO5PTlO8cQy\nWfPkYwd4smHDhmnw4MFauHChFi5cqAcffFDDhg1zd7GqvfJU/DvyfCutUbej5fE0JTWaKouj81Tk\nnWruVtlElTOHSjSrqmPlyPCgZR0fe3F1SUOkVvU1UVY9b0msGy96Gk8sE9yr0omzJ554whnlcLk1\nx/6tqPiBejVpYZVts0AF6hhxlwpUoIu5F/RnzjmlZacq8rquMhikJz99XHGHF6qmfy298W2czl0+\nK0kaHP+A/p70hvp92Evj94zV/RvvtVQAmd/jYU4CmXt6bf8lQTEJUYoIbqB59/yfJFmmmyt1rJez\nrWgwJ9zMlS/WFUTmFtzbf0mwbE8q+rBZ2/d9Le/xVpF3upl7Z5mXMa/TdvvmabYtxW0fDua/bZOA\n5t5m9pT28LH+d2kBoPXQmiWtq7wPjLIqlcyfu7IliTtaJbljm96AhzNQvXh67GT9bBr1SazSslN1\n+tJ/dTb3rEu3W6ACXcg5rwu55yXJ0rvNJJM2nFirkxdPaPbBGRrz6ZOafXCGJu55Rmez0/X4jljN\n+WqWBjQdrDlfzdLYnaN1KeeSRn/2uEZ9EitJighuoNibR+r/kuapa8N7Jf0Vp9j+kDUn3Gx7kkmy\n/Ejd/kuCBm7uq4Gb+2rsztGW0QLM6zDHZdYxkLWYhCg9tHWg5Tl/NP2ITmecUlpWqiWuMCfz7MU+\n5vJM7TC9yHz2WCf+rOOKlIxkRcUPLHEZ63+X99ltPk6ufO5XRazkDI5WhFRmXa5GnFJxHDug/GJi\nYhQVFaV169Zp1apVGjRokGJiYtxdrGrFkeSMvboa27/L8wwsT/LHVfGFM9bn6EgD5ZnHvF5ve6ea\nVHxkh5KUtv+VGSrR3jLOUJ7vzd6/raeV9b1aH7+Shkit7DVhey2u7fu+U3rTObK98i7jqTF/abyx\nzChkMJXS7Llly5ZFXmAfEBAgPz8/5ebmKiQkRImJpQ9bd+TIEcXFxWn16tVFpu/atUtLly5VQECA\nhgwZooceekhGo1EvvviifvzxRwUGBmru3Llq0qSJTp06pSlTpshgMKh58+aaNWuW/PxKz/elp//1\nzrGktESlZ5/RI9tjdE1QuK4UXFZmfkYpS7uen8FffZo8oEN/fKU/L59TvgqHIDLIoI8Gf6btv3ys\nZUfe0HUhjfTIzY/pvR9XK/rGEfrXD28rJfM3bR20Q+nZZzTti+f12C1P6O1jKxTgF6AAQw09fcc4\nTfligiJqX6cr+Vf0Z85Z1Q4I1pX8y1p53yqF166nUZ/EauV9q5SefUbzD83VpZxLGnnrE2p2dTNL\nGecfmqupHaZLkmYfmKWn7xinyXvH6+qa18jPr7CcUmEr5/G7x2lqh+nq3bSvpdX1gKaD9crhl7Wi\n5zuaf2iu8o152tBvsyRp0Ja+Wt7jrcJKrO3DtSAyTqM+iVVoYB0t6rpYEcENLJVSt4W3KtLS2bYV\nt/nf1p+ZmW/e5pbdDUMbKSkt0bJde63CbZW2XfPf5kTi2r7vS1KRdSelJdrtBWe9Xuty2q7b/Lmr\ngiBnrbs863Hl/gAoztOvuSuBF3R93evdXQyn8dbY6dtf/lPsmSRJ/T7speTM3yp4NCrPIINMMqll\n3Zt0/OJ/5Cc/hQVdqxuvbqmDfxyQ0VQgf4O/6gZerXM56WoceoMG/m2IPvpvvLLzList63fFdXlD\n/734X+1O2am24e319g8rFBZ0jbLyM7Wy5yr1btpXkizvSRu542EZDAaNvn2cPjn9sdb2fV+7T+9S\n18bdFZMQpajmw7Tg0ByZDCZdU/Na1QyoKZNJ2jywcPmj6Uf0+CePqF7t+prX6RVLHDX8locl/ZWw\ne/LTx/WPnm9JKhxRwDx9yr6JluN/NP2IXk16xe6oAeYYzBw7xSREaVHXxZa4IyUj2TJPXkHhMFc1\n/GtY5klKS9SgLX314YCEIrGKbVxSUpxlntdeXJRXkKf3+28uFsfYm78y9yfrWCktK9Wh96w4a9uO\nbiMmIarEyofyxk/24sXS5vfkez/gDo5eF55+/YSHh7q7CC41efJk5eTkqH///jIajdqyZYsiIiL0\nwgsvuLtoRVjXO/kK8zO/pOeNuX7DXqxgbzSf0p5bzooBnDVveZ+zjnJ02558z6kMR45rRY99RY6b\nM491VcWSjh6b0spT1mclbcPRdboyprVexvwaHVfdV1zBVfcWV/DE4+cMlYmbSk2cmc2aNUtt2rRR\n//79ZTAYtGPHDu3bt09z584tcZmVK1cqPj5etWrV0oYNGyzT8/Ly1KdPH33wwQeqVauWoqOj9eab\nb+rrr7/Wrl27tGDBAn377bd68803tXz5cj355JN69NFH1bFjR82cOVORkZHq2bNnqeU1BzDmXlRv\n9nhb5y6f04sHputi7gVHj02VCA4IVlZ+luXvexvdp73JnytPeZrZcY6urnm15h2crYu5FxTkX1MZ\neZfUsu5N+vniCUvCLcAQoPq1G2hiu8lacXS5Ludn65nWEzT3qxd1LqewdXjdwLoKqVFHuQU5Sr9y\nRhG1G+hCznn1bzpYG06slSRdHXiNLuT+WVgpVfMaXbhyXmG1rtX5nHMaffs4LT3yuowmo4wy6rUu\nf9fCxJcVGhiqy/mXlX75jFb2LEzMpWef0ROfPqqQgDpa3fe9Yvs84MP71bjODZp510tF5l/R8x3d\nFt5KsduHK/bmkZryxQQ1Dm2ixd2Xa9yu0VrcfXmRIG1C2+eLJNasW4pbVxrtPr1LK44u16Kuiy1d\nmtOzz1iWNc9vm+SyvjGXVBFjDgqtK2vMZTG/s25T/21FPpNULKFXmZtTaUm/qrjhedNDwMzdDwN7\n26/KMjm74hKey9Ovz5SMZD3+2QgdfuKwu4vidN4WO92xrE2xRh9JaYmK3vagpQeYpwk0BCnXlKPH\nbn5C639aq6z8TN0T0Vn70/bp0ZtHKTMvUxtOrFUt/9q6XJCth5rHaE/y5/rjcqoCFKCra4bpqppX\naUO/zVp19G298W2crql5rc5dOau6gVfpYu4F1atVX1M7zNBze8bomTsm6oMT63X2yhmFBtRVUECQ\nagXUsjRa+v/YO/e4qOr8/78GGBDGQQ2hQYhatqtGuJF0MYqfl2RFBeuLW/jVzFrTTXEXzNuilvpV\nQ+G7YS1lZmZf/W7yLdGipTSXsqs0368sae2NVoMYxUs6gHKd3x+zn+NnPvM5Z86ZCwzj5/l49Ehm\nzvnczuec85736/N+f5bcUYj//ssbuNBuxdlLp3HVgKGIDDPi7MWzONN+Gjsy7DbRkwdm4+mU5Vh3\neDXiBsYjNFgvLS4iwtz8lIWSHbFu9EZEhUdJi58GBIdj5d3PYs0Xq9DZ04k9WXbBbvKeCTCGRkoL\neUg02eaxZTAZYmFpbZL+Twt1xLZi3wsE3mIj+jvaTqLFOlYsYp9HZkuN7HFqFjPREJuf2Fyu3rNk\noZenTghXuBLO3ClPrdOf1Av47yr0vkYs/LqyUGsT+bvtBAS+cJaRkYGqqirp756eHkyaNAnvvfde\nH7bKmUATzngLj2nUvmvlBDX6mN68tzwRHvz12e+v7ZKjL8TD/jZGgO98e2rFS1f3szdx59lAvlcK\nUPDX93d/mI/+PH6e4ondpCpV45///GdkZWVJK6gnTJiAuro6xXMSEhKwefNmp8//8Y9/ICEhAYMG\nDUJoaChSUlJQU1MDs9mMtLQ0AMDIkSPx9ddfAwCOHj2K1NRUAMB9992Hzz77THXnTIZYvDxuG9Yf\nXouo8CgYQ/3LwAwLCvuXaKZDpH4QwoMj8GHDB9Dp7Jel2PwcfvPRfDRfOgWjPhLtXZcQFx6Hb89/\n4yCaPZX8a3TburD+8BqUpJdi5i2zse7L1ZJoBgDnO87jZFsTzlyyfzYn6Vd4Mmk+/udvbyIYwZg9\nfA5W3v0sghCEwWFDAAA2nQ0hQSEYFDoYLxz5T8AGhAdHAAA+/+EzWNqa8MhNM/DO1Pex5I5CrPxs\nObL3TkR0RAyeTJoPa9d5zN3/BPIOzsPc/U9g6t5MNLedwjBjHHJvnoHH35+JX34wC+sPr8WW8a9J\nbd2Qtglb6srwyvjt2D25At+e+QYnLhyX9k+LM8ajIGUx5ux/DDn7sh1SPBJjbufRHQDsK7XzP1qA\nC+0XpDRNc/c/gV/un4XJeybYUy3tnYidR3cge+9EabU5cDkkWm5fM/JQIQ4i+phGawOKzUV4edw2\nB2cSCQtn906Rc1a5gpTJniP3uS8goeK9gTf605tjo7b+3mwTW5dcewSBAbk//dXwiTPGY88v9vR1\nM3xCf7OdaDFj6aFF9nfrH/8dwUHBmvrdm3TY2gEAb/7FLpoBwNdn/wwbbNh2bIu0MOhS9yUAwPv/\nfA9nLjXDFBGLjff/DoMHDEZHdyc2m3+H0iPFACDZSZe67OecvXgG/zz/T4ToQvDikd+hs6cD0296\nFAPDDNg6YTtyb56Bc5fOITo8Bs99tRYX2q04134Gy1JXAgDShqXjfMePCAKx7YrwdMpyvPr1FnTZ\nOjH2mvGw2S7vU3bu0lms/nIFdh7dgWJzEdaN3ojf/W8xHq3Kxcz3HkFjSwPOt/+INV+sQlvnRXT1\ndMHS2gRLa5N9IVLSPOl+77J1OoxXfnUeqk8clOyeB/dNAsB/D9Q110o2zvaMnVL7SArKae/YRTmy\n6rPRejlNi8kQKwlF9LuGthcarQ0OkXAEufchSXkpt1F5immUgyNP6T3baJXfG4LF0/dznFE+1Y07\nZWp9lltam7xiXwSiXaDl2va17SjwDmptIn+3na4EYmNjcfz4cenv06dP4+qrr+7DFl0Z0HOfjXIH\nHN+15HjA8R3B3j9yjnitz1NPnr9a7mnegh9/e/bzfs9rObcv0Dr2nuKra+fL8VNqszciM+UWwSnV\nQZ/nbt/lznPn2UDaQe9NLddeX9IbNnxfIOwfPqqEs/DwcLz11ltoa2tDS0sLdu7cicGDByueM2HC\nBISEhDh93tLSAqPxsoBlMBjQ0tKClpYWDBw4UPo8ODgYXV1dsNlsktPJYDDAalW3qoestIyOiEFn\ndyfWH16LdfcWwRA80PXJPiYYdidUe4/d4aMDcKHzPNq72xGqC5UcQcQJBABZP30QEaEG/HDxB+j+\nddmCEYynkn+Nin+8BUtbE05dPIkX/7cUa75cieZLp3BHdOq/yrePnz4oFDroEBU2FK9+vcUeQYZu\ndKMbBxv247ma/wB0wPn2H3Gu4xx0Nh0W3bEE3bZu9KAHM4fPRmdPB6IGDMVjSY8jfuA1uDvuHtQ1\n12JDzRp09XRhw73F+PbMN9jy9YvYcG8x1oxeh92TK/DS+K3Yk1WJ6IgYhOj02HZ0C3psPQjWhaAk\nvRRnLp7Bo1W5yK6wi2ud3Z1Iik6GpbUJyz99GhvSilE+pULa+yMpOhl7siqRe/MMFJuLJOdHimkU\n1o3eiOWfPg2zpQYZiZl4PWMXtk7YjrrmWiw5lA+dDlhyRyFOX2wGACQYr8XNUbcg1jAM6w+vdXgI\n8vY1o7/fnrHT4RhWHCPpnwBnQ5T3UNJqBCk92OTCrNWU6w5knxFfYLbUqDZ+PBkzrWW5A6/+3nxB\n8X7MsEaUP/5AELiPvxs+gZSmkaa/2U70D5GClMV4+qN8nLzYhO6eboQiVG23e42hoUOlf7d22+0l\no96Ilg57X4MQhGCEICpsKCL+tejnfOeP6Lb14Bc3Tsf0ETOx4q5n0d59Ca8dewVDwq7CoNDL16e9\n5xKCdMEwhg7Ci0d+B6N+EHrQg1MXT2LbsS1ouPA9yr99E6u/XIHVX67A7BFzsOHeYmy8vwSxhjg0\nWL/HyYtN2HbMbu8QW2zW8MdR/rc/IDwkHAtHLsLOb19HS6cV+dV5qGuuhTHUCFNELKLCo7A9YyfS\nE8agIrsSJfdvxo6J/42YcBMGDxiMiddNQbetC6faTmLBh/PQ3HYKJsMw/L62FI3WBtQ11yI8JAIr\n734WSw8tgqW1CZ3dnfh9bSmGhF2F9IQxkvOLfQ+YLTWYs/8xe/T/oUWoPnFQWpT05IHZmJM0DyFB\negB2O4m36TmZT7w9yIidtCuz3CGdI8B/H5J5WZJeqhi5RTvy5N6z9L95e0OwsIuDvPVDmR4bX0AE\nuxTTKI/tC612ga/sB2+Xq9WR6mqVtqB/oPZe8HfbKdDp6upCVlYWnnjiCcydOxeZmZk4efIkZs6c\niZkzZ/Z18wIa3m9C+hmnZl8lpfvHnd+9SouF1eLOPa1mgbC3FvZqOZYVM9TsoUb+f6X8zvf24m56\ngZivRF+le8MT0YpdrK+1H2RhpZpzWLtWS13uLm5hy+4N0SyQ7yNh/zijSjjbuHEj9u/fj9GjR+P+\n++/HF198gaKiIrcqHDhwIFpbL6cmbG1thdFodPq8p6dH2heEPjYyMtJlHazwUT6lArsyy3Hm4hm0\ndbcqnNk72GDj/t2DbnTYOhy+m3RdFkJ0Idh+bCt+bD+HMfHjAdgw7YZczB/5G3xw4o9YM3od/vP+\nFxATfjW+PfcNhhniMXv4HJibaxCpH4ShA6IRrAtGZ3cHdEFBGBASjgh9OJ5K/jUi9YMQP/AaLPxZ\nAU61nYIOOmy8/3fY9sAOvJqxA1HhUTjXfhY5NzyCBSm/xjBjHAYEh8NkiMUrD2xHfnUeVn++ClHh\nQ2Gz2aPkln5SgHWjN+LmqFswZ/9jqD5xEEsPLUJz2yksPbQIK+9+FsG6EAQHBWPN6HUwGWLx+9pS\nDA69Cr++vcC+J9q/VkmTVU3TR8yEpbUJU/dmIrsiE7mVOfj2zDdY8+VKzBr+OEyGWOm6Tx8x02El\nVFJ0MvKr87Dys+WADVh9zzrMT1mIPVmVyEjMxO7JFUgxjcKerEonhwy7Uph1AhHIMazTiIX9jPe3\nFiOILoM1iFjkHFjegETnya388AQSRUhWvbsK3da6QaunZbmDmrnhS5TmoVhl0r8IVIMtEOhvthNw\n+f5Pik5GZJgRg0OHwNpxAdC5PteXhEDv9NnpjstR9caQSAwKHQRrpxU/v84eRdWDHjx0wzQMCAmX\nhLVJ12XBhh48f2QTXjA/j9Wfr7Iv4Ll/M3Zm7sbmMWUYEhqFYF0IrgqLgs4G6IND0I1uBOl0uMZ4\nLRaOXAS9To/IsEH44MQfJUHs27PfYOknBSioXoiuni7s/PZ1xIRfjZV3rsGCkfnoQQ+e/igfSz8p\nwLLUQpRPqcCspNmIjojBQL0Ry1ILUWwuwoq77DYSsZ9I1NeWujI0t51CZJgRj9w0A88f2YQzl07j\nubQSbB5bhmJzEX59ewFCgvSoa67FkwdmS/vPEpGofEoFfpWchx/bz0lpGwlkYRJgt53jBybg5qhb\nUJCyGFvqyvDyuG2SfTV9xEzsyiyXoplo2HcHqZu2jQpSFjssImLft7x3FEkH6anTmxfpD7h+53vb\nfvKl3UTXQf/fk3K0LDjy1QpvX5SrZWxYMdfXbRMIrmQWLFiALVu2YM6cOZg9ezZefPFFPPPMM5g/\nfz7mz5/f1827IqCFB6VnnDu/HbW+l9QsOObhreeyXD3eeP67G/0stwjW1Tl9vQiEfX96WoYrvBV1\nTy+O94boK4fSYit3+8GKTFrvWbKAzVWmBuK3o8UzrXVpPc6dsfF0Plxp/jJh36rc4wyw769RX1+P\n7u5u3HjjjdwV0SwNDQ3Iz8932qcjMzMTu3fvRkREBB5++GGUlZXhyJEj+NOf/iTt0/HCCy9g69at\nTvt03HXXXZg4caJivWSfDuDyDWW21GDKngx02bqchKveJEw3AD3oRkhQCC52X5Q+10GHIAQjQh+B\nnBsexvvH/4jHb52DXd++gXOXzuJs+xkMCh2MoeHRyL15BrYd3YKGlu8RPSBG2p+DQJwh43en40Ln\nj4g1xGHN6HUAgOiIGJgMsdLG9R09HYgfeA1mj5iD1V+uwJDQKDw/5gWsP2zfg2VXZjn2/PUtbDSv\nw9tT3pX2IyMbyZNVzas/t++zoQ/SY+XdzyIjMRON1gZkV2QiQh8hOYQKUhZL39U11yIpOhkAMOnt\nCfihtQFxA+Ox7t4irP58lbQKmcZsqXHYp2Pq3kyUjduK/Oo8qb1KKzS0bhxPzpX7sQzw9/7wxkNU\nzoEkd6yrXOJs210JUO70gV3J4q1x0HLdvFWvt8vyNf2prQLvwN73/ZVA3qejP9lOZJ8O8iwxW2rw\nyw9moaHle88GwQ2CEIQe9Eh7kxHGxj+AjxoPostmT1cdHhSBDlsHdDoddD3Ar0YuxG/vWYmdR3dg\nw+H/wPmOc8i9aSb+69vtWDpqBe6OuwdT9mRgUOgQDAwzoLO7C80XT+K5tBJsqStDZ3cnOnvsC3d+\nfXsBFn/8G7w6YQf+fu7veO6rtXhl/HZkJGbiBfPz2HB4DWIMV8NmA0YPS8OyuwqR8T9jceqiBSX3\nb8bNUbcAgGSf5NzwMO6Ouwdz9z+BiuxKaYwXfDhPspuIjZNbmYM5SfOw/dir2JC2CSZDLHL2ZaPL\n1gmbDajIrkRdcy2iI2KkdyNJsUkEpqr6Soeod+DyM4PsEUueH2RhUvzABJRPsduTJA01GZOXxm+F\nyRDr9ENVbh9Y+vlEIH2m90ehj3fl9JHbc0ULSvXI7ZdAn+eqnVrb5qt3d1/aBIHYJ7YdvrL9BQK1\nXAr9MWAj9vsTgbbHmRw8f4g/PPNcvdPV7mXqST1afDbulK/2WE/tk974Xcnac+7Up9ROuYVR3vJJ\nuVuON9rgro3J+gjdvcZq61eypdWUpeZ8d9tGjlUaA395tvkLgeJvAjzzOakSzurq6rBw4UIMHjwY\nPT09OH36NF588UUkJycrnkc7f9555x20tbXhF7/4BQ4ePIgXX3wRNpsNDz30EKZPn46enh4888wz\n+Otf/wqbzYZ169bhpz/9Kb777jusWLECnZ2dSExMxNq1axEcrLzfxpH6b5wualV9JR7/YCa6e3rQ\ng24VQ+MZRn0kum3daOtqRRCCYNRHoqWzBbEDY/HQ9b/AByf+iKSoZMQahuH5I5twVVgU5o/8NZ77\nai1iDcMQotNj89gy5B2cB5sNyL15Bl79egsi9OFYcdezWH94LXJueBg7vtmGEJ3eIdUNcWTkV+dh\nWWohkqKTUX3iILYfe9XB6WC21KC57RSSopMRZ4zHC+bn8d9/eQMhQXqUpJcCgINDhvdv1gnDChy0\nIUEbL8QhA0DauLz6xEFsqStDSXopd7N4ukzWgaJWENIK72UDgCtS8b7zVr1qz/GGQeSNh6O3HrCB\n9KD2Jb01TsKQ8D8C4ZoEqnDW32yn5mar07OEiGfWDitaOqzo7gX7iWZI2FX4sf1H5I3Mx976t7An\nyy4YLTu0GJ09Hfix/Ryiw6/GojuW4MUjpdg8tszBVvn2zDdY/unTeDplOabe+JDUJ8AuaM1Jmodi\n83MwhkaiJL3UKQIra8/PsXXC65KoRRYM5VbmoK2zDWtGr8Pqz1dBH6x3sGWmj7icRoos/iCiFrFv\niB3U1tkGfZDd5iM2EgAH+4L0Z8GH86DTAbsnX15YxNogrn4Yyy1wIaIdAAcby9LahAUfzgMAqZ9q\nxALaLpv2TjZCgi6fy/tRrOR4osvy5QblnpTtTbvHG84VYTv5lkB49wr6L43WBjxxYAa+mvNVXzfl\niqc/CmeePr88dTyrWYjiSdtYnxix3XwtBsktJOoN/MkG0VKHNxdrV9VXothc5Fe2jyvb1lf3Aa8e\nub89Kc8bYh6N3OI6b6MkPrt6tgFXXirDQLF5fS6cPfzww1i2bJnk7Dly5AjWrl2L//mf/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DvY0ndlOQmoNiY2Nx/Phx6e/Tp0/j6quvdrtSfyLOGI9lqYXQB+sdHshxxnhJNCPMqpoO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/7aAApP7iZx7xxpQ6lEWDZW1JZTWl0i91+sL7LIIxN9d71YL3hPyo+QC/MPbXmA10evI7RnmDy+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\nRERERERERERERAQA+D9nDXmK/svmdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3d41f6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, axarr = plt.subplots(2, 3, figsize=(30,10)) #1 row, 2 cols, x, y\n",
+    "irow, icol = 0,0\n",
+    "fig.suptitle(\"pca against features\")\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "    \n",
+    "    icol = pltGraph(\"ohlc_price\", \"pca\", irow, icol, df)\n",
+    "    icol = pltGraph(\"avg_bo_spread\", \"pca\", irow, icol, df)\n",
+    "    icol = pltGraph(\"avg_bo_spread\", \"ohlc_price\", irow, icol, df)\n",
+    "    irow+=1\n",
+    "    icol=0\n",
+    "    icol = pltGraph(\"avg_bo_spread\", \"period_return\", irow, icol, df)\n",
+    "    icol = pltGraph(\"avg_bo_spread\", \"period_return\", irow, icol, df, yval=df['period_return'].shift(periods=1).fillna(method=\"bfill\"), title=\"avg bo spread v future period return\")\n",
+    "    icol = pltGraph(\"ohlc_price\", \"pca\", irow, icol, df, xval=df['ohlc_price'].shift(periods=1).fillna(method=\"bfill\"), title=\"ohlc 15 min future v pca\")\n",
+    "       \n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+b67bU1iOC2cvN5q8/R/WDfiE71qPJvbcVeoN6phtWo/y/jzx+WDKPVE30/Ki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kBlZoq1dyOjdfvMeIvsuCRitv9t+MzGx5OwaSsMP5dX106PdOD6L38byo/btR/Xp7pg6mSPZcO6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7APgt+Zib32zj5KBphH60GM+PAnSW/TJuOyuSUiWp8v+qAnEC+h/35ZdfVnYK5eLUxTy8PRTU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Pl/R8ELSbaIAKkvrkk08A9Zd1hw4dNMlWpFK1alWqVq1Kz549KVOmjKR1v0mhUBAdHU1K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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3d26a5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ZshynfCcgJSoaVUf1yjemwso5tW2MBvnsn7X8RyAtOg5/95uKC6Pno/rEAf95G+pqvzC2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\nICIiIiKie2LhQERERERE9/Q/QpkTAwOFubsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a408df4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check feature correlation, to see what correlates with the close price\n",
+    "colormap = plt.cm.inferno\n",
+    "plt.figure(figsize=(15,15))\n",
+    "plt.title('Pearson correlation of features', y=1.05, size=15)\n",
+    "sns.heatmap(df.corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(15,5))\n",
+    "corr = df.corr()\n",
+    "sns.heatmap(corr[corr.index == 'close_bid'], linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Start running datascience methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_shape(*argv):\n",
+    "    for el in argv:\n",
+    "        print(el.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "this creates training examples and actuals for the model\n",
+    "if nb_lookback_rows is above 1, X will have examples each of which is a 20 row dataframe\n",
+    "so the regression model needs to be able to use all those rows to train on\n",
+    "\"\"\"\n",
+    "\n",
+    "def create_training_set(df, nb_lookback_rows=1):\n",
+    "    \n",
+    "    dataX, dataY = [], [] # for training\n",
+    "    \n",
+    "    # it creates for each row a 20 row lookback dataset\n",
+    "    # this expands the dataset by 20 faculty\n",
+    "    for iRow in range(len(df)-nb_lookback_rows-1): \n",
+    "        \n",
+    "        df_lookback_rows = df[iRow:(iRow+nb_lookback_rows)] # from example 1 to 21\n",
+    "        dataX.append(df_lookback_rows)\n",
+    "        next_row = df[iRow + nb_lookback_rows] #get example 1+20, so the next point that is to be forecasted\n",
+    "        dataY.append(next_row) \n",
+    "    \n",
+    "    return np.array(dataX), np.array(dataY) # convert to numpy arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Use Random Forest for feature importance:\n",
+    "Check which feature is most important, based on predicting the next closing price using just one example as training\n",
+    "Do this for each example, and check which features are the best on average\n",
+    "Looking back more than 1 example for each example requires a decision how to use the features. Do recent examples features get more weight?\n",
+    "\n",
+    "- scale all features to range 0-1 for faster convergence\n",
+    "- use random forest to find best decision tree to explain closing price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# create random forest regressor - random decision trees, like weak learner, ada boost\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Scale and create datasets\n",
+    "idx_close_bid = df.columns.tolist().index('close_bid') # predict this, should it be return?\n",
+    "df_np = df.values.astype('float32') # so regressor can use it\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "df_scaled = scaler.fit_transform(df_np) # scale features to between 0 and 1 for faster convergence\n",
+    "\n",
+    "# Set look_back to 100 which is 100 ticks\n",
+    "# look back is 1 period, to check which features predict best a 1 period return\n",
+    "look_back_rows = 1 # to work with more than one, use alternative reshape\n",
+    "X, y = create_training_set(df_scaled, nb_lookback_rows=look_back_rows) # look back only 1 row\n",
+    "y = y[:,idx_close_bid]\n",
+    "#TODO:X = np.reshape(X, (X.shape[0], X.shape[2]* look_back_rows)) # to get back rows and columns\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[2])) # to get back rows and columns\n",
+    "# extend extra rows into columns, as all the prices during lookback periodd should be used as features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10202, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_shape(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# fit model\n",
+    "forest = RandomForestRegressor(n_estimators = 100)\n",
+    "forest = forest.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature ranking:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>close_bid</th>\n",
+       "      <th>avg_price</th>\n",
+       "      <th>ohlc_price</th>\n",
+       "      <th>high_bid</th>\n",
+       "      <th>low_bid</th>\n",
+       "      <th>open_bid</th>\n",
+       "      <th>last_10_tick_avg_bo_spread</th>\n",
+       "      <th>last_10_tick_avg_bid_return</th>\n",
+       "      <th>range</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>nb_ticks</th>\n",
+       "      <th>pca</th>\n",
+       "      <th>oc_diff</th>\n",
+       "      <th>period_return</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>15_min</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.935566</td>\n",
+       "      <td>0.02643</td>\n",
+       "      <td>0.019724</td>\n",
+       "      <td>0.01465</td>\n",
+       "      <td>0.002517</td>\n",
+       "      <td>0.000504</td>\n",
+       "      <td>0.000092</td>\n",
+       "      <td>0.000088</td>\n",
+       "      <td>0.000063</td>\n",
+       "      <td>0.000058</td>\n",
+       "      <td>0.000054</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.00004</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.000038</td>\n",
+       "      <td>0.000038</td>\n",
+       "      <td>0.00002</td>\n",
+       "      <td>0.000018</td>\n",
+       "      <td>0.000008</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   close_bid  avg_price  ohlc_price  high_bid   low_bid  open_bid  \\\n",
+       "0   0.935566    0.02643    0.019724   0.01465  0.002517  0.000504   \n",
+       "\n",
+       "   last_10_tick_avg_bo_spread  last_10_tick_avg_bid_return     range  \\\n",
+       "0                    0.000092                     0.000088  0.000063   \n",
+       "\n",
+       "   avg_bo_spread  nb_ticks       pca  oc_diff  period_return       day  \\\n",
+       "0       0.000058  0.000054  0.000052  0.00004       0.000039  0.000038   \n",
+       "\n",
+       "       hour  weekday    15_min     month  \n",
+       "0  0.000038  0.00002  0.000018  0.000008  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find feature with best explanatory power to predict close price\n",
+    "importances = forest.feature_importances_\n",
+    "std = np.std([forest.feature_importances_ for forest in forest.estimators_], axis=0)\n",
+    "indices = np.argsort(importances)[::-1] # get indices for importances\n",
+    "#print(indices)\n",
+    "\n",
+    "column_list = df.columns.tolist()\n",
+    "#print(column_list)\n",
+    "print(\"Feature ranking:\")\n",
+    "feature_dict = OrderedDict()\n",
+    "for f in range(X.shape[1]-1):\n",
+    "    #print(\"%d. %s %d (%f)\" % (f, column_list[indices[f]], indices[f], importances[indices[f]]))\n",
+    "    feature_dict[column_list[indices[f]]] = importances[indices[f]]\n",
+    "display(pd.DataFrame([feature_dict]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Try linear regression\n",
+    "- sklearn requires numpy arrays as input\n",
+    "- check how close we can get with linear regression\n",
+    "- resources: http://bigdata-madesimple.com/how-to-run-linear-regression-in-python-scikit-learn/\n",
+    "- problem: my features are note independent of each other, eg ohlc price, open bid, close bid etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q6rwhk7e7j5IHpJYJlOx/JmQCAAAAGi+CJgBoZCrrwzR0w93ab9kvyVbl/WOu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\nAPRIZjAAoFzKGjS1tLSkrq6ua7u6ujodHR2pqanJhg0b8thjj6W5uTk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AA9K/f/9UV1fn\noIMOyvPPP99dpRLn+u5kBqs85q/KZAarPOavymQG27/s6bn+lWujKtDYsWOzZMmSTJgwIaVSKdOm\nTcv8+fPT1taWpqamTJ06Neeff35KpVIaGxtz6KGHdnfJvcLO+nLsscdm7ty5Of744/PRj340yUsn\n2bFjx3Zz1T3brn5W6B676ssVV1yRKVOmpFQq5bjjjstJJ53U3SX3eLvqSVNTUyZOnJi+ffumoaEh\n48eP7+6SeyXn+u5nBqs85q/KZAarPOavymQG2z/s7bm+qlQqlcpQJwAAAAA93H5x6RwAAAAAlU/Q\nBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFOL/B/kpfyDRnEyXAAAA\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cf3a5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot all against close_bid\n",
+    "fig, axarr = plt.subplots(2, 2, figsize=(20,10)) #1 row, 2 cols, x, y\n",
+    "#plt.figure(figsize=(20, 4))\n",
+    "irow, icol = 0,0\n",
+    "icol = pltGraph(\"ohlc_price\", \"close_bid\", irow, icol, df)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- we may have to address feature correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10202, 20)\n",
+      "(10202,)\n",
+      "(10099, 20)\n",
+      "(103, 20)\n",
+      "(10099,)\n",
+      "(103,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score\n",
+    "\n",
+    "# df only\n",
+    "X = df.drop(\"close_bid\", axis=1)\n",
+    "y = df.close_bid.shift().values\n",
+    "cols = X.columns\n",
+    "\n",
+    "\n",
+    "#convert to numpy first\n",
+    "df_np = df.copy().values.astype('float32')\n",
+    "X, y = create_training_set(df_np, 1)\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[2]))\n",
+    "idx_close_bid = df.columns.tolist().index('close_bid') # find index of columns in dataframe\n",
+    "y = y[:,idx_close_bid] # select column to predict\n",
+    "cols = df.columns # i have all here, because close_bid is included as features\n",
+    "\n",
+    "\n",
+    "# create train and test\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, shuffle=False)\n",
+    "check_shape(X, y, X_train, X_test, y_train, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mV3Cgn354e7DiIu9SOz/fpiwZwE2oycI+MzNTZWVliomJadDrEhMTNX/+fC1Z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\nwh4AAMP9fzJ0kNjjsjViAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a365080f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>oc_diff</th>\n",
+       "      <th>low_bid</th>\n",
+       "      <th>last_10_tick_avg_bo_spread</th>\n",
+       "      <th>avg_price</th>\n",
+       "      <th>last_10_tick_avg_bid_return</th>\n",
+       "      <th>nb_ticks</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>year</th>\n",
+       "      <th>day</th>\n",
+       "      <th>15_min</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>month</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>pca</th>\n",
+       "      <th>high_bid</th>\n",
+       "      <th>range</th>\n",
+       "      <th>period_return</th>\n",
+       "      <th>ohlc_price</th>\n",
+       "      <th>open_bid</th>\n",
+       "      <th>close_bid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>linear regression coefficients</th>\n",
+       "      <td>-0.203845</td>\n",
+       "      <td>-0.01149</td>\n",
+       "      <td>-0.002238</td>\n",
+       "      <td>-0.002219</td>\n",
+       "      <td>-0.001859</td>\n",
+       "      <td>-0.001265</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.480864e-07</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>0.000603</td>\n",
+       "      <td>0.001265</td>\n",
+       "      <td>0.007099</td>\n",
+       "      <td>0.018637</td>\n",
+       "      <td>0.183853</td>\n",
+       "      <td>0.200127</td>\n",
+       "      <td>0.300594</td>\n",
+       "      <td>0.504454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lasso regression coefficients</th>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000003</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000000e+00</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 oc_diff  low_bid  last_10_tick_avg_bo_spread  \\\n",
+       "linear regression coefficients -0.203845 -0.01149                   -0.002238   \n",
+       "lasso regression coefficients  -0.000000  0.00000                   -0.000000   \n",
+       "\n",
+       "                                avg_price  last_10_tick_avg_bid_return  \\\n",
+       "linear regression coefficients  -0.002219                    -0.001859   \n",
+       "lasso regression coefficients    0.000000                    -0.000000   \n",
+       "\n",
+       "                                nb_ticks   weekday  year           day  \\\n",
+       "linear regression coefficients -0.001265 -0.000002   0.0  9.480864e-07   \n",
+       "lasso regression coefficients  -0.000003  0.000000   0.0 -0.000000e+00   \n",
+       "\n",
+       "                                  15_min      hour     month  avg_bo_spread  \\\n",
+       "linear regression coefficients  0.000002  0.000007  0.000017       0.000603   \n",
+       "lasso regression coefficients  -0.000000 -0.000000  0.000000      -0.000000   \n",
+       "\n",
+       "                                     pca  high_bid     range  period_return  \\\n",
+       "linear regression coefficients  0.001265  0.007099  0.018637       0.183853   \n",
+       "lasso regression coefficients  -0.000000  0.000000 -0.000000       0.000000   \n",
+       "\n",
+       "                                ohlc_price  open_bid  close_bid  \n",
+       "linear regression coefficients    0.200127  0.300594   0.504454  \n",
+       "lasso regression coefficients     0.000000  0.000000   0.000000  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_lasso = linear_model.Lasso(alpha = 0.1) # Lasso takes care of regularisation\n",
+    "reg_linear = linear_model.LinearRegression()\n",
+    "\n",
+    "reg_linear.fit(X_train, y_train)\n",
+    "reg_lasso.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "df_coeff = pd.DataFrame(columns=cols\n",
+    "                     , data=[list(reg_linear.coef_), list(reg_lasso.coef_)]\n",
+    "                     , index=[\"linear regression coefficients\", \"lasso regression coefficients\"])\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# predict all examples and compare to actuals\n",
+    "plt.scatter(reg_linear.predict(X_test), y_test)\n",
+    "plt.xlabel(\"predicted\")\n",
+    "plt.ylabel(\"actual\")\n",
+    "plt.title(\"predicted v actual close_bid\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "df_coeff.sort_values(by='linear regression coefficients', axis=1)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- what mae is acceptible as a result? If i invest based on my prediction, and it goes the other way i lose money. \n",
+    "- Therefore, check the directional error, not MAE. Compare close with next close. If my prediction - actual next close has the same sign and value of that measure, count as accurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check regression errors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_error_metrics(df, y_train, y_test, X_train_pred, X_test_pred, y_prev):\n",
+    "\n",
+    "    #compute direction of next step\n",
+    "#     if len(X_test.shape) >2:\n",
+    "#         y_prev = X_test[:,0,idx_close_bid]\n",
+    "#     else:\n",
+    "#         y_prev = X_test[:,idx_close_bid]\n",
+    "    err_list = []\n",
+    "    \n",
+    "    pred_directions = X_test_pred - y_prev\n",
+    "    act_directions = y_test - y_prev\n",
+    "    pred_returns = X_test_pred / y_prev-1\n",
+    "    act_returns =  y_test / y_prev -1\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    sign_error = np.sign(pred_directions) != np.sign(act_directions)\n",
+    "    actual_minus_pred = act_directions - pred_directions\n",
+    "    abs_actual_minus_prod = abs(act_directions) - abs(pred_directions)\n",
+    "    return_vals = act_returns - pred_returns\n",
+    "\n",
+    "    # how often do you make a negative 1 percent return when a positive return was predicted\n",
+    "\n",
+    "    err_list= [\n",
+    "        [\"mse train all feature: \", mean_squared_error(y_train, X_train_pred)]\n",
+    "        ,[\"mse test all feature: \", mean_squared_error(y_test, X_test_pred)]\n",
+    "        ,[\"mae train all feature: \", mean_absolute_error(y_train, X_train_pred)]\n",
+    "        ,[\"mae test all feature: \", mean_absolute_error(y_test, X_test_pred)]\n",
+    "        ,[\"mean avg bo spread: \", df.avg_bo_spread.mean()]\n",
+    "        \n",
+    "\n",
+    "        ,[\"how often sign of price change is same: \", (sign_error==False).sum() / len(sign_error)]\n",
+    "\n",
+    "    # if correct sign, how often larger than actual value, smaller than actual value\n",
+    "    # generally good profit\n",
+    "        ,[\"if same sign, how often is actual better than 0.1 percent in both directions: \"\n",
+    "          , (abs(act_directions[~sign_error]) > 0.001).sum() / len(act_directions[~sign_error])]\n",
+    "\n",
+    "    # positive surprise\n",
+    "        ,[\"if same sign, how often is actual better than predicted in both directions: \"\n",
+    "          , (abs_actual_minus_prod[~sign_error] > 0).sum() / len(abs_actual_minus_prod[~sign_error])]\n",
+    "\n",
+    "    # positive suprise of least 10 bp\n",
+    "        ,[\"if same sign, how often is actual better than predicted by more than 0.001 USD per EUR in both directions: \", \n",
+    "          (abs_actual_minus_prod[~sign_error] > 0.001).sum() / len(abs_actual_minus_prod[~sign_error])]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "        ,[\"if not same sign, how often is actual worse than -0.1 percent return from predicted in both directions\",\n",
+    "         (abs(return_vals[sign_error]) > 0.001).sum() / len(return_vals[sign_error])]\n",
+    "         \n",
+    "        ,[\"if not same sign, how often is actual worse than -0.1 percent return in both directions\",\n",
+    "         (abs(act_returns[sign_error]) > 0.001).sum() / len(act_returns[sign_error])]\n",
+    "    ]   \n",
+    "    # show histogram of returns if sign error\n",
+    "    plt.hist(act_returns[sign_error], bins=20)\n",
+    "    plt.title(\"returns if pred and act sign mismatches\")\n",
+    "    plt.show()\n",
+    "    \n",
+    "    df_err = pd.DataFrame(err_list)\n",
+    "    \n",
+    "    for el in err_list:\n",
+    "        print(el)\n",
+    "    \n",
+    "    \n",
+    "    return df_err\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KtmzZUjt37pQkbd26Vffff7/nKwMAoAa64fgmJiZq4cKFio2NVUFBgXr16lUV6wIAwGuV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\nAGAZ8QUAwLL/B36M6wikk3reAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cff9fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['mse train all feature: ', 4.4521161e-07]\n",
+      "['mse test all feature: ', 6.5240918e-08]\n",
+      "['mae train all feature: ', 0.00042638468]\n",
+      "['mae test all feature: ', 0.00020227849]\n",
+      "['mean avg bo spread: ', 3.9868658581951545e-05]\n",
+      "['how often sign of price change is same: ', 0.55339805825242716]\n",
+      "['if same sign, how often is actual better than 0.1 percent in both directions: ', 0.0]\n",
+      "['if same sign, how often is actual better than predicted in both directions: ', 0.98245614035087714]\n",
+      "['if same sign, how often is actual better than predicted by more than 0.001 USD per EUR in both directions: ', 0.0]\n",
+      "['if not same sign, how often is actual worse than -0.1 percent return from predicted in both directions', 0.0]\n",
+      "['if not same sign, how often is actual worse than -0.1 percent return in both directions', 0.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_err = check_error_metrics(df, y_train, y_test, reg_linear.predict(X_train), reg_linear.predict(X_test), X_test[:,idx_close_bid])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# log results\n",
+    "log=True\n",
+    "\n",
+    "if log:\n",
+    "    \n",
+    "    simname= \"linear regression\"\n",
+    "    sim_desc = \"1 row lookback\"\n",
+    "    \n",
+    "    dict_err= OrderedDict(zip(df_err[0], df_err[1]))\n",
+    "    \n",
+    "    list_stats=OrderedDict()\n",
+    "    \n",
+    "    list_stats[\"simname\"] = simname\n",
+    "    list_stats[\"sim_desc\"] = sim_desc\n",
+    "    list_stats[\"MSE\"] = dict_err[\"mse test all feature: \"]\n",
+    "    list_stats[\"MAE\"] = dict_err[\"mae test all feature: \"]\n",
+    "    \n",
+    "    differences_described = pd.Series(reg_linear.predict(X_test) - y_test).describe()\n",
+    "\n",
+    "    list_stats.update(OrderedDict(differences_described))\n",
+    "    list_stats.update(dict_err)\n",
+    "    \n",
+    "    results = pd.DataFrame([list_stats])\n",
+    "    #results.to_excel(\"log_results.xlsx\")\n",
+    "    if os.path.isfile(\"log_results.xlsx\"):\n",
+    "        log_results = pd.read_excel(\"log_results.xlsx\")\n",
+    "        log_results.loc[len(log_results),:] = list_stats.values()\n",
+    "        log_results.to_excel(\"log_results.xlsx\")\n",
+    "    #log_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>simname</th>\n",
+       "      <td>500_epochs</td>\n",
+       "      <td>500_epoch_lookback_40</td>\n",
+       "      <td>linear regression</td>\n",
+       "      <td>linear regression</td>\n",
+       "      <td>500_epochs_40_lookback_pca_unshuffled</td>\n",
+       "      <td>linear regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sim_desc</th>\n",
+       "      <td>\\nkaggle params but with 500 epochs to account...</td>\n",
+       "      <td>500 iterations, lookback 40</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "      <td>added directional errors checking and pca as f...</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSE</th>\n",
+       "      <td>1.59188e-07</td>\n",
+       "      <td>1.97246e-07</td>\n",
+       "      <td>6.79626e-07</td>\n",
+       "      <td>6.55241e-08</td>\n",
+       "      <td>4.82937e-07</td>\n",
+       "      <td>6.52409e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MAE</th>\n",
+       "      <td>0.000285781</td>\n",
+       "      <td>0.000340846</td>\n",
+       "      <td>0.000536307</td>\n",
+       "      <td>0.000202905</td>\n",
+       "      <td>0.000594482</td>\n",
+       "      <td>0.000202278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>102</td>\n",
+       "      <td>102</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>102</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-9.29131e-07</td>\n",
+       "      <td>-3.83515e-05</td>\n",
+       "      <td>-5.08408e-05</td>\n",
+       "      <td>-2.90581e-05</td>\n",
+       "      <td>0.000506372</td>\n",
+       "      <td>-2.31451e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.000400953</td>\n",
+       "      <td>0.00044465</td>\n",
+       "      <td>0.000826849</td>\n",
+       "      <td>0.000255566</td>\n",
+       "      <td>0.000478296</td>\n",
+       "      <td>0.000255616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-0.00135148</td>\n",
+       "      <td>-0.00127888</td>\n",
+       "      <td>-0.00317997</td>\n",
+       "      <td>-0.000772953</td>\n",
+       "      <td>-0.00072515</td>\n",
+       "      <td>-0.000772119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-0.000158489</td>\n",
+       "      <td>-0.000304043</td>\n",
+       "      <td>-0.000402606</td>\n",
+       "      <td>-0.000200629</td>\n",
+       "      <td>0.000192821</td>\n",
+       "      <td>-0.000199616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>6.07371e-05</td>\n",
+       "      <td>-5.84126e-06</td>\n",
+       "      <td>-3.07747e-05</td>\n",
+       "      <td>-2.43187e-05</td>\n",
+       "      <td>0.000540495</td>\n",
+       "      <td>-1.14441e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.000239968</td>\n",
+       "      <td>0.00017789</td>\n",
+       "      <td>0.00031692</td>\n",
+       "      <td>0.000116229</td>\n",
+       "      <td>0.000830978</td>\n",
+       "      <td>0.000123918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.000755668</td>\n",
+       "      <td>0.00128782</td>\n",
+       "      <td>0.00327372</td>\n",
+       "      <td>0.0005337</td>\n",
+       "      <td>0.00159335</td>\n",
+       "      <td>0.000535846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mse train all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3917e-07</td>\n",
+       "      <td>4.45245e-07</td>\n",
+       "      <td>5.16241e-07</td>\n",
+       "      <td>4.45212e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mse test all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.79626e-07</td>\n",
+       "      <td>6.55241e-08</td>\n",
+       "      <td>4.82937e-07</td>\n",
+       "      <td>6.52409e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mae train all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000423565</td>\n",
+       "      <td>0.000426773</td>\n",
+       "      <td>0.000505385</td>\n",
+       "      <td>0.000426385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mae test all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000536307</td>\n",
+       "      <td>0.000202905</td>\n",
+       "      <td>0.000594482</td>\n",
+       "      <td>0.000202278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean avg bo spread:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>how often sign of price change is same:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.446602</td>\n",
+       "      <td>0.533981</td>\n",
+       "      <td>0.882353</td>\n",
+       "      <td>0.553398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than 0.1 percent in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.655556</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than predicted in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.978261</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.366667</td>\n",
+       "      <td>0.982456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than predicted by more than 0.001 USD per EUR in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0666667</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if not same sign, how often is actual worse than -0.1 percent return from predicted in both directions</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if not same sign, how often is actual worse than -0.1 percent return in both directions</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                    0  \\\n",
+       "simname                                                                                    500_epochs   \n",
+       "sim_desc                                            \\nkaggle params but with 500 epochs to account...   \n",
+       "MSE                                                                                       1.59188e-07   \n",
+       "MAE                                                                                       0.000285781   \n",
+       "count                                                                                             102   \n",
+       "mean                                                                                     -9.29131e-07   \n",
+       "std                                                                                       0.000400953   \n",
+       "min                                                                                       -0.00135148   \n",
+       "25%                                                                                      -0.000158489   \n",
+       "50%                                                                                       6.07371e-05   \n",
+       "75%                                                                                       0.000239968   \n",
+       "max                                                                                       0.000755668   \n",
+       "mse train all feature:                                                                              0   \n",
+       "mse test all feature:                                                                               0   \n",
+       "mae train all feature:                                                                              0   \n",
+       "mae test all feature:                                                                               0   \n",
+       "mean avg bo spread:                                                                                 0   \n",
+       "how often sign of price change is same:                                                             0   \n",
+       "if same sign, how often is actual better than 0...                                                  0   \n",
+       "if same sign, how often is actual better than p...                                                  0   \n",
+       "if same sign, how often is actual better than p...                                                  0   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "\n",
+       "                                                                              1  \\\n",
+       "simname                                                   500_epoch_lookback_40   \n",
+       "sim_desc                                            500 iterations, lookback 40   \n",
+       "MSE                                                                 1.97246e-07   \n",
+       "MAE                                                                 0.000340846   \n",
+       "count                                                                       102   \n",
+       "mean                                                               -3.83515e-05   \n",
+       "std                                                                  0.00044465   \n",
+       "min                                                                 -0.00127888   \n",
+       "25%                                                                -0.000304043   \n",
+       "50%                                                                -5.84126e-06   \n",
+       "75%                                                                  0.00017789   \n",
+       "max                                                                  0.00128782   \n",
+       "mse train all feature:                                                        0   \n",
+       "mse test all feature:                                                         0   \n",
+       "mae train all feature:                                                        0   \n",
+       "mae test all feature:                                                         0   \n",
+       "mean avg bo spread:                                                           0   \n",
+       "how often sign of price change is same:                                       0   \n",
+       "if same sign, how often is actual better than 0...                            0   \n",
+       "if same sign, how often is actual better than p...                            0   \n",
+       "if same sign, how often is actual better than p...                            0   \n",
+       "if not same sign, how often is actual worse tha...                            0   \n",
+       "if not same sign, how often is actual worse tha...                            0   \n",
+       "\n",
+       "                                                                    2  \\\n",
+       "simname                                             linear regression   \n",
+       "sim_desc                                               1 row lookback   \n",
+       "MSE                                                       6.79626e-07   \n",
+       "MAE                                                       0.000536307   \n",
+       "count                                                             103   \n",
+       "mean                                                     -5.08408e-05   \n",
+       "std                                                       0.000826849   \n",
+       "min                                                       -0.00317997   \n",
+       "25%                                                      -0.000402606   \n",
+       "50%                                                      -3.07747e-05   \n",
+       "75%                                                        0.00031692   \n",
+       "max                                                        0.00327372   \n",
+       "mse train all feature:                                     4.3917e-07   \n",
+       "mse test all feature:                                     6.79626e-07   \n",
+       "mae train all feature:                                    0.000423565   \n",
+       "mae test all feature:                                     0.000536307   \n",
+       "mean avg bo spread:                                       3.98687e-05   \n",
+       "how often sign of price change is same:                      0.446602   \n",
+       "if same sign, how often is actual better than 0...           0.152174   \n",
+       "if same sign, how often is actual better than p...           0.978261   \n",
+       "if same sign, how often is actual better than p...           0.152174   \n",
+       "if not same sign, how often is actual worse tha...           0.105263   \n",
+       "if not same sign, how often is actual worse tha...           0.105263   \n",
+       "\n",
+       "                                                                    3  \\\n",
+       "simname                                             linear regression   \n",
+       "sim_desc                                               1 row lookback   \n",
+       "MSE                                                       6.55241e-08   \n",
+       "MAE                                                       0.000202905   \n",
+       "count                                                             103   \n",
+       "mean                                                     -2.90581e-05   \n",
+       "std                                                       0.000255566   \n",
+       "min                                                      -0.000772953   \n",
+       "25%                                                      -0.000200629   \n",
+       "50%                                                      -2.43187e-05   \n",
+       "75%                                                       0.000116229   \n",
+       "max                                                         0.0005337   \n",
+       "mse train all feature:                                    4.45245e-07   \n",
+       "mse test all feature:                                     6.55241e-08   \n",
+       "mae train all feature:                                    0.000426773   \n",
+       "mae test all feature:                                     0.000202905   \n",
+       "mean avg bo spread:                                       3.98687e-05   \n",
+       "how often sign of price change is same:                      0.533981   \n",
+       "if same sign, how often is actual better than 0...                  0   \n",
+       "if same sign, how often is actual better than p...                  1   \n",
+       "if same sign, how often is actual better than p...                  0   \n",
+       "if not same sign, how often is actual worse tha...                  0   \n",
+       "if not same sign, how often is actual worse tha...                  0   \n",
+       "\n",
+       "                                                                                                    4  \\\n",
+       "simname                                                         500_epochs_40_lookback_pca_unshuffled   \n",
+       "sim_desc                                            added directional errors checking and pca as f...   \n",
+       "MSE                                                                                       4.82937e-07   \n",
+       "MAE                                                                                       0.000594482   \n",
+       "count                                                                                             102   \n",
+       "mean                                                                                      0.000506372   \n",
+       "std                                                                                       0.000478296   \n",
+       "min                                                                                       -0.00072515   \n",
+       "25%                                                                                       0.000192821   \n",
+       "50%                                                                                       0.000540495   \n",
+       "75%                                                                                       0.000830978   \n",
+       "max                                                                                        0.00159335   \n",
+       "mse train all feature:                                                                    5.16241e-07   \n",
+       "mse test all feature:                                                                     4.82937e-07   \n",
+       "mae train all feature:                                                                    0.000505385   \n",
+       "mae test all feature:                                                                     0.000594482   \n",
+       "mean avg bo spread:                                                                       3.98687e-05   \n",
+       "how often sign of price change is same:                                                      0.882353   \n",
+       "if same sign, how often is actual better than 0...                                           0.655556   \n",
+       "if same sign, how often is actual better than p...                                           0.366667   \n",
+       "if same sign, how often is actual better than p...                                          0.0666667   \n",
+       "if not same sign, how often is actual worse tha...                                           0.333333   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "\n",
+       "                                                                    5  \n",
+       "simname                                             linear regression  \n",
+       "sim_desc                                               1 row lookback  \n",
+       "MSE                                                       6.52409e-08  \n",
+       "MAE                                                       0.000202278  \n",
+       "count                                                             103  \n",
+       "mean                                                     -2.31451e-05  \n",
+       "std                                                       0.000255616  \n",
+       "min                                                      -0.000772119  \n",
+       "25%                                                      -0.000199616  \n",
+       "50%                                                      -1.14441e-05  \n",
+       "75%                                                       0.000123918  \n",
+       "max                                                       0.000535846  \n",
+       "mse train all feature:                                    4.45212e-07  \n",
+       "mse test all feature:                                     6.52409e-08  \n",
+       "mae train all feature:                                    0.000426385  \n",
+       "mae test all feature:                                     0.000202278  \n",
+       "mean avg bo spread:                                       3.98687e-05  \n",
+       "how often sign of price change is same:                      0.553398  \n",
+       "if same sign, how often is actual better than 0...                  0  \n",
+       "if same sign, how often is actual better than p...           0.982456  \n",
+       "if same sign, how often is actual better than p...                  0  \n",
+       "if not same sign, how often is actual worse tha...                  0  \n",
+       "if not same sign, how often is actual worse tha...                  0  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_excel(\"log_results.xlsx\").T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JlKXlHMOWFhcguXx5dqBYXZ30mc9YNTRkvm6z7XPm1J+uXZK69uNs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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cd37e48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,10))\n",
+    "plt.scatter(reg_linear.predict(X_train), reg_linear.predict(X_train) - y_train, c='b', s=40, alpha=0.5)\n",
+    "plt.scatter(reg_linear.predict(X_test), reg_linear.predict(X_test) - y_test, c='g', s=40)\n",
+    "plt.hlines(y=0, xmin=1.07, xmax = 1.17)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pred: 1.110524, actual: 1.110180\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"pred: %f, actual: %f\" % (reg_linear.predict(X_test[0,:].reshape(1,-1)), y_test[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Try ridge regression, to be more robust to correlation in features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "hideCode": true,
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
+      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
+      "Reciprocal condition number/precision: 6.576732214380598e-11 / 5.960464477539063e-08\n",
+      "  RuntimeWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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q/OzQ0Bby82u4/9EMDw+u7xLqBH1cnvvuaavVn31zWdv4+kgT47urZfNmNa7L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\ndQYEqmhTAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cc854a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mse train all feature:  9.51813e-07\n",
+      "mse test all feature:  1.49011e-06\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LQ/CFZW2uyqp8Hijm8+DNePhOIZUVC60InO0756oYz3WDXcyKyVzf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\nqJEIfYJPf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OKLeOWVV7Bz50709vbiz/7sz/CDH/wADz30EG655ZaK32ltbcX27dvxjW98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\n+tKXEI/HAQDxeBxNTU0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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cf90390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reg = linear_model.Ridge (alpha = .5)\n",
+    "reg.fit(X_train, y_train) \n",
+    "reg.coef_\n",
+    "\n",
+    "\n",
+    "df_coeff = pd.DataFrame(columns=cols\n",
+    "                     , data=[list(reg.coef_)]\n",
+    "                     , index=[\"ridge regression coefficients\"])\n",
+    "\n",
+    "\n",
+    "\n",
+    "# predict all examples and compare to actuals\n",
+    "plt.scatter(reg.predict(X_test), y_test)\n",
+    "plt.xlabel(\"predicted\")\n",
+    "plt.ylabel(\"actual\")\n",
+    "plt.title(\"predicted v actual close_bid\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "df_coeff.sort_values(by='ridge regression coefficients', axis=1)\n",
+    "\n",
+    "print(\"mse train all feature: \", np.mean((reg.predict(X_train) - y_train) ** 2))\n",
+    "print(\"mse test all feature: \", np.mean((reg.predict(X_test) - y_test) ** 2))\n",
+    "\n",
+    "plt.figure(figsize=(30,10))\n",
+    "plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train, c='b', s=40, alpha=0.5)\n",
+    "plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, c='g', s=40)\n",
+    "plt.hlines(y=0, xmin=1.07, xmax = 1.17)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run LSTM Model to predict close bid in next 15 min\n",
+    "- scale features to range 0-1 to speed up convergence\n",
+    "- set a larger lookback windows, so LSTM has something to work with and can take a decision which part of history to prioritise\n",
+    "- todo: make it do error on the sign- for that need to get the sign between next and X[bid]\n",
+    "- 1% test size, 10% of 99% validation size\n",
+    "- try to also predict direction. y_pred - X_test close versus y_act - X_test close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict sign only"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if runLSTMBinary:\n",
+    "    # Scale and create datasets\n",
+    "    idx_close_bid = df.columns.tolist().index('close_bid')\n",
+    "    df_np = df.values.astype('float32')\n",
+    "\n",
+    "    # Scale the examples\n",
+    "    df_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    df_scaled = df_scaler.fit_transform(df_np)\n",
+    "\n",
+    "    # Scale the actuals columns, but not the real values\n",
+    "    y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    t_y = df['close_bid'].values.astype('float32')\n",
+    "    t_y = np.reshape(t_y, (-1, 1))\n",
+    "    y_scaler = y_scaler.fit(t_y) # create a fitted y scaler\n",
+    "\n",
+    "    # Set look_back to 20 which is 5 hours (15min*20)\n",
+    "    X, y_orig = create_training_set(df_scaled, nb_lookback_rows=40)\n",
+    "    #y_return_sign = np.sign(y[:,idx_close_bid] - X[:,0,idx_close_bid]) # these are the actuals\n",
+    "    y_return_sign = y_orig[:,idx_close_bid] - X[:,0,idx_close_bid] # these are the actuals\n",
+    "    y = np.sign(y_return_sign) # an array of -1, 1 and 0\n",
+    "\n",
+    "    y = pd.get_dummies(y).values.astype('float32')\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # need to create a binarised vector, one positive class, one negative class\n",
+    "    #y_pred_return_sign = # comes from model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict exact price value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if not runLSTMBinary:\n",
+    "    # Scale and create datasets\n",
+    "    idx_close_bid = df.columns.tolist().index('close_bid')\n",
+    "    #idx_high = df.columns.tolist().index('high_bid')\n",
+    "    #idx_low = df.columns.tolist().index('low_bid')\n",
+    "    df_np = df.values.astype('float32')\n",
+    "\n",
+    "    # Scale the examples\n",
+    "    df_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    df_scaled = df_scaler.fit_transform(df_np)\n",
+    "\n",
+    "    # Scale the actuals columns, but not the real values\n",
+    "    y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    t_y = df['close_bid'].values.astype('float32')\n",
+    "    t_y = np.reshape(t_y, (-1, 1))\n",
+    "    y_scaler = y_scaler.fit(t_y) # create a fitted y scaler\n",
+    "\n",
+    "    # Set look_back to 20 which is 5 hours (15min*20)\n",
+    "    X, y = create_training_set(df_scaled, nb_lookback_rows=40)\n",
+    "    y = y[:,idx_close_bid]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10163, 40, 20)\n",
+      "(10163,)\n",
+      "(10061, 40, 20)\n",
+      "(102, 40, 20)\n",
+      "(10061,)\n",
+      "(102,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set training data size\n",
+    "# We have a large enough dataset. So divid into 99% training and val (10% of those 99%) / 1% test set\n",
+    "import sklearn\n",
+    "sklearn.__version__\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.01, shuffle=False)       \n",
+    "check_shape(X, y, X_train, X_test, y_train, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.backend import categorical_crossentropy\n",
+    "# ensure there is a 1st derivative! else get none error\n",
+    "# it will check in which direction this error goesn down and walk there\n",
+    "# maybe it has issue because sign is not continuous...\n",
+    "# just use categorical crossentropy, it does exactly what i need much better\n",
+    "def ret_direction_error(y_true, y_pred):\n",
+    "    \n",
+    "    # this guy puts everything into numpy before working on it https://stackoverflow.com/questions/46411573/keras-custom-loss-function-not-working\n",
+    "     \n",
+    "    out = categorical_crossentropy(y_true, y_pred)\n",
+    "    \n",
+    "    return out\n",
+    "    \n",
+    "    # y_true is y_train, y_pred is what the model gives me\n",
+    "    # so should set y_true to the return sign? and stop training on absolute value, but make sure sign is right"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "lstm_6 (LSTM)                (None, 40, 40)            9760      \n",
+      "_________________________________________________________________\n",
+      "lstm_7 (LSTM)                (None, 40, 20)            4880      \n",
+      "_________________________________________________________________\n",
+      "lstm_8 (LSTM)                (None, 40, 10)            1240      \n",
+      "_________________________________________________________________\n",
+      "lstm_9 (LSTM)                (None, 40, 10)            840       \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 40, 10)            0         \n",
+      "_________________________________________________________________\n",
+      "lstm_10 (LSTM)               (None, 5)                 320       \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 5)                 30        \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 1)                 6         \n",
+      "=================================================================\n",
+      "Total params: 17,076\n",
+      "Trainable params: 17,076\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Input, LSTM, Dense\n",
+    "import keras.backend as K\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# create a small LSTM network\n",
+    "# shoudl first input number match nb of lookback rows?\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(40, input_shape=(X.shape[1], X.shape[2]), return_sequences=True)) # does not take into account nb examples\n",
+    "model.add(LSTM(20, return_sequences=True))\n",
+    "model.add(LSTM(10, return_sequences=True))\n",
+    "model.add(LSTM(10, return_sequences=True)) # a second layer of 10 really helps get the loos to 7 by 10th epoch\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(LSTM(5, return_sequences=False))\n",
+    "model.add(Dense(5, kernel_initializer='uniform', activation='relu'))\n",
+    "\n",
+    "# for price prediction\n",
+    "if not runLSTMBinary:\n",
+    "    model.add(Dense(1, kernel_initializer='uniform', activation='relu')) # this compresses everything to one output in the final layer\n",
+    "    #model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mae', 'mse', 'accuracy'])\n",
+    "    model.compile(loss='mse', optimizer='adam', metrics=['mae', 'mse', 'accuracy'])\n",
+    "    \n",
+    "    \n",
+    "# for direction prediction\n",
+    "if runLSTMBinary:\n",
+    "    # need a softmax output for category predictions\n",
+    "    model.add(Dense(3, activation=\"softmax\"))   \n",
+    "\n",
+    "    # loss: optimises this - https://keras.io/losses/\n",
+    "    # loss will show up in the history under 'loss'\n",
+    "    model.compile(loss=ret_direction_error, optimizer='adam', metrics=['mae', 'mse', ret_direction_error, 'accuracy'])\n",
+    "    \n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simname = \"500_epochs_40_lookback_pca_unshuffled_binary\"\n",
+    "sim_desc = \"added directional errors checking and pca as feature with unshuffled data\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error improved from inf to 0.06875, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00001: val_mean_squared_error improved from 0.06875 to 0.01363, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00002: val_mean_squared_error improved from 0.01363 to 0.01334, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00003: val_mean_squared_error improved from 0.01334 to 0.01233, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00004: val_mean_squared_error did not improve\n",
+      "Epoch 00005: val_mean_squared_error did not improve\n",
+      "Epoch 00006: val_mean_squared_error improved from 0.01233 to 0.01223, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00007: val_mean_squared_error improved from 0.01223 to 0.00177, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00008: val_mean_squared_error improved from 0.00177 to 0.00150, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00009: val_mean_squared_error improved from 0.00150 to 0.00090, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00010: val_mean_squared_error improved from 0.00090 to 0.00043, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00011: val_mean_squared_error did not improve\n",
+      "Epoch 00012: val_mean_squared_error did not improve\n",
+      "Epoch 00013: val_mean_squared_error did not improve\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error improved from 0.00043 to 0.00028, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00016: val_mean_squared_error did not improve\n",
+      "Epoch 00017: val_mean_squared_error did not improve\n",
+      "Epoch 00018: val_mean_squared_error improved from 0.00028 to 0.00018, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00019: val_mean_squared_error improved from 0.00018 to 0.00017, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error did not improve\n",
+      "Epoch 00022: val_mean_squared_error did not improve\n",
+      "Epoch 00023: val_mean_squared_error improved from 0.00017 to 0.00012, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00024: val_mean_squared_error did not improve\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "Epoch 00026: val_mean_squared_error did not improve\n",
+      "Epoch 00027: val_mean_squared_error did not improve\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error did not improve\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error did not improve\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n",
+      "Epoch 00033: val_mean_squared_error improved from 0.00012 to 0.00011, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00034: val_mean_squared_error did not improve\n",
+      "Epoch 00035: val_mean_squared_error did not improve\n",
+      "Epoch 00036: val_mean_squared_error did not improve\n",
+      "Epoch 00037: val_mean_squared_error improved from 0.00011 to 0.00008, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00038: val_mean_squared_error did not improve\n",
+      "Epoch 00039: val_mean_squared_error did not improve\n",
+      "Epoch 00040: val_mean_squared_error did not improve\n",
+      "Epoch 00041: val_mean_squared_error did not improve\n",
+      "Epoch 00042: val_mean_squared_error improved from 0.00008 to 0.00008, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00043: val_mean_squared_error improved from 0.00008 to 0.00007, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00044: val_mean_squared_error did not improve\n",
+      "Epoch 00045: val_mean_squared_error improved from 0.00007 to 0.00007, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00046: val_mean_squared_error did not improve\n",
+      "Epoch 00047: val_mean_squared_error did not improve\n",
+      "Epoch 00048: val_mean_squared_error did not improve\n",
+      "Epoch 00049: val_mean_squared_error improved from 0.00007 to 0.00006, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00050: val_mean_squared_error did not improve\n",
+      "Epoch 00051: val_mean_squared_error did not improve\n",
+      "Epoch 00052: val_mean_squared_error did not improve\n",
+      "Epoch 00053: val_mean_squared_error did not improve\n",
+      "Epoch 00054: val_mean_squared_error did not improve\n",
+      "Epoch 00055: val_mean_squared_error did not improve\n",
+      "Epoch 00056: val_mean_squared_error did not improve\n",
+      "Epoch 00057: val_mean_squared_error did not improve\n",
+      "Epoch 00058: val_mean_squared_error did not improve\n",
+      "Epoch 00059: val_mean_squared_error did not improve\n",
+      "Epoch 00060: val_mean_squared_error did not improve\n",
+      "Epoch 00061: val_mean_squared_error improved from 0.00006 to 0.00005, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00062: val_mean_squared_error improved from 0.00005 to 0.00004, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00063: val_mean_squared_error did not improve\n",
+      "Epoch 00064: val_mean_squared_error did not improve\n",
+      "Epoch 00065: val_mean_squared_error did not improve\n",
+      "Epoch 00066: val_mean_squared_error did not improve\n",
+      "Epoch 00067: val_mean_squared_error did not improve\n",
+      "Epoch 00068: val_mean_squared_error did not improve\n",
+      "Epoch 00069: val_mean_squared_error did not improve\n",
+      "Epoch 00070: val_mean_squared_error did not improve\n",
+      "Epoch 00071: val_mean_squared_error did not improve\n",
+      "Epoch 00072: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00073: val_mean_squared_error did not improve\n",
+      "Epoch 00074: val_mean_squared_error did not improve\n",
+      "Epoch 00075: val_mean_squared_error did not improve\n",
+      "Epoch 00076: val_mean_squared_error did not improve\n",
+      "Epoch 00077: val_mean_squared_error did not improve\n",
+      "Epoch 00078: val_mean_squared_error did not improve\n",
+      "Epoch 00079: val_mean_squared_error did not improve\n",
+      "Epoch 00080: val_mean_squared_error did not improve\n",
+      "Epoch 00081: val_mean_squared_error did not improve\n",
+      "Epoch 00082: val_mean_squared_error did not improve\n",
+      "Epoch 00083: val_mean_squared_error did not improve\n",
+      "Epoch 00084: val_mean_squared_error did not improve\n",
+      "Epoch 00085: val_mean_squared_error did not improve\n",
+      "Epoch 00086: val_mean_squared_error did not improve\n",
+      "Epoch 00087: val_mean_squared_error did not improve\n",
+      "Epoch 00088: val_mean_squared_error did not improve\n",
+      "Epoch 00089: val_mean_squared_error did not improve\n",
+      "Epoch 00090: val_mean_squared_error did not improve\n",
+      "Epoch 00091: val_mean_squared_error did not improve\n",
+      "Epoch 00092: val_mean_squared_error did not improve\n",
+      "Epoch 00093: val_mean_squared_error did not improve\n",
+      "Epoch 00094: val_mean_squared_error did not improve\n",
+      "Epoch 00095: val_mean_squared_error did not improve\n",
+      "Epoch 00096: val_mean_squared_error did not improve\n",
+      "Epoch 00097: val_mean_squared_error did not improve\n",
+      "Epoch 00098: val_mean_squared_error did not improve\n",
+      "Epoch 00099: val_mean_squared_error did not improve\n",
+      "Epoch 00100: val_mean_squared_error did not improve\n",
+      "Epoch 00101: val_mean_squared_error did not improve\n",
+      "Epoch 00102: val_mean_squared_error did not improve\n",
+      "Epoch 00103: val_mean_squared_error did not improve\n",
+      "Epoch 00104: val_mean_squared_error did not improve\n",
+      "Epoch 00105: val_mean_squared_error did not improve\n",
+      "Epoch 00106: val_mean_squared_error improved from 0.00004 to 0.00003, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00107: val_mean_squared_error did not improve\n",
+      "Epoch 00108: val_mean_squared_error did not improve\n",
+      "Epoch 00109: val_mean_squared_error did not improve\n",
+      "Epoch 00110: val_mean_squared_error did not improve\n",
+      "Epoch 00111: val_mean_squared_error did not improve\n",
+      "Epoch 00112: val_mean_squared_error did not improve\n",
+      "Epoch 00113: val_mean_squared_error did not improve\n",
+      "Epoch 00114: val_mean_squared_error did not improve\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00115: val_mean_squared_error did not improve\n",
+      "Epoch 00116: val_mean_squared_error did not improve\n",
+      "Epoch 00117: val_mean_squared_error did not improve\n",
+      "Epoch 00118: val_mean_squared_error did not improve\n",
+      "Epoch 00119: val_mean_squared_error did not improve\n",
+      "Epoch 00120: val_mean_squared_error did not improve\n",
+      "Epoch 00121: val_mean_squared_error did not improve\n",
+      "Epoch 00122: val_mean_squared_error did not improve\n",
+      "Epoch 00123: val_mean_squared_error did not improve\n",
+      "Epoch 00124: val_mean_squared_error did not improve\n",
+      "Epoch 00125: val_mean_squared_error did not improve\n",
+      "Epoch 00126: val_mean_squared_error did not improve\n",
+      "Epoch 00127: val_mean_squared_error did not improve\n",
+      "Epoch 00128: val_mean_squared_error did not improve\n",
+      "Epoch 00129: val_mean_squared_error did not improve\n",
+      "Epoch 00130: val_mean_squared_error did not improve\n",
+      "Epoch 00131: val_mean_squared_error did not improve\n",
+      "Epoch 00132: val_mean_squared_error did not improve\n",
+      "Epoch 00133: val_mean_squared_error did not improve\n",
+      "Epoch 00134: val_mean_squared_error did not improve\n",
+      "Epoch 00135: val_mean_squared_error did not improve\n",
+      "Epoch 00136: val_mean_squared_error did not improve\n",
+      "Epoch 00137: val_mean_squared_error did not improve\n",
+      "Epoch 00138: val_mean_squared_error did not improve\n",
+      "Epoch 00139: val_mean_squared_error did not improve\n",
+      "Epoch 00140: val_mean_squared_error did not improve\n",
+      "Epoch 00141: val_mean_squared_error did not improve\n",
+      "Epoch 00142: val_mean_squared_error did not improve\n",
+      "Epoch 00143: val_mean_squared_error did not improve\n",
+      "Epoch 00144: val_mean_squared_error did not improve\n",
+      "Epoch 00145: val_mean_squared_error did not improve\n",
+      "Epoch 00146: val_mean_squared_error did not improve\n",
+      "Epoch 00147: val_mean_squared_error did not improve\n",
+      "Epoch 00148: val_mean_squared_error did not improve\n",
+      "Epoch 00149: val_mean_squared_error did not improve\n",
+      "Epoch 00150: val_mean_squared_error did not improve\n",
+      "Epoch 00151: val_mean_squared_error did not improve\n",
+      "Epoch 00152: val_mean_squared_error did not improve\n",
+      "Epoch 00153: val_mean_squared_error did not improve\n",
+      "Epoch 00154: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00155: val_mean_squared_error did not improve\n",
+      "Epoch 00156: val_mean_squared_error did not improve\n",
+      "Epoch 00157: val_mean_squared_error did not improve\n",
+      "Epoch 00158: val_mean_squared_error did not improve\n",
+      "Epoch 00159: val_mean_squared_error did not improve\n",
+      "Epoch 00160: val_mean_squared_error did not improve\n",
+      "Epoch 00161: val_mean_squared_error did not improve\n",
+      "Epoch 00162: val_mean_squared_error did not improve\n",
+      "Epoch 00163: val_mean_squared_error did not improve\n",
+      "Epoch 00164: val_mean_squared_error did not improve\n",
+      "Epoch 00165: val_mean_squared_error did not improve\n",
+      "Epoch 00166: val_mean_squared_error did not improve\n",
+      "Epoch 00167: val_mean_squared_error did not improve\n",
+      "Epoch 00168: val_mean_squared_error did not improve\n",
+      "Epoch 00169: val_mean_squared_error did not improve\n",
+      "Epoch 00170: val_mean_squared_error did not improve\n",
+      "Epoch 00171: val_mean_squared_error did not improve\n",
+      "Epoch 00172: val_mean_squared_error did not improve\n",
+      "Epoch 00173: val_mean_squared_error did not improve\n",
+      "Epoch 00174: val_mean_squared_error did not improve\n",
+      "Epoch 00175: val_mean_squared_error did not improve\n",
+      "Epoch 00176: val_mean_squared_error did not improve\n",
+      "Epoch 00177: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00178: val_mean_squared_error did not improve\n",
+      "Epoch 00179: val_mean_squared_error did not improve\n",
+      "Epoch 00180: val_mean_squared_error did not improve\n",
+      "Epoch 00181: val_mean_squared_error did not improve\n",
+      "Epoch 00182: val_mean_squared_error did not improve\n",
+      "Epoch 00183: val_mean_squared_error did not improve\n",
+      "Epoch 00184: val_mean_squared_error did not improve\n",
+      "Epoch 00185: val_mean_squared_error did not improve\n",
+      "Epoch 00186: val_mean_squared_error did not improve\n",
+      "Epoch 00187: val_mean_squared_error did not improve\n",
+      "Epoch 00188: val_mean_squared_error did not improve\n",
+      "Epoch 00189: val_mean_squared_error did not improve\n",
+      "Epoch 00190: val_mean_squared_error did not improve\n",
+      "Epoch 00191: val_mean_squared_error did not improve\n",
+      "Epoch 00192: val_mean_squared_error did not improve\n",
+      "Epoch 00193: val_mean_squared_error did not improve\n",
+      "Epoch 00194: val_mean_squared_error did not improve\n",
+      "Epoch 00195: val_mean_squared_error did not improve\n",
+      "Epoch 00196: val_mean_squared_error did not improve\n",
+      "Epoch 00197: val_mean_squared_error did not improve\n",
+      "Epoch 00198: val_mean_squared_error did not improve\n",
+      "Epoch 00199: val_mean_squared_error did not improve\n",
+      "Epoch 00200: val_mean_squared_error did not improve\n",
+      "Epoch 00201: val_mean_squared_error did not improve\n",
+      "Epoch 00202: val_mean_squared_error did not improve\n",
+      "Epoch 00203: val_mean_squared_error did not improve\n",
+      "Epoch 00204: val_mean_squared_error did not improve\n",
+      "Epoch 00205: val_mean_squared_error did not improve\n",
+      "Epoch 00206: val_mean_squared_error did not improve\n",
+      "Epoch 00207: val_mean_squared_error did not improve\n",
+      "Epoch 00208: val_mean_squared_error did not improve\n",
+      "Epoch 00209: val_mean_squared_error did not improve\n",
+      "Epoch 00210: val_mean_squared_error did not improve\n",
+      "Epoch 00211: val_mean_squared_error did not improve\n",
+      "Epoch 00212: val_mean_squared_error did not improve\n",
+      "Epoch 00213: val_mean_squared_error did not improve\n",
+      "Epoch 00214: val_mean_squared_error did not improve\n",
+      "Epoch 00215: val_mean_squared_error did not improve\n",
+      "Epoch 00216: val_mean_squared_error did not improve\n",
+      "Epoch 00217: val_mean_squared_error did not improve\n",
+      "Epoch 00218: val_mean_squared_error did not improve\n",
+      "Epoch 00219: val_mean_squared_error did not improve\n",
+      "Epoch 00220: val_mean_squared_error did not improve\n",
+      "Epoch 00221: val_mean_squared_error did not improve\n",
+      "Epoch 00222: val_mean_squared_error did not improve\n",
+      "Epoch 00223: val_mean_squared_error did not improve\n",
+      "Epoch 00224: val_mean_squared_error did not improve\n",
+      "Epoch 00225: val_mean_squared_error did not improve\n",
+      "Epoch 00226: val_mean_squared_error did not improve\n",
+      "Epoch 00227: val_mean_squared_error did not improve\n",
+      "Epoch 00228: val_mean_squared_error did not improve\n",
+      "Epoch 00229: val_mean_squared_error did not improve\n",
+      "Epoch 00230: val_mean_squared_error did not improve\n",
+      "Epoch 00231: val_mean_squared_error did not improve\n",
+      "Epoch 00232: val_mean_squared_error did not improve\n",
+      "Epoch 00233: val_mean_squared_error did not improve\n",
+      "Epoch 00234: val_mean_squared_error did not improve\n",
+      "Epoch 00235: val_mean_squared_error did not improve\n",
+      "Epoch 00236: val_mean_squared_error did not improve\n",
+      "Epoch 00237: val_mean_squared_error did not improve\n",
+      "Epoch 00238: val_mean_squared_error did not improve\n",
+      "Epoch 00239: val_mean_squared_error did not improve\n",
+      "Epoch 00240: val_mean_squared_error did not improve\n",
+      "Epoch 00241: val_mean_squared_error did not improve\n",
+      "Epoch 00242: val_mean_squared_error did not improve\n",
+      "Epoch 00243: val_mean_squared_error did not improve\n",
+      "Epoch 00244: val_mean_squared_error did not improve\n",
+      "Epoch 00245: val_mean_squared_error did not improve\n",
+      "Epoch 00246: val_mean_squared_error did not improve\n",
+      "Epoch 00247: val_mean_squared_error did not improve\n",
+      "Epoch 00248: val_mean_squared_error did not improve\n",
+      "Epoch 00249: val_mean_squared_error did not improve\n",
+      "Epoch 00250: val_mean_squared_error did not improve\n",
+      "Epoch 00251: val_mean_squared_error did not improve\n",
+      "Epoch 00252: val_mean_squared_error did not improve\n",
+      "Epoch 00253: val_mean_squared_error did not improve\n",
+      "Epoch 00254: val_mean_squared_error did not improve\n",
+      "Epoch 00255: val_mean_squared_error did not improve\n",
+      "Epoch 00256: val_mean_squared_error did not improve\n",
+      "Epoch 00257: val_mean_squared_error did not improve\n",
+      "Epoch 00258: val_mean_squared_error did not improve\n",
+      "Epoch 00259: val_mean_squared_error did not improve\n",
+      "Epoch 00260: val_mean_squared_error did not improve\n",
+      "Epoch 00261: val_mean_squared_error did not improve\n",
+      "Epoch 00262: val_mean_squared_error did not improve\n",
+      "Epoch 00263: val_mean_squared_error did not improve\n",
+      "Epoch 00264: val_mean_squared_error did not improve\n",
+      "Epoch 00265: val_mean_squared_error did not improve\n",
+      "Epoch 00266: val_mean_squared_error did not improve\n",
+      "Epoch 00267: val_mean_squared_error did not improve\n",
+      "Epoch 00268: val_mean_squared_error did not improve\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00269: val_mean_squared_error did not improve\n",
+      "Epoch 00270: val_mean_squared_error did not improve\n",
+      "Epoch 00271: val_mean_squared_error did not improve\n",
+      "Epoch 00272: val_mean_squared_error did not improve\n",
+      "Epoch 00273: val_mean_squared_error did not improve\n",
+      "Epoch 00274: val_mean_squared_error did not improve\n",
+      "Epoch 00275: val_mean_squared_error did not improve\n",
+      "Epoch 00276: val_mean_squared_error did not improve\n",
+      "Epoch 00277: val_mean_squared_error did not improve\n",
+      "Epoch 00278: val_mean_squared_error did not improve\n",
+      "Epoch 00279: val_mean_squared_error did not improve\n",
+      "Epoch 00280: val_mean_squared_error did not improve\n",
+      "Epoch 00281: val_mean_squared_error did not improve\n",
+      "Epoch 00282: val_mean_squared_error did not improve\n",
+      "Epoch 00283: val_mean_squared_error did not improve\n",
+      "Epoch 00284: val_mean_squared_error did not improve\n",
+      "Epoch 00285: val_mean_squared_error did not improve\n",
+      "Epoch 00286: val_mean_squared_error did not improve\n",
+      "Epoch 00287: val_mean_squared_error did not improve\n",
+      "Epoch 00288: val_mean_squared_error did not improve\n",
+      "Epoch 00289: val_mean_squared_error did not improve\n",
+      "Epoch 00290: val_mean_squared_error did not improve\n",
+      "Epoch 00291: val_mean_squared_error did not improve\n",
+      "Epoch 00292: val_mean_squared_error did not improve\n",
+      "Epoch 00293: val_mean_squared_error did not improve\n",
+      "Epoch 00294: val_mean_squared_error did not improve\n",
+      "Epoch 00295: val_mean_squared_error did not improve\n",
+      "Epoch 00296: val_mean_squared_error did not improve\n",
+      "Epoch 00297: val_mean_squared_error did not improve\n",
+      "Epoch 00298: val_mean_squared_error did not improve\n",
+      "Epoch 00299: val_mean_squared_error did not improve\n",
+      "Epoch 00300: val_mean_squared_error did not improve\n",
+      "Epoch 00301: val_mean_squared_error did not improve\n",
+      "Epoch 00302: val_mean_squared_error did not improve\n",
+      "Epoch 00303: val_mean_squared_error did not improve\n",
+      "Epoch 00304: val_mean_squared_error did not improve\n",
+      "Epoch 00305: val_mean_squared_error did not improve\n",
+      "Epoch 00306: val_mean_squared_error did not improve\n",
+      "Epoch 00307: val_mean_squared_error did not improve\n",
+      "Epoch 00308: val_mean_squared_error did not improve\n",
+      "Epoch 00309: val_mean_squared_error did not improve\n",
+      "Epoch 00310: val_mean_squared_error did not improve\n",
+      "Epoch 00311: val_mean_squared_error did not improve\n",
+      "Epoch 00312: val_mean_squared_error did not improve\n",
+      "Epoch 00313: val_mean_squared_error did not improve\n",
+      "Epoch 00314: val_mean_squared_error did not improve\n",
+      "Epoch 00315: val_mean_squared_error did not improve\n",
+      "Epoch 00316: val_mean_squared_error did not improve\n",
+      "Epoch 00317: val_mean_squared_error did not improve\n",
+      "Epoch 00318: val_mean_squared_error did not improve\n",
+      "Epoch 00319: val_mean_squared_error did not improve\n",
+      "Epoch 00320: val_mean_squared_error did not improve\n",
+      "Epoch 00321: val_mean_squared_error did not improve\n",
+      "Epoch 00322: val_mean_squared_error did not improve\n",
+      "Epoch 00323: val_mean_squared_error did not improve\n",
+      "Epoch 00324: val_mean_squared_error did not improve\n",
+      "Epoch 00325: val_mean_squared_error did not improve\n",
+      "Epoch 00326: val_mean_squared_error did not improve\n",
+      "Epoch 00327: val_mean_squared_error did not improve\n",
+      "Epoch 00328: val_mean_squared_error did not improve\n",
+      "Epoch 00329: val_mean_squared_error did not improve\n",
+      "Epoch 00330: val_mean_squared_error did not improve\n",
+      "Epoch 00331: val_mean_squared_error did not improve\n",
+      "Epoch 00332: val_mean_squared_error did not improve\n",
+      "Epoch 00333: val_mean_squared_error did not improve\n",
+      "Epoch 00334: val_mean_squared_error did not improve\n",
+      "Epoch 00335: val_mean_squared_error did not improve\n",
+      "Epoch 00336: val_mean_squared_error did not improve\n",
+      "Epoch 00337: val_mean_squared_error did not improve\n",
+      "Epoch 00338: val_mean_squared_error did not improve\n",
+      "Epoch 00339: val_mean_squared_error did not improve\n",
+      "Epoch 00340: val_mean_squared_error did not improve\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-76-841b8888eacc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \"\"\"\n\u001b[1;32m     22\u001b[0m \u001b[0mcallbacks_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'time err = model.fit(X_train, y_train, epochs=epoch, batch_size=100, verbose=0, callbacks=callbacks_list, validation_split=0.1)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2156\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2157\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2160\u001b[0m     \u001b[1;31m#-------------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2077\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2078\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2080\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<decorator-gen-60>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\IPython\\core\\magics\\execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1178\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m             \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mexec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m             \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    861\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 863\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    864\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1428\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1429\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1430\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1432\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1077\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1078\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1079\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1080\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2266\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2267\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2268\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2269\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1122\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1124\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1125\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1319\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1322\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1325\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1328\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1329\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1304\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1305\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Save the best weight during training. Cant handel custom functions for errors\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "epoch = 500\n",
+    "\n",
+    "# write custom errors as string, they seem to refer to the key in err.history dict\n",
+    "if not runLSTMBinary:\n",
+    "    #checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_mean_absolute_error', verbose=1, save_best_only=True, mode='min')\n",
+    "    checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_mean_squared_error', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "if runLSTMBinary:\n",
+    "    checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_ret_direction_error', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "# Fit\n",
+    "\"\"\"\n",
+    "it seems batch size controls convergence speed a lot! Batch size tells how many examples are propagated through the network.\n",
+    "Weights are adjusted based on results with these examples. This is useful if the full dataset takes too much memory\n",
+    "It also speeds up training, as you will converge quicker (dont have to wait for a full iteration of each example to adjust weights).\n",
+    "\n",
+    "With more features to train on, convergence seems slower. To get to the same level, i take more epochs.\n",
+    "\"\"\"\n",
+    "callbacks_list = [checkpoint]\n",
+    "%time err = model.fit(X_train, y_train, epochs=epoch, batch_size=100, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check testing errors\n",
+    "- it converges to a low number very fast - how can i get more detail\n",
+    "- but if i split using cross val split with random state, it takes ages to converge. Maybe i should only allow training on the past, as the model will always be used to predict the future. So training on random parts of the timeseries to predict other random parts might destroy historical trends that influence the future, and can be learned by the model.\n",
+    "- train it on directional error for more useful results. Need y_train - X_train[:,idx_close_bid] as feature and evaluate against y_true - X_train[:,idx_close_bid]\n",
+    "- it seems the model always predicts negative, so column zero\n",
+    "- its easier to optimise over mse than mae, because values bigger and would decline more, so better gradients.\n",
+    "\n",
+    "Issues:\n",
+    "- cannot checkpoint custom error functions\n",
+    "- cannot write custom error functions\n",
+    "- cannot debug custom error functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    4945.484375\n",
+      "dtype: float32\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>acc</th>\n",
+       "      <th>loss</th>\n",
+       "      <th>mean_absolute_error</th>\n",
+       "      <th>mean_squared_error</th>\n",
+       "      <th>val_acc</th>\n",
+       "      <th>val_loss</th>\n",
+       "      <th>val_mean_absolute_error</th>\n",
+       "      <th>val_mean_squared_error</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.108767</td>\n",
+       "      <td>0.108767</td>\n",
+       "      <td>0.018983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.121210</td>\n",
+       "      <td>0.121210</td>\n",
+       "      <td>0.019710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.075678</td>\n",
+       "      <td>0.075678</td>\n",
+       "      <td>0.009418</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.064085</td>\n",
+       "      <td>0.064085</td>\n",
+       "      <td>0.005192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.046499</td>\n",
+       "      <td>0.046499</td>\n",
+       "      <td>0.003853</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.075462</td>\n",
+       "      <td>0.075462</td>\n",
+       "      <td>0.006855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.039816</td>\n",
+       "      <td>0.039816</td>\n",
+       "      <td>0.002767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.023818</td>\n",
+       "      <td>0.023818</td>\n",
+       "      <td>0.000790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.039482</td>\n",
+       "      <td>0.039482</td>\n",
+       "      <td>0.002768</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.030328</td>\n",
+       "      <td>0.030328</td>\n",
+       "      <td>0.001532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.033949</td>\n",
+       "      <td>0.033949</td>\n",
+       "      <td>0.002212</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.031965</td>\n",
+       "      <td>0.031965</td>\n",
+       "      <td>0.001608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.032927</td>\n",
+       "      <td>0.032927</td>\n",
+       "      <td>0.002076</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020286</td>\n",
+       "      <td>0.020286</td>\n",
+       "      <td>0.000780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.032203</td>\n",
+       "      <td>0.032203</td>\n",
+       "      <td>0.001955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.021150</td>\n",
+       "      <td>0.021150</td>\n",
+       "      <td>0.000868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.030479</td>\n",
+       "      <td>0.030479</td>\n",
+       "      <td>0.001798</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.019870</td>\n",
+       "      <td>0.019870</td>\n",
+       "      <td>0.000778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.030525</td>\n",
+       "      <td>0.030525</td>\n",
+       "      <td>0.001765</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.019043</td>\n",
+       "      <td>0.019043</td>\n",
+       "      <td>0.000673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.031537</td>\n",
+       "      <td>0.031537</td>\n",
+       "      <td>0.001842</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.040543</td>\n",
+       "      <td>0.040543</td>\n",
+       "      <td>0.002230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.029722</td>\n",
+       "      <td>0.029722</td>\n",
+       "      <td>0.001696</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020245</td>\n",
+       "      <td>0.020245</td>\n",
+       "      <td>0.000786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.028403</td>\n",
+       "      <td>0.028403</td>\n",
+       "      <td>0.001580</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.035359</td>\n",
+       "      <td>0.035359</td>\n",
+       "      <td>0.001807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.027680</td>\n",
+       "      <td>0.027680</td>\n",
+       "      <td>0.001506</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.038440</td>\n",
+       "      <td>0.038440</td>\n",
+       "      <td>0.002015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.027289</td>\n",
+       "      <td>0.027289</td>\n",
+       "      <td>0.001464</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.030535</td>\n",
+       "      <td>0.030535</td>\n",
+       "      <td>0.001432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.026903</td>\n",
+       "      <td>0.026903</td>\n",
+       "      <td>0.001394</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020303</td>\n",
+       "      <td>0.020303</td>\n",
+       "      <td>0.000738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.025939</td>\n",
+       "      <td>0.025939</td>\n",
+       "      <td>0.001288</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020524</td>\n",
+       "      <td>0.020524</td>\n",
+       "      <td>0.000752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.026659</td>\n",
+       "      <td>0.026659</td>\n",
+       "      <td>0.001294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.023020</td>\n",
+       "      <td>0.023020</td>\n",
+       "      <td>0.000686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.026742</td>\n",
+       "      <td>0.026742</td>\n",
+       "      <td>0.001302</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.022634</td>\n",
+       "      <td>0.022634</td>\n",
+       "      <td>0.000842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.023688</td>\n",
+       "      <td>0.023688</td>\n",
+       "      <td>0.001061</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.019073</td>\n",
+       "      <td>0.019073</td>\n",
+       "      <td>0.000619</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         acc      loss  mean_absolute_error  mean_squared_error  val_acc  \\\n",
+       "0   0.000221  0.108767             0.108767            0.018983      0.0   \n",
+       "1   0.000221  0.075678             0.075678            0.009418      0.0   \n",
+       "2   0.000221  0.046499             0.046499            0.003853      0.0   \n",
+       "3   0.000221  0.039816             0.039816            0.002767      0.0   \n",
+       "4   0.000221  0.039482             0.039482            0.002768      0.0   \n",
+       "5   0.000221  0.033949             0.033949            0.002212      0.0   \n",
+       "6   0.000221  0.032927             0.032927            0.002076      0.0   \n",
+       "7   0.000221  0.032203             0.032203            0.001955      0.0   \n",
+       "8   0.000221  0.030479             0.030479            0.001798      0.0   \n",
+       "9   0.000221  0.030525             0.030525            0.001765      0.0   \n",
+       "10  0.000221  0.031537             0.031537            0.001842      0.0   \n",
+       "11  0.000221  0.029722             0.029722            0.001696      0.0   \n",
+       "12  0.000221  0.028403             0.028403            0.001580      0.0   \n",
+       "13  0.000221  0.027680             0.027680            0.001506      0.0   \n",
+       "14  0.000221  0.027289             0.027289            0.001464      0.0   \n",
+       "15  0.000221  0.026903             0.026903            0.001394      0.0   \n",
+       "16  0.000221  0.025939             0.025939            0.001288      0.0   \n",
+       "17  0.000221  0.026659             0.026659            0.001294      0.0   \n",
+       "18  0.000221  0.026742             0.026742            0.001302      0.0   \n",
+       "19  0.000221  0.023688             0.023688            0.001061      0.0   \n",
+       "\n",
+       "    val_loss  val_mean_absolute_error  val_mean_squared_error  \n",
+       "0   0.121210                 0.121210                0.019710  \n",
+       "1   0.064085                 0.064085                0.005192  \n",
+       "2   0.075462                 0.075462                0.006855  \n",
+       "3   0.023818                 0.023818                0.000790  \n",
+       "4   0.030328                 0.030328                0.001532  \n",
+       "5   0.031965                 0.031965                0.001608  \n",
+       "6   0.020286                 0.020286                0.000780  \n",
+       "7   0.021150                 0.021150                0.000868  \n",
+       "8   0.019870                 0.019870                0.000778  \n",
+       "9   0.019043                 0.019043                0.000673  \n",
+       "10  0.040543                 0.040543                0.002230  \n",
+       "11  0.020245                 0.020245                0.000786  \n",
+       "12  0.035359                 0.035359                0.001807  \n",
+       "13  0.038440                 0.038440                0.002015  \n",
+       "14  0.030535                 0.030535                0.001432  \n",
+       "15  0.020303                 0.020303                0.000738  \n",
+       "16  0.020524                 0.020524                0.000752  \n",
+       "17  0.023020                 0.023020                0.000686  \n",
+       "18  0.022634                 0.022634                0.000842  \n",
+       "19  0.019073                 0.019073                0.000619  "
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#pd.DataFrame(model.predict(X_train))\n",
+    "print(pd.DataFrame(y_train).sum()) # classes are quite balanced\n",
+    "pd.DataFrame(err.history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "smallest validation MAE:  0.0190429590761\n",
+      "smallest validation MSE:  0.000619442572553\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"smallest validation MAE: \", min(err.history['val_mean_absolute_error']))\n",
+    "print(\"smallest validation MSE: \", min(err.history['val_mean_squared_error']))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": false,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "for error_metric in list(err.history.keys()):\n",
+    "    if 'val' not in error_metric:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(err.history[error_metric])\n",
+    "        plt.plot(err.history['val_' + error_metric])\n",
+    "        plt.title(error_metric, fontsize=30)\n",
+    "        plt.ylabel(error_metric)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left', fontsize=30)\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Rerun LSTM with decaying learning rate:\n",
+    "\n",
+    "As seen from the above, the model seems to have converged nicely, but the mean absolute error on the development data remains at ~0.003X which means the model is unusable in practice. Ideally, we want to get ~0.0005. Let's go back to the best weight, and decay the learning rate while retraining the model\n",
+    "\n",
+    "We need this to get inside the average bid offer spread for EUR/USD, so 1.10115 - 1.10110. But lets not forget the data is scaled. Maybe it looks better when we unscale it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# tune model by starting from best weights and rerunning with decaying learning rate\n",
+    "# Load the weight that worked the best\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "#epoch=60\n",
+    "\n",
+    "# Train again with decaying learning rate\n",
+    "from keras.callbacks import LearningRateScheduler\n",
+    "import keras.backend as K\n",
+    "\n",
+    "def scheduler(epoch):\n",
+    "    if epoch%2==0 and epoch!=0:\n",
+    "        lr = K.get_value(model.optimizer.lr)\n",
+    "        K.set_value(model.optimizer.lr, lr*.9)\n",
+    "        print(\"lr changed to {}\".format(lr*.9))\n",
+    "    return K.get_value(model.optimizer.lr)\n",
+    "lr_decay = LearningRateScheduler(scheduler) # do sth to learning rate\n",
+    "\n",
+    "callbacks_list = [checkpoint, lr_decay] # checkin with these once in a while\n",
+    "err_decay_lr = model.fit(X_train, y_train, epochs=int(epoch/3), batch_size=500, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check testing errors after decaying learning rate\n",
+    " - here error chart resolution is better, as we start from the trained model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "smallest validation MAE:  0.0190429590761\n",
+      "smallest validation MSE:  0.000619442572553\n",
+      "decay lr: smallest validation MAE:  0.0123163131533\n",
+      "decay lr: smallest validation MSE:  0.000265824883329\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"smallest validation MAE: \", min(err.history['val_mean_absolute_error']))\n",
+    "print(\"smallest validation MSE: \", min(err.history['val_mean_squared_error']))\n",
+    "print(\"decay lr: smallest validation MAE: \", min(err_decay_lr.history['val_mean_absolute_error']))\n",
+    "print(\"decay lr: smallest validation MSE: \", min(err_decay_lr.history['val_mean_squared_error']))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "for error_metric in list(err_decay_lr.history.keys()):\n",
+    "    if 'val' not in error_metric:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(err_decay_lr.history[error_metric]) # this is for train\n",
+    "        plt.plot(err_decay_lr.history['val_' + error_metric]) # this is for test\n",
+    "        plt.title(error_metric, fontsize=30)\n",
+    "        plt.ylabel(error_metric)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left', fontsize=30)\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "The variance should have improved slightly. However, unless the mean absolute error is small enough, the model is not usable in practice. This is mainly due to only using the sample data for training and limiting epoch to a few hundreds.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Check scaled predictions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#simname = \"500_epochs_40_lookback\"\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "if not runLSTMBinary:\n",
+    "    # Benchmark\n",
+    "    model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "\n",
+    "    X_test_pred = model.predict(X_test) # predict on testset\n",
+    "\n",
+    "    predictions = pd.DataFrame()\n",
+    "    predictions['predicted'] = pd.Series(np.reshape(X_test_pred, (X_test_pred.shape[0])))\n",
+    "    predictions['actual'] = y_test\n",
+    "    predictions = predictions.astype(float)\n",
+    "\n",
+    "\n",
+    "    fig, axarr = plt.subplots(1, 2, figsize=(15,5)) #1 row, 2 cols, x, y\n",
+    "    i_row, icol = 0,0\n",
+    "    fig.suptitle(\"predictions on test set\", fontsize=20)\n",
+    "    predictions.plot(ax=axarr[icol])\n",
+    "    axarr[icol].set_title(\"Predicted close vs actual over time\")\n",
+    "\n",
+    "    icol +=1\n",
+    "    predictions['diff'] = predictions['actual'] - predictions['predicted']\n",
+    "    sns.distplot(predictions['diff'], ax=axarr[icol]);\n",
+    "    axarr[icol].set_title('Distribution of differences: actual minus predicted')\n",
+    "    plt.show()\n",
+    "\n",
+    "    print(\"MSE scaled : \", mean_squared_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "    print(\"MAE scaled: \", mean_absolute_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "    #predictions['diff'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Check unscaled predictions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#unscale predictions and actuals\n",
+    "X_test_pred = model.predict(X_test)\n",
+    "X_test_pred_unscaled = y_scaler.inverse_transform(X_test_pred)\n",
+    "X_test_pred_unscaled = np.reshape(X_test_pred_unscaled, (X_test_pred_unscaled.shape[0]))\n",
+    "\n",
+    "actual = y_scaler.inverse_transform(np.reshape(y_test, (y_test.shape[0], 1)))\n",
+    "actual = np.reshape(actual, (actual.shape[0]))\n",
+    "\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(X_test_pred_unscaled)\n",
+    "predictions['close_bid'] = pd.Series(actual)\n",
+    "\n",
+    "\n",
+    "# get low and high bid from untransformed dataframe\n",
+    "p = df[-X_test_pred_unscaled.shape[0]:].copy()\n",
+    "predictions.index = p.index # get the date index  from the dataframe\n",
+    "predictions = predictions.astype(float)\n",
+    "predictions = predictions.merge(p[['low_bid', 'high_bid']], right_index=True, left_index=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if not runLSTMBinary:\n",
+    "    fig, axarr = plt.subplots(1, 4, figsize=(5,10)) #1 row, 2 cols, x, y\n",
+    "    irow, icol = 0,0\n",
+    "\n",
+    "    predictions.plot(x=predictions.index, y='close_bid', c='red', figsize=(40,10), ax=axarr[icol])\n",
+    "    predictions.plot(x=predictions.index, y='predicted', c='blue', figsize=(40,10), ax=axarr[icol])\n",
+    "    index = [str(item) for item in predictions.index]\n",
+    "    #plt.fill_between(x=predictions.index, y1='low_bid', y2='high_bid', data=predictions, alpha=0.4)\n",
+    "    axarr[icol].set_title('Prediction vs Actual (low and high as blue region)')\n",
+    "\n",
+    "    icol += 1\n",
+    "    predictions['diff'] = predictions['predicted'] - predictions['close_bid']\n",
+    "    sns.distplot(predictions['diff'], ax=axarr[icol]);\n",
+    "    axarr[icol].set_title('Distribution of differences between actual and prediction ')\n",
+    "    #plt.savefig(simname+\"__histogram__actual_minus_pred.jpg\")\n",
+    "\n",
+    "    icol += 1\n",
+    "    sns.kdeplot(predictions[\"diff\"], predictions[\"predicted\"], kind=\"kde\", space=0, ax=axarr[icol])\n",
+    "    #sns.jointplot(predictions[\"diff\"], predictions[\"predicted\"], kind=\"kde\", space=0, ax=axarr[icol]) # must be by itself\n",
+    "    axarr[icol].set_title('Distribution of error and price')\n",
+    "    #plt.savefig(simname+\"__contour__error_v_price.jpg\")\n",
+    "\n",
+    "\n",
+    "    icol +=1\n",
+    "    predictions['correct'] = (predictions['predicted'] <= predictions['high_bid']) & (predictions['predicted'] >= predictions['low_bid'])\n",
+    "    predictions.correct.value_counts().plot(kind=\"bar\", ax=axarr[icol])\n",
+    "    axarr[icol].set_title(\"True (in high low range), False prediction counts\")\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "    print(\"MSE unscaled : \", mean_squared_error(predictions['predicted'].values, predictions['close_bid'].values))\n",
+    "    print(\"MAE unscaled: \", mean_absolute_error(predictions['predicted'].values, predictions['close_bid'].values))\n",
+    "    #predictions['diff'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## check binary predictions and confusion matrix\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if runLSTMBinary:\n",
+    "    from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "    #check_shape(y_test, model.predict(X_test))\n",
+    "\n",
+    "    y_pred_class = np.argmax(model.predict(X_test), axis=1)  # find position of largest argument\n",
+    "\n",
+    "    y_test_class = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "    test_acc = 100 * np.sum(y_pred_class==y_test_class) / len(y_test)\n",
+    "\n",
+    "    print(\"acc \", test_acc )\n",
+    "\n",
+    "    confusion_matrix(y_pred_class, y_test_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not runLSTMBinary:\n",
+    "    df_unscaled = df_scaler.inverse_transform(df_scaled)\n",
+    "\n",
+    "    X_test_unscaled = df_scaler.inverse_transform(np.reshape(X_test[:,0,:], (X_test.shape[0], X_test.shape[2])))\n",
+    "    y_prev = X_test_unscaled[:,idx_close_bid]\n",
+    "\n",
+    "    y_train_unscaled =  y_scaler.inverse_transform(np.reshape(y_train, (y_train.shape[0], 1)))\n",
+    "    y_train_unscaled = np.reshape(y_train_unscaled, (y_train_unscaled.shape[0]))\n",
+    "\n",
+    "    y_test_unscaled =  y_scaler.inverse_transform(np.reshape(y_test, (y_test.shape[0], 1)))\n",
+    "    y_test_unscaled = np.reshape(y_test_unscaled, (y_test_unscaled.shape[0]))\n",
+    "\n",
+    "\n",
+    "    X_train_pred = model.predict(X_train)\n",
+    "    X_train_pred_unscaled = y_scaler.inverse_transform(X_train_pred)\n",
+    "    X_train_pred_unscaled = np.reshape(X_train_pred_unscaled, (X_train_pred_unscaled.shape[0]))\n",
+    "\n",
+    "    #check_shape(df,y_train_unscaled, y_test_unscaled, X_train_pred_unscaled, X_test_pred_unscaled, y_prev)\n",
+    "\n",
+    "    df_err = check_error_metrics(df\n",
+    "                        , y_train_unscaled, y_test_unscaled\n",
+    "                        , X_train_pred_unscaled, X_test_pred_unscaled\n",
+    "                        , y_prev)\n",
+    "    #idx_close_bid\n",
+    "    #X_test[:,0,idx_close_bid].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "Sim results:\n",
+    "- runing at 500 epochs converges a bit better. seems extra features need more time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check logs and compare to previous simulations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log = True\n",
+    "initval=False\n",
+    "#sim_desc = \"500 iterations, lookback 40\"\n",
+    "#simname = \"500_epoch_lookback_40\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "list_stats=OrderedDict()\n",
+    "\n",
+    "if log:\n",
+    "    #simname= \"linear regression\"\n",
+    "    #sim_desc = \"1 row lookback\"\n",
+    "    \n",
+    "    dict_err= OrderedDict(zip(df_err[0], df_err[1]))\n",
+    "    \n",
+    "    list_stats=OrderedDict()\n",
+    "    \n",
+    "    list_stats[\"simname\"] = simname\n",
+    "    list_stats[\"sim_desc\"] = sim_desc\n",
+    "    list_stats[\"MSE\"] = dict_err[\"mse test all feature: \"]\n",
+    "    list_stats[\"MAE\"] = dict_err[\"mae test all feature: \"]\n",
+    "    \n",
+    "    differences_described = predictions[\"diff\"].describe()\n",
+    "\n",
+    "    list_stats.update(OrderedDict(differences_described))\n",
+    "    list_stats.update(dict_err)\n",
+    "    \n",
+    "    results = pd.DataFrame([list_stats])\n",
+    "    #results.to_excel(\"log_results.xlsx\")\n",
+    "    if os.path.isfile(\"log_results.xlsx\"):\n",
+    "        log_results = pd.read_excel(\"log_results.xlsx\")\n",
+    "        log_results.loc[len(log_results),:] = list_stats.values()\n",
+    "        log_results.to_excel(\"log_results.xlsx\")\n",
+    "    #log_results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.read_excel(\"log_results.xlsx\").T"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "celltoolbar": "Hide code",
+  "hide_code_all_hidden": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {
+    "height": "947px",
+    "left": "0px",
+    "right": "1568px",
+    "top": "67px",
+    "width": "264px"
+   },
+   "toc_section_display": "block",
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/capstone_project/500_epoch_lookback_40.weights.best.hdf5 b/capstone_project/500_epoch_lookback_40.weights.best.hdf5
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diff --git a/capstone_project/500_epochs_40_lookback_pca_unshuffled.weights.best.hdf5 b/capstone_project/500_epochs_40_lookback_pca_unshuffled.weights.best.hdf5
new file mode 100644
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diff --git a/capstone_project/500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5 b/capstone_project/500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5
new file mode 100644
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diff --git a/capstone_project/aws_v1 - fx spot prediction notebook.ipynb b/capstone_project/aws_v1 - fx spot prediction notebook.ipynb
new file mode 100644
index 0000000..b0f4034
--- /dev/null
+++ b/capstone_project/aws_v1 - fx spot prediction notebook.ipynb	
@@ -0,0 +1,2140 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Machine learning capstone project - fx spot prediction\n",
+    "\n",
+    "The goal is to create features that can help predict the bid price, using a lookback period of a few minutes.\n",
+    "\n",
+    "Try to include the bid offer spread - from the benchmark model it seems volume is not an important feature so it is not a problem that i dont have this data point.\n",
+    "\n",
+    "I took inspiration from : https://www.kaggle.com/kimy07/eurusd-15-minute-interval-price-prediction/notebook\n",
+    "\n",
+    "Introduction\n",
+    "This notebook trains a LSTM model that predicts the bid price of EURUSD 15 minutes in the future by looking at last five hours of data. While there is no requirement for the input to be contiguous, it's been empirically observed that having the contiguous input does improve the accuracy of the model. I suspect that having day of the week and hour of the day as the features mitigates some of the seasonality and contiguousness problems.\n",
+    "\n",
+    "Disclaimer: This exercise has been carried out using a small sample data which only contains 14880 samples (2015-12-29 00:00:00 to 2016-05-31 23:45:00) and lacks ASK prices. Which restricts the ability for the model to approach a better accuracy.\n",
+    "\n",
+    "Improvements\n",
+    "\n",
+    "To tune the model further, I would recommend having at least 5 years worth of data, have ASK price (so that you can compute the spread), and increasing the epoch to 3000.\n",
+    "Adding more cross-axial features. Such as spread.\n",
+    "If you are looking into classification approach (PASS, BUY, SELL), consider adding some technical indicators that is more sensitive to more recent data.\n",
+    "Consider adding non-numerical data, e.g. news, Tweets. The catch is that you have to get the data under one minute for trading, otherwise the news will be reflected before you even make a trade. If anybody knows how to get the news streamed really fast, please let me know.\n",
+    "\n",
+    "Credits : Dave Y. Kim, Mahmoud Elsaftawy,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run on EC2:\n",
+    "Enter the repo directory: cd aind2-cnn\n",
+    "Activate the new environment: source activate aind2\n",
+    "Start Jupyter: jupyter notebook --ip=0.0.0.0 --no-browser\n",
+    "Find this line in output and copy url to browser: \n",
+    "Copy/paste this URL into your browser when you connect for the first time to login with a token: \n",
+    "http://0.0.0.0:8888/?token=3156e...\n",
+    "\n",
+    "change the 0.0.0.0 with EC2 IP.\n",
+    "\n",
+    "you should see the checked out repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:90% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "from subprocess import check_output\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "  \n",
+       "    $('.ctb_hideshow').toggle();\n",
+       "    \n",
+       "    </script>\n",
+       " "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pypyodbc\n",
+    "display(HTML(\"\"\"<script>\n",
+    "  \n",
+    "    $('.ctb_hideshow').toggle();\n",
+    "    \n",
+    "    </script>\n",
+    " \"\"\"))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get data from mongodb\n",
+    "import io\n",
+    "import pymongo\n",
+    "from pymongo import MongoClient\n",
+    "import datetime\n",
+    "\n",
+    "#client = MongoClient(connect=False) #Makes it \"good enough\" for our multi-threaded use case. \n",
+    "\n",
+    "# mng_client = pymongo.MongoClient('localhost', 27017)\n",
+    "# mng_db = mng_client['fx_prediction'] # Replace mongo db name\n",
+    "# collection_name = 'fx_tick_data_typed' # Replace mongo db table name\n",
+    "# db = mng_db[collection_name]\n",
+    "\n",
+    "#print(db.count())\n",
+    "#min_date = datetime.datetime(2016, 1, 1, 0)\n",
+    "#max_date = datetime.datetime(2016, 12, 1, 0)\n",
+    "min_date = \"1Jan16\"\n",
+    "max_date = \"1Feb16\"\n",
+    "\n",
+    "#https://bitbucket.org/djcbeach/monary/wiki/Home use to speed up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simname = \"mine\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1Jan16\n",
+      "1Feb16\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(min_date)\n",
+    "print(max_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# # each of these is a stage in the pipeline - match, project, group, project.\n",
+    "\n",
+    "# cursor_group = db.aggregate(\n",
+    "#    [\n",
+    "      \n",
+    "#        {\"$match\":{\n",
+    "#            \"date\": {\n",
+    "#                \"$gte\": min_date\n",
+    "#                , \"$lte\": max_date\n",
+    "#            }\n",
+    "#        }           \n",
+    "#        },\n",
+    "       \n",
+    "#        {\n",
+    "#            \"$project\": {\n",
+    "#                \"_id\" : 0\n",
+    "#                , \"bo_spread\": {\"$subtract\": [\"$ask\", \"$bid\"]}\n",
+    "#                , \"bid\": 1\n",
+    "#                , \"ask\": 1\n",
+    "#                , \"date\": 1\n",
+    "               \n",
+    "#            }\n",
+    "#        },\n",
+    "       \n",
+    "       \n",
+    "#        {\n",
+    "#         \"$group\" : {\n",
+    "#            #\"_id\" : \"null\",\n",
+    "#            \"_id\": {\n",
+    "#                \"dateAgg\": { \"$dateToString\": { \"format\": \"%G/%m/%d %H:%M\", \"date\": \"$date\" } }\n",
+    "#                },\n",
+    "#            #\"high\": { \"$sum\": { \"$multiply\": [ \"$price\", \"$quantity\" ] } },\n",
+    "#             \"dateSample\": {\"$first\": \"$date\"},\n",
+    "#             \"high\": { \"$max\": \"$bid\"},\n",
+    "#             \"low\": { \"$min\": \"$bid\"},\n",
+    "#             \"open\": { \"$first\": \"$bid\"},\n",
+    "#             \"close\": { \"$last\": \"$bid\"},\n",
+    "#            \"avg_bo_spread\": { \"$avg\": \"$bo_spread\" },\n",
+    "#            \"max_bo_spread\": { \"$max\": \"$bo_spread\" },\n",
+    "#            \"min_bo_spread\": { \"$min\": \"$bo_spread\" },\n",
+    "#            \"count\": { \"$sum\": 1 }\n",
+    "#         }\n",
+    "#       },\n",
+    "       \n",
+    "#        {\n",
+    "#            \"$project\": {\n",
+    "#                \"_id\" : 0\n",
+    "#                , \"date\": \"$dateSample\"\n",
+    "#                , \"high\": 1\n",
+    "#                , \"low\": 1\n",
+    "#                , \"open\": 1\n",
+    "#                , \"close\": 1\n",
+    "#                , \"avg_bo_spread\": 1\n",
+    "#                , \"max_bo_spread\": 1\n",
+    "#                , \"min_bo_spread\": 1\n",
+    "#                , \"count\": 1\n",
+    "#            }\n",
+    "#        }\n",
+    "       \n",
+    "#    ], allowDiskUse=True\n",
+    "# )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# cursor_group = db.aggregate(\n",
+    "#    [\n",
+    "#       {\"$match\":{\n",
+    "#            \"date\": {\n",
+    "#                \"$gte\": min_date\n",
+    "#                , \"$lte\": max_date\n",
+    "#            }\n",
+    "#        }           \n",
+    "#        },\n",
+    "       \n",
+    "             \n",
+    "#        {\n",
+    "#         \"$group\" : {\n",
+    "#            #\"_id\" : \"null\",\n",
+    "#            \"_id\": {\n",
+    "#                #\"month\": {\"$month\": \"$date\"}, \n",
+    "#                #\"day\"  : {\"$dayOfMonth\": \"$date\"}, \n",
+    "#                #\"year\" : {\"$year\": \"$date\"},\n",
+    "#                \"time\": { \"$dateToString\": { \"format\": \"%G/%m/%d %H:%M\", \"date\": \"$date\" } }\n",
+    "#                #\"date\": { \"$dateFromParts\": {\"year\": \"$date\", \"month\": \"$date\", \"day\": \"$date\", \"hour\": \"$date\", \"minute\": \"$date\"}}\n",
+    "#                },\n",
+    "#            #\"high\": { \"$sum\": { \"$multiply\": [ \"$price\", \"$quantity\" ] } },\n",
+    "#             \"high\": { \"$max\": \"$date\"},\n",
+    "#             \"low\": { \"$min\": \"$date\"},                                \n",
+    "#            \"count\": { \"$sum\": 1 }\n",
+    "#         }\n",
+    "#       }\n",
+    "#    ], allowDiskUse=True\n",
+    "# )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "strConnDef = \"DRIVER={ODBC Driver 13 for SQL Server};SERVER=localhost,1433;DATABASE=kai_dw;uid=kai_ta;pwd=tenpen12\"\n",
+    "def getQueryRaw(strQuery, params=None, strConn=strConnDef, commitOn=None):\n",
+    "\n",
+    "    if commitOn is None:\n",
+    "        commitOn = False\n",
+    "\n",
+    "    if params is None:\n",
+    "        params = []\n",
+    "\n",
+    "    pypyodbc.lowercase = False\n",
+    "    conn = pypyodbc.connect(strConn)\n",
+    "    cursor = conn.cursor()\n",
+    "    cursor.execute(strQuery, params)\n",
+    "\n",
+    "    if commitOn:\n",
+    "        conn.commit()\n",
+    "        return \"sql insert was successful.\", \"sql insert was successful.\"\n",
+    "    try:\n",
+    "        rows = cursor.fetchall()\n",
+    "        #print(\"rows\", rows)\n",
+    "        # print(\"PARAMS:\", params)\n",
+    "        description = cursor.description\n",
+    "        conn.close()\n",
+    "        return rows, description\n",
+    "    except:\n",
+    "        # print(\"THE QUERY: \" + strQuery) TODO: add query\n",
+    "        conn.close()\n",
+    "        raise ValueError(\"There was an error fetching a sql query. Make sure the index exists for your selected dates. THE PARAMS: \", params)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def getQueryDataframe(strQuery, params=None, strConn=strConnDef, columnMustAlwaysExist=None, commitOn=None):\n",
+    "\n",
+    "    rows, cursorDescription = getQueryRaw(strQuery, params, strConn, commitOn)\n",
+    "    if commitOn:\n",
+    "        return \"sql insert was successful.\"\n",
+    "\n",
+    "    if len(rows) == 0:\n",
+    "        print(\"No rows were returned.\")\n",
+    "        print(\"THE PARAMS: \", params)\n",
+    "        print(\"THE QUERY: \" + strQuery)\n",
+    "        print(\"Rows length is zero. No records returned\")\n",
+    "\n",
+    "        if columnMustAlwaysExist is None:\n",
+    "            columnMustAlwaysExist = \"Empty\"\n",
+    "\n",
+    "        columns = [\"Information\", columnMustAlwaysExist]\n",
+    "        rows = [\n",
+    "            [\"No results were returned.\", \"There is no data.\"]\n",
+    "            , [\"No results were returned.\", \"There is no data.\"]\n",
+    "        ]\n",
+    "\n",
+    "    else:\n",
+    "        # bytes conversion needed because of the linux pypyodbc bug\n",
+    "        columns = [column[0].decode(\"cp1252\") if type(column[0]) == bytes else column[0] for column in\n",
+    "                   cursorDescription]\n",
+    "\n",
+    "    results = pd.DataFrame(data=rows, columns=columns)\n",
+    "\n",
+    "\n",
+    "    return results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>bid_price</th>\n",
+       "      <th>ask_price</th>\n",
+       "      <th>bo_spread</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>count</th>\n",
+       "      <th>open</th>\n",
+       "      <th>close</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:15.493</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08751</td>\n",
+       "      <td>0.00050</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:38.993</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08749</td>\n",
+       "      <td>0.00046</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:41.493</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08749</td>\n",
+       "      <td>0.00036</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:41.993</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08745</td>\n",
+       "      <td>0.00032</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:44.743</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08745</td>\n",
+       "      <td>0.00042</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08703</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  month  day  hour  weekday                       date  bid_price  \\\n",
+       "0  2016      1    3    17        1  0 2016-01-03 17:00:15.493    1.08701   \n",
+       "1  2016      1    3    17        1  0 2016-01-03 17:00:38.993    1.08703   \n",
+       "2  2016      1    3    17        1  0 2016-01-03 17:00:41.493    1.08713   \n",
+       "3  2016      1    3    17        1  0 2016-01-03 17:00:41.993    1.08713   \n",
+       "4  2016      1    3    17        1  0 2016-01-03 17:00:44.743    1.08703   \n",
+       "\n",
+       "   ask_price  bo_spread     high      low  avg_bo_spread  count     open  \\\n",
+       "0    1.08751    0.00050  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "1    1.08749    0.00046  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "2    1.08749    0.00036  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "3    1.08745    0.00032  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "4    1.08745    0.00042  1.08723  1.08661       0.000165    142  1.08701   \n",
+       "\n",
+       "     close  \n",
+       "0  1.08701  \n",
+       "1  1.08703  \n",
+       "2  1.08713  \n",
+       "3  1.08713  \n",
+       "4  1.08703  "
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str_query = \"\"\"\n",
+    "\n",
+    "\n",
+    "select\n",
+    "    --distinct\n",
+    "    const.year, const.month, const.day, const.hour, const.weekday, round(const.minute/15,0) * 15\n",
+    "    , const.snaptime 'date'\n",
+    "    , const.bid_price\n",
+    "    , const.ask_price\n",
+    "    , const.ask_price - const.bid_price 'bo_spread'\n",
+    "\t, max(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0))'high'\n",
+    "\t, min(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'low'\n",
+    "    , avg(const.ask_price - const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'avg_bo_spread'\n",
+    "\t--, min(const.snaptime) 'open_datetime'\n",
+    "\t--, max(const.snaptime) 'close_datetime'\n",
+    "\t, count(*) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'count'\n",
+    "    , first_value(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0) order by const.snaptime) 'open'\n",
+    "    , last_value(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0) order by const.snaptime) 'close'\n",
+    "from dbo.fx_spot_data_features const\n",
+    "where\n",
+    "    const.snaptime >= '\"\"\"+min_date+\"\"\"'\n",
+    "    and const.snaptime <= '\"\"\"+max_date+\"\"\"'\n",
+    "    \n",
+    "--group by const.year, const.month, const.day, const.hour, round(const.minute/15,0)\n",
+    "--order by const.year, const.month, const.day, const.hour, round(const.minute/15,0)\n",
+    "order by const.snaptime\n",
+    "\n",
+    "\"\"\"\n",
+    "res = getQueryDataframe(str_query)\n",
+    "print(res.count())\n",
+    "res.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'date'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2441\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2442\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'date'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-141-6cacc01fd7ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#cursor = list(cursor_group)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf_res\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m#df_res.rename(columns={\"_id\": \"date\"}, inplace=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/data/eurusd_features.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   2828\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2829\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2830\u001b[0;31m                 \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2831\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2832\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1962\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1964\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1966\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1969\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1970\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1973\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1643\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1645\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1646\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3590\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3591\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2442\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2444\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2446\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'date'"
+     ]
+    }
+   ],
+   "source": [
+    "#cursor = list(cursor_group)\n",
+    "df_res =  pd.DataFrame(res)\n",
+    "df_res.set_index('date', inplace=True)\n",
+    "#df_res.rename(columns={\"_id\": \"date\"}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_res.to_csv(\"eurusd_features.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_res = pd.read_csv(\"data/eurusd_features.csv\")\n",
+    "df_res.set_index('date', inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# load kaggle reference dataset for comparison\n",
+    "#df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_sample.csv')\n",
+    "df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_01.01.2010-31.12.2016.csv')\n",
+    "\n",
+    "# Rename bid OHLC columns\n",
+    "df_kaggle.rename(columns={'Time' : 'date', 'Open' : 'open', 'Close' : 'close', \n",
+    "                   'High' : 'high', 'Low' : 'low', 'Close' : 'close', 'Volume' : 'volume'}, inplace=True)\n",
+    "df_kaggle['date'] = pd.to_datetime(df_kaggle['date'], infer_datetime_format=True)\n",
+    "df_kaggle.set_index('date', inplace=True)\n",
+    "df_kaggle = df_kaggle.astype(float)\n",
+    "\n",
+    "simname = \"bm_kaggle\"\n",
+    "\n",
+    "df_res = df_kaggle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5f75faee80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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U52TgncDGqro9ySO7qVaSJC1FH4+InA3MVtW+qroHuArYNNLnRcDVVXU7QFV9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\nkiSpM/8/Q2Ep4tIP3CQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5f6b3bfeb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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rtu+G59fZxrx8+KSmsbppXUwIIdOduBbrYddG3T+pbXqU9XmrvlGP9OjUvt22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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5f6987be80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#df_res.date = pd.to_datetime(df_res.date.dateAgg, format='%Y%m%d %H:%M')\n",
+    "df_res.head()\n",
+    "sns.pairplot(df_res.loc[:,[\"bo_spread\", \"hour\", \"close\"]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "year             2081682\n",
+      "month            2081682\n",
+      "day              2081682\n",
+      "hour             2081682\n",
+      "weekday          2081682\n",
+      "Unnamed: 6       2081682\n",
+      "bid_price        2081682\n",
+      "ask_price        2081682\n",
+      "bo_spread        2081682\n",
+      "high             2081682\n",
+      "low              2081682\n",
+      "avg_bo_spread    2081682\n",
+      "count            2081682\n",
+      "open             2081682\n",
+      "close            2081682\n",
+      "avg_price        2081682\n",
+      "range            2081682\n",
+      "ohlc_price       2081682\n",
+      "oc_diff          2081682\n",
+      "period_return    2081682\n",
+      "pca              2081682\n",
+      "dtype: int64\n",
+      "2016-01-03 17:00:15.493 2016-01-31 23:59:41.170\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>Unnamed: 6</th>\n",
+       "      <th>bid_price</th>\n",
+       "      <th>ask_price</th>\n",
+       "      <th>bo_spread</th>\n",
+       "      <th>high</th>\n",
+       "      <th>...</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>count</th>\n",
+       "      <th>open</th>\n",
+       "      <th>close</th>\n",
+       "      <th>avg_price</th>\n",
+       "      <th>range</th>\n",
+       "      <th>ohlc_price</th>\n",
+       "      <th>oc_diff</th>\n",
+       "      <th>period_return</th>\n",
+       "      <th>pca</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:15.493</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08751</td>\n",
+       "      <td>0.00050</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086965</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1322.240112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:38.993</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08749</td>\n",
+       "      <td>0.00046</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086970</td>\n",
+       "      <td>-0.00002</td>\n",
+       "      <td>1.000018</td>\n",
+       "      <td>-1322.160522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:41.493</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08749</td>\n",
+       "      <td>0.00036</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086995</td>\n",
+       "      <td>-0.00012</td>\n",
+       "      <td>1.000110</td>\n",
+       "      <td>-1322.161377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:41.993</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08745</td>\n",
+       "      <td>0.00032</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08713</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086995</td>\n",
+       "      <td>-0.00012</td>\n",
+       "      <td>1.000110</td>\n",
+       "      <td>-1322.161865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-03 17:00:44.743</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08745</td>\n",
+       "      <td>0.00042</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08703</td>\n",
+       "      <td>1.08692</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>1.086970</td>\n",
+       "      <td>-0.00002</td>\n",
+       "      <td>1.000018</td>\n",
+       "      <td>-1322.161011</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         year  month  day  hour  weekday  Unnamed: 6  \\\n",
+       "date                                                                   \n",
+       "2016-01-03 17:00:15.493  2016      1    3    17        1           0   \n",
+       "2016-01-03 17:00:38.993  2016      1    3    17        1           0   \n",
+       "2016-01-03 17:00:41.493  2016      1    3    17        1           0   \n",
+       "2016-01-03 17:00:41.993  2016      1    3    17        1           0   \n",
+       "2016-01-03 17:00:44.743  2016      1    3    17        1           0   \n",
+       "\n",
+       "                         bid_price  ask_price  bo_spread     high  \\\n",
+       "date                                                                \n",
+       "2016-01-03 17:00:15.493    1.08701    1.08751    0.00050  1.08723   \n",
+       "2016-01-03 17:00:38.993    1.08703    1.08749    0.00046  1.08723   \n",
+       "2016-01-03 17:00:41.493    1.08713    1.08749    0.00036  1.08723   \n",
+       "2016-01-03 17:00:41.993    1.08713    1.08745    0.00032  1.08723   \n",
+       "2016-01-03 17:00:44.743    1.08703    1.08745    0.00042  1.08723   \n",
+       "\n",
+       "                            ...       avg_bo_spread  count     open    close  \\\n",
+       "date                        ...                                                \n",
+       "2016-01-03 17:00:15.493     ...            0.000165    142  1.08701  1.08701   \n",
+       "2016-01-03 17:00:38.993     ...            0.000165    142  1.08701  1.08703   \n",
+       "2016-01-03 17:00:41.493     ...            0.000165    142  1.08701  1.08713   \n",
+       "2016-01-03 17:00:41.993     ...            0.000165    142  1.08701  1.08713   \n",
+       "2016-01-03 17:00:44.743     ...            0.000165    142  1.08701  1.08703   \n",
+       "\n",
+       "                         avg_price    range  ohlc_price  oc_diff  \\\n",
+       "date                                                               \n",
+       "2016-01-03 17:00:15.493    1.08692  0.00062    1.086965  0.00000   \n",
+       "2016-01-03 17:00:38.993    1.08692  0.00062    1.086970 -0.00002   \n",
+       "2016-01-03 17:00:41.493    1.08692  0.00062    1.086995 -0.00012   \n",
+       "2016-01-03 17:00:41.993    1.08692  0.00062    1.086995 -0.00012   \n",
+       "2016-01-03 17:00:44.743    1.08692  0.00062    1.086970 -0.00002   \n",
+       "\n",
+       "                         period_return          pca  \n",
+       "date                                                 \n",
+       "2016-01-03 17:00:15.493       1.000000 -1322.240112  \n",
+       "2016-01-03 17:00:38.993       1.000018 -1322.160522  \n",
+       "2016-01-03 17:00:41.493       1.000110 -1322.161377  \n",
+       "2016-01-03 17:00:41.993       1.000110 -1322.161865  \n",
+       "2016-01-03 17:00:44.743       1.000018 -1322.161011  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#plt.savefig(\"test.png\")\n",
+    "print(df_res.count())\n",
+    "print(df_res.index.min(), df_res.index.max())\n",
+    "df_res.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['year', 'month', 'day', 'hour', 'weekday', 'Unnamed: 6', 'bid_price',\n",
+       "       'ask_price', 'bo_spread', 'high', 'low', 'avg_bo_spread', 'count',\n",
+       "       'open', 'close', 'avg_price', 'range', 'ohlc_price', 'oc_diff',\n",
+       "       'period_return', 'pca'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#df.drop([\"vol\"], axis=1, inplace=True)\n",
+    "df_res.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df_res\n",
+    "# all these should refer to the price prediction period, so for tick it doesnt exist\n",
+    "#df['low'] = df.bid.min()\n",
+    "#df['high'] = df.bid.max()\n",
+    "#df['open'] = df.bid.iat[1]\n",
+    "#df['close'] = df.bid.iat[-1]\n",
+    "\n",
+    "# to include seasonality as a feature\n",
+    "#df['hour'] = df.index.hour\n",
+    "#df['day']  = df.index.weekday\n",
+    "#df['week'] = df.index.week\n",
+    "#df['month'] = df.index.month\n",
+    "\n",
+    "#df['momentum']  = df['volume'] * (df['open'] - df['close'])\n",
+    "df['avg_price'] = (df['low'] + df['high'])/2\n",
+    "df['range']     = df['high'] - df['low']\n",
+    "df['ohlc_price'] = (df['low'] + df['high'] + df['open'] + df['close'])/4\n",
+    "df['oc_diff']    = df['open'] - df['close']\n",
+    "#df['bo_spread'] = df.ask - df.bid\n",
+    "df['period_return'] = df.close / df.open"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2081682\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Z/uECZy1pn/bx5RHD6gNIBo0EFokpd+fwSIG27PS/57VkUmTTprEACaEEIBJTI4Ui+WJp\n2vMAAZgZPe051QASQglAJKZmOgisbFFHTlcBJYQSgEhMHZ0KegY1ACCqAagJKBGUAERiaqbzAJWp\nBpAcSgAiMTUwEn2Iz7QJqKcjy6FhXQaaBEoAIjFVbRPQovYc/cN5iiVNCBd3SgAiMXUwNON0THMx\nmLKejhwlP7aWgMSXEoBITB0czNOeS5PLzOxtvqgjB6CxAAmgBCASUwcHx1i8IDfjx/W0R4/RWID4\nUwIQiamDQ3kWdbTM+HFHawBKALGnBCASUwcH8yzpqKIGEB6jsQDxpwQgElN9Q/mj3+ZnYlF7uQag\nTuC4UwIQiSF35+DQGIsXzLwJqC2XpjWbUg0gAZQARGLoyNg4haKzpIpOYIhqAeoDiD8lAJEYOjgY\nfXhX0wQEUT+ArgKKPyUAkRg6ODgGUFUTEIT5gNQEFHtKACIxVB4FvLjaGoDWBEgEJQCRGCo3AVUz\nEAw0I2hSKAGIxFDfUNQEVHUfQHuOw6PjFIqleoYlTUYJQCSGDgzm6WzJ0JKZ2UygZYs6ssCxNQUk\nnpQARGLo4FC+6uYf0GjgpFACEImhvqGxqpt/oHI0sBJAnCkBiMTQwcF81ZeAwrEaQL9qALGmBCAS\nQweH8lVfAgqVM4KqDyDOlABEYqZUcvpq7APobo86gQ+EAWUSTzNbK05Emt7h0QLFkle1FgDALRu3\nA9CRS/PDZw6wJDQlveeSM+oWozSHmmsAZpY2s5+a2X+E+2eZ2UYz22ZmXzOzXChvCfe3hf2ra31u\nETnZgTAIrNqJ4Mq62rJHF5aXeKpHE9AfAFsr7n8S+LS7nwMcAq4K5VcBh0L5p8NxIlJnR+cBqrIG\nUKYEEH81JQAzWwm8DfhSuG/AG4FvhENuAq4I25eH+4T9l4bjRaSOypdu1nIZKEBXe5b+EV0FFGe1\n1gA+A/wJUB4vvhjod/fxcH8nsCJsrwB2AIT9A+H445jZ1Wa2ycw27d+/v8bwRJLnwFCdmoBas4wW\nSoyNF+sRljShqhOAmf0KsM/dH6pjPLj79e6+zt3X9fb21vPUIonQF/oAeupQAwDUDBRjtVwF9Frg\n7WZ2GdAKLAT+Hug2s0z4lr8S2BWO3wWsAnaaWQboAg7W8PwiMoGDQ2N0tWXJpmur4He1RQlkYKTA\naZ2t9QhNmkzVCcDdPwZ8DMDM3gB81N1/y8y+DrwD+CqwAfh2eMjt4f5Pwv7vubtXH7qIVCpfvvnT\n7f1k03b0frW62qIawGHVAGJrNgaC/SnwETPbRtTGf0MovwFYHMo/Alw7C88tkniDY+N0tNQ+xGdh\na3SOfiWA2KrLQDB3/z7w/bD9HPCqCY4ZBd5Zj+cTkckNjY3T21nbJaAAmXSKBS0ZBjQldGxpKgiR\nmBkaG6cjV59B/hoLEG9KACIxUnJnOF+sSxMQKAHEnRKASIwM54s40NFS3UpgJ1ICiDclAJEYGRqL\nxmAuqGMNYGy8xGhBg8HiSAlAJEYGQwKoWxOQBoPFmhKASIwM1TsBtCoBxJkSgEiM1L0JSDWAWFMC\nEImRvqE8mZTRnqtPJ/DC1iyGEkBcKQGIxMiu/lGWdbWSqtNM6+mU0dmqwWBxpQQgEhMld/YMjLC8\nu62u513YlmVgVAkgjpQARGKibzDP2HiJFXVOAF1tWdUAYkoJQCQmdvWPANS9BtAdBoNp8t74UQIQ\niYnd/SNkUsbShfWdu39hW5Z8scTh0fGpD5Z5RQlAJCZ29Y9welcr6VR9l9ourwuwZ2CkrueVxlMC\nEIkBd2f3wAjLu+rb/ANRExDAnv7Rup9bGksJQCQGtvcNM1qofwcwQFd7tDTkbtUAYkcJQCQGHt81\nAMDynvongAUtGVKmGkAcKQGIxMATuw6TNmNpHVYCO1E6ZSzqyPH0i0fqfm5pLCUAkRh4YtcAS7ta\nyKRn5y29rKuNLXsOz8q5pXGUAETmOXfn8V0Ds9IBXLa8u42dh0Y0ICxmlABE5rmdh0YYGCmwYhba\n/8uWdUVjCzbvGZi155C5pwQgMs89ETqAZ+MKoLLy6OItu9UMFCdKACLz3BO7B2ZlBHClBS0Zli5s\nUQKIGSUAkXluy+7DnHPaArKz1AFctnbZQjYrAcSKEoDIPLdlz2HWLl84689z/vIutu0f1ALxMaIE\nIDKPHRgc48XDY6xdNhcJYCHFkms8QIxUnQDMbJWZ3WtmW8xss5n9QShfZGZ3m9kz4WdPKDcz+6yZ\nbTOzx8zs4nr9EiJJtTVcmz8XNYDyc6gZKD5qqQGMA//T3dcCrwauMbO1wLXAPe6+Brgn3Ad4K7Am\n3K4GvlDDc4sIx67KmYsawKqedjpbMuoIjpGqE4C773H3h8P2EWArsAK4HLgpHHYTcEXYvhy42SP3\nA91mtqzqyEWELXsOs6K7je4wYdtsSqWM85YvZPNujQWIi7r0AZjZauDlwEZgqbvvCbv2AkvD9gpg\nR8XDdoYyEanS5t2HOW8Ovv2XrV22kK17jlAsaXWwOKg5AZjZAuDfgD909+Pqhh6tITejV4qZXW1m\nm8xs0/79+2sNTyS2RvJFnts/OCft/2XnL1/ISKHI8weH5uw5ZfbUlADMLEv04f8Vd/9mKH6x3LQT\nfu4L5buAVRUPXxnKjuPu17v7Ondf19vbW0t4IrH21ItHKPnctP+Xnb+8C1BHcFzUchWQATcAW939\n7yp23Q5sCNsbgG9XlL8vXA30amCgoqlIRGao3Bl7/hzWAKIBZ6Z+gJjI1PDY1wLvBR43s0dC2Z8B\nnwBuM7OrgBeA3wj77gAuA7YBw8CVNTy3SOJt2TNAZ0uGlbM4CVylWzZuB+D0ha3c8dgezlzUAcB7\nLjljTp5f6q/qBODuPwQmW3360gmOd+Caap9PRCLlD+L7nj7A4gUt3PrAjikeUV/nLu3ke0/uY3hs\nnPaWWr5DSqNpJLDIPFRyZ+/AKMu6Z28CuMmcu7QTB57ZPzjnzy31pQQgMg/1DebJF0ss75r7BLCi\np422bJpnNCXEvKcEIDIP7R4YAaKlGudayow1Sxfw9IuDlFzjAeYzJQCReWjPwCgpg9NmYRH46Th3\naSeDY+PsHRhtyPNLfSgBiMwz7s7WPYdZ2dM+a4vAT2XNaQsA1Aw0zykBiMwzO/qG2XdkjFec2dOw\nGDpbsyzvauXpfeoIns+UAETmmU0vHCKXTnHhiq6GxrFmaScvHBziyGihoXFI9ZQAROaRwbFxHts5\nwM+v7KIlm25oLOcu7aTk8KNtBxsah1RPCUBkHvnOY7vJF0usa2DzT9kZi9ppyaS498l9Ux8sTUkJ\nQGQe+dqDO+jtbOGMRe2NDoV0yjh/eRe3P7qbvqF8o8ORKigBiMwTz7x4hIe397PuzB6iuRgb7xfX\nLGGkUOTLP36+0aFIFZQAROaJWx7YTjZtvPyMxjf/lC1d2Mqb1i7lph8/z9DYeKPDkRlSAhCZB54/\nMMRX7t/Or75sOQuabAK2D77hJQyMFLj1ge2NDkVmSAlApMm5O3/x75vJZVJcu/7nGh3OSS4+o4dX\nn72If/rv5xgbLzY6HJkBJQCRJvdfW/dx71P7+cNfXsNpC+d+8rfp+NAbzuHFw2P8v5+etMifNLHm\nqkuKyHFGC0X+4t83c+7SBWz4hdWNDmdCt2zcjruzvLuVT9z5JCP5ErlMSgvFzAOqAYg0qVs2bueD\n//owOw+N8Lo1vXx9086ji8E0GzPjsp9fxqHhAj94WuMC5gslAJEm9dyBQX7w9D4uWtXN2b0LGh3O\nlM5esoCXr+rmvmcOcODIWKPDkWlQAhBpQvsOj/K1B3awqCPH21+2vNHhTNv6C04nmzZuf2w3rrUC\nmp4SgEiTGS+W+PCtP2V0vMh7LjmT1gbP+TMTna1Z3nTeUrbtG+Q7j+9pdDgyBSUAkSbzN999mgd+\n1scVF63g9Ca96udULjl7Mcu7W/nIbY/y13duZWBEs4U2KyUAkSZy1+a9fPEHz/KeS85oqhG/M5Ey\n432vWc2vXLiM6+97jtd/6l6++INnOaT5gpqOEoBIk3j+wBAfve1RLlzZxcd/dW2jw6nJwtYs685c\nxDVvOIfeBS184s4neeV1/8Xln/shP952gEKx1OgQBY0DEGkKI/kiH/jXh0injc//1sW0ZOZPu/+p\nLO9u48rXnsWegREefP4Qj+w4xHu+tJHO1gyvO7eX9eefztt+fhmpVHNMbpc01sw99evWrfNNmzY1\nOgyRWePufOqup/jek/t4au8RNvzCas5d2tnosGZNfrzE0y8e4akXj/DU3iMMjo2zqqeNyy9awUff\n8tJGhxcbZvaQu6+b6jjVAETm2ODYOJue7+Mnzx3kzsf3sr1vmLQZ6y84PdYf/gC5TIoLVnRxwYou\nSu48uqOfO57Yyz/eu43BsXF+85Wr+LnTO5tmuuu4m/MagJmtB/4eSANfcvdPTHasagASF4dHC9z+\nyG6++fBOHtnRT8khkzJe85LFnNbZwtplXbTl4tHsM1Mj+SJ3bdnLpuf7KDmcubidXz5vKecvX8jZ\nvQs4u7eDha3ZRoc5r0y3BjCnCcDM0sDTwJuAncCDwLvdfctExysByHzh7owWShwZLXB4tED/cIFd\n/SPs6BvmqRcHuXvLXkYLJU5f2Mp5yzo5a8kCzljUTi6j6zDKjowWeHLPETbvGeDZ/UMUS8c+m5Ys\naOHsJR2sXtLOyp52VnS3saKnjdZsGiO68qi7PctpC1ti039Si2ZtAnoVsM3dnwMws68ClwMTJgCR\nueDuuEPlVyEDSu7kiyXy4yXGxkuMFUqMjRfZf2SMB58/xIPP97Flz2GGxsYZG5/8qpbOlgwXruhm\n3eoeVnS3qXljEp2tWV551iJeedYiiiWnbyjP/iNj7B8c4+DgGPuOjLF1z2GOTLHwzKKOHMu7W1nZ\n3c6qRW2c1tlKV3uWnvYc7bk0KTPSKSObNlqzaVoyKXKZFGaGAWYcfT2kzVjQmqEjl47l/22uE8AK\nYEfF/Z3AJfV+kkNDea74/I8m3HeqCo+HjwD38s0peVSeOvriOPYiKO8vhZ9UPn6iY0rHtt0Bi15g\nKeOkF1e5ZpYyI5UyMinDgWLJj34zSqeiF3LKOO453KHox47JplOkU8de3IaFn5GpXtjl3eW/nXv0\nl/Lwt6n8m5px9G9VeW6zk89z4vmPPepYWVR+ssq/xdGbO8WiU/Jou+SQMsikwu9vx/9fC8UShWKJ\n0ileE6f6m5y+sJWX9HbQmk2TTafIplO0ZlO0ZtO0ZdN0tUUfOvqWP3PplNHb2UJvZ8tJ+wrFEgPD\nBfpHCoyXSuDR6384P87h0QKHR8bpH8nz0AuH+K+tLzJezT/4BCmD9lzmuM+EbCpFJn3s/RW9V489\npvw5Em0fH0P5fZGa4KVRfn+uXbaQL/z2K2qO/VSarhPYzK4Grg53B83sqRpPuQQ4UOM5ZlMzx9fM\nsUGD43t+6kP096tNouO7D/jie6t++JnTOWiuE8AuYFXF/ZWh7Ch3vx64vl5PaGabptMW1ijNHF8z\nxwaKr1aKrzbNHt90zHXd9EFgjZmdZWY54F3A7XMcg4iIMMc1AHcfN7MPA3cRXQZ6o7tvnssYREQk\nMud9AO5+B3DHHD5l3ZqTZkkzx9fMsYHiq5Xiq02zxzelpp4KQkREZo+uTxMRSah5lQDMbL2ZPWVm\n28zs2gn2n2lm95jZY2b2fTNbWbHvk2b2RLj9ZkX5WWa2MZzza6Fzupni+0o45xNmdqOZVT0mfjbi\nq9j/WTMbrDa22YrPIteZ2dNmttXMfr/J4rvUzB42s0fM7Idmdk6Vsd1oZvvM7IlJ9lv4H20L8V1c\nsW+DmT0Tbhsqyl9hZo+Hx3zWphowMofxmVm7mX3HzJ40s81mNumUMo2I74TH3j7ZeRsuGgXZ/Dei\nTuNngbOBHPAosPaEY74ObAjbbwT+JWy/DbibqM+jg+hqpIVh323Au8L2F4EPNll8lxGNhTLg1maL\nL+xfB/wLMNiE/98rgZuBVLh/WpPF9zRwXtj+EPDlKuN7HXAx8MQk+y8D7gyvo1cDG0P5IuC58LMn\nbPeEfQ+EYy089q01/H/rGh/QDvxSOCYH/HczxVfxuF8HbpnsvI2+zacawNFpJNw9D5Snkai0Fvhe\n2L63Yv9a4D53H3f3IeAxYH34RvNG4BvhuJuAK5olPog6zT0gekOupDqzEp9F8zt9CviTKuOa1fiA\nDwJ/6e4lAHff12TxObAwbHcIPHYTAAAF4klEQVQBu6sJzt3vA/pOccjlwM3hpXQ/0G1my4C3AHe7\ne5+7HyJKVOvDvoXufn947d1M9e+Nusfn7sPufm84dx54mOrfG3WPD8DMFgAfAf6q2rhm23xKABNN\nI7HihGMeJcq4AL8GdJrZ4lC+PlQblwC/RDQgbTHQ7+7jpzhnI+M7KjT9vBf4zyaL78PA7e5e6wrg\nsxXfS4DfNLNNZnanma1psvh+F7jDzHYS/X9rasqoIv5Tle+coHy2zDS+o8ysG/hV4J4mi+//AH8L\nDM9iXDWZTwlgOj4KvN7Mfgq8nmiUcdHdv0t06emPiZpRfgIU51l8nyf6FvnfzRKfmS0H3gn8wyzG\nVHV84TEtwKhHIzb/CbixyeL7I+Ayd18J/DPwd7MYX+yYWYbob/pZD5NMNgMzuwh4ibt/q9GxnMp8\nSgDTmUZit7v/uru/HPjzUNYffl7n7he5+5uI2vGeBg4SVeUyk52zwfEBYGYfB3qJqpPVmo34Xg6c\nA2wzs+eBdjPb1kTxQfSN7Jth+1vAhc0Sn5n1Ai9z943hFF8DfqHK+KqN/1TlKycony0zja/seuAZ\nd//MLMZWTXyvAdaF98UPgXPN7PuzHOPM1btTYbZuRB1ozwFncawT7vwTjlnCsc6+64jafiHqwFsc\nti8EngAy4f7XOb4T+ENNFt/vEn1zbGvGv98Jj6+lE3i2/n6fAN4ftt8APNgs8YXbAeDcsO8q4N9q\n+BuuZvJOzLdxfCfmA6F8EfAzog7MnrC9KOw7sRP4shpfg/WO76+Afyv/zWu91Tu+6Zy30beGBzDD\nf9BlRN/sngX+PJT9JfD2sP0O4JlwzJeAllDeSrTmwBbgfuCiinOeHV7o24iSQUuTxTcezvdIuP2v\nZorvhPNXnQBm8e/XDXwHeJyo6eVlTRbfr4XYHgW+D5xdZWy3AnuAAlGt5yrgA8AHwn4D/jHE/jiw\nruKx7w+v/23AlRXl64iS1bPA5wgDR5shPqJv2g5srXhv/G6zxHfCuVfTpAlAI4FFRBJqPvUBiIhI\nHSkBiIgklBKAiEhCKQGIiCSUEoCISEIpAYiIJJQSgCSSmX3AzN43g+NX12NKXzPrNrMP1XoekXpQ\nApDEMbOMu3/R3W+erfOfYnc30bTPMz1nuvqIRCamBCDzUvhG/qRFC+ZsNbNvhNk2X2FmPzCzh8zs\nrjBlLxYt0PIZM9sE/IGZ/W8z+2jYd5GZ3R8W+viWmfWE8leY2aNm9ihwzRTx/E5Y+ON7hFkpzeyP\nzezBcN6/CId+AniJRQvAfMrM3mBm/1Fxns+Z2e+E7ectWkjmYeCd4Xf4pJk9YNECN79Y1z+qJI4S\ngMxnLwU+7+7nAYeJPqT/AXiHu7+CaObP6yqOz7n7Onf/2xPOczPwp+5+IdEw/4+H8n8Gfs/dXzbN\neC4Oz/16M3szsIZoHYGLgFeY2euAa4FnPZoY7o+ncc6D7n6xu3813M+4+6uAP6yIU6Qqp6qqijS7\nHe7+o7D9r8CfARcAd0dr/ZAmmt+l7GsnnsDMuoBud/9BKLoJ+HqYY77bo4VCIFrx7K1TxHO3u5cX\nFXlzuP003F9AlBC2T/N3myzm8symDxHNMSNSNSUAmc9OnMjqCLDZ3V8zyfFDsxxP5fkN+Gt3/7+V\nB5jZ6hMeM87xNfHWU5wTYCz8LKL3r9RITUAyn51hZuUP+/cQzbTZWy4zs6yZnX+qE7j7AHCooj39\nvcAPPJrHv9/M/kco/60ZxnYX8P6wLCBmtsLMTiNKUp0Vx70ArDWzllDruHSGzyNSNX2DkPnsKeAa\nM7uRaKrlfyD64P1saNrJAJ8BNk9xng3AF82snWjO/ytD+ZXAjWbmwHdnEpi7f9fMzgN+EpqjBoHf\ndvdnzexH4ZLSO939j83sNqJpl3/GsSYjkVmn6aBlXgpNKf/h7hc0OBSReUtNQCIiCaUagMgMmNlb\ngE+eUPwzd/+1RsQjUgslABGRhFITkIhIQikBiIgklBKAiEhCKQGIiCSUEoCISEL9f4tOzVoPcWjQ\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8dd194c358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create ohlc prices\n",
+    "df_res.head()\n",
+    "print(df.high.count())\n",
+    "sns.distplot(df.period_return)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#df.drop([\"_id\"], axis=1, inplace=True)\n",
+    "df.head()\n",
+    "#df.to_excel(\"df_res.xlsx\")\n",
+    "import dill as pickle\n",
+    "with open(simname+'_fx_features.pkl', 'wb') as file:\n",
+    "    pickle.dump(df, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add PCA as a feature instead of for reducing the dimensionality. This improves the accuracy a bit.\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "dataset = df.copy().values.astype('float32')\n",
+    "pca_features = df.columns.tolist()\n",
+    "\n",
+    "pca = PCA(n_components=1)\n",
+    "df['pca'] = pca.fit_transform(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jTVjoAozKxfopdVr65o+/efWr2UDgWLGAMsd+8YIw99WeFCwY2W8b0/5ewZPf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8dec67bcf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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GW+tc5tgd2MeemHTfP3n9uTMef/eTzmf3k9Yd2brjheetnf4L4J7Dj+eew8eS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\n6CRJkmru/wNh7xHC3s7IPwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8dec722748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ZvEmSJDURkzdJkqQm4pw3SZUw4/9+0OgQJKkp2PMmSZLUREzeJEmS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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e22e71cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ZgKmTR/VZaGZmpebEzswqS0szOxub9it+29tOZFtaXAHwpW/9nJqaGr77lQv7Mjozs5Jy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\nMzMzM6sSTuzMzMzMqsT/DwTnGDSgc8FCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e25268208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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mtzY3sKWpvleGt5eaTx6Yfs2sYvlQrJmVob0Pfardi5HDsnPkqqo6PibbAjyz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\nswrx/wBqQ1b+/IecaAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e25007e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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m9X0fqdP6qmvLN5Cb3o5sT9O/1tbGmABl1T+SnTEq4THxl8cyjuDNOu53vX9r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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e22f4d780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.colors as colors\n",
+    "import matplotlib.cm as cm\n",
+    "import pylab\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "norm = colors.Normalize(df['ohlc_price'].values.min(), df['ohlc_price'].values.max())\n",
+    "color = cm.viridis(norm(df['ohlc_price'].values))\n",
+    "plt.scatter(df['ohlc_price'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "plt.title('ohlc_price vs pca')\n",
+    "plt.show()\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs pca')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['ohlc_price'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs ohlc_price')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['period_return'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs period_return')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['period_return'].shift().values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs period_return shift')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['bo_spread'].values.min(), df['bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['bo_spread'].values))\n",
+    "    plt.scatter(df['bo_spread'].values, df['period_return'].shift().values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('bo_spread vs period_return shift')\n",
+    "    plt.show()\n",
+    "\n",
+    "#plt.figure(figsize=(10,5))\n",
+    "#norm = colors.Normalize(df['volume'].values.min(), df['volume'].values.max())\n",
+    "#color = cm.viridis(norm(df['volume'].values))\n",
+    "#plt.scatter(df['volume'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "#plt.title('volume vs pca')\n",
+    "#plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "norm = colors.Normalize(df['ohlc_price'].values.min(), df['ohlc_price'].values.max())\n",
+    "color = cm.viridis(norm(df['ohlc_price'].values))\n",
+    "plt.scatter(df['ohlc_price'].shift().values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "plt.title('ohlc_price - 15min future vs pca')\n",
+    "plt.show()\n",
+    "\n",
+    "#plt.figure(figsize=(10,5))\n",
+    "#norm = colors.Normalize(df['volume'].values.min(), df['volume'].values.max())\n",
+    "#color = cm.viridis(norm(df['volume'].values))\n",
+    "#plt.scatter(df['volume'].shift().values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "#plt.title('volume - 15min future vs pca')\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this creates a training dataset for the model\n",
+    "def create_dataset(dataset, look_back=20):\n",
+    "    dataX, dataY = [], []\n",
+    "    for i in range(len(dataset)-look_back-1):\n",
+    "        a = dataset[i:(i+look_back)]\n",
+    "        dataX.append(a)\n",
+    "        dataY.append(dataset[i + look_back])\n",
+    "    return np.array(dataX), np.array(dataY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iIiIiIhICFLyJiIiIiIiEAA2bFBERERGRxqVHBTSIet5ERERERERCgII3ERERERGREKBh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eJLNxAaWvt+y9rsv/jycMaNXYbDrnDnXV1ISNDx1ZfOB5R77u3O6VN6Zjz/AwJBfIKW2a/f6tKf\nNPE/mEyl+PqomfnK8PrZdzhIe+cD2r45B9QqjKvXYE5JQ3vrTQDof1xF/s7faNKrBx2/+Lj8pwIW\nuNTbvPoiPqGhKHYbaW+/7/o5gBZPP4HK15e2f3fe+0WHj5G2sPLcrIaIvYwff6HDzCfotewtHDYb\nh2c5dWyFxZz98id6/PMN1/4ohXM1+t+b9V8Xf8bYPzV/KR3nvwIqNdkr11Fy5iwxtzv3rmb98D/y\ntu8mvE8SSV9/iMNs4cTcRZeoNBdOQ/g/Z+s+ovt1YegP810/FVBBr7ens2/2UiwGE4cXfUnS3Il0\neHIU+cdSSft+IwCxQ3vSauQwFLsdu6WM3TMu4Gw6u4PTC5aQ+NaroFKRs3I9pSlnibnd2fZm/fCz\n0/+9u9PtK6f/T85znpTsGx5Gh7kzAGfbq1+7GdOuuhZ4OfF27AMr6+8kyZWGUGo4JacxUL588QzQ\nV1GU7UKIj3CuJ7oVmAacA3bgnBouADYA+xVFmVBLep8COuAW4CpgExAP3AN0r9Art1ux5+1xnLNv\n91QsmwSKcL7ReLA8X75AW0VRDlE7jdPJl4mc0d57M6v75Hc29PHe+TNDtn/D0VsGe81++582sr73\nKK/ZH7pjOXaWec2+mvu9bn/7wNu8YrvPZucx0mWOz7xi31f1kNd9nzzkJq/Z775hlddjz1t1D42j\n/r1tf2v/O7xmv//W771WfjXOnxpYkeSdn3y4bfcytg2o/nujl49+W37weuzjnGho9OjHJjbq52Pt\nR4capR8b+7LJY8BTQogjOH/p1zUfXn5AyKvAdmAbFRtFzk8asAtYDTyuKEpdP2D4UbnOASHEfuC+\n8iWaI4E3y6/tA+SxehKJRCKRSCQSiaRBabTLJhVFSaFyT1pVBleR+SfwzwtIdp2iKG6/fqkoyqfA\np9Xsdiz/3wZMLf+rqrMPGHgBdiUSiUQikUgkEonkD9HYZ94kEolEIpFIJBKJREIjnnm7WIQQLwLV\nFxsvVxTlES9kRyKRSCQSiUQikVRDkT8VcFH86QZviqK8jvN31yQSiUQikUgkEonkT4NcNimRSCQS\niUQikUgkVwB/upk3iUQikUgkEolE0rhRFDmHdDFIr0kkEolEIpFIJBLJFYAcvEkkEolEIpFIJBLJ\nFYBcNimRSCQSiUQikUguK/K0yYtDzrxJJBKJRCKRSCQSyRWAUBTF23n4KyCdLJFIJBKJRCK5HFwR\nU1pZD3du1M/HMf/a3yj9KJdNShqcnNEdvWZb98nvbOgz0mv2h2z/hqO3DPaa/fY/bWR97+q/WX/5\nGLpjOXaWec2+mvu9bn/7wNu8YrvP5hUAlDk+84p9X9VDXvf97qHDvWY/af1qr8eet+oeGkf9e9v+\n1v53eM1+/63fe638au4HYEXS/V6xf9vuZWwbcLtXbAP02/KD12P/SkFRGuXYqNEjl01KJBKJRCKR\nSCQSyRWAHLxJJBKJRCKRSCQSyRWAXDYpkUgkEolEIpFILity2eTFIWfeJBKJRCKRSCQSieQKQA7e\nJBKJRCKRSCQSieQKQA7eJBKJRCKRSCQSieQKQO55k0gkEolEIpFIJJcVxSH3vF0McuZNIpFIJBKJ\nRCKRSK4A5OBNIpFIJBKJRCKRSK4A5LJJiUQikUgkEolEcllRFDmHdDHIwZvEa/h17Efwfc+DUGPe\n8i0lqz52+17T+2aCho8BAYq5hMLPZ2M7ewyAgGEPEDDwLhCC0s3fULr2i3rZjOjdhYTJjyLUKjJX\nrCf18+89ZBKmjCayb1ccZiuHZ79L0fEzALR/8Umi+iZhzctn1wNTXfLaIX24aszfCGrVjOQxMyg8\neqpeeQnq1hPd+AkIlRrTmpXkfvNvd/80b0Hs5OfQtEnA8NnH5H73tes7VVAwMU9PR9PiKkAh8+03\nMR89XGuZ2055FKFSkVFLmdtOfZTIPt2wWywcmf0ehcfOnFdXN6Q3V411lvm30TMoPHoagOgb+tPy\n/turpR4O5NXLJxW8OGMFmzYeJyIyiBU/PXFBupeChrAf1rMbrZ4ei1CpyV65hoxl33rItHp6HOG9\nu2O3WDg1byHFx51+VQcH0ebZCQRe1RIFhVNvLKLo0DEiBvcj7tF7CWjZnIOPTaP42Ml65WXrllO8\nMXcNdofCXSO7MHZcX7fv8/NLeenFnzh71oRGo2b2nFtIaKsD4PPPdvHt8n0oisLIUV158OGeF+yL\nLZtPMu/1/2F3OBg5qivjxvf3sD/zhRWcTctDo/FhztzbKu3/ayfLl+9BUWDUqK489EjvetkM7ZFE\n3FOPg0qFYdXPZH+13EMm7qnHCe3VA4fFQsr/vUXpCWcs60bcTtRNN4IQGFb+TM5/nXEQ+9D9RN18\nIzZTPgDnPv4XBbt+c6XXELEH0HzUjTS/60YUhwPjr3s4+e4X+IQGc828ZwjpEE/myo3n9YW36/98\n/Cljv1dXWk8ai1CpyP5pLelf/NdDpvWksYT3ScJhtnB87iJX7AOgUtHlo39g1Rs5/NzrbnrN7rmd\nqyY8yo6bH8SWX/iH89pQ/u84/SGi+3XGbray99XF5B9N8ZAJbKolad4E/JoEYzqSwp6X3kex2YlM\n6kDP+VMpOacHIPOX3zi+9LtKRZVg0OdzKNXnsWvyPzzSDevZldaTxkG5/8/V0PZeNWkc4b2TcFgs\nnJj7tsv/Sf9Zgr2kFMXhALuD/eOecdNrevftXDVhNDtveaBW/1/OdkDy10AOeSXeQagIeWAmpgVP\nkDvzNjS9bkLdtLWbiF1/jrw3HyH35REU//ghIQ+/AoC6WTwBA+8id8695L5yF5rOg1Dr4uq2qVLR\n7pmx7J/6OjvvnYLuuv4EtmruJhLZpyuBcbHsGDWRo298SLtnx7u+y1r5C/umzPFItvhUGr/P+Dum\nfUfqX36ViugnJpH+ynOcfvJhQgcNwS+upXv5CwvIXryI3P9+7aEePX4Cxbt3ceaJhzgzcQzWs2m1\nmmo3bQz7przOjnunEH19P4JqKHNAXCzbR03k6LzFtHt2nCuPtekWnT7Lwef/4VHm7P9tZddD09n1\n0HQOvfZO+dULG7gB3DmiM0s+uv+C9S4Vl9y+SsVVUx7jyPTX2PfQU0QNHUhAS/d7Nqx3Ev7Nm7L3\nvsc4/ff3uGpq5YNTq6fHYdq5h30PPsmBRydRmpoOQOmZVI7NnEfB/kP1zord7mDO7J/5YMk9rPjx\nMVatPMSpk3o3maVLfqV9h2i++2Ecc9+4jTfmrQXgxPEcvl2+jy//8yjffj+OTRtPkJaae0GusNsd\nzJm1msUf3cePK59k1U+HOFnN/pIPt9K+Qwzf//g48968g7mv/+yyv3z5Hr5ePpbvfniMjRtPkFof\n+yoVLZ5+ihMzXuLw6MeIGDIY/5Yt3ERCe/ZA07wphx4aQ9r8RbScNAEA/1YtibrpRo48NZnD456k\nSe+eaJrGuvRyvvmeI49N4MhjE9wGbtAwsRfeLRHtwB7sfHAaO++bSuqyFQA4rGWcWvI1J9/5rG7/\ne7H+6+LPGPttpj7GoWmz2PPARLTDBhBQ7T4I752Ef1wsu+95gpN/f5/4aY+7fd901C2UlMd8Vfx0\nUYT16II5K+eSZbch/K/r15mguBjW3/EM++d8zDUzHq1RrsPT93Bq2WrW3/EMZQXFtLxjsOs7495j\nbLrvBTbd94L7wA1ofe+NFKZk1GxcpaL11Mc4NO019j44odz/7m1veO8kAprHsufexzn5f+/R5hn3\nQevvk2ayf/QUj4Gbny6KsJ5dz+//88RyBZeyHZD8NbiiB29CiI1CiO6XOM3HhRAPXco0JZ74tO6E\nLScNhz4d7DYsO1ej6TLETcZ2ah9KSQEAZacOoAqPdurGtqbszEGwmsFhx3osGU23YXXaDL06npL0\nLMwZOSg2GznrtqEd2MNNJmpgD7JWbwSg4NAJfIID8YsMA8C07wi2giKPdEtSz1GSVkvHUQv+bdtj\nzTxHWXYm2GwUbN5AcO9+bjL2fBPmE8fAbne7rgoMIiCxM/lrVjov2Gw4ij3zVUFplTJnr91G1ED3\nkNEO7EHWqk1VyhyEX2QYoVfH16pbklJ3mWOu63fe789H9x4tadIk4KL1/yiX2n5whwTM5zKxZGaj\n2GwY1m8hvH8vN5mI/r3Q/+8XAIoOH8MnOAjfyHDUQYGEdk4kZ6XzAVqx2bAXFQNQmpqO+ey5C8rL\nwQMZtGgRQVxcOL5+aobfdDUbNhx3kzl1Uk+vXq0AaN06inPnTBgMRZw+baTTNU0JCPDFx0dF9x4t\nWLf22AXaP0eLluHExYXj56dm+M2JbFjvnsapU3p69S633yaKjHP5GAxFnDpl4Jprmrns9+jRknVr\n6n5pEtS+LeZzGVgzs1BsNvJ+2URYX/cZu7B+vTGuWQ9A8ZGjqIOD8YkIx79FHMVHj6FYLOBwUHjg\nIGED6ndvN0TsNRtxPSmffY9SZgOgLM/ZRjrMFvL3H8VhLTtvnrxd/3XxZ4v9kA4JmNMzsWQ4Y1+/\nbiuR1WN/QE9yft4IQOGh46jLYx/ATxtJRJ/uZP+41iPt1hNHk/LBv0C5ZNltEP/HDEoifeUWAPJ+\nP4lvcCCaqDAPuageiWSu3wXA2Z82EzO47sc7f10E0f27kPb9LzV+H9IhAfO5LFfbq1+/hYj+7rPF\nEf17kvNzRdt73NX21sVVE8eQ8v6noNReAeeL5QouZTtwpaE4RKP+a6xc0YO3S40QwkdRlA8VRTn/\nq0vJH0YdpsORm+X67MjLRhWuq1Xef8AIrAe3AmA7dxLfhG6IoCbg54+m0wBUETF12tRoI7DkGFyf\nLTlGNNqIajKRmLONlTL6XDTayHqXq774Rmqx6SvfdtsMenwjtfXTjY7FXmAidvLztHp7KTETpyM0\n/rXKm3OqlCfHszwabUQ1Gadf/D2uX5gvdMP61i30F8EvKtLt3rPqDR6+9IuKxJqjryJjxC8qEk1s\nNDZTPm1mTOKajxbS+tkJqPw1F52XnJxCYmJCXJ+jo0PJyXZf7tOufbTrofzggXNkZuSTnV1IfIKW\nPbvPYsorobS0jC2bT5GVdWEPDdnZhcTENHF9jqnN/pqjABw4cI6MDBPZWQUktNWye3eay/7mzSfI\nrId936goyvRVfWvANyqymkwkVr17HflFRWFOSSW4UyLq0BCERkOTXj3w01bGqvbOW+mw9H1aTpuC\nOjjYLc2GiL3AFk0J69yB7h/Ppdv7rxHSoU2d5a+Kt+v/r4Zf9X5Hb8Sver8TFYG1avuQY0QT5ZRp\n/fQYznzwL48BQkT/nlgNRopPpjRc5i8R/roISqv0q6U5ufhr3QdHfmHB2AqLUeyOGmUirklg8Ffz\n6LXoWUJaN3Nd7/jMgxx++0sUR80DKD9tpLtv9UY01WLfTxtZrY4MlTIKJC6YReeP3iL61usr89O/\nJ1a9kZJTKecvez36UW+0A5Irm0Y5eBNCfC+E2C2EOCSEGC+EUAshPhVC/C6EOCiEmFJNXlX+veea\ntkqZIiHEgvI01wshtOXXNwohFgohkoFJQohXhRDTyr+LF0KsE0LsF0LsEUK0Kb8+XQjxmxDigBDi\ntVrsjRdCJAshkpcsWXLJfPNXxLd9DwIGjKBo+XwA7JmnKVn9CWHPLCFsyoeUnT0GisPLubx8CLUa\n/zZtyVv1AymTxuGwlBI56j5vZ8uN0MR4HGart7Pxp0Co1QQltCH7+9UcGDsZh9lMs/tHNqjNseP6\nUlho5q47l7Lsi2Tad4hBrRK0aRPF6LF9GD/2Sx4f9yXt2kejUl36t5PjxvenoNDMnbcvZtnnu+jQ\nIRaVWkWbNlrGju3H2DHLGD92Ge3bx6BWNWw3Zk47S9ZXy0l483US3phN6cnTzv0vgP7Hlfz+wGiO\njH+Kstxcmj8+rkHzAiDUKnybBJM85gVOvvs5nV6fWrfSBeLt+pc4Ce/bnTJTPsXH3PdRqzR+xD00\nktSPvvRSzi4v+UdTWHvz02y8ZwZnvv4fPd5y3vPRA7piycuvcf/cpeLgU8+zf5fLl0sAACAASURB\nVPQUDk+bReyImwjtfDUqjR/NHxxF2sf/rjuBBuJytAOSxktjPbBktKIouUKIAOA3YDfQTFGUjgBC\niKrz7T7AMuB3RVFe90zKRRCQrCjKFCHEy8ArwITy7/wURelenvarVXSWAW8oivKdEMIfUAkhrgcS\ngJ6AAFYIIQYqirK5qjFFUZYAFaO2S7io4c+B3ZTjNlumCo/Gkee5blzdvC2hj8zCtOBxlOJ813Xz\nlv9i3uLc9B00YhKOvCwP3epY9LlodFGuzxpdJBZ9bjUZI/7RkVRY0mgjsOiNXGrKjHp8qry994nS\nUmbUn0ejiq5Bj82gx3zcuVyscNsmIkfWPnjz11W+5dPoPMtj0efir6tS5nK/CB+fOnVrI3pYP7LW\nbiU+vnENKr2F1WB0u/f8tFEevrQajPjptMCRcplIrAYjKAoWvYGiI86lbcaNv9Ls/rsuOi86XQhZ\nWZUzLdnZBeiiQ9xkgoM1zJl7KwCKonDDsPdoHud8C37XyC7cNbILAAsX/EJMNd26iI4OISurMpaz\narE/d97tLvvXDV1EXIX9UV25a1RXABbMX09MdGidNssMBnyrxJufNooyg7GajBE/bRTFVWSsBufb\neOPqNRhXrwGg6ZiHKSufobPlmVz6hpWriX/d/V1eQ8SeJScX/S87ASg4fBLF4cA3LJQyU/1mwLxd\n/381rNX7HW0k1ur9jiEXv6rtgy4SiyGXyMF9iOjXg/DeSaj8fFEHBdL2pcmkL/sOTayOrp8udKXZ\n5ZP57B83nbJcE42FQf+eC4Dp8GkCoivv5wBdBGa9+15oq6kIn5AghFqFYne4ydiKS11yOdv2o3pe\njV9YMBGd2xIzMInofl1Q+fniExxAt9nu+9WseqO7b7WRWKrFvlXvbJ8rokKjjXLJWA3Ouioz5WPc\nvIPgDm2xFRajidXR5Z8LXfJdPl7A/vHTPPxvLo/xChpLO9BYUBT58udiaJQzb8DTQoj9wA4gDvAD\nWgsh3hFC3AhUvTsXU/fADcABVJz88AVQ9XgzjxMhhBAhOAeM3wEoimJWFKUEuL78by+wB2iPczAn\nuQBsZ37HJ7oFqqhmoPZB02s4ln3ua9ZVETE0eWoh+UtnYM9OdftOhES4ZDRJQzHvWFWnzcIjJwmM\ni8U/Vofw8UE3rB+GLe4HDBi2JBMzfDAAoYkJ2ItLsBovfWdoPn4Mv6bN8Y2OAR8fQgcOoWjnr/XS\ntZtyKTPk4NfMuek6qHMSlrTUWuWrljn6un4YtiS7fa/fkkzMTYMAZ5ltRc4yV/dXTbo1IgS6oX3J\nXrutXuX5K1B09AT+zZuiiY1G+PgQNXQAedt2usnkbt2F9oZrAQi+uh324hLKjHmU5Zqw5hjwj3Mu\nFWqS1JnSlLMXnZeOnZqSlppLerqJMqud1asOc+21bd1kCgrMlFmdey2/Xb6PpO4tCA52LtU0Gp3D\nm8yMfNavPcZNt3S8QPvNSE3JJf1sHlarndUrD3HtEE/71nL73yzfS/fuLT3sZ2Tks27NUW6+tVOd\nNouPHse/WVP8Ypz+D792EKZfd7jJmH7dQeT1QwEI6tAee3Extlznw6NPmHOZp69OS3j/fuSu3+i8\nHlG5rCusf19KU9zjsCFiT795F+FJTp8HxMWi8vW5oAc2b9f/X43CoycIiItFU16X2mH9yd22y00m\nd+sudDcOBiAksS32omLKjHmkLv6C30aMJXnUeI69+hb5uw9wfPZCSk6nsuvWR0geNZ7kUeOx6I3s\nGz21UQ3cANcBI5kbk2l+8wAAwjvGU1ZUisXgmVdj8mFihzr3o8XdMpCsTbsB0ERWLrMOS2wNKoHV\nVMSRd79m7U0TWXfrZHa/8C6G3w6z56UP3NIsPHqCgOZV/D90ALlbq/l/2y50N1a0vW2xlftf5a9B\nHeDc/6fy1xDWoyslp1MpOZ3Kb7c9zO6/jWf338Zj0RvYN2ZKjf6vTz/qjXZAcmXT6GbehBCDgWFA\nH0VRSoQQGwEN0Bm4AXgc+BswulzlV+BaIcRbiqKYL8BU1dmw4lqlasgiME9RlMUXoCOpjsNO4Rdz\nCZu6GKFSU7r1O+wZp/Af/DcAzBv/Q9BtT6AKbkLIgzNdOnmz7gagyVMLUAWHodhtFH7xOkpp3Uck\nK3YHx9/6iC4LZzqP3f1pA8Vn0ml6p3Mde8Z3azD+uofIvt3os/xd55G9c9536Se+Npmwbon4hoXQ\n94fFnPnoazJ/3EDUoJ60nToGv7BQOr81g8LjKeyv4VTK6uXP/vBt4mb9HVQq8teuxpqWQtjw2wAw\nrV6BOiyCVgsXowoMBIdC+O0jOfPEwzhKS8j+cBGx02YifHwoy8okc+EbtZo69o+P6fr2i6BSkfnT\nLxSfSafZndcBcO67tRh/3UNU3670+eYd588jzHnP5a+adAG0g3rS9pnR+IWF0mW+s8z7Jjvfn4R1\n7YAlx4A54+JPQJs29Vt27UrFlFfCtQMXMGHiYNdsy+Xgktu3OzizcDEd/vEqQqUiZ9U6SlPOEn3b\njQBkr/gZ045kwvsk0fXLxTgsFk7OW+RSP/P2EhJemorw9cWSkcXJeW8DEDGgN60mjcc3rAnt33yZ\nkpOnOTLt1fNmxcdHxQszb+CxsV9idzi4c0Rn4hO0fP2V80Hp7nuSOH3KwIszfkQIaBOvZdacm136\nUyZ9i8lUio+PihdfuoHQ0Nr3W9Zm/8WXhzNu7DIcdoU77+pCQoKOr750PpTcc293Tp/SM+P5HxAI\n4hO0zH79Vpf+pIn/wWQqxddHzcxXhtfPvsNB2jsfkPDmHIRKjWH1GsypaUTdchMAhp9WUbDzN5r0\n6kHHzz/BYTaT8vcFLvXWr87EJzQUxWYjbdH72IudXUbz8WMIbNMaBbBmZZO6YJGb2YaIvYwff6HD\nzCfotewtHDYbh2e957LX97v38AkMRPhWdO1NgPyqWfJ6/dfFnzH2T81fSsf5r4BKTfbKdZScOUvM\n7TcAkPXD/8jbvpvwPkkkff0hDrOFE3MX1ZFow9EQ/s/Zuo/ofl0Y+sN8108FVNDr7ensm70Ui8HE\n4UVfkjR3Ih2eHEX+sVTSvt8IQOzQnrQaOQzFbsduKWP3jHfrb9zu4PSCJSS+9SqoVOSsXE9pylli\nbne2vVk//Oz0f+/udPvK6f+T85wnJfuGh9Fh7gzAuXxdv3Yzpl17L6jstcVyQ7YDkj8/QjnPKTne\nQAhxOzBWUZRbhRDtgX3AA8AaRVEKhBAdgS8URelSPrCbBgwEBgMjFEWx1ZKuAtyrKMpXQoiZQLSi\nKBMr0lAUJblc7lWgSFGUfwghduBcNvm9EEIDqHHO2M0GhiqKUiSEaAaUKYpyvifVxuXky0zOaO+9\nmdV98jsb+jTs/qDzMWT7Nxy9ZbDX7Lf/aSPre4/ymv2hO5ZjZ5nX7Ku53+v2tw+8zSu2+2x2Hh1d\n5vDO+Uu+qoe87vvdQ4d7zX7S+tVejz1v1T00jvr3tv2t/e/wmv3+W7/3WvnVOH9qYEWSd37y4bbd\ny9g2oPrvjV4++m35weuxj3OiodGT+rdejfr5uOV/djZKPza6mTfgZ+BxIcQR4BjOpZPNgI1CiIpl\nnjOqKiiKMl8I0QT4XAhxv6LUeHpFMdCzfOCWA9xdj7w8CCwWQswCyoBRiqKsEUJ0ALYLIQCKcA4u\nL90PrUgkEolEIpFIJBJJNRrd4E1RFAtQ0+vSt2uQHVzl/1fqkbbHcTxV0yj//GqV/08AQ6qpoCjK\n2zXlRyKRSCQSiUQikUgaikY3eJNIJBKJRCKRSCR/bhrzD2E3Zv50gzchxE6cB5xU5UFFUYJrkpdI\nJBKJRCKRSCSSK4E/3eBNUZRe3s6DRCKRSCQSiUQikVxqGuvvvEkkEolEIpFIJBKJpAp/upk3iUQi\nkUgkEolE0rhRFLnn7WKQM28SiUQikUgkEolEcgUgB28SiUQikUgkEolEcgUgl01KJBKJRCKRSCSS\ny4qiyDmki0F6TSKRSCQSiUQikUiuAISiKN7Ow18B6WSJRCKRSCQSyeXgijgJ5PSIfo36+bj1f7c1\nSj/KZZOSBmdTvxFesz1o2385estgr9lv/9NGVna/z2v2b07+N+t7j/Ka/aE7lrN94G1es99n8wqv\n27ezzCu21dwP4LXyNwrffxvhNfvqu3L5otNor9l/4OAnXvf/X93+hj4jvWZ/yPZvvBr7ABv73uUV\n+4N//Zafe97jFdsAN+76yuuxf6XgkKdNXhRy2aREIpFIJBKJRCKRXAHIwZtEIpFIJBKJRCKRXAHI\nZZMSiUQikUgkEonksqI45LLJi0HOvEkkEolEIpFIJBLJFYAcvEkkEolEIpFIJBLJFYBcNimRSCQS\niUQikUguK4o8bfKikDNvEolEIpFIJBKJRHIFIAdvEolEIpFIJBKJRHIFIAdvEolEIpFIJBKJRHIF\nIPe8SSQSiUQikUgkksuK3PN2ccjBm+SyEt6rK/GTRyNUKjJ/XMfZL77zkGkzeQyRfbphN1s49vq7\nFB0/jUYXSfuXnsY3PAxQyPxhLeeWrwSgw6xnCGzRFACf4CBsRcXsfuSZOvMS1K0nuvETECo1pjUr\nyf3m327f+zVvQezk59C0ScDw2cfkfve16ztVUDAxT09H0+IqZ37efhPz0cP18sHV0x5C168LdrOV\n/a9+SMGxFA+ZgKZaus6diF+TYPKPnGHfy++j2OxED0qi7eOjUBwOFLuDw299Tt7+Y+VlD+Sal8YR\n1jEeTXgolryCWvPQduqjTh9bLByZ/R6Fx84AENG7C22nPIpQqchYsZ7Uz793ph0aTMc5UwiI1VKa\nqef3F+djKywm+ob+tLz/dle6wfEt2PXwc5SkZdBprrMOOv/rXfJ+3UXa4s8I69mNVk+PRajUZK9c\nQ8aybz3y1urpcYT37o7dYuHUvIUUHz8NQNevl+IoLUWxO1Dsdg6Od6af8Op0AuKaAaAODsJeVMyB\nMZM90v0jttXBQbR5dgKBV7VEQeHUG4soOnSMiMH9iHv0XgJaNufgY9MoPnayVp9fCC/OWMGmjceJ\niAxixU9PXJI0G8L3gW1a0fqZJ1EH+mPOzOHk7Lewl5RePvvxV9H6mSdR+fmi2O2cWfAhRUdO1OmL\nLcd9mfdTMHaHYGSPUsYN8szzrtO+zFsZjM0O4YEOPhufj6UMHloahtUmsDng+o4WJg4rqdNeTXR/\n/j6aDeiEzWxl+8yPyT2S5iHT9t4hdHjgOkJaRLN8wNNYTEVOP93cm8TRw0EIbMVmds7+HNPxs/W2\n3RCx0JA2/2jsN5T9+t5/Eb27kDD5UYRaRWaVdrUqCVNGE9m3Kw6zlcOz36Xo+Jnz6gbHt6Tds+PL\nY0/PoVfevryxdwGxH9Gri7PfV6vI/HE9aZ979vvxU0aX9/tWjs55h6LjZ1z9vl9EE1AgY8Vazv3H\n2e9rr+1DqzF3E9iqGXvGPk/h0VM12q6gwzMPE9W3Kw6zhYOzPqi13+08ZxK+TYIpOHqGA6+8i2Kz\nE3tDP1o/dJsz3krMHH7zIwpPOOO148zH0PbvhjWvgG33Tj9vHirwZuxL/hzIwZvk8qFSkfDMOA5M\nfg1LjpFuH/0fxq2/UZKS7hKJ6NONwOax7Lr7KUIS25IwbTx7xz+PYndw6p1/UXT8NOpAf7p9/A/y\nfttPSUo6R15+y6XfesIj2IuL65WX6CcmcXbmNMqMelot+JCinduwnk11idgLC8hevIjg3v091KPH\nT6B49y4y5r0CPj6oNP71coG2XxeC4mLYeOdUwjrG03HGaH595GUPufYT7+XMv1eTuWY7HWeMJu72\na0n7dh2GXb+TvWk3ACHxcXR7YxKbRk4DIHHaQ+i3HyC0XSs23fMcZQXFXL9uMUGtmlNcxceRfboS\nEBfL9lETCU1MoN2z40ge8wKoVLSbNoa9T8/GkpNLj3/Ow7AlmeKUdFo9dAd5vx1k3+ff0/LBO2j5\n0B2cem8Z2f/bSvb/tgIQ1KYF17w5naITKag0fqQtW0FUv24cGDOZqxfMJqx3EldNGs/hqS9j1Rvp\ntOQt8rbuojS1suMJ652Ef/Om7L3vMYKvbsdVU5/g98crO8RDk17Ell/o5qsTr/7d9X/Lp0ZjL6qh\n/lUqrpry2EXbbvX0OEw793D85TcRPj6o/DUAlJ5J5djMebSe9mS96r++3DmiM/c/0IPnn/N8yLso\n/mD5oWbft3l2Iqnvf0LB/kNobxpG03tHcPbjZZfNfssnHiH90y8x7dxDWO8kWjz+CIcnvXheV9gd\nMGdFCB+NNhEd6uDu98O5tr2V+Gi7S6agVDDrh2CWPJpP0zAHxiLn22E/H/hkjIkgDZTZ4YHFYQxs\na6VzC9t5bVan6YBOhLSM5oebZxB1TWt6znyIn++f4yGn33uSc5v2c90nz7ldL0rXs/bRN7EWlNC0\nfyd6v/Jwjfo10kCx0JA24Q/EfgPar9f9p1LR7pmx7J00C0tOLt0/eQP9lmS3fi+yT1cC42LZ4WqT\nx7N77Izz6raf8QQn3/0M097DxN4yhBYP3M6ZJV9dtrJfSOwnTBvH/kmzsOQYSfr4TQxbPPv9gOax\n7PzbBEITE2g7fTx7xs1Asds59c6nFB0/gzrQn6RP/k7eLme/X3w6jd9f+D/aPftYzXVehai+XQiM\ni2XLXZNp0jGeq58by47RMz3k2k64j5QvV5K1djtXPz+G5rcP4ey3aynN0LPz8VnYCouJ6tOFxBnj\nXfrnVm4ibfn/6PTqU3XmA7wc+5I/DV7d8yaEaCWE+N2befgjCCEeEUK86+18XCmEdoinND0Tc0Y2\nis1GzvqtRA7o6SYT2b8nWT9vBKDw0HF8QoLwiwzHasyjqPxNoL3ETElqOhptpIcN7ZC+5KzdWmde\n/Nu2x5p5jrLsTLDZKNi8geDe/dxk7PkmzCeOgd3udl0VGERAYmfy1zjfAGKz4SguqpcPogclcW7V\nFgBMv5/ENyQQTWSYh1xUj0Sy1u8EIP2nLcQM7u7MU6nFJaMO8AdFAcAnKICIru0pPJVOydlsStKy\nKDM5O9uogd3d0tYO7EHWqk0AFBw6gU9wEH6RYYReHU9pehbmjBwUm43stdtculEDepC5aiMAmas2\noh3oXm8AMdf1I3vdrwA4LFby9hwCQLHZKD5xipBrEjGfy8SS6ax/w/othPfv5ZZGRP9e6P/3CwBF\nh4/hExyEb2R4vXwLEHltPwzrN3tcD+6QcNG21UGBhHZOJGflWld5Kh4SS1PTMZ89V+/81ZfuPVrS\npEnAJUvvj5T/fPjHNaVgv7Oe85P3ETGoz2W1j6KgDgoEQB0URJkh9/zywMF0H1pE2omLcODnA8Ov\nMbPhiJ+bzMr9Gq5LtNA0zAFAZLAzzoSAoPKxis0ONgdwEat+4q7typkVzlgxHDiNX0ggAVFNPOTy\njqZRnGH0uG7YfwprQUm5/ikCo+sfIw0VCw1ls77UFvsNar8e91/o1fGUVGlXc9ZtQzuwh5tM1MAe\nZK3eCFS0yYGuNrk23cAWsZj2Old75O7aj26we3kauuz1jf3KfqW831+3lagB1co/oAfZP3v2SVaj\nyTUDWdnvRwBQknqO0rSM8+axguiB3clY5bw38s/T70Z2TyR7g7PfzVi5mehBzv7PdPA4tkLnfW76\n/QT+ugiXTt7eo5QV1OOFcTnejP3GiKKIRv3XWPnLHlgihJCzjpcZP20klpzKxsiSY3Q1xBVotBFY\ncgxuMn7VZWK0BCdcRcGh427Xm3S+mrI8E6XpmXXmxTdSi02vd322GfT4RmrrVQ7f6FjsBSZiJz9P\nq7eXEjNxOqKeM2/+2nBKsyo7eHN2Lv4698bXt0kIZYXFKHbng6M5x+gmEz24O4O++Qc9Fk5n/6wl\nAAQ202E1FdLuqb/RpMNVdJo5DnX5G/Hqg1yNNgJzDfXg73E916XrF9EEq9EEgNVoci5jqYZuWF+y\n13gOnNXBQYT37Yk1R+9Wt1a9wSNvflGRWHP0VWSM+EVVylw9fzadls5Hd+sNHnZCOidSlmvCXEP9\n+0VFXrRtTWw0NlM+bWZM4pqPFtL62Qn1m21oRPyR8ldQk+9LU9JcD4KRg/uh0UVdVvsp73xEyyce\npds3H9PqyUdJXfLZ+R0BZOeriGlS+UImpomDnAK1m0yKQU1BqYqHlzZh5Lth/LCnsr7tDrjznXD6\nz42ib3wZneMubNYNIEAXTnGVdqA4O5cA3cU9hLW5cwAZWw/WW94bsdBQ9V/B+WK/Ie3X5/6rqU/z\n7PciMWdXaXv1zrb3fLrFZ9KJKh/I6Yb0ueyxV9/Y12gjsGRXKYM+t8Y+yV3G6CHj7+r3614W7ZEH\nXQSlVfxrzslFo3OvA2e/W1LZ72bnetQTQPPbrkW/fd8F56ECb8a+5M9DYxi8+QghlgkhjgghvhFC\nBAohhgoh9gohDgohPhFC1No7CCHeEEIcFkIcEEL8o/zap0KID4UQyUKI40KIW8qvPyKEWCGE2ACs\nL782XQjxW7n+a1XS/V4IsVsIcUgIMb7K9UfL09wFuE/VuOdrfLn95CVLlvxxL0kAUAX4k/j6s5xa\n9InH+nrddf3rNev2RxFqNf5t2pK36gdSJo3DYSklctR9DW63guyNyWwaOY3d0+bT7vFR5XlSEdqu\nFYbtB8namIy91EKbR25ruEyUz/hVEJoYj8Nspfi059r7hJenkfntT5Tl5f8hk4eeeo4DYyZzZPpr\nxNx5EyGdE92+jxo6EMP6LX/IRk0ItZqghDZkf7+aA2Mn4zCbaXb/yEtupzFTm+9PvrGImDtvotPS\n+agDA3CUXfhA5o/Yj759OCnvfsSekWNIefcj2jw38ZLYszsEhzJ8+ODhfJY+ms8HvwSSYnAO8NQq\n+G5iHr88Z+TgWR9OZKnrSK3hiO7RnvgRA9izYPllseetWPBW7Ndlv6Huv/pw5PX3aD7iRrr/803U\ngQEotssbe5cr9sG5yiRx7nROvv3PWvfVXQ4ikq6m+W3Xcvzdf9ct3MBc7tiXNC4aw+xTO2CMoijb\nhBCfAFOBx4ChiqIcF0J8BjwBLKyuKISIBO4E2iuKogghqs6DtwJ6Am2AX4QQ8eXXuwHXKIqSK4S4\nHkgolxPACiHEQEVRNgOjy2UCgN+EEN8CfsBrQBKQD/wC7K2pUIqiLAEqRm1KTTJ/Nax6Ixpd5ds0\njS4Si959mYlFn+v2Bk+ji8RaLiPUahJfn07Oms0YNu10T1ytImpQb3aPrt+G4TKjHh9t5UybT5SW\nMqP+PBpVdA16bAY95uNHACjctonIkbUP3lqOuo64O64FIP/waQJiIsjb7/zOPzoCc06ee/r5hfiG\nBCHUKhS7A39dpIcMQO7eowQ20+HbJARzTi7mnFyMe44Q2f1qTnz8HfHlgzeL3n3phUWfi78ukoqh\nVEU9CB8f/N3qJ8Kla83Ndy1j8YsMw1rtMJToYf3IqmXgbE7PIGv5CoIT27nVrZ82yiNvVoMRP50W\nOFIuE4nVUJ6H8iVJNlM+uVt2ENwhgcLyZTuoVUQM7MPBcVNqzIPVYLx424qCRW+g6Ihzpte48Vea\n3X9XjXYaK3+o/NTue3PaOY488woA/s2bEt7HfYluQ9vX3jiElEVLATD+so3Wz9b98BzdxEFWfuWA\nKytfhS7UXk3GTpNAB4F+EOin0L1VGUcz1bSKqpQLDVDo2bqMLSf8SIip+4Gy7T1DiL9roDOvv58h\nKCaCihYnKDqC0hpi/HyEtW1O79ceYcMTC7Dm13/ZljdioaHqH6gz9hvSfn3uv5r6NM9+z4h/dJU2\nWetse4WPulbdktQM9k2eDUBAXCxR/bpd1rLXN/Yt+lw00VXKoI2osU9yl4l0yQi1msS508les8Wz\n3z8PLUZeT/M7hgCQf/gUAdGRmMq/89dFYMlxrwNnvxtY2e9GR7jVU3B8Czq++BjJk9+gLL9+2yQq\naCyx3xhxKI1hDunKozF47ayiKNvK//8CGAqcURSlYk3cv4CBtejmA2bgYyHECKDqsV//URTFoSjK\nCeA00L78+lpFUSoi8vryv73AnnKZhPLvnhZC7Ad2AHHl13sBGxVF0SuKYgUqjx+U1EnB0ZMENI/F\nP1aH8PFBN7Q/xq2/uckYt/5GzI2DAQhJbIutqASr0dmwtZ3xFCWp50j/+kePtMO7d6Yk9RxWveca\n8ZowHz+GX9Pm+EbHgI8PoQOHULTz13rp2k25lBly8GsWB0BQ5yQsaam1yqcuX8vW+19g6/0vkL0x\nmWY3DQAgrGM8tqJSLEaTh44x+TAxQ51LUprfMoDsTckABDaPdsmEtmuFys+HsvxCLMZ8zNlGbEUl\nBMXFEDO0J4Upzv0Ahi3JbmnrtyQTc9MgZxqJCeU+NlF45CSBcZX1E31dP5euYUsysTcNBiD2psEY\ntlSpNyHQDe1L9tptbnZaP3YP4FxaBFB09AT+zZuiiY1G+PgQNXQAedvcO+PcrbvQ3uAc6AZf3Q57\ncQllxjxU/hpUAc49YCp/DWE9ulB6uvKErrCkLpjT0mut/z9iuyzXhDXHgH/5qXZNkjpTmnJlne7V\nUL73CStfPisEzR/6G1k//HxZ7VuNuYR26QhAaLdrMKfXvQemYzMbqQY16bkqrDZYfcCfaztY3WSG\ndLCyJ8UXmx1KrXDgrC9ttHZyiwQFpc59EOYy+PWkH6219prMeHD8qw2sGvUqq0a9SvqGvVx1W18A\noq5pjbWohFJD/WemA2MiGLTgKbbNWEphana99cA7seDN2G9I+/W5/6q3q7ph/dzbT5zta8zwwc50\nEhOwF9fcJlfV9Q0PdSoLQatHR3Luu7WXtez1jf3CI9X6/WH9MWx175MMW38j+sYqfVJ5+QHavfAk\nJSnppH/l2e+fj7Rv1vDrA8/z6wPPk7MpmaY3OR8jm3SMp6yopMZ+N3f3YaKHOPvdpjcPdPW7/tGR\ndH1zKgdeeY+StLq3ZVSnscS+5M9DY5h5qz4rZQI8T6KoSVFRbEKInjgH1aGZBgAAIABJREFUfCOB\nCcCQWtKt+Fz1NYUA5imKsriqoBBiMDAM6KMoSokQYiNQv01NktqxOzi54CM6zX8ZoVaR9dN6Ss6c\nJfaO6wHI/H4Nudt3E9GnGz3/877zpwLmOs+DCb2mPTHDB1N0MoWkT52nS55ZvIzc7XsA0A3rR866\nC1g247CT/eHbxM36O6hU5K9djTUthbDhztkq0+oVqMMiaLVwMarAQHAohN8+kjNPPIyjtITsDxcR\nO20mwseHsqxMMhe+US+zOdv2oe3XhcHfL8ButnDgtcpbr8fbz3Jg9hIsBhNH3vmSbnMn0u6JURQc\nS+XsD//P3n2HR1H8Dxx/76WHhPRCQuihdwKhgyAqoCgCiiIoCIKCCCgqon5Rmqg/UMQCYhdBAVGk\nSTP0IiC9BAgESL/0dpfc3f7+uHjJpRBQyCXyeT0PD8ntzH5mZneyO7uzexEABPbuQM1+3TAZDJj0\n+RyZ9pEl/6n3vqHVW8+CArUevMtydyz70jWCB/YBIGbNFpL3HsG3cxs6rfrI/FrqWR8DoBpNnHv/\nC9p8OB00GuLW/UH2JfMbwS5/u4YWs6cQNKAXuvgkTkxfYInr2aYJ+kQtuthEy2dOft7UHWm+It9y\nqTlt/M/rufTBYpq8PwNFoyFxw1ZyL18lYMB9ACSs3UTa/kN4dWpHm+WLMen1XJi7EAAHL08azX4N\nMF+J1W7dQdrBI5Z4Pr27od1a+ssKADCa/nFsgEsfLiH0jSkoDg7oY+O5MPdDALy7daTOC8/g4OlB\n43lvknMhijMvzSi7HDfopSmrOXgwmrTUHO7qvoAJz/dk0JA2/3yF/6L+12t737u7EziwHwApO/eR\ntGFrhcaPencRdSaOQbGzw5SXR9R7H5fbFPZ2MH1AFmO+8sCkKgxspyM0wMiKA+Y/8UPDddT3N9K1\nYR4PLfRCo8Dg9jpCA42ci7Nj2ip3TKqCyQT3tdDTs3FeORFLitl1nKDuLXlwwzsFrwv/0rLsrk8m\nsf9/X5OblEajx++m6aj7cPHxoP/qt4nddZz9M76m5bgBOHq60eH14YC5724c+vaNBb9NfeF2xfzX\nff82xr+R/U81moj8v6W0/uB181ewrNtO9qVrBA00H/di12wmee8RfDq3pdPKReavb5n1yXXzAgT0\n6UrNQebyJ0UcIG7d9gqt+432fdVo4vz8pbRc8Ib5qwLWbSfn0lWCCo77sb9sJmXvEXw6tSV85ccF\nXxFkbkcPy3E/mrCv3ze3+eIfSNl3BN/uHQidMhoHz+q0eP81ss5f5vjkmaWWIWnPX/h2bk33nz/E\nqNNzYuZnlmXtFrzCydlL0GtTOffRD7SaPZHQcY+SGXmZa2vNL3GpP3oQjh5uNH1lVEGdjOx70vxW\n0VYzn8erXVMcPd3p+dvHnP98VenboYBN+774z1BU1XYz+hRFqQNcAjqrqrpPUZSlBb+PBXqpqnpB\nUZSvgb9UVS1xhFAUxQ1wVVU1UVEUDyBKVVWfgjz+wP1AXWAH0AAYCoSpqjqhIP89wEzMUzSzFEUJ\nBvKBTsBoVVUfUBSlMXAUuA84h/lOXFsgA9gOHPt7fddxR0+b3NHlYZvF7rHnZ87e39Nm8Ruvi2B9\nWMU9D1dc/0M/sK3jEJvF771/Jfu638Zn78rRaedam8c3UsrrsyuAHcMAbFb/StH2q0u+cKCi2A1K\n4fsWo2wW/4kTX9q8/e/0+Ns72e7Z2F77Vtm07wNEdLbN9PKee1ezqcNQm8QGuO/gCpv3ff7Re3Ar\n3qm+vSv1+XGzjdsqZTtWhjtv54DxBc+7nQYmYh4grSx4I+SfwGdl5HUHflUUxRnzjjqlyLIrwEGg\nOjBOVVWdolhvA1VVNyuK0gTYV7AsC3gC2ASMUxTlDIUDNlRVjVMUZQawD/Mdwn/+yiEhhBBCCCGE\nuAk2HbypqnqZwmfRitoGlDtHSFXVOMwvGynNVlVVxxVL/zXwdbHPPgRKm/fRt4yYXwFflVc2IYQQ\nQgghhLiVKsOdNyGEEEIIIcQdpDJ/EXZlVmUGb4qirMH8/FpRr6iq+nvxtKqqPlUhhRJCCCGEEEKI\nClJlBm+qqg60dRmEEEIIIYQQwlaqzOBNCCGEEEII8d8g0yb/mcrwJd1CCCGEEEIIIcohgzchhBBC\nCCGEqAJk8CaEEEIIIYQQVYA88yaEEEIIIYSoUCZ55u0fkTtvQgghhBBCCFEFKKqq2roMdwJpZCGE\nEEIIURGqxC2tY/fcU6nPj1tt3lwp21GmTYrbLmFkS5vFDvjqOFvCH7FZ/D4HfiJyQHebxW+4difb\nOg6xWfze+1eSb/rWZvEdNCNsHn9f9wE2id1p51oAjCyzSXw7hpFn/NImsQEc7UZxqFc/m8UP277B\nZtsezNvf1vu+rePbat8H8/5/qm9vm8VvtnGbTfs+wPZOg20Sv9e+VWzu8KhNYgPcc/BHm/f9qkK+\nKuCfkWmTQgghhBBCCFEFyOBNCCGEEEIIIaoAmTYphBBCCCGEqFAybfKfkTtvQgghhBBCCFEFyOBN\nCCGEEEIIIaoAmTYphBBCCCGEqFDyJd3/jNx5E0IIIYQQQogqQAZvQgghhBBCCFEFyLRJIYQQQggh\nRIWSt03+M3LnTQghhBBCCCGqALnzJmzGsXkX3B9/BTQacnf+TM6GL62WO3fsh2u/UaAoqLpsMr+d\nheFqJAAufYbh2n0QKJC742dytnx/3ViNpozEt3MbjDo9p2Z+Qua5SyXSONfwo+WsSTh4uJNxNoqT\nMz5CNRivm9+nYysaTRmJotEQs3Ybl7/9FYD6Yx/Fr1sYAMFv/R/xH87BmJJsFc+1bQf8R08EOw3p\nm9eTunqZ1XKH4FoEvvAqTvUbkvzdUlJ/WWFZ5jlgCB733A+qij46ioQP30HNzyux3rI0nDISn05t\nMer1nJn5saU+3h1b03CyuT6xa7cR/d0vANhXd6P5rMm41PAjNy6Jk9PnY8jMpnrTBjR+dax5pQpc\nWrqSpB0HAWj7yQxzXuV+AAzqVkBnKcPuXRd5Z85mjCaVQYNbM3pMZ6sypqfn8sb0dVy9moaTkx0z\nZ91PaEN/AL779iCrVx5FVVUGD2nD8Cc7lFnXslR0fM8ObakzcTSKxo6E9ZuJXba6RJo6E8fg1TEM\no17PxbkfkB0ZBUCbHz/HlJuLajShGo2ceOZFAFzr16Hei89h5+qMLi6RCzP/D2NO7k23RXHTp61l\nR0Qk3j7VWLvu2X+9vuJ274pi3txtGI0mHh7citFjOlotT0/X8ebrGwra3p63Z/UlNNQPgG+/+ZOf\nVx1DURRCG/oxc3Y/nJzKP5RVb9+OWhPGgkaDdsPvxC9fWSJNyISxeIS3x6TTc/nd+eScvwiA/8MP\n4tf/XlAUktZvInG1uZ8HjRyOZ+eOoJrIT0vn8rz55CenlBq/Mm3/O63v3Yzbve8DuLVrT+C48aDR\nkLZpA9qVK6yWO9YMIXjKyzg3aEDiN1+SvLrYvqrRUG/hJxi0yVyZMf2Wlu121N+7Y2tCJ41EsdMQ\nV+S4UlTo5FH4dG6DSZfH6ZmLyIo0H5MaT38O387tyEtN5+ATUyzp608Yjm/XMNR8A7kx8ZyZ9TGG\nrJwyy9DoxafwKziGn3z701LPAVyC/Gg56wXLOcCJ/y1CNRhxrR1E8zefpXqjupz/dAXRy9YB4OTv\nQ4sZ43H09gBUrq3ZxpUfN5ZYb2Xq++K/4Y6886YoSh1FUU6W8vnbiqLcXU7eGYqivHT7SneHUDS4\nD3+NtAXPkjz9IZzD+2IXVM8qiVEbQ+o7I0l5YxDZa5dQ/cn/AWAX3ADX7oNInvk4yW8OwbFVd+z8\nQ8oM5du5Da4hgewZPJEz7yyhycujS00XOuEJolesZ8/giRgyswke0Ov6+TUKjac+zV+T5rB36GQC\n7+lCtbrBAFz+fi37n5gKQPafe/F59CnrYBoN/mMnE/PWVC6PH0H17r1xDKltlcSUlUHikoWkrrE+\nsNt7++L1wGCuTBlD9PNPoWg0uHfrVep6AarVqWmV36dTG1xCarBvyPOcnbuYRi+PseRt9NLTHJ08\nm/2PTSbgni6WvHVGPETqnyfYN2QiqX+eoPaIhwDIuniFP0e+wsERUzk6aTaNX3kGpdig0aCuw6Cu\no+jAzWg0MWvmJj5dMpS1v41lw/pTXLyQZJXv8yV7adwkgDW/jmHOOwN4Z+4WAM5HJrJ65VGW/zSS\n1b+MYUfEea5El37CXJYKj6/RUHfyWM5MfYujI8bj27s7LrWt91nPju1wrhnEX4+PJeq9j6k7xfrE\n6dQL0zn+9CTLwRug/svPc2XxNxx7aiIpu/YT9NjDN9UOZRn4cCuWLB12S9ZVnNFoYvasLXyyeAi/\n/jaajRtOc/GC1irN0iX7aNzYn59/GcXsuf2ZN2cbAAkJmfzw/WFWrHySNWufxmg0sXHDmfKDajTU\neuE5Il99k1Mjx+HdqwfOxdrfIzwM5+BgTg4fTfT8hdSaNAEA5zq18et/L2eem8yp0ePx7NgBp6Aa\nAMT/uIrTY8Zz+pnnSd93kBrDHy8zfmXZ/ndc37tJt3PfB0Cjocb4iUS/MY2LY0fh0bMXTrWs//Yb\nMzOJ+2xRyUFbAZ8HH0Z/5cptKd4tr79GQ6MXR3NsymwOPDYZ/z5dcS3lmOQaUoP9Q57n7Duf0ejl\nZyzL4tf/wdHJs0qsNvXgcQ4Om8zB4S+ScyWO2iPK3vd9O7emWkgguwe9wOm5n9P0ladLTRc6YRjR\nyzewe9AL5GdmE/yg+bhqyMji7Ptfc3nZb1bpVaORcx9+x96hL3Jg1OuEDLnHcg5QtP6Vpe+L/447\ncvBWFlVV31RVdauty3EncKjXHGPiFYxJMWA0oDu4Cac2d1mlyb9wDDUn0/zzxWNovM1XXu1r1CU/\n6jjk6cBkJP/cIZzalT3m9useRtzGnQCknzyPvXs1HH08S6TzDmtG4vb9AMSuj8CvR/vr5vdo2oCc\na/HkxiaiGozEb9mLX3dzHmN24RUwxdkZUK1iOYc2IT8uhvyEODAYyNi1jWrhXa3SGNPT0F84C0Zj\nyUpp7FAcncz/OzljKLirV3y9AL7dw4q1R3viN+wAIOPUeezdzPWp3rQBudfi0cUmohoMJGzZY8nr\n2609cRsiAIjbEIFfd/PVbpM+D9VoMhfJ0bFEPcty4ngstWp5ExLihYOjHX37NWX79kirNBcvJBEe\nXgeAevV8iYlJQ6vNIioqmRYtg3BxccDeXkNY+1ps3XLuhuLaKr5bk1B0MXHo4xJQDQa023bh1TXc\nKo1313CSfv8DgKzT57B3q4aDj9d11+scEkTGsVMApB86inePTjfTDGUKa18bDw+XW7Ku4k6ciKNW\nLU9CQjzNbd+3CX9sP2+V5uJFLR3CzSe09er5EBObjlabDYDBaEKvM2AwmNDpDPj7u5Ubs1rjhuhj\nYsmLi0c1GEjZvhPPztZt5dm5I8lbzIPE7DMF7e/thUvtELLOnMOk14PJROaxk3h16wKAqciVbk0p\n/fxvlWn732l972bdzn0fwKVhY/JiY8iPj0M1GEjf8QfuHa3vPBrT09BFnkMt+BtelL2vL24dwkn7\nfcNtKd+trn/1guPk38eVxK17LMfJv/l2b0/8xgjg72OSq+UYnXb0DIaMrBLrTTl4zHLsST8ViZO/\nT5ll8OventgNN3YOkGA5B9iBf8E5QF5qBhlnLlpm4vwtLznNcgfPmKMj+1IMTn7eVmkqU9+vjFRV\nqdT/Kqs7efBmpyjK54qinFIUZbOiKC6KonytKMpgAEVR+imKclZRlMOKoixUFGVdkbxNFUWJUBQl\nSlGUiTYqf5Wm8QrAlJJg+d2UkoCdl3+Z6V26P0zeiT0AGGIu4NCwLUo1D3B0xrFlN+y8A8rM6+Tn\njS6h8Mq+LjEZ52J/YB083DFk5lgOBrrEFEuasvI7+XujTyicCqlPTLb6w11/3FAAqvfoQ/KyL6zi\n2fv4YtAmWn43aJNw8PErsw5FGVK0pP6ygnpfrKTeN2swZWeTc/TPUtdrLr9Psd+90SWWLLdzic9T\nLHkdvT3IS04DzAcs8zQRs+rNGhD+w3zCl/0fZ+d9bmlDS12V+9HQwuqzxMRMAgPdLb8HBFQnMSHT\nKk2jxgGWE7MTx2OIi00nISGTBqF+HDl8lbTUHHJz89m18yLx8Rk30HK2i+/o64M+sXAfykvSltgu\njr4+5CUmFUmTjKNvYZqm82fS4vP5+D9wr+Wz3MtXLCcCPj274OTve6NNYDOJCZkEBla3/B4Q6E5C\novXJWaNG/mzdaj6hP3E81tL2AQHuPDWyA316f0qvHotwc3Oic5e65cY0t22R9tdqcSzW/g6+vsXa\nX4uDry+5l6Jxb9Ecu+ruaJyc8AgPw6FIOwePGkHLFd/gc3dPYr/6rsz4lWX732l9r7Jx8PUlP6lw\nO+drk7D3ufF+Gzh2PAlfLEE13diFMltz8vO22veLHyfNaXzQFT2WJqWU6B/XE3R/L5L3HSlzubO/\nl9X6dYnJOPuXcw6QkFLiPOF6nGv44d6oLumnLlh9Xpn6vvjvuJMHb6HAx6qqNgPSgEF/L1AUxRlY\nDPRVVbUdUPysujFwL9AB+J+iKA7FV64oyjOKohxSFOXQkiVLblcd7ggOjdvj0m0gmT8tAMAYd4ns\nDV/h9dJivKZ8iuHKOVSTqZy1VLyLn5mnO2bs2IJn/1s3pUFTzQ238K5cGvMoUU8NROPsjHvPPrds\n/TdELTxxyDh1gQOPT+HPUa9Se8RANI7m7nDqfwsBMKibUJQAFOqVuqqyjB7TmcxMHYMGfs6y7w/R\nuEkgdhqF+vV9GTW6E8+MXs64Mctp1DgAjebWXyGzdfyiTo1/heNPT+LM1LcIHNgP91bNALjwzkIC\nB/ajxefzsXN1wZRf8kp9VfT0mI5kZugYPPArflh2hMZNArDTKKSn6/hj+3k2bRnHtojx5Obm89va\nU7e1LLorV4lfsZKG784idN5Mci5GQZG/NzFffsvxoU+SvDUC/4ceuC1lqOjtb+t939bxKyu3Dh0x\npqWiu3C+/MR3iNpPPoxqNJLw+y6blcHOxYnW70zh3PxvrGbd3Ap32t9+cWPu5BeWXFJV9WjBz4eB\nOkWWNQaiVFX9+4nW5cAzRZavV1VVD+gVRUkEAoBrRVeuquoS4O9RW9W4RFaBTKkJaIrcLdN4B2BM\nTSyRzr5mKNVHziBt/nOo2emWz3W71qDbtQYAt0ETMRa5iwfg0utRXHqYx+N6bRrOAb6A+Uqus78P\nuiTr5yTy0zOxd3dFsdOgGk04+3tb0uiTUkrNr9jb4RRQeHXMyd8HfVLJ5y8yI7YQ/L93SV7+leUz\nQ7IWe9/CO432vn7kJyeVyFsa19Zh5CfEYcwwt0fmvp24NG5OZsSWEus1lz+52O8pOPv78Hdr/l1u\nxd4eZ/+i9fG25M1LScfRx9N8183Hk7zUkle7cy7HYMzVUa1eCJlno4q0hQGTeglF8UVVzQ9h+/u7\nEx9feLU9ISED/wB3q/W5uTkxa475ZFhVVe69+2Nqhpinkgwa3JpBg1sD8MGCPwgslrc8FR0/T5ts\ndWXU0c+3xHbJ0ybj6O8HnClI40OetqD9tea2NKSlk7JrP25NQsk8dgrdlRjOvGh+FtS5ZhBenayn\nyFZG/gHuVndLEuIzCSg29dHc9v0Bc9vf1+czaoZ4smf3JYKDPfD2dgXg7j4NOXY0hgcGNLtuTHPb\nFml/X1/yirV/vlZb0P4Fafx8ydear5hrN25Gu3EzAMFPP0lekvUzegAp2/4gdO5bxH6zrMSyyrT9\n77S+V9nka7U4+BXuZw6+fhiSS+5PpXFt2gz3jp1xax+O4uCInasrwVOnEfPe3NtV3H9Nn5Rite+X\ndpzUJyXjHFDkmOTnXaJ/lCawX098u7Tjr+ffKrEsZPA9BD/UG4CM0xdxLnKsdvb3QZdYzjlAgHeJ\n84TSKHZ2tJr3InG/7yYx4mCJ5ZWp71dGpko8NbEyu5PvvOmL/Gzk5gay/yavAPIvncLOvzYa32Cw\ns8e5w33o/4qwSqPxDsRjwgIyPn8NY0K01TLF3duSxqldb3T7ref/527/kZT/PQJA0s6D1OjbHQCP\n5qEYsnIsUwCLSj18Cv9e5rfeBfXvSdLOQ+b8uw6Vmj/jzEVcQ2rgXMMPxd6OwD6dLXlcQwIt63UL\n70reNeuHy3Xnz+IQVBP7gBpgb0/1br3JPrDnhtrOkJSAc6Om5mfeANdW7ci7Gl3qegG0uw5Z5U/a\ndYjAfj0AqN6ssD6ZZy4U1Mcfxd6egD5dLHm1uw5Ro19PAGr064l2l3mapnMNf8sLSpwDfalWOwhd\nXBKKnQYHj79PqhQ0Sk1QC9u8eYsgrkSncO1aGvl5RjZuOM1ddzW0KmdGho78PPMzBqtXHqVdWC3c\n3Mx1Tk42P/8UF5vOti3n6Hd/8xtqO1vFzzp7HueaQTjVCECxt8e3dzdS9xywSpOy+yB+95qf+3Rr\n2ghjdg75yalonJ3QuJifQdE4O+HZvjW5Ueb9yd6zYPqqolBzxCPE/7rpptrBFpo3r0F0dGph2288\nQ8+7GlilsWr7VcdoFxaCm5sTNWpU5/ixWHJz81FVlQP7o6lbr/zpVdlnI3EODsIx0Nz+3r26k7Zv\nv1WatL0H8OljPtmr1qQRxuxs8lNSgcJ2dvT3w7NbZ1K2RQDgFBxkye/ZpSO5V6yu4VlUpu1/p/W9\nyiY38iyOQcE4BASi2Nvj0eMuMvfvvaG8iV9/QeTwoZx/ahjX3plF9rGjlXrgBpQ4rvjf3cVy/Pib\ndtchAvv2BMzHJGN26cfoorw7tqb2Ew9y/OV5mPR5JZZfXbWZ/U+8wv4nXiFxx58E9Sv/HCDl8GkC\nLOcAPUjacahEmuKavTGO7EsxRP+wvtTllanvi/8OGXSU7hxQT1GUOqqqXgYetXF5/ntMRjKXzcHr\nxU9BY4du1y8YYy/i0nMIALkRK3F7cBwaN0/chxe8CtloJOXtxwDwnDAfTTUPVKOBzO/moOZmlhUJ\n7Z6/8O3cli6rF2LU5XF65ieWZW0WvMrp2YvRa1M5v2gZLWZNosHYoWRGXiJm7fbr5leNJs69/yVt\nF043v1r/tz/IvmQ+eWswfhjVapnfSOfapj2Jn/xfifonLf6AmjPeB42GjK0byLt6GY/7BgCQvmkt\ndp7e1Jq/BI1rNTCZ8BwwmOjxI9BFniFrTwS1P1iKajSijzpP+u+/lbpegOxL1wgeaJ5WGbNmC8l7\nj+DbuQ2dVn1kfi3zrI+L1OcL2nw4HTQa4tYV1ufyt2toMXsKQQN6oYtP4sR08xRWz1aNqT3iIVSD\nEVU1cfa9peSnZ6JxdqL1h68DYK88gIk4TBRO9bG31/Da6/cydvRyjCYTAx9uRYNQP35ccRiAR4e2\nI+qilunTfkNRoH4DP96e1d+Sf/ILq0lLy8XeXsP0N+6lenXnMrd/aSo8vtHEpQ8W0+T9GSgaDYkb\ntpJ7+SoBA+4DIGHtJtL2H8KrUzvaLF+MSa/nwlzztFMHL08azX4NMF/l1W7dQdpB8/Mdvnd3J3Bg\nPwBSdu4jacOted/SS1NWc/BgNGmpOdzVfQETnu/JoCFtbsm67e01vDa9D+PG/ITRpDJwYAsahPrx\n04q/AHhkaBuiopJ5fdp6FEWhfgNf3prZF4CWrYLoc08jHhn8NfZ2Gho3CWDII63KD2oyceWjT2k4\nbxbYaUjeuBnd5Sv4PWBuu6TfNpB+4E88wtvT/PsvCr4qYIEle/0Z07GvXh3VaODKh59gzDYPIGqO\nGYlzSDCqSSUvMZHoBYtKj1+Jtv8d1/du0u3c9wEwmYj79CNqz5qHYqchdfNG9Fei8epn/kqV1A3r\nsPfyot7CT9G4uoJJxeehQVwYOwpTTtmvwr9VbnX9VaOJyP9bSusPXjcfJ9dtJ/vSNYIG3gNA7JrN\nJO89gk/ntnRaucj89TWzCo/Rzd6ahGfbZjh4utP518VcWvojcb9tp+GLT6NxcKD1h28A5hednHu3\n9EdUzMfwNnT9+UOMujxOzfzUsszqHOCjZbSc/QINxj1KRuRlrhWcAzj6eNDx67nYV3NBVVVqD+3H\nnqEv4t6gFkH9upN5PpqO388D4MIny62DV6K+L/47FFW982b0KYpSB1inqmrzgt9fAtwwT51cp6rq\nKkVRHgDeA7KBPwF3VVWHKYoyA8hSVfX9grwngfsLBnllufMauYiEkS1tFjvgq+NsCX/EZvH7HPiJ\nyAHdbRa/4dqdbOs4xGbxe+9fSb7pW5vFd9CMsHn8fd0H2CR2p51rATBSchpfRbBjGHnGL8tPeJs4\n2o3iUK9+Nosftn2DzbY9mLe/rfd9W8e31b4P5v3/VN/eNovfbOM2m/Z9gO2dBtskfq99q9jcwXbX\n3O85+KPN+z5QJeYj7u/xQKU+P+6447dK2Y535J23goFW8yK/v19Ksj9UVW2sKIoCfAwcKkg7o9i6\nqtacDSGEEEIIIUSVdCc/81aeMYqiHAVOAR6Y3z4phBBCCCGEEDZxR955uxGqqi4AFpSbUAghhBBC\nCHFTKvMXYVdmcudNCCGEEEIIIaoAGbwJIYQQQgghRBUg0yaFEEIIIYQQFUq+pPufkTtvQgghhBBC\nCFEFyOBNCCGEEEIIIaoAGbwJIYQQQgghRBUgz7wJIYQQQgghKpR8VcA/I3fehBBCCCGEEKIKUFRV\ntXUZ7gTSyEIIIYQQoiJUiVtau7s+VKnPj7vu/qVStqNMmxS3nXZMU5vF9v38NBGdB9ksfs+9qznV\nt7fN4jfbuI1tHYfYLH7v/Ssxssxm8e0YZvP4+7oPsEnsTjvXApBn/NIm8R3tRtm87Q/16mez+GHb\nN7Cu3TCbxb//8DLyTd/aLL6DZoTNt7+t468Pe9xm8fsf+sFm9bfDvN9vCX/EJvH7HPjJ5sc9W/f9\nqkK+KuCfkWmTQgghhBBCCFEFyOBNCCGEEEIIIaoAmTYphBBCCCEFYG+QAAAgAElEQVSEqFBq1Xg0\nr9KRO29CCCGEEEIIUQXI4E0IIYQQQgghqgCZNimEEEIIIYSoUPIl3f+M3HkTQgghhBBCiCpABm9C\nCCGEEEIIUQXItEkhhBBCCCFEhZIv6f5nZPAmbMahWVeqDZ2GorFDt2sVuZuWWi13bNUL14eeB1VF\nNRrI/vEdDBeOAOD25CwcW/bAlJlC2owHbzimd3hrGkwahWKnIe63bVz5bk2JNA0mj8KnU1uMujzO\nzvqIrMhLADR67Tl8uoSRn5rOn09MtqSv1qA2DV8ei52LM7q4JM7M+ABjTm65ZXFr157AceNBoyFt\n0wa0K1dY179mCMFTXsa5QQMSv/mS5NUrrVeg0VBv4ScYtMlcmTG97Dp3bE3DySNRNBpi124j+rtf\nSqRpOGWkuc56PWdmfkzmuUvXzVt39BCCBtxNfloGABc//YHkfX/h3aEl9Z8bhsbeHpPBcN3679p5\ngbmzf8doMjF4SBvGPNPVanl6ei6vv7aWq1dScXKyZ9acAYQ29Afgu28OsHLlEVQVhgxpw4inOl43\nVmWI79mhLXUmjkbR2JGwfjOxy1aXSFNn4hi8OoZh1Ou5OPcDsiOjAGjz4+eYcnNRjSZUo5ETz7wI\ngGuDutR78Tk0jg6oRiOXFnxG1pnz5ZZl964o5s3dhtFo4uHBrRg9xrr86ek63nx9A1evpuHkZM/b\ns/oSGuoHwLff/MnPq46hKAqhDf2YObsfTk639lAyfdpadkRE4u1TjbXrnr0l66zevh21JowFjQbt\nht+JX76yRJqQCWPxCG+PSafn8rvzyTl/EQD/hx/Er/+9oCgkrd9E4upfAfDq0ZWgJ4fhXCuEM89N\nJify+m3fbOoI/Lu0wqjL4+iMxWScvVwijUuQH23nTsDRw430M5f5641PUA1Gy3KPpvXo8tUM/npt\nEXHbDgJg7+ZKqzfG4N6gJqqqcuytJdctx+5dF3lnzmaMJpVBg1szekxnq+Xp6bm8MX1dwfa3Y+as\n+wv3/W8PsnrlUVRVZfCQNgx/ssN1Y5XG1n3/em7HvgfQ9KUR+HdpjVGXx7EZn5Fx7nKJNC5BfrSZ\n83zBtr/E0TfN2z6gRzsajhuCajKhGk2c/r/vSD12DoA6Q++j1sC7AIUrv2zn8vJN/6qct7L+jaaM\nxLdzG4w6PadmfmI5rhTlXMOPlrMm4eDhTsbZKE7O+Miyv183v0Yh/Ot30CelcPTFeQDUH/soft3C\nAGj/9Tzs3VxB5ZYd9+o98yi+3duDSSUvNZ3TMz8mT5uKYm9P41efoXrj+qiqqdS2qCx9X/w3yLTJ\nAoqiZNm6DHcURYPb46+T8eFYUt98AKcO/bCrUd8qSd7Z/aS9NZC0tx8m6+vXcRvxtmWZbu8a0j98\n5uZiajSEvjSG4y/O5uDjk/C/uyuudWpaJfHu1BaXmjU48MgEIud9SsOphTHiN0RwfPLMEqttNO05\noj75nkPDp6DdcYCQYTcwmNRoqDF+ItFvTOPi2FF49OyFU63aVkmMmZnEfbao5KCtgM+DD6O/cqXc\nUI1eepqjk2ez/7HJBNzThWrF6uzTqQ0uITXYN+R5zs5dTKOXx1jKeL28V1es4+CIqRwcMZXkfX8B\nkJeWwbGX3uHAEy9y+u1FZZbJaDQx6+2NLF76OL+tf44N605x4UKSVZoln+2mcZNAfvltHHPnPcSc\n2eaTkvORiaxceYQfV45mza9jiYg4T3R0SrntYNP4Gg11J4/lzNS3ODpiPL69u+NSO8QqiWfHdjjX\nDOKvx8cS9d7H1J1ifeJ06oXpHH96kmXgBlD72ae49vVyjj89iatf/kCtcU/dUN1nz9rCJ4uH8Otv\no9m44TQXL2it0ixdso/Gjf35+ZdRzJ7bn3lztgGQkJDJD98fZsXKJ1mz9mmMRhMbN5wpN+bNGvhw\nK5YsHXbrVqjRUOuF54h89U1OjRyHd68eOBdrf4/wMJyDgzk5fDTR8xdSa9IEAJzr1Mav/72ceW4y\np0aPx7NjB5yCagCQeymaC/+bRdbxk+UWwb9LK6qFBPLHQy9yfNYXtJg2stR0TSYO5dKyjfzx0Ivk\nZ2RT66GeReqh0GTiULT7T1jlaTZ1OIn7jhExaCo7h04j61JsmeUwGk3MmrmJT5cMZe1vY9mw/hQX\ni+37ny/ZS+MmAaz5dQxz3hnAO3O3AOZ9f/XKoyz/aSSrfxnDjojzXKnsfe8m3fJ9D/Dr0ppqIYFE\nDJzCidlLaT5tVKnpGj//GJd+2EjEwCnkZ2YT8uBdAGgPnmTXY6+ye9hrHH97MS3fMP+Ndqtfk1oD\n72L3iDfY9firBHRti2vNgH9V1ltZf9eQQPYMnsiZd5bQ5OXRpaYJnfAE0SvWs2fwRAyZ2QQP6AWA\nb+c2181f69F+ZF+Osfrs8vdr2f/EVACcA31JP3n+lh73or9fy8EnXuLgiKlo9xym7qjBAAQ/2BuA\nA0+8yF8TC84RlMI7SpWl74vbQ1GU+xRFOacoygVFUV4tZbmiKMrCguXHFUVp+29jyuBN2IR93RYY\nk65g0l4DYz76Pzfi2LqXdSJ9juVHxckFUC2/G84fRs1Ov6mY1Zs2IPdaPLrYBFSDgcStu/Ht1t4q\njW+39iRs2gFAxqnz2LtVw9HHE4D0o6cxZJQc47uG1CD96GkAUv88hl/P8q8EuzRsTF5sDPnxcagG\nA+k7/sC9o/XVb2N6GrrIc6il3MGy9/XFrUM4ab9vKDeWuc6JqAYDCVv24Ns9zGq5X/f2xG8oWefC\n9io7b3FZkZfJ06YCkB11teDTkn9mThyPoVZtL0JCvHB0tKNv/2Zs33bOKs3Fi0mEd6wDQL36vsTG\npKPVZnHxopaWLYNxcXHA3l5D+/a12br55gYQFR3frUkoupg49HHmfU+7bRdeXcOt0nh3DSfp9z8A\nyDp9Dnu3ajj4eF2/IqqKXTVXAOyqVSNfW/6J7IkTcdSq5UlIiCcOjnb07duEP7Zb3zG6eFFLh3Dz\nxYR69XyIiU1Hq80GwGA0odcZMBhM6HQG/P3dyo15s8La18bDw+WWra9a44boY2LJi4tHNRhI2b4T\nz86drNJ4du5I8hbzIDX7TEH7e3vhUjuErDPnMOn1YDKReewkXt26AKC7chX91ZgS8UoT0KMd19bv\nAiDt5AUc3Fxx8vUskc63fTPLVfWr63YS0LOwz9V99F7itv2JPjXD8pm9mws+bRpz9ZcIAFSDEUNW\nDmU5cTyWWrW8CQnxMm//fk3Zvj3SKs3FC0mEh9cBoF49X2Ji0tBqs4iKSqZFyyDLvh/WvhZbt5wr\nJUrZbN33y3Or9z0wb/uYDUW2vbsrTj6lb/v4bQcAuLZuF4EF296Yq7eksXNxBtV8LHSrE0zayQuY\n9HmoRhPJR84Q2Kt9ifXejFtZ/7iNOwFIP3kee/fCY2lR3mHNSNy+H4DY9RH49TCX3697WJn5nfy9\n8e3Slphft1mty5hdOOPFkJGFMSf3lh73is6osXN2svxcrW5NUg+ZL+DkF/RNz6Z1LcsrS9+vjFRV\nqdT/yqMoih3wMdAXaAo8pihK02LJ+gKhBf+eAT79t+0mg7diCkbI7ymKclJRlBOKojxa8PnHiqIM\nKPh5jaIoXxb8PEpRlNm2LHNVpPEMwJQSb/ndlBqPxtO/RDrHNr3xfHsd1Sd+RtbXr/+rmE5+3ugT\nCu8w6JNScPLzKSdNcok0xWVfuopvd/PUIb9enXHy9y23LA6+vuQnFV5tztcmYe9Tfr6/BY4dT8IX\nS1BNarlpdYnJlp/1iaXX2TpNMk5+3jiX+Nw6b80hfenw/fs0mf4s9u7VSsT1v+vvQWzJaSQJCZkE\nBnoU1iegOokJmVZpGjUOYOvmswAcPx5DbGwaCfEZhDb04/DhK6Sl5pCbm8/OneeJi8/gZlR0fEdf\nH/SJhftVXpK2xHZw9PUhLzGpSJpkHH0L0zSdP5MWn8/H/4F7LZ9d/mgptZ8dSdtVX1DnuZFEL/m2\n3LonJmQSGFjd8ntAoDsJidYXJRo18mfrVvMJ/YnjscTFppOQkElAgDtPjexAn96f0qvHItzcnOjc\npS6Vnblti7S/VotjsfZ38PUt1v5aHHx9yb0UjXuL5thVd0fj5IRHeBgON9DHi3P29yY3obA/6RJT\ncPazHpw7eLqRn5mNajSVSOPs50XgXWFEr9pqlcc1yJ+81ExazRhLt2WzafnGaKsTy+ISEzMJDHS3\n/B5Q1r5fMCg7cTzGsv0bhPpx5PBVy76/a+dF4it536sMnP28yI0vvLCiS0jB2b/YtvdwL7btk63S\nBPQMo8eq92n/wVSOvW2eGpd18SperRvj4OGGxskR/y6tcQm4/vGqIumKHEt1ick4+3lbLXfwcMeQ\nmVNsfzencfLzLjN/o8lPcX7R95ZBbFH1xw01r9uzOlFLfgRu7XGv3rjH6PLrpwTe282y/szz0fh2\nC0Ox0+Bcw3we41xkO1SWvi9uiw7ABVVVo1RVzQNWAMWnXz0IfKua7Qc8FUWp8W+CyuCtpIeB1kAr\n4G7gvYJG3gV0K0gTjHmETcFnOyu6kHeKvL+2kfbm/WR8PAHXByfaujilOjfnE4Ievpd2X76Lnatz\nqXfKbiW3Dh0xpqWiu1D+s023S8zPm9k7aAIHh09Fn5xG6MQRVsur1a1J/fH/burNmGe6kpGpY+CD\ni1n23UGaNKmBxk5D/fp+jB7dhdFPL+OZ0cto3DgQO82t/1Nm6/hFnRr/CsefnsSZqW8ROLAf7q2a\nARDwYF8uL1rKkcFPc3nRUuq/8vwtiff0mI5kZugYPPArflh2hMZNArDTKKSn6/hj+3k2bRnHtojx\n5Obm89vaU7ckZmWlu3KV+BUrafjuLELnzSTnYhSYSn+u5XZq+tJwzixcUeKEVbHTUL1xHaJXbWXX\nsOkYc/XUH/nAv4o1ekxnMjN1DBr4Ocu+P0TjJoHYaRTq1/dl1OhOPDN6OePGLKdR4wA0mlv/woHK\n1Pcqi4SIQ+wY/BKHX5pPo3FDAMi6HEvUt78RvmgaHT56hYzIaMvJ/3+Vb5e25KWkk3m25PNzABc/\nMz87nnMtnpqD77vl8aM+W86eB58l/vddlvXHrduOPjGZ9l/No+HkpwBQb+HfiIrs++KmBQNXi/x+\nreCzm01zU+SFJSV1BZarqmoEEhRF2QG0xzx4m1RwO/Q04FUwqOsElBhVKIryDObboyxevJhnnrnJ\n57P+40xpCWi8Ay2/a7wCMaUllpnecP4wdn41Udw8UbPS/lFMfVIKTgGFV8yd/LzRJyWXk8anRJri\ncqJjOD7JPM/dJaQGPp3blVuWfK0WBz8/y+8Ovn4YkrXXyVHItWkz3Dt2xq19OIqDI3aurgRPnUbM\ne3NLTe/sX3gF0Mm/9Do7+/uQbknjgz4pBcXevsy8eSmFU1Zjf91Kq/cLp3k7+XnTct5UTr+9iLAl\ns0otU0CAO/HxheuIT8jAP8DdKo2bmxNz5povYKmqSp/eCwkJMV+JHDSkDYOGtAFgwfxtBAZU52ZU\ndPw8bbLVHVlHP98S2yFPm4yjvx9wpiCND3nagvYumA5pSEsnZdd+3JqEknnsFH739eLyws8BSP5j\nD/VeLn/w5h/gbnW3JCE+k4BiUx/d3JyYNae/pe739fmMmiGe7Nl9ieBgD7y9zVM17+7TkGNHY3hg\nQLNy49qSuW2LtL+vL3nF2j9fqy1o/4I0fr7ka819UrtxM9qNmwEIfvpJ8pJurK/WHtKn4GUSkH46\nCpcAH1ILljn7e6NLSrVKn5+WhYN7NRQ7DarRZJXGs0ld2s41P4fn6OmOf5dWmIxG0k5cQJeYQtpJ\n88tV4rYevO4JnL+/O/HxhXe6EsrY92fNMa9DVVXuvftjav697w9uzaDBrQH4YMEfBBbLWx5b9/2K\n1HXZHKBg2wd6k3rM/LlzgDe6xGLbPj2z2Lb3KZEGIOWvs7gG+5vv1KVncvXXCK7+GgFAo+cetbpr\nVNEUGqJQ+Oy6c4AvYL6D6+zvgy7Jelp3fnom9u6uxfZ3cxp9Ukqp+f17hePXPQzfzm3QODliX82F\n5jOe5+SMj6zWbdLn4X9XOJeW/nTLjntFxf++m9bzp3Fp6U+oRhPnP/zGsqz3/pV4NKlLw2cGAZWn\n74ubV/RcvsASVVVt/laYO+OS1S2gqmoM4Anch/lO2y7gESBLVdXMUtIvUVU1TFXVMBm4lWS4fBI7\n/9pofIPBzgGn9n3JO/aHVRqNXy3Lz3a1moC94z8euAFknrmAS80aONfwR7G3x//urmh3H7JKo939\nJwH39QCgerNQDNk55CVfP6aDV8HJg6JQ+6nBxK7ZXG5ZciPP4hgUjENAIIq9PR497iJz/94bqkfi\n118QOXwo558axrV3ZpF97GiZAzcwP5P3d50D+nRBu8u6zkm7DhHYr0ids8x1zjxzocy8RZ9d8OvR\nwfJ8m72bK63mT+PCJ8tIP172szDNWwQTfTmFa1dTycszsnH9Ke7q1dAqTUaGjrw885u2Vq38i7Cw\n2ri5maeEJCebn7+KjU1n6+az9H+gxQ21na3iZ509j3PNIJxqBKDY2+Pbuxupew5YpUnZfRC/e80n\n+m5NG2HMziE/ORWNsxMaF/MzKBpnJzzbtyY3yvyimrzkFKq3bg5A9bYt0V0r/2H15s1rEB2dyrVr\naeTnGdm48Qw972pQou75BXVfveoY7cJCcHNzokaN6hw/Fktubj6qqnJgfzR161WeaVplyT4biXNw\nEI6B5vb37tWdtH37rdKk7T2ATx/ziweqNWmEMTub/BTzyZO9p3man6O/H57dOpOyLeKG4kav3MKu\nx19j1+OvER9xiJr9zZM3PJs3wJCVi15b8m+L9tBpavQ2T8MOub87CTsOA7B9wGS2PzCJ7Q9MIm7b\nQU6+8zUJEYfRJ6eTm5BMtdrmWTi+HZqRFVX2c3jNWwRxJTqlcPtvOM1dd5Xc9y3bf+VR2oXVKrHv\nx8Wms23LOfrd3/yG2qIwvm37fkXaPew1dg97jYSIQwT3K7btSzmuJB86TWBv87OwNe/vRsIO89/b\noi8hqd6oDhpHe/LTzacdjgXHH+cAHwJ7tSdm040dR24HlUhMbLT8XqNvdwA8mhceV4pLPXwK/17m\nKfZB/XuStNNc56Rdh0rNf+GT5ex64Fl2D5zAidc/IOXQScvAzTWk8IJwtbo10SUk39LjnkuR9ft1\nDyMn2vz3VuPkiKZguqJ3h5YARH66qtL1/crIpCqV+l/Rc/mCf8UHbjFA0bdf1Sz47GbT3BS581bS\nLmCsoijfAN5Ad2BqwbL9wCSgF+ADrCr4J26WyUjWD7PxmPQ5KBp0e9ZgjL2Ac49HAdDt+BGndn1w\n6vQgGA2oeToylxS+Zc99zHs4NOyA4uaJ17vbyVm7CP3un68bUjWaOD9/KS0XvGH+qoB128m5dJWg\nh+4BIPaXzaTsPYJPp7aEr/wYo07PudkfW/I3eWsynm2a4eDpTqdflnBp6Y/Er9uGf59uBD9snj6h\n3XGA+PXbb6D+JuI+/Yjas+ah2GlI3bwR/ZVovPrdD0DqhnXYe3lRb+GnaFxdwaTi89AgLowdhSnn\n5h5IPvf+F7T5cDpoNMSt+4PsS9cIHtgHgJg1W0jeewTfzm3otOojTLo8Ts/62NJepeUFaDBhOO6h\ndVBR0cUlcfadxQDUHHIfrjUDqTtqCHVHDSkogROgtyqTvb2G6W/2ZczoZZiMKgMHtSY01J8Vy80H\nyaGPhRF1MYlpr/6KgkKDUD9mzi68ovjC8z+RlpaLg70dr/+vL9WrO99Um1R4fKOJSx8spsn7M1A0\nGhI3bCX38lUCBpj3m4S1m0jbfwivTu1os3wxJr2eC3MXAuDg5Umj2a8BoNjZod26g7SD5q/MiHp3\nEXUmjkGxs8OUl0fUex+XHr9Y3V+b3odxY37CaFIZOLAFDUL9+GmF+Y2hjwxtQ1RUMq9PW4+iKNRv\n4MtbM/sC0LJVEH3uacQjg7/G3k5D4yYBDHmk1U20/I15acpqDh6MJi01h7u6L2DC8z0td1v+EZOJ\nKx99SsN5s8BOQ/LGzeguX8HvgX4AJP22gfQDf+IR3p7m339R8FUBCyzZ68+Yjn316qhGA1c+/ARj\ntnkA4dm1E7WefxZ7Dw9C58wg52IU5195o9QiJO4+in+X1tz16/yC18Uvtizr8OFUjs38HL02jbML\nl9N2zvM0em4I6eeiLS8juJ5T735Lm1nPoXGwJycmkWMzFlN/xP2lprW31/Da6/cydvRyjCYTAx9u\nRYNQP35cYT5RfHRoO6Iuapk+7TcUBeo38OPtWf0t+Se/sJq0tFxzH3rj3srf927SLd/3gMQ9R/Hr\n0pqevyzAqNNz/K3Cbd/+w5c5PnMJem0aZz4q2PbPDiHjXLTljlpg7w7U7NcNk8GASZ/PkWmFd5na\nvTsJBw83VIORk/O++tcvrLiV9c+NTaTL6oUYdXmcnvmJ5fM2C17l9OzF6LWpnF+0jBazJtFg7FAy\nIy8Rs9Z8/NTu+Qvfzm1LzV+WBuOHUa1WwZtgYxKoVjuIjisW3Lrj3nPDcK0VhKqq6OKTODfPPOvB\n0duD1h+8DqoJfVLJl0ZVlr4vbos/gVBFUepiHpANBR4vlmYtMEFRlBVAOJCuqmrcvwmqqKU88Hkn\nUhQlS1VVN0VRFOBdzG+HUYFZqqr+WJDmaWCmqqpBiqI4AGnAcFVVrz9qKPqaxDuQdkzxF+9UHN/P\nTxPReZDN4vfcu5pTfXvbLH6zjdvY1nFI+Qlvk977V2Jkmc3i2zHM5vH3dR9gk9iddq4FIM/4pU3i\nO9qNsnnbH+rVz2bxw7ZvYF27W/vK+Ztx/+Fl5JvKf4HN7eKgGWHz7W/r+OvDip/DVZz+h36wWf3t\nMO/3W8IfsUn8Pgd+svlxz9Z9H6gS3369JfyRSn1+3OfAT+W2o6Io/YAPADvgS1VVZyuKMg5AVdXP\nCsYVizDP3MsBRqqqeqjMFd4AufNWQFVVt4L/Vcx32qaWkuYL4IuCn/OBkq/YE0IIIYQQQlzXjbyO\nv7JTVXUDsKHYZ58V+VkFxt/KmPLMmxBCCCGEEEJUATJ4E0IIIYQQQogqQKZNCiGEEEIIISqUqWo8\nmlfpyJ03IYQQQgghhKgCZPAmhBBCCCGEEFWATJsUQgghhBBCVKj/wtsmbUHuvAkhhBBCCCFEFSCD\nNyGEEEIIIYSoAmTapBBCCCGEEKJCmWTa5D8id96EEEIIIYQQogqQwZsQQgghhBBCVAGKqqq2LsOd\nQBpZCCGEEEJUhCoxH3F92OOV+vy4/6EfKmU7yjNv4rbb3fUhm8XuuvsXYoe3sVn8oO/+4te2T9gs\n/oNHvmdbxyE2i997/0oO9epns/hh2zdwuHdfm8Vvt20jxtXeNoltNygFwGbtH7Z9g823vZFlNotv\nxzAWNRpns/gTzn1m833f1tvf1vG3dxpss/i99q2yad8HiOg8yCbxe+5dzaYOQ20SG+C+gyts3ver\nCvmqgH9Gpk0KIYQQQgghRBUggzchhBBCCCGEqAJk2qQQQgghhBCiQplsXYAqSu68CSGEEEIIIUQV\nIIM3IYQQQgghhKgCZNqkEEIIIYQQokLJ2yb/GbnzJoQQQgghhBBVgAzehBBCCCGEEKIKkGmTQggh\nhBBCiAplkmmT/4gM3kSF8gxvQ70XRqNoNCSs28K1738ukabeC6Px6tQOk05P5JyFZEdGFS7UaGi9\n9H3ykpI5/cpsAOo89yTeXdqj5hvQxcYTOecjjFnZ5ZbFqUVnPIZPBY2GnIhfyFr3ldVyl859cev/\nFCgKqi6HtK/nYLgSCYDi6obn0//DvmZ9UFXSlr5F/oXjN9QGLaYOx79ra4w6PX/9bwnpZy+XSOMa\n5EfY3PE4eLqTfuYSh1//FNVgxKddE8LnTyYnNgmA2O1/Evn5L7jVrkHYOxMK8wf7c/azVQB4d2xN\nw8kjUTQaYtduI/q7X0rEazhlJD6d2mLU6zkz82Myz126bl7/Xh2pO/oRqtUJ5s9R08g8a95G9tXd\naDn3RdybNCBufUSJONXbt6PWhLGg0aDd8Dvxy1eWSBMyYSwe4e0x6fRcfnc+OecvmmM+/CB+/e8F\nRSFp/SYSV/8KQM2xo/DoFI6ab0AfF8fleQswZpfc/tXbtyNk/LiC2JtIWFFK7PHjqB7eHpNez+V3\n/4/cIrF9+90HioJ2/SYSfza3Q40Rw/Dtfx+GtHQAYr74hoyDf5ZYb2l2RTowd50bRpPC4Pa5jOmR\nWyLNwSgH5q53w2AEL1cT3z6Tjj4fRnzuSZ5BwWCCe5rref7unHLj3Y62Dxo5HM/OHUE1kZ+WzuV5\n88lPTqmw+F49uhL05DCca4Vw5rnJ5ESeL7cdbsT0aWvZERGJt0811q579pas83q6TX+E2j2aY9Dl\nse3Vb0g6fbVEmj7vj8K/eS1M+UYSTlwm4s1lmAw3/qJtW+//tuz7tyv+je7/3h1bEzppJIqdhrgy\n/gaHTh6FT+c2mHR5nJ65iKxI89/gxtOfw7dzO/JS0zn4xJSSZX7sAUInPsmu+0aSn55Z6eoO4B3e\nmgaTRpnr/9s2rny3pkSaBpNHmY9BujzOzvqIrMhLOPn70PiNiTh6e4AKsWu3EPPTegD87upEnacf\nxbVOMEdGv0rm2Yulxv5bkxefxLdzG0w6PSfe/pSMc5dLpHEJ8qPVrBdw8HAj4+wljv9vEarBSI17\nu1BvxABQFAw5Ok7PW0rm+SsANH99LH5d25KXmsGex6ZetwylqYi+L/57ZNqkqDgaDfWnjOXUS29z\n5Inn8bu7Gy51alol8erYDueQGhwe+iwX3vuEBi+Ns1oeNOR+cqKvWX2W9ucxjoyYyF9PTSL3aiwh\nwweVXxZFg8eTr5L83gQSXxmES6f7sA+qZ5XEkBSLdvZokl57hMxfPsdz1OuWZR5PvIz++F6SXnmY\npOmPYoiNKh6hVP5dWlGtViDbHnyRY7O+oNW0p0pN13TiULFS7a4AACAASURBVC4u28S2B18kLyOb\n2g/1tCxLPnqOiMemE/HYdCI/N58EZEXHWT6LGPY6Rp2euD8OAdDopac5Onk2+x+bTMA9XahWrM19\nOrXBJaQG+4Y8z9m5i2n08hjzAo2mzLxZUVc58er7pB09Y7UuU14+F5f8yIWPvi1ZKY2GWi88R+Sr\nb3Jq5Di8e/XAuXaIVRKP8DCcg4M5OXw00fMXUmuSeUDqXKc2fv3v5cxzkzk1ejyeHTvgFFQDgIzD\nf3Fq1LOcHjMe3dUYAh9/pPTYE8dzftobnB41Fu9ePXGuXcsqSfUO7XGqGcSpEU9zZf5Car9QGNu3\n332cGT+J02Oew6NIbOD/2Tvv8Kaq/4+/TpKmK91tWgpll7333i5coOAARWWJbBVUnCiC46e4B6g4\ncXzFzVD2XrL3pmV0pnslaZL7++OGtmlaWpASquf1PD7Sez/nvM8593zuuWeGlEW/cvjhiRx+eGKl\nO252B7z8ewDzHszij6npLN3rw4lkrYtNdoHgpd8MfHB/Fn9MzeCtYdkA6HWwYFQmv0zO4OdJGWw8\npmfvmQrG4aqo7JN+WMShMRM4NHYSWVu2U+P+YVdVv+B0PCdeeJncfQcqLvRLYPAdrZn/6fArGmd5\n1OnVguC6Rr65/nnWPLeQ3jPLLsNjv29n4Y0z+e7WWei89TQb2qPyIp6u/570/SrUr1T912ho/Pho\n9j42m233Porxuh74lfEO9oupwdahkzjy6sc0fmJs0b2kJWvY8+jLZWbL2xhGaKfWmBNTy863p/Pu\n1I+dNoZ9j89m+7CpGAe45z+0azt8a9Vg210TOfbaRzSaruZfsds5+d4X/D18KrvGPkXNO24sCpt3\n6gwHnn6drD2Hys+7k/BubfCLqcGGO6dy4JVPaPbk6DLtGk0cRtx3S9hw51QKc3KpdXs/AAoSUtk2\n7iU2DXuCk5/9TPMZxc/n/JJ17JzySoVpKIur4vuSfyUe7bwJIdYKITp4Mg3/FCFErqfTUF0IaBqL\n+VwiloRkFJuN1JUbCevR2cUmtGcnUv5cC0DOwWNoDf54hYUAoI8II7RrB5L/WOESJvPvPerXMJBz\n8Cj6iLAK0+LVoAW25LPYU8+D3UbB1r/wad/Hxabw+F6UfHUk03piH9qQSACErwF9k3bkr3OOHtpt\nKPmVqwY1+rTn7OKNAGTsP4lXgD/e4cFuduEdm5GwajsAZxdvoEbf9pWKHyCiU3PyzqVQkJgGQMG5\nJMwJKSg2G8krNhHey9XlInp1JGnpOgCyDx5HZ/BHHxZMYLOG5YbNjztP/pkEN22H2ULW3iM4rIVu\n9/ybNMJyPgFrYhKKzUb66vUEd+vqYhPcrQtpK1YBkHf4KDqDP16hIfjWiSH38FEcFgs4HOTsPUBI\nz+5qmnfsBofDGeYI+ojwMrXNJbQz1qxTR41LanfvQtryVUXxaA0GdKEh+NSOIe/IUZQL2vv2E+zU\nvlz2n9NRO8xOTKgDvQ5uamVm9WG9i82Svd5c19xCdLCatzCDAoAQ4O+t2tjsYHMAFaw8qaqyd+QX\nzxZqfHwA5arqm8+cxXL2/MUzfxl06FiHoCDfKx5vWdTr34ojv24FIHnvabwDffGLCHSzi19f3EFN\n3heHITKk0hqerv+e9P2q1K9M/Q9s1pD8Eu/RlJWbiOjV0cUmvFdHkpatVfN08Dg6gx/6MLVdyNxz\nGFt22e1L7JQHOfnB1yjl+J2n834h/2o7kuzM/0bCe5bKf8+OJP/p3gZZ0zKLZiDt+Wby48/hHREK\nQH78eQrKaIPKIrJXBxKWrgcg68AJvAL88A5zb3fDOjQnefU2ABKWrCeyt9reZe4/hi1HndHNPHAc\nH2NoUZiM3UcozK54pU9ZXA3fv9ZRENf0f9cq/+mZNyGEtmIryZVCHxGKJcVU9LclNQ19RKiLjXd4\nKNYSNtaUNLzDVZv6k0dx+qMvQSm/oYq8eQAZW3dVmBZtiBF7enLR3/b0ZLQhEeXa+/UZhHnfJjVs\nRDSO7AyCx75IxKzvCBr1PMLbp0JNAB9jCAXJaUV/F6Sk4xvh+iLWBxsozM1HcXZIC5LT8SlhE9oq\nlj4/zKHLe9MJqF/TTaPmDV05/9eWor/NKcV6lpR0vEt1br0jQkvZpOEdEYqP23X3sJeCPjzM9dma\nTG4dba/wcKwpxaPI1lQTXuHhFJyOJ6BlC7SBAWi8vQnq3AEvo/uHWvhN15O1fYfbda/wcApTS8db\nWjsMa6rJxUYfHo45Lh5Dy+ZoAwMQ3t4Ede6IPqK4rkQMvpWmn3xInWmPojUYKlUWyVkaooLsRX9H\nBTlIyXZ9HcWZtGQXaHjgkyCGvB/Mb7u8i+7ZHTD4vRB6zAmnW8NCWsfYLqpXlWVfc+QIWn3/JWED\n+pDw+ddXXb+6Y4gMJjcpo+jv3KRMDJHuH5YX0Og0NL69M/EbDlZaw9P135O+X9X6FdV/79LtnvP9\n6moThrlEu2BJrfhdG96zI5bUdHJPxF/UztO+7x0RiiW5ZLtfdhvkapPmZuMTFYEhth7ZBy99abS3\nMdSl3TWnpONtdH0GXkEBFOYUt7vm5HS35wRQ67a+pG7Zc8lpKIur4fuSfyeV2vMmhPgViAF8gHdQ\nO30NFEWZ7rz/INBBUZSJQojngPuAVOAssFNRlDcuEv39QohPnWkZqSjKdiFEKLAAqA/kA2MVRSlz\nQ5EQorczTaAO/fQC2gMvATlAQ2ANMF5RFIdzpmweMACYIIQoAOYCBsAEPKgoSqIQYgwwFtADJ4D7\nFUXJF0LUA7512v9WmfKT/HNCunWgMDOLvKMnCWrbokybWiOGoNjtpC5fd0W19U074NdrEKaXRwIg\ntDq86jYh6+vXKDx5gMD7pmO4ZSQ5P314RXXLIutIHMsHTsFeYMHYvTWd5j7KqkHTiu4LnZaoXu04\n/N4PVZ6Wq4n5zFmSvv+RRq+/jMNsIf/kqaIR9wvUGH43it1O+so1VaId+9psHGYzBSdOoTi1U/9Y\nQuI334GiEP3QCGqNG0P8G29dEV27Q3AwQceCUZlYCgX3fhxM69o26obb0Wrgl0kZZBcIJn8TyPEk\nLbFR9oojvQwqKvvzC77i/IKviLr3LoyDbiXhy4VXVf+/Ru8XhpGw4ziJO09cFT1P1f/S+p7w/cro\nV3X9LwuNt546D9zBnimzqlTnWsm71teH5nOmc+Kdz7Hnu+8NvlqEtm9Grdv6sm3sCx7Rv9q+L7l2\nqeyBJSMVRUkXQvgCfwP9gU3Ahd2ZdwOzhRAdgTuB1oAXsAvYWUHcfoqitBFC9ELtsLUAXgR2K4oy\nSAjRD/gKaFNO+GnABEVRNgkhDIDZeb0T0AyIB/4E7gAWAf7ANkVRHhdCeAHrgNsVRUkVQtwNzAZG\nAj8rivIJgBDiZWAU8B5qR/EjRVG+EkJMKC9TQoixqJ0/5s2bx9ixY8sz/c9gTU3Hu8SonXdEGNZU\n1w3OFlM6+hI2emMYFlM6YX26Etq9IyFd2qPRe6H196PRc1M5NuttAIw39SO0WwcOTHm+UmmxZ6Sg\nDY0s+lsbGok9w33fgC4mluBRz5P2xkSUXHVTvj09GXt6CoUn1aUM5u0rMdz6ULla9e4aQJ3BfQHI\nOHgK38jiEUVfYygFqRku9tbMXLwMfgitBsXuwDcyFLPTxpZX3HClbNqLZsaD6IMNWDPVZTWR3VuT\ndSQOS3p2kZ2PsVjP2xiKJbV4BBLUkVAfYxhZRTZhWFLTETpdhWEvBaspzfXZhodjLRVfocmE3lg8\nqq+PCKfQpI7ImpYtx7RsOQA1Rz3gMksQdsMAgrp04ti0p8vULjSZ8IooHW9p7TT0EeHklbCxOrXT\nli0nzakdPeoBCp3atozMovCmJctoOPvFigsCiAxykJRVPNOWlKXBGGgvZWMnyM+Bnx789Aod6hZy\nJFFL3fBiu0BfhU71C9lwXE9sVPkfNVVZ9hdIX7WG2FdeLPMD7mroVydaDutNs7vUfSsp++MxRBXP\nrBuigslNziwzXMcJN+MbamDNxEv7SPZ0/fek71e1/gXKq/+W0u2e8/3qapOGT2SJd3DExd+1vrWi\n8K1hpNPXbzjtw+j4xevsGDUDa7pr3fG071tS0/GOLNnul90GudqEFdkIrZbmc6aTvHwDpnXbyi2T\n0tQecj21Bql71rIOncQ3MowLJeNjDMWS4voMCrNy8Aoobnd9IkNdnpOhYW1aPPMwO6a+SmHW5e+W\nudq+f60jT5u8PCq7bHKyEGIvsBV1Bq4ecEoI0UUIEQY0Qe3MdQd+UxTFrChKDvBHJeL+DkBRlPVA\noBAiGOgBfO28vhoIE0K4LwRW2QTMFUJMBoIVRbmwfmi7oiinFEWxOzUu7PC0Az85/90YtbO4Qgix\nB3gWuLCTtoUQYoMQYj8wHGjuvN79QpovpLEsFEWZryhKB0VROsiOm0rOkeP4xtTAu4YRodMRMaAH\n6Zu2u9ikb9yO8cY+AAQ0b4Q9N4/CtAzi533D33eMZsfQsRyd+SZZO/cVddyCO7el1rDBHHpqDg6L\ntVJpKTx1EF1UbbQR0aDV4dvlBsy71rrYaMOiCJ3yBhnznsOedKbouiMrDXt6EtqoOgB4N++E7Xz5\nB5ac/t/KosNEktbuJOYWtSqGtGxAYW4+FpP7y9q04xDR/TsBEHNLTxLXqktBvcOCimyCm9cHIYo6\nbgA1b3RdMgngF1MDH2eZR17XHdMG16VFqRt2EDWwNwCBzWOx5eZjTcsk5/CJCsNeCnlHjuFTMxp9\nVCRCpyO0Xy8yt2x1scncvI2w6/oD4N+0Mfa8PArT1Y6rLljNu94YQXDPbqSvWqumuWN7ou4ewoln\nX1T3ZlRCO6RvbzI3l9beStj1F7SbYM/Lw1ZK28sYQUiP7kXautDihje4RzcK4i6+hOkCLWraiDdp\nOZeuwWqDZft86NvUte72a2plV5wXNjsUWGHfWS8aRNhJzxVkF6gNnrkQNp/QUz/i4rNuVVX23jWj\ni/PfvQsFZ1wPE6pq/erK/m/X8cOg2fwwaDanVu6hySB1/1lk63pYc8zkp2a7hWk2pDu1ezTjr8c+\nu+jS8bLwdP33pO9XpX5l6n/p96hxQHdMG1wPdjFt2EHUTX3UPDWPxZ6nvoPLzc/JM2y8eRRb7hjP\nljvGY0lN4+8Hn3DruHk67xfy71urZP57YNro2o6YNv5N5I0l2qAS+W/89Hjy485x7vvKfE4Wc2bR\ncjbf9xSb73uKlHU7iB7YC4CgFg3VdreM8k3feYjIfuo+/Oibe5G8Tk2nT2QYbV97jH0vfED+mcRL\nSkdprrbvS/6dVDjzJoTog7rEsKtz2eBa1OWT3wN3AUeAXxRFUYS4rB506Zp4STVTUZRXhRBLgIHA\nJiHEDRXEa3Z26EDd5n9QUZSuuPMFMEhRlL3OZaF9LjeNEid2ByfnfkKLuS+ARkvykpXknz5L1O3q\nI0v67S8ytuwkpGt72v/wMQ6zheNz3q0w2gaPjkXj5UWLt9RR35yDRzn5xscXD+Swk/XVa4RN/1D9\nqYD1v2E7fwq/fkMAyF+9CMOgsWgMwQQ/MANQT74yvaCeQJf11WuEPDIHodNhSz1P5vzKLaNI3riH\nyB6tGfDbm9jNVnbPnF90r8u709jz0qeYTZkcevd7OrwykSYThpJ1JI4zv64FIHpAJ+oO6Y9it2O3\nFLJjxgdF4bU+3hg7t2Dv7AUumkff+Iy27zwDGg2Ji9eQd/ocNQdfB8D5X1aQtnkX4d3a0nXRe+ox\n1S9/4Myvo8ywABG9O9Ho8ZHogwNpM3cGOcfi2DNV/emGbr98gM7PD+Glvl586sRgjj8LDgdn3vuI\nRq+9DFoNacuWY447Q8StAwFI/WMpWdv+JqhzR1p885nzyOriJVgNZj6DLjAQxW7jzDsfFh0JXnvy\nI2i8vGj0f6p+7qGjnHn7/VLPW9WOfe1lhEaLadlyzPFnCL9F1TYtXkr2Be2vF+Awm4n7v2Lt+jOf\nVbVtNs68W6xda+wo/BrURwGsScnEv1VxfQXQaeGZ23IZ83kQDkUwuL2Z2Eg7329T907e09lMA6Od\nHo2sDHo3BI2AIR3NxEbZOZqoZcaiAByKwOGAG1ta6NOkgkGLKir7WmMewiemJopDwZqSQvxb75cp\nX1X6wT26UnvSI+iCgoidM5P8k6c4/uRzlXoGF2PaYz+xfXs8mRn59O31FhMn9eHOoW3/cbxlEb/u\nAHV6t+D+FbOwFVhZ9fSXRfdumT+RNc9+TV5KFn1eHEZOQjpDfngCgFMrdvP3B0srJ+Lp+u9J369C\n/crUf8Xu4Nibn9Lm7WfVn1xZvJq80+eIHnw9AAm/LCdt8y7CurWj64/vqz/X8nLxEvzmL04luF1z\nvIID6PbbPE5/+gOJf6wu/1lfQ3m/kP/jcz+l1VvPqT8VsHg1+afPEj3Imf9fl5O+eRdhXdvR+ccP\nsJstHJ2ttkFBrZoQdVMfck/E0+ELdZbx1LxvSd+yi/BenYh9bDRewYG0fONpco/Hse/RspeRpm7a\nTXi3NvT6+R3sZgv7ZxV/H7R/60kOzJ6PxZTB0fe+pfXsycSOu5ucY3Gc+11dhttg9J3ogww0e3Kk\nM092tjzwDACtZ00ipH0z9MEB9PnjA45/sqjSj+aq+L7kX4lQKujFCyFuB0YrinKrEKIJsAe4EdgL\n7ADOAE8696p1RN1P1g21Y7gLmF/enjdnR/CIoijjhBA9UJcjthRCvAukKooyy9l5fEtRlDJbTiFE\nA0VRTjr/vQj4BsgEllG8bHKZMx0/CSFyFUUxOO31wCHU/WxbnMsoGymKclAIYXKGzwCWAucVRXlQ\nCPE78D9FUb4RQjwC/N+F+C7Cf7qzt7HHII9p99j4Kwn3V81HV2WI/no3v7W7z2P6t+/6hlVdhnpM\nv//WH9nRb6DH9DusXsrO/jd5TL/9qmXYf3Lf9H410N6pLvnxVPl3WL3U48/ejueWGGkZzvuNx1Vs\nWEVMPPqxx+u+p5+/p/VXdx3iMf1+WxZ51PcB1narxM/2VAF9Nv/En53u8Yg2wI3bv/e471PhGcTX\nBovaPHBNfx8P2fPlNVmOldnz9icwTghxGDiKunQSRVEynNeaKYqy3Xntb2fnZh+QDOyHomXc5WEW\nQuxG3SM30nltJrBACLEP9cCSBy4SfqoQoi/gAA6idtS6ou7Ne5/iA0vcfhVSURSrEGII8K4QIgi1\nPN52xvMcsA314JVtQIAz2BTgWyHEk8gDSyQSiUQikUgkkkvGcU133a5dKuy8KYpiAcocvlMU5ZYy\nLr+hKMpMIYQfsJ6LHFiiKEqfcq6nA5WarlEUZVLpa87lm9llpa/0LJmiKHtQT6gsbfcR8FEZ10+j\ndg4v8GxpG4lEIpFIJBKJRCK50lT2tMlLYb4QohnqvrgvFUWp+Ee3JBKJRCKRSCQSiURyUa54501R\nlGGlrwkhPkA9pbEk7yiK8nll4xVCPIS6ZLEkmxRFcTuuX1GUtcDaysYtkUgkEolEIpFIrh5K9dia\nd81RFTNvbpTVwbqMOD4HKt3Zk0gkEolEIpFIJJJ/E5X9nTeJRCKRSCQSiUQikXiQqzLzJpFIJBKJ\nRCKRSCQXcChy2eTlIGfeJBKJRCKRSCQSiaQaIDtvEolEIpFIJBKJRFINkMsmJRKJRCKRSCQSyVVF\nkT/SfVnImTeJRCKRSCQSiUQiqQYIRXZ7rwaykCUSiUQikUgkV4NqcRLIty0fuqa/j4ft//yaLEe5\nbFIikUgkEolEIpFcVRzVo495zSE7b5Iq57d293lM+/Zd3/Bh43Ee0x9/9GPO3N3JY/q1f9jO4vbD\nPaZ/y86FrOoy1GP6/bf+6HH9b1qO9Ij2ffsXALCl120e0e+6/neP1733Pej7E49+jJ2FHtPXMtzj\ndf+/rm//wsdj+toHzR7Lf/+tPwKwpMMwj+jfvONb1ncf7BFtgF6bfvG470v+3cg9bxKJRCKRSCQS\niURSDZCdN4lEIpFIJBKJRCKpBshlkxKJRCKRSCQSieSqoihyz9vlIGfeJBKJRCKRSCQSiaQaIDtv\nEolEIpFIJBKJRFINkMsmJRKJRCKRSCQSyVXFIZdNXhZy5k0ikUgkEolEIpFIqgGy8yaRSCQSiUQi\nkUgk1QC5bFIikUgkEolEIpFcVRRPJ6CaIjtvkqtOy+n3Y+zRBrvZwu4X5pN1JM7Nxi86gg6vTMAr\nOICsw6fZ+exHKDY7Ye2b0nnuo+QnpAKQsPpvjn3yKwD1h99InUF9QFHIPnGO3TPnV5iWHs/cRZ3e\nLbCZrax66ktMh8662bQY3ofWD/QjqI6RBV0ex5yRB4B3oB9954wgqHY4NouNNU9/RfrxhEqXg0/r\nLoQ8+DhoNOSt/o3s375yLYMeNxB42wgQAqUgn/TPXqMw/jh46YmcOQ/hpQeNloJtq8j68ZNK6zaf\nPgJj99bYzVb2zJxHdhnl7xsdQbtXJqIPMpB1OI7dz32IYrMX3Q9qVp/un89k99Pvk7hqO/51atDu\nlUnFaa9p5NjHiwDo8sM7CI2GhN9XEf/1r25ajR57iLCu7bBbLBye9QE5R08DENqlDY0efajMsLWG\n3kitO29EcThI27yLE+9/A4ChYW2aPPkwWn9fcChXVV8XaKDVK48T0LQhiUvWcuzNzyp8Fh2eGkbN\nni2xma1sefYz0g+fcU/fvf1oet91BNSO5Meek7Fk5gJQ9+YuNB95EwiBLc/Mtllfk3nMvf5eILhT\nO+pOHo3QaElespyEhT+52dSdPIaQLh2wWyycfOVt8o6dAqDtD5/gKChAsTtQ7Hb2j30cAL8Gdan/\n+Hi0fj6YE1M4MetN7PkF5aahKuoegM7gR+vnxhDQsBaKorD3xYp9vyQ9S70HUst4D1z3xkiMLWrj\nKLSTvD+Otc8vxGFzXJJOZXlmxu+sW3uM0DB/fl/8yGXHc7E6fIFLrf/Gfl2oN/ou/OvW5O+RM8g5\notaR0vUf/ju+X55+eWw46cMrK0OxO2BIm1zGdM12ub893puJPxmpGWQD4LrG+YzvkcXpNB2P/RpR\nZHcuU8eknpmM6JRTps6Vzn+90UOJvm0AhZlqek9+9C1pW3YT2qkVDcYPR6PT4bDZykxLs2kjMHZv\ng91sZe/Mj8k+Gudm4xsdQds5k5y+f5o9z6u+H9m7PY3GDUVxOFDsDg69+TUZe4+qaRp2EzG39wUU\nsk+cZd+L89ziDenclgZTRyE0GpL+WMnZb352s2kwdRShXdtjN1s4Nvs9co+dQui9aP3BbDReOoRO\ni2nNFuI/+x4A/9i6xE4fh0avR7HbOfHGfHIOHy8z75fClfJ9yb8b2Xm7TIQQU4H5iqLkezot1Qlj\n99b4145i1e2PE9KyAa1nPMj6B2a62TWbfA8nF/7J+eVbafX0Q9QZ1Ie4RasASNtzlG1T3nSx94kI\nof4917N6yJM4LIV0eHUSNW/octG01O7VgqC6RhZe/zyRrevRe+YwfrrrNTe7pF0niV+7n9u/eszl\nertxN2I6fJY/J35McP1Iej1/L78/+HblCkJoCBn5BCmzJ2JPSyHqlS/J37EB2/nTRSa2lASSXxyH\nkpeDT5uuhI6ZQfKzI6HQSspL41EsBaDVEvniJxTs2YL1+IEKZY3dW+MfE8WaQY8T3KIhLWc8xKYH\nXnCzazr5Hk4vXEbC8q20nDGS2oP6EO8sfzSCppPvwbR1f5F9XnwiG4Y9XXR/wLL3SVq3k+bT7mfP\no7OxpKTT8fNXMG3YQV7cuaJwYV3b4htTgy1DJxHYPJbGT4xhx6inQaOh8bRR7J48yy1sSLvmRPTq\nyLb7p6EU2vAKCVSLVKuh2czJHJr5Hrkn4tEFB9D7zwVXTd9hLeTk/B8w1I/Bv37tCp9FdM+WBNSJ\n5LebZxDeqj6dnh3Bn8NfdrNL3X2C8+v2ct2CJ12u555LZcVDr2HNzie6R0u6vPBAmeHVZ6Kh3qMP\nc+ix57GmptFy/ptkbNxOQXxxJyW4S3t8akWze9jDGJo1pt5jj3Bg3PSi+wenPIMty/UjscETk4j/\ncAHZew8SMXAA0ffewdnPFpaZhKqqewDNp99Pypa97HzyHYROi9bHu+xyKIM6vVoQXNfINyXeA4vK\neA8c+307K6YtAOD6N0fRbGgPDny3vtI6l8LgO1oz/L6OPPWk+wf3pVBeHb7A5dT/3FNn2f/UGzR5\naqyLVln1/7/i+276gQZ6L/+8zGdid8DLy0P59J4UIgNt3P1FDfrGFtAwvNDFrn0tMx/dlepyrV6Y\njV9GJRbF0+f9WvRvXMYniNBUSfkDnP1+MWe+/cNFzpqZzd5pr2I1ZeBfP4Yu3851uR/RvQ3+MVGs\nHfwYwS0a0mLGSDY/+LxbsptMupfT3y4jcfkWWswYScztfTnz00pM2w+QvG4nAAENY2j36hTWDZmG\nd0QIde++gXV3TcdhKaTtK5OJvr6ra6QaDQ0fH8v+qTOxpKTR9tPXSdu4nfwSZRHStR2+taL5++7x\nBDRvRMNpD7Nn7JMo1kL2TX4eR4EZodXS+qM5pG/dRc7BY9Qf/wDxC/5HxtZdhHRtR73xI9g36Tn3\nZ3GJXCnfl/y7kXveLp+pgJ+nE1HdqNGnPWcXbwQgY/9JvAL88Q4PdrML79iMBOeo+tnFG6jRt32F\ncWu0WrTeeoRWg9ZXjzk146L29fq34uivWwFI3nsafaAvfhGBbnamw2fJOZ/mdj20QQ3Ob1VH/zJP\nJRNQMwzfsIAK0wmgb9gcW/I57CkJYLeRv3k5fh17udhYj+1HyVM/li3HD6ANMxbdUyzq7IbQ6hA6\nHSiVW3wQ2bs955ZsUNN84AReBr9yyr950azG2cXriezToehevbtvIHHV31gyst3CAYR3akH+uRS8\nw4IAMCekoNhsJK/YRHivDi62Eb06krR0HQDZB4+jM/ijDwsmsFlDCs4llRm25h3XE/fVryiF6ghv\noTMdoZ1ak3sintwT8QD41apxVfUdZgtZe4/gsLp+n5ttMwAAIABJREFUhJVHTN+2nP59MwCmfafQ\nB/jhGx7kZpdx5Ax5Ce71z7T3JNbsfGf4k/hFhpSrZWgai/l8IpbEZBSbDdOqDYT06OxiE9qjM6l/\nrQEg99BRdAZ/vMLKjxPAJyaa7L0HAcjasYfQ3l3Lta2quqcz+BLWtglnf10LgGKzY8ut/Jhavf6t\nOFLiPeBdznsgfn3x4EjyvjgMFynvf0qHjnUICvL9x/GUV4cvcDn1Pz/uPPln3FcYlKz/+lD1uf5X\nfL+0vi07t9xnsj9BT+0QGzEhNvRauKlpHquPXfqz3hrnQ+3gQmoG2d1vhneskvyXR+6xOKwmtb3N\nO+U+ax3Zuz3nl5bw/QA/vMPK9v2kVdsAOLd4A1FO37cXWIpstL4+Lu2dKNnu+7i3+wFNYyk4l4g5\nQX33pa7aSFjPTq66PTqR/Kf67ss5eAxdgD9657vPUWBWdXRahE5bpK0oCjp/9bnp/P2wmtIvWkaV\n5Ur5fnXBoYhr+r9rlX/1zJsQYgQwDXVZ7T7gOWABEA6kAg8pinJGCPEFsFhRlEXOcLmKohiEEH2A\nmYAJaAHsBO4DJgHRwBohhElRlL5XM1/VGR9jCAXJxR+iBSnp+EaEYDFlFl3TBxsozM1HsatLkgqS\n0/GJKP5QCm0VS58f5mBOyeDgW9+Sc+o85tQMTny9lOuXvoPdYiVly35St158Jso/MpjcpOIXfV5S\nJv6RweSnlt0pKY3pyDnqX9+WxJ0nMLasS0B0KIaoEArSyl7CUhJtaAT2tOSiv21pKXg3bF6uvaHv\nbZj3bCm+IDREvfoVuqha5P61COuJg5VKs48x1KX8zSlq2ZYsf69gA4U5eUXlf8EG1BnOqL4d2PLw\nbIKbu468XyD6+i4k/LUZX2Ooy3VLSjqBzWNdrnlHhGJOSSthk4Z3RCg+bteLw/rVjia4dVMajLsX\nh6WQ4+99Rc7hk/jVrgEKtHn7GbxCAt0+Iqpa/1LxNYaQl1Tc4Oclp+NrDKHAlHXJcTUY3JOEjfvL\nva8PD8OSYir625pqIqBZYzcba0pqCZs09OFhFKapPtJs7iwUh4Pk3/8i5Y+/ACiIO0NIj85kbNxG\nWJ/ueBvDy01DVdU9v2gj1owcWs98mMDY2mQdOc3B//u6/MIqhaHUeyA3KRPDRd4DGp2Gxrd3ZsPs\n/1Vaw1OUV4cvcDn1vzJofV1nPv/tvl9aP3nFpnLLJjlXR1Rg8dLCqAA7+xL0bna7z3sz6NMaGAPs\nTO+XQWyE66DQ0sP+DGxWziCFX3SV5B+g1tCbiBrYm5zDJzn+7lfYcvJc4jX2dV/x4hMRQkGJd505\nOR0fYwiWtBK+HxRQyvfT8DEWt/uRfTrQZOI96EMC+Xvq/6lpS83g1DdL6Lf4PewWK6at+zFtc30P\nekeEurz7LClpBDRv5GKjjwjDUqos9BGhWNMyQKOh3YI38K0ZRcLPy8g5pC6NPPnOAlrOfZ76Ex4E\njWDPwzPc8i2RVBX/2pk3IURz4Fmgn6IorYEpwHvAl4qitAIWAu9WIqq2qLNszYD6QHdFUd4FEoC+\nsuN2dck6EsfygVNYe/fTnPp+OZ3mPgqAV4AfUX3aseKWR/nrhknofL2pNbB7laZl1/y/0Af4ctev\nz9Dy/j6YDp/FYb/ye2C8m7fH0O82Mhe+X3xRcZD05H2cf+QW9A2b4RVT/4rrlkWzafdz+N3vy53p\nEzotUb3bk7ByW5WlQWg1eAUZ2DHqaU68/zUtZz/mvK4luHUTDr7wLjvHPkdgs8p/bF4JfU8R2bEJ\nDe/oya63fqwyjYMTnmTfqKkcnv4iUYMHEtBaHWg48eq7RA0eSMtP5qL188VRWPZ+lytBeXVPaDUE\nNqlL/KKVbBj+DPYCCw0eurXK0tH7hWEk7DhO4s4TVaYhKZtr1fdL6xt7d64gpovTLMrKqgnn+XV0\nIsPbZzPppwiX+1Y7rDnuyw1N88qJoWo4//NyNt85ke33T8eSlkns5BEu9/3r1aLBhOFVop28dgfr\nhkxj57S5NB43FABdgD+Rvduz5rYprLpxAlpfb2redIXbfYeDXQ8+xtbBowloFotfPXVJcPTgGzj1\n3gK23TGGk+8uoNGMCVdWVyK5CP/mmbd+wI+KopgAFEVJF0J0Be5w3v8aeL0S8WxXFOUcgBBiD1AX\n2FhRICHEWGAswLx58xg7tuxZiv8C9e4aQJ3Bah834+ApfCPDiu75GkMpKLXMwZqZi5fBD6HVoNgd\n+EaGFi2FsOUVH4aQsmkvmhkPog82EN6hGfnnU7FmqrNeiat3ENrKvQFvMaw3ze7qoYbfH48hqnhk\nzz8qmLzkTLcw5VGYZ2bN08WHjNy3ajbZZ00XCVGMPT0VbVhk0d+6MCP2jFQ3O6/aDQkd+wypr07F\nkes+I6Pk52I+uBOf1l0pPHuqTK06Q6+jtrP8sw6p5X+hxH2MoW7LTAozc/EK8C8q/5I2wU3r0e6V\niQDogwMwdm+Nw24nea26H8HYvQ1ZR+KwpmdTkOK6jMTbGIol1XX5nyU1HR9jGFlFNmFYUtMROh0+\nxrAyw1pS0kldo3YOsw+dQHE48AoOxJKSRubuQxQ692Vl7D6If53oMuOoCv0Lm/gvRqN7+tHwTnV5\nbNqB0/hHhXLhqftHhlKQcvGlvqUJblSLLi8+yOpH3sKaVf6HnNWU5jIrpo8IdysLqykNvTECOOy0\nCcNqSnPeU5+lLTOL9A1bMTSNJWfvQcxnznP4cXXfmk+taEK6ui6vuhp1L3P/Ccwp6WQeUGc/E1du\nr7Dz1vIi7wFDVDC55bwHOk64Gd9QA2smlr2v71qjvDp8gcup/5Wh5DK3K6l9rfp+aX3T5l0EtXSd\n3blApMFGUnbxp1dSjhZjgOvSR4N38QBF74ZmZi0XZORrCPFTBwc3nPSlWaSVcP9yBgvzXZe1Xqn8\nW9OL26CE31bS+o2niu0iQmn12nQOvfQ+Heare297LJwDOH0/KpSMvaqtT2Qo5lLvusKsnFK+H+Zm\nA5C++wh+NY14BQUQ1qEZBQkpRe1+0pq/CWnlWu6W1HSXd5+3MQxr6XdfahreLnkOw5rq2n7Zc/PJ\n3HWA0C5tyT99hsib+nLybfVQKtPqzTR6SnbeLoeqOfLp38+/dubtErHhLAshhAYouYahZCtkp5Id\nXkVR5iuK0kFRlA7/5Y4bwOn/rWTtvc+w9t5nSFq7k5hb1I+mkJYNKMzNd1k2dQHTjkNE91fXpcfc\n0pPEtbsAivZRAQQ3rw9CYM3MpSApjZCWDdH6qI8uvFNzck6fd4v3wLfr+N+g2fxv0GxOr9xD40Hq\nEo/I1vWw5pgrvWQSQB/gi8ZLC0DToT1I3HGcwjxzpcJaTx7CKyoGbUQ0aHX4dbuegh0bXGy0YZGE\nP/4aaR+8gC2x+BRCTUAwws8AgPDyxqdlZwoT4svViv9xBRuGPc2GYU+TtHYHtW7uCUBwi4bYcgvK\nLf8aReXfq2iz+OrbHmX1rVNZfetUEldt58CrXxR13ACib+jK+T/VfVxZh9TOpE8NI0KnI/K67pg2\n7HDRSd2wg6iBvQEIbB6LLTcfa1omOYdP4BdTo8ywqeu3E9K+BQC+MTXQeOkozMwmbdte/BvWRuPc\n/+BXM/Kq6leGY9+vZunQmSwdOpNzq3dT77ZuAIS3qo81N/+Slkz6RYXS+60JbJrxCTnxyRe1zT1y\nHJ9a0XjXiETodIT370nGJtfZ0fSN24m4Qe1oGZo1xp6XT2FaBhofbzS+6h4MjY83wR3bUHBKrY+6\nYKc/CkGtEXeR9NufLnFejbpnScuiIDkN/zrqPqfwTs3JPeXu+yXZ/+06fhg0mx8GzebUyj00qcR7\noNmQ7tTu0Yy/Hvus0ntMPU15dfgCl1P/K4M1Q63H/xXfL60f0q5ZuWXTItpKfIaOc5k6rHZYdtif\nvrGuJ7Sm5mqKqti+BD0OBYJ9iz9zlx7yZ2Dzi8y6mXZUSf71JfapRfTuVLQ8VWfwo/XcGZz4cCFZ\n+44W2Wwc/jQbhz9N8tod1BxYyvfT3H0/bcchovqrs5a1bulJ8jpV169W8UBnYOO6aPQ6CrNyMCeZ\nCG4Ri8bb2e53bE5unKvv5xw5jm+t4vxE9O9B2sa/XXU3/k3kjeq7L6B5I2dZZOAVHIjWoB5toNHr\nCenYmvx4NX6rKYOgtuoKhOD2LSk4m1ju45BIrjT/5pm31cAvQoi5iqKkCSFCgc3APaizbsOBC1/L\ncUB74H/AbYBXJeLPAQJQ98NJKknyxj1E9mjNgN/exG62uhzn3+Xdaex56VPMpkwOvfs9HV6ZSJMJ\nQ8k6EscZ52EE0QM6UXdIfxS7HbulkB0zPgAg48BJElZtp/fCl1HsdrKOxhP/8xpaPflAuWmJX3eA\n2r1bMHzFLGwFVlY//WXRvZvnT2TNs1+Tn5JFy/v70nb09fiFB3L3788Rv+4Aa5/9hpAGUfR/9UEU\nFDKOJ7Lmmcrvs8FhJ33B/2F8+l31pwLW/kHhuVMYBqgTw7krfyZoyGi0hiBCR6mnDCp2O8lPP4A2\nJJyw8S+ARgMaDflbVmLeVeFkMAApG/dg7N6Gvr/NdR7ZXHyscqd3prN31idYTJkcefc72s2ZROPx\nQ8k6Gl90GMTF0Pp4E9G5BfvnfOZMr/qx0fadZ0CjIXHxGvJOn6Pm4OsAOP/LCtI27yK8W1u6LnoP\nh9nKoZc/KAp79I3P3MICJPyxhqbPPkLnhW/isNk49JIaxpaTx9nvFtPx81dBUdQjrDu2umr6AN1+\n+QCdnx/CS0dE744XLa/zG/YR3asVty991flTAQuK7vX9cCpbX/iCgtRMGg8bQLORN+IbFsTNP71E\nwoZ9bJ35Ba3G3YY+2ECnZ+8vSvOye14qW8zu4PTb82j6xkyERkPK0pUUxJ0l8rYbAUj+/U8yt+4g\npGt72n43D4fFwolX1FXlXiHBNJ6tniQqtFpMK9eRuV0dTAkf0IuowQMBSF+/hdSlK8vNb1XWvYOv\nf0Xbl8ej8dKRfz6FvTPn0WDELRWGA/U9UKd3C+53vgdWlXgP3OJ8D+SlZNHnxWHkJKQz5IcnADi1\nYjd/f7C0UhqXyrTHfmL79ngyM/Lp2+stJk7qw51D215yPGXV4X9a/yN6d6LR4yPRBwfSZu4Mco7F\nsWfqbMC1/gO0+/AFFLvjX+/75emXhU4Dz1yXzpjvjTgUGNwql9iIQr7fpQ7I3dMul+VH/Pl+twGd\nBrx1Cm/ebkI4z07Itwo2n/Zh5o0XmQlV1Jm8K53/hhPvJyC2LgoK5sRUjryq+nCtoTfiVyuKeiOH\nUm/kULfkpGzaQ0T3NvT59S3sZovLcf4d33mCfbPmYzFlcvg9p+8/MpTso/Gc/W0tAFH9O1FrYE8c\nNhsOSyG7ZrwHQObBkySu2kbPhXOc7X4cZ35eTfPpDxaL2x2ceOsTWsx9AaHVkLR4Ffmnz1Jj0A0A\nJP76F+lbdhLatT0d//cRDrOFo3PU+PVhITR+djJoNAiNhtTVm0jfrHYoj732IQ2mjEJoNTishRx/\n/cPyn8clcKV8X/LvRijVZATxchBCPABMR50x2w28AHyO+4ElkcBvgC/wJzChxIEl0xRFucUZ3/vA\nDkVRvhBCTAImAgmV2Pf27y3kSvBbu/s8pn37rm/4sPE4j+mPP/oxZ+7uVLFhFVH7h+0sbl81exAq\nwy07F7Kqi3tjfrXov/VHj+t/03KkR7Tv2692Brf0us0j+l3X/+7xuve+B31/4tGPseO55ZVahnu8\n7v/X9e1f+HhMX/ug2WP5779V3X+7pMMwj+jfvONb1ncf7BFtgF6bfvG47wPX7lGJJfi02Zhr+vt4\n9KFPrsly/DfPvKEoypfAl6Uu9yvDLhkoeUTSk87ra4G1Jewmlvj3e6gHoEgkEolEIpFIJJJLQLmG\nj+O/lpF73iQSiUQikUgkEomkGiA7bxKJRCKRSCQSiURSDfhXL5uUSCQSiUQikUgk1x4OuWzyspAz\nbxKJRCKRSCQSiURSDZCdN4lEIpFIJBKJRCKpBshlkxKJRCKRSCQSieSqck3/TsA1jJx5k0gkEolE\nIpFIJJJqgOy8SSQSiUQikUgkEkk1QC6blEgkEolEIpFIJFcVedrk5SFn3iQSiUQikUgkEomkGiAU\nRW4XvArIQpZIJBKJRCKRXA2qxZTWh43HXdPfx+OPfnxNlqNcNimpclJHN/eYdsSnB1nddYjH9Ptt\nWcSRW/p4TL/J4rWs6jLUY/r9t/5IoeMrj+l7aUZ4XH9Lr9s8ot11/e8AHsv/tVD2O/vf5DH99quW\nedz37Cz0mL6W4R5//p7Ov6d8H1T/91T+tQwHYEe/gR7R77B6Kf9r/YBHtAHu2vulx32/uuDwdAKq\nKXLZpEQikUgkEolEIpFUA2TnTSKRSCQSiUQikUiqAXLZpEQikUgkEolEIrmqKPK0yctCzrxJJBKJ\nRCKRSCQSSTVAdt4kEolEIpFIJBKJpBogO28SiUQikUgkEolEUg2Qe94kEolEIpFIJBLJVUX+VMDl\nIWfeJBKJRCKRSCQSiaQaIDtvEolEIpFIJBKJRFINkMsmJRKJRCKRSCQSyVVF/lTA5SE7b5JrAq/m\nPTDc+xRCo6Vgw08ULPvU5b6+TV/8B00Ch4LisJH7/WvYTuyqVNyhXdoQO/UhhFZD4u+riP/6Vzeb\n2EdHEtatLQ6zlUOz3if32OmLhjU0rEPjJ8ai9fPBnJjKwRfewZ5fUBSfd2Q4nb9966Lp8m/XCePY\niQiNlszlS0hf9K1rnmvVpsbUJ/FuEIvpq89I/+WHonsafwNRk6fjXbseoJD4zmuYjxwqN/+NHn0I\nodGQUE7+Gz32EGFd22G3WDg86wNyjp6+aNgWLz+KX+1oAHQBfthy8tk+YjqRN/SgzvDbS8UeAmS4\naW7ccJJX5yzH7lC4c0gbRo/p5nI/K6uA555ZzNmzmXh7a5n18i3ENjIC8PVX2/npxz0oisKQoW25\n/4FO5ZZzeXhaP7hTO+pOHo3QaElespyEhT+52dSdPIaQLh2wWyycfOVt8o6dAkBr8KfBExPxq1cH\nBYWTr75L7sGj1SbvntAP7NiemAnjQKPBtPRPkr//0c0mZsI4Ajt3xGGxEPf6mxQcPwmA8Y7bCR94\nIwiBacmfpPys+kGNEcMJv/lGbJlZAJz/7Euyt/9dFF9V+J6xXxfqjb4L/7o1+XvkDHKOqHVCF2ig\n1SuPE9C0IYlL1laqTMrimRm/s27tMULD/Pl98SOXHc/F8HT927D+BK/M/gu7w8GQoW0ZM7aHm/6z\nT//O2TMZeHvreHnObcX6X27jxx93oSgwdGhbRjzYpUK9f+LrbX/4BEdBAYrdgWK3s3/s4wDEzpyO\nb0xNQH0f2HPz2Ddq6iWXRWmq4vkHdmxP7YkPO33vL5K+K8P3Jj5MUOeOOMwW4l6fS77T9yKHDCJ8\n4A2gKOSfjiPutbdQCgvxrV+POo9OROPrizU5mVOzX8dRog0uTdsnhxPVozV2s5Xtz31C5pF4Nxv/\nmuF0eW08+iADGYfj2P70PBw2O14GXzrPeRi/qDCETsvRL5cR99sGAGKHXUf9O/uAEJz6aS3HFy4H\nqqfvS6oPctmkxPMIDQHDnyHr7XGkP3cbPp0Goq3RwMXEengbGTPvIOOlO8n54jkCHnix0tE3fnw0\nex+bzbZ7H8V4XQ/86tZyuR/WtS1+MTXYOnQSR179mMZPjFVvaDTlhm0y4xFOfrSQ7fc9Tuq67dS+\nz7XDEjv5AdK37ik/URoNkY9M4dwLT3Jq/AME9u6HPqaOi4k9J5vkee+S/vMPbsEjx04kb+d2Tj8y\ngtOTRmE9e6b8/E8bxZ5HZ7P13keJvL47/mXk3zemBluGTuLIK/No/MSY4vyXE/bAs2+xfcR0to+Y\nTsqabaSu3QZA8l8bi64ffPE9p4J7x81ud/DyrD/5aP49/P7HwyxdcpCTJ1JdbD6Zv5kmTSP55bcx\nzHn1Nl59ZQUAx4+l8NOPe/jufw/x069jWLf2OGfi08sv6zLwtD4aDfUefZjD019kz4gJhPfvhW+d\nGBeT4C7t8akVze5hD3Pq/z6g3mPFH1J1J48hc9su9tw/nn0PTaEg/ly1ybtH9DUaak+ewPEZz3Fo\n5MOE9uuDT53aLiaBnTriXSuagyNGcWbuu9SZMhEAn7p1CB94I4cnTOXQmPEEdemEd3SNonApi37l\n8MMTOfzwRJeOG1SN7+WeOsv+p94gc89hl7gc1kJOzv+BE+99VXF5XITBd7Rm/qfD/1EcF+OaqH8v\nLWPep8P4Y8l4li4+yIlS+vM/3kiTplH8+sc4XnltEHNm/1mk/+OPu/jhx9H88tvDrF17nPiK9P+h\nrwMcnPIM+0ZNLeq4ARyf+X/sGzWVfaOmkr5+C+nrt1xSOZTHFX/+Gg21p4zn2FPPc/ChcYT2641P\nqfwHde6AT82aHLh/NPFz36X2VNX3vMLDMA6+jUPjpnBw1HiERktov94A1J02hXOffM6h0ePJ2LCZ\nqLuHlJuEqB6tMNSOYtmtT7Djpc9p/+wDZdq1mnI3x775i2W3PkFhdh71BqtaDe/uT/apBJbf9Rxr\nR71C68fvQaPTEtiwJvXv7MPK4S+yfOizRPdqgyFG7eRXR9+XVB9k503icXT1WmJPOYvDdA7shZi3\nL0Xfpq+rkSW/6J9C7wsolY4//1wS5oQUFJuNlJWbiOjV0eV+eK+OJC1bC0D2wePoDH7ow4IJbNaw\n3LB+tWuQuVud6Urfvhdjn84u8RUkppB36my5afJp1ARr4nkKkxPBZiN7/WoMXbq72NizMjEfPwp2\nu8t1jZ8/vs1bk7V8iXrBZsORl1uuVkGJPCSv2ER4rw4u9yN6dSRp6boS+fcvyn9FYQEi+3clacVG\nt+tR13V3u3aB/fsSqF07lJiYELz0Wm4a2IzVq4+52Jw8kUrnznUBqF8/nPPnMzGZcjl1Ko2WraLx\n9fVCp9PQoWNtVq6o/KzTtaBvaBqL+XwilsRkFJsN06oNhPTo7GIT2qMzqX+tASD30FF0Bn+8wkLQ\n+vsR2Lo5KUvUD1rFZsOem1dt8u4Jff8mjTCfT8CamIRis5GxZh3B3VxnTIK7dyFt+SoA8g4fQWsw\noAsNwad2DHlHjqJYLOBwkLNvP8E9y6/bJakK38uPO0/+mQQ3LYfZQtbeIzishZVKW3l06FiHoCDf\nfxTHxfB8/TtP7TohxMSEoNdruenm5qxe5RrHyZOpdO7i1G8QTsL5LEymXE6eNNGqVc0i/Y4d67By\n+eEyVIr5J75eWcL6dse0an2l7S/GlX7+/k0aYSnhe+mr1xPcrauLTXC3LqStuOB7zvyHqvkXWi0a\nbz1oNGi8vSlMSwPAu1ZNcvcdACB7525CLuKTNfu2I+6PTQCk7z+JV4AfPuFBbnbGTk05t0IdgIn7\nfSM1+7UDQFFA5+cDgM7PG2tWHg67g8B60aTtP4ndbEWxO0jdeYSa/VU/rY6+7wkcyrX937XKf6rz\nJoR4TAhxwPnfVCFEXSHEESHEQiHEYSHEIiGEn9O2vRBinRBipxDiLyFEDef1tUKI14QQ24UQx4QQ\nPT2bq+qPJiQSe0Zi0d+OjGS0IZFudvq2/QmZ9QdBUz4i5/PnKh2/JcVU4t9peEeEutz3jgjDnJxW\nbJOajndEGN4RoeWGzTt9jnBnR87YryvexnAAtL4+1LlvEHGfuS8LKYlXWAS21OLRXpspFa+wiErl\nxyuyBvbsTGpMfYq673xC1KTpCG+fcu3NKSXylqLmrSTeEaGlbNR8+rhddw8b3KYp1vQsCs4mueka\nB3Rzu3aBlJQcoqICiv6OjAwkJTnHxaZxk8iiD7P9+86TmJBFcnIODWMj2LXzLJkZ+RQUFLJh/UmS\nkrLL1boW9fXhYS51y5pqcitbfXgY1pTUEjZp6MPD8K4RiS0ziwYzptDq07ep/8REND7e1SbvntD3\nCg+nMLVkWZrwCg8rZROGNdX1mejDwzHHxWNo2RxtYADC25ugzh3RRxT7asTgW2n6yYfUmfYoWoPB\nJc6q9L3qiqfrX3JyDlFRxR/uUeXpLz8CwL5950lIyCQ5KZvYRhHs3HmmSH/9+uMkVqD/T3z9As3m\nzqLlJ3Mx3nqDW/wBrZtTmJ6J+Vyi271rATVvJfJvMqGPKO174aXyb1J91pRG0v9+ptX3X9J60ULs\neXlk79gNgDk+nuDuaicwtHdP9M42uCx8jSEUlGjjC5LT8TW6do71wQasOfkodvXw+vzkjCKbE9+v\nJLB+NLeufIfrF81mz+sLQVHIOnGOiHaN0Qf5o/XRE9WjNX5R6jeC9H1JVfKf2fMmhGgPPAR0BgSw\nDVgHNAZGKYqySQixABgvhHgHeA+4XVGUVCHE3cBsYKQzOp2iKJ2EEAOBF4ABVzk7/0msu1dh3b0K\nr9j2+A+aRNbc0R5Ly+HZH9Do0VHUfWgIpg07UGw2AOqNvouzPyzGXmCuMm2h1eLToBHJH7+L+dhh\njGMnEjZ0GKZvFlSZZnlEXt+D5DJm3QKbN8Rhtv6juEeP6carc5Zz5+BPiI010qRpFFqNoEGDcEaO\n7srY0d/h6+tF4yaRaDRXftOzp/XLQ2i1+Mc24PTb88k9fIy6k0dTc/gQzn628IppeDrvntYvifnM\nWZK+/5HY12bjMJspOHEKxaF+4KX+sYTEb74DRSH6oRHUGjeG+DcuvtdVUjGefv5jxvZgzuw/GXz7\nPBo1MtK0aQ00Wg0NGkQwenR3Ro9aiK+vF02aRKHVVO0Y+MEJT2I1paMLDqLZ3JcoOHOOnL0Hi+6H\n9++FadWGKk2Dp9AaDAR378L+YQ9hz82j/gtPEzqgL+kr1xD3+tvETBpHjfvvIXPzNpRCW5WlI6pb\nCzKPnGHt6FcxxBjpNe8JUncdJed0Ikc+X0Kvj5/AXmAh8+iZos6fRFKV/Gc6b0AP4BdFUfIAhBA/\nAz2Bs4qibHLafANMBv4EWgArhBAAWqDksNa+UI0VAAAgAElEQVTPzv/vBOqWJSaEGAuMBZg3bx5j\nx469knn5V6HOtBXvIVFn4pLLtS88vhNtRC2EIRglN7PC+L1LjMh5G8OwpLruUbCkpuETGUbWBZuI\nUCypaQidttyw+fEJ7Jk6CwDfmBqEd1eXVwQ2iyWibxcaTLgfncEfgOBbBpO5+BfXPKSloisxeq8L\nj6AwzXXfRbn5N6ViM6ViPqYu18nZtI6wIcPKtfcxFo/aeRvVvLnmPx0fY4n8O/MpdLqLhhVaDcY+\nndj+wJNumpEDupO0YiMNG5adLqMxgKSk4tHu5ORsjJEBLjYGgzcvz7kVAEVRuGHAB9SKUUdC7xzS\nhjuHtAHg7bfWEFUqbEV4Wt9qSnOpW/qIcLfnYjWloTdGAIedNmFYTWmgKFhSTeQeVpeapa3dTM3h\nd1Za29N594R+ocmEVwl/00eoo/quNmnoI8LJK2FjNakzBmnLlvP/7N13eFTF+sDx7+ymQgjphd4C\nBFAICRCqCKKCCoJgwU4VpIsVvKI0O0UsYPldr3JR0YsgRelVIAIC0msIJW3TIG2T7M7vjw1JNkWK\nwCbwfp6Hh+w5M+edmT2z58yeOWeTVtgeRFBt4NPk5l+hy0sp/PwxLVtBg6n29+Jer75XkTl6/wsM\nrEJcXFrB67gy4k+b3qsgfreus6l5MX6/MB7qFwbAjA/XEBTo+bfx/lFfB3JMtmNOXmoayZu24REa\nUjh4Mxrw6dSWvwaPvZImuKFsdStSfz8/chKL9z1Tfv3z0/j7kWsy4RneAnNsHHlptqubqZu24NE0\nlOTV68g+fYajL00EbFMovSLtb4do8EhX6vax3bOWsv8k7oG+wFEA3AN9yEqwvxc7JzUdlyqVUEYD\n2mKlUqB3QZo6vTpy6CvbbQrppxPIOJuIZ91qJO87wclFGzm5yDZl9baRfcmMt71f0vcvTzmemViu\n3VLTJstQfN/R2K7M7ddat8j/d5vW+u4iacz5/1soYwCstZ6ntY7QWkfIwO3v5UXvwxhYC4NfdTA6\n49a6Bzl71tmlMQQUPlzAqVYoOLlc1sANoFLNYNyCA1BOTgTc1R7TJvuHCpg27SCoe2cAPJuGYMnI\nJCcplQsHj5WZ19k7/4CtFHWe7cvZRbb7j3YNe52tfYaztc9wznxv+7AvPnADyD5yGJdqNXAODAIn\nJzw7dSF9+++XVR9LajK5pgRcqttu+q7cPBxzTMknZ5VW/8Bu7TFt2mG3PnHTDoJ63FFQ/7z00utf\nPK93q9vJiD5XYjCMUgR0bUf8qi2Updlt1Yg5lcyZM6nk5lhYsfwAd97Z0C7N+fPZ5ObY7vf7aeFu\nwiNq4eFhmx6YlGQ7xY49l8aaVYfpcX+zv2uychc//dBR3GpUwzU4EOXkhF/XjqRs2W6XJnlzFP73\n2O799GjSCEtGJrlJKeQmp5KTYMIt/0lzVcObkxVd9v2V5a3ujoifcegIbtWr4RJka2/vO+8g9fdt\ndmlSf9+G791dAagc2hhLRgZ5ybaTNycv2zQ75wB/vDu0J3nNettyn8KpV14d2pEVbd8Pr1ffq8gc\nv/9V51R0MmdOp5CTY2HFsv3c2aVk/Jz8+D8u/JOIiNol4p87l8bqlYe474Hb/jbeP+nrBjdXDO62\n+88Mbq54tWpB1onCh1N5hbcgO+ZMicFQeVK87/l06UTq1uJ9bzu+3S72vUZYMjLITU4hJz4RjyaN\nMbja2r5KyxZkx9g+6y72SZQi+IlHSViy3G6bx75fw6pH/sWqR/7F2XW7qPOA7Z44n9vqk5ueRbYp\njeIS/jhIjW62QWCdnh04u872ROvMuGQC2zQBwNXHkyp1gkk/k5D/2jbwrxTkQ/Wu4cSssNVN+r64\nnm6lK2+bgH8rpd7GNjjrDTwJzFJKtdVabwX6A5uBw4D/xeVKKWegodZ6f1kbF/+A1UL6f6dSdcw8\nlMFA9pZFWM4dx+2OhwHI3vADri274da2J1jy0LnZnJ87/rI3f+SDL2gxc6LtsbtL15Jx8gzVetvG\n4ucWrSTp9134tmtJ24VzbI/snfIJANpiLTUvQGC3DtR46F4AEtdvJ3bp2iuuc/xns6j51ntgMJC2\nagU5MdF4de8JQOqKJRi9fKgzcy6GSpXAqvHu1ZeTw57GmpVJ/GezCR4/EeXkRG5cLLEz3y4z1OH3\nvyRs1gQwGIhduo6Mk2eo3rsbAGcXrSLp9134tQuj7Y8f2X4qYcrHBfUvLe9Fgd3alzpl0issFHOC\niexzCWWWycnJwGsT72HooAVYrFZ692lOgxB/vv9uJwCPPBrOieMmJrz6C0pB/Qb+vDXlvoL8Y0f/\nRGpqFk5OBia8fg+enmXf81ce42OxcnLmXELfn4QyGEhYvpqs6NME9rTtU/FLfiV12w6824YTtmAu\nVrOZY9NnF2Q/OWseIa+PQzk7Yz4Xx7HpsypM3R0S32ol5qNPCXlnCspgxLRiJdmnYvC7vwcApqXL\nOb/9D6q2aUWzb77Cmp1N9HuF0x/rTZqIk6cnOi+PmNmfYMmwncDXGDKQSvXroYGcuHhOzZhtF/Z6\n9D3/O1rT8IUBuHh50uLDV7lwJJrdY6YC0G7RxzhVqoRyvnho9wSu7J6w8eN+IirqFKkpmdzZaQYj\nRnYuuNJ0LZSH/W/Cv7ozeNB8rBZN74daEBISwHcLbCfIjz4WwYnjibz6ymIUigYh/kye+kBB/tEj\nfyA1NQtnJyMT3+h+6fj/oK87e3vRaOprgG26tGn1BlKjCn8ix7drR0yrr82DSi665u9/ft9r+M4U\nMBpIWrGS7OgY/B+w9b3EX5aTdrHvfftl/k8F2PpexqHDpGzYTOjc2WCxkHnsBIlLVwDg06UzAb3u\nByBl8xaSfl1VZhFiN+0huMPt9Fj6HnnZZv74V+FPEXWcM44/3vyK7MRU9s78gch3h9Ps+YdIPXSq\n4IragXmLaT15MHf/OAWlFHtn/kBOqu0hYe0+GIlLVQ90noVd074h94Lt4WqO7/s0AUr//SBR4Smt\nb52LlkqpcRTet/YF8DO2KZI7gHBsO/qTWutMpVQLYDZQFdsgd6bW+nOl1HpgvNZ6h1LKD9ihta5z\nidC3TiOXInFQU4fF9v9iP2vblv0I4euty9YfOXR/Z4fFb7x0PWsi+zksftdtC8m1Ou7xxc6Gpxwe\nf2unng6J3XbjEgCH1b88tP3Ort0dFj98zQqH9z0L1+4+yCtl5HGHv/+Orr+j+j7Y+r+j6m/E9lMD\nO7r0cEj8iLXL+aF56T8HcCM8vOdrh/d9bBcpyr336j9frs+PXzz+cblsx1vpyhta6w+BDy++VkrV\nAfK01k+UknY30KmU5Z2L/G2ijHvehBBCCCGEEOJaknvehBBCCCGEEKICuKWuvBWntY7G9lRJIYQQ\nQgghhCjXbunBmxBCCCGEEOLGk1/FuzoybVIIIYQQQgghKgAZvAkhhBBCCCFEBSDTJoUQQgghhBA3\nlNbl8kn85Z5ceRNCCCGEEEKICkAGb0IIIYQQQghRAci0SSGEEEIIIcQNJU+bvDpy5U0IIYQQQggh\nKgCltXZ0GW4F0shCCCGEEOJGqBBPAplWb0S5Pj9+7cScctmOMm1SXHcJA5o5LHbAV/tY27avw+J3\n2fojh+7v7LD4jZeuZ01kP4fF77ptIRbmOyy+kccdHn9rp54Oid124xIAcq3/cUh8Z8NTDm/7HV16\nOCx+xNrlDu97jnrvoXy8/46Ov7nDgw6L32Hzzw6rv5HHAVgS/rhD4vfcOZ8tHXs5JDZA+02LHd73\nKwq5fnR1ZNqkEEIIIYQQQlQAMngTQgghhBBCiApApk0KIYQQQgghbihrxbg1r9yRK29CCCGEEEII\nUQHI4E0IIYQQQgghKgCZNimEEEIIIYS4oazytMmrIlfehBBCCCGEEKICkMGbEEIIIYQQQlQAMngT\nQgghhBBCiApA7nkTQgghhBBC3FBa7nm7KjJ4Ew7j0qw9Hv1fAWUke9NPZC7/0m69a+R9VO4+EBTo\n7EwufDOZvNOHAXC/6wncOz0ESpG18UeyVn17xfF9IlsQMuZZlNFA7JI1nPrm5xJpQsYOwLddGNbs\nHA5MnkP6kZMANJ4wHL924eSkpBH1xLirqD1UbtmagCEjUAYjqSuXkfzjf+3Wu9SoRfCYl3GtH4Lp\nP1+SvOj7gnWGyh4EjXoR11p1AU3srHfIPnSgzHo2HPssymDgXBn1bDjuWXzbtsRiNnNw8sdcOHzy\nb/MGdImk7qCHqVynOn8MeJULh04AEHhPB2o/3qvY1r2BlCtqmwmvLmHD+iP4+FZmydJhV5T3Wrge\n8b1at6TOqEEog5H4ZSs5N/+nEmnqjBqMd2QEFrOZ49NnknHE1q5h33+ONSsLbbGiLRb+GvICACGT\nXsS9ZnUAjB6VsaRnsHfgmEuWZfOm47w9bSUWq+ahvi0YNLid3fq0tCxen7CU06dTcXU1MnnK/YQ0\nDADgm/9E8dPC3Wit6dsvjCefbn3FbbFp4zGmT/0Ni9VK335hDB7SoUT8ia8t4XRMCq6uTkyZ1rMw\n/tfbWbhwF1pDv35hPPVM5GXF9GwVTq0RQ8FgwLT8N+IWLCyRpuaIoVRt0wprtpnodz8k8+hxAAL6\n9ML/vntAKRKX/UrCT4sBqDF0AFXbtkHn5mGOjSX6nRlYMjIKtnc9+h5AjX73UuOhe9FWK0m/7+LY\nHNvnn0eDWjR+eSjGyu75KQ2AtURMR7//f+em7Pttwqg3ehDKYCB+6SrOfPu/EmnqjR6Ed9twrNlm\njkybXdD3ATAYaPHF++QkJnHg5al2+ao/2ou6I55l231Pkpd24R+X9Xq1f7MXnyKwfXMs2Tn8OWku\naYeiS6SpVM2f8OkjcKnqQerBaHa9/gk6z4JveCitPxxH5tlEAGLX/cGRzxcVZjQo7vhmClmJKUSN\neb/Edr1ah1Fv9GDIb/+zpXz21h09GO/IcKxmM0enzSpo//Af5mHJzEJbrWCxsmfwC3b5qj3Si7oj\nBrD9/ifs2t/xfR83ILtEUHFTuGWnTSqlJimlxju6HLcsZaDKExNJnTGM5Ik9cW3TA2O1enZJLIln\nSXnnGZL/1YeMXz6jytNvAGCs3gD3Tg+RPOUxkt94CNfmd2AMqHll8Q0GGr0wiD3jprL9sbEEdOtA\npTo17JL4tg2jUs1gtvUbyaG3P6PRS0MK1sUtW8fusVOuru758QOHjebMGy9zYvjTeN7RBZeate2S\nWC6cJ37ubJL/932J7IFDRpCxM4qTw57i5MiB5JyOKTNUo/ED2T12KtseG0vg3e2pXEo93WsGs7Xf\nSA5Nn0ujlwYXlLGsvOknTvPXK++Tuvug3bbif9tM1FMvEvXUi+x/86P8pVc2cAPo3ac58754/Irz\nXSvXPL7BQN2xQzn44pvsfup5/Lp2wr22/T7rFRmOW41q/Nl/KCfe+5i64+xPnPaPnsDegWMKBm4A\nRye9x96BY9g7cAzJG7eSvHHrJYtisViZMvlXPp33KEt+GcryZfs5fizRLs3n836ncWggixYPZtrb\nPXl7+ipbvCMJ/LRwNwt+eJaffh7MhvVHiTmVfEVNYbFYmfLWCuZ+0Z9flg1n+dL9HCsWf95nm2kc\nGsTPvzzH9HceZNrUXwviL1y4i+8XDmLR4qGsX3+UU5cT32Cg1ujhHHnlX+x/9jl8utyBW7H2r9om\nArfq1dn35CBOfTibWmNGAOBWpzb+993DweFj2T/oebwiW+NaLRiA8zv/ZP+AYRwY/DzZp88S1P9h\nu21ej77n3bIp/p1asf3J8WzvP45T85cAoIwGmkwaxaF35rG9/8UvlEp+re3o9/9Sbsa+X3/cUPaP\nf4tdT4zE/66OuBfbD7wjw3GrGczOR4dx7L1PaDD+Obv11frdT+apMyU27RLgh1erFmTHJVyz4l6P\n9g9o35zKNYNY8+AL7JnyJbe/+myp6UJHPcrx+StY8+AL5J7PoPaDnQvWJf15mA39X2ND/9fsB25A\nvcfu5UL0udKDGwzUGzeU/ePf5M8nR+S3v33f944Mx71GMLsee45j735M/RfsP3v3jZ7IngFjSwzc\nXAL88GodVmr7O77vk1t6g4ibwS07eBOO5VTvNvISYrAmngFLHubtK3Bt0cUuTd7x3ejM8wDkHt+L\nwTvQlje4Hrkn/4KcbLBayDm8A9eWd11RfM8mDcg8E0f2uQR0Xh4Jq7fg36mVXRq/Tq2IW7EegPP7\nj+LkUQkXXy8AUncfJO98+tVUHQC3ho3JiT1Lbnws5OVxfuNaPCLb26WxpKWSffQwWCx2yw2VKuPe\ntDlpK5fZFuTlYc0ouyxZReoZv2oLfp0i7Nb7d2pF3PINRepZGRdfLzybNCgzb2b0WTJjyjhY5gvq\n1v5v1/+diFa1qVrV/dIJr5NrHd8jNITss7GYY+PReXmY1mzCu0MbuzQ+HdqQ+Ns6ANIPHMbJozLO\nvt6XHcP3zvaY1my8ZLq/9p6jVi0fatb0xtnFSPceTVi79ohdmuPHEmnTpg4A9er5cfZsKiZTOidO\nJHHb7dVwd3fGyclARKtarF51+LLLaIt/llq1valZ0xsXFyPd72vK2jX22zh+PJE2kfnx6/tx7mwa\nJlM6x4+buP326gXxW7WqzeqVB0uJYq9y44aYz54jJzYOnZdH8tqNeLVra5fGq10kSavWAJBxML/9\nfbxxr12T9IOHsZrNYLVyYc8+vDva9u3zO/4EqzU/zyFc/P3stnk9+l71PncT/Z+f0bl5AOSm2D4j\nfVo3J/3YKdKPnSoSoeTgzdHv/6XcbH2/SmgI2WdiMZ+z9f3E1ZvxLd73O7Ym4df1AFzYfwRjkb7v\n4u+LT9sI4n9ZVWLb9UYOIPrTr0t7m6/a9Wj/oDvCObNsEwAp+47h7FEJVz+vEun8WjUldk0UAKeX\nbiSoc0SJNMW5BfgQ2KEFMT+vK3V9ldAQss/GFXz2Jq7ZhE8H+6vFPh1ak/Drxc/eI5f92Vt35ECi\nP/l3qXP/HN/3sT9xKKesqHL9r7y6ZQZvSqmnlFJ7lVJ7lFLfFFvXQim1LX/9IqWUd/7yUUqpA/nL\nv8tfVlkp9ZVSKkop9adSqvgcMXEZjF4BWJPjCl5bU+IxeAeUmd6tYx9y/toMQN7ZYziHtERVrgou\nbrje1hGDT9AVxXf198GcYCp4bU5IwtXfp1gaX7LjkwrTJCbj6u97RXHK4uzrT15i4bfdeaZEnH39\nLy9vYDCW86kEj3mFOrM+J2jkiyhXtzLTZycUqUNCyTq4+vsUS2NrC7cSy6+s/gF3tbt0oluEi5+v\n3f6Wk2gq0ZYufr7kJCQWSZOEi19hmiYfTua2zz8k4IF7Smy/SvOm5Cankn0m9pJlSUi4QFBQlYLX\ngYGeJMTbT7dq1Diw4KT8r71niT2XRnz8BRqE+LNr52lSUzLJyspl08bjxMWdv2TMouLjLxAUVLXg\ndVBZ8VceAmDv3rOcO5dKfNx5Qhr6s3NnTEH8jRuPEnsZ8W1tW6T9TSZcirW/s59fsfY34eznR9bJ\nU1S5rRlGzyoYXF2p2iYC5wD7QRqAX/e7SYvaYbfsevS9SrWq4dU8lIgvp9HykzepElo/f3kwaGgx\ncwKtvn6nzLZw9Pt/q3EpfqxJTMKl+LHGz8d+/0xIwtXPlqbeqIGc/PTrEgMEnw6tyTElkXEs+voV\n/hpxC/Ahq8ixNCshGTd/+8GRi5cHeRcy0BZrqWl8bg+h83fTaTP7JarUq16wvNkLT3Jg1gJ0GT8Y\n5uJfrO8nJuHq51sijf17ZCpMo6HpjLdo/sUHBD5wd2F5OrQmJzGJzOPRpcYtj31f3DxuiXvelFJN\ngYlAO621SSnlA4wqkuQ/wEit9Qal1FvAG8AY4BWgrtbarJS6+DXRBGCt1npA/rIopdRqrXUG4rpw\nbtwK9459SJn+JACW2BNkrvgKrxfmoc1Z5J4+DLrkfR03K2U04la/IfGfzSb7yEEChozAt19/TN9+\n5eiiFfBs2gBrdo6ji3HT2P/8y+SYknHyqkqTD98iK+YMF/bsL1jv17UTpjWbrlm8QYPb8fa0lTzU\n+3NCQgJoHBqE0aCoX9+PAYPaMmTQAtzdnWnUOBCD4dp/Ozl4SAemTf2V3r3m0rBhAKGhwRiMBurX\n92fQoPYMGjgfd3dnGjcOwmi4vt9BZsecJu67hTR8dwrWbDOZx08UXG27KPjxR9AWC8mrS//2/1pS\nRgPOVT3YMfA1PJs04Lap4/i9z/MooxGv5o3549lXsGSbuXPDfBRBaOIuvdFiHP3+CxvvdhHkpqaR\ncfg4VcOaFSw3uLpQ86m+7Bs7yXGFu4HSDkWz6r5RWLLMBLRvTqsPxrG29wsEdgzDnJJG2qFofMND\nr0vsv55/hRxTMs5eVWk6402yYs6QfugYNZ7sx/5xb1yXmGW5kr4PdAXW3NACihvmlhi8AV2AhVpr\nE4DWOlkp2wFHKVUV8NJab8hP+zVw8U72vcB8pdTPwMU7Ru8Geha5X84NqAXYzd1RSg0BhgDMnTuX\nIUOGIApZUhPsrpYZvAOxppScN26s0RDPZ94idcZz6Iy0guXZm/5H9ibbTd+V+4zGmnJlJyjmxGRc\ni3x77hrgizkxuViaJNwCfbkY1dXfB3NiEtdCblIiTv6FV9qc/PzJTUr8mxxF8poSyTMlkn3Etstd\n2LIB3779y0zvFlD4jZ9rQMk6mBOTcQsoUs/8tlBOTpfMW5bAu9oTt2ozDRqUXa5bSY4pyW5/c/H3\nK9GWOaYkXAL8ufhR4uLvS44pKX+dbd/MS00jedM2PEJDCgdvRgM+ndry1+Cxl1WWgIAqxMUVXmmJ\njz9PQGAVuzQeHq5MmfYAAFpr7rnrY2rUtH0L/lDfFjzUtwUAM2esI6hY3ksJDKxCXFxhX44rI/60\n6b0K4nfrOpuaF+P3C+OhfmEAzPhwDUGBnpeMaWvbIu3v50dOsfbPNZny2z8/jb8fuSbbt/GmFSsx\nrVgJQPWBT5OTWPgtve89d1E1sjVHxr9WIu716HvmhGQS120H4PyBY2irFWcvT8wJSaT+eYDcIg9N\nUPiWGLw5+v2/1eQUP9b4+5JT/FhjSrbfPwN8MZuS8e3cFp/2rfCODMfg4oyxciUavj6GM/MX4Roc\nQNi/ZxZss8VXH7Jn8IvkJqfemIpdhjv+Ow2A1AMncA8s3J/dA3zITrS/FzonNR2nKpVRRgPaYrVL\nk5eRVZAuYcseDK8YcfHywKd5Q4I6hRPYvgUGF2ecPNxpOdn+frWcxGJ9398XsympRBrXAD8u9gpX\nf7+CNBc/e3NT00jauA2P0IbkXcjANTiAFv83syB9iy9nsGfI+IL2d3TfB1pSAQZv8rTJq3PLTJu8\nSvcBH2PrBH8opZwABTyktW6R/6+W1rrETRda63la6witdYQM3ErKO7kPp8BaGPyqg9EJ1zbdMe+2\n/9ba4BNE1ednkvb5q1ji7eZyo6r4FKRxDe9K9rblVxT/wsFjVKoZjFtwAMrJiYC72mPa9IddGtOm\nHQR17wyAZ9MQLBmZ5CRdmwNj9pHDuFSrgXNgEDg54dmpC+nbf7+svJbUZHJNCbhUt910Xbl5OOaY\nU2WmL1rPwG7tMW2yn9qVuGkHQT3uAGz1zEu31bN4G5WWt1RKEdC1HfGrtlxWfW4F6YeO4lajGq7B\ngSgnJ/y6diRly3a7NMmbo/C/504APJo0wpKRSW5SCgY3VwzutntQDG6ueLVqQdaJwgfUeIW3IDvm\nTInBSFma3VaNmFPJnDmTSm6OhRXLD3DnnQ3t0pw/n01uju2WiZ8W7iY8ohYeHq4AJCXZJhnEnktj\nzarD9Li/GVei2W3VORWdzJnTKeTkWFixbD93dikZPyc//o8L/yQionaJ+OfOpbF65SHue+C2S8bM\nOHQEt+rVcAmytb9Pl06kbt1mlyb19+34dusKQOXQRlgyMshNtp08OnnZpnm6BPjj1bEdyWvWA7Yn\nWAY90pdjE9+03RNXzPXoe4kbo/AOt7W5e81gDM5O5KaeJ2n7Hio3qIXB1QVltB3aNSU/rxz9/t9q\nLhw6invNYFzz30v/uzqQvCXKLk3y5igC7u0MQJWmDbGkZ5CblMKpud/yR59B7Og3hMOTPiBt516O\nTJ5J5olTRD3wDDv6DWFHvyGYE5PYPWBcuRq4AQUPGIldv4Ma93UEwLtZA3LTszCbSpY1accBgrva\n7kereX8n4jbsBMDVt3CatVfTemBQ5KSmc3DO96zqMZLVD4xh52tzMP1xgF2vf2q3zQuHjuJeo0j7\nd+1I8uZi7b8lioB7L372NiQvv/0Nbq4Y7T57w8g8cYrME6f4o+fT7Hx4CDsfHoI50cTugWPt2t/R\nfR8o/fHT4qZwq1x5WwssUkp9qLVOyp82CYDWOk0plaKU6qi13gQ8CWxQShmAmlrrdUqpzcCjgAfw\nGzBSKTVSa62VUmFa6z8dUakKzWrhwrfT8Bo3F2UwkrV5EZZzx3HrbHtaW/b6H6jccxgGj6pUeXJi\nQZ6Utx4BoOrzMzB4eKEteVz4dio668oekawtVo588AUtZk60PY536VoyTp6hWm/bnPZzi1aS9Psu\nfNu1pO3CObZH+U75pCB/0zfH4NWyKc5eVWi3eC4nv/ie2F/WXlH94z+bRc233gODgbRVK8iJicar\ne08AUlcswejlQ52ZczFUqgRWjXevvpwc9jTWrEziP5tN8PiJKCcncuNiiZ35dpmhDr//JWGzJoDB\nQOzSdWScPEP13t0AOLtoFUm/78KvXRhtf/zI9pMIUz4uaKPS8gL439Gahi8MwMXLkxYfvsqFI9Hs\nHmN7hLVXWCjmBBPZ567+CWjjx/1EVNQpUlMyubPTDEaM7FxwteVGuObxLVZOzpxL6PuTUAYDCctX\nkxV9msCe9wIQv+RXUrftwLttOGEL5mI1mzk2fTYAzt5eNJpqu6qjjEZMqzeQGrWrYNO+XTtiWn3p\nB5Vc5ORk4LWJ9zB00AIsViu9+zSnQdCqkpYAACAASURBVIg/339nO1F65NFwThw3MeHVX1AK6jfw\n560p9xXkHzv6J1JTs3ByMjDh9Xvw9Cz7fsuy4k/4V3cGD5qP1aLp/VALQkIC+G6B7QTl0cciOHE8\nkVdfWYxC0SDEn8lTHyjIP3rkD6SmZuHsZGTiG90vL77VSsxHn9LwnSlgNJC0YiXZ0TH4P9ADgMRf\nlpO2/Q+qtmlFs2+/zP+pgBkF2etPmoCTpyfakkfMrE8Kfg6g1qhhGJydafiebd9PP3CYmJlzCvJd\nj7537pd1hE4cRpv5H2DNy+PAW7Y8eRcyOL1gKa3+7+2Cr7M1Z0ttf0e+/5dyM/b94x9+TrMP3wCD\nkfhlq8k8eZqgXrZ7V+MW/0bK1p14tw0n/PvPsGabOTpt9jWqzZW7Hu2fsHk3ge1b0HXxhwU/FXBR\nm1kvsnvy55hNqRyYvYDwaSMJHd6PtMOniPl5PQDBXVtTp+9daIsFizmXna/OKSNSKSxWTsyYR9MP\nJoHBQMKyNWRFnyaol+2zN27xr7b2j4yg5Xe29j823fakZGdvL0KnvQrYPnsTV20kNeryTvcc3feB\nZZffSKKiUfoWuWaplHoaeBHbE3j+BKKBdK31+0qpFsBnQCXgBPAskA6sA6piu9r2rdb6baWUOzAT\naIftyuVJrfX9lwh/azRyGRIGOO6b2YCv9rG2bV+Hxe+y9UcO3d/ZYfEbL13Pmsh+DovfddtCLMx3\nWHwjjzs8/tZOPR0Su+1G22Okc63/cUh8Z8NTDm/7HV16OCx+xNrlDu97jnrvoXy8/46Ov7nDgw6L\n32Hzzw6rvxHbTw0sCXfMTz703DmfLR0d9yy59psWO7zvQzl+VGIRE2qNKtfnx1NjZpfLdrxVrryh\ntf4a2/1spa3bDZT2S68dSkmbBQy9tqUTQgghhBBCiL8n97wJIYQQQgghRAVwy1x5E0IIIYQQQpQP\nZfw8n7gEufImhBBCCCGEEBWADN6EEEIIIYQQogKQwZsQQgghhBBCVAByz5sQQgghhBDihpJb3q6O\nXHkTQgghhBBCiApABm9CCCGEEEIIUQHItEkhhBBCCCHEDWXVytFFqJDkypsQQgghhBBCVABKa7ld\n8AaQRhZCCCGEEDdChbik9VKN0eX6/PjdM7PKZTvKtElx3SUMaOaw2AFf7WNt274Oi99l648cur+z\nw+I3XrqeNZH9HBa/67aFWJjvsPhGHnd4/K2dejokdtuNSwAcVv/y0PY7uvRwWPyItcul793i8bd0\n7OWw+O03LXZo3wcctv933baQVW0edkhsgG7bf3B4368o5PrR1ZFpk0IIIYQQQghRAcjgTQghhBBC\nCCEqAJk2KYQQQgghhLihrI4uQAUlV96EEEIIIYQQogKQwZsQQgghhBBCVAAybVIIIYQQQghxQ8nT\nJq+OXHkTQgghhBBCiApABm9CCCGEEEIIUQHI4E0IIYQQQgghKgC5500IIYQQQghxQ8lPBVwdGbyJ\ncsGlWXs8+r8Cykj2pp/IXP6l/foWd+LReyRaW8FqIX3B2+Qe/fOytu0T2YKQMc+ijAZil6zh1Dc/\nl0gTMnYAvu3CsGbncGDyHNKPnPzbvB4NatPopSEYK7mRHZvI/jdmYcnMKtiea6Afbf4742/LVbll\nawKGjEAZjKSuXEbyj/+1r3ONWgSPeRnX+iGY/vMlyYu+L1hnqOxB0KgXca1VF9DEznqH7EMHyqx/\nw7HPogwGzpVR/4bjnsW3bUssZjMHJ3/MhcMn/zZvgxFP4tchHGteHlln4jk45WPy0jNRRiOhrz1H\nlUb1UE5Xf2F/wqtL2LD+CD6+lVmydNhVb6c8xfdq3ZI6owahDEbil63k3PyfSqSpM2ow3pERWMxm\njk+fScaREwCEff851qwstMWKtlj4a8gLAFRqUJd6LwzH4OKMtlg4OeMz0g8e/cdlvRnb37NVOLVG\nDAWDAdPy34hbsLBEmpojhlK1TSus2Wai3/2QzKPHAQjo0wv/++4BpUhc9isJPy0GoNqzT+LVLhK0\nldzUNKLf+ZDcpOSC7V2PvgdQo9+91HjoXrTVStLvuzg251sAPBrUovHLQzFWds9PaaC006NNG48x\nfepvWKxW+vYLY/CQDnbr09KymPjaEk7HpODq6sSUaT0JaRgAwDdfb2fhwl1oDf36hfHUM5GX+Q5c\nnptx3/NqHUa90YPBYCB+6SrOltL3644ejHdkOFazmaPTZhX0/fAf5mHJzEJbrWCxsmewre/XGtgf\nn45t0FYruSlpHJs2m5wi+97Vupb1j/x+1jXd9+sNeQS/Tq3AqslJSePA5I/JMaWUedxpNO5Z/NqF\nYck2s3/yJwXbL8ot2J/bp4zBuWoVzh86wb5JH6HzLJfOb1C0+ffbmBOT2f3COwAEdImk/uB+QH4f\n7dvdgX0fNyD7Mt4mUQHJtEnheMpAlScmkjpjGMkTe+LapgfGavXskuQe3EbyG31ImdSX81+9TpVn\n3rzszTd6YRB7xk1l+2NjCejWgUp1atit920bRqWawWzrN5JDb39Go5eG2FYYDGXmbfzqMI5/Op+o\nJ14gcUMUtZ7oZbfNkFFPk7xtd9mFMhgIHDaaM2+8zInhT+N5Rxdcata2S2K5cJ74ubNJ/t/3JbIH\nDhlBxs4oTg57ipMjB5JzOqbs+o8fyO6xU9n22FgC725P5VLq714zmK39RnJo+lwavTS4sP5l5E2O\n2sP2x8cR9cR4Mk+fo/bTvQEI6NoWg4sz2594gainX86PULnsdihD7z7NmffF41ec71q55vENBuqO\nHcrBF99k91PP49e1E+61a9ol8YoMx61GNf7sP5QT731M3XH2J077R09g78AxBQM3gNrDnuHMvxew\nd+AYTn/1X2o998w1Ke7N2P61Rg/nyCv/Yv+zz+HT5Q7cirV/1TYRuFWvzr4nB3Hqw9nUGjMCALc6\ntfG/7x4ODh/L/kHP4xXZGtdqwQDEff8jBwY/z4EhI0nbGkXwk/3ttnk9+p53y6b4d2rF9ifHs73/\nOE7NXwKAMhpoMmkUh96Zx/b+4/IjlHyUm8ViZcpbK5j7RX9+WTac5Uv3c+xYol2aeZ9tpnFoED//\n8hzT33mQaVN/BeDokQQWLtzF9wsHsWjxUNavP8qpU/98wFDUzbjv1Rs3lP3j3+TPJ0fgf1dH3OvY\n73vekeG41whm12PPcezdj6n/gn3f3zd6InsGjC0YuAGcXbCI3c+MZs+AsaT8voOazzxyTYp7Let/\nrff9U98uIeqJ8UQ99SKmLTupO6AvUPpxJ/j+zlSqGcSWvqM4+PY8Ql8aVGoZQ0Y8wanvlrGl7yjy\nLmRQvWcXAPzahf1t/lqP9CAj+qzdsowTp9nz8vsA1H7yQQf3fXIv9f6IiksGb0UopSKUUrMdXY5b\njVO928hLiMGaeAYseZi3r8C1RRe7NNpceFVLubqXdk5SpswzcWSfS0Dn5ZGwegv+nVrZrffr1Iq4\nFesBOL//KE4elXDx9cKzSYMy81aqFUzqn7YrXclRewjo3MZue1mxCWScOF1mmdwaNiYn9iy58bGQ\nl8f5jWvxiGxvl8aSlkr20cNgsdgtN1SqjHvT5qStXGZbkJeHNSO9zFhZReoQv2oLfp0i7Nb7d2pF\n3PINRepfuaD+ZeVNjtqLtti+0T+/7yhuAb62jWmNwd0VZTRgcHXJj3Dlx5CIVrWpWtX90gmvk2sd\n3yM0hOyzsZhj49F5eZjWbMK7Qxu7ND4d2pD42zoA0g8cxsmjMs6+3n+/Ya0xVq4EgLFyZXJN1+ZE\n+mZr/8qNG2I+e46c2Dh0Xh7Jazfi1a6tXRqvdpEkrVoDQMbB/Pb38ca9dk3SDx7GajaD1cqFPfvw\n7mjrq9YiV9sNbm4U/2C6Hn2vep+7if7Pz+jcPAByU84D4NO6OenHTpF+7FSRCCU/KP/ae5Zatb2p\nWdMbFxcj3e9ryto1h+3SHD+eSJvIOgDUq+/HubNpmEzpHD9u4vbbq+Pu7oyTk4FWrWqzeuXBSzX/\nFbnZ9r0qoSFkn40r6PuJazbh06G1XRqfDq1J+PVi3z9yWX2/6EwPg7sr+koOin/jWtb/Wu/7Rets\ndHMt3Fgpxx3vsCbErtgIQNq+ozhVsW2/OJ+IpiSs3QbAuWXr8b+jVX75IsrM7xrgg1/7lpxdvMZu\nWxnRZ8mMiQXAHJ/k4L6P/YlDOWXV5ftfeSXTJvMppZy01juAHY4uy63G6BWANTmu4LU1JR6nereV\nSOfSsiseD43GUMWX1FnDL3v75gRTkb+T8GwaYrfe1d+X7PikwjSJybj6++Lq71Nm3oyTZ/Dr1ArT\nxj8I6NIW1wA/W13c3aj9xIPsHj2ZWv17llkmZ19/8hILv+3OMyXi3qjJZdXHOTAYy/lUgse8gmvd\n+mQfO0L8vI/Q5tJnSGQnFKlbQnIp9fcpliYJV38f3EosL5kXIPiBO0lY/TsACWu34d+pFR2Wfo7R\n7eLgLeey6nUzc/HztduXchJNVGnSqESanITEImmScPHzJTcpBYAmH05GW63EL/mNhF9+AyD6oy8I\nff9Nag9/FqUM/DX8pRtQm4rH1rZF2t9kwiPUvv2d/fyKtb8JZz8/sk6eovqApzF6VkGbc6jaJoKM\nI4VTU6sPeArfu7tiycjg8LhX7LZ5PfpepVrV8GoeSv3nHsNqzuXoR//hwsHjVKoVDBpazJyAs7dn\nmW0RH3+BoKCqBa+DAj3Zu9f+CkKjxoGsXnmIiIja7N17lnPnUomPO09IQ39mzVxLakomrm7ObNx4\nlKbNqpUZS4CLf7F9LzGJKqENS6SxO9YkmnC92Pc1NJ3xFlitxC3+jfhfVhakqzX4CQLuuZO8jAz2\njZ54/Stzla7lcafec48R3L0TeemZ7HreNgOntOOOi1cVsuML2zQ7IQk3fx9yklILljlXrULehcyC\nLyKzE5Jx8/cpLF8Z+RuNfYajc77FqVLZg1xzcmGc8tL3xc2jXF15U0r9rJTaqZTar5QaopR6Tin1\nXpH1zyil5uT//bpS6rBSarNSaoFSavzfbHe9UmqWUmq3UmqfUqp1/vJJSqlvlFJbgG+UUp2VUkvz\n13kopf5PKfWXUmqvUuqh/OV3K6W2KqV2KaUWKqU8rmujiAI5u9aQPKEnaXNG4dF7hEPLcnDqx9To\ncy8R//cOxkru6DzbN2F1Bz3M6e+XYsm6flPNldGIW/2GpCxfTPTowVjNWfj263/pjNdBnWf6oPOs\nxP26CQDPpg3QViub7x/Clj7P56eSLvJP7X/+ZfYOHMPBF98kqHcPqjRvCkBgr+5Ez/mCXX0HEj3n\nC+q/PNLBJb35ZMecJu67hTR8dwoh70wm8/gJsBbeR3b2q/+w99GnSVq9noAHH7ju5VFGA85VPdgx\n8DWOzfmG26aOy19uxKt5Y/a/MZudQ17PTx14VTEGD+nA+QvZ9O41l/nfRBEaGozBaKB+fX8GDWrP\noIHzGTJoPo0bB2E0lKvTiJvOX8+/wp4BYzkw/i2C+/TAs3nhl3wxn3/Ljr4DSVy1geA+9zmwlDfO\nic8WsKXXMOJ+20SNvvcCpR937K7MXUN+7VuSk5zGhUMl75+73q6s79P1hhdQ2FFK+SilVimljub/\nX+bldKWUUSn158UxyKWUt0/dAVrrcCACGAUsAnoXWf8I8J1SqhXwENAc6J6f/lIqaa1bAMOBr4os\nbwLcpbV+rFj614E0rfVtWuvbgbVKKT9gYn76ltiu0o2jFPmDzx1KqR3z5s27jOLduiypCRh8ggpe\nG7wDsaYklJk+98hOjP41UB4lp0CU5uJVMdvfvpgT7aeWmROTcAv0LUzj74M5Mcl2Ba6MvJmnzrF7\nzGR2PPsy8as2k3XWduXQs0kI9Z9/krb/+4Qaj9gOpl73F92F8+uQlIiTv3/Bayc/f3KTEkukK02u\nKZE8UyLZR2zTlS5s2YBb/ZJXxC4qmNKIbbqHOTHJbr05MblYGls9s0sst88bfF9n/NqHs/+NWQXL\ngu7uQNLW3WiLpWBKh8Lnsup1M8sxJdntSy7+fiXehxxTEi4B/kXS+JJjSspfZ9vv8lLTSN60DY9Q\n2/vtf28XkjdsBSBp3RY8in2jL2xsbVuk/f38yCnW/rkmU7H29yPXZPvm3bRiJQefG83hMS9huZBO\n9mn7K1UAyWvW4d3Jfurz9eh75oRkEtdtB+D8gWNoqxVnL0/MCUmk/nmA3LQLWM22q92l9b3AwCrE\nxaUVvI6LP09AYBW7NB4erkyb3otFi4fy9rsPkpySQc2atvOOh/qF8eP/BvPN/GfwrOpGnTrSv/9O\nTmKxfc/fF7MpqUQau2ONv19Bmot9Pzc1jaSN20rt44krN+B7R9sSy8uLa3ncuSjut80E3Gmben7x\nuFP9wbsIm2UbvGitcQssbFO3AF+yix37c9Mu4FSlEspoyE/jU5DGnJhcan6v5o3w7xRBh0VzuG3K\nGHwimtFsUskvzVx9Cs9PHNH3gZYlClUO6XL+7x96BVijtQ4B1uS/Lsto4LLnoJe3wdsopdQeYBtQ\nE6gLnFBKRSqlfIHGwBagPbBYa52ttb4A/HIZ214AoLXeCHgqpS72rCVa66xS0t8FfHzxhdY6BYjE\nNtjbopTaDTwN1C4lL1rreVrrCK11xJAhQy6jeLeuvJP7cAqshcGvOhidcG3THfPudXZpjAGFN3g7\n1QoFJxd0emrxTZWqUs1g3IIDUE5OBNzVHtOmP+zWmzbtIKh7ZwA8m4ZgycgkJymVCwePlZm3YGqC\nUtR5ti9nF60CYNew19naZzhb+wznzPe2e9JSly4qUabsI4dxqVYD58AgcHLCs1MX0rf/fln1saQm\nk2tKwKW6rU0qNw/HHHOqzPRF6xDYrT2mTfYzgxM37SCoxx0F9c9LL73+RfP6RLag9hO92PPiO0UP\nFmTHm/COaAaAIf+bT835y6rXzSz90FHcalTDNTgQ5eSEX9eOpGzZbpcmeXMU/vfcCYBHk0ZYMjLJ\nTUrB4OaKwd02Pcfg5opXqxZknbA9oCYnKRnPFrb29mx5O9lnzt3AWlUcGYeO4Fa9Gi5Btvb36dKJ\n1K3b7NKk/r4d3262L6srhzbCkpFBbrJtyqqTl22aoUuAP14d25G8Zj0ArtULpwx6tY8kK+aM3Tav\nR99L3BiFd7jtPXevGYzB2Ync1PMkbd9D5Qa1MLi6FJyMatIortlt1TkVncyZ0ynk5FhYsWw/d3ax\nHxCcP59NTo7tlpkfF/5JRERtPDxs/TkpKQOAc+fSWL3yEPc9UHKKuyh04dBR3GsE45r/Xvp37Ujy\n5ii7NMlbogi492Lfb0heekZB3zfa9f0wMk/YPuvdagQX5Pft2IasmJJfKDjatd733WsWfsnr3ymC\nzFO2z7uLx50zP/3GjiG26aMJ66MI7t4JgKrNCrdfXMrO/QR0sT0xtdp9nUncuKOgfKXlP/bJAjY9\nMIzNvUfw18SZJO/Yx75JH5XYrmugr0P7PlD646fFjdQL+Dr/76+BB0tLpJSqAdwHfHG5Gy4397wp\npTpjGzC11VpnKqXWY3vU6XfAw8AhYJHWWiulriZE8UH0xdcZV1JMYFUpV+nEP2G1cOHbaXiNm4sy\nGMnavAjLueO4dX4YgOz1P+Aa3g23dj3RljzIyeb8Z2XOki3hyAdf0GLmRNtjd5euJePkGar1vhuA\nc4tWkvT7LnzbtaTtwjm2R/ZO+QQAbbGWmhcgsFsHajxkm7KRuH47sUvXXnGd4z+bRc233gODgbRV\nK8iJicaru+0+udQVSzB6+VBn5lwMlSqBVePdqy8nhz2NNSuT+M9mEzx+IsrJidy4WGJnvl1mqMPv\nf0nYrAlgMBC7dB0ZJ89QvXc3AM4uWkXS77vwaxdG2x8/sv1UwpSPC+pfWl6ARi8MxODiRNhs27ec\nafuOcPjdzznz42+EThxOm/9+SGE/vbxBdlHjx/1EVNQpUlMyubPTDEaM7MxD/cKueDtX65rHt1g5\nOXMuoe9PQhkMJCxfTVb0aQJ72vah+CW/krptB95twwlbMBer2cyx6bZnJzl7e9Fo6muAbXqMafUG\nUqN2AXDi3TnUGTUYZTRizcnhxHsflx7/Ct107W+1EvPRpzR8ZwoYDSStWEl2dAz+D/QAIPGX5aRt\n/4OqbVrR7Nsv838qoPCnPupPmoCTpyfakkfMrE+wZNgOGzUGP4tbzepoqyYnIYFTM+bYhb0efe/c\nL+sInTiMNvM/wJqXx4G3bHnyLmRwesFSWv3f26AvHt5KDuadnAxM+Fd3Bg+aj9Wi6f1QC0JCAvhu\nge0E8dHHIjhxPJFXX1mMQtEgxJ/JUwung44e+QOpqVk4OxmZ+EZ3PD3drv59KcVNt+9ZrJyYMY+m\nH0wCg4GEZWvIij5NUC9b349b/CspW3fiHRlBy+8+w5pt5th022DA2duL0GmvAra+n7hqI6lRtp/I\nqT30KdxrVQetMcclcPz9T/9RvS+6lvW/1vt+g+GPU6lWNbTWZMclcvidzwFKPe6c+fE3POrWoP1P\ns7Fk53Bg8ieF5ZrxCgemzsVsSuHonPncNmUMDYY+yoUjJzm7xHYsN235E792LUvNXxb/O1rRePwA\nAIzurkT+9wPMSakO6vssu6o3TVxLgVrr2Py/4yh7HvtM4CWgShnrS1BaX4MLg9eAUqoXMEhr/YBS\nqjGwG7gX2INtemIM8LLWOip/2uRcoB22AeguYJ7W+v0ytr0eOKS1fk4p1QH4VGt9m1JqEpB+MV/+\nAHK81vp+pdTbgJvWekz+Ou/8WDuBLlrrY0qpykB1rfWRS1SvfDSygyQMaOaw2AFf7WNt274Oi99l\n648cur+zw+I3XrqeNZH9HBa/67aFWJjvsPhGHnd4/K2dyn5wzfXUdqPtMdKOqn95aPsdXXo4LH7E\n2uXS927x+Fs69rp0wuuk/abFDu37gMP2/67bFrKqzcMOiQ3QbfsPDu/72C42lHvPB40u1+fHn8TP\nHgoUnT43T2tdcC+UUmo1EFQiI0wAvtZaexVJm6K1trvvTSl1P9BDaz286BjkUuUqN1fegF+B55RS\nB4HD2KZOorVOyV/WRGsdlb/sD6XUEmAvEA/8BaXMD7GXrZT6E3AGBlxGeaYAHyul9mF75OqbWuv/\nKaWeARYopS7eDTsRuNTgTQghhBBCCFFB5A/Uynxwhdb6rrLWKaXilVLBWutYpVQwUNrDHNoDPZVS\nPbDNNvRUSn2rtX7i78pVbgZvWmsztoePlLautFHo+1rrSUqpSsBGbFfE/s63F6+iFdnupGKv1wPr\n8/9Ox3ZPW/GyrAVaFV8uhBBCCCGEEMASbOOIt/P/X1w8gdb6VeBVsJv997cDNyhHg7erME8p1QTb\nSPVrrfUuRxdICCGEEEIIcWnl5M6t6+Vt4Ael1EDgFLbnd6CUqgZ8obW+6nn9FXbwprUu8cNWSqmP\nsV2CLGqW1rrzDSmUEEIIIYQQ4pamtU6ilN/b01qfA0oM3IrO/ruUCjt4K43W+vlLpxJCCCGEEEKI\niuemGrwJIYQQQgghyj+rowtQQZW3H+kWQgghhBBCCFEKGbwJIYQQQgghRAUggzchhBBCCCGEqADk\nnjchhBBCCCHEDWW9uX8q4LqRK29CCCGEEEIIUQEofZP/Ql45IY0shBBCCCFuBOXoAlyOIQGjy/X5\n8byEWeWyHWXapLjuvm/+jMNiP7Ln3yy4/VmHxX9s7/9xok/x342/cer9bwsrWj3msPjd/1jA5g4P\nOix+h80/Ozz+2rZ9HRK7y9YfAdjfvcRvhN4QTVesYVlEf4fEBrhvx38d1vZga3/Lv90cFt/4TDZb\nO/V0WPy2G5c4vO9t6djLYfHbb1qMhfkOi2/kcYfVv/2mxQDs6FLid4hviIi1y/mh+dMOiQ3w8J6v\nHd73K4pyPXIrx2TapBBCCCGEEEJUADJ4E0IIIYQQQogKQKZNCiGEEEIIIW4oedrk1ZErb0IIIYQQ\nQghRAcjgTQghhBBCCCEqAJk2KYQQQgghhLih5NfKro5ceRNCCCGEEEKICkAGb0IIIYQQQghRAci0\nSSGEEEIIIcQNZXV0ASooufImhBBCCCGEEBWAXHkTN1zYy48T3OF2LNk5RL3+BSmHTpVIU7m6H23f\nGYZLVQ9SDkaz/bV5WPMsOFepROu3BuJRIwBLTi5/vPElacfOAnD/8vfJzcxCWzTaYmFV/zcvWZaW\nL/enWkdbWba9/iUpB0uWJeTRrjR6ohtVagXyU6eR5KSmA1ClThCRkwfiHVqbvR/9j0Nf/3rVbeIe\n1gbfAWNQBgPnV/9C2qJv7dZ7dLqbqg8+jlIKa1YmpnnvkxN97Kpihb7wNP7tW2DJzuGvNz/l/OHo\nkuWp5k+LqaNwrurB+UMn2fOvj9F5Fqrd2566T/VEKcjLzGb/219y4WgMlWsH02LaqIL8laoFcHTe\njyW269UmjHqjB6EMBuKXruLMt/8rkabe6EF4tw3Hmm3myLTZZBw5UbjSYKDFF++Tk5jEgZen2uWr\n/mgv6o54lm33PUle2oVyFRvAJ7IFIWOeRRkNxC5Zw6lvfi6RJmTsAHzbhWHNzuHA5DmkHzn5t3k9\nGtSm0UtDMFZyIzs2kf1vzMKSmVVq/KI8wlsR9NzzYDCQ+utyTAu/s1vvUqMm1ce9hFuDBiR8/RVJ\nPy2034DBQL3Zn5BnSiJm0oRLxruoyfinCMjf9/ZM+qzMfS9s2khcqnqQdvAku//1CTrPQuAd4TR8\nrh/aakVbrBz44BtS9hwGoM6j91Kr952AIubntUQvKNkX/0n7N54wHL924eSkpBH1xLgS+Wo+9gAh\no55m073PklvG+1/UpuNuTF/tg8UKfVukM7jtebv1UadcGfFTANWr5gHQrVEmwzukcTLJiXE/+xek\nO5PqxMiOqTzV+tIxvVq3pM6oQSiDkfhlKzk3/6cSaeqMGox3ZAQWs5nj02cW7P9h33+ONSsLbbGi\nLRb+GvKCrb0mvYh7zeoAGD0qp4i55QAAIABJREFUY0nPYO/AMaXHd3D/82odRr3RgyE//tlS6l93\n9GC8I8Oxms0cnTarIH74D/OwZGahrVawWNkz2Fb/WgP749OxDdpqJTcljWPTZpOTlFxq/Ms14dUl\nbFh/BB/fyixZOuwfbeui61H3OsOfwbtdK3ReHtln4zg6fTaW9IxS43u2CqfWiKFgMGBa/htxCxaW\nSFNzxFCqtmmFNdtM9Lsfknn0OACBfR/Er8c9oDWZJ6OJfmcGOjcX9/r1qD12BAYXZ7TFSsysj8k4\ndKTMNgh7+XGCOjTPP+/4nNQyzjsi3xlecN4R9dpc23mHhzttpg2lUpAvysnI4a9XEL14E1VqBxH5\n7vCC/B41Atj3Scn9uihH9H1x85HBm7ihgjvcTpVagSx/4GV8b6tP+MSnWP3E5BLpbh/9MIe/Xcnp\nX7cTPvFp6vbuxPGF62gy6AFSD8WwZexHVKkTTPhrT7J+yLsF+dYNeqdgcHVZZakdyNL7X8H39npE\nTHySVY9PKZHOtPso5zbupsuXr9gtzzmfwc63/0uNLmFX2ArFGAz4DX6B2DfH8P/snXd8U9X7x983\nHUkXtM1oC2Uvochqy16COHCCIiqoDBkiyBBQ9OsE3CAqigx/7q1fvyqggiBToJQpe7ZAZ5IOupI0\nyf39cdO0aVpApKTgefviZXrvc87nnOeeJ/eee0bs5mzqv7aM4u2bKD2T4jYpzUon45mJOIsKCOrY\nFd34maQ/OfZvS+m7dyCkYTQbBk8lvG1z4p4czZaRz3jZtZp4PylfrCRj9RbinhxNgzuu49T3v1Oc\nns22cS9iLyhC1709bZ8aw5aRz1CUmsHmYbNc9ZHot/I9Mv/YTutpD3rUs9m0ceyb+hy2bDMdlr2O\neVMSJSln3CYRXePRNIhhx72PEBbXkubTx7Nn7Ez3+XpDbqU49Qz+wUEe5Q006AhP7IAlM7taH/tM\n26Xf6vGH2TX5RazZOST83ysYNyZTXEFf260jwQ1i2DpkEnXiWtBq5lh2PDzrnGmvmfUIxxZ+Qt6u\nA8Tc2o+Gw+/g5JKvqi+Hqywxjz5GylMzsZuMNH3rPQq2bcF6qvxhxlFQQMb7C6nTrUeVWWjvGIz1\n1Cn8gkPOrVUBfY8OhDSIZt2gaYS3bU7bWaP4c8SzXnbXTLqPk1/8QsaqLbSdNcrd9kxJ+8havwOA\nsOYN6PTKZNbfPZ3QZrE0HHQdmx58Btlup/PbT5K9cZdXnS/a/0Dmij848+0vtHl2kld51QYtkZ3b\nY8kwXpAfHE6YsyqSZfdmE1XHztCPYriuRQnNdaUedvGxFhbd45lnE62dH0ZnuPPpuzCW/q2Kzy+q\nUtFk6jgOTHsWm9HMtUvmkbspiZLU026T8K7xaGLrsev+cYS2aUWTaY+wb/wM9/n9k5/26hgdff51\n9+dGj46q9uG9NsRf02nj2D/1OWxGM+2XvkHO5iRKUsrrH9E1nqDYGHbeN57QNi1p9vgj7B1XXv99\nk//jVf+0L3/g1AdfABBz1600GDGU4/MWVV+OC2DQ4PYMG57Ik094v1y4KGqo7nnbd5Oy+BNwOGk0\n/kFih99F6vufVKnfcPIEjsx4mlKjidaLFpD351YsFdpe3S4JaOrXZ98DDxPSuhUNp0zk0KNTCdBp\nMQy6nX0jxyPbbDR9dhaR/fpg/u13YseNIv2TLziblEzdLgnEjh3F4WlPeusD0T3bEdowml9um0nk\ntc2I/89DrBn+opddu8lDOfLZbxWeO/pw/Nu1NB/an7Mn0tn02ALUEWHc9OMrnFrxJwWpmaweqnyH\nSSqJW1cvIG3tDjrOHFZlOXwS+4Krkn/FtElJ4V9R19pO/es6kvLzZgDMfx0nICwYja6ul11U59ac\nWb0dgJSfNlG/XycA6jStR1bSQQAKUjIIqadDHVnnosoSe11HUn7+UynL3hMEVlOW3EOnKEo3ex23\n5hSQs/8kTrvjovTLUDdvTWnGGexZ6WC3U7RpDSGde3lqHd6Hs0i5eVqP7Mdfa7goLUOfeNJWbAQg\nb98x/MOCUWvDvey0iXFkrt0GQNqKDRj6JChp9h7FXqA8oOX9dQyNIdIrrS6xLcVnsrBkmjyOh7Vu\ngeVMBtb0LGS7HePvm9D27OJhE9mrM9m/rgOgYP8R/EJDCNBGABCo1xLZLYGsn1d7aTadNIqURR9D\nNdsO+1IboE6b5hSfycSSno1st5P9+2b0vRM9bHS9E8n8RdE/u/8o/qHBBGrDz5k2uGEMebsOAJCT\ntAdDX886VUVQy2uwpadRmpmBbLeTv/4Pwrp297Bx5OdhOXIY2W73Su+v0xHauQt5v608r1ZFovrE\nk7ayvO0FVNP2dIlxZK5R2t6Z5RuJ7qu0PUeJ1W3jF6Rx7zEd2rg+efuO4bTakB1OzDsPEt3P07f/\nxP8AebsPYj9b9UuhFpNHcPzdT5HP1QAq8Fd6IA0j7DSIsBPoBze3LmLtkaDzJ6zE1hQNDcNLqV/3\n/N8/oa1bYEnLwJqhtH/Tmo1EVG7/Pbtg/O0PAAoPHMa/Qvu/ELTX9cC0ZkOV53wdf2GtW2BJy3TX\n37hmI5E9O3vq9+xM9q9l9T9yQfWvOMqtClJfcBs4FwmJjahb9++3h+qoqbrnbd+t9CJQrpdar6vS\nLuSalljT0rFlZCLb7eSs3UB4924eNuHdu2JevQaAooOuthep6Et+fqjUgaBSoVKrKTW77sWyjF9w\nMAB+ISHnHPGsf10n93NHzjmeOwzVPHfIMvgHawDwD1Zjyy/C6fBcrWXoEkfRaSPFGd7PCmX4IvZr\nO065dv+rrVy1HRpJkhpLknRYkqRPgH3AB5IkJUuStF+SpBcq2KVIkvSCJEk7JUn6S5Kka1zH9ZIk\nrXbZL5MkKVWSJJ3r3HBJkpIkSdotSdJiSZL8fFPLK48gQwTFWeVfsiVZuQQZPG8SgeGh2AqKkV1f\njsVZuQS7bPKOnCK2fzwAkW2bEByjJThKOScj03fxTAZ8+TxN7+pzAWUJpyizvCwVdS4n/lo9dnP5\nW2O7ORu/SH219mHX30rxrq0XpaXRR2LJKr+5WLJzUFfqgAXUDaO0oMjtf0u2ucpOWoM7+mL8c7fX\n8ZgbupP+259exwP1kVizyzt0VqOZQL1nvmpdJLYKNrZsM2qdYtP0sdGcXPSx1w/DRPbsjM1kpuhY\nSnXV9qk2gLqyfrYZdWV9vdbj2liNOaj12nOmLTp5Bp2rE2Lo1w21oeoHqIoE6HSUGsvf6paajPhr\nz5+ujOhxj5L1wRLkv3ln0+gjKKkQb5asHDSV4q3qtlduE9U3gT7fvUHighnseXEJAIXHTxPR4RoC\n6oaiUgdi6NGBoCitR77/xP/nQtcrEasxh8Jj3lOwqiOr0J/oOuWd4ugwB9kF3reQXWlq7lwWw9iv\nDRw1BnidX3kwhIFtLuzNe6BO61F/m9HkVbdAnRZbtrGCjZlAXblNm/mzuXbpfAy33eiVf1j7OEpz\n8rCcyaha38fxF6jXeuZtNKPWab1sPMtoKreRIe7NF2m/bB5Rt93gka7hmOEkfPcB+gF93KNwtYma\nrHsZUbf0J3fbjqr1dZX0TSYCK7W9AJ2uUtszKd9TJjOZ3/yXdl99TPvvPsdRVMTZZGVU/fS7S4gd\nN4p2X31M7PjRpC37qFofBBkiKKkQ2yVZORf03FFmc+yr36nTtB63/f4WN3w3l92vfe7VFhve1IVT\nv577vuyL2BdcnVy1nTcXLYD3ZFmOAx6XZTkBaAf0kSSpXQU7kyzLnYBFwHTXseeAta603wENASRJ\nag0MBXrIstwBcABeY+SSJI11dRaTlyxZUkPV+/dx8P9WEFgnmBu+fpEW9w0g71Cq+yFy7Yi5rBr6\nLBsenUeLof3Rd2rp49JeejRtOxHW/1ZyPnnPp+WIjG9D7O3XcXjhlx7HJX8/DL3j3SMnl4qI7gmU\n5uVTdPi4x3GVOpAGD95N6rIvq0l5ZWufj4Nz3yV28E0kfPgqfsFBVY6UXUpCO3fFkZeL5djRGtWp\njqx1yay/ezo7ps+n1fghABSmpHPik5/psnAWnd95grNHUt0PYDWJSh1Io4cGc2Lp15c87zbRNtY8\nmsb/Hs5gWPxZJn3v+TLH5oA/jgZxY+tqpileYvY/+gR7R0/h4IwXiB40kLD2cR7ndf17Y1qzsUa0\na0P8/fXok+wZNZUD018kZvBA6rRv4z53aulnJN89GuPq9cQMvqXGy3K5OVfdAWIfGILscGJctf6S\na/uFhhLeoyt/3T+SvUOGo9JoiLz+OgD0tw/k9HtL2XvvQ5x+dymNp0++5PplRHdvS96hU/x8/WRW\n3/MMHWc9gH+Ixn1e5e9HvT4dOb0q6R9r1bbYF9ROrvY1b6myLJe9CrlHkqSxKHWOAdoAe13nylaY\n7gAGuz73BAYByLL8qyRJua7j/YF4YLskSQBBgNdke1mWlwBlvbZaPPha8zQf2p+mg5WRsJz9JwmO\nKn/jGhQVQUl2roe9La+QwLBgJD8VssNJcFQExS4be5GFpGc/cNveuvINCs8o7i/JzgOU6Yxn1u4k\nsm1Tr7K0GNqPZq5ROfP+k4RER1L2TrCizuXEbjZ6TIP01xpw5Hivnwls1Az9hCfJnP04zsKzXuer\no+GQATS4sx8A+QdOoKkwKqExRGLN9pxuUppfQEBYiNv/GoMWSwWbsOYNufY/Y9k++RVK8z2nkum7\nd+DsoZPYcvK9ymEz5niMDKn1WmxGT22rKYfACjaBBi1WUw7avt2I7JFIRNd4VIEB+IUE0/KZKZz5\n/AfUMQY6frTAnWeH/5vPnjEzKM3JqxXa4BrFqahv0GKtrG80o4nSUuY5tT4Sq9GM5O9Xbdri1HR2\nT1HWjAY1iEHXo5OX3ytTajIRoC9/IAjQ6bGbTedIUU5wmzjCunYnNLELUkAgfsHB1J8xi7TXX67S\nvtGQATS4U3nYyj9wgqDoSHL3KOc0UZFYKsVb1W3POyZzdh0iuL5BGanLL+D0j+s4/eM6AFpNGIol\n23Pq0j/xf3UExUYTFGOg86dvuOy1JH70GsmjZ2GrdP0rEhVqJ/Ns+a03s8APQ5jn9KdQdfkto09z\nC7NXSeQWq4gIVjqlG48H0SbKhi7kwjqpNpPZo/6Bep1X3WwmM4EGPXDQZaPFZjK7zim+suflk7Nx\nK6GtW1CwZ7+S0E9FZO9u/DVmavX6Po4/m9Hsmbdei9Vk9rJRG3SUrexS63Vum7L6l+blY96wldDW\nLTm754BHeuOq9bR5/VlO/5/vXuZURU3W3XBzPyK6J7B/ive6aXfepkr6Oh22Sm2v1GRytb2yMuoo\nNZmoE98Ba0Ym9nzlfpe3cTOhca3J+f0PtDdcz+mFiwHIXb/Rq/PWfGh/mrieO3L3n3SNxisvnYKi\nIi/ouaPMpvEdvTj0fysAKDydTVGakTpN6pGzT9nUJbpnO3IPpWLNOfd92RexX9v5Vz8c/wOu9pG3\nIgBJkpqgjKj1l2W5HbAC0FSwK1tM4eD8HVoJ+FiW5Q6uf61kWX7+0hb76uLY12tYNfRZVg19lrQ/\ndtL4NmUTBO21zSgtLMFi8n7Qz95+iNgBynSwxrf3JP0PZapEQFgwKn9lmkHTwX0w7jyMvciCX1Cg\ne066X1Ag0d3i3LtQVuTo12v59Z7n+PWe50hbu5PGtylrfbTtmlJaUHVZahrrsUMExMTib4gBf39C\nevanaPsmDxs/XRRRM18i+60XKc04XU1OVXPq29VsHjaLzcNmkbUumfq3KOvpwts2x15YjNXs/aBp\nTt5PdD9lTUr9W3qTvUGZEqOJ0tLxtansee5dik9leqWLubE76au8p0wCFBw6SlCDGNQxBiR/f/TX\n9yRns+ebypxNSRhu6gtAWFxLHIVFlJpzSV38GdsHP0zykLEcfn4e+Tv2cmT2AopPpJJ02wiSh4wl\nechYrEYzu0dN83p486U2QMHBYwQ3iEHj0jdc3wPTxu0eNqaNyUTfrOjXiWuBo6gYmznvnGkDIlzr\nPSWJxiPvJu0H7zVBlSk5cojAevUJiIpG8venbp/rKNha9TWrTPZHH3DkgXs5OmIYZ16ZQ9Ge3dV2\n3ABSv13NpmFPsWnYU0rbG1ix7ZVU0/YOEN1faXuxt/Yia30yAMGxUW6bOq0aowr0d+/sGOjygyZK\nS3S/RNJ+9azPP/F/dRQdP8WmW0azZfAEtgyegNVoZvuImefsuAG0rWcjNdefM3n+2Bzwy8EQrmvh\nuUOosVDlnpW1Nz0QpwzhQeUPaysPhDAw7sLfvBceOoomth7qmCgkf390/XuRu9lzdDxnUxL6G5WO\ndmibVjiKiik156LSqFEFKetyVBo14YkdKDlxyp0uPL4DllNnvB7IK+Lz+Dt0lKDYCvr9e5GzqZL+\n5iQMN5XVvyV2l75Ko8bPo/4dKT6hTJPVxMa402t7daHklPc9x9fUVN3DO3ek/v2DOThrLk6rrVr9\nokNH0NSvR2C00vYi+/Umb4vn9MK8P7ehHdAfgJDWrXAUFVGak4sty0hom2tQqdUAhHXqgOWUcv8r\nNZsJa3+tcrxjeyxpnr4/9vUaVg99ltWVnjsiz/nccdDjuSPtj50AFGfmENVFGXFUR9YhrHGM+6Ux\nQMObu3Lql/MvZfBF7AuuTq72kbcy6qB05PIlSYoCbgbWnSfNZuAe4FVJkm4AyiZIrwF+lCTpTVmW\nsyVJigTCZFm+8EUP/2IyNu4hpmc7bln+GnaL1WMUrdfCqWx/4UMsxjz2LPiGbq89wrWPDibv0ClO\n/KAshK/TJIYuc8YgyzJnj6eR9Nz/AaCJrEvPN5Wd4CR/P1JXbiXzz7/OWZb0jXuJ6dWOW1e8isNi\nY9sz5WXp8+5Ukp7/kBJjHi3vv57WI29Go63Lzd+9SMamv0h6/kM02jrc+NVzBIQEITtlWg0fwIo7\nn8ZeZPl7TnE6MC17k+hn5yOp/ChYs5zS0ycJu+FOAApW/Y+Ie0aiCquDbqxrVq/DQdrM0X9PBzBu\n3oW+Rwf6/LAAh8XK3hcXu8/FL5jJvjlLsZpyObzwSzrMnUSLR+7h7OEUzvyoLGZv/vBgAuuGEvfE\nKABku5M/H1K2ivfTqNF1vpb9Ly2rWtzh5Pj8pbSd/xyo/Mha8TvFJ08TfYeyhibzx9/I3bKDiG7x\nxH/9Pk6LlaMvvf2361jrtAHZ4eTIvGV0WPAfJJWK9OVrKTp5hnqDlDUk6T+swvznTrTdO9Ht24U4\nrFYOznnvnGkBogb0JPaumwAwrttGxvK15y+M00nGondoNOdVJD8Vuat+wXoqlYiBtwKQu3I5/hER\nNH17EargYHDKaO+8i2PjRuEsvvh1Ftmbd6Pv0YG+/3tTaXsvlLe9xLdmsnf2EqymPA6+8yWdXppE\nq0eGcPZwqntELbp/Z2IH9sJpt+O0lrJz1jvu9PGvTSGgbiiy3cG+Vz/EXuhZzn/if4C4F6YQ3imO\ngPAwuv+4mJPLvibj5wvwdRX4q+DpATmM+cqAU4ZB7QppoS/lq52hANzbqZBVh0L4alco/ipQ+8vM\nu8OEMtEDim0Sf57U8PxN1XeWvHA4OblgMa3feB5JpSJ75e+UpJwm6nal7WT99Ct5W5OJ6BZPxy8X\n47RaOfay0v4DIsJpNfcpQNk8wvT7evKSdrqz1vbvhen3qjcqqajvy/jD4eTEm0uIm/c8qFRkr1hD\nScppou+4yaX/q6LfNYFOXyn6x15+x13/1i/NctffuHoDeUnKy8RG4x4kqGF9kGWsmdkcf+Of7TQJ\nMH3a9yQlpZKXW8x1vd9k4qS+3DXkH+xoXEN1bzp1HKqAAOLmK1sIFO4/UvVOm04np95ZRMtX54Cf\nCvMvq7CknEJ/20AAjD+vJH/bdup2SaTtZx+4firgTQCKDh0md/0mWi9+GxwOio+dwLj8FwBS571N\ng4njkPz8cNpKSZ33jre2i7LnjoHLX8dusbL92fJ7VK+F09j+wv9hMeaxd8E3dH1tAm0fvYu8Q6mc\ndD13HFjyI51nj+GG7+YgSRJ7F3zj3tXaLyiQqK5t2TH7o/NeCp/EvuCqRJLlq3PQUpKkxsByWZbb\nuv7+COgOnAbygZ9kWf5IkqQUIEGWZZMkSQnAG7Is95UkyQB8CUQBW4BbgcayLFslSRoKzEIZuSwF\nHq0wPbMqrk4nXyBftx/hM+2hez7iy3YjfaZ/394POTG46u3WLwdN/7uZXxLv85n+zdu/ZFPPO32m\n33PT/3yuv7bb3T7R7rdF+Z29/Tf394l+3C9rWJFwv0+0AW5J/sJnvgfF/46PNOc3rCH8RljY0vt2\nn+l32/CTz2Nvc687fKbfY+OPOPjcZ/p+DPNZ/Xts/BGA5H4DfaKfsHYl37R/yCfaAPfs+djnsY8y\nS6zWMyxycq1+Pv48561a6cerduRNluUUoG2Fv0dUY9e4wudkoK/rz3zgRlmW7ZIkdQMSZVm2uuy+\nBi79KnWBQCAQCAQCgUAgqIartvN2CWgIfOP6fTgbMMbH5REIBAKBQCAQCAT/YkTnrRpkWT4K/IOJ\n5gKBQCAQCAQCgaAqrtKVWzXO1b7bpEAgEAgEAoFAIBBcFYjOm0AgEAgEAoFAIBBcAYhpkwKBQCAQ\nCAQCgeCycnX81PjlR4y8CQQCgUAgEAgEAsEVgOi8CQQCgUAgEAgEAsEVgJg2KRAIBAKBQCAQCC4r\nTrHd5EUhRt4EAoFAIBAIBAKB4ApAdN4EAoFAIBAIBAKB4ApAksWQ5eVAOFkgEAgEAoFAcDmQfF2A\nC+Ge8Mdq9fPxN3lv10o/ijVvghrH+HCcz7T1y/azttvdPtPvt+U7Dt3a12f61yxfx5quQ3ym33/r\ntzj43Gf6fgzzuf6W3rf7RLvbhp8AfFb/2uD75H4DfaafsHaliL1/uf7mXnf4TL/Hxh99GvuAz+69\n/bZ8x6rOQ32iDXBD0tc+j/0rhVrdc6vFiGmTAoFAIBAIBAKBQHAFIDpvAoFAIBAIBAKBQHAFIKZN\nCgQCgUAgEAgEgsuKU8ybvCjEyJtAIBAIBAKBQCAQXAGIzptAIBAIBAKBQCAQXAGIaZMCgUAgEAgE\nAoHgsiKL/SYvCjHyJhAIBAKBQCAQCARXAKLzJhAIBAKBQCAQCARXAGLapEAgEAgEAoFAILisiN0m\nLw4x8iYQCAQCgUAgEAgEVwBi5E1QKwiI60nofU8iqfwo2fg9Jb8s8zgf2OE6Qu6cBE4Z2Wmn8KtX\nsR/beUF5R3btQIspI5H8VGT8tIbUT//nZdNi6ii03TvitNg4MHshhUdOnjNtaPNGtJo5Fr9gDZYM\nI/ufewtHcQmaaD1dvlpAcWr6ecsV0qkzhrETkVR+5K1aQc53X3jWObYhMVOeQN2sBaZPPiDnh6/d\n51QhoUQ/NgN1wyaATMZbr2I5dKDa+recOhJJpSK9mvq3nDYSbbdOOKxWDs5+l4LDJ8+ZtunYoeh6\nJ4JTxpabz4HZ72Iz5RJ1Y08aDbujUu4RQO55/VGRp2f9xPp1R4jUhvDT8kf+VtpLQU3oh3fuROPH\nHkZS+ZG1YhXpn3/vZdP4sTFEdE3AYbVy/OUFFB05AUDHr5fiLClBdjiRHQ7+Gvs4AMHNGtP08Qmu\ndpjNsdnzcBSX/OOyXo3+r5MYT8OJ40ClwrTyNzK//NbLpsHEcdTtkojTYiXltfkUHz0OgGHwHehv\nuREkCeOKX8n+/kcA6o18gPDuXUF2UpqXT8qr8yk157jzq4nYa/LwEOrdfj2leWcBOL7oC8xbdhHZ\nuR3NJgxD5e+P026/aD9djdfe1/rhnTvSdPIYUKnIWr6atCpiv8nkMUR0jcdptXL0pbfcsR//zRIc\nxSXITic4nOwZo8R+4wkjiOieiGy3Y0nL5OjLb+MoLPrHZa2J+v+Te/A1T09A1z0eW24+ScOnue31\n/brRZPQ9hDSuT/LoWRQcOn7OMrR6fAT67h1xWKzse3GRO84qElRPT7s5kwmoG8bZQyf467mFyHYH\nwY3q0fbZR6jTqglHF31F6ufL3Wni/jMefc9O2HLP8ud90z3qfKXFvuDKQYy8CXyPpCJs2NPkLxhP\nzjO3o+k8EL+YZh4mtoPbyH1+MLkv3kXBR88Q9tALF5x9q8cfZs+0uWy7byqGAT0JbhzrcV7brSPB\nDWLYOmQSh155n1YzxyonVKpq014z6xGOL/qcpOGPY1yfRMPh5R2WkjNZbH9oBtsfmlF9oVQqoh6Z\nzJnnnuDEhIeo06cfgQ0aeZg4Cs6Stfhtcv77tVfyqLETKdqRxMlHHuTkpNHYTp+qvv7TR7N76ly2\n3jeVqBt6EFJF/YMaxLBlyCQOvbyYVjPHlNe/mrSpn/1E0vDpJD04A9PmHTQZdTcAWb9tIunBGSQ9\nOIP9L7zjUvh7HTeAQYPbs2TZsL+d7lJxyfVVKppMHcfBGS+w+8FH0fXvTVCjBh4m4V3j0cTWY9f9\n4zjx+rs0meb54LR/8tPsHT3F3XEDaDZzEqcWf8yeEY+Rs3Er9e4bfEmKezX6v+HkCRx58ln2jxxP\nZL8+aCr5v26XBDT167PvgYdJnf82DadMBEDTuBH6W27k4ISp7H/4UcK7dkZdLwaAzK+/48CYRzkw\ndhL5W5KIeeB+jzxrIvYATn+13B1n5i27ALDlnWXP9FfYNvxxDry48KJdddVde1/rq1Q0nTaO/dNf\nYNcDE9Ff34ugxp5tL6JrPEGxMey8bzzHXnuXZo97xv6+yf9hz6ip7o4bQN723ex6aBK7R0ym5HQa\nscPvuiTFrYn6X/Q9GMhc8Qe7p87xyrbo+Cn2zXqdvN0Hz1sEXfcOhDSIZtNdkznw8lLaPDG6SrsW\nE4eR+uVKNt01mdKCIurf0Q8A+9lCDr3xESmf/+yVJn3FenZMftnr+JUY+77AWcv/1VZqZedNkqTG\nkiTtq+bcR5Ik3V2D2islSQqvqfwF3vg3uRZH9mmcpjPgKMWStJLADtd5GlmL3R+lwCD4G9vLFp/J\nxJKejWy3k/37ZvS9Ez1yE+qZAAAgAElEQVTO63onkvnLOgDO7j+Kf2gwgdpw6rRpXm3a4IYx5O1S\nRrpykvZg6Nvlb9VZ0/IabBlplGZlgN3O2Q1rCe3aw8PGkZ+H5ehhcDg8jquCQwiKa0/+qhXKAbsd\nZ1FhtVolFeqQtXozut4JHuf1vRPJXLm+Qv1D3PWvLm3F0R0/jbpK3egBPao8fiEkJDaibt2gi07/\nT7nU+qGtW2BJy8CakYVst2Nas5GInp5tJrJnF4y//QFA4YHD+IeGEKCNOGe+mgb1OLtnPwD5ybuJ\n7NPtkpT3avN/yDUtsaalY8vIRLbbyVm7gfDunr4K794V8+o1ABQddPk/MoKgRg0oPHgYp9UKTicF\ne/YR0Utp284KcaDSaKj8vVQTsVcdhUdSsJmUFyVFJ06Xlepv+Qmuvmvva/2w1i2wpGW6Y9+4ZiOR\nPTt72ET27Ez2r2Wxf+SCYj9v+25wKI+XBfuPoNbrLkl5L3X9z3UfLaO6ezBA3u6D2M9639+KU9Mo\nPnX+GS6gxFn6yg0A5O87in9YiDv/ikQmxJG1diugdMoMfZRy2nLPcvbgcWS7wytN7q6DlFZRPt/H\nPlXfmAVXBbWy8+YLJAWVLMsDZVnO83V5/k2oIqJw5Ga4/3bmZuEXEeVlF9ixPxGzf6bu5EUUfPjM\nBedvzTZV+GxGrY/0OK/Wa7FkmcttjDmo9VrU+shq0xadPKNMGwQM/bqhNpTfOIPqGUj8+HU6vlf9\n6GCAVo/daHT/bTcZCdDqL6g+AVExOM7mETPlSRq/tZToSTOQ1Jpq7S3ZFeqWrdStImp9ZCUbpZ4a\nr+OeaZuOv48ePy4i+sZenFjiPTpouL77BdXn30CgTuvRlmxGk9d1CNRpsWUbK9iYCdSV27SZP5tr\nl87HcNuN7mMlKafcnUBt3x4e7VBQjuLbCv43mQis5P8Ana6S/00E6HSUnEwl7Nq2+NUJQ6VWU7dL\nAgEV/Fx/1IO0++pjtNf3Jf3DTz3yrKnYix1yM50/e4PWTz+Cf1iIV30N13V1farN747/HQTqK7U9\noxm1Tutl43GvMZrKbWSIe/NF2i+bR9RtN1SpEXVLf3K37bj0hb8EnOs+Wm5T9T34UqExRHjkb8k2\nozF4liGgbhj2gmJkV4fYkpWDplI5/w6+j32sF114Qa2nVnTeJEmaJknSPte/Ka7DfpIkLZUkab8k\nSaskSfJ6FSRJUqIkSX9KkrRHkqQkSZLCqsl/hCRJP0qStE6SpKOSJD3nOt5YkqTDkiR9AuwDGkiS\nlCJJks51/kFJkva68v/UdUwvSdL3kiRtd/2rcnhBkqSxkiQlS5KUvGTJkkvgJYFt1xpyn7mNswsn\nKevffMjBue8SO/gmEj58Fb/gIGTXPHOrOZfNd45n+0MzOPbWxwCogoIvqbbk54emWUtyV/5IyuQx\nOK0laIfcf/6El5gT73/J5jseIfO3jcTefZPHuTpxzXFabJe9TFcr+x99gr2jp3BwxgtEDxpIWPs4\nAI698jbRgwZy7dL5+AUH4SwV6x0uNZZTp8n86ltavjaHFq/Opvj4CXCWd4rS/u8T9t77EObf12G4\n87YaL0/af1fx510TSXpgBlZzHi0ee9DjfEiTWJo96rtph4JLy1+PPsmeUVM5MP1FYgYPpE77Nh7n\nYx8YguxwYly13kclFFwuROwLyvB5502SpHhgJNAF6AqMQdnhoAXwrizLcUAecFeldIHA18BkWZbb\nA9cD51qp39mVRztgiCRJZePQLYD3ZFmOk2U5tUL+ccB/gH6u/Ce7Tr0FvCnLcqIrP8+dNVzIsrxE\nluUEWZYTxo4dW5WJwIUy0hbj/lsZicuq1r706A789LFIoRc2u7XiaITaoMVqzPE4bzWa0USVv9lS\n6yOxGs3K279q0hanprN7ymySRz5B1upNlKRlAiCX2t1TPAoOKwvOA+t7rm8AKDUb8deXj7T56/SU\nmo1edlVRajJiNxmxHFHm+hdsXo+mWYtq7TWGCnUzKHXzrH9OJRulnhav495pATJ/24ThOs8pgFHX\n9yBz9aYLqs+/AZvJ7NGWAvU6L1/aTGYCDfoKNlpsJrPrnNLu7Hn55GzcSmhr5XpbTqVx8PHn+GvM\nNEy/b8CanlnTVbkiUXxbwf86HbZK/i81mSr5X0epSRkxMP2yioPjJ3N4ykwcBYVYTqd5aeSs+YOI\n3p7v8moi9mw5+UrnUZZJ//F36rRpXm6nj6TdqzOuuHUvVzM2Y6W2p9diNZm9bDzuNXqd26Ys9kvz\n8jFv2Epo65ZuO8PN/YjonsCRF+fVZBX+Eee6j5bbVH0P/ic0uPsGun72Kl0/exWrKc8jf41BiyXb\nswyl+QX4hwUj+SmPxZqoSCyVyvl3ELF/YciyXKv/1VZ83nkDegI/yLJcJMtyIfBfoBdwUpbl3S6b\nHUDjSulaARmyLG8HkGX5rCzL53rtvFqWZbMsyyUujZ6u46myLG+twr4f8K0syyZX/mVRfD2wUJKk\n3cBPQB1JkkL/Rn0FlbCn7MMvqiEqXX3wC0DTeSC2PX942KgMDd2f/Ru2Bv9A5MILm90a3CAGTYwB\nyd8fw/U9MG3c7nHetDGZ6Jv7AlAnrgWOomJs5jwKDh6rNm1ARB0lsSTReOTdpP2wWjkeXgdUri//\negYAbJne8/ItRw4TWC+WgKho8PenTu9+FG7784Lq48jLodSU7e4UhrSPx3oqtVr7inWIGtAD08Zk\nj/PGjclED+zjrr+9sOr6V0wb1CDanV7fO8Fzd01JwtC/O1mrN19Qff4NFB46iia2HuqYKCR/f3T9\ne5G7eZuHTc6mJPQ3Kms9Q9u0wlFUTKk5F5VGjSpImXig0qgJT+xAyQllgxr/8LpKYkki9sF7yPzx\n18tXqSuIokNH0NSvR2C04v/Ifr3J2+L5tZ/35za0A/oDENK6FY6iIkpzlHUkZX4ONOgJ79WdnDXr\nAFDXr+dOH96jKyWnznjkWROxV3Gtjr5PZ/caF//QYNrPn8Wx9z4nf+/hf+QvwaWj4NBRgmJjULuu\npb5/L3I2JXnY5GxOwnBTWey3xF5Y5I59P4/Y70jxCeW7PrxzR+rfP5iDs+bitNbeWQ7nuo+WUd09\n+J9w+rtVbB3+BFuHP0H2+u3UG9gbgLpty+OsMjk7DhDVT5l2WO+WPhjXJ3vZXCgi9gU1SW3+qYCK\n83UdwD9dQVu5C13299/dW1cFdJVl2fIPyyMow+mg8Iu51J2yBEmlwrL5Bxzpx9H0uQcAy/pvUHca\ngKbb7eCwI5daOLt4+nkyLefIvGV0WPAfZdvd5WspOnmGeoOUtQPpP6zC/OdOtN070e3bhcqWvXPe\nA0B2OKtMCxA1oCexdylTBY3rtpGxfC0A4R1a02TMvco0StdbG2dhQZV1znr/LRq8+DqoVOSv/gXb\nqRTCb74dgLxffsIvPJLGCxajCg4Gp0zEHXdz8pGHcJYUk/X+28RM/w+Svz+lmRlkLHil2voffuMD\nOr71NKhUZCz/g6KTZ6g/aAAAaT+sxvznTnTdO9Ltu3eUbZrnvOuuf1VpAZpPGEZww3rIsowl08jh\nV5e69cI7tsaabcKSnn3B16gy06d9T1JSKnm5xVzX+00mTurLXUM6XnR+Ptd3ODm5YDGt33geSaUi\ne+XvlKScJup2pQ1l/fQreVuTiegWT8cvF+O0Wjn28tsABESE02ruU4AyZdb0+3rykpSfydBd35vo\nQQMByNmwBePK3/9Brcu56vzvdHLqnUW0fHUO+Kkw/7IKS8op9LcpvjP+vJL8bdup2yWRtp994Pqp\ngDfdyZs9/zT+deogO+yceus9HEXKbSN2zEg0DeojO2Vs2dmkvun51rtGYm/iA4S1aIyMjCXDyKFX\nFitlGXITwbHRNBk1hCajhrhKoObvLn256q69r/UdTk68uYS4ec+DSkX2ijWUpJwm+g4l9jN//JXc\nLTuI6JpAp6/ex2mxcuxlZafegIhwWr80C1Bi37h6A3lJyg6DTaeOQxUQQNx8ZW114f4jHJ+36OLL\n6eJS17+6++iF3IMB4l6YQninOALCw+j+42JOLvuajJ/XouvTmZbTRhMYXof282ZRcCSFPVXsSglg\n2rwLXfeO9PzvWzgsNvbPLvdTxzef5MDcxVhNuRx953PazZ1M8/FDOXskhTM/Kff1QG1dun70Mv4h\nQciyTKN7B7L53sdxFJVw7ezHiIxvQ0B4GL1/fo/jS5WfIPF97GMALv4mLKjVSL4eFpQkqRPwEcqU\nSQnYBjwAfCrLcluXzXQgVJbl5yVJ+ghYjjLqdQgYKsvydtd6t5KqRt8kSRoBvAS0RZlauQ0YBZiA\n5WU6LtsUIAGIAn4AusmybJYkKVKW5RxJkr4Adsmy/LrLvkOFEcLqqL1jr5cB48NxPtPWL9vP2m41\ntjnpeem35TsO3drXZ/rXLF/Hmq5Dzm9YQ/Tf+i0OPveZvh/DfK6/pfftPtHutuEnAJ/Vvzb4Prnf\nQJ/pJ6xdKWLvX66/uVfl37y8fPTY+KNPYx/w2b2335bvWNV5qE+0AW5I+trnsY/yPF3ruSVsYq1+\nPl5RsLBW+tHn0yZlWd6J0nlLQulULeMCfhhKlmUbMBR4R5KkPcBqoPot95T8vwf2At/LsnzO8XBZ\nlvcDc4H1rvznu049BiS4NjI5AIw/X1kFAoFAIBAIBAKB4J9SK6ZNyrI8n/LOURltK5x/o8LnERU+\nb0cZsbsQzsiyfGcl3ZSKOq5jjSt8/hj4uNJ5E0qnUSAQCAQCgUAgEAguG7Wi8yYQCAQCgUAgEAj+\nPfh66daVylXVeZMk6Ubg1UqHT8qyPAhlaqZAIBAIBAKBQCAQXJFcVZ03WZZ/A37zdTkEAoFAIBAI\nBAKB4FJzVXXeBAKBQCAQCAQCQe3H6esCXKH4fLdJgUAgEAgEAoFAIBCcH9F5EwgEAoFAIBAIBIIr\nADFtUiAQCAQCgUAgEFxWnGK3yYtCjLwJBAKBQCAQCAQCwRWA6LwJBAKBQCAQCAQCwRWAJH4g77Ig\nnCwQCAQCgUAguBxIvi7AhXBDyIRa/Xy8qui9WulHseZNUONs6DHIZ9q9N//A6i73+Ex/wLZvWNV5\nqM/0b0j6mk097/SZfs9N/+On+GE+0799x+c+11/X/S6faPf983sA1na72yf6/bZ85/PY85XvQfH/\nioT7faZ/S/IXJPcb6DP9hLUrfR57a7oO8Zl+/63f+tz/vox9AAef+0Tfj2FIUoBPtAFkudTnsX+l\nIIuxjYtCTJsUCAQCgUAgEAgEgisA0XkTCAQCgUAgEAgEgisAMW1SIBAIBAKBQCAQXFacvi7AFYoY\neRMIBAKBQCAQCASCKwDReRMIBAKBQCAQCASCKwAxbVIgEAgEAoFAIBBcVpxit8mLQoy8CQQCgUAg\nEAgEAsEVgOi8CQQCgUAgEAgEAsEVgJg2KRAIBAKBQCAQCC4rTllMm7wYROdNcFmJ6NKRZlNGI6lU\nZP78O6c/+6+XTbMpo4nsFo/DYuXI3HcoPHICKTCA9u/ORRXgj+Tvh+mPLaR+8BUAjcbch7ZnZ5Bl\nSnPzOTz3bWymXI88W00bia57RxwWK/tnv0fB4ZNeupoYPe3mTCGgbhhnD51g3/PvINsd1aZXBQaQ\n8P4LqAL9kfz8yFq7lRNLvwUgtEUjWj8xBoCO82ZiMeagTbwWh8XKvhcXVakfVE9PuzmT3fp/Pbew\nXP/xEehd+hXTN7xvILF39AMZCo6dYv/sRThtpbScNAx9r3gAWr/0JEdeegdHYRHhXTrSdPLDSCoV\nWctXc6YK/zed/DAR3eJxWqwceeltio6cKD+pUtFh2RvYjGYOPDEXgMYTHiKyRyJyqR1LeqZbqzra\nzniQqB7tcVhs7Hp+MfmHUrxsguvpiX95IoF1Q8k7mMLOZ95DtjvQxrem8/xpFKcZAcj4YztHlv5Q\noXwSfT6dQ4kxl6Qpb9Qq/cguHWg+ZRSSn4qMn9dw6tMfqEzzqaPQduuEw2Lj0Jx3KDxyErVByzXP\nPEZgZF2QIf2n1aR9swIA/XXdaDx6KMGN67Pz4ScpOHS8Wr9Hdu1AiykjFf2f1pD66f+8bFpMHYW2\ne0ecFhsHZi+k8IjSzq55egK67vHYcvNJGj7Nbd9s4gPoeiYgl9opScvk4Jx3sRcWe+RZE7FX0d9d\nPnoFqzGH3Y+/qpRp3FD0vRIAaLfgGQ7NWYjNlOtz/wO0mf4ghh4dcFhs7Hn+fc4eTvGyCaqnp+NL\nkwisG0r+wZPsflZpe1F94mk5fgiy04nscHJg3qfk7jkMQJP7b6bBHdcBMmePnWbvC4u98q2TGE/D\nieNApcK08jcyv/zWy6bBxHHU7ZKI02Il5bX5FB9V6hN1953oBt4IskzxyRRSXn0TubSUoKZNaDR1\nIqqgIGxZWZyY+xrO4pJq63+5Y6/r128hqVSkV9PeW04bqVxvq5WDs991t63Irh1oOXWkV9qmY4ei\n650IThlbbj4HZr+LzZSL5OdH66fGE9aqKZK/ioyV62ve982aKr4PDEB2ODn11rsUHTpSpd9rIvb1\n/brRZPQ9hDSuT/LoWedt+xfK07N+Yv26I0RqQ/hp+SOXJM+/Q6tWrfjww2V06tSRp59+hnnz3rwk\n+foy9gVXH2LapODyoVLR/PGx7Ht8NsnDHkN/fU+CG8d6mER060RQbD22D53A0dcW0Xz6OABkWyl7\nH3uWnSOmsfOhaUR06UhYXEsAznz+P3Y+NJWdI6Zh3pxMw5FDPfLUde9IcINoNt/9GAdfWULrmQ9X\nWbwWE4eT+tUKNt/9GPaCIurf3u+c6Z22UnY8+gJbh89k6/CZ6Lp2oG7bFgC0eWocx979HICiM5lo\nE69l012TOfDyUto8Mboa/WGkfrmSTXdNprSgiPp3lOl3IKRBtFd6tT6CRkNvZutDs/jzvulIfiqi\nB3QHwJz0F3/eNx2AktPpNHjgLlCpaDZtHPunv8jO4ZPQX9+LoMr+7xqPpkEMO+59hGOvv0fz6eM9\nztcbcivFqWc8juVt38POBx9j14gp5VrVYOjRnpAG0ay583H2zPmAdrNGVmnX+rF7Of75L6y583FK\nzxbR6M6+7nPmXYdZf/9TrL//Kc+HN6DpfTdRkJJe+/RVKlpMH8Pex+eSdP8UDFW0/chunQiKjWHb\nPRM58uoiWs4YC4DscHD8nY/YPmwKO8c+Sf3BN7nTFp04xb6nXiN/94Fq61ym3+rxh9kzbS7b7puK\nYYC3vrZbR4IbxLB1yCQOvfI+rWaOdZ/LXPEHu6fO8co2N2kvScOmkvTA4xSfyqDRg4M9ztdU7JXR\ncOhAilLSPI6lfPYTW4fPAMC8eQeNRw7xvf8BfQ8ljtcNmsZfc5fRdtaoKu2umXQfJ7/4hXWDplFa\nUOR6MANT0j423vckm4Y9xd4XF9PuGeXlkFofQeOhN7LpwafZMPQJJJWKejd088xUpaLh5AkcefJZ\n9o8cT2S/PmgaNfAwqdslAU39+ux74GFS579NwykTAQjQaTEMup0D4yezf/QEJJUfkf36ANB4+mTO\nLP2QAw9PIHfjn0QPvbva+l/W2FNJAOyeOpet900l6oYehFTR3oMaxLBlyCQOvbyYVjPHuH3Vavro\nKtOmfvYTScOnk/TgDEybd9BklFJfQ/9uqAID2Db8cZIeeoL6gwbUuO9jx40i/ZMvODB2EukffUrs\n2KrbU03FftHxU+yb9Tp5uw9WrXuRDBrcniXLhl3SPP8OOTk5PPbYVN54Y/4ly9OnsS+4KhGdt2qQ\nJKnQ9f96kiR9V+H4l5Ik7ZUkaaokSddIkrRbkqRdkiQ1811prwzCWreg5EwGlvQsZLsd45pNaHt1\n9rDR9exM1q9/AFCw/wj+YSEEaiMAcJZYAJD8/ZD8/cA13O6o8KbXL0jtPl6GvncCGb9sACB/31FX\nnuFe5YtMiCN77VYA0lesQ98n8bzpHSVWjzLJLu3ghvXI3aXc1ALD66AKDLgg/Sy3/noMbv1E0ldW\nrS/5qVCpA5H8VPhpArG6RhzN2/YiO5wuPx4mUK8lrHULLGcysJb5//dNaHt28SxDr85k/7rO7X+/\n0BACXP4P1GuJ7JZA1s+rPdLkbd8NlbSqI7pPPGdWbAQgd98xAkKDUeu8faFLjCNjTRIAp5dvILpv\nQrV5lqExRBLVswOn/vdHrdOv06Y5JWcy3W0/+/dN6Holemr2SiTrV+WN/dn9R/EPVa6zzZznfgvu\nKLZQnHoGtT4SgOLUNEpOVd9ZrahffCYTS3q2S38z+t6V9HsnkvnLugr6we52lrf7IPazhV755iTt\ncbez/P1HUBs8r31Nxp7aEImuRyfSflzjkZejqML3gUaNLPve/wBRfeJJW6m0vbx9xwgIC0ZdhS90\niXFkrtkGwJnlG91tr+y7BsAvSOPxPSf5+eFX4XvAYvSceRByTUusaenYMjKR7XZy1m4gvLvnQ154\n966YVyu+LDp4GP/QEAIiI9z5q9SBoFKhUqspNZsBUMfWp3DvPsVnO3YR0atHtfW/nLEXEafcjsva\ne9bqzeh6e+aj751I5krv613eVrzTetxrNOryzGQZVZDa/X0sl9rdp2rK98gyfsHBSllCQrCZc6r0\nTU3FfnFqGsUX2Pb/DgmJjahbN+iS53uhGI1GkpOTKS0tvWR5+jL2aztyLf+vtiI6b+dBluV0WZbv\nBpAkKRpIlGW5nSzLbwJ3At/JstxRluVLM2fgKkatj8SabXL/bc02ez3oB+q1WLPNlWyUByVUKjp9\nNJ9uyz8ib/seCg4cdds1HjuMLv9diuGGPqQu+9JL15JVrmvJNqMpy9NFQN0w7AXF7gdRS3aO2+ac\n6VUSXT99jT6/LsOc9Bdn9x8DoOjEafcNsk6rJvjXCfVMbziPfla5vsYQgSXL7JXeaswl5bPl9P7p\nPfqsXIy9sATztr1UJuqW68ndupPAyv43VvBtma90kdgq2Niyzah1ik3Tx0ZzctHHXp3jqrSqQ2OI\npKRCXUqyc9DoIzxsAsNDsRcUuX1R2SayXQv6fvUyXd6eSVjT+u7jbR9/gANvfYnsrL58vtJX6yOx\nZlX0fQ7qSm3f28bsZaOJ1hPaogln9x/l71BV7KkrX3u91qOdVVXGc1Hv1n6Yt3he+5qMvVZTR3B0\n4WdVtsdm4+8FIOrG3qQs+8rn/gfQ6CMoySx/wLZk5aAxeLa9gLphlFZoe0qsl9tE9U2gz3dvkLhg\nBnteXOIqZy4nPltBv+Xv0P/X97AXlmDa9pdHvoE6rWdcm0xe370BOh22bGO5jdFEgE5HqclM5jf/\npd1XH9P+u89xFBVxNnmXUr7UVMJ7KB2RyD69CDToqq//ZYy9yt+v1uyqr7el0r1GrY9E43XcM23T\n8ffR48dFRN/YixNLvgYge+1WnCVWei5fSs8fF5H6+c/ldaoh359+dwmx40bR7quPiR0/mrRlH1EV\nlyP2BefGl7EvuDq5qjpvkiRNkyRpn+vfFNexB10jZXskSfr0HGmbSJK0RZKkvyRJmlPheGNJkva5\n/lwF1HeNtj0HTAEekSSp+lf9gkuH08nOEdPYOuhhwtq0ILhJQ/eplCWfs23wGLJXrafeXQMvY5lk\ntj4wk423jaduXDNCmirTYfbPWUSDu28AlNEx2eG45NL+YSEY+iSw8c6JrB84Hr8gNTE39fSykx0O\njKvWV5HDhRPRPYHSvHyKDlf/jiL2wbsvida5yD+UwupbHmPdvbM4+fVvJM5T1mBE9eqINTe/yjU0\nV4u+X5CGuJdmcOytDz1GAGoDjR4ajOxwkPXbxsuip+vRCVtOPgWHvNfPARx/X1kPm/XbBurfdfMl\n0awN/s9al8z6u6ezY/p8Wo0fAijfA1F94vnj9smsuelR/ILU1L+5+hGwv4tfaCjhPbry1/0j2Ttk\nOCqNhsjrlelcKa8tQH/HLbR+/y1UwUEeI06XGl/Hfhkn3v+SzXc8QuZvG4m9+yYA6sQ1R3Y62XTr\nWDYPfpSG9992SbTO5Xv97QM5/d5S9t77EKffXUrj6ZMviaagduKL2BfUXq6aDUskSYoHRgJdAAnY\nJknSduA/QHdZlk2SJEWeI4u3gEWyLH8iSdKj1djcDiyXZbmDS1MCCmVZ9toZQZKkscBYgMWLFzN2\n7NjKJv86rMYc1BXezKoNWmxGs4eNzWj2mHql2HhOB3EUFpO3cx+RXTtSfPKUx7nsVRto+8Yz2HLz\nibldWXdgNeWhidIBygJfjUGLpVKepfkF+IcFuzpaTjSGSLeN1Zhz3vT2wmJyd+xH160DRSdOE5l4\nLYERdQEoOJpKHVX5exKNQYsl+zz6UeX6luxcNFFar/TaztdSnJ5NaV4BAFl/JBHerhUZv24CoN4t\nytqIwy/Md/m2kv/13r61mnI83p4HGrRYTTlo+3YjskciEV3jUQUG4BcSTMtnpnBk9gIADDf3I7J7\nAvsmP0tlGg8ZQKNBygNH3oETBFWoS5Ah0muahy2vEP+wELcvKtrYK0yJy968B9WTfgSGhxLZviXR\nveOJ6tEBVWAA/qFBdJr9SK3QB1fbj6ro+0isldq+t43WbSP5+RH30gyyVm3EtH6bl4/PR1WxZ618\n7Y1mNFFa8s9RxqqIHtgXXY94dk16AYD6d91Evdv7K3nWUOwZ+nVB3zsBXfeOqNSB+IcE0fb5Sex7\n/h2PvLNWbaTdvKfJ2bbLJ/5vNGQADe5U2l7+gRMERUeSu0c5p4mKxJLt2fZK8wsIqND2lFj3ngaV\ns+sQwfUNBNQNQ5vQhpL0bGyu74HMP7YT0a6lh73NZPaMa53O67u31GQi0KAvt9HrKDWZqBPfAWtG\nJvb8swDkbdxMaFxrcn7/A8vpMxyd+R/FX7H1Ce/qOR3PV7Gn8vN8L602VH29NYYK7d0VE5K/PxqP\ne1DVcZD52yY6zJ/FyWXfEH1DT8xbdiM7HJTmniV/7yFCGtWrUd9rb7ie0wuVzSly12+stvNWk7F/\ntTBhwiOMGaOsJT09EY4AACAASURBVB848DYyMjL+cZ61JfZrO+JHui+Oq2nkrSfwgyzLRbIsFwL/\nBRKAb2VZNgHIslz1pHCFHkDZfLtqR+guFFmWl8iynCDLcoLouCkUHDpKUGwMmhgDkr8/+v49MW/a\n7mFj3rSdqJuUL7ywuJbYC4uxmXMJCK+DX6gyv18VGEhEYnuKU5WNCjSxMe702l6dKU49Q8Z/f2Hn\nCOXNrHFDEjE39wagbtsWrjzzvMqXu2M/hn5dAah3S1+MG5KV9BuTq0wfEB6Gf1mZ1AFEdm7n3jwh\na80Wtj4wE1CmQzistvPq5+w4QJRbvw/G9eX69QZ661syTYS3baGshwC0iW0pdOlru7an8QO3A+B0\naRccOkpQgxjUZf6/vic5m5M8y7ApCcNNfd3+dxQWUWrOJXXxZ2wf/DDJQ8Zy+Pl55O/Y6+64hXfp\nSOz9gzjw5EturYqkfLvavclAxrpkYm/pBUBE2+aUFpZgNXn7wpx8gJj+ynrIBrf2JnP9DgDU2rpu\nm/C4pqCSsOUVcnDh16weOInfb5vCjqcWYtp+gJ3PLKoV+gAFB495tH3D9T0xbUr20DRt2k7UTUqH\nu05cC+xF5e2k1VMTKE45w5mvfuZiKDh4jOAGFfV7YNroGXumjclE39zXre8oqrqdViSyawcaDb+D\nvTNfdV/7tO9/ZftDyoYhNRV7x977ko23PcKmQRP56z8LyEne5+64BTeIduer65VIcWqaz/yf+u1q\nNg17ik3DniJrXTL1ByptL7xtc+yFJVir8IU5+QDR/ZW1qLG39iLL9T0QHBvltqnTqjGqQH9K8wu8\nvgd0iXHu74Eyig4dQVO/HoHRUUj+/kT2603elq0eNnl/bkM7QOl0h7RuhaOoiNKcXGxZRkLbXINK\nrazxCuvUAcup0wD4h7viQZKIGX4v2T+t9MjTV7G3zbXbZNn1jhrQA9NGz+tt3JhM9MAK19vVtirH\nSsW0QRXalr53AsWpypovS5aJiIS2AKg0auq2LX+Arinfl5rN/D979x0fRdE/cPwzl94gPaFKR2oI\nhN5F1AcrjyIKKoiKSJOqgqgoIuhPQUSRYn0Ee0Ox0XsNvdcQWkgP6e1ufn/skeRyCSCSXMDv25ev\nF7mbne/s3Mzezs7snk9YM+P18DCyz9p+5heVVd+/kcyZ8yHh4RGEh0dck4EbVJy+L25MN8zM2zUi\nlwDKktnCsZkLaDrjFZSTifNLVpAZdZoq990OQMzPf5G0aTv+7VvR+tsPsWTncPgN44TMNcCPhpNG\ngsmEMpmIX7mBpI3Gga32M4/iWbMa2mIh53w8R/9vrk3YhA07CezQko4/vIc5O5cDU+YUvBc+8wUO\nTJ1HTkIyR99fRLPXR1Hv6YdIOxLF2V9WXnJ7t0A/mrw8DGUyoUyK2BWbSNhg3PMTeltHajxg7Ffq\ngWM4eXrQ6cdZmLNz2T/lw5Ljz15E86nPUm9IX1KPnOSMTfxwu+0v7D9G7IottP9iOtpsIfVwFGd+\nWg5Ao/GDMLka3bvFpzNJ23+Y42/P5fgMo/4xORH723Iyo04Teq9RzvOL/yJ503b82rei1TdzsWTn\ncPSN9y77sdYdPRiTiwtNZxozLxdjlSRu/S5COragx+IZBY8Lv6jtrPHsmrKAnIQUDrz3Fa3eGEGj\noX24cDiaUz+vBqBKjzbUeuBWtNmMOSeP7RPev2z5KkJ8bbZwdMZHNJ/5kvG47iUryYw6TdX7jKW1\n535eStLGHQS0b0nb7z7AnJ3D4akfAFC5+c2E/qcb6ceiifjMOCk9Me9LkjbtILBLG+qPeRIX30o0\ne3si6UdPsmf0lBLjH3nnI1q8O8l4/PmSlWREnaFqb2v8n5aSuHEHAR1a0v67941Hp79e2E+avDoK\n35ZNcPH1ocPieUR99A0xv66kwdgnMLm40GLWS4DxsIPDb80v2K6s+t6l1BvWH6+axgUdvzYtOPLW\nPIfXP0Dchl0EdWxBt59nYs7OsXmkd+tZz7FnynxyElI4OPsrWr4xgobP9CH1cDSnF68GILRHG6r3\n6owlPx9LTh47JhjHxpT9x4lZsYXOi95Am81cOHySUz+upMn4gYXBLRZOzf6QBm++Dk4mEv9YSvbJ\nUwTdbSwxj//1dy5s2Ubltq1puvBj6+PqjUekZxw6TPKa9TSa9x6YzWQeO0H8kj8A8L+lG8H33gVA\n8voNJP5p+zAjm/0vx7538b6h8FkvgslEzJJVZESdKXgK5NmflpG4cQeBHcJp//1s4/H4r39QsO3h\ntz+22xag3tD+eNasitaa7PPxHH5zAQBnvv+LRpOG0vbLGSilOLdkFfVHPFqmdR/9znvUGP40yskJ\nS24e0e/YzjoXrYuy6PuBXdvQYMwTuPpWIuydCaQdOcnuEp5K+XeNG/MDW7dGk5KcSfcuMxk+ohv3\n9wn/x/leqZCQECIjN1OpUiUsFgujRo2kcePmpKWlXXWeDu374oak9A3yA3lKqZbAZ0A7rMsmgaeB\nT4H2WutEpZR/abNvSqlfgG+11guVUs8A/6e19lZK1cJYKtm06L+t20ymlGWTxdwYlXyV1nbs7bDY\nXTb8xLK2Dzosfs8t37K0Td/LJywjt239hvWd7nNY/E7rf+aXVo577PM92xc5PP7qDqX/dEJZ6rbx\nBwBWti/98e1l6ZZN3zu87zmq7sGo/98i+jks/p2RXxJ5Szne/1tMxMrfHd73VrTr47D4PTZ/5/D6\nd2TfBzCzyCHxneiPUi4OiQ2gdZ7D+z7GeXCF18nzqQp9frw+c0GFrMcbZuZNa71DKfUZcHEd2Eda\n6w1KqanAGqWUGdgJDCwli2eBL5VSzwOLy7q8QgghhBBC/FvJPW9X54YZvAForWcAM4q99jnw+RVs\nGwUU/fGVSdbXTwJNi//b+vfkf1hkIYQQQgghhLgiN9IDS4QQQgghhBDihnVDzbxdCaXUi0DxhfDf\naa2nOqI8QgghhBBC/NtoWTZ5Vf51gzfrIE0GakIIIYQQQojriiybFEIIIYQQQojrwL9u5k0IIYQQ\nQgjhWPK0yasjM29CCCGEEEIIcR2QwZsQQgghhBBCXAdk2aQQQgghhBCiXFmUxdFFuC7JzJsQQggh\nhBBCXAdk8CaEEEIIIYQQ1wGltTzppRxIJQshhBBCiPKgHF2AK9HKa0CFPj/envF5haxHuedNlLnf\nIvo5LPadkV+yol0fh8Xvsfk7VrZ/wGHxb9n0PUta9XdY/Lu2L2JD53sdFr/jusUOj/9nm4ccEvuO\nrV8DsLRNX4fEv23rNw7ve46qezDqf23H3g6L32XDT3wbNsBh8R/c/bnD+96ytg86LH7PLd86vP4d\n2fcBlHJxSHyt8zCzyCGxAZzo7/C+L25ssmxSCCGEEEIIIa4DMngTQgghhBBCiOuALJsUQgghhBBC\nlCuN/FTA1ZCZNyGEEEIIIYS4DsjgTQghhBBCCCGuA7JsUgghhBBCCFGuLPJLWldFZt6EEEIIIYQQ\n4joggzchhBBCCCGEuA7IskkhhBBCCCFEubIoedrk1ZDBmyh3jcc9RnDHFpizc9k9eS6ph0/apfGo\nGkT4GyNwrezNhYNR7Hp5DjrfTEjXVjQY0gdtsaDNFg688wXJuw9jcnWh/YKXMbk4o5yciFmxhaPz\nf7DL179dCxqMfhxlMnHulxVEf/GzXZoGYx4noH1LzDk5HJzyAWmHo65o25r97qL+yAGsvX0QeRfS\nbGLWH/U4yslETCkx648eRECHcCzZuRyY8j7pR6Iuua13/Vo0fG4wJlcXtNnC4bcXkHbgGD6N63Hz\n808bmSpVYv03Gf8YwR3DMGfnsmvyPFIPlVz/LacNt9b/SXa+ZNT/RZUb16Hjp5PZOfF9YlZsxT3E\nnxavPYObf2XQmlM/rSTqq7/s8vVtE06dZ58Ck4nYJcs4u8j+M6r97FP4tWuFJSeHo2/MIuPICQBa\nfTsfc2YW2mIBs4XdT4212a5q33upPXwQW+56hPwi9V8RYl/UaOwAAjuEY8nOYe9rH5ba9sNefxaX\nyt6kHopizyvvo/PNVLm9I3UeuweUIj8zmwNvfkTa0VMANJ30NEGdWpKbnMqGh8eXGr/h2IEEdQjH\nnJ3Dvtc+LGjbxeM3f/1ZXCr7kHroBHut8T1vqkrTl5+hUsPaHP3wa6IXLQHALTiAZpOH4epfGdCc\n+WkFp775Ayib/lZncF8Cu7QGiyY3+QIHpnxAbkIyytmZm18YTKWb66J1yScEjqx/v7bh1B31BMpk\n4vyvyzm98Ee7NHVHPYF/+1aYs3M4MnU26UdOoFxdCPtgqnFsc3YiYdUmoj/+GgCv+rWoP34IJldX\ntNnMsbfnk3bwaInxAcKf709oJ6Pvb31pASmHou3SeFULpN2bQ3Gt7E3ywZNsnTgPS74ZF28P2r7x\nNJ6hAShnJw5//gcnF68DoH6/ntS5vxsoxYkfVnN00VK7fB3R/xqOeZxAa3vfP2VOie3dvUoQzV8f\nVdDe902eXXCsu+T2JkXbz6aTE5/ErrFvAhB8SzvqPtUHr1rVyqXufW4Kpd1bQwu2964ezL45P5ZY\n/2XR9wGaTBpS0PY3PjzOLs+/q2HDhnz66Ue0bBnOiy++xDvvzPzHef4dL074hTWrj+Af4MUvS565\nJnlWhL4vbiyybFKUq6COLfCqEcrq3mPYO/Ujmk4YVGK6m0c8TNSXf7C69xjy0jKocW93ABK27mPd\nwy+wvv9E9rw2j+YvPQWAJTePzUNeZ12/CazrN4GgDmH4Nq1nm6nJRMNxT7Br9FQ2PzyakNs64lWr\nuk2SgPbheNSowqY+Izg0bR4Nn3vqirZ1Cw7Av00YWTHxdvvScOyT7B4zlS0Pjya4Zyc8S4jpWaMK\nm/uM4ND0uTR8bnBhzFK2rTfsUaI+/o5tA8YTteBr6g17FICM46eIHPQ82waMZ/fo1wFQToXdPLhj\nGF41Qll131j2vP4xzSY8XmL9Nxr5EFGL/mDVfWPJS82g5n3ditSjotHIh0jYvLfgJW22cGDmItb0\neY71A1/hpj498a5d7ATGZKLOmKfZP+5Vdj46nKBbO+NRq4ZNEr92rfCoXoUdDw/h2FsfUHes7Zfn\nvmcnsXvQaLuTN9fgQHzbhJN9Pq7E/XFobKvADi3wrFGFdfePYt+0BTR+/skS0zUY3o+TX/3GuvtH\nkZeWTvV7bwEg61w8W4a8xoZ+z3H84x9pMmFwwTZnf1vD9menXTa+V41Q1t//LAemLaDx80+UmK7+\n8P5Ef/U76+9/lry0DKpZ4+enpnPo7c84uehXm/TabObwrC/Y+NBYtgyaRI0+t+Fl/ezLor9FL/yF\nrY+MY+tj40nYsJ3agx4AoNq9PQDY8shYdo6cYuRT5AKGQ+vfZKLe2MHsGzuFyP4jCbrV/jjg174l\nHtWrsq3vUI6+9SH1xhkXYXRuHntGvsyOgWPYMWAMfm3D8WnSAIA6QwcQ/cm37Bg4hpMffUXtoY+V\nWoTQTs3xrhnKH3c/R+Rrn9Jq0oAS0zV/ti9HFv7FH3c/R15qBrV7dwWgXt8epJ44x9IHX2L1E9MI\nG/sQJmcnKtWrRp37u7G8/6ss7TOJql1a4F0j2G7/HdH/PGuEsuGBkRycPp9Gz5X8edcf/gjRX//G\nhgdGkp+WQbV7jM87sEP4Jbev2bcXGSfP2ryWceI0u59/m+SdB21eL6u6T4s+z7K+L7Os78ssf/gV\n8rNzOLtyu12+ZdX3Ac5dwbHn70hKSmLkyNG8/faMa5bn39H7v2HM/6j/tcuwAvR9ceNx6OBNKTVE\nKXXFLU4pVUspte8axPVVSg29fEpxrYV0bcXZ342rtSn7juHi44lbgK9dusDWTTi/YgsAZ5asI7Rb\nBADmrJyCNE4e7qALn1R08T3l7ITJ2cnmPYBKjeuRdeY82efi0Pn5xC7bQGCXCJs0QV1ac/73NQCk\n7j+Ks7cXrgG+l922waiBHHt/IZTw5KTMItvFLd9AUJfWtvvapTXn/1hdJKZnQczSttVa4+zlAYCz\ntyc5CUkAWHJy0WZj1sHk6mpXlpCurTjzW5H69/bELbDk+o9ZsRWA00vWEtKtcF9r972dmBXbyElO\nLXgtJyGlYAbPnJlNetQ53IP9bPL0aVSf7LPnyYmJRefnE79iHf6d2tik8e/Uhrg/VwGQfuAIzt5e\nuATY5lOS2iOe4OScz+w+84oQ+6KQLhGc+30tABcu0fYDIpoQu9Jo++d+W0tIV6PuU/YeIT8tw/j3\nvqO4B/sXbJO88xB5qRmXjB/UpXWR+Edx9jHadnH+EU2IXbnZGn8NwV2NNpebnErqweM2M7AAuYkp\nBVfxzZnZZESdxS3IKFtZ9DdzZlbB9k7ubgX/9qpdneRI4+shz9o2KzeqU/C+I+vfp1F9ss7EkH3u\nYvtbT0Bn2/YX2KkNsdb2l7b/iPXzMdqfJSsbMI5tqsixzeY44OVJrvU4UJJq3Vty8tcNACTtPY6L\njyfugZXt0gW3acSZZdsAOPnLeqrd0tIaC5w93Y1Ynm7kXsjAYrZQqXZVEvcex5xtHHvitx+iWg/b\nz9lR/S/mjytr73EF7X01Qdb2HtQlotTt3YL9CezYkrOLV9jklXHyLJmnYuxilFXd22zbtgkZp+PJ\njEm0y7es+j5A8s6D5KWm271+teLj44mMjCQvL++a5fl3RLS+icqVPa5ZfhWh71dklgr+X0XlsMGb\nUspZaz1Xa/2/ssr/Em/7An978KaUcrr6EgkA9yA/ss4XHmSyY5PsTvJdKvuQl5ZRMAjJjku0SRPS\nLYKu379N63fHs/u1+YUbmhSdFr1Bz2VzSdiyl5T9x4vF9ic7rvCLLScuCbegAJs0bnZpEnEL8r/k\ntoGdI8iJTyL9mP0yGCNtgl1+tjEDyI4tkne8kbdbkH+p2x5991PqDX+UDj/Ppd6Ixzjx4aKCdJUa\n16fNopm0WfgOQEE9ArgH+5NVJFZ2XBLuQcXq39e7WP0XpnEP8iO0ewTR3y8vcV8BPKoEUvnmm0jZ\nZ1v/rkEB5BbZn9z4RNwCA+zS2OxzfEJhGg1NZr5G2EfvEHL3bQVp/Du1ITc+kczjJ0stkyNjX+RW\nQt27Bdu2BaPtZxbWfWySXXsBqH5Pd+I37bpszKLcg/1s2pnRr+zj5xeL715C/FJjVAnCp2FtLuw/\nVhDjomvV3wDqDHmYjos/JPT2zpyY/w0AaUejCewcgXIy4V7FmPlxDyncxpH1X1Jfdg0qqf0lFktj\njW0y0fKzGbRf8hkp23aTdsBYHnV81ifUHjqAtj8uoM7wgUTNXVhqGTyC/Wz2Pys2CY9ix15XX29y\ni+x/ZmxyQZpjXy+nUp2q3L18Frd9P5Vdby0Crblw7AxBLRviWtkLJ3dXQjuF4RlqW2eO6n/ZsYX5\nZccl2rVlu/YeV9je3YL8S92+4eiBHH1/4WUv2FxUVnVfVM072nLqz80lxi+Pvi9KVhH6vrjx/KN7\n3pRStYA/ge1AS2A/8BjQCJgBeAMJwECtdYxSajWwC+gEfKWU8gHStdZvK6VaAHMBT+A4MEhrnayU\nagV8Yg1pv5DbtjwDgf9a4zoBXZVS44EHATfgJ631K8B0oK5SahewDPgNGKe1vsuaz/tApNb6M6XU\nSeAboCfwllJqCLAF6I4xCHxCa73uqipQXJXY1ZHEro7EP/xmGg7pw5ZhbxhvWDTr+0/E2duTiLdH\n4123+qUzugZMbq7UGvhfdo58vcxjFVXtv7dzdNZnxK/eQnCP9tw8cSi7Rr4GQOqBo2ztPxrPm6rR\n7utZmFxdsORem6uYjcc9ysH3vi71pMXJw41W/zeK/W9/QX5GVolprtbeYS+Qm5CEi29lmsx8laxT\nZ0g/dIzqj/Zh/5hXrmmsihS7OP9Wjal+T3e2DC7fuJfj5OFGi+ljODzjc8zX+LMv7sTcrzgx9ytu\neuw+qj9wB1EffUvMkpV41apG60/fJPu8sXxZW679lVOH1L/Fwo6BY3Dy9qTJtBfwrF2TzKhTVO19\nOydmf0LC6s0E3tKBBhOGsXfU5DIpQmiHpqQcOsXqJ6fjXSOYLvOeI37HYdKiYjj06W90mfsc5qwc\nUg6fsrlgdC1UpP4X2LEluUkXSDsUhV/LxuUSs7S6z88wZmVMzk5U7RrOnlnflUt5RDmqAH1fVDzX\n4oElDTEGMBuUUp8Aw4DewL1a63ilVF9gKnDx5iZXrXUEgFJqcpF8/geM0FqvUUq9BrwCjAI+BYZr\nrdcqpf7vCsrTEmiutU5SSt0G1AfaAAr4RSnVBXgBaKq1bmEtR7fL5JmotW5pTTsEcNZat1FK9bKW\n89biGyilBgODAebNm8fgwYOLJ/nXuKlPT2rcZ9yzduHACTxC/UnebbznHuJPdlyyTfq8C2m4+Hih\nnExoswX34AC7NABJOw/hWS3YuFpe5Cb1/PRMEiIPENw+zCZ9dnwS7sG2V+Jz4m2XmORY01woSBNA\nTnwSytm5xG09qofiUSWYtguNpukWFECbz99i26AJ5CalWNMGFtnOyM82ZiLuIUViBhl5K2enUret\n0qsrR2ca1zTiVmzi5gn2N1ZnRhv3YzR4+n6C2jcHrPUfEsDF2nQP9ic7vlj9p6QXq//CNL6NatNy\n2nAAXH19CO4YhsVsJnb1dpSzE63+bxRn/9jA+VWRduXJjU/Etcj+uAYFkJOQaJfGLTiQi5+mW1Bg\nQZqLy0LyUi6QuHYz3o0akJ+WgVuVYFp8+m5B+hYfz2T34HHkWevf0bE7LJxurfvjeIQEcPEd92B/\ncuJs24LR9j0L6z7E36a9eNerSdMXnyZy1HTyLlzZUqV2C42HKaQeOG4zE2X0K/v4zsXiZ8dffjmO\ncnIi7M2xxPy1nrjVW21iXHQt+ltx5/9aT4sZE4j66Fu02cLRWZ8XvNdj83dUurkO9Z4y7olzVP1f\n3M/ifTk3vqT2F1AsjW35zOmZpOzYh3+7cDKjThHyn+4cf/djABJWbqTBC8Ns0tfr24Pa/zXum0re\nH4VHSABgXLn3CPEnq9hxNTclHdci++8Z4leQpta9nTn0yW8ApJ+OI+NsPJVqVyVp3wmiflpL1E/G\nsrxmIx4gM9a23OXV/1p++SG5CUlos7G8zz0kEDhs/Ds4wK4t27X34ML2nhOfVOL2wbe0JahLBIEd\nwjG5ueLs5UHTySPYN3k2xfX85rUyr3sw7qlLPhRNTlKqTZ7l0fevhaFDn+Gpp4z78Hr1upuYGPul\np9crR/X960VFXppYkV2LZZOntdYbrP9eCNwONAWWWWe2JgFFp0C+KZ6BUqoy4Ku1XmN96XOgi1LK\n1/r6WuvrX1xBeZZprS+2+tus/+8EdgA3Ywzm/q7iZb74qKDtQK2SNtBaz9daR2itI/7NAzeA6O+W\nsb7/RNb3n0js6kiq9eoMgG/TeuSnZ5GTmGK3TWLkAUJ7tAWg+l2diV1jDAY8q4cUpKnUsBYmV2fy\nLqTh6uuDs7cnACY3F4LaNiP95DmbPNMOHsOzRhXcqwSjnJ0J6dmRhHW2g4z4dZGE9jJOdio1qU9+\neqZxT08p22YcP8W6Xk+ysfcwNvYeRk58IlsHPFcwcANstgu+tSMJ67bZxExYF0nof7oVxDRnlByz\n6LY5Ccn4hjcBwC+iGZmnjS879yrBBQ8ocQ81vjCOf7GEdf0msq7fRM6vjqT6ncXqP8G+/hMiD1Cl\nh7Euv8ZdXYhdY9wEv/Ke0ay8exQr7x5FzIqt7Jv+GbGrjffCXnqK9KizRC36wy4/gLRDR/GoXgU3\n6/4E9ehM0vqtNmmSNmwl+A5joO/duAH56RnkJSZjcnfDycNY329yd8O3dTiZJ6LJPBHNtnsGsP3B\nwWx/cDA58QnsemK0zeDJ0bE3PvICGx95gbg1kVTt1QWAyk3rkZeeWWLbT9p+gJBbjLZf9c4uBW3f\nPSSA8DfHsOeVD0q8r6Y0mx95ns2PPE/cmm1F4he27ZLjt7PG70r8GvuBeHFNXhpCRtRZor/8zeb1\na93fADxqhBZsH9Qlgsxoo5+b3FwxWe+B829jXKw4Nu9bh9c/FLY/94L214nE9bbHgcT12wixtj+f\nJg2sdZGMi28lnC4e21xd8WsdVnBhJjchmcrW44Bvq2ZknbYt17FvVhQ80OLsqh3UurujUT/N6pKX\nnkV2wgWKi9t2kOo9jXudat3TibOrdgCQeT6JkLbGTJObfyV8alUh/Uyc9W8fADxD/anWoxWn/rBd\nvlde/W9Hv2fYNWAkuweNBqDKfy7f3pO37ye4oL13I36t8XnHr4sscftjc75i3d3PsL73cPZOepek\nyH0lDtyAcql7gJr/aWdX51A+ff9amDPnQ8LDIwgPj7ihBm7guL4vbmzXYuat+PqpNGC/1rp9Kekv\nfVf9P1c0fwVM01rPK5rAutyzqHxsB7Lul8gT4OJTM8zIzy38LXEbdhHUsQXdfp6JOTuHPa8WfjSt\nZz3HninzyUlI4eDsr2j5xggaPtOH1MPRnF68GoDQHm2o3qszlvx8LDl57JhgfGm6BfoS9uozKJMJ\nZVKcW7aZuPU7bWIbj9T/mPBZL4LJRMySVWREnaFa754AnP1pGYkbdxDYIZz23882Htv/+geX3PZK\nHHnnI1q8O8l45PmSlWREnaFqb+O+jXM/LSVx4w4COrSk/XfvG49Lf31OQcyStgU4NG0u9Uc/jnJy\nwpKbx+HpRj36ht1MzUd7o/PzC5Y25qUUzhDErd9FcMcWdF88w/pTDYX132bWeHZPWUBOQgqH3rPW\n/9A+XDgczemfV19yH/1aNKD6XZ1JPXqKzl8ay1gPf1DsmofZwomZ82nyzmQwmYj7bQVZJ08Teu8d\nAJxf/CfJm7bj1y6Cll/PxZKdw7Fpxufr4udLozcmAMYsT/yytaRstf18L8mRsa3iN+wksEMLuvw4\nC3N2DnunzC14r9XM59k3dT45Cckcnv0lYVNHUn9IX9KOnOTML8aN7HWfvB/Xyt40ft5YxKDNZjYN\neBGAsCkjQ+kULgAAIABJREFU8GvVGFdfH7r9+gFHF3xvFz9hw04CO4TT6cdZmLNz2T/lw4L3wme+\nwIGp88hJSObo7EU0n/os9Yb0JfXISc78shIA14DKtPtsGs5eHmituemhXmx4aCw+9WpStVcX0o5G\nF1zpPzbnK4Ay6W/1hvbHs2ZVtNZkn4/n8JsLjPL5V6bFu5NAW+xmtx1e/2YLx2YuoOmMV1BOJs4v\nWUFm1Gmq3Hc7ADE//0XSpu34t29F628/xJKdw+E3Zlvr3Y+Gk0aCyYQymYhfuYGkjcZJ9ZE351D3\n2SdQTiYsuXkcfWtOCS3PELNuN1U6NafXkv8jPzuHbS9/VPBe5/fHsO3VT8iOT2HPu9/S7q2hNB12\nPymHogtm1A7MX0ybKU9x2/evo5Riz7vfkms9tnR4x/hZF51vZscbX5CXlmm3/47of1nn4uj4w3uY\ns3M5MKWwbmza+/uLaPb6KOo9/RBpR6I4a23vRn9pWeL2pQnq2pqbxw3C1bcSAF0+HMfaZ94u07p3\n8nAlpF1Ttk/5rNRylVXfN2dk0WzKSPxbNcbF14cuv87h+IJ/tnQzJCSEyMjNVKpUCYvFwqhRI2nc\nuDlpaaX/BMu1NG7MD2zdGk1Kcibdu8xk+Ihu3N8n/OozrAB9X9x4lL7CG25L3NgYBEUBHbTWm5RS\nH2GsC3gKeNT6mgvQQGu933rP2zitdaR1+8kU3vO2G2N55Drr65W11qOVUnuAoVrr9UqpN4E7tdZN\nSynPQCBCaz3c+vdtwBSgh9Y6XSlVDcjDGHTt0FrfZE1XA1iHsQTUA2Om7tUi97xFaK0TrGkL9kEp\nFYhxb1yty1TV1VfyDeC3iH4Oi31n5JesaNfHYfF7bP6Ole0fcFj8WzZ9z5JW1/Cxx3/TXdsXsaHz\nvQ6L33HdYofH/7PNQw6JfcdW4/eAlrbp65D4t239xuF9z1F1D0b9r+3Y22Hxu2z4iW/DSn4kfXl4\ncPfnDu97y9o+6LD4Pbd86/D6d2TfBzBO/8qf1nmYWXT5hGXEif4O7/sYkxcVXiPvByr0+fHB9O8r\nZD1ei1mjw8Aw6/1uB4DZwF/Ae9blkM7AuxgPM7mUAcBcpZQncAK4+ANUjwOfKKU0l3lgSXFa66VK\nqUbAJmX83k868IjW+rhSaoP1Zwf+0FqPV0p9C+zDGIz+/cvqQgghhBBCCFGGrsXgLV9r/Uix13YB\nXYon1Fp3K/b35CL/3gW0K2Gb7UDRJ088V1pBtNafAZ8Ve20WMKuEtP2K/f1cSXkXn1Urug/W2bha\nCCGEEEIIIUQZc+iPdAshhBBCCCGEuDL/aOZNa30S48mS5UopdTvwZrGXo7TWjltkLIQQQgghhLgi\nFiU/FXA1rssnJWqt/8K4r04IIYQQQggh/hVk2aQQQgghhBBCXAeuy5k3IYQQQgghxPXLgiybvBoy\n8yaEEEIIIYQQ1wEZvAkhhBBCCCHEdUCWTQohhBBCCCHKlcbs6CJcl2TmTQghhBBCCCGuA0pr7egy\n/BtIJQshhBBCiPKgHF2AK1HP5+4KfX58LO3XClmPsmxSCCGEEEIIUa7kaZNXRwZvoswtbvmIw2Lf\nu2MhcxoOcVj8oYfncqpvG4fFr/nNVpa06u+w+HdtX8SKdn0cFr/H5u8cHn9hs0EOif3I3k8A2NTl\nHofEb7/2F4e3vfcd2PeHH56LmUUOi+9Ef4e3/X97fPNn7g6L7zQw22H732PzdwD8FtHPIfHvjPyS\ntR17OyQ2QJcNPzm874sbm9zzJoQQQgghhBDXAZl5E0IIIYQQQpQrWTZ5dWTmTQghhBBCCCGuAzJ4\nE0IIIYQQQojrgAzehBBCCCGEEOI6IPe8CSGEEEIIIcqVxuzoIlyXZOZNCCGEEEIIIa4DMngTQggh\nhBBCiOuALJsUQgghhBBClCv5qYCrI4M3Ue6ajX+U4E4tMGfnsPOV+Vw4dNIujWfVICKmDcPF14cL\nB6PYPulDdL6ZgFaNaDtjNJnn4gE4t3IbRxb8DECd/ndw033dQGtSj51h5+T5ly1Lpxcf5KauTcnP\nzmXFC5+TcOC0XZqm/bsRNuAWKt8UzCftxpKdnAGAWyVPur/xGJVrBpKfk8+qif8j6ei5K64H97B2\n+A0cCyYTGSsXk7r4f7Z10Ol2Kt3zGCiFzsok6eM3yYs+Ci6uhEyeh3JxBZMTWVtWcOG7BVcct8n4\nxwjuGIY5O5ddk+eRWkL9e1QNouW04bhW9ubCwZPsfGkOOr9wbXrlxnXo+Olkdk58n5gVW/G6qQot\np40oLHu1YI7M/R6Adt/MQplMnPtlBdFf/GwXq8GYxwlo3xJzTg4Hp3xA2uEoAPzbtaDB6MdL3LZ6\nnzuofv8daIuFxI07OPb+QgC869Xk5uefxsnLAyzaJs6l8iuLslyJiBf6Ua1zM/Kzc9k06WOSDp6y\nL9PDt9DokZ741Azhu84jyUlJB6DWne1oMug/oBT5GdlsmfIFKUfs2+9Fvm1aUmvkkyiTE7G/LeXc\noh/s0tQa+RR+7SIw5+RwfNq7ZBw5AUD4NwuwZGWhzRa02czewWMB8Kxbizpjh+Lk6U52TBzHpryD\nOTOr1DKURdsDcPb2JOylp/CpVx2tNbtfvXzfL6pzseNAfAnHgZ5vDyK4aU0seWZi955k9cuLsOSX\nzYnHixN+Yc3qI/gHePHLkmeuOp+yaPPBt7Sj9pMP4lWrGtsGTSDtkNFGnCt503zaWHwa1SPmt9WA\n4/u+o+OXZt1xd6Yt98dsgQdapPNU+1Sb97dGuzH8h2CqVc4HoGfDTIZ2ukBUojNjfg4qSHcmxZkR\nnVN4rE1aiXGu9f7XfrIPVe+5lbwUo7zHP/ySxE078W/TnLpD+2NydsaSn19iWRqPe4zgji0wZ+ey\ne/JcUg+ftEvjUTWI8DdGWPt+FLteNvp+SNdWNBjSB22xoM0WDrzzBcm7Dxtl6vcfatzbHdCkHjvN\nnlfn2eXr1zacuqOeQJlMnP91OacX/miXpu6oJ/Bv3wpzdg5Hps4m/cgJlKsLYR9MxeTijHJ2ImHV\nJqI//hoAr/q1qD9+CCZXV7TZzLG355N28GiJ+/53XKu+L25sMngT5Sq4YxheNUNZce9Y/JrVJWzC\nQNYOmGyXrvHIhzi+6E/OLt1M84mPc9N93Tj5/QoAEncdZsuz79ikdw/yo85Dt7Hygeex5OQRMX0E\n1W5vd8my1OzSlMq1gll028uEhNWm6+R+/PDgm3bpzu84TvTqvdz7vzE2r7cccgcJB0/z5/C5+NYJ\nocvLD/PLwHevrCKUCb9BzxE3dTjmxDhCp31OZuQ68s9GFSTJjztH7KtD0BlpuLdoj/9TE4idNAjy\ncol7bSg6JwucnAh5dQFZuzaRe3TfZcMGdwzDq0Yoq+4bi2/TejSb8DgbBrxil67RyIeIWvQH55Zu\nptmEQdS8rxvR1vrHpGg08iESNu8tSJ8RHcO6fhML3r/1j/c5v2Y7TcY9yq7RU8mJS6L1p9NIWBdJ\nxskzBdsFtA/Ho0YVNvUZQaUm9Wn43FNEPjERTCYajnuCnSOn2G3r17IJQV1as+XRcei8fFz8KhlV\n6mSi8eSRHJg8m/Rj0ThX8qbr0k+tZSo9v7Ioy5Wo2rkZPjeFsPjOCQQ2r0ObSY/xZ//X7dLF7zzG\n2TW76fnJ8zavp5+JZ9njb5KbmknVTs1o98qAEre/uP+1Rz/NgTEvkxufSLP575C8fitZ0YWDFN92\nrXCvXpWd/Z7Gu3FDao95hn1Dxhe8v//ZF8m/YHuSWPe5EUTP+YTU3fsJ6nUrVR/+L6c/XlRiEcqq\n7QE0Gf8ocZt2s/35WShnJ5zc3UquhxLc1KUpvrWCWVjkOPB9CceBI79sZdm4TwC47Z0naNynE/u+\nWnvFcf6O3v8No/8jrXnhefsT7r+jLNp8+onT7H3hbW5+YbBNLEtuHsfnf4N3nRp41akJ4Li+7+tD\n1z8/qRjHnmLMFnh9qT8fPRRHSKV8+n5Whe71s6gXmGeTrlX1bD58MN7mtdoB+fz0RExBPt3er06P\nhpn2QZSpTOof4PTXSzj15a824XJTUtk9bjq5Ccl41alBuy9n2Lwf1LEFXjVCWd17DL5N69F0wiA2\nDnzZrtg3j3iYqC//IGbpJppOGESNe7tz6oflJGzdR+ya7QD41KtBy+nPsuaBcbgF+VGr7+2seXA8\nlpw8wqeNpOpt7W0zNZmoN3Ywe0dNJicukfCP3iJx/VYyi9SFX/uWeFSvyra+Q/Fp0oB6455m1+Dn\n0bl57Bn5MpasbJSTE2EfvkHS5h2k7T9CnaEDiP7kW5I378CvfUtqD32MPSNesv8s/qZr1ffFjU3u\neRPlqkq3Vpxesh6A5L3HcfHxwi3Q1y5dYOvGnLNeVT+9ZB1Vure6bN4mJyec3FxRTiacPFzJjk++\nZPraPZpz+OfNAMTujsK1kgeeQfYn3wkHT5N2NtHudf+6VTi72bj6l3IiFp9qAXgE+Fy2nACu9ZqQ\nH3sGc9w5MOeTuXEpnq272KTJPbIXnWGcLOcc3YdTQHDBezrHmN1QTs4oZ2fQl77Se1FI11ac+W2d\nUeZ9x3Dx9iyl/psUzGqcXrKWkG4RBe/V7ns7MSu2kZOcarcdQGCbpmSeicMtoDIA2efi0Pn5xC7b\nQGCXCJu0QV1ac/73NQCk7j+Ks7cXrgG+VGpcj6wz50vcttp/b+Pk/35G5xlXePOs5fBvE0b6sWjS\nj0UDkJ+aXhDnUvmVRVmuRI3u4UT9shGAhD0ncPXxxCOwsl265EOnyDhn3/4Sdh8nNzXTuv1xPEP8\nSo3l3ag+2WdjyImJRefnk7BiHX6d2tqk8e/Ulvi/VgGQfuAwzt5euASUnieAe42qpO7eD8CFyF34\nd21fatqyanvO3h4EhN/M6Z9XA6DzzeSnl3BCW4raPZpzqMhxwK2U40D02sKLI7F7TuJ9ifr+pyJa\n30Tlyh7/OJ+yaPOZJ8+Secp+hYElO4cLuw9hyc3D1d/4XB3V9z2rV3Fo/KLHnuL2nnOlpl8+Nfzy\ncXWC/zTKYOWRv/9Zbz7pTk3fPKpVLuFpfYGty2T/S5N+5CS5Ccb3bcYJ+1nrkK6tOPt7kb7v44lb\nQMl9//yKLQCcWbKOUGvfN2flFKRx8nC3+b5TRb/33e2/930a1SfrTAzZ54xjX/yK9QR0bmMbt1Mb\nYv80jn1p+4/g7OOFq/XYZ8nKNuI4O6GcnQpia61x9jI+N2cvT3ITki5ZR1fqWvX964XGUqH/r6hk\n5g1QStUC/gS2Ay2B/cBjQBNgFuAF5AA9gADgC+trAMO11hvLt8TXL/dgP7JiC09Es+KS8AjyIych\npeA1V19v8tIz0Waj42TFJuEeVHii5N+8Pt2+eYPsuGT2z/yStBNnyY5P5tgXv3Pb77Mw5+QSt2kv\n8ZsvPRPlFeJL+vnCA33G+RS8QnzJjL+yE/CEQ2eoc1s4MduPEdysFj5V/fEO9SMrseQlLEU5+Qdh\nTowt+Ds/MQ63ek1KTe/d/R6yd20qfEGZCJ3+P5xDq5P+1/fkHtt/RWV2D/a3qf/sOKNui9a/i683\neWkZBfV/MQ0YM5yh3SPY9PRUfJvYXnm/qOpt7Tj310Y8gv1tXs+JS6JSk/o2r7kF+ZMdl1gkTSJu\nQf64271euK1nzar4hjWi7pCHseTkcXT2/0g7eBzPmlVAQ4t3X8TFrxKxyzYU7vcl8iuLslwJj2A/\nMs4XfuFnxCbhEexHVsKFK9q+qLq9O3Nu/d5S33cNDCAnLqHg79z4BHwaN7RLkxsXXyRNIq6BAeQl\nGn2k8YwpaIuF2F/+Iu7XvwDIOnkKv05tSV6/hYBuHXELDiy1DGXV9jyrBpObnEbY5KepVL8mFw5F\nsf//vii9sorxLnYcSD+fgvcljgMmZxMN723LuqnfXnEMRymLNn8lnDxsZz7Lu+8XH0A48thTXGy6\nM6GVCpcWhvqY2XPO1S7dzrNu3PdRFYJ9zIy/JZn6QbYzc78f9KJX41IuUnhWLZP9B6je5z+E9upK\n2sHjHH3vf+SnZdjkG9zdfsWLe5AfWUWOddmxSbgH+5GTWKTvV/Yp1vcTcQ8u/N4P6RbBzcMfwtWv\nEttG/Z9RtvhkTiz8jVuWzMack0vC5r0kbLE9DroF+dsc+3LiEvFp0sAmjWtQADnF6sI1yJ/cxGQw\nmWj5ydt4VAvl3I9/kHbAWBp5fNYnNJvxMnWGDQSTYtfTE+z2W4iyIjNvhRoCc7TWjYBUYDjwDfCs\n1joMuBXIAuKAnlrrlkBf4D0Hlfdf6cKhkyzt9Syr+07kxNdLaTNjNAAuPp6EdmvJsrtG89ftI3D2\ncKN6r45lWpYd8//C1ceDB39+kWaPdiPh4Gks5mt/pcatSSu8b7mHlEXvF76oLZx//hHOPnMXrvUa\n41KjzjWPW5LG4x7l4HtflzrTp5ydCO3ainPLt5RZGZSTCZfK3kQ+MZFj739Bs6ljrK874Rt2M/tf\neY/tg18iuGvby+RUdmUpTyGtb6befzuzY+Z3ZRZj/7Dn2fPEKA6Of5XQ3r3wCTMuNByb/h6hvXvR\nbMEMnDw9sOSVfL/LtVBa21NOJirdXIvo75ezrv+LmLNyqPv43WVWjq6v9ONc5FFith8rsxiiZFfa\n9ys1vvKBZlnE/6fHnsahuawYdpafn4yhf6tURvwQZPN+rhlWHfXg9kYZpeRQNs7+uJSN9w9n66Pj\nyUlMof7Ix2ze96pdnbrD+pdJ7NjVkax5YBzbx82g4ZA+ADj7eBHStRWr7nmWFXcMw8nDjWr/ucbf\n+xYLOwaOYXPvJ/FpXB/P2saS4Kq9b+fE7E/Y8t+nOP7eJzSYMOzaxhXiEmTmrdBprfXFy2ULgReB\nGK31NgCtdSqAUsoLeF8p1QIwAw1KykwpNRgYDDBv3jwGDy55luLfoPaDt3JT7+4AJO8/gUdIQMF7\nHsH+ZBVb5pCbko6LtyfKyYQ2W/AI8S9YCpGfUfgwhLgNuzFNGIirrzeBEY3JPBtPboox6xWzMhL/\n5vZf4E37daXxg52M7fdG4x1aeGXPK9SXjNgUu21Kk5eRzaqJhQ8ZeWTFVFJPJ1xii0LmpHicAkIK\n/nYOCMacHG+XzqVmPfwHv0j89FFY0u1nZHRmOtn7t+Me1p680ydKjHVTn57UtNb/hQNG/V+scfdg\nf7tlJnkp6bj4eBXUf9E0vo1q03LacABcfX0I7hiGxWwmdrVxP0JwxxZcOHSS3KRUsuJsl5G4BfuT\nE2+7/C8nPgn34AAuFKQJICc+CeXsjHtwQInb5sQlEb/KGBymHjiGtlhw8a1ETlwiKTsPkGe9Lyth\n4w4qNzO6Z7Y1TnmV5eIN/cU1eOgW6t1vLI9N3BeFV6g/Fz91rxB/suIuvdS3ON8G1Wn36kBWPjOT\n3Auln8jlJiTazIq5BgXa7X9uQiKuwUHAQWuaAHITEq3vGZ9lfsoFktZtxrtRfdJ27yf71FkOjjXu\nW3OvXhW/9rbLq8qj7aXsPUZ2XBIp+4wZz5jlWy87eGt2ieOAd6gv6aUcB1oPuxMPf29WDS/5vr6K\npiza/JUousztWsa+0r6fvHM/XjdVLTGP8ohf9NhTXIh3PudTC0+9zqc5Eexju/TR263wAkXXetlM\nWapIzjTh52lcHFx33IPGIbkEepVysTDTdlnrtdr/3KTC76Bzi5cT9vYLhemC/Gn+5ngOvPY+EfON\ne287LXoDsPb9UH+Sdxtp3UP8yS52rMu7kFas7wfYpQFI2nkIz2rBuFT2ISCiMVnn4gq+98+v2oZf\nc9t6z4lPsjn2uQUHkFv82BefiJvNPgeQG2/7/WVOzyRlxz7824WTGXWKkP905/i7HwOQsHIjDV6Q\nwdvVsMiPdF8VmXkrVHwqobS1c6OBWCAMiADs1zsAWuv5WusIrXXEv3ngBhD17XJWP/wiqx9+kfOr\nt1PjLuOkya9ZXfLSM22WTV2UEHmAqj2Mdek17upMzOodAAX3UQH4NqkDSpGbkk7W+UT8mtXDyd34\nOALbNCEt6qxdvvu+XMO3903l2/umErV8Fw3vM5Z4hITVJjct+4qXTAK4+nhgcnECoFGfTsREHiUv\nI/uKts09fgCX0Bo4BVUFJ2c8O9xGVuQ6mzROASEEjn2TxA9eIT+m8CmEJh9flKc3AMrFDfdmbck7\nF11qrOjvlrGu30TW9ZvI+dWRVL+zMwC+TeuRn55Vav1XKaj/LgU3i6+8ZzQr7x7FyrtHEbNiK/um\nf1YwcAOoent7zv5prCK+cMAYTLpXCUY5OxPSsyMJ6yJt4sSviyS0V1cAKjWpT356JrmJKaQdPIZn\njSolbhu/dit+rZoC4FGjCiYXZ/JSUkncshuvejUxWe9/8GvZuCDOpfIri7KU5sjXK/m9z2R+7zOZ\nMyt3UvueDgAENq9Dbnrm31oy6RnqT9eZw9gwYQFp0bGXTJt+6Cju1aviViUE5exMYI/OJG+wnR1N\nWr+VoNuNgZZ344aYMzLJS0zG5O6GycO4B8Pk7oZv6xZknTDao7OvtT8qRfXHHuT84j9t8iyPtpeT\neIGs2ES8bjLucwps04T0E/Z9v6i9X67hm/um8s19UzmxfBc3X8FxoPEDHanZqTF/jfn4iu8xdbSy\naPNXIjfZaMeO6vue1UIcGr/osae4plVziU525kyKM7lm+OOgF93r2z6hNT7dVNDE9pxzxaLB16Nw\noPb7AS96NbnErFtCZJnsv2uR+9SCurYpWJ7q7O1J2IwJHJuziAt7DhekWd9/Iuv7TyR2dSTVehXr\n+4n2fT8x8gChPYxZy+p3dSZ2jRHXs3rhhc5KDWthcnUm70Ia2ecT8G1aH5Ob9Xu/dRPST9r2/bRD\nR/GoXrg/QT06kbh+m23c9dsIucM49vk0aWCti2RcfCvh5O0JgMnVFb/WYWRGG/nnJiRTOdxYgeDb\nqhlZp2NK/TiEuNZk5q1QTaVUe631JqAfsBl4WinVWmu9TSnlg7FssjJwRmttUUoNAJwcWObrTuz6\nXYR0CuPWxe9gzs61eZx/u/fGseu1j8hOSOHAe18TMW04Nw/rw4VDJzllfRhB1VvbUOuBHmizGXNO\nHpETPgAged9xzq3YStdFr6PNZi4cjib6x1U0f35AqWWJXrOPml2b0n/ZFPKzclk58fOC9+6cP5xV\nk74gM+4CzR7tTviTt+EZWIm+v7xE9Jp9rJ60EL+6ofSYPhCNJvloDKtevPL7bLCYSfrk/wie+J7x\nUwGrfyXvzAm8b/0vAOnLf6TyA0/i5F0Z/yeMpwxqs5nYiQNw8gskYOgrYDKByUTmpuVk71h/RWHj\n1u8iuGMLui+eYX1kc+FjldvMGs/uKQvISUjh0Htf0fKNETQc2ocLh6MLHgZxKU7ubgS1bcreNz62\nltc42Qif9SKYTMQsWUVG1Bmq9e4JwNmflpG4cQeBHcJp//1sLNm5HHj9g4JtD7/9sd22AOd+XUWj\nSc/QdtE7WPLzOfCasU1+Wganv1pC60+ng9bGI6xbN79kfmVVlitxdt0eqnZpzr2/T7f+VMAnBe91\nnzOKza98RlZ8Cg373UrjQXfgEVCZO394jXPr9rB58mc0H3IPrr7etJn0aEE5/3jotZKDmS1EvTuP\nRm9PRplMxP2+nKyTpwm55w4AYn/5k5TNkfi1b0X4V/Ow5ORwbJqxItzFz5eGU40niSonJxKWryFl\nq3ExJfDWLoT27gVA0tpNxP++vNT9Lcu2t/+t/xH++lBMLs5kno1j9+R51H3srstuB8Zx4KauTXnU\nehxYUeQ4cJf1OJARd4Fur/Yj7VwSD3zzHAAnlu1k2we/X1GMv2vcmB/YujWalORMuneZyfAR3bi/\nT/jfzqcs2nxQ1zY0GDsIV99KtJgxgbQjJ9k1aioAHX76AGdPT5SLcWrRcs4raLPFYX2/Ihx7inM2\nwYs9k3jq62AsGno3T6d+UB5f7zAuyD3UMp2lh7z4eqc3ziZwc9a8c28CShnbZ+YqNka5M/mOS8yE\namM241rvf73hj+JTvxYaTXZMPIemG324ep878KweSu1Bfag9qI9dceI27CKoYwu6/TwTc3aOzeP8\nW896jj1T5pOTkMLB2da+/0wfUg9Hc3rxagBCe7Sheq/OWPLzseTksWPCbABS9h8nZsUWOi96w/q9\nf5JTP66kyfiBhcHNFo7NXEDTGa+gnEycX7KCzKjTVLnvdgBifv6LpE3b8W/fitbffoglO4fDbxj5\nuwb40XDSSDCZUCYT8Ss3kLTRGFAeeXMOdZ99AuVkwpKbx9G35pT+efwN16rvixub0tfJFcSyVOSB\nJZFAK+AA8CjGA0tmAx4YA7dbgSrADxgzdX8Cw7TW3pcJ8a+u5MUtH3FY7Ht3LGROwyEOiz/08FxO\n9W1z+YRlpOY3W1nSqmzuQbgSd21fxIp29l/m5aXH5u8cHn9hs0EOif3IXmMwuKnLPQ6J337tLw5v\ne+87sO8PPzwXM45bXulEf4e3/X97fPNn7g6L7zQw22H732Ozcf/tbxH9HBL/zsgvWduxt0NiA3TZ\n8JPD+z6gHFaAv6GKT+cKfX4ck7auQtajzLwVytdaFx9lbAOKPzrpKFD0ktrzCCGEEEIIIQSglPLH\nePBhLeAk8KDW2u5GTqXUaOBJjImevcDjWutL3oMj97wJIYQQQgghxLXzArBCa10fWGH924ZSqhow\nEojQWjfFuBXroctlLIM3QGt90lppQgghhBBCCPFP3AtcvIn6c+C+UtI5Ax5KKWfAEzhXSjqbDYQQ\nQgghhBCi3Fh0xf6pgKI/+2U1X2s9v7T0xYRorS8+hvQ8EFI8gdb6rFLqbeAUxrM1lmqtl14uYxm8\nCSGEEEIIIUQR1oFaqYM1pdRyILSEt14slo9WStk9nEUp5YcxQ1cbSAG+U0o9orVeeKlyyeBNCCGE\nEEIIIf4GrfWtpb2nlIpVSlXRWscopaoAcSUkuxWI0lrHW7f5EegAXHLwJve8CSGEEEIIIcqVxlKh\n//8xV4PdAAAgAElEQVSHfgEu/tjwAGBxCWlOAe2UUp5KKQX0AA5eLmMZvAkhhBBCCCHEtTMd6KmU\nOooxwzYdQClVVSn1O4DWegvwPbAD42cCTFximeZFsmxSCCGEEEIIIa4RrXUixkxa8dfPAb2K/P0K\n8MrfyVsGb0IIIYQQQohypanYT5usqGTZpBBCCCGEEEJcB5TWdk+uFNeeVLIQQgghhCgPytEFuBJB\n3q0r9PlxfPq2ClmPMvNWPtQ/+V8p9fQ/zUPiS/zrLbbEl/gS/98b/9+87xJf4l+D+NcFi7ZU6P8r\nKhm8XR8GXz6JxJf4N1xsiS/xJf6/N/6/ed8lvsR3dHxRgcngTQghhBBCCCGuA/K0SSGEEEIIIUS5\nugY/hP2vJDNv14fL/mCfxJf4N2BsiS/xJf6/N/6/ed8lvsR3dHxRgcnTJoUQQgghhBDlyt8rvEIP\nQpIydlbIh7/IskkhhBBCCCFEudJafqT7asiySSGEEEIIIYS4DsjgrYJRSpmUUh0cXQ4hhBBCiLKm\nlKrt6DIIcT2RwVsFo7W2AB84uhwASqkOSql+SqnHLv5fTnGdyiPOpeIrpVY5MH6Ao2ILg1Kqk1Lq\nceu/g/5tJxfWPlBVKVXz4v/lGHuKUqqnUsqrvGJWFEqpFVfy2o1MKeWhlGrooNieSqmXlFILrH/X\nV0rdVY7xlVLqEaXUy9a/ayql2pRX/CLl8CznkN9b4/6r2roQV0vueauYViil7gd+1A56ooxS6gug\nLrALuLgoWQP/K4fwR5VSPwCfaq0PlEM8G1prs1LKopSqrLW+UN7xgc1KqV3Ap8AfjmoDjqSUCgHe\nAKpqrf+jlGoMtNdaf1wOsV8BIoCGGJ+BC7AQ6FjGcWdj9LESaa1HlmX8IuUYAbwCxELBc5w10Lw8\n4gMngIeB95RSacA6YK3WenFZBrXGulT9VyrD2O6AJxColPIDLt4kXwmoVlZxSyhHR2AycBPG+YEC\ntNa6TjnFvxt4G3AFaiulWgCvaa3vKY/4GP19O9De+vdZ4DtgSTnFn4PR524BXgPSgB+A1uUR3Lrq\n5yPAG6iplAoDntZaDy3j0Cal1ESggVJqTPE3tdYzyjg+UCHafztgNtAIow84ARlleexxNIv8VMBV\nkcFbxfQ0MAbIV0plU3gAKc8OHAE0dtDAIQx4CPhIKWUCPgG+1lqnlmMZ0oG9SqllQMbFF8vpBLoB\ncCswCOME9lvgM631kXKIXVF8hnEi9aL17yPAN//f3plH2VVVafz3BcIghBlBG0GJNIjIEEHCoIiK\nouKAiMio4AQog4oKDcqg4hJFm6EVRAigIIZWEBuZlNEwJyBRBhuQqZ0VQgiBEPj6j3Nu6qVIJSHm\nnnNTb//WqvXq3FRl76p6776zz97720DrwRuwI7AJMAnA9h8ljSpg99b8uBWwPunnBdgZKHmIcRCw\nru1/FLQ5C9vjgHGSVgc+ABwCfBxo9W9gexSkzB/wJ+AHpHvv7sBL2rRNuucfDLyUFDw0wdvjwMkt\n2+7ldODT2YcaSgJHAa8DrgawfXvhrPdo27tI2jXbf1JSSbW5zW2PkXRbtv+opCUK2v828Dbgomz/\nN5LeUMDuB4H3kvakJe61Q1H7+X8y6XdxPmkPthdpPxAEsxHBWwdpNhGV+S2wOmkTUxTbU4HTgNMk\nbQOcC3xb0n8DX7Z9bwE3fpo/ipMD5iuAKyRtS8r67C/pN8Chtm+o4VdhVrE9XtJhALZnSir1ZjrD\ntiUZoFT5nu2zsr39gK1tz8zrU0jZp1I8DNTIOAMg6fuk4PUvpJ/7/eRAuhDvtr1Rz/q7+bX3pbYM\n2j4BOEHSAbZPasvOfDDF9iUV7T9je8qgeKnkAeIMSUs3NiWNBp4uaP+Z3DbQ2F8VyqYmbD886Pdf\n4r67ve2vS1rS9jEF7A1F7ec/tu+VtJiTDOO4HMgfVtOnoHtE8NZRcunMOsBSzTXb1xaw+3PSG8co\n4E5JN9Pz5lWifCW/eb0T2Bt4OXA8cA7weuAXFDiJajbSNcg9b3sAe5I2sAeQTkI3Jp3I9UP/1bT8\ne2g2MWMpF1CMl3QqsIKkj5EyoKcVsg2wIqlc7p95vWy+1io95Ur3A1dLupjZX/tFSpeAlUnlQo+R\nfgd/bwLZQkyTtDtwHun5tys92fc2sX1SLl17OT3vz7ZLlKsDXCXpG6SDq96/fang+XeSdgMWk7QO\ncCBwfSHbkMqFLwVeJukcUhb8wwXtnwhcALxY0ldJBxdHFLT/cH7+WdJIUhb+rgJ29wZOIGXfagZv\ntZ//T+ZM6+2SjiMdng9rbYok8xC8UGJIdweR9FHSTXMNUs/ZWOAG228qYHubuf277WsK+HA/cBVw\nuu3rB/3biSVKFyX9gTmc+JaofZf0e1LJ1jjbjwz6ty/Y/nrbPtRG0hhS7f8GpCzwqsD7bd9RyP52\nwFtJ5WuX2b6ihN1se29S+dhV2f4bgKPaPlDIvX5DYvvoNu0PRtKrSCVcnwYWs71GIbsvJ20ktyLd\nAyYAB9t+oIDtOfYaF+x3nJNQk0u892T7LyKVSs967ZGqLZ4qYT/7sDLpPVfAjbb/Xsp2tr8e8OZs\n/1e2SwRPje1VSM/9t2T7lwMHtV1CLelHpDLBlwL39f4T6flXpN+2A8//tUgHtkuQ7nvLA98pVG1U\nheWWXr/TQcjj0+/s5JDuCN46iKTJpAblG21vnG/mx9p+X0Efvm77C/O61pLtZW0/0badefjQq/i4\nFKnvaCXbrZVO9dhWP4qUDEbS4iTREAH32H6mkN1XAH9qNoy5jGq1Epv3Hh9WBzbPy5ts/7mU7doo\nqfu9nhS0rgDcCFxn+4yqjhVA0l3U6zXuFLkCY5mSvc6SdgSubISqJK0AvNH2hYXsrzSHy1NL3ftq\nku95lwHPq+6x/WAB+yNIB4Tj27Y1Fx+WAaZn1fHmNbCk7Sdr+dQ2EbwtGBG8dRBJt9jeLCsObm77\naUm/s/3qgj5Msj1m0LU7SpyAZeW1jwCvZvay0X3atj03JE20/doCdlYFPs/zf/4ip39dQNIngXNs\nP5bXKwK72v5OAdu3AlvanpHXSwATbBdRfMs2q5RNZ9tN6XQvU0iCKqe2nQWRdDKp1+06239s09YQ\n9qvdfySdDxxou3ivcbZfTeU12z8X2JeUdbyFVD58gu1vFLJ/u+2NB127zfYmhew/ALwMeJR0aLUC\n8GdSNuZjtie2bP8sUqat9757fO333lJIutX2phXt3wi8pTm8lrQscLntYTv7d9TS63Y6CJk6/Z5O\nBm/DupZ2EeaRfOJ3IUm04mdA6ydPkMQScuZvXUl39Hz8AZhcwgdSyeDqpJKpa0jlo1ML2QZS2V7P\nx6aS9qVcj+g5wN2k3rajgQdIG5l+4mPNBgKS6hrwsUK2F28Ct2x7BqmMpQi5bPpa0in00fnxqFL2\nST1vT5BFg0iKh1NJvaat9/7Z/hRJbXCMpB0kvbhtm4Ooef9ZhdRrfJmki5qPQrYhqbxeRipfg6Ty\nenBB++vnTNt7gUtI98A9C9qf056opDbAFcA7bK9ie2Xg7aQxBfuTxgi0zYZzuO+2HrgqKSojafKg\nfcdkSUVK5TO/lHSIpJdJWqn5KGh/qd6qo/x56Zl7wSJACJZ0ENs75k+PyjXYy5OaqEtwLulN82vA\noT3Xp9r+55y/ZaHzSts7S3qP7bPyaWxJtT1IIikNM0kB1AcK2V7Z9umSDso9htdI6rfgbbHe8tFc\nPlIqgPqbpHfbvijbfg9Qsu/lIAbKprdtyqYL2t9yUJbx5z3VAL9r27iknUmzvq4mZR9OkvQ52//d\ntu1MzfvPUYXsDEVNlVeAkUpCGe8FTrb9jLLqayFulfQt4L/y+pMk2fhSjLU965DK9uWSvmn7E5KW\nLGB/hKQVc9DWlHGW2CcelB+LDUQfgl3y4yd7rhkoMueNJJY0phFIkbQpML2Q7WARIoK3jiJpa2Ad\n2+NyGd2/AX9o226u9Z8C7Jo3zKuRnifL5l60h9r2AWjq+x+TtAGpbKTo6bvtbUvaG0Tz8/9J0juB\nPwIlT/+6wKXAj5VUHyHNwSp1gLEvcE4u3xNJOn+vQrYBnrL9lCSUpLPvlrRuQfvLSlqzea1LWpOk\neAkwY+hvW2gcAWxm+6/Z/qrAL4FSwVu1+08JQah5UFPlFeBU0kHZb4BrlQQcSs73PAD4IgMzFq9g\n9o182/xJ0hdISqeQgom/5PfiErJ8xwM35PJdkdQuv9q20aZMuERv2zz8qK3kfDBwvqSmXPwlDASU\nw5JQm1wwInjrIEqqb5uSxBrGASNJs762KujDp0inwH9h4E3DQAnVp+/lWvsjSBL5y5LeUIshaXmS\nbHQzoPQa4Jimkb1lvpLtf5akuLgcSXmqn/gCKWDbL6+vAL5fwrDt+4Cxud+gKV0pyeCy6UcpVDad\n+Szwa0n3kTZwryDNGVwGKDFCY0QTuGX+QdkS/+b+80UG7j+tCxUBSJrKQL/hEqR7/zTby5WwD3yG\n9DOPljSBrPJayDa2TyTJ5Tc8qDTrspT9acxecVKa3UjvO41AyoR8bTEKVH7YPjv3/Db91e+zfWfb\ndgc97+fkV5Hnv6Q5HtK53KiOycAppJLtx0mvxdarHYJFjxAs6SBKQiWbAJOaRmkVEgvp8eFeklhK\nqxLBg2x+Zk6X86Ndbs4Ukn5CkqhvNqt7AhuVVPwMyiJpD9s/HOJ5WHLOWa9P25DLpnv78ArYXRJY\nLy/vaVukZJDtb5AOiX6UL+0C3FFC6bZLSBLwHlIpXbGAQpVUXrPtKodmkv7T9sGas1hPkfmmNZG0\nnO3Hh+rvKtUyIenLpNlmPyA9/3YHXlJC5TnbP6lnuRRpZMMk20UOMHLv3+OkvndIgfsKtncuYb8G\nyy61TqeDkCee+t9OCpZE5q2bzLDtptY/n3iX5mHKlstAGgwOaeOwGenUCeBdwM2FfRlte6ee9dE5\nqG6N/MYxt9PHIrOeaiJpvO0PZNGcOW2i2jzAaF5no+b6VQWoUTYt6U22r5Q0+IBitCRs/7RN+w22\nPydpJwYqDb5n+4IStqG+4mJD7ve8MFdiFAnelJQ29we2Jr3+rpN0SsHg/QzSoVmTZdqTVH3S9qHZ\nD/LjN1u2M1ck/TtwCM8f0t620vC5pH6zicx+3xVle77ebXujnvV3Jf2GQplv2wf0rnMFxHlDfHkb\nbGB7/Z71VZJaz3zWxEWqgYcfEbx1k/G512cFSR8D9qGAytsg7geulnQx8HRzsc3sg/MQYEnXAmNs\nT83ro4CL27I7BNMlbW3719mHrWi/cfjW/LgVsD4DfRc7A8P6Bt5DtcZ126fm3pLHbX+7tP2GimXT\n2wBXkg5LYGAT12zgigRvALZ/AvyklL1BnEn6vR+e178nvRZbD94GBc4jSM+DYllP4GySsmaTgdiN\nFNiUOvkvfmgGYHtifu1/3PbubdubC+eTyua+z8CQ9taxvUPO9G5TqK99KKZJ2p0UMBnYFZhW0x9S\n2XgpJkkaa/tGAEmbM7AvCIJZRPDWTWaQGvQfJ23gvmT7isI+PJQ/lqCgTHpmNWYXRpiRr5VkP+Cs\nXMYDae7Oh9o0aPssSOMagK1tz8zrUyivtlkF23/Km6gza4jG2H5W0q5AteAN2JFcNp19+qOk1rOB\nto/Mn+4H7MTsp/+tl7bMpe9Fyb1ifV81FRff1fN5o3L7nkK2of7Jf41DM2DWa38tSUuULFEexEzb\n361hOFf7XAy8pob9zG7ACfnDDPT8FWFQ2ewI0iHq+aXsA68FrpfUBNBrAvc0lSglW2eCbhPBWzd5\nMXAgafN2BimQK0pPFqyGaMPZwM2SmlKp95JOw0tyF3AcMJo0KHVK9qPEzJkVSSIlTZ/BsvlaX5A3\nUc9JWr6QQMxgJmSlyR/Tc+rbyDcXoHbZ9IXAY6T7T5P1aT14s129XDVTTXHR9t4l7MyF2if/vYdm\nIt0DP1zQ/v2k1/9FzP7aL9Xv+nNJ+wMXMHvFS6kxPZMkbWa7ymga2w8wl8MKSYfZ/lqLLvSWzc4E\nHrT9SIv2BrN9QVvBIkwIlnSUXMLwVmBvUunMeOD0rIRXwv4GpHKZpoH578BetosoH0kaA7w+L6+1\nfVsJuz32L2VgAzvr1N328UN+08KzvTdJ6fMq0gbmDcDRts9s23ZXUBpMvwlJZbJ3E9V635/SbMXB\nuEDfSWP/EGAdYDvSvMV9gHNtnzTXb1x49n9re4MStrpIvvecBGxA6r9aFXi/7dYPbiStkW03JbLX\nAQeV2kBKuotU7fEQKXhdC7iHtJEtdvIvaTmSwZJjApqS5efRHGYWsD+nvlbbLtJzJulu4JUkddtp\nDGS9O5HxkTTJ9pgW//+vDxZGmtO1YOHxoiVf3ukg5MmnH+ikYEkEbx1G0kak4G170kZ+LHCF7c8X\nsH09cLjtq/L6jcCxtrds23YXqL2BlbQ6sHle3mT7z7V8qYGkOZaoNqWlwx1J25EObwRcVrJsWtL3\ngJNsTy5lsytIGkG6z95MBcVFSVeQxCMaAY09gN1tb1fI/lqkLP+sgzPSIRbQ3hyuoRRee+wWVXrN\nwaObvut+If/9n0dbf/cXiqTbnBW4W/r/nxccqrDSd78RwduCEWWTHUTSQaShwH8nNS5/zvYzeWPx\nv0DrwRuwTBO4Adi+upLqZS2ul/SaGhtYScdkaeSf5fUISedUbqQviu2zJC1Bkqs3aQNdpA8ll8wd\nyYDi3q9JcuWtj83I/X6/zP1+RftcexQ+Fwf2lnQ/qXSrU6fvbWL7OUn/lTeINeYrrWp7XM/6TEkH\nF7T/XuCjJHEakYLI0wpkfZuSWTMwHoaea0WQtClJrGZUXk8B9rE9saAPG5B6rZZqrrnQnDHbD+bM\nc3Pvm1CwXHx+aOW5kPvM9wfWltSbYR9F6rsLgk4RwVs3WYk0HHO20668sSilwne/pC8y+wnw/YVs\nV6MjG9iXNbX9SvO2xgNFy0ZrI+kdwKnArEHRkj5h+5IC5s8jZRwa1bvdSf1vb2nbcOV+v+IKnx3l\nV0qjCn7q8qUp/5C0BwMz7nYlDSkvxUdIc+WmQSoZA25gQH2yFXp6rM8ilYk+ltcrAq2XqvdwBrC/\n7euy/a1JwVypctEjgTeSgrdfAG8nHR4VCd4kfYmkLNooy46TdL7tr5SwPx+0lQU5F7iEVKbeO5Zj\nasF+w77EjlEBC0KUTQZzJL9pHs3svRdHNW+qw5WhykYaSpSP5H7Hc4DJwLbAJTWl62uQey92sH1v\nXo8GLra93ty/c6HYfl7JrKTJtouosNXs9wtmqV4uQ+p1nU5Btct8/zkJ2IJ0iHQ9cIDth9u2ne1P\nBjZznuumNPftloLP/eeVxbVdKjcf9lvtsxpkazKwEXCb7Y2UZg7+sGDZ7D3ARj1//6WB222vW8L+\nvJD0H7aPbdlG74zNVYBRtludsdnPLL3Emp0OQqbPeCjKJoNFitHAy0hyuYsDbwbeRKETyFrUrO3P\n5SoNJ5AyTxOAaySN6Vj5SttMbQK3zP2k+VMluFzSB0kZT4D3A5cVsg3p1LvYTLVgdiqrXh4DfMj2\nowCSViIp4O1TyP444KZBSr8lh5OPkLTioJ+/5D7lGqUZqz8iBc+7kOadjoEiirNP5Qqbmbnv7q+k\n9+FS/JFUrtmozC4J/F8p40NlXm3vA1AgcBs8Y3MJyszYDIIXRGTegjmST+AOIamtzcprd6VxeTgy\nhMphQzG1wy4g6bskpbvxpE3UziQFvF8C2G4tuBmUeQFYjIEMWKkMTJV+vyChNCy76fu5zvaFhexW\nzTxle03PE6SfvVjJtqS9gP9gYLbWzsBXbf9g6O9aqPar3YNzxcX3gc8CH8yPT5AyX0VGSEi6ENiM\nlPU3SfH2ZuARaD/7X/v5rzQQfhNgUmMzBEvaZakl1uh0EPLUjEci8xYsUvzN9s9rO9FPuMJQ6g6z\nFPAXYJu8/huwNGmIsWkxMzWvzIukV7vFkRmV+/36HknfIcmlN31n+0razvYnC5ivnXlqsktVsvy2\nz5Z0K6nKA1Lvd7Eh4fO6B0v6UFuKt7Yt6XU563SK0ria5VxgREUPF+SPhqsL2ob6z//aMzaDYL6I\nzFswRyS9mdQs/ytmHxYa5Vwtk/scjgVeavvtktYHtrBdsnyp06j9Ya1zs932rKFq/X7BrN//qxqx\nkqzy+zvbrypgu2rmKZg7BV77ZwEnu9KQ7Hkh6Se2d5r3Vy7w/78XcDip4kKkkvWSmdeqMzb7kci8\nLRiReQuGYm9S2dZIBsomW814BLM4k1Rvf3he/56kdhjB2wA7k95ca9D2zbxmv18A9wJrkgYVQ+o5\nunfoL1941M48BfOk7df+5sDukjo5JBtodVh4fv7fS+o7M7C37RvatDnI/jeVZmw+Tup7+5ILztjs\nR0JtcsGI4C0Yis26ojDVh6xie7ykwwBsz5T07Ly+qc+oeRrW9knhrZJ+wez9frfkPqzIfrfPKOAu\nSTfn9Wakv8lFALbf3abxHKxFwNZN2n7tv63l//9fpdWfX2nGbe+cwVMllZgzWHXGZhC8UCJ4C4bi\neknrx6lvFaYpDYpuyrbGAqVnfnWdTpda/ItU6/cLAPhSbQeCztLqoVEIgtWZMwjVZ2wGwQsigrdg\nKMYCt0v6A+WHVPc7nwEuAkZLmgCsSqr9DwaomXlrVfmxlLJcMCS3AtOzZPu/k8rHL7H9TGW/gvpM\nqO1AZdq+74oBlV/y5yXv9U8AkyXFjM1CmCibXBAieAuGYvvaDvQrtidJ2oZUcy+SVHxsHGfn/Hl/\nyYIh6RjbX+pZLwacbXt3ANtj27Kd7R0HfIU0IPpS0mzFT9v+YZt2g1lcC7w+z5i6HLiFNO9r96pe\nBa0j6TNzuDwFmGj7dtufKu1TSbK64nTnRqQs1rOU7Sfzl3yhZRdqzxmMGZvBIkGoTQZBx5D0IlL2\nbS3bH5O0DrCu7f+p7FrrSDqJuZREljgBlTQO+L3tr0laktR7dpvto9q2ne3fbntjSTsCO5CeC9fa\n3qiE/X6nURSUdACwtO3jJP0mfv/DH0nnksQymjE5OwB3AC8Hzrd9XCXXiiDpRuAttp/I62WBy21v\nWdCHanMG50Xbapv9yBIjV+90EDLjmT+H2mQQBPPFOGAisEVe/x8p0zTsgzdSyRrAVsD6JJVNSKId\npfov9wHOyYIx2wK/sP2fhWzDwH35naQN45Q0vzcohCRtQcq0fSRfG1HRn6AcawBjeoKXI4GLgTeQ\n7snDOngjZdmeaBa2n8iHicWoOWdwPmhVbbMfsUOLbUGI4C0Iusdo27tI2hXA9pPqk917MwBX0n7A\n1rZn5vUpwHVt2s4nvg0nkAZlTwCulTQmbypK8D951th0YD9JqwJPFbIdwEHAYcAFtn8naW3gqso+\nBWV4MT1zTYFngNVsT5f09BDfM5yY1nuvk/Ra0n0oSHQ6SxT0DxG8BUH3mCFpaQbUJkcz+4aiH1gR\nWA74Z14vm6+1yfGD1o+Ssn/Hk/4Wb3red7SA7UNz39uUrID2JPCe5t8lbRezh9rD9rWkvrdmfT8w\nq1xX0km2D6jhW9A655B6rn6W1+8Czs29YP2gvHwwcL6kP5L6rVcn9XsGQdAhouctCDpGHhJ6BClw\nuJxUQvhh21fX9KskkvYGjiJlPEQqWzqqycz1M01PVm0/+pX4/Q9vJG1KuucCTLB969y+frghaSRJ\nLAtCLGs2JN1me5PafgwnRi6+aqeDkGdm/q2TVU8RvAVBx5D0Q1KT/HTgfuAm23+v61V5JK0ObJ6X\nN9n+cyG7B5H6DqcCpwFjgENtX17C/ryIDURdIngbvkg6ETjP9vW1fSmJpPfN7d9thwIjIOmtXXkf\nGC6MXHzlTgchz8z8RyeDtyibDILucTrwemA7YDRwm6RrbZ9Q1632kbSe7bt7+s8ezo8vlfTSQn1n\n+9g+QdLbgJWBPYEfkLKgXaDTb3ZBsAgzEThC0rrABaRArh8yb++ay7+ZYS6fL2kyc1c53jA/duU9\nIOhzIvMWBB0kzxbbjKR2uC9p9s56db1qH0nfs/1xSVcx+5tpMyS+9b4zSXfY3lDSCcDVti/oUrYr\nMj916dJzIWgHSSsBOwEfBNa0vU5ll4IWkbRW/vST+fEH+bGZ7Xlocaf6hMi8LRiReQuCjiHpV8Ay\nwA0khcXNbP+1rldlsP3x/Ok7gP1J835M+j18t5AbEyVdDrwCOEzSKOC5QrbnhwdqO9AP5BlX9Eqn\nZ4Z9BjzglcB6wFrAXZV9KUaea7kTaa7drP2h7WNq+VQC2w/CLDGo3oOZQyVNAiJ4a4k8Dz54gUTw\nFgTd4w7gtcAGwBTgMUk32O4nyeazgMeBE/N6N+Bs4AMFbH8E2BgYSRrYuwpwZgG7wCzBgP1IIi0A\n1wCnNMIBtufanxL8a0h6Dem5tlJa6m/Ah2z/FsD2mRXdC1okq7zuCNwHnAd82fZjdb0qys9I7zkT\n6T+FY0iv961sT8iLLYkZj0EHibLJIOgoOePzYeAQYHXbS9b1qByS7rS9/ryutWT7o6RZX2sAtwNj\ngRtKlGxm+98nBY6NsuaewLO2P1rCfr8j6XrgcNtX5fUbgWNtb1nVsaB1JH2C1N+1NjDrfpvHRwx7\nJP3W9ga1/ahFnmt3BrA8qVT/UVIPdFeHhi/yLL7Yip0OQmY++2iUTQZBMG8kfYokWPJaUoncGbQ8\noLqDTJI01vaNAJI2B0oJBxxE6je80fa2ktYDji1kG1KZ7EY96ysl/aag/X5nmSZwA7B9dZ7zFQx/\nngOuZNDBDYVmPHaA6yW9xvbk2o7UwPZEYCNJy+f1lMouDXvcqY6ERYcI3oKgeywFfAuYaHtmbWdK\n0qP6NZK0kXgor9cC7i7kxlO2n5KEpCWz+uW68/62hcazkkbbvg9A0trAswXt9zv3S/oiA6IFe5BG\ndgTDnwOpe3BThZ777uLA3pLuJ5VNNkJRG9b0rxQ5aDuSXLIu6RrgmAjigq4RwVsQdAzb36ztQ4NT\n4RgAAAWkSURBVEV2qO0A8IikFYALgSskPQo8WND+54Cr8gYKknjA3gXt9zv7AEczII9+Xb4WDH9q\nH9zUove+uyKp8gPgWqCfev7OAH7LQG/1nqSZn9FnHHSK6HkLgiAYAknbkPofLrU9o5DNpYDPAm8m\nbZxuAb5t+6kS9oNEPoV/zvbU2r4EZZB0Aemg5GBSqeSjwEjb76jqWCEkHQR8lHRwIeC9wGm2T6rq\nWCEk3W5743ldCxYeiy02qtNByLPPTu1kz1sEb0EQBB1C0niS0uY5+dJuwAq2d67nVf8gaTPSCfyo\nfGkKSbRgYj2vgtLUOLipjaQ7gC1sT8vrZUhiTf1SNnkD8Dnbv87rrYBv2t6irmfDlwjeFowomwyC\nIOgWGwxS1bxK0p3VvOk/Tgf2t30dgKStSaVTfbGBDRK2r6ntQwXE7P21z+Zr/cJ+wFk56y7gnyTF\n5yDoFBG8BUEQdIuaSptBGsswS93V9q8l9ZVwUNC3jANuyuWjkMomT6/oT1Fs305Sm1wurx+v7NLw\nJ4Z0LxBRNhkEQdABBiltrgvMprRZYsZdPyNpTP50L2Bp4Eek3/8uJCGLz9TyLQhKkV8HW+fldbZv\nq+lPCSTtYfuHkub4Grf9rdI+9QuLjVim00HIs89N62TmOTJvQRAE3aALSpv9zPGD1kf2fN7pDUYQ\nLCzyQOp+G0rdzHEcNdevCoKOEJm3IAiCIJhPJH3I9lm1/QiCYOEhaTHgQNvfru1LPxGZtwUjgrcg\nCIIgmE8kTbI9Zt5fGQTBooSkm22/rrYf/cSIEUt3Ogh57rnpnQzeomwyCIIgCOafTr6ZB0HwLzNB\n0snAj4FpzcVcShoEnSGCtyAIgiCYfzp9UhwEwQLTDOM+pueaSQPbg6AzRPAWBEEQBPNPZN6CYBhi\ne9vaPvQfMSpgQRhR24EgCIIgWISYUNuBIAgWPpJWk3S6pEvyen1JH6ntVxAMJgRLgiAIgiAzxKyn\nKcDEPMQ3CIJhSA7axgGH295I0uLAbbZfU9m1YcuIEUt2Ogh57rmnO1lpEZm3IAiCIBhgU2Bf4N/y\nxyeA7YHTJH2+pmNBELTKKrbHk2v5bM8Enq3r0jDH7vZHR4metyAIgiAYYA1gjO0nACQdCVwMvAGY\nCBxX0bcgCNpjmqSVyaJEksaSsu5B0CkieAuCIAiCAV4MPN2zfgZYzfZ0SU8P8T1BECz6fAa4CFhb\n0gRgVeD9dV0KgucTwVsQBEEQDHAOcJOkn+X1u4BzJS0D3FnPrSAIWuZO4ALgSWAqcCHw+6oeDXMc\nk1cWiBAsCYIgCIIeJG0KbJWXE2zfWtOfIAjaR9J44HHSAQ7AbsAKtneu59XwRhrZ6SDEfqaTgiUR\nvAVBEARBRtKJwHm2r6/tSxAE5ZB0p+3153UtWHhE8LZgRNlkEARBEAwwEThC0rqkEqrzIvMWBH3B\nJEljbd8IIGlzIF77LdLV4KjrROYtCIIgCAYhaSVgJ+CDwJq216nsUhAELSLpLmBd4KF8aU3gHmAm\nYNsb1vItCHqJzFsQBEEQPJ9XAusBawF3VfYlCIL22b62A0EwP0TmLQiCIAgyko4DdgTuA84DLrT9\nWF2vgiAIgiARmbcgCIIgGOA+YEtgbWBJYENJ2L62rltBEARBEMFbEARBEPTyHHAlsAZwOzAWuAF4\nU02ngiAIggBgRG0HgiAIgqBDHAhsBjxoe1tgEyDKJoMgCIJOEMFbEARBEAzwlO2nACQtaftukgJd\nEARBEFQnyiaDIAiCYIBHJK0AXAhcIelR4MHKPgVBEAQBEGqTQRAEQTBHJG0DLA9cantGbX+CIAiC\nIIK3IAiCIAiCIAiCRYDoeQuCIAiCIAiCIFgEiOAtCIIgCIIgCIJgESCCtyAIgiAIgiAIgkWACN6C\nIAiCIAiCIAgWASJ4C4IgCIIgCIIgWAT4f2+l+5OX/kY/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d61dd20b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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TLzyCs//6b35b9ftacVuln2PWceydcOJRAHQsOPYWMWH8lHXu+8rwt+jcOYyZ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8dd8659c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check feature correlation, to see what correlates with the close price\n",
+    "colormap = plt.cm.inferno\n",
+    "plt.figure(figsize=(15,15))\n",
+    "plt.title('Pearson correlation of features', y=1.05, size=15)\n",
+    "sns.heatmap(df.corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(15,5))\n",
+    "corr = df.corr()\n",
+    "sns.heatmap(corr[corr.index == 'close'], linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create random forest regressor - random decision trees, like weak learner, ada boost\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Scale and create datasets\n",
+    "target_index = df.columns.tolist().index('close')\n",
+    "dataset = df.values.astype('float32')\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "dataset = scaler.fit_transform(dataset)\n",
+    "\n",
+    "# Set look_back to 20 which is 5 hours (15min*20)\n",
+    "X, y = create_dataset(dataset, look_back=1)\n",
+    "y = y[:,target_index]\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[2]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-113-c9eeae1a0f99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# fit model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mforest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mforest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mforest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                     verbose=self.verbose, class_weight=self.class_weight)\n\u001b[0;32m--> 327\u001b[0;31m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m    328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m             \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    777\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    623\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    330\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight)\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0mcurr_sample_weight\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mcompute_sample_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'balanced'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1122\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1125\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    360\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 362\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# fit model\n",
+    "forest = RandomForestRegressor(n_estimators = 100)\n",
+    "forest = forest.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature ranking:\n",
+      "0. pca 19 (0.453368)\n",
+      "1. ask_price 7 (0.217941)\n",
+      "2. ohlc_price 16 (0.075821)\n",
+      "3. close 13 (0.069535)\n",
+      "4. period_return 18 (0.064586)\n",
+      "5. bid_price 6 (0.060840)\n",
+      "6. oc_diff 17 (0.057908)\n",
+      "7. month 1 (0.000000)\n",
+      "8. day 2 (0.000000)\n",
+      "9. hour 3 (0.000000)\n",
+      "10. weekday 4 (0.000000)\n",
+      "11.  5 (0.000000)\n",
+      "12. low 9 (0.000000)\n",
+      "13. high 8 (0.000000)\n",
+      "14. avg_bo_spread 10 (0.000000)\n",
+      "15. count 11 (0.000000)\n",
+      "16. open 12 (0.000000)\n",
+      "17. avg_price 14 (0.000000)\n",
+      "18. range 15 (0.000000)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3zeuTPK+7/890/U+SPKu7d+33WOdltgdRHvjABz7yxhtvXORaYF127NiRJHnz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8e03d9b940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find feature with best explanatory power to predict close price\n",
+    "importances = forest.feature_importances_\n",
+    "std = np.std([forest.feature_importances_ for forest in forest.estimators_], axis=0)\n",
+    "indices = np.argsort(importances)[::-1]\n",
+    "\n",
+    "column_list = df.columns.tolist()\n",
+    "print(\"Feature ranking:\")\n",
+    "for f in range(X.shape[1]-1):\n",
+    "    print(\"%d. %s %d (%f)\" % (f, column_list[indices[f]], indices[f], importances[indices[f]]))\n",
+    "\n",
+    "# Plot the feature importances coming from the forest of decision trees\n",
+    "plt.figure(figsize=(20,10))\n",
+    "plt.title(\"Feature importances\")\n",
+    "plt.bar(range(X.shape[1]), importances[indices],\n",
+    "       color=\"salmon\", yerr=std[indices], align=\"center\")\n",
+    "plt.xticks(range(X.shape[1]), indices)\n",
+    "plt.xlim([-1, X.shape[1]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-116-93770cf0dfc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'low'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'high'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# plot first 200 entries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    251\u001b[0m     \"\"\"\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    300\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    169\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    330\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2198\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2199\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2200\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2201\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2202\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    465\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1144\u001b[0;31m                 renderer, self, dsu, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1146\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2424\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2426\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2428\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/legend.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    481\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegendPatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 483\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legend_box\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'legend'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    280\u001b[0m                                                         renderer)\n\u001b[1;32m    281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 282\u001b[0;31m         \u001b[0mpx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_offset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mget_offset\u001b[0;34m(self, width, height, xdescent, ydescent, renderer)\u001b[0m\n\u001b[1;32m    219\u001b[0m         \"\"\"\n\u001b[1;32m    220\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/legend.py\u001b[0m in \u001b[0;36m_findoffset_best\u001b[0;34m(self, width, height, xdescent, ydescent, renderer)\u001b[0m\n\u001b[1;32m    432\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_findoffset_best\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0;34m\"Helper function to locate the legend at its best position\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m         \u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_find_best_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mox\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mxdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mydescent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/legend.py\u001b[0m in \u001b[0;36m_find_best_position\u001b[0;34m(self, width, height, renderer, consider)\u001b[0m\n\u001b[1;32m    948\u001b[0m             \u001b[0;31m# take their into account when checking vertex overlaps in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m             \u001b[0;31m# the next line.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m             \u001b[0mbadness\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlegendBox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_contains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m             \u001b[0mbadness\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlegendBox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_contains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m             \u001b[0mbadness\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlegendBox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_overlaps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbboxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mcount_contains\u001b[0;34m(self, vertices)\u001b[0m\n\u001b[1;32m    658\u001b[0m             \u001b[0mdy0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    659\u001b[0m             \u001b[0mdx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 660\u001b[0;31m             \u001b[0mdy1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    661\u001b[0m         \u001b[0minside\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdx0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdx1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdy0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdy1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minside\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# plot close price, compare to low and high price\n",
+    "ax = df.plot(x=df.index, y='close', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in df.index]\n",
+    "plt.fill_between(x=index, y1='low',y2='high', data=df, alpha=0.4)\n",
+    "plt.show()\n",
+    "\n",
+    "# plot first 200 entries \n",
+    "p = df[:200].copy()\n",
+    "ax = p.plot(x=p.index, y='close', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in p.index]\n",
+    "plt.fill_between(x=index, y1='low', y2='high', data=p, alpha=0.4)\n",
+    "plt.title('zoomed, first 200')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale and create datasets\n",
+    "target_index = df.columns.tolist().index('close')\n",
+    "high_index = df.columns.tolist().index('high')\n",
+    "low_index = df.columns.tolist().index('low')\n",
+    "dataset = df.values.astype('float32')\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "dataset = scaler.fit_transform(dataset)\n",
+    "\n",
+    "# Create y_scaler to inverse it later\n",
+    "y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "t_y = df['close'].values.astype('float32')\n",
+    "t_y = np.reshape(t_y, (-1, 1))\n",
+    "y_scaler = y_scaler.fit(t_y)\n",
+    "    \n",
+    "# Set look_back to 20 which is 5 hours (15min*20)\n",
+    "X, y = create_dataset(dataset, look_back=1)\n",
+    "y = y[:,target_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set training data size\n",
+    "# We have a large enough dataset. So divid into 98% training / 1%  development / 1% test sets\n",
+    "train_size = int(len(X) * 0.99)\n",
+    "trainX = X[:train_size]\n",
+    "trainY = y[:train_size]\n",
+    "testX = X[train_size:]\n",
+    "testY = y[train_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "lstm_9 (LSTM)                (None, 1, 20)             3360      \n",
+      "_________________________________________________________________\n",
+      "lstm_10 (LSTM)               (None, 1, 20)             3280      \n",
+      "_________________________________________________________________\n",
+      "lstm_11 (LSTM)               (None, 1, 10)             1240      \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 1, 10)             0         \n",
+      "_________________________________________________________________\n",
+      "lstm_12 (LSTM)               (None, 4)                 240       \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_6 (Dense)              (None, 1)                 5         \n",
+      "=================================================================\n",
+      "Total params: 8,145\n",
+      "Trainable params: 8,145\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Input, LSTM, Dense\n",
+    "\n",
+    "# create a small LSTM network\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(20, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))\n",
+    "model.add(LSTM(20, return_sequences=True))\n",
+    "model.add(LSTM(10, return_sequences=True))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(LSTM(4, return_sequences=False))\n",
+    "model.add(Dense(4, kernel_initializer='uniform', activation='relu'))\n",
+    "model.add(Dense(1, kernel_initializer='uniform', activation='relu'))\n",
+    "\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mae', 'mse'])\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error improved from inf to 0.02140, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00001: val_mean_squared_error improved from 0.02140 to 0.00056, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00002: val_mean_squared_error improved from 0.00056 to 0.00035, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00003: val_mean_squared_error improved from 0.00035 to 0.00024, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00004: val_mean_squared_error improved from 0.00024 to 0.00011, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00005: val_mean_squared_error improved from 0.00011 to 0.00006, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00006: val_mean_squared_error improved from 0.00006 to 0.00005, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00007: val_mean_squared_error improved from 0.00005 to 0.00003, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00008: val_mean_squared_error did not improve\n",
+      "Epoch 00009: val_mean_squared_error did not improve\n",
+      "Epoch 00010: val_mean_squared_error did not improve\n",
+      "Epoch 00011: val_mean_squared_error did not improve\n",
+      "Epoch 00012: val_mean_squared_error did not improve\n",
+      "Epoch 00013: val_mean_squared_error did not improve\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error improved from 0.00003 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00016: val_mean_squared_error did not improve\n",
+      "Epoch 00017: val_mean_squared_error improved from 0.00002 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00018: val_mean_squared_error improved from 0.00002 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00019: val_mean_squared_error improved from 0.00002 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error did not improve\n",
+      "Epoch 00022: val_mean_squared_error improved from 0.00002 to 0.00002, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00023: val_mean_squared_error did not improve\n",
+      "Epoch 00024: val_mean_squared_error improved from 0.00002 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "Epoch 00026: val_mean_squared_error did not improve\n",
+      "Epoch 00027: val_mean_squared_error did not improve\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error did not improve\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error did not improve\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n",
+      "Epoch 00033: val_mean_squared_error did not improve\n",
+      "Epoch 00034: val_mean_squared_error did not improve\n",
+      "Epoch 00035: val_mean_squared_error did not improve\n",
+      "Epoch 00036: val_mean_squared_error did not improve\n",
+      "Epoch 00037: val_mean_squared_error did not improve\n",
+      "Epoch 00038: val_mean_squared_error did not improve\n",
+      "Epoch 00039: val_mean_squared_error did not improve\n",
+      "Epoch 00040: val_mean_squared_error did not improve\n",
+      "Epoch 00041: val_mean_squared_error did not improve\n",
+      "Epoch 00042: val_mean_squared_error did not improve\n",
+      "Epoch 00043: val_mean_squared_error did not improve\n",
+      "Epoch 00044: val_mean_squared_error did not improve\n",
+      "Epoch 00045: val_mean_squared_error did not improve\n",
+      "Epoch 00046: val_mean_squared_error did not improve\n",
+      "Epoch 00047: val_mean_squared_error did not improve\n",
+      "Epoch 00048: val_mean_squared_error did not improve\n",
+      "Epoch 00049: val_mean_squared_error did not improve\n",
+      "Epoch 00050: val_mean_squared_error did not improve\n",
+      "Epoch 00051: val_mean_squared_error did not improve\n",
+      "Epoch 00052: val_mean_squared_error did not improve\n",
+      "Epoch 00053: val_mean_squared_error did not improve\n",
+      "Epoch 00054: val_mean_squared_error did not improve\n",
+      "Epoch 00055: val_mean_squared_error did not improve\n",
+      "Epoch 00056: val_mean_squared_error did not improve\n",
+      "Epoch 00057: val_mean_squared_error did not improve\n",
+      "Epoch 00058: val_mean_squared_error did not improve\n",
+      "Epoch 00059: val_mean_squared_error did not improve\n",
+      "Epoch 00060: val_mean_squared_error did not improve\n",
+      "Epoch 00061: val_mean_squared_error did not improve\n",
+      "Epoch 00062: val_mean_squared_error did not improve\n",
+      "Epoch 00063: val_mean_squared_error did not improve\n",
+      "Epoch 00064: val_mean_squared_error did not improve\n",
+      "Epoch 00065: val_mean_squared_error did not improve\n",
+      "Epoch 00066: val_mean_squared_error did not improve\n",
+      "Epoch 00067: val_mean_squared_error did not improve\n",
+      "Epoch 00068: val_mean_squared_error did not improve\n",
+      "Epoch 00069: val_mean_squared_error did not improve\n",
+      "Epoch 00070: val_mean_squared_error did not improve\n",
+      "Epoch 00071: val_mean_squared_error did not improve\n",
+      "Epoch 00072: val_mean_squared_error did not improve\n",
+      "Epoch 00073: val_mean_squared_error did not improve\n",
+      "Epoch 00074: val_mean_squared_error did not improve\n",
+      "Epoch 00075: val_mean_squared_error did not improve\n",
+      "Epoch 00076: val_mean_squared_error did not improve\n",
+      "Epoch 00077: val_mean_squared_error did not improve\n",
+      "Epoch 00078: val_mean_squared_error did not improve\n",
+      "Epoch 00079: val_mean_squared_error did not improve\n",
+      "Epoch 00080: val_mean_squared_error did not improve\n",
+      "Epoch 00081: val_mean_squared_error did not improve\n",
+      "Epoch 00082: val_mean_squared_error did not improve\n",
+      "Epoch 00083: val_mean_squared_error did not improve\n",
+      "Epoch 00084: val_mean_squared_error did not improve\n",
+      "Epoch 00085: val_mean_squared_error did not improve\n",
+      "Epoch 00086: val_mean_squared_error did not improve\n",
+      "Epoch 00087: val_mean_squared_error did not improve\n",
+      "Epoch 00088: val_mean_squared_error did not improve\n",
+      "Epoch 00089: val_mean_squared_error did not improve\n",
+      "Epoch 00090: val_mean_squared_error did not improve\n",
+      "Epoch 00091: val_mean_squared_error did not improve\n",
+      "Epoch 00092: val_mean_squared_error did not improve\n",
+      "Epoch 00093: val_mean_squared_error did not improve\n",
+      "Epoch 00094: val_mean_squared_error did not improve\n",
+      "Epoch 00095: val_mean_squared_error did not improve\n",
+      "Epoch 00096: val_mean_squared_error did not improve\n",
+      "Epoch 00097: val_mean_squared_error did not improve\n",
+      "Epoch 00098: val_mean_squared_error did not improve\n",
+      "Epoch 00099: val_mean_squared_error did not improve\n",
+      "CPU times: user 16min 23s, sys: 1min 23s, total: 17min 46s\n",
+      "Wall time: 15min 1s\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Save the best weight during training.\n",
+    "simname = \"bm_kaggle_4\"\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_mean_squared_error', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "# Fit\n",
+    "callbacks_list = [checkpoint]\n",
+    "%time history = model.fit(trainX, trainY, epochs=100, batch_size=10000, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 100\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d9849c5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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OGbPTyT1/k3zjI8m3/zo5MpWsvSC5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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8daa0cde48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d98422cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epoch = len(history.history['loss'])\n",
+    "print(\"epoch\", epoch)\n",
+    "for k in list(history.history.keys()):\n",
+    "    if 'val' not in k:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(history.history[k])\n",
+    "        plt.plot(history.history['val_' + k])\n",
+    "        plt.title(k)\n",
+    "        plt.ylabel(k)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left')\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0022660818189899888"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(history.history['val_mean_absolute_error'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As seen from the above, the model seems to have converged nicely, but the mean absolute error on the development data remains at ~0.003X which means the model is unusable in practice. Ideally, we want to get ~0.0005. Let's go back to the best weight, and decay the learning rate while retraining the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00001: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0009000000427477062\n",
+      "Epoch 00002: val_mean_squared_error did not improve\n",
+      "Epoch 00003: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0008100000384729356\n",
+      "Epoch 00004: val_mean_squared_error did not improve\n",
+      "Epoch 00005: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0007290000503417104\n",
+      "Epoch 00006: val_mean_squared_error did not improve\n",
+      "Epoch 00007: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0006561000715009868\n",
+      "Epoch 00008: val_mean_squared_error did not improve\n",
+      "Epoch 00009: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0005904900433961303\n",
+      "Epoch 00010: val_mean_squared_error did not improve\n",
+      "Epoch 00011: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0005314410547725857\n",
+      "Epoch 00012: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "Epoch 00013: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00047829695977270604\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0004304672533180565\n",
+      "Epoch 00016: val_mean_squared_error did not improve\n",
+      "Epoch 00017: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00038742052274756136\n",
+      "Epoch 00018: val_mean_squared_error did not improve\n",
+      "Epoch 00019: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0003486784757114947\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00031381062290165574\n",
+      "Epoch 00022: val_mean_squared_error did not improve\n",
+      "Epoch 00023: val_mean_squared_error improved from 0.00001 to 0.00001, saving model to bm_kaggle_4.weights.best.hdf5\n",
+      "lr changed to 0.0002824295632308349\n",
+      "Epoch 00024: val_mean_squared_error did not improve\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00025418660952709616\n",
+      "Epoch 00026: val_mean_squared_error did not improve\n",
+      "Epoch 00027: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00022876793809700757\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00020589114428730683\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00018530203378759326\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n"
+     ]
+    }
+   ],
+   "source": [
+    "# tune model by starting from best weights and rerunning with decaying learning rate\n",
+    "# Load the weight that worked the best\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "#epoch=60\n",
+    "\n",
+    "# Train again with decaying learning rate\n",
+    "from keras.callbacks import LearningRateScheduler\n",
+    "import keras.backend as K\n",
+    "\n",
+    "def scheduler(epoch):\n",
+    "    if epoch%2==0 and epoch!=0:\n",
+    "        lr = K.get_value(model.optimizer.lr)\n",
+    "        K.set_value(model.optimizer.lr, lr*.9)\n",
+    "        print(\"lr changed to {}\".format(lr*.9))\n",
+    "    return K.get_value(model.optimizer.lr)\n",
+    "lr_decay = LearningRateScheduler(scheduler)\n",
+    "\n",
+    "callbacks_list = [checkpoint, lr_decay]\n",
+    "history = model.fit(trainX, trainY, epochs=int(epoch/3), batch_size=10000, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d983efef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d9836e2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d982bcf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epoch = len(history.history['loss'])\n",
+    "for k in list(history.history.keys()):\n",
+    "    if 'val' not in k:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(history.history[k])\n",
+    "        plt.plot(history.history['val_' + k])\n",
+    "        plt.title(k)\n",
+    "        plt.ylabel(k)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left')\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0021724022948290786"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(history.history['val_mean_absolute_error'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The variance should have improved slightly. However, unless the mean absolute error is not small enough. The model is still not an usable model in practice. This is mainly due to only using the sample data for training and limiting epoch to a few hundreds.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3N9ne78BjU9q+nnB77Dj/oLNjx/1NZfYHFmX67Q2Ax+Ntpaet2vJSVV3N+tLa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\nV21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zvtHaM9RkNIiLP4uIZ/O1/0ZEzM/lp0XE26V24+7SPnWvf6MYU2drEBdNu29ExPKI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\n8lwR3hyQgDYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d982b3ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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XyVsvd1be98rv6cIfWz8lb2puTt57yJ9f4nF/Tt4U8pf8x/49SSE/mL9Z0r/730f1a+/k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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8ddc98c2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE :  8.5686068097e-06\n",
+      "MAE :  0.00260148191793\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    20817.000000\n",
+       "mean         0.002528\n",
+       "std          0.001476\n",
+       "min         -0.010324\n",
+       "25%          0.001668\n",
+       "50%          0.002496\n",
+       "75%          0.003467\n",
+       "max          0.010337\n",
+       "Name: diff, dtype: float64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+    "\n",
+    "# Benchmark\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "\n",
+    "pred = model.predict(testX)\n",
+    "\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(np.reshape(pred, (pred.shape[0])))\n",
+    "predictions['actual'] = testY\n",
+    "predictions = predictions.astype(float)\n",
+    "\n",
+    "predictions.plot(figsize=(20,10))\n",
+    "plt.show()\n",
+    "\n",
+    "predictions['diff'] = predictions['predicted'] - predictions['actual']\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.distplot(predictions['diff']);\n",
+    "plt.title('Distribution of differences between actual and prediction')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"MSE : \", mean_squared_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "print(\"MAE : \", mean_absolute_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "predictions['diff'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GJUy+5iLev+6qdNsm3WZwxYCzAZj99PyM8evpK1/jDAlJkiRJkiRJkiRJkqSV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1nu50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i6Zxo3p6+n+R0D2uWRNa+gqBFsWiWPjSf//yQKqppDkO70iIjx5ILwM+/ZIasM33GUoY+eBOr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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8ddca30240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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HsikNZZI6zspMoGMIZgAQA4VyTdlUQn1mHX3cy3cM6NCp+Y4+JtDLCGYAEAPF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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8ddcacffd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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86euF77v3dyxcuJCEhASOOmoMp512OikpKSx97z2OO/ZozIxf/PJ/6N27N2vX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\n0cpEPOB1F297X7+jAR6pcXGvf54SOfU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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8d8377b400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE :  6.42847482193e-09\n",
+      "MAE :  7.12554587774e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    20817.000000\n",
+       "mean         0.000069\n",
+       "std          0.000040\n",
+       "min         -0.000283\n",
+       "25%          0.000046\n",
+       "50%          0.000068\n",
+       "75%          0.000095\n",
+       "max          0.000283\n",
+       "Name: diff, dtype: float64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred = model.predict(testX)\n",
+    "pred = y_scaler.inverse_transform(pred)\n",
+    "close = y_scaler.inverse_transform(np.reshape(testY, (testY.shape[0], 1)))\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(np.reshape(pred, (pred.shape[0])))\n",
+    "predictions['close'] = pd.Series(np.reshape(close, (close.shape[0])))\n",
+    "\n",
+    "p = df[-pred.shape[0]:].copy()\n",
+    "predictions.index = p.index\n",
+    "predictions = predictions.astype(float)\n",
+    "predictions = predictions.merge(p[['low', 'high']], right_index=True, left_index=True)\n",
+    "\n",
+    "ax = predictions.plot(x=predictions.index, y='close', c='red', figsize=(40,10))\n",
+    "ax = predictions.plot(x=predictions.index, y='predicted', c='blue', figsize=(40,10), ax=ax)\n",
+    "index = [str(item) for item in predictions.index]\n",
+    "plt.fill_between(x=index, y1='low', y2='high', data=p, alpha=0.4)\n",
+    "plt.title('Prediction vs Actual (low and high as blue region)')\n",
+    "plt.show()\n",
+    "\n",
+    "predictions['diff'] = predictions['predicted'] - predictions['close']\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.distplot(predictions['diff']);\n",
+    "plt.title('Distribution of differences between actual and prediction ')\n",
+    "plt.show()\n",
+    "\n",
+    "g = sns.jointplot(\"diff\", \"predicted\", data=predictions, kind=\"kde\", space=0)\n",
+    "plt.title('Distributtion of error and price')\n",
+    "plt.show()\n",
+    "\n",
+    "# predictions['correct'] = (predictions['predicted'] <= predictions['high']) & (predictions['predicted'] >= predictions['low'])\n",
+    "# sns.factorplot(data=predictions, x='correct', kind='count')\n",
+    "\n",
+    "print(\"MSE : \", mean_squared_error(predictions['predicted'].values, predictions['close'].values))\n",
+    "print(\"MAE : \", mean_absolute_error(predictions['predicted'].values, predictions['close'].values))\n",
+    "predictions['diff'].describe()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "celltoolbar": "Hide code",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/capstone_project/bm_kaggle.weights.best.hdf5 b/capstone_project/bm_kaggle.weights.best.hdf5
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diff --git a/capstone_project/chromedriver.exe b/capstone_project/chromedriver.exe
new file mode 100644
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HcmV?d00001

diff --git a/capstone_project/data preparation/add_row_number_to_existing_query.sql b/capstone_project/data preparation/add_row_number_to_existing_query.sql
new file mode 100644
index 0000000..34d53d4
--- /dev/null
+++ b/capstone_project/data preparation/add_row_number_to_existing_query.sql	
@@ -0,0 +1,12 @@
+
+--update const set row_num = row_number() over (order by snaptime asc)
+--from kai_dw.dbo.fx_spot_data_features const
+WITH upd AS
+(
+    SELECT
+        row_num
+        ,ROW_NUMBER() over (order by snaptime asc) seq
+    FROM kai_dw.dbo.fx_spot_data_features const
+)
+UPDATE upd
+SET row_num = Seq
diff --git a/capstone_project/data preparation/convert types to datetime.sql b/capstone_project/data preparation/convert types to datetime.sql
new file mode 100644
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diff --git a/capstone_project/data preparation/feature creation.sql b/capstone_project/data preparation/feature creation.sql
new file mode 100644
index 0000000..22f4178
--- /dev/null
+++ b/capstone_project/data preparation/feature creation.sql	
@@ -0,0 +1,68 @@
+
+
+select
+	
+	const.snaptime
+	, const.bid_price
+	, const.ask_price
+	, datepart(month, const.snaptime) 'month'
+	, datepart(year, const.snaptime) 'year'
+	, datepart(WEEK, const.snaptime) 'week'
+	, datepart(HOUR, const.snaptime) 'hour'
+	, datepart(day, const.snaptime) 'day'
+	, datepart(WEEKDAY, const.snaptime) 'weekday'
+	, datepart(MINUTE, const.snaptime) 'minute'
+	, datepart(QUARTER, const.snaptime) 'quarter'
+	, ROW_NUMBER() over (order by snaptime asc) 'row_num'
+
+
+	--, count(*)
+into kai_dw.dbo.fx_spot_data_features
+
+--drop table kai_dw.dbo.fx_spot_data_features
+from kai_dw.dbo.fx_spot_data_typed const
+
+--group by const.snaptime
+--order by 2 desc
+
+
+
+/*
+select top 100
+	
+	const.year, const.month, const.day, const.hour, round(const.minute/15,0) * 15
+	, max(const.bid_price) 'high'
+	, min(const.bid_price) 'low'
+	, min(const.snaptime) 'open_datetime'
+	, max(const.snaptime) 'close_datetime'
+	, count(*)
+	--, max(constMin.bid_price)
+
+from kai_dw.dbo.fx_spot_data_features const
+--left join kai_dw.dbo.fx_spot_data_features constMin
+--	on constMin.snaptime = max(const.snaptime)
+
+group by const.year, const.month, const.day, const.hour, round(const.minute/15,0)
+order by const.year, const.month, const.day, const.hour, round(const.minute/15,0)
+*/
+
+--select round(datepart(minute, '2000-05-30 20:57:47.000')  /15, 0) * 15
+
+USE [kai_dw]
+
+GO
+
+CREATE NONCLUSTERED INDEX [NonClusteredIndex-20171112-181301] ON [dbo].[fx_spot_data_features]
+(
+	[snaptime] ASC,
+	[row_num] ASC
+)WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, SORT_IN_TEMPDB = OFF, DROP_EXISTING = OFF, ONLINE = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON)
+
+GO
+
+
+select count(*) from kai_dw.dbo.fx_spot_data_features
+select count(*) from kai_dw.dbo.fx_spot_data_typed
+select count(*) from kai_dw.dbo.fx_spot_data
+
+
diff --git a/capstone_project/data preparation/get_data.sql b/capstone_project/data preparation/get_data.sql
new file mode 100644
index 0000000..bbd8a85
--- /dev/null
+++ b/capstone_project/data preparation/get_data.sql	
@@ -0,0 +1,89 @@
+
+
+declare @min_date datetime = '1Jan16'
+declare @max_date datetime = '1Jan17'
+
+/*
+set @min_date = ? -- '1Jan16'
+set @max_date = ? -- '1Jan17'
+*/
+
+set nocount on
+drop table kai_dw.dbo.fx_spot_data_15_min 
+
+--insert into kai_dw.dbo.fx_spot_data_15_min 
+select
+    --distinct
+    const.year, const.month, const.day, const.hour, const.weekday, round(const.minute/15,0) * 15 '15_min'
+	, DATETIMEFROMPARTS(const.year, const.month, const.day, const.hour, round(const.minute/15,0) * 15, 0, 0) 'datestamp'
+	
+    --, const.snaptime 'date'
+    --, const.bid_price
+    --, const.ask_price
+    --, const.ask_price - const.bid_price 'bo_spread'
+	, max(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0))'high_bid'
+	, min(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'low_bid'
+    , avg(const.ask_price - const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'avg_bo_spread'
+	--, min(const.snaptime) 'open_datetime'
+	--, max(const.snaptime) 'close_datetime'
+	, count(*) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0)) 'nb_ticks'
+    , first_value(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0) order by const.snaptime asc rows between unbounded preceding and unbounded following) 'open_bid'
+	, last_value(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0) order by const.snaptime asc rows between unbounded preceding and unbounded following) 'close_bid'
+
+    -- this is current bid price 
+	, const.bid_price
+	, constPrev.bid_price 'bid_price_prev'
+	, const.ask_price
+	, const.snaptime
+	, constPrev.snaptime 'snaptime_prev'
+	
+	, row_number() over  (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0) order by const.snaptime desc) row_num_window
+	--, max(const.bid_price) over (partition by const.year, const.month, const.day, const.hour, round(const.minute/15,0) order by const.snaptime asc) 'close'
+
+into kai_dw.dbo.fx_spot_data_15_min
+from kai_dw.dbo.fx_spot_data_features const
+left join kai_dw.dbo.fx_spot_data_features constPrev
+	on const.row_num = constPrev.row_num - 1
+
+where
+    const.snaptime >= @min_date
+    and const.snaptime <= @max_date
+    
+--group by const.year, const.month, const.day, const.hour, round(const.minute/15,0)
+order by const.year, const.month, const.day, const.hour, 6
+--order by const.snaptime
+
+
+
+--select * from #tmp1
+select
+distinct
+    const.[year]
+	, const.[month]
+	, const.[day]
+	, const.[hour]
+	, const.[weekday]
+	, const.[15_min]
+	, const.datestamp
+	
+    
+	, const.high_bid
+	, const.low_bid
+    , const.avg_bo_spread
+	, const.nb_ticks
+    , const.[open_bid]
+	, const.[close_bid]
+
+	-- last 5 ticks return average
+    
+	-- this is current bid price 
+	--, const.bid_price
+	--, const.snaptime
+	
+	--, const.row_num_window
+	, avg(case when const.row_num_window <= 10 then const.bid_price / const.bid_price_prev-1 else NULL end) over (partition by const.datestamp) 'last_10_tick_avg_bid_return'
+	, avg(case when const.row_num_window <= 10 then const.ask_price - const.bid_price else NULL end) over (partition by const.datestamp) 'last_10_tick_avg_bo_spread'
+
+
+from kai_dw.dbo.fx_spot_data_15_min const
+order by const.datestamp
diff --git a/capstone_project/data preparation/get_data_1y.sql b/capstone_project/data preparation/get_data_1y.sql
new file mode 100644
index 0000000..7c8f6fd
--- /dev/null
+++ b/capstone_project/data preparation/get_data_1y.sql	
@@ -0,0 +1,54 @@
+
+
+declare @min_date datetime = '1Jan16'
+declare @max_date datetime = '3Jan17'
+
+/*
+set @min_date = ? -- '1Jan16'
+set @max_date = ? -- '1Jan17'
+*/
+
+set nocount on
+set ansi_warnings off -- to remove message when avg ignores the NULL
+
+
+--select * from #tmp1
+select
+distinct
+    const.[year]
+	, const.[month]
+	, const.[day]
+	, const.[hour]
+	, const.[weekday]
+	, const.[15_min]
+	, const.datestamp
+	
+    
+	, const.high_bid
+	, const.low_bid
+    , const.avg_bo_spread
+	, const.nb_ticks
+    , const.[open_bid]
+	, const.[close_bid]
+
+	-- last 5 ticks return average
+    
+	-- this is current bid price 
+	--, const.bid_price
+	--, const.snaptime
+	
+	--, const.row_num_window
+	, avg(case when const.row_num_window <= 10 then const.bid_price / const.bid_price_prev-1 else NULL end) over (partition by const.datestamp) 'last_10_tick_avg_bid_return'
+	, avg(case when const.row_num_window <= 10 then const.ask_price - const.bid_price else NULL end) over (partition by const.datestamp) 'last_10_tick_avg_bo_spread'
+
+
+from kai_dw.dbo.fx_spot_data_15_min const
+
+
+where
+    const.snaptime >= @min_date
+    and const.snaptime <= @max_date
+    
+order by const.datestamp
+
+
diff --git a/capstone_project/data preparation/import csv to sql server.sql b/capstone_project/data preparation/import csv to sql server.sql
new file mode 100644
index 0000000..90b53ac
--- /dev/null
+++ b/capstone_project/data preparation/import csv to sql server.sql	
@@ -0,0 +1,71 @@
+
+
+--q1-2011 is the last file that has two description columns
+
+--declare @varDay as int = 1
+declare @varMonth as int = 1
+declare @varYear as int = 2000
+declare @file as nvarchar(max)
+declare @sqlstring as nvarchar(max)
+
+
+
+while @varYear <> 2018
+
+begin
+
+
+	set @file = 'D:\Python Projects\kai_code\capstone_project\data\DAT_ASCII_EURUSD_T_' + convert(nvarchar,@varYear) + format(@varMonth, '0#')+'.csv'
+
+	if @file not in ('sds')
+	begin
+
+		set @sqlstring = 
+
+		'
+		BULK INSERT dbo.fx_spot_data FROM '+CHAR(39)+@file+char(39)+'
+			 WITH
+			 (
+				 FIELDTERMINATOR = '','',
+				 ROWTERMINATOR = ''0x0A'',
+				 MAXERRORS = 100,
+				 DATAFILETYPE = ''char'',
+				 KEEPIDENTITY
+				 --FIRSTROW = 1
+			 ) 
+		 '
+		 print(@sqlstring)
+		 exec(@sqlstring)
+		 
+		 
+	 end
+	 
+	 
+	 --set @varDay= @varDay + 1
+	 set @varMonth = @varMonth + 1
+		 if @varMonth = 13
+		 begin 
+			set @varMonth = 1
+
+			set @varYear = @varYear + 1
+		 end
+
+
+ 
+ end
+
+
+
+
+--BULK INSERT dbo.fx_tick_data
+--FROM 'D:\Python Projects\kai_code\capstone_project\data\DAT_ASCII_EURUSD_T_200005.csv'
+----WITH (FORMAT = 'CSV'); 
+--WITH  
+--( 
+--	FIELDTERMINATOR = ',',
+--				 ROWTERMINATOR = '0x0A',
+--				 MAXERRORS = 100,
+--				 DATAFILETYPE = 'char',
+--				 KEEPIDENTITY
+--				 --FIRSTROW = 1
+--);  
\ No newline at end of file
diff --git a/capstone_project/data preparation/screenscraper_fx_spot.py b/capstone_project/data preparation/screenscraper_fx_spot.py
new file mode 100644
index 0000000..3559a0a
--- /dev/null
+++ b/capstone_project/data preparation/screenscraper_fx_spot.py	
@@ -0,0 +1,192 @@
+__author__ = 'kai.aeberli'
+
+from selenium import webdriver
+from selenium.webdriver.common.by import By
+
+from selenium.webdriver.common.keys import Keys
+import itertools
+from collections import OrderedDict
+import sys
+import pandas as pd
+import pymongo
+import json, os
+
+
+def Webscraping():
+
+
+    webstr = "http://www.histdata.com/download-free-forex-historical-data/?/ascii/tick-data-quotes/EURUSD"
+    driver.get(webstr)
+
+
+    # get all hrefs that contain "historical"
+    from selenium.webdriver.common.by import By
+    all_links = driver.find_elements(By.CSS_SELECTOR, 'a[href*="eurusd"')
+
+    listHref = []
+    for link in all_links:
+        #print(link.get_attribute("href"))
+        listHref.append(link.get_attribute("href"))
+
+    # travel down each of them
+    listHRefMonth = []
+    # append months to each of them
+    for strLink in listHref:
+        for month in range(12):
+            listHRefMonth.append(strLink + '/' + str(month+1))
+
+
+
+    #liElements=driver.find_element_by_id("listed-islamic-securities").find_element_by_class_name("listed-securities").find_elements_by_xpath(".//li")
+    #lisElementsDeep = liElements.copy()
+
+
+
+
+    # listHref = []
+    # for li in lisElementsDeep:
+    #     href = li.find_element_by_tag_name("a").get_attribute("href")
+    #     listHref.append(href)
+
+
+    all_values = []
+    for href in listHRefMonth[1:]:
+
+        try:
+
+
+            driver.get(href)
+            import time
+            time.sleep(2)  # give time to load - need this else animations are still running
+
+            zip_file = driver.find_element(By.CSS_SELECTOR, 'a[id="a_file"')
+            zip_file.click()
+
+
+            # go through months
+            #each_year_month_links = driver.find_elements(By.CSS_SELECTOR, 'a[href*="eurusd"')
+
+            # securityTable = driver.find_element_by_id("currencies-all").find_element_by_tag_name("tbody").find_elements_by_xpath(".//tr")
+            #
+            # #dailyValues = []
+            #
+            # for htmlRow in securityTable:
+            #     #if len(htmlRow.find_elements(By.XPATH, "th")) == 0:
+            #
+            #     # read out all the td elements - each is one column of that row
+            #
+            #     # can i do it in one go???
+            #     #htmlRow.find_elements(By.CSS_SELECTOR, "td")[2].text but each needs special treatment
+            #     row = OrderedDict()
+            #     row["Link"] = href
+            #     row["hash"] = htmlRow.find_element_by_xpath("td[1]").text # #
+            #     row["name"] = htmlRow.find_element_by_xpath("td[2]").find_element_by_tag_name('a').text # name
+            #     row["symbol"] = htmlRow.find_element_by_xpath("td[3]").text # symbol
+            #     row["market_cap"] = htmlRow.find_element_by_xpath("td[4]").get_attribute("data-usd") # market cap
+            #     row["price"] = htmlRow.find_element_by_xpath("td[5]").find_element_by_tag_name("a").get_attribute("data-usd") # price
+            #     row["available_supply"] = htmlRow.find_element_by_xpath("td[6]").text # available supply
+            #     row["volume_24h"] = htmlRow.find_element_by_xpath("td[7]").find_element_by_tag_name("a").text  # low vol???
+            #     row["percent_change_1h"] = htmlRow.find_element_by_xpath("td[8]").get_attribute("data-usd") # % 1h
+            #     row["percent_change_24h"] = htmlRow.find_element_by_xpath("td[9]").text # % 24h
+            #     row["percent_change_7d"] = htmlRow.find_element_by_xpath("td[10]").text  # % 7d
+            #     row["date"] = href[-9:-1]
+            #
+            #     all_values.append(row)
+
+
+            #all_values.append(dailyValues)
+
+        except:
+            print("exc")
+
+
+
+
+    return all_values
+
+
+def zip_extract(path):
+
+
+    import zipfile
+    archive = zipfile.ZipFile(path)
+    archive.extractall()
+
+
+
+
+
+def get_zipfiles():
+
+    global driver
+    #driver=webdriver.Firefox()
+    chromedriver_path = os.path.dirname(__file__)
+    driver=webdriver.Chrome(chromedriver_path+"/chromedriver.exe")
+    webstr = "http://www.histdata.com/download-free-forex-historical-data/?/ascii/tick-data-quotes/EURUSD"
+    driver.get(webstr)
+
+    import time
+    time.sleep(15)
+
+    listSecurities=Webscraping()
+
+    import pandas as pd
+    df = pd.DataFrame.from_records(listSecurities)
+    df.to_csv("fx_spot_quotes.csv")
+
+    for val in listSecurities:
+        print(val)
+
+
+    driver.quit()
+
+
+
+def run_function_on_all_files_in_dir(file_ending, file_function, dir = None):
+
+    if dir == None:
+        foldername = os.path.join(os.path.dirname(__file__), "")
+    for base, dirs, files in os.walk(foldername):
+        for file in files:
+            if file[-3:] == file_ending:
+                longfilepath = os.path.join(base, file)
+                #zip_extract(longfilepath)
+                #import_content(longfilepath)
+                print("Processing "+longfilepath)
+                file_function(longfilepath)
+
+
+
+
+def import_content(filepath):
+
+
+    mng_client = pymongo.MongoClient('localhost', 27017)
+    mng_db = mng_client['fx_prediction'] # Replace mongo db name
+    collection_name = 'fx_tick_data_typed' # Replace mongo db table name
+    db_cm = mng_db[collection_name]
+
+    data = pd.read_csv(filepath)
+
+    data.columns = ["date", "bid", "ask", "vol"]
+    #data.date = pd.to_datetime(data.date, format='%Y%m%d %H%M%S%f')
+    data.bid = data.bid.astype(float)
+    data.ask = data.ask.astype(float)
+    data.vol = data.vol.astype(float)
+    import datetime
+    data_json = json.loads(data.to_json(orient='records'))
+    for row in data_json:
+        row["date"] = datetime.datetime.strptime(row['date'], '%Y%m%d %H%M%S%f')
+
+    #db_cm.remove()
+    db_cm.insert_many(data_json)
+
+
+def main():
+    #get_zipfiles() # goes to default download folder
+    #run_function_on_all_files_in_dir("zip", zip_extract)
+    run_function_on_all_files_in_dir("csv", import_content)
+    pass
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/capstone_project/kaggle_bm.ipynb b/capstone_project/kaggle_bm.ipynb
new file mode 100644
index 0000000..55f379d
--- /dev/null
+++ b/capstone_project/kaggle_bm.ipynb
@@ -0,0 +1,1864 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Machine learning capstone project - fx spot prediction\n",
+    "\n",
+    "The goal is to create features that can help predict the bid price, using a lookback period of a few minutes.\n",
+    "\n",
+    "Try to include the bid offer spread - from the benchmark model it seems volume is not an important feature so it is not a problem that i dont have this data point.\n",
+    "\n",
+    "I took inspiration from : https://www.kaggle.com/kimy07/eurusd-15-minute-interval-price-prediction/notebook\n",
+    "\n",
+    "Introduction\n",
+    "This notebook trains a LSTM model that predicts the bid price of EURUSD 15 minutes in the future by looking at last five hours of data. While there is no requirement for the input to be contiguous, it's been empirically observed that having the contiguous input does improve the accuracy of the model. I suspect that having day of the week and hour of the day as the features mitigates some of the seasonality and contiguousness problems.\n",
+    "\n",
+    "Disclaimer: This exercise has been carried out using a small sample data which only contains 14880 samples (2015-12-29 00:00:00 to 2016-05-31 23:45:00) and lacks ASK prices. Which restricts the ability for the model to approach a better accuracy.\n",
+    "\n",
+    "I will use 1 year of data, from 1Jan16 to 1Jan17, also in 15 minute intervals, but with tick data features.\n",
+    "\n",
+    "Improvements\n",
+    "\n",
+    "To tune the model further, I would recommend having at least 5 years worth of data, have ASK price (so that you can compute the spread), and increasing the epoch to 3000.\n",
+    "Adding more cross-axial features. Such as spread.\n",
+    "If you are looking into classification approach (PASS, BUY, SELL), consider adding some technical indicators that is more sensitive to more recent data.\n",
+    "Consider adding non-numerical data, e.g. news, Tweets. The catch is that you have to get the data under one minute for trading, otherwise the news will be reflected before you even make a trade. If anybody knows how to get the news streamed really fast, please let me know.\n",
+    "\n",
+    "Credits : Dave Y. Kim, Mahmoud Elsaftawy,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run on EC2:\n",
+    "Enter the repo directory: cd aind2-cnn\n",
+    "Activate the new environment: source activate aind2\n",
+    "Start Jupyter: jupyter notebook --ip=0.0.0.0 --no-browser\n",
+    "Find this line in output and copy url to browser: \n",
+    "Copy/paste this URL into your browser when you connect for the first time to login with a token: \n",
+    "http://0.0.0.0:8888/?token=3156e...\n",
+    "\n",
+    "change the 0.0.0.0 with EC2 IP.\n",
+    "\n",
+    "you should see the checked out repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "hideCode": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:70% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd, numpy as np\n",
+    "import pypyodbc\n",
+    "import io, datetime\n",
+    "import matplotlib.colors as colors, matplotlib.cm as cm, pylab, matplotlib.pyplot as plt\n",
+    "\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "from subprocess import check_output\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "  \n",
+       "    $('.ctb_hideshow').toggle();\n",
+       "    \n",
+       "    </script>\n",
+       " "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pypyodbc\n",
+    "display(HTML(\"\"\"<script>\n",
+    "  \n",
+    "    $('.ctb_hideshow').toggle();\n",
+    "    \n",
+    "    </script>\n",
+    " \"\"\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "hideCode": false
+   },
+   "outputs": [],
+   "source": [
+    "#kaggle dates: 2015-12-29 00:00:00 to 2016-05-31 23:45:00\n",
+    "min_date = \"29Dec15\"\n",
+    "max_date = \"31May16\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simname = \"mine\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29Dec15\n",
+      "31May16\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(min_date)\n",
+    "print(max_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "strConnDef = \"DRIVER={ODBC Driver 13 for SQL Server};SERVER=localhost,1433;DATABASE=kai_dw;uid=kai_ta;pwd=tenpen12\"\n",
+    "def getQueryRaw(strQuery, params=None, strConn=strConnDef, commitOn=None):\n",
+    "\n",
+    "    if commitOn is None:\n",
+    "        commitOn = False\n",
+    "\n",
+    "    if params is None:\n",
+    "        params = []\n",
+    "\n",
+    "    pypyodbc.lowercase = False\n",
+    "    conn = pypyodbc.connect(strConn)\n",
+    "    cursor = conn.cursor()\n",
+    "    cursor.execute(strQuery, params)\n",
+    "\n",
+    "    if commitOn:\n",
+    "        conn.commit()\n",
+    "        return \"sql insert was successful.\", \"sql insert was successful.\"\n",
+    "    try:\n",
+    "        rows = cursor.fetchall()\n",
+    "        #print(\"rows\", rows)\n",
+    "        # print(\"PARAMS:\", params)\n",
+    "        description = cursor.description\n",
+    "        conn.close()\n",
+    "        return rows, description\n",
+    "    except:\n",
+    "        # print(\"THE QUERY: \" + strQuery) TODO: add query\n",
+    "        conn.close()\n",
+    "        raise ValueError(\"There was an error fetching a sql query. Make sure the index exists for your selected dates. THE PARAMS: \", params)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def getQueryDataframe(strQuery, params=None, strConn=strConnDef, columnMustAlwaysExist=None, commitOn=None):\n",
+    "\n",
+    "    rows, cursorDescription = getQueryRaw(strQuery, params, strConn, commitOn)\n",
+    "    if commitOn:\n",
+    "        return \"sql insert was successful.\"\n",
+    "\n",
+    "    if len(rows) == 0:\n",
+    "        print(\"No rows were returned.\")\n",
+    "        print(\"THE PARAMS: \", params)\n",
+    "        print(\"THE QUERY: \" + strQuery)\n",
+    "        print(\"Rows length is zero. No records returned\")\n",
+    "\n",
+    "        if columnMustAlwaysExist is None:\n",
+    "            columnMustAlwaysExist = \"Empty\"\n",
+    "\n",
+    "        columns = [\"Information\", columnMustAlwaysExist]\n",
+    "        rows = [\n",
+    "            [\"No results were returned.\", \"There is no data.\"]\n",
+    "            , [\"No results were returned.\", \"There is no data.\"]\n",
+    "        ]\n",
+    "\n",
+    "    else:\n",
+    "        # bytes conversion needed because of the linux pypyodbc bug\n",
+    "        columns = [column[0].decode(\"cp1252\") if type(column[0]) == bytes else column[0] for column in\n",
+    "                   cursorDescription]\n",
+    "\n",
+    "    results = pd.DataFrame(data=rows, columns=columns)\n",
+    "\n",
+    "\n",
+    "    return results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create 15 minute data - this fills the 15 minutes table\n",
+    "str_query = open(\"get_data.sql\", \"r\").read() # returns prepared data\n",
+    "str_query = str_query.replace(\"/*\", \"\").replace(\"*/\", \"\")\n",
+    "\n",
+    "df = getQueryDataframe(str_query, [min_date, max_date])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "hideCode": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>15_min</th>\n",
+       "      <th>datestamp</th>\n",
+       "      <th>high_bid</th>\n",
+       "      <th>low_bid</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>nb_ticks</th>\n",
+       "      <th>open_bid</th>\n",
+       "      <th>close_bid</th>\n",
+       "      <th>last_10_tick_avg_bid_return</th>\n",
+       "      <th>last_10_tick_avg_bo_spread</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 17:00:00</td>\n",
+       "      <td>1.08723</td>\n",
+       "      <td>1.08661</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>142</td>\n",
+       "      <td>1.08701</td>\n",
+       "      <td>1.08664</td>\n",
+       "      <td>-2.760660e-06</td>\n",
+       "      <td>0.000139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>2016-01-03 17:15:00</td>\n",
+       "      <td>1.08714</td>\n",
+       "      <td>1.08662</td>\n",
+       "      <td>0.000149</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1.08666</td>\n",
+       "      <td>1.08674</td>\n",
+       "      <td>1.104220e-05</td>\n",
+       "      <td>0.000154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2016-01-03 17:30:00</td>\n",
+       "      <td>1.08699</td>\n",
+       "      <td>1.08671</td>\n",
+       "      <td>0.000141</td>\n",
+       "      <td>109</td>\n",
+       "      <td>1.08674</td>\n",
+       "      <td>1.08674</td>\n",
+       "      <td>-9.201579e-07</td>\n",
+       "      <td>0.000153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2016-01-03 17:45:00</td>\n",
+       "      <td>1.08687</td>\n",
+       "      <td>1.08655</td>\n",
+       "      <td>0.000102</td>\n",
+       "      <td>93</td>\n",
+       "      <td>1.08674</td>\n",
+       "      <td>1.08662</td>\n",
+       "      <td>7.362178e-06</td>\n",
+       "      <td>0.000094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2016-01-03 18:00:00</td>\n",
+       "      <td>1.08665</td>\n",
+       "      <td>1.08523</td>\n",
+       "      <td>0.000113</td>\n",
+       "      <td>459</td>\n",
+       "      <td>1.08662</td>\n",
+       "      <td>1.08574</td>\n",
+       "      <td>1.013142e-05</td>\n",
+       "      <td>0.000093</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  month  day  hour  weekday  15_min           datestamp  high_bid  \\\n",
+       "0  2016      1    3    17        1       0 2016-01-03 17:00:00   1.08723   \n",
+       "1  2016      1    3    17        1      15 2016-01-03 17:15:00   1.08714   \n",
+       "2  2016      1    3    17        1      30 2016-01-03 17:30:00   1.08699   \n",
+       "3  2016      1    3    17        1      45 2016-01-03 17:45:00   1.08687   \n",
+       "4  2016      1    3    18        1       0 2016-01-03 18:00:00   1.08665   \n",
+       "\n",
+       "   low_bid  avg_bo_spread  nb_ticks  open_bid  close_bid  \\\n",
+       "0  1.08661       0.000165       142   1.08701    1.08664   \n",
+       "1  1.08662       0.000149        80   1.08666    1.08674   \n",
+       "2  1.08671       0.000141       109   1.08674    1.08674   \n",
+       "3  1.08655       0.000102        93   1.08674    1.08662   \n",
+       "4  1.08523       0.000113       459   1.08662    1.08574   \n",
+       "\n",
+       "   last_10_tick_avg_bid_return  last_10_tick_avg_bo_spread  \n",
+       "0                -2.760660e-06                    0.000139  \n",
+       "1                 1.104220e-05                    0.000154  \n",
+       "2                -9.201579e-07                    0.000153  \n",
+       "3                 7.362178e-06                    0.000094  \n",
+       "4                 1.013142e-05                    0.000093  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# dates only have an effect if a subset of dates is needed.\n",
+    "str_query = open(\"get_data_1y.sql\", \"r\").read() # returns prepared data\n",
+    "str_query = str_query.replace(\"/*\", \"\").replace(\"*/\", \"\")\n",
+    "#print(str_query)\n",
+    "df = getQueryDataframe(str_query, [min_date, max_date])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min date 2016-01-03 17:00:00\n",
+      "max date 2016-05-30 23:45:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.set_index('datestamp', inplace=True)\n",
+    "print(\"min date\", min(df.index))\n",
+    "print(\"max date\", max(df.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"eurusd_features.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df_res = pd.read_csv(\"data/eurusd_features.csv\")\n",
+    "df_res.set_index('date', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min date 2015-12-29 00:00:00\n",
+      "max date 2016-05-31 23:45:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load kaggle reference dataset for comparison\n",
+    "df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_sample.csv')\n",
+    "#df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_01.01.2010-31.12.2016.csv')\n",
+    "\n",
+    "# Rename bid OHLC columns\n",
+    "df_kaggle.rename(columns={'Time' : 'date', 'Open' : 'open_bid', 'Close' : 'close_bid', \n",
+    "                   'High' : 'high_bid', 'Low' : 'low_bid', 'Volume' : 'volume'}, inplace=True)\n",
+    "df_kaggle['date'] = pd.to_datetime(df_kaggle['date'], infer_datetime_format=True)\n",
+    "df_kaggle.set_index('date', inplace=True)\n",
+    "df_kaggle = df_kaggle.astype(float)\n",
+    "\n",
+    "simname = \"bm_kaggle\"\n",
+    "\n",
+    "df = df_kaggle\n",
+    "print(\"min date\", min(df.index))\n",
+    "print(\"max date\", max(df.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to include seasonality as a feature\n",
+    "if simname == \"bm_kaggle\":\n",
+    "    df['hour'] = df.index.hour\n",
+    "    df['day']  = df.index.weekday\n",
+    "    df['week'] = df.index.week\n",
+    "    df['month'] = df.index.month\n",
+    "\n",
+    "    df['momentum']  = df['volume'] * (df['open_bid'] - df['close_bid'])\n",
+    "df['avg_price'] = (df['low_bid'] + df['high_bid'])/2\n",
+    "df['range']     = df['high_bid'] - df['low_bid']\n",
+    "df['ohlc_price'] = (df['low_bid'] + df['high_bid'] + df['open_bid'] + df['close_bid'])/4\n",
+    "df['oc_diff']    = df['open_bid'] - df['close_bid']\n",
+    "#df['bo_spread'] = df.ask - df.bid\n",
+    "df['period_return'] = df.close_bid / df.open_bid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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KVgq4GjZs8SoUBF7UpyOPPDKLFi3KCSeckGKxmCuuuCL33XdfVq1a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\nXocz114PPPBAFi1aNHx7vByrKhnJ8SuTQ815662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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22a03a09a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb Rows:  14880\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create ohlc prices, analyse distribution, think about feature transformation and de-trending\n",
+    "\n",
+    "fig, axarr = plt.subplots(2, 5, figsize=(30,10)) #1 row, 2 cols, x, y\n",
+    "#plt.figure(figsize=(20, 4))\n",
+    "i_row, i_col = 0,0\n",
+    "fig.suptitle(\"frequency distributions\")\n",
+    "\n",
+    "\n",
+    "sns.distplot(df.period_return-1, ax=axarr[i_row, i_col])\n",
+    "#axarr[0, 0].set_title('Axis [0,0] Subtitle')\n",
+    "\n",
+    "# i_col += 1\n",
+    "# sns.distplot(df.bo_spread, ax=axarr[i_row, i_col])\n",
+    "\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "    i_col += 1\n",
+    "    sns.distplot(df.avg_bo_spread, ax=axarr[i_row, i_col])\n",
+    "\n",
+    "    x_axis_col = \"ohlc_price\"\n",
+    "    y_axis_col = \"avg_bo_spread\"\n",
+    "    i_col += 1\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    x_axis_col = \"period_return\"\n",
+    "    y_axis_col = \"avg_bo_spread\"\n",
+    "    i_col += 1\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "\n",
+    "    i_row, i_col = 1, 0 # move down one row\n",
+    "\n",
+    "    x_axis_col = \"hour\"\n",
+    "    y_axis_col = \"avg_bo_spread\"\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "\n",
+    "    x_axis_col = \"hour\"\n",
+    "    y_axis_col = \"nb_ticks\"\n",
+    "    i_col += 1\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "    x_axis_col = \"day\"\n",
+    "    y_axis_col = \"nb_ticks\"\n",
+    "    i_col += 1\n",
+    "    norm = colors.Normalize(df[x_axis_col].values.min(), df[x_axis_col].values.max())\n",
+    "    color = cm.viridis(norm(df[x_axis_col].values))\n",
+    "    axarr[i_row, i_col].scatter(df[x_axis_col].values, df[y_axis_col].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    axarr[i_row, i_col].set_xlabel(x_axis_col)\n",
+    "\n",
+    "\n",
+    "#plt.tight_layout() # reduce overlap\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Nb Rows: \", df.high_bid.count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-95-d9adaf9b6402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# all at once\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"bo_spread\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, size, aspect, dropna, plot_kws, diag_kws, grid_kws)\u001b[0m\n\u001b[1;32m   2071\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"scatter\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2072\u001b[0m         \u001b[0mplot_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"edgecolor\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"white\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2073\u001b[0;31m         \u001b[0mplotter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2074\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"reg\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2075\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mregression\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mregplot\u001b[0m  \u001b[0;31m# Avoid circular import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_offdiag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m   1491\u001b[0m         \"\"\"\n\u001b[1;32m   1492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1493\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_lower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1494\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_upper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_lower\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m   1423\u001b[0m                 \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkw_color\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mkw_color\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1424\u001b[0m                 func(data_k[x_var], data_k[y_var], label=label_k,\n\u001b[0;32m-> 1425\u001b[0;31m                      color=color, **kwargs)\n\u001b[0m\u001b[1;32m   1426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1427\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3432\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3433\u001b[0m                          \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3434\u001b[0;31m                          edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[1;32m   3435\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3436\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4061\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ymargin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4062\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4063\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4064\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_collection\u001b[0;34m(self, collection, autolim)\u001b[0m\n\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mautolim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1762\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_datalim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_datalim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remove_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mget_datalim\u001b[0;34m(self, transData)\u001b[0m\n\u001b[1;32m    227\u001b[0m             result = mpath.get_path_collection_extents(\n\u001b[1;32m    228\u001b[0m                 \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m                 offsets, transOffset.frozen())\n\u001b[0m\u001b[1;32m    230\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transformed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36mget_path_collection_extents\u001b[0;34m(master_transform, paths, transforms, offsets, offset_transform)\u001b[0m\n\u001b[1;32m   1008\u001b[0m     return Bbox.from_extents(*_path.get_path_collection_extents(\n\u001b[1;32m   1009\u001b[0m         \u001b[0mmaster_transform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1010\u001b[0;31m         offsets, offset_transform))\n\u001b[0m\u001b[1;32m   1011\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36mvertices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_nonfinite\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vertices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvertices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \"\"\"\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# all at once\n",
+    "#sns.pairplot(df, hue=\"bo_spread\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import dill as pickle\n",
+    "with open(simname+'_eurusd_features.pkl', 'wb') as file:\n",
+    "    pickle.dump(df, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add PCA as a feature instead of for reducing the dimensionality. This improves the accuracy a bit.\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "dataset = df.copy().values.astype('float32')\n",
+    "pca_features = df.columns.tolist()\n",
+    "\n",
+    "pca = PCA(n_components=1)\n",
+    "df['pca'] = pca.fit_transform(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "hideCode": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3A2fLmjWshOEVVy4eDGYiVqU8hF/yYVxt7Xg1ShFVDCmjFbeLggQB/Tf7VQ/n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\nOedQg97MAwukkMFGheHGNF44/zh9UtVRF6mztRJH8ttmtfC5XD1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Wq9VIp9OtrxVFwfd9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\nCIIgCBtMBFyCIAiCIAgb7P8D7tjtUAeyfHoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21314e6bf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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h9I4HcSOFlFn/womQEUGGLc1zTEgrUcC1gkTDyaJX5IZh4tTRAEJTsZaPYdPI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\nJEmSJEnb7P8DWHwd8XsUmLQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21314fe7f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "norm = colors.Normalize(df['ohlc_price'].values.min(), df['ohlc_price'].values.max())\n",
+    "color = cm.viridis(norm(df['ohlc_price'].values))\n",
+    "plt.scatter(df['ohlc_price'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "plt.title('ohlc_price vs pca')\n",
+    "plt.show()\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs pca')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['ohlc_price'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs ohlc_price')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['period_return'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs period_return')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    norm = colors.Normalize(df['avg_bo_spread'].values.min(), df['avg_bo_spread'].values.max())\n",
+    "    color = cm.viridis(norm(df['avg_bo_spread'].values))\n",
+    "    plt.scatter(df['avg_bo_spread'].values, df['period_return'].shift().values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "    plt.title('avg_bo_spread vs period_return shift')\n",
+    "    plt.show()\n",
+    "    \n",
+    "  \n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "norm = colors.Normalize(df['ohlc_price'].values.min(), df['ohlc_price'].values.max())\n",
+    "color = cm.viridis(norm(df['ohlc_price'].values))\n",
+    "plt.scatter(df['ohlc_price'].shift().values, df['pca'].values, lw=0, c=color, cmap=pylab.cm.cool, alpha=0.3, s=1)\n",
+    "plt.title('ohlc_price - 15min future vs pca')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this creates a training dataset for the model\n",
+    "def create_dataset(dataset, look_back_rows=20):\n",
+    "    dataX, dataY = [], [] # for training\n",
+    "    # it creates for each row a 20 row lookback dataset\n",
+    "    # this expands the data by 20!\n",
+    "    for i in range(len(dataset)-look_back_rows-1): # \n",
+    "        a = dataset[i:(i+look_back_rows)] # from example 1 to 21\n",
+    "        dataX.append(a)\n",
+    "        dataY.append(dataset[i + look_back_rows]) #get example 1+20, so the next point that is to be forecasted\n",
+    "    return np.array(dataX), np.array(dataY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ubi6PPvooq1aton79+hw5cgSr1corr7yCv78/CxYsYOfOnVgsFh555BHuuOMO\nBg8eTOPGjTl8+DCpqam89tpr1KxZs8jj/fDDD6xfv56MjAwmT55My5Yt6dKlC5s3b2bnzp3MmjUL\nb29vTCYTrVu3vl5lELn+DAWH2QBYL/8fvB1x3o3r03beGI6t/j/ObPoNgF1h82k0sh/BT4dy9rc/\nSNyxH0t26b/glBvGwgcGFKinHXG/T3kT0+zltJs7mobD+nL47c/YNf5V2/qkPQdJijhMlZAWxHz1\nDxnv/7ei6mOxlC6mEJc2xJIj/iB570F8O7Ti1Nc/lz5PwGAs/Nm4/EuyvXGXu7Qhdm7PQZIjDuHX\noSUn/7uhNGmW3We9iLrkv9ZFxxRW1wLndJmgkQM59/sBzm6PwKdt05JzvIzBjnvPnhjP4AY0mxXO\nyc/Wc/bXXRhcXWgyPYw/XlzI2V934dWsEc3nTCQlMirfHxeuiKGIQUuFfCE2uLnjN/IFTH5ViZ/1\nTL51iYte4OzSl6gSNodKDzxG8pqlV5dXwaOXmKex1XCw5GDZ9wF418kXlvvLREw3zcI8eAtcOI3l\n2E8Ya4Rcg7TsuE/tibl8t2YTFWpWI+dCOr89OYkKNQNovXgmaSdOkXrwz6tKWcqP69bg+uSTT/D1\n9WX+/PmkpqbSt29fXF1duf/++5k+fTorV65kyZIldO3alZiYGFatWkVmZiYPPfQQXbrkDXVo2bIl\nkyZN4pVXXuHrr7/m8ccfL/J4NWvWZPr06Rw+fJjx48fz+eef29ZNmzaN119/nfr16/PCCy84/NxF\nrreGTzxA1W7tADBXrEBK1AnbOjd/X7KSU8nNyMy3TUZcIj7Ng4qMq96jE03Dh3Jg3ruc+r9f84IM\nBnLTM9j+5Azbdl1XzyftryFg/xSNnniAqt3aAuBS0YPz0RcnCnAvqp6nE/BpHlhoXJWOLUmJOk5m\nwjly0zM5+X+/EtC9A2ZPD+o+2IPod7+8uCMDWK/gL/RlXcbpM3g3vTgczc3fl+zzKVguqaM9MZcz\ne3pQ8/7bOfb+2osLDYZS93IEPv4g/t1uyNtnxQqkRl285m7+vmQXcc0rNQsqMe7yfGs/0JMj731x\nSb72X/Py8KxnxiVQqdll1zE5Nd91LC4mIy4hX0+am79viY2TgNu7kZWUjP9NIZgquOPm70uHD+ax\n/eFxJeYLefee16X3XhW/Qu/P4mL8b+1Cw7GPE/XyMuK//x8AFRvUweTuxtlfdwGQsv8QaUdO4NW0\nEZnxW+y0UauzAAAgAElEQVTKrSg5CadxDbo4LNjk609uajLWzPzvA5r8quEf/jLZsUeInzYSa3Ze\nvu6tOpJ9PIrcpASsmemkbf4/PEK6X1VOhUqJwVC9/cXfPWtgTT8LOWm2RcZmg8DFA/PgLRhMLnnD\nCwdvIWdtHzCayd04GTKS8mLbj8F67uobLplxCXhfcg+6+he85vbEXC4rIS/P03/9wSc99jTJEZF4\nN22oBte/yHV7hys6Opr27fMeME9PTwIDAzl+/DgdO3YEoG3bthw5coRDhw6xf/9+Bg8ezLBhw8jJ\nySE2NhaApk3z/koVEBBAZmbRNzdgO1bDhg05c+ZMvnUJCQnUr1/fdlyRf5rDSz5lc+hENodOZMuj\nU/Bp3hCP2gEA1Ln/NuI37iywTcLWiCLjArp3oMnYIez4z+yLX8AArFZueDUc7yYN8uJuDcGSk3td\nZim8ng4t+dQ2gcXmR6dQOV+dbiXul10FtjmzdW+RcTV6hNDw8fsBMLqYqd6jI4k795OTlk69B3sQ\n0D3v88s7uC4+zQKJ/zXiepzmdXV2+x4qNW9IhVp59anRpycJG3eUOuZyOWkZ1Lq/F/435/3F27NR\nfbybBHF2a+lmgYt+e41tAovtQydT6ZJrWatvj0KfocRtEXbF5c83ndoP9KLqLR0A8GpUj0pNg0jY\nsqfY7f5WHp71xG1/Xce/jlezT0/O/G+H3TFnNu6g+t235L375OlBtR5dOFPCfbDprsfZPngc2x8e\nR+TsxaTHnra7sQWQtH0P3s0aUaFW3sx4Nfr0JPGynIuLqXJzJ4JGDyNi9HRbYwsgPeYU5ooeeDcP\nBsC9ZjU86tUi9fDVf/HOiNiGW8PmmANqA+DZo2/ecMFLGCt6U23qEtK2/0zia5NtjS0Aj4634f3A\nsLxfzC54dLqNjH3F379XwnL0RwzVO4BP3h+kTK2GYYn+Ol9Mzkc3kfN+e3JWdCJ7bV/ISSdnRSe4\ncBpTq2GYOk/+K+mqmFo8giXyk6vO6+z23/Nfz/t6kvC/yz+TSo65XMapeFL+iCbgzpsBcKlciUot\ngkn5I+qqcy4TLLll+6eMuG49XIGBgezcuZMePXqQmprKoUOHqFWrFvv27SMgIIDdu3cTFBREgwYN\nCAkJYcaMGVgsFt58801q165d6uNFRERw9913c/DgQWrUqJFvXbVq1YiOjiYwMJC9e/dSqVKla3Wa\nImVOVtJ59k5/izYvPYvRxUxaTBwRU98EwLtJA1pMHs7m0InFxjV6qj8Gg4EWk4fb9pu05xAH5r7L\nnucX0WLScAwuZjITktg9rvD3VP4pspLOs2f6EtrNyZuh7UJMHHteyHs3o1KT+rSYPJxNoc8VG3fg\nlZW0eO4xun0yB6vVStyGXRxZ9S1YrewMW0CzcY/Q6IkHsOTk8tvEhbaphf9JspPOEznzDZrPGovR\nxUx6bBwHpi/Eq3EgjSc+yY4h44qMKZbFQsT4uTQaM5T6w/phzc1l3/MvX1UNs5LOs3/GYlq9NAaD\n2Ux67Gn2Tn0DyHuGmk56gq2DwouNKzpfK7+Pm0fjsY8S9PhDWHJz2TPptSvKt6w+69lJ5zkw401a\nzArLu44xceyfvgivxg1o8twItj88rsgYyJtAo0LNADqsmI/RxUzs599z7rcDpa5PaWSfS+bgrEU0\nnTkOg4uZjNjT/DHjdTwbBxI8YSS7HgkrMgag/pN5MywGTxhp22dyxB9EvbyU/c/NIfDZxzC6umDN\nyeXQ3LfIiI276pwt55NIXDyDKmNewmA2k3M6lsQ3puLaoAm+T0zidPggPHvej6lKNTza34xH+5tt\n28bPeIqkFa/iO3wCAfNXgdVK+o5fSFn/8VXnVUD6GXL+70nMd6/EYHLBeu4IOd8Ox1CtDaaeb+Y1\nrIqRu20+5juXYR6S19DJ3TILa9zuq04rO+k8f7z4Bs1eHGu7npF/fSYFTxjBzkfGFhlTkn0T59Iw\nbDg17usFRgNH31lDSmT0Vecs5YfBarVar8eBsrKyeP755zl+/DiZmZkMHjyYtWvX4u3tTXJyMhUq\nVGDu3Ln4+Pjw0ksvsXfvXtLS0rjtttsYNWoUgwcPZurUqQQGBrJq1SoSEhL4z3/+U+ixFi5cyIED\nB7hw4QJZWVlMnTqVxo0b297hioiIYNq0aXh6elKxYkWaNGlS5L4ulcvKa12Wa8pEaJnPEZTntWYi\nlPXtC079XNbcsWNVuann1zcUnPK7LOm98yOgfHwm/dTpAWenUaLuWz7luw6FzJJWxvTc/km5edZ/\n7Pigs9Mo0a1b1/BLl77OTqNEN21ey/F+HZydRrHqfLIdgKwFFZ2cSfFcwy6wofP9zk6jRDf/+pmz\nU7Bb5u6Ozk6hWG5ttzo7BeA69nC5uroyZ07+f5Bw7dq1jBkzhsDAwHzLJ06cWGD7FSsuTus6YEDx\n/8MpqvG0efNmIO9dsM8+Kz83s4iIiIiIlE/lelr4UaNGkZycnG+Zp6cnixcvdlJGIiIiIiL/DoYS\nZo2VPE5tcF3aa3Ulysq/qyUiIiIiIlKY6zZLoYiIiIiIyL9NuR5SKCIiIiIiTlKGpl4vy9TDJSIi\nIiIi4iBqcImIiIiIiDiIhhSKiIiIiEjpaUihXdTDJSIiIiIi4iBqcImIiIiIiDiIhhSKiIiIiEip\nGaz6h4/toR4uERERERERB1GDS0RERERExEE0pFBEREREREpPsxTaRT1cIiIiIiIiDqIGl4iIiIiI\niIMYrFar1dlJiIiIiIhI+ZK9uZmzUyiWS5f9zk4B0DtcpZLLSmenUCwToWU+R1Ce15qJUL7r0M/Z\naZSo5/ZPyk09v+3Q39lpFOv27R8D5eMz6fuQh5ydRol6bFvNjx0fdHYaJbp165py86yXl3r+0qWv\ns9Mo0U2b13K8Xwdnp1GsOp9sByBrQUUnZ1I817AL5ebeLDcsmhbeHhpSKCIiIiIi4iBqcImIiIiI\niDiIhhSKiIiIiEjpaUihXdTDJSIiIiIi4iBqcImIiIiIiDiIhhSKiIiIiEipGSy5zk6hXFAPl4iI\niIiIiIOowSUiIiIiIuIgGlIoIiIiIiKlp1kK7aIeLhEREREREQdRg0tERERERMRBNKRQRERERERK\nT0MK7aIeLhEREREREQdRg0tERERERMRBNKRQRERERERKT0MK7aIGl5NYrVYmTVxHUEN/hj7W2dnp\nFEl5XlvOyrNKlzY0HDkAo6sLKVHH2T/zLXIvpJc6zq2qHyHvzGRL6Hiyk1MA8L+xLc1feIr0uARb\n3I7HXyA3LcOh5+TMa+7fpQ2NRva31WnvzCWF1rOkOPeqfnR8ZwabQ8Nt9XTxrkiTsY/iWb8mRjdX\n/nz3C06u/5/Dz+l61rNKlzYEjRiI0dWF1Khj7H+x6Pux0DijgeBnh+AX0gqDycSxlV8R8/n3AHjU\nDqDp5BG4VPIiNy2DfdMWkXbsJAA+rZvQ8D+hmNxcyUlNY//0N0k/GW87nl/ntgSOHIjRJe94kS8u\nJjctf15FxhiNNHpmCL5/5XT8o3XE/pVThdoBNJ00EpdKXuSkZXBg+kJbTi1mh+EZVI/c9LznJWnX\nPg6/9j7u1f1pPP5x3KtXITctg2Mr19ld27L4rDuqtn+rftct+N/cgYixc2zLGjzRn2q3dSY3PZPk\nvQc5/Nr7WLKy7aojgG+ndtR/MhSjqwsXoo5xcPYbBXIuKsbo6kpQ2HC8mgRhMBo4v/8wUQuWYsnK\nsm3rXr0qbd+ZR8To6aT+EW13XsVxb9MFnwEjMbi4kn08isS3ZmJNv5AvxuPG2/G+ZzBYrVgzM0h6\nbwFZf0ZiqFARvycnY65ZD4PBQOov35Cy7oNrktflDPV7Yeo6HYPJFeuZfeR8NxKyUgqPDboL8+1L\nyV5U/a+TrIzptlcx+rfEmp2GZf8KLL+9VarjO+NZ92ndhKBRgzD+9flzYMYbZJyMp93bMzG5u9mO\n61GnRqnORcoHpw0pXLt2LfPnz8+3bPTo0WRd8mF0uS5duti9/+7du5OZmZlv2caNG/nkk08KxD70\n0EPExMTYve+rFR19hqFDVvDt+v3X7ZhXQnleW87K08XHi+bPj2DPhJfZ/OBo0mPjaPTUwFLHVb+z\nGx3enop7Vd9821VqGczRlV+xdVC47cfRjS1nXvO8Oj3JbxNe4X8PjiEtNp7gpwaUOq7GnV0JKaSe\nLaaMICP+LL8OnsiOUS/SJGwIbpfFXGvXs54uPl40mzySiIkL+PWhZ0mLjafhyMLvx6LiavXpgUft\nALYMDGPboxOp0/9OvJsGAtB82tPEfPYdW/qPIXrpalq9FAaAW1VfWs0dyx9zl7N10Hjift5G4/HD\nLjmeN00nj2TvxPls7fcM6SfjCHoq9LKcio6p2ec2KtQOYFvoGHYMnUDtfr3xbhoEQLOpzxCz9ju2\nDhjNkWWf0GL2WNs+KzVvxK4RU9j+8Di2PzyOw6+9D0DT50eRvP8QW/uPZveoadQddK9dtS2Lz7oj\na2v29iR4/HCCw4ZiwHDxHHrfTJUu7djx6AS2PzyOzIQkGjzRv8RcL80neNIoDkyax44B/yH9ZBz1\nRwy2O6bOkPsxmEzsGjKGnQ+PweTmSp2H+9q2Nbi60HjKsxjN1+7v3kYvH/xGPE/CyxM4NfpBcuJi\n8Rn4VL4Yc/U6VB70NPGznuZ0+CCS175DlbC8RqpPvyfJORvP6bEDOP3cI3j16ItrwxbXLD+bClUw\n376EnHUDyX63Ddbko5i6Ti881icQc7dZYLj4ddV08xzIukD2e+3I+ehmjPV6Ymhwu92Hd8az7ubv\nS8s54zg4bxnbB4/jzM/baDxuOAC7Hp9se/7/XPoJGafikX+eMvUO1yuvvIKrq6vD9t+tWzf69evn\nsP3ba9XKnfTp25rb72jm7FSKpTyvLWfl6RfSiuQD0aSdOA3Aic++J+D2G0sV51alMlVvas/u0S8V\n2M6nZSN8b2hOx/dn0/7tqVRu08SBZ5PHmde8SkjLAnWqXkg9i4v7u547L6uni3dF/Dq0JGrppwBk\nxp9ly9DnyU5OdeQpXdd6+oW0IjnyYl1i1n5HwO1dSxVX9aYOxH61AWuuhZyUC5z+/leq394NN//K\nVKxXg9Pf/wpA4pbfMbm74RVcn2rdO5L46++kHDwCQOznP3Dwlfdsx/MNacn5yGjS/zpe7NrvCOiV\nP6/iYvxvCuHUf3+25RT3w2YCbu+Km78vFevVIO77zRdzqpCXk3v1qpg8KtA4/HE6fDifJpNHYvb2\nBMCrcQNOfb0BgNy0DJJ2ldwYLqvPuqNqC1Dt1k5kJSZxeOGKfPvzahzImY3byUlNA+DMhm1U7d7R\nrnwBKndoTUpkFOkxpwA4+fm3VOvZ1e6Y5D0HOP7+GrBawWIh9dAR3AL8bds2HDOc09/8ZOs9vBbc\nW4WQFX2AnNMnAEj5/jMq3pi/IWLNySZxyYtYziUCkPVnJCYfPzCZSXpvAedWvA6AyacKBhdXLGnX\n/rPHWPdWrKd3wbm8Xr3cPUsxNinku5m5AuY7l5Pzy4R8iw3V2mA5sAqsFrBkYznyLcaGfew+vjOe\n9ardO5Kw5beLnz9ffM+hV9/Nf7renjQeP5z90xbafS5Sfjh1SOGePXsYOnQoZ8+eZcCAASxZsoT1\n69dz+vRpJkyYgNlspmbNmsTGxrJixQqysrIICwvj5MmT+Pj48Prrr+Pi4lLk/qdMmUJsbCx+fn7M\nmTOHb775hj///JOxY8fyyiuv8L///Y+AgACSkpKu41nD5Cl3ALB165HretzSUp7XlrPydK/mR0Z8\nou33zPhEXDw9MFWskH94WzFxmQlJ7AlfUOj+s5NTObV+I/EbduDTKpjW88exJXQ8mfFnHXZOzrzm\nl9cpw856ZlxWz9/DXy6wb49aAWQmJlEvtDf+nVpjdDVz5MP/knb8lEPP6XrW072aH5lx9t2PRcW5\nV/Mj87J71TOoDu7VqpB5JinvS+5fMs6cxb2qLx61q5ObkUGLmc/gUacGGXEJHHrl/YvHq1qFjEuG\nymXGJ2L29MDkUcE21Ki4GPeqfmTEXZ5TXdyq+hXIKTP+LG5V/TCYTZzdsZeD85aSlXSeRqMfoemk\nEUSEz+P8/sNU730LR5atxsXHG7/ObeyqbVl81h1VW8A2lKt675vzHfP8/sPUHtCbmDXfkn0+lYA7\nb8LNr3KxeV7KraofmfGX5HMmEbNnxXw5FxeTtH3PxX1V86dmv7s4NGcxAAF334bBbOb0Vz9Qd8gD\ndudUErNfNXISL/aO5CbGY/TwxFChom1YYe6ZU+Seufh5UvnhZ0nfuRFyc/IWWHLxGzUNj5DupO3Y\nQM7JY9csPxvvWlhTLhlVlBKLwa0SuHrlG1Zo6rEQS8Q7WM/sy7e59dQOjE0HkHtyC5jcMDa8Dyz2\nDxV1xrPuUacGlvRMms949uLnz6vv5cur7uB78xplf/xp97mUCZZcZ2dQLji1h8tsNrN8+XIWLVrE\n++9f/B/f3LlzefLJJ1mxYgVt27a1LU9LS2P06NGsWrWK1NRUIiMji93/gAED+PDDD6lZsyarV6+2\nLd+7dy87duzg008/Ze7cuVy4cKGYvYiUbwajofAVuZYrirvcnvAFxG/YAcC5PQdJjjiEX4eWpc6z\nvDAYi/jYLFBP++LybWM24VGzGrmp6Wwb/gJ7Jr1O49EP4924/pWmW/YYCq+L9fK6FBdX2L1qsYCh\n8HvYarFgMJvx79ae6CWfsO3hcM7u2EerOReH9hW6z7+2tSemsOfHmlv48r/Xnd8fxd4J88hKPAcW\nC38uXY1fl7YYzGYOTF9ExXo1CflwAU0mjSBh865C93OpMvusO6i2xTn97Ubif9xCmzde4Ia3Z5J2\nNBZLdk7Juf6lqOf30pztifEMbkDrN2dy8rP1nP11F56NGlDjvp4cnle6d47sUsQzU9gXYoObO1VG\nz8YcUIvEJS/mW5e46AVihvXE6FmJSg88du3zpIj775I8ja2GgyUHy76C75Dl/jIRsGIevAXzvR9j\nOfYT5Bb9OkoBTnjWDWYTVbq1J/rtj9k+ZDxnd+6l5UvjLh7O1YWa997G0ffW2n8eUq44tYeradOm\nGAwG/P39yci4OA48OjqaNm3y/prXrl07vvrqKwAqVapErVq1AKhSpQrp6QVfBP6bi4sLrVu3BqBt\n27Zs3ryZFi3yxiIfPXqU5s2bYzQa8fT0pFGjRg45PxFnCXz8Qfy73QCAuWIFUqOO29a5+fuSnZxK\nbkb+dxwzTidQqVlQiXGXMnt6UPuBnhx574uLCw1gzbH/i015EPT4g1Tt1g7Iq2dK1AnbOjd/X7IK\nqVN6IfUsLO5SmQl5ve0xX/8CQFpMHOf2HKRSsyDO/1G2e3CLE/j4Q/h3zbsfTRUrkBpd8H60XH4/\nxiVQqXnB+9GSkUnG6QRc/XzyrcuIP0tGXP7lAO5/rctMOMu5iIO2YXSx636icdijGN3yRklkxiVQ\nqVnDYvMqLiYjLgG3KpXzrcuMTyyQ66XrfFo1xuztScL/dgJgMBjAYsVqsWB0d+XAzDdtxw8eP7yI\n2pb9Z91RtS2O2duTuO82ceyDvHy9mwWRHnO6xFz/lnH6DF5NL8mnih/Z51Py5VxSjP+tXWg49nGi\nXl5G/Pd5E99Uu+NmTB4etFkyGwDXKpVp8sKz/PnGByRu2mF3foXJSTiNa9DFYcEmX39yU5OxZuZ/\nz87kVw3/8JfJjj1C/LSRWLPz8nVv1ZHs41HkJiVgzUwnbfP/4RHS/apyKlRKDIbq7S/+7lkDa/pZ\nyEmzLTI2GwQuHpgHb8FgcskbXjh4Czlr+4DRTO7GyZCR93lpbD8G6zn7e4Wc8axnnkkiee9B2xDF\nk+t+InjMUIxurlgys/Dr1IbUw0fJOKn3t/6pnNrDZSjir5GNGjXit99+A/KGHZYUX5js7GxbD9jO\nnTtp2PDigxMUFERERAQWi4W0tDSioqKuJH2RMiv67TW2l9q3D51MpeYN8agdAECtvj2I37izwDaJ\n2yLsirtUTlo6tR/oRdVbOgDg1agelZoGkbBlT7HblTdRb6/h10ET+HXQBLYOfR6f5kG2OtXpe1uR\n9bQn7lLpJ8+QHPknNXt3A8DVtxI+LRqRfODazGDmLNFvr2br4PFsHTye7Y9NKnif/a/gF83EbXuK\njDuzcSc17+6OwWTE7OlBtR6dOfPLdjLjz5IeG0e1HnmzLPqFtMJqsZAadZz4DdvxaRWMe/W892iq\n3dKB1OjjWDKz8x2vwl/Hq9mnJ2cuy6u4mDMbd1D97lsuyakLZzbuIPPMXzndlpeT7985RR/H5OFO\nozFDbe9t1Rl0D/E/bwWLhQbD+lGrb08AKtSubmuwFqxt2X/WHVXb4ng3bkCLOeMwmEwYTEbqPdyH\n0/9n/2yfSdv34N2sERVq5c2MV6NPTxIvy7m4mCo3dyJo9DAiRk+3NbYAol97hx0DRrHrkTB2PRJG\nVkISkdNeverGFkBGxDbcGjbHHFAbAM8effOGC17CWNGbalOXkLb9ZxJfm2xrbAF4dLwN7wf+mkjG\n7IJHp9vI2Ff8fXElLEd/xFC9A/jkTXRjajUMS/TX+WJyPrqJnPfbk7OiE9lr+0JOOjkrOsGF05ha\nDcPUefJfSVfF1OIRLJEFJ0QrijOe9TO/bMenZTDu1asCUPXmkL8+f/J65nzaNOXszr2lKWOZYbBY\nyvRPWVEmp4UfO3Yszz33HO+88w5eXl6Yr2AWHxcXF1asWMGxY8eoUaMGYWFhtp6yJk2a0K1bNx54\n4AGqVq2Kn5/ftT4FkTIjK+k8+2csptVLYzCYzaTHnmbv1DcA8G7SgKaTnmDroPBi44pksfL7uHk0\nHvsoQY8/hCU3lz2TXrumL4KXNVlJ59k74y1avzQao9lMWmxcvno2n/Q4vw6aUGxccX4bv4Cm44dS\nu+9tGAxGopd/xvnIcjamvxjZSec5MGMxLWf/fZ/FsW/aIiDvS3LTSU+ydfD4YuNi1n5HhVrV6Pjh\nPIwuZmI+/4Gk3/L+wLZ38qs0mfgEDR7tiyUrm4jnXgGrldTDx/hjzjJazx2HwWwiO+VC3rp8eb1J\ni1lhGF3MpMfEsX/6IrwaN6DJcyPY/vC4ImMg76X6CjUD6LBiPkYXM7Gff8+53w4AsO/5V2gy8Unq\nPXo/lqxs9k16GaxWErf8Tsyab7jh7RlgMHIh+jiRs/OGmkUtWkHTF/5D9Ttvxpqby4GZb9LmtcnF\n1rasPuuOrG1Rzm6PwKdtM0JWzgeDkTMbt3P846+L3SZfzueSOThrEU1njsPgYiYj9jR/zHgdz8aB\nBE8Yya5HwoqMAaj/ZN6MdsETRtr2mRzxB1EvL7U7h9KynE8icfEMqox5CYPZTM7pWBLfmIprgyb4\nPjGJ0+GD8Ox5P6Yq1fBofzMe7W+2bRs/4ymSVryK7/AJBMxfBVYr6Tt+IWX9x9c+0fQz5Pzfk5jv\nXonB5IL13BFyvh2OoVobTD3fzGtYFSN323zMdy7DPCSvAZS7ZRbWuN12H94Zz3rq4aP8MXcpLefk\nff7kpFxg76SL7/F61K5O3DX6pwGkbDJYrZe83VdGrFu3jlatWlG3bl3WrFnD7t27mT17trPTIpeV\nzk6hWCZCy3yOoDyvNROhfNfB+bNvlqTn9k/KTT2/7WD/9NHOcPv2vC9BZb2eJkL5PuQhZ6dRoh7b\nVvNjxwednUaJbt26ptw86+Wlnr906VtyoJPdtHktx/t1cHYaxarzyXYAshZUdHImxXMNu1Bu7s3y\nIvfr6s5OoVim3o6ddMpeZbKHq3r16owePZoKFSpgNBqZNWtWoXERERHMmzevwPI77riDgQML/tsj\nIiIiIiJyjZShYXtlWZlscLVv3561a0ueqaVly5asWLGixDgRERERERFnKFP/8LGIiIiIiMg/SZns\n4RIRERERkTJOQwrtoh4uERERERERB1GDS0RERERExEE0pFBEREREREpPQwrtoh4uERERERERB1GD\nS0RERERExEHU4BIREREREXEQvcMlIiIiIiKlZ7E6O4NyQT1cIiIiIiIiDqIGl4iIiIiIiINoSKGI\niIiIiJSepoW3i3q4REREREREHMRgtVr1tpuIiIiIiJRK7hofZ6dQLNOD55ydAqAhhSIiIiIiciU0\npNAuanCVQi4rnZ1CsUyElvkcQXleayZC+W+7UGenUaK7dq0sN/VcV8brec+uvDqW9XqaCOXHjg86\nO40S3bp1Dd+HPOTsNErUY9vqcvOsl5frvrFLH2enUaJumz/nRP/2zk6jWLU/3gFA1oKKTs6keK5h\nF8rNvSn/LHqHS0RERERExEHUwyUiIiIiIqWnf/jYLurhEhERERERcRA1uERERERERBxEQwpFRERE\nRKT0rJql0B7q4RIREREREXEQNbhEREREREQcRA0uERERERERB9E7XCIiIiIiUnqaFt4u6uESERER\nERFxEDW4REREREREHERDCkVEREREpPQ0pNAu6uESERERERFxEDW4REREREREHERDCkVEREREpPQ0\npNAuanA5idVqZdLEdQQ19GfoY52dnU6RlOe15aw8q97Ymsaj+mF0MXM+6gQR05eScyH9iuLazXuW\nzDNJ7Jv7PgB+NzSlyTMDMJpN5GZms3/e+5zb/6fDz6msXPOqN7am6SU1+72Y2hYV1+uHxWTEJ9li\no1b8l9j1v163c4DrV0+/zm0JHDkQo4sLqVHHiHxxMblp6fbFGI00emYIviGtMJhMHP9oHbGff59v\n2+p33YL/zR2IGDvHtqzF7DA8g+qRm54BQNKufRx+7f0Sc63SpQ1BIwZidM3LY/+Lb5FbyLUtMs5o\nIPjZIfj9le+xlV8Rc1m+Ne6+hao3deD3S/Jt+VIYXkF1bfme3bWfQ6+WnC+U3WfdGdf9b7UfupMa\n997KttAwu3L9m2+ndtR7chBGVxcuRB3j0OxFBXIuKsZU0YNGE5/Co24tMBiIW/8zMSs/B8CzcRCB\nz0ywxMoAACAASURBVAzFVMEdg9HIiQ8/J/67X0qVW1Hc23ShUv+nMLi4kn38MGeXzMSafiFfjMeN\n/8/efUdHVa19HP9OS2+kh05CKAFCb6KICAhYaVJCQEAQEZXem4B0Ua+KoIgFERBBX8tFio0OCQKh\nlyAJBNJ7z5T3j9EJIW0SEibxPp+1WIuZeWbO7+wz58zs2fuc9MHx6eFgAENuNkmfrSHv+sUCNW5T\nVqFLiiP509UVkuteigZPoHpkMQqVFYa4c2j3ToDctKJrGz6FuvfH5L3v8/dK1kDV4x2UHoEY8jLR\nn9+M/tT6cmep7PemjY8nHT5byanXl5B2KX9/UWjUtHxrNre/3Ufsb8fKnV9UD1VmSuGuXbtYs2ZN\nhb3erFmzOHDgQIH74uLiWLRoUaHaNWvWsGvXrgpbdmnCw+MYPXIzP+8+/8CWWR6Ss2JZKqeViyMt\nF47j5PR3+H3AdDJvxdLk1cHlqvMb8RSurRubbivUKtosn0jY0o0cGDqHq598R6vFL1f6OlWVbW7l\n4kjrheMImf4Ovw6YTsatWJoW07bF1dnX8yEvNYM/hs0x/XvQna0H1Z4aFycC5k3g7Ow1HBv8Olm3\nY2j4SpDZNbX69cC2jjfHg6YQMnoWdQY/iVNAQwDUTg40njGWxlNHo0BR4DWdmzfi5MsLODFiOidG\nTDers6VxcaTZvAmEzX6LI89PIjMqFv8Jw8pUV7tfT+zqeHN02FSOj5pN3SF9cQrw+zuvPU1njqXJ\n1FHcExeX5v6Ejl/IseAZHAueYXZnq6ru65ba7gDOgY2pF/ysWTnvzdNo7qtcmLuK0KETyb4dTYOX\ng82uqT92KDlxCZwMfp1TL06nZr/eODYztmfAmzOI+GQbf74whbNTl+D72ihsavuUOeO9lI4uuI5f\nQMLbM4meMhBtbBQuQycWqFH71MMl6DXilr9GzKwgUnd9gvuUVQVqHJ8OxrpJq/vOUyxbd9S9N6D9\nfhh5n7bGkHID1SOLi6518UPddRko8r+uqrqthNwM8j5ri/arbijr90Lh27tcUSrzvQmgtNLQ7I1X\nUWgKjm84NW9E+43LcAlsUq7covqpMh2uB8HDw6PIDteDtnVLKP36t6J3n2aWjlIiyVmxLJXTo3ML\nki9cJ+NmDAAR3+ynVp8uZa5zaxeAx0OBROz8xXSfQatjf59XSb0cAYBdLU9yU9Irc3WAqrPN722z\nG9/sp7YZbXt3nWugPwa9noc2zKXbtuU0GtsPlIW/OFamB9Werh0DSb0YTtbNaACidu3F+4lHzK7x\neLQjd378DYNOjzYtg5j9h/HubXzM6/HO5CYkcfW9zQVez8bHE5WdLU1mjqPDl2toOm8CaieHUrO6\ndWxJysVwMv/OcWvXXtOyzK3zfLQDUT/8bsobve8IPr27AuD9+EPkxCdx5T/35vVAZWdL05lj6fTl\nagLmv4zayb7UvFB193VLbHcAK1dnGk97kavvF36sNDU6tCLt4lWyb90B4Pa3P+PZq6vZNeHvfML1\n9z8z5nCrgUKjRpeRgcJKQ+Sn20kODQMgNy6BvORUrD3dypzxXjaBncgNv4A2+iYA6ft2YvdwwY6I\nQZtL4kdL0ScnGJd//SIqFzdQGTsE1gFtsWnZmfT9lfcjtLLe4xiiT0JyOAC6Mx+jbFr4hwHUtqj7\nfoL2j1kF7lZ4tUZ/YSsY9KDPQ//Xzyj9+5UrS2W+NwEaT3uROz/9Tl5KaoHXrPN8H8I3bCP1wtVy\n5a5KDPqq/a+qqHIdrk2bNjFgwAAGDx7M6tWr0el09OzZE61WS2xsLE2bNiUpKYnc3Fz69St5B/vq\nq68YOXIkw4cPJyIiglu3bvH8888DsGfPHp577jlGjx7NmTNnHsSqmcxb0Idnngt8oMssD8lZsSyV\n08bLjezoRNPt7NhENA52qO1tza6zdneh2bRgTs1bB7qCRzCDVoeVqxM9dr9H09eHEv7Fj5W7QlSd\nbW7r5UaWGW1bUp1CrSLu+DmOTVzJoReX4NkpEN/BTzywdYAH1542nu5kx8SbbufEJqB2sENlZ2tW\njY2nG9kxCQUe++eLatS3+/jrk2/Q5+QWWKaVqxOJIWe5tGIDJ0bMQJeVTcDc0kdmbLzcyLlnWRoH\nO1RF7DfF1dl4uZETe29eVwBufbuP6598g65QXmcSQ85yYcVHHBsxA11mNs3mTSg17z9ZquK+bont\njlJJszde59r7m8mJS6SsrD3dC267uATUDvYFMpdao9PTeMEk2m1+l5RT58mMvI0hN4/oH/M7st7P\n9ERla0PauStlzngvlZsXuoQY021dQixKOwcUtvkddl3cHbJPHTbddgmeTNbJA6DToqzhjsvIqSS8\nPx/0uvvOUyyn2hjSbuXfTotCYe0MVo4F16fne+jDNmGIO1fgfsOdEJQBQ0GpBo09Sv/nUDh4lytK\nZb43az7THYVaxe3/y9/e/zi/4F0SjvxZrsyieqpSHa6IiAh2797Ntm3b2LZtGxERERw4cIB27dpx\n+vRpDh48iL+/P0ePHuXo0aN06VL4l7u7tWnThs8//5yxY8eyenX+POS8vDxWrFjBp59+yieffIKN\njU1lr5oQFqNQFD1aYrjny1RxdSigzfJXOf/WZnLik4ssyU1MZX+fVzk8ahEtF76Efd3yffhVN/fb\ntgadnshvf+Pc6i/Q52nRpmcSvuW/eD/WrsKzVgnFjNwZ9HqzahRFPHZvW98r9fw1zs5aTW5CMuj1\nXP/4a9y6tEGhLuUUZkXRH4+FlldSXVHroi8975mZa/7Oa+D6xztw79IahVpVcl6q8L5uge3ecMIw\nkk9fIPFEWOn5inKfmf9xefE7HHlyJGonB+qNer5AXZ3h/ak3ZgjnZy5Dn5t778tUWOaiOk8Kaxvc\nJi1H7V2bxA1LQaXC7bU3Sf5irWn0q/KUnlPZcizotejPfVGoTPfHbMCAOvgo6me3oY/4FXTlbL9K\nem86Nm5ArX69uLTyo/LlEv86VeqiGRcvXqRbt25oNBoA2rVrx9WrV+nVqxd//PEHt27dYvLkyfzy\nyy8olUoGDhxY4uu1a2f80tK6dWtWrcqfo5yYmIizszM1atQwPS7Ev0mj8QPw6toWALW9LWnXbpoe\ns/FwJTclHV12ToHnZEUn4NK8YaE6hwa1sKvpQcDk4QBYuzmjUClRWmu48PYW3Ns3I/q3UABSL90g\n7UoEjg3rkBEZXdmraRGNxw/A+662Tb2PttVl51C778OkXonIfx2FAoO2En9dtqCcmHicm/mbblt7\nuJKXko7+rvYqqSY7Jh5r9xoFHrt7hKEoLi2boHZyIP6g8T2qUChAbyj4hepvfuOex+MR4+eGyt6W\n9PDIErMCZMfE43zXti2QNzoeKzeXAo9lx5Y82uLSqgkaR3viDp7k78DF5oXqsa9bYrt79+5KblIK\nHo92RGVrg7WHKx2+WM2JEdNLfJ4pT3Q8jgGN8pfp7kZealrBzCXU1OjQiozrEeTGJ6HPyiZu/0Hc\nH+0MGC+W0Hjua9jVr83pl2aREx1nVqbS6OJjsG7Y3HRb5eqBLj0FQ052gTqVmxfuM9aijbpB3OKX\nMeTlYOXfArVnLVyCJxtrXNxAqUShsSLpozcrJJ9J2i0UPu3zbzvUxJCVCNpM013KZsNBY4c6+CgK\nlcY4vTD4KNpd/UCpRndgHmQbLzSkbD8FQ3L5LtRUWe9N7z6Pora3pd3Hxrazdnc1jbj+cyz615Cr\nFJqlSo1wNW3alLCwMLRaLQaDgZCQEBo0aECXLl0ICQkhKSmJRx99lPPnz3Pp0iUCA0ueAhMWZvxl\nKzQ0FH///J3Fzc2N1NRUEhONH3xnz56tvJUSwgKurN/JwWFzODhsDodfWEiNFg2xr+MFQL2BjxPz\nx8lCz4k7drbIuuSz1/jlyddMrxe58xfu7D1G2JKNGHR6AheMo0ZL45cOB99a2NevSfK58Ae3sg/Y\n5fU7TRe3OPjCQlzvarP6Ax8nuoi2jT12ttg6R7/aNH55ICgVKK01NHi+J1H7/p1XrEo4fgbn5v7Y\n1jGOitTq14u4gyFm18QdCMHn6cdQqJSoHezw6tmFuAMFn38vlZ0NjaaMNp23VXf4M8YrghXRgQn/\n6GvThSpOjJmLc3N/7P7OUbt/T2IPFl7WP3mLqos7EEqtp7vflfch4v44UXJeWxsaTx1tOm+r3vBn\niPn1WLFfaqrDvm6J7X7oqXGcCDZeJOXi8g/Jioo2u7MFkHTiNE7NGpkuZuHT7wkSDp4wu8ajexfq\njTKel6TQqPHo3oXkP43fNQKWTkdlb8vp8bMrrLMFkB12DKuGzVF71wHAoccAskMLXjxMae+E58IN\nZJ34jYT/zMWQZ+xY5F49y51XniJmVhAxs4JI37+TzKP7Kr6zBehv/ILCpwO4GC8go2r5IvrwnwrU\naL96FO3n7dFu7kzerv6gzUK7uTNkRKNq+SKqh+YZC+08UbV4Af3F7eXKUlnvzavvfMbR5183Xagn\nJz6R8wvf/fd1toTZqtQIV7169WjTpg1Dhw5Fr9fTtm1bevTogUKhwNvbm5o1a6JUKmnQoAGurq6l\nvt6ZM2cYMWIECoWCZcuWYTAYP7DUajULFixgzJgxODs7oy5taokQ1VhuUipn3thA21Wvo9CoybwV\ny+kFHwLg3LQBgfPHcnDYnBLriqPLyiF06lqaTR2OQq1Gn5fHqXkflPor/r9FblIqp97YQLtVr6PU\nqMm4Fcupu9q21fyx/PF32xZXd+XjXbSYMZLHtq80zvfff5zIb3+z5GpVmrykVC4sWUeLZVNRatRk\n3Yrh/OL3cWziS9M5L3NixPRia8B4srptLW86bF6DUqMm6tt9JJ+6UOIyE46e5taO/9LuoyWgUJIR\nHsnF5aVfQtqY40MCl09BoVaTFRXDuTeMOZya+BIwdzzHgmeUWHdr115sa3vR6cvVKDVqbn27n6RT\nF0taLAlHT3Pz6920/2gJCqWS9PBILizbYE7zVtl93RLb/X7lJadwedl7BCydjlKjISsqmstL3sWh\niR+NZr3Cny9MKbYGIPz9T/GfPp62m98Fg4H4g8eJ+vpHnFo0we3hDmRGRtFq/XLT8v5a9wVJJ07f\nV2Z9ahKJ6xfjNnkFCrUGbcwtEj9YhMa3Ka7j5hEzKwj7ngNQuXtj2/4xbNs/Znpu3NIJ6NNT7mv5\nZsuKQ7tnPOqnt6BQaTAk/4X257EovFqj6rXO2LEqge74GtR9N6Ieaez06I4uwxBTvvOhquN7U1RP\nCsM/vRBRKh1bLB2hRCqCqnxGkJwVTUUQP7YNKr3Qwp46uaXatOf3Vbw9nzlpbMeq3p4qgvil0yBL\nxyjV48d2sK/j86UXWljP419Xm329umz3A13Kd3W7B6nr4W+5OaR96YUWVGebsfOT+5Z5V9S0FKup\nGdXmvVldaD+ytnSEEqnH5ZRe9ABU66Gd3NxcxowZU+j+Bg0asHhxMX/TQQghhBBCCCEekGrd4bKy\nsmLz5rL/fQ0hhBBCCCGEeBCq1EUzhBBCCCGEEOLfpFqPcAkhhBBCCCEspOQ/iSf+JiNcQgghhBBC\nCFFJpMMlhBBCCCGEEJVEphQKIYQQQgghyk7+uJRZZIRLCCGEEEIIISqJdLiEEEIIIYQQopLIlEIh\nhBBCCCFEmRn0CktHqBZkhEsIIYQQQgghKol0uIQQQgghhBCiksiUQiGEEEIIIUTZyR8+NouMcAkh\nhBBCCCFEJZEOlxBCCCGEEEJUEoXBYJA/WSaEEEIIIYQok7x3bS0doUSa17MsHQGQc7jKRMcWS0co\nkYqgKp8RJGdFUxHE7vZDLR2jVH1Ctlab9vyp3TBLxyjRk6FfAdXjmLS3w2BLxyhVrxPb+bXzQEvH\nKFX3o99Um339l06DLB2jVI8f28EfXfpbOkapHj28i8jBHSwdo0R1t58AIPctewsnKZnV1Ixqc0wS\n/y4ypVAIIYQQQgghKol0uIQQQgghhBCiksiUQiGEEEIIIUSZGfQKS0eoFmSESwghhBBCCCEqiXS4\nhBBCCCGEEKKSyJRCIYQQQgghRNnJlEKzyAiXEEIIIYQQQlQS6XAJIYQQQgghRCWRKYVCCCGEEEKI\nsjPIlEJzyAiXEEIIIYQQQlQS6XAJIYQQQgghRCWRKYVCCCGEEEKIMpM/fGweGeESQgghhBBCiEoi\nHS4hhBBCCCGEqCQypVAIIYQQQghRdnoZuzGHtJIQQgghhBBCVBIZ4bIQg8HA3Nnf09Dfg9FjHrJ0\nnGJJzoplqZweXVrT6JUhKK3UpF2N5NzSj9BmZJldp7TW0GzGaJwDfEGpJOXcNc6v2oQ+Jw/XtgE0\nmTQchUpFXkoaF9d+QdrVyEpfJ0tuc88urWg88Z92uknYkqLbs7g6tb0tgQvG4VC/JigU3PrpINc/\n/wEAt7YBNHl9GEq1Cl1OLufXfEHK+fBKXydLtad7l9b4TxiK0kpD2rVIzi9dj66ItiytztrTjY6b\nlnI0aAZ5KWkAODX1o/GUkahsrVEoldz44v+48/OhMmd0e6gNfi8HodCoyQiP5OKb69BlZpWpxtrT\njXYbl3EieJopn2NTP/wnjUJlY41CpSRi83fE7DlY5nx3q6r7uttDbfCbMAylRkP6tQguvvlh0W1Y\nVI1SSaPXR+LasSUKlYrIr74n6tt9BZ7r89RjeHTrQNi0lab7XFo1peHE4SitrdCmZ3JhyQdk3441\nuy1dO7elwfgglFYaMq5FcHn5B4UyF1ejtLKi4dSxODZtiEKpIPX8Va699TH63Fzs6tem0YyXUdnZ\nYDAY+OvDL0k6cdrsXCWxad0Fl6ETUGisyIu8RsL6pRiyMgrU2D3cG6dngsFgwJCTTdJnb5F7/SIK\nW3vcxs9DXas+CoWC9D/+S9r3X1RIrnspGjyB6pHFKFRWGOLOod07AXLTiq5t+BTq3h+T977P3ytZ\nA1WPd1B6BGLIy0R/fjP6U+vvK899H4eUChpPGoF7J+N79MaWH7i1az8A9g1qETB7HCo7GzAYuPrB\nVhKOnaH+iGfx7pV/rLVycUJtZ8Ov3Ufd17qIqskiI1y7du1izZo1lfb6x48fZ/LkyYXuf/PNN7l9\n+3aB+8LDwwkODq60LEUJD49j9MjN/Lz7/ANdbllJzoplqZxWLo60WPASp2a+zcGBU8mKiqXRxKFl\nqvMb1Q+FSsmhYbM4NHQGSmsr/F54FrW9LW1WTebyf7ZweNhMzq/YRKvlr6PUVO5vOZbc5lYujgQu\nfImTM97hjwHTyIyKocnEIWWqa/TyILJjEjkweCaHR8yn3oAeuLTwR6FW0Xr5q5x982MODpvNtU++\no9Xilyt9nSzVnhoXR5rPf5kzs9ZyeNBksqJiaPTKsDLX+fTtSoePFmHj6VrgeS1XTiH8ox0cGz6T\nPyctp/GkEdjV8S5jRieazn2Fs7NXc3zI62RFxeA3IahMNd59HqXN+iVYe7gVeF6LZdP4a+N2QkZO\n58zkN/F/7QVsa5ct392q6r6ucXEiYN4Ezs5ew7HBr5N1O4aGrxRuw+JqavXrgW0db44HTSFk9Czq\nDH4Sp4CGAKidHGg8YyyNp45GQf7V0qw9XAlcOZ3LqzdyIng6cb8dp8n0sWa3pcbFicZzJ3Jh7mpC\nhr5K1u0YGrwcbHZN3ZEDUKhUnBw5hdARU1BZW1F3RH8A/KeOI/qnXzj5wlSuLPuAgCVTQXX/X8eU\nji64vTyf+LWzuDN5ENqYKFyGvVKgRu1TlxrDXyN22WtEzxxOyq5NuE81dlJdBo9HmxhL9LShRM95\nAcee/bHyb3HfuQqxdUfdewPa74eR92lrDCk3UD2yuOhaFz/UXZeBIr99VN1WQm4GeZ+1RftVN5T1\ne6Hw7V3uOBVxHKrTryd2dXw4MnQax16YQ70hfXEK8AOg6YwxRP3wG8eGz+T8kvUELpuEQmX8AejY\n8JkcGz6T0PFvoMvOJmzuu+VeD4vRK6r2vyrif2pK4dy5c6lZs6alY7B1Syj9+reid59mlo5SIslZ\nsSyV071TICkXrpN5MxqAyJ37qNm7S5nqkk5d5Nqmb8FgAL2B1Ms3sPH2wK6uD3npWSSEGL+oZ0Tc\nRpuRhUsL/0pdJ0tu83vbKeKb/dTsU3p73l13Yc0XXHx3CwDW7i4ordRo0zMxaHX80mciqZcjALCr\n5Ulecnqlr5Ol2tOtY0tSLoSb2ujmzn149364THXW7jXwfLQ9f05eUeA5SisN1zd+Q2LIWQByYhPJ\nTU7D2rNgp6c0rh1aknrxGlm3jMuO2rUH7yceMbvGyr0G7l07cGbKskL5/tq0g6R/8sUlkpeSWuZ8\nd6uq+7prx0BSL4aTdfOf9tlbuA1LqPF4tCN3fvwNg06PNi2DmP2H8e5tfMzr8c7kJiRx9b3NBV7P\ns3sn4o+eIu3yX8bX+24fV9751IxWNKrRoRVpF6+RdesOALe//RmvXo+YXZNy5gKRn+/4ux31pF/5\nC2tvDwAUKiVqRwcAVHa26HPzzM5VEpuWHckNv4A2+iYAaft2Yv9wwY6IQZtHwoY30ScnAJB7/SIq\nFzdQqUn67C2SN//HmMvFHYXGCn1mxR9/lPUexxB9EpKNI/e6Mx+jbDq4cKHaFnXfT9D+MavA3Qqv\n1ugvbAWDHvR56P/6GaV/v3LnqYjjkGe39tz+8XfTezR63xF8+hjfCwqVEs3f21ttb4s+J7fQazd6\nPZj4I6eJP1oxI52i6nkgUwqzs7OZPXs2t2/fJi8vjyeeeML02KZNm/jpp59Qq9W0a9eO6dOnc/Lk\nSVauXIlarcbW1pZ3330Xa2trFi5cSEREBHq9nkmTJtGxY8dilxkREcGYMWNISkpi6NChDBo0iODg\nYBYtWoSjoyPTpk3DYDDg4eHxIJqggHkL+gBw7NhfD3zZZSE5K5alctp4uZEdk2C6nR2biMbBDrW9\nbYGpRiXVxR8/m1/n7U79oX04t+xjMiPvoLazwb1jC+KPn8U5wBdH39pYu9eo1HWy5Da39XIly4z2\nLK3OoNPTavEEvB/vQPTvoaRHGEffDTodVq5OPPLlMjQujpya/V6lr5NF35ux+W2UE5uAxsEOlb1t\ngek8JdXlxCdxZuZbhV5bn5tH1Pe/mW7Xeu5xVHY2pJy7UuaMOXcvOy4BtYM9Kjtb0/Sykmpy45M4\nN3t1kfnu/PCr6XbNZ3ugsrUh9fzVMuW7N2tV3NdtPN3Jjok33c6JTUDtYFewDUuosfEsmDcnNgGH\nhvUATFMLfZ7sVmCZdnVros/KofmSSdjVrUl2TDxX3vms1Kz/sPZ0Iyf2rjxFbPeSapJOnMl/LS8P\nag1+iisrPwTg6lsf0/I/b1B78NNoajhxceFa0OnNzlYctZsX2oT8KZO6hFiUdg4obO1N0wp1cXfQ\nxd0x1dQYMYms0AOg0xrv0Otwm/gGdh27kxnyO9rbEfedqxCn2hjSbuXfTotCYe0MVo4FphWqer6H\nPmwThrhzBZ5uuBOCMmAouttHQWWN0v850Je/01oRx6Gi9in3v9+jF1dtot26+dQb2hcrV2fC5r6L\n4a7tbe9bG89H23Go32vlXgdR9T2QEa5t27ZRq1Yttm/fztq1a7G2tgbg8uXL7N69m23btrFt2zYi\nIiL47bff2L9/P3369OHLL79k6NChpKamsmPHDmrUqMGWLVtYt24dixcXM/z8t7y8PD788EO++uor\nNm7cSGJioumx9evX89RTT7F582Z69OhRqesuhMUpih5SN9z7AW9GnVOTBnT6eCERX+8h7tAptBlZ\nnJy6Bt9Rz9Flywpq9u1KQsh59HnaCotf5SiLPmwWak8z6k4vWMe+Hi9h5eSA/4v9TffnJqbyS9+J\nHBm1kJYLX8K+bvmnmVVlCmUx0z3uaUtz64pTf8SzNBw3iFNTV6HPKeMXs+K2o15ftpoS1At+jgYv\nDiZs+ooif/02W1Xd14vZfgXbsPiaorZ/oXW6h0Ktwr1re8I/2saJkTNIDD1L4IrppWf95/lmbFNz\nahwa+9Jq3VJu79xN4pGTKKw0NF08lUtvvsexfmM5/cp8/KePv6+RzbsCFX2/Xle41NoG98nLUXvX\nJmHDmwUeS3h/Ibde7IXSwRnngWPuP1fhpZeaU9lyLOi16M8VPodM98dswIA6+CjqZ7ehj/gVdOXf\nbyrkOFTUe1SvR2mlIfDNSZxb/CEHnp5AyEuLCJg9tsD2rje4Dzd37CnyXEvx7/FARriuX79O165d\nAahfvz5OTk7Ex8dz/fp1WrZsiUajAaBdu3ZcvXqV8ePHs379ekaOHImXlxeBgYFcuXKFkydPEhYW\nBoBWqyUxMRFXV9cil9mqVSusrKwA8PPz49at/F9Tbty4wfPPPw9AmzZt2Lp1a6WtuxCW4P/SQDy7\ntgWMUxjSrt00PWbt4UpuSjq67JwCz8mOScClecNi63x6diZg5mgurP6UO3uOGIsUCnRZ2ZwYv8T0\nvEe+XkPm31Or/i0avTQQz65tANDY25Eann+hAJvi2jM6HpfmfkXWuXcKJO1aJDnxyeiycri95wje\n3TugtrfFrX0zYn4PBSD18g1Sr0bg2LAOGZH/jjb1GzcIj67tAON7M/1afltae7iSV0xbOjdrWGrd\nvRQaNc0XTMDBtzbHx8wn+05cmfNmR8fhFJA/bc7aw5W81DT0dy3bnJri8gXMm4hdg9qcHDuH7Oiy\n56sO+3pOTDzOze5pn5T0Au1TUk12THyBkTRrD9cCI4pFLjMuiZSzl01TFG9//yuNp4xGaW1lVqc2\nOzoOx7u3qbtbkdu9pBqPx7vgP20c19ZuJHaf8WIo9r51UdlYk3jkJABp56+Q+ddNHAMakRN7tNRc\nJdHGR2PVMH9asMrVA116Coac7AJ1KjcvPGauJS/qL2LfmIAhz5jXpmUn8iKvoUuKx5CTRebhPdh1\n7H5fmYqUdguFT/v82w41MWQlgjbTdJey2XDQ2KEOPopCpTFOLww+inZXP1Cq0R2YB9lJxtr26ZRa\nbwAAIABJREFUUzAkXy9ThIo+DmVHJ2Dt7lLgsZzYBBz86qCysSL+0J8ApJy7Svr1m7g0b0jMrwmg\nVODZvSPHRswuU/6qxGCoOudJVWUPZITLz8+Ps2eN0xRu3rzJ2rVrAfD19SUsLAytVovBYCAkJIQG\nDRrw/fff069fPzZv3oy/vz9ff/01vr6+PPnkk2zevJmPP/6Y3r174+LiUuwyL1y4gFarJTMzk/Dw\ncOrWrVsgz6lTpwBMuYT4N7m64RsOB83mcNBsjo5agEtzf9PFAuoO6EHsgdBCz4k/FlZsnXf3DjSd\nNpKQV5fnfwEDMBho985MnJr6Guse74heq3sgVyl8kK5s+IZDQXM4FDSHw6MWUKNAOz1OzB8nCz0n\n7tjZYutq9uyI/7gBACg1anx6diIh9DwGvZ6WC16iRstGADj41sK+Xk2Sz1X+VQoflH8uYnFs+ExO\njJ6H811tVLt/zyLfmwnHw8yqu1fL5ZNR29tyopydLYDEE2dwbu5vuphFzX69iD8QUuaaojR/cyoq\ne1tOjptbrs4WVI99PeH43+3z9/Jq9etF3MEQs2viDoTg8/RjxnOfHOzw6tmFuFLaN+6PE7gENsbG\nxxMAz24dSQ+PNHsEMenEGZyaNcK2tvHKeDX79SLhnswl1bh360zDyS8SNnmxqbMFkHXrDmp7O5ya\nNwbAppYXdvVrk361bB2GomSHHcfavzlq7zoAOPTsb5wueBelvRNeizaQeeI3Et6dZ+psAdh16oHT\nwBeNN9Qa7Dr3IPtc6ftZWelv/ILCpwO4GH+QUrV8EX34TwVqtF89ivbz9mg3dyZvV3/QZqHd3Bky\nolG1fBHVQ/P+Du2JqsUL6C9uL1OGij4OxR4IpdZd71Hvng8R+3sImTejUTvY4dzCeEy3reWFff1a\npF6+AYCjX120qRnlPj6J6uOBjHANGTKEOXPmMHz4cHQ6HaNGjSIpKYnGjRvTp08fhg4dil6vp23b\ntvTo0YOwsDDmzZuHra0tSqWSxYsX4+Xlxbx58xg+fDjp6ekMGzYMZTHD+QDW1taMHTuW1NRUXn31\n1QKds5dffpnp06fz3//+l9q1az+IJhDCYnKTUjm7eD2tV0xCqVGTeSuGsEXrAHBq6kuLeWM5HDS7\nxLpGrwxBoVDQYl7+Vb6SzlzhwqpPOTP/fVrMHYtCoyYnPok/pxc+n+bfJDcplTOLN9B2pfEKbRm3\nYjiz0HhuhnPTBrSYN5ZDQXNKrLvw9hZazBlD1+0rMRgMxPx+kr+2/gwGA6HT3iJgSjAKtQp9npbT\n894nOzaxpEjVVm5SKueXfEjLFVNQqNVkRUVzdtEHgPG9GTD3JY4Nn1liXXFcAhvj2bUdGRG3ab8x\nfwr61fe/IuHYmRKeWVBeUioXl35A82XTUGrUZEXFcGHxezg28aPJ7PGEjJxebE1JnAMb4/FIezIi\nomi7Yanp/vB1X5J43Px8d6uq+3peUioXlqyjxbKpxva5FcP5xe/j2MSXpnNe5sSI6cXWgPECGra1\nvOmweQ1KjZqob/eRfOpCictMv3qDS6s+JnDldBRqFdq0DM7OXWt2W+Ylp3B52fsELJ2OQqMmOyqa\nS0v+g0MTPxrPmsDJF6YWWwPQYLzxCouNZ00wvWZK2CWurf2Y83NW4jdpDEorDQatjiur1pMdFWN2\ntuLoU5NI+HAJ7lNWoFCr0UZHkfDBIqx8m+L60lyiZw7HodcAVO5e2LXvhl37bqbnxi55haTN7+A6\ndhbea7aCwUBWyB+k7d5237kKyYpDu2c86qe3oFBpMCT/hfbnsSi8WqPqtc7YsSqB7vga1H03oh5p\n7Nzqji7DEPNnueNUxHHo1s692NXyovOWVSjUam59u5+kUxcBOD3jLZpMfcG0vS+s+Jisv7e3XV0f\nsqSz9T9BYTAYDJYOUV3o2GLpCCVSEVTlM4LkrGgqgtjdvvCln6uaPiFbq017/tSu8CWBq5InQ78C\nqscxaW+HIq4+VsX0OrGdXzsPtHSMUnU/+k212dd/6TTI0jFK9fixHfzRpX/phRb26OFdRA7uYOkY\nJaq7/QQAuW/ZWzhJyaymZlSbY1J1kb2kAs4/rEQ280uefvygVOs/fPz+++9z/PjxQvcvW7aMOnXq\nWCCREEIIIYQQQuSr1h2uiRMnMnHiREvHEEIIIYQQQogiVesOlxBCCCGEEMIyDHq5SqE5HshVCoUQ\nQgghhBDif5F0uIQQQgghhBCiksiUQiGEEEIIIUTZyZRCs8gIlxBCCCGEEEJUEulwCSGEEEIIIUQl\nkSmFQgghhBBCiDIzGGRKoTlkhEsIIYQQQgghKol0uIQQQgghhBCikkiHSwghhBBCCCEqiZzDJYQQ\nQgghhCg7vYzdmENaSQghhBBCCCEqiXS4hBBCCCGEEKKSKAwGg8HSIYQQQgghhBDVS8YcH0tHKJH9\nsjuWjgDIOVxlsrv9UEtHKFGfkK3s7TDY0jFK1evEdn5sG2TpGKV66uSWKr/NwbjddWyxdIxSqQiq\nNu2ZdaGHpWOUyDZgP1A9jkk/dxhi6Ril6n1iG78/NMDSMUrV7cjOarOv/9p5oKVjlKr70W9YVO9V\nS8co1aKI91jf5CVLxyjR+EsbANgaOMrCSUo2NOxTfmo3zNIxSvVk6FeWjiAqmEwpFEIIIYQQQvzP\n0ev1LFiwgMGDBxMcHExERESRdfPnz2fNmjXlXo50uIQQQgghhBBlZjAoqvS/0uzfv5/c3Fy2b9/O\n1KlTWbFiRaGabdu2ceXKlftqJ+lwCSGEEEIIIf7nnDx5kkceeQSAVq1ace7cuQKP//nnn5w5c4bB\ng+/vlB3pcAkhhBBCCCH+56Snp+Pg4GC6rVKp0Gq1AMTGxvLBBx+wYMGC+16OXDRDCCGEEEIIUXbV\n/A8fOzg4kJGRYbqt1+tRq43do59//pmkpCTGjRtHXFwc2dnZ+Pr60r9//zIvRzpcQgghhBBCiP85\nbdq04bfffqNv376cPn2aRo0amR4bMWIEI0aMAGDXrl1cv369XJ0tkA6XEEIIIYQQ4n9Qz549OXz4\nMEOGDMFgMLBs2TJ++OEHMjMz7/u8rbtJh0sIIYQQQghRZgZ96VcCrMqUSiWLFy8ucJ+fn1+huvKO\nbJmWc1/PFkIIIYQQQghRLOlwCSGEEEIIIUQlkQ6XEEIIIYQQQlQSOYdLCCGEEEIIUWYGQ/U+h+tB\nkREuIYQQQgghhKgk0uESQgghhBBCiEoiUwqFEEIIIYQQZaeXsRtzSIergnh0aU2jV4agtFKTdjWS\nc0s/QpuRZXad0lpDsxmjcQ7wBaWSlHPXOL9qE/qcPFzbBtBk0nAUKhV5KWlcXPsFaVcjy5XTvUtr\n/CcMRWmlIe1aJOeXrkdXRM7S6qw93ei4aSlHg2aQl5JmXLeH29B84StkxcSb6kLGLUSXmV3mnJ4P\nt6LJxMEoNWpSr90kbPHHRbanOXVtV08iJy6Jc6s+B8CtXQBNXx+KUq1Cl5PH+dWfk3z+epkzQvXZ\n7mVhMBiYO/t7Gvp7MHrMQ5W+vLtVt/Y8EJrFe1+mkJtnwL+ehkUTXXGwy//w+eG3DDZ/n2a6nZ6p\nJzZBx56NNVGr4M0NSVz+Kw9bGwXPdrdn6JOO95XnbtWlLT26tKbRhCGmY83ZpRuKPCYVV6e01hAw\nfTTOAX6gVJBy7hoXVhtz/qPW093w6taeP6euLldG14fa4Dt+OEqNmvTwCC4vW4cuM8usGqWVFf7T\nXsSxaUMUCiWpF65wdc1G9Lm5ODb1o+Hro1HZWINKyc0vvyNmz4FyZSyPB7mvuz3UBr+Xg1Bo1GSE\nR3LxzcJtWGyNUon/ayNx7dQKhUpJ5Fc/cPvbvQC4tGmG/2sjTe/Fq+98Svq1CNNrKjRqWq6ZTdR3\n+4j77ViFrY9/92b0mPE0Kis1MZdu8/2Mr8hJL/xZ12FkV9oNfxgMBhIj4vlh1lYyEtIrLEdR6j7a\nnI5T+qGyUpNwOYrf535BXkbxn8OPLR9J4tXbnNm0DwCFUsHD84fi094fgMgD5zi2ameF56z5SCAt\nXx+I0kpN8pVbHF+4CW0JOTsuGUPKtSguff5zgfvtvFzp+eU8dg9aQG7y/betZ5dWNJ74zzHxJmFL\nij52FlentrclcME4HOrXBIWCWz8d5PrnPwDgHOBLwNRgVDbWKFRKrn/+A1G7D993ZlH1Sbe0Ali5\nONJiwUucmvk2BwdOJSsqlkYTh5apzm9UPxQqJYeGzeLQ0Bkora3we+FZ1Pa2tFk1mcv/2cLhYTM5\nv2ITrZa/jlJT9r6yxsWR5vNf5systRweNJmsqBgavTKszHU+fbvS4aNF2Hi6Fniec2Bjbmz5gWPD\nZ5r+laezZeXiSMuF4zg5/R1+HzCdzFuxNHm18F/7NqfOb8RTuLZubLqtUKtos3wiYUs3cmDoHK5+\n8h2tFr9c5oz/LL86bPeyCA+PY/TIzfy8+3ylLqco1a09E1N0LHwvkTUz3Pi/D3yo7a3m3c3JBWqe\nfsyer9/25uu3vdmy2gt3FxWzxtbAzUXF6k3J2Nko2fUfbzav8OLQn9kcCCn8oV4e1aUtjcea8Zya\n9TYHB00hMyqWxq8UzllSnd+ofijUKg4HzeTwsBmorK3wHfmc8XlO9gTMGkPTaS+AonwndmtcnGgy\ndyLn56zmxNDXyL4dg++E4WbX1HthAAqVitARUwkZMQWltTV1Rxj/gGazN6dzY+N2Ql+YxtkpS/F7\n7QVsa/uUK2dZPch9XePiRNO5r3B29mqOD3mdrKgY/CYEmV1T67me2Nbx4UTQZEJHz6LO4CdxDGiI\nyt6OFsunc+39zZwInsrl1R/RbOkUFH+/F52aN6LdxuU4t2xSoetj5+rAc6uD2D7+E97vvpSkyHh6\nzHqmUJ1P8zo8NLY7n/Rfy7pey0m8EcdjU5+s0Cz3sqnhwGPLRrL3tQ1s67OQ1JvxdJrar8haF19v\nnv5sMr692xW4v9GznXBp4MWOZxbzzXNLqNm+Eb5PtKnQnNY1HOm4ZAwHp3zAT8/MIf1WHK0mDSqy\n1qmBD903zqBur/aFHqv/9EM8/tls7LxqVEguKxdHAhe+xMkZ7/DHgGlkRsXQZOKQMtU1enkQ2TGJ\nHBg8k8Mj5lNvQA9cWhg7r21XTeLKhp0cCppDyGuraDp5OHZ1vCsku6jaqmWHKzg4mPDwcEvHMHHv\nFEjKhetk3owGIHLnPmr27lKmuqRTF7m26VswGEBvIPXyDWy8PbCr60NeehYJIcYPxYyI22gzskw7\nb1m4dWxJyoVw0/Jv7tyHd++Hy1Rn7V4Dz0fb8+fkFYWe5xLYCNd2zen0+XLaf7SIGq2bljkjgEfn\nFiRfuE7GzRgAIr7ZT60+hduztDq3dgF4PBRIxM5fTPcZtDr293mV1MvGX0DtanmSm1K+X8Sqy3Yv\ni61bQunXvxW9+zSr1OUUpbq159HT2TTzt6JeTQ0Ag3o7sPtAJgaDocj6z75NxdVZycAnHAC4GJ7L\nk93sUKkUaDQKHmlrw76jmeXOc7fq0pbuHQMLHWt8ijgmlVSXeOoS4XfnvHIDWx93ALx7dCYnPpnL\n/9lS5mz/qNGhJWkXr5F16w4At3ftwavXI2bXJJ++QMRn3/ydT0/6levYeLujtNJwY9MOkkLDAMiJ\nSyQvORVrT7dyZy2LB7mvu3ZoSerFa2TdMm6/qF178H7iEbNrPB7twJ2ffsOg06NNyyB232G8n+iK\nXR0ftBmZJIWeBSAz4ja6jCycmxt/ZKszqC/XN2wl9fy1Cl0fv65NiAqLJPFGHAChXx6ixbPtCtXd\nOXeT/3RbTE5aNmprNY5eLmQlVcw+Xpw6XQKIPRtBSkQsABe2/UHDpzsWWds8qBuXdh3h+s+hBe5X\nKJWoba1RWalRWmlQalTocrUVmtO7czMSzv1FeqTx8/va179Sr2+nImv9hzzO9e8OErk3pMD9th4u\n1H6sDX+88naF5br3mBjxzX5qFvH9o6S6C2u+4OK7xmOOtbsLSis12vRMlFYarn68i4QT5wDIjk0k\nNzkN23t+vK5uDHpFlf5XVVTLDldVY+PlRnZMgul2dmwiGgc71Pa2ZtfFHz9LZqRxx7Xxdqf+0D5E\n/3KMzMg7qO1scO/YAjAORzv61sbavey/5th4uZEdm7/8nNgENA52qIrKWUxdTnwSZ2a+RcZfUYVe\nPy8lnZvf7OHYyNlc/WArLVdNxbocBxIbLzeyoxNNt0tsz2LqrN1daDYtmFPz1oFOX+B5Bq0OK1cn\neux+j6avDyX8ix/LnNG0/Gqw3cti3oI+PPNcYKUuozjVrT1j4nV4u6lMt73cVKRnGsjIKtzhSkrV\n8cX/pTF9TP7yWjSy5qffM8nTGsjM0vPL0Szik/SFnlse1aUt7z3WZJt5TLq7LuF4GJmRd0w56w3p\nQ/QvxwG4uWs/4Rt3os/OLXO2/GW7k3PXNOmcuATUDvao7GzNqkk6cYasm8Z81t4e1H7+KeJ+PYo+\nN4/oH/N/DPJ5ticqWxtSz10pd9ayeJD7uo2XGzl3f6YU2YbF11jf077ZsQlYe7qRGXkbla0Nrh1a\nAuDY1A973zpYu7sAcH7hOyQc+bPC18fZpwapt5NMt1PvJGPjZIu1g02hWr1WT5NegUw5toR6Hf04\ntaPipjUWxd6nBul3fS6mRydh7WiLxr5wtkNLtnH1++OF7r/87RFyUjMJ/mMlIw6uIjUyjojfwio0\np523K5l35cyMScLK0Q51ETlPLv+SGz8eLXR/Vlwyh6a8T+r12xWWy9bLlSwzjp2l1Rl0elotnkDX\n7StJOHmR9Ijb6HPzuPl/v5ueU6dfd9R2NiSdu1ph+UXVVaXO4Zo4cSIjRoygQ4cOnD17lvfeew8n\nJydu3bqFTqdj1KhR9O3b11T/3nvv4e7uztChQwkPD2fRokVs3ryZp59+mnbt2nH58mV8fX1xc3Mj\nNDQUKysrPvroI7Kzs5k7dy5JScYD5rx582jcuHFxsUpXzFQVwz1f9M2pc2rSgDarpxDx9R7iDp0C\n4OTUNTSaMJjGrwWReOoSCSHn0eeV/dcmhbKYnv49Oc2tu9eZmW+Z/p985jIpYVdw6xDI7R9/L0tM\nFGa2Z3F1KKDN8lc5/9ZmcuKTiyzJTUxlf59XcWpSn04fzuHw9QVk/P3lsgxBzcpp6e1ebVSz9tQX\nPZCFqoifsXbuzaBbB1tqeeUfcqeMcuHtz5IZMiUad1cVnVrZcOZSTrnzFFBN2lKhLOY3v0LHpNLr\nnJo0oPWqqUTu2EvcoQr8kl1cG+n1ZapxaOxL8+UziNq5m4QjJwvU1Q3uR61BTxI2ZQn63PJ3Dqus\nYrZfgTYsoabIY71ejy4zi7MzV+L70jD8JgaTfPoCSSfPVfpxsrjPSH0xn5GX9oZxaW8YbYY8RPDm\nCfyn6+JiR8LvP5sZbV2Ktq88RXZiGp8/PB21tYYnPphA4KgehH26v6JiFtuGZclZKYprv3u3rRl1\npxesQ7X8E9qumoz/i/25+lH+eXB+I5+m/tDenHh1ZYHzTcW/V5XqcA0aNIhvv/2WDh06sGvXLrp2\n7UpkZCRr1qwhPT2d/v3706lT0UPOd8vIyOCpp55i4cKF9O7dm9mzZzN58mSGDx/OtWvX+PHHH+nU\nqRPDhg3jxo0bzJ49m61bt5Ypq/9LA/Hs2hYAtb0taddumh6z9nAlNyUdXXbBL0/ZMQm4NG9YbJ1P\nz84EzBzNhdWfcmfPEWORQoEuK5sT45eYnvfI12vIvGVe58Bv3CA8urYz5Uy/ln9iu7WHK3lF5YyO\nx7lZw1Lr7qZ2sKPOwF789dl3+XcqwKA174Ov0fgBeBXTnjbFtGdWdMH2/KfOoUEt7Gp6EDDZeA6F\ntZszCpUSpbWGC29vwb19M6J/M06hSL10g7QrETg2rGNWh6u6bPfqojq3p4+7inNX8rPFJuhwclBi\na1P4g3jv4UxmjHEpcF9Gpp5JI5xxdjSOkn26K5U6PuU/JFeXtmw4blCZc2YVcUy6u867Z2cCZozh\n4ppPubOnYk9Az4mJx6lZ/nRJKw838lLT0N+VsbQazx5d8J82lqtvbSR23yFTnUKjpsm8V7GvX5tT\n42aTHR1XodmriuzoOJwC8tvH2sO1UBuWVJMdE4/VXSOo1h6uxhFPhQJdZjanXlloeqzj1ndM0xIr\n0mNT+tK4h3FU19rRhphL+aMqjt7OZCVnkJdVsLPsWs8dBw8nIkONF2U69fVRnlo2GBtnW7KSK25q\nYbtXn6Z+d+Mon5WDDQlX8meg2Hu5kJ2cgTbL/I68b8/WHHpzG/o8Hbl5Oq58dxTfJ9rcd4erxYTn\nqNWtNQAaBxuSr+bntPWsQU5KOroy5KwojV4aiGdX4zlqGns7UsPzvycV9/0jOzoel+Z+Rda5dwok\n7VokOfHJ6LJyuL3nCN7dOwCg1KgJXDQexwa1ODJqIVl34qnu5A8fm6dKTSl85JFHOHv2LMnJyYSG\nhnL16lXatzeeJOng4ICfnx83b94s5VWMmjUzzkt3cnLCz8/P9P+cnByuXLnCzp07CQ4OZv78+aSk\npJQ569UN33A4aDaHg2ZzdNQCXJr7m058rDugB7EHQgs9J/5YWLF13t070HTaSEJeXZ7/xQbAYKDd\nOzNxauprrHu8I3qtzuwrgoV/tMN0AYsTo+fhfNfya/fvWWTOhONhZtXdTZuZRZ2BT+D5mPGg4tio\nPs4BDYk/esasnFfW7+TgsDkcHDaHwy8spEaLhtjX8QKg3sDHifnjZKHnxB07W2Rd8tlr/PLka6bX\ni9z5C3f2HiNsyUYMOj2BC8ZRo2UjABx8a2FfvybJ58w7J7C6bPfqojq3Z+dWNoRdySXitvHXyW/2\npNOtQ+HpMKnpeiLvaGnZxLrA/Tv2pLNuayoACck6du3LoM8jduXOU13a8tpHOzgyfBZHhs/i2Oj5\nuDRvmL/8/kXnTDgeVmydV/eONJ36AqGvLavwzhZA4onTODVrZLqYRc3nehF/MMTsGo/HOtFw8hjC\nJi0p0NkCaLZ0Gmp7W/58ac6/trMFkHjiDM7N/bGtbdx+Nfv1Iv7AvW1YfE38gRBqPtUdhUqJ2sEO\nr55diD9wAgwGWq6dg2MT42e8R/fOGLS6AlcprCi/rf0v6/uuZH3flWx87i1qt66Pa30PANoFPcyl\nvWcLPcfB05mB77+AXQ17AAKfa0/s5TsV2tkCCH3vB77pt5Rv+i1l1+CVeLX0xbmeJwABQ7py41fz\nPof/EXchEr+/L6ShVCup91hLYk7/dd85z677jp+fX8jPzy9k7/CluAf64lDX+PntP+gxon47dd/L\nKI8rG77hUNAcDgXN4fCoBdQocEws4ftHMXU1e3bEf9wAwNjB8unZiYRQ4zmvbVa+jsbeliOjF/0r\nOlvCfFVqhEupVNK7d28WLVpEjx49TFMBe/bsSXp6OleuXKF27dqmemtra+LijB9S588XvNJSsdPN\nAF9fX5555hmefvppEhIS2LFjx33lzk1K5ezi9bReMQmlRk3mrRjCFq0DwKmpLy3mjeVw0OwS6xq9\nMgSFQkGLeWNNr5t05goXVn3Kmfnv02LuWBQaNTnxSfw5/a0ic5iT8/ySD2m5YgoKtZqsqGjOLvrA\nlDNg7kscGz6zxLpi6Q2cnr6aJtNG0XDc8+h1Os7Mfdd0yfiy5jzzxgbarnodhUZN5q1YTi/4EADn\npg0InD+Wg8PmlFhXHF1WDqFT19Js6nAUajX6vDxOzfuA7NjEEp9XXM7qsN2ri+rWnq4uKt541ZXp\nqxPIyzNQ21vN0tddOX8tlzc+SOTrt40fxJF38vCooUKjLnhMGjPAibnvJDLgtTsYgPGDnWjub13E\nksquurRlblIqZ5esp9WKySjVajKjYgock5rPHceR4bNKrGs0wZiz+dxxd+W8zMXVn5Yr073yklK5\n9OYHNHtzGgqNmuyoaC4ufg/HJn40nvUyoS9MK7YGoMF440h741n5V0NNOXuJmL0HcX+kPZkRUbRZ\n/6bpsfAPvyTp+OkKyV5V5CWlcnHpBzRfNg2lRk1WVAwX/m7DJrPHEzJyerE1AFHf7sG2thftv3gL\npUZN1Hf7SD51AYDzC9+lyezxKNRqchOSCJu5stLXJyMhnf+bvoXnPxyDykpFUkQ8307eDEDNFnV4\nZuUw1vddSWRIOAfe38sL219Dr9WTFpvCtnEfV2q27MQ0fp/zOT3fHYdKoyb1Zhy/zjTuCx7N6/Ho\nkmC+6be0xNc4smIHD88bwuD/voFBpyfq2CVOb/y5xOeUVU5iGsfmb+LhtyYY/5TCzViOzd0IgGtA\nfTosGsXPzy8s5VUqXm5SKmcWb6DtSuOVVzNuxXBmYf73jxbzxnIoaE6JdRfe3kKLOWPoun0lBoOB\nmN9P8tfWn6nRshFeXduSHnGbzp/kr9ul97YRf6xiz5ETVY/CUFkTicvpzp079OjRgz179uDp6cn8\n+fOJjIwkJyeH4OBg+vXrR3BwMIsWLcLKyopJkyZhZ2dHs2bNOH/+PJs3b6Z79+7s3r0ba2trnn/+\nedauXUvt2rWZMGEC48aNo169esydO5e0tDTS09OZOHEijz/+eKnZdrcvfLniqqRPyFb2dih8+fSq\npteJ7fzYNqj0Qgt76uSWKr/NwbjddZT/KmwPioqgatOeWRd6WDpGiWwDjFN7qnp79gnZys8dCl9S\nuarpfWIbvz80wNIxStXtyM5qs6//2nmgpWOUqvvRb1hU71VLxyjVooj3WN/kJUvHKNH4SxsA2Bo4\nysJJSjY07FN+alf4z+FUNU+GfmXpCGZLfrVh6UUW5PJexV6ltLyq1AgXgI+PT4HRqpUrC/9atXnz\nZtP/d+4s/Mf4fv31V9P/v/76a9P/161bV+T/hRBCCCGEEKIyVKlzuIQQQgghhBDi36TKjXAJIYQQ\nQgghqr6q9MeFqzIZ4RJCCCGEEEKISiIdLiGEEEIIIYSoJNLhEkIIIYQQQohKIudwCSGcSitHAAAg\nAElEQVSEEEIIIcrMYJBzuMwhI1xCCCGEEEIIUUmkwyWEEEIIIYQQlUSmFAohhBBCCCHKTC4Lbx4Z\n4RJCCCGEEEKISiIdLiGEEEIIIYSoJDKlUAghhBBCCFFmBoOM3ZhDWkkIIYQQQgghKol0uIQQQggh\nhBCiksiUQiGEEEIIIUTZyVUKzaIwGAwGS4cQQgghhBBCVC/xYwMsHaFE7h9fsHQEQEa4ykTHFktH\nKJGKoCqfESRnRVMRxO72Qy0do1R9QrZWm/b8qd0wS8co0ZOhXwHV45j0S6dBlo5RqseP7WBfx+ct\nHaNUPY9/XW329eqy3f/o0t/SMUr16OFdRA7uYOkYJaq7/QQAuW/ZWzhJyaymZlSb96b4d5EOlxBC\nCCGEEKLMDAaZUmgOuWiGEEIIIYQQQlQS6XAJIYQQQgghRCWRDpcQQgghhBBCVBI5h0sIIYQQQghR\nZga5LLxZZIRLCCGEEEIIISqJdLiEEEIIIYQQopLIlEIhhBBCCCFEmRkMMnZjDmklIYQQQgghhKgk\n0uESQgghhBBCiEoiUwqFEEIIIYQQZSZXKTSPjHAJIYQQQgghRCWRDpcQQvw/e/cdHUXZ9nH8uy29\n99AJCYEQCEgXKVKUovLQpHepotJ7kd6kPCKKooAgYEUfGyiogHSC1IBAKIEE0nvPlvePhYSQDlmT\n+F6fc3IOu3vtzG/vmdnZe+aeQQghhBDCRGRIoRBCCCGEEKLEDAYZUlgccoZLCCGEEEIIIUxEznCV\nEYPBwJxZ3+Pt48qIkc+WdZwCSc7SVVY5XVs1ovbr/VCaqUm6fodLSz5Cm5JW7DqluYZ600dg7+cF\nSiUJl4IJWrUFfUYWTo39qDNxEAqViqyEJK6s3U7S9Tsm/0xluczdWjXEd8LDdrrLhcX5t2dBdWpr\nSxrMH41NjUqgUBD605/c/PQHAJwb+1HnrQEo1Sp0GZkEvbOdhKAbJv9MpmxP52efodb4ASg1GpKD\nQ7iy9AN0qWnFq1Eqqf3WUJyaB6BQqbiz63vCvt0PgGVVD/zmjEdjb4s2NZ3LizaQGnIv13SrvtqV\nSt07cHLgFABU1la0/nlznjoAl1aN8B43AKWZMUPQ0k3o8lmuBdYpFfhOHIrzg6whO38g9EFWq6oe\n+M0dh8beFl1qOpcWvpc3a98uVOnegeMDphqzWprjN3c8NjUrg7J4x0fL67ZuqnXgIc+Xnse1XTMu\nTF2Z/ZxDw7p4TxiE0twMbXIqlxdvJP1eZLHyAji1bEzNsQNRmmlICQ7h6vKNeTIXVKM0M8N7yihs\n63qjUCpIDLpO8JrN6DMzsapRhdrTx6GyssBgMHDrg8+IO3Wu2LkKY9GoFQ79x6PQmJF1J5iYTUsw\npKXkqrF6rjN2rwwGgwFDRjpx29aQefMKCktrnMfORV25BgqFguRDP5P0/fZSyfU4Rc0XUbVehEJl\nhiHqEtpfx0NmUv613i+h7ryZrPc8H3xIR1Qd16N0bYAhKxV90A70Zzc9cRZTrZu2dWtRe9IwVBYW\nKJRKQj77jvB9f+aa7uPfT+LfS85wlYEbN6IYMXQH+/YGlXWUQknO0lVWOc0cbKk/fwxnZ6zjz95T\nSAuLpPaE/iWqqzW8BwqVkiMDZnKk/3SU5mbUGtYdtbUlz6yaxNV3d3J0wAyCVmyh4fK3UGpMeyyn\nLJe5mYMtDRaM4cz09RzqNZXUsAjqTOhXorra4/qQHhHL4b4zODpkHtV7dcShvg8KtYpGy9/g4tLN\n/DlgFsGffEfDReNM/plM2Z4aBzv85o7n4qx3ONH3LdLuReD9+sBi11Tu0RHLqh6cHDiZ0yNmUrVv\nN+z8vAGo9/ZbhO75lRP9J3Hr4y+ov3xqrunaN/Cl+uDuuZ/z9yH+3BVODZmW/WfMYEu9ueO5MGsN\nx16dSGpYJD7jB+TzeQquq9KjE1ZVPTg+YAonh8+iWr+u2PnVAsB/4ZuEfvMrx/tN5sbmLwlYkfsH\nln0DX2o+lrX6wFfQZ2RyfMBUTo2cY6zz8yqwrcvrtm7KdUBtZ4Pv9FH4ThmBgpyhTeauTjRYOY2r\nqz/m1OBpRP1xkjrTRhWZ9dE8vnMmcHnOak73f4O0exHUHDe42DXVhvZCoVJxZuhkAodMRmVuRrUh\nPQHwmTKa8J9+48ywKVxbthG/xVNA9fQ/x5S2DjiPm0f02pncn9QHbUQYDgNez1Wj9qyG46A3iVz2\nJuEzBpGwZwsuU4ydVIe+Y9HGRhI+tT/hs4dh26knZj71nzpXHpYuqDt/iPb7AWRtbYQh4Taq1ovy\nr3WohbrNMlDktI+q3UrITCFrW2O0u9qhrPECCq/OTxTFlOtmg+VTubn5S04Nmca5SUvxeXMollU9\nsqeb3/dTRWQwKMr1X3nxr+1wffTRR1y4cKGsY+Rr985AevRsSOcu9co6SqEkZ+kqq5wuLRqQcPkm\nqXfDAbjzzX4qdW5Vorq4s1cI3vItGAygN5B49TYWHq5YVfMkKzmNmNPGH+opIffQpqThUN/HpJ+p\nLJf54+0U8vUBKnUpuj0frbv8znau/HcnAOYuDijN1GiTUzFodfzWZQKJV0MAsKrsRlZ8ssk/kynb\n06l5AxKv3CDtQTuE7fkVjxdbF7vGtW1z7v/4BwadHm1SChEHjuLRuTXmrk5Y16hExP6jAMQcP4fK\n0hxb35oAmDnZ4zv1Na6/tyPXvOzr+6Kxs6Hxh4tp9ukqKvd8AQDn5gEkXLmRvbxC9/yKR+fcOYuq\nc2vbjLAfDmZnDd9/DM/ObTB3dcS6RiXC9x/LyWqRO2vdaSO5tuGzXPNSqJSorCxQqJQozTQA6LO0\nBbZ1ed3WTbUOALh3aElmTBzXN+Rezm7tWxB9/CxJV28Zp/fdfq6t31pk1occmzUk6UowaaH3Abj3\n7T7cX2hd7JqE85e58+lXD9pRT/K1W5h7uALG5aq2tQFAZWWJPjOr2LkKYxHQnMwbl9GG3wUgaf83\nWD+XuyNi0GYR8+FS9PExAGTevILKwRlUauK2rSF+x7vGXA4uKDRm6FNL//tHWb0DhvAzEG88c687\nvxll3b55C9WWqLt+gvbQzFxPK9wbob+8Gwx60Gehv7UPpU+PJ8piqnVTaabh5idfEXf6IgAZUbFk\nJSRh4eoMFPz9JP69/rVDCkePHl3WEQo0d34XAE6cuFXGSQonOUtXWeW0cHcmPSIm+3F6ZCwaGyvU\n1pa5hhoVVhd98mJOnYcLNfp34dKyzaTeuY/aygKX5vWJPnkRez8vbL2qYO7iaNLPVJbL3NLdibRi\ntGdRdQadnoaLxuPRoRnhBwNJfjC8zKDTYeZkR+vPlqFxsOXsrA0m/0ymbE8LNxfSI6KzH2dExqC2\nsUJlZZk9bKewGgu33OtlRmQMNt7VMXdzJiMqzviDNvu1WMzdnEm6HkK9hW8R/N4O9NrcHRSDTkf0\nkUBubd2DubMDz2xcYMzg7kzGY/PR2FihsrbMNaywsDoLd2cyIh/PWg0Ld5c8WdOjYrFwcyLp+m38\nF73JtQ07MGh1ubLe3vE/mnzwNm1+/BCVtSVAoUP4yuu2bqp1AMgevuXZrV2ueVpVq4Q+LQP/xROx\nqlaJ9Ihorq3fVmTWh8zdnMmIfCRPVAxqG+tcmQuriTt1Pmda7q5U7vsS11Z+AMD1NZsJeHchVfq+\njMbRjisL1oJOX+xsBVE7u6ONyRkyqYuJRGllg8LSOntYoS7qPrqo+9k1jkMmkhZ4GHQPthO9DucJ\nC7Fq3p7U0wfR3gt56lx52FXBkBSa8zgpDIW5PZjZ5hpWqOq0Af2FLRiiLuV6u+H+aZR+/dHdOw4q\nc5Q+/wH9k3VaTbVu6jOzuP/D79nPV+reEZWlBQlB10GpLPD7Sfx7mbzDlZyczJw5c0hKSiIyMpIu\nXbrw448/8vPPP6NQKFi0aBEtW7bE3d2dhQsXYm1tjbOzM+bm5qxYsSLfaW7YsIGbN28SExNDYmIi\nc+fOpUmTJjz//PN4eXlRq1YtEhMT6dq1K82aNWPWrFncu3ePrKws5s2bh7+/PwsWLCAkJAS9Xs/E\niRNp3ry5qZtCiLKhyP+UuuHxHXwx6uzq1OSZ1ZMJ+fIXoo6cBeDMlHeoPb4vvm8OJPbs38ScDir0\nKHyFV8C1NHnasxh15+a/j2r5JzReNQmf13py/aNvAMiMTeS3rhOw861Biw/mcHRYKCl3wksn/z9N\nWcB6pdcXq0aRz2sGXf7PP3zNe/wA4s9dJvbUBRye8cv1+u2t32T/OyMqlrDv9uPz5tBcQ5Yen14u\nhdXll0mvL3jb0uvxGT+A+LNXiD11EcfHstaZNpKYkxcI/mA3Zk72tP35I9yfb0bEH6fynV653dZN\ntA4URqFW4fJcE86MnUfa3XCqvNqFBityhpAWRVHQ9vtI5uLU2Ph6UW/ZDO59s5fYY2dQmGmou2gK\nfy/dQOyxM9jWq43/ylkkXQnO1Vl/IgWsm+h1eZ5SmFvgPH4BKmc3Ipe9leu1mPcWELt5BS5TVmLf\neyQJX21+ulx5515kTmXAKNBr0V/aDnbVcpXpDs1C1XYZ6sHHISUcfcjvKCs94W+4f2DdrD74P1Tt\n25VzE5eiz8jEe8KgAr+fKiL5j4+Lx+QdrpCQELp168YLL7xAREQEgwcPxs/Pj8DAQAICAjh58iSz\nZ8+mT58+rFq1Ch8fH9atW0dERESh07WwsGD79u1cv36dKVOm8P3333P//n327NmDo6MjM2caT0F/\n/vnnVK5cmXXr1nH79m0OHjzIlStXcHR0ZNmyZcTFxTFo0CB++uknUzeFEP8YnzG9cWvTGAC1tSVJ\nwXezXzN3dSIzIRldekau96RHxODg711gnWenlvjNGMHl1Vu5/4txaBQKBbq0dE6NXZz9vtZfvkNq\naAXtHBSg9pjeuLV5BgCNtRWJN3LOMlgU1J7h0Tj418q3zqVFA5KC75ARHY8uLYN7vxzDo30z1NaW\nODetR8TBQAASr94m8XoItt5VK2yHKyMiGvt6OcPOzF2dyEpIRv9IexVWkx4RnessirmrExmRMaSH\nR2Pm7JBrXg9f8+jchsy4BFzbNkdlaYG5qxPNtq/m1JBpVOnTmajDgWREROM1qi+VXmkPQOXu7Ul+\nZLnmlxMgPSIa+8e2k+ysj2Uyd3UiPTKW9Ii8WS0evObZxZjVrV2z7KwtdqzixODpuLVrzvEBU8Bg\nIDMmHgDnJn65OlwVYVs31TpQ6Dyj4ki4eDV7GNi973/Hd/IIlOZm6DMyi8ycHh6Frd8jeVycyUpM\nypW5qBrXDq3wmTqa4LUfE7nfeLMEa69qqCzMiT12BoCkoGuk3rqLrV9tMiKPF5mrMNrocMy8c4YF\nq5xc0SUnYMhIz1WncnbHdcZassJuEblwPIYsY16LgBZk3QlGFxeNISON1KO/YNW8/VNlyldSKArP\npjmPbSphSIsFbWr2U8p6g0BjhXrwcRQqjXF44eDjaPf0AKUa3eG5kB5nrG06GUP8zSeKYsp1U6FR\n4zfvdaxrViFw1BzS70cBFPr9JP69TH4Nl4uLCwcOHGDq1Kl88MEHaLVaXn31Vb799lsOHDhA+/bt\nUavVREZG4uNjXKEbN25c5HRbtGgBgI+PD9HRxlO9jo6OODrmHt5w8+ZNGjZsCECNGjUYNmwY165d\n4/DhwwwePJg333wTrVZLbGxsaX5sIcrU9Q+/5ujAWRwdOIvjw+fj4O+D1YOLdav16kjk4cA874k+\ncaHAOo/2zag7dSin31ie8wMMwGCgyfoZ2NU1Xsjv0aE5eq3uH7lL4T/p2odfc2TgbI4MnM3R4fNx\nzNVOHYg4dCbPe6JOXCywrlKn5viM7gWAUqPGs1MLYgKDMOj1BMwfg2NAbQBsvCpjXb0S8ZdMf5dC\nU4k5eR57f5/si8Ur93iBqD9PF7sm6vBpPF9+3njdi40V7p1aEXX4NBlRsaSFReDe0XhHRafmARj0\nepJv3OHIS6M5Ndh4NuPK8g9ICwvP/jHjEFCX6oNeAeDOFz+hTTH+yDs1cg72jyyvKj07EflYzkez\n5lcXdTiQyi+3fyTrs0QdOkVG5IOsnYxZnR9mDb7D4W5jODFoOicGT+fysk2khYVzYvB0AJKu3sTj\nwXuUFuYAxF8KzpWnImzrploHChN16BQODXyx8HQDwK1dc5Jv3ClWZwsg7tR57OrVxrKK8c54lXq8\nQMxjmQurcWnXEu9Jr3Fh0qLszhZAWuh91NZW2Pn7AmBR2R2rGlVIvv5kHYZHpV84ibmPP2qPqgDY\ndOppHC74CKW1He5vf0jqqT+I+e/c7M4WgFWLjtj1fs34QK3BqmVH0i/lXX+elv72byg8m4GD8YCU\nKuA19DdyH/TW7mqL9tOmaHe0JGtPT9Cmod3RElLCUQW8hurZuQ9Cu6GqPwz9lS+eKIsp1836y6ag\ntrYicNTc7M4WUOj3k/j3MvkZri1bttCwYUMGDBjAiRMnOHToEC1btmT16tVERESwYIFx/LyHhwfB\nwcF4e3tz/vz5IqYKQUFBdO/enWvXruHu7g6AMp/T+7Vq1eLixYt07NiRu3fvsn79egICAvDw8GDs\n2LGkp6fzwQcf4ODgkOe9QvwbZMYlcnHRJhqtmIhSoyY1NIILb78PgF1dL+rPHcXRgbMKrav9ej8U\nCgX15+bc5Svu/DUur9rK+XnvUX/OKBQaNRnRcfw1bU2ZfM5/SmZcIucXfUjjlcY7tKWERnB+gfHa\nDPu6Nak/dxRHBs4utO7yup3Unz2SNl+sxGAwEHHwDLd27wODgcCpa/CbPBiFWoU+S8u5ue+RHllx\nDwhlxSVyefH71F82BaVGTVpoBEGL3sO2jhd1Z4/j1JBpBdaA8QJ1y8oeNNvxDkqNmrBv9xN/9jIA\nl+ato+6ssdQY3gt9ZhaX5qzNdZ1Ufq6+8wl1Zo6m+a61KNUq7n69D9/JIx5k+IAGyyejUKtJC4vg\n0kJjBrs6XvjNGcuJwdMLrQvd8yuWVdxp8dlqlBo1od8eIO7sFQAuzl1P3Vlj8BreE31mFhdmrysy\n66WFG6kzbSQtu7Y1Dk0E7u09UmB9ed3WTbkOFCT5+m3+XrWZBiunoVCr0CalcHHO2mLlBciKT+Dq\nsvfwWzINhUZNelg4fy9+F5s6tfCdOZ4zw6YUWANQc6zxLna+M8dnTzPhwt8Er91M0OyV1Jo4EqWZ\nBoNWx7VVm0gPK3xUT3HoE+OI+WAxLpNXoFCr0YaHEbPxbcy86uI0Zg7hMwZh80IvVC7uWDVth1XT\ndtnvjVz8OnE71uM0aiYe7+wGg4G004dI2vv5U+fKIy0K7S9jUb+8E4VKgyH+Ftp9o1C4N0L1wvvG\njlUhdCffQd31Y9RDjR0b3fFlGCL+eqIoplo37Rv44tq6CSkh92jy0ZLs+QVv/IzYk0X/xhX/PgqD\noYhv/Kd04sQJlixZgoODA7a2tly/fp2ff/6ZLVu2cOzYMbZvN/4fDxcuXGDJkiVYWVmh0Whwd3dn\nyZIl+U5zw4YNnDp1CqVSSVpaGvPnz8ff359WrVpx9KjxjlUzZ86ka9euNG/enNmzZxMREYFOp2P2\n7Nn4+voyd+5c7t27R3JyMgMGDODVV18t8rPo2Fl6DWMCKgaW+4wgOUubioHsbZr31s/lTZfTuytM\ne/7UJO/twMuTboG7gIrxnfRbiz5lHaNIHU58xf7mRe8Dylqnk19WmG29oiz3Q616lnWMIrU9uoc7\nfZuVdYxCVfvCOMw1c411GScpnNmUlAqzblYUoQOalHWEQlXZVfpnaZ+Eyc9wtWjRgh9//DHP82PH\njmXs2LHZjy9evMimTZtwcnJi3bp1aDSaQqfbtWtX+vfPveN52NkCct1wY82avEfhVq1aVezPIIQQ\nQgghhBBPotzcFt7Z2ZkRI0ZgZWWFra0tK1asYMKECSQkJOSqs7Gxwc+v4t/VRQghhBBCCPHvV246\nXJ07d6Zz59z/Qd97771XRmmEEEIIIYQQhZHbwhePye9SKIQQQgghhBD/X0mHSwghhBBCCCFMpNwM\nKRRCCCGEEEJUHAaDDCksDjnDJYQQQgghhBAmIh0uIYQQQgghhDARGVIohBBCCCGEKDEZUlg8coZL\nCCGEEEIIIUxEOlxCCCGEEEIIYSIypFAIIYQQQghRYvIfHxePnOESQgghhBBCCBORDpcQQgghhBBC\nmIh0uIQQQgghhBDCROQaLiGEEEIIIUSJyW3hi0dhMBgMZR1CCCGEEEIIUbHc6tWyrCMUquY3x8s6\nAiBnuErkpyYDyjpCoboF7mJfs35lHaNInU99zveNB5Z1jCK9cmZnuV/mYFzuaZc7lnWMIln6Hagw\n7aljZ1nHKJQK4/ZT3tuzW+Au/vfMoLKOUaTuf33G8TavlHWMIrU8/H2F2dZ/b9m7rGMUqf3xr1nr\nPb6sYxRpcvD77PAfWdYxCjX40icA5X577/7XZ3wRMKysYxSp7/ltZR1BlDLpcAkhhBBCCCFKzGCQ\n20EUh7SSEEIIIYQQQpiIdLiEEEIIIYQQwkRkSKEQQgghhBCixPRyl8JikTNcQgghhBBCCGEi0uES\nQgghhBBCCBORIYVCCCGEEEKIEjPoZUhhccgZLiGEEEIIIYQwEelwCSGEEEIIIYSJyJBCIYQQQggh\nRIkZ5C6FxSJnuIQQQgghhBDCRKTDJYQQQgghhBAmIh0uIYQQQgghhDARuYZLCCGEEEIIUWJyDVfx\nyBkuIYQQQgghhDAROcNVStxaNcR3Qj+UZmqSrt/lwuKP0KakFbtObW1Jg/mjsalRCRQKQn/6k5uf\n/mB8T+tnCHh7LGnh0dnTOT5qEbrU9BLndG3ViNrj+6E005AUfIeLSz5El0/Oouos3JxpsWUxRwfO\nICshCQCNnTV1pw7HpmZllOZm3Nz6Hff2/lnijI9ze64hfhP6otSoSQy+y7lFm/Nv20LqXjzwAemR\ncdm1wTt+JGzvsafPZsLl7tzYjzpvDUCpVqHLyCTone0kBN14qryHA9PY8FkCmVkGfKpreHuCEzZW\nOcddfvgjhR3fJ2U/Tk7VExmj45ePK6FWwdIP47h6KwtLCwXd21vTv5vtU+V5XEVrz+IwGAzMmfU9\n3j6ujBj5rMnn91BFbUv35xpS941XUWk0JFy/w7lFH+ebu7C6Gn06Uv0/7VBZaIi/cptzCzejz9I+\ncSaHFk2oNmYISo2a1Bsh3Fj5LrrUtGLXuP+nC24vvYDS3IyUqze4sfJdDFlarOt4U+ONUagszFGo\nlITt3EP0/oNPnPNR5Xlbd372GWqNG4hCoyblxh2uLH0/T3sWVWPu5kyTj5dxavDU7H2Q83ON8Zs3\ngfRH9pV/jZv3RPvK/NRs589zU7ujMlMTfTWMX2d9RmZy3mnX7d6MJq91xGAwoE3P4o9FXxJx6Q5m\nNha8sGIQTl4eKJQKLu85wemP9pdKtkdVbtOARhN7otRoiL8WyvH5W8lKKbgNnl0ygvjgMC5v+wUA\njY0lLRcNw76mJygV3PzfMYK27H3qXKWxbXf+7f3c+/LtPxG69xi2NSsRMHckaisLDAYDlzd8QdTx\ni0+U07N1AA3e7I3STE3CtVBOvf0J2nzar6A6MztrGs8dgoNvNXRpGdz63xGu7z4AgFvTOjSc0g+F\nSkVGQjLnVu0i/trdJ8opKhY5w1UKzBxsabBgDGemr+dQr6mkhkVQZ0K/EtXVHteH9IhYDvedwdEh\n86jeqyMO9X0AcGzgw83PfuLIwNnZf0+yA9E42OI/byxnZ67jzz6TSQ2LxPf1/iWuq9S1Nc0/ehsL\nN6dc76s/fxzpkbEcGzyL0xOWUnfKUMwfqykpMwdbGi0Yzelp6/m91zRSQiOp+0bfEtVZV/ckKzGF\nQwNmZ/+VRmfLlMtdoVbRaPkbXFy6mT8HzCL4k+9ouGjcU+WNTdCxYEMs70x35n8bPanioea/O+Jz\n1bz8vDVfrvPgy3Ue7FztjouDipmjHHF2ULF6SzxWFkr2vOvBjhXuHPkrncOn8+4sn1RFa8/iuHEj\nihFDd7Bvb5DJ5/WoitqWZg62NHp7FKen/pffek4jNSwSv4K29wLqPNs3watfJ46NW87vvWeiMtdQ\na2CXJ86ktrfDe9abXJu3nHODxpN+P5xqY4YWu8apTUs8er3ElUnzOD9kAkpzMzxf7Q6A7+JZhG7Z\nxYWRE7kybSE1JozAoornE2d9qDxv6xoHO+rOeZ2Ls1Zzst9bpIVFUGv8wBLVeHRpyzObFmPu6pzr\nffb1fbmz6wdOD52W/VdanS1LJxteXDmYH17/iG0vLCThTjTPTftPnjrHmm60ntGDPSPe47NXlnNy\n415efn80AK0mvUzy/Xi2d13Czh4raTCgDZ6NapZKvofMHW14dvFwDk18n+9fnkNSaBSNJvXOt9bO\ny5NOn0yl+otNcj3f8I3/kBoRxw895rO332Jq922HS0Ctp8pVGtu2zYN9+cH+c7L/Qh/syxvMGs6d\n7w9xsP8czi3cTNMVb6BQlfwnrrmjLc0WjeTolPfY230WyWGRBLzVp0R1Daf1R5uawb4eszkwaDEe\nrerj2SYAjY0lrda+wbm1X/BLn3mcWbKdlqvHo9RU7HMfBoOiXP+VF9LhKgUuLRqQcPkmqXfDAQj5\n+gCVurQqUd3ld7Zz5b87ATB3cUBppkabnAqAY4PauDSpx3M7ltJy83ycGtV5spzNG5Bw+Ub2/O9+\nsx/Pzs+VqM7cxRG3tk0JnLQi13s0dtY4N2tA8OavAciIjOX4iHlkJSQ/UdaHXFvWJ/7yTVLuRgBw\n++sDVMmnbQurc2rgg0Gv59kP59Du8+XUHtUDlE+/EZpyuRu0On7rMoHEqyEAWFV2Iyv+6dry+Ll0\n6vmYUb2SBoA+nW3YezgVg8GQb/22bxNxslfS+0UbAK7cyKRbOytUKgUajYLWjS3Yfzz1qTI9qqK1\nZ3Hs3hlIj54N6dylnsnn9aiK2pZuLesTF3Qrezu+9dVvVOmS96xgYXVVuz1H8MX+I+4AACAASURB\nVI69ZCWmgMHA+aVbufvTkSfO5NCsEcl/Xyc99D4AEd/txaVT22LXuL74PPc//w5tUjIYDNx8532i\nf/kDhZmG0G2fk3DmPACZUTFkJSRi5uryxFkfKs/bulOzABKvBJMWalznwvb8gseLrYtdY+biiEub\nZpyfvCzPtO3r++LY2J8mW1fyzAeLcWhYt1QyA1R/ri7hF0KID4kC4Pyuw9R9pWmeOl2mlv2zd5IS\nlQhA+MUQrF3sUGpU/LH4Kw6t2AOAjZs9KjM1GUmld9AKoNKz9YgOuk3SnUgArn3xBzW7Nc+31rff\n8wR/d5SQXwJzPX96+W7OvPMlAJYPtv2spKdb/qWxbTsFPNyXz6bdF8uoPeo/2ftyhUqJxtYaALW1\nBbrMrCfK6dHSn9hLt0i+Y5x/8Jd/UK1ryxLVOfnV4PaPxzDoDei1Ou7/eYGqHZtiU82drKQ0Ik9d\nASDp9n20yWk4B3g/UVZRsVTsbnU5YenuRFpETPbj9MhYNDZWqK0tc50uL6rOoNPTcNF4PDo0I/xg\nIMkh9wDISkgi9OcjRBwMxDHAlyZrJvPngFmkR8aWKKeFuzPpkY/OPwaNjRUqa8vcwwULqcuIjuPc\njLV5pm1VxYOMmDhqDOyGa8uGKM3U3PrsR1Lv3C9RxsdZujuTFp7zOQtu24LrFGoVUScvcXn9LpTm\nZrT47zS0yWnc3L3vKbOZdrkbdDrMnOxo/dkyNA62nJ214anyRkTr8HBWZT92d1aRnGogJc2AjVXu\nDmhcoo7t/0vi8zUe2c/Vr23OTwdTaVjHnKwsA78dT0OtLr2jRxWtPYtj7nzjmZUTJ26ZfF6Pqqht\naenunDePbQHbewF1NtU9MQ+6SYv3pmPh6kDs2asErf/8iTOZubmQEZkzRC0jKhq1jTUqK8vsIW6F\n1VhUrYTG0YG6q99G4+JE0oUgQj7YhiEzi8ifcoaTub38IipLS5KDrj5x1ofK87Zu4e5MxiP7l4yo\nmDztWVhNZnQcl2atznfaWQnJhO87RPShU9g3qEODVTM4NXgKGVEl21fmx9bTkaT7OUPZksLjMbe1\nxMzGItewwsSwWBLDcubXbk5vbvx+AX2WDgCDTk+XNcPw6dyI4F/PEXcz4qmzPcrKw4nUR/aFqRFx\nmNlaobG2yDOs8PSyXQB4Ns/bMTXo9LRa8RrVOzXhzm9/kXg7/Klylca2rVApiTpxiaD1u4378nen\nok1J4+auX7iwYhvPbppNrYFdMHeyI3DWexh0+pLn9HAiNSKn/dIiYjGztUJtbZFrWGFhdTEXb1Lj\npWeJPncdlUZNlY6N0Wt1JIWEo7Yyx71lPSKOB+FUryZ2tSpj6WJf4pyi4qkwZ7j27NnDwIED6d+/\nP59++ilDhgyhT58+jB49mszMTPbs2cNbb73FmDFj6NKlC3v2GI8iXbhwgV69ejFkyBAmTZrEzJkz\nAdixYwd9+/alX79+bN++/enCKfNvxjwbezHqzs1/n/0dx2BmZ4PPaz0BODN9PREHjUeg4s5fJe7C\ndVya1y9xTEUB8+exnMWty/UetQqryu7oktM4OWoB5+e8S51JQ7Cr83TDJRSK/Hfyj7dtYXV3vv2D\nS6u3o8/Sok1O5cbOn/F4vkm+9SVi4uUOkBmbyG9dJ3Bs+AICFozBuppHfpMqFn3+B7fJb9TFN7+m\n0K6ZJZXdc47JTB7ugEIB/SaHM2llNC0aWlCqIyEqWHuWaxW1LQs485w3d8F1CrUK1+b+BM7YwKGB\n89DY2VB3Qt4hQcWlUBTQRnp9sWoUajX2TQK4tmAlF0dNRm1rS7VRg3PVVRrYi6oj+vP3zMXoMzOf\nOOtD5XpbL2ide6Q9i1WTj0uzVhN96BQACRf+JuHiVZyaBTxZzscoCljn9AXsF9WWZry04TUcqruy\nf9bOXK/tnbKND5pOx8LBmhZvdC2VfEXlLKrt8nN05sd8+dxbmNtbU3/cK08XrBS27ZBvD3Jx9Y6c\nfflne/F8vglKMw1NVkzg7Nsf8muXNzny2mIC5ozAwr3klzQU+FtCX8zfHHo959Z8DgYDL36xkFbr\n3iDieBD6LB3alHSOTPwvfiNf5sUvF1Hj5VZEnr6S3RmvqPQGZbn+Ky8q1BkuOzs7Nm7cyPvvv8+2\nbdtQKpWMHDmSixeNF0YmJyfzySefcPv2bcaOHUvPnj1ZsGABq1atwsfHh3Xr1hEREUFwcDA///wz\nu3YZj+4MHz6c5557Di8vr2JnqT2mN25tngFAY21F4o072a9ZuDqRmZCMLj0j13vSw6Nx8K+Vb51L\niwYkBd8hIzoeXVoG9345hkf7ZqhtrKjepxM3tv4vZ0IKMGiLdwG49+g+uLVpDIDa2pKk4JyLM80L\nyJkWHo19Pe8i6x6VEW088hf60yEAUkMjiD9/Fft63iT+XbKj+75je+HxSObERzIX1LZp4TE4+Hvn\nW1el63MkXgvJmY5CgUH7ZF9w/9hyt7bEuWm97I524tXbJF4Pwda7Kil3nuxIo6eLikvXcrJFxuiw\ns1FiaZH3C+nXo6lMH+mQ67mUVD0Th9hjb2s8cr51TyJVPZ/uK6Qit2d5U1Hbss7YXni0NebOs727\nORa4vTs+mvuRuvSoOML/CMw+ah7681F8R+W91qa4MiKisPGrnf3YzMUZbWIS+kcyFVaTFR1L7J8n\nss/eRP16kCrDjNekKDRqvGdNxLJGVS6Nm05GeOQT53xUedzWH0oPj8LOzyf7sbmrE1mPtWdxah6n\ntrGicq/OhHy6J+dJhQJ9MfeV+Xn2rZfw6mA8uGlmY0n0tbDs12zcHUiPT0GblreDbOvpyH8+GkfM\njXC+GrgebYZxeFv11nWJvnqPlMgEslIz+PuHQHw6N3rifA8FvN6dKs83BEBjbUn89dDs16zcHMlI\nyD9nQTyfrUf89TDSouLRpmVw6+dTVO/0TIlzlfa2XaVbKxKv3SHx+sN9ORi0OuxqVUFlYU7En+cA\niLt4g6QbYTj61+J+RNFnN/3H96BSW+Ny0NhYkPBI+1m6OZKRkIzusfZLDY/Bub5XvnXmHjacX/cl\nmYkpxnYY3tU49FChQJuawR+v5VyS0eXbZSTfLd2znKJ8Kj9dv2KoWbMmSqUSjUbD5MmTmT17NuHh\n4WgffKHWqWO8tsnT05PMB0cJIyMj8fExfnE3bmz8EX/t2jXu3bvHsGHDGDZsGPHx8YSEhJQoy7UP\nv86+gcXR4fNx9PfBqqrxCG+1Xh2IOHQmz3uiTlwssK5Sp+b4jO4FgFKjxrNTC2ICg9CmplGjTyc8\n2hvHitv5VsehXi0ij10oVs7gj77i2KCZHBs0kxMj5uHg750z/54diTwcmOc9MScvFKvuUWn3oki4\ncpPK3doAYOZkj0P92iRcLvmdy65u+ib75hZ/DluAU31vrKu6A1CjdwfC82nbyBMXC6yzrVUF33G9\nQalAaa6h5qudCNt/osS54J9b7ga9noD5Y3AMMP6Is/GqjHX1SsRfevI7wbVsaMGFa5mE3DPu/L/+\nJZl2zSzy1CUm67lzX0tAHfNcz3/1SzLv7zZelxATr2PP/hS6tLZ64jxQsduzvKmobfn3pm+yL4A/\nPPRtHB/djnt1IPzQX3neE3n8YoF19w6colKn5ijNjdcvebRrTNzlm0+UDSD+9Fls/Hyzb2bh0b0L\nsUdOFrsm5uBRnNu1QmlmBoBT6+ak/B0MQO1FM1BZW3JpfOl1tqB8busPxZ46j72/D5ZVjOtcpR4v\nEH34dIlrHqdNTadKrxdxbWe8Xsmmdk3s6noTe+LcE2c99t8f+eyV5Xz2ynJ2916FZ8OaOFR3BSBg\nQGuCD+TdD1vYW/Hqrklc//UcP0/ckt3ZAvDt2piWD85oqczU+HZ9hrvHn34I6fmN/+On3gv5qfdC\n9g1cikuAF7bV3ACo3bctd38/W6Lp1ejclAbjXgaM236NF5sQfvLvEucq7W3brlYV6oztlb0v9+r7\nAmG/niD5bgQaG0scGxh/61lVccOmZiUSrhbvd92l97/l177z+bXvfA4MXoxzg1rYVDPOv1af57l3\nMG/7hR+/VGBdrT7P4/96DwDMnezw6tmWO3tPgMFA642TcfSrAUCVTk3Ra3Vyl8L/JyrUGS6lUsnf\nf//NgQMH+Oqrr0hLS6Nnz57ZFwLnd4rXw8OD4OBgvL29OX/eeHGyl5cX3t7efPzxxygUCrZt24av\nr+8T58qMS+T8og9pvPItlBo1KaERnF/wAQD2dWtSf+4ojgycXWjd5XU7qT97JG2+WInBYCDi4Blu\n7d4HBgOBU9ZQb9owao/pjV6r4+ysDdm3wS1pzouLN9FwxSSUajWpYRFcfHsjAHZ1vfCfM5pjg2YW\nWleYs9PX4Dd9BFV7dkShUHLjk29IvPLkP3QeZj678EOarHrYZpGcnZ/Ttg3njeLQgNmF1l3bvIf6\n04fy/BcrUahV3Dtwkjvf/vFUuR5mM+lyn7oGv8mDUahV6LO0nJv7Xomv23uUk4OKhW84MW11DFlZ\nBqp4qFnylhNBwZks3BjLl+uMP27u3M/C1VGF5rFrNkb2smPO+lh6vXkfAzC2rx3+Pub5zOnJVLT2\nLM8qaltmxiVy9u2PaLr6zezt+K95mwBwqFuThvNf42D/OYXW3frqAGb2NrTbuQSFUkn837c5v3TX\nE2fSxidwY8V/qb1oJgqNmoywcIKXrsPa15ta0ydwYeTEAmsAwr/bi9rOlvofr0WhVJJy7SY3N27E\n1r8uTq2ak3YnFP+NK7PnF7LpUxJOl+wH8uPK87aeFZfIlSUb8V82FaVGTVpYBJcXbcC2Ti3qzBrL\n6aHTCqwplF7PhemrqD15BDVf64tBp+PSvLVPtK/MT1psMr/O2MHL741CqVGTcCeKfdM+BcDdvxqd\nlg3ks1eW02BAG2wrOeHdKQDvTjnDGb8e8i6Hln1Dh8X9GfLzXDAYCN5/nr+2Pf2+6FHpsUkcm7uV\nNuvGo9KoSLobxdFZnwDgVK86LRcO46feCwudRuDqL2gxfwgvf7sIg8HA3d/PcuWzA0+VqzS27asf\nfUv9GUNp/+WKB/vyU4R8exCAU1PWU3/aYFRmGvRaHeeXbiE1tOQHMTJikzg1/xNavfM6So2a5NBI\nTs7ZDICjXw2aLhjBr33nF1p35ZOfaL50NJ2/WQIKBUGbviM2yDjS58TMTTRdMNy4XkfFc2Tiu0/V\nruWBQV9+7gRYnikMBd22qJzZs2cPN2/e5PXXX2fMmDHZZ7DMzMzo3bs3Wq2WmzdvMnXqVDIyMujS\npQu///47Fy5cYMmSJVhZWaHRaHB3d2fJkiV8/PHHHDhwgMzMTBo0aMC8efNQqVSFZvipyYB/4qM+\nsW6Bu9jXLO+tn8ubzqc+5/vGA4suLGOvnNlZ7pc5GJd72uWOZR2jSJZ+BypMe+rYWXRhGVJh3H7K\ne3t2C9zF/54ZVNYxitT9r8843uYpr1H5B7Q8/H2F2dZ/b5n/rcjLk/bHv2at9/iyjlGkycHvs8N/\nZFnHKNTgS8ZOXXnf3rv/9RlfBAwr6xhF6nt+W1lHKLagLh3KOkKh6u39rawjABXoDFfPnjkXaxd1\nkwtzc3N+//13AC5evMimTZtwcnJi3bp1aDTGYSavvfYar732mukCCyGEEEIIIf7fqzAdrifl7OzM\niBEjsLKywtbWlhUrVhT9JiGEEEIIIUShytN/Llye/es7XJ07d6Zz585lHUMIIYQQQgjx/1CFukuh\nEEIIIYQQQlQk//ozXEIIIYQQQojSJ0MKi0fOcAkhhBBCCCGEiUiHSwghhBBCCCFMRDpcQgghhBBC\nCGEicg2XEEIIIYQQosT0cg1XscgZLiGEEEIIIYQwEelwCSGEEEIIIYSJyJBCIYQQQgghRInJbeGL\nR85wCSGEEEIIIYSJSIdLCCGEEEIIIUxEhhQKIYQQQgghSkyGFBaPnOESQgghhBBCCBNRGAwGQ1mH\nEEIIIYQQQlQsZzt2LusIhWp0YF9ZRwBkSGGJ6NhZ1hEKpWJguc8IkrO0qRjI3qb9yzpGkbqc3l1h\n2vOnJgPKOkahugXuAirGd9JvLfqUdYwidTjxFfubv1rWMYrU6eSXFWZbryjL/VCrnmUdo0htj+7h\nTt9mZR2jUNW+OAVA5hrrMk5SOLMpKRVm3awo5D8+Lh4ZUiiEEEIIIYQQJiIdLiGEEEIIIYQwERlS\nKIQQQgghhCgxuUth8cgZLiGEEEIIIYQwEelwCSGEEEIIIYSJSIdLCCGEEEIIIUxEruESQgghhBBC\nlJhcw1U8coZLCCGEEEIIIUxEOlxCCCGEEEIIYSIypFAIIYQQQghRYnoZUlgscoZLCCGEEEIIIUxE\nOlxCCCGEEEIIYSIypFAIIYQQQghRYnKXwuKRM1xCCCGEEEIIYSLS4RJCCCGEEEIIE5EhhWXEYDAw\nZ9b3ePu4MmLks2Udp0CSs3SVVU7XVo2o/Xo/lGZqkq7f4dKSj9CmpBW7Tmmuod70Edj7eYFSScKl\nYIJWbUGfkYVTYz/qTByEQqUiKyGJK2u3k3T9jsk/U1kuc7dWDfGd8LCd7nJhcf7tWVCd2tqSBvNH\nY1OjEigUhP70Jzc//QEA58Z+1HlrAEq1Cl1GJkHvbCch6IbJP5Mp29P52WeoNX4ASo2G5OAQriz9\nAF1qWvFqlEpqvzUUp+YBKFQq7uz6nrBv9wNgWdUDvznj0djbok1N5/KiDaSG3APAa0w/3Ds+iy4t\ng4SLV7n+30/RZ2Zh7uZM3TnjMHOyR6FUErLz++wMLq0a4T1uAEozY4agpZvQ5bNcC6xTKvCdOBTn\nB1lDdv5A6IOsVlU98Js7Do29LbrUdC4tfC87a60xffHoZMwaf+Eq1/67HX1mFigVeI3ojWvrxqgs\nLYrV1uV1WzfVOvCQ50vP49quGRemrsx+zqFhXbwnDEJpboY2OZXLizeSfi+yWHkBnFo2pubYgSjN\nNKQEh3B1+cY8mQuqUZqZ4T1lFLZ1vVEoFSQGXSd4zWb0mZlY1ahC7enjUFlZYDAYuPXBZ8SdOlfs\nXIWxaNQKh/7jUWjMyLoTTMymJRjSUnLVWD3XGbtXBoPBgCEjnbhta8i8eQWFpTXOY+eirlwDhUJB\n8qGfSfp+e6nkepyi5ouoWi9CoTLDEHUJ7a/jITMp/1rvl1B33kzWe54PPqQjqo7rUbo2wJCVij5o\nB/qzm0oll6nWU8dn6uH9xmAUahX6jEyurd1K4uXgUslc1mRIYfH8o2e4Bg8ezI0buX84nDx5kkmT\nJpX6vPbs2cNvv/1W6tMtDTduRDFi6A727Q0q6yiFkpylq6xymjnYUn/+GM7OWMefvaeQFhZJ7Qn9\nS1RXa3gPFColRwbM5Ej/6SjNzag1rDtqa0ueWTWJq+/u5OiAGQSt2ELD5W+h1Jj2WE5ZLnMzB1sa\nLBjDmenrOdRrKqlhEdSZ0K9EdbXH9SE9IpbDfWdwdMg8qvfqiEN9HxRqFY2Wv8HFpZv5c8Asgj/5\njoaLxpn8M5myPTUOdvjNHc/FWe9wou9bpN2LwPv1gcWuqdyjI5ZVPTg5cDKnR8ykat9u2Pl5A1Dv\n7bcI3fMrJ/pP4tbHX1B/+VQAPLu1w6VVY04Pn8mpIdPIiI7Da4yx7X2nvUbMsb84NXgaZ99YhO+U\nEQ8y2FJv7nguzFrDsVcnkhoWic/4Afl8noLrqvTohFVVD44PmMLJ4bOo1q8rdn61APBf+Cah3/zK\n8X6TubH5SwJWTAGg0kvtcH2uMSeHzeLE4OlkxMRTa6wxa7W+XXF8xo/To+dxfOCDz9apZYFtXV63\ndVOuA2o7G3ynj8J3yggU5PzwM3d1osHKaVxd/TGnBk8j6o+T1Jk2qsisj+bxnTOBy3NWc7r/G6Td\ni6DmuMHFrqk2tBcKlYozQycTOGQyKnMzqg3pCYDPlNGE//QbZ4ZN4dqyjfgtngKqp/85prR1wHnc\nPKLXzuT+pD5oI8JwGPB6rhq1ZzUcB71J5LI3CZ8xiIQ9W3CZYuykOvQdizY2kvCp/QmfPQzbTj0x\n86n/1LnysHRB3flDtN8PIGtrIwwJt1G1XpR/rUMt1G2WgSKnfVTtVkJmClnbGqPd1Q5ljRdQeHV+\n6limWk8VajX+SyZxZfkmTg2exq2t3+C34I2nzisqln/tkMKePXvSoUOHso6Rr907A+nRsyGdu9Qr\n6yiFkpylq6xyurRoQMLlm6TeDQfgzjf7qdS5VYnq4s5eIXjLt2AwgN5A4tXbWHi4YlXNk6zkNGJO\nG3+op4TcQ5uShkN9H5N+prJc5o+3U8jXB6jUpej2fLTu8jvbufLfnQCYuzigNFOjTU7FoNXxW5cJ\nJF4NAcCqshtZ8ckm/0ymbE+n5g1IvHKDtAftELbnVzxebF3sGte2zbn/4x8YdHq0SSlEHDiKR+fW\nmLs6YV2jEhH7jwIQc/wcKktzbH1rYlunFlGHT6FNTgUg6uBJ3Nq3AODC9FXc/WofAObuLhi0egCc\nmweQcOVG9vIK3fMrHp1z5yyqzq1tM8J+OJidNXz/MTw7t8Hc1RHrGpUI338sJ6vFw6xeRB46nZ01\n8o+TuD/fHIBKXdtya+se9BlZGLK0xveevlRgW5fXbd1U6wCAe4eWZMbEcX3DjlzTc2vfgujjZ0m6\ness4ve/2c2391iKzPuTYrCFJV4JJC70PwL1v9+H+Quti1yScv8ydT7960I56kq/dwtzDFQCFSona\n1gYAlZWl8WxmKbAIaE7mjctow+8CkLT/G6yfy90RMWiziPlwKfr4GAAyb15B5eAMKjVx29YQv+Nd\nYy4HFxQaM/Sppf/9o6zeAUP4GYg3HoDXnd+Msm7fvIVqS9RdP0F7aGaupxXujdBf3g0GPeiz0N/a\nh9Knx1PnMtV6atBqOfLyGJKv3QbAsrI7WQn5n80T/14mOwydlZXFrFmzCA0NRafTMXz4cAA2btxI\ndHQ0aWlprF27Ntd7vvrqK3bv3o1er6d9+/a8+eab+U57z549HDhwgJSUFOLi4nj99dd58cUXeeml\nl6hRowYajQYvLy9cXFzo168fixcv5sKFC2RlZfHGG2/QsWNH1qxZQ2BgIHq9nmHDhtGlSxdTNUUe\nc+cb53XixK1/bJ5PQnKWrrLKaeHuTHpETPbj9MhYNDZWqK0tcw01Kqwu+uTFnDoPF2r078KlZZtJ\nvXMftZUFLs3rE33yIvZ+Xth6VcHcxdGkn6ksl7mluxNpxWjPouoMOj0NF43Ho0Mzwg8GkvxgeJlB\np8PMyY7Wny1D42DL2VkbTP6ZTNmeFm4upEdEZz/OiIxBbWOFysoye6hOYTUWbrnXy4zIGGy8q2Pu\n5kxGVJzxB232a7GYuzmTGHSdqv27EfrVPrISk/Ho2hZz5wfrpMEABgPPvP829g3qcPfzH6k+qDsW\n7s5kPDYfjY0VKmvLXMMKC6uzcHcmI/LxrNWwcHfJkzU9KhYLNycSg65TrV837j7I6tm1bfb2Y1XN\nE+uaVagx9D+YOdgBkJlY8A/g8rqtm2odALKHbHl2a5drnlbVKqFPy8B/8USsqlUiPSKaa+u3FZn1\nIXM3ZzIiH8kTFYPaxjpX5sJq4k6dz5mWuyuV+77EtZUfAHB9zWYC3l1Ilb4vo3G048qCtaDTFztb\nQdTO7mhjcoZM6mIiUVrZoLC0zh5WqIu6jy7qfnaN45CJpAUeBp2xQ49eh/OEhVg1b0/q6YNo74U8\nda487KpgSArNeZwUhsLcHsxscw0rVHXagP7CFgxRuQ8yGO6fRunXH92946AyR+nzH9A/fafVlOup\n8XvdnqbbVmHmYMvFueueOm95If/xcfGY7AzXF198gZOTE59//jlbt25l/fr1xMXF0bZtW7Zv306b\nNm3Yt29fdn1MTAybN29m165dfPvtt2RmZpKSklLg9NPS0ti6dStbtmxhxYoVaLVaUlNTGT9+POvW\n5azIBw4cIC4ujq+//prt27dz6dIlDh06RGhoKLt372b79u1s2rSJxMREUzWFEGVLkf+XoeHxHXwx\n6uzq1KTF5gWEfPkLUUfOok1J48yUd/Aa/h9a7VxBpa5tiDkdhP7B0fh/JWX+X5t52rMYdefmv8/+\njmMws7PB57We2c9nxibyW9cJHBu+gIAFY7Cu5vH0ucuKsoD1Sq8vVo0in9cMuvyff/ha+L7DRP52\nnEYbF9DkoyWk3g7Ls07+Nf5tjrw0GqdmAcYnFMVcroXV5ZdJry9429Lrub/3TyJ+P0HjjfNpunkx\nqSE5WRVqFfb+PpydtJzTo+cBUKNvIUOnyuu2bqJ1oDAKtQqXNk258dHnnBo6ndjAizRYMa3orA/f\nX9D2+0jm4tTY+HrR8P0l3PtmL7HHzqAw01B30RT+XrqBEz1Gce71efhMG4u5m3OxsxUSOv/n9bq8\npeYWuExajtqjCjEfLs31Wsx7Cwh97QWUNvbY9x759Lnyzr3InMqAUaDXor+U9xoy3aFZgAH14OOo\nu3+OPuR30GU+fSwTr6eZsQkcfWUMgaPm4Dd3PJZVPZ8ur6hQTHaG68aNGzz7rPHCaxsbG2rVqsXR\no0fx9/cHwMXFhejonKMEd+/excfHBwsL44XBU6dOLXT6TZs2RalU4uLigp2dHbGxsQDUrFkzV92t\nW7do2LAhAPb29kycOJHNmzcTFBTE4MHGsdZarZawsDDs7OxK4ZMLUfZ8xvTGrU1jANTWliQF381+\nzdzVicyEZHTpGbnekx4Rg4O/d4F1np1a4jdjBJdXb+X+L8ahUSgU6NLSOTV2cfb7Wn/5Dqmh4ab6\naGWi9pjeuLV5BgCNtRWJN3JuFGBRUHuGR+PgXyvfOpcWDUgKvkNGdDy6tAzu/XIMj/bNUFtb4ty0\nHhEHAwFIvHqbxOsh2HpXJeVOxWzTjIho7OvlDDszd3UiKyEZ/SPtVVhNekR0rrMo5q5OZETGkB4e\njZmzQ655PXxNbWdDxK9HCNn+HQB29bxJe7BOuj3fgpiT59ClplO1TxfMTBTQ/wAAIABJREFUnO0B\nqNy9PcmPLNf8cgKkR0Rj/9h2kp31sUzmrk6kR8aSHpE3q8WD19R21oT/coTbn+Zkfbj9ZETFEbH/\nKIYsLboHHRuH+j6we2/2dCrCtm6qdaDQeUbFkXDxavbQr3vf/47v5BEozc3QZxT94zw9PApbv0fy\nuDiTlZiUK3NRNa4dWuEzdTTBaz8mcv+fAFh7VUNlYU7ssTMAJAVdI/XWXWz9apMRebzIXIXRRodj\n5p0zLFjl5IouOQFDRnquOpWzO64z1pIVdovIheMxZBnzWgS0IOtOMLq4aAwZaaQe/QWr5u2fKlO+\nkkJReDbNeWxTCUNaLGhTs59S1hsEGivUg4+jUGmMwwsHH0e7pwco1egOz4X0OGNt08kY4m8+dSxT\nracqayucmvgTdeiU8eNfvUVycAg23tVIu5tztlH8u5nsDFetWrUIDDT+aEhOTubatWtUqVKlwPpq\n1apx8+ZNMjONX4RvvvkmERERBdYHBRnHkUdHR5OcnIyzs/HokPKxI05eXl5cvGgcIpGUlMTIkSPx\n8vKiefPm7Nixg08//ZQuXbpQtWrVJ/+wQpQz1z/8mqMDZ3F04CyOD5+Pg78PVlWNZ0mq9epI5OHA\nPO+JPnGhwDqP9s2oO3Uop99YnvMDDMBgoMn6GdjV9TLWdWiOXqv7R+5S+E+69uHXHBk4myMDZ3N0\n+Hwcc7VTByIOncnznqgTFwusq9SpOT6je/F/7N13dBRl28fx77b0RnogFJNACqEKoShFmiI2QHoH\nQZpKb6FXBRSxF8SCKEXQR/RVARuIhAQEQi+hJkI6Cekhu+8fC0tC2i5ks8nzXJ9zOIfsXjPz23tm\nd+beuWcWQKlR49O1NckHT6DTamky/0VqNGkAgINfLezr1uTGcfPfpdBckg8cxTm0Pra326FWz24k\n7o0yuiZxTxQ+Tz+mv+7FwQ6vro+QuCeK3MQUsuPi8eqi/2LPtVUTdFotGTFXcAryo9Fr01GoVChU\nSuoN7cn1X/QHvLV6dcO3j34I5eWvdpCXdAOAyFHhOBdaX769upJwT87CWUuqS9xzkFpPdyqUtS2J\nf0aSm3A7a1d9Vrc7Wc9fwSnYnyavTTNkfWhYT67/8hcACb9F4P1Ee1AoUKhUAKSdLLotVIf3urm2\ngbIk/hmJS+NAbHw8AfDs2IqMmCtGdbYAUiOP4tSwAba++rMQNXt2I/mezGXVuHdsQ8DkF4ievNjQ\n2QLIjr2G2t4Op9BAAGxqeWFXz5eMcw/eYciJPoB1/VDU3vrjGYeuvfTDBQtR2jvhtfBDsiJ/J3nt\nXENnC8CudRecnn9B/4dag12bLuQcL779PCjtpV9R+ISBi/4LKVWTF9DG/Fik5tZXHbj1eUtubWhD\n/vZecCubWxvaQOZ1VE1eQNV27u3QnqgaDUd7avMD5zLbdqrVEhw+DufG+nVu/5AvdnVrkX783ANn\nrgp0OkWV/ldVmO0MV9++fZk3bx4DBgwgNzeXiRMnsn379lLrXV1dGT16NIMHD0ahUPDYY4/h5eVV\nan1SUhLDhg3j5s2bLFiwANXtndG9OnfuzP79+xkwYAAFBQVMmDCB9u3bExkZycCBA8nKyqJLly44\nODg88GsWoirKS03n2OIPaPbqJJQaNVmx8UQvfA8Ap2A/Gs0dzb5Bs8usazChPwqFgkZz797lK/Xo\nWU6u/JSj896hUfhoFBo1uUmp/DP9dYu8zsqSl5rO0cUf8vBr+ju0ZcbGc3SB/toM5+CHaDR3NH8N\nmlNm3ck1G2k0ZxTtN7+GTqcj/o9DXPz6Z9DpODjtdUKm3L59cP4tjsx9h5yEFEu+5AeSn5rOySXv\n0Wj5VJQaNdmx8ZxY/A6OQX4EzxlH5NDppdaA/qJ021rehG1YjVKjJu7bXdw4fBKA4/PWEDx7LPVG\n9Eabl8/x8DdApyMlMhqX5g1ptXE1KJQk7onkyib9Ad3JJe8SNGsMXl+uBuDf/+wmMHDU7Qzv03jF\nFBRqNdlx8RxfpM/gFORHSPhYIobMKLMudvtObH29aP3lKpQaNbHf7ib18CkAjs19k+DZL+I3ohfa\nvHyi56zRZz0QTVKzEFpvXIVCqSThzyguf/0DAOc/3ET9CYNp8/XrKG7fxe5SobNb96qq73VzbgOl\nyTh3idMrP6bxa9NRqFXcupnJsfA3ypymSOYbaZxZ/g4hS6ej0KjJibvO6SVv4RDkT+Cs8RwaPrXU\nGoCHxurvXBc4a7xhnmnRpzn/xsecmPMa/pNGobTSoLtVwNmVH5ATV/oXzMbSpqeS/P4S3Ke8ikKt\n5tb1OJLfXYiVXzCuL4ZzfeZgHLr1RuXuhV3Ljti17GiYNmHJBFI3vInr6Fl4r/4adDqyo/7k5k+b\nHjhXMdmJ3PplLOqnN6JQadDduMitn0ej8GqGqtt7+o5VGQoOrEb95DrUw/QdnYL9y9HF//PAscy5\nnUbPXEWDScNRqNVo8/M5MX8tuYnV93NdmE6h0xW6irea2L59OxcuXCh32GFFK2BjpS7PVCoGVfmM\nIDkrmopB/NSy+K2fq5ruUV9Xm/b8sUXx24FXJT0OfgVUj8+kX1v3sXSMcnWO2MquVn0tHaNcXQ9s\nqTbv9eqy3v98pFf5hRbWYd92rvQLs3SMMtXZrB8ul/e6vYWTlM1qama12Tari78efc7SEcr06F/f\nWToCUMV/+HjhwoXFfrcLqNQ7CgohhBBCCCHE/aryHS4hhBBCCCFE1SO3hTfOf+0PHwshhBBCCCGE\npUmHSwghhBBCCCHMpEoPKRRCCCGEEEJUTbrSfshaFCFnuIQQQgghhBDCTKTDJYQQQgghhBBmIkMK\nhRBCCCGEECbTyV0KjSJnuIQQQgghhBDCTKTDJYQQQgghhBBmIkMKhRBCCCGEECaTHz42jpzhEkII\nIYQQQggzkQ6XEEIIIYQQQpiJDCkUQgghhBBCmKy636VQq9WycOFCzpw5g5WVFUuXLqVu3bqG53/7\n7Tfeffdd1Go1vXv3pm/fvve1HDnDJYQQQgghhPifs3v3bvLy8ti8eTNTp07l1VdfNTyXn5/PihUr\nWL9+PRs2bGDz5s0kJSXd13KkwyWEEEIIIYT4n3Po0CHatWsHQNOmTTl+/LjhuZiYGOrUqYOzszNW\nVlY8/PDDREVF3ddyZEihCVQMsnSEclWHjCA5K1r3qK8tHcEo1aU9exz8ytIRjFId2rNzxFZLRzBK\n1wNbLB3BKNXlvV5d1nuHfdstHcEodTZHWjqCUaymZlo6Qrmqy7YpKkdGRgYODg6Gv1UqFbdu3UKt\nVpORkYGjo6PhOXt7ezIyMu5rOdLhMsFvbZ63dIQyddr/Dbta3d/Y0srU9cAWfm3dx9IxytU5Yis7\nw/pZOka5ukVu5uew/paOUa4nIjdVm/X+n+aDLR2jTM/+8yVAlW/PzhFbKWCjpWOUS8UgFAqNpWOU\nS6fLrzbvddkXVZzOEVurxfEHVI/jpPTkly0do1xObm9ZOoLRqvtt4R0cHMjMvPtFgVarRa1Wl/hc\nZmZmkQ6YKWRIoRBCCCGEEOJ/TvPmzdmzZw8AR44coUGDBobn/P39uXz5Mjdu3CAvL4+DBw/SrFmz\n+1qOnOESQgghhBBC/M/p2rUr+/bto3///uh0OpYvX86OHTvIysqiX79+zJo1i1GjRqHT6ejduzde\nXl73tRzpcAkhhBBCCCFMVt1vC69UKlm8eHGRx/z9/Q3/79SpE506dXrw5TzwHIQQQgghhBBClEg6\nXEIIIYQQQghhJjKkUAghhBBCCGEyLdV7SGFlkTNcQgghhBBCCGEm0uESQgghhBBCCDORIYVCCCGE\nEEIIk1X3uxRWFjnDJYQQQgghhBBmIh0uIYQQQgghhDATGVIohBBCCCGEMJlWhhQaRc5wCSGEEEII\nIYSZSIdLCCGEEEIIIcxEOlxCCCGEEEIIYSZyDVcFcWvbHP9xg1Bo1GTGXOHUsvcoyMo2qcba040W\n65YTOWQa+Wk39dM8+jAh8yaScz3JUPfPuHkUZOUYnc39kWYEjBuI0kpDxvnLnFj2AQWZ2cbXKRUE\nThqGW6smKFQqLm/cQey3uwCwq+1NyNxxaJwdKcjK4fiid8i6/C8ALk2Dqf/SIFTWVtzKyOLE4vfI\n/jeh9PYbPxClRr/sU8veL7n9SqpRKmnwyjBcb+e78tX3xN3Od4fPU4/h0TGM6GmvGR5rtGIqDgH1\nKMjWt2XqoeOcW/u50e16p83qjx+A0krDzfNXOLG09LYtq87a041W65eyf9AMw7p3CvYncMowVLbW\nKJRKLn3xH679/JdJ+e7weKQZDcb3Nyz/2NIPS8xZWp3SWkPI9JE4h/iDUkHa8fOcXLUebW6+Ydpa\nT3fEq2NL/pm6qtQc5lrPtrW9CQkfj8bZkVtZOZxc/LZhO7yjdt8nqflsZw4MmgqAyt6Odv/3cZG6\nc29+ZnLbej3alOCX+qLSaEg7d4Uji9dxq4S2LauuXp8u1H2uIyobDTdOXeLIoo/R5t8qd9mWaE+/\nF/vj1aUtBdm5pB07w7m1n6PNy8fa043g8HFYuTqjUCq5vPF7k9vSGDqdjvDZ3xNQ34ORo9qaZRkV\n6dNPP+H48eO8/vqaSlleVXmv38uc+yG1kz1BU0di/5AvKmsrLn62nWs/7dVn7dmFOv2eRHergOxr\nCZxc+oHhM/ZeltgPASg0apq8Ppt/v91Fwu8RRrdpkUxmOP5wDPan/qQRqGysUaiUXN7wHfG/7DU5\nX2Vmrmh/7Uvk3Q/Okpevpb6/I3PnhOJgX/TQ+fc/4/lo3XkUSgVOjmrmzgrF19eOtPQ8Xl11irPn\n0rG1UfF0j1r061PXLDktSW4Lb5z/iTNcffv2JTY2lu3bt/Prr78CMGXKFHr37s3Zs2cZMmQI/fv3\nJy0t7b7mr3FxIjh8Asdmr+JA/1fIjovHf/wgk2q8u3eg+QdLsPZwKzKdc6NArny1g6hh0w3/TOls\naVwcaTh3PNGzX+fvvpPIikug/viBJtX59uyKXW1v9g+cyoERs6nT/0mcQvwBCF30MrHbdrK//xRi\nPt5Ck1f1B7PWnq40WTmN0ys/IWLwDOJ/P0DQjBdKbb+QueM5Nns1Ef1eIfvfeAImFG+/0mpq9eyC\nbW1vDgyaQtTIWdTu1wOnkAAA1E4OBM4YTeDUkSgo+qHgHNqAQ+PmEzl0OpFDp5vc2dK4OBI6bxxH\nZ73Bvj6TyY6Lp8GEktu2rDqfJ9sT9tFCbDxdi0zX5LUpxHy0lYjBM/ln0goCJw3Frra3SRnvLn8s\nh2etYW+fKWTFJRA4YYBJdf4jeqJQq9g3aCb7Bs5AZW2F37Dn9NM52RMyaxTB04aDouwPXnOt54YL\nXyF2+04iBkzm4rrNNFoxrch8nRsHUnfIs0UfC63PjSOnDOs/cuh0Uv85YXzDAlYujjRbOJqoaWv5\ntdd0suISCHmpn0l1Pp1a4Ne/K3+PW8Fvz89CZa3Bf1B3o5Zf2e3p06Mj7o88TNSIWUQOnU5uUip+\nL/YHIHD6CyT//Q+RQ6Zz+KXFBE4daVJbGiMmJpGRwzbw80+mrSdLCAoK4tdfd9K37/OVtsyq9F6/\nd3lm3Q/Nm0BOQgoHhs7k0EtLCJwyAmtPV2x8PAgY25+DY+YTMXg6OdcS8R/Tt5SMltkPOYU2oOW6\n5bg0DjK6Pe/NZK7jj0bLp3Fx3Waihk3n6ORl1H95OLa+pu+DKjNzRUpNzWPxsuO8trwp2za1o1ZN\nW95572yRmpzcAuYvOsbKFU356vO2tH/Uk9VrTgGwZu0Z7GxVbNn4KJ9+3Jq/I5LYu6/kL53Ff7//\niQ7XHb169aJz584A/P3332zbtg0HBwcyMzPZtGkTzs7O9zVf17AmpJ86T3bsdQDitv+C9+PtjK6x\ncq+Be/swjk5ZXmzezo0CqfFwKC0+fY3m7y/BpWmwSdncWjUh7VQMWVf1y43dvhPvJ9qZVOfZIYy4\nHX+gK9By62Ym13f9jc8T7bH2qIF9vZpc3/U3AMn7j6CyscYx8CG8OrUm+e8j3DxzUf96v93NmTWf\nldx+rRqTfiqG7Kt32mZn8fYro8ajQyuu/fC7IV/87n2G7F6d25CXnMq5tzcUmZ+NjycqO1uCZo4h\n7MvVBM8dj9rJwfS2PXm3za5u24X3E4+aVGftXgPPDi35Z/KrRaZRWmm4sO4bUqKOAZCbkELejZtY\ne5q+c3Fv1bjY8n1KyFlWXcrh08Ss/xZ0OtDqSD97CVsfdwC8u7QhN+kGZ97aWG4Wc6xnaw9X7OvV\nJH7XPuD2dmir3w4BrFydCZz2AufeKboNODcKROPkwMMfLiHs85XU6tXNiNYsyrNNI1JPXCTzajwA\nF7f+im/34mddyqqr3eNRzm/4ifz0TNDpOLrsU67+aNyZzMpuT8cgfxL3RHIrIwuAxD8O4NmpNQDR\nM1ZydevPAFh7uaO7pTWyFY339caD9OzVlCe6N6zweVe0CRPG8emnn7NlyzeVtsyq9F4vzJz7IbWT\nPa5hjbmwbiug/6yMHBlOfloGCpUShVqNyt4WFAqU1tZoc/NKzGiJ/RBA7b7diflwE+knz5nUpoZM\nZjr+UFppuLh+K6l39kGJKeSnpd/XPqiyMle0iMgkQoKdqFPbHoDeverw885r6HQ6Q422QIdOpyMj\nQz8iISu7ACtr/aH1qdPpPPlETVQqBRqNkkfaevDr7/FmzSyqrio7pDA/P5/Zs2cTGxtLQUEBI0aM\noFatWixfvhytVouXlxerV6/GxsamxOnXrFnD3r178fb2JjU1FYC3334bd3d3zpw5Q0ZGBuPGjePW\nrVtcunSJ+fPns3jx4vvKauPlRm5CsuHv3MRk1A72qOxsDae/y6rJS0rl+OySh2bkp2Vw/ec/Sfoz\nEufGQTReOZPIIVPJTUwxPlt8oeUmJKNxsENlb1tkOEdZdcWyJyTjEFAHGy93chNT9Tvm23ISU7Dx\ndMWutg8FOTk0WvoKdnVqkhOfxNk1JZ9BsvF0Jyf+7pDJ3IRk1A52RduvjBobTzdy4u/Npz9tf2dI\nh0+PjkWWaeXqRErUMc6s+pi81HQaTB5OSPg4omcaP0TGxsuNnATj2ra0utykVI7OfL3YvLV5+cR9\n/7vh71rPdUZlZ0Pa8bPFak3NmWNkzsJ1yQei79Z5u1O3f3dOrFgHwNXtu/UZe3QoN4s51rO1p1ux\n7TA3IQVrTzdunrtMw0WvcP6dDWhvFR2ipysoIOmvg1z8dDvWbi40f3cBuUmp5b6Gwmy93MiOL9xm\nKWgc7VDb2xYZVlhWnUNdH6xPXKD1OzOw8XAh5fAZTry5yajlV3Z7pp84R+0BPYjd+jP56Rl4P9kB\na7ca+iKdDnQ6mr+3EOfGQVzd9AN1Bxc9q/ig5s7Xn/mLiLhYofM1h5deegWAzp07Vdoyq9J7/d5c\n5toP2fl6k5ucSt2BT+HepilKKw2XNu4g6+o1smPjufzl9zyy5U3yMzK5lZFF1Ki5JWe0wH4I4MT8\ntQDUHfxM+Q1ZUm4zHX9o8/K5tuM3w981n+2CytaG9BP31zGsjMwVLT4+By+vu8eYnh7WZGbeIjOr\nwDCs0M5OzewZIYx68QDOzlZoC3Ss+zAMgNCGzvzfz//SpLELeXlafv89HrX6v2/4XcV/tfbfqcp2\nuDZv3oyrqyurV68mIyODXr16YWVlxdq1a/H392fr1q3ExMTQsGHxbzqPHTtGVFQU33zzDVlZWXTr\nVvSb64ULF7Jr1y7ef/99YmNjmTJlyn13tgBQlnyiUKfVmlZTgsIfKmnRp0k7dgbXsCZc+/H3MqYq\nRFHKcgu0xtcpS/iA0GpLHVKi02pRqNV4tHuYgy/OJ+vqdWr37U6T16YRMWRG8QlKmj/3tl8Zyyrh\nuWKv7x7pJ85zbNbdtr3w8Rba/d/HKNRqdLfKv3YGKHG5ANyzbGPrSlNv6LPU7d+dQ6+sKHIdhbEU\npWx7xXOWX+cU9BDNVk7lytadJP71j8lZSvKg67m09tUVaAkYP5AbR06SEhmNS/OQIs9f+nSb4f+5\niSnEfbcLzw5hpoUvY9nG1inUKjxahRI5ZQ0FuXk0XzyW4Il9OL76S9Oy3JmnGdvz+s97sPZ0pdm7\nC9Bm5xL33a5i15r9M34hGhcnmr01777yi/tXZd/rZtwPKdRq7Gp5cSszm6gx87H19aLlh4vJunoN\njbMjno+1Ys8z48i/cZP6EwfRcP4EjtxzDRVgkf1QhTDj8ccddYc8h2/fHhydvLTUM4QmqYTMFaHQ\nd05FqApFOx9zk3XrY9iy8VF8fe3YtOUyM+ccYePnbZn0UiBr3znDoGH7cXe3JizMjehjNyonvKhy\nqmyHKyYmhrZt9UNuHBwc8Pf357fffsPfXz9mu0+fPqVOe+nSJUJDQ1EqlTg4ONCgQQOzZs25nohT\nSH3D39YeruSn30Sbk2tSzb3UDnbU6v0Elz/ffvdBhaLYN/X38h/TF492LQBQ2duSEXOl6HLTMoot\nNyc+CefQgBLrcq4nYeXmUuS5nIQUcuKLPg5gc/u53KQUbkSfMQwNifv+N4KmjkBprSmWNzc+CeeG\n97TNPRnLqsmJT8LavUaR5wp/M1YSlyZBqJ0cSNp7EACFQgFaXbkf5v5j+uDRXt+2antbMs4Xb9uC\ne9v2ehLODYu37b1191Jo1ITOH4+Dny8HRs0j51pimfWFBYzpg2f7hw05b56/WmT5eSUsP7uEnIXr\nvLu2IWTGKE6t/pRrv+wzOkth966niljP926fhZ/zfqI9ealpeHRohcrWBmsPV8K+WEXk0On49nmC\nxD0HyTV8Y61AW1BQ7msIGtsb7w7NAX3bphdqWxvPGqW0bTI1Qv1LrMtJTOX67wcNZ8Ri/28fgaOf\nKzcHVH57qp0ciN/5F5e/+A4Ap4YBhiE/no+1JvnAEQqycsi/kU7inkgcG9Qz6nX8N1i0aAHPPPM0\nAN9/v4MFCxZVynKr6nu9svZDd0Z7/PvjH/rXFhvPjaOncQ4JwN6vNol7D5Kfmg7A1W9+oc1XxUcU\ngGX2QxXBXMcfoN8HhcydiN1DvhwaPYec68bvgyyVuSJ5edlw/MTdDlJiYi5Ojmpsbe8eOu8/kEST\nxjXw9bUDoE/vOqx56zRpafnk5BTw0oQGODtZAfD5hgvUvl0n/vdU2Wu4/P39OXhQfzCckZHB2bNn\n8fX15dKlSwB89NFH7Nq1q8RpAwICiI6ORqvVkpWVxfnz582aNSXyKM6h9Q0Xk9bs2Y2kPVEm19zr\nVlYOvr0fx6NjKwAcGjyEU3AAKRFHypwu5qMtRAyZQcSQGUSOCsc5tL7hZgu+vbqSsLf4cpMPHC21\nLnHPQWo93QmFSonawQ6vrm1J/DOS3IQUsuPi8eqq7xi7tWqCTqsl4/wVEv6IxKVJIDY+HgB4PRZG\nRsyVEs/Q3Fm27e1l1+rZjcR7MpZVk7gnCp+nHyuU7xESy2lblZ0NDaaMNFy3VWfwM/q7Q5XT4bpz\nE4uIwTOJHDm3eJvtOVjC64s2qu5eTVZMRm1vS6SJnS2A8x9t5e/Bs/h78CwiRs7DJTTAsPw6vbqU\nmrO0Oq9OrQieOpyDLy+/7wMwwCzrOTfx9nbYRb8dut7ZDmOu8NdTY4gcor8hxqkV75Mdd53IodMB\ncGkSbBjGo3ZyoOYznUjY/Xe5r+H0B9v4Y0A4fwwIZ8+whdRoFIB9bS8A6vXuzPU/i58NSNh/rNS6\nf3dHUrNrK8OXEd4dHyb15IUq2Z5OQX40em06CpUKhUpJvaE9uX77rmW1enXDt49+yJ/K3g6Pdi2N\neg3/LRYsWESzZi1o1qxFpXW2oOq+1ytrP5RzLZH00xeo+aR+mKOVqzPOjQJJP3WBm2cu4vFIc1S2\n1vrX9lirUodlW2I/VBHMdfwBELpsKip7Ww6NCa+wzpa5M1ek1mFuHD+RxpWrmQBs++4q7dt5FqkJ\nauDEP4dTSE7RdwT/3BNPTR9bXFys2PbdVT78WH/8mZySy3ffx/J4V59KfQ2VQadTVOl/VUWVPcPV\nt29f5s2bx4ABA8jNzWXixIn4+/szZ84clEolHh4eDB8+vMRpg4ODad++Pc8//zyenp64uZnvLjYA\n+anpnFr6LqHLp6HUqMmOi+fk4rdxDPInaPZYooZNL7WmTFot0TNW0mDKSB56oR+6ggKOz3vDpNuf\n5qemc3LJ+zReMQWFWr/c44veAcApyI+Q8LFEDJlRZl3s9p3Y+nrR+stVKDVqYr/dTeph/V14js19\nk+DZL+I3ohfavHyi56wBnY6Mc5c5/do6mq6cjkKtIv9mpv65UjO+R6PlU/VtExvPicXv4BjkR/Cc\ncUQOnV5qDegvXLat5U3YhtUoNWrivt3FjcMny2yX5P1HiN36f7T4aAkolPpbzq74wOh2BchLTefE\nkvdp8uqdNrvOsYXv6ts22I+Q8BeJGDyzzLrSuDQOxLN9CzIv/0vLdXeHu5575yuSI46anPPYkg9o\n+upklGo1WXHxRXKGho/h78GzyqxrML4/CoWC0PAxhvmmHj3DqVWfmpTFXOv5+Lw1BM8eS70RvdHm\n5XM8/I3Sx4Pcdmb1JwTNGkOrr95AqVZx9ZufSYmMLnOae+WlpnN44Ue0XPUySo2azNgE/pmn345c\ngh+i6fwX+GNAeJl1F7fuxsrZgY4bl6JQKrlx+hJHl31VJdtTPzSzIa02rgaFksQ9kVzZ9OPtLO8S\nNGsMXl+uBuDf/+wmMHCUSe0pHkxVeq8XZu790NEZqwia/gK+vbqCQsGF9d+QfiqG9FMx2Pp40Orz\n19Dm5ZNzPYkTS94rI2Pl7ocqgrmOP5wbB+LRriWZl+N4+MOlhsdj3vuSlAOm7YMqK3NFc3W1Zn54\nKLPCj5Cfr8O3lh0L54dy8lQaS189wVeft6VlCzcGD3qIsROi0GgUODlpWP2afgTE8CF+LFh8jH6D\n9qFDx+hRATQMub+bs4nqT6HTlXNUIgx+a1N5t/e9H532f8OuViU6P3zCAAAgAElEQVTf8rYq6Xpg\nC7+2Ln1IaFXROWIrO8OK3+K7qukWuZmfw/pbOka5nojcVG3W+3+aD7Z0jDI9+4/++q6q3p6dI7ZS\ngGl3tLMEFYNQKIoPd65qdLr8avNel31RxekcsbVaHH9A9ThOSk9+2dIxyuXk9palIxituuwvLa3K\nnuEyxubNm/nhhx+KPT5lyhSaNWtmgURCCCGEEEL8b9BWoWF7VVm17nD169ePfv2q/hkIIYQQQggh\nxP+mKnvTDCGEEEIIIYSo7qr1GS4hhBBCCCGEZeiQIYXGkDNcQgghhBBCCGEm0uESQgghhBBCCDOR\nIYVCCCGEEEIIk8ldCo0jZ7iEEEIIIYQQwkykwyWEEEIIIYQQZiIdLiGEEEIIIYQwE7mGSwghhBBC\nCGEyrc7SCaoHOcMlhBBCCCGEEGYiHS4hhBBCCCGEMBMZUiiEEEIIIYQwmQ65Lbwx5AyXEEIIIYQQ\nQpiJQqfTyeVuQgghhBBCCJNsbjLc0hHK1O/oZ5aOAMiQQpPsDOtn6Qhl6ha5mV9b97F0jHJ1jtjK\nrlZ9LR2jXF0PbOG3Ns9bOka5Ou3/hj/a9rZ0jHJ1/HtbtVnv+9s/Y+kYZWqz53uAKt+eXQ9sQaHQ\nWDpGuXS6fArYaOkY5VIxqNq812VfVHG6HthSLY4/oHocJ9lZ17N0jHJl5V6ydASjaXUypNAYMqRQ\nCCGEEEIIIcxEOlxCCCGEEEIIYSYypFAIIYQQQghhMrkThHHkDJcQQgghhBBCmIl0uIQQQgghhBDC\nTGRIoRBCCCGEEMJkWvnhY6PIGS4hhBBCCCGEMBPpcAkhhBBCCCGEmUiHSwghhBBCCCHMRK7hEkII\nIYQQQphMp5NruIwhZ7iEEEIIIYQQwkykwyWEEEIIIYQQZiJDCoUQQgghhBAm08qQQqPIGS4hhBBC\nCCGEMBPpcAkhhBBCCCGEmciQQiGEEEIIIYTJdJYOUE1Ih6uCuD/SjPrjB6C00nDz/BVOLP2Agsxs\nk+usPd1otX4p+wfNID/tJgAejzYndMEEsuOTDHVRYxZQkJVTYha3ts3xHz8QpUZDxvnLnFr2PgVZ\n2cbVKJU0eGUYrq2aoFCpuPLV98R9uwsA29rehISPR+PsyK2sHE4ufpusy/8C0GjFVBwC6lGQrc+U\neug459Z+jo2PB0EzxmDj405BVg6XN35fZhsGjBuI0kqf6cSy0tuwxDqlgsBJw3C7nf3yxh3E3s5+\nR82nH8OzQxhHpr1meKzxq1NxDKhryJ5y6ARn3/y81JxF2nDcIBQaNZkxVzi17L2S27mMGmtPN1qs\nW07kkGmG9e0Y7E/9SSNQ2VijUCm5vOE74n/ZW26e0ri2bY7f2MEoNWoyYi5zZnnxnKXVKK2sqD/t\nBRyDA1AolKSfPMu51evQ5uXhGOxPwCsjUdlYg0rJ1S+/I/6XPeXmMed6tqvtTcjccWicHSnIyuH4\noncM2+gdtft1x/fZzuwfOA0Ala01IXPH4/BQLVCWf9LfpXUL6rw4FKVGTVbMZWJee6tYe5ZV4/Vc\ndzyf6obS2orMMzHEvPYWuvxb2AcFUO+l0Yb1HrdxO0m7/qiy7en/Yj+8u7alIDuXG9FnOLv2C7R5\n+aBU4DfyeTzaPQzAG2+sZsqUaeW+DlN9+uknHD9+nNdfX1Ph864IOp2O8NnfE1Dfg5Gj2lbKMqva\ne70wc+2X1E4OBE4diX09X5TWVlz6bDvXf9Zn83uxP15d9Nto2rEznFv7uX4bLYOl9kMACo2aZq/P\nIva7XST8dsDotjXn8YdTsD+BU4ahsrVGoVRy6Yv/cO3nv4zOVlk576j5dEe8OoZxeOrK+8pYkie6\nP8aiJTOwtrbi+LHTjHtxJjdvZhSre+aZxwmfPwmdVkdqahrjx83k4oUr1KjhzNq3l9G4STBZmdl8\n8cVWPniv/GMM8d/FbEMKJ06caHRt3759iY2NfaDl7dq1i/j4+Aeax/3SuDgSOm8cR2e9wb4+k8mO\ni6fBhIEm1/k82Z6wjxZi4+laZDrnxoFc2riDiMEzDf9K62xpXJwImTueY7NXE9HvFbL/jSdgwiCj\na2r17IJtbW8ODJpC1MhZ1O7XA6eQAAAaLnyF2O07iRgwmYvrNtNoxd2DKOfQBhwaN5/IodOJHDqd\nc2v1HyYh8yaSduIsEf0n88/ERdQd/Gypbdhw7niiZ7/O330nkRWXQP3xJbdhaXW+PbtiV9ub/QOn\ncmDEbOr0fxKnEH8A1E72BM8cTdDUEXDP9Z0uofU5OHYBEUNmEDFkhlGdLY2LE8HhEzg2exUH+r9C\ndlw8/uOLt3NZNd7dO9D8gyVYe7gVma7R8mlcXLeZqGHTOTp5GfVfHo6tr3e5mUrLGRQ+kRNzVhE5\n4GVy/o3Hb/xgo2vqDu+NQqXi4NCpRA2dgtLamjpDewHQcNl0Lq3bzMHh0zg2ZSn+Lw/H1tennDzm\nXc+hi14mdttO9vefQszHW2jy6tQi83VuHMhDQ4pug3UHPYM2N4/9A6cROSocAPuggBLzq52dCJj9\nMmfnreDI4PHkXLtOnReHGV3j2r4N3r2f4tTkeRwdOhGltRU+ffV5ApfMJnb9V0SPmsSp6YuoN3Ek\nNlW0PWs+1RGPRx/mwPDZRAyZQW7yDfzH9gegTr8nqdE8hKgx8wBo06Y1/fr1LfN1mCIoKIhff91J\n377PV9g8K1pMTCIjh23g559OVNoyq9p7/d7lmmu/FDJvAjkJyUQOm8HhlxbTYMoIrD1c8enREfdH\nHiZqxCwih04nNykVvxf7l5PTcvsh59D6hH2yDJcmQUa3650s5jz+aPLaFGI+2krE4Jn8M2kFgZOG\nYlfb9P2RuXOqnewJnvUCwdOKt+2DcHd35YOPVjGw/ziaNurMxYtXWbJsZrE6GxtrPvlsDQP6jaV1\n2JP8+ONuXn9jIQCvrZpPZkYmzZt0pUO7njz+eEe6P9mp4kKKasFsHa533nnHXLMu0RdffEFGRvFv\nHCqDW6smpJ2MIevqdQCubtuF9xOPmlRn7V4Dzw4t+Wfyq8Wmc2ncANcWobT+fAUtP1pIjWbBpWZx\nbdWY9FMxZN9eRtz2nXg/3s7oGo8Orbj2w+/oCrTcuplJ/O59eD/RDmsPV+zr1SR+1z4AkvcfQWVr\njWPgQ9j4eKKysyVo5hjCvlxN8NzxqJ0cAHAM8uPaj38AUJCVQ+qhkg9A3Fo1Ie3U3baJ3b4T7yfa\nmVTn2SGMuB1/GLJf3/U3Pk+0B8C7c1tyk1I5+9aGIvOz8fFAZWdL8MzRtP5yFSHzxqF2si+1fQ1t\nGNaE9FPnyY6904a/FG/nMmqs3Gvg3j6Mo1OWF5lGaaXh4vqtpEYdAyA3MYX8tHSsPYt2yoxVI6wJ\nN0+dJzv2GgD/bv8Fr27tjK65ceQklz/7BnQ60GrJOHsBG293lFYaLq3fSurB6Ls5b5Sf05zr2dqj\nBvb1anJ919/A7W3URr+NAli5OhM8fRRn3/6yyLIUKiUqOxsUKiVKKw0AuvxbJeZ3CWtGxulz5Nxu\nq/jvfsK9awejazwef4xrm77j1s0M0Om4sPo9kn75HYWVhtjPNpF26CgAeYnJ5KelY+XhXiXb0zHI\nj4Q/o7iVkQVAwu8H8HqsFQA1n+zAxU+3o83Vn0no3bsvv/76W5mvwxQTJozj008/Z8uWbypsnhXt\n640H6dmrKU90b1hpy6xq7/XCzLVfUjs54NqyMRfXbTVkixo1h/z0DByD/EncE2nYRhP/OIBnp9Zl\n5rTUfgj0X1TEfLiJtBPnjGjRe7KY6fhDaaXhwrpvSLmzP0pIIe/GzfvaH5n7OMm7Sxtyk25w5q0v\niz33IDp3acc/h6KJOX8JgI8/+pJ+/Yt/caxSqVAoFDg7OQLgYG9HTk4uAM2ah/LVV9+i1WrJz8/n\n559+47meT1ZoTkvS6hRV+l9VUe6Qwu3bt7N7924yMzNJTU1lwoQJ1KhRgzVr1qBSqahduzaLFy9m\nx44dbNu2Da1Wy8svv8y0adPYt28fJ0+eZMmSJahUKqytrVmyZAk1a9ZkzZo17N27F29vb1JTU8vM\n8NRTT1GvXj00Gg2LFy8mPDzcMM3cuXO5du0ap06dYubMmaxatYqZM2eyZcsWQH/27I033uDbb7/l\n8OHDZGVlsWzZMubMmYO3tzdXr16lUaNGLFq06L4b0cbLjZyEZMPfuQnJaBzsUNnbFjkNXlZdblIq\nR2e+XuL889MyuPbTHhL+iMKlSSBNV09n/6AZ5CakFM/i6U5OoaGHuQnJqB3sUNnZGoZvlFVj4+lG\nTnzRjA4BdbH2dCM3MVW/MzY8l4K1pxsKtYqUqGOcWfUxeanpNJg8nJDwcUTPXEX6iXP49HiMi+u2\noHFxwq1ts1LbMDfeuDYsrc7Gy43chHuz1wEwDOnw6VH04NjK1ZmUqGOcWrmOvNQ0AicPp+Hc8Ryd\nsarEnEVyFF5WYjJqB/ui7VxGTV5SKsdnF1+GNi+fazvuHpzWfLYLKlsb0k3cCd/N6U5u4XVdYs7S\na1Ijjxoet/b2wLfvU5x97QO0eflc/+FXw3M+z3bV5zx+tpw85lvPNl7uxbbRnMQUbDxduXnuEqGL\nX+bs2xvQ3SookunShv/Q4v2FtP/hQ1T2tgBkxVwqMb+Vpzu5CYXbKqlYe5ZVY1O7JpoaLgSvWojG\n3ZWb0Se4/P5n6PLySfjx7rAjz6cfR2VrS8aJM1WyPdNPnKNO/x5c3foz+ekZ+DzZAWv3GgDY1fHB\n/iFf6g17DoBx415kwYL7/3y910svvQJA585V9xviufO7AxARcbHSllnV3utFsplpv2Tn601ecip1\nBj6FW5tmKDUarmz8nvir10g/cY7aA3oQe3sb9X6yA9ZuNcppQ8vshwCOzVsL6M+4m8Kcxx/avHzi\nvv/d8Het5zqjsrMhzYR1Xxk5AWK37wagZglt+yB8fWsSe/sLCoC42Gs4Ozvh6OhQZFhhZmYWL08M\n57c/t5GSfAOlSknnx/Rn4Q9GHmHgwJ7s//sg1tZWPPtcd/JvlfylnvjvZdQ1XNnZ2Xz66aekpKTQ\np08flEolW7Zswc3NjTfffJNvv/0WtVqNk5MT77//fpFp586dy7JlywgODmb37t28+uqrjB49mqio\nKL755huysrLo1q1bmcvPyspi/PjxhISEsGrVKlq3bs3AgQO5dOkSs2fP5uuvvyY4OJiFCxei0WhK\nnY+fnx9z584lNjaWS5cu8cknn2Bra0uXLl1ITEzEw8PDmOYoRqEspQddoL2vunsV/oC5cfQMadFn\ncQtrzL8//FG8uJRl6LRao2pKyqgrKPnxO8+lnzjPsVl3Ow8XPt5Cu//7GIVazcnF71D/lWG0+vJ1\nsq8lkLTvEA5+tYvPSFHyyVbdvW1TVl1JGbVlt236ifMcnbm6UPattP/pIxRqVZnTlXatT9F2NqKm\nDHWHPIdv3x4cnbwUbW6eUdMUozBiezCixiHQj9AVM4jb9hPJfx8qUldnSE9q9elB9JQlaPPKyWnO\n9VzG66g/fiA3Dp8iJfIYNZqHFHk+aPookg9Ec/79r7FydabD/32Ea4c2pPy5v3j80nIVaquyahRq\nNc4tmnBmzjK0efkEzJlEndFDuPT2OkNdzUG98Xn+aU5NW1hl2/PaT3ux9nTj4XfnU5CTS9x3u9He\nPiuoUKtwDq3P4ckr6PLXVzz66CO89NJE1q59q+zXIh5MVXuvF2au/ZJahW0tL25lZnNozDxsfb15\n+IPFZF29zvWf92Dt6Uqzdxegzc4l7rtdhm20VBbaDz0Icx9/3FFv6LPU7d+dQ6+sMJy9NkVl5axo\nylLyFBQU/eKuYcNAZoe/TPOmXbl44QrjJgznq00f0Lpld2bNXMaKV+ewP/JHrl9P4Ldf/6J1m+aV\nEV9UIUZ1uFq2bIlSqcTd3R1bW1suX77MpEmTAMjJyaFt27bUrVuXhx56qNi0CQkJBAcHG+bz+uuv\nc+nSJUJDQ1EqlTg4ONCgQYNyM9yZ99mzZ4mIiOCnn34CIC0trczpdIW+nS2cr06dOjg46Ie9eXh4\nkJubW26GwvzH9MGjfQsA1Pa2ZJy/YnjO2sOV/LQMCnKKzjPnehLODQPKrStM7WBH7ee7cfGz7+4+\nqABdKd+O5MYn4dywfrFlaAsto6yanPgkwzfVd57LTUgm53oSVm4uRZZ15zmXJkGonRxI2ntQH0+h\nAK0OnVaL0saKk0vfMyw/cMZow/T+Y/ri0U7fhip7WzJiireh9t42jE/CObR4G2pzcotltPZwJaeE\ns4CFuTQNQuNoT+Le2wcWhbKXJed6Ik4h97Rh+s0ieY2pKYlCoyZk7kTsHvLl0Og55FxPLLO+LLnx\nSTgVWtdWHm7FMpRX49nlEepPG82519eRsOvuxdIKjZqguS9hX8+Xw2Nml5qz3gv9cX9Uv55rPdvJ\nbOs5J774Nmpz+zmf7u3JS03Ds2MYKlsbrD1cab1hJRFDZuDZsRX7B04FnY685BsAODdrXGKHKzc+\nEYeQu59XVu5u3CrWnqXX5CelkLI3wvCtfuLOP/Ad3s/QngGzJ2FbrzbHx80g93pCie1Ze+Td6xks\n1Z5qJ3uu//IXlz7Xfy45NQwg6/bQ2dzEVOJ37TMMy9y6dRvt2z/K2rUlvpxyLVq0gGeeeRqA77/f\nUaFny/6bVIX3elnZzLFfyk3Uj3S5dvvLx+zY69w4ehqnhgFkX0sgfudfXP7i7jZ6Z3h3YVVhP2Sq\nyjr+AP26D50/Hgc/Xw6MmkfONePXfWXmrEjz5k+mx1NdAXB0cuDE8bsjDWrW8iYl5QZZ99zwpUu3\n9uz/+xAXL+hf44fvf8HKVfNwc6uBrZ0t4XNWkJqqP16dMnUsMTGXK+nVmJ9lusLVj1HXcJ04ob/u\nJikpidzcXOrUqcN7773Hhg0bGDt2LK1b68dFK0v4Rt/T05PTp08DEBUVRb169QgICCA6OhqtVktW\nVhbnz58vP+jtefv5+TF8+HA2bNjAm2++yTPP6E+/KxQKdDod1tbWJCcnU1BQQHp6epGbcRTOpyjl\nmz5j3bmINGLwTCJHzsU5tL7hQlLfXl1J2HOw2DTJB6KNqivsVlY2tZ9/HM/HwgBwbFAP55AAkvYf\nLbE++cBRnEPrY3t7GbV6diNxb5TRNYl7ovB5+jEUKiVqBzu8uj5C4p4ochNTyI6Lx6uL/m5brq2a\noNNqyYi5gsrOhgZTRhqu26oz+BkSfo8ArRa/F/rh20t/BtO2to9hx6Zvwy2GG1VEjgov3jb35C6c\nvaS6xD0HqfV0p0LZ25L4Z2SZ7auytSFw6kjDdVt1Bz9D/G8RoC37Rqcpkbfb8PbNLGr27EbSniiT\na0oSumwqKntbDo0Jf6DOlj7DEZwaNjBc4F7zuW4k7b03Z+k1Ho+1JmDyKKInLSlyAAbQcOk01Pa2\n/PNi2Z3CS+s2cXC4/gYr5lzPuQm3t9Gu+m3U7c42ev4Ke3q8SMRg/bZ2cvkHZMddJ2LIDABunrmA\n9+1plDbW+sdKGcp3I+owDiGBhptZeD/bnZS/Dhhdk/zHPtw6PoLSygoA13atyDyt//xrsHgmKntb\njo8vvbMFcHX9V4b/W6o9nYL9afLaNBQqFQqVkoeG9eT6L/rtI+G3CLyfaG84m/LUU08SFVX251xZ\nFixYRLNmLWjWrIV0tspQFd7rpTHXfinnWgLppy8YhuhZuTrj3CiQ9FMxOAX50ei16YZttN7Qnlwv\n4W6vVWE/ZKrKOv4AaLJiMmp7WyJN7GxVds6KtGTxGlqHPUnrsCfp2K4nLcOa4h9QD4AXRg/ixx27\nik1z5PBx2rVrhaen/rrbp5/pxqVLV0lOTmX06EHMWzAFAE9Pd0aM6s+WTf+ptNcjqgajznAlJSUx\nbNgwbt68yYIFC1AqlYwZMwadToe9vT0rV67k2rVrJU67dOlSlixZgk6nQ6VSsXz5cmrXrk379u15\n/vnn8fT0xM3N+Aswx44dS3h4OFu2bCEjI8NwN8RmzZoxY8YM1q9fzyOPPMLzzz9P7dq1qVu3rtHz\nvl95qemcWPI+TV6dgkKtJjvuOscWvguAU7AfIeEvEjF4Zpl1pdLqODJ9FUHTRhAwpi/aggKOhq8t\ndivUO/JT0zm55D0aLZ+KUqMmOzaeE4vfwTHIj+A544gcOr3UGtBfqGxby5uwDatRatTEfbuLG4dP\nAnB83hqCZ4+l3ojeaPPyOR7+Buh0JO8/QuzW/6PFR0tAodTf/nzFBwCcf2cDIQtewufJjugKCji5\n9D2arZ1bSu73abziTtvEc3yRPpNTkB8h4WOJGDKjzLrY7Tux9fWi9ZerUGrUxH67m9TDp8ps3uT9\nR7i65SdafrQEhVJJRswVTi7/sOx1cjvvqaXvErp8mr4N4+I5ufhtHIP8CZo9lqhh00utKYtz40A8\n2rUk83IcD3+41PB4zHtfknKg5E52eTlPL3uXhsumodCoyYm7zqnbOQNnjePg8Gml1gA8NFZ/B7PA\nWeMM80w7dpr4nXtxb9eSrMtxNP9g2d2c739J6oEjZeYx53o+NvdNgme/iN+IXmjz8omes6bINUgl\nOb7oXYKmj6LNkx0MQ39Kux37rRtpxLy6lgaLZ6HQqMmNu875ZWuwDwzAf8ZEokdNKrUG4Pp3P6F2\ncqTRujdQKJVknr3AhXffxTE0GNdHWpF9JZbQd+/eKvryB5+TFnW4yrVnyoFokpqF0HrjKhRKJQl/\nRnH56x8AOP/hJupPGEybr/VDoS9cuMibb8pwQnOrau/1e7OZa78UPXMVgdNHUatnNxRKBZfWb+Xm\nqRgAXJo3pNXG1aBQkrgnkiubfjQiZ+Xvhx6EOY8/XBoH4tm+BZmX/6XlusWGx8+98xXJEabtj8x6\nnGRGiYnJjB0znY1fv4+VlYaLFy7zwkh956l580a898FrtA57kj//2M+baz7k512byMvLJzXlBn17\n60f0rFr5Hp98uoaof35BoVCwbMmbHDoUbbHXJCxDodOVfTSyfft2Lly4wLRpFf87KtXNzrB+lo5Q\npm6Rm/m1dR9LxyhX54it7GpVcbeJNpeuB7bwW5uqe+vpOzrt/4Y/2va2dIxydfx7W7VZ7/vbm3bh\nemVrs0f/e3ZVvT27HtiCQlH6dbVVhU6XTwEbLR2jXCoGVZv3uuyLKk7XA1uqxfEHVI/jJDvrepaO\nUa6s3EuWjmC0j4PHWDpCmUaf+sjSEYAq9MPH0dHRrFpV/K5t3bt3Z+DA4r/VIIQQQgghhBBVXbkd\nrl69elVGDho3bsyGDcV/m0IIIYQQQgghqiuz/fCxEEIIIYQQQvyvqzJDCoUQQgghhBDVh1b3YHf9\n/l8hZ7iEEEIIIYQQwkykwyWEEEIIIYQQZiJDCoUQQgghhBAmK/uXLsUdcoZLCCGEEEIIIcxEOlxC\nCCGEEEIIYSYypFAIIYQQQghhMrlLoXHkDJcQQgghhBBCmIl0uIQQQgghhBDCTGRIoRBCCCGEEMJk\nWksHqCbkDJcQQgghhBBCmIl0uIQQQgghhBDCTBQ6nU5+s0wIIYQQQghhkrcbjLN0hDK9dPZ9S0cA\n5Bouk/zUcoClI5Spe9TX7AzrZ+kY5eoWuZkfHh5k6RjleurQxiq/zkG/3gvYaOkY5VIxqNq0Z/bJ\nLpaOUSbbkN1A9fhM+jmsv6VjlOuJyE380ba3pWOUq+Pf26rNe/23Ns9bOka5Ou3/hoV1X7J0jHIt\nvPw2HwS9aOkYZRp7+kMAvm48wsJJyjYg+lN+bDHQ0jHK1ePgV5aOICqYDCkUQgghhBBCCDORDpcQ\nQgghhBBCmIkMKRRCCCGEEEKYTG4Lbxw5wyWEEEIIIYQQZiIdLiGEEEIIIYQwExlSKIQQQgghhDCZ\nTqewdIRqQc5wCSGEEEIIIYSZSIdLCCGEEEIIIcxEhhQKIYQQQgghTKbVWTpB9SBnuIQQQgghhBDC\nTKTDJYQQQgghhBBmIkMKhRBCCCGEECaTEYXGkTNcQgghhBBCCGEm0uESQgghhBBCCDORIYVCCCGE\nEEIIk2nlh4+NIme4hBBCCCGEEMJM5AxXBfF4pBkNJvRHaaXm5rkrHF/6Ebcys42uU1praDhjJM4h\nfqBUknb8PCdWrkebm4/rwyEETRqMQqUiP+0mp974gpvnrtxXTvdHmlF//ACUVhpunr/CiaUfUFBC\nzvLqrD3daLV+KfsHzSA/7ab+tT3anNAFE8iOTzLURY1ZQEFWjsk5PR9tStDEfig1atLPXyV68ccl\ntqcxdQ+vmkRuYirHV34OgFuLEIJfGYBSraIgN58Tqz7nxokLJmeE6rPeTaHT6Qif/T0B9T0YOaqt\n2ZdXWHVrzz0Hs3n7yzTy8nXUr6th4URXHOzufo+14/dMNnx/0/B3RpaWhOQCfllXE7UKln2YypmL\n+djaKHi2kz0Dejg+UJ7CqktbejzSjAbj+xs+a44t/bDEz6TS6pTWGkKmj8Q5xB+UCtKOn+fkKn3O\nO2o93RGvji35Z+qq+8ro2rY5fmMHo9SoyYi5zJnl71GQlW1UjdLKivrTXsAxOACFQkn6ybOcW70O\nbV4ejsH+BLwyEpWNNaiUXP3yO+J/2XNfGe9HZb7X3do2x3/cIBQaNZkxVzi1rHgbllqjVFL/5WG4\ntm6KQqXkylc7+PfbnQC4NG9I/ZeHGbbFc29+Ssb5y4Z5KjRqmqyeTdx3u0j8PaLCXk/9Tg3pMuNp\nVFZq4k//y/czviI3o/i+LmxYe1oMfhR0OlIuJ7Fj1tdkJmdUWI6S1OkQSqspPVFZqUk+E8cf4V+Q\nn1n6fvixFcNIOfcvR9fvAkChVPDovAH4tKwPwJU9x4lYua3Cc9Zs15gmrzyP0krNjbOxHFiwnltl\n5Gy1ZBRp5+M4/fnPRR6383Kl65dz+anPfPJuPHjbej7SlMCJdz4TrxK9pOTPztLq1Pa2NJ4/Bod6\nNUGhIPbHvVz4fAcAziF+hEwdgsrGGoVKyYXPdxD3074HziNJK/QAACAASURBVCyqPjnDVQGsXBxp\nNP9FDs9cw97np5Idl0CDiQNMqvMf0ROFSslfA2fx14AZKK2t8B/+LGp7W5qvnMyZt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JU4A/AHaNG2BTvxZaF0cATi9cQ/z+/0rNZkyfjKl5UL0skvs+e5sTl8ip2avJuBpe5Llzk9MI\n/34Hh4fPJHT9Fpq+NgOtq5Nxucyon5buzmTftSyNrbVhIGRkXZEex8SjdXPC2tOD7PhE6gx8njYf\nLKHtpyux86mHLjuHzPBorn3xEx2+XUPH/22kWsvGXPl0+wPtp7WnBznxidQe+DytPlhKm02vYe9t\nyHdP+04z2Nbv17wFXXmhRzOTPb8p10dLdxeyYxMLfevLik3A0s2wXZ+cv5a4fUeLZIrZfbDgiJBd\no7oApJw6T+KRU4S+/yUhg4NIPnUR/9dnFHpcyplLOLXyw9LDMHip8fwTKC00aBxsqTukB/p8HYeG\nziRkcBA5sQlFjtoCaBzsQaEgNynlduZb62/t6uTEJ9F4zljabHqNFu/OR6FSFdtXjYMdtQd058Lb\nnxa5T+viVOJredA5wfDeZsUYtw6UVGfpXnibz4pJwNLNmfSwSFTWlji3NazD9o0bYFvfs2Bb0ufn\nU6vPM3T86T0sHO2I+TukxJzCvD0U53AdP36cESNGkJCQwIABA/D09GTNmjVotVocHR1ZsWIFZ8+e\n5euvv+bttw2HrTt06MC+ffuYNWsWSUlJJCUlsXHjRhwcHMpeoLL4EwT1Op1RNYpi7tPn64qpvs1r\n3ECSjp0hIeQEji19y85YSTmjduxB6+ZEi/cWosvMJuKHXWV/CZN+lkihVuHyaGuOjJlP5vUoPPt2\npdlrhikYJecs/neUwjlLrlEoinkNOh35GZmcnLmK+qMH0mDCYJKOnSHxyKlyD1oMyzeTXhbJfX+9\nLc2p2asL/j/5xDmST57HKcCfyF93G5HLjPqpKKE/dy+vtLriXotOh0KtxrqmO3npmRwatQArT3fa\nbFxCxvVINA52uD3Rlj0vjCU3KZWGEwJpsmA8x4o5r9NU/VSoVVjdzHdk1HysPD1otWEJGdej7nHf\naQbbelVnwvWR4vpL2fuCW5zb+uO3eCIA8fuPEr//9uAsbMtP1BvxEpbV3ciKNJw3mXTsLJc/3krT\nVUGg03Pjl7/ITU5Fl5uHc4dWaOyscQowfPFXatTkFDrwhwAAIABJREFUJCYXXWgJ6zX5OpRqNc6P\ntOC/8YtIOX0Jl8da4//WHPb1GFukvEaPp4jde7gg252yImM4PnVlsa/lQecEit1ebz2X0XXFbfM6\nHfnpmRyb/gZeY/vRaNIgEo+eJeFw4W3p+tbfub71d7zG9MP/takcHrO4+OUIs/ZQDLjUajUff/wx\nERERjBw5kuzsbL766ivc3d357LPPWL9+PZ06dSrx8e3atWPYsGFGLy87Og6HJg0L/q11dSI3OQ1d\nVrZRNVnRcWhdqhW6785fx4rj8WxHchKTcX28LSorS7SuTgR8vrrULzeVkVNtb0v0zn+59vkPANg3\n8SqYylKVcppLP7NjE0k+eb5g2tKNn/7Ce+oIlFqLEqdDZUXFYu97V4aU1EI5S6vJio7D4q6cWTHx\noFCQn5HF0fELC+5r+9WaMt/fYl+XmfTybvfb25Koba2p+dKzXPts2+0bFQp0ecZ9wa3q/Wwwqi+u\nj7UGQGVjRVpoWKlZAbKi43Dw8yo+b1QcFs6Ohe7LikkoOFp949e/AcgMjybp+DkcfL2wqV+L2L2H\nyU00/CJ+/bvfaf/lm8W/NhP1Mzs2EYDIX27liyLp+DnDfjIyptz7TnPY1qsiBc1QUBOAmi92Ntn6\nmBVd+HYAy5v3laX2gOeoN6QHJ+evpdW6+dh61cbWq27BBVZuvRL9HfsIlbUlSUfPEPnzX4Bh2nyD\nUf3JSzFMqb3w9ibiDxwz1FpZorTQYOdTn8Zzbg9EDg03nDOmtrMhLzW9IHNMTDyarGzSr0UUTCWN\n23sYxZyxWNV0L5Lf/alHuPBW8VOMS3stDypng1F9cO1o2CepbaxIu1R0Hci/ex2IisOhSdF1ID8r\nm6yo+IKjVrfuy765LeVlZnF47JKC+x755i0ywqOxbVgHhUJB6oWrAIT/+Be1+3UttmdVmXE/H4iH\nYkqhr68vCoUCV1dXIiMjsbW1xd3dsGG1adOGixcvFnmM/o5D/PXq1SvX8uIPHsfBryFWtTwAqNnz\naWL3HjK6JnbPIap3fwKFSona1hr3Lh2I3VP48Xf79/lRhAw2/Hp8duV6MiOiyvwluTJy2vvUp+mq\nIBQqFQqVkrpDehL1+94ql9Nc+hn7TwiOzbyxrO4GgFuntqSFhpU6QEgIuZnB05ChRs+nibtrOaXV\nxO05RI3nOxfKGbcnBPR6/N+ag52P4WRw187t0eflF7pymbHMpZd3u9/eliQvIwvPl57BtVNbAGwb\n1cO+sRcJwceMylXV+xn6wbcED55B8OAZhLw8Fwe/hljfzOHZqwsxe4su61be4upi9xymZvc719FH\niP0nhKzIWFLOXaZGt8cBwxdOh6bepJy9TOr5K7h2aInKSguA+xNtST514YH2MysyhpRzl6n+3N35\nQu9p32kO23pVpOcEOn4DMOn6mB2TQGZENO5dDFdWdG7rj16nK/Tlvji1BzxHrd7PEPLyXBIOGc6j\n0+v0NJo6vGB7q/nS06SFXis0JV7r4kTL9xcVTCmtO7w3UTv3AZAQfAzP3l1RqNWgUOAzezQNxg0k\n9dzlggtXhAwJQp+vI37/f9Ts0QUwDI5s6nmS+N8Z4g8cw8rDDTvv+oDhKqXo9UWuTKq2s8Ha04Pk\nE+eL738pr+VB5Qz9YCvBg2YSPGgmISPmFX1v9xwuZh04UWJdzJ7D1Lxjm/fo8ggxfx8CvZ6Wb8/C\nvrEhi/uT7dDn5ZF28Rp2XrVpsmAsSq0FADW6dSTh8KlS1w1hvh6KI1x3TouoVq0aaWlpxMTE4Obm\nRkhICHXr1kWr1RIbGwtAREQEycnJxT7eGLmJKZxZ+j5NV0xDqVGTGR7N6SXrCn6BCRkSVGINGE6u\ntqrpQcDmN1Bq1ERs30XS0TMV0InKz2mYoteEtlveAIWS2D0hhH39a5XLeS8qI2faxauce/1Dmq0K\nQqFWkZeazsm5JZ/8eyvn2WXv4bdiuiFDRDRnlryLnU8DfGaP4dDQoBJrACK2/26YivX5m4acP9zO\neXrhWnxmj0GhVpMTn8iJmcVMx3qIellc7vvpbYl0Ok7MeJ1GU0dQ75V+6PPzOTX/rRIvWV5cLnPp\npyHHepqtnIpCbejPqcWGHPY+9fGdO4bgwTNKrQvfthMrT3fafbEapUZN+PY/SDx6FoDjM1bjE/QK\nnr26gELB5U++I+VsKClnQ7Gq7krbz1ahy8klKyqO00vff+D9PDFzNd5BL1Oz59MolAqufrKV1LOh\nAPe076zq23pVZ+r18eS8NTSePZr6w3uhy8nlxJy3Sz2TX6FW4TW6H7mpGfivml5wu2vHNlx46xP8\n35iJQqUkKyaBU/PXFlonM8JucO3zH2jzyQpQKEk+fo7zb34MwJVN39Nw4mACPn8dhVJJ2sWrXFz7\nebEZzq/+CJ85Y2j77Jugh9OL3iU/PYP89AxOzHwd7xmvoLLUosvN48TsN4r86QIrTw+y45LQ5+cX\n3HZnzvTL14t9LQ865y05iSmcXroe/9duvbdRnFz0nmEdaFwf37mjCR40s9S68O93Yl3TnfZbXkeh\nvmsdmP8OvnNGodSoyY5L4liQ4UJvkb/txdrTg3afrUSfn0/a5XBOL9tY4rohzJtCr69K1/Aov23b\ntnH58mWmT59OdnY2Xbt2ZdmyZaxduxaFQoGDgwMrV67E3t6eiRMnEhcXR4MGDTh69Ci///47s2bN\nolu3bnTs2LHMZf3Zrs8DeEX37sngrVU+I0jOivZk8Fb+at+7smOUqfOB76SfFaTzge8A89gn7Wpb\n/KXXq5IuB7+t8r0E81g3wbB+5rOlsmOUSUWgrJ8V5Mlgw5U3zSHnzoB+lR2jTE+HfFPZEYw2tcar\nlR2hVG/dWFvZEYCH4AhXr169Cv5fq9Xy11+GucuPPFL0jyOuX7++yG2vvfaa6cIJIYQQQggh/l97\nKM7hEkIIIYQQQoiqyOyPcAkhhBBCCCEePLlKoXHkCJcQQgghhBBCmIgMuIQQQgghhBDCRGTAJYQQ\nQgghhBAmIudwCSGEEEIIIcpNZ9Z/XOrBkSNcQgghhBBCCGEiMuASQgghhBBCCBORAZcQQgghhBCi\n3PRV/L97kZWVxcSJExk4cCAjR44kISGh2DqdTscrr7zCV199VeZzyoBLCCGEEEIIIYCvvvqKRo0a\n8eWXX9KjRw/ef//9YuvWrFlDSkqKUc8pAy4hhBBCCCGEAI4cOcJjjz0GQMeOHTlw4ECRmh07dqBQ\nKArqyiJXKRRCCCGEEEKUm7lfpXDr1q189tlnhW5zdnbGzs4OABsbG1JTUwvdf+HCBX755Rfeeecd\n3nvvPaOWIwMuIYQQQgghxP87ffr0oU+fPoVumzBhAunp6QCkp6djb29f6P4ffviB6Ohohg4dSkRE\nBBqNhpo1a9KxY8cSlyMDLiGEEEIIIYQAWrZsyT///EOzZs3Ys2cPrVq1KnT/jBkzCv7/3XffxcXF\npdTBFsg5XEIIIYQQQoh7oNdX7f/uxYABA7h48SIDBgzgm2++YcKECQBs2rSJP//8856eU6HX32sc\nIYQQQgghxP9X49xfrewIpXo/em1lRwBkSmG5/NOhV2VHKNXj+7ZV+YxgyLmnQ8/KjlGmjvu2m00/\nF9WZWNkxyrTo2rtm08+3vMZVdoxSTb1kuERtVe/n4/u28We7PmUXVrIng7eyq23fyo5Rpi4HvzWb\nbd1c+pnPlsqOUSYVgWaxrQNsbzG4kpOUrufRzWazTxIPFxlwCSGEEEIIIcpNV9kBzIScwyWEEEII\nIYQQJiIDLiGEEEIIIYQwERlwCSGEEEIIIYSJyDlcQgghhBBCiHLTybXOjSJHuIQQQgghhBDCRGTA\nJYQQQgghhBAmIlMKhRBCCCGEEOUmMwqNI0e4hBBCCCGEEMJEZMAlhBBCCCGEECYiUwqFEEIIIYQQ\n5SZXKTSOHOESQgghhBBCCBORAZcQQgghhBBCmIhMKRRCCCGEEEKUm16mFBpFjnAJIYQQQgghhInI\nEa4K4tS+FfXGBKK00JB+6RrnV75HfkamUTVKCwu8po3ErrEXCqWClNMXufTmh+hycrCu60mjGWNR\nWVui1+u5sv4LEkOOVamMt1hWd6PlJ6s5MWUJaedC7ynjrQx1xwwqyHBh5bpicxZXo7KxptHs8VjX\n8QSFgujfdhO+ZTsAtj5eNHh1BCorSxRKJde/2E7Mzn/uK2dVf8/L0rBzE56a0R2VhZroczf4acaX\nZKdlFakLGNqR1oMeBb2ehGtx/DzrK9Lj0yo0i7n2s14nPx6d/iIqCzVx5yPYOfsLcorpYeMXA2j9\nylPo9XrysnLZveRbok+FYWFrydOvDcKpvgcKpYIz24I59MGu+85VVfvp/EhLGowbiFKjIe3SNc4u\nX18kV4k1SiWNXh2KU1t/FCoVYV/+RMT2wr2q/vwTuHYK4MT0VYVuV2jU+L85mxvbdxGzO9iorC4d\nWuA1diBKC0OO08s3kJ+eaXydUoH35KE438x7bcvPhN+Vt0b3J3B7PIBjxeRt8eYswn/YRcxfB43K\nW5YHvb2bsn/WtTzwnTcWjYMd+RlZnFq8joxrNwqes7j+aRztaDxrFNaeHijUqnK/nrLo9Xrmzv4J\nr4aujHj5kQp//pKYy2e7+6P+NJnYF6WFhpSL1/lv8YfkpRdd/0qrq9fnSer27IRKqyHx7FWOLv4I\nXW4eLq0b4zelP0q1mvysHE68vpnE05eNymXqfZJldTcCPl3F0VeXknrOkKnpymnYetUlP9PwuhKP\nnOLi2s/K3VNhPuQIVwXQONrjPXcCZ+au5tCAiWTeiKbe2MFG19Qe+hIKlYojQ6dyeMhUVFoLag/p\nBUDDaaOI+vVPjgybxoUV7+G7dBqoyv+2mTIjgMJCg8+CySjV9zeG1zja02juRM7MfZ3DAyaQdSOq\n2Jwl1dQdOYDs2HiODH6Vo68EUaPns9g18QbAd/kMrn38Nf8Nm8rJaUupP2k4lp7V7zlnVX/Py2Lt\nZEuP1YF8M+Zj1nVeRmJYHE/NeqFIXXW/WjwysjMf93qL959eScLVWJ6Y9lyFZjHXflo52fLMqsH8\nPP4DPn16MclhcTwa1KNIXbV6bjw2syfbRqzjixdWcvC93+j+/igAOkzpTlpkEp93W8aWnqtoNrAj\n1VvUu69cVbWfGkd7fOeN4+TsNwju9yqZN6LxGh9odE3Nnk9hVcuDg4FTOTRiFrX6PYe9rxcAantb\nvGeMxHvaCBQoCj2nvV8j2ny0AsdmPuXooR1N5o3jxOw32d93MhkRMTQcN7BcdZ49u2Bdy4MDA6dx\ncPhsavfvhr1vg5t5bWg8cyQ+04ZzV1wc/BoS8PFyHP2Nz1uWB729m7p/fosnEf79Tg70n0roh9/i\n/9q0gucsqX/ek4eRfiWc4EFBHBwyEwAF9cv92ooTGhrLiKGb2fHb6Qp5PmOZy2e7RTU7Wi0excGg\nd/ij5wzSw2NoMqlfuepqdG5Ng/5d+HfMa/zRezYqSwu8Bj2LQq0iYNUEji75hL/6zeX8Rz/SatkY\no3KZcp8EoLTQ0GTxRBSawv1z8GvEkbELCBkSRMiQILMebOmq+H9VxUM74MrOzqZz584PZFnVApqT\nevYSmeGRANzYvgP3px8zuib5+BnCPttqmAir05F24QpaD1cAFColajtbAFTWVuhycqtcRoCGU0cS\n9b+/yE1Ovad8hTNcJOuODG5PdzS6JnTNx1xe9ykAFs7VUGjU5Keno7DQELbpG5IOnwAgJzae3KQU\ntG7O95Gzar/nZWnQ0YeIE2EkXI0F4PAX/9L0xdZF6iJPXeedTkvITs1CrVVj5+5IZmJGhWYx137W\nebQxUSeukXTN0MPjX+6h8QttitTl5+Sxa84W0mNTAIg6eQ0bF3uUGhW7l27ln9e2AWDr5oDKQk12\natGjAOVRVfvp1LYZKWdDybweBUDEtp14PPOY0TWuj7cl8pfd6PN15KWmE/3HPjyeNdzn/mR7cuIT\nufju5iLLrdW3K6EbvyblzEWjszq39Sf5bCgZN3OEb9tZsCxj69weDyDi578L8kbt2k/1Zw37Ko8n\nHyE7LpEL7xTNW7tfN0I3fk3yaePzluVBb++m7J/WtRo2dWsQtWs/APEHjqGy1GLnbfihoqT+xfwT\nwvWtOwDuWG9tyv3aivPVlsP07NWcZ7s2qZDnM5a5fLa7tWtK4unLpIdFA3Bl65/U6lr0KGBpdbWe\nf5SLX/xGbko66PUcW76JsF/2oc/L57dnJpF8/hoA1p5u5CQbd0TWlPskAO/prxD569/kJqcU3GZZ\n3Q2VtRU+M0cR8MUbNJ43DrW9rVF5hfl6aAdcD5LWzZnsmLiCf2fHxqO2tUFlbWVUTWLIcTKvG3aE\nWndXavZ7nti/DB8kF9/8kNqDe9Fu+4c0W7uQi29shPzyj9lNmdGj+1Mo1Gqifv6j3LmK5nQhOya+\njJxl1OTr8F4wmdab15J89DQZYTfQ5+QS9cufBY/xeKELKitLUk9duMecVf89L4tD9Wqk3Egs+HdK\nZBKW9lZobS2L1OrydPg83YypwUup07YBR7caNyXLWObaT7vq1UiNvN3D1KgktHZWWNzVw5SIBK78\nfarg353m9ib0rxPocvMB0Ofr6PrmMIb8bx7hBy+QeDn6vnJV1X5aurmQFX3HMmPiUdtaF8pVWo2l\nmzNZ0fGF7rv1o0nE9l1c+fg7dNm3p0LdcnrBWuL3/2dUxoIc7s5k37Usja01Khsro+ss3Z0L76ti\n4tG6OQEQvn0Xlz/+jvxi8p6cv5a4fUfLlbcsD3p7N2X/LN1dyI5NLHS2flZsApY3e1tS/2J2HyQn\nIRkAu0Z1AdATXu7XVpx5C7ryQo9mFfJc5WEun+3WHk5k3vE+Z8YkoLGzRm1jaXSdbR0PtNXseWRd\nEJ2/WU7j0b3ITTX8GKDPy0frZM+zv6/Fb3J/Ln76q1G5TLlPqvFCZxRqFTd+vP3dA8DCyZ6EQyc5\n99pGQobMID8zC9+5Y43KK8zXQzXgSk9PZ+zYsQQGBrJo0SIAQkJCGDJkCIMHD6ZXr15cuXKFb775\nhlWrDPPl8/Pz6d69O9nZ2fe8XIWy+Dbqdbpy1dh616f5+8u48f1vJOw/gsJCQ+Ml0zi3/F2Ce47k\n2Pj5NAwac09HZUyV0bZRfWr0eJqLqzeUO1OxlIpib74zgzE155esYf9zQ1Hb21JneN9CdbUG9aLO\ny/05PXNFoXnq5WEO73lZFCX0UVfCl+dzO0/weovZ/P32bwzePA6FovjH31sW8+xneXuotrLg+Xdf\nwbGOK7tmbyl032/TPmV9mxlYOtrQbmK3+8xVRft5n9t3cf3Wm+DHCAAUJfTn7uWVVlfca9FVziSX\nB769m7J/JWTRG9lb57b+tHxn3s1/JZZaW9WZzWd7Se9Zvt7oOqVahVs7P0JmvsvuwAVoHGzwndC7\noCY7IYUdz7zKP0MX03LxSGxre5Sdy0T7JDvvetTs+TTnVn1Q5P6U05c4OWs1OfFJoNNx+cNvce7Q\nEsV9TtsUVdtD9e5+/fXXNGrUiClTpnD8+HEOHjzIxYsXWb16Ne7u7mzYsIEdO3YUDL6mT5/O3r17\nadu2LVqt9p6XmxUVi51vw4J/a12cyU1JRZeVbXSN65MdaDh9FJfe+oiYXXsBsKlfG5WlloT9RwBI\nPX2BjCvXsfNtRHbMgSqR0b1rJ1TW1rTYuBIAC5dqNF44mcvvfU78v4fKlREgOyoOO99GpeYsraZa\nQHPSL18jJy4RXWYWsX/sxeXx9oDhJGrvuZOwruvJsdGzyI6KLXe+W8zhPS/OE1O74f1UU0MeO0ui\nz90+ydzOw4HMpHRyMwsPQp3quGDrak/YYcPJvke/PcDzK/ph6WBFZlLFTC00p34+8urz1H/S0EML\nWyviLkQU3Gfr7khWUjp5mUUH8nbVq9Hjg7HEh0axNXANedmGKU11HmtM3PkbpMckk5uRzbmfD9Pw\n2Rb3lO2WqtrP7Og4HJrcsUxXJ3KT0wpv36XUZEXHoXWpVui+O4+A3K8Go/ri+phhmp3Kxoq00LBS\nswJkRcfh4OdVbF1WVBwWzo6F7suKSaiwvGV50Nv7ncur+WJnk/UvK7rw7QCWRva29oDnqDekByfn\nr6XVuvll1ld1VfmzvfHYXng83hIAjY0VKZeuF9xn6VaNnOQ08u9aHzKj4nFq2qDYuqzYJG7sPlJw\nAY3rv+7DZ1RP1LZWuLbxJXK3Yb+UfO4ayRfCsG/oSVpYVKkZTbVP8uj6OGobK1p/uNxwu4sTTRa/\nyqV1m8lLSUNtb0vc3sMAhh8zdHqjfzCoanRyWXijPFRHuK5evUrTpoadvb+/P2q1Gnd3d5YvX86s\nWbM4ePAgeXl52Nra0qZNG/7991+2bdtG7969y3jm0iWGHMe+SSOsbl6AoUbPp4nfe8joGpdO7fGa\n8gonpiwp2NkBZIZHoraxxt7PcNEHy5ruWNf1JO2icVfeeRAZQ9d+wqEBEzgybBpHhk0jJy6Rs4vX\n3NNgy5DhGPZNGhVczKJ6z2eI3xtidI1r5w7UGW44wVahUePauQNJ/50EwHdZECobK46NmX1fgy1D\nhqr/nhdn91v/Y0O3VWzotoqPeryJZ4u6ONU1zNdvHfgo53aeLPIYWzcHeq8bhnU1w7kOzXq0IeZ8\nZIUNtsC8+rl/7S988cJKvnhhJV/1fp3qzevhWMfQQ/+Bj3HpjxNFHmPpYE3fL6dwcecx/jf5k4LB\nFoB3t1a0v3lES2WhxrtbS64fOH/P+aDq9jP+4HEc/BpiVcvwy3PNnk8Te1eu0mpi9xyievcnDOeR\n2Vrj3qUDsXvubV9TnNAPviV48AyCB88g5OW5OPg1xPpmDs9eXYjZW3RZt/IWVxe75zA1u3e+I+8j\nxP4TUuQ5TOVBb++3lgeYtH/ZMQlkRkTj3sVwbo9zW3/0Oh1pl8KKPP+dag94jlq9nyHk5bkkHCr6\n2s1RVf5sP7t+G7v7z2N3/3n8PWQx1Zp6YVPbHYB6vZ8k8u+i03yjD5wqsS7ijxBqPhWAUqsxvI4n\nWpF4+jL6fB0tF43Eyd8wKLKrXxO7utVJOFn2FRVNtU+6uOZTDvR9teCiGNlxCZxeuJa4vYdRWVvS\naOqIgvO2ag96wXDlVDMdcAnjPFRHuBo0aMCxY8d46qmnOHPmDHl5ecyfP59du3Zha2vLzJkz0d+c\n8923b18+/PBDEhMT8fG5vytB5SYlc37FOnyXBaHQqMmKiOLc0new9WmA96xxHBk2rcQagHpjDFe7\n8Z41ruA5k0+c49JbH3J6zioaTH4ZpYUGfV4+F17fQFZE+c/vMGXGimTI8C6+y4JQajRkRkRxfula\nbH0a0GjWeP4bNrXEGoDQdZtoGDSGVpvXgl5P3N6DRHz7C/ZNfXB+NICMsAiab1hZsLwr739+T5cI\nN4f3vCzp8Wn8GLSFvutfRmWhIvFaHNunGE7gr9G0Fi+sGsiGbqsIOxTKnnU7GfbNJHR5OlJjkvl6\nlCned/PrZ2ZCGjtnbqb7upEoNWqSw2LZEWS42pS7X226rAjkixdW0mxgR+xqOOHVxR+vLv4Fj/9u\nyDv8s+J7nlw6gCH/mwd6PZd2Hee/T3ffV66q2s/cxBTOLH2fpiumodSoyQyP5vSSddj51KfxnLGE\nDAkqsQYMJ6tb1fQgYPMbKDVqIrbvIunomfvqVelZ19Ns5VQUajWZEdGcWmzIYe9TH9+5YwgePKPU\nuvBtO7HydKfdF6tRatSEb/+DxKNnTZK3LA96ezd1/07OW0Pj2aOpP7wXupxcTsx5u9S/wKpQq/Aa\n3Y/c1Az8V02/fTtN0PNgryxYkczlsz0nMYX/Fn1I29WTUKpVpIfHcHj+RgAcfevRYsHL7O4/r9S6\ny9/+gYW9LU98uRSFUknSuaucfOsT8jOzCZ66hmZBg1CoVehy8jg0Zz1ZMWVPF62MfVL8gWOEb/0f\nrT9YCgol6aFhnF1ZQVM3RZWl0Osfnr8RnZ2dzYwZM4iJiaF+/focPnyYTp06ERwcjJWVFS4uLjg6\nOrJs2TIAunfvTmBgIP379zfq+f/p0Kvsokr0+L5tVT4jGHLu6dCzsmOUqeO+7WbTz0V1JlZ2jDIt\nuvau2fTzLa9xZRdWoqmX3gfMY5/0Z7s+lR2jTE8Gb2VX275lF1ayLge/NZtt3Vz6mc+WsgsrmYpA\ns9jWAba3GFxGZeXqeXSz2eyTzMVg51crO0KpNsevrewIwEN2hEur1bJ2rXGN1el0WFtb8/zzz5s4\nlRBCCCGEEOL/q4fqHC5jXb9+nZ49e9KtWzdsbeVvHwghhBBCCCFM46E6wmWsWrVq8eOPP1Z2DCGE\nEEIIIcyWXKXQOP8vj3AJIYQQQgghxIMgAy4hhBBCCCGEMJH/l1MKhRBCCCGEEPfn4bnWuWnJES4h\nhBBCCCGEMBEZcAkhhBBCCCGEiciUQiGEEEIIIUS56So7gJmQI1xCCCGEEEIIYSIy4BJCCCGEEEII\nE5EphUIIIYQQQohy08llCo0iR7iEEEIIIYQQwkRkwCWEEEIIIYQQJiIDLiGEEEIIIYQwETmHSwgh\nhBBCCFFucgaXcRR6vZztJoQQQgghhCifPo6TKjtCqbYmvVPZEQA5wlUuYf0CKjtCqWp/E1LlM4Ih\n5/X+bSo7RplqfX3IbPq5wWd0Zcco05hzG82mn5v9Xq7sGKUafOpjwDz2SX+1713ZMcrU+cB37Azo\nV9kxyvR0yDdms63/2a5PZcco05PBW/mnQ6/KjlGmx/dtI58tlR2jVCoCAcjd27iSk5RO89hZs9kn\niYeLDLiEEEIIIYQQ5aaTeXJGkYtmCCGEEEIIIYSJyIBLCCGEEEIIIUxEphQKIYQQQgghyk0v1yk0\nihzhEkIIIYQQQggTkQGXEEIIIYQQQpiITCkUQgghhBBClJtcpdA4coRLCCGEEEIIIUxEBlxCCCGE\nEEIIYSIypVAIIYQQQghRbrrKDmAm5AiXEEJ09aeiAAAgAElEQVQIIYQQQpiIDLiEEEIIIYQQwkRk\nwCWEEEIIIYQQJiLncAkhhBBCCCHKTa+X68IbQ45wCSGEEEIIIYSJyBEuE7Bs0QHHAeNQaCzIDbtE\n/IZl6DPTC9VYP/os9i8MBr0efXYWiZ++Sc7lsyisbHAeMw91zbooFArS/vkfqT99/v8+p0P/8Tdz\nXiRhY3E5u2LXfRDoQZ+TReKnb5B7+WyhGuepr5OfGEvSptUmyWgOvQSo/bgfbaf2RGWhJv58BH/P\n/Zzc9KwS659YOZSEizc4/skuABRKBY/OH0D1Ng0BCNtziuDXv6/QjObUz5odm9Fici+UGg1JF8I5\nsGBTqf18ZNkIki5FcObT3wHQ2FrRfskwHOpVB6WCyz/u5/Qnv1VoxqraT+dHWtJgbCAKjZr00DDO\nLn+f/IzMctVo3Zxp/dEKQgZPJzc5FQC7xg1oOHk4KkstCpWSa5t/IPr3vfeV1aVDCxqOG4DSQkPq\npTBOL9tAfnpmueu0bs60/WQZBwJnFOS1b9wA76lDUVlpUSiVXP38RyJ3/HtfeaFqbOtek4bg3rk9\nuSlpAGSE3eDUvLcL1Xj2eRbP3l3RZeeQfjWc8298TN7NemNpHO3xXTgBKw9X9Dod517bSPLJCwDY\nNKiN97QRqG2sb973AannL5f5nE7tW1FvTCBKCw3pl65xfuV7RdbPkmqUFhZ4TRuJXWMvFEoFKacv\ncunND9Hl5BQ81rK6Gy0/Wc2JKUtIOxdartd7P/R6PXNn/4RXQ1dGvPzIA1vunf45oWfN93py86CR\nJywZpsDWSlFw/4/79Xy+6/aRk7RMiE6EP15XYGsFy7boOX3V8Ed3m9aDeYEKLC0UxSypbPe1H1Iq\naThpKE7tmqNQKQn78mdubN8JgHVdT3xmjUZlZQlA6PtfkHDwOAB+K6Zj27AO+RmG7THxv9NcWvvp\nPeUX5kGOcFUwpZ0jzmPnE/fWLCKn9CEvOgLHgeML1air16baoEnErJhE1MxBJG/7BJdpqwBw7DeG\nvIQYoqYPIGrOMOy69MKiYdP/1zmdxiwg/u2ZRE3tTV5MBI4DJtyVsw6OgZOIXTmJ6FmBpGz7GJep\nrxeqses+GK1P8wrPdyujOfQSwLKaLU+sGMrOSRv5uutCUq7H0W5az2JrHet70P3TKdR/tnWh2xu9\n2A7Heu5sfWEJ3/VYSo02jaj/TMsKy2hO/dRWs+WRpcP5Z/L7/NR9LqnhsbSY0rvYWvv61eny8XTq\nPFO4n80n9iAjOpGfey7gt/5LadSvEy7+DSosY1Xtp8bRnsZzx3Ny9moO9n+VzIhoGowLLFeNR9fH\nablhKVpX50KPa7piOlc++oZDQ4M4PmU5DScNw8rT4z6y2uE3fyzHZ73Fvj5TyIyIptH4geWuq96t\nIwEfLMLSzanQ4/xXTSX0g60ED5rJf5NX4j15CNa17j0vVJ1t3bGpN6fmv03IkCBChgQVGWxVa9mE\nOoN7cHTCYkKGBBG//yiNZ40u34sFvKe/TNKxswQPmMLpRe/it3waSq0FSq0FLdbO49rmHwkZOoMr\nn3xHk8WTynw+jaM93nMncGbuag4NmEjmjWjqjR1sdE3toS+hUKk4MnQqh4dMRaW1oPaQXgWPVVho\n8FkwGaX6wf7uHRoay4ihm9nx2+kHutw7JaTqmb9Jz5pxCn5ZrsTTFd7+vvC0tBcfUfD9QiXfL1Ty\n9VwFLvYwZ6ACFwcFH/yqJ18H3y9UsG2Rguxc+Oh/9zat7X73QzV7dMGqVnVCAqdweMQsavV7Djtf\nLwC8g0YS+ctuDg0N4uzy9/FbNhWFyvC128GvEf+NXcChoUEcGhpk1oMtXRX/r6r4fzngCg8Pp2/f\nviZ5bkv/tuSEniEv6joAqbu+x+bRZwvV6PNyid+4HF1SPAA5l8+icnQGlZrET98kafM7AKgcXVBo\nLNBllO+XvocqZ7N2hXKm7foe6yI5c/g/9u47Oqpq7eP4d1p6IYUkQKgJJRAINTQp0hQ7KgLSFRCw\n0nsHqVawUFQQFQHB9nJBVHoJCUgNPZSQhPTek5nz/jEwENIhY5J7n89aruXMPDPnlz2zz8w+e59D\n/JqFBeYEsGzcCiu/9qT+tb3M80HlaUuAmh0bE332Jkk3owE4/+N+vJ9tW2Ct78CuXNx+hGu7jue5\nX6VWo7W2RGOhRW2hQ63ToM/OLbOMlak9q3doQmzwDVJCje15efNe6j5dcHs27P84V385zM0/8rZn\n0OJNnFixBQBr1yqoLbTkpKSXWcaK2p7O/n4kX7hKRlgkAOHb/8DjiU4lrrFwdcK1sz+nx7+f5zlq\nCx3Xv95KQtBZALJi4slJSsbSLe+grDRc2vqRdD6E9FvGHLe2/YnHk4+Vqs7S1Qm3Lm34Z9ySfHmv\nrfuJ+Lt5o+PJTkx5pLxQMfq6SqfFrkEdag18Dv+Ny2m6eAKW7q55auwb1SM+6CxZMfEARO87hutj\nrVBptai0Wuq/O5Q2G5biv3E5PrPeRGNjnX87GjWuj7Ui4te/AUi9coOMsNu4tG+Oc1s/MsKjiDt6\nEoDYg8c5N/PDYrM7+Tcn5cJVMsJuAxDx8y7ce3UqcU3S6fOEbtgKigIGA6mXr2PpUdX03PrjRxL5\nnz2mWc5/y6bvj9PnxeY82bvJv7rd+x0JhiZ1oLa7cUaqX1cVO44Vfi7Q17vA2QFe6WKsb9VAxRtP\nq1CrVWjUKnxqqoiIe7gsj7ofqtrFn9s79qLoDeSmpBH952E8nugM3Ok/9rYAaG2sMGTnAMaZTY2N\nNQ0nj8J/4wf4zBiL1sHu4f4AUWnIksIypnVxJzcu2nRbHxeN2sYOlbWtaQmPPuY2+pjbphqnIe+R\ncfwA6O98kRn0uLw1D5u23UgP2kduxM3/2ZwaF3f0cVGlylll8DgyThhzqp1cqTJ0AjGL38aux4v5\nXr8sVJa2BLCt5kRqZLzpdmpkApb21uhsrfItNTq04EcAPNs3ynP/pZ+PUO/JVgzevxSVVkPY4fPc\n3HumzDJWpva08XAm/b72TI9KwMLepsD2DHr/BwCqtfXJ9zqK3kDHJSOo3bM1oX//Q/KNyDLLWFHb\n08rdhazoe7+SsmLi0NrZorGxNi3nKaomOzaBc9PyLw82ZOdw+/c9ptvVn++BxtqK5OArj5Q18/4c\n0XHo7GzQ2FrnWS5YVF1WbAKnp3xQYN7w3/aabtd4oTsaGyuSzl1+6LxQMfq6paszCSfOEfL5D6SH\nRlBr4HP4LZtM4NDJpprk81ep+cpTWHm4khkZS/VnHkdtoUPnaEeN53ug6A0EDZ0CgNfoAXi/OZBL\ny9fl2Y7O0QFUKnISk033ZUbHYenmgtpCR3ZcIj7Tx2BXvza5qWlcXfVd8dndXMiKjjXdLujzWVRN\nQuDpe6/lXpUa/Z7h8tIvAPB4tgcqrZbI3/+i9tCCZ8TNZebs3gAEBFz/V7d7v8h48Lhvktfdybhk\nMC0T7B4YTyekKGzYrbBl1r3lgh2b3Pv/iDiFjX8pzBnycMsJH3U/ZOnuSlbUvc9AZnQcLt61Abi0\nYh0tVs2hZv9nsHByIHjWxyh6AxZOjiQcP8Ol5WvJTkim/nvD8Jk+lrNT867MEf9dKt0M14svvkhc\nXBw5OTm0bNmS4GDjtHifPn3YsGED/fr1o3///nz7rfEcg9u3bzNixAgGDx7MiBEjuH373o8KvV7P\npEmTWLNmTdkFVBXSpAZ9/lJLK1zHLUbr4Unc6kV5HotbNYewEb1Q2zni+PLrZZevsuVUF7ITLSSn\ny3vGnPGrF4JGg8s7i0j89kPTkXuzqCxtifGIW0EUQ8kn3lu9+QyZ8SlseGwS33WZgqWjLc2G9yir\niJWsPQv+fJamPe86PHUdWx57F0tHW5qOee5Ro91TUduzJJ/FR/y81h78AnVH9OPMpCUYsrKLf0Ih\nCnuf0Rseqq4wdYY8j/eovpycsAxDVk5pIuZTEfp65u1oTo9fTHpoBACh3/+Gtac7VtXcTDWJpy5w\n7autNF06iTbfLEFRDOQkpWDIycWlYyuqdm6N/7fL8f92OVW7+GNb1zP/hopod7VWi0uHFoT/+idB\nw6dya8tO/D6cjkpX9PHmkrRfSWrsGtaj+ecLidi2k/gjJ7BrUI/qL/TiyvIvi9z+fzNDIav/CmrO\nrQfg8ebgWTX/exx8Q2HIUoUB3VR09Xu4Adej7odUqgK2azCgttDhu3AcFxZ+xpHn3+CfMbNpOGUU\nlm4uJJ+/wtmpy8mOSwSDgevrtuDSsSWqf3l5aVlRFKVC/1dRVLp3t1u3bhw8eBAPDw88PT05cuQI\nlpaW1KpVi127dvHDD8ajyMOHD+exxx7j008/ZfDgwXTp0oWjR4+yYsUKxo0bR25uLhMnTqR169YM\nHDiwmK2WXG5sJBbe96bqNc5V0acmoWTlPaKocXGn6pQPyQm/TvS8sSg5WQBY+bUjJ/Qq+oRYlKwM\n0g//gU3bbmWWr7Ll1MdGYentW6KcrpM/JDf8BjHzx6DkZGFRvylatxpUGTzOWFPFBdRqVDoLEtbk\n/TH5KCp6W7Z++1nqdPMDwMLOirjL4abHbN2rkJmYRm5GyX+M1uvZgkOLfsSQoyc7R8/lX45S74mW\nnPnmrzLJW9Hb0+/N5/F83Hg+oM7WmsQrYabHbNycyEoqXXtW69CExCvhZMQkkpuRxfX/BFK7Z9md\nE1dR2zMzMgaHxvVNty2rOpOTnIIhM6tUNQVR6bQ0nvkWNnU9OTFyOpmRMaXO5zWqL1U7G89p0tpa\nk3o1NG+OpFT0D+TIjIzFsYl3sXUF5fWdPRa7ep4ce30WmbdLnxcqXl+3866FnXcdIncduO9eFUru\nvWWJGhsrEk+eN81KWjg74jWqP7nJqag0ai5/9A1xR08Za62tUFvosG9UD5/pY0yvETTcOAOmtbcl\nN8U4a2tV1Zno6Dh0mVmk3QwnOfgqYFxSqJo+Busa7kVmz4yMwf7+z56rS4Gfz6JqqnbvSP2Jo7j6\n4Tqi/zRetMW9d1c0Nja0WL3Y+Pe6OuEz5z2uffYtcYeCStCqlV81Zzh73wRbdCI42ICNZf7By64g\nhWkD8t//n0CFhd8pzBio4um2DznY4tH3Q5lRsVi4OuV5LDM6Dtt6tdBYWhJ3+AQAycFXSLsehkOT\n+mRXc0Nnb0vsIeMSXpVKBQbloQ7Uicqj0s1w9erViwMHDnDw4EHGjRvH0aNH2bNnD0888QQREREM\nGzaMYcOGkZiYyM2bN7l8+TKrV69m8ODBfPbZZ8TFGWc6Ll26RFxcHOnpZXeuBEDmmWNY1vdF61ET\nALueLxqX5txHbeuA+9zVpAfuJe6TmaYfNgA27Xrg8PII4w2tDpv2Pcg8l3dd/f9WzgAsvO/L2eMl\nMgvI6TZnNRmBe4n7dIYpZ/aVs9x+8xmipg4kaupAUv/aRvrRP8t0sGXMWLHb8vjK3/mpz0J+6rOQ\n7f2W4u5XD8faxiPMjft35sae08W8Ql4x50PxunNyvVqrpvbjfkSdKrvlKRW9PU9/9is7Xp7Hjpfn\nsWvgIlz96mFfy9ieDfp14daek6V6vTpPtqHZmGcBUOu01HmiNZHHLpZZ3oranvGBp3H0rW+6mEX1\nPr2IPRBU6pqC+C6agMbWmhOjZjzUYAswXcQiYNAUAl+biaNvfdOFLDxf7En0gfxtEHfsTInqHuS3\neBxaW2sCH2GwBRWvrysGhQbjh5tmtGq81IvUkJum87XAuOyw5edzTedm1Rn+MpG7DwMQH3AKz5d7\nG4/8q1Q0mvYGXmNfJeXiNdNFOAKHTELRG4g78g81XugJGAd6tnU9SfjnPHFHT2Ht4YZ9w3oAVGnu\nA4pCZkQ0RUkIPI1DkwZYe1YDjJ+9uINBJa5x7doe73EjODNuvmmwBRDyydcEDXiLE8MmcGLYBLJj\nE7gw7+P/mcEWQIcmcDoEbkYZZx8271PoVsA1rZLSFG5FQ/MHriG0+7jCkk0Ka8Y/2mALHn0/FHsg\niOrPdEOlUaO1s8G9Z0diDwSSEXYbjZ0NDk0bAmBdwx3bOjVIvXwdjbUVDca/bjpvq9bA54neGwAy\n4PqvVulmuBo0aMCtW7eIiYlhwoQJrF69mr///pt58+bh7e3NunXrUKlUrF+/noYNG1KvXj1ee+01\nWrZsSUhICEFBxk7SpEkT1qxZQ9++fenUqRONGjUqZsslY0hOIO6LBbiOX4JKqyU3Mpy4z+ZiUc8H\n5zdmEDllEHa9XkLj6o5Nm67YtOlqem70gjdJ2PgxziOn4rFiEygKGUH7Sdn5Y5lkq6w547+cj8u4\nJai0OnKjwoj/bC66ej44j5pJ1NSB2PZ8CY2rB9ZtHse6zeOm58YsHIshNanMMxWUsTK0JUBmfAr7\npm+g5yej0Oi0JN+KYc+UbwCo6lubLgsG81OfhUW+xpElW3lsZn/6/Wceit5AeMBFTq3bVWYZK1t7\nHpn5DZ0/GotGpyHlVgyHp30FgHOT2rSfN4wdL88r8jWOL99Mu9lDePbn+SiKwq09J7nwXdnMFkLF\nbc+chGQuLPwM3/cnotZpyQiP4vz8ldg38qLRtNEEDZ1UaE1RHJs1pGqnNqTdDKfV6nuf5fsvyVxa\n2QnJBC/4Ar8l41FptWSER3J27mcAOPjUo/GMNwgYNKXIusJUadYQt86tSbsZQZt18033X1n1A3EB\nD5cXKkZfT7t2i8sffo3fiimoNGoyo+M5N+sT0wxV4JBJpIdGcPPbX2jz9fugUpN0+iKXPjD2oevf\nbKP+24Px/3YZKrWa1Cs3uPJJwf8kwaXl62g0fTRtn/wAFAieuxJ9Wjr6tHTOTFlGw8kj0FhZYsjJ\n5cy0FaYLGBQmJzGJS++vovHCSah0WjLDI7m44FPsGnnRcOpYTgybUGgNQN3RxpUzDaeONb1m0pmL\nXP1wbYnb77+Vi4OKhcNh3BcKObkKNd1g8Wsqzt1QmLNBYdsc41xAaDS4OoJOm3dQ9fF2BUWBORsU\nwDhoa+ENMweWfg7hUfdD4T//gbWnO22+/QC1Tkv4L3+SePI8AGenLqPBe8NRW+pQcvVcXLqajPAo\nMsKjuLX1P8b9k0pFWkgoF5dU3iWmMkwsGZVSkRY4ltDy5csJCwvjk08+4YMPPuDq1at88cUXrFu3\njr/++ovs7GyaNWvGrFmziIiIYO7cuWRlZZGZmcmMGTOoWrUq48ePZ8uWLRw/fpwFCxawdetWLCws\nitxuaD//f+kvfDi1NgdW+IxgzHmrf5vyjlGsmj8GVZr2/LJR6S+j/G8bfXF1pWnPjb7mOeerrAw+\nZ/xBWtHbs9bmQPa0/3cvCvAwuh39id3+/co7RrF6BW6uNH3973Z9yztGsboHbGV/R/NcTKksdTm8\nHT3fl3eMImkwDjBzDua/KFBFout0odLskyqLJ+3eLL6oHO1KLfqg17+l0s1wAUyaNMn0/xMmTDD9\n/4gRIxgxYkSe2po1a/LVV1/le40tW4yXYW7dujW//vqrmZIKIYQQQggh/pdVygGXEEIIIYQQonwZ\nKt9CuXJR6S6aIYQQQgghhBCVhQy4hBBCCCGEEMJMZEmhEEIIIYQQotQUZElhScgMlxBCCCGEEEKY\niQy4hBBCCCGEEMJMZMAlhBBCCCGEEGYi53AJIYQQQgghSs1Q3gEqCZnhEkIIIYQQQggzkQGXEEII\nIYQQQpiJLCkUQgghhBBClJpBLgtfIjLDJYQQQgghhBBmIgMuIYQQQgghhDATWVIohBBCCCGEKDWD\nIksKS0JmuIQQQgghhBDCTFSKIkNTIYQQQgghROl0sXmjvCMUaX/66vKOAMiSwlLJ/sC2vCMUyWJC\nWoXPCJKzrFlMSGNTs+HlHaNYA858U2na89eWg8o7RpGe/+c7oHLsk/a0f7m8YxSr29Gf2O3fr7xj\nFKtX4OZK09f/bte3vGMUq3vAVn5uMbi8YxSrz8mN5Bz0Ke8YRdJ1ugCAnu/LOUnRNAysNJ/NykKR\nqxSWiCwpFEIIIYQQQggzkQGXEEIIIYQQQpiJLCkUQgghhBBClJr8w8clIzNcQgghhBBCCGEmMuAS\nQgghhBBCCDORAZcQQgghhBBCmImcwyWEEEIIIYQoNTmHq2RkhksIIYQQQgghzEQGXEIIIYQQQghh\nJrKkUAghhBBCCFFqiiwpLBGZ4RJCCCGEEEIIM5EBlxBCCCGEEEKYiSwpFEIIIYQQQpSaXKWwZGSG\nSwghhBBCCCHMRAZcQgghhBBCCGEmsqTQDFR1n0DTaT4qjQVKzDlyd4+F7JSCa72fQfvkWnJWVTPe\nYeWEpsfHqKs2Q8lJxxC8EcPJLyVnBc9ZGTLeVb1TM/zefRm1hZbEy2Ecm/M1uWmZhda3XfA6SVfD\nubhhV577bdyd6fndTHb2nU12YmqZZqzo7en+WHN83n4FjU5H0pVQTs1fR25aRqnqnvz7czKjE0y1\nV7/dQdjOI9jXrY7fzNfR2lihKArnV24m5ujZR8pb0dvzLpcOLfEaMxCVTktaSCgXFn2OPj2jVDWW\nbi60Xvc+gYMnkpNU8N/4MFw7tqD+2AGoLXSkXA0leOGX6At4z4urs3Rzoe3XCzk6cHK+fNWf7Yp7\nV39OTlhWJpnLu6979O5MrQHPmm5r7WywdHPm8HOjyY5PMt3v/c4Q3Lu1JyfZ+NrpoRGcm/lRibcD\noKviQOM5b2HtURXFYODiktUknb0MgK1XLRpOeA2trc2dx9aQculaiV7X/TE/mrz9CmoLHclXbvHP\nvLUFtmFRdXX7dqdOn65oLHUkXLjByXnrMOTk4traB99x/VFrtegzszmzbCMJwSXLVZj9ZxQ+3qaQ\nkwsNPGH+MBV21irT478eUfj2z3tLwFIzICoB/lqmws4aFn6vEHwDDAo0rQszB6qwslAVsCXzUxSF\nGdN+w7t+VV57vYPZtuPSoSVeY19FrdORevUmFxZ9UfB+p6AatZoG7w7Fua0fKo2G0B9+I/znP7Gt\n40mT+e+anq9Sq7HzrsWZqcuJ2RdI08UTsPOugz7D+BlJOHGOK59sMNvfaE4GlaG8I1QK/1MzXJcu\nXSIoKAiAbt26kZWVVfYbsXZF++Rqcn97lZxvWqAk3UDTaX7BtVW80HZ+H1T33gZN16WQnUbO+lbk\n/tAVdZ1eqOo9KTkrcs7KkPEOSyd72i54nYPjP2PHc9NJDYuh+Xt9C6x1qFuNbusmU6tXm3yP1Xm2\nA93XT8PG3ansQ1bw9rSoYk+LuSMJmvgJf784ifTwaBq/3a9UdXa1q5GTnMa+ATNM/4XtPAJAs2nD\nCf1tP/sGzODUvLW0WfI2Ks0j7KoreHvepavigM+MNzk7bTnH+r9LRngUXmMHlqrGo3cXWn65AMuq\nLmWczR7fWWM4PfVDDvcdR0Z4FA3efLXUddWe6oz/mrlYuTnneZ7WwRafqSPwmTgcyui3bUXo65E7\nDxA4ZBKBQyYRNHwq2XGJXF7xVZ7BFkCVpg05N+sjU21pB1sADSe+TuKpCwQMGEfw3JX4LpqA2tIC\ntaUFLT6Zyc2NvxI4dDLXv/6JJvPeKdFrWjjZ02reKI5N+pS/+kwmLSyaJu8U0NeLqKverTVe/Xty\naPQS/np5GhorC7wHPYlKq8F/6VucnP81e/rN4NK6X2m1cHSp/+77xacozPpG4eOxKv5vkRrPqvDR\ntrzn1zzfQcW2OWq2zVHz4wwVrg4w/VUVro4q1uxQ0Btg2xwV2+eqyMqBdf8pn/NzQkJieG3oRnbt\nDDbrdnRVHGg8cyxnp60goN+7ZERE4f1m/v1OYTU1+vTAuqYHxwaOJ+i1qdTs9zQOjb1JuxFm+jwH\nDplEXOBpIv84RMy+QAAcfRtwYsxs0+OVdbAlSu5/asC1e/durl69atZtqGt3R4k8AYkhAOhPr0Xt\nk38HjdYa7VNfkbt/ap67Ve4tMJzfBIoBDDkYru9CXb+P5KzAOStDxrs82jch7tx1UkOjALi6ZQ+1\nn2pXYG39/t259stBQncH5bnfumoVPB9vyf43S/+jqCQqenu6tW9KQvB10m4Z2/D61r/x7J3/6GtR\ndc5+9VEMBjqsnk7Xze/TYOQLoDb+0lZp1OjsbY1/oq0V+uycR8pb0dvzLmd/P5IvXCUjLBKA8O1/\n4PFEpxLXWLg64drZn9Pj3y/zbC5t/Ug6H0L6LeN2b237E48nHytVnaWrE25d2vDPuCX5nufRoz1Z\nsYlc+vS7Mstc0fp67SHPk52QRPgvf+W5X6XTYtegDrUGPof/xuU0XTwBS3dX42NaLfXfHUqbDUvx\n37gcn1lvorGxzvfaKo0a18daEfHr3wCkXrlBRthtXNo3x7mtHxnhUcQdPQlA7MHjnJv5YYkyu7Vr\nSkLwNdJC7/XhmgX19SLqaj7zGFe+20lOchooCqcWfUPo/x1GydWz84l3SLp0EwAbTzeykx5tpcCR\nYGhSB2q7G/cl/bqq2HHMOFNUkK93gbMDvNLFWN+qgYo3nlahVqvQqFX41FQREfdIkR7apu+P0+fF\n5jzZu4lZt+PcthnJF0LIuHV3n7I7/36niJqqXdpy+//2ougN5KakEfXXYTyezPv8Kn6NcHu8HReX\nrgHAqpobGhtrGk0Zhf93K/CZORatg51Z/05R/ir8ksLt27ezd+9eMjMziYmJYciQIfz9999cuXKF\nyZMnk56ezoYNG7CwsKBOnTrMnz+f33//nf3795OZmUloaCgjR46kY8eO/Pzzz+h0Opo0MXbguXPn\nEhYWBsCqVatwdHR89MAOnigpYfdup4SjsnQEC/s8S3g0PVdiOPM1Ssy5PE9XbgehbjwAfcRR0Fii\nrv8CGB7tB5fkNHPOypDxDhsPZ9Ij402306MSsLC3QWtrlW+ZzInFxh9/7m0b57k/IyaRQ+NXmSUf\nUOHb09rdhYyoe79CMqPj0dnboLW1zrOssKg6lUZNTMA5gj/ehNrSgnafTiQ3LYNrP/zBmSXr6fDl\ndLwG9sbS2YHj01ah6B9hyUYFb8+7rAf/0DkAACAASURBVNxdyIq+115ZMXFo7WzR2FiblvcUVZMd\nm8C5acvLPNfd7Wbev93oOHR2NmhsrfMsFyyqLis2gdNTPijw9cO2Gwch1Z/uUmaZK1Jf1znaU2vA\nswQOnZLvMUtXZxJOnCPk8x9ID42g1sDn8Fs2mcChk6kz5AUUvYGgO8/zGj0A7zcHcmn5ugde3wFU\nKnISk033ZUbHYenmgtpCR3ZcIj7Tx2BXvza5qWlcXVWyga2Nh3OePpxh6sN527CoOrvaHliec6DD\nqklYVa1C3MnLnPv4RwCUXD2Wzg48vmkBFlXsCZryWYlyFSYyHjzumzx1dzIuGUzLBLsHxqkJKQob\nditsmXVvSrVjk3v/HxGnsPEvhTlDymc54czZvQEICLhu1u1YubmSGRVrup0VHYfWzibvfqeIGis3\nFzKj8vZ5O+/aebbh/c4Qrq3eZHo9C2cH4oPOcmn5WrITkmkwbhiNZ4zhzBTz7L/MTa5SWDKVYoYr\nLS2NtWvXMnLkSDZt2sSqVauYP38+P/30EytXrmTDhg1s2rQJe3t7Nm/eDEBqaiqrV6/miy++YM2a\nNbi7u9OnTx+GDRtGs2bNAHjppZfYuHEjNWrU4PDhw2WUtpCdk0Fv+l+130gw5GI4922+Mv3+aYCC\ndvBRtM//iOHmHtBnl1E2yWmenJUh452k6oKzKoaKtAa7grdnYW344KCoiLqbP+/j7PKNGHJyyU1N\nJ+S7nVR7vDVqCx2tl7zFybmr2d37HQ6NWIDfjNewcncu8LVKpoK3pylEwV9HeT6bJakxg8L6DQ+8\n5yWt+zdUpL5e/YUexBw8Tubt6HyPZd6O5vT4xaSHRgAQ+v1vWHu6Y1XNDZeOrajauTX+3y7H/9vl\nVO3ij21dz/wbKKLd1VotLh1aEP7rnwQNn8qtLTvx+3A6Kl0JjjerCuvDSonr1FoNbu18CZyykr0D\nZ6NztKXxWy+barLik9n1xLvsHzqPlvNGYlfLo/hchTAU8ru3oG6z9QA83hw8q+bPHnxDYchShQHd\nVHT1K58B17+mJP2kiJqC+tn93wWOTRugc7Qn8o9DpvuSg69ydupysuMSwWDg2totuHRsiUpb4edA\nxCOoFO+uj48PAPb29nh5eaFSqXB0dCQjIwNvb2/s7IxTsW3atOHQoUP4+fnRqFEjAKpVq0Z2dsE/\nDnx9fQFwdXUlM7PwE4lLJSUMVbX71sHbVUfJiIfcdNNd6iaDQGeDdvBRVBqdcSnP4KPkbu8Dai36\nAzMh03gyvbrNeJTERzuJVnKaOWcFz9h07AvU6NoCAJ2dFYlXwk2PWbs5kZWUij7DPAO8h1IB27PR\n6Jfw6NISAK2tNclXb5kes3JzIjspFX1m3nNCMyLjcPL1KrDO8+mOJF8OJfnKnddRGY92O3h5orGy\nJOrgKQASzoaQEhKOk68Xt6PieSgVsD0LkhkZg0Pj+qbbllWdyUlOwXBfu5akpqx4jepL1c6tAeN7\nnno1NO92C3jPMyNjcWziXWyduVTUvu7eowOXP/ymwMfsvGth512HyF0H7rtXhZKbi0qj5vJH3xB3\n1NgfNNZWqC102Deqh8/0MabqoOHGGTCtvS25KWkAWFV1Jjo6Dl1mFmk3w0kONp5OEHvwOKrpY7Cu\n4V5gHp8xL5r6uq4Ufd25acF9PTMmkYi9J0wzYrd2HKbRqD5o7ayp2qYxt/eeACDp4k2SLofiUN+T\n1NDIohu0ENWc4ex9E0LRieBgAzaW+QcFu4IUpg3If/9/AhUWfqcwY6CKp9v+lw+2gKyoWBybPLBP\nSUrNs08pqiYzKhZLV6c8j90/C+/eoyORO/fDfcs6q/g1QutgR+zB4wCoVCowKBXswKcoa5VihktV\nyNEjlUpFSEgI6enGHw6BgYHUrVu30OeoVCoM932gC3vdR2G48Teqav5Qxbjz1fiNwBCyI09N7g9d\nyN3QhtyN7cnZ/iLkZpC7sT2kRaLxG4Gmw0xjoY0bmqbDMFzYLDkrcM6KnvHs57+w65U57HplDrsH\nLcS1WT3sahl/bNTv+zjhe0+W2bbKQkVsz4tfbjNd3OLA0Lk4NfXGtqaxDeu81J3I/f/ke0700bOF\n1jl4edJo9EugVqG21FGvXy/CdweQeisKnZ01Ts2MX+42nm7Y1a1uOs/jYVTE9ixIfOBpHH3rY+1p\nPMJfvU8vYg8ElbqmrISs2UrAoCkEDJpC4GszcfStj01N43Y9X+xJ9IHj+Z4Td+xMierMpSL2da29\nLTaeHiSduVTg44pBocH44VhVcwOgxku9SA25SVZMPPEBp/B8ubfxyL9KRaNpb+A19lVSLl7Lc0EC\nRW8g7sg/1HihJ2AcxNnW9SThn/PEHT2FtYcb9g3rAVCluQ8oCpkR+WfbAC58sZ29/Weyt/9M9g2Z\nZ+zDd9qw7svdub0vf1+POnqu0LrwvwKp0cMftaUOgOqPtyIh+BqK3kDLuSNx9jP2dft6NbCvU434\nsyEP1c4AHZrA6RC4GWX8cb95n0K35vnrktIUbkVDc6+89+8+rrBkk8Ka8f8bgy2AuGN39il3+myN\nPr2IORhU4pqYA0FUe/ZxVBo1Wjsb3Ht2JOa+fVKVFo2JP553mbbGxooG418znbdVa9BzRO8NABlw\n/VerFDNchdFoNLz99tsMGTIEtVpNrVq1mDhxIjt27Ciw3tfXl2XLluHl5VXg42UiI4bcP0ajffZ7\nVBodSuJ1cneNROXeAk2vz40/YoqgP7YC7VPr0A41dlj90fdRovLv4CVnBcpZGTLekRWfQsCsr3ns\ng7GodVpSb0UTMMN4PoRz4zr4zx3OrlfmmGXbJVbB2zM7IZmTc9fQZvk7qHVa0sKi+WeW8TLpVXzq\n0nz2CPYNmFFk3aU1P9N0ylC6bVmCSqsh4q9Abv68D4DACR/TdNJgNBY6DLl6Ti/6mvSwgn8clkgF\nb8+7chKSubDwM3zfn4hapyUjPIrz81di38iLRtNGEzR0UqE15padkEzwgi/wWzIelVZLRngkZ+ca\nz7dx8KlH4xlvEDBoSpF1/7aK0tetPT3Iik1E0d9bwnp3hipwyCTSrt3i8odf47diCiqNmszoeM7N\n+gSA699so/7bg/H/dhkqtZrUKze48kn+Za8Al5avo9H00bR98gNQIHjuSvRp6ejT0jkzZRkNJ49A\nY2WJISeXM9NWYCjBxWiyE5L5Z+5a2i5/B7VWQ1pYNMdnrQagSuO6tJj9Onv7zyyy7tqWv7BwsOPx\nHxagUqtJvHiDsx9+jT4ji4DxH9Ns0iBUWg2G7FyCpn+R55+KKC0XBxULh8O4LxRychVqusHi11Sc\nu6EwZ4PCtjnGY+yh0eDqCDpt3kHVx9sVFAXmbFDgznk5Lbxh5sBKcWz+oeQkJHN+wec0fX+CcZ8S\nFkXw/FV5PqOF1YDxAhrWNTzw37gCtU5L+M9/knjyvOn1bWp65Bvcxx09RdjW/9B6zQJQqY3/vMVi\n8/1TMOamIAPFklAphV2+RuST/YFteUcoksWEtAqfESRnWbOYkMamZsPLO0axBpz5ptK0568tB5V3\njCI9/4/xpP+K3p4WE9LY0/7l4gvLWbejP7Hbv4ArN1YwvQI3V5q+/ne7gi9BX5F0D9jKzy0Gl3eM\nYvU5uZGcgz7lHaNIuk4XANDzfTknKZqGgZXms1lZtLSt2H3on7SN5R0BqCRLCoUQQgghhBCiMqrU\nSwqFEEIIIYQQ5UMuC18yMsMlhBBCCCGEEGYiAy4hhBBCCCGEMBNZUiiEEEIIIYQoNYNKrlJYEjLD\nJYQQQgghhBBmIgMuIYQQQgghhDATWVIohBBCCCGEKDWD/MPHJSIzXEIIIYQQQghhJjLgEkIIIYQQ\nQggzkSWFQgghhBBCiFKTJYUlIzNcQgghhBBCCGEmMuASQgghhBBCCDORJYVCCCGEEEKIUlNkSWGJ\nyAyXEEIIIYQQQpiJDLiEEEIIIYQQwkxUiqIo5R1CCCGEEEIIUbn42L1c3hGKdCH1p/KOAMg5XKWy\nr8NL5R2hSF2PbOPvdn3LO0axugdsrTQ5d/v3K+8YxeoVuJkdrV8t7xjFevr4D5Xmfd/sN6y8YxSp\n3+n1ABW+PbsHbCU57p3yjlEsB5dPsbGsU94xipWedaPS9PXKsu+s6H0IjP1oT/uK/aO221Hjj9qK\n3p7dA7ai5/vyjlEsDQPLO0KJGVRyDldJyJJCIYQQQgghhDATGXAJIYQQQgghhJnIkkIhhBBCCCFE\nqRnksvAlIjNcQgghhBBCCGEmMuASQgghhBBCCDORJYVCCCGEEEKIUlPQl3eESkFmuIQQQgghhBDC\nTGTAJYQQQgghhBBmIksKhRBCCCGEEKUmVyksGZnhEkIIIYQQQggzkQGXEEIIIYQQQpiJLCkUQggh\nhBBClJosKSwZmeESQgghhBBCCDORAZcQQgghhBBCmIkMuIQQQgghhBDCTOQcrjLi3KEl9UYPQq3T\nkhpyk0vvf44+PaNUNZZuLrRcu5jjQyaQk5QCgNbejvrjX8emricaS0tubthG1K79RWZx6dASr7Gv\notbpSL16kwuLvsiXpdAatZoG7w7Fua0fKo2G0B9+I/znPwGwrulB4xlj0Tnak5ueyfn5K0m/GQFA\nleY+eL81CLWlBbmp6Zxf8BmZEdG0WrMQjZWlabs2tar/q5nvsqrmhv/6pZx8dwEpF6+Z7lfptPh9\nMI2In/8kem9Ake16l2vHFtQfOwC1hY6Uq6EEL/wSfVpGyevUKhq+NwTXdsa8N77/nbDtfwFgW7cG\njaeNQmNjBYrClc82ERdwmjpDnsejVwfTa1tUcUBrY8WebsNLlNmtY3MavtUftYWWlCu3OLNgDbkF\nZC6sTmtrTbPZo7CrUx1UKsJ2HOTaht8BcGxcj8YTBqOxskSlUXNtw++E7zxcbCZzvef2Pl40GDcM\njZUVKrWam9/9QuSug3let+YrT1H9+e4cGzihRO33oGqd/Gj2zsuoLbQkXQ4jcO5X5KZllrjOwsGW\nVjOHUKVhLfQZWVz/9RBXNhk/A25tGtF8Qn9UGg1ZSamcWvYDiZdvlTqjudrXqWUTvN8ejEqrwZCV\nzeUPvyH5/NWHaMX8Dh2O4bMvL5OdY6C+lz0zp/tiZ5v3a2rv/ijWrLuKSq3CwV7LzKm+eHrakJSc\nzZLlF7h8JRlrKw3PPl2Dfn1rl0muBz3Z+3HmLZiMpaUF585eZMwbU0hJSc1X99xzTzBj9nsoBoWE\nhCTGjpnC9WuhODk58snKRTTz8yE9LYNvv93Kl59vKJNsFbGvg3n3m06tmtDw3cGoNGpyklK5+NEG\nUq/cBMB7dD/cuvoDkHwhhPNL1mHIyi4wY3l8DzVdPAE77zroM4z7j4QT57jySdGfBZcOLfEaMxCV\nTktaSCgXFuX/vVFojVpN/XeG4tyuOSqNmtAffifi590A2NTxpNHUN9BYWwEQ8vl3xB87DYDv+xOx\nq18bffqdnP8Ec/WT9cXnLOP2tK3jSZP575qer1KrsfOuxZmpy4nZF/hQ7fmoFEVhxrTf8K5fldde\n71D8E/7LKOjLO0Kl8F8zwxUUFMTFixfLZdu6Kg40mvEWwdOXEzjgHTIjoqg3dlCpatyf7EKLLxZi\nWdUlz/MazXyLrJg4TgybxOl35uL93mtYVnUuMkvjmWM5O20FAf3eJSMiCu83B5a4pkafHljX9ODY\nwPEEvTaVmv2exqGxNwBN5r5L2PbdBAwYx/V1m2m6eCIAllWdabZ0EpeWryNw8CRi9h6j0aSRAJwY\nNZPAIZMIHDKJa2s3k3k7+l/NDKC20NFk3tuodHl/uDn4NqDNuvep0qxRoe2ZP6s9vrPGcHrqhxzu\nO46M8CgavPlqqepq9umJTc1qHBkwkYBh06nd/ykcGnsB4DP5dcJ/30vAoCkEL/iSZu+/h0qj5sa3\nvxIwaAoBg6ZwfPQ89JmZnJnxSYkyW1Sxp9mcNzgx+WP2vzSR9PAoGr3Vv1R1Dcb0JTMqngP9pnB4\nyCxqv9SDKk3rA9Bq2XtcXr2NQwOnE/TOMnzGDcKmpkcx7Wi+97zZ4olcW7uFwCGTODVuEfXfGYr1\nfXkcmzWk9uDnS9R2BbF0ssd//uscnrCKnc9PIzU8Gr93+5aqrvmkAeSmZ7Grz3T+GrQAj45NqdbZ\nD52dNR0/fJtTH27mj76zOLHwW9ovH4taV7pjY+ZqX5VWi+/CcVxY/CWBgydx/ZttNJ7z9kO2ZF4J\nCdnMX3SOpe83Z9uPnahR3ZpVn1/OU5OZpWf2vLMsW9ycHzZ0oPNjbqz46AIAH31yCRtrDVu+f4xv\n1rbjSEAsBw/n3988KldXZ75cs5xX+4+hedPuXL9+iwWLpuSrs7Ky5Kv1HzGg32ja+T/Fjh1/8cGH\ncwFYunw2aalptPTrSZdOfXjiia70fqrbI2eriH0dzLvf1Npa03zpeC6v/I6jAydzfuk6/N5/D5VO\ni1tXf1zaNuPooMkc6T8BtZUltfs/VUjG8vkecvRtwIkxs03fk8UNDnRVHPCZ8SZnpy3nWP93yQiP\nwmts/pyF1dR4oSfWNasROHAcx+/ktL+Ts+Gkkdz+v70EDZ3EhUWf47twPCqN2pTznzGzCRo6iaCh\nk4odbJmrPdNuhJnaKnDIJOICTxP5xyFi9gU+VHs+qpCQGF4bupFdO4PNuh1R+f3XDLi2bdtGdHTZ\nf7mWhJO/HykXrpIRdhuAiO1/4N6rU4lrLFydcO3sz5kJi/I8R2tvh5N/M258tQWArJh4/hk5lZzk\n/EdS73Ju24zkCyFk3IoEIHz7bjye6FTimqpd2nL7//ai6A3kpqQR9ddhPJ7shGVVZ2zrVCfqT+PR\nzLijp9BYW2LfsC5u3doRe/QkKZeuG1/vlz+5/PE3ef8WBzsaTR5J8LyV/1rmuxpOHMHtHfvISUrO\n85o1X+lNyOofST5/pdD2fJBLWz+SzoeQfifHrW1/4vHkY6Wqc+vahoj/22fKG/nnEar1NuZVadTo\n7O2MbWZrXeCR2AbvDib2yClij54qUWbXds1IOn/NlOXmT39RvXfHUtWdX/EtFz75HgBL1yqoLbTk\npqajttBxZe124gLPAZAZHU92YgrWboUfFADzvedqCx3XvtpKQtBZwNhncpJSsLpzIMPC2ZGGE0dw\nZdXGErVdQTza+xJ/7jqpoVEAXN2yl1pPtS9VnXPjOtz4vyMoBgVDrp7bB89Qs0cb7Gq5k5OSQXSg\ncRCRcuM2uakZuPh553v9opirfZXcXA49+wapl28AYF3D3TQb/6gCAmNp7ONArZq2ALz0Yi127b6N\noiimGoNeQVEUUlNzAUjP0GNhafwau3AxmaeerI5Go0KnU9OxQ1X+3htVJtnu171HJ/45cYaQqzcA\nWLvmO/r1zz+A12g0qFQqHB3sAbCztSEzMwuAFi19+eGHnzEYDOTk5LBr5x5e6FPwQKA0KmJfB/Pu\nN21qVSM3NZ34IGOu9JsR5KZlUKVpA6L3BRI4YjZKrh6NrTUWTg5kF/J5LY/vIatqbmhsrGk0ZRT+\n363AZ+ZYtA52Rbals78fyReukhF2N8Mf+XMWUVO1iz+3d9zLGf3nYTye6AwYZ4u09sb+p7WxwpCd\nkydnw8mj8N/4AT4zSpDTzO0JUMWvEW6Pt+Pi0jUP3Z6PatP3x+nzYnOe7N3ErNsRlV+ZLCncvn07\ne/fuJTMzk5iYGIYMGcLff//NlStXmDx5Munp6WzYsAELCwvq1KnD/Pnz+f3334t8To8ePdi5cyfr\n169HrVbTqlUrJk6cyMqVKwkLCyMuLo6IiAimTZuGk5MTBw8eJDg4GG9vb/r27cvhw8aBwbhx4+jf\nvz/h4eHFbu9hWbm7khUVa7qdFROH1s4WjY21afq8qJrs2ASCpy/P97rWnh5kxyZSc8CzOLdriVqn\n5dam38i4dbvwLG6uZN6/neg4tHY2ebMUUWPl5kJmVFyex+y8a2Pp5kJWTALc9+MnKzoeSzcXbGpV\nx5CRhe+C97CpVZ3MqFguf7w+T67ag583DsruW85n7swA1Z/rhkqrIeLXv6kz7MU82w2ebZwhqj3o\nuULbM19Wdxcyo/NuS2dng8bWOs/ymKLqrNzz5s2Mjsf1Tt4Ly76m9eezqD3gKSycHTkz4xMU/b1L\nrtrW88StS2sO9XmnxJmt3Z3JeGB7OjsbtLbWeZYaFVen6A00nz8Wj+7+RO47TurNCDAo3Pp1n+k5\nNft0Q2tjRcK5ogex5nrPDdk53P59j+n+6s/3QGNtRVLwFVCraTLvXa6u2oghN7fE7fcgaw9n0qPi\nTbczouKxsLdBa2uVZ1lhUXVxZ69R55kOxJ66gkanxbNHKwy5elJuRqK1scS9fROijgbj3KQuDl41\nsHZ1LFVGc/YpRa/HwtmRNuuXYVHFnrMzPypVtsJERWXi7m5luu1W1ZK0tFzS0vWmZYU2NlqmTW7M\n628cw9HRAoNeYd1q43Ix3yaO/GdXBH7NqpCdbWDv3ii0WlWZZLufp2d1wsLu7YPDw27j6OiAvb1d\nnmWFaWnpvPPWDPbs30Z8XCJqjZruj78MwPHAU7z6ah+OHjmOpaUFz7/Qm5xH+EzeVRH7Oph3v5kW\nehuNjRUubZsRd+wMDj5e2NXzxNK1CmD8vNbs+wTeo/uRFRNP9J2ZkHwZy+F7yMLZgfigs1xavpbs\nhGQajBtG4xljODMl/++B+9so6/42KvD3RuE1lg/8FsmMjsPlTs5LK9bRYtUcavZ/BgsnB4JnfYyi\nN2Dh5EjC8TOmnPXfG4bP9LGcnbqs8JxmbM+7vN8ZwrXVm0yv9zDt+ahmzu4NQEDAdbNto6KTy8KX\nTJnNcKWlpbF27VpGjhzJpk2bWLVqFfPnz+enn35i5cqVbNiwgU2bNmFvb8/mzZuLfM727dtJTExk\n5cqVrF+/nk2bNhEVFWUaRFlYWLBu3TpmzJjB+vXr8fX1pVOnTkyaNInq1Qs+R6i47T0SVcFf6orB\nULqaB19Wq8G6hju5aRmcHD2D87M/wuud4dg1rFd4FnUJtlNEjaqAxxR9wfebHtNqcO3chpA1PxI4\ndDLxx8/SbMmke5uz0FHj+R7cWF9IO5sps33DutTo08t09KssFNYO6A0lrysor8GA2kJHs0XvcW7+\nFxx4dixBb8yl8bSRWLrdW2Zau19vbm39o8BzMgqlLribKw9kLkndqdmf82ePN7BwsKP+iLwDWK+h\nz9LgjZcIGrcCQ1ZOMZnM857fr/bgF6g38hVOT1yCISsb77GvknjqPPGBZ4rOVgxVCftyUXWnPvgR\nFIUnNs+j40dvE3U0GEOOnty0TA699wmNX3+WJ7bMp86zHYkOuoAhp5Rr5M3cvtnxSRx+7g2Oj5xB\n45ljsa5ZrXT5CtquUvD9mvs+lldDUlj3dQhbvn+Mnb91ZfjQekyZfgpFUXjv7YaoVDBw6FEmTTuF\nv78LWl3ZL+JQF9Juen3e96hJk4ZMm/EOLZv3xKtuW5Yt/YwffvwSgKlTFqEoCkcDd/Dj1tXs+fsQ\nOdkFn1dUynAF3l2ufR3z7jf1aRmcmriCusNeoP33y6j+dGfij5/DkHNvAHtr6x/s7f4a0fuC8Fsy\nvuBtlMP3UHLwVc5OXU52XCIYDFxbuwWXji1RaYs4Fl7Ye5cnZ+E1Be6X7nz/+C4cx4WFn3Hk+Tf4\nZ8xsGk4ZhaWbC8nnr+TJeX1dSXKadx/k2LQBOkd7Iv84ZLrvodpTiH9JmX0KfXx8ALC3t8fLy8u4\nlMLRkYyMDLy9vbGzM07rtmnThkOHDuHn51foc7KysggNDSU+Pp5Ro0YBxsFSaGhonm15eHiQXcyX\n1P3LUYra3qPIiorFoUl9022Lqi7kJKdgyMwqVc2DsmMTAIjcsReAjPBIks5cwKFxfVIv5Z8pursd\nx/u2Y1nVmZyk1HxZCqvJjIrF0tUpz2NZ0XFkRsZi4VIlz7buPpYVk0DS2UumZQERv+2h4fjXUFta\nYMjKxqV9C1Kv3CAzouAln+bK7NG7C1pba1qvNS7VtHR1Ns1wxB48XmCWgniN6kvVzq0B4zK/1Kuh\n+XLoH3gfMyNjcWziXWBdZmSc6ejr/XntvGqisbIg9tA/ACSdu0LqtVtU8fUmak8cqFW4dWtLwJBp\nxWZu8MbLuHVuCYDO1obkkHuZrao6k11I5iq+XgXWubZrRsrVULJiE9FnZBHxxxE8uhlnFtQ6Lc3m\njsa+bg2ODJ9Dxu1YimOu9xyMF0JpPOtNbOt6cnzkDDJvxwDg8WRnshOSqNqlLRprKyyrOuP/7XIC\nh9w7OFAY37F9qN6lhbE97axIuhJmeszazYmspFT0GXn3RemRcbg0rVdgnaWHHac/2kJ2choAjYY/\nZVx6qFKRm57F3hFLTM/r/fP7pN4q3dI4c7WvxtYG59a+xOw3zhKkXLpO6tWb2HnXKnLmvSTc3a04\nF5xouh0Tk4WDvRZr63tfU0ePxeLXzAlPTxsA+r5Ui48+vUhSUg6ZmXrefrMBjg4WAGzYeI2ad+oe\n1azZ43j6mZ4A2DvYEXzukumx6jU8iI9PJP2BiwH06NWZo0dOcP2ase+t/uJbli2fhYuLE9Y21syY\nvpiEhCQAxk8YTUjIzYfKVlH7+r+130SlIjcjk+Nj5pse67D5Q9LDorCrXxuVSkXKnSWwYb/uoVa/\n3gXmLY/vodzkVLQOdqbvI5VKBQalyAOxmZExODR+IMMDvyWKqsmMisXigZyZ0XHY1quFxtKSuMMn\nAEgOvkLa9TAcmtQnu5obOntbYg+VPKc59/EA7j06Erlzf54jNVX8GpW6PYX4t5TZ4b/CjuaqVCpC\nQkJIT08HIDAwkLp16xb5HABPT0+qVavG119/zcaNGxk0aBDNmzcv9Hkqlco0uMrNzSUtLY3s7Gyu\nXr2ap8Yc4gNP4dCkAdaexqO81V/oRezBoFLXPCjzdjQpF0PweKorADonRxybNiTlYuFXBIs7dhpH\n3/qmiwTU6NOLmAe2U1RNzIEgF8S7ZQAAIABJREFUqj37OCqNGq2dDe49OxJzIIismHgywqNw72G8\nAo9zWz8Ug4HUkFBi9gdSpVlDrKq5AeDWtS2pIaGm84+qtGhM/PGz/3rmKx+v5+gr75pOns2KjSd4\nzielGmwBhKzZarpgReBrM3H0rW86UdzzxZ5EH8j/enHHzhRaF33gODXuy+vRswPR+4JIvxWJ1s4G\nx6YNAOP5MbZ1apB86QYA9l61yE1OMw0ginJ59U8cGjidQwOnc3j4bJzuy1Lrpe5E7T+R7zkxAWcL\nravesy31R70EGH90VevZjrjjxpOEWy59F52tNUdem1uiwZaxfczzngM0fX8CWlsbjo+cmaetDj0z\nisDBxs/ChcVfkBEeWaLBFsC5z39md7/Z7O43m78GL8ClmRd2tdwB8Or7OBH7TuZ7TuTRc4XWefV9\nHN83+wBg6exAvRe7ELozABSFTp+Nx6lxHQA8e7bBkKsv9VUKzda+BgM+M8bg2KwhALZ1PbGpXYPk\nEiwrK047fxfOBScRess4CN32yy06d3LLU9OogQP/nIwnLt74o23/gSiqV7OmShULtv1yi9VrjfvG\nuPgsfvktjCd6PvrMG8CC+R/Rzv8p2vk/RddOfWjj3xwv7zoAjBg5kB2//5nvOadOnqNTp7a4ubkC\n8Oxzvbhx4xZxcQmMHDmQWXOMMy1ubq4Mf70/W3789aGyVdS+/m/tN1EUWn40FQcf48EN9+7tUHJz\nSb1yE3vvWjSZPQa1pXEQXv0p4+xXQcrje0hjY0WD8a+ZzjOqNeg549VyixggxAfeyeBpzFC9Ty9i\nDzz4e6PwmtgDQVR/pluenLEHAskIu43GzgaHpsa+fff7J/XydTTWVjQY//q9nAOfLzanOffxcPd3\nRd738mHaUzw6BUOF/q+iMPs8q0aj4e2332bIkCGo1Wpq1arFxIkT2bFjR5HPc3Z2ZtiwYQwePBi9\nXk+NGjXo3bvgI1MAfn5+rFixAk9PT4YMGUK/fv3w9PQscolhWclJSObios9osmgiKp2WzPBILsxf\niX0jLxpOHcPxYRMLrSnOuWnLqD9hJNVfeALUKm58vZWUCyFFZjm/4HOavj8BtU5LRlgUwfNXYd+o\nHj7TxxA4ZFKhNWA8adW6hgf+G1eg1mkJ//lPEk+eN2aZ9RE+00ZTZ/hLGLJzODfjQ1AUUq/c4OKy\ntTRbOgmVVkNuShpnZ3xoymRTsxpRF8snc1nLTkgmeMEX+C0Zj0qrJSM8krNzPwPAwacejWe8QcCg\nKUXWhW3bjU0Nd9p/vwyVVkvYz3+RcNJ4kYRTkz+g0YRhqC10KLl6zi9ZS0a4cXbDplY1Mkow2Coo\n8+n5q2m19F3UOi1pYVGcnvMFAI4+dWk6cySHBk4vsu78R9/TdPrrdN68FEVRiNp3guubduHk1wD3\nzq1IvRlB+6/mmLZ5ceWPxAYUvnTPXO+5Y7OGVO3UmrSbEbRes9C0vauf3bu88aPKik8hcPZXdFzx\npvGfeAiL5tiMtQA4Na5Dmzmvsbvf7CLrLny1g7aLRvHktoWgUhH85S/EBxvPAQiY+iVt5gw3/s0x\niRx679NSZzRnnzozZTkN3huGSqvFkJND8OxPyIqJLypOiTg7WzJ7hi9TZ5wiJ0fBs4YNc2f7cv5C\nEguXBPPDhg60ae3CoIF1Gf1mEDqdCgcHHSuWGmd3hg2ux5z5Z+k38DAKCiNf96ZJ49Kd+1YSMTFx\njB41ie83fYGFhY7r124y4jXj4Klly6Z8/uVS2vk/xf59R/n4o9Xs+vNHsrNzSIhP5JWXjFdvXb7s\nc7765iOC/vkDlUrFogUfc+LEoy11hYrZ1+/mMud+8+ysT2k8fRRqnZas2EROTVoBwO2dB7Hx9KDd\nhsUoej2p18IIXri6wIzl8T0Ud/QUYVv/Q+s1C0ClNl6+ffGXRT4nJyGZCws/w/f9icYM4VGcv/N7\no9G00QQNnVRoDUD4z39g7elOm28/MOb85V7Os1OX0eC94agtjd8/F5euJiM8iozwKG5t/Q+tVhv3\nV2khoVxcUnxOc7anTU2PfKtmHqY9hfi3qBSlsJXz4kH7OrxU3hGK1PXINv5ul//y1BVN94CtlSbn\nbv9+5R2jWL0CN7Ojdf5LLFc0Tx//odK875v9hpV3jCL1O70eoMK3Z/eArSTHlfwCL+XFweVTbCzr\nlHeMYqVn3ag0fb2y7Dsreh8CYz/a0/7l8o5RpG5HfwIqxz5Jz/flHaNYGgYWX1RBeNo/+j9pYU7/\n396dR0VV/38cf84AIrIoguKCqCCWZmqItlhaaibW91uSkJio5bGs1COKOyaikmuYWG5pKqdUTDjH\nXMq00lJzaaOfkQsGilruIYsoML8/PMwXZEBGm9B6Pc7xnLoz87nvz713PnPf9/O+l8zLX9z8TX8D\n3UkoIiIiIiJWK9IfPq6Uf8zf4RIREREREbnTKOESERERERGxEZUUioiIiIiI1e6kJwHeyTTDJSIi\nIiIiYiOa4RIREREREQGuXLnC6NGjOX/+PM7OzsycOZPatWuXes/y5cvZuHEjBoOBIUOG8OSTT1bY\nphIuERERERGxWpHpn/eUwtWrV9O8eXOGDRvGpk2beO+994iKijK/npWVxapVq9i6dSt5eXk899xz\nN024VFIoIiIiIiICfPfddzz22GMAdOrUiT179pR63cnJiQYNGpCXl0deXh4Gg+GmbWqGS0RERERE\n/nXWrVvHypUrSy3z8PDA1dUVAGdnZy5fvlzmc/Xr1+fpp5+msLCQV1999abrUcIlIiIiIiL/OiEh\nIYSEhJRaNnToUHJycgDIycnBzc2t1Os7d+7kzJkzbN++HYBBgwYREBBA69aty12PSgpFRERERMRq\nJoru6H+3IiAggB07dgDXk6t27dqVer1mzZpUr16datWq4ejoiKurK1lZWRW2qRkuERERERERICws\njLFjxxIWFoaDgwNz584F4IMPPsDHx4euXbuye/duQkNDMRqNBAQE0LFjxwrbVMIlIiIiIiLC9Ydi\nzJ8/v8zyl156yfzfw4cPZ/jw4ZVuUwmXiIiIiIhYzcQ/77HwtqB7uERERERERGzEYDKZTFUdhIiI\niIiI3F3quLSv6hAqdDZ7f1WHAKikUEREREREbkGR6daeBPhvo5JCERERERERG1HCJSIiIiIiYiMq\nKRQREREREavd6h8X/rfRDJeIiIiIiIiNKOESERERERGxEZUUioiIiIiI1Uwm/eHjytAMl4iIiIiI\niI0o4bpDxcfHs3r16jLLhw4dWmbZ6tWriY+PL7M8KSmJOXPmlFoWERHB1atXy11vx44dKx1jly5d\nyM/PL7Vs586drF27tsx7Q0NDyczMLLctS7HejnHjxrFz585Sy86ePUt0dHSZ986ZM4ekpKRKtftX\nx3mjvXv3EhERUWb59OnTOXXqVKllaWlphIeH39b6wsPDSUtLu6027nRLliwhJSWlqsOwOUv7srzj\n6XYlJSWxffv2v7zdW1U8vpSMa+TIkTz//PMcPnyY8PBw+vTpw59//mlVu5bG25vFcDs+//xz/vjj\nj9tq425k63HVVvLz8+nSpUtVh/GXyczMJDQ0tKrDqJRDhw6xf//1P2hr6VzkTrN//35+/fXXqg5D\nqpBKCu8yCxYsuK3Px8XF/UWRWNapUyebtn876tSpYzHhuhtMnDixqkO4a73yyitVHcI/TnBwcFWH\nYFHJuHbv3s23337LqVOnyMnJqfRFlZJud7y11qpVq4iOjsbLy+tvXa/I3Wbr1q14enrSvn37qg6l\nUtavX0/Pnj259957qzoUqSJKuKxw7do1xo8fT2ZmJoWFhbz00kusXr2apk2b8ttvv2EymYiLi6NO\nnTrMnTuXAwcOUFRUxMCBAwkKCiI8PJx7772XI0eOkJ2dzTvvvEPDhg3LXd+2bdvYsmULV65cISoq\nitatW9OxY0d27drFgQMHiI2Nxc3NDTs7O9q2bWuxjZ9++omXX36ZCxcuEBYWxuLFi9myZQu///47\n48aNw97enoYNG3Ly5EkSEhK4evUqo0aN4tSpU9SqVYv58+fj4OBQboxvvvkmJ0+exMPDg5kzZ7J5\n82aOHTtGZGQkcXFxfP3119SrV4+LFy9WahsvX76cTZs2YW9vT2BgICNHjqRHjx5s2bKFCxcu0Llz\nZ3bv3o2zszMvvPACycnJ5bb10UcfsWzZMgoLC5k+fTp2dnaMHDmSxMREPvvsMxYuXEjt2rW5du0a\nvr6+Ftu4cuUK48eP59SpU1y7do2nnnqq3FhHjx7Nd999x8yZM7G3t8fJyYl33nkHR0dHJk+eTEZG\nBkVFRYwYMYIHH3yw3LgzMjIYNGgQFy9eJCwsjJCQEMLDw4mOjsbV1ZXIyEhMJhN16tQpt42hQ4fS\nv39/OnTowM8//0x8fDxubm6ljt2ePXua3x8fH4+npydhYWGkpaURHR1NQkIC//nPfwgMDOTQoUP4\n+vri4eHBgQMHqFatGkuWLOHKlStMnDjRvH+joqK45557LMaUnZ3NxIkTuXz5MmfOnCEoKIiNGzey\nefNmDAYDMTExPPzww3h5eTFlyhScnZ3x8PDA0dGRGTNmWGwzPj6eY8eOcf78ebKysoiKiiIwMJAn\nnngCX19f/Pz8yMrKomfPnnTo0KHUvpw0aRKtWrWyat8US0pKYv369RQVFdGjRw+2b99OXl4e7u7u\nLFiwgI0bN7Jjxw6uXLnC8ePHGTx4MMHBwaSkpFjsW0JCAhs3bsRgMNCzZ0/69+9f4fotjUUA7777\nLufOnSMvL4+333671GfWrVvH6tWrKSoqokuXLgwfPrzcvm3bto2cnBwuXrzIG2+8wVNPPcUzzzxD\nkyZNcHBwwNfXF09PT/r06cPUqVNJSUnh2rVrDBs2jG7dulkc/yrTh4YNGxIbG0tRURFeXl7MmTOH\n6tWrW4zT0vhSfBwfOnSI7OxsXnvtNQoKCjhy5AhdunShUaNG5j65u7sTFxeHnZ0djRo1IiYmhk8+\n+cS8X4cPH05kZCS7du3il19+YerUqdjZ2eHo6MjUqVNp0KCBVWNcye0XExNT5ntz+vRpUlNTGTt2\nLLNnz2bs2LEkJiYC12fP3n77bZKTk/nhhx/Izc1l+vTpTJgwgXr16nHixAnuv/9+pkyZUul9Wq1a\nNRYsWIDJZOK+++5jypQpbN26lQ8//JCCggIMBgMLFiygdu3aFfbrr3Ljb5W3tzfz5s3D0dGRWrVq\nERsbS2pqKmvWrDFfOCz+PRw3bhyXLl3i0qVLLF68mJo1a9oszpycHCIjI8nKysLHxweAffv2mbdl\nTk4Oc+fOZd++faSnpzN27FgKCwt57rnn+Pjjj3F0dLRJXMHBwSxduhQ3NzcefPBBEhISuO++++jV\nqxfPPfeceZwtHl9Onz7NpEmTyM/PNx/TxQoLCxk3bhz+/v5/6QWrpKQkvvzyS65cucLZs2fp378/\n27dv58iRI4wZM4bc3FxWrlxJtWrVaNKkifk7eeNY2rFjR5KTk3FwcOC+++4DIDo62jzDvGDBglLH\nwK2ut6LPdOvWjS1btrBixQqMRiPt2rUjMjKS+Ph4MjMzOX/+PKdOnWL8+PG4u7vz9ddfc/DgQZo1\na0ZISAi7du0Crlcd9enTh5MnT950fXeqIj0WvlKUcFlh7dq11K5dmzlz5pCdnU1wcDDVqlXj+eef\nJyYmhg8//JDFixfz2GOPkZmZyerVq8nPzyc0NNRcqte6dWsmTpxIXFwcmzZtqnAwa9iwITExMeYv\nXMnkYsqUKcyfP5+mTZsyefLkctuwt7dn2bJlnDx5stS6Zs2axZAhQ+jcuTOJiYmcPHkSgNzcXCIi\nIvD29iY8PJzU1FRat25dbvthYWG0bduWWbNmkZiYiIuLCwA///wz+/fv5+OPPyY3N5fu3bvfdPtm\nZGSwd+9e1qxZg729PcOGDWPnzp0EBgby448/kpGRgb+/P3v27MHZ2fmm5Y8BAQG88sor7Nixg9mz\nZzNu3Djg+onejBkzSEpKolatWhXugzVr1tCwYUPi4uJIT0/nq6++4vLlyxw6dIgtW7aUivXLL79k\n3759BAUFMWDAAL744guysrL46quvcHd3JzY2losXL9KvXz82bdpU7jqvXbvGwoULKSoq4tlnn6Vr\n167m1xYtWsQzzzxDaGgomzdvtlh2ChASEkJycjIdOnQgKSmJTp06cfz48VLH7kMPPVTh9oPrJxfP\nPPMMkydPpkePHowfP56IiAj69evH0aNH2bhxIw899BB9+/YlPT2d8ePHlxtTRkYGTz/9NN27d+eP\nP/4gPDycli1bcuDAAdq0acPevXuZMGECISEhzJo1C39/f+Li4m5aYlW9enVWrVrFkSNHGDVqFBs2\nbOD06dMkJSXh7u5u3u+W9mVqaqpV+6YkNzc33n33Xd577z3zj+6gQYP4+eefgesJ5rJly0hPT2fI\nkCEEBwczefLkMn07evQomzdv5qOPPgLgpZde4tFHHy33IgCUPxb17t2bZ599lvj4eD799FPzd/f8\n+fMsXbqUDRs24OjoyNy5c8nJycHZ2dli+3l5eXzwwQdcuHCBkJAQunbtSm5uLq+//jotW7Y0lzBv\n27aNixcv8vHHH/Pnn3/ywQcf4ODgYHH8c3Nzq1Qf3nnnHfz8/Fi3bh1paWnmE6qSbja+REdH8/nn\nn7Nw4UIyMzMZOHAgjRo1YtmyZeY+GY1GEhMT8fDwYN68eSQnJ2Nvb4+bmxsLFy4s1V5UVBTTp0+n\nRYsWbNu2jRkzZjB48GCrxriS22/27NkWvzctWrQgOjq6wotcvr6+REVFkZmZSXp6OsuWLcPJyYlu\n3bpx9uzZci/ElNynvXr1wmAwkJycjIeHB0uXLuX3338nPT2dJUuW4OTkxJtvvsk333zDf//73wr7\n9Vcp+Vs1ePBg8vPzWb16NV5eXqxcuZKFCxfy+OOPl/v5hx56iIEDB9o8zjVr1tC8eXMiIiL46aef\n2Lt3L0eOHGH27Nl4eXmxaNEiPv30U8LDwwkODiYyMpKvv/6aBx980GbJFlwvqytO/r29vdm9ezeO\njo74+Pjw6aeflhlf5s+fT3h4OJ07d2bPnj3MmTOHiIgICgoKiIyMJDAwkBdffPEvjzMnJ8d8sXLF\nihUkJiayd+9eVqxYQVpaGsnJybi4uBAbG8vatWupUaOGxbG0V69eeHp6mse4559/nsDAQMaNG8eu\nXbtKXVC81fWW95lVq1YRGBhIfHw869evx8nJidGjR5uTqGrVqvH++++za9culi9fzrJly3jsscfo\n2bMnDRo0sHrbrFq16o5OuKRylHBZIS0tjUceeQQAFxcX/Pz82LVrl/nENSAggC+++AIvLy8OHjxo\nvr+moKDAnNC0bNkSgHr16nHu3LkK11c8Ve7v78/Zs2dLvXbu3DmaNm1qXu/x48ctttGyZUsMBgN1\n6tThypUrpfrywAMPANCuXTs++eQTAGrWrIm3tzcAnp6e5OXllRufg4ODeWYtICCAXbt2cf/99wOQ\nnp5Oq1atMBqNuLi40Lx58wr7CpCamsrjjz9uPtkIDAzkyJEjdO/enR07dpCZmUlERATbt2/HaDTS\nu3fvCtsLDAwE4IEHHmDWrFnm5RcuXKBmzZq4u7ubXy/PsWPHzGWSTZo0wc3NjXPnznHs2DHatGlT\nJtYhQ4awaNEiBgwYgJeXF61bt+bw4cN899135vuICgoKuHDhQrlXjtu2bUu1atUA8PPzK3VfSHp6\nurnGPiAgoNzk5rHHHmP27NlcunTJPNPw6KOPAv87dk+cOFHh9itWfMLr5uaGn5+f+b/z8/M5fPgw\n3377LVu2bAGo8B4ZT09PVq5cydatW3FxcaGgoIDQ0FCSk5M5e/YsXbp0wd7enjNnzuDv7w9cPzY3\nb95cYXzF3z9/f3/zd8rd3d28f4vduC8HDhxIdHS0VfumpKZNm2I0GnFwcGDkyJHUqFGD33//nYKC\nAgBz6Uj9+vXN901a6tvhw4c5deqU+WTxzz//JCMjo8KEq7yxqFWrVsD1bV1yfDlx4gT+/v7m2aLI\nyMgK+9a+fXuMRiOenp64ublx4cIFc59L+u2338xjQM2aNRkxYgRLly61OP7dmHBZ6sMXX3xhPsZC\nQkLKje9WxpeSfXJyciIjI4MRI0YA12eyH3nkERo3blymj3B9v7Vo0cLczty5c28phuK2rfneAJhM\npjJtAPj4+JgvctWpU6fC+1hK9t/Z2ZmrV6/i4eEBwODBgwHw8PBg7NixODs7c+zYsXIrJ2yh5G/V\n6dOn8fHxMZdWtm/fnrfffrtMwlXedrGl9PR0OnfuDECbNm2wt7fHy8uL6dOnU6NGDf744w8CAgJw\ncXGhffv2fPPNNyQlJfH666/bNK7u3buzaNEi6tevT0REBAkJCZhMJp566ilmzpxZZnw5fPgwixcv\n5v3338dkMmFvf/108NChQ7i4uJCbm2uTOIu/R66urvj5+WEwGKhZsyZ5eXk0a9bMfDwXb7s2bdpY\nHEtvVHLsK3muczvrLe8z+fn5HD9+nAsXLpgv2Obk5JjPw4o/V69evQrvmYfSx3BF65O7nx6aYQU/\nPz8OHDgAXL96ffjwYby9vfm///s/AL7//nuaNWuGr6+veUp/5cqVBAUF0ahRI6vXV3wSeOjQoTJX\nRby8vMw3yBdfUbfEYDBYXN68eXN++OEH4Hopx83eb8m1a9dITU0F4MCBA+YTSYBmzZqRkpJCUVER\nubm5HD169KbttWjRgpSUFAoKCjCZTOzfv5+mTZvSsWNH9u/fz8WLF+ncuTMHDx7k119/rXDmDf63\n/W6MzcPDg6ysLPNJZEXbz8/Pz/z6iRMnzGVavr6+FmPdsGEDvXr1IiEhAX9/fxITE/H19eXpp58m\nISGBpUuX0qNHD2rVqlXuOn/55RcKCgrIzc0lLS3NXLZSHE/xfqsobqPRSI8ePYiOjqZbt274+/tb\nPHaLOTo6mpP6gwcPlmqromPC19eXgQMHkpCQwLx58yq8Gr58+XLatm3LnDlz6NGjByaTiYcffpjU\n1FTWr19vPsGuV6+e+XgpeWyWpzjew4cPm0/QjMayQ9uN+3LUqFFW75uSjEYjv/76K9u2bWPevHlM\nmjSJoqIi8w+ope1mqW++vr40a9aMVatWkZCQQHBwcLllmSX7UtH+vJGPjw/Hjh0z//gPHz68wpnD\n4m167tw5srOzzSfmN25XX19f8za9fPkygwYNqvT4V14f0tPTgesPO/n8888txncr40vJPuXn5+Pj\n48N7771HQkICQ4YMMSfulo6dunXrmm94379/P02aNLmlGIrbLu97YzAYMJlMODo6cv78eQoLC8nK\nyip10aVkfNaM1yX7f+3aNQAuXboEwLRp09i3bx/z588nLi6OadOm4ejoWOpk0NZK9sXd3Z3s7GzO\nnDkDXC/Za9KkSalx6uTJk6USVWu2xe3w8/Pjxx9/BP43Vk+aNInY2FhmzJhB3bp1zdstNDSUdevW\ncf78eZvfu9O8eXNOnDhBSkoKnTt3Jjc3l+3bt5c7vvj6+hIZGUlCQgJTpkyhR48ewPULbEuWLGHD\nhg02echDefvJYDCQlpZmTvT27dtnTqItfcZgMFBUVFTq//+O9Rbz9vamfv36LF++nISEBPr162e+\nQFFevMXHRUFBATk5OVy9erXUuPF3HcN/NZOp6I7+d6fQDJcVQkNDmTRpEmFhYeTn5zN06FCSkpJI\nTk5mxYoVODk5MWvWLGrVqsW+ffvo27cvubm5dOvWzXz1xBqZmZn079+fq1evEhMTU+q1mJgYxowZ\ng4uLC87OzlbXrEdGRjJhwgSWL1+Oq6ur+eqWNRwcHEhISCAjI4MGDRowatQo80xZixYt6NSpE717\n96Zu3brmE7aKNG7cmICAAMLCwigqKqJdu3Z069YNg8FAvXr1aNCgAUajkaZNm1ZqBuKnn36if//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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21314b4ffd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Vu3fvPJeXmzx7HC5evKhKlSopLi5Offr0kc1mk8lk0vXr1/OdOAAAAADIUBST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\nAQAAAIAhEgcAAAAAhv4/nFu8rOoxCoYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2133d2ce518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check feature correlation, to see what correlates with the close price\n",
+    "colormap = plt.cm.inferno\n",
+    "plt.figure(figsize=(15,15))\n",
+    "plt.title('Pearson correlation of features', y=1.05, size=15)\n",
+    "sns.heatmap(df.corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(15,5))\n",
+    "corr = df.corr()\n",
+    "sns.heatmap(corr[corr.index == 'close_bid'], linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create random forest regressor - random decision trees, like weak learner, ada boost\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Scale and create datasets\n",
+    "target_index = df.columns.tolist().index('close_bid') # predict this, should it be return?\n",
+    "dataset = df.values.astype('float32') # so regressor can use it\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "dataset = scaler.fit_transform(dataset) # scale features to between 0 and 1 for faster convergence\n",
+    "\n",
+    "# Set look_back to 100 which is 100 ticks\n",
+    "# look back is 1 period, to check which features predict best a 1 period return\n",
+    "look_back_rows = 1 # to work with more than one, use alternative reshape\n",
+    "X, y = create_dataset(dataset, look_back_rows=look_back_rows) # look back only 1 row\n",
+    "y = y[:,target_index]\n",
+    "#TODO:X = np.reshape(X, (X.shape[0], X.shape[2]* look_back_rows)) # to get back rows and columns\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[2])) # to get back rows and columns\n",
+    "# extend extra rows into columns, as all the prices during lookback periodd should be used as features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(14878, 11)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape\n",
+    "#y.shape\n",
+    "#X[0].shape\n",
+    "#X.shape[2]\n",
+    "#np.reshape(X, (X.shape[0]*10, X.shape[2]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fit model\n",
+    "forest = RandomForestRegressor(n_estimators = 100)\n",
+    "forest = forest.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature ranking:\n",
+      "0. close_bid 3 (0.816391)\n",
+      "1. ohlc_price 7 (0.067108)\n",
+      "2. high_bid 1 (0.064573)\n",
+      "3. avg_price 5 (0.050164)\n",
+      "4. low_bid 2 (0.001339)\n",
+      "5. open_bid 0 (0.000077)\n",
+      "6. range 6 (0.000056)\n",
+      "7. momentum 15 (0.000053)\n",
+      "8. volume 4 (0.000046)\n",
+      "9. pca 10 (0.000045)\n",
+      "10. hour 11 (0.000038)\n",
+      "11. oc_diff 8 (0.000028)\n",
+      "12. period_return 9 (0.000028)\n",
+      "13. week 13 (0.000023)\n",
+      "14. day 12 (0.000023)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21314b3e320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find feature with best explanatory power to predict close price\n",
+    "importances = forest.feature_importances_\n",
+    "std = np.std([forest.feature_importances_ for forest in forest.estimators_], axis=0)\n",
+    "indices = np.argsort(importances)[::-1] # get indices for importances\n",
+    "#print(indices)\n",
+    "\n",
+    "column_list = df.columns.tolist()\n",
+    "#print(column_list)\n",
+    "print(\"Feature ranking:\")\n",
+    "for f in range(X.shape[1]-1):\n",
+    "    print(\"%d. %s %d (%f)\" % (f, column_list[indices[f]], indices[f], importances[indices[f]]))\n",
+    "\n",
+    "    \n",
+    "# Plot the feature importances coming from the forest of decision trees and their standard deviation\n",
+    "plt.figure(figsize=(20,10))\n",
+    "plt.title(\"Feature importances\")\n",
+    "plt.bar(range(X.shape[1]), importances[indices],\n",
+    "       color=\"salmon\", yerr=std[indices], align=\"center\")\n",
+    "plt.xticks(range(X.shape[1]), indices)\n",
+    "plt.xlim([-1, X.shape[1]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IiIiIiEKCQSgmM2BNnBThH7mjttx68x2QY1od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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22a04aa9ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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AtQ3vNKqWy2Vr/P39ZTabnR+bzWYFBARowIABioyM1IgR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\neO4pAMDm087P6LoGrRolueaMrrm/c2UZUFWctdvHGg8/HgBQ8uk7Ka2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CNJXrdL4QtHu4S27N5heZ9YIiuzRK/HgLlmmKbCsdKMpd7TE1LfULyo9yul\nCAUBAID5gWAOAADAApBrKS6Yk8svjFFWI8msGjv3KOetVby944yfB9dvlCS17N5uab+3jvsBKsFI\nIqvCyZPLvt5Daurco4GLLyuq482YobddosjKc7Xk2V/KM3Si6PWReEadfZGi140xTVPBSEqvdQW1\nbf+g+ocTpx7beLqPRRVLELgDAGAmGaapY0PWw7BDkZR2dg2fGr87OvYyrMxb3sOs+NUjMu129b7n\nujP26Ds5VnPpbx6fRuWlt/YH35F/9w71vetq9Vy9pWT7vjWUM8a5bJkSre1q2fPKaJdQC/KGoXgq\nV7LagLkUjWeVN+buwqRYIrtgOhYDALDQEcwBAABYAAr+4oI52QVyYigdDMt3rEfh1Wsl+5kvjYfW\nj15B6t9l7QrSaCJbsm4eQLmIvimY0vH0VknSkc1nfsBlic2mzus/KrtR0KrHfjilLY6Hkjo6GLd8\nfC5vKJbIqm8wrpf2DWh3T0iRuLUQnWGa2tMTWjBhRQAA5sJAKHlGqGYysWRWr3QOKZHO6XB/TJG3\nBOTrejrVdGCXTlz8+0qf7B76ZtGV5yq2dKUWvfQbOZPl0SGvZdd2nf/Df1QisEg77vjypKN0rZoo\nlCNJPq9LwXUbVR0Ny9d32PKedApFpQjNcccaU1JohK45AAAsBM65LgAAAAAzrxAISJI8FoM5hmkq\nlzfkclZ2jtu263XZTFPhNevG/Xl01bnKebyWW7sXDFPxVE513qpSlgnMqVMhFtNUxzM/V97tVf+m\nzVPer/fdf6ALvvt1rXr8Ie279VMquD1F79HdH5Wn2qnm+tEPmHJ5Q6lMXqlsfvTPdF6pbEGpTF65\nwvSChplcQXt7wrpwVbNsJfqADAAAvOFoEd1y3iydLeiVg0PjjsBa9uTPJEk9E43dtNnUd8U1WvfA\nt7Xod0+p98oPTKmGUnHFInrH335Wpmx66fN/r1xtXUn2PVsoRzoZzLlgo5Y98x9q2b1dIx0rLe0b\nSWS0RLUlqRGYS5ONknIm4nIl43KmknKmk6N/JhMnv06Mfn/yT9ls2n/Df1eurqGoGkLRtAKN3uk8\nDAAAMA8QzAEAAFgAzEC7JOsdcyQpmy9UdDCnYBjy7n1dkiYM5pgOp4bP36C2Hc+rOjysTGPzpPtG\n41mCOagYpmme6pjTtO811R7v05HN16ngmfqJY6OqWt3X3aR1D/4/Lfv1ozp07U3F1yVp75GQatyu\nkoRvJhOOZ3T4+IhWLirNh2QAAGDUcDStRHrqY5HGC+XYCnkte+oxZX316r/03ROu7T0ZzFn628fn\nNphjmrr4m1+Sd+i4dn/0zzS8bkNJtp0slCNJPm+Vjq57uySpZfcrOnzNDZb2pmMOKkEqk1cyk5/w\n5+f96J+1/vvflK2IrrjphhZ1/n8fK6qO0EhGpmlyEQAAABWOYA4AAMACYK+vU97tlTsUtLwmmzNU\nM/E53HkvkcqrsXOPpImDOZIUXL9RbTueV8ueHTr2+++bdN9oPKOlrVw9isowksqd+sBr2dP/IUnq\n3XzttPftvvYmrf3xfVrzyAM6dM0N446Sm0zBMBVLWvtQyDN4XLXH+zT0tkuKvp8xvYMjqvO61NJQ\nfIcfAAAwvr4ixlNaFdjxvDyhoLquu1lGVfWEx8WXrlB49Vq1bX9erlik6C4XpbLi8Z9oyXNPauiC\ni7Xv5k+WZE8roRxJ8lQ7lVp5jrK+estdQiUpVzCUSOdU43ZNt1RgzgxHJ+6WU3f4oNbdf6/Sjc0a\nfNulynu8J/+peeNrt/fU1850Spfd9adqOvB60XXkCqOjd+trJ36+AgAA8x/BHAAAgAWgymlXqqnF\n8igrabRjTiUbSeW0snOvch6v4ouXTXjc0AUbJUktuywGcxJcPYrKMXY1tC2f09Lf/kLp+iYNbHzn\ntPfNNPnVe8U1Wv7rRxXY8bwGfu+/THvPidgzaV3xlx9V7Ymj+u0939XgxsumvNf+3oje7nbJ6+at\nNAAA0xVLZhVJZEq+7/JfTTLG6k36rrhGjV1f15Lnn9Th9/9hyWuZjO9Ily76zj3K+ur10uf+t+Rw\nTHtPq6GcMbU11Qqev0GLXvqN3MEBpVsCltZF4lmCOZjXJhxjZRja+H+/LHshr+2f/l86ccm7Jt/M\nNJXx1avp4O4p1RKMpQnmACiJXL6gXN6Ql7+jgbJTubMJAAAAcIrLaVe6ya/qaEgqWAvcZHMzOxpm\nriWHI/L1HVJk1dqzdusInXuhDKfL8hWkuYKheGrq7fiBchKNj35YFtjxvKqjYfVdcY1MR2lCKZ3X\n3yZJOueR+0uy30TO+7fvqvbEUUnSxd/8khypxJT3yhuG9vaEVDAq+/kRAIDZMBPdclyxiBa98JSi\ny1YpfM76yWu4/P2SpKW/ebzktUzGns3o0q9+Rs5MWtv+4itKtbZPf88iQznS6Dir4NjFCHtesbwu\nFi99qAqYLfmCMeFFNSt++e9q2fOKjv7++6yFciTJZlP43AtUe7xPVbFw0fWEYvw+AZg+wzS153BY\nXceic10KULRc3lB4JKO+wbj29YS0bf+gnt3Zr51dQfUHE8pVwEXEBHMAAAAWgLFgjs0w5I4MW1qT\nzc3/F7tnY3/9ddlM86xjrCTJqHYrdO4FauzeJ2fS2gf6UU5SowKYpqnIyY45pRxjNSayZp2GLrhY\nbdufk+9IV8n2fbOa/l6d92/3KdXcqoPX36aagX5d8C//Z1p7xtM5HeyNlKhCAAAWplQmr2AkVfJ9\nO37zuBy5nHree71ks016fLJtsYLnX6TWnS+ruojuoqVw4X1fU8Phg+q+9kb1//57p72fTSo6lCNJ\nPq9LwXUngzlFjLOK0CkU81h4JCPDNM+4vSoS0oXf/bpyHq9e/ZMvFLVn6GQYsPFA8V1zEumcUpl8\n0esA4M06+yKKJDIKjWQUmqgrGFAG0tnR9wI9J2LafWhYL+45oed3H9fO7qC6+6MaiKSUSOdkmKbC\n8YwOHo3od7tPzPuQDsEcAACABcDldCjd5JckuS2ecM7mK7cjhGGacu/ZKUmTBnMkaWj9RtkMQ817\nX7O0P+OsUAkS6bzyhiFHKqFFv3ta8UUdCp13YUnv4+D1H9X/z959x0dV5f8ff01PJn3SSEIKJdTQ\ni6BYsLv2dddddXV1i6hfu2t3bauu2MvPtrqWVdd1i32tyAoKAtJJQgqEkF4m03u59/fHQKQk5M5k\nEkg4z8eDByVzzr2EMLn3nvf5fABKP3grrvPuNv2FP6MJBth0+S1s+e0fcBSNofTDt8nasrZf87bb\nvDR3xn+XvyAIgiAcLpo6Xey/JN5/JV99gKTW0HDCmYrHNB77E1SSROHyLwbgjHqW9/1SSj98G3vx\nWDYuui0uc47OT4s6lAORYI61dDJhvYGscuUVc/zBsAgSCENWl73nBetpLz+K3mmn/NLrFLd1280y\nfgoApuotsZ2TWEQXBKEfGtqdtFo83b+va3Eg9xBAFISDxRcIUdfiYGV5K6sq2ymvt1Df5sTs8OFT\nsEFYhr1COhuHYEhHBHMEQRAEQRAOA3qtGm+0wZxhXDHH4wuRXlMBgLV0Up+v7y7tXq5sMd/uEsEc\nYeiz7ar8VLDia7R+LzuPP0PRzvNotMw/HnduAcVLPoyp5PuB5H3/P/JXf0PHtCNoPO4nSHo9P9z0\nILJazezH70Tj698u/e0tDhHCEwRBEIQYBENh2ro8fb8wSqn1tZiqt9A2ZwG+zBzF45qOOQVZrR60\ndlYJ5nbmPH4nYZ2eVXc8jmSIPkyzr+y0RApzkmM7H70WrTFSJTS9rgqt26l4rLgWEoYqi3P/EEz2\npjWUfPUB1rET2X7WhVHPubt9nqkm+oo5gKhuIQhCzMw2LztaHXv9mcsXpM0S/+stQYiWzeWnYoeF\n1ZXtNHQ4FW0GNljNFH/5ATOfuZe0uur9Pi7vmneohXREMEcQBEEQBOEwsLuVFUCiwmBOcBhXzHF5\ng2RsqySUYMQ5clSfr++aNANZpSJbYZUNf0jsHhWGvt0Bs6IBaGPVTaOh9pxfofX7GPXpv+M2rdrv\nY8bzDyJptKy/+q7uQJFl4jRqfvprUloamPy3Z/t1DEmWqay3DOsQoyAIgiAMhBazh/AA7OAu/uoD\ngEgbqyj4MnPomDqXrMoNGNub435ee1KFghyx+FYMDhubFt2KY9S4fs9pNGgZX5TerzmSE/WYy2ah\nkmXFVUJBtPAVhiaHO7DfoqA6EGDmM/chq1Ssu/ZeZI026nl9mTl4skZEKubE8B5ncwUIS8P3OYwg\nCAPD5Q2ydae1x0qE9W1O8b4iHBRhSaK1y83aqg42bjPTafcesFqmKhwis2I9k197ihOvOo+zfnE0\ncx+7nTGfvMvCG39F9qY1vY7tKaTTfIiGdEQwRxAEQRAE4TCg1agJmLIASLCYFY3xD+PFZo/FQWrD\ndmxjJoBG0+frg8mp2EeNx1S1GXVA2a5Qm3hILQxxNpcfg9XMiPUrsIyfgktBiK0n6j6q7Ow49TyC\niUbGfvQ2qlAwpmPsa8K7r5DU3kztuZfgLB6718cqLrkGZ34R4957A9NW5QtPPfEHw1TutIry0IIg\nCIKgUFiSaBqAdpCqcIjirz8ikJJG67yFUY9vPO40AEYu+zzep9ZNFQoy76E/kLNpNc3zT2D7mRf0\ne06NSsXkUSa0mv495k8x6ugsi1QJzS5fp3icTVQKFYagnlpGjfv3q6Q21rH9zAuw9qN9r2V8GQlW\nM4mdbVGPlWQZq0M8RxAEQTl/MEx5XVevgWd/MExTh3uQz0o4nPkCIba32FlV0U51ow2Xr/fnfAZL\nJ8Vfvs+8B2/grJ8fxfE3XMSkd14irb6W9unz2PT7m1l/9R/RBPwcfcfvKPj2yz6PvzukU3uIhnRE\nMEcQBEEQBOEwEcqJ9EdX2soqGJaQhulis2rLZlSShLV0suIxnVNmoQkGyKhVVpZatLMShjKPL0gw\nLFH4zWeoJCnSxioGapWKWeOzKcpJobd4TigphfpTzsNobmekgpvsviS1NDDh3ZfxZuZQ+aur9vt4\nOCGRtTc+gEqSmPP4XYrDdr2xufzU7VMyWhAEQRCAYXstDZGATUO7E38guofcbRYvwXD8d27nrltB\nosVMw8LTkfT6qMc3LzgJSaOl6Jv/xv3c4MdQzsjvvqRj6hxW3/ZIXFqEji9KJylB1+95Uow6uiZN\nR1apyIoimOMNhIb1hg5heNq3ZVRSSwOT/v4iXlMWWy67vl9zW8dNAcBUsyWm8T2FhgRBEHoiSTIV\nOyz4+vg+3NDhFJV+hQFndfop39HF6sp2GjtcvV7v622WXVVxfspZvzyGuY/dQeGyzwkak9h++i9Y\nce//48N/f8/yR16j5ue/YftZF/LtAy8iaXXMf+B6Rn/8juJzOhRDOiKYIwiCIAiCcJgIRxnMAQgG\nh2e5U8OWTQBYSycpHmPetYM0a4uyB9U2t9jpJgxd1j3aWMlqNY3HnhbTPAVZSSQl6Bidn8rUMVkY\ndD1XqKo951fIKhWl7/0tprLve5r+wkNoggE2LrqVkDGpx9eYp85h21kXktqwnYlvP9+v4wE0drgw\n27z9nkcQBEEY+vYs275pm5nQAIRQDgXbmuzUtTpYvbWdinqLopZGsizT1BH/ajkAJV+8D0D9ydG1\nsdotkJpB+6wjydi2leTGHfE8tf1COd/96UXCicZ+zzsyK5mcjP7PA5Bi1BNKSsE2egKm6i1RBZdF\nOythKAlLEi7vHrv3ZZmZ/+9PaAJ+Ni26jVBSSr/mt0yIBHMyqpVt6NlvvKiYIwiCQlUNVhyevr9f\nhyWZ+jbnIJyRcLhxuANsb7azqqKNTdvNmO2+A7ar0tutHHfzJUx65yVSd26jfcY8Nl1+C5+//DGf\nvvk166+7l5YjT9jvWV7HzCP55tG/4U8zMevZ+5n0t2ejfnbYU0jnYNyniWCOIAiCIAjC4SIzE0mj\nJTGKYI7/ECnzGE9ef4jU2goArGOjD+Zkl69V9HpfIBz1DmJBOFTYXX6Sm+vJrN5M+4z5+E3ZUc9h\n0GooHvHjg+2MFAOzx+eQnZa432vd+UW0zDuezOrN/Wovlff9/8hfvYz26fNo6iNMtOU3N+LOzWfC\nu6+Qvq0y5mPuVtVgw+ML9XseQRAEYWjqqWy73R1gY6152F0Ttls8tFo8QKQqUKfNy4ZtZtZWddDa\n5UaSen5Qbrb78Abi/71S77CSv2op9uKxUVXE3FfDcT8BoHDZp/E6tQEL5aQl6RldkBqHM4ww6DQY\ntBrMZTPRBPxk7LpfUsLuFpVChaHD7QvttWg4cvnnjFj7HW2zjqJx13tAf+x+D4q1Yo4/FMapYKFd\nEITDW32bg44oNge1WTx4DtBSSBCUcrgDbNsVxllf20ljp6vPqk0AOpeDY27/LWk7t1N79q8iVXEW\nv0bNzy6LtKDvo5Kkbdxklj75Nq68Qia/9Twzn74XVTi2+4rdIZ2DUe1eBHMEQRAEQRAOEzqdFl9G\n1mFfMcflDZJRW0HIkICzaLTicb7MHFz5RWRWbICwssUVUTVHGKrsrgBFX38CQEOMbaxG5aei1ex9\ny6nTqpk8ysT4wnQ0+9x01/70EgDmPXQTpq2boj6e2u9jxvMPImm0bLj6rj5v6kPGJNZefz9qKcyc\nx+5EFerfQ6qQJFFZbyEsDb/3TUEQBKF3Vqefih2WXsu2u3xBNtR2DpvFEI8vSE2jrcePuXxBqhtt\nfF/RRl2LA98+IZzGAaqWU/jNp2iCwUi1nH60h2qZfwJhvYGi/33a7wp+MHChHINWw6QSE+o4tMLa\nU0qS7scqoRXK21mJFr7CUOLeo1qO1u1k+gt/JqzTs/7qP8alvVwwORXnyBJM1eUQ432BaGclCMKB\ndFg9UVfAkWSZuhbRgluIjX2fME6TwjDObhqvmwV3LSJj21bqTvs5G6+6g3BizxWu9xqnUjF1dCb5\nmZHXuguKWfrU37GOnciYT//J/D9dj9of+/dM20Go+iiCOYIgCIIgCIcJnVaDz5RFgsWs+EFzYBhW\nzHFbHaTWb8M2ZgKyRhvV2M4ps9G7naTV1yp6vXhILQxFXn8IfzBE0dKPCRkSaD7qpKjnSDXqGWHq\nfeEpLzOJWeNzSEnUd/9Z59Q5bLnseozmdhbedDFj338zqkWxCe++TFJ7MzU/vQRn0RhFYzpmHUXd\nqeeRXlfFhHdfUXys3rh8QWoael6sFARBEIaPvdpVbTfTafcesGy7Lxhmw5YNkgAAIABJREFUQ60Z\nxyBXFvH6Q1TssGB1xuehc1iSqKy3Eu7j+3MwLNHQ4WR1ZTsVOyy7dqT6FbVbAEjo6kDj9Sg+r5Iv\nP0BSa2IOE+8WSkqmde6xpDbWkbajpl9zDVQoR61SMbEko9f2oP2RkqiPun0vRK5/giERTBaGBrf3\nx8Bg2evPkGjpZOsFi3AXFMftGJZxU9B5XCQ374xpfJddbPARBKFnDneAqhifOZgdvrhdEwpDk6ex\nhfY2K20Wj6IfVfUWVlW0sSGGMM5uar+PBXf/H1mVG9l5/Jmsu/YeRUFYFTCxOANTagLjCtOZUZpN\ncoIOf0YW3zz6N9pnzKNg5dccc/vv0DntMXw2RDBHEARBEARBGEA6rRqfKRtNMIDOpWyXRGAYVsxR\nbdmCWgrHVOa+u53VFmXtrERZd2Eosrn8ZFRvIaWlgZb5x+/X21mJsQVpfb7GmKBlxrgsinJSUAGo\nVFRdsIhlf/4rgeRUZrzwEPMeuAGtu+/d9UktDUx49xU8WblsveiqqM5106Jb8WTlMuntF0jt5yIc\nQLvNS1PnwFQEEARBEA4iWUb9+qt0/eM/e7WrUioYlti0zUyXfeArIUiyTEO7k7VVHXTavZTXdcVl\nIWZbkz2qv7MMdNq9bNxmZvP2LkVjCr79kjMuWsi558zmlN/8hCMeuonx/3yF3LUr0Nss+70+dUcN\npppy2uYswJeZo/jcetPdzuqb2NtZRUI5N0VCOdPmxi2UAzAqL5X0ZENc5tpXilEXqRKaV0hW5Yao\nqn3YRaVQYYjY/R6WUVPO2I/exjFyFNXn/y6ux7CMnwKAqTq2dlZObwB/DIufgiAMb75AJHAt9aOq\nX11LbAEGYehzfPYVhUdMYdz8MlKuXoT9n+9Rs62dqgZrrz/qWx0xhXF2UwUDHPmn68jZtJqmo07k\nh5sfAo2ycPnYgjSy0hO7f5+WpGfm+GzG5KchJ6fw3Z9eovHYU8kuX8fCmy4mwdwe9fm5vIMfLhfB\nHEEQBEEQhMOEXqfGa8oGUNzOajhWzNFv2QyAbeykqMd27t5BWq5sB6nbFyQ4DD+HwvBmdwUoXhp7\nG6sRGUZSk/R9v5DIru/R+alMG5tFwq6d350z5vHVC+/RWTaLwm+/4MT/O4+07VW9TyLLzHj+QTTB\nAJsW3Rp1kCiUlML6a+9BHQoy54m7Yu5Rvae6FocI5gmCIAwTYUmizeLBecsdZN5yPWNuuwbJ7Y5t\nLlmmot5Cm0V5NZho2V1+1lV3Utfq6K5sE5blfodz2i0eWvtx3n1V2QHIrNjAEYtvIWRIoHPKbBKs\nZoq++ZSprzzOMXf8jrPPP4rTLzyOo/54BZNff5qC776k9IO3AKg/6dyYz21PbXOPIZhojARzYlj4\n+jGU81UklHP/C3EL5WSnJ1KYkxyXuXqSYtQBYJ48E73TTurObYrHikqhw4cky8hxaOV2qHJ7gxAO\nM+upe1DJMuuvvQdJr+zeRSnL+DIATDXlsc8h2lkJgrCHsCRRXmfB389njE5vkHbrwF2HCoceSZZp\nWF9FwXWXgywTMiZTsuRDFtx9FWf+YgFzHrmVvFX/Qx2I77WcKhxi3p9vJm/NclpnH83q2x9XXLm+\nMDuZguz9r3nVKhWFOcnMmZiDKSuVVbc/Tu3ZvyKtvpbjr7+AlIa6qM5RJnLvNJhEMEcQBEEQBOEw\nodNEKuYAJCoN5gyzijmBYJjkXbvWYqmY484vwmvKIqt8reIH9eIhtTDU2O1uCr/5FH9qOm2zF0Q1\nVqNWMSo/NepjpicbmDU+h5xdu2F8mTkse/R1qn7xO1JaGjjhul9S8tm/e/x/l7fqf+StWU779Hk0\nHXNq1McGaJ23kJ0nnImpegul/3kjpjn2JMkylfUWAmKnqyAIwpBldweobrDyfXk7LF7M6DeeQ1ar\n0Xlc5K/8OuZ5JVmmqsFKQ7szjmcLwVCY6gYrG7aZcfdQ1aY/4RyPL0hN48C2akxurueoe65CFQrx\n/V1Pseyxv/Hhe6v57xtfsfLup6m88ApajjgOgPzVy5j09xc58v7rGP3Zv/CnpNE6b2FcziOckEjL\n/BNIbmvCVLU5qrEDGcoxGrSML0yPy1y90Wk1JOg0mMtmApBVsV7xWJu45xk2drQ6aDHHFj481PmD\nYYJhibEfv0PGtkrqTzyLzulHxP04tjETkTRaTNXRvYfsqUsEcwRB2ENNY3RVCw9kR4sDSRq+AUzh\nR8FQmC1VrRTfuIgEWxebLr+F/771NV8//Q+qz7uUYFIyJUs+YsHdV3HW+UfFL6QjScx+/M7u6pEr\n73lGcQg2Oz2RMX1U4U7QaykbnUnZ6CyqrvsjWy67gaSOVhbecCHpNRVRnepgX8MqiyYJgiAIgiAI\nQ55Oq8a+u2JOl9JgzvBaVHZ5g+TUVhLWG3AUj4l+ApUKc9lsCpd/TlJLg6I+9DZ3YK/Sm0o4PQES\nDVq0GpGjFwaXLxAidfV3JNi62HbGL5G1uqjGF+emYNApK0u7L51WzaQSE4WeAA3tLsx2L1t+exPm\nSTOZ+9jtzHnyj2SXr2P9NXcTToj8n1L7fcx4/iEkjZYNV9+lqE91bzZeeTu561ZS9sYztMw/Hlfh\nqJjngsiD/8qdVqaNyUTVj/MSBEEQBo8/GKbd4qHN4sHjj1RQG/v+m0x57UncOXmsvelBjr31N4z6\n4j0aY6gqt6e6VgeBoMTYkX23f+xLa5ebuhYHwfCBQ/W7wzllozPJSFHWDiksSVTWWxVVvImV3mbh\n6DsXYXDYWHvD/bTPOTryAZUKT95IPHkjaV5w8l6vT9++lYztW0mrq6F17jFxrXjRsPAnFC/9mMJv\nPsUycZqiMQMZytGoVUweZRqUe4OUJD3mstkAZG1ZR90Zv1Q0zu0LEpYkNGpx/zKUWZ1+Gjtc6DRq\ncjIS0Wlju64/VLm9QRLM7ZS9/hSB5FQ2//6WATmOZEjAXlJK+vYqVKFg1PdUEPm3kGQZtbiPEITD\nXmuXO65VbnzBME2dLopyU+I2p3DocXmDlO/oYsKTD5BVuYGGhaez7ZxfgUqFZeI0LBOnsfnyWzBV\nbWbkt18wcvnnlCz5iJIlHxE0JtN85PF0nXwm7klzo7vOlmVmPns/JUs+omviNFbc9zySIUHR0LQk\nPROLMhQfKis9kYxUA/XX3shaUxazH7+TqX99jOWLX1M8h3WQK+aIYI4gCIIgCMJhQqfV4DNlAZBg\nNSsaExjkPqsDzW1zklZfi7V0kuLymfsyl82icPnnZJevUxTMibYkptnuZWu9lRnjsklOFA+2hcEV\naWP1MQANJ5wV1VijQcvIOLRXSDHqmTzKhMcXpKHdRfuRx/PVc/9h/gPXU/LVB2TUVvD9XU/hLBrN\nhHdfJqm9marzf4uzKIaw3R4CqRmsv+ZujvzTdcx54i6+eeQ1ZF3/FvlsLj91rQ7G5Pd/0VUQBGEo\nsDh8uLxBEgxajAYtCXrN4AeNw2F0y/+Hf858MPYdjJCRsTj8tFk8WBw+9oyflHz2b2a88BBeUzbL\nFr+Gu6AY8+SZ5GxcRWJHC96c/H6dapPZRTAUZnxxRkyLr5FKNnZsbuXXm9GGc7Y1xW+Hdk80Pi8L\n7r6S5JYGKi+8gh2n/bzPMYF0Ex2zjqJj1lEDck7tM48kkJJG4bJPsY2ZqGhMwYqvKPh+adxDOQDj\nizJISoh+YT8WKYk6OgtH4U/LIKtCWfteiFSCsrsCmFKVLbwIh55gKEzVTmvk12GJHa1Oxg1wlabB\n5vaFmPLqk+g8btZedx/+jMwBO5Zl/JRIeLC+NqY22mFJxub0i/9TgnCYc3mDbGuyx33ehnYXeZnG\nYRfAFCI6bF6qd1rJX/oJpR++hb14LGuvv3//zWx7hnR+f3OPIZ2pu0I6TUefSvusow4c0pFlpr20\nmDH/fRfrmIl8++BfFLebNxq0lI0yoVZHd0+kUasZU5CG69orcH7wJlkVG1AH/Eh6ZZsQ3L4ggWAY\nfYybDKMlgjmCIAiCIAiHCZ1WjXd3xRzFrayGV8UcubwCdTiENYYHY7t1TpkFQFb5OupP+Wmfr3d5\ng4TCkqJFqaZOF9ub7ciAPxAmOXFwHsALQ0tYkggEJQLBMP6QRDAYJhCK/L7756BEMCyRkWJgVF6q\n4q8lR6eVCSu+xjViJF2Tpkd1XmML0uK6o9OYoGNCcQYleSk0ZhpZ/uTfKXvpYcZ+9HdOuObnVF58\nNRPefQVPVi6VF10Zl2M2H30yjUefQuG3X3DKorPZdPkttB5xXL8q8TR2uEg16smOsnKWIAjCUCLL\nMvU7zeTedj2h/CIqL/6/7vdOg1ZDgkGD0aAl0aAdkNBOMBTG7gpgdwfIffR+Rv39ZZz5Rfzwh4fo\nKpsV05yFSz9h9lN3409NZ9niV7sD2fUnn0NWxXqKl3xE1YVX9Pvc221ejOvXMOWdF/HcfhehmbP7\nHBOWJBraXTR2uJBiqGSjNJzTbvHQaonfDu39TyTMEQ/fTGbVZupPPIuKX18b9RQ56YmYUhPY1mQn\nJMVnU4Gs09N49CmM+fSfzH3sdsXjognlqFUq9Do1eq2m+2eDLvJrnVaNXqfBoNWg06kHtWJGilEf\nqRI6eSYFK7+OKoBmd4tgzlBW1WDDH/rx/r+1y01+VtKwuid1eYNM2LgarymbHaf9bECPZR1fBp/+\nk4zq8piCOQAWhwjmCMLhLBSWqKy3DEjVwpAksbPNFZfKjcKhQ5ZldrQ6aehwkrqjhtlP/JGgMYmV\n9zzT9/VpDyGd0auWkPP1f/erpNNbSGfy355l3Htv4Cgaw/I/v0IwWVmre71WzZTRmf0KiiUn6mDh\nQjSvVJG5dROd0+YqHmtz+cnJiF+o/kBEMEcQBEEQBOEwodOqCWTmAJCoMJgTlmXFoZKhQL9lIwDW\n0skxz2EvGUfQmEz2lrWKXi8Djj4eUsuyzLZmO81md/ef+YZZKEqIj/o2B/VtTsWv73L46HL4yElP\npGREKsaEA98CJn75GVqfh4aFF0cVRslMTRiwh8YJei2lI9Mpzk2h6f5HsEyZw4wn7mTaXx4BYNOi\nWwknKtuBo0T7w0+R9dcnSH7jVRbcfRVtM49k0xW34SgpjXnO6gYbSQm6Pj//giAIQ5E/GGbrTitF\nj/+pu+paormNddfdBxoN/lAYfyiM3R3Yb2ysoR1fINQdxLG5/N1tpzIrNlDyziv4U9JIbm1k4U0X\nU3vOxZRfdn13G0Ql8lcsYe4jtxE0JrP84b/iLB7b/bHGY05l+vMPUfLl+1RdsKhf4U0AZJnRT/4J\nQ/UWtCu/Y8P199Fw8rkHHgIxBXL21Fc4J1KNx9avYxyQLDP9xYcpWPk1HdOOYO0Nf4rpczkyO5nU\nJD0ZyQZqmmx0OXxxOb0tv70Rc9lM1GFl1+ShhERa5i3ss1R/epKBMQWpkQDMISjFGAlhmMtmUbDy\na7LK19N4vMJgjmv//+PC0NDU6drv/45MpGLW9NKsg3NSA8BrdWA0t9Ex7QgY4LZrlnFTADBVb2bH\n6efHNEeXw8dYxKK5IByuahtt3de4A6Gly01BdhKJBvGcYjgIhSW27rTS5fChdbs48k/XofV7WXn3\n07hGRtmqfVdIJzjnCNZeeuMB213tDumUvv8Gk95+AVd+EcsWv0og3aToUBqVirJRmXH5OpSOPQ5e\neYHsTaujDOYERDBHEARBEARBiL9wVnQVcwACweERzAmFJZIqtwBgHRd7MAeNBvPkGeT98C0JXR34\ndoWdDsR2gLLue9447Wm4VSsS4qPd4o1pXIfNi9nuIzcjEtAx6PffheIPhsn98kMAGk44U/HcapVq\nUFo16XUaRuenErr6MqrmziT/9utx5+bTdMypcTvGiAwjxcUZuB5+HO+lv0N72y2MWLmM3CvOYfvp\nv6DikmsIpCnvd71bSJKoqLcwc1wWmgFeBBAEQRhMVqefqp1W0r//hvH/eR3nyBKCiUmM/vw/aH1e\n1tzyMLK292oL0YR2NGoVDnckjNNTgFnj9TDn0dsAWHH/84CKOY/fwbj3/0be6m8UV8/JXfsd8x66\nEUlv4NsHX9qv0kEoKYXmo06ieOnHZFZuoGvyzD7nPJDMivWYqrdgHTuJpLYmZj96O6nbKtl8+S0x\nt15VqrdwTliSqKi3DsgO7d1K33uju7T+ynueial9ZKpRT2pSZJxBr2HK6EzaLJ64VM8JpqTRcOLZ\n/ZpjTwl6DWPy0w75CnpajRqjQUtn2Y9VQhuPP0PRWIcngCTJUbcgEA4ulzdIXYujx4/Z3H46rJ5B\nW6waSJIso66vA8CpoCV1fzlKxhIyJGCqKY95Dm8ghMcXxDhIrewEQTh0tHa5abfF9vxHKUmWqWtx\nMHmUsgCFcOjy+IKU77BEglyyzJzHbielqZ6q839L84KT+zf5PpV0Mqq3ULj8871DOolGdF4Pnuw8\nli1+VdGzcgAVMLEko/t6vr+C849EVqvJ2biaykuuUTzO5lLeFri/xBNBQRAEQRCEw4g2MQF/ajoJ\nXcqDOcHQ8AiIuL1BMmorCOt0OPbY9RyLzilzgMiDaiXs7p4v8P2BMBtrzT3u7PUFhsfnXYgfhyeA\nNxD7bilJlmm1eFi9tZ1tzfb9/m+7GpoZ8cN3WMdOxFk0RvG8BdlJg1oJRqtRM2L+DIJL/8eOxc/1\nv1LBLhnJBsYVpXf/PjxhIv73P2LnS2/hyi9i7MfvcNqlp1D6n9dRBaPfEe72BalqsBEKx6fNhiAI\nwsG2s83J5u1m6Ghj7qO3E9bpWHXH4yx75DU6y2ZR9M2nHHn/dagDsT3o3B3YabV4qGt1UNtsp93m\n7bWq4JRXnyClpYGa8y6ja/JMuibP4MsX3qf6Z7/prp4z7YWH0Ph6X+TI2ryGo+69GlRqvrv/eSyT\nZvT4uvpTIhVtSr58P6a/257G//s1ADZeeTtLnv0n9uIxjHv/TY6+/ffo7dZ+z9+X3eEcq/PHf6dt\nTXbcvuCAHbNg+RdM+8sjeDNz+PbBvyguc7+vkdn7V8wbYTIyZ0IOmYdI+xeNWsXovFTmTsg95EM5\nu6Uk6rCNnUjIkEi2wvsdiFxrOj2ias5QEpYibVIOVIGrrsVBOE5t4g4mrz+EsWknAK4ogjmaGO81\nZI0W29hJpNZvQ+ONvSWg2R6fKmCCIAwdLm+QbU32QTlWp91Lh83b70qMwsFjdfpZX2Purq40/l9/\nZeSKJXRMO4Lyy66P78FUKqwTprL58lv49M2vWfLMu1T/7DICKWl4skawbPFf8eQWKJ5ubEEaWWnx\nuz6WU9NwTZhCZtXmqL73evwh/IP0HF4EcwRBEARBEA4jOq0anymbBKtZ8Rh/aOg/hANw292k1ddi\nHzX+gDu3lTBP+XEHqRJOTxBJkvf5swDrazpx9bLo4RcVc4R9dFjjs1tKkmWaOl2sqmxnR6ujOyii\n++B91FKYncefpXgug1ZDcW5KXM4rWhq1mkklJkZmJfd7ruQEHZNHmVDv++BdpcJ47lm0LlnJ5qvu\nAJWK6S8t5pTLzyLv+/9BlA+vOm1eVpa3UVlvweLwIYuHX4IgDEHBUJjN27vY0eZAliTmPno7CbYu\ntvz2JmxjJxFKSuHbB/9C28wjyV/1Pxb88Yp+LUoqkb1hFaUfvo2jaAzll17b/eeSIYHNl9/M0iff\nxlVQzLj33+TkK84hq4eWpKatm1jwxytRSRIr736GzulH9Hq8jmlH4MnOo3DZZwcM+vQlqXkn+d8v\nxTJ+CuayWbgLiln69Ls0H3kCuRtXceLVPyetrjrm+ZXaM5zTZvHQahm4f6/MivUcsfgWQgmJfPvA\ni3hz8mKax6DTkNVL0GV39ZwJRRloD1KlOhWQZzJyxMRcinJThlQVmWSjHlmro2viNNLqa9E5lS8O\n2kQ7qyFlW5O9zzYpvmCYhnbXIJ3RwHF7g6S0RB/MGVeUji7GCsKWcWWopTDp27fGNB7A4hi8XfyC\nIBx8oXAkMDmQVQv3VVlvYeWWNip2WGjtcg9aQEHoP1mWqW2ydVeKzN64mimvPok3M4dVdzzWr+qb\nGpWKpATd/s/JdtsnpPPft76OqmVWYXYyBdn9f563L8/8BahDQbIqNkQ1zjpIVXNEMEcQBEEQBOEw\notOq8Zqy0budqP3Kdl4Nl5ZKUkU56lAQa2k/2ljtYi0tI6zTk71FWTBHkmUce+weNdu9bKw14z9A\nNSJxIyzsSZZlOuMUzNktLMnsbHeyurKdhnYnpk/eQ1apaFz4E8VzjMpPPeit7saOTGN0Xmw77SGy\nsDdldOYB/x4ZpmRSbr2Jr9/8itqzLyKptYkF91zF0bf/jtQdNVEdT5JlOmxeNtd1sXpXOMo7gH3j\nhaFhOOxEFw4PDneAddWdWJyR68hx773BiHUraJ1zNLXnXtL9unCikRX3P0/z/OPJ3bCKY+74PVq3\nc0DOSet2MefxO5DUGtbc8jCS3rDfayyTfqyek9TWxHF/uCRSPWdXYCht+1aOvvNyNH4/q+54jLa5\nxxz4oGo19Sedjc7jpmDFkpjPfdz7f0Mly9T89NfdFeBCxiRW3v0MFRdfTVJ7M8dfdwEjl38e8zGU\n2h3OqW20Ddgxkpt2cNTdV6EKh/n+j09jHzMx5rkKspJ6XyjYZYTJyJyJsVXP0ahUJOg0xBKnSU82\nMGt8DuOLMtDr9m8feqhLMUY2MZjLIm3asirWKx7bW6XQ3kiyLILKB0mHzas4hNfY4Rry16sub4jk\n5kgwJ5pWVqlGPWWjM/t8v+mJZfwUAEzVW6Ieu5vDExAVNwXhMFLbaOszMDkQQpJEp91LdaON7yvb\nWFvVwfYWO1anX1TTOYR12X3dXy8J5nbmPXQTskrN93c9hT8jK+Z5E/VaZozL5ugZBSyYmscRE3OZ\nOjqT0oI0RmYlk5magNGg/fF7o0oFfYTh1SoVOo2aBJ2GPJORMQVpMZ/fgQSOOhqAnE2rohpncw5O\nMGfw6o0LgiAIgiAIB51eq8FnygYg0dKJO6+wzzGBYOwPgcKShCxz0BfuAXSbNgJgLZ3U77kkvR7L\nhKlkla9D53IoKr9vdwVITzbQ1OFie4udvm5rRcWcgSfLMqo4tUEaaDZX4IBBrv4IhiXa11cwq2ID\n7TPmKe4FnWrUM8JkHJBzilZRbgo6rZqaRluf/7f2pFGrmDI6E4O+70Wz5EQdk2eXssV0L9vP+CXT\nX1rMiLXfkXvlubjyi3DlF+EsKMaVX4yroBhXfhGe3PwD7lDyBcPsbHeys91JepKBEZlGstMT0Byk\n3f3CwREKS6zZ2o4xQUeeyUiW+BoQDlFNnS7qWhzdD+fTayqY8uqT+DKy+OEPf96vtaCkN/D9H59i\n7qO3U/S//3LczZey/M+vEEjLiOt5TfvLYpI6Wqm86Eqs48p6fd3u6jlNC05kzmN3Mu79N8lfvYzK\ni65k6l8eRed2submh2lecLKi49afdA6T/v4iJV++T8MJZ0Z93jqHjZIv3sedk0fTMafs/UG1msqL\n/w/b6PHMfeRW5j9wA1svqKL819f2+dC7PwZyd7bB2sXRdy7C4LTzww1/on32gpjn0qhU5GXu38aq\nx+PuCuC2WTxsa7J37yjePU+iQbvHD033rw27AjWSLOPzh/D6w3j9IbyBUORnfxhfILTXdUeiXsvo\n/NQh07KqN8mJOlSAefKPwZzWeQsVjbW7A4qusV3eIG1dHtqtHkoL08kZ4p+zocYXCFHToDyEJ8ky\ndS0OJo8yxXzMsCRhdwUwHaQ2c25fkFHNO5FVKkXPQXbTadUkGrSML0xna0N07QW7gzk15VGN25Mk\ny1gcPnIyDo37LkEQBk5rl5t2W3w3ZMXK5Qvi8gVp7HChVatJT9GTmZqAKTWh+xpJOPgaOyIV7VTB\nAPMfuJ4EWxfr/+8uuib33I5XCVOKgYnFJnTayD2Heo/r5X1Jsow/ELlGDoYlNCoVGo0ajVqFRqOK\n/KxWo9GoYgq4xkJ99AIkjZbsjWuiGmcbpIo5IpgjCIIgCIJwGNFp1fgyI8GcBIXBnGA/wgDbmuzd\nu/AiF+e7LsjVe/xao0KjUqHVqsnJSCTVqI/5eL2RZBljZWSXWjwq5gB0ls0ie8taMis30Db32D5f\nb3X58TeGaelyK5pfkmWCoTA6rbjhHSiddh9NHS4KspLIzkgctJvEWHRYB7YFSNHXHwPQcLzyhcWx\nIwdmd0us8jKT0GnVbK23KlpYVKtUTC4xkZyovLVdokHLjNIsNqsn8e1DLzNizXImvPsyqTu3kddU\nz77NOCSNFveIAlwFxd2hneYFJ/UYfrK5/djcfmqbVOSkJzIiM4m0pPi/HwqHnvo2J4GQRMDlx+by\no21Sk52eQF5mEqnia0A4BITCEtWNNjr3WCjQetzMe+hG1KEga25+GH9GZo9jZa2O1bcsJpSQyOjP\n/s1xf7iY5Q+/qjgE2pcRa5Yx+rN/Yx0zkcoLr1A0xjJpBl+98B5lbzzLuPdeZ+6jtwOw9rr7aDhR\neTtHd0ExnWWzyNm4isSOFrw5+VGd++hP/4XW76XinGt6DXG2HHUiS5/+B0fdezUT33mJtLoqVt/2\nKKGkg9NGMlYan5ej7r6K5NZGKi+6kvrTftav+XJNxu7FAqVGmIxkpBiwOHz7hW8ORK1SYUzQYUzY\n/3ohEtqJLEaEJInstMQh1bKqN1qNGmOCDsvE6UhqDVkKq4RCpCKj0xvs8Z4uGJJot3pot3hwen9s\n59vS6RbBnEEkyzJbd1r3Cqkp0Wn3YnX6yUjZvypZX/zBMOV1FnyBEEdMyj0oG3fc3iApzTvxZI/o\nsbJaT9QqVfe55pqMePwhdrYrr/7mzi8ikJJGRj8q5kCkIoII5gjC8ObyBqltUt46cjCFJAmz3YfZ\n7kOtUlEyIoXCnOQhs9FtuLK7/Nh3VWef9pdHyKrcyM6FZ7D9rAtjnrMwO5nR+amK/20PFNo5WPRp\nqVgnTMW0dSNat1PxfZMvGLmmH+i/y6HzmRIEQRAEQRAGnE6rxrchHjqjAAAgAElEQVSrlGWCxaxo\njL8fFXMse5SBDMsy4ZAM9D5fU6eLjGQDRbkpMT3w643HFyJ9WyWSVoejuDQuc5qnzIZ3XiJryzpF\nwRzbrgXXaPgCIpgzkJzuAA5PAEdDgO0tdvIyk8jPSjrkdv9IskynTVnruVhklq9j/L9fJWRIoGnB\nSYrG5JmMAxKi66+stESmjtVQXtdFsI+S76Uj02LasavTapg2NpPKegttc4/pbneic9pJbmmI/Giu\nJ7mlgZTmnSS3NJC3Znl3aCd3/UpW3vdcr/OHJZlWi4dWi4e5E3IxJojb9uHM4wvSYt47sBmSpO6v\nAaNBywiTkVyT8ZB7bxIOD20WDztaHPtVbZvx3AOktDRQ9fPf0D77qANPotGw7vr7CSUkMu79N1l4\n08UsW/wqntyCfp2bzmFj9hN/RNLqWHPLw8g65d+X9qyeM+W1p2g89jR2nH5+1OdQf9I5ZJevo3jJ\nR1QpDAZBZFdr6YdvETQmUddHSMVRUsqSZ95l3p//QP7qZZxwzS9Ycd9zuApHRX2+B4MqFGTegzeQ\nWb2Z+hPPouKSa/o958hsZdVy9mXQaRRX2lEiEtrRDsvv1SmJOtzGJGxjJmCqKUft9yEZlF032V2B\n7utEWZaxOv20Wjx02X09tsOwuf24vMGowtJC7OrbnNjdgb5f2IPtzXZmjs+OakODyxukvK4L366K\nsC1mN0W5gxsuDIUlgg4niV0dtM+Yp3jcvgHAUXmpeHwhOu0KK1qoVFhKJzNi/Up0DhvB1PRoTrub\nxeknFJYOiUrEgiDEXygsUVlvGRItoyRZpq7VgdnuY0JReo/BZWFwNHZGquUUff0xpR++jb2klHXX\n37dfFVMl1CoV4wvTyT1EqmL3l23OkWRWrCd781pa5yur+giRZ/cDHcwR38kFQRAEQRAOIzqtGq/p\nx4o5SsRaMcflDcbUjsnq8rNpu5n1NZ2YlT7w6oPT7iatrhp7SSmSPj5hgq6J05HVarIq1sdlvp6I\ndlYDy+H58YF0ICSxs93J6sp2ttZbcMT4sHogWOy+qHe0KpWzbgXH3P47NH4/P/zhIUU7SXQaNaPz\n+27fdrCkJemZUZpFwgFCDMW5Kf1amNNq1JSNziR3j93dwZQ0rOOn0LjwdLb+6v/44ZbFLH36H3z0\nr5V88N5qlvy/f+HOySOrfB0ofOC259eoMDxta7Yf8AGsxx+irtXB6sp2ttR10WnzDokHtsLQZ3cH\nWFfdSVWDdb9QTuHSTyj56gMs48oov/Q6ZROqVGy64nYqL7yC5JYGFt54MclNO/p1jjOef4hESycV\nF1+NY9S4mOawTJrBskffoO6MX8Y0vumYUwkZEij58n3F7+0Ahcs+I7Grgx2n/kzR995gajrfPfAi\n1T/7DalNOzjxmvMZ969X0TkPzZ3V3SSJ2U/cRf7qZbTNXsDaG/4U02LBnkwpBrEINAiSjZHPsbls\nFupQMKpWPHa3H48vRF2Lg1WV7WxW8P1r35CqMDBsLj8NUVR82Zerh0DxgVgcPjbWmrtDORBpuxEe\noHub3ri9QZJbGgBwFRQrHqfrIQgzoTidlChCZD+2s6pQPGZfwbDE9uZD/P1eEISY1Tba8PhDvX48\nuXEHcx69nclvPIMqFOz1dYPJ4QmwtrqThnYn8iDen8qyjGPXJrvDmccXosvuI6WhjllP3UPQmMzK\nu58mnBh9sMag0zC9NGvYhHIAPPOPBiBn0+qoxtmcA9/OSgRzBEEQBEEQDiN6rRrfrmBOosJgTiDG\nijkWR/8qfDg8Acp3WFhb1UG71dOvGz25ohJNMBC3NlYAoaRk7CXjIjtIAwNzQ9ifakXCgUmyjMuz\n/wMNSZZpt3lZX9vJuupO2i2eg74IPlA9xvNXfs2Cu69EJUmsvOcZmo49TdG4sSPTDvlKTsYEHTNK\ns0nqYeFuRIaRUXn9DxapVSomlpgoykmhryXGYHIq1nFlmCfPwuC0k9yyU9ExDqWAmBB/Zpt3r8py\nByLJMl0OHxX1FlZVtLGj1YE/IMKbQvz5AiEq6y1sqO3E6d3/PSiptZFZz9xLMNHIqtsfi6pKDSoV\nFZdex+bf3oixs5WFN11C6o6amM6z4LsvKV76MV3jp1J9/m9imiMeQknJNC84iZSWBjKVhrVlmfH/\nfh1Zrab23IsVH0vWaNl8+c2svvURVJLEtJcf5YwLj2PmU/fE/HkcaFNfeYySJR/RNWEqK//4dHRf\nL70YmZ0chzMT+pKyq+KNuWwWQCRYrFCX3ceaqnYaOpyKNxq0Wz2E+qh2KPRPMCRRtdNKf+9sdrY5\nFW3eaTa7Kd9h2W+DQTAs0Woe2Da9+3L5Qt3BHGd+ieJxet3+y2cadSSgr7SSoXVXMCejpn/trHZX\nnRIEYXhpMbt7feZjbG9m9uN3curvz6Dkqw+Y9PYLHHfzpSSY2wf5LHu2u3rOhlozbt/ABYYCwTDt\nFg9b6y2sLG9jfW0nG2o6qW9zDGoo6FDS1OlClmWmP/8gWr+XH258ANfI6KtppiXpmTUu+5CsiN0f\n4TlzCOv05GyMMpjjGvhncCKYIwiCIAiCcBjRadX4MqOsmBOWYrrRsTj9EIedcC5fkK07razZ2kGL\n2Y0kKTsXSZLx+kPY3QHUGyILJfEM5gCEjpiHJuAnfVvsu98ORCy6Dhy3N0i4j69rpzfA1gZr9yL4\nwVgsCIUlLAPwALbwf/9l/v3XIWt0fPfAi7TOU1baNSstgdyMobGLxqDXMH1sFmlJPz5gyEg2MK4o\nthLyvRmdn8rsCTmYFLTfs0ycBoBp6yZFc4tgzvAlSTLbWxwxje2u8LW1nYp6C/Yo2yQKQk/CksSO\nVgdrtnbQ0cvigCoU5IiH/oDO42b9NXfjjqLqwJ6qf/F71l99FwlWMyde/XNmPHMfxtYmxeMN1i5m\nPn0fYb2BH27+M7Lm4LYRqj/5XIBI1RwFsjeuJr2uiqajT46pnVfDCWfyydtL2XT5Lfgyshjz6T85\nZdHZHHvzpeSvWALhQ+P6cdw//8r4f7+Go2gM3z3wYkw7ePeVlKCLqQ2lEL3kRC1qlQpz2UwgumBO\nLEtkYUmmzTK4YY3DhiSR+MwTtH26dK/KNbEKhiV2tPZedUeWZbY326ltsvW6waGxw6X4vj4e3N4g\nyc2RYHx/K+ZApLpA2SgTGgUVwLor5lQrrzrVm5pGG8GQCLAJwnDh9gXZ1kM1LIOlk+nPPchpl53G\nqC/ew1E4mu/vfILGY08lq2I9J195LjnrVsTlHHJ/+JapLy0msaMl5jkcnkilzX5VzwkGu6tPyrKM\n3R1gR6uDddWdrKxoY2uDlXabt7tluUykNePGWjPeA1QbGo4CwTBtFg8j1ixjxPqVtM06iuajT456\nnvzMJKaNzUI/DFtmG9NSME+eSXpdFXq7VfE4fyiMZwBDZgDDrwGuIAiCIAiC0CudVvNjK6suZcEc\nSZYJhqSoLtTDkoR6/TrOu/aXrLj/OdrmHhvT+e7JGwhR02RjZ5uTSdVrSO5qw3LqOXgNiQRCEoFg\n+Mefg9JeO/NmbI3sTrOWTur3eexmSjGgP2YBvPM6WRUbsEyaEbe5d/MHDq+by8Hk6KFaTm92L4Kb\n7T4ml5gwJgzebVSX3ddngChaoz77V3ep2+8eeImuycq+dnUaNaUj4xtqGWg6rZqpYzLZWm/FFwgz\neZQJdT9baPQkKUHH1DFZmO1e6locvZah7toVzMms3EjDiWf3Oa/bFyQUltD2siggDF2NHS68/XyP\nl2SZTpuXTpuXlEQdBdnJ5KQnolbH/2tcGN7aLB52tDj2a1m1r8l/+39kVm9m5/FnKnoPO5DWn/+a\n7aMKKXjiQcZ+8g9Gf/ovGo4/napf/B5n8djeB8oyM5+9jwS7hY1X3IazaHS/ziMeOqYdgSc7j8Ll\nn7Pxyjv6DKCM/89rAFSfd1nMxwympFHzs8uoOfcS8tYso/SDN8ndsIqcTatx5+az7cwL2XHqeQRT\nD8737ZIv3mPaK4/hyRrB8odeJpCaEZd5C7Jib0MpREejVmM0aHFlZOEsKCarYkMk9KUZuMWbFrNb\nVEQaAPr/fkzyA/dSMn4K25/9Z1zmbO1yk5+VRPI+LZ3CksTWnVbMfWws8IfCtHa5KRikf2+3N0h+\ncz0QZTDnAM9AUox6JhRnUFFvOeAcvswcvJk5mKo3Kz5ub/yhMNuabEwsMfV7LkEQDr7Wrr0rNOuc\ndsb/61VK338Trd+LK6+QiouvpmHh6aDR0HTMqXSWzWb6S4s55o7fU3nRVVRedGVM35tTdm5j2kuP\nkLf2WwDGfPIuFZdcTe25FyNro28Zurt6jtnuY3xReo/Vi/elrtuOfulX6L/+Ct2K7winZ1Bzw93U\nzjqOoMLwpn1XKKh0ZNqwasV0IM1mN3IwwLSXHkFWq9m06NaoWsWqVSrGFKQN6+tqY4KWjulHkLtx\nFdmbf4gquGR1BQa0ba54uicIgiAIgnAY0WnVyMZkQglGEqxmxeMCUe7KsjkD5K1YgloKM/LbL6M9\nzQOfi99P8U2LGHHXHyg9Zhopd9+GdWMFHTYvNpcfjz+0X7nsjNoKJI0W+6hxcTuP4twUgnPnAZCl\ntHVBlEQrq4HjjKESidsXZH1NZ7/btEWj3RrfNlal773B7CfvJpCSxrJHXlMcygEYU5CmuGT7oUSj\nVjN5lIlpYzMHPOCSlZbI7Ak5jM1PQ6ve/1i20eMJ6w1kVimrmCMDzihCZMLQ4A+EaWjvfad5LJze\nIFUNVr4Xba6EKNhdftZVd1LVYO0zlJO9YRUT3n0ZV14h66+5O6bjadQq8kxGZpRmM3diLqkXX4Dz\nh42Yn34JT/EYSpZ8xKm/P5P5911DRnXPLT+Kln7CyO++onPKbGrPUd4GakCp1dSfdDY6j5uCFUsO\n+NKUhu3krVmOefJMrBOm9v/YGg2t849n+eLX+OIvH7H9jF9gsFmZ9spjnHHRQmY9efegt7nK+34p\ns568G39KGsv//DLenLy4zKvTqMk1JcZlLkGZFGNkUcJcNgudx0Vafe2AHs/jDw3qdfbhQAqF0fz5\nQQBM1Vuiqk52IDJQ22Tb68/8wTAba7v6DOXs1tjhGrSWwW5fiOTmnchqNe4RIxWP661izm7Z6YmM\nVtAi1zJ+ComWzri0n2nfFcoWBGHoM+/6v6zxupnwzkv85JKTmPiPvxBMSmbdtffw+Suf0HDiWT8G\nb1Qqtp99EUufeAtPTh6T33qOY+74PQZrl+Jj6h1Wpj/3ICcvOoe8td/SPn0eG6+4jbDBwLSXH+XE\nq3+OqXJDzH+n3qrnSLKM1+bA9/GnyDfcQPLsaWTOm0HKHbdg+PorXNl5qLs6mXzrFcy98wqMbc2K\njxmSJLY2WNlabxn2bTHDkkSL2c2Yj/9BatMOtv/kfBwlpYrH6zRqpo3JHNahHIhUtrPOjDyzz9m4\nKqqxNoXtxmMlKuYIgiAIgiAcZrQaFV5TFokKW1lBpEwmicrT4hanj7G7buTiHVpJ216NzuPGNno8\nBpuFce+/ybj336R17jHUnnMx7TOPhD0WxFXhEOl11dhLSpH0fbeaUSI92UBasgEpqZBAzggyKzZE\nSq7GuRKHPw6lxoWeuaxOjr/2QpoXnET1+b9TPC4kSWyp62JUXipFuSkDeIYQDIWx7dGiJrGjlWNv\n+w3OgmLqT/4pLfOOQ9Yp7AMty0x45yWmvP40XlM2yxa/euCKBPswpSQwYgjvPlKpVOi0gxMqUqtU\njMxJJteUyI5WJ61d7u6WDrJOj7V0Mqatm9B4PYraejjcATIUtMkSho66FnvcK2HtFgxHKnw1drjI\nTEtgZHbyXu3cBAEiD3RrGu20W5W1jdHbrRyx+BZktYZVtz9GKCm6CgfpSQZGZBrJTk9As29oUatF\nvuACvL/4BV3/+YDUZ59g5IoljFyxhLaZR1J1wSI6p84BlYqErg5mPPcAoQQjP/zhob2u9w62+pPO\nYdLfX6Tkq/cjCyi9GPefNwCoPu/SuJ+Do6SU9dfey5bLbqDki/cZ+9HbjP7sX4z+7F9UXrCIisuu\nj/sx95W1ZS3zH7wRSafnuwdejOpaoy95mUn7f/0IAyrZqAeLB3PZLEZ98R4nXnM+ksJd+dvP+CWb\nF90a9TFbzG7RrixOgqEwHa/+nenbqvBlZJFgNTPy2y+oOf+3cZnf7g7QbvWQm2HE5Q1SXtcVVass\nXzBMW5eH/AFeHPTu2riT3LITT3Yekl75dZFe1/d7TlFuCh5fiLYDfE+1jJ9CwcqvMVVvoSUrV/Hx\ne1PTaCM9WT9o9zeCIMSfwxMg4PYy9r/vMvGdl0iwdeFPSWPT729m21kXIhl6/15onTCVr577D3Mf\nvY381cs46aqf8v2dT9BVNqvXMapQkDEf/4PJbz2H3mnHlV/EpstvoWX+8aBSsfPEs5jyyuOM/vw/\nnHD9hWz/yfls+e2NBFPSov677a6e02H1ktbWQMp3S8n6fhn5m39A64+EN4OJRpqPPIHWOcfQNmcB\n3px8kht3MPPZ+8lf/Q05G1dR+aurqDnvUsUVfNptXuyeABOLMkhLHp7PUFq7PGC1MPmt5wgkpVBx\nyTVRjR9flD5sPzf78k+bQSjBSM6mNVGNsw1wq3ARzBEEQRAEQTjM6LUafKZskivWowqHkDV9XxJG\nWzHHanFhqoqUak5pqsdg7cKfkRnT+e4rq2IdANU/u4zGY09j5HdfMfbDt8lbs5y8Nctxjiyh9uxf\nsfOkcwgZk0jduR1NwB/XNlbFuwMZKhX+ufNI+eQDklt24iooidsxQARzBkooLGGo2ERm1WbS6rdR\nd9rPo3rYIAN1rQ7c3iDjitIHbJGow+bbaxfp2I/eJqWpnpSmevJXL8OflsHO48+g/uSfYh8z4QAn\nLDPl1SeY8O4ruHPzWbb4Ndz5RYrPQ6NWMa4w+ocxhzudVsO4wnQKspPY3mzHsmvXTdfEaWRVrCej\ntgLz1Dl9zmOPobqTcOiyu/y0D8Iu5z3bXE0dnSkWOYVuXn+Iih0WXL69q3GpggG0Xg9anwedx939\na63Xw5hP/kGipZPNv71RcZWXBJ2GXJORESYjiQYFjx/Vaow//ym+c8+m/MPPyf7L04xYv5IR61fS\nNXEaW3+5iDGfvIPe5WDdtffgziuM5a/fLxq1iuy0RCxO337Xxu6CYjrLZpGzcTXG9mY8uQX7jdfb\nLBQv+RBXflFkEWSABFPSqP3ZpdSeezF5a5Yx4/mHmPjuyzQuPD2qHbXRSqur5qi7r0IVDrPi/mex\nTJwet7nVKtWw39l7KErdVTGnef7xFM5egN5pVzTO2NbM+P+8TvNRJx5wkbAnXQ4fvkCIBL1YtugP\njy/Ilm1mFvz1aWS1mhX3PsvCG35FYRyDOQB1LQ7UKhXVDbb9qtYq0dDhZESmcUBaze7m9gXRetwk\nWsy0zTwyqrF9VczZbVxROt5AqNfrdsu4MgBMNeW0HHViVOfQk2BYorrRRtmo+DxjEQRh8Fl3tnLy\n5WeS0tJAMNFIxa/+j5rzfk0oSdkGsGBqOivue57x//wrU15/iuP+8Gu2/PZGan522X6bBkesWc60\nlxaT2lhH0JjMpstvYdtZF+0VVAykZrDuxgeoP/lcZj19L2M+/ScFK5awadGtNJxwpuKNiGq/j5xN\naxjxw7eM+GE5KS0N3R+zF4+lbc4xtM49BvPkGfttNHMVjmL54lcp+vpjpr20mKl/fYLirz9m3XX3\n0jV5pqLj+wJhNm4zU5SbQsmIFFQD+P1lsMmyTFOni0lvP4/eaWfT5bcQSFfe2jA7PZGstMOn+qQx\n2Uhn2Szy1n5LQlcHvswcReOCYQmXN7hfu854EVe4giAIgiAIhxmtVo0vMxuVLGOwWRRdmAaiCIh4\n/SEMlVvQ+n2EdTo0wSCZFetpWXBSf067W/aWSDDHXDYbWaenceHpNC48nYyacsZ+8BaFyz5l5nMP\nMOW1J6k/+acEkiM3tdbSyXE5flqSfq/qFfL8I+GTD8is2BD3YI4kywSCYfRDsH3QoczpCXYHx7S+\nyIJj1QWLop6n3ebF4w8xeZRpQBYPOvbYdakO+Bn1xXv4U9NZ/tArFP3vE4qXfNRdMco6dhI7Tvkp\nDQtPJ5ia/uMkksSM5x9k7Ed/xzmyhGUPvxp1S4nR+WlicaQfkv4/e2ceHldZt//P7JOZSSaZ7GuT\npnu6l26UspWyyY6AigqyKCryvqK+oj8V3BXcUEARRQVkpyAF2feltNA9S5MmafY022T2feb8/pgk\ntDTLOZOZbnk+19UraXK+z3nSTuac8zz3976NOhZW5jDgDNDU5WRgziIAsvfslCXMcfuEMOd4QZIk\nGjvlbWomk/o2ByfMyRVd1QIG3UFqW+zkv/Ycqx77G3q3A63fh87vQx0ZPzavZ8kq6i8bfzPXZNCS\naTGQYzWSlW5IaCFcq9WQf+mncJ29nqaX3mTav/5M8abXOOnWrwGwf+mJNH/qCsXjTgarWU+BzURu\nZhpajRqHJ8iupoFDIlha1l9EbvVWpr36LHVXfvWQcWZsfARNOETDxV/8OJIglQzFXKFScdKPvsai\ne3/NO7+4L+kOjwCm7g7Wfv869F43H9xyBz3L1yZ1/ByrEYNevIcdbsxpOtQqFeGMzPhrRya22u2s\n+9/PsezO23jlnqfkOzwSF8B39fuYXjRxPJBgdAbdQWr22cl/8wUy9zXQcsaF2OcupnfJKgq2voep\nuwNfofw4p/EIhqPUtNgTrg+EovQO+lPqzOn1RzB3xzeGPcXTFNXqZD6Hq1Uq5lfY2NrQR2CUONHB\nYWHO0DNoMuh3BkYciwQCwbFH2pOPkd7VRuvp57PjhlsUCSxGUKup/8z1DMxbxKpffItF991BTvVW\nPvz2LwinW0lvbWTRvbdT+NE7SGo1TeddQfUXbxr3XAPzl/HKPU8xa8O/mPfQPay8/btUvLSBrTfd\niqe0YtQac2crhUNCnLydW9CE4k1Jo7niTIhKRdsZF9C94mQW3P97Kv/7OKd/80qaz/k0u6+9mVBG\n1oRDSEBrjxuHO8icaVnymgSOAfocfrRNjcx49hE8RWU0XnCl7FqtWs2M4qnVcGc2auldvJLCj94h\nd+cW2k8/T3atwx0UwhyBQCAQCAQCQXLQadX4bbkAGAf65AlzFDjm2F2Bkfiq1jMuZPoLT5KTLGGO\nJJFTsw1fTj6+/IMf6AZnzefD//sVu67/DtP/+ziVGx9h5jMPjnzfMSM5jjnTPhFfFF0Zz6zNqdlG\n65kXJ+UcBxIUwpyk4/KGyGuoBiCq0zHzmYdouPTqhKLO3P4wW+v7qKqwkZlEO9jAJzouS95+CYNz\nkD2XX4tjVhWOWVXsvuabFG55m/KXn6Zw81ssvftnLPrrr+k6cR37zrqU3kUrWHbnbVS8/DSOilm8\n/au/E8zKUTSPTItBdKgniWyrkQyzjm0dcWGOrW6nrLpwNIYvEMZkTM2igODwsd/uw+0fX/yQCoKR\nKHWtDhZWiq7qqUxHr4d9LX0s/MsvmfHco0R1OgK2PALZeXjSTESMJiJpH/8Jp5njnxvNhNMzaD/5\nrIOio9QqFWajjkyLHqtZjzXJkRoZZj2Wi9fTedKJ1L3/ETMfu4+M1kY+uvlnKRGWfBKDTkPBGI4/\nmRYDFYUZNHUdLLTrOOUcltzzC8pfeYa6z91w0DzVoSCVzz5MyJJBy5kXpXz+B+I6ZT09S1ZTsPU9\nCj58m/0rTknq+IbBfk7+3rWk2fvZ/tXvK1r0lktJrrL4NEFyGP49d/uViYTt85bQdO7lVP73cWZt\n+Bf1V1yvqL57wEt5QTpq9fHT5X642G/30dDuIBaNUvXQ3UhqNXVX3gBAx8lnUbD1vaTGWSWDth43\n+VlpKXM18ATCpHe2xj9XKsyR6ZgDcbfMuWVZbG/sP+R74XQr7uJpZO2tSWoEdmOHk0yLAYNYLxAI\njik8/jBFL2wgptaw8yvKXE9Go3/hCl65ZwOrfvkdije9jvXrn6Z3yWrKX9qAOhalZ8kqdtzwPVwV\ns2SNJ+n01F9xPe2nnMuSu39K0ea3OPOGC9lzxfXs+cyXQZLGdsUpn0n38rXsXz66K45cwhmZbPvf\nH9O6/iKW/vE2pr/wJMXvv8bO6/+P1vUXynofdfpCfFTfS1W57bhwkG3v9bDor7ejjkbYef13FEUz\nVhZnTLlrhcmoo3VxfM0+b+dmZcIcT5CSvNQ8fwhhjkAgEAgEAsEUQ69VE8gaEuYM9smqUeKYY3cH\nmVWzHYCGS66i/OVnRoQ6k8Xc1YZxsJ+2U84Z8yEsmJVN3ZVfZc8V11H87ivM/M+/UYeCOCrnTvr8\n6Wn6Qx7mIvPmE00zkTP0MyebYChKumiCSypuX4g59bsJpVtpPvtS5jxxP9Nee5Z951yW0HjhaIxd\nTQNUFmVQnKSNo97Bg6NuKp97FEmlOsglQNLp6VpzBl1rzsBg72PaaxupeGkDpW+9SOlbLxJOM6Hz\n+7DPXsDbP//rwU46MtCoVMwuVVYjGB+dVoO2rBRfTj7ZdTtkL8w7vSEhzDnGiURjNHe5jtj57e4A\nHb2epC0u+QJhXL5wSjvcpzoxScLtDaHTajAZE1++i8UkGtoduGobOPXn38TWUI2zfCbv//DOMbte\nR0OjUpFp1g8JcQxkmHUpi3IcRq1SUZpnIffck2hctIB+ZyDl58u2Gim0mSZ0/CnNs+Dyhuhzfny9\njpjMdKxdT/mrz5Jds+2gCJ9prz2L0Wmn7orriaYdHsGrWqWiojCDklwzjd/8IXlXn8eie2+nZ+mJ\nSNrkXFO0Xg9r/9+XSe9qo/azX6Hx4i8kZdwDyTDpyTAntqkjmDzpJuXCHIDd13yT4vdeZd5D99B+\nyrn4Cg6NdxuLcDRGryO1LirHI/u6XbT2uAEofvcVrC17aTnjwhFX1841Z7D0zh9T+vaLR5UwxxeM\n0Ovwp8z5xesPkz0kzHErFObodcquc9Yh17jRrlf2WQuY9g9HSPIAACAASURBVMZzSY3ADkdj1LcJ\n8bVAcKzh/Wg7FY21dK08VXHz1FgEbbm89au/U/XgXcx7+C9YXngCd/E0dn75u3SvOjUhQaCvoJj3\nfvJnit97hcX3/IKqh+5h+n+fQO9xfeyKYzLTseYM9i9fy/4T1ip2aJ6IgaolvHr3k8x8+kGqHriL\nFb/5HuUvb2DbTbfiLqucsD4ak2jv9RzzwpxBd5C0d9+kaPOb9C5aQdeJ62TXZpoNFGZPvYY7S5oW\nR+UcQpYM8nZsVlTr8ISQJCklomEhzBEIBAKBQCCYYui1GgJDjjlpdpnCHJmOOTFJwuEKkF27DX92\nHu6ySgZnziNrby2agJ+ocXJZtrnVQzFWC5ZNcCRIWh0dp55Lx6nnTuqcBzKtYJQNTa2WwJJlZLz/\nDnrXoCxbVSUEFYiiBPLwd/dg6Wpj/7I17L3kqngc1BP/YN9Zlx7kBqCEmCSxt9OJxx9mZmkm6kk+\nvB0ozLE27SGndjvdy9fiLSwd9figLZeGy66h4dNfwrZnF+UvP03ZG8/Tu2gl7912FxGz8s34isKM\n48by92giM92Afc4iSt59mbS+bll2zi5vaEoupBxPtHS7CUdHuZZGoxRtep38HR/gKSpjcMZcHJVz\niZjTDz12kjR3u7Ba9KSbJrfBHQxF2dU0gEqlSmmH+1QjEo3h8oZwekM4PSFcvtBIVJIt3UhJrlnx\ngnIwFKV6nx3L6y+x/o5b0Htc7DvzYrbf+ENF92QzSzIpzDZN+tqWKEa9lvkV2fgCYWLSxMcPE41J\nRKOx+MdDPpeIxuJ/j8UkstIN5GWZ0Gnl3wfMLsvE2xDGF4yMfK1l/cWUv/os5S8//bEwR5KY9dS/\niGm0NF4o33J+MlhNemaXZY6IOm2rT6D5nMuofP4xKp97jMaLPj/pc6hDQdbcdiNZjXU0n3MZNVf/\nz6THHI2SXHH9O5Kkm3QwoLwunJHJzq98l5W3f5cld/+U937yZ0Wbgp19XiHMkUksJrGnbZBeh3/4\nC4e45QCEMrJSEmeVDNp6PCkR5sRiEv5gBEsCjjlqlQqtAsecYaYXZmB3BQ+JOxycPZ9pbzxHVn11\nUiOw7e4A3QNe8awgEBxDmJ58HCDu/JJMNBpqrv4fepesxtLVSssZFx7kWKMC1GoVWrUajUaFRq1C\nrY5/HA//Oeez5eTTmP63P1Dy1EN4SsrpXr6W7hPWTsoVRy6SVkfDZdfQfsrZLLn75xRvep11N32G\n/z7wsqw1WIcnSOgYdyPv6HKw7N5fI6lU7LjhFtn3VGqVillTtOFOp9Wg0+voW7Cc4k2vYerpxJcv\nTygeicVw+8IpaQ4Qq6wCgUAgEAgEUwytVo3rgCgrOch1zHF6Qhi720mz99N+8tmgUtE/fxnZe3Zh\nq99N36IVCc8bIGdYmFM1sTAn2ViMOnKso29ixVathvffIbtmB92rT0vqeYNh+TFigokJhCJYancB\nYJ+9gEB2Hq2nn0fFy09TuPlNulefPqnxu+0+fIEIVRW2hB/6fYEwnsDHcTeVzz0KQNN5n524WKXC\nPncR9rmL2HbjD+NCowQ2Uq0mPcViIywlZFkMDMyNC3Oy63bSIUeY4zv88UeC5OENhOka8B70NZ3L\nwfQXn6Ry4yOYe7oOqfEUluKonIujck5crDNjXlxUOwlhREySqGsdZNns3ISdTsKRGLuaBwgM3RcM\nOAPkZE5OdDtVCYWjOLwhXJ4QTm8Qjz/MWJoTuzuA3R3AZNBSlGOmwGaacLPQ6Q1R19jLzPt+y5zH\n/05Ub+DDm39Gy9mXKppneUH6URNpeLQ5h2k1aqoqbGyr7yM6tAHbt2gF3rxCSt9+kR1f/T7RNBMF\nH75DRlsTLWdcQCAnP6VzOtAl50DRXLbVyK4v30zZG88z78G7aF13PuF0a+InkiSW//b/kbdzMx0n\nrWfrTbemJGLMqNOI95gjzGTEnG3rzqfipQ0UbX6LovdeVRRt7PaHcHlDwi1pAsKRKNXNdpy+j12N\nRnPLGab95LOPyjgrbyBMr8NPXpJ/372B+LU1vbMVSa3GK3NDDpTFWB2IyaijwGY65N7PPnshALb6\nXUmP/GvsdJKVbsCoF9t9AsHRjs8bpPDlZwhZMuhalfj6oVqlQq9Vo9dp0OvU6LUHfCw/G51WzTKN\nGs2Q8CYuxJmM22UO/PEPDNz5e1CpyJIk0kJRCoMR/MEI/mAUfyj+eSAUPUScmAz8eUW8/+O7mX//\n75n76F8p2PI2bWdMLG6SgD5n4Kh5plGKxx/G+sRDWFv20nz2pTgVOMJPy0+flPvqsY45TUfv4pUU\nb3qN3J1baD3zYtm1Dk9QCHMEAoFAIBAIBJNHp1Xjzx4S5sh1zJEpDrG7A2QPxVb1Vy2Jf5y3hNn8\ng5zqrUkR5oTM6TjLZ05qnESYVjC2e0F05WoAcmq3JV+YE4pMfJBANi5fGFv9sDAnvjja8OkvUfHy\n08x+4v5JC3MgnmO9q2mAxTNzEuqy7DnALUfr9TDttY148wrpXnGysoE0iQmD1CoVs8syhQtGirBa\n9HTPWwwQF+accs6ENd5AmEg0ltDrSXDkaexwjixMZuxrYOYzD1H2+ka0wQARQxpN511B22nnkdbf\nQ2ZjLVmNdWQ21VHy7suUvPvyyDiBzOy4UGdmFXsv/kJCtue+YITGDiezy5S7u0VjMXY3D+A9QDjY\n0ecVm+YKCT36OO1RPe3zTlAsZPAFIzR2OtnX7aLQZqYoxzzqQmv3gJf2HQ2s+vnN5FZvxV08jU0/\n+APOyjmKzldoM1FekKGoZqphNuqYXZZJbetg/AtqNa3rL2Lev+O2/21nXMisp/4JQMOlV6d0Lp90\nyfkk+bOnUfu5G1j0t98w76F72PnV7yV8rtlP/J2yN56nf94SNt9yR8L3HBNRlGM+Yk5NgjhmoxaN\nSjUiPlOESsXWm27lzBsuZMk9P6d36YlETPI3xTr7vUKYMw6+QJjdzXb8Bz4vjuGWM0zXmnXE7rzt\nqIuzAmjb706BMCf+b2PpbMWbX6zI1UGJg9onqShMp3fQTyT28TqKo3IOMbUGW311wuOORTQmsafN\nweIZyYnEEQgEqSP48iukDfTS9KkriOkNsuvS9FpmlWaOiG8m8x41KYbuy1QqFWkG7aguy5IkEQhF\n8QUidPR5GPQEkzqF1nXnM/fRv1K06Q1ZwhyAvkH/MSvM6W7uYtm//kg4zUS1AodKi1FHaX5yoqyP\nVcxGLb2LVwKQt+MDxcKcsvzkOxkLYY5AIBAIBALBFEOvVRMY2syTG2UVicWIxSTUE9ibDrqCzKrZ\nDsBA1dKDPmYPfT1RDIP9pHe20r18bcoW/8fCYtSRO84iYeSE5Uhq9aR/xtEIiCirpOL2higbWgy1\nz54PgKt8Jt0rTqZwy9vY6nZgn7t40ufxBMJUN9tZWJk94e/NJzkwxmraa/9BG/BR99kvH7bXfXlB\n+lHnSnA8oVGriS5cTEyjxbZnp+w6lzd0zOeiT0X6HX4cTi9Fm95g5n8eIm/nFgA8BSU0XnAlLWdd\nfJBrRftpn4p/IklDQp24SCezqY6sxjoKtr4X//PRO7zxu38nFBHZbfeRlWFUtPkVkyRq9g3iOqAj\nH8DhDeL2hSYdjzVV0Lz9JsU3XUcxMHdaJY0Xfp7WdRcQTVMW3xGNSXT0e+jo9xwUcxWTJBo7nERe\neYV1v/wORqed9rVn8dHNP1McaWhLNzBzitqeKyUvy4TLG6aj3wNAy5Awp/zlZ3BWzCZ/+yZ6Fq9S\n1N2qhLFccj5JblYaH112NZXPP8aMZx+m6bzP4CmtUHy+/A/fYcHff4cvJ5/3f3Sn7E2lomwzoUiU\nQXeQqIxMMo1KJaJZjgJUKhWWNN1BjixK8JRWUH/5dcz795+peuBP7LzhFtm1fQ4/M4oz0GmP3eiJ\nVDHoDlKzz36Q8APGd8uBozvOyhMI0+/0j+lSmwhefxit14PRMcD+Gcregyez6a3TaijNs7Bvv2vk\na1FjGq7ymWQ21aGKhJG0yX3ecniCdPR5KMmd2puwAsHRjuXJxwBolSkoGabAZiIrXb6Q50hyoGgn\n22qk3+mnuct1UPzrZHCXVeIpKqNg67uoQyFi+omfRZ3eIMFwFMMRjLOSJElxA1wwFCXnnt9jcA6y\n+0vfJDjkgD8RKmBWaeaUF7ibjDo6p80gYLWRt2MLSJLs5hinJx4tnex/Q9FuJxAIBAKBQDDF0GnV\nhDIyiWm0GAf7ZdcFJxCIBMNRPIEwOTXbiBjScEyfHf96VjbuknJyardDNHGRSU71kBPP/BMSHiNR\nyiboMJDSMwjMnoetfjfqUGKL1mMx0b+7QBkubxBb/W68eYUHPdDWXxbvGJ39xP1JO5fDG6S21Y6k\noMPY5Q193PUqSVRufISYVsc+hdEjiZKepqc0TyzmphprbiaO6bPJ2lsr+z3jk4IIwdFPtH8A6be/\n4ZyrzmTNT24ib+cWepas5t0f380L/3iRvZ++euwoGZUKf24B3atPo+7zX2PTrX/ivw++yjMbNtN8\n9qVkNdZxwu9/GF9YSoCGNgd+BQuj9a2D2N2BUb/X2ecd9euCTyBJ6H/2EwA6V68jvbONZX/8Medd\neRoL7/015u72hIa1uwPsah5gS10PO+p7sd55Oyd/7zr0Xjfbv/p9PvjB7xWLcixGHfPKbVN+IVcJ\n04szsA45e3iLyuibv4z8HR+w6K+/BqDh0qtScl6rSc8Js3MpzbNMuNCvVqkoKrGx6/pvo45GWHTf\nHYrPZ+lsYdUvvkVMq+P92+6SvTmQYdIzqzST+RXZrFlQyKLKHErzLFjGEQLn20xHrhtccBCTFV/W\nffYreIrKmPnMg2Q21squi0kS3QO+SZ37eKR7wMvu5oFDRDkTueUM037y2QCUvvNiKqeZEK37PUkd\nz+MPY+lqBcBdPE1RrX6S7z8leeZDNoDtsxegDQbIaG2c1Nhjsa/LhS8gHHcFgqOV4ICdvLdfw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TNplxTJ+Do3IujhlzaSyr5MTf/IKIwUj1l74p+1zTizKO+DPG0cjwGlXv4lVMe20jeTs2yxbm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\n8Whu745r/6b2bvY2dMR9HgOJ1BwhHg2t3fQEPk9vynv3JVI3rWb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jygCw7tx6XBbmdHr9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\nAIDholTKltFj0/DSiyls6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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22a04aa67b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot close price, compare to low and high price\n",
+    "ax = df.plot(x=df.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in df.index]\n",
+    "plt.fill_between(x=index, y1='low_bid',y2='high_bid', data=df, alpha=0.4)\n",
+    "plt.title(\"entire history\", fontsize=30)\n",
+    "plt.show()\n",
+    "\n",
+    "# plot first 200 entries \n",
+    "p = df[:200].copy()\n",
+    "ax = p.plot(x=p.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in p.index]\n",
+    "plt.fill_between(x=index, y1='low_bid', y2='high_bid', data=p, alpha=0.4)\n",
+    "plt.title('zoomed, first 200', fontsize=30)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale and create datasets\n",
+    "target_index = df.columns.tolist().index('close_bid')\n",
+    "high_index = df.columns.tolist().index('high_bid')\n",
+    "low_index = df.columns.tolist().index('low_bid')\n",
+    "dataset = df.values.astype('float32')\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "dataset = scaler.fit_transform(dataset)\n",
+    "\n",
+    "# Create y_scaler to inverse it later\n",
+    "y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "t_y = df['close_bid'].values.astype('float32')\n",
+    "t_y = np.reshape(t_y, (-1, 1))\n",
+    "y_scaler = y_scaler.fit(t_y)\n",
+    "    \n",
+    "# Set look_back to 20 which is 5 hours (15min*20)\n",
+    "X, y = create_dataset(dataset, look_back_rows=20)\n",
+    "y = y[:,target_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set training data size\n",
+    "# We have a large enough dataset. So divid into 98% training / 1%  development / 1% test sets\n",
+    "train_size = int(len(X) * 0.99)\n",
+    "trainX = X[:train_size]\n",
+    "trainY = y[:train_size]\n",
+    "testX = X[train_size:]\n",
+    "testY = y[train_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "lstm_9 (LSTM)                (None, 20, 20)            2960      \n",
+      "_________________________________________________________________\n",
+      "lstm_10 (LSTM)               (None, 20, 20)            3280      \n",
+      "_________________________________________________________________\n",
+      "lstm_11 (LSTM)               (None, 20, 10)            1240      \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 20, 10)            0         \n",
+      "_________________________________________________________________\n",
+      "lstm_12 (LSTM)               (None, 4)                 240       \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_6 (Dense)              (None, 1)                 5         \n",
+      "=================================================================\n",
+      "Total params: 7,745\n",
+      "Trainable params: 7,745\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Input, LSTM, Dense\n",
+    "\n",
+    "# create a small LSTM network\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(20, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))\n",
+    "model.add(LSTM(20, return_sequences=True))\n",
+    "model.add(LSTM(10, return_sequences=True))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(LSTM(4, return_sequences=False))\n",
+    "model.add(Dense(4, kernel_initializer='uniform', activation='relu'))\n",
+    "model.add(Dense(1, kernel_initializer='uniform', activation='relu'))\n",
+    "\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mae', 'mse'])\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error improved from inf to 0.26399, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00001: val_mean_squared_error improved from 0.26399 to 0.17870, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00002: val_mean_squared_error improved from 0.17870 to 0.07720, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00003: val_mean_squared_error improved from 0.07720 to 0.02238, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00004: val_mean_squared_error improved from 0.02238 to 0.01324, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00005: val_mean_squared_error did not improve\n",
+      "Epoch 00006: val_mean_squared_error did not improve\n",
+      "Epoch 00007: val_mean_squared_error improved from 0.01324 to 0.01135, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00008: val_mean_squared_error improved from 0.01135 to 0.00217, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00009: val_mean_squared_error improved from 0.00217 to 0.00062, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00010: val_mean_squared_error improved from 0.00062 to 0.00048, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00011: val_mean_squared_error improved from 0.00048 to 0.00037, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00012: val_mean_squared_error improved from 0.00037 to 0.00028, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00013: val_mean_squared_error improved from 0.00028 to 0.00020, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error did not improve\n",
+      "Epoch 00016: val_mean_squared_error improved from 0.00020 to 0.00020, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00017: val_mean_squared_error improved from 0.00020 to 0.00018, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00018: val_mean_squared_error did not improve\n",
+      "Epoch 00019: val_mean_squared_error did not improve\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error improved from 0.00018 to 0.00016, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00022: val_mean_squared_error did not improve\n",
+      "Epoch 00023: val_mean_squared_error did not improve\n",
+      "Epoch 00024: val_mean_squared_error did not improve\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "Epoch 00026: val_mean_squared_error improved from 0.00016 to 0.00016, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00027: val_mean_squared_error did not improve\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error improved from 0.00016 to 0.00015, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error improved from 0.00015 to 0.00014, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n",
+      "Epoch 00033: val_mean_squared_error did not improve\n",
+      "Epoch 00034: val_mean_squared_error improved from 0.00014 to 0.00013, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00035: val_mean_squared_error improved from 0.00013 to 0.00013, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00036: val_mean_squared_error did not improve\n",
+      "Epoch 00037: val_mean_squared_error did not improve\n",
+      "Epoch 00038: val_mean_squared_error did not improve\n",
+      "Epoch 00039: val_mean_squared_error improved from 0.00013 to 0.00011, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00040: val_mean_squared_error did not improve\n",
+      "Epoch 00041: val_mean_squared_error did not improve\n",
+      "Epoch 00042: val_mean_squared_error did not improve\n",
+      "Epoch 00043: val_mean_squared_error did not improve\n",
+      "Epoch 00044: val_mean_squared_error did not improve\n",
+      "Epoch 00045: val_mean_squared_error did not improve\n",
+      "Epoch 00046: val_mean_squared_error did not improve\n",
+      "Epoch 00047: val_mean_squared_error did not improve\n",
+      "Epoch 00048: val_mean_squared_error did not improve\n",
+      "Epoch 00049: val_mean_squared_error improved from 0.00011 to 0.00011, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00050: val_mean_squared_error did not improve\n",
+      "Epoch 00051: val_mean_squared_error did not improve\n",
+      "Epoch 00052: val_mean_squared_error improved from 0.00011 to 0.00010, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00053: val_mean_squared_error did not improve\n",
+      "Epoch 00054: val_mean_squared_error improved from 0.00010 to 0.00009, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00055: val_mean_squared_error did not improve\n",
+      "Epoch 00056: val_mean_squared_error did not improve\n",
+      "Epoch 00057: val_mean_squared_error did not improve\n",
+      "Epoch 00058: val_mean_squared_error did not improve\n",
+      "Epoch 00059: val_mean_squared_error did not improve\n",
+      "Epoch 00060: val_mean_squared_error did not improve\n",
+      "Epoch 00061: val_mean_squared_error improved from 0.00009 to 0.00009, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00062: val_mean_squared_error did not improve\n",
+      "Epoch 00063: val_mean_squared_error did not improve\n",
+      "Epoch 00064: val_mean_squared_error did not improve\n",
+      "Epoch 00065: val_mean_squared_error did not improve\n",
+      "Epoch 00066: val_mean_squared_error did not improve\n",
+      "Epoch 00067: val_mean_squared_error improved from 0.00009 to 0.00008, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00068: val_mean_squared_error did not improve\n",
+      "Epoch 00069: val_mean_squared_error did not improve\n",
+      "Epoch 00070: val_mean_squared_error did not improve\n",
+      "Epoch 00071: val_mean_squared_error did not improve\n",
+      "Epoch 00072: val_mean_squared_error did not improve\n",
+      "Epoch 00073: val_mean_squared_error did not improve\n",
+      "Epoch 00074: val_mean_squared_error did not improve\n",
+      "Epoch 00075: val_mean_squared_error improved from 0.00008 to 0.00008, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00076: val_mean_squared_error did not improve\n",
+      "Epoch 00077: val_mean_squared_error did not improve\n",
+      "Epoch 00078: val_mean_squared_error did not improve\n",
+      "Epoch 00079: val_mean_squared_error did not improve\n",
+      "Epoch 00080: val_mean_squared_error did not improve\n",
+      "Epoch 00081: val_mean_squared_error did not improve\n",
+      "Epoch 00082: val_mean_squared_error improved from 0.00008 to 0.00007, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00083: val_mean_squared_error did not improve\n",
+      "Epoch 00084: val_mean_squared_error did not improve\n",
+      "Epoch 00085: val_mean_squared_error did not improve\n",
+      "Epoch 00086: val_mean_squared_error did not improve\n",
+      "Epoch 00087: val_mean_squared_error did not improve\n",
+      "Epoch 00088: val_mean_squared_error did not improve\n",
+      "Epoch 00089: val_mean_squared_error did not improve\n",
+      "Epoch 00090: val_mean_squared_error improved from 0.00007 to 0.00006, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00091: val_mean_squared_error did not improve\n",
+      "Epoch 00092: val_mean_squared_error did not improve\n",
+      "Epoch 00093: val_mean_squared_error did not improve\n",
+      "Epoch 00094: val_mean_squared_error did not improve\n",
+      "Epoch 00095: val_mean_squared_error did not improve\n",
+      "Epoch 00096: val_mean_squared_error did not improve\n",
+      "Epoch 00097: val_mean_squared_error did not improve\n",
+      "Epoch 00098: val_mean_squared_error did not improve\n",
+      "Epoch 00099: val_mean_squared_error improved from 0.00006 to 0.00006, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00100: val_mean_squared_error did not improve\n",
+      "Epoch 00101: val_mean_squared_error improved from 0.00006 to 0.00006, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00102: val_mean_squared_error did not improve\n",
+      "Epoch 00103: val_mean_squared_error did not improve\n",
+      "Epoch 00104: val_mean_squared_error did not improve\n",
+      "Epoch 00105: val_mean_squared_error did not improve\n",
+      "Epoch 00106: val_mean_squared_error did not improve\n",
+      "Epoch 00107: val_mean_squared_error did not improve\n",
+      "Epoch 00108: val_mean_squared_error improved from 0.00006 to 0.00005, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00109: val_mean_squared_error did not improve\n",
+      "Epoch 00110: val_mean_squared_error did not improve\n",
+      "Epoch 00111: val_mean_squared_error did not improve\n",
+      "Epoch 00112: val_mean_squared_error did not improve\n",
+      "Epoch 00113: val_mean_squared_error did not improve\n",
+      "Epoch 00114: val_mean_squared_error did not improve\n",
+      "Epoch 00115: val_mean_squared_error did not improve\n",
+      "Epoch 00116: val_mean_squared_error did not improve\n",
+      "Epoch 00117: val_mean_squared_error improved from 0.00005 to 0.00005, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00118: val_mean_squared_error did not improve\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00119: val_mean_squared_error did not improve\n",
+      "Epoch 00120: val_mean_squared_error did not improve\n",
+      "Epoch 00121: val_mean_squared_error did not improve\n",
+      "Epoch 00122: val_mean_squared_error did not improve\n",
+      "Epoch 00123: val_mean_squared_error did not improve\n",
+      "Epoch 00124: val_mean_squared_error did not improve\n",
+      "Epoch 00125: val_mean_squared_error improved from 0.00005 to 0.00005, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00126: val_mean_squared_error improved from 0.00005 to 0.00005, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00127: val_mean_squared_error did not improve\n",
+      "Epoch 00128: val_mean_squared_error improved from 0.00005 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00129: val_mean_squared_error did not improve\n",
+      "Epoch 00130: val_mean_squared_error did not improve\n",
+      "Epoch 00131: val_mean_squared_error did not improve\n",
+      "Epoch 00132: val_mean_squared_error did not improve\n",
+      "Epoch 00133: val_mean_squared_error did not improve\n",
+      "Epoch 00134: val_mean_squared_error did not improve\n",
+      "Epoch 00135: val_mean_squared_error did not improve\n",
+      "Epoch 00136: val_mean_squared_error did not improve\n",
+      "Epoch 00137: val_mean_squared_error did not improve\n",
+      "Epoch 00138: val_mean_squared_error did not improve\n",
+      "Epoch 00139: val_mean_squared_error did not improve\n",
+      "Epoch 00140: val_mean_squared_error did not improve\n",
+      "Epoch 00141: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00142: val_mean_squared_error did not improve\n",
+      "Epoch 00143: val_mean_squared_error did not improve\n",
+      "Epoch 00144: val_mean_squared_error did not improve\n",
+      "Epoch 00145: val_mean_squared_error did not improve\n",
+      "Epoch 00146: val_mean_squared_error did not improve\n",
+      "Epoch 00147: val_mean_squared_error did not improve\n",
+      "Epoch 00148: val_mean_squared_error did not improve\n",
+      "Epoch 00149: val_mean_squared_error did not improve\n",
+      "Epoch 00150: val_mean_squared_error did not improve\n",
+      "Epoch 00151: val_mean_squared_error did not improve\n",
+      "Epoch 00152: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00153: val_mean_squared_error did not improve\n",
+      "Epoch 00154: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00155: val_mean_squared_error did not improve\n",
+      "Epoch 00156: val_mean_squared_error did not improve\n",
+      "Epoch 00157: val_mean_squared_error did not improve\n",
+      "Epoch 00158: val_mean_squared_error did not improve\n",
+      "Epoch 00159: val_mean_squared_error did not improve\n",
+      "Epoch 00160: val_mean_squared_error did not improve\n",
+      "Epoch 00161: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00162: val_mean_squared_error did not improve\n",
+      "Epoch 00163: val_mean_squared_error did not improve\n",
+      "Epoch 00164: val_mean_squared_error did not improve\n",
+      "Epoch 00165: val_mean_squared_error did not improve\n",
+      "Epoch 00166: val_mean_squared_error did not improve\n",
+      "Epoch 00167: val_mean_squared_error did not improve\n",
+      "Epoch 00168: val_mean_squared_error did not improve\n",
+      "Epoch 00169: val_mean_squared_error did not improve\n",
+      "Epoch 00170: val_mean_squared_error did not improve\n",
+      "Epoch 00171: val_mean_squared_error did not improve\n",
+      "Epoch 00172: val_mean_squared_error did not improve\n",
+      "Epoch 00173: val_mean_squared_error did not improve\n",
+      "Epoch 00174: val_mean_squared_error improved from 0.00004 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00175: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00176: val_mean_squared_error did not improve\n",
+      "Epoch 00177: val_mean_squared_error did not improve\n",
+      "Epoch 00178: val_mean_squared_error did not improve\n",
+      "Epoch 00179: val_mean_squared_error did not improve\n",
+      "Epoch 00180: val_mean_squared_error did not improve\n",
+      "Epoch 00181: val_mean_squared_error did not improve\n",
+      "Epoch 00182: val_mean_squared_error did not improve\n",
+      "Epoch 00183: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00184: val_mean_squared_error did not improve\n",
+      "Epoch 00185: val_mean_squared_error did not improve\n",
+      "Epoch 00186: val_mean_squared_error did not improve\n",
+      "Epoch 00187: val_mean_squared_error did not improve\n",
+      "Epoch 00188: val_mean_squared_error did not improve\n",
+      "Epoch 00189: val_mean_squared_error did not improve\n",
+      "Epoch 00190: val_mean_squared_error did not improve\n",
+      "Epoch 00191: val_mean_squared_error did not improve\n",
+      "Epoch 00192: val_mean_squared_error did not improve\n",
+      "Epoch 00193: val_mean_squared_error did not improve\n",
+      "Epoch 00194: val_mean_squared_error did not improve\n",
+      "Epoch 00195: val_mean_squared_error did not improve\n",
+      "Epoch 00196: val_mean_squared_error did not improve\n",
+      "Epoch 00197: val_mean_squared_error did not improve\n",
+      "Epoch 00198: val_mean_squared_error did not improve\n",
+      "Epoch 00199: val_mean_squared_error did not improve\n",
+      "Wall time: 8min 11s\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Save the best weight during training.\n",
+    "#simname = \"15_min_replication_1\"\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_mean_squared_error', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "# Fit\n",
+    "\"\"\"\n",
+    "it seems batch size controls convergence speed a lot! Batch size tells how many examples are propagated through the network.\n",
+    "Weights are adjusted based on results with these examples. This is useful if the full dataset takes too much memory\n",
+    "It also speeds up training, as you will converge quicker (dont have to wait for a full iteration of each example to adjust weights).\n",
+    "\"\"\"\n",
+    "callbacks_list = [checkpoint]\n",
+    "%time history = model.fit(trainX, trainY, epochs=200, batch_size=500, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 200\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131c22ef28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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68GJ2dP6+uHv6OjqT611mzQ+zY9rBubdl4Vt+O1sX362qFvac7Jak/TTt3depu+deT7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\nIiIiIiIiIiIiKrL/B8QZwLn1LW59AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21313c366d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131381b828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epoch = len(history.history['loss'])\n",
+    "print(\"epoch\", epoch)\n",
+    "for k in list(history.history.keys()):\n",
+    "    if 'val' not in k:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(history.history[k])\n",
+    "        plt.plot(history.history['val_' + k])\n",
+    "        plt.title(k, fontsize=30)\n",
+    "        plt.ylabel(k)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left', fontsize=30)\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0034859505006226755"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(history.history['val_mean_absolute_error'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As seen from the above, the model seems to have converged nicely, but the mean absolute error on the development data remains at ~0.003X which means the model is unusable in practice. Ideally, we want to get ~0.0005. Let's go back to the best weight, and decay the learning rate while retraining the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error did not improve\n",
+      "Epoch 00001: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0005904900433961303\n",
+      "Epoch 00002: val_mean_squared_error did not improve\n",
+      "Epoch 00003: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0005314410547725857\n",
+      "Epoch 00004: val_mean_squared_error did not improve\n",
+      "Epoch 00005: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00047829695977270604\n",
+      "Epoch 00006: val_mean_squared_error did not improve\n",
+      "Epoch 00007: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0004304672533180565\n",
+      "Epoch 00008: val_mean_squared_error did not improve\n",
+      "Epoch 00009: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00038742052274756136\n",
+      "Epoch 00010: val_mean_squared_error did not improve\n",
+      "Epoch 00011: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0003486784757114947\n",
+      "Epoch 00012: val_mean_squared_error did not improve\n",
+      "Epoch 00013: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00031381062290165574\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0002824295632308349\n",
+      "Epoch 00016: val_mean_squared_error did not improve\n",
+      "Epoch 00017: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00025418660952709616\n",
+      "Epoch 00018: val_mean_squared_error did not improve\n",
+      "Epoch 00019: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00022876793809700757\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00020589114428730683\n",
+      "Epoch 00022: val_mean_squared_error did not improve\n",
+      "Epoch 00023: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00018530203378759326\n",
+      "Epoch 00024: val_mean_squared_error did not improve\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00016677183302817866\n",
+      "Epoch 00026: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "Epoch 00027: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to bm_kaggle.weights.best.hdf5\n",
+      "lr changed to 0.00015009464841568844\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error did not improve\n",
+      "lr changed to 0.0001350851875031367\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00012157666351413355\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n",
+      "Epoch 00033: val_mean_squared_error did not improve\n",
+      "lr changed to 0.00010941899454337544\n",
+      "Epoch 00034: val_mean_squared_error did not improve\n",
+      "Epoch 00035: val_mean_squared_error did not improve\n",
+      "lr changed to 9.847709443420172e-05\n",
+      "Epoch 00036: val_mean_squared_error did not improve\n",
+      "Epoch 00037: val_mean_squared_error did not improve\n",
+      "lr changed to 8.862938630045391e-05\n",
+      "Epoch 00038: val_mean_squared_error did not improve\n",
+      "Epoch 00039: val_mean_squared_error did not improve\n",
+      "lr changed to 7.976644701557234e-05\n",
+      "Epoch 00040: val_mean_squared_error did not improve\n",
+      "Epoch 00041: val_mean_squared_error did not improve\n",
+      "lr changed to 7.178980231401511e-05\n",
+      "Epoch 00042: val_mean_squared_error did not improve\n",
+      "Epoch 00043: val_mean_squared_error did not improve\n",
+      "lr changed to 6.461082011810504e-05\n",
+      "Epoch 00044: val_mean_squared_error did not improve\n",
+      "Epoch 00045: val_mean_squared_error did not improve\n",
+      "lr changed to 5.8149741380475466e-05\n",
+      "Epoch 00046: val_mean_squared_error did not improve\n",
+      "Epoch 00047: val_mean_squared_error did not improve\n",
+      "lr changed to 5.233476658759173e-05\n",
+      "Epoch 00048: val_mean_squared_error did not improve\n",
+      "Epoch 00049: val_mean_squared_error did not improve\n",
+      "lr changed to 4.7101289601414466e-05\n",
+      "Epoch 00050: val_mean_squared_error did not improve\n",
+      "Epoch 00051: val_mean_squared_error did not improve\n",
+      "lr changed to 4.239116096869111e-05\n",
+      "Epoch 00052: val_mean_squared_error did not improve\n",
+      "Epoch 00053: val_mean_squared_error did not improve\n",
+      "lr changed to 3.815204618149437e-05\n",
+      "Epoch 00054: val_mean_squared_error did not improve\n",
+      "Epoch 00055: val_mean_squared_error did not improve\n",
+      "lr changed to 3.4336842873017304e-05\n",
+      "Epoch 00056: val_mean_squared_error did not improve\n",
+      "Epoch 00057: val_mean_squared_error did not improve\n",
+      "lr changed to 3.0903160222806036e-05\n",
+      "Epoch 00058: val_mean_squared_error did not improve\n",
+      "Epoch 00059: val_mean_squared_error did not improve\n",
+      "lr changed to 2.7812844200525434e-05\n",
+      "Epoch 00060: val_mean_squared_error did not improve\n",
+      "Epoch 00061: val_mean_squared_error did not improve\n",
+      "lr changed to 2.5031560107890984e-05\n",
+      "Epoch 00062: val_mean_squared_error did not improve\n",
+      "Epoch 00063: val_mean_squared_error did not improve\n",
+      "lr changed to 2.2528404588229024e-05\n",
+      "Epoch 00064: val_mean_squared_error did not improve\n",
+      "Epoch 00065: val_mean_squared_error did not improve\n"
+     ]
+    }
+   ],
+   "source": [
+    "# tune model by starting from best weights and rerunning with decaying learning rate\n",
+    "# Load the weight that worked the best\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "#epoch=60\n",
+    "\n",
+    "# Train again with decaying learning rate\n",
+    "from keras.callbacks import LearningRateScheduler\n",
+    "import keras.backend as K\n",
+    "\n",
+    "def scheduler(epoch):\n",
+    "    if epoch%2==0 and epoch!=0:\n",
+    "        lr = K.get_value(model.optimizer.lr)\n",
+    "        K.set_value(model.optimizer.lr, lr*.9)\n",
+    "        print(\"lr changed to {}\".format(lr*.9))\n",
+    "    return K.get_value(model.optimizer.lr)\n",
+    "lr_decay = LearningRateScheduler(scheduler) # do sth to learning rate\n",
+    "\n",
+    "callbacks_list = [checkpoint, lr_decay] # checkin with these once in a while\n",
+    "history = model.fit(trainX, trainY, epochs=int(epoch/3), batch_size=500, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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W1DcAZvUw6lvP8Ky+99oVTc1GVFtRrG89v0Ub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\n6urquu06hw8f1smTJ2WxWPTGG2/onXfe0R//8R/rnXfe0datW/Xiiy/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2DL1T8B8u2KhWIe7LsvUwvTq+tjqT/Xqvyq2NHem4HuPXnl0rGMz01s1GhrNjyx/IO3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n5bUdw2wbZE7ITOQyMfR1VzKZjBtSribKEMH+xkz2NBw86XMtdiuvFb6NAwfrU9e6ffUTV6VR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\nDwBgRGW7x8skk4pbSvR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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131978a320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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Uuh9KhRxPHd2CA60ufPuVXrzdP4PfGzqPn3m4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WBMWzPc2ILRlOwq3YMaeoK+gFZiZ7RdO69evye/zz+p8UY2qUDP3sotAj0AAJx05fYg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7VmJeTIXOsB5n06Ql8oEyV2O2WVvwhd7bkcym8M3B+xGmekNSBMFUfjFaRg9Nzib0eBlPQipF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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131aa8a5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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iIBoNPLFzFc/ur8awyJ1h7lUoEuNy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yHX/MPTUERERJvCaC52dDWTB+XoLxejv4iIiIiowFy+YQBY8xT8\nNmv2fqE7d/+QiIiIaKNg8w9RGSt25JfMaWmCWlBjOOAp2p6rNZA7ASI/iMgXOforlo7h/IIrr2sX\ny7n5Abw9fwEAMBL0IJ5OKFxR/rkXm3+c61pnf/0eAFBk+o/cwNS6jgamfHhn8s+sonUQERFRcXhD\n4wDW1vwjfwYnIiIiIiqEtJjGcMCNBlMdLDrzmtZoMjdCI6jhDpbuwVYiIiKitWDzD1EZUyLyCwC0\nai2cliZ4w+NIZpJF23c1XL5haFQatK+z+WM58pSlcoz+EiURDw8+DgECtldvgyiJGPQPK11W3snN\nP851Ts3ZUb0NBo0eR6ZPQJTEfJS2Yp7cqfvWSoUn/+Saf2YZ+0VERLQpeMOrb/6pMzpg0Znh8g1B\nkqRClUZEREREm9xoaAxJMYVuW8ea19CqNGixNGEsPIFkJpXH6oiIiIiUxeYfojKmROSXrN3aClES\nFxsUSkkkFcV4eBLtlU5o1dq8r99W2QJ7hQ2n5s4iVWbRX29OHMFEZAoHGvbiQ84PAgDO+8pzgtHF\niJKI0aAXdUYHjFrDutbSqrXord0JfyKAgSLHWHhCXqgFNZrMjUXd972sukroVFrGfhEREW0S3tAE\nbBVWVOosK36PIAjosXUikAxxWiARERERFYwc+dW9zmnvbZVOiJKIsVzjOxEREdFGwOYfojKlVOSX\nrMPaBgAYKcHoL5d/GBKkvEd+yVSCCr2OHYil4zi/MFCQPQohno7jseGD0Kl1uLvjVrRb26BVaXFh\nYVDp0vJqOjqLeCaBtjzFZe2v7wNQ3OivtJjGeGgCjeZ6aFWaou27HEEQUGuswWx0jif5iYiINrhg\nMgR/IoAWy+qbj7sXo7823lRJIiIiIioNrtxnzfXeD2+zZqfFjwQY/UVEREQbB5t/iMqUUpFfsvbc\nBdJw0F30vS/HlZvQ0mPvKtgefY5dAIATM6cLtke+Pet5CaFUGLc6b4CtwgqtSoMuWzsmIlMIJIJK\nl5c3cuRXvpp/Om1tqNLb0T97umgxdxPhKaSlDFrXGVuWLw5DDZJiCoHkxvl9QkRERO/nDU0AAFrM\nK4/8kvXkohdcRZ6WSERERESbQ0bMYCjgRr3Rsaoplctpq8ze23YH2fxDREREGwebf4jKlJKRXwBg\nq7CiWm/HcMBTctNABnxD0Ko0aM1T88dy5Oivk7PlEf21EPfhee8rsFVY8SHndYtf31rVDQC44Ns4\n03/ki/Z8/fdXCSrsr+tFIpPEydmzeVnzcjyhbANTIX8Pr0atsQYAMBNl9BcREdFG5g1lI31bLKtv\n/nEYa2HVWTDgHyq56wMiIiIiKn+joTEkM0l02TvWvVa13g6z1rR4iJCIiIhoI2DzD9F7iJKodAmX\npXTkl6zd2opIKoqZWOk0BISSYUxEptBhbStoXJIgCOir24l4pjyivx4dehopMY17O26HTq1b/PpW\ne7b55/yCS6nS8s4T9EKj0qDJ3JC3NYsd/TUazD54K5XmH4exFgAwy+YfIiKiDc0bGgewtuYfQRDQ\nbe9EKBnGdHQm36URFY0kSXAHR8vi3gAREdFmIkd+yRMn10MQBLRbnViI+xBIhNa9HhEREVEpYPMP\n0RJvTBzBn7725zgzd07pUi5J6cgvWYe1DQAwHPAoVsN7DfpHAAA99s6C79WXm7p0bPpUwfdaD3dw\nFEemT6DF0oR99b3v+l6juR5mrQnnF1wb4oR2MpPCeHgSLeZGaPLY/FVncqDV0oJzCwNFuSHgCY1B\nq9Ki3ugo+F4r4TDkJv+UUKMfERER5Z83NA6L1gxbhXVN7++xZT+DDzD6i8rY0el+/M3R7+I5z8tK\nl0JERERLuHzZ5p8uW37u+zL6i4iIiDYaNv8Q5cTScfxq6AlEUlE8eObfcWGhdGOQ5MivPoUiv2Qd\n1lYAwEjArWgdS8kPGrrzdBF4Ka2WFlTp7Tg9dxapTKrg+62FJEl42PU4AOCjXXe/r1lMJaiwtaob\ngWQQUxvghPZYeByiJC5evOfT/vo+SJBwbPpE3tdeKplJYjIyjRZLE9QqdUH3WikHY7+IVuzodD8O\nul9ARswoXQoR0apEUlHMx31osTRBEIQ1rdFtZ/MPlb8juc/7z4y+iEgqqnA1REREBGQn4Q8FRlBn\nrIW1wpKXNdn8Q0RERBsNm3+Icl70vopIKopdtdsBScI/nv7XkppoI1sa+dWpYOQXADSa6qFT60rq\n12nAPwSdSovWyuaC7yUIAvocOxHPJHCuRKO/+mfPYCjgxq6aKxcfxrzXlg0U/SXndBciLmtP3S6o\nBFXBo7+8oQmIkliU38MrZdaaoFfrOfmH6DKSmSR+ev4XeHT4aTzQ/yBCjwtROwAAIABJREFUybDS\nJRERrdh6Ir9ktYZq2CqscPmHN8RUSdp8oqkozi+4oBJUiKXjeNbzktIlEREREQBveByJTBJdeYj8\nkrVWtkCAAHeAzT9ERES0MbD5hwhAOBXB86OvwKw14dPbPoHPbP8k0mIa3z/5I3hDE0qX9y6lEvkF\nAGqVGm2VTkxGphFNxRStBQBCyTCmItPosLblNfLpUuTpS/I0plKSEtP41eATUAkqfLjrzou+bmtV\nF4AN0vyTu1gvxOQfi86MK6q2wBuewER4Ku/ryzyhbAOT01I6zT+CIMBhrMFcbB6iJCpdDlHJOjX3\nNhKZJCp1Frj8w/jm0QdK7nMEEdHF5KP5RxAEdNs6EU5FMBmZzldpREVzcu5tZKQMbmu9EbYKK14a\nex2BRFDpsoiIiDY9OfKrJ4/NPwaNHnUmBzwhL+93ERER0YbA5h8iAM96XkI8k8DtbR+CXlOBXbXb\n8altH0c8ncB3+x/EVKR04pCOz5wEoHzkl2wx+qsExqPK8QI9F5lwUwhOSzOq9VU4NXcWyRKL/npl\n7A3MxRdwfdM1cBhrL/q6Kr0dDmMNXP6hso+p8QS9MGmNqDFUFWT9/fV9AFDQ6T+eAk4vWg+HsQZp\nMQ1fPKB0KUQl68hUNibkq72fx93tt2Ih7sPfHvteSTaIEhG9Vz6af4B3Posz+ovK0Ync39n76/fg\njrYPISWm8JT7eYWrIiIiIpc/2/zTZc9f8w8AtFc6kcgk2bhOREREGwKbf2jT8ycCeHnsddgrbLi2\n8arFr++v78Ovb7kf4VQED/Q/iLnYgoJVZmUjv86WROSXbLH5J+BWthC8cxFYzOYfOforkUni3MKF\nou17OeFkBE+5n4NRY8Ad7Tdf9vVb7T1IZJIl0cS1VqFkGHPxhezIXkEoyB47aq6AXq3HkekTBTsR\nNBocg0GjR62huiDrr1WtoQYAMMvoL6JlhZMRvL1wAS3mRjSY6nBH+834/I7fhCAI+NGZn+Cx4YM8\nSUhEJc0bGodBY0C13r6udeTP4i4/m3+ovMiRXy3mRjiMNbi6YR9qDdV4feIQ5mLzSpdHRES0aWXE\nDIb8bjgMNbBVWPO6dlvu8J27jO+JEhEREcnY/EOb3tPuF5AS07iz/WZo1dp3fe/apgO4v+su+BMB\nfOfEP8GfUHbixTuRXzsVj/yStefilYYDHoUryZ4u1ql1RY9LKsXoryfdzyGWjuOO9pth0hov+3o5\n+utCGUd/yRNz2iyFm5ijU2vR59gBfyKwOG44n6KpGGZic3Bamkvmz7jMYcw2/8xEZxWuhKg0HZ85\nBVESsbe+d/Fru2qvxNf2fBk1hmo87X4e/3T6x4il4wpWSUS0vFg6jpnYHFosTetuoq7W22GvsMHl\nG2bTI5UVOfKrN3d9p1apcXf7rRAlEY8PP6twdURERJvXWHgC8UwcXXmM/JK15w62ugNs/iEiIqLy\nV1pPFomKbC42j9cnDsFhqMFV9XuWfc3NzutxZ9vNmI8v4DsnHkQoGS5yle94J/Jrh2I1vJdRa0S9\nqQ7u4KiikVGBRBDT0Rl0WduhVqmLuneLpQk1+iqcmnu7JKK/piMzeHX8TdQaqnFd09Urek+PvRMC\nBJz3lW/zj1tu/rE6C7qPHP11aOpY3tceDY0BKL3IL2BJ8w8n/xAt68j0CQgQsLdu97u+3miux9f3\nfgVb7d04PXcO3zr2PcxE+eeIiErLWGgCAOBcZ+QXkJ2M2WPvRCQdxUR4at3rERWLHPnVuyRiu69u\nF5rMDTg6fQLj4UmlSiMiItrU5Gnv3XmO/AKABlMddGrd4n1FIiIionLG5h/a1J4ceQ6iJOKujlsv\n2TByZ/st+FDLdZiOzuC7/T9ENBUrYpVZGTGD/tkzJRX5JeuobEUik8SEgtnISkR+yQRBQF/dLiQz\nSbxdAtFfvxx6EqIk4sNdd0Gj0qzoPQaNAW2VLXAHvWU7lUKe/NNawMk/ANBpa4e9wob+2dNIZpJ5\nXXs0mGv+KfL0qpVwyLFfbFogep/52AKGA2502zqWHUFu0hrxu7s+g5taPoipyDS+efQBnJsfUKBS\nIqLlecPjALJN7fnwTvRX/iclEhXCeyO/ZCpBhXs7bocECY8NH1SwQiIios1Lnr7dXYDJPypBhVZL\nMyYj04iX6T1RIiIiIhmbf2jTmghP4fDUcTSZGxZjmy5GEATc33UXrm28CmPhCXz/5I8QTyeKVGnW\ngH8IkVS0pCK/ZB258agjAbdiNQz4hgAU5gTISixGf02fVGR/2YBvEKfn3kaXrR27aq5c1Xu3VnVD\nlES4cr+W5USSJHiCXtQYqmHWmQq6l0pQYX99HxKZJE7Ons3r2p5QroGpBCf/GLVGmLRGTv4hWsaR\n6X4AwL4lkV/vpVap8dHue/CpbR9HKpPE907+CM+PvgJJkopVJhHRRXlD+W3+6bZlm38GyvBzJW1O\n7438WurK6q3osLbh9NzbJRF3TUREtJmIkoihwAhqDNWw620F2aOt0gkJEjy5Q3lERERE5aq0OgiI\niuiJkWcgQcI9HbetqJlGEAR8Ysv92FfXi5HgKH5w+sdIFTHiSR5BXkqRXzK5+UfJG6Eu3xD06gq0\nmPPzwGK1ms2NqDVU4/T8ubxPg1kpURLxkOtxAMBHuu6GIAirev8WezcA4LxvMO+1FdpsbB6RdBRt\nRWqakaO/Dk8dz+u6nuAYLFrzspNDSoHDUIO52IKiEX9EpUaSJByZPgGNoMbu2sv/HX2gYS9+v++L\nqNSZ8fDg4/j3c/+3qJ8niIiW4w2No0KtQ62hOi/rVRvsqNZXweUfhiiJeVmTqJCWi/ySCYKA+zrv\nAAA8OvQUG3eJiIiKaCw8gVg6XpCpP7I2qxMA4A6OFmwPIiIiomJg8w9tSp6gF/2zZ9Be2Yrt1dtW\n/D6VoMKntn0cu2quxIBvED8885OiPASXI78qdZaSi/wCAIexFiaNUbHmH38igJnYHLps7ZeMbysk\nQRDQ69iJZCaJs/PKRH8dmjqOsfAE9tf3rWlyTLvVCZ1ahwsLrgJUV1jyxXlbpbMo+9WbHGi1tODc\nwgACiVBe1gwmQ/Al/GitbF5141ax1BprIEoi5uM+pUshKhlj4UlMRaaxvWYbjFrDit7TbnXi6/u+\nirZKJw5NHcPfHf9H+BOBAldKRLS8ZCaJqcgMms1NeZ0w2mPvRCwdw3h4Mm9rEhXCxSK/luqyteOK\n6i1w+Ydx3ld+10tERETlarCAkV8y+TDhCJt/iIiIqMyx+Yc2pceGDwIA7u28fdUP2dUqNX5r+yex\nraoHZ+bP4cdv/1fBT7PKkV+7a3eUXOQXkG18abe2Yj6+gEAiWPT934n86iz63kv1OXYBAI7PFD/6\nK5FJ4rGhp6BVaXFvx+1rWkOj0qDb1oGp6Ax8cX+eKywsT7D4cVn76/sgQcKx6RN5WU/+OThLMPJL\n5jDUAgBmGf1FtOjIdHYC2L66i0d+LcdWYcXv934BV9XvgSfkxV8f+Q5GGCVCRAoYC09CggRnniK/\nZPIDGkZ/Uam7VOTXUvJ11qNDT3P6D5U9SZJwaPIY/sfrf4mfnHxY6XKIiC5qwJ9r/rEXrvnHVmGF\nvcIGd3CUf8cTERFRWSu9LgKiAnP5hnBuYQBb7d3oWWOziFalwed3fBqd1jYcmzmJn55/qKANQKUc\n+SWTo7+UeHDpyj1Q6LEp2/zTbG6Aw1CDM3PFj/56zvMSAskQPuS8bl3511urstFfF8os+ssd9EIl\nqNBibizannvqdkElqPIW/TWayxVvtTTnZb1CcBizUSAzUTb/EAHZuMVj0ydh0OhxZfXWVb9fq9bi\nU9s+jo9234NQMoz/c/wf8ebEkQJUSkR0cd7QOACgJc/NP/K1Fpt/qNRdKvJrqRZLE/ocOzEaGkP/\n7JlilEZUELPReXy3/4f4t3M/gz8RwEHXy4in40qXRUT0PqIkYsg/gmp9Far09oLu1VbZglAyjIUy\nOxBJREREtBSbf2hTkSQJj+am/tzTedu61tKpdfjirt+C09KENyeP4CHXYwU5GVDqkV8yuflHieiv\nAf8wDBo9mi3Fa/xYjiAI6HPsRFJM4cz8+aLt608E8Nzoy6jUWXCL84Z1rbXVnm3+OV9G0V9pMY2x\n0DiazQ3QqrVF29eiM+OKqi3whicwEZ5a93qeUK75p4Qn/9TmYhDY/EOUNegfgT8RQG/tjjX//0cQ\nBNzU8kF8efdnoVPr8JPzP8cvBh4tSqwoERFQuOYfu96GWkM1Bv0jBZ+USrRWK4n8WurujtugElR4\nbPgg/66mspMRMzjofgF/efjbOO9z4YrqLfhg09VIZJI4PnNa6fKIiN5nPDyFaDpW0MgvWZvVCQBw\nBzmRl4iIiMoXm39oUzk7fx7DATd21VyJtkrnutczaAz40u7PotFUj5fGXsfjucaifCr1yC9Za2UL\nVIKq6M0/vrgfc7F5dNnaS+LXp69Ojv46VbQ9Hxs6iKSYwj0dt0GvqVjXWg2mOlTqLDjvc5XNmNvx\n8CTSUgatefgzvVr76/sAYN3TfyRJgifoRZXeDovOnI/SCsJhyD4QYewXUdaRqWzs37761UV+LWdr\nVTe+vveraDDV4cWx1/Ddkz9COBVZ97pERJfjDY1Dq9Kizlib97W7bZ2IZ+KLDUZEpWalkV+yOmMt\nDtTvxXR0Jm8TQImKYSTgwf935O/x6PDT0Gv0+MyVv4Hf3fkZ3Np6AwQIeGvyqNIlEhG9z2ARIr9k\n8rMCd9Bb8L2IiIiICkX5J+VERSJKIh4bPggBAu7uWN/Un6XMWhO+vPtzqDVU42nPC3jG/WLe1gaW\nRn6t7GakUnRqHZrNDfCGxpDKpIq270CJRH7JGk31qDPW4szcOSSKEP3lDY3j0NQxNJkbcKBh77rX\nEwQBW+zdCCXDmIisf5pNMcgX5W0KTMzZUXMF9Go9jkyfWNeJ9oW4H+FUpKQjvwBAr9GjUmfh5B8i\nACkxjROzp2GrsKIrT6cQa43V+NqeL2FnzZUY8A3ib448kJfJYkREF5MS05iITKHZ3AC1Sp339Rn9\nRaVupZFfS93ZfjM0Kg2eGHkWKTFdqNKI8iKWjuFnF36Jbx/7PiYiU/hA43782VVfw5663RAEAVV6\nO7bXbcFQYITXeURUcly5z5DFmPzjtDRBJagwEhgt+F5EREREhcLmH9o0Tsycxlh4AnvretFors/r\n2tYKC76y+/OwV9jwyPBTeGns9bys++7Ir7a8rFlI7dY2pKUMvOHinewd8OcuAu2l0fwjCAJ6HTuR\nElM4M3euoHtJkoSHXY9DgoSPdN2dt8lHW6u6AAAXyiT6yx3MXpQr0fyjU2vR59gBfyIAl294zet4\nQtkGJmdlaTf/AECtoQYLcR/SfNBBm9zZ+fOIpWPYU7crr5Pn9Bo9PrfjU7ij7WbMxRfwN8e+i/7Z\nM3lbn4hoqcnwFERJzHvkl0w+pe3yr/1zElGhrDbyS2bX23Bd09XwJfx4bfytAlZItHaSJKF/5jT+\n4q1v45XxN1FnrMUf9H0Rv7H112DUGt/12hvargYAHJo6pkSpRETLEiURg/4RVOntqDZUFXw/nVqH\nJnMDvOFx3vMiIiKissXmH9oUMmIGT4w8A5Wgwl3ttxRkj2qDHV/t/RwsOjN+PvAI3szDyORyifyS\ndVhbAaCo0V8u3xCMGgOazA1F2/Ny5ClNhY7+Oj33Ngb8Q9hevRVbq7rztq681jlfeTT/eIJe6NV6\nOAoQVbES+Yj+Gg2OAQBaLcVvYFoth7EGEiTMxRaULoVIUYuRX3V9eV9bJahwd8et+Oz2TwGShAdP\n/xueHHl2XRPGiIiWMxrKfgYpVPOPrcIKh7EGQ/4RZMRMQfYgWqvVRn4tdWvrjahQ6/C0+3nE04kC\nVEe0dr64Hz84/WM8eObfEUlFcHf7rfjT/b+PLlv7sq/f37wberUehyaP8fMmEZWMycg0IuloUab+\nyNoqnUiLaYyHJ4u2JxEREVE+lX43AVEeHJ46junoLK5p2IdaY3XB9nEYa/GV3Z+DSWPEf5z7+bqb\nP45Pl0fkl6zYzT/zsQXMx33otnWUVHNUNvrLgbPz5wp2IzgjZvDLoSegElS4v+uuvK5tq7Ci3ujA\noG+45E+6RFNRTEdn0VrZrNjvgU5bO+wVNpyYPYXkGqPePEF58k9hHrzlk8OQPRU9E51VuBIi5cTS\nMZyZP4d6Ux2aC9h82uvYga/t/TKq9XY8MfIsfnTmJ3zASER55Q1lJ3YWqvkHyMbzxjMJjIaKNx2U\naCXWEvkls+jM+FDLdQinInjR+1q+SyNaE1ES8aL3NfzFoW/h9Nzb6LZ14P/Z/we4o/1maFWai76v\nQqPDnrqd8CX8jGkkopIhT9guZvNPe6UTADASZPQXERERlafSeVpOVCApMY0nRp6FRqXBHe03F3y/\nJnMDvrT7t1Gh1uFfzv50zdFPGTGDk3PlE/kFAPYKG2wVVgwH3JAkqeD7DeTiA0ol8ksmCAL6HDuR\nEtM4O1+Y6K9Xx9/CTHQO1zZehXpTXd7X31rVjaSYwkgRpzithSd3Wr0td3GuBJWgwv76PiQySZya\nPbvq94uSiNHQOOqMtTBoDAWoML/kSISZ2JzClRAp58TMGaTFNPbV9UIQhILu1WRuwNf3fhU9tk70\nz57Bt499D3Ox+YLuSUSbhzc0AY2gRkMBPk/KenKf1V1+PlCm0rHWyK+lbnJeB5PWiOdGX0Y4Fclz\nhUSr4w1N4FtHv4dfuB6FWlDjk1s/ht/r/QLqTI4Vvf9Aw14AwFt5mGJNRJQP8mfHYt73bavMTuR2\nB7xF25OIiIgon9j8Qxve6+OH4Ev4cX3TNbBVWIuyZ2tlC35n529BLajxwzP/vqaTU3LkV6+jPCK/\ngGzTS7u1FaFkGPPxwkcCuXK/rj0l1vwDFDb6K5qK4smRZ6FX63FngWLs5Oiv877BgqyfL/LFeGul\nsnFZcvTXoenVR3/NRucQz8ThLIPILwCozT0cmY2y+Yc2ryPT2civvXW7i7KfWWfCl3d/Ftc3X4OJ\nyBS+eeQBXFgo7f8/E1Hpy4gZjEcm0Wiuh+YSEyHWq8uW/azOaRJUStYT+SUzaPS4rfUmxDNxPOt5\nKX/FEa1CIpPELwefwDePfgeekBf76nrxZwf+GNc07ltVk3p7ZSscxhr0z55GLB0rYMVERJcnSiIG\n/SOwV9hQrbcXbd9aYw0MGgPcwdI+DElERER0MeXRUUC0RolMEk+7n0eFWodbW28s6t7d9g58fsen\nIUoS/uHUv6x6gooc+dVbWx6RX7JiRX9JkoQB3xBMWmNBTyqvVaO5HvWmOpydP494Op7XtZ9yP49I\nOorb226CRWfO69oyOUrt/IKrIOvniyeUHcPbpnDzT73JAaelGecXXAgmQ6t6rzy9qLWyuRCl5V2t\nIRudOMPJI7RJ+RMBuHxD6LC2osZQVbR91So1Pt7zYfzG1o8inknguyd/iJe8rxdl0h4RbUxT0Rmk\nxXRBI78AwFphQb3RgaGAGxkxU9C9iFZqPZFfS13XdDVsFVa8PPY6/IlAPkojWrGz8xfwl4e+jedG\nX4a9woYv7/os/vuV/21N9wkEQcCB+r1IienF+1FEREqZiswgnIqg295R8Gm7S6kEFdoqWzAbm+dU\nPyIiIipLbP6hDe0l72sIpcL4UMt1MOtMRd//iuot+Mz2TyItpvG9k/8Mb2hiRe8rx8gvmdz8U+i4\nqPn4AnwJ/2KTSinqq92BlJhec/Tbcmaic3h57A1U6+24ofkDeVv3vfQaPdoqnfAEvYimSvPUnyRJ\ncAe8sFfYYK2oVLoc7K/vgyiJODrdv6r3eYKlMb1opXRqHWwVVsxEZ5UuhUgRR6f7IUHCvrpeRfb/\nQONV+P2+L8CkNeLnrkfwH+d/gZSYVqQWIipvo6FxACh48w+QndSZzCQXm56JlJSPyC+ZVq3Fne03\nIyWm8ZT7+TxVSHRpwWQI/3L2p/j+yR/BlwjgFucN+MZVf4ht1T3rWnd/fR8ECHhritFfRKQsl38Y\nQPZwYrG1VToBvHO/joiIiKiclOYTc6I8iKZieHb0ZZg0RtzkvE6xOnbXbsentn0c8XQc3+1/EFOR\nmcu+Z8BXfpFfsmZzI7QqTcEn/8ixAcXMfV6t3gJEfz0y9BQyUgb3dd4JrVqbt3WXs7WqGxIkDPhL\nM6JhIe5HKBVWfOqPbG/dbqgEFQ5PrS76yxMcg0pQodncUKDK8s9hqIE/EUAyk1S6FKKiOzp1AipB\nhT7HLsVq6LC24U/2fhVOSxPenDyCvz/+AwQSQcXqIaLy5C1i84/8mZ3RX1QK8hH5tdSB+r1wGGrw\nxsRhzEY5HZMKR5REvD5xCH/+1rdwdLofrZUt+JO9X8WHu+6ETq1b9/p2vQ1bq7oxHPBgegX3roiI\nCsUl3/e1Ff++b7s12/wzEhgt+t5ERERE61VeXQVEq/D86MuIpWO4pfUGGDR6RWvZX9+HT2y5H+FU\nBA/0P4i52MIlXy83i5Rb5BcAaFQaOC0tGA9P5j3uaqkBX/YESI8CF4Er1WiuR4OpDmcXLuTl12LQ\nP4L+2dNor2xFX55uVF/KVns3AOBCiUZ/uYPZi/BSmZhj0ZlxRVUPvKFxTEamV/SejJjBWHgcDaa6\nvNysLRb5hPQso79ok5mKTMMbnsAVVT2KTBRcyq634Q/6fhf76noxEvTgm0cf4MlEIloVbyjbgNxo\nKnwDsnxq28XmHyoB+Yr8kqlVatzdcStEScTjIwfzsibRe01FZvD3J36An55/CJIk4mM99+Fre76E\nZktjXvc50LAXAPDW1LG8rktEtFKSJMHlH4atwlrUqG2ZfJ9Rvu9IREREVE7Y/EMbUjAZwgtjr8Gq\ns+D65muULgcA8MGmA7i/6y74EwE8cOKf4E8Eln2dHPllLcPIL1mHtRUSJLgL9BAyexE4BLPWhAZT\nXUH2yJc+x06kxTROrzP6S5REPOR6DADw0e67i5J33VbZAr26AudLtPlHfsgtj+MtBfvr+wBgxdN/\nJiLTSIlptFpKo4FppWrl5p/onMKVEBXXkVysn1KRX++lU2vxm1f8Ou7vuguBRBB/e/wfcGiSD2qI\n6PJEScRYaAL1Rgd0BZ4mCWSbpBtN9RgKuJFmVCEpKJ+RX0v1Onai2dyIY9MnMR6ezNu6RCkxjSeG\nn8FfHf47DPpHsKt2O75x1R/hhuYPFGRS9M6aK2HQ6HF46jhEScz7+kRElzMVnUE4FUG3raMo9z/f\ny6w1odZQDXfQy/8PEhERUdlh8w9tSM+4X0Qyk8TtbTeX1DSNm53X4462mzEXX8ADJx5EKBl+32vk\nyK/dZRj5JeuwtgIARgoU/TUbm4M/EUC3vVORi8DV6MtT9NfR6X6Mhsawx7EL7blf30JTq9Totndg\nJjaH+ZivKHuuhjs4CgFCUaIqVmpHzZXQq/U4MnViRTcIRkPZBqbWyuZCl5ZXDkP2QckMm39oE5Ek\nCUenTkCn1mFH7ZVKl7NIEATc7LweX9z1GWhVGvzbuZ/hB6d+jIV46f1/m4hKx0x0FkkxVdTPUd32\nDqTEVMEOCBCtRL4jv2QqQYV7O2+HBAmPDj2d17Vp83L5hvBXh/8OT7qfg1lnxud3fBqf3/Fp2PW2\ngu2pU2uxx7EL/kSgZA8CEdHG5spNe5cnRyqhrbIVsXSMh96IiIio7JRnZwHRJSzEfXh1/E1U66tw\nTeM+pct5n7vab8FNLR/EVHQG3+v/IaKp2Lu+X86RXzK5OWW4QM0/rsXIL+UuAleq3lSHRlM93p4/\nj9gao7+SmSQeGXoKGpUG93XekecKL22rvQcAcMFXWjf9MmIGo6FxNJrroddUKF3OIp1ai17HDvgS\nfgz6hy/7ek9wDEDpRJetlHxKeibGmyC0eYwERzEXX8Cumu2oKKHGYtmV1Vvw9b1fQZetHafmzuIv\nDn0bz42+jIyYUbo0IipBo6FxAIDTUrwGZDmul9FfpKR8R34tdUXVFnRa23Fm/hyGA+68r0+bRyQV\nxX+c+zn+z4kfYCY6h+ubr8E3rvoj7KrdXpT9DzRk76W9NXm0KPsRES3l8mc/K3bbFWz+scrRX2xa\nJyIiovLC5h/acJ4aeQ5pKYO72m+BRqVRupz3EQQBH+m6Gx9ovAre8AT+4dQ/I55OAMhFfs2Wd+QX\nkB3r7zDUYCToKch41IHcRWCPvTPvaxdCn2Mn0lIGp+feXtP7X/C+Cn8igBubr0V1kbOut1Z1AUDJ\nnfibjEwjJaZKMi7rqlz016EVRH+NBr3QqjRoNNUXuqy8qjZUQ4DAyT90UdFUDD8593OMhSaULiVv\njkydAADsqy+NyK/lOIy1+P3e38Gntn0cWpUGvxx8An999DsFm8RHROXLm2v+Kebkn67cA5yBFTRI\nExVCoSK/ZIIgLB7WeHToaUiSlPc9aGOTJAlHpk7gL976Ft6YPIImcwP+aM+X8PGeD8Og0RetjrbK\nFtQZHTg5dxbRVLRo+xIRSZIEl38YVp0FtYb8/129Uu2VTgDZQ0BERERE5YTNP7ShTEdn8dbUMdSb\n6kr64ZwgCPj1Lfdjb91uDAc8+MHpHyOVSWUjv9LlHfkla7e2IpaOYyoyk9d1JUnCgG8IFp0ZdUZH\nXtculN7F6K+Tq35vIBHCM54XYdaacFvbjfku7bLqjA5YdZW44BssqZxrd+7iWz6JU0o6be2wV9jQ\nP3MayUzyoq9LZVIYj0yh2dwItUpdxArXT6vSoEpvxywn/9BFvOh9FW9OHsFPLzy0IR58ZcQMjs+c\nhEVrxlZ7l9LlXJIgCDjQsBd/duCPcU3DPoyHJ/HtY9/Hf55/iA9viGiRNzQOAQKazA1F29OsNaHJ\n3ICRgBspMV20fYlkhYr8WqrT1obt1Vvh8g/j3MJAwfahjWcutoDvnfwR/vXt/0Q8k8CHO+/En+z9\nKtqtzqLXIggCrm7Yi7SYxrE13McgIlqr6egsQskwuu2dEARBsTpAz4fgAAAgAElEQVSazA3QqDSL\n9x+JiIiIykV5dxcQvccTw89AlETc035ryTfPqAQVPr3tE9hZcyUGfIP40dmf4Mh0dqpAn2OXwtWt\nX0cu+ivf0wZmorMIJkPosSl7Ebga9SYHmswNODc/gFg6dvk3LPHEyEEkMknc3XErDBpDgSq8OEEQ\nsLWqG+FUBOPhqaLvfzGe3Njdtsri3wi9HJWgwv76PsQzCZy6xLSnsfAEREmEs7J4cRv55DDWIJgM\nrTnOjjauZCaJl8ffAJD9s3qpPwfl4tzCAMKpCPrqdpVNs55Za8Int30Mf9D3RdSbHHht4hD+/K1v\n4fDU8Q3RkEVEaydKIryhCTiMtUWPT+2xdSIlpuHmRDJSQCEjv5a6u+N2AMCjw0+X1AEKKk2iJOJZ\nz0v4X4e+jXMLA9hW1YNvXPWHuKX1BkU/d+6r74UAAW9NHlOsBiLafFy5CZFdNuUivwBAo9KgxdyE\n8fDkJQ/2EREREZWa0u6OIFqFsdAEjs2chNPSVLQc9PVSq9T4zPZPYqu9G6fnzuHQ1DFYdZbFxply\n1mFtAwAM5/nG/sBi7nN5RH7J5OivU7Mrfwg+Hp7EGxNHUG+qwzUN+wtY3aVtreoGAFzwlU70lzvo\nhU6tQ4OpTulSlrU/F/11+BLRX57gGACUZHTZSsjjlzn9h97rzcmjiKSi2OPYBQECHh8+WPYPvuTm\n3H11pTtV8GK6bO34032/h/s670A8k8CP3/4vPND/IKajs0qXRkQKmYstIJ6Jo8XSWPS95c/wjP6i\nYit05NdSLZZG7HHsgjc0jv7ZMwXdi8qbJEn4+cAj+NXQk6hQ6/CbV/w6vrTrt1FjqFa6NNgqrNhW\n3QN3cBRTkWmlyyGiTcLly9737VG4+QcA2q1OiJKI0VxcLhEREVE5YPMPbRiPDR8EANzbcUfZTIQB\nsvE5n9/5m+jMNctshMgvIDvtRq/WYzjozuu6A/JFYJk1/7wT/XVqRa+XJAkPux6HBAkf6bpL0RN/\nW+zZ5p/zC6XR/BNPxzEZmYbT0lSyf1bqTQ44Lc04tzCAYDK07Gs8oez0otYynvwDALNRNv/QOzJi\nBs+PvgKtSoOP9dyH/fV9mIhM4fh0+cYFxNMJnJo9ixpDNdoqy7NZT6PS4NbWG/GNq/4IV1ZvxQXf\nIP73ob/FEyPPIpVJKV0eERWZN/cAo8XSVPS9u23tECAsPtghKpZiRH4tdXdHdhrx48MHkREzRdmT\nys/jI8/glfE30WRuwP+86mvYX99XUvezrm7YBwCc/kNERSFJEgb9w7DozHAYa5UuZ/H6n9FfRERE\nVE5K86kp0SoNB9w4M38O3baOxSkl5aRCrcMXd/0W7u24Hbe13qR0OXmhElRotzoxE51DOBnJy5qS\nJMHlG4ZVZ4HDUNjTmvlWZ6xFs7kR5xYGEE1dPvrr7YULOO9zYVtVD66o2lKECi/OWmFBo6keg/6R\nknhIPBoahwSpJCO/ltpf3wdREnF0un/Z748Gx6BXV5TEDY21kJt/ZqLzCldCpaR/9jTm4ws40LAP\nFp0Zd7bfArWgxuMjz5Ttg69Tc2eRFFPYV7e7pB7GrEWNoQpf3Plb+Oz2T8GkNeHJkWfxvw//Xck0\ndxJRccjNP05L8RuQjVojms0NGAl4kCyBz5W0eRQr8kvmMNbi6oZ9mI7O4tAlpoHS5vX86Ct42v08\nagzV+NKuz8KsMyld0vvsqN4Go8aAw1PHyvazPBGVj5nYHALJEHpsnSVx7d1WmZ3M7w6w+YeIiIjK\nB5t/qOxJkoRHh54GANzTcXtJXByshUFjwG1tN8FaUal0KXkjx5eNBPMT/TUVnUEoFUa3vTQuAler\n17ETGSmDU3NnL/m6jJjBw67HIUDA/V13lcTPdWtVN1JiKu8xbmvhCcoTc0p7Asfeut1QCaplo7/i\n6Timo7NoKeHpRZfD2C96L0mS8OzoyxAg4KaWDwLINpt8oHE/ZmPzeGvqqMIVrk05R34tRxAE9Dp2\n4M8OfA03tlyL2dg8Huh/EP969j8vOqmMiDYWufmn2Vz82C8gG/2VljJw5+kagehyihn5tdSd7TdD\nq9LgSU7ao/d4Y+IIHh58HFZdJb66+3OwVliULmlZWrUWe+t2I5AM4dzCgNLlENEGN+jLxsJ2lUDk\nFwBU6W2w6Mxw5+5DEhEREZWD8nziSLTEBd8gXP5hXFm9FZ22NqXLoSU6clFm+WoYeSf3ubwiv2R9\njh0ALh/99frEYUxFZ3BN4z40mRuKUdplbbF3AQDO+5SfDiGP220v8ck/Fp0ZV1T1wBsax2Rk+l3f\nk6cXlXoD06VU6+1QCSrMRGeVLoVKxIBvCN7QOHbXbn/Xg7Xb2m7KPfh6ruwefIWSYZxfcMFpaUKd\nyaF0OXml1+jxa9334uv7vgKnpRlHpk/gz9/6Fl4dfwuiJCpdHhEViCRJ8IbHUWOohlFrUKQGOb53\ngNFfVCTFjvyS2SqsuK75GvgSfrw68VZR96bSdWLmNH56/hcwaY34Su/nUG2oUrqkSzrQsBcA8NYU\no7+IqLAG/Ln7vvbSaP4RBAFtlU74En74EwGlyyEiIiJaETb/UFl799Sf2xSuht6rtbIFAgQMB9x5\nWU9+QNBtL8/mH4exFi3mRpxfcCGaii77mlg6hidGnkGFWoe72kvn93SXrQNqQV0S0TDuoBeVOgts\nFValS7ms/fV9APC+6T/lMr3oUtQqNWr0VZjh5B/KeXb0JQDALa03vOvr8oMvfyKA1yYOFb+wdTg2\ncxKiJG6YqT/LcVqa8cd7v4yP93wYkiThvy48jL899n2MhSaULq1shJJhxNKXj/QkKgW+hB+RVBQt\nlibFauiytUOAwOYfKppiR34tdavzRujVFTjofgHxdLzo+1NpObcwgH89+1Po1Fp8addvo8FUp3RJ\nl+W0NKPBVIfTs2cRuch9DCKi9ZIkCS7fMCxaM+qMpXPwRj54yOk/REREVC7Y/ENl7dTcWXhCXvQ5\ndip6A5uWZ9Do0WiuhyfoXXc+vCiJcPmHYauwotZQnacKi6/PsQsZKYOTc28v+/2D7hcRTkVwa+uN\nJTX6W6+pQLvVCW9oXNEbfv5EAP5EINtYVgJxaJezo+ZK6NV6HJk68a5JGp7QGIDsjdRy5jDWIJKK\nXrSZjTaPsdAEzi0MoNvWsWxT27sffCUUqHBtjk71Q4CAPXW7lS6loFSCCtc3X4M/O/A17HHswkhw\nFH999Dt42PV4Wf33UkIgEcRfHPoWvvH6X+GF0VfW/XmHqNBGc5FfTrNy104GjQEtlia4g14kM0nF\n6qDNQanIL5lZZ8KHnNchnIrgBe+rRd+fSsdwwIN/OvVjQBDwOzv/e9kcBBEEAQca9iItZXB0ul/p\ncohog5qNzSOQDKLL3lFS9/va5OafwKjClRARERGtjEbpAojWSpREPDZ8EAIE3N1+q9Ll0EV0WNsw\nHp7EWHhiXTe3piIzCKci2FfXV1IXgavV69iJR4afwvGZk7g6Nz5bNh9bwIveV2GvsOGmlusUqvDi\nttp7MOgfwQXfIPoUODULvDMxp63EI79kOrUWvY4deHPyCAb9w+jJxaeNBr0waY2o1tsVrnB9ao01\nwDwwE5tDm7Y8/ptQYTw3+goA4Gbn9ct+36wz4aaWD+JJ93N4aex13N52UzHLW5O52DxGgh5stXfD\nWlGpdDlFYa2oxGe2fxIH5vfiZwO/wvPeV3Bs5iQ+3nMfdtVuV7q8kiNJEn524ZeIpKLQqjR4aPBx\nvDZxGL/WfQ+uqN6idHlEy/Lmmn+UPjjRY+/EaGgMwwEPtlZ1K1oLbWxKRX4tdVPLB/Hy2Bt4fvQV\nXNd0Dcw6k2K1XMpUZAa/cD2Kycg01IIKgqB6148qCFAJaqgEASpBtcw/8vff/drl1hIEAWpBnXvd\nO2s4jLXYXbu9rK/5lzMensT3T/4z0lIGn9v+qcXrwnKxr64Pjww9hbcmj+L65muULoeINiBXLvKr\n21YakV+y1spmCBDgDrL5h4iIiMoDm3+obB2d7sdkZBpXN+xDnal0xoHSu3VYW/Hq+JsYDnjW1fwj\nxwL0lGnkl6zWWA2npQnnF1yIpKIwaY2L33tk6CmkpQzu7bwdOrVWwSqXt7WqC4+PHMSFBZdizT/u\nxeaf8jglCWSjv96cPIJDU8fRY+9CMB7CfNyHK6q2lP1NbYche3p6JjpXNg1ZlH/zMR+OzfSj0VSP\nK6u3XvR1Nzmvw8tjb+C50ZdxXdPVMGoNRaxy9Y5MZU82763fuJFfF3NF9Rb8j/1/iIOeF/Cs5yX8\n0+l/w46aK/Cx7vtQbSjvpsV8Oj5zCifnzqLL1o7Pbf80nhh5Bq+Ov4XvnfwRdtRsw0e67lFkygTR\npZRK80+3rQPPjb6MAd8Qm3+ooJSM/JLpNXrc1nYTHnI9hmdGX8RHuu5WrJblpMU0nvG8iIPuF5CW\nMrBX2CBKEkQxhQREiJKY/Xcpk/sx+zUJUkHq2V/fh9/Y8lFoS/CaeC1monN4oP9BxNIxfHrbJ7Cz\n9kqlS1o1a4UFV1RtwZn5c5gIT6HRXK90SUQrJkoi4ukEYukYDBo9jEvuw1HpcPlGAJRe849eo0eD\nqQ6e0BgyYgZqlVrpkoiIiIguic0/VJYyYgZPDD8DtaDGHW03K10OXUKHtRUAMBxw48aWa9e8zoB/\nYzT/ANkbz6OhcZycPYtrGvcByI4APzZzEk5LM/aWaLyM09IMg0aP8wsuxWqQm39aK8snLqvL1g57\nhQ39M6fxiZ4PY9w3BaC8fg4XU2t8p/mHNq8Xx16FKIm42Xn9JRvaDBo9bmm9Ab8aehLPj76Mezpv\nL2KVqyNJEo5Mn4BWpcHuTTrxRqfW4p6O27Cvrhf/deFhnJ57GxcWXLir41bc2Hztpr/pGU5G8H8H\nfgWtSotPbv0YzDoTPrHlfnyg8Sr8wvUoTs+dw7n5AdzkvA63td4EvaZC6ZKJAGSbf+wVNsUnj3TZ\n2qESVIunvIkKQenIr6U+2HgAL4y+ilfG3sBNLR+ErcKqaD2yIb8bP73wEKYi07DqKvGJLR9e8bQ/\nURIh5ZqBMpIICbkfJQkZKZP7UVxsFlr8510NRe98PS2m8ZT7eRyeOo7p6Cw+v+PTJfPrtFb+RADf\n7X8QoWQYH+u+D1c17FG6pDU70LAXZ+bP4a3Jo/hId2k1sNHGJkkSUmIK0XQM0VQMsXQc0XQ092MM\nsVQs+7109nvyv8fSMUTTccTT8cVmRbPWhP954Gswa0tzAttmJUkSXP4hmLUmNJjqlC7nfdoqWzAR\nmcJkZBrNlkalyyEiIiK6JDb/UFl6Y/II5uILuL75AzyBXuKq9VWw6MwYDnjWvIYoiRj0DcNeYSv7\nmCQA6HPsxCND2eivaxr3QZIkPOx6DADw0e57oBJUCle4PLVKjR5bJ07OncVcbB41huqi7i9KIkaD\nXtQZHTBoSntiyFIqQYV99b14xvMiTs29jYgQAoB1TcIqFQ5DLQBgNsbmn80qmori9YnDsFVYsadu\n12Vff33zNXjB+ypeGHsNN7RcC4vOXIQqV88bHsd0dAa9jp0waPRKl6OoepMDv9f7BRyeOo6HBx/H\nLwefwKHJY/hvWz+CDmub0uUp5ueuRxBORfCRrrvf9UC52dKI3+v9Ao7PnMIvB5/AM54XcWjyGD7c\ndSf21fWW/cQ3Km+BRBDBZAi7apSfOqHX6OG0NMMd9CKeTrBBjgqiFCK/ZFq1Fne234L/OP9zPDny\nHH5j60cVrSeWjuGRoafx6vibECDguqarcW/n7au6zlIJKkAA1FAjXzN6tti78NMLD+Hw1HF888gD\n+MLO3yzb66ZwKoIH+n+I+bgPd7XfghtaPqB0Seuyo2YbTFojDk8fx32dd2z6RnBavYyYgT8RQCgV\nRiwVX9KwIzf1xBYbet799TgyUmZVe1WodTBoDLBXWGEw1cOo1SORTmLAP4SD7hfw0e57CvSzpLWY\njy/Anwhgd+2OkrxearM68cbkEbiDo2z+ISIiopLH5h8qO8lMCk+NPAedSovbWm9Suhy6DEEQ0GFt\nw8nZM/DF/bDrbateYyI8hUg6iu0120ryInC1agzVcFqaccE3iHAqggsLgxgJjmJ37Q502dqVLu+S\ntlZ14+TcWZxfcOHapuI2/0xHZxHPJLCrDG/+XlXfh2c8L+Lw1HHoK7K3xp2W8vt5vJddb4VGpeHk\nn03slfG3kMwkcVf7LdCoLv+xUqfW4Y62D+FnA7/CQc8L+LXue4tQ5eodmToBANhXt/kiv5YjCAKu\natiD7TXb8MjQk3h94jC+fez7+EDjVbiv8453RVhuBqfn3sbR6X60VTqXnWooCAL21O3CjppteNbz\nEp4dfQk/fvu/8MrYm/hYz71l+xCTyl+pRH7Jum0dcAdHMRxw44rqLUqXQxtQKUR+LXVVfR+eG30J\nb04ewc3O6+Aw1ipSx8nZM/jZhV8hkAyi3lSHT279aMk09GrVWnx62yfQZG7ArwafxN8e/wd8cuuv\nYX99n9KlrUo8Hcf3+/8ZU5Fp3Nhy7YaYWK1RabC3rhcvj72OtxcuYEfNFUqXRCVGkiRE0lHMxxYw\nF1vI/hjP/Ribx0LCD1ESV7SWWlDj/2fvvuOrrM//j7/us5KTc05O9t47zEDYICBLUZyIAxx1VWu1\njq/+7LLt92tbu2xtq611VUUcuBUVRUCQnTACCQnZe+95krN+fyTBBZLAObnPOfk8H48+tJ7c9/0O\nCefc4/pcl49Ki07tQ5A2cHBcl0qLVq3FRzX4P63KG61Ki4/6q//vo/JBq/I+ZXGa2Wbh0X1/Zmf1\nHhZFzSdIG+DoPwLhLBW2lQKuN/Jr2PCY+/LOKhZEzpE5jSAIgiAIwvcTxT+C29lZs4eOgU5WxJ6P\n0csgdxxhBBKMseQ05VLaUU6m9+hHWnnSyK9h00OmUNlVzcGGHLZW7kApKbk88SK5Y51RakAyAAVt\nxWN+wVveUQkMttt1N2G6UGIMkeS3FqJVeeHnZfSI9y+FpCBIG0hjbzN2u90jivOEkTNbzXxRtQut\nypv5EbNHvN28iFl8XrmDL6v3sjR64VkVhTqTzW7jYMMRfFRaJoqH0d+gU/uwNu0qZofN4PUT77C7\ndj85TbmsTr5k3BRK9Zr7eK3gHVSSknVpV31vtz6NUsPFCSuYEz6Dd4s/4nDTMf6c/SRzw2dwaeJK\nl+18JXguVyv+SfFPZEvlFxS1l4riH8HhXGnk1zClQsmqhAt4PvcVNpV+xi2T1o3p8dv7O9hY+D45\nTbmoJCUXxy9neez5qEdQwD2WJEliWcwiwnVh/DdvAy8df53a7nouTbzQZbvkfp3ZauY/R1+ioquK\nOWEzuDJplcdcJ80Jz2RH9W721WWL4p9xymw102pqo9n0zQKf5r4WWvraMFlNp9zOoNYTa4giUBuA\nUeOLVqVFq/b+WiHPYBHPcJGPWqF2+N8btULFJQkX8uLx1/iwdDM3T1zr0P0LZ6+4faj4x981i3/C\ndaF4KTWUdVbKHUUQBEEQBOGMXOsKXxDOoM9i4rOK7WhV3iyPWSR3HGGEEoyxAJR2VJAZOvrin6KT\nK0A8q/jnvZKPebf4I8w2M0uizyPYZ2w76ZyNEG0Q/l5+FLYWY7PbxvTma3lXFfDViht3Myssk8qi\nD+gx9zE1yDVvaJyNEG0Q9T0NdJt7xIPscWZ//UG6zN2siD1/VKOxVAoVK+OX80r+Rj4p/5y1aVc5\nMeXoFbaV0DHQxfyIWSPqZjQeJfrF8dOZ97Kt6ks+KtvCS8dfZ29tFnfNvR41OrnjOdW7xZvoGOhk\nVfwFROjDRrRNoDaA2ybfQGFbMW8WfsCeuiwONx3jorhlLIqaL8ZmCGOm0sWKfxKMcSgkBYVtJXJH\nETyQK438+rqM4ElEGyI52JjD8q7ziR6D8SE2u43dtft5r/gTTFYTicY41qatJkwX6vRjn4uJgak8\nlHk3Tx97kS2VX1DbU8/NE69z6RHQVpuVF/JepbC9hKnBk1ibttotCpZGKlofSaQ+nGPN+XQP9KDX\nePZ533hks9voHOg6Reee1pOjmU5Fo1ATqA0gSBtPkHfg0L8HEOgdQKA2AC+lZoy/k1PLDJ3K1qqd\nZDccYWn0QmJ8o+SOJDB4Da5T+RDuop9LCklBrCGaovZS+ix9Lv05JAiCIAiCIJ5oCG5lW9WX9Jh7\nuSThAnzG2YgJdxatj0QlKSntqBj1tja7jaL20qEbBv5OSCePQG0AsYZoKrqq0Kl8WBm3VO5IIyJJ\nEmkByeyty6K6q3ZMb5RUdFSiUqhG/LDV1cwIzeCd4k3Y7DZi3LB70ekMr6Ru7G0WxT/jiM1uY2vl\nTlSSksVR80e9/azQaWyp+IK9ddksi1nsMivyAbIaxMivkVAqlCyPXcz0kClsLHyf3JZ8Hvz0d9w+\n6QYmBaXLHc8p8lsL2VOXRZQ+ghWxi0e9fYp/Ej+deS9f1u7jo9LPeLt4E7tqD7Am+VLSA1McH1gQ\nvqWqqwZfjQGjl6/cUQDwVnmdPB82WUx4j6KQVBDOxNVGfg1TSAouTbiQp3KeZ1PpZn409RanHq++\np4FXC96mpKMcb6U316ZeyfyIWW5TkBKqC+GhzHt4IW8DeS0F/Dn7Ke6YchOhMo1M+z42u40NBW9x\ntDmPVP8kbp5wnccV+EqSxJywTN4u3kRWw+FTjj8VXF+/dYCm3mZahrr3DBf2DP/TYrN8ZxsJCX9v\nP5L9EgjSBhLoPVjcE6QdLO4xqPVu0eFKISm4IvFi/nHkGd4t+ZifZNzuFrk9WUtfK2397UwNnuTS\nn01xxhgK20uo6KwmbagruiAIgiAIgisSxT+C2+g297Ctcid6tY7FUeIGgztRK9VEG6Ko6Kqi3zow\nqhU/1d219Fn6mBo80YkJ5TEzbBoVXVVcFL/crYrZ0vyT2FuXRUFb0ZgV/wxYzdT01BNriHbbThwG\njZ70gBTyWgqI9aDVZSHaoeKfvmYS/eLkDSOMmaPNx2nsa2Ze+Myzeog8OPZiBc/nvsJHZZ+5TMt1\ns9XMkcZc/L38SPSLlzuOWwjUBnDnlB+Q05TLi/mv8+Lx1/h/M+4hxAUfyp0Lk6WfVwveRiEpuD59\nzVk/zFMqBgvmZoRksKnsM3bV7OPJnOeYHDSB1UmXuEUXQME9dQ1009bfzsTANLmjfEOKfyJlnRWU\ndJS7XDbBffWa+1xu5NfXpQekkOyXQG5LASXt5U45hzbbLHxWsZ3PyrdhsVvJCJ7MmpRL8fMyOvxY\nzuaj1nLX1Ft4r/hjtlbt5M/ZT3LrxHUuVThrt9t5u+hD9tcfJM43hh9Ovgm1Ui13LKeYGTadd0s+\nZn9dtij+cUNVXbX84/B/6LX0fec1ncqHCF0ogdpAgoY69gx37wnw9nPbezHflhqQxITAVI63nOB4\na6EY9SyzwuGRX36u3SF7uAt5eWelKP4RBEEQBMGlecZZuzAubKn4ApO1n6sSLsBb5SV3HGGUEoyx\nlHVWUNlZRbL/yMd3DY/8SvGgkV/DFkXNI9Y3injfWLmjjErq0EVuQWsRK2LPH5NjVnXVYLPbiHPz\njjlXJq0iPSyRVP8kuaM4TPDQA5Wm3maZkwhjxW63s6XiCwCWnsMIzozgSUTpIzjYkMMFsUtcoqvX\nsZZ8TFYT50XOcelVh65GkiQyQiZzh07Jk/tf5JljL/Ng5t0edb72QekntJrauCB2iUNGJuk1Oq5N\nvYIFEbN5s+h9jjUfJ7/lBEtiFnJB7BKP+rMTXEN1Vy0AMS4y8mtYin8in1Zso7CtRBT/CA5ztDnP\nJUd+DZMkiUsTV/L4wad4v+QT7p9+p0M7T5S0l/NqwVvU9zbi52Xk6pTL3X4xjUJScGXyKiL14bxa\n8BZP5TzPlUkXc370eS7RtePjsi18Ub2bcF0od029xaM/xw0aPZMC0znanEd1Vy1RYzC6TnAMs9XM\nS8dfo9fSx7zwWYTqgoeKfAIJ0vqPq1FGlydeRH5LIe8Vf0R6QLK49pNRcZv7Ff8IgiAIgiC4MnFm\nK7iF9v4OdlTvxt/LjwURs+WOI5yFeONggctoR38VtpUAgw8GPI1CUpBgjHOJm5WjYdDoidSHU9JR\nzoDVPCbHrBi6uHb34p8wXQhXT1rlUTeWvhr71SRzEmGslHSUU95ZyeSgCYTpQs56PwpJwSUJF2DH\nzqbSTx2Y8Oxl1w+N/AoTI7/OxsK42SyKmk9dTwMbCt7EbrfLHckhitvL2FG9h1CfEIeP6YwyRHDf\ntDu5ZeJaDBoDn1Vs5//2/ZkD9Yc85s9PcA1VXTUADilec6QEYyxKSXnynF8QHOGQi478+roEYyyT\ng9Ip6SjjeOsJh+yzz9LHayfe4a+H/kVDbxMLI+fxy9n/4/aFP183OzyT+6bfiUGj5+3iTbyS/ybm\nU4woGkvbq3bxcfnnBHkHcHfGbejcqKvv2ZoTngnAvvpsmZMIo/F+6SfU9TSwKGoe69KvYlnMIjJC\nJhNtiBhXhT8AkfpwZodlUttTz/76Q3LHGdeK2kvwUWldYjHQ9zF6GQjw9qeso1JcpwmCIAiC4NI8\n5+mj4NE2l2/DbLNwUfwyj22d7OnijYMrJEZT/GO1WSluLyNIG4i/t5+zoglnIS0gGYvNQklH2Zgc\nr7yzCoDYoZU2guswanzRKNQ09onOP+PF55VfALA8ZvE572tiYBoJxlhymvNkX0HXa+4lr6WACF0Y\nkfpwWbO4s9VJq0g0xnGo8Shbq3bKHeecDVjNbMh/EwmJ69PXOOU8VJIkMkMz+NWcB1kZt4xeSy8v\nHX+dvx76F5Wd1Q4/njA+VXa7ZvGPRqkhzjeaqq4a+k4xgkQQRmt45FeUi478+rpLEi5EQuKDks3Y\n7LZz2teRplwe3fc4u2r2EaYL5YHMH3FN6uVoVd4OSus64o2xPDzzJ8QYothXn83fD/2Hjv4uWbLs\nq8vmraIPMGoM3DPtdrccq3Y2JgWmo1fryKo/jEXm4ithZBjiarkAACAASURBVPJbC9letYtQnxAu\nT7xI7jguYVXCCtQKFZtKPx2zhW3CN7X0tdFiaiPJL8EtFsnF+UbTbe6hxdQmdxRBEARBEITTcv2z\nKmHca+5rYXftfkK0QcwOy5Q7jnCW/LyMBHr7U9ZZMeIVEtXdtZisJo8c+eXu0vwHR3+daC0ek+OV\nd1ahU/sQpA0Yk+MJIydJEsE+QTT1tYjVT+NAXU8Dx5rzSTDGkugXd877kySJSxIuBODDEnm7/xxu\nOobFbmVmqOj6cy6UCiW3TroBo8bAe8UfU9BaJHekc/Jx2RYa+5pZHD2fBKNzx3RqlBpWJazgkdkP\nkhE8mdKOCv6U/U825L9F10C3U48teL6qrhp0ah/8vVyvoD7FPxE7dorbx6aoXPBswyO/prtw159h\nkfpwMkOnUt1dy+HGY2e1j/b+Dp459jLPHnuZHnMPq+JX8LOZ95JgjHNsWBfj52Xk/uk/YkZoBmWd\nFfwp+x9UDC0YGSs5TblsKHgLH5WWuzNuJ0gbOKbHl5NSoWRm2DS6zT3ktRTIHUc4gx5zL+uPb0Qh\nKfjBxGvRKDVyR3IJ/t5+nB99Hu39HXxRtUvuOONScfvQyC9/1x75Nezk6K9RdrUXBEEQBEEYS6L4\nR3B5H5VtwWa3cXHCCpQKpdxxhHMQb4ylx9w74g4hnjzyy90l+cWjkpQUtBY6/VhdA920mFqJ9Y12\nuxFp40WINogB6wAdA51yRxGc7PPKHQAsc0DXn2Ep/omk+SdT0FYk69iXrKGRX5mhGbJl8BRGLwO3\nTb4RhaTghbwNtPS558rIis4qPq/cQZB3wMkitbEQqA3g9sk38JOMHxKmC2FP3QH+d9+f2Fb1JVab\ndcxyCJ6j19xHc18L0fpIlzyXSh4q9BejvwRHcIeRX1+3Kv4CFJKCTWWfjuo93ma38WXNXh7d9zg5\nTbkkGuP52az7WRm/DJVC5cTErkOjVPODCddxWeJKOvo7+duhf5PdcGRMjl3QWsQLuRtQKVTcNfVW\nlx9X4wxzwmYAsK/uoMxJhO9jt9t5/cQ7dAx0cnH8CmIMUXJHcikrYhejU/vwacV2ugd65I4z7hQN\nF//4uUfxz3BX+/IxLjYVBEEQBEEYDVH8I7i02u56suoPE6kPd4uVe8L3G159ONLRX0VutgJkPNEo\nNSQY46jqrnX6DZLhFZxxYuSXywoeGqnQ2CtGf3my9v4OsuoPE+oTzOSgdIfu+5LECwD4sHSzLB2k\n2kztFLeXkWiMJ1DrP+bH90QJxljWpFxKj7mX53JfdrtW+habhVfy38SOnXXpV+Elwwrp1IAkfjbz\nPtakXAZIvF30Ib8/8Dfyx6DwVvAs1S468mtYvDEWlaQ8ee4vCGfLnUZ+DQv2CWRexCwae5vZV589\nom3qexp44tDTvH7iXSQJ1qau5r7pdxCmC3FyWtcjSRIrYs/njik3oZSU/DfvVd4v+eScx6h9n7KO\nSv5z7CUA7ph808mHweNNlCGCaH0EuS35okOhC8tqOMyhxqMkGONYEbtY7jguR6vSsjJuGSaric3l\nW+WOM+4UtZWgVWndZux2lD4ShaSQfWS5IAiCIAjC9xHFP4JL+6jsM+zYuSThAreY/St8v+FxGWUd\n5Wf8WqvNSkl7GSE+Qfh5GZ2cTDgbaQFDo7/anDvSZfiiOs432qnHEc5eiHbw4UqTKP7xaF9U7cZq\nt7I0ZqHDP5PjfGOYEjSR0o4KWUYHZDccwY6dmWFi5JcjLYiYw5zwGVR21fDGiXfdajTgp+XbqO2p\nZ0HEbFL8k2TLoVQoWRw1n9/M+X8siJxDQ28TTx55jv8cfYnmvhbZcgnupbLLtYt/NEo18cZYqrtq\n6TX3yh1HcGPuNPLr61bGLUWtUPNx2eeYv6dY1myz8FHpZ/z+wBOUdJQzLXgyj8x+kPmRs8f9/ZLJ\nQRN4aMbdBGsD+axiO88ce4k+i8nhx6ntruffOS9gtpq5eeLak9fE49Xs8BnY7Day6g/JHUU4hZa+\nNt448R5eSg03Tbh23L9PnM55kXMI8g5gZ81emnrF+fVYaTO102xqJckvzm1+NzVKNVH6CKq6ajDb\nLHLHEQRBEARBOCX3OLMSxqWKziqONOUS7xvLpEDHdhgQ5BGhC0Oj1Iyo809lVw0maz8pfmLkl6sa\nvtFZ0Frs1OMMt9ONFcU/LivEJxhgxCP9BPfTZ+njy5p9+GoMzAqd7pRjrEpYgYTEh6WfOnW19qlk\nNRxGKSnd7mGhq5MkiWtTriDGEMW++my+rNknd6QRqemuY3PFNvy8jFyedLHccQDQa3Rcl3olD8+8\nl0RjPEeb83h031/4oGQzJku/3PEEF1c1VPzjyqM+kv0SsGOnqL1M7iiCG3O3kV/D/LyMLI6aT3t/\nBztr9p7ya4rby/jDgSf4uPxzDBo9d0y+idsm34DRy3eM07quMF0oD824hzT/ZI415/OXg085tDNp\nc18LTx55lh5LL+vS15ARMtlh+3ZXM0OnoZSU7K3Ldqsi7/HAZrexPv8NTFYTa5IvI0gbIHckl6VS\nqLg08UKsdisflm6WO8648dXIL/e67xvnG4PFbqWmu1buKIIgCIIgCKckin8El/Vh6acAXJp4IZIk\nyZxGcASlQkmcbwx1PQ30mvu+92uL2ksASPZ3r4vA8STaEImPSktBW5HTbvTZ7XYqOqsI0gaiV+uc\ncgzh3A2PVRCdfzzXrpr9mKwmzo9agFqpdsoxIvXhZIZOpbq7liNNuU45xqnUdtdT013HhMBUdGqf\nMTvueKFWqrl98g3o1TreKvqA0hF0/5OT1WbllfyN2Ow2rku9Eq3KW+5I3xBtiOD+6Xdyy8S16DV6\nPq3YxqP7/yJLxyzBfVR11aJVebv0g7+UoXP+4WsAQRgtdxz59XXLYhfhrfTm04pt3+hY02fp47UT\n7/C3Q/+mobeJhZHz+OXs/2FK8EQZ07oundqHu6bewvnRC6jvaeDP2f+koPXcO9V29Hfyz8PP0jHQ\nxerkS5gbPsMBad2fXqNjclA6tT31VA2NmBRcw9bKnRS1l5IRPIk54vf1jKaFTCHWEM3BxpyTo+cF\n5ypqGy7+SZA5yegMdyUv6xCjvwRBEARBcE2i+EdwSXmNheS3FpLmn3zyRrDgGU6O/jrDfOTCtqHi\nHzdbATKeKCQFKf5JtJraaHLS6JGmvmZ6LX1i5JeL06t1eCu9aRCdfzySxWZhe9UuvJQaFkTOceqx\nLo5fgUJSsKn0M6w2q1OPNSyr4TAAM0MzxuR441GAtz+3TFyHzW7juWPr6ejvlDvSaW2r+pLKrhpm\nhU1nUpBrdp6UJInM0Ax+NechLoxbSudAF//KeYGXj79BjxiZJHyLydJPY28TUfoIl15QEecbg1qh\nOnkNIAij5a4jv4bp1TqWxSyix9zLtqovATjSlMuj+x5nV80+wnWhPJB5F9ekXu5yhamuRqlQclXy\npaxLW0O/dYCncp5ne9Wus16w0mPu5ckjz9FsamVl3FKWRJ/n4MTubbiwZF/dQZmTCMOqumr5sPRT\nfDUGrktd7dKf/65CISm4POkiAN4t/kh0shoDRe0leCu9iTJEyB1lVOKNMQCUn+G+tiAIgiAIglxE\n8Y/gcux2O68ffR+ASxIvkDmN4Ggni3++Z+W/1WalpKOcUJ8QjF6GMUomnI2vRn+d+2rKUxke+RXn\nG+OU/QuOIUkSIT6BNPe1jPm4JsH5shqO0DHQyfyI2fiotU49VohPEHPDZ9DQ28iBoaIcZ7LZbWQ3\nHMFLqWFy0ASnH288Sw1I4vKki+gY6OK53Few2CxyR/qOhp5GNpV9hkGj56rkS+WOc0ZeSg2XJFzA\nT2feS4whkv31B/nt/sfJGcPOWYLrq+6uxY6daEOk3FG+l1qpJt43lpruOrrNPXLHEdyQu478+rrz\noxdgUOvZVrmT/xx9iWePvUyPuYdV8YPv9cPX0sLIzIuYyb3T7kCn8uGtog94teAtzKM8/zBZ+vlX\nzgvU9tSzKGo+F8evcFJa9zUhIBWDWk92/eFR//kKjme2mnnp+GtY7VauT78avUZ0UB6pFP9EJgWm\nU9ReKrpqOll7fwdNfS0k+cWhkNzr8VSwNgidyufk/UpBEARBEARX415nV8K4kNdSwImWUqYGTRQP\n/D1Q/NDPtLSj4rRfU9FVzYB1QHR9cgNp/oPFPyfanF38Izr/uLoQn2AsNgttpg65owgOZLPb+Lxy\nBwpJMWarnFfGLUMlKfm4bIvTHyCUdlTQamojI3gyGqXGqccSYGn0QjJDplLaUc47xZvkjvMNNruN\nVwrewmKzcE3KFW41Ai5SH86DmXdzWcJKes29PHPsZV7I3UDXQLfc0QQXUNU1OIbF1Yt/4KvRX8Xt\nZTInEdyNu4/8Guat8uKCuCWYrP0cbc4jyS+en8+6n5XxS1EpVHLHc0uJfnE8PPMnRBsi2VOXxT8O\nP0PnQNeItjXbLDx77GXKOyuZFTadq5IvER1UTkGpUDIrbDo9ll5ym/PljjPuvV/6CXU9DSyMnMfE\nwFS547idyxJXIiHxbsnHY9aJdjw6OfLLDe/7SpJErDGa5r4Wcb0lCAwu4q7raRAd0wRBEFyIKP4R\nXM7BxhwkJFYliK4/nshH7UOYLpTyzsrTXkgPt/sXxT+uL0gbQKC3PyfaSpzS8aWiswqFpCBK715t\ngMejYO3gg5YmMfrLo+S1FFDf08CM0Az8vf3G5Jj+3n6cFzWXVlMbe2oPOPVYX438mubU4wiDJEli\nXfoaInRh7Kjew34XGg+xs3ovpR3lTAuezLSQyXLHGTWlQsmKuPP52az7iPeN4WBjDr/d/zgHG3LE\nTbhxbrj4J8YNin+GHwCJ0V/CaLn7yK+vWxA5h/OjFrAubQ33TruDUF2I3JHcnr+3Hw9M/9HJAuQ/\nZf3z5Hvj6VhtVl7Me5WCtiImB03g+rQ1btedYix9NforW+Yk41tBaxHbq3YR6hPCFUMjrITRi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7qqnqriFaH0lZZyWR+nB0ah+5owlnITUgCUqgoK2ImWFn31HDbrdT0VmFv5cfRi/DmTcQXMbw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\n5yafqbOTVqqotVjrazerqLVEB7u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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131ab49a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epoch = len(history.history['loss']) # get epoch length from any of the columns\n",
+    "for k in list(history.history.keys()):\n",
+    "    if 'val' not in k:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(history.history[k]) # this is for train\n",
+    "        plt.plot(history.history['val_' + k]) # this is for test\n",
+    "        plt.title(k, fontsize=30)\n",
+    "        plt.ylabel(k)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left', fontsize=30)\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0035122722055874436"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(history.history['val_mean_absolute_error'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The variance should have improved slightly. However, unless the mean absolute error is not small enough. The model is still not an usable model in practice. This is mainly due to only using the sample data for training and limiting epoch to a few hundreds.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visually compare the delta between the prediction and actual (scaled values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ViYiIiIiIiIi0TKFQJ/j22685diylu8sQERERERERkT5kU9Y2Ht/6VyrqKjt0v46kb0Fl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\niIiIiIiILEAKhUREREREREREFqD/CyM3fMbHfs4CAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21319777f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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mFxUASCJQAXmlrjnNI1TdgaqVQAUgzxGogDxS272GKl0jVEV+j1z0ogIAAhWQ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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131aa08470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE :  1.47740581385e-05\n",
+      "MAE :  0.00256412901334\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    149.000000\n",
+       "mean       0.000289\n",
+       "std        0.003846\n",
+       "min       -0.009856\n",
+       "25%       -0.001409\n",
+       "50%        0.000065\n",
+       "75%        0.001438\n",
+       "max        0.015438\n",
+       "Name: diff, dtype: float64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+    "\n",
+    "# Benchmark\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "\n",
+    "pred = model.predict(testX) # predict on testset\n",
+    "\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(np.reshape(pred, (pred.shape[0])))\n",
+    "predictions['actual'] = testY\n",
+    "predictions = predictions.astype(float)\n",
+    "\n",
+    "predictions.plot(figsize=(20,10))\n",
+    "plt.title(\"Predicted close vs actual\")\n",
+    "plt.show()\n",
+    "\n",
+    "predictions['diff'] = predictions['actual'] - predictions['predicted']\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.distplot(predictions['diff']);\n",
+    "plt.title('Distribution of differences: actual minus predicted')\n",
+    "plt.show()\n",
+    "# if predicted minus actual is positive, this is \n",
+    "\n",
+    "print(\"MSE : \", mean_squared_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "print(\"MAE : \", mean_absolute_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "predictions['diff'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare the unscaled values and see if the prediction falls within the Low and High\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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teZ0TMBp0dM+Kw2r3UlXvorbBQ7AdAi9+n4Zn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px8so+HH3bTr98/41zln0wEcwRBEARBEARBEJohgjmCIAjCP011g5PtJQ14fAf/gjgjwUinjGO/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\nkNz5s9jUsz9xnz+tWgm3hynX3E3S7sBTsZGNJ5yGd+89LVhlw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ECYQi2laRuA/u03ds1cj771TR8s8lSabLLc/pZyl8wUWKnHCibE6nmhvIVF1taM4c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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x213137f9828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Cy+TJk7Vq1ao2X3/06NH64x//GHv9Rx55RK+//rouvPBCvfrqqwoEApJa7vp86KGH1NTU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\nRERESfD/AXZaow4oj9opAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131a8cbac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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161ZMnjy5wXMtXLgQ/v7+SExMhF6vx2OPPYbPPvsM8+bNw+nTpzFt2jTcfPPNDbZJS0vD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\ns/8HkrAgsxv62WMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2131a8d43c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE :  1.16343369599e-07\n",
+      "MAE :  0.000227541731508\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    149.000000\n",
+       "mean      -0.000026\n",
+       "std        0.000341\n",
+       "min       -0.001370\n",
+       "25%       -0.000128\n",
+       "50%       -0.000006\n",
+       "75%        0.000125\n",
+       "max        0.000875\n",
+       "Name: diff, dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred = model.predict(testX)\n",
+    "pred = y_scaler.inverse_transform(pred)\n",
+    "close = y_scaler.inverse_transform(np.reshape(testY, (testY.shape[0], 1)))\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(np.reshape(pred, (pred.shape[0])))\n",
+    "predictions['close_bid'] = pd.Series(np.reshape(close, (close.shape[0])))\n",
+    "\n",
+    "p = df[-pred.shape[0]:].copy()\n",
+    "predictions.index = p.index\n",
+    "predictions = predictions.astype(float)\n",
+    "predictions = predictions.merge(p[['low_bid', 'high_bid']], right_index=True, left_index=True)\n",
+    "\n",
+    "ax = predictions.plot(x=predictions.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "ax = predictions.plot(x=predictions.index, y='predicted', c='blue', figsize=(40,10), ax=ax)\n",
+    "index = [str(item) for item in predictions.index]\n",
+    "plt.fill_between(x=index, y1='low_bid', y2='high_bid', data=p, alpha=0.4)\n",
+    "plt.title('Prediction vs Actual (low and high as blue region)')\n",
+    "plt.show()\n",
+    "\n",
+    "predictions['diff'] = predictions['predicted'] - predictions['close_bid']\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.distplot(predictions['diff']);\n",
+    "plt.title('Distribution of differences between actual and prediction ')\n",
+    "plt.show()\n",
+    "\n",
+    "g = sns.jointplot(\"diff\", \"predicted\", data=predictions, kind=\"kde\", space=0)\n",
+    "plt.title('Distributtion of error and price')\n",
+    "plt.show()\n",
+    "\n",
+    "# predictions['correct'] = (predictions['predicted'] <= predictions['high']) & (predictions['predicted'] >= predictions['low'])\n",
+    "# sns.factorplot(data=predictions, x='correct', kind='count')\n",
+    "\n",
+    "print(\"MSE : \", mean_squared_error(predictions['predicted'].values, predictions['close_bid'].values))\n",
+    "print(\"MAE : \", mean_absolute_error(predictions['predicted'].values, predictions['close_bid'].values))\n",
+    "predictions['diff'].describe()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "so it looks i improved on the previous results, by using lookback of 1 tick, 100 iterations, and my additional features. However, can i predict fast enough to make trading decisions?\n",
+    "\n",
+    "Sim results:\n",
+    "\n",
+    "- it got a lot worse when i changed lookback to 20 ticks"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "celltoolbar": "Hide code",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/capstone_project/log_results.xlsx b/capstone_project/log_results.xlsx
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diff --git a/capstone_project/main_fx_spot_prediction_notebook.ipynb b/capstone_project/main_fx_spot_prediction_notebook.ipynb
new file mode 100644
index 0000000..ad1113a
--- /dev/null
+++ b/capstone_project/main_fx_spot_prediction_notebook.ipynb
@@ -0,0 +1,3605 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "# Machine learning capstone project - fx spot prediction\n",
+    "\n",
+    "The goal is to create features that can help predict the bid price, using a lookback period of a few minutes.\n",
+    "\n",
+    "Try to include the bid offer spread - from the benchmark model it seems volume is not an important feature so it is not a problem that i dont have this data point.\n",
+    "\n",
+    "I took inspiration from : https://www.kaggle.com/kimy07/eurusd-15-minute-interval-price-prediction/notebook\n",
+    "\n",
+    "Introduction\n",
+    "This notebook trains a LSTM model that predicts the bid price of EURUSD 15 minutes in the future by looking at last five hours of data. While there is no requirement for the input to be contiguous, it's been empirically observed that having the contiguous input does improve the accuracy of the model. I suspect that having day of the week and hour of the day as the features mitigates some of the seasonality and contiguousness problems.\n",
+    "\n",
+    "Disclaimer: This exercise has been carried out using a small sample data which only contains 14880 samples (2015-12-29 00:00:00 to 2016-05-31 23:45:00) and lacks ASK prices. Which restricts the ability for the model to approach a better accuracy.\n",
+    "\n",
+    "I will use 1 year of data, from 1Jan16 to 1Jan17, also in 15 minute intervals, but with tick data features.\n",
+    "\n",
+    "Improvements\n",
+    "\n",
+    "To tune the model further, I would recommend having at least 5 years worth of data, have ASK price (so that you can compute the spread), and increasing the epoch to 3000.\n",
+    "Adding more cross-axial features. Such as spread.\n",
+    "If you are looking into classification approach (PASS, BUY, SELL), consider adding some technical indicators that is more sensitive to more recent data.\n",
+    "Consider adding non-numerical data, e.g. news, Tweets. The catch is that you have to get the data under one minute for trading, otherwise the news will be reflected before you even make a trade. If anybody knows how to get the news streamed really fast, please let me know.\n",
+    "\n",
+    "Credits : Dave Y. Kim, Mahmoud Elsaftawy,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "To run on EC2:\n",
+    "- Enter the repo directory: cd aind2-cnn\n",
+    "- Activate the new environment: source activate aind2\n",
+    "- Start Jupyter: jupyter notebook --ip=0.0.0.0 --no-browser\n",
+    "- Find this line in output and copy url to browser: \n",
+    "- Copy/paste this URL into your browser when you connect for the first time to login with a token: http://0.0.0.0:8888/?token=3156e...\n",
+    "- change the 0.0.0.0 with EC2 IP.\n",
+    "- you should see the checked out repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:70% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd, numpy as np\n",
+    "import pypyodbc\n",
+    "import io, datetime, os\n",
+    "import matplotlib.colors as colors, matplotlib.cm as cm, pylab, matplotlib.pyplot as plt\n",
+    "from collections import OrderedDict\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "from subprocess import check_output\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "  \n",
+       "    $('.ctb_hideshow').css({'display': 'block'});\n",
+       "    \n",
+       "    </script>\n",
+       " "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pypyodbc\n",
+    "display(HTML(\"\"\"<script>\n",
+    "  \n",
+    "    $('.ctb_hideshow').css({'display': 'block'});\n",
+    "    \n",
+    "    </script>\n",
+    " \"\"\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "initval = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "#kaggle dates: 2015-12-29 00:00:00 to 2016-05-31 23:45:00\n",
+    "min_date = \"29Dec15\"\n",
+    "max_date = \"31May16\"\n",
+    "\n",
+    "if initval:\n",
+    "    rerunSQL = False\n",
+    "    log = False\n",
+    "    useKaggle = False\n",
+    "    runLSTMBinary = False\n",
+    "    simname = \"500_epochs\"\n",
+    "    sim_desc = \"\"\"\n",
+    "    kaggle params but with 500 epochs to account for more features\n",
+    "    \"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if log:\n",
+    "    #log = {\"simname\": [\"mine_initial\", simname], \"sim_desc\": [\"kaggle params\", sim_desc]}\n",
+    "    #df_log = pd.DataFrame(log)\n",
+    "    if os.path.isfile(\"sim_log.xlsx\"):\n",
+    "        df_log = pd.read_excel(\"sim_log.xlsx\")\n",
+    "        df_log.loc[len(df_log)]= [simname, sim_desc] \n",
+    "        df_log.to_excel(\"sim_log.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>simname</th>\n",
+       "      <td>500_epochs</td>\n",
+       "      <td>500_epoch_lookback_40</td>\n",
+       "      <td>linear regression</td>\n",
+       "      <td>linear regression</td>\n",
+       "      <td>500_epochs_40_lookback_pca_unshuffled</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sim_desc</th>\n",
+       "      <td>\\nkaggle params but with 500 epochs to account...</td>\n",
+       "      <td>500 iterations, lookback 40</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "      <td>added directional errors checking and pca as f...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSE</th>\n",
+       "      <td>1.59188e-07</td>\n",
+       "      <td>1.97246e-07</td>\n",
+       "      <td>6.79626e-07</td>\n",
+       "      <td>6.55241e-08</td>\n",
+       "      <td>4.82937e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MAE</th>\n",
+       "      <td>0.000285781</td>\n",
+       "      <td>0.000340846</td>\n",
+       "      <td>0.000536307</td>\n",
+       "      <td>0.000202905</td>\n",
+       "      <td>0.000594482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>102</td>\n",
+       "      <td>102</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-9.29131e-07</td>\n",
+       "      <td>-3.83515e-05</td>\n",
+       "      <td>-5.08408e-05</td>\n",
+       "      <td>-2.90581e-05</td>\n",
+       "      <td>0.000506372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.000400953</td>\n",
+       "      <td>0.00044465</td>\n",
+       "      <td>0.000826849</td>\n",
+       "      <td>0.000255566</td>\n",
+       "      <td>0.000478296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-0.00135148</td>\n",
+       "      <td>-0.00127888</td>\n",
+       "      <td>-0.00317997</td>\n",
+       "      <td>-0.000772953</td>\n",
+       "      <td>-0.00072515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-0.000158489</td>\n",
+       "      <td>-0.000304043</td>\n",
+       "      <td>-0.000402606</td>\n",
+       "      <td>-0.000200629</td>\n",
+       "      <td>0.000192821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>6.07371e-05</td>\n",
+       "      <td>-5.84126e-06</td>\n",
+       "      <td>-3.07747e-05</td>\n",
+       "      <td>-2.43187e-05</td>\n",
+       "      <td>0.000540495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.000239968</td>\n",
+       "      <td>0.00017789</td>\n",
+       "      <td>0.00031692</td>\n",
+       "      <td>0.000116229</td>\n",
+       "      <td>0.000830978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.000755668</td>\n",
+       "      <td>0.00128782</td>\n",
+       "      <td>0.00327372</td>\n",
+       "      <td>0.0005337</td>\n",
+       "      <td>0.00159335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mse train all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3917e-07</td>\n",
+       "      <td>4.45245e-07</td>\n",
+       "      <td>5.16241e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mse test all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.79626e-07</td>\n",
+       "      <td>6.55241e-08</td>\n",
+       "      <td>4.82937e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mae train all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000423565</td>\n",
+       "      <td>0.000426773</td>\n",
+       "      <td>0.000505385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mae test all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000536307</td>\n",
+       "      <td>0.000202905</td>\n",
+       "      <td>0.000594482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean avg bo spread:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>how often sign of price change is same:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.446602</td>\n",
+       "      <td>0.533981</td>\n",
+       "      <td>0.882353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than 0.1 percent in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.655556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than predicted in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.978261</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.366667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than predicted by more than 0.001 USD per EUR in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if not same sign, how often is actual worse than -0.1 percent return from predicted in both directions</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if not same sign, how often is actual worse than -0.1 percent return in both directions</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                    0  \\\n",
+       "simname                                                                                    500_epochs   \n",
+       "sim_desc                                            \\nkaggle params but with 500 epochs to account...   \n",
+       "MSE                                                                                       1.59188e-07   \n",
+       "MAE                                                                                       0.000285781   \n",
+       "count                                                                                             102   \n",
+       "mean                                                                                     -9.29131e-07   \n",
+       "std                                                                                       0.000400953   \n",
+       "min                                                                                       -0.00135148   \n",
+       "25%                                                                                      -0.000158489   \n",
+       "50%                                                                                       6.07371e-05   \n",
+       "75%                                                                                       0.000239968   \n",
+       "max                                                                                       0.000755668   \n",
+       "mse train all feature:                                                                              0   \n",
+       "mse test all feature:                                                                               0   \n",
+       "mae train all feature:                                                                              0   \n",
+       "mae test all feature:                                                                               0   \n",
+       "mean avg bo spread:                                                                                 0   \n",
+       "how often sign of price change is same:                                                             0   \n",
+       "if same sign, how often is actual better than 0...                                                  0   \n",
+       "if same sign, how often is actual better than p...                                                  0   \n",
+       "if same sign, how often is actual better than p...                                                  0   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "\n",
+       "                                                                              1  \\\n",
+       "simname                                                   500_epoch_lookback_40   \n",
+       "sim_desc                                            500 iterations, lookback 40   \n",
+       "MSE                                                                 1.97246e-07   \n",
+       "MAE                                                                 0.000340846   \n",
+       "count                                                                       102   \n",
+       "mean                                                               -3.83515e-05   \n",
+       "std                                                                  0.00044465   \n",
+       "min                                                                 -0.00127888   \n",
+       "25%                                                                -0.000304043   \n",
+       "50%                                                                -5.84126e-06   \n",
+       "75%                                                                  0.00017789   \n",
+       "max                                                                  0.00128782   \n",
+       "mse train all feature:                                                        0   \n",
+       "mse test all feature:                                                         0   \n",
+       "mae train all feature:                                                        0   \n",
+       "mae test all feature:                                                         0   \n",
+       "mean avg bo spread:                                                           0   \n",
+       "how often sign of price change is same:                                       0   \n",
+       "if same sign, how often is actual better than 0...                            0   \n",
+       "if same sign, how often is actual better than p...                            0   \n",
+       "if same sign, how often is actual better than p...                            0   \n",
+       "if not same sign, how often is actual worse tha...                            0   \n",
+       "if not same sign, how often is actual worse tha...                            0   \n",
+       "\n",
+       "                                                                    2  \\\n",
+       "simname                                             linear regression   \n",
+       "sim_desc                                               1 row lookback   \n",
+       "MSE                                                       6.79626e-07   \n",
+       "MAE                                                       0.000536307   \n",
+       "count                                                             103   \n",
+       "mean                                                     -5.08408e-05   \n",
+       "std                                                       0.000826849   \n",
+       "min                                                       -0.00317997   \n",
+       "25%                                                      -0.000402606   \n",
+       "50%                                                      -3.07747e-05   \n",
+       "75%                                                        0.00031692   \n",
+       "max                                                        0.00327372   \n",
+       "mse train all feature:                                     4.3917e-07   \n",
+       "mse test all feature:                                     6.79626e-07   \n",
+       "mae train all feature:                                    0.000423565   \n",
+       "mae test all feature:                                     0.000536307   \n",
+       "mean avg bo spread:                                       3.98687e-05   \n",
+       "how often sign of price change is same:                      0.446602   \n",
+       "if same sign, how often is actual better than 0...           0.152174   \n",
+       "if same sign, how often is actual better than p...           0.978261   \n",
+       "if same sign, how often is actual better than p...           0.152174   \n",
+       "if not same sign, how often is actual worse tha...           0.105263   \n",
+       "if not same sign, how often is actual worse tha...           0.105263   \n",
+       "\n",
+       "                                                                    3  \\\n",
+       "simname                                             linear regression   \n",
+       "sim_desc                                               1 row lookback   \n",
+       "MSE                                                       6.55241e-08   \n",
+       "MAE                                                       0.000202905   \n",
+       "count                                                             103   \n",
+       "mean                                                     -2.90581e-05   \n",
+       "std                                                       0.000255566   \n",
+       "min                                                      -0.000772953   \n",
+       "25%                                                      -0.000200629   \n",
+       "50%                                                      -2.43187e-05   \n",
+       "75%                                                       0.000116229   \n",
+       "max                                                         0.0005337   \n",
+       "mse train all feature:                                    4.45245e-07   \n",
+       "mse test all feature:                                     6.55241e-08   \n",
+       "mae train all feature:                                    0.000426773   \n",
+       "mae test all feature:                                     0.000202905   \n",
+       "mean avg bo spread:                                       3.98687e-05   \n",
+       "how often sign of price change is same:                      0.533981   \n",
+       "if same sign, how often is actual better than 0...                  0   \n",
+       "if same sign, how often is actual better than p...                  1   \n",
+       "if same sign, how often is actual better than p...                  0   \n",
+       "if not same sign, how often is actual worse tha...                  0   \n",
+       "if not same sign, how often is actual worse tha...                  0   \n",
+       "\n",
+       "                                                                                                    4  \n",
+       "simname                                                         500_epochs_40_lookback_pca_unshuffled  \n",
+       "sim_desc                                            added directional errors checking and pca as f...  \n",
+       "MSE                                                                                       4.82937e-07  \n",
+       "MAE                                                                                       0.000594482  \n",
+       "count                                                                                             102  \n",
+       "mean                                                                                      0.000506372  \n",
+       "std                                                                                       0.000478296  \n",
+       "min                                                                                       -0.00072515  \n",
+       "25%                                                                                       0.000192821  \n",
+       "50%                                                                                       0.000540495  \n",
+       "75%                                                                                       0.000830978  \n",
+       "max                                                                                        0.00159335  \n",
+       "mse train all feature:                                                                    5.16241e-07  \n",
+       "mse test all feature:                                                                     4.82937e-07  \n",
+       "mae train all feature:                                                                    0.000505385  \n",
+       "mae test all feature:                                                                     0.000594482  \n",
+       "mean avg bo spread:                                                                       3.98687e-05  \n",
+       "how often sign of price change is same:                                                      0.882353  \n",
+       "if same sign, how often is actual better than 0...                                           0.655556  \n",
+       "if same sign, how often is actual better than p...                                           0.366667  \n",
+       "if same sign, how often is actual better than p...                                          0.0666667  \n",
+       "if not same sign, how often is actual worse tha...                                           0.333333  \n",
+       "if not same sign, how often is actual worse tha...                                                  0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_log = pd.read_excel(\"sim_log.xlsx\")\n",
+    "display(pd.read_excel(\"log_results.xlsx\").T)\n",
+    "#display(df_log)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# create 15 minute data - this fills the 15 minutes table\n",
+    "if rerunSQL:\n",
+    "    str_query = open(\"get_data.sql\", \"r\").read() # returns prepared data\n",
+    "    str_query = str_query.replace(\"/*\", \"\").replace(\"*/\", \"\")\n",
+    "\n",
+    "    df = getQueryDataframe(str_query, [min_date, max_date])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# dates only have an effect if a subset of dates is needed.\n",
+    "if rerunSQL:\n",
+    "    str_query = open(\"get_data_1y.sql\", \"r\").read() # returns prepared data\n",
+    "    str_query = str_query.replace(\"/*\", \"\").replace(\"*/\", \"\")\n",
+    "    #print(str_query)\n",
+    "    df = getQueryDataframe(str_query, [min_date, max_date])\n",
+    "    df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if rerunSQL:\n",
+    "    df.set_index('datestamp', inplace=True)\n",
+    "    df.index = pd.to_datetime(df.index) # else fill betweeen doesnt work\n",
+    "    print(\"min date\", min(df.index))\n",
+    "    print(\"max date\", max(df.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if rerunSQL:\n",
+    "    df.to_csv(\"data/eurusd_features.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"data/eurusd_features.csv\")\n",
+    "df.set_index('datestamp', inplace=True)\n",
+    "df.index = pd.to_datetime(df.index) # else fill betweeen doesnt work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if useKaggle:\n",
+    "    # load kaggle reference dataset for comparison\n",
+    "    df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_sample.csv')\n",
+    "    #df_kaggle = pd.read_csv('data/bm_kaggle/EURUSD_15m_BID_01.01.2010-31.12.2016.csv')\n",
+    "\n",
+    "    # Rename bid OHLC columns\n",
+    "    df_kaggle.rename(columns={'Time' : 'date', 'Open' : 'open_bid', 'Close' : 'close_bid', \n",
+    "                       'High' : 'high_bid', 'Low' : 'low_bid', 'Volume' : 'volume'}, inplace=True)\n",
+    "    df_kaggle['date'] = pd.to_datetime(df_kaggle['date'], infer_datetime_format=True)\n",
+    "    df_kaggle.set_index('date', inplace=True)\n",
+    "    df_kaggle = df_kaggle.astype(float)\n",
+    "\n",
+    "    simname = \"bm_kaggle\"\n",
+    "\n",
+    "    df = df_kaggle\n",
+    "    print(\"min date\", min(df.index))\n",
+    "    print(\"max date\", max(df.index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# to include seasonality as a feature\n",
+    "if simname == \"bm_kaggle\":\n",
+    "    df['hour'] = df.index.hour\n",
+    "    df['day']  = df.index.weekday\n",
+    "    df['week'] = df.index.week\n",
+    "    df['month'] = df.index.month\n",
+    "    df['momentum']  = df['volume'] * (df['open_bid'] - df['close_bid'])\n",
+    "    \n",
+    "df['avg_price'] = (df['low_bid'] + df['high_bid'])/2\n",
+    "df['range']     = df['high_bid'] - df['low_bid']\n",
+    "df['ohlc_price'] = (df['low_bid'] + df['high_bid'] + df['open_bid'] + df['close_bid'])/4\n",
+    "df['oc_diff']    = df['open_bid'] - df['close_bid']\n",
+    "df['period_return'] = df.close_bid / df.open_bid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Explore dataset - show some graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
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RERERERENSEqVnvGl7ebbUrZVHXYAhOhkjfzl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\nvFj6C1G9P8ndO4ic8VFp00bFzvzYmE5JCAcAgKkqv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cdif0jSeagwK84rkAIu8//Wk8fL/YSgZX5otwe7KGuAowcmtruuklS/3icwCA7bNPoY2bgKqr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\nALW1GQBgjG4EACg/fJf2uJJmVgy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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3c366ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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Yz75Icp2rKeZ/7yBmmvv8cxPpedOZPKD0J0MtSVlfmGzPc6ERHpn2Qqy+J1zWQP0im4JZXi7EvP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Mqx4ZVl3M6yKioc0XaA/jNLXFF8bpjk6tQKJRjWSzJr62V5IE5aMPwfjg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\nLj8Jx4xJRXGuBSlmLeSyI3crXdIb4Hr6eQDAzFsux9H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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3c476d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot close price, compare to low and high price\n",
+    "ax = df.plot(x=df.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in df.index]\n",
+    "plt.fill_between(x=index, y1='low_bid',y2='high_bid', data=df, alpha=0.4)\n",
+    "plt.title(\"entire history\", fontsize=30)\n",
+    "plt.show()\n",
+    "\n",
+    "# plot first 200 entries \n",
+    "p = df[:200].copy()\n",
+    "ax = p.plot(x=p.index, y='close_bid', c='red', figsize=(40,10))\n",
+    "index = [str(item) for item in p.index]\n",
+    "plt.fill_between(x=index, y1='low_bid', y2='high_bid', data=p, alpha=0.4)\n",
+    "plt.title('zoomed, first 200', fontsize=30)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- there are periods where the price doesnt move, probably weekends. Maybe dont consider these for training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pltGraph(xname, yname, irow, icol, df, xval=None, yval=None, title=None, norm_axis=None):\n",
+    "    x_axis_col = xname\n",
+    "    y_axis_col = yname\n",
+    "    if xval is None:\n",
+    "        xval = df[x_axis_col]\n",
+    "    if yval is None:\n",
+    "        yval = df[y_axis_col]\n",
+    "    if title is None:\n",
+    "        title = x_axis_col + \" vs \" + y_axis_col\n",
+    "    if norm_axis is None:\n",
+    "        norm_axis = \"x\"\n",
+    "  \n",
+    "    axarr[irow, icol].scatter(xval.values, yval.values, color=\"green\", lw=0, cmap=pylab.cm.cool, alpha=0.8, s=2)\n",
+    "    axarr[irow, icol].set_xlim(xval.values.min(), xval.values.max())\n",
+    "    axarr[irow, icol].set_ylim(yval.values.min(), yval.values.max())\n",
+    "    axarr[irow, icol].set_xlabel(x_axis_col)\n",
+    "    axarr[irow, icol].set_ylabel(yname)\n",
+    "    axarr[irow, icol].set_title(title)\n",
+    "    axarr[irow, icol].grid(False)\n",
+    "    return icol + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AlBzaj6DQmAMBubPyPTjd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RAAAg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SpA1h/rf/ZM0vqxje6RnSd4xm03GXk+K9/svQqLSkbh3J8I1/Z+LusQxq8wgPtx9Mq5AbWXDv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Z4GtHqw0tt7wj5UVrYtSwkbYg1fksxoRueg6vA3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0dYZZI5h4nZV48jtPs/Z7I1LBGcGrO17RWQ440lAMFlbwNYyat+QZlGIrN4u/XjM3EpMl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\nS1jtpXit7I/MZTN5hUy89xL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sZPz2Oxpq4PKy2/mqIcPhg7f+fDC66liSRXVdFcZuHNvZ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4lV01CNOu8nok/m5w8tQFKfZNFFKZfHiecdzympXDPvPdl2aADQ7msXjaeC1WwTT/pUscAN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\n7JOoGSWo2Pa59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8weuJvrRJPfXB4n5r33PfFrA8nWaqqo+j7w0kerwv7a3WJM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\nuHABq1atwsaNG/HGG29g586dcDqdKC8vR0JCA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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3c79bac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb Rows:  10204\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create ohlc prices, analyse distribution, think about feature transformation and de-trending\n",
+    "\n",
+    "fig, axarr = plt.subplots(4, 4, figsize=(40,20)) #1 row, 2 cols, x, y\n",
+    "#plt.figure(figsize=(20, 4))\n",
+    "irow, icol = 0,0\n",
+    "fig.suptitle(\"frequency distributions\")\n",
+    "\n",
+    "\n",
+    "sns.distplot(df.period_return-1, ax=axarr[irow, icol])\n",
+    "axarr[irow, icol].set_title(\"dist period_return\")\n",
+    "#axarr[0, 0].set_title('Axis [0,0] Subtitle')\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "    icol+=1\n",
+    "    sns.distplot(df.avg_bo_spread, ax=axarr[irow, icol])\n",
+    "    axarr[irow, icol].set_title(\"dist avg_bo_spread\")\n",
+    "    icol+=1\n",
+    "    icol = pltGraph(\"ohlc_price\", \"avg_bo_spread\", irow, icol, df)\n",
+    "    qlow, qhigh = 0.01, 0.99\n",
+    "    df_mask = df.loc[(df.period_return < df.period_return.quantile(qhigh) ) & (df.period_return > df.period_return.quantile(qlow)) & (df.avg_bo_spread < df.avg_bo_spread.quantile(qhigh) ) & (df.avg_bo_spread > df.avg_bo_spread.quantile(qlow)),:]\n",
+    "    #df_mask = df.loc[(df.period_return < df.period_return.quantile(qhigh) ) & (df.period_return > df.period_return.quantile(qlow)) & (df.avg_bo_spread < 0.0001 ) & (df.avg_bo_spread > -0.0001),:]\n",
+    "    icol = pltGraph(\"period_return\", \"avg_bo_spread\", irow, icol, df_mask)\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    irow, icol = 1, 0 # move down one row\n",
+    "    \n",
+    "    icol = pltGraph(\"ohlc_price\", \"avg_bo_spread\", irow, icol, df)\n",
+    "    icol = pltGraph(\"hour\", \"avg_bo_spread\", irow, icol, df)\n",
+    "    icol = pltGraph(\"hour\", \"nb_ticks\", irow, icol, df)\n",
+    "    icol = pltGraph(\"day\", \"nb_ticks\", irow, icol, df)\n",
+    "    \n",
+    "    irow, icol = 2, 0\n",
+    "    df_mask = df.loc[(df.period_return < df.period_return.quantile(qhigh) ) & (df.period_return > df.period_return.quantile(qlow)),:]\n",
+    "    icol = pltGraph(\"hour\", \"period_return\", irow, icol, df_mask)\n",
+    "    icol = pltGraph(\"hour\", \"ohlc_price\", irow, icol, df)\n",
+    "    icol = pltGraph(\"weekday\", \"period_return\", irow, icol, df)\n",
+    "    icol = pltGraph(\"weekday\", \"nb_ticks\", irow, icol, df)\n",
+    "    \n",
+    "    irow, icol = 3, 0\n",
+    "    res = df.loc[:,[\"weekday\", \"avg_bo_spread\"]].groupby(\"weekday\").std()\n",
+    "    res.plot(kind=\"bar\", ax=axarr[irow, icol])\n",
+    "    #display(res)\n",
+    "    \n",
+    "    icol = pltGraph(\"weekday\", \"avg_bo_spread\", irow, icol+1, df)\n",
+    "            \n",
+    "\n",
+    "#plt.tight_layout() # reduce overlap\n",
+    "    plt.show()\n",
+    "\n",
+    "print(\"Nb Rows: \", df.high_bid.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- it seems the hours with the least number of ticks have the highest bo spread. This is expected, as during low activity traders might set spreads wide to avoid surprises.\n",
+    "- period return is between two closing bids, 15 minutes apart. It might be a spurious measure.\n",
+    "- do i have to stick to 15 minute intervals to complete this?\n",
+    "- weekday 1 seems to have higher stdev of avg bo spread"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Add PCA as a feature and show graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# Add PCA as a feature instead of for reducing the dimensionality. This improves the accuracy a bit.\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "df_np = df.copy().values.astype('float32')\n",
+    "pca_features = df.columns.tolist()\n",
+    "\n",
+    "pca = PCA(n_components=1)\n",
+    "df['pca'] = pca.fit_transform(df_np)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kWG3MerVXRJuyrZCm1sY+Nar9NrNig93RwKFT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gghhBACkI4zIYQQQgghhBBCCCGEEEIIIQDpOBNCCCGEEEIIIYQQQgghhBACkI4zIYQQQggh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\n19a0adPslvXw4cOaP3++d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5rZcY7GtH3G8p6zKfsmakLb53VbeCvF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DvY0ndlOQmoNiY2Nx/Phx6e/Tp0/j6quvdrtSfyLOGI9lqYXQB+sdHshxxnhJNCPMqpoO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/7aAApP7iZx7xxpQ6lEWDZW1JZTWl0i91+sL7LIIxN9d71YL3hPyo+QC/MPbXmA10evI7RnmDy+\nuBZgzmQT9plgruqZlB9BYlASaQcXE+UfzaslWZhMkDsu76rtEvsX7RDXLmHPHAvxUFT9XO+4FGNh\n4tZwaYXX2j0gRNbY3eYsqtTPFsjtrbebuDXc4pysF/T1hkrZR4lBSRbXSuSa1Rvr0dhqWDkq26JK\nSZzj0Zoj9PX0k9sL8ShiSzgaOw1R/tHM3/cCS+56ib6efsQVxsrrJoSnuMJYEoOSpNjUZDKyaXy+\nbCOYq91sbCBrpFlwC9QN5sX9i/jLsBSL8ao3VFr0+7X633quKdYXMWt7JOV1p5g9YA4fnthC1shs\nOXYB+bO45iIvUMwfwnow7uPZuDm60tTSxBmDno5OnbjQeJ604cs5f+k8bx5Zx+w74wnpNpKILeHm\nHDL/GFZ9/QpL7noJgJzDa2XbhBhaXneStOHLmdx/apvn1Nr8JQRrcY2s50DxxwPPDIhl7p7n8XLW\nmYWxS+dZ980qztTrWTv2TQB5r8Xsiqa2sZaahmo8nbVo7Ow5U6fHx8MXZ3sXeSxo+48jJuVHXNOa\nsTVL1Gt9R1hvB7T6841+f7TVJuvXrreNNwu/5LnJ7q9//etff72m3BzU1zf+r5ugoqKioqKich24\nujr+r5uggvrspKKioqKi8ntBfXa6ObjWs5OHo0ebr3s5ezFQN+iqnxULk6O6jbnm4qnyWNbHNZlM\nPNx7otyH2Hdrn/Fw9GBUtzGAeeG1v+ft9PlPdpjJZOKdoxvx6+jHkZrDzNg+lQOn9/Hn3o/wwfdb\n2HVqB8bmJgrLd3NHpwDcHNzxcfdlVLcx9PH0o2e7nnxQtoVFw9Lo26EfByr3kjA4mT6efXmg5zju\n7XEfqZ8tkKJZwp45BGgH8OTO6dza7jZWfJlJQ1MDD946ni+rvuTPfR5hWv/p+Lj7Utt4kfeP5WJn\na4+7gweJQclkf/V3+nveThc3H4u+UvbpqG5j6NHuFj74fguP9H2MJwqmsOl4LmnDl3O79g5qGy/K\n/qptvEjUjunc0SmAqB3T+fjkLkJvuZ/axos8u/spKSYdO/8tz+5+ilHdxrQ5BpQU64uI2jGdsd1D\n8XD0oLbxIu8c3cjOk9u5t8d9jOw6mozidPp73k7CnjmyP8d2D+UunxEsL15GfOBc/LUBzNsTz1Dv\nYXx0YhtPD4iR7ycEJbG/ch8P954o22Q9BgCLvhJjoY+nn+wvD0cP2Q4vZy/m7Ym/5nmKPr+1XS/+\ncWQ9j/adbLFPsY3otwDtAD45Vcgn5YWkBC/EVeN6RX96OHoQ2uN+JvZ5VP5e23hRHuf5whg+OrGN\nzJAsurv3YMWXmdzRKYCvqg4xZdsjBOmG8tBtf+ajE9u41NxA4and3OUzgqgd0+ndoQ/DutzFqYsn\nifskhrHdQxmoG4SDjQPLi5cx1HsYO09uVwiW81hVms1HJ7YR5R/NkoOpfHRiG55OnVhevIwZt0eR\n/dXfyQzJYrAuiI1HNzBEFyzbONArkP2V+3jqjmdZXryM0d3HUll3mjmfzqazs475e+fiYOPA/L1z\n+ejENu7oFCDvydrGi3KMtnZNxTUU/dzFzYfu7j3w9xzAP7/bSGNLI1F3RDPU25xB1sXNh1HdxuDm\n4M5Q72H08fSjV4fejO0eio+7L38vXkHC3nh2ndxJzeWzPHTrRBKGJLH9hwI8HN156o5nWff1avL/\nvYULly6w69R2Hrx1PPf3fICdP+yg3HCKWbdHs6r0VfK+f48Z/Z/Ez7MftY0XAZjY51F6tbuNnMNr\n2xxXbb12R6cA7u1xH3GFsTzceyL7K/bgbO9CbeNFpn80hUvNDWjsNDjZOfNV1SEMzXUcOH2Aj07m\ng8mGZlMT/Tr68/wnz+Js68Lf/vUSJloY3e1eiquLuNx8CRvsyB6zmj91HcVDt/2ZhD1zGOo9zGJ8\nimsg/o3tHoreUEkXN59W77nWzkl53dr6jPV2yp9rGy8S3uu5vOf8AAAgAElEQVRhiz8QUM6F1vts\n7XcxTypfV44167Gl/Kxyu5uFX/LcpFo1qqioqKioqKioqKioqKioqKio/MpYV8IUlOXz5M7pV1jn\nKbe7Vg6Z8jNXs3FsLSvrWp8R1nCt2S02mYzM3D6NFz59Dk/nTiQPXUigbjArR2Vjb6NBY2dPlH80\nMbuimbg13KJCwV8bgJezjldLssj/9xY6OXux9utVhOfdT+zun6wkUz9bQFxhrMxle330OnIOryXi\ntkc5e6mK5H3zeWZArLQrFMdIHrqQDk4dWTkqm8n9p8r2K/tZ2afK8xd2arnj8tg0Pl9WyYTnhUlL\nPKUtY2ZIFho7DXpDpYXdobCSE9U710JUjhmbjRZtzB2XR2ZIFnGFsfhrA+T+rcdDoG4wS0csJ6M4\nHTBbSApbOJH5FB8415y/psjbam0MtDYelFlZyiwncUzrLL7WEP0T2jOMvPEfXlGhI6qF4gpjiQ+c\nKzPwhCWgGAttZf4JK0QxZjOK00kMSpKViIl753Dx8kWid85k0YEFLLnrJdIOLgbM1Uf2tvayb+qN\n9czYPpXpBVNIO5hKF1df/LUBFJTlM3/fC0T2myFtQleVZhMfOJfJ/aeSGZJFZkgWOYfXkhmSRWJQ\nktxeVAOKSr5OTuZKSrFdRnE6Uf7RvHIoi8h+M4jdHc3RmiN4aNqRUbyMakM1iXvNmXqJQUkW2Voi\nN0tpV3k160azSPsEAJWGCk7XVVBaXSL3qezTuMJYi3unoCyf1M9TeNL/WVw0LnR09OQfR3M4WnOE\n85fO8VifKeQee4eVo7LZHL6NdaEb8Hb1QefqzdGaI3Ju2Hh0AxpbDfMGJbHsC7Od6APv38uk/AhK\nq0vIObz2usaVEnHfARibjRSe3M20gkmE54VReHI3JhM42Tlzf49xrDu8iin9nmDt2DfxcPSgo6Mn\nl5obWDYik2cDZ5M8ZBG5x94hMySLpwNieevb9XR09MTZ3oXzjTUUnvyYJ3dOB7C4L0U7lPmOAKXV\nJTy05QFpVXm9lrtK0etqn2ktt9B6zl86YvkVtqbi59aO0dp3T1vbWbdbOSb/KKjCmYqKioqKioqK\nioqKioqKioqKyq+I9WJjRW05GcXpvD563RWZTWI7ZdZUaxZ01lxLXLN+33pR1Fo8Uf4u7P/EQnr2\n6DUkDE4GE9hiR9rBxVTUlqNz9Zbi2YovM2hoasBkQgpbAo2dPU8HxPLMgFjSRqTjonFm9dgcskZm\nS1s9e1uNtECLLJiM1sWL+MC5vP3tBp7yj8HGBilaxBXGMik/goKyfDKK08kMyZL9KgQeZV6cOP9i\nfRETt4bLxfrGZiOl1SVSbAKzpZ7ecFr2ocguStgzB52rt/zZ2jYvJ/Qt2farXbuK2nJp7bdyVLbF\ntfZx97WwDlQKWIBFvpVYuNcbKpm5fRrheWFS5BMibUFZvuwrsaivFBDFgve1BFjxvs7VW1bYXQ/K\n8Wd9HGXGldbFi6UjlhPaM4x3H8xj5ahsKYAp2yIW50urS2hqMVpkU0X2m0FGcbq0Ndw0Pp819+Zg\nb6OhyWSkr6cfxmYjMz+KZN6eOCoulssMs9S7ltDRsRMttFDVcIY5g+bJjLQld73Eii8zmFrwGEdr\njtDUYmTRgQUWmVyiT0J7hsl8PCHmFZTlM/OjaZypN1+nmR9FknZw8X/au4wfLv6bjOJllP34PS98\n+hzVl6o4XXeaC43nmTvoL7xakkXinrk0tRiv6DchdrcmCivRuXqbBeJj7zC223200MLx88evuFYb\nw3KlcCvuFYDuHj0I1AVSZTjDswOeo6WlBU9nTzo4dWTj0Q2cv3TOYty6aFwoPLmb5z95lh8unuDl\nf2VgbDGSNTKbYJ9heLt24bmB8dRcqibKP5q0g4st7E6vF+V9p7HTENJtJOtDN5J61xLm73uBZwaY\n55xAXSCdnb3ZXb4DMAvmyUMX4u3qg6ezJ8X6IjYe3UC9sR6A5V8sQ+vshbO9C5eaG5jeL4oD+n28\nPnqdtOJUjmsxBpR/cCDme5Hndy1BUHmvtTZXK79LrtYXShGtur6K8rqTFkI//JQF11q7Wvv9WoKm\n9Zj8o6AKZyoqKioqKioqKioqKioqKioqKr8irf3lvqi+aWu7QN1g3h/3gaxgakuAUVYWXO390uqS\nKwQx65yaYn0RxfoiKUgUlOUTvvl+Zm2PpPDkbrkYCvDmkXXY2tqSNiJdVupMyo+wEM9cNM48MyCW\nmF3R5uyx/1Ssna6rYM4ns4n7JIbEPXNpbDbirw2QC+WiWkoIMvGBc5m1PZK5n8bx/Y/HebVkBcbm\nJplZtTEsl8SgJFI/W9CqkKOsBlH2S1xhLCaTOfsqed98ThvKmbU9UopKPu6+zL4znrzwDwnUDaa0\nusRif+I6WS9QKyvsrjf7p7q+SlbGWVerWItGov2iqs1aUMMETS1NxOyKplhfRNrBxXLRXimGiEV+\n0W69oVIKMm1VninPdVJ+BLG7o2+oqkT0u7K6Trn/0uoSJmwOk5UxAHGFseSUrpNCsthWnEtGcTrJ\nQxda5G+JSq+EPXMoKMtH5+pNoG6wubrMRgNASvBCPBw9SBicTI/2twDIdjnZO2Fva0/KkFRCuo0k\nYc8c4gPn0tfTj9l3xuPj2pWQbiN5OiCWhqYGUj9bQGS/GcQVxlpUbilz/nJC38JfG4DWpTMv3f0y\na8aux8PRgyj/aF7+Vwb6uko6u+qID5yHva09zwQ8h9bJiy5uXeji5kuwzzAam42cvVTFY32mXHnd\nFf1oXVGkvKY+7r5sGp9PYlASO04VYIMNvTr0umKcif0am41U11fxw8UTJO+bz+w746lpqMHG1gaA\nJpooPPkx5y+fY6DXIM7U68kpXcf4vPuYtT2SKP9o+nr64enUCRMmhuiC0dhqpFBpMkFfTz9Z5Smu\nwfVWZinHlmizOJfQnmGE9gzj9dHrWPFlBnGfxDBz+zSevONpTtdVkLJ/PvXGBpZ/sYxmUxMzCqYy\na3skTSYjGlsNBWXbqDCcIj5wHmkj0qEF/LV3AKB18SJiSzjj8+6T41K0XTkvKKstW7terWH9nWFd\nmSnE76tVjCo/mxP6FloXL4vMRmXbhJh2PX1s/UcIbbX/j4aacaaioqKioqLyP0PN6bg5UJ+dVFRU\nVFRUfh+oz043B9f77KTMH4osmEx4r4evmXMjsmjaypFRvge0mkcjstRm7Ygkv2wr732XS+gt91+x\nTX/P24krjOWjE9tIDEriodv+zN1dQ7jH908M0A5k/r4XCOs5DjcHd6J2TKepxcjfQv6OvzZAZkLt\nrfiUh3tPpI+nH6G33E/fjn6s+DKT+iYDekMlD946nkDdYDo76zh09ktc7N2Y2PsxTtb9wMO9JwJw\nl88I3BzcAXOWzqT8CLZ+n8dpQwX1xgYAvFw702Jq4cDp/QzwuhM3B3ee2jmDUxdP8pjf4xYZXQKR\nfSX6R+QO+XX0Q+vixe5TO3np7r8x7tZw7u4aApgrtaJ3zWRs91AKyrbx/CfP4u95B7069G4188c6\nk+hq11nZ994u3mQUpxMfOJe7u4bIa+Ht4s28PfG0d+jA/L1zGds9VOZZ6Q2VPN5vmsy5KtYXYTKZ\n6OPpxz1d/0Rwl2Hsr9zHvT3u44Pvt/DswOdkG7u4+XBHpwCLbLjwXg/j5uDOu9++w92+97SZz6bM\nNrqjUwA7fthukZnWFsrMpzs6BTDdf5aFGCByoebtiWdOYAJzgxLlZ9eUvs6uU9uZP3gBg72DLPq7\ni5sPXs5eZBSnE97rYWobL/J8YQyzbo9mZsCT2GHH7MKn2X6igIbGBhZ+loSDrSNby/LYf3ovfxmy\ngNdLX+UvQxaQdnAx9U0GDuo/Z+WobEZ3G8s93f4EwK3tepH62QJeK3mFnT98RHun9uhcvJn3aRx1\nxjoc7Zz4uqaUzJAs2a/iukRsDeeNr1cz1DuYIzWH2fz9+3xT8zWzA+O4y2cESw6mUmes5VLzJVb8\n6VW6t+vBAO1Acg6v5eylalKGphJ5+wwAHrh1HAO0A0n9PIW7fUJkZpb1vS/Got5QybO7n6K/5+3y\nmtY2XsTH3ZdeHXrT2VnH7lM7OVR1SF73/p63YzKZqG28yJRtj9JCCxP7PMauH3ZyufkSH3y/mZ0n\nP2LZiEzu7DyQfx7dyFdnD/F430hyj7+Nh0M7yutOYW9nz4LgxbxaksWHZfkA2NvaU1x1EAc7B/ZU\nfMIzAbHsO72HHSc+Yrr/LADyjr/HtP7TZT7X9aDM8IraMZ27fEZYjOFeHXrjYufKoeov+Wvwi9zX\nM4yh3sH8qesodp/agd5wmtl3xrO38hPaObTntTFr6NnuVtKKFmGDDYM7D6GPZ1/e+fYtDp/7mkl9\nphDe+yF83HzYWraZR/tOxs3Bnbzj78n7QTnn/xwhqa3vh7HdQ+U8a50R2Np3AMCx89/y0JYHeKTP\npCty1o6d/5bwzfdzj++frsiCvFru2vVcj+vNePxv8Uuem2xMJpPpV2zLTUF1de3/ugkqKioqKioq\n14FW6/6/boIK6rOTioqKiorK7wX12enm4HqfnZSLp9ezkNpWZdi1ss7ael9URMQVxrZpoVVRW05p\ndYnMfhJVG6KKx18bAJitF8V+ACK2hKOx01hYJIqFU2XOl6goi9gSzr8vltHBoSM1l6tJHrKICb0f\nZsLmMOxtNNjY/GTVKNo8ttt9bDjyBucu1zC9XxQbjrxBZ1cd7g4e5iypvWY7ya0TPrKwTGytf8T/\nfy9eQfoXL+Lr1o1nBsQS0m0kkQWTLSo9Csry0bp4Eb75fjo4dmTbw7tazfxprbLsatdDvFesL5LV\nTBnF6bIyTvRplH80iXvn0NnFm9fGrCGuMJaGpnoqDadlVpjeUMmEzWH4unUjd1weekMlT+2YSepd\nS6hpqOGVQ2YbSJ2rt4Wop7RqEz9Pyo+wuO7X4nrHcmvHa60tekMlCXvmML7nQ+Qee0fmfl1qbmD1\n2BxZrZY7Ls+i8k2MvYraclndmH7333jlUBbPDIjl/KXzLC1KpaOTJ1H+T7O0KBUvl85ynxvDcmW/\nvTZmDTpXbyblR2BsNmJsMeKicSEzJIujNUfo+x+xMq4wltrGi9jZ2JN61xK0Ll5XWK+G54VxsvYE\nWmcvNLYOnL1UhYemHe2d2vPug3lyDMQVxpIYlCTHWicnLzR29jS1NAFgMkF1wxm8XX3IC8+ntLqE\n0J5hFmO6tLoEf22AxXwRsSWclOCFclvr6y3ugwm9zSLVW9+8yarSbMBciTlreyQ61y68NmYNs7ZH\nUmU4Q0JQMhuPbiB3XB6l1SXM/TQOe1t77G3tGd/zYbaf3EbEbY+y7IvFrB6TQ/K++Tw3MJ4VX2Zg\nbG7ictNl/jI0hbmfPk8nFy1n66tlFVxb9+71IM5v4tZw3n0wT54fmOe/cZtCwQa8nHW00IyDrVlE\n+fHyeS40/sjE2yaxv3IPq8fmSJvVEJ9RvPPtW1Q16Mm8ZyXnL53nta/+TlXDGd4MfRt/bYC0AxXH\nac1+V1TC/txzE/u6nnvtat8Bygpm5T04cWs4yUMXyqq4653XrmeevZn4Jc9NqnCmoqKioqKi8j9D\nXfy5OVCfnVRUVFRUVH4fqM9ONwfX8+zU1iJkWwuQQgwwNhvR2GmkkNHWQmZbx2xLHLuamCMWeXWu\n3lJEE9Z+TS1GTCbIHWe5KK20gFOiFOsSg5JIO7iYjWG5Mu9o9oA5vHpoBT3a30Ly0IVE7XiCuYP+\nwoTeD1NaXSKFpMKTu3nlUBYnLpbhau+Gu6M7+rpKXrrnZfp6+hGzK5ofLv6bLm6+5IXny76KD5x7\nhR2mOMfxPR8i9fMUYgfE06NdDxL3zmHT+HwpLinPC8yCjvI9630KrvfaCLFH5JtZL6or+7RYX2Qh\neinbI8aE+F1vqCR650x+uHiCTk5azl6qprOLDjeNuxxLykVt62v3Wy12X22x3VrQKTy5m7hPYtA6\ne3Gh8UdWj8kBzLZ7xfoiKW6JPhNCiVLsnLl9GlrnzlQ3nKGjkyfnLtXQ0tJCJxct9rb2xAfO49WS\nLLJG/iQoFuuLGJ93H5vDtxGoG0xBWT7Hzx8n99g7ZIZkUV1fxZM7p/P+uA+uyLYS2XzCPlR5nWZt\nj2TJ8HRSP1vAY32myP1ZCyzieoTnhWFjA9mj13CgYj9Li1Jp79gBja0DGjt7skevIWZXNCnBC+U9\nUlpdwrSCSXT36CEFqGJ9EeGb76erW3cpNAJyPEUWTDbbS34SQ+Y9K+nr6cdDWx7g9dHrLERyIXhP\n2Gy+l2bfGU9It5EUntzNvD1xmEwmEoNSWFqUig02PHVHDIUVu4jsN4O+nn6Eb74frbMXl4yX0djb\nU9NwloTBybx5ZB0mEzSbmlg9Nue6s/LaGlvivCK2hEtRT4yZ6voqZm2PZHLfaWz74QMqDafROnnh\naO+IyQR3agfy0YkPwQbW3vsmoT3DKCjLZ9aOSFaPyaGmoYYVX2ZQXluOCRMdHTuSEbLCQuxWjmHl\n+BbiuMg9u9453PocrQXPG+2fq92DIgdR2AO3to2yHeI7wrpN4n0x995s4tkveW5SrRpVVFRUVFRU\n/meodkM3B+qzk4qKioqKyu8D9dnp5uB6np2sLfyELZ2wsjp2/luidkxnbPdQCxvBiX0e5eHeE/Fx\n972qRVaxvugKi622bLKuZRs4qtsY+nj6cez8tzz+4UQc7ZyYeceTPNx7IiN876HgRD4jfO+RFoHi\nc8r9VtSWU9t4kScKprDt3/nUNxnY+cMO7GxtGeF7D/f2vB9nWxce6zeZB3uF88TtsxioG4SXc2cW\nfpZEr3a9yf7q7ywdsZw6Yx1RO5/gsT5TOKDfSwvN2JhsaWy5zLHz3zHdfxZezl58d/47Fg5bTGdX\nHT7uvng5e/HkzunSzk7Z1v6et/PSF0uwxY6TtT9QpP8cF40Lvdrdxl2+I2T7J+VH8I8j63nvu1xp\nLdiafVlt40UitoSTd/w9eQ2vRm3jRd47lstAr0D2VnzKtP7T8XD0kDaM4mdxrbu4+VhY8AmLQnEu\nfTz96OLmQ23jRZ7d/RRP3fEMQbpgYgOfZ+cP20m/O5M/93mEe3vch5uDu9xHa3aS4vhKRH/8Ess1\na2tQZT+K9vT3vB03B3d6tLuFD8q2sOzuDGb4R+GqcWXyhxH069ifeqOBt7/dwL6KvdKmc5PCHg+g\nV4fe3OP7J6ICohnqHcwXZ4p4tPfjBOmGcvjc15w2VBDa434O6j9ny/d5hHQdSWXdaQD+eXQjj/ad\nzFdVh5jx0VQ+Lt/JX4IW0MlFS+pnC1gyPJ1b2/diUn4EecffY/fJnUzrb7YGHOgVSNSOJ/Dr2I/n\nC2PkNd51cgeP9p3M5uN5HK75mhUjX5Gin4ejB8X6Ivp4+skMwF7tb+OrsyUM9AokYW88k/tOo6jq\nc1KGLqL4zBcM1gXxj6PrOVT1JYlBSQzUDcLZ3oU+HfxIGJJkMW7u8f0T9/d8ADcHdzm+nt39FEO9\nh/F4v2nc5TuCLi4+rCrNZlr/6YT1HMet7XsBZhHo8X7TpJ3nlu/zmNRnCvP3vUBnZx0LDyTxeN9I\nzjToefrOGO7UBjK6+72kF79IfOA8cg6vxdetK6O73cuByn3oGypx07ihsXHk4/KdZIasZGS3URzU\nfy7vAWtau9+s348smIyXsxdTtj1CkG4on5QXorHR8Pwnz+Js60Lsx0/xZdWXTOo7lTcOr8LV3p3n\nB77A4XPfYKKF6oYzXGyspc5YS0dnT4qrvuCOTgHUGw1sPv4+43tNYEiXYN45upHaxgtgMuFk58LB\nM5+REZJFnbGOZ3c/RXivh6+wl6yoLaePpx/9PW9H5+p9Q/aT1vdPa5a819s/wqqzv+ftcq5Qfq5X\nh97c7RNiIV62ZRXZ3/N2EvbMkecL5krVsd1DAbOt6ZM7p+PXoR/z9sTfVHaNv+S5yfZXbIeKioqK\nioqKioqKioqKioqKiooKln+NL/4KPz7QbC8o7OeUFSw+7r7yn/I1a4r1RTy05QFZ3SW2+zlVDYBs\nQ6BuMJvG5/PamDXyPZ2rN8YWI3GFsfJ44n9RYSPOUW+oRGOn4ZkBsdjb2nO2oYrH+kwhYc8cCsry\nWVqUyoOb7qW6vgq9oZKK2nI8nT3xdu3Cy//KkBaPgbrBvD56HYG6QHxcuzKl7xPUN9fRyVlLSvBC\n9IZKc/7aLeNIO7iYiC3hFOuL8NcGWFRPKBHn0c6xPStHZbNyVDZT/aYT90kMb33zJpEFkwHIDMmS\n1pHKc1OeuzhvjZ2GxKCk6+pzH3dfMkOyyChOl3aUogpNtF+ME3EMZVWboKK2nIQ9c+Q2Ykwl7Iln\naVEquUf/yfnL50jeN5/onTOJ3R3NpPwIiyqzpSOWX2HtpzyWaJfyc8r3WmvXtRD7tD5O9M6ZTMqP\nQG+oxMnOmUUHFqBz9Ubn6k0Hx47M3zuXRQcWsHpMDinBC0nYMwdA2iwqCdQNxsfdl9CeYUT5R7Pi\n0HJWHFrOJeNlOjt709fTzzx+6k8z86NIwvPuByAv3Gx/mXbQLMKmDEnF09mTuMJYWUWjN1SSGZKF\ns70LiUFJ0rpU6+KFztUbrYsXmSFZJAYlMffTOCrqyjlQsR8HOw3GFiPV9VUUlOUTWTCZgrJ8Htry\nAAVl+UzcGs64TaHM2xNHlH80x88fx8Xele0nt+Ht2oW+nn5o7DT4awPIG/8hK0dlk3ZwMQVl+UzK\nj2BVaba8l5TE7IomYks4k/IjAIgPnCsr5AA8nT1paKoHkBaVgIWVn4+7LxvDcunVoRd22NHBqQPN\ntLD+8Foq607zRMHjJOyNx9PZk/fHfWC2Pf1PNdvyL5YRHzgPH9euPNJ7Mk4aR9KGm+9vcQ+0Vckp\n7oO2EHNdaM8w3h/3Af7aAJpMRjYe3UBnFx3BPsPYND6flOCFfHhiC+0dOqKxs6eDUwc0thpWj81h\nzdj1rLk3B1+PrvKaxhXGsujAAjq76kj9bMF/xkY+L939MjrXLlw0/khjsxFAVpNZz9ei/QVl+cQV\nxsp+/bmI/VvbnF6rf0R12NIRy+XcLT6n/KwQc69FoG5wq5VvekMlkQWT5dwb2jPsZ38P3YyoVo0q\nKioqKioq/zNUu6GbA/XZSUVFRUVF5feB+ux0c3Cjz05KW7EJm8NYNeYNmZNztfyxqyHyx673c23Z\nZ4k2bRqfLxdRJ+VHcK7hHB2dO0prwcSgJDKK04kPnCutzIRlmXKhvVhfRMyuaJkxlXvsHRKDkgCY\nsX0qLS0mbG1ssMEGL9fOVDdUMbnPNN76dj3zBiWx7IvFLB2ewSuHsjh58QQdnT0511AjM5aE9WDa\nZ4vZ+u9NLLnrJV45lGWRSWUtnImF4Un5ESQGJeGvDWDi1nAuXq7l3KWzbJlQAFy5iKy0AVSee1e3\n7qwcZc6EulEbNuvcOevsOL2hkvDN9+Pt2gVne5dWx0ZBWT5pBxdb5H4VlOXz3MfPcv7yOWIHxBOo\nC5SWm9X1VXKstJYvJsak0uKyNStOMTaA6x6zyoylBzfdK/PoxHvKY4uxIzLbxufdR0tLC74eXVk0\nbAkZxekyN0p8Nm34ckK6jbyincIaFEBjq8HTqRMdnDrK/tC6eBG9cyaLhi2RFoWl1SUk75tv7pf6\n07jbt0PrqiUxKImoHU+wabzZEjRmVzRNJrOF6WtjzBaKNjbmTLKGpgaqG87gpnGns6tOCmnnLtVg\ngw2rx+bIcxX9Io57ufkS+nqzGGiPPetCN1xhfSeETiHaal28pCAmxtCk/AiaWozSklK8Vm+sl6J4\neN79mDCxOXybxXhUjhEhZD9R8Dhn6vUkD1nEum9WoTfowWRC52bet8gcFNfnQMV+3jyyDnsbDecv\nnaPm8lkAOjl6Udt0gdVjclqdu35uTlZFbTkPvH8vaSPSWXRggcxejCuMpd5YT0NTPQ52Dpwx6PFx\n92X2nfHkHF5LfOBc2X/iXhR2nMJOVfSNctyIPm2rjWK+EOLVryEiXc3itzW73Njd0TIzUnku1laL\ncGX+XVsI+0nrOf9GbWv/26gZZ1aoiz8qKioqKiq/D9TFn5sD9dlJRUVFRUXl94H67HRz8HOfnUSW\nkYvGRS5yW2c1Xe9+bkTAaCtvTbwn8oHAvPD51jdvygykyf2nXrFAOmFzGNmj17S6gFysL+KB98di\ngw22trZonTvjbO9Mk8nI7DvjWfFlBouGLQFA6+LFrO2RVNWbhYaOTp5cam7Ayc6ZlOCFzN87l4d7\nPcKrX61gS3iBPMaBiv0s+jyZ2QPmEOk/Hb2h8or8J72h0kII3BiWKzPUlo5YzsyPIqluOEPC4GQm\n9H5Y5o9ZC2EFZfmkfrZAihARW8JbPc71XreILeE0mcxVK0KIse5DIYpav668lpH9ZvBqSZYcOyKb\naXzPh/lX1RdSWAGI2vEEvm7dWDkq20IcVOaL5RxeK0Wpa51Da+1qDVEZ+frodQBMK5jE+tCNFgKd\nEJCU/ZM7Lo9N371Hrw690Lp4UV1fZSGaiW3ve28U5y+fs8jzEtc7MSiJYn0xed+/x7R+03n72w0k\nD10oBceVo7KJ3PY45y/XoHXujMbOXlYaPh1gFn3Tv3iRtOHLZabeylHmMTBxazj1xgZcNM68+6D5\nvtEbKqmurzJXQN72KBuPbiAleCFaFy8p8PX19JOin/LeEXaNBWXbePWrFUzp+wQh3f7Ualaf2F5k\nEYp5RGCdYafMXYvdHS3zCkurSwCkcCRy2sRxxP7rjfVUGipwtnOhi7sPiUFJ1DTU0Fdh2wpmATk+\ncC4p++fLfnyszxTSv3iRSX2m4u7gwdvfbsDBzoElw9MtRHdxzOvNc7QWi8R8tT50oxS2hIh/tOYI\nz3/yLClDUln3zSriA+cxb08c0XfEsurrV3ghcD7BPsPkOYjjF5TlSxHVwU5D1shs4gpjuXj5Is72\n5vmpNfFPiEutZS3+XK6WlynmttayMsX9rhTSlONFmYVIsHcAACAASURBVFsmfm+r/5Viamt/mCCO\nqczVu1n4Jc9NqlWjioqKioqKioqKioqKioqKiorKb4yPuy+vjVljsdBpb6v5WfvZGJZ73VU/V7Nx\n9HH3lRU+wpJwcv+prA/dyOT+U+U2yv9NJnMVhviMsuLgaM0RmmnGzcGdNWPXM2fQPFKCF1JpOI2n\nsycmk3mxPnHPXKrrq1gyPJ32jh2obbzIMwNiWT02R+6rsu40fz/0N4wtRt4oXUv0zpnE7Ipm3Ter\nsLOxp0e7HlJAFHZ2QsyytrLUGypl1ZjO1RsbG+jo5EnusXcsLP+U/VSsL2Lm9mn8cOGE3MfKUdny\nOGBebL+W1ZmwRxPHMTY3yf0Ji0vltmkHF1v0t3hdvLZ0xHJWlWYjSiEqasvJKE7nKf8YNpe9x6Xm\nBhKDkkg7uNj8b/hynhkQK9uqtH8DZPWN9bm0dl7W1nRXQ1huZhSn468NYH3oRikIKrexZmXxyyz6\nPJnnPn5WilHiugkKT+7G0c6J1WNyLBbr9YZK6o31zNoRycajb9LQVM/6w+tkXwkx8WjNEX5sPEdH\nJ0+MLY3Y22hICV7I0wGxJOyN5+1vNzB30F9YVZotRdmEPXPY9N17JA9diIvGmeShC+Vx4wpjpWi2\n7ptVNJmMLDqwgOr6KjaNzyek20gCdYMpKMsnPO9+wvPCmLA5jLe+edNsi/nBRFYcWs6Uvk9wQL+P\nRQcWWIxfYfOoFLWM/7ENFG1QWsIqK1wjtoRTXV9F1shs2XdpBxeTsn8+kdsep6GpnrSDi6X956T8\nCNIOLibKP5q88HwSBidzqbmBsd3uI/WzBSTsjedozRHiCmNltZsYS5WG06QELyRrZDaby97nSf9n\neevb9aw8lElVwxkuGS+TdnCxhV2oOAfx2tXuJ2urworaclaVZuPj5itFwMKTu6moLSeuMBZPZ098\n3brSwakD1fVVnLhwgsaWRt47/k9G+Y4l9fMUnih4nJhd0dJGt1hfRPK++Zw2lNPU0kTy0IUE6gYT\n5R/N2YYqDE11zNoe2arFqRDNlP35S7kRG17x3ZA7Lk+K5DpXb+xtf7KVFf+EjaPyGNdqQ2v3q7Ch\n1djd+HfZzc5vVnHW0tLCX//6V7799lscHBxYvHgx3bt3l+/n5eWxdu1a3N3dmTBhAhERETQ2NpKY\nmMipU6dwc3MjJSWFHj168MMPP5CQkICNjQ233XYbCxYswNa2bc1P/atpFRUVFRWV3wfqX02b+V8+\nN4H67KSioqKiovJ7QX12ujn4uVaNbVUI/JK/0G/L3uxGKoPEYnR84FxZCdLWZ8U5CEvE1tr/4v5F\nZH+VRcLgZFI/T5GVINX1VUTteIJJfaay7vAq7LFH5+aNvq6SDk4d6eSildZiEbc9SurnKXRy0tLY\ncpkLjRewx56X7nmZFV9mcL7hPF6uXrKCRqAUvcQi71vfvElIt5HSgi3KP5p5e+Lo4uZD9ug1sirD\nutpMVECJyqHWLBRF9dDVbOdENYaNDSQPXSirclqze2xrjFhXg1gfV9g3Rtz2KLnH3iEzJEvazsXs\niqa87uQVdoxXG0PXqlJsa3y0hlI8uFpVS7G+iOidM3G2d+FWj17sOFmAzrULzaYmnO2dLaqlphY8\nhsZWw5bwAosqNDE2C8q2seKQ2WrQx7UrM26PYtkXi9E6e2Fva09TSxNLhqdz/PxxFn2eTMqQVHKP\nvUO9sZ7nBsYDZkFxfM+HyD32DhvDcskpXceKQ8vp7OyNs8ZJXkMxfo7WHGHenjiMLUaShyxi7der\nqG44w7IRmbKiL64wltrGi7LyyWQy8XTAbNlWrZMXGSErLCwHAVm5JyrYlHavAjEehEgbuzuaf/9Y\nhpuDO5eaG1g15g3m753L2YZq5g76C+u+WUVFXTmZ96ykr6efhWVoaXUJs3ZEMm9QEhuPbuBi4wUu\nNP7I6jE5AKQdXCxFSCFIZxSnE9lvhrTOFNabKcELeffoO3xwYjOjfMey/E8vt1lJqZyD2hpfbc11\nAPe9N4oz9XpiB8Tz/479k2ZTEzY2NphMJkwm6OjckYjbHmXt16uoMJyio6Mn7g4eNJuasLc1Vx0C\nXGpuwNjchLPGSWagCTExpNtISqtLWq0KFX0/86NI7G3tZXXxr1mFVVCWb1Ed2Na+xZy2dMRyWbVp\nPb8p55obqfprjRudF/5b3JQVZzt37qSxsZF//vOfxMfHs3TpUvneuXPnyMrKYsOGDfzjH/9g69at\nlJeX8+677+Li4sK7775LUlISqampAKSlpfHcc8+xceNGTCYTu3bt+q2araKioqKioqLyX0d9blJR\nUVFRUVFR+WNiXSGhfL2gLL+NT93Yvov1RVdUYUzKj2BSfsR1VTyI6oPQnmFSNGutzYKNYblSrGjN\nqizSfzrdPXowoffDJA9ZRGjPMHSu3rL66YB+H7MHzGFd6AaWDE9H5+ZNO8f2UoxLDErizSPr8HHz\nJSNkBV3cfJk9YA5bH/qIkG4jqbts4ILxR0Z0CSEl+KeqH9FmUWUB5kXm5z95lgmbzQvNTS1GXv5X\nBl4unaVoJrZXildiQToleCGhPcMI1A0mb/yHbBqfb7HQHFcYe0U/K6+HqABZOSpbVhc627vICjDl\ncUV/KvcvXrNe8E7YM8diHAhbPZEpJyoCA3WDzdaH4/PbtI6zrihs7ZjKY93I2BJEFkwGaHOfFbXl\nzNoeibG5iSj/aE4ZTjK57zReG7MGdwcPng6IlRUtoT3DeDP0bdaOfdOiAkaISEdrjpD3/Xt4OnYC\n4N7u97G57H2e8o/B3cGD8T0fpqKunJqGGt7+dgMpQ1J5NnA2iUFJ6OtPs6zoRRL3zmF8z4dI/+JF\nKRBtP7mN2QPmUPDnXWSPXkOTyUhcYSzF+iLiCmN55VAWy0ZkknnPSib0fhh7W3taMPHKoSxpcZcZ\nkoWzvTN9Pf1YM3Y9Pu6+RPpPJ2VIKhNvm0St8SJaFy9WjsomMShJjpH3x30g7yNft274awMorS4h\nPO9+xuWFMj7vPvSGSor1RURsCSeuMJbkoQvp4NSRC40/MnfQX/DXBuDu4EHa8OVsLnuf+MB5dHPv\nQV9PP4tqPh93c/VWB8eOLCtajKGpDo2tgxTNABqa6kn9bAFpBxczvudDpB1cTGS/GSz/YplFJdap\nuh8oPPkx3188ziBtELvKt0ubSOsxuHTEcmnJeTWulqvlpnHHQ9OOV0tWUGk4jb6+kjMGPWfq9bSY\nmskMySLYZxhpI9LxdevKW2Hv8tqYNTjZOcuqQyFoAywatoTMkCzSDi7m/KVzvPyvDDZ99x6pny2g\ntLqk1Xkydnc0VQY9NjaQGZL1q4tm0womye8OZV8o/xdzRGS/GbIaMj5w7hX3uBBKxVz1c0Uzgfg+\n+qPwmwlnxcXFjBgxAoABAwbw9ddfy/fKy8vp06cP7du3x9bWFn9/f0pKSjh+/Dh33303AD179uT7\n778H4JtvviEoKAiAu+++m/379/9WzVZRUVFRUVFR+a+jPjepqKioqKioqPwxUS5GCksrvaGSB96/\nl2kFkwjPC5MLnT9n32JBvjXx5XorHYQA1JqNn7UVmbBGbA2RaaU3VJI8dCGbvnuPl4qXUKwvkqJG\nX08/jM1GNpe9x/y9c5n7aZzMNNO5elNRW07qZws4XVfBkuFmi78o/2i2n9wmF/ft7eyws7Fjw5E3\nmLUjUi4iizYrhR1/bQA9PG6RIlny0IVobDXY29qjc/WmWF8kt1ee46T8CN765k2e3DldLgRbZ7q1\n1s9iwVppRefj7itFk4zidBKDkmQFUmvWhyJjSbkofzVRS7QjL9ws6vlrA6g31hOzK1pe0+vNYbNu\nR2uvbQzLvSFBwPoeUFJQlk/ElnBWFr9Med0pztRXsuLLDEJ8RrHu8CqO1hwhyj+anMNrLY7prw0g\nozhdXnvR7xG3PUrCnnhOG8p5ZsBsHugxnre+Xc/4ng+x6utX5FjKvGclAD9cPMHGoxvkWFk6PIMO\nTh1JG76cXh164evWTbbV2Gwk/99bAPNYcLZ3kWJvZkgWNjaQUbyMVaVm4eW5gfH0cL/FQogBaGw2\nErMrGjCLqIUnd7O0KJX3v88lbbhZNHpqx0wLS0Pl2BMVlmkHF7N6bA5bwgtYNiITQO43MyQLf22A\nuYrzP0KemH9Cuo2Udp+pdy2RIqzoXzEfuGncWToiAzsbO/SG0xTri5lWMInpBVMwNjeRNdIs7i37\nYjFn66tJO5hKheEUl5obZOabk50z6w6vorz2JF+dPYQd9gBX2BhW1JYTqBssM7euJty3Ztco7t9n\nBsTS0FyPu4MHNv/Z/k++ownrMY7zl85xoGI/4/JCmftpnNyfztWblaOyWTkqm7SDi6mur6KyrpIz\nDZVyuyj/aKrrqyivPUnq5ykc//EYiw4skNdHzBE+7r5kjcymR/tbWDRsyc+6765GaM8wC8tT6z+e\nEPMGwNIRy8k5vJYo/2ii/KPJKE5vtU8jtoRLsfOXiGbi+yhhz5w/jHj2mwlndXV1uLm5yd/t7Oxo\najJ7+Hbv3p3jx49z9uxZGhoaOHDgAPX19fj5+fHxxx9jMpk4dOgQZ86cobm5GZPJhI2Nebi7urpS\nW6vaCamoqKioqKj8cVCfm1RUVFRUVFRU/rgoRZW4wliid87E3taezHtWkhduXvi/2kJxWwixoLUF\nT2uR4kb2LRY9lQKUMqOrLQJ1g3l/3AccqNjPzO3TWPR5Mi8EzgfMGVDCdm7lqGyaWprQ11Vypl7P\n3T4hLDqwQC5+v/tgHmvGrkfr4sXEreHM2xNHbeNF9IZKSqtLqGk4i86lC+l3/42lwzNIO7hYflYp\nZgk2jc+XotyiAwtICV6IvY2G0uoS2S4hfimFoZzDa3l99Dr52Un5EURsCbdY9Lfu56tlAYFZ3BMZ\nadbbiH0W64t4cud0iwoRa5HB+nW9oVK2RW+oxEGRN/Rr5CxZcz3ZbkrasmactT2Sf18s443Dq5ne\nL4r5QQuwt9EQ2vM+Ojt74+nsScLeeCL7zbDoLx93X+ID51oIm0tHmCuplo7IoLOLjhc//ysfnNiM\nsdlIsM8wXh+9jpBuI2U2WOLeOWidO/PMAHN13qT8CF4tySLKP5rlXywz24r2nULUjicorS5h5ahs\nbGzMGWGAtMME89h/OiCWKsMZEoOSKK0uIWFvPCnBCzlac4TwzfdTUJbPUztm0mxqoqGpgUUHFpAY\nlMSq0mxc7d1pajFy/tJ5YndHc7qunIjbHpXirrWIKu5FIaAk7p3DUztmYmODtI8Ec3VoSLeRUlSJ\n2RXNxK3hHKjYT22jubpt6Yjl0sZU9OXGsFxSghcS0m0k9cZ6mmnm/x37J8lDFtHR2RN7W3uq66sA\n0Dp78ePl85xrqCFlSCrxgfN4asdMZnw0lYameh7oMZ5aYy3RAbF09ehK3MezGZ93HxO3/nQvCfFH\niHhXq3wSAo01ekMlq0qz8XLpTEcnT54d8DwAu8q388GJzTTRRNahTIwtRs42VHPGoGdK/mNM2BxG\nXGGsPB8AW1sb2jt2oKbhLLO2R7Ks6EWaaebh2x7hiX6zMGHimQGx0qZTmakoxkLawcW/yb2ndfGS\n87JyvlFW7AmxPLLfDBL2xpOwJ/6KijMlNjatvnxdKM9RtONG54ebFfvfasdubm4YDAb5e0tLC/b2\n5sO1a9eOxMREYmJiaN++Pf3796dDhw6EhITw/fffM2nSJAYOHEj//v2xs7OzyOUwGAx4eHj8Vs1W\nUVFRUVFRUfmvoz43qaioqKioqKj88REVHzG7omnCKLOA4OoLxVfbn6iyag1ldtXVsmuU9oBiEfj9\ncR+QGJQkF+YjtoSjsdNI27q2qhOO1hxh0efJTO8Xxa5TO+jg1IG4wliMzUaq66t4cud0Xh+9DncH\nD6YPieLouSO8dXQ93m5dWBmSLdsrrAdfG7OGozVHeOVQFtE7Z2IygckGZtweJSt7hIChzOlSVqGI\nfKLEoCTK607Kti46sADgiowy66o70XeZIeY2xOyKRmOnabOiry2RSORUKXPklO+LrDMhQFpndwkx\nULRH/CwEpPfHfYDO1Zu4wliyRmbLjDPr7LZfyq9h6QbmRfbVY3MAOH7+OOsPr6PSUIGXS2eq66vo\n6NwRAG/XLrxyKMvifgFz9Y04Z9EnogLT09mT+Xvn0tjciMbWATBXaInKsFcOZbFqzBvUNNSQuHcO\nvm7dSAleSPK++WQUL+NsQxXzBicR7DMMz6+1cvxcvFxL3CcxdHH1pdnUhLuDO+8+aK4Ae/lfGYgy\np0UHFmAymTh+/jhLi1Jpbmnm+Pnj6A2naefYHheNC2AWvjJDspi1PZLzjTWs/XoVcwbN44VPnmNp\nUSrrD6/DRWPO2FLm2yXsmSNf83H3lZln4vpY3/MiX63JZKSh8RKLPk8GYNb2SJzsnAFzrpcY24lB\nSUTteIK5g/5CnbEWW2yZM2geAFUNZ9A6eTFj+1RsTDasuXc9NQ01rPgygw5OHUjYG08nJy983H2Z\n6jedXh168cGJzQTqAunRrgdxn8TQyUlL8tCFMldNtPN6xlVFbbnMwovyj2Zy/6nyXlRmtCXvm4/W\nyYvqS2ZBzNOpExpbDRpbDYmDUyivPWXOWrTzZqrfdDKK0+Vc4uWsQ2NnT+yAODYe3cAzgbHM+XQ2\nm46bj+Pl3Jm+nn5yHCvv12J9EXM/ff43yfqqqC0ndnc0JpOlWC7aYZ2DuKo0m3mDzONYaccpUFYw\n/txcM+V81Fo7fs/8ZhVnAwcO5NNPPwXg0KFD9O7dW77X1NTE4cOH2bhxIytWrKCsrIyBAwdSWlpK\ncHAwb7/9NqGhoXTt2hWAfv368fnnnwPw6aefMmjQoN+q2SoqKioqKioq/3XU5yYVFRUVFRUVlf8b\nBOoGy4onJb80V6a1nBtlzta1FjKVi6/vj/sAgCd3Tpd5RMYWoxSgJm4NbzXjqqK2nFWl2ehcvDmg\n38dzA+OZv+8FovyjWTkqG39tgMxqSgxKYmlRKu8e24iHQzsLWzMfd19z3pThNNX1VeQcXktK8EKc\n7V2Y1m86mEysP7xOVoopRTPrbDGRT9TUYq4yEllfuePyWDkqm9xxeRbVW9bZScq+E/Z8K0dlX7cN\npkD0q782wMLGEX4SQpSvt1WxZm17mBP6lhSQrD8jRDPr4/0a/Br7EwLpogML2Hh0AzY20MHREzAL\nTxG3PUpGcTqLhi2R+WbWBOoGy34orS6RNnEp+82VjolByXg4enC05oj8TNbIbLm/VaXZpA1fTu64\nPPy1ATSbmnC2d2be4CRyj71DzK5o7G3t5dh31jiRec9K5gyah76+kguXL8jqLxsbWDYiE39tALnj\n8kgYnEzusXfo6OSJDTa8/e0G5g1O4sfL55l9Z7wce0JA9HHtiovGnH92S/uerBm7nrzwfBKDkqTN\nnqgyjQ+cS0ZxOsX6IgrK8skoTre4Ltb3vMgOXDRsCTn3/YNOjl54u3Zh9dgccsflkRK8EHcHD1aO\nyiYzJAutixedXbzJPfYOCYOTscWWjOJlrPgyA1+3rmSErMDLWYeXa2eOnz8uRexXDmXh7dqFNffm\nsGjYEpZ9sZhifTFgFkc9nT2xs7HD1cEVrYvXFePpesaV3lBJpeE0Y7vdR9wnMRSU5Vt8Xm+oZNGB\nBejrT/Ngz3BsscXLuTPPBMzGzsae9BF/Y1Xpq2w48gYAhkYD6V+8yPieDxGoG4zeUImLxpm6ywbe\n/nYDTSYjJy6cwGQykX7335h9ZzwXLv9IzK5oiyozMabNY8GGRcOW/Cbikb2tRmYkWs/B1tXNZ+ur\nWfaF2X7y51Q1Xw0xz4rqv4gt4RaVcH8EbEwmk+m32HFLSwt//etf+e677zCZTCxZsoTDhw9TX1/P\nI488wt///nd27tyJo6MjTzzxBKGh/7+9Ow+Iqlz/AP4dFpFNcWFLpaSupkZqaLllSi5wQRGRQAwy\n3FPJwARN8Lov99pNLbe0SNNKEnEBMdRM3JJIzcxKJTVNEEFDFmGGOb8/uOf8ZoaZgUEZBvx+/rky\nM+ec9z3bfTvPeZ7XGwUFBYiKikJpaSns7e2xePFiODs7448//kBcXBzkcjnc3d2xaNEimJub69x2\nXh5LEhERETUEjo729d0Ek1Cf4yaAYyciIqKGgmMn0/AwYycxe0ihlGPHMPWgjSEPG7U9MFXNWlLN\nvqqttOwUKdNmYvqbUslD1W3qaldO8S14uvSU1lEiLwEAJI9Ikdp25PphzMp4B842LrBv0kzKFhH7\nMuXgeKwbtEkKjImf3Sy8gSfs2yJ5REqVjCptfc7KycTJmyew4ofF2OWfAk+XnlKQTcxQUs1Q05yz\nTDPIpZmhVtP9rG0bqu1QncdKc52qf2flZFYJkqm2KysnE1FHIgFAmoPLVKmexznFtzDhm7EQhMog\n1O2SXGwa8hk8HLtK55MuadkpUjajo40ThiUNBQC42LkiostE/DtrCd71nIPd2UlI8N4mlSYsVZTA\nQmYpzUPmn+yDKc9HIvXqHsT1mg9HGycpKyqn+BamH5oiZej8M2kQ8kvuwNHGGTN7xGBWxjtwsnZB\nM6tm0jUjzlm2Mms5Ph6SgF/zLyImIwpu9k8hcXgyjlw/jFbWreDh2BXn887Bw7FrlcxJzfMU+P9r\nZHL6eNwqvolNQz6Dt7uv2n5VPX/SslMw4ZuxgAyI6VEZtHayccbegAMAKsuyiqVUVc8d0fBkbzg0\naYHmVg5Y8+o65JXcxpxjs1CmKMOdB3mIe2kBAjoESr8X2x6w2xel8gfILb0FSzNLONu4wFxmgfWD\nN0llDlWPa02vJXG5tOyUKv0W+3Ly5gks/X4B5JDDDOYwNzODXCnH291mYvXZlQj6x2jsuLQdABDR\neSK++H0rNgz6BEtPL8IQNx+sPrsScS8twMbza5FTUhkc9XvKH1cKL0uZt2L2qOaxmpw+Xurjo6Ya\naNe3/m0XtiAmI0rtOGvuW133veqIfY32nCVlcoplb8Xz1FSCZw8zbqqzwFl94sMfIiKihoEPf0wD\nx05EREQNA8dOpuFhx06qD7/Fv/WVUtS2fGhKEOQVcqlkIAC1B+wP2z7xQfBre0dgx7DkagMX2ton\ntiun+BYmp49HTslfSPZPVSutdz7vHBxtnKSgkfjQWwxqWFvYqJWRjDoSKT209nDsqjXopNmHEcm+\nyCn+C442TtgbcED6jRjUUw1iaa5D9bjoC3yJpcqqC1hqbkP1MzGgutprnRSs0RYEFEtpqpaGG7H7\nn3C1fQK7/FOkfaUZ4DNVN+/f+N/cdfmIzYiGi+0TmPFCNNaeWy3tC0D3g33Vh/je7r5SwKZEXoK7\npQV4yqE93uoaiYRfNqtdH1k5mcgruY34E3NgbVFZDvHNtNcr54sWgDb27aTgrBgMUL0egMrSpLEZ\n0XC0cUJucQ6cbV2wpF9l9lfc8Tn/a9+fMDMzw/KX36/MwHxuKsZ6RODI9cN457tpAAAXG1fkl97B\niv7/xZgu4VK/xPMpp/iW2nWjFki+fwObh26RAkjazltx/+SX5mPj+XXIK86DQ1MHKWAoBj3E/3Wx\ndcWR64elffZm2uvIL72DzUO3VB6nY9GoqKhAaxtHTPR4C4mXvqxyfG7ev4Fdv+/EF79tRX5pPuJ6\nzcfac6sR12u+dJy0XV/VBXBUryFt16zqPaSwrBCj/hGMry99hZk9YqQAZljKaLzXKx7/OjEXhfK/\n8Zl3ZQDN0cYJk9PHAwBmvBCNAW5eCE0JQm+Xvki89CXuywvR1q4dlvRbIc0nphn4O593DlHfvg2H\npg5qGa2PkrbgueY+Ck0JQmFZITYNTdB7L9B2T6oJ8f4pr5CrlXs09P/P6trDjJvqrFQjERERERER\nERERqVOdl0b8W99cZdqW3+6biMThydJDZnEdjyJoplqGUZxLRwwcaJYz1Cen+BbGpo2Bi60rkkek\nINk/Va20HlA575QYGFG13TcRu/xT1AJUsRkzpSyYCeljkVN8Cwne2+Bi64rQlCCp3aolGwHAxtIG\nHw9JkIJm4m9USziK+1XfcRH3u+qDfdXfjE0bg6ycTL0l0TS3ofrZ+wNWw8LMEnklt3XuU835lMTP\nlvVbCWuLynmzxH3VEGTlZCJgty/C00bj3e9mwP/pQCzsuwQbz6/Daq918HTpWWWfa1ItWSlaN2gT\nLM2awMnWBXG95mNMl3C160MsZbfg5DxYyCzR26UvAMDMzAxKQYlW1o6QyYAj1w+rlcQThMr5s/yT\nfTDl4HgMcPNC8ohU7A04gBX9/wtzmQXmHJuFCeljMeOFaCzsuwQyMxlie8ZhgJsXJj43FRvOf4jz\neecwwM0LEZ0nwkJmgeAOYyAX5IjJiEJWTqbaOZxTfAsj9/hJ57t4Dk8/NAVvd4/GE3ZtsfT0IrUA\niGrQQvw7KycLH51djYkeU1CkKMTUbpGYfmiKWrAMqJzz73zeOUR9Nx1jO4+Di63r/7IAZbh89zLm\nHH8XMT3mopW1I+6WFaBF0xZaj6tf0lAs/D4e3R17oKAsH1f/vgq5Uo6lpxchKydTKvOnGgCrjnh/\nCk0JQlp2itp1L/Z1u28iPF16/u96ssDu7J24XZoDAGhqbo2TN08gvywP73w3DSWKYsS9tAAejl2x\n9PQiTD80BRWCAn8V30Ar68qyobNfnIttv32GUnkJnKydEe0Zg5VZKxDtOUtredSIA2G4U3YbRfK6\neTlVzDjTt8/E+4m1ReX8deK9SddvDQ1wZeVkSnPCicFBXWVCGzJmnBEREVG94VvTpoFjJyIiooaB\nYyfTUBdjp7p8S9+Qslma2WKv7R0BCzNLKYNDNcutuqwQMUtGXzlKzWwHcb4mbftBXOf0Q1Nw/f5V\n7B6xX21+M9X1qG5L89+qGWKqy+jqR02Pi2qGUG2Podh/MYuuphmImtmG+ko9mgrV7KC07P3Y/usW\n5D24jdZWTrCzspWy5/RRzbwRzwExu2pC+lg4Xrjk+wAAHqVJREFUWjvBvkkzrSUrxay8pacWYcel\n7WjexAHmMnMUlhcivNObSL26F/fK7mLj4E/h7e6rlvEYf2IOLGSWUtBA7EuJvAQVggIRXSZid3YS\noj1nYfyBN+Bk6wx5hQL3ygvg0KQlrC2bwkJmiQcVpcgtzsHmoVuQlZMFTxdPtcwxXeU5xSzDdnZP\nIr73fKnEoy4fZq3Cgu/jYA5z7Bv5jfS5ank9zW2mZadIZTIDdvtiVo/3pD6JJSyHuPngm+v71dYh\nlpAsq3iAnJJbaGvXDsXyYpTIi6VsQjGTTSw5KJZbBarPfBLvA+Jcb7r6Lh6TiR5T8O53M9C2WeWc\n4IIAFDzIx315IVpbOSH9tSNqyyWc/wQfnfsAbe3bSRmvR64fxsqs5TCXWcDG0gYTPaYg4ZfNVe4L\nYlbiy08MQNCzwQa9yFCTa1Qzu7K634rXV17JbbXs2ppuT18b9N1vTAlLNWrgwx8iIqKGgQ9/TAPH\nTkRERA0Dx06m4VGNnYwR3KhNQE4z0ASol0UT/9ZHs6Sg5vxr+tqqq9yk6kNjANLDe0Pn8arNfHLG\neCis+qBbsyxfdW3TLOunGWTRVfqxPon9nf3iXGl+sqycLOzO3qkWlNJF9RxT3V8ApBKgIs3+iwGO\nuF7zEXEgHApBAQszCyiVFQAAAQLMYIY3Oo/DdM8ZUqBmbOdx2Hh+nXSMVNsnln6ckD4W7eyexNRu\nkXi2VSfkldxGTEY08kvvILZnHHq36SMF4BacnIdSRSkqBAVyS3LgZO2CBJ/PdZ7/mtsDUO11JS7r\ns/NVxPR8D2O6hKuda7quHdV7h2rWKVAZnPR3H4llmQvhattGbe7C1/aOwNW//8DsF+PxyYWNiOgy\nEct/WITJHtPh7e6jdS4sQ6+xtOwUONo4ScEzXUEk8Xf+yT7YNOQzAJUlNB8oHiCvNBcJ3tuk9Sx7\n+T/4Nf8i3vluGsxhgU+9t0plZENTglCqKMG6QZuQV3JbyjjTtt207BRMTH8TLrauNQr+au7rmtxb\na3rMz+edw4KT86TPVAO9tX1RQ/MeU9MSm/WFpRqJiIiIiIiIiIgaCM1ygoDhc8xUt35xnYY+HNUs\nV6hagkuc26k6LrauUtBMLB0olmWrrq26HuSLpd0WnJyHhafmYcrB8SgsK0TUkcgal3rT7F9d/P5h\nKJRyKSCjLZtFtZ+q55D4MDxozwgE7PaVgipiSUJ5hbzKtuqbWFbP290XScP3wcOxK765vh8WMkvE\n955f7X5XLVupur+OXD+MN9JCkZa9XyoDqtn/nOJbuH7/Go5c/xYKQQ4XGxdMff5tPGHXBk42zgj6\nx2hABnzyy0b47HwVUUciEe05CxvPr4O8Qo68kttVjk1sxkx4OHbFx4MTMLVbJGIzojFi9z+RX5qP\nOyV5aNm0FRIvfQkXW1ep34nDk7FpaAIAwM7SHvkP8jD90BS9x1q1/54uPWt0fbexb4tPvT9Hwi+b\n1dbhYuuqs/yqailFzTkEoz1nYfuvWyEIAhb2XaJ2j4jrNR8AsOXiJxAE4IvftmKyx3Rs/PkjqQyp\n5voMDZqFp41GXsltRHvOwsT0N7WWIczKycTE9DeRV3IbbvZPwdHGCUtPLwIAKIUKCBBw+e5lKSA6\n/dAUfHR2NZytXdGuWTspoAZUlmu0kFkCABaemodoz1lYmbVC637zdvfFxsGfSr+vCUPu0zU55mIA\nc8HJeVAIcqx5dZ1aILq25RTFIJl4jjZ2DJwREREREREREREZkaHzmhlC8yH7owr8iOXhXts7Qu9D\nU3H7qg/HdQXENOcEq66tLrausDS3RFyv+RAEwMbSGu8PWG2SmQ61IdYF05aNqG0+J82Sk4nDk7HL\nP0Xa12JwqrrsrfoitkkMfm33TcSaV9dhZdYKnXMyqVI9p8TgYcIvmxHZLRrrz6/B7eJc5JXchkxW\ndbll/VZi26+fwdnaFcEdxmD9T2tQIVQgr+Q2Ei99gQqhAs0sm8PKvCneH7Aa3u6+2O6biPje8zHp\nYATSslPUti0eiznHKgNsHw9JQLJ/Kga4eeGp5u3xqffnanMSisvlldxGTkkO7ssLoRAUmNqtMtin\neV3oCnTU9LiqBlzEfQ2gynklEoOBqteoeB4uPb1ICm6qZvbdvH8DHo5dsScgrXKeOXMLlMhL8c31\n/dgw6BNpH+rLOtX2b1Uejl3xVLP28HDsCg/Hrmhr5ybda1S52LqirZ0bPBy7InF4MoDKuRMX9l0C\nh6YOaGHVEl/8tlUKiD6oKEV87/lIG3UI6wZtkvYXAClr69f8i7h+/xocbZz0Bp48HLtq/Vyf2r7c\noIuFWWUAWpz7UHOZ2twPxLnTLM0tpZcoxHPJFO8vD4uBMyIiIiIiIiIionqgmUXyKKg+ZH+U6/Z0\n6Ylk/1TsGKY/CKPtIb+uTDMx26OmD13Fh7SONk6wsbTBW10jtT4018XUsyTEB9LashG3+yZqfUCt\nGWDRFqBsCA+1xWChp0tPRHvOQmzGTIOPl5gl9V6fePi7ByK/7A7e/S4K2iYqGuDmBVe7JxDScQzW\nnlsFGws7THhuCpRQQkDlAiWKYshk6hlSHo5dsWHQJ1UyjtrYt8X5vHO4UfQnJnpMgbe7r5Q9uGNY\nsvQbTY42Tmhj2xaOTZ3Q1q4dnm3VCQBqdA0ZSrwniPta9bzS9lsx4K0ZcNvumwgPx66wkFlKGZ+q\nwd28kttwsXWFQqmAtUVlcLu6YJJqwF9bhp1qu8QSiGKwWFdAUfzufN45jNzjh/N557AyawXe6hqJ\nYkURSuSl8HDsitkvzkVe6W0sODkPOcW3pD6J67U0rwxCbTy/Dh8PTpACvfpYmtc84+xRU83mfH/A\n6lpdS7p4uvSsss6GcH+pDc5xRkRERPWG83SYBo6diIiIGgaOnUzDoxw71eX8WQ8zj42xiPMBqWZJ\nVUfs19jO4xB7LBpu9k9WG8xTXc6U94ehcz5pm9NM83tT7zOg3k4A0lx3YuDJkP0hzoM3Mf1N2FjY\nYpvvDgDq2WniA/8Ryb74q+gG5EJlKcdWVq2RX5YPQICDVQvM67UQA9y8APx/0Clgty92+adIn2lK\ny06Bt7uvWp/O553DpIMRUmlJbe1dcHIeQp8Nw5aLn8DawqZOsnjE4BYAtfnNajtPlWbgRJxXa9LB\nCCzp+2/EHovGx4MT4OHYVZpTTd/8XJpzKj6K/qvuYzF418a+LdKyU7D09CKpz2nZKdL34vx34hxm\nYruqmxdOc7uP6vg97Lrqav5MU76niDjHGRERERERERERUQNTlw8eazuPjTHpK7WmT4L3NozpEl6j\nDDhRQ9gfhgaJQlOC9M7x1hD6DKi3U/y3i60rxqaNQVp2So0zM8VlPRy7YpLHNDjbugCAWnaMGEgB\ngPWDN2Hz0C140r493u42Ey2atsTb3aJhBjPcK7uLVWdWqmUAns87h6uFf+B83jmd+1QMtqiWb1yZ\ntQIbBn1SJeAi/sbRxglypRzLMhfi5v0bmP3i3Do5ZmImkrYsJH3z4Ok7vzSz4sQ56wa4eaGd3ZNV\nMs2qOx9V5+17FFTPCfG4A1ArG3nz/g2szFoBoHL+uxJ5aZW508QyhTWdU/FRBs2qK49rrLbU9TpN\nDTPOiIiIqN7wrWnTwLETERFRw8Cxk2ng2OnRMuQheUPJoKotQ/unWSqwscnKyUTUkcgaZ/kAkLLC\nrhb+gf++8iHGdAnXOmccAGlfi+X5SuQlmPFCNKK+m47IbtEY6xFRJZi57cIWjOkSblA/9J3j4jFf\n9vJ/pM9q2teHodomfRlnWTmZerPEarqNtOwUrMxaoXc9dV36r7rjAFRmlckr5IjvPb9K5qD4vTHn\n9BLnlkz2TzXKedHYPMy4iYEzE9ZQUh6JiIhqiw9/TMPDjp04ZiEiIjIOjp1MQ2N57tRQNfaxp6H9\na8zBxNqWEMzKycSUg+OlubD0rV/8XgzSTfSYgpiMKOwesb9KoKKu9rUpnNPa2qAa1HuYoE1N1mMq\n57G24J1mkNHY7cvKyWTQrJYYOGuE/vz7TwR8FYBdwbvQrnm7+m4OERERkVYcsxARERER1a8///6z\n0Y7Fa9u32iz3/Y3vMTV1KuL7x2P4s8MfaXsaqkfV35qs53Hbt2TaGDgjIiIiIiIiIiIiIiIiAmBW\n3w0gIiIiIiIiIiIiIiIiMgUMnBERERERERERERERERGBgTMiIiIiIiIiIiIiIiIiAAycERERERER\nEREREREREQFg4IyIiIiIiIiIiIiIiIgIAANnRERERERERERERERERAAYOHvsKJVKxMfHIzg4GGFh\nYbh27Zra94cPH0ZgYCCCg4OxY8cOvctcu3YNo0ePRmhoKObNmwelUimtp6CgAEOHDkVZWZnxOtfA\nGePYJCQkICgoCEFBQfjwww+N28EGzBjHZtu2bQgMDMSoUaOQmppq3A42UMa6nymVSowfPx5ffPGF\n8TpHRCaDYyfTxbGTaeK4yXRx7ETU+J07dw5hYWFVPtd2fcvlckRHRyMkJAShoaG4cuWKsZtrMEP6\nV15ejujoaLz22muIiIjA1atXjdxaw+jqGwCUlpYiJCREOkbV3c9NkSH9q8kypsaQ/snlcrz77rsI\nDQ3FqFGjcOjQIWM21WCG9K2iogKzZ89GSEgIRo8ejd9//92YTa2V2pyb+fn5eOWVV0z+vmlo3wIC\nAhAWFoawsDDMnj3bWM2sNUP7t2HDBgQHB2PkyJFITEysfgMCPVYOHDggxMTECIIgCGfOnBEmT54s\nfVdeXi4MGjRIuHfvnlBWViaMHDlSyMvL07nMpEmThFOnTgmCIAhxcXHCN998IwiCIBw9elTw9/cX\nunfvLjx48MCY3WvQ6vrYXL9+XQgICBAUCoWgVCqF4OBg4eLFi0buZcNU18cmPz9f8PX1FcrLy4X7\n9+8L/fv3F5RKpZF72fAY434mCIKwcuVKISgoSNi+fbuxukZEJoRjJ9PFsZNp4rjJdHHsRNS4bdy4\nUfDz8xOCgoLUPtd1faenpwuRkZGCIAjCsWPHhGnTptVHs2vM0P5t3bpVmDt3riAIgnDlyhUhIiKi\nPppdI7r6JgiC8NNPPwkBAQFCnz59hMuXLwuCoP9+booM7V91y5gaQ/v39ddfC4sWLRIEQRDu3r0r\nvPLKK8ZsrkEM7Vt6eroQGxsrCIIgnDp1qlGem+Xl5cJbb70lDBkyRO1zU2No3x48eCD4+/sbu5m1\nZmj/Tp06JUyaNEmoqKgQioqKhNWrV1e7DWacPWaysrLw8ssvAwC6deuGn3/+WfruypUrcHNzQ/Pm\nzdGkSRN4enoiMzNT5zIXLlzAiy++CADo378/Tpw4AQAwMzPDp59+CgcHB2N2rcGr62Pj4uKCTZs2\nwdzcHDKZDAqFAlZWVkbuZcNU18emZcuWSE5OhqWlJe7cuQMrKyvIZDIj97LhMcb9LC0tDTKZTFqG\niB4/HDuZLo6dTBPHTaaLYyeixs3NzQ1r1qyp8rmu67t9+/aoqKiAUqlEUVERLCws6qHVNWdo/y5f\nvoz+/fsDANzd3U06M0RX34DKzLmPPvoI7u7u0mf67uemyND+VbeMqTG0f97e3nj77bcBAIIgwNzc\n3CjtrA1D+zZo0CAsXLgQAPDXX3+hWbNmRmlnbdXm3Fy+fDlCQkLg5ORkjCbWmqF9+/XXX1FaWoqI\niAiEh4fj7NmzxmpqrRjav2PHjqFDhw6YOnUqJk+ejAEDBlS7DQbOHjNFRUWws7OT/jY3N4dCoZC+\ns7e3l76ztbVFUVGRzmUEQZD+I9XW1hb3798HAPTt2xctWrQwRncalbo+NpaWlmjZsiUEQcDy5cvR\nuXNntG/f3ki9a9iMcd1YWFjg888/R3BwMIYPH26MbjV4dX1cfv/9d+zbt08a0BLR44ljJ9PFsZNp\n4rjJdHHsRNS4DR06VGvwS9f1bWNjg5s3b8LHxwdxcXEmXxLP0P516tQJ3377LQRBwNmzZ5Gbm4uK\nigpjNrnGdPUNADw9PeHq6qr2mb77uSkytH/VLWNqDO2fra0t7OzsUFRUhMjISMyYMcMYzayV2hw7\nCwsLxMTEYOHChRg2bFhdN/GhGNq/pKQktGzZskG8IGRo35o2bYpx48Zh8+bNmD9/PmbOnNmo7it3\n797Fzz//jFWrVkn9EwRB7zYYOHvM2NnZobi4WPpbqVRKJ5nmd8XFxbC3t9e5jJmZmdpvTf0tAlNn\njGNTVlaGmTNnori4GPPmzavrLjUaxrpuXn/9dWRkZCAzMxOnTp2qyy41CnV9XJKTk5Gbm4s33ngD\nu3btQkJCAo4ePWqEnhGRKeHYyXRx7GSaOG4yXRw7ET2edF3fCQkJ6NevHw4cOIDdu3cjNja2Qc61\nqqt/gYGBsLOzQ2hoKNLT09GlSxeTzuwxhL77OTUMt27dQnh4OPz9/U0+uFQby5cvx4EDBxAXF4eS\nkpL6bs4js3PnTpw4cQJhYWG4ePEiYmJikJeXV9/NeiTat2+P4cOHQyaToX379nBwcGg0fQMABwcH\n9OvXD02aNIG7uzusrKxQUFCgdxkGzh4zL7zwgvQfL2fPnkWHDh2k755++mlcu3YN9+7dQ3l5OX74\n4Qd0795d5zKdO3fG999/DwA4evQoevToYeTeNC51fWwEQcBbb72Fjh07YsGCBY1mwGgMdX1ssrOz\nMW3aNAiCAEtLSzRp0kTtYQRpV9fHZdasWUhMTMTWrVsREBCAsWPHSqU+iOjxwbGT6eLYyTRx3GS6\nOHYiejzpur6bNWsmZWo1b94cCoXCZDOy9NHVv/Pnz6N379744osv4O3tjXbt2tV3Ux8ZffdzMn13\n7txBREQE3n33XYwaNaq+m/NIJScnY8OGDQAAa2tryGSyRjVO27ZtGz7//HNs3boVnTp1wvLly+Ho\n6FjfzXokvv76ayxbtgwAkJubi6KiokbTN6AyCy0jIwOCICA3NxelpaXVTpXA1xEeM4MHD8bx48cR\nEhICQRCwZMkS7N27FyUlJQgODkZsbCzGjRsHQRAQGBgIZ2dnrcsAQExMDOLi4vD+++/D3d0dQ4cO\nrefeNWx1fWwOHjyI06dPo7y8HBkZGQCAqKgodO/evT673SDU9bExNzfHs88+i+DgYGlOCHHOCNKN\n9zMiMgbea0wXx06mieMm08X7GdHjpbrre+zYsZgzZw5CQ0Mhl8vxzjvvwMbGpr6bXWPV9c/S0hKr\nVq3C+vXrYW9vj8WLF9d3k2tMtW/a6Lo3NxTV9a+hq65/69evR2FhIdauXYu1a9cCAD7++GM0bdrU\nmM2sler6NmTIEMyePRtjxoyBQqHAnDlzGkS/RI353Kyub6NGjcLs2bMxevRoyGQyLFmypEFlslbX\nv4EDByIzMxOjRo2CIAiIj4+v9sVImVBdMUciIiIiIiIiIiIiIiKix0DjyZUkIiIiIiIiIiIiIiIi\neggMnBERERERERERERERERGBgTMiIiIiIiIiIiIiIiIiAAycEREREREREREREREREQFg4IyIiIiI\niIiIiIiIiIgIAANnRGRkSUlJiI2Nre9mPLSwsDB8//339d0MIiIiauQ4diIiIiLS7saNG/Dy8tL6\nXceOHet02/7+/nW6fiKqXwycERERERERERERERHV0O7du+u7CURUhyzquwFEZHoUCgX+9a9/4dKl\nS7hz5w7at28Pd3d3ODs7Y9y4cQCAyMhI+Pn54fnnn8fMmTPx999/o0OHDsjMzMTRo0f1rv/atWsY\nM2YM7t27h4EDByI6OhoymQw7d+7Ep59+CplMhi5duiAuLg62trY617N8+XIcP34c5ubmePXVVzFt\n2jSsWbMGV69exfXr13Hv3j0EBwdj/PjxSEpKwq5du6RthoeHIz4+Hjk5OZDJZIiOjkafPn2Qm5uL\nOXPm4P79+8jLy4Ovry9mzpyJ8vJyvPfee/j555/Rpk0b3L1795HucyIiImq4OHbi2ImIiIjq3vr1\n67Fnzx6Ym5ujb9++CA0NxYMHD/DOO+/g0qVLaNasGT766CO0aNFCWubevXt47733kJ2djSZNmiA2\nNha9e/fWuQ0vLy94eXnhhx9+AAAsWbIEnTt3RlhYGJo3b45Lly7hgw8+wIgRI/Dbb7/pXP/Ro0ex\nevVqKBQKtG3bFgsXLlRrFxGZNmacEVEVZ86cgaWlJb766iukp6ejrKwMLi4uSElJAQAUFRXhxx9/\nxIABA7B48WL4+Phg79698Pb2Rm5ubrXrv3HjBtasWYNdu3YhKysLhw4dwm+//Yb169dj69at2Lt3\nL6ytrfHhhx/qXMfNmzdx9OhR7NmzB19++SWuXr2KsrIyAMDvv/+OhIQEJCUl4auvvsKFCxcAALm5\nudi1axeioqKwePFiBAYGIikpCevWrUN8fDyKioqwb98++Pn5YceOHdizZw+2b9+OgoICbN26FQCw\nf/9+zJ07F9evX3/Y3UxERESNBMdOHDsRERFR3fruu+9w+PBh6eWea9euISMjAwUFBXjzzTexb98+\ntG7dGqmpqWrLrVq1Cm5ubti/fz9WrFiBDz74oNptOTg4IDk5GZGRkYiJiZE+79ixIw4cOIBOnTrp\nXX9BQQFWrlyJzZs3Izk5Gf369cN//vOfR7cziKjOMeOMiKro2bMnHBwcsG3bNmRnZ+Pq1ato0aIF\nysvLce3aNZw5cwYDBw5EkyZNcPz4cSxduhQAMHjwYDRr1qza9Xt5eaFly5YAAB8fH5w+fRo5OTkY\nOHCg9PZNcHAwZs+erXMdzs7OsLKyQkhICAYOHIgZM2bAysoKAODn5ye9be3l5YVTp06hRYsW6Ny5\nMywsKm97J06cQHZ2NlavXg2g8k3xP//8E+PGjcOpU6ewefNmXLp0CXK5HKWlpTh9+jSCg4MBAE89\n9RS6d+9em11LREREjRDHThw7ERERUd06deoUfH190bRpUwBAYGAgkpOT4eTkhOeffx4A8Mwzz1TJ\ncs/MzJSCVh07dsRXX31V7bZee+01AJXjotjYWBQUFACAtJ3q1v/tt9/i1q1bCA8PBwAolUo0b968\nNt0monrCwBkRVXHo0CGsXr0a4eHhGDlyJO7evQtBEDB8+HCkpqbizJkzmDBhAgDA3NwcgiAYtH7x\nAQwACIIACwsLKJVKtd8IggCFQqF3HYmJiTh9+jSOHj2KkJAQ6c1mc3Nz6XdKpVL6WxxciZ9/9tln\ncHBwAFD5RnXr1q2xbNky/Pnnn/Dz88OgQYNw4sQJCIIAmUym1kbVPhAREdHjjWMnjp2IiIiobmmO\nfYDKF3lUxxgymazKOEtzDHLlyhW0b98eZma6C7GpLqNrbKRv/RUVFXjhhRewfv16AEBZWRmKi4t1\nbo+ITA9LNRJRFSdPnoSPjw8CAwPRunVrZGZmoqKiAsOGDUNqaiquXbuGHj16AAD69OmDvXv3AqhM\nmy8sLKx2/eLvysrKkJKSgj59+uDFF1/E4cOHce/ePQDAjh078NJLL+lcxy+//ILXX38dPXv2RExM\nDJ5++mn88ccfAICDBw+ivLwcf//9N7799lv069evyvK9evXC9u3bAQCXL1/G8OHDUVpaiuPHj2Pc\nuHHw8fHBrVu3kJubC6VSid69e2Pfvn1QKpW4efMmfvzxR8N2KhERETVaHDtx7ERERER1q1evXkhJ\nScGDBw+gUCiwc+dO9OrVq9rlevToIZVvvHLlCiZMmACZTKZ3GbHcdnp6Op5++mm92WLa1v/888/j\n7Nmz0lhr7dq1WLFiRY36SUSmga/9EVEVQUFBmDlzJtLS0tCkSRN069YNN27cgKurK1q0aIFu3bpJ\ng4w5c+YgJiYGO3bswLPPPlujckPu7u6YOHEiCgsL4efnJz2cmTRpEsLCwiCXy9GlSxfMnz9f5zo6\nd+6Mbt26wc/PD9bW1ujUqRP69++PCxcuwMrKCqGhoSgqKsKkSZPwzDPP4KefflJbfu7cuYiPj8ew\nYcMAACtWrICdnR0mTZqEWbNmoVmzZmjVqhWee+453LhxA6Ghobh06RJ8fHzQpk0bdOjQoba7l4iI\niBoZjp04diIiIqK6NXDgQFy8eBGBgYFQKBR4+eWXMXDgQGzZskXvcpGRkZg7dy6GDx8OCwsLrFix\notrA2Y8//oivv/4a1tbWWLZsmcHrd3JywpIlSzBjxgwolUo4Ozvj3//+t8F9JqL6IxMMrRNCRKRi\ny5Yt6NOnD5555hlcuHABcXFxSEpKqrf2rFmzBgAwffr0emsDERERkS4cOxERERGZLi8vL2zZsgVt\n27at76YQUT1ixhkRPZQnn3wSUVFRMDMzg5WVFRYuXIjU1FRs2LBB6+93795t0PrDwsK0ljAKCQnB\n6NGja9VmIiIiovrCsRMRERFR/dI3XiIiAphxRkRERERERERERERERAQAMKvvBhARERERERERERER\nERGZAgbOiIiIiIiIiIiIiIiIiMDAGREREREREREREREREREABs6IiIiIiIiIiIiIiIiIADBwRkRE\nRERERERERERERAQA+D9nDXmK/svmdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3d41f6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, axarr = plt.subplots(2, 3, figsize=(30,10)) #1 row, 2 cols, x, y\n",
+    "irow, icol = 0,0\n",
+    "fig.suptitle(\"pca against features\")\n",
+    "\n",
+    "if simname != \"bm_kaggle\":\n",
+    "    \n",
+    "    icol = pltGraph(\"ohlc_price\", \"pca\", irow, icol, df)\n",
+    "    icol = pltGraph(\"avg_bo_spread\", \"pca\", irow, icol, df)\n",
+    "    icol = pltGraph(\"avg_bo_spread\", \"ohlc_price\", irow, icol, df)\n",
+    "    irow+=1\n",
+    "    icol=0\n",
+    "    icol = pltGraph(\"avg_bo_spread\", \"period_return\", irow, icol, df)\n",
+    "    icol = pltGraph(\"avg_bo_spread\", \"period_return\", irow, icol, df, yval=df['period_return'].shift(periods=1).fillna(method=\"bfill\"), title=\"avg bo spread v future period return\")\n",
+    "    icol = pltGraph(\"ohlc_price\", \"pca\", irow, icol, df, xval=df['ohlc_price'].shift(periods=1).fillna(method=\"bfill\"), title=\"ohlc 15 min future v pca\")\n",
+    "       \n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+b67bU1iOC2cvN5q8/R/WDfiE71qPJvbcVeoN6phtWo/y/jzx+WDKPVE30/Ki\nZYuTEpPAso7jbf/upPNZ0HXxV+16bm1/yQ6PUaSUP9tDB7Hz+eGU6tSaolUrEn/6PDt6DLH9u7Zj\nH5d+3sSVDTs4M/972/Ld/d4kI8ka2726rsefirCdC9u6DyVq+z4u/ryJyA07OP3lMttygNTEFH4Z\nOCtLufZuN+X+pQ7ov9jRo0fZuXMnAM2bNyc5OdnOERWs/1u4iw4da/NEq2p/a76+ITW5cTichHOX\nADj33S8EPPHQHaVz8vWi2MP12TXwnUzruJQOIC0ukaidBwHrL/hp8Yl41agMwI2wP/M7v2Q1/k80\nyVKuT0itHNMVe7gBESs2YEnPIC02nku/bCHgiaYYHc2cnL2YqJ0HAEiOjCLlRixOxXwAcPT2oMqQ\nFwDISEq5JzE4+XnhWrYEl37ZAsC1rXsxOTvhHlSO5GvXOTJ5VubRY38/AGKPnmTrM/1Jj0/A6GjG\nyc/bNppscHCg0ms9qT9vMg3mT6XK6JcxuRTJEi/AheVrufTzJgDS4xNIOH8JZ39fvENqEhMWTuIf\n2xKxZDX+LbNu801b9yVTvaKZMiWsPxo819KFH39LxGKxZJt+7vdxeHuYePZxVwAOh6fS5uEimEwG\nzGYDTeo6s2Zr3jp+meLYm0S1So6UKWEG4Nkn3Fi1MSHHOL5YGoO3h5FnWlpHDMLCU2jdzCVTHL9s\nTchT2cUa1SD60Cniz1lHqE8tXktgq6wzEHJLV6r1Q5yYv4rUmHiwWNj39lzOrbTun5ItQki+ep1D\n7/9fnuujVOOqRB44w40zkQAc/vpXKrYNyTZt9dBmHFmyhZM/7cq03K9qaY4v344lw0JGajpnfj1I\nhZZ18lR+oTku7BSHT0gtEiIuc3XLHgCubNzF/jfeB6wdt8oDehIy7x0aLphCtdH9MLlmPU8NJiN+\nD9UhYtkaAOKOnyHh3EV8G9XGYHbAvXJZyoS2peGCKdR8ZzDOxX2y5OHXqAbXD5+0HXNnvl1DyVaN\n7zidT72q+D1YkzPfrbUts6Sls6bVq8QcPQOAS8lipNyIIzeF5bgo0bgaVw+eJuaP8+PI1xuo0Cb7\n86NKl0c4vnQzp376PdPy4sEVsKRn0GruYNovfZPafdtgMObtByIo+Lr4q3Y9t7bf7+EQLv6w3nYd\nu7xmc5ZroWetByj2SEOOTJ6ZpeyK/Xtwbete4N5d1zPFUvsBijVvSNjkrJ1MgHMbD3Lut6w/1tu7\n3ZT7lzqR54WLAAAgAElEQVSg/2KrV6/mxIkT9g7DbkaNaUW79jX/9nydi/uQFHnN9jkp8hpmN5cs\nN0y5pUu+Gs3eYe8Rfyoi0zrxZy9icnHCJ8Qad9Eq5XErH4iTrycAyZf/zC85l3JzSudc3IfkyMzf\nORXzJiMllQsr/pwSU7L9o5iKOHPj4DEwGqg+vj/Hps8HwJKWlm3e+Y3BubgvyVei4ZabjaQrUTgX\n8yb+5Dmi94QB2EaDI9dttaWzpKfj27Q+jZd/imftqlxYad2Wsj3aY0nPYGfPYezoPoSUK1E5jl5e\nXLmBjOQUALwb1sajRhDXtu3FuZgvSZevZorXwc0l2zwALl1Np7ivyfa5uI+JuAQL8YlZb6KiY9L5\ncnkcQ3oVtS2rUcmRH35NJDXNQkJiBmu2JXIl+s6n/Fy+mo6/zx3EsSyWIS94/RlHZSdWbkiwxbF2\nayJX8xhHkeI+JF6+9diPwuzugsNtx0lu6dzKBODkXZSGM4bS7JuJPPBSR1JjrR3g09+t4+jMpaT/\nsb/ywjXAi7hLUbbPcZeicXIvgtnVOUvaTW99zfHlWUduLu8/RaV2IRgdjDi4OFH+8WBc/DzyVH5h\nOS7sFYdL6QBSrl2n6hsvEfLFJOpMH4XBZI2jXM/2WNLT2d5zONu6DSX5anT2o0Ae7mAwkHr9z2nj\nyZFROBXzwcnXi+jfD3Li46/Y1m0oNw4eo9bUoVnycC7uQ9Itx0FSZBRmt6zHZm7pnHw9qfZ6d/aM\n+hjSM2+7JS0dR++iPLZqOlVe60L4lz/kWi+F5bhw8/ci/lK07XP85Wgc3V2yPT+2vf1/hK/YlmW5\nwWQiYuthfn7xQ37sMYWSjatRJbR5nmMo6LrIrl2/9QfKnNp+k0sRnIv5kHT59utY5h88KvbvwcnP\n/i/LtF7XcoH4Na1P+MxvrOveo+v6rSq/2p0Tn36dZWqva7lAAHZ+tDy7KrJ7u1kYGTLSC/2/wkDP\ngN5nlixZwvr160lKSuLKlSv06NGDtWvXcvz4cYYOHUpCQgLz5s3D0dGRsmXLMn78eFasWMGvv/5K\nUlISZ8+epU+fPjRu3JilS5diNpupVs06Ajh27FjOnz8PwIwZM/DwuH8bAHsyGHP4Xee2G5G8psv0\nVXwiu1+fRuW+nQnqH0r0njCu7TqU6bm321luz8+QfbmW9AzI7tfo255nKNvjKUp3epLdAyaSkZxK\npVdCub4njKgdB+5tDDlMpbTcEp/Z051ak6zTcsM/yTz6dXXjTn7buJMSTz1K8Aej2PLMq/g0rovZ\n3QXvBtYOvdHsQEr0jRy3A8D/yYep1L8HB0a+S8q169nHm4scfqwnu8Phu18SeKS+M4HF/2yqB/+n\nKO/Ni6HT61fw8zLSqJYTe4/kvaN1U0YOcZiyi2N1PM0aFKHkLXEM6uXJ+19cp/OgS/h6m2hY25l9\nR/I4iyKHOstynOSSzuBgwi+kOjsGvU96cgp1xr9ElVee5eC0BXmL4TY5nY+WO3ieZ+vkb2k09Bme\nWTKKhCs3OL8ljOLBFfK0bmE5LuwVh9HBhO+DwezqN46YQyfwa1qP4PdH8NtT/fBtXBcHdxd8/jhP\nDWYHUqKynqe57cOki1fYc8uMkjMLVlD++aez5pFTO3N7+53T1G4D1Jn0KofenU/y1evZJkmJimFN\nq1cp+kBZGn4yks0nx2SfF4XnuMi2QO7s/Dj27Z/vDEhJTePgvF+o2q05h+evzWWtW8oqBHWRaXtz\nap8yMrId2b31GPKoURmzh7ttVs2tSnVqzflvfyI9PucZJX/ndd2jRmXMntnHUrrzkwCkxGU/Vdne\n7abcv9QBvQ/Fx8fz+eefs3LlSr744gsWLVrE9u3b+eKLLwgPD2fp0qW4ubkxceJEvvnmG1xcXIiL\ni2POnDmcPn2al156iY4dO9KhQwd8fX2pWdN6UX/66aepV68ew4cPZ/PmzTz55JN23tL7R8UXn6VY\nU+vzLg6uRYg9cc72nZOfNyk34khPynxznnjpKh7VKv5lukwMBtITk9jRd7yt3MCnqtuexXT8YyT0\nZn6pN+LIuC2/pMtX8aheMdt0SZeu4uiTOY+kSOuvmwazA9XHvIxruZLs6D2KpItXAAh8+nEMJhNl\ne7S3br+bKw3nT2Fb96F/awxJlzMvB3C+JT63iqWpPXUokb/uxKtOVep/PgmA+FPnOb/kZ27sOwLA\nhRXreWDoizi4u2IwGTn2/lzbVCdTEWeMjmbcHyhPlZF/Ppu1o8cQa33370GxRxqy59W3iDt+GoDk\ny1fxqFYpy7aYPbJ/uYW/r4kDx1NtnyOvpVPUzYCLc9YL+c+bExn2QuYfguITMhjYvSge7tb0ny+N\npXTAnTflAb4mDh77c79Y4zBSJJs4Vm9OYOgLmes+PiGDAT088HC3jkjMXRJDqVzieOClp/F/2Dqt\nysG1CDG3nCPOxbxyOEeu4VW9Qrbpkq5Ec2n9LttLX87/uJmgPu3zuvkA1Hu1LWWb1wLA0c2Za8f+\nnHHgWtyTpOvxpCXm/QbV0c2ZbdO+I/mG9caxdu+WtimLf6WwHBcFHUfD+VMAMHu4EX86gphD1lk5\nVzbuourIl3ApWRyDycjR97645Tx1wujoSNEHylP1jZdseW3/z3AAHNxdSYuNB8CpmBfJkddwq1ga\n90pluLjq1henWW/MK7/0NMVzaL+dc2y/r+F5Sxt2M51buZK4lPCj6sBu1vJ9PDCYjBidzBx+fyG+\n9atxab11CmLMkdPEHjuDe8VSOdaPPY+L4FfaUbp5bQAcXZ2JOv7n+eFS3JPkG3d2flRo25Coo+eI\n/uM8MxisI8J5VdB14eT754yP7K5lObX9GUnJJF2+mmX9W0chiz/WmEurfs3aqzYa8X+iCYkXI/Fr\n2gC4t9d1AP8WD3Lxx43ZxGKg2CNZp9MWpnZT7l+agnsfqlKlCgDu7u5UqFABg8GAh4cHiYmJVKxY\nETc3641v/fr1OX78OAAPPPAAAAEBAaSkZN8wVK9eHQBfX1+Sku78JQX/ZidmLmZLt+Fs6Tacbc+P\nxrN6RVunsHTHx4jcuCvLOte2789TukwsFuq+P5yiVcoDEBt+loSzF9n07CAAPKpXsuUX2LEFkb/t\nzKbcfTmmu7JxFyXbNsdgMuLg5kLxFg9y5dcdANSaOAiTaxF29B5t63wCrH+kJ+uadmPdw93+CNHC\n/pHv/e0xJEdGkRhxmeItrM8A+oTUwpKRQdyJsxQJLE7dj9/k5OffcewD69smb77MIWLJz1R/a4B1\neh7g3/Ih4k6eJS0mjqhtewl8phUGBwcwGHhgxH+p0K8rsUdOZnpRBEDlQb3wql2Fnb2G2zqft25L\nkT+2pWSHx7mSzTbf1Ki2E/uPpXDmgnXUevHqBJrVzzpdKSYug7OX0qkV5Jhp+eLVCfzva+uLN65d\nT2fJmgRaNcn+udXcNKrt/Ecc1hu6b3+Oo1mDHOK4mEatB5wyx/FzHB//3y1x/BJPqyY5Tz0+8ul3\nbOjyBhu6vMHGnmPxqlER11LFASj79KNc+nV3lnUitx7IMd2FNTso0SIEo5P1GVb/ZnWJPpz720Rv\nt2v6Cr7tMIFvO0xgSafJFK9VHo8yxQCo2rkpp9ftu6P8qnZ+mPr92wFQxMedKs8+xPEfduRp3cJz\nXBRsHDdfdLL9PyMoElAM9wfKAeBZu4r1rdUXIrm2bR+lnn0Cg4MJDAaqjnyJiv26EnPkZKaXqFjS\nM7i6ZQ+BHR4DrD9KuZYLJPr3Q1gyLAQN6mV9Oy7WH87iTlifxTz26Xf81nUkv3Udyeb/vJnpmCvz\nzKNc/vX3LHFf2XYg23TXD5xgbev+tvzOfreWi6u3sf+t2VjSM6g55kW8almf2XcrXxLXsiW4fjC8\n0OyPW+2Zsdz2sqAVXSZRrGZ5iv5xfjzQ6WHOrNubp3xu8qpUkjqvPoXBaMDkZKZK1+acXPUX17xb\nFHRd/FW7nlvbf2XjTgLaPnLLdawxVzb+ub5ncFWidh3MUqZbhdIkX4lme5dBtmvPvbyuA3gFVyVq\nV9YZTG4VSpMWE59leWFqN+X+pRHQ+1BOU38MBgPh4eEkJCTg4uLCjh07KFeuXI7rGAwGMm6ZJpHj\nlCK5IynRMRx461NqvzMQo4MDCRGXOTD2f4D1mc3qb7zIlm7Dc02Xm32jp1N9ZB8MZgeSr15n95B3\nbd8dfusTak4ahMHBgcSIyxwcN8Na7h8jBdu6DyU1OibHdOeXrKZIYHEaLpiK0ezA+aVriN4ThkfN\nIPya1iP+zAUazHrLVt7xGQu5tj3zxSb5yrV7EgPAgVEfUGXEfynfqyMZKansH/k+WCyU7d4ek5MT\npZ9rRennrG9CrTdnIrteGMn1fUc4/cUS6nw8Fkt6BslXo9g/dCoAp+Z+R6VXu9PgyykYjEbijp/m\n+IdfZqlzp2I+BD7zBEmXrhL80Wjb8nPfrOTiyg0cfutjakwcjNHsQOL5yxwaP4MSbR7Jdv/5eJgY\n/7Inr0+LIjUNAv1NvP2qF4dOpDDuk+ssetd6IT97KQ0/LyNmh8zn5Qsd3Xjjw+t0HBCJxQIvPedO\n9YqO2RWVK29PE+Ne9WbI1GukploI9Hdgwmve1jj+F8Wi9603MmcvpuLnZcoax9NFeeODKJ7ufxEL\n8FKnolSv5JRNSVmlRMewZ+xM6k/tj9HsQPz5SHaP/hQAzyrlqD2mNxu6vJFrulOL1+Do4UazhRMw\nGI1cP3KafW9/dcf1cFNSVCwbRs6jxYcvYjI7EHPuCuuGzQXAr3oZHn6rO992mJBrHntmrqL55Od5\nbvkYMBjYNeMHrhw8k6fyC8txYa84UqJusHfoVKoM6Y2piBMZqWnsGz6NjJRUTn7+LZX796DhfOt5\nGnv8NMc+ynqeAhyZMpuqI1+i0VdNsFjg4NgZpMUnknbyHEfenUvwu8PAaCQ5MooDoz+kyfLMfzIp\nJTqGfeM+o+6U1zCYHUg4H8neMdY0HlXKUXN0H37rOjLXdDlJT0xm1+D3qDa4GwYHBzJSU9kz6n+Z\nRqMKy/64XVJULL+Nmkvz91+y/tmZc1fYOGKONcZqZXjorZ4s6zg+1zz2fLyCRqO60H7ZWIwOJk7/\n/Humabl/paDrIrt2/ebsmB09hvxxHcuaBqwvJCpS0p8G86dhNDsQsfQXru85bMvbpZQ/SReyjvK5\nlPIn6VLm5ffqun5rmYm3/Kj85/IAEi9GZnrb/e3s3W4WSvozLHlisOT0+jAplJYsWcLJkyd5/fXX\n2bhxIz/++CPvvPMOYWFhTJs2jfbt2zNv3jyMRiOlS5fm7bffZuXKlbZ1kpOTadWqFevWrWPDhg1M\nmTKFMWPGMHLkSFatWoWTkxPTpk2jfPnydOyY/SvWb7pyJTbX7wuCn5876Sy0awwmQvmpwZ3/WY+/\n2xM7vuaXkOfsGkOL7YvsHsPNONY2fNauMTy6bTFJB7PvhBYk5+rrSTz8mF1jKFJ1DcvqdLNrDE/t\ntj4b+ukD/7VrHC8d+czux4VzdetLuApDHPZuL1psX8QPdXP+s0kFpc3vCwvF/vi8ah+7xgDw/OFZ\nhaIu7H0NAet1pDCcI/ZuN8Hadt4P0taWt3cIf8nh0TubLXRPYrB3AHJnbu0UNm3alKZNra/SrlKl\nCnPmWH+NbNu2bY7rODk5sW7dOgCaNWtGs2bNAGzLAF5//fV7EruIiIiIiPy7qQMqIiIiIiKSX5qC\nmyd6CZGIiIiIiIgUCHVARUREREREpEBoCq6IiIiIiEh+aQpunmgEVERERERERAqEOqAiIiIiIiJS\nIDQFV0REREREJL8y0u0dwX1BI6AiIiIiIiJSINQBFRERERERkQKhKbgiIiIiIiL5ZNBbcPNEI6Ai\nIiIiIiJSIAwWi8Vi7yBERERERETuZ+krA+wdwl8ytb5o7xA0BVfu3pUrsfYOAT8/d35q0NmuMTyx\n42vSWWjXGABMhPJLyHN2jaHF9kVsbNzBrjEANN28lLUNn7VrDI9uW8wPdUPtGgNAm98X2j2ONr8v\ntPtx0XTzUgCWBPewaxwd93xZKPYHUCjiWFW/i11jaLXz/1jX6Bm7xgDQfOu3hWJ/fFPrP3aNAaDT\nvi8KRV0UluNidYNOdo3h8R3f2L3dBGvbeV/QFNw80RRcERERERERKRDqgIqIiIiIiEiBUAdURERE\nRERECoSeARUREREREckvPQOaJxoBFRERERERkQKhDqiIiIiIiIgUCE3BFRERERERyS9Nwc0TjYCK\niIiIiIhIgVAHVERERERERAqEpuCKiIiIiIjkV4bF3hHcFzQCKiIiIiIiIgVCI6Dyj+LXOJjK/Tpj\ndDQTe+IsByZ8Rnp84h2ncy7mQ8PP32Jz6DBSb8QC4F23KkGvdcdoMpJyI44j788j9vjZfMdssVh4\nY8RyKlby4/kXHryLHEpgpDYANScO5NDbn2a7zb6Ng6nYtytGRzNxJ878mc5oIGhAT3xCamEwmTiz\ncAXnl/4CgEspf6qO6ovZw530hCQOjptBwpkLAJTp2oYSbR/Bkp5OSnQMYe/MylSea8Wy1PxoPMlX\nrmF0NBN/4gzHJs0gPSFzbN6N6lL2pW5Z0phcXag84mVcygSCwcDlVes5v3ApAA7ublQc1BuXsqUw\nOjlydt63RP78q7U2nm4FQMjCd0mMuEzYpE9JjY7JUh8V+/egePNGpMbEAZBw9gIHR71/F/UP3g1q\nUvGV7uzoMSTT8mbfTSU9OZW4UxEcnPwFXjUr8cArnTCaHYg5cY7942eRls2+KvZQ7b9MV3fqAJKv\nRHNwyjzrOk2CqT3uJRIvXbOl2dJ7/F3lfbcx3FSq3cP4P1KPnQPfzb6+ctjneUlzt8dFdvwfqkW1\nV5/F6GjmxvFz7B43m7T4pDynC5n6Cq6litvSuZbw4+ruIxz8aBH1J/a1LTcYjXhUKsW2wR9lG8e9\n3Cc+9apS5bUuGB1MpCencmjqvCz52iOG64dOZhuHX+NgKr/cGaOjA7HHz3Jwwsxs48gpndHJTLWh\nz+NRtTwYjdw4eIJDUz4nIzmVYk3qUOPNviRdvppt2QA+D9ahQt9QDGYH4sPPEvb2x1mOzb9K41TM\nh3qzJ7Kj++u264fPQ3WpOvoVki79WfbuvqNJT8h6vN1JXec13d0eFwFNalGz/zMYHR24cew8O8bO\nyfYcySmdY1FX6o7qgWdQadITkzm1bBPH/28NAEXLl6DemP/gUMQZsLD/w8Vc2nKw0NRFvo4Fo5FK\n/Xvi3bA2BpORs1+t4MLS1QB41qlGpf49MZhMpN6I5fgHc4k7cQaAUl3aEtCmOZb0dFKvW69bvo2D\nqdSvi+1+5dCEnK/x2aYzGgga0APfhtZr/OmFKzi/xLoPvOpWI+i17hhMRlL/uK+JO26NxSu4CpVe\nCQWg6ZyR7Bozi4SIK0DhaTvl/qUR0H+R5ORkmjdvbu8w7hmzpzvVR7/EnuHv89uzg0iIiCTo5S53\nnK7Ek00ImTkW52LetmUOrkUInjyIo9MXsjl0GIcnz6H2xAEYzPn7DSc8/ArP95zPT6sO3WUOThhp\nRAa/AZAQEUmlfl2zpDJ7ulNtVD/2j3iXLc8NyJQusEMLXEr5s7XrYLb3GkHpzk9StGoFAKqP68/5\n71aztfMgwmctotY7gwHwrl+DEu2as6P3KLZ1G0rkhh1UG/3HRcNkpGSnttT4YCwORd04/MYUdnV5\nhaQLlyjXt/ttcRWl8huvZpumbJ8uJF+5xu/dX2NP7yGU6PAE7tWCAAga9SrJkdfY3Wsw+18bS4UB\nvXH088EtqDyBXdoDsD10MAnnLlLhxc7Z1pxnjSAOjn6fHT2GsKPHkLvqfBqdHCn/385Uf3sQBtOf\nzalXnWoAbOs7id+6jiRy8z5qj32JWm++yO9DPmDD00NIOB/JA692ypKno6f7X6ar0KMN3sFBmZZ5\n16rMyfkr+a3rSNu/229u85J3fmIwF3WlxojnqTa0BxgM2dZZbvs8L2nu5rjIjqOXO3XG9WHbkOn8\n0mEY8ecjqd4/m7rIJd32ITNY13k06zqPZs/4z0mNS2DvpC+JPXnBtnxd59FEbjvIuVVbubBu113V\n9d3uE4ODiTqTXmH/hNls7DKS43O+p/b4vrdnXShiuJl/jTH/Zc+w9/ntmcEkRkRS+ZWsbXhu6Sr0\n6oDBZGRT1+Fs6jIUo5MjFf7zFACeNStzasEPbA4dYft3K7NnUaq88TIHRkxle+fXSIy4TIV+oXeU\nxr/Vw9T59C2cbjvuPGoEcfarFezsOcT2L7fOZ2HYJ05e7jQY/wKbB89g1VMjiIuIpNZrz95RutpD\nupCWkMxPHUaypttb+DeuQUDTWgDUHdmDU9//xupOY9jx5hwaTemXqR21d13k51go2b4FRUoFsCN0\nILueH06pTq1xr1oRk6sLNSYN4cSM+ezoPpijU2dSbcIgDGYHvOrXoETb5vzeZyQ7e7zOlQ3bAag+\nui/7hr/H5mcHkhhxmcovZ3+NzyldqQ4tcCkVwJYur7PtPyMp88c13sG1CLUnD+LY9AVsDR3K4cmz\nqfXHfY1TMW9qTRlM2JQ5AESs2UXwiJ7Wei4kbWehlZFR+P8VAuqAyj+Gb0hNbhwOJ+HcJQDOffcL\nAU88dEfpnHy9KPZwfXYNfCfTOi6lA0iLSyRqp/XX2fgzF0iLT8SrRuV8xfx/C3fRoWNtnmhV7a7W\nNxAAXAOsv7KfX7Ia/yeaZEnnE1KLG2F/bvOt6Yo93ICIFRuwpGeQFhvPpV+2EPBEU5z8vHAtW4JL\nv2wB4NrWvZicnXAPKkfytescmTzL9itsTFg4zv5+ALhXroBrhTJc/P4nyMgg6fxFAC4s/YlijzfN\nFJdXg9rEhh3PNk34B3M4OeMLABx9vDCYHUiPj8fB3Q3P+rU48/k3AKRcucbeF4eRFhNL3NGT7OzU\nDwCjoxknP2/bCESmejM74Fa5LKVD29Fg/lRqTBqMU3Ff63cODlR6rSf1502mwfypVBn9MiaXItnW\nv3dILUzOToS9/XGm5e4PlAcgKTIKgEvrduLXuCY3wk4Rf+4yAGe+XUPJVo2z5OnXqAbXD5/MMZ1P\nvar4PViTM9+tzVyXNSvhU78aDy2YQKPZo/EOfuCO885vDAEtGpJ09TphH3yVXXVZ48xln+clzd0c\nF9kp3rA61w+dJP6sdRtPLV5HqVaN7iqdwcFE3bdeZP/UhSRejsr0nU9wZUo+Vp89b8/NNo57uU8s\naemsafUqMUetIxouJYuRciOuUMYA4NuwJjcOn7S1U2e/+4UST2SNI7d00XvCOPH5UrBYIMNCzNHT\ntrbJq2ZlfOpX48Ev3yZk5pt43XaOeDeoRUzYCRLPW/ONWPIz/i2b5DmNo68Xvk0bsG/QxCwxe9QI\nwqtuderNnUydT97Cs3aVbOvgpsKwT/wbVSfq4Cni/jj2TyxaT+kns54juaXzrlqW0z9swZJhISMt\nnYu/7afUY/UBMJgMOBZ1BcDs4kxGSmqhqov8HAt+Dzfg4sr1tutq5C+b8W/ZFJdSAaTFJxC96wAA\nCWcukB6fiEf1IFKuXefo1Fm2UdaYI+EAWe5X/LO5r/EJqZVjumLN6nPhh9uu8a2a/HFfk2C7r0n4\n477Gs0ZlijdvyNUte4k9egqAU9+tZ9+0hUDhaTvl/qYO6D9cfHw8ffv2JTQ0lLFjxwKwY8cOevTo\nQffu3enYsSOnTp3im2++YfLkyQCkp6fTtm1bkpOT7Rj5nXMu7kNS5J/TD5Mir2F2c8HkWiTP6ZKv\nRrN32HvEn4rItE782YuYXJzwCakJQNEq5XErH4iTr2e+Yh41phXt2tfMRw4uWEiwfUrOZZuTL1/L\nNp1zcR+SIzN/51TMG+fiviRfibbeyP0h6UoUzsW8iT95jug9YYC1M1fp5VAur9sGQGzYcY5NnIHB\nbMZyy8P4yVeu4eDmmqkz51TMN3PZt6dJzyBozADqzf+QG3sOkXD2AkUCA0i5Gk1g53bU+mQiwXOm\n4la5PBnJKQBY0tMBaLz8UzxrV+XCyvVZas3J15vo3w8S/vFX7Og+hBsHj1NrylAAyvZojyU9g509\nh7Gj+xBSrkRR8eXQLHkAXN24k+MfzrNN470p5vAJAIr4Wzu1pdo1xejgQMr1PztDSZFRmN1ccMju\n+LwUlW06J19Pqr3enT2jPob0zL9iptyI48ziX9jUbRRHZnxDvWkDMo3i/1Xef0cMZ79by/FZS0hP\nzv5GEvKwz/OS5i6Oi9sV8fch4ZYbnsTIKMzuLji4Ot9xurIdHibpynUurP89Szk1Bnbh0Ixvs52e\nBvd+n1jS0nH0Lspjq6ZT5bUuhH/5Q6GMwZb/5Vvb5lziyCHd1e0HSDhrvQl39velbJdWXFprbZtS\nbsRydvFqtvR4g2P/+5o6UwZlyfevjs3c0qRcjebgiKkknD6fZdtSb8Rx/ruf2NVrGOGfLKTGO0Nx\n8vPOki7TNtp5nxTx98587F+OwjHbcyTndNcOnKRsmwcxOJhwKOJE4GN1cfbzAOD3ifOp8nxr2q5+\nj4dnDmXX219iSc86OmOvusjPseBU3JfkW6Z6J0Vew6mYDwlnL2Aq4ox3A+sosHuVCriWL4WTryfx\nJ89xfc9hwHpdrdg31LaurYw83tfcfo2//XxxLubzx32N8y33NRVs9zWupQNIT0ymxoTXAGgw+WUy\nUtOAwtN2yv1NHdB/uK+//prKlSuzcOFCOne2TkU8fvw4U6dOZf78+Tz++OP89NNPtG7dmrVr15Ke\nns5vv/1GSEgITk5Odo7+zhiMORzOt11s8pou01fxiez+f/buOzqKqn3g+Hdbeu9AaEmAEAKhhioI\nSFO6FCmhiVSV3rv0Ik2KgrwKiA0Ey/v+pFpABAJKCB1CIJDeezZld39/LGyy7G4SDCSRcz/ncDS7\nz8x9dubunblz78zO3ID3qL60ObCWam+0J+nSdV2DXHGMT3M0OIhLjH9mjUoNUiPrUKtNTqHUFJm+\noZxzpF8AACAASURBVHCwpdnWhaiylYTt0B/1kpRieaNlPxVz+4PN/PnGSOR2NtQcPQiJXIZlNQ8K\nsnK4MnE+Nxd/iNf7o7Gp56W3jjPd3+b+nm9psnmhwWdRxsRzZfpqsh9q72d9eOBHLD3dsajihnPb\nZri2b07gvvUE7luPa4dArGt7Gs3TlNQQbee8+YdTabd/ORqNhoKcXKMnV0+/Zmq7IYGmq9/j+of7\nyU1MNXj7r1mbif1VO00pJeQOKaF3cWnpX6p1P68cSqUU+/xF1gvdRynjtiga5zOsO7d2/2AQ4xTg\ng5mDDY9+Pmd0Hc8jj9Lsk7zkdE72eI+zo5cSsGR8pcjBuoaHkfWUtj0rOc7Otzatdi8h4ttjJPxx\nGYDLszcR99vj78iV26RevaO/AhPHBv26WYoYI67NW0/i78EApIXeIu3qbV0nxJhKXS/UpfyOqNWE\nfPg1aDR0+2YZbTe9R9y566jzVUjNFLRZN4kLiz/lp67T+XX0apovHImlu2GnvDJsi6KfSaeYumA0\nF7UaVXYOV+espebI/rTYtwGPHh1I+eua3rmEwsGOxlsWUZBTTMfL4LzGxGc3cYzXqNWosnIImbmB\n2qP60vrAOqq+0Z7kS9pcJHIZbh2ac+8T7YyShODrtPrwfW1ZlaTtrLQqenrtv2QKrngI0UvuwYMH\ndOjQAYCAgADkcjnu7u6sXLkSKysr4uLiaNq0KTY2NrRo0YI//viDw4cPM2nSpArOvPTafKGdLiu3\ntiQj7JHudXNXJ/LSMlEp9Udyc2ITsW/gU2KcHokEVY6S4ImFD3Vp982HZD+eelOeJDRCQrXHfymA\nVJ6MM2qnnGaifuqzKOMSsffX/8xP4pSxiZg5O+i9p4xPRhmn/zqAxeP3AGx8atB4/Wzif7/Ina37\nDB49npuYrHcAMndxJj89Qy+33NhEbP3qGo1xDGxMVngEeYkpqHOUJJw8g0uH1sT93y8Auv+69+iI\nzNycBmvmkRZyg+gjR3Xri/7pV3xnj0Nua01BkVFKG58a2PjUIvboab0tqykoQCKTcmfTZySdCwFA\nZmmB1EyBra8X9ecX3if19AOHipJZaa/wxp25jHv7ZtQc+BoyMwUKOxu9bWm8fibhUGRfPYmzqV0N\nq6qu+E0brt1WzvZIZFKk5gpubPqSWgNfI+yzH4t8HAmaAu1o8CtfaqcEPv0deZ45hC7/1OT2KKq4\nfV6amNLWC2VULOmht7CtX4fM2+G6dXX6ejkACmtL0sIKR6os3Bwfbwv9EdPs2CQcG3qbjLOvVxOp\nTEriX7cMPqtn15Y8/O9ZvVkEAHUnvIl7+2bAi90nNzYdwKVFA92FifRbD8i4E4Fzcz+gfOqFqRxs\nfaoDUGf8ANxMbAtTbbMyTj+Pp+OqdGmN35wx3Fj/GTHHtLcQyG2sqDGgC+GfFz3ZferCVGwCdn51\n9Nb7dN0sTczT5DZWVHuzOxF7DxcpWoK6QP8CZmWoF/6T+lG1QxMAFDYWpN0t/I5YujmSm5aJKsfw\nO+Lc0MtonLmHDVc2fUteehYAvqNfJ/NhHPY+1ZBZmBFz+goASVfvkX4vWreeyrAtzJwddet61rqg\njEvEzEV/eWV8kvZcIlvJ5clLdO+1/GqzbhqvtXdNmu78AE1BAbmJKY/z1T8+5xv7Thg5r3kSp4xN\n0putZe7qpB25lUgoyFFyqch5TZtvNpIdGUduQgqpoXd0U3otXBxwqFeTzt+sQG5lUWFtp/DyECOg\nLzlvb29CQrQn0jdu3KCgoIBFixaxatUq1qxZg5ubG5rHX/BBgwZx8OBBkpKS8PU1vH+ssvpz+Fz+\nHD6X82MW4eDvg1V17ZX1Gv1fI/604Y3rSRdCSxWnR6Oh2aa52NXXHhzdO7dEU1DwXJ6C+6w0hKLm\n58f/jgEugC0Anv27EH/mosEySReuYO9fR/eZi8YlnL5EtV6dkMikyG2scO/ShoTfg8mNTyYnKg73\nLton8zq3DECjVpMZ9hBLT3ea7VhC+H++487mvUZ/9yot5AbIpFh4VgGgSr9uJJ0J1otJCQ7BrkFd\nozGundpSc7T2gQUShRzXTm1J/fsqyph4Mm7dw/31jgBEf/d/qJRKrs9bS/T3R6m/rHBanUe3dmSG\nP9TrfAJo1BrqTh+NRRU3AKq92ZXMexHkJiSTfD4EzwE9kMjlIJHgO2883pOGknErXPfAouI6n6Cd\n4gsQvv//ODN0PsmXbvLop9M4NvTRPfmv5oDOxP1uOO0o4fxVo3GpV8M49cb7ugcMPfzuFDHHzxO6\n/FMKsnOoNagLHp2091bZ1auJQwMv4s9pT+6eLHN21JIXlkNpFbfPSxNT2nqhcLTHrmE9Mh7fR/XE\nk4db/DZiGU4NvbGuof2MXgM6EfPb3wb5xp+7WmycSzNfEi7eMPpZXZr5khBs+N6dj78rl32iUalp\ntHgcjgHazryNVzWsa1XVrbMic0i9pt0vdz85pHsg0LnRi3Eo0k7VeNN425x4PtRknEenQOrPHMnF\n91brOp8ABdk51BzYFfeOgQDY1a2FfQNvvfUmB2vbSUtP7Xqr9utK4umLzxzztIJsJZ5vdsP11Zba\nbVC3Nnb1fUg+H6IXVxnqxbUdRzg+eDHHBy/mZNBynBt5Y/O47nsP7Ej0b5cNcog9d81knPfAjvhP\n7geAuZMdXv078PDn82Q+ikdhY4VzgLbTZO3pip1XFVJuRVSabVGWupB4+iJVexY9rrYl8XQwaDQE\nbJyPra+27rl2ao2mQEVmWASWnh403b6UsK17+eP1t7k4YqYuD71jt4nzGlNx8acvUa1XR10uHl3a\nEP/bRdBoaKp3XtMKTUEBmXcjiP8tGIdGdbGsqr1/Oj08irSwSE4NXlihbafw8hAjoC+5IUOGMHv2\nbIYMGYKXlxcKhYIuXbowbNgwLC0tcXFxIT4+HtCOkEZERDBsmPH73Sq7vJR0ri7/mMZrpiGVy8mO\niuPq0u2A9p5N/wXj+HP43GLjinNl0Uf4z38HiUJObmIqf88y/hMT5SsXNeeRon3wgY13Da4t2waA\nna8XfgsmcD5oNvkp6dxYvpNGq6cjkcvJiYrTxUUePo6lpzutvliPVCEn8shJ3f2dVxdupv688XiN\n7o86L5/Q+ZtAo6FWUF9k5ubUGNSDGoO0P3vy9AMkCtIzUOfm4bdiFlKFgpyoWG4v34KNrzd1507m\n71HTyU9N4/aqjwxiAO5t+4w6sybQbP8W0GhIPHOBqG+19+bcmL8Gn+njqNK3GxKJlIeffUvmLe19\nlw/3HqLOrAkE7ltPbmIyobPXA+hGMINHzCIr/BF3Nv6HgA1zkMikKOOTubZIW+79z76jzntBBO5b\nh0QqJfPuA+5u2fdMe+XJ1N62e5chkUhIDrnDtXWfE/tLMM3WTUGikJMdGU/I4p0A2NevTaNF73Bm\n6HzyUtK5suwTo3EmqTVcnL4R/9kjqTv+TTQqNX/P20Z+qn7Hu7h1lzmHUjK1z190vXhabkoGfy3d\nTcv17yGVy8mKjOfSok8AcPCrTdPFY/jlrUXFxgHY1HAnK9r4z3rY1PAgOzqh2O3xIveJKieXSzM2\n0mDGcCRyOer8fC4v3E7rjxdUeA5PZlI8ncfVDz6myZqpSBVysiPjCF2qfcCXXX0vGi58h7PD5hUb\nV3fyW0gkEhoufEe33pQrd7ix7jP+mvkhfjNHUmf8ADQqFSHzt9Ly40W6uPyUdG6u2I7/qplIFdp2\n8sYHH2Hr643vvAlcHDnLZEyx1GpCZ6+j7vQx1B47GI1KxbVFG40+IK0i98nT9SI3OYPgxXtou2Ey\nUoWczMh4LizQ/tyWo18tWiwZw/HBi4uNu7nnf7RcOY7u360AiYTrH39P8nXtg23OTt9K09lDkZor\n0BSouLR8L1mRht+XitoWZakLUUeOYenpTot9HyJVyIn6/oTu/s7rS7bgO28CErmcvKQUQudon79R\nc3hfpBZmeA7sgefAHrocry/fScCaJ8fuWL3zGr8F4zk/fA55Kekm4yK/O45VNXdaH1iHRP7UMX7R\nVvzmj0P6+LwmZNYGADLuRnBz7R4C1mk7wbXf7ETwbO05Q2VpO4V/N4lGI8a3BS21Ws2QIUPYs2cP\nNjY2JcYnJJg+eJYXV1dbjgYa/5mN8tI9+GtUHKjQHABkDONEy0EVmkOXC99yum2/Cs0BoP3ZI5xq\nZfhzAeWp8/mD/LdZxV/M6fnXgQrPo+dfByq8XrQ/q/2t0MNNRlRoHv0v76sU+wOoFHn83MLwZ1bK\nU4+LX/FL6wEVmgNAp3OHKsX++CZgVIXmADD4yueVYltUlnpxPNDwJ07KU9fgbyq83QRt2/lvoPrK\ntqJTKJFsSMWfv4spuAIAjx49ol+/frz++uul6nwKgiAIgiAIgiA8KzEFVwCgevXq/PCD4ZPIBEEQ\nBEEQBEEQnhfRARUEQRAEQRAEQSgrTeX4mZPKTkzBFQRBEARBEARBEMqF6IAKgiAIgiAIgiAI5UJM\nwRUEQRAEQRAEQSgrI7+LLhgSI6CCIAiCIAiCIAhCuRAdUEEQBEEQBEEQBKFciCm4giAIgiAIgiAI\nZSWm4JaKGAEVBEEQBEEQBEEQyoXogAqCIAiCIAiCIAjlQkzBFQRBEARBEARBKCsxBbdUJBqNRmwp\nQRAEQRAEQRCEMlB9blHRKZRINkpZ0SmIEVDhn0tIyKjoFHB1teVEy0EVmkOXC99WeA5P8lBxoEJz\nkDGM9d6TKzQHgFn3tnOq1cAKzaHz+YMVnsOTPH5pPaBCc+h07hDh/dtWaA5eh88CMNtzSoXmsS5y\nS4XXi87nDwJUijwqQw5Xu3Wp0BwAGh47USm2xeY6kyo0B4Cpd3dUim1xPHBwheYA0DX4m0rRfld0\nuwnatlN4eYgOqCAIgiAIgiAIQhlp1BWdwb+DeAiRIAiCIAiCIAiCUC5EB1QQBEEQBEEQBEEoF6ID\nKgiCIAiCIAiCIJQLcQ+oIAiCIAiCIAhCWYmfYSkVMQIqCIIgCIIgCIIglAvRARUEQRAEQRAEQRDK\nhZiCKwiCIAiCIAiCUFbiZ1hKRYyACoIgCIIgCIIgCOVCdEAFQRAEQRAEQRCEciGm4Ar/ei5tm+Az\ncShSMwWZYRFcX/kxqqyc0sdJJdSbOhLnlgFIZDIiDvxE5JETANjV96betJHILC1AKuXB/h+IPXpG\nb30AHU99zuneE59buVbVPfBbOBGFvS2qbCXXlm0jOyIagJpDe1K1V0c0KhV5KencXLObnKi4IiU6\nIKUTag4/03bUaDQsmPcjPnVcGfN2m2datrS8Xm1A+1l9kJnJSbgVxdF5B8jLVBrE+fVpQYt3XgMN\n5CvzOPXBQeKuPtSL6bPjHTLj0ji17NtSl19/0WSy7j3k4Zc//ePP4BTYCJ93gwgeMQsAjx7tqTGk\nl+59uY0V5m5OADi3aYr3pKFIFdp9f3PlTlTZ+nXEZIxUSt0pI3F6XD8efvkjUY/rxxNVenbE9dVA\nQmeu1b3WcPUMbHxqocox3K7ObZriPXEYEoWcrHsPublyh/F8jMVIpdR5fyROrRojkUl5+OVPRB85\nDoBD0wbUeX8kEpmM/LQM7m7+jMywiGfetpbNWuM0bAIShRl5EWEkbF+NJidbL8amfVfs+w4FDWhy\nlSTu2UzevVvPXJYxvp386DGvF3IzGTE3ozk48ytyM3MN4tqMeoVWQW1BA0kRiRya/TVZSZkALL6y\nkvTYVF3s7x//wuUjf+ktH/jFhudeJ1zaNcNv0bso4xJ16/lrwiJU2UpqDO1JlZ6d0KhU5KemV1ge\nxurmi8jhCYsqbgR+vpbLU5aTcSucmkF9ce/SVve+wsHOYN8+zTYwEPfRbyNVKFDev0/kpg9RZ2cb\njfWcMQtlxH0SDx0CQGplhef0GZhXrw4SKSknT5D47Tcmyyrv/eE1/i3cXm0JQPqNsBK3Ra1X/Wk7\nQ9t+J96O4uT8L4y23769A2k29jVAQ35OPr8t/5b4aw+RmSvotHQw7g1rIpFKiL3ygF+WfoMqN/9f\nsS1c2jahzqQhSM0UZIQ95PoK0+caxcWZuznT8j8rODdsNvlpGXrLVu31Ku6vBnJ5xjqjOZSpDS9S\nfvNPVxEcNNOg/Co9O+HaIZDQWWuMlm9MebWb/xpiCm6piBFQ4V+vwcJJhM77kD8HTSU7Kp46k4Ya\nxCgcbE3GefbrglV1D84NncGF0fOo8dbr2Pl5A9BozQzu7T7I+aDZXJ62inpTRmBV3UO3vvDPtZ08\ndX7Bcy3Xf9n7RH53nHNvTefe7m8JWDMDAKcWDanauxPBYxdyfvhs4n8LpsGiibryJPgipRPPem3p\n3r0Exozcz9Gfrz/Tcs/C0smG7uuC+H7ybvZ0+YDUR4m0n9XHIM6xthsd5vbj0Ojt7O21mnPbj9J3\nxzt6MYHjXsOzuXepy7aqVY0m25bg3rn1P85fam6G1/i38F85HYmssOmM/fk0wSNmETxiFhdHzyUv\nKZU7G/YA4LdwElfnbeD84CnkRMfhM3mY3joVDnYmY6r1ew3L6h5cGDadi2PmUn3wG9j5+QAgt7Oh\n3ux3qDdjDBIkeuu096/LXxMX63IqWlb9BZO5Om89F96aQk5UHN6TDPMxFVOtbxcsq1cheNg0Lj3O\nx9bPB5m1FQ1XzyJs236Cg2Zwe/0uGqyYjkTxbHVQaueA27sLiFu/gMj3hlAQF41T0ES9GEXVGjiN\nnEzs8hlEzRhFyqG9eMxe+UzlmGLtZM2gjUPZP+4/rO+wiqSHSfSY19sgrlpDT9qP78iOvpvZ+Noa\nEu8n0G3W6wC4ermRk5bN5m7rdf+KnkRZO1kDvJA6Yd+wHhFf/qjb78EjZqHKVuLYoiFVe3Xm0tgF\nBAfNIv63C7qyyjMP0K+bl99f/sJyAJCaKWiw7D29ehix/3tdTn9PWoJaadh5Kkpmb4/njJk8XP4B\nd8aOIS82Bo8xbxvEmVevQe2167Bv317vdfeRo8hPTOTu+HGEvfcuzm/0xKp+faPllPf+cH01EKfA\nAC4EzeL8kGlILcyL3RaWTjZ0XRPE/97dxb5uy0h/lEjbmX0N4hxru/HKnH4ceXsbB3qvJnjHz/Tc\nPg6AwEndkchkfNFrFV/0XIncQkGLCd0MyqmM20LhYIv/oolcmbuRswOnkRMVR93Jxo/5xcVVeb09\ngbuWYvH4IuUTcjtr6s8dS/2Zo3mqSdf73GVpwwE8enSg6cfLMXd1fqp8G+rNHkfd6WNAYiIBI8qj\n3RReTqID+pI5fPgwGzZsqOg0ylXazXtkP4oFIPLwcTy6v2IQ49wywGScW4dAon76DY1KTUFGFrEn\n/qRK9/ZIzRSEf3qQ5ItXAciNTyYvLQNzN2ecWwaQERaB18h+AOSnZzy3cs1dHbGuVZXYE38CkHQu\nBJmFObb1apOblMqttbt1V1PTb97DwsO1SIkOqDnzzNvwqwOX6Ne/Md17NHjmZUurVrv6xIZGkPog\nAYCQA2fw69PCIE6VV8CxeQfIStCO1MRdjcDaxQ6pQgZA9VZ1qNXej5Cv/ih12Z5vdifmv78Sd+qc\n3usSuZw6U0bSYu9aAvevp/6iycisLI2uw6llADILc26u3GGynJoj+pCXkkbU9ycB7f7Jebzvow4f\nx6Obfh1xatnIZIxrh5bE/PdXXf2IO3lWV3fcO7cmLymFux/t11ufRRU3ZFaW+M4ZR+AXG6i/cFJh\nWYEBpN8MIyfySVnHDPMpJsa1QyAx/yvMJ/7EWTy6tceqehUKsrJJuaT9nmRHRKPKysHev57J7WSM\nVeNAcsNuUhATqd12R49g+0pXvRhNfh4JO9agSkkCIPfeTWQOziAv+2Seuh18eXTlIYn3tfXz/L6z\nNOnXzCAu6mok615ZgTJDidxcjr2HPdkp2hGxms1ro1apGf/tu0w7MYfXpnZDIpXolQG8kDph37Ae\nTs39afH5Wpp9/AEOjbUdnbykVG6t260bAcm4Ga4rqzzzeLpuNlw944XlAFBv5lhi/vcb+WnpGOPz\n/giSzoUYfe8J26bNyL59h7zoKACS/vsTDp06G8Q59+5NyvHjpJ0+rfd6zM4dxOz6BACFsxMShQJV\nVpbRcl7UtjC1PxJ+C+avcQvRFBQgs7LEzNG+2G1Ro1194q5GkBqh/X6Efnka397G2+8TCw6QbaT9\njroYRvCOn0GjQaPWEH8jEruqTgblVMZt4dwygLQbhcfyR9+dwKN7u2eKM3dxxK1DC/6eZji66PFa\na3ITU7m99QuD93Sfu4xtuJmLIy7tA7kyfZXBut06tyE3MYWwj/aZLN+Y8mg3hZeT6IAK/3q5cUmF\n/x+fhMLGCpm1fifCwt3ZZJyFuzO58frvmbs5oc7LJ/qnX3WvV+vbGZmlBWnX7mDh4YJ1rWrcedwB\n0BSonlu5Fu4u5CakgKbwx4yVCclYuDmRFf6IlMs3AZAo5NSZPIy4X87r4jScB4xPDyvOwsU96N23\n0TMv9yxsqziQEZOi+zsjNhVzW0vMbCz04tKjkgn/rXAktuP8Nwk7dRV1vgprN3s6LxrI/6Z9jkZV\n+nkudz7cQ+zR0wav1xrRF41KzcWRcwgOmkVeQrLB1fYnEk9f5O6WveSnZxp9X2FvS40hvbiz6XPd\na0WneuXGJyG3sdLr4Fq4uZiMsXBzRhn3dP3QXrWOOnKC+3sOoc7N08vBzMmO5ItXubXmE4JHzNab\n6mhQ3xKSkNtY6+dTTIy5uwu5RXJVPs4n+2E0MksLnAIDALCt7421V3XMXRyMbidTZM5uFCTG6/4u\nSEpAam2DxNKq8LWEWHL+KryI4DzqfbIu/QEFBc9UljH2VR1Jiy6cApYWk4qlnSXmNoajIeoCNQ26\nNWTBxWXUbuXNpW+1o4pSuZS7Z27z6fCd7HxzK3U7+NJ2dHu9Mop6nnUiPz2DyEPHuDhqDmE7v6TR\n2lmYu2rbjNTLNwBtm/H0iEl55fF03ZTIZC8sh6q9OyGRy4j+4ZTBZwWwru2Ja/sW3NtlejosgMLV\nlfzEBN3f+QkJyKytkVpZ6cVFb99G6qmTxleiVuM5ew51PtlNVmgouZGRRsspqjz2B4BGpcJzQHfa\n/rAThYNtsdvC1sOx1O33g9+u6f5uP38A4b+Eos5X8fCPm6Q+0H7Hbas60WRkR+4e/dugnMq4LSzc\nnVHGl+5cw1RcbmIKV+Z8SNb9KIP1Rx4+Sfinh1Ar8wzeK7rusrTheYkpXJu3nuwHhnUw+shxHvzn\nIKpc0+UbUx7t5r+O5l/wrxIQHdCX0JUrVxgzZgx9+/blm2++4ezZswwcOJDhw4fz7rvvkp6ezoUL\nF5g2bZpumbZttffFzJ07lwkTJvDWW2+RlpZWUR+hzAw6JxLjVV2jUoOxK21q/eVrjeiD9zuDCJm5\nFnVuPi6tG5ObmEpy8NXnX66J6S+aIjkpHGxptnUhqmwlYTu+NBpf2UikxWwLIxSWZvT+6G0carpy\nbN4BpHIpvbaM4ZcVh3Sjo2Xl3LYZru2bE7hvPYH71uPaIRDr2p7/aF1V+75GwplLKGPii40ruh+N\n1oHHMcauAJfU6U6/HsbVuevJS0oFtZrw3dr7YyVyOZja/nr5mI6RGKuXajWq7ByuzllLzZH9abFv\nAx49OpDy1zXU+c/WKTRVP57+LgJIzC1wm7kcRRVPEreX/l6lYss38b1Tq4wfra8fu8qyRgs4sfEo\nb38xAYlEQvCX5/hx8WFUeSqU6Tmc3v0b/j0KL+yYKuN51ImrczeQ8HswAGlXbpF69TZOgYVlKxzs\naLJlkdF7g8sjj6fr5pMRc8lTo9dlzcG2Xm2q9evKrbW7TH7O6oPfIPLQUVRZJVyse8Y2y5TIdWu5\nOfBNZLa2uA0bXvpyyqFeRB46yukuo3QxppgakVKb2BZySzNe3zoWh5qunJx/QO89twbVGfjVdK58\n8Tv3f72m956pcip6W5gckXvq85c27h8pYxv+IpRHuym8nMRDiF5CcrmcPXv2EBUVxTvvvENubi5f\nffUV7u7u7N27l507d/Lqq6+aXL5Vq1aMGjWq3PJ9VlZWZpibF1ZdsyIjLeauTuSnZaJW6t8Ar4xL\nxN7fx2icMjYRM2f9dSjjkwHtiIH/4slY165G3Klz+C+eDICNd3UKcnJptV/7oADLah5oVKrnUq4y\nTv91AIsiOdn41KDx+tnE/36RO1v3gbqSXM4you3UN/DprD2QmNlYkHA7WveerbsDOalZ5OcYXnG1\nreJI/90TSLoXyzfDtlCQm0/VJrWxr+5Mx/lvAmDtaodEKkFuLufYfMNOeOC+9QAknrlE+G7jIx0S\nmZQ7mz7TTcWTWVogNVNg6+tF/fmF9x8WvZfSFPfX2nBn42d6r5m7FF7NN1Y3c+MSsW9Qx2iMMi7R\nYPmiV7aNcQjwRW5nQ+KZS9rP9/jkQKNWo4xNwM7vqbLSM/TyKS5GGZeI2VP5KOOTQCJBla3k8uQl\nuvdafrVZNwWstAoSYjGv46f7W+7sgiojHU2ufodJ5uKOx/y15EdGELP4XTR5z3bFvqiuM3vg18Vf\n+3lsLIi9FaN7z87Dnmwj9dO5lgu2rnY8uKidynrx6/P0Xz0IS3tLfDv7EX0jmtib2noukYAqX6Vb\nNjU6RW9dz6tOyG2sqPZmNyL2HtG9J0GCRqUt28anBo3WzyHht2DufrSfzn/qfx/KI4+n62ZugrY9\ne3Ji/Lxy8OjRAbm1Jc13a+8NNndxosGyKYRt268tWyrFrWNLgkfNoST58fFY+frq/la4uFBgpE6a\nYtOsOcr79ylITkKtVJL626/YtzOctpkfr3/Rqjz2h41PTZBKyLzzAIDoH09Re/Sbenm0mtIT784N\nATCzsSTxduHInY27A8rULApMtN+9P5lI8r1YDg3frPeQobpvNKPT0rf49YNvuP3TJYNlMyrgO2Jq\nW3iPG4hr++YAyK0tyQx7aFCm6uljfmwi9g0Mj/lPx/0TZW3Dn5fybjeFl5MYAX0J+fn5IZFIhNid\n1QAAIABJREFUcHV1JSYmBhsbG9zd3QFo0aIFd+/eNVhGU2S6Z+3atcst138iOzuPlJRsUh7fP2Dv\nXwer6h4AePbvQvyZiwbLJF24YjIu4fQlqvXqhEQmRW5jhXuXNroroAGrpiOztiR47CJub/yc80Gz\nOR80m9/fGI86L4/Q+RsBUOXkEP2z4RTPf1JubnwyOVFxuHfRPonWuWUAGrWazLCHWHq602zHEsL/\n8x13Nu+t1J1PgLOb/8feXqvZ22s1Bwasp2qTWjjU0k43CxjajrCToQbLWNhb8dZXU7l77Ar/nfIZ\nBY9PXqIv3+eTdgt16wv58gy3/ve30c4noHvIhKnOJ0Dy+RA8B/TQjsJIJPjOG4/3pKFk3ArXe1BF\nSeS21lh5epAWelvvdXv/Olg+3vfV+nUl4am6+aR+GItJOH2RKr06FqkfbUk4bVi3i5JZWVB3+hjk\ndtoHedQY/vhhEGo1ycGPy/LUllW1X1cSn1pfcTGJpy9StWcnvXwSTweDRkPAxvnY+mofCuXaqTWa\nAtUzPwU3+0ow5nUbIK+iHYG27dqP7Iv69zNLbWypunwbWed/J37jkjJ1PgGOb/hZ99CLbb03UaNp\nLVxqa+tnq6C2XD92zWAZWzc7hu4YiZWj9oFCTfo1J/Z2DNmp2bjXq0LXGT20F0YsFLQZ9QpXfrqs\nW/bO79qn9T7vOlGQrcTzze64dtQ+xdOmbi3s/HxIOheCpacHTbcv5f6eQ9zdsldvRLk883i6blp5\nafezZTW355rD3c2fc27QFN13NzcxmetLtug6vjbeNchPz0IZk0BJMv76C0vf+phVrQaA0xs9ST93\nroSlCtm3b4/bcO2Ip0ShwKF9BzJDDO87zfhL+8CV8twfNj418Vs4Gam5GQBVenQwyOv8lv9yoPdq\nDvRezdcD1uHRuDYONbXfj0ZDXuHeKcP229zeigEHphF2PISfp/1Hr/Pp070Jry4axOHRHxntfAJE\n/HGj0myLe7sOcn74HM4Pn0PwmIWGx/LThp8h6UJoqeL+ibK24c9Lebeb/zYataTS/6sMxAjoS6jo\nlAhHR0cyMzOJj4/Hzc2N4OBgatWqhbm5OQkJ2gNwVFSU3nRbU1MqKqsby3fSaPV0JHI5OVFxXFu2\nDQA7Xy/8FkzgfNBs8lPSTcZFHj6Opac7rb5Yj1QhJ/LISVIu38S+UT1c2zcnKyKawN3LdeXd3XaA\npAtXdOsDkJqZcWfLvudSLsDVhZupP288XqP7o87LJ3T+JtBoqBXUF5m5OTUG9aDGoB4AqPPyCX57\nQfls7DLITsrk5zlf0GfbWGQKOakPE/i/mdpt5t6wBt1XDWNvr9U0HvYKdlWdqNM1gDpdA3TLfxO0\nFWWq4QM8yuL+Z99R570gAvetQyKVknn3AXe3PNtDGAAsPT3ITUzVjTg9cWP5DhqumoFUIScnMo7r\nH2zTja4Gj5j1uH4YxoD2wRqW1TwI3L8BqUJO1JETunv5TEk6F0Lkwf+j+a7lIJGSda/win1+Sjo3\nV2zHf9VMbVlRcdz44CNsfb3xnTeBiyNnmYwBiDpyDEtPd1rs+1Cbz/eF+VxfsgXfeROQyOXkJaUQ\nOmet0fyKo05LJWHbKtxnrUAiV5AfG0XC1uWYefviOmkuUTNGYdetH3IXd6xbdsC6ZeEJc8yS91Fn\nlm1adlZSJgdnfMnwT0YjU8hIjkji66naB4J4NqrOgPVvsbnbeh4Eh/PL1uNMOPgeapWK9Lh09r79\nKQAnNx6l74oBTD85F5lCRuh/Qwj+8pxeGcALqROhs9dSb8bbeI0dhEal5trCTeSnZWh/qsLcnOqD\nXqf6oNf1PnN55mGqbpbn9wO0P3GljC1+mvwTqrRUoj7cQI1Fi5DIFeTFRBO5fh2WdepSbdp0wiZN\nKHb5mF2fUO39KdT5ZBdoIP3PsyR9f8QgTpWWWu77I/boaSw9PQj8fC1qlYqs8EfFfpac5ExOzN3P\nGx+9g8xM234fm7UXADf/GnRZNYwDvVfTaGh7bKs64dM1AJ8i7fd3I7bSdkYfkECXVYX3IUf/Fc6v\ny77RK6cybou8lHSuL99JwJonx/JYri7dDoBdfS/8Fozn/PA5xcaVVVnb8BehPNpN4eUk0RQd+hL+\n9Q4fPkx4eDgzZ84kNzeXHj16sGLFCrZs2YJEIsHe3p7Vq1djZ2fHe++9R2JiIt7e3ly+fJljx44x\nd+5cXn/9ddq3L/kG8ISEjBJjXjRXV1tOtBxUoTl0ufBthefwJA8VB0oOfIFkDGO99+QKzQFg1r3t\nnGo1sEJz6Hz+YIXn8CSPX1oPqNAcOp07RHj/tiUHvkBeh88CMNtzSoXmsS5yS4XXi87nDwJUijwq\nQw5Xu3Wp0BwAGh47USm2xeY6k0oOfMGm3t1RKbbF8cDBFZoDQNfgbypF+13R7SZo285/g/ztFiUH\nVTDF5NLdRvAiiRHQl0z//v11/29ubs4vv/wCQJs2bQxid+7cafDamjXP54EegiAIgiAIgiAITxMd\nUEEQBEEQBEEQhLJ6MQ8cfumIhxAJgiAIgiAIgiAI5UJ0QAVBEARBEARBEIRyIabgCoIgCIIgCIIg\nlFUl+ZmTyk6MgAqCIAiCIAiCIAjlQnRABUEQBEEQBEEQhHIhpuAKgiAIgiAIgiCUkUZMwS0VMQIq\nCIIgCIIgCIIglAvRARUEQRAEQRAEQRDKhZiCKwiCIAiCIAiCUFZiCm6piBFQQRAEQRAEQRAEoVxI\nNBqNpqKTEARBEARBEARB+DfL+9C6olMokdmMrIpOQUzBFf65hISMik4BV1dbTrQcVKE5dLnwLafb\n9qvQHADanz3Ceu/JFZrDrHvbUXGgQnMAkDGMU60GVmgOnc8f5OwrfSo0B4C2Z37gj3Z9KzSHdn98\nz6HGIys0hwEhewF49FaLCs2j+tcXK7xetD3zA0ClyOO3Nm9WaA6v/vkdxwMHV2gOAF2Dv6kU++N2\nrw4VmgNAvZ9+rxTboqKPIaA9jlSGc5yKbjdB23b+K2jEFNzSEFNwBUEQBEEQBEEQhHIhOqCCIAiC\nIAiCIAhCuRAdUEEQBEEQBEEQBKFciHtABUEQBEEQBEEQykgjfoalVMQIqCAIgiAIgiAIglAuRAdU\nEARBEARBEARBKBdiCq4gCIIgCIIgCEJZqcXYXmmIrSQIgiAIgiAIgiCUC9EBFQRBEARBEARBEMqF\nmIIrCIIgCIIgCIJQVuIpuKUiRkAFQRAEQRAEQRCEciFGQIV/PZe2TfCZOBSpmYLMsAiur/wYVVZO\n6eOkEupNHYlzywAkMhkRB34i8sgJAKyqe+C3cCIKe1tU2UquLdtGdkQ0ADWH9qRqr44ANNy8lLvr\nP0YZFYtT62bUmjAcqZmCrLAI7qzehipbPx9TMTJrK+rOm4xVTU+QSIj7+VciDxwBQG5rg8/0sVjV\nqo7U3IyHew8Rf+z3Um8nr1cb0H5WH2RmchJuRXF03gHyMpUGcX59WtDinddAA/nKPE59cJC4qw/1\nYvrseIfMuDROLfu21OWXhkajYcG8H/Gp48qYt9s813WXlnObpnhPGopUoa0nN1fuNNh/ZeXYuhk1\nx49AqlCQde8BYWs+MiijuJjAn/aRm5Cki43+6nsSTpRcFxxbN6PW+CAkZgqy7z3grpG6WZoY35Vz\nyEtMJnzTbr3Xzau40XjPh1yftpTM2/eKzcXjlQD83xuIzExO2t1HXFq6h4Isw/poMk4qocncEbg2\nqwdA7B+hhG76Wm/ZWn1eoWqnZvw5ZXOJ28aiSVvs35qMRGFG/sO7JH+yAk1Oll6MVbse2PYaDhrQ\n5ClJ+XwD+eE39WKcp69DlZJA6mfrSywTyl4XnvBdMVe7TzbvwrJWdeounq57TyKVYu1di5sLVpN8\n+nylzAHAqU1TvCYMR6qQk3kvgturdhi2nSZipGZm1Jk5Ftv6PkgkUtJv3OHuhk9R5+Xh0NQf78lB\nSORy1Ll53N20h4ybYSb3iUvbJtSZNASpmYKMsIdcX2H6mFJcnLmbMy3/s4Jzw2aTn5YBgGu7pvgv\nmUxOXKIu7uK4JQbrriz7pCjr5q1wHTEOiUJB7oNwYreuRZ2TbTTWY+pcciPuk3LkGwCqzl2Goko1\n3fsK9yrkXLtC1Ir5JZZbUduiNMcCkzFSKXWnjMTp8XnFwy9/JOrxeYVtfW/qThuFzMICiVRKxBff\nE3v0TGEuCjkBH84j+nH8izzHeaJqr464dQgkZOZa3Wve4wfj0UV7HHYcM4eU/ZsgP09vuYpqN4V/\nPzEC+hKIjIxk0KBBxcYMGjSIyMjIcsqofDVYOInQeR/y56CpZEfFU2fSUIMYhYOtyTjPfl2wqu7B\nuaEzuDB6HjXeeh07P28A/Je9T+R3xzn31nTu7f6WgDUzAHBq0ZCqvTsRPHYhAIm/n6fe/HdRONhR\nd8F73FiwjktD3kUZHUvtiUFP5WI6ptY7Q8hNSOKvoClcHjuLqv26Y9tAe4Jdb+F75MYn8ffoGYRO\nWYr31LGYuTqXahtZOtnQfV0Q30/ezZ4uH5D6KJH2s/oYxDnWdqPD3H4cGr2dvb1Wc277UfrueEcv\nJnDca3g29y5Vuc/i3r0Exozcz9Gfrz/3dZeWwsEOv4WTuDpvA+cHTyEnOg6fycOeaxlyBzt85r3P\nrYVr+HvYJJTRsdScMKLUMZbVq1GQkcmVMdN0/0rT+ZQ72FFn/nvcXLiWv4dORhkdR62JhuWWFFNt\naD/sG/kZrF9ipqDeomlI5SVf1zRztKX5srGcn/kRx/rOJSsygYZTDNuw4uJq9myLbS0Pjg9cwInB\ni3BpXo9qXVoAoLCzpsmCkTSeG4REUvJ0KKmtA04TFpO0aQ6x0wdQEB+Fw5B39bdNlZo4DHufhNXv\nEzd3GOmH9+AyfZ1ejG2vIMx9G5dYnm6dZawLT1Qb2g+7gMJ9kvPgkV79SL0YQsKJ3412MipDDqD9\n7vkueJfr89cTPOR9lNFxeE0aXuqYmqPeRCKTcWnEDC6OmI7U3JwaI/ojkcvxWz6d22s/5tLIGUR8\nfoj6i983tUtQONjiv2giV+Zu5OzAaeRExVF3svFjSnFxVV5vT+CupVi4OektZ9+oHg8O/MT54XN0\n/1TZ+hdeKss+KUpmZ4/HlLlErV7E/YlB5MVG4zJqvEGcmWdNPFdswrZdR73Xo9csIWLKWCKmjCVu\n2wbUWZnEfbypxHIrcluUdCwo7nhRrd9rWFb34MKw6VwcM5fqg9/Azs8HgEarZxK++1uCR8wiZNpK\n6rw/EsvqHgDY+delxaercGjkqyvnRZ7jyO2sqT/nHXxnjIYiTWXVnq/i2q4ZF0bNA0CVmoj94Il6\nZVZUu1nZaTSSSv+vMhAdUOFfL+3mPbIfxQIQefg4Ht1fMYhxbhlgMs6tQyBRP/2GRqWmICOL2BN/\nUqV7e8xdHbGuVZXYE38CkHQuBJmFObb1apOblMqttbt1VyEzb93D3MMVx8DGZNy8izIyBoDoI0dx\n69peL5fiYu5t3kP4ts8BMHN2RKKQo8rKQm5rg0OLACL+o72anJeQRMi4ORSkZ5RqG9VqV5/Y0AhS\nHyQAEHLgDH59WhjEqfIKODbvAFkJ6QDEXY3A2sUOqUIGQPVWdajV3o+Qr/4oVbnP4qsDl+jXvzHd\nezR47usuLaeWjUi/eY+cx/Uk6vBxPLoZ1qeycGzRhMxbYbr9H/v9UVy7dCh1jG1DXzQqNf5bVtD4\n8y1UHzUYpCU35Y4tGpN5s3CdMUeO4tql/TPF2Dfxx7FlE2J+OGawfu/p44n7+RfdSE9x3Fv7k3I9\nnMyHcQDcO/gLNXq0fqY4iVSK3NIcmZkCqUKOVC5HnZsPQPWugSgT0wjd+LXBOo2xaNSKvHs3KIh9\nBEDmie+watddL0ZTkEfyrhWoU7Ujz3nhN5E5OINM2+E292uGRUBrMk8eLlWZUPa6AGDfpCEOgU2J\n/f6o0TLsGvnh/Gob7m3YWWlzAHAMDCDjZhg5T9rFw8dw7/pKqWNSQ24Q8fkh0GhArSbzTjgWHi5o\nCgo41/sdMu/cB8Cimjv5xbSbzi0DSLtReKx49N0JPLq3e6Y4cxdH3Dq04O9pawyWc2hUF6fm/rTa\nu5oWu5bi2KS+4baoJPukKKsmLVDevUV+TBQAqT//gF2H1ww/3xt9ST/1Mxl//Gp8RXI5HlPnEb97\nGwWJCSWWW5HboqRjQXHHC9cOLYn576+684q4k2fx6P4KUjMF4XsOknLxKgC5Ccnkp2Vg8fhicvVB\nPbj3ydek37irK+dFneMAeHRuQ25iCne27tdbn62vF/G/X6QgUzvCnR38K1YtO+nFVFS7KbwcRAe0\ngvXv35+kpCTy8/Np2rQp169rR3/69evH3r17GTx4MG+99Rb79u0DICYmhrFjxxIUFMTYsWOJiYnR\nrUulUjFr1ix27doFwKZNm+jfvz+TJk0iJSUFgNjYWCZMmMDo0aPp2bMnJ0+e5P79+wwYMEC3nqlT\npxIaGlpem6DMcuMKpyLmxiehsLFCZm2pF2Ph7mwyzsLdmdx4/ffM3ZywcHchNyFFe0LzmDIhGQs3\nJ7LCH5FyuXAKSe2JQST88ifmbi7660pIQm5jjcyqMJ8SY1Rq6i2eSvP9W0i7fJ3sh9FYelYhLzEF\nz7d6E7BzFU32rMemrhfqXP3pMKbYVnEgIyZF93dGbCrmtpaY2VjoxaVHJRP+W+EIZMf5bxJ26irq\nfBXWbvZ0XjSQ/037HI1KXapyn8XCxT3o3bfRc1/vs7Bwc0FZZGpcbnwSchsrvf1XVmZuLuQVLSMh\n0aCOFBcjkUlJvXSF6zOXcvXd+TgENqHKm2+UWK65uwu58cWXW1yMmbMjXlPGcvuDTaDW3//uPV9D\nKpcR95P+tC5TrNydyI5N1v2dE5eMwtYKubVFqeMe/HiGvPQs3ji+mZ4nt5D5KI6Y0yEAhB/6lZuf\nfI+qlN8PmbM7qqQ43d+qpHikVjZILK0LX0uIQXn5rO5vh6Bp5Px1GlQFSB1dcBg5g6Rti0CtKlWZ\nUPa6YObsRO0pY7mzfCMatfHvZK3Jo3m4+wuT08grQw6Atr3VK8Ow7SwuJiX4CjmPtMdDcw9XPAf1\nJOGXcwBoVCoUjva0/mEX3pNH8OjAD8Xk4YwyvnTHFFNxuYkpXJnzIVn3owzWn5+WyaNDxzg/ch53\nt39FwLoZmD81SlpZ9klRClc3ChLjdX8XJCYgs7ZBammlFxf/yRbSfz1ucj0OXd6gIDmRzPNnTMYU\nVZHboqRjQXHHCws3Z5RxT59XOKPOyyfmp190r1ft8xoySwvSrms7nNcXbyHpz7/18nhR5zgAkUdO\nEL7nkEFbmX79Lq6vNENhbwuAdfvXkTm46MVUVLspvBzEPaAVrFOnTpw5cwYPDw88PT35888/MTc3\np0aNGhw9epQvv/wSgNGjR9OuXTu2bt1KUFAQHTp04Ny5c2zYsIFp06ZRUFDAzJkzad68OcOGDePq\n1atcvHiRQ4cOkZ2dTdeuXQEIDw9n9OjRtGzZkr///puPPvqIzz77DAsLC8LCwnBxcSEyMpJGjSq2\nI1BWBh0kifFrLRqVGqRGpiOo1WBi6l7Rg5jCQds4q3JyePDJATyHGk5rfXoZo+U9FXP7g83cXf8x\nfitnU3P0IFKCQ7Cs5kFBVg5XJs7HopoHATtWkhMZTebtcKPrK0piYoTMVEdSYWlGj3VB2FZx5NDo\n7UjlUnptGcMvKw7pRkdfSqXYN2UlKUUZxcUU7eSp8guI/uYHqgzoSczBn0oquMRyTcUgkVBv2UzC\nt+4hPylF7y3rul549O3O1ckl38tVWIyJz/dUfSwuzm98X3JTMvip03vILMxos2kKdYK6c3e/8VGO\nYpkox9hJkcTcAqeJS5A5u5Ow+n2QyXB+fyWp+zbqrvKXVlnqAhIJdZfO5P7WTw32yRO2/r7I7W1J\nOHG6UufwZF0l5VGaGJt6Xvivnk3Udz+T9OdfutfzU9I412ccNnVrE7B1KVn3H+k6rHppmPqspayb\nT8c97cqcD3X/n3rlNmmhd3AO1D/eVpp9op9UiTmVhmOfgcRu2/AMxVaubVHaY7mxnJ5u32oG9aX6\n4NcJmbqy1BeTTa3rH53jFCPm5zOYuznTbPtiAAqiItAU5OsHVVC7WempxdheaYgOaAXr2rUrH3/8\nMVWqVGHatGns378fjUZDt27dWLt2LaNGjQIgLS2NiIgI7ty5wyeffMKnn36KRqNB/vh+q9u3b2Nj\nY0N2tna6xIMHD/D390cqlWJjY0PdunUBcHV1ZefOnRw6dAiJREJBQQEAAwcO5PDhw1StWpXevXuX\n/4Z4BlZWZpibF1ZdMxcH3f+buzqRn5aJWpmrt4wyLhF7fx+jccrYRMyc9dehjE9GGaf/OoDF4/cA\nbHxq0Hj9bACuz1sLajW5sYnY+tUtXJeLM/npGXr5FBfjGNiYrPAI8hJTUOcoSTh5BpcOrYn7P+0V\n0yf/VUbFkh56C9v6dUx2QNtOfQOfztoTGzMbCxJuR+ves3V3ICc1i/wcw4OebRVH+u+eQNK9WL4Z\ntoWC3HyqNqmNfXVnOs5/EwBrVzskUglycznH5n9ptPx/o9y4ROwb1NH9bao+la2MBGzql1BHiolx\n7fYqWWH3yb4XoX1TIkFTUPLV49y4BGz9inw2E+Uai7GqVR2LKu7Ufm8MAGZODkikUqRmZqhylMit\nLWn0sfbhFWYujtRdMp0H2z8n+exF3br8Jvaj6qtNAJBbW5J+t/CedEs3R/LSMlEp9etjdkwyTv7e\nRuOqdW5OyJr9aApUFGTmEPHTH3i+1uIfdUBViXGY+/jr/pY5uaLKTEOTq39vnszZHZfZGymIekDC\nBxPR5OdiVqchcrdqOARN08Y4OINUikRhRsqulcWWW5a68GSf1Hr3yT5xRCKTIjU3I2ztNgBcOrUj\n4eiverM4KmMO2jISsSvy3TNzNZZH8TFur7Wlzsx3uPvhp8Sf0N4mILO2wrGZP4mngwHIvHOfrLAH\nWHvX1HVAvccNxLV9c0BbNzPDCh+69qQNUD19TIlNxL6B4THl6bii5DZWVB/Qlfuff1/4ogQ0j4/D\nhZ+zcuyTogoS4rCoWzhdWO7sgioj3eA7Uhxzrzogk5FzLaTUy1TktjB3cSz8fyPHguKOF8q4RIPl\nn4xCShRy/BZNxrq2J5feWYAyRn8qstc7g7H19cLnPe2zIV7UOU5x5HbWxB77gwd7v6fLhW/Jjwqn\nIFb/OSIV1W4KLwfRTa9gdevW5dGjR4SGhtKhQweys7M5deoUXl5e+Pj4sG/fPvbv30///v2pV68e\nXl5ezJw5k/3797Ns2TK6d9fOt2/QoAG7du3ixx9/5NatW/j4+BAaGoparSY7O5uwMO0T/7Zs2UKf\nPn1Yv349LVu2RPO40e3evTtnz57lxIkTlb4Dmp2dR0pKNikp2s62vX8drB7fwO/ZvwvxZy4aLJN0\n4YrJuITTl6jWqxMSmRS5jRXuXdqQ8HswufHJ5ETF4f74KXDOLQPQqNVkhj3E0tOdZjuWEP6f77QF\nPL6amBIcgl2Dulh4VgGgSr9uJJ0J1suluBjXTm2pOXowoD1IuXZqS+rfV1HGxJNx6x7ur2sf7KBw\ntMeuYT0ybpl+0ujZzf9jb6/V7O21mgMD1lO1SS0carkCEDC0HWEnDadZW9hb8dZXU7l77Ar/nfIZ\nBY/vqYu+fJ9P2i3UrS/kyzPc+t/fL1XnEwrryZMHQlTr15UEI/WpLFKDQ7BtUE+3/z36dif5j+BS\nx1jVrkGNt4fC4w5glf6vk/hLydPZDNfZjeQzJZWrjcm4fpuLb44lZPQ0QkZPI/aHYyT88gdha7dz\nf+se/hoyWfdeXmIKd5Zt1Ot8AtzYeYSTgxdzcvBifg36AKdG3tjUcAfAa0Anon+7bJBz3LmrJuNS\nb0bg2bUlABK5jKodmpAUWvyTd01Rhp7HzMcfuUd1AGxeexPlJf0REam1HW5LPiEn+FeSti5Ak689\nAcy7e5WYyT2JmzuMuLnDyDz5HdnnTpTqJKosdSHj+m0uDXhb9xCV2B+OknjqD92JNYBd4wak/lX8\n7RSVIQeA5MftouXjMqr27UriU9+94mJcO7bCZ9rbhE5drut8AqBWU2/+ZOwaah/mZlW7OlY1q5Fx\n/Y4u5N6ug7oHAgWPWWh4rDh9ySDfpAuhpYorqiA7h+oDuuHWMRAA27q1sPfzIfHcFb24yrJPisq6\nfBHLen66J9k69OhN5oWzJSylz8o/gOzQv0sOLKIit0VJx4LijhcJpy9SpVfHIucVbUk4rX2v4aoZ\nyK2tuPTOQoPOJ0D47m/IuBVO2Ef7dXm8iHOc4tjV9yZg7UwkMu3zH2z7jCL7rP7FvYpqN4WXgxgB\nrQQCAwOJjIxEKpXSokULwsLC8PX1pXXr1gwZMoS8vDwaNWqEu7s7c+bMYenSpeTm5qJUKlmwYIFu\nPRYWFixZsoQ5c+Zw8OBB2rdvz4ABA3Bzc8PZWXuDe/fu3Vm3bh27du3Cw8NDd2+oubk5LVq0IDk5\nGQcHB6N5VlY3lu+k0erpSORycqLiuLZMe3Cx8/XCb8EEzgfNJj8l3WRc5OHjWHq60+qL9UgVciKP\nnNTd33l14WbqzxuP1+j+qPPyCZ2/CTQaagX1RWZuTo1BPQBo+vlG1Hn5hIybw+1VH+G3YhZShYKc\nqFhuL9+Cja83dedO5u9R08lPTTMaA3Bv22fUmTWBZvu3gEZD4pkLRH37X+3nnL8Gn+njqNK3GxKJ\nlIeffUvmLdM/JVBUdlImP8/5gj7bxiJTyEl9mMD/zdTeV+zesAbdVw1jb6/VNB72CnZVnajTNYA6\nXQN0y38TtBVlapap1b80tPVkBw1XzUCqkJMTGcf1D7aVvOCzlJGaRtjqrfgun4NELkcZHcvdFZux\nqeeD95zJXBkzzWQMwKPPvsZr2nia7N2KRCYj8bezpbr3Mj81jburPqL+itnadUbFcmefGe1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kBlZoq1dyOjdfvMeIvsuCRitv9t+MzGx5OwaSsMP5dX106PdOD6L38byo/btR/Xp7pg6mSPZcO6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7APgt+Zib32zj5KBphH60GM+PAnSW/TJuOyuSUiWp8v+qAnEC+h/35ZdfVnYK5eLUxTy8PRTU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Pl/R8ELSbaIAKkvrkk08A9Zd1hw4dNMlWpFK1alWqVq1Kz549KVOmjKR1v0mhUBAdHU1K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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3d26a5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ZshynfCcgJSoaVUf1yjemwso5tW2MBvnsn7X8RyAtOg5/95uKC6Pno/rEAf95G+pqvzC2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\nICIiIiKie2LhQERERERE9/Q/QpkTAwOFubsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a408df4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check feature correlation, to see what correlates with the close price\n",
+    "colormap = plt.cm.inferno\n",
+    "plt.figure(figsize=(15,15))\n",
+    "plt.title('Pearson correlation of features', y=1.05, size=15)\n",
+    "sns.heatmap(df.corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(15,5))\n",
+    "corr = df.corr()\n",
+    "sns.heatmap(corr[corr.index == 'close_bid'], linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Start running datascience methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_shape(*argv):\n",
+    "    for el in argv:\n",
+    "        print(el.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "this creates training examples and actuals for the model\n",
+    "if nb_lookback_rows is above 1, X will have examples each of which is a 20 row dataframe\n",
+    "so the regression model needs to be able to use all those rows to train on\n",
+    "\"\"\"\n",
+    "\n",
+    "def create_training_set(df, nb_lookback_rows=1):\n",
+    "    \n",
+    "    dataX, dataY = [], [] # for training\n",
+    "    \n",
+    "    # it creates for each row a 20 row lookback dataset\n",
+    "    # this expands the dataset by 20 faculty\n",
+    "    for iRow in range(len(df)-nb_lookback_rows-1): \n",
+    "        \n",
+    "        df_lookback_rows = df[iRow:(iRow+nb_lookback_rows)] # from example 1 to 21\n",
+    "        dataX.append(df_lookback_rows)\n",
+    "        next_row = df[iRow + nb_lookback_rows] #get example 1+20, so the next point that is to be forecasted\n",
+    "        dataY.append(next_row) \n",
+    "    \n",
+    "    return np.array(dataX), np.array(dataY) # convert to numpy arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Use Random Forest for feature importance:\n",
+    "Check which feature is most important, based on predicting the next closing price using just one example as training\n",
+    "Do this for each example, and check which features are the best on average\n",
+    "Looking back more than 1 example for each example requires a decision how to use the features. Do recent examples features get more weight?\n",
+    "\n",
+    "- scale all features to range 0-1 for faster convergence\n",
+    "- use random forest to find best decision tree to explain closing price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# create random forest regressor - random decision trees, like weak learner, ada boost\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Scale and create datasets\n",
+    "idx_close_bid = df.columns.tolist().index('close_bid') # predict this, should it be return?\n",
+    "df_np = df.values.astype('float32') # so regressor can use it\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "df_scaled = scaler.fit_transform(df_np) # scale features to between 0 and 1 for faster convergence\n",
+    "\n",
+    "# Set look_back to 100 which is 100 ticks\n",
+    "# look back is 1 period, to check which features predict best a 1 period return\n",
+    "look_back_rows = 1 # to work with more than one, use alternative reshape\n",
+    "X, y = create_training_set(df_scaled, nb_lookback_rows=look_back_rows) # look back only 1 row\n",
+    "y = y[:,idx_close_bid]\n",
+    "#TODO:X = np.reshape(X, (X.shape[0], X.shape[2]* look_back_rows)) # to get back rows and columns\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[2])) # to get back rows and columns\n",
+    "# extend extra rows into columns, as all the prices during lookback periodd should be used as features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10202, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_shape(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "# fit model\n",
+    "forest = RandomForestRegressor(n_estimators = 100)\n",
+    "forest = forest.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature ranking:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>close_bid</th>\n",
+       "      <th>avg_price</th>\n",
+       "      <th>ohlc_price</th>\n",
+       "      <th>high_bid</th>\n",
+       "      <th>low_bid</th>\n",
+       "      <th>open_bid</th>\n",
+       "      <th>last_10_tick_avg_bo_spread</th>\n",
+       "      <th>last_10_tick_avg_bid_return</th>\n",
+       "      <th>range</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>nb_ticks</th>\n",
+       "      <th>pca</th>\n",
+       "      <th>oc_diff</th>\n",
+       "      <th>period_return</th>\n",
+       "      <th>day</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>15_min</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.935566</td>\n",
+       "      <td>0.02643</td>\n",
+       "      <td>0.019724</td>\n",
+       "      <td>0.01465</td>\n",
+       "      <td>0.002517</td>\n",
+       "      <td>0.000504</td>\n",
+       "      <td>0.000092</td>\n",
+       "      <td>0.000088</td>\n",
+       "      <td>0.000063</td>\n",
+       "      <td>0.000058</td>\n",
+       "      <td>0.000054</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.00004</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.000038</td>\n",
+       "      <td>0.000038</td>\n",
+       "      <td>0.00002</td>\n",
+       "      <td>0.000018</td>\n",
+       "      <td>0.000008</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   close_bid  avg_price  ohlc_price  high_bid   low_bid  open_bid  \\\n",
+       "0   0.935566    0.02643    0.019724   0.01465  0.002517  0.000504   \n",
+       "\n",
+       "   last_10_tick_avg_bo_spread  last_10_tick_avg_bid_return     range  \\\n",
+       "0                    0.000092                     0.000088  0.000063   \n",
+       "\n",
+       "   avg_bo_spread  nb_ticks       pca  oc_diff  period_return       day  \\\n",
+       "0       0.000058  0.000054  0.000052  0.00004       0.000039  0.000038   \n",
+       "\n",
+       "       hour  weekday    15_min     month  \n",
+       "0  0.000038  0.00002  0.000018  0.000008  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# find feature with best explanatory power to predict close price\n",
+    "importances = forest.feature_importances_\n",
+    "std = np.std([forest.feature_importances_ for forest in forest.estimators_], axis=0)\n",
+    "indices = np.argsort(importances)[::-1] # get indices for importances\n",
+    "#print(indices)\n",
+    "\n",
+    "column_list = df.columns.tolist()\n",
+    "#print(column_list)\n",
+    "print(\"Feature ranking:\")\n",
+    "feature_dict = OrderedDict()\n",
+    "for f in range(X.shape[1]-1):\n",
+    "    #print(\"%d. %s %d (%f)\" % (f, column_list[indices[f]], indices[f], importances[indices[f]]))\n",
+    "    feature_dict[column_list[indices[f]]] = importances[indices[f]]\n",
+    "display(pd.DataFrame([feature_dict]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Try linear regression\n",
+    "- sklearn requires numpy arrays as input\n",
+    "- check how close we can get with linear regression\n",
+    "- resources: http://bigdata-madesimple.com/how-to-run-linear-regression-in-python-scikit-learn/\n",
+    "- problem: my features are note independent of each other, eg ohlc price, open bid, close bid etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q6rwhk7e7j5IHpJYJlOx/JmQCAAAAGi+CJgBoZCrrwzR0w93ab9kvyVbl/WOu\nH6dHIh/lWBwAAACAcgiaAKARsfdhsjfpNlmMSsp+S4t3Lar0Hm+Dt1r4tNL8bgslSTFhfWurXAAA\nAAD1DEETADQy9pDp4U8e1Np978hWxQ4mP09/FZdaNb/bQgImAAAAAOfl1LfOZWVladSoURWOFRUV\nafjw4dq/f78k6fTp03rqqac0fPhwjRgxQnv37nVmaQDQaJgsRsc/B33QV+v3rtP1fw3Tmn2rKw2Z\n3OSuFTHvaNvwndow4ENCJgAAAADV4rSgafny5Xr22Wd16tSpcmPZ2dkaOXKkfv31V8e1LVu2SJLe\nffddPfroo3r55ZedVRoANBrpB9KUkD5SJotRU7Y8qp9P/KTndk7XYevhcnOvbXadJnWcoibuTfRi\n98WKCeurYP8QmnsDAAAAqDanHZ0LDQ1VYmKipk6dWm7MarVq6dKlZcbuvPNORUdHS5J+++03XXbZ\nZc4qDQAahUxzhu7bdI8Ghg/VzSuvr3Luy92XKDq0h4L9QxQT1ptwCQAAAMBFcVrQ1KtXLxmNxgrH\nIiMjKy7Gw0NPPvmkPvnkE7366qvOKg0AGjSTxaitv3ymN3YtlbXUqjX7Vlc4b8ats7Xn6G499adn\nJcnRJJyQCQAAAMDFcrlm4M8//7ymTJmiYcOGKS0tTT4+PnVdEgC4DJPFqGD/kAqvS9L6vev0/L/m\n6pTtZJXPmXHrbD0cOanMNXuTcAAAAAC4WC4TNG3YsEG5ubl64IEH5O3tLYPBIDc3p/YqB4B6xWQx\nOnYd2QOhTHOGJOm+jxN09OQRFZ0urPR+D4OnJnSYpA3712lQ+yHlxgmZAAAAAFyqWguaUlNTVVhY\nqLi4uArH77rrLj311FMaOXKkSkpK9PTTT6tp06a1VR4AuLxg/xBHyGSyGGUuyFG/9XfJw+ChU6Xl\nX7xg5+3mo+SBqZKkyMAoJUSMIVQCAAAA4BQGm81W8but64m8PEtdlwAAtSr9QJqmfP6oQv3a6Ou8\nf1U4x0MeKlGJhrWL11N/epZgCfVaQIB/XZeASvA7zLUEBPizJi6GNXFNrIvrYU1c08X+BuNsGgDU\nE5nmDI35cJRGp4/QoaLcSkOmgKatlTr4Y7Vr3p6QCQAAAECtcpkeTQCAitnfIvfY5w9XOc/Xw1et\nvFvr9Z5/VWRglNb030DIBAAAAKBWETQBgIuo6I1ySzIXa/7O2SqWtcJ7vAxe+st1CYoIuEnRoT0k\n/a+pNyGvEcaxAAAgAElEQVQTAAAAgNpG0AQALsD+RrmE68cqOrSH5n81R/859I32HN9d6T392g7Q\nhE4TFRkYVYuVAgAAAEDlCJoAwAUE+4doQNhgPf75I7Kp8nc0GGRQbLsR6hfeXxEBHZSQPtLxJjoA\nAAAAqGsETQBQR+xH5UwWo8wFOVqatbjKkKllk1Z6+c+Jignr67hGyAQAAADAlRA0AUAtM1mMkqT4\ntFiNixiv2V/N1IlTx1WiknJzL/Nspv/rMlfXtrxOgb5B5UIlQiYAAAAAroSgCQBqkcliVHxarJ66\n5VkdLsyr8E1ybnJXkHegxt70oAa1H0KYBOCSlZaWatasWfrhhx/k5eWlOXPmqE2bNo7x//znP1qw\nYIFsNpsCAgL0wgsvqEmTJnVYMQAAqK8ImgCghlT01rizr6UfSNOPx35UboFZj3z2oI5bj5d7xh0h\nd2nRn18hXAJQoz799FNZrVa999572rVrlxYsWKBly5ZJkmw2m6ZPn65XX31Vbdq00dq1a2UymRQW\nFlbHVQMAgPqIoAkAaoD9rXFn90w6+9r0bU9p488fVHjvTS06KLfokLqH/FlLer5em2UDaCQyMzPV\nrVs3SVLHjh317bffOsZ++uknNW/eXElJSdq3b5+6d+9e7ZApIMDfKfXi4rEmroc1cU2si+thTRoO\ngiYAqAHB/iGOkMm+i8lckHPekGlSxylKiBjjeAYAOEN+fr78/Pwcn93d3VVSUiIPDw8dO3ZM33zz\njWbMmKHQ0FA9+OCDuvHGG9W5c+fzPjcvz+LMsnGBAgL8WRMXw5q4JtbF9bAmruliwz+3Gq4DABot\ne8gUnxarJZmL1X99L92fnlBhyDSp4xS93H2JNv3ykeLTYuugWgCNiZ+fnwoKChyfS0tL5eFx5r83\nNm/eXG3atFF4eLg8PT3VrVu3MjueAAAALgQ7mgCghqQfSNPWX7bowO/79dzO6ZKkr/P+5Ri/ttl1\nuvry9prQaaIiA6MkSdGhPSSxmwmAc3Xq1ElbtmxRnz59tGvXLrVv394xduWVV6qgoEAHDx5UmzZt\n9PXXX2vo0KF1WC0AAKjPCJoA4BKZLEYlZr6it75/s9xYE0MTPdjhETVr0kwPR04qN15V83AAqCk9\ne/bU9u3bNXz4cNlsNs2bN0+pqakqLCxUXFyc5s6dq8mTJ8tms+nmm29WdHR0XZcMAADqKYPNZrNV\nNtijRw8ZDIZKb968ebNTiroQnOMEUJvODoIyzRlKP/CRlu5arBIVl5vr73GZ1ty93rF7qTrPPreh\nOACag7oyfoe5FnqcuB7WxDWxLq6HNXFNF/sbrModTStXrpTNZtPSpUt15ZVXavDgwXJ3d1dqaqqM\nRuNFfSEA1Df2cMnef+mu0N7a8ssn+s/RrArne8hTBoPU0qelAn2Dqv09ZzcUBwAAAID6qMqgKTg4\nWJL0ww8/aP78+Y7rY8aM0eDBg51bGQC4APsuo4Trx+rYyWP68eg+7T76XaXz+7UdoNnd5stckKNA\n36ALDo0ImQAAAADUZ9Xu0fTVV1/pT3/6kyTp888/l7u7u9OKAgBXcqVvqB77/OEq53i7+Whet4Ua\necNoSQRGAAAAABqnagVNc+bM0ZNPPqm8vDzZbDYFBwdr4cKFzq4NAGrFuQ24M80ZCvQNUmLmK/rH\n90myylrpvcPaxeveiLEXtXsJAAAAABqaagVN119/vVJTU3Xs2DEZDAY1b97c2XUBQK04twF3pjlD\nAzf00anSU1Xe1zXwdj3bZWa1G30DAAAAQGNQZdA0ffp0zZ49W6NGjarw7XMrVqyo8uFZWVlatGiR\nVq5cWW6sqKhI9957r+bOnavw8HAVFxfr6aeflslkktVq1fjx43XHHXdc4L8OAFyYYP8QLei2SJK0\n6rsVWvLvVyoNmTzkoQkdH1WzJs30wYHkC2r0DQAAAACNQZVBU1xcnCTpkUceueAHL1++XCkpKfL2\n9i43lp2drZkzZyo3N9dxLSUlRc2bN9cLL7yg33//XQMHDiRoAlDjMs0ZigyMksliVHbembfGPf3F\nVB0qOCSrrfJdTCF+V6qpu7diwnpr2rYpWtBtEUflAAAAAOAcVQZNN954oyTplltu0Z49e5SRkSEP\nDw/deuutCgsLq/LBoaGhSkxM1NSpU8uNWa1WLV26tMxYTEyMevXqJUmy2Ww0GwdQ4zLNGRqc0k9v\n3PmWnv5iqoz5v1brvhm3ztag9kMkndkBZT9mBwAAAAAoq1o9mlasWKFVq1bpz3/+s2w2m5KSkvTg\ngw9q0KBBld7Tq1cvGY3GCsciIyPLXfP19ZUk5efna+LEiXr00UerUxoAnJe92Xegb5DmdX1BPx77\nUTn5v1U6/4omV8jT00vL70rSniO7HW+SsyNkAgAAAICKVStoWrt2rdatWyc/Pz9J0kMPPaS//OUv\nVQZNFyMnJ0cTJkxQfHy8+vfvX6PPBtBwnfvWuLOvb/3lM72ZvUwvRb+q+z5OkKng/LuYkvqudrxF\njmbfAAAAAFB9btWZ5O3tLU9PzzKfvby8arSQw4cPa8yYMXriiSc0dOjQGn02gIbL/tY4k6XsDspM\nc4YGfdBXj33+sA4X5mnRv56vNGTydvfRpI5T1Payq7Qi5h1FBkaxawkAAAAALkKVO5qWLFkiSWre\nvLlGjBihPn36yMPDQ+np6Wrbtu0FfVFqaqoKCwsdDcbP9frrr+vEiRN67bXX9Nprr0k601C8adOm\nF/Q9ABqXinommSxGPb51osIva6efT/ykvJOHtNm4qcL7h7WLV/aRLCVEjFFMWG92MAEAAADAJTDY\nbDZbZYP2oKkyDz/8cI0XdKHy8ix1XQIAF5JpztAO05d6buf0Csc95KESndaMW5/TOz+s1Ks9linQ\nN0iSlJA+kkbfgAsKCPCv6xJQCX6HuZaAAH/WxMWwJq6JdXE9rIlrutjfYFXuaKpOkPTAAw/ojTfe\nuKgvB4CLdXZfJpPFKHNBjt7O/pvW7Ftd6T1uBje91WulAnxaKzIwSp2Du2jatimOcImQCQAAAAAu\nTbWagVclNze3JuoAgGozWYyKT4vV6r5rlZ2XpUc2j9fx4t+rvGfM9eP0T9NWRQR0cIRJkYFRZcIl\nQiYAAAAAuDSXHDQZDIaaqANAI1LZW+Kqy1yQo+LTxZq+7Slt/PmDSud5yFOpg9MlnQmVKvpewiUA\nAAAAqDmXHDQBwIWwvyXuQo+p2UOi9ANpjh1MPx7fW+n8l7svUXRojzLfQagEAAAAAM5F0ASgVl1M\nLyT7UTlPeeg/R7MqnXdTiw6acss0Rw8mAAAAAEDtuuSgqYqX1gFAhaobMtnfIHd508u1++h3lc67\nqUUHPR/90gWHS5d6hA8AAAAAUNYFBU3Hjx9Xs2bNylwbOHBgjRYEoHEzWYySpPV71+m5ndOrnBvu\nf7Vmdp1dpsH3hXzPxRzhAwAAAABUzq06k3bv3q2YmBgNGDBAubm56tmzp7777szugoSEBGfWB6AR\nyTRnKD4tVl1W/7HSkMnL4KU7Qu7Sy92XaMeofysioIMS0kc6AqrqupgjfAAAAACAqlUraJozZ46W\nLl2q5s2b64orrtCsWbM0c+ZMZ9cGoIGzh0Mmi1Fzv3xOQz8YoL1H96jodGGF84e1i9fOv+zSO3e/\nr5E3jJZ0aYERIRMAAAAA1KxqBU1FRUUKDw93fO7atausVqvTigLQsJksRmWaM5SQPlLpB9J088rr\ntXjXIhWcztdpnS4z11Ne8pCHAn2ClH2k4kbgBEYAAAAA4Bqq1aOpefPm2rNnjwwGgyQpJSWlXK8m\nAKgOk8Wo2JSBKijJV2nJaY1OH1Hp3Je7L1F0aA+ZC3IU6BskiVAJAAAAAFxZtYKmWbNm6cknn9S+\nffsUGRmptm3b6oUXXnB2bQAaGPtRuV9OHJTVdqrCOeH+V+vhTo+qpXdLxYT1lUS4BAAAAAD1RbWC\nptDQUL3zzjsqLCxUaWmpJMnPz8+phQGo/0wWo4L9Q5R+IE2S9MTnjyv/1IlKQiaDJnWcrISIMYpP\ni5Wki3qbHAAAAACg7lQraNqyZYu+/vprPfTQQ4qNjdXRo0c1ceJEjRw50tn1AainMs0ZmrZtiiID\novTW929WMdOgZl6XKbHH644dTC9Fv6pA3yBCJgAAAACoZ6rVDHzJkiUaPHiwPvzwQ91000367LPP\ntG7dOmfXBqCesR+NyzRn6PGtE9WqSUClIZNBBg1rF6+PBn+qAO8rNP9fc2SyGGWyGDVt25TaLBsA\nAAAAUEOqtaNJksLDw/XSSy/p7rvvlq+vr4qLi51ZF4B6wH40LtOcoUDfIMWnxWpcxHg9nzFXuYVm\n7T76Xbl7DDKotXegXuj+kmMH09q7N0j6Xy+mpJhV7GYCAAAAgHqoWkFTq1atNHv2bGVnZ+uFF17Q\nggUL9Ic//MHZtQFwYSaLUQnpI5Vw/Vg99cUUTf3jMzrw+4967POHy829I+Qu7Tm2W2NvHKfOwV3K\nHYs7N1QiZAIAAACA+qlaQdOLL76oTz/9VPfcc498fHx05ZVX6uGHy/+fSQD1j31X0sXMmRw5VZO3\nTpKnwUvP7Zxe4b1uclO/8Lu1KPQVAiQAAAAAaOCq1aPJ19dXBQUFWrRokR566CGVlJTIx8fH2bUB\ncDL7riR7b6XqzMk0Zyj9QJqi3+2i0ekjlHfykCwlJ8rdZ5BBwb5X6tlb/09Pb39C5oIcp/17AAAA\nAABcQ7V2NC1cuFAHDx7UkCFDZLPZlJycLKPRqGeeecbZ9QFwomD/kGr1Q0qKWSVJSj+QpjEfj1KJ\nraTCeV6GJmrWpJnGd3hEq/esVOIdyxQZGKXOwV0UGRhV4/UDAAAAAFxLtYKm7du3a8OGDXJzO7MB\nKjo6Wv379z/vfVlZWVq0aJFWrlxZbqyoqEj33nuv5s6dq/Dw8GrdA6DmVRQymSxGmQtyFOgb5OjD\ntOjr5/VbgVE22Sp8zqSOU5QQMcbxzEHthzieTcgEAAAAAI1DtYKm06dPq6SkRF5eXo7P7u7uVd6z\nfPlypaSkyNvbu9xYdna2Zs6cqdzc3GrfA6B2mCxGDfqgr3Lyf9Pyu5KUcP1YTfl8kk7rdIXzh7WL\n178Pfa2EiDFVNvgGAAAAADR81erR1L9/f40ePVorV67UypUrdc8996hv375V3hMaGqrExMQKx6xW\nq5YuXaqwsLBq3wPAuTLNGZIkc0GOPAye8vP016NbHtbjn0+sNGQK9r1ST/3pWa29ewPBEgAAAACg\nejuaHnzwQV133XX66quvZLPZ9OCDDyo6OrrKe3r16iWjseIGw5GRkRd8DwDnyTRnaHBKP83r+oJm\nfzVTl3tdriOnDpeb5yEPDW43TNt/26Ypf3xS0aE9CJgAAAAAAA5VBk0ZGRmOP/v4+KhHjx5lxqKi\n6LsCNASRgVHqf9UgTf58kkp1WkdPHVErr1Y6bP1f2OTt7qPkAamKDIySyWIkYAIAAAAAlFNl0PTq\nq686/nzkyBG1bNlSRUVFOnTokNq2basVK1Y4vUAAzmEPizLNGVr0r+e12bipzPjvJcclSTNunS1J\nZZp7EzIBAAAAACpSZdBkf/PbihUrlJycrJUrV8poNOr+++9Xnz59LuiLUlNTVVhYqLi4uIuvFkCN\nyDRn6JHN49Wp9R+1Zt/qcuNuctOEmyZp2X9eVefgLrw1DgAAAABQLQabzVbxu8rP0q9fP61du9bx\nNriioiINGzZMqampTi/wfPLyLHVdAlBvmCxGmQtyNPGz8dr/+48qVWm5Of3aDtCEThMVGRilTHMG\nIROAOhcQ4F/XJaAS/A5zLQEB/qyJi2FNXBPr4npYE9d0sb/BqtUMvLi4WJ6eno7PZ/8ZgGuzH5Ez\nWYwa9EFfeXv46KEOE/Vi5vPKyf9N0SF3qF/43Y75I28Y7fgzIRMANAylpaWaNWuWfvjhB3l5eWnO\nnDlq06ZNuXnTp09Xs2bNNGXKlDqoEgAANATVCpruvPNO3XPPPerdu7ckadOmTbrjjjucWhiAS2ey\nGDUsdaDW9N+g7Lws/WYxadot0/Vm9jItvytJkhToG6SE9JFKillF7yUAaKA+/fRTWa1Wvffee9q1\na5cWLFigZcuWlZnz7rvvau/evbzsBQAAXJJqBU1PPPGE0tPTlZGRIQ8PD40ePVp33nmns2sDcAlM\nFqOy87L0i+WgsvOy9NyOmbIZbFqx+y15GDwV6BvkCJYImQCgYcvMzFS3bt0kSR07dtS3335bZvzf\n//63srKyFBcXpwMHDlT7uRxrdD2siethTVwT6+J6WJOGo1pBkyTFxMQoJibGmbUA9Zr9iFpdf/+S\nzMU6fuq4NuxfJy93Ty3vmaSYsL6KCOggc0GOJJUJmSTeIgcADV1+fr78/Pwcn93d3VVSUiIPDw8d\nOnRIS5cu1ZIlS/TRRx9d0HPpp+Fa6HHielgT18S6uB7WxDU5tUcTgKqZLMY6O35mb/D9+NaJCr/s\nam38+QNJkrvBXYE+f1BEQIcyIZi9TgBA4+Hn56eCggLH59LSUnl4nPkZmJ6ermPHjmncuHHKy8vT\nyZMnFRYWpsGDB9dVuQAAoB4jaAJqQLB/iFNDpop2S5ksRklSbMpAldiKVWAt0O6j30mS3OSmVk1b\ny8fTW+aCHE3bNsVRH8fkAKDx6dSpk7Zs2aI+ffpo165dat++vWNs9OjRGj36zIsgkpOTdeDAAUIm\nAABw0QiagBrizJDp3N1SmeYMPb51osZFjFdRSZEkyd3NXZ5unnoqaoY6B3dRoG+Qo66z7yVkAoDG\np2fPntq+fbuGDx8um82mefPmKTU1VYWFhYqLi6vr8gAAQANisNlstrou4lJwjhMNmX0nk333UrB/\niCNk+vn4TzptK5Gt1KZWvgHy9fDTjM7/p5iwvnVcNQDULJqDui5+h7kWepy4HtbENbEuroc1cU0X\n+xvMrYbrAFBD7DuZ0g+kSZLi02KVac7QI5vH6w8+wSo6Xai7QnvrbzEr1LzJ5Uq8YxkhEwAAAACg\nTnF0DnAhZ/diCvYPUcL1YzV202gNCo/V0aKj2nNkt37NPyhJGtYuXkt6vi5JigjowJE4AAAAAECd\nI2gCXIR9B9PkyKmSpACf1nruqxkqLi3Wmn2rJUkvZj6v5T2TFODTWpGBUY57CZkAAAAAAK6AoAmo\nY2fvYooMiNK96X/RaZ2Wn6e/8ov/d055zPXj9Ejko5JUrjk4AAAAAACugB5NQB3KNGcoNmWgVn23\nQre9E6W3vn9Tp3Vakhwh0x0hd8nLzUux18Yp2D+k3FvkAAAAAABwFexoAuqAyWLU1l8+07Pbp6mg\nJF+Pff6wJMkgg2yyydfDT6dKTmpCx0f1TJcZyjRncFQOAAAAAODyCJqAWmQPmJ7ZPlWFJYXlxm2y\nqV/bAdp/4kc9dcuzjrfInR0yAQAAAADgqgiagFqSfiBN932cIKvtVKVzWjZtpdnd5kti1xIAAAAA\noP4haAKcxGQxylyQo7zCQ3pj1zJtN/+z0rkzbp2tzsFdFOgbVCZgOrtROAAAAAAAro6gCXACk8Wo\nO9Z009FTR6qcZw+YKjoaZ7IYebscAAAAAKBeIWgCLsK5O41MFqOkM8fdTBajpmx5tNKQycvgpRJb\niV7s/qpG3jC60u/g7XIAAAAAgPrGzZkPz8rK0qhRoyocKyoq0vDhw7V//35JUmlpqWbMmKG4uDiN\nGjVKBw8edGZpwEWz7zSyh0smi1GxKQM16IO+mvvlc4pa2UGbjZvK3edl8FKgT5D+2uvvCmt2taJD\ne5z3uwiZAAAAAAD1idOCpuXLl+vZZ5/VqVPlGx9nZ2dr5MiR+vXXXx3XPv30U1mtVr333nuaPHmy\nFixY4KzSgEsS7B+iBd0WSfrfW+ROni7Szyd+0uJdi1Si4nL32AOmy5u2UERAB629ewMhEgAAAACg\nwXHa0bnQ0FAlJiZq6tSp5casVquWLl1aZiwzM1PdunWTJHXs2FHffvuts0oDLonJYtTjWyeqqKRQ\nJ06d0NFTR+Qhz3Lzugberme7zNSeI7sVHdpDwf4higjoQMAEAAAAAGiwnBY09erVS0ajscKxyMjI\nctfy8/Pl5+fn+Ozu7q6SkhJ5eNBGCq4ntt1wLf73izpe/LskqUTF8jJ4qdRWKn+vZprR+f8c4dLZ\njb4JmQAAAAAADZnLpDh+fn4qKChwfC4tLSVkgksxWYwyF+RoaMoAFZTklxsP8gvW7K7zFBHQQZJ4\nYxwAAAAAoNFxmSSnU6dO2rJli/r06aNdu3apffv2dV0SIOlMwJSdl6XHt0zSsVNHdFqnJUnh/lfr\noOVnPX3rTHUO7qJA36AyoRIhEwAAAACgsam1oCk1NVWFhYWKi4urcLxnz57avn27hg8fLpvNpnnz\n5tVWaYCDyWJUsH+IMs0ZkqQdpi/1t2/fVE6BSaUqdczz8fDR+wNTZC7IKRcw2REyAQAAAAAaG4PN\nZrPVdRGXIi/PUtclwEXZQ6MLmR+fFqvYdsM1Z+cslf5355Kb3Bwh07XNrlNR6Um93vOvigyMksli\n5IgcADhZQIB/XZeASvA7zLUEBPizJi6GNXFNrIvrYU1c08X+BnOr4ToAl2APgEyWihvS29l3LqUf\nSJO5IEe/HD+oOTtnOkImSWri3lQvd1+il7svkcHdzREySWd2LREyAQAAAABwhsv0aAJqUmUB0Nm7\nnDLNGRqwobcGhg/Vmn2r5SFPlahYkuRp8FSxrVju8lCIf4iiQ3tIkt7MXqZA36By3wUAAAAAANjR\nhAasopApIX2k0g+kyWQxKq/wkIpLS7Rm32pJcoRMkvRQh0m63KulNg7+WGv6b1Cwf4iC/UO0uu9a\ngiUAAAAAACrBjiY0WPZjc8H+IY4/J1w/Vvd9fI/8vS5TYUmhbGc1+Jakfm0H6Ju8fyshYowSIsaU\nC5UImQAAAAAAqBxBExoce6g0LHWgPNw8NS5ivF7LelU2m5RXdEhWm1VHTh0+6w6D3A1uerjDY3qm\ny4wLbiIOAAAAAADO4OgcGhT7m+PMBTnycPNU58CumrrtMR0r+l3NvZrruPV3x1xvd295GjwV7Bui\nRbcv1lbTZkImAAAAAAAuATua0GB1Duyqt75/U5J0+NQhHc47JEnyMjTR+A6P6PXsRC28/WVFh/ZQ\n8H8bfhMyAQAAAABw8Qia0CDYj8uZC3I0LmK8Bn/QX0WnCx3jXgYveXv66IT1uAJ8WmvTLx9pec8k\nxYT1dcwhZAIAAAAA4NIQNKFeswdMsSkDlVd0SCesx2WTrcwcD3mq2FasaTdP1js/rNSrPZYp0DeI\nYAkAAAAAgBpG0IR6K9OcoYmfjVeftnfrlxM/y2qzlpvj7e6j1j5X6NFOkzXyhtEa1H4IARMAAAAA\nAE5CM3DUK/YdTJnmDMVvHKZ9v+/V4l2LyoRMzTyaySCD+rUdoGC/EHm5eyo6tIckjscBAAAAAOBM\n7GhCvWGyGDVwQ19FtLxJm35Ol1WnHGPucpcM0mnbabX2u0KzOsxV0vd/U+IdHJMDAAAAAKC2EDTB\npWWaMyRJO0xfas/R3Tpo+UkHLT+VmXNHyF36IudzLbjtRV3b8jpHsMRb5AAAAAAAqF0ETah1Joux\n0gAo05yhyMAoZZozlFd4SGM/Hq1iW3G5eR7y0Gmd1sSOk/VMlxmO+85GyAQAAAAAQO0iaEKtMlmM\nSkgfqaSYVeWCoPQDaXrg0zHqf9Ugrf9xrdwNHpWETJ5KHZyuvMJDejFzoWLMvcuFTAAAAAAAoPYR\nNKFWBfuHVBgymSxGzf/XHHVqFaU1+1ZLUrmQyU1u+nPInfol/2cF+gYpMjBKAT6tNW3blAqfCQAA\nAAAAahdBE5yiquNxZ1/PNGc4+i/tP7ZPu23fVXiPv+dlusyrmabc8qQe3zrRcT0yMIqQCQAAAAAA\nF+FW1wWg4bEfjzNZjFXOW5K5WL2T79BzO6drzb7Vstqs5eZ4yFNt/K/S0jve0GVNLlOgb5BW911b\nJlgiZAIAAAAAwDWwowk1rqLjcfYdTiaLUev3rtOeo7sdR+Qq0sStiZ6MelaD2g9xPDMioAOhEgAA\nAAAALoygCU5hD5WC/UOUac7Q41sn6qlbntXY9NEqVvkG33ZuclNSzKoKQyVCJgAAAAAAXJtTj85l\nZWVp1KhR5a5/9tlnGjJkiOLi4rRmzRpJktVq1eTJkzVs2DCNGTNGP//8szNLg5OZLEbFpgz8//bu\nP07Lus4X/2uYARyYETLROum4QuKxrEWws3mMMpK0pU1xskEUMt312z52j/2gEi0nd03EH53VyB9l\nmkdOCkhmgCtuiKbLVgskJKcfFhodSI0SkplRhmHu7x8eJkkBgYt7bmaez8ejx6Prvu4f78u3M9d7\nXn6u687CJ+/L3y/62zy5YXU+unDiDkOmflX9c95bLsjySaty6tBxQiUAAADYD+2zFU233HJL5s2b\nl9ra2u0e37JlS6688srMnTs3tbW1OeusszJmzJgsXLgwAwYMyJw5c/Lkk0/m8ssvz6233rqvyqNA\nL78sbltA9Pj6lfn1H5/MBd/7WF7c+uIOX1tdVZ3P/7fLMn54o3AJAAAA9nP7bEVTQ0NDZsyY8YrH\nV69enYaGhgwaNCj9+vXLqFGjsnTp0vzqV7/Ku9/97iTJ0KFDs3r16n1VGgXaduPvhU/el4n3nZnl\nzyzNt/7PHfnbB85NRzp2GDIN7v+6NP/V5Vkw/t/yj6M+IWQCgH2os7Mzzc3NaWpqyqRJk7JmzZrt\n9i9YsCBnnnlmJkyYkObm5nR2dnZTpQDA/m6frWg65ZRTsnbtK791rKWlJfX19V3bAwcOTEtLS445\n5pg89NBDOfnkk7Ny5co8++yz2bp1a6qrq/dVieyhl69cSpIpoz6XK//zS3nuhedy+nf+OptLm3f4\n2iMGHpF7z7g/iXsuAUC5LFq0KO3t7Zk9e3ZWrFiR6dOn56abbkqSvPjii7nuuusyf/781NbW5tOf\n/p3ukEwAACAASURBVHQeeuihvO997+vmqgGA/VHZbwZeV1eX1tbWru3W1tbU19fn5JNPzurVqzNx\n4sSMHDkyb33rW4VMFWjbCqbbT/1WkuRvvnNKOjo7UtramWc3P7vD173vsPdn1R8ez71n3C9gAoAy\nW758eUaPHp0kGTFiRFatWtW1r1+/fpk1a1bX7Q46OjrSv3//bqkTANj/lT1oGjZsWNasWZONGzdm\nwIABWbZsWc4///w8/vjjOeGEE3LJJZfk8ccfz29/+9tyl8Zr8Kb6wzJl1OfyTOvTufvns7O25f/u\n8jXnveWC3PXEzHzt5NtecS8nAGDfa2lpSV1dXdd2dXV1Ojo6UlNTkz59+uTggw9OksycOTNtbW05\n8cQTX9P7DhlSv+snUVZ6Unn0pDLpS+XRk56jbEHT/Pnz09bWlqampkydOjXnn39+SqVSGhsbc+ih\nh6Zv3765/vrrc/PNN6e+vj5XXHFFuUpjF9Zt+tMlkI+vX5nzHpiUjlLHTl9z3lsuyH1Pzc/U//b5\nnNQwJmf+16aMesM7tlsRJWwCgPL48xXlnZ2dqamp2W77mmuuyVNPPZUZM2akqqrqNb3v+vWbCq+V\nPTdkSL2eVBg9qUz6Unn0pDLtafhXVSqVSgXXUlb+ZSzWn3+D3LpNazPxvjOz4cXnUtOnJs+0PJ2O\n7Dhk6tunb259/x05dei4roDqz4MlK5oA2B3+C+fee+CBB/LQQw9l+vTpWbFiRb761a/mG9/4Rtf+\nL3zhC+nXr1++8IUvpE+f1/5dMeawyuIPtcqjJ5VJXyqPnlQmQRN7bdtqo+mjr82nH74wd467Ow//\nZnEeX/+T3PbTr+/wdbVVtXmh9ELeNPDwXDn66q6QSbAEQBEETXuvs7Mzl112WZ544omUSqVMmzYt\nP/3pT9PW1pZjjz02jY2NOf7447tWMk2ePDljx47d5fuawyqLP9Qqj55UJn2pPHpSmQRNFGLbKqSP\nzD897Vs2Z03rmp0+v77mwBx24OG54G1/n5MaxnStgnJ5HABFETRVLnNYZfGHWuXRk8qkL5VHTyrT\nns5gZb8ZOJVj+TNLM+oN70iy/aqjzzz0yfxy4xM7fW1VqvI/3zMjJzWMSZLtAqU31R8mZAIAAIBe\nSNDUC63btDbPtD6dM+Z9MF87+ba8bchf5sx5p2fkIcdn7i9npTOdr/q6YfVvzj+O/GReX/v6DBlw\nSFdI9WqETAAAAND7CJp6mZdf1vbZUZfkn3/wxQzuNzi/+uMT+dUfd7yKaXD/12Xu6fMESAAAAMAO\nCZp6oemjr83Dv1mcL/3oiztcvbRNVapy4Ygp+bff3F+m6gAAAID9laCpF1m3aW3OnHd6fvPHNWnP\n5h0+rzrVObDf4Gza8sdc8+7rcvZbJ+fct51nNRMAAACwU4KmHm7bt8g90/p0Fj55/04vj+tX1T9v\nrPsvuXnsN5IkFy7++1e92TcAAADAqxE09WDLn1mav33g3Pxx88a0dOz4qyKrU5PP/9UXM354Y5I/\nhUpz/uZeARMAAADwmgmaepjlzyzNGwa+Md954tu5YcX1+cPm3+/0+c1/dfkrAqZt9mXItG7TWiEW\nAAAA9DCCph5k4ZP35fyFk9Ovpn9aO1p2+tx/ec9X8/ra1+fUoeO2+ya6coQ/5f48AAAAoDwETT3A\nwifvyx9e+EO+vPyqbMmWbOnY8qrPq051BvQdmBve97WcOnRc1+Nvqj+srKFPuT8PAAAAKA9B035q\n26VnX11+ff75R5fu8HkDagbm5pO/kSEDDkmSvGHgG1814Cl36CNkAgAAgJ5H0LQfWrdpbc6cd3pG\nHnJ85vzyzh0+b3C/12VQ/8G58j+/lDvH3S3cAQAAAPYpQdN+YuGT92XIgEPyhoFvzOPrV+ZXf3wi\nv/rjE6/63Oa/ujxvft2bu56fWEEEAAAA7HuCpgq27fK4b/2fO/Kp7/9jkqS2ujYvbn3xFc+tSlUa\n6v8inxw5Jbf/9NZMH31tpj76GfdCAgAAAMpG0FShtn0z2/TR1+bz/35R1+MvbH3hFc/94F+clstH\nX5nkpZVLJzWMccNtAAAAoOwETRVm2yqmJJky6nP5x+/9f2nb2vqK59VWD8hnj784rzvgdTn7rZO3\n27ft9UImAAAAoJwETRVk+TNL8/eL/janDW3Mbau+npaOTSmltN1zqqtq8tFjzssPnlmSE9703zP1\n0c90rWB6LV4eZAEAAAAUSdDUzbYFP+s2rc3HFp6TZ9qezvUrru3af2j/Q/Ps5mfz9oP+Mn/c8nwu\nP3FaTh06rut1u3N53LbL8VxSBwAAAOwLffblm69cuTKTJk16xeOLFy9OY2NjmpqaMmfOnCTJli1b\nMmXKlEyYMCETJ07M6tWr92Vp3WLdprXb/f9twc+6TWvz+PqV2bD5udSk73avmfrOS3PHqXdl0YRH\nc+/p9+XUoeOS7Nnlce7bBAAAAOxL+yxouuWWW/KFL3whmzdv3u7xLVu25Morr8xtt92WmTNnZvbs\n2fn973+f73//++no6MisWbPyD//wD7nuuuv2VWnd4uWh0rpNa3PmvNPzTOvTmTLqc/nOE9/Ol5df\nnbOP/mi2piNJMrj/69L8V5fnpIYx+fLyqwu75E3IBAAAAOwr++zSuYaGhsyYMSOf+9zntnt89erV\naWhoyKBBg5Iko0aNytKlSzN8+PBs3bo1nZ2daWlpSU1Nz7qqb9tqomdan876tt9lzfNP5WMLz8nv\nX1ifjlJHPjHiM3l43YP5n++ZkdfXvj5X/ueXMn54o1VIAAAAwH5jn6U5p5xyStauXfuKx1taWlJf\nX9+1PXDgwLS0tGTAgAFZt25dPvCBD2TDhg25+eab91VpZfPyVUjrNq3Nw79ZnIse/XRqa2qTJDV9\nanJw7ZBUVVXl3Ledl3Pfdl7X89825C99exwAAACwX9mn92h6NXV1dWltbe3abm1tTX19fW6//fa8\n613vygMPPJDvfve7mTp16isuu6tEL7/v0p8/fu7Cs7P8maVZt2ltxs45KZ/+/v9Ie2d7/tj+x4x/\n85m55f2353UHHJTbTpmZN9Uftl2gJFwCAAAA9jdlD5qGDRuWNWvWZOPGjWlvb8+yZcty3HHH5cAD\nD+xa6TRo0KB0dHRk69at5S5vt7z8vkvbtl/u3Lecn08/fGEuffTi/H7z71JKKdVV1fnIURPzi40/\nyxsGvjF3jrs7o97wju4oHwAAAKBQZbsR0vz589PW1pampqZMnTo1559/fkqlUhobG3PooYfm3HPP\nzSWXXJKJEydmy5Yt+dSnPpUBAwaUq7w98vL7J63btDYT7zszd467O0ky/rvj8nTrb1NXU5+fPfd/\n8pGjJuaNA/9L7ntqXj72tvPzhoFvtGoJAAAA6FGqSqVSqbuL2Bvr12/q7hKSvLSa6W++c0puef/t\nSZL/8eDfp/mEf8qQAYfk7/7t3Mwf/0DeVH9Ylj+zNFMf/YwbfAPAazRkSP2un0S3qJQ5jJcMGVKv\nJxVGTyqTvlQePalMezqD9ayvdusmy59Zmm8+fmvWtvzfTLrvrAw+YHCqqv50Q+9tIVOSjHrDO4RM\nAAAAQI8kaNoL275J7jPf/0S25qX7ST2/ZWM+/87mnNQwZoffGidkAgAAAHoiQdMeWrdpbT4y//T8\n5vk1Obj2kGzt3Jr/+d7rkyRfXn51TmoY080VAgAAAJSXoOlVrNu09lVXHS1/ZmmSly5/e1P9YfnK\nmJuSJG8Y+MYkf1qptO2SOQAAAIDepE93F1Bp1m1am3MXnt0VKi1/ZmnWbVqbhU/el9O+84Gc/t2/\n7nps6qOf6fr2uJcHS0ImAAAAoDeyounPvKn+sEwffW2mPvqZnDb0jEz/z8tzyMBDU11Vk/9S/6b8\n83+fllFveEeSuKk3AAAAwMtY0fQyC5+87/+tXro/o4a8I1f+5z9na2lrOjo70rdP39x08jdy6tBx\nXc8XMgEAAAD8iRVNeSlgWv7M8nxlxZfTr0//bO58MUlSXVWdIQMOSV3f+sx4301dK5kAAAAAeKVe\nHTSt27Q233ni2/nnH12aJDmgT21e7HwhB/YblE8e95mc8Kb//oobfQMAAADw6npN0LTtm+TWbVqb\nJHl8/cr83b+dmy2dW7qe82LnCznvLRfkzP/aZPUSAAAAwG7q8UHTtmDp3IVnZ8qoz6X5Py5JR2dH\n1rWsTSmlruf1qeqT//GXn86//eb+/OCZJblz3N1WMQEAAADshh59M/B1m9bm3IVnJ0mmj742//yD\nL2bdprXZsrUjBx8wJEnyiRGfyR2n3pWhB745577tvNw57m4hEwAAAMAe6NFBU5JMGfW5PL5+Zda3\n/S7NJ/xTDh34htT3q88lf9Wcfn365dShH8ipQ8fl7g/dmzfVH9b1PwAAAAB2T4+8dG7b5XLjvzsu\n61rWdt2H6bC6w1NdVdP1DXL/9fXHdN2LSbgEAAAAsHd63IqmhU/el4n3nZnH169MqZRcPfpf8i/v\n+Wr+4sAjM+1dV2dA3wFd3yTnht8AAAAAxakqlUqlXT+tcq148mddq5GWP7M0p3/3rzOk9pBUV9Xk\nt61rc0T9kbn7Q/cmSde3zlm9BAD7jyFD6ru7BHZg/fpN3V0CLzNkSL2eVBg9qUz6Unn0pDLt6Qy2\n369oOnfh2V2Xyo16wzty72n/mvnjH8jNY7+RI+qPzIz33bTdfZeETABAb9PZ2Znm5uY0NTVl0qRJ\nWbNmzXb7Fy9enMbGxjQ1NWXOnDndVCUA0BPs9/douv3Ub223/fJ7Lm27wTcAQG+2aNGitLe3Z/bs\n2VmxYkWmT5+em266KUmyZcuWXHnllZk7d25qa2tz1llnZcyYMTn44IO7uWoAYH+03wdNr399XcbP\nHp/vNH0nhw86fLt9Q4Yc001VAQBUjuXLl2f06NFJkhEjRmTVqlVd+1avXp2GhoYMGjQoSTJq1Kgs\nXbo0H/jAB3b5vi5rrDx6Unn0pDLpS+XRk55jvw+aDh90eJZdsKy7ywAAqFgtLS2pq6vr2q6urk5H\nR0dqamrS0tKS+vo/DfcDBw5MS0tLd5QJAPQA+/09mgAA2Lm6urq0trZ2bXd2dqampuZV97W2tm4X\nPAEA7A5BEwBADzdy5Mg88sgjSZIVK1Zk+PDhXfuGDRuWNWvWZOPGjWlvb8+yZcty3HHHdVepAMB+\nrqpUKpW6uwgAAPadzs7OXHbZZXniiSdSKpUybdq0/PSnP01bW1uampqyePHi3HDDDSmVSmlsbMzZ\nZ5/d3SUDAPspQRMAAAAAhXDpHAAAAACFEDQBAAAAUAhB0z60cuXKTJo06RWPL168OI2NjWlqasqc\nOXOSJFu2bMmUKVMyYcKETJw4MatXry53uXtsd46zvb09U6ZMyUc+8pGcd955+fWvf13mavfMjo4x\nSV544YVMmDChq2ednZ1pbm5OU1NTJk2alDVr1pSz1D22O8f4Wl5TiXbnGLds2ZLPfvazmThxYj78\n4Q/nwQcfLGepe2V3jnPr1q25+OKLM2HChJx11ll54oknylnqHtuTf1//8Ic/5D3vec9+8/t1d49x\n/PjxmTRpUiZNmpSLL764XGXuld09xq997WtpamrKGWeckbvvvrtcZfZquzqnvdq5nn1rVz1ZsGBB\nzjzzzEyYMCHNzc3p7Ozspkp7l9c6/1166aW59tpry1xd77SrnvzkJz/JxIkTc9ZZZ+XCCy/M5s2b\nu6nS3mVXfZk3b17Gjx+fxsbG3Hnnnd1UZe+0O3/X70pN0cXxkltuuSXz5s1LbW3tdo9v2bIlV155\nZebOnZva2tqcddZZGTNmTFasWJGOjo7MmjUrS5YsyXXXXZcZM2Z0U/Wv3e4e58KFCzNgwIDMmTMn\nTz75ZC6//PLceuut3VT9a7OjY0ySxx9/PF/84hfz7LPPdj22aNGitLe3Z/bs2VmxYkWmT5+em266\nqZwl77bdPcZdvaYS7e4xzps3L4MHD84111yTjRs35vTTT8/73ve+cpa8R3b3OB966KEkyaxZs/Kj\nH/0o//Iv/9Ij/33dsmVLmpubc8ABB5SrzL2yu8e4efPmlEqlzJw5s5xl7pXdPcYf/ehHeeyxx3LX\nXXflhRdeyG233VbOcnutnZ3TdnSuP/jgg7u56p5tZz158cUXc91112X+/Pmpra3Npz/96Tz00EP7\nxflrf/da5r9Zs2bliSeeyDve8Y5uqrJ32VlPSqVSLr300nzlK1/JEUcckbvvvjvr1q3L0KFDu7nq\nnm9XPytXX311FixYkAEDBmTcuHEZN25cBg0a1I0V9w67+3f9rs71VjTtIw0NDa8aFK1evToNDQ0Z\nNGhQ+vXrl1GjRmXp0qU58sgjs3Xr1nR2dqalpSU1NftHBri7x/mrX/0q7373u5MkQ4cO3S9WFuzo\nGJOXVmjdcMMN252Uli9fntGjRydJRowYkVWrVpWlzr2xu8e4q9dUot09xlNPPTWf+MQnkrw0jFRX\nV5elzr21u8d58skn5/LLL0+S/Pa3v82BBx5Yljr3xp78+3rVVVdlwoQJOeSQQ8pR4l7b3WP8+c9/\nnhdeeCHnnXdeJk+enBUrVpSr1D22u8f47//+7xk+fHj+4R/+IR//+Mdz0kknlanS3m1n57QdnevZ\nt3bWk379+mXWrFldfyh0dHSkf//+3VJnb7Or+e/HP/5xVq5cmaampu4or1faWU+eeuqpDB48OLff\nfnvOOeecbNy4UchUJrv6WTn66KOzadOmtLe3p1QqpaqqqjvK7HV29+/6XRE07SOnnHLKq4ZFLS0t\nqa+v79oeOHBgWlpaMmDAgKxbty4f+MAHcumll+43lyPt7nEec8wxeeihh1IqlbJixYo8++yz2bp1\nazlL3m07OsYkGTVqVN74xjdu91hLS0vq6uq6tqurq9PR0bFPa9xbu3uMu3pNJdrdYxw4cGDq6urS\n0tKSCy+8MJ/85CfLUeZe25Ne1tTU5KKLLsrll1+ev/mbv9nXJe613T3Ge+65JwcddFDXULM/2N1j\nPOCAA3L++efn1ltvzT/90z/lM5/5TI/7vbNhw4asWrUq119/fdcx+uLcfW9n57QdnevZt3bWkz59\n+nT9V+aZM2emra0tJ554YrfU2dvsrC+/+93vcsMNN6S5ubm7yuuVdtaTDRs25LHHHss555yTb37z\nm/nhD3+YH/zgB91Vaq+yq7+VjjrqqDQ2NmbcuHE56aST9ov/CNoT7O7f9bsiaCqzurq6tLa2dm23\ntramvr4+t99+e971rnflgQceyHe/+91MnTp1v75OeEfH2djYmLq6ukycODHf+9738ta3vnW/WSny\nWv35sXd2du5XgQx/8vTTT2fy5Mk57bTT9osAZm9cddVVeeCBB3LppZemra2tu8sp1Le//e38x3/8\nRyZNmpSf/exnueiii7J+/fruLqtQRx55ZD70oQ+lqqoqRx55ZAYPHtzjjnHw4MF517velX79+mXo\n0KHp379/nnvuue4uq8fb2TltR+d69q1dzRmdnZ256qqrsmTJksyYMcNqgDLZWV8WLlyYDRs25IIL\nLsjXv/71LFiwIPfcc093ldpr7KwngwcPzhFHHJFhw4alb9++GT169H5xFUJPsLO+/PznP8/DDz+c\nBx98MIsXL85zzz2X+++/v7tKJXt+rhc0ldmwYcOyZs2abNy4Me3t7Vm2bFmOO+64HHjggV0NGzRo\nUDo6Oip+pc/O7Og4H3/88Zxwwgm56667cuqpp+bwww/v7lILN3LkyDzyyCNJkhUrVmT48OHdXBF7\n4ve//33OO++8fPazn82HP/zh7i5nn7n33nvzta99LUlSW1ubqqqq9OnTs04N3/rWt/K///f/zsyZ\nM3PMMcfkqquuypAhQ7q7rELNnTs306dPT5I8++yzaWlp6XHHOGrUqDz66KMplUp59tln88ILL2Tw\n4MHdXVaPt7Nz2o7O9exbu5ozmpubs3nz5tx44437zX0Ue4Kd9WXy5Mm55557MnPmzFxwwQX54Ac/\nmDPOOKO7Su01dtaTww8/PK2trV03ol62bFmOOuqobqmzt9lZX+rr63PAAQekf//+qa6uzkEHHZTn\nn3++u0ole36ut8yiTObPn5+2trY0NTVl6tSpOf/881MqldLY2JhDDz005557bi655JJMnDgxW7Zs\nyac+9akMGDCgu8vebbs6zr59++b666/PzTffnPr6+lxxxRXdXfJue/kxvpqxY8dmyZIlmTBhQkql\nUqZNm1bmCvfero6xJ9jVMd588815/vnnc+ONN+bGG29M8tJN8vaXm0lvs6vjfP/735+LL744Z599\ndjo6OnLJJZf0uGPsCXZ1jB/+8Idz8cUX56yzzkpVVVWmTZu2362k3NUxvve9783SpUvz4Q9/OKVS\nKc3NzT1uRWwlerVz2q7O9exbO+vJsccem7lz5+b444/PRz/60SQvhRxjx47t5qp7vl39rFB+u+rJ\nFVdckSlTpqRUKuW4445z778y2VVfmpqaMnHixPTt2zcNDQ0ZP358d5fcK+3tub6q5AYHAAAAABSg\nZ10fAQAAAEC3ETQBAAAAUAhBEwAAAACFEDQBAAAAUAhBEwAAAACFEDQB+9zatWszZsyYV9139NFH\n79PPPu200/bp+wMAAPAngiagR/vud7/b3SUAAAD0GjXdXQDQ89x8882ZN29eqqurc+KJJ2bixIl5\n8cUX86lPfSq//OUvc+CBB+aGG27I6173uq7XbNy4MZ///Ofz5JNPpl+/fpk6dWpOOOGEHX7GmDFj\nMmbMmCxbtixJMm3atLzlLW/JpEmTMmjQoPzyl7/Mddddl9NPPz2/+MUvdvj+jzzySL7yla+ko6Mj\nhx12WC6//PLt6gIAAOC1s6IJKNT3v//9LF68OPfcc0++853vZM2aNXn00Ufz3HPP5WMf+1gWLFiQ\ngw8+OP/6r/+63euuv/76NDQ05P7778/VV1+d6667bpefNXjw4Nx777258MILc9FFF3U9fvTRR+eB\nBx7IMcccs9P3f+655/LlL385t956a+699968613vyrXXXlvcPwwAAIBeRtAEFOqHP/xhxo0blwMO\nOCA1NTVpbGzMD37wgxxyyCF5+9vfniR585vfnA0bNmz3uqVLl3bdT+noo4/O7Nmzd/lZH/nIR5K8\ntLrp2WefzXPPPZckXZ+zq/dfuXJlnn766UyePDmnnXZavvWtb2XNmjV7fvAAAAC9nEvngEJ1dna+\n4rGOjo7U1Pzp101VVVVKpdJ2z3n5/iRZvXp1jjzyyPTps+M8/OWv6ezsTHV1dZLkgAMO2Olzt73/\n1q1bM3LkyNx8881Jks2bN6e1tXWHnwcAAMDOWdEEFOqd73xn7rvvvrz44ovp6OjIt7/97bzzne/c\n5euOP/74rsvpVq9enb/7u79LVVXVTl9z3333JUm+973vZdiwYRk0aNBuvf/b3/72rFixIk899VSS\n5MYbb8zVV1/9mo4TAACAV7KiCSjUe9/73vzsZz9LY2NjOjo6Mnr06Lz3ve/NHXfcsdPXXXjhhfnC\nF76QD33oQ6mpqcnVV1+9y6Dpxz/+cebOnZva2tpMnz59t9//kEMOybRp0/LJT34ynZ2dOfTQQ3PN\nNdfs9jEDAADwkqrSn1+/ArAfGDNmTO64444cdthh3V0KAAAA/48VTUDFmjRpUp5//vlXPD5hwoRu\nqAYAAIBdsaIJAAAAgEK4GTgAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAA\nAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0\nAQAAAFAIQRMAAAAAheiWoGnlypWZNGnSKx5fvHhxGhsb09TUlDlz5nRDZQAAPZcZDADY12rK/YG3\n3HJL5s2bl9ra2u0e37JlS6688srMnTs3tbW1OeusszJmzJgcfPDB5S4RAKDHMYMBAOVQ9hVNDQ0N\nmTFjxiseX716dRoaGjJo0KD069cvo0aNytKlS8tdHgBAj2QGAwDKoexB0ymnnJKamlcupGppaUl9\nfX3X9sCBA9PS0rLL9yuVSoXWBwDQE5nBAIByKPulcztSV1eX1tbWru3W1tbthp4dqaqqyvr1m/Zl\naeymIUPq9aQC6Uvl0ZPKpC+VZ8iQXc8D7DkzWM/h91fl0ZPKpC+VR08q057OYBXzrXPDhg3LmjVr\nsnHjxrS3t2fZsmU57rjjurssAIAezQwGABSp21c0zZ8/P21tbWlqasrUqVNz/vnnp1QqpbGxMYce\nemh3lwcA0COZwQCAfaGq1AMusLfErrJY9liZ9KXy6Ell0pfK49K5yuVnpbL4/VV59KQy6Uvl0ZPK\ntN9fOgcAAADA/k3QBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQ\nBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAA\nFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0A\nAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAh\nBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAA\nAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhyho0dXZ2prm5OU1NTZk0aVLWrFmz3f55\n8+Zl/PjxaWxszJ133lnO0gAAeiwzGABQLjXl/LBFixalvb09s2fPzooVKzJ9+vTcdNNNXfuvvvrq\nLFiwIAMGDMi4ceMybty4DBo0qJwlAgD0OGYwAKBcyho0LV++PKNHj06SjBgxIqtWrdpu/9FHyvcu\nawAAEt1JREFUH51NmzalpqYmpVIpVVVVr+l9hwypL7xW9o6eVCZ9qTx6Upn0hZ7GDNZ76Enl0ZPK\npC+VR096jrIGTS0tLamrq+varq6uTkdHR2pqXirjqKOOSmNjY2prazN27NgceOCBr+l916/ftE/q\nZc8MGVKvJxVIXyqPnlQmfak8Bs+9ZwbrHfz+qjx6Upn0pfLoSWXa0xmsrPdoqqurS2tra9d2Z2dn\n14Dz85//PA8//HAefPDBLF68OM8991zuv//+cpYHANAjmcEAgHIpa9A0cuTIPPLII0mSFStWZPjw\n4V376uvrc8ABB6R///6prq7OQQcdlOeff76c5QEA9EhmMACgXMp66dzYsWOzZMmSTJgwIaVSKdOm\nTcv8+fPT1taWpqamNDU1ZeLEienbt28aGhoyfvz4cpYHANAjmcEAgHKpKpVKpe4uYm+5lrOyuL62\nMulL5dGTyqQvlcc9miqXn5XK4vdX5dGTyqQvlUdPKtN+cY8mAAAAAHouQRMAAAAAhRA0AQAAAFAI\nQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAA\nAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0\nAQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAA\nhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMA\nAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFAIQRMAAAAAhRA0AQAAAFCI\nmnJ+WGdnZy677LL84he/SL9+/fKlL30pRxxxRNf+n/zkJ5k+fXpKpVKGDBmSa665Jv379y9niQAA\nPY4ZDAAol7KuaFq0aFHa29sze/bsTJkyJdOnT+/aVyqVcumll+bKK6/MXXfdldGjR2fdunXlLA8A\noEcygwEA5VLWFU3Lly/P6NGjkyQjRozIqlWruvY99dRTGTx4cG6//fb88pe/zHve854MHTq0nOUB\nAPRIZjAAoFzKGjS1tLSkrq6ua7u6ujodHR2pqanJhg0b8thjj6W5uTk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AA9K/f/9UV1fn\noIMOyvPPP99dpRLn+u5kBqs85q/KZAarPOavymQG27/s6bn+lWujKtDYsWOzZMmSTJgwIaVSKdOm\nTcv8+fPT1taWpqamTJ06Neeff35KpVIaGxtz6KGHdnfJvcLO+nLsscdm7ty5Of744/PRj340yUsn\n2bFjx3Zz1T3brn5W6B676ssVV1yRKVOmpFQq5bjjjstJJ53U3SX3eLvqSVNTUyZOnJi+ffumoaEh\n48eP7+6SeyXn+u5nBqs85q/KZAarPOavymQG2z/s7bm+qlQqlcpQJwAAAAA93H5x6RwAAAAAlU/Q\nBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFELQBAAAAEAhBE0AAAAAFOL/B/kpfyDRnEyXAAAA\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cf3a5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot all against close_bid\n",
+    "fig, axarr = plt.subplots(2, 2, figsize=(20,10)) #1 row, 2 cols, x, y\n",
+    "#plt.figure(figsize=(20, 4))\n",
+    "irow, icol = 0,0\n",
+    "icol = pltGraph(\"ohlc_price\", \"close_bid\", irow, icol, df)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- we may have to address feature correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10202, 20)\n",
+      "(10202,)\n",
+      "(10099, 20)\n",
+      "(103, 20)\n",
+      "(10099,)\n",
+      "(103,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score\n",
+    "\n",
+    "# df only\n",
+    "X = df.drop(\"close_bid\", axis=1)\n",
+    "y = df.close_bid.shift().values\n",
+    "cols = X.columns\n",
+    "\n",
+    "\n",
+    "#convert to numpy first\n",
+    "df_np = df.copy().values.astype('float32')\n",
+    "X, y = create_training_set(df_np, 1)\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[2]))\n",
+    "idx_close_bid = df.columns.tolist().index('close_bid') # find index of columns in dataframe\n",
+    "y = y[:,idx_close_bid] # select column to predict\n",
+    "cols = df.columns # i have all here, because close_bid is included as features\n",
+    "\n",
+    "\n",
+    "# create train and test\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, shuffle=False)\n",
+    "check_shape(X, y, X_train, X_test, y_train, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mV3Cgn354e7DiIu9SOz/fpiwZwE2oycI+MzNTZWVliomJadDrEhMTNX/+fC1Z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\nwh4AAMP9fzJ0kNjjsjViAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a365080f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>oc_diff</th>\n",
+       "      <th>low_bid</th>\n",
+       "      <th>last_10_tick_avg_bo_spread</th>\n",
+       "      <th>avg_price</th>\n",
+       "      <th>last_10_tick_avg_bid_return</th>\n",
+       "      <th>nb_ticks</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>year</th>\n",
+       "      <th>day</th>\n",
+       "      <th>15_min</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>month</th>\n",
+       "      <th>avg_bo_spread</th>\n",
+       "      <th>pca</th>\n",
+       "      <th>high_bid</th>\n",
+       "      <th>range</th>\n",
+       "      <th>period_return</th>\n",
+       "      <th>ohlc_price</th>\n",
+       "      <th>open_bid</th>\n",
+       "      <th>close_bid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>linear regression coefficients</th>\n",
+       "      <td>-0.203845</td>\n",
+       "      <td>-0.01149</td>\n",
+       "      <td>-0.002238</td>\n",
+       "      <td>-0.002219</td>\n",
+       "      <td>-0.001859</td>\n",
+       "      <td>-0.001265</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.480864e-07</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>0.000603</td>\n",
+       "      <td>0.001265</td>\n",
+       "      <td>0.007099</td>\n",
+       "      <td>0.018637</td>\n",
+       "      <td>0.183853</td>\n",
+       "      <td>0.200127</td>\n",
+       "      <td>0.300594</td>\n",
+       "      <td>0.504454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lasso regression coefficients</th>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000003</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000000e+00</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 oc_diff  low_bid  last_10_tick_avg_bo_spread  \\\n",
+       "linear regression coefficients -0.203845 -0.01149                   -0.002238   \n",
+       "lasso regression coefficients  -0.000000  0.00000                   -0.000000   \n",
+       "\n",
+       "                                avg_price  last_10_tick_avg_bid_return  \\\n",
+       "linear regression coefficients  -0.002219                    -0.001859   \n",
+       "lasso regression coefficients    0.000000                    -0.000000   \n",
+       "\n",
+       "                                nb_ticks   weekday  year           day  \\\n",
+       "linear regression coefficients -0.001265 -0.000002   0.0  9.480864e-07   \n",
+       "lasso regression coefficients  -0.000003  0.000000   0.0 -0.000000e+00   \n",
+       "\n",
+       "                                  15_min      hour     month  avg_bo_spread  \\\n",
+       "linear regression coefficients  0.000002  0.000007  0.000017       0.000603   \n",
+       "lasso regression coefficients  -0.000000 -0.000000  0.000000      -0.000000   \n",
+       "\n",
+       "                                     pca  high_bid     range  period_return  \\\n",
+       "linear regression coefficients  0.001265  0.007099  0.018637       0.183853   \n",
+       "lasso regression coefficients  -0.000000  0.000000 -0.000000       0.000000   \n",
+       "\n",
+       "                                ohlc_price  open_bid  close_bid  \n",
+       "linear regression coefficients    0.200127  0.300594   0.504454  \n",
+       "lasso regression coefficients     0.000000  0.000000   0.000000  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_lasso = linear_model.Lasso(alpha = 0.1) # Lasso takes care of regularisation\n",
+    "reg_linear = linear_model.LinearRegression()\n",
+    "\n",
+    "reg_linear.fit(X_train, y_train)\n",
+    "reg_lasso.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "df_coeff = pd.DataFrame(columns=cols\n",
+    "                     , data=[list(reg_linear.coef_), list(reg_lasso.coef_)]\n",
+    "                     , index=[\"linear regression coefficients\", \"lasso regression coefficients\"])\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# predict all examples and compare to actuals\n",
+    "plt.scatter(reg_linear.predict(X_test), y_test)\n",
+    "plt.xlabel(\"predicted\")\n",
+    "plt.ylabel(\"actual\")\n",
+    "plt.title(\"predicted v actual close_bid\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "df_coeff.sort_values(by='linear regression coefficients', axis=1)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- what mae is acceptible as a result? If i invest based on my prediction, and it goes the other way i lose money. \n",
+    "- Therefore, check the directional error, not MAE. Compare close with next close. If my prediction - actual next close has the same sign and value of that measure, count as accurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check regression errors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_error_metrics(df, y_train, y_test, X_train_pred, X_test_pred, y_prev):\n",
+    "\n",
+    "    #compute direction of next step\n",
+    "#     if len(X_test.shape) >2:\n",
+    "#         y_prev = X_test[:,0,idx_close_bid]\n",
+    "#     else:\n",
+    "#         y_prev = X_test[:,idx_close_bid]\n",
+    "    err_list = []\n",
+    "    \n",
+    "    pred_directions = X_test_pred - y_prev\n",
+    "    act_directions = y_test - y_prev\n",
+    "    pred_returns = X_test_pred / y_prev-1\n",
+    "    act_returns =  y_test / y_prev -1\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    sign_error = np.sign(pred_directions) != np.sign(act_directions)\n",
+    "    actual_minus_pred = act_directions - pred_directions\n",
+    "    abs_actual_minus_prod = abs(act_directions) - abs(pred_directions)\n",
+    "    return_vals = act_returns - pred_returns\n",
+    "\n",
+    "    # how often do you make a negative 1 percent return when a positive return was predicted\n",
+    "\n",
+    "    err_list= [\n",
+    "        [\"mse train all feature: \", mean_squared_error(y_train, X_train_pred)]\n",
+    "        ,[\"mse test all feature: \", mean_squared_error(y_test, X_test_pred)]\n",
+    "        ,[\"mae train all feature: \", mean_absolute_error(y_train, X_train_pred)]\n",
+    "        ,[\"mae test all feature: \", mean_absolute_error(y_test, X_test_pred)]\n",
+    "        ,[\"mean avg bo spread: \", df.avg_bo_spread.mean()]\n",
+    "        \n",
+    "\n",
+    "        ,[\"how often sign of price change is same: \", (sign_error==False).sum() / len(sign_error)]\n",
+    "\n",
+    "    # if correct sign, how often larger than actual value, smaller than actual value\n",
+    "    # generally good profit\n",
+    "        ,[\"if same sign, how often is actual better than 0.1 percent in both directions: \"\n",
+    "          , (abs(act_directions[~sign_error]) > 0.001).sum() / len(act_directions[~sign_error])]\n",
+    "\n",
+    "    # positive surprise\n",
+    "        ,[\"if same sign, how often is actual better than predicted in both directions: \"\n",
+    "          , (abs_actual_minus_prod[~sign_error] > 0).sum() / len(abs_actual_minus_prod[~sign_error])]\n",
+    "\n",
+    "    # positive suprise of least 10 bp\n",
+    "        ,[\"if same sign, how often is actual better than predicted by more than 0.001 USD per EUR in both directions: \", \n",
+    "          (abs_actual_minus_prod[~sign_error] > 0.001).sum() / len(abs_actual_minus_prod[~sign_error])]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "        ,[\"if not same sign, how often is actual worse than -0.1 percent return from predicted in both directions\",\n",
+    "         (abs(return_vals[sign_error]) > 0.001).sum() / len(return_vals[sign_error])]\n",
+    "         \n",
+    "        ,[\"if not same sign, how often is actual worse than -0.1 percent return in both directions\",\n",
+    "         (abs(act_returns[sign_error]) > 0.001).sum() / len(act_returns[sign_error])]\n",
+    "    ]   \n",
+    "    # show histogram of returns if sign error\n",
+    "    plt.hist(act_returns[sign_error], bins=20)\n",
+    "    plt.title(\"returns if pred and act sign mismatches\")\n",
+    "    plt.show()\n",
+    "    \n",
+    "    df_err = pd.DataFrame(err_list)\n",
+    "    \n",
+    "    for el in err_list:\n",
+    "        print(el)\n",
+    "    \n",
+    "    \n",
+    "    return df_err\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KtmzZUjt37pQkbd26Vffff7/nKwMAoAa64fgmJiZq4cKFio2NVUFBgXr16lUV6wIAwGuV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\nAGAZ8QUAwLL/B36M6wikk3reAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cff9fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['mse train all feature: ', 4.4521161e-07]\n",
+      "['mse test all feature: ', 6.5240918e-08]\n",
+      "['mae train all feature: ', 0.00042638468]\n",
+      "['mae test all feature: ', 0.00020227849]\n",
+      "['mean avg bo spread: ', 3.9868658581951545e-05]\n",
+      "['how often sign of price change is same: ', 0.55339805825242716]\n",
+      "['if same sign, how often is actual better than 0.1 percent in both directions: ', 0.0]\n",
+      "['if same sign, how often is actual better than predicted in both directions: ', 0.98245614035087714]\n",
+      "['if same sign, how often is actual better than predicted by more than 0.001 USD per EUR in both directions: ', 0.0]\n",
+      "['if not same sign, how often is actual worse than -0.1 percent return from predicted in both directions', 0.0]\n",
+      "['if not same sign, how often is actual worse than -0.1 percent return in both directions', 0.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_err = check_error_metrics(df, y_train, y_test, reg_linear.predict(X_train), reg_linear.predict(X_test), X_test[:,idx_close_bid])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# log results\n",
+    "log=True\n",
+    "\n",
+    "if log:\n",
+    "    \n",
+    "    simname= \"linear regression\"\n",
+    "    sim_desc = \"1 row lookback\"\n",
+    "    \n",
+    "    dict_err= OrderedDict(zip(df_err[0], df_err[1]))\n",
+    "    \n",
+    "    list_stats=OrderedDict()\n",
+    "    \n",
+    "    list_stats[\"simname\"] = simname\n",
+    "    list_stats[\"sim_desc\"] = sim_desc\n",
+    "    list_stats[\"MSE\"] = dict_err[\"mse test all feature: \"]\n",
+    "    list_stats[\"MAE\"] = dict_err[\"mae test all feature: \"]\n",
+    "    \n",
+    "    differences_described = pd.Series(reg_linear.predict(X_test) - y_test).describe()\n",
+    "\n",
+    "    list_stats.update(OrderedDict(differences_described))\n",
+    "    list_stats.update(dict_err)\n",
+    "    \n",
+    "    results = pd.DataFrame([list_stats])\n",
+    "    #results.to_excel(\"log_results.xlsx\")\n",
+    "    if os.path.isfile(\"log_results.xlsx\"):\n",
+    "        log_results = pd.read_excel(\"log_results.xlsx\")\n",
+    "        log_results.loc[len(log_results),:] = list_stats.values()\n",
+    "        log_results.to_excel(\"log_results.xlsx\")\n",
+    "    #log_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>simname</th>\n",
+       "      <td>500_epochs</td>\n",
+       "      <td>500_epoch_lookback_40</td>\n",
+       "      <td>linear regression</td>\n",
+       "      <td>linear regression</td>\n",
+       "      <td>500_epochs_40_lookback_pca_unshuffled</td>\n",
+       "      <td>linear regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sim_desc</th>\n",
+       "      <td>\\nkaggle params but with 500 epochs to account...</td>\n",
+       "      <td>500 iterations, lookback 40</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "      <td>added directional errors checking and pca as f...</td>\n",
+       "      <td>1 row lookback</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSE</th>\n",
+       "      <td>1.59188e-07</td>\n",
+       "      <td>1.97246e-07</td>\n",
+       "      <td>6.79626e-07</td>\n",
+       "      <td>6.55241e-08</td>\n",
+       "      <td>4.82937e-07</td>\n",
+       "      <td>6.52409e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MAE</th>\n",
+       "      <td>0.000285781</td>\n",
+       "      <td>0.000340846</td>\n",
+       "      <td>0.000536307</td>\n",
+       "      <td>0.000202905</td>\n",
+       "      <td>0.000594482</td>\n",
+       "      <td>0.000202278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>102</td>\n",
+       "      <td>102</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>102</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-9.29131e-07</td>\n",
+       "      <td>-3.83515e-05</td>\n",
+       "      <td>-5.08408e-05</td>\n",
+       "      <td>-2.90581e-05</td>\n",
+       "      <td>0.000506372</td>\n",
+       "      <td>-2.31451e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.000400953</td>\n",
+       "      <td>0.00044465</td>\n",
+       "      <td>0.000826849</td>\n",
+       "      <td>0.000255566</td>\n",
+       "      <td>0.000478296</td>\n",
+       "      <td>0.000255616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-0.00135148</td>\n",
+       "      <td>-0.00127888</td>\n",
+       "      <td>-0.00317997</td>\n",
+       "      <td>-0.000772953</td>\n",
+       "      <td>-0.00072515</td>\n",
+       "      <td>-0.000772119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-0.000158489</td>\n",
+       "      <td>-0.000304043</td>\n",
+       "      <td>-0.000402606</td>\n",
+       "      <td>-0.000200629</td>\n",
+       "      <td>0.000192821</td>\n",
+       "      <td>-0.000199616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>6.07371e-05</td>\n",
+       "      <td>-5.84126e-06</td>\n",
+       "      <td>-3.07747e-05</td>\n",
+       "      <td>-2.43187e-05</td>\n",
+       "      <td>0.000540495</td>\n",
+       "      <td>-1.14441e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.000239968</td>\n",
+       "      <td>0.00017789</td>\n",
+       "      <td>0.00031692</td>\n",
+       "      <td>0.000116229</td>\n",
+       "      <td>0.000830978</td>\n",
+       "      <td>0.000123918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.000755668</td>\n",
+       "      <td>0.00128782</td>\n",
+       "      <td>0.00327372</td>\n",
+       "      <td>0.0005337</td>\n",
+       "      <td>0.00159335</td>\n",
+       "      <td>0.000535846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mse train all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3917e-07</td>\n",
+       "      <td>4.45245e-07</td>\n",
+       "      <td>5.16241e-07</td>\n",
+       "      <td>4.45212e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mse test all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.79626e-07</td>\n",
+       "      <td>6.55241e-08</td>\n",
+       "      <td>4.82937e-07</td>\n",
+       "      <td>6.52409e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mae train all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000423565</td>\n",
+       "      <td>0.000426773</td>\n",
+       "      <td>0.000505385</td>\n",
+       "      <td>0.000426385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mae test all feature:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000536307</td>\n",
+       "      <td>0.000202905</td>\n",
+       "      <td>0.000594482</td>\n",
+       "      <td>0.000202278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean avg bo spread:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "      <td>3.98687e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>how often sign of price change is same:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.446602</td>\n",
+       "      <td>0.533981</td>\n",
+       "      <td>0.882353</td>\n",
+       "      <td>0.553398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than 0.1 percent in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.655556</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than predicted in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.978261</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.366667</td>\n",
+       "      <td>0.982456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if same sign, how often is actual better than predicted by more than 0.001 USD per EUR in both directions:</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.152174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0666667</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if not same sign, how often is actual worse than -0.1 percent return from predicted in both directions</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>if not same sign, how often is actual worse than -0.1 percent return in both directions</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                    0  \\\n",
+       "simname                                                                                    500_epochs   \n",
+       "sim_desc                                            \\nkaggle params but with 500 epochs to account...   \n",
+       "MSE                                                                                       1.59188e-07   \n",
+       "MAE                                                                                       0.000285781   \n",
+       "count                                                                                             102   \n",
+       "mean                                                                                     -9.29131e-07   \n",
+       "std                                                                                       0.000400953   \n",
+       "min                                                                                       -0.00135148   \n",
+       "25%                                                                                      -0.000158489   \n",
+       "50%                                                                                       6.07371e-05   \n",
+       "75%                                                                                       0.000239968   \n",
+       "max                                                                                       0.000755668   \n",
+       "mse train all feature:                                                                              0   \n",
+       "mse test all feature:                                                                               0   \n",
+       "mae train all feature:                                                                              0   \n",
+       "mae test all feature:                                                                               0   \n",
+       "mean avg bo spread:                                                                                 0   \n",
+       "how often sign of price change is same:                                                             0   \n",
+       "if same sign, how often is actual better than 0...                                                  0   \n",
+       "if same sign, how often is actual better than p...                                                  0   \n",
+       "if same sign, how often is actual better than p...                                                  0   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "\n",
+       "                                                                              1  \\\n",
+       "simname                                                   500_epoch_lookback_40   \n",
+       "sim_desc                                            500 iterations, lookback 40   \n",
+       "MSE                                                                 1.97246e-07   \n",
+       "MAE                                                                 0.000340846   \n",
+       "count                                                                       102   \n",
+       "mean                                                               -3.83515e-05   \n",
+       "std                                                                  0.00044465   \n",
+       "min                                                                 -0.00127888   \n",
+       "25%                                                                -0.000304043   \n",
+       "50%                                                                -5.84126e-06   \n",
+       "75%                                                                  0.00017789   \n",
+       "max                                                                  0.00128782   \n",
+       "mse train all feature:                                                        0   \n",
+       "mse test all feature:                                                         0   \n",
+       "mae train all feature:                                                        0   \n",
+       "mae test all feature:                                                         0   \n",
+       "mean avg bo spread:                                                           0   \n",
+       "how often sign of price change is same:                                       0   \n",
+       "if same sign, how often is actual better than 0...                            0   \n",
+       "if same sign, how often is actual better than p...                            0   \n",
+       "if same sign, how often is actual better than p...                            0   \n",
+       "if not same sign, how often is actual worse tha...                            0   \n",
+       "if not same sign, how often is actual worse tha...                            0   \n",
+       "\n",
+       "                                                                    2  \\\n",
+       "simname                                             linear regression   \n",
+       "sim_desc                                               1 row lookback   \n",
+       "MSE                                                       6.79626e-07   \n",
+       "MAE                                                       0.000536307   \n",
+       "count                                                             103   \n",
+       "mean                                                     -5.08408e-05   \n",
+       "std                                                       0.000826849   \n",
+       "min                                                       -0.00317997   \n",
+       "25%                                                      -0.000402606   \n",
+       "50%                                                      -3.07747e-05   \n",
+       "75%                                                        0.00031692   \n",
+       "max                                                        0.00327372   \n",
+       "mse train all feature:                                     4.3917e-07   \n",
+       "mse test all feature:                                     6.79626e-07   \n",
+       "mae train all feature:                                    0.000423565   \n",
+       "mae test all feature:                                     0.000536307   \n",
+       "mean avg bo spread:                                       3.98687e-05   \n",
+       "how often sign of price change is same:                      0.446602   \n",
+       "if same sign, how often is actual better than 0...           0.152174   \n",
+       "if same sign, how often is actual better than p...           0.978261   \n",
+       "if same sign, how often is actual better than p...           0.152174   \n",
+       "if not same sign, how often is actual worse tha...           0.105263   \n",
+       "if not same sign, how often is actual worse tha...           0.105263   \n",
+       "\n",
+       "                                                                    3  \\\n",
+       "simname                                             linear regression   \n",
+       "sim_desc                                               1 row lookback   \n",
+       "MSE                                                       6.55241e-08   \n",
+       "MAE                                                       0.000202905   \n",
+       "count                                                             103   \n",
+       "mean                                                     -2.90581e-05   \n",
+       "std                                                       0.000255566   \n",
+       "min                                                      -0.000772953   \n",
+       "25%                                                      -0.000200629   \n",
+       "50%                                                      -2.43187e-05   \n",
+       "75%                                                       0.000116229   \n",
+       "max                                                         0.0005337   \n",
+       "mse train all feature:                                    4.45245e-07   \n",
+       "mse test all feature:                                     6.55241e-08   \n",
+       "mae train all feature:                                    0.000426773   \n",
+       "mae test all feature:                                     0.000202905   \n",
+       "mean avg bo spread:                                       3.98687e-05   \n",
+       "how often sign of price change is same:                      0.533981   \n",
+       "if same sign, how often is actual better than 0...                  0   \n",
+       "if same sign, how often is actual better than p...                  1   \n",
+       "if same sign, how often is actual better than p...                  0   \n",
+       "if not same sign, how often is actual worse tha...                  0   \n",
+       "if not same sign, how often is actual worse tha...                  0   \n",
+       "\n",
+       "                                                                                                    4  \\\n",
+       "simname                                                         500_epochs_40_lookback_pca_unshuffled   \n",
+       "sim_desc                                            added directional errors checking and pca as f...   \n",
+       "MSE                                                                                       4.82937e-07   \n",
+       "MAE                                                                                       0.000594482   \n",
+       "count                                                                                             102   \n",
+       "mean                                                                                      0.000506372   \n",
+       "std                                                                                       0.000478296   \n",
+       "min                                                                                       -0.00072515   \n",
+       "25%                                                                                       0.000192821   \n",
+       "50%                                                                                       0.000540495   \n",
+       "75%                                                                                       0.000830978   \n",
+       "max                                                                                        0.00159335   \n",
+       "mse train all feature:                                                                    5.16241e-07   \n",
+       "mse test all feature:                                                                     4.82937e-07   \n",
+       "mae train all feature:                                                                    0.000505385   \n",
+       "mae test all feature:                                                                     0.000594482   \n",
+       "mean avg bo spread:                                                                       3.98687e-05   \n",
+       "how often sign of price change is same:                                                      0.882353   \n",
+       "if same sign, how often is actual better than 0...                                           0.655556   \n",
+       "if same sign, how often is actual better than p...                                           0.366667   \n",
+       "if same sign, how often is actual better than p...                                          0.0666667   \n",
+       "if not same sign, how often is actual worse tha...                                           0.333333   \n",
+       "if not same sign, how often is actual worse tha...                                                  0   \n",
+       "\n",
+       "                                                                    5  \n",
+       "simname                                             linear regression  \n",
+       "sim_desc                                               1 row lookback  \n",
+       "MSE                                                       6.52409e-08  \n",
+       "MAE                                                       0.000202278  \n",
+       "count                                                             103  \n",
+       "mean                                                     -2.31451e-05  \n",
+       "std                                                       0.000255616  \n",
+       "min                                                      -0.000772119  \n",
+       "25%                                                      -0.000199616  \n",
+       "50%                                                      -1.14441e-05  \n",
+       "75%                                                       0.000123918  \n",
+       "max                                                       0.000535846  \n",
+       "mse train all feature:                                    4.45212e-07  \n",
+       "mse test all feature:                                     6.52409e-08  \n",
+       "mae train all feature:                                    0.000426385  \n",
+       "mae test all feature:                                     0.000202278  \n",
+       "mean avg bo spread:                                       3.98687e-05  \n",
+       "how often sign of price change is same:                      0.553398  \n",
+       "if same sign, how often is actual better than 0...                  0  \n",
+       "if same sign, how often is actual better than p...           0.982456  \n",
+       "if same sign, how often is actual better than p...                  0  \n",
+       "if not same sign, how often is actual worse tha...                  0  \n",
+       "if not same sign, how often is actual worse tha...                  0  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_excel(\"log_results.xlsx\").T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JlKXlHMOWFhcguXx5dqBYXZ30mc9YNTRkvm6z7XPm1J+uXZK69uNs\nCr35Oi9+2W1oMDfbWpJktWlT9v3KtC1+fZvafpzd/nwpAivR1vKvn4kJJdYl3rTJ6vJl6cYNT5s2\nuTq8vj6qqamZOcfWbwMntu5mHegfYzdAzj0TJLcvWlqs/uzPXOCumDSI2c7p0JB08aJX1fSHlU7p\nuFil3xvdNWYT16Kv0HIcxvMS5D6raiDVIVAbSHWIRauWpp4HeaHaUhWSIqWS56jQEWZDQ65T3f3f\ny7sItv/Z5Yx+TH9/f7+nrVttSnqr5BFm6fvS12f0939vdPx4RH5j3T10uaBDKekH06Vfo7//vZuR\nU1+f+nfu964jNddIN0mJ16x16SMuX5bq660GB93slM98ZkZ9fSbjdheTjkLSvKYyKab8lnpNZyov\nbgTi3PcWMpK5kG1OPhbXr3saHJROnZJWrXKjZf2ZOQ89FFNnp0mZhSi5B6Dkay/bd+7ZM31zFodV\nf/9sR0ZTk9XwsFEk4nf6uzSi1Rhh29ZmtW2b1N8/vWDfKUlHjxr98If1N8uV0erVcXV2Gn3jG7PX\nY7b6Zz5GJxf7mfmuM2Nc4Dw5dasx5uZ6WuV/fymqNRo5vYw3N7sg5Y0bRq2ttqD6Ivn4zI6GdzMl\nPc/9fmjI/U4yifpamnscS00pmOvYpb9vbMyN0t+6NZ7SoZh8PfgpvKTZ2aG+kRGj3/42olhMamy0\nmp52gfHbbvPU2zs7GzZTnVjtkdjZruVc6dMyKfV6bW626ux0acK2bZvRl76U/3vS65mNG4tP/1mO\nWk6tl2whZ4ZU4pikl/WNG2cyzqzMlqbP1fNuBr61RufOlV+WOjoiGh72Eilf/dk6xR1D1xaJROI3\nB2ZZNTXNaO/ezAH5bPVBttSfmzZlb5vO17WaXA6bmwtLM55pW2bbapm3v9BZHfnuE+mvSXPb41//\nekwPPzxbl1y75mnt2tQUv8nfnW/WWzxu9frr0cQM4A0b4hocTJ5VHldbW/aZ5dl+n3wcR0bcjLPB\nQaOxMekP/9Btb6XvI4U+MzALpzLS73Nr18a1ZMncQGah5bi726iry5sz4zDI52Ux9lkBCDcCXwit\nQjs8Fio4Vus5r+dDvoZTNTqlyumsKLezNPn9r7xSl/GBOVtDua3NNaZ7etzDeHLjev36ynTipl+j\nTU1WS5e6FFqTk1IsJt24IU1MGP3Lv3has8bq1Kl6NTQoYwDP/7/kghmXLxtdvOhpfNzN9HEz3Tz9\nl/9Sr//4H2cf6iV3no4d8zQ2ZhL7mS0dxZtvulkN/nd1dXn6h3+o0yc+EdfatdKePVPasWP2PW++\nGdHJk56S0641N+e+Bgotv+Vc05nKy9q1cfnpAH2FPnwUss3J5cFaqytX3CjIy5fjN4OVVmvX+sfE\nJP4tXeq2LX2/cn9nTEeO1CsW0820me49ExNWt93mHjKT04gG+aGxUEePGv3X/9qgixe9xKyYkRHX\n0bJmjdXXvz57PWaqf8rpCMl27yv2M/NdZ8a4oEd656bJEvkKcueOf0y7u03GzspMZXzFCqu9e12w\ntaMjol/9Klpw51dyIGlmxtWrUnIa0bmS1z3JVVeVeq9Mf9/atW6gQ2/vbCemMdLq1bOBsOQ6/dZb\nUz+vp8coFlMiON7cbDU56YJ7K1a4/cxWJ1Y7bVUp6dMKka/dmnxu77jDH+RTWPlJrmeq0UYLSoeb\nf27T07hmq9fKUe4xyXQeDxyIaO3azG23TGWjmLJU6HNVd7dJrN8kubbl4KBRc3O8oM/xt2ly0mjV\nKn/bjLq6IpKKrw/S72MtLa6Nmiw5Rbm1ViMjs2uwSsVfq9n2sdjnjUzXiLvXzv1bv0O/0MGJhQxu\n81/Lty7tI4/E1NdndOpUvU6fdtkE1qyxamxM/W5/f9z9wx/g4drrIyNWIyNeYhbWlSvuHrFhw4w2\nbYrLGKOPPorolVeMTp3y5jyjpO9jpuOYvLbYlSuuvXr8uJdYj7NS95Fi6tigDAoIglz3Oam4tcxP\nnvQS58Wvw7ZujWvDhnhNDcAuxmLsswIQbgS+EFqFPKRV6qG+r891vnd2RnKOrK3lnNfzIV/DqRqd\nUvPRqVpKw7bQB5jkzz52zLsZGEhNM1Buh3C27Y/Hrf7pn6KanpYGB6XpaZeGrq7O6Pr1iOrq4hoc\ndEGZrVvdNp06ZXT5sqe6urjWrLFas8YFVE6fdiMnBwZcwGv5cqm+Xhod9fSv/+qpoWFG1sZTHppb\nW616e40GB72k2QLuwTdZZ2ck0bl36ZLR//7fblbc6Kg0MSE9++wSffe7N7Rjh8v3/+670cQI0oEB\no48/djOccq0tJc2OevaPVUdHJGcgyVdMYDVTefE/d7Yz3aZ8d67rL1+dM/facT/PzPgdyq6TuqMj\noqYmacuW1Gsv035l+86zZyPascNq06ZYSkfh+vXShg2lzWpLF6SHzL4+t/5Gf7+neNwoHpdiMauG\nBjdD5he/iOqWW5RzMfpiOnWS3xeP20TaISn13ldK50qu68x9Xuo6dbk+L6idO357IrnD7NQpo6Gh\neM51UzZunNGbb0b07rtRNTS4QNHAgC2o8yt5NHxrq9XoqKuPo1H3nljMavXqzMc9X11V6r0y/fWm\nJpce8do1Nyr/5EklOmOPHDEaGjIyxlNXl9XatXHt2TOVMhNlctLtj3+tLlni6uvJSaNPfMJqw4Z4\n1hkIv/udp098Ym75yLQPmdZNK3dttEzXsgsApv7d0JDRj35Ur82b4ynfl202RbZ2qz9TrlIzzcq5\nn5VaF+e6D77+ejSl3Jw9W726vqXFtRn8Nesk1+l58qSyzmIvVKZjV04nZKaZVYOD7rXkenlkxOjd\ndzN/R7Yg7oEDbt09f33J++4rfCbZlSuzQS+fCzR4Kc9nIyNGhw+7e+KOHTE1N7uyeeyYp6tXpeSB\nQZI0Oekl2kjJxyx9HU+fn70g+f44MODKZVeXSawn6Kco37rVL9eunXn1qhscduut0p490zlnQ2UL\nUOc7VvlkKjcPPJB6LqTUDv1CAqq56gD//8lyrUvb3j57r7PWHbeJCU9dXdKnPy2tX6/Ed+ea9TYw\n4L7n6FHXBpqZkRoapAsX3PUwPGy0bJnV+Lgrp6dPe9q6dfY+kCuo4X/vj35UL8lqbMwoHndp5o1R\nynqclRiMU0wdG5RBAeVa6HZ8OUGejo6I1qxxbTa/HrJW6umR/vRPZwK99tdi67MCEG4EvhBahXTa\nVCLw4lLQRdXZOZuC7uJFl2/+G9+YDkTjZj7lajhVY2S/3xGVPkr3j/4odc2jQhrAfX1u1OzBg5HE\nqMVCG7bFpqGTpPFxd235Iw6T9ymTQvYj14P3fffN6J//Oaq6OrduQjzu/jU1uRk6LnAlNTZKnZ1G\nly659IOeZ9TU5NYDu3BBNz/X040bsw/JsZjbDmtdJ8Ply56keMpDc3Oz6yg9ccLTb37jZqBt2DB3\nnYHkjpMTJ2bLoR+8sdbo1VfrtWPHDXV0uHM1MCBdu+Y6NjzPKBazam2Na//+aNZzV0gnRa5rupDz\nka28ZEsztHNnrOBOpkySO2aNMbrtNquhIamuzqq11c3MGRpyHUzXr3spMw1z7W+2YyC59G6u42C2\nE0JSQQ/zpaZmy3Uskj9z3Trp7rvL67TMx/++d9+N6Nw512njcx0sVg0Nnm7csDp3zlNnp0nMkLLW\nzXz0R1MvWWJvjkLO3amT3on4/vuehoeNNmxwaxwmj2DOVTeV0iFRbGdNUDt3/PbEbPpBSXJBzOZm\nZeyI9dONHTkSSax/d+qU1X/4DzO6/fbMgeXk4zM7Gt5q+/a4/t//c53Xq1ZZLVniyu7mzbPnJ/k4\n5rv/lhqATH+fu9dKy5a5tV527pzS2bMukN/c7OoDN0vG1QmrV0s7d8b06qv1unpVmppyKVX92aiS\nC36tXj2jp5+eytmRXOg9M718uMClC9iVUo/4ZaOQ9GnDw24mwdKlbi0h//uy1e0rVtis7dZt29zP\nuWaabdpkEwGQAwci+uY3Z2dEJ89YPHvWU1eXkeRp9erZeiLb5ycfh3yB3FJSp/nnZ3hYevttT6dP\n12n5cumWW9z57Oxc2DZ3e/uMDhyYbW9IfqrezOW2UKWmGM31eQcORNTVFUnMtJ6YkC5edOsL+m3g\n+np7s/1kdeqUW5Mz+frIVK6PHPE0POwl1sDq7XXrLW3eXNhMslWr4jp1ygWQZlmtWhVP1Kez5VGa\nmjL6X/+rTitXurI5NmZ07pyn5ctdINyfTb52rRts0N2dehy7ukxilpufxm5y0qi1Na4LF+rU1OSX\nx0hipvLatS7Ycdddcd24kZqifHjYrYXT2jo7KOjgwahWr848Gyq9Hqn04L/M7cfZujQ9MFdIh3+x\nz2mzs5BTy2F3t1FnZ1Tvvx/RtWsu88PYmBvUsGSJ0cWLSqSgl1LrgfXrrfbuna3r//t/r9Px426m\nVzTqMlGMjrrr6Pp1dy+55Rb33DEw4J4bJieN1q3LnU44+TsvXHA/G2M0NmYViXianJSWLJnd70oM\nxinm+C6GWTjVSk9capDHzVB1KauT+xTuuiuus2erO+McADCLwBdqSjmjfNLfG4/Pjr5JltxQLTXw\nkj7i/sKF1Ac3N9rHC13jptBAyltvSRcu1OU9h5Uc2V/otdPePqPOztS1siYnjXp7XWeMlDl1x5o1\n8ZTR3/7fnTwZ0cSE0cSENDDgHsaT0+blSmNSaBo6P0g3OGh05YqbReB3VBnj1jP49reXJD3YTmn1\n6vwP3cnfkcxvmA8MuE7J8XEX5FqxwioWc6PV6+vdZzQ2SpOTVj09Ec3M6ObMFTcLYXra6ve/d4Gl\nZctm03B5nkvFNTPj74ObUTYyYlIemoeHpXPnPDU2ullHdXVuptPtt8fV0OA6S12qwhl1dbkFqfv6\npKkpPxhmbm6vS9koubLd2mr1u995GhoymplxndQTE36KLU9vvhlJSTFXyLHyy3lLi9WFC7Mjq+3N\nHvBly6wOH65PjBwu9mEu23e/+mp92sLrxc0eSO6YtdaNAp+ZcR1Ha9a4WQ8nT7oT565x13kUjUp1\nda6zp9DR7ZnK+/CwW+B81aq4+vuNVq2Kq7nZLVyenvJtPlKzpX/m1JTU2RktuYO2kPckr6k2PW0S\nAWVXLiTJyPOsVq+OJzrhenrcse7qcuXS1TOufN59d/5OnfROxOFhVzZ6elyAzZ+x6Y/2d+fABdsK\nmWmS6/wX21kT1M4dv90wOGjU3++ObzRqE0Gd48eNDhyo1+SkCyC3tlr9j//hBhacP29UV+dm1MZi\nbk2rBx6YKajza+3aGVnrRrnfuGE1OWm1bNns7Itss2Ly3X8zBW1GRqTr112q3mznJfl9flDHGLeO\nxrlzXlKarEiifpT8juiIvvMd18m9dq30mc9YjYy4z0hfw+Wb33QLiSfPAhoYSA2g+yOxk0fpZwqi\nJtcdyeum9fa69+WrR2bX6XN15erVs4GYXOnThoel3/zG07VrnjzPamLCU2urmymdqW631g1CSO4g\n9iVfK9lmmlnrjuXEhPvumZmInnxyif7bf7uRaDMMDRl98IGn/n6XarihQRoZiWh4OK777nMBxFyD\nbVy7yEu0i/y0T01Ns8HfYlKnuQCpTbmeLl3yNDTkaWzMzXJ03xPRgQNx/eVfLkybu63NdW6ePJma\nwjXb2i6F3i+uX5+9hpODM1eueHriiamC6sHkAObJk57OnnX1USzmAq+trbNpqycmrIaG3N9Eo1aR\niNHkpKfr161u3LDat69eP/jB1Jz6oLfXpYPzUxY7bk3BpUvnzvDNdEzWr7caGkrtMF6zxqXv9v8+\neSDB0JCfxtV9/9q1Vl1d7m88zyTuo5GI0XvvuTZpf7+rV5ubXQDEBdo8nTvnKRqVmpvjsla6dCly\ns/Pa7Yd7jjPasiWuLVvctra0xFPKVW+ve/abnJzdp1yzodLrkWxrA3V3m5R6Ldf9L1974+BBN2DK\nnw3qB+YKzQqQa7CgCy6l7mO2dWnPnvV08aJb28uvx6117fiZGTfgyp+VKkm//nVUY2OeGhvtnMC2\nP1NwctJdx5I0M2M1NuaeVZYsUdKsWvfs9LnPxfUXf5F9/dbkttnwsHTyZFQTE+4ZJxIxGh93gzfc\nOnKVW+O32OfgsM/CqUYmmHL45y99QF9yHZYuCGm7ASBsCHyhZpQzyifTe0dGpOQ849LchmopgZf0\n7+rpMeru9nTLLbPBAMkFU8LUuCnk/Ph/4z8c5DuHlVi3IPnhvpCgQltbtrWyNOdhdXhYOn3aU3e3\np6amuP7wD2dHf/ujrpPXWEnuKOvuNvqHf4hmnA22c2cspTPyoYcyH5/BQfcAdvRoRENDruPCzfiQ\n7rvPqrnZqrdXevrpBk1NuVlZS5dKJ04s0c6dsYIeHvxrNLmDpaHB6ve/tzp6NKKJiYiiURc48jty\nY0nPHq2trtPp8mWbSEkVjfrpqfyOXBc0aWqyN9cJcx0U1rqAlnswlV59NaJ43D20Xr3qHngloxs3\nXKfuypVSU5PRmTMRLVvmZoO1tkpHj0blrz01Pm40Pe0eqOvrpatXXWrG0VGrH/4wqu5uTydOeImA\nlx90MMY9kNfXW737rtHDD89NSfPeexFdu5a8LljqMZSkjRtn9NOfRm9eG9KlS26mW0uLledJly5Z\n/cEfuKBFIUGZfCm7rl5VokPDv2aLmT2wd69b1+LNNyO6ccN1UN9yi9XkpKfjx60aGqw2b3bpKi9d\nsrp0ybs5SldqbLQ6d87NeP3TP43lTTuVXt6T0wYNDRn197vytmSJEiPGkwPPnZ2RlDXf3DXr6fnn\nPf2n/zSTM41RNtkesg8ccGlrs81akDJ30OY6zpL0ox/Vq6fHBT6sddeQn5LT81w6Uc+T1qxxMyx6\nelI71vz6xq9nJBf8am3N3amT3okYidhER6ifku/GDZPUsWdkjE2pQ19/PVpyh0SxnTUL0blTiXQ6\nyfegd9+N6OpVT1evunpv6VIpGjW6csXNhj1+3AXxJSXK9OSkEsFPN1PWJurMnh6je+4prPPLv/cm\npwwdHjZavVrasSPzcfQHgfT0eIlOSD+96euvRxPp1Q4ciOjYsYjGx911aq2X8z6bHJh7772IWltd\nIN2vu/xrxr+/zQ7qMGpqcte4Cw4pMVNr61b3+899Lj4nEDs0ZBIdspcuSdu3W91++2zZ8Gdttbba\nxCy79POeXEf4ZWxqys0mmZx09V9Xl8naufzDH9br9OnZR6qREVenrV3rAjFzz5VL8Xj8uAt6jYwY\nNTYa9fS4YMPAgNXSpTalbvfv0X190vi425+Bgdl7dnv77HdkqmvPnXP3xqkpt2/RqP+ap3376vVH\nfybePdMAACAASURBVDQja92xvH7d3ByI4uqnZcvctezPfMnWRvPr0+R20WwAwSZSBGerc0+fjiTq\nR7+et9bdA+64wyYCDW52h7t3S/4sWenYsYik/PXG0aMmbRZM6jqghVq/3sra+Jzf55pRKOW+X3z4\noadPfcqtU5S8/lVnp9F//s9LdOutbt2mBx6Y0shI5kDa3/99nU6d8vTxx56mpoymptwMUH+2yuXL\nrn20alVcra0uIB2JuADEkiX+mlZG165JExOefvCDen3hCzMpbddly9zMUn8mlh+UmpqyunxZklID\nOi0t8TnBHP86dW0pt6MjIy6A/dFHrq2RXDZjMaNIJJ4om4OD7vocHTWqrzeqq7Oqq9PNwKi9GaRw\n9zK/PbZkiVU87srBkiXS9LSn69fdTLFTp9w17wYu6GYZng1mpj8zZlqjUMrd5vCDWt3dRm+9FVFd\nnWt3+msDbdjg6it/UIB/rWzdGtM779QXNMDNP0/vvZfaZpKKDyLkGix4333TBa9Le/26UX+/G3B2\n48ZsMNMfMDc0JB08GFF9fUQrVtib2R+Mrl1zAzoGB41++tO4NmxwaeQuXlTK2r7+IJPGRptI9Zt8\nnuJxmzOYmFwv9faam+0xF2xtaHDXx9hYXOPjVv39JmXmnO/oUZe+OjmIm2+mayVnuAcp1bcvfZtL\nacdXehuKOW65zp8bQFqZwb1hFcRrFkAwEfhCzShnlE+m9zY1ucZFa6vNekMtpcGZ/l0NDVae5zo5\nkjspGhqyj4r1pd/wq71mQa4GSCHnp9hzWOjI/kzb9fvf6+YDhqdr14wiEZs04yr39xpj5qxVJLmG\n9eCgEh1cbsFk98+t26LEjC5/1LW/xor/4H/1akQTE+7Bb+lSM2c2mOQejPxO62wBxI6OiI4dc2vF\nDA25zo8bN9wD4pIl0vCwe7A8dKhOQ0MuB30s5h76r12LaGxMevjheN6F1/0ZSkeOeIngmmQ1Oupm\nabl1h1wngDHu4S+auHO4B7t/+zejT386nuhA/f/svWuQXld5Jvqstfd36Xu3Wmqp5W5JLSwDxhZg\nATKmuNmQSQR1hhkOGZIfM3OKSoVKcA41OVOTk4RAhlsqVKZyipA4YaiZOScwJIbAGMvEgG8CyZaR\nZLkl69KtW9/v3V9/99ve6/x49rv3/m7drYstCb63ymW7++tv39Ze613v8z7Pk8kAADeasRg9iyIR\nbh67u/m5XM54hRY+q2KRoBUA5HJk/BSLLJjk88rvKs7lyDSybWB2VmNpySASoRn68jIByYUFAmoC\n0OXzPIfvfz+K/fsdLC6y6CHyjXJdLKaxQPJbvxVDuczP3HVXGdPTFubmLBSLlApaWXH9sRZ+zy9d\nsnDPPexgPn9eI5vluczMcKPvOAR3BgYM9u1z0dNTfzO3UckustSCkMJguBDTiBkWfkfoJeUilTIh\nAJSfk+N1dxtMTUlHNTw5NbJe1xrT4feXgLGB1iyC3Hsv/198Uubn+X4mkwR2CwW+h4ODDpRSfnGo\nr48SmOUyu8vFD05kjKrHeKMIF99lnLW2Kpw4QcC6EWtB7l84VlcVPvOZmD/+t21zsWePQWcnu5iT\nSRaOczl4cwbH3ubNBvPzfJ9d18XrXmfw9rezUJXPswgpz1PmG/G7E0BFa4OREd2wgCsFOynUdXYC\nCwvGf5fn5jS6utwK/7zqOfRW6l69Xj+m65XTCcvcGsNC6eIiDc4ti2B9Ps9mGMtiMbunx/jFvuVl\nFu9Z9GNHebHI97yjA17zADYsL1lv7RXmZ19fpT9K5d8LsMr3rLPTxeoqpVSPHFFYWdEYGSFg19bm\nwhiFU6fqr7P1zjORUHULP+KjI8XUhQWeA+dqrpNhwKSjw2DHDlMB7j76qO2DR2GPpR//GLjzTgJk\n0qDw5jc7vjTdV74SrWlG6eoKCrXxOBkwc3NkXl64AKyuEpCPRskK/e53LTz4IL1cjxyxMDtrVV2h\nwuqq8YGY6nsjUo4tLcpfg7JZ3k82iShEowZ33umgsxMVcm+bNrmYm9MYHqbEWi7HNTqXs/CBD1CK\n68gRC6kUpegSCSCb1XBdjjthzHR2GrS3y1jTGB4Gdu7kPVleZnFaIp+H52FifBC/XhFZ5oKw9xz/\nnv/d3V2/Cz6V4pwLBPPj3JwwT9m4smkTUNloFLDIA09K/v9a78zx4wqf/3zMf1+Wl4E//mM2C3EN\navSu1AJmDz1U9CRo195DrMXYjsdR0XRkTLCOy3yRSgFTU3wPs1nmuk891YL3vtdBf39lg8t//a9R\nHD1KwKNY5BzDhiOD3l4X0Sjnmr4++v8RkOR9yGR4zFgseC6RCLC8rHH5cmUR/9FHbRw8SOB8cpI5\nqNZcU86e5fOLx4GWFoUzZww2bSIQX91EEt4DJBIGIyMWTp3iXLq4GJxDLMb8KRYjUNXSwuaXTEbD\ntg0iEePlfS5aW8lqtiy+X45DZlu5rLx8kWMnlSKo6zguYjHOdZGI8d+RYlGkhF3fSzC8Z4zHmZ8O\nDtbPOarnPgGvBUi3LIW5OWDrVr7vxrAZaP/+Wqni730v7u8xl5fpXUuwu3bel3xsaSnImcK549Uw\nEtdqFrx0yaq7h5udRdV7UsLjj8eQzzMfNgYe2wsecApPglyhpwcYHeVzAnjMsTGOpZGRKPr62BTB\nJgzmj5EI90X8TlPFQuTnJic1VlfhP5e1ZMpXVriGc/ywYa9UArq7FfbsMdiyxdQw58SzdWVFFBKU\nv+9bj+l6IxjuN0si8Hqi3jmH5UjDsV5D8lr3r9q/Tyn4agYiN90IPF7vmUgdQxqE6HXI+sLtKtv9\nWsXtOGab0Yxm3L7RBL6accvE9RTVGn1GKYWPfaxxF3w94KVeN/BaeucDA2RChDc4SrEDfy1JoOoF\nf2xM4ZvftH39+Nc6AVgvAdnI87mWZ7heZ3+98xoeZrFdisIEbIC+vkomRKPN3cgIpegEnJTiXKlE\n7ydhWGSzlASS7n2yXYD773f9AsvgoMHcHDf5ABCLEdjp6HArCkfCBgPgnXeQ+FYXDx95JIKJCcqB\ncJPMoo7rBoWQ730vio98pIzlZYJhpRK/p1AQvXuNf/5nmkenUixK2LbB2BhgjI0rV5ig79zpYHgY\nnscWIyiYUDJUa+UV/diF/t73lpHLBcWhHTuAiQkLySRBI9fldWoNbN3KYtnKika5zMJhT4/rF9wz\nGZ572OfIGBa64nGCiYODQcGYzwUeCML/dxyFzZs1+voMdu3iJnd5Wb4L0JqFjHye71V3N/29ALKw\nKLXCjTSBCY6njg7+e3jYQlcXu6NZnFXYulVjctLUdL+L3vvAgMHPfw6/s7Vclu50eL4ULEps3752\n57xEI8muj3+8VLFpk8JCuBCTTAIvv6ywaVNjjy75d0eH8Twt+F3pNItDxlC2slxmJ2x7u/GLY7Oz\nCv39qu6YfuABxx/PQfe3i09+sgTA9je6YV82PisWjuWaLl+m1JkURo8csRGN8lilUiCFxvsT3Mf1\nNpnh4juPT2nBwUHHL7Lm87yH8/Mara3A5s0KmzahgoGXTALPPWfh0qXgPZqYUJifd/Gud7kYG7Ow\nc6epKAjz/Flk7+2ltGE+z/s5MkIfoHBhLZVS/hhViuczNcWC/Pbt7Fb//Odj+MxnCjXgl2zA5fjx\nOLBlC8EBpQiaC5gRjvXk0+TnjeLV6Oa8EX5MBw9aeOUVq0piamPePHJ88eVaWhLGAX8vEq6lEuee\nbNagVKL8nYypdBqgpCUAcFwDnNvKZYPeXoK5s7OUpgqzmqp9mYDatS4sMSgeOqOjGsmk69+fI0cs\ndHSIhKYwS3ic7m6DQ4ciKBaBaJTr4ZUrFnbsIFgyOakwMECw+exZym+GvefqAUphVvHgoIstWxwP\nNAjky4BwY0Ul0EHgvZIFu7SkUCyyoJnPk+nvusynLlzQeOEFztu//dulNaWJjaG0pzF810ZGxIeS\nDRmFAufwH/3I9n0mjxwhawuA32jB5hSufcZQHuvv/s7GE09YSKfpm9TTY9DTo7CywqYMehMpf8wU\nCmSu9PYanD6t/UaKoECsPKkwztO9vXwmExMW/t2/A7Zvj2L7duDcOY25OcoBUr5L+WsQIP6WnAOW\nlsisyGSYAxFIYlgWvMaVoLkrzLRLJBT+9m9t7N3r4MoVystx3uIaDsBruqnfBT8zo3DokOUzcLq6\nODfOzXEN2LLFoK2NUqGy5rS3GxQKAUBjWcwz9u511s1lv/3taMXaWihwLv3BDyLo6TF135U77jB1\nAbPR0Rg++clCXeYVEMx/3/0u2aDt7aYCkOU6G7C6pCGiUOC9k7WHICzzETZi8TxPn7bQ31/23nk2\noFy+rPwcLp9HVY6jsHWr6/nqwVtLLVy4IAAZ/zabZRNVJMLnQaCGOenzz1t45zsddHYaRKMuJict\nH2DjP8yBikVe1NISwa/FRbJnzp7V2LXLxZ49ASDAOQx48kkbxmgsLHAsplKyBnPdsm2DYpHHyWYV\n4nG+F1QXMJ70tvLnENflOyUNXADfIQFAolHe69ZW5tilEjwmJO8vrx0YGaH06hNP2BXNO+98p4vJ\nSa4dEuGco7roPTLCf584oTEzw33Fli08bleX8WW5q9dhNvnQB1fCGIVDh2y85z2VTXxcJ7jHkPU+\n3EQAbJyRKGOf85z8jfHZaImEqss+DssrplIKf/EXcWSzxp+LJFyX871lwR8/pRLzdT43frZcZv4j\nAJTrcixHo/TmpSIF0N/vQikyhEdGKLsJGAwNVXoBy/2rlimXeSmTIRAnexbvycJ1XT+3rtf4mc9r\n/511HDK/R0cVdu5szHSV77hehvvtJhEI1D/ngQFTk8eLxPJXvhKpaUwA1pbgrpawDPv3rawYHDxo\nVSh4AMFculaDanWsripfgnh1VeNb37pxoObtEleb79+OY7YZzWjG7RtN4KsZt0xcj9/TjSjIHTjA\nRfaRR+yKIu3wsMInPxkkO9XH6uwE3v52F0tLxit2ALt2OUilFFZXWQTdiKkxCxvK83K5NkmK64n1\nEpCN3OMb4dkVlo6an9dYWODPJTGlJ4aF2VnKLXV1wZPfY1Lb3c2NTjIJLCzoCuARAB55JILxcRbS\npZDU20vwiybJ3Fwlk9wAy8YZkIIcmV/velcZSlGaqbPTYHWVXYE7dtAAO5fjsxRQKhKBN9bgFVeC\nSCaBZ58NinmXLvHclpYE2AGkeKgUpbEyGRY2ZcMobCLppNRa4+xZ5RewRFLw3DmF5WXLT9AnJxXG\nxsiyohwIPBlCbty6u8ULgtJ2v/mbpRr/K5FuesMbgExGI5kM3oN3vIOb5NFRglG5nMLQEDuMz51T\nVeAgQ47f2cku3vC4YnFM+UVGgNdIaVPpYidTqFhksUM2ya4LzxPHRT5Pf4dYzGBxkUUbssj4fWGp\nFGNYpMxm2YVKhicwNIQaVhOLsdwEZbMByytccAxLA9FMvvb9TiRUjfzk7t0s+ohkl2wqtm0L+/2w\neCKAzPQ0cPQoZcrSaRakwt2/8m7KPU6l6O+ytESATmuOkd7ewDutXBaAVcCvSoZZ+BoOHrRw6pRV\nUdgTL5aeHoOxMbJLWNRiga1UYoe3+F0BLIx4d8/zpwqAo85OFqkSCcpwibb+ep2f3d18jlJ8lxCm\nYDzOeyJ+TbkcC1WzswodHZSi2r/fRX8/u8KvXNEVBfxSiQBWPg+0t7OYwvscgAGrqxpbtrCbnLJq\nHKfnz2vMziq8+90lr9BCgCeV4ljXmvMjO+Fr2X3bthUr5tFy2cX58xYSCZ7Hrl2B5KZSLrq6DMR3\nKBzhuftqu1erCw6HD+sKpsy1bv6v14+JhTnLByDDHfEb8VY5csRCMqn955XJcJ6XIjMBL865rst3\nvVAAEgnXe08CoDcSMejqYvMMr4lriuuy8Pbnfx71AA7LB/ktC/j939f4i7/I++BX+P0dGeE8z/fI\nYNs2Ap2FgsILL1jo7dV+8UhCAKZCgZJ4q6vanzOl8Kc1C/Dt7TyWSGL29BgcOWJXeM+JDKlSBtks\nPBnCwFPnzjsNTpywfFmvRILFX77LwPy8FNC5lnd1sSmpmgU7Oqr9eyLPwXGM57UV+Dd+4QtxvPvd\nbo008eioRksL54D773d8X7vWVgJF+bxGNsv3rVDgPZieZsGV8pCcH7q6CADIuVHel6yOS5ciXn6h\nsLxskEoZDA8TdCQ4QCCbgCnPmUCbwhvewAYapYz/DBMJAm5kllIaM5FQHmgBzM9bOHYMPrtbxp88\nQ2GYyXNPJOj5VCrRb4heO8FdYt7Dtbivz8Xjj1s4dkzj7Fmyysplnv/lyxa2bzeYnw/kxvr7WZR+\n4AEH+/Y5/pwkXf1zc8Bzz9nIZglicY0OxgHnYoM9ewhsLS1xLR4aooy5NKns2OHirrtcfPjDzrq5\n7OJi5XwgfodTU7wfliWglMLdd3MN6+kB/vt/t7GyotHZGTR95HIKf/d3MfzarzkVeaZI2b3yikZ3\nt8GlSwFzv7vbYHaWfml8NoFMIAFYoKuLkoHT09qXiuY8atDaarw8hM9G8oQrV7S3fsp7EDxvywqY\n2vm8wXveU8axYzbyeY5fx+E4iESUn28WCsab+7jmrq4C8/Mcx4cPc75cXWWzF9nyymevypon810q\nZVAs8v13HGB52cIrr2gcO+b49/f4cfFGJYM5DL7Kd5fL/PuWFo5r5tbGUzzgHFAocA62LIIo8rtw\nSJ4r4zseZx6RSgFKUVoZEEaS8d47HcrJgPe/v4wPf9gB0LhZMlz0dl2DQqHSTzSVolzk5s3Afffx\nmAsLtTmxgH7VIe91OCRfBIKGKTYPKd9f8qGH6jMSKe+uvPnT4NgxjXiccutK8VwjETYG9PUxfzxx\nIoaVFT6PN73J8Y4BX4762DE26DgOrznY08DfM5fLBu3tbAxwHO3dK4SaSaR5TPmy0PT4Yp795je7\nfkNXdzc920ZHKf07OGgwOqrx858bv9FHgLuzZ5lYVksOtrcDy8tsKIjFOP5tm4B1mIlU3fhpjMHC\nQsBALpd53123vl9n9Xc0iqkpqgYMDzOXfstbOJ+GGUk3QyLweqPeuXV2oiKPNyaQNRd2d7gxQWwH\nwhGe76slLKvZ5Pm8xuSkW+FJKO942KturbzyRoGat7Ps37WwtzbyTtzO96QZzWjGrRVN4KsZt0xc\nDyX8av5WEsh63i3GsCARls1ZWVF4/HHjb6hl097TA69jNzBalyLUo4/auHyZ3xHIaOkKc+rqBT9g\nF1T+/FqS1mtJFNZLQDZyj+Uz4bhazy7pJD51SqNY1BgbY0H82DGDrVtdZLOUiEqnlSf/Aq+rnBuj\neJyFsmPHaLo9OspO629+00Z3t8HcnEY0CmzaxG51FpWA/n54koksDBijUSzyvNiVz6JDuay8jQ18\neYPlZcqjDA4abN8OnDihMD0dFMC0VshkmGAvLBjcdRcT+Y4O43fm9/QA4+Mahw5ZXhEr6HYMhwBb\nxrBIdeedLpaWtL+JDxeswoWDQE+fnd2bN7uYmyOrTdgH1cHuShZ5cjmyx154wcLqKtDdHUhFPPCA\ng+3by/j7v2/xZBdZTFhZ0UinHbS1UZbmjjsAKaAND9solXTF+VYemwXMu+9m4Wx0VKGlhWyx2VkL\n6TTvKwuVxjsGi7aFAjvtg2tigYTFLYVf/VUXi4v0oVha4nG0ZvFH7mOpFIBpwc8C74tk0kVrK8cr\n/QjIdujtBU6e1H7XsXSn1nuOpRIlM48fVzWSGiL/VwkYKbzrXSXP/yZgVEnH7dSUgjGW5yvH73rx\nRXp3bd5s/Pmqr8/4Xi27dzt+ke7MGc5309PKN/BuayP7rlCgDJbjGE8CRnzXXGzbRpZYdXR3Gzzz\njFXzjI2hF8snPlHEN79po1gUSVF5X1gsk4JruQwPoGGx7ckn2VVbKBivAMaiaT7PjkvZZNaTkq31\nUwk8NUTqUGuCWgMDLs6f5/POZvksCX4pKEWmyZNPKtx7r4OLFzl+tK5kL5ZKNHR/y1scrKxorKwY\nDA25HmMjYFoJm4dFr8Bf4mc/i+DAgRLOnLEwP28wPR2A2aUS/76lhQUrggQKi4sKCwtR9PQAly+z\nWDc2Zns+PWSSUA6UoItS3Og//7z2GSkDA/xdeO7eqDStRLiYFpajE6bMtbKZ6/kxBf/dmO0bPi/K\nWAY/E5+zlhaNsLfK8DDB5PBcFzBxg87u6ndcwAUZ121t9Cz5lV9x/Hs3P0+23vy8yHEZRKNcw3p7\nCVpfvmwhleKcJmwiyuJp/Of/HMVf/zXzid27HXznOxZGRixvrHJdKxQUZmcpp7m4qDzPEnphdXeT\nxaMUPchSKRZio1EXxaL2QaRyOZC4TafJ0CHbRgq4lJEqlRSmpjTuu8/B/HwwdyWTXNu0Jms4FuP3\nLC4qjI9TUrelxfj+U/IeJpMsno+OKuzb5+Ib34gimw2aYAYGDI4dY1Faaz5PNqoor/kD3vmx6Pqz\nn2ns2eNgYUF7jAKDbFYkfQ3On+f1bNnC+7+0RDBAnq80MWQyChcuKGzbZhCPa/T0OFhZsbzf86Id\nh0w5gJKJ2Sw8Bo3y5MKUX5SPRHjupRKPH42yOLy0pHD0qIUPfKCEAwcMDh60kUjAAyqYH7iuwoUL\n0ijFfyxL+6Cr/FueBX/P/ybYBj+X3bIFaG0VZoXxwBLjM75iMc4n09Ms5i4vB36afP+A8XGe//Iy\n57HlZYV9+xwkEsD3vx/xmTGRiMFjj1lYXOQ1s+lBpC7Z4d/TY9DZ6VYwUXfudHHgQNnPxS9fJjDZ\n1gZfprUe+3F0VOHHP7bxzDMWJiYIanKNMUinA5lJYVQtLHBtEQnG++5jY4CwoGMx4zMM43E2lAwO\nGgwPa28O0n6ulE6HwRcBuTRGRw127XLw7LOcG8tl+M0yvb3cjywvB1J93lPz1w16lwI//znnkWwW\nvj8gYCrGrcg2k7Hv4N57AWO47pCtz/FUKLCBT2uOhVjMIBplMV/ehY4O1y8cFwrKY8fwZSsUOKYo\nVc3msXIZPjAmzTO5HCWFR0e5zr30EjzpV2m+UjV5g/jCcuxyrlSKUoXptPGAPoVYjOCf5AScB4Jm\nE3k/AR67VCKge++94iHnVkihHz9OOcUjR0QOlnPY889Tvvg3f7NxYTucm/3lX0Z9j6tolCxF5uME\nfk6cICj18Y8XcfhwZQOmZfF5SxOObVPOb88e12eqylhfXOS8eeYMWblDQy5efNFCJCLsZtTI9CUS\nCtPTwOHDFhIJ7QNLo6OqAoRcWOB3RCLA/LwLYyz/2ZRK/HutmY/v2eNiepr7Xs5FzLPDz9V1CZBb\nlsKuXfzM9HTQMOg4zE/kHML5lYC6pRLX6HQaXmMH2Y47dwZejwJIpVIE5GQPt2cPG46qJQfHxhRi\nsQD8FN/Ori42M8hzeec7g3ESNAlVzj+2TYbhwoKu8S+s/Lv6QRUQu6I2cemSxve+Z+Ntb+PfHj7M\nOXnbNu4vw+Dcrewn1ahZNpzHSyNSWBkiAK6Yt4qvpORuSpH13ci/M/zfoqoQBrn4fOvbIdSLq/15\nvRoNsDZzbSPfcTMBoWthb63XLN2UQmxGM5pxI8P63Oc+97mbfRJXG9ls8WafQjNehejsZLKTTgsQ\nYXDgwMYW8o3+rSyiJ05YSKW072vR3c3N+qFDFrSuXoRZSJybU552NxPyF1+0YFnsDBscZBfwrl3G\nk8ChhJIU/PJ5FlqyWW5ydu1i4bFSU5zJVkeHqfDi6e+nnFr4Gp580saRI5TV6u6upOjLNY6Nabzy\nisapUxYOHbIwOOhi+/bG9/DSJV03SZPjN7rHAPzzWVhQuO8+B/F4FMVi+aqeoXzPyorGxYsKyaSF\nuTl297GTm7JGAIvzsgmmvJ/yN/WbNxucPctryeU0xsZoHj8zw4SJnewshhUKLFq4LguDlNELiiLS\ngc3CAzf90SjHSiKhsGOHgx/+MIKlJRb9SiWFc+c0CgWRL+L4KZcNikUm1tu2sViRyXDcEXzT6O9n\n0URkY6oTyOqwbW4a77wzKNyEgTLZuIZDurwdBx5woP2u9UaRzyvvfsFn/zz/PN8h22ah4Xvfs/A/\n/kcUyaTlFcL4TNJpYHSUm+U9e4BEgkWz48ctj+lVWzCWsCwWVLNZFh83bWIBhoUu7TO5XFd5ne3G\nk3gU0KA+aJhOB2B0KiXyMcovGlZ/vhEw57oKS0sak5McW3Nzwbso/ihybfVYbeFjPP20jRdf1Hjl\nFctjI/Lz4+MBWMBnwUJYS4vyOvUVTp+m11wqxfc+n9dob+cYPnuWxvZaw/eT0ppA4cCAwb/+15RJ\nlHktFgNefFF7hWQCjbbNTl7Loo/bW9/q+KBMS4vBv/yXLPoQ6K0ExQ8ccPCzn1ne+1QZXV3iwyAM\nS75bW7bw2Stl0NdHkIYd2SxYaA288oqFlhYFxyHbI5mkz1dvr8G///dldHYG8+DKivbv1cGDNmKx\nQB4LgA94DQ4aDzCw4Dj06Bsa4rPMZHgMpZTHAuVc3tICf+zInMTxWHmtbW1S/FB+cXJoiMdrb+e1\nJxIsEheLlFNNpwWsBcbHFc6fJwAiY99xAqaTdLeT/aC8YjolXbUmUCEgM4vxwX1ubaXMF+UeNZaX\nle839fGPl/D611e+ANKNu2+f668LjULWwYsXRXpJxoZCfz/XkvDaBqy/vgGVa1XYt4xrJz8TXjer\nv/PKFeUDTuExu7SkcM89rj8+RBZncVGjowP++xaNGpw+bWF+XiOdrpw7+I4J6xY+q7S1lfJsu3YB\nQ0MuHEd5xWTO/5y7lT+n5nLwWCIsLBeLlXOl1mz+2LSJxb3HHrMxPa19OVLH4bMF4DMhcjntj81U\nymB62sLCgsbiIv2DpOHCtgnOCPPScWR88T2kxySLi21tnKfSafFpBC5e1J4MLfOdbFZ7Umi8xmSS\nQFtLi8bMDK89k6G32fw8JYu3bTN4y1tc9PWxeDoxoXH6tIUrVyyMjGi0tjKfmZmht5oUQcMg8f2z\n0gAAIABJREFUZPiZAAEjWtZ4AZ/ZKKGRTBIkXljQWFjQPrAjPjQSlGPlOzoxwXeToInyWSiWFcyd\nwtZhMZ/rYi5HUEUk27RGKF9gjkH5LkrE7d1bwj/9UwRTU/SNdF3eX/HIFLagzEdAJeBVHZZlQsV3\nzrHCSqfPE9+R9nbeg1iMDROuS8lngm+6Zv2WeyDzkMjnjY4yD9u8mUz2F16wkcmIXLXyn41l8WHF\nYmRxvf71Lubn2e2/sgIMDLh4//tdnxEwOUn5Ra2BM2c0nnjC8iQcCcKcPq3wwgs2JiZYNC4UmIMk\nEsrLMZTX/EJWpIA+AJlfuRznhdlZgjRLS3zvVldl3mHR1bIInF++zLHK/J5z9eoq72FbG9fNfB6+\nDKFtA4uL2gPI+CxbW/kspqe1p0BQXUwMvIdcl0X5TIZrZns7rzuT4XE4NuD7mApjmYV8voejozqU\nLyl/PNk2/PePa5f2pDi5Ls7M8L5ks1yHq5ntXV0cY5QjDcCsoAlLis4Be0vWs7VCVA/KZfhAuTTj\nRCL0ghOlAscxofOqn4fJcxEGcFsbGaOjoxovv8x/K8U5L5fjvcvlmHNv3cpzqbfWHD9O79xnnrHw\n3/6bjZ//nPNdNit5gKgRAHfcAX8vOTFBsPzCBTZQjo1ZKBTgN8XIe5ZMAm99axmxGNl+Fy8qvPyy\nhVJJe9KlGhcvMj8FFN7+dsdTDaAM7NNPWzh82MbLL/M8f/pTNvEVChyvMs9IriH/CNgt3sbyeZnf\nJf+fn9eezLqMW1VnThJvOO7F4nE2Bcmx5W+1FtCrsplPKT6vSIRSnPSlVZieJiO7t5cylDMzCsvL\nzEEIqHHe6e012L6d+7x0WuH++zm3vOUtLpaWFPr7mUv09bERhfsa7ctMj47yvi8tad8mwbYD1qRS\nlGIcHmb+lUjw2UndIR5nnly9hw/nKy+/rD1Z5iCHWl7m/q1c5jwhY5cSofzuWCzIw9fK025mdHdT\nzrd673DffQ4OH+Y9eOEFC1rDUy4If471l5MnOe7Sab47y8satq18udxo1Ph7MKmzAPBrLfE43+Ww\nRzvXGlOxVwBq6zES69VPwnH8uMKXvxzFqVMWZmY4p4yOaoyPV+bJ3lU2zJOr9zeyF7xZz1ry/epQ\nCti3r/5Gv9HzlzErNaGqb6x7T17LqLdP2bYt2qxPN6MZt0C0tcUa/q7J+GrGLRXr+T1Vx7XqCYcX\n5zDlvVAIikXVxwGC7rtcjklSd7fxOwPryQKOjmq/u9i26W0T9r4JM6gGBgwSCVPhzVPNltpI98uR\nI1Zdw/evfjWKhx8uNjRr3Qijq56We73zefhhIB5fZ/eK2ucnbDH6+wSfkw2TeHC5rvK7kKX7z3UN\nWlpcTE5qXLqk/OJUeBNCBhMLDj093EQVCiyaAywKjI+rhhtl8fHI58mK+dM/jSGbJRBHdhiLJyxe\nGt8nK/g5mV8iEbK0xPE2OEhvotXV2iLbWqE1PQOWlytZJkDt//M+Bv8tG4FGwFP4b2SzSWklJqHF\nIvD00wpAGc8/b3sdw5V/y+KXhakpg3/+Z3YPJxLa3zjTYLje9RrPs4xF00QCOH+eXZvidxbu/KMU\nUCA1VmyQe0rH/oULyvM1WhtcXC/K5aDwOjQETE4SUEqn+bv2duNJEQGNwMVymcbxwk64dEnj6FHK\nNb7udcbzRoE/7yhV3x9O/hvgho6G9dpjkwTG8/G4Qnu7g5kZhf/wH2JIpykVQt+uQB6rrS04R+mY\nzufZ2S6bmKEh158PRG5RpPX6+gyOHLGwc6fjy4sAgcTjpk3AM89Y6O3l9+3Z43oSO4Es0fw8N63s\n/uMgef55y5PD4f0VT7lkEnj44WJDKVmAwOvkJM3Wpet2YMDg9GkWKQkMcfzs3m3Q1eViYIDvrFLB\n3CN+epkMZacAskSmpmqBXBmPc3MaXV3sWqYPGAv309MsWon3RT4fdMVnMmQUZLMsrnIcVX6/MTyn\nVIrn7rq8L9PTypPbNT5ownsF7zkaXLyoMDSk/OcTjRp0dbFwEo9rXzKx2q9mo+utrIPVm2GRYZJC\nQVje9pVXtG9sHl7f5JmKZBQ7t8myEDkcYXyE161G5und3Xy3xANk2zYyPCYnpRnAYGZGeZ3GLKp3\ndpKd19XlYHmZY8+yApBLTNNjMd7zWEz577VIqU5MECiamdGeJJeqYOGUSgrLy4HniVKVY0qevxQB\nf/ADG88/b+HyZQsLCyLHyrUsk2FxVd4nrTmv0iOR/l+FgkG5rEPgEN8lPiPjeQMZ73OU9JOmhUKB\nuZMAF7z3fD/m5/kMWdDmOZAlyXcnm2W+1dLCeS2XY+G/q4vMRcmrzpxhofHKFfEQ4tz97LM22trK\n0JqSW4DxgGMgvG7LnC9MAWnkyOWCdU2aAoCgeYRyXAGTqjrIjoEnx6WwsMDvoESr8uXW+H7KvRGp\nNj7TSKTSC8ayTAXjuFwmG8IY4GtfiyMalTxI+QyKRuvKWuu6MMYLBYOdO10kk2RQz81x3BGgML5H\nHdcPF52dxsuX4AEZjb+fY4nXNjPDezQ3Z5BMctwT+FGhz/N8APqKDg6SjXzpUsByyOcVJic5z95x\nh/Fz3dFRAk6WRWB+ZcXgqacIMJTLgDFBwTuV4n3v7uZx8nkWSLVmoTiT4dqnFFkaV65oTwVA+cVl\nCfEkikQMxsfJ+KY8Fo9BNlEgBZnLGa+BiPNCKqXxwgvaX0+EKeU4QcG10T3m+ySNFsZnuc/MhNlS\nPF8B4B2Ha69t0/uO4EKtDKA8E7LMg2ctkn/STCPNV8IIFYah45ARRCCPfxOehyQoZ2eQTuu6DK9G\nIe+0XJ+8p8L8TCbJUoxG2RiQycBXIqjzbSiVKNGdzZI5OzxMFuHKivLkJwMwUVhlmQwZr6dOaV9x\n4vhx7rNkjpme5nMnO57ArVIia0oFiZYWrjuFApskz5yR+x1cE6CQyVj+PByNAps3u+jqAiYmbPT0\nGJw8aWFpie8VJduDec22gV27yNYEyHAqFLjOrKyw8Dw7S9llmRPX2xewkW/tqLcHWev7rlwBYjG+\nq1ornxFHsI3PCggAOLk2gH6Gtk1QXWvly9OeOqXR3+9AKfoNC6syHmfjVaGgfO+6Q4e4zoh3VJjd\nPjTkoqMDOH7cxuio8sHWVErhu9+N4NQpg+5uC3feyXy3q0vBGNeTCOU15fPGk6ylokA+jwobBaB+\nvnLihPbnleDeclzMzmqfHROPcz2VveXeve5rxgK6VvZRI5/1sDJDJsPmqdVV+J6abW2Ufp+YUJ53\nKvyGVYBz99vexvVRVEGMUb70p1LKr7V0drp4+OFyRW3koYcqzwEI8sp617pR1aGpKc4RAuZQVYh5\n5eXL2rcgCEc9QO1W9Ma6FquL9VQkNsqkey3Zb43qXr298ID3ZjSjGbdqNIGvZty2cT16wuLdIkWH\nRAIYGAD27GHBUDZg0tlIho5o+7PTsafH1BT0wrKAw8P0fJGiT6EQdPMoZfCxj5mqxNrgX/2rUkNg\nCthYspNIqIpCc3ButWatw8PUsteax3vwwfKax6+ORufz3HPAv/gX9f/m+HF60ExMsFP13nvZsS4F\nycFBPh+RiGIhy/gbwKCoYvzNt0h4TUzY6OjgMxUDbOm+liKBdMvmctxAZjIszp08qSDGylL8ly7y\n4Nq40UmnDaJR5bGFAsDBdQO5GTHFZtEIfnGA3dNMyiVJnJggo3BpKfDiWC9EkmdqyvI30xstHAD1\nvQHWj+BZuy43GgcP2n5xoF64LvyOUNdtXKirdxyyIgKQq1HHrnShbiS0VldVZFkvpLv9zBmD0VHL\nZ9EUCgrRqPK66U2D8+P9IFswvKGnDNbly+xGffvb6cclUjjhSKUUnnnG8lkFIqGSSrEYSEaKsJCU\n531nYfNm+EDr3ByLALEYC/ci00i5TR6nrY1AQdjjoqvL9ZoCOBcMD9P4vr0dGB+nz0yhwA0/CwAs\nCPX0GM9Tip25Q0OuL+sGBMXhfJ73MpWSIrzxfMeCeTwW47u/Y4frS80CtRujVIpdtiI7l8vxnevs\nZGHMGAJGmzYBW7aQybRpE/CHf1jE7/9+FJEIZauE/cnzVH6Bk/4bHKvhsWUMizcs9CuvIEdJ3Rdf\n5P1vb+d8Mj/PtUHYjADHQiolAEstEE8vKdcHT2Ix+qe4LouPgPb8guCBvSzUTkywgH3ihIWZGcpO\nAeyQtW0ylEZHFf7oj6Ies4tydh0d2LBflxQD4nE+a/Fmk3E0NORWrOOU2iTQLR50xtBjZ3U1XFQg\nWNXdbdDTozA46HjgU+26VW+N6umh7x2bV4L3Iho1PoC+sKCxshJI4ebz9PygRKTrg+1SgKM/kUJL\ni4uhIYP5+cBzSmtUFOKkeByARfDlYaU4LMzBAFSrvLelkvEL8+PjLA4JiMaOdpGWMt5YdWHbXEOl\nGUTYQgLOEfQI1sp43EW5zPeuVAJ27GAelEoFbBIp1hsDYPtROPv/CugaB1Z3oHjqUyiPvcNvMJAm\nBYDv9/IyJa4iEa7JjiNMeGAh+iLmd/81kjsnUG7bAZX6FGKL+z0gg3PUiRMamzbRQ3JuLpgX6gW9\n1sh24X0Iy8LVrp3hwvraEczdIh0oz6q68BsU7IN7IWNTnoecl7CTe3vhs3PYdBP+vqtr3Aj8vaSA\nrHDliuWvDQBZ1rZN0MC2meO2t3OdO3UqKCjbtmnYYFIdwubRmuAXYCreHWGBSB5DWU2D48fppdfa\nyiYFSnjBz3evXOH7JM1l5TKbT4SZFJZhBIQRy/mvrU3k/CgzJ+xz/pwAJEGfQIa3XoTlTCnXKQAJ\nC66WpfyifbHIcS4SvgBCoLA8Ux5zfh5+o0S94HNU/n1LpRqzmmTNEvnBQoGfr74/9WJxUePOO8l2\noXx0MI7lHQn8NwPWYqlkfElyMrTq3j2f8Xd9EeTfmQwBykjE+E1o6+WdVFgwGB8HrlyxfKlIYT47\njvLnVIBzPZvoyDD5X/9Led7DNmyb33X5svZZt+m0sM+CfYU0fRUK8NjZwZ6tVGIOEI/DazwI7qtl\nsUElmdTo6KBX7pkzZKcmk8G9CIfjUA51aoogjxSiXZfrFFntlb5nNyMch2MzaBaovBZKmwdronib\nRiLKb0oC4LP6s1myDc+cIfheKhns3+9iZQWYmyNQmE5rPx+JRNiEtn+/49cSwiDC178eQT7PMSYN\nV7JeTk8TYM/lgLvuIkgmkojlMhUreG5stLz7bkpUVhfrH3+c3nfSZDswwHG8uFjZjGZZBPFWVzmv\niS9hfz+b3np6rq6J+HrieuXoqhtqH32Udgci/53L8T0rFGTNY5NEZydzxHvu4Zry059qn73c1xfI\nPa6usrnv5Ek2+LzrXTzWlSsWlpcV9u51sW0bsG9f5f0KeydvRI5wIzLgZEVpD6gLml5GRxU2b+Zn\nAnuMWjlNievxi3u14lrtStZqeN8ImPZayCGGgbWREY1YrJJZt17dqxnNaMatEU3gqxm3ZYhe+sRE\nkBx2dm5cT7i722B4WPmduem0wtNPA7/7u3mcOxfByAglzFZWuImJRAhKLCwQ8ALYWeY4wJEjLC63\nt1MvXLpRBwZcdHS4vnQEOzgp2QIY/O3f2n6h7sCByo72RknTRpKd7u5aQA7g5os6/ExCUikWDYTt\ndi160o3OZ3m57o9x/LjCH/9xDKurgV/A/LzB/fc7KBaZsExOAvfcQ6kxek0ERWCtTaiTVTaEQeeu\n1gHgFS46SWFFCnxA0H1bLEq3e7A5li7NRsFuaTmPoGghoRTZINVFcAGIpqY0Hnss8EBJp4F8Xl9V\nh6R3JO98rvbvblxsBHCq7tDdaIS7lm9UXP09XjvC1yYeBAQ/jceu0V5hrTYiEeOxLoLvkn9bFosi\nq6sERimnSm+Qy5c1tm1zsX07Cyzd3XxBZmZYQOnqMlhaUl7HcnA8YT2I55wUc7Sm+XwspmBZLrq7\nuVmMRIQlxnllcLCM0VEL8TglNldXNR55JACkn37a8nzseDzLMujpAZaWWOiUYuTCgsIPf6iwfTtl\nbS5csNHaSmAomVR+xzuLFwapFN+V5WXXL95aFufteDwArNbanExMEORaXWXxeGWF9ycWc0OSJgo7\ndwZdxDK/bdrETvquLoPVVeOxcAKz+c5Osk0IbJuKsU5gk/8WYIGsSX5eKRZnolHjF8Ori0/CeK03\n9rQ2nhxg4LcibKBCIfBLCxfmLYvdsy0t9H2bmYHv6WbbLLYLM88YhVdekftBkPLSpaBIt5Zfl3Rz\nfvObFs6csfzvzucVTp0CPvIRpwKYyueVXxBYWeGxBgYMrlyxajphOzqUV9yprKg2YhGHY2VFeazb\nwKNT/L4SCbI3slnl/4z+Q/DkSxWeeML2ATF5DsZwHNg2gepUyuDoUQvZLJl3gPIAI/53NUgCAKUt\nRwEPODKrO6COfQqY3N9gfueYWF013jlyfAnLRD4TzCmWBz4ExwzLToXZtzJHtrTwHtu28tYo/jzw\nlwquH9uPAr/+MaB70j/D3O4fA8u7gC0jQCQHlFqAyXcAz/4pML0fIlccZq9oDWR7jmL17o8DXd53\nbQVwx3PA9x+FPbcfpRLX2IUFhfe8p4zjx7XPal17fucDkzmxet2+1gjP21ezFtd+PgDBpDieTMKb\nE2ubmYBGjOn6EZYcC9ak6u8MJFbpeynMRvpIsThN8Np1GzV01A/xRwsXxsJ5FkEfg9ZWFxMTllfQ\nZWG3pYXr3cSExtmz/HsyZyvXuFQqYBXWAzO1DuY/ARWM0R4TAx7rLLhPtq3WBPh4zvw32aPh38k7\nEjTvUNqSneHh97/6XMtl5fmkhRt/ao8t/14vVwreC3r+bSQcB15DjaxN1ZJTwedqwfnGYGFl3PhC\nbdBsFXjpNX5HgrxEfHXlmRGMD0DqSIRre7FIlmCpJP64wE9+YvvNeQTbRA6Q74+A//K+SjMc5UWD\n8Rqw+OCzu9hkwOPIWFSKe6WWFvhSnWuF43AOp3wnAQI2BNYysm9mrDV/uy58/zZKTgf+bmRj8joK\nBd5HyalGRiykUnxuhw8r3HWXg7k5zkUiNUpGtsHqKqUlu7s1jh/X+OhHHb9w/9JLGsPDYZl1ycEU\nFhf5nra0aOzbV8LQEJuKslkeI5kMvOHEG3BkhLmV5CBHjihcvhzkQ8IG2r3beGME/jXyuQsIq3xf\nwje+kQPltfT1ul72kfiuDw/TJ3NpSZryKAFLedmgUUFrMiW1VujvD+S2h4bI5gKC6yeIpHH33cbP\nIVMp1mDk/1dXNb71rdocth4g8+ij9prX+sADjp9/Hjxo1TRkiToBPeYkH6G8/b59Ra8u05jlLCG1\nrKARkWPogQduDtsLuHoP4I3ERsC0V5v9Vg2sTUxo5HKmwnsUaFz3akYzmnHrRBP4asZtF7IIcfGp\npIp3dq5NgTbGIJWiBFh3dyBNRb8ZhR//OIqPf7yIiYkobNtFVxcTi5UV7QMny8uBsXVXl/jwsBt8\n1y6DRx6xMThoPJNdg2TSoK0tOKd8nonW88/DB5zChtiSxBw8aOHhh4sVLIaNGIFKIbNYpOwLN/Pw\nZFDY8Tc4aHxj6suXtWe2zq7agwct9PRgzS6nsTGeH0NVGPUCLBTXe26f+UwMFy5YFX4WxSLws59Z\n2LOHf9/ezmP39DiYn7d9fW7ZEMZixi8OS3FG/JTCBb1whDe9ti3dcoE0XnV34ca6HoOiRG3RQrr3\naztNCcppzM6SkbF+sa4Zt1PI2CmXlc8kql+wlOffWEorHqevWSJhcOkSgRrKHSpMTnJT1dFh8IY3\nsOguHhCJBHypLSmIsLBt/C7xVEpVFLpZRGdRsKuLG0thSnZ1Gezd6yKft7BnD0GkCxc0RA6OEl8B\n4C4F8rY2dvgTJOJcKu+b+GV0dblwXc5bADe18m5ms5X+EoWC9nxzeB2RSNDFnky6eOSRCDo6+D3x\nOKWD7rmH60I+z7njnntcvPKK5XvBRaPK80tg8Wt8HNizR2HPHrLSvvUtG5s2EdBIpymBxAIVn590\n3K+u8pm3tgrDt7aoGS4+cowEjId6XkLhv20UlH/VSCYDOaswKzYe5/0RkJXHIHMik2FRIZsNCprl\nMgu4mYzBwAAABM/VGN7TcOdxIqHwyisWvvQljfe9z6nZ6N5xB73SfuVXyhVdrAMDBseOsdixtCTg\nk/EkAIUlwPGzbVv9CbneWl9P1lCkEyXyeYXubtdnPwMsUtHXitKSYb9E6eoGhDGh0dICD4CBP/47\nOw0+8hEWvRIJhTvuKOEHP7B9pne1B1zFOvPmbwAHfg+I5fwfmZ3PAf/4KDC9v+71Ayy+imRYLBa6\nH9sJornd40BiB3DpfcDuZ+F6bCwc/RT0LL83LB0VsILIjJSinVIEjdvbawE7AATsQqAXAKBjnv9I\nRArAXT8C+l8C/udjwHQA6sm/XRfAez8bgF4S3ZMoPfBZlL71zwB4fum0xsGDEWQyLBJvRH4rHDez\nYWS9KJWMB2DWBxskrv0a1r5X4YYOKfyHgQwC5Y3As8bnWT1uqtc/11VYXrb8745G+b5cuKCxvEzW\nYU+Pwcsv0/smk1GepF5Y5rgxk0mAO2meEMCBxzUeY4eM/Xx+Y8Di+s+g9v7k82QlNco1bybzJhyi\nhgCsfZ23yvlWR5hptV5Uzh+1zSauq9DaanwpR9eFv75mMsrzQpUGN+P7XlUywIM1Qxr4SqX6N1YU\nKyrPIfAoyuXgN6dsNMSLjcDkrQN4bTTYzAOQNSqAV+0cKY0qpRLljIVRPDurMD1toaPD+PtAznXB\n/TCG0rU/+xkbNP/+76mqsbKi/ecZBr4A7unKZc7ZJ07YeOihAuJxC62tzH8nJ4PG2f5+g0OHpLGW\neSxVTjRaWlQVaMX6xoEDZLULOAQQ9Jqe5h7etrkHYN7h1lgk1AMjbpQ83NWyj8LS1uK5mkgov15x\n8WLQTJfNBk1ZAJvcWlqYm2cywNmzzPG2bRPlmEDGMJVSeOEFhfZ2KnJInUKaj8P530aBkrWuNZx/\nSmOxMaxNSWNxZ2ftminKNmfOWGhpgcf0DGoyYZazhKgK8Rj8GZlxqgYku9bYyPio95l69/BGSmFW\n/608k2oQUJjt13ptEtXAGvcqfKfDY6he3asZzWjGrRVN4KsZt2SstSjJIiSLD8AE94UXNNrbmRQn\nEsCuXbVa0eFko1BgwhmN8nuXligR9YUvxLF5MwEN265lTZRK3AD19rq+5IsknTMzYm7Nz7KDXaFU\ncn2GQGen8ru+ZHMvIJ7IPAA0ov/qV6P44hcDj5W1ul/CSdd99xkcPUpN+Z4eFysr7Exsb6fXTCJB\nf425ORaDBUCcmGAxescO+MXJ4WEa9y4v855HIganT1ugGTg9bubmtO//IDr4yaRdkWALWCkdlSJB\n6DjSWc9rpHwFGXb07mDXFiUjKG/4+te7uHhR+ebu4QjLxlQHNyvG77av1xV8oyKfVw3Pg7G+iXcz\nbu9Ya2yF2Yn1wnHoR6I1MDYmm93AHFz+n54WFiYm+C7Oz7NQzc8EoBeAmkJHNdsgl2Mn6coKx2Y0\nyo1mKqXwk58oX4ZPZNqEhdDTw/kik1Ge5Bu/D2DxR2vjyyQBYYCaUkj9/exoJagUdFmHPyv3JCjG\nUuIsGqW8y+wsGS35PDA2pj0vFTYq3HOPgTGcBy9dsr1OfgJFuRxZeYUCZWgcBzh71sLlywZDQwot\nLWRjLSwo//wAzvkdHewAv3yZ8165rOp0vjeOasbHtc1Fyi9KF4vGYxpybYtEpEuWX9zWJh2vxpMe\n1XDdgAkIBECcyK0MDHANAbhG5HLK/56AFcb7Pjxs4eBBC296k+uvv5cuWXjiCY5fMrODOf2ZZ2zE\n48H3jo/DA0cDmbV8Hti6tf6VV3c11+v8HBgwHqs5+Fk87vp+YMHPDBYXORbi8UDKSgpcvE/GXzOE\nBSbhukBfn4uHHnI8uWBe3+Ki8t6BoDgv0nh+bD8KHPg/K0AvXuAkAaXvNQa+wk0bHE8G2P5iJftq\nJ4B7vg1Yoclm53Nw//FRYPodCOdFUiywLHiFOem0VyhvPYrld3wNxYgHph39VADKbTnd8BxromMB\n+M3/Dbj4K5XfIfdi97P1/273M/y9B5gVCgpTU1L0vP2KuGu/77f69dRnoF1tNJ4r+d0yj2tNBlRf\nn4tIxODJJy3MzmpPpi4An6s98WrD+FKS4rUm72YgRce1jMyh677EhrExttzNHgfr3c9bN27keUvj\nUCTiorWVgBYBd/jee+LlBtRnbofPK5ApBho/4/We/au3f7l1Q/xK2Si6Xrgu976838pn4LguGasi\nf1rvWRWLCsPDtj8/iDQuGy6rGZcGmYzxvEOBb387ioEBg7k5StVSapcKJwRE4fvKKmXQ0aE84JUq\nCeHnms8jJCfNYsTXvx7B+DgbcLZvF+sGsp7f+c7gjxvJwD34YLmiNsIGXAJGSlU2va4FEExNUV2h\nWn2HkuoaX/96xN9zr64S6Lp8WSGX0z4LT7zVCwWDLVuY01NRp5Ztu7REJYrFReN5mQafX1118eCD\nZXR3M3c9csRCKsVmtUKBTVQi2W3bBj09lFifnSUQNjSkNuxbKyFgS1ubwchIFPE4PHBN1As0fvpT\n5akXsIlQKYOtW0V9gtff10cPPgFi77nHQbFIn3hjDE6coKS9UsCmTQ4mJiycPMnGD/qiGezZ46Kj\n48YwnTYiH7hRicEbLYVZHd3dBuPj1SAg/YLrgYD1zids+yEMZ2HqVatGiK9wuIanlMF737vupTSj\nGc24ydEEvppxy8V6i2QiwW6aXI4MJkAYBgoTE0ygZmYU9u93cPCgVdPt3dHB4i6BJiYmksjGYi4i\nEe0VaVnsrN6cUrqKBeC2NnqhAPyuc+c0YjEWA9/6Vgdzc8qXv2CXPLWp83n4kok8f8qP0G8EFT8P\nJzGNul9mZ4EvfjGGuTmN1lYmTQ895GBkROHcOR572zbeu7k5ha1baT4PBFJgxSI7WGxoaVnUAAAg\nAElEQVTb9YGwmRkmrLmc9hO+K1eYeJZKLLC3tBivG5z3KR4HHnsMACL4q7+K4EMfKnl+V4GXCCCg\nV1BInJ9ncmrb1DJncqn8Z0OQiBsWdt2vb64OBKbbYTbYa9Xp+Mu3KW3GRmMj0o/0q7B82RwJy4LP\ndqJUnngRVgJeEhsZh1LYKRSUz1jMZvlPJEKZI2FayeclVlaM5wMDT2qPBaBUiqxH8ewTqb/Av4p/\nk05zA2lMcF1h8KvBGaOjg8XOlRXlyRuKBGHATsjlgB07HJw7Z3kADT/Dbn7jyXkF0nNAMA9xPifT\nIB6nl4RIc7kuQbV8XhoReLJhgO+1DpGN49zKc6a0F2V8RNpXmAwik1MtNyZFuXKZckp79xI0W1kx\naGkJPru6Ch/IyuWAp56iP8qlS+xsnp2NYNcurnfz8wpnzgBDQy727DGYnKRM5eAg/ZlWV8m843xv\nfJ+fTZuMPzbX8w6o143b2QkfiFvLuLynx8XIiMbiokImo3wvqXAELDDl+U34dw22zaLX979vI5VS\neO45FuaD99zAf9+3B5KGKLYAW4eBWIOB0zVR/+d1olhkEagu+8qqQtg9UE19//8NgcwsJsVifC8F\nsFOK8oOlD/860BkC0+79n0CuE5i6H+ioOt560b4AvPmbQJjVtv0o8L//BmA30JazS8D7/hT41hOh\nH95sYKAZr3bImiFr3ZUrNjIZXVEUpeTg2l6nwmIslxViMeUzk8N5pBS0Ke/46l1TM26f0Bp+kb2z\n0/hAhjRB5nJXD7IJWHK1LNVmSFw9u1fyO/Fidl3l7ysbRXivGG5UDCRMyfinNDfVFt78ZheLi8Ab\n38jGWplnxJ/XcTSWlkxIKpnSh7EY97b33utWydfVgjHGGL/QXyyKnDIZQiLd9+CDZc9Pu9YS4tvf\njiIWMz4L3xgq4UxMAHff7VYo0XR0NAYI+N3Mg5mDMydMJhX273cxNqZw/LiNuTkCxWwEhmcnoEL+\nxzxOMmn85tcK/9BQiDRpocA6QEsL/6GXF0HCP/qjKNraqFyQz2tkMnzOIg2dyTBfVSrIf0dGDB55\nJIJPfpIPuh7gJ83HyaTGyAgbnQGgv9/F4iK9JbdvdzE9rT25eI6VXM7GyIjBQw+V8OCDDo4cUejq\nUlhaoucsZdcNAEq7/+hHNjZtYg6cSrExmowig+Vl28/R2SRsUC4bz0NXobeXA/16ZAY3Ih8Y/kyY\nbTU/r/HpTxdrmtXX+q7riQcecHxZSYlikTWsL30pWqNEUX0+YduPwUHUMPWqVSM6Ovg7qeMReHTw\n3HPA+Hjkhkg8NqMZzXh1wvrc5z73uZt9Elcb2ewGHZWbcVvGk0/aWFmp7uRiUfdNb3Lx1FMaP/xh\nxNO/hweCsVuqtVUkDQjwWBaTwi1bKhegS5eo4y3dXpVSLuyYZydSbUedbII2bWLBltIJCtPTGrkc\ni6rCZLIs4ydonZ30NxGplT17SLmfmNBYWuImPCwhBXCB7esD9u0LdlVSyNu3z8Wb3uRidFTh85+P\nYWpKI5tlV+z581y4bZuJ5pYtgeGv41BeJRqlXEA8zvtIg2OFlhbKvAQ/IxCYyzFJy+XoaUbDZz6X\nTCa4xkSCCR916SmJNjOj0dXFpF7k2MRHy3V5P9vbecx8XmN+nslxLqdRKgXgo0gsJRIbl+moltK5\nWkP4taO5aW3Gqxmq7niV+UqkkhIJzoFi/HwtYYzILpHZFUgM8l2rBuqErSQyUSJNRyDb+MXK/v6g\n61Y8TwRcIXOGm1zxKRC/vUomXP1rknlafF3Ee0EYNWIKPz7O75YNszDW5H5Jx69lsXgRi5Hlls9z\n/chmg27cQkF+LkWRWjbxzQ7KucI3PL/jDnYhLy0JYMPnJMyH6ucCcH2IRtnY0dVF5lQk4iIapZyK\nUpR9EpBkclIjmeRasLKisbSkoZTyJGA0UinO47kcmXMiq3n5ssbYGH9PyUUWpdlQQu/Mvj6D3/md\nEtJpjo/+fkr/VG8sL13SdcGvoSFuRBcWlP+u3HefAxZCgDNnKJu2usrfry2NxfFazZZUiuzIqSmN\n06d5/Vy3qsaHeGHtOgx0jwO9F4F4uvHhXAVM3A+kBtY4p+A8XBfA/v+H371e5HuAk/9H1d8rn+kW\nZkSX3/9/AzsOVx3OANE80HsBiOau7RWIJwE7DyTv4H3pWee8Y0ngyP91DQf6ZYlbZx56NSKT0R77\n/+qvk00ALtrbmc/WY1uH5Uh9BuUv+D1txtohDXuSTzgO5eHDMszXEjd2L/KLEK/d/SDb83pZq/zb\n1lbu7eNxg95e5l1btnA/f+oUi+yxGBUURKqSDWainBCW7uc+enDQYNcuF319Lj76UcdvUJ2aUnjy\nSRtPP21hfDzwD5P5sLeXTWinT2v80z9FMD2tPfa6wvw8gaaJCYWXXyZTa2GBOdP0tPb3/93dwLFj\nGs8+a+PllzXOn2eNYnmZ+drKCpvOTpywMDam0ddn0NbG/CuTIcsmn9cYH6fqzOysheVl5kPSeCbK\nL4FKAnM++u8C2Wz9vU+958gaC/OWqSlKb589G1xPPg+/1hONAt3dLrJZ5pvyM4D1kTNnNH7wAwuP\nPWajUGCzWyKhcPq0RjxuMDzM+/7887wnbKJTXkM2Zcfn55WvjJHNKr9mVC6zhvJv/20JMzOs18zN\n8blkMqKYwyYwY7gGZTIEU0slAoeSR4syBpvZeByqE/E8ZmYIni0tMV8Oe1FtJI4csep6/ykV1KPk\nM6mUwrFjGtPTvCfz86zzvP71PO5Gvut6orMTGB0lg1Ap+HUveL66ExMaTzwR3ItTpyrP5+JF1u6U\ngufty78tleDXziYnWUeTiMcNfuu3Snjf+xx0dxs89piNVMrG1JSLY8esiuNd7b1vRjOacX3R1hZr\n+LtrYny5rovPfe5zOH/+PKLRKL7whS9g586d/u+ffvppfO1rX4Nt2/joRz+KX//1X2/4N2NjY/iD\nP/gDKKWwZ88efPazn4XW69PXm/GLG+tpKB8/bvkML6DSJyWXo6wWpYoU0mluWs+c0X731OCg8RIi\nFjfDnXqy4WWhs9YQmZIr7MyyLPqRSMcfP2u8LiTl+WpRxmtoyHgJmPK6oUhrl++3LNcHyGL++8oO\nrbB/V70OpG9/O+p3+q+sBDJhx49biMeB1lYXHR383nic/7S0MLEulQxOnKCMIe8pu9NmZphQp9NM\nvETGTBJ2ud8Bc4PJwtgYO8YF0OLzoQfD5KTC3r0G6bSLuTkmb+3tLrZscaGU9gqq1FSnfE1lcVG+\nL5BYa0Yzri024ttxO4QxnKduzPsQvNdS2N/oPWLXKYE4yzIe6MLNHqXepNvW+McRiRelCISUy6bi\nuWhNoCnstVQdAnCRlVI9l/Pf9LQQyRTlS7nJccJdvKUSZXIp/UdgiB4h8Hy7bj2Qq14Ia4rG9caT\n0jGIRCgjyI5b3gD6glUCmQG4wzVieprNFu3tGv39BgMDDiYnybCzbeX5bCqPPcdzKBQIfjoOP2NZ\nInGo0N/PtVEpjUuXlFcQCZ5ZOh0wDXM54P3vd9aVOwEaSwHv3u1UsMjHxzW+8x22+U5MaKysaGjN\n46413rxvrPtT1wXOnbMQjRqk07VMcT/qsbHWik1XCAit4/UlYYyih9dGYnWwwS8CBqPfKd+5DiB1\nPa9E18TG78svwLxdGU1gZaMhxdJrDaXo4cVmq7XZNsLoqGaaNuOXK4QxLflCNqtr9ibN+OUNehC6\niMVYD5iaAi5etPB7v5dDLmdXyd2zUWh1tTbPEMlVqtEYjI4CDz7ohiQOK9VwRJJ5cpJ5q2276Otj\njnrqFMEUx6GkXzIptgEKR49a6O7m70WqrasrUFCZmzNYWtKeXLjy5MiBuTkW+9vaDKJR/p2AB5OT\nBAosCx5rn6BPJgMsLNC3Spr1qq8ZCN6t6Wm5L1dXA6SMJI974YKFyUl4fms8r64uMqkAhc5OF297\nm4tTpzRGR7m/iMe5Z5meJkiVy2nE4wqHDmm85z1lbN8OTE9r/Kf/FMPmzQqLiwTEwo0T9PiTBmru\ny6SUGVYGaGsDjh2jh6V49CUSst8IlCck9xYAh9epqvYtwRgSv/NUivvBiQk2fD//vIVkcuOyghLr\n+cmHPzMyQgAvkCdWGB628PjjBr/92+UNfdf1xq5dxmtSMV69r1KKHYB/L6hSEZyPjON43Ph+vMHP\nTV3ViHoMstVVVMgtXuu9b0YzmvHqxTUBXz/5yU9QLBbxD//wDzh58iT+7M/+DH/zN38DACiVSvjy\nl7+M73znO2hpacFv/MZv4MEHH8SJEyfq/s2Xv/xlfPrTn8b+/fvxJ3/yJ3jqqafwwQ9+8IZeZDNu\njxBg56WXyDASDw6h4g8OGhhjQSmNLVsMkkku8JGI+IIE3ZrpNMGpSIQSemHZxJERhVyOJrf1gpR4\noLoYIQms47CTK5kUI2Pjd+dQO9yFZXERBHj8vj7KZNx5p4vOTnga11xE02mar1Knm91WIkvQ1RX4\ndz3yCD2yBMAbHlb4yEfKePllhcVFGueK9Iv4FUjyNT/PxFdAtXjcxUMPFfGVr8QwN2ehXGbhkRJn\nxk9Sk0mRmVJeQq7876/2piEAGeh1h/HrbJYdOYDB3r2kj+fzwPvfX0YyqZBMAiMj7M4RWYjG0SxC\nNOP64helaEGfgxv3PlzPfeHGjfNbKkVgQxhYjY5jTKVknDHwjN4FhGt8bfx9cP22vTEAMFzErJWy\nC66F/698OcnbqfNfWFzFIjfWuRwBSNtWvn+V+JV1dbnIZNgEwsIEN7AiBdzSopDPs4vxrrsA2QgO\nDtIjcnYWvo8VfTCM55VGWV87lGW2tXFty2Y1enpcLCwIoFivKBKMjQsXarX6GzWC1JMCrpZkOXZM\nY26O0p2lkvIbYK5n/EsHbiKxTlG0awNMrOrYgNdXRRz9FCUEw0CSY1XKHSYG+LmGUTXWNwqmXUus\nDm78vsy85dU7j5sSt8ec8osQwZxEdkEms96c3gQ4ftmDMmgcBLEYGwCvh+XVjF+skD336CgbabgX\nVvjLv2zFhz9cwK5dDs6c0bAsMlPEM7xe850xZI4tLWls3gyMjgJAsGaH8xhjjCetx30+QPlExzHo\n6VFewxFBrXyee2yAed/yMvM7aQabn5d6CdlG2az2WWgSlIdk4+uFCwZ79wY+6+JlPjkZMLmkXuK6\nym9iavTOBMe5vrXQdRUcJ5D8EwnDpSX4HpC2DU9m23h+WwQbR0e15zvONSEe5/mcPq3R0WFw9Chl\nvNvagu+3beOp3/DYYX9xNuKxCdu2mfd2dlL2/Qc/sGHbrMsI2w2o9HsmCGaqgNO1wxjj1aQklze+\nFPvVygqu5Sdf/Zm5OSo1eGfhMZwIfgHlDX3X9Ub4GAJkra7S2kRC7gXva9DQwnHMmuPkpPHrXCJN\nChBYa3T/pFl/bKxyjF/rvW9GM5rx6oUy5urTty9/+cvYu3cvPvShDwEA3v3ud+OnP/0pAODcuXP4\nyle+gm984xsAgC996Ut461vfipMnT9b9m3e/+904dOgQlFL4yU9+gsOHD+Ozn/3smsdfWEhd7Sk3\noyr27bvnZp9CRRQKgccLUJlwycJPqjj/OzxqKT1R+3lhZ0WjJkSrF2mu6ztf8QGo9/aIhF/ARKj0\nw4lEjJ/shNkGEi0txj9GLMakKZWCJ2VWKd0n/lXVSWp12HZwXkpRQrFYxA0p+NULuWZJ7rUOzsEY\nJhQtLfxZJsPnks/XGtk2oxnNaEYzri+kKUR8ASrZXUFXe6N1RAoIkQjXU4DSKyIPXL1+BN4V4UJA\nsAaJvK106q61/sjftLcbfxNbLocbVIIQUE+6p8Prp1wXpXMr78FrWsRsXQQidU5+vSjHgMy2jX/e\nKgCxFKDKgLGBUhyI5IP/L3QATmM5iJqIpIHWpas/741EvguwC5Q8XCuMBjJ9V3fezWhGM5rRjGa8\nxmFZ3M+LHHilpUJtBL5NwX7ZtoPmoWJRfLJNjVRruO4hLH6pPwSS5LUsq+rjN/pd9eeiUfheuvSD\nRd16xq0Ucn8ln5X7Uu95hPPWaDRQlwgDluF7G75n4fsodagwazT8N/XkdsPfczW5abjOJKoblFlk\nzSkMAm0kpCmtOp+u/szSUuDHJmNOzqG312z4u6435BgEnGr3MuF70dJS6RMv4zhcl5PPAmzaa3S+\nUsOiUlH9413tvW9GM8Jx/Pjpm30Kt1Vs2dL4hbumaSedTqNdDHkAWJaFcrkM27aRTqfREXrD29ra\nkE6nG/6NMZTjkc+mUuuDWj09rbDtpuvw9QRlpG6NcByRGKr8eRjckQVcQJ5wsiAFvUqZrEA2QMZX\noVD5vdcaG0kOKZVVmQTIzyMRdiJl63jZWxZg26rG6yvsWSNBdgSLfRtJnqJRMrHicXYTZTKVoNyN\nKv5VJz9A4IsWJPXK19VubQWSyVs/aW5GM5rRjNsxwsWOeoWPcEGkXsgGnpvbgPUb7uytZgCHCwyy\n2ZV1IQy0bTRKJeWvH2KOHj5/ekLCl9o1hmtsRwdzCJGsFP+q6pzhNYtCB2DlAX2V3a7mKtN1JwYU\nEIBfkfzVg13hiF4DWLfRiK+u/xnXBrKbm6BXM647ZC/RjGY0oxmvVtA3Vvn5jnjDrhfh3KZUUn6j\nEBCwtOrNX5Ificy41A3CeVogZ14bV5sLGUMAw7KYe9VrgrrVQnI+yROz2cbsO8kRw6CXfIfcU2nI\nrgeyyPcICCVNxgIU3sh7Vc0MqwRQaV1xtS4y0Wjgf7bWZ9ra6tezYrGg1riR77rekGO0trLZLZDY\nZ4TvRfX5OA4BLMcJGsQti4pSrJk1Pq4cT/ZI9Y7XdPBpxvXEWkBOM64urgn4am9vRybUbuu6LmwP\nCq/+XSaTQUdHR8O/Cft5ZTIZdG7ABXBlpc4M24yrip///NTNPgU/Hn3Uxne/a/v0YonVVYVt21zc\nd1+QUczMKBw+rLFpE6UGEwnS7wcHXR9AcRzqOH/sY2Vs2kQvLQA4eFBjdtbytZg3EtI9ZQw7r6JR\nFtJyOYNkUldQ2xkGW7bQU6urS7Swmax2dhLk3bTJxSc+QZnByms2uPdeFzt30jQzHL/zOzG89JJG\noRC8L7LQDg66SCapT10sBr+XImNbm8HWrS4+/elSBd36d383hpdesny/rXAipjVp4JKohZM+vrIu\nXFdXAI0EKA1+9VeLaG2N4/TpEubnFVpbDbZupZ7zwABp8D09vMapKYXHH7fwne/YOH1aw3FuRHZw\n+0iSNeOXJW7XMXnrnrdIpzTqnr32oK777RdyzmrNZgZjjMeEViiX15aHlIJNNOrirrvoX3ngAHd2\nx49rHD1KiRNpSAGU193o4t57Hdx1l4uJCY3ubsr6ikzvBz9YxKOPxpBKaZw719gPKxYDWlsNensN\n7rrLwYc+5CCRUDh5UqOnx3iymgqnTmnMzXHt7++nPMy997ro6DAYGnKxe7eDz38+BmMoCbywQKnd\n9naD5eXAm+BVj+1HKVnYfxTovQRYG6y+Jwbo8ZUNSR3Kd3WNU4bw6KcqPcC2H6U3WLfk3QUg0QP8\n47c35BVWcYwtp4GtE8DN6jfLtQP/34+B1AbP+5c6btf567UJpag4IB3azWjG2tE4B2oCqDc6fnHm\nLs4zLj7+cQc7dri4774yHn44jnRaI5kMPE1ZFKctQ2cn99BbtlASLx4PfIZ6euiTdOGCgjHaK9Jz\nXAoTv7fXYHDQ8f3EczmD6WkNrRV27XKxuqpw7pxGNttoPFOJJZttLPEaiRhs3uzijW/k8f7wD4u4\n4w6DRx+18eMfW/jRj2w4jgop39BXvVx2sbyskcspzztMjvnavEOWxXyyr8/F7t2sK125ovDyyxZS\nKTYCZ7PBeUUi9DMbGnI9SUkDywpyxXKZefG/+TdlAAaPPx5BLkc7B4mhIRdve1sZXV3AyZMWLlxQ\n6OkBtm+ndOLMjPYkx6vPNmAlFYt8zmspDGlNicPubsBxOJba2w3273e9fNi8qj5TtOKIVFhxDA66\n+OQnSzfN22pqSuHgQQtPP02ve6k91bsXV/PZtY733HPteOyx4jV/RzOa0SiaSndXFzec8XXffffh\nmWeewYEDB3Dy5EncReMHAMDrXvc6jI2NIZFIoLW1FceOHcMnPvEJKKXq/s3dd9+No0ePYv/+/Th0\n6BDuv//+azmlZtzGkUgEWtHhEEm8ZDLw+VpcVOjvd9HTA3R3sxvDtplMSaIIGLS0GHz4wyzQBdrC\n1ybrJybXxiiUStQCTqWCzo4wjb211WDPHoM3vclgYoJ63oUCtZxXV2k2urQEfPWrUQAGmQzQ3l4J\nCtUz/HzLWxwcP86qU0DnZtKzvKyxdavx7o/xfWkAJsOtrZRbEK8w8T9hN5qLUsnyu5cCQ+/qLv/g\nWuNxJlbijyCgmG0b7N7toFDQ2LuXiddTT2k/ATdGYWWF4N7QkMHx4wp//udRXL6skU7fyHaYZjGj\nGbda3F5jMmDjXPt5v9psGts2DQGTX8bQOpiPgbXkB+k9QLBq/fvnuuyMbGkBPvCBEnbt4poKGESj\n9OsKzoFeEbt2ufgv/4UFkXpeXEeOWBgaMjh8GFirqGjbBrGYQTzuIpcLmlgyGfpJ3Huvi8lJBfG0\nFCkSY4CJCYW77ybgdumShXvu4WcLBYWFBe3lDexIvRrm2TWHD0RNrv9ZAHAUsHQnMPOONUCt0Hft\n/hEws4/MrNUdQMtS7bGuxivsas/31YyJBzYO1jWjGetEo8JvM5pRG7VjJfD8rP/7ZjTDGIOODoNT\npzQ6Ohx88YsxLC+zWbS93XgMMAWlXGzbZvCe95Rx5Qr34lIPuHDBQqHAJmAZZ9GowsoK9/aFgqpQ\nw8nleOzBQWBiApiZ0YhEFPbvd9Dfb3DmjMb0NPz9eDiE3SSSb/VyokiENY73v9/B9u0EdiTHu3wZ\nOH+e3q2rq9pnncVibJiKxxV27wYKBRfnzyu4rvbu06v5FIIQVYPZWYVsVmFmRiMaFe8yAFCIxVhX\nMcagpcXFr/0ar/PIEYWZGeav4i0fjxs89FAZ//E/skn5wQed/5+9dw2O4zzPRJ+ve64YDGYGIO4A\nAZAEeBPvFCmRSiRRiuQV5RPJyrFlKbHlSiQ7dnzis7V2ks16400q2a3EldqU95xKzo9UspWoXCWn\nknIkZZVay5dd0ZZD0pIoSjJJgRcAJIjrYAaXuXV/58fTb3fPYAYESEqinHmrVDbJmZ7ur7/Le3me\n58Uf/3HI6R1P0PbQkI0jRyy8/HIA/f0ai4vA7KyB8+cVbrvNwuXLqsz/9MdMDQ1kC4VC7CNPsDZ7\nuVXKlQcCQHMzC3UHD9qIxbTj6/IdSf/b98q6uzU+97li1Z671axWf97V2mq+392t8cwzJRw9aq34\n2bExhWefDeD0aRNLS+x9J3mqpqbV9+fq7tb4zd8E7r67cEPPVre61e29tesqfP3CL/wCXnnlFTz+\n+OPQWuOP/uiP8I//+I9YXFzEJz7xCfz2b/82fvVXfxVaazz22GNob2+v+h0A+K3f+i189atfxZ/+\n6Z9iw4YNePDBB2/qA9bt1jdx8mZny5F1HR0WUikbp06Z7t9nMgqBgEJPj42mJo233mIxJR7ndajt\nq9HcDLz4YgDJpMaRIyUMD5vYsEFhetpwaem1kl2e8+Hp+7IfCZOFmQxcp01M+ojl8xpjY7zPgQEb\nAJlhgHIKVrbbCDYS0Whs5P360SHVGn4ePWrhO9+xMDxMdBIR+0xisgAF93DNZGyEw8opUilEozb+\n3b+jzuOzzwZch3fdOjreiYSNTIbOWjhMhlg2KxR95eogm6ZGQwOb5cbjGpcuefKELS0a7e1s3trb\ny78bGVG+xrrabRY7Oqrw6KMWvvGNEEZGTORybH77viQf61a3utW0QEC7vZekJ0F1k6bLtT/zXga0\n0oB6LY2fr9fInLWdItGtm+QKBJSbAJFeXf53IOAM2WcF7FDrPXlyH9zbd+yw8MQTlovu1Vpjbk7j\nrbc8kEQ4zGTPPfd4CMfu7uVNoS9cUHjzTRPz88oBTZRL8ti2RjBItGpvL6/jw1eht1cjnTZcQAzv\nV5pq0+TvhW3W1MSE0OioQjxOMMbSEuf8agqAN2wH/9vqikg2gGwH8PIfAK//qo/Z9RWP2VXtWvFJ\nIP4/vD8Xa+jKJEauzRZby/2+Hxa6Ru+vutVtlXarS3HV7YO15Wzp6oyvaFTDMJQroS+SaHW7lUzD\ne383/4yXYlE1xlIkotHdDeRyGidOUFmFCgXMBTQ2slChNXDggI0779R48sk8Xn7Zi9EJ/FVlfk0k\nohGPK0QiQKGgHeYYY/d43MbQEOPzbds0cjkD6TTw5psmrlzR0JpMsoYGr2e4SPHJs0grBhaAxLfT\njqSdxl13ldDV5eUqyPYJ4I03TESjlGZUir/T1GRjcZGFpqtXlXOf5X77e9FfvNY1czmRnDSQzdL3\ny+Xo/3nqBvz+unXA+LiJdFo7YC6y4VIphUjERm+vxs6d3g91dABDQxrRKNl8kQjn3PHjpvs+Jc+l\nNVlvfX0aly97sofivxeLnjSiqDPYNhWDpGfc0hLzP5GIxubNlqsuNDtL1YNt28gSXE3RBrjxYlQ1\nP7/W7/jzULOzChcuqDUxq9by/Wvd17FjfD/My4lqFBmVd9xhI51e276x2nGoW93q9sHYdRW+DMPA\n7//+75f93caNG93/f+TIERw5cuSa3wGAgYEB/M3f/M313Ebdfkbs0CELFy546G0pCn3xiwWcOGFi\ndtZD4UQiZDj96Ec8lMSRSCaBbdtsR/ZIobmZsgCzswpvvKHQ26uxbZuNmZkSzpwxoRQPOcpklR9s\n7EWiEQholEoK4bBGc7NGoaAwN8e5LAk6aXpKBKlGNEpHM5fjfe7bV0I6TUe3qUkjkaAzChDNs2OH\njZER3ueuXVZNZ6O7W+MrXyngt34rDKVMx2Hj/UciRBKRVq+hlI2lJQNTU3TeHovFgHYAACAASURB\nVH+8gH37JFnpPWs8Tsd8fh7YuFFjfl6jsVEjmdRIpWwMDxs4fVrYYHSG29s1tm618eabJlpbNYJB\nSsYEgywwMhHO38jlFEIhyhwuLpIxEIlobN9u48QJE+fPm5icJALf34T3w25r7V1Tt7qt1ljMBopF\nFtotq3pS5nrlQ5qaKAfCZr1eccmyytdmMKictc89srYMB69RiVC8XhNN/UiEMiPZrEI0imVs4Ztp\nWmuEwx7wQZ53hW/ggyiQWRZBDrbtSbHIf/7eXrLPWpbXX7XSKJViO/I7Gk8+WXQZ1M89F8CLL5pu\nf4dNmyxMTCiUSjy3777bQipV+/nHxhSOHzcwNqYwP+/NLemDYJpkkvX1aTz4oIX+fu1jbdPicSIy\np6eBhgbbOf+0wwjzkkaSnDl2zMSlSwZOnTIckAfPI9MkGjmffx/eV+LS6j5nAEiMA/f+J/753v9U\nXoDq+z6w2HLt6wRr6DkXIsuZXH3fp5Siv/i12vt9P2yu94O+g7rdAiZFcu5d3Gffl6J13f7VmN9X\nqSUZrDXPDDlTBYhTt1vFNAxDY/16C6apMDZmur5QNf9s7bGndthM9D1lzgDMG0QiBIN2dtqYnQXG\nxw3Mz1O2rljk70txIxYDWlrIZr9wQblA3QsX2CagVILrs0QiQCJBnyebJauIiXrG/5s322hs9J5N\na42JCcMBRbGIls+z4NPSYjuAUwEO2SgUGI8Hg/SPCBJmPuDTny6iqYkMeX9h5LnnAhgZod8VDgOt\nrRzIxUUCXwGq+uTzLPawt7i+qRKHoRDfYaHAMZUWFPJOpT+T+LwC5gKAixcVUimNYpG5HPZlYjGM\n4CiF+Xm+15ERw8ll8N8eecQL9I8dMxGPMw/ltzfeMN0C15tvGpieJquooQFobrYRiRiIRuXTysnD\nkBnmN1HVYdGTykEAsGGDjfZ25rsAD/AFVFcPqmY3Woxai0mhyW9ar55ZdaPfrzQpbGmtMTnpxQ+Z\njIFTp4De3noRq251+1my6yp81a1utWy1qJHKz4mz19dX/r2TJxW2baNzd+qUgVhMYX5euYfShg1E\nFgEivwTcdpvtIqT4PRMjI3RIdu4EACbqFhfpjKXTPDj9ElHS9NIwyMgKBplsy2ToQDY1MTmYyQAM\nvm0Eg8pJprHwFYspnD9vYGDARjptYMcOG2fPegc2kVurR+bs28dE4OnTcGQNpc+NdllkIyPAoUMa\nzzyTX/b9asiVZJJOur+PGsAk4oMPFvAf/2PElZIMhfj8V68qtLbaDhXc+05/PwBonD/vJR6JKmOB\nzBldABovv2y6jqZlec1j/RYIeP17lFouzXCrGmUi6oFw3W6+RaNEXobDZKtI4Fi5dtYeULK4UywS\nidrczH2sUJAg0usB6KFcyRadmfHk4irvgyhEBnJLS7zmwoJexphdrcXjGqYpvQw0pqYUlpYUJieB\n+XntSoMEAkxIiLyJN0ba1xvMW5/+IFyagrsj46AzAX0NFpzYB7fueY7JOGgHMarKxkAKTLX62xgG\n0N5uobeX59IXv0jghD84pmytwsQEEzEbN3JfT6U0Ojv1igH3s8+aGB42neKqdw+2TQS9UsIC59+T\nAW2654pYPK6xcydlXOS+GhsFPMPvHT1KP+LQIQsvvGA6z6+d3+W8ZLD7Ptjc+rV9PjkK3P2H1eUK\n7VW67qUQEPAVwNI9AIzaEoivwmOCNY2s7X6vZZYCcgmgFAXyjWSehVfB5Mo3kJFWt59pE8R9JWtG\nig9MlmqnL45CczMwM7PaPbludbu2mSaLC/k855VhEOSTq7JN+eMRFg/qPv+tYoYBtLUplEoGAgGq\nE4TDlINbWCCwS96XVxTx/m5l04jFbChlIJlk/C09sQIBMrGkN1QyqfHjHwdgWfSjFxfpSzO2pa+2\ne7eFeJwbmNaUZSYYmPJ409PKAceyuLVzJ5lb7E3K/XHnTgsPP0yAT6WfxOtqTE4q53k1Nm3SmJsD\nBgcBrW1cuGBAawNzc14rBWGGdXfb2LfPxpe/XD0/Ib1bK21xkT5mNMp/CwS4rgRsxXVGv+96YntR\nppAidTBIsJ7WyumZptz8QSCg3eIk+6FRpQdgX6xgkC00mLOgtGSpRIaX1uwjOzlpYGGBrLWlJY7N\nP/xDAB0d/JHvfc/E9DSLUqLgw7EHLl8GfvCDAEolFv5sm0o9yaSN1lYbpRLn365dGv39Fq5cMfHi\ni/wtUXDQmoo68TjQ2cnfDwYJRvYrJkk+rJZ6UDW72cWklawWg2q1zKob/X6lJZNe0dC/9lkAfX9U\nRepWt7q9f1YvfNXtptlqUSNrQZfYNnWpz59n8rGpiQwiScaeOkXmlzgZJ08qtLRIEEJZI8ALXOJx\njcOHgWTSwssvm/jRjwIIBOi0+gPuYlE5vVDozObzlDGMRpk0a2lhwS2bNZy+IgqhED/T0EAUlzTQ\nvvNOC6OjdFgiESZklYIr4QSsHpnT36+hNbOYmQxw/LiBiQnDuS7/GxlRGBtTy8ay/ICn9fayF5nf\nslmN2Vngr/86jEBAYWCASU06xSx83X8/Haq33vIYekrxHUoBsrdX4+pVhcuXmYymTCWvHYnI+HjO\nqd/8iWjT1K72+PXZexuQihwmCwLa7ZNWD4LrtlYjW7D63AmHNVIpgKhSr+i+WlsuL6KdPkoMBAcG\nGPQFgyxU796tcfGiwvS0wvi4glLU7U8khPnEPTOVspHJGO5+KUlIKZCZJgPeVIprfmFBIZ2uvvZX\nQt4GgyyobNxouXIu2SyTAm1tCjMz3HvDYZ4Vw8Ms1DMQhoPE1ejqAqanFbJZ7RZepDm0FIoqjX0O\nmaioNeYfNGPVNIkiZXLFhmEA8bjI4SindwMTH7EY2b3z8wZCIe2+y0BAY88eC3fdZaO/vxy4cuyY\niUzGwMiIQjqtMDnJBM7oKNDQwL29vd3GW28pKKXw3HOBqnr6zz/P5ueNjcDcnPYVv3iOxGIaLS08\nPyT4Fla4PziXwL67m82jBUiza5eNDRsoCyxyx4cOWdi+3cbp03yPExM2olHK7gpCuJbdtPf66m+Q\nWbUW+cDoTPW/zyVZxLrWtc7fAyy2ssg018t7eOAr1T/beno5E8wyAdOXPMlHgenNQCQNNF9Y/XMA\nwNVdwP930vtz16vAJ38RiE/U/k4hBLz4Z/X+Xv8KLBjkGZHNkk1jWdyPkknuaTybyPDSGpiaUg4r\nuZrJ39d9sLqtzkxTuwlyYRYmEuLnKMcH0C7op7rV/f5bwQh8YksCy2LPo4UFhUxGlFrod9BfJZjM\nMLBiz9hQSCMWo3+eSil0dVmYnWXfcUC5DDBhAs7NASMjpjN3PIYXCzX0Vfv6tNuXVCydVnjhBROn\nT7MNQD6vXWWa6WnmNhIJjV//9eKyGL/ST1JKoa2NijC5HH00Ks8o7N9vI5cD3n5bIRajX59IaFy8\naMAw6DMPDLAY9PjjBM9UApY3bLBw5oyBK1cUikXmZ8Jh3ktDAwtf0p5CcjQsTmm3lYJp8reqFZfF\nKhVhRMFAlHdMU7tFLa35nrQmi6tQIBPNNLVbSAoGvWv39rKf17ZtvPjJkybCYY39+5lXeust9oIl\nmBmQs2VpSWFkxMDzz5sYHTVw5oyJuTnlMMM01q2zoRTVh06eJJOIsqjcS0xT4eJF5quammzccYeN\nXbsEyKVw990l/K//ZWJhgePT1UXVoYYGjU2bLGzaRAZZby9zS6KYtH37ct/9WraaYtKNSiGKVctD\nyd+/H9+vNFkzSrGIKMXf3l4bQ0O1VTHqVre6fTjN/NrXvva1D/om1mqLizUkXOr2gdpLLwWW0bNJ\n3eZhvNrPjY0pvPQS5ZReecXEzIyBTMZAsUiauTTwLBSYsO3v9w682Vk6WkK3HxkxUCrRGUmn6ajM\nztJBu3jRwNSU6TaHFcYC5bQ0AgEPDUYqOpFElgV0dlIikHrWcHSX6QBrTXmo1lZqQP/O7xSxebON\n+XkWgLJZYHBQuygvpTQeesgqY0/VsmRS4803PVmBmRlKCrS1EW0/OKjR1LR8zCu/K0antgiRNAuH\ntZvEfu01A7kcUU6NjXDQRkTdplLA//7fAVy9yp5jpRLfzX33Wdixw0apFAJQxMwMKftKGU4gqTAx\nYUApjZkZA7mcX6pG2Bh07gMBBhJKaTeZfT1mmisnrK9tK39XKX/xS5B/xk2TcajbB2Pvt78rPfSC\nQQnI/Ul+2y2or1tHJOLEBPcbowphxTSZNHS+DUAkXOW5uL4EZd/YCDQ3c42nUmTSzM4qbN1KRujs\nrHIYXixaM0lARtfgoI3ZWQOWpcrmvDSAD4VstLRQmo4MGz4bUZflrB8/ul+eLRQSMAGLMv/23xZw\n+TKfXaRV1q+38eu/XkQiwYA0HCayV9hfwmiLxTypRIITPF3/xkb+bqlkVJUq9ebDrRkIhcO2e14F\nAkw8bN1qY8sWMpzZp9Hr5xAOUy4yEuH4HTpk48tfzmPLFoBFKCYF5Fx68UUTx4+brqwkG4UbiEaJ\nXI1ENEZHDWzaRCZVOq3w5psG+vu9a7z0UgA//rGBQsFw93evtxcD+A0bbOzdy7n77rsGzp1TDivZ\ngpxTnZ08M6WxemUy5uWX6WOQBW3g7//edBDPwKZNGgMD/B5lgrTzvivfq3YLhaZZPreFNcix5tkv\nhV757rLrZXuAS4eA+AgLUcYq0LjZTiCaXv73w/dBfefrMMM56MUUMNcOxKbKi1TpHuCF/xf4ly8C\nrz0FvPMx3sPAy0DHqeXXLEaAVIW8oaGBmX7g6k7g4l3AC/8P8L0/AHKNwIb/CQTWgEYZvo/34D5b\nD3Dx54BADsilgInNwEIrYBaAfAy4eBj4h78Gzn109b9xi5ph2A44iD5WKARfwXWtbJFrffbW3J9W\nssZG2+mJwvUYjZJ53NLCs2ZhwWMUGwaTyFyP5c8q4KNYjMnpQqH65+pWt0qLRCj1nEgwQZ1K0UdY\nWPCAGV7CXLk9eYDV+QaUQ1z5M34zDHvVn/3ZsRt/XgK5lFtwohQ/CxfFonJjdoB7STBIn1opVGUu\n0eibh0Ia69ZxTzl8mGCpyUnltj0gQxCueoLsafk8/VwpqEajbF/Q1KRdfyaTAd59l2Cc73zHxNWr\nBiYnFS5fpj8aCsEB4jJm37x5eaK/qYn5EOmdWiqxjxTvhfM5ECBorKeH/83MKMfPgbvv8vc0Dh+2\n8fGPF3D1qokXXzTxzW8GkMsxtr10SeFb3woikaCvNzNjuAoMhQKwfj3bPExNGSgUvLGJRPj7UoQS\nwJPEPNXiLiluERCrHDaO/53zrGhu1mhrI3Cvo8OGbYufK0AqAYixOKYUlYA+/WkvB1IqscgkPuvI\niIGZGeYfyouUCvG4xvi4wqVLbAmRzXIOSQuHaJTv48wZA4bBIhyZZ56CRiTCObm4yPjrIx+hvHco\nJGAPYP16vqstW2y3lcIdd9h45JGiK/PY36/x1FMlfOQjBHmtJp8kNjxsVC1+RSIaw8PGsndfzbdf\nrVXLQ11vDux6vl9psmbefttEPq/Q3q6xdy+Lh+Ew12ZlLq2WxWLhen66bnW7BSwWC9f8tzrjq243\nzVZLQV7pc342GNlEjCzYx0VQeCzA5HIGIpHyA6mnR+PcufLCztwcnRZBFC0tscfIlStEw2utyhKC\ngYAkSDXm5+kIRiJ0IuNxjWeeyeG110L4l39R6OjQTuILuHiRCQnbJlJQKQ8p5W94eSPImUp0e0uL\nwtCQ7RbRVhrjyu/6f3vfPt7bc88FMDcHZ8zJAAGIXmtt9a7z6quGGySUSgq5nMaWLXCp8bt3A5OT\nRbz2WhidnWSMiQUCCuPjBjo6OL7i8AqSVxw5CR5CIY7n4qKugbRkMU5rzpVyxokgdrzi2lqDKylm\n1TJhivCzXkEhFNJOYW9NP1e3W8CECWVZZAPVRoJqBx2pnTlcfX4ZBoM2KfL40YtsBM3G1KUSJepM\nk/tdqcT7aGykc06Wl3Z7+0kRQnpflUqcd2SG8c/ptHZl5ERepKFBPus1VZY1zqbUcJGDfX0M4M6c\ngYO0Y2JIKRajduzg5H/nHYIKBCEngWkkYqBY1OjpKQHQOHMm4Aa8ZCHBQUAy0CDoAL49jYnhzk4b\nsRgwPGy60rjV97ESnnsugKWloIPUpB7+wgIlSuJxhQMHLLzzjoGrVylJGw7bSCaB6WkW5tjboNr7\nvvbeIfuOXz735pjso9Ij0vuXYFC7Ek35PAuCLS0aQ0MaIyMKfX2UwTUMsids20A+r7FhA2UN//AP\neU6txMSemDDKnieXI1K4uRl44AEyvSIR5TbXBpbLpaTTPDOzWW+dKMX52NlpI5Xi/JmfZ78urYFU\nSrv9L/ys8LExhb/4iwC++92AW6SbndV44QXTlZphz0/DSTBwbGZnOWfXraPsJs9stUxiDfCaiweD\nXqKmoUE7TdwpBSq9ziQxz7UGlxVZNgcuHwSe/R9kOx38b2RZRdJAKQgkRoHwkvfZdA/w/d9d3uNr\nrgeh134D9pUDML/932G3/Rj6l/5PIOiTN85Hge/+nsuUKutVU415lu6hDGE1y6wH/up73p+7XuU9\n+e+1GAYu7wF0iD3EOl8rZ3Kle6rLFV4+APz9f/dJGV9/4vODZlwuN+3IWmk0NhKpXSxqWBbPE9nb\n/XKsqzEWzby9/2bf8/udbFeKLIlkksCIxUXKeMdiZKzOzvKZKYtVXVKXBX/+ZTTKOICJbl5PxrjS\nF5M5IwUMYRAA7+dcuj52mjArbHu5PGTd1m65nCefJlL3pRIZKcJcl/khc6N83S4vwgICpuT53NDA\nsy+fL2cvV1ogQLnphQXGqCsxkeoGVwJV3pUUxgV8ZZr0iQoF2d+4H2sNp3c3C1u1+k4Fg2S1R6ME\nUT74YAE7dwKjo6YLkvUXRSRP0d2t8frrplPsEIAMwWWpFCdRJKLdVg6iKjA9TcCuFHpKJfrag4Nk\nJg0Pm27MXmmVuYZnnw0gEjHKYgVRm0kmWcib8RHL43H63kNDNr70pUJZPmZ2lj3MyS6CI3lNgNX0\ntHJ8Io1wWOP0aRNtbSzsWRaZaz09GsGgjURCY3ycBTqtlQOuo38o743MSulrzPXAnrDaaZ+g3PfV\n2Kjxb/6NhdFRATbTb9y/38apUwZSKbgtM86eZTzT0ADs3u3JYct4VioSRSLaBQf6QXqiIDE1xfxR\nOAy0tWmMjrKgWihwnOJxAroWF1mYI4tP5i3/V3oETkwYZe8wnVaOhKFnnZ3lrTFqzYNqViv3VE1R\nIZul/5xOL3/3bPth4L/+1xCGhuw15bFWykOtxm70+7Wu6Z/rYmuRi6xb3er24bA646tuN81qoUYq\nERMrfY6MLB7+wtYyTTpoDQ1wdY07OymZ19PDZGk2q/Duu5T9S6Wor80A2Mbly+UHeqHApN38vAHT\npFSfbWuHJcHEc0sLETetrXQ4OzuZmDt6tITPfMbGAw9YiMXINmhvp5MWiTCwbmmxsWePjS98gb1R\nKk2Syvv22WtG5lR+f3GRvcquNea1fjubJbvu2DETw8OGI1PI60WjZMVJoBCPs6H43r0WpqcNl4EV\nCtGhCwTo+O3bZ7vIlxdeCCCbLb+/YJB91dra4F67UJAgRTnIOSZVw2HtFq/m56sHfw0N1OkOBCQB\nwiCESSWPOUJbewAZi2kn2VL+3UBAO2wy0Q2Xohf/VxrtClOQwXL133+/2UVk1AD/+hClqzcpQEnw\nCXgBGeCxqoJBCWA4l/2JNH6HjZRTKRbrhSEibCth56xfT+m+hQXlJoHZ80i5iEYyRllgmJvjvxmG\nrBNhb2n09jKABYgobG/X+NjHii7asKmJa1V6XRHhqRCP2xgc5J46MECnXxCt6TRcCZFCgcHxXXex\ngBAMAhcumE7ByGO/hEK8v8FBMnQzGYW5OcMdJ5Hfi8U0urpsbNyo0drKvgJdXQwYk0lPkrGvjwnf\nCxcMPPSQhXvuqY5uPHbMxJUrniSf1gaCQen/pbFli0ZXF9dAqQRs2mTjgQdKeOSRIr73vZDTA6Lc\nRE5lZVS3jDnnDxOSN77GhFUsTDwyYeH0IeEcLBSYpAsEFADDSSpot3BPaUHAsgxX7rClhSzhQgFl\nZ6/vl1328OuvG7h0yTsfZmakUTkLk7OzRLlOTRnIZlkAi0SYENi3j2fR8LCBYlE596HcnhfJpMaB\nA5bjFyiMjRkOul6789F/L5KcOHnSRDZLEMbEBPf9t982cemScmV5JVkRDAJbt1IuxrKI8u7stHHu\nHOdjZfJYkmVa83O2zWRKUxOTZiwuMrFumvQb4nEgGLTdIrfX9L7Csj1kP534LPDqb5KVNXw/EMwB\nSymyq/7562Q7XTrksaIu3YXgd7+OyPRB57zT0Ef+PezeV8qvHygBhSaXYUX2J+eBmu/xrplPwhj9\nOYS//3XYyQvQbVWYYBfvKmdq3f87QH/F75kWcPFu4JvfBk79cjmTS57l8kGX7dTTo7Fhg41wmD5a\nMKgQDConIVx+6fIxrAUs4Dyj9J1y3p//u+9PQYdofxvNzV7hOxzmfp7N8jMDA14/WIIlyvvwXcsM\nQ5KJQPVnWv1zmibvWUBflIFa/b2sPK6UBieApDaS3zC4llIpYN06nlu2TcACzzp/z0ZvfnjsYJ6L\nDQ1AWxvPjYceYjIwEGBi0s/yEAuHtbOPcv9qbASkTw+gXQDWey9rxz2byfuVryeMwUhEznH6pgIa\nW/MvV/25W12u7726v3KfTKTNKHHIOKC1lcVrgv6W3wN9BG//kZ6SyaTXU6ipSbvn5vICLuWcQyGq\nXFgWfMyYcr/Svesa6+rDaWu/cdMkyKelxXbaCChX2cCyRLqS12WcST8hGJT+0nwP8bj0/lKuOoJc\nPxRijNnZyeL8/v02PvWpEopF+h3pNP2MpSUW6LXW2LzZRnMzpfIsSzn9wuEyvNrbWcjavt3GxYvM\nRQwOapw5Y7rAiGJRubGFbVPpRnw/8alWMmGzLC0Bo6MG4nFPbUZYMv39Fn7wA7Ns7JXS+MIXCnjj\nDXNZPkbGkfLZHG9h1XG90B+icoJCY6NGLqddUNC+fTa2bNG4/XYbbW3eOguFKEEu789TJqBf1d9v\nu2C93l6Njg76r8Ui/YlNm7gvUgnDY0GlUjZuu8122Ve/9mslfOpTJRw9amHfvuWxQyVrrrfXdiXa\nyf7k9VtaNLZutRzgL/+OfczoyzQ1adx2G99RQwNw8aJCMinFbM69eBwOMAaODCcZeMePG/jJT0yc\nOWPAtpULchBbCwtJTHxmUULws7a6u3XZM3d2kjUnYOLKdx+JsFC7uMhze60MsJuZA7ue79e6ZuUY\niKrEaq3O+Kpb3W4NW4nxVS981e2m2WopyCt97tQp03UiZmc96YFgUGHrViZPEwmNPXtsfOQjJVy5\nYiCbNXDqFBFNpZJ2pQ8/9rES7r2XjVuzWTpC8bh2ixGRiHb6U3lMr0BA4957LezYQUcnFKKj1t+v\n0dZm47HHvGeR52CBjPJJO3ZY+NrXinjssRK6ut7DwV7FWF7LGajmCL31loFIhI5WPA40N9vIZol6\n2r6dznA6Tdkow1CIxz35BKUU9u5lIlocgLExhXPnzLLfDQQoZxWN0rmdnSXrAqDzTAeaCL7ubgaC\nc3Nw2X9+Mwz+/tCQhVxOIZWSgFO5vcEAtQypWWlsnO5JbxkGiw3r1tEBLBS8XkGS1I9EmKBnIoKF\njcZG20HoKQfd5y+M1U5QyPyrlXCqHch6hb21JD4Mwysw2vb7p2PNRNfq7lVQkmsNhgMB2/eur++5\nmEjTTqNg7hkSNMv7NwzOzWKR846Ickl6sFAvshrNzSzamKbtNHv2ZE4jEYXeXn5eCl/SU0IC5WhU\nY/167kkCBkgkWDCW/kSNjZQXaW6Go2nPtZFM2vjP/zmPJ5+0kMkwSJO5HokQHTs/z72PwSRRm0tL\nwF/9VQjnz3NPaG5mkHnXXTY6O4EtWxh4ZrMKP/0p91jT5PWU4t6pFO+1p0dj40YbxaLHrunsFDS+\nBM58nmBQoatL42tfKyAeB65epXQhZVzlDVWXcxUbHuae9s47DMDFolHg4EEWMSipZ+NLXyrivvss\nTE0ZmJgwMTYWwPw8550kvTheNgIBVYb6XD73vIS79Di4VjITkHlWPckqSZr2dhtPPlnCnXcWcPq0\niXyeSQHpgQMYbrGT12EipqfHRi6nsG4dUc2c35yz+/bZThKEZ201qR9Jsly+TOZUschkxdQUEAwa\njvQl2drptHITRVKMGhy03CRNMqlx9qyBlhayqBYXFaJRjTvvtNHVBWc9EYHc2Vn5zr17Eblkf0Ce\nz/O92zZcWZqREcOVuxR5Hzmrf+VXirh40cDYGMEjhQJQuV9EIhrbtmk8+WQJzc02JiYMF4UcizGx\nv3u37cpqsjeEBdvm3Jb9gNtr7b3MMIDAUjeC5z6G8NufhnX6Y9CZHn53vgd4+2Mw3ngKxk8fRbjQ\nDaUo0ZRIAIV9f4Ziw6XlF82nYLzxlG+/gpcUnO9Gw8WPIfrOU4heeBRJoxuhXDfy3S9BhzPefWV7\noP7n12HM93h79h1/BiSr/F4uRUlFwCvs+SQWGxo0urstbNhgI5Nh0TSZJPtOaxYoWDD03oOci4C3\ntmRf9xc+AgFPMlNkm+TfWlrY46WtjXN3ZfbE9TDCvQRdLMZiaDxOaW1h4goyPZnUTqKW/lWxqBAM\nkp3kST5X3o/8jgB61DUYI6sD17CXI+e4sIWFuVz7fNbueifiHVX3w0CA/lBzsxSGZZz812bhiX1n\nPHk5QHwl7lOA7DmVPf48+Syy63iePvZYEU88YWF8HHj99YDb31CeS87TSEQ784YMhdZWD7gkMYH4\nfbXG4mYVYGRtrlRwpOSqzGGuhaYmvi8pXlYDaNW+nsdwkiS/rKNg0CvevLeS3WTLSO/klcwve3yz\nGVBcv/yvsdErfq5bx2KHsHqvXlVYWjKqrlUBv61bZ7t9fsNhG42NymEG+B/GVQAAIABJREFU0xfJ\nZg33PfnXpGkSaLl+vY2ODvpiLNryPJc9Tfxisow4B2VvFBCV1iz6cI18OKpgfAe8V1mnQGUP5cpv\n2WhutvGZzxTR0ECfp1SCy8ABpHiknPEvB0UIUEt8BL9/J+eOaTIOjMU8nySZpB+STGqcP+8VvwhE\nolRefz9Bt4GAwvw83LMglWJMefhwCW1tVGK4ckWho4PnxpkzHuC3VNKuGk0qZWP3bm6enZ2MPfyg\n1WSyetGhqYmFujvvtBCNwpVvk6R+VxcwOGjhyhUDWmv09moXtHvsWPV8jFJe/oTqOxz3mRk4RS+4\ncXCxCESjbJcQDBLk9vjjRezdS1/QNJmL4DXI5CKITyGR4F4cjdpOgZ8xd2Mjz6x4nEWjeJxnyego\nlX0WFtiH/cABG48+auHee233fR07Zq5qzKSwsm+fjR07qE5z5Qqv39Ji4/77S/jMZywsLQFnz3o5\nGBmLvj4PXBePA9u3W2hsVE5Bif482XU8IxMJ5l3eecfE2bOGczayj1cy6c3n65X1u1aLkcpi0k9+\nUvvds+cm370oAV0rJvswmIxBRwdB+KdOrTxPKq1e+Kpb3W4Nqxe+6va+2GoREyt9zs8GC4e9PjaS\ntGprs/H5zxdxxx1MlEUiGn/3dwFMTCgXZbVuHeA/hC9fJtq/s5OH9Pi4gVKJiO8tW5hMDgbpUP3e\n7+XQ08PfHhy0MDhoI5Wq/iw3AyHyfo15NavmCInzKM5MPE42xFe/6hXzhodZZJR3IxaP23jqqRKa\nmjwHgA4EkfcXL1IrWyng4x/PIxplwp+JfgbziQTQ3S0IPr6j9nYmJQqFauw29nTZv59yEW1tdIDn\n5hhMA16xqTKIl+CHSWqNu++23ISS9FJqb6fOvqDaAC8BFYkAH/1oCffdZ2FoiGyCxUVgbs5wpQ6E\n/UM2mNzHcvlDssT4XzTqyT0KSrB2IsoLFFdrwp4R9LQkQ0WayTCYGFpLckGSUKapnb5N3r2JBYMa\nzc3a1TqvZaEQCxTxuHalwuS+rlU0M00Wa7u7OQdWKjowibo8eSWBdnOzja1bWajKZg1Xo59BmY2B\nASK0m5qY9CyVBB2sXFZqKsXnbWrS2LxZ49OfLmDjRpGBY5Klq4vjzQSuxuQkgygp3jExpZ0G0UTn\nSW8sYUEqpV1G0s//fAmFgsLMDJ9l584SFhcNnDxJBGE47EmhWhaDxHicAb1SBqanuQ9cumSUsWna\n21nAGhigzMaZM1z/775ruLr+ra0ekrZU8pLS7K3ARsJkdBGpPjfHPktSqMtmFWZmFDo6bNx3n4U7\n7uD+HY97gZ9/ztVCvSaTGmfO8Fr5PO8zFNI4eNBCZyfPkqefLrqsVz8AYGoqgEzGdgrZDLyTSRY4\nTFM7Dcarz6tgULsyt9JMm+/Q+7ysP5HaaWvT2LBBO0lIjaYm292volHOnfXrGbwnEhoLCyYKBeUg\ni0XGS8M0DTeBJ0wz02TwPzmpXGaXYXAeSdEL4FyNRKpL5Aq6VIpWra0slBoGC2stLfLugMVFA52d\nHvONjC0b+/fzPcl5VSwqtLUxSTc0xAIqAKdnG9HVorFf7V4kIeMPyGdnmWhJJvmcsRgcWRnlyAR5\n1+vs1LjjDgJa8nngzTdNJzHu7afhMPerTZv4znt7mdDyChxkqe3bp13fYv9+/vnjHy8inVauXGk4\nTMCOv7gmxTApmqxbp519wnL3uEhEu/uRSAI1NXHfisU0jh4tYir+PcwE31z23oyRu2Ce/RgaGji+\nIscUiXAP4brwpJwDi90YCB5Ecl0OyXASvfowkj/+Y4QmKJcojdlr9girZIbBOxdCIY1t20oYGtJY\nXDQRj/M5CgWFc+cMzM8TKS2JR69/mifjyYQ057mgx7nXegdCNKrddSNjxV6CGuk093BKKy+/fcDr\ngUdZMgE6eMViT87KYxLIeZlIwDnDRf7ZcBgbnnwsmW28PsACcigEl+XrFfacd2hwDvK++b788pnV\nk8G1i6vyPphM1O65wzFWjpRg9cJXMKjR0GDjjjssBAJM6sn+JuxGYbunUgRqSG+dhgaFhgZeRxjB\nkQh9LBbe+XfJpPfeW1ttt58IQVjcd6SAJT0CpZdlWxulXTs7gWPHDJw4YWJmxnQT3yJD3djIdVYq\nEczR3Q1s2MBCw4EDFuJxJvUaGujzSdGs2lxhoag6m221JkXRWjJr3ueUM3Y8f0TesaPDxuCgjXXr\nCEKRIsu1fjMS0b7zg38vvpbIhy0vVi6/jhQfV/ZTq31Xu3tsJKIcFkTtoq2MdypFxlStZ6z2LsJh\n7bLd/UVxj1mi3fVNmUOe/Z2dCgcO2Egk4IArjbJrcL9gst40bWzZYuMXf5GMFgDo6qIfVihQIaNQ\n8M5mggX9/SE9ZkksRnaIbbMXsoyL14vaK6rzHhRSKdstlMg+H49znYdClIcTMM4HY6spFHt+rxS/\nwmHGRFJMAeRM4Ry94w4bBw5onDxpYHqaAB9hAxUK2gHBqLIeUVKIB3iddeu0U/ySflPakbjjvt7f\nr7Ftm+34wswfbN9uo7ubfv1bb5nI5Qw3Xt69m/Hr3BzjAwGIdndr9PVp7NplIRzmGZHLKVy+rHDp\nEgsc+TwwM8OeUFIsCoXIkO7q4pzZu9fCt79dnb1TK0G/Ekumqwt44AELjzxi4YEHLBe0e618jPiV\ni4v0tTIZ7uFkrClH9YRnQ3s7/Y5t2zgP7riD8/0HPwggFDLQ3s5Cc2MjczG2zXM8keA7a2lhz3Se\n45RX7OkRMLPGT39qOuBY5eRvbDz6aLk0di3G07WKGtmswptvmtiwAbjtNo2NG7k37thhu3LiolrT\n1GQjEAB27tRlxaonnrDw2GMl/NIvWXjwQQtLSwobNxJgSCAJXN8BjgJBTw/9xnxeoadH31COyV/E\n9FutOGqldy9FSr9PvdK1Pkx2I/OkXviqW91uDasXvur2vtlqKci1PudnMIXDHgJ8+3Ymmv2H/tiY\nwre/HcDEhOkkN5Sr+euXA6hkRc3OErUzOEhHq7XVRjDI/9/VxeTsPfcQpb5//8rP8l5Qrtdq13sP\n1Rwh0akeGPCa7lY6WpIAFaRWJsOi4zPPFLB3Lz8nDgCTWzb+6Z+CLkosFgPOnDFx9ChRetPTTMRu\n3267aD2AAe727RY+//kiolGirbJZj/EiTAgpWv3yLxfR2QkMDGhs3VpCPM6ky/w8ky5LS+UBvCTl\nQyGiOvfssTE7C3R0MDErPYckQciCg3aDXUkeRyLsCxQOs3eZaVK2IB6n086gRyOZtF3WEFAuCScB\n38CAhUTC6+FERK/XoP1m9J0Q6ScmDCXZpp3gkPcqjI3VGJGy/I4wKiWJLL8VCGjcdx+ZlFNTyklc\neGMh7yIYZKI/meQ8keSmJMYk8F/JpD+f9AVcbbANSCKB73loSGPrVkpPLC56BeJYjKzSVIo9oTo6\n+J5DIUr5iayTYVDurb1d49AhFsEmJgw89piFT32qhAcesHD5soF83is8jY7yt6S4yt5XHoNoYQHY\ntInz4/Jlw0WYskjJtTE5aSCfBw4cIHLt1CkT586ZiET4bGfOGBgY4ESammKxZOdOkWcDpqcNVz5p\nfp6Jm0yGkoGtrWQniPb75KSBmRm+940bbczPM7ErRYhgkMEr+y8AXV1k4wlLdmmJ6zMSIeqRCVHt\n9NtS6O9ncL0aCV2/SYHl7FkTSil0dtrYvdt2Cyz+7/oBANmswoULpsOgYvJ2wwburffea6OtzcbJ\nk2ZNdDqlBrlHMoGj3ESiP5HT1cXguavLdtDEnO9HjpTwiU9Y6Ojgvba3a6xfb2P7dgZdZ84YSCSA\nCxcYlAYCnkScMO0aGjzkfk+PjaNHLbcJNqUyPbkbwEOQ9vauzB72gyzOnTPQ2Ai32bZSLMS1tjIR\nqxTcOS3MMv+78Z9XUkD1/+ZHP1rEhQu170WCcn9Anslw7EViRpKlhQJldeRcrHymw4dtjI9TynNh\nAc77g1OoAPbutZHLwUXdtrbCLXSlUsCv/EoJhw9T/vjwYT5TVxewZ4/tyGhqtw8Piztw9zO+P6Cl\nRaGvz8bjj5fwJ39SxMMPW5ieZl+FaJTnYyTCPbalxcaBAza+9rU8nnzSRne8E98d/Q5y2mNqIdOD\n8Pf/BK3hTtx1l+X6Q+EwpZmbmlh4yOW4127axPNva3cXvvaJo/i/730S93X9H7jwRi+Ghw3k83wX\ntg1grhvY9BIQ8X5PZXsQ/+HXYc12l+3VIt31yU8WsGWLxvHjAUxNsSBoWXAYgsqV6Zyb4xwVlpQU\nseJx6V0nZwrnOxPO3BtDIY2+Ppljksgk86G3l4xd9okS4EH5Gpb1QV9Qu/uvsLU5htqd72RMSxHb\nRkMDz9CWFp47nI/8TxDspqnR22u7wBB5zqkpzmF/Mj4YJJrdNCnLLWdUeWGs2i7kJYcliexnT4TD\ncBk2sRhBOOGwgaUlr99qZQFGGBSbNtn49rfzeOABAhh6eynrmEzaKBa5/gcHLXR1sajW0sJk+/y8\ncpN7whqNx5kIjUbJWO3rs33rnWdvTw/9heZmmQfsYxiLabegrBRcUNzWrZQCe/ddA+PjpiMtRV+E\ncmW8n3CYwCj2AYQjHehJbTc1EegwMKAduVRvbACuo6YmjYYG5QB0tAt2iERsp4jChLUAQQiy8YBU\nwtKhP6Pd919LEUDevcyRhgZKQ27aZCMYBPJ5ysiKxLcfKMS55wFopOhpWUzy+6WcZU5xnLxzTIBR\n/nuiQoPXG0lUEa4FThK2WlMTCwudnRwXyvMu/474ZVLkWVysXfgiaMwDcYVCLKzFYh47nr4QnHjE\ndpnSsRiL7M3NGm1tHKPWVmBkhPK7AqDxA1nEx2luJoBF+qTu2mWjtRU4d45xixSU5fm0JhPGL1Pd\n3Mx9YGGBv3X5Mv8sygz+orQAakQu1bZZwKHUHBlHwh7bvp3+hsS+tQqMN9sEzMU1sPJvevMMkD7J\npsl3EA6jTGZO4iatudd2dQGzswZKJQOZDNlFkYjHKNeakt+NjdoFU5Bdyvfd3q7dYk0opHDbbVz7\n4tdv3mzj/HlPVaarizGwyMRduWKgt5d9sQiO4TMtLrIw1NtL/7i3lyDOuTnDVXh4910D6bSBiQnu\nM0NDBHFaFmPRtjYbLS3MRUgOxC9B6BvBm864WSkfs2WL9vmVGtksC5FS1F1Y8KT+5SyTQonkZl55\nJeADJbPYPD1NNYI9eywEgxozMyyEbd3KHE0yCVeues8eFrdmZ5UTy3ixVDyOsvG4FuNpJVvpu3fc\nYWPzZp5jXV0ae/fa+KVfKrrg1WuBps+do2rC4CCL3B7Alt+lHLQH1LveHNNqW5GIrfTuAwE+a+W9\nXI8E461mNzJP6oWvutXt1rB64atuHxqrZDD192s89VQJH/nI8l4uckDRmZcDXTkBuHcIV9NsZtCr\n3Kay+Tyd2MXFtWkVf5itliMkjI5ahTQmzcqRWtu3a8zNKXfc/A7AX/xF2JFE8GQRmag08KUvFbF7\nt42rVxXiceU6V0pp/PzPW3jsMcuVO/znfw5gfl65xYBQiMmSri4mJ++/33YTqocP23j4YaLXjh9X\nZZJYYkrBCWw1tmyxEQ4r9PUxUdnbq52gXjnN6D35LKLyGHAOD5NFEwqxr1w2SyexoQEu0i8e55zb\nuRNOQKNcybfKJFYmY6C1lbIXzc1M1nV2Ut+8WISjYe9PNpQXjshqWxlZKY2229vh9hAAPERtLMZ7\nYe+7alfwEiAi+dXfz4QqwCBI2FHBIIO39nYyfMbHuebIBjLKnp+Ics6/ZFKju5vJfxZWPTbVSoUv\nScCEQnw+PzK48lkkGUn0PpMFsZhIVjKRVCoxwSbrJJlkIWRpSSGXY2DM5CcT7zLuDQ3alRrs6fHk\nV/0OtBQApqe5X4XDHJ+FBeUm4qT/SCjEObpli410mui/s2dZ+Jqf9xIikQivz2Ktdu6TTnyhwHtJ\nJIgy3rOHqMr1622MjhrI5bhGpqe5L0QiNq5cMWBZhiNPyCLYzIxy5BqZTJqd5f0wKGWSYXxcAeAz\nitRjJMI18/nPF929OJNR2LTJdhlEsRivE40yGJ6fVzh0yLouOddq41vtuwIAyGaV02DcQDZLWUPp\niZBIEGX7xhsBjI0ZTjK1fC6xvw8TFZs325ib4/NKfxmRW+Wa4DrZudPCM88UHUQnE7LpNJMhMm/8\n905gB/D220SfiwUC2gUExGLcnzs7bfzRH+Vx//2ejGA2y8Tu3ByTQH4wSTX2MJ/Zk4bp7SXCmhJt\n3NtaW4lELZWI1t22zWNAiazOtQqUly4pnDnDedffb2PvXkrg1WIyS1AeDlPuaWxMOZK5bGLe1QUn\nMU8WR39/bSAHwGJvWxsTLFJAk/OlpcXGwoLC+fMGZmc9SSxgdc9WLLJwNTHB4g4LwdrtbxcM8nka\nG4GhIdvtO3HxIgtE8Tjf5e23s+D1wAMWvvKVoovK3tTehUM9BzCbzWNphn3AOl//Y+xouR133sni\n6qc/XcTSksLMDBODmQwlG/ftI/q4r4/36h+bl14KYGZGYXRU+lMxadgR60J78U7YKo8wkmhbOoyD\n6f+C4NWDTsHGdooMNvbutfDJT5bw5JMW/vqvQxgfN52eggpTU3AKtspl75RKUrzkn9nrhkny1lYN\nkSFeWhLQinJYPDxzRcK0o4OJL8vi2RsMGohGgVSKyU0CbDzmNhkRXJ8NDWRqtLRo3Habhbk5rq94\nXLtyxfE4MDDAHiUEfAC7dpElyn5U/Jw/2RuJaPT08P21t3vgFjkPBcQgBTgyEbh/+eVa/SaFJAGY\nmKaCH0wSDvN9BIMagYCNWIyIdOkrBnjMY7I2DXev8pv4W7fdZuORR7h3yn6SShFk8dRTRWzZorF+\nPf2xX/u1ksNsYz9KYaPE42Sw7tnDpPbAgMbTT+fR1ka1AUlcip85NGQhm+U+0NLCPpDZrOHOCdPk\nfN2xQztqD5Q4zWaVAyjQTm8Yvu+BARayGxuZeE4kOB9GRgxcvcpzRhQNenpYfCMqn7/T2EgfUAAd\n3d3sLcZkvXL7vFiWMOsEvKIcVqAHhhApXSl6+cedhSvbJ0novS+RwdIaGBsznOIinMQ+fVHZC2Mx\nsl4ojUrAk1L87OCg7YB6PDlw8SUjEe7BDQ1M4EvhSPwpKdY0NcEB33g9byuLV1Ivk0IIGWfKGVvl\nskkXF3mtREK7DEGRoI/F6Bf4WVMecEw+y/sVMJDWnLeRCJPqQ0MWNm60sWuXjZ4eJqz377fcQmJz\ns8QoZPh8+tNFAExOT0wodw4Jc5SsHM7hjg7bLZY+9JCFoSHKqh0/bqJUMtx36y9Gk/HFOdLertHR\nweI89w6+83we7p5VqWAg4yygpaYm+ojt7UzE33efhd/4jRJGRxXeecfEwoLhnjsy91T5JSusdizh\nL5JW+x7ZpNoBouplUqV+80vYeu+SMVhjo0Y8LgBLKeDy30UpYMcOAhCnpwn8iUTIzlpcNJBKcd1Z\nFs/rzk4b27bZTs9d5Z5H0ajHwN2/30JPD0FHTz9dwFtvEQzl35v8vnytWLqvz14moawU9yR/iwYZ\ng3Saz7J7t4Vduyxs3qzdvdSfA1kre+d67Vr5mK4u+tiHD9M/KRbZSyyZJChqft5AsSiMOQ9sVcnc\nF5MCi2VRSn3nThttbWTxis9F8JHIVVNh5uRJssilgOb5+t543MiYXeu7lWAuGZfVgKbFlw6HUZbL\n8ssI3oyC0lrbYlzr3fvlHa91rQ+T3cg8qRe+6la3W8NWKnwFav5L3er2AVl3Nwsv1zJxNHt72U9B\ngoBcTkEpG4cOeRnyymuOjSkcO2bie98zkUoxOSJOmdb8t9Xcw4fZDh2ycOFCZd8EXTZutWx42HQk\nC7zPVo6bjPG//AsR3U1N5bT4qSn+b3e3xhNPlHDsmIl0WmHXLr47f3Kyu1vj3ntLuHIlCEAYV0ws\nRCK6atAh3zt82MLrr5P5URmQh0IaDz5YwiOPFPGXfxnCyAiLDj09dNQzGfYWCwSAuTkmoC5fJvq8\nqYkJLiYUFdraCti508J3vxvAwoLHGAOILjx1ykBLi41AwMbCgolczgs6iRTm/796lckVgAFvNqtx\n4IDGbbeV8M//bGJ62nQLPCLTItJcgkwWdli1BBaT8RyIjg6NiQkNaere3MzCiiTeBC1cLHpBNhOU\ngmJm8ujcOWD9eqJkx8fhJgCbmlgku3rVgGGwN9TFiwqFAiNmDynMsdiyxXJkqjS++MUCTpwwMTdn\nYG6OQercnHYCVT6j3yi5w+CqpcVGezuf4Z13lIsQrCw0SvKGfUJEWorvtFgEzp4l/bClhUGJZWmM\njRlO8ZLBxPQ0EaZLSwz2u7qA229nYLC0tDyx4J+rlXN/aIjSnZcvs9+eaUpixXad7u5uSrSNjlqO\npj0Dd0mGCZtgfNxAZ6f3wOLMNzWxX8DTTxfx3HMBvPBCeWNrkY6anS2/8UCAiU7KUXnX7e0lInrb\nNq7Jbds0LlzQTg+x8gWndfle/NxzAZw/b2B8nAVUMfleOq2WjVEyqZftD36TfUc+e+RICcPDtb+b\nTBIJPTIixUOyXksl7bBigEceKeGb3+T+0NtrO32ovEQMZYXIdjhyhL3Url41kM8zATU3B1duKxzW\nmJ7WOHSohM99zsKxY+ayhJB/TMWUIgshnTbQ2EgkrFhjI5nLhqHdIuTjj7NPg4zJs88G3N9pbeXf\nV9tn/fu3/zuzswoXLig88USp6tnR2+tnbHj3vJrzJJNR6OuTd27g2Wf5O7XOYJkTzz9v4uTJAHp6\nmDA6f97A+fMGGhuZbFBK4+jRa0vDyPN0dSkcOWK5vSL27bOQyRhYt46M46UlhdlZJoQTiWs/m388\n+/s13njDxKlTHoo2n2fivrWVTJUf/pD+yKFDFgxD3n/5vVeedWNjCsefvxO57/4cdka4984mef89\nPRYefliev4gzZygXHAxSTrS9Xfv+vdwuXGAh2LIMt/dSqcT526tux+75v8S//78Y5D/7bACldf41\nbGPHDttJinM9RiLcQ5iQJIJfKeUyjGdny8+sQIDnCaVGbXfMJBlr2wQ2hMNMGts21/HQkCTYKH+1\naZPG6CgT7KEQpe2EgSd7uIBibJvFqWIRuP12MpQ/+9kCjh838cYbJs6cUQ6DhICMUkkYgkxQaq1x\n7FgAlqXQ0CAJX55Lg4Ma+/fb+OxnS+7aymQ4X5UiM42FKpHFsvHVr+bx8Y9H3WS5sGl4LpNFMDrK\n/d+2yeahZBw/K0nnlhbKgL/zjuFIdSmnB6V2ZBqVw6DSbqHezxQLh8kgkeTbSnuDfy7Jurr9du5n\nuRyT0l/8orc3eVbC0aPWsn2e88vba956y0Brqze/3npLuQA4Oe8iERYiyBLkfp7JsAh4330laM21\nFg7TD5mcJJAjEGAvl1gMAFj03baNa2RyUmFyEmVo8HyeRSFhXEcilLqWAq5t83eV4rkrxU3T9MYZ\ngFP00mXS1kyQe58VZpbfnyC7hUAW3jPfczCo0NZWwsQEC76trRZyOeDKFfqyiQRceTIBkkUiHvts\n3TrtsEwsnD5toK/PxmuvGVhcNBwZOP6+1vQ5e3ttzM1xHkjRz/O3+FyGoR1JRGGFcN2bpsbFiwai\nUY3mZgJG5ueVu/aV4jyOx8msKZVYpLAs7TBFpUjItTs+bqBYNCCFYCkwHz5so7OTvlTluTI2pvDC\nCyZee82EUsDOnd6+uW8fP7u4CJw9y/fc2KhdGdtolJLohw5ZOHqU8/XYMRMXLiicOSM9/LQ7f2UN\ni+QoYGNgwJOebGoCJie1w5jXTvHcduaJ9/LFl5U4IBLh3iHrKpOh//riiwo//KHpsI21W5yVsW9s\nJOPUXwRTiv1A5+c9SfRKCwRE6lS7c4e958jYF2ZfMqmwtKSd3kvLJTGlMLm46Emm8vrKKXiymJ7J\neKoAMo6ArFUWCnbssDEyQqZcVxewYYOF8+cJsKOEMvewI0dK+Id/CDggLl57fl6ht9fCXXdZSCbL\nfcWTJ220ti6PMeUsrhVLP/ywNx/8e9qxYyZeeKF8XCMRFjj6+6+d/xCftdrff1DW3a3x2c+W8PDD\n3h6+dauFkRGCWsX8PmEyqXHxonLjGIJDNO65x6qIEVZ+1tWMx42M2Xs53v654+WyOA4Ax2vDBgvP\nPRdYVfxTy9YaR8l3qs3F67nWh8VuxbVVt7rV7eZZvfBVtw+tyQHld3hzOToPlQF4pcmBnk6rqodc\nrULKrWCVid3rdThW47zU+q1a4yN/PzICNzEibJhczuvBBcBF58q9VHOw/L8v/QyEwUKjg7iSU5JK\nsTAhBRYPJUkmxyOPFPHyyyxWLS2VJzdHR5no7+315tfJkwaCQa/5McCA7R//MYgnn7SwaRN79Fy4\nQPYCJezYOHhwkAm5xUXg3XdZVIlG4UgxehI2gkZMJJhMlKIsgybloAhZuGtpoWRMLqdw9qzhSglV\nFr2kkbtSykVJl0oad9/NZMP58wpzc4YbiPb22kinPUkVsrT4fhcXy2VXcjkmvpqbi+jrA0ZGTKfZ\nschT8n5ERtIfvLIApZFKUTNfEg8dHcDRoxZGRgyMjBjI5VjEamzUjkyW1+RcKWl0TImZ7dsZWGUy\nBubnyWgqFuH0j9EwDEEnK1fCCiA7KpvlmPp7OS0uEiFKBhTvPRhUGB9nwLq05PUq2brVQjzO5OjS\nklpW/Kmcq9WKDZ2dDMi5NxGZLSaB8aFDFp59NoDnnzcdRgatsVESud49AOVFKLmHQ4cs/O3fBjA5\nqZxknXZ6QjCpJglDSW5lMuyRePKkcuYIn3v7dgbssk8cPWrjlVeCy1hRu3eXFwok6PPfJ1m5uuw+\nVwuGWG1Ctto9lCNPmaSW4snLLwcwMmI40pksjHd12RgZYS+G7dvZZyWRYLLj+edNJBIac3NANqsR\niTARl0ppNzk7MmJifNzC975nYnraC/rJFF0+pv4kcDKpkc9rpxjKy54kAAAgAElEQVTPdzM0RAaZ\nf5xOnFD45jdDeP11Mkx37LDd378WwKNaQc7/nWpnh3xvLWfTtX6nlnV3s0i9d6+3NhobuU/Pzirs\n2mWt+mysBb44dszE+fMAUO5j5PMKR44Uaz5rtXNT5tmOHcDCguncL1lFV6/yutEocP68gTfeIPNy\nZqZ8XgDl+4fM99OnDefs4hm8YweBPM3NcO9peNh0kqL+8fDGufKeh4dZoBM2IcBkZCxmY+9eGwMD\ntnvtJ55gkp3AkeX3m07TLxsZoXSc9Jlhfyg4UmOUS1JK+nNpBwFuuyCIl182HbALxyuXsx1ZUf7u\nffdZGBjg2cukP8/eVMoDR4VCXFv5PJnUb75pIpNhwvbgQfY3yWSApSUD6bSN4WETDz9s4bOfLeG5\n5wL41rcCZWjnUolym+k0i2Gzs3AYyvz3vj4b+/ZZLoIa4JzPZIj4X1gg0IIAE+6/S0vcg4aHTaRS\nlMosFFDGGmloAB5+2MKJEwbOnjXQ0KAxOSnFLyauUyn2gTtyxMKJEyZOnybzJBwmw725maAXstqk\nrxn3fLFQiMwH6Ykn9y9rlgl2A+PjCt//vomPfrTkFg3862r9+mvvCbX2ef/ajMXK51dPj8bsrC7b\nv70ivHLXbEeH5RbcxsYU/umfTOfcI1uKjCiOuwCHeE0yV+i70jeUdx8OUy60t5eJfRbQWAxhPzKO\nqdbSQ0o77GLO8UJBuzJ2folJFiHI9kmluBaWlhTSabjrBmBhQZj2AJlmr75qoq+P4IbWVu2c+1Q9\n8NY+xy+XI9hpZka5TPlEglJuDz1UwjPPePtCoQCMjpK1t7TE4lQ4zLmxb5/GiRMKU1NUPZBxZZ8e\n7dwb75vFLz5bPs8YjpKlLHBcvsw5Rek+G6bJgmIqBezYUcKPfmRgcpKypwALb4WCjLNy3iGlbgsF\nvrNIhGdCV5ddFazQ3a3xzDMlANXPm0OHLLzxBuXUrl6F0zeOvk9vL5zxNfHnf84xjscJXMrlGHvI\nmhIlBPr89FcTCY3bb9cYGaG/Eonw3VkWv6M1i0dzc14xUf6TArcA2tJp5a7dU6cok9vayv3j6lWO\nIYFrfCeWxbGLRm23F55pihwo50M6LcoQ3nhI3JFOA6GQjVRKuUX0pSVgbs50ZSxDIe3IlJbw7rsB\nVzpeiqKJBKV2z5wxHHYbxysUEoUCYO9eC1orvPMO40jbVg6b0nYLBADc9zE1xc/NzpLhKUyahgZv\nL4nHlRvjpVJUOLjzTu7zlXatZPi1YunKPe3QIQt/93eV6Tfua6vJPdwIaHUtdj3+dC2QcbVx2bCB\n8Ydcn3ucxqOPFtf0rDfrM7XsvRzvyrnT22u5zHYqjFh4+eW1vYOVfutmAbpv5rVuxG5WPkzs/Vpb\ndatb3T4Yq0sd1u1Da5UaxK2tTEY+/XRx1QdfLYmCcFjj/HnDlXhKJm8N6cMbabxZzVbqD7bSb12r\n7853vhPG+DgdhYYGsnwAIkFjMToSX/hCwZVrWs2z5vMMGpeW4MhsaVeGbCWK/fCwgZ/8xHCTS9JE\nNhikRM0775h47bUAlpaYwJqf53+Tkwz2BgdFdoBJ69OniTr1s9eE2bNrF5H7XV0MwGIxYONG9onq\n69OuhFNHB1GuhqFcqRii6dkjZPt2G4uL/P35eaCtjd+jbKKF+++3MDTESHTrVjZSnphQmJ6mpI/0\n+/E3iWafBOX2+OjvJ6KYjZNZjOP3+H7iceDgQcrBPPwwE7ccf4X5ecAvHclCFuUJAwFgYsJEoUCJ\nIylosP+Xcvs/yX2JHE1jI5PL09MK586Z+MEPTGzfbuGee2wsLZFZduWK9/7CYeXKtlBai8+1ezdR\nzw89ZKFQYN+DVEo7SRQ4PW60Mx84psJus22iku+91y6T4IlEmPhaXKT8YUODF4Dz2ZhE5ZxQTsIJ\nmJigdGYtmb1q61HkJUQ335NWKf++fPbtt01cvaqc/j9eL7C+PsruTEyQWeHp63vXyGYVvvMd05U+\no3QlZUpKJSAWY4+yvXtttLRQYs+2ldsDbmKCheHNm+0yedT+fmn4zN/mWrXxxBOlsmeXZ1haokSK\nSAuyiLh26Yzr0Wf3xtHA4iKQSplobS1hYoLJwosXDSQSykXIA0w0tbZqHDzIMR4ctJHJGIjFiGB9\n5x0DU1OGIw/FcZA5JAXzfB544w0TxSKLw9548j1Vk5z1j9eVK9676e9ngtM/XidOKPzBH4QxPc17\nWVgwcPEiJdykp8lK8h1rlXeR+1trv8mbKUFTTQZntVbt3v3Xl2t3djJhSenD5WdjNlv93NyxgyyV\nQoGSbQCL9EzKkfUzOMiCzKlTnBf5vCe7lkwuf8cy3yulfAsF3utqpX46OvSye377bcOZt8rprwII\nG6Svzy67D09WVFWVFZ2cJNNsbo7PlMsBgHLZAUoxKWtZRHvHYlwn8TiZvx0d7EfIvY3fn5pSWLcO\nGBrimmhr03jySfYFef75AC5fVrh82XAl7AYGbLfvxpEjJXzyk0XE4xzb1layKdetgyt/TZksXfZu\ne3s1XnrJxMyM6RtBnr+AQnu7MMiEYURQSrHIAkw0auP0aQPf/S6LUJYFV5JLpMO0ppRsSwsTxuPj\nlOIMBpXbbywQ0Lj99hK2bxdZO0q8xeMaiYSJ1tYi9uwRmU8CaX78YxPDw6bLUiVrnf5AYyPQ3Gwj\nHGYhrK2N/kM0yjP94EG7zM+SuZTJACdOmLh0ief90pLC+DifdfNmveY9YWxM4aWXAsv8bv81Fhc9\nn0LWpfQE7enh+nz0UQv793OttbVRsvWpp0rYvJlxQTZLhs/Vq6bD7uaYJBIstgwOahQKLALt2cO5\nns3ydz05bo43pbkoIZfJEGDA3nTaYWyT6dvZCbcwIIwwAaeYpieLKfNA5N+iUeCeeyxEo7bLtE8k\nbHR1MekfCnnjR1a61zOH46PQ1sZ9RnqrSX/jiQkWZuJx7fQkY6Fg2zbvrJaxn5w00N0NbNignV5I\n0k8Vzp5IdnRXl3aKMhrRqHJ7vEmcJqy1aJTPt7DAMQsEgJYW+j6BAL/3cz9nYWaG31VKOeucBXfK\nV8JRV+AabmqCyxZrbeW8JtCJigSf//zy2LDWnPNbUxPnwg9/aLjS2/m8doAALIRNTHB/W1xUTl8w\nw71H6V0sCgOplMbWrVxXd95pufNY+lWSuWXj8mWFdeu8Pq4i2amUdueLMMiKRfpM6TT9ZNtWSKU4\nD7JZj7nV3s55JHLN0ah2emJRKjGR8PqcNTbSd8vlGJ8YBpmsHR3cC4tFvrP167XbS2xsTMEwKIEv\n/ZpFiUAKxCKBODjIPmQHD/K+5+cNGAaLat3dLA7u2MEelbEY+/ExhmHBNR7nOGSzyikkK5w6JbKF\n9KlmZ4HBQfpIe/awgC/7V2XPzoaG6j7HamTi1rLPNTWxP7Aw7fwSiquRtasmS11NwvlG7Ub6Hfnv\ntda4vPJKAIBye1/LOIRCcK+/mmddy2cqZbWHhq6dO3mvx9s/RpW95V955cbfwc+q3ex8GHBj77ou\ndVi3ut0aVpc6rNvPpN0MunU1dEc2S/3tuTn++UYQNjfbrhcdf7N/61qoGL8MV2enxt13Wzh1ikWD\noSFdJsO1lt/v7AS2brWQSmHV7/zQIQvf+pbp9D7ykIuhEJMlb79tukGlZcFp+s5Ak+jfcmtuBsbH\nGXBJ8aNQ0GhuZrLLL9uwfbsnKXf+vOe8NjXBZRRIIYfIeGBggGMliMjbbiPTQBgg/ucVRsfUFNDV\nxcTehQsmgkE4SHK4SQr2imIw2dWlXVZZQwPQ02Pj7NkA2tr4ObHZWYXDhy08/TQReKkU58Dly6ZP\nl5/BdzTKfhMf+YiFTEbYMF6hqrmZSYDWVo3xcSaWWLQS9pnC8eOGg+Rlge0b3wjhi18suFJoU1Ps\nbUU5PyJryagho+ngQRtNTZ5UZjVJG5Gh+du/DboJF8Dr4yLBvvSrIlOHPQMoA8j7k6KXbfP5lCKC\ndHaWCYeBAfZDWUlmr5pVMsBW2t+6uzX+w38o4M//POiw4shos20WW199lVKB27eXlsm3AFxjg4Ma\nuVy5TN3sLPC7v5t3kYbZrMKPfmQ6RWcm58Nhvv+REeBzn7OWPcPnPlebEVP52UqJlOtF7l2LiVrL\nurs1vvSlAp59NoBiMYhXX2WSQwq6p04xcV4pqZtI2DhyhIxR9iBS+OEP2axckMbpNJNxkQjKiuXz\n80AkYmBw0H9dyr5s374cme6fC83NGl/+cn7FufXNb4bc/dMvgXnqlIHOTk9qppa9X5Ift4oETbW1\nVuv6ExOGm1gWk7MRYA+1cvkeuGd05doeGyOrp7dXQ2uNH/3IQCbDIs+ePZaLVs/nFZ54ojxxK/Pa\nz5gEvALtaqV+qp21ySST9NEoARkLC2TbDA5WZ9Sv5I+Jv6AUk/KmSblg+Q2lFNrbKRPa319+tgMe\nezybZTFLa409eyyk0wpnzwJHjtiuzNg3vhHC1asEAwBM2BYKGj/9qcKTT5bKpC9Fxsx/No+OMvns\nZ8j6faz9+20AltPHkEXDoSGN0VF+tqkJDrOb5/bwsMK2bWRavvJKAErRr8hk6A+xxxYTwpQKo3Sj\nsKIPHACAEqamCN6JRjW2bi3h53+eZ1EqBfzyL3tyrrFYEG+/zeK21mRyf+tbJubmlOPnCJNZY2GB\nwKHeXu5/AFzJt8VFnukbNtjL/A6ZS6OjTPaLSf/WkRFjzT7pWuUT/fM1kdDL1gawnGkh9sILJvJ5\nA8GgRjConEIAk9H79nHstm+3y35bfrepyZMgVYrjd+wYi2LePgtHYlKhocF2ew4BLMpIAaSz00I+\nr3DpkpzdngSzUvRJenpspzcRpVeV8tYZpVO9M4nSm9plTIsZhsI991hl81xYrFRisDExwaJS5bsW\n8+8f/gK6rJOmJmD/fn7/wgUbf//3JubnAbJGPRWCcFg7ignKvV+ywTSuXPH6fLEPM1xWDvu/2tiw\nwcLx45zrb70lzHMyrkdH2YsrkxEZdDhsMRv33LP8mU6coI/pl1mrFesdP05J6P5+YHISKBapQjAz\nwxiHvU0NdHYS1CB7sgDhIhEb58+bSCSAO+6wXHCPXx4xkdCYmDDc90AWGOUpCwWNYtFwJDG108+X\neyelMikhnslwfEsl9ngDPFZkNEoA1MSEgXXr6OOyQMRxGhiwoZRygXwDA5YjHUw/LxTiXtrSwvgh\nEuFcFtZ1JsO9u1gk+1EYX2KHD7MflN+U0s6cIZChULBcBm9vr8aOHZ48IMdHIxq1cemS4bC9NEZH\neQ40N7MPoVLKty6W+1Rr9RveC2m3hx+23L5l/rFYLbPk/WDcXK8/LXat+CWd/v/Zu/sgu8rzMODP\n2V1pBasVK4RkYJGC/KHEJggjUYLFhwN47Ia4GWJXNqih4+mMa1JDE9ch7qRxII3d2O3g8Yxbx67H\nZTx0hAJ1MkkNHcYNLh4sxk5kY/ER2zGgINbGyGIX7QqtpN09/ePVuR/7eff73qPfb4YZdO/d3XPP\nx3ve8z7v+zzpWas2pfdkv7+R79ro/pgqrfZsnsuW0nyPQZkt1nhYs6xmAxaewBctbb43qMk6tP39\nEzsVzVL3ayk7QdP9rZkeBM4+Oz0oFdKD++ik+fUL4zvJBw5M/vezLIudO09O+t5kenvz+P3fPxH/\n+T+vjJdeSqlI0qzqFDBJRcuzSgqOrq70ML95c5pRm2rtVH/ftm0j8Xd/1xYvv9x+apVMfqp4fJr9\nXMzAPXYsi6efTt9rssGaN71pLN7znhPxN3+TAlfnnBNx/fXp30eO5HUpo84/f2Lu976+rDLgvn59\nmsV8+HCauT062hbnnjt26gE9q8xCL1JMpZRtKcXIJZekgffxg6cRaYCj9mEwpXxpiyxLQa3CihXV\nYuRr1qQBmtdei8rvO3myqGeQBvVGRsZOpU1M+214OA2aHTlSP6A8PNwWe/asrLy2ZUseR47kpwbb\n8jjzzLHYtCmPzZvTQ/tUgza150KxD/M8j7/8yxUxOppVasadPJnSKH73u+2R5yk4t3JlOg4vvpgG\n2EZGiloE+amaHWlWf56nVUtdXXnd9VAMrs5Fow98RZDpwIEUPMyyLM48MwV6i9mNv/ZrI5M+eNYO\nLtUGbLdvz+Pcc0fiwQfb47vfbY8TJ/JTgdGUGmvDhjT4cdFFY5Pu79m2zQvxsDGfQEjRpn3xixFn\nnFEdeClSEPX3Z5Om1B3/8FUMYPb3pwHvCy6IeOSRrDIgmuSVdmZ8qt4iHc/4lLOzTTlT1FCMiFOp\nUCMiqqslZhpkWaqUH8uZgqY2EP70022V9rbYv9ddNzLp79+wIZ/wwB2RrqeBgajU8Spe+9GPIn78\n46yyzUUauOJ8f/75tspKoxQQSZMpnn++7VTawpRqdarB6GJgM93H0t986KH2uPDCNOP91399dNp9\n9dBDEx8FLrggjx//OKsMnhafn02ao9rXa9MhbtqU2vDOziy6u9PAdAo0TGy7H3iget4Xdfgi0uze\nYsBs7dr0Nx54oCOGh6uTliLSAPuKFSlwUHyu1vj9kmrExoTgwYEDWTzwQEccOJDqJl1zTbUubETE\nOeekny+OY5Fqr7s7/c6f/jRtdxGkKFJInjiR0g4W9dFWrYpKHbGItBrgHe/I48ILU6BvbCylbyz6\nZ+PbgocfXlVZ0Tk4mFZfPfNM26mVXNV2oKMjrRzZvn207phOl/Jt/D4bHq5daZjug8U+nG2ftNFB\nrIUYhH7iifbKCs4jR4rVTimI9Qu/kE/6O6f6uxFpdc/AQHtNO1uksxuLl15KwZJi4sTwcMR1143G\nT39a1CZL/adUV6m6Cn50NK1Wv/DCPHp6Un9psmDyxRdH5d5x1llpolTteRkRlZ8bf/2vWTMWt97a\n2IS+2p+v9hXzulRztX3U555ri+9/Pz9VByud12nlU35q0kx+qq5XHj/8YUesXFms4E/1tF7/+uqK\njyIIGVENgr/5zfmp2lFFXb90zf7sZ/mpVWLFdqXA2GQTST73uZWV1RS1Kc4ne9bbv79aC7WoRTU2\nlp0K7qW/ldK0pv+vrT29dm0a3N+y5WRs3JhPOJYRkwdp+/sj/uIv2io16FavTivF29vTBLaUBSOt\nvFqxoprh4ayzUn+6vz9NFiz6eMePp2eEVAs3HZPiXFm1Kq1czbI8jh6NShrriLH49rfbK3WIi3T1\nRftYPOcV7V7x/FME1FKt3bF44xsnP8dqU3f/1V+tjKGh+vvNZJPYHnigI1avrv6+4j6QAtPpeEzX\np5pLv2GhB8NboU7SfPrTjfRZl7qe0lJOHF4oak5NTVAQmC2pDjntjV+K/93vzj310mKbKjVjI+kR\nFvpvTZfC4IILOuM73xmJ6VJDFPr6stizpyO++MUVlULEx46lwZpVq+pTCtb+/dk4//yIHTvSzNkt\nW1KQ4g1vyKO/P/29115LqX/yvLoq49JL03fasCGlziqWvb///SNx/HhKU9HZmdJWXXrpaLz0Ulsc\nP57qFbzySnaq8Hh62L/22rFJl89v25bHO985GjfeOBrvfOdovPGNEf/4j22xdm3UpYxK+6/+/KtN\nQzE4mJ1aUZdqYJ19dh4dHXm8850jsXlzmjW5Zk0eBw60R56n33/yZKrFcOONJyu1zYpUK4Xu7rH4\nwAdSyptikPjYsYiXXkoDW0VR7a6uNJB38cVjle1Os3nTwN/rXpcetLMsi82b83jzm0dj5crqe2vW\n5KcG9aIyaJf+floRsH59+ndnZwoQrliRBoVuvnk0Pvzhkbj55pG48sqpU4xMls5m69Y8XnklKqme\nVq1KA5Fbt6Yg4ehompl/xRWp3tgFF4zF6tVphv/PfpbqIYyOpgfYFLxLM11/4RfSoNB8Uy40qjZg\n/MILxaBvqpfw8stphn9KmRbxs59lddtUXOPjU75s3ly9xp97LtWKGBkp6oCl410M5hSfnWo/L2WK\n2EZS0kxnzZqIl17qjK6uk7F+fRrgKVIQpVRLYxNS6o5PIdffXwyep9UtnZ1Rqfd11lnVtDYnT0Zs\n3BiVv1Gk0SvS8dSaS8qZb32r41SdujQIVhSgX7t2LK69dnTG9B1LlU5nPn9nPj9bmyblmWfao7+/\nrS7VZBG4uOGG0Qm/f3Bw6lS/P/pRexw+nI5V9RpM95l162JCu1Ccs88+2xbDw6mO3OhoSnnb3l5N\nWzjZfa/42c7OtN1HjkS88EJqR9O9J6WNPXgwi8suS6kWJ9tXk93rOzsjfumXRuP88/MFOf616RDP\nOy+lyEop4/K45prRuPLK0di/v31C21F7fRXpwyKisk3F/2/fngasf/rTLH72s7a6mjTps2OxYcPE\nPtz4c2hkJE3sqQ0eDA5m8eMfp3t7W1tK3fzyy23R0xOV9LH/7J+djAMHqscxIs0uX7s2Hcef/zyr\nTP5YsSKLX/qlsfjZz1J7unJlqsc2OppWSbS355XUtBH1aU+ff74tXntt/LlXbQv+9m8748iR0Uqf\n4NixLH7+85RuNdULqn6vs8/O4w/+oPHU4OP32d//fXu8/HLaJ2efXd3e7u48tm4dm1U/bTYpT9es\nSd+jSLl96FA2q3vNQw91xJEj1XtZd3f6nZs35/Hxj5+Ysh8xVXrXX/zFtMrn+PG0GnfDhmpAdsuW\n1K8YGMji6qtH4/bbR2Lr1jz2708phrMsi3POSasgV6xIq/NHR1Mg9HWvK1YPRrznPSNxxRXV7SqO\nwUsvpUHlM86oTrIpVs2n/Ve9bufTns8mDXNESot8+HDq461YkVYAFamji0lS118/GuvWZdHbO1ap\n17p2barpdNZZ1dSVxXaOvw/296dnhaKNLPqH6VxIgcyrrhqdNKD+8MMd8eSTHeMmpKQ+3GTtxIMP\ndsTgYBEcr9a9bW8vUp2meoSvf30KZKcUnGml1kUXpZTIv/mbo3HttY2nAj733DwefTSlXS1WoZ53\nXkotmAJgWaWvnWXp3HnTm9LvL+79RRu5alUeH/zgyRgaSjXmalPjRqQ26YYbUu3OInV2RJzK3JD6\nJSdOpD59RLovbdw4FjfddDIiUnrn9vaU4nRwMAXChofTPnrrW0fjAx9I7eNUfbMUnOuMl18+OeP5\nOVVb8eqrKfV2RLVPtXp1Cqa+8kpWua/M91oozLe/O5fU0EtpPv3pRvqs8+2vz9Z80movl6XeR61k\nKcfDGiHVITQHqQ5pGQtdqHIumnmGzVIW3pzP39q4MRqazVYMPj79dPup4EvET39aTcHx4osRV1xR\n/3A91+9aO2OvSD2Y53klZ//Y2Fh0dBT546t/c/xKq4iItWuzeOc76ztWL7+cBmIOHy5S+6Xv8Mgj\n7ZX0So3MKmv0/Kvt8FVnwqcH4GKW/oUXRrz+9Sdjz56V8fTTxez60Vi1Kj1Er12bx549K2PDhrTi\npzorOb1/++0norc3nzB77+1vzyPPR+PIkbY4eTKlMNy2bSQ+9KGTlfR4xSqELCtm5OZ1aXpWr67u\nvzQzvZpGMH2Pak2AWkVqjOlWD9aabubhrbdWz9Ef/aitsgLnvPMiilm8F14YlRWGRft0/vlj8fLL\nbadSGLXFoUPpbxUrRiKWZibh+O928GBbPP98WnF55Eg1iDkykp1aiVS/TY1c48V5VjuDOaII7lRn\nUs9lVdJCW4hZtONXq9bOll67duJqgPHXa3HeF4PcESkNaW06sp6eNAhWW7Q6Yur2bS4zG2+66UT8\nyZ90Vn5/Z2dKafXxjx+fMc1sYTZpN+djPrOp5/qztbN/q4MhKS1SMYO8WN08fqVtf3/Ed7+bAhnF\nNV8cuwMHsviHf0iBrtprsJihPv4aLM7ZT35yZQwPp1o0R46M1c2cn+q8GH++p1XLed3gTkpHWk09\nN9m+mqodePe7F7b/NX57L7mk2n5M1XbUXl+1q5JrUxEW98aenrRK80c/yusGdtvb08qU6VJZ1Z7n\nX/hCRzzzTFvlPnjsWDUAUbtC9vDhiK1bq6l1zz23ehzzPLUBhw9np2rdVO9vxQD29deP1qSaa6tL\n4TVVn2emtqBov2r7BKtXx6kJEWkVRlH/7YYb5t42F+lhv/CFFfHkk+2VvzXVCpvxxrcneV5//y9M\ndszG32teeKEtHnywvVJfcqa2aevW0UpKy6o8tm6de9+ydqXc+NTWRfDh7LOrKxO7u6NuNeWWLVkc\nPjwaL7yQUnoWKTS7u/Np+xK1qbsi0t8866yxaGub2E4vRIaMRu8HF16Yx6uvpuvk0KG0Mj7L0mSh\nY8dSyscLLsijrS2lBH7jG0cqKxSPHGmLjo6xCefnwEBWl048z/MYHq5v7xpdxdZoloPCW986eirN\naprAMjycVl319IydWtFWrUdY7Jfx6a6LNPGNXnO9vXlcdtlYnHFGtU9erLR66aXqhJrh4SxGR6tt\nXLGKfHg4JvRZenryuhXChVWrqu1YbRvd1VXNPLF6dTUzQFdXdfXv9u0jMTCQVsUPDmaVGsyjo0UN\nsTzOPXf6Z8K+viyeeqqxFPbj+1zFeZNleTzzTBYbN6a/n4L/WfzyL49VVu3X9knncy00Q393sc2n\nP91In3WpV70189jOVFphZeByWcrxMKAcBL5oGs3SkWzmm+lSdoLm+7caebAoBh+LB9fh4YhDh9pi\ncLA6W6/Iz97IgEajUsq+tCrr+PFUWL6rK73X05Nm6UZMfdwn60CvXZvHa6+lh+Faq1ZFfO1r7XH2\n2Y091DV6/s1UcyEipf44cCClQ1y3Lotjx9LD7pvelAZcnnwyBXvWr08Pi6mW2MR9PVmKiNWrs+js\nHItzzkl/83Wvm/hwu3HjyKl6FZOn6Sl+Z3d3Hr/yK6Pxk59kkWVtlQf8NWvG4sYbRxoOEExmpvQW\nxTn6pS+tmPShaPyD2mTndCM/O1+TDTSN/26rVuXR3p7O69qZzB0d1dRZs33wLM6z8Sn5ilR/050j\ny5FGZL4DGm9/e8T+/fWp7KaqIRMx8XpdsyYNrE6W0mh82hrpZwkAACAASURBVMtzz22sfZ3LA/v2\n7Xl8/OPHKzUAzzknGqqtOJlmuTcvpNrroHYANLWl1UBKrdr98KY3pfbyxz/O4tprRypBogsvzE/V\nOMxOrfLJ46yz6n/XZDUsrr12NJ5/Pn2mdnB3/HU2Xu35/qUvrYiDB9snfGam1HPj24GxsRQYeeih\njmknrcylbzDZ9VmbzrAwWT3RavC9mmKt9l5QfPZXfiXi299ur9QQuvzy0TjrrNn04bKa/+LUSpXq\nd1uzJqXXWru2/rsUx/H73091Abu60gz3tFJ2LIaHU52dYvB6skH6mfbrTG1B0X4VK6FGRlIQbsOG\ntIo5y+JUTblqXbS5KtLsPvjgWDzxRHtkWWr7Lrts+nqNk7Ung4Pp/doV31Pd52vvNcXKtjyPePrp\n9F1napve/e4U+CrqYhZ1ot797oXp48806DvZ+93dacLVpZdGw32JvXvTqrHinlz0m84+uzpZZ7HM\ndJ+t1kSL+OEP04qFFStSRoL29lS76vHH06SwImBSrW2bsjDs3t1RdxzHxvJ48slqysFUhzPVYJ1s\nUsp0iiB57WSeiHQvmOyc+/VfH42DB9tOnTPp2o3IK3XNJktbuBD3zQsvzCPPJ65e+PVfH41nnmmP\ns85KffeUOrbaLta2LbVtSp6nCWjj01sXk9wiJp8kmH5nVOrbbd48Nmm7dPBgdmqSTURRH7e7e2J/\nu1axn848M+Lo0YkBqvFq7wnV8yZltogonmNSQPSXf7l+JdWrr2bx2c+ujC1bxub1/Nws/d3FNtf+\ndKN91oVOITmdZh7bmc5S7qOFsFQT2AUFgdkS+KJpNEtHstlvpkvZCVqsv1V0jB56KB3zYrbvkSMR\nEVmMjFQHHt/ylon1reart7eo35NHZ+dYHD2aZjCmguR5bNo0/UPRZB3ojRvHTj1Y1s8i7unJ4xvf\n6KgUgJ7poa7R86+Rmgsvv1ytmVV8Jq0ASNuY59VAWVpJNfkKt/GDLgcPplQoZ51VLWwdMfXD7b59\nWezZszIeeqi9MgA//jv+i39RLfI9/ns3GiCYTKOrZeYzG3CxZxJONoCyf38x4FUNFG7cmMdLL2Xx\nyitp1WER/DrrrOp5MdsHz9rzrFhtN1mtn7LkW290tWphPveLRtvXuT6wb9+ex/btx2f8/TNplnvz\nQqq9ZmsHQIv2cLL9W7sfaouyFys5IqrH6i1vqV6r49vlydqFon5i7WD8m940Frfe2ngqup6efNJV\nDKtWTb3aqVCcizMN1vb1ZfG1r7XHN77REatWpX3X3z9zsGE6s6knunHjaOR5TLmipfhssSp3w4bZ\nTZrZu7d9wmqcZ55pq1sJWJjqOD74YBqcX7kypatLNRHjVCrTkUlr/BRm0x4Xas/VjRsjrrtuJB5+\nuD1GRqr1K1NfZCzWrYu49trRBevTjl/t1Mhg/2TtSXd3FmedNdbQBKGpVrsXQeuZ2qYUsFu8Pv5M\n/YE8z+OZZ9rrglXd3dVrtNG+xIED9cGiY8eyU3U7lz9tV+21ePJk+6l0u/mp1Y/Jz39erX978GD1\nexTt5fjjWEyiqg9UZXHppSPxwQ/O7j40WY20VavG6gJA479PUUt1fI239CwzcZLAQtw3p7red+0a\njYjRuoDWZO3i+OuxCBpu3DgWv/ALCzcRr1r3r/75p7jvTdcPnO1+qj23vvGN9ujpGatbJVs8M/b0\njNVdS0UdsjPOyGL9+nxeE3jK0t9dLM0YZGr2sZ0yWOpJcq0WFASWl8AXTaOZOpKtcjNthtSQs1Xb\nMcrz1DE6cSot8uhoOtYdHdVUdxGLcw7UpgSstXZtyoU/nak60F/7Wh6PP14d0LjggjxefLFa26Mw\n/qFusuM40/lXuw1ZlsfTT09MkbRhQ3XlSm16k+rDaf2AbMTk+7oYyClSihSpvDZuHJvxZ/fty+pS\nrr3ySsSf/ElnfPzjxyf9jlM96I5/vdFzf6q0KF1dKeXQVKvQIhp/UFvsh7zxAwPFLNehoTy6uqoD\nXhdfPBb/5J+Mxc9/HnHsWMTzz2dx7rmp7kNtOrbZaPRhsRXTiExltu3/Yt8vlvuBvZnuzQtlfEA3\nrWaMaVcXzzZ9z1Tt8tTXYB7VlUW1/9/4d9q/Pzt1HWan/l40lHquMN0g5I4do6dSE7dVUhMX7U53\nd8w5EDpT2zGb62su12LtveR732uLdeuirsbXxo15/MM/1P/MdOknL7poLJ5+Ot1n164di8suS4GN\n1LeYXzvRSFvw3HPt8ba35fHkk3lNkCDVgfoP/2Hygf2F0sgg9lTXUVtb1tBKpUZWu8/UNi1mmz1d\nf6CvL/U/+vvT69V792jlfGqkL9HXl8Xjj7fHT36SapWtWZNqXOV5mvDUDIp9/OCD7bF/f7VNSvI4\n55y8cj7/p/+UVlIV/eaivaw9jlmW1a06L4KGWTb7+1DtdbRpU2P31PHnzEyDvAtx35zpep/pHP7a\n19rj7/6uPV56Kf3NIoVmo6sCG+17FJ97+eU0IWv8cZyuHziX/VQciyLF4mQ/O/6+UqQ3rW0n5jqB\np0z93cWw3H3W6barFcZ2WlUZJ8kB5SHwRdPQkZydVk0/VdsxKoIxK1emh5GTJ9OsvI0bxyr1DSIW\n5xyY7/k2WQf63e8ePVUzrPp7h4er9UFqFQ918zmOtdswVSq855+vpmAr0puceWZK7djZObEY83Sr\nEYrZxXmevlequTD9w+2ePSsn7Qjv2bNyzqtRZrPPaldSDAxk8fLLaUb0ZZfl8fzzbXU/N5+VO4v5\nkDfZirs8T7VbitWSxUq+iy4anTTFzXy2qZGHxWac4Vkmy/nAXsZ7c29vHtddN1KXCvL226dPBTmX\n9D2NXoNppVFWSSWVTD1gMNXvvfXWkfja1/LYvz/VXXrrW0crNSYb2Z7pBiHHpyaOiEq785a3TD3Q\nO5PlbDvG30teey3iJz9pOxXMq66Gvu66sVi7trF0xVOlJ1uo62WmtmBgoD417cBAFkNDEWecEbOu\nMzRbjQxiz7c9aWS1+3K2TdP1B1J9r2xCqrkLLqimjpupL1Gcs2NjKZVlquEZsWFDylqwYcPyr/iq\n9ba3jcZLL6XaXSn9aErF97a3peu7tzePX/3V0bq6aIXa45jOm7yhlZeNmO89daZB3oW6b851O/v6\nsvg//6cjXnihusxucDCPV1/NG14VONX9YqrXf/d3T4xbYTZzW75Y2RbG31dSvczqhMrCXO5b+rsz\nE2Q6/ZRxkhxQHgJfNA0dydlp1Zk1tR2g2mBMlqUUPS++2NZQnYf5WozzbbIBj56eiIGBqR/oF+o4\nTvaQMVn9oYsuGotdu9LnGn1A7e1NAzO19Z1STYGskgJqqp/9+c8n396DB1P9gLkEZWa/z9Jgcn9/\nNSXMZD83nwe1xXzIG/9wXww89/RUVxWOLzi+2Ns0XrPO8GT+Wu3e3Eiwqa8vi0ceSfUP169Prz3y\nSEece+7UEw7msh8avQZnM2AwU+D/Qx+qpp6bzc9FTD+QWGzL+HSKte3RXCxn2zH+XlJMximCeRHp\nGNcGD2ey3NdLbV3G9H1SvbFVq7IJkz0W629P9nphvvtnfqsql8ZU131xDdXWS4pIq91m+tlCcc72\n9ORx7FjEkSOpftvISLXm1FJppK2t1lTLp6yp1sg5sdzX1XgztdnLvb1797bH0aPjnz2yePXVvKFV\ngVPdL667rr7u7vj7yGzb8mI/1W1llsfrXz864zPCdPt4YprclAq4djVvxPT3ranOb/3d5tOKGXDK\npoyT5IDyaL/rrrvuWu6NmK3XXjux3JvAIlizJs2UHRpKQZDzzsvjhht0XKayd2/7uHzqSZZFbN++\nvDM+u7o6p7xOn3uure6BsbMzYv36iMsvH4t/+S9H4hd/cWxJzoHFOt+K4NL27WNx0UVjce65eTz1\nVCruXciy9LfWrFnc4zjdd5zt9//e91Ltk/POy+P88/M455yIEyfSdl5++dikP9vXl8Vf/mVH/OQn\n7TE8HLFiRRbt7RHHj0cMDUWsW5cGTQcGsnjqqba48MKJK9AmM5t99vDDHfHaa22xfn3E0FBbdHZG\ndHRkcfJkVAa8m+GamU5PT/051N+fVVYSrlmTvkeqHzMWV1yxfN9j/LnfyLFsNtO1XaerVro3F4N1\n/f1t07YtDz+cPhORUoc++2xbHDzYHn//922xbt1YfOtbHbF3b3s891xb9PSkn13M/TD+vlg477yU\nOq9W7bZXZTE0lE347Gx/bnxbE1G9Xx06lPZnZ2fEyy9XU5d1d6dVJsU9bS6Wq+0Yfy/p7Ez7YHQ0\nYvPmfE7HeDmvl66uzlix4njlGD77bKoDmWXpftHZGdHIuTJX050/xTFdiP1TnC9XXpnS+5482fxt\nU8TsrvOpFOfsqlURr7ySRVdXRHd3qrm6eXP9ddjXl8XDD09syxZCo23tmjURv/iLeZxxRsT55+ex\ndetY/OZv1h+jRs6JZrsPzXQsl3t79+5tjwMH2uLVVyPGX4+XXz4aV145/fk21f3iO9/piDPPHP/p\napsy27a82E8jIyvjxImROO+8PLZtG41HHmns3JpuH4/flh/9qD7l5vi2qdZM53cZ+rtl0WhbxOJq\n5P5fVp4doTl0dXVO+Z4VXzQVS+Mb16oza2aaBbnUq1TG5+yf6yqk6f7GdDMDF/s4Trc/Z7Ovi+08\nciTqUvRcccXk9ciKB5FNm8biwIG2Sjqe170uzVD+lV+p/36zWeU2m31WOzBRu1IhDXYuXirNhTT+\nHNqxYyQOHsyiu7s5Zj4vlulmcJrdubRa5d7c6GrQol0o6uUVq0D/4R9STcKLL04zw8fPZl+s/TCb\n1QFzTScz2xpl46+tYhvXrKlN1Za2fTYrouZisa73ye4la9ZEXHLJzHU2p7Oc10vtMfz7v2+PtWtT\nLaTalQ6LlXpoNjWBFmr/tErbFLEwq4BqV/TV1rzauLF+xfdip0Ofzcr7Ro7RQn1mqTRyLJc7R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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cd37e48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,10))\n",
+    "plt.scatter(reg_linear.predict(X_train), reg_linear.predict(X_train) - y_train, c='b', s=40, alpha=0.5)\n",
+    "plt.scatter(reg_linear.predict(X_test), reg_linear.predict(X_test) - y_test, c='g', s=40)\n",
+    "plt.hlines(y=0, xmin=1.07, xmax = 1.17)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pred: 1.110524, actual: 1.110180\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"pred: %f, actual: %f\" % (reg_linear.predict(X_test[0,:].reshape(1,-1)), y_test[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Try ridge regression, to be more robust to correlation in features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "hideCode": true,
+    "hideOutput": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:40: RuntimeWarning: scipy.linalg.solve\n",
+      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
+      "Reciprocal condition number/precision: 6.576732214380598e-11 / 5.960464477539063e-08\n",
+      "  RuntimeWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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q/OzQ0Bby82u4/9EMDw+u7xLqBH1cnvvuaavVn31zWdv4+kgT47urZfNmNa7L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\ndQYEqmhTAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cc854a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mse train all feature:  9.51813e-07\n",
+      "mse test all feature:  1.49011e-06\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LQ/CFZW2uyqp8Hijm8+DNePhOIZUVC60InO0756oYz3WDXcyKyVzf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\nqJEIfYJPf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OKLeOWVV7Bz50709vbiz/7sz/CDH/wADz30EG655ZaK32ltbcX27dvxjW98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\n+tKXEI/HAQDxeBxNTU0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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23a3cf90390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reg = linear_model.Ridge (alpha = .5)\n",
+    "reg.fit(X_train, y_train) \n",
+    "reg.coef_\n",
+    "\n",
+    "\n",
+    "df_coeff = pd.DataFrame(columns=cols\n",
+    "                     , data=[list(reg.coef_)]\n",
+    "                     , index=[\"ridge regression coefficients\"])\n",
+    "\n",
+    "\n",
+    "\n",
+    "# predict all examples and compare to actuals\n",
+    "plt.scatter(reg.predict(X_test), y_test)\n",
+    "plt.xlabel(\"predicted\")\n",
+    "plt.ylabel(\"actual\")\n",
+    "plt.title(\"predicted v actual close_bid\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "df_coeff.sort_values(by='ridge regression coefficients', axis=1)\n",
+    "\n",
+    "print(\"mse train all feature: \", np.mean((reg.predict(X_train) - y_train) ** 2))\n",
+    "print(\"mse test all feature: \", np.mean((reg.predict(X_test) - y_test) ** 2))\n",
+    "\n",
+    "plt.figure(figsize=(30,10))\n",
+    "plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train, c='b', s=40, alpha=0.5)\n",
+    "plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, c='g', s=40)\n",
+    "plt.hlines(y=0, xmin=1.07, xmax = 1.17)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run LSTM Model to predict close bid in next 15 min\n",
+    "- scale features to range 0-1 to speed up convergence\n",
+    "- set a larger lookback windows, so LSTM has something to work with and can take a decision which part of history to prioritise\n",
+    "- todo: make it do error on the sign- for that need to get the sign between next and X[bid]\n",
+    "- 1% test size, 10% of 99% validation size\n",
+    "- try to also predict direction. y_pred - X_test close versus y_act - X_test close"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict sign only"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if runLSTMBinary:\n",
+    "    # Scale and create datasets\n",
+    "    idx_close_bid = df.columns.tolist().index('close_bid')\n",
+    "    df_np = df.values.astype('float32')\n",
+    "\n",
+    "    # Scale the examples\n",
+    "    df_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    df_scaled = df_scaler.fit_transform(df_np)\n",
+    "\n",
+    "    # Scale the actuals columns, but not the real values\n",
+    "    y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    t_y = df['close_bid'].values.astype('float32')\n",
+    "    t_y = np.reshape(t_y, (-1, 1))\n",
+    "    y_scaler = y_scaler.fit(t_y) # create a fitted y scaler\n",
+    "\n",
+    "    # Set look_back to 20 which is 5 hours (15min*20)\n",
+    "    X, y_orig = create_training_set(df_scaled, nb_lookback_rows=40)\n",
+    "    #y_return_sign = np.sign(y[:,idx_close_bid] - X[:,0,idx_close_bid]) # these are the actuals\n",
+    "    y_return_sign = y_orig[:,idx_close_bid] - X[:,0,idx_close_bid] # these are the actuals\n",
+    "    y = np.sign(y_return_sign) # an array of -1, 1 and 0\n",
+    "\n",
+    "    y = pd.get_dummies(y).values.astype('float32')\n",
+    "\n",
+    "\n",
+    "\n",
+    "    # need to create a binarised vector, one positive class, one negative class\n",
+    "    #y_pred_return_sign = # comes from model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict exact price value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if not runLSTMBinary:\n",
+    "    # Scale and create datasets\n",
+    "    idx_close_bid = df.columns.tolist().index('close_bid')\n",
+    "    #idx_high = df.columns.tolist().index('high_bid')\n",
+    "    #idx_low = df.columns.tolist().index('low_bid')\n",
+    "    df_np = df.values.astype('float32')\n",
+    "\n",
+    "    # Scale the examples\n",
+    "    df_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    df_scaled = df_scaler.fit_transform(df_np)\n",
+    "\n",
+    "    # Scale the actuals columns, but not the real values\n",
+    "    y_scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "    t_y = df['close_bid'].values.astype('float32')\n",
+    "    t_y = np.reshape(t_y, (-1, 1))\n",
+    "    y_scaler = y_scaler.fit(t_y) # create a fitted y scaler\n",
+    "\n",
+    "    # Set look_back to 20 which is 5 hours (15min*20)\n",
+    "    X, y = create_training_set(df_scaled, nb_lookback_rows=40)\n",
+    "    y = y[:,idx_close_bid]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10163, 40, 20)\n",
+      "(10163,)\n",
+      "(10061, 40, 20)\n",
+      "(102, 40, 20)\n",
+      "(10061,)\n",
+      "(102,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set training data size\n",
+    "# We have a large enough dataset. So divid into 99% training and val (10% of those 99%) / 1% test set\n",
+    "import sklearn\n",
+    "sklearn.__version__\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.01, shuffle=False)       \n",
+    "check_shape(X, y, X_train, X_test, y_train, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.backend import categorical_crossentropy\n",
+    "# ensure there is a 1st derivative! else get none error\n",
+    "# it will check in which direction this error goesn down and walk there\n",
+    "# maybe it has issue because sign is not continuous...\n",
+    "# just use categorical crossentropy, it does exactly what i need much better\n",
+    "def ret_direction_error(y_true, y_pred):\n",
+    "    \n",
+    "    # this guy puts everything into numpy before working on it https://stackoverflow.com/questions/46411573/keras-custom-loss-function-not-working\n",
+    "     \n",
+    "    out = categorical_crossentropy(y_true, y_pred)\n",
+    "    \n",
+    "    return out\n",
+    "    \n",
+    "    # y_true is y_train, y_pred is what the model gives me\n",
+    "    # so should set y_true to the return sign? and stop training on absolute value, but make sure sign is right"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "lstm_6 (LSTM)                (None, 40, 40)            9760      \n",
+      "_________________________________________________________________\n",
+      "lstm_7 (LSTM)                (None, 40, 20)            4880      \n",
+      "_________________________________________________________________\n",
+      "lstm_8 (LSTM)                (None, 40, 10)            1240      \n",
+      "_________________________________________________________________\n",
+      "lstm_9 (LSTM)                (None, 40, 10)            840       \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 40, 10)            0         \n",
+      "_________________________________________________________________\n",
+      "lstm_10 (LSTM)               (None, 5)                 320       \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 5)                 30        \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 1)                 6         \n",
+      "=================================================================\n",
+      "Total params: 17,076\n",
+      "Trainable params: 17,076\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Input, LSTM, Dense\n",
+    "import keras.backend as K\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# create a small LSTM network\n",
+    "# shoudl first input number match nb of lookback rows?\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(40, input_shape=(X.shape[1], X.shape[2]), return_sequences=True)) # does not take into account nb examples\n",
+    "model.add(LSTM(20, return_sequences=True))\n",
+    "model.add(LSTM(10, return_sequences=True))\n",
+    "model.add(LSTM(10, return_sequences=True)) # a second layer of 10 really helps get the loos to 7 by 10th epoch\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(LSTM(5, return_sequences=False))\n",
+    "model.add(Dense(5, kernel_initializer='uniform', activation='relu'))\n",
+    "\n",
+    "# for price prediction\n",
+    "if not runLSTMBinary:\n",
+    "    model.add(Dense(1, kernel_initializer='uniform', activation='relu')) # this compresses everything to one output in the final layer\n",
+    "    #model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mae', 'mse', 'accuracy'])\n",
+    "    model.compile(loss='mse', optimizer='adam', metrics=['mae', 'mse', 'accuracy'])\n",
+    "    \n",
+    "    \n",
+    "# for direction prediction\n",
+    "if runLSTMBinary:\n",
+    "    # need a softmax output for category predictions\n",
+    "    model.add(Dense(3, activation=\"softmax\"))   \n",
+    "\n",
+    "    # loss: optimises this - https://keras.io/losses/\n",
+    "    # loss will show up in the history under 'loss'\n",
+    "    model.compile(loss=ret_direction_error, optimizer='adam', metrics=['mae', 'mse', ret_direction_error, 'accuracy'])\n",
+    "    \n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simname = \"500_epochs_40_lookback_pca_unshuffled_binary\"\n",
+    "sim_desc = \"added directional errors checking and pca as feature with unshuffled data\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00000: val_mean_squared_error improved from inf to 0.06875, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00001: val_mean_squared_error improved from 0.06875 to 0.01363, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00002: val_mean_squared_error improved from 0.01363 to 0.01334, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00003: val_mean_squared_error improved from 0.01334 to 0.01233, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00004: val_mean_squared_error did not improve\n",
+      "Epoch 00005: val_mean_squared_error did not improve\n",
+      "Epoch 00006: val_mean_squared_error improved from 0.01233 to 0.01223, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00007: val_mean_squared_error improved from 0.01223 to 0.00177, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00008: val_mean_squared_error improved from 0.00177 to 0.00150, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00009: val_mean_squared_error improved from 0.00150 to 0.00090, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00010: val_mean_squared_error improved from 0.00090 to 0.00043, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00011: val_mean_squared_error did not improve\n",
+      "Epoch 00012: val_mean_squared_error did not improve\n",
+      "Epoch 00013: val_mean_squared_error did not improve\n",
+      "Epoch 00014: val_mean_squared_error did not improve\n",
+      "Epoch 00015: val_mean_squared_error improved from 0.00043 to 0.00028, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00016: val_mean_squared_error did not improve\n",
+      "Epoch 00017: val_mean_squared_error did not improve\n",
+      "Epoch 00018: val_mean_squared_error improved from 0.00028 to 0.00018, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00019: val_mean_squared_error improved from 0.00018 to 0.00017, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00020: val_mean_squared_error did not improve\n",
+      "Epoch 00021: val_mean_squared_error did not improve\n",
+      "Epoch 00022: val_mean_squared_error did not improve\n",
+      "Epoch 00023: val_mean_squared_error improved from 0.00017 to 0.00012, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00024: val_mean_squared_error did not improve\n",
+      "Epoch 00025: val_mean_squared_error did not improve\n",
+      "Epoch 00026: val_mean_squared_error did not improve\n",
+      "Epoch 00027: val_mean_squared_error did not improve\n",
+      "Epoch 00028: val_mean_squared_error did not improve\n",
+      "Epoch 00029: val_mean_squared_error did not improve\n",
+      "Epoch 00030: val_mean_squared_error did not improve\n",
+      "Epoch 00031: val_mean_squared_error did not improve\n",
+      "Epoch 00032: val_mean_squared_error did not improve\n",
+      "Epoch 00033: val_mean_squared_error improved from 0.00012 to 0.00011, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00034: val_mean_squared_error did not improve\n",
+      "Epoch 00035: val_mean_squared_error did not improve\n",
+      "Epoch 00036: val_mean_squared_error did not improve\n",
+      "Epoch 00037: val_mean_squared_error improved from 0.00011 to 0.00008, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00038: val_mean_squared_error did not improve\n",
+      "Epoch 00039: val_mean_squared_error did not improve\n",
+      "Epoch 00040: val_mean_squared_error did not improve\n",
+      "Epoch 00041: val_mean_squared_error did not improve\n",
+      "Epoch 00042: val_mean_squared_error improved from 0.00008 to 0.00008, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00043: val_mean_squared_error improved from 0.00008 to 0.00007, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00044: val_mean_squared_error did not improve\n",
+      "Epoch 00045: val_mean_squared_error improved from 0.00007 to 0.00007, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00046: val_mean_squared_error did not improve\n",
+      "Epoch 00047: val_mean_squared_error did not improve\n",
+      "Epoch 00048: val_mean_squared_error did not improve\n",
+      "Epoch 00049: val_mean_squared_error improved from 0.00007 to 0.00006, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00050: val_mean_squared_error did not improve\n",
+      "Epoch 00051: val_mean_squared_error did not improve\n",
+      "Epoch 00052: val_mean_squared_error did not improve\n",
+      "Epoch 00053: val_mean_squared_error did not improve\n",
+      "Epoch 00054: val_mean_squared_error did not improve\n",
+      "Epoch 00055: val_mean_squared_error did not improve\n",
+      "Epoch 00056: val_mean_squared_error did not improve\n",
+      "Epoch 00057: val_mean_squared_error did not improve\n",
+      "Epoch 00058: val_mean_squared_error did not improve\n",
+      "Epoch 00059: val_mean_squared_error did not improve\n",
+      "Epoch 00060: val_mean_squared_error did not improve\n",
+      "Epoch 00061: val_mean_squared_error improved from 0.00006 to 0.00005, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00062: val_mean_squared_error improved from 0.00005 to 0.00004, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00063: val_mean_squared_error did not improve\n",
+      "Epoch 00064: val_mean_squared_error did not improve\n",
+      "Epoch 00065: val_mean_squared_error did not improve\n",
+      "Epoch 00066: val_mean_squared_error did not improve\n",
+      "Epoch 00067: val_mean_squared_error did not improve\n",
+      "Epoch 00068: val_mean_squared_error did not improve\n",
+      "Epoch 00069: val_mean_squared_error did not improve\n",
+      "Epoch 00070: val_mean_squared_error did not improve\n",
+      "Epoch 00071: val_mean_squared_error did not improve\n",
+      "Epoch 00072: val_mean_squared_error improved from 0.00004 to 0.00004, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00073: val_mean_squared_error did not improve\n",
+      "Epoch 00074: val_mean_squared_error did not improve\n",
+      "Epoch 00075: val_mean_squared_error did not improve\n",
+      "Epoch 00076: val_mean_squared_error did not improve\n",
+      "Epoch 00077: val_mean_squared_error did not improve\n",
+      "Epoch 00078: val_mean_squared_error did not improve\n",
+      "Epoch 00079: val_mean_squared_error did not improve\n",
+      "Epoch 00080: val_mean_squared_error did not improve\n",
+      "Epoch 00081: val_mean_squared_error did not improve\n",
+      "Epoch 00082: val_mean_squared_error did not improve\n",
+      "Epoch 00083: val_mean_squared_error did not improve\n",
+      "Epoch 00084: val_mean_squared_error did not improve\n",
+      "Epoch 00085: val_mean_squared_error did not improve\n",
+      "Epoch 00086: val_mean_squared_error did not improve\n",
+      "Epoch 00087: val_mean_squared_error did not improve\n",
+      "Epoch 00088: val_mean_squared_error did not improve\n",
+      "Epoch 00089: val_mean_squared_error did not improve\n",
+      "Epoch 00090: val_mean_squared_error did not improve\n",
+      "Epoch 00091: val_mean_squared_error did not improve\n",
+      "Epoch 00092: val_mean_squared_error did not improve\n",
+      "Epoch 00093: val_mean_squared_error did not improve\n",
+      "Epoch 00094: val_mean_squared_error did not improve\n",
+      "Epoch 00095: val_mean_squared_error did not improve\n",
+      "Epoch 00096: val_mean_squared_error did not improve\n",
+      "Epoch 00097: val_mean_squared_error did not improve\n",
+      "Epoch 00098: val_mean_squared_error did not improve\n",
+      "Epoch 00099: val_mean_squared_error did not improve\n",
+      "Epoch 00100: val_mean_squared_error did not improve\n",
+      "Epoch 00101: val_mean_squared_error did not improve\n",
+      "Epoch 00102: val_mean_squared_error did not improve\n",
+      "Epoch 00103: val_mean_squared_error did not improve\n",
+      "Epoch 00104: val_mean_squared_error did not improve\n",
+      "Epoch 00105: val_mean_squared_error did not improve\n",
+      "Epoch 00106: val_mean_squared_error improved from 0.00004 to 0.00003, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00107: val_mean_squared_error did not improve\n",
+      "Epoch 00108: val_mean_squared_error did not improve\n",
+      "Epoch 00109: val_mean_squared_error did not improve\n",
+      "Epoch 00110: val_mean_squared_error did not improve\n",
+      "Epoch 00111: val_mean_squared_error did not improve\n",
+      "Epoch 00112: val_mean_squared_error did not improve\n",
+      "Epoch 00113: val_mean_squared_error did not improve\n",
+      "Epoch 00114: val_mean_squared_error did not improve\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00115: val_mean_squared_error did not improve\n",
+      "Epoch 00116: val_mean_squared_error did not improve\n",
+      "Epoch 00117: val_mean_squared_error did not improve\n",
+      "Epoch 00118: val_mean_squared_error did not improve\n",
+      "Epoch 00119: val_mean_squared_error did not improve\n",
+      "Epoch 00120: val_mean_squared_error did not improve\n",
+      "Epoch 00121: val_mean_squared_error did not improve\n",
+      "Epoch 00122: val_mean_squared_error did not improve\n",
+      "Epoch 00123: val_mean_squared_error did not improve\n",
+      "Epoch 00124: val_mean_squared_error did not improve\n",
+      "Epoch 00125: val_mean_squared_error did not improve\n",
+      "Epoch 00126: val_mean_squared_error did not improve\n",
+      "Epoch 00127: val_mean_squared_error did not improve\n",
+      "Epoch 00128: val_mean_squared_error did not improve\n",
+      "Epoch 00129: val_mean_squared_error did not improve\n",
+      "Epoch 00130: val_mean_squared_error did not improve\n",
+      "Epoch 00131: val_mean_squared_error did not improve\n",
+      "Epoch 00132: val_mean_squared_error did not improve\n",
+      "Epoch 00133: val_mean_squared_error did not improve\n",
+      "Epoch 00134: val_mean_squared_error did not improve\n",
+      "Epoch 00135: val_mean_squared_error did not improve\n",
+      "Epoch 00136: val_mean_squared_error did not improve\n",
+      "Epoch 00137: val_mean_squared_error did not improve\n",
+      "Epoch 00138: val_mean_squared_error did not improve\n",
+      "Epoch 00139: val_mean_squared_error did not improve\n",
+      "Epoch 00140: val_mean_squared_error did not improve\n",
+      "Epoch 00141: val_mean_squared_error did not improve\n",
+      "Epoch 00142: val_mean_squared_error did not improve\n",
+      "Epoch 00143: val_mean_squared_error did not improve\n",
+      "Epoch 00144: val_mean_squared_error did not improve\n",
+      "Epoch 00145: val_mean_squared_error did not improve\n",
+      "Epoch 00146: val_mean_squared_error did not improve\n",
+      "Epoch 00147: val_mean_squared_error did not improve\n",
+      "Epoch 00148: val_mean_squared_error did not improve\n",
+      "Epoch 00149: val_mean_squared_error did not improve\n",
+      "Epoch 00150: val_mean_squared_error did not improve\n",
+      "Epoch 00151: val_mean_squared_error did not improve\n",
+      "Epoch 00152: val_mean_squared_error did not improve\n",
+      "Epoch 00153: val_mean_squared_error did not improve\n",
+      "Epoch 00154: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00155: val_mean_squared_error did not improve\n",
+      "Epoch 00156: val_mean_squared_error did not improve\n",
+      "Epoch 00157: val_mean_squared_error did not improve\n",
+      "Epoch 00158: val_mean_squared_error did not improve\n",
+      "Epoch 00159: val_mean_squared_error did not improve\n",
+      "Epoch 00160: val_mean_squared_error did not improve\n",
+      "Epoch 00161: val_mean_squared_error did not improve\n",
+      "Epoch 00162: val_mean_squared_error did not improve\n",
+      "Epoch 00163: val_mean_squared_error did not improve\n",
+      "Epoch 00164: val_mean_squared_error did not improve\n",
+      "Epoch 00165: val_mean_squared_error did not improve\n",
+      "Epoch 00166: val_mean_squared_error did not improve\n",
+      "Epoch 00167: val_mean_squared_error did not improve\n",
+      "Epoch 00168: val_mean_squared_error did not improve\n",
+      "Epoch 00169: val_mean_squared_error did not improve\n",
+      "Epoch 00170: val_mean_squared_error did not improve\n",
+      "Epoch 00171: val_mean_squared_error did not improve\n",
+      "Epoch 00172: val_mean_squared_error did not improve\n",
+      "Epoch 00173: val_mean_squared_error did not improve\n",
+      "Epoch 00174: val_mean_squared_error did not improve\n",
+      "Epoch 00175: val_mean_squared_error did not improve\n",
+      "Epoch 00176: val_mean_squared_error did not improve\n",
+      "Epoch 00177: val_mean_squared_error improved from 0.00003 to 0.00003, saving model to 500_epochs_40_lookback_pca_unshuffled_binary.weights.best.hdf5\n",
+      "Epoch 00178: val_mean_squared_error did not improve\n",
+      "Epoch 00179: val_mean_squared_error did not improve\n",
+      "Epoch 00180: val_mean_squared_error did not improve\n",
+      "Epoch 00181: val_mean_squared_error did not improve\n",
+      "Epoch 00182: val_mean_squared_error did not improve\n",
+      "Epoch 00183: val_mean_squared_error did not improve\n",
+      "Epoch 00184: val_mean_squared_error did not improve\n",
+      "Epoch 00185: val_mean_squared_error did not improve\n",
+      "Epoch 00186: val_mean_squared_error did not improve\n",
+      "Epoch 00187: val_mean_squared_error did not improve\n",
+      "Epoch 00188: val_mean_squared_error did not improve\n",
+      "Epoch 00189: val_mean_squared_error did not improve\n",
+      "Epoch 00190: val_mean_squared_error did not improve\n",
+      "Epoch 00191: val_mean_squared_error did not improve\n",
+      "Epoch 00192: val_mean_squared_error did not improve\n",
+      "Epoch 00193: val_mean_squared_error did not improve\n",
+      "Epoch 00194: val_mean_squared_error did not improve\n",
+      "Epoch 00195: val_mean_squared_error did not improve\n",
+      "Epoch 00196: val_mean_squared_error did not improve\n",
+      "Epoch 00197: val_mean_squared_error did not improve\n",
+      "Epoch 00198: val_mean_squared_error did not improve\n",
+      "Epoch 00199: val_mean_squared_error did not improve\n",
+      "Epoch 00200: val_mean_squared_error did not improve\n",
+      "Epoch 00201: val_mean_squared_error did not improve\n",
+      "Epoch 00202: val_mean_squared_error did not improve\n",
+      "Epoch 00203: val_mean_squared_error did not improve\n",
+      "Epoch 00204: val_mean_squared_error did not improve\n",
+      "Epoch 00205: val_mean_squared_error did not improve\n",
+      "Epoch 00206: val_mean_squared_error did not improve\n",
+      "Epoch 00207: val_mean_squared_error did not improve\n",
+      "Epoch 00208: val_mean_squared_error did not improve\n",
+      "Epoch 00209: val_mean_squared_error did not improve\n",
+      "Epoch 00210: val_mean_squared_error did not improve\n",
+      "Epoch 00211: val_mean_squared_error did not improve\n",
+      "Epoch 00212: val_mean_squared_error did not improve\n",
+      "Epoch 00213: val_mean_squared_error did not improve\n",
+      "Epoch 00214: val_mean_squared_error did not improve\n",
+      "Epoch 00215: val_mean_squared_error did not improve\n",
+      "Epoch 00216: val_mean_squared_error did not improve\n",
+      "Epoch 00217: val_mean_squared_error did not improve\n",
+      "Epoch 00218: val_mean_squared_error did not improve\n",
+      "Epoch 00219: val_mean_squared_error did not improve\n",
+      "Epoch 00220: val_mean_squared_error did not improve\n",
+      "Epoch 00221: val_mean_squared_error did not improve\n",
+      "Epoch 00222: val_mean_squared_error did not improve\n",
+      "Epoch 00223: val_mean_squared_error did not improve\n",
+      "Epoch 00224: val_mean_squared_error did not improve\n",
+      "Epoch 00225: val_mean_squared_error did not improve\n",
+      "Epoch 00226: val_mean_squared_error did not improve\n",
+      "Epoch 00227: val_mean_squared_error did not improve\n",
+      "Epoch 00228: val_mean_squared_error did not improve\n",
+      "Epoch 00229: val_mean_squared_error did not improve\n",
+      "Epoch 00230: val_mean_squared_error did not improve\n",
+      "Epoch 00231: val_mean_squared_error did not improve\n",
+      "Epoch 00232: val_mean_squared_error did not improve\n",
+      "Epoch 00233: val_mean_squared_error did not improve\n",
+      "Epoch 00234: val_mean_squared_error did not improve\n",
+      "Epoch 00235: val_mean_squared_error did not improve\n",
+      "Epoch 00236: val_mean_squared_error did not improve\n",
+      "Epoch 00237: val_mean_squared_error did not improve\n",
+      "Epoch 00238: val_mean_squared_error did not improve\n",
+      "Epoch 00239: val_mean_squared_error did not improve\n",
+      "Epoch 00240: val_mean_squared_error did not improve\n",
+      "Epoch 00241: val_mean_squared_error did not improve\n",
+      "Epoch 00242: val_mean_squared_error did not improve\n",
+      "Epoch 00243: val_mean_squared_error did not improve\n",
+      "Epoch 00244: val_mean_squared_error did not improve\n",
+      "Epoch 00245: val_mean_squared_error did not improve\n",
+      "Epoch 00246: val_mean_squared_error did not improve\n",
+      "Epoch 00247: val_mean_squared_error did not improve\n",
+      "Epoch 00248: val_mean_squared_error did not improve\n",
+      "Epoch 00249: val_mean_squared_error did not improve\n",
+      "Epoch 00250: val_mean_squared_error did not improve\n",
+      "Epoch 00251: val_mean_squared_error did not improve\n",
+      "Epoch 00252: val_mean_squared_error did not improve\n",
+      "Epoch 00253: val_mean_squared_error did not improve\n",
+      "Epoch 00254: val_mean_squared_error did not improve\n",
+      "Epoch 00255: val_mean_squared_error did not improve\n",
+      "Epoch 00256: val_mean_squared_error did not improve\n",
+      "Epoch 00257: val_mean_squared_error did not improve\n",
+      "Epoch 00258: val_mean_squared_error did not improve\n",
+      "Epoch 00259: val_mean_squared_error did not improve\n",
+      "Epoch 00260: val_mean_squared_error did not improve\n",
+      "Epoch 00261: val_mean_squared_error did not improve\n",
+      "Epoch 00262: val_mean_squared_error did not improve\n",
+      "Epoch 00263: val_mean_squared_error did not improve\n",
+      "Epoch 00264: val_mean_squared_error did not improve\n",
+      "Epoch 00265: val_mean_squared_error did not improve\n",
+      "Epoch 00266: val_mean_squared_error did not improve\n",
+      "Epoch 00267: val_mean_squared_error did not improve\n",
+      "Epoch 00268: val_mean_squared_error did not improve\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00269: val_mean_squared_error did not improve\n",
+      "Epoch 00270: val_mean_squared_error did not improve\n",
+      "Epoch 00271: val_mean_squared_error did not improve\n",
+      "Epoch 00272: val_mean_squared_error did not improve\n",
+      "Epoch 00273: val_mean_squared_error did not improve\n",
+      "Epoch 00274: val_mean_squared_error did not improve\n",
+      "Epoch 00275: val_mean_squared_error did not improve\n",
+      "Epoch 00276: val_mean_squared_error did not improve\n",
+      "Epoch 00277: val_mean_squared_error did not improve\n",
+      "Epoch 00278: val_mean_squared_error did not improve\n",
+      "Epoch 00279: val_mean_squared_error did not improve\n",
+      "Epoch 00280: val_mean_squared_error did not improve\n",
+      "Epoch 00281: val_mean_squared_error did not improve\n",
+      "Epoch 00282: val_mean_squared_error did not improve\n",
+      "Epoch 00283: val_mean_squared_error did not improve\n",
+      "Epoch 00284: val_mean_squared_error did not improve\n",
+      "Epoch 00285: val_mean_squared_error did not improve\n",
+      "Epoch 00286: val_mean_squared_error did not improve\n",
+      "Epoch 00287: val_mean_squared_error did not improve\n",
+      "Epoch 00288: val_mean_squared_error did not improve\n",
+      "Epoch 00289: val_mean_squared_error did not improve\n",
+      "Epoch 00290: val_mean_squared_error did not improve\n",
+      "Epoch 00291: val_mean_squared_error did not improve\n",
+      "Epoch 00292: val_mean_squared_error did not improve\n",
+      "Epoch 00293: val_mean_squared_error did not improve\n",
+      "Epoch 00294: val_mean_squared_error did not improve\n",
+      "Epoch 00295: val_mean_squared_error did not improve\n",
+      "Epoch 00296: val_mean_squared_error did not improve\n",
+      "Epoch 00297: val_mean_squared_error did not improve\n",
+      "Epoch 00298: val_mean_squared_error did not improve\n",
+      "Epoch 00299: val_mean_squared_error did not improve\n",
+      "Epoch 00300: val_mean_squared_error did not improve\n",
+      "Epoch 00301: val_mean_squared_error did not improve\n",
+      "Epoch 00302: val_mean_squared_error did not improve\n",
+      "Epoch 00303: val_mean_squared_error did not improve\n",
+      "Epoch 00304: val_mean_squared_error did not improve\n",
+      "Epoch 00305: val_mean_squared_error did not improve\n",
+      "Epoch 00306: val_mean_squared_error did not improve\n",
+      "Epoch 00307: val_mean_squared_error did not improve\n",
+      "Epoch 00308: val_mean_squared_error did not improve\n",
+      "Epoch 00309: val_mean_squared_error did not improve\n",
+      "Epoch 00310: val_mean_squared_error did not improve\n",
+      "Epoch 00311: val_mean_squared_error did not improve\n",
+      "Epoch 00312: val_mean_squared_error did not improve\n",
+      "Epoch 00313: val_mean_squared_error did not improve\n",
+      "Epoch 00314: val_mean_squared_error did not improve\n",
+      "Epoch 00315: val_mean_squared_error did not improve\n",
+      "Epoch 00316: val_mean_squared_error did not improve\n",
+      "Epoch 00317: val_mean_squared_error did not improve\n",
+      "Epoch 00318: val_mean_squared_error did not improve\n",
+      "Epoch 00319: val_mean_squared_error did not improve\n",
+      "Epoch 00320: val_mean_squared_error did not improve\n",
+      "Epoch 00321: val_mean_squared_error did not improve\n",
+      "Epoch 00322: val_mean_squared_error did not improve\n",
+      "Epoch 00323: val_mean_squared_error did not improve\n",
+      "Epoch 00324: val_mean_squared_error did not improve\n",
+      "Epoch 00325: val_mean_squared_error did not improve\n",
+      "Epoch 00326: val_mean_squared_error did not improve\n",
+      "Epoch 00327: val_mean_squared_error did not improve\n",
+      "Epoch 00328: val_mean_squared_error did not improve\n",
+      "Epoch 00329: val_mean_squared_error did not improve\n",
+      "Epoch 00330: val_mean_squared_error did not improve\n",
+      "Epoch 00331: val_mean_squared_error did not improve\n",
+      "Epoch 00332: val_mean_squared_error did not improve\n",
+      "Epoch 00333: val_mean_squared_error did not improve\n",
+      "Epoch 00334: val_mean_squared_error did not improve\n",
+      "Epoch 00335: val_mean_squared_error did not improve\n",
+      "Epoch 00336: val_mean_squared_error did not improve\n",
+      "Epoch 00337: val_mean_squared_error did not improve\n",
+      "Epoch 00338: val_mean_squared_error did not improve\n",
+      "Epoch 00339: val_mean_squared_error did not improve\n",
+      "Epoch 00340: val_mean_squared_error did not improve\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-76-841b8888eacc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \"\"\"\n\u001b[1;32m     22\u001b[0m \u001b[0mcallbacks_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'time err = model.fit(X_train, y_train, epochs=epoch, batch_size=100, verbose=0, callbacks=callbacks_list, validation_split=0.1)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2156\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2157\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2160\u001b[0m     \u001b[1;31m#-------------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2077\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2078\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2080\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<decorator-gen-60>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\IPython\\core\\magics\\execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1178\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m             \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mexec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m             \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    861\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 863\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    864\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1428\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1429\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1430\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1432\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1077\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1078\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1079\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1080\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2266\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2267\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2268\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2269\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1122\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1124\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1125\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1319\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1322\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1325\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1328\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1329\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1304\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1305\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Save the best weight during training. Cant handel custom functions for errors\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "epoch = 500\n",
+    "\n",
+    "# write custom errors as string, they seem to refer to the key in err.history dict\n",
+    "if not runLSTMBinary:\n",
+    "    #checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_mean_absolute_error', verbose=1, save_best_only=True, mode='min')\n",
+    "    checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_mean_squared_error', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "if runLSTMBinary:\n",
+    "    checkpoint = ModelCheckpoint(simname + \".weights.best.hdf5\", monitor='val_ret_direction_error', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "# Fit\n",
+    "\"\"\"\n",
+    "it seems batch size controls convergence speed a lot! Batch size tells how many examples are propagated through the network.\n",
+    "Weights are adjusted based on results with these examples. This is useful if the full dataset takes too much memory\n",
+    "It also speeds up training, as you will converge quicker (dont have to wait for a full iteration of each example to adjust weights).\n",
+    "\n",
+    "With more features to train on, convergence seems slower. To get to the same level, i take more epochs.\n",
+    "\"\"\"\n",
+    "callbacks_list = [checkpoint]\n",
+    "%time err = model.fit(X_train, y_train, epochs=epoch, batch_size=100, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check testing errors\n",
+    "- it converges to a low number very fast - how can i get more detail\n",
+    "- but if i split using cross val split with random state, it takes ages to converge. Maybe i should only allow training on the past, as the model will always be used to predict the future. So training on random parts of the timeseries to predict other random parts might destroy historical trends that influence the future, and can be learned by the model.\n",
+    "- train it on directional error for more useful results. Need y_train - X_train[:,idx_close_bid] as feature and evaluate against y_true - X_train[:,idx_close_bid]\n",
+    "- it seems the model always predicts negative, so column zero\n",
+    "- its easier to optimise over mse than mae, because values bigger and would decline more, so better gradients.\n",
+    "\n",
+    "Issues:\n",
+    "- cannot checkpoint custom error functions\n",
+    "- cannot write custom error functions\n",
+    "- cannot debug custom error functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    4945.484375\n",
+      "dtype: float32\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>acc</th>\n",
+       "      <th>loss</th>\n",
+       "      <th>mean_absolute_error</th>\n",
+       "      <th>mean_squared_error</th>\n",
+       "      <th>val_acc</th>\n",
+       "      <th>val_loss</th>\n",
+       "      <th>val_mean_absolute_error</th>\n",
+       "      <th>val_mean_squared_error</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.108767</td>\n",
+       "      <td>0.108767</td>\n",
+       "      <td>0.018983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.121210</td>\n",
+       "      <td>0.121210</td>\n",
+       "      <td>0.019710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.075678</td>\n",
+       "      <td>0.075678</td>\n",
+       "      <td>0.009418</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.064085</td>\n",
+       "      <td>0.064085</td>\n",
+       "      <td>0.005192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.046499</td>\n",
+       "      <td>0.046499</td>\n",
+       "      <td>0.003853</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.075462</td>\n",
+       "      <td>0.075462</td>\n",
+       "      <td>0.006855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.039816</td>\n",
+       "      <td>0.039816</td>\n",
+       "      <td>0.002767</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.023818</td>\n",
+       "      <td>0.023818</td>\n",
+       "      <td>0.000790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.039482</td>\n",
+       "      <td>0.039482</td>\n",
+       "      <td>0.002768</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.030328</td>\n",
+       "      <td>0.030328</td>\n",
+       "      <td>0.001532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.033949</td>\n",
+       "      <td>0.033949</td>\n",
+       "      <td>0.002212</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.031965</td>\n",
+       "      <td>0.031965</td>\n",
+       "      <td>0.001608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.032927</td>\n",
+       "      <td>0.032927</td>\n",
+       "      <td>0.002076</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020286</td>\n",
+       "      <td>0.020286</td>\n",
+       "      <td>0.000780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.032203</td>\n",
+       "      <td>0.032203</td>\n",
+       "      <td>0.001955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.021150</td>\n",
+       "      <td>0.021150</td>\n",
+       "      <td>0.000868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.030479</td>\n",
+       "      <td>0.030479</td>\n",
+       "      <td>0.001798</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.019870</td>\n",
+       "      <td>0.019870</td>\n",
+       "      <td>0.000778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.030525</td>\n",
+       "      <td>0.030525</td>\n",
+       "      <td>0.001765</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.019043</td>\n",
+       "      <td>0.019043</td>\n",
+       "      <td>0.000673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.031537</td>\n",
+       "      <td>0.031537</td>\n",
+       "      <td>0.001842</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.040543</td>\n",
+       "      <td>0.040543</td>\n",
+       "      <td>0.002230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.029722</td>\n",
+       "      <td>0.029722</td>\n",
+       "      <td>0.001696</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020245</td>\n",
+       "      <td>0.020245</td>\n",
+       "      <td>0.000786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.028403</td>\n",
+       "      <td>0.028403</td>\n",
+       "      <td>0.001580</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.035359</td>\n",
+       "      <td>0.035359</td>\n",
+       "      <td>0.001807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.027680</td>\n",
+       "      <td>0.027680</td>\n",
+       "      <td>0.001506</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.038440</td>\n",
+       "      <td>0.038440</td>\n",
+       "      <td>0.002015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.027289</td>\n",
+       "      <td>0.027289</td>\n",
+       "      <td>0.001464</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.030535</td>\n",
+       "      <td>0.030535</td>\n",
+       "      <td>0.001432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.026903</td>\n",
+       "      <td>0.026903</td>\n",
+       "      <td>0.001394</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020303</td>\n",
+       "      <td>0.020303</td>\n",
+       "      <td>0.000738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.025939</td>\n",
+       "      <td>0.025939</td>\n",
+       "      <td>0.001288</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020524</td>\n",
+       "      <td>0.020524</td>\n",
+       "      <td>0.000752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.026659</td>\n",
+       "      <td>0.026659</td>\n",
+       "      <td>0.001294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.023020</td>\n",
+       "      <td>0.023020</td>\n",
+       "      <td>0.000686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.026742</td>\n",
+       "      <td>0.026742</td>\n",
+       "      <td>0.001302</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.022634</td>\n",
+       "      <td>0.022634</td>\n",
+       "      <td>0.000842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.000221</td>\n",
+       "      <td>0.023688</td>\n",
+       "      <td>0.023688</td>\n",
+       "      <td>0.001061</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.019073</td>\n",
+       "      <td>0.019073</td>\n",
+       "      <td>0.000619</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         acc      loss  mean_absolute_error  mean_squared_error  val_acc  \\\n",
+       "0   0.000221  0.108767             0.108767            0.018983      0.0   \n",
+       "1   0.000221  0.075678             0.075678            0.009418      0.0   \n",
+       "2   0.000221  0.046499             0.046499            0.003853      0.0   \n",
+       "3   0.000221  0.039816             0.039816            0.002767      0.0   \n",
+       "4   0.000221  0.039482             0.039482            0.002768      0.0   \n",
+       "5   0.000221  0.033949             0.033949            0.002212      0.0   \n",
+       "6   0.000221  0.032927             0.032927            0.002076      0.0   \n",
+       "7   0.000221  0.032203             0.032203            0.001955      0.0   \n",
+       "8   0.000221  0.030479             0.030479            0.001798      0.0   \n",
+       "9   0.000221  0.030525             0.030525            0.001765      0.0   \n",
+       "10  0.000221  0.031537             0.031537            0.001842      0.0   \n",
+       "11  0.000221  0.029722             0.029722            0.001696      0.0   \n",
+       "12  0.000221  0.028403             0.028403            0.001580      0.0   \n",
+       "13  0.000221  0.027680             0.027680            0.001506      0.0   \n",
+       "14  0.000221  0.027289             0.027289            0.001464      0.0   \n",
+       "15  0.000221  0.026903             0.026903            0.001394      0.0   \n",
+       "16  0.000221  0.025939             0.025939            0.001288      0.0   \n",
+       "17  0.000221  0.026659             0.026659            0.001294      0.0   \n",
+       "18  0.000221  0.026742             0.026742            0.001302      0.0   \n",
+       "19  0.000221  0.023688             0.023688            0.001061      0.0   \n",
+       "\n",
+       "    val_loss  val_mean_absolute_error  val_mean_squared_error  \n",
+       "0   0.121210                 0.121210                0.019710  \n",
+       "1   0.064085                 0.064085                0.005192  \n",
+       "2   0.075462                 0.075462                0.006855  \n",
+       "3   0.023818                 0.023818                0.000790  \n",
+       "4   0.030328                 0.030328                0.001532  \n",
+       "5   0.031965                 0.031965                0.001608  \n",
+       "6   0.020286                 0.020286                0.000780  \n",
+       "7   0.021150                 0.021150                0.000868  \n",
+       "8   0.019870                 0.019870                0.000778  \n",
+       "9   0.019043                 0.019043                0.000673  \n",
+       "10  0.040543                 0.040543                0.002230  \n",
+       "11  0.020245                 0.020245                0.000786  \n",
+       "12  0.035359                 0.035359                0.001807  \n",
+       "13  0.038440                 0.038440                0.002015  \n",
+       "14  0.030535                 0.030535                0.001432  \n",
+       "15  0.020303                 0.020303                0.000738  \n",
+       "16  0.020524                 0.020524                0.000752  \n",
+       "17  0.023020                 0.023020                0.000686  \n",
+       "18  0.022634                 0.022634                0.000842  \n",
+       "19  0.019073                 0.019073                0.000619  "
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#pd.DataFrame(model.predict(X_train))\n",
+    "print(pd.DataFrame(y_train).sum()) # classes are quite balanced\n",
+    "pd.DataFrame(err.history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "smallest validation MAE:  0.0190429590761\n",
+      "smallest validation MSE:  0.000619442572553\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"smallest validation MAE: \", min(err.history['val_mean_absolute_error']))\n",
+    "print(\"smallest validation MSE: \", min(err.history['val_mean_squared_error']))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": false,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "for error_metric in list(err.history.keys()):\n",
+    "    if 'val' not in error_metric:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(err.history[error_metric])\n",
+    "        plt.plot(err.history['val_' + error_metric])\n",
+    "        plt.title(error_metric, fontsize=30)\n",
+    "        plt.ylabel(error_metric)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left', fontsize=30)\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Rerun LSTM with decaying learning rate:\n",
+    "\n",
+    "As seen from the above, the model seems to have converged nicely, but the mean absolute error on the development data remains at ~0.003X which means the model is unusable in practice. Ideally, we want to get ~0.0005. Let's go back to the best weight, and decay the learning rate while retraining the model\n",
+    "\n",
+    "We need this to get inside the average bid offer spread for EUR/USD, so 1.10115 - 1.10110. But lets not forget the data is scaled. Maybe it looks better when we unscale it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# tune model by starting from best weights and rerunning with decaying learning rate\n",
+    "# Load the weight that worked the best\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "#epoch=60\n",
+    "\n",
+    "# Train again with decaying learning rate\n",
+    "from keras.callbacks import LearningRateScheduler\n",
+    "import keras.backend as K\n",
+    "\n",
+    "def scheduler(epoch):\n",
+    "    if epoch%2==0 and epoch!=0:\n",
+    "        lr = K.get_value(model.optimizer.lr)\n",
+    "        K.set_value(model.optimizer.lr, lr*.9)\n",
+    "        print(\"lr changed to {}\".format(lr*.9))\n",
+    "    return K.get_value(model.optimizer.lr)\n",
+    "lr_decay = LearningRateScheduler(scheduler) # do sth to learning rate\n",
+    "\n",
+    "callbacks_list = [checkpoint, lr_decay] # checkin with these once in a while\n",
+    "err_decay_lr = model.fit(X_train, y_train, epochs=int(epoch/3), batch_size=500, verbose=0, callbacks=callbacks_list, validation_split=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check testing errors after decaying learning rate\n",
+    " - here error chart resolution is better, as we start from the trained model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "smallest validation MAE:  0.0190429590761\n",
+      "smallest validation MSE:  0.000619442572553\n",
+      "decay lr: smallest validation MAE:  0.0123163131533\n",
+      "decay lr: smallest validation MSE:  0.000265824883329\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"smallest validation MAE: \", min(err.history['val_mean_absolute_error']))\n",
+    "print(\"smallest validation MSE: \", min(err.history['val_mean_squared_error']))\n",
+    "print(\"decay lr: smallest validation MAE: \", min(err_decay_lr.history['val_mean_absolute_error']))\n",
+    "print(\"decay lr: smallest validation MSE: \", min(err_decay_lr.history['val_mean_squared_error']))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "for error_metric in list(err_decay_lr.history.keys()):\n",
+    "    if 'val' not in error_metric:\n",
+    "        plt.figure(figsize=(40,10))\n",
+    "        plt.plot(err_decay_lr.history[error_metric]) # this is for train\n",
+    "        plt.plot(err_decay_lr.history['val_' + error_metric]) # this is for test\n",
+    "        plt.title(error_metric, fontsize=30)\n",
+    "        plt.ylabel(error_metric)\n",
+    "        plt.xlabel('epoch')\n",
+    "        plt.legend(['train', 'test'], loc='upper left', fontsize=30)\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "The variance should have improved slightly. However, unless the mean absolute error is small enough, the model is not usable in practice. This is mainly due to only using the sample data for training and limiting epoch to a few hundreds.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Check scaled predictions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#simname = \"500_epochs_40_lookback\"\n",
+    "model.load_weights(simname+\".weights.best.hdf5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "if not runLSTMBinary:\n",
+    "    # Benchmark\n",
+    "    model.load_weights(simname+\".weights.best.hdf5\")\n",
+    "\n",
+    "    X_test_pred = model.predict(X_test) # predict on testset\n",
+    "\n",
+    "    predictions = pd.DataFrame()\n",
+    "    predictions['predicted'] = pd.Series(np.reshape(X_test_pred, (X_test_pred.shape[0])))\n",
+    "    predictions['actual'] = y_test\n",
+    "    predictions = predictions.astype(float)\n",
+    "\n",
+    "\n",
+    "    fig, axarr = plt.subplots(1, 2, figsize=(15,5)) #1 row, 2 cols, x, y\n",
+    "    i_row, icol = 0,0\n",
+    "    fig.suptitle(\"predictions on test set\", fontsize=20)\n",
+    "    predictions.plot(ax=axarr[icol])\n",
+    "    axarr[icol].set_title(\"Predicted close vs actual over time\")\n",
+    "\n",
+    "    icol +=1\n",
+    "    predictions['diff'] = predictions['actual'] - predictions['predicted']\n",
+    "    sns.distplot(predictions['diff'], ax=axarr[icol]);\n",
+    "    axarr[icol].set_title('Distribution of differences: actual minus predicted')\n",
+    "    plt.show()\n",
+    "\n",
+    "    print(\"MSE scaled : \", mean_squared_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "    print(\"MAE scaled: \", mean_absolute_error(predictions['predicted'].values, predictions['actual'].values))\n",
+    "    #predictions['diff'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Check unscaled predictions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#unscale predictions and actuals\n",
+    "X_test_pred = model.predict(X_test)\n",
+    "X_test_pred_unscaled = y_scaler.inverse_transform(X_test_pred)\n",
+    "X_test_pred_unscaled = np.reshape(X_test_pred_unscaled, (X_test_pred_unscaled.shape[0]))\n",
+    "\n",
+    "actual = y_scaler.inverse_transform(np.reshape(y_test, (y_test.shape[0], 1)))\n",
+    "actual = np.reshape(actual, (actual.shape[0]))\n",
+    "\n",
+    "predictions = pd.DataFrame()\n",
+    "predictions['predicted'] = pd.Series(X_test_pred_unscaled)\n",
+    "predictions['close_bid'] = pd.Series(actual)\n",
+    "\n",
+    "\n",
+    "# get low and high bid from untransformed dataframe\n",
+    "p = df[-X_test_pred_unscaled.shape[0]:].copy()\n",
+    "predictions.index = p.index # get the date index  from the dataframe\n",
+    "predictions = predictions.astype(float)\n",
+    "predictions = predictions.merge(p[['low_bid', 'high_bid']], right_index=True, left_index=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "if not runLSTMBinary:\n",
+    "    fig, axarr = plt.subplots(1, 4, figsize=(5,10)) #1 row, 2 cols, x, y\n",
+    "    irow, icol = 0,0\n",
+    "\n",
+    "    predictions.plot(x=predictions.index, y='close_bid', c='red', figsize=(40,10), ax=axarr[icol])\n",
+    "    predictions.plot(x=predictions.index, y='predicted', c='blue', figsize=(40,10), ax=axarr[icol])\n",
+    "    index = [str(item) for item in predictions.index]\n",
+    "    #plt.fill_between(x=predictions.index, y1='low_bid', y2='high_bid', data=predictions, alpha=0.4)\n",
+    "    axarr[icol].set_title('Prediction vs Actual (low and high as blue region)')\n",
+    "\n",
+    "    icol += 1\n",
+    "    predictions['diff'] = predictions['predicted'] - predictions['close_bid']\n",
+    "    sns.distplot(predictions['diff'], ax=axarr[icol]);\n",
+    "    axarr[icol].set_title('Distribution of differences between actual and prediction ')\n",
+    "    #plt.savefig(simname+\"__histogram__actual_minus_pred.jpg\")\n",
+    "\n",
+    "    icol += 1\n",
+    "    sns.kdeplot(predictions[\"diff\"], predictions[\"predicted\"], kind=\"kde\", space=0, ax=axarr[icol])\n",
+    "    #sns.jointplot(predictions[\"diff\"], predictions[\"predicted\"], kind=\"kde\", space=0, ax=axarr[icol]) # must be by itself\n",
+    "    axarr[icol].set_title('Distribution of error and price')\n",
+    "    #plt.savefig(simname+\"__contour__error_v_price.jpg\")\n",
+    "\n",
+    "\n",
+    "    icol +=1\n",
+    "    predictions['correct'] = (predictions['predicted'] <= predictions['high_bid']) & (predictions['predicted'] >= predictions['low_bid'])\n",
+    "    predictions.correct.value_counts().plot(kind=\"bar\", ax=axarr[icol])\n",
+    "    axarr[icol].set_title(\"True (in high low range), False prediction counts\")\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "    print(\"MSE unscaled : \", mean_squared_error(predictions['predicted'].values, predictions['close_bid'].values))\n",
+    "    print(\"MAE unscaled: \", mean_absolute_error(predictions['predicted'].values, predictions['close_bid'].values))\n",
+    "    #predictions['diff'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## check binary predictions and confusion matrix\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if runLSTMBinary:\n",
+    "    from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "    #check_shape(y_test, model.predict(X_test))\n",
+    "\n",
+    "    y_pred_class = np.argmax(model.predict(X_test), axis=1)  # find position of largest argument\n",
+    "\n",
+    "    y_test_class = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "    test_acc = 100 * np.sum(y_pred_class==y_test_class) / len(y_test)\n",
+    "\n",
+    "    print(\"acc \", test_acc )\n",
+    "\n",
+    "    confusion_matrix(y_pred_class, y_test_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not runLSTMBinary:\n",
+    "    df_unscaled = df_scaler.inverse_transform(df_scaled)\n",
+    "\n",
+    "    X_test_unscaled = df_scaler.inverse_transform(np.reshape(X_test[:,0,:], (X_test.shape[0], X_test.shape[2])))\n",
+    "    y_prev = X_test_unscaled[:,idx_close_bid]\n",
+    "\n",
+    "    y_train_unscaled =  y_scaler.inverse_transform(np.reshape(y_train, (y_train.shape[0], 1)))\n",
+    "    y_train_unscaled = np.reshape(y_train_unscaled, (y_train_unscaled.shape[0]))\n",
+    "\n",
+    "    y_test_unscaled =  y_scaler.inverse_transform(np.reshape(y_test, (y_test.shape[0], 1)))\n",
+    "    y_test_unscaled = np.reshape(y_test_unscaled, (y_test_unscaled.shape[0]))\n",
+    "\n",
+    "\n",
+    "    X_train_pred = model.predict(X_train)\n",
+    "    X_train_pred_unscaled = y_scaler.inverse_transform(X_train_pred)\n",
+    "    X_train_pred_unscaled = np.reshape(X_train_pred_unscaled, (X_train_pred_unscaled.shape[0]))\n",
+    "\n",
+    "    #check_shape(df,y_train_unscaled, y_test_unscaled, X_train_pred_unscaled, X_test_pred_unscaled, y_prev)\n",
+    "\n",
+    "    df_err = check_error_metrics(df\n",
+    "                        , y_train_unscaled, y_test_unscaled\n",
+    "                        , X_train_pred_unscaled, X_test_pred_unscaled\n",
+    "                        , y_prev)\n",
+    "    #idx_close_bid\n",
+    "    #X_test[:,0,idx_close_bid].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "Sim results:\n",
+    "- runing at 500 epochs converges a bit better. seems extra features need more time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check logs and compare to previous simulations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log = True\n",
+    "initval=False\n",
+    "#sim_desc = \"500 iterations, lookback 40\"\n",
+    "#simname = \"500_epoch_lookback_40\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "list_stats=OrderedDict()\n",
+    "\n",
+    "if log:\n",
+    "    #simname= \"linear regression\"\n",
+    "    #sim_desc = \"1 row lookback\"\n",
+    "    \n",
+    "    dict_err= OrderedDict(zip(df_err[0], df_err[1]))\n",
+    "    \n",
+    "    list_stats=OrderedDict()\n",
+    "    \n",
+    "    list_stats[\"simname\"] = simname\n",
+    "    list_stats[\"sim_desc\"] = sim_desc\n",
+    "    list_stats[\"MSE\"] = dict_err[\"mse test all feature: \"]\n",
+    "    list_stats[\"MAE\"] = dict_err[\"mae test all feature: \"]\n",
+    "    \n",
+    "    differences_described = predictions[\"diff\"].describe()\n",
+    "\n",
+    "    list_stats.update(OrderedDict(differences_described))\n",
+    "    list_stats.update(dict_err)\n",
+    "    \n",
+    "    results = pd.DataFrame([list_stats])\n",
+    "    #results.to_excel(\"log_results.xlsx\")\n",
+    "    if os.path.isfile(\"log_results.xlsx\"):\n",
+    "        log_results = pd.read_excel(\"log_results.xlsx\")\n",
+    "        log_results.loc[len(log_results),:] = list_stats.values()\n",
+    "        log_results.to_excel(\"log_results.xlsx\")\n",
+    "    #log_results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.read_excel(\"log_results.xlsx\").T"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "celltoolbar": "Hide code",
+  "hide_code_all_hidden": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {
+    "height": "947px",
+    "left": "0px",
+    "right": "1568px",
+    "top": "67px",
+    "width": "264px"
+   },
+   "toc_section_display": "block",
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/capstone_project/mine_initial.weights.best.hdf5 b/capstone_project/mine_initial.weights.best.hdf5
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diff --git a/customer_segments/customer_segments.html b/customer_segments/customer_segments.html
new file mode 100644
index 0000000..2cdb73e
--- /dev/null
+++ b/customer_segments/customer_segments.html
@@ -0,0 +1,20007 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>customer_segments</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
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+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
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+  content: "\e227";
+}
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+  content: "\e227";
+}
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+  content: "\e227";
+}
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+  content: "\00a5";
+}
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+  content: "\00a5";
+}
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+}
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+  content: "\20bd";
+}
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+  content: "\e230";
+}
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+  content: "\e231";
+}
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+  content: "\e232";
+}
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+  content: "\e233";
+}
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+  content: "\e234";
+}
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+  content: "\e235";
+}
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+  content: "\e236";
+}
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+  content: "\e237";
+}
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+  content: "\e238";
+}
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+  content: "\e239";
+}
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+  content: "\e240";
+}
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+  content: "\e241";
+}
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+  content: "\e242";
+}
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+  content: "\e243";
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+}
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+  content: "\e245";
+}
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+  content: "\e246";
+}
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+  content: "\e247";
+}
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+  content: "\e248";
+}
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+  content: "\e249";
+}
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+  content: "\e250";
+}
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+  content: "\e251";
+}
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+  content: "\e252";
+}
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+}
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+  content: "\e254";
+}
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+  content: "\e255";
+}
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+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
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+h6 .small,
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+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
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+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
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+h3 .small,
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+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
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+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
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+  width: 75%;
+}
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+  width: 66.66666667%;
+}
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+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
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+  right: 66.66666667%;
+}
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+  right: 58.33333333%;
+}
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+  right: 50%;
+}
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+  right: 41.66666667%;
+}
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+  right: 33.33333333%;
+}
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+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
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+  left: 91.66666667%;
+}
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+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
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+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
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+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
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+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
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+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 21ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
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+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Unsupervised-Learning">Unsupervised Learning<a class="anchor-link" href="#Unsupervised-Learning">&#182;</a></h2><h2 id="Project:-Creating-Customer-Segments">Project: Creating Customer Segments<a class="anchor-link" href="#Project:-Creating-Customer-Segments">&#182;</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with <strong>'Implementation'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a <code>'TODO'</code> statement. Please be sure to read the instructions carefully!</p>
+<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
+<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#182;</a></h2><p>In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in <em>monetary units</em>) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.</p>
+<p>The dataset for this project can be found on the <a href="https://archive.ics.uci.edu/ml/datasets/Wholesale+customers">UCI Machine Learning Repository</a>. For the purposes of this project, the features <code>'Channel'</code> and <code>'Region'</code> will be excluded in the analysis — with focus instead on the six product categories recorded for customers.</p>
+<p>Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import libraries necessary for this project</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span> <span class="c1"># Allows the use of display() for DataFrames</span>
+
+<span class="c1"># Import supplementary visualizations code visuals.py</span>
+<span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>
+
+<span class="c1"># Pretty display for notebooks</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="c1"># Load the wholesale customers dataset</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;customers.csv&quot;</span><span class="p">)</span>
+    <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;Region&#39;</span><span class="p">,</span> <span class="s1">&#39;Channel&#39;</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
+    <span class="k">print</span> <span class="s2">&quot;Wholesale customers dataset has {} samples with {} features each.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;It shows the spending per wholesale customer on that category per year.&quot;</span><span class="p">)</span>
+    <span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)))</span>
+<span class="k">except</span><span class="p">:</span>
+    <span class="k">print</span> <span class="s2">&quot;Dataset could not be loaded. Is the dataset missing?&quot;</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Wholesale customers dataset has 440 samples with 6 features each.
+It shows the spending per wholesale customer on that category per year.
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Fresh</th>
+      <th>Milk</th>
+      <th>Grocery</th>
+      <th>Frozen</th>
+      <th>Detergents_Paper</th>
+      <th>Delicatessen</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>12669</td>
+      <td>9656</td>
+      <td>7561</td>
+      <td>214</td>
+      <td>2674</td>
+      <td>1338</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>7057</td>
+      <td>9810</td>
+      <td>9568</td>
+      <td>1762</td>
+      <td>3293</td>
+      <td>1776</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Data-Exploration">Data Exploration<a class="anchor-link" href="#Data-Exploration">&#182;</a></h2><p>In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.</p>
+<p>Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: <strong>'Fresh'</strong>, <strong>'Milk'</strong>, <strong>'Grocery'</strong>, <strong>'Frozen'</strong>, <strong>'Detergents_Paper'</strong>, and <strong>'Delicatessen'</strong>. Consider what each category represents in terms of products you could purchase.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display a description of the dataset</span>
+<span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span> <span class="c1"># shows per column statistics from dataset. Columns contain usd values for spending.</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
+
+<span class="c1"># normalise columns around mean to check distribution</span>
+<span class="n">norm_data</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="n">norm_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">norm_data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
+<span class="c1"># plot</span>
+<span class="c1">#norm_data.hist(bins=200)</span>
+<span class="c1"># they all seems quite biased to low values</span>
+
+<span class="c1"># describe</span>
+<span class="c1">#display(norm_data.describe())</span>
+
+
+<span class="c1">#norm_data.head(10)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Fresh</th>
+      <th>Milk</th>
+      <th>Grocery</th>
+      <th>Frozen</th>
+      <th>Detergents_Paper</th>
+      <th>Delicatessen</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>440.000000</td>
+      <td>440.000000</td>
+      <td>440.000000</td>
+      <td>440.000000</td>
+      <td>440.000000</td>
+      <td>440.000000</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>12000.297727</td>
+      <td>5796.265909</td>
+      <td>7951.277273</td>
+      <td>3071.931818</td>
+      <td>2881.493182</td>
+      <td>1524.870455</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>12647.328865</td>
+      <td>7380.377175</td>
+      <td>9503.162829</td>
+      <td>4854.673333</td>
+      <td>4767.854448</td>
+      <td>2820.105937</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>3.000000</td>
+      <td>55.000000</td>
+      <td>3.000000</td>
+      <td>25.000000</td>
+      <td>3.000000</td>
+      <td>3.000000</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>3127.750000</td>
+      <td>1533.000000</td>
+      <td>2153.000000</td>
+      <td>742.250000</td>
+      <td>256.750000</td>
+      <td>408.250000</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>8504.000000</td>
+      <td>3627.000000</td>
+      <td>4755.500000</td>
+      <td>1526.000000</td>
+      <td>816.500000</td>
+      <td>965.500000</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>16933.750000</td>
+      <td>7190.250000</td>
+      <td>10655.750000</td>
+      <td>3554.250000</td>
+      <td>3922.000000</td>
+      <td>1820.250000</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>112151.000000</td>
+      <td>73498.000000</td>
+      <td>92780.000000</td>
+      <td>60869.000000</td>
+      <td>40827.000000</td>
+      <td>47943.000000</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Selecting-Samples">Implementation: Selecting Samples<a class="anchor-link" href="#Implementation:-Selecting-Samples">&#182;</a></h3><p>To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add <strong>three</strong> indices of your choice to the <code>indices</code> list which will represent the customers to track. It is suggested to try different sets of samples until you obtain customers that vary significantly from one another.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[27]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Select three indices of your choice you wish to sample from the dataset</span>
+<span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">401</span><span class="p">,</span> <span class="mi">338</span><span class="p">,</span> <span class="mi">65</span><span class="p">]</span>
+
+<span class="n">retailer</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;Frozen&quot;</span><span class="p">]</span><span class="o">&gt;=</span><span class="mi">9000</span><span class="p">]</span>
+<span class="c1">#print(retailer)</span>
+<span class="n">market</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;Detergents_Paper&quot;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">8000</span><span class="p">]</span>
+<span class="c1">#print(market)</span>
+
+
+<span class="c1"># Create a DataFrame of the chosen samples</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;Chosen samples of wholesale customers dataset:&quot;</span>
+<span class="n">display</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
+
+
+<span class="c1"># show samples values as bars, also show whole dataset mean values as bars</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;50%&quot;</span><span class="p">])</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">])</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;25%&quot;</span><span class="p">])</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;75%&quot;</span><span class="p">])</span>
+
+
+<span class="n">samples_as_barchart</span> <span class="o">=</span> <span class="n">samples</span>
+
+<span class="c1">#samples_as_barchart = samples</span>
+<span class="n">samples_as_barchart</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">indices</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="s1">&#39;25%&#39;</span><span class="p">,</span> <span class="s1">&#39;75%&#39;</span><span class="p">]</span> <span class="c1"># add mean to x axis</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">samples_as_barchart</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;bar&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Chosen samples of wholesale customers dataset:
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Frozen</th>
+      <th>Fresh</th>
+      <th>Delicatessen</th>
+      <th>Detergents_Paper</th>
+      <th>Grocery</th>
+      <th>Milk</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>13223</td>
+      <td>27167</td>
+      <td>1902</td>
+      <td>92</td>
+      <td>2128</td>
+      <td>2801</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>15601</td>
+      <td>3</td>
+      <td>550</td>
+      <td>15</td>
+      <td>7021</td>
+      <td>333</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>36</td>
+      <td>85</td>
+      <td>1423</td>
+      <td>24231</td>
+      <td>45828</td>
+      <td>20959</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-1">Question 1<a class="anchor-link" href="#Question-1">&#182;</a></h3><p>Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers.<br>
+<em>What kind of establishment (customer) could each of the three samples you've chosen represent?</em><br>
+<strong>Hint:</strong> Examples of establishments include places like markets, cafes, and retailers, among many others. Avoid using names for establishments, such as saying <em>"McDonalds"</em> when describing a sample customer as a restaurant.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong></p>
+<ul>
+<li>Example 401 is above median on Fresh, Frozen, Deli, and below on rest. This is could be a food market as it would be higher than the median in those categories . </li>
+<li>Example 338 are above mean and median and 75 percentile on Frozen, above median on Grocery, below on rest so could be a fish shop selling frozen fish.</li>
+<li>Example 65 is a high multiple of the IQR on Grocery and milk and detergents paper - it could be a very popular cofee shop.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Feature-Relevance">Implementation: Feature Relevance<a class="anchor-link" href="#Implementation:-Feature-Relevance">&#182;</a></h3><p>One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.</p>
+<p>In the code block below, you will need to implement the following:</p>
+<ul>
+<li>Assign <code>new_data</code> a copy of the data by removing a feature of your choice using the <code>DataFrame.drop</code> function.</li>
+<li>Use <code>sklearn.cross_validation.train_test_split</code> to split the dataset into training and testing sets.<ul>
+<li>Use the removed feature as your target label. Set a <code>test_size</code> of <code>0.25</code> and set a <code>random_state</code>.</li>
+</ul>
+</li>
+<li>Import a decision tree regressor, set a <code>random_state</code>, and fit the learner to the training data.</li>
+<li>Report the prediction score of the testing set using the regressor's <code>score</code> function.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Make a copy of the DataFrame, using the &#39;drop&#39; function to drop the given feature</span>
+<span class="n">testDependentFeature</span> <span class="o">=</span> <span class="s2">&quot;Detergents_Paper&quot;</span>
+
+<span class="n">new_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="n">testDependentFeature</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">testDependentFeature</span><span class="p">]</span>
+<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="c1"># TODO: Split the data into training and testing sets using the given feature as the target</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">new_data</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+
+<span class="c1"># TODO: Create a decision tree regressor and fit it to the training set</span>
+<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
+<span class="n">regressor</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
+<span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+<span class="c1"># TODO: Report the score of the prediction using the testing set</span>
+<span class="n">score</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s2">&quot;R-squared for explaining {} is {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">testDependentFeature</span><span class="p">,</span> <span class="n">score</span><span class="p">))</span>
+<span class="c1"># Detergents_Paper seems to have highest R-squared.</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-2">Question 2<a class="anchor-link" href="#Question-2">&#182;</a></h3><p><em>Which feature did you attempt to predict? What was the reported prediction score? Is this feature necessary for identifying customers' spending habits?</em><br>
+<strong>Hint:</strong> The coefficient of determination, <code>R^2</code>, is scored between 0 and 1, with 1 being a perfect fit. A negative <code>R^2</code> implies the model fails to fit the data.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> I attempted to predict Detergent_Paper. The prediction score was 0.6606. This means this feature can be explained with around 66% accuracy using all the other features combined. I would say it is still required as this is only slightly better than chance.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Visualize-Feature-Distributions">Visualize Feature Distributions<a class="anchor-link" href="#Visualize-Feature-Distributions">&#182;</a></h3><p>To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Produce a scatter matrix for each pair of features in the data</span>
+<span class="c1"># plot each feature against each other feature in pairs.</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">scatter_matrix</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">diagonal</span> <span class="o">=</span> <span class="s1">&#39;kde&#39;</span><span class="p">);</span> 
+
+<span class="c1"># feature against feature produces the distribution of that feature</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># check feature distributions</span>
+<span class="n">data</span><span class="o">.</span><span class="n">Detergents_Paper</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-3">Question 3<a class="anchor-link" href="#Question-3">&#182;</a></h3><p><em>Are there any pairs of features which exhibit some degree of correlation? Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? How is the data for those features distributed?</em><br>
+<strong>Hint:</strong> Is the data normally distributed? Where do most of the data points lie?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> Yes, Detergents Paper seems to correlate with Grocery, and maybe Grocery with Milk a bit. This confirms my suspicions for Detergents ag Grocery by looking at the plot. Further, the data for these features is clearly not normally distributed, but heavily skewed towards lower values, so positively skewed. This means the median falls below the mean. This lack of normal distribution applies to all features in fact, probably due to the fact that there are a lot more small food shops than large ones.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Data-Preprocessing">Data Preprocessing<a class="anchor-link" href="#Data-Preprocessing">&#182;</a></h2><p>In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Feature-Scaling">Implementation: Feature Scaling<a class="anchor-link" href="#Implementation:-Feature-Scaling">&#182;</a></h3><p>If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most <a href="http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics">often appropriate</a> to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a <a href="http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html">Box-Cox test</a>, which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.</p>
+<p>In the code block below, you will need to implement the following:</p>
+<ul>
+<li>Assign a copy of the data to <code>log_data</code> after applying logarithmic scaling. Use the <code>np.log</code> function for this.</li>
+<li>Assign a copy of the sample data to <code>log_samples</code> after applying logarithmic scaling. Again, use <code>np.log</code>.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Scale the data using the natural logarithm</span>
+<span class="n">log_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+
+<span class="c1"># TODO: Scale the sample data using the natural logarithm</span>
+<span class="n">log_samples</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="k">print</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>
+
+<span class="c1"># Produce a scatter matrix for each pair of newly-transformed features</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">scatter_matrix</span><span class="p">(</span><span class="n">log_data</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">diagonal</span> <span class="o">=</span> <span class="s1">&#39;kde&#39;</span><span class="p">);</span>
+
+<span class="c1"># now the features are much more normally distributed.</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen
+0  27167   2801     2128   13223                92          1902
+1      3    333     7021   15601                15           550
+2     85  20959    45828      36             24231          1423
+       Fresh      Milk    Grocery    Frozen  Detergents_Paper  Delicatessen
+0  10.209758  7.937732   7.662938  9.489713          4.521789      7.550661
+1   1.098612  5.808142   8.856661  9.655090          2.708050      6.309918
+2   4.442651  9.950323  10.732651  3.583519         10.095388      7.260523
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_png output_subarea ">
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+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).</p>
+<p>Run the code below to see how the sample data has changed after having the natural logarithm applied to it.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[14]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the log-transformed sample data</span>
+<span class="n">display</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Fresh</th>
+      <th>Milk</th>
+      <th>Grocery</th>
+      <th>Frozen</th>
+      <th>Detergents_Paper</th>
+      <th>Delicatessen</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>10.209758</td>
+      <td>7.937732</td>
+      <td>7.662938</td>
+      <td>9.489713</td>
+      <td>4.521789</td>
+      <td>7.550661</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1.098612</td>
+      <td>5.808142</td>
+      <td>8.856661</td>
+      <td>9.655090</td>
+      <td>2.708050</td>
+      <td>6.309918</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>4.442651</td>
+      <td>9.950323</td>
+      <td>10.732651</td>
+      <td>3.583519</td>
+      <td>10.095388</td>
+      <td>7.260523</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Outlier-Detection">Implementation: Outlier Detection<a class="anchor-link" href="#Implementation:-Outlier-Detection">&#182;</a></h3><p>Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many "rules of thumb" for what constitutes an outlier in a dataset. Here, we will use <a href="http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/">Tukey's Method for identfying outliers</a>: An <em>outlier step</em> is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.</p>
+<p>In the code block below, you will need to implement the following:</p>
+<ul>
+<li>Assign the value of the 25th percentile for the given feature to <code>Q1</code>. Use <code>np.percentile</code> for this.</li>
+<li>Assign the value of the 75th percentile for the given feature to <code>Q3</code>. Again, use <code>np.percentile</code>.</li>
+<li>Assign the calculation of an outlier step for the given feature to <code>step</code>.</li>
+<li>Optionally remove data points from the dataset by adding indices to the <code>outliers</code> list.</li>
+</ul>
+<p><strong>NOTE:</strong> If you choose to remove any outliers, ensure that the sample data does not contain any of these points!<br>
+Once you have performed this implementation, the dataset will be stored in the variable <code>good_data</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[15]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># For each feature find the data points with extreme high or low values</span>
+<span class="n">indices</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">log_data</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+    
+    <span class="c1"># TODO: Calculate Q1 (25th percentile of the data) for the given feature</span>
+    <span class="n">Q1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="mi">25</span><span class="p">)</span>
+    
+    <span class="c1"># TODO: Calculate Q3 (75th percentile of the data) for the given feature</span>
+    <span class="n">Q3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="mi">75</span><span class="p">)</span>
+    
+    <span class="c1"># TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)</span>
+    <span class="n">step</span> <span class="o">=</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">Q3</span> <span class="o">-</span> <span class="n">Q1</span><span class="p">)</span>
+    
+    
+    <span class="c1"># Display the outliers</span>
+    <span class="k">print</span> <span class="s2">&quot;Data points considered outliers for the feature &#39;{}&#39;:&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">feature</span><span class="p">)</span>
+    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Q1-step is {}, Q3+step is {}, the step size is {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">Q1</span><span class="o">-</span><span class="n">step</span><span class="p">,</span> <span class="n">Q3</span><span class="o">+</span><span class="n">step</span><span class="p">,</span> <span class="n">step</span><span class="p">))</span>
+    <span class="c1"># the ~ operator flips the booleans</span>
+    <span class="n">outliers</span> <span class="o">=</span> <span class="n">log_data</span><span class="p">[</span><span class="o">~</span><span class="p">((</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="n">Q1</span> <span class="o">-</span> <span class="n">step</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="p">(</span><span class="n">Q3</span> <span class="o">+</span> <span class="n">step</span><span class="p">)))][</span><span class="n">feature</span><span class="p">]</span>
+    <span class="n">display</span><span class="p">(</span><span class="n">outliers</span><span class="p">)</span>
+    
+    <span class="c1"># add indices</span>
+    <span class="n">indices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">outliers</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+    
+<span class="c1"># OPTIONAL: Select the indices for data points you wish to remove</span>
+<span class="n">outliers</span>  <span class="o">=</span> <span class="n">indices</span>
+
+<span class="c1"># Remove the outliers, if any were specified</span>
+<span class="n">good_data</span> <span class="o">=</span> <span class="n">log_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">log_data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">outliers</span><span class="p">])</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
+<span class="k">print</span><span class="p">(</span><span class="n">Counter</span><span class="p">(</span><span class="n">outliers</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Data points considered outliers for the feature &#39;Fresh&#39;:
+Q1-step is 5.51455083361, Q3+step is 12.2705718166, the step size is 2.53350786861
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_text output_subarea ">
+<pre>65     4.442651
+66     2.197225
+81     5.389072
+95     1.098612
+96     3.135494
+128    4.941642
+171    5.298317
+193    5.192957
+218    2.890372
+304    5.081404
+305    5.493061
+338    1.098612
+353    4.762174
+355    5.247024
+357    3.610918
+412    4.574711
+Name: Fresh, dtype: float64</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Data points considered outliers for the feature &#39;Milk&#39;:
+Q1-step is 5.01673296722, Q3+step is 11.1987283614, the step size is 2.31824827282
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_text output_subarea ">
+<pre>86     11.205013
+98      4.718499
+154     4.007333
+356     4.897840
+Name: Milk, dtype: float64</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Data points considered outliers for the feature &#39;Grocery&#39;:
+Q1-step is 5.27575998758, Q3+step is 11.672709891, the step size is 2.3988562138
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_text output_subarea ">
+<pre>75     1.098612
+154    4.919981
+Name: Grocery, dtype: float64</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Data points considered outliers for the feature &#39;Frozen&#39;:
+Q1-step is 4.26035024816, Q3+step is 10.5252235842, the step size is 2.34932750101
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_text output_subarea ">
+<pre>38      3.496508
+57      3.637586
+65      3.583519
+145     3.737670
+175     3.951244
+264     4.110874
+325    11.016479
+420     3.218876
+429     3.850148
+439     4.174387
+Name: Frozen, dtype: float64</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Data points considered outliers for the feature &#39;Detergents_Paper&#39;:
+Q1-step is 1.45874266385, Q3+step is 12.3636993597, the step size is 4.08935876094
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_text output_subarea ">
+<pre>75     1.098612
+161    1.098612
+Name: Detergents_Paper, dtype: float64</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Data points considered outliers for the feature &#39;Delicatessen&#39;:
+Q1-step is 3.76959400251, Q3+step is 9.74900908097, the step size is 2.24228065442
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_text output_subarea ">
+<pre>66      3.295837
+109     1.098612
+128     1.098612
+137     3.583519
+142     1.098612
+154     2.079442
+183    10.777768
+184     2.397895
+187     1.098612
+203     2.890372
+233     1.945910
+285     2.890372
+289     3.091042
+343     3.610918
+Name: Delicatessen, dtype: float64</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Counter({154: 3, 128: 2, 65: 2, 66: 2, 75: 2, 193: 1, 264: 1, 137: 1, 142: 1, 145: 1, 412: 1, 285: 1, 161: 1, 420: 1, 38: 1, 171: 1, 429: 1, 175: 1, 304: 1, 305: 1, 439: 1, 184: 1, 57: 1, 187: 1, 203: 1, 325: 1, 289: 1, 81: 1, 338: 1, 86: 1, 343: 1, 218: 1, 95: 1, 96: 1, 353: 1, 98: 1, 355: 1, 356: 1, 357: 1, 233: 1, 109: 1, 183: 1})
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-4">Question 4<a class="anchor-link" href="#Question-4">&#182;</a></h3><p><em>Are there any data points considered outliers for more than one feature based on the definition above? Should these data points be removed from the dataset? If any data points were added to the <code>outliers</code> list to be removed, explain why.</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> There are 5 data points that are outliers in more than one dimension. We should remove them too as the would distort the data in more than 1 dimension. The points in the outliers list should all be remove given we use Tukey's outlier definition.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Feature-Transformation">Feature Transformation<a class="anchor-link" href="#Feature-Transformation">&#182;</a></h2><p>In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-PCA">Implementation: PCA<a class="anchor-link" href="#Implementation:-PCA">&#182;</a></h3><p>Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the <code>good_data</code> to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the <em>explained variance ratio</em> of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new "feature" of the space, however it is a composition of the original features present in the data.</p>
+<p>In the code block below, you will need to implement the following:</p>
+<ul>
+<li>Import <code>sklearn.decomposition.PCA</code> and assign the results of fitting PCA in six dimensions with <code>good_data</code> to <code>pca</code>.</li>
+<li>Apply a PCA transformation of <code>log_samples</code> using <code>pca.transform</code>, and assign the results to <code>pca_samples</code>.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[16]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Apply PCA by fitting the good data with the same number of dimensions as features</span>
+<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
+<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>
+
+<span class="c1"># TODO: Transform log_samples using the PCA fit above</span>
+<span class="c1"># this applies dimensionality reduction to each sample</span>
+<span class="n">pca_samples</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>
+<span class="c1">#print(pca_samples)</span>
+
+<span class="c1"># Generate PCA results plot, indicating how much each feature contributes to each dimension.</span>
+<span class="n">pca_results</span> <span class="o">=</span> <span class="n">vs</span><span class="o">.</span><span class="n">pca_results</span><span class="p">(</span><span class="n">good_data</span><span class="p">,</span> <span class="n">pca</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA0wAAAHyCAYAAADYwk6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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+Ww6ALWQaYhERy5O6NrsXwbjBdIZIKgYeB2ad4hfK3wPFkvon5bWU1P0k++wKfBARM4H5QOkxq/wa
+uFZSQVK/K4Dlp1A3q6c05kDSlWSeVfhRROyv/6Hkt5Rm4YJakyOBjfXdNl+lLQcRsS4iOkbE+RFx
+PpkvS70j4rMGHVWeSVsOkvI71Zr8CZnbdO0E0pgD4GVgULKvC8nc9tfsXgLjXoPTq1XSRdmSzK1K
+/wlUnEpBEXFY0lXATElFZP52M4D1J9jsGuBGSUeAz4B/PWb5PKA/sIZMl/nPIuIzSd3qUydJPyFz
+72sx8Kqk1RExrCHHlSdSnQNgFnA239wCsDT8ZrRs0p6FaZJ+QOYK4keAc1C3tOfA6iftOXhQUq9k
+2yrg7+t7PHkm7Tl4CnhK0jvAYeCnzfFOFL9W3MzMzMzMLAvfkmdmZmZmZpaFG0xmZmZmZmZZuMFk
+ZmZmZmaWhRtMZmZmZmZmWbjBZGZmZmZmloUbTGZmZmZmZlm4wWRmZmZmZpaFG0xmZmZmZmZZ/D8v
+MucZ7RipCwAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-5">Question 5<a class="anchor-link" href="#Question-5">&#182;</a></h3><p><em>How much variance in the data is explained</em> <strong><em>in total</em></strong> <em>by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending.</em><br>
+<strong>Hint:</strong> A positive increase (between examples) in a specific dimension corresponds with an <em>increase</em> of the <em>positive-weighted</em> features and a <em>decrease</em> of the <em>negative-weighted</em> features. The rate of increase or decrease is based on the indivdual feature weights.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> Together the first and second principal component explain 72.52% of total variance. The first four pc explain 92.79% of total variance.</p>
+<p>I dont understand the hint: dimensions are not increasing, they are new features that have values which are either high or low. Ah, maybe it means if you compare two examples, if a dimension increases this indicates that the positively weighted features in its portfolio increase and the negative weight features decrease. The rate of increase depends on the rate of the weight changes in its feature portfolio i guess..</p>
+<ul>
+<li>Dimension 1 is Detergents_paper, Milk, Grocery with some representation of Deli. This is probably consumer retail spending.</li>
+<li>Dimension 2 is Fresh, Frozen and Deli, with some representation for Milk and Grocery. This is probably a hotel or restaurant.</li>
+<li>Dimension 3 is Deli with some Frozen and Milk, with very low Fresh and low Detergents_Paper. This could be a speciality shop such as a butcher. </li>
+<li>Dimension 4 is Deli with some Fresh but very low Frozen and low Detergents_Paper. This could be a market, where they sell fresh food and meat.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions. Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[17]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display sample log-data after having a PCA transformation applied</span>
+<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">pca_results</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
+<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">samples</span><span class="p">))</span>
+
+
+<span class="c1"># hard to see how it works...</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Dimension 1</th>
+      <th>Dimension 2</th>
+      <th>Dimension 3</th>
+      <th>Dimension 4</th>
+      <th>Dimension 5</th>
+      <th>Dimension 6</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-2.4072</td>
+      <td>2.4079</td>
+      <td>0.5245</td>
+      <td>-0.0848</td>
+      <td>0.5462</td>
+      <td>-0.0377</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-3.4491</td>
+      <td>-3.6586</td>
+      <td>6.7584</td>
+      <td>-2.9439</td>
+      <td>0.6921</td>
+      <td>2.9431</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>5.3109</td>
+      <td>-4.5845</td>
+      <td>2.0274</td>
+      <td>1.1669</td>
+      <td>-0.0343</td>
+      <td>0.3363</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Fresh</th>
+      <th>Milk</th>
+      <th>Grocery</th>
+      <th>Frozen</th>
+      <th>Detergents_Paper</th>
+      <th>Delicatessen</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>27167</td>
+      <td>2801</td>
+      <td>2128</td>
+      <td>13223</td>
+      <td>92</td>
+      <td>1902</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>3</td>
+      <td>333</td>
+      <td>7021</td>
+      <td>15601</td>
+      <td>15</td>
+      <td>550</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>85</td>
+      <td>20959</td>
+      <td>45828</td>
+      <td>36</td>
+      <td>24231</td>
+      <td>1423</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Dimensionality-Reduction">Implementation: Dimensionality Reduction<a class="anchor-link" href="#Implementation:-Dimensionality-Reduction">&#182;</a></h3><p>When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the <em>cumulative explained variance ratio</em> is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.</p>
+<p>In the code block below, you will need to implement the following:</p>
+<ul>
+<li>Assign the results of fitting PCA in two dimensions with <code>good_data</code> to <code>pca</code>.</li>
+<li>Apply a PCA transformation of <code>good_data</code> using <code>pca.transform</code>, and assign the results to <code>reduced_data</code>.</li>
+<li>Apply a PCA transformation of <code>log_samples</code> using <code>pca.transform</code>, and assign the results to <code>pca_samples</code>.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[18]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Apply PCA by fitting the good data with only two dimensions</span>
+<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>
+
+<span class="c1"># TODO: Transform the good data using the PCA fit above</span>
+<span class="n">reduced_data</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>
+
+<span class="c1"># TODO: Transform log_samples using the PCA fit above</span>
+<span class="n">pca_samples</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>
+
+<span class="c1"># Create a DataFrame for the reduced data</span>
+<span class="n">reduced_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Dimension 1&#39;</span><span class="p">,</span> <span class="s1">&#39;Dimension 2&#39;</span><span class="p">])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[19]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display sample log-data after applying PCA transformation in two dimensions</span>
+<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Dimension 1&#39;</span><span class="p">,</span> <span class="s1">&#39;Dimension 2&#39;</span><span class="p">]))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Dimension 1</th>
+      <th>Dimension 2</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-2.4072</td>
+      <td>2.4079</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-3.4491</td>
+      <td>-3.6586</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>5.3109</td>
+      <td>-4.5845</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Visualizing-a-Biplot">Visualizing a Biplot<a class="anchor-link" href="#Visualizing-a-Biplot">&#182;</a></h2><p>A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case <code>Dimension 1</code> and <code>Dimension 2</code>). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.</p>
+<p>Run the code cell below to produce a biplot of the reduced-dimension data.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[20]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Create a biplot</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">biplot</span><span class="p">(</span><span class="n">good_data</span><span class="p">,</span> <span class="n">reduced_data</span><span class="p">,</span> <span class="n">pca</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt output_prompt">Out[20]:</div>
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0xe5c9ba8&gt;</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
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+<div class="output_png output_subarea ">
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on <code>'Milk'</code>, <code>'Grocery'</code> and <code>'Detergents_Paper'</code>, but not so much on the other product categories.</p>
+<p>From the biplot, which of the original features are most strongly correlated with the first component? What about those that are associated with the second component? Do these observations agree with the pca_results plot you obtained earlier?</p>
+<ul>
+<li><p>First Dimension: detergents, grocery and milk (positively) - agrees with previous plot feature weights.</p>
+</li>
+<li><p>Second Dimension: Deli, Frozen, Fresh, also positively. Agrees too.</p>
+</li>
+</ul>
+<p>So the length of the arrow is the weight of that feature in the dimension, the direction indicates how much each dimension is exposed to that feature.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Clustering">Clustering<a class="anchor-link" href="#Clustering">&#182;</a></h2><p>In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-6">Question 6<a class="anchor-link" href="#Question-6">&#182;</a></h3><p><em>What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> The K-means algorithm will try to find clusters of points that are close to each other: a point can belong to only one cluster at a time. The Gaussian Mixture model allows points to belong to several clusters at a time, with a certain probability. Given wholesale customers might belong to several possible clusters, such as the "small place" cluster or the "deli" cluster at the same time, I would use the gaussian mixture model which allows this. Further, the mixture model incorporates the covariance information of the data (<a href="http://scikit-learn.org/stable/modules/mixture.html">http://scikit-learn.org/stable/modules/mixture.html</a>) which I believe is desirable, given we have some feature correlation. We should have enough points per cluster, so calculating the covariance matrix should work.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Creating-Clusters">Implementation: Creating Clusters<a class="anchor-link" href="#Implementation:-Creating-Clusters">&#182;</a></h3><p>Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known <em>a priori</em>, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the "goodness" of a clustering by calculating each data point's <em>silhouette coefficient</em>. The <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html">silhouette coefficient</a> for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the <em>mean</em> silhouette coefficient provides for a simple scoring method of a given clustering.</p>
+<p>In the code block below, you will need to implement the following:</p>
+<ul>
+<li>Fit a clustering algorithm to the <code>reduced_data</code> and assign it to <code>clusterer</code>.</li>
+<li>Predict the cluster for each data point in <code>reduced_data</code> using <code>clusterer.predict</code> and assign them to <code>preds</code>.</li>
+<li>Find the cluster centers using the algorithm's respective attribute and assign them to <code>centers</code>.</li>
+<li>Predict the cluster for each sample data point in <code>pca_samples</code> and assign them <code>sample_preds</code>.</li>
+<li>Import <code>sklearn.metrics.silhouette_score</code> and calculate the silhouette score of <code>reduced_data</code> against <code>preds</code>.<ul>
+<li>Assign the silhouette score to <code>score</code> and print the result.</li>
+</ul>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[23]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Use the ouput of PCA reduced to 2 components as input for the Gaussian Mixture Model</span>
+
+<span class="n">nb_comp</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">]</span>
+
+<span class="n">val_out</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="k">for</span> <span class="n">val_comp</span> <span class="ow">in</span> <span class="n">nb_comp</span><span class="p">:</span>
+    <span class="c1"># TODO: Apply your clustering algorithm of choice to the reduced data </span>
+    <span class="kn">from</span> <span class="nn">sklearn.mixture</span> <span class="kn">import</span> <span class="n">GaussianMixture</span>
+    <span class="n">clusterer</span> <span class="o">=</span> <span class="n">GaussianMixture</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="n">val_comp</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Predict the cluster for each data point</span>
+    <span class="n">preds_loc</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>
+    <span class="c1">#print(preds)</span>
+
+    <span class="c1"># TODO: Find the cluster centers</span>
+    <span class="n">centers_loc</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">means_</span>
+
+    <span class="c1"># TODO: Predict the cluster for each transformed sample data point</span>
+    <span class="n">sample_preds_loc</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">)</span>
+
+    <span class="c1"># TODO: Calculate the mean silhouette coefficient for the number of clusters chosen</span>
+    <span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">silhouette_score</span>
+    <span class="n">score</span> <span class="o">=</span> <span class="n">silhouette_score</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">preds_loc</span><span class="p">)</span>
+    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Nb comps: {}, score: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val_comp</span><span class="p">,</span> <span class="n">score</span><span class="p">))</span>
+    
+    <span class="k">if</span> <span class="n">val_out</span> <span class="o">==</span> <span class="n">val_comp</span><span class="p">:</span>
+        <span class="n">preds</span> <span class="o">=</span> <span class="n">preds_loc</span>
+        <span class="n">centers</span> <span class="o">=</span> <span class="n">centers_loc</span>
+        <span class="n">sample_preds</span> <span class="o">=</span> <span class="n">sample_preds_loc</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Nb comps: 2, score: 0.447411995571
+Nb comps: 3, score: 0.359479670374
+Nb comps: 4, score: 0.312405270688
+Nb comps: 5, score: 0.3285065946
+Nb comps: 6, score: 0.28969365136
+Nb comps: 7, score: 0.328311049677
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-7">Question 7<a class="anchor-link" href="#Question-7">&#182;</a></h3><p><em>Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> 2 clusters has the best silhouette score. I am guessing 1 cluster would be even better as it has no alternatives, but it throws an error.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Cluster-Visualization">Cluster Visualization<a class="anchor-link" href="#Cluster-Visualization">&#182;</a></h3><p>Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[24]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the results of the clustering from implementation</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">cluster_results</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">centers</span><span class="p">,</span> <span class="n">pca_samples</span><span class="p">)</span>
+
+<span class="c1"># the black X are my samples, the round circles the cluster centers.</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Data-Recovery">Implementation: Data Recovery<a class="anchor-link" href="#Implementation:-Data-Recovery">&#182;</a></h3><p>Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the <em>averages</em> of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to <em>the average customer of that segment</em>. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.</p>
+<p>In the code block below, you will need to implement the following:</p>
+<ul>
+<li>Apply the inverse transform to <code>centers</code> using <code>pca.inverse_transform</code> and assign the new centers to <code>log_centers</code>.</li>
+<li>Apply the inverse function of <code>np.log</code> to <code>log_centers</code> using <code>np.exp</code> and assign the true centers to <code>true_centers</code>.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[25]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># This shows the cluster centers on the first 2 components of PCA.</span>
+
+<span class="c1"># TODO: Inverse transform the centers</span>
+<span class="n">log_centers</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">centers</span><span class="p">)</span> <span class="c1"># recovers original features from pca dimensions</span>
+<span class="c1">#print(&quot;log centers&quot;, log_centers)</span>
+
+<span class="c1"># TODO: Exponentiate the centers</span>
+<span class="n">true_centers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">log_centers</span><span class="p">)</span> <span class="c1"># recover original values from log values</span>
+<span class="c1">#print(&quot;true centers&quot; ,true_centers)</span>
+
+<span class="c1"># Display the true centers</span>
+<span class="n">segments</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Segment {}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">centers</span><span class="p">))]</span>
+<span class="n">true_centers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">true_centers</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+<span class="n">true_centers</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">segments</span>
+
+<span class="c1"># order features, so can focus on comparison not retrieval</span>
+<span class="n">tmp</span> <span class="o">=</span> <span class="n">true_centers</span>
+<span class="n">ordered</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">Frozen</span><span class="p">)</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Fresh&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Fresh</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Delicatessen&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Delicatessen</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Detergents_Paper&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Detergents_Paper</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Grocery&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Grocery</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Milk&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Milk</span>
+<span class="n">true_centers</span> <span class="o">=</span> <span class="n">ordered</span>
+
+
+<span class="n">tmp</span> <span class="o">=</span> <span class="n">data</span>
+<span class="n">ordered</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">Frozen</span><span class="p">)</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Fresh&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Fresh</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Delicatessen&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Delicatessen</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Detergents_Paper&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Detergents_Paper</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Grocery&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Grocery</span>
+<span class="n">ordered</span><span class="p">[</span><span class="s2">&quot;Milk&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">Milk</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">ordered</span>
+
+<span class="n">display</span><span class="p">(</span><span class="n">true_centers</span><span class="p">)</span>
+
+<span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Frozen</th>
+      <th>Fresh</th>
+      <th>Delicatessen</th>
+      <th>Detergents_Paper</th>
+      <th>Grocery</th>
+      <th>Milk</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Segment 0</th>
+      <td>2196.0</td>
+      <td>9468.0</td>
+      <td>799.0</td>
+      <td>343.0</td>
+      <td>2624.0</td>
+      <td>2067.0</td>
+    </tr>
+    <tr>
+      <th>Segment 1</th>
+      <td>1068.0</td>
+      <td>5174.0</td>
+      <td>1101.0</td>
+      <td>4536.0</td>
+      <td>11581.0</td>
+      <td>7776.0</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Frozen</th>
+      <th>Fresh</th>
+      <th>Delicatessen</th>
+      <th>Detergents_Paper</th>
+      <th>Grocery</th>
+      <th>Milk</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>440.000000</td>
+      <td>440.000000</td>
+      <td>440.000000</td>
+      <td>440.000000</td>
+      <td>440.000000</td>
+      <td>440.000000</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>3071.931818</td>
+      <td>12000.297727</td>
+      <td>1524.870455</td>
+      <td>2881.493182</td>
+      <td>7951.277273</td>
+      <td>5796.265909</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>4854.673333</td>
+      <td>12647.328865</td>
+      <td>2820.105937</td>
+      <td>4767.854448</td>
+      <td>9503.162829</td>
+      <td>7380.377175</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>25.000000</td>
+      <td>3.000000</td>
+      <td>3.000000</td>
+      <td>3.000000</td>
+      <td>3.000000</td>
+      <td>55.000000</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>742.250000</td>
+      <td>3127.750000</td>
+      <td>408.250000</td>
+      <td>256.750000</td>
+      <td>2153.000000</td>
+      <td>1533.000000</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>1526.000000</td>
+      <td>8504.000000</td>
+      <td>965.500000</td>
+      <td>816.500000</td>
+      <td>4755.500000</td>
+      <td>3627.000000</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>3554.250000</td>
+      <td>16933.750000</td>
+      <td>1820.250000</td>
+      <td>3922.000000</td>
+      <td>10655.750000</td>
+      <td>7190.250000</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>60869.000000</td>
+      <td>112151.000000</td>
+      <td>47943.000000</td>
+      <td>40827.000000</td>
+      <td>92780.000000</td>
+      <td>73498.000000</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-8">Question 8<a class="anchor-link" href="#Question-8">&#182;</a></h3><p>Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project. <em>What set of establishments could each of the customer segments represent?</em><br>
+<strong>Hint:</strong> A customer who is assigned to <code>'Cluster X'</code> should best identify with the establishments represented by the feature set of <code>'Segment X'</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> Comparing the feature values for each segment with the statistical description of each feature, it seems that:</p>
+<p>To be fair, i am not sure how to answer these very well, as I find it hard to get myself interested in food establishments and what they might buy. It might be more interesting to segment drivers for economic performance, predictors of burglary or maybe internet user groups. However, here my attempt.</p>
+<p>PC:</p>
+<ul>
+<li>Dim 2 represents Frozen, Fresh and Deli. A higher value means more of these.</li>
+<li>Dim 1 represents Detergents_Paper, Grocery and milk. A higher value means more of these.</li>
+</ul>
+<p>Clusters:</p>
+<ul>
+<li>Segment 0 (any dim2, &lt;0.5 dim1). Here I expect  it to have any value for dim2 features be lower on dim1 features than Segment 1 and on the lower end of the distribution for those features. Thus, it would have low Detergents_Paper, Grocery and Milk, so low consumer retail spending. This could represent a restaurant, food market, hotel.</li>
+<li>Segment 1 (any dim2, &gt;=0.5 dim1). Here I expect any dim2 features and higher values on dim1 features, and also at the higher end of the distribution for those features. So high Detergents_Paper, Grocery and Milk, indicating retail spending like a supermarket.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-9">Question 9<a class="anchor-link" href="#Question-9">&#182;</a></h3><p><em>For each sample point, which customer segment from</em> <strong><em>Question 8</em></strong> <em>best represents it? Are the predictions for each sample point consistent with this?</em></p>
+<p>Run the code block below to find which cluster each sample point is predicted to be.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[26]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the predictions</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample_preds</span><span class="p">):</span>
+    <span class="k">print</span> <span class="s2">&quot;Sample point&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;predicted to be in Cluster&quot;</span><span class="p">,</span> <span class="n">pred</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Sample point 0 predicted to be in Cluster 0
+Sample point 1 predicted to be in Cluster 0
+Sample point 2 predicted to be in Cluster 1
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> Point 0 is a market as it has a lot of frozen fresh and deli and little detpaper, grocery and milk. This matches the prediction in cluster 0. Point 1 an outlier(as found earlier) so was removed and 2 was and outlier on 2 dimensions so was removed as well. However, Point 2 was high on Det_Paper, Grocery and Milk, so would be correctly classified as retail in cluster 1.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#182;</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the <strong><em>customer segments</em></strong>, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which <em>segment</em> that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the <strong><em>customer segments</em></strong> to a hidden variable present in the data, to see whether the clustering identified certain relationships.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-10">Question 10<a class="anchor-link" href="#Question-10">&#182;</a></h3><p>Companies will often run <a href="https://en.wikipedia.org/wiki/A/B_testing">A/B tests</a> when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. <em>How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service?</em><br>
+<strong>Hint:</strong> Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> The distributor could call, survey etc a representative customer, or several, from each segment and ask them. They could use this to extrapolate to all customers from this segment and change his schedule by segment.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-11">Question 11<a class="anchor-link" href="#Question-11">&#182;</a></h3><p>Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a <strong><em>customer segment</em></strong> it best identifies with (depending on the clustering algorithm applied), we can consider <em>'customer segment'</em> as an <strong>engineered feature</strong> for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a <strong><em>customer segment</em></strong> to determine the most appropriate delivery service.<br>
+<em>How can the wholesale distributor label the new customers using only their estimated product spending and the</em> <strong><em>customer segment</em></strong> <em>data?</em><br>
+<strong>Hint:</strong> A supervised learner could be used to train on the original customers. What would be the target variable?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> They can transform the estimated spending to principal components using the trained pca on the original data. This would allow them to assign new customers to their clusters.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Visualizing-Underlying-Distributions">Visualizing Underlying Distributions<a class="anchor-link" href="#Visualizing-Underlying-Distributions">&#182;</a></h3><p>At the beginning of this project, it was discussed that the <code>'Channel'</code> and <code>'Region'</code> features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the <code>'Channel'</code> feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.</p>
+<p>Run the code block below to see how each data point is labeled either <code>'HoReCa'</code> (Hotel/Restaurant/Cafe) or <code>'Retail'</code> the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[28]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the clustering results based on &#39;Channel&#39; data</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">channel_results</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">outliers</span><span class="p">,</span> <span class="n">pca_samples</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAzwAAAH4CAYAAACPEOhlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-12">Question 12<a class="anchor-link" href="#Question-12">&#182;</a></h3><p><em>How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? Would you consider these classifications as consistent with your previous definition of the customer segments?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> It seems my cluster 0, or "small place" is gone. However, my two other clusters "supermarket" and "market" are still there. Just that market was Hotels in the end - i guess they also use less Det_Paper, Milk and Grocery than a retailer (to be honest i dont know). There are segments of purely retailers below -2.5 on dim1, and purely horeca above 2 on dim1. These classifications are consistent with my previous definition of segments, although there is a larger overlap region as expected.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
+<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+
+ 
+
+
+</html>
diff --git a/customer_segments/customer_segments.ipynb b/customer_segments/customer_segments.ipynb
new file mode 100644
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+++ b/customer_segments/customer_segments.ipynb
@@ -0,0 +1,1821 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning Engineer Nanodegree\n",
+    "## Unsupervised Learning\n",
+    "## Project: Creating Customer Segments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
+    "\n",
+    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
+    "\n",
+    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting Started\n",
+    "\n",
+    "In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in *monetary units*) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.\n",
+    "\n",
+    "The dataset for this project can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers). For the purposes of this project, the features `'Channel'` and `'Region'` will be excluded in the analysis — with focus instead on the six product categories recorded for customers.\n",
+    "\n",
+    "Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wholesale customers dataset has 440 samples with 6 features each.\n",
+      "It shows the spending per wholesale customer on that category per year.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicatessen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12669</td>\n",
+       "      <td>9656</td>\n",
+       "      <td>7561</td>\n",
+       "      <td>214</td>\n",
+       "      <td>2674</td>\n",
+       "      <td>1338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7057</td>\n",
+       "      <td>9810</td>\n",
+       "      <td>9568</td>\n",
+       "      <td>1762</td>\n",
+       "      <td>3293</td>\n",
+       "      <td>1776</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
+       "0  12669  9656     7561     214              2674          1338\n",
+       "1   7057  9810     9568    1762              3293          1776"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import libraries necessary for this project\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from IPython.display import display # Allows the use of display() for DataFrames\n",
+    "\n",
+    "# Import supplementary visualizations code visuals.py\n",
+    "import visuals as vs\n",
+    "\n",
+    "# Pretty display for notebooks\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Load the wholesale customers dataset\n",
+    "try:\n",
+    "    data = pd.read_csv(\"customers.csv\")\n",
+    "    data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n",
+    "    print \"Wholesale customers dataset has {} samples with {} features each.\".format(*data.shape)\n",
+    "    print(\"It shows the spending per wholesale customer on that category per year.\")\n",
+    "    display(pd.DataFrame(data.head(2)))\n",
+    "except:\n",
+    "    print \"Dataset could not be loaded. Is the dataset missing?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Exploration\n",
+    "In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.\n",
+    "\n",
+    "Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: **'Fresh'**, **'Milk'**, **'Grocery'**, **'Frozen'**, **'Detergents_Paper'**, and **'Delicatessen'**. Consider what each category represents in terms of products you could purchase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicatessen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>12000.297727</td>\n",
+       "      <td>5796.265909</td>\n",
+       "      <td>7951.277273</td>\n",
+       "      <td>3071.931818</td>\n",
+       "      <td>2881.493182</td>\n",
+       "      <td>1524.870455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>12647.328865</td>\n",
+       "      <td>7380.377175</td>\n",
+       "      <td>9503.162829</td>\n",
+       "      <td>4854.673333</td>\n",
+       "      <td>4767.854448</td>\n",
+       "      <td>2820.105937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3127.750000</td>\n",
+       "      <td>1533.000000</td>\n",
+       "      <td>2153.000000</td>\n",
+       "      <td>742.250000</td>\n",
+       "      <td>256.750000</td>\n",
+       "      <td>408.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>8504.000000</td>\n",
+       "      <td>3627.000000</td>\n",
+       "      <td>4755.500000</td>\n",
+       "      <td>1526.000000</td>\n",
+       "      <td>816.500000</td>\n",
+       "      <td>965.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>16933.750000</td>\n",
+       "      <td>7190.250000</td>\n",
+       "      <td>10655.750000</td>\n",
+       "      <td>3554.250000</td>\n",
+       "      <td>3922.000000</td>\n",
+       "      <td>1820.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>112151.000000</td>\n",
+       "      <td>73498.000000</td>\n",
+       "      <td>92780.000000</td>\n",
+       "      <td>60869.000000</td>\n",
+       "      <td>40827.000000</td>\n",
+       "      <td>47943.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Fresh          Milk       Grocery        Frozen  \\\n",
+       "count     440.000000    440.000000    440.000000    440.000000   \n",
+       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
+       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
+       "min         3.000000     55.000000      3.000000     25.000000   \n",
+       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
+       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
+       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
+       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
+       "\n",
+       "       Detergents_Paper  Delicatessen  \n",
+       "count        440.000000    440.000000  \n",
+       "mean        2881.493182   1524.870455  \n",
+       "std         4767.854448   2820.105937  \n",
+       "min            3.000000      3.000000  \n",
+       "25%          256.750000    408.250000  \n",
+       "50%          816.500000    965.500000  \n",
+       "75%         3922.000000   1820.250000  \n",
+       "max        40827.000000  47943.000000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Display a description of the dataset\n",
+    "display(data.describe()) # shows per column statistics from dataset. Columns contain usd values for spending.\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# normalise columns around mean to check distribution\n",
+    "norm_data = scaler.fit_transform(data)\n",
+    "\n",
+    "norm_data = pd.DataFrame(norm_data, columns=data.columns)\n",
+    "# plot\n",
+    "#norm_data.hist(bins=200)\n",
+    "# they all seems quite biased to low values\n",
+    "\n",
+    "# describe\n",
+    "#display(norm_data.describe())\n",
+    "\n",
+    "\n",
+    "#norm_data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Selecting Samples\n",
+    "To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add **three** indices of your choice to the `indices` list which will represent the customers to track. It is suggested to try different sets of samples until you obtain customers that vary significantly from one another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chosen samples of wholesale customers dataset:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Delicatessen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Milk</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13223</td>\n",
+       "      <td>27167</td>\n",
+       "      <td>1902</td>\n",
+       "      <td>92</td>\n",
+       "      <td>2128</td>\n",
+       "      <td>2801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15601</td>\n",
+       "      <td>3</td>\n",
+       "      <td>550</td>\n",
+       "      <td>15</td>\n",
+       "      <td>7021</td>\n",
+       "      <td>333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>36</td>\n",
+       "      <td>85</td>\n",
+       "      <td>1423</td>\n",
+       "      <td>24231</td>\n",
+       "      <td>45828</td>\n",
+       "      <td>20959</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Frozen  Fresh  Delicatessen  Detergents_Paper  Grocery   Milk\n",
+       "0   13223  27167          1902                92     2128   2801\n",
+       "1   15601      3           550                15     7021    333\n",
+       "2      36     85          1423             24231    45828  20959"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xf1e6240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# TODO: Select three indices of your choice you wish to sample from the dataset\n",
+    "indices = [401, 338, 65]\n",
+    "\n",
+    "retailer = data[data[\"Frozen\"]>=9000]\n",
+    "#print(retailer)\n",
+    "market = data[data[\"Detergents_Paper\"] >= 8000]\n",
+    "#print(market)\n",
+    "\n",
+    "\n",
+    "# Create a DataFrame of the chosen samples\n",
+    "samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)\n",
+    "print \"Chosen samples of wholesale customers dataset:\"\n",
+    "display(samples)\n",
+    "\n",
+    "\n",
+    "# show samples values as bars, also show whole dataset mean values as bars\n",
+    "import seaborn as sns\n",
+    "samples = samples.append(data.describe().loc[\"50%\"])\n",
+    "samples = samples.append(data.describe().loc[\"mean\"])\n",
+    "samples = samples.append(data.describe().loc[\"25%\"])\n",
+    "samples = samples.append(data.describe().loc[\"75%\"])\n",
+    "\n",
+    "\n",
+    "samples_as_barchart = samples\n",
+    "\n",
+    "#samples_as_barchart = samples\n",
+    "samples_as_barchart.index = indices + ['median', 'mean', '25%', '75%'] # add mean to x axis\n",
+    "_ = samples_as_barchart.plot(kind='bar', figsize=(14,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1\n",
+    "Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers.  \n",
+    "*What kind of establishment (customer) could each of the three samples you've chosen represent?*  \n",
+    "**Hint:** Examples of establishments include places like markets, cafes, and retailers, among many others. Avoid using names for establishments, such as saying *\"McDonalds\"* when describing a sample customer as a restaurant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** \n",
+    "- Example 401 is above median on Fresh, Frozen, Deli, and below on rest. This is could be a food market as it would be higher than the median in those categories . \n",
+    "- Example 338 are above mean and median and 75 percentile on Frozen, above median on Grocery, below on rest so could be a fish shop selling frozen fish.\n",
+    "- Example 65 is a high multiple of the IQR on Grocery and milk and detergents paper - it could be a very popular cofee shop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Feature Relevance\n",
+    "One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.\n",
+    "\n",
+    "In the code block below, you will need to implement the following:\n",
+    " - Assign `new_data` a copy of the data by removing a feature of your choice using the `DataFrame.drop` function.\n",
+    " - Use `sklearn.cross_validation.train_test_split` to split the dataset into training and testing sets.\n",
+    "   - Use the removed feature as your target label. Set a `test_size` of `0.25` and set a `random_state`.\n",
+    " - Import a decision tree regressor, set a `random_state`, and fit the learner to the training data.\n",
+    " - Report the prediction score of the testing set using the regressor's `score` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n",
+    "testDependentFeature = \"Detergents_Paper\"\n",
+    "\n",
+    "new_data = data.drop([testDependentFeature], axis=1)\n",
+    "target = data[testDependentFeature]\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "\n",
+    "# TODO: Split the data into training and testing sets using the given feature as the target\n",
+    "X_train, X_test, y_train, y_test = train_test_split(new_data, target, test_size=0.25, random_state=5)\n",
+    "\n",
+    "# TODO: Create a decision tree regressor and fit it to the training set\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "regressor = DecisionTreeRegressor(random_state=6)\n",
+    "regressor.fit(X_train, y_train)\n",
+    "\n",
+    "# TODO: Report the score of the prediction using the testing set\n",
+    "score = regressor.score(X_test, y_test)\n",
+    "print(\"R-squared for explaining {} is {}\".format(testDependentFeature, score))\n",
+    "# Detergents_Paper seems to have highest R-squared."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2\n",
+    "*Which feature did you attempt to predict? What was the reported prediction score? Is this feature necessary for identifying customers' spending habits?*  \n",
+    "**Hint:** The coefficient of determination, `R^2`, is scored between 0 and 1, with 1 being a perfect fit. A negative `R^2` implies the model fails to fit the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** I attempted to predict Detergent_Paper. The prediction score was 0.6606. This means this feature can be explained with around 66% accuracy using all the other features combined. I would say it is still required as this is only slightly better than chance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize Feature Distributions\n",
+    "To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Produce a scatter matrix for each pair of features in the data\n",
+    "# plot each feature against each other feature in pairs.\n",
+    "pd.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde'); \n",
+    "\n",
+    "# feature against feature produces the distribution of that feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check feature distributions\n",
+    "data.Detergents_Paper.hist(bins=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3\n",
+    "*Are there any pairs of features which exhibit some degree of correlation? Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? How is the data for those features distributed?*  \n",
+    "**Hint:** Is the data normally distributed? Where do most of the data points lie? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** Yes, Detergents Paper seems to correlate with Grocery, and maybe Grocery with Milk a bit. This confirms my suspicions for Detergents ag Grocery by looking at the plot. Further, the data for these features is clearly not normally distributed, but heavily skewed towards lower values, so positively skewed. This means the median falls below the mean. This lack of normal distribution applies to all features in fact, probably due to the fact that there are a lot more small food shops than large ones."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Feature Scaling\n",
+    "If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most [often appropriate](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a [Box-Cox test](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html), which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.\n",
+    "\n",
+    "In the code block below, you will need to implement the following:\n",
+    " - Assign a copy of the data to `log_data` after applying logarithmic scaling. Use the `np.log` function for this.\n",
+    " - Assign a copy of the sample data to `log_samples` after applying logarithmic scaling. Again, use `np.log`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
+      "0  27167   2801     2128   13223                92          1902\n",
+      "1      3    333     7021   15601                15           550\n",
+      "2     85  20959    45828      36             24231          1423\n",
+      "       Fresh      Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
+      "0  10.209758  7.937732   7.662938  9.489713          4.521789      7.550661\n",
+      "1   1.098612  5.808142   8.856661  9.655090          2.708050      6.309918\n",
+      "2   4.442651  9.950323  10.732651  3.583519         10.095388      7.260523\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6sTeD1vVYdDbfab0X8u9eX6IXREQD63nT22dpXKE0bUIBXiyJkuxnx9DQNSg7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/PmWG26xEJwfDxP2wuYq1sDg3pupUnTCmjYPm0vJI4FxydypDSFtpNwe7teyIWVFoeHs1xc66Ar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U1oGJrAjzJ0ITA1jX6YMKMpEcJjMxUKjkGjF3J4uoQuBF98YQU/ybhc67N/osh0xebT981y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\nlVjqPZZllYBSVZ/QHcA35//9DHbmaEs7P1uhB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1T8bidT2RKXZrLE8ypvTmfxuZykZJVX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fe20OW0qefHPeVQ5ZyLOQK7lhVcBwVwRdFcxkSnSHPAQ9GgFdo2SaGJbEdizGF4ukiyam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\nhvApMagcfeedLt0woeUnuLahK5OxfrHlVkjONjk8Xril8vtRcWB3O7w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vf+PMCmMFeyQa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\nH41Go9FoNBqNRrMq+P8BgiKWD2uC/EAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9c55588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# TODO: Scale the data using the natural logarithm\n",
+    "log_data = data.apply(lambda x: np.log(x))\n",
+    "\n",
+    "# TODO: Scale the sample data using the natural logarithm\n",
+    "log_samples = samples.apply(lambda x: np.log(x))\n",
+    "print(samples)\n",
+    "print(log_samples)\n",
+    "\n",
+    "# Produce a scatter matrix for each pair of newly-transformed features\n",
+    "pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');\n",
+    "\n",
+    "# now the features are much more normally distributed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Observation\n",
+    "After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).\n",
+    "\n",
+    "Run the code below to see how the sample data has changed after having the natural logarithm applied to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicatessen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10.209758</td>\n",
+       "      <td>7.937732</td>\n",
+       "      <td>7.662938</td>\n",
+       "      <td>9.489713</td>\n",
+       "      <td>4.521789</td>\n",
+       "      <td>7.550661</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.098612</td>\n",
+       "      <td>5.808142</td>\n",
+       "      <td>8.856661</td>\n",
+       "      <td>9.655090</td>\n",
+       "      <td>2.708050</td>\n",
+       "      <td>6.309918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.442651</td>\n",
+       "      <td>9.950323</td>\n",
+       "      <td>10.732651</td>\n",
+       "      <td>3.583519</td>\n",
+       "      <td>10.095388</td>\n",
+       "      <td>7.260523</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Fresh      Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
+       "0  10.209758  7.937732   7.662938  9.489713          4.521789      7.550661\n",
+       "1   1.098612  5.808142   8.856661  9.655090          2.708050      6.309918\n",
+       "2   4.442651  9.950323  10.732651  3.583519         10.095388      7.260523"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Display the log-transformed sample data\n",
+    "display(log_samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Outlier Detection\n",
+    "Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many \"rules of thumb\" for what constitutes an outlier in a dataset. Here, we will use [Tukey's Method for identfying outliers](http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/): An *outlier step* is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.\n",
+    "\n",
+    "In the code block below, you will need to implement the following:\n",
+    " - Assign the value of the 25th percentile for the given feature to `Q1`. Use `np.percentile` for this.\n",
+    " - Assign the value of the 75th percentile for the given feature to `Q3`. Again, use `np.percentile`.\n",
+    " - Assign the calculation of an outlier step for the given feature to `step`.\n",
+    " - Optionally remove data points from the dataset by adding indices to the `outliers` list.\n",
+    "\n",
+    "**NOTE:** If you choose to remove any outliers, ensure that the sample data does not contain any of these points!  \n",
+    "Once you have performed this implementation, the dataset will be stored in the variable `good_data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data points considered outliers for the feature 'Fresh':\n",
+      "Q1-step is 5.51455083361, Q3+step is 12.2705718166, the step size is 2.53350786861\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "65     4.442651\n",
+       "66     2.197225\n",
+       "81     5.389072\n",
+       "95     1.098612\n",
+       "96     3.135494\n",
+       "128    4.941642\n",
+       "171    5.298317\n",
+       "193    5.192957\n",
+       "218    2.890372\n",
+       "304    5.081404\n",
+       "305    5.493061\n",
+       "338    1.098612\n",
+       "353    4.762174\n",
+       "355    5.247024\n",
+       "357    3.610918\n",
+       "412    4.574711\n",
+       "Name: Fresh, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data points considered outliers for the feature 'Milk':\n",
+      "Q1-step is 5.01673296722, Q3+step is 11.1987283614, the step size is 2.31824827282\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "86     11.205013\n",
+       "98      4.718499\n",
+       "154     4.007333\n",
+       "356     4.897840\n",
+       "Name: Milk, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data points considered outliers for the feature 'Grocery':\n",
+      "Q1-step is 5.27575998758, Q3+step is 11.672709891, the step size is 2.3988562138\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "75     1.098612\n",
+       "154    4.919981\n",
+       "Name: Grocery, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data points considered outliers for the feature 'Frozen':\n",
+      "Q1-step is 4.26035024816, Q3+step is 10.5252235842, the step size is 2.34932750101\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "38      3.496508\n",
+       "57      3.637586\n",
+       "65      3.583519\n",
+       "145     3.737670\n",
+       "175     3.951244\n",
+       "264     4.110874\n",
+       "325    11.016479\n",
+       "420     3.218876\n",
+       "429     3.850148\n",
+       "439     4.174387\n",
+       "Name: Frozen, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data points considered outliers for the feature 'Detergents_Paper':\n",
+      "Q1-step is 1.45874266385, Q3+step is 12.3636993597, the step size is 4.08935876094\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "75     1.098612\n",
+       "161    1.098612\n",
+       "Name: Detergents_Paper, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data points considered outliers for the feature 'Delicatessen':\n",
+      "Q1-step is 3.76959400251, Q3+step is 9.74900908097, the step size is 2.24228065442\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "66      3.295837\n",
+       "109     1.098612\n",
+       "128     1.098612\n",
+       "137     3.583519\n",
+       "142     1.098612\n",
+       "154     2.079442\n",
+       "183    10.777768\n",
+       "184     2.397895\n",
+       "187     1.098612\n",
+       "203     2.890372\n",
+       "233     1.945910\n",
+       "285     2.890372\n",
+       "289     3.091042\n",
+       "343     3.610918\n",
+       "Name: Delicatessen, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Counter({154: 3, 128: 2, 65: 2, 66: 2, 75: 2, 193: 1, 264: 1, 137: 1, 142: 1, 145: 1, 412: 1, 285: 1, 161: 1, 420: 1, 38: 1, 171: 1, 429: 1, 175: 1, 304: 1, 305: 1, 439: 1, 184: 1, 57: 1, 187: 1, 203: 1, 325: 1, 289: 1, 81: 1, 338: 1, 86: 1, 343: 1, 218: 1, 95: 1, 96: 1, 353: 1, 98: 1, 355: 1, 356: 1, 357: 1, 233: 1, 109: 1, 183: 1})\n"
+     ]
+    }
+   ],
+   "source": [
+    "# For each feature find the data points with extreme high or low values\n",
+    "indices = []\n",
+    "for feature in log_data.keys():\n",
+    "    \n",
+    "    # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n",
+    "    Q1 = np.percentile(log_data[feature], 25)\n",
+    "    \n",
+    "    # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n",
+    "    Q3 = np.percentile(log_data[feature], 75)\n",
+    "    \n",
+    "    # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n",
+    "    step = 1.5 * (Q3 - Q1)\n",
+    "    \n",
+    "    \n",
+    "    # Display the outliers\n",
+    "    print \"Data points considered outliers for the feature '{}':\".format(feature)\n",
+    "    print(\"Q1-step is {}, Q3+step is {}, the step size is {}\".format(Q1-step, Q3+step, step))\n",
+    "    # the ~ operator flips the booleans\n",
+    "    outliers = log_data[~((log_data[feature] >= (Q1 - step)) & (log_data[feature] <= (Q3 + step)))][feature]\n",
+    "    display(outliers)\n",
+    "    \n",
+    "    # add indices\n",
+    "    indices.extend(outliers.index)\n",
+    "    \n",
+    "# OPTIONAL: Select the indices for data points you wish to remove\n",
+    "outliers  = indices\n",
+    "\n",
+    "# Remove the outliers, if any were specified\n",
+    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)\n",
+    "\n",
+    "from collections import Counter\n",
+    "print(Counter(outliers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4\n",
+    "*Are there any data points considered outliers for more than one feature based on the definition above? Should these data points be removed from the dataset? If any data points were added to the `outliers` list to be removed, explain why.* "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** There are 5 data points that are outliers in more than one dimension. We should remove them too as the would distort the data in more than 1 dimension. The points in the outliers list should all be remove given we use Tukey's outlier definition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Transformation\n",
+    "In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: PCA\n",
+    "\n",
+    "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the `good_data` to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the *explained variance ratio* of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data.\n",
+    "\n",
+    "In the code block below, you will need to implement the following:\n",
+    " - Import `sklearn.decomposition.PCA` and assign the results of fitting PCA in six dimensions with `good_data` to `pca`.\n",
+    " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hwJ+Bh4HTMnNFZxuLiBMiYlZEzFq6dGk96pUkSZLUxzT7RR8OAOYAWwOjgPMjYuPOGmbm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\nvvtu1l9/fU4++WT2228/Bg0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6zczMzMwanRtMTcSCBQtYu3Ytc+fOBWD37t1s3ryZCy+8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\nMucZ7RipCwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe279dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# TODO: Apply PCA by fitting the good data with the same number of dimensions as features\n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA().fit(good_data)\n",
+    "\n",
+    "# TODO: Transform log_samples using the PCA fit above\n",
+    "# this applies dimensionality reduction to each sample\n",
+    "pca_samples = pca.transform(log_samples)\n",
+    "#print(pca_samples)\n",
+    "\n",
+    "# Generate PCA results plot, indicating how much each feature contributes to each dimension.\n",
+    "pca_results = vs.pca_results(good_data, pca)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 5\n",
+    "*How much variance in the data is explained* ***in total*** *by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending.*  \n",
+    "**Hint:** A positive increase (between examples) in a specific dimension corresponds with an *increase* of the *positive-weighted* features and a *decrease* of the *negative-weighted* features. The rate of increase or decrease is based on the indivdual feature weights. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** Together the first and second principal component explain 72.52% of total variance. The first four pc explain 92.79% of total variance.\n",
+    "\n",
+    "I dont understand the hint: dimensions are not increasing, they are new features that have values which are either high or low. Ah, maybe it means if you compare two examples, if a dimension increases this indicates that the positively weighted features in its portfolio increase and the negative weight features decrease. The rate of increase depends on the rate of the weight changes in its feature portfolio i guess..\n",
+    "\n",
+    "- Dimension 1 is Detergents_paper, Milk, Grocery with some representation of Deli. This is probably consumer retail spending.\n",
+    "- Dimension 2 is Fresh, Frozen and Deli, with some representation for Milk and Grocery. This is probably a hotel or restaurant.\n",
+    "- Dimension 3 is Deli with some Frozen and Milk, with very low Fresh and low Detergents_Paper. This could be a speciality shop such as a butcher. \n",
+    "- Dimension 4 is Deli with some Fresh but very low Frozen and low Detergents_Paper. This could be a market, where they sell fresh food and meat.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Observation\n",
+    "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions. Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Dimension 1</th>\n",
+       "      <th>Dimension 2</th>\n",
+       "      <th>Dimension 3</th>\n",
+       "      <th>Dimension 4</th>\n",
+       "      <th>Dimension 5</th>\n",
+       "      <th>Dimension 6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-2.4072</td>\n",
+       "      <td>2.4079</td>\n",
+       "      <td>0.5245</td>\n",
+       "      <td>-0.0848</td>\n",
+       "      <td>0.5462</td>\n",
+       "      <td>-0.0377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-3.4491</td>\n",
+       "      <td>-3.6586</td>\n",
+       "      <td>6.7584</td>\n",
+       "      <td>-2.9439</td>\n",
+       "      <td>0.6921</td>\n",
+       "      <td>2.9431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.3109</td>\n",
+       "      <td>-4.5845</td>\n",
+       "      <td>2.0274</td>\n",
+       "      <td>1.1669</td>\n",
+       "      <td>-0.0343</td>\n",
+       "      <td>0.3363</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
+       "0      -2.4072       2.4079       0.5245      -0.0848       0.5462   \n",
+       "1      -3.4491      -3.6586       6.7584      -2.9439       0.6921   \n",
+       "2       5.3109      -4.5845       2.0274       1.1669      -0.0343   \n",
+       "\n",
+       "   Dimension 6  \n",
+       "0      -0.0377  \n",
+       "1       2.9431  \n",
+       "2       0.3363  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Milk</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Delicatessen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27167</td>\n",
+       "      <td>2801</td>\n",
+       "      <td>2128</td>\n",
+       "      <td>13223</td>\n",
+       "      <td>92</td>\n",
+       "      <td>1902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>333</td>\n",
+       "      <td>7021</td>\n",
+       "      <td>15601</td>\n",
+       "      <td>15</td>\n",
+       "      <td>550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>85</td>\n",
+       "      <td>20959</td>\n",
+       "      <td>45828</td>\n",
+       "      <td>36</td>\n",
+       "      <td>24231</td>\n",
+       "      <td>1423</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
+       "0  27167   2801     2128   13223                92          1902\n",
+       "1      3    333     7021   15601                15           550\n",
+       "2     85  20959    45828      36             24231          1423"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Display sample log-data after having a PCA transformation applied\n",
+    "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))\n",
+    "display(pd.DataFrame(samples))\n",
+    "\n",
+    "\n",
+    "# hard to see how it works..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Dimensionality Reduction\n",
+    "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the *cumulative explained variance ratio* is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.\n",
+    "\n",
+    "In the code block below, you will need to implement the following:\n",
+    " - Assign the results of fitting PCA in two dimensions with `good_data` to `pca`.\n",
+    " - Apply a PCA transformation of `good_data` using `pca.transform`, and assign the results to `reduced_data`.\n",
+    " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# TODO: Apply PCA by fitting the good data with only two dimensions\n",
+    "pca = PCA(n_components=2).fit(good_data)\n",
+    "\n",
+    "# TODO: Transform the good data using the PCA fit above\n",
+    "reduced_data = pca.transform(good_data)\n",
+    "\n",
+    "# TODO: Transform log_samples using the PCA fit above\n",
+    "pca_samples = pca.transform(log_samples)\n",
+    "\n",
+    "# Create a DataFrame for the reduced data\n",
+    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Observation\n",
+    "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Dimension 1</th>\n",
+       "      <th>Dimension 2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-2.4072</td>\n",
+       "      <td>2.4079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-3.4491</td>\n",
+       "      <td>-3.6586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.3109</td>\n",
+       "      <td>-4.5845</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Dimension 1  Dimension 2\n",
+       "0      -2.4072       2.4079\n",
+       "1      -3.4491      -3.6586\n",
+       "2       5.3109      -4.5845"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Display sample log-data after applying PCA transformation in two dimensions\n",
+    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing a Biplot\n",
+    "A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case `Dimension 1` and `Dimension 2`). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.\n",
+    "\n",
+    "Run the code cell below to produce a biplot of the reduced-dimension data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xe5c9ba8>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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GBho1aoQOHTrg0ksvxc+2ixM7d+7E6NGj0bFjRzRs2BAtWrRAnz598MILL9TY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\nGW+jOWWMDeeBOb+46CI5Mdu3N3VLAGehbsRjjz1GdHQ0jzz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0p2Pv2raKChEX+/aJgWj4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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe34ea90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a biplot\n",
+    "vs.biplot(good_data, reduced_data, pca)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Observation\n",
+    "\n",
+    "Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on `'Milk'`, `'Grocery'` and `'Detergents_Paper'`, but not so much on the other product categories. \n",
+    "\n",
+    "From the biplot, which of the original features are most strongly correlated with the first component? What about those that are associated with the second component? Do these observations agree with the pca_results plot you obtained earlier?\n",
+    "\n",
+    "- First Dimension: detergents, grocery and milk (positively) - agrees with previous plot feature weights. \n",
+    "\n",
+    "- Second Dimension: Deli, Frozen, Fresh, also positively. Agrees too.\n",
+    "\n",
+    "So the length of the arrow is the weight of that feature in the dimension, the direction indicates how much each dimension is exposed to that feature.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering\n",
+    "\n",
+    "In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 6\n",
+    "*What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** The K-means algorithm will try to find clusters of points that are close to each other: a point can belong to only one cluster at a time. The Gaussian Mixture model allows points to belong to several clusters at a time, with a certain probability. Given wholesale customers might belong to several possible clusters, such as the \"small place\" cluster or the \"deli\" cluster at the same time, I would use the gaussian mixture model which allows this. Further, the mixture model incorporates the covariance information of the data (http://scikit-learn.org/stable/modules/mixture.html) which I believe is desirable, given we have some feature correlation. We should have enough points per cluster, so calculating the covariance matrix should work."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Creating Clusters\n",
+    "Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known *a priori*, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the \"goodness\" of a clustering by calculating each data point's *silhouette coefficient*. The [silhouette coefficient](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html) for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the *mean* silhouette coefficient provides for a simple scoring method of a given clustering.\n",
+    "\n",
+    "In the code block below, you will need to implement the following:\n",
+    " - Fit a clustering algorithm to the `reduced_data` and assign it to `clusterer`.\n",
+    " - Predict the cluster for each data point in `reduced_data` using `clusterer.predict` and assign them to `preds`.\n",
+    " - Find the cluster centers using the algorithm's respective attribute and assign them to `centers`.\n",
+    " - Predict the cluster for each sample data point in `pca_samples` and assign them `sample_preds`.\n",
+    " - Import `sklearn.metrics.silhouette_score` and calculate the silhouette score of `reduced_data` against `preds`.\n",
+    "   - Assign the silhouette score to `score` and print the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb comps: 2, score: 0.447411995571\n",
+      "Nb comps: 3, score: 0.359479670374\n",
+      "Nb comps: 4, score: 0.312405270688\n",
+      "Nb comps: 5, score: 0.3285065946\n",
+      "Nb comps: 6, score: 0.28969365136\n",
+      "Nb comps: 7, score: 0.328311049677\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use the ouput of PCA reduced to 2 components as input for the Gaussian Mixture Model\n",
+    "\n",
+    "nb_comp = [2,3,4,5,6,7]\n",
+    "\n",
+    "val_out = 2\n",
+    "for val_comp in nb_comp:\n",
+    "    # TODO: Apply your clustering algorithm of choice to the reduced data \n",
+    "    from sklearn.mixture import GaussianMixture\n",
+    "    clusterer = GaussianMixture(random_state=5, n_components=val_comp).fit(reduced_data)\n",
+    "\n",
+    "    # TODO: Predict the cluster for each data point\n",
+    "    preds_loc = clusterer.predict(reduced_data)\n",
+    "    #print(preds)\n",
+    "\n",
+    "    # TODO: Find the cluster centers\n",
+    "    centers_loc = clusterer.means_\n",
+    "\n",
+    "    # TODO: Predict the cluster for each transformed sample data point\n",
+    "    sample_preds_loc = clusterer.predict(pca_samples)\n",
+    "\n",
+    "    # TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n",
+    "    from sklearn.metrics import silhouette_score\n",
+    "    score = silhouette_score(X=reduced_data, labels=preds_loc)\n",
+    "    print(\"Nb comps: {}, score: {}\".format(val_comp, score))\n",
+    "    \n",
+    "    if val_out == val_comp:\n",
+    "        preds = preds_loc\n",
+    "        centers = centers_loc\n",
+    "        sample_preds = sample_preds_loc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 7\n",
+    "*Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score?* "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** 2 clusters has the best silhouette score. I am guessing 1 cluster would be even better as it has no alternatives, but it throws an error."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cluster Visualization\n",
+    "Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IiFRBd3c3u+66q4KgIpgZu+66a0m9aQqERERERESqREFQ8Uo9dgqEREREREQa\n1LPPPstZZ53Fvvvuy6RJkzjhhBN47LHHeOqppzjkkEOKWuf8+fNZv359Se1yznHBBRew33778fa3\nv51//vOfJa0vEwVCIiIiIiINyDnHaaedxtSpU3niiSdYtmwZl1xyCc8991xJ6y0mEOrp6Rnw8+23\n387jjz/O448/zlVXXcWnPvWpktqUiQIhEREREZFa0LkBZl0Mk8/03zs3lLS6u+66i+bmZs4///xt\nr02cOJGjjjpqwHLz58/ns5/97LafTzrpJO6++256e3s577zzOOSQQ5gwYQKXXnopixcvpqOjg7PP\nPptDDz2U119/nWXLlnH00UczadIkpk2bxoYNvt1Tp05l9uzZtLe3c/nllw/Y5s0338xHPvIRzIx3\nvOMdbNq0advn4qKqcSIiIiIiSde5ASaeBlteg609sHw1LLgVVtwIbSOLWuWqVauYNGlS0U1avnw5\n69atY9WqVQBs2rSJESNG8MMf/pDvf//7tLe3s3XrVmbNmsXNN9/M7rvvzvXXX8+FF17Iz3/+cwDe\nfPNNOjo6Bq173bp1tLW1bft59OjRrFu3jpEji9vXTBQIiYiIiIgk3dyr+4Mg8N+3vOZfv+IrVWnS\nPvvsw5o1a5g1axYnnngi73vf+wYt8+ijj7Jq1SqOO+44AHp7ewcEM2eeeWbF2ptOgZCIiIiISNIt\nWdkfBKVs7YGlK4te5fjx41m8eHHe5YYOHUpfX9+2n1Mlq3feeWdWrFjBnXfeyZVXXsmiRYu29fSk\nOOcYP348999/f8Z1Dxs2LOPre+21F52dndt+Xrt2LXvttVfethZCOUIiIiIiIkk3ZQI0p/VhNA+F\nyROKXuUxxxzDG2+8wVVXXbXttQcffJB77rlnwHJjx45l+fLl9PX10dnZydKlSwF44YUX6OvrY/r0\n6Vx88cXbKru1trbS1dUFwIEHHsjzzz+/LRDaunUrDz30UN62nXLKKfzqV7/COcc//vEPdtppp1iH\nxYF6hEREREREkm/ODJ8TlBoe1zwUhu/gXy+SmXHjjTcye/Zsvvvd79LS0sLYsWO57LLLBix35JFH\nsvfee3PwwQczbtw4DjvsMMDn8XzsYx/b1lt0ySWXAHDeeedx/vnns/3223P//fezePFiLrjgAjZv\n3kxPTw+zZ89m/PjxOdt2wgkn8Pvf/5799tuPHXbYgV/84hdF72fW/XfOxb7Scmlvb3eZkqlERERE\nRGrN6tXhc8TXAAAgAElEQVSrGTduXPQPdG7wOUFLV/qeoDkzii6UUC8yHUMzW+aca8/3WfUIiYiI\niIjUgraRVSuMUI+UIyQiIiJSis5umPUYTF7mv3d2V7tFIhKBeoREREREitXZDRM7YEsPbAWWd8GC\njbCiHdpaqt06EclBPUIiIiIixZr7TH8QBP77ll7/uogkmgIhERERkWIt6eoPglK2OljaVZXmiEh0\nCoREREREijWlFZrTXms2mNxaleaISHQKhERERESKNWcMDB/aHww1Gwwf4l8XqQHPPvssZ511Fvvu\nuy+TJk3ihBNO4LHHHuOpp57ikEMOKWqd8+fPZ/369SW165FHHuGII45gu+224/vf/35J68pGgZCI\niIhIsdpafGGEmaN8L9DMkSqUIDXDOcdpp53G1KlTeeKJJ1i2bBmXXHIJzz33XEnrLSYQ6unpGfDz\nLrvswg9+8AO+8IUvlNSWXBQIiYiIiJSirQWuOACWTPLfFQRJucRcqv2uu+6iubmZ888/f9trEydO\n5Kijjhqw3Pz58/nsZz+77eeTTjqJu+++m97eXs477zwOOeQQJkyYwKWXXsrixYvp6Ojg7LPP5tBD\nD+X1119n2bJlHH300UyaNIlp06axYcMGAKZOncrs2bNpb2/n8ssvH7DNPfbYg8MPP5zm5vSxp/FR\n+WwRERERkaQrQ6n2VatWMWnSpKKbtHz5ctatW8eqVasA2LRpEyNGjOCHP/wh3//+92lvb2fr1q3M\nmjWLm2++md13353rr7+eCy+8kJ///OcAvPnmm3R0dBTdhlIoEBIRERERSbpcpdqvOKAqTdpnn31Y\ns2YNs2bN4sQTT+R973vfoGUeffRRVq1axXHHHQdAb28vI0eO3Pb+mWeeWbH2plMgJCIiIiKSdGUo\n1T5+/HgWL16cd7mhQ4fS19e37efubj8kb+edd2bFihXceeedXHnllSxatGhbT0+Kc47x48dz//33\nZ1z3sGHDim5/qZQjJCIiIiKSdGUo1X7MMcfwxhtvcNVVV2177cEHH+See+4ZsNzYsWNZvnw5fX19\ndHZ2snTpUgBeeOEF+vr6mD59OhdffDH//Oc/AWhtbaWrywdoBx54IM8///y2QGjr1q089NBDRbc5\nTuoREhERERFJujljfE5QanhcDKXazYwbb7yR2bNn893vfpeWlhbGjh3LZZddNmC5I488kr333puD\nDz6YcePGcdhhhwGwbt06Pvaxj23rLbrkkksAOO+88zj//PPZfvvtuf/++1m8eDEXXHABmzdvpqen\nh9mzZzN+/PicbXv22Wdpb2/nlVdeoampicsuu4yHH36YHXfcsej9HbT/zrnYVlZu7e3trlrJVCIi\nIiIicVq9ejXjxo2L/oHObp8TtLTL9wTNGdPwVQozHUMzW+aca8/3WfUIiYiIiIjUglSpdomFcoRE\nRERERKThKBASEREREZGGo0BIRERERKRKailfP2lKPXYKhEREREREqqClpYUXX3xRwVARnHO8+OKL\ntLQUXyyi6sUSzGwI0AGsc86dVO32iIiIiIhUwujRo1m7di3PP/98tZtSk1paWhg9enTRn696IAT8\nB7AaiK8ouIiIiIhIwjU3N7P33ntXuxkNq6pD48xsNHAi8LNqtkNERERERBpLtXOELgPmAH1VboeI\niIiIiDSQqgVCZnYSsNE5tyzPcp80sw4z69D4SRERERERiUM1e4SOBE4xs6eAhcAxZnZt+kLOuauc\nc+3Oufbdd9+90m0UEREREZE6VLVAyDn3JefcaOfcWOAs4C/OuXOq1R4REREREWkc1c4REhERERER\nqbgklM/GOXc3cHeVmyEiIiIiIg1CPUIiIiIiItJwFAiJiIiIiEjDUSAkIiIiIiINR4GQiIiIiIg0\nHAVCIiIiIiLScBQIiYiIiIhIw1EgJCIiIiIiDUeBkIiIiIiINBwFQiIiIiIi0nAUCImIiIiISMNR\nICQiIiIiIg1HgZCIiIiIiDQcBUIiIiIiItJwFAiJiIiIiEjDUSAkIiIiIiINR4GQiIiIiIg0HAVC\nIiIiIiLScBQIiYiIiIiUW2c3zHoMJi/z3zu7q92ihje02g0QEREREalrnd0wsQO29MBWYHkXLNgI\nK9qhraXarWtY6hESERERESmnuc/0B0Hgv2/p9a9L1SgQEhEREREppyVd/UFQylYHS7uq0hzxFAiJ\niIiIiJTTlFZoTnut2WBya1WaI54CIRERERGRcpozBoYP7Q+Gmg2GD/GvS9UoEBIRERERKae2Fl8Y\nYeYo3ws0c6QKJSSAqsaJiIgkSecGmHs1LFkJUybAnBnQNrLarRKRUrW1wBUHVLsVEqJASEREGk9S\ng43ODTDxNNjyGmztgeWrYcGtsOLGZLRPRKSOaGiciIg0llSwMW8RPLDSf594mn+92uZe3R8Egf++\n5TX/ukg6TdApUhIFQiIi0liSHGwsWdnfrpStPbB0ZXXaI8mVmqBz3np4oMt/n9ihYEikAAqERESk\nsSQ52JgyAZrTRq03D4XJE6rTHkkuTdApUjIFQiIi0liSHGzMmQHDd+hvX/NQ//OcGdVtlySPJugU\nKZkCIRERaSxJDjbaRvrCCDPP8IHZzDNUKEEy0wSdIiUz51y12xBZe3u76+joqHYzRESk1qWqxi1d\n6QOOpFSNE4kqlSOUGh6XmqBTc9OIYGbLnHPt+ZZT+WwREWk8bSPhiq9UuxUixUtN0Dn3GT8cbnIr\nzBmjIEikAAqERERERGqRJugUKYlyhEREJBk6N8Csi2Hymf57Eub1ERGRuqUeIRERqb7UJKep+X2W\nr4YFt6pQgIiIlI16hEREpPqSPMmpiIjUJQVCIiJSfaVMcqohdSIiUgQNjRMRkeqbMsEPhwsHQ1Em\nOdWQOhERKZJ6hEREJJpy9rwUO8mphtSJiEiR1CMkIiL5pfe8dKyCnyyEc06Gb15Qeu9L20jfi1Po\nJKelDKkTEZGGpkBIRETyS+95cQ56HVzzO/jdXfEMRStmktNih9SJiEjD09A4ERHJL1PPC0Cfq+5Q\ntGKH1ImISMNTICQiIvlNmdAfbKSr5lC01JC6mWf4XqCZZ6hQgoiIRKKhcSIikt+cGb4a26YuPywu\nrNpD0YoZUiciIg1PPUIiIpJfquflI6fAkCZoMv+6hqKJiEiNUiAkIiLRtI2E+ZfAk3+ET39IQ9FE\nRKSmaWiciIgURkPRRESkDqhHSKQRlXNiTJEk0bUuIiJZmEtPek2w9vZ219HRUe1miNS29IkxUzke\nGt4k9UbXuohIQzKzZc659nzLqUdIpNGkT4y5tae688BIciSx96SUNulaFxGRHJQjJNJoMk2MWc15\nYCQZ0ntPlq/25bLj6j3p3OADkCUr/ZxEc2bkX2+pbdK1LiIiOahHSKTRZJoYs9rzwEj1lbP3JBXQ\nzFsED6z03yeelr93p9Q26VoXEZEcFAiJNJo5M3yeROoGUfPACJS396TYgKbUNulaFxGRHBQIidSq\nYnMnUhNjzjxD88BIv3L2nhQb0JTapijXehLzokREpCJUNU6kFpWxGpZzDjOLbTmpEeWssDbrYj8c\nLhwMNQ/1gUmu+YjKXfVNVeVEROqSqsaJ1LMy5XN0d3dz0kknsXDhwpzLLVy4kJNOOonu7u6SticJ\nUs6ewmKHqJW791JV5eLV2Q2zHoPJy/z3Tv19EJFkU9U4kVpUhnyO7u5uTj31VO68807uuOMOAM46\n66xByy1cuJCzzz6bvr4+Tj31VG666SZaWlqK3q4kSNvI3D00pax3xY0+wFi60gc1UarGlbNNoKpy\ncershokdsKUHtgLLu2DBRljRDm36+yAiyaQeIZFaFHM+h3OO6dOnc+eddwLQ19fH2WefPahnKBwE\nAdx5551Mnz6dWhpiK1WSCmiWXO+/J2HomarKxWfuM/1BEPjvW3r96yIiCaVASKQWxVwNy8w499xz\naWrq/5OQHgylB0EATU1NnHvuucoVSjIVA8hOVeXis6SrPwhK2epgaVdVmiMiEoWKJYjUqtQElYUO\nNcohY7Bjxsk7jeSWzRvoC/29aGpqYsGCBRmHz0lCqBhAfmX4PWpIsx6DeesHBkPNBjNHwhUHVK1Z\nItKYohZLUCAkIgNkCobSKQiqEcVWaxMpVHqOULPB8CHKERKRqlDVOBEpyllnncWCBQtoyjLcrclM\nQVCtUDEAqZS2Fh/0zBwFk1t9T5CCIBFJOAVCIjLIWWedxck7ZR4edPJOIxUE1QoVA5BKamvxw+CW\nTPLfFQSJSMIpEBKRQRYuXMgtmzMn1d+yeUPeeYYkIVQMQEREJCsFQiIywLYcoSz5g33OZSytLQlU\n7glJRUREapgmVBWRbaJWjUuV1obMk65KgpRzQlIREZEaph4hEQGyzxO04LrruOnldSy47rqc8wyJ\niIiI1BIFQiKCc45rrrlmcBAUqg63rZpcWjB0zTXXUEtl+EVERERAgZCIAGbGDTfcwLRp04Ds8wSl\nB0PTpk3jhhtuwLKU2pY60bnBz0k0+Uz/vTNzIQ0REZFaoglVRWSb7u5upk+fzrnnnpsz92fhwoVc\nc8013HDDDbS0qERuXevcABNPgy2v+TmIUpXnVHRBREQSKuqEqgqERGQA51ykHp6oyyVK5waYe7Wf\naHTKBF9GWjfzuc26GOYtGjgxa/NQX4GulCIMOhciUq86u2HuM7CkC6a0wpwxmlerwqIGQqoaJyID\nRA1uajIICvdsLF8NC25Vz0Y+S1YODILA/7x0ZfHr1LkQkXrV2Q0TO2BLD2wFlnfBgo2wol3BUAIp\nR0hEGsPcq/tvvMF/3/Kaf12ymzKhf0LWlOahfl6iYulciEi9mvtMfxAE/vuWXv+6JI4CIRFpDOXo\n2WgEc2b4nKBUMJTKEZozo/h16lzkpwIVIrVpSVd/EJSy1cHSrqo0R3JTICQijaGUno1q3ZQm4Wa4\nbaQfsjbzDH+sZp5R+hC2cvQy1ZPU0MF5i+CBlf77xNMUDJWqsxtmPQaTl/nvnd3VbpHUoymt0Jz2\nWrPB5NaqNEdyU7EEEWkMxVY/q1bVtHqu1lbP+xaHchWoaGTpeRvNwPChytuQ+A261gyGD9G1VmFR\niyVUrUfIzNrM7C4ze9jMHjKz/6hWW0SkARTbs1GtfJZ6zqMpRy9TPdHQwfgpb0Mqpa3FBz0zR/le\noJkjFQQlWDWrxvUAn3fO/dPMWoFlZvZH59zDVWyTiNSztpGFP1Gv1k1pvd8MF3MuGsWUCb6SXnqP\nUDFDB1XG11PehlRSWwtccUC1WyERVK1HyDm3wTn3z+DfXcBqYK9qtUdEYpSE3Ja4VCufRXk0jSuu\nAhWpITrz1sMDXf77xI7GzI1R3oaIZJCIHCEzGwv8DTjEOfdK2nufBD4JMGbMmElPP/10xdsnIgWo\nt/wP5QjFSxOpRpM6TktX+uC3mOM06zEf/IR7QprND9VptKfVytsQaShRc4SqHgiZ2XDgr8C3nHO/\nzbWsiiWI1IB6TPSO46a0lrZbLsUEdwqcijd5me8JGvR6KyyZVPn2VFtqmODSLn8MGnWYoEgDiBoI\nVTNHCDNrBm4AFuQLgkSkRlQqt6WSN8jVymeptzyaXAUgMu1neuC0fDUsuLW6vWK1FJhNafWz2qf3\nCDXqcDDlbYhImmpWjTPgamC1c+5/qtUOkaqrp3waqExui+ZZiS5J11ehQXLSKufV2nU3Z4wvEZ3K\njUkNB5szpqrNEikbzRUlBarmhKpHAucCx5jZ8uDrhCq2R6Tyau3GKoq4Er1zSdoNclLlur6qESAV\nGiQnrXJeUq67qOdOZXylkWQrDrJks4IjyapqQ+Occ/cCVq3tiyRCoUOFakFqjphy5rYk7QY5qbJd\nXxf9AH53V+WHnM2Z4beTniOULUiOs4x0HJJw3RU6XFDDwaRRZJwrqgeOXg59zv+8vAsWbNQDAdmm\nmj1CIpKEG6tySOW2LLne3+TOvTrengeVlo4m2/V1+73F92yU0pNU6ESqlehdLEQSrruk9EqJJE3G\nuaKAN5wm0pWsqlosQaThJe2Jd9zKlexeaM9Co0hP5D94n8zXF664ADyO81lIAYhiexfLVdAgCddd\nvT48ESlVpuIgmWgiXQlRICRSTUm4sSqncg39q8Twu3Ip1016piBlhxb/9Vr3wOvr+KPgutsKD8Cr\nMZSz0Mp55aw0l4Trrt4fnogUa84YP+wtPFdUE9DrIPzsoJErJ8ogVZ9HqBCaR0jqUr3NFRM2+Uyf\npD/o9Ql+2FyjKeckqdnmb/rwidA6bOD1BcW1oxbOZ6bj0GSw285wxvtr//erXifarXepOYyWdPme\nC81hVB7pc0Wdsyccv1IT6TagmphHSESov7liwvT0eqBy9qhkGzK1ek3mIKWYno1aOJ+ZjkOfg40v\nwY9+Ddf8DlbeXLtBQxJ6paQwqWpmqZtxJeyXT6biICvaNZGuZKVASETKJ6lD/6o1KWY58zsKDVKK\nCcCTej7DMh2HFOdg8xZfNW/+JbU1OWpYPT88qUcZq5kFCfuq6Fd+4eBIPXOSRkPjRKS8kjb0r5pD\ni7INX5t5Ruk3tpXar6Sdz3TpxyGTPXaBjt9oiJlUxuRlfl6bQa+3wpJJlW9Po0rvmWvGTzisnrm6\nFHVonMpni0h5hUtpX/GV6t9kVrP8cDnLQRdamrqU7STpfKYLH4chQzIv09dXf2WoO7s1aWRSTWn1\nN91hStivvFw9c9KwNDRORBpLNcsPlyu/I32I1+LLkhegVFIqWPvj/fDok4Pf33Xn+ipDrRyUZMtU\nzWz4EP+6VE7GeYZUSrvRKRASkcZS7YT/uPM7ylkuuhbkyvPZ7i2ZP9PylupfB3FSDkqytbUoYT8J\nMs0zpJ65hqehcSLSWMo5PK0a6m2IVyFSQeC8Rb6s97xF/ufODf79d0+CoWnD44YOgaMm1dd1oCfd\nyZdK2F8yyX9XEFR5c8b4nKDUMEX1zAkKhESk0VQql6ZS6mmIV7rODb7AxOQz/fdUgJOSLwicM8PP\noRQOdlqH9fca1ct1oBwUkfxSPXMzR/nfjZkjNXxUVDVORKSmlbMSXTVFqYIXZYLXpFe5i8Ogalia\nNFJEGpuqxomI1Lp8PSJQX0O8wqIM+ZsyoX+/U9LzfNpG+mMxeYLvPZt7debjWMv0pFtEpCjqERIR\nSaJC5gWqx16PqL09+Y5RNeeNEhGRqlCPkIhILSukCELS5/YpRtTennx5Po1cTEJERHJS+WwRkVLk\nKt9cinoughDFnBm+DHh6T076kL985cgb/TiKiEhW6hESkfoTJbcmru3kKt9ciig9IvUsrqpujX4c\n49bZDbMeg8nL/PfO7mq3SESkaMoREpH6UsmckGIrtuXqRUq997dl8Oga6O2Dnt7B+1Gunqh6oxyh\n+AyqToefl0WFGUQkYaLmCGlonIjUl1w5IXGXky5m2FX6jfny1X4I2Iob/fvh94YOgSFNMH5/OOqw\n/mAn1zp0cz9Qqmep3opJVMPcZ/qDIPDft/T61684oJotq4zObr+vS7r83E1zxigAFKlxCoREpL5U\nMidkygQfhKT3COUadpUveT/8Xk8vmPkgKBzEVTLYiyrJPVT58ogkmiVd/UFQylYHS7uq0pyKSu8N\nW94FCzaqN0ykxilHSETqSyVzQoqZwydXoBY1iEtaAYBy5kpJckxp9cPhwprNz11U73L1holIzVIg\nJPWpUsnykjyVnGC0mIT+XIFa1CAuaQUAVKK6McwZ43OCUsFQs8HwIf71QtVa0YVG7g0TqWMqliD1\nR8nRja1zA1z0A7j9HsDg+HfBNy9IzrnPdX1CtGs3add4lMlPkyzJw/qSJpUns7TL9wQVkydTi0UX\nZj0G89YPDIaaDWaObIz8KJEaowlVpXHp6XTjSgUI190GG1+ClzfD7+6qdqsGytWLFLWHKa7S0nFJ\nWg9VITSsrzBtLf7Gf8kk/72YwKWQYWZx9BzFsY44e8NEJDHUIyT1p9afTkvxii1nXQj1HgyWtB6q\nQlTimpGBJi+DBzIMKZvc6gOslDh6juLsfYqjN0xEKkLls6VxFVPJS+pDtiICi+4sLnBJD3rOORmO\nn6my1elquUR10gpPNIIprb7qWvows/SiC3GU646z5HeqN0xE6oYCIak/c2b4m9P0p9PlSJaXZMkU\nBAM8/xJsfLGwwCXTXD0//U3/BKeQjLLVSVGrJaqT9uCkEXoc54zxpae39dJkGWYWR4ECFTkQKZ86\nmFtLOUJSf5KWPyHlk14d8JyTB1aMazL/PTUEuJB8sUy5Zm9s7Q+CUtR7UNsqWWUwn0bJV2pr8UPT\nZo7yvUAf3gNO2RWmPzQwhyeOct2NXPJbpJxSw07nrfdDXeet9z8nvQJkGuUIiUhtypaXcvs8uPYW\nH5w80Qkvbhr82Sj5YtlyzdIpn6T2pXphqj2srxHzlXLl8EDae0HPUUk5QkWsQ0QGS3glReUIiUh9\ny1Yd8Npb/E1j5wbY//2DPxd12NOUCbBsFfRleFhk5nuZNOyyPiRlWF+ufKU6GIKSUb4cnhXtpRUo\nSPU+qciBSLzqZNipAiERGahWchSy3TTes8z/e+7VPp8nXZNFC1zmzICfLAQyBEK77wJjR9VWUQBJ\nvmz5SuMmDezVWN7lc2zqoVcj381UHAUKVORAJH5Ri54knHKERKRfUnIU0nN/Mm1/ygQYOmTw64+s\n8csvWTk4nwfgoH2iBS5tI33OkdnA15uHwhnT/NC6K76iIEjiky1fifdFn3en1iiHR6Q21cncWgqE\nRKRfEiajjRqMzZkBQzL8Cetzvr3ZJvk8atLgz2TzzQtgRGsyEulrUZSAVvplK/TycG/0IShxTB5a\nSXVyMyXScNKLnswcWZO91BoaJyL9kjCnSq5gLJzH0TbS9+6seDRzexdfVnoZ9VqeH6faMpUf15xL\n+WXKV5rSFW0ISnphgFoYQqccHpHaVQfDTtUjJCL9svWiVHJOlUKCsaMmZW9vXGXUUzemGgpXmCT0\nLtaLqL0muQoPhCWt1yh1M7Vkkv+uIEhEKkQ9QiKVluRiBEmYjLaQCS7ztTfOamBJPm9JlITexXoR\ntdckShWnWuw1EhEpEwVCIpWU9OFCSRgKVkgwVqn2VuO8lRp4VTtwKySglfyiDEGJUsUpX7nqWlGv\n5cRFpKI0oapIJTXihInFSMoElymVPm/ZJouNGniV+vk4JKENjSbT5KRNBgfuAO/eyQcL0x/ys8Cn\nm9zqh6bVglyTsCoYEhGiT6iqHCGRStJwoWiSlpdT6fNWan5NEvJz4srRkujCVZwmDvNBUK+DB1/1\nM8BP7ICDd4ivXHW1co2i5kJJ4ZKWPyZSZhoaJ1JJGi6UXbWHcuVS6fNWauCVlIA7zhwtiSY1hG7W\nY/Dwq5C6DFLBAvjek229KUWWq65mrlGdzGifOMofkwakHiGRSso2YWKjz0sT90Succ9fU8h5i2Pb\npVbvS0L1P6mubMHC6tfimfujmr0ySZ+EtVZ7VdTTJg1IOUIi5ZKthyNp+S9JEGcOTrlyU6Kct7i2\nXQ85QlJdsx7zw+HSCyfMHBlPUYTJy6qXazQoRyjo1UpCz0Ut5y9V85yKxCxqjpCGxomUQ74qY0ke\nLlTMELVSh7XFOZQr6oSshYpy3uLadtRqeNmOexKq/0l1zRnjhzWVOgQumygV6solyZOw1nJVvmqe\nU5EqUSAkUg7luhkvt2LKRMdRWjrOHJxq5sfEue18gVe+4570gLvSkpyDVg7lDhbKHWjlk9QZ7Ws5\nf6na51SkCpQjJFIOSUlWL1Qx1cbiqFAWZ+5UNfNjKrntJFSGqxVx56DVilSwsGSS/x5nj0m4Ql0p\nuUb1Jun5S7nonEoDUiAkUg61mqxeTAAXR9AXpdRy1CIE1SxIUclt12qwXQ0KGssjV6BVqwUDSjVn\njM8JSgVD6b0qST8u5QyeRRJIQ+NEymHODD9MKT1ZPenV4YoZohbXsLZcQ7kKGX5XzfyYSm476nFv\ntCFhmShoHKyz2w+bW9LlezHiHDbXyGWYcw1JbOTjIpJQqhonUi61WB2umGpjlahQFmdVuWxqLWCI\nctzjPDe1dnzCKnH91JIolc1KCZTKXbGuVtXLcSlnEC0Sk6hV4xQIichAxQRw5Q76Jp/pczsGvT4B\nllxf+vprtdx0vuMeVwBQq8cnpdbbH7d8N+SlloBWGebM6uG41HJ5cGkoKp8tIsUpptpYuSuUZRoG\nBvD6G/4mNzw/UzE9FrVa5S/fcY9rSFitHp+UWionXomn7fkqm5VaAlplmDOrh+NSy+XBRTJQICQi\nyZEtmEnlXHW9Cj29/cuvfsI/6b99Hhw/s/gS3tXKISn3cLNi87fS2/W3ZbWfY1ML5cQrlUOS74a8\n1BLQ1SjDXAvDteqhPHUtlwcXyUBV40QkGXKVOE490R+378DP9PT64OcTXy2tKli2Kn+vv5G/Sl24\n/VGq2kXZ37gUU8UuU7seXQNDhwxcrhaqIKYUem6qJdfT9jjlq2xWagnouMsw56u0lgog5633Q8/m\nrfc/J7EiW62Xp67l8uAiGShHSESSIUo+S7ZcoWHbw6uvD349ag5RphySnh4YMsQHW/lySorJQalU\nAn+h+VvZ2tVk0OdqL8emlvKDKplDkupByTTZ6qA8kCBQSt20V7L3JUpOSjFFCGqhBymJ8l0bIgkR\nNUdIPUIikgzZhqctuqP/CX62npt92gqbtym9hwAGzmN00D79QVCqHbl6mIqZp6aQ4Xil9GikhoQt\nud5/z3fzn61dB+2be56nuMTde1NLcwhV8ml7rvlicvVcZOt9WbK5PPPjROklK3S4Vq30ICVRPfRq\niYRkzREysx2BLwGjgdudc9eF3vuxc+7TFWifiDSKbAURXnjZP9FfcWP2+Zl++vWBOUK5hoDlmpMo\n3PMUzkWC3Dkx+YKaTLlAhcwDFHUOpThka9dRh5U/x6Yc+1pLcwglKYckFSilyxiY9MDRy4MeQ+LN\nbfqMVAUAACAASURBVIoS5GTKeQJ4vdcHN+ltUMJ/abJdG6CeNqk5uXqEfgEYcANwlpndYGbbBe+9\no+wtE5HkKWeuRSqfxWzg632u/wl+KlcovWdiysTMr2e6ec7VQ5Dav6fX+aFgYbl6mDL1VJnBuH2y\n5wKdc3K0/J1K92gUk1cUl3Lsa7ZexCTmN1XiaXu+fJt8MgYmwBuuPLlNmXrJoD/Igf6cp/RHu6tf\ny9zTky24WvS8eoVKoZ42qUFZc4TMbLlz7tDQzxcCJwCnAH90zh1WmSb2U46QSBVVIteicwO0nw4b\nXxr8XlxzBmXLM5p4EDyzfuCNeEqUHKEJH4DNWwa+vtNwOPVYuO62zLlAc2bkz98p9xxKmVRrMuBy\n7Gst5QiVW6Z8mx2GwKm7wcOvRXuCnykfJ5uouU25ehFSbe7qgfCv5VCgdejAIXsnPggrXxu47ky5\nQtn2wYARmhMnsvTz1tUL1z1X+xPGSl2II0doOzPb9r5z7lvAT4G/AbuW3kQRqZpienYq0TPRNhLO\neH95n+Bn6yHo6xscBJnBHrvmz4lpG+kDnvRepNe64fZ7sg/NipK/U40ejULziuJSjn3N1otYS0FQ\nqb04KZmGhG3uhWuei/4EP1PFue0s80D7cK9NNvl6EVK9ZON2GPi5Hgb2OrW1QEtaZUPInCuU2of0\nOyBHfD1ZcYnr3Mct03m79rnMPW0qrS0JlisQugU4JvyCc24+8HngzTK2SUTKqdiyzZXKtSj30Kxs\n6zcbvH/OwdhR0YKBh9f4YXxhqfVFvbnPFKBWc6hapZVrXzMFdrVSUjvO4UaZhoQB9AXfowxpyzR8\n76+H+t6ZqEPTwqIUQ4ga5EQtNpHah90yjLlL0o17koeaZTpvDt+rFlbO0tpJDRKlpmQNhJxzc5xz\nf8rw+h3Ouf3L2ywRKZtie3aiPK2P4+ay3E/ws63/3ZNK643IdnyOPyrazX22ABVqv0cjqkr13lRi\nDqe4xDm3ULZ8m7AogUB6xbkpO0XrtckkasW3KEFOvvmR0vfhjN2TPSdO3PNKxRk4ZDpvffi7yijH\nv1RJDhKlpmgeIZFGU2weRr5ci1rPxSi1/bk+D/lzbio1r5DU1rGOc26h9Bwhwz/FDyslp6OYtkad\nAyjq/DW55kdKl/Q5ccp57jPNx1SIbOftw3tA65Box78UxcwdJQ0lao5Q1vLZIlKnopZtTpd6Wp+6\noR+3j399+my/zq5Xs/c0Rb25zFRmOltxgijLFbqt8P4VWiQg/fikfz7fMailMs+1rpaOdabS0MX2\nWqSGhKUChXE7wE0vwGu98ZTrLqatUUuGp7c92012rtLO6aKus1riPPdxlwzPdt6+uXdljl+hc0eJ\nZKEeIZFGE0fPTaZ19PVBb9/gZaNW/IrarnK1P9864gi+cqmlXopSlftY5lNLxzruXov0Sl/n7OmT\n3OMIBIpta65enEaelybOcx9n71K4fdUKItUjJHlE7RGKFAiZ2TuBsYR6kJxzvyqlgcVQICQSk1LL\nI2e6kTTzQ23CBQMKubmMenMax01soeuoVOnwSgwtjDMIKWZdce9nEtpQbnHdcMY9PKqcba1Ue8sp\njiAuruNZb4FD0oc1StXFFgiZ2TXAvsByIDXVunPOXVByKwukQEgkAXLN9TOkCZqairu5jJq7FMdc\nM4WuI2Lg1NPTw2233UZHRwdbtmxh+PDhtLe3c+KJJzJ0aIaRyOk38eecDNfeMjhAjSt4iTMAKHZd\ncfbGlLI/1ZorqZpq7WY4X3uT3FuUfqPehH9QdM6elRs+lqs99RA4VLNHShIvzhyhduBgV0tj6ESk\nPFI3npsyDLFoHgofPhFahxV3cxk1d6nYHKdS1pEnp+TVV1/l0ksvZd68eaxdu3bQx0ePHs3MmTP5\n3Oc+x7Bhw/yL6Tfxy1fDglvzDwXMtlwUuSoGFhqEFLuuOPNzStmfVEntRlJreRW52pt+Y7+8y+es\nJOXGPj0nJzVq+FfPwe9erHw7k54PVYxC8sFEssg1j1DKKuCt5W6IiFRJISWvUzee6c9Fmsw/if/m\nBcVPxBl1Dpk45popdB05Sodv3LiRqVOnctFFF7F27Vr2339/LrzwQv77v/+bCy+8kP3335+1a9dy\n0UUXMXXqVDZu3Og/H7WMeZwT2cYZhORaV65rKl8Z9kKux1oqepAEUefZSYpc7Y27tHTcss3ZVMik\nrXHPk5Ne9ryWgyCRmETpEdoNeNjMlgJvpF50zp1StlaJlKraydi1otDehkw3ngC77Qwdvyk9x+OU\n9/jXVq/J3qOUrzpblO1MmQC3z8s8DC2TOTP8cUkbgvXqZz/EiSeeSEdHB3vvvTfz5s3joEnv5Fu3\nreb6RzbSZMYHLzmdKds/x+c/ez4dHR2ceOKJ3H333Qy7Z1m0m/g4b/bj6E3Lt65x++S+prIcS+bM\nKPx6jHN/GkHUCm1Jkau90x9Kdu9WpopvKVHamfQer2IkeSijNKwogdDXyt0IkVjFOZSo3hU6tCjb\njecZ748nZ2X56mg5HoUOayr1msgSfF36y6u3BUH33Xcfw3fejaO/dxcvbXmTJjN66eP6jrX8c4/h\n3HPv3znqXUfS0dHBZV//Jhc+smbwdso1FDAlVxAS17og9zWVK5CddXFh12Oc+9MIam14VK72xlla\nOl0cN+ypIG5zT/+wuELaGXe562qrx8BO6kLUqnF7AocHPy51zm0sa6uyULEEiaRSpXHrodep0KIB\ncVfbKvZcFXrsy3BN9PT0sPfee7N27Vr+8Ic/cNxxx/HL+57kW7c9QlMTXP/JI3jptTeZec0yhjYZ\nPzr7MLY+vZxp06bRNnwn1rw+mqHp5ca3a4bH74i/XHhYnEUCOjfART+A2+8FHBx/FPzvI/Dgo4OX\njVLMophCGI1Y9KDeFBN4lCv5P85KdZ3dcNGTvjx5H35YXNR2lqPcdVTl6LmptUIdUvNiK5ZgZmcA\n3wPuxtc8ucLMvuicW1xyK0Xi1rkBFt1R/ryBeul1KrS3odhhadlEHfa1ZAV84quwphNGvxXWPwfd\nb0Y/9mXIJbnttttYu3YtBxxwAMceeywAdz3yPG/29vHeA/ZgYtsIAN62yw48vnEL9/3rBb50/HvZ\nf//9efzxx/k9rZxC2lPhg/aJbyhgNnEXCfjdXf2/B9fd5vPFhg6Bnt7+ZaL2YBXT+9WIRQ/qSbE9\nBYX0bhVyYx9nT0xbC8wf56vEFdoLF7XHK+6gpVw9N/kKdWjYnFRJlKFxFwKHp3qBzGx34E+AAiFJ\nllRwsjlLRbM48wbirL5VTcUMLYrzxjPKje+SFXDEh/zTVIBHnxy4jijHvgy5JKne6dNPP52mJl93\n5uENrwAwYa+dti13wJ6tPL5xC8ue3kRTUxOnn3463/72t+loepNTwh1CzUPhqCxPestxsx9Hj2am\n34Pmob6Mulnhw9U01K3xlBJ4RKkaVuiNfTkq6xVT3SxKPlc5gpZyDcnLFdhp2JxUUZSqcU1pQ+Fe\njPg5kcpK3ZT1pQ33NIv/ZqpeqlWlehtmnuGDgplnVLZXK0r1tk98tT8IyibfsY+j0lyaLVu2ALDL\nLrtse+2Nnj6GNhmtLf2lrnYe5v/92ps9A5bvam6KtT0FST00mLfID0Wbt8j/nKtCWybZfg8O2re4\na6ra16NUXqmBR77KaoVWl0tKZb1Uj9fMUX7bM0cODgzKUTmvXCXW54zxQwxTxzYc2CW9AqDUtSg9\nQneY2Z3Ar4OfzwR+X74miRQpW0Wz3XeBjkXx3kzVU7WqYgoPxJUbFWXY15rOaOsat09p2ynQ8OHD\nAXjppf6JZbcb2sTm1x1d3f13Ei+/6v+9w1uGDFi+9fyzoHfX6uS2xNWjme334KjDiu/BStJQt3rI\nA0y6UooeROlJKPTGPkmV9fL1JJUjaClXEYpcQxlrbX4rqSt5AyHn3BfNbDpwZPDSVc65G8vbLJEi\nZK1oNi3+m5dGHcJTjtyofDe++7TByseKW3dKGW5o29t9DuaiRYv4xje+QVNTE+NG7sjGrudZuW7z\ntuUee87/Z37YmJ3p6+vjN7/5jf/8Me+BU2KchaCQfYyrR7Oefw9S13rXqz7f6YGV8NPfwF9/BVMm\nVrt12dVarkUpgUeUYVyF3thnumE/Z89kHtNyBC3lDASzBXblrAAokkekqnFl27jZ+4HLgSHAz5xz\n38m1vKrGSU5xV9dKrTPbzWUjVquqVEW+sPQcoWwqVeku0NPTw95jxrB2w4a8VeOGNBk/DleNa2tj\nzZo1DB0apVM+gkL3Mc7zWK+/B7MuhiuvH1j0ATJX9kuKOCueVVIqeCu0pHeUymqlVpdL8jEtZ+W8\nSpZYL9d+SEOLWjUua66Pmd0bfO8ys1dCX11m9koMDRwC/Ag4HjgY+JCZHVzqeqWBxZ1fkC+PItWT\nseT6/vlR6l01cqOmTISbfgi77OQrkm33Fp+MH5ZrWGKuYWDZdG7wN8KTz/TfM+TODN3wPDNf9sPd\nZs6cybPPPsv0SW20bj+UN7b28cEf38eM+Q+wtbePtp2358Ade5k5c+a25WMLgorZx6g5UxGOQ0m/\nB1HWXy1LVg4OggDe2Jr72sklXz5LqWo11yLVU7Bkkv8e9eY3Sj5PlFybXJJ8TEvdt1zrLeZ8lLK9\ncuyHSARZ/yd2zr0r+F6uvsnJwL+cc2sAzGwh8AHg4TJtTxpBnPkF9VIZLqooQ6uqkRvVuQHO+3J/\nIYzeXujr6y/RnG84VqHBW9Thf3Ov5nM9O3EzL9Dx5JO8853v5Morr+SmTx/Jt37/CH9e/RxN1sQH\n/20Uh7c8y7uOPJKnnnqKww8/nNmzZ8dzbIrdxyg5U3EPg0y/vs45GY6fmdwS9FMmZJ7TCIoL/CtR\nGavWci1KHcYXdRhXMVXbUpJ+TEvZtySpl/2QmpO3+puZ7Wtm2wX/nmpmF5jZiBi2vRcQzoJeG7wm\nSZLkJ7blVi+V4aKIWkWsDNXX8koPSHt6YcgQGLdv7p6/1LX79DpfOTAsjh6kJSsZ1tPHbbTRTgtP\nPvkk06ZN49h3/Bu7rv4t5++ymv/j7uWGL53O9JPez5NPPsnhe+zFbbfdxrBhw0o/LmFTJvSfkyj7\nCPl7corpScsm0/V19Ed8/k0c6y+HOTP8MLh0Q4cUF/hXomchKRXPokgFhvPW++Ft89b7nwvpJatE\nT0ItHdM4lLvXUiRhoozNuAFoN7P9gKuAm4HrgBPK2bAU+//t3X+cXHV97/H3Z7O5LISUSgGJsinQ\nB8hFA7Ysm3Ipyq1ilCQQTAlRjFebW9Y+aqxW71KLPKo0pY/ifYhKvWW1VNsQDcGUQPgVhQKlpS5s\nNCFCgFgoLrKKFoUlYTGb/d4/zhx2Mntm5szM+X1ez8cjj2VnZ+d8Z+YknM98Pt/Px+wSSZdI0vz5\nKXRtKbOiDA1tV5E6wzUTNvsVQ/e1poIC0sn90sEHBe8Jkmaeu9UalYFddZ301ZvCBcCV8+OofdK9\n+nV9Xs/rWv1Cu3fv1pVXXnnAXXs1WwM9r9VH/+VezTnyyJBPvAWDq6V1t0gv7pGc8wK/Q3rqB6hh\nsn9RfhAQdH4FydIHDb3zvMYIb32/Vw4neUHQ3DntBf5JZBay1PGsmajm1cSVSfCzVfe/UBkS7KRJ\ntfeaRtXAIu5GGMzzQQmFCYSmnHOTZnaBpGucc9eY2fciOPaPJPVWfX9M5bYDOOe+LC8AU19fX3qd\nHcqobKVhtYrcEatWKxe9Sbc3bicgrT13Je9i5ojXSCve2bwMrFbQ8arOjzn7JnXZ7KN16ZzjdPv/\n/UONPP2kxp/9seY+vFt9v9inc885R91/ekn8HyCYvKYS1uA+YT/giPKDgHqt7Wtl7YOGhad6jRGi\nCPyj7IxV74K4UYvirIk6MIwySKgNCLolzTLpjYdIZx3W2mNHFVwkEaTENUwVyLAwgdA+M3uPpP8l\naWnltoB6gZY9JOkEMztOXgC0UtJ7I3hcRKVMpWFB0sh+pCXL2a92AtKgc3fKSce+PjiICwqcfPWO\nF3B+dA+u1nm98xRhU+xwrrpO2jsxPUx4ynnfB31oEfYDjig/CAg6v7pneU0vptz04x/S45XL9V+U\nnbk9UQX+UWVrai+IvzcufWVMesMh0lsqF+l5uGiNOjCMMkioDQgm5X24cNZhrb+2UQUXrTxOu0Fh\n1vdDATFoukdI0gclnSHpL51zT1UCl3WdHtg5Nynpw5K2StolaaNz7pFOHzcRZdk3086+g6IpS2e4\nNPb+hNVON8BWz916GYs5Bzc+XrPzI6l/K1r50CLsfaPswhh0fs2d45We+Y//3sXez75+W+N9ankV\n1X6WoIv0V5z08J729tmkZXC+14ba/1i1kzK+qPdfRRkQRPVYYR+nk71XZdsPBSjcQNVHJX2k6vun\nJP11FAd3zt0u6fYoHisxZdo3U6bSsLLLevar1U/lWz1362XEPnhB+9mAJP+taCWj18p9o8qGNDq/\n/OGka9Z6Wawil+JGsZ8l6ILYl6dSpojK+CYnJ3XbnVs0sm+HXtLLOlQHq08navG+M9TdbiYjymxV\nVI8V9nE6yUDlaY8ZEJGmA1XN7ExJn5b06/ICJ5PknHPHx766GpkYqJrGQMk0FXVYIoptdEy6/IvS\nHfdLMuldvyP9xUfqn7txDF2N4t+KME0NWl1/TANmO9Z/UXC76nqDcstqzRPep/z1giHpwIGiBbVn\nzx5dffXVGhoa0jPPPDPj58foSA0sfK8+dvdftt6lMcoBn1E9VtjHCTNkttlx8rDHDGgi7EDVMHuE\nrpP0MUnbJAVMlyuZsu2bSXpjPNCpoAv9W+7xAqF6OsmI1QtWOv23opWMUivrz2r2L8v71LKk9lP7\nWiUoZXruuee0ePFi+R+MnnDCCVqxYoUOP/xwPf/889q4caN2796ty4e/oJvP/jfddtttOuqoo8If\nIMqmE1E9VtjH6TQDxTwflEyYjNCwc25hQutpiIwQCi9sBgD1Jfl3tFF25arrOltH2f6tyWqmKouq\nWzs/tlfaX9PaucDtjvfs2aOzzz5bIyMjOu6443Tdlz6ns/f9s+yH/y49/5Q0tU9Tb/+M7n75jRoY\nGNBTTz2lvr4+3XvvvdHP78qiKLNZQI6FzQiFaZZwj5l91szOMLPf8v9EsMZ8yvKmcuRb2KGmaCzJ\nrG2jDmyd/ltRxuxzVM0ZiiRowKX/qf3206XdC6UPxThQNGOuvvrqV4OgBx54QP/zTa+Tfe966b9+\nIHV5lzRdL/9C55xzjh544AEdd9xxGhkZ0ec///mUV56QJIbMAgUSpjTOzwZVR1VO0u9Gv5wcyGpZ\nCfKvSHOb0sxsJVliVS9YuX+b95518m9FGUvFKMU9UJi20CUqZZqcnNTQ0JAkaWhoSEcffbT0opNO\nfKf0ujdL3/lbafKVV+9/9NFH69prr9WiRYs0NDSkSy+9VN3dYS57cq5E5wTQqaalcVmSidI4IC6d\nbBbPUkld2iVOSR5/zVrp2hukyZrtkwfN9gZxdnK8tF9HpC+oMcJs8z7lL+GF7s0336xly5bpxBNP\n1K5du9TVVVPUcvWbpBdGpTM/Jp3zaUnS1NSUTjrpJO3evVs333yzzjsv8SlfAFIQWWmcmb3WzK4z\nszsq359sZtSBAVFrd25TvZK64R3pzLtqlNlKQpIlVoOrvaGgtaZc58+XUjG0OoMmqIwui9pcp/9B\n6IUXXjgzCKqjq6tLF1544QG/j4jk5XwDGgiTI/6apK9Kuqzy/ROSbpDXTQ5AVNqd2xQUeIzvkd76\nfu+CPOl5V1nY25JUiVXvPOmk46Udjx94e1TPt2ilYn7m8l+2Sc5JXSaddVryGcwsZVAbaaUDWJgy\nuizoYJ0vvfSSJOnwww9v6ZD+/cfH25wrhJnycr4BTYT5SOUI59xGSVOS5JybFG20gei1mwEICjwm\n90uv7EsnK9NuZiuvzjqtXM+3XX7m8tobpIcfl3Y+4QWQ196QXFOQ0THpA5+UjjtH+n/fyH5TksH5\n0qHd0uzK940GXDYapJklHazz0EMPlSQ9//zzLR3Sv//cucVuK56ovJxvQBNhAqE9ZvZr8hokyMx+\nW9ILsa4KKCs/AzB8g/c1zKfUQYFHkKSyMmXrrFi259suP3NZu59qcn8yQbofiP3jLdL+KS9bKiVf\nuhm4tjolRq10AGu1jC4tHayzr88r99+4caOmpqZCHW5qako33njjAb+PCOTlfAOaCBMI/YmkWyT9\nhpn9m6R/lLQm1lUBCC/oQvyg2VL3rAPvl1SWIut7W0bHWts71ez+WX++WXH/tpmZS18SQbofiAU1\nCEqydLM26Bl+wSsxGnpWemjc+3rqyIHB0DUnSsOneV/rlR0tnDudOfJlcbhqB+tcvHixjjnmGO3e\nvVt333339A/Gfyw9/6Q0Vbkyn/iF9/3Ei7rrrru0e/du9fb26txzz43ueZRdXs43oImmHyM7575r\nZm+V9AZJJulx51zQPGsASRveIf3Bn0sTv5TmzpFee4T0toXS+5ZK7xpofb9RFLK8/6K2E1uzvVNh\n71+0vTzVong/R8ekx56s//MkgvSgEtIkjy8F76v4ytj0QFTpwBKjVjrDDc739mjUDtIMKqNLmj8A\ndnhcOvkQ6ZBZ0t79La+zu7tbAwMDuvzyyzUwMKAHHnhARx/5a9LnTpZm90h+lmjnRmnHN/TKUado\n4K9+IEkaGBgoR+vspGT5fANa0LR9tpnNkrRY0rGqCpycc5+LdWUBaJ8NVBneIZ3xnkrRaoVJ+vdv\nSAtPnb6AbXWGTScXvllv+bxmrbcnpHY2z8CK4ECm1fsXTb33844h6fot4c+Rem3GpQPPESm+IDro\nvZS8hg2HzU3mHA1qh11P/1wvC9QKP+B4cNz7/cH57W9crw5eFnbwWLXB32x5gdCyI6Rde1te5549\ne3T22We/OlR16G+/pLeP/L5s394Z9717dLbe/vf/pdNPP1333HOP5syZ0/r6iyKq9zPoMaM434CI\nhW2fHSYQul3ShKSdqjRMkCTn3Gc6XWSrCISAKqcs8zac11pwovTw5vYes9NAJuuBQ6uzmjqZ7VQE\n9d7PLpvuSBjmHKn3Oh7cI61+93SmMs4guvbcNvOex/uWSn/xkWQC9f5tXvlbM2nPCgoKXg7tbq8j\nWAyzkJ577jktXrz41XbYJ5xwgi688EIdfvjhev7553XjjTdq9+7dkqTTTz9dt912m4488si2jlUI\nUb6fQE5ENkdI0jHOuXc75/7cOfcZ/08EawTQiSdHW7s9jE5nAKXVOjvsvp9WO9qFuX+re47ypN77\n2WpHwnqv4+p3TzcFiXv+VO1erj96j/TUt6Wv/VVy2cqgfRXdkg6ycJ3hGolypkuUHcFi2FR/1FFH\n6d5779XatWtf3TN05ZVX6hOf+ISuvPLKV/cErV27Vvfcc09+gqC45vLQ4Q2oK0zB7B1m9g7n3Ldi\nXw2A8I7vDc4IHd/b/mN2GsgsXODto6nNIITdf9FOWV4r+35andXU7P6t7jnKm6D3M0izcyTM655E\nEJ32Xq56+yruWCBd/5P2S4yinukSZfDSyiykFsyZM0eXXXaZLr30Ut1+++0aGRnR+Pi45s6dq76+\nPp177rn52hMU51weOrwBdYXJCH1H0k1m9rKZvWhm42b2YtwLA9DEVz7j7QmqZpXb29XpDKBOWkn7\nQcXQxtbmu7SSSeid5+1vOel4ac7B3tc7huoHLc06wtUbZrv4D4uRIfLfz+oOhF1d0qya/3U0O0fC\ndNYrw/ypeu2wFx4WrjNcPVF/4h9lR7BWZiE1Uidb0t3drfPOO09XXHGFrr76al1xxRU677zz4g+C\nos7exJm1ocMbUFeYPUJPSTpf0k7X7M4xY48QUMPvGvfkqJcJ+spnvEYJ7Yqi2UG7TRra3V/Uyj6e\n2ufXPcu7qD/peG8waqub8+sdu3r9WWoW0Y7hHdJb3++Vw0ne6zU1Jc2a5TU/iOo5Zr3RRpbV23vU\nTsMFKWBPSSV4aTc70emm+qztcYljPVG/h9Wifj+BHAi7RyjMRyajkr6fdhAEIMDCU9tvjBDE/+S+\nnUCm+jHaKT+qVxr11Uo3sXrraKUcrzaDM7nf+7PjcemRH0hfuVF6w/HSW0IGRc1Kx6qzU1loFtGO\n67dMDx+VvGGks7u94PHgg9o7R3y1pZB+N7p2z72yirr8zM9cRdURzJ+F1K5G2ZJrToynI1on62lH\nTCWEkqJ/P4ECCZMR+pqk4yXdIekV/3baZwOIVL32xlLj7EArmYRmGZwwx2t07Hry3GUurs55Yd+3\nLM+lyoqif+LfKFvyzTcmny2KI3tT9PcQSFiUXeOeknS3pP8maW7VHwBFk1QHtKDj1O4vqtZs30+z\n/Se+oH0oQcJ2LKs99oITD9xPI+V/n0tce3fC7O1qd99Y2dTbe5SlC+hO9tQ02uOSRke0OPbc5OE9\nBAqoaUYoS8gIATFKao9Go+NI3oXwV2+S9rw883ejzkI00+rxirjPJa7nFCbTlOZcKjJR0el0T02j\nbMnyR+LbW9POelrd+5RkSR9QIh1nhMzs85WvW8zslto/US4WQAbEPcclzHH8/UUfvCB8FqKVLFZ1\nBufUN0gHzZ6ZwWl2vEZayU7Vk7W5RFE8pyBhMk1pzqUiExWdTrM2jbIlSXVEq85oXfVDr+V50Hrq\nZb5qbx9+wQumhp71ArmhZ73vo5odBCCUuhkhMzvNObfNzN4a9HPn3H2xriwAGaGM4xPUfItrL0g7\nx2ll/0ir2Yrq8/Tk473btj8uPfYfXiOAKDuhtaqIGaV6wjzXtDJCaWaiiiiLHdFaycaEzWjVu98d\nC6R37Tzw9i6T9jupOs6fbV5Q1UljCQCSIsgIOee2Vb7eJ+lRSY865+7z/0S3VBRC2T9Bzdqn+O1I\nao5LmOOEzUIEZZd+/qI3xyfoPag9T79+m3TLPdKWL0m775Q+dNGBx5OkD3xSeu3veH8+8Ml4n5fE\negAAIABJREFU39uksnJZEOY97mQuVSfSykR1Iuq5NlGKM2vTzt4aP2AJm40Jm9Gqd78/eHzm7a/U\nBEESQ06BFDTcI2Rmn5b0YXkBk8n7a3uNc+6KRFZXg4xQhpX5E9SifIqfhT1CrR6nURe41/zKzMds\n5TwdHZMWnC+98NKBtx92qLTz5nje26SycnlSby5VnBnovP17lrU5O7Wy1hFtzRNe8FPbqrpeNiZs\nRqve/eZ0SXummq+LjBAQmSj2CP2JpDMlne6cO9w59xpJCyWdaWYfi26pKIQ8foIalaJ8ih/XXpA4\nj9OoC1zQe3D/tvDn6VXXSeN7Zt7+4p743tuksnJ54u8bG77B++oHQXFmoNPKRLUrjc5prchaR7Th\nmnk9UuNsTNiMVr37HXPQzKut2ZIOsun7+8Hh4PywzwJABBq1z14l6T3Ouaf8G5xzT0p6n6T3x70w\n5EyZL+DSDAKjLsmrveiU4in5C7q4bYd/wRqk9j0YHZMee3Lm/eqdp8M7Dxwk6nMuvvc2bxfgaYn7\nw4ekPhSISqsX9mnwh6oOn+Z9TTNT1Wqp3uB8L8PWLGgJut8hXdKzr0i1CaFDZkn3vTk7wSFQUo0G\nasx2zv2s9kbn3E/NrPafEJTd4Gpp/a0zy53KcAG3cIG0fdfMMpq4g8DaErPtu7z3IKoLtrgfPwr+\nBeviP5R2PnHgz2rfg6uu85oh1Oqy4PN04QJp2/dnBkNmzd/bdsu2/OcTVAqGaUl8+OAH63mwcK60\nfXxmqVfUndOKYnC+tP65maV69bIxfkbrqh96wWV/neYKQfcb3y99/ScH3s8kLTtCWniY9wdAahpl\nhH7Z5s9QRnn7BDVK7XyKH0UmJ+5PxdMo+WvndemdJ932t96eoEbvwfBOryNcrZOODz5PB1dLc+fM\nvP1X5jR/bzsp24oqW9auPDT+KFsGulkjhLAZC3jaKdULm9Gqvd+je2dm65ykXXujejYAOtCoffZ+\nSQEF8jJJPc65xLNCNEtAJgR92i+F/xQ/qmYBcW+sT3rjfqevS71N9b52NsCPjkmXf1G6418lOeld\nZ0l/8ZHG68nbRvtqUTayiLOZQVEalITRSuvmZhmLosnDQNJWGzMAiETYZgl1S+Occ3WmDAIl1qhc\nLOxFbqNMSysXynGX5CVd8tfp69KslKmd8s3eedLX/qq155HnxiHN3oOwwU3cZZVlKiFs1Aih+kLa\nz0SURW2AuH3cK3fL2j6bVsvwACSqUWkcgFpRlItFdaEc98b6JDfuj45JG+9s/Lp0WrKVVPlmnsu2\nGp2brZT8JVFWmXYJYTvamfWTh0YIach6pzxf1jrmAThAo2YJAGpFEcRElWmJ+1PxpD519y+wXwi4\nsPNfl6gyDElsgI+rcUicpWa+RudmKxm7PGfF4lKbwfjeuPSVMemkQ6SzDqtf1hVlI4Q8lJKFlacA\nsWzZOiBHCISAVkQRxER5oRz3hX0SgYN/gR3Unc1/XaIqJ0xCHAFku4Fgq8FTo3Nz+UfDBzdpdVLM\nstoMxqSkSSft2CM9uqd+WVdUpVV5KSULi055ACJAaRzQiijKxcrcYS9IUPZAko48fPp1yVuGIeqy\nrXZKzdrpXtfo3Gyl5I95SDMFZTB8jcq6oiqtykspWVh0ygMQATJCQCui+rQ/TzNK4lYve7Bi0fTr\nWvYMQzuBYLtZtHrnZiuZzDI1MwgrKINRrVFZVxSlVVkvJWu1bC/sbB8AaIBACGhVGkFMEvtD0hLm\nArvMA3ul9gLBqLNorQY3BPsHqi1xqxV3WVeWS8naLdtrN0As0l4pAB2pO0coi5gjhFIqw8yUZjOA\nwt6nqNo5B1qZZ5RGoF3k4L4e/wL87p9Lj78sTVVurzcbKOpjHzCPqFJKloU9QknO2gk7lwlAroWd\nI0QgBGRdngd0IjqtBoJhg6c0Au2sBvdJZAr8C/HxSa9hgiQdZNJ9b5YWHhbtsYKOncVSsv5t0kMB\nJXr9c6Xh06I9FgNOgVLoeKAqgIzIW6MA1NdJFqTVUrOwpWxpdOTLYhfApLqq+U0Lqv9KT0m6/ifx\nB0JZbeOcZNle1vdKAUgUgRCQdWVvFFAUUc1CqvfYQQFWmOApqUC7eo1P/yh7wX2jrmpRBg9ciM8U\nVYvwMLK8V6oR9jUBsSAQArKuXqOA9y31yubKtMciz+LKgnQaYCURaNeu0WzmfdII7qsvLp+eSCZA\nyeuFeJyS7ACXZNAVlaLNgAIyhEAIyLqgEqf3LZXeNRBPdgHxiCvz0mmAlURHvto1+ntTu8wbpJtG\nF8Dai8ugqXpxBCh5vBBPQlJle3lsux2Urfz5pLT4Yem2U7K9diDjCISAPKgtcVqzNnt7LNBYXJmX\nTgOsJGb+1Buae8RrpGNfn04XwNqLS7+Dm0lyii9AyeOFeNFkda9UPfWG8e7c6wXzZIaAthEIAXlE\nA4X4xNXWOa7MSxQBVtwzf+oOzX1nvK28Gz1WvYvLI2dLx/bEG6Bk4UKcPSf50WgYbxz72IASoX02\nOlPGWSBZQEvteMTd1jmOWUhJtKLu9O95Gq28mz1WmdsoM0snX/z36+cBWVUpnjbjaB0fLmQKc4QQ\nv7TmjxB4ZXcOS97lNcCMc9hsVOdamDVG+fo3e6wsDxiNW5mDwLwanfD2BO3ce+DtvG/ZwIcLmcMc\nIcQv6VkgcbYfzpsk9nWUUV5LDuMsbYvq73nSrbybPVaZ9+rQwjt/enu8xghBwXvZG21kQVLt9xE5\nAiG0L+mLxiwOYUxTFBe/Se3HSHot7WJm00xJ/j2P8vUP81hZ2KuTBlp451OZg/es48OF3CIQQvuS\nvmjM66f1WRVlhq3Tx8pKti+JVtJ5k+Tf8yhff97L+mjhnV9lDd6zjg8XcitocgIQzuBq78JidiWe\njvtCY+GC6WP5Or0gGx3z9hL0X+R9HR3rbI150ijDlvRjdfL7Ub6HfsnhwArvvBpYkW7pZRbOzyT/\nnkf5+mftvcwSP7Mw8DrvQm1gHnsZgE4Mzvf2BM2ufM+HC7lBswR0Js5N2kHHirJBQNkbDvRfJD0U\nkE3rXyAN35DsY7X7+3l/DxuVA2bpuSX59xzlQIet5PGax8t/fSlbzASaJSAZcc8fqT1WlA0Cyr7n\nKOn9GHH8fp7fw2blgFl6bkn+PUfx1XbY2j7uleqRlYoPr3n8KFvMJUrjkC/+BdnwDd7XTj6VLvue\noyhLnjp9rHZ/P8/vYbNywDw/N6CRRh22EA9ecyAQgRCik4X9DK2IY89RnmRpP0a7v5/UexjHud0s\n0Cn7+YniosNW8uJ+zUcnvPlU/du8r6MT0TwuEDP2CCEaWdrPEFYe14wDJfEexnWMpgM/A457SI+0\n7G3So0+We6Aw8o2BrsmL8zVnmCgyKOweITJCiEaUHciSQlep7Gg345LEexjXud2sHLD2ub13sXf7\n12/zGksMbfQCpaxnXoFadNhKXpyvOWV3yDEyQohGlB3IUC5Zz8zFeW630o2tWQYJweiUlU102Epe\nXK95/zbpoYASu/650vBpnT8+0Aa6xiFZSQ9XRXFkqTtakHrn9suveEFSJyVqrXRjo3lC6zrplEUA\nFS86bCUvrtecYaLIMUrjEI2kh6uiOLJ+gR90bk9OSrv+I9kSNZontK7dkh0/gBp61vuke+hZ73s2\ngAMzUeqIHCMQQjTYb4N2Zf0Cv/bcPul4adYsaXK/9/Ok9sPxYUPr2u2UxZ4HILzeHi/LOvA6Lws0\nMI9GCcgNSuMQHYYuoh2Dq71BorV7hLJ0gV99bvdfNB0E+ZLIYEU9ULgM2i3Zob0z0BpKHZFTZIQA\npCutbGK7neqykMHKT4+bdLVbsrNw7vTv+Lok/ecEM1IAoEDoGgegfDrpVJdWl7usd9fLqnY6ZdU2\nWajGjJSZaCwBIGOYIwQA9XQyGyitDFYeZ3VlgV+yM3ya9zXMBXr1noejZktW9TP2Cx0o640lRitZ\nvP5tZPMAzMAeIQDl02mnujT2w2W9u17RvBpAjUvP1aSF2C80rVFjibT3jHTSPh1AKZARAlA+Wdjn\n06o8rrkIgvYLMSNlWpYbS9D9D0ATBEIAyiePrajzuOYiYEZKY1kOFLMcpFGyB2QCgRCA8snj3Ks8\nrrkImJHSWJYDxawGaVnfVwWUCF3jAABA+9rpzJfUuqr3CPlBWtqB7JonvOCndr7VwLz091UBBRG2\naxzNEgAAwUbHvK50wzu9PUoMcEWQrA7T9LN5WQvSslyyB5QMgRAAFE0UAUzt3KLtu6T1t1KOh3zJ\nYpC2cK7Xwa42I5R2yR5QQuwRAoAi8QOYoY3SQzu9r6de4N3eCuYWIQ40Ccj2viqgZAiEAKBIogpg\nmFuEqGWxSUAagRkNONpHII2IURoHAEUSVQCzcIFXDlf9WMwtQieyNnw16oGrftOI4XGv/K3RfqQ4\nSvZaOX4eMSAXMSAjBABFEtXgVeYWIWpZaxIQ5cDVtLNdaR8/CQzIRQxSCYTM7LNm9piZPWxmN5nZ\nr6axDgAonKgCGOYWIWpZm+sTZWCW9kV62sdPQtYCaRRCWhmhb0t6k3PuFElPSPpkSusAgGKJMoDp\nnSdd8ylp+AbvK0EQOpG1JgFRBmZpX6SnffwkZC2QRiGkEgg5577lnPMLz78j6Zg01gEAhdQ7z8sA\n9S/w9gxddV3rXeOAqGWtSUCUgVnaF+lpHz8JWQukUQjmnEt3AWZbJN3gnLu+2X37+vrcyMhIAqsC\ngByrnQHkl8dR2gYcyG8w0OnA1dqN/P5FelKBXtrHT0pU7xcKz8y2Oef6mt4vrkDIzO6SdHTAjy5z\nzt1cuc9lkvokvdvVWYiZXSLpEkmaP3/+aU8//XQs6wWAwliz1psfVNvxbWCFV+IGIHppX6SnfXwg\nQ1IPhJoe2OwDkgYkvc05tzfM75ARAoAQ+i/yhqnOuH2Bt98HAIACCxsIpdU17p2SBiWdFzYIAgCE\nFFULbQAACiytrnF/I2mupG+b2XYzuzaldQBA8TADCKMT0ponpP5t3tc8z5Mp0nMBkCmpN0toBaVx\nABDS6JjXLe7BnV4maHA1jRLKYsbGeXndtvK4cb5Iz8Xn7+UZHve6vbGXB4hc2NK47mZ3AIDc8oOB\n4Z1euViZggF/BhCyJ+4L4UbDNa85MbrjJKFIz0WaGdhtH5fWP5fvwA7IMQIhAMVU20J6+y5p/a20\nkEa6krgQLtJwzSI9F6l4gR2Qc2ntEQKAeF113XQQJHlfX9rr3Q6kpdGFcFSKNFyzSM9FKl5gB+Qc\ngRCAYhreeeAcHcn7/sGAttJxGR3zZvr0X+R9HR1L7tjIpiQuhAfne/to/ADCH645OD+6YySlSM9F\nKl5gB+QcgRCAYkq7hbRfmje00ZvpM7TR+55gqNySuBDu7fFK7QZe5z3uwLz87kEp0nORihfYATlH\n1zgAxVS7R8hvIZ3UHqE1a73gpzorNbtbGlhBE4Mym9EFrXIhnOeLe7TGb5bx4LgX3NE1DogcXeMA\nlFvvPC/oSauFdJKleUXpjleU59GIn+EoyoUwraBb19tDYwQgI8gIAUAcksoIBWW+DumRlr1NevTJ\n/AQUaWfw0LoizvgBUAhhM0LsEQKAOAyu9i7k/X1K/oX94OpojxPUHe+Fl6R1t0S7Nynuxg90+cuf\nJDrgAUCMCIQAIA5+ad7ACq8sb2BFPNmNoBI8SZqqZPujCCiSaPyQhS5/mGl0QlrzhNS/zfs6OjH9\nsyy3gm60bgCoYI8QAMSld178jREWLvCGxQYFQ75OA4pG2Zqonl/Q80iyyx9majb8deFc77bqYCgL\nraCTGFoLoBDICAFAntWW4JnNvE+nAUUS2ZqkSgkRXrPSt6y2gqZkD0BIBEIAkGe1JXjvP0867NBo\nA4okZjIlVUqI8JqVvmV1xk+WS/YAZAqlcQCQd7UleH4b6qjahg+ultbfOrOjW9TZmiRKCRFemNK3\nLLaCzmrJHoDMoX02AKC5qIMrZF+Wh782ml8U97qZnQRkXtj22QRCQBzKMBgSQHH5F/v3v+B1IOwy\n6azDsnHRH2Z+kb/+qIfWMjsJyIWwgRClcUDUagdDbt/llRWx3wFAHtS72M9CECQ1bobgl+nFVbIX\neOxJafHDUs8sMkRAztAsAYgagyEB5FnWu66l2Qwh8NiSdu6VHhqXhp71gkjmFgG5QCAERI3BkEDx\nFXlgZ9a7ri2cO92y25dUM4SgY1fbJ2m8kiEq4rkBFAyBEIpldExas1bqv8j7OjqW/BqSaDUMID1+\n6djQs8XMAqQZaISRxPyieoFu7bGDTIoMEZATNEtAcdTuzfFb/Ca9Nycr6wAQjzVPeBe4te2ZB+Zl\nr5V0O7LcLc4XphlCu93dmjVEqD72y/ulXXu94KeeIp0bQE7QLAHl02hvTpKzSfzBkLQaBoop66Vj\nnfIHpcbRdS0qzZoh1AYz28el9c+FC+aaNWOoPnbtcYIU6dwACoZACMWRpb05DIYEiqsMAzuzOCi1\nFWE6y9XTSqBbGzQGZYiKdm4ABcIeIRQHe3MAJCGJPSroTCdZu1b3SPlB4/Bp0m2nSHM5N4C8IBBC\ncQyu9vbi+MGQvzdncHW66wJQLH4WYOB13sXxwLxs7Z9BZw0fOgl0OTeAXKFZAopldIy9OQBQdp02\nfAjTjAFAZoVtlkAgBAAAiodgBigtusYBmMnPmA3v9PZUkTEDUDS1bbO/+UYCIACBCISAsqidb7R9\nl7T+VuYbASiOTtpmAygdmiUAZdFozhIAFEGjttkAUINACCiLducsjY5Ja9ZK/Rd5X0fH4lsjAHSi\n6MNuAUSK0jigLBYu8MrhqoOhZnOWKKcDkCdFHHZbu+eJpg9AZMgIAWXRzpwlyukA5EnRht36e56G\nnpUeGve+njri3Q6gYwRCQFn0zvMyOQMrvCzQwIrmmZ12y+kAIA1RDTQdnZDWPCH1b/O+phV4hNnz\nlJW1AjlEaRxQJr3zpGs+Ff7+7ZTTAUCaenuka05s//ez1Hmu2Z6nLK0VyCEyQgDqa6ecDgDyLEud\n5xbOnS7z81XvecrSWoEcIhACUF875XQAkGdZ6jzXbM9TltYK5BClcQAaa7WcDgDyLEud5/w9T1f9\n0Atu+mu6xmVprUAOmXMu7TWE1tfX50ZGRtJeBgAAKKrafTd+FiaL+27ytFYgQWa2zTnX1+x+lMYB\nAAD4ouo8l4Q8rRXIIErjAAAAqnXaea4VnQ5MTXKtQMEQCAEAAKSB9tdAqiiNAwAASAPtr4FUEQgB\nAACkgfbXQKoIhAAAANLQbGAqgFgRCAEAAKSh2cBUALEiEAIAAEgD7a+BVNE1DgAAIC20vwZSQ0YI\nAAAAQOkQCAEAAAAoHQIhAAAAAKVDIAQAAACgdAiEAAAAAJQOgRAAAACA0iEQAgAAAFA6BEIAAAAA\nSodACAAAAEDpEAgBAAAAKB0CIZSecy7S+wEAACD7CIRQahMTE1qyZIk2bNjQ8H4bNmzQkiVLNDEx\nkdDKAAAAEKfutBcApGViYkLLli3T1q1bdeedd0qSVq5cOeN+GzZs0MUXX6ypqSktW7ZMmzdvVk9P\nT9LLBQAAQITICKGUnHNavny5tm7dKkmamprSxRdfPCMzVB0ESdLWrVu1fPlyyuQAAAByjkAIpWRm\nWrVqlbq6pv8K1AZDtUGQJHV1dWnVqlUys8TXDAAAgOgQCKG0Vq5cqfXr1wcGQ8uWLQsMgtavXx9Y\nPoeCGh2T1qyV+i/yvo6Opb0iAAAQEctTiU9fX58bGRlJexkomKDMTy2CoBIaHZNOvUB6aa+0b1Ka\n3S0deoi04yapd17aqwMAAHWY2TbnXF+z+5ERQukFZYaqEQSV1FXXTQdBkvf1pb3e7QAAIPcIhAB5\nwdDSpUsDf7Z06VKCoDIa3jkdBPn2TUoP7kxnPQAAIFIEQoC88rgtW7YE/mzLli1N5wyhgBYu8Mrh\nqs3ulvoXpLMeAAAQKQIhlF6zPUL1Wmuj4AZXe3uC/GDI3yM0uDrddQEAgEgQCKHU6rXIPv/88xu2\n1kYJ9M7zGiMMrPCyQAMraJQAAECBdDe/C1BM9YIgvzFC7c/9YEgSe4bKoneedM2n0l4FAACIARkh\nlJJzTuvWrWs4J6jenKF169YpT23nAQAAMBOBEErJzLRp0yYtWrRIUv0W2bXB0KJFi7Rp0yaZWeJr\nBgAAQHQojUN8Rse8mSvDO70OXIOrM7W/oqenR5s3b9by5cu1atWquuVu/u3r1q3Tpk2b1NPTk+Qy\nAQAAEAPLU4lPX1+fGxkZSXsZCGN0TDr1gumBlH7HrQxuNnfOhcrwhL0fAAAA0mNm25xzfc3uR2kc\n4nHVddNBkOR9fWmvd3vGhA1uCIIAAACKg0AI8RjeOR0E+fZNSg/uTGc9AAAAQJVUAyEz+7iZOTM7\nIs11IAYLF0wPovTN7vbmsQAAAAApSy0QMrNeSe+Q9MO01oAYDa729gT5wZC/R2hwdbrrAgAAAJRu\nRuhqSYOS8tOtAeH1zvMaIwys8LJAAysy2SgBAAAA5ZRK+2wzO1/Sj5xzO5ptQDezSyRdIknz589P\nYHWITO886ZpPpb0KAAAAYIbYAiEzu0vS0QE/ukzSn8kri2vKOfdlSV+WvPbZkS0QAAAAQGnFFgg5\n594edLuZLZB0nCQ/G3SMpO+aWb9z7sdxrQcAAAAAfImXxjnndko6yv/ezP5TUp9z7mdJrwUAAABA\nOTFHCAAAAEDppNIsoZpz7ti01wAAAACgXMgIAQAAACgdAiEAAAAApUMgBAAAAKB0CIQAAAAAlA6B\nEAAAAIDSIRACAAAAUDoEQgAAAABKh0AIAAAAQOkQCAEAAAAoHQIhAAAAAKVDIAQAAACgdAiEAAAA\ngBJxzkV6v7wiEAIAAABKYmJiQkuWLNGGDRsa3m/Dhg1asmSJJiYmElpZ8rrTXgAAAACA+E1MTGjZ\nsmXaunWr7rzzTknSypUrZ9xvw4YNuvjiizU1NaVly5Zp8+bN6unpSXq5sSMjBAAAABScc07Lly/X\n1q1bJUlTU1O6+OKLZ2SGqoMgSdq6dauWL19eyDI5AiEAAACg4MxMq1atUlfX9OV/bTBUGwRJUldX\nl1atWiUzS3zNcaM0DgAAACgBvwyuOtipDoa2bNkyIwhav359YPlcERAIAQAAACVRLxi6+eabD7hf\n0YMgidI4AAAAoFRWrlyp9evXH1AmV60MQZBEIAQAAACUzsqVK7V06dLAny1durTwQZBEIAQAAACU\njr8nKMiWLVuazhkqAgIhAAAAoESCusNVq9dau2gIhAAAAICSqNci+/zzz2/YWruICIQAAACAEqgX\nBK1fv16bN2+e0UCh6MEQgRAAAABQcM45rVu3ruGcoKBuclNTU1q3bp2cc4mvOW4EQgAAAEDBmZk2\nbdqkRYsWSarfIrs2GFq0aJE2bdokM0t8zXFjoCoAAABQAj09Pdq8ebOWL1+uVatW1W2R7d++bt06\nbdq0ST09PUkuMzGWpzRXX1+fGxkZSXsZAAAAQG4550JleMLeL2vMbJtzrq/Z/SiNAwAAAEokbHCT\nxyCoFQRCAAAAAEqHQAgAAABA6RAIAQAAACgdAiEAAAAApUMgBAAAAKB0CIQAAAAAlA6BEAAAAIDS\nIRACAAAAUDoEQgAAAABKh0AIAAAAQOkQCAEAAAAoHQIhAAAAAKVDIAQAAACgdAiEAAAAAJQOgRAA\nAACA0iEQAgAAAFA65pxLew2hmdlPJT2d9joQuSMk/SztRSAzOB/g41xANc4H+DgX4Kt3Lvy6c+7I\nZr+cq0AIxWRmI865vrTXgWzgfICPcwHVOB/g41yAr9NzgdI4AAAAAKVDIAQAAACgdAiEkAVfTnsB\nyBTOB/g4F1CN8wE+zgX4OjoX2CMEAAAAoHTICAEAAAAoHQIhZIqZfdzMnJkdkfZakB4z+6yZPWZm\nD5vZTWb2q2mvCckys3ea2eNm9gMz+9O014N0mFmvmd1jZo+a2SNm9sdprwnpMrNZZvY9M7s17bUg\nXWb2q2b2zcr1wi4zO6PVxyAQQmaYWa+kd0j6YdprQeq+LelNzrlTJD0h6ZMprwcJMrNZkr4k6V2S\nTpb0HjM7Od1VISWTkj7unDtZ0m9L+iPOhdL7Y0m70l4EMuELku50zp0k6VS1cV4QCCFLrpY0KImN\nayXnnPuWc26y8u13JB2T5nqQuH5JP3DOPemc+6WkDZLOT3lNSIFzbsw5993Kf4/Lu9B5fbqrQlrM\n7BhJiyX9XdprQbrM7DBJb5F0nSQ5537pnPtFq49DIIRMMLPzJf3IObcj7bUgc35f0h1pLwKJer2k\n0arvnxEXv6VnZsdK+k1Jw+muBCn6vLwPTKfSXghSd5ykn0r6aqVU8u/MbE6rD9Id/bqAYGZ2l6Sj\nA350maQ/k1cWh5JodD44526u3OcyeaUx65NcG4BsMbNDJW2S9FHn3ItprwfJM7Mlkp5zzm0zs7PT\nXg9S1y3ptyStcc4Nm9kXJP2ppMtbfRAgEc65twfdbmYL5EX2O8xM8sqgvmtm/c65Hye4RCSo3vng\nM7MPSFoi6W2OPv9l8yNJvVXfH1O5DSVkZrPlBUHrnXP/lPZ6kJozJZ1nZudK6pH0K2Z2vXPufSmv\nC+l4RtIzzjk/Q/xNeYFQS5gjhMwxs/+U1Oec+1naa0E6zOydkj4n6a3OuZ+mvR4ky8y65TXJeJu8\nAOghSe91zj2S6sKQOPM+HfsHSc875z6a9nqQDZWM0Cecc0vSXgvSY2b3S/rfzrnHzezTkuY45/5P\nK49BRghAFv2NpIMkfbuSJfyOc+5D6S4JSXHOTZrZhyVtlTRL0t8TBJXWmZJWSdppZtsrt/2Zc+72\nFNcEIBvWSFpvZv9N0pOSPtjqA5ARAgAAAFA6dI0DAAAAUDoEQgAAAABKh0AIAAAAQOkQCAEAAAAo\nHQIhAAAAAKVDIAQA6JiZ7Tez7Wb2iJntMLOPm1lX5Wd9ZvbFlNb1QESPc2HluU2ZWV8tU9CgAAAC\nsUlEQVQUjwkASBftswEAHTOzl5xzh1b++yhJX5f0b865P093ZdEws/8uaUrSkLxBjiMpLwkA0CEy\nQgCASDnnnpN0iaQPm+dsM7tVkszs02b2D2Z2v5k9bWbvNrOrzGynmd1pZrMr9zvNzO4zs21mttXM\n5lVuv9fM/trMHjSzJ8zsrMrtb6zctt3MHjazEyq3v1T5amb2WTP7fuVYF1VuP7vymN80s8fMbL1V\npvjWPKddzrnHk3j9AADJIBACAETOOfekpFmSjgr48W9I+l1J50m6XtI9zrkFkl6WtLgSDF0j6fec\nc6dJ+ntJf1n1+93OuX5JH5XkZ5w+JOkLzrk3S+qT9EzNMd8t6c2STpX0dkmf9YMrSb9ZeayTJR0v\n6cx2nzcAID+6014AAKB07nDO7TOznfKCpTsrt++UdKykN0h6k6RvV5IzsySNVf3+P1W+bqvcX5L+\nXdJlZnaMpH9yzu2uOebvSPqGc26/pJ+Y2X2STpf0oqQHnXPPSJKZba885r9G8kwBAJlFRggAEDkz\nO17SfknPBfz4FUlyzk1J2uemN6tOyfuAziQ94px7c+XPAufcO2p/v/L43ZXH+rq8DNPLkm43s99t\nYbmvVP33q48JACg2AiEAQKTM7EhJ10r6G9deR57HJR1pZmdUHm+2mb2xyTGPl/Skc+6Lkm6WdErN\nXe6XdJGZzaqs7y2SHmxjbQCAgiAQAgBE4WC/fbakuyR9S9Jn2nkg59wvJf2epL82sx2Stkv6H01+\nbYWk71dK294k6R9rfn6TpIcl7ZD0z5IGnXM/DrsmM7vAzJ6RdIak28xsa9jfBQBkE+2zAQAAAJQO\nGSEAAAAApUMgBAAAAKB0CIQAAAAAlA6BEAAAAIDSIRACAAAAUDoEQgAAAABKh0AIAAAAQOkQCAEA\nAAAonf8PUbL7DMWbB6EAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xeecb860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Display the results of the clustering from implementation\n",
+    "vs.cluster_results(reduced_data, preds, centers, pca_samples)\n",
+    "\n",
+    "# the black X are my samples, the round circles the cluster centers.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Data Recovery\n",
+    "Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the *averages* of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to *the average customer of that segment*. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.\n",
+    "\n",
+    "In the code block below, you will need to implement the following:\n",
+    " - Apply the inverse transform to `centers` using `pca.inverse_transform` and assign the new centers to `log_centers`.\n",
+    " - Apply the inverse function of `np.log` to `log_centers` using `np.exp` and assign the true centers to `true_centers`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Delicatessen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Milk</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Segment 0</th>\n",
+       "      <td>2196.0</td>\n",
+       "      <td>9468.0</td>\n",
+       "      <td>799.0</td>\n",
+       "      <td>343.0</td>\n",
+       "      <td>2624.0</td>\n",
+       "      <td>2067.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Segment 1</th>\n",
+       "      <td>1068.0</td>\n",
+       "      <td>5174.0</td>\n",
+       "      <td>1101.0</td>\n",
+       "      <td>4536.0</td>\n",
+       "      <td>11581.0</td>\n",
+       "      <td>7776.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Frozen   Fresh  Delicatessen  Detergents_Paper  Grocery    Milk\n",
+       "Segment 0  2196.0  9468.0         799.0             343.0   2624.0  2067.0\n",
+       "Segment 1  1068.0  5174.0        1101.0            4536.0  11581.0  7776.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frozen</th>\n",
+       "      <th>Fresh</th>\n",
+       "      <th>Delicatessen</th>\n",
+       "      <th>Detergents_Paper</th>\n",
+       "      <th>Grocery</th>\n",
+       "      <th>Milk</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "      <td>440.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3071.931818</td>\n",
+       "      <td>12000.297727</td>\n",
+       "      <td>1524.870455</td>\n",
+       "      <td>2881.493182</td>\n",
+       "      <td>7951.277273</td>\n",
+       "      <td>5796.265909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4854.673333</td>\n",
+       "      <td>12647.328865</td>\n",
+       "      <td>2820.105937</td>\n",
+       "      <td>4767.854448</td>\n",
+       "      <td>9503.162829</td>\n",
+       "      <td>7380.377175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>55.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>742.250000</td>\n",
+       "      <td>3127.750000</td>\n",
+       "      <td>408.250000</td>\n",
+       "      <td>256.750000</td>\n",
+       "      <td>2153.000000</td>\n",
+       "      <td>1533.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1526.000000</td>\n",
+       "      <td>8504.000000</td>\n",
+       "      <td>965.500000</td>\n",
+       "      <td>816.500000</td>\n",
+       "      <td>4755.500000</td>\n",
+       "      <td>3627.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3554.250000</td>\n",
+       "      <td>16933.750000</td>\n",
+       "      <td>1820.250000</td>\n",
+       "      <td>3922.000000</td>\n",
+       "      <td>10655.750000</td>\n",
+       "      <td>7190.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>60869.000000</td>\n",
+       "      <td>112151.000000</td>\n",
+       "      <td>47943.000000</td>\n",
+       "      <td>40827.000000</td>\n",
+       "      <td>92780.000000</td>\n",
+       "      <td>73498.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Frozen          Fresh  Delicatessen  Detergents_Paper  \\\n",
+       "count    440.000000     440.000000    440.000000        440.000000   \n",
+       "mean    3071.931818   12000.297727   1524.870455       2881.493182   \n",
+       "std     4854.673333   12647.328865   2820.105937       4767.854448   \n",
+       "min       25.000000       3.000000      3.000000          3.000000   \n",
+       "25%      742.250000    3127.750000    408.250000        256.750000   \n",
+       "50%     1526.000000    8504.000000    965.500000        816.500000   \n",
+       "75%     3554.250000   16933.750000   1820.250000       3922.000000   \n",
+       "max    60869.000000  112151.000000  47943.000000      40827.000000   \n",
+       "\n",
+       "            Grocery          Milk  \n",
+       "count    440.000000    440.000000  \n",
+       "mean    7951.277273   5796.265909  \n",
+       "std     9503.162829   7380.377175  \n",
+       "min        3.000000     55.000000  \n",
+       "25%     2153.000000   1533.000000  \n",
+       "50%     4755.500000   3627.000000  \n",
+       "75%    10655.750000   7190.250000  \n",
+       "max    92780.000000  73498.000000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This shows the cluster centers on the first 2 components of PCA.\n",
+    "\n",
+    "# TODO: Inverse transform the centers\n",
+    "log_centers = pca.inverse_transform(centers) # recovers original features from pca dimensions\n",
+    "#print(\"log centers\", log_centers)\n",
+    "\n",
+    "# TODO: Exponentiate the centers\n",
+    "true_centers = np.exp(log_centers) # recover original values from log values\n",
+    "#print(\"true centers\" ,true_centers)\n",
+    "\n",
+    "# Display the true centers\n",
+    "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n",
+    "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n",
+    "true_centers.index = segments\n",
+    "\n",
+    "# order features, so can focus on comparison not retrieval\n",
+    "tmp = true_centers\n",
+    "ordered = pd.DataFrame(tmp.Frozen)\n",
+    "ordered[\"Fresh\"] = tmp.Fresh\n",
+    "ordered[\"Delicatessen\"] = tmp.Delicatessen\n",
+    "ordered[\"Detergents_Paper\"] = tmp.Detergents_Paper\n",
+    "ordered[\"Grocery\"] = tmp.Grocery\n",
+    "ordered[\"Milk\"] = tmp.Milk\n",
+    "true_centers = ordered\n",
+    "\n",
+    "\n",
+    "tmp = data\n",
+    "ordered = pd.DataFrame(tmp.Frozen)\n",
+    "ordered[\"Fresh\"] = tmp.Fresh\n",
+    "ordered[\"Delicatessen\"] = tmp.Delicatessen\n",
+    "ordered[\"Detergents_Paper\"] = tmp.Detergents_Paper\n",
+    "ordered[\"Grocery\"] = tmp.Grocery\n",
+    "ordered[\"Milk\"] = tmp.Milk\n",
+    "data = ordered\n",
+    "\n",
+    "display(true_centers)\n",
+    "\n",
+    "display(data.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 8\n",
+    "Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project. *What set of establishments could each of the customer segments represent?*  \n",
+    "**Hint:** A customer who is assigned to `'Cluster X'` should best identify with the establishments represented by the feature set of `'Segment X'`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** Comparing the feature values for each segment with the statistical description of each feature, it seems that:\n",
+    "\n",
+    "To be fair, i am not sure how to answer these very well, as I find it hard to get myself interested in food establishments and what they might buy. It might be more interesting to segment drivers for economic performance, predictors of burglary or maybe internet user groups. However, here my attempt.\n",
+    "\n",
+    "PC:\n",
+    "- Dim 2 represents Frozen, Fresh and Deli. A higher value means more of these.\n",
+    "- Dim 1 represents Detergents_Paper, Grocery and milk. A higher value means more of these.\n",
+    "\n",
+    "Clusters:\n",
+    "- Segment 0 (any dim2, <0.5 dim1). Here I expect  it to have any value for dim2 features be lower on dim1 features than Segment 1 and on the lower end of the distribution for those features. Thus, it would have low Detergents_Paper, Grocery and Milk, so low consumer retail spending. This could represent a restaurant, food market, hotel.\n",
+    "- Segment 1 (any dim2, >=0.5 dim1). Here I expect any dim2 features and higher values on dim1 features, and also at the higher end of the distribution for those features. So high Detergents_Paper, Grocery and Milk, indicating retail spending like a supermarket."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 9\n",
+    "*For each sample point, which customer segment from* ***Question 8*** *best represents it? Are the predictions for each sample point consistent with this?*\n",
+    "\n",
+    "Run the code block below to find which cluster each sample point is predicted to be."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample point 0 predicted to be in Cluster 0\n",
+      "Sample point 1 predicted to be in Cluster 0\n",
+      "Sample point 2 predicted to be in Cluster 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the predictions\n",
+    "for i, pred in enumerate(sample_preds):\n",
+    "    print \"Sample point\", i, \"predicted to be in Cluster\", pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** Point 0 is a market as it has a lot of frozen fresh and deli and little detpaper, grocery and milk. This matches the prediction in cluster 0. Point 1 an outlier(as found earlier) so was removed and 2 was and outlier on 2 dimensions so was removed as well. However, Point 2 was high on Det_Paper, Grocery and Milk, so would be correctly classified as retail in cluster 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the ***customer segments***, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which *segment* that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the ***customer segments*** to a hidden variable present in the data, to see whether the clustering identified certain relationships."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Question 10\n",
+    "Companies will often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. *How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service?*  \n",
+    "**Hint:** Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** The distributor could call, survey etc a representative customer, or several, from each segment and ask them. They could use this to extrapolate to all customers from this segment and change his schedule by segment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 11\n",
+    "Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a ***customer segment*** it best identifies with (depending on the clustering algorithm applied), we can consider *'customer segment'* as an **engineered feature** for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a ***customer segment*** to determine the most appropriate delivery service.  \n",
+    "*How can the wholesale distributor label the new customers using only their estimated product spending and the* ***customer segment*** *data?*  \n",
+    "**Hint:** A supervised learner could be used to train on the original customers. What would be the target variable?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** They can transform the estimated spending to principal components using the trained pca on the original data. This would allow them to assign new customers to their clusters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizing Underlying Distributions\n",
+    "\n",
+    "At the beginning of this project, it was discussed that the `'Channel'` and `'Region'` features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the `'Channel'` feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.\n",
+    "\n",
+    "Run the code block below to see how each data point is labeled either `'HoReCa'` (Hotel/Restaurant/Cafe) or `'Retail'` the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ROSMGHiIiK5k5c6bu3507d0ZcXBxCQ0PtcuykpCS0adMGCxcutMvxSmPmzJlo3769bj2b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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xf137940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Display the clustering results based on 'Channel' data\n",
+    "vs.channel_results(reduced_data, outliers, pca_samples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 12\n",
+    "*How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? Would you consider these classifications as consistent with your previous definition of the customer segments?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** It seems my cluster 0, or \"small place\" is gone. However, my two other clusters \"supermarket\" and \"market\" are still there. Just that market was Hotels in the end - i guess they also use less Det_Paper, Milk and Grocery than a retailer (to be honest i dont know). There are segments of purely retailers below -2.5 on dim1, and purely horeca above 2 on dim1. These classifications are consistent with my previous definition of segments, although there is a larger overlap region as expected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
+    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/deep_learning_examples/__init__.py b/deep_learning_examples/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/deep_learning_examples/cross_entropy.py b/deep_learning_examples/cross_entropy.py
new file mode 100644
index 0000000..f8d5f5d
--- /dev/null
+++ b/deep_learning_examples/cross_entropy.py
@@ -0,0 +1,26 @@
+import numpy as np
+
+
+# Write a function that takes as input two lists Y, P,
+# Y is a list of labels, P is the list of probabilities
+# and returns the float corresponding to their cross-entropy.
+def cross_entropy(Y, P):
+    # get prediction for each point using model
+
+    # get probability for each point
+
+    # get -ln(prob) for each point, and sum them
+    val = 0
+    for i in range(len(P)):
+        val += Y[i] * np.log(P[i]) + (1-Y[i]) * np.log(1-P[i])
+
+    cross_entropy = -val
+
+    return cross_entropy
+
+
+# Trying for Y=[1,0,1,1] and P=[0.4,0.6,0.1,0.5].
+
+Y = [1,0,1,1]
+P = [0.4,0.6,0.1,0.5]
+print(cross_entropy(Y, P))
\ No newline at end of file
diff --git a/deep_learning_examples/example_keras_neural_networks/XOR_neural_network.py b/deep_learning_examples/example_keras_neural_networks/XOR_neural_network.py
new file mode 100644
index 0000000..5cc8a4b
--- /dev/null
+++ b/deep_learning_examples/example_keras_neural_networks/XOR_neural_network.py
@@ -0,0 +1,50 @@
+import numpy as np
+from keras.utils import np_utils
+import tensorflow as tf
+#tf.python.control_flow_ops = tf
+
+# Set random seed
+np.random.seed(42)
+
+# Our data
+X = np.array([[0,0],[0,1],[1,0],[1,1]]).astype('float32')
+y = np.array([[0],[1],[1],[0]]).astype('float32')
+
+# Initial Setup for Keras
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation, Flatten
+
+# One-hot encoding the output
+y = np_utils.to_categorical(y)
+
+# Building the model
+xor = Sequential()
+
+# each of the .add() functions is listed in the model architecture
+
+# input and first hidden layer, specify input values, output nodes, and activation function
+xor.add(Dense(8, input_shape=(2,))) # can also put function here
+xor.add(Activation("relu"))
+
+# output layer with 2 output nodes
+xor.add(Dense(2))
+xor.add(Activation("sigmoid")) # add sigmoid to output
+
+
+
+xor.compile(loss="categorical_crossentropy", optimizer="adam", metrics = ['accuracy'])
+
+# Uncomment this line to print the model architecture
+xor.summary()
+
+# Fitting the model
+# Hint: This next line is where you can change the number of epochs, it's set to 10 now.
+history = xor.fit(X, y, nb_epoch=100, verbose=0)
+
+# Scoring the model
+score = xor.evaluate(X, y)
+print("\nAccuracy: ", score[-1])
+
+# Checking the predictions
+print("\nPredictions:")
+print(xor.predict_proba(X))
\ No newline at end of file
diff --git a/deep_learning_examples/example_keras_neural_networks/__init__.py b/deep_learning_examples/example_keras_neural_networks/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/deep_learning_examples/example_keras_neural_networks/basic_examples.py b/deep_learning_examples/example_keras_neural_networks/basic_examples.py
new file mode 100644
index 0000000..2f2874f
--- /dev/null
+++ b/deep_learning_examples/example_keras_neural_networks/basic_examples.py
@@ -0,0 +1,45 @@
+
+
+
+
+
+def buildNNInLayers():
+    from keras.models import Sequential
+    from keras.layers.core import Dense, Activation, Flatten
+
+    #Create the Sequential model
+    model = Sequential()
+
+    #1st Layer - Add an input layer of 32 nodes. So there are 32 inputs.
+    model.add(Dense, input_dim=32)
+
+    #2nd Layer - Add a fully connected layer of 128 nodes
+    model.add(Dense(128)) # convert 32 inputs to 128 nodes.
+
+    #3rd Layer - Add a softmax activation layer
+    model.add(Activation('softmax'))
+
+    #4th Layer - Add a fully connected layer
+    model.add(Dense(10)) # there are 10 final output nodes
+
+    #5th Layer - Add a Sigmoid activation layer
+    model.add(Activation('sigmoid'))
+
+
+    # compile the model, using as loss (or error) function cross entropy and performance metric accuracy
+    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])
+
+
+    # see the model architecture
+    model.summary()
+
+    # fit model to data
+    model.fit(X, y, nb_epoch=1000, verbose=0)
+
+
+    # evaluate model performance using accuracy score as defined on compilation
+    model.evaluate()
+
+
+
+buildNNInLayers()
\ No newline at end of file
diff --git a/deep_learning_examples/logistic_regression_algo/__init__.py b/deep_learning_examples/logistic_regression_algo/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/deep_learning_examples/logistic_regression_algo/data.csv b/deep_learning_examples/logistic_regression_algo/data.csv
new file mode 100644
index 0000000..c4324e9
--- /dev/null
+++ b/deep_learning_examples/logistic_regression_algo/data.csv
@@ -0,0 +1,100 @@
+0.78051,-0.063669,1
+0.28774,0.29139,1
+0.40714,0.17878,1
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+0.73156,0.71782,0
+0.44556,0.57991,0
+0.85275,0.85987,0
+0.51912,0.62359,0
diff --git a/deep_learning_examples/logistic_regression_algo/logistic_regression_algo.py b/deep_learning_examples/logistic_regression_algo/logistic_regression_algo.py
new file mode 100644
index 0000000..40b2789
--- /dev/null
+++ b/deep_learning_examples/logistic_regression_algo/logistic_regression_algo.py
@@ -0,0 +1,105 @@
+import numpy as np
+# Setting the random seed, feel free to change it and see different solutions.
+np.random.seed(42)
+
+def sigmoid(x):
+    return 1/(1+np.exp(-x))
+def sigmoid_prime(x):
+    return sigmoid(x)*(1-sigmoid(x))
+
+#prediction with sigmoid
+def prediction(X, W, b):
+    return sigmoid(np.matmul(X,W)+b)
+
+# cross entropy function
+def error_vector(y, y_hat):
+    return [-y[i]*np.log(y_hat[i]) - (1-y[i])*np.log(1-y_hat[i]) for i in range(len(y))]
+
+# error function
+def error(y, y_hat):
+    ev = error_vector(y, y_hat)
+    return sum(ev)/len(ev)
+
+# TODO: Fill in the code below to calculate the gradient of the error function.
+# The result should be a list of three lists:
+# The first list should contain the gradient (partial derivatives) with respect to w1
+# The second list should contain the gradient (partial derivatives) with respect to w2
+# The third list should contain the gradient (partial derivatives) with respect to b
+def dErrors(X, y, y_hat):
+
+    # this does all the partial derivs at the same time
+    # so each points partial deriv wrt x1, wrt x2 and wrt b
+    DErrorsDx1 = (y - y_hat[:,0]) * (X[:,0])
+    DErrorsDx2 = (y-y_hat[:,0]) * X[:,1]
+    DErrorsDb = (y-y_hat[:,0])
+
+    """
+    seems to be some issue with the quiz - above gives an error
+    DErrorsDx1 = [X[i][0] * (y[i] - y_hat[i]) for i in range(len(y))]
+    DErrorsDx2 = [X[i][1] * (y[i] - y_hat[i]) for i in range(len(y))]
+    DErrorsDb = [y[i] - y_hat[i] for i in range(len(y))]
+    """
+
+    return DErrorsDx1, DErrorsDx2, DErrorsDb
+
+# TODO: Fill in the code below to implement the gradient descent step.
+# The function should receive as inputs the data X, the labels y,
+# the weights W (as an array), and the bias b.
+# It should calculate the prediction, the gradients, and use them to
+# update the weights and bias W, b. Then return W and b.
+# The error e will be calculated and returned for you, for plotting purposes.
+def gradientDescentStep(X, y, W, b, learn_rate = 0.01):
+
+    # this does the prediction for all points at the same time
+    # TODO: Calculate the prediction
+    y_hat = prediction(X, W, b)
+
+    # gets partial derivs for each point
+    # TODO: Calculate the gradient
+    grad = dErrors(X, y, y_hat)
+
+    # update weights for equation, must be sequentially after using each points
+    #  partial derivs
+    # TODO: Update the weights
+    # actually, could also update weights with sum * learning_rate
+
+    # see: weights are changed even for correctly classified points.
+    for iPoint in range(len(y)):
+        W[0] += learn_rate * grad[0][iPoint]
+        W[1] += learn_rate * grad[1][iPoint]
+        b += learn_rate * grad[2][iPoint]
+
+    # This calculates the error
+    e = error(y, y_hat)
+    return W, b, e
+
+# This function runs the perceptron algorithm repeatedly on the dataset,
+# and returns a few of the boundary lines obtained in the iterations,
+# for plotting purposes.
+# Feel free to play with the learning rate and the num_epochs,
+# and see your results plotted below.
+def trainLR(X, y, learn_rate = 0.01, num_epochs = 100):
+    x_min, x_max = min(X.T[0]), max(X.T[0])
+    y_min, y_max = min(X.T[1]), max(X.T[1])
+    # Initialize the weights randomly
+    W = np.array(np.random.rand(2,1))*2 -1
+    b = np.random.rand(1)[0]*2 - 1
+    # These are the solution lines that get plotted below.
+    boundary_lines = []
+    errors = []
+    for i in range(num_epochs):
+        # In each epoch, we apply the gradient descent step.
+        W, b, error = gradientDescentStep(X, y, W, b, learn_rate)
+        boundary_lines.append((-W[0]/W[1], -b/W[1]))
+        errors.append(error)
+    return boundary_lines, errors
+
+
+
+import numpy as np
+raw_data = open("data.csv", 'rt')
+data = np.loadtxt(raw_data, delimiter=",")
+X = data[:,[0,1]]
+y = data[:,2]
+
+print(trainLR(X, y)[1])
\ No newline at end of file
diff --git a/deep_learning_examples/perceptron_algorithm/__init__.py b/deep_learning_examples/perceptron_algorithm/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/deep_learning_examples/perceptron_algorithm/data.csv b/deep_learning_examples/perceptron_algorithm/data.csv
new file mode 100644
index 0000000..c4324e9
--- /dev/null
+++ b/deep_learning_examples/perceptron_algorithm/data.csv
@@ -0,0 +1,100 @@
+0.78051,-0.063669,1
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diff --git a/deep_learning_examples/perceptron_algorithm/perceptron_classification.py b/deep_learning_examples/perceptron_algorithm/perceptron_classification.py
new file mode 100644
index 0000000..7ae41a7
--- /dev/null
+++ b/deep_learning_examples/perceptron_algorithm/perceptron_classification.py
@@ -0,0 +1,70 @@
+import numpy as np
+
+# Setting the random seed, feel free to change it and see different solutions.
+np.random.seed(42)
+
+
+def stepFunction(t):
+    if t >= 0:
+        return 1
+    return 0
+
+# this does one prediction for one point only
+def prediction(X, W, b):
+    return stepFunction((np.matmul(X, W) + b)[0])
+
+
+# TODO: Fill in the code below to implement the perceptron trick.
+# The function should receive as inputs the data X, the labels y,
+# the weights W (as an array), and the bias b,
+# update the weights and bias W, b, according to the perceptron algorithm,
+# and return W and b.
+def perceptronStep(X, y, W, b, learn_rate=0.01):
+    # Fill in code
+
+    for iPoint, point in enumerate(X):
+        predicted = prediction(X[iPoint], W, b)
+
+        # Learning in perceptron is only applied to misclassified points (compared to gradient descent)
+        if predicted != y[iPoint]:
+            if predicted == 1:  # predicted 1, real 0, subtract
+                W[0] -= learn_rate * X[iPoint][0]
+                W[1] -= learn_rate * X[iPoint][1]
+                b -= learn_rate
+
+            elif predicted == 0:  # predicted 0, real 1, add
+                W[0] += learn_rate * X[iPoint][0]
+                W[1] += learn_rate * X[iPoint][1]
+                b += learn_rate
+
+    return W, b
+
+
+# This function runs the perceptron algorithm repeatedly on the dataset,
+# and returns a few of the boundary lines obtained in the iterations,
+# for plotting purposes.
+# Feel free to play with the learning rate and the num_epochs,
+# and see your results plotted below.
+def trainPerceptronAlgorithm(X, y, learn_rate=0.01, num_epochs=25):
+    x_min, x_max = min(X.T[0]), max(X.T[0])
+    y_min, y_max = min(X.T[1]), max(X.T[1])
+    W = np.array(np.random.rand(2, 1))
+    b = np.random.rand(1)[0] + x_max
+    # These are the solution lines that get plotted below.
+    boundary_lines = []
+    for i in range(num_epochs):
+        # In each epoch, we apply the perceptron step.
+        W, b = perceptronStep(X, y, W, b, learn_rate)
+        boundary_lines.append((-W[0] / W[1], -b / W[1]))
+    return boundary_lines
+
+
+
+import numpy as np
+raw_data = open("data.csv", 'rt')
+data = np.loadtxt(raw_data, delimiter=",")
+X = data[:,[0,1]]
+y = data[:,2]
+trainPerceptronAlgorithm(X, y)
+
+
diff --git a/deep_learning_examples/softmax.py b/deep_learning_examples/softmax.py
new file mode 100644
index 0000000..6b823b4
--- /dev/null
+++ b/deep_learning_examples/softmax.py
@@ -0,0 +1,16 @@
+import numpy as np
+
+
+# Write a function that takes as input a list of numbers, and returns
+# the list of values given by the softmax function.
+def softmax(L):
+    # first do softmax function as e^(z_i) / (sum(e^(Z))
+    sumE = np.sum(np.exp(L))
+    res = []
+    for elem in L:
+        res.append(np.exp(elem) / sumE)
+
+    return res
+
+
+softmax([1,2,3,4])
\ No newline at end of file
diff --git a/examples/testdb_kai.sql b/examples/testdb_kai.sql
new file mode 100644
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diff --git a/finding_donors/finding_donors.html b/finding_donors/finding_donors.html
new file mode 100644
index 0000000..43d3e2f
--- /dev/null
+++ b/finding_donors/finding_donors.html
@@ -0,0 +1,15124 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>finding_donors</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  content: "\e260";
+}
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+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
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+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
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+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
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+button,
+select,
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+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
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+  color: #337ab7;
+  text-decoration: none;
+}
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+  color: #23527c;
+  text-decoration: underline;
+}
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+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
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+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
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+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
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+h4,
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+h6,
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+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
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+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
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+  margin-bottom: 9px;
+}
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+}
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+  font-size: 33px;
+}
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+  font-size: 27px;
+}
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+}
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+}
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+}
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+  font-size: 12px;
+}
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+  margin: 0 0 9px;
+}
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+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
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+  font-size: 92%;
+}
+mark,
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+  background-color: #fcf8e3;
+  padding: .2em;
+}
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+  text-align: left;
+}
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+  text-align: right;
+}
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+  text-align: center;
+}
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+  text-align: justify;
+}
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+  white-space: nowrap;
+}
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+  text-transform: lowercase;
+}
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+  text-transform: uppercase;
+}
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+  text-transform: capitalize;
+}
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+  color: #777777;
+}
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+  color: #337ab7;
+}
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+  color: #286090;
+}
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+  color: #3c763d;
+}
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+  color: #2b542c;
+}
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+  color: #31708f;
+}
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+  color: #245269;
+}
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+  color: #8a6d3b;
+}
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+a.text-warning:focus {
+  color: #66512c;
+}
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+  color: #a94442;
+}
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+a.text-danger:focus {
+  color: #843534;
+}
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+  color: #fff;
+  background-color: #337ab7;
+}
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+}
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+  background-color: #dff0d8;
+}
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+  background-color: #c1e2b3;
+}
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+  background-color: #d9edf7;
+}
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+}
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+  background-color: #fcf8e3;
+}
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+a.bg-warning:focus {
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+}
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+  background-color: #f2dede;
+}
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+}
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+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
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+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
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+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
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+  padding-left: 0;
+  list-style: none;
+}
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+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
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+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
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+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
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+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
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+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
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+  font-size: 90%;
+  text-transform: uppercase;
+}
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+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
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+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
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+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
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+blockquote small:before,
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+}
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+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
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+}
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+}
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+  font-style: normal;
+  line-height: 1.42857143;
+}
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+}
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+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
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+  color: #888;
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+}
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+  font-weight: bold;
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+}
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+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
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+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
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+}
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+}
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+  margin-left: auto;
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+  padding-right: 0px;
+}
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+  }
+}
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+  }
+}
+@media (min-width: 1200px) {
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+  }
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
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+  content: "\f177";
+}
+.fa-long-arrow-right:before {
+  content: "\f178";
+}
+.fa-apple:before {
+  content: "\f179";
+}
+.fa-windows:before {
+  content: "\f17a";
+}
+.fa-android:before {
+  content: "\f17b";
+}
+.fa-linux:before {
+  content: "\f17c";
+}
+.fa-dribbble:before {
+  content: "\f17d";
+}
+.fa-skype:before {
+  content: "\f17e";
+}
+.fa-foursquare:before {
+  content: "\f180";
+}
+.fa-trello:before {
+  content: "\f181";
+}
+.fa-female:before {
+  content: "\f182";
+}
+.fa-male:before {
+  content: "\f183";
+}
+.fa-gittip:before {
+  content: "\f184";
+}
+.fa-sun-o:before {
+  content: "\f185";
+}
+.fa-moon-o:before {
+  content: "\f186";
+}
+.fa-archive:before {
+  content: "\f187";
+}
+.fa-bug:before {
+  content: "\f188";
+}
+.fa-vk:before {
+  content: "\f189";
+}
+.fa-weibo:before {
+  content: "\f18a";
+}
+.fa-renren:before {
+  content: "\f18b";
+}
+.fa-pagelines:before {
+  content: "\f18c";
+}
+.fa-stack-exchange:before {
+  content: "\f18d";
+}
+.fa-arrow-circle-o-right:before {
+  content: "\f18e";
+}
+.fa-arrow-circle-o-left:before {
+  content: "\f190";
+}
+.fa-toggle-left:before,
+.fa-caret-square-o-left:before {
+  content: "\f191";
+}
+.fa-dot-circle-o:before {
+  content: "\f192";
+}
+.fa-wheelchair:before {
+  content: "\f193";
+}
+.fa-vimeo-square:before {
+  content: "\f194";
+}
+.fa-turkish-lira:before,
+.fa-try:before {
+  content: "\f195";
+}
+.fa-plus-square-o:before {
+  content: "\f196";
+}
+.fa-space-shuttle:before {
+  content: "\f197";
+}
+.fa-slack:before {
+  content: "\f198";
+}
+.fa-envelope-square:before {
+  content: "\f199";
+}
+.fa-wordpress:before {
+  content: "\f19a";
+}
+.fa-openid:before {
+  content: "\f19b";
+}
+.fa-institution:before,
+.fa-bank:before,
+.fa-university:before {
+  content: "\f19c";
+}
+.fa-mortar-board:before,
+.fa-graduation-cap:before {
+  content: "\f19d";
+}
+.fa-yahoo:before {
+  content: "\f19e";
+}
+.fa-google:before {
+  content: "\f1a0";
+}
+.fa-reddit:before {
+  content: "\f1a1";
+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 21ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
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+    <!-- End of mathjax configuration --></head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Supervised-Learning">Supervised Learning<a class="anchor-link" href="#Supervised-Learning">&#182;</a></h2><h2 id="Project:-Finding-Donors-for-CharityML">Project: Finding Donors for <em>CharityML</em><a class="anchor-link" href="#Project:-Finding-Donors-for-CharityML">&#182;</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with <strong>'Implementation'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a <code>'TODO'</code> statement. Please be sure to read the instructions carefully!</p>
+<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
+<blockquote><p><strong>Note:</strong> Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#182;</a></h2><p>In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features.</p>
+<p>The dataset for this project originates from the <a href="https://archive.ics.uci.edu/ml/datasets/Census+Income">UCI Machine Learning Repository</a>. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article <em>"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid"</em>. You can find the article by Ron Kohavi <a href="https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf">online</a>. The data we investigate here consists of small changes to the original dataset, such as removing the <code>'fnlwgt'</code> feature and records with missing or ill-formatted entries.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Exploring-the-Data">Exploring the Data<a class="anchor-link" href="#Exploring-the-Data">&#182;</a></h2><p>Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, <code>'income'</code>, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import libraries necessary for this project</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span> <span class="c1"># Allows the use of display() for DataFrames</span>
+
+<span class="c1"># Import supplementary visualization code visuals.py</span>
+<span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>
+
+<span class="c1"># Pretty display for notebooks</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="c1"># Load the Census dataset</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;census.csv&quot;</span><span class="p">)</span>
+
+<span class="c1"># Success - Display the first record</span>
+<span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
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+<div class="prompt"></div>
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+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>age</th>
+      <th>workclass</th>
+      <th>education_level</th>
+      <th>education-num</th>
+      <th>marital-status</th>
+      <th>occupation</th>
+      <th>relationship</th>
+      <th>race</th>
+      <th>sex</th>
+      <th>capital-gain</th>
+      <th>capital-loss</th>
+      <th>hours-per-week</th>
+      <th>native-country</th>
+      <th>income</th>
+    </tr>
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+    <tr>
+      <th>0</th>
+      <td>39</td>
+      <td>State-gov</td>
+      <td>Bachelors</td>
+      <td>13.0</td>
+      <td>Never-married</td>
+      <td>Adm-clerical</td>
+      <td>Not-in-family</td>
+      <td>White</td>
+      <td>Male</td>
+      <td>2174.0</td>
+      <td>0.0</td>
+      <td>40.0</td>
+      <td>United-States</td>
+      <td>&lt;=50K</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>50</td>
+      <td>Self-emp-not-inc</td>
+      <td>Bachelors</td>
+      <td>13.0</td>
+      <td>Married-civ-spouse</td>
+      <td>Exec-managerial</td>
+      <td>Husband</td>
+      <td>White</td>
+      <td>Male</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>13.0</td>
+      <td>United-States</td>
+      <td>&lt;=50K</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>38</td>
+      <td>Private</td>
+      <td>HS-grad</td>
+      <td>9.0</td>
+      <td>Divorced</td>
+      <td>Handlers-cleaners</td>
+      <td>Not-in-family</td>
+      <td>White</td>
+      <td>Male</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>40.0</td>
+      <td>United-States</td>
+      <td>&lt;=50K</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Data-Exploration">Implementation: Data Exploration<a class="anchor-link" href="#Implementation:-Data-Exploration">&#182;</a></h3><p>A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \$50,000. In the code cell below, you will need to compute the following:</p>
+<ul>
+<li>The total number of records, <code>'n_records'</code></li>
+<li>The number of individuals making more than \$50,000 annually, <code>'n_greater_50k'</code>.</li>
+<li>The number of individuals making at most \$50,000 annually, <code>'n_at_most_50k'</code>.</li>
+<li>The percentage of individuals making more than \$50,000 annually, <code>'greater_percent'</code>.</li>
+</ul>
+<p><strong>Hint:</strong> You may need to look at the table above to understand how the <code>'income'</code> entries are formatted.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Total number of records</span>
+<span class="n">n_records</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="c1"># TODO: Number of records where individual&#39;s income is more than $50,000</span>
+<span class="n">n_greater_50k</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;income&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;50K&quot;</span><span class="p">])</span>
+
+<span class="c1"># TODO: Number of records where individual&#39;s income is at most $50,000</span>
+<span class="n">n_at_most_50k</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;income&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&lt;=50K&quot;</span><span class="p">])</span>
+
+<span class="c1"># TODO: Percentage of individuals whose income is more than $50,000</span>
+<span class="n">greater_percent</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;income&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;&gt;50K&quot;</span><span class="p">])</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+
+<span class="c1"># Print the results</span>
+<span class="k">print</span> <span class="s2">&quot;Total number of records: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_records</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;Individuals making more than $50,000: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_greater_50k</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;Individuals making at most $50,000: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_at_most_50k</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;Percentage of individuals making more than $50,000: {:.2f}%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">greater_percent</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Total number of records: 45222
+Individuals making more than $50,000: 11208
+Individuals making at most $50,000: 34014
+Percentage of individuals making more than $50,000: 24.78%
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Preparing-the-Data">Preparing the Data<a class="anchor-link" href="#Preparing-the-Data">&#182;</a></h2><p>Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as <strong>preprocessing</strong>. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Transforming-Skewed-Continuous-Features">Transforming Skewed Continuous Features<a class="anchor-link" href="#Transforming-Skewed-Continuous-Features">&#182;</a></h3><p>A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '<code>capital-gain'</code> and <code>'capital-loss'</code>.</p>
+<p>Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Split the data into features and target label</span>
+<span class="n">income_raw</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;income&#39;</span><span class="p">]</span>
+<span class="n">features_raw</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;income&#39;</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Visualize skewed continuous features of original data</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">distribution</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
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+<div class="output_png output_subarea ">
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+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For highly-skewed feature distributions such as <code>'capital-gain'</code> and <code>'capital-loss'</code>, it is common practice to apply a <a href="https://en.wikipedia.org/wiki/Data_transformation_(statistics)">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of <code>0</code> is undefined, so we must translate the values by a small amount above <code>0</code> to apply the the logarithm successfully.</p>
+<p>Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Log-transform the skewed features</span>
+<span class="n">skewed</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;capital-gain&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-loss&#39;</span><span class="p">]</span>
+<span class="n">features_raw</span><span class="p">[</span><span class="n">skewed</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">skewed</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="c1"># applies column wise, incr x by 1</span>
+
+<span class="c1"># moves values closer together so algo doesnt get confused</span>
+
+<span class="c1"># Visualize the new log distributions</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">distribution</span><span class="p">(</span><span class="n">features_raw</span><span class="p">,</span> <span class="n">transformed</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Normalizing-Numerical-Features">Normalizing Numerical Features<a class="anchor-link" href="#Normalizing-Numerical-Features">&#182;</a></h3><p>In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as <code>'capital-gain'</code> or <code>'capital-loss'</code> above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.</p>
+<p>Run the code cell below to normalize each numerical feature. We will use <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html"><code>sklearn.preprocessing.MinMaxScaler</code></a> for this.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import sklearn.preprocessing.StandardScaler</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
+
+<span class="c1"># Initialize a scaler, then apply it to the features</span>
+<span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
+<span class="n">numerical</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;education-num&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-gain&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-loss&#39;</span><span class="p">,</span> <span class="s1">&#39;hours-per-week&#39;</span><span class="p">]</span>
+<span class="n">features_raw</span><span class="p">[</span><span class="n">numerical</span><span class="p">]</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">numerical</span><span class="p">])</span>
+
+<span class="c1"># Show an example of a record with scaling applied</span>
+<span class="n">display</span><span class="p">(</span><span class="n">features_raw</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>age</th>
+      <th>workclass</th>
+      <th>education_level</th>
+      <th>education-num</th>
+      <th>marital-status</th>
+      <th>occupation</th>
+      <th>relationship</th>
+      <th>race</th>
+      <th>sex</th>
+      <th>capital-gain</th>
+      <th>capital-loss</th>
+      <th>hours-per-week</th>
+      <th>native-country</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.30137</td>
+      <td>State-gov</td>
+      <td>Bachelors</td>
+      <td>0.8</td>
+      <td>Never-married</td>
+      <td>Adm-clerical</td>
+      <td>Not-in-family</td>
+      <td>White</td>
+      <td>Male</td>
+      <td>0.02174</td>
+      <td>0.0</td>
+      <td>0.397959</td>
+      <td>United-States</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Data-Preprocessing">Implementation: Data Preprocessing<a class="anchor-link" href="#Implementation:-Data-Preprocessing">&#182;</a></h3><p>From the table in <strong>Exploring the Data</strong> above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called <em>categorical variables</em>) be converted. One popular way to convert categorical variables is by using the <strong>one-hot encoding</strong> scheme. One-hot encoding creates a <em>"dummy"</em> variable for each possible category of each non-numeric feature. For example, assume <code>someFeature</code> has three possible entries: <code>A</code>, <code>B</code>, or <code>C</code>. We then encode this feature into <code>someFeature_A</code>, <code>someFeature_B</code> and <code>someFeature_C</code>.</p>
+<table>
+<thead><tr>
+<th style="text-align:center"></th>
+<th style="text-align:center">someFeature</th>
+<th></th>
+<th style="text-align:center">someFeature_A</th>
+<th style="text-align:center">someFeature_B</th>
+<th style="text-align:center">someFeature_C</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:center">0</td>
+<td style="text-align:center">B</td>
+<td></td>
+<td style="text-align:center">0</td>
+<td style="text-align:center">1</td>
+<td style="text-align:center">0</td>
+</tr>
+<tr>
+<td style="text-align:center">1</td>
+<td style="text-align:center">C</td>
+<td>----&gt; one-hot encode ----&gt;</td>
+<td style="text-align:center">0</td>
+<td style="text-align:center">0</td>
+<td style="text-align:center">1</td>
+</tr>
+<tr>
+<td style="text-align:center">2</td>
+<td style="text-align:center">A</td>
+<td></td>
+<td style="text-align:center">1</td>
+<td style="text-align:center">0</td>
+<td style="text-align:center">0</td>
+</tr>
+</tbody>
+</table>
+<p>Additionally, as with the non-numeric features, we need to convert the non-numeric target label, <code>'income'</code> to numerical values for the learning algorithm to work. Since there are only two possible categories for this label ("&lt;=50K" and "&gt;50K"), we can avoid using one-hot encoding and simply encode these two categories as <code>0</code> and <code>1</code>, respectively. In code cell below, you will need to implement the following:</p>
+<ul>
+<li>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies"><code>pandas.get_dummies()</code></a> to perform one-hot encoding on the <code>'features_raw'</code> data.</li>
+<li>Convert the target label <code>'income_raw'</code> to numerical entries.<ul>
+<li>Set records with "&lt;=50K" to <code>0</code> and records with "&gt;50K" to <code>1</code>.</li>
+</ul>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: One-hot encode the &#39;features_raw&#39; data using pandas.get_dummies()</span>
+<span class="n">features</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">features_raw</span><span class="p">)</span> <span class="c1"># creates a non sparse matrix</span>
+
+<span class="c1"># TODO: Encode the &#39;income_raw&#39; data to numerical values</span>
+<span class="n">income</span> <span class="o">=</span> <span class="n">income_raw</span><span class="o">.</span><span class="n">map</span><span class="p">({</span><span class="s1">&#39;&lt;=50K&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;&gt;50K&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">})</span>
+
+<span class="c1"># or, good for multiple classes. Also provides easy reverse transform.</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="n">le</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span>
+<span class="n">income</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">income_raw</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s2">&quot;income one hot&quot;</span><span class="p">,</span> <span class="n">income</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s2">&quot;income rev transformed&quot;</span><span class="p">,</span> <span class="n">le</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">income</span><span class="p">))</span>
+
+<span class="c1"># just be careful: it gives unecessary order to things.</span>
+
+<span class="c1"># Print the number of features after one-hot encoding</span>
+<span class="n">encoded</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
+<span class="k">print</span> <span class="s2">&quot;{} total features after one-hot encoding.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">encoded</span><span class="p">))</span>
+
+<span class="c1"># Uncomment the following line to see the encoded feature names</span>
+<span class="c1">#print encoded</span>
+<span class="c1">#print(income.head(10))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>(&#39;income one hot&#39;, array([0, 0, 0, ..., 0, 0, 1], dtype=int64))
+(&#39;income rev transformed&#39;, array([&#39;&lt;=50K&#39;, &#39;&lt;=50K&#39;, &#39;&lt;=50K&#39;, ..., &#39;&lt;=50K&#39;, &#39;&lt;=50K&#39;, &#39;&gt;50K&#39;], dtype=object))
+103 total features after one-hot encoding.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Shuffle-and-Split-Data">Shuffle and Split Data<a class="anchor-link" href="#Shuffle-and-Split-Data">&#182;</a></h3><p>Now all <em>categorical variables</em> have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.</p>
+<p>Run the code cell below to perform this split.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+
+<span class="c1"># Split the &#39;features&#39; and &#39;income&#39; data into training and testing sets</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">income</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># Show the results of the split</span>
+<span class="k">print</span> <span class="s2">&quot;Training set has {} samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="k">print</span> <span class="s2">&quot;Testing set has {} samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Training set has 36177 samples.
+Testing set has 9045 samples.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Evaluating-Model-Performance">Evaluating Model Performance<a class="anchor-link" href="#Evaluating-Model-Performance">&#182;</a></h2><p>In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a <em>naive predictor</em>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Metrics-and-the-Naive-Predictor">Metrics and the Naive Predictor<a class="anchor-link" href="#Metrics-and-the-Naive-Predictor">&#182;</a></h3><p><em>CharityML</em>, equipped with their research, knows individuals that make more than \$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \$50,000 accurately. It would seem that using <strong>accuracy</strong> as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that <em>does not</em> make more than \$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \$50,000 is <em>more important</em> than the model's ability to <strong>recall</strong> those individuals. We can use <strong>F-beta score</strong> as a metric that considers both precision and recall:</p>
+$$ F_{\beta} = (1 + \beta^2) \cdot \frac{precision \cdot recall}{\left( \beta^2 \cdot precision \right) + recall} $$<p>In particular, when $\beta = 0.5$, more emphasis is placed on precision. This is called the <strong>F$_{0.5}$ score</strong> (or F-score for simplicity).</p>
+<p>Looking at the distribution of classes (those who make at most \$50,000, and those who make more), it's clear most individuals do not make more than \$50,000. This can greatly affect <strong>accuracy</strong>, since we could simply say <em>"this person does not make more than \$50,000"</em> and generally be right, without ever looking at the data! Making such a statement would be called <strong>naive</strong>, since we have not considered any information to substantiate the claim. It is always important to consider the <em>naive prediction</em> for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \$50,000, <em>CharityML</em> would identify no one as donors.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-1---Naive-Predictor-Performace">Question 1 - Naive Predictor Performace<a class="anchor-link" href="#Question-1---Naive-Predictor-Performace">&#182;</a></h3><p><em>If we chose a model that always predicted an individual made more than \$50,000, what would that model's accuracy and F-score be on this dataset?</em><br>
+<strong>Note:</strong> You must use the code cell below and assign your results to <code>'accuracy'</code> and <code>'fscore'</code> to be used later.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#from sklearn.metrics import accuracy_score, recall_score</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+
+<span class="n">income_all_true</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">income</span><span class="p">),),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span>
+<span class="c1">#print(income_all_true.head())</span>
+<span class="c1">#print(income.head())</span>
+
+<span class="c1"># TODO: Calculate accuracy</span>
+<span class="c1">#accuracy = accuracy_score(income, income_all_true, normalize=True, sample_weight=None) # normalise=True means show percentage</span>
+<span class="c1">#recall = recall_score(income, income_all_true)</span>
+
+<span class="n">income_pred_naive</span> <span class="o">=</span> <span class="n">income_all_true</span>
+
+<span class="c1"># by hand</span>
+<span class="n">tp</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">income</span><span class="o">==</span><span class="n">income_pred_naive</span><span class="p">)</span>
+<span class="n">fp</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">income_pred_naive</span><span class="p">)</span> <span class="o">-</span> <span class="n">tp</span>
+<span class="n">fn</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">income</span><span class="p">)</span> <span class="o">-</span> <span class="n">tp</span>
+<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tp</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">tp</span> <span class="o">+</span> <span class="n">fp</span><span class="p">)</span>
+<span class="n">recall</span> <span class="o">=</span> <span class="n">tp</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">tp</span> <span class="o">+</span> <span class="n">fn</span><span class="p">)</span>
+
+
+
+<span class="c1"># TODO: Calculate F-score using the formula above for beta = 0.5</span>
+<span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span>
+<span class="n">fscore</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">beta</span><span class="o">**</span><span class="mf">2.0</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">accuracy</span> <span class="o">*</span> <span class="n">recall</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">((</span><span class="n">beta</span><span class="o">**</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">accuracy</span><span class="p">)</span> <span class="o">+</span> <span class="n">recall</span><span class="p">)</span>
+
+
+<span class="c1"># Print the results </span>
+<span class="k">print</span> <span class="s2">&quot;Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">fscore</span><span class="p">)</span>
+
+<span class="c1"># accuracy matches the share of &gt;50k incomes, if you predict true always.</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Supervised-Learning-Models">Supervised Learning Models<a class="anchor-link" href="#Supervised-Learning-Models">&#182;</a></h3><p><strong>The following supervised learning models are currently available in</strong> <a href="http://scikit-learn.org/stable/supervised_learning.html"><code>scikit-learn</code></a> <strong>that you may choose from:</strong></p>
+<ul>
+<li>Gaussian Naive Bayes (GaussianNB)</li>
+<li>Decision Trees</li>
+<li>Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)</li>
+<li>K-Nearest Neighbors (KNeighbors)</li>
+<li>Stochastic Gradient Descent Classifier (SGDC)</li>
+<li>Support Vector Machines (SVM)</li>
+<li>Logistic Regression</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-2---Model-Application">Question 2 - Model Application<a class="anchor-link" href="#Question-2---Model-Application">&#182;</a></h3><p>List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen</p>
+<ul>
+<li><em>Describe one real-world application in industry where the model can be applied.</em> (You may need to do research for this — give references!)</li>
+<li><em>What are the strengths of the model; when does it perform well?</em></li>
+<li><em>What are the weaknesses of the model; when does it perform poorly?</em></li>
+<li><em>What makes this model a good candidate for the problem, given what you know about the data?</em></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> The three supervised learning models are Decision Tree, KNN and AdaBoost with Decision Tree as the weak learner.</p>
+<ul>
+<li>Decision trees are applied in finance, for example, where they have been used to model investment decision making: <a href="http://www.cfapubs.org/doi/pdf/10.2469/faj.v50.n6.75">http://www.cfapubs.org/doi/pdf/10.2469/faj.v50.n6.75</a>. In this paper, they are considered superior to a weighted combination of features, as they more closely model the thinking of a portfolio manager when selecting securities. Decision Trees are helpful as they can visualise the path taken to arrive at a classification. They perform well on datasets with nominal, ordinal, interval and ratio data instead of just one of the types. The weaknesses are that they can overfit if too many nodes are allowed, and that they perform poorly on unbalanced datasets, such as ours. However, we can address this by tweaking the fbeta score to give more emphasis to accuracy and checking our final accuracy against the accuracy by predicting the same outcome every time. We can also limit the number of nodes allowed, or set the minimum examples required per leaf to stop the tree from growing too deep and overfitting.</li>
+</ul>
+<ul>
+<li>KNN. An industry application is concept searching. <a href="https://www.kcura.com/relativity/Portals/0/Documents/8.0%20Documentation%20Help%20Site/Content/Features/Analytics/Concept%20searching.htm">https://www.kcura.com/relativity/Portals/0/Documents/8.0%20Documentation%20Help%20Site/Content/Features/Analytics/Concept%20searching.htm</a>. Based on a search query, Knn will find documents that contain a similar topic, rather than exact word matches. It works by setting up a feature space of related words, and checking for nearest neighbours in that space. Strengths are that it is non parametric so can fit any decision boundary. Further, it is resistant to correlated attributes, which our dataset may have. Overfitting is not a problem, as it finds locally optimal solutions for points that do not affect points elsewhere through any global parameters. We also don't have any missing data, so KNN should work well. Weaknesses are that all features are equally important. This assumption is ok, as we dont have an a priori idea what features matter in our dataset. Further, it is not obvious what K should be, so we would have to tune this.
+<a href="https://www.quora.com/Classification-machine-learning-When-should-I-use-a-K-NN-classifier-over-a-Naive-Bayes-classifier">https://www.quora.com/Classification-machine-learning-When-should-I-use-a-K-NN-classifier-over-a-Naive-Bayes-classifier</a></li>
+</ul>
+<ul>
+<li>Ada Boost is applied in industry to predict whether customers might leave and preventatively offer them enticements to stay. <a href="https://www.cs.rit.edu/~rlaz/PatternRecognition/slides/churn_adaboost.pdf">https://www.cs.rit.edu/~rlaz/PatternRecognition/slides/churn_adaboost.pdf</a>. Advantages are that instead of trying to find a a strong model that fits the weight distribution of the entire dataset we only have to find weak learners, i.e a model that performs better than chance. Each subsequent iteration will apply a weak learner to a slightly changed distribution, increasing the weights of examples we got wrong. We will also have continuous information gain, as each learner will improve on the previous. We also dont need any prior knowledge of the dataset, due to information gain guaranteed by the weak learner. Weaknesses are that a weak learner needs to exist for AdaBoost to work. A weak learner that is too complex, such a huge neural network, can lead to overfitting. It also seems that AdaBoost is sensitive to uniformly distributed noise (<a href="http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf">http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf</a>). For our problem, we will use AdaBoost with a Decision Tree as the weak learner. By controlling leafs and node splits, as above, we should get an improved solution to using just a Decision tree, as we can focus on difficult examples.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation---Creating-a-Training-and-Predicting-Pipeline">Implementation - Creating a Training and Predicting Pipeline<a class="anchor-link" href="#Implementation---Creating-a-Training-and-Predicting-Pipeline">&#182;</a></h3><p>To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.
+In the code block below, you will need to implement the following:</p>
+<ul>
+<li>Import <code>fbeta_score</code> and <code>accuracy_score</code> from <a href="http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics"><code>sklearn.metrics</code></a>.</li>
+<li>Fit the learner to the sampled training data and record the training time.</li>
+<li>Perform predictions on the test data <code>X_test</code>, and also on the first 300 training points <code>X_train[:300]</code>.<ul>
+<li>Record the total prediction time.</li>
+</ul>
+</li>
+<li>Calculate the accuracy score for both the training subset and testing set.</li>
+<li>Calculate the F-score for both the training subset and testing set.<ul>
+<li>Make sure that you set the <code>beta</code> parameter!</li>
+</ul>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import two metrics from sklearn - fbeta_score and accuracy_score</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">fbeta_score</span><span class="p">,</span> <span class="n">accuracy_score</span>
+
+<span class="c1"># a generic learning function for any algo</span>
+<span class="k">def</span> <span class="nf">train_predict</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">):</span> 
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    inputs:</span>
+<span class="sd">       - learner: the learning algorithm to be trained and predicted on</span>
+<span class="sd">       - sample_size: the size of samples (number) to be drawn from training set</span>
+<span class="sd">       - X_train: features training set</span>
+<span class="sd">       - y_train: income training set</span>
+<span class="sd">       - X_test: features testing set</span>
+<span class="sd">       - y_test: income testing set</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    
+    <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+    
+    <span class="c1"># Fit the learner to the training data using slicing with &#39;sample_size&#39;</span>
+    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get start time</span>
+    <span class="c1"># we want the same samples every time, not shuffled, so we can compare the learners.    </span>
+    <span class="n">learner</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:</span><span class="n">sample_size</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[:</span><span class="n">sample_size</span><span class="p">])</span>
+    <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get end time</span>
+    
+    <span class="c1"># Calculate the training time</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;train_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span><span class="o">-</span><span class="n">start</span>
+        
+    <span class="c1"># Get the predictions on the test set,</span>
+    <span class="c1">#       then get predictions on the first 300 training samples</span>
+    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get start time</span>
+    <span class="n">predictions_test</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+    <span class="n">predictions_train</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">])</span>
+    <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get end time</span>
+    
+    <span class="c1"># Calculate the total prediction time</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;pred_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span><span class="o">-</span><span class="n">start</span>
+            
+    <span class="c1"># Compute accuracy on the first 300 training samples</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;acc_train&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_pred</span><span class="o">=</span><span class="n">predictions_train</span><span class="p">,</span> <span class="n">y_true</span><span class="o">=</span><span class="n">y_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">])</span>
+        
+    <span class="c1"># Compute accuracy on test set</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;acc_test&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_pred</span><span class="o">=</span><span class="n">predictions_test</span><span class="p">,</span> <span class="n">y_true</span><span class="o">=</span><span class="n">y_test</span><span class="p">)</span>
+    
+    <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="c1"># place more emphasis on precision, as there are very &quot;&gt;50k&quot; examples in the dataset.</span>
+    
+    <span class="c1"># Compute F-score on the the first 300 training samples</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;f_train&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">y_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">],</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">predictions_train</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="n">beta</span><span class="p">)</span>
+        
+    <span class="c1"># Compute F-score on the test set</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;f_test&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">predictions_test</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="n">beta</span><span class="p">)</span>
+       
+    <span class="c1"># Success</span>
+    <span class="k">print</span> <span class="s2">&quot;{} trained on {} samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">learner</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">)</span>
+        
+    <span class="c1"># Return the results</span>
+    <span class="k">return</span> <span class="n">results</span>
+
+<span class="k">print</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="nb">len</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span><span class="o">*</span><span class="mf">0.63</span><span class="p">)))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>3
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Initial-Model-Evaluation">Implementation: Initial Model Evaluation<a class="anchor-link" href="#Implementation:-Initial-Model-Evaluation">&#182;</a></h3><p>In the code cell, you will need to implement the following:</p>
+<ul>
+<li>Import the three supervised learning models you've discussed in the previous section.</li>
+<li>Initialize the three models and store them in <code>'clf_A'</code>, <code>'clf_B'</code>, and <code>'clf_C'</code>.<ul>
+<li>Use a <code>'random_state'</code> for each model you use, if provided.</li>
+<li><strong>Note:</strong> Use the default settings for each model — you will tune one specific model in a later section.</li>
+</ul>
+</li>
+<li>Calculate the number of records equal to 1%, 10%, and 100% of the training data.<ul>
+<li>Store those values in <code>'samples_1'</code>, <code>'samples_10'</code>, and <code>'samples_100'</code> respectively.</li>
+</ul>
+</li>
+</ul>
+<p><strong>Note:</strong> Depending on which algorithms you chose, the following implementation may take some time to run!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import the three supervised learning models from sklearn</span>
+<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
+<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">AdaBoostClassifier</span>
+<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
+<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+
+
+<span class="c1"># TODO: Initialize the three models</span>
+<span class="n">clf_A</span> <span class="o">=</span> <span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<span class="c1">#clf_B = SVC(random_state=5)</span>
+<span class="n">clf_B</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">()</span>
+<span class="n">clf_C</span> <span class="o">=</span> <span class="n">AdaBoostClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<span class="c1">#clf_A = GaussianNB()</span>
+<span class="c1">#clf_A = LogisticRegression(random_state=5)</span>
+
+<span class="c1"># TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data</span>
+<span class="n">samples_1</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.01</span><span class="p">))</span>
+<span class="n">samples_10</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.1</span><span class="p">))</span>
+<span class="n">samples_100</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">1</span><span class="p">))</span>
+
+<span class="c1"># Collect results on the learners</span>
+<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="p">[</span><span class="n">clf_A</span><span class="p">,</span> <span class="n">clf_B</span><span class="p">,</span> <span class="n">clf_C</span><span class="p">]:</span>
+    <span class="n">clf_name</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
+    <span class="n">results</span><span class="p">[</span><span class="n">clf_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">samples</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="n">samples_1</span><span class="p">,</span> <span class="n">samples_10</span><span class="p">,</span> <span class="n">samples_100</span><span class="p">]):</span>
+        
+        <span class="c1"># bundles up useful algo metrics</span>
+        <span class="n">results</span><span class="p">[</span><span class="n">clf_name</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> \
+        <span class="n">train_predict</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">samples</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+
+<span class="c1"># Run metrics visualization for the three supervised learning models chosen</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">,</span> <span class="n">fscore</span><span class="p">)</span>
+
+<span class="c1"># Compared to the naive predictor (25% accuracy), we have improved considerably.</span>
+<span class="k">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>DecisionTreeClassifier trained on 362 samples.
+DecisionTreeClassifier trained on 3618 samples.
+DecisionTreeClassifier trained on 36177 samples.
+KNeighborsClassifier trained on 362 samples.
+KNeighborsClassifier trained on 3618 samples.
+KNeighborsClassifier trained on 36177 samples.
+AdaBoostClassifier trained on 362 samples.
+AdaBoostClassifier trained on 3618 samples.
+AdaBoostClassifier trained on 36177 samples.
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_png output_subarea ">
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+>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>{&#39;KNeighborsClassifier&#39;: {0: {&#39;pred_time&#39;: 0.4960000514984131, &#39;f_test&#39;: 0.59304818869202103, &#39;train_time&#39;: 0.0009999275207519531, &#39;acc_train&#39;: 0.8666666666666667, &#39;acc_test&#39;: 0.80464344941956878, &#39;f_train&#39;: 0.74999999999999989}, 1: {&#39;pred_time&#39;: 3.5820000171661377, &#39;f_test&#39;: 0.6270768571122306, &#39;train_time&#39;: 0.032000064849853516, &#39;acc_train&#39;: 0.85999999999999999, &#39;acc_test&#39;: 0.8180210060807076, &#39;f_train&#39;: 0.72368421052631571}, 2: {&#39;pred_time&#39;: 27.072999954223633, &#39;f_test&#39;: 0.63166816232924516, &#39;train_time&#39;: 1.7300000190734863, &#39;acc_train&#39;: 0.87333333333333329, &#39;acc_test&#39;: 0.82012161415146489, &#39;f_train&#39;: 0.75320512820512819}}, &#39;AdaBoostClassifier&#39;: {0: {&#39;pred_time&#39;: 0.07000017166137695, &#39;f_test&#39;: 0.61047338962147801, &#39;train_time&#39;: 0.1099998950958252, &#39;acc_train&#39;: 0.89666666666666661, &#39;acc_test&#39;: 0.81039248203427305, &#39;f_train&#39;: 0.81168831168831157}, 1: {&#39;pred_time&#39;: 0.07699990272521973, &#39;f_test&#39;: 0.7018820838099199, &#39;train_time&#39;: 0.21799993515014648, &#39;acc_train&#39;: 0.83999999999999997, &#39;acc_test&#39;: 0.84986180210060802, &#39;f_train&#39;: 0.68014705882352933}, 2: {&#39;pred_time&#39;: 0.07000017166137695, &#39;f_test&#39;: 0.72455089820359275, &#39;train_time&#39;: 1.678999900817871, &#39;acc_train&#39;: 0.84999999999999998, &#39;acc_test&#39;: 0.85760088446655613, &#39;f_train&#39;: 0.71153846153846156}}, &#39;DecisionTreeClassifier&#39;: {0: {&#39;pred_time&#39;: 0.0, &#39;f_test&#39;: 0.5187038764950378, &#39;train_time&#39;: 0.019999980926513672, &#39;acc_train&#39;: 1.0, &#39;acc_test&#39;: 0.76174682144831396, &#39;f_train&#39;: 1.0}, 1: {&#39;pred_time&#39;: 0.0, &#39;f_test&#39;: 0.60533669881907515, &#39;train_time&#39;: 0.019999980926513672, &#39;acc_train&#39;: 0.9966666666666667, &#39;acc_test&#39;: 0.80751796572692092, &#39;f_train&#39;: 0.99719101123595499}, 2: {&#39;pred_time&#39;: 0.007999897003173828, &#39;f_test&#39;: 0.62747080996598326, &#39;train_time&#39;: 0.3990001678466797, &#39;acc_train&#39;: 0.96999999999999997, &#39;acc_test&#39;: 0.81835268103924819, &#39;f_train&#39;: 0.96385542168674709}}}
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Improving-Results">Improving Results<a class="anchor-link" href="#Improving-Results">&#182;</a></h2><p>In this final section, you will choose from the three supervised learning models the <em>best</em> model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (<code>X_train</code> and <code>y_train</code>) by tuning at least one parameter to improve upon the untuned model's F-score.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-3---Choosing-the-Best-Model">Question 3 - Choosing the Best Model<a class="anchor-link" href="#Question-3---Choosing-the-Best-Model">&#182;</a></h3><p><em>Based on the evaluation you performed earlier, in one to two paragraphs, explain to </em>CharityML<em> which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \$50,000.</em><br>
+<strong>Hint:</strong> Your answer should include discussion of the metrics, prediction/training time, and the algorithm's suitability for the data.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> I believe AdaBosst is the best model for finding individuals who make above 50k USD. It is not the fastest trainer, but all models train below 2 seconds on the full datasets which I believe is acceptable, and prediction time is negligible. It has the best accuracy on the testing set, so you get better generalisation than KNN or Decision Tree. However, given that we have an unbalanced dataset, with only around 25% of people sampled earning above 50k, we need to also check the F score, given recall should influence our prediction, as it is important to measure how many of the true positives are found. Here AdaBoost does quite a bit better than the other models on the testing set. Further, it addresses the overfitting issue with the Decision Tree by training several of them (like basis splines) and weighting them according to the prediction accuracy they give.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-4---Describing-the-Model-in-Layman's-Terms">Question 4 - Describing the Model in Layman's Terms<a class="anchor-link" href="#Question-4---Describing-the-Model-in-Layman's-Terms">&#182;</a></h3><p><em>In one to two paragraphs, explain to </em>CharityML<em>, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical or technical jargon, such as describing equations or discussing the algorithm implementation.</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> AdaBoost is trained using in this case a Decision Tree on the dataset. A Decision Tree attempts to seperate a dataset into two classes (here income &gt;50, below 50k) by asking a serious of questions to its features, such as whether or not some is degree level educated. It will ask questions first that improve the dataset separation by the most.</p>
+<p>To run, AdaBoost will set the weight of each person the same initially. It will then calibrate a decision tree to that data and check how large the error is. Next it will iteratively increase the weights of people that it got wrong, and focus on getting those right in the next iteration. In contrast, people that it got right will have their weight reduced. Predictions are made using a weighted combination of all those fitted decision trees, where higher weight is given to those trees that have make more accurate predictions.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation:-Model-Tuning">Implementation: Model Tuning<a class="anchor-link" href="#Implementation:-Model-Tuning">&#182;</a></h3><p>Fine tune the chosen model. Use grid search (<code>GridSearchCV</code>) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:</p>
+<ul>
+<li>Import <a href="http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html"><code>sklearn.grid_search.GridSearchCV</code></a> and <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html"><code>sklearn.metrics.make_scorer</code></a>.</li>
+<li>Initialize the classifier you've chosen and store it in <code>clf</code>.<ul>
+<li>Set a <code>random_state</code> if one is available to the same state you set before.</li>
+</ul>
+</li>
+<li>Create a dictionary of parameters you wish to tune for the chosen model.<ul>
+<li>Example: <code>parameters = {'parameter' : [list of values]}</code>.</li>
+<li><strong>Note:</strong> Avoid tuning the <code>max_features</code> parameter of your learner if that parameter is available!</li>
+</ul>
+</li>
+<li>Use <code>make_scorer</code> to create an <code>fbeta_score</code> scoring object (with $\beta = 0.5$).</li>
+<li>Perform grid search on the classifier <code>clf</code> using the <code>'scorer'</code>, and store it in <code>grid_obj</code>.</li>
+<li>Fit the grid search object to the training data (<code>X_train</code>, <code>y_train</code>), and store it in <code>grid_fit</code>.</li>
+</ul>
+<p><strong>Note:</strong> Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[12]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import &#39;GridSearchCV&#39;, &#39;make_scorer&#39;, and any other necessary libraries</span>
+<span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span>
+
+
+
+<span class="n">dt_stump</span> <span class="o">=</span> <span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">min_samples_leaf</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">dt_stump</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">clf_knn</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+<span class="n">clf_knn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+<span class="c1"># TODO: Initialize the classifier</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">AdaBoostClassifier</span><span class="p">(</span><span class="n">base_estimator</span><span class="o">=</span><span class="n">dt_stump</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<span class="c1">#clf = AdaBoostClassifier(base_estimator=clf_knn, random_state=5)</span>
+
+<span class="c1">#clf = clf_knn</span>
+
+<span class="c1"># Create the parameters list you wish to tuned</span>
+<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_estimators&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">90</span><span class="p">]</span>
+    <span class="p">,</span> <span class="s2">&quot;learning_rate&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">,</span> <span class="mf">0.09</span><span class="p">]</span>
+    <span class="p">,</span> <span class="s2">&quot;base_estimator__max_depth&quot;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span> <span class="c1"># use __ to access nested parameters of sub classifier</span>
+    <span class="p">,</span> <span class="s2">&quot;base_estimator__min_samples_leaf&quot;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">90</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">900</span><span class="p">]</span>
+    <span class="c1">#, &quot;base_estimator__criterion&quot; : [&quot;gini&quot;, &quot;entropy&quot;]</span>
+    <span class="c1">#, &quot;base_estimator__splitter&quot; :  [&quot;best&quot;, &quot;random&quot;]</span>
+    
+    <span class="c1">#&quot;n_neighbors&quot;: [3, 5, 7, 10, 13, 15, 20]</span>
+    <span class="c1">#, &quot;weights&quot;: [&quot;uniform&quot;, &quot;distance&quot;]</span>
+    
+    <span class="c1">#&quot;base_estimator__n_neighbors&quot;: [3, 5, 7, 10, 13, 15, 20]</span>
+    
+    
+    
+
+<span class="p">}</span>
+
+<span class="c1"># TODO: Make an fbeta_score scoring object</span>
+<span class="n">scorer</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="c1"># check this, which is the right score?</span>
+<span class="n">stratifiedShuffling</span> <span class="o">=</span> <span class="bp">False</span>
+<span class="k">if</span> <span class="n">stratifiedShuffling</span><span class="p">:</span>
+    <span class="c1"># make sure test labels are evenly split labels between validation set, given dataset is unbalanced</span>
+    <span class="c1"># this ensure none of them has a large concentration of &gt;50k or &lt;=50k earners.</span>
+    <span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">StratifiedShuffleSplit</span>
+    <span class="n">cv</span> <span class="o">=</span> <span class="n">StratifiedShuffleSplit</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">grid_obj</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parameters</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scorer</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">)</span>
+
+    
+<span class="c1"># base case</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="c1"># TODO: Perform grid search on the classifier using &#39;scorer&#39; as the scoring method</span>
+    <span class="c1"># default uses 3 fold cross validation</span>
+    <span class="n">grid_obj</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parameters</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scorer</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+
+    
+
+<span class="c1"># TODO: Fit the grid search object to the training data and find the optimal parameters</span>
+<span class="n">grid_fit</span> <span class="o">=</span> <span class="n">grid_obj</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+<span class="c1"># Get the estimator</span>
+<span class="n">best_clf</span> <span class="o">=</span> <span class="n">grid_fit</span><span class="o">.</span><span class="n">best_estimator_</span>
+
+<span class="c1"># Make predictions using the unoptimized and optimised model</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">best_predictions</span> <span class="o">=</span> <span class="n">best_clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+<span class="c1"># Report the before-and-afterscores</span>
+<span class="k">print</span> <span class="s2">&quot;Unoptimized model</span><span class="se">\n</span><span class="s2">------&quot;</span>
+<span class="k">print</span> <span class="s2">&quot;Accuracy score on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
+<span class="k">print</span> <span class="s2">&quot;F-score on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
+<span class="k">print</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Optimized Model</span><span class="se">\n</span><span class="s2">------&quot;</span>
+<span class="k">print</span> <span class="s2">&quot;Final accuracy score on the testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">))</span>
+<span class="k">print</span> <span class="s2">&quot;Final F-score on the testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Fitting 3 folds for each of 525 candidates, totalling 1575 fits
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>[Parallel(n_jobs=8)]: Done   2 tasks      | elapsed:    2.0s
+[Parallel(n_jobs=8)]: Done   9 tasks      | elapsed:    7.5s
+[Parallel(n_jobs=8)]: Done  16 tasks      | elapsed:    8.3s
+[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:   10.3s
+[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:   11.6s
+[Parallel(n_jobs=8)]: Done  45 tasks      | elapsed:   13.3s
+[Parallel(n_jobs=8)]: Done  56 tasks      | elapsed:   15.7s
+[Parallel(n_jobs=8)]: Done  69 tasks      | elapsed:   18.0s
+[Parallel(n_jobs=8)]: Done  82 tasks      | elapsed:   20.3s
+[Parallel(n_jobs=8)]: Done  97 tasks      | elapsed:   22.6s
+[Parallel(n_jobs=8)]: Done 112 tasks      | elapsed:   25.4s
+[Parallel(n_jobs=8)]: Done 129 tasks      | elapsed:   28.1s
+[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   30.6s
+[Parallel(n_jobs=8)]: Done 165 tasks      | elapsed:   34.2s
+[Parallel(n_jobs=8)]: Done 184 tasks      | elapsed:   37.2s
+[Parallel(n_jobs=8)]: Done 205 tasks      | elapsed:   41.4s
+[Parallel(n_jobs=8)]: Done 226 tasks      | elapsed:   45.2s
+[Parallel(n_jobs=8)]: Done 249 tasks      | elapsed:   49.4s
+[Parallel(n_jobs=8)]: Done 272 tasks      | elapsed:   52.8s
+[Parallel(n_jobs=8)]: Done 297 tasks      | elapsed:   57.5s
+[Parallel(n_jobs=8)]: Done 322 tasks      | elapsed:  1.0min
+[Parallel(n_jobs=8)]: Done 349 tasks      | elapsed:  1.1min
+[Parallel(n_jobs=8)]: Done 376 tasks      | elapsed:  1.2min
+[Parallel(n_jobs=8)]: Done 405 tasks      | elapsed:  1.3min
+[Parallel(n_jobs=8)]: Done 434 tasks      | elapsed:  1.4min
+[Parallel(n_jobs=8)]: Done 465 tasks      | elapsed:  1.4min
+[Parallel(n_jobs=8)]: Done 496 tasks      | elapsed:  1.5min
+[Parallel(n_jobs=8)]: Done 529 tasks      | elapsed:  1.6min
+[Parallel(n_jobs=8)]: Done 562 tasks      | elapsed:  1.8min
+[Parallel(n_jobs=8)]: Done 597 tasks      | elapsed:  2.0min
+[Parallel(n_jobs=8)]: Done 632 tasks      | elapsed:  2.2min
+[Parallel(n_jobs=8)]: Done 669 tasks      | elapsed:  2.5min
+[Parallel(n_jobs=8)]: Done 706 tasks      | elapsed:  2.7min
+[Parallel(n_jobs=8)]: Done 745 tasks      | elapsed:  2.9min
+[Parallel(n_jobs=8)]: Done 784 tasks      | elapsed:  3.1min
+[Parallel(n_jobs=8)]: Done 825 tasks      | elapsed:  3.4min
+[Parallel(n_jobs=8)]: Done 866 tasks      | elapsed:  3.6min
+[Parallel(n_jobs=8)]: Done 909 tasks      | elapsed:  3.9min
+[Parallel(n_jobs=8)]: Done 952 tasks      | elapsed:  4.2min
+[Parallel(n_jobs=8)]: Done 997 tasks      | elapsed:  4.4min
+[Parallel(n_jobs=8)]: Done 1042 tasks      | elapsed:  4.7min
+[Parallel(n_jobs=8)]: Done 1089 tasks      | elapsed:  5.5min
+[Parallel(n_jobs=8)]: Done 1136 tasks      | elapsed:  6.3min
+[Parallel(n_jobs=8)]: Done 1185 tasks      | elapsed:  7.2min
+[Parallel(n_jobs=8)]: Done 1234 tasks      | elapsed:  8.1min
+[Parallel(n_jobs=8)]: Done 1285 tasks      | elapsed:  9.1min
+[Parallel(n_jobs=8)]: Done 1336 tasks      | elapsed: 10.0min
+[Parallel(n_jobs=8)]: Done 1389 tasks      | elapsed: 11.0min
+[Parallel(n_jobs=8)]: Done 1442 tasks      | elapsed: 11.7min
+[Parallel(n_jobs=8)]: Done 1497 tasks      | elapsed: 12.6min
+[Parallel(n_jobs=8)]: Done 1552 tasks      | elapsed: 13.2min
+[Parallel(n_jobs=8)]: Done 1575 out of 1575 | elapsed: 13.6min finished
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Unoptimized model
+------
+Accuracy score on testing data: 0.8576
+F-score on testing data: 0.7246
+
+Optimized Model
+------
+Final accuracy score on the testing data: 0.8651
+Final F-score on the testing data: 0.7448
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[34]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="n">score</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">grid_fit</span><span class="o">.</span><span class="n">grid_scores_</span><span class="p">)</span><span class="o">.</span><span class="n">mean_validation_score</span>
+<span class="n">params</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">grid_fit</span><span class="o">.</span><span class="n">grid_scores_</span><span class="p">)</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+<span class="n">resGrid</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">resGrid</span> <span class="o">=</span> <span class="n">resGrid</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;mean_validation_score&quot;</span><span class="p">],</span> <span class="n">ascending</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+
+
+<span class="kn">import</span> <span class="nn">datetime</span>
+<span class="n">st</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%A_</span><span class="si">%d</span><span class="s2">_%B_%Y_%I_%M%p&quot;</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="n">st</span><span class="p">)</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">importances = best_clf.feature_importances_</span>
+<span class="sd">indices = np.argsort(importances)[::-1]</span>
+<span class="sd">print(X_train.columns[indices[:5]])</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="c1">#print(pd.DataFrame(best_clf.feature_importances_))</span>
+<span class="n">clf_name</span> <span class="o">=</span> <span class="n">best_clf</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> 
+<span class="c1"># create excel sheet of parameter grid</span>
+<span class="n">writer</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">ExcelWriter</span><span class="p">(</span>
+        <span class="n">clf_name</span> <span class="o">+</span> <span class="s1">&#39;resGrid.xlsx&#39;</span>
+     <span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;xlsxwriter&quot;</span>     
+    <span class="p">)</span>
+ 
+<span class="n">pd</span><span class="o">.</span><span class="n">formats</span><span class="o">.</span><span class="n">format</span><span class="o">.</span><span class="n">header_style</span> <span class="o">=</span> <span class="bp">None</span>    
+ 
+<span class="n">resGrid</span><span class="o">.</span><span class="n">to_excel</span><span class="p">(</span><span class="n">writer</span><span class="p">,</span> <span class="n">sheet_name</span><span class="o">=</span><span class="s2">&quot;elem&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+<span class="n">workbook</span> <span class="o">=</span> <span class="n">writer</span><span class="o">.</span><span class="n">book</span>
+<span class="n">worksheet</span> <span class="o">=</span> <span class="n">writer</span><span class="o">.</span><span class="n">sheets</span><span class="p">[</span><span class="s2">&quot;elem&quot;</span><span class="p">]</span>
+<span class="n">formatObject</span> <span class="o">=</span> <span class="n">workbook</span><span class="o">.</span><span class="n">add_format</span><span class="p">()</span>
+<span class="n">formatObject</span><span class="o">.</span><span class="n">set_text_wrap</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">formatObject</span><span class="o">.</span><span class="n">set_bold</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="n">worksheet</span><span class="o">.</span><span class="n">set_column</span><span class="p">(</span><span class="s2">&quot;A:F&quot;</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+<span class="n">worksheet</span><span class="o">.</span><span class="n">set_row</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="n">formatObject</span><span class="p">)</span>
+ 
+<span class="n">writer</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
+
+<span class="c1">#open excel</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">win32com.client</span>
+
+<span class="n">cwd</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\\</span><span class="s2">&quot;</span>
+
+<span class="n">xl</span><span class="o">=</span><span class="n">win32com</span><span class="o">.</span><span class="n">client</span><span class="o">.</span><span class="n">Dispatch</span><span class="p">(</span><span class="s2">&quot;Excel.Application&quot;</span><span class="p">)</span>
+<span class="n">xl</span><span class="o">.</span><span class="n">Visible</span> <span class="o">=</span> <span class="bp">True</span>
+<span class="n">xl</span><span class="o">.</span><span class="n">Workbooks</span><span class="o">.</span><span class="n">Open</span><span class="p">(</span><span class="n">Filename</span><span class="o">=</span><span class="n">cwd</span><span class="o">+</span><span class="n">clf_name</span><span class="o">+</span><span class="s2">&quot;resGrid.xlsx&quot;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Monday_05_June_2017_09_33PM
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt output_prompt">Out[34]:</div>
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>&lt;COMObject Open&gt;</pre>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[35]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># close any excel sheets without asking questions and quit excel</span>
+<span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">book</span><span class="p">:</span> <span class="n">book</span><span class="o">.</span><span class="n">Close</span><span class="p">(</span><span class="bp">False</span><span class="p">),</span> <span class="n">xl</span><span class="o">.</span><span class="n">Workbooks</span><span class="p">)</span>
+<span class="n">xl</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-5---Final-Model-Evaluation">Question 5 - Final Model Evaluation<a class="anchor-link" href="#Question-5---Final-Model-Evaluation">&#182;</a></h3><p><em>What is your optimized model's accuracy and F-score on the testing data? Are these scores better or worse than the unoptimized model? How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in <strong>Question 1</strong>?</em><br>
+<strong>Note:</strong> Fill in the table below with your results, and then provide discussion in the <strong>Answer</strong> box.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="Results:">Results:<a class="anchor-link" href="#Results:">&#182;</a></h4><table>
+<thead><tr>
+<th style="text-align:center">Metric</th>
+<th style="text-align:center">Benchmark Predictor</th>
+<th style="text-align:center">Unoptimized Model</th>
+<th style="text-align:center">Optimized Model</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:center">Accuracy Score</td>
+<td style="text-align:center">0.2917</td>
+<td style="text-align:center">0.8576</td>
+<td style="text-align:center">0.8687</td>
+</tr>
+<tr>
+<td style="text-align:center">F-score</td>
+<td style="text-align:center">0.2478</td>
+<td style="text-align:center">0.7246</td>
+<td style="text-align:center">0.7430</td>
+</tr>
+</tbody>
+</table>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer: </strong> The optimised model's scores are better than the benchmark and the unoptimised scores. Accuracy improves beyond just predicting "&gt;50k" for all points, and especially the F-Score improves, showing the model is adding value. However, i believe that the benchmark predictor does not illustrate the issue of unbalanced datasets well, as we will see a large improvement by using as benchmark the prediction of the less frequent part of the dataset across the entire dataset. However, if we were to predict the more frequent part as a benchmark, i believe it would be a lot more difficult to achieve such performance gains, given we have already 75% accuracy.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[24]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="c1"># confusion matrix best</span>
+<span class="n">pred</span> <span class="o">=</span> <span class="n">best_clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">),</span> <span class="n">annot</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span> <span class="n">fmt</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>C:\Anaconda3\envs\udacity\lib\site-packages\IPython\html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
+  &#34;`IPython.html.widgets` has moved to `ipywidgets`.&#34;, ShimWarning)
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+<div class="prompt output_prompt">Out[24]:</div>
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x9b297b8&gt;</pre>
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+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Feature-Importance">Feature Importance<a class="anchor-link" href="#Feature-Importance">&#182;</a></h2><p>An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \$50,000.</p>
+<p>Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a <code>feature_importance_</code> attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-6---Feature-Relevance-Observation">Question 6 - Feature Relevance Observation<a class="anchor-link" href="#Question-6---Feature-Relevance-Observation">&#182;</a></h3><p>When <strong>Exploring the Data</strong>, it was shown there are thirteen available features for each individual on record in the census data.<br>
+<em>Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> I believe the important features would be:</p>
+<ul>
+<li>Capital Gain - most important as a large capital gain can provide income for years to come, and reduces reliance on intelligence, hard work or biological properties like race or attractiveness.</li>
+<li>Age - also important as usually people tend to get better at what they do with age.</li>
+<li>Education Level - fairly good predictor as it can replace experience and is sometimes a proxy for family wealth which would have a positive effect on income.</li>
+<li>Work class - private sector pays better</li>
+<li>race - biological attributes might provide some noise</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Implementation---Extracting-Feature-Importance">Implementation - Extracting Feature Importance<a class="anchor-link" href="#Implementation---Extracting-Feature-Importance">&#182;</a></h3><p>Choose a <code>scikit-learn</code> supervised learning algorithm that has a <code>feature_importance_</code> attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.</p>
+<p>In the code cell below, you will need to implement the following:</p>
+<ul>
+<li>Import a supervised learning model from sklearn if it is different from the three used earlier.</li>
+<li>Train the supervised model on the entire training set.</li>
+<li>Extract the feature importances using <code>'.feature_importances_'</code>.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[28]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import a supervised learning model that has &#39;feature_importances_&#39;</span>
+
+<span class="n">clfAda</span> <span class="o">=</span> <span class="n">AdaBoostClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+
+<span class="c1"># TODO: Train the supervised model on the training set </span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">clfAda</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+<span class="c1"># TODO: Extract the feature importances</span>
+<span class="n">importances</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">feature_importances_</span>
+
+<span class="c1"># best fit features</span>
+<span class="n">best_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="c1">#importances = best_clf.feature_importances_</span>
+
+<span class="c1"># interesting - my best fit has a different feature order - it includes marital status! A hidden feature.</span>
+<span class="c1"># other ways of feature selection: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection</span>
+
+
+<span class="c1">#print(importances.shape)</span>
+<span class="c1"># importances show the contribution of each feature to the model.</span>
+<span class="c1"># There are 103 features, as we did one hot encoding on the enums.</span>
+
+<span class="c1"># Plot</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">feature_plot</span><span class="p">(</span><span class="n">importances</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
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+<div class="output_area">
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+
+
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+<h3 id="Question-7---Extracting-Feature-Importance">Question 7 - Extracting Feature Importance<a class="anchor-link" href="#Question-7---Extracting-Feature-Importance">&#182;</a></h3><p>Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \$50,000.<br>
+<em>How do these five features compare to the five features you discussed in <strong>Question 6</strong>? If you were close to the same answer, how does this visualization confirm your thoughts? If you were not close, why do you think these features are more relevant?</em></p>
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+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> It seems that race is not even listed, and age is the most prominent feature. However, it does seem right that capital gain and education are both fairly important as expected. Interesting is that Age dominates compared to capital gain, but i suppose this makes sense as an income as low as 50k can be achieved irrespective of capital gain, education and other predictors if you live long enough. I would suspect capital gain and loss taking over at higher incomes.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Feature-Selection">Feature Selection<a class="anchor-link" href="#Feature-Selection">&#182;</a></h3><p>How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of <strong>all</strong> features present in the data. This hints that we can attempt to <em>reduce the feature space</em> and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set <em>with only the top five important features</em>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[29]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import functionality for cloning a model</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">clone</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reduce the feature space - could also use PCA for this,</span>
+<span class="sd">    in order to reduce dimensionality while maintaining other features&#39; information</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">X_train_reduced</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">X_train</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">importances</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[:</span><span class="mi">5</span><span class="p">]]]</span>
+<span class="n">X_test_reduced</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[</span><span class="n">X_test</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">importances</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[:</span><span class="mi">5</span><span class="p">]]]</span>
+
+<span class="c1"># Train on the &quot;best&quot; model found from grid search earlier</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="p">(</span><span class="n">clone</span><span class="p">(</span><span class="n">best_clf</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_reduced</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+<span class="c1"># Make new predictions</span>
+<span class="n">reduced_predictions</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_reduced</span><span class="p">)</span>
+
+<span class="c1"># Report scores from the final model using both versions of data</span>
+<span class="k">print</span> <span class="s2">&quot;Final Model trained on full data</span><span class="se">\n</span><span class="s2">------&quot;</span>
+<span class="k">print</span> <span class="s2">&quot;Accuracy on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">))</span>
+<span class="k">print</span> <span class="s2">&quot;F-score on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
+<span class="k">print</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Final Model trained on reduced data</span><span class="se">\n</span><span class="s2">------&quot;</span>
+<span class="k">print</span> <span class="s2">&quot;Accuracy on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">reduced_predictions</span><span class="p">))</span>
+<span class="k">print</span> <span class="s2">&quot;F-score on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">reduced_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Final Model trained on full data
+------
+Accuracy on testing data: 0.8651
+F-score on testing data: 0.7448
+
+Final Model trained on reduced data
+------
+Accuracy on testing data: 0.8420
+F-score on testing data: 0.7020
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-8---Effects-of-Feature-Selection">Question 8 - Effects of Feature Selection<a class="anchor-link" href="#Question-8---Effects-of-Feature-Selection">&#182;</a></h3><p><em>How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?</em><br>
+<em>If training time was a factor, would you consider using the reduced data as your training set?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> The final model's accuracy is 0.8433 and its F score is 0.7032. This is only a little bit lower than the metrics for the model trained on the full data. If training time was a factor i would consider reducing features, as the accuracy and f score are almost unchanged. It would depend how expensive it would be to get a few classifications wrong, in terms of letters sent out.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
+<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+
+ 
+
+
+</html>
diff --git a/finding_donors/finding_donors.ipynb b/finding_donors/finding_donors.ipynb
new file mode 100644
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+++ b/finding_donors/finding_donors.ipynb
@@ -0,0 +1,1382 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning Engineer Nanodegree\n",
+    "## Supervised Learning\n",
+    "## Project: Finding Donors for *CharityML*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
+    "\n",
+    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
+    "\n",
+    ">**Note:** Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting Started\n",
+    "\n",
+    "In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. \n",
+    "\n",
+    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## Exploring the Data\n",
+    "Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>education_level</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>marital-status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>native-country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>39</td>\n",
+       "      <td>State-gov</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>2174.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>50</td>\n",
+       "      <td>Self-emp-not-inc</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          workclass education_level  education-num       marital-status  \\\n",
+       "0   39          State-gov       Bachelors           13.0        Never-married   \n",
+       "1   50   Self-emp-not-inc       Bachelors           13.0   Married-civ-spouse   \n",
+       "2   38            Private         HS-grad            9.0             Divorced   \n",
+       "\n",
+       "           occupation    relationship    race    sex  capital-gain  \\\n",
+       "0        Adm-clerical   Not-in-family   White   Male        2174.0   \n",
+       "1     Exec-managerial         Husband   White   Male           0.0   \n",
+       "2   Handlers-cleaners   Not-in-family   White   Male           0.0   \n",
+       "\n",
+       "   capital-loss  hours-per-week  native-country income  \n",
+       "0           0.0            40.0   United-States  <=50K  \n",
+       "1           0.0            13.0   United-States  <=50K  \n",
+       "2           0.0            40.0   United-States  <=50K  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import libraries necessary for this project\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from time import time\n",
+    "from IPython.display import display # Allows the use of display() for DataFrames\n",
+    "\n",
+    "# Import supplementary visualization code visuals.py\n",
+    "import visuals as vs\n",
+    "\n",
+    "# Pretty display for notebooks\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Load the Census dataset\n",
+    "data = pd.read_csv(\"census.csv\")\n",
+    "\n",
+    "# Success - Display the first record\n",
+    "display(data.head(n=3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Data Exploration\n",
+    "A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \\$50,000. In the code cell below, you will need to compute the following:\n",
+    "- The total number of records, `'n_records'`\n",
+    "- The number of individuals making more than \\$50,000 annually, `'n_greater_50k'`.\n",
+    "- The number of individuals making at most \\$50,000 annually, `'n_at_most_50k'`.\n",
+    "- The percentage of individuals making more than \\$50,000 annually, `'greater_percent'`.\n",
+    "\n",
+    "**Hint:** You may need to look at the table above to understand how the `'income'` entries are formatted. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of records: 45222\n",
+      "Individuals making more than $50,000: 11208\n",
+      "Individuals making at most $50,000: 34014\n",
+      "Percentage of individuals making more than $50,000: 24.78%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Total number of records\n",
+    "n_records = len(data)\n",
+    "\n",
+    "# TODO: Number of records where individual's income is more than $50,000\n",
+    "n_greater_50k = len(data[data[\"income\"] == \">50K\"])\n",
+    "\n",
+    "# TODO: Number of records where individual's income is at most $50,000\n",
+    "n_at_most_50k = len(data[data[\"income\"] == \"<=50K\"])\n",
+    "\n",
+    "# TODO: Percentage of individuals whose income is more than $50,000\n",
+    "greater_percent = 100 * len(data[data[\"income\"] == \">50K\"]) / float(len(data))\n",
+    "\n",
+    "# Print the results\n",
+    "print \"Total number of records: {}\".format(n_records)\n",
+    "print \"Individuals making more than $50,000: {}\".format(n_greater_50k)\n",
+    "print \"Individuals making at most $50,000: {}\".format(n_at_most_50k)\n",
+    "print \"Percentage of individuals making more than $50,000: {:.2f}%\".format(greater_percent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## Preparing the Data\n",
+    "Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as **preprocessing**. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transforming Skewed Continuous Features\n",
+    "A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '`capital-gain'` and `'capital-loss'`. \n",
+    "\n",
+    "Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KK9Az9TNgY+DmUsqEJ9fUffT1Uspn4Zb7Jv6W5fcIwMT7dnCiv0nn/XB/y6ny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\n2gQEAADQJiAAAIA2AQEAALQJCAAAoO0Ga7UEP7pgqRcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb54d320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Split the data into features and target label\n",
+    "income_raw = data['income']\n",
+    "features_raw = data.drop('income', axis = 1)\n",
+    "\n",
+    "# Visualize skewed continuous features of original data\n",
+    "vs.distribution(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For highly-skewed feature distributions such as `'capital-gain'` and `'capital-loss'`, it is common practice to apply a <a href=\"https://en.wikipedia.org/wiki/Data_transformation_(statistics)\">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of `0` is undefined, so we must translate the values by a small amount above `0` to apply the the logarithm successfully.\n",
+    "\n",
+    "Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CibbVdOff1H3BZGKXUM0CszA/9KOcZs48o/a134XSBWsx8JuIeEYtNv4J2I7S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\nlt95AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb6a6ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Log-transform the skewed features\n",
+    "skewed = ['capital-gain', 'capital-loss']\n",
+    "features_raw[skewed] = data[skewed].apply(lambda x: np.log(x + 1)) # applies column wise, incr x by 1\n",
+    "\n",
+    "# moves values closer together so algo doesnt get confused\n",
+    "\n",
+    "# Visualize the new log distributions\n",
+    "vs.distribution(features_raw, transformed = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Normalizing Numerical Features\n",
+    "In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `'capital-gain'` or `'capital-loss'` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.\n",
+    "\n",
+    "Run the code cell below to normalize each numerical feature. We will use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) for this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>education_level</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>marital-status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>native-country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.30137</td>\n",
+       "      <td>State-gov</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.02174</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.397959</td>\n",
+       "      <td>United-States</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       age   workclass education_level  education-num  marital-status  \\\n",
+       "0  0.30137   State-gov       Bachelors            0.8   Never-married   \n",
+       "\n",
+       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
+       "0   Adm-clerical   Not-in-family   White   Male       0.02174           0.0   \n",
+       "\n",
+       "   hours-per-week  native-country  \n",
+       "0        0.397959   United-States  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import sklearn.preprocessing.StandardScaler\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "# Initialize a scaler, then apply it to the features\n",
+    "scaler = MinMaxScaler()\n",
+    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
+    "features_raw[numerical] = scaler.fit_transform(data[numerical])\n",
+    "\n",
+    "# Show an example of a record with scaling applied\n",
+    "display(features_raw.head(n = 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Data Preprocessing\n",
+    "\n",
+    "From the table in **Exploring the Data** above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called *categorical variables*) be converted. One popular way to convert categorical variables is by using the **one-hot encoding** scheme. One-hot encoding creates a _\"dummy\"_ variable for each possible category of each non-numeric feature. For example, assume `someFeature` has three possible entries: `A`, `B`, or `C`. We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`.\n",
+    "\n",
+    "|   | someFeature |                    | someFeature_A | someFeature_B | someFeature_C |\n",
+    "| :-: | :-: |                            | :-: | :-: | :-: |\n",
+    "| 0 |  B  |  | 0 | 1 | 0 |\n",
+    "| 1 |  C  | ----> one-hot encode ----> | 0 | 0 | 1 |\n",
+    "| 2 |  A  |  | 1 | 0 | 0 |\n",
+    "\n",
+    "Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:\n",
+    " - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_raw'` data.\n",
+    " - Convert the target label `'income_raw'` to numerical entries.\n",
+    "   - Set records with \"<=50K\" to `0` and records with \">50K\" to `1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('income one hot', array([0, 0, 0, ..., 0, 0, 1], dtype=int64))\n",
+      "('income rev transformed', array(['<=50K', '<=50K', '<=50K', ..., '<=50K', '<=50K', '>50K'], dtype=object))\n",
+      "103 total features after one-hot encoding.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: One-hot encode the 'features_raw' data using pandas.get_dummies()\n",
+    "features = pd.get_dummies(features_raw) # creates a non sparse matrix\n",
+    "\n",
+    "# TODO: Encode the 'income_raw' data to numerical values\n",
+    "income = income_raw.map({'<=50K':0, '>50K':1})\n",
+    "\n",
+    "# or, good for multiple classes. Also provides easy reverse transform.\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le = LabelEncoder()\n",
+    "income = le.fit_transform(income_raw)\n",
+    "print(\"income one hot\", income)\n",
+    "print(\"income rev transformed\", le.inverse_transform(income))\n",
+    "\n",
+    "# just be careful: it gives unecessary order to things.\n",
+    "\n",
+    "# Print the number of features after one-hot encoding\n",
+    "encoded = list(features.columns)\n",
+    "print \"{} total features after one-hot encoding.\".format(len(encoded))\n",
+    "\n",
+    "# Uncomment the following line to see the encoded feature names\n",
+    "#print encoded\n",
+    "#print(income.head(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shuffle and Split Data\n",
+    "Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.\n",
+    "\n",
+    "Run the code cell below to perform this split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set has 36177 samples.\n",
+      "Testing set has 9045 samples.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import train_test_split\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "\n",
+    "# Split the 'features' and 'income' data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features, income, test_size = 0.2, random_state = 0)\n",
+    "\n",
+    "# Show the results of the split\n",
+    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
+    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## Evaluating Model Performance\n",
+    "In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a *naive predictor*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metrics and the Naive Predictor\n",
+    "*CharityML*, equipped with their research, knows individuals that make more than \\$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \\$50,000 accurately. It would seem that using **accuracy** as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that *does not* make more than \\$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \\$50,000 is *more important* than the model's ability to **recall** those individuals. We can use **F-beta score** as a metric that considers both precision and recall:\n",
+    "\n",
+    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n",
+    "\n",
+    "In particular, when $\\beta = 0.5$, more emphasis is placed on precision. This is called the **F$_{0.5}$ score** (or F-score for simplicity).\n",
+    "\n",
+    "Looking at the distribution of classes (those who make at most \\$50,000, and those who make more), it's clear most individuals do not make more than \\$50,000. This can greatly affect **accuracy**, since we could simply say *\"this person does not make more than \\$50,000\"* and generally be right, without ever looking at the data! Making such a statement would be called **naive**, since we have not considered any information to substantiate the claim. It is always important to consider the *naive prediction* for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \\$50,000, *CharityML* would identify no one as donors. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1 - Naive Predictor Performace\n",
+    "*If we chose a model that always predicted an individual made more than \\$50,000, what would that model's accuracy and F-score be on this dataset?*  \n",
+    "**Note:** You must use the code cell below and assign your results to `'accuracy'` and `'fscore'` to be used later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#from sklearn.metrics import accuracy_score, recall_score\n",
+    "import numpy as np\n",
+    "\n",
+    "income_all_true = pd.Series(np.ones((len(income),), dtype=np.int))\n",
+    "#print(income_all_true.head())\n",
+    "#print(income.head())\n",
+    "\n",
+    "# TODO: Calculate accuracy\n",
+    "#accuracy = accuracy_score(income, income_all_true, normalize=True, sample_weight=None) # normalise=True means show percentage\n",
+    "#recall = recall_score(income, income_all_true)\n",
+    "\n",
+    "income_pred_naive = income_all_true\n",
+    "\n",
+    "# by hand\n",
+    "tp = sum(income==income_pred_naive)\n",
+    "fp = sum(income_pred_naive) - tp\n",
+    "fn = sum(income) - tp\n",
+    "accuracy = tp / float(tp + fp)\n",
+    "recall = tp / float(tp + fn)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# TODO: Calculate F-score using the formula above for beta = 0.5\n",
+    "beta = 0.5\n",
+    "fscore = (1+beta**2.0) * (accuracy * recall) / float((beta**2.0 * accuracy) + recall)\n",
+    "\n",
+    "\n",
+    "# Print the results \n",
+    "print \"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore)\n",
+    "\n",
+    "# accuracy matches the share of >50k incomes, if you predict true always."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Supervised Learning Models\n",
+    "**The following supervised learning models are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **that you may choose from:**\n",
+    "- Gaussian Naive Bayes (GaussianNB)\n",
+    "- Decision Trees\n",
+    "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n",
+    "- K-Nearest Neighbors (KNeighbors)\n",
+    "- Stochastic Gradient Descent Classifier (SGDC)\n",
+    "- Support Vector Machines (SVM)\n",
+    "- Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2 - Model Application\n",
+    "List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen\n",
+    "- *Describe one real-world application in industry where the model can be applied.* (You may need to do research for this — give references!)\n",
+    "- *What are the strengths of the model; when does it perform well?*\n",
+    "- *What are the weaknesses of the model; when does it perform poorly?*\n",
+    "- *What makes this model a good candidate for the problem, given what you know about the data?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** The three supervised learning models are Decision Tree, KNN and AdaBoost with Decision Tree as the weak learner. \n",
+    "\n",
+    "- Decision trees are applied in finance, for example, where they have been used to model investment decision making: http://www.cfapubs.org/doi/pdf/10.2469/faj.v50.n6.75. In this paper, they are considered superior to a weighted combination of features, as they more closely model the thinking of a portfolio manager when selecting securities. Decision Trees are helpful as they can visualise the path taken to arrive at a classification. They perform well on datasets with nominal, ordinal, interval and ratio data instead of just one of the types. The weaknesses are that they can overfit if too many nodes are allowed, and that they perform poorly on unbalanced datasets, such as ours. However, we can address this by tweaking the fbeta score to give more emphasis to accuracy and checking our final accuracy against the accuracy by predicting the same outcome every time. We can also limit the number of nodes allowed, or set the minimum examples required per leaf to stop the tree from growing too deep and overfitting.\n",
+    "\n",
+    "\n",
+    "- KNN. An industry application is concept searching. https://www.kcura.com/relativity/Portals/0/Documents/8.0%20Documentation%20Help%20Site/Content/Features/Analytics/Concept%20searching.htm. Based on a search query, Knn will find documents that contain a similar topic, rather than exact word matches. It works by setting up a feature space of related words, and checking for nearest neighbours in that space. Strengths are that it is non parametric so can fit any decision boundary. Further, it is resistant to correlated attributes, which our dataset may have. Overfitting is not a problem, as it finds locally optimal solutions for points that do not affect points elsewhere through any global parameters. We also don't have any missing data, so KNN should work well. Weaknesses are that all features are equally important. This assumption is ok, as we dont have an a priori idea what features matter in our dataset. Further, it is not obvious what K should be, so we would have to tune this.\n",
+    "https://www.quora.com/Classification-machine-learning-When-should-I-use-a-K-NN-classifier-over-a-Naive-Bayes-classifier\n",
+    "\n",
+    "\n",
+    "- Ada Boost is applied in industry to predict whether customers might leave and preventatively offer them enticements to stay. https://www.cs.rit.edu/~rlaz/PatternRecognition/slides/churn_adaboost.pdf. Advantages are that instead of trying to find a a strong model that fits the weight distribution of the entire dataset we only have to find weak learners, i.e a model that performs better than chance. Each subsequent iteration will apply a weak learner to a slightly changed distribution, increasing the weights of examples we got wrong. We will also have continuous information gain, as each learner will improve on the previous. We also dont need any prior knowledge of the dataset, due to information gain guaranteed by the weak learner. Weaknesses are that a weak learner needs to exist for AdaBoost to work. A weak learner that is too complex, such a huge neural network, can lead to overfitting. It also seems that AdaBoost is sensitive to uniformly distributed noise (http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf). For our problem, we will use AdaBoost with a Decision Tree as the weak learner. By controlling leafs and node splits, as above, we should get an improved solution to using just a Decision tree, as we can focus on difficult examples.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation - Creating a Training and Predicting Pipeline\n",
+    "To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.\n",
+    "In the code block below, you will need to implement the following:\n",
+    " - Import `fbeta_score` and `accuracy_score` from [`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics).\n",
+    " - Fit the learner to the sampled training data and record the training time.\n",
+    " - Perform predictions on the test data `X_test`, and also on the first 300 training points `X_train[:300]`.\n",
+    "   - Record the total prediction time.\n",
+    " - Calculate the accuracy score for both the training subset and testing set.\n",
+    " - Calculate the F-score for both the training subset and testing set.\n",
+    "   - Make sure that you set the `beta` parameter!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import two metrics from sklearn - fbeta_score and accuracy_score\n",
+    "from sklearn.metrics import fbeta_score, accuracy_score\n",
+    "\n",
+    "# a generic learning function for any algo\n",
+    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
+    "    '''\n",
+    "    inputs:\n",
+    "       - learner: the learning algorithm to be trained and predicted on\n",
+    "       - sample_size: the size of samples (number) to be drawn from training set\n",
+    "       - X_train: features training set\n",
+    "       - y_train: income training set\n",
+    "       - X_test: features testing set\n",
+    "       - y_test: income testing set\n",
+    "    '''\n",
+    "    \n",
+    "    results = {}\n",
+    "    \n",
+    "    # Fit the learner to the training data using slicing with 'sample_size'\n",
+    "    start = time() # Get start time\n",
+    "    # we want the same samples every time, not shuffled, so we can compare the learners.    \n",
+    "    learner = learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
+    "    end = time() # Get end time\n",
+    "    \n",
+    "    # Calculate the training time\n",
+    "    results['train_time'] = end-start\n",
+    "        \n",
+    "    # Get the predictions on the test set,\n",
+    "    #       then get predictions on the first 300 training samples\n",
+    "    start = time() # Get start time\n",
+    "    predictions_test = learner.predict(X_test)\n",
+    "    predictions_train = learner.predict(X_train[:300])\n",
+    "    end = time() # Get end time\n",
+    "    \n",
+    "    # Calculate the total prediction time\n",
+    "    results['pred_time'] = end-start\n",
+    "            \n",
+    "    # Compute accuracy on the first 300 training samples\n",
+    "    results['acc_train'] = accuracy_score(y_pred=predictions_train, y_true=y_train[:300])\n",
+    "        \n",
+    "    # Compute accuracy on test set\n",
+    "    results['acc_test'] = accuracy_score(y_pred=predictions_test, y_true=y_test)\n",
+    "    \n",
+    "    beta = 0.5 # place more emphasis on precision, as there are very \">50k\" examples in the dataset.\n",
+    "    \n",
+    "    # Compute F-score on the the first 300 training samples\n",
+    "    results['f_train'] = fbeta_score(y_true=y_train[:300], y_pred=predictions_train, beta=beta)\n",
+    "        \n",
+    "    # Compute F-score on the test set\n",
+    "    results['f_test'] = fbeta_score(y_true=y_test, y_pred=predictions_test, beta=beta)\n",
+    "       \n",
+    "    # Success\n",
+    "    print \"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size)\n",
+    "        \n",
+    "    # Return the results\n",
+    "    return results\n",
+    "\n",
+    "print(int(round(len([1,1,1,1])*0.63)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Initial Model Evaluation\n",
+    "In the code cell, you will need to implement the following:\n",
+    "- Import the three supervised learning models you've discussed in the previous section.\n",
+    "- Initialize the three models and store them in `'clf_A'`, `'clf_B'`, and `'clf_C'`.\n",
+    "  - Use a `'random_state'` for each model you use, if provided.\n",
+    "  - **Note:** Use the default settings for each model — you will tune one specific model in a later section.\n",
+    "- Calculate the number of records equal to 1%, 10%, and 100% of the training data.\n",
+    "  - Store those values in `'samples_1'`, `'samples_10'`, and `'samples_100'` respectively.\n",
+    "\n",
+    "**Note:** Depending on which algorithms you chose, the following implementation may take some time to run!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DecisionTreeClassifier trained on 362 samples.\n",
+      "DecisionTreeClassifier trained on 3618 samples.\n",
+      "DecisionTreeClassifier trained on 36177 samples.\n",
+      "KNeighborsClassifier trained on 362 samples.\n",
+      "KNeighborsClassifier trained on 3618 samples.\n",
+      "KNeighborsClassifier trained on 36177 samples.\n",
+      "AdaBoostClassifier trained on 362 samples.\n",
+      "AdaBoostClassifier trained on 3618 samples.\n",
+      "AdaBoostClassifier trained on 36177 samples.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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zzz8f5OjoWMvZ2bnWs88+G7J///48Iaiw/HwSYAGCKVVGjRrlNWTIkIrTpk2L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q+fV9U97UVXnwJ2Wcu6S6tqUVUtWrBgwWReTlKHqurtwPY0s8vuC/w4yXuTbDfQwCQNVJIjgIOB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\nCEmSJEl9s4CQJEmS1Lf/D/FCACf0YnbeAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb880198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'KNeighborsClassifier': {0: {'pred_time': 0.4960000514984131, 'f_test': 0.59304818869202103, 'train_time': 0.0009999275207519531, 'acc_train': 0.8666666666666667, 'acc_test': 0.80464344941956878, 'f_train': 0.74999999999999989}, 1: {'pred_time': 3.5820000171661377, 'f_test': 0.6270768571122306, 'train_time': 0.032000064849853516, 'acc_train': 0.85999999999999999, 'acc_test': 0.8180210060807076, 'f_train': 0.72368421052631571}, 2: {'pred_time': 27.072999954223633, 'f_test': 0.63166816232924516, 'train_time': 1.7300000190734863, 'acc_train': 0.87333333333333329, 'acc_test': 0.82012161415146489, 'f_train': 0.75320512820512819}}, 'AdaBoostClassifier': {0: {'pred_time': 0.07000017166137695, 'f_test': 0.61047338962147801, 'train_time': 0.1099998950958252, 'acc_train': 0.89666666666666661, 'acc_test': 0.81039248203427305, 'f_train': 0.81168831168831157}, 1: {'pred_time': 0.07699990272521973, 'f_test': 0.7018820838099199, 'train_time': 0.21799993515014648, 'acc_train': 0.83999999999999997, 'acc_test': 0.84986180210060802, 'f_train': 0.68014705882352933}, 2: {'pred_time': 0.07000017166137695, 'f_test': 0.72455089820359275, 'train_time': 1.678999900817871, 'acc_train': 0.84999999999999998, 'acc_test': 0.85760088446655613, 'f_train': 0.71153846153846156}}, 'DecisionTreeClassifier': {0: {'pred_time': 0.0, 'f_test': 0.5187038764950378, 'train_time': 0.019999980926513672, 'acc_train': 1.0, 'acc_test': 0.76174682144831396, 'f_train': 1.0}, 1: {'pred_time': 0.0, 'f_test': 0.60533669881907515, 'train_time': 0.019999980926513672, 'acc_train': 0.9966666666666667, 'acc_test': 0.80751796572692092, 'f_train': 0.99719101123595499}, 2: {'pred_time': 0.007999897003173828, 'f_test': 0.62747080996598326, 'train_time': 0.3990001678466797, 'acc_train': 0.96999999999999997, 'acc_test': 0.81835268103924819, 'f_train': 0.96385542168674709}}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Import the three supervised learning models from sklearn\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "\n",
+    "# TODO: Initialize the three models\n",
+    "clf_A = DecisionTreeClassifier(random_state=5)\n",
+    "#clf_B = SVC(random_state=5)\n",
+    "clf_B = KNeighborsClassifier()\n",
+    "clf_C = AdaBoostClassifier(random_state=5)\n",
+    "#clf_A = GaussianNB()\n",
+    "#clf_A = LogisticRegression(random_state=5)\n",
+    "\n",
+    "# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
+    "samples_1 = int(round(len(y_train) * 0.01))\n",
+    "samples_10 = int(round(len(y_train) * 0.1))\n",
+    "samples_100 = int(round(len(y_train) * 1))\n",
+    "\n",
+    "# Collect results on the learners\n",
+    "results = {}\n",
+    "for clf in [clf_A, clf_B, clf_C]:\n",
+    "    clf_name = clf.__class__.__name__\n",
+    "    results[clf_name] = {}\n",
+    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
+    "        \n",
+    "        # bundles up useful algo metrics\n",
+    "        results[clf_name][i] = \\\n",
+    "        train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
+    "\n",
+    "# Run metrics visualization for the three supervised learning models chosen\n",
+    "vs.evaluate(results, accuracy, fscore)\n",
+    "\n",
+    "# Compared to the naive predictor (25% accuracy), we have improved considerably.\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## Improving Results\n",
+    "In this final section, you will choose from the three supervised learning models the *best* model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (`X_train` and `y_train`) by tuning at least one parameter to improve upon the untuned model's F-score. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3 - Choosing the Best Model\n",
+    "*Based on the evaluation you performed earlier, in one to two paragraphs, explain to *CharityML* which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \\$50,000.*  \n",
+    "**Hint:** Your answer should include discussion of the metrics, prediction/training time, and the algorithm's suitability for the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** I believe AdaBosst is the best model for finding individuals who make above 50k USD. It is not the fastest trainer, but all models train below 2 seconds on the full datasets which I believe is acceptable, and prediction time is negligible. It has the best accuracy on the testing set, so you get better generalisation than KNN or Decision Tree. However, given that we have an unbalanced dataset, with only around 25% of people sampled earning above 50k, we need to also check the F score, given recall should influence our prediction, as it is important to measure how many of the true positives are found. Here AdaBoost does quite a bit better than the other models on the testing set. Further, it addresses the overfitting issue with the Decision Tree by training several of them (like basis splines) and weighting them according to the prediction accuracy they give."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4 - Describing the Model in Layman's Terms\n",
+    "*In one to two paragraphs, explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical or technical jargon, such as describing equations or discussing the algorithm implementation.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** AdaBoost is trained using in this case a Decision Tree on the dataset. A Decision Tree attempts to seperate a dataset into two classes (here income >50, below 50k) by asking a serious of questions to its features, such as whether or not some is degree level educated. It will ask questions first that improve the dataset separation by the most. \n",
+    "\n",
+    "To run, AdaBoost will set the weight of each person the same initially. It will then calibrate a decision tree to that data and check how large the error is. Next it will iteratively increase the weights of people that it got wrong, and focus on getting those right in the next iteration. In contrast, people that it got right will have their weight reduced. Predictions are made using a weighted combination of all those fitted decision trees, where higher weight is given to those trees that have make more accurate predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation: Model Tuning\n",
+    "Fine tune the chosen model. Use grid search (`GridSearchCV`) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:\n",
+    "- Import [`sklearn.grid_search.GridSearchCV`](http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).\n",
+    "- Initialize the classifier you've chosen and store it in `clf`.\n",
+    " - Set a `random_state` if one is available to the same state you set before.\n",
+    "- Create a dictionary of parameters you wish to tune for the chosen model.\n",
+    " - Example: `parameters = {'parameter' : [list of values]}`.\n",
+    " - **Note:** Avoid tuning the `max_features` parameter of your learner if that parameter is available!\n",
+    "- Use `make_scorer` to create an `fbeta_score` scoring object (with $\\beta = 0.5$).\n",
+    "- Perform grid search on the classifier `clf` using the `'scorer'`, and store it in `grid_obj`.\n",
+    "- Fit the grid search object to the training data (`X_train`, `y_train`), and store it in `grid_fit`.\n",
+    "\n",
+    "**Note:** Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 525 candidates, totalling 1575 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=8)]: Done   2 tasks      | elapsed:    2.0s\n",
+      "[Parallel(n_jobs=8)]: Done   9 tasks      | elapsed:    7.5s\n",
+      "[Parallel(n_jobs=8)]: Done  16 tasks      | elapsed:    8.3s\n",
+      "[Parallel(n_jobs=8)]: Done  25 tasks      | elapsed:   10.3s\n",
+      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:   11.6s\n",
+      "[Parallel(n_jobs=8)]: Done  45 tasks      | elapsed:   13.3s\n",
+      "[Parallel(n_jobs=8)]: Done  56 tasks      | elapsed:   15.7s\n",
+      "[Parallel(n_jobs=8)]: Done  69 tasks      | elapsed:   18.0s\n",
+      "[Parallel(n_jobs=8)]: Done  82 tasks      | elapsed:   20.3s\n",
+      "[Parallel(n_jobs=8)]: Done  97 tasks      | elapsed:   22.6s\n",
+      "[Parallel(n_jobs=8)]: Done 112 tasks      | elapsed:   25.4s\n",
+      "[Parallel(n_jobs=8)]: Done 129 tasks      | elapsed:   28.1s\n",
+      "[Parallel(n_jobs=8)]: Done 146 tasks      | elapsed:   30.6s\n",
+      "[Parallel(n_jobs=8)]: Done 165 tasks      | elapsed:   34.2s\n",
+      "[Parallel(n_jobs=8)]: Done 184 tasks      | elapsed:   37.2s\n",
+      "[Parallel(n_jobs=8)]: Done 205 tasks      | elapsed:   41.4s\n",
+      "[Parallel(n_jobs=8)]: Done 226 tasks      | elapsed:   45.2s\n",
+      "[Parallel(n_jobs=8)]: Done 249 tasks      | elapsed:   49.4s\n",
+      "[Parallel(n_jobs=8)]: Done 272 tasks      | elapsed:   52.8s\n",
+      "[Parallel(n_jobs=8)]: Done 297 tasks      | elapsed:   57.5s\n",
+      "[Parallel(n_jobs=8)]: Done 322 tasks      | elapsed:  1.0min\n",
+      "[Parallel(n_jobs=8)]: Done 349 tasks      | elapsed:  1.1min\n",
+      "[Parallel(n_jobs=8)]: Done 376 tasks      | elapsed:  1.2min\n",
+      "[Parallel(n_jobs=8)]: Done 405 tasks      | elapsed:  1.3min\n",
+      "[Parallel(n_jobs=8)]: Done 434 tasks      | elapsed:  1.4min\n",
+      "[Parallel(n_jobs=8)]: Done 465 tasks      | elapsed:  1.4min\n",
+      "[Parallel(n_jobs=8)]: Done 496 tasks      | elapsed:  1.5min\n",
+      "[Parallel(n_jobs=8)]: Done 529 tasks      | elapsed:  1.6min\n",
+      "[Parallel(n_jobs=8)]: Done 562 tasks      | elapsed:  1.8min\n",
+      "[Parallel(n_jobs=8)]: Done 597 tasks      | elapsed:  2.0min\n",
+      "[Parallel(n_jobs=8)]: Done 632 tasks      | elapsed:  2.2min\n",
+      "[Parallel(n_jobs=8)]: Done 669 tasks      | elapsed:  2.5min\n",
+      "[Parallel(n_jobs=8)]: Done 706 tasks      | elapsed:  2.7min\n",
+      "[Parallel(n_jobs=8)]: Done 745 tasks      | elapsed:  2.9min\n",
+      "[Parallel(n_jobs=8)]: Done 784 tasks      | elapsed:  3.1min\n",
+      "[Parallel(n_jobs=8)]: Done 825 tasks      | elapsed:  3.4min\n",
+      "[Parallel(n_jobs=8)]: Done 866 tasks      | elapsed:  3.6min\n",
+      "[Parallel(n_jobs=8)]: Done 909 tasks      | elapsed:  3.9min\n",
+      "[Parallel(n_jobs=8)]: Done 952 tasks      | elapsed:  4.2min\n",
+      "[Parallel(n_jobs=8)]: Done 997 tasks      | elapsed:  4.4min\n",
+      "[Parallel(n_jobs=8)]: Done 1042 tasks      | elapsed:  4.7min\n",
+      "[Parallel(n_jobs=8)]: Done 1089 tasks      | elapsed:  5.5min\n",
+      "[Parallel(n_jobs=8)]: Done 1136 tasks      | elapsed:  6.3min\n",
+      "[Parallel(n_jobs=8)]: Done 1185 tasks      | elapsed:  7.2min\n",
+      "[Parallel(n_jobs=8)]: Done 1234 tasks      | elapsed:  8.1min\n",
+      "[Parallel(n_jobs=8)]: Done 1285 tasks      | elapsed:  9.1min\n",
+      "[Parallel(n_jobs=8)]: Done 1336 tasks      | elapsed: 10.0min\n",
+      "[Parallel(n_jobs=8)]: Done 1389 tasks      | elapsed: 11.0min\n",
+      "[Parallel(n_jobs=8)]: Done 1442 tasks      | elapsed: 11.7min\n",
+      "[Parallel(n_jobs=8)]: Done 1497 tasks      | elapsed: 12.6min\n",
+      "[Parallel(n_jobs=8)]: Done 1552 tasks      | elapsed: 13.2min\n",
+      "[Parallel(n_jobs=8)]: Done 1575 out of 1575 | elapsed: 13.6min finished\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unoptimized model\n",
+      "------\n",
+      "Accuracy score on testing data: 0.8576\n",
+      "F-score on testing data: 0.7246\n",
+      "\n",
+      "Optimized Model\n",
+      "------\n",
+      "Final accuracy score on the testing data: 0.8651\n",
+      "Final F-score on the testing data: 0.7448\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
+    "from sklearn.grid_search import GridSearchCV\n",
+    "from sklearn.metrics import make_scorer\n",
+    "\n",
+    "\n",
+    "\n",
+    "dt_stump = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)\n",
+    "dt_stump.fit(X_train, y_train)\n",
+    "clf_knn = KNeighborsClassifier(n_jobs=8)\n",
+    "clf_knn.fit(X_train, y_train)\n",
+    "\n",
+    "# TODO: Initialize the classifier\n",
+    "clf = AdaBoostClassifier(base_estimator=dt_stump, random_state=5)\n",
+    "#clf = AdaBoostClassifier(base_estimator=clf_knn, random_state=5)\n",
+    "\n",
+    "#clf = clf_knn\n",
+    "\n",
+    "# Create the parameters list you wish to tuned\n",
+    "parameters = {\n",
+    "    \"n_estimators\": [1, 3, 9, 30, 90]\n",
+    "    , \"learning_rate\": [0.003, 0.009, 0.1, 0.03, 0.09]\n",
+    "    , \"base_estimator__max_depth\":[1, 3, 9] # use __ to access nested parameters of sub classifier\n",
+    "    , \"base_estimator__min_samples_leaf\":[1, 3, 9, 30, 90, 300, 900]\n",
+    "    #, \"base_estimator__criterion\" : [\"gini\", \"entropy\"]\n",
+    "    #, \"base_estimator__splitter\" :  [\"best\", \"random\"]\n",
+    "    \n",
+    "    #\"n_neighbors\": [3, 5, 7, 10, 13, 15, 20]\n",
+    "    #, \"weights\": [\"uniform\", \"distance\"]\n",
+    "    \n",
+    "    #\"base_estimator__n_neighbors\": [3, 5, 7, 10, 13, 15, 20]\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "\n",
+    "}\n",
+    "\n",
+    "# TODO: Make an fbeta_score scoring object\n",
+    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
+    "\n",
+    "# check this, which is the right score?\n",
+    "stratifiedShuffling = False\n",
+    "if stratifiedShuffling:\n",
+    "    # make sure test labels are evenly split labels between validation set, given dataset is unbalanced\n",
+    "    # this ensure none of them has a large concentration of >50k or <=50k earners.\n",
+    "    from sklearn.cross_validation import StratifiedShuffleSplit\n",
+    "    cv = StratifiedShuffleSplit(y_test, n_iter=10, test_size=0.25, random_state=4)\n",
+    "    grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=scorer, verbose=10, n_jobs=8, cv=cv)\n",
+    "\n",
+    "    \n",
+    "# base case\n",
+    "else:\n",
+    "    # TODO: Perform grid search on the classifier using 'scorer' as the scoring method\n",
+    "    # default uses 3 fold cross validation\n",
+    "    grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=scorer, verbose=10, n_jobs=8)\n",
+    "\n",
+    "    \n",
+    "\n",
+    "# TODO: Fit the grid search object to the training data and find the optimal parameters\n",
+    "grid_fit = grid_obj.fit(X_train, y_train)\n",
+    "\n",
+    "# Get the estimator\n",
+    "best_clf = grid_fit.best_estimator_\n",
+    "\n",
+    "# Make predictions using the unoptimized and optimised model\n",
+    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
+    "best_predictions = best_clf.predict(X_test)\n",
+    "\n",
+    "# Report the before-and-afterscores\n",
+    "print \"Unoptimized model\\n------\"\n",
+    "print \"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions))\n",
+    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5))\n",
+    "print \"\\nOptimized Model\\n------\"\n",
+    "print \"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
+    "print \"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Monday_05_June_2017_09_33PM\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<COMObject Open>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score = pd.DataFrame(grid_fit.grid_scores_).mean_validation_score\n",
+    "params = pd.DataFrame.from_records(pd.DataFrame(grid_fit.grid_scores_).parameters.values)\n",
+    "resGrid = pd.concat([params, score],axis=1)\n",
+    "resGrid = resGrid.sort_values(by=[\"mean_validation_score\"], ascending=False)\n",
+    "\n",
+    "\n",
+    "import datetime\n",
+    "st = datetime.datetime.utcnow().strftime(\"%A_%d_%B_%Y_%I_%M%p\")\n",
+    "print(st)\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "importances = best_clf.feature_importances_\n",
+    "indices = np.argsort(importances)[::-1]\n",
+    "print(X_train.columns[indices[:5]])\n",
+    "\"\"\"\n",
+    "\n",
+    "#print(pd.DataFrame(best_clf.feature_importances_))\n",
+    "clf_name = best_clf.__class__.__name__ \n",
+    "# create excel sheet of parameter grid\n",
+    "writer = pd.ExcelWriter(\n",
+    "        clf_name + 'resGrid.xlsx'\n",
+    "     , engine=\"xlsxwriter\"     \n",
+    "    )\n",
+    " \n",
+    "pd.formats.format.header_style = None    \n",
+    " \n",
+    "resGrid.to_excel(writer, sheet_name=\"elem\", index=False)\n",
+    "workbook = writer.book\n",
+    "worksheet = writer.sheets[\"elem\"]\n",
+    "formatObject = workbook.add_format()\n",
+    "formatObject.set_text_wrap(1)\n",
+    "formatObject.set_bold(1)\n",
+    "\n",
+    "worksheet.set_column(\"A:F\", 30)\n",
+    "worksheet.set_row(0, 60, formatObject)\n",
+    " \n",
+    "writer.save()\n",
+    "\n",
+    "#open excel\n",
+    "import os\n",
+    "import win32com.client\n",
+    "\n",
+    "cwd = os.getcwd() + \"\\\\\"\n",
+    "\n",
+    "xl=win32com.client.Dispatch(\"Excel.Application\")\n",
+    "xl.Visible = True\n",
+    "xl.Workbooks.Open(Filename=cwd+clf_name+\"resGrid.xlsx\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# close any excel sheets without asking questions and quit excel\n",
+    "map(lambda book: book.Close(False), xl.Workbooks)\n",
+    "xl.quit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 5 - Final Model Evaluation\n",
+    "_What is your optimized model's accuracy and F-score on the testing data? Are these scores better or worse than the unoptimized model? How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in **Question 1**?_  \n",
+    "**Note:** Fill in the table below with your results, and then provide discussion in the **Answer** box."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Results:\n",
+    "\n",
+    "|     Metric     | Benchmark Predictor | Unoptimized Model | Optimized Model |\n",
+    "| :------------: | :-----------------: | :---------------: | :-------------: | \n",
+    "| Accuracy Score |     0.2917          |   0.8576          |   0.8687        |\n",
+    "| F-score        |     0.2478          |   0.7246          |   0.7430        |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer: ** The optimised model's scores are better than the benchmark and the unoptimised scores. Accuracy improves beyond just predicting \">50k\" for all points, and especially the F-Score improves, showing the model is adding value. However, i believe that the benchmark predictor does not illustrate the issue of unbalanced datasets well, as we will see a large improvement by using as benchmark the prediction of the less frequent part of the dataset across the entire dataset. However, if we were to predict the more frequent part as a benchmark, i believe it would be a lot more difficult to achieve such performance gains, given we have already 75% accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda3\\envs\\udacity\\lib\\site-packages\\IPython\\html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
+      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x9b297b8>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HFHDWaih7tWnNmCEXb34+eshgDj4wsL68nA9XrKDZLrsAMGfhInK5HId//Wubt923fTvW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\n3IYB7nYI4QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9b290b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# confusion matrix best\n",
+    "pred = best_clf.predict(X_test)\n",
+    "sns.heatmap(confusion_matrix(y_test, pred), annot = True, fmt = '')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## Feature Importance\n",
+    "\n",
+    "An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \\$50,000.\n",
+    "\n",
+    "Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 6 - Feature Relevance Observation\n",
+    "When **Exploring the Data**, it was shown there are thirteen available features for each individual on record in the census data.  \n",
+    "_Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** I believe the important features would be: \n",
+    "- Capital Gain - most important as a large capital gain can provide income for years to come, and reduces reliance on intelligence, hard work or biological properties like race or attractiveness.\n",
+    "- Age - also important as usually people tend to get better at what they do with age.\n",
+    "- Education Level - fairly good predictor as it can replace experience and is sometimes a proxy for family wealth which would have a positive effect on income.\n",
+    "- Work class - private sector pays better\n",
+    "- race - biological attributes might provide some noise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation - Extracting Feature Importance\n",
+    "Choose a `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.\n",
+    "\n",
+    "In the code cell below, you will need to implement the following:\n",
+    " - Import a supervised learning model from sklearn if it is different from the three used earlier.\n",
+    " - Train the supervised model on the entire training set.\n",
+    " - Extract the feature importances using `'.feature_importances_'`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZPvyj3HPPXTz99P9YeullWXTRxbj44gtYa62P8KMfncMhhxzBySfPXBN68skn\ncPjhR3P66WfzrnfNeH/y5Mkvc9hhR3POORdy3333MnHiBHbeeTc233wrgz9pAWINoKQFwtJLL8s/\n//nITMOefvp/PPvsM7NM29gvcJVVVgVg0KDB09/NO3jwYF577fUufwflDR2XXXYJt99+KwMHvq3L\nfnaf+MR2nHzyiaywwlDe854VePvb38Hjjz/GqFEX8qtf/ZzXX59Cv36zHlI7agK+6abryXyEAw4o\n7wyeMmUKY8c+zZAhQzj11O8zYMBAxo17lg9+cPVO89N+eZZffgUAHn/8sQ7THjw4pk87bNg6jB59\nH5mPMHLk8bS2tjJu3DOMHn0/w4evOz2dBx64j1tuuQmASZNmfjf0+PHjWXHFlQBYffU1p0+37LLL\nsfjiiwPQ2trKq6++2uUySOodTQsAI6IvcCawOvAasGdmPtYw/hvAnsC4atA+mZnNyo+kBdt6663P\nqFEXMGLEZ1huuXczZcoUfvSjU1h77XVYZZVVee655wAYO3YMEydOmP67Pn06fM0lAIsssminvwO4\n9NKf8YEPfIgRIz7DAw/cx9133wFA3759mTp15mCxNE1P45JLRjFiROmTuPzyQ/n853dmk03W5777\nHmL06Pu7tawrrDCUNdccxre+dQRTp07loovOY7nl3s03vnEAl132WwYOfBvHHnt0wzL2Zdq0aV0u\nT58+fbtMu9Eaa3yYUaMuBEqQBrDaau/nmmuu5NBDvz09nS22+D+22GIrXnjhea6++rczpbHUUkvz\nxBOP8973rsjDDz/UkI9Zy6NPnz5Mmza1W+tG0vzRzBrA7YDFMnPdiBgO/AD4VMP4tYBdMrN7R0xJ\n89X8fmzL2942iCOO+A7f+96xTJ06lcmTJ7Peeh9jxIjP8OabbzJo0CD22mtXhg59L8suu9zsEwRW\nXXW1Ln+33nobcMopJ3HLLTcxaNAg+vXrx+uvv07Eapx55mkMHfremabfZptPcf75Z/PhDw8DYP/9\nv8YPfnAiF1xwNi+99DJf+9pB3crXeuttwOjR97PffnvyyiuT2WCDjRk48G1sueXH2W+/vRgwYDFa\nW9/J+PHl+nj11dfgoIO+yqmnnjnb9dBZ2o0GDBhAS0sLq6++5vRhw4evx733/ml6Leouu+zBiSd+\nl6uuuoLJk19mjz32nimNAw/8FieccAwDBgykf/8WhgxZqtPlXWmllbn44gtYZZVVu7zBRNL806cZ\nj1gAiIhvUu9RAAAZ5klEQVQfAn/OzEur7//LzOUaxv8DeBhYBrg2M7u81W7cuEnNyegCaMiQwYwb\nN2n2E+otx7JdONWxXC+//DI22WRzWltbOffcM+nfvz+7775Xb2erR9WxXLurp58T2l4zL0DrVq5D\nhgzusJmkmTWAiwON7S1vRkRLZrZ1srkUOAOYCPwmIrbNzGs6S6y1dSAtLf2al9sFzJAhg3s7C2oS\ny3bhVLdyHTp0OQ455KsMHDiQwYMHc+KJJ9LauvCtg7qV64Ki2evdcm1uADgRaFzDfduCv4joA5ya\nmROq79cCawKdBoAvvDC5iVldsNTt6qROLNuFUx3Lda211uMnP1lv+vcpU1jo1kEdy3VB0cz1Xrdy\n7SzYbeZjYO4Etgao+gA+1DBuceBvETGoCgY3AewLKEmSNB80swbwN8DmEXEX0AfYPSK+AAzKzHMj\n4nDgVsodwrdk5nVNzIskSZIqTQsAM3MqsG+7wY80jB8FjEKSJEnzlW8CkSRJqhkDQEmSpJoxAJQk\nSaoZA0BJkqSaMQCUJEmqGQNASZKkmjEAlCRJqhkDQEmSpJoxAJQkSaoZA0BJkqSaMQCUJEmqGQNA\nSZKkmmnp7QxIkhZeS113RVPTf3br7ZuavrSwsgZQkiSpZgwAJUmSasYAUJIkqWYMACVJkmrGAFCS\nJKlmDAAlSZJqxgBQkiSpZgwAJUmSasYAUJIkqWYMACVJkmrGAFCSJKlmDAAlSZJqxgBQkiSpZgwA\nJUmSasYAUJIkqWYMACVJkmrGAFCSJKlmDAAlSZJqxgBQkiSpZgwAJUmSasYAUJIkqWYMACVJkmrG\nAFCSJKlmDAAlSZJqxgBQkiSpZgwAJUmSasYAUJIkqWYMACVJkmrGAFCSJKlmDAAlSZJqxgBQkiSp\nZlqalXBE9AXOBFYHXgP2zMzHOpjuXOD5zDy0WXmRJEnSDM2sAdwOWCwz1wUOBX7QfoKI2Af4YBPz\nIEmSpHaaVgMIrA/cAJCZ90TEsMaREfFRYB3gHGDV2SXW2jqQlpZ+zcjnAmnIkMG9nQU1iWXbsT4/\n/WnT0p62665NS7uN5do7mr3eLdfeYbk2XzMDwMWBCQ3f34yIlsycEhHLAkcDI4DPdiexF16Y3IQs\nLpiGDBnMuHGTejsbagLLtnc0e51brr2nmevdcu09lmvP6SzYbWYAOBFonGvfzJxSfd4BWBK4DlgG\nGBgRj2TmRU3MjyRJkmhuAHgn8AngsogYDjzUNiIzTwdOB4iI3YBVDf4kSZLmj2YGgL8BNo+Iu4A+\nwO4R8QVgUGae28T5SpIkqQtNCwAzcyqwb7vBj3Qw3UXNyoMkSZJm5YOgJUmSasYAUJIkqWYMACVJ\nkmrGAFCSJKlmDAAlSZJqxgBQkiSpZgwAJUmSasYAUJIkqWYMACVJkmrGAFCSJKlmDAAlSZJqxgBQ\nkiSpZgwAJUmSasYAUJIkqWYMACVJkmrGAFCSJKlmDAAlSZJqxgBQkiSpZgwAJUmSasYAUJIkqWYM\nACVJkmrGAFCSJKlmDAAlSZJqxgBQkiSpZgwAJUmSasYAUJIkqWYMACVJkmrGAFCSJKlmDAAlSZJq\nxgBQkiSpZgwAJUmSasYAUJIkqWYMACVJkmrGAFCSJKlmDAAlSZJqxgBQkiSpZgwAJUmSasYAUJIk\nqWYMACVJkmrGAFCSJKlmZhsARsRyHQz7v+ZkR5IkSc3W0tmIiFii+nhdRGwE9Km+9weuBN7X3KxJ\nkiSpGToNAIFfAJtXn59rGD4F+E3TciRJkqSm6jQAzMwtASLigszcY/5lSZIkSc3UVQ0gAJm5R9UP\ncClmNAOTmQ909buI6AucCawOvAbsmZmPNYz/NHAoMA34eWaeNldLIEmSpDnSnZtAvgs8Rmn2vbz6\n+3U30t4OWCwz16UEej9oSLMfcCKwGbAusF9ELDnHuZckSdIcm20NIPBFYGhmPjOHaa8P3ACQmfdE\nxLC2EZn5ZkSslplTImIpoB/weleJtbYOpKWl3xxm4a1ryJDBvZ2FBVKfn/60qelP23XXpqYPlm1v\nmB/r3HLtHc1e75Zr77Bcm687AeC4uQj+ABYHJjR8fzMiWjJzCkAV/G0PnAFcC7zcVWIvvDB5LrLw\n1jRkyGDGjZvU29mopWavd8u2d1iuC69mrnfLtfdYrj2ns2C3q8fAfLj6ODoiTgMuAd5oGz+7PoDA\nRKBxrn3bgr+GNK6IiN8CFwG7ABfOJk1JkiTNo65qAC9v9/2TDZ+nASvOJu07gU8Al0XEcOChthER\nsThwNbBFZr4WES8DU7uda0mSJM21rh4D8955TPs3wOYRcRfl7uHdI+ILwKDMPDcifg78ISLeAB4E\nfjaP85MkSVI3zLYPYERc0G7QNGAy8DfgvMx8s6PfZeZUYN92gx9pGH8ucO4c5VaSJEnzbLaPgaHU\n3n2Y0oT7F+D9wPLAlsCpzcuaJEmSmqE7dwGvBnwsMycBRMR5wE3Axyi1gJIkSXoL6U4NYGtb8Fd5\nBXh7Zk5jNs/ukyRJ0oKnOzWA90TEz4DzqW7mAP4UER9nNs/ukyRJ0oKnOzWA+wJPAacAJwGPAwdQ\nHvS8T/OyJkmSpGaYbQ1gZr4CHFb9NfplU3IkSZKkpurqTSB3ZOb6ETGJ8uiXNn2AaZm5eNNzJ0mS\npB7XVQ3gDtX/D8yPjEiSJGn+6LQPYGaOqf7/G1gb2AsYB3y0GiZJkqS3oNneBBIRhwJfBj4LDACO\njohvNztjkiRJao7u3AX8OWBr4OXMfA4YDnyhqbmSJElS03QnAHwjM19r+5KZLwJvNC9LkiRJaqbu\nPAj6qYjYBpgWEYsCBwH2AZQkSXqL6rQGMCIGVx8PAL4JfIjy5o+PA/s3P2uSJElqhq5qAMdHxB3A\ntcB+lLeB9Gv3XmBJkiS9xXQVAL4b2ATYlFLjNw24NiKuBW7LzNfnQ/4kSZLUwzoNADNzHOV1b78E\niIgVgM2A7wErA4M7+60kSZIWXLO9CSQihgKfArYA1gQeAM5tbrYkSZLULF29C/g44JOUmr7rgTOB\n32fmK/Mpb5IkSWqCrmoADwOuAk7MzHvmU34kSZLUZF0FgAF8AjghIlYBfgdcA9zoncCSJElvXZ0+\nBzAzH83MH2bmxsD7gRuBEcDfI+J38yuDkiRJ6lndeRUcwPLAEGAx4HVgStNyJEmSpKbq6iaQrwIb\nARsCz1FuBDkPuDUzX50vuZMkSVKP66oP4FaUoO+QzHxsPuVHkiRJTdbVg6C3np8ZkSRJ0vzR3T6A\nkiRJWkgYAEqSJNWMAaAkSVLNGABKkiTVjAGgJElSzRgASpIk1YwBoCRJUs0YAEqSJNWMAaAkSVLN\nGABKkiTVjAGgJElSzRgASpIk1YwBoCRJUs0YAEqSJNWMAaAkSVLNGABKkiTVjAGgJElSzbQ0K+GI\n6AucCawOvAbsmZmPNYz/PPB1YArwELBfZk5tVn4kSZJUNLMGcDtgscxcFzgU+EHbiIgYABwLbJyZ\n6wFvB7ZtYl4kSZJUaWYAuD5wA0Bm3gMMaxj3GvDRzJxcfW8BXm1iXiRJklRpWhMwsDgwoeH7mxHR\nkplTqqbeZwAi4ivAIOB3XSXW2jqQlpZ+TcvsgmbIkMG9nYVamh/r3bKd/yzXhVez17vl2jss1+Zr\nZgA4EWhcw30zc0rbl6qP4EnAKsCnM3NaV4m98MLkrkYvVIYMGcy4cZN6Oxu11Oz1btn2Dst14dXM\n9W659h7Lted0Fuw2swn4TmBrgIgYTrnRo9E5wGLAdg1NwZIkSWqyZtYA/gbYPCLuAvoAu0fEFyjN\nvfcBXwL+CPw+IgBOy8zfNDE/kiRJookBYNXPb992gx9p+OwzCCVJknqBQZgkSVLNGABKkiTVjAGg\nJElSzRgASpIk1YwBoCRJUs0YAEqSJNWMAaAkSVLNGABKkiTVjAGgJElSzRgASpIk1YwBoCRJUs0Y\nAEqSJNVMS29noFmWuu6KJs9htyanv+B4duuJvZ0FSZLUg6wBlCRJqhkDQEmSpJoxAJQkSaoZA0BJ\nkqSaMQCUJEmqGQNASZKkmjEAlCRJqhkDQEmSpJoxAJQkSaoZA0BJkqSaMQCUJEmqGQNASZKkmjEA\nlCRJqhkDQEmSpJoxAJQkSaoZA0BJkqSaMQCUJEmqmZbezoC0IFnqusV7OwvzzbNbT+ztLEiSeok1\ngJIkSTVjAChJklQzBoCSJEk1YwAoSZJUMwaAkiRJNWMAKEmSVDMGgJIkSTVjAChJklQzBoCSJEk1\nYwAoSZJUMwaAkiRJNWMAKEmSVDMGgJIkSTXT0qyEI6IvcCawOvAasGdmPtZumoHA74AvZeYjzcqL\nJEmSZmhmDeB2wGKZuS5wKPCDxpERMQz4A7BSE/MgSZKkdpoZAK4P3ACQmfcAw9qNXxQYAVjzJ0mS\nNB81rQkYWByY0PD9zYhoycwpAJl5J0BEdCux1taBtLT06/FMavaGDBnc21lQE9SpXJe6bvHezsJ8\nM23Xab2dhfmq2dtxnfaTBYnl2nzNDAAnAo1ruG9b8Dc3Xnhh8rznSHNl3LhJvZ0FNYHlunCqW7k2\nc3mHDBlcu/W5oLBce05nwW4zm4DvBLYGiIjhwENNnJckSZK6qZk1gL8BNo+Iu4A+wO4R8QVgUGae\n28T5SpIkqQtNCwAzcyqwb7vBs9zwkZkbNSsPkiRJmpUPgpYkSaoZA0BJkqSaMQCUJEmqGQNASZKk\nmjEAlCRJqhkDQEmSpJoxAJQkSaoZA0BJkqSaMQCUJEmqGQNASZKkmjEAlCRJqhkDQEmSpJpp6e0M\nSJI0t5a6bvHezsJ88+zWE3s7C/ON5dp81gBKkiTVjAGgJElSzRgASpIk1YwBoCRJUs0YAEqSJNWM\nAaAkSVLNGABKkiTVjAGgJElSzRgASpIk1YwBoCRJUs0YAEqSJNWMAaAkSVLNGABKkiTVjAGgJElS\nzRgASpIk1YwBoCRJUs0YAEqSJNWMAaAkSVLNGABKkiTVjAGgJElSzRgASpIk1YwBoCRJUs0YAEqS\nJNWMAaAkSVLNGABKkiTVjAGgJElSzRgASpIk1YwBoCRJUs0YAEqSJNWMAaAkSVLNGABKkiTVTEuz\nEo6IvsCZwOrAa8CemflYw/hPAEcBU4ALMvMnzcqLJEmSZmhmDeB2wGKZuS5wKPCDthER0R84BdgC\n2BDYOyKWbmJeJEmSVGlmALg+cANAZt4DDGsYtxrwWGa+kJmvA3cAGzQxL5IkSar0mTZtWlMSjojz\ngMsz8/rq+3+AFTNzSkSsD3wlM3esxh0D/Cczz2tKZiRJkjRdM2sAJwKDG+eVmVM6GTcYeLGJeZEk\nSVKlmQHgncDWABExHHioYdw/gPdFxBIRsQil+ffuJuZFkiRJlWY2AbfdBfwhoA+wO/BhYFBmnttw\nF3Bfyl3AZzQlI5IkSZpJ0wJASZIkLZh8ELQkSVLNGABKkiTVjAGgJElSzRgASvMoItaIiKOqzyMi\n4l1dTDsyIvadx/kdGhEfmZc01D09UbYR8WRELNbMfNZRRJwYEbv1UFojIuJdEbFMRJzZE2lqVhGx\nW0Sc2Nv56C0RcVtErNrb+WjTtHcBS3WRmX8B/lJ9/RqwL/B0E+dX2wPo/Da/y1a95mvAvpn5CLBf\nb2dGmh8MAHtBRCwOnAe8A3gXcAZwf/V/EvAs8Gpm7hYRXwG+AEwDLs3M03sn1wu3iBgAXAisACwC\nfBPYn4YyysyzIuI24BFgVcrjjXasPu8LjALWAC6u3nbzHcorEN8J/DUzd+9i/tsCxwATgBeAB4Hv\nAucA7wGWBa7KzCMj4iLgUmAZyrM2BwIrAd/LzIt6ZIUsRHq7bBvyMRS4gHLcnQZ8NTP/GhEXAisD\nA4DTMnNURBwHbFxNe3lmfq8HVsVbSvXO+LOB91Faq46krO8jgXGUsnwkIjaiBG+fq343NjOXiYj3\nUY6ziwCTgc8BSwM/BPoBSwJfBlqZUbY7Axdn5vCI2Bw4FngVeA7Yo5ruW8DrwIqUY/Jx7fK9UUfT\ntO23mXlDRGwFfK46xj8G3AWsAtwCvB34CJCZ+cUeWp0LkuERcRMwBDgLeIKO13NHZXoRZRt4J/Ap\n4JeUbWOxavq/NM6oo306M8dGxAnAxyjbwQ8z81fVtM8CSwBbZuabVRpfA/pn5skRcTbwemZ+NSKO\nqPL+EHB6lf5zwB6ZOaGjeTTk6xOU49CIzOy1l2DYBNw7VqYcCLYAtqBsCGcDu2XmJsC/ACLi/ygn\nofUpG9J2ERG9k+WF3r7Ak5m5LuVEsRazllGbuzJzI8rB5/C2gZl5LaW2aBfKAemFzNycEigMj4jl\nOppxRPSjHEA+npkbA69Uo94D3JOZW1JOCB01Hb89M7cFPgkcOjcLXgO9VrbtnEwJ8Dag1DidHxGD\nKQ/C3x7YCnizmnYnyoXfx6jvW5L2BMZX6+tTlAvkHwKbAVtSgrqunAycUJX7acCawPuBAzNzU+B7\nwO7tyvZ1gIjoA5wLbJ+ZGwK3UwJPKBcSnwaGA4d0Mu/uTNNmaJX2x4CvUp6fuw6wfkS8Yza/fSt6\ng1J+I4Bv0Pl67szvM/OjlGPic8DHKRd0b+tk+pn26Yj4OPDezFyfcpF1RMN6/kVmbtYW/FV+Q9k3\nAYJSNlTDrgF+AuxfzeM64JDZzGN74ABg294M/sAawN7yDPD1iNie8lq8/sC7MvPhavwfKSeqD1AO\nJLdUw1spV8M5f7NbCwFcD5CZj0bEL4ET2pVRm99X/++inJg68gqwVET8AngJGNSYRkQcAHym+roT\nMDEzn6m+/5FSu/c8sHZEbFzlYdEO5tN2xfsUJTDRrHq7bNusBvyhysdfIuI9mTkpIr5OOQkuDvys\n4XcnUraD6+d4iRcOHwQ+FhFtJ9xFgKmZ+RxARNzVye/6VP+D6g1TmXlV9Zv1gW9HxCuUV5BO7CSN\nJSn75P+q738Ajqec8B+qXms6pUqHiLiGsh08BFze0TSd5BHgucz8T5XOy5n59+rzBBbOffqBzJwW\nEWOB5YHHOlnPjRrXV9v573rK+fBKSlB5bER8hhJcARxY/W+/T/8XWKuq8YOy7w5tTDsijqVUvABs\nCgys+l3/A1g+ItYGJmTmxIhYDTizqpvpDzxK2XY7m8emlH39jU7X0HxiDWDvOBC4OzN3Bn5F2bif\nqmr8oFw1QtkYHwY2rq4uLqI0Darn/QNYGyAiVgR+xKxl1Gat6v96lPJpNJWyX30ceE9mfp5SkzSg\nMY3M/HFmblSV6xhgcEQMqUa3lf9uwIuZuRPwA8pBqDEfUJoS1bVeK9uGE1tbPj5W5WMNYGxELAus\nlZkjgG2AkyJiUWAH4POU2oPdImKFeVwHb0WPUGpkNqKs818CNOwna1f/X6V0kaBaT0tUwxvLfaeq\nO83pwNGZuSslWGsrt7aybTMeWLwqH4ANgX9Wn2fZ5zJz26q8v9LZNI35pLwViy6mXZg1Lm9n67mz\nMoVSVgAbAWOqmvxjgeMz89cN+9791XTt9+lHgFur7WoT4DKqVre2tDPzyIZ03gSuBU4Cbqr+fkSp\nGYRynt6lSu8QSvDa1Tz2B26kdPnpVdYA9o6rgR9FxOcozTtTKFctF0TES5RmiP9V/YNuAe6oTgp/\nBv7XWaKaJ+dQ1v/tlD4bVwL7N5ZRVQZQTsjfBF4Gvki52mtzF3AxpUn22xHxB8oB73FKf7NZZObU\nqtbouuqqvy/lKvIW4JKIWBd4rRrW6V2o6lSvlW07BwE/iYiDKDUCXwLGAstUtVlvAidn5msR8Txw\nD6W28SbgP3O99G9d51DW1+2UGpMzKcfJG6v101aDch/wYkT8iRL0PVENPxg4JyKOpDQX70xZ77+K\niBcoNUFLVtO2le3eAFUN1V7AFRExldIvdzdKq8zcOo+yHe7EjGCy7qYBHa3nF+m4TBv9Fbg0Ir5M\niWU6C6ja79PPAxtFxB8ptba/qWriu8rnFcBIyr6/LKUrwrbVuC9T+o+29e39EuVY3dU8jgH+HBHX\nZOYdXc24mXwV3AIiIvYHLsvMcVX18+uZ2etXCJpZVaXfdrdgT6Z7GKWj8GsR8TPgpsy8uCfnoa41\nq2wl9Q736a5ZA7jgeAa4qaoBnADs2sv50fw1CbgnIiYDT1I1d0mS1AzWAEqSJNWMN4FIkiTVjAGg\nJElSzRgASpIk1Yw3gUiqnYiYBvyNGW/eALgvM/ecy/TWBr6UmR29rUWSFjgGgJLqauPMHN9Dab0f\neHcPpSVJTWcAKEkNqlc7nUZ54Xw/4PTMvCAi+gKnUN7UMpjyFok9KQ9pPgZ4e0RcCPwU+HFmfqBK\nb6O27xExEliX8jDZBzNz5+ql8p+mdMl5EtgvM5+eT4srqaYMACXV1a0R0dgEvAXlLQG/Br6YmQ9E\nxNuBuyPi75SA713AutXbWw4FDs3MT0TEUcBnMnP3KuDrygrABzJzSkTsQnnbyEeq73tT3hixdY8u\nqSS1YwAoqa5maQKu3se9EuWVXW2DBwBrZuZZ1WvF9omIlSjvIp00F/O9JzOnVJ+3BT4C3FfNrx8w\ncC7SlKQ5YgAoSTP0A17MzDXaBkTE0sCEiNiG0jT8A8r7hB+hvF+2vWmU2sI2i7Qb/1K7+X0vM8+q\n5rUo0DqvCyFJs+NjYCRphgRejYidASLiPZS7hdcCNgeuroK1e4HtKAEcwBSgf/V5HLB8RCwVEX2q\n6TpzI7BnRCxefT8GGNWDyyNJHTIAlKRKZr4OfIoSlD0I3AR8OzPvBM4GNqyG3w38C3hvdXPI3cCq\nEfGbzPw7cA5wH3APMKaLWZ4HXEN5D/TDwIeA3ZqycJLUwHcBS5Ik1Yw1gJIkSTVjAChJklQzBoCS\nJEk1YwAoSZJUMwaAkiRJNWMAKEmSVDMGgJIkSTXz/1Ik4EFeBBSHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd07d9b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# TODO: Import a supervised learning model that has 'feature_importances_'\n",
+    "\n",
+    "clfAda = AdaBoostClassifier(random_state=5, n_estimators=30)\n",
+    "\n",
+    "# TODO: Train the supervised model on the training set \n",
+    "model = clfAda.fit(X_train, y_train)\n",
+    "\n",
+    "# TODO: Extract the feature importances\n",
+    "importances = model.feature_importances_\n",
+    "\n",
+    "# best fit features\n",
+    "best_clf.fit(X_train, y_train)\n",
+    "#importances = best_clf.feature_importances_\n",
+    "\n",
+    "# interesting - my best fit has a different feature order - it includes marital status! A hidden feature.\n",
+    "# other ways of feature selection: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection\n",
+    "\n",
+    "\n",
+    "#print(importances.shape)\n",
+    "# importances show the contribution of each feature to the model.\n",
+    "# There are 103 features, as we did one hot encoding on the enums.\n",
+    "\n",
+    "# Plot\n",
+    "vs.feature_plot(importances, X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 7 - Extracting Feature Importance\n",
+    "\n",
+    "Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \\$50,000.  \n",
+    "_How do these five features compare to the five features you discussed in **Question 6**? If you were close to the same answer, how does this visualization confirm your thoughts? If you were not close, why do you think these features are more relevant?_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** It seems that race is not even listed, and age is the most prominent feature. However, it does seem right that capital gain and education are both fairly important as expected. Interesting is that Age dominates compared to capital gain, but i suppose this makes sense as an income as low as 50k can be achieved irrespective of capital gain, education and other predictors if you live long enough. I would suspect capital gain and loss taking over at higher incomes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Selection\n",
+    "How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of **all** features present in the data. This hints that we can attempt to *reduce the feature space* and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set *with only the top five important features*. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final Model trained on full data\n",
+      "------\n",
+      "Accuracy on testing data: 0.8651\n",
+      "F-score on testing data: 0.7448\n",
+      "\n",
+      "Final Model trained on reduced data\n",
+      "------\n",
+      "Accuracy on testing data: 0.8420\n",
+      "F-score on testing data: 0.7020\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import functionality for cloning a model\n",
+    "from sklearn.base import clone\n",
+    "\n",
+    "\"\"\"\n",
+    "    Reduce the feature space - could also use PCA for this,\n",
+    "    in order to reduce dimensionality while maintaining other features' information\n",
+    "\"\"\"\n",
+    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
+    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
+    "\n",
+    "# Train on the \"best\" model found from grid search earlier\n",
+    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
+    "\n",
+    "# Make new predictions\n",
+    "reduced_predictions = clf.predict(X_test_reduced)\n",
+    "\n",
+    "# Report scores from the final model using both versions of data\n",
+    "print \"Final Model trained on full data\\n------\"\n",
+    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
+    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))\n",
+    "print \"\\nFinal Model trained on reduced data\\n------\"\n",
+    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions))\n",
+    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 8 - Effects of Feature Selection\n",
+    "*How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?*  \n",
+    "*If training time was a factor, would you consider using the reduced data as your training set?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** The final model's accuracy is 0.8433 and its F score is 0.7032. This is only a little bit lower than the metrics for the model trained on the full data. If training time was a factor i would consider reducing features, as the accuracy and f score are almost unchanged. It would depend how expensive it would be to get a few classifications wrong, in terms of letters sent out."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
+    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/git commit and push with dummy message.bat b/git commit and push with dummy message.bat
new file mode 100644
index 0000000..03ef02d
--- /dev/null
+++ b/git commit and push with dummy message.bat	
@@ -0,0 +1,2 @@
+git commit -am "latest commit dummy message" && git push 
+PAUSE
\ No newline at end of file
diff --git a/git pull.bat b/git pull.bat
new file mode 100644
index 0000000..d258290
--- /dev/null
+++ b/git pull.bat	
@@ -0,0 +1,2 @@
+git pull
+PAUSE
\ No newline at end of file
diff --git a/git rebase fetch.bat b/git rebase fetch.bat
new file mode 100644
index 0000000..7e6b912
--- /dev/null
+++ b/git rebase fetch.bat	
@@ -0,0 +1,3 @@
+git fetch --all
+git reset --hard origin/master
+PAUSE
\ No newline at end of file
diff --git a/smartcab/agent.py b/smartcab/agent.py
new file mode 100644
index 0000000..e6db273
--- /dev/null
+++ b/smartcab/agent.py
@@ -0,0 +1,406 @@
+import random
+import math
+from environment import Agent, Environment
+from planner import RoutePlanner
+from simulator import Simulator
+import numpy as np
+
+class LearningAgent(Agent):
+    """ An agent that learns to drive in the Smartcab world.
+        This is the object you will be modifying. """ 
+
+    def __init__(self, env, learning=False, epsilon=1.0, alpha=0.5, epsilon_scalar=0.01):
+        super(LearningAgent, self).__init__(env)     # Set the agent in the evironment 
+        self.planner = RoutePlanner(self.env, self)  # Create a route planner
+        self.valid_actions = self.env.valid_actions  # The set of valid actions
+
+        # Set parameters of the learning agent
+        self.learning = learning # Whether the agent is expected to learn
+        self.Q = dict()          # Create a Q-table which will be a dictionary of tuples
+
+        """
+        Q has this structure:
+        
+        { 'state-1': { 
+            'action-1' : Qvalue-1,
+            'action-2' : Qvalue-2,
+             ...
+           },
+          'state-2': {
+            'action-1' : Qvalue-1,
+             ...
+           },
+           ...
+        }
+        
+        as the agent encounters states, it will take various actions and record their q values here.
+        the q value is the rewards received at this state, for taking this action.
+        
+        
+        
+        """
+
+        self.epsilon = epsilon   # Random exploration factor
+        """
+        
+        this factor should decay to 0 between trials, as the number of trials grows. This is because the agent is expected to learn,
+        and do less exploring as it matures.
+        
+        """
+
+        self.alpha = alpha       # Learning factor
+
+        ###########
+        ## TO DO ##
+        ###########
+        # Set any additional class parameters as needed
+
+        self.nb_trials = 1.0
+        self.epsilon_scalar = epsilon_scalar
+
+        print(self)
+
+
+    def reset(self, destination=None, testing=False):
+        """ The reset function is called at the beginning of each trial.
+            'testing' is set to True if testing trials are being used
+            once training trials have completed. """
+
+        # Select the destination as the new location to route to
+        self.planner.route_to(destination)
+        
+        ########### 
+        ## TO DO ##
+        ###########
+
+
+
+        # Update epsilon using a decay function of your choice
+        #self.epsilon = self.epsilon - 0.01  # reduce epsilon by 0.05 after each trial
+
+        # math.pow(self.nb_trials, 2) # decays too fast
+        #self.epsilon = math.pow(self.alpha, self.nb_trials) # get to 30%
+        #self.epsilon -= 0.05 # get to 10% but bad rating, doesnt get there on time
+        #self.epsilon = math.exp(-self.alpha*self.nb_trials) # goes to below 10%, smoother decay
+        #self.epsilon = math.cos(self.alpha*self.nb_trials) # goes to 25%
+        #self.epsilon = math.exp(-self.epsilon_scalar*self.nb_trials) # make epsilon independent of alpha
+        # negative of gompertz function decay
+        g_a = -1.0  # flips to negative, converges to 0
+        g_b = 40.0  # controls x direction displacement, higher goes left
+        g_c = 4.0  # controls slops of transition between 1 and 0, higher is steeper
+        g_val = self.nb_trials / 700
+        self.epsilon = (g_a * math.exp(-g_b * math.exp(-g_c * g_val))) + 1
+
+        # like this you exponentially slower decay, so you have enough time to learn from q after exploring
+        # but alpha decrease should only affect how much q is updated with new info, not how much epsilon decays
+
+        if self.nb_trials % 300 == 0:
+            #self.alpha -= (self.alpha * 0.50)
+            pass
+            # problem is, makeing alpha smaller increases epsilon again, so it will wobble
+
+
+        # Update additional class parameters as needed
+        self.nb_trials +=1.0
+
+
+
+        # If 'testing' is True, set epsilon and alpha to 0
+        if testing:
+            self.epsilon = 0.0
+            self.alpha = 0.0
+
+        return None
+
+
+
+    # figure out what state i am currently in
+    def build_state(self):
+        """ The build_state function is called when the agent requests data from the 
+            environment. The next waypoint, the intersection inputs, and the deadline 
+            are all features available to the agent. """
+
+        # Collect data about the environment
+        waypoint = self.planner.next_waypoint() # The next waypoint, or direction the smartcab should drive to get to the destination at some point
+        # this is relative to the current heading of the smartcab
+
+        inputs = self.env.sense(self)           # Visual input - intersection light and traffic
+
+        """
+        inputs has:
+            light
+            left: where vehicle to left of smartcab wants to go
+            right: where vehicle to left of smartcab wants to go
+            oncoming: where vehicle to left of smartcab wants to go
+            
+            these values are None if there are no vehicle in those positions of the intersection
+        
+        """
+
+        deadline = self.env.get_deadline(self)  # Remaining deadline in units of remaining number of actions until out of time
+
+        ########### 
+        ## TO DO ##
+        ###########
+        # Set 'state' as a tuple of relevant data for the agent        
+
+        # i think we need light and oncoming for safety, and we need deadline, waypoint for efficiency.
+
+        #state = (waypoint, inputs["light"], inputs["oncoming"], deadline, inputs["left"], inputs["right"])
+        # forward from left could make problems if you run over a red light specifically then. What about crashes when they go left?
+
+        # a priori knowledge approach: not reinforcement learning. Left=forward because in U.S you can turn right on red if no traffic.
+        state = (waypoint, inputs["light"], inputs["oncoming"], inputs["left"]=="forward")
+
+        # reinforcement learning: use everything and let it figure out whats useful. Deadline can lead to recklessness.
+        #state = (waypoint, inputs["light"], inputs["oncoming"], inputs["left"], inputs["right"], deadline)
+
+        return state
+
+
+    def get_maxQ(self, state):
+        """ The get_max_Q function is called when the agent is asked to find the
+            maximum Q-value of all actions based on the 'state' the smartcab is in. """
+
+        ########### 
+        ## TO DO ##
+        ###########
+        # Calculate the maximum Q-value of all actions for a given state
+
+        # break ties between max q if they are the same values. Else you might always prefer a certain action, and will keep picking it, although a different one is better
+        # say the first action reward is net 0, but the second action would start accumulating positive Q thereafter. If you never pick it, you have a problem.
+        # implement this in choose_action
+        maxQ = max(self.Q[state].values())
+
+        return maxQ
+
+
+    def createQ(self, state):
+        """ The createQ function is called when a state is generated by the agent. """
+
+        ########### 
+        ## TO DO ##
+        ###########
+        # When learning, check if the 'state' is not in the Q-table
+        if self.learning: # need to remember Qs when learning...
+            if state not in self.Q:
+                self.Q[state] = {}
+                for action in self.valid_actions:
+                    self.Q[state][action] = 0.0
+
+        # If it is not, create a new dictionary for that state
+        #   Then, for each action available, set the initial Q-value to 0.0
+
+        return
+
+
+    def choose_action(self, state):
+        """ The choose_action function is called when the agent is asked to choose
+            which action to take, based on the 'state' the smartcab is in. """
+
+        # Set the agent state and default action
+        self.state = state
+        self.next_waypoint = self.planner.next_waypoint() # ask planner for next waypoint
+        action = None
+
+        ########### 
+        ## TO DO ##
+        ###########
+        # When not learning, choose a random action
+        if not self.learning:
+            action = random.choice(self.valid_actions)
+
+        # When learning, choose a random action with 'epsilon' probability
+        #   Otherwise, choose an action with the highest Q-value for the current state
+        else:
+
+            # there is also a random.random() function that generates uniformly random numbers between 0 and 1. Much easier than below ;)
+
+            outcomes =['random', 'highest']
+            positive_epsilon = abs(self.epsilon)  # if epsilon is 0 its -0.0000
+            prob = [positive_epsilon, 1-positive_epsilon]
+
+            if np.random.choice(outcomes, p= prob) == "random":
+
+                action = random.choice(self.valid_actions)
+            else:
+
+                # pick best action as dictated by Q, if its a tie pick a random action
+                maxQ = max(self.Q[state].values())
+                maxQs = [key for key, m in self.Q[state].items() if m == maxQ]
+                action_with_highest_q_value_in_current_state = random.choice(maxQs)
+                action = action_with_highest_q_value_in_current_state
+                print(action)
+
+                # you will check this action against reality next, and get a reward for it.
+                # later this reward will influence your Q learning for this round.
+
+
+        return action
+
+
+    def learn(self, state, action, reward):
+        """ The learn function is called after the agent completes an action and
+            receives an award. This function does not consider future rewards 
+            when conducting learning. """
+
+        """
+            we are given the type <s, a, r, s'>. At this point, the agent has already chosen an action, and received a reward for it
+            
+            the content of state will be the state the agent was in before chosing this action (old state).
+            
+            we do not have access to the new state.
+            
+            Q for this state has not been updated yet with the reward of the current action.
+            
+        """
+
+        ###########
+        ## TO DO ##
+        ###########
+        # When learning, implement the value iteration update rule
+        #   Use only the learning rate 'alpha' (do not use the discount factor 'gamma')
+        """
+         the q function is :
+         Q(s, a) = R(state) + gamma * sum_s_prime[ T(s,a,s') * max_a_prime Q(s',a')                      ]
+         gamma is the discount factor of future rewards, T the transition probability for each state,action,next state tuple.
+         max a prime means chose best action after getting to s' to maximise Q in state s prime.
+         notice that we are leaving state s via the specific action a.
+
+         q needs to be updated by the current learning rate alpha
+        
+            alpha decreases to 0 over time, so that the learned value converges to the expected (average) value
+            also, things you learn earlier matter more than those you learn later
+            
+        the idea is to move to the expected q value over time, but problem is q is also changing over time.
+        but it still works (proven somewhere)
+        
+        
+        """
+
+        current_q = self.Q[state][action]
+
+        reward_for_current_action = reward # reward we got at this state for chosing action
+
+        gamma = 0  # complete discounting here, we dont use the max q at this state, only the current reward
+
+        """
+        this is the best Q (future rewards) you can get based on current knowledge of Q, if you are in the current state
+        there is an associated action to this max Q, which is the action you took in this step (so its already done).
+        
+        """
+        max_q_at_this_state = self.get_maxQ(state) # should this be the max q of the next state s prime?
+        """
+         what i do now is just pick the best action at the current state and add its q to current q. I am also adding it again through reward, but this is the real world reward, wheras Q is the learned reward.
+         so i keep updating Q with information from real reward, as well as historical rewards that accumulate in the Q dictionary for this state,action pair
+         
+         we give a weight of 1-alpha to current_q, so that we are not yet completely certain, but as alpha is small it gets a fairly large weight.
+         
+         """
+
+        updated_q = ((1.0 - self.alpha) * current_q) + (self.alpha * (reward_for_current_action + gamma * max_q_at_this_state))
+
+        """
+        so somehow the max_q_at this state tells me what action is best to do next based on future rewards
+        but as we dont use it, the current reward is what i learn
+        the action chosen was done using max Q, so max Q is also current q, so dont have to add it again in function.
+        maybe max Q could somehow help with the future?
+        
+        """
+
+        # now we update the q value for this state,action pair with the Q from the best action
+        self.Q[state][action] = updated_q
+
+        return
+
+
+    def update(self):
+        """ The update function is called when a time step is completed in the 
+            environment for a given trial. This function will build the agent
+            state, choose an action, receive a reward, and learn if enabled. """
+
+        state = self.build_state()          # Get current state
+        self.createQ(state)                 # Create 'state' in Q-table
+        action = self.choose_action(state)  # Choose an action
+        reward = self.env.act(self, action) # Receive a reward
+
+        if self.learning:
+            self.learn(state, action, reward)   # Q-learn
+
+        return
+        
+
+def run():
+    """ Driving function for running the simulation. 
+        Press ESC to close the simulation, or [SPACE] to pause the simulation. """
+
+    ##############
+    # Create the environment
+    # Flags:
+    #   verbose     - set to True to display additional output from the simulation
+    #   num_dummies - discrete number of dummy agents in the environment, default is 100
+    #   grid_size   - discrete number of intersections (columns, rows), default is (8, 6)
+    #   reward_late - gradient of late punishment, not needed if deadline not part of state variables
+    env = Environment(verbose=False)
+    
+    ##############
+    # Create the driving agent
+    # Flags:
+    #   learning   - set to True to force the driving agent to use Q-learning
+    #    * epsilon - continuous value for the exploration factor, default is 1
+    #    * alpha   - continuous value for the learning rate, default is 0.5
+    #    * epsilon_scalar - multiplier in epsilon decay function, controls speed of decay
+    agent = env.create_agent(LearningAgent, learning=True, epsilon=1, alpha=0.01, epsilon_scalar=0.001)
+    # nb of trials before testing is controled by epsilon decay rate
+    """"
+        epsilon_scalar = 0.0005 gives 10k trials, with A+ rating for safety and reliability
+        0.001 gives about 5000 trials
+        
+        it seems with epsilon scalar 0.05, and only 100 trials, but removing deadline from state, you get A rating!
+        this is because the feature space is much smaller, so all states can be visited fairly quickly
+        
+        
+    """
+    
+    ##############
+    # Follow the driving agent
+    # Flags:
+    #   enforce_deadline - set to True to enforce a deadline metric
+    env.set_primary_agent(agent, enforce_deadline=True)
+
+    ##############
+    # Create the simulation
+    # Flags:
+    #   update_delay - continuous time (in seconds) between actions, default is 2.0 seconds, smallest 1 millisecond
+    #   display      - set to False to disable the GUI if PyGame is enabled
+    #   log_metrics  - set to True to log trial and simulation results to /logs
+    #   optimized    - set to True to change the default log file name
+    sim = Simulator(env, update_delay=0.001, log_metrics=True, optimized=True, display=False)
+    
+    ##############
+    # Run the simulator
+    # Flags:
+    #   tolerance  - epsilon tolerance before beginning testing, default is 0.05 
+    #   n_test     - discrete number of testing trials to perform, default is 0
+    sim.run(n_test=10, tolerance=0.01)
+
+    # reliability gets worse as epsilon goes below alpha
+    # key is to increase nb of trials, this improves reliability a lot. But need to find optimal stopping point
+
+
+if __name__ == '__main__':
+    run()
+
+    # you need to run this from a command prompt inside the smartcab folder. From there call:
+    # python smartcab/agent.py
+    # just set up the working directory to parent in config for pycharm, then it works
+
+    import sys, os
+    #sys.path.append("D:/Python Projects/machine_learning/udacity_ml_projects/machine-learning/projects/smartcab/")
+    root_dir = os.path.dirname(os.path.dirname(__file__))
+    sys.path.append(root_dir)
+
+    import visuals as vs
+    #vs.plot_trials("sim_improved-learning.csv")
+    vs.plot_trials("sim_improved-learning.csv")
+
diff --git a/smartcab/logs/sim_default-learning.csv b/smartcab/logs/sim_default-learning.csv
new file mode 100644
index 0000000..8199437
--- /dev/null
+++ b/smartcab/logs/sim_default-learning.csv
@@ -0,0 +1,31 @@
+trial,testing,parameters,initial_deadline,final_deadline,net_reward,actions,success
+1,False,"{'a': 0.5, 'e': 0.95}",20,0,-99.77425496299104,"{0: 15, 1: 0, 2: 2, 3: 1, 4: 2}",0
+2,False,"{'a': 0.5, 'e': 0.8999999999999999}",25,0,-184.17438505776124,"{0: 14, 1: 2, 2: 5, 3: 1, 4: 3}",0
+3,False,"{'a': 0.5, 'e': 0.8499999999999999}",25,0,-93.59244660561683,"{0: 13, 1: 4, 2: 7, 3: 1, 4: 0}",0
+4,False,"{'a': 0.5, 'e': 0.7999999999999998}",20,0,-86.20542885364848,"{0: 12, 1: 3, 2: 3, 3: 1, 4: 1}",0
+5,False,"{'a': 0.5, 'e': 0.7499999999999998}",30,0,-125.81904908326409,"{0: 23, 1: 0, 2: 3, 3: 2, 4: 2}",0
+6,False,"{'a': 0.5, 'e': 0.6999999999999997}",20,0,-169.7754900391223,"{0: 13, 1: 0, 2: 3, 3: 0, 4: 4}",0
+7,False,"{'a': 0.5, 'e': 0.6499999999999997}",25,0,-101.96242417600288,"{0: 14, 1: 3, 2: 5, 3: 3, 4: 0}",0
+8,False,"{'a': 0.5, 'e': 0.5999999999999996}",20,0,-51.669021382597236,"{0: 16, 1: 0, 2: 2, 3: 1, 4: 1}",0
+9,False,"{'a': 0.5, 'e': 0.5499999999999996}",30,0,-114.6857068012144,"{0: 24, 1: 2, 2: 1, 3: 0, 4: 3}",1
+10,False,"{'a': 0.5, 'e': 0.4999999999999996}",25,0,8.523929218718036,"{0: 22, 1: 2, 2: 1, 3: 0, 4: 0}",0
+11,False,"{'a': 0.5, 'e': 0.4499999999999996}",20,6,2.6372405042559306,"{0: 11, 1: 2, 2: 1, 3: 0, 4: 0}",1
+12,False,"{'a': 0.5, 'e': 0.39999999999999963}",20,12,3.885614862099901,"{0: 7, 1: 0, 2: 1, 3: 0, 4: 0}",1
+13,False,"{'a': 0.5, 'e': 0.34999999999999964}",20,6,-82.49861561860719,"{0: 10, 1: 0, 2: 2, 3: 0, 4: 2}",1
+14,False,"{'a': 0.5, 'e': 0.29999999999999966}",25,5,-30.325002712884658,"{0: 17, 1: 1, 2: 1, 3: 0, 4: 1}",1
+15,False,"{'a': 0.5, 'e': 0.24999999999999967}",20,4,-7.753947988884446,"{0: 14, 1: 0, 2: 1, 3: 1, 4: 0}",1
+16,False,"{'a': 0.5, 'e': 0.19999999999999968}",20,0,17.135866450902693,"{0: 19, 1: 0, 2: 1, 3: 0, 4: 0}",0
+17,False,"{'a': 0.5, 'e': 0.1499999999999997}",35,5,-21.04164919169808,"{0: 27, 1: 0, 2: 2, 3: 0, 4: 1}",1
+18,False,"{'a': 0.5, 'e': 0.09999999999999969}",25,1,15.181717259316402,"{0: 23, 1: 0, 2: 0, 3: 1, 4: 0}",1
+19,False,"{'a': 0.5, 'e': 0.049999999999999684}",35,15,33.86188035362803,"{0: 20, 1: 0, 2: 0, 3: 0, 4: 0}",1
+20,False,"{'a': 0.5, 'e': -3.191891195797325e-16}",20,15,10.035833471014122,"{0: 5, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1,True,"{'a': 0.0, 'e': 0.0}",30,20,19.121080280295335,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+2,True,"{'a': 0.0, 'e': 0.0}",25,13,-23.739057300270957,"{0: 11, 1: 0, 2: 0, 3: 0, 4: 1}",1
+3,True,"{'a': 0.0, 'e': 0.0}",25,20,9.768667094901527,"{0: 5, 1: 0, 2: 0, 3: 0, 4: 0}",1
+4,True,"{'a': 0.0, 'e': 0.0}",20,4,23.904146087827254,"{0: 16, 1: 0, 2: 0, 3: 0, 4: 0}",1
+5,True,"{'a': 0.0, 'e': 0.0}",20,16,6.958688575491094,"{0: 4, 1: 0, 2: 0, 3: 0, 4: 0}",1
+6,True,"{'a': 0.0, 'e': 0.0}",35,3,47.84290493713636,"{0: 32, 1: 0, 2: 0, 3: 0, 4: 0}",1
+7,True,"{'a': 0.0, 'e': 0.0}",20,8,19.40669845167018,"{0: 12, 1: 0, 2: 0, 3: 0, 4: 0}",1
+8,True,"{'a': 0.0, 'e': 0.0}",25,16,14.768318913000536,"{0: 9, 1: 0, 2: 0, 3: 0, 4: 0}",1
+9,True,"{'a': 0.0, 'e': 0.0}",25,1,32.27705221109443,"{0: 24, 1: 0, 2: 0, 3: 0, 4: 0}",1
+10,True,"{'a': 0.0, 'e': 0.0}",20,0,29.9382788888541,"{0: 20, 1: 0, 2: 0, 3: 0, 4: 0}",0
diff --git a/smartcab/logs/sim_default-learning.txt b/smartcab/logs/sim_default-learning.txt
new file mode 100644
index 0000000..b719c7f
--- /dev/null
+++ b/smartcab/logs/sim_default-learning.txt
@@ -0,0 +1,256 @@
+/-----------------------------------------
+| State-action rewards from Q-Learning
+\-----------------------------------------
+
+('right', 'red', 'left', True)
+ -- forward : -19.59
+ -- right : -10.38
+ -- None : 1.08
+ -- left : -30.48
+
+('right', 'green', None, False)
+ -- forward : 0.55
+ -- right : 1.38
+ -- None : -4.03
+ -- left : 0.40
+
+('forward', 'green', 'left', False)
+ -- forward : 0.00
+ -- right : 0.97
+ -- None : 0.00
+ -- left : 0.31
+
+('forward', 'green', None, False)
+ -- forward : 1.62
+ -- right : 0.89
+ -- None : -4.16
+ -- left : -0.14
+
+('left', 'red', 'right', False)
+ -- forward : -14.46
+ -- right : 0.68
+ -- None : 1.92
+ -- left : 0.00
+
+('forward', 'green', 'forward', True)
+ -- forward : 0.00
+ -- right : 0.00
+ -- None : 0.00
+ -- left : 0.00
+
+('left', 'green', 'left', True)
+ -- forward : 0.00
+ -- right : 0.65
+ -- None : 0.00
+ -- left : 0.00
+
+('left', 'green', 'forward', False)
+ -- forward : 0.56
+ -- right : 0.00
+ -- None : -2.48
+ -- left : 0.00
+
+('right', 'red', None, False)
+ -- forward : -32.47
+ -- right : 2.08
+ -- None : 1.80
+ -- left : -9.65
+
+('left', 'green', None, True)
+ -- forward : -0.20
+ -- right : 1.36
+ -- None : -2.96
+ -- left : 0.00
+
+('right', 'green', 'forward', False)
+ -- forward : 0.00
+ -- right : 1.97
+ -- None : -2.90
+ -- left : -15.16
+
+('right', 'red', 'left', False)
+ -- forward : -4.83
+ -- right : 1.51
+ -- None : 0.00
+ -- left : 0.00
+
+('forward', 'red', None, False)
+ -- forward : -9.68
+ -- right : 0.76
+ -- None : 1.66
+ -- left : -7.36
+
+('forward', 'green', 'left', True)
+ -- forward : 0.00
+ -- right : 0.00
+ -- None : 0.00
+ -- left : 0.00
+
+('left', 'green', 'right', False)
+ -- forward : 1.26
+ -- right : 0.00
+ -- None : 0.00
+ -- left : -9.83
+
+('forward', 'red', 'forward', True)
+ -- forward : -19.64
+ -- right : 0.00
+ -- None : 0.00
+ -- left : 0.00
+
+('forward', 'red', 'left', False)
+ -- forward : -7.81
+ -- right : 0.87
+ -- None : 0.57
+ -- left : -7.86
+
+('forward', 'green', None, True)
+ -- forward : 2.19
+ -- right : 0.00
+ -- None : -3.58
+ -- left : 0.89
+
+('left', 'red', 'forward', False)
+ -- forward : -7.37
+ -- right : 0.96
+ -- None : 1.08
+ -- left : -5.19
+
+('forward', 'green', 'forward', False)
+ -- forward : 1.93
+ -- right : 0.41
+ -- None : -2.49
+ -- left : -9.81
+
+('left', 'red', None, True)
+ -- forward : -20.39
+ -- right : -9.60
+ -- None : 1.66
+ -- left : -30.13
+
+('left', 'green', 'left', False)
+ -- forward : 0.55
+ -- right : 0.00
+ -- None : 0.41
+ -- left : 1.53
+
+('left', 'green', 'forward', True)
+ -- forward : 0.00
+ -- right : 0.46
+ -- None : -2.06
+ -- left : -10.49
+
+('right', 'red', 'forward', False)
+ -- forward : 0.00
+ -- right : 1.46
+ -- None : 0.00
+ -- left : 0.00
+
+('forward', 'red', 'right', False)
+ -- forward : 0.00
+ -- right : 0.93
+ -- None : 0.00
+ -- left : 0.00
+
+('left', 'red', 'left', True)
+ -- forward : 0.00
+ -- right : 0.00
+ -- None : 0.97
+ -- left : 0.00
+
+('left', 'green', None, False)
+ -- forward : 0.88
+ -- right : 0.40
+ -- None : -3.57
+ -- left : 2.17
+
+('right', 'green', 'left', True)
+ -- forward : 0.00
+ -- right : 0.61
+ -- None : 0.00
+ -- left : 0.00
+
+('right', 'green', 'forward', True)
+ -- forward : 0.00
+ -- right : 1.08
+ -- None : -2.13
+ -- left : 0.00
+
+('right', 'green', 'right', True)
+ -- forward : 0.00
+ -- right : 0.00
+ -- None : 0.00
+ -- left : 0.00
+
+('forward', 'red', None, True)
+ -- forward : -19.94
+ -- right : -14.41
+ -- None : 2.29
+ -- left : -19.94
+
+('right', 'green', None, True)
+ -- forward : 1.60
+ -- right : 0.00
+ -- None : -2.05
+ -- left : -0.20
+
+('forward', 'red', 'forward', False)
+ -- forward : -22.75
+ -- right : 0.00
+ -- None : 1.99
+ -- left : 0.00
+
+('right', 'red', 'right', False)
+ -- forward : 0.00
+ -- right : 1.55
+ -- None : 0.00
+ -- left : 0.00
+
+('left', 'red', 'forward', True)
+ -- forward : 0.00
+ -- right : -9.87
+ -- None : 0.00
+ -- left : -20.03
+
+('forward', 'green', 'right', False)
+ -- forward : 0.00
+ -- right : 0.00
+ -- None : 0.00
+ -- left : -10.12
+
+('left', 'red', None, False)
+ -- forward : -24.17
+ -- right : 1.06
+ -- None : 1.95
+ -- left : -17.00
+
+('right', 'red', 'forward', True)
+ -- forward : 0.00
+ -- right : 0.00
+ -- None : 2.00
+ -- left : 0.00
+
+('left', 'red', 'left', False)
+ -- forward : -30.32
+ -- right : 0.51
+ -- None : 0.00
+ -- left : -7.41
+
+('right', 'red', None, True)
+ -- forward : -29.54
+ -- right : 0.00
+ -- None : 1.51
+ -- left : 0.00
+
+('right', 'green', 'left', False)
+ -- forward : 0.28
+ -- right : 0.00
+ -- None : 0.49
+ -- left : 0.37
+
+('right', 'green', 'right', False)
+ -- forward : 1.36
+ -- right : 0.00
+ -- None : -3.49
+ -- left : -9.60
+
diff --git a/smartcab/logs/sim_improved-learning.csv b/smartcab/logs/sim_improved-learning.csv
new file mode 100644
index 0000000..22cca5b
--- /dev/null
+++ b/smartcab/logs/sim_improved-learning.csv
@@ -0,0 +1,1462 @@
+trial,testing,parameters,initial_deadline,final_deadline,net_reward,actions,success
+1,False,"{'a': 0.01, 'e': 1.0}",25,0,-108.96780636635536,"{0: 17, 1: 2, 2: 4, 3: 0, 4: 2}",0
+2,False,"{'a': 0.01, 'e': 1.0}",25,0,-79.44208005231125,"{0: 14, 1: 6, 2: 3, 3: 2, 4: 0}",0
+3,False,"{'a': 0.01, 'e': 1.0}",25,0,-71.26535670961611,"{0: 19, 1: 0, 2: 4, 3: 1, 4: 1}",0
+4,False,"{'a': 0.01, 'e': 1.0}",30,0,-118.62828529207832,"{0: 20, 1: 2, 2: 6, 3: 0, 4: 2}",0
+5,False,"{'a': 0.01, 'e': 1.0}",20,0,-176.2321540821477,"{0: 10, 1: 3, 2: 3, 3: 1, 4: 3}",0
+6,False,"{'a': 0.01, 'e': 1.0}",20,0,-101.06856112572181,"{0: 12, 1: 1, 2: 5, 3: 1, 4: 1}",0
+7,False,"{'a': 0.01, 'e': 1.0}",20,0,-160.12334839012843,"{0: 11, 1: 3, 2: 2, 3: 1, 4: 3}",0
+8,False,"{'a': 0.01, 'e': 1.0}",25,0,-47.2862905706929,"{0: 19, 1: 0, 2: 5, 3: 1, 4: 0}",0
+9,False,"{'a': 0.01, 'e': 1.0}",25,0,-126.98979744496599,"{0: 17, 1: 0, 2: 5, 3: 1, 4: 2}",0
+10,False,"{'a': 0.01, 'e': 1.0}",20,0,-52.955842538919626,"{0: 15, 1: 1, 2: 3, 3: 0, 4: 1}",0
+11,False,"{'a': 0.01, 'e': 1.0}",25,0,-180.79863248488812,"{0: 14, 1: 1, 2: 4, 3: 4, 4: 2}",0
+12,False,"{'a': 0.01, 'e': 0.9999999999999999}",25,0,-110.6186934862661,"{0: 15, 1: 1, 2: 8, 3: 0, 4: 1}",0
+13,False,"{'a': 0.01, 'e': 0.9999999999999999}",20,0,-148.7739488468915,"{0: 11, 1: 4, 2: 2, 3: 0, 4: 3}",0
+14,False,"{'a': 0.01, 'e': 0.9999999999999999}",25,0,-95.76464261045741,"{0: 15, 1: 3, 2: 6, 3: 0, 4: 1}",0
+15,False,"{'a': 0.01, 'e': 0.9999999999999999}",20,0,-98.03746155492769,"{0: 15, 1: 0, 2: 2, 3: 1, 4: 2}",0
+16,False,"{'a': 0.01, 'e': 0.9999999999999999}",20,5,-16.55328191935883,"{0: 12, 1: 2, 2: 0, 3: 1, 4: 0}",1
+17,False,"{'a': 0.01, 'e': 0.9999999999999998}",20,0,-112.05363146068042,"{0: 12, 1: 0, 2: 6, 3: 1, 4: 1}",0
+18,False,"{'a': 0.01, 'e': 0.9999999999999998}",20,0,-92.06018285540392,"{0: 14, 1: 0, 2: 3, 3: 2, 4: 1}",0
+19,False,"{'a': 0.01, 'e': 0.9999999999999998}",20,0,-66.15076014526754,"{0: 12, 1: 2, 2: 5, 3: 1, 4: 0}",0
+20,False,"{'a': 0.01, 'e': 0.9999999999999997}",25,0,-172.84638028424484,"{0: 13, 1: 2, 2: 6, 3: 2, 4: 2}",0
+21,False,"{'a': 0.01, 'e': 0.9999999999999996}",20,0,-134.21634678453134,"{0: 11, 1: 0, 2: 7, 3: 0, 4: 2}",0
+22,False,"{'a': 0.01, 'e': 0.9999999999999996}",35,15,-116.98209042275873,"{0: 11, 1: 3, 2: 4, 3: 0, 4: 2}",1
+23,False,"{'a': 0.01, 'e': 0.9999999999999994}",20,0,-169.64357986425009,"{0: 10, 1: 2, 2: 5, 3: 0, 4: 3}",0
+24,False,"{'a': 0.01, 'e': 0.9999999999999993}",20,0,-129.21595031363003,"{0: 12, 1: 1, 2: 4, 3: 1, 4: 2}",0
+25,False,"{'a': 0.01, 'e': 0.9999999999999991}",20,0,-113.98824938232467,"{0: 13, 1: 2, 2: 2, 3: 1, 4: 2}",0
+26,False,"{'a': 0.01, 'e': 0.9999999999999989}",25,0,-73.27414358840714,"{0: 19, 1: 2, 2: 2, 3: 1, 4: 1}",0
+27,False,"{'a': 0.01, 'e': 0.9999999999999987}",25,0,-162.83063202861362,"{0: 13, 1: 2, 2: 7, 3: 1, 4: 2}",0
+28,False,"{'a': 0.01, 'e': 0.9999999999999984}",20,7,-54.652256405060136,"{0: 10, 1: 0, 2: 1, 3: 1, 4: 1}",1
+29,False,"{'a': 0.01, 'e': 0.9999999999999981}",25,0,-101.79003872952535,"{0: 16, 1: 1, 2: 6, 3: 1, 4: 1}",0
+30,False,"{'a': 0.01, 'e': 0.9999999999999977}",20,0,-42.703308831712945,"{0: 14, 1: 2, 2: 3, 3: 1, 4: 0}",0
+31,False,"{'a': 0.01, 'e': 0.9999999999999972}",30,0,-187.0427907308196,"{0: 15, 1: 3, 2: 9, 3: 1, 4: 2}",0
+32,False,"{'a': 0.01, 'e': 0.9999999999999966}",20,0,-140.23607505584158,"{0: 9, 1: 1, 2: 8, 3: 1, 4: 1}",0
+33,False,"{'a': 0.01, 'e': 0.9999999999999959}",25,0,-128.35398010580616,"{0: 15, 1: 2, 2: 3, 3: 5, 4: 0}",0
+34,False,"{'a': 0.01, 'e': 0.999999999999995}",20,0,-18.252238540540105,"{0: 16, 1: 1, 2: 3, 3: 0, 4: 0}",0
+35,False,"{'a': 0.01, 'e': 0.999999999999994}",25,0,-36.70860351309524,"{0: 18, 1: 2, 2: 5, 3: 0, 4: 0}",0
+36,False,"{'a': 0.01, 'e': 0.9999999999999928}",20,0,-166.53539182336664,"{0: 11, 1: 2, 2: 3, 3: 1, 4: 3}",0
+37,False,"{'a': 0.01, 'e': 0.9999999999999913}",25,0,-260.2427088824185,"{0: 8, 1: 6, 2: 4, 3: 4, 4: 3}",0
+38,False,"{'a': 0.01, 'e': 0.9999999999999896}",25,0,-74.36599580970785,"{0: 14, 1: 4, 2: 7, 3: 0, 4: 0}",0
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+40,False,"{'a': 0.01, 'e': 0.9999999999999849}",20,0,-29.41290642262633,"{0: 14, 1: 3, 2: 3, 3: 0, 4: 0}",0
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+49,False,"{'a': 0.01, 'e': 0.9999999999999257}",20,0,-226.3626057572194,"{0: 8, 1: 1, 2: 7, 3: 0, 4: 4}",0
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+51,False,"{'a': 0.01, 'e': 0.9999999999998953}",25,0,-153.89634124979844,"{0: 17, 1: 0, 2: 3, 3: 3, 4: 2}",0
+52,False,"{'a': 0.01, 'e': 0.9999999999998759}",30,0,-138.07562229077638,"{0: 19, 1: 4, 2: 4, 3: 1, 4: 2}",0
+53,False,"{'a': 0.01, 'e': 0.999999999999853}",25,0,-101.25625003775188,"{0: 15, 1: 3, 2: 6, 3: 0, 4: 1}",0
+54,False,"{'a': 0.01, 'e': 0.999999999999826}",20,0,-38.838260442743184,"{0: 15, 1: 3, 2: 0, 3: 2, 4: 0}",0
+55,False,"{'a': 0.01, 'e': 0.9999999999997943}",20,0,-72.17017896381708,"{0: 12, 1: 2, 2: 5, 3: 1, 4: 0}",0
+56,False,"{'a': 0.01, 'e': 0.9999999999997571}",20,0,-59.318358736030895,"{0: 15, 1: 1, 2: 3, 3: 0, 4: 1}",0
+57,False,"{'a': 0.01, 'e': 0.9999999999997133}",25,0,-42.224720968582204,"{0: 17, 1: 4, 2: 4, 3: 0, 4: 0}",0
+58,False,"{'a': 0.01, 'e': 0.999999999999662}",30,0,-91.7095043223214,"{0: 22, 1: 4, 2: 2, 3: 0, 4: 2}",0
+59,False,"{'a': 0.01, 'e': 0.999999999999602}",25,0,-216.6501475963622,"{0: 14, 1: 2, 2: 4, 3: 1, 4: 4}",0
+60,False,"{'a': 0.01, 'e': 0.9999999999995317}",25,0,-124.4603138000312,"{0: 11, 1: 3, 2: 10, 3: 1, 4: 0}",0
+61,False,"{'a': 0.01, 'e': 0.9999999999994494}",20,0,-44.37307152097844,"{0: 15, 1: 3, 2: 1, 3: 0, 4: 1}",0
+62,False,"{'a': 0.01, 'e': 0.9999999999993534}",20,0,-139.78319474531597,"{0: 13, 1: 2, 2: 1, 3: 1, 4: 3}",0
+63,False,"{'a': 0.01, 'e': 0.9999999999992412}",30,0,-212.92277622837292,"{0: 18, 1: 2, 2: 5, 3: 1, 4: 4}",0
+64,False,"{'a': 0.01, 'e': 0.9999999999991104}",25,10,-85.12144621650658,"{0: 10, 1: 2, 2: 1, 3: 0, 4: 2}",1
+65,False,"{'a': 0.01, 'e': 0.9999999999989581}",20,0,-107.2658028142508,"{0: 12, 1: 3, 2: 3, 3: 0, 4: 2}",0
+66,False,"{'a': 0.01, 'e': 0.9999999999987806}",20,0,-83.81566326761974,"{0: 12, 1: 2, 2: 5, 3: 0, 4: 1}",0
+67,False,"{'a': 0.01, 'e': 0.9999999999985744}",30,0,-240.21180129866255,"{0: 15, 1: 3, 2: 8, 3: 0, 4: 4}",0
+68,False,"{'a': 0.01, 'e': 0.9999999999983347}",25,0,-129.31307169172828,"{0: 16, 1: 3, 2: 3, 3: 1, 4: 2}",0
+69,False,"{'a': 0.01, 'e': 0.9999999999980563}",25,0,-124.52878674890424,"{0: 14, 1: 2, 2: 7, 3: 1, 4: 1}",0
+70,False,"{'a': 0.01, 'e': 0.9999999999977336}",25,0,-125.85929629307923,"{0: 12, 1: 5, 2: 7, 3: 0, 4: 1}",0
+71,False,"{'a': 0.01, 'e': 0.9999999999973594}",30,0,-136.2449357065508,"{0: 18, 1: 4, 2: 5, 3: 2, 4: 1}",0
+72,False,"{'a': 0.01, 'e': 0.9999999999969262}",20,0,-67.42724910031566,"{0: 14, 1: 1, 2: 4, 3: 0, 4: 1}",0
+73,False,"{'a': 0.01, 'e': 0.9999999999964251}",20,0,-157.14391803457784,"{0: 8, 1: 5, 2: 4, 3: 1, 4: 2}",0
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+1366,False,"{'a': 0.01, 'e': 0.016163950843422148}",20,12,18.188945793689065,"{0: 8, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1367,False,"{'a': 0.01, 'e': 0.016072592977629307}",20,10,18.054442464755518,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1368,False,"{'a': 0.01, 'e': 0.01598174725705037}",30,8,40.65140014468502,"{0: 22, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1369,False,"{'a': 0.01, 'e': 0.015891410858297705}",30,16,24.85622518777923,"{0: 14, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1370,False,"{'a': 0.01, 'e': 0.015801580973007212}",20,4,24.76907693321467,"{0: 16, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1371,False,"{'a': 0.01, 'e': 0.015712254807763837}",20,13,12.789842801246015,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1372,False,"{'a': 0.01, 'e': 0.01562342958402907}",30,18,24.358886203279596,"{0: 12, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1373,False,"{'a': 0.01, 'e': 0.015535102538066892}",25,15,19.56703848719643,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1374,False,"{'a': 0.01, 'e': 0.015447270920871725}",35,25,18.972794670803566,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1375,False,"{'a': 0.01, 'e': 0.015359931998095155}",20,13,12.587824334067307,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1376,False,"{'a': 0.01, 'e': 0.015273083049974212}",25,11,27.42467716457614,"{0: 14, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1377,False,"{'a': 0.01, 'e': 0.015186721371259093}",25,16,18.684911693680295,"{0: 9, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1378,False,"{'a': 0.01, 'e': 0.015100844271141223}",25,18,14.249444129374998,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1379,False,"{'a': 0.01, 'e': 0.01501544907318253}",25,11,21.210510188218944,"{0: 13, 1: 1, 2: 0, 3: 0, 4: 0}",1
+1380,False,"{'a': 0.01, 'e': 0.014930533115243505}",20,15,7.9341331423338675,"{0: 5, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1381,False,"{'a': 0.01, 'e': 0.014846093749412925}",20,14,11.213679973660543,"{0: 6, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1382,False,"{'a': 0.01, 'e': 0.01476212834193713}",20,0,34.06926784739895,"{0: 20, 1: 0, 2: 0, 3: 0, 4: 0}",0
+1383,False,"{'a': 0.01, 'e': 0.014678634273149527}",25,7,31.588754348032825,"{0: 18, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1384,False,"{'a': 0.01, 'e': 0.014595608937400972}",20,5,28.8557355871431,"{0: 15, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1385,False,"{'a': 0.01, 'e': 0.014513049742989725}",30,9,35.815859094078036,"{0: 21, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1386,False,"{'a': 0.01, 'e': 0.014430954112092276}",20,14,5.035263914830354,"{0: 6, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1387,False,"{'a': 0.01, 'e': 0.014349319480694067}",20,8,20.002130362870147,"{0: 12, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1388,False,"{'a': 0.01, 'e': 0.014268143298520775}",20,13,13.489974839248532,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1389,False,"{'a': 0.01, 'e': 0.01418742302896936}",30,15,26.349814342834865,"{0: 15, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1390,False,"{'a': 0.01, 'e': 0.014107156149040234}",30,7,32.83749975417698,"{0: 22, 1: 1, 2: 0, 3: 0, 4: 0}",1
+1391,False,"{'a': 0.01, 'e': 0.014027340149269318}",25,1,36.75567829601171,"{0: 23, 1: 1, 2: 0, 3: 0, 4: 0}",1
+1392,False,"{'a': 0.01, 'e': 0.013947972533659647}",20,13,13.486769582569547,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1393,False,"{'a': 0.01, 'e': 0.013869050819614648}",20,7,23.00007481757514,"{0: 13, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1394,False,"{'a': 0.01, 'e': 0.013790572537870638}",20,6,22.835060481892583,"{0: 14, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1395,False,"{'a': 0.01, 'e': 0.01371253523242999}",25,8,21.900444561760644,"{0: 16, 1: 0, 2: 1, 3: 0, 4: 0}",1
+1396,False,"{'a': 0.01, 'e': 0.013634936460494518}",25,13,11.897547768585998,"{0: 11, 1: 0, 2: 1, 3: 0, 4: 0}",1
+1397,False,"{'a': 0.01, 'e': 0.013557773792399086}",20,8,26.14125844165046,"{0: 12, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1398,False,"{'a': 0.01, 'e': 0.013481044811545995}",20,11,14.556672069664437,"{0: 9, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1399,False,"{'a': 0.01, 'e': 0.013404747114338478}",20,8,25.225373858253068,"{0: 12, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1400,False,"{'a': 0.01, 'e': 0.013328878310115977}",20,11,17.881156985222976,"{0: 9, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1401,False,"{'a': 0.01, 'e': 0.013253436021088416}",25,11,29.76998557127509,"{0: 14, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1402,False,"{'a': 0.01, 'e': 0.013178417882271365}",20,7,26.804067544992726,"{0: 13, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1403,False,"{'a': 0.01, 'e': 0.013103821541421645}",30,15,20.65922316876076,"{0: 15, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1404,False,"{'a': 0.01, 'e': 0.013029644658972828}",20,13,13.941076618520993,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1405,False,"{'a': 0.01, 'e': 0.012955884907970838}",20,10,19.850371609126913,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1406,False,"{'a': 0.01, 'e': 0.012882539974010454}",20,9,21.492986707378705,"{0: 11, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1407,False,"{'a': 0.01, 'e': 0.012809607555171798}",20,3,31.1795254906815,"{0: 17, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1408,False,"{'a': 0.01, 'e': 0.012737085361956946}",30,14,32.85161690405128,"{0: 16, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1409,False,"{'a': 0.01, 'e': 0.012664971117226642}",20,2,34.42126118376929,"{0: 18, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1410,False,"{'a': 0.01, 'e': 0.01259326255613824}",20,0,34.29284603047597,"{0: 20, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1411,False,"{'a': 0.01, 'e': 0.01252195742608242}",20,10,18.554563195287308,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1412,False,"{'a': 0.01, 'e': 0.012451053486621677}",20,13,13.043231130738413,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1413,False,"{'a': 0.01, 'e': 0.012380548509428158}",20,0,34.081354079287344,"{0: 20, 1: 0, 2: 0, 3: 0, 4: 0}",0
+1414,False,"{'a': 0.01, 'e': 0.012310440278221924}",20,12,12.557509557947064,"{0: 8, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1415,False,"{'a': 0.01, 'e': 0.012240726588709672}",20,6,13.320972393326771,"{0: 13, 1: 0, 2: 1, 3: 0, 4: 0}",1
+1416,False,"{'a': 0.01, 'e': 0.01217140524852367}",25,16,14.367411051218134,"{0: 9, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1417,False,"{'a': 0.01, 'e': 0.012102474077160585}",20,12,15.436337943396087,"{0: 8, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1418,False,"{'a': 0.01, 'e': 0.01203393090592153}",35,15,35.61108302488062,"{0: 20, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1419,False,"{'a': 0.01, 'e': 0.011965773577851224}",20,9,19.476063008072146,"{0: 11, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1420,False,"{'a': 0.01, 'e': 0.01189799994767815}",20,7,24.037473095513313,"{0: 13, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1421,False,"{'a': 0.01, 'e': 0.011830607881754829}",30,22,18.161097946424892,"{0: 8, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1422,False,"{'a': 0.01, 'e': 0.011763595257997972}",30,0,44.798622757893376,"{0: 30, 1: 0, 2: 0, 3: 0, 4: 0}",0
+1423,False,"{'a': 0.01, 'e': 0.011696959965829978}",25,12,27.052122349991492,"{0: 13, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1424,False,"{'a': 0.01, 'e': 0.01163069990611909}",20,0,35.43648638150181,"{0: 20, 1: 0, 2: 0, 3: 0, 4: 0}",0
+1425,False,"{'a': 0.01, 'e': 0.01156481299112111}",20,16,6.124497153608251,"{0: 4, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1426,False,"{'a': 0.01, 'e': 0.011499297144421106}",20,10,16.071800841229134,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1427,False,"{'a': 0.01, 'e': 0.011434150300874801}",30,8,37.98898990500734,"{0: 22, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1428,False,"{'a': 0.01, 'e': 0.01136937040655095}",20,9,19.839893268980717,"{0: 11, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1429,False,"{'a': 0.01, 'e': 0.01130495541867349}",25,2,38.28965712395873,"{0: 23, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1430,False,"{'a': 0.01, 'e': 0.011240903305564265}",30,4,43.47074750510962,"{0: 26, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1431,False,"{'a': 0.01, 'e': 0.011177212046585727}",20,13,12.567749985856336,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1432,False,"{'a': 0.01, 'e': 0.0111138796320841}",25,17,13.784477546168537,"{0: 8, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1433,False,"{'a': 0.01, 'e': 0.011050904063332978}",30,17,26.686600697158788,"{0: 13, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1434,False,"{'a': 0.01, 'e': 0.010988283352476369}",20,13,12.128093881916175,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1435,False,"{'a': 0.01, 'e': 0.010926015522473076}",25,17,1.5438536208312659,"{0: 7, 1: 0, 2: 1, 3: 0, 4: 0}",1
+1436,False,"{'a': 0.01, 'e': 0.010864098607040629}",20,9,18.73825849385846,"{0: 11, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1437,False,"{'a': 0.01, 'e': 0.010802530650599662}",25,18,14.433055503959444,"{0: 7, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1438,False,"{'a': 0.01, 'e': 0.010741309708218738}",30,16,28.920115052149523,"{0: 14, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1439,False,"{'a': 0.01, 'e': 0.010680433845558834}",20,14,11.63370926705957,"{0: 6, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1440,False,"{'a': 0.01, 'e': 0.010619901138818943}",20,6,25.083314067804523,"{0: 14, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1441,False,"{'a': 0.01, 'e': 0.010559709674680895}",35,18,36.69170778876012,"{0: 17, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1442,False,"{'a': 0.01, 'e': 0.010499857550255731}",25,17,10.84547817155854,"{0: 7, 1: 1, 2: 0, 3: 0, 4: 0}",1
+1443,False,"{'a': 0.01, 'e': 0.010440342873029085}",30,24,12.93778209738799,"{0: 6, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1444,False,"{'a': 0.01, 'e': 0.010381163760807222}",20,0,34.12673200703016,"{0: 20, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1445,False,"{'a': 0.01, 'e': 0.01032231834166375}",35,21,25.564248672008993,"{0: 14, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1446,False,"{'a': 0.01, 'e': 0.01026380475388644}",20,14,11.055833276139854,"{0: 6, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1447,False,"{'a': 0.01, 'e': 0.010205621145923494}",25,16,15.683955847899902,"{0: 9, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1448,False,"{'a': 0.01, 'e': 0.010147765676331022}",35,21,24.74456605204462,"{0: 14, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1449,False,"{'a': 0.01, 'e': 0.01009023651372043}",20,10,16.94754059285982,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1450,False,"{'a': 0.01, 'e': 0.010033031836706008}",20,3,34.05901635449409,"{0: 17, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1451,False,"{'a': 0.01, 'e': 0.009976149833852865}",20,7,22.716400147985727,"{0: 13, 1: 0, 2: 0, 3: 0, 4: 0}",1
+1,True,"{'a': 0.0, 'e': 0.0}",20,10,19.62537736103818,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
+2,True,"{'a': 0.0, 'e': 0.0}",25,9,30.67095725223201,"{0: 16, 1: 0, 2: 0, 3: 0, 4: 0}",1
+3,True,"{'a': 0.0, 'e': 0.0}",25,13,23.22037616969927,"{0: 12, 1: 0, 2: 0, 3: 0, 4: 0}",1
+4,True,"{'a': 0.0, 'e': 0.0}",25,12,23.559397510423857,"{0: 13, 1: 0, 2: 0, 3: 0, 4: 0}",1
+5,True,"{'a': 0.0, 'e': 0.0}",30,13,33.89727489897414,"{0: 17, 1: 0, 2: 0, 3: 0, 4: 0}",1
+6,True,"{'a': 0.0, 'e': 0.0}",30,21,16.333634294102367,"{0: 9, 1: 0, 2: 0, 3: 0, 4: 0}",1
+7,True,"{'a': 0.0, 'e': 0.0}",20,14,14.099413707846358,"{0: 6, 1: 0, 2: 0, 3: 0, 4: 0}",1
+8,True,"{'a': 0.0, 'e': 0.0}",20,12,14.160381947784604,"{0: 8, 1: 0, 2: 0, 3: 0, 4: 0}",1
+9,True,"{'a': 0.0, 'e': 0.0}",30,14,32.008362107071896,"{0: 16, 1: 0, 2: 0, 3: 0, 4: 0}",1
+10,True,"{'a': 0.0, 'e': 0.0}",30,17,26.825034128726344,"{0: 13, 1: 0, 2: 0, 3: 0, 4: 0}",1
diff --git a/smartcab/logs/sim_improved-learning.txt b/smartcab/logs/sim_improved-learning.txt
new file mode 100644
index 0000000..b61a23c
--- /dev/null
+++ b/smartcab/logs/sim_improved-learning.txt
@@ -0,0 +1,292 @@
+/-----------------------------------------
+| State-action rewards from Q-Learning
+\-----------------------------------------
+
+('right', 'red', 'left', True)
+ -- forward : -7.59
+ -- right : -3.34
+ -- None : 0.68
+ -- left : -7.58
+
+('right', 'green', None, False)
+ -- forward : 0.60
+ -- right : 1.89
+ -- None : -4.80
+ -- left : 0.74
+
+('forward', 'green', 'left', False)
+ -- forward : 1.85
+ -- right : 0.52
+ -- None : 0.50
+ -- left : 0.43
+
+('left', 'green', 'right', True)
+ -- forward : 0.09
+ -- right : 0.05
+ -- None : -0.68
+ -- left : -0.99
+
+('forward', 'green', None, False)
+ -- forward : 1.82
+ -- right : 0.76
+ -- None : -4.70
+ -- left : 0.69
+
+('forward', 'green', 'right', True)
+ -- forward : 0.32
+ -- right : 0.00
+ -- None : -0.30
+ -- left : -0.60
+
+('left', 'red', 'right', False)
+ -- forward : -2.77
+ -- right : 0.23
+ -- None : 1.22
+ -- left : -12.38
+
+('forward', 'green', 'forward', True)
+ -- forward : 0.66
+ -- right : 0.07
+ -- None : -0.61
+ -- left : -2.94
+
+('left', 'green', 'left', True)
+ -- forward : 0.14
+ -- right : 0.12
+ -- None : 0.16
+ -- left : 0.87
+
+('left', 'green', 'forward', False)
+ -- forward : 0.72
+ -- right : 0.34
+ -- None : -2.76
+ -- left : -10.86
+
+('right', 'red', None, False)
+ -- forward : -12.38
+ -- right : 1.63
+ -- None : 1.81
+ -- left : -12.19
+
+('left', 'green', None, True)
+ -- forward : 0.48
+ -- right : 0.46
+ -- None : -2.70
+ -- left : 1.55
+
+('right', 'red', 'right', True)
+ -- forward : -2.70
+ -- right : -0.79
+ -- None : 0.25
+ -- left : -0.79
+
+('right', 'green', 'forward', False)
+ -- forward : 0.34
+ -- right : 1.59
+ -- None : -2.61
+ -- left : -12.31
+
+('right', 'red', 'left', False)
+ -- forward : -10.00
+ -- right : 1.83
+ -- None : 1.29
+ -- left : -9.28
+
+('forward', 'red', None, False)
+ -- forward : -12.14
+ -- right : 0.69
+ -- None : 1.81
+ -- left : -13.31
+
+('forward', 'green', 'left', True)
+ -- forward : 1.06
+ -- right : 0.15
+ -- None : 0.08
+ -- left : 0.18
+
+('left', 'green', 'right', False)
+ -- forward : 0.13
+ -- right : 0.44
+ -- None : -1.44
+ -- left : -6.68
+
+('forward', 'red', 'forward', True)
+ -- forward : -4.93
+ -- right : -2.64
+ -- None : 0.80
+ -- left : -4.17
+
+('forward', 'red', 'left', False)
+ -- forward : -9.09
+ -- right : 0.73
+ -- None : 1.87
+ -- left : -9.62
+
+('forward', 'green', None, True)
+ -- forward : 1.68
+ -- right : 0.35
+ -- None : -2.43
+ -- left : 0.35
+
+('forward', 'red', 'left', True)
+ -- forward : -4.53
+ -- right : -2.47
+ -- None : 1.04
+ -- left : -4.56
+
+('left', 'red', 'forward', False)
+ -- forward : -8.35
+ -- right : 0.55
+ -- None : 1.82
+ -- left : -8.68
+
+('forward', 'green', 'forward', False)
+ -- forward : 1.86
+ -- right : 0.30
+ -- None : -2.18
+ -- left : -10.04
+
+('left', 'red', None, True)
+ -- forward : -16.59
+ -- right : -9.61
+ -- None : 1.44
+ -- left : -16.05
+
+('left', 'green', 'left', False)
+ -- forward : 0.57
+ -- right : 0.52
+ -- None : 0.44
+ -- left : 1.78
+
+('left', 'green', 'forward', True)
+ -- forward : 0.32
+ -- right : 0.10
+ -- None : -0.45
+ -- left : -4.30
+
+('right', 'red', 'forward', False)
+ -- forward : -8.58
+ -- right : 1.21
+ -- None : 1.83
+ -- left : -9.31
+
+('forward', 'red', 'right', False)
+ -- forward : -3.86
+ -- right : 0.26
+ -- None : 1.48
+ -- left : -10.40
+
+('left', 'red', 'left', True)
+ -- forward : -8.87
+ -- right : -3.70
+ -- None : 0.82
+ -- left : -3.81
+
+('left', 'green', None, False)
+ -- forward : 0.68
+ -- right : 0.63
+ -- None : -4.73
+ -- left : 1.82
+
+('right', 'green', 'left', True)
+ -- forward : 0.13
+ -- right : 0.82
+ -- None : 0.12
+ -- left : 0.08
+
+('right', 'green', 'forward', True)
+ -- forward : 0.10
+ -- right : 0.56
+ -- None : -0.43
+ -- left : -1.53
+
+('right', 'green', 'right', True)
+ -- forward : 0.09
+ -- right : 0.34
+ -- None : -0.15
+ -- left : -2.09
+
+('forward', 'red', None, True)
+ -- forward : -20.60
+ -- right : -6.76
+ -- None : 1.56
+ -- left : -14.52
+
+('right', 'green', None, True)
+ -- forward : 0.35
+ -- right : 1.43
+ -- None : -2.81
+ -- left : 0.45
+
+('forward', 'red', 'forward', False)
+ -- forward : -8.90
+ -- right : 0.46
+ -- None : 1.89
+ -- left : -7.98
+
+('right', 'red', 'right', False)
+ -- forward : -5.16
+ -- right : 0.55
+ -- None : 1.17
+ -- left : -12.99
+
+('left', 'red', 'forward', True)
+ -- forward : -3.82
+ -- right : -3.47
+ -- None : 0.69
+ -- left : -3.45
+
+('forward', 'green', 'right', False)
+ -- forward : 1.35
+ -- right : 0.15
+ -- None : -1.48
+ -- left : -5.52
+
+('left', 'red', 'right', True)
+ -- forward : -3.07
+ -- right : -1.34
+ -- None : 0.31
+ -- left : -2.34
+
+('left', 'red', None, False)
+ -- forward : -12.62
+ -- right : 0.70
+ -- None : 1.82
+ -- left : -12.65
+
+('right', 'red', 'forward', True)
+ -- forward : -4.54
+ -- right : -2.94
+ -- None : 0.78
+ -- left : -3.45
+
+('forward', 'red', 'right', True)
+ -- forward : -3.11
+ -- right : -0.59
+ -- None : 0.31
+ -- left : -3.81
+
+('left', 'red', 'left', False)
+ -- forward : -9.33
+ -- right : 0.56
+ -- None : 1.82
+ -- left : -11.30
+
+('right', 'red', None, True)
+ -- forward : -18.70
+ -- right : -7.89
+ -- None : 1.27
+ -- left : -15.31
+
+('right', 'green', 'left', False)
+ -- forward : 0.49
+ -- right : 1.60
+ -- None : 0.45
+ -- left : 0.40
+
+('right', 'green', 'right', False)
+ -- forward : 0.13
+ -- right : 0.98
+ -- None : -0.99
+ -- left : -4.65
+
diff --git a/smartcab/logs/sim_improved-learning_ref.csv b/smartcab/logs/sim_improved-learning_ref.csv
new file mode 100644
index 0000000..b1f1809
--- /dev/null
+++ b/smartcab/logs/sim_improved-learning_ref.csv
@@ -0,0 +1,4617 @@
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+4605,False,"{'a': 0.01, 'e': 0.010001702004705479}",25,15,22.08324180787924,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
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+6,True,"{'a': 0.0, 'e': 0.0}",30,21,18.85495444814331,"{0: 9, 1: 0, 2: 0, 3: 0, 4: 0}",1
+7,True,"{'a': 0.0, 'e': 0.0}",20,2,26.62957430279652,"{0: 18, 1: 0, 2: 0, 3: 0, 4: 0}",1
+8,True,"{'a': 0.0, 'e': 0.0}",20,8,20.42049781373089,"{0: 12, 1: 0, 2: 0, 3: 0, 4: 0}",1
+9,True,"{'a': 0.0, 'e': 0.0}",30,20,21.161776577693406,"{0: 10, 1: 0, 2: 0, 3: 0, 4: 0}",1
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diff --git a/smartcab/logs/sim_improved-learning_ref.txt b/smartcab/logs/sim_improved-learning_ref.txt
new file mode 100644
index 0000000..3976e9e
--- /dev/null
+++ b/smartcab/logs/sim_improved-learning_ref.txt
@@ -0,0 +1,292 @@
+/-----------------------------------------
+| State-action rewards from Q-Learning
+\-----------------------------------------
+
+('right', 'red', 'left', True)
+ -- forward : -6.29
+ -- right : -2.60
+ -- None : 1.29
+ -- left : -8.27
+
+('right', 'green', None, False)
+ -- forward : 0.68
+ -- right : 2.01
+ -- None : -4.62
+ -- left : 0.69
+
+('forward', 'green', 'left', False)
+ -- forward : 1.87
+ -- right : 0.73
+ -- None : 0.70
+ -- left : 0.71
+
+('left', 'green', 'right', True)
+ -- forward : 0.39
+ -- right : 0.08
+ -- None : -0.46
+ -- left : -1.15
+
+('forward', 'green', None, False)
+ -- forward : 1.82
+ -- right : 0.84
+ -- None : -4.82
+ -- left : 0.79
+
+('forward', 'green', 'right', True)
+ -- forward : 1.37
+ -- right : 0.09
+ -- None : -0.39
+ -- left : -1.54
+
+('left', 'red', 'right', False)
+ -- forward : -3.20
+ -- right : 0.22
+ -- None : 1.90
+ -- left : -12.99
+
+('forward', 'green', 'forward', True)
+ -- forward : 1.64
+ -- right : 0.10
+ -- None : -0.88
+ -- left : -2.77
+
+('left', 'green', 'left', True)
+ -- forward : 0.13
+ -- right : 0.14
+ -- None : 0.09
+ -- left : 1.60
+
+('left', 'green', 'forward', False)
+ -- forward : 0.53
+ -- right : 0.98
+ -- None : -2.71
+ -- left : -11.58
+
+('right', 'red', None, False)
+ -- forward : -12.03
+ -- right : 1.86
+ -- None : 1.60
+ -- left : -12.51
+
+('left', 'green', None, True)
+ -- forward : 0.36
+ -- right : 0.35
+ -- None : -2.36
+ -- left : 1.91
+
+('right', 'red', 'right', True)
+ -- forward : -0.79
+ -- right : -1.39
+ -- None : 0.73
+ -- left : -1.56
+
+('right', 'green', 'forward', False)
+ -- forward : 0.34
+ -- right : 1.77
+ -- None : -2.31
+ -- left : -10.89
+
+('right', 'red', 'left', False)
+ -- forward : -9.37
+ -- right : 1.85
+ -- None : 1.03
+ -- left : -8.93
+
+('forward', 'red', None, False)
+ -- forward : -12.35
+ -- right : 0.83
+ -- None : 1.91
+ -- left : -12.56
+
+('forward', 'green', 'left', True)
+ -- forward : 1.75
+ -- right : 0.16
+ -- None : 0.17
+ -- left : 0.21
+
+('left', 'green', 'right', False)
+ -- forward : 0.69
+ -- right : 0.19
+ -- None : -1.51
+ -- left : -5.68
+
+('forward', 'red', 'forward', True)
+ -- forward : -2.35
+ -- right : -3.97
+ -- None : 1.75
+ -- left : -6.59
+
+('forward', 'red', 'left', False)
+ -- forward : -11.63
+ -- right : 0.76
+ -- None : 1.89
+ -- left : -11.97
+
+('forward', 'green', None, True)
+ -- forward : 1.83
+ -- right : 0.49
+ -- None : -3.33
+ -- left : 0.51
+
+('forward', 'red', 'left', True)
+ -- forward : -8.27
+ -- right : -5.76
+ -- None : 1.83
+ -- left : -7.61
+
+('left', 'red', 'forward', False)
+ -- forward : -8.95
+ -- right : 0.56
+ -- None : 1.86
+ -- left : -9.58
+
+('forward', 'green', 'forward', False)
+ -- forward : 1.89
+ -- right : 0.54
+ -- None : -3.24
+ -- left : -13.41
+
+('left', 'red', None, True)
+ -- forward : -16.95
+ -- right : -6.49
+ -- None : 1.87
+ -- left : -16.03
+
+('left', 'green', 'left', False)
+ -- forward : 0.41
+ -- right : 0.52
+ -- None : 0.57
+ -- left : 1.86
+
+('left', 'green', 'forward', True)
+ -- forward : 0.08
+ -- right : 0.61
+ -- None : -0.55
+ -- left : -2.98
+
+('right', 'red', 'forward', False)
+ -- forward : -6.69
+ -- right : 1.96
+ -- None : 0.87
+ -- left : -6.21
+
+('forward', 'red', 'right', False)
+ -- forward : -4.60
+ -- right : 0.27
+ -- None : 1.94
+ -- left : -12.99
+
+('left', 'red', 'left', True)
+ -- forward : -3.07
+ -- right : -3.46
+ -- None : 1.65
+ -- left : -7.31
+
+('left', 'green', None, False)
+ -- forward : 0.76
+ -- right : 0.78
+ -- None : -4.68
+ -- left : 1.94
+
+('right', 'green', 'left', True)
+ -- forward : 0.11
+ -- right : 1.49
+ -- None : 0.09
+ -- left : 0.05
+
+('right', 'green', 'forward', True)
+ -- forward : 0.03
+ -- right : 0.99
+ -- None : -0.54
+ -- left : -1.92
+
+('right', 'green', 'right', True)
+ -- forward : 0.01
+ -- right : 0.78
+ -- None : -0.24
+ -- left : -0.98
+
+('forward', 'red', None, True)
+ -- forward : -16.98
+ -- right : -9.52
+ -- None : 1.92
+ -- left : -19.57
+
+('right', 'green', None, True)
+ -- forward : 0.26
+ -- right : 1.81
+ -- None : -2.39
+ -- left : 0.42
+
+('forward', 'red', 'forward', False)
+ -- forward : -9.87
+ -- right : 0.61
+ -- None : 1.92
+ -- left : -9.80
+
+('right', 'red', 'right', False)
+ -- forward : -2.53
+ -- right : 0.46
+ -- None : 1.71
+ -- left : -12.16
+
+('left', 'red', 'forward', True)
+ -- forward : -6.27
+ -- right : -3.81
+ -- None : 1.55
+ -- left : -5.92
+
+('forward', 'green', 'right', False)
+ -- forward : 1.93
+ -- right : 0.29
+ -- None : -1.54
+ -- left : -7.30
+
+('left', 'red', 'right', True)
+ -- forward : -1.93
+ -- right : -1.72
+ -- None : 0.89
+ -- left : -4.55
+
+('left', 'red', None, False)
+ -- forward : -12.82
+ -- right : 0.80
+ -- None : 1.88
+ -- left : -12.24
+
+('right', 'red', 'forward', True)
+ -- forward : -4.53
+ -- right : -1.52
+ -- None : 1.00
+ -- left : -4.54
+
+('forward', 'red', 'right', True)
+ -- forward : -3.43
+ -- right : -1.75
+ -- None : 1.22
+ -- left : -4.20
+
+('left', 'red', 'left', False)
+ -- forward : -9.92
+ -- right : 0.67
+ -- None : 1.93
+ -- left : -10.43
+
+('right', 'red', None, True)
+ -- forward : -14.26
+ -- right : -6.93
+ -- None : 1.71
+ -- left : -11.28
+
+('right', 'green', 'left', False)
+ -- forward : 0.38
+ -- right : 1.91
+ -- None : 0.45
+ -- left : 0.40
+
+('right', 'green', 'right', False)
+ -- forward : 0.20
+ -- right : 1.59
+ -- None : -0.79
+ -- left : -6.21
+
diff --git a/smartcab/logs/sim_no-learning.csv b/smartcab/logs/sim_no-learning.csv
new file mode 100644
index 0000000..f56fd99
--- /dev/null
+++ b/smartcab/logs/sim_no-learning.csv
@@ -0,0 +1,31 @@
+trial,testing,parameters,initial_deadline,final_deadline,net_reward,actions,success
+1,False,"{'a': 0.5, 'e': 1.0}",30,0,-189.18766695428616,"{0: 15, 1: 5, 2: 6, 3: 2, 4: 2}",0
+2,False,"{'a': 0.5, 'e': 1.0}",20,0,-32.028728542392514,"{0: 14, 1: 2, 2: 4, 3: 0, 4: 0}",0
+3,False,"{'a': 0.5, 'e': 1.0}",20,12,-6.61394572212318,"{0: 6, 1: 1, 2: 1, 3: 0, 4: 0}",1
+4,False,"{'a': 0.5, 'e': 1.0}",20,0,-105.96250760705891,"{0: 10, 1: 4, 2: 4, 3: 1, 4: 1}",0
+5,False,"{'a': 0.5, 'e': 1.0}",20,0,-148.94203511584809,"{0: 11, 1: 1, 2: 4, 3: 2, 4: 2}",0
+6,False,"{'a': 0.5, 'e': 1.0}",20,0,-50.92941811419935,"{0: 13, 1: 0, 2: 7, 3: 0, 4: 0}",0
+7,False,"{'a': 0.5, 'e': 1.0}",20,0,-47.545391189460986,"{0: 14, 1: 1, 2: 4, 3: 1, 4: 0}",0
+8,False,"{'a': 0.5, 'e': 1.0}",20,0,-77.40255656893456,"{0: 10, 1: 6, 2: 2, 3: 2, 4: 0}",0
+9,False,"{'a': 0.5, 'e': 1.0}",30,0,-139.0246068390602,"{0: 20, 1: 2, 2: 5, 3: 1, 4: 2}",0
+10,False,"{'a': 0.5, 'e': 1.0}",20,0,-61.37583009358228,"{0: 12, 1: 3, 2: 4, 3: 1, 4: 0}",0
+11,False,"{'a': 0.5, 'e': 1.0}",25,0,-81.4946198129636,"{0: 17, 1: 4, 2: 2, 3: 1, 4: 1}",0
+12,False,"{'a': 0.5, 'e': 1.0}",20,0,-65.60843961080717,"{0: 15, 1: 1, 2: 2, 3: 1, 4: 1}",0
+13,False,"{'a': 0.5, 'e': 1.0}",30,0,-122.25152672899478,"{0: 18, 1: 3, 2: 8, 3: 0, 4: 1}",0
+14,False,"{'a': 0.5, 'e': 1.0}",25,0,-130.54699646076554,"{0: 11, 1: 5, 2: 8, 3: 0, 4: 1}",0
+15,False,"{'a': 0.5, 'e': 1.0}",25,0,-159.93976619888116,"{0: 17, 1: 4, 2: 0, 3: 0, 4: 4}",0
+16,False,"{'a': 0.5, 'e': 1.0}",20,0,-41.230151913538315,"{0: 11, 1: 6, 2: 3, 3: 0, 4: 0}",0
+17,False,"{'a': 0.5, 'e': 1.0}",30,0,4.418273616665318,"{0: 26, 1: 2, 2: 2, 3: 0, 4: 0}",0
+18,False,"{'a': 0.5, 'e': 1.0}",25,0,-51.82444395636539,"{0: 17, 1: 2, 2: 6, 3: 0, 4: 0}",0
+19,False,"{'a': 0.5, 'e': 1.0}",25,0,-174.23957942445747,"{0: 11, 1: 3, 2: 9, 3: 0, 4: 2}",0
+20,False,"{'a': 0.5, 'e': 1.0}",20,0,-92.19225710620512,"{0: 11, 1: 3, 2: 5, 3: 0, 4: 1}",0
+1,True,"{'a': 0.5, 'e': 1.0}",20,0,-272.6295078055859,"{0: 5, 1: 3, 2: 6, 3: 2, 4: 4}",0
+2,True,"{'a': 0.5, 'e': 1.0}",20,0,-89.70516983314789,"{0: 13, 1: 2, 2: 3, 3: 1, 4: 1}",0
+3,True,"{'a': 0.5, 'e': 1.0}",20,0,-40.372218124024705,"{0: 15, 1: 0, 2: 5, 3: 0, 4: 0}",0
+4,True,"{'a': 0.5, 'e': 1.0}",20,4,-84.79055985923387,"{0: 10, 1: 1, 2: 3, 3: 1, 4: 1}",1
+5,True,"{'a': 0.5, 'e': 1.0}",20,0,-93.53703439272299,"{0: 11, 1: 4, 2: 4, 3: 0, 4: 1}",0
+6,True,"{'a': 0.5, 'e': 1.0}",20,0,-99.59745080640693,"{0: 13, 1: 2, 2: 3, 3: 0, 4: 2}",0
+7,True,"{'a': 0.5, 'e': 1.0}",20,0,-58.149657299144096,"{0: 15, 1: 1, 2: 3, 3: 0, 4: 1}",0
+8,True,"{'a': 0.5, 'e': 1.0}",25,0,-65.96100362059256,"{0: 16, 1: 2, 2: 6, 3: 1, 4: 0}",0
+9,True,"{'a': 0.5, 'e': 1.0}",20,0,-188.629556046148,"{0: 7, 1: 5, 2: 5, 3: 0, 4: 3}",0
+10,True,"{'a': 0.5, 'e': 1.0}",25,10,-91.40865250704636,"{0: 10, 1: 1, 2: 2, 3: 0, 4: 2}",1
diff --git a/smartcab/report.html b/smartcab/report.html
new file mode 100644
index 0000000..83f8217
--- /dev/null
+++ b/smartcab/report.html
@@ -0,0 +1,19184 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>smartcab</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    /*!
+*
+* Twitter Bootstrap
+*
+*/
+/*!
+ * Bootstrap v3.3.7 (http://getbootstrap.com)
+ * Copyright 2011-2016 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: 1px dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    background: transparent !important;
+    color: #000 !important;
+    box-shadow: none !important;
+    text-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: 'Glyphicons Halflings';
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
+  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Glyphicons Halflings';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
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+.h4 small,
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+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
+.fa-pencil-square-o:before {
+  content: "\f044";
+}
+.fa-share-square-o:before {
+  content: "\f045";
+}
+.fa-check-square-o:before {
+  content: "\f046";
+}
+.fa-arrows:before {
+  content: "\f047";
+}
+.fa-step-backward:before {
+  content: "\f048";
+}
+.fa-fast-backward:before {
+  content: "\f049";
+}
+.fa-backward:before {
+  content: "\f04a";
+}
+.fa-play:before {
+  content: "\f04b";
+}
+.fa-pause:before {
+  content: "\f04c";
+}
+.fa-stop:before {
+  content: "\f04d";
+}
+.fa-forward:before {
+  content: "\f04e";
+}
+.fa-fast-forward:before {
+  content: "\f050";
+}
+.fa-step-forward:before {
+  content: "\f051";
+}
+.fa-eject:before {
+  content: "\f052";
+}
+.fa-chevron-left:before {
+  content: "\f053";
+}
+.fa-chevron-right:before {
+  content: "\f054";
+}
+.fa-plus-circle:before {
+  content: "\f055";
+}
+.fa-minus-circle:before {
+  content: "\f056";
+}
+.fa-times-circle:before {
+  content: "\f057";
+}
+.fa-check-circle:before {
+  content: "\f058";
+}
+.fa-question-circle:before {
+  content: "\f059";
+}
+.fa-info-circle:before {
+  content: "\f05a";
+}
+.fa-crosshairs:before {
+  content: "\f05b";
+}
+.fa-times-circle-o:before {
+  content: "\f05c";
+}
+.fa-check-circle-o:before {
+  content: "\f05d";
+}
+.fa-ban:before {
+  content: "\f05e";
+}
+.fa-arrow-left:before {
+  content: "\f060";
+}
+.fa-arrow-right:before {
+  content: "\f061";
+}
+.fa-arrow-up:before {
+  content: "\f062";
+}
+.fa-arrow-down:before {
+  content: "\f063";
+}
+.fa-mail-forward:before,
+.fa-share:before {
+  content: "\f064";
+}
+.fa-expand:before {
+  content: "\f065";
+}
+.fa-compress:before {
+  content: "\f066";
+}
+.fa-plus:before {
+  content: "\f067";
+}
+.fa-minus:before {
+  content: "\f068";
+}
+.fa-asterisk:before {
+  content: "\f069";
+}
+.fa-exclamation-circle:before {
+  content: "\f06a";
+}
+.fa-gift:before {
+  content: "\f06b";
+}
+.fa-leaf:before {
+  content: "\f06c";
+}
+.fa-fire:before {
+  content: "\f06d";
+}
+.fa-eye:before {
+  content: "\f06e";
+}
+.fa-eye-slash:before {
+  content: "\f070";
+}
+.fa-warning:before,
+.fa-exclamation-triangle:before {
+  content: "\f071";
+}
+.fa-plane:before {
+  content: "\f072";
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+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 21ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+  text-overflow: ellipsis;
+  overflow: hidden;
+  height: 80px;
+}
+.tooltipbuttons {
+  position: absolute;
+  padding-right: 15px;
+  top: 0px;
+  right: 0px;
+}
+.tooltiptext {
+  /*avoid the button to overlap on some docstring*/
+  padding-right: 30px;
+}
+.ipython_tooltip {
+  max-width: 700px;
+  /*fade-in animation when inserted*/
+  -webkit-animation: fadeOut 400ms;
+  -moz-animation: fadeOut 400ms;
+  animation: fadeOut 400ms;
+  -webkit-animation: fadeIn 400ms;
+  -moz-animation: fadeIn 400ms;
+  animation: fadeIn 400ms;
+  vertical-align: middle;
+  background-color: #f7f7f7;
+  overflow: visible;
+  border: #ababab 1px solid;
+  outline: none;
+  padding: 3px;
+  margin: 0px;
+  padding-left: 7px;
+  font-family: monospace;
+  min-height: 50px;
+  -moz-box-shadow: 0px 6px 10px -1px #adadad;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  border-radius: 2px;
+  position: absolute;
+  z-index: 1000;
+}
+.ipython_tooltip a {
+  float: right;
+}
+.ipython_tooltip .tooltiptext pre {
+  border: 0;
+  border-radius: 0;
+  font-size: 100%;
+  background-color: #f7f7f7;
+}
+.pretooltiparrow {
+  left: 0px;
+  margin: 0px;
+  top: -16px;
+  width: 40px;
+  height: 16px;
+  overflow: hidden;
+  position: absolute;
+}
+.pretooltiparrow:before {
+  background-color: #f7f7f7;
+  border: 1px #ababab solid;
+  z-index: 11;
+  content: "";
+  position: absolute;
+  left: 15px;
+  top: 10px;
+  width: 25px;
+  height: 25px;
+  -webkit-transform: rotate(45deg);
+  -moz-transform: rotate(45deg);
+  -ms-transform: rotate(45deg);
+  -o-transform: rotate(45deg);
+}
+ul.typeahead-list i {
+  margin-left: -10px;
+  width: 18px;
+}
+ul.typeahead-list {
+  max-height: 80vh;
+  overflow: auto;
+}
+ul.typeahead-list > li > a {
+  /** Firefox bug **/
+  /* see https://github.com/jupyter/notebook/issues/559 */
+  white-space: normal;
+}
+.cmd-palette .modal-body {
+  padding: 7px;
+}
+.cmd-palette form {
+  background: white;
+}
+.cmd-palette input {
+  outline: none;
+}
+.no-shortcut {
+  display: none;
+}
+.command-shortcut:before {
+  content: "(command)";
+  padding-right: 3px;
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+
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#194;&#182;</a></h1><h2 id="Reinforcement-Learning">Reinforcement Learning<a class="anchor-link" href="#Reinforcement-Learning">&#194;&#182;</a></h2><h2 id="Project:-Train-a-Smartcab-to-Drive">Project: Train a Smartcab to Drive<a class="anchor-link" href="#Project:-Train-a-Smartcab-to-Drive">&#194;&#182;</a></h2><p>Welcome to the fourth project of the Machine Learning Engineer Nanodegree! In this notebook, template code has already been provided for you to aid in your analysis of the <em>Smartcab</em> and your implemented learning algorithm. You will not need to modify the included code beyond what is requested. There will be questions that you must answer which relate to the project and the visualizations provided in the notebook. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide in <code>agent.py</code>.</p>
+<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#194;&#182;</a></h2><p>In this project, you will work towards constructing an optimized Q-Learning driving agent that will navigate a <em>Smartcab</em> through its environment towards a goal. Since the <em>Smartcab</em> is expected to drive passengers from one location to another, the driving agent will be evaluated on two very important metrics: <strong>Safety</strong> and <strong>Reliability</strong>. A driving agent that gets the <em>Smartcab</em> to its destination while running red lights or narrowly avoiding accidents would be considered <strong>unsafe</strong>. Similarly, a driving agent that frequently fails to reach the destination in time would be considered <strong>unreliable</strong>. Maximizing the driving agent's <strong>safety</strong> and <strong>reliability</strong> would ensure that <em>Smartcabs</em> have a permanent place in the transportation industry.</p>
+<p><strong>Safety</strong> and <strong>Reliability</strong> are measured using a letter-grade system as follows:</p>
+<table>
+<thead><tr>
+<th style="text-align:center">Grade</th>
+<th style="text-align:center">Safety</th>
+<th style="text-align:center">Reliability</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:center">A+</td>
+<td style="text-align:center">Agent commits no traffic violations,<br/>and always chooses the correct action.</td>
+<td style="text-align:center">Agent reaches the destination in time<br />for 100% of trips.</td>
+</tr>
+<tr>
+<td style="text-align:center">A</td>
+<td style="text-align:center">Agent commits few minor traffic violations,<br/>such as failing to move on a green light.</td>
+<td style="text-align:center">Agent reaches the destination on time<br />for at least 90% of trips.</td>
+</tr>
+<tr>
+<td style="text-align:center">B</td>
+<td style="text-align:center">Agent commits frequent minor traffic violations,<br/>such as failing to move on a green light.</td>
+<td style="text-align:center">Agent reaches the destination on time<br />for at least 80% of trips.</td>
+</tr>
+<tr>
+<td style="text-align:center">C</td>
+<td style="text-align:center">Agent commits at least one major traffic violation,<br/> such as driving through a red light.</td>
+<td style="text-align:center">Agent reaches the destination on time<br />for at least 70% of trips.</td>
+</tr>
+<tr>
+<td style="text-align:center">D</td>
+<td style="text-align:center">Agent causes at least one minor accident,<br/> such as turning left on green with oncoming traffic.</td>
+<td style="text-align:center">Agent reaches the destination on time<br />for at least 60% of trips.</td>
+</tr>
+<tr>
+<td style="text-align:center">F</td>
+<td style="text-align:center">Agent causes at least one major accident,<br />such as driving through a red light with cross-traffic.</td>
+<td style="text-align:center">Agent fails to reach the destination on time<br />for at least 60% of trips.</td>
+</tr>
+</tbody>
+</table>
+<p>To assist evaluating these important metrics, you will need to load visualization code that will be used later on in the project. Run the code cell below to import this code which is required for your analysis.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import the visualization code</span>
+<span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>
+
+<span class="c1"># Pretty display for notebooks</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Understand-the-World">Understand the World<a class="anchor-link" href="#Understand-the-World">&#194;&#182;</a></h3><p>Before starting to work on implementing your driving agent, it's necessary to first understand the world (environment) which the <em>Smartcab</em> and driving agent work in. One of the major components to building a self-learning agent is understanding the characteristics about the agent, which includes how the agent operates. To begin, simply run the <code>agent.py</code> agent code exactly how it is -- no need to make any additions whatsoever. Let the resulting simulation run for some time to see the various working components. Note that in the visual simulation (if enabled), the <strong>white vehicle</strong> is the <em>Smartcab</em>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-1">Question 1<a class="anchor-link" href="#Question-1">&#194;&#182;</a></h3><p>In a few sentences, describe what you observe during the simulation when running the default <code>agent.py</code> agent code. Some things you could consider:</p>
+<ul>
+<li><em>Does the Smartcab move at all during the simulation?</em></li>
+<li><em>What kind of rewards is the driving agent receiving?</em></li>
+<li><em>How does the light changing color affect the rewards?</em>  </li>
+</ul>
+<p><strong>Hint:</strong> From the <code>/smartcab/</code> top-level directory (where this notebook is located), run the command</p>
+<div class="highlight"><pre><span></span><span class="s1">&#39;python smartcab/agent.py&#39;</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> The smartcab does not move, it seems the simulation is in a state where we can observe how a fixed position still generates variable rewards. The only way to move the car is to start another trial. The driver is receiving both positive and negative rewards. For example, if the light in front of her changes to red, doing nothing will create a positive reward. In contrast, if the light is green, and there is no oncoming traffic, doing nothing will create a negative reward.</p>
+<p>To force agent to move, could use simulating annealing algo, something to do with heating and refreezin slowly in metallurgy.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Understand-the-Code">Understand the Code<a class="anchor-link" href="#Understand-the-Code">&#194;&#182;</a></h3><p>In addition to understanding the world, it is also necessary to understand the code itself that governs how the world, simulation, and so on operate. Attempting to create a driving agent would be difficult without having at least explored the <em>"hidden"</em> devices that make everything work. In the <code>/smartcab/</code> top-level directory, there are two folders: <code>/logs/</code> (which will be used later) and <code>/smartcab/</code>. Open the <code>/smartcab/</code> folder and explore each Python file included, then answer the following question.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-2">Question 2<a class="anchor-link" href="#Question-2">&#194;&#182;</a></h3><ul>
+<li><em>In the </em><code>agent.py</code><em> Python file, choose three flags that can be set and explain how they change the simulation.</em></li>
+<li><em>In the </em><code>environment.py</code><em> Python file, what Environment class function is called when an agent performs an action?</em></li>
+<li><em>In the </em><code>simulator.py</code><em> Python file, what is the difference between the </em><code>'render_text()'</code><em> function and the </em><code>'render()'</code><em> function?</em></li>
+<li><em>In the </em><code>planner.py</code><em> Python file, will the </em><code>'next_waypoint()</code><em> function consider the North-South or East-West direction first?</em></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong></p>
+<p>agent.py: I chose to set verbose, update_delay and log_metrics. Verbose=True will produce more debug output for agent movement in the simulation. update_delay sets the time between state changes in the simulation, and corresponding screen updates. log_metrics=True will write simulation metrics to log files.</p>
+<p>environment.py: the act() function is called when an agent performs an action.</p>
+<p>simulator.py: the difference is that render() renders the GUI of pygame, wherease render_text() updates the console.</p>
+<p>planner.py: it checks the east-west direction first.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Implement-a-Basic-Driving-Agent">Implement a Basic Driving Agent<a class="anchor-link" href="#Implement-a-Basic-Driving-Agent">&#194;&#182;</a></h2><p>The first step to creating an optimized Q-Learning driving agent is getting the agent to actually take valid actions. In this case, a valid action is one of <code>None</code>, (do nothing) <code>'Left'</code> (turn left), <code>'Right'</code> (turn right), or <code>'Forward'</code> (go forward). For your first implementation, navigate to the <code>'choose_action()'</code> agent function and make the driving agent randomly choose one of these actions. Note that you have access to several class variables that will help you write this functionality, such as <code>'self.learning'</code> and <code>'self.valid_actions'</code>. Once implemented, run the agent file and simulation briefly to confirm that your driving agent is taking a random action each time step.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Basic-Agent-Simulation-Results">Basic Agent Simulation Results<a class="anchor-link" href="#Basic-Agent-Simulation-Results">&#194;&#182;</a></h3><p>To obtain results from the initial simulation, you will need to adjust following flags:</p>
+<ul>
+<li><code>'enforce_deadline'</code> - Set this to <code>True</code> to force the driving agent to capture whether it reaches the destination in time.</li>
+<li><code>'update_delay'</code> - Set this to a small value (such as <code>0.01</code>) to reduce the time between steps in each trial.</li>
+<li><code>'log_metrics'</code> - Set this to <code>True</code> to log the simluation results as a <code>.csv</code> file in <code>/logs/</code>.</li>
+<li><code>'n_test'</code> - Set this to <code>'10'</code> to perform 10 testing trials.</li>
+</ul>
+<p>Optionally, you may disable to the visual simulation (which can make the trials go faster) by setting the <code>'display'</code> flag to <code>False</code>. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!</p>
+<p>Once you have successfully completed the initial simulation (there should have been 20 training trials and 10 testing trials), run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!
+Run the agent.py file after setting the flags from projects/smartcab folder instead of projects/smartcab/smartcab.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Load the &#39;sim_no-learning&#39; log file from the initial simulation results</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">plot_trials</span><span class="p">(</span><span class="s1">&#39;sim_no-learning.csv&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
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+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-3">Question 3<a class="anchor-link" href="#Question-3">&#194;&#182;</a></h3><p>Using the visualization above that was produced from your initial simulation, provide an analysis and make several observations about the driving agent. Be sure that you are making at least one observation about each panel present in the visualization. Some things you could consider:</p>
+<ul>
+<li><em>How frequently is the driving agent making bad decisions? How many of those bad decisions cause accidents?</em></li>
+<li><em>Given that the agent is driving randomly, does the rate of reliabilty make sense?</em></li>
+<li><em>What kind of rewards is the agent receiving for its actions? Do the rewards suggest it has been penalized heavily?</em></li>
+<li><em>As the number of trials increases, does the outcome of results change significantly?</em></li>
+<li><em>Would this Smartcab be considered safe and/or reliable for its passengers? Why or why not?</em></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong>
+The agent has almost 40% bad actions, with more violations than accidents as would be expected from a random agent. It does not learn from mistakes, so the rates dont improve but fluctuate randomly. The reliability rate is about 10%, but given it is driving randomly you would expect this as it does not chose its action based on a destination. The average rolling rewards are negative, due to the high number of bad actions committed and inability to learn, so it also doesnt improve over trials. The rewards suggest it is not penalised heavily on average, so not actively seeking out accidents, but rewards are still consistently negative. The number of trials does not affect the outcome - as expected, as no learning is happening. The only visible change across trials seems like random fluctuation. This smartcab is not safe or reliable, as it cannot learn, and thus gets safety and reliability ratings of F.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Inform-the-Driving-Agent---Learning-policies">Inform the Driving Agent - Learning policies<a class="anchor-link" href="#Inform-the-Driving-Agent---Learning-policies">&#194;&#182;</a></h2><p>The second step to creating an optimized Q-learning driving agent is defining a set of states that the agent can occupy in the environment. Depending on the input, sensory data, and additional variables available to the driving agent, a set of states can be defined for the agent so that it can eventually <em>learn</em> what action it should take when occupying a state. The condition of <code>'if state then action'</code> for each state is called a <strong>policy</strong>, and is ultimately what the driving agent is expected to learn. Without defining states, the driving agent would never understand which action is most optimal -- or even what environmental variables and conditions it cares about!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Identify-States">Identify States<a class="anchor-link" href="#Identify-States">&#194;&#182;</a></h3><p>Inspecting the <code>'build_state()'</code> agent function shows that the driving agent is given the following data from the environment:</p>
+<ul>
+<li><code>'waypoint'</code>, which is the direction the <em>Smartcab</em> should drive leading to the destination, relative to the <em>Smartcab</em>'s heading.</li>
+<li><code>'inputs'</code>, which is the sensor data from the <em>Smartcab</em>. It includes <ul>
+<li><code>'light'</code>, the color of the light.</li>
+<li><code>'left'</code>, the intended direction of travel for a vehicle to the <em>Smartcab</em>'s left. Returns <code>None</code> if no vehicle is present.</li>
+<li><code>'right'</code>, the intended direction of travel for a vehicle to the <em>Smartcab</em>'s right. Returns <code>None</code> if no vehicle is present.</li>
+<li><code>'oncoming'</code>, the intended direction of travel for a vehicle across the intersection from the <em>Smartcab</em>. Returns <code>None</code> if no vehicle is present.</li>
+</ul>
+</li>
+<li><code>'deadline'</code>, which is the number of actions remaining for the <em>Smartcab</em> to reach the destination before running out of time.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-4">Question 4<a class="anchor-link" href="#Question-4">&#194;&#182;</a></h3><p><em>Which features available to the agent are most relevant for learning both <strong>safety</strong> and <strong>efficiency</strong>? Why are these features appropriate for modeling the </em>Smartcab<em> in the environment? If you did not choose some features, why are those features</em> not <em>appropriate?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong>
+Safety: most relevant are light and oncoming, given that respecting traffic lights avoids accidents and the direction of ongoing traffic affects whether you can turn left on green or not. I dont need waypoint as direction of travel is irrelevant for safety, and dont need left and right as this is handled by the color of the light. Also dont need deadline, unless you allow for reckless actions in order to meet a deadline.</p>
+<p>Efficiency: most relevant are deadline, waypoint as efficiency depends on how much time is left, and if the next waypoint is an efficient step towards the destination. Anything in inputs doesnt matter, as they dont impact the time to destination. One exception is oncoming, as if it is none you can turn left immediately, else have to wait one timestep.</p>
+<p>Interestingly, after running simulations, it seems removing deadline as a state variable reducing the variance in my reliability estimates. It also makes it easier to check states, as there are a lot less. It should also be noted that including the deadline could lead to the agent making illegal moves to meet the deadline, like running over a red light, however removing deadline ensures good behaviour after only 100 trials!</p>
+<p>Including right and left would lead to an increase in state space, making it harder to interpret state action reward tuples. The edge case with left is if cars on your left go straight, and you turn right on a red light, which is allowed.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Define-a-State-Space">Define a State Space<a class="anchor-link" href="#Define-a-State-Space">&#194;&#182;</a></h3><p>When defining a set of states that the agent can occupy, it is necessary to consider the <em>size</em> of the state space. That is to say, if you expect the driving agent to learn a <strong>policy</strong> for each state, you would need to have an optimal action for <em>every</em> state the agent can occupy. If the number of all possible states is very large, it might be the case that the driving agent never learns what to do in some states, which can lead to uninformed decisions. For example, consider a case where the following features are used to define the state of the <em>Smartcab</em>:</p>
+<p><code>('is_raining', 'is_foggy', 'is_red_light', 'turn_left', 'no_traffic', 'previous_turn_left', 'time_of_day')</code>.</p>
+<p>How frequently would the agent occupy a state like <code>(False, True, True, True, False, False, '3AM')</code>? Without a near-infinite amount of time for training, it's doubtful the agent would ever learn the proper action!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-5">Question 5<a class="anchor-link" href="#Question-5">&#194;&#182;</a></h3><p><em>If a state is defined using the features you've selected from <strong>Question 4</strong>, what would be the size of the state space? Given what you know about the evironment and how it is simulated, do you think the driving agent could learn a policy for each possible state within a reasonable number of training trials?</em><br>
+<strong>Hint:</strong> Consider the <em>combinations</em> of features to calculate the total number of states!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> The state space would be: 2 (for light) <em> 4 (for oncoming direction) </em> 3 (for next waypoints) * 2 (for left=forward). I think the agent could learn policies for each state in a reasonable numer of trails.</p>
+<p>Thus a total of 48 states.</p>
+<p>To see if how long it takes to learn 48 states, you can run Monte Carlo, see below.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">random</span>
+
+<span class="k">def</span> <span class="nf">percent_visited</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span>
+    <span class="n">visited</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">steps</span><span class="p">):</span>
+        <span class="n">current_state</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">states</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># random visiting</span>
+        <span class="n">visited</span><span class="p">[</span><span class="n">current_state</span><span class="p">]</span> <span class="o">=</span> <span class="bp">True</span> <span class="c1"># add to visited list</span>
+    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">visited</span><span class="p">)</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">states</span><span class="p">)</span> <span class="c1"># return share</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="n">states</span> <span class="o">=</span> <span class="mi">48</span>
+<span class="n">n_steps</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span><span class="o">*</span><span class="mi">40</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">)]</span> <span class="c1"># jump a little bit</span>
+<span class="n">coverage</span> <span class="o">=</span> <span class="p">[</span><span class="n">percent_visited</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span><span class="n">states</span><span class="p">)</span> <span class="k">for</span> <span class="n">steps</span> <span class="ow">in</span> <span class="n">n_steps</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">n_steps</span><span class="p">,</span> <span class="n">coverage</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;coverage of states&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;n steps&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+<div class="prompt output_prompt">Out[3]:</div>
+
+
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>&lt;matplotlib.legend.Legend at 0x2782390&gt;</pre>
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>So this means, it takes around 250 steps for an agent to visit all 48 states if it does something random at each step. This corroborates the idea that without deadline convergence happens after around 250 trials.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Update-the-Driving-Agent-State">Update the Driving Agent State<a class="anchor-link" href="#Update-the-Driving-Agent-State">&#194;&#182;</a></h3><p>For your second implementation, navigate to the <code>'build_state()'</code> agent function. With the justification you've provided in <strong>Question 4</strong>, you will now set the <code>'state'</code> variable to a tuple of all the features necessary for Q-Learning. Confirm your driving agent is updating its state by running the agent file and simulation briefly and note whether the state is displaying. If the visual simulation is used, confirm that the updated state corresponds with what is seen in the simulation.</p>
+<p><strong>Note:</strong> Remember to reset simulation flags to their default setting when making this observation!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Implement-a-Q-Learning-Driving-Agent">Implement a Q-Learning Driving Agent<a class="anchor-link" href="#Implement-a-Q-Learning-Driving-Agent">&#194;&#182;</a></h2><p>The third step to creating an optimized Q-Learning agent is to begin implementing the functionality of Q-Learning itself. The concept of Q-Learning is fairly straightforward: For every state the agent visits, create an entry in the Q-table for all state-action pairs available. Then, when the agent encounters a state and performs an action, update the Q-value associated with that state-action pair based on the reward received and the interative update rule implemented. Of course, additional benefits come from Q-Learning, such that we can have the agent choose the <em>best</em> action for each state based on the Q-values of each state-action pair possible. For this project, you will be implementing a <em>decaying,</em> $\epsilon$<em>-greedy</em> Q-learning algorithm with <em>no</em> discount factor. Follow the implementation instructions under each <strong>TODO</strong> in the agent functions.</p>
+<p>Note that the agent attribute <code>self.Q</code> is a dictionary: This is how the Q-table will be formed. Each state will be a key of the <code>self.Q</code> dictionary, and each value will then be another dictionary that holds the <em>action</em> and <em>Q-value</em>. Here is an example:</p>
+
+<pre><code>{ 'state-1': { 
+    'action-1' : Qvalue-1,
+    'action-2' : Qvalue-2,
+     ...
+   },
+  'state-2': {
+    'action-1' : Qvalue-1,
+     ...
+   },
+   ...
+}</code></pre>
+<p>Furthermore, note that you are expected to use a <em>decaying</em> $\epsilon$ <em>(exploration) factor</em>. Hence, as the number of trials increases, $\epsilon$ should decrease towards 0. This is because the agent is expected to learn from its behavior and begin acting on its learned behavior. Additionally, The agent will be tested on what it has learned after $\epsilon$ has passed a certain threshold (the default threshold is 0.01). For the initial Q-Learning implementation, you will be implementing a linear decaying function for $\epsilon$.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Q-Learning-Simulation-Results">Q-Learning Simulation Results<a class="anchor-link" href="#Q-Learning-Simulation-Results">&#194;&#182;</a></h3><p>To obtain results from the initial Q-Learning implementation, you will need to adjust the following flags and setup:</p>
+<ul>
+<li><code>'enforce_deadline'</code> - Set this to <code>True</code> to force the driving agent to capture whether it reaches the destination in time.</li>
+<li><code>'update_delay'</code> - Set this to a small value (such as <code>0.01</code>) to reduce the time between steps in each trial.</li>
+<li><code>'log_metrics'</code> - Set this to <code>True</code> to log the simluation results as a <code>.csv</code> file and the Q-table as a <code>.txt</code> file in <code>/logs/</code>.</li>
+<li><code>'n_test'</code> - Set this to <code>'10'</code> to perform 10 testing trials.</li>
+<li><code>'learning'</code> - Set this to <code>'True'</code> to tell the driving agent to use your Q-Learning implementation.</li>
+</ul>
+<p>In addition, use the following decay function for $\epsilon$:</p>
+$$ \epsilon_{t+1} = \epsilon_{t} - 0.05, \hspace{10px}\textrm{for trial number } t$$<p>If you have difficulty getting your implementation to work, try setting the <code>'verbose'</code> flag to <code>True</code> to help debug. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!</p>
+<p>Once you have successfully completed the initial Q-Learning simulation, run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Load the &#39;sim_default-learning&#39; file from the default Q-Learning simulation</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">plot_trials</span><span class="p">(</span><span class="s1">&#39;sim_default-learning.csv&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-6">Question 6<a class="anchor-link" href="#Question-6">&#194;&#182;</a></h3><p>Using the visualization above that was produced from your default Q-Learning simulation, provide an analysis and make observations about the driving agent like in <strong>Question 3</strong>. Note that the simulation should have also produced the Q-table in a text file which can help you make observations about the agent's learning. Some additional things you could consider:</p>
+<ul>
+<li><em>Are there any observations that are similar between the basic driving agent and the default Q-Learning agent?</em></li>
+<li><em>Approximately how many training trials did the driving agent require before testing? Does that number make sense given the epsilon-tolerance?</em></li>
+<li><em>Is the decaying function you implemented for $\epsilon$ (the exploration factor) accurately represented in the parameters panel?</em></li>
+<li><em>As the number of training trials increased, did the number of bad actions decrease? Did the average reward increase?</em></li>
+<li><em>How does the safety and reliability rating compare to the initial driving agent?</em></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong>
+From the q table it seems that the agent learned for example that continuing to drive on a red light produces negative rewards. 
+Compared to the basic agent, the default Q learning agent has improved total bad actions (down to 10% after 20 trials vs 40% in the base case). The reliability is also improving over time now, so are the rewards.</p>
+<p>Exploration factor declines as expected across trials to zero so the decaying function is implemented correctly. I had to apply one fix as epsilon 0 was internally represented as -0.0000 which cause issue with probabilitistic action choice.</p>
+<p>The number of bad actions descreased with increasing number of trials, the average reward increased.</p>
+<p>Safety is unchanged at F, but Reliability has improved to A.</p>
+<p>The lack of learning enough shows up in sim_default-learning.txt, as meaning states are still 0, so no accumulated rewards as the agent has not visited them yet. With slower decay and more training could probably get better value out of this default agent.</p>
+<p>Here the bellman equation iterative update algo:
+<img src="attachment:image.png" alt="image.png"></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h2 id="Improve-the-Q-Learning-Driving-Agent">Improve the Q-Learning Driving Agent<a class="anchor-link" href="#Improve-the-Q-Learning-Driving-Agent">&#194;&#182;</a></h2><p>The third step to creating an optimized Q-Learning agent is to perform the optimization! Now that the Q-Learning algorithm is implemented and the driving agent is successfully learning, it's necessary to tune settings and adjust learning paramaters so the driving agent learns both <strong>safety</strong> and <strong>efficiency</strong>. Typically this step will require a lot of trial and error, as some settings will invariably make the learning worse. One thing to keep in mind is the act of learning itself and the time that this takes: In theory, we could allow the agent to learn for an incredibly long amount of time; however, another goal of Q-Learning is to <em>transition from experimenting with unlearned behavior to acting on learned behavior</em>. For example, always allowing the agent to perform a random action during training (if $\epsilon = 1$ and never decays) will certainly make it <em>learn</em>, but never let it <em>act</em>. When improving on your Q-Learning implementation, consider the impliciations it creates and whether it is logistically sensible to make a particular adjustment.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Improved-Q-Learning-Simulation-Results">Improved Q-Learning Simulation Results<a class="anchor-link" href="#Improved-Q-Learning-Simulation-Results">&#194;&#182;</a></h3><p>To obtain results from the initial Q-Learning implementation, you will need to adjust the following flags and setup:</p>
+<ul>
+<li><code>'enforce_deadline'</code> - Set this to <code>True</code> to force the driving agent to capture whether it reaches the destination in time.</li>
+<li><code>'update_delay'</code> - Set this to a small value (such as <code>0.01</code>) to reduce the time between steps in each trial.</li>
+<li><code>'log_metrics'</code> - Set this to <code>True</code> to log the simluation results as a <code>.csv</code> file and the Q-table as a <code>.txt</code> file in <code>/logs/</code>.</li>
+<li><code>'learning'</code> - Set this to <code>'True'</code> to tell the driving agent to use your Q-Learning implementation.</li>
+<li><code>'optimized'</code> - Set this to <code>'True'</code> to tell the driving agent you are performing an optimized version of the Q-Learning implementation.</li>
+</ul>
+<p>Additional flags that can be adjusted as part of optimizing the Q-Learning agent:</p>
+<ul>
+<li><code>'n_test'</code> - Set this to some positive number (previously 10) to perform that many testing trials.</li>
+<li><code>'alpha'</code> - Set this to a real number between 0 - 1 to adjust the learning rate of the Q-Learning algorithm.</li>
+<li><code>'epsilon'</code> - Set this to a real number between 0 - 1 to adjust the starting exploration factor of the Q-Learning algorithm.</li>
+<li><code>'tolerance'</code> - set this to some small value larger than 0 (default was 0.05) to set the epsilon threshold for testing.</li>
+</ul>
+<p>Furthermore, use a decaying function of your choice for $\epsilon$ (the exploration factor). Note that whichever function you use, it <strong>must decay to </strong><code>'tolerance'</code><strong> at a reasonable rate</strong>. The Q-Learning agent will not begin testing until this occurs. Some example decaying functions (for $t$, the number of trials):</p>
+$$ \epsilon = a^t, \textrm{for } 0 &lt; a &lt; 1 \hspace{50px}\epsilon = \frac{1}{t^2}\hspace{50px}\epsilon = e^{-at}, \textrm{for } 0 &lt; a &lt; 1 \hspace{50px} \epsilon = \cos(at), \textrm{for } 0 &lt; a &lt; 1$$<p>You may also use a decaying function for $\alpha$ (the learning rate) if you so choose, however this is typically less common. If you do so, be sure that it adheres to the inequality $0 \leq \alpha \leq 1$.</p>
+<p>If you have difficulty getting your implementation to work, try setting the <code>'verbose'</code> flag to <code>True</code> to help debug. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!</p>
+<p>Once you have successfully completed the improved Q-Learning simulation, run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Load the &#39;sim_improved-learning&#39; file from the improved Q-Learning simulation</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">plot_trials</span><span class="p">(</span><span class="s1">&#39;sim_improved-learning_ref.csv&#39;</span><span class="p">)</span> <span class="c1"># e decay</span>
+<span class="n">vs</span><span class="o">.</span><span class="n">plot_trials</span><span class="p">(</span><span class="s1">&#39;sim_improved-learning.csv&#39;</span><span class="p">)</span> <span class="c1"># gompertz decay parametrized to go to 0 at around 1600 trials</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-7">Question 7<a class="anchor-link" href="#Question-7">&#194;&#182;</a></h3><p>Using the visualization above that was produced from your improved Q-Learning simulation, provide a final analysis and make observations about the improved driving agent like in <strong>Question 6</strong>. Questions you should answer:</p>
+<ul>
+<li><em>What decaying function was used for epsilon (the exploration factor)?</em></li>
+<li><em>Approximately how many training trials were needed for your agent before begining testing?</em></li>
+<li><em>What epsilon-tolerance and alpha (learning rate) did you use? Why did you use them?</em></li>
+<li><em>How much improvement was made with this Q-Learner when compared to the default Q-Learner from the previous section?</em></li>
+<li><em>Would you say that the Q-Learner results show that your driving agent successfully learned an appropriate policy?</em></li>
+<li><em>Are you satisfied with the safety and reliability ratings of the </em>Smartcab<em>?</em></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong></p>
+<p>The epsilon decay function used was e^(-0.0005 * nb_trials). I also attempted gompertz decay, but needed to parametrize the function quite a bit. With the first i need about 4k trials, with gompertz about 2k to converge.</p>
+<p>Epsilon tolerance was 0.01, alpha was 0.01 and constant over time. I used these values so that the simulation could learn a lot from historical mistakes before it started testing. A low alpha was chosen so that learning could be spread out better over the large number of trials.</p>
+<p>Improvement over default q learner was vast. Initially I tried using better decay functions, but it turned out i needed more training. Accident rates are now will below 5% and reliability at worst is around 80% - 90% and i feel i narrowed the standard deviation of rolling reliability. Rewards are almost constant at 2.</p>
+<p>Safety and Reliability rating are at A+, which is incredible. I would certainly say the learner has learned an appropriate policy, although it still has a rather high variance in reliability.</p>
+<p>N.B: Removing deadline from the state variables reduces variance in the reliability running average, so I did this for the output.</p>
+<p>Including all features is possible with advances in computing power, as opposed to using a priori knowledge to reduce space dimensionality. But a priori is NOT reinforcement learning - the agent must come up with all rules by itself, so you should not remove any features.</p>
+<p>It seems running on all features except deadline, leads to a slightly lower reliability after 10k trials, and reliability variance is up again (not shown here).</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Define-an-Optimal-Policy">Define an Optimal Policy<a class="anchor-link" href="#Define-an-Optimal-Policy">&#194;&#182;</a></h3><p>Sometimes, the answer to the important question <em>"what am I trying to get my agent to learn?"</em> only has a theoretical answer and cannot be concretely described. Here, however, you can concretely define what it is the agent is trying to learn, and that is the U.S. right-of-way traffic laws. Since these laws are known information, you can further define, for each state the <em>Smartcab</em> is occupying, the optimal action for the driving agent based on these laws. In that case, we call the set of optimal state-action pairs an <strong>optimal policy</strong>. Hence, unlike some theoretical answers, it is clear whether the agent is acting "incorrectly" not only by the reward (penalty) it receives, but also by pure observation. If the agent drives through a red light, we both see it receive a negative reward but also know that it is not the correct behavior. This can be used to your advantage for verifying whether the <strong>policy</strong> your driving agent has learned is the correct one, or if it is a <strong>suboptimal policy</strong>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-8">Question 8<a class="anchor-link" href="#Question-8">&#194;&#182;</a></h3><p>Provide a few examples (using the states you've defined) of what an optimal policy for this problem would look like. Afterwards, investigate the <code>'sim_improved-learning.txt'</code> text file to see the results of your improved Q-Learning algorithm. <em>For each state that has been recorded from the simulation, is the <strong>policy</strong> (the action with the highest value) correct for the given state? Are there any states where the policy is different than what would be expected from an optimal policy?</em> Provide an example of a state and all state-action rewards recorded, and explain why it is the correct policy.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong></p>
+<p>I have now removed deadline from the states, so answers are based on this: I find it surprising that its not required to know how close it is from failing in order to get good reliability.</p>
+<p>I have defined states as waypoint, light, oncoming.
+Examples:</p>
+
+<pre><code>waypoint=left, light=green, oncoming=forward, left!=forward =&gt; should wait for oncoming or go right or forward. In the simulation, going forward has the biggest reward, followed by right. Waiting actually has quite a negative reward.
+
+waypoint=right, light=green, oncoming=None, left!=forward =&gt; should turn right. Simulation agrees, right has the biggest reward.
+
+waypoint=right, light=red, oncoming=forward, left!=forward =&gt; should wait. Simulation agrees.
+
+
+</code></pre>
+<p>States: The few i checked seem to have the right policies. Removing deadline, it seems all states chose the best action.</p>
+<p>A state i found where the optimal policy is different from the learned is:</p>
+
+<pre><code>waypoint=left, light=green, oncoming=right, left!=forward. It prefers forward instead of wait, although the next waypoint is left.
+
+('left', 'green', 'right', False)
+ -- forward : 0.13
+ -- right : 0.44
+ -- None : -1.44
+ -- left : -6.68
+
+
+</code></pre>
+<p>An example of a state with all actions and rewards that has the correct policy is:</p>
+
+<pre><code>('forward', 'red', 'left', False)
+ -- forward : -9.09
+ -- right : 0.73
+ -- None : 1.87
+ -- left : -9.62
+
+
+</code></pre>
+<p>here the light is red, car wants to go forward, oncoming goes left, so best action is chosen correctly (do nothing).</p>
+<p>Papers on learning rates for later:</p>
+
+<pre><code>http://www.jmlr.org/papers/volume5/evendar03a/evendar03a.pdf
+http://karpathy.github.io/2016/05/31/rl/
+https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
+http://mnemstudio.org/path-finding-q-learning-tutorial.htm
+https://www-s.acm.illinois.edu/sigart/docs/QLearning.pdf
+http://www.umiacs.umd.edu/~hal/courses/ai/out/cs421-day10-qlearning.pdf</code></pre>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<h3 id="Optional:-Future-Rewards---Discount-Factor,-'gamma'">Optional: Future Rewards - Discount Factor, <code>'gamma'</code><a class="anchor-link" href="#Optional:-Future-Rewards---Discount-Factor,-'gamma'">&#194;&#182;</a></h3><p>Curiously, as part of the Q-Learning algorithm, you were asked to <strong>not</strong> use the discount factor, <code>'gamma'</code> in the implementation. Including future rewards in the algorithm is used to aid in propogating positive rewards backwards from a future state to the current state. Essentially, if the driving agent is given the option to make several actions to arrive at different states, including future rewards will bias the agent towards states that could provide even more rewards. An example of this would be the driving agent moving towards a goal: With all actions and rewards equal, moving towards the goal would theoretically yield better rewards if there is an additional reward for reaching the goal. However, even though in this project, the driving agent is trying to reach a destination in the allotted time, including future rewards will not benefit the agent. In fact, if the agent were given many trials to learn, it could negatively affect Q-values!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Optional-Question-9">Optional Question 9<a class="anchor-link" href="#Optional-Question-9">&#194;&#182;</a></h3><p><em>There are two characteristics about the project that invalidate the use of future rewards in the Q-Learning algorithm. One characteristic has to do with the </em>Smartcab<em> itself, and the other has to do with the environment. Can you figure out what they are and why future rewards won't work for this project?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong></p>
+<p>Smartcab: it cannot see what is happening elsewhere in the city, so no point optimising now to make some headway a few roads down the line. It would need traffic reports to do this for example. Further, the agent cannot see how far it is from the destination, thus cannot optimise the path.</p>
+<p>As traffic and lights and other cars movements are random, there is no benefit to learning to go towards the goal directly, as the environment keeps evolving randomly, so the agent cannot control the environments future. So lets say driving at the same speed to keep traffice flowing has no benefit down the line, as no traffic jams are avoided or created. Further, as each trial has a new destination, the learned Q cannot incorporate how to get there fast, as this info is useless in the next trial.</p>
+<p>I wonder if there is a way to parallelize this implementation?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
+<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+
+ 
+
+
+</html>
diff --git a/smartcab/smartcab.ipynb b/smartcab/smartcab.ipynb
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+++ b/smartcab/smartcab.ipynb
@@ -0,0 +1,690 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning Engineer Nanodegree\n",
+    "## Reinforcement Learning\n",
+    "## Project: Train a Smartcab to Drive\n",
+    "\n",
+    "Welcome to the fourth project of the Machine Learning Engineer Nanodegree! In this notebook, template code has already been provided for you to aid in your analysis of the *Smartcab* and your implemented learning algorithm. You will not need to modify the included code beyond what is requested. There will be questions that you must answer which relate to the project and the visualizations provided in the notebook. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide in `agent.py`.  \n",
+    "\n",
+    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----\n",
+    "\n",
+    "## Getting Started\n",
+    "In this project, you will work towards constructing an optimized Q-Learning driving agent that will navigate a *Smartcab* through its environment towards a goal. Since the *Smartcab* is expected to drive passengers from one location to another, the driving agent will be evaluated on two very important metrics: **Safety** and **Reliability**. A driving agent that gets the *Smartcab* to its destination while running red lights or narrowly avoiding accidents would be considered **unsafe**. Similarly, a driving agent that frequently fails to reach the destination in time would be considered **unreliable**. Maximizing the driving agent's **safety** and **reliability** would ensure that *Smartcabs* have a permanent place in the transportation industry.\n",
+    "\n",
+    "**Safety** and **Reliability** are measured using a letter-grade system as follows:\n",
+    "\n",
+    "| Grade \t| Safety \t| Reliability \t|\n",
+    "|:-----:\t|:------:\t|:-----------:\t|\n",
+    "|   A+  \t|  Agent commits no traffic violations,<br/>and always chooses the correct action. | Agent reaches the destination in time<br />for 100% of trips. |\n",
+    "|   A   \t|  Agent commits few minor traffic violations,<br/>such as failing to move on a green light. | Agent reaches the destination on time<br />for at least 90% of trips. |\n",
+    "|   B   \t| Agent commits frequent minor traffic violations,<br/>such as failing to move on a green light. | Agent reaches the destination on time<br />for at least 80% of trips. |\n",
+    "|   C   \t|  Agent commits at least one major traffic violation,<br/> such as driving through a red light. | Agent reaches the destination on time<br />for at least 70% of trips. |\n",
+    "|   D   \t| Agent causes at least one minor accident,<br/> such as turning left on green with oncoming traffic.       \t| Agent reaches the destination on time<br />for at least 60% of trips. |\n",
+    "|   F   \t|  Agent causes at least one major accident,<br />such as driving through a red light with cross-traffic.      \t| Agent fails to reach the destination on time<br />for at least 60% of trips. |\n",
+    "\n",
+    "To assist evaluating these important metrics, you will need to load visualization code that will be used later on in the project. Run the code cell below to import this code which is required for your analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Import the visualization code\n",
+    "import visuals as vs\n",
+    "\n",
+    "# Pretty display for notebooks\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Understand the World\n",
+    "Before starting to work on implementing your driving agent, it's necessary to first understand the world (environment) which the *Smartcab* and driving agent work in. One of the major components to building a self-learning agent is understanding the characteristics about the agent, which includes how the agent operates. To begin, simply run the `agent.py` agent code exactly how it is -- no need to make any additions whatsoever. Let the resulting simulation run for some time to see the various working components. Note that in the visual simulation (if enabled), the **white vehicle** is the *Smartcab*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1\n",
+    "In a few sentences, describe what you observe during the simulation when running the default `agent.py` agent code. Some things you could consider:\n",
+    "- *Does the Smartcab move at all during the simulation?*\n",
+    "- *What kind of rewards is the driving agent receiving?*\n",
+    "- *How does the light changing color affect the rewards?*  \n",
+    "\n",
+    "**Hint:** From the `/smartcab/` top-level directory (where this notebook is located), run the command \n",
+    "```bash\n",
+    "'python smartcab/agent.py'\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** The smartcab does not move, it seems the simulation is in a state where we can observe how a fixed position still generates variable rewards. The only way to move the car is to start another trial. The driver is receiving both positive and negative rewards. For example, if the light in front of her changes to red, doing nothing will create a positive reward. In contrast, if the light is green, and there is no oncoming traffic, doing nothing will create a negative reward. \n",
+    "\n",
+    "To force agent to move, could use simulating annealing algo, something to do with heating and refreezin slowly in metallurgy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Understand the Code\n",
+    "In addition to understanding the world, it is also necessary to understand the code itself that governs how the world, simulation, and so on operate. Attempting to create a driving agent would be difficult without having at least explored the *\"hidden\"* devices that make everything work. In the `/smartcab/` top-level directory, there are two folders: `/logs/` (which will be used later) and `/smartcab/`. Open the `/smartcab/` folder and explore each Python file included, then answer the following question."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2\n",
+    "- *In the *`agent.py`* Python file, choose three flags that can be set and explain how they change the simulation.*\n",
+    "- *In the *`environment.py`* Python file, what Environment class function is called when an agent performs an action?*\n",
+    "- *In the *`simulator.py`* Python file, what is the difference between the *`'render_text()'`* function and the *`'render()'`* function?*\n",
+    "- *In the *`planner.py`* Python file, will the *`'next_waypoint()`* function consider the North-South or East-West direction first?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:**\n",
+    "\n",
+    "agent.py: I chose to set verbose, update_delay and log_metrics. Verbose=True will produce more debug output for agent movement in the simulation. update_delay sets the time between state changes in the simulation, and corresponding screen updates. log_metrics=True will write simulation metrics to log files.\n",
+    "\n",
+    "environment.py: the act() function is called when an agent performs an action.\n",
+    "\n",
+    "simulator.py: the difference is that render() renders the GUI of pygame, wherease render_text() updates the console.\n",
+    "\n",
+    "planner.py: it checks the east-west direction first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----\n",
+    "## Implement a Basic Driving Agent\n",
+    "\n",
+    "The first step to creating an optimized Q-Learning driving agent is getting the agent to actually take valid actions. In this case, a valid action is one of `None`, (do nothing) `'Left'` (turn left), `'Right'` (turn right), or `'Forward'` (go forward). For your first implementation, navigate to the `'choose_action()'` agent function and make the driving agent randomly choose one of these actions. Note that you have access to several class variables that will help you write this functionality, such as `'self.learning'` and `'self.valid_actions'`. Once implemented, run the agent file and simulation briefly to confirm that your driving agent is taking a random action each time step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic Agent Simulation Results\n",
+    "To obtain results from the initial simulation, you will need to adjust following flags:\n",
+    "- `'enforce_deadline'` - Set this to `True` to force the driving agent to capture whether it reaches the destination in time.\n",
+    "- `'update_delay'` - Set this to a small value (such as `0.01`) to reduce the time between steps in each trial.\n",
+    "- `'log_metrics'` - Set this to `True` to log the simluation results as a `.csv` file in `/logs/`.\n",
+    "- `'n_test'` - Set this to `'10'` to perform 10 testing trials.\n",
+    "\n",
+    "Optionally, you may disable to the visual simulation (which can make the trials go faster) by setting the `'display'` flag to `False`. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!\n",
+    "\n",
+    "Once you have successfully completed the initial simulation (there should have been 20 training trials and 10 testing trials), run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!\n",
+    "Run the agent.py file after setting the flags from projects/smartcab folder instead of projects/smartcab/smartcab.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ISCIjI/3CVJWVK1d6lq+55hq/bebNm0eVKlUYOnQotWrVKta0elu9ejU33HAD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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x922e320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the 'sim_no-learning' log file from the initial simulation results\n",
+    "vs.plot_trials('sim_no-learning.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3\n",
+    "Using the visualization above that was produced from your initial simulation, provide an analysis and make several observations about the driving agent. Be sure that you are making at least one observation about each panel present in the visualization. Some things you could consider:\n",
+    "- *How frequently is the driving agent making bad decisions? How many of those bad decisions cause accidents?*\n",
+    "- *Given that the agent is driving randomly, does the rate of reliabilty make sense?*\n",
+    "- *What kind of rewards is the agent receiving for its actions? Do the rewards suggest it has been penalized heavily?*\n",
+    "- *As the number of trials increases, does the outcome of results change significantly?*\n",
+    "- *Would this Smartcab be considered safe and/or reliable for its passengers? Why or why not?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:**\n",
+    "The agent has almost 40% bad actions, with more violations than accidents as would be expected from a random agent. It does not learn from mistakes, so the rates dont improve but fluctuate randomly. The reliability rate is about 10%, but given it is driving randomly you would expect this as it does not chose its action based on a destination. The average rolling rewards are negative, due to the high number of bad actions committed and inability to learn, so it also doesnt improve over trials. The rewards suggest it is not penalised heavily on average, so not actively seeking out accidents, but rewards are still consistently negative. The number of trials does not affect the outcome - as expected, as no learning is happening. The only visible change across trials seems like random fluctuation. This smartcab is not safe or reliable, as it cannot learn, and thus gets safety and reliability ratings of F."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----\n",
+    "## Inform the Driving Agent - Learning policies\n",
+    "The second step to creating an optimized Q-learning driving agent is defining a set of states that the agent can occupy in the environment. Depending on the input, sensory data, and additional variables available to the driving agent, a set of states can be defined for the agent so that it can eventually *learn* what action it should take when occupying a state. The condition of `'if state then action'` for each state is called a **policy**, and is ultimately what the driving agent is expected to learn. Without defining states, the driving agent would never understand which action is most optimal -- or even what environmental variables and conditions it cares about!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Identify States\n",
+    "Inspecting the `'build_state()'` agent function shows that the driving agent is given the following data from the environment:\n",
+    "- `'waypoint'`, which is the direction the *Smartcab* should drive leading to the destination, relative to the *Smartcab*'s heading.\n",
+    "- `'inputs'`, which is the sensor data from the *Smartcab*. It includes \n",
+    "  - `'light'`, the color of the light.\n",
+    "  - `'left'`, the intended direction of travel for a vehicle to the *Smartcab*'s left. Returns `None` if no vehicle is present.\n",
+    "  - `'right'`, the intended direction of travel for a vehicle to the *Smartcab*'s right. Returns `None` if no vehicle is present.\n",
+    "  - `'oncoming'`, the intended direction of travel for a vehicle across the intersection from the *Smartcab*. Returns `None` if no vehicle is present.\n",
+    "- `'deadline'`, which is the number of actions remaining for the *Smartcab* to reach the destination before running out of time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4\n",
+    "*Which features available to the agent are most relevant for learning both **safety** and **efficiency**? Why are these features appropriate for modeling the *Smartcab* in the environment? If you did not choose some features, why are those features* not *appropriate?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:**\n",
+    "Safety: most relevant are light and oncoming, given that respecting traffic lights avoids accidents and the direction of ongoing traffic affects whether you can turn left on green or not. I dont need waypoint as direction of travel is irrelevant for safety, and dont need left and right as this is handled by the color of the light. Also dont need deadline, unless you allow for reckless actions in order to meet a deadline.\n",
+    "\n",
+    "Efficiency: most relevant are deadline, waypoint as efficiency depends on how much time is left, and if the next waypoint is an efficient step towards the destination. Anything in inputs doesnt matter, as they dont impact the time to destination. One exception is oncoming, as if it is none you can turn left immediately, else have to wait one timestep.\n",
+    "\n",
+    "Interestingly, after running simulations, it seems removing deadline as a state variable reducing the variance in my reliability estimates. It also makes it easier to check states, as there are a lot less. It should also be noted that including the deadline could lead to the agent making illegal moves to meet the deadline, like running over a red light, however removing deadline ensures good behaviour after only 100 trials!\n",
+    "\n",
+    "Including right and left would lead to an increase in state space, making it harder to interpret state action reward tuples. The edge case with left is if cars on your left go straight, and you turn right on a red light, which is allowed. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a State Space\n",
+    "When defining a set of states that the agent can occupy, it is necessary to consider the *size* of the state space. That is to say, if you expect the driving agent to learn a **policy** for each state, you would need to have an optimal action for *every* state the agent can occupy. If the number of all possible states is very large, it might be the case that the driving agent never learns what to do in some states, which can lead to uninformed decisions. For example, consider a case where the following features are used to define the state of the *Smartcab*:\n",
+    "\n",
+    "`('is_raining', 'is_foggy', 'is_red_light', 'turn_left', 'no_traffic', 'previous_turn_left', 'time_of_day')`.\n",
+    "\n",
+    "How frequently would the agent occupy a state like `(False, True, True, True, False, False, '3AM')`? Without a near-infinite amount of time for training, it's doubtful the agent would ever learn the proper action!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 5\n",
+    "*If a state is defined using the features you've selected from **Question 4**, what would be the size of the state space? Given what you know about the evironment and how it is simulated, do you think the driving agent could learn a policy for each possible state within a reasonable number of training trials?*  \n",
+    "**Hint:** Consider the *combinations* of features to calculate the total number of states!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** The state space would be: 2 (for light) * 4 (for oncoming direction) * 3 (for next waypoints) * 2 (for left=forward). I think the agent could learn policies for each state in a reasonable numer of trails. \n",
+    "\n",
+    "Thus a total of 48 states.\n",
+    "\n",
+    "To see if how long it takes to learn 48 states, you can run Monte Carlo, see below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import random\n",
+    "\n",
+    "def percent_visited(steps, states):\n",
+    "    visited = np.zeros(states, dtype=bool)\n",
+    "    for _ in range(steps):\n",
+    "        current_state = random.randint(0, states-1) # random visiting\n",
+    "        visited[current_state] = True # add to visited list\n",
+    "    return sum(visited)/float(states) # return share"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2782390>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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eZjYaGEvkoEhn1NbPzAY03CZyEG5LUMO8oNk84Jmuri1Kkx5Ud9hvUY5oPwVf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\nFO4iIiGkcBcRCaH/DzmPP6RgAACvAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x499c550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "states = 48\n",
+    "n_steps = [s*40 for s in range(50)] # jump a little bit\n",
+    "coverage = [percent_visited(steps,states) for steps in n_steps]\n",
+    "plt.plot(n_steps, coverage, label=\"coverage of states\")\n",
+    "plt.xlabel(\"n steps\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So this means, it takes around 250 steps for an agent to visit all 48 states if it does something random at each step. This corroborates the idea that without deadline convergence happens after around 250 trials."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Update the Driving Agent State\n",
+    "For your second implementation, navigate to the `'build_state()'` agent function. With the justification you've provided in **Question 4**, you will now set the `'state'` variable to a tuple of all the features necessary for Q-Learning. Confirm your driving agent is updating its state by running the agent file and simulation briefly and note whether the state is displaying. If the visual simulation is used, confirm that the updated state corresponds with what is seen in the simulation.\n",
+    "\n",
+    "**Note:** Remember to reset simulation flags to their default setting when making this observation!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----\n",
+    "## Implement a Q-Learning Driving Agent\n",
+    "The third step to creating an optimized Q-Learning agent is to begin implementing the functionality of Q-Learning itself. The concept of Q-Learning is fairly straightforward: For every state the agent visits, create an entry in the Q-table for all state-action pairs available. Then, when the agent encounters a state and performs an action, update the Q-value associated with that state-action pair based on the reward received and the interative update rule implemented. Of course, additional benefits come from Q-Learning, such that we can have the agent choose the *best* action for each state based on the Q-values of each state-action pair possible. For this project, you will be implementing a *decaying,* $\\epsilon$*-greedy* Q-learning algorithm with *no* discount factor. Follow the implementation instructions under each **TODO** in the agent functions.\n",
+    "\n",
+    "Note that the agent attribute `self.Q` is a dictionary: This is how the Q-table will be formed. Each state will be a key of the `self.Q` dictionary, and each value will then be another dictionary that holds the *action* and *Q-value*. Here is an example:\n",
+    "\n",
+    "```\n",
+    "{ 'state-1': { \n",
+    "    'action-1' : Qvalue-1,\n",
+    "    'action-2' : Qvalue-2,\n",
+    "     ...\n",
+    "   },\n",
+    "  'state-2': {\n",
+    "    'action-1' : Qvalue-1,\n",
+    "     ...\n",
+    "   },\n",
+    "   ...\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Furthermore, note that you are expected to use a *decaying* $\\epsilon$ *(exploration) factor*. Hence, as the number of trials increases, $\\epsilon$ should decrease towards 0. This is because the agent is expected to learn from its behavior and begin acting on its learned behavior. Additionally, The agent will be tested on what it has learned after $\\epsilon$ has passed a certain threshold (the default threshold is 0.01). For the initial Q-Learning implementation, you will be implementing a linear decaying function for $\\epsilon$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q-Learning Simulation Results\n",
+    "To obtain results from the initial Q-Learning implementation, you will need to adjust the following flags and setup:\n",
+    "- `'enforce_deadline'` - Set this to `True` to force the driving agent to capture whether it reaches the destination in time.\n",
+    "- `'update_delay'` - Set this to a small value (such as `0.01`) to reduce the time between steps in each trial.\n",
+    "- `'log_metrics'` - Set this to `True` to log the simluation results as a `.csv` file and the Q-table as a `.txt` file in `/logs/`.\n",
+    "- `'n_test'` - Set this to `'10'` to perform 10 testing trials.\n",
+    "- `'learning'` - Set this to `'True'` to tell the driving agent to use your Q-Learning implementation.\n",
+    "\n",
+    "In addition, use the following decay function for $\\epsilon$:\n",
+    "\n",
+    "$$ \\epsilon_{t+1} = \\epsilon_{t} - 0.05, \\hspace{10px}\\textrm{for trial number } t$$\n",
+    "\n",
+    "If you have difficulty getting your implementation to work, try setting the `'verbose'` flag to `True` to help debug. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation! \n",
+    "\n",
+    "Once you have successfully completed the initial Q-Learning simulation, run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2bVv69evHc889x4ABA2jbti0BAQHcdtttBeadlJSEn5+fY3revHmICCNGjMg1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Qbeew+ys0s8nGEYRqkwgWUYhrfE1oKWw6Hv32HSNhj2NpxyMcTWhl0fwMIrYFYL+PwnOp4rN8dv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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9882438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the 'sim_default-learning' file from the default Q-Learning simulation\n",
+    "vs.plot_trials('sim_default-learning.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 6\n",
+    "Using the visualization above that was produced from your default Q-Learning simulation, provide an analysis and make observations about the driving agent like in **Question 3**. Note that the simulation should have also produced the Q-table in a text file which can help you make observations about the agent's learning. Some additional things you could consider:  \n",
+    "- *Are there any observations that are similar between the basic driving agent and the default Q-Learning agent?*\n",
+    "- *Approximately how many training trials did the driving agent require before testing? Does that number make sense given the epsilon-tolerance?*\n",
+    "- *Is the decaying function you implemented for $\\epsilon$ (the exploration factor) accurately represented in the parameters panel?*\n",
+    "- *As the number of training trials increased, did the number of bad actions decrease? Did the average reward increase?*\n",
+    "- *How does the safety and reliability rating compare to the initial driving agent?*"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:**\n",
+    "From the q table it seems that the agent learned for example that continuing to drive on a red light produces negative rewards. \n",
+    "Compared to the basic agent, the default Q learning agent has improved total bad actions (down to 10% after 20 trials vs 40% in the base case). The reliability is also improving over time now, so are the rewards.\n",
+    "\n",
+    "Exploration factor declines as expected across trials to zero so the decaying function is implemented correctly. I had to apply one fix as epsilon 0 was internally represented as -0.0000 which cause issue with probabilitistic action choice.\n",
+    "\n",
+    "The number of bad actions descreased with increasing number of trials, the average reward increased.\n",
+    "\n",
+    "Safety is unchanged at F, but Reliability has improved to A.\n",
+    "\n",
+    "The lack of learning enough shows up in sim_default-learning.txt, as meaning states are still 0, so no accumulated rewards as the agent has not visited them yet. With slower decay and more training could probably get better value out of this default agent.\n",
+    "\n",
+    "Here the bellman equation iterative update algo:\n",
+    "![image.png](attachment:image.png)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----\n",
+    "## Improve the Q-Learning Driving Agent\n",
+    "The third step to creating an optimized Q-Learning agent is to perform the optimization! Now that the Q-Learning algorithm is implemented and the driving agent is successfully learning, it's necessary to tune settings and adjust learning paramaters so the driving agent learns both **safety** and **efficiency**. Typically this step will require a lot of trial and error, as some settings will invariably make the learning worse. One thing to keep in mind is the act of learning itself and the time that this takes: In theory, we could allow the agent to learn for an incredibly long amount of time; however, another goal of Q-Learning is to *transition from experimenting with unlearned behavior to acting on learned behavior*. For example, always allowing the agent to perform a random action during training (if $\\epsilon = 1$ and never decays) will certainly make it *learn*, but never let it *act*. When improving on your Q-Learning implementation, consider the impliciations it creates and whether it is logistically sensible to make a particular adjustment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Improved Q-Learning Simulation Results\n",
+    "To obtain results from the initial Q-Learning implementation, you will need to adjust the following flags and setup:\n",
+    "- `'enforce_deadline'` - Set this to `True` to force the driving agent to capture whether it reaches the destination in time.\n",
+    "- `'update_delay'` - Set this to a small value (such as `0.01`) to reduce the time between steps in each trial.\n",
+    "- `'log_metrics'` - Set this to `True` to log the simluation results as a `.csv` file and the Q-table as a `.txt` file in `/logs/`.\n",
+    "- `'learning'` - Set this to `'True'` to tell the driving agent to use your Q-Learning implementation.\n",
+    "- `'optimized'` - Set this to `'True'` to tell the driving agent you are performing an optimized version of the Q-Learning implementation.\n",
+    "\n",
+    "Additional flags that can be adjusted as part of optimizing the Q-Learning agent:\n",
+    "- `'n_test'` - Set this to some positive number (previously 10) to perform that many testing trials.\n",
+    "- `'alpha'` - Set this to a real number between 0 - 1 to adjust the learning rate of the Q-Learning algorithm.\n",
+    "- `'epsilon'` - Set this to a real number between 0 - 1 to adjust the starting exploration factor of the Q-Learning algorithm.\n",
+    "- `'tolerance'` - set this to some small value larger than 0 (default was 0.05) to set the epsilon threshold for testing.\n",
+    "\n",
+    "Furthermore, use a decaying function of your choice for $\\epsilon$ (the exploration factor). Note that whichever function you use, it **must decay to **`'tolerance'`** at a reasonable rate**. The Q-Learning agent will not begin testing until this occurs. Some example decaying functions (for $t$, the number of trials):\n",
+    "\n",
+    "$$ \\epsilon = a^t, \\textrm{for } 0 < a < 1 \\hspace{50px}\\epsilon = \\frac{1}{t^2}\\hspace{50px}\\epsilon = e^{-at}, \\textrm{for } 0 < a < 1 \\hspace{50px} \\epsilon = \\cos(at), \\textrm{for } 0 < a < 1$$\n",
+    "You may also use a decaying function for $\\alpha$ (the learning rate) if you so choose, however this is typically less common. If you do so, be sure that it adheres to the inequality $0 \\leq \\alpha \\leq 1$.\n",
+    "\n",
+    "If you have difficulty getting your implementation to work, try setting the `'verbose'` flag to `True` to help debug. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation! \n",
+    "\n",
+    "Once you have successfully completed the improved Q-Learning simulation, run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jOJ3OwJgc+oVSah1pq1tgCDv5gO0YPjjTcW8i+BPGC3cPhhPtCQxhy8Y0DG3PKTEi2Llj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\nKRdJKQOllIHFi8Pzz0OuH52Ko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5E94jUDGJTdlmNz3jyDbx4bNBtViVxZKQp5AvwLtZbw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0tu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\nzMzMzMw65P8DZcFf3XHMSTYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xaddf4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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NlU925dDL/bAVck6Vcr78FNmzRJQrMDNYV0Rqaiq7du0CIDk5uYjcBoOhJImK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TtE5C8YIhedgT8wRC8WV+Scrmhfm6/X78PudJHjcNKtcSrjh3ag1yuLDBVBZ4GBlRAT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\nz+G2r5edXbyRpaMs9iuwXjZjF8HK2w4FByxFYFRUVEBg1alThyZNmgCQn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8B0sVceF8FY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\nd7noIhWr+N//iq5bEElJQd6vQw+NPZcnFmVpYBXEmDHqh09MVOMxPP8pfKy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MzL2P4/9v\nHDmJDZgwYQIJ2xfC+PBkNXjggjQ6JgfLzuVpYmtgzq8/0mFmdeK8+aIrgIn77cfAiy6CVZ8RJ448\nB8mJEL/uG+bOnVukgXXlFZfyzIX6WeKEuKSakL2BFYvn8uSz/yZz0xK6dOnCBVfdyYGd2wOz2OHS\nAC/eMi9avcQwDMOIxtfhatlSp1z75OVppqD58zXYxX/Nng0ffBBkHwK93WjSRGey+K9mzSI/16pl\nHrCiMANrb5KYWHLlwcrO3vLUHXmk+sULE67YXcp69uioUSr/f8QRuuxLUm/cqPOloh8lnXJK5D8n\nqL9/7FiV0u/SBUaMiPSyhb1JoAItU6fq5y+/jFzXq5fGGtTYAMfVg8lroH4OLAvNv2rXVg2TRo10\nPzNmBOvaA4OuU4GPtLTA0EqJg0zvWDZIUIXA76aDr5Yfl5x/VmjtWrDRCyT/ZnEwRwtgxAWQdmdk\n/bAHrUUHWDM7UBZs90+9IrT/V6AmCHDPPYGhiq/WFzIsN2+GLl0Ye1Bj+sZ7c+O6gVvbBFm8Aj4H\nDjtJjdS4ZBi8AhbDIPS1NS2N1M8zVMo+8U0SRS2bYX3h5m2/wdi5urvNk+HQ4PcyqFc2zIfZnTrR\nackSzZ+2H9DmVOIOug8+1kTl99w2iAlT5nNQo+dg9Sp4GIQ5LF4xh14tWtBx35WMDE3/BDix8XcR\nyyJxfDj+K05tCx03P0LORY8T5823awKMiofDevakfa4+Ap05ri5d/lzPGdtqsO2ppzTtQEEqox92\nYOw5Qc42SawJiToPcPTzQ7nrkG+JjwP4Ej4Zx0UX3AeTx7Bfx06w7nvdKNdUBA3DMHaXuDgVK27V\nSoWNw+TmwrJlanD98Yd6wJYt0/fvvtMUqNlRz1pTUvS2o6hX/fplc1tWGTADyygbXntNf62dOu2d\n/SUm6qss2BuPaf72NxVPufLKYI7U44+XvJ3bb1cBjB49VCJ/yhR95BT9D3fTTaqoCNC5M8yaFaxr\n2FDP3VdnQ1oTuGuJJ38fSshR6zGYngldH4Fp0yKNwDvqq/rkm29qWz6N0mCR5/7pocYDAKsaAasg\nPjm/MmH3Q2H8eP086iQY/gn4Oh6TN8G7n0bWbwa0A2bWhXjPmKt7PDABOnrj7foo5O6E7wZCj5cD\n71sq+RMYJySotP3vv5N+zwus+l8fGpELySBNG8PiFTpX680XIGtfOOV4WPpZRBNpi7eqEuTvwEE7\niU+upUZf9RYkvLkEPvBr3q5vHZrA9hXU8KardapTR9VLAX4BcrbBAVlQpyts+ImeHdPp+cVcmLxK\n66yAXT1YuZJJnhP5xS/hopOaEbd5GTwFE46Po0c7qJGcR+0u/+L1F+7m1LbADkjIycUlwp81Umm4\ncRvXHQ7z5s2jfZIaWJ0+2AQ7oCEZMO0dnXV91lkR43744YcREXrV3ZcuCYGB1eGgIyFbRTja1FxD\nfBxs3g5p1SAu449QDrHQlyHPDCzDMIyyID4+8Hodd1z+9Xl5eluxdGlgfK1apbm8Vq/W24XJkwue\noZCSorc10a86dfKX1aypz5Vr1tSp8nHRD10rEWVqYIlIb+Ax9Er5gnPuvqj14q0/GZ1mf6Fzbnph\n24pIHeAtoCV623K2c26jt24IcAmaMvVfzrnxZTk+oxDOPbe8e1B67C0v3IAB+vI58cSSiWl06aKP\nn26+Wed0QZCrqzD23TcwsC68EK69Vv/Vjvak7etflj/bYVJWMJ8p+h9w3wlqFI0YoS+fPkfD42Nh\n6BXQ8TnIPgBe/wU2pwGrIO4PTXgcZn1IlfDoi+Hp32CxVykJoDE8OAJuvAn2Bf6Mg6cehg6D4JyO\n8D2QehAc+X+QEjL25oyAVZ5x5ueMqkWkgSWic/rmzIE5czimcWNY1g6Yp/u+ZQi8+gHM+hl+mAGL\nFkHLByHv35pP7EVgzT/IXbGc+Ee+0XlsuZlqSAK0uRTWerLy+7aF+Z6E+7wVEYdg+vTpHOx7Ud8C\n2v0MjfoF45n7ALzzlapBHhNsN/tAIW2mYx8v9djJowFpCFuXwQw4/pNvoe6hkPknUq0hZ//7EKb8\nfAY9PW0SqQFTM7dxOpAu8NioUXQ8cibt6kHSjlwQyOtci7hfNwVhmyEeuf922tTN4I4lsCmUVz1x\nwzcQp1bfCX/rCKt/Z0vakVRnFnG5GyDTU0CU0PeqHD1Ye3IdMwzDqOzExWmwSqNGkWk8o8nOhjVr\nAsNr1So1utavj3zNmqXvGzao96ww0tIija709PyfU1P1Vb160e9+OtO9QZkZWCISDzwFHI9qX/0o\nIuOcc3NC1U5Cnze3Aw4FngEOLWLbwcDnzrn7RGSwt3yziHQE+gOdgMbARBHZ1zlXxOkzjAIYPVpn\nhvohe3ubTz8tuk6Y1q01FPCww4pXf+RIePJJTYb87ruaNr537/weu0GnQPy38NzcoKxGbmBggQpd\nnHIKvPUWPPGEinCsiDQU2L+DvrddCAJ02A/4JZDMz8uJNHCSEkFCKnPvLIEfQhZYNSBjJ1x/I+w3\nHR7+EnKrqXG1ZQvQCpgL1ISmfSL7krsNsjZpmKNvYHWJgxWeIXPTGXDi1VCnrhqcX32lYZC/eSGC\nyUDH2nBDe/jxMPjhCg3p9Nuqht6O9zmcuVPG0xn01tuxy8CaN38BHbzxLu/Vi6a+gdW0FtTcDrM1\ntnFLWBIqHk34nLEQmvSFdVPIWz+NuB1AHeDQu4Bb4HSofWInHn6rLvumfkUnB/tsEeLqHAstT4c3\nboHUA/VcV1NDrW/fvtBgAEx+ddcYXGJN2LGZJmlpJCQkkBSfG4hsVIO4/dLg101sWbqUjJUrqVOn\nDikpKeTm5tKmbgZf3gJH3x156LOpzua1K6iXBrN++JSDm0Oz1p1g1UrI2EDG+kUq7JG+n3qu1v9Y\nbgbWnlzHCmt3yeYlXPnhlWXTacMwjIpAQ++FPpNs5L18HJCdpXpdmZkqjpyVpa/s7OBzVhbsyILN\n2Vo/KwuytkDWuoJ1zQojPl4DnhIS9LP/7r+il3eXsvRgdQcWOOcWAojIm8DpQPjCdDrwinPOAd+L\nSC0RaYR6pwra9nSgl7f9y8CXwM1e+ZvOuZ3AIhFZ4PUhcrKBYRSXs87KF/ZUoRk9Wg2G4j6euewy\nfX3vzXPJyoo0rmbeoje3ae2gQ8i4SkiAajmB5DnAyScH6eW/+06NjSuuUJ//Pfdo+Sbv7+bD+fCP\nOtD9IuCt4BFWVL5fqlcjYi6Ur7joUwsgXv+ZN/WBjfMh3VcO/Aje/lg/v/ACfP659qV2bU/b9mvI\nyILHOsKvnjDGPmnsUrBIe0+VGxP+hPpem4sWwUZPdGEzsPC/sOgVmHedlk2YAGf4KoZeH5e+Q5Np\nc3AJIDnAXUDSYkiAuvGv7hrzjl8mBOPK3YRLrYZ/JrrUBnw7czOwOQMerMas3DfoXBfiNm6DPwFJ\ngTc9j98f0HjMbzyYWg/WpMBdmcTlOk0H4AvPHHEQtGighuzWHbBhqxqdvrMyB07sUQfe30y7X7Yz\nqvp4Umah8QHeocf9CUDSC/fDqHs15iMpnri8LMa7RBiZzUtxsOYOPTUpiRAfv5Ha2UAcHFB/p867\ny/0YNqyGPEjMeRESYGfcGyQ1S0UygZFnU07s9nXMObeqoEY3ZW5i7LyxZdlvwzCMyomgFlkMjTRB\nn2+GNJpwbvdeOU4zr2SGyqLb26NhuD1toaCGRfoBvZ1zl3rL5wOHOueuCdX5ELjPOTfZW/4cNZZa\nFrStiGxyztXyygXY6JyrJSJPAt8751711r0IfOKcGx3Vr8vR1K2g0/F/o/Ki+s5Vg6oylqoyDqg6\nY7FxVDx2ZywtnHN7KbGesifXMefctKi2wteezsAsKj9V5Ttp46hYVJVxQNUZy195HLt17anUIhfO\nOSciJbIQnXMjgZFl1KW9iohMc84dUnTNik9VGUtVGQdUnbHYOCoeVWksxSV87akq47dxVCxsHBWP\nqjIWG0fJKcupXitQTS+fpl5ZceoUtu2fXhgh3vuaEuzPMAzDMIrLnlzHDMMwjL8oZWlg/Qi0E5FW\nIpKEClCMi6ozDrhAlMOAzV7cemHbjgMGep8HAu+HyvuLSLKItEInHIckyAzDMAyjROzJdcwwDMP4\ni1JmIYLOuRwRuQYYj06Hfsk5N1tErvTWPwt8jErbLkA1ti4qbFuv6fuAt0XkEnSK+NneNrNF5G10\n8nEOcPVfQEGwSoQ6elSVsVSVcUDVGYuNo+JRKcayJ9exIqgU4y8GNo6KhY2j4lFVxmLjKCFlJnJh\nGIZhGIZhGIbxV6MS50g2DMMwDMMwDMOoWJiBZRiGYRiGYRiGUUqYgVVBEZFmIvKFiMwRkdkiMsgr\nryMiE0Tkd++9dmibISKyQER+E5ETy6/3sRGReBGZ4eWNqZRj8ZKIjhaReSIyV0R6VMZxAIjIdd53\na5aIvCEiKZVhLCLykoisEZFZobIS91tEuorIr966x728ehVhLA94369fROQ9EalV0ccSaxyhdTeI\niBOReqGyCjmOskZEentjXiAig8u7P4VR1a5Bdv2pUOOolNcery9V4vpj1569MA7nnL0q4AtoBBzs\nfU4D5gMdgRHAYK98MHC/97kjMBNNcN0K+AOIL+9xRI3peuB14ENvudKNBXgZuNT7nATUqqTjaAIs\nAqp5y28DF1aGsQBHAgcDs0JlJe43qjJ6GJoc/hPgpAoylhOABO/z/ZVhLLHG4ZU3QwUilgD1Kvo4\nyvgYxXtjbe39d8wEOpZ3vwrpb5W6BmHXnwoxDirxtcfrT5W4/hQwDrv2lOI4zINVQXHOrXLOTfc+\nbwXmon9Mp6N/snjvfb3PpwNvOud2OucWoYpW3fdurwtGRJoCpwAvhIor1VhEpCb6Y34RwDmX5Zz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CJvAicCV8dKIWAYhmEYVRELETQMwzAMwzAMwyglLETQMAzDMAzDMAyjlPhLhwjWq1fPtWzZ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9088438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the 'sim_improved-learning' file from the improved Q-Learning simulation\n",
+    "vs.plot_trials('sim_improved-learning_ref.csv') # e decay\n",
+    "vs.plot_trials('sim_improved-learning.csv') # gompertz decay parametrized to go to 0 at around 1600 trials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 7\n",
+    "Using the visualization above that was produced from your improved Q-Learning simulation, provide a final analysis and make observations about the improved driving agent like in **Question 6**. Questions you should answer:  \n",
+    "- *What decaying function was used for epsilon (the exploration factor)?*\n",
+    "- *Approximately how many training trials were needed for your agent before begining testing?*\n",
+    "- *What epsilon-tolerance and alpha (learning rate) did you use? Why did you use them?*\n",
+    "- *How much improvement was made with this Q-Learner when compared to the default Q-Learner from the previous section?*\n",
+    "- *Would you say that the Q-Learner results show that your driving agent successfully learned an appropriate policy?*\n",
+    "- *Are you satisfied with the safety and reliability ratings of the *Smartcab*?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:**\n",
+    "\n",
+    "The epsilon decay function used was e^(-0.0005 * nb_trials). I also attempted gompertz decay, but needed to parametrize the function quite a bit. With the first i need about 4k trials, with gompertz about 2k to converge.\n",
+    "\n",
+    "Epsilon tolerance was 0.01, alpha was 0.01 and constant over time. I used these values so that the simulation could learn a lot from historical mistakes before it started testing. A low alpha was chosen so that learning could be spread out better over the large number of trials.\n",
+    "\n",
+    "Improvement over default q learner was vast. Initially I tried using better decay functions, but it turned out i needed more training. Accident rates are now will below 5% and reliability at worst is around 80% - 90% and i feel i narrowed the standard deviation of rolling reliability. Rewards are almost constant at 2.\n",
+    "\n",
+    "Safety and Reliability rating are at A+, which is incredible. I would certainly say the learner has learned an appropriate policy, although it still has a rather high variance in reliability.\n",
+    "\n",
+    "\n",
+    "N.B: Removing deadline from the state variables reduces variance in the reliability running average, so I did this for the output.\n",
+    "\n",
+    "Including all features is possible with advances in computing power, as opposed to using a priori knowledge to reduce space dimensionality. But a priori is NOT reinforcement learning - the agent must come up with all rules by itself, so you should not remove any features.\n",
+    "\n",
+    "It seems running on all features except deadline, leads to a slightly lower reliability after 10k trials, and reliability variance is up again (not shown here).\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define an Optimal Policy\n",
+    "\n",
+    "Sometimes, the answer to the important question *\"what am I trying to get my agent to learn?\"* only has a theoretical answer and cannot be concretely described. Here, however, you can concretely define what it is the agent is trying to learn, and that is the U.S. right-of-way traffic laws. Since these laws are known information, you can further define, for each state the *Smartcab* is occupying, the optimal action for the driving agent based on these laws. In that case, we call the set of optimal state-action pairs an **optimal policy**. Hence, unlike some theoretical answers, it is clear whether the agent is acting \"incorrectly\" not only by the reward (penalty) it receives, but also by pure observation. If the agent drives through a red light, we both see it receive a negative reward but also know that it is not the correct behavior. This can be used to your advantage for verifying whether the **policy** your driving agent has learned is the correct one, or if it is a **suboptimal policy**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 8\n",
+    "Provide a few examples (using the states you've defined) of what an optimal policy for this problem would look like. Afterwards, investigate the `'sim_improved-learning.txt'` text file to see the results of your improved Q-Learning algorithm. _For each state that has been recorded from the simulation, is the **policy** (the action with the highest value) correct for the given state? Are there any states where the policy is different than what would be expected from an optimal policy?_ Provide an example of a state and all state-action rewards recorded, and explain why it is the correct policy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** \n",
+    "\n",
+    "I have now removed deadline from the states, so answers are based on this: I find it surprising that its not required to know how close it is from failing in order to get good reliability.\n",
+    "\n",
+    "I have defined states as waypoint, light, oncoming.\n",
+    "Examples:\n",
+    "\n",
+    "    waypoint=left, light=green, oncoming=forward, left!=forward => should wait for oncoming or go right or forward. In the simulation, going forward has the biggest reward, followed by right. Waiting actually has quite a negative reward.\n",
+    "    \n",
+    "    waypoint=right, light=green, oncoming=None, left!=forward => should turn right. Simulation agrees, right has the biggest reward.\n",
+    "    \n",
+    "    waypoint=right, light=red, oncoming=forward, left!=forward => should wait. Simulation agrees.\n",
+    "    \n",
+    "    \n",
+    "States: The few i checked seem to have the right policies. Removing deadline, it seems all states chose the best action. \n",
+    "\n",
+    "A state i found where the optimal policy is different from the learned is:\n",
+    "\n",
+    "    waypoint=left, light=green, oncoming=right, left!=forward. It prefers forward instead of wait, although the next waypoint is left.\n",
+    "    \n",
+    "    ('left', 'green', 'right', False)\n",
+    "     -- forward : 0.13\n",
+    "     -- right : 0.44\n",
+    "     -- None : -1.44\n",
+    "     -- left : -6.68\n",
+    "    \n",
+    "    \n",
+    "An example of a state with all actions and rewards that has the correct policy is:\n",
+    "\n",
+    "    ('forward', 'red', 'left', False)\n",
+    "     -- forward : -9.09\n",
+    "     -- right : 0.73\n",
+    "     -- None : 1.87\n",
+    "     -- left : -9.62\n",
+    "\n",
+    "\n",
+    " here the light is red, car wants to go forward, oncoming goes left, so best action is chosen correctly (do nothing).\n",
+    "    \n",
+    "    \n",
+    "Papers on learning rates for later:\n",
+    "\n",
+    "    http://www.jmlr.org/papers/volume5/evendar03a/evendar03a.pdf\n",
+    "    http://karpathy.github.io/2016/05/31/rl/\n",
+    "    https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0\n",
+    "    http://mnemstudio.org/path-finding-q-learning-tutorial.htm\n",
+    "    https://www-s.acm.illinois.edu/sigart/docs/QLearning.pdf\n",
+    "    http://www.umiacs.umd.edu/~hal/courses/ai/out/cs421-day10-qlearning.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----\n",
+    "### Optional: Future Rewards - Discount Factor, `'gamma'`\n",
+    "Curiously, as part of the Q-Learning algorithm, you were asked to **not** use the discount factor, `'gamma'` in the implementation. Including future rewards in the algorithm is used to aid in propogating positive rewards backwards from a future state to the current state. Essentially, if the driving agent is given the option to make several actions to arrive at different states, including future rewards will bias the agent towards states that could provide even more rewards. An example of this would be the driving agent moving towards a goal: With all actions and rewards equal, moving towards the goal would theoretically yield better rewards if there is an additional reward for reaching the goal. However, even though in this project, the driving agent is trying to reach a destination in the allotted time, including future rewards will not benefit the agent. In fact, if the agent were given many trials to learn, it could negatively affect Q-values!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optional Question 9\n",
+    "*There are two characteristics about the project that invalidate the use of future rewards in the Q-Learning algorithm. One characteristic has to do with the *Smartcab* itself, and the other has to do with the environment. Can you figure out what they are and why future rewards won't work for this project?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:**\n",
+    "\n",
+    "Smartcab: it cannot see what is happening elsewhere in the city, so no point optimising now to make some headway a few roads down the line. It would need traffic reports to do this for example. Further, the agent cannot see how far it is from the destination, thus cannot optimise the path.\n",
+    "\n",
+    "As traffic and lights and other cars movements are random, there is no benefit to learning to go towards the goal directly, as the environment keeps evolving randomly, so the agent cannot control the environments future. So lets say driving at the same speed to keep traffice flowing has no benefit down the line, as no traffic jams are avoided or created. Further, as each trial has a new destination, the learned Q cannot incorporate how to get there fast, as this info is useless in the next trial.\n",
+    "\n",
+    "I wonder if there is a way to parallelize this implementation?\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
+    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
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+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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diff --git a/titanic_survival_exploration/titanic_data.csv b/titanic_survival_exploration/titanic_data.csv
new file mode 100644
index 0000000..5cc466e
--- /dev/null
+++ b/titanic_survival_exploration/titanic_data.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
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+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
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+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
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+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
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+  content: "\00a5";
+}
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+  content: "\20bd";
+}
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+  content: "\20bd";
+}
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+  content: "\e230";
+}
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+  content: "\e231";
+}
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+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
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+  content: "\e235";
+}
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+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
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+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
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+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
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+  content: "\e242";
+}
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+  content: "\e243";
+}
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+  content: "\e244";
+}
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+  content: "\e245";
+}
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+  content: "\e246";
+}
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+  content: "\e247";
+}
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+  content: "\e248";
+}
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+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
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+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
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+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #000;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 3px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 18px;
+  margin-bottom: 18px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  margin: -1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
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+h6 .small,
+.h1 .small,
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+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: normal;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 18px;
+  margin-bottom: 9px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
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+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 9px;
+  margin-bottom: 9px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 33px;
+}
+h2,
+.h2 {
+  font-size: 27px;
+}
+h3,
+.h3 {
+  font-size: 23px;
+}
+h4,
+.h4 {
+  font-size: 17px;
+}
+h5,
+.h5 {
+  font-size: 13px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 9px;
+}
+.lead {
+  margin-bottom: 18px;
+  font-size: 14px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 19.5px;
+  }
+}
+small,
+.small {
+  font-size: 92%;
+}
+mark,
+.mark {
+  background-color: #fcf8e3;
+  padding: .2em;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 8px;
+  margin: 36px 0 18px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 9px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-left: 5px;
+  padding-right: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 18px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: bold;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 541px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+  border-bottom: 1px dotted #777777;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 9px 18px;
+  margin: 0 0 18px;
+  font-size: inherit;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: '\2014 \00A0';
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+  text-align: right;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: '';
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: '\00A0 \2014';
+}
+address {
+  margin-bottom: 18px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 2px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #888;
+  background-color: transparent;
+  border-radius: 1px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: bold;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 8.5px;
+  margin: 0 0 9px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  word-break: break-all;
+  word-wrap: break-word;
+  color: #333333;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 768px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 940px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1140px;
+  }
+}
+.container-fluid {
+  margin-right: auto;
+  margin-left: auto;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
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+  width: 75%;
+}
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+  width: 66.66666667%;
+}
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+  width: 58.33333333%;
+}
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+  width: 50%;
+}
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+  width: 41.66666667%;
+}
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+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
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+  right: 91.66666667%;
+}
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+  right: 83.33333333%;
+}
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+  right: 75%;
+}
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+  right: 66.66666667%;
+}
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+  right: 58.33333333%;
+}
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+  right: 50%;
+}
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+  right: 41.66666667%;
+}
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+  right: 33.33333333%;
+}
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+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
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+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
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+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
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+  margin-left: 83.33333333%;
+}
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+  margin-left: 75%;
+}
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+  margin-left: 66.66666667%;
+}
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+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
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+  margin-left: 41.66666667%;
+}
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+  margin-left: 33.33333333%;
+}
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+  margin-left: 25%;
+}
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+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 18px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+table col[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-column;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  float: none;
+  display: table-cell;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  overflow-x: auto;
+  min-height: 0.01%;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 13.5px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  padding: 0;
+  margin: 0;
+  border: 0;
+  min-width: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 18px;
+  font-size: 19.5px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: bold;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: none;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 32px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 45px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio label,
+.checkbox label {
+  min-height: 18px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-left: -20px;
+  margin-top: 4px \9;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  vertical-align: middle;
+  font-weight: normal;
+  cursor: pointer;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.form-control-static {
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+  min-height: 31px;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 30px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-lg {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.form-group-lg select.form-control {
+  height: 45px;
+  line-height: 45px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 45px;
+  min-height: 35px;
+  padding: 11px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 40px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 32px;
+  height: 32px;
+  line-height: 32px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 45px;
+  height: 45px;
+  line-height: 45px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  border-color: #3c763d;
+  background-color: #dff0d8;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  border-color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  border-color: #a94442;
+  background-color: #f2dede;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 23px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #404040;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-top: 7px;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 25px;
+}
+.form-horizontal .form-group {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    text-align: right;
+    margin-bottom: 0;
+    padding-top: 7px;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 0px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 17px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  vertical-align: middle;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  white-space: nowrap;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  border-radius: 2px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  outline: 0;
+  background-image: none;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  background-image: none;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  background-image: none;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  background-image: none;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  background-image: none;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  background-image: none;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  background-image: none;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  color: #337ab7;
+  font-weight: normal;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 13px;
+  text-align: left;
+  background-color: #fff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 2px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  text-decoration: none;
+  color: #262626;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  cursor: not-allowed;
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  left: auto;
+  right: 0;
+}
+.dropdown-menu-left {
+  left: 0;
+  right: auto;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  top: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+  content: "";
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 541px) {
+  .navbar-right .dropdown-menu {
+    left: auto;
+    right: 0;
+  }
+  .navbar-right .dropdown-menu-left {
+    left: 0;
+    right: auto;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-left: 8px;
+  padding-right: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-left: 12px;
+  padding-right: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  float: none;
+  display: table-cell;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-left: 0;
+  padding-right: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+  border-radius: 3px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 45px;
+  line-height: 45px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 1px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 17px;
+  border-radius: 3px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-top-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-bottom-left-radius: 0;
+  border-top-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  margin-bottom: 0;
+  padding-left: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  background-color: transparent;
+  cursor: not-allowed;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 8px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 2px 2px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+  cursor: default;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 2px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  text-align: center;
+  margin-bottom: 5px;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 2px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 2px 2px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 30px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+}
+@media (min-width: 541px) {
+  .navbar {
+    border-radius: 2px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  overflow-x: visible;
+  padding-right: 0px;
+  padding-left: 0px;
+  border-top: 1px solid transparent;
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 541px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-left: 0;
+    padding-right: 0;
+  }
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 540px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: 0px;
+  margin-left: 0px;
+}
+@media (min-width: 541px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 541px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+@media (min-width: 541px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.navbar-brand {
+  float: left;
+  padding: 6px 0px;
+  font-size: 17px;
+  line-height: 18px;
+  height: 30px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 541px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: 0px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  margin-right: 0px;
+  padding: 9px 10px;
+  margin-top: -2px;
+  margin-bottom: -2px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 541px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 3px 0px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 18px;
+}
+@media (max-width: 540px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 18px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 6px;
+    padding-bottom: 6px;
+  }
+}
+.navbar-form {
+  margin-left: 0px;
+  margin-right: 0px;
+  padding: 10px 0px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 540px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-form {
+    width: auto;
+    border: 0;
+    margin-left: 0;
+    margin-right: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: -1px;
+  margin-bottom: -1px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 0px;
+  margin-bottom: 0px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 4px;
+  margin-bottom: 4px;
+}
+.navbar-text {
+  margin-top: 6px;
+  margin-bottom: 6px;
+}
+@media (min-width: 541px) {
+  .navbar-text {
+    float: left;
+    margin-left: 0px;
+    margin-right: 0px;
+  }
+}
+@media (min-width: 541px) {
+  .navbar-left {
+    float: left !important;
+    float: left;
+  }
+  .navbar-right {
+    float: right !important;
+    float: right;
+    margin-right: 0px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  background-color: #e7e7e7;
+  color: #555;
+}
+@media (max-width: 540px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  background-color: #080808;
+  color: #fff;
+}
+@media (max-width: 540px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 18px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  content: "/\00a0";
+  padding: 0 5px;
+  color: #5e5e5e;
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 18px 0;
+  border-radius: 2px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  line-height: 1.42857143;
+  text-decoration: none;
+  color: #337ab7;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  margin-left: -1px;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-bottom-right-radius: 2px;
+  border-top-right-radius: 2px;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+  cursor: default;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  background-color: #fff;
+  border-color: #ddd;
+  cursor: not-allowed;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 17px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-bottom-left-radius: 3px;
+  border-top-left-radius: 3px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-bottom-right-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-bottom-left-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-bottom-right-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.pager {
+  padding-left: 0;
+  margin: 18px 0;
+  list-style: none;
+  text-align: center;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  background-color: #fff;
+  cursor: not-allowed;
+}
+.label {
+  display: inline;
+  padding: .2em .6em .3em;
+  font-size: 75%;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: .25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  color: #fff;
+  line-height: 1;
+  vertical-align: middle;
+  white-space: nowrap;
+  text-align: center;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 20px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  border-radius: 3px;
+  padding-left: 0px;
+  padding-right: 0px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-left: 60px;
+    padding-right: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 59px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 18px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-left: auto;
+  margin-right: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #000;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 18px;
+  border: 1px solid transparent;
+  border-radius: 2px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+  color: #3c763d;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+  color: #31708f;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+  color: #8a6d3b;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  background-color: #f2dede;
+  border-color: #ebccd1;
+  color: #a94442;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  overflow: hidden;
+  height: 18px;
+  margin-bottom: 18px;
+  background-color: #f5f5f5;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 18px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  zoom: 1;
+  overflow: hidden;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  margin-bottom: 20px;
+  padding-left: 0;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-right-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 2px;
+  border-bottom-left-radius: 2px;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  text-decoration: none;
+  color: #555;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  background-color: #eeeeee;
+  color: #777777;
+  cursor: not-allowed;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 18px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 2px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 15px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-right-radius: 0;
+  border-top-left-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-left: 15px;
+  padding-right: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-right-radius: 1px;
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 1px;
+  border-top-right-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 1px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 1px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 1px;
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-left-radius: 1px;
+  border-bottom-right-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 1px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 1px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  border: 0;
+  margin-bottom: 0;
+}
+.panel-group {
+  margin-bottom: 18px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 2px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  height: 100%;
+  width: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 3px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 1px;
+}
+.close {
+  float: right;
+  font-size: 19.5px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  display: none;
+  overflow: hidden;
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -moz-transition: -moz-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  background-clip: padding-box;
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.modal-backdrop.in {
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-left: 5px;
+  margin-bottom: 0;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 12px;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+.tooltip.in {
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.tooltip.top {
+  margin-top: -3px;
+  padding: 5px 0;
+}
+.tooltip.right {
+  margin-left: 3px;
+  padding: 0 5px;
+}
+.tooltip.bottom {
+  margin-top: 3px;
+  padding: 5px 0;
+}
+.tooltip.left {
+  margin-left: -3px;
+  padding: 0 5px;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 2px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  bottom: 0;
+  right: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: normal;
+  letter-spacing: normal;
+  line-break: auto;
+  line-height: 1.42857143;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  white-space: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  font-size: 13px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 3px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover-title {
+  margin: 0;
+  padding: 8px 14px;
+  font-size: 13px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 2px 2px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+.popover.top > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-width: 0;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  bottom: -11px;
+}
+.popover.top > .arrow:after {
+  content: " ";
+  bottom: 1px;
+  margin-left: -10px;
+  border-bottom-width: 0;
+  border-top-color: #fff;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-left-width: 0;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.popover.right > .arrow:after {
+  content: " ";
+  left: 1px;
+  bottom: -10px;
+  border-left-width: 0;
+  border-right-color: #fff;
+}
+.popover.bottom > .arrow {
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  top: -11px;
+}
+.popover.bottom > .arrow:after {
+  content: " ";
+  top: 1px;
+  margin-left: -10px;
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  content: " ";
+  right: 1px;
+  border-right-width: 0;
+  border-left-color: #fff;
+  bottom: -10px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  overflow: hidden;
+  width: 100%;
+}
+.carousel-inner > .item {
+  display: none;
+  position: relative;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -moz-transition: -moz-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    -moz-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    -moz-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  width: 15%;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+}
+.carousel-control.right {
+  left: auto;
+  right: 0;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  outline: 0;
+  color: #fff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  margin-top: -10px;
+  z-index: 5;
+  display: inline-block;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  line-height: 1;
+  font-family: serif;
+}
+.carousel-control .icon-prev:before {
+  content: '\2039';
+}
+.carousel-control .icon-next:before {
+  content: '\203a';
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  margin-left: -30%;
+  padding-left: 0;
+  list-style: none;
+  text-align: center;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  border: 1px solid #fff;
+  border-radius: 10px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+}
+.carousel-indicators .active {
+  margin: 0;
+  width: 12px;
+  height: 12px;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  left: 15%;
+  right: 15%;
+  bottom: 20px;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    left: 20%;
+    right: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after,
+.item_buttons:before,
+.item_buttons:after {
+  content: " ";
+  display: table;
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after,
+.item_buttons:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*!
+*
+* Font Awesome
+*
+*/
+/*!
+ *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */
+/* FONT PATH
+ * -------------------------- */
+@font-face {
+  font-family: 'FontAwesome';
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
+  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+.fa {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+/* makes the font 33% larger relative to the icon container */
+.fa-lg {
+  font-size: 1.33333333em;
+  line-height: 0.75em;
+  vertical-align: -15%;
+}
+.fa-2x {
+  font-size: 2em;
+}
+.fa-3x {
+  font-size: 3em;
+}
+.fa-4x {
+  font-size: 4em;
+}
+.fa-5x {
+  font-size: 5em;
+}
+.fa-fw {
+  width: 1.28571429em;
+  text-align: center;
+}
+.fa-ul {
+  padding-left: 0;
+  margin-left: 2.14285714em;
+  list-style-type: none;
+}
+.fa-ul > li {
+  position: relative;
+}
+.fa-li {
+  position: absolute;
+  left: -2.14285714em;
+  width: 2.14285714em;
+  top: 0.14285714em;
+  text-align: center;
+}
+.fa-li.fa-lg {
+  left: -1.85714286em;
+}
+.fa-border {
+  padding: .2em .25em .15em;
+  border: solid 0.08em #eee;
+  border-radius: .1em;
+}
+.pull-right {
+  float: right;
+}
+.pull-left {
+  float: left;
+}
+.fa.pull-left {
+  margin-right: .3em;
+}
+.fa.pull-right {
+  margin-left: .3em;
+}
+.fa-spin {
+  -webkit-animation: fa-spin 2s infinite linear;
+  animation: fa-spin 2s infinite linear;
+}
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+    transform: rotate(0deg);
+  }
+  100% {
+    -webkit-transform: rotate(359deg);
+    transform: rotate(359deg);
+  }
+}
+.fa-rotate-90 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
+  -webkit-transform: rotate(90deg);
+  -ms-transform: rotate(90deg);
+  transform: rotate(90deg);
+}
+.fa-rotate-180 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
+  -webkit-transform: rotate(180deg);
+  -ms-transform: rotate(180deg);
+  transform: rotate(180deg);
+}
+.fa-rotate-270 {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
+  -webkit-transform: rotate(270deg);
+  -ms-transform: rotate(270deg);
+  transform: rotate(270deg);
+}
+.fa-flip-horizontal {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.fa-flip-vertical {
+  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(1, -1);
+  transform: scale(1, -1);
+}
+:root .fa-rotate-90,
+:root .fa-rotate-180,
+:root .fa-rotate-270,
+:root .fa-flip-horizontal,
+:root .fa-flip-vertical {
+  filter: none;
+}
+.fa-stack {
+  position: relative;
+  display: inline-block;
+  width: 2em;
+  height: 2em;
+  line-height: 2em;
+  vertical-align: middle;
+}
+.fa-stack-1x,
+.fa-stack-2x {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  text-align: center;
+}
+.fa-stack-1x {
+  line-height: inherit;
+}
+.fa-stack-2x {
+  font-size: 2em;
+}
+.fa-inverse {
+  color: #fff;
+}
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+   readers do not read off random characters that represent icons */
+.fa-glass:before {
+  content: "\f000";
+}
+.fa-music:before {
+  content: "\f001";
+}
+.fa-search:before {
+  content: "\f002";
+}
+.fa-envelope-o:before {
+  content: "\f003";
+}
+.fa-heart:before {
+  content: "\f004";
+}
+.fa-star:before {
+  content: "\f005";
+}
+.fa-star-o:before {
+  content: "\f006";
+}
+.fa-user:before {
+  content: "\f007";
+}
+.fa-film:before {
+  content: "\f008";
+}
+.fa-th-large:before {
+  content: "\f009";
+}
+.fa-th:before {
+  content: "\f00a";
+}
+.fa-th-list:before {
+  content: "\f00b";
+}
+.fa-check:before {
+  content: "\f00c";
+}
+.fa-remove:before,
+.fa-close:before,
+.fa-times:before {
+  content: "\f00d";
+}
+.fa-search-plus:before {
+  content: "\f00e";
+}
+.fa-search-minus:before {
+  content: "\f010";
+}
+.fa-power-off:before {
+  content: "\f011";
+}
+.fa-signal:before {
+  content: "\f012";
+}
+.fa-gear:before,
+.fa-cog:before {
+  content: "\f013";
+}
+.fa-trash-o:before {
+  content: "\f014";
+}
+.fa-home:before {
+  content: "\f015";
+}
+.fa-file-o:before {
+  content: "\f016";
+}
+.fa-clock-o:before {
+  content: "\f017";
+}
+.fa-road:before {
+  content: "\f018";
+}
+.fa-download:before {
+  content: "\f019";
+}
+.fa-arrow-circle-o-down:before {
+  content: "\f01a";
+}
+.fa-arrow-circle-o-up:before {
+  content: "\f01b";
+}
+.fa-inbox:before {
+  content: "\f01c";
+}
+.fa-play-circle-o:before {
+  content: "\f01d";
+}
+.fa-rotate-right:before,
+.fa-repeat:before {
+  content: "\f01e";
+}
+.fa-refresh:before {
+  content: "\f021";
+}
+.fa-list-alt:before {
+  content: "\f022";
+}
+.fa-lock:before {
+  content: "\f023";
+}
+.fa-flag:before {
+  content: "\f024";
+}
+.fa-headphones:before {
+  content: "\f025";
+}
+.fa-volume-off:before {
+  content: "\f026";
+}
+.fa-volume-down:before {
+  content: "\f027";
+}
+.fa-volume-up:before {
+  content: "\f028";
+}
+.fa-qrcode:before {
+  content: "\f029";
+}
+.fa-barcode:before {
+  content: "\f02a";
+}
+.fa-tag:before {
+  content: "\f02b";
+}
+.fa-tags:before {
+  content: "\f02c";
+}
+.fa-book:before {
+  content: "\f02d";
+}
+.fa-bookmark:before {
+  content: "\f02e";
+}
+.fa-print:before {
+  content: "\f02f";
+}
+.fa-camera:before {
+  content: "\f030";
+}
+.fa-font:before {
+  content: "\f031";
+}
+.fa-bold:before {
+  content: "\f032";
+}
+.fa-italic:before {
+  content: "\f033";
+}
+.fa-text-height:before {
+  content: "\f034";
+}
+.fa-text-width:before {
+  content: "\f035";
+}
+.fa-align-left:before {
+  content: "\f036";
+}
+.fa-align-center:before {
+  content: "\f037";
+}
+.fa-align-right:before {
+  content: "\f038";
+}
+.fa-align-justify:before {
+  content: "\f039";
+}
+.fa-list:before {
+  content: "\f03a";
+}
+.fa-dedent:before,
+.fa-outdent:before {
+  content: "\f03b";
+}
+.fa-indent:before {
+  content: "\f03c";
+}
+.fa-video-camera:before {
+  content: "\f03d";
+}
+.fa-photo:before,
+.fa-image:before,
+.fa-picture-o:before {
+  content: "\f03e";
+}
+.fa-pencil:before {
+  content: "\f040";
+}
+.fa-map-marker:before {
+  content: "\f041";
+}
+.fa-adjust:before {
+  content: "\f042";
+}
+.fa-tint:before {
+  content: "\f043";
+}
+.fa-edit:before,
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+}
+.fa-reddit-square:before {
+  content: "\f1a2";
+}
+.fa-stumbleupon-circle:before {
+  content: "\f1a3";
+}
+.fa-stumbleupon:before {
+  content: "\f1a4";
+}
+.fa-delicious:before {
+  content: "\f1a5";
+}
+.fa-digg:before {
+  content: "\f1a6";
+}
+.fa-pied-piper:before {
+  content: "\f1a7";
+}
+.fa-pied-piper-alt:before {
+  content: "\f1a8";
+}
+.fa-drupal:before {
+  content: "\f1a9";
+}
+.fa-joomla:before {
+  content: "\f1aa";
+}
+.fa-language:before {
+  content: "\f1ab";
+}
+.fa-fax:before {
+  content: "\f1ac";
+}
+.fa-building:before {
+  content: "\f1ad";
+}
+.fa-child:before {
+  content: "\f1ae";
+}
+.fa-paw:before {
+  content: "\f1b0";
+}
+.fa-spoon:before {
+  content: "\f1b1";
+}
+.fa-cube:before {
+  content: "\f1b2";
+}
+.fa-cubes:before {
+  content: "\f1b3";
+}
+.fa-behance:before {
+  content: "\f1b4";
+}
+.fa-behance-square:before {
+  content: "\f1b5";
+}
+.fa-steam:before {
+  content: "\f1b6";
+}
+.fa-steam-square:before {
+  content: "\f1b7";
+}
+.fa-recycle:before {
+  content: "\f1b8";
+}
+.fa-automobile:before,
+.fa-car:before {
+  content: "\f1b9";
+}
+.fa-cab:before,
+.fa-taxi:before {
+  content: "\f1ba";
+}
+.fa-tree:before {
+  content: "\f1bb";
+}
+.fa-spotify:before {
+  content: "\f1bc";
+}
+.fa-deviantart:before {
+  content: "\f1bd";
+}
+.fa-soundcloud:before {
+  content: "\f1be";
+}
+.fa-database:before {
+  content: "\f1c0";
+}
+.fa-file-pdf-o:before {
+  content: "\f1c1";
+}
+.fa-file-word-o:before {
+  content: "\f1c2";
+}
+.fa-file-excel-o:before {
+  content: "\f1c3";
+}
+.fa-file-powerpoint-o:before {
+  content: "\f1c4";
+}
+.fa-file-photo-o:before,
+.fa-file-picture-o:before,
+.fa-file-image-o:before {
+  content: "\f1c5";
+}
+.fa-file-zip-o:before,
+.fa-file-archive-o:before {
+  content: "\f1c6";
+}
+.fa-file-sound-o:before,
+.fa-file-audio-o:before {
+  content: "\f1c7";
+}
+.fa-file-movie-o:before,
+.fa-file-video-o:before {
+  content: "\f1c8";
+}
+.fa-file-code-o:before {
+  content: "\f1c9";
+}
+.fa-vine:before {
+  content: "\f1ca";
+}
+.fa-codepen:before {
+  content: "\f1cb";
+}
+.fa-jsfiddle:before {
+  content: "\f1cc";
+}
+.fa-life-bouy:before,
+.fa-life-buoy:before,
+.fa-life-saver:before,
+.fa-support:before,
+.fa-life-ring:before {
+  content: "\f1cd";
+}
+.fa-circle-o-notch:before {
+  content: "\f1ce";
+}
+.fa-ra:before,
+.fa-rebel:before {
+  content: "\f1d0";
+}
+.fa-ge:before,
+.fa-empire:before {
+  content: "\f1d1";
+}
+.fa-git-square:before {
+  content: "\f1d2";
+}
+.fa-git:before {
+  content: "\f1d3";
+}
+.fa-hacker-news:before {
+  content: "\f1d4";
+}
+.fa-tencent-weibo:before {
+  content: "\f1d5";
+}
+.fa-qq:before {
+  content: "\f1d6";
+}
+.fa-wechat:before,
+.fa-weixin:before {
+  content: "\f1d7";
+}
+.fa-send:before,
+.fa-paper-plane:before {
+  content: "\f1d8";
+}
+.fa-send-o:before,
+.fa-paper-plane-o:before {
+  content: "\f1d9";
+}
+.fa-history:before {
+  content: "\f1da";
+}
+.fa-circle-thin:before {
+  content: "\f1db";
+}
+.fa-header:before {
+  content: "\f1dc";
+}
+.fa-paragraph:before {
+  content: "\f1dd";
+}
+.fa-sliders:before {
+  content: "\f1de";
+}
+.fa-share-alt:before {
+  content: "\f1e0";
+}
+.fa-share-alt-square:before {
+  content: "\f1e1";
+}
+.fa-bomb:before {
+  content: "\f1e2";
+}
+.fa-soccer-ball-o:before,
+.fa-futbol-o:before {
+  content: "\f1e3";
+}
+.fa-tty:before {
+  content: "\f1e4";
+}
+.fa-binoculars:before {
+  content: "\f1e5";
+}
+.fa-plug:before {
+  content: "\f1e6";
+}
+.fa-slideshare:before {
+  content: "\f1e7";
+}
+.fa-twitch:before {
+  content: "\f1e8";
+}
+.fa-yelp:before {
+  content: "\f1e9";
+}
+.fa-newspaper-o:before {
+  content: "\f1ea";
+}
+.fa-wifi:before {
+  content: "\f1eb";
+}
+.fa-calculator:before {
+  content: "\f1ec";
+}
+.fa-paypal:before {
+  content: "\f1ed";
+}
+.fa-google-wallet:before {
+  content: "\f1ee";
+}
+.fa-cc-visa:before {
+  content: "\f1f0";
+}
+.fa-cc-mastercard:before {
+  content: "\f1f1";
+}
+.fa-cc-discover:before {
+  content: "\f1f2";
+}
+.fa-cc-amex:before {
+  content: "\f1f3";
+}
+.fa-cc-paypal:before {
+  content: "\f1f4";
+}
+.fa-cc-stripe:before {
+  content: "\f1f5";
+}
+.fa-bell-slash:before {
+  content: "\f1f6";
+}
+.fa-bell-slash-o:before {
+  content: "\f1f7";
+}
+.fa-trash:before {
+  content: "\f1f8";
+}
+.fa-copyright:before {
+  content: "\f1f9";
+}
+.fa-at:before {
+  content: "\f1fa";
+}
+.fa-eyedropper:before {
+  content: "\f1fb";
+}
+.fa-paint-brush:before {
+  content: "\f1fc";
+}
+.fa-birthday-cake:before {
+  content: "\f1fd";
+}
+.fa-area-chart:before {
+  content: "\f1fe";
+}
+.fa-pie-chart:before {
+  content: "\f200";
+}
+.fa-line-chart:before {
+  content: "\f201";
+}
+.fa-lastfm:before {
+  content: "\f202";
+}
+.fa-lastfm-square:before {
+  content: "\f203";
+}
+.fa-toggle-off:before {
+  content: "\f204";
+}
+.fa-toggle-on:before {
+  content: "\f205";
+}
+.fa-bicycle:before {
+  content: "\f206";
+}
+.fa-bus:before {
+  content: "\f207";
+}
+.fa-ioxhost:before {
+  content: "\f208";
+}
+.fa-angellist:before {
+  content: "\f209";
+}
+.fa-cc:before {
+  content: "\f20a";
+}
+.fa-shekel:before,
+.fa-sheqel:before,
+.fa-ils:before {
+  content: "\f20b";
+}
+.fa-meanpath:before {
+  content: "\f20c";
+}
+/*!
+*
+* IPython base
+*
+*/
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+code {
+  color: #000;
+}
+pre {
+  font-size: inherit;
+  line-height: inherit;
+}
+label {
+  font-weight: normal;
+}
+/* Make the page background atleast 100% the height of the view port */
+/* Make the page itself atleast 70% the height of the view port */
+.border-box-sizing {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.corner-all {
+  border-radius: 2px;
+}
+.no-padding {
+  padding: 0px;
+}
+/* Flexible box model classes */
+/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* This file is a compatability layer.  It allows the usage of flexible box 
+model layouts accross multiple browsers, including older browsers.  The newest,
+universal implementation of the flexible box model is used when available (see
+`Modern browsers` comments below).  Browsers that are known to implement this 
+new spec completely include:
+
+    Firefox 28.0+
+    Chrome 29.0+
+    Internet Explorer 11+ 
+    Opera 17.0+
+
+Browsers not listed, including Safari, are supported via the styling under the
+`Old browsers` comments below.
+*/
+.hbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+.hbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.vbox {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+.vbox > * {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+}
+.hbox.reverse,
+.vbox.reverse,
+.reverse {
+  /* Old browsers */
+  -webkit-box-direction: reverse;
+  -moz-box-direction: reverse;
+  box-direction: reverse;
+  /* Modern browsers */
+  flex-direction: row-reverse;
+}
+.hbox.box-flex0,
+.vbox.box-flex0,
+.box-flex0 {
+  /* Old browsers */
+  -webkit-box-flex: 0;
+  -moz-box-flex: 0;
+  box-flex: 0;
+  /* Modern browsers */
+  flex: none;
+  width: auto;
+}
+.hbox.box-flex1,
+.vbox.box-flex1,
+.box-flex1 {
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex,
+.vbox.box-flex,
+.box-flex {
+  /* Old browsers */
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+.hbox.box-flex2,
+.vbox.box-flex2,
+.box-flex2 {
+  /* Old browsers */
+  -webkit-box-flex: 2;
+  -moz-box-flex: 2;
+  box-flex: 2;
+  /* Modern browsers */
+  flex: 2;
+}
+.box-group1 {
+  /*  Deprecated */
+  -webkit-box-flex-group: 1;
+  -moz-box-flex-group: 1;
+  box-flex-group: 1;
+}
+.box-group2 {
+  /* Deprecated */
+  -webkit-box-flex-group: 2;
+  -moz-box-flex-group: 2;
+  box-flex-group: 2;
+}
+.hbox.start,
+.vbox.start,
+.start {
+  /* Old browsers */
+  -webkit-box-pack: start;
+  -moz-box-pack: start;
+  box-pack: start;
+  /* Modern browsers */
+  justify-content: flex-start;
+}
+.hbox.end,
+.vbox.end,
+.end {
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+}
+.hbox.center,
+.vbox.center,
+.center {
+  /* Old browsers */
+  -webkit-box-pack: center;
+  -moz-box-pack: center;
+  box-pack: center;
+  /* Modern browsers */
+  justify-content: center;
+}
+.hbox.baseline,
+.vbox.baseline,
+.baseline {
+  /* Old browsers */
+  -webkit-box-pack: baseline;
+  -moz-box-pack: baseline;
+  box-pack: baseline;
+  /* Modern browsers */
+  justify-content: baseline;
+}
+.hbox.stretch,
+.vbox.stretch,
+.stretch {
+  /* Old browsers */
+  -webkit-box-pack: stretch;
+  -moz-box-pack: stretch;
+  box-pack: stretch;
+  /* Modern browsers */
+  justify-content: stretch;
+}
+.hbox.align-start,
+.vbox.align-start,
+.align-start {
+  /* Old browsers */
+  -webkit-box-align: start;
+  -moz-box-align: start;
+  box-align: start;
+  /* Modern browsers */
+  align-items: flex-start;
+}
+.hbox.align-end,
+.vbox.align-end,
+.align-end {
+  /* Old browsers */
+  -webkit-box-align: end;
+  -moz-box-align: end;
+  box-align: end;
+  /* Modern browsers */
+  align-items: flex-end;
+}
+.hbox.align-center,
+.vbox.align-center,
+.align-center {
+  /* Old browsers */
+  -webkit-box-align: center;
+  -moz-box-align: center;
+  box-align: center;
+  /* Modern browsers */
+  align-items: center;
+}
+.hbox.align-baseline,
+.vbox.align-baseline,
+.align-baseline {
+  /* Old browsers */
+  -webkit-box-align: baseline;
+  -moz-box-align: baseline;
+  box-align: baseline;
+  /* Modern browsers */
+  align-items: baseline;
+}
+.hbox.align-stretch,
+.vbox.align-stretch,
+.align-stretch {
+  /* Old browsers */
+  -webkit-box-align: stretch;
+  -moz-box-align: stretch;
+  box-align: stretch;
+  /* Modern browsers */
+  align-items: stretch;
+}
+div.error {
+  margin: 2em;
+  text-align: center;
+}
+div.error > h1 {
+  font-size: 500%;
+  line-height: normal;
+}
+div.error > p {
+  font-size: 200%;
+  line-height: normal;
+}
+div.traceback-wrapper {
+  text-align: left;
+  max-width: 800px;
+  margin: auto;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+body {
+  background-color: #fff;
+  /* This makes sure that the body covers the entire window and needs to
+       be in a different element than the display: box in wrapper below */
+  position: absolute;
+  left: 0px;
+  right: 0px;
+  top: 0px;
+  bottom: 0px;
+  overflow: visible;
+}
+body > #header {
+  /* Initially hidden to prevent FLOUC */
+  display: none;
+  background-color: #fff;
+  /* Display over codemirror */
+  position: relative;
+  z-index: 100;
+}
+body > #header #header-container {
+  padding-bottom: 5px;
+  padding-top: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+body > #header .header-bar {
+  width: 100%;
+  height: 1px;
+  background: #e7e7e7;
+  margin-bottom: -1px;
+}
+@media print {
+  body > #header {
+    display: none !important;
+  }
+}
+#header-spacer {
+  width: 100%;
+  visibility: hidden;
+}
+@media print {
+  #header-spacer {
+    display: none;
+  }
+}
+#ipython_notebook {
+  padding-left: 0px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+@media (max-width: 991px) {
+  #ipython_notebook {
+    margin-left: 10px;
+  }
+}
+[dir="rtl"] #ipython_notebook {
+  float: right !important;
+}
+#noscript {
+  width: auto;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-align: center;
+  font-size: 22px;
+  color: red;
+  font-weight: bold;
+}
+#ipython_notebook img {
+  height: 28px;
+}
+#site {
+  width: 100%;
+  display: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  overflow: auto;
+}
+@media print {
+  #site {
+    height: auto !important;
+  }
+}
+/* Smaller buttons */
+.ui-button .ui-button-text {
+  padding: 0.2em 0.8em;
+  font-size: 77%;
+}
+input.ui-button {
+  padding: 0.3em 0.9em;
+}
+span#login_widget {
+  float: right;
+}
+span#login_widget > .button,
+#logout {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button:focus,
+#logout:focus,
+span#login_widget > .button.focus,
+#logout.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:hover,
+#logout:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+span#login_widget > .button:active:hover,
+#logout:active:hover,
+span#login_widget > .button.active:hover,
+#logout.active:hover,
+.open > .dropdown-togglespan#login_widget > .button:hover,
+.open > .dropdown-toggle#logout:hover,
+span#login_widget > .button:active:focus,
+#logout:active:focus,
+span#login_widget > .button.active:focus,
+#logout.active:focus,
+.open > .dropdown-togglespan#login_widget > .button:focus,
+.open > .dropdown-toggle#logout:focus,
+span#login_widget > .button:active.focus,
+#logout:active.focus,
+span#login_widget > .button.active.focus,
+#logout.active.focus,
+.open > .dropdown-togglespan#login_widget > .button.focus,
+.open > .dropdown-toggle#logout.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+span#login_widget > .button:active,
+#logout:active,
+span#login_widget > .button.active,
+#logout.active,
+.open > .dropdown-togglespan#login_widget > .button,
+.open > .dropdown-toggle#logout {
+  background-image: none;
+}
+span#login_widget > .button.disabled:hover,
+#logout.disabled:hover,
+span#login_widget > .button[disabled]:hover,
+#logout[disabled]:hover,
+fieldset[disabled] span#login_widget > .button:hover,
+fieldset[disabled] #logout:hover,
+span#login_widget > .button.disabled:focus,
+#logout.disabled:focus,
+span#login_widget > .button[disabled]:focus,
+#logout[disabled]:focus,
+fieldset[disabled] span#login_widget > .button:focus,
+fieldset[disabled] #logout:focus,
+span#login_widget > .button.disabled.focus,
+#logout.disabled.focus,
+span#login_widget > .button[disabled].focus,
+#logout[disabled].focus,
+fieldset[disabled] span#login_widget > .button.focus,
+fieldset[disabled] #logout.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+span#login_widget > .button .badge,
+#logout .badge {
+  color: #fff;
+  background-color: #333;
+}
+.nav-header {
+  text-transform: none;
+}
+#header > span {
+  margin-top: 10px;
+}
+.modal_stretch .modal-dialog {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  min-height: 80vh;
+}
+.modal_stretch .modal-dialog .modal-body {
+  max-height: calc(100vh - 200px);
+  overflow: auto;
+  flex: 1;
+}
+@media (min-width: 768px) {
+  .modal .modal-dialog {
+    width: 700px;
+  }
+}
+@media (min-width: 768px) {
+  select.form-control {
+    margin-left: 12px;
+    margin-right: 12px;
+  }
+}
+/*!
+*
+* IPython auth
+*
+*/
+.center-nav {
+  display: inline-block;
+  margin-bottom: -4px;
+}
+/*!
+*
+* IPython tree view
+*
+*/
+/* We need an invisible input field on top of the sentense*/
+/* "Drag file onto the list ..." */
+.alternate_upload {
+  background-color: none;
+  display: inline;
+}
+.alternate_upload.form {
+  padding: 0;
+  margin: 0;
+}
+.alternate_upload input.fileinput {
+  text-align: center;
+  vertical-align: middle;
+  display: inline;
+  opacity: 0;
+  z-index: 2;
+  width: 12ex;
+  margin-right: -12ex;
+}
+.alternate_upload .btn-upload {
+  height: 22px;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+[dir="rtl"] #tabs li {
+  float: right;
+}
+ul#tabs {
+  margin-bottom: 4px;
+}
+[dir="rtl"] ul#tabs {
+  margin-right: 0px;
+}
+ul#tabs a {
+  padding-top: 6px;
+  padding-bottom: 4px;
+}
+ul.breadcrumb a:focus,
+ul.breadcrumb a:hover {
+  text-decoration: none;
+}
+ul.breadcrumb i.icon-home {
+  font-size: 16px;
+  margin-right: 4px;
+}
+ul.breadcrumb span {
+  color: #5e5e5e;
+}
+.list_toolbar {
+  padding: 4px 0 4px 0;
+  vertical-align: middle;
+}
+.list_toolbar .tree-buttons {
+  padding-top: 1px;
+}
+[dir="rtl"] .list_toolbar .tree-buttons {
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-right {
+  padding-top: 1px;
+  float: left !important;
+}
+[dir="rtl"] .list_toolbar .pull-left {
+  float: right !important;
+}
+.dynamic-buttons {
+  padding-top: 3px;
+  display: inline-block;
+}
+.list_toolbar [class*="span"] {
+  min-height: 24px;
+}
+.list_header {
+  font-weight: bold;
+  background-color: #EEE;
+}
+.list_placeholder {
+  font-weight: bold;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+}
+.list_container {
+  margin-top: 4px;
+  margin-bottom: 20px;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+}
+.list_container > div {
+  border-bottom: 1px solid #ddd;
+}
+.list_container > div:hover .list-item {
+  background-color: red;
+}
+.list_container > div:last-child {
+  border: none;
+}
+.list_item:hover .list_item {
+  background-color: #ddd;
+}
+.list_item a {
+  text-decoration: none;
+}
+.list_item:hover {
+  background-color: #fafafa;
+}
+.list_header > div,
+.list_item > div {
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+.list_header > div input,
+.list_item > div input {
+  margin-right: 7px;
+  margin-left: 14px;
+  vertical-align: baseline;
+  line-height: 22px;
+  position: relative;
+  top: -1px;
+}
+.list_header > div .item_link,
+.list_item > div .item_link {
+  margin-left: -1px;
+  vertical-align: baseline;
+  line-height: 22px;
+}
+.new-file input[type=checkbox] {
+  visibility: hidden;
+}
+.item_name {
+  line-height: 22px;
+  height: 24px;
+}
+.item_icon {
+  font-size: 14px;
+  color: #5e5e5e;
+  margin-right: 7px;
+  margin-left: 7px;
+  line-height: 22px;
+  vertical-align: baseline;
+}
+.item_buttons {
+  line-height: 1em;
+  margin-left: -5px;
+}
+.item_buttons .btn,
+.item_buttons .btn-group,
+.item_buttons .input-group {
+  float: left;
+}
+.item_buttons > .btn,
+.item_buttons > .btn-group,
+.item_buttons > .input-group {
+  margin-left: 5px;
+}
+.item_buttons .btn {
+  min-width: 13ex;
+}
+.item_buttons .running-indicator {
+  padding-top: 4px;
+  color: #5cb85c;
+}
+.item_buttons .kernel-name {
+  padding-top: 4px;
+  color: #5bc0de;
+  margin-right: 7px;
+  float: left;
+}
+.toolbar_info {
+  height: 24px;
+  line-height: 24px;
+}
+.list_item input:not([type=checkbox]) {
+  padding-top: 3px;
+  padding-bottom: 3px;
+  height: 22px;
+  line-height: 14px;
+  margin: 0px;
+}
+.highlight_text {
+  color: blue;
+}
+#project_name {
+  display: inline-block;
+  padding-left: 7px;
+  margin-left: -2px;
+}
+#project_name > .breadcrumb {
+  padding: 0px;
+  margin-bottom: 0px;
+  background-color: transparent;
+  font-weight: bold;
+}
+#tree-selector {
+  padding-right: 0px;
+}
+[dir="rtl"] #tree-selector a {
+  float: right;
+}
+#button-select-all {
+  min-width: 50px;
+}
+#select-all {
+  margin-left: 7px;
+  margin-right: 2px;
+}
+.menu_icon {
+  margin-right: 2px;
+}
+.tab-content .row {
+  margin-left: 0px;
+  margin-right: 0px;
+}
+.folder_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f114";
+}
+.folder_icon:before.pull-left {
+  margin-right: .3em;
+}
+.folder_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+}
+.notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.running_notebook_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f02d";
+  position: relative;
+  top: -1px;
+  color: #5cb85c;
+}
+.running_notebook_icon:before.pull-left {
+  margin-right: .3em;
+}
+.running_notebook_icon:before.pull-right {
+  margin-left: .3em;
+}
+.file_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f016";
+  position: relative;
+  top: -2px;
+}
+.file_icon:before.pull-left {
+  margin-right: .3em;
+}
+.file_icon:before.pull-right {
+  margin-left: .3em;
+}
+#notebook_toolbar .pull-right {
+  padding-top: 0px;
+  margin-right: -1px;
+}
+ul#new-menu {
+  left: auto;
+  right: 0;
+}
+[dir="rtl"] #new-menu {
+  text-align: right;
+}
+.kernel-menu-icon {
+  padding-right: 12px;
+  width: 24px;
+  content: "\f096";
+}
+.kernel-menu-icon:before {
+  content: "\f096";
+}
+.kernel-menu-icon-current:before {
+  content: "\f00c";
+}
+#tab_content {
+  padding-top: 20px;
+}
+#running .panel-group .panel {
+  margin-top: 3px;
+  margin-bottom: 1em;
+}
+#running .panel-group .panel .panel-heading {
+  background-color: #EEE;
+  padding-top: 4px;
+  padding-bottom: 4px;
+  padding-left: 7px;
+  padding-right: 7px;
+  line-height: 22px;
+}
+#running .panel-group .panel .panel-heading a:focus,
+#running .panel-group .panel .panel-heading a:hover {
+  text-decoration: none;
+}
+#running .panel-group .panel .panel-body {
+  padding: 0px;
+}
+#running .panel-group .panel .panel-body .list_container {
+  margin-top: 0px;
+  margin-bottom: 0px;
+  border: 0px;
+  border-radius: 0px;
+}
+#running .panel-group .panel .panel-body .list_container .list_item {
+  border-bottom: 1px solid #ddd;
+}
+#running .panel-group .panel .panel-body .list_container .list_item:last-child {
+  border-bottom: 0px;
+}
+[dir="rtl"] #running .col-sm-8 {
+  float: right !important;
+}
+.delete-button {
+  display: none;
+}
+.duplicate-button {
+  display: none;
+}
+.rename-button {
+  display: none;
+}
+.shutdown-button {
+  display: none;
+}
+.dynamic-instructions {
+  display: inline-block;
+  padding-top: 4px;
+}
+/*!
+*
+* IPython text editor webapp
+*
+*/
+.selected-keymap i.fa {
+  padding: 0px 5px;
+}
+.selected-keymap i.fa:before {
+  content: "\f00c";
+}
+#mode-menu {
+  overflow: auto;
+  max-height: 20em;
+}
+.edit_app #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+.edit_app #menubar .navbar {
+  /* Use a negative 1 bottom margin, so the border overlaps the border of the
+    header */
+  margin-bottom: -1px;
+}
+.dirty-indicator {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-dirty {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-dirty.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-dirty.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  width: 20px;
+}
+.dirty-indicator-clean.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean.pull-right {
+  margin-left: .3em;
+}
+.dirty-indicator-clean:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f00c";
+}
+.dirty-indicator-clean:before.pull-left {
+  margin-right: .3em;
+}
+.dirty-indicator-clean:before.pull-right {
+  margin-left: .3em;
+}
+#filename {
+  font-size: 16pt;
+  display: table;
+  padding: 0px 5px;
+}
+#current-mode {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#texteditor-backdrop {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+@media not print {
+  #texteditor-backdrop {
+    background-color: #EEE;
+  }
+}
+@media print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
+  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
+    background-color: #fff;
+  }
+}
+@media not print {
+  #texteditor-backdrop #texteditor-container {
+    padding: 0px;
+    background-color: #fff;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+/*!
+*
+* IPython notebook
+*
+*/
+/* CSS font colors for translated ANSI colors. */
+.ansibold {
+  font-weight: bold;
+}
+/* use dark versions for foreground, to improve visibility */
+.ansiblack {
+  color: black;
+}
+.ansired {
+  color: darkred;
+}
+.ansigreen {
+  color: darkgreen;
+}
+.ansiyellow {
+  color: #c4a000;
+}
+.ansiblue {
+  color: darkblue;
+}
+.ansipurple {
+  color: darkviolet;
+}
+.ansicyan {
+  color: steelblue;
+}
+.ansigray {
+  color: gray;
+}
+/* and light for background, for the same reason */
+.ansibgblack {
+  background-color: black;
+}
+.ansibgred {
+  background-color: red;
+}
+.ansibggreen {
+  background-color: green;
+}
+.ansibgyellow {
+  background-color: yellow;
+}
+.ansibgblue {
+  background-color: blue;
+}
+.ansibgpurple {
+  background-color: magenta;
+}
+.ansibgcyan {
+  background-color: cyan;
+}
+.ansibggray {
+  background-color: gray;
+}
+div.cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-radius: 2px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  border-width: 1px;
+  border-style: solid;
+  border-color: transparent;
+  width: 100%;
+  padding: 5px;
+  /* This acts as a spacer between cells, that is outside the border */
+  margin: 0px;
+  outline: none;
+  border-left-width: 1px;
+  padding-left: 5px;
+  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
+}
+div.cell.jupyter-soft-selected {
+  border-left-color: #90CAF9;
+  border-left-color: #E3F2FD;
+  border-left-width: 1px;
+  padding-left: 5px;
+  border-right-color: #E3F2FD;
+  border-right-width: 1px;
+  background: #E3F2FD;
+}
+@media print {
+  div.cell.jupyter-soft-selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected {
+  border-color: #ababab;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  div.cell.selected {
+    border-color: transparent;
+  }
+}
+div.cell.selected.jupyter-soft-selected {
+  border-left-width: 0;
+  padding-left: 6px;
+  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
+}
+.edit_mode div.cell.selected {
+  border-color: #66BB6A;
+  border-left-width: 0px;
+  padding-left: 6px;
+  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
+}
+@media print {
+  .edit_mode div.cell.selected {
+    border-color: transparent;
+  }
+}
+.prompt {
+  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
+  min-width: 14ex;
+  /* This padding is tuned to match the padding on the CodeMirror editor. */
+  padding: 0.4em;
+  margin: 0px;
+  font-family: monospace;
+  text-align: right;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+  /* Don't highlight prompt number selection */
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  /* Use default cursor */
+  cursor: default;
+}
+@media (max-width: 540px) {
+  .prompt {
+    text-align: left;
+  }
+}
+div.inner_cell {
+  min-width: 0;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_area {
+  border: 1px solid #cfcfcf;
+  border-radius: 2px;
+  background: #f7f7f7;
+  line-height: 1.21429em;
+}
+/* This is needed so that empty prompt areas can collapse to zero height when there
+   is no content in the output_subarea and the prompt. The main purpose of this is
+   to make sure that empty JavaScript output_subareas have no height. */
+div.prompt:empty {
+  padding-top: 0;
+  padding-bottom: 0;
+}
+div.unrecognized_cell {
+  padding: 5px 5px 5px 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.unrecognized_cell .inner_cell {
+  border-radius: 2px;
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+  border: 1px solid #cfcfcf;
+  background: #eaeaea;
+}
+div.unrecognized_cell .inner_cell a {
+  color: inherit;
+  text-decoration: none;
+}
+div.unrecognized_cell .inner_cell a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+@media (max-width: 540px) {
+  div.unrecognized_cell > div.prompt {
+    display: none;
+  }
+}
+div.code_cell {
+  /* avoid page breaking on code cells when printing */
+}
+@media print {
+  div.code_cell {
+    page-break-inside: avoid;
+  }
+}
+/* any special styling for code cells that are currently running goes here */
+div.input {
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.input {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+/* input_area and input_prompt must match in top border and margin for alignment */
+div.input_prompt {
+  color: #303F9F;
+  border-top: 1px solid transparent;
+}
+div.input_area > div.highlight {
+  margin: 0.4em;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+div.input_area > div.highlight > pre {
+  margin: 0px;
+  border: none;
+  padding: 0px;
+  background-color: transparent;
+}
+/* The following gets added to the <head> if it is detected that the user has a
+ * monospace font with inconsistent normal/bold/italic height.  See
+ * notebookmain.js.  Such fonts will have keywords vertically offset with
+ * respect to the rest of the text.  The user should select a better font.
+ * See: https://github.com/ipython/ipython/issues/1503
+ *
+ * .CodeMirror span {
+ *      vertical-align: bottom;
+ * }
+ */
+.CodeMirror {
+  line-height: 1.21429em;
+  /* Changed from 1em to our global default */
+  font-size: 14px;
+  height: auto;
+  /* Changed to auto to autogrow */
+  background: none;
+  /* Changed from white to allow our bg to show through */
+}
+.CodeMirror-scroll {
+  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
+  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
+  overflow-y: hidden;
+  overflow-x: auto;
+}
+.CodeMirror-lines {
+  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
+  /* we have set a different line-height and want this to scale with that. */
+  padding: 0.4em;
+}
+.CodeMirror-linenumber {
+  padding: 0 8px 0 4px;
+}
+.CodeMirror-gutters {
+  border-bottom-left-radius: 2px;
+  border-top-left-radius: 2px;
+}
+.CodeMirror pre {
+  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
+  /* .CodeMirror-lines */
+  padding: 0;
+  border: 0;
+  border-radius: 0;
+}
+/*
+
+Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
+Adapted from GitHub theme
+
+*/
+.highlight-base {
+  color: #000;
+}
+.highlight-variable {
+  color: #000;
+}
+.highlight-variable-2 {
+  color: #1a1a1a;
+}
+.highlight-variable-3 {
+  color: #333333;
+}
+.highlight-string {
+  color: #BA2121;
+}
+.highlight-comment {
+  color: #408080;
+  font-style: italic;
+}
+.highlight-number {
+  color: #080;
+}
+.highlight-atom {
+  color: #88F;
+}
+.highlight-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.highlight-builtin {
+  color: #008000;
+}
+.highlight-error {
+  color: #f00;
+}
+.highlight-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.highlight-meta {
+  color: #AA22FF;
+}
+/* previously not defined, copying from default codemirror */
+.highlight-def {
+  color: #00f;
+}
+.highlight-string-2 {
+  color: #f50;
+}
+.highlight-qualifier {
+  color: #555;
+}
+.highlight-bracket {
+  color: #997;
+}
+.highlight-tag {
+  color: #170;
+}
+.highlight-attribute {
+  color: #00c;
+}
+.highlight-header {
+  color: blue;
+}
+.highlight-quote {
+  color: #090;
+}
+.highlight-link {
+  color: #00c;
+}
+/* apply the same style to codemirror */
+.cm-s-ipython span.cm-keyword {
+  color: #008000;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-atom {
+  color: #88F;
+}
+.cm-s-ipython span.cm-number {
+  color: #080;
+}
+.cm-s-ipython span.cm-def {
+  color: #00f;
+}
+.cm-s-ipython span.cm-variable {
+  color: #000;
+}
+.cm-s-ipython span.cm-operator {
+  color: #AA22FF;
+  font-weight: bold;
+}
+.cm-s-ipython span.cm-variable-2 {
+  color: #1a1a1a;
+}
+.cm-s-ipython span.cm-variable-3 {
+  color: #333333;
+}
+.cm-s-ipython span.cm-comment {
+  color: #408080;
+  font-style: italic;
+}
+.cm-s-ipython span.cm-string {
+  color: #BA2121;
+}
+.cm-s-ipython span.cm-string-2 {
+  color: #f50;
+}
+.cm-s-ipython span.cm-meta {
+  color: #AA22FF;
+}
+.cm-s-ipython span.cm-qualifier {
+  color: #555;
+}
+.cm-s-ipython span.cm-builtin {
+  color: #008000;
+}
+.cm-s-ipython span.cm-bracket {
+  color: #997;
+}
+.cm-s-ipython span.cm-tag {
+  color: #170;
+}
+.cm-s-ipython span.cm-attribute {
+  color: #00c;
+}
+.cm-s-ipython span.cm-header {
+  color: blue;
+}
+.cm-s-ipython span.cm-quote {
+  color: #090;
+}
+.cm-s-ipython span.cm-link {
+  color: #00c;
+}
+.cm-s-ipython span.cm-error {
+  color: #f00;
+}
+.cm-s-ipython span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+div.output_wrapper {
+  /* this position must be relative to enable descendents to be absolute within it */
+  position: relative;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  z-index: 1;
+}
+/* class for the output area when it should be height-limited */
+div.output_scroll {
+  /* ideally, this would be max-height, but FF barfs all over that */
+  height: 24em;
+  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
+  width: 100%;
+  overflow: auto;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+  display: block;
+}
+/* output div while it is collapsed */
+div.output_collapsed {
+  margin: 0px;
+  padding: 0px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+div.out_prompt_overlay {
+  height: 100%;
+  padding: 0px 0.4em;
+  position: absolute;
+  border-radius: 2px;
+}
+div.out_prompt_overlay:hover {
+  /* use inner shadow to get border that is computed the same on WebKit/FF */
+  -webkit-box-shadow: inset 0 0 1px #000;
+  box-shadow: inset 0 0 1px #000;
+  background: rgba(240, 240, 240, 0.5);
+}
+div.output_prompt {
+  color: #D84315;
+}
+/* This class is the outer container of all output sections. */
+div.output_area {
+  padding: 0px;
+  page-break-inside: avoid;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+div.output_area .MathJax_Display {
+  text-align: left !important;
+}
+div.output_area .rendered_html table {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area .rendered_html img {
+  margin-left: 0;
+  margin-right: 0;
+}
+div.output_area img,
+div.output_area svg {
+  max-width: 100%;
+  height: auto;
+}
+div.output_area img.unconfined,
+div.output_area svg.unconfined {
+  max-width: none;
+}
+/* This is needed to protect the pre formating from global settings such
+   as that of bootstrap */
+.output {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: vertical;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: vertical;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.output_area {
+    /* Old browsers */
+    display: -webkit-box;
+    -webkit-box-orient: vertical;
+    -webkit-box-align: stretch;
+    display: -moz-box;
+    -moz-box-orient: vertical;
+    -moz-box-align: stretch;
+    display: box;
+    box-orient: vertical;
+    box-align: stretch;
+    /* Modern browsers */
+    display: flex;
+    flex-direction: column;
+    align-items: stretch;
+  }
+}
+div.output_area pre {
+  margin: 0;
+  padding: 0;
+  border: 0;
+  vertical-align: baseline;
+  color: black;
+  background-color: transparent;
+  border-radius: 0;
+}
+/* This class is for the output subarea inside the output_area and after
+   the prompt div. */
+div.output_subarea {
+  overflow-x: auto;
+  padding: 0.4em;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+  max-width: calc(100% - 14ex);
+}
+div.output_scroll div.output_subarea {
+  overflow-x: visible;
+}
+/* The rest of the output_* classes are for special styling of the different
+   output types */
+/* all text output has this class: */
+div.output_text {
+  text-align: left;
+  color: #000;
+  /* This has to match that of the the CodeMirror class line-height below */
+  line-height: 1.21429em;
+}
+/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
+div.output_stderr {
+  background: #fdd;
+  /* very light red background for stderr */
+}
+div.output_latex {
+  text-align: left;
+}
+/* Empty output_javascript divs should have no height */
+div.output_javascript:empty {
+  padding: 0;
+}
+.js-error {
+  color: darkred;
+}
+/* raw_input styles */
+div.raw_input_container {
+  line-height: 1.21429em;
+  padding-top: 5px;
+}
+pre.raw_input_prompt {
+  /* nothing needed here. */
+}
+input.raw_input {
+  font-family: monospace;
+  font-size: inherit;
+  color: inherit;
+  width: auto;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+}
+input.raw_input:focus {
+  box-shadow: none;
+}
+p.p-space {
+  margin-bottom: 10px;
+}
+div.output_unrecognized {
+  padding: 5px;
+  font-weight: bold;
+  color: red;
+}
+div.output_unrecognized a {
+  color: inherit;
+  text-decoration: none;
+}
+div.output_unrecognized a:hover {
+  color: inherit;
+  text-decoration: none;
+}
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul {
+  list-style: disc;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin: 0em 2em;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+  margin: 0em 2em;
+}
+.rendered_html ol {
+  list-style: decimal;
+  margin: 0em 2em;
+  padding-left: 0px;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+  margin: 0em 2em;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+  margin: 0em 2em;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  background-color: #fff;
+  color: #000;
+  font-size: 100%;
+  padding: 0px;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: 1px solid black;
+  border-collapse: collapse;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  border: 1px solid black;
+  border-collapse: collapse;
+  margin: 1em 2em;
+}
+.rendered_html td,
+.rendered_html th {
+  text-align: left;
+  vertical-align: middle;
+  padding: 4px;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+div.text_cell {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+}
+@media (max-width: 540px) {
+  div.text_cell > div.prompt {
+    display: none;
+  }
+}
+div.text_cell_render {
+  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
+  outline: none;
+  resize: none;
+  width: inherit;
+  border-style: none;
+  padding: 0.5em 0.5em 0.5em 0.4em;
+  color: #000;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+a.anchor-link:link {
+  text-decoration: none;
+  padding: 0px 20px;
+  visibility: hidden;
+}
+h1:hover .anchor-link,
+h2:hover .anchor-link,
+h3:hover .anchor-link,
+h4:hover .anchor-link,
+h5:hover .anchor-link,
+h6:hover .anchor-link {
+  visibility: visible;
+}
+.text_cell.rendered .input_area {
+  display: none;
+}
+.text_cell.rendered .rendered_html {
+  overflow-x: auto;
+  overflow-y: hidden;
+}
+.text_cell.unrendered .text_cell_render {
+  display: none;
+}
+.cm-header-1,
+.cm-header-2,
+.cm-header-3,
+.cm-header-4,
+.cm-header-5,
+.cm-header-6 {
+  font-weight: bold;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+.cm-header-1 {
+  font-size: 185.7%;
+}
+.cm-header-2 {
+  font-size: 157.1%;
+}
+.cm-header-3 {
+  font-size: 128.6%;
+}
+.cm-header-4 {
+  font-size: 110%;
+}
+.cm-header-5 {
+  font-size: 100%;
+  font-style: italic;
+}
+.cm-header-6 {
+  font-size: 100%;
+  font-style: italic;
+}
+/*!
+*
+* IPython notebook webapp
+*
+*/
+@media (max-width: 767px) {
+  .notebook_app {
+    padding-left: 0px;
+    padding-right: 0px;
+  }
+}
+#ipython-main-app {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook_panel {
+  margin: 0px;
+  padding: 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  height: 100%;
+}
+div#notebook {
+  font-size: 14px;
+  line-height: 20px;
+  overflow-y: hidden;
+  overflow-x: auto;
+  width: 100%;
+  /* This spaces the page away from the edge of the notebook area */
+  padding-top: 20px;
+  margin: 0px;
+  outline: none;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  min-height: 100%;
+}
+@media not print {
+  #notebook-container {
+    padding: 15px;
+    background-color: #fff;
+    min-height: 0;
+    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  }
+}
+@media print {
+  #notebook-container {
+    width: 100%;
+  }
+}
+div.ui-widget-content {
+  border: 1px solid #ababab;
+  outline: none;
+}
+pre.dialog {
+  background-color: #f7f7f7;
+  border: 1px solid #ddd;
+  border-radius: 2px;
+  padding: 0.4em;
+  padding-left: 2em;
+}
+p.dialog {
+  padding: 0.2em;
+}
+/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
+   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
+ */
+pre,
+code,
+kbd,
+samp {
+  white-space: pre-wrap;
+}
+#fonttest {
+  font-family: monospace;
+}
+p {
+  margin-bottom: 0;
+}
+.end_space {
+  min-height: 100px;
+  transition: height .2s ease;
+}
+.notebook_app > #header {
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+}
+@media not print {
+  .notebook_app {
+    background-color: #EEE;
+  }
+}
+kbd {
+  border-style: solid;
+  border-width: 1px;
+  box-shadow: none;
+  margin: 2px;
+  padding-left: 2px;
+  padding-right: 2px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+}
+/* CSS for the cell toolbar */
+.celltoolbar {
+  border: thin solid #CFCFCF;
+  border-bottom: none;
+  background: #EEE;
+  border-radius: 2px 2px 0px 0px;
+  width: 100%;
+  height: 29px;
+  padding-right: 4px;
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  /* Old browsers */
+  -webkit-box-pack: end;
+  -moz-box-pack: end;
+  box-pack: end;
+  /* Modern browsers */
+  justify-content: flex-end;
+  display: -webkit-flex;
+}
+@media print {
+  .celltoolbar {
+    display: none;
+  }
+}
+.ctb_hideshow {
+  display: none;
+  vertical-align: bottom;
+}
+/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
+   Cell toolbars are only shown when the ctb_global_show class is also set.
+*/
+.ctb_global_show .ctb_show.ctb_hideshow {
+  display: block;
+}
+.ctb_global_show .ctb_show + .input_area,
+.ctb_global_show .ctb_show + div.text_cell_input,
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border-top-right-radius: 0px;
+  border-top-left-radius: 0px;
+}
+.ctb_global_show .ctb_show ~ div.text_cell_render {
+  border: 1px solid #cfcfcf;
+}
+.celltoolbar {
+  font-size: 87%;
+  padding-top: 3px;
+}
+.celltoolbar select {
+  display: block;
+  width: 100%;
+  height: 32px;
+  padding: 6px 12px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 1px;
+  width: inherit;
+  font-size: inherit;
+  height: 22px;
+  padding: 0px;
+  display: inline-block;
+}
+.celltoolbar select:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.celltoolbar select::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.celltoolbar select:-ms-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-webkit-input-placeholder {
+  color: #999;
+}
+.celltoolbar select::-ms-expand {
+  border: 0;
+  background-color: transparent;
+}
+.celltoolbar select[disabled],
+.celltoolbar select[readonly],
+fieldset[disabled] .celltoolbar select {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.celltoolbar select[disabled],
+fieldset[disabled] .celltoolbar select {
+  cursor: not-allowed;
+}
+textarea.celltoolbar select {
+  height: auto;
+}
+select.celltoolbar select {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.celltoolbar select,
+select[multiple].celltoolbar select {
+  height: auto;
+}
+.celltoolbar label {
+  margin-left: 5px;
+  margin-right: 5px;
+}
+.completions {
+  position: absolute;
+  z-index: 110;
+  overflow: hidden;
+  border: 1px solid #ababab;
+  border-radius: 2px;
+  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
+  box-shadow: 0px 6px 10px -1px #adadad;
+  line-height: 1;
+}
+.completions select {
+  background: white;
+  outline: none;
+  border: none;
+  padding: 0px;
+  margin: 0px;
+  overflow: auto;
+  font-family: monospace;
+  font-size: 110%;
+  color: #000;
+  width: auto;
+}
+.completions select option.context {
+  color: #286090;
+}
+#kernel_logo_widget {
+  float: right !important;
+  float: right;
+}
+#kernel_logo_widget .current_kernel_logo {
+  display: none;
+  margin-top: -1px;
+  margin-bottom: -1px;
+  width: 32px;
+  height: 32px;
+}
+#menubar {
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  margin-top: 1px;
+}
+#menubar .navbar {
+  border-top: 1px;
+  border-radius: 0px 0px 2px 2px;
+  margin-bottom: 0px;
+}
+#menubar .navbar-toggle {
+  float: left;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  border: none;
+}
+#menubar .navbar-collapse {
+  clear: left;
+}
+.nav-wrapper {
+  border-bottom: 1px solid #e7e7e7;
+}
+i.menu-icon {
+  padding-top: 4px;
+}
+ul#help_menu li a {
+  overflow: hidden;
+  padding-right: 2.2em;
+}
+ul#help_menu li a i {
+  margin-right: -1.2em;
+}
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+}
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+.dropdown-submenu > a:after {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  display: block;
+  content: "\f0da";
+  float: right;
+  color: #333333;
+  margin-top: 2px;
+  margin-right: -10px;
+}
+.dropdown-submenu > a:after.pull-left {
+  margin-right: .3em;
+}
+.dropdown-submenu > a:after.pull-right {
+  margin-left: .3em;
+}
+.dropdown-submenu:hover > a:after {
+  color: #262626;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+}
+#notification_area {
+  float: right !important;
+  float: right;
+  z-index: 10;
+}
+.indicator_area {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#kernel_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  border-left: 1px solid;
+}
+#kernel_indicator .kernel_indicator_name {
+  padding-left: 5px;
+  padding-right: 5px;
+}
+#modal_indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+}
+#readonly-indicator {
+  float: right !important;
+  float: right;
+  color: #777;
+  margin-left: 5px;
+  margin-right: 5px;
+  width: 11px;
+  z-index: 10;
+  text-align: center;
+  width: auto;
+  margin-top: 2px;
+  margin-bottom: 0px;
+  margin-left: 0px;
+  margin-right: 0px;
+  display: none;
+}
+.modal_indicator:before {
+  width: 1.28571429em;
+  text-align: center;
+}
+.edit_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f040";
+}
+.edit_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.edit_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.command_mode .modal_indicator:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: ' ';
+}
+.command_mode .modal_indicator:before.pull-left {
+  margin-right: .3em;
+}
+.command_mode .modal_indicator:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_idle_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f10c";
+}
+.kernel_idle_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_idle_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_busy_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f111";
+}
+.kernel_busy_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_busy_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_dead_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f1e2";
+}
+.kernel_dead_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_dead_icon:before.pull-right {
+  margin-left: .3em;
+}
+.kernel_disconnected_icon:before {
+  display: inline-block;
+  font: normal normal normal 14px/1 FontAwesome;
+  font-size: inherit;
+  text-rendering: auto;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  content: "\f127";
+}
+.kernel_disconnected_icon:before.pull-left {
+  margin-right: .3em;
+}
+.kernel_disconnected_icon:before.pull-right {
+  margin-left: .3em;
+}
+.notification_widget {
+  color: #777;
+  z-index: 10;
+  background: rgba(240, 240, 240, 0.5);
+  margin-right: 4px;
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget:focus,
+.notification_widget.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.notification_widget:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.notification_widget:active:hover,
+.notification_widget.active:hover,
+.open > .dropdown-toggle.notification_widget:hover,
+.notification_widget:active:focus,
+.notification_widget.active:focus,
+.open > .dropdown-toggle.notification_widget:focus,
+.notification_widget:active.focus,
+.notification_widget.active.focus,
+.open > .dropdown-toggle.notification_widget.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.notification_widget:active,
+.notification_widget.active,
+.open > .dropdown-toggle.notification_widget {
+  background-image: none;
+}
+.notification_widget.disabled:hover,
+.notification_widget[disabled]:hover,
+fieldset[disabled] .notification_widget:hover,
+.notification_widget.disabled:focus,
+.notification_widget[disabled]:focus,
+fieldset[disabled] .notification_widget:focus,
+.notification_widget.disabled.focus,
+.notification_widget[disabled].focus,
+fieldset[disabled] .notification_widget.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.notification_widget .badge {
+  color: #fff;
+  background-color: #333;
+}
+.notification_widget.warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning:focus,
+.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.notification_widget.warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.notification_widget.warning:active:hover,
+.notification_widget.warning.active:hover,
+.open > .dropdown-toggle.notification_widget.warning:hover,
+.notification_widget.warning:active:focus,
+.notification_widget.warning.active:focus,
+.open > .dropdown-toggle.notification_widget.warning:focus,
+.notification_widget.warning:active.focus,
+.notification_widget.warning.active.focus,
+.open > .dropdown-toggle.notification_widget.warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.notification_widget.warning:active,
+.notification_widget.warning.active,
+.open > .dropdown-toggle.notification_widget.warning {
+  background-image: none;
+}
+.notification_widget.warning.disabled:hover,
+.notification_widget.warning[disabled]:hover,
+fieldset[disabled] .notification_widget.warning:hover,
+.notification_widget.warning.disabled:focus,
+.notification_widget.warning[disabled]:focus,
+fieldset[disabled] .notification_widget.warning:focus,
+.notification_widget.warning.disabled.focus,
+.notification_widget.warning[disabled].focus,
+fieldset[disabled] .notification_widget.warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.notification_widget.warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.notification_widget.success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success:focus,
+.notification_widget.success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.notification_widget.success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.notification_widget.success:active:hover,
+.notification_widget.success.active:hover,
+.open > .dropdown-toggle.notification_widget.success:hover,
+.notification_widget.success:active:focus,
+.notification_widget.success.active:focus,
+.open > .dropdown-toggle.notification_widget.success:focus,
+.notification_widget.success:active.focus,
+.notification_widget.success.active.focus,
+.open > .dropdown-toggle.notification_widget.success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.notification_widget.success:active,
+.notification_widget.success.active,
+.open > .dropdown-toggle.notification_widget.success {
+  background-image: none;
+}
+.notification_widget.success.disabled:hover,
+.notification_widget.success[disabled]:hover,
+fieldset[disabled] .notification_widget.success:hover,
+.notification_widget.success.disabled:focus,
+.notification_widget.success[disabled]:focus,
+fieldset[disabled] .notification_widget.success:focus,
+.notification_widget.success.disabled.focus,
+.notification_widget.success[disabled].focus,
+fieldset[disabled] .notification_widget.success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.notification_widget.success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.notification_widget.info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info:focus,
+.notification_widget.info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.notification_widget.info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.notification_widget.info:active:hover,
+.notification_widget.info.active:hover,
+.open > .dropdown-toggle.notification_widget.info:hover,
+.notification_widget.info:active:focus,
+.notification_widget.info.active:focus,
+.open > .dropdown-toggle.notification_widget.info:focus,
+.notification_widget.info:active.focus,
+.notification_widget.info.active.focus,
+.open > .dropdown-toggle.notification_widget.info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.notification_widget.info:active,
+.notification_widget.info.active,
+.open > .dropdown-toggle.notification_widget.info {
+  background-image: none;
+}
+.notification_widget.info.disabled:hover,
+.notification_widget.info[disabled]:hover,
+fieldset[disabled] .notification_widget.info:hover,
+.notification_widget.info.disabled:focus,
+.notification_widget.info[disabled]:focus,
+fieldset[disabled] .notification_widget.info:focus,
+.notification_widget.info.disabled.focus,
+.notification_widget.info[disabled].focus,
+fieldset[disabled] .notification_widget.info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.notification_widget.info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.notification_widget.danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger:focus,
+.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.notification_widget.danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.notification_widget.danger:active:hover,
+.notification_widget.danger.active:hover,
+.open > .dropdown-toggle.notification_widget.danger:hover,
+.notification_widget.danger:active:focus,
+.notification_widget.danger.active:focus,
+.open > .dropdown-toggle.notification_widget.danger:focus,
+.notification_widget.danger:active.focus,
+.notification_widget.danger.active.focus,
+.open > .dropdown-toggle.notification_widget.danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.notification_widget.danger:active,
+.notification_widget.danger.active,
+.open > .dropdown-toggle.notification_widget.danger {
+  background-image: none;
+}
+.notification_widget.danger.disabled:hover,
+.notification_widget.danger[disabled]:hover,
+fieldset[disabled] .notification_widget.danger:hover,
+.notification_widget.danger.disabled:focus,
+.notification_widget.danger[disabled]:focus,
+fieldset[disabled] .notification_widget.danger:focus,
+.notification_widget.danger.disabled.focus,
+.notification_widget.danger[disabled].focus,
+fieldset[disabled] .notification_widget.danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.notification_widget.danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+div#pager {
+  background-color: #fff;
+  font-size: 14px;
+  line-height: 20px;
+  overflow: hidden;
+  display: none;
+  position: fixed;
+  bottom: 0px;
+  width: 100%;
+  max-height: 50%;
+  padding-top: 8px;
+  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+  /* Display over codemirror */
+  z-index: 100;
+  /* Hack which prevents jquery ui resizable from changing top. */
+  top: auto !important;
+}
+div#pager pre {
+  line-height: 1.21429em;
+  color: #000;
+  background-color: #f7f7f7;
+  padding: 0.4em;
+}
+div#pager #pager-button-area {
+  position: absolute;
+  top: 8px;
+  right: 20px;
+}
+div#pager #pager-contents {
+  position: relative;
+  overflow: auto;
+  width: 100%;
+  height: 100%;
+}
+div#pager #pager-contents #pager-container {
+  position: relative;
+  padding: 15px 0px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+div#pager .ui-resizable-handle {
+  top: 0px;
+  height: 8px;
+  background: #f7f7f7;
+  border-top: 1px solid #cfcfcf;
+  border-bottom: 1px solid #cfcfcf;
+  /* This injects handle bars (a short, wide = symbol) for 
+        the resize handle. */
+}
+div#pager .ui-resizable-handle::after {
+  content: '';
+  top: 2px;
+  left: 50%;
+  height: 3px;
+  width: 30px;
+  margin-left: -15px;
+  position: absolute;
+  border-top: 1px solid #cfcfcf;
+}
+.quickhelp {
+  /* Old browsers */
+  display: -webkit-box;
+  -webkit-box-orient: horizontal;
+  -webkit-box-align: stretch;
+  display: -moz-box;
+  -moz-box-orient: horizontal;
+  -moz-box-align: stretch;
+  display: box;
+  box-orient: horizontal;
+  box-align: stretch;
+  /* Modern browsers */
+  display: flex;
+  flex-direction: row;
+  align-items: stretch;
+  line-height: 1.8em;
+}
+.shortcut_key {
+  display: inline-block;
+  width: 21ex;
+  text-align: right;
+  font-family: monospace;
+}
+.shortcut_descr {
+  display: inline-block;
+  /* Old browsers */
+  -webkit-box-flex: 1;
+  -moz-box-flex: 1;
+  box-flex: 1;
+  /* Modern browsers */
+  flex: 1;
+}
+span.save_widget {
+  margin-top: 6px;
+}
+span.save_widget span.filename {
+  height: 1em;
+  line-height: 1em;
+  padding: 3px;
+  margin-left: 16px;
+  border: none;
+  font-size: 146.5%;
+  border-radius: 2px;
+}
+span.save_widget span.filename:hover {
+  background-color: #e6e6e6;
+}
+span.checkpoint_status,
+span.autosave_status {
+  font-size: small;
+}
+@media (max-width: 767px) {
+  span.save_widget {
+    font-size: small;
+  }
+  span.checkpoint_status,
+  span.autosave_status {
+    display: none;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  span.checkpoint_status {
+    display: none;
+  }
+  span.autosave_status {
+    font-size: x-small;
+  }
+}
+.toolbar {
+  padding: 0px;
+  margin-left: -5px;
+  margin-top: 2px;
+  margin-bottom: 5px;
+  box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+}
+.toolbar select,
+.toolbar label {
+  width: auto;
+  vertical-align: middle;
+  margin-right: 2px;
+  margin-bottom: 0px;
+  display: inline;
+  font-size: 92%;
+  margin-left: 0.3em;
+  margin-right: 0.3em;
+  padding: 0px;
+  padding-top: 3px;
+}
+.toolbar .btn {
+  padding: 2px 8px;
+}
+.toolbar .btn-group {
+  margin-top: 0px;
+  margin-left: 5px;
+}
+#maintoolbar {
+  margin-bottom: -3px;
+  margin-top: -8px;
+  border: 0px;
+  min-height: 27px;
+  margin-left: 0px;
+  padding-top: 11px;
+  padding-bottom: 3px;
+}
+#maintoolbar .navbar-text {
+  float: none;
+  vertical-align: middle;
+  text-align: right;
+  margin-left: 5px;
+  margin-right: 0px;
+  margin-top: 0px;
+}
+.select-xs {
+  height: 24px;
+}
+.pulse,
+.dropdown-menu > li > a.pulse,
+li.pulse > a.dropdown-toggle,
+li.pulse.open > a.dropdown-toggle {
+  background-color: #F37626;
+  color: white;
+}
+/**
+ * Primary styles
+ *
+ * Author: Jupyter Development Team
+ */
+/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
+ * of chance of beeing generated from the ../less/[samename].less file, you can
+ * try to get back the less file by reverting somme commit in history
+ **/
+/*
+ * We'll try to get something pretty, so we
+ * have some strange css to have the scroll bar on
+ * the left with fix button on the top right of the tooltip
+ */
+@-moz-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-webkit-keyframes fadeOut {
+  from {
+    opacity: 1;
+  }
+  to {
+    opacity: 0;
+  }
+}
+@-moz-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+@-webkit-keyframes fadeIn {
+  from {
+    opacity: 0;
+  }
+  to {
+    opacity: 1;
+  }
+}
+/*properties of tooltip after "expand"*/
+.bigtooltip {
+  overflow: auto;
+  height: 200px;
+  -webkit-transition-property: height;
+  -webkit-transition-duration: 500ms;
+  -moz-transition-property: height;
+  -moz-transition-duration: 500ms;
+  transition-property: height;
+  transition-duration: 500ms;
+}
+/*properties of tooltip before "expand"*/
+.smalltooltip {
+  -webkit-transition-property: height;
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+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Introduction-and-Foundations">Introduction and Foundations<a class="anchor-link" href="#Introduction-and-Foundations">&#182;</a></h2><h2 id="Project:-Titanic-Survival-Exploration">Project: Titanic Survival Exploration<a class="anchor-link" href="#Project:-Titanic-Survival-Exploration">&#182;</a></h2><p>In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive. To complete this project, you will need to implement several conditional predictions and answer the questions below. Your project submission will be evaluated based on the completion of the code and your responses to the questions.</p>
+<blockquote><p><strong>Tip:</strong> Quoted sections like this will provide helpful instructions on how to navigate and use an iPython notebook.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#182;</a></h1><p>To begin working with the RMS Titanic passenger data, we'll first need to <code>import</code> the functionality we need, and load our data into a <code>pandas</code> DataFrame.<br>
+Run the code cell below to load our data and display the first few entries (passengers) for examination using the <code>.head()</code> function.</p>
+<blockquote><p><strong>Tip:</strong> You can run a code cell by clicking on the cell and using the keyboard shortcut <strong>Shift + Enter</strong> or <strong>Shift + Return</strong>. Alternatively, a code cell can be executed using the <strong>Play</strong> button in the hotbar after selecting it. Markdown cells (text cells like this one) can be edited by double-clicking, and saved using these same shortcuts. <a href="http://daringfireball.net/projects/markdown/syntax">Markdown</a> allows you to write easy-to-read plain text that can be converted to HTML.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import libraries necessary for this project</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span> <span class="c1"># Allows the use of display() for DataFrames</span>
+
+<span class="c1"># Import supplementary visualizations code visuals.py</span>
+<span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>
+
+<span class="c1"># Pretty display for notebooks</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="c1"># Load the dataset</span>
+<span class="n">in_file</span> <span class="o">=</span> <span class="s1">&#39;titanic_data.csv&#39;</span>
+<span class="n">full_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">in_file</span><span class="p">)</span>
+
+<span class="c1"># Print the first few entries of the RMS Titanic data</span>
+<span class="n">display</span><span class="p">(</span><span class="n">full_data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>PassengerId</th>
+      <th>Survived</th>
+      <th>Pclass</th>
+      <th>Name</th>
+      <th>Sex</th>
+      <th>Age</th>
+      <th>SibSp</th>
+      <th>Parch</th>
+      <th>Ticket</th>
+      <th>Fare</th>
+      <th>Cabin</th>
+      <th>Embarked</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0</td>
+      <td>3</td>
+      <td>Braund, Mr. Owen Harris</td>
+      <td>male</td>
+      <td>22.0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>A/5 21171</td>
+      <td>7.2500</td>
+      <td>NaN</td>
+      <td>S</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>1</td>
+      <td>1</td>
+      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
+      <td>female</td>
+      <td>38.0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>PC 17599</td>
+      <td>71.2833</td>
+      <td>C85</td>
+      <td>C</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>1</td>
+      <td>3</td>
+      <td>Heikkinen, Miss. Laina</td>
+      <td>female</td>
+      <td>26.0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>STON/O2. 3101282</td>
+      <td>7.9250</td>
+      <td>NaN</td>
+      <td>S</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>1</td>
+      <td>1</td>
+      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
+      <td>female</td>
+      <td>35.0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>113803</td>
+      <td>53.1000</td>
+      <td>C123</td>
+      <td>S</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>0</td>
+      <td>3</td>
+      <td>Allen, Mr. William Henry</td>
+      <td>male</td>
+      <td>35.0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>373450</td>
+      <td>8.0500</td>
+      <td>NaN</td>
+      <td>S</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>From a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship:</p>
+<ul>
+<li><strong>Survived</strong>: Outcome of survival (0 = No; 1 = Yes)</li>
+<li><strong>Pclass</strong>: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)</li>
+<li><strong>Name</strong>: Name of passenger</li>
+<li><strong>Sex</strong>: Sex of the passenger</li>
+<li><strong>Age</strong>: Age of the passenger (Some entries contain <code>NaN</code>)</li>
+<li><strong>SibSp</strong>: Number of siblings and spouses of the passenger aboard</li>
+<li><strong>Parch</strong>: Number of parents and children of the passenger aboard</li>
+<li><strong>Ticket</strong>: Ticket number of the passenger</li>
+<li><strong>Fare</strong>: Fare paid by the passenger</li>
+<li><strong>Cabin</strong> Cabin number of the passenger (Some entries contain <code>NaN</code>)</li>
+<li><strong>Embarked</strong>: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)</li>
+</ul>
+<p>Since we're interested in the outcome of survival for each passenger or crew member, we can remove the <strong>Survived</strong> feature from this dataset and store it as its own separate variable <code>outcomes</code>. We will use these outcomes as our prediction targets.<br>
+Run the code cell below to remove <strong>Survived</strong> as a feature of the dataset and store it in <code>outcomes</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Store the &#39;Survived&#39; feature in a new variable and remove it from the dataset</span>
+<span class="n">outcomes</span> <span class="o">=</span> <span class="n">full_data</span><span class="p">[</span><span class="s1">&#39;Survived&#39;</span><span class="p">]</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">full_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Survived&#39;</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Show the new dataset with &#39;Survived&#39; removed</span>
+<span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+<div class="output_html rendered_html output_subarea ">
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>PassengerId</th>
+      <th>Pclass</th>
+      <th>Name</th>
+      <th>Sex</th>
+      <th>Age</th>
+      <th>SibSp</th>
+      <th>Parch</th>
+      <th>Ticket</th>
+      <th>Fare</th>
+      <th>Cabin</th>
+      <th>Embarked</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>3</td>
+      <td>Braund, Mr. Owen Harris</td>
+      <td>male</td>
+      <td>22.0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>A/5 21171</td>
+      <td>7.2500</td>
+      <td>NaN</td>
+      <td>S</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>1</td>
+      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
+      <td>female</td>
+      <td>38.0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>PC 17599</td>
+      <td>71.2833</td>
+      <td>C85</td>
+      <td>C</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>3</td>
+      <td>Heikkinen, Miss. Laina</td>
+      <td>female</td>
+      <td>26.0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>STON/O2. 3101282</td>
+      <td>7.9250</td>
+      <td>NaN</td>
+      <td>S</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>1</td>
+      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
+      <td>female</td>
+      <td>35.0</td>
+      <td>1</td>
+      <td>0</td>
+      <td>113803</td>
+      <td>53.1000</td>
+      <td>C123</td>
+      <td>S</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>3</td>
+      <td>Allen, Mr. William Henry</td>
+      <td>male</td>
+      <td>35.0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>373450</td>
+      <td>8.0500</td>
+      <td>NaN</td>
+      <td>S</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The very same sample of the RMS Titanic data now shows the <strong>Survived</strong> feature removed from the DataFrame. Note that <code>data</code> (the passenger data) and <code>outcomes</code> (the outcomes of survival) are now <em>paired</em>. That means for any passenger <code>data.loc[i]</code>, they have the survival outcome <code>outcomes[i]</code>.</p>
+<p>To measure the performance of our predictions, we need a metric to score our predictions against the true outcomes of survival. Since we are interested in how <em>accurate</em> our predictions are, we will calculate the proportion of passengers where our prediction of their survival is correct. Run the code cell below to create our <code>accuracy_score</code> function and test a prediction on the first five passengers.</p>
+<p><strong>Think:</strong> <em>Out of the first five passengers, if we predict that all of them survived, what would you expect the accuracy of our predictions to be?</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[44]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">accuracy_score</span><span class="p">(</span><span class="n">truth</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Returns accuracy score for input truth and predictions. &quot;&quot;&quot;</span>
+    
+    <span class="c1"># Ensure that the number of predictions matches number of outcomes</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">truth</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred</span><span class="p">):</span> 
+        
+        <span class="c1"># Calculate and return the accuracy as a percent</span>
+        <span class="k">return</span> <span class="s2">&quot;Predictions have an accuracy of {:.2f}%.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">truth</span> <span class="o">==</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="s2">&quot;Number of predictions does not match number of outcomes!&quot;</span>
+    
+<span class="c1"># Test the &#39;accuracy_score&#39; function</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="nb">int</span><span class="p">))</span>
+<span class="k">print</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">outcomes</span><span class="p">[:</span><span class="mi">5</span><span class="p">],</span> <span class="n">predictions</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Predictions have an accuracy of 60.00%.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p><strong>Tip:</strong> If you save an iPython Notebook, the output from running code blocks will also be saved. However, the state of your workspace will be reset once a new session is started. Make sure that you run all of the code blocks from your previous session to reestablish variables and functions before picking up where you last left off.</p>
+</blockquote>
+<h1 id="Making-Predictions">Making Predictions<a class="anchor-link" href="#Making-Predictions">&#182;</a></h1><p>If we were asked to make a prediction about any passenger aboard the RMS Titanic whom we knew nothing about, then the best prediction we could make would be that they did not survive. This is because we can assume that a majority of the passengers (more than 50%) did not survive the ship sinking.<br>
+The <code>predictions_0</code> function below will always predict that a passenger did not survive.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[45]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">predictions_0</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Model with no features. Always predicts a passenger did not survive. &quot;&quot;&quot;</span>
+
+    <span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">passenger</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+        
+        <span class="c1"># Predict the survival of &#39;passenger&#39;</span>
+        <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    
+    <span class="c1"># Return our predictions</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
+
+<span class="c1"># Make the predictions</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="n">predictions_0</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-1">Question 1<a class="anchor-link" href="#Question-1">&#182;</a></h3><p><em>Using the RMS Titanic data, how accurate would a prediction be that none of the passengers survived?</em><br>
+<strong>Hint:</strong> Run the code cell below to see the accuracy of this prediction.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[46]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">print</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">outcomes</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Predictions have an accuracy of 61.62%.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer:</strong> <em>61.62%</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<p>Let's take a look at whether the feature <strong>Sex</strong> has any indication of survival rates among passengers using the <code>survival_stats</code> function. This function is defined in the <code>titanic_visualizations.py</code> Python script included with this project. The first two parameters passed to the function are the RMS Titanic data and passenger survival outcomes, respectively. The third parameter indicates which feature we want to plot survival statistics across.<br>
+Run the code cell below to plot the survival outcomes of passengers based on their sex.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="n">vs</span><span class="o">.</span><span class="n">survival_stats</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">outcomes</span><span class="p">,</span> <span class="s1">&#39;Sex&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+
+
+<div class="output_png output_subarea ">
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+TkSuQmCC
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Examining the survival statistics, a large majority of males did not survive the ship sinking. However, a majority of females <em>did</em> survive the ship sinking. Let's build on our previous prediction: If a passenger was female, then we will predict that they survived. Otherwise, we will predict the passenger did not survive.<br>
+Fill in the missing code below so that the function will make this prediction.<br>
+<strong>Hint:</strong> You can access the values of each feature for a passenger like a dictionary. For example, <code>passenger['Sex']</code> is the sex of the passenger.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[47]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">predictions_1</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Model with one feature: </span>
+<span class="sd">            - Predict a passenger survived if they are female. &quot;&quot;&quot;</span>
+    
+    <span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">passenger</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+        
+        <span class="c1"># Remove the &#39;pass&#39; statement below </span>
+        <span class="c1"># and write your prediction conditions here</span>
+        <span class="k">if</span> <span class="n">passenger</span><span class="p">[</span><span class="s1">&#39;Sex&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;female&#39;</span><span class="p">:</span>
+            <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    
+    <span class="c1"># Return our predictions</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
+
+<span class="c1"># Make the predictions</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="n">predictions_1</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-2">Question 2<a class="anchor-link" href="#Question-2">&#182;</a></h3><p><em>How accurate would a prediction be that all female passengers survived and the remaining passengers did not survive?</em><br>
+<strong>Hint:</strong> Run the code cell below to see the accuracy of this prediction.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[48]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">print</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">outcomes</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Predictions have an accuracy of 78.68%.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer</strong>: <em>78.68%</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<p>Using just the <strong>Sex</strong> feature for each passenger, we are able to increase the accuracy of our predictions by a significant margin. Now, let's consider using an additional feature to see if we can further improve our predictions. For example, consider all of the male passengers aboard the RMS Titanic: Can we find a subset of those passengers that had a higher rate of survival? Let's start by looking at the <strong>Age</strong> of each male, by again using the <code>survival_stats</code> function. This time, we'll use a fourth parameter to filter out the data so that only passengers with the <strong>Sex</strong> 'male' will be included.<br>
+Run the code cell below to plot the survival outcomes of male passengers based on their age.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[56]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="n">vs</span><span class="o">.</span><span class="n">survival_stats</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">outcomes</span><span class="p">,</span> <span class="s1">&#39;Parch&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Sex == &#39;male&#39;&quot;</span><span class="p">])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Examining the survival statistics, the majority of males younger than 10 survived the ship sinking, whereas most males age 10 or older <em>did not survive</em> the ship sinking. Let's continue to build on our previous prediction: If a passenger was female, then we will predict they survive. If a passenger was male and younger than 10, then we will also predict they survive. Otherwise, we will predict they do not survive.<br>
+Fill in the missing code below so that the function will make this prediction.<br>
+<strong>Hint:</strong> You can start your implementation of this function using the prediction code you wrote earlier from <code>predictions_1</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[49]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">predictions_2</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Model with two features: </span>
+<span class="sd">            - Predict a passenger survived if they are female.</span>
+<span class="sd">            - Predict a passenger survived if they are male and younger than 10. &quot;&quot;&quot;</span>
+    
+    <span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">passenger</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+        
+        <span class="c1"># Remove the &#39;pass&#39; statement below </span>
+        <span class="c1"># and write your prediction conditions here</span>
+        <span class="k">if</span> <span class="n">passenger</span><span class="p">[</span><span class="s1">&#39;Sex&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;female&#39;</span><span class="p">:</span>
+            <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">passenger</span><span class="p">[</span><span class="s1">&#39;Age&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">10.0</span><span class="p">:</span>
+                <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    
+    <span class="c1"># Return our predictions</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
+
+<span class="c1"># Make the predictions</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="n">predictions_2</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-3">Question 3<a class="anchor-link" href="#Question-3">&#182;</a></h3><p><em>How accurate would a prediction be that all female passengers and all male passengers younger than 10 survived?</em><br>
+<strong>Hint:</strong> Run the code cell below to see the accuracy of this prediction.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[51]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">print</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">outcomes</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Predictions have an accuracy of 79.35%.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer</strong>: <em>79.35%</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+<p>Adding the feature <strong>Age</strong> as a condition in conjunction with <strong>Sex</strong> improves the accuracy by a small margin more than with simply using the feature <strong>Sex</strong> alone. Now it's your turn: Find a series of features and conditions to split the data on to obtain an outcome prediction accuracy of at least 80%. This may require multiple features and multiple levels of conditional statements to succeed. You can use the same feature multiple times with different conditions.<br>
+<strong>Pclass</strong>, <strong>Sex</strong>, <strong>Age</strong>, <strong>SibSp</strong>, and <strong>Parch</strong> are some suggested features to try.</p>
+<p>Use the <code>survival_stats</code> function below to to examine various survival statistics.<br>
+<strong>Hint:</strong> To use mulitple filter conditions, put each condition in the list passed as the last argument. Example: <code>["Sex == 'male'", "Age &lt; 18"]</code></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[40]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">checkSurvived</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">filters</span><span class="p">):</span>
+    <span class="n">all_data</span> <span class="o">=</span> <span class="n">data</span>
+    <span class="k">for</span> <span class="n">condition</span> <span class="ow">in</span> <span class="n">filters</span><span class="p">:</span>
+        <span class="n">all_data</span> <span class="o">=</span> <span class="n">vs</span><span class="o">.</span><span class="n">filter_data</span><span class="p">(</span><span class="n">all_data</span><span class="p">,</span> <span class="n">condition</span><span class="p">)</span>
+    
+    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;nb&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_data</span><span class="p">),</span> <span class="s2">&quot;percent survived&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_data</span><span class="p">[</span><span class="n">all_data</span><span class="p">[</span><span class="s2">&quot;Survived&quot;</span><span class="p">]</span> <span class="o">==</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">all_data</span><span class="p">[</span><span class="n">all_data</span><span class="p">[</span><span class="s2">&quot;Survived&quot;</span><span class="p">]])),</span> <span class="s2">&quot; in cat &quot;</span> <span class="o">+</span> <span class="s1">&#39; | &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">filters</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">all_data</span>
+
+<span class="n">vs</span><span class="o">.</span><span class="n">survival_stats</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">outcomes</span><span class="p">,</span> <span class="s1">&#39;Age&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Sex == &#39;female&#39;&quot;</span><span class="p">,</span> <span class="s2">&quot;Age &gt; 0&quot;</span><span class="p">,</span> <span class="s2">&quot;Age &lt; 35&quot;</span><span class="p">])</span>
+<span class="c1">#vs.survival_stats(data, outcomes, &#39;Age&#39;, [&quot;Sex == &#39;male&#39;&quot;, &quot;Age &gt;= 20&quot;])</span>
+
+<span class="k">print</span><span class="p">(</span><span class="s2">&quot;full:&quot;</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">full_data</span><span class="p">))</span>
+<span class="n">checkSurvived</span><span class="p">(</span><span class="n">full_data</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Sex == &#39;male&#39;&quot;</span><span class="p">,</span> <span class="s2">&quot;Age &gt;= 10&quot;</span><span class="p">])</span>
+<span class="n">checkSurvived</span><span class="p">(</span><span class="n">full_data</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Sex == &#39;male&#39;&quot;</span><span class="p">,</span> <span class="s2">&quot;Age &gt; 20&quot;</span><span class="p">])</span>
+<span class="n">checkSurvived</span><span class="p">(</span><span class="n">full_data</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Sex == &#39;male&#39;&quot;</span><span class="p">,</span> <span class="s2">&quot;Age &gt;= 10&quot;</span><span class="p">,</span> <span class="s2">&quot;Age &lt; 30&quot;</span><span class="p">])</span>
+<span class="n">t</span> <span class="o">=</span> <span class="n">checkSurvived</span><span class="p">(</span><span class="n">full_data</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Sex == &#39;male&#39;&quot;</span><span class="p">,</span> <span class="s2">&quot;Age &gt;= 10&quot;</span><span class="p">,</span> <span class="s2">&quot;Age &lt; 30&quot;</span><span class="p">,</span> <span class="s2">&quot;Pclass == 3&quot;</span><span class="p">])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
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+"
+>
+</div>
+
+</div>
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>(&#39;full:&#39;, 891)
+(&#39;nb&#39;, 421, &#39;percent survived&#39;, 0.17577197149643706, &#34; in cat Sex == &#39;male&#39; | Age &gt;= 10&#34;)
+(&#39;nb&#39;, 351, &#39;percent survived&#39;, 0.18233618233618235, &#34; in cat Sex == &#39;male&#39; | Age &gt; 20&#34;)
+(&#39;nb&#39;, 205, &#39;percent survived&#39;, 0.15609756097560976, &#34; in cat Sex == &#39;male&#39; | Age &gt;= 10 | Age &lt; 30&#34;)
+(&#39;nb&#39;, 144, &#39;percent survived&#39;, 0.1388888888888889, &#34; in cat Sex == &#39;male&#39; | Age &gt;= 10 | Age &lt; 30 | Pclass == 3&#34;)
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>After exploring the survival statistics visualization, fill in the missing code below so that the function will make your prediction.<br>
+Make sure to keep track of the various features and conditions you tried before arriving at your final prediction model.<br>
+<strong>Hint:</strong> You can start your implementation of this function using the prediction code you wrote earlier from <code>predictions_2</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[52]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">predictions_3</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Model with multiple features. Makes a prediction with an accuracy of at least 80%. &quot;&quot;&quot;</span>
+    
+    <span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">test</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">passenger</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+        
+        <span class="c1"># Remove the &#39;pass&#39; statement below </span>
+        <span class="c1"># and write your prediction conditions here</span>
+        <span class="k">if</span> <span class="n">passenger</span><span class="p">[</span><span class="s1">&#39;Sex&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;female&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">passenger</span><span class="p">[</span><span class="s2">&quot;Pclass&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">passenger</span><span class="p">[</span><span class="s2">&quot;SibSp&quot;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">passenger</span><span class="p">[</span><span class="s1">&#39;Age&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">10.0</span><span class="p">:</span>
+                <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+               
+               
+        <span class="n">i</span> <span class="o">+=</span><span class="mi">1</span>           
+    
+    <span class="c1"># Return our predictions</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">predictions</span><span class="p">),</span> <span class="n">test</span>
+
+<span class="c1"># Make the predictions</span>
+<span class="n">predictions</span><span class="p">,</span><span class="n">test</span> <span class="o">=</span> <span class="n">predictions_3</span><span class="p">(</span><span class="n">full_data</span><span class="p">)</span>
+<span class="k">print</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">outcomes</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="c1">#testdata = checkSurvived(full_data, [&quot;Sex == &#39;male&#39;&quot;, &quot;Age &gt;= 10&quot;])</span>
+<span class="n">testdata</span> <span class="o">=</span> <span class="n">checkSurvived</span><span class="p">(</span><span class="n">full_data</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Sex == &#39;female&#39;&quot;</span><span class="p">])</span>
+
+<span class="n">surv</span> <span class="o">=</span> <span class="n">checkSurvived</span><span class="p">(</span><span class="n">testdata</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Survived == 0&quot;</span><span class="p">])</span>
+
+<span class="n">gb</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Pclass&quot;</span><span class="p">,</span> <span class="s2">&quot;SibSp&quot;</span><span class="p">,</span> <span class="s2">&quot;Parch&quot;</span><span class="p">]</span>
+<span class="n">dfall</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">testdata</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">gb</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
+<span class="n">dfSurv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">surv</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">gb</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
+
+<span class="c1">#dfSurv / dfall</span>
+<span class="c1">#dfSurv</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Predictions have an accuracy of 80.58%.
+(&#39;nb&#39;, 314, &#39;percent survived&#39;, 0.7420382165605095, &#34; in cat Sex == &#39;female&#39;&#34;)
+(&#39;nb&#39;, 81, &#39;percent survived&#39;, 0.0, &#39; in cat Survived == 0&#39;)
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Question-4">Question 4<a class="anchor-link" href="#Question-4">&#182;</a></h3><p><em>Describe the steps you took to implement the final prediction model so that it got an accuracy of at least 80%. What features did you look at? Were certain features more informative than others? Which conditions did you use to split the survival outcomes in the data? How accurate are your predictions?</em><br>
+<strong>Hint:</strong> Run the code cell below to see the accuracy of your predictions.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[53]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython2"><pre><span></span><span class="k">print</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">outcomes</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+<div class="prompt"></div>
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Predictions have an accuracy of 80.58%.
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer</strong>: <em>I got an accuracy of 80.58%. Initially i used the filter function to check various features and compute the percent survived. However, to get a better idea of cells of survival / death, I then moved to using group by count to compare dataframes for dead against all female. I then tried to find cells that gave more than 50% dead females. One such area of cells gave me the result - see code. Turns out class 3 with more than 1 increases your probability of death as a woman.</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#182;</a></h1><p>After several iterations of exploring and conditioning on the data, you have built a useful algorithm for predicting the survival of each passenger aboard the RMS Titanic. The technique applied in this project is a manual implementation of a simple machine learning model, the <em>decision tree</em>. A decision tree splits a set of data into smaller and smaller groups (called <em>nodes</em>), by one feature at a time. Each time a subset of the data is split, our predictions become more accurate if each of the resulting subgroups are more homogeneous (contain similar labels) than before. The advantage of having a computer do things for us is that it will be more exhaustive and more precise than our manual exploration above. <a href="http://www.r2d3.us/visual-intro-to-machine-learning-part-1/">This link</a> provides another introduction into machine learning using a decision tree.</p>
+<p>A decision tree is just one of many models that come from <em>supervised learning</em>. In supervised learning, we attempt to use features of the data to predict or model things with objective outcome labels. That is to say, each of our data points has a known outcome value, such as a categorical, discrete label like <code>'Survived'</code>, or a numerical, continuous value like predicting the price of a house.</p>
+<h3 id="Question-5">Question 5<a class="anchor-link" href="#Question-5">&#182;</a></h3><p><em>Think of a real-world scenario where supervised learning could be applied. What would be the outcome variable that you are trying to predict? Name two features about the data used in this scenario that might be helpful for making the predictions.</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Answer</strong>: <em>I could use supervised learning on a set of bonds. The outcome would be outperformance of a bond against the benchmark index by 3 % over 1 year. Two features that might be helpful to make this prediction is the credit rating and expected remaining life of a bond.</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
+<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+    </div>
+  </div>
+</body>
+
+ 
+
+
+</html>
diff --git a/titanic_survival_exploration/titanic_survival_exploration.ipynb b/titanic_survival_exploration/titanic_survival_exploration.ipynb
new file mode 100644
index 0000000..49b3547
--- /dev/null
+++ b/titanic_survival_exploration/titanic_survival_exploration.ipynb
@@ -0,0 +1,865 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning Engineer Nanodegree\n",
+    "## Introduction and Foundations\n",
+    "## Project: Titanic Survival Exploration\n",
+    "\n",
+    "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive. To complete this project, you will need to implement several conditional predictions and answer the questions below. Your project submission will be evaluated based on the completion of the code and your responses to the questions.\n",
+    "> **Tip:** Quoted sections like this will provide helpful instructions on how to navigate and use an iPython notebook. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Getting Started\n",
+    "To begin working with the RMS Titanic passenger data, we'll first need to `import` the functionality we need, and load our data into a `pandas` DataFrame.  \n",
+    "Run the code cell below to load our data and display the first few entries (passengers) for examination using the `.head()` function.\n",
+    "> **Tip:** You can run a code cell by clicking on the cell and using the keyboard shortcut **Shift + Enter** or **Shift + Return**. Alternatively, a code cell can be executed using the **Play** button in the hotbar after selecting it. Markdown cells (text cells like this one) can be edited by double-clicking, and saved using these same shortcuts. [Markdown](http://daringfireball.net/projects/markdown/syntax) allows you to write easy-to-read plain text that can be converted to HTML."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import libraries necessary for this project\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from IPython.display import display # Allows the use of display() for DataFrames\n",
+    "\n",
+    "# Import supplementary visualizations code visuals.py\n",
+    "import visuals as vs\n",
+    "\n",
+    "# Pretty display for notebooks\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Load the dataset\n",
+    "in_file = 'titanic_data.csv'\n",
+    "full_data = pd.read_csv(in_file)\n",
+    "\n",
+    "# Print the first few entries of the RMS Titanic data\n",
+    "display(full_data.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship:\n",
+    "- **Survived**: Outcome of survival (0 = No; 1 = Yes)\n",
+    "- **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n",
+    "- **Name**: Name of passenger\n",
+    "- **Sex**: Sex of the passenger\n",
+    "- **Age**: Age of the passenger (Some entries contain `NaN`)\n",
+    "- **SibSp**: Number of siblings and spouses of the passenger aboard\n",
+    "- **Parch**: Number of parents and children of the passenger aboard\n",
+    "- **Ticket**: Ticket number of the passenger\n",
+    "- **Fare**: Fare paid by the passenger\n",
+    "- **Cabin** Cabin number of the passenger (Some entries contain `NaN`)\n",
+    "- **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
+    "\n",
+    "Since we're interested in the outcome of survival for each passenger or crew member, we can remove the **Survived** feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets.  \n",
+    "Run the code cell below to remove **Survived** as a feature of the dataset and store it in `outcomes`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                               Name  \\\n",
+       "0            1       3                            Braund, Mr. Owen Harris   \n",
+       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
+       "2            3       3                             Heikkinen, Miss. Laina   \n",
+       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
+       "4            5       3                           Allen, Mr. William Henry   \n",
+       "\n",
+       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
+       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
+       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
+       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
+       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Store the 'Survived' feature in a new variable and remove it from the dataset\n",
+    "outcomes = full_data['Survived']\n",
+    "data = full_data.drop('Survived', axis = 1)\n",
+    "\n",
+    "# Show the new dataset with 'Survived' removed\n",
+    "display(data.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The very same sample of the RMS Titanic data now shows the **Survived** feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now *paired*. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.\n",
+    "\n",
+    "To measure the performance of our predictions, we need a metric to score our predictions against the true outcomes of survival. Since we are interested in how *accurate* our predictions are, we will calculate the proportion of passengers where our prediction of their survival is correct. Run the code cell below to create our `accuracy_score` function and test a prediction on the first five passengers.  \n",
+    "\n",
+    "**Think:** *Out of the first five passengers, if we predict that all of them survived, what would you expect the accuracy of our predictions to be?*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions have an accuracy of 60.00%.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def accuracy_score(truth, pred):\n",
+    "    \"\"\" Returns accuracy score for input truth and predictions. \"\"\"\n",
+    "    \n",
+    "    # Ensure that the number of predictions matches number of outcomes\n",
+    "    if len(truth) == len(pred): \n",
+    "        \n",
+    "        # Calculate and return the accuracy as a percent\n",
+    "        return \"Predictions have an accuracy of {:.2f}%.\".format((truth == pred).mean()*100)\n",
+    "    \n",
+    "    else:\n",
+    "        return \"Number of predictions does not match number of outcomes!\"\n",
+    "    \n",
+    "# Test the 'accuracy_score' function\n",
+    "predictions = pd.Series(np.ones(5, dtype = int))\n",
+    "print accuracy_score(outcomes[:5], predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Tip:** If you save an iPython Notebook, the output from running code blocks will also be saved. However, the state of your workspace will be reset once a new session is started. Make sure that you run all of the code blocks from your previous session to reestablish variables and functions before picking up where you last left off.\n",
+    "\n",
+    "# Making Predictions\n",
+    "\n",
+    "If we were asked to make a prediction about any passenger aboard the RMS Titanic whom we knew nothing about, then the best prediction we could make would be that they did not survive. This is because we can assume that a majority of the passengers (more than 50%) did not survive the ship sinking.  \n",
+    "The `predictions_0` function below will always predict that a passenger did not survive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def predictions_0(data):\n",
+    "    \"\"\" Model with no features. Always predicts a passenger did not survive. \"\"\"\n",
+    "\n",
+    "    predictions = []\n",
+    "    for _, passenger in data.iterrows():\n",
+    "        \n",
+    "        # Predict the survival of 'passenger'\n",
+    "        predictions.append(0)\n",
+    "    \n",
+    "    # Return our predictions\n",
+    "    return pd.Series(predictions)\n",
+    "\n",
+    "# Make the predictions\n",
+    "predictions = predictions_0(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1\n",
+    "*Using the RMS Titanic data, how accurate would a prediction be that none of the passengers survived?*  \n",
+    "**Hint:** Run the code cell below to see the accuracy of this prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions have an accuracy of 61.62%.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print accuracy_score(outcomes, predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer:** *61.62%*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "Let's take a look at whether the feature **Sex** has any indication of survival rates among passengers using the `survival_stats` function. This function is defined in the `titanic_visualizations.py` Python script included with this project. The first two parameters passed to the function are the RMS Titanic data and passenger survival outcomes, respectively. The third parameter indicates which feature we want to plot survival statistics across.  \n",
+    "Run the code cell below to plot the survival outcomes of passengers based on their sex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P1BPwLd34RwPXZqbHpZckaYCrZxT99RHxIPAy8LGIGAa80tiyJElST3Tags/M\ns4CDgObMXAmsAI5pdGGSJKn76hlkdxywMjNXR8QXgKuA7RtemSRJ6rZ69sF/MTOXR8TbgXcCPwD+\nrbFlSZKknqgn4FsOS3s0cHlm3oCnjZUkaUCrJ+CfjIjvA5OAGyNi4zqXkyRJ/aSeoD4euBk4MjOf\nB7bC38FLkjSg1TOK/q+Z+R/ACxGxEzCY8tzwkiRpYKpnFP3EiFgAPAbcXv69qdGFSZKk7quni/4C\n4ADgvzNzFMVI+t81tCpJktQj9QT8ysxcBrwhIt6Qmb8GmhtclyRJ6oF6DlX7fERsBtwBXB0RSyiO\nZidJkgaoelrwxwB/Bf438J/AI8B7GlmUJEnqmQ5b8BHxXorTw/4xM28GpvZJVZIkqUfabcFHxKUU\nrfatgQsi4ot9VpUkSeqRjlrwhwJjy5PMvBH4DcWIekmSNMB1tA/+b5m5GoqD3QDRNyVJkqSe6qgF\n/7aIuL+8HsAu5e0AMjPHNLw6SZLULR0F/G59VoUkSepV7QZ8Zv65LwuRJEm9x9O+SpJUQQa8JEkV\n1NHv4G8t//5L35UjSZJ6Q0eD7IZHxEHAxIiYRqufyWXmfQ2tTJIkdVtHAf8l4IvACOCbre5L4LBG\nFSVJknqmo1H0M4AZEfHFzPQIdpJUIXG+xy7rC3lu9tu2Oz1dbGZeEBETKQ5dC3BbZl7f2LIkSVJP\ndDqKPiK+BkwB5peXKRHx1UYXJkmSuq/TFjxwNDAuM18DiIipwB+AcxpZmCRJ6r56fwe/Rc31zRtR\niCRJ6j31tOC/BvwhIn5N8VO5Q4GzGlqVJEnqkXoG2f0kIm4D9i0nfS4zn25oVZIkqUfqacGTmYuB\nmQ2uRZIk9RKPRS9JUgUZ8JIkVVCHAR8RTRHxYF8VI0mSekeHAZ+Zq4GHImKnPqpHkiT1gnoG2W0J\nzIuIe4EVLRMzc2LDqpIkST1ST8B/seFVSJKkXlXP7+Bvj4idgV0z81cR8UagqfGlSZKk7qrnZDMf\nBWYA3y8n7QBc18iiJElSz9TzM7lPAAcDLwJk5gJg20YWJUmSeqaegH81M//WciMiBgH9dwZ7SZLU\nqXoC/vaIOAfYJCL+HrgW+EVjy5IkST1RT8CfBSwF/gicBtwIfKGzhSJix4j4dUTMj4h5ETGlnL5V\nRPwyIhaUf7esWebsiHg4Ih6KiCO795AkSVI9o+hfi4ipwD0UXfMPZWY9XfSrgE9n5n0RMRSYHRG/\nBE4Bbs0nz2A/AAALQElEQVTMiyLiLIovEJ+LiN2BycBoYHvgVxHxlvJgO5IkqQvqGUV/NPAI8G3g\nu8DDEfGuzpbLzMWZeV95fTnwAMUI/GOAqeVsU4H3ltePAaZl5quZ+RjwMLBf1x6OJEmC+g50cwnw\nPzPzYYCI2AW4Abip3o1ExEhgL4pegO3K088CPA1sV17fAfhdzWKLymmt13UqcCrATjt5BF1JktpS\nzz745S3hXnoUWF7vBiJiM+CnwD9l5ou195Vd/V0akZ+Zl2dmc2Y2Dxs2rCuLSpK0wWi3BR8R7y+v\nzoqIG4HpFGF8HPD7elYeEYMpwv3qzPyPcvIzETE8MxdHxHBgSTn9SWDHmsVHlNMkSVIXddSCf095\nGQI8A7wDGE8xon6TzlYcEQH8AHggM79Zc9dM4OTy+snAz2umT46IjSNiFLArcG/dj0SSJK3Rbgs+\nMz/Uw3UfDJwE/DEi5pTTzgEuAqZHxIeBPwPHl9ubFxHTgfkUI/A/4Qh6SZK6p9NBdmVr+gxgZO38\nnZ0uNjPvBKKduw9vZ5kLgQs7q0mSJHWsnlH011F0tf8CeK2x5UiSpN5QT8C/kpnfbnglkiSp19QT\n8N+KiHOBW4BXWya2HMRGkiQNPPUE/J4Ug+UO4/Uu+ixvS5KkAaiegD8OeHPtKWMlSdLAVs+R7P4E\nbNHoQiRJUu+ppwW/BfBgRPyetffBd/gzOUmS1H/qCfhzG16FJEnqVfWcD/72vihEkiT1nnqOZLec\n18/4thEwGFiRmW9qZGGSJKn76mnBD225Xp5A5hjggEYWJUmSeqaeUfRrZOE64MgG1SNJknpBPV30\n76+5+QagGXilYRVJkqQeq2cU/Xtqrq8CHqfoppckSQNUPfvge3peeEmS1MfaDfiI+FIHy2VmXtCA\neiRJUi/oqAW/oo1pmwIfBrYGDHhJkgaodgM+My9puR4RQ4EpwIeAacAl7S0nSZL6X4f74CNiK+BT\nwInAVGDvzHyuLwqTJEnd19E++IuB9wOXA3tm5kt9VpUkSeqRjg5082lge+ALwFMR8WJ5WR4RL/ZN\neZIkqTs62gffpaPcSZKkgcMQlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4\nSZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmS\nKsiAlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJkirI\ngJckqYIMeEmSKsiAlySpggx4SZIqaFB/F6ANU5wf/V3CBiHPzf4uQVI/sQUvSVIFGfCSJFVQwwI+\nIn4YEUsi4k8107aKiF9GxILy75Y1950dEQ9HxEMRcWSj6pIkaUPQyBb8lcBRraadBdyambsCt5a3\niYjdgcnA6HKZSyOiqYG1SZJUaQ0L+My8A/hLq8nHAFPL61OB99ZMn5aZr2bmY8DDwH6Nqk2SpKrr\n633w22Xm4vL608B25fUdgIU18y0qp60jIk6NiFkRMWvp0qWNq1SSpPVYvw2yy8wEuvwbnsy8PDOb\nM7N52LBhDahMkqT1X18H/DMRMRyg/LuknP4ksGPNfCPKaZIkqRv6OuBnAieX108Gfl4zfXJEbBwR\no4BdgXv7uDZJkiqjYUeyi4ifAOOBbSJiEXAucBEwPSI+DPwZOB4gM+dFxHRgPrAK+ERmrm5UbZIk\nVV3DAj4zT2jnrsPbmf9C4MJG1SNJ0obEI9lJklRBBrwkSRVkwEuSVEEGvCRJFWTAS5JUQQa8JEkV\nZMBLklRBDfsdvCR1S0R/V7BhOK+/C1Cj2YKXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIq\nyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiA\nlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJkirIgJck\nqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmC\nDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4\nSZIqyICXJKmCBlzAR8RREfFQRDwcEWf1dz2SJK2PBlTAR0QT8D3gXcDuwAkRsXv/ViVJ0vpnQAU8\nsB/wcGY+mpl/A6YBx/RzTZIkrXcGWsDvACysub2onCZJkrpgUH8X0FURcSpwannzpYh4qD/rUTed\n198FdMs2wLP9XURXxHnR3yVooDqvvwvoFj+DhZ3rmWmgBfyTwI41t0eU09bIzMuBy/uyKAkgImZl\nZnN/1yFtqPwMds1A66L/PbBrRIyKiI2AycDMfq5JkqT1zoBqwWfmqoj4JHAz0AT8MDPn9XNZkiSt\ndwZUwANk5o3Ajf1dh9QGdw1J/cvPYBdEZvZ3DZIkqZcNtH3wkiSpFxjwUjdExPiIuL6/65DWJxFx\nZkQ8EBFXN2j950XEPzdi3eujAbcPXpJUWR8H3pmZi/q7kA2BLXhtsCJiZEQ8GBFXRsR/R8TVEfHO\niPhtRCyIiP3Ky90R8YeIuCsi3trGejaNiB9GxL3lfB5eWWolIi4D3gzcFBGfb+szExGnRMR1EfHL\niHg8Ij4ZEZ8q5/ldRGxVzvfRiPh9RMyNiJ9GxBvb2N4uEfGfETE7In4TEW/r20fc/wx4bej+DrgE\neFt5+QDwduCfgXOAB4FDMnMv4EvAV9tYx+eB/8rM/YD/CVwcEZv2Qe3SeiMzTweeoviMbEr7n5k9\ngPcD+wIXAn8tP393A/9YzvMfmblvZo4FHgA+3MYmLwfOyMx9KD7PlzbmkQ1cdtFrQ/dYZv4RICLm\nAbdmZkbEH4GRwObA1IjYFUhgcBvrOAKYWLPvbwiwE8U/Hknrau8zA/DrzFwOLI+IF4BflNP/CIwp\nr+8REV8BtgA2ozh2yhoRsRlwEHBtxJpDxW7ciAcykBnw2tC9WnP9tZrbr1F8Pi6g+IfzvogYCdzW\nxjoC+IfM9LwIUn3a/MxExP50/pkEuBJ4b2bOjYhTgPGt1v8G4PnMHNe7Za9f7KKXOrY5r58P4ZR2\n5rkZOCPKpkJE7NUHdUnrs55+ZoYCiyNiMHBi6zsz80XgsYg4rlx/RMTYHta83jHgpY59HfhaRPyB\n9nu8LqDour+/7Oa/oK+Kk9ZTPf3MfBG4B/gtxTiZtpwIfDgi5gLzgA1u8KtHspMkqYJswUuSVEEG\nvCRJFWTAS5JUQQa8JEkVZMBLklRBBrykNpXHC58XEfdHxJzyICSS1hMeyU7SOiLiQGACsHdmvhoR\n2wAb9XNZkrrAFryktgwHns3MVwEy89nMfCoi9omI28szdN0cEcMjYlB5Zq/xABHxtYi4sD+Ll+SB\nbiS1oTxZx53AG4FfAdcAdwG3A8dk5tKImAQcmZn/KyJGAzOAM4CLgf0z82/9U70ksIteUhsy86WI\n2Ac4hOJ0ntcAX6E4lecvy0OINwGLy/nnRcSPgOuBAw13qf8Z8JLalJmrKc6ed1t5+txPAPMy88B2\nFtkTeB7Ytm8qlNQR98FLWkdEvDUidq2ZNI7i/PbDygF4RMTgsmueiHg/sBVwKPCdiNiir2uWtDb3\nwUtaR9k9/x1gC2AV8DBwKjAC+DbFaXQHAf8K/Ixi//zhmbkwIs4E9snMk/ujdkkFA16SpAqyi16S\npAoy4CVJqiADXpKkCjLgJUmqIANekqQKMuAlSaogA16SpAoy4CVJqqD/D4fPhy9k8RkrAAAAAElF\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2e10240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vs.survival_stats(data, outcomes, 'Sex')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examining the survival statistics, a large majority of males did not survive the ship sinking. However, a majority of females *did* survive the ship sinking. Let's build on our previous prediction: If a passenger was female, then we will predict that they survived. Otherwise, we will predict the passenger did not survive.  \n",
+    "Fill in the missing code below so that the function will make this prediction.  \n",
+    "**Hint:** You can access the values of each feature for a passenger like a dictionary. For example, `passenger['Sex']` is the sex of the passenger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def predictions_1(data):\n",
+    "    \"\"\" Model with one feature: \n",
+    "            - Predict a passenger survived if they are female. \"\"\"\n",
+    "    \n",
+    "    predictions = []\n",
+    "    for _, passenger in data.iterrows():\n",
+    "        \n",
+    "        # Remove the 'pass' statement below \n",
+    "        # and write your prediction conditions here\n",
+    "        if passenger['Sex'] == 'female':\n",
+    "            predictions.append(1)\n",
+    "        else:\n",
+    "            predictions.append(0)\n",
+    "    \n",
+    "    # Return our predictions\n",
+    "    return pd.Series(predictions)\n",
+    "\n",
+    "# Make the predictions\n",
+    "predictions = predictions_1(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2\n",
+    "*How accurate would a prediction be that all female passengers survived and the remaining passengers did not survive?*  \n",
+    "**Hint:** Run the code cell below to see the accuracy of this prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions have an accuracy of 78.68%.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print accuracy_score(outcomes, predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer**: *78.68%*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "Using just the **Sex** feature for each passenger, we are able to increase the accuracy of our predictions by a significant margin. Now, let's consider using an additional feature to see if we can further improve our predictions. For example, consider all of the male passengers aboard the RMS Titanic: Can we find a subset of those passengers that had a higher rate of survival? Let's start by looking at the **Age** of each male, by again using the `survival_stats` function. This time, we'll use a fourth parameter to filter out the data so that only passengers with the **Sex** 'male' will be included.  \n",
+    "Run the code cell below to plot the survival outcomes of male passengers based on their age."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nO/3InnHfH9gNeEzS3hGxtjk7ZGZmZsVdgx8N7BMRy5tTcEQsJj3SNiJWSppD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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa33feb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vs.survival_stats(data, outcomes, 'Parch', [\"Sex == 'male'\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Examining the survival statistics, the majority of males younger than 10 survived the ship sinking, whereas most males age 10 or older *did not survive* the ship sinking. Let's continue to build on our previous prediction: If a passenger was female, then we will predict they survive. If a passenger was male and younger than 10, then we will also predict they survive. Otherwise, we will predict they do not survive.  \n",
+    "Fill in the missing code below so that the function will make this prediction.  \n",
+    "**Hint:** You can start your implementation of this function using the prediction code you wrote earlier from `predictions_1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def predictions_2(data):\n",
+    "    \"\"\" Model with two features: \n",
+    "            - Predict a passenger survived if they are female.\n",
+    "            - Predict a passenger survived if they are male and younger than 10. \"\"\"\n",
+    "    \n",
+    "    predictions = []\n",
+    "    for _, passenger in data.iterrows():\n",
+    "        \n",
+    "        # Remove the 'pass' statement below \n",
+    "        # and write your prediction conditions here\n",
+    "        if passenger['Sex'] == 'female':\n",
+    "            predictions.append(1)\n",
+    "        else:\n",
+    "            if passenger['Age'] < 10.0:\n",
+    "                predictions.append(1)\n",
+    "            else:\n",
+    "                predictions.append(0)\n",
+    "    \n",
+    "    # Return our predictions\n",
+    "    return pd.Series(predictions)\n",
+    "\n",
+    "# Make the predictions\n",
+    "predictions = predictions_2(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3\n",
+    "*How accurate would a prediction be that all female passengers and all male passengers younger than 10 survived?*  \n",
+    "**Hint:** Run the code cell below to see the accuracy of this prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions have an accuracy of 79.35%.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print accuracy_score(outcomes, predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer**: *79.35%*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "***\n",
+    "Adding the feature **Age** as a condition in conjunction with **Sex** improves the accuracy by a small margin more than with simply using the feature **Sex** alone. Now it's your turn: Find a series of features and conditions to split the data on to obtain an outcome prediction accuracy of at least 80%. This may require multiple features and multiple levels of conditional statements to succeed. You can use the same feature multiple times with different conditions.   \n",
+    "**Pclass**, **Sex**, **Age**, **SibSp**, and **Parch** are some suggested features to try.\n",
+    "\n",
+    "Use the `survival_stats` function below to to examine various survival statistics.  \n",
+    "**Hint:** To use mulitple filter conditions, put each condition in the list passed as the last argument. Example: `[\"Sex == 'male'\", \"Age < 18\"]`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+9BuA84G3RcQCih8I53e9bEmSVKueS78ujojvUrSwvxIRm1HfvvcHgL3b6L8C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\nSRVgoEuSVAEGuiRJFWCgS5JUAf8fDd+qD278eEEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9688ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('full:', 891)\n",
+      "('nb', 421, 'percent survived', 0.17577197149643706, \" in cat Sex == 'male' | Age >= 10\")\n",
+      "('nb', 351, 'percent survived', 0.18233618233618235, \" in cat Sex == 'male' | Age > 20\")\n",
+      "('nb', 205, 'percent survived', 0.15609756097560976, \" in cat Sex == 'male' | Age >= 10 | Age < 30\")\n",
+      "('nb', 144, 'percent survived', 0.1388888888888889, \" in cat Sex == 'male' | Age >= 10 | Age < 30 | Pclass == 3\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "def checkSurvived(data, filters):\n",
+    "    all_data = data\n",
+    "    for condition in filters:\n",
+    "        all_data = vs.filter_data(all_data, condition)\n",
+    "    \n",
+    "    print(\"nb\", len(all_data), \"percent survived\", len(all_data[all_data[\"Survived\"] ==1]) / float(len(all_data[all_data[\"Survived\"]])), \" in cat \" + ' | '.join(filters))\n",
+    "    return all_data\n",
+    "\n",
+    "vs.survival_stats(data, outcomes, 'Age', [\"Sex == 'female'\", \"Age > 0\", \"Age < 35\"])\n",
+    "#vs.survival_stats(data, outcomes, 'Age', [\"Sex == 'male'\", \"Age >= 20\"])\n",
+    "\n",
+    "print(\"full:\",len(full_data))\n",
+    "checkSurvived(full_data, [\"Sex == 'male'\", \"Age >= 10\"])\n",
+    "checkSurvived(full_data, [\"Sex == 'male'\", \"Age > 20\"])\n",
+    "checkSurvived(full_data, [\"Sex == 'male'\", \"Age >= 10\", \"Age < 30\"])\n",
+    "t = checkSurvived(full_data, [\"Sex == 'male'\", \"Age >= 10\", \"Age < 30\", \"Pclass == 3\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After exploring the survival statistics visualization, fill in the missing code below so that the function will make your prediction.  \n",
+    "Make sure to keep track of the various features and conditions you tried before arriving at your final prediction model.  \n",
+    "**Hint:** You can start your implementation of this function using the prediction code you wrote earlier from `predictions_2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions have an accuracy of 80.58%.\n",
+      "('nb', 314, 'percent survived', 0.7420382165605095, \" in cat Sex == 'female'\")\n",
+      "('nb', 81, 'percent survived', 0.0, ' in cat Survived == 0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "def predictions_3(data):\n",
+    "    \"\"\" Model with multiple features. Makes a prediction with an accuracy of at least 80%. \"\"\"\n",
+    "    \n",
+    "    predictions = []\n",
+    "    test = []\n",
+    "    i = 0\n",
+    "    for _, passenger in data.iterrows():\n",
+    "        \n",
+    "        # Remove the 'pass' statement below \n",
+    "        # and write your prediction conditions here\n",
+    "        if passenger['Sex'] == 'female':\n",
+    "            if passenger[\"Pclass\"] == 3 and passenger[\"SibSp\"] >= 2:\n",
+    "                predictions.append(0)\n",
+    "            else:\n",
+    "                predictions.append(1)\n",
+    "        else:\n",
+    "            if passenger['Age'] < 10.0:\n",
+    "                predictions.append(1)\n",
+    "            else:\n",
+    "                predictions.append(0)\n",
+    "               \n",
+    "               \n",
+    "        i +=1           \n",
+    "    \n",
+    "    # Return our predictions\n",
+    "    return pd.Series(predictions), test\n",
+    "\n",
+    "# Make the predictions\n",
+    "predictions,test = predictions_3(full_data)\n",
+    "print accuracy_score(outcomes, predictions)\n",
+    "import pandas as pd\n",
+    "#testdata = checkSurvived(full_data, [\"Sex == 'male'\", \"Age >= 10\"])\n",
+    "testdata = checkSurvived(full_data, [\"Sex == 'female'\"])\n",
+    "\n",
+    "surv = checkSurvived(testdata, [\"Survived == 0\"])\n",
+    "\n",
+    "gb = [\"Pclass\", \"SibSp\", \"Parch\"]\n",
+    "dfall = pd.DataFrame(testdata).groupby(gb).count()\n",
+    "dfSurv = pd.DataFrame(surv).groupby(gb).count()\n",
+    "\n",
+    "#dfSurv / dfall\n",
+    "#dfSurv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4\n",
+    "*Describe the steps you took to implement the final prediction model so that it got an accuracy of at least 80%. What features did you look at? Were certain features more informative than others? Which conditions did you use to split the survival outcomes in the data? How accurate are your predictions?*  \n",
+    "**Hint:** Run the code cell below to see the accuracy of your predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions have an accuracy of 80.58%.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print accuracy_score(outcomes, predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Answer**: *I got an accuracy of 80.58%. Initially i used the filter function to check various features and compute the percent survived. However, to get a better idea of cells of survival / death, I then moved to using group by count to compare dataframes for dead against all female. I then tried to find cells that gave more than 50% dead females. One such area of cells gave me the result - see code. Turns out class 3 with more than 1 increases your probability of death as a woman.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "After several iterations of exploring and conditioning on the data, you have built a useful algorithm for predicting the survival of each passenger aboard the RMS Titanic. The technique applied in this project is a manual implementation of a simple machine learning model, the *decision tree*. A decision tree splits a set of data into smaller and smaller groups (called *nodes*), by one feature at a time. Each time a subset of the data is split, our predictions become more accurate if each of the resulting subgroups are more homogeneous (contain similar labels) than before. The advantage of having a computer do things for us is that it will be more exhaustive and more precise than our manual exploration above. [This link](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) provides another introduction into machine learning using a decision tree.\n",
+    "\n",
+    "A decision tree is just one of many models that come from *supervised learning*. In supervised learning, we attempt to use features of the data to predict or model things with objective outcome labels. That is to say, each of our data points has a known outcome value, such as a categorical, discrete label like `'Survived'`, or a numerical, continuous value like predicting the price of a house.\n",
+    "\n",
+    "### Question 5\n",
+    "*Think of a real-world scenario where supervised learning could be applied. What would be the outcome variable that you are trying to predict? Name two features about the data used in this scenario that might be helpful for making the predictions.*  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Answer**: *I could use supervised learning on a set of bonds. The outcome would be outperformance of a bond against the benchmark index by 3 % over 1 year. Two features that might be helpful to make this prediction is the credit rating and expected remaining life of a bond.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
+    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}