diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..c1832336 --- /dev/null +++ b/.gitignore @@ -0,0 +1,19 @@ +# file extensions +**/*.pyc +**/*.h5 +**/*.p +!test/test_data/*.p +*.swp +.coverage + +# folder +build/* +dist/* +neuralhydrology.egg-info/* +.vscode/* +.idea/* +runs/* +configs/* +.ipynb_checkpoints/* +data/* +docs/build/* diff --git a/.style.yapf b/.style.yapf new file mode 100644 index 00000000..20617f1f --- /dev/null +++ b/.style.yapf @@ -0,0 +1,3 @@ +[style] +column_limit = 120 +based_on_style = google \ No newline at end of file diff --git a/CODEOWNERS b/CODEOWNERS new file mode 100644 index 00000000..b1693b31 --- /dev/null +++ b/CODEOWNERS @@ -0,0 +1 @@ +* @kratzert diff --git a/README.md b/README.md new file mode 100644 index 00000000..3b0cbf55 --- /dev/null +++ b/README.md @@ -0,0 +1,118 @@ +![#](docs/source/_static/img/neural-hyd-logo-black.png) + +Python library to train neural networks with a strong focus on hydrological applications. + +This package has been used extensively in research over the last year and was used in various academic publications. +The core idea of this package is modularity in all places to allow easy integration of new datasets, new model +architectures or any training related aspects (e.g. loss functions, optimizer, regularization). +One of the core concepts of this code base are configuration files, which lets anyone train neural networks without +touching the code itself. The `NeuralHydrology` package is build on top of the deep learning framework +[Pytorch](https://pytorch.org/), since it has proven to be the most flexible and useful for research purposes. + +We (AI for Earth Science group at Institute for Machine Learning, Johannes Kepler University, Linz, Austria) are using +this code in our day-to-day research and will continue to integrate our new research findings into this public repository. + +**Note:** We will gradually add more examples/documentation over the next couple of days/weeks. + +- Documentation: [neuralhydrology.readthedocs.io](neuralhydrology.readthedocs.io) +- Research Blog: [neuralhydrology.github.io](neuralhydrology.github.io) +- Bug reports/Feature requests [https://github.com/neuralhydrology/neuralhydrology/issues](https://github.com/neuralhydrology/neuralhydrology/issues) + +# Getting started + +## Requirements + +We recommend to use Anaconda/Miniconda. With one of the two installed, a dedicated environment with all requirements +installed can be set up from the environment files provided in +[environments](https://github.com/neuralhydrology/neuralhydrology/environments). + +If you have no CUDA capable GPU available run + +```bash +conda env create -f environments/environment_cpu.yml +``` + +With a CUDA capable GPU available, check which CUDA version your GPU supports and then run e.g. (for CUDA 10.2) + +```bash +conda env create -f environments/environment_cuda10_2.yml +``` + +If neither Minicoda/Anaconda are available, make sure to Python environment with all packages installed that are listed +in one of the environment files. + +## Installation + +For now download or clone the repository to your local machine and install a local, editable copy. +This is a good idea if you want to edit the ``neuralhydrology`` code (e.g., adding new models or datasets).:: + +```bash + git clone https://github.com/neuralhydrology/neuralhydrology.git + cd neuralhydrology + pip install -e . +``` +Besides adding the package to your Python environment, it will also add three bash scripts: +`nh-run`, `nh-run-scheduler` and `nh-results-ensemble`. For details, see below. + + +## Data + +Training and evaluating models requires a dataset. +If you're unsure where to start, a common dataset is CAMELS US, available at +[CAMELS US (NCAR)](https://ral.ucar.edu/solutions/products/camels). +Download the "CAMELS time series meteorology, observed flow, meta data" and place the actual data folder +(`basin_dataset_public_v1p2`) in a directory. +This directory will be referred to as the "data directory", or `data_dir`. + +## Configuration file + +One of the core concepts of this package is the usage of configuration files (`.yml`). Basically, all configurations +required to train a neural network can be specified via these configuration files and no code has to be touched. +Training a model does require a `.yml` file that specifies the run configuration. We will add a detailed explanation +for within the next weeks that explains the config files and arguments in more detail. For now refer to the +[example config](https://github.com/neuralhydrology/neuralhydrology/blob/master/examples/config.yml.example) for a full +list of all available arguments (with inline documentation). For an example of a configuration file that can be used to +train a standard LSTM for a single CAMELS US basin, check +[1_basin_config.yml](https://github.com/neuralhydrology/neuralhydrology/blob/master/examples/1_basin_config.yml.example). + +## Train a model + +To train a model, prepare a configuration file, then run:: + +```bash + nh-run train --config-file /path/to/config.yml +``` +If you want to train multiple models, you can make use of the ``nh-run-scheduler`` command. +Place all configs in a folder, then run:: +```bash + nh-run-scheduler train --config-dir /path/to/config_dir/ --runs-per-gpu X --gpu-ids Y +``` +With X, you can specify how many models should be trained on parallel on a single GPU. +With Y, you can specify which GPUs to use for training (use the id as specified in ``nvidia-smi``). + +## Evaluate a model + +To evaluate a trained model on the test set, run:: + + nh-run evaluate --run-dir /path/to/run_dir/ + +If the optional argument ``--epoch N`` (where N is the epoch to evaluate) is not specified, +the weights of the last epoch are used. You can also use ``--period `` if you want to evaluate the model on the +train period ``--period train``) or validation period (``--period validation``) + +To evaluate all runs in a specific directory you can, similarly to training, run:: + + nh-run-scheduler evaluate --run-dir /path/to/config_dir/ --runs-per-gpu X --gpu-ids Y + + +To merge the predictons of a number of runs (stored in ``$DIR1``, ...) into one averaged ensemble, +use the ``nh-results-ensemble`` script:: + + nh-results-ensemble --run-dirs $DIR1 $DIR2 ... --save-file /path/to/target/file.p --metrics NSE MSE ... + +``--metrics`` specifies which metrics will be calculated for the averaged predictions. + +# Contact + +If you have any questions regarding the usage of this repository, feature requests or comments, please open an issue. +You can also reach out to Frederik Kratzert (kratzert(at)ml.jku.at) by email. \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d0c3cbf1 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..6247f7e2 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/source/_static/img/neural-hyd-logo-black.png b/docs/source/_static/img/neural-hyd-logo-black.png new file mode 100644 index 00000000..745a3564 Binary files /dev/null and b/docs/source/_static/img/neural-hyd-logo-black.png differ diff --git a/docs/source/_static/img/neural-hyd-logo-white.png b/docs/source/_static/img/neural-hyd-logo-white.png new file mode 100644 index 00000000..d04b9f9d Binary files /dev/null and b/docs/source/_static/img/neural-hyd-logo-white.png differ diff --git a/docs/source/api/neuralhydrology.data.basedataset.rst b/docs/source/api/neuralhydrology.data.basedataset.rst new file mode 100644 index 00000000..8aaf33f1 --- /dev/null +++ b/docs/source/api/neuralhydrology.data.basedataset.rst @@ -0,0 +1,7 @@ +BaseDataset +=========== + +.. automodule:: neuralhydrology.data.basedataset + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/api/neuralhydrology.data.camelsgb.rst b/docs/source/api/neuralhydrology.data.camelsgb.rst new file mode 100644 index 00000000..dffefa3c --- /dev/null +++ b/docs/source/api/neuralhydrology.data.camelsgb.rst @@ -0,0 +1,7 @@ +CamelsGB +======== + +.. automodule:: neuralhydrology.data.camelsgb + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/api/neuralhydrology.data.camelsus.rst b/docs/source/api/neuralhydrology.data.camelsus.rst new file mode 100644 index 00000000..b3540506 --- /dev/null +++ b/docs/source/api/neuralhydrology.data.camelsus.rst @@ -0,0 +1,7 @@ +CamelsUS +======== + +.. automodule:: neuralhydrology.data.camelsus + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.data.caravan.rst b/docs/source/api/neuralhydrology.data.caravan.rst new file mode 100644 index 00000000..ef3bab55 --- /dev/null +++ b/docs/source/api/neuralhydrology.data.caravan.rst @@ -0,0 +1,7 @@ +Caravan +======= + +.. automodule:: neuralhydrology.data.caravan + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.data.climateindices.rst b/docs/source/api/neuralhydrology.data.climateindices.rst new file mode 100644 index 00000000..253ed06c --- /dev/null +++ b/docs/source/api/neuralhydrology.data.climateindices.rst @@ -0,0 +1,7 @@ +climateindices +============== + +.. automodule:: neuralhydrology.data.climateindices + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.data.dischargeinput.rst b/docs/source/api/neuralhydrology.data.dischargeinput.rst new file mode 100644 index 00000000..4f470eeb --- /dev/null +++ b/docs/source/api/neuralhydrology.data.dischargeinput.rst @@ -0,0 +1,7 @@ +dischargeinput +============== + +.. automodule:: neuralhydrology.data.dischargeinput + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.data.hourlycamelsus.rst b/docs/source/api/neuralhydrology.data.hourlycamelsus.rst new file mode 100644 index 00000000..7876745f --- /dev/null +++ b/docs/source/api/neuralhydrology.data.hourlycamelsus.rst @@ -0,0 +1,7 @@ +HourlyCamelsUS +============== + +.. automodule:: neuralhydrology.data.hourlycamelsus + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/api/neuralhydrology.data.pet.rst b/docs/source/api/neuralhydrology.data.pet.rst new file mode 100644 index 00000000..7c05d758 --- /dev/null +++ b/docs/source/api/neuralhydrology.data.pet.rst @@ -0,0 +1,7 @@ +pet +=== + +.. automodule:: neuralhydrology.data.pet + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.data.rst b/docs/source/api/neuralhydrology.data.rst new file mode 100644 index 00000000..8eed95f4 --- /dev/null +++ b/docs/source/api/neuralhydrology.data.rst @@ -0,0 +1,20 @@ +nh.data +======= + +.. automodule:: neuralhydrology.data + :members: + :undoc-members: + :show-inheritance: + +.. toctree:: + :maxdepth: 4 + + neuralhydrology.data.basedataset + neuralhydrology.data.camelsus + neuralhydrology.data.hourlycamelsus + neuralhydrology.data.camelsgb + neuralhydrology.data.caravan + neuralhydrology.data.climateindices + neuralhydrology.data.dischargeinput + neuralhydrology.data.pet + neuralhydrology.data.utils diff --git a/docs/source/api/neuralhydrology.data.utils.rst b/docs/source/api/neuralhydrology.data.utils.rst new file mode 100644 index 00000000..258676ee --- /dev/null +++ b/docs/source/api/neuralhydrology.data.utils.rst @@ -0,0 +1,7 @@ +utils +===== + +.. automodule:: neuralhydrology.data.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.evaluation.evaluate.rst b/docs/source/api/neuralhydrology.evaluation.evaluate.rst new file mode 100644 index 00000000..cedba9f1 --- /dev/null +++ b/docs/source/api/neuralhydrology.evaluation.evaluate.rst @@ -0,0 +1,7 @@ +evaluate +======== + +.. automodule:: neuralhydrology.evaluation.evaluate + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.evaluation.metrics.rst b/docs/source/api/neuralhydrology.evaluation.metrics.rst new file mode 100644 index 00000000..f182644f --- /dev/null +++ b/docs/source/api/neuralhydrology.evaluation.metrics.rst @@ -0,0 +1,7 @@ +metrics +======= + +.. automodule:: neuralhydrology.evaluation.metrics + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.evaluation.plots.rst b/docs/source/api/neuralhydrology.evaluation.plots.rst new file mode 100644 index 00000000..ace9d9eb --- /dev/null +++ b/docs/source/api/neuralhydrology.evaluation.plots.rst @@ -0,0 +1,7 @@ +plots +===== + +.. automodule:: neuralhydrology.evaluation.plots + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.evaluation.rst b/docs/source/api/neuralhydrology.evaluation.rst new file mode 100644 index 00000000..13a9302f --- /dev/null +++ b/docs/source/api/neuralhydrology.evaluation.rst @@ -0,0 +1,16 @@ +nh.evaluation +============= + +.. automodule:: neuralhydrology.evaluation + :members: + :undoc-members: + :show-inheritance: + +.. toctree:: + :maxdepth: 4 + + neuralhydrology.evaluation.evaluate + neuralhydrology.evaluation.metrics + neuralhydrology.evaluation.plots + neuralhydrology.evaluation.signatures + neuralhydrology.evaluation.tester diff --git a/docs/source/api/neuralhydrology.evaluation.signatures.rst b/docs/source/api/neuralhydrology.evaluation.signatures.rst new file mode 100644 index 00000000..bf60ea2c --- /dev/null +++ b/docs/source/api/neuralhydrology.evaluation.signatures.rst @@ -0,0 +1,7 @@ +signatures +========== + +.. automodule:: neuralhydrology.evaluation.signatures + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.evaluation.tester.rst b/docs/source/api/neuralhydrology.evaluation.tester.rst new file mode 100644 index 00000000..cb532d9c --- /dev/null +++ b/docs/source/api/neuralhydrology.evaluation.tester.rst @@ -0,0 +1,7 @@ +Tester +====== + +.. automodule:: neuralhydrology.evaluation.tester + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.basemodel.rst b/docs/source/api/neuralhydrology.modelzoo.basemodel.rst new file mode 100644 index 00000000..4211b281 --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.basemodel.rst @@ -0,0 +1,7 @@ +BaseModel +========= + +.. automodule:: neuralhydrology.modelzoo.basemodel + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.cudalstm.rst b/docs/source/api/neuralhydrology.modelzoo.cudalstm.rst new file mode 100644 index 00000000..8324a18e --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.cudalstm.rst @@ -0,0 +1,7 @@ +CudaLSTM +======== + +.. automodule:: neuralhydrology.modelzoo.cudalstm + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.ealstm.rst b/docs/source/api/neuralhydrology.modelzoo.ealstm.rst new file mode 100644 index 00000000..dc805b41 --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.ealstm.rst @@ -0,0 +1,7 @@ +EALSTM +====== + +.. automodule:: neuralhydrology.modelzoo.ealstm + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.embcudalstm.rst b/docs/source/api/neuralhydrology.modelzoo.embcudalstm.rst new file mode 100644 index 00000000..4027859c --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.embcudalstm.rst @@ -0,0 +1,7 @@ +EmbCudaLSTM +=========== + +.. automodule:: neuralhydrology.modelzoo.embcudalstm + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.fc.rst b/docs/source/api/neuralhydrology.modelzoo.fc.rst new file mode 100644 index 00000000..2bfa58a7 --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.fc.rst @@ -0,0 +1,7 @@ +FC +== + +.. automodule:: neuralhydrology.modelzoo.fc + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.head.rst b/docs/source/api/neuralhydrology.modelzoo.head.rst new file mode 100644 index 00000000..ab931b9a --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.head.rst @@ -0,0 +1,7 @@ +head +==== + +.. automodule:: neuralhydrology.modelzoo.head + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.lstm.rst b/docs/source/api/neuralhydrology.modelzoo.lstm.rst new file mode 100644 index 00000000..583059ee --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.lstm.rst @@ -0,0 +1,7 @@ +LSTM +==== + +.. automodule:: neuralhydrology.modelzoo.lstm + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.multifreqlstm.rst b/docs/source/api/neuralhydrology.modelzoo.multifreqlstm.rst new file mode 100644 index 00000000..40909071 --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.multifreqlstm.rst @@ -0,0 +1,7 @@ +MultiFreqLSTM +============= + +.. automodule:: neuralhydrology.modelzoo.multifreqlstm + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.odelstm.rst b/docs/source/api/neuralhydrology.modelzoo.odelstm.rst new file mode 100644 index 00000000..f6cb6969 --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.odelstm.rst @@ -0,0 +1,7 @@ +ODELSTM +======= + +.. automodule:: neuralhydrology.modelzoo.odelstm + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.modelzoo.rst b/docs/source/api/neuralhydrology.modelzoo.rst new file mode 100644 index 00000000..66e50c77 --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.rst @@ -0,0 +1,21 @@ +nh.modelzoo +=========== + +.. automodule:: neuralhydrology.modelzoo + :members: + :undoc-members: + :show-inheritance: + +.. toctree:: + :maxdepth: 4 + + neuralhydrology.modelzoo.basemodel + neuralhydrology.modelzoo.cudalstm + neuralhydrology.modelzoo.ealstm + neuralhydrology.modelzoo.embcudalstm + neuralhydrology.modelzoo.fc + neuralhydrology.modelzoo.head + neuralhydrology.modelzoo.lstm + neuralhydrology.modelzoo.multifreqlstm + neuralhydrology.modelzoo.odelstm + neuralhydrology.modelzoo.template diff --git a/docs/source/api/neuralhydrology.modelzoo.template.rst b/docs/source/api/neuralhydrology.modelzoo.template.rst new file mode 100644 index 00000000..05bb8433 --- /dev/null +++ b/docs/source/api/neuralhydrology.modelzoo.template.rst @@ -0,0 +1,7 @@ +Template +======== + +.. automodule:: neuralhydrology.modelzoo.template + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.nh_run.rst b/docs/source/api/neuralhydrology.nh_run.rst new file mode 100644 index 00000000..13c15cee --- /dev/null +++ b/docs/source/api/neuralhydrology.nh_run.rst @@ -0,0 +1,7 @@ +nh\_run +======= + +.. automodule:: neuralhydrology.nh_run + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.nh_run_scheduler.rst b/docs/source/api/neuralhydrology.nh_run_scheduler.rst new file mode 100644 index 00000000..fcbe2381 --- /dev/null +++ b/docs/source/api/neuralhydrology.nh_run_scheduler.rst @@ -0,0 +1,7 @@ +nh\_run\_scheduler +================== + +.. automodule:: neuralhydrology.nh_run_scheduler + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.rst b/docs/source/api/neuralhydrology.rst new file mode 100644 index 00000000..21746964 --- /dev/null +++ b/docs/source/api/neuralhydrology.rst @@ -0,0 +1,27 @@ +neuralhydrology +=============== + +.. automodule:: neuralhydrology + :members: + :undoc-members: + :show-inheritance: + + +.. toctree:: + :maxdepth: 4 + + neuralhydrology.data + neuralhydrology.evaluation + neuralhydrology.modelzoo + neuralhydrology.training + neuralhydrology.utils + +Main Entry Points +----------------- + +.. toctree:: + :maxdepth: 4 + + neuralhydrology.nh_run + neuralhydrology.nh_run_scheduler + neuralhydrology.utils.nh_results_ensemble diff --git a/docs/source/api/neuralhydrology.training.basetrainer.rst b/docs/source/api/neuralhydrology.training.basetrainer.rst new file mode 100644 index 00000000..801b89ba --- /dev/null +++ b/docs/source/api/neuralhydrology.training.basetrainer.rst @@ -0,0 +1,7 @@ +BaseTrainer +=========== + +.. automodule:: neuralhydrology.training.basetrainer + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.training.logger.rst b/docs/source/api/neuralhydrology.training.logger.rst new file mode 100644 index 00000000..5f65070b --- /dev/null +++ b/docs/source/api/neuralhydrology.training.logger.rst @@ -0,0 +1,7 @@ +Logger +====== + +.. automodule:: neuralhydrology.training.logger + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.training.loss.rst b/docs/source/api/neuralhydrology.training.loss.rst new file mode 100644 index 00000000..83b72843 --- /dev/null +++ b/docs/source/api/neuralhydrology.training.loss.rst @@ -0,0 +1,7 @@ +loss +==== + +.. automodule:: neuralhydrology.training.loss + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.training.regularization.rst b/docs/source/api/neuralhydrology.training.regularization.rst new file mode 100644 index 00000000..d769c99a --- /dev/null +++ b/docs/source/api/neuralhydrology.training.regularization.rst @@ -0,0 +1,7 @@ +regularization +============== + +.. automodule:: neuralhydrology.training.regularization + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.training.rst b/docs/source/api/neuralhydrology.training.rst new file mode 100644 index 00000000..86daf1f2 --- /dev/null +++ b/docs/source/api/neuralhydrology.training.rst @@ -0,0 +1,16 @@ +nh.training +=========== + +.. automodule:: neuralhydrology.training + :members: + :undoc-members: + :show-inheritance: + +.. toctree:: + :maxdepth: 4 + + neuralhydrology.training.basetrainer + neuralhydrology.training.logger + neuralhydrology.training.loss + neuralhydrology.training.regularization + neuralhydrology.training.train diff --git a/docs/source/api/neuralhydrology.training.train.rst b/docs/source/api/neuralhydrology.training.train.rst new file mode 100644 index 00000000..6cce4438 --- /dev/null +++ b/docs/source/api/neuralhydrology.training.train.rst @@ -0,0 +1,7 @@ +train +===== + +.. automodule:: neuralhydrology.training.train + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.utils.config.rst b/docs/source/api/neuralhydrology.utils.config.rst new file mode 100644 index 00000000..c4ad8a45 --- /dev/null +++ b/docs/source/api/neuralhydrology.utils.config.rst @@ -0,0 +1,7 @@ +config +====== + +.. automodule:: neuralhydrology.utils.config + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.utils.configutils.rst b/docs/source/api/neuralhydrology.utils.configutils.rst new file mode 100644 index 00000000..bfee364c --- /dev/null +++ b/docs/source/api/neuralhydrology.utils.configutils.rst @@ -0,0 +1,7 @@ +configutils +=========== + +.. automodule:: neuralhydrology.utils.configutils + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.utils.errors.rst b/docs/source/api/neuralhydrology.utils.errors.rst new file mode 100644 index 00000000..2d2f9dd2 --- /dev/null +++ b/docs/source/api/neuralhydrology.utils.errors.rst @@ -0,0 +1,7 @@ +errors +====== + +.. automodule:: neuralhydrology.utils.errors + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.utils.nh_results_ensemble.rst b/docs/source/api/neuralhydrology.utils.nh_results_ensemble.rst new file mode 100644 index 00000000..c3600db1 --- /dev/null +++ b/docs/source/api/neuralhydrology.utils.nh_results_ensemble.rst @@ -0,0 +1,7 @@ +nh\_results\_ensemble module +============================ + +.. automodule:: neuralhydrology.utils.nh_results_ensemble + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/api/neuralhydrology.utils.rst b/docs/source/api/neuralhydrology.utils.rst new file mode 100644 index 00000000..f3076b79 --- /dev/null +++ b/docs/source/api/neuralhydrology.utils.rst @@ -0,0 +1,15 @@ +nh.utils +======== + +.. automodule:: neuralhydrology.utils + :members: + :undoc-members: + :show-inheritance: + + +.. toctree:: + :maxdepth: 4 + + neuralhydrology.utils.config + neuralhydrology.utils.configutils + neuralhydrology.utils.errors diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 00000000..75568bd9 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,63 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('.')) +sys.path.insert(0, os.path.abspath('../../')) +# -- Project information ----------------------------------------------------- + +project = 'NeuralHydrology' +copyright = '2020, Frederik Kratzert' +author = 'Frederik Kratzert' + +# The full version, including alpha/beta/rc tags +release = '0.9.0-beta' + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', # autodocument + 'sphinx.ext.napoleon', # google and numpy doc string support + 'sphinx.ext.mathjax' # latex rendering of equations using MathJax +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# -- Napoleon autodoc options ------------------------------------------------- +napoleon_numpy_docstring = True + +# -- Other settings ----------------------------------------------------------- + +# Path to logo image file +html_logo = '_static/img/neural-hyd-logo-white.png' diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 00000000..c57b6085 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,15 @@ +.. NeuralHydrology documentation master file, created by + sphinx-quickstart on Mon Aug 17 14:00:15 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to NeuralHydrology's documentation! +=========================================== + +.. toctree:: + :maxdepth: 5 + :caption: Contents: + + usage/quickstart + usage/models + api/neuralhydrology diff --git a/docs/source/usage/models.rst b/docs/source/usage/models.rst new file mode 100644 index 00000000..567b77bb --- /dev/null +++ b/docs/source/usage/models.rst @@ -0,0 +1,108 @@ +Models +====== + +The following section gives an overview over all implemented models. + +CudaLSTM +-------- +:py:class:`neuralhydrology.modelzoo.cudalstm.CudaLSTM` is a network using the standard PyTorch LSTM implementation. +All features (``x_d``, ``x_s``, ``x_one_hot``) are concatenated and passed at each time step. +Initial forget gate bias can be set (in config.yml) and will be set during model initialization. + +EA-LSTM +------- +:py:class:`neuralhydrology.modelzoo.ealstm.EALSTM` is an implementation of the Entity-Aware LSTM, as used +in the 2019 HESS paper. The static features (``x_s`` and/or ``x_one_hot``) are used to compute the input gate +activations, while ``x_d`` is used in all other gates of the network. +Initial forget gate bias can be set, and if ``embedding_hiddens`` is passed, the input gate consists of the so-defined +FC network and not a single linear layer. + +LSTM +---- +:py:class:`neuralhydrology.modelzoo.lstm.LSTM` is an own LSTM implementation. +Momentarily, the only advantage compared to CudaLSTM is the return of the entire cell state array. +This class will be most likely adapted/changed in the near future to provide much more flexibility for various settings. + +EmbCudaLSTM +----------- +:py:class:`neuralhydrology.modelzoo.embcudalstm.EmbCudaLSTM` is similar to `CudaLSTM`_, +with the only difference that static inputs (``x_s`` and/or ``x_one_hot``) are passed through an embedding network +(defined, for instance, by ``embedding_hiddens``) before being concatenated to the dynamic inputs ``x_d`` +at each time step. + + +Implementing a new model +------------------------ +The listing below shows the skeleton of a template model you can use to start implementing your own model. +Once you have implemented your model, make sure to modify :py:func:`neuralhydrology.modelzoo.__init__.get_model`. +Furthermore, make sure to select a *unique* model abbreviation that will be used to specify the model in the config.yml +files. + +:: + + from typing import Dict + + import torch + + from neuralhydrology.modelzoo.basemodel import BaseModel + + + class TemplateModel(BaseModel): + + def __init__(self, cfg: dict): + """Initialize the model + + Each model receives as only input the config dictionary. From this, the entire model has to be implemented in + this class (with potential use of other modules, such as FC from fc.py). So this class will get the model inputs + and has to return the predictions. + + Each Model inherits from the BaseModel, which implements some universal functionality. The basemodel also + defines the output_size, which can be used here as a given attribute (self.output_size) + + Parameters + ---------- + cfg : dict + Configuration of the run, read from the config file with some additional keys (such as number of basins). + """ + super(TemplateModel, self).__init__(cfg=cfg) + + ########################### + # Create model parts here # + ########################### + + def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + """Forward pass through the model + + By convention, each forward pass has to accept a dict of input tensors. Usually, this dict contains 'x_d' and, + possibly, x_s and x_one_hot. If x_d and x_s are available at multiple frequencies, the keys 'x_d' and 'x_s' + have frequency suffixes such as 'x_d_1H' for hourly data. + Furthermore, by definition, each model has to return a dict containing the network predictions in 'y_hat', + potentially in addition to other keys. LSTM-based models should stick to the convention to return (at least) + the following three tensors: y_hat, h_n, c_n (or, in the multi-frequency case, y_hat_1H, y_hat_1D, etc.). + + Parameters + ---------- + data : Dict[str, torch.Tensor] + Dictionary with tensors + - x_d of shape [batch size, sequence length, features] containing the dynamic input data. + - x_s of shape [batch size, features] containing static input features. These are the concatenation + of what is defined in the config under camels_attributes and static_inputs. In case not a single + camels attribute or static input feature is defined in the config, x_s will not be present. + - x_one_hot of shape [batch size, number of basins] containing the one hot encoding of the basins. + In case 'use_basin_id_encoding' is set to False in the config, x_one_hot will not be present. + Note: If the input data are available at multiple frequencies (via use_frequencies), each input tensor + will have a suffix "_{freq}" indicating the tensor's frequency. + + Returns + ------- + The network prediction has to be returned under the dictionary key 'y_hat' (or, if multiple frequencies are + predicted, 'y_hat_{freq}'. Furthermore, make sure to return predictions for each time step, even if you want + to train sequence-to-one. Which predictions are used for training the network is controlled in the train_epoch() + function in neuralhydrology/training/basetrainer.py. Other return values should be the hidden states as 'h_n' and cell + states 'c_n'. Further return values are possible. + """ + ############################### + # Implement forward pass here # + ############################### + pass + diff --git a/docs/source/usage/quickstart.rst b/docs/source/usage/quickstart.rst new file mode 100644 index 00000000..25c396c5 --- /dev/null +++ b/docs/source/usage/quickstart.rst @@ -0,0 +1,63 @@ +Quick Start +============ + +Installation +------------ +The neuralhydrology project is available on PyPI. +Hence, installation is as easy as:: + + pip install neuralhydrology + +Alternatively, you can clone the repository and install the local, editable copy. This is a good idea if you want to +edit the ``neuralhydrology`` code (e.g., adding new models or datasets).:: + + git clone https://github.com/kratzert/lstm_based_hydrology.git + cd lstm_based_hydrology + pip install -e . + + +Data +---- +Training and evaluating models requires a dataset. +If you're unsure where to start, a common dataset is CAMELS US, available at +`CAMELS US (NCAR) `_. +Download the "CAMELS time series meteorology, observed flow, meta data" and place the actual data folder +(``basin_dataset_public_v1p2``) in a directory. +This directory will be referred to as the "data directory", or ``data_dir``. + + +Training a model +---------------- +To train a model, prepare a configuration file, then run:: + + nh-run train --config-file /path/to/config.yml + +If you want to train multiple models, you can make use of the ``nh-run-scheduler`` command. +Place all configs in a folder, then run:: + + nh-run-scheduler --config-dir /path/to/config_dir/ --runs-per-gpu X --gpu-ids Y + +With X, you can specify how many models should be trained on parallel on a single GPU. +With Y, you can specify which GPUs to use for training (use the id as specified in ``nvidia-smi``). + + +Evaluating a model +------------------ +To evaluate a trained model on the test set, run:: + + nh-run evaluate --run-dir /path/to/run_dir/ + +If the optional argument ``--epoch N`` (where N is the epoch to evaluate) is not specified, +the weights of the last epoch are used. + +To evaluate all runs in a specific directory you can, similarly to training, run:: + + nh-run-scheduler --mode evaluate --run-dir /path/to/config_dir/ --runs-per-gpu X --gpu-ids Y + + +To merge the predictons of a number of runs (stored in ``$DIR1``, ...) into one averaged ensemble, +use the ``nh-results-ensemble`` script:: + + nh-results-ensemble --run-dirs $DIR1 $DIR2 ... --save-file /path/to/target/file.p --metrics NSE MSE ... + +``--metrics`` specifies which metrics will be calculated for the averaged predictions. diff --git a/environments/environment_cpu.yml b/environments/environment_cpu.yml new file mode 100644 index 00000000..cb1e768b --- /dev/null +++ b/environments/environment_cpu.yml @@ -0,0 +1,30 @@ +name: neuralhydrology +channels: + - pytorch + - defaults + - anaconda +dependencies: + - bokeh + - cpuonly + - h5py + - hdf5 + - jupyter + - matplotlib + - netcdf4 + - numba + - pandas + - pip + - pytest + - pytest-cov + - python=3.7 + - pytorch + - ruamel.yaml + - scipy + - sphinx + - tqdm + - xarray + - yapf + - pip: + - tensorboard + - sphinx-rtd-theme + diff --git a/environments/environment_cuda10_2.yml b/environments/environment_cuda10_2.yml new file mode 100644 index 00000000..0b2f5a8f --- /dev/null +++ b/environments/environment_cuda10_2.yml @@ -0,0 +1,30 @@ +name: neuralhydrology +channels: + - pytorch + - defaults + - anaconda +dependencies: + - bokeh + - cudatoolkit=10.2 + - h5py + - hdf5 + - jupyter + - matplotlib + - netcdf4 + - numba + - pandas + - pip + - pytest + - pytest-cov + - python=3.7 + - pytorch + - ruamel.yaml + - scipy + - sphinx + - tqdm + - xarray + - yapf + - pip: + - tensorboard + - sphinx-rtd-theme + diff --git a/environments/environment_cuda9_2.yml b/environments/environment_cuda9_2.yml new file mode 100644 index 00000000..5e06c5ff --- /dev/null +++ b/environments/environment_cuda9_2.yml @@ -0,0 +1,30 @@ +name: neuralhydrology +channels: + - pytorch + - defaults + - anaconda +dependencies: + - bokeh + - cudatoolkit=9.2 + - h5py + - hdf5 + - jupyter + - matplotlib + - netcdf4 + - numba + - pandas + - pip + - pytest + - pytest-cov + - python=3.7 + - pytorch + - ruamel.yaml + - scipy + - sphinx + - tqdm + - xarray + - yapf + - pip: + - tensorboard + - sphinx-rtd-theme + diff --git a/examples/config.yml.example b/examples/config.yml.example new file mode 100644 index 00000000..b7b3e770 --- /dev/null +++ b/examples/config.yml.example @@ -0,0 +1,275 @@ +# --- Experiment configurations -------------------------------------------------------------------- + +# experiment name, used as folder name +experiment_name: development_run + +# place to store run directory (if empty runs are stored in code_dir/runs/) +run_dir: + +# files to specify training, validation and test basins (relative to code root or absolute path) +train_basin_file: ./data/8_basins_dev_set.txt +validation_basin_file: ./data/8_basins_dev_set.txt +test_basin_file: ./data/8_basins_dev_set.txt + +# training, validation and test time periods (format = 'dd/mm/yyyy') +train_start_date: '01/10/1999' +train_end_date: '30/09/2008' +validation_start_date: '01/10/1980' +validation_end_date: '30/09/1989' +test_start_date: '01/10/1989' +test_end_date: '30/09/1999' + +# if you want to use different (continuous or split) periods per basin (and period) define path to pickle files here. +per_basin_train_periods_file: +per_basin_validation_periods_file: +per_basin_test_periods_file: + +# fixed seed, leave empty to use a random seed +seed: + +# which GPU (id) to use [in format of cuda:0, cuda:1 etc, or cpu or None] +device: cuda:0 + +# --- Validation configuration --------------------------------------------------------------------- + +# specify after how many epochs to perform validation +validate_every: 1 + +# specify how many random basins to use for validation +validate_n_random_basins: 4 + +# By default, validation is cached (even is this argument is empty). Set to False, if you do not want to use it. +cache_validation_data: True + +# specify which metrics to calculate during validation (see neuralhydrology.evaluation.metrics) +# this can either be a list or a dictionary. If a dictionary is used, the inner keys must match the name of the +# target_variable specified below. Using dicts allows for different metrics per target variable. +metrics: +- NSE +- KGE +- Alpha-NSE +- Beta-NSE + +# --- Model configuration -------------------------------------------------------------------------- + +# base model type [lstm, ealstm, cudalstm, embcudalstm, multifreqlstm] +# (has to match the if statement in modelzoo/__init__.py) +model: cudalstm + +# path to weight file that should be used as initial weights. Leave empty to start from random weights +checkpoint_path: + +# prediction head [regression]. Define the head specific parameters below +head: regression + +# ----> Regression settings <---- +output_activation: linear + +# ----> Embedding network settings <---- + +# define number of neurons per layer in the FC network used as embedding/shortcut network +embedding_hiddens: +- 30 +- 20 +- 64 + +# activation function of embedding network (currently only tanh supported) +embedding_activation: tanh + +# dropout applied to embedding network +embedding_dropout: 0.0 + +# ----> MultiFreqLSTM settings <---- + +# Use an individual LSTM per frequencies (True) vs. use a single shared LSTM for all frequencies (False) +per_frequency_lstm: True + +# how to transfer states from lower to higher frequencies. One of [identity, linear, None]. +transfer_multifreq_states: + h: identity + c: identity + +# ----> ODE-LSTM settings <---- +# method to use to solve the ODE. One of [euler, rk4, heun] +ode_method: euler + +# number of iterations to break each ODE solving step into. +ode_num_unfolds: 4 + +# lowest frequency that will be used to randomly aggregate the first slice of the input sequence. +# See ODELSTM for more details on the frequency randomization. +ode_random_freq_lower_bound: 6D + +# ----> General settings <---- + +# Number of cell states of the LSTM +hidden_size: 64 + +# Initial bias value of the forget gate +initial_forget_bias: 3 + +# Dropout applied to the output of the LSTM +output_dropout: 0.4 + +# --- Training configuration ----------------------------------------------------------------------- + +# specify optimizer [Adam] +optimizer: Adam + +# specify loss [MSE, NSE, RMSE] +loss: MSE + +# add regularization terms. +# Options: tie_frequencies (couples the different frequencies' predictions in an MSE error term) +regularization: +#- tie_frequencies + +# specify learning rates to use starting at specific epochs (0 is the initial learning rate) +learning_rate: + 0: 1e-3 + 10: 5e-4 + 20: 1e-4 + +# Mini-batch size +batch_size: 256 + +# Number of training epochs +epochs: 30 + +# adds noise with given std to the labels during training. Leave empty or set to 0 if not used. +target_noise_std: 0.005 + +# If a value, clips the gradients during training to that norm. +clip_gradient_norm: 1 + +# Defines the time step frequencies to use (daily, hourly, ...). If used, predict_last_n and seq_length must be dicts. +# Use pandas frequency strings to define frequencies. Note: the strings need to include values, e.g. '1D' instead of 'D' +# https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html?highlight=frequency#timeseries-offset-aliases +#use_frequencies: +#- 1D +#- 1H + +# Subset of frequencies from use_frequencies that are "evaluation-only", i.e., the model will get input and produce +# output in the frequencies listed here, but they will not be considered in the calculation of loss and regularization +# terms. +#no_loss_frequencies: +#- 1H + +# Defines which time steps are used to calculate the loss. Can't be larger than seq_length. +# If use_frequencies is used, this needs to be a dict mapping each frequency to a predict_last_n-value, else an int. +predict_last_n: 1 + +# Length of the input sequence +# If use_frequencies is used, this needs to be a dict mapping each frequency to a seq_length, else an int. +seq_length: 365 + +# Number of parallel workers used in the data pipeline +num_workers: 16 + +# Log the training loss every n steps +log_interval: 5 + +# If true, writes logging results into tensorboard file +log_tensorboard: True + +# If a value and greater than 0, logs n random basins as figures during validation +log_n_figures: 2 + +# Save model weights every n epochs +save_weights_every: 1 + +# Store the results of the validation to disk +save_validation_results: False + + +# --- Data configurations -------------------------------------------------------------------------- + +# which data set to use [camels_us, camels_gb, global, hourly_camels_us] +dataset: camels_us + +# Path to data set root +data_dir: /datadisk/data/CAMELS + +# Set to True, if train data file should be save to disk. If empty or False, train data is not saved. +save_train_data: False + +# If existing train data file exist, specify path to the pickle file here +train_data_file: + +# Forcing product [daymet, maurer, maurer_extended, nldas, nldas_extended, nldas_hourly] +# can be either a list of forcings or a single forcing product +forcings: maurer_extended + +# variables to use as time series input (names match the data file column headers) +# Note: In case of multiple input forcing products, you have to append the forcing product behind +# each variable. E.g., 'prcp(mm/day)' of the daymet product is 'prcp(mm/day)_daymet' +# To use different dynamic inputs per frequency, this variable must contain a dict mapping each frequency to its +# list of variables. E.g., to use precipitation from daymet for daily and from nldas_hourly for hourly predictions: +# 1D: +# - prcp(mm/day)_daymet +# 1H: +# - total_precipitation_nldas_hourly +dynamic_inputs: +- prcp(mm/day) +- srad(W/m2) +- tmax(C) +- tmin(C) +- vp(Pa) + +# which columns to use as target +target_variables: +- QObs(mm/d) + +# clip negative predictions to zero for all variables listed below. Should be a list, even for single variables. +clip_target_to_zero: +- QObs(mm/d) + +zero_center_target: True + +# Which CAMELS attributes to use. Leave empty if none should be used +camels_attributes: +- elev_mean +- slope_mean +- area_gages2 +- frac_forest +- lai_max +- lai_diff +- gvf_max +- gvf_diff +- soil_depth_pelletier +- soil_depth_statsgo +- soil_porosity +- soil_conductivity +- max_water_content +- sand_frac +- silt_frac +- clay_frac +- carbonate_rocks_frac +- geol_permeability +- p_mean +- pet_mean +- aridity +- frac_snow +- high_prec_freq +- high_prec_dur +- low_prec_freq +- low_prec_dur + +# Path to pickle file(s) containing additional data. Each pickle file must contain a dictionary +# with one key for each basin and the value is a time indexed data frame, where each column is a +# feature. +# Convention: If a column is used as static input, the value to use for specific sample should be in +# same row (datetime) as the target discharge value. +additional_feature_files: + +# columns of the data frame to use as (additional) static inputs for each sample. Must be present in +# the above linked additional feature files. Leave empty to not use any +static_inputs: + +# whether to use basin id one hot encoding as (additional) static input +use_basin_id_encoding: False + + + + + diff --git a/neuralhydrology/__about__.py b/neuralhydrology/__about__.py new file mode 100644 index 00000000..ea68199a --- /dev/null +++ b/neuralhydrology/__about__.py @@ -0,0 +1 @@ +__version__ = "0.9.0-beta" diff --git a/neuralhydrology/__init__.py b/neuralhydrology/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/neuralhydrology/data/__init__.py b/neuralhydrology/data/__init__.py new file mode 100644 index 00000000..15f0d181 --- /dev/null +++ b/neuralhydrology/data/__init__.py @@ -0,0 +1,70 @@ +from neuralhydrology.data.basedataset import BaseDataset +from neuralhydrology.data.camelsus import CamelsUS +from neuralhydrology.data.camelsgb import CamelsGB +from neuralhydrology.data.hourlycamelsus import HourlyCamelsUS +from neuralhydrology.utils.config import Config + + +def get_dataset(cfg: Config, + is_train: bool, + period: str, + basin: str = None, + additional_features: list = [], + id_to_int: dict = {}, + scaler: dict = {}) -> BaseDataset: + """Get data set instance, depending on the run configuration. + + Currently implemented datasets are 'camels_gb', 'camels_us' and 'hourly_camels_us' + + Parameters + ---------- + cfg : Config + The run configuration. + is_train : bool + Defines if the dataset is used for training or evaluating. If True (training), means/stds for each feature + are computed and stored to the run directory. If one-hot encoding is used, the mapping for the one-hot encoding + is created and also stored to disk. If False, a `scaler` input is expected and similarly the `id_to_int` input + if one-hot encoding is used. + period : {'train', 'validation', 'test'} + Defines the period for which the data will be loaded + basin : str, optional + If passed, the data for only this basin will be loaded. Otherwise the basin(s) is(are) read from the appropriate + basin file, corresponding to the `period`. + additional_features : List[Dict[str, pd.DataFrame]], optional + List of dictionaries, mapping from a basin id to a pandas DataFrame. This DataFrame will be added to the data + loaded from the dataset and all columns are available as 'dynamic_inputs', 'static_inputs' and + 'target_variables' + id_to_int : Dict[str, int], optional + If the config argument 'use_basin_id_encoding' is True in the config and period is either 'validation' or + 'test', this input is required. It is a dictionary, mapping from basin id to an integer (the one-hot encoding). + scaler : Dict[str, Union[pd.Series, xarray.DataArray]], optional + If period is either 'validation' or 'test', this input is required. It contains the means and standard + deviations for each feature and is stored to the run directory during training (train_data/train_data_scaler.p) + + Returns + ------- + BaseDataset + A new data set instance, depending on the run configuration. + + Raises + ------ + NotImplementedError + If no data set class is implemented for the 'dataset' argument in the config. + """ + if cfg.dataset == "camels_us": + Dataset = CamelsUS + elif cfg.dataset == "camels_gb": + Dataset = CamelsGB + elif cfg.dataset == "hourly_camels_us": + Dataset = HourlyCamelsUS + else: + raise NotImplementedError(f"No dataset class implemented for dataset {cfg.dataset}") + + ds = Dataset(cfg=cfg, + is_train=is_train, + period=period, + basin=basin, + additional_features=additional_features, + id_to_int=id_to_int, + scaler=scaler) + return ds diff --git a/neuralhydrology/data/basedataset.py b/neuralhydrology/data/basedataset.py new file mode 100644 index 00000000..651c7ea9 --- /dev/null +++ b/neuralhydrology/data/basedataset.py @@ -0,0 +1,562 @@ +import logging +import pickle +import sys +import warnings +from typing import List, Dict, Union + +import numpy as np +import pandas as pd +import torch +import xarray +from numba import NumbaPendingDeprecationWarning +from numba import njit, prange +from torch.utils.data import Dataset +from tqdm import tqdm + +from neuralhydrology.data import utils +from neuralhydrology.utils.config import Config + +LOGGER = logging.getLogger(__name__) + + +class BaseDataset(Dataset): + """Base data set class to load and preprocess data. + + Use subclasses of this class for training/evaluating a model on a specific data set. E.g. use `CamelsUS` for the US + CAMELS data set and `CamelsGB` for the CAMELS GB data set. + + Parameters + ---------- + cfg : Config + The run configuration. + is_train : bool + Defines if the dataset is used for training or evaluating. If True (training), means/stds for each feature + are computed and stored to the run directory. If one-hot encoding is used, the mapping for the one-hot encoding + is created and also stored to disk. If False, a `scaler` input is expected and similarly the `id_to_int` input + if one-hot encoding is used. + period : {'train', 'validation', 'test'} + Defines the period for which the data will be loaded + basin : str, optional + If passed, the data for only this basin will be loaded. Otherwise, the basin(s) is(are) read from the + appropriate basin file, corresponding to the `period`. + additional_features : List[Dict[str, pd.DataFrame]], optional + List of dictionaries, mapping from a basin id to a pandas DataFrame. This DataFrame will be added to the data + loaded from the dataset and all columns are available as 'dynamic_inputs', 'static_inputs' and + 'target_variables' + id_to_int : Dict[str, int], optional + If the config argument 'use_basin_id_encoding' is True in the config and period is either 'validation' or + 'test', this input is required. It is a dictionary, mapping from basin id to an integer (the one-hot encoding). + scaler : Dict[str, Union[pd.Series, xarray.DataArray]], optional + If period is either 'validation' or 'test', this input is required. It contains the means and standard + deviations for each feature and is stored to the run directory during training (train_data/train_data_scaler.p) + """ + + def __init__(self, + cfg: Config, + is_train: bool, + period: str, + basin: str = None, + additional_features: List[Dict[str, pd.DataFrame]] = [], + id_to_int: Dict[str, int] = {}, + scaler: Dict[str, Union[pd.Series, xarray.DataArray]] = {}): + super(BaseDataset, self).__init__() + self.cfg = cfg + self.is_train = is_train + + if period not in ["train", "validation", "test"]: + raise ValueError("'period' must be one of 'train', 'validation' or 'test' ") + else: + self.period = period + + if period in ["validation", "test"]: + if not scaler: + raise ValueError("During evaluation of validation or test period, scaler dictionary has to be passed") + + if cfg.use_basin_id_encoding and not id_to_int: + raise ValueError("For basin id embedding, the id_to_int dictionary has to be passed anything but train") + + if basin is None: + self.basins = utils.load_basin_file(getattr(cfg, f"{period}_basin_file")) + else: + self.basins = [basin] + self.additional_features = additional_features + self.id_to_int = id_to_int + self.scaler = scaler + + # check and extract frequency information from config + self.frequencies = [] + self.seq_len = None + self._predict_last_n = None + self._initialize_frequency_configuration() + + # during training we log data processing with progress bars, but not during validation/testing + self._disable_pbar = cfg.verbose == 0 or not self.is_train + + # initialize class attributes that are filled in the data loading functions + self.x_d = {} + self.x_s = {} + self.attributes = {} + self.y = {} + self.per_basin_target_stds = {} + self.dates = {} + self.num_samples = 0 + self.one_hot = None + self.period_starts = {} # needed for restoring date index during evaluation + + # get the start and end date periods for each basin + self._get_start_and_end_dates() + + # if additional features files are passed in the config, load those files + if (not additional_features) and cfg.additional_feature_files: + self._load_additional_features() + + if cfg.use_basin_id_encoding: + if self.is_train: + # creates lookup table for the number of basins in the training set + self._create_id_to_int() + + # create empty tensor of the same length as basins in id to int lookup table + self.one_hot = torch.zeros(len(self.id_to_int), dtype=torch.float32) + + # load and preprocess data + self._load_data() + + if self.is_train: + self._dump_scaler() + + def __len__(self): + return self.num_samples + + def __getitem__(self, item: int) -> Dict[str, torch.Tensor]: + basin, indices = self.lookup_table[item] + + # for catchment attributes and one-hot-encoding we need the raw basin_id. + basin_id = basin.split('.')[0] + + sample = {} + for freq, seq_len, idx in zip(self.frequencies, self.seq_len, indices): + # if there's just one frequency, don't use suffixes. + freq_suffix = '' if len(self.frequencies) == 1 else f'_{freq}' + # slice until idx + 1 because slice-end is excluding + sample[f'x_d{freq_suffix}'] = self.x_d[basin][freq][idx - seq_len + 1:idx + 1] + sample[f'y{freq_suffix}'] = self.y[basin][freq][idx - seq_len + 1:idx + 1] + + # check for static inputs + static_inputs = [] + if self.attributes: + static_inputs.append(self.attributes[basin_id]) + if self.x_s: + static_inputs.append(self.x_s[basin][freq][idx]) + if static_inputs: + sample[f'x_s{freq_suffix}'] = torch.cat(static_inputs, dim=-1) + + if self.per_basin_target_stds: + sample['per_basin_target_stds'] = self.per_basin_target_stds[basin] + if self.one_hot is not None: + x_one_hot = self.one_hot.zero_() + x_one_hot[self.id_to_int[basin_id]] = 1 + sample['x_one_hot'] = x_one_hot + + return sample + + def _load_basin_data(self, basin: str) -> pd.DataFrame: + """This function has to return the data for the specified basin as a time-indexed pandas DataFrame""" + raise NotImplementedError + + def _load_attributes(self) -> pd.DataFrame: + """This function has to return the attributes in a basin-indexed DataFrame.""" + raise NotImplementedError + + def _create_id_to_int(self): + self.id_to_int = {b: i for i, b in enumerate(np.random.permutation(self.basins))} + + # dump id_to_int dictionary into run directory for validation + file_path = self.cfg.train_dir / "id_to_int.p" + file_path.parent.mkdir(parents=True, exist_ok=True) + with file_path.open("wb") as fp: + pickle.dump(self.id_to_int, fp) + + def _dump_scaler(self): + # dump scaler dictionary into run directory for validation + file_path = self.cfg.train_dir / "train_data_scaler.p" + file_path.parent.mkdir(parents=True, exist_ok=True) + with file_path.open("wb") as fp: + pickle.dump(self.scaler, fp) + + def _get_start_and_end_dates(self): + + # if no per-basin periods file exist, same periods are taken for all basins from the config + if getattr(self.cfg, f"per_basin_{self.period}_periods_file") is None: + + # even if single dates, everything is mapped to lists, so we can iterate over them + if isinstance(getattr(self.cfg, f'{self.period}_start_date'), list): + if self.period != "train": + raise ValueError("Evaluation on split periods currently not supported") + start_dates = getattr(self.cfg, f'{self.period}_start_date') + else: + start_dates = [getattr(self.cfg, f'{self.period}_start_date')] + if isinstance(getattr(self.cfg, f'{self.period}_end_date'), list): + end_dates = getattr(self.cfg, f'{self.period}_end_date') + else: + end_dates = [getattr(self.cfg, f'{self.period}_end_date')] + + self.dates = {b: {'start_dates': start_dates, 'end_dates': end_dates} for b in self.basins} + + # read periods from file + else: + with open(getattr(self.cfg, f"per_basin_{self.period}_periods_file"), 'rb') as fp: + self.dates = pickle.load(fp) + + def _load_additional_features(self): + for file in self.cfg.additional_feature_files: + with open(file, "rb") as fp: + self.additional_features.append(pickle.load(fp)) + + def _load_or_create_xarray_dataset(self) -> xarray.Dataset: + # if no netCDF file is passed, data set is created from raw basin files + if (self.cfg.train_data_file is None) or (not self.is_train): + data_list = [] + + # list of columns to keep, everything else will be removed to reduce memory footprint + keep_cols = self.cfg.target_variables + self.cfg.static_inputs + if isinstance(self.cfg.dynamic_inputs, list): + keep_cols += self.cfg.dynamic_inputs + else: + # keep all frequencies' dynamic inputs + keep_cols += [i for inputs in self.cfg.dynamic_inputs.values() for i in inputs] + # make sure that even inputs that are used in multiple frequencies occur only once in the df + keep_cols = list(sorted(set(keep_cols))) + + if not self._disable_pbar: + LOGGER.info("Loading basin data into xarray data set.") + for basin in tqdm(self.basins, disable=self._disable_pbar, file=sys.stdout): + df = self._load_basin_data(basin) + + # add columns from dataframes passed as additional data files + df = pd.concat([df, *[d[basin] for d in self.additional_features]], axis=1) + + # remove unnecessary columns + df = df[keep_cols] + + # make end_date the last second of the specified day, such that the + # dataset will include all hours of the last day, not just 00:00. + start_dates = self.dates[basin]["start_dates"] + end_dates = [date + pd.Timedelta(days=1, seconds=-1) for date in self.dates[basin]["end_dates"]] + + native_frequency = utils.infer_frequency(df.index) + if not self.frequencies: + self.frequencies = [native_frequency] # use df's native resolution by default + if any([pd.to_timedelta(freq) < pd.to_timedelta(native_frequency) for freq in self.frequencies]): + raise ValueError(f'Frequency is higher than native data frequency {native_frequency}.') + + # get maximum warmup-offset across all frequencies + offset = max([(self.seq_len[i] - self._predict_last_n[i]) * pd.to_timedelta(freq) + for i, freq in enumerate(self.frequencies)]) + + # create xarray data set for each period slice of the specific basin + for i, (start_date, end_date) in enumerate(zip(start_dates, end_dates)): + # add warmup period, so that we can make prediction at the first time step specified by period + warmup_start_date = start_date - offset + df_sub = df[warmup_start_date:end_date] + + # make sure the df covers the full date range from warmup_start_date to end_date, filling any gaps + # with NaNs. This may increase runtime, but is a very robust way to make sure dates and predictions + # keep in sync. In training, the introduced NaNs will be discarded, so this only affects evaluation. + full_range = pd.date_range(start=warmup_start_date, end=end_date, freq=native_frequency) + df_sub = df_sub.reindex(pd.DatetimeIndex(full_range, name=df_sub.index.name)) + + # as double check, set all targets before period start to NaN + df_sub.loc[df_sub.index < start_date, self.cfg.target_variables] = np.nan + + # For multiple slices per basin, a number is added to the basin string starting from the 2nd slice + xr = xarray.Dataset.from_dataframe(df_sub) + basin_str = basin if i == 0 else f"{basin}.{i}" + xr = xr.assign_coords({'basin': basin_str}) + data_list.append(xr.astype(np.float32)) + + # create one large dataset that has two coordinates: datetime and basin + xr = xarray.concat(data_list, dim="basin") + + if self.is_train and self.cfg.save_train_data: + self._save_xarray_dataset(xr) + + else: + with self.cfg.train_data_file.open("rb") as fp: + d = pickle.load(fp) + xr = xarray.Dataset.from_dict(d) + if not self.frequencies: + native_frequency = utils.infer_frequency(xr["date"].values) + self.frequencies = [native_frequency] + + return xr + + def _save_xarray_dataset(self, xr: xarray.Dataset): + """Store newly created train data set to disk""" + file_path = self.cfg.train_dir / "train_data.p" + + file_path.parent.mkdir(parents=True, exist_ok=True) + + # netCDF has issues with storing variables with '/' in the var names. Solution: convert to dict, then pickle + with file_path.open("wb") as fp: + pickle.dump(xr.to_dict(), fp) + + def _calculate_per_basin_std(self, xr: xarray.Dataset): + basin_coordinates = xr["basin"].values.tolist() + if not self._disable_pbar: + LOGGER.info("Calculating target variable stds per basin") + for basin in tqdm(self.basins, file=sys.stdout, disable=self._disable_pbar): + coords = [b for b in basin_coordinates if b.startswith(basin)] + if len(coords) > 0: + obs = np.concatenate([xr.sel(basin=coord)[self.cfg.target_variables].to_array() for coord in coords]) + if np.sum(~np.isnan(obs)) > 2: + # calculate std for each target + per_basin_target_stds = torch.tensor([np.nanstd(obs, axis=1)], dtype=torch.float32) + # we store duplicates of the std for each coordinate of the same basin, so we are faster in getitem + for coord in coords: + self.per_basin_target_stds[coord] = per_basin_target_stds + + def _create_lookup_table(self, xr: xarray.Dataset): + lookup = [] + if not self._disable_pbar: + LOGGER.info("Create lookup table and convert to pytorch tensor") + for basin in tqdm(self.basins, file=sys.stdout, disable=self._disable_pbar): + + # store data of each frequency as numpy array of shape [time steps, features] + x_d, x_s, y = {}, {}, {} + + # keys: frequencies, values: array mapping each lowest-frequency + # sample to its corresponding sample in this frequency + frequency_maps = {} + lowest_freq = utils.sort_frequencies(self.frequencies)[0] + + # converting from xarray to pandas DataFrame because resampling is much faster in pandas. + df_native = xr.sel(basin=basin).to_dataframe() + for freq in self.frequencies: + if isinstance(self.cfg.dynamic_inputs, list): + dynamic_cols = self.cfg.dynamic_inputs + else: + dynamic_cols = self.cfg.dynamic_inputs[freq] + + df_resampled = df_native[dynamic_cols + self.cfg.target_variables + + self.cfg.static_inputs].resample(freq).mean() + x_d[freq] = df_resampled[dynamic_cols].values + y[freq] = df_resampled[self.cfg.target_variables].values + if self.cfg.static_inputs: + x_s[freq] = df_resampled[self.cfg.static_inputs].values + + # number of frequency steps in one lowest-frequency step + frequency_factor = pd.to_timedelta(lowest_freq) // pd.to_timedelta(freq) + # array position i is the last entry of this frequency that belongs to the lowest-frequency sample i. + frequency_maps[freq] = np.arange(len(df_resampled) // frequency_factor) \ + * frequency_factor + (frequency_factor - 1) + + # store first date of sequence to be able to restore dates during inference + if not self.is_train: + self.period_starts[basin] = pd.to_datetime(xr.sel(basin=basin)["date"].values[0]) + + # we can ignore the deprecation warning about lists because we don't use the passed lists + # after the validate_samples call. The alternative numba.typed.Lists is still experimental. + with warnings.catch_warnings(): + warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning) + + # checks inputs and outputs for each sequence. valid: flag = 1, invalid: flag = 0 + # manually unroll the dicts into lists to make sure the order of frequencies is consistent. + # during inference, we want all samples with sufficient history (even if input is NaN), so + # we pass x_d, x_s, y as None. + flag = validate_samples(x_d=[x_d[freq] for freq in self.frequencies] if self.is_train else None, + x_s=[x_s[freq] for freq in self.frequencies] if self.is_train and x_s else None, + y=[y[freq] for freq in self.frequencies] if self.is_train else None, + frequency_maps=[frequency_maps[freq] for freq in self.frequencies], + seq_length=self.seq_len, + predict_last_n=self._predict_last_n) + valid_samples = np.argwhere(flag == 1) + for f in valid_samples: + # store pointer to basin and the sample's index in each frequency + lookup.append((basin, [frequency_maps[freq][int(f)] for freq in self.frequencies])) + + self.x_d[basin] = {freq: torch.from_numpy(_x_d.astype(np.float32)) for freq, _x_d in x_d.items()} + self.y[basin] = {freq: torch.from_numpy(_y.astype(np.float32)) for freq, _y in y.items()} + if x_s: + self.x_s[basin] = {freq: torch.from_numpy(_x_s.astype(np.float32)) for freq, _x_s in x_s.items()} + + self.lookup_table = {i: elem for i, elem in enumerate(lookup)} + self.num_samples = len(self.lookup_table) + + def _load_hydroatlas_attributes(self): + if self.is_train: + # sanity check attributes for NaN in per-feature standard deviation + utils.attributes_sanity_check(data_dir=self.cfg.data_dir, + attribute_set="hydroatlas", + basins=self.basins, + attribute_list=self.cfg.hydroatlas_attributes) + + df = utils.load_hydroatlas_attributes(self.cfg.data_dir, basins=self.basins) + + # remove all attributes not defined in the config + drop_cols = [c for c in df.columns if c not in self.cfg.hydroatlas_attributes] + df = df.drop(drop_cols, axis=1) + return df + + def _load_combined_attributes(self): + """This function loads data set specific attributes and combines them with hydroatlas attributes""" + dfs = [] + + # load dataset specific attributes from the subclass + df = self._load_attributes() + if df is not None: + dfs.append(df) + + # Hydroatlas attributes can be used everywhere + if self.cfg.hydroatlas_attributes: + dfs.append(self._load_hydroatlas_attributes()) + + if dfs: + # combine all attributes into a single dataframe + df = pd.concat(dfs, axis=1) + + # fix the order of the columns to be alphabetically + df = df.sort_index(axis=1) + + # calculate statistics and normalize features + if self.is_train: + self.scaler["attribute_means"] = df.mean() + self.scaler["attribute_stds"] = df.std() + + if any([k.startswith("camels_attr") for k in self.scaler.keys()]): + LOGGER.warning( + "Deprecation warning: Using old scaler files won't be supported in the upcoming release.") + + # Here we assume that only camels attributes are used + df = (df - self.scaler['camels_attr_means']) / self.scaler["camels_attr_stds"] + else: + df = (df - self.scaler['attribute_means']) / self.scaler["attribute_stds"] + + # preprocess each basin feature vector as pytorch tensor + for basin in self.basins: + attributes = df.loc[df.index == basin].values.flatten() + self.attributes[basin] = torch.from_numpy(attributes.astype(np.float32)) + + def _load_data(self): + # load attributes first to sanity-check those features before doing the compute expensive time series loading + self._load_combined_attributes() + + xr = self._load_or_create_xarray_dataset() + + if "NSE" in self.cfg.loss: + # get the std of the discharge for each basin, which is needed for the NSE loss. + self._calculate_per_basin_std(xr) + + if self.is_train: + # calculate feature means and stds for normalization + self.scaler["xarray_stds"] = xr.std(skipna=True) + self.scaler["xarray_means"] = xr.mean(skipna=True) + + if not self.cfg.zero_center_target: + # make centering a no-op by setting the target mean to zero + self.scaler["xarray_means"] = self.scaler["xarray_means"].assign( + {var: 0.0 for var in self.cfg.target_variables}) + + # performs normalization + xr = (xr - self.scaler["xarray_means"]) / self.scaler["xarray_stds"] + + self._create_lookup_table(xr) + + def get_period_start(self, basin: str) -> pd.Timestamp: + """Return the first date in the period for a given basin + + Parameters + ---------- + basin : str + The basin id + + Returns + ------- + pd.Timestamp + First date in the period for the specific basin. Necessary during evaluation to restore the dates. + """ + return self.period_starts[basin] + + def _initialize_frequency_configuration(self): + """Checks and extracts configuration values for 'use_frequency', 'seq_length', and 'predict_last_n'""" + + # If use_frequencies is not supplied, we'll fill it with the native frequency while loading the df. + self.frequencies = self.cfg.use_frequencies + + self.seq_len = self.cfg.seq_length + self._predict_last_n = self.cfg.predict_last_n + if not self.frequencies: + if not isinstance(self.seq_len, int) or not isinstance(self._predict_last_n, int): + raise ValueError('seq_length and predict_last_n must be integers if use_frequencies is not provided.') + self.seq_len = [self.seq_len] + self._predict_last_n = [self._predict_last_n] + else: + # flatten per-frequency dictionaries into lists that are ordered as use_frequencies + if not isinstance(self.seq_len, dict) \ + or not isinstance(self._predict_last_n, dict) \ + or any([freq not in self.seq_len for freq in self.frequencies]) \ + or any([freq not in self._predict_last_n for freq in self.frequencies]): + raise ValueError('seq_length and predict_last_n must be dictionaries with one key per frequency.') + self.seq_len = [self.seq_len[freq] for freq in self.frequencies] + self._predict_last_n = [self._predict_last_n[freq] for freq in self.frequencies] + + +@njit() +def validate_samples(x_d: List[np.ndarray], x_s: List[np.ndarray], y: List[np.ndarray], seq_length: List[int], + predict_last_n: List[int], frequency_maps: List[np.ndarray]) -> np.ndarray: + """Checks for invalid samples due to NaN or insufficient sequence length. + + Parameters + ---------- + x_d : List[np.ndarray] + List of dynamic input data; one entry per frequency + x_s : List[np.ndarray] + List of additional static input data; one entry per frequency + y : List[np.ndarray] + List of target values; one entry per frequency + seq_length : List[int] + List of sequence lengths; one entry per frequency + predict_last_n: List[int] + List of predict_last_n; one entry per frequency + frequency_maps : List[np.ndarray] + List of arrays mapping lowest-frequency samples to their corresponding last sample in each frequency; + one list entry per frequency. + + Returns + ------- + np.ndarray + Array has a value of 1 for valid samples and a value of 0 for invalid samples. + """ + # number of samples is number of lowest-frequency samples (all maps have this length) + n_samples = len(frequency_maps[0]) + + # 1 denote valid sample, 0 denote invalid sample + flag = np.ones(n_samples) + for i in range(len(frequency_maps)): # iterate through frequencies + for j in prange(n_samples): # iterate through lowest-frequency samples + # find the last sample in this frequency that belongs to the lowest-frequency step j + last_sample_of_freq = frequency_maps[i][j] + if last_sample_of_freq < seq_length[i] - 1: + flag[j] = 0 # too early for this frequency's seq_length (not enough history) + continue + + # any NaN in the dynamic inputs makes the sample invalid + if x_d is not None: + _x_d = x_d[i][last_sample_of_freq - seq_length[i] + 1:last_sample_of_freq + 1] + if np.any(np.isnan(_x_d)): + flag[j] = 0 + continue + + # all-NaN in the targets makes the sample invalid + if y is not None: + _y = y[i][last_sample_of_freq - predict_last_n[i] + 1:last_sample_of_freq + 1] + if np.prod(np.array(_y.shape)) > 0 and np.all(np.isnan(_y)): + flag[j] = 0 + continue + + # any NaN in the static features makes the sample invalid + if x_s is not None: + _x_s = x_s[i][last_sample_of_freq] + if np.any(np.isnan(_x_s)): + flag[j] = 0 + + return flag diff --git a/neuralhydrology/data/camelsgb.py b/neuralhydrology/data/camelsgb.py new file mode 100644 index 00000000..613298d5 --- /dev/null +++ b/neuralhydrology/data/camelsgb.py @@ -0,0 +1,84 @@ +from typing import Dict, List, Union + +import pandas as pd +import xarray + +from neuralhydrology.data.basedataset import BaseDataset +from neuralhydrology.data import utils +from neuralhydrology.utils.config import Config + + +class CamelsGB(BaseDataset): + """Data set class for the CAMELS GB dataset by [#]_. + + Parameters + ---------- + cfg : Config + The run configuration. + is_train : bool + Defines if the dataset is used for training or evaluating. If True (training), means/stds for each feature + are computed and stored to the run directory. If one-hot encoding is used, the mapping for the one-hot encoding + is created and also stored to disk. If False, a `scaler` input is expected and similarly the `id_to_int` input + if one-hot encoding is used. + period : {'train', 'validation', 'test'} + Defines the period for which the data will be loaded + basin : str, optional + If passed, the data for only this basin will be loaded. Otherwise the basin(s) are read from the appropriate + basin file, corresponding to the `period`. + additional_features : List[Dict[str, pd.DataFrame]], optional + List of dictionaries, mapping from a basin id to a pandas DataFrame. This DataFrame will be added to the data + loaded from the dataset, and all columns are available as 'dynamic_inputs', 'static_inputs' and + 'target_variables' + id_to_int : Dict[str, int], optional + If the config argument 'use_basin_id_encoding' is True in the config and period is either 'validation' or + 'test', this input is required. It is a dictionary, mapping from basin id to an integer (the one-hot encoding). + scaler : Dict[str, Union[pd.Series, xarray.DataArray]], optional + If period is either 'validation' or 'test', this input is required. It contains the means and standard + deviations for each feature and is stored to the run directory during training (train_data/train_data_scaler.p) + + References + ---------- + .. [#] Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., + Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: Hydrometeorological time series and landscape + attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data Discuss., + https://doi.org/10.5194/essd-2020-49, in review, 2020. + """ + + def __init__(self, + cfg: Config, + is_train: bool, + period: str, + basin: str = None, + additional_features: List[Dict[str, pd.DataFrame]] = [], + id_to_int: Dict[str, int] = {}, + scaler: Dict[str, Union[pd.Series, xarray.DataArray]] = {}): + super(CamelsGB, self).__init__(cfg=cfg, + is_train=is_train, + period=period, + basin=basin, + additional_features=additional_features, + id_to_int=id_to_int, + scaler=scaler) + + def _load_basin_data(self, basin: str) -> pd.DataFrame: + """Load input and output data from text files.""" + df = utils.load_camels_gb_timeseries(data_dir=self.cfg.data_dir, basin=basin) + + return df + + def _load_attributes(self) -> pd.DataFrame: + if self.cfg.camels_attributes: + if self.is_train: + # sanity check attributes for NaN in per-feature standard deviation + utils.attributes_sanity_check(data_dir=self.cfg.data_dir, + attribute_set=self.cfg.dataset, + basins=self.basins, + attribute_list=self.cfg.camels_attributes) + + df = utils.load_camels_gb_attributes(self.cfg.data_dir, basins=self.basins) + + # remove all attributes not defined in the config + drop_cols = [c for c in df.columns if c not in self.cfg.camels_attributes] + df = df.drop(drop_cols, axis=1) + + return df diff --git a/neuralhydrology/data/camelsus.py b/neuralhydrology/data/camelsus.py new file mode 100644 index 00000000..05a461a6 --- /dev/null +++ b/neuralhydrology/data/camelsus.py @@ -0,0 +1,106 @@ +from typing import Dict, List, Union + +import numpy as np +import pandas as pd +import xarray + +from neuralhydrology.data.basedataset import BaseDataset +from neuralhydrology.data import utils +from neuralhydrology.utils.config import Config + + +class CamelsUS(BaseDataset): + """Data set class for the CAMELS US data set by [#]_ and [#]_. + + Parameters + ---------- + cfg : Config + The run configuration. + is_train : bool + Defines if the dataset is used for training or evaluating. If True (training), means/stds for each feature + are computed and stored to the run directory. If one-hot encoding is used, the mapping for the one-hot encoding + is created and also stored to disk. If False, a `scaler` input is expected and similarly the `id_to_int` input + if one-hot encoding is used. + period : {'train', 'validation', 'test'} + Defines the period for which the data will be loaded + basin : str, optional + If passed, the data for only this basin will be loaded. Otherwise the basin(s) are read from the appropriate + basin file, corresponding to the `period`. + additional_features : List[Dict[str, pd.DataFrame]], optional + List of dictionaries, mapping from a basin id to a pandas DataFrame. This DataFrame will be added to the data + loaded from the dataset and all columns are available as 'dynamic_inputs', 'static_inputs' and + 'target_variables' + id_to_int : Dict[str, int], optional + If the config argument 'use_basin_id_encoding' is True in the config and period is either 'validation' or + 'test', this input is required. It is a dictionary, mapping from basin id to an integer (the one-hot encoding). + scaler : Dict[str, Union[pd.Series, xarray.DataArray]], optional + If period is either 'validation' or 'test', this input is required. It contains the means and standard + deviations for each feature and is stored to the run directory during training (train_data/train_data_scaler.p) + + References + ---------- + .. [#] A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, + L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan: Development of a large-sample watershed-scale + hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional + variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, + doi:10.5194/hess-19-209-2015, 2015 + .. [#] Addor, N., Newman, A. J., Mizukami, N. and Clark, M. P.: The CAMELS data set: catchment attributes and + meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293-5313, doi:10.5194/hess-21-5293-2017, + 2017. + """ + + def __init__(self, + cfg: Config, + is_train: bool, + period: str, + basin: str = None, + additional_features: List[Dict[str, pd.DataFrame]] = [], + id_to_int: Dict[str, int] = {}, + scaler: Dict[str, Union[pd.Series, xarray.DataArray]] = {}): + super(CamelsUS, self).__init__(cfg=cfg, + is_train=is_train, + period=period, + basin=basin, + additional_features=additional_features, + id_to_int=id_to_int, + scaler=scaler) + + def _load_basin_data(self, basin: str) -> pd.DataFrame: + """Load input and output data from text files.""" + # get forcings + dfs = [] + for forcing in self.cfg.forcings: + df, area = utils.load_camels_us_forcings(self.cfg.data_dir, basin, forcing) + + # rename columns + if len(self.cfg.forcings) > 1: + df = df.rename(columns={col: f"{col}_{forcing}" for col in df.columns}) + dfs.append(df) + df = pd.concat(dfs, axis=1) + + # add discharge + df['QObs(mm/d)'] = utils.load_camels_us_discharge(self.cfg.data_dir, basin, area) + + # replace invalid discharge values by NaNs + qobs_cols = [col for col in df.columns if "qobs" in col.lower()] + for col in qobs_cols: + df.loc[df[col] < 0, col] = np.nan + + return df + + def _load_attributes(self) -> pd.DataFrame: + if self.cfg.camels_attributes: + if self.is_train: + # sanity check attributes for NaN in per-feature standard deviation + utils.attributes_sanity_check(data_dir=self.cfg.data_dir, + attribute_set=self.cfg.dataset, + basins=self.basins, + attribute_list=self.cfg.camels_attributes) + + df = utils.load_camels_us_attributes(self.cfg.data_dir, basins=self.basins) + + # remove all attributes not defined in the config + drop_cols = [c for c in df.columns if c not in self.cfg.camels_attributes] + df = df.drop(drop_cols, axis=1) + + return df diff --git a/neuralhydrology/data/climateindices.py b/neuralhydrology/data/climateindices.py new file mode 100644 index 00000000..6c6b9c32 --- /dev/null +++ b/neuralhydrology/data/climateindices.py @@ -0,0 +1,130 @@ +import logging +import pickle +import sys +from pathlib import Path +from typing import List + +import numpy as np +from numba import njit +from tqdm import tqdm + +from .pet import get_priestley_taylor_pet +from .utils import load_camels_us_attributes, load_camels_us_forcings, load_basin_file + +LOGGER = logging.getLogger(__name__) + + +def precalculate_dyn_climate_indices(data_dir: Path, basin_file: Path, window_length: int, forcings: str): + basins = load_basin_file(basin_file=basin_file) + camels_attributes = load_camels_us_attributes(data_dir=data_dir, basins=basins) + additional_features = {} + new_columns = [ + 'p_mean_dyn', 'pet_mean_dyn', 'aridity_dyn', 't_mean_dyn', 'frac_snow_dyn', 'high_prec_freq_dyn', + 'high_prec_dur_dyn', 'low_prec_freq_dyn', 'low_prec_dur_dyn' + ] + for basin in tqdm(basins, file=sys.stdout): + df, _ = load_camels_us_forcings(data_dir=data_dir, basin=basin, forcings=forcings) + lat = camels_attributes.loc[camels_attributes.index == basin, 'gauge_lat'].values + elev = camels_attributes.loc[camels_attributes.index == basin, 'elev_mean'].values + df["PET(mm/d)"] = get_priestley_taylor_pet(t_min=df["tmin(C)"].values, + t_max=df["tmax(C)"].values, + s_rad=df["srad(W/m2)"].values, + lat=lat, + elev=elev, + doy=df.index.dayofyear.values) + + for col in new_columns: + df[col] = np.nan + + x = np.array([ + df['prcp(mm/day)'].values, df['srad(W/m2)'].values, df['tmax(C)'].values, df['tmin(C)'].values, + df['vp(Pa)'].values, df['PET(mm/d)'].values + ]).T + + new_features = numba_climate_indexes(x, window_length=window_length) + + if np.sum(np.isnan(new_features)) > 0: + raise ValueError(f"NaN in new features of basin {basin}") + + df.loc[df.index[window_length - 1]:, 'p_mean_dyn'] = new_features[:, 0] + df.loc[df.index[window_length - 1]:, 'pet_mean_dyn'] = new_features[:, 1] + df.loc[df.index[window_length - 1]:, 'aridity_dyn'] = new_features[:, 2] + df.loc[df.index[window_length - 1]:, 't_mean_dyn'] = new_features[:, 3] + df.loc[df.index[window_length - 1]:, 'frac_snow_dyn'] = new_features[:, 4] + df.loc[df.index[window_length - 1]:, 'high_prec_freq_dyn'] = new_features[:, 5] + df.loc[df.index[window_length - 1]:, 'high_prec_dur_dyn'] = new_features[:, 6] + df.loc[df.index[window_length - 1]:, 'low_prec_freq_dyn'] = new_features[:, 7] + df.loc[df.index[window_length - 1]:, 'low_prec_dur_dyn'] = new_features[:, 8] + + drop_cols = [c for c in df.columns if c not in new_columns] + + df = df.drop(drop_cols, axis=1) + + additional_features[basin] = df + + filename = f"dyn_climate_indices_{forcings}_{len(basins)}basins_{window_length}lookback.p" + + output_file = Path(__file__).parent.parent.parent / 'data' / filename + + with output_file.open("wb") as fp: + pickle.dump(additional_features, fp) + + LOGGER.info(f"Precalculated features successfully stored at {output_file}") + + return additional_features + + +@njit +def numba_climate_indexes(features: np.ndarray, window_length: int) -> np.ndarray: + n_samples = features.shape[0] + new_features = np.zeros((n_samples - 365 + 1, 9)) + + for i in range(new_features.shape[0]): + x = features[i:i + window_length] + + p_mean = np.mean(x[:, 0]) + pet_mean = np.mean(x[:, -1]) + aridity = pet_mean / p_mean + t_mean = (np.mean(x[:, 1]) + np.mean(x[:, 2])) / 2 + + precip_days = x[x[:, 0] > 0] + frac_snow = precip_days[precip_days[:, 2] < 0, :].shape[0] / precip_days.shape[0] + + high_prec_freq = precip_days[precip_days[:, 0] > 5 * p_mean].shape[0] / precip_days.shape[0] + low_prec_freq = precip_days[precip_days[:, 0] < 1].shape[0] / precip_days.shape[0] + + idx = np.where(x[:, 0] < 1)[0] + groups = split_list(idx) + low_prec_dur = np.mean(np.array([len(p) for p in groups])) + + idx = np.where(x[:, 0] >= 5 * p_mean)[0] + groups = split_list(idx) + high_prec_dur = np.mean(np.array([len(p) for p in groups])) + + new_features[i, 0] = p_mean + new_features[i, 1] = pet_mean + new_features[i, 2] = aridity + new_features[i, 3] = t_mean + new_features[i, 4] = frac_snow + new_features[i, 5] = high_prec_freq + new_features[i, 6] = high_prec_dur + new_features[i, 7] = low_prec_freq + new_features[i, 8] = low_prec_dur + + return new_features + + +@njit +def split_list(alist: List) -> List: + newlist = [] + start = 0 + end = 0 + for index, value in enumerate(alist): + if index < len(alist) - 1: + if alist[index + 1] > value + 1: + end = index + 1 + newlist.append(alist[start:end]) + start = end + else: + newlist.append(alist[start:len(alist)]) + return newlist diff --git a/neuralhydrology/data/dischargeinput.py b/neuralhydrology/data/dischargeinput.py new file mode 100644 index 00000000..b198a5fa --- /dev/null +++ b/neuralhydrology/data/dischargeinput.py @@ -0,0 +1,37 @@ +import logging +import pickle +import sys +from pathlib import Path + +from tqdm import tqdm + +from neuralhydrology.data.utils import load_basin_file, load_camels_us_forcings, load_camels_us_discharge + +LOGGER = logging.getLogger(__name__) + + +def create_discharge_files(data_dir: Path, basin_file: Path, out_dir: Path, shift: int = 1): + + out_file = out_dir / f"discharge_input_shift{shift}.p" + if out_file.is_file(): + raise FileExistsError + + basins = load_basin_file(basin_file) + + data = {} + + for basin in tqdm(basins, file=sys.stdout): + + df, area = load_camels_us_forcings(data_dir=data_dir, basin=basin, forcings="daymet") + df["QObs(mm/d)"] = load_camels_us_discharge(data_dir=data_dir, basin=basin, area=area) + + df[f"QObs(t-{shift})"] = df["QObs(mm/d)"].shift(shift) + + drop_columns = [col for col in df.columns if col != f"QObs(t-{shift})"] + + data[basin] = df.drop(labels=drop_columns, axis=1) + + with out_file.open("wb") as fp: + pickle.dump(data, fp) + + LOGGER.info(f"Data successfully stored at {out_file}") diff --git a/neuralhydrology/data/hourlycamelsus.py b/neuralhydrology/data/hourlycamelsus.py new file mode 100644 index 00000000..c02dcfa8 --- /dev/null +++ b/neuralhydrology/data/hourlycamelsus.py @@ -0,0 +1,138 @@ +import logging +import pickle + +import numpy as np +import pandas as pd + +from neuralhydrology.data import utils, CamelsUS +from neuralhydrology.utils.config import Config + +LOGGER = logging.getLogger(__name__) + + +class HourlyCamelsUS(CamelsUS): + """Data set class providing hourly data for CAMELS US basins. + + This class extends the `CamelsUS` dataset class by hourly in- and output data. Currently, only NLDAS forcings are + available at an hourly resolution. + + Parameters + ---------- + cfg : Config + The run configuration. + is_train : bool + Defines if the dataset is used for training or evaluating. If True (training), means/stds for each feature + are computed and stored to the run directory. If one-hot encoding is used, the mapping for the one-hot encoding + is created and also stored to disk. If False, a `scaler` input is expected and similarly the `id_to_int` input + if one-hot encoding is used. + period : {'train', 'validation', 'test'} + Defines the period for which the data will be loaded + basin : str, optional + If passed, the data for only this basin will be loaded. Otherwise the basin(s) are read from the appropriate + basin file, corresponding to the `period`. + additional_features : List[Dict[str, pd.DataFrame]], optional + List of dictionaries, mapping from a basin id to a pandas DataFrame. This DataFrame will be added to the data + loaded from the dataset and all columns are available as 'dynamic_inputs', 'static_inputs' and + 'target_variables' + id_to_int : Dict[str, int], optional + If the config argument 'use_basin_id_encoding' is True in the config and period is either 'validation' or + 'test', this input is required. It is a dictionary, mapping from basin id to an integer (the one-hot encoding). + scaler : Dict[str, Union[pd.Series, xarray.DataArray]], optional + If period is either 'validation' or 'test', this input is required. It contains the means and standard + deviations for each feature and is stored to the run directory during training (train_data/train_data_scaler.p) + """ + + def __init__(self, + cfg: Config, + is_train: bool, + period: str, + basin: str = None, + additional_features: list = [], + id_to_int: dict = {}, + scaler: dict = {}): + self._netcdf_dataset = None # if available, we remember the dataset to load faster + self._warn_slow_loading = True + super(HourlyCamelsUS, self).__init__(cfg=cfg, + is_train=is_train, + period=period, + basin=basin, + additional_features=additional_features, + id_to_int=id_to_int, + scaler=scaler) + + def _load_basin_data(self, basin: str) -> pd.DataFrame: + """Load input and output data from text files.""" + # get forcings + dfs = [] + if not any(f.endswith('_hourly') for f in self.cfg.forcings): + raise ValueError('Forcings include no hourly forcings set.') + for forcing in self.cfg.forcings: + if forcing[-7:] == '_hourly': + df = self.load_hourly_data(basin, forcing) + else: + # load daily CAMELS forcings and upsample to hourly + df, _ = utils.load_camels_us_forcings(self.cfg.data_dir, basin, forcing) + df = df.resample('1H').ffill() + if len(self.cfg.forcings) > 1: + # rename columns + df = df.rename(columns={col: f"{col}_{forcing}" for col in df.columns if 'qobs' not in col.lower()}) + dfs.append(df) + df = pd.concat(dfs, axis=1) + + # replace invalid discharge values by NaNs + qobs_cols = [col for col in df.columns if 'qobs' in col.lower()] + for col in qobs_cols: + df.loc[df[col] < 0, col] = np.nan + + # add stage, if requested + if 'gauge_height_m' in self.cfg.target_variables: + df = df.join(utils.load_hourly_us_stage(self.cfg.data_dir, basin)) + df.loc[df['gauge_height_m'] < 0, 'gauge_height_m'] = np.nan + + # convert discharge to 'synthetic' stage, if requested + if 'synthetic_qobs_stage_meters' in self.cfg.target_variables: + attributes = utils.load_camels_us_attributes(data_dir=self.cfg.data_dir, basins=[basin]) + with open(self.cfg.rating_curve_file, 'rb') as f: + rating_curves = pickle.load(f) + df['synthetic_qobs_stage_meters'] = np.nan + if basin in rating_curves.keys(): + discharge_m3s = df['qobs_mm_per_hour'].values / 1000 * attributes.area_gages2[basin] * 1e6 / 60**2 + df['synthetic_qobs_stage_meters'] = rating_curves[basin].discharge_to_stage(discharge_m3s) + + return df + + def load_hourly_data(self, basin: str, forcings: str) -> pd.DataFrame: + """Load a single set of hourly forcings and discharge. If available, loads from NetCDF, else from csv. + + Parameters + ---------- + basin : str + Identifier of the basin for which to load data. + forcings : str + Name of the forcings set to load. + + Returns + ------- + pd.DataFrame + Time-indexed DataFrame with forcings and discharge values for the specified basin. + """ + fallback_csv = False + try: + if self._netcdf_dataset is None: + self._netcdf_dataset = utils.load_hourly_us_netcdf(self.cfg.data_dir, forcings) + df = self._netcdf_dataset.sel(basin=basin).to_dataframe() + except FileNotFoundError: + fallback_csv = True + if self._warn_slow_loading: + LOGGER.warning('## Warning: Hourly NetCDF file not found. Falling back to slower csv files.') + self._warn_slow_loading = False # only warn once + except KeyError: + fallback_csv = True + LOGGER.warning(f'## Warning: NetCDF file does not contain data for {basin}. Trying slower csv files.') + if fallback_csv: + df = utils.load_hourly_us_forcings(self.cfg.data_dir, basin, forcings) + + # add discharge + df = df.join(utils.load_hourly_us_discharge(self.cfg.data_dir, basin)) + + return df diff --git a/neuralhydrology/data/pet.py b/neuralhydrology/data/pet.py new file mode 100644 index 00000000..818b615f --- /dev/null +++ b/neuralhydrology/data/pet.py @@ -0,0 +1,354 @@ +import numpy as np +from numba import njit + +LAMBDA = 2.45 # Kept constant, MJkg-1 +ALPHA = 1.26 # Calibrated in CAMELS, here static +STEFAN_BOLTZMAN = 4.903e-09 +PI = np.pi + + +@njit +def get_priestley_taylor_pet(t_min: np.ndarray, t_max: np.ndarray, s_rad: np.ndarray, lat: float, + elev: float, doy: np.ndarray) -> np.ndarray: + """Calculate potential evapotranspiration (PET) as an approximation following the Priestley-Taylor equation. + + The ground head flux G is assumed to be 0 at daily time steps (see Newman et al. 2015). The + equation follow FAO-56 (Allen et al. (1998)) + + Parameters + ---------- + t_min : np.ndarray + Daily min temperature (degree C) + t_max : np.ndarray + Daily max temperature (degree C) + s_rad : np.ndarray + Solar radiation (Wm-2) + lat : float + Latitude in degree + elev : float + Elevation in m + doy : np.ndarray + Day of the year + + Returns + ------- + np.ndarray + Array containing PET estimates in mm/day + """ + lat = lat * (PI / 180) # degree to rad + + # Slope of saturation vapour pressure curve + t_mean = 0.5 * (t_min + t_max) + slope_svp = get_slope_svp_curve(t_mean) + + # incoming netto short-wave radiation + s_rad = s_rad * 0.0864 # conversion Wm-2 -> MJm-2day-1 + in_sw_rad = get_net_sw_srad(s_rad) + + # outgoginng netto long-wave radiation + sol_dec = get_sol_decl(doy) + sha = get_sunset_hour_angle(lat, sol_dec) + ird = get_ird_earth_sun(doy) + et_rad = get_extraterra_rad(lat, sol_dec, sha, ird) + cs_rad = get_clear_sky_rad(elev, et_rad) + a_vp = get_avp_tmin(t_min) + out_lw_rad = get_net_outgoing_lw_rad(t_min, t_max, s_rad, cs_rad, a_vp) + + # net radiation + net_rad = get_net_rad(in_sw_rad, out_lw_rad) + + # gamma + atm_pressure = get_atmos_pressure(elev) + gamma = get_psy_const(atm_pressure) + + # PET MJm-2day-1 + pet = (ALPHA / LAMBDA) * (slope_svp * net_rad) / (slope_svp + gamma) + + # convert energy to evap + pet = pet * 0.408 + + return pet + + +@njit +def get_slope_svp_curve(t_mean: np.ndarray) -> np.ndarray: + """Slope of saturation vapour pressure curve + + Equation 13 FAO-56 Allen et al. (1998) + + Parameters + ---------- + t_mean : np.ndarray + Mean temperature (degree C) + + Returns + ------- + np.ndarray + Slope of the saturation vapor pressure curve in kPa/(degree C) + """ + delta = 4098 * (0.6108 * np.exp((17.27 * t_mean) / (t_mean + 237.3))) / ((t_mean + 237.3)**2) + return delta + + +@njit +def get_net_sw_srad(s_rad: np.ndarray, albedo: float = 0.23) -> np.ndarray: + """Calculate net shortwave radiation + + Equation 38 FAO-56 Allen et al. (1998) + + Parameters + ---------- + s_rad : np.ndarray + Incoming solar radiation (MJm-2day-1) + albedo : float, optional + Albedo, by default 0.23 + + Returns + ------- + np.ndarray + Net shortwave radiation (MJm-2day-1) + """ + net_srad = (1 - albedo) * s_rad + return net_srad + + +@njit +def get_sol_decl(doy: np.ndarray) -> np.ndarray: + """Get solar declination + + Equation 24 FAO-56 Allen et al. (1998) + + Parameters + ---------- + doy : np.ndarray + Day of the year + + Returns + ------- + np.ndarray + Solar declination in rad + """ + # equation 24 FAO Allen + sol_dec = 0.409 * np.sin((2 * np.pi) / 365 * doy - 1.39) + return sol_dec + + +@njit +def get_sunset_hour_angle(lat: float, sol_dec: np.ndarray) -> np.ndarray: + """Sunset hour angle + + + + Parameters + ---------- + lat : float + Latitude in rad + sol_dec : np.ndarray + Solar declination in rad + + Returns + ------- + np.ndarray + Sunset hour angle in rad + """ + term = -np.tan(lat) * np.tan(sol_dec) + term[term < -1] = -1 + term[term > 1] = 1 + sha = np.arccos(term) + return sha + + +@njit +def get_ird_earth_sun(doy: np.ndarray) -> np.ndarray: + """Inverse relative distance between Earth and Sun + + Equation 23 FAO-56 Allen et al. (1998) + + Parameters + ---------- + doy : np.ndarray + Day of the year + + Returns + ------- + np.ndarray + Inverse relative distance between Earth and Sun + """ + ird = 1 + 0.033 * np.cos((2 * PI) / 365 * doy) + return ird + + +@njit +def get_extraterra_rad(lat: float, sol_dec: np.ndarray, sha: np.ndarray, + ird: np.ndarray) -> np.ndarray: + """Extraterrestrial Radiation + + Equation 21 FAO-56 Allen et al. (1998) + + Parameters + ---------- + lat : float + Lat in rad (pos for northern hemisphere) + sol_dec : np.ndarray + Solar declination in rad + sha : np.ndarray + Sunset hour angle in rad + ird : np.ndarray + Inverse relative distance of Earth and Sun + + Returns + ------- + np.ndarray + Extraterrestrial radiation MJm-2day-1 + """ + term1 = (24 * 60) / PI * 0.082 * ird + term2 = sha * np.sin(lat) * np.sin(sol_dec) + np.cos(lat) * np.cos(sol_dec) * np.sin(sha) + et_rad = term1 * term2 + return et_rad + + +@njit +def get_clear_sky_rad(elev: float, et_rad: np.ndarray) -> np.ndarray: + """Clear sky radiation + + Equation 37 FAO-56 Allen et al. (1998) + + Parameters + ---------- + elev : float + Elevation in m + et_rad : np.ndarray + Extraterrestrial radiation in MJm-2day-1 + + Returns + ------- + np.ndarray + Clear sky radiation MJm-2day-1 + """ + cs_rad = (0.75 + 2 * 10e-5 * elev) * et_rad + return cs_rad + + +@njit +def get_avp_tmin(t_min: np.ndarray) -> np.ndarray: + """Actual vapor pressure estimated using min temperature + + Equation 48 FAO-56 Allen et al. (1998) + + Parameters + ---------- + t_min : np.ndarray + Minimum temperature in degree C + + Returns + ------- + np.ndarray + Actual vapor pressure kPa + """ + avp = 0.611 * np.exp((17.27 * t_min) / (t_min + 237.3)) + return avp + + +@njit +def get_net_outgoing_lw_rad(t_min: np.ndarray, t_max: np.ndarray, s_rad: np.ndarray, + cs_rad: np.ndarray, a_vp: np.ndarray) -> np.ndarray: + """Net outgoing longwave radiation + + Expects temperatures in degree and does the conversion in kelvin in the function. + + Equation 49 FAO-56 Allen et al. (1998) + + Parameters + ---------- + t_min : np.ndarray + Min temperature in degree C + t_max : np.ndarray + Max temperature in degree C + s_rad : np.ndarray + Measured or modeled solar radiation MJm-2day-1 + cs_rad : np.ndarray + Clear sky radiation MJm-2day-1 + a_vp : np.ndarray + Actuatal vapor pressure kPa + + Returns + ------- + np.ndarray + Net outgoing longwave radiation MJm-2day-1 + """ + term1 = ((t_max + 273.16)**4 + (t_min + 273.16)**4) / 2 # conversion in K in equation + term2 = 0.34 - 0.14 * np.sqrt(a_vp) + term3 = 1.35 * s_rad / cs_rad - 0.35 + net_lw = STEFAN_BOLTZMAN * term1 * term2 * term3 + return net_lw + + +@njit +def get_net_rad(sw_rad: np.ndarray, lw_rad: np.ndarray) -> np.ndarray: + """Net radiation + + Equation 40 FAO-56 Allen et al. (1998) + + Parameters + ---------- + sw_rad : np.ndarray + Net incoming shortwave radiation MJm-2day-1 + lw_rad : np.ndarray + Net outgoing longwave radiation MJm-2day-1 + + Returns + ------- + np.ndarray + [description] + """ + return sw_rad - lw_rad + + +@njit +def get_atmos_pressure(elev: float) -> float: + """Atmospheric pressure + + Equation 7 FAO-56 Allen et al. (1998) + + Parameters + ---------- + elev : float + Elevation in m + + Returns + ------- + float + Atmospheric pressure in kPa + """ + temp = (293.0 - 0.0065 * elev) / 293.0 + return np.power(temp, 5.26) * 101.3 + + +@njit +def get_psy_const(atm_pressure: float) -> float: + """Psychometric constant + + Parameters + ---------- + atm_pressure : float + Atmospheric pressure in kPa + + Returns + ------- + float + Psychometric constant in kPa/(degree C) + """ + return 0.000665 * atm_pressure + + +@njit +def srad_from_t(et_rad, cs_rad, t_min, t_max, coastal=False): + # equation 50 + if coastal: + adj = 0.19 + else: + adj = 0.16 + + sol_rad = adj * np.sqrt(t_max - t_min) * et_rad + + return np.minimum(sol_rad, cs_rad) diff --git a/neuralhydrology/data/utils.py b/neuralhydrology/data/utils.py new file mode 100644 index 00000000..b0e3b398 --- /dev/null +++ b/neuralhydrology/data/utils.py @@ -0,0 +1,514 @@ +from pathlib import Path +from typing import List, Tuple, Union + +import numpy as np +import pandas as pd +import xarray + +######################################################################################################################## +# CAMELS US utility functions # +######################################################################################################################## + + +def load_camels_us_attributes(data_dir: Path, basins: List[str] = []) -> pd.DataFrame: + """Load CAMELS US attributes from the dataset provided by [#]_ + + Parameters + ---------- + data_dir : Path + Path to the CAMELS US directory. This folder must contain a 'camels_attributes_v2.0' folder (the original + data set) containing the corresponding txt files for each attribute group. + basins : List[str], optional + If passed, return only attributes for the basins specified in this list. Otherwise, the attributes of all basins + are returned. + + Returns + ------- + pandas.DataFrame + Basin-indexed DataFrame, containing the attributes as columns. + + References + ---------- + .. [#] Addor, N., Newman, A. J., Mizukami, N. and Clark, M. P.: The CAMELS data set: catchment attributes and + meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293-5313, doi:10.5194/hess-21-5293-2017, + 2017. + """ + attributes_path = Path(data_dir) / 'camels_attributes_v2.0' + + if not attributes_path.exists(): + raise RuntimeError(f"Attribute folder not found at {attributes_path}") + + txt_files = attributes_path.glob('camels_*.txt') + + # Read-in attributes into one big dataframe + dfs = [] + for txt_file in txt_files: + df_temp = pd.read_csv(txt_file, sep=';', header=0, dtype={'gauge_id': str}) + df_temp = df_temp.set_index('gauge_id') + + dfs.append(df_temp) + + df = pd.concat(dfs, axis=1) + # convert huc column to double digit strings + df['huc'] = df['huc_02'].apply(lambda x: str(x).zfill(2)) + df = df.drop('huc_02', axis=1) + + if basins: + # drop rows of basins not contained in the passed list + drop_basins = [b for b in df.index if b not in basins] + df = df.drop(drop_basins, axis=0) + + return df + + +def load_camels_us_forcings(data_dir: Path, basin: str, forcings: str) -> Tuple[pd.DataFrame, int]: + """Load the forcing data for a basin of the CAMELS US data set. + + Parameters + ---------- + data_dir : Path + Path to the CAMELS US directory. This folder must contain a 'basin_mean_forcing' folder containing one + subdirectory for each forcing. The forcing directories have to contain 18 subdirectories (for the 18 HUCS) as in + the original CAMELS data set. In each HUC folder are the forcing files (.txt), starting with the 8-digit basin + id. + basin : str + 8-digit USGS identifier of the basin. + forcings : str + Can be e.g. 'daymet' or 'nldas', etc. Must match the folder names in the 'basin_mean_forcing' directory. + + Returns + ------- + pd.DataFrame + Time-indexed DataFrame, containing the forcing data. + int + Catchment area (m2), specified in the header of the forcing file. + """ + forcing_path = data_dir / 'basin_mean_forcing' / forcings + if not forcing_path.is_dir(): + raise OSError(f"{forcing_path} does not exist") + + files = list(forcing_path.glob('**/*_forcing_leap.txt')) + file_path = [f for f in files if f.name[:8] == basin] + if file_path: + file_path = file_path[0] + else: + raise FileNotFoundError(f'No file for Basin {basin} at {file_path}') + + df = pd.read_csv(file_path, sep='\s+', header=3) + df["date"] = pd.to_datetime(df.Year.map(str) + "/" + df.Mnth.map(str) + "/" + df.Day.map(str), format="%Y/%m/%d") + df = df.set_index("date") + + # load area from header + with open(file_path, 'r') as fp: + content = fp.readlines() + area = int(content[2]) + + return df, area + + +def load_camels_us_discharge(data_dir: Path, basin: str, area: int) -> pd.Series: + """Load the discharge data for a basin of the CAMELS US data set. + + Parameters + ---------- + data_dir : Path + Path to the CAMELS US directory. This folder must contain a 'usgs_streamflow' folder with 18 + subdirectories (for the 18 HUCS) as in the original CAMELS data set. In each HUC folder are the discharge files + (.txt), starting with the 8-digit basin id. + basin : str + 8-digit USGS identifier of the basin. + area : int + Catchment area (m2), used to normalize the discharge. + + Returns + ------- + pd.Series + Time-index pandas.Series of the discharge values (mm/day) + """ + + discharge_path = data_dir / 'usgs_streamflow' + files = list(discharge_path.glob('**/*_streamflow_qc.txt')) + file_path = [f for f in files if f.name[:8] == basin] + if file_path: + file_path = file_path[0] + else: + raise FileNotFoundError(f'No file for Basin {basin} at {file_path}') + + col_names = ['basin', 'Year', 'Mnth', 'Day', 'QObs', 'flag'] + df = pd.read_csv(file_path, sep='\s+', header=None, names=col_names) + df["date"] = pd.to_datetime(df.Year.map(str) + "/" + df.Mnth.map(str) + "/" + df.Day.map(str), format="%Y/%m/%d") + df = df.set_index("date") + + # normalize discharge from cubic feed per second to mm per day + df.QObs = 28316846.592 * df.QObs * 86400 / (area * 10**6) + + return df.QObs + + +######################################################################################################################## +# HOURLY CAMELS US utility functions # +######################################################################################################################## + + +def load_hourly_us_forcings(data_dir: Path, basin: str, forcings: str) -> pd.DataFrame: + """Load the hourly forcing data for a basin of the CAMELS US data set. + + The hourly forcings are not included in the original data set by Newman et al. (2017). + + Parameters + ---------- + data_dir : Path + Path to the CAMELS US directory. This folder must contain an 'hourly' folder containing one subdirectory + for each forcing, which contains the forcing files (.csv) for each basin. Files have to contain the 8-digit + basin id. + basin : str + 8-digit USGS identifier of the basin. + forcings : str + Must match the folder names in the 'hourly' directory. E.g. 'nldas_hourly' + + Returns + ------- + pd.DataFrame + Time-indexed DataFrame, containing the forcing data. + """ + forcing_path = data_dir / 'hourly' / forcings + if not forcing_path.is_dir(): + raise OSError(f"{forcing_path} does not exist") + + files = list(forcing_path.glob('*.csv')) + file_path = [f for f in files if basin in f.stem] + if file_path: + file_path = file_path[0] + else: + raise FileNotFoundError(f'No file for Basin {basin} at {forcing_path}') + + return pd.read_csv(file_path, index_col=['date'], parse_dates=['date']) + + +def load_hourly_us_discharge(data_dir: Path, basin: str) -> pd.DataFrame: + """Load the hourly discharge data for a basin of the CAMELS US data set. + + Parameters + ---------- + data_dir : Path + Path to the CAMELS US directory. This folder must contain a folder called 'hourly' with a subdirectory + 'usgs_streamflow' which contains the discharge files (.csv) for each basin. File names must contain the 8-digit + basin id. + basin : str + 8-digit USGS identifier of the basin. + + Returns + ------- + pd.Series + Time-index Series of the discharge values (mm/hour) + """ + discharge_path = data_dir / 'hourly' / 'usgs_streamflow' + files = list(discharge_path.glob('**/*usgs-hourly.csv')) + file_path = [f for f in files if basin in f.stem] + if file_path: + file_path = file_path[0] + else: + raise FileNotFoundError(f'No file for Basin {basin} at {discharge_path}') + + return pd.read_csv(file_path, index_col=['date'], parse_dates=['date']) + + +def load_hourly_us_stage(data_dir: Path, basin: str) -> pd.Series: + """Load the hourly stage data for a basin of the CAMELS US data set. + + Parameters + ---------- + data_dir : Path + Path to the CAMELS US directory. This folder must contain a folder called 'hourly' with a subdirectory + 'usgs_stage' which contains the stage files (.csv) for each basin. File names must contain the 8-digit basin id. + basin : str + 8-digit USGS identifier of the basin. + + Returns + ------- + pd.Series + Time-index Series of the stage values (m) + """ + stage_path = data_dir / 'hourly' / 'usgs_stage' + files = list(stage_path.glob('**/*_utc.csv')) + file_path = [f for f in files if basin in f.stem] + if file_path: + file_path = file_path[0] + else: + raise FileNotFoundError(f'No file for Basin {basin} at {stage_path}') + + df = pd.read_csv(file_path, + sep=',', + index_col=['datetime'], + parse_dates=['datetime'], + usecols=['datetime', 'gauge_height_ft']) + df = df.resample('H').mean() + df["gauge_height_m"] = df["gauge_height_ft"] * 0.3048 + + return df["gauge_height_m"] + + +def load_hourly_us_netcdf(data_dir: Path, forcings: str) -> xarray.Dataset: + """Load hourly forcing and discharge data from preprocessed netCDF file. + + Parameters + ---------- + data_dir : Path + Path to the CAMELS US directory. This folder must contain a folder called 'hourly', containing the netCDF file. + forcings : str + Name of the forcing product. Must match the ending of the netCDF file. E.g. 'nldas_hourly' for + 'usgs-streamflow-nldas_hourly.nc' + + Returns + ------- + xarray.Dataset + Dataset containing the combined discharge and forcing data of all basins (as stored in the netCDF) + """ + netcdf_path = data_dir / 'hourly' / f'usgs-streamflow-{forcings}.nc' + if not netcdf_path.is_file(): + raise FileNotFoundError(f'No NetCDF file for hourly streamflow and {forcings} at {netcdf_path}.') + + return xarray.open_dataset(netcdf_path) + + +######################################################################################################################## +# CAMELS GB utility functions # +######################################################################################################################## + + +def load_camels_gb_attributes(data_dir: Path, basins: List[str] = []) -> pd.DataFrame: + """Load CAMELS GB attributes from the dataset provided by [#]_ + + Parameters + ---------- + data_dir : Path + Path to the CAMELS GB directory. This folder must contain an 'attributes' folder containing the corresponding + csv files for each attribute group (ending with _attributes.csv). + basins : List[str], optional + If passed, return only attributes for the basins specified in this list. Otherwise, the attributes of all basins + are returned. + + Returns + ------- + pd.DataFrame + Basin-indexed DataFrame, containing the attributes as columns. + + References + ---------- + .. [#] Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., + Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: Hydrometeorological time series and landscape + attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data Discuss., + https://doi.org/10.5194/essd-2020-49, in review, 2020. + """ + attributes_path = Path(data_dir) / 'attributes' + + if not attributes_path.exists(): + raise RuntimeError(f"Attribute folder not found at {attributes_path}") + + txt_files = attributes_path.glob('*_attributes.csv') + + # Read-in attributes into one big dataframe + dfs = [] + for txt_file in txt_files: + df_temp = pd.read_csv(txt_file, sep=',', header=0, dtype={'gauge_id': str}) + df_temp = df_temp.set_index('gauge_id') + + dfs.append(df_temp) + + df = pd.concat(dfs, axis=1) + + if basins: + # drop rows of basins not contained in the passed list + drop_basins = [b for b in df.index if b not in basins] + df = df.drop(drop_basins, axis=0) + + return df + + +def load_camels_gb_timeseries(data_dir: Path, basin: str) -> pd.DataFrame: + """Load the time series data for one basin of the CAMELS GB data set. + + Parameters + ---------- + data_dir : Path + Path to the CAMELS GB directory. This folder must contain a folder called 'timeseries' containing the forcing + files for each basin as .csv file. The file names have to start with 'CAMELS_GB_hydromet_timeseries'. + basin : str + Basin identifier number as string. + + Returns + ------- + pd.DataFrame + Time-indexed DataFrame, containing the time series data (forcings + discharge) data. + """ + forcing_path = data_dir / 'timeseries' + if not forcing_path.is_dir(): + raise OSError(f"{forcing_path} does not exist") + + files = list(forcing_path.glob('**/CAMELS_GB_hydromet_timeseries*.csv')) + file_path = [f for f in files if f"_{basin}_" in f.name] + if file_path: + file_path = file_path[0] + else: + raise FileNotFoundError(f'No file for Basin {basin} at {file_path}') + + df = pd.read_csv(file_path, sep=',', header=0, dtype={'date': str}) + df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d") + df = df.set_index("date") + + return df + + +######################################################################################################################## +# General utility functions # +######################################################################################################################## + + +def load_hydroatlas_attributes(data_dir: Path, basins: List[str] = []) -> pd.DataFrame: + """Load HydroATLAS attributes into a pandas DataFrame + + Parameters + ---------- + data_dir : Path + Path to the root directory of the dataset. Must contain a folder called 'hydroatlas_attributes' with a file + called `attributes.csv`. The attributes file is expected to have one column called `basin_id`. + basins : List[str], optional + If passed, return only attributes for the basins specified in this list. Otherwise, the attributes of all basins + are returned. + + Returns + ------- + pd.DataFrame + Basin-indexed DataFrame containing the HydroATLAS attributes. + """ + attribute_file = data_dir / "hydroatlas_attributes" / "attributes.csv" + if not attribute_file.is_file(): + raise FileNotFoundError(attribute_file) + + df = pd.read_csv(attribute_file, dtype={'basin_id': str}) + df = df.set_index('basin_id') + + if basins: + drop_basins = [b for b in df.index if b not in basins] + df = df.drop(drop_basins, axis=0) + + return df + + +def load_basin_file(basin_file: Path) -> List[str]: + """Load list of basins from text file. + + Parameters + ---------- + basin_file : Path + Path to a basin txt file. File has to contain one basin id per row. + + Returns + ------- + List[str] + List of basin ids as strings. + """ + with basin_file.open('r') as fp: + basins = fp.readlines() + basins = sorted(basin.strip() for basin in basins) + return basins + + +def attributes_sanity_check(data_dir: Path, attribute_set: str, basins: List[str], attribute_list: List[str]): + """Utility function to check if the standard deviation of one (or more) attributes is zero. + + This utility function can be used to check if any attribute has a standard deviation of zero. This would lead to + NaN's, when normalizing the features and thus would lead to NaN's when training the model. The function will raise + a `RuntimeError` if one or more zeros have been detected and will print the list of corresponding attribute names + to the console. + + Parameters + ---------- + data_dir : Path + Path to the root directory of the data set + attribute_set : {'hydroatlas', 'camels_us', 'hourly_camels_us', 'camels_gb'} + Name of the attribute set to check. + basins : + List of basins to consider in the check. + attribute_list : + List of attribute names to consider in the check. + + Raises + ------ + ValueError + For an unknown 'attribute_set' + RuntimeError + If one or more attributes have a standard deviation of zero. + """ + if attribute_set == "hydroatlas": + df = load_hydroatlas_attributes(data_dir, basins) + elif attribute_set in ["camels_us", "hourly_camels_us"]: + df = load_camels_us_attributes(data_dir, basins) + elif attribute_set == "camels_gb": + df = load_camels_gb_attributes(data_dir, basins) + else: + raise ValueError(f"Unknown 'attribute_set' {attribute_set}") + drop_cols = [c for c in df.columns if c not in attribute_list] + df = df.drop(drop_cols, axis=1) + attributes = [] + if any(df.std() == 0.0) or any(df.std().isnull()): + for k, v in df.std().iteritems(): + if (v == 0) or (np.isnan(v)): + attributes.append(k) + if attributes: + msg = [ + "The following attributes have a std of zero or NaN, which results in NaN's ", + "when normalizing the features. Remove the attributes from the attribute feature list ", + "and restart the run. \n", f"Attributes: {attributes}" + ] + raise RuntimeError("".join(msg)) + + +def sort_frequencies(frequencies: List[str]) -> List[str]: + """Sort the passed frequencies from low to high frequencies. + + Use `pandas frequency strings + `_ + to define frequencies. Note: The strings need to include values, e.g., '1D' instead of 'D'. + + Parameters + ---------- + frequencies : List[str] + List of pandas frequency identifiers to be sorted. + + Returns + ------- + List[str] + Sorted list of pandas frequency identifiers. + """ + deltas = {freq: pd.to_timedelta(freq) for freq in frequencies} + return sorted(deltas, key=deltas.get)[::-1] + + +def infer_frequency(index: Union[pd.DatetimeIndex, np.ndarray]) -> str: + """Infer the frequency of an index of a pandas DataFrame/Series or xarray DataArray. + + Parameters + ---------- + index : Union[pd.DatetimeIndex, np.ndarray] + DatetimeIndex of a DataFrame/Series or array of datetime values. + + Returns + ------- + str + Frequency of the index as a `pandas frequency string + `_ + + Raises + ------ + ValueError + If the frequency cannot be inferred from the index or is zero. + """ + native_frequency = pd.infer_freq(index) + if native_frequency is None: + raise ValueError(f'Cannot infer a legal frequency from dataset: {native_frequency}.') + if native_frequency[0] not in '0123456789': # add a value to the unit so to_timedelta works + native_frequency = f'1{native_frequency}' + if pd.to_timedelta(native_frequency) == pd.to_timedelta(0): + raise ValueError('Inferred dataset frequency is zero.') + return native_frequency diff --git a/neuralhydrology/evaluation/__init__.py b/neuralhydrology/evaluation/__init__.py new file mode 100644 index 00000000..c0aed7ba --- /dev/null +++ b/neuralhydrology/evaluation/__init__.py @@ -0,0 +1,35 @@ +from pathlib import Path + +from neuralhydrology.evaluation.tester import BaseTester, RegressionTester, UncertaintyTester +from neuralhydrology.utils.config import Config + + +def get_tester(cfg: Config, run_dir: Path, period: str, init_model: bool) -> BaseTester: + """Get specific tester class objects depending on the model (head) type. + + Parameters + ---------- + cfg : Config + The run configuration. + run_dir : Path + Path to the run directory. + period : {'train', 'validation', 'test'} + The period to evaluate. + init_model : bool + If True, the model weights will be initialized with the checkpoint from the last available epoch in `run_dir`. + + Returns + ------- + BaseTester + `RegressionTester` if the model head is 'regression'. `UncertaintyTester` if the evaluation is run in MC-Dropout + mode. + """ + if cfg.head.lower() == "regression": + if cfg.mc_dropout: + Tester = UncertaintyTester + else: + Tester = RegressionTester + else: + NotImplementedError(f"No evaluation method implemented for {cfg.head} head") + + return Tester(cfg=cfg, run_dir=run_dir, period=period, init_model=init_model) diff --git a/neuralhydrology/evaluation/evaluate.py b/neuralhydrology/evaluation/evaluate.py new file mode 100644 index 00000000..5d8c1b3a --- /dev/null +++ b/neuralhydrology/evaluation/evaluate.py @@ -0,0 +1,23 @@ +from pathlib import Path + +from neuralhydrology.evaluation import get_tester +from neuralhydrology.utils.config import Config + + +def start_evaluation(cfg: Config, run_dir: Path, epoch: int = None, period: str = "test"): + """Start evaluation of a trained network + + Parameters + ---------- + cfg : Config + The run configuration, read from the run directory. + run_dir : Path + Path to the run directory. + epoch : int, optional + Define a specific epoch to evaluate. By default, the weights of the last epoch are used. + period : {'train', 'validation', 'test'}, optional + The period to evaluate, by default 'test'. + + """ + tester = get_tester(cfg=cfg, run_dir=run_dir, period=period, init_model=True) + tester.evaluate(epoch=epoch, save_results=True, metrics=cfg.metrics) diff --git a/neuralhydrology/evaluation/metrics.py b/neuralhydrology/evaluation/metrics.py new file mode 100644 index 00000000..833a83cf --- /dev/null +++ b/neuralhydrology/evaluation/metrics.py @@ -0,0 +1,680 @@ +from typing import List, Dict + +import numpy as np +import pandas as pd +from scipy import stats, signal +from xarray.core.dataarray import DataArray + + +def get_available_metrics() -> List[str]: + """Get list of available metrics. + + Returns + ------- + List[str] + List of implemented metric names. + """ + metrics = ["NSE", "MSE", "RMSE", "KGE", "Alpha-NSE", "Pearson-r", "Beta-NSE", "FHV", "FMS", "FLV", "Peak-Timing"] + return metrics + + +def _validate_inputs(obs: DataArray, sim: DataArray): + if obs.shape != sim.shape: + raise RuntimeError("Shapes of observations and simulations must match") + + if (len(obs.shape) > 1) and (obs.shape[1] > 1): + raise RuntimeError("Metrics only defined for time series (1d or 2d with second dimension 1)") + + +def _mask_valid(obs: DataArray, sim: DataArray) -> (DataArray, DataArray): + # mask of invalid entries. NaNs in simulations can happen during validation/testing + idx = (~sim.isnull()) & (~obs.isnull()) + + obs = obs[idx] + sim = sim[idx] + + return obs, sim + + +def _get_fdc(da: DataArray) -> np.ndarray: + return da.sortby(da, ascending=False).values + + +def nse(obs: DataArray, sim: DataArray) -> float: + r"""Calculate Nash-Sutcliffe Efficiency [#]_ + + Nash-Sutcliffe Efficiency is the R-square between observed and simulated discharge. + + .. math:: \text{NSE} = 1 - \frac{\sum_{t=1}^{T}(Q_m^t - Q_o^t)^2}{\sum_{t=1}^T(Q_o^t - \overline{Q}_o)^2}, + + where :math:`Q_m` are the simulations (here, `sim`) and :math:`Q_o` are observations (here, `obs`). + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + + Returns + ------- + float + Nash-Sutcliffe Efficiency + + References + ---------- + .. [#] Nash, J. E.; Sutcliffe, J. V. (1970). "River flow forecasting through conceptual models part I - A + discussion of principles". Journal of Hydrology. 10 (3): 282-290. doi:10.1016/0022-1694(70)90255-6. + + """ + + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + denominator = ((obs - obs.mean())**2).sum() + numerator = ((sim - obs)**2).sum() + + value = 1 - numerator / denominator + + return float(value) + + +def mse(obs: DataArray, sim: DataArray) -> float: + r"""Calculate mean squared error. + + .. math:: \text{MSE} = \frac{1}{T}\sum_{t=1}^T (\widehat{y}_t - y_t)^2, + + where :math:`\widehat{y}` are the simulations (here, `sim`) and :math:`y` are observations + (here, `obs`). + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + + Returns + ------- + float + Mean squared error. + + """ + + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + return float(((sim - obs)**2).mean()) + + +def rmse(obs: DataArray, sim: DataArray) -> float: + r"""Calculate root mean squared error. + + .. math:: \text{RMSE} = \sqrt{\frac{1}{T}\sum_{t=1}^T (\widehat{y}_t - y_t)^2}, + + where :math:`\widehat{y}` are the simulations (here, `sim`) and :math:`y` are observations + (here, `obs`). + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + + Returns + ------- + float + Root mean sqaured error. + + """ + + return np.sqrt(mse(obs, sim)) + + +def alpha_nse(obs: DataArray, sim: DataArray) -> float: + r"""Calculate the alpha NSE decomposition [#]_ + + The alpha NSE decomposition is the fraction of the standard deviations of simulations and observations. + + .. math:: \alpha = \frac{\sigma_s}{\sigma_o}, + + where :math:`\sigma_s` is the standard deviation of the simulations (here, `sim`) and :math:`\sigma_o` is the + standard deviation of the observations (here, `obs`). + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + + Returns + ------- + float + Alpha NSE decomposition. + + References + ---------- + .. [#] Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error + and NSE performance criteria: Implications for improving hydrological modelling. Journal of hydrology, 377(1-2), + 80-91. + + """ + + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + return float(sim.std() / obs.std()) + + +def beta_nse(obs: DataArray, sim: DataArray) -> float: + r"""Calculate the beta NSE decomposition [#]_ + + The beta NSE decomposition is the difference of the mean simulation and mean observation divided by the standard + deviation of the observations. + + .. math:: \beta = \frac{\mu_s - \mu_o}{\sigma_o}, + + where :math:`\mu_s` is the mean of the simulations (here, `sim`), :math:`\mu_o` is the mean of the observations + (here, `obs`) and :math:`\sigma_o` the standard deviation of the observations. + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + + Returns + ------- + float + Beta NSE decomposition. + + References + ---------- + .. [#] Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error + and NSE performance criteria: Implications for improving hydrological modelling. Journal of hydrology, 377(1-2), + 80-91. + + """ + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + return float((sim.mean() - obs.mean()) / obs.std()) + + +def beta_kge(obs: DataArray, sim: DataArray) -> float: + r"""Calculate the beta KGE term [#]_ + + The beta term of the Kling-Gupta Efficiency is defined as the fraction of the means. + + .. math:: \beta_{\text{KGE}} = \frac{\mu_s}{\mu_o}, + + where :math:`\mu_s` is the mean of the simulations (here, `sim`) and :math:`\mu_o` is the mean of the observations + (here, `obs`). + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + + Returns + ------- + float + Beta NSE decomposition. + + References + ---------- + .. [#] Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error + and NSE performance criteria: Implications for improving hydrological modelling. Journal of hydrology, 377(1-2), + 80-91. + + """ + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + return float(sim.mean() / obs.mean()) + + +def kge(obs: DataArray, sim: DataArray, weights: List[float] = [1., 1., 1.]) -> float: + r"""Calculate the Kling-Gupta Efficieny [#]_ + + .. math:: + \text{KGE} = 1 - \sqrt{[ s_r (r - 1)]^2 + [s_\alpha ( \alpha - 1)]^2 + + [s_\beta(\beta_{\text{KGE}} - 1)]^2}, + + where :math:`r` is the correlation coefficient, :math:`\alpha` the :math:`\alpha`-NSE decomposition, + :math:`\beta_{\text{KGE}}` the fraction of the means and :math:`s_r, s_\alpha, s_\beta` the corresponding weights + (here the three float values in the `weights` parameter). + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + weights : List[float] + Weighting factors of the 3 KGE parts, by default each part has a weight of 1. + + Returns + ------- + float + Kling-Gupta Efficiency + + References + ---------- + .. [#] Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error + and NSE performance criteria: Implications for improving hydrological modelling. Journal of hydrology, 377(1-2), + 80-91. + + """ + if len(weights) != 3: + raise ValueError("Weights of the KGE must be a list of three values") + + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + r, _ = stats.pearsonr(obs.values, sim.values) + + alpha = sim.std() / obs.std() + beta = sim.mean() / obs.mean() + + value = (weights[0] * (r - 1)**2 + weights[1] * (alpha - 1)**2 + weights[2] * (beta - 1)**2) + + return 1 - np.sqrt(float(value)) + + +def pearsonr(obs: DataArray, sim: DataArray) -> float: + """Calculate pearson correlation coefficient (using scipy.stats.pearsonr) + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + + Returns + ------- + float + Pearson correlation coefficient + + """ + + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + r, _ = stats.pearsonr(obs.values, sim.values) + + return float(r) + + +def fdc_fms(obs: DataArray, sim: DataArray, lower: float = 0.2, upper: float = 0.7) -> float: + r"""Calculate the slope of the middle section of the flow duration curve [#]_ + + .. math:: + \%\text{BiasFMS} = \frac{\left | \log(Q_{s,\text{lower}}) - \log(Q_{s,\text{upper}}) \right | - + \left | \log(Q_{o,\text{lower}}) - \log(Q_{o,\text{upper}}) \right |}{\left | + \log(Q_{s,\text{lower}}) - \log(Q_{s,\text{upper}}) \right |} \times 100, + + where :math:`Q_{s,\text{lower/upper}}` corresponds to the FDC of the simulations (here, `sim`) at the `lower` and + `upper` bound of the middle section and :math:`Q_{o,\text{lower/upper}}` similarly for the observations (here, + `obs`). + + + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + lower : float, optional + Lower bound of the middle section in range ]0,1[, by default 0.2 + upper : float, optional + Upper bound of the middle section in range ]0,1[, by default 0.7 + + Returns + ------- + float + Slope of the middle section of the flow duration curve. + + References + ---------- + .. [#] Yilmaz, K. K., Gupta, H. V., and Wagener, T. ( 2008), A process-based diagnostic approach to model + evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, + doi:10.1029/2007WR006716. + """ + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + if any([(x <= 0) or (x >= 1) for x in [upper, lower]]): + raise ValueError("upper and lower have to be in range ]0,1[") + + if lower >= upper: + raise ValueError("The lower threshold has to be smaller than the upper.") + + # get arrays of sorted (descending) discharges + obs = _get_fdc(obs) + sim = _get_fdc(sim) + + # for numerical reasons change 0s to 1e-6. Simulations can still contain negatives, so also reset those. + sim[sim <= 0] = 1e-6 + obs[obs == 0] = 1e-6 + + # calculate fms part by part + qsm_lower = np.log(sim[np.round(lower * len(sim)).astype(int)]) + qsm_upper = np.log(sim[np.round(upper * len(sim)).astype(int)]) + qom_lower = np.log(obs[np.round(lower * len(obs)).astype(int)]) + qom_upper = np.log(obs[np.round(upper * len(obs)).astype(int)]) + + fms = ((qsm_lower - qsm_upper) - (qom_lower - qom_upper)) / (qom_lower - qom_upper + 1e-6) + + return fms * 100 + + +def fdc_fhv(obs: DataArray, sim: DataArray, h: float = 0.02) -> float: + r"""Calculate the peak flow bias of the flow duration curve [#]_ + + .. math:: \%\text{BiasFHV} = \frac{\sum_{h=1}^{H}(Q_{s,h} - Q_{o,h})}{\sum_{h=1}^{H}Q_{o,h}} \times 100, + + where :math:`Q_s` are the simulations (here, `sim`), :math:`Q_o` the observations (here, `obs`) and `H` is the upper + fraction of flows of the FDC (here, `h`). + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + h : float, optional + Fraction of upper flows to consider as peak flows of range ]0,1[, be default 0.02. + + Returns + ------- + float + Peak flow bias. + + References + ---------- + .. [#] Yilmaz, K. K., Gupta, H. V., and Wagener, T. ( 2008), A process-based diagnostic approach to model + evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, + doi:10.1029/2007WR006716. + """ + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + if (h <= 0) or (h >= 1): + raise ValueError("h has to be in range ]0,1[. Consider small values, e.g. 0.02 for 2% peak flows") + + # get arrays of sorted (descending) discharges + obs = _get_fdc(obs) + sim = _get_fdc(sim) + + # subset data to only top h flow values + obs = obs[:np.round(h * len(obs)).astype(int)] + sim = sim[:np.round(h * len(sim)).astype(int)] + + fhv = np.sum(sim - obs) / np.sum(obs) + + return fhv * 100 + + +def fdc_flv(obs: DataArray, sim: DataArray, l: float = 0.3) -> float: + r"""Calculate the low flow bias of the flow duration curve [#]_ + + .. math:: + \%\text{BiasFMS} = -1 \frac{\sum_{l=1}^{L}[\log(Q_{s,l}) - \log(Q_{s,L})] - \sum_{l=1}^{L}[\log(Q_{o,l}) + - \log(Q_{o,L})]}{\sum_{l=1}^{L}[\log(Q_{o,l}) - \log(Q_{o,L})]} \times 100, + + where :math:`Q_s` are the simulations (here, `sim`), :math:`Q_o` the observations (here, `obs`) and `L` is the lower + fraction of flows of the FDC (here, `l`). + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + l : float, optional + Fraction of lower flows to consider as low flows of range ]0,1[, be default 0.3. + + Returns + ------- + float + Low flow bias. + + References + ---------- + .. [#] Yilmaz, K. K., Gupta, H. V., and Wagener, T. ( 2008), A process-based diagnostic approach to model + evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, + doi:10.1029/2007WR006716. + """ + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations + obs, sim = _mask_valid(obs, sim) + + if (l <= 0) or (l >= 1): + raise ValueError("l has to be in range ]0,1[. Consider small values, e.g. 0.3 for 30% low flows") + + # get arrays of sorted (descending) discharges + obs = _get_fdc(obs) + sim = _get_fdc(sim) + + # for numerical reasons change 0s to 1e-6. Simulations can still contain negatives, so also reset those. + sim[sim <= 0] = 1e-6 + obs[obs == 0] = 1e-6 + + obs = obs[-np.round(l * len(obs)).astype(int):] + sim = sim[-np.round(l * len(sim)).astype(int):] + + # transform values to log scale + obs = np.log(obs) + sim = np.log(sim) + + # calculate flv part by part + qsl = np.sum(sim - sim.min()) + qol = np.sum(obs - obs.min()) + + flv = -1 * (qsl - qol) / (qol + 1e-6) + + return flv * 100 + + +def mean_peak_timing(obs: DataArray, sim: DataArray, window: int = None, resolution: str = '1D') -> float: + """Mean difference in peak flow timing. + + Uses scipy.find_peaks to find peaks in the observed time series. Starting with all observed peaks, those with a + prominence of less than the standard deviation of the observed time series are discarded. Next, the lowest peaks + are subsequently discarded until all remaining peaks have a distance of at least 100 steps. Finally, the + corresponding peaks in the simulated time series are searched in a window of size `window` on either side of the + observed peaks and the absolute time differences between observed and simulated peaks is calculated. + The final metric is the mean absolute time difference across all peaks. For more details, see Appendix of [#]_ + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + window : int, optional + Size of window to consider on each side of the observed peak for finding the simulated peak. That is, the total + window length to find the peak in the simulations is :math:`2 * \\text{window} + 1` centered at the observed + peak. The default depends on the temporal resolution, e.g. for a resolution of '1D', a window of 3 is used and + for a resolution of '1H' the the window size is 12. + resolution : str, optional + Temporal resolution of the time series in pandas format, e.g. '1D' for daily and '1H' for hourly. + + + Returns + ------- + float + Mean peak time difference. + + References + ---------- + .. [#] Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G. S.: A note on leveraging synergy in multiple + meteorological datasets with deep learning for rainfall-runoff modeling, Hydrol. Earth Syst. Sci. Discuss., + https://doi.org/10.5194/hess-2020-221, in review, 2020. + """ + # verify inputs + _validate_inputs(obs, sim) + + # get time series with only valid observations (scipy's find_peaks doesn't guarantee correctness with NaNs) + obs, sim = _mask_valid(obs, sim) + + # heuristic to get indices of peaks and their corresponding height. + peaks, properties = signal.find_peaks(obs.values, distance=100, prominence=np.std(obs.values)) + + if window is None: + # infer a reasonable window size + window = max((0.5 * pd.to_timedelta('1D')) // pd.to_timedelta(resolution), 3) + + # evaluate timing + timing_errors = [] + for idx in peaks: + # skip peaks at the start and end of the sequence and peaks around missing observations + # (NaNs that were removed in obs & sim would result in windows that span too much time). + if (idx - window < 0) or (idx + window >= len(obs)) or (pd.date_range(obs[idx - window]['datetime'].values, + obs[idx + window]['datetime'].values, + freq=resolution).size != 2 * window + 1): + continue + + # check if the value at idx is a peak (both neighbors must be smaller) + if (sim[idx] > sim[idx - 1]) and (sim[idx] > sim[idx + 1]): + peak_sim = sim[idx] + else: + # define peak around idx as the max value inside of the window + values = sim[idx - window:idx + window + 1] + peak_sim = values[values.argmax()] + + # get xarray object of qobs peak, for getting the date and calculating the datetime offset + peak_obs = obs[idx] + + # calculate the time difference between the peaks + delta = peak_obs.coords['datetime'] - peak_sim.coords['datetime'] + + timing_error = np.abs(delta.values / pd.to_timedelta(resolution)) + + timing_errors.append(timing_error) + + return np.mean(timing_errors) if len(timing_errors) > 0 else np.nan + + +def calculate_all_metrics(obs: DataArray, sim: DataArray, resolution: str = "1D") -> Dict[str, float]: + """Calculate all metrics with default values. + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + resolution : str, optional + Temporal resolution of the time series in pandas format, e.g. '1D' for daily and '1H' for hourly. + + Returns + ------- + Dict[str, float] + Dictionary with keys corresponding to metric name and values corresponding to metric values. + """ + results = { + "NSE": nse(obs, sim), + "MSE": mse(obs, sim), + "RMSE": rmse(obs, sim), + "KGE": kge(obs, sim), + "Alpha-NSE": alpha_nse(obs, sim), + "Beta-NSE": beta_nse(obs, sim), + "Pearson-r": pearsonr(obs, sim), + "FHV": fdc_fhv(obs, sim), + "FMS": fdc_fms(obs, sim), + "FLV": fdc_flv(obs, sim), + "Peak-Timing": mean_peak_timing(obs, sim, resolution=resolution) + } + + return results + + +def calculate_metrics(obs: DataArray, sim: DataArray, metrics: List[str], resolution: str = "1D") -> Dict[str, float]: + """Calculate specific metrics with default values. + + Parameters + ---------- + obs : DataArray + Observed time series. + sim : DataArray + Simulated time series. + metrics : List[str] + List of metric names. + resolution : str, optional + Temporal resolution of the time series in pandas format, e.g. '1D' for daily and '1H' for hourly. + + Returns + ------- + Dict[str, float] + Dictionary with keys corresponding to metric name and values corresponding to metric values. + """ + values = {} + for metric in metrics: + if metric == 'all': + values = calculate_all_metrics(obs, sim, resolution=resolution) + break + else: + if metric.lower() == "nse": + values["NSE"] = nse(obs, sim) + elif metric.lower() == "mse": + values["MSE"] = mse(obs, sim) + elif metric.lower() == "rmse": + values["RMSE"] = rmse(obs, sim) + elif metric.lower() == "kge": + values["KGE"] = kge(obs, sim) + elif metric.lower() == "alpha-nse": + values["Alpha-NSE"] = alpha_nse(obs, sim) + elif metric.lower() == "beta-nse": + values["Beta-NSE"] = beta_nse(obs, sim) + elif metric.lower() == "pearson-r": + values["Pearson-r"] = pearsonr(obs, sim) + elif metric.lower() == "fhv": + values["FHV"] = fdc_fhv(obs, sim) + elif metric.lower() == "fms": + values["FMS"] = fdc_fms(obs, sim) + elif metric.lower() == "flv": + values["FLV"] = fdc_flv(obs, sim) + elif metric.lower() == "peak-timing": + values["Peak-Timing"] = mean_peak_timing(obs, sim, resolution=resolution) + else: + raise RuntimeError(f"Unknown metric {metric}") + + return values diff --git a/neuralhydrology/evaluation/plots.py b/neuralhydrology/evaluation/plots.py new file mode 100644 index 00000000..ea43df0e --- /dev/null +++ b/neuralhydrology/evaluation/plots.py @@ -0,0 +1,235 @@ +from typing import Tuple + +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np + + +def percentile_plot(y: np.ndarray, + y_hat: np.ndarray, + title: str = '') -> Tuple[mpl.figure.Figure, mpl.axes._subplots.Axes]: + """Plot the time series of simulated percentiles and observed values. + + Parameters + ---------- + y : np.ndarray + Array of observed values. + y_hat : np.ndarray + Array of simulated values, where the last dimension contains the samples for each time step. + title : str, optional + Title of the plot. + + Returns + ------- + Tuple[mpl.figure.Figure, mpl.axes.Axis] + The percentile plot. + """ + fig, ax = plt.subplots() + + y_median = np.median(y_hat, axis=-1).flatten() + y_25 = np.percentile(y_hat, 25, axis=-1).flatten() + y_75 = np.percentile(y_hat, 75, axis=-1).flatten() + y_10 = np.percentile(y_hat, 10, axis=-1).flatten() + y_90 = np.percentile(y_hat, 90, axis=-1).flatten() + y_05 = np.percentile(y_hat, 5, axis=-1).flatten() + y_95 = np.percentile(y_hat, 95, axis=-1).flatten() + + x = np.arange(len(y_05)) + ax.fill_between(x, y_05, y_95, color='#35B779', label='05-95 percentile') + ax.fill_between(x, y_10, y_90, color='#31688E', label='10-90 percentile') + ax.fill_between(x, y_25, y_75, color="#440154", label='25-75 percentile') + ax.plot(y_median, '-', color='red', label="median") + ax.plot(y.flatten(), '--', color='black', label="observed") + ax.legend() + ax.set_title(title) + + return fig, ax + + +def regression_plot(y: np.ndarray, + y_hat: np.ndarray, + title: str = '') -> Tuple[mpl.figure.Figure, mpl.axes._subplots.Axes]: + """Plot the time series of observed and simulated values. + + Parameters + ---------- + y : np.ndarray + Array of observed values. + y_hat : np.ndarray + Array of simulated values. + title : str, optional + Title of the plot. + + Returns + ------- + Tuple[mpl.figure.Figure, mpl.axes.Axis] + The regression plot. + """ + + fig, ax = plt.subplots() + + ax.plot(y.flatten(), label="observed", lw=1) + ax.plot(y_hat.flatten(), label="simulated", alpha=.8, lw=1) + + box = ax.get_position() + ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9]) + ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.08), ncol=2) + ax.set_title(title) + + return fig, ax + + +def uncertainty_plot(y: np.ndarray, y_hat: np.ndarray, title: str = '') -> Tuple[mpl.figure.Figure, np.ndarray]: + """Plots probability plot alongside a hydrograph with simulation percentiles. + + The probability plot itself is analogous to the calibration plot for classification tasks. The plot compares the + theoretical percentiles of the estimated conditional distributions (over time) with the respective relative + empirical counts. + The probability plot is often also referred to as probability integral transform diagram, Q-Q plot, or predictive + Q-Q plot. + + + Parameters + ---------- + y : np.ndarray + Array of observed values. + y_hat : np.ndarray + Array of simulated values. + title : str, optional + Title of the plot, by default empty. + + Returns + ------- + Tuple[mpl.figure.Figure, np.ndarray] + The uncertainty plot. + """ + + fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(7, 3), gridspec_kw={'width_ratios': [3, 5]}) + + # only take part of y to have a better zoom-in + y_flat = y.flatten() + x_bnd = np.arange(0, 400) + y_bnd_len = len(x_bnd) + + # hydrograph: + y_r = [0, 0, 0, 0, 0, 0] # used later for probability-plot + quantiles = [0.9, 0.80, 0.50, 0.20, 0.1] + labels_and_colors = { + 'labels': ['05-95 PI', '10-90 PI', '25-75 PI', '40-60 PI', '45-55 PI'], + 'colors': ['#FDE725', '#8FD744', '#21908C', '#31688E', '#443A83'] + } + for idx in range(len(quantiles)): + lb = round(50 - (quantiles[idx] * 100) / 2) + ub = round(50 + (quantiles[idx] * 100) / 2) + y_lb = np.percentile(y_hat[x_bnd, :, :], lb, axis=-1).flatten() + y_ub = np.percentile(y_hat[x_bnd, :, :], ub, axis=-1).flatten() + y_r[idx] = np.sum(((y_flat[x_bnd] > y_lb) * (y_flat[x_bnd] < y_ub))) / y_bnd_len + if idx <= 3: + axs[1].fill_between(x_bnd, + y_lb, + y_ub, + color=labels_and_colors['colors'][idx], + label=labels_and_colors['labels'][idx]) + + y_median = np.median(y_hat, axis=-1).flatten() + axs[1].plot(x_bnd, y_median[x_bnd], '-', color='red', label="median") + axs[1].plot(x_bnd, y_flat[x_bnd], '--', color='black', label="observed") + axs[1].legend(prop={'size': 5}) + axs[1].set_ylabel("runoff") + axs[1].set_xlabel("time index") + # probability-plot: + quantiles = np.arange(0, 101, 5) + y_r = quantiles * 0.0 + for idx in range(len(y_r)): + ub = quantiles[idx] + y_ub = np.percentile(y_hat[x_bnd, :, :], ub, axis=-1).flatten() + y_r[idx] = np.sum(y_flat[x_bnd] < y_ub) / y_bnd_len + + axs[0].plot([0, 1], [0, 1], 'k--') + axs[0].plot(quantiles / 100, y_r, 'ro', ms=3.0) + axs[0].set_axisbelow(True) + axs[0].yaxis.grid(color='#ECECEC', linestyle='dashed') + axs[0].xaxis.grid(color='#ECECEC', linestyle='dashed') + axs[0].xaxis.set_ticks(np.arange(0, 1, 0.2)) + axs[0].yaxis.set_ticks(np.arange(0, 1, 0.2)) + axs[0].set_xlabel("cumulative nominal quantiles") + axs[0].set_ylabel("cumulative empirical quantiles") + + axs[0].set_title(title) + + fig.tight_layout() + + return fig, axs + + +def prediction_intervall_plot(y: np.ndarray, + y_hat: np.ndarray, + title: str = '') -> Tuple[mpl.figure.Figure, np.ndarray]: + fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(7, 3), gridspec_kw={'width_ratios': [3, 5]}) + + y_flat = y.flatten() + + # only take part of y to have a better zoom-in + x_bnd = np.arange(0, 1400) + y_len = len(x_bnd) + + # hydrograph: + y_r = [0, 0, 0, 0, 0, 0] # used later for probability-plot + quantiles = [0.95, 0.9, 0.80, 0.50, 0.20, 0.1] + labels_and_colors = { + 'labels': [ + '2.5-97.5 percentile', '05-95 percentile', '10-90 percentile', '25-75 percentile', '40-60 percentile', + '45-55 percentile' + ], + 'colors': ['#FDE725', '#8FD744', '#35B779', '#21908C', '#31688E', '#443A83'] + } + for idx in range(len(quantiles)): + lb = round(50 - (quantiles[idx] * 100) / 2) + ub = round(50 + (quantiles[idx] * 100) / 2) + y_lb = np.percentile(y_hat[x_bnd, :, :], lb, axis=-1).flatten() + y_ub = np.percentile(y_hat[x_bnd, :, :], ub, axis=-1).flatten() + y_r[idx] = np.sum(((y_flat[x_bnd] > y_lb) * (y_flat[x_bnd] < y_ub))) / y_len + if idx <= 3: + axs[1].fill_between(x_bnd, + y_lb, + y_ub, + color=labels_and_colors['colors'][idx], + label=labels_and_colors['labels'][idx]) + + y_median = np.median(y_hat, axis=-1).flatten() + axs[1].plot(x_bnd, y_median[x_bnd], '-', color='red', label="median") + axs[1].plot(x_bnd, y_flat[x_bnd], '--', color='black', label="observed") + axs[1].legend(prop={'size': 5}) + + # probability-plot: + axs[0].plot([0, 1], [0, 1], 'k--') + for idx in range(1, len(quantiles) - 1): + # move description out of the way: + is_quantile_small = quantiles[idx] <= 0.5 + ha_argument = 'right' if is_quantile_small else 'left' + text_pos = 1 if is_quantile_small else 0 + l_coord = [text_pos, quantiles[idx]] if is_quantile_small else [quantiles[idx], text_pos] + + axs[0].plot(l_coord, [y_r[idx], y_r[idx]], ':', color='#ffb95a') + axs[0].text(text_pos, + y_r[idx], + f'{round(y_r[idx], 2)}', + fontsize=8, + va='center', + ha=ha_argument, + c='#ffb95a', + backgroundcolor='w') + + axs[0].plot(quantiles, y_r, 'ro-') + axs[0].set_axisbelow(True) + axs[0].yaxis.grid(color='#ECECEC', linestyle='dashed') + axs[0].xaxis.grid(color='#ECECEC', linestyle='dashed') + axs[0].xaxis.set_ticks(np.arange(0, 1, 0.2)) + axs[0].yaxis.set_ticks(np.arange(0, 1, 0.2)) + axs[0].set_xlabel("prediction intervals") + axs[0].set_ylabel("obs in quantiles") + axs[0].set_title(title) + + fig.tight_layout() + + return fig, axs diff --git a/neuralhydrology/evaluation/signatures.py b/neuralhydrology/evaluation/signatures.py new file mode 100644 index 00000000..603b0964 --- /dev/null +++ b/neuralhydrology/evaluation/signatures.py @@ -0,0 +1,697 @@ +import warnings +from datetime import datetime +from typing import Dict, List, Tuple + +from dateutil.relativedelta import relativedelta + +import numpy as np +from numba import njit +from xarray.core.dataarray import DataArray + +from neuralhydrology.data.utils import infer_frequency + + +def get_available_signatures() -> List[str]: + """Return a list of available signatures. + + Returns + ------- + List[str] + List of all available signatures. + """ + signatures = [ + "high_q_freq", "high_q_dur", "low_q_freq", "low_q_dur", "zero_q_freq", "q95", "q5", "q_mean", "hfd_mean", + "baseflow_index", "slope_fdc", "stream_elas", "runoff_ratio" + ] + return signatures + + +def calculate_all_signatures(da: DataArray, prcp: DataArray, datetime_coord: str = 'date') -> Dict[str, float]: + """Calculate all signatures with default values. + + Parameters + ---------- + da : DataArray + Array of discharge values for which the signatures will be calculated. + prcp : DataArray + Array of precipitation values. + datetime_coord : str, optional + Datetime coordinate in the passed DataArray. + + Returns + ------- + Dict[str, float] + Dictionary with signature names as keys and signature values as values. + """ + results = { + "high_q_freq": high_q_freq(da, coord=datetime_coord), + "high_q_dur": high_q_dur(da), + "low_q_freq": low_q_freq(da, coord=datetime_coord), + "low_q_dur": low_q_dur(da), + "zero_q_freq": zero_q_freq(da), + "q95": q95(da), + "q5": q5(da), + "q_mean": q_mean(da), + "hfd_mean": hfd_mean(da, coord=datetime_coord), + "baseflow_index": baseflow_index(da)[0], + "slope_fdc": slope_fdc(da), + "stream_elas": stream_elas(da, prcp, coord=datetime_coord), + "runoff_ratio": runoff_ratio(da, prcp, coord=datetime_coord) + } + return results + + +def calculate_signatures(da: DataArray, + signatures: List[str], + datetime_coord: str = 'date', + prcp: DataArray = None) -> Dict[str, float]: + """Calculate the specified signatures with default values. + + Parameters + ---------- + da : DataArray + Array of discharge values for which the signatures will be calculated. + signatures : List[str] + List of names of the signatures to calculate. + datetime_coord : str, optional + Datetime coordinate in the passed DataArray. + prcp : DataArray, optional + Array of precipitation values. Required for signatures 'runoff_ratio' and 'streamflow_elas'. + + Returns + ------- + Dict[str, float] + Dictionary with signature names as keys and signature values as values. + + Raises + ------ + ValueError + If a passed signature name does not exist. + """ + values = {} + for signature in signatures: + if signature == "high_q_freq": + values["high_q_freq"] = high_q_freq(da, coord=datetime_coord) + elif signature == "high_q_dur": + values["high_q_dur"] = high_q_dur(da) + elif signature == "low_q_freq": + values["low_q_freq"] = low_q_freq(da, coord=datetime_coord) + elif signature == "low_q_dur": + values["low_q_dur"] = low_q_dur(da) + elif signature == "zero_q_freq": + values["zero_q_freq"] = zero_q_freq(da) + elif signature == "q95": + values["q95"] = q95(da) + elif signature == "q5": + values["q5"] = q5(da) + elif signature == "q_mean": + values["q_mean"] = q_mean(da) + elif signature == "hfd_mean": + values["hfd_mean"] = hfd_mean(da, coord=datetime_coord) + elif signature == "baseflow_index": + values["baseflow_index"] = baseflow_index(da, coord=datetime_coord)[0] + elif signature == "slope_fdc": + values["slope_fdc"] = slope_fdc(da) + elif signature == "runoff_ratio": + values["runoff_ratio"] = runoff_ratio(da, prcp, coord=datetime_coord) + elif signature == "stream_elas": + values["stream_elas"] = stream_elas(da, prcp, coord=datetime_coord) + else: + ValueError(f"Unknown signatures {signature}") + return values + + +@njit +def _split_list(alist: list, min_length: int = 0) -> list: + """Split a list of indices into lists of consecutive indices of at least length `min_length`. """ + newlist = [] + start = 0 + for index, value in enumerate(alist): + if index < len(alist) - 1: + if alist[index + 1] > value + 1: + end = index + 1 + if end - start >= min_length: + newlist.append(alist[start:end]) + start = end + else: + if len(alist) - start >= min_length: + newlist.append(alist[start:len(alist)]) + return newlist + + +def high_q_dur(da: DataArray, threshold: float = 9.) -> float: + """Calculate high-flow duration. + + Average duration of high-flow events (number of consecutive steps >`threshold` times the median flow) [#]_, + [#]_ (Table 2). + + Parameters + ---------- + da : DataArray + Array of flow values. + threshold : float, optional + High-flow threshold. Values larger than ``threshold * median`` are considered high flows. + + Returns + ------- + float + High-flow duration + + References + ---------- + .. [#] Clausen, B. and Biggs, B. J. F.: Flow variables for ecological studies in temperate streams: groupings based + on covariance. Journal of Hydrology, 2000, 237, 184--197, doi:10.1016/S0022-1694(00)00306-1 + .. [#] Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures. + Hydrology and Earth System Sciences, 2015, 19, 3951--3968, doi:10.5194/hess-19-3951-2015 + """ + median_flow = float(da.median()) + idx = np.where(da.values > threshold * median_flow)[0] + if len(idx) > 0: + periods = _split_list(idx) + hqd = np.mean([len(p) for p in periods]) + else: + hqd = np.nan + return hqd + + +def low_q_dur(da: DataArray, threshold: float = 0.2) -> float: + """Calculate low-flow duration. + + Average duration of low-flow events (number of consecutive steps <`threshold` times the median flow) [#]_, + [#]_ (Table 2). + + Parameters + ---------- + da : DataArray + Array of flow values. + threshold : float, optional + Low-flow threshold. Values below ``threshold * median`` are considered low flows. + + Returns + ------- + float + Low-flow duration + + References + ---------- + .. [#] Olden, J. D. and Poff, N. L.: Redundancy and the choice of hydrologic indices for characterizing streamflow + regimes. River Research and Applications, 2003, 19, 101--121, doi:10.1002/rra.700 + .. [#] Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures. + Hydrology and Earth System Sciences, 2015, 19, 3951--3968, doi:10.5194/hess-19-3951-2015 + """ + + mean_flow = float(da.mean()) + idx = np.where(da.values < threshold * mean_flow)[0] + if len(idx) > 0: + periods = _split_list(idx) + lqd = np.mean([len(p) for p in periods]) + else: + lqd = np.nan + return lqd + + +def zero_q_freq(da: DataArray) -> float: + """Calculate zero-flow frequency. + + Frequency of steps with zero discharge. + + Parameters + ---------- + da : DataArray + Array of flow values. + + Returns + ------- + float + Zero-flow frequency. + """ + + # number of steps with zero flow + n_steps = (da == 0).sum() + + return float(n_steps / len(da)) + + +def high_q_freq(da: DataArray, coord: str = 'date', threshold: float = 9.) -> float: + """Calculate high-flow frequency. + + Frequency of high-flow events (>`threshold` times the median flow) [#]_, [#]_ (Table 2). + + Parameters + ---------- + da : DataArray + Array of flow values. + coord : str, optional + Datetime coordinate in `da`. + threshold : float, optional + High-flow threshold. Values larger than ``threshold * median`` are considered high flows. + + Returns + ------- + float + High-flow frequency + + References + ---------- + .. [#] Clausen, B. and Biggs, B. J. F.: Flow variables for ecological studies in temperate streams: groupings based + on covariance. Journal of Hydrology, 2000, 237, 184--197, doi:10.1016/S0022-1694(00)00306-1 + .. [#] Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures. + Hydrology and Earth System Sciences, 2015, 19, 3951--3968, doi:10.5194/hess-19-3951-2015 + """ + + # determine the date of the first January 1st in the data period + first_date = da.coords[coord][0].values.astype('datetime64[s]').astype(datetime) + last_date = da.coords[coord][-1].values.astype('datetime64[s]').astype(datetime) + + if first_date == datetime.strptime(f'{first_date.year}-01-01', '%Y-%m-%d'): + start_date = first_date + else: + start_date = datetime.strptime(f'{first_date.year + 1}-01-01', '%Y-%m-%d') + + # end date of the first full year period + end_date = start_date + relativedelta(years=1) - relativedelta(seconds=1) + + # determine the median flow over the entire period + median_flow = da.median(skipna=True) + + hqfs = [] + while end_date < last_date: + + data = da.sel({coord: slice(start_date, end_date)}) + + # number of steps with discharge higher than threshold * median in a one year period + n_steps = (data > (threshold * median_flow)).sum() + + hqfs.append(float(n_steps)) + + start_date += relativedelta(years=1) + end_date += relativedelta(years=1) + + return np.mean(hqfs) + + +def low_q_freq(da: DataArray, coord: str = 'date', threshold: float = 0.2) -> float: + """Calculate Low-flow frequency. + + Frequency of low-flow events (<`threshold` times the median flow) [#]_, [#]_ (Table 2). + + Parameters + ---------- + da : DataArray + Array of flow values. + coord : str, optional + Datetime coordinate in `da`. + threshold : float, optional + Low-flow threshold. Values below ``threshold * median`` are considered low flows. + + Returns + ------- + float + Low-flow frequency + + References + ---------- + .. [#] Olden, J. D. and Poff, N. L.: Redundancy and the choice of hydrologic indices for characterizing streamflow + regimes. River Research and Applications, 2003, 19, 101--121, doi:10.1002/rra.700 + .. [#] Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures. + Hydrology and Earth System Sciences, 2015, 19, 3951--3968, doi:10.5194/hess-19-3951-2015 + """ + + # determine the date of the first January 1st in the data period + first_date = da.coords[coord][0].values.astype('datetime64[s]').astype(datetime) + last_date = da.coords[coord][-1].values.astype('datetime64[s]').astype(datetime) + + if first_date == datetime.strptime(f'{first_date.year}-01-01', '%Y-%m-%d'): + start_date = first_date + else: + start_date = datetime.strptime(f'{first_date.year + 1}-01-01', '%Y-%m-%d') + + # end date of the first full year period + end_date = start_date + relativedelta(years=1) - relativedelta(seconds=1) + + # determine the mean flow over the entire period + mean_flow = da.mean(skipna=True) + + lqfs = [] + while end_date < last_date: + + data = da.sel({coord: slice(start_date, end_date)}) + + # number of steps with discharge lower than threshold * median in a one year period + n_steps = (data < (threshold * mean_flow)).sum() + + lqfs.append(float(n_steps)) + + start_date += relativedelta(years=1) + end_date += relativedelta(years=1) + + return np.mean(lqfs) + + +def hfd_mean(da: DataArray, coord: str = 'date') -> float: + """Calculate mean half-flow duration. + + Mean half-flow date (step on which the cumulative discharge since October 1st + reaches half of the annual discharge) [#]_. + + Parameters + ---------- + da : DataArray + Array of flow values. + coord : str, optional + Datetime coordinate name in `da`. + + Returns + ------- + float + Mean half-flow duration + + References + ---------- + .. [#] Court, A.: Measures of streamflow timing. Journal of Geophysical Research (1896-1977), 1962, 67, 4335--4339, + doi:10.1029/JZ067i011p04335 + """ + + # determine the date of the first October 1st in the data period + first_date = da.coords[coord][0].values.astype('datetime64[s]').astype(datetime) + last_date = da.coords[coord][-1].values.astype('datetime64[s]').astype(datetime) + + if first_date > datetime.strptime(f'{first_date.year}-10-01', '%Y-%m-%d'): + start_date = datetime.strptime(f'{first_date.year + 1}-10-01', '%Y-%m-%d') + else: + start_date = datetime.strptime(f'{first_date.year}-10-01', '%Y-%m-%d') + + end_date = start_date + relativedelta(years=1) - relativedelta(seconds=1) + + doys = [] + while end_date < last_date: + + # compute cumulative sum for the selected period + data = da.sel({coord: slice(start_date, end_date)}) + cs = data.cumsum(skipna=True) + + # find steps with more cumulative discharge than the half annual sum + hf_steps = np.where(~np.isnan(cs.where(cs > data.sum(skipna=True) / 2).values))[0] + + # ignore days without discharge + if len(hf_steps) > 0: + # store the first step in the result array + doys.append(hf_steps[0]) + + start_date += relativedelta(years=1) + end_date += relativedelta(years=1) + + return np.mean(doys) + + +def q5(da: DataArray) -> float: + """Calculate 5th flow quantile. + + Parameters + ---------- + da : DataArray + Array of flow values. + + Returns + ------- + float + 5th flow quantile. + """ + + return float(da.quantile(0.05)) + + +def q95(da: DataArray) -> float: + """Calculate 95th flow quantile. + + Parameters + ---------- + da : DataArray + Array of flow values. + + Returns + ------- + float + 95th flow quantile. + """ + return float(da.quantile(0.95)) + + +def q_mean(da: DataArray) -> float: + """Calculate mean discharge. + + Parameters + ---------- + da : DataArray + Array of flow values. + + Returns + ------- + float + Mean discharge. + """ + return float(da.mean()) + + +@njit +def _baseflow_index_jit(streamflow: np.ndarray, alpha: float, warmup: int, n_passes: int) -> Tuple[float, np.ndarray]: + non_nan_indices = np.where(~np.isnan(streamflow))[0] + non_nan_streamflow_runs = _split_list(non_nan_indices, min_length=warmup + 1) + if len(non_nan_streamflow_runs) == 0: + # no consecutive run of non-NaN values is long enough to calculate baseflow. + return np.nan, np.full_like(streamflow, np.nan) + + streamflow_sum = 0 + overall_baseflow = np.full_like(streamflow, np.nan) + for non_nan_run in non_nan_streamflow_runs: + streamflow_run = streamflow[non_nan_run] + + # mirror discharge of length 'window' at the start and end + padded_streamflow = np.zeros((len(streamflow_run) + 2 * warmup)) + padded_streamflow[warmup:-warmup] = streamflow_run + padded_streamflow[:warmup] = streamflow_run[1:warmup + 1][::-1] + padded_streamflow[-warmup:] = streamflow_run[-warmup - 1:-1][::-1] + + baseflow = padded_streamflow + for _ in range(n_passes): + new_baseflow = np.zeros_like(padded_streamflow) + quickflow = baseflow[0] + for i in range(1, len(padded_streamflow)): + quickflow = alpha * quickflow + (1 + alpha) * (baseflow[i] - baseflow[i - 1]) / 2 + if quickflow > 0: + new_baseflow[i] = baseflow[i] - quickflow + else: + new_baseflow[i] = baseflow[i] + + # switch between forward and backward passes. Next iteration's input is the baseflow generated in this iteration + baseflow = np.flip(new_baseflow) + + overall_baseflow[non_nan_run] = baseflow[warmup:-warmup] + streamflow_sum += np.sum(streamflow_run) + + bf_index = np.nansum(overall_baseflow) / streamflow_sum + + return bf_index, overall_baseflow + + +def baseflow_index(da: DataArray, + alpha: float = 0.98, + warmup: int = 30, + n_passes: int = None, + coord: str = 'date') -> Tuple[float, DataArray]: + """Calculate baseflow index. + + Ratio of mean baseflow to mean discharge [#]_. If `da` contains NaN values, the baseflow is calculated for each + consecutive segment of more than `warmup` non-NaN values. + + Parameters + ---------- + da : DataArray + Array of flow values. + alpha : float, optional + alpha filter parameter. + warmup : int, optional + Number of warmup steps. + n_passes : int, optional + Number of passes (alternating forward and backward) to perform. Should be an odd number. If None, will use + 3 for daily and 9 for hourly data and fail for all other input frequencies. + coord : str, optional + Datetime coordinate in `da`, used to infer the frequency if `n_passes` is None. + + Returns + ------- + Tuple[float, DataArray] + Baseflow index and baseflow array. The baseflow array contains NaNs wherever no baseflow was + calculated due to NaNs in `da`. + + Raises + ------ + ValueError + If `da` has a frequency other than daily or hourly and `n_passes` is None. + + References + ---------- + .. [#] Ladson, T. R., Brown, R., Neal, B., and Nathan, R.: A Standard Approach to Baseflow Separation Using The + Lyne and Hollick Filter. Australasian Journal of Water Resources, Taylor & Francis, 2013, 17, 25--34, + doi:10.7158/13241583.2013.11465417 + """ + + if n_passes is None: + freq = infer_frequency(da[coord].values) + if freq == '1D': + n_passes = 3 + elif freq == '1H': + n_passes = 9 + else: + raise ValueError(f'For frequencies other than daily or hourly, n_passes must be specified.') + if n_passes % 2 != 1: + warnings.warn('n_passes should be an even number. The returned baseflow will be reversed.') + + # call jit compiled function to calculate baseflow + bf_index, baseflow = _baseflow_index_jit(da.values, alpha, warmup, n_passes) + + # parse baseflow as a DataArray using the coordinates of the streamflow array + da_baseflow = da.copy() + da_baseflow.data = baseflow + + return bf_index, da_baseflow + + +def slope_fdc(da: DataArray, lower_quantile: float = 0.33, upper_quantile: float = 0.66) -> float: + """Calculates flow duration curve slope. + + Slope of the flow duration curve (between the log-transformed `lower_quantile` and `upper_quantile`) [#]_ (Eq. 3). + + Parameters + ---------- + da : DataArray + Array of flow values. + lower_quantile : float, optional + Lower quantile to use in slope calculation. + upper_quantile : float, optional + Upper quantile to use in slope calculation. + + Returns + ------- + float + Slope of the flow duration curve. + + References + ---------- + .. [#] Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical + analysis of hydrologic similarity based on catchment function in the eastern USA. + Hydrology and Earth System Sciences, 2011, 15, 2895--2911, doi:10.5194/hess-15-2895-2011 + """ + # sort discharge by descending order + fdc = da.sortby(da, ascending=False) + + # get idx of lower and upper quantile + idx_lower = np.round(lower_quantile * len(fdc)).astype(int) + idx_upper = np.round(upper_quantile * len(fdc)).astype(int) + + value = (np.log(fdc[idx_lower].values + + 1e-8)) - np.log(fdc[idx_upper].values + 1e-8) / (upper_quantile - lower_quantile) + + return value + + +def runoff_ratio(da: DataArray, prcp: DataArray, coord: str = 'date') -> float: + """Calculate runoff ratio. + + Runoff ratio (ratio of mean discharge to mean precipitation) [#]_ (Eq. 2). + + Parameters + ---------- + da : DataArray + Array of flow values. + prcp : DataArray + Array of precipitation values. + coord : str, optional + Datetime dimension name in `da`. + + Returns + ------- + float + Runoff ratio. + + References + ---------- + .. [#] Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical + analysis of hydrologic similarity based on catchment function in the eastern USA. + Hydrology and Earth System Sciences, 2011, 15, 2895--2911, doi:10.5194/hess-15-2895-2011 + """ + # rename precip coordinate name (to avoid problems with 'index' or 'date') + prcp = prcp.rename({list(prcp.coords.keys())[0]: coord}) + + # slice prcp to the same time window as the discharge + prcp = prcp.sel({coord: slice(da.coords[coord][0], da.coords[coord][-1])}) + + # calculate runoff ratio + value = da.mean() / prcp.mean() + + return float(value) + + +def stream_elas(da: DataArray, prcp: DataArray, coord: str = 'date') -> float: + """Calculate stream elasticity. + + Streamflow precipitation elasticity (sensitivity of streamflow to changes in precipitation at + the annual time scale) [#]_. + + Parameters + ---------- + da : DataArray + Array of flow values. + prcp : DataArray + Array of precipitation values. + coord : str, optional + Datetime dimension name in `da`. + + Returns + ------- + float + Stream elasticity. + + References + ---------- + .. [#] Sankarasubramanian, A., Vogel, R. M., and Limbrunner, J. F.: Climate elasticity of streamflow in the + United States. Water Resources Research, 2001, 37, 1771--1781, doi:10.1029/2000WR900330 + """ + # rename precip coordinate name (to avoid problems with 'index' or 'date') + prcp = prcp.rename({list(prcp.coords.keys())[0]: coord}) + + # slice prcp to the same time window as the discharge + prcp = prcp.sel({coord: slice(da.coords[coord][0], da.coords[coord][-1])}) + + # determine the date of the first October 1st in the data period + first_date = da.coords[coord][0].values.astype('datetime64[s]').astype(datetime) + last_date = da.coords[coord][-1].values.astype('datetime64[s]').astype(datetime) + + if first_date > datetime.strptime(f'{first_date.year}-10-01', '%Y-%m-%d'): + start_date = datetime.strptime(f'{first_date.year + 1}-10-01', '%Y-%m-%d') + else: + start_date = datetime.strptime(f'{first_date.year}-10-01', '%Y-%m-%d') + + end_date = start_date + relativedelta(years=1) - relativedelta(seconds=1) + + # mask only valid time steps (only discharge has missing values) + idx = (da >= 0) & (~da.isnull()) + da = da[idx] + prcp = prcp[idx] + + # calculate long-term means + q_mean_total = da.mean() + p_mean_total = prcp.mean() + + values = [] + while end_date < last_date: + q = da.sel({coord: slice(start_date, end_date)}) + p = prcp.sel({coord: slice(start_date, end_date)}) + + val = (q.mean() - q_mean_total) / (p.mean() - p_mean_total) * (p_mean_total / q_mean_total) + values.append(val) + + start_date += relativedelta(years=1) + end_date += relativedelta(years=1) + + return np.median([float(v) for v in values]) diff --git a/neuralhydrology/evaluation/tester.py b/neuralhydrology/evaluation/tester.py new file mode 100644 index 00000000..e001bc71 --- /dev/null +++ b/neuralhydrology/evaluation/tester.py @@ -0,0 +1,458 @@ +import logging +import pickle +import random +import re +import sys +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Union + +import numpy as np +import pandas as pd +import torch +import xarray +from torch.utils.data import DataLoader +from tqdm import tqdm + +from neuralhydrology.data import get_dataset +from neuralhydrology.data.basedataset import BaseDataset +from neuralhydrology.data.utils import load_basin_file, sort_frequencies +from neuralhydrology.evaluation import plots +from neuralhydrology.evaluation.metrics import calculate_metrics +from neuralhydrology.modelzoo import get_model +from neuralhydrology.modelzoo.basemodel import BaseModel +from neuralhydrology.training.logger import Logger +from neuralhydrology.utils.config import Config +from neuralhydrology.utils.errors import NoTrainDataError + +LOGGER = logging.getLogger(__name__) + + +class BaseTester(object): + """Base class to run inference on a model. + + Use subclasses of this class to evaluate a trained model on its train, test, or validation period. + For regression settings, `RegressionTester` is used; for uncertainty prediction, `UncertaintyTester`. + + Parameters + ---------- + cfg : Config + The run configuration. + run_dir : Path + Path to the run directory. + period : {'train', 'validation', 'test'}, optional + The period to evaluate, by default 'test'. + init_model : bool, optional + If True, the model weights will be initialized with the checkpoint from the last available epoch in `run_dir`. + """ + + def __init__(self, cfg: Config, run_dir: Path, period: str = "test", init_model: bool = True): + self.cfg = cfg + self.run_dir = run_dir + self.init_model = init_model + if period in ["train", "validation", "test"]: + self.period = period + else: + raise ValueError(f'Invalid period {period}. Must be one of ["train", "validation", "test"]') + + # determine device + self._set_device() + + if self.init_model: + self.model = get_model(cfg).to(self.device) + + # pre-initialize variables, defined in class methods + self.basins = None + self.scaler = None + self.id_to_int = {} + self.additional_features = [] + + # placeholder to store cached validation data + self.cached_datasets = {} + + self._load_run_data() + + def _set_device(self): + if self.cfg.device is not None: + if "cuda" in self.cfg.device: + gpu_id = int(self.cfg.device.split(':')[-1]) + if gpu_id > torch.cuda.device_count(): + raise RuntimeError(f"This machine does not have GPU #{gpu_id} ") + else: + self.device = torch.device(self.cfg.device) + else: + self.device = torch.device("cpu") + else: + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + def _load_run_data(self): + """Load run specific data from run directory""" + + # get list of basins + self.basins = load_basin_file(getattr(self.cfg, f"{self.period}_basin_file")) + + # load feature scaler + scaler_file = self.run_dir / "train_data" / "train_data_scaler.p" + with scaler_file.open('rb') as fp: + self.scaler = pickle.load(fp) + + # load basin_id to integer dictionary for one-hot-encoding + if self.cfg.use_basin_id_encoding: + file_path = self.run_dir / "train_data" / "id_to_int.p" + with file_path.open("rb") as fp: + self.id_to_int = pickle.load(fp) + + for file in self.cfg.additional_feature_files: + with open(file, "rb") as fp: + self.additional_features.append(pickle.load(fp)) + + def _get_weight_file(self, epoch: int): + """Get file path to weight file""" + if epoch is None: + weight_file = sorted(list(self.run_dir.glob('model_epoch*.pt')))[-1] + else: + weight_file = self.run_dir / f"model_epoch{str(epoch).zfill(3)}.pt" + + return weight_file + + def _load_weights(self, epoch: int = None): + """Load weights of a certain (or the last) epoch into the model.""" + weight_file = self._get_weight_file(epoch) + + LOGGER.info(f"Using the model weights from {weight_file}") + self.model.load_state_dict(torch.load(weight_file, map_location=self.device)) + + def _get_dataset(self, basin: str) -> BaseDataset: + """Get dataset for a single basin.""" + ds = get_dataset(cfg=self.cfg, + is_train=False, + period=self.period, + basin=basin, + additional_features=self.additional_features, + id_to_int=self.id_to_int, + scaler=self.scaler) + return ds + + def evaluate(self, + epoch: int = None, + save_results: bool = True, + metrics: Union[list, dict] = [], + model: torch.nn.Module = None, + experiment_logger: Logger = None) -> dict: + """Evaluate the model. + + Parameters + ---------- + epoch : int, optional + Define a specific epoch to evaluate. By default, the weights of the last epoch are used. + save_results : bool, optional + If True, stores the evaluation results in the run directory. By default, True. + metrics : Union[list, dict], optional + List of metrics to compute during evaluation. Can also be a dict that specifies per-target metrics + model : torch.nn.Module, optional + If a model is passed, this is used for validation. + experiment_logger : Logger, optional + Logger can be passed during training to log metrics + + Returns + ------- + dict + A dictionary containing one xarray per basin with the evaluation results. + """ + if model is None: + if self.init_model: + self._load_weights(epoch=epoch) + model = self.model + else: + raise RuntimeError("No model was initialized for the evaluation") + + # during validation, depending on settings, only evaluate on a random subset of basins + basins = self.basins + if self.period == "validation": + if len(basins) > self.cfg.validate_n_random_basins: + random.shuffle(basins) + basins = basins[:self.cfg.validate_n_random_basins] + + # force model to train-mode when doing mc-dropout evaluation + if self.cfg.mc_dropout: + model.train() + else: + model.eval() + + results = defaultdict(dict) + + pbar = tqdm(basins, file=sys.stdout) + pbar.set_description('# Validation' if self.period == "validation" else "# Evaluation") + + for basin in pbar: + + if self.cfg.cache_validation_data and basin in self.cached_datasets.keys(): + ds = self.cached_datasets[basin] + else: + try: + ds = self._get_dataset(basin) + except NoTrainDataError as error: + # skip basin + continue + if self.cfg.cache_validation_data and self.period == "validation": + self.cached_datasets[basin] = ds + + loader = DataLoader(ds, batch_size=self.cfg.batch_size, num_workers=0) + + y_hat, y = self._evaluate(model, loader, ds.frequencies) + + predict_last_n = self.cfg.predict_last_n + seq_length = self.cfg.seq_length + # if predict_last_n/seq_length are int, there's only one frequency + if isinstance(predict_last_n, int): + predict_last_n = {ds.frequencies[0]: predict_last_n} + if isinstance(seq_length, int): + seq_length = {ds.frequencies[0]: seq_length} + lowest_freq = sort_frequencies(ds.frequencies)[0] + for freq in ds.frequencies: + if predict_last_n[freq] == 0: + continue # this frequency is not being predicted + results[basin][freq] = {} + + # rescale predictions + y_hat_freq = y_hat[freq] * self.scaler["xarray_stds"][self.cfg.target_variables].to_array().values + y_freq = y[freq] * self.scaler["xarray_stds"][self.cfg.target_variables].to_array().values + if self.cfg.zero_center_target: + y_hat_freq = y_hat_freq + self.scaler["xarray_means"][self.cfg.target_variables].to_array().values + y_freq = y_freq + self.scaler["xarray_means"][self.cfg.target_variables].to_array().values + + # create xarray + data = self._create_xarray(y_hat_freq, y_freq) + + # get maximum warmup-offset across all frequencies + offsets = { + freq: (seq_length[freq] - predict_last_n[freq]) * pd.to_timedelta(freq) for freq in ds.frequencies + } + max_offset_freq = max(offsets, key=offsets.get) + start_date = ds.get_period_start(basin) + offsets[max_offset_freq] + + # determine the end of the first sequence (first target in sequence-to-one) + # we use the end_date stored in the dataset, which also covers issues with per-basin different periods + end_date = ds.dates[basin]["end_dates"][0] \ + + pd.Timedelta(days=1, seconds=-1) \ + - pd.to_timedelta(max_offset_freq) * (predict_last_n[max_offset_freq] - 1) + date_range = pd.date_range(start=start_date, end=end_date, freq=lowest_freq) + if len(date_range) != data[f"{self.cfg.target_variables[0]}_obs"][1].shape[0]: + raise ValueError("Evaluation date range does not match generated predictions.") + + frequency_factor = pd.to_timedelta(lowest_freq) // pd.to_timedelta(freq) + freq_range = pd.timedelta_range(end=(frequency_factor - 1) * pd.to_timedelta(freq), + periods=predict_last_n[freq], + freq=freq) + + xr = xarray.Dataset(data_vars=data, coords={'date': date_range, 'time_step': freq_range}) + results[basin][freq]['xr'] = xr + + # only warn once per freq + if frequency_factor < predict_last_n[freq] and basin == basins[0]: + tqdm.write(f'Metrics for {freq} are calculated over last {frequency_factor} elements only. ' + f'Ignoring {predict_last_n[freq] - frequency_factor} predictions per sequence.') + + if metrics: + for target_variable in self.cfg.target_variables: + # stack dates and time_steps so we don't just evaluate every 24H when use_frequencies=[1D, 1H] + obs = xr.isel(time_step=slice(-frequency_factor, None)) \ + .stack(datetime=['date', 'time_step'])[f"{target_variable}_obs"] + obs['datetime'] = [c[0] + c[1] for c in obs.coords['datetime'].values] + # check if not empty (in case no observations exist in this period + if not all(obs.isnull()): + sim = xr.isel(time_step=slice(-frequency_factor, None)) \ + .stack(datetime=['date', 'time_step'])[f"{target_variable}_sim"] + sim['datetime'] = [c[0] + c[1] for c in sim.coords['datetime'].values] + + # clip negative predictions to zero, if variable is listed in config 'clip_target_to_zero' + if target_variable in self.cfg.clip_targets_to_zero: + sim = xarray.where(sim < 0, 0, sim) + + if 'samples' in sim.dims: + sim = sim.mean(dim='samples') + values = calculate_metrics( + obs, + sim, + metrics=metrics if isinstance(metrics, list) else metrics[target_variable], + resolution=freq) + # add variable identifier to metrics if needed + if len(self.cfg.target_variables) > 1: + values = {f"{target_variable}_{key}": val for key, val in values.items()} + # add frequency identifier to metrics if needed + if len(ds.frequencies) > 1: + values = {f"{key}_{freq}": val for key, val in values.items()} + if experiment_logger is not None: + experiment_logger.log_step(**values) + for k, v in values.items(): + results[basin][freq][k] = v + + if (self.period == "validation") and (self.cfg.log_n_figures > 0): + self._create_and_log_figures(results, experiment_logger, epoch) + + if save_results: + self._save_results(results, epoch) + + return results + + def _create_and_log_figures(self, results: dict, experiment_logger: Logger, epoch: int): + basins = list(results.keys()) + random.shuffle(basins) + for target_var in self.cfg.target_variables: + max_figures = min(self.cfg.validate_n_random_basins, self.cfg.log_n_figures, len(basins)) + for freq in results[basins[0]].keys(): + figures = [] + for i in range(max_figures): + xr = results[basins[i]][freq]['xr'] + obs = xr[f"{target_var}_obs"].values + sim = xr[f"{target_var}_sim"].values + figures.append( + self._get_plots( + obs, sim, title=f"{target_var} - Basin {basins[i]} - Epoch {epoch} - Frequency {freq}")[0]) + # make sure the preamble is a valid file name + experiment_logger.log_figures(figures, freq, preamble=re.sub(r"[^A-Za-z0-9\._\-]+", "", target_var)) + + def _save_results(self, results: dict, epoch: int = None): + # use name of weight file as part of the result folder name + weight_file = self._get_weight_file(epoch=epoch) + + result_file = self.run_dir / self.period / weight_file.stem / f"{self.period}_results.p" + result_file.parent.mkdir(parents=True, exist_ok=True) + + with result_file.open("wb") as fp: + pickle.dump(results, fp) + + LOGGER.info(f"Stored results at {result_file}") + + def _evaluate(self, model: BaseModel, loader: DataLoader, frequencies: List[str]): + """Evaluate model""" + predict_last_n = self.cfg.predict_last_n + if isinstance(predict_last_n, int): + predict_last_n = {frequencies[0]: predict_last_n} # if predict_last_n is int, there's only one frequency + + preds, obs = {}, {} + with torch.no_grad(): + for data in loader: + + for key in data: + data[key] = data[key].to(self.device) + predictions = self._generate_predictions(model, data) + + for freq in frequencies: + if predict_last_n[freq] == 0: + continue # no predictions for this frequency + freq_key = '' if len(frequencies) == 1 else f'_{freq}' + y_hat_sub, y_sub = self._subset_targets(model, data, predictions, predict_last_n[freq], freq_key) + + if freq not in preds: + preds[freq] = y_hat_sub.detach().cpu() + obs[freq] = y_sub.cpu() + else: + preds[freq] = torch.cat((preds[freq], y_hat_sub.detach().cpu()), 0) + obs[freq] = torch.cat((obs[freq], y_sub.detach().cpu()), 0) + + for freq in preds.keys(): + preds[freq] = preds[freq].numpy() + obs[freq] = obs[freq].numpy() + + return preds, obs + + def _generate_predictions(self, model: BaseModel, data: Dict[str, torch.Tensor]): + raise NotImplementedError + + def _subset_targets(self, model: BaseModel, data: Dict[str, torch.Tensor], predictions: np.ndarray, + predict_last_n: int, freq: str): + raise NotImplementedError + + def _create_xarray(self, y_hat: np.ndarray, y: np.ndarray): + raise NotImplementedError + + def _get_plots(self, qobs: np.ndarray, qsim: np.ndarray, title: str): + raise NotImplementedError + + +class RegressionTester(BaseTester): + """Tester class to run inference on a regression model. + + Use the `evaluate` method of this class to evaluate a trained model on its train, test, or validation period. + + Parameters + ---------- + cfg : Config + The run configuration. + run_dir : Path + Path to the run directory. + period : {'train', 'validation', 'test'} + The period to evaluate. + init_model : bool, optional + If True, the model weights will be initialized with the checkpoint from the last available epoch in `run_dir`. + """ + + def __init__(self, cfg: Config, run_dir: Path, period: str = "test", init_model: bool = True): + super(RegressionTester, self).__init__(cfg, run_dir, period, init_model) + + def _generate_predictions(self, model: BaseModel, data: Dict[str, torch.Tensor]): + return model(data) + + def _subset_targets(self, model: BaseModel, data: Dict[str, torch.Tensor], predictions: np.ndarray, + predict_last_n: np.ndarray, freq: str): + y_hat_sub = predictions[f'y_hat{freq}'][:, -predict_last_n:, :] + y_sub = data[f'y{freq}'][:, -predict_last_n:, :] + return y_hat_sub, y_sub + + def _create_xarray(self, y_hat: np.ndarray, y: np.ndarray): + data = {} + for i, var in enumerate(self.cfg.target_variables): + data[f"{var}_obs"] = (('date', 'time_step'), y[:, :, i]) + data[f"{var}_sim"] = (('date', 'time_step'), y_hat[:, :, i]) + return data + + def _get_plots(self, qobs: np.ndarray, qsim: np.ndarray, title: str): + return plots.regression_plot(qobs, qsim, title) + + +class UncertaintyTester(BaseTester): + """Tester class to run inference on an uncertainty model. + + Use the `evaluate` method of this class to evaluate a trained model on its train, test, or validation period. + + Parameters + ---------- + cfg : Config + The run configuration. + run_dir : Path + Path to the run directory. + period : {'train', 'validation', 'test'} + The period to evaluate. + init_model : bool, optional + If True, the model weights will be initialized with the checkpoint from the last available epoch in `run_dir`. + """ + + def __init__(self, cfg: Config, run_dir: Path, period: str = "test", init_model: bool = True): + super(UncertaintyTester, self).__init__(cfg, run_dir, period, init_model) + + def _generate_predictions(self, model: BaseModel, data: Dict[str, torch.Tensor]): + if self.cfg.mc_dropout: + return model.sample(data, self.cfg.n_samples) + else: + raise ValueError(f"Unknown uncertainty head {self._head}") + + def _subset_targets(self, + model: BaseModel, + data: Dict[str, torch.Tensor], + predictions: np.ndarray, + predict_last_n: int, + freq: str = None): + y_hat_sub = predictions # predictions are already subset by the sample functions + y_sub = data['y'][:, -predict_last_n:, :] + return y_hat_sub, y_sub + + def _create_xarray(self, y_hat: np.ndarray, y: np.ndarray): + data = {} + var = self.cfg.target_variables[0] + data[f"{var}_obs"] = (('date', 'time_step'), y[:, :, 0]) + data[f"{var}_sim"] = (('date', 'time_step', 'samples'), y_hat[:, :, :]) + return data + + def _get_plots(self, qobs: np.ndarray, qsim: np.ndarray, title: str): + return plots.uncertainty_plot(qobs, qsim, title) diff --git a/neuralhydrology/modelzoo/__init__.py b/neuralhydrology/modelzoo/__init__.py new file mode 100644 index 00000000..0a30816e --- /dev/null +++ b/neuralhydrology/modelzoo/__init__.py @@ -0,0 +1,45 @@ +import torch.nn as nn + +from neuralhydrology.modelzoo.cudalstm import CudaLSTM +from neuralhydrology.modelzoo.ealstm import EALSTM +from neuralhydrology.modelzoo.embcudalstm import EmbCudaLSTM +from neuralhydrology.modelzoo.lstm import LSTM +from neuralhydrology.modelzoo.odelstm import ODELSTM +from neuralhydrology.modelzoo.multifreqlstm import MultiFreqLSTM +from neuralhydrology.utils.config import Config + +SINGLE_FREQ_MODELS = ["cudalstm", "ealstm", "lstm", "embcudalstm"] + + +def get_model(cfg: Config) -> nn.Module: + """Get model object, depending on the run configuration. + + Parameters + ---------- + cfg : Config + The run configuration. + + Returns + ------- + nn.Module + A new model instance of the type specified in the config. + """ + if cfg.model in SINGLE_FREQ_MODELS and len(cfg.use_frequencies) > 1: + raise ValueError(f"Model {cfg.model} does not support multiple frequencies.") + + if cfg.model == "cudalstm": + model = CudaLSTM(cfg=cfg) + elif cfg.model == "ealstm": + model = EALSTM(cfg=cfg) + elif cfg.model == "lstm": + model = LSTM(cfg=cfg) + elif cfg.model == "embcudalstm": + model = EmbCudaLSTM(cfg=cfg) + elif cfg.model == "multifreqlstm": + model = MultiFreqLSTM(cfg=cfg) + elif cfg.model == "odelstm": + model = ODELSTM(cfg=cfg) + else: + raise NotImplementedError(f"{cfg.model} not implemented or not linked in `get_model()`") + + return model diff --git a/neuralhydrology/modelzoo/basemodel.py b/neuralhydrology/modelzoo/basemodel.py new file mode 100644 index 00000000..212112d0 --- /dev/null +++ b/neuralhydrology/modelzoo/basemodel.py @@ -0,0 +1,27 @@ +from typing import Dict + +import torch +import torch.nn as nn + +from neuralhydrology.utils.config import Config + + +class BaseModel(nn.Module): + + def __init__(self, cfg: Config): + super(BaseModel, self).__init__() + self.cfg = cfg + + self.output_size = len(cfg.target_variables) + + def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + raise NotImplementedError + + def sample(self, data: Dict[str, torch.Tensor], n_samples: int) -> torch.Tensor: + predict_last_n = self.cfg.predict_last_n + samples = torch.zeros(data['x_d'].shape[0], predict_last_n, n_samples) + for i in range(n_samples): + prediction = self.forward(data) + samples[:, -predict_last_n:, i] = prediction['y_hat'][:, -predict_last_n:, 0] + + return samples \ No newline at end of file diff --git a/neuralhydrology/modelzoo/cudalstm.py b/neuralhydrology/modelzoo/cudalstm.py new file mode 100644 index 00000000..47818f6e --- /dev/null +++ b/neuralhydrology/modelzoo/cudalstm.py @@ -0,0 +1,68 @@ +import logging +from typing import Dict + +import torch +import torch.nn as nn + +from neuralhydrology.modelzoo.head import get_head +from neuralhydrology.modelzoo.basemodel import BaseModel +from neuralhydrology.utils.config import Config + +LOGGER = logging.getLogger(__name__) + + +class CudaLSTM(BaseModel): + + def __init__(self, cfg: Config): + super(CudaLSTM, self).__init__(cfg=cfg) + + if cfg.embedding_hiddens: + LOGGER.warning("## Warning: Embedding settings are ignored. Use EmbCudaLSTM for embeddings") + + input_size = len(cfg.dynamic_inputs + cfg.static_inputs + cfg.hydroatlas_attributes + cfg.camels_attributes) + if cfg.use_basin_id_encoding: + input_size += cfg.number_of_basins + + if cfg.head.lower() == "umal": + input_size += 1 + + self.lstm = nn.LSTM(input_size=input_size, hidden_size=cfg.hidden_size) + + self.dropout = nn.Dropout(p=cfg.output_dropout) + + self.head = get_head(cfg=cfg, n_in=cfg.hidden_size, n_out=self.output_size) + + self.reset_parameters() + + def reset_parameters(self): + if self.cfg.initial_forget_bias is not None: + self.lstm.bias_hh_l0.data[self.cfg.hidden_size:2 * self.cfg.hidden_size] = self.cfg.initial_forget_bias + + def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + # transpose to [seq_length, batch_size, n_features] + x_d = data['x_d'].transpose(0, 1) + + # concat all inputs + if 'x_s' in data and 'x_one_hot' in data: + x_s = data['x_s'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_one_hot = data['x_one_hot'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_s, x_one_hot], dim=-1) + elif 'x_s' in data: + x_s = data['x_s'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_s], dim=-1) + elif 'x_one_hot' in data: + x_one_hot = data['x_one_hot'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_one_hot], dim=-1) + else: + pass + + lstm_output, (h_n, c_n) = self.lstm(input=x_d) + + # reshape to [batch_size, seq_length, n_hiddens] + h_n = h_n.transpose(0, 1) + c_n = c_n.transpose(0, 1) + + pred = {'h_n': h_n, 'c_n': c_n} + pred.update(self.head(self.dropout(lstm_output.transpose(0, 1)))) + + return pred diff --git a/neuralhydrology/modelzoo/ealstm.py b/neuralhydrology/modelzoo/ealstm.py new file mode 100644 index 00000000..64c0e4fc --- /dev/null +++ b/neuralhydrology/modelzoo/ealstm.py @@ -0,0 +1,108 @@ +from typing import Dict + +import torch +import torch.nn as nn + +from neuralhydrology.modelzoo.basemodel import BaseModel +from neuralhydrology.modelzoo.fc import FC +from neuralhydrology.modelzoo.head import get_head +from neuralhydrology.utils.config import Config + + +class EALSTM(BaseModel): + + def __init__(self, cfg: Config): + super(EALSTM, self).__init__(cfg=cfg) + self._hidden_size = cfg.hidden_size + + input_size_dyn = len(cfg.dynamic_inputs) + input_size_stat = len(cfg.static_inputs + cfg.camels_attributes + cfg.hydroatlas_attributes) + if cfg.use_basin_id_encoding: + input_size_stat += cfg.number_of_basins + + # If hidden units for a embedding network are specified, create FC, otherwise single linear layer + if cfg.embedding_hiddens: + self.input_net = FC(cfg=cfg) + else: + self.input_net = nn.Linear(input_size_stat, cfg.hidden_size) + + # create tensors of learnable parameters + self.weight_ih = nn.Parameter(torch.FloatTensor(input_size_dyn, 3 * cfg.hidden_size)) + self.weight_hh = nn.Parameter(torch.FloatTensor(cfg.hidden_size, 3 * cfg.hidden_size)) + self.bias = nn.Parameter(torch.FloatTensor(3 * cfg.hidden_size)) + + self.dropout = nn.Dropout(p=cfg.output_dropout) + + self.head = get_head(cfg=cfg, n_in=cfg.hidden_size, n_out=self.output_size) + + # initialize parameters + self.reset_parameters() + + def reset_parameters(self): + nn.init.orthogonal_(self.weight_ih.data) + + weight_hh_data = torch.eye(self.cfg.hidden_size) + weight_hh_data = weight_hh_data.repeat(1, 3) + self.weight_hh.data = weight_hh_data + + nn.init.constant_(self.bias.data, val=0) + + if self.cfg.initial_forget_bias is not None: + self.bias.data[:self.cfg.hidden_size] = self.cfg.initial_forget_bias + + def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + + if 'x_s' in data and 'x_one_hot' in data: + x_s = torch.cat([data['x_s'], data['x_one_hot']], dim=-1) + elif 'x_s' in data: + x_s = data['x_s'] + elif 'x_one_hot' in data: + x_s = data['x_one_hot'] + else: + raise ValueError('Need x_s or x_one_hot in forward pass.') + + x_d = data['x_d'].transpose(0, 1) + + seq_len, batch_size, _ = x_d.size() + + # TODO: move hidden and cell state initialization to init and only reset states in forward pass to zero. + h_0 = x_d.data.new(batch_size, self._hidden_size).zero_() + c_0 = x_d.data.new(batch_size, self._hidden_size).zero_() + h_x = (h_0, c_0) + + # empty lists to temporally store all intermediate hidden/cell states + h_n, c_n = [], [] + + # expand bias vectors to batch size + bias_batch = (self.bias.unsqueeze(0).expand(batch_size, *self.bias.size())) + + # calculate input gate only once because inputs are static + i = torch.sigmoid(self.input_net(x_s)) + + # perform forward steps over input sequence + for t in range(seq_len): + h_0, c_0 = h_x + + # calculate gates + gates = (torch.addmm(bias_batch, h_0, self.weight_hh) + torch.mm(x_d[t], self.weight_ih)) + f, o, g = gates.chunk(3, 1) + + c_1 = torch.sigmoid(f) * c_0 + i * torch.tanh(g) + h_1 = torch.sigmoid(o) * torch.tanh(c_1) + + # store intermediate hidden/cell state in list + h_n.append(h_1) + c_n.append(c_1) + + h_x = (h_1, c_1) + + h_n = torch.stack(h_n, 0) + c_n = torch.stack(c_n, 0) + + h_n = h_n.transpose(0, 1) + c_n = c_n.transpose(0, 1) + + pred = {'h_n': h_n, 'c_n': c_n} + pred.update(self.head(self.dropout(h_n))) + + return pred diff --git a/neuralhydrology/modelzoo/embcudalstm.py b/neuralhydrology/modelzoo/embcudalstm.py new file mode 100644 index 00000000..e098daf2 --- /dev/null +++ b/neuralhydrology/modelzoo/embcudalstm.py @@ -0,0 +1,63 @@ +from typing import Dict + +import torch +import torch.nn as nn + +from neuralhydrology.modelzoo.basemodel import BaseModel +from neuralhydrology.modelzoo.fc import FC +from neuralhydrology.modelzoo.head import get_head +from neuralhydrology.utils.config import Config + + +class EmbCudaLSTM(BaseModel): + + def __init__(self, cfg: Config): + super(EmbCudaLSTM, self).__init__(cfg=cfg) + + # embedding net before LSTM + if not cfg.embedding_hiddens: + raise ValueError('EmbCudaLSTM requires config argument embedding_hiddens.') + + self.embedding_net = FC(cfg=cfg) + + input_size = len(cfg.dynamic_inputs) + cfg.embedding_hiddens[-1] + self.lstm = nn.LSTM(input_size=input_size, hidden_size=cfg.hidden_size) + + self.dropout = nn.Dropout(p=cfg.output_dropout) + + self.head = get_head(cfg=cfg, n_in=cfg.hidden_size, n_out=self.output_size) + + self.reset_parameters() + + def reset_parameters(self): + if self.cfg.initial_forget_bias is not None: + self.lstm.bias_hh_l0.data[self.cfg.hidden_size:2 * self.cfg.hidden_size] = self.cfg.initial_forget_bias + + def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + + x_d = data['x_d'].transpose(0, 1) + + if 'x_s' in data and 'x_one_hot' in data: + x_s = torch.cat([data['x_s'], data['x_one_hot']], dim=-1) + elif 'x_s' in data: + x_s = data['x_s'] + elif 'x_one_hot' in data: + x_s = data['x_one_hot'] + else: + raise ValueError('Need x_s or x_one_hot in forward pass.') + + embedding = self.embedding_net(x_s) + + embedding = embedding.unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, embedding], dim=-1) + + lstm_output, (h_n, c_n) = self.lstm(input=x_d) + + # reshape to [batch_size, seq_length, n_hiddens] + h_n = h_n.transpose(0, 1) + c_n = c_n.transpose(0, 1) + + pred = {'h_n': h_n, 'c_n': c_n} + pred.update(self.head(self.dropout(lstm_output.transpose(0, 1)))) + + return pred diff --git a/neuralhydrology/modelzoo/fc.py b/neuralhydrology/modelzoo/fc.py new file mode 100644 index 00000000..d024b0e4 --- /dev/null +++ b/neuralhydrology/modelzoo/fc.py @@ -0,0 +1,67 @@ +import numpy as np +import torch +import torch.nn as nn + +from neuralhydrology.utils.config import Config + + +class FC(nn.Module): + + def __init__(self, cfg: Config, input_size: int = None): + super(FC, self).__init__() + + # If input size is not passed, will use number of all static features as input + if input_size is None: + input_size = len(cfg.camels_attributes + cfg.hydroatlas_attributes + cfg.static_inputs) + if cfg.use_basin_id_encoding: + input_size += cfg.number_of_basins + + if len(cfg.embedding_hiddens) > 1: + hidden_sizes = cfg.embedding_hiddens[:-1] + else: + hidden_sizes = [] + + output_size = cfg.embedding_hiddens[-1] + + # by default tanh + activation = self._get_activation(cfg.embedding_activation) + + # create network + layers = [] + if hidden_sizes: + for i, hidden_size in enumerate(hidden_sizes): + if i == 0: + layers.append(nn.Linear(input_size, hidden_size)) + else: + layers.append(nn.Linear(hidden_sizes[i - 1], hidden_size)) + + layers.append(activation) + # by default 0.0 + layers.append(nn.Dropout(p=cfg.embedding_dropout)) + + layers.append(nn.Linear(hidden_size, output_size)) + else: + layers.append(nn.Linear(input_size, output_size)) + + self.net = nn.Sequential(*layers) + self.reset_parameters() + + def _get_activation(self, name: str) -> nn.Module: + if name.lower() == "tanh": + activation = nn.Tanh() + elif name.lower() == "sigmoid": + activation = nn.Sigmoid() + else: + raise NotImplementedError(f"{name} currently not supported as activation in this class") + return activation + + def reset_parameters(self): + for layer in self.net: + if isinstance(layer, nn.modules.linear.Linear): + n_in = layer.weight.shape[1] + gain = np.sqrt(3 / n_in) + nn.init.uniform_(layer.weight, -gain, gain) + nn.init.constant_(layer.bias, val=0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.net(x) diff --git a/neuralhydrology/modelzoo/head.py b/neuralhydrology/modelzoo/head.py new file mode 100644 index 00000000..cc112de4 --- /dev/null +++ b/neuralhydrology/modelzoo/head.py @@ -0,0 +1,55 @@ +import logging + +import torch +import torch.nn as nn + +from neuralhydrology.utils.config import Config + +LOGGER = logging.getLogger(__name__) + + +def get_head(cfg: Config, n_in: int, n_out: int) -> nn.Module: + """Get specific head module, depending on the run configuration. + + Parameters + ---------- + cfg : Config + The run configuration. + n_in : int + Number of input features. + n_out : int + Number of output features. + + Returns + ------- + nn.Module + The model head, as specified in the run configuration. + """ + if cfg.head.lower() == "regression": + head = Regression(n_in=n_in, n_out=n_out, activation=cfg.output_activation) + else: + raise NotImplementedError(f"{cfg.head} not implemented or not linked in `get_head()`") + + return head + + +class Regression(nn.Module): + """ + Regression head with different output activations. + """ + + def __init__(self, n_in: int, n_out: int, activation: str = "linear"): + super(Regression, self).__init__() + + layers = [nn.Linear(n_in, n_out)] + if activation != "linear": + if activation.lower() == "relu": + layers.append(nn.ReLU()) + elif activation.lower() == "softplus": + layers.append(nn.Softplus()) + else: + LOGGER.warning(f"## WARNING: Ignored output activation {activation} and used 'linear' instead.") + self.net = nn.Sequential(*layers) + + def forward(self, x: torch.Tensor): + return {'y_hat': self.net(x)} \ No newline at end of file diff --git a/neuralhydrology/modelzoo/lstm.py b/neuralhydrology/modelzoo/lstm.py new file mode 100644 index 00000000..edc91d7a --- /dev/null +++ b/neuralhydrology/modelzoo/lstm.py @@ -0,0 +1,174 @@ +import math +from collections import defaultdict + +from typing import Dict + +import torch +import torch.nn as nn + +from neuralhydrology.modelzoo.basemodel import BaseModel +from neuralhydrology.modelzoo.head import get_head +from neuralhydrology.utils.config import Config + + +class LSTM(BaseModel): + """Replication of the CuDNN LSTM + + The idea of this model is to be able to train an LSTM using the nn.LSTM layer, which uses the + optimized CuDNN implementation, and later to copy the weights into this model for a more + in-depth network analysis. + + Note: Currently only supports one-layer CuDNN LSTMs + + Parameters + ---------- + cfg : Config + The run configuration. + + Example + ------- + >>> # copy weights from PyTorch LSTM to this LSTM implementation + >>> cudnn_lstm = nn.LSTM(input_size=15, hidden_size=128, num_layers=1) + >>> lstm = LSTM(cfg=cfg) + >>> lstm.copy_weights(cudnn_lstm) + """ + + def __init__(self, cfg: Config): + super(LSTM, self).__init__(cfg=cfg) + + input_size = len(cfg.dynamic_inputs + cfg.static_inputs + cfg.camels_attributes + cfg.hydroatlas_attributes) + if cfg.use_basin_id_encoding: + input_size += cfg.number_of_basins + + self._hidden_size = cfg.hidden_size + + self.cell = _LSTMCell(input_size=input_size, + hidden_size=self._hidden_size, + initial_forget_bias=cfg.initial_forget_bias) + + self.dropout = nn.Dropout(p=cfg.output_dropout) + + self.head = get_head(cfg=cfg, n_in=self._hidden_size, n_out=self.output_size) + + def forward(self, + data: Dict[str, torch.Tensor], + h_0: torch.Tensor = None, + c_0: torch.Tensor = None) -> Dict[str, torch.Tensor]: + """Forward pass through the LSTM network + + Parameters + ---------- + data : Dict[str, torch.Tensor] + Dictionary, containing input features as key-value pair. + h_0 : torch.Tensor, optional + Initial hidden state, by default 0. + c_0 : torch.Tensor, optional + Initial cell state, by default 0. + + Returns + ------- + Dict[str, torch.Tensor] + Model output and all intermediate states and gate activations as a dictionary. + """ + + x_d = data['x_d'].transpose(0, 1) + + # concat all inputs + if 'x_s' in data and 'x_one_hot' in data: + x_s = data['x_s'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_one_hot = data['x_one_hot'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_s, x_one_hot], dim=-1) + elif 'x_s' in data: + x_s = data['x_s'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_s], dim=-1) + elif 'x_one_hot' in data: + x_one_hot = data['x_one_hot'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_one_hot], dim=-1) + else: + pass + + seq_len, batch_size, _ = x_d.size() + + # TODO: move hidden and cell state initialization to init and only reset states in forward pass to zero. + if h_0 is None: + h_0 = x_d.data.new(batch_size, self._hidden_size).zero_() + if c_0 is None: + c_0 = x_d.data.new(batch_size, self._hidden_size).zero_() + h_x = (h_0, c_0) + + output = defaultdict(list) + for x_t in x_d: + h_0, c_0 = h_x + cell_output = self.cell(x_t=x_t, h_0=h_0, c_0=c_0) + + h_x = (cell_output['h_n'], cell_output['c_n']) + + for key, cell_out in cell_output.items(): + output[key].append(cell_out.detach()) + + # stack to [batch size, sequence length, hidden size] + pred = {key: torch.stack(val, 1) for key, val in output.items()} + pred.update(self.head(self.dropout(pred['h_n']))) + return pred + + def copy_weights(self, cudnnlstm: nn.Module): + """Copy weights from a PyTorch nn.LSTM into this model class + + Parameters + ---------- + cudnnlstm : nn.Module + Model instance of a Pytorch nn.LSTM + """ + + assert isinstance(cudnnlstm, nn.modules.rnn.LSTM) + assert cudnnlstm.num_layers == 1 + + self.cell.copy_weights(cudnnlstm, layer=0) + + +class _LSTMCell(nn.Module): + + def __init__(self, input_size: int, hidden_size: int, initial_forget_bias: float = 0.0): + super(_LSTMCell, self).__init__() + + self.input_size = input_size + self.hidden_size = hidden_size + self.initial_forget_bias = initial_forget_bias + + self.w_hh = nn.Parameter(torch.FloatTensor(4 * hidden_size, hidden_size)) + self.w_ih = nn.Parameter(torch.FloatTensor(4 * hidden_size, input_size)) + + self.b_hh = nn.Parameter(torch.FloatTensor(4 * hidden_size)) + self.b_ih = nn.Parameter(torch.FloatTensor(4 * hidden_size)) + + self.reset_parameters() + + def reset_parameters(self): + stdv = math.sqrt(3 / self.hidden_size) + for weight in self.parameters(): + if len(weight.shape) > 1: + weight.data.uniform_(-stdv, stdv) + else: + nn.init.zeros_(weight) + + if self.initial_forget_bias != 0: + self.b_hh.data[self.hidden_size:2 * self.hidden_size] = self.initial_forget_bias + + def forward(self, x_t: torch.Tensor, h_0: torch.Tensor, c_0: torch.Tensor) -> Dict[str, torch.Tensor]: + gates = h_0 @ self.w_hh.T + self.b_hh + x_t @ self.w_ih.T + self.b_ih + i, f, g, o = gates.chunk(4, 1) + + c_1 = c_0 * torch.sigmoid(f) + torch.sigmoid(i) * torch.tanh(g) + h_1 = torch.sigmoid(o) * torch.tanh(c_1) + + return {'h_n': h_1, 'c_n': c_1, 'i': i, 'f': f, 'g': g, 'o': o} + + def copy_weights(self, cudnnlstm: nn.Module, layer: int): + + assert self.hidden_size == cudnnlstm.hidden_size + assert self.input_size == cudnnlstm.input_size + + self.w_hh.data = getattr(cudnnlstm, f"weight_hh_l{layer}").data + self.w_ih.data = getattr(cudnnlstm, f"weight_ih_l{layer}").data + self.b_hh.data = getattr(cudnnlstm, f"bias_hh_l{layer}").data + self.b_ih.data = getattr(cudnnlstm, f"bias_ih_l{layer}").data diff --git a/neuralhydrology/modelzoo/multifreqlstm.py b/neuralhydrology/modelzoo/multifreqlstm.py new file mode 100644 index 00000000..fba41871 --- /dev/null +++ b/neuralhydrology/modelzoo/multifreqlstm.py @@ -0,0 +1,183 @@ +import logging +from typing import Dict + +import pandas as pd +import torch +import torch.nn as nn + +from neuralhydrology.data.utils import sort_frequencies +from neuralhydrology.modelzoo.head import get_head +from neuralhydrology.modelzoo.basemodel import BaseModel +from neuralhydrology.utils.config import Config + +LOGGER = logging.getLogger(__name__) + + +class MultiFreqLSTM(BaseModel): + + def __init__(self, cfg: Config): + super(MultiFreqLSTM, self).__init__(cfg=cfg) + self.lstms = None + self.transfer_fcs = None + self.heads = None + self.dropout = None + + self._slice_timestep = {} + self._frequency_factors = [] + + self._seq_lengths = cfg.seq_length + self._per_frequency_lstm = self.cfg.per_frequency_lstm # default: a distinct LSTM per frequency + self._transfer_multifreq_states = self.cfg.transfer_multifreq_states # default: linear transfer layer + transfer_modes = [None, "None", "identity", "linear"] + if self._transfer_multifreq_states["h"] not in transfer_modes \ + or self._transfer_multifreq_states["c"] not in transfer_modes: + raise ValueError(f"MultiFreqLSTM supports state transfer modes {transfer_modes}") + + if len(cfg.use_frequencies) < 2: + raise ValueError("MultiFreqLSTM expects more than one input frequency") + self._frequencies = sort_frequencies(cfg.use_frequencies) + + # start to count the number of inputs + input_sizes = len(cfg.camels_attributes + cfg.hydroatlas_attributes + cfg.static_inputs) + + # if not per_frequency_lstm, the LSTM gets an additional frequency flag as input. + if not self._per_frequency_lstm: + input_sizes += len(self._frequencies) + + if cfg.use_basin_id_encoding: + input_sizes += cfg.number_of_basins + if cfg.head.lower() == "umal": + input_sizes += 1 + + if isinstance(cfg.dynamic_inputs, list): + input_sizes = {freq: input_sizes + len(cfg.dynamic_inputs) for freq in self._frequencies} + else: + if not self._per_frequency_lstm: + raise ValueError(f'Different inputs not allowed if per_frequency_lstm is False.') + input_sizes = {freq: input_sizes + len(cfg.dynamic_inputs[freq]) for freq in self._frequencies} + + if not isinstance(cfg.hidden_size, dict): + LOGGER.info("No specific hidden size for frequencies are specified. Same hidden size is used for all.") + self._hidden_size = {freq: cfg.hidden_size for freq in self._frequencies} + else: + self._hidden_size = cfg.hidden_size + + if (not self._per_frequency_lstm + or self._transfer_multifreq_states["h"] == "identity" + or self._transfer_multifreq_states["c"] == "identity") \ + and any(size != self._hidden_size[self._frequencies[0]] for size in self._hidden_size.values()): + raise ValueError("All hidden sizes must be equal if per_frequency_lstm=False or state transfer=identity.") + + # create layer depending on selected frequencies + self._init_modules(input_sizes) + self.reset_parameters() + + # frequency factors are needed to determine the time step of information transfer + self._init_frequency_factors_and_slice_timesteps() + + def _init_modules(self, input_sizes: Dict[str, int]): + self.lstms = nn.ModuleDict() + self.transfer_fcs = nn.ModuleDict() + self.heads = nn.ModuleDict() + self.dropout = nn.Dropout(p=self.cfg.output_dropout) + for idx, freq in enumerate(self._frequencies): + freq_input_size = input_sizes[freq] + + if not self._per_frequency_lstm and idx > 0: + self.lstms[freq] = self.lstms[self._frequencies[idx - 1]] # same LSTM for all frequencies. + self.heads[freq] = self.heads[self._frequencies[idx - 1]] # same head for all frequencies. + else: + self.lstms[freq] = nn.LSTM(input_size=freq_input_size, hidden_size=self._hidden_size[freq]) + self.heads[freq] = get_head(self.cfg, n_in=self._hidden_size[freq], n_out=self.output_size) + + if idx < len(self._frequencies) - 1: + for state in ["c", "h"]: + if self._transfer_multifreq_states[state] == "linear": + self.transfer_fcs[f"{state}_{freq}"] = nn.Linear(self._hidden_size[freq], + self._hidden_size[self._frequencies[idx + 1]]) + elif self._transfer_multifreq_states[state] == "identity": + self.transfer_fcs[f"{state}_{freq}"] = nn.Identity() + else: + pass + + def _init_frequency_factors_and_slice_timesteps(self): + for idx, freq in enumerate(self._frequencies): + if idx < len(self._frequencies) - 1: + frequency_factor = pd.to_timedelta(freq) / pd.to_timedelta(self._frequencies[idx + 1]) + if frequency_factor != int(frequency_factor): + raise ValueError('Adjacent frequencies must be multiples of each other.') + self._frequency_factors.append(int(frequency_factor)) + # we want to pass the state of the day _before_ the next higher frequency starts, + # because e.g. the mean of a day is stored at the same date at 00:00 in the morning. + slice_timestep = int(self._seq_lengths[self._frequencies[idx + 1]] / self._frequency_factors[idx]) + self._slice_timestep[freq] = slice_timestep + + def reset_parameters(self): + if self.cfg.initial_forget_bias is not None: + for freq in self._frequencies: + hidden_size = self._hidden_size[freq] + self.lstms[freq].bias_hh_l0.data[hidden_size:2 * hidden_size] = self.cfg.initial_forget_bias + + def _prepare_inputs(self, data: Dict[str, torch.Tensor], freq: str) -> torch.Tensor: + """Concat all different inputs to the time series input""" + suffix = f"_{freq}" + # transpose to [seq_length, batch_size, n_features] + x_d = data[f'x_d{suffix}'].transpose(0, 1) + + # concat all inputs + if f'x_s{suffix}' in data and 'x_one_hot' in data: + x_s = data[f'x_s{suffix}'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_one_hot = data['x_one_hot'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_s, x_one_hot], dim=-1) + elif f'x_s{suffix}' in data: + x_s = data[f'x_s{suffix}'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_s], dim=-1) + elif 'x_one_hot' in data: + x_one_hot = data['x_one_hot'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_one_hot], dim=-1) + else: + pass + + if not self._per_frequency_lstm: + # add frequency one-hot encoding + idx = self._frequencies.index(freq) + one_hot_freq = torch.zeros(x_d.shape[0], x_d.shape[1], len(self._frequencies)).to(x_d) + one_hot_freq[:, :, idx] = 1 + x_d = torch.cat([x_d, one_hot_freq], dim=2) + + return x_d + + def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + x_d = {freq: self._prepare_inputs(data, freq) for freq in self._frequencies} + + # initial states for lowest frequencies are set to zeros + batch_size = x_d[self._frequencies[0]].shape[1] + lowest_freq_hidden_size = self._hidden_size[self._frequencies[0]] + h_0_transfer = x_d[self._frequencies[0]].new_zeros((1, batch_size, lowest_freq_hidden_size)) + c_0_transfer = torch.zeros_like(h_0_transfer) + + outputs = {} + for idx, freq in enumerate(self._frequencies): + if idx < len(self._frequencies) - 1: + # get predictions and state up to the time step of information transfer + slice_timestep = self._slice_timestep[freq] + lstm_output_slice1, (h_n_slice1, c_n_slice1) = self.lstms[freq](x_d[freq][:-slice_timestep], + (h_0_transfer, c_0_transfer)) + + # project the states through a hidden layer to the dimensions of the next LSTM + if self._transfer_multifreq_states["h"] is not None: + h_0_transfer = self.transfer_fcs[f"h_{freq}"](h_n_slice1) + if self._transfer_multifreq_states["c"] is not None: + c_0_transfer = self.transfer_fcs[f"c_{freq}"](c_n_slice1) + + # get predictions of remaining part and concat results + lstm_output_slice2, _ = self.lstms[freq](x_d[freq][-slice_timestep:], (h_n_slice1, c_n_slice1)) + lstm_output = torch.cat([lstm_output_slice1, lstm_output_slice2], dim=0) + + else: + # for highest frequency, we can pass the entire sequence at once + lstm_output, _ = self.lstms[freq](x_d[freq], (h_0_transfer, c_0_transfer)) + + outputs[f'y_hat_{freq}'] = self.heads[freq](self.dropout(lstm_output.transpose(0, 1)))['y_hat'] + + return outputs diff --git a/neuralhydrology/modelzoo/odelstm.py b/neuralhydrology/modelzoo/odelstm.py new file mode 100644 index 00000000..afa31999 --- /dev/null +++ b/neuralhydrology/modelzoo/odelstm.py @@ -0,0 +1,308 @@ +import logging +from typing import Dict, Tuple, Union + +import numpy as np +import pandas as pd +import torch +import torch.nn as nn + +from neuralhydrology.data.utils import sort_frequencies +from neuralhydrology.modelzoo.basemodel import BaseModel +from neuralhydrology.modelzoo.head import get_head +from neuralhydrology.modelzoo.lstm import _LSTMCell +from neuralhydrology.utils.config import Config + +LOGGER = logging.getLogger(__name__) + + +class ODELSTM(BaseModel): + """ODE-LSTM from [#]_. + + An ODE-RNN post-processes the hidden state of a normal LSTM. + Parts of this code are derived from https://github.com/mlech26l/learning-long-term-irregular-ts. + + The forward pass in this model works somewhat differently than the other models, because ODE-LSTM relies on + irregularly timed samples. To simulate such irregularity, we aggregate parts of the input sequence to random + frequencies. While doing so, we try to take care that we don't aggregate too coarsely right before the model + should create a high-frequency prediction. + + Since this aggregation means that parts of the input sequence are at random frequencies, we cannot easily + return predictions for the full input sequence at each frequency. Instead, we only return sequences of length + predict_last_n for each frequency (we do not apply the random aggregation to these last time steps). + + The following describes the aggregation strategy implemented in the forward method: + + 1. slice one: random-frequency steps (cfg.ode_random_freq_lower_bound <= freq <= lowest-freq) until beginning + of the second-lowest frequency input sequence. + 2. slice two: random-frequency steps (lowest-freq <= freq <= self._frequencies[1]) until beginning of + next-higher frequency input sequence. + 3. repeat step two until beginning of highest-frequency input sequence. + 4. slice three: random-frequency steps (self._frequencies[-2] <= freq <= highest-freq) until predict_last_n + of the lowest frequency. + 5. lowest-frequency steps to generate predict_last_n lowest-frequency predictions. + 6. repeat steps four and five for the next-higher frequency (using the same random-frequency bounds but + generating predictions for the next-higher frequency). + + References + ---------- + .. [#] Lechner, M.; Hasani, R.: Learning Long-Term Dependencies in Irregularly-Sampled Time Series. arXiv, 2020, + https://arxiv.org/abs/2006.04418. + """ + + def __init__(self, cfg: Config): + super(ODELSTM, self).__init__(cfg=cfg) + if len(cfg.use_frequencies) < 2: + raise ValueError('ODELSTM needs at least two frequencies.') + if isinstance(cfg.dynamic_inputs, dict) or isinstance(cfg.hidden_size, dict): + raise ValueError('ODELSTM does not support per-frequency input variables or hidden sizes.') + + # Note: be aware that frequency_factors and slice_timesteps have a slightly different meaning here vs. in + # multifreqlstm. Here, the frequency_factor is relative to the _lowest_ (not the next-lower) frequency. + # slice_timesteps[freq] is the input step (counting backwards) in the next-*lower* frequency from where on input + # data at frequency freq is available. + self._frequency_factors = {} + self._slice_timesteps = {} + self._frequencies = sort_frequencies(cfg.use_frequencies) + self._init_frequency_factors_and_slice_timesteps() + + # start to count the number of inputs + self.input_size = len(cfg.dynamic_inputs + cfg.camels_attributes + cfg.hydroatlas_attributes + + cfg.static_inputs) + + if cfg.use_basin_id_encoding: + self.input_size += cfg.number_of_basins + if cfg.head.lower() == 'umal': + self.input_size += 1 + + self.lstm_cell = _LSTMCell(self.input_size, self.cfg.hidden_size, cfg.initial_forget_bias) + self.ode_cell = _ODERNNCell(self.cfg.hidden_size, + self.cfg.hidden_size, + num_unfolds=self.cfg.ode_num_unfolds, + method=self.cfg.ode_method) + self.dropout = nn.Dropout(p=cfg.output_dropout) + self.head = get_head(cfg=cfg, n_in=self.cfg.hidden_size, n_out=self.output_size) + + def _init_frequency_factors_and_slice_timesteps(self): + for i, freq in enumerate(self._frequencies): + frequency_factor = pd.to_timedelta(self._frequencies[0]) / pd.to_timedelta(freq) + if frequency_factor != int(frequency_factor): + raise ValueError('Frequencies must be multiples of the lowest frequency.') + self._frequency_factors[freq] = int(frequency_factor) + + if i > 0: + prev_frequency_factor = pd.to_timedelta(self._frequencies[i - 1]) / pd.to_timedelta(freq) + if self.cfg.predict_last_n[freq] % prev_frequency_factor != 0: + raise ValueError( + 'At all frequencies, predict_last_n must align with the steps of the next-lower frequency.') + + if self.cfg.seq_length[freq] > self.cfg.seq_length[self._frequencies[i - 1]] * prev_frequency_factor: + raise ValueError('Higher frequencies must have shorter input sequences than lower frequencies.') + + # we want to pass the state of the day _before_ the next higher frequency starts, + # because e.g. the mean of a day is stored at the same date at 00:00 in the morning. + slice_timestep = self.cfg.seq_length[freq] / prev_frequency_factor + if slice_timestep != int(slice_timestep): + raise ValueError('At all frequencies, seq_length must align with the next-lower frequency steps.') + self._slice_timesteps[freq] = int(slice_timestep) + + # in theory, the following conditions would be possible, but they would make the implementation + # quite complex and are probably hardly ever useful. + if self.cfg.predict_last_n[self._frequencies[ + i - 1]] < self.cfg.predict_last_n[freq] / prev_frequency_factor: + raise NotImplementedError( + 'Lower frequencies cannot have smaller predict_last_n values than higher ones.') + + if any(self.cfg.predict_last_n[f] / self._frequency_factors[f] > self._slice_timesteps[self._frequencies[-1]] / + self._frequency_factors[self._frequencies[-2]] for f in self._frequencies): + raise NotImplementedError('predict_last_n cannot be larger than sequence length of highest frequency.') + + def _prepare_inputs(self, data: Dict[str, torch.Tensor], + freq: str) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """Concat all different inputs to the time series input. """ + suffix = f"_{freq}" + # transpose to [seq_length, batch_size, n_features] + x_d = data[f'x_d{suffix}'].transpose(0, 1) + + # concat all inputs + if f'x_s{suffix}' in data and 'x_one_hot' in data: + x_s = data[f'x_s{suffix}'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_one_hot = data['x_one_hot'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_s, x_one_hot], dim=-1) + elif f'x_s{suffix}' in data: + x_s = data[f'x_s{suffix}'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_s], dim=-1) + elif 'x_one_hot' in data: + x_one_hot = data['x_one_hot'].unsqueeze(0).repeat(x_d.shape[0], 1, 1) + x_d = torch.cat([x_d, x_one_hot], dim=-1) + else: + pass + + # add frequency indicator. This will not be used as normal input, but the ODE-RNN uses it to determine the + # elapsed time since the last sample. + frequency_factor = 1 / self._frequency_factors[freq] + frequency_encoding = torch.ones(x_d.shape[0], x_d.shape[1], 1).to(x_d) * frequency_factor + return torch.cat([x_d, frequency_encoding], dim=-1) + + def _randomize_freq(self, x_d: torch.Tensor, low_frequency: str, high_frequency: str) -> torch.Tensor: + """Randomize the frequency of the input sequence. """ + frequency_factor = pd.to_timedelta(low_frequency) // pd.to_timedelta(high_frequency) + possible_aggregate_steps = list(filter(lambda n: frequency_factor % n == 0, range(1, frequency_factor + 1))) + + t = 0 + max_t = x_d.shape[0] / frequency_factor + x_d_randomized = [] + while t < max_t: + highfreq_slice = x_d[t * frequency_factor:(t + 1) * frequency_factor] + + # aggregate to a random frequency between low and high + random_aggregate_steps = np.random.choice(possible_aggregate_steps) + if highfreq_slice.shape[0] % random_aggregate_steps == 0: + randfreq_slice = highfreq_slice.view(-1, random_aggregate_steps, highfreq_slice.shape[1], + highfreq_slice.shape[2]).mean(dim=1) + # update the frequency indicators. + randfreq_slice[:, :, -1] = random_aggregate_steps / self._frequency_factors[high_frequency] + else: + # do not randomize last slice if it doesn't align with aggregation steps + randfreq_slice = highfreq_slice + x_d_randomized.append(randfreq_slice) + + t += 1 + + return torch.cat(x_d_randomized, dim=0) + + def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + + x_d = {freq: self._prepare_inputs(data, freq) for freq in self._frequencies} + + slice_one = self._randomize_freq(x_d[self._frequencies[0]][:-self._slice_timesteps[self._frequencies[1]]], + self.cfg.ode_random_freq_lower_bound, self._frequencies[0]) + + batch_size = slice_one.shape[1] + h_0 = slice_one.data.new(batch_size, self.cfg.hidden_size).zero_() + c_0 = slice_one.data.new(batch_size, self.cfg.hidden_size).zero_() + h_n, c_n = self._run_odelstm(slice_one, h_0, c_0) + + prev_freq = self._frequencies[0] + i = 1 + for freq in self._frequencies[1:-1]: + to_randomize = x_d[freq][:-self._slice_timesteps[self._frequencies[i + 1]]] + + # slice_timestep of this and the next frequency are identical, so we can move on to the next frequency + if len(to_randomize) == 0: + continue + # random-frequency steps until the beginning of the highest-frequency input sequence. + slice_two = self._randomize_freq(to_randomize, prev_freq, freq) + h_n, c_n = self._run_odelstm(slice_two, h_n[:, -1], c_n) + prev_freq = freq + i += 1 + + pred = {} + prev_freq_end_step = 0 + for freq in self._frequencies: + # run random-frequency steps until predict_last_n of this frequency + end_step = -int(self.cfg.predict_last_n[freq] * self._frequency_factors[self._frequencies[-1]] / + self._frequency_factors[freq]) + if end_step == 0: + continue # this means the current frequency should not be predicted, so we skip it. + + # check that the slice wouldn't be empty. If it wouldn't, this means that the previous frequency's + # predict_last_n is not aligned with this frequency's predict_last_n, so we can run random-frequency + # inputs until the beginning of this frequency's predict_last_n. + if end_step != prev_freq_end_step and end_step > -self.cfg.seq_length[self._frequencies[-1]]: + slice_three = self._randomize_freq(x_d[self._frequencies[-1]][prev_freq_end_step:end_step], prev_freq, + self._frequencies[-1]) + h_n, c_n = self._run_odelstm(slice_three, h_n[:, -1], c_n) + prev_freq_end_step = end_step + + # run predict_last_n steps at the target frequency + pred_slice = x_d[freq][-self.cfg.predict_last_n[freq]:] + h_n_out, _ = self._run_odelstm(pred_slice, h_n[:, -1], c_n) + pred[f'y_hat_{freq}'] = self.head(self.dropout(h_n_out))['y_hat'] + + return pred + + def _run_odelstm(self, input_slice: torch.Tensor, h_0: torch.Tensor, + c_0: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Ingest `input_slice` into the ODE-LSTM and return hidden states and last cell state. """ + h_x = (h_0, c_0) + + h_n = [] + for x_t in input_slice: + h_0, c_0 = h_x + + # separate frequency indicator from input variables + t_input = x_t[:, :-1] + t_elapsed = x_t[0, -1] + + lstm_out = self.lstm_cell(x_t=t_input, h_0=h_0, c_0=c_0) + ode_out = self.ode_cell(lstm_out['h_n'], h_0, t_elapsed) + + h_x = (ode_out, lstm_out['c_n']) + h_n.append(ode_out) + + # stack h to [batch size, sequence length, hidden size] + return torch.stack(h_n, 1), h_x[1] + + +class _ODERNNCell(nn.Module): + """An ODE-RNN cell (Adapted from https://github.com/mlech26l/learning-long-term-irregular-ts). """ + + def __init__(self, input_size: int, hidden_size: int, num_unfolds: int, method: str, tau=1): + super(_ODERNNCell, self).__init__() + self.method = { + 'euler': self.euler, + 'heun': self.heun, + 'rk4': self.rk4, + }[method] + self.input_size = input_size + self.hidden_size = hidden_size + self.num_unfolds = num_unfolds + self.tau = tau + + self.w_ih = nn.Parameter(torch.FloatTensor(hidden_size, input_size)) + self.w_hh = nn.Parameter(torch.FloatTensor(hidden_size, hidden_size)) + self.bias = nn.Parameter(torch.FloatTensor(hidden_size)) + self.scale = nn.Parameter(torch.FloatTensor(hidden_size)) + + self.reset_parameters() + + def reset_parameters(self): + nn.init.orthogonal_(self.w_hh) + nn.init.xavier_uniform_(self.w_ih) + nn.init.zeros_(self.bias) + nn.init.constant_(self.scale, 1.0) + + def forward(self, new_hidden_state: torch.Tensor, old_hidden_state: torch.Tensor, elapsed: float) -> torch.Tensor: + delta_t = elapsed / self.num_unfolds + + hidden_state = old_hidden_state + for i in range(self.num_unfolds): + hidden_state = self.method(new_hidden_state, hidden_state, delta_t) + return hidden_state + + def dfdt(self, inputs: torch.Tensor, hidden_state: torch.Tensor) -> torch.Tensor: + h_in = torch.matmul(inputs, self.w_ih) + h_rec = torch.matmul(hidden_state, self.w_hh) + dh_in = self.scale * torch.tanh(h_in + h_rec + self.bias) + if self.tau > 0: + dh = dh_in - hidden_state * self.tau + else: + dh = dh_in + return dh + + def euler(self, inputs: torch.Tensor, hidden_state: torch.Tensor, delta_t: float) -> torch.Tensor: + dy = self.dfdt(inputs, hidden_state) + return hidden_state + delta_t * dy + + def heun(self, inputs: torch.Tensor, hidden_state: torch.Tensor, delta_t: float) -> torch.Tensor: + k1 = self.dfdt(inputs, hidden_state) + k2 = self.dfdt(inputs, hidden_state + delta_t * k1) + return hidden_state + delta_t * 0.5 * (k1 + k2) + + def rk4(self, inputs: torch.Tensor, hidden_state: torch.Tensor, delta_t: float) -> torch.Tensor: + k1 = self.dfdt(inputs, hidden_state) + k2 = self.dfdt(inputs, hidden_state + k1 * delta_t * 0.5) + k3 = self.dfdt(inputs, hidden_state + k2 * delta_t * 0.5) + k4 = self.dfdt(inputs, hidden_state + k3 * delta_t) + + return hidden_state + delta_t * (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 diff --git a/neuralhydrology/modelzoo/template.py b/neuralhydrology/modelzoo/template.py new file mode 100644 index 00000000..b3db8dbe --- /dev/null +++ b/neuralhydrology/modelzoo/template.py @@ -0,0 +1,73 @@ +from typing import Dict + +import torch + +from neuralhydrology.modelzoo.basemodel import BaseModel +from neuralhydrology.utils.config import Config + + +class TemplateModel(BaseModel): + + def __init__(self, cfg: Config): + """Initialize the model + + Each model receives as only input the config dictionary. From this, the entire model has to be implemented in + this class (with potential use of other modules, such as FC from fc.py). So this class will get the model inputs + and has to return the predictions. + + Each Model inherits from the BaseModel, which implements some universal functionality. The basemodel also + defines the output_size, which can be used here as a given attribute (self.output_size). + + To be generally useable within this codebase, the output layer should not be implemented in this Module, + but rather using the get_head() function from neuralhydrology.modelzoo.head. + + Parameters + ---------- + cfg : Config + Configuration of the run, read from the config file with some additional keys (such as number of basins). + """ + super(TemplateModel, self).__init__(cfg=cfg) + + ########################### + # Create model parts here # + ########################### + + def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + """Forward pass through the model + + By convention, each forward pass has to accept a dict of input tensors. Usually, this dict contains + 'x_d' and, possibly, x_s and x_one_hot. If x_d and x_s are available at multiple frequencies, + the keys 'x_d' and 'x_s' have frequency suffixes such as 'x_d_1H' for hourly data. + Furthermore, by definition, each model has to return a dict containing the network predictions in + 'y_hat', potentially in addition to other dictionary keys. LSTM-based models should stick to the + convention to return (at least) the following three tensors: y_hat, h_n, c_n (or, in the multi- + frequency case, y_hat_1H, y_hat_1D, etc.). + + Parameters + ---------- + data : Dict[str, torch.Tensor] + Dictionary with tensors + - x_d of shape [batch size, sequence length, features] containing the dynamic input data. + - x_s of shape [batch size, features] containing static input features. These are the + concatenation of what is defined in the config under camels_attributes and static_inputs. + In case not a single camels attribute or static input feature is defined in the config, + x_s will not be present. + - x_one_hot of shape [batch size, number of basins] containing the one hot encoding of the + basins. In case 'use_basin_id_encoding' is set to False in the config, x_one_hot will + not be present. + + Note: If the input data are available at multiple frequencies (via use_frequencies), each input + tensor will have a suffix "_{freq}" indicating the tensor's frequency. + + Returns + ------- + The network prediction has to be returned under the dictionary key 'y_hat' (or, if multiple frequencies + are predicted, 'y_hat_{freq}'. Furthermore, make sure to return predictions for each time step, even if + you want to train sequence-to-one. Which predictions are used for training the network is controlled in + the train_epoch() function in neuralhydrology/training/basetrainer.py. Other return values should be the + hidden states as 'h_n' and cell states 'c_n'. Further return values are possible. + """ + ############################### + # Implement forward pass here # + ############################### + pass diff --git a/neuralhydrology/nh_run.py b/neuralhydrology/nh_run.py new file mode 100644 index 00000000..c9c4efc8 --- /dev/null +++ b/neuralhydrology/nh_run.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python +import argparse +import sys +from pathlib import Path + +# make sure code directory is in path, even if the package is not installed using the setup.py +sys.path.append(str(Path(__file__).parent.parent)) +from neuralhydrology.evaluation.evaluate import start_evaluation +from neuralhydrology.training.train import start_training +from neuralhydrology.utils.config import Config +from neuralhydrology.utils.logging_utils import setup_logging + + +def _get_args() -> dict: + parser = argparse.ArgumentParser() + parser.add_argument('mode', choices=["train", "continue_training", "evaluate"]) + parser.add_argument('--config-file', type=str) + parser.add_argument('--run-dir', type=str) + parser.add_argument('--epoch', type=int, help="Epoch, of which the model should be evaluated") + parser.add_argument('--period', type=str, choices=["train", "validation", "test"], default="test") + parser.add_argument('--gpu', type=int, help="GPU id to use (see nvidia-smi). Does override config argument.") + args = vars(parser.parse_args()) + + if (args["mode"] == "train") and (args["config_file"] is None): + raise ValueError("Missing path to config file") + + if (args["mode"] == "continue_training") and (args["run_dir"] is None): + raise ValueError("Missing path to run directory file") + + if (args["mode"] == "evaluate") and (args["run_dir"] is None): + raise ValueError("Missing path to run directory") + + return args + + +def _main(): + args = _get_args() + if (args["run_dir"] is not None) and (args["mode"] == "evaluate"): + setup_logging(str(Path(args["run_dir"]) / "output.log")) + + if args["mode"] == "train": + start_run(config_file=Path(args["config_file"]), gpu=args["gpu"]) + elif args["mode"] == "continue_training": + continue_run(run_dir=Path(args["run_dir"]), + config_file=Path(args["config_file"]) if args["config_file"] is not None else None, + gpu=args["gpu"]) + elif args["mode"] == "evaluate": + eval_run(run_dir=Path(args["run_dir"]), period=args["period"], epoch=args["epoch"], gpu=args["gpu"]) + else: + raise RuntimeError(f"Unknown mode {args['mode']}") + + +def start_run(config_file: Path, gpu: int = None): + """Start training a model. + + Parameters + ---------- + config_file : Path + Path to a configuration file (.yml), defining the settings for the specific run. + gpu : int, optional + GPU id to use. Will override config argument 'device'. + + """ + + config = Config(config_file) + + # check if a GPU has been specified as command line argument. If yes, overwrite config + if gpu is not None: + config.device = f"cuda:{gpu}" + + start_training(config) + + +def continue_run(run_dir: Path, config_file: Path = None, gpu: int = None): + """Continue model training. + + Parameters + ---------- + run_dir : Path + Path to the run directory. + config_file : Path, optional + Path to an additional config file. Each config argument in this file will overwrite the original run config. + gpu : int, optional + GPU id to use. Will override config argument 'device'. + + """ + # load config from base run and overwrite all elements with an optional new config + base_config = Config(run_dir / "config.yml") + + if config_file is not None: + base_config.update_config(config_file) + + base_config.is_continue_training = True + + # check if a GPU has been specified as command line argument. If yes, overwrite config + if gpu is not None: + base_config.device = f"cuda:{gpu}" + + start_training(base_config) + + +def eval_run(run_dir: Path, period: str, epoch: int, gpu: int = None): + """Start evaluating a trained model. + + Parameters + ---------- + run_dir : Path + Path to the run directory. + period : {'train', 'validation', 'test'} + The period to evaluate. + epoch : int, optional + Define a specific epoch to use. By default, the weights of the last epoch are used. + gpu : int, optional + GPU id to use. Will override config argument 'device'. + + """ + config = Config(run_dir / "config.yml") + + # check if a GPU has been specified as command line argument. If yes, overwrite config + if gpu is not None: + config.device = f"cuda:{gpu}" + + start_evaluation(cfg=config, run_dir=run_dir, epoch=epoch, period=period) + + +if __name__ == "__main__": + _main() diff --git a/neuralhydrology/nh_run_scheduler.py b/neuralhydrology/nh_run_scheduler.py new file mode 100644 index 00000000..03678f34 --- /dev/null +++ b/neuralhydrology/nh_run_scheduler.py @@ -0,0 +1,129 @@ +#!/usr/bin/env python +import argparse +import random +import subprocess +import sys +import time +from pathlib import Path +from typing import List + +import numpy as np + + +def _get_args() -> dict: + + parser = argparse.ArgumentParser() + parser.add_argument('mode', choices=["train", "evaluate"]) + parser.add_argument('--directory', type=str, required=True) + parser.add_argument('--gpu-ids', type=int, nargs='+', required=True) + parser.add_argument('--runs-per-gpu', type=int, required=True) + + args = vars(parser.parse_args()) + + args["directory"] = Path(args["directory"]) + if not args["directory"].is_dir(): + raise ValueError(f"No folder at {args['directory']}") + + return args + + +def _main(): + args = _get_args() + schedule_runs(**args) + + +def schedule_runs(mode: str, directory: Path, gpu_ids: List[int], runs_per_gpu: int): + """Schedule multiple runs across one or multiple GPUs. + + Parameters + ---------- + mode : {'train', 'evaluate'} + Use 'train' if you want to schedule training of multiple models and 'evaluate' if you want to schedule + evaluation of multiple trained models. + directory : Path + If mode is 'train', this path should point to a folder containing the config files (.yml) to use for model + training. For each config file, one run is started. If mode is 'evaluate', this path should point to the folder + containing the different model run directories. + gpu_ids : List[int] + List of GPU ids to use for training/evaluating. + runs_per_gpu : int + Number of runs to start on a single GPU. + + """ + + if mode == "train": + processes = list(directory.glob('**/*.yml')) + elif mode == "evaluate": + processes = list(directory.glob('*')) + else: + raise ValueError("'mode' must be either 'train' or 'evaluate'") + + # if used as command line tool, we need full path's to the fils/directories + processes = [str(p.absolute()) for p in processes] + + # for approximately equal memory usage during hyperparam tuning, randomly shuffle list of processes + random.shuffle(processes) + + # array to keep track on how many runs are currently running per GPU + n_parallel_runs = len(gpu_ids) * runs_per_gpu + gpu_counter = np.zeros((len(gpu_ids)), dtype=np.int) + + # for command line tool, we need full path to the main.py script + script_path = str(Path(__file__).absolute().parent / "nh_run.py") + + running_processes = {} + counter = 0 + while True: + + # start new runs + for _ in range(n_parallel_runs - len(running_processes)): + + if counter >= len(processes): + break + + # determine which GPU to use + node_id = np.argmin(gpu_counter) + gpu_counter[node_id] += 1 + gpu_id = gpu_ids[node_id] + process = processes[counter] + + # start run via subprocess call + if mode == "train": + run_command = f"python {script_path} train --config-file {process} --gpu {gpu_id}" + else: + run_command = f"python {script_path} evaluate --run-dir {process} --gpu {gpu_id}" + print(f"Starting run {counter+1}/{len(processes)}: {run_command}") + running_processes[(run_command, node_id)] = subprocess.Popen(run_command, + stdout=subprocess.DEVNULL, + shell=True) + + counter += 1 + time.sleep(2) + + # check for completed runs + for key, process in running_processes.items(): + if process.poll() is not None: + print(f"Finished run {key[0]}") + gpu_counter[key[1]] -= 1 + print("Cleaning up...\n\n") + try: + _ = process.communicate(timeout=5) + except TimeoutError: + print('') + print("WARNING: PROCESS {} COULD NOT BE REAPED!".format(key)) + print('') + running_processes[key] = None + + # delete possibly finished runs + running_processes = {key: val for key, val in running_processes.items() if val is not None} + time.sleep(2) + + if (len(running_processes) == 0) and (counter >= len(processes)): + break + + print("Done") + sys.stdout.flush() + + +if __name__ == "__main__": + _main() diff --git a/neuralhydrology/training/__init__.py b/neuralhydrology/training/__init__.py new file mode 100644 index 00000000..ec42e0e7 --- /dev/null +++ b/neuralhydrology/training/__init__.py @@ -0,0 +1,92 @@ +import logging +from typing import List + +import torch + +import neuralhydrology.training.loss as loss +from neuralhydrology.training import regularization +from neuralhydrology.utils.config import Config + +LOGGER = logging.getLogger(__name__) + + +def get_optimizer(model: torch.nn.Module, cfg: Config) -> torch.optim.Optimizer: + """Get specific optimizer object, depending on the run configuration. + + Currently only 'Adam' is supported. + + Parameters + ---------- + model : torch.nn.Module + The model to be optimized. + cfg : Config + The run configuration. + + Returns + ------- + torch.optim.Optimizer + Optimizer object that can be used for model training. + """ + if cfg.optimizer == "Adam": + optimizer = torch.optim.Adam(model.parameters(), lr=cfg.learning_rate[0]) + else: + raise NotImplementedError(f"{cfg.optimizer} not implemented or not linked in `get_optimizer()`") + + return optimizer + + +def get_loss_obj(cfg: Config) -> loss.BaseLoss: + """Get loss object, depending on the run configuration. + + Currently supported are 'MSE', 'NSE', 'RMSE', 'WeightedNSE'. + + Parameters + ---------- + cfg : Config + The run configuration. + + Returns + ------- + loss.BaseLoss + A new loss instance that implements the loss specified in the config or, if different, the loss required by the + head. + """ + if cfg.loss == "MSE": + loss_obj = loss.MaskedMSELoss(cfg) + elif cfg.loss == "NSE": + loss_obj = loss.MaskedNSELoss(cfg) + elif cfg.loss == "WeightedNSE": + loss_obj = loss.MaskedWeightedNSELoss(cfg) + elif cfg.loss == "RMSE": + loss_obj = loss.MaskedRMSELoss(cfg) + + else: + raise NotImplementedError(f"{cfg.loss} not implemented or not linked in `get_loss()`") + + return loss_obj + + +def get_regularization_obj(cfg: Config) -> List[regularization.BaseRegularization]: + """Get list of regularization objects. + + Currently, only the 'tie_frequencies' regularization is implemented. + + Parameters + ---------- + cfg : Config + The run configuration. + + Returns + ------- + List[regularization.BaseRegularization] + List of regularization objects that will be added to the loss during training. + + """ + regularization_modules = [] + for reg_name in cfg.regularization: + if reg_name == "tie_frequencies": + regularization_modules.append(regularization.TiedFrequencyMSERegularization(cfg)) + else: + raise NotImplementedError(f"{reg_name} not implemented or not linked in `get_regularization_obj()`.") + + return regularization_modules diff --git a/neuralhydrology/training/basetrainer.py b/neuralhydrology/training/basetrainer.py new file mode 100644 index 00000000..6cdb1ee6 --- /dev/null +++ b/neuralhydrology/training/basetrainer.py @@ -0,0 +1,329 @@ +import logging +import random +import sys +from datetime import datetime +from pathlib import Path +from typing import Dict + +import numpy as np +import torch +from torch.utils.data import DataLoader +from tqdm import tqdm + +import neuralhydrology.training.loss as loss +from neuralhydrology.data import get_dataset +from neuralhydrology.data.basedataset import BaseDataset +from neuralhydrology.data.utils import load_basin_file +from neuralhydrology.evaluation import get_tester +from neuralhydrology.evaluation.tester import BaseTester +from neuralhydrology.modelzoo import get_model +from neuralhydrology.training import get_loss_obj, get_optimizer, get_regularization_obj +from neuralhydrology.training.logger import Logger +from neuralhydrology.utils.config import Config +from neuralhydrology.utils.logging_utils import setup_logging + +LOGGER = logging.getLogger(__name__) + + +class BaseTrainer(object): + """Default class to train a model. + + Parameters + ---------- + cfg : Config + The run configuration. + """ + + def __init__(self, cfg: Config): + super(BaseTrainer, self).__init__() + self.cfg = cfg + self.model = None + self.optimizer = None + self.loss_obj = None + self.experiment_logger = None + self.loader = None + self.validator = None + self.noise_sampler_y = None + self._target_mean = None + self._target_std = None + self._allow_subsequent_nan_losses = cfg.allow_subsequent_nan_losses + + # load train basin list and add number of basins to the config + self.basins = load_basin_file(cfg.train_basin_file) + self.cfg.number_of_basins = len(self.basins) + + # check at which epoch the training starts + self._epoch = self._get_start_epoch_number() + + self._create_folder_structure() + setup_logging(str(self.cfg.run_dir / "output.log")) + LOGGER.info(f"### Folder structure created at {self.cfg.run_dir}") + + if self.cfg.is_continue_training: + LOGGER.info(f"### Continue training of run stored in {self.cfg.base_run_dir}") + + LOGGER.info(f"### Run configurations for {self.cfg.experiment_name}") + for key, val in self.cfg.as_dict().items(): + LOGGER.info(f"{key}: {val}") + + self._set_random_seeds() + self._set_device() + + def _get_dataset(self) -> BaseDataset: + return get_dataset(cfg=self.cfg, period="train", is_train=True) + + def _get_model(self) -> torch.nn.Module: + return get_model(cfg=self.cfg) + + def _get_optimizer(self) -> torch.optim.Optimizer: + return get_optimizer(model=self.model, cfg=self.cfg) + + def _get_loss_obj(self) -> loss.BaseLoss: + return get_loss_obj(cfg=self.cfg) + + def _set_regularization(self): + self.loss_obj.set_regularization_terms(get_regularization_obj(cfg=self.cfg)) + + def _get_tester(self) -> BaseTester: + return get_tester(cfg=self.cfg, run_dir=self.cfg.run_dir, period="validation", init_model=False) + + def _get_data_loader(self, ds: BaseDataset) -> torch.utils.data.DataLoader: + return DataLoader(ds, batch_size=self.cfg.batch_size, shuffle=True, num_workers=self.cfg.num_workers) + + def initialize_training(self): + """Initialize the training class. + + This method will load the model, initialize loss, regularization, optimizer, dataset and dataloader, + tensorboard logging, and Tester class. + If called in a ``continue_training`` context, this model will also restore the model and optimizer state. + """ + self.model = self._get_model().to(self.device) + if self.cfg.checkpoint_path is not None: + LOGGER.info(f"Starting training from Checkpoint {self.cfg.checkpoint_path}") + self.model.load_state_dict(torch.load(str(self.cfg.checkpoint_path), map_location=self.device)) + + self.optimizer = self._get_optimizer() + self.loss_obj = self._get_loss_obj() + + # Add possible regularization terms to the loss function. + self._set_regularization() + + # restore optimizer and model state if training is continued + if self.cfg.is_continue_training: + self._restore_training_state() + + ds = self._get_dataset() + if len(ds) == 0: + raise ValueError("Dataset contains no samples.") + self.loader = self._get_data_loader(ds=ds) + + self.experiment_logger = Logger(cfg=self.cfg) + if self.cfg.log_tensorboard: + self.experiment_logger.start_tb() + + if self.cfg.is_continue_training: + # set epoch and iteration step counter to continue from the selected checkpoint + self.experiment_logger.epoch = self._epoch + self.experiment_logger.update = len(self.loader) * self._epoch + + if self.cfg.validate_every is not None: + if self.cfg.validate_n_random_basins < 1: + warn_msg = [ + f"Validation set to validate every {self.cfg.validate_every} epoch(s), but ", + "'validate_n_random_basins' not set or set to zero. Will validate on the entire validation set." + ] + LOGGER.warning("".join(warn_msg)) + self.cfg.validate_n_random_basins = self.cfg.number_of_basins + self.validator = self._get_tester() + + if self.cfg.target_noise_std is not None: + self.noise_sampler_y = torch.distributions.Normal(loc=0, scale=self.cfg.target_noise_std) + self._target_mean = torch.from_numpy( + ds.scaler["xarray_means"][self.cfg.target_variables].to_array().values).to(self.device) + self._target_std = torch.from_numpy( + ds.scaler["xarray_stds"][self.cfg.target_variables].to_array().values).to(self.device) + + def train_and_validate(self): + """Train and validate the model. + + Train the model for the number of epochs specified in the run configuration, and perform validation after every + ``validate_every`` epochs. Model and optimizer state are saved after every ``save_weights_every`` epochs. + """ + for epoch in range(self._epoch + 1, self._epoch + self.cfg.epochs + 1): + # set new learning rate + if epoch in self.cfg.learning_rate.keys(): + LOGGER.info(f"Setting learning rate to {self.cfg.learning_rate[epoch]}") + for param_group in self.optimizer.param_groups: + param_group["lr"] = self.cfg.learning_rate[epoch] + + self._train_epoch(epoch=epoch) + avg_loss = self.experiment_logger.summarise() + LOGGER.info(f"Epoch {epoch} average loss: {avg_loss}") + + if epoch % self.cfg.save_weights_every == 0: + self._save_weights_and_optimizer(epoch) + + if (self.validator is not None) and (epoch % self.cfg.validate_every == 0): + self.validator.evaluate(epoch=epoch, + save_results=self.cfg.save_validation_results, + metrics=self.cfg.metrics, + model=self.model, + experiment_logger=self.experiment_logger.valid()) + + valid_metrics = self.experiment_logger.summarise() + if valid_metrics: + print_msg = f" -- Median validation metrics:" + print_msg += ", ".join(f"{key}: {val:.5f}" for key, val in valid_metrics.items()) + LOGGER.info(print_msg) + + # make sure to close tensorboard to avoid losing the last epoch + if self.cfg.log_tensorboard: + self.experiment_logger.stop_tb() + + def _get_start_epoch_number(self): + if self.cfg.is_continue_training: + if self.cfg.continue_from_epoch is not None: + epoch = self.cfg.continue_from_epoch + else: + weight_path = [x for x in sorted(list(self.cfg.run_dir.glob('model_epoch*.pt')))][-1] + epoch = weight_path.name[-6:-3] + else: + epoch = 0 + return int(epoch) + + def _restore_training_state(self): + if self.cfg.continue_from_epoch is not None: + epoch = f"{self.cfg.continue_from_epoch:03d}" + weight_path = self.cfg.base_run_dir / f"model_epoch{epoch}.pt" + else: + weight_path = [x for x in sorted(list(self.cfg.base_run_dir.glob('model_epoch*.pt')))][-1] + epoch = weight_path.name[-6:-3] + + optimizer_path = self.cfg.base_run_dir / f"optimizer_state_epoch{epoch}.pt" + + LOGGER.info(f"Continue training from epoch {int(epoch)}") + self.model.load_state_dict(torch.load(weight_path, map_location=self.device)) + self.optimizer.load_state_dict(torch.load(str(optimizer_path), map_location=self.device)) + + def _save_weights_and_optimizer(self, epoch: int): + weight_path = self.cfg.run_dir / f"model_epoch{epoch:03d}.pt" + torch.save(self.model.state_dict(), str(weight_path)) + + optimizer_path = self.cfg.run_dir / f"optimizer_state_epoch{epoch:03d}.pt" + torch.save(self.optimizer.state_dict(), str(optimizer_path)) + + def _train_epoch(self, epoch: int): + self.model.train() + self.experiment_logger.train() + + # process bar handle + pbar = tqdm(self.loader, file=sys.stdout) + pbar.set_description(f'# Epoch {epoch}') + + # Iterate in batches over training set + nan_count = 0 + for data in pbar: + + for key in data.keys(): + data[key] = data[key].to(self.device) + + # apply possible subclass pre-processing + data = self._pre_model_hook(data) + + # get predictions + predictions = self.model(data) + + if self.noise_sampler_y is not None: + for key in filter(lambda k: 'y' in k, data.keys()): + noise = self.noise_sampler_y.sample(data[key].shape) + # make sure we add near-zero noise to originally near-zero targets + data[key] += (data[key] + self._target_mean / self._target_std) * noise.to(self.device) + + loss = self.loss_obj(predictions, data) + + # early stop training if loss is NaN + if torch.isnan(loss): + nan_count += 1 + if nan_count > self._allow_subsequent_nan_losses: + raise RuntimeError(f"Loss was NaN for {nan_count} times in a row. Stopped training.") + LOGGER.warning(f"Loss is Nan; ignoring step. (#{nan_count}/{self._allow_subsequent_nan_losses})") + else: + nan_count = 0 + + # delete old gradients + self.optimizer.zero_grad() + + # get gradients + loss.backward() + + if self.cfg.clip_gradient_norm is not None: + torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.cfg.clip_gradient_norm) + + # update weights + self.optimizer.step() + + pbar.set_postfix_str(f"Loss: {loss.item():.4f}") + + self.experiment_logger.log_step(loss=loss.item()) + + def _set_random_seeds(self): + if self.cfg.seed is None: + self.cfg.seed = int(np.random.uniform(low=0, high=1e6)) + + # fix random seeds for various packages + random.seed(self.cfg.seed) + np.random.seed(self.cfg.seed) + torch.cuda.manual_seed(self.cfg.seed) + torch.manual_seed(self.cfg.seed) + + def _set_device(self): + if self.cfg.device is not None: + if self.cfg.device.startswith("cuda"): + gpu_id = int(self.cfg.device.split(':')[-1]) + if gpu_id > torch.cuda.device_count(): + raise RuntimeError(f"This machine does not have GPU #{gpu_id} ") + else: + self.device = torch.device(self.cfg.device) + else: + self.device = torch.device("cpu") + else: + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + LOGGER.info(f"### Device {self.device} will be used for training") + + def _create_folder_structure(self): + # create as subdirectory within run directory of base run + if self.cfg.is_continue_training: + folder_name = f"continue_training_from_epoch{self._epoch:03d}" + + # store dir of base run for easier access in weight loading + self.cfg.base_run_dir = self.cfg.run_dir + self.cfg.run_dir = self.cfg.run_dir / folder_name + + # create as new folder structure + else: + now = datetime.now() + day = f"{now.day}".zfill(2) + month = f"{now.month}".zfill(2) + hour = f"{now.hour}".zfill(2) + minute = f"{now.minute}".zfill(2) + second = f"{now.second}".zfill(2) + run_name = f'{self.cfg.experiment_name}_{day}{month}_{hour}{minute}{second}' + + # if no directory for the runs is specified, a 'runs' folder will be created in the current working dir + if self.cfg.run_dir is None: + self.cfg.run_dir = Path().cwd() / "runs" / run_name + else: + self.cfg.run_dir = self.cfg.run_dir / run_name + + # create folder + necessary subfolder + if not self.cfg.run_dir.is_dir(): + self.cfg.train_dir = self.cfg.run_dir / "train_data" + self.cfg.train_dir.mkdir(parents=True) + else: + raise RuntimeError(f"There is already a folder at {self.cfg.run_dir}") + if self.cfg.log_n_figures is not None: + self.cfg.img_log_dir = self.cfg.run_dir / "img_log" + self.cfg.img_log_dir.mkdir(parents=True) + + def _pre_model_hook(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + return data diff --git a/neuralhydrology/training/logger.py b/neuralhydrology/training/logger.py new file mode 100644 index 00000000..6fe102bf --- /dev/null +++ b/neuralhydrology/training/logger.py @@ -0,0 +1,162 @@ +import subprocess +from collections import defaultdict +from pathlib import Path +from typing import Dict, Union, List + +import matplotlib as mpl +import numpy as np +from torch.utils.tensorboard import SummaryWriter + +from neuralhydrology.__about__ import __version__ +from neuralhydrology.utils.config import Config + + +class Logger(object): + """Class that logs runs to tensorboard and saves plots to disk. + + Parameters + ---------- + cfg : Config + The run configuration. + """ + + def __init__(self, cfg: Config): + self._train = True + self.log_interval = cfg.log_interval + self.log_dir = cfg.run_dir + self._img_log_dir = cfg.img_log_dir + + # get git commit hash if folder is a git repository + current_dir = str(Path(__file__).absolute().parent) + if subprocess.call(["git", "-C", current_dir, "branch"], stderr=subprocess.DEVNULL, + stdout=subprocess.DEVNULL) == 0: + git_output = subprocess.check_output(["git", "-C", current_dir, "describe", "--always"]) + cfg.log_only('commit_hash', git_output.strip().decode('ascii')) + + # Additionally, the package version is stored in the config + cfg.log_only("package_version", __version__) + + # store a copy of the config into the run folder + cfg.dump_config(folder=self.log_dir) + + self.epoch = 0 + self.update = 0 + self._metrics = defaultdict(list) + self.writer = None + + @property + def tag(self): + return "train" if self._train else "valid" + + def train(self) -> 'Logger': + """Set logging to training period. + + Returns + ------- + Logger + The Logger instance, set to training mode. + """ + self._train = True + return self + + def valid(self) -> 'Logger': + """Set logging to validation period. + + Returns + ------- + Logger + The Logger instance, set to validation mode. + """ + self._train = False + return self + + def start_tb(self): + """ Start tensorboard logging. """ + self.writer = SummaryWriter(log_dir=str(self.log_dir)) + + def stop_tb(self): + """ Stop tensorboard logging. """ + if self.writer is not None: + self.writer.flush() + self.writer.close() + self.writer = None + + def log_figures(self, figures: List[mpl.figure.Figure], freq: str, preamble: str = ""): + """Log matplotlib figures as to disk. + + Parameters + ---------- + figures : List[mpl.figure.Figure] + List of figures to save. + freq : str + Prediction frequency of the figures. + preamble : str, optional + Prefix to prepend to the figures' file names. + """ + if self.writer is not None: + self.writer.add_figure(f'validation/timeseries/{freq}', figures, global_step=self.epoch) + + for idx, figure in enumerate(figures): + figure.savefig(Path(self._img_log_dir, preamble + f'_freq{freq}_epoch{self.epoch}_{idx + 1}'), dpi=300) + + def log_step(self, **kwargs): + """Log the results of a single step within an epoch. + + Parameters + ---------- + **kwargs + Key-value pairs of metric names and values. + """ + for k, v in kwargs.items(): + self._metrics[k].append(v) + + if not self._train: + return + + self.update += 1 + + if self.log_interval <= 0 or self.writer is None: + return + + if self.update % self.log_interval == 0: + tag = self.tag + for k, v in kwargs.items(): + self.writer.add_scalar('/'.join([tag, k]), v, self.update) + + def summarise(self) -> Union[float, Dict[str, float]]: + """"Log the results of the entire training or validation epoch. + + Returns + ------- + Union[float, Dict[str, float]] + Average loss if training is summarized, else a dict mapping metric names to median metric values. + """ + # summarize statistics of training epoch + if self._train: + self.epoch += 1 + + # summarize training + avg_loss = np.nanmean(self._metrics["loss"]) if self._metrics["loss"] else np.nan + + if self.writer is not None: + self.writer.add_scalar('/'.join([self.tag, 'avg_loss']), avg_loss, self.epoch) + + # summarize validation + else: + if self.writer is not None: + for k, v in self._metrics.items(): + means = np.nanmean(v) if v else np.nan + medians = np.nanmedian(v) if v else np.nan + self.writer.add_scalar('/'.join([self.tag, f'mean_{k.lower()}']), means, self.epoch) + self.writer.add_scalar('/'.join([self.tag, f'median_{k.lower()}']), medians, self.epoch) + + # return value for print in console + if self._train: + value = avg_loss + else: + value = {key: np.median(val) for key, val in self._metrics.items()} + + # clear buffer + self._metrics = defaultdict(list) + + return value diff --git a/neuralhydrology/training/loss.py b/neuralhydrology/training/loss.py new file mode 100644 index 00000000..5af7b362 --- /dev/null +++ b/neuralhydrology/training/loss.py @@ -0,0 +1,252 @@ +from typing import Dict, List, Tuple + +import numpy as np +import torch + +from neuralhydrology.training.regularization import BaseRegularization +from neuralhydrology.utils.config import Config + +ONE_OVER_2PI_SQUARED = 1.0 / np.sqrt(2.0 * np.pi) + + +class BaseLoss(torch.nn.Module): + """Base loss class. + + All losses extend this class by implementing `_get_loss`. + + Parameters + ---------- + cfg : Config + The run configuration. + prediction_keys : List[str] + List of keys that will be predicted. During the forward pass, the passed `prediction` dict + must contain these keys. Note that the keys listed here should be without frequency identifier. + ground_truth_keys : List[str] + List of ground truth keys that will be needed to compute the loss. During the forward pass, the + passed `data` dict must contain these keys. Note that the keys listed here should be without + frequency identifier. + additional_data : List[str], optional + Additional list of keys that will be taken from `data` in the forward pass to compute the loss. + For instance, this parameter can be used to pass the variances that are needed to compute an NSE. + """ + + def __init__(self, + cfg: Config, + prediction_keys: List[str], + ground_truth_keys: List[str], + additional_data: List[str] = None): + super(BaseLoss, self).__init__() + self._predict_last_n = _get_predict_last_n(cfg) + self._frequencies = [f for f in self._predict_last_n.keys() if f not in cfg.no_loss_frequencies] + + self._regularization_terms = [] + + # names of ground truth and prediction keys to be unpacked and subset to predict_last_n items. + self._prediction_keys = prediction_keys + self._ground_truth_keys = ground_truth_keys + + # subclasses can use this list to register inputs to be unpacked during the forward call + # and passed as kwargs to _get_loss() without subsetting. + self._additional_data = [] + if additional_data is not None: + self._additional_data = additional_data + + def forward(self, prediction: Dict[str, torch.Tensor], data: Dict[str, torch.Tensor]) -> torch.Tensor: + """Calculate the loss. + + Parameters + ---------- + prediction : Dict[str, torch.Tensor] + Dictionary of predictions for each frequency. If more than one frequency is predicted, + the keys must have suffixes ``_{frequency}``. For the required keys, refer to the documentation + of the concrete loss. + data : Dict[str, torch.Tensor] + Dictionary of ground truth data for each frequency. If more than one frequency is predicted, + the keys must have suffixes ``_{frequency}``. For the required keys, refer to the documentation + of the concrete loss. + + Returns + ------- + torch.Tensor + The calculated loss. + """ + # unpack loss-specific additional arguments + kwargs = {key: data[key] for key in self._additional_data} + + losses = [] + prediction_sub, ground_truth_sub = {}, {} + for freq in self._frequencies: + if self._predict_last_n[freq] == 0: + continue # no predictions for this frequency + freq_suffix = '' if freq == '' else f'_{freq}' + + # apply predict_last_n and mask + freq_pred, freq_gt = self._subset({key: prediction[f'{key}{freq_suffix}'] for key in self._prediction_keys}, + {key: data[f'{key}{freq_suffix}'] for key in self._ground_truth_keys}, + self._predict_last_n[freq]) + # remember subsets for multi-frequency component + prediction_sub.update({f'{key}{freq_suffix}': freq_pred[key] for key in freq_pred.keys()}) + ground_truth_sub.update({f'{key}{freq_suffix}': freq_gt[key] for key in freq_gt.keys()}) + + losses.append(self._get_loss(freq_pred, freq_gt, **kwargs)) + + loss = torch.mean(torch.stack(losses)) + for regularization in self._regularization_terms: + loss = loss + regularization(prediction_sub, ground_truth_sub, + {k: v for k, v in prediction.items() if k not in self._prediction_keys}) + return loss + + def _subset(self, prediction: Dict[str, torch.Tensor], ground_truth: Dict[str, torch.Tensor], predict_last_n: int) \ + -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: + ground_truth_sub = {key: gt[:, -predict_last_n:, :] for key, gt in ground_truth.items()} + prediction_sub = {key: pred[:, -predict_last_n:, :] for key, pred in prediction.items()} + + return prediction_sub, ground_truth_sub + + def _get_loss(self, prediction: Dict[str, torch.Tensor], ground_truth: Dict[str, torch.Tensor], **kwargs): + raise NotImplementedError + + def set_regularization_terms(self, regularization_modules: List[BaseRegularization]): + """Register the passed regularization terms to be added to the loss function. + + Parameters + ---------- + regularization_modules : List[BaseRegularization] + List of regularization functions to be added to the loss during `forward`. + """ + self._regularization_terms = regularization_modules + + +class MaskedMSELoss(BaseLoss): + """Mean squared error loss. + + To use this loss in a forward pass, the passed `prediction` dict must contain + the key ``y_hat``, and the `data` dict must contain ``y``. + + Parameters + ---------- + cfg : Config + The run configuration. + """ + + def __init__(self, cfg: Config): + super(MaskedMSELoss, self).__init__(cfg, ['y_hat'], ['y']) + + def _get_loss(self, prediction: Dict[str, torch.Tensor], ground_truth: Dict[str, torch.Tensor], **kwargs): + mask = ~torch.isnan(ground_truth['y']) + loss = 0.5 * torch.mean((prediction['y_hat'][mask] - ground_truth['y'][mask])**2) + return loss + + +class MaskedRMSELoss(BaseLoss): + """Root mean squared error loss. + + To use this loss in a forward pass, the passed `prediction` dict must contain + the key ``y_hat``, and the `data` dict must contain ``y``. + + Parameters + ---------- + cfg : Config + The run configuration. + """ + + def __init__(self, cfg: Config): + super(MaskedRMSELoss, self).__init__(cfg, ['y_hat'], ['y']) + + def _get_loss(self, prediction: Dict[str, torch.Tensor], ground_truth: Dict[str, torch.Tensor], **kwargs): + mask = ~torch.isnan(ground_truth['y']) + loss = torch.sqrt(0.5 * torch.mean((prediction['y_hat'][mask] - ground_truth['y'][mask])**2)) + return loss + + +class MaskedNSELoss(BaseLoss): + """Basin-averaged Nash--Sutcliffe Model Efficiency Coefficient loss. + + To use this loss in a forward pass, the passed `prediction` dict must contain + the key ``y_hat``, and the `data` dict must contain ``y`` and ``per_basin_target_stds``. + + A description of the loss function is available in [#]_. + + Parameters + ---------- + cfg : Config + The run configuration. + eps: float, optional + Small constant for numeric stability. + + References + ---------- + .. [#] Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: "Towards learning + universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets" + *Hydrology and Earth System Sciences*, 2019, 23, 5089-5110, doi:10.5194/hess-23-5089-2019 + """ + + def __init__(self, cfg: Config, eps: float = 0.1): + super(MaskedNSELoss, self).__init__(cfg, ['y_hat'], ['y'], additional_data=['per_basin_target_stds']) + self.eps = eps + + def _get_loss(self, prediction: Dict[str, torch.Tensor], ground_truth: Dict[str, torch.Tensor], **kwargs): + mask = ~torch.isnan(ground_truth['y']) + y_hat = prediction['y_hat'][mask] + y = ground_truth['y'][mask] + per_basin_target_stds = kwargs['per_basin_target_stds'] + # expand dimension 1 to predict_last_n + per_basin_target_stds = per_basin_target_stds.expand_as(prediction['y_hat'])[mask] + + squared_error = (y_hat - y)**2 + weights = 1 / (per_basin_target_stds + self.eps)**2 + scaled_loss = weights * squared_error + return torch.mean(scaled_loss) + + +class MaskedWeightedNSELoss(BaseLoss): + """Basin-averaged Nash--Sutcliffe Model Efficiency Coefficient loss. + + This loss function weights multiple outputs according to user-specified weights in the + config argument 'target_loss_weights'. To use this loss in a forward pass, the passed + `prediction` dict must contain the key ``y_hat``, and the `data` dict must contain + ``y`` and ``per_basin_target_stds``. + + Parameters + ---------- + cfg : Config + The run configuration. + eps: float, optional + Small constant for numeric stability. + """ + + def __init__(self, cfg: Config, eps: float = 0.1): + super(MaskedWeightedNSELoss, self).__init__(cfg, ['y_hat'], ['y'], additional_data=['per_basin_target_stds']) + + self.eps = eps + + if cfg.target_loss_weights is None: + raise ValueError('target_loss_weights must be specified for WeightedNSELoss') + elif len(cfg.target_loss_weights) != len(cfg.target_variables): + raise ValueError("Number of loss weights must be equal to the number of target_variables.") + else: + self._loss_weights = torch.tensor(cfg.target_loss_weights).to(cfg.device) + + def _get_loss(self, prediction: Dict[str, torch.Tensor], ground_truth: Dict[str, torch.Tensor], **kwargs): + + mask = ~torch.isnan(ground_truth['y']) + y_hat = prediction['y_hat'] * torch.sqrt(self._loss_weights) + y = ground_truth['y'] * torch.sqrt(self._loss_weights) + + per_basin_target_stds = kwargs['per_basin_target_stds'] + per_basin_target_stds = per_basin_target_stds.expand_as(prediction['y_hat']) + + squared_error = (y_hat[mask] - y[mask])**2 + norm_factor = 1 / (per_basin_target_stds[mask] + self.eps)**2 + scaled_loss = norm_factor * squared_error + + return torch.mean(scaled_loss) + + +def _get_predict_last_n(cfg: Config) -> dict: + predict_last_n = cfg.predict_last_n + if isinstance(predict_last_n, int): + predict_last_n = {'': predict_last_n} + if len(predict_last_n) == 1: + predict_last_n = {'': list(predict_last_n.values())[0]} # if there's only one frequency, we omit its identifier + return predict_last_n diff --git a/neuralhydrology/training/regularization.py b/neuralhydrology/training/regularization.py new file mode 100644 index 00000000..e39485a3 --- /dev/null +++ b/neuralhydrology/training/regularization.py @@ -0,0 +1,105 @@ +from typing import Dict + +import pandas as pd +import torch + +from neuralhydrology.data.utils import sort_frequencies +from neuralhydrology.utils.config import Config + + +class BaseRegularization(torch.nn.Module): + """Base class for regularization terms. + + Regularization terms subclass this class by implementing the `forward` method. + + Parameters + ---------- + cfg: Config + The run configuration. + """ + + def __init__(self, cfg: Config): + super(BaseRegularization, self).__init__() + self.cfg = cfg + + def forward(self, prediction: Dict[str, torch.Tensor], ground_truth: Dict[str, torch.Tensor], + other_model_data: Dict[str, torch.Tensor]) -> torch.Tensor: + """Calculate the regularization term. + + Parameters + ---------- + prediction : Dict[str, torch.Tensor] + Dictionary of predicted variables for each frequency. If more than one frequency is predicted, + the keys must have suffixes ``_{frequency}``. For the required keys, refer to the documentation + of the concrete loss. + ground_truth : Dict[str, torch.Tensor] + Dictionary of ground truth variables for each frequency. If more than one frequency is predicted, + the keys must have suffixes ``_{frequency}``. For the required keys, refer to the documentation + of the concrete loss. + other_model_data : Dict[str, torch.Tensor] + Dictionary of all remaining keys-value pairs in the prediction dictionary that are not directly linked to + the model predictions but can be useful for regularization purposes, e.g. network internals, weights etc. + + Returns + ------- + torch.Tensor + The regularization value. + """ + raise NotImplementedError + + +class TiedFrequencyMSERegularization(BaseRegularization): + """Regularization that penalizes inconsistent predictions across frequencies. + + This regularization can only be used if at least two frequencies are predicted. For each pair of adjacent + frequencies f and f', where f is a higher frequency than f', it aggregates the f-predictions to f' and calculates + the mean squared deviation between f' and aggregated f. + + Parameters + ---------- + cfg : Config + The run configuration. + + Raises + ------ + ValueError + If the run configuration only predicts one frequency. + """ + + def __init__(self, cfg: Config): + super(TiedFrequencyMSERegularization, self).__init__(cfg) + self._frequencies = sort_frequencies( + [f for f in cfg.use_frequencies if cfg.predict_last_n[f] > 0 and f not in cfg.no_loss_frequencies]) + + if len(self._frequencies) < 2: + raise ValueError("TiedFrequencyMSERegularization needs at least two frequencies.") + + def forward(self, prediction: Dict[str, torch.Tensor], ground_truth: Dict[str, torch.Tensor], + *args) -> torch.Tensor: + """Calculate the sum of mean squared deviations between adjacent predicted frequencies. + + Parameters + ---------- + prediction : Dict[str, torch.Tensor] + Dictionary containing ``y_hat_{frequency}`` for each frequency. + ground_truth : Dict[str, torch.Tensor] + Dictionary continaing ``y_{frequency}`` for each frequency. + + Returns + ------- + torch.Tensor + The sum of mean squared deviations for each pair of adjacent frequencies. + """ + + loss = 0 + for idx, freq in enumerate(self._frequencies): + if idx == 0: + continue + frequency_factor = pd.to_timedelta(self._frequencies[idx - 1]) // pd.to_timedelta(freq) + freq_pred = prediction[f'y_hat_{freq}'] + mean_freq_pred = freq_pred.view(freq_pred.shape[0], freq_pred.shape[1] // frequency_factor, + frequency_factor, -1).mean(dim=2) + lower_freq_pred = prediction[f'y_hat_{self._frequencies[idx - 1]}'][:, -mean_freq_pred.shape[1]:] + loss = loss + torch.mean((lower_freq_pred - mean_freq_pred)**2) + + return loss diff --git a/neuralhydrology/training/train.py b/neuralhydrology/training/train.py new file mode 100644 index 00000000..e98419a6 --- /dev/null +++ b/neuralhydrology/training/train.py @@ -0,0 +1,19 @@ +from neuralhydrology.training.basetrainer import BaseTrainer +from neuralhydrology.utils.config import Config + + +def start_training(cfg: Config): + """Start model training. + + Parameters + ---------- + cfg : Config + The run configuration. + + """ + if cfg.head.lower() in ['regression']: + trainer = BaseTrainer(cfg=cfg) + else: + raise ValueError(f"Unknown head {cfg.head}.") + trainer.initialize_training() + trainer.train_and_validate() diff --git a/neuralhydrology/utils/__init__.py b/neuralhydrology/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/neuralhydrology/utils/config.py b/neuralhydrology/utils/config.py new file mode 100644 index 00000000..85abd525 --- /dev/null +++ b/neuralhydrology/utils/config.py @@ -0,0 +1,604 @@ +import warnings +from collections import OrderedDict +from pathlib import Path +from typing import Dict, List, Union, Any + +import pandas as pd +from ruamel.yaml import YAML + + +class Config(object): + + def __init__(self, cfg_path: Path): + """Read run configuration from the specified path and parse it into a dict. + + During parsing, config keys that contain 'dir', 'file', or 'path' will be converted to pathlib.Path instances. + Configuration keys ending with '_date' will be parsed to pd.Timestamps. The expected format is DD/MM/YYYY. + + Parameters + ---------- + cfg_path : Path + Path to the config file. + """ + self._cfg = self._read_config(cfg_path=cfg_path) + + def as_dict(self) -> dict: + """Return run configuration as dictionary. + + Returns + ------- + dict + The run configuration, as defined in the .yml file. + """ + return self._cfg + + def dump_config(self, folder: Path, filename: str = 'config.yml'): + """Save the run configuration as a .yml file to disk. + + Parameters + ---------- + folder : Path + Folder in which the configuration will be stored. + filename : str, optional + Name of the file that will be stored. Default: 'config.yml'. + + Raises + ------ + FileExistsError + If the specified folder already contains a file named `filename`. + """ + cfg_path = folder / filename + if not cfg_path.exists(): + with cfg_path.open('w') as fp: + temp_cfg = {} + for key, val in self._cfg.items(): + if any([key.endswith(x) for x in ['_dir', '_path', '_file', '_files']]): + if isinstance(val, list): + temp_list = [] + for elem in val: + temp_list.append(str(elem)) + temp_cfg[key] = temp_list + else: + temp_cfg[key] = str(val) + elif key.endswith('_date'): + if isinstance(val, list): + temp_list = [] + for elem in val: + temp_list.append(elem.strftime(format="%d/%m/%Y")) + temp_cfg[key] = temp_list + else: + # Ignore None's due to e.g. using a per_basin_period_file + if isinstance(val, pd.Timestamp): + temp_cfg[key] = val.strftime(format="%d/%m/%Y") + else: + temp_cfg[key] = val + + yaml = YAML() + yaml.dump(dict(OrderedDict(sorted(temp_cfg.items()))), fp) + else: + FileExistsError(cfg_path) + + def log_only(self, key: str, value: Any): + """Log any key-value pair into config. These are not accessible as properties and are set-only. + + This can be used to e.g. log git commit hash id or package version to the config, with the sole purpose to + document these values in the config that is saved to disk. + + Parameters + ---------- + key : str + The dictionary key + value : Any + The argument to store + """ + self._cfg[key] = value + + def update_config(self, cfg_path: Path): + """Update config arguments. + + Useful e.g. in the context of fine-tuning or when continuing to train from a checkpoint to adapt for example the + learning rate, train basin files or anything else. + + Parameters + ---------- + cfg_path : Path + Path to the new config file. Each argument specified in this file will overwrite the existing config + argument. + """ + new_config = self._read_config(cfg_path=cfg_path) + for key, val in new_config.items(): + self._cfg[key] = val + + def _as_default_list(self, value: Any) -> list: + if value is None: + return [] + elif isinstance(value, list): + return value + else: + return [value] + + def _get_value_verbose(self, key: str) -> Union[float, int, str, list, dict, Path, pd.Timestamp]: + """Use this function internally to return attributes of the config that are mandatory""" + if key not in self._cfg.keys(): + raise ValueError(f"{key} is not specified in the config (.yml).") + elif self._cfg[key] is None: + raise ValueError(f"{key} is mandatory but 'None' in the config.") + else: + return self._cfg[key] + + @staticmethod + def _parse_config(cfg: dict) -> dict: + for key, val in cfg.items(): + # convert all path strings to PosixPath objects + if any([key.endswith(x) for x in ['_dir', '_path', '_file', '_files']]): + if (val is not None) and (val != "None"): + if isinstance(val, list): + temp_list = [] + for element in val: + temp_list.append(Path(element)) + cfg[key] = temp_list + else: + cfg[key] = Path(val) + else: + cfg[key] = None + + # convert Dates to pandas Datetime indexs + elif key.endswith('_date'): + if isinstance(val, list): + temp_list = [] + for elem in val: + temp_list.append(pd.to_datetime(elem, format='%d/%m/%Y')) + cfg[key] = temp_list + else: + cfg[key] = pd.to_datetime(val, format='%d/%m/%Y') + + else: + pass + + # Add more config parsing if necessary + return cfg + + def _read_config(self, cfg_path: Path): + if cfg_path.exists(): + with cfg_path.open('r') as fp: + yaml = YAML(typ="safe") + cfg = yaml.load(fp) + else: + raise FileNotFoundError(cfg_path) + + cfg = self._parse_config(cfg) + + return cfg + + @property + def additional_feature_files(self) -> List[Path]: + return self._as_default_list(self._cfg.get("additional_feature_files", None)) + + @property + def allow_subsequent_nan_losses(self) -> int: + return self._cfg.get("allow_subsequent_nan_losses", 0) + + @property + def base_run_dir(self) -> Path: + return self._get_value_verbose("base_run_dir") + + @base_run_dir.setter + def base_run_dir(self, folder: Path): + self._cfg["base_run_dir"] = folder + + @property + def batch_size(self) -> int: + return self._get_value_verbose("batch_size") + + @property + def cache_validation_data(self) -> bool: + return self._cfg.get("cache_validation_data", True) + + @property + def camels_attributes(self) -> List[str]: + return self._as_default_list(self._cfg.get("camels_attributes", [])) + + @property + def checkpoint_path(self) -> Path: + return self._cfg.get("checkpoint_path", None) + + @property + def clip_gradient_norm(self) -> float: + return self._cfg.get("clip_gradient_norm", None) + + @property + def clip_targets_to_zero(self) -> List[str]: + return self._as_default_list(self._cfg.get("clip_targets_to_zero", [])) + + @property + def continue_from_epoch(self) -> int: + return self._cfg.get("continue_from_epoch", None) + + @property + def data_dir(self) -> Path: + return self._get_value_verbose("data_dir") + + @property + def dataset(self) -> str: + return self._get_value_verbose("dataset") + + @property + def device(self) -> str: + return self._cfg.get("device", None) + + @device.setter + def device(self, device: str): + if device == "cpu" or device.startswith("cuda:"): + self._cfg["device"] = device + else: + raise ValueError("'device' must be either 'cpu' or a 'cuda:X', with 'X' being the GPU ID.") + + @property + def dynamic_inputs(self) -> Union[List[str], Dict[str, List[str]]]: + return self._get_value_verbose("dynamic_inputs") + + @property + def embedding_activation(self) -> str: + return self._cfg.get("embedding_activation", "tanh") + + @property + def embedding_dropout(self) -> float: + return self._cfg.get("embedding_dropout", 0.0) + + @property + def embedding_hiddens(self) -> List[int]: + return self._as_default_list(self._cfg.get("embedding_hiddens", [])) + + @property + def epochs(self) -> int: + return self._get_value_verbose("epochs") + + @property + def experiment_name(self) -> str: + if self._cfg.get("experiment_name", None) is None: + return "run" + else: + return self._cfg["experiment_name"] + + @property + def forcings(self) -> List[str]: + return self._as_default_list(self._get_value_verbose("forcings")) + + @property + def head(self) -> str: + return self._get_value_verbose("head") + + @property + def hidden_size(self) -> Union[int, Dict[str, int]]: + return self._get_value_verbose("hidden_size") + + @property + def hydroatlas_attributes(self) -> List[str]: + return self._as_default_list(self._cfg.get("hydroatlas_attributes", [])) + + @property + def img_log_dir(self) -> Path: + return self._cfg.get("img_log_dir", None) + + @img_log_dir.setter + def img_log_dir(self, folder: Path): + self._cfg["img_log_dir"] = folder + + @property + def initial_forget_bias(self) -> float: + return self._cfg.get("initial_forget_bias", None) + + @property + def is_continue_training(self) -> bool: + return self._cfg.get("is_continue_training", False) + + @is_continue_training.setter + def is_continue_training(self, flag: bool): + self._cfg["is_continue_training"] = flag + + @property + def learning_rate(self) -> Dict[int, float]: + if ("learning_rate" in self._cfg.keys()) and (self._cfg["learning_rate"] is not None): + if isinstance(self._cfg["learning_rate"], float): + return {0: self._cfg["learning_rate"]} + elif isinstance(self._cfg["learning_rate"], dict): + return self._cfg["learning_rate"] + else: + raise ValueError("Unsupported data type for learning rate. Use either dict (epoch to float) or float.") + else: + raise ValueError("No learning rate specified in the config (.yml).") + + @property + def log_interval(self) -> int: + return self._cfg.get("log_interval", 10) + + @property + def log_n_figures(self) -> int: + if (self._cfg.get("log_n_figures", None) is None) or (self._cfg["log_n_figures"] < 1): + return 0 + else: + return self._cfg["log_n_figures"] + + @property + def log_tensorboard(self) -> bool: + return self._cfg.get("log_tensorboard", True) + + @property + def loss(self) -> str: + return self._get_value_verbose("loss") + + @loss.setter + def loss(self, loss: str): + self._cfg["loss"] = loss + + @property + def mc_dropout(self) -> bool: + return self._cfg.get("mc_dropout", False) + + @property + def metrics(self) -> Union[List[str], Dict[str, List[str]]]: + return self._cfg.get("metrics", []) + + @metrics.setter + def metrics(self, metrics: Union[str, List[str], Dict[str, List[str]]]): + self._cfg["metrics"] = metrics + + @property + def model(self) -> str: + return self._get_value_verbose("model") + + @property + def n_distributions(self) -> int: + return self._get_value_verbose("n_distributions") + + @property + def n_samples(self) -> int: + return self._get_value_verbose("n_samples") + + @property + def n_taus(self) -> int: + return self._get_value_verbose("n_taus") + + @property + def negative_sample_handling(self) -> str: + return self._cfg.get("negative_sample_handling", None) + + @property + def negative_sample_max_retries(self) -> int: + return self._get_value_verbose("negative_sample_max_retries") + + @property + def no_loss_frequencies(self) -> list: + return self._as_default_list(self._cfg.get("no_loss_frequencies", [])) + + @property + def num_workers(self) -> int: + return self._cfg.get("num_workers", 0) + + @property + def number_of_basins(self) -> int: + return self._get_value_verbose("number_of_basins") + + @number_of_basins.setter + def number_of_basins(self, num_basins: int): + self._cfg["number_of_basins"] = num_basins + + @property + def ode_method(self) -> str: + return self._cfg.get("ode_method", "euler") + + @property + def ode_num_unfolds(self) -> int: + return self._cfg.get("ode_num_unfolds", 4) + + @property + def ode_random_freq_lower_bound(self) -> str: + return self._get_value_verbose("ode_random_freq_lower_bound") + + @property + def optimizer(self) -> str: + return self._get_value_verbose("optimizer") + + @property + def output_activation(self) -> str: + return self._cfg.get("output_activation", "linear") + + @property + def output_dropout(self) -> float: + return self._cfg.get("output_dropout", 0.0) + + @property + def per_basin_test_periods_file(self) -> Path: + return self._cfg.get("per_basin_test_periods_file", None) + + @property + def per_basin_train_periods_file(self) -> Path: + return self._cfg.get("per_basin_train_periods_file", None) + + @property + def per_basin_validation_periods_file(self) -> Path: + return self._cfg.get("per_basin_validation_periods_file", None) + + @property + def per_frequency_lstm(self) -> bool: + return self._cfg.get("per_frequency_lstm", True) + + @property + def predict_last_n(self) -> Union[int, Dict[str, int]]: + return self._get_value_verbose("predict_last_n") + + @property + def rating_curve_file(self) -> Path: + return self._get_value_verbose("rating_curve_file") + + @property + def regularization(self) -> List[str]: + return self._as_default_list(self._cfg.get("regularization", [])) + + @property + def run_dir(self) -> Path: + return self._cfg.get("run_dir", None) + + @run_dir.setter + def run_dir(self, folder: Path): + self._cfg["run_dir"] = folder + + @property + def save_train_data(self) -> bool: + return self._cfg.get("save_train_data", False) + + @property + def save_validation_results(self) -> bool: + return self._cfg.get("save_validation_results", False) + + @property + def save_weights_every(self) -> int: + return self._cfg.get("save_weights_every", 1) + + @property + def seed(self) -> int: + return self._cfg.get("seed", None) + + @seed.setter + def seed(self, seed: int): + if self._cfg.get("seed", None) is None: + self._cfg["seed"] = seed + else: + raise RuntimeError("Seed was already specified and can't be replaced") + + @property + def seq_length(self) -> Union[int, Dict[str, int]]: + return self._get_value_verbose("seq_length") + + @property + def static_inputs(self) -> List[str]: + return self._as_default_list(self._cfg.get("static_inputs", [])) + + @property + def target_loss_weights(self) -> List[float]: + return self._cfg.get("target_loss_weights", None) + + @property + def target_noise_std(self) -> float: + if (self._cfg.get("target_noise_std", None) is None) or (self._cfg["target_noise_std"] == 0): + return None + else: + return self._cfg["target_noise_std"] + + @property + def target_variables(self) -> List[str]: + if "target_variables" in self._cfg.keys(): + return self._cfg["target_variables"] + elif "target_variable" in self._cfg.keys(): + warnings.warn("'target_variable' will be deprecated. Use 'target_variables' in the future", FutureWarning) + return self._cfg["target_variable"] + else: + raise ValueError("No target variables ('target_variables') defined in the config.") + + @property + def tau_down(self) -> float: + return self._get_value_verbose("tau_down") + + @property + def tau_up(self) -> float: + return self._get_value_verbose("tau_up") + + @property + def test_basin_file(self) -> Path: + return self._get_value_verbose("test_basin_file") + + @property + def test_end_date(self) -> pd.Timestamp: + return self._get_value_verbose("test_end_date") + + @property + def test_start_date(self) -> pd.Timestamp: + return self._get_value_verbose("test_start_date") + + @property + def train_basin_file(self) -> Path: + return self._get_value_verbose("train_basin_file") + + @property + def train_data_file(self) -> Path: + return self._cfg.get("train_data_file", None) + + @property + def train_dir(self) -> Path: + return self._cfg.get("train_dir", None) + + @train_dir.setter + def train_dir(self, folder: Path): + self._cfg["train_dir"] = folder + + @property + def train_end_date(self) -> pd.Timestamp: + return self._get_value_verbose("train_end_date") + + @property + def train_start_date(self) -> pd.Timestamp: + return self._get_value_verbose("train_start_date") + + @property + def transfer_multifreq_states(self) -> Dict[str, str]: + return self._cfg.get("transfer_multifreq_states", {'h': 'linear', 'c': 'linear'}) + + @property + def umal_extend_batch(self) -> bool: + return self._cfg.get("umal_extend_batch", False) + + @property + def use_basin_id_encoding(self) -> bool: + return self._cfg.get("use_basin_id_encoding", False) + + @property + def use_frequencies(self) -> List[str]: + return self._as_default_list(self._cfg.get("use_frequencies", [])) + + @property + def validate_every(self) -> int: + if (self._cfg.get("validate_every", None) is None) or (self._cfg["validate_every"] < 1): + return None + else: + return self._cfg["validate_every"] + + @property + def validate_n_random_basins(self) -> int: + if (self._cfg.get("validate_n_random_basins", None) is None) or (self._cfg["validate_n_random_basins"] < 1): + return 0 + else: + return self._cfg["validate_n_random_basins"] + + @validate_n_random_basins.setter + def validate_n_random_basins(self, n_basins: int): + self._cfg["validate_n_random_basins"] = n_basins + + @property + def validation_basin_file(self) -> Path: + return self._get_value_verbose("validation_basin_file") + + @property + def validation_end_date(self) -> pd.Timestamp: + return self._get_value_verbose("validation_end_date") + + @property + def validation_start_date(self) -> pd.Timestamp: + return self._get_value_verbose("validation_start_date") + + @property + def verbose(self) -> int: + """Defines level of verbosity. + + 0: Only log info messages, don't show progress bars + 1: Log info messages and show progress bars + + Returns + ------- + int + Level of verbosity. + """ + return self._cfg.get("verbose", 1) + + @property + def zero_center_target(self) -> bool: + return self._cfg.get("zero_center_target", True) diff --git a/neuralhydrology/utils/configutils.py b/neuralhydrology/utils/configutils.py new file mode 100644 index 00000000..4223fc24 --- /dev/null +++ b/neuralhydrology/utils/configutils.py @@ -0,0 +1,45 @@ +"""Utility script to generate config files from a base config and a defined set of variations""" +import itertools +from pathlib import Path +from typing import Dict + +from neuralhydrology.utils.config import Config + + +def create_config_files(base_config_path: Path, modify_dict: Dict[str, list], output_dir: Path): + """Create configs, given a base config and a dictionary of parameters to modify. + + This function will create one config file for each combination of parameters defined in the modify_dict. + + Parameters + ---------- + base_config_path : Path + Path to a base config file (.yml) + modify_dict : dict + Dictionary, mapping from parameter names to lists of possible parameter values. + output_dir : Path + Path to a folder where the generated configs will be stored + """ + if not output_dir.is_dir(): + output_dir.mkdir(parents=True) + + # load base config as dictionary + base_config = Config(base_config_path) + experiment_name = base_config.experiment_name + option_names = list(modify_dict.keys()) + + # iterate over each possible combination of hyper parameters + for i, options in enumerate(itertools.product(*[val for val in modify_dict.values()])): + + for key, val in zip(option_names, options): + base_config.log_only(key, val) + + # create a unique run name + name = experiment_name + for key, val in zip(option_names, options): + name += f"_{key}{val}" + base_config.log_only("experiment_name", name) + + base_config.dump_config(output_dir, f"config_{i+1}.yml") + + print(f"Finished. Configs are stored in {output_dir}") diff --git a/neuralhydrology/utils/errors.py b/neuralhydrology/utils/errors.py new file mode 100644 index 00000000..bd9b4a07 --- /dev/null +++ b/neuralhydrology/utils/errors.py @@ -0,0 +1,3 @@ +class NoTrainDataError(Exception): + """Raised, when basin contains no valid discharge in training period""" + pass diff --git a/neuralhydrology/utils/logging_utils.py b/neuralhydrology/utils/logging_utils.py new file mode 100644 index 00000000..c4e94c8d --- /dev/null +++ b/neuralhydrology/utils/logging_utils.py @@ -0,0 +1,26 @@ +import logging +import sys + +LOGGER = logging.getLogger(__name__) + + +def setup_logging(log_file: str): + """Initialize logging to `log_file` and stdout. + + Parameters + ---------- + log_file : str + Name of the file that will be logged to. + """ + file_handler = logging.FileHandler(filename=log_file) + stdout_handler = logging.StreamHandler(sys.stdout) + + logging.basicConfig(handlers=[file_handler, stdout_handler], level=logging.INFO, format="%(asctime)s: %(message)s") + + # Make sure we log uncaught exceptions + def exception_logging(type, value, tb): + LOGGER.exception(f"Uncaught exception", exc_info=(type, value, tb)) + + sys.excepthook = exception_logging + + LOGGER.info(f"Logging to {log_file} initialized.") diff --git a/neuralhydrology/utils/nh_results_ensemble.py b/neuralhydrology/utils/nh_results_ensemble.py new file mode 100644 index 00000000..255a0089 --- /dev/null +++ b/neuralhydrology/utils/nh_results_ensemble.py @@ -0,0 +1,210 @@ +"""Utility script to average the predictions of several runs. """ +import argparse +import pickle +import sys +from collections import defaultdict +from pathlib import Path +from typing import List + +import numpy as np +import pandas as pd +import xarray as xr +from tqdm import tqdm + +sys.path.append(str(Path(__file__).parent.parent.parent)) +from neuralhydrology.data.utils import sort_frequencies +from neuralhydrology.evaluation.metrics import calculate_metrics +from neuralhydrology.utils.config import Config + + +def create_results_ensemble(run_dirs: List[Path], + best_k: int = None, + metrics: List[str] = None, + period: str = 'test', + epoch: int = None) -> dict: + """Average the predictions of several runs for the specified period and calculate new metrics. + + If `best_k` is provided, only the k runs with the best validation NSE will be used in the generated ensemble. + + Parameters + ---------- + run_dirs : List[Path] + List of directories of the runs to be merged + best_k : int, optional + If provided, will only merge the k best runs based on validation NSE. + metrics : List[str], optional + Use this parameter to override the metrics from the config files in the run directories. + period : {'test', 'validation', 'train'}, optional + One of train, val, test. If best_k is used, only 'test' is allowed. + The run_directories must contain results files for the specified period. + epoch : int, optional + If provided, will ensemble the model predictions of this epoch otherwise of the last epoch + + Returns + ------- + dict + Dictionary of ensemble predictions and metrics per basin and frequency. + """ + if len(run_dirs) < 2: + raise ValueError('Need to provide at least two run directories to be merged.') + + if period not in ['train', 'validation', 'test']: + raise ValueError(f'Unknown period {period}.') + if best_k is not None: + if period != 'test': + raise ValueError('If best_k is specified, the period must be test.') + print('Searching for best validation runs.') + best_val_runs = _get_best_validation_runs(run_dirs, best_k, epoch) + best_runs = [_get_results_file(run_dir, period, epoch) for run_dir in best_val_runs] + else: + best_runs = [_get_results_file(run_dir, period, epoch) for run_dir in run_dirs] + + config = Config(run_dirs[0] / 'config.yml') + if metrics is not None: + # override metrics from config + config.metrics = metrics + + # get frequencies from a results file. + # (they might not be stored in the config if the native data frequency was used) + run_results = pickle.load(open(best_runs[0], 'rb')) + frequencies = list(run_results[list(run_results.keys())[0]].keys()) + + return _create_ensemble(best_runs, frequencies, config) + + +def _create_ensemble(results_files: List[Path], frequencies: List[str], config: Config) -> dict: + """Averages the predictions of the passed runs and re-calculates metrics. """ + lowest_freq = sort_frequencies(frequencies)[0] + ensemble_sum = defaultdict(dict) + target_vars = config.target_variables + + print('Loading results for each run.') + for run in tqdm(results_files): + run_results = pickle.load(open(run, 'rb')) + for basin, basin_results in run_results.items(): + for freq in frequencies: + freq_results = basin_results[freq]['xr'] + + # sum up the predictions of all basins + if freq not in ensemble_sum[basin]: + ensemble_sum[basin][freq] = freq_results + else: + for target_var in target_vars: + ensemble_sum[basin][freq][f'{target_var}_sim'] += freq_results[f'{target_var}_sim'] + + # divide the prediction sum by number of runs to get the mean prediction for each basin and frequency + print('Combining results and calculating metrics.') + ensemble = defaultdict(lambda: defaultdict(dict)) + for basin in tqdm(ensemble_sum.keys()): + for freq in frequencies: + ensemble[basin][freq]['xr'] = ensemble_sum[basin][freq] + + # combine date and time to a single index to calculate metrics + frequency_factor = pd.to_timedelta(lowest_freq) // pd.to_timedelta(freq) + ensemble[basin][freq]['xr'] = ensemble[basin][freq]['xr'].isel( + time_step=slice(-frequency_factor, None)).stack(datetime=['date', 'time_step']) + ensemble[basin][freq]['xr']['datetime'] = [ + c[0] + c[1] for c in ensemble[basin][freq]['xr'].coords['datetime'].values + ] + + for target_var in target_vars: + # average predictions + ensemble[basin][freq]['xr'][ + f'{target_var}_sim'] = ensemble[basin][freq]['xr'][f'{target_var}_sim'] / len(results_files) + + # clip predictions to zero + sim = ensemble[basin][freq]['xr'][f'{target_var}_sim'] + if target_var in config.clip_targets_to_zero: + sim = xr.where(sim < 0, 0, sim) + + # calculate metrics + ensemble_metrics = calculate_metrics( + ensemble[basin][freq]['xr'][f'{target_var}_obs'], + sim, + metrics=config.metrics if isinstance(config.metrics, list) else config.metrics[target_var], + resolution=freq) + # add variable identifier to metrics if needed + if len(target_vars) > 1: + ensemble_metrics = {f'{target_var}_{key}': val for key, val in ensemble_metrics.items()} + for metric, val in ensemble_metrics.items(): + ensemble[basin][freq][f'{metric}_{freq}'] = val + + return dict(ensemble) + + +def _get_medians(results: dict, metric='NSE') -> dict: + """Calculates median metric across all basins. """ + medians = {} + key = metric + frequencies = list(results[list(results.keys())[0]].keys()) + for freq in frequencies: + # if the one freq was resampled, there still is a freq suffix + if len(frequencies) > 1 or (len(frequencies) == 1 and + f'{metric}_{freq}' in results[list(results.keys())[0]][freq]): + key = f'{metric}_{freq}' + metric_values = [v[freq][key] for v in results.values() if freq in v.keys() and key in v[freq].keys()] + medians[freq] = np.median(metric_values) + + return medians + + +def _get_best_validation_runs(run_dirs: List[Path], k: int, epoch: int = None) -> List[Path]: + """Returns the k run directories with the best median validation metrics. """ + val_files = list(zip(run_dirs, [_get_results_file(run_dir, 'validation', epoch) for run_dir in run_dirs])) + + # get validation medians + median_sums = {} + for run_dir, val_file in val_files: + val_results = pickle.load(open(val_file, 'rb')) + val_medians = _get_medians(val_results) + print('validation', val_file, val_medians) + median_sums[run_dir] = sum(val_medians.values()) + + if k > len(run_dirs): + raise ValueError(f'best_k k is larger than number of runs {len(val_files)}.') + return sorted(median_sums, key=median_sums.get, reverse=True)[:k] + + +def _get_results_file(run_dir: Path, period: str = 'test', epoch: int = None) -> Path: + """Returns the path of the results file in the given run directory. """ + if epoch is not None: + dir_results_files = list(Path(run_dir).glob(f'{period}/model_epoch{str(epoch).zfill(3)}/{period}_results.p')) + else: + dir_results_files = list(Path(run_dir).glob(f'{period}/model_epoch*/{period}_results.p')) + if len(dir_results_files) == 0: + raise ValueError(f'{run_dir} is missing {period} results.') + return sorted(dir_results_files)[-1] + + +def _main(): + parser = argparse.ArgumentParser() + parser.add_argument('--run-dirs', type=str, nargs='+', help='Directories of the runs to be averaged.') + parser.add_argument('--period', type=str, choices=['train', 'validation', 'test'], default='test') + parser.add_argument('--save-file', type=str, help='Path to target pickle file for averaged results.') + parser.add_argument('--metrics', + type=str, + nargs='+', + required=False, + help='Option to override the metrics from the config.') + parser.add_argument('--best-k', + type=int, + required=False, + help='If provided, will only use the k results files with the best median validation NSEs.') + parser.add_argument('--epoch', + type=int, + required=False, + help='If provided, will return results of this specific epoch otherwise of the last epoch') + args = vars(parser.parse_args()) + + ensemble_results = create_results_ensemble([Path(f) for f in args['run_dirs']], + args['best_k'], + metrics=args['metrics'], + period=args['period'], + epoch=args['epoch']) + + pickle.dump(ensemble_results, open(args['save_file'], 'wb')) + print(f'Successfully written results to {args["save_file"]}.') + + +if __name__ == '__main__': + _main() diff --git a/neuralhydrology/utils/ratingcurve.py b/neuralhydrology/utils/ratingcurve.py new file mode 100644 index 00000000..a6bcf35e --- /dev/null +++ b/neuralhydrology/utils/ratingcurve.py @@ -0,0 +1,91 @@ +from typing import Sequence + +import numpy as np + + +class RatingCurve(object): + """Class to estimate rating curves from stage and discharge data and to convert stage to discharge and vice-versa. + + Parameters + ---------- + stages : Sequence + Stage data to estimate a rating curve + discharges : Sequence + Discharge data to estimate a rating curve + move_stages_to_zero : bool, optional + Option to account for any offset in the stage data. This will automatically set the minimum measured stage to + zero. + """ + + def __init__(self, stages: Sequence[float], discharges: Sequence[float], move_stages_to_zero: bool = True): + + # Validate input. + if len(stages) != len(discharges): + raise ValueError("The sequence 'stages' and 'discharges' must have the same length") + + self.stages = np.array(stages, dtype=np.float32) + self.discharges = np.array(discharges, dtype=np.float32) + + # In the interpolation, x-axis is stage and y-axis is discharge. + if move_stages_to_zero: + use_stages = self.stages - np.min(self.stages) + self.zero_stages = np.min(self.stages) + else: + use_stages = self.stages + self.zero_stages = False + + self.coeffs = np.polyfit(use_stages, self.discharges, deg=2) + + def stage_to_discharge(self, stage: np.ndarray) -> np.ndarray: + """Convert stage to discharge. + + Parameters + ---------- + stage : np.ndarray + Stage data to convert to discharge + + Returns + ------- + np.ndarray + Estimated discharge + """ + + if self.zero_stages: + stage -= self.zero_stages + + return self.coeffs[2] + stage * self.coeffs[1] + stage**2 * self.coeffs[0] + + def discharge_to_stage(self, discharge: np.ndarray) -> np.ndarray: + """Convert discharge to stage. + + Parameters + ---------- + discharge : np.ndarray + Discharge data to convert to stage + + Returns + ------- + np.ndarray + Estimated stage + """ + + # init storage + stage = np.full(discharge.shape, np.nan) + solutions = np.full([discharge.shape[0], 2], np.nan) + + centered_bias = self.coeffs[2] - discharge + radicand = self.coeffs[1]**2 - 4 * self.coeffs[0] * centered_bias + radicand[np.isnan(radicand)] = -9999 + mask = radicand >= 0 + + solutions[mask, 0] = (-self.coeffs[1] + np.sqrt(radicand[mask])) / (2 * self.coeffs[0]) + solutions[mask, 1] = (-self.coeffs[1] - np.sqrt(radicand[mask])) / (2 * self.coeffs[0]) + + best_idx = np.argmin(np.abs(solutions[mask] - np.expand_dims(discharge[mask], -1)), axis=1) + + stage[mask] = solutions[mask, best_idx] + + if self.zero_stages: + stage += self.zero_stages + + return stage diff --git a/setup.py b/setup.py new file mode 100644 index 00000000..2c89b207 --- /dev/null +++ b/setup.py @@ -0,0 +1,54 @@ +from pathlib import Path + +from setuptools import setup + +# read the description from the README.md +readme_file = Path(__file__).absolute().parent / "README.md" +with readme_file.open("r") as fp: + long_description = fp.read() + +about = {} +with open("neuralhydrology/__about__.py", "r") as fp: + exec(fp.read(), about) + +# TODO: Add Classifier, License and update Authors etc. + +setup(name='neuralhydrology', + version=about["__version__"], + packages=[ + 'neuralhydrology', 'neuralhydrology.data', 'neuralhydrology.utils', 'neuralhydrology.modelzoo', + 'neuralhydrology.training', 'neuralhydrology.evaluation' + ], + url='neuralhydrology.readthedocs.io', + license='', + author='Frederik Kratzert', + author_email='f.kratzert@gmail.com', + description='Library for training deep learning models with environmental focus', + long_description=long_description, + long_description_content_type='text/markdown', + entry_points={ + 'console_scripts': [ + 'nh-schedule-runs=neuralhydrology.nh_run_scheduler:_main', 'nh-run=neuralhydrology.nh_run:_main', + 'nh-results-ensemble=neuralhydrology.utils.nh_results_ensemble:_main' + ] + }, + python_requires='>=3.6', + install_requires=[ + 'matplotlib', + 'numba', + 'numpy', + 'pandas', + 'ruamel.yaml', + 'torch', + 'scipy', + 'tensorboard', + 'tqdm', + 'xarray', + ], + classifiers=[ + 'Programming Language :: Python :: 3', + 'Operating System :: OS Independent', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Scientific/Engineering :: Hydrology', + ], + keywords='deep learning hydrology lstm neural network streamflow discharge rainfall-runoff') diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 00000000..b3ef946f --- /dev/null +++ b/test/__init__.py @@ -0,0 +1,4 @@ +from typing import Union + +# Type alias to allow explicit type annotations of fixtures that don't break an IDE's type inspections +Fixture = Union diff --git a/test/conftest.py b/test/conftest.py new file mode 100644 index 00000000..feb18906 --- /dev/null +++ b/test/conftest.py @@ -0,0 +1,101 @@ +from pathlib import Path +from typing import List, Union, Dict, Callable + +import pytest + +from neuralhydrology.utils.config import Config +from test import Fixture + + +def pytest_addoption(parser): + parser.addoption('--smoke-test', + action='store_true', + default=False, + help='Skips some tests for faster execution. Out of the single-frequency ' + 'models and forcings, only test cudalstm on forcings that include daymet.') + + +@pytest.fixture +def get_config(tmpdir: Fixture[str]) -> Fixture[Callable[[str], dict]]: + """Fixture that provides a function to fetch a run configuration specified by its name. + + The fetched run configuration will use a tmp folder as its run directory. + + Parameters + ---------- + tmpdir : Fixture[str] + Name of the tmp directory to use in the run configuration. + + Returns + ------- + Fixture[Callable[[str], dict]] + Function that returns a run configuration. + """ + + def _get_config(name): + config_file = Path(f'./test/test_configs/{name}.test.yml') + if not config_file.is_file(): + raise ValueError(f'Test config file not found at {config_file}.') + config = Config(config_file) + config.run_dir = Path(tmpdir) + return config + + return _get_config + + +@pytest.fixture(params=['lstm', 'ealstm', 'cudalstm', 'embcudalstm']) +def single_freq_model(request) -> str: + """Fixture that provides models that support predicting only a single frequency. + + Returns + ------- + str + Name of the single-frequency model. + """ + if request.config.getoption('--smoke-test') and request.param != 'cudalstm': + pytest.skip('--smoke-test skips this test.') + return request.param + + +@pytest.fixture(params=[('daymet', ['prcp(mm/day)', 'tmax(C)']), ('nldas', ['PRCP(mm/day)', 'Tmax(C)']), + ('maurer', ['PRCP(mm/day)', 'Tmax(C)']), ('maurer_extended', ['prcp(mm/day)', 'tmax(C)']), + (['daymet', + 'nldas'], ['prcp(mm/day)_daymet', 'tmax(C)_daymet', 'PRCP(mm/day)_nldas', 'Tmax(C)_nldas'])], + ids=lambda param: str(param[0])) +def single_freq_forcings(request) -> Dict[str, Union[str, List[str]]]: + """Fixture that provides daily forcings. + + Returns + ------- + Dict[str, Union[str, List[str]]] + Dictionary ``{'forcings': , 'variables': }``. + """ + if request.config.getoption('--smoke-test') and 'daymet' not in request.param[0]: + pytest.skip('--smoke-test skips this test.') + return {'forcings': request.param[0], 'variables': request.param[1]} + + +@pytest.fixture(params=['multifreqlstm', 'odelstm']) +def multi_freq_model(request) -> str: + """Fixture that provides multifrequency models. + + Returns + ------- + str + Name of the multifrequency model. + """ + return request.param + + +@pytest.fixture(params=[('camels_us', ['QObs(mm/d)'])], ids=lambda param: param[0]) +def daily_dataset(request) -> Dict[str, List[str]]: + """Fixture that provides daily datasets. + + Returns + ------- + Dict[str, List[str]] + Dictionary ``{'dataset: , 'target': }``. + """ + if request.config.getoption('--smoke-test') and request.param[0] != 'camels_us': + pytest.skip('--smoke-test skips this test.') + return {'dataset': request.param[0], 'target': request.param[1]} diff --git a/test/test_config_runs.py b/test/test_config_runs.py new file mode 100644 index 00000000..d2f9b600 --- /dev/null +++ b/test/test_config_runs.py @@ -0,0 +1,155 @@ +"""Integration tests that perform full runs. """ +import pickle +from datetime import datetime +from pathlib import Path +from typing import Dict, Tuple, Callable + +import pandas as pd +import pytest +from pytest import approx + +from neuralhydrology.data.utils import load_camels_us_forcings, load_camels_us_discharge, load_hourly_us_netcdf +from neuralhydrology.evaluation.evaluate import start_evaluation +from neuralhydrology.training.train import start_training +from neuralhydrology.utils.config import Config +from test import Fixture + + +def test_daily_regression(get_config: Fixture[Callable[[str], dict]], single_freq_model: Fixture[str], + daily_dataset: Fixture[str], single_freq_forcings: Fixture[str]): + """Test regression training and evaluation for daily predictions. + + Parameters + ---------- + get_config : Fixture[Callable[[str], dict] + Method that returns a run configuration to test. + single_freq_model : Fixture[str] + Model to test. + daily_dataset : Fixture[str] + Daily dataset to use. + single_freq_forcings : Fixture[str] + Daily forcings set to use. + """ + config = get_config('daily_regression') + config.log_only('model', single_freq_model) + config.log_only('dataset', daily_dataset['dataset']) + config.log_only('data_dir', config.data_dir / daily_dataset['dataset']) + config.log_only('target_variables', daily_dataset['target']) + config.log_only('forcings', single_freq_forcings['forcings']) + config.log_only('dynamic_inputs', single_freq_forcings['variables']) + + basin = '01022500' + test_start_date, test_end_date = _get_test_start_end_dates(config) + + start_training(config) + start_evaluation(cfg=config, run_dir=config.run_dir, epoch=1, period='test') + + results = _get_basin_results(config.run_dir, 1)[basin]['1D']['xr'].isel(time_step=-1) + + assert pd.to_datetime(results['date'].values[0]) == test_start_date.date() + assert pd.to_datetime(results['date'].values[-1]) == test_end_date.date() + + discharge = _get_discharge(config, basin) + + assert discharge.loc[test_start_date:test_end_date].values \ + == approx(results[f'{config.target_variables[0]}_obs'].values.reshape(-1), nan_ok=True) + + # CAMELS forcings have no NaNs, so there should be no NaN predictions + assert not pd.isna(results[f'{config.target_variables[0]}_sim']).any() + + +def test_daily_regression_additional_features(get_config: Fixture[Callable[[str], dict]]): + """Tests #38 (training and testing with additional_features). + + Parameters + ---------- + get_config : Fixture[Callable[[str], dict]] + Method that returns a run configuration + """ + config = get_config('daily_regression_additional_features') + + basin = '01022500' + test_start_date, test_end_date = _get_test_start_end_dates(config) + + start_training(config) + start_evaluation(cfg=config, run_dir=config.run_dir, epoch=1, period='test') + + results = _get_basin_results(config.run_dir, 1)[basin]['1D']['xr'].isel(time_step=-1) + + assert pd.to_datetime(results['date'].values[0]) == test_start_date.date() + assert pd.to_datetime(results['date'].values[-1]) == test_end_date.date() + + discharge = _get_discharge(config, basin) + + assert discharge.loc[test_start_date:test_end_date].values \ + == approx(results[f'{config.target_variables[0]}_obs'].values.reshape(-1), nan_ok=True) + + # CAMELS forcings have no NaNs, so there should be no NaN predictions + assert not pd.isna(results[f'{config.target_variables[0]}_sim']).any() + + +def test_multifreq_regression(get_config: Fixture[Callable[[str], dict]], multi_freq_model: Fixture[str]): + """Test regression training and evaluation for multifrequency predictions. + + Parameters + ---------- + get_config : Fixture[Callable[[str], dict] + Method that returns a run configuration to test. + multi_freq_model : Fixture[str] + Model to test. + multi_freq_forcings : Fixture[str] + Forcings set to use. + """ + config = get_config('multifreq_regression') + config.log_only('model', multi_freq_model) + + basin = '01022500' + test_start_date, test_end_date = _get_test_start_end_dates(config) + + start_training(config) + start_evaluation(cfg=config, run_dir=config.run_dir, epoch=1, period='test') + + results = _get_basin_results(config.run_dir, 1)[basin] + discharge = load_hourly_us_netcdf(config.data_dir, config.forcings[0]) \ + .sel(basin=basin, date=slice(test_start_date, test_end_date))['qobs_mm_per_hour'] + + hourly_results = results['1H']['xr'].to_dataframe().reset_index() + hourly_results.index = hourly_results['date'] + hourly_results['time_step'] + assert hourly_results.index[0] == test_start_date + assert hourly_results.index[-1] == test_end_date.floor('H') + + daily_results = results['1D']['xr'] + assert pd.to_datetime(daily_results['date'].values[0]) == test_start_date + assert pd.to_datetime(daily_results['date'].values[-1]) == test_end_date.date() + assert len(daily_results['qobs_mm_per_hour_obs']) == len(discharge) // 24 + + assert len(discharge) == len(hourly_results) + assert discharge.values \ + == approx(hourly_results['qobs_mm_per_hour_obs'].values, nan_ok=True) + + # Hourly CAMELS forcings have no NaNs, so there should be no NaN predictions + assert not pd.isna(hourly_results['qobs_mm_per_hour_sim']).any() + assert not pd.isna(daily_results['qobs_mm_per_hour_sim'].values).any() + + +def _get_test_start_end_dates(config: Config) -> Tuple[datetime, datetime]: + test_start_date = pd.to_datetime(config.test_start_date, format='%d/%m/%Y') + test_end_date = pd.to_datetime(config.test_end_date, format='%d/%m/%Y') + pd.Timedelta(days=1, seconds=-1) + + return test_start_date, test_end_date + + +def _get_basin_results(run_dir: Path, epoch: int) -> Dict: + results_file = list(run_dir.glob(f'test/model_epoch{str(epoch).zfill(3)}/test_results.p')) + if len(results_file) != 1: + pytest.fail(f'Results file not found.') + + return pickle.load(open(str(results_file[0]), 'rb')) + + +def _get_discharge(config: Config, basin: str) -> pd.Series: + if config.dataset == 'camels_us': + _, area = load_camels_us_forcings(config.data_dir, basin, 'daymet') + return load_camels_us_discharge(config.data_dir, basin, area) + else: + raise NotImplementedError diff --git a/test/test_configs/daily_regression.test.yml b/test/test_configs/daily_regression.test.yml new file mode 100644 index 00000000..8e9f0864 --- /dev/null +++ b/test/test_configs/daily_regression.test.yml @@ -0,0 +1,76 @@ +# --- Experiment configurations -------------------------------------------------------------------- +experiment_name: test_run + +run_dir: + +train_basin_file: ./test/test_data/4_basins_test_set.txt +validation_basin_file: ./test/test_data/4_basins_test_set.txt +test_basin_file: ./test/test_data/4_basins_test_set.txt + +train_start_date: '01/01/2000' +train_end_date: '31/12/2001' +validation_start_date: '01/01/2001' +validation_end_date: '31/12/2002' +test_start_date: '01/01/2001' +test_end_date: '31/12/2002' + +seed: 111 +device: cpu + +# --- Validation configuration --------------------------------------------------------------------- +validate_every: 1 +validate_n_random_basins: 4 +cache_validation_data: True +metrics: + - NSE + - KGE + - Alpha-NSE + - Beta-NSE + +# --- Model configuration -------------------------------------------------------------------------- +model: cudalstm +head: regression +output_activation: linear + +hidden_size: 64 +initial_forget_bias: 3 +output_dropout: 0.4 +embedding_hiddens: + - 30 + - 20 + - 64 +embedding_activation: tanh +embedding_dropout: 0.0 + +# --- Training configuration ----------------------------------------------------------------------- +optimizer: Adam +loss: MSE +learning_rate: + 0: 1e-3 + 10: 5e-4 + 20: 1e-4 +batch_size: 256 +epochs: 1 +clip_gradient_norm: 1 + +predict_last_n: 1 +seq_length: 30 + +num_workers: 0 +log_interval: 5 +log_tensorboard: True +log_n_figures: 2 +save_weights_every: 1 +save_validation_results: False + +# --- Data configurations -------------------------------------------------------------------------- +dataset: # will be filled by conftest +data_dir: ./test/test_data +forcings: # will be filled by conftest +dynamic_inputs: # will be filled by conftest +target_variables: + - QObs(mm/d) +zero_center_target: True +camels_attributes: + - elev_mean + - slope_mean diff --git a/test/test_configs/daily_regression_additional_features.test.yml b/test/test_configs/daily_regression_additional_features.test.yml new file mode 100644 index 00000000..97f1a4f0 --- /dev/null +++ b/test/test_configs/daily_regression_additional_features.test.yml @@ -0,0 +1,74 @@ +# --- Experiment configurations -------------------------------------------------------------------- +experiment_name: test_run + +run_dir: + +train_basin_file: ./test/test_data/4_basins_test_set.txt +validation_basin_file: ./test/test_data/4_basins_test_set.txt +test_basin_file: ./test/test_data/4_basins_test_set.txt + +train_start_date: '01/01/2000' +train_end_date: '31/12/2001' +validation_start_date: '01/01/2001' +validation_end_date: '31/12/2002' +test_start_date: '01/01/2001' +test_end_date: '31/12/2002' + +seed: 111 +device: cpu + +# --- Validation configuration --------------------------------------------------------------------- +validate_every: 1 +validate_n_random_basins: 4 +cache_validation_data: True +metrics: + - NSE + - KGE + - Alpha-NSE + - Beta-NSE + +# --- Model configuration -------------------------------------------------------------------------- +model: cudalstm +head: regression +output_activation: linear + +hidden_size: 64 +initial_forget_bias: 3 +output_dropout: 0.4 + +# --- Training configuration ----------------------------------------------------------------------- +optimizer: Adam +loss: MSE +learning_rate: + 0: 1e-3 + 10: 5e-4 + 20: 1e-4 +batch_size: 256 +epochs: 1 +clip_gradient_norm: 1 + +predict_last_n: 1 +seq_length: 30 + +num_workers: 0 +log_interval: 5 +log_tensorboard: True +log_n_figures: 2 +save_weights_every: 1 +save_validation_results: False + +# --- Data configurations -------------------------------------------------------------------------- +dataset: camels_us +data_dir: ./test/test_data/camels_us +forcings: daymet +dynamic_inputs: + - prcp(mm/day) + - tmax(C) + - rand_col # this is the column from the additional_features file +additional_feature_files: ./test/test_data/additional_features.p +target_variables: + - QObs(mm/d) +zero_center_target: True +camels_attributes: + - elev_mean + - slope_mean diff --git a/test/test_configs/multifreq_regression.test.yml b/test/test_configs/multifreq_regression.test.yml new file mode 100644 index 00000000..69aaf8d0 --- /dev/null +++ b/test/test_configs/multifreq_regression.test.yml @@ -0,0 +1,85 @@ +# --- Experiment configurations -------------------------------------------------------------------- +experiment_name: test_run + +run_dir: + +train_basin_file: ./test/test_data/4_basins_test_set.txt +validation_basin_file: ./test/test_data/4_basins_test_set.txt +test_basin_file: ./test/test_data/4_basins_test_set.txt + +train_start_date: '01/01/2000' +train_end_date: '31/12/2001' +validation_start_date: '01/01/2001' +validation_end_date: '31/12/2002' +test_start_date: '01/01/2001' +test_end_date: '31/12/2002' + +seed: 111 +device: cpu + +# --- Validation configuration --------------------------------------------------------------------- +validate_every: 1 +validate_n_random_basins: 4 +cache_validation_data: True +metrics: + - NSE + - KGE + - Alpha-NSE + - Beta-NSE + +# --- Model configuration -------------------------------------------------------------------------- +model: multifreqlstm +head: regression +output_activation: linear + +hidden_size: 64 +initial_forget_bias: 3 +output_dropout: 0.4 + +ode_method: euler +ode_num_unfolds: 4 +ode_random_freq_lower_bound: 5D + +# --- Training configuration ----------------------------------------------------------------------- +optimizer: Adam +loss: MSE +regularization: + - tie_frequencies +learning_rate: + 0: 1e-3 + 10: 5e-4 + 20: 1e-4 +batch_size: 256 +epochs: 1 +clip_gradient_norm: 1 + +use_frequencies: + - 1D + - 1H +predict_last_n: + 1D: 1 + 1H: 24 +seq_length: + 1D: 30 + 1H: 48 + +num_workers: 0 +log_interval: 5 +log_tensorboard: True +log_n_figures: 2 +save_weights_every: 1 +save_validation_results: False + +# --- Data configurations -------------------------------------------------------------------------- +dataset: hourly_camels_us +data_dir: ./test/test_data/camels_us +forcings: nldas_hourly +dynamic_inputs: + - total_precipitation + - temperature +target_variables: + - qobs_mm_per_hour +zero_center_target: True +camels_attributes: + - elev_mean + - slope_mean diff --git a/test/test_data/4_basins_test_set.txt b/test/test_data/4_basins_test_set.txt new file mode 100644 index 00000000..0ed10f49 --- /dev/null +++ b/test/test_data/4_basins_test_set.txt @@ -0,0 +1,4 @@ +01022500 +01547700 +02064000 +03015500 diff --git a/test/test_data/additional_features.p b/test/test_data/additional_features.p new file mode 100644 index 00000000..778528db Binary files /dev/null and b/test/test_data/additional_features.p differ diff --git a/test/test_data/camels_us/basin_mean_forcing/daymet/01022500_lump_cida_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/daymet/01022500_lump_cida_forcing_leap.txt new file mode 100644 index 00000000..807ed6a7 --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/daymet/01022500_lump_cida_forcing_leap.txt @@ -0,0 +1,1100 @@ + 44.82 + 133.00 + 587675987 +Year Mnth Day Hr dayl(s) prcp(mm/day) srad(W/m2) swe(mm) tmax(C) tmin(C) vp(Pa) +2000 01 01 12 31185.97 0.00 189.56 0.00 -2.36 -14.36 202.51 +2000 01 02 12 31302.05 0.00 200.48 0.00 4.81 -8.61 319.42 +2000 01 03 12 31370.88 5.50 126.63 0.00 9.25 -1.10 566.46 +2000 01 04 12 31449.59 2.19 182.02 0.00 10.43 -2.10 524.66 +2000 01 05 12 31449.59 28.85 154.59 0.00 10.74 -3.62 467.68 +2000 01 06 12 31449.59 0.00 205.84 0.00 -0.62 -13.89 211.67 +2000 01 07 12 31596.23 3.51 163.35 0.00 1.49 -11.94 247.92 +2000 01 08 12 31716.49 1.09 139.45 0.00 1.18 -6.37 383.49 +2000 01 09 12 31795.20 0.00 161.64 0.00 1.21 -7.73 342.46 +2000 01 10 12 31795.20 0.00 178.67 0.00 4.12 -6.08 386.57 +2000 01 11 12 31805.06 26.02 140.02 0.00 8.14 -2.87 495.17 +2000 01 12 12 32025.18 0.11 172.47 0.00 9.20 -0.59 586.39 +2000 01 13 12 32140.80 0.00 195.12 0.00 0.19 -10.89 267.18 +2000 01 14 12 32140.80 0.00 207.21 0.00 -7.61 -19.41 131.99 +2000 01 15 12 32287.44 0.00 164.76 0.00 -13.23 -21.57 114.94 +2000 01 16 12 32464.01 0.00 230.14 0.00 -7.98 -21.92 109.73 +2000 01 17 12 32486.41 16.83 178.60 0.00 -2.67 -17.07 160.57 +2000 01 18 12 32496.27 7.44 144.61 0.00 -7.45 -17.20 160.00 +2000 01 19 12 32809.61 0.00 221.01 0.00 -10.43 -22.12 112.28 +2000 01 20 12 32832.01 0.00 240.52 0.00 -9.55 -22.93 97.28 +2000 01 21 12 32841.88 0.47 222.65 0.00 -8.00 -20.21 127.29 +2000 01 22 12 33155.20 7.95 147.63 0.00 -11.11 -20.41 119.82 +2000 01 23 12 33177.60 0.00 214.45 0.00 -12.11 -22.46 107.00 +2000 01 24 12 33324.24 0.06 269.50 0.00 -5.55 -21.33 117.74 +2000 01 25 12 33523.20 0.41 229.54 0.00 -1.72 -13.19 223.27 +2000 01 26 12 33523.20 19.06 194.11 0.00 4.75 -8.87 313.80 +2000 01 27 12 33753.20 0.38 240.87 0.00 2.69 -8.97 309.65 +2000 01 28 12 33868.81 0.00 250.67 0.00 -6.51 -18.40 145.31 +2000 01 29 12 33950.76 0.00 247.99 0.00 -6.66 -18.15 147.04 +2000 01 30 12 34214.41 0.00 286.91 0.00 0.14 -15.06 200.00 +2000 01 31 12 34214.41 0.00 301.01 0.00 4.37 -12.75 226.23 +2000 02 01 12 34481.29 11.51 151.76 0.00 3.53 -4.95 426.56 +2000 02 02 12 34560.00 0.00 278.44 0.00 1.89 -11.38 256.84 +2000 02 03 12 34758.06 0.00 228.76 0.00 -6.38 -15.73 180.10 +2000 02 04 12 34905.61 0.00 276.70 0.00 -5.18 -17.37 155.44 +2000 02 05 12 34987.57 0.00 308.36 0.00 -4.34 -19.18 137.93 +2000 02 06 12 35251.21 0.00 304.12 0.00 -2.21 -16.43 172.41 +2000 02 07 12 35251.21 0.00 297.30 0.00 -2.27 -15.74 180.94 +2000 02 08 12 35596.81 0.00 349.90 0.00 2.05 -19.30 133.90 +2000 02 09 12 35596.81 0.00 306.27 0.00 -2.15 -16.17 177.29 +2000 02 10 12 35942.42 0.00 284.83 0.00 1.80 -10.52 271.53 +2000 02 11 12 35942.42 7.95 171.72 0.00 -0.65 -9.46 299.91 +2000 02 12 12 36288.00 12.87 246.73 0.00 1.75 -13.22 221.15 +2000 02 13 12 36288.00 0.00 334.41 0.00 -2.78 -17.78 151.95 +2000 02 14 12 36633.59 10.57 241.64 0.00 2.27 -11.68 254.25 +2000 02 15 12 36633.59 41.44 201.13 0.00 3.74 -6.24 383.06 +2000 02 16 12 36979.17 0.00 304.96 0.00 0.82 -10.70 268.64 +2000 02 17 12 36979.17 3.75 255.45 0.00 0.43 -12.59 234.20 +2000 02 18 12 37324.79 0.00 355.76 0.00 -7.79 -21.97 107.31 +2000 02 19 12 37324.79 9.29 253.38 0.00 -4.34 -17.08 163.32 +2000 02 20 12 37670.39 3.60 268.52 0.00 -1.32 -14.65 194.97 +2000 02 21 12 37670.39 0.00 366.87 0.00 -0.50 -14.75 193.41 +2000 02 22 12 38016.01 0.00 391.83 0.00 -1.48 -17.53 152.53 +2000 02 23 12 38016.01 0.00 417.20 0.00 3.80 -14.87 192.13 +2000 02 24 12 38361.60 0.00 311.05 0.00 9.87 -1.24 560.90 +2000 02 25 12 38361.60 0.00 378.56 0.00 11.89 -3.08 487.06 +2000 02 26 12 38707.20 0.00 335.54 0.00 4.31 -7.47 348.54 +2000 02 27 12 38707.20 0.00 300.81 0.00 6.98 -3.09 491.01 +2000 02 28 12 39052.79 2.07 219.75 0.00 9.79 2.56 736.00 +2000 02 29 12 39062.66 6.86 204.00 0.00 8.72 0.13 613.80 +2000 03 01 12 39398.37 0.26 195.32 0.00 4.64 -0.99 572.53 +2000 03 02 12 39628.39 1.69 226.91 0.00 6.17 -0.44 590.23 +2000 03 03 12 39744.00 7.55 87.05 0.00 1.71 -1.26 561.56 +2000 03 04 12 40089.58 10.87 118.55 0.00 3.14 -1.00 570.49 +2000 03 05 12 40089.58 0.88 256.26 0.00 4.22 -2.67 502.83 +2000 03 06 12 40435.20 0.01 241.95 0.00 4.00 -2.26 518.30 +2000 03 07 12 40435.20 0.00 358.78 0.00 5.02 -4.86 427.64 +2000 03 08 12 40780.79 0.00 434.72 0.00 8.66 -4.86 427.81 +2000 03 09 12 40780.79 0.00 364.42 0.00 7.63 -2.02 527.60 +2000 03 10 12 41126.39 5.31 319.82 0.00 8.04 -4.17 451.56 +2000 03 11 12 41126.39 0.00 260.01 0.00 0.31 -5.66 403.40 +2000 03 12 12 41472.01 20.46 185.56 0.00 1.58 -3.99 455.88 +2000 03 13 12 41817.59 15.27 297.98 0.00 4.65 -5.15 422.59 +2000 03 14 12 41817.59 0.00 504.18 0.00 2.85 -12.51 234.02 +2000 03 15 12 42163.20 0.00 509.01 0.00 6.36 -9.42 300.91 +2000 03 16 12 42163.20 0.00 422.37 0.00 11.73 0.89 651.96 +2000 03 17 12 42508.81 13.57 393.81 0.00 10.69 -6.36 378.63 +2000 03 18 12 42508.81 5.18 327.12 0.00 -3.06 -13.41 219.89 +2000 03 19 12 42854.43 0.00 517.28 0.00 0.62 -13.75 213.15 +2000 03 20 12 42854.43 0.00 515.12 0.00 4.24 -9.95 285.76 +2000 03 21 12 43199.99 0.00 501.78 0.00 6.44 -6.80 365.80 +2000 03 22 12 43545.61 0.00 527.02 0.00 10.36 -4.66 432.56 +2000 03 23 12 43545.61 0.00 541.38 0.00 12.52 -3.67 464.12 +2000 03 24 12 43891.18 0.00 548.44 0.00 14.85 -2.01 528.60 +2000 03 25 12 43891.18 0.00 541.27 0.00 11.37 -4.14 452.94 +2000 03 26 12 44236.80 1.67 535.18 0.00 13.03 -2.01 530.21 +2000 03 27 12 44236.80 2.40 492.21 0.00 15.21 -1.59 545.35 +2000 03 28 12 44582.40 0.00 501.03 0.00 12.94 0.01 612.82 +2000 03 29 12 44582.40 32.66 322.90 0.00 10.90 0.90 646.59 +2000 03 30 12 44927.99 6.80 314.62 0.00 9.10 -0.42 592.72 +2000 03 31 12 45273.61 0.00 439.80 0.00 9.66 -0.58 587.98 +2000 04 01 12 45273.61 0.00 499.52 0.00 10.40 -2.24 519.23 +2000 04 02 12 45619.19 0.00 497.30 0.00 12.68 -0.24 600.76 +2000 04 03 12 45619.19 5.84 380.06 0.00 14.47 0.71 639.31 +2000 04 04 12 45964.80 10.61 252.29 0.00 10.92 3.20 767.38 +2000 04 05 12 45964.80 13.51 240.65 0.00 10.35 3.08 766.14 +2000 04 06 12 46310.38 3.59 346.63 0.00 9.87 -1.55 545.47 +2000 04 07 12 46310.38 0.17 459.58 0.00 10.88 -0.39 600.00 +2000 04 08 12 46656.00 2.78 350.69 0.00 10.76 0.54 628.08 +2000 04 09 12 46771.62 4.49 356.05 0.00 13.78 1.87 696.67 +2000 04 10 12 47001.59 13.15 403.20 0.00 13.82 -1.14 564.71 +2000 04 11 12 47337.32 0.00 459.90 0.00 7.35 -3.89 458.43 +2000 04 12 12 47347.18 6.73 293.96 0.00 8.35 -0.75 582.12 +2000 04 13 12 47692.79 1.82 419.08 0.00 6.37 -3.27 479.86 +2000 04 14 12 47692.79 0.00 511.17 0.00 8.28 -4.48 439.27 +2000 04 15 12 48038.42 0.00 573.07 0.00 14.31 -2.23 522.13 +2000 04 16 12 48038.42 0.00 550.95 0.00 20.31 4.36 838.42 +2000 04 17 12 48383.98 0.00 500.01 0.00 9.79 -2.62 506.20 +2000 04 18 12 48383.98 0.00 476.55 0.00 7.65 -3.64 467.18 +2000 04 19 12 48729.59 0.00 466.96 0.00 9.91 -1.10 564.67 +2000 04 20 12 48729.59 1.34 277.04 0.00 7.53 1.13 655.23 +2000 04 21 12 49075.20 0.00 482.33 0.00 12.91 1.57 682.94 +2000 04 22 12 49075.20 14.41 301.54 0.00 9.09 0.52 629.47 +2000 04 23 12 49420.80 54.77 172.45 0.00 6.30 1.90 698.09 +2000 04 24 12 49420.80 20.77 178.60 0.00 7.14 2.68 738.48 +2000 04 25 12 49766.41 4.65 238.17 0.00 5.43 -0.44 591.44 +2000 04 26 12 49766.41 0.00 443.33 0.00 6.80 -1.75 539.53 +2000 04 27 12 50112.01 2.03 495.62 0.00 9.16 -1.00 570.42 +2000 04 28 12 50112.01 1.00 385.70 0.00 6.86 -0.21 601.39 +2000 04 29 12 50457.57 0.00 563.29 0.00 12.08 -0.62 585.51 +2000 04 30 12 50457.57 1.00 596.54 0.00 17.33 1.93 699.43 +2000 05 01 12 50803.18 0.00 605.49 0.00 13.46 -1.94 535.85 +2000 05 02 12 50803.18 0.52 545.22 0.00 15.04 2.66 743.83 +2000 05 03 12 51148.78 0.00 588.65 0.00 13.98 -0.46 591.38 +2000 05 04 12 51148.78 0.00 574.57 0.00 15.38 1.22 668.30 +2000 05 05 12 51412.40 0.61 553.24 0.00 18.44 4.65 852.48 +2000 05 06 12 51494.39 0.00 591.44 0.00 23.58 5.93 929.40 +2000 05 07 12 51641.94 2.28 434.02 0.00 20.19 7.29 1023.52 +2000 05 08 12 51839.99 6.10 398.02 0.00 22.09 8.19 1095.43 +2000 05 09 12 51862.39 5.61 405.94 0.00 22.07 7.41 1032.47 +2000 05 10 12 52185.61 14.20 274.88 0.00 9.32 1.83 693.63 +2000 05 11 12 52185.61 7.83 221.86 0.00 7.85 2.06 708.09 +2000 05 12 12 52449.24 14.71 237.65 0.00 10.58 4.26 829.68 +2000 05 13 12 52531.20 0.00 533.19 0.00 15.61 2.60 735.46 +2000 05 14 12 52646.80 7.86 338.39 0.00 14.17 4.32 826.00 +2000 05 15 12 52876.81 0.00 529.34 0.00 17.67 4.76 861.89 +2000 05 16 12 52876.81 0.00 570.01 0.00 18.48 3.24 770.19 +2000 05 17 12 53075.77 0.00 549.53 0.00 18.49 4.39 839.94 +2000 05 18 12 53222.39 0.00 512.83 0.00 19.09 6.53 962.70 +2000 05 19 12 53244.79 15.45 372.76 0.00 16.05 4.27 835.19 +2000 05 20 12 53558.15 0.00 530.08 0.00 14.62 1.20 662.12 +2000 05 21 12 53568.00 0.00 550.35 0.00 18.34 3.08 762.70 +2000 05 22 12 53590.40 1.14 459.13 0.00 18.06 6.86 988.69 +2000 05 23 12 53903.74 0.00 455.54 0.00 16.91 5.67 919.03 +2000 05 24 12 53913.61 4.30 201.88 0.00 10.91 5.07 881.47 +2000 05 25 12 53936.00 15.66 172.20 0.00 11.54 6.60 970.47 +2000 05 26 12 54112.55 10.34 236.58 0.00 14.10 6.96 996.83 +2000 05 27 12 54259.18 0.00 477.67 0.00 19.32 6.66 975.12 +2000 05 28 12 54259.18 0.00 389.58 0.00 16.19 6.84 988.46 +2000 05 29 12 54374.80 0.00 357.95 0.00 14.57 6.26 940.88 +2000 05 30 12 54594.89 0.00 507.81 0.00 16.04 2.17 715.46 +2000 05 31 12 54604.77 0.00 492.27 0.00 18.43 5.02 881.64 +2000 06 01 12 54604.77 0.00 471.09 0.00 21.72 8.72 1137.56 +2000 06 02 12 54683.48 0.00 524.11 0.00 27.13 9.47 1197.44 +2000 06 03 12 54803.77 0.00 419.82 0.00 21.25 10.28 1262.31 +2000 06 04 12 54950.41 0.00 492.45 0.00 21.25 6.97 997.94 +2000 06 05 12 54950.41 6.18 326.34 0.00 18.39 7.25 1021.79 +2000 06 06 12 54950.41 0.00 488.09 0.00 18.42 4.78 862.35 +2000 06 07 12 55029.13 11.45 321.65 0.00 16.40 5.61 919.73 +2000 06 08 12 55097.95 14.32 348.11 0.00 16.64 4.44 835.28 +2000 06 09 12 55214.08 2.65 397.85 0.00 22.75 9.11 1168.59 +2000 06 10 12 55296.01 0.00 438.96 0.00 21.97 9.92 1233.59 +2000 06 11 12 55296.01 4.21 256.38 0.00 14.14 5.97 927.42 +2000 06 12 12 55296.01 5.94 253.69 0.00 11.65 3.79 800.48 +2000 06 13 12 55296.01 0.00 487.78 0.00 17.72 3.83 800.00 +2000 06 14 12 55296.01 0.00 490.72 0.00 20.00 5.61 912.50 +2000 06 15 12 55318.41 0.00 436.04 0.00 18.93 7.35 1028.20 +2000 06 16 12 55374.72 3.12 270.89 0.00 19.49 10.93 1314.04 +2000 06 17 12 55374.72 0.00 464.79 0.00 28.05 13.85 1588.18 +2000 06 18 12 55411.63 6.08 392.35 0.00 30.65 10.90 1312.16 +2000 06 19 12 55411.63 3.13 338.45 0.00 23.90 11.90 1391.76 +2000 06 20 12 55411.63 0.00 453.26 0.00 24.13 11.14 1331.58 +2000 06 21 12 55411.63 0.00 453.08 0.00 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136.64 0.00 2.92 -4.44 440.28 +2002 12 25 12 31025.30 0.00 168.21 0.00 -0.79 -10.35 279.18 +2002 12 26 12 31081.61 16.58 105.23 0.00 -2.03 -9.40 300.62 +2002 12 27 12 31104.01 0.00 161.22 0.00 -2.16 -10.85 267.46 +2002 12 28 12 31104.01 0.00 218.75 0.00 -2.10 -17.75 160.00 +2002 12 29 12 31104.01 0.00 200.54 0.00 -0.61 -13.23 220.55 +2002 12 30 12 31104.01 0.00 208.83 0.00 0.83 -13.15 224.48 +2002 12 31 12 31104.01 0.40 175.60 0.00 -4.51 -14.95 192.32 diff --git a/test/test_data/camels_us/basin_mean_forcing/daymet/01547700_lump_cida_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/daymet/01547700_lump_cida_forcing_leap.txt new file mode 100644 index 00000000..aa65110c --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/daymet/01547700_lump_cida_forcing_leap.txt @@ -0,0 +1,1100 @@ + 40.98 + 383.00 + 114169652 +Year Mnth Day Hr dayl(s) prcp(mm/day) srad(W/m2) swe(mm) tmax(C) tmin(C) vp(Pa) +2000 01 01 12 32832.00 0.00 254.92 0.00 6.30 -6.65 360.00 +2000 01 02 12 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+2000 01 19 12 34190.73 0.00 256.77 0.00 -5.50 -16.01 179.57 +2000 01 20 12 34214.39 3.01 139.48 0.00 -2.29 -9.29 298.68 +2000 01 21 12 34214.39 3.86 207.81 0.00 -3.91 -15.74 180.67 +2000 01 22 12 34214.39 1.33 230.54 0.00 -9.74 -18.29 150.56 +2000 01 23 12 34560.01 1.00 256.68 0.00 -7.14 -17.02 160.00 +2000 01 24 12 34560.01 0.00 199.75 0.00 -2.99 -10.04 280.00 +2000 01 25 12 34560.01 0.00 249.60 0.00 -1.48 -10.81 271.06 +2000 01 26 12 34905.61 3.96 100.62 0.00 -5.30 -9.75 289.67 +2000 01 27 12 34905.61 0.00 295.95 0.00 -4.22 -15.89 182.24 +2000 01 28 12 34905.61 0.00 227.74 0.00 -8.12 -15.90 182.24 +2000 01 29 12 35251.20 0.00 330.12 0.00 -3.26 -17.50 160.00 +2000 01 30 12 35251.20 0.00 348.51 0.00 -0.48 -17.02 160.00 +2000 01 31 12 35327.63 8.00 147.64 0.00 -2.01 -8.40 320.00 +2000 02 01 12 35596.80 0.00 153.03 0.00 -2.61 -7.19 355.18 +2000 02 02 12 35596.80 0.00 232.73 0.00 -2.62 -9.74 292.38 +2000 02 03 12 35942.40 0.00 276.04 0.00 -3.76 -12.65 239.05 +2000 02 04 12 35942.40 5.02 180.86 0.00 -1.59 -8.62 320.00 +2000 02 05 12 35942.40 10.75 241.44 0.00 0.53 -10.26 277.46 +2000 02 06 12 36288.00 0.00 301.87 0.00 -0.70 -10.03 282.42 +2000 02 07 12 36288.00 0.00 260.88 0.00 1.02 -6.60 360.00 +2000 02 08 12 36633.59 0.00 395.26 0.00 0.89 -16.35 168.23 +2000 02 09 12 36633.59 0.00 399.51 0.00 1.53 -16.39 170.78 +2000 02 10 12 36633.59 0.00 397.88 0.00 8.32 -9.82 280.00 +2000 02 11 12 36979.21 1.02 331.87 0.00 7.44 -3.93 445.68 +2000 02 12 12 36979.21 0.00 387.52 0.00 4.81 -10.72 269.92 +2000 02 13 12 37324.80 0.00 308.65 0.00 -1.50 -11.12 264.68 +2000 02 14 12 37324.80 22.02 202.84 0.00 0.81 -7.12 360.00 +2000 02 15 12 37646.73 7.74 219.18 0.00 3.38 -5.21 416.18 +2000 02 16 12 37670.41 0.00 333.94 0.00 4.13 -5.71 404.07 +2000 02 17 12 37670.41 0.00 412.13 0.00 7.03 -7.95 337.70 +2000 02 18 12 38016.00 2.28 253.45 0.00 -0.14 -7.59 344.61 +2000 02 19 12 38016.00 12.78 127.81 0.00 -0.33 -4.60 439.44 +2000 02 20 12 38361.60 3.00 150.48 0.00 -0.26 -5.18 415.90 +2000 02 21 12 38361.60 0.00 253.92 0.00 1.07 -5.19 416.90 +2000 02 22 12 38707.19 0.00 397.20 0.00 6.10 -5.74 400.00 +2000 02 23 12 38707.19 0.00 430.99 0.00 11.03 -3.69 464.43 +2000 02 24 12 39052.79 0.00 398.34 0.00 13.11 0.68 636.96 +2000 02 25 12 39052.79 4.09 319.63 0.00 16.90 1.50 680.31 +2000 02 26 12 39052.79 0.00 398.63 0.00 16.03 3.22 778.00 +2000 02 27 12 39398.40 0.00 168.55 0.00 8.20 3.99 800.00 +2000 02 28 12 39398.40 18.05 302.33 0.00 12.77 0.33 624.31 +2000 02 29 12 39744.00 0.00 428.60 0.00 9.28 -3.97 442.50 +2000 03 01 12 39744.00 0.00 457.87 0.00 12.19 -3.76 459.30 +2000 03 02 12 40089.61 6.44 265.09 0.00 8.76 -1.00 560.00 +2000 03 03 12 40089.61 0.00 267.13 0.00 3.54 -3.10 487.87 +2000 03 04 12 40435.20 0.00 439.35 0.00 8.32 -5.28 421.51 +2000 03 05 12 40435.20 0.00 463.75 0.00 11.97 -3.89 448.81 +2000 03 06 12 40780.80 0.00 466.35 0.00 12.71 -3.36 472.85 +2000 03 07 12 40780.80 0.00 481.77 0.00 14.72 -3.16 479.44 +2000 03 08 12 41126.39 0.00 502.47 0.00 22.46 -0.80 576.12 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b/test/test_data/camels_us/basin_mean_forcing/daymet/02064000_lump_cida_forcing_leap.txt @@ -0,0 +1,1100 @@ + 37.24 + 226.00 + 427165365 +Year Mnth Day Hr dayl(s) prcp(mm/day) srad(W/m2) swe(mm) tmax(C) tmin(C) vp(Pa) +2000 01 01 12 34214.41 0.00 299.00 0.00 16.14 -2.24 520.00 +2000 01 02 12 34214.41 0.00 245.17 0.00 15.41 1.60 688.22 +2000 01 03 12 34214.41 0.00 209.46 0.00 18.96 6.85 987.98 +2000 01 04 12 34214.41 0.00 206.08 0.00 20.06 8.15 1091.73 +2000 01 05 12 34214.41 17.15 203.51 0.00 18.62 -0.05 599.91 +2000 01 06 12 34276.89 0.00 240.66 0.00 7.76 -5.81 400.00 +2000 01 07 12 34557.21 0.00 258.21 0.00 9.85 -5.52 400.00 +2000 01 08 12 34560.00 0.00 274.81 0.00 11.60 -5.90 400.00 +2000 01 09 12 34560.00 0.00 250.43 0.00 9.38 -4.89 439.86 +2000 01 10 12 34560.00 18.09 120.26 0.00 8.77 0.88 640.00 +2000 01 11 12 34560.00 16.31 181.11 0.00 16.00 2.23 722.21 +2000 01 12 12 34709.48 0.00 270.11 0.00 15.69 -0.92 570.81 +2000 01 13 12 34905.60 0.00 270.53 0.00 15.50 -0.91 566.88 +2000 01 14 12 34905.60 0.00 309.15 0.00 19.94 -4.99 439.99 +2000 01 15 12 34905.60 0.00 229.02 0.00 3.92 -7.63 349.76 +2000 01 16 12 34905.60 0.00 231.02 0.00 5.70 -5.95 399.94 +2000 01 17 12 35168.47 0.00 296.94 0.00 14.55 -4.58 440.00 +2000 01 18 12 35251.20 4.35 166.80 0.00 2.73 -6.68 360.00 +2000 01 19 12 35251.20 6.12 104.46 0.00 -1.64 -7.00 359.91 +2000 01 20 12 35251.20 10.79 187.71 0.00 4.68 -5.94 400.00 +2000 01 21 12 35400.68 0.00 305.01 0.00 3.53 -10.46 280.00 +2000 01 22 12 35596.79 0.00 260.51 0.00 -2.01 -12.78 235.05 +2000 01 23 12 35596.79 1.57 183.42 0.00 -1.77 -9.37 290.35 +2000 01 24 12 35596.79 4.91 157.83 0.00 -0.49 -8.20 326.17 +2000 01 25 12 35939.61 14.42 182.11 0.00 2.61 -6.90 360.00 +2000 01 26 12 35942.40 0.00 275.34 0.00 0.58 -10.40 280.00 +2000 01 27 12 35942.40 0.00 313.59 0.00 0.75 -12.39 240.00 +2000 01 28 12 36091.88 0.00 351.19 0.00 0.41 -15.46 199.77 +2000 01 29 12 36288.00 0.00 381.96 0.00 3.51 -15.89 170.11 +2000 01 30 12 36288.00 8.18 262.53 0.00 2.34 -12.85 232.37 +2000 01 31 12 36292.46 21.00 113.28 0.00 0.50 -4.58 440.00 +2000 02 01 12 36633.60 0.00 294.11 0.00 2.41 -8.40 320.00 +2000 02 02 12 36633.60 0.00 309.34 0.00 3.52 -7.97 324.34 +2000 02 03 12 36633.60 0.00 325.88 0.00 5.17 -7.18 360.00 +2000 02 04 12 36979.20 0.00 328.32 0.00 7.75 -4.56 440.00 +2000 02 05 12 36979.20 0.00 308.58 0.00 6.86 -4.15 440.33 +2000 02 06 12 36979.20 0.00 293.66 0.00 6.00 -4.00 440.00 +2000 02 07 12 37324.80 0.00 324.18 0.00 7.70 -3.57 474.60 +2000 02 08 12 37324.80 0.00 361.51 0.00 10.44 -2.99 482.04 +2000 02 09 12 37329.27 0.00 396.40 0.00 9.45 -6.66 360.00 +2000 02 10 12 37670.43 0.00 418.30 0.00 14.94 -4.21 440.00 +2000 02 11 12 37670.43 0.00 408.89 0.00 17.89 -0.27 597.68 +2000 02 12 12 37933.27 1.85 363.00 0.00 18.50 -0.49 600.00 +2000 02 13 12 38016.01 2.29 133.16 0.00 1.03 -3.01 480.00 +2000 02 14 12 38016.01 6.13 142.22 0.00 3.89 -1.50 560.00 +2000 02 15 12 38361.60 0.00 392.64 0.00 14.12 -0.25 600.00 +2000 02 16 12 38361.60 0.00 409.76 0.00 13.59 -1.71 543.51 +2000 02 17 12 38361.60 0.00 442.66 0.00 19.18 -0.93 565.49 +2000 02 18 12 38707.19 16.68 249.58 0.00 9.15 -1.50 560.00 +2000 02 19 12 38707.19 11.68 254.37 0.00 10.95 0.00 600.00 +2000 02 20 12 39049.99 0.00 450.04 0.00 17.53 -2.35 520.00 +2000 02 21 12 39052.79 0.00 411.53 0.00 10.32 -4.59 440.00 +2000 02 22 12 39052.79 0.00 422.85 0.00 11.89 -4.04 440.00 +2000 02 23 12 39398.40 0.00 429.31 0.00 14.58 -2.25 520.00 +2000 02 24 12 39398.40 0.00 433.97 0.00 18.59 0.74 639.85 +2000 02 25 12 39741.22 0.00 437.45 0.00 22.94 3.79 801.41 +2000 02 26 12 39744.01 0.00 425.60 0.00 25.19 6.27 941.68 +2000 02 27 12 39744.01 0.00 390.61 0.00 21.08 5.59 920.00 +2000 02 28 12 40089.58 4.63 296.53 0.00 21.27 4.67 853.44 +2000 02 29 12 40089.58 0.00 422.33 0.00 16.84 -2.00 520.26 +2000 03 01 12 40435.19 0.00 423.65 0.00 19.16 -1.34 560.00 +2000 03 02 12 40435.19 0.00 400.85 0.00 19.49 0.91 645.04 +2000 03 03 12 40435.19 0.00 381.52 0.00 14.91 -1.37 559.97 +2000 03 04 12 40780.81 0.00 370.22 0.00 14.54 -0.80 576.14 +2000 03 05 12 40780.81 0.00 379.41 0.00 16.10 0.06 608.56 +2000 03 06 12 41126.39 0.00 419.16 0.00 20.92 0.82 640.00 +2000 03 07 12 41126.39 0.00 429.06 0.00 22.54 1.00 640.00 +2000 03 08 12 41188.88 0.00 421.21 0.00 24.02 3.11 768.89 +2000 03 09 12 41472.01 0.00 395.58 0.00 28.51 9.12 1169.83 +2000 03 10 12 41472.01 0.00 384.05 0.00 26.34 8.50 1119.78 +2000 03 11 12 41817.60 1.51 332.32 0.00 23.41 8.43 1114.12 +2000 03 12 12 41817.60 5.72 263.42 0.00 20.31 5.92 926.77 +2000 03 13 12 42163.20 0.00 368.34 0.00 10.27 -4.00 440.00 +2000 03 14 12 42163.20 0.00 379.12 0.00 11.50 -3.59 472.62 +2000 03 15 12 42163.20 0.00 432.38 0.00 17.87 -1.82 534.28 +2000 03 16 12 42508.80 0.00 410.14 0.00 20.79 2.66 733.01 +2000 03 17 12 42508.80 23.40 231.73 0.00 18.54 6.53 963.12 +2000 03 18 12 42854.40 0.00 431.95 0.00 15.63 -3.05 480.00 +2000 03 19 12 42854.40 0.00 307.44 0.00 8.64 -2.53 517.63 +2000 03 20 12 43199.98 0.00 197.69 0.00 7.28 0.53 640.00 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1.50 680.00 +2000 04 07 12 45619.17 0.00 518.01 0.00 26.84 4.56 845.11 +2000 04 08 12 45964.80 0.00 491.04 0.00 27.06 7.56 1045.16 +2000 04 09 12 45964.80 18.51 381.97 0.00 20.71 1.06 645.01 +2000 04 10 12 45964.80 0.00 441.13 0.00 15.34 1.00 640.00 +2000 04 11 12 46310.42 0.00 475.68 0.00 22.50 5.37 909.72 +2000 04 12 12 46310.42 0.00 429.55 0.00 23.02 8.49 1119.08 +2000 04 13 12 46656.05 0.00 464.40 0.00 21.10 5.00 879.66 +2000 04 14 12 46656.05 2.05 177.64 0.00 9.25 4.50 840.00 +2000 04 15 12 46658.84 1.48 265.32 0.00 12.63 5.30 904.18 +2000 04 16 12 47001.61 3.09 217.17 0.00 16.44 9.00 1159.61 +2000 04 17 12 47001.61 23.98 321.80 0.00 26.31 12.50 1440.31 +2000 04 18 12 47347.21 30.12 337.46 0.00 22.00 7.84 1067.52 +2000 04 19 12 47347.21 8.75 134.85 0.00 9.45 5.00 880.00 +2000 04 20 12 47349.99 0.00 495.88 0.00 22.41 5.50 920.00 +2000 04 21 12 47692.73 0.00 469.28 0.00 23.58 7.95 1076.10 +2000 04 22 12 47692.73 0.00 492.05 0.00 22.80 5.82 919.70 +2000 04 23 12 48038.36 0.00 402.72 0.00 17.36 5.50 920.00 +2000 04 24 12 48038.36 0.00 458.66 0.00 18.76 4.50 840.25 +2000 04 25 12 48038.36 16.00 268.68 0.00 15.36 5.50 920.00 +2000 04 26 12 48383.98 8.94 153.76 0.00 11.29 6.20 935.64 +2000 04 27 12 48383.98 0.00 348.03 0.00 14.64 5.57 920.00 +2000 04 28 12 48466.72 0.00 408.93 0.00 17.92 6.73 978.67 +2000 04 29 12 48729.60 3.74 283.47 0.00 15.46 5.84 920.00 +2000 04 30 12 48729.60 0.00 502.22 0.00 20.39 4.97 877.41 +2000 05 01 12 49070.75 0.00 555.27 0.00 24.98 4.62 843.91 +2000 05 02 12 49075.20 0.00 543.29 0.00 26.36 6.92 978.27 +2000 05 03 12 49075.20 0.00 536.88 0.00 25.57 6.52 961.41 +2000 05 04 12 49420.78 0.00 512.67 0.00 24.78 7.50 1040.00 +2000 05 05 12 49420.78 0.00 508.16 0.00 26.51 9.18 1158.58 +2000 05 06 12 49420.78 0.00 488.73 0.00 29.01 12.30 1418.45 +2000 05 07 12 49766.39 0.00 487.73 0.00 31.33 14.33 1610.04 +2000 05 08 12 49766.39 0.00 489.28 0.00 31.53 14.51 1624.56 +2000 05 09 12 49766.39 0.00 471.32 0.00 31.27 15.45 1741.05 +2000 05 10 12 50107.54 0.00 446.47 0.00 30.02 15.73 1779.71 +2000 05 11 12 50112.00 0.00 554.47 0.00 29.11 8.00 1023.52 +2000 05 12 12 50112.00 0.00 526.07 0.00 28.00 9.51 1160.50 +2000 05 13 12 50308.11 0.00 498.90 0.00 31.37 13.85 1537.98 +2000 05 14 12 50457.59 0.00 507.49 0.00 31.37 13.00 1442.39 +2000 05 15 12 50457.59 0.00 510.54 0.00 24.45 7.08 993.54 +2000 05 16 12 50460.38 0.00 504.43 0.00 22.53 5.56 900.25 +2000 05 17 12 50803.18 0.00 495.29 0.00 23.50 6.87 989.39 +2000 05 18 12 50803.18 0.00 443.84 0.00 26.51 12.00 1400.00 +2000 05 19 12 50803.18 0.00 374.64 0.00 29.00 16.80 1903.83 +2000 05 20 12 50999.31 6.17 287.50 0.00 29.47 16.95 1916.28 +2000 05 21 12 51148.80 14.01 335.18 0.00 28.60 13.57 1557.35 +2000 05 22 12 51148.80 18.46 227.67 0.00 22.99 14.16 1612.31 +2000 05 23 12 51148.80 4.33 298.62 0.00 24.00 11.88 1390.64 +2000 05 24 12 51231.53 0.00 345.38 0.00 24.21 14.01 1600.76 +2000 05 25 12 51494.39 0.00 420.43 0.00 29.10 15.39 1751.60 +2000 05 26 12 51494.39 2.22 455.95 0.00 25.50 9.22 1176.70 +2000 05 27 12 51494.39 0.00 454.74 0.00 26.26 11.09 1327.29 +2000 05 28 12 51494.39 13.73 249.61 0.00 24.13 14.00 1600.00 +2000 05 29 12 51690.52 13.49 175.33 0.00 18.17 11.58 1366.77 +2000 05 30 12 51840.01 0.24 304.26 0.00 15.79 7.35 1028.24 +2000 05 31 12 51840.01 0.00 442.75 0.00 18.78 5.54 920.00 +2000 06 01 12 51840.01 0.00 505.59 0.00 25.66 8.51 1120.53 +2000 06 02 12 51840.01 0.00 466.64 0.00 30.99 14.98 1712.44 +2000 06 03 12 51842.80 0.25 432.87 0.00 31.91 17.00 1920.33 +2000 06 04 12 52123.12 0.00 346.99 0.00 26.77 16.50 1880.00 +2000 06 05 12 52185.60 0.00 232.93 0.00 22.29 16.00 1800.00 +2000 06 06 12 52185.60 19.07 136.14 0.00 20.68 16.02 1803.82 +2000 06 07 12 52185.60 0.00 455.15 0.00 23.00 9.87 1229.58 +2000 06 08 12 52185.60 0.00 491.18 0.00 24.50 9.54 1202.81 +2000 06 09 12 52185.60 0.00 495.16 0.00 27.01 11.34 1346.99 +2000 06 10 12 52185.60 0.00 490.66 0.00 30.43 14.39 1631.59 +2000 06 11 12 52185.60 0.00 474.80 0.00 32.01 16.50 1879.70 +2000 06 12 12 52268.33 0.00 444.84 0.00 32.50 18.42 2113.53 +2000 06 13 12 52381.72 0.00 438.56 0.00 33.14 19.30 2248.14 +2000 06 14 12 52468.71 0.00 415.57 0.00 32.56 19.93 2314.56 +2000 06 15 12 52526.73 14.97 308.71 0.00 31.04 19.00 2200.00 +2000 06 16 12 52531.19 13.62 284.62 0.00 29.54 19.03 2205.04 +2000 06 17 12 52531.19 0.02 336.71 0.00 29.00 20.02 2322.75 +2000 06 18 12 52531.19 0.00 354.82 0.00 30.55 20.86 2457.89 +2000 06 19 12 52531.19 10.90 298.15 0.00 30.24 19.22 2236.07 +2000 06 20 12 52531.19 9.46 289.78 0.00 27.79 17.64 2021.97 +2000 06 21 12 52531.19 0.00 336.17 0.00 26.47 18.00 2080.00 +2000 06 22 12 52531.19 0.00 374.46 0.00 29.32 19.38 2261.14 +2000 06 23 12 52531.19 0.00 446.29 0.00 28.50 16.10 1815.27 +2000 06 24 12 52531.19 0.00 472.25 0.00 30.50 16.54 1883.58 +2000 06 25 12 52526.73 0.00 454.44 0.00 31.51 18.21 2096.91 +2000 06 26 12 52468.71 0.00 440.63 0.00 32.05 19.28 2245.09 +2000 06 27 12 52381.72 13.98 313.88 0.00 31.41 19.60 2287.97 +2000 06 28 12 52268.33 16.02 265.11 0.00 29.34 20.00 2320.57 +2000 06 29 12 52188.39 19.19 176.67 0.00 24.98 19.31 2249.46 +2000 06 30 12 52185.60 0.51 459.84 0.00 25.98 13.53 1562.37 +2000 07 01 12 52185.60 0.00 466.60 0.00 27.01 14.25 1619.74 +2000 07 02 12 52185.60 0.00 480.90 0.00 28.00 14.45 1638.73 +2000 07 03 12 52185.60 0.00 487.00 0.00 29.68 15.49 1759.00 +2000 07 04 12 52185.60 0.00 443.42 0.00 31.20 18.57 2131.72 +2000 07 05 12 52185.60 0.00 369.22 0.00 29.79 20.00 2320.00 +2000 07 06 12 52181.14 5.21 277.12 0.00 29.41 19.56 2284.44 +2000 07 07 12 51922.74 0.00 376.35 0.00 29.08 19.12 2218.77 +2000 07 08 12 51840.01 0.00 440.13 0.00 27.11 15.31 1744.58 +2000 07 09 12 51840.01 0.00 435.24 0.00 27.46 15.90 1791.69 +2000 07 10 12 51840.01 0.00 452.67 0.00 32.05 18.91 2186.17 +2000 07 11 12 51840.01 0.00 404.35 0.00 32.76 21.50 2560.00 +2000 07 12 12 51835.56 29.31 276.50 0.00 28.67 19.39 2263.00 +2000 07 13 12 51497.18 0.00 304.87 0.00 26.16 19.05 2207.93 +2000 07 14 12 51494.39 0.00 335.63 0.00 25.77 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-6.50 360.00 +2002 12 28 12 34018.30 0.00 255.28 0.00 5.50 -7.00 360.00 +2002 12 29 12 34131.68 0.00 258.19 0.00 8.13 -5.05 432.22 +2002 12 30 12 34214.41 0.00 272.09 0.00 12.65 -3.43 480.00 +2002 12 31 12 34214.41 0.00 266.39 0.00 11.81 -3.30 480.00 diff --git a/test/test_data/camels_us/basin_mean_forcing/daymet/03015500_lump_cida_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/daymet/03015500_lump_cida_forcing_leap.txt new file mode 100644 index 00000000..7c0f7242 --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/daymet/03015500_lump_cida_forcing_leap.txt @@ -0,0 +1,1100 @@ + 41.91 + 477.00 + 831030801 +Year Mnth Day Hr dayl(s) prcp(mm/day) srad(W/m2) swe(mm) tmax(C) tmin(C) vp(Pa) +2000 01 01 12 32486.40 0.00 230.58 0.00 3.00 -7.58 345.40 +2000 01 02 12 32486.40 4.38 202.13 0.00 10.15 -5.15 417.17 +2000 01 03 12 32486.40 6.95 165.16 0.00 13.99 3.53 792.52 +2000 01 04 12 32486.40 20.08 151.25 0.00 13.11 3.99 813.96 +2000 01 05 12 32613.94 4.98 205.67 0.00 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3.54 175.23 0.00 -11.05 -19.30 129.97 +2000 01 23 12 34214.40 0.18 268.96 0.00 -7.56 -17.66 157.56 +2000 01 24 12 34214.40 0.42 260.66 0.00 -3.08 -12.62 235.33 +2000 01 25 12 34543.44 0.33 311.25 0.00 -6.20 -19.24 126.86 +2000 01 26 12 34560.00 0.96 238.94 0.00 -7.36 -15.45 189.22 +2000 01 27 12 34560.00 2.48 251.21 0.00 -8.44 -18.80 139.26 +2000 01 28 12 34905.60 1.47 224.44 0.00 -10.76 -18.01 152.78 +2000 01 29 12 34905.60 0.00 340.05 0.00 -5.51 -20.43 120.00 +2000 01 30 12 34907.52 0.10 351.12 0.00 -0.16 -16.84 166.56 +2000 01 31 12 35251.19 2.07 233.61 0.00 -0.96 -8.55 318.90 +2000 02 01 12 35251.19 1.00 212.27 0.00 -2.64 -8.91 310.89 +2000 02 02 12 35450.02 2.78 194.81 0.00 -5.10 -11.88 244.95 +2000 02 03 12 35596.80 0.00 254.63 0.00 -4.84 -12.33 237.68 +2000 02 04 12 35596.80 3.87 137.33 0.00 -1.87 -6.74 364.34 +2000 02 05 12 35942.39 1.38 371.91 0.00 -1.03 -16.15 169.12 +2000 02 06 12 35942.39 1.05 340.00 0.00 -1.90 -13.42 216.48 +2000 02 07 12 36209.89 0.00 316.51 0.00 -1.90 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379.13 0.00 13.39 1.85 697.23 +2000 02 25 12 39052.80 6.38 287.47 0.00 14.73 2.84 751.63 +2000 02 26 12 39052.80 0.00 402.69 0.00 14.85 1.97 704.61 +2000 02 27 12 39398.40 0.00 410.78 0.00 17.13 3.34 778.55 +2000 02 28 12 39398.40 5.72 313.72 0.00 12.87 -0.65 589.55 +2000 02 29 12 39727.45 0.00 409.20 0.00 6.04 -5.95 394.01 +2000 03 01 12 39744.01 0.00 450.28 0.00 11.27 -4.02 451.14 +2000 03 02 12 40011.48 7.23 300.23 0.00 9.05 -2.80 498.27 +2000 03 03 12 40089.61 0.00 243.71 0.00 -0.61 -6.53 372.98 +2000 03 04 12 40288.41 0.00 426.81 0.00 4.96 -7.71 340.32 +2000 03 05 12 40435.20 0.00 457.38 0.00 9.27 -5.82 396.69 +2000 03 06 12 40562.76 0.00 459.57 0.00 8.46 -6.40 375.21 +2000 03 07 12 40780.80 0.00 480.10 0.00 12.82 -4.15 451.18 +2000 03 08 12 40908.34 0.00 500.07 0.00 21.67 -0.07 609.64 +2000 03 09 12 41126.42 0.00 464.62 0.00 23.50 6.25 940.27 +2000 03 10 12 41184.78 2.27 477.43 0.00 19.29 -2.42 513.50 +2000 03 11 12 41471.99 0.00 202.69 0.00 1.64 -3.14 484.11 +2000 03 12 12 41473.91 8.79 188.66 0.00 0.89 -4.96 429.08 +2000 03 13 12 41817.60 0.74 365.94 0.00 0.60 -8.22 328.55 +2000 03 14 12 41819.52 0.00 401.56 0.00 4.35 -5.65 403.37 +2000 03 15 12 42163.18 0.07 449.41 0.00 9.79 -2.60 506.28 +2000 03 16 12 42165.10 0.00 482.97 0.00 15.48 0.73 638.46 +2000 03 17 12 42508.81 10.82 367.27 0.00 7.09 -7.06 356.71 +2000 03 18 12 42508.81 0.00 453.24 0.00 -1.03 -12.66 234.37 +2000 03 19 12 42854.41 0.00 518.51 0.00 6.32 -9.42 297.17 +2000 03 20 12 42854.41 0.00 391.35 0.00 9.58 -0.40 592.58 +2000 03 21 12 43200.00 0.00 361.64 0.00 10.44 1.26 666.93 +2000 03 22 12 43543.68 0.74 249.51 0.00 8.40 2.38 724.45 +2000 03 23 12 43545.59 0.00 330.99 0.00 11.46 3.22 777.31 +2000 03 24 12 43889.26 0.00 523.32 0.00 15.44 -1.46 550.01 +2000 03 25 12 43891.19 2.19 485.05 0.00 19.50 2.00 692.35 +2000 03 26 12 44234.88 0.61 516.81 0.00 18.41 1.46 663.94 +2000 03 27 12 44236.79 0.00 450.92 0.00 10.13 -2.26 520.36 +2000 03 28 12 44580.45 4.80 381.47 0.00 11.87 -3.49 470.18 +2000 03 29 12 44582.38 5.94 349.06 0.00 9.90 -2.93 493.55 +2000 03 30 12 44926.09 3.64 153.69 0.00 3.50 -1.04 569.24 +2000 03 31 12 44928.00 0.00 491.65 0.00 6.73 -6.59 371.49 +2000 04 01 12 45215.24 0.00 551.53 0.00 12.02 -5.37 414.26 +2000 04 02 12 45273.59 2.55 495.23 0.00 16.51 -1.75 529.19 +2000 04 03 12 45491.64 9.31 171.33 0.00 11.87 6.43 954.74 +2000 04 04 12 45619.22 22.34 294.12 0.00 15.20 5.21 889.06 +2000 04 05 12 45837.25 5.79 328.50 0.00 7.28 -3.67 462.58 +2000 04 06 12 45964.81 1.00 443.36 0.00 8.39 -2.58 506.49 +2000 04 07 12 46111.60 0.00 531.13 0.00 11.89 -2.86 495.96 +2000 04 08 12 46310.42 19.70 342.01 0.00 9.52 -1.51 550.61 +2000 04 09 12 46388.54 20.66 366.16 0.00 7.24 -4.52 437.78 +2000 04 10 12 46656.01 4.40 248.51 0.00 3.28 -3.75 460.36 +2000 04 11 12 46672.57 0.00 320.07 0.00 3.55 -3.21 481.92 +2000 04 12 12 47001.61 2.68 314.28 0.00 4.53 -3.46 471.11 +2000 04 13 12 47001.61 0.00 476.64 0.00 5.72 -5.36 415.37 +2000 04 14 12 47347.20 0.00 544.57 0.00 12.01 -2.44 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SRAD(W/m2) SWE(mm) Tmax(C) Tmin(C) Vp(Pa) +2000 01 01 12 31185.94 0.00 233.66 0.00 -8.49 -8.49 192.89 +2000 01 02 12 31302.07 4.21 198.52 0.00 -4.08 -4.08 308.07 +2000 01 03 12 31370.89 2.68 181.57 0.00 3.23 3.23 536.80 +2000 01 04 12 31449.60 22.18 192.75 0.00 3.75 3.75 526.92 +2000 01 05 12 31449.60 9.32 205.01 0.00 3.51 3.51 460.48 +2000 01 06 12 31449.60 2.14 202.13 0.00 -5.76 -5.76 227.77 +2000 01 07 12 31596.25 0.64 201.30 0.00 -5.37 -5.37 242.34 +2000 01 08 12 31716.49 0.58 178.65 0.00 -2.78 -2.78 326.60 +2000 01 09 12 31795.20 0.13 226.16 0.00 -3.41 -3.41 321.57 +2000 01 10 12 31795.20 14.74 204.88 0.00 -1.80 -1.80 355.91 +2000 01 11 12 31805.08 7.88 203.32 0.00 1.95 1.95 470.63 +2000 01 12 12 32025.19 1.05 238.30 0.00 3.60 3.60 511.35 +2000 01 13 12 32140.80 0.00 246.52 0.00 -4.58 -4.58 262.11 +2000 01 14 12 32140.80 0.00 262.92 0.00 -12.74 -12.74 121.35 +2000 01 15 12 32287.45 6.70 196.11 0.00 -17.02 -17.02 87.75 +2000 01 16 12 32464.00 11.31 224.85 0.00 -15.77 -15.77 94.05 +2000 01 17 12 32486.40 6.00 229.57 0.00 -11.58 -11.58 124.37 +2000 01 18 12 32496.28 0.92 265.88 0.00 -11.85 -11.85 126.72 +2000 01 19 12 32809.60 0.00 274.00 0.00 -16.64 -16.64 85.34 +2000 01 20 12 32832.00 0.26 235.67 0.00 -17.29 -17.29 79.88 +2000 01 21 12 32841.88 6.93 230.21 0.00 -14.49 -14.49 103.20 +2000 01 22 12 33155.20 3.63 224.38 0.00 -14.84 -14.84 105.37 +2000 01 23 12 33177.60 0.68 229.28 0.00 -17.17 -17.17 86.50 +2000 01 24 12 33324.25 0.17 245.16 0.00 -14.19 -14.19 113.80 +2000 01 25 12 33523.20 14.59 235.82 0.00 -7.67 -7.67 214.54 +2000 01 26 12 33523.20 5.16 243.81 0.00 -2.74 -2.74 300.25 +2000 01 27 12 33753.19 0.52 243.04 0.00 -3.20 -3.20 268.92 +2000 01 28 12 33868.80 0.04 304.84 0.00 -11.89 -11.89 134.23 +2000 01 29 12 33950.74 0.00 298.35 0.00 -12.96 -12.96 129.21 +2000 01 30 12 34214.40 0.00 326.22 0.00 -8.95 -8.95 166.16 +2000 01 31 12 34214.40 9.31 268.41 0.00 -5.15 -5.15 231.93 +2000 02 01 12 34481.29 2.45 287.39 0.00 -1.06 -1.06 344.90 +2000 02 02 12 34560.00 0.00 324.72 0.00 -4.99 -4.99 233.47 +2000 02 03 12 34758.07 0.00 299.70 0.00 -10.51 -10.51 153.49 +2000 02 04 12 34905.60 0.06 270.40 0.00 -12.35 -12.35 122.24 +2000 02 05 12 34987.54 0.02 350.17 0.00 -13.05 -13.05 109.34 +2000 02 06 12 35251.20 0.00 346.97 0.00 -10.33 -10.33 139.42 +2000 02 07 12 35251.20 0.00 346.59 0.00 -9.16 -9.16 151.18 +2000 02 08 12 35596.80 0.00 364.06 0.00 -9.96 -9.96 125.73 +2000 02 09 12 35596.80 0.00 355.12 0.00 -8.71 -8.71 164.45 +2000 02 10 12 35942.40 0.92 280.83 0.00 -4.97 -4.97 241.35 +2000 02 11 12 35942.40 10.13 251.92 0.00 -4.99 -4.99 250.42 +2000 02 12 12 36288.00 2.60 376.05 0.00 -7.37 -7.37 163.61 +2000 02 13 12 36288.00 1.48 303.54 0.00 -11.56 -11.56 117.93 +2000 02 14 12 36633.60 31.57 313.87 0.00 -7.64 -7.64 187.56 +2000 02 15 12 36633.60 14.50 317.02 0.00 -1.39 -1.39 317.35 +2000 02 16 12 36979.20 6.29 295.52 0.00 -4.80 -4.80 244.74 +2000 02 17 12 36979.20 1.27 343.52 0.00 -6.95 -6.95 179.33 +2000 02 18 12 37324.80 5.91 320.86 0.00 -14.61 -14.61 97.99 +2000 02 19 12 37324.80 3.99 324.70 0.00 -12.41 -12.41 126.85 +2000 02 20 12 37670.40 0.73 302.73 0.00 -8.38 -8.38 183.42 +2000 02 21 12 37670.40 0.02 403.57 0.00 -8.40 -8.40 167.57 +2000 02 22 12 38016.00 0.06 340.53 0.00 -10.26 -10.26 136.71 +2000 02 23 12 38016.00 0.16 346.86 0.00 -7.01 -7.01 216.80 +2000 02 24 12 38361.60 0.04 348.00 0.00 3.08 3.08 504.48 +2000 02 25 12 38361.60 0.00 419.43 0.00 2.99 2.99 436.19 +2000 02 26 12 38707.20 0.06 315.02 0.00 -0.72 -0.72 363.60 +2000 02 27 12 38707.20 0.08 303.45 0.00 1.30 1.30 484.63 +2000 02 28 12 39052.80 5.21 228.89 0.00 5.51 5.51 682.16 +2000 02 29 12 39062.68 2.71 222.14 0.00 4.11 4.11 630.19 +2000 03 01 12 39398.40 0.22 213.62 0.00 2.71 2.71 578.20 +2000 03 02 12 39628.39 6.55 247.41 0.00 2.75 2.75 568.64 +2000 03 03 12 39744.00 8.88 167.55 0.00 0.69 0.69 518.73 +2000 03 04 12 40089.60 2.58 190.48 0.00 0.33 0.33 506.43 +2000 03 05 12 40089.60 0.45 267.88 0.00 0.39 0.39 472.04 +2000 03 06 12 40435.20 0.07 331.55 0.00 0.73 0.73 456.46 +2000 03 07 12 40435.20 0.00 406.97 0.00 -0.59 -0.59 390.13 +2000 03 08 12 40780.80 0.00 450.61 0.00 1.64 1.64 427.03 +2000 03 09 12 40780.80 3.30 319.48 0.00 2.77 2.77 484.59 +2000 03 10 12 41126.40 1.21 348.83 0.00 1.22 1.22 409.25 +2000 03 11 12 41126.40 9.16 284.76 0.00 -2.41 -2.41 358.79 +2000 03 12 12 41472.00 21.46 241.45 0.00 -1.48 -1.48 402.53 +2000 03 13 12 41817.60 5.43 390.72 0.00 -1.59 -1.59 324.91 +2000 03 14 12 41817.60 0.11 495.64 0.00 -5.05 -5.05 228.44 +2000 03 15 12 42163.20 0.00 491.83 0.00 -2.01 -2.01 345.01 +2000 03 16 12 42163.20 9.53 335.70 0.00 5.34 5.34 579.96 +2000 03 17 12 42508.80 8.77 396.44 0.00 1.72 1.72 351.72 +2000 03 18 12 42508.80 1.70 401.62 0.00 -7.30 -7.30 195.34 +2000 03 19 12 42854.40 0.01 513.34 0.00 -7.56 -7.56 187.83 +2000 03 20 12 42854.40 0.00 515.53 0.00 -3.54 -3.54 261.32 +2000 03 21 12 43200.00 0.00 505.64 0.00 0.11 0.11 357.24 +2000 03 22 12 43545.60 0.00 509.49 0.00 2.65 2.65 421.58 +2000 03 23 12 43545.60 0.00 520.62 0.00 4.17 4.17 463.06 +2000 03 24 12 43891.20 0.00 507.93 0.00 6.00 6.00 527.37 +2000 03 25 12 43891.20 0.57 409.49 0.00 3.95 3.95 469.67 +2000 03 26 12 44236.80 0.23 405.35 0.00 5.19 5.19 520.85 +2000 03 27 12 44236.80 0.02 526.08 0.00 6.28 6.28 551.35 +2000 03 28 12 44582.40 22.90 389.65 0.00 6.54 6.54 611.55 +2000 03 29 12 44582.40 8.89 378.79 0.00 6.11 6.11 616.16 +2000 03 30 12 44928.00 0.80 396.35 0.00 4.39 4.39 564.71 +2000 03 31 12 45273.60 0.06 381.44 0.00 4.17 4.17 548.87 +2000 04 01 12 45273.60 0.01 496.41 0.00 4.87 4.87 566.25 +2000 04 02 12 45619.20 4.81 387.43 0.00 6.02 6.02 609.00 +2000 04 03 12 45619.20 5.34 408.89 0.00 6.36 6.36 611.52 +2000 04 04 12 45964.80 15.13 321.11 0.00 7.02 7.02 704.92 +2000 04 05 12 45964.80 5.41 302.26 0.00 6.13 6.13 677.18 +2000 04 06 12 46310.40 0.92 389.55 0.00 4.17 4.17 552.51 +2000 04 07 12 46310.40 0.60 378.60 0.00 4.72 4.72 578.89 +2000 04 08 12 46656.00 1.37 376.01 0.00 5.61 5.61 629.44 +2000 04 09 12 46771.61 10.58 399.39 0.00 7.29 7.29 648.92 +2000 04 10 12 47001.60 3.05 404.77 0.00 5.67 5.67 548.76 +2000 04 11 12 47337.32 3.71 391.82 0.00 2.23 2.23 459.93 +2000 04 12 12 47347.20 3.13 362.36 0.00 2.79 2.79 491.04 +2000 04 13 12 47692.80 0.61 407.53 0.00 1.08 1.08 438.86 +2000 04 14 12 47692.80 0.01 534.26 0.00 1.75 1.75 451.34 +2000 04 15 12 48038.40 0.00 546.02 0.00 6.38 6.38 600.56 +2000 04 16 12 48038.40 0.00 539.53 0.00 9.84 9.84 666.91 +2000 04 17 12 48384.00 0.00 518.01 0.00 3.56 3.56 458.37 +2000 04 18 12 48384.00 0.00 542.68 0.00 1.84 1.84 432.85 +2000 04 19 12 48729.60 0.76 380.58 0.00 3.91 3.91 548.90 +2000 04 20 12 48729.60 0.36 349.75 0.00 4.57 4.57 627.38 +2000 04 21 12 49075.20 3.76 390.94 0.00 6.48 6.48 647.42 +2000 04 22 12 49075.20 24.71 343.81 0.00 4.79 4.79 615.37 +2000 04 23 12 49420.80 40.49 236.88 0.00 4.23 4.23 654.94 +2000 04 24 12 49420.80 17.23 276.57 0.00 4.17 4.17 630.74 +2000 04 25 12 49766.40 2.37 361.64 0.00 2.36 2.36 535.06 +2000 04 26 12 49766.40 0.78 383.23 0.00 2.18 2.18 510.07 +2000 04 27 12 50112.00 1.14 391.41 0.00 3.55 3.55 546.56 +2000 04 28 12 50112.00 0.25 447.62 0.00 3.47 3.47 563.34 +2000 04 29 12 50457.60 0.19 447.98 0.00 5.68 5.68 608.36 +2000 04 30 12 50457.60 0.18 455.14 0.00 8.41 8.41 663.27 +2000 05 01 12 50803.20 0.20 473.54 0.00 5.98 5.98 573.80 +2000 05 02 12 50803.20 0.07 471.70 0.00 7.97 7.97 650.36 +2000 05 03 12 51148.80 0.01 584.81 0.00 6.53 6.53 574.02 +2000 05 04 12 51148.80 0.39 433.22 0.00 7.90 7.90 698.20 +2000 05 05 12 51412.46 0.23 422.36 0.00 11.33 11.33 871.38 +2000 05 06 12 51494.40 0.16 440.43 0.00 13.33 13.33 919.19 +2000 05 07 12 51641.93 3.93 424.65 0.00 14.36 14.36 1010.92 +2000 05 08 12 51840.00 2.43 404.64 0.00 14.40 14.40 1046.23 +2000 05 09 12 51862.40 9.76 423.25 0.00 13.46 13.46 908.07 +2000 05 10 12 52185.60 5.93 324.53 0.00 5.97 5.97 664.98 +2000 05 11 12 52185.60 14.00 290.40 0.00 4.79 4.79 683.90 +2000 05 12 12 52449.26 3.45 392.36 0.00 7.21 7.21 759.72 +2000 05 13 12 52531.20 3.00 415.54 0.00 8.69 8.69 764.79 +2000 05 14 12 52646.81 3.02 400.99 0.00 9.94 9.94 813.77 +2000 05 15 12 52876.80 0.59 524.74 0.00 10.28 10.28 790.20 +2000 05 16 12 52876.80 0.00 566.20 0.00 10.08 10.08 747.39 +2000 05 17 12 53075.75 0.03 493.85 0.00 11.72 11.72 844.83 +2000 05 18 12 53222.40 11.15 391.95 0.00 11.92 11.92 882.38 +2000 05 19 12 53244.80 3.93 458.13 0.00 9.47 9.47 747.15 +2000 05 20 12 53558.12 0.26 567.43 0.00 7.82 7.82 648.46 +2000 05 21 12 53568.00 0.27 423.19 0.00 10.15 10.15 790.67 +2000 05 22 12 53590.40 0.48 411.27 0.00 12.65 12.65 926.46 +2000 05 23 12 53903.73 2.85 385.28 0.00 10.43 10.43 821.89 +2000 05 24 12 53913.60 11.50 301.45 0.00 7.96 7.96 805.75 +2000 05 25 12 53936.00 10.90 227.04 0.00 9.01 9.01 921.11 +2000 05 26 12 54112.55 5.00 337.81 0.00 10.25 10.25 953.13 +2000 05 27 12 54259.20 0.76 515.92 0.00 12.62 12.62 951.65 +2000 05 28 12 54259.20 0.00 472.04 0.00 11.88 11.88 919.31 +2000 05 29 12 54374.81 0.00 433.47 0.00 10.47 10.47 845.64 +2000 05 30 12 54594.93 0.00 553.98 0.00 9.23 9.23 733.02 +2000 05 31 12 54604.80 0.00 529.62 0.00 11.30 11.30 884.82 +2000 06 01 12 54604.80 0.00 537.97 0.00 15.81 15.81 1073.49 +2000 06 02 12 54683.51 0.00 552.72 0.00 17.37 17.37 1085.16 +2000 06 03 12 54803.75 0.00 502.22 0.00 15.81 15.81 1066.60 +2000 06 04 12 54950.40 0.40 405.26 0.00 13.36 13.36 943.07 +2000 06 05 12 54950.40 0.20 417.01 0.00 12.81 12.81 899.79 +2000 06 06 12 54950.40 5.35 418.20 0.00 11.09 11.09 802.59 +2000 06 07 12 55029.11 13.82 394.17 0.00 11.42 11.42 841.83 +2000 06 08 12 55097.93 3.82 406.16 0.00 10.73 10.73 874.20 +2000 06 09 12 55214.06 0.33 417.42 0.00 15.38 15.38 1101.92 +2000 06 10 12 55296.00 1.47 371.14 0.00 15.23 15.23 1060.24 +2000 06 11 12 55296.00 6.13 333.36 0.00 9.82 9.82 816.08 +2000 06 12 12 55296.00 1.64 379.29 0.00 8.22 8.22 742.32 +2000 06 13 12 55296.00 0.03 546.80 0.00 10.44 10.44 755.19 +2000 06 14 12 55296.00 0.00 556.58 0.00 11.99 11.99 823.87 +2000 06 15 12 55318.40 0.19 382.52 0.00 13.54 13.54 1032.12 +2000 06 16 12 55374.71 0.09 375.17 0.00 16.25 16.25 1308.30 +2000 06 17 12 55374.71 4.30 384.76 0.00 21.32 21.32 1511.11 +2000 06 18 12 55411.61 3.54 405.03 0.00 20.25 20.25 1318.71 +2000 06 19 12 55411.61 1.16 390.89 0.00 18.60 18.60 1305.31 +2000 06 20 12 55411.61 0.14 503.89 0.00 17.67 17.67 1261.83 +2000 06 21 12 55411.61 6.22 381.67 0.00 18.37 18.37 1357.10 +2000 06 22 12 55411.61 4.24 358.32 0.00 19.20 19.20 1458.55 +2000 06 23 12 55411.61 0.77 402.45 0.00 20.00 20.00 1381.00 +2000 06 24 12 55374.71 0.02 535.83 0.00 18.04 18.04 1192.13 +2000 06 25 12 55318.40 0.19 382.36 0.00 18.13 18.13 1351.46 +2000 06 26 12 55296.00 0.18 364.37 0.00 21.04 21.04 1611.22 +2000 06 27 12 55296.00 12.12 369.13 0.00 21.99 21.99 1583.35 +2000 06 28 12 55296.00 3.18 479.70 0.00 19.18 19.18 1391.20 +2000 06 29 12 55296.00 0.08 384.46 0.00 19.53 19.53 1420.29 +2000 06 30 12 55296.00 3.57 339.99 0.00 17.80 17.80 1332.81 +2000 07 01 12 55286.12 1.49 391.66 0.00 17.00 17.00 1269.55 +2000 07 02 12 55149.35 0.15 516.71 0.00 19.01 19.01 1338.59 +2000 07 03 12 55066.01 2.27 358.41 0.00 19.83 19.83 1511.04 +2000 07 04 12 54972.80 3.02 355.29 0.00 21.17 21.17 1580.15 +2000 07 05 12 54950.40 3.24 346.76 0.00 18.98 18.98 1365.72 +2000 07 06 12 54950.40 0.69 506.79 0.00 16.56 16.56 1189.71 +2000 07 07 12 54940.52 11.25 353.52 0.00 18.19 18.19 1325.32 +2000 07 08 12 54752.33 3.43 342.14 0.00 16.04 16.04 1199.52 +2000 07 09 12 54627.19 3.52 374.19 0.00 15.57 15.57 1181.09 +2000 07 10 12 54604.80 5.85 356.33 0.00 17.95 17.95 1310.15 +2000 07 11 12 54604.80 1.39 362.83 0.00 16.41 16.41 1210.72 +2000 07 12 12 54458.15 0.29 382.63 0.00 17.37 17.37 1224.57 +2000 07 13 12 54281.60 0.14 439.90 0.00 18.86 18.86 1300.25 +2000 07 14 12 54259.20 0.05 387.99 0.00 21.00 21.00 1522.83 +2000 07 15 12 54249.32 0.54 377.84 0.00 20.00 20.00 1428.62 +2000 07 16 12 54029.21 15.85 361.74 0.00 18.47 18.47 1335.66 +2000 07 17 12 53913.60 14.13 320.70 0.00 16.56 16.56 1306.90 +2000 07 18 12 53913.60 5.28 266.04 0.00 16.25 16.25 1347.20 +2000 07 19 12 53715.53 0.89 362.53 0.00 15.84 15.84 1209.90 +2000 07 20 12 53568.00 0.05 528.90 0.00 14.94 14.94 1035.92 +2000 07 21 12 53568.00 0.84 402.02 0.00 16.49 16.49 1189.08 +2000 07 22 12 53369.93 2.62 329.35 0.00 18.85 18.85 1420.99 +2000 07 23 12 53222.40 1.03 371.16 0.00 17.28 17.28 1293.89 +2000 07 24 12 53222.40 0.10 511.46 0.00 18.80 18.80 1272.43 +2000 07 25 12 52955.51 0.00 502.59 0.00 20.08 20.08 1322.99 +2000 07 26 12 52876.80 0.00 518.18 0.00 19.76 19.76 1284.46 +2000 07 27 12 52794.86 0.51 347.25 0.00 18.92 18.92 1382.00 +2000 07 28 12 52531.20 1.55 291.86 0.00 18.03 18.03 1436.33 +2000 07 29 12 52531.20 0.37 456.24 0.00 19.26 19.26 1431.90 +2000 07 30 12 52301.21 0.00 452.93 0.00 19.67 19.67 1395.99 +2000 07 31 12 52185.60 0.00 497.01 0.00 18.07 18.07 1207.01 +2000 08 01 12 52103.66 0.27 380.71 0.00 16.94 16.94 1177.97 +2000 08 02 12 51840.00 1.02 338.42 0.00 17.72 17.72 1353.85 +2000 08 03 12 51840.00 0.27 239.84 0.00 17.66 17.66 1543.26 +2000 08 04 12 51494.40 0.19 311.06 0.00 19.38 19.38 1548.37 +2000 08 05 12 51494.40 0.05 493.82 0.00 18.80 18.80 1314.91 +2000 08 06 12 51227.51 0.18 365.16 0.00 19.25 19.25 1394.48 +2000 08 07 12 51148.80 5.94 323.57 0.00 19.87 19.87 1548.75 +2000 08 08 12 50918.81 2.51 224.43 0.00 19.06 19.06 1646.86 +2000 08 09 12 50803.20 2.39 343.81 0.00 21.26 21.26 1655.19 +2000 08 10 12 50605.13 3.58 324.12 0.00 20.83 20.83 1527.82 +2000 08 11 12 50457.60 0.80 486.29 0.00 18.11 18.11 1234.01 +2000 08 12 12 50310.95 0.00 450.64 0.00 17.82 17.82 1263.18 +2000 08 13 12 50112.00 0.00 414.47 0.00 17.60 17.60 1349.03 +2000 08 14 12 50030.06 1.94 253.89 0.00 18.09 18.09 1531.90 +2000 08 15 12 49766.40 3.94 226.42 0.00 18.61 18.61 1612.71 +2000 08 16 12 49684.46 3.61 279.47 0.00 18.64 18.64 1508.65 +2000 08 17 12 49420.80 1.03 299.63 0.00 16.67 16.67 1249.76 +2000 08 18 12 49338.86 0.08 494.90 0.00 14.69 14.69 1009.98 +2000 08 19 12 49075.20 0.03 380.45 0.00 15.88 15.88 1117.65 +2000 08 20 12 48993.26 9.91 329.09 0.00 14.55 14.55 1080.65 +2000 08 21 12 48729.60 2.79 345.12 0.00 14.34 14.34 1050.28 +2000 08 22 12 48582.95 0.18 437.97 0.00 15.53 15.53 1054.91 +2000 08 23 12 48384.00 5.53 353.07 0.00 17.02 17.02 1189.67 +2000 08 24 12 48185.93 2.78 336.19 0.00 17.46 17.46 1261.95 +2000 08 25 12 48038.40 0.38 364.16 0.00 17.76 17.76 1273.43 +2000 08 26 12 47715.19 0.01 466.35 0.00 19.29 19.29 1315.93 +2000 08 27 12 47692.80 0.72 350.26 0.00 19.93 19.93 1317.75 +2000 08 28 12 47347.20 0.19 460.92 0.00 17.41 17.41 1123.84 +2000 08 29 12 47347.20 0.00 479.76 0.00 16.39 16.39 1039.94 +2000 08 30 12 47001.60 0.00 460.34 0.00 17.14 17.14 1145.14 +2000 08 31 12 47001.60 0.00 448.18 0.00 18.86 18.86 1333.90 +2000 09 01 12 46656.00 2.57 328.93 0.00 21.45 21.45 1543.16 +2000 09 02 12 46656.00 8.59 340.70 0.00 21.01 21.01 1401.93 +2000 09 03 12 46310.40 3.41 195.43 0.00 15.54 15.54 1242.50 +2000 09 04 12 46310.40 3.34 251.70 0.00 13.11 13.11 1060.89 +2000 09 05 12 45964.80 0.79 442.29 0.00 9.95 9.95 756.98 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b/test/test_data/camels_us/basin_mean_forcing/maurer/01547700_lump_maurer_forcing_leap.txt @@ -0,0 +1,1100 @@ + 40.98 + 383.00 + 114169652 +Year Mnth Day Hr Dayl(s) PRCP(mm/day) SRAD(W/m2) SWE(mm) Tmax(C) Tmin(C) Vp(Pa) +2000 01 01 12 32832.00 0.48 224.65 0.00 -0.51 -0.51 356.45 +2000 01 02 12 32832.00 0.77 224.57 0.00 0.39 0.39 396.03 +2000 01 03 12 32832.00 6.45 195.57 0.00 5.40 5.40 649.88 +2000 01 04 12 32832.00 3.18 132.94 0.00 11.18 11.18 955.96 +2000 01 05 12 32832.00 0.40 252.66 0.00 5.73 5.73 551.57 +2000 01 06 12 33177.60 0.16 203.87 0.00 -1.66 -1.66 339.35 +2000 01 07 12 33177.60 0.05 255.42 0.00 -1.85 -1.85 345.07 +2000 01 08 12 33177.60 0.00 227.84 0.00 -0.31 -0.31 392.21 +2000 01 09 12 33177.60 1.02 189.98 0.00 -0.81 -0.81 404.10 +2000 01 10 12 33177.60 5.82 175.30 0.00 2.28 2.28 513.77 +2000 01 11 12 33499.54 1.55 181.68 0.00 3.68 3.68 579.96 +2000 01 12 12 33523.20 0.84 164.77 0.00 3.18 3.18 554.92 +2000 01 13 12 33523.20 0.92 156.89 0.00 1.43 1.43 461.76 +2000 01 14 12 33523.20 0.19 265.23 0.00 -6.29 -6.29 227.78 +2000 01 15 12 33599.62 0.00 235.71 0.00 -8.55 -8.55 193.42 +2000 01 16 12 33868.80 0.00 258.20 0.00 -7.51 -7.51 203.13 +2000 01 17 12 33868.80 0.00 284.07 0.00 -6.25 -6.25 166.13 +2000 01 18 12 33868.80 0.01 221.78 0.00 -10.81 -10.81 141.22 +2000 01 19 12 34190.73 2.08 221.23 0.00 -12.05 -12.05 156.19 +2000 01 20 12 34214.40 3.52 231.68 0.00 -7.26 -7.26 211.58 +2000 01 21 12 34214.40 1.15 253.52 0.00 -9.45 -9.45 154.50 +2000 01 22 12 34214.40 0.52 228.54 0.00 -13.07 -13.07 122.87 +2000 01 23 12 34560.00 0.11 321.24 0.00 -12.69 -12.69 136.60 +2000 01 24 12 34560.00 0.00 234.64 0.00 -7.87 -7.87 223.60 +2000 01 25 12 34560.00 2.53 248.35 0.00 -5.97 -5.97 245.73 +2000 01 26 12 34905.60 0.67 164.91 0.00 -6.68 -6.68 248.35 +2000 01 27 12 34905.60 0.01 298.48 0.00 -9.74 -9.74 170.36 +2000 01 28 12 34905.60 0.00 279.61 0.00 -10.97 -10.97 152.56 +2000 01 29 12 35251.20 0.00 342.86 0.00 -11.32 -11.32 138.70 +2000 01 30 12 35251.20 6.39 280.46 0.00 -9.21 -9.21 157.12 +2000 01 31 12 35327.62 1.68 281.27 0.00 -4.90 -4.90 267.90 +2000 02 01 12 35596.80 0.57 155.13 0.00 -4.02 -4.02 328.30 +2000 02 02 12 35596.80 0.15 276.62 0.00 -5.26 -5.26 277.15 +2000 02 03 12 35942.40 2.19 257.40 0.00 -7.41 -7.41 221.52 +2000 02 04 12 35942.40 6.10 226.20 0.00 -6.32 -6.32 255.28 +2000 02 05 12 35942.40 1.63 272.17 0.00 -3.86 -3.86 288.35 +2000 02 06 12 36288.00 0.05 325.82 0.00 -4.57 -4.57 274.94 +2000 02 07 12 36288.00 0.00 330.39 0.00 -2.99 -2.99 297.20 +2000 02 08 12 36633.60 0.00 388.33 0.00 -6.55 -6.55 182.97 +2000 02 09 12 36633.60 0.00 390.67 0.00 -7.21 -7.21 170.14 +2000 02 10 12 36633.60 1.02 308.47 0.00 -3.70 -3.70 242.03 +2000 02 11 12 36979.20 0.28 334.65 0.00 1.23 1.23 360.36 +2000 02 12 12 36979.20 0.00 394.04 0.00 -2.44 -2.44 265.26 +2000 02 13 12 37324.80 15.90 275.24 0.00 -5.12 -5.12 245.49 +2000 02 14 12 37324.80 8.80 263.36 0.00 -4.71 -4.71 295.82 +2000 02 15 12 37646.73 2.35 245.80 0.00 -1.52 -1.52 380.19 +2000 02 16 12 37670.40 0.30 344.53 0.00 -1.72 -1.72 357.74 +2000 02 17 12 37670.40 0.55 324.59 0.00 -0.36 -0.36 339.96 +2000 02 18 12 38016.00 9.11 271.20 0.00 -2.21 -2.21 332.74 +2000 02 19 12 38016.00 3.52 189.80 0.00 -2.50 -2.50 381.46 +2000 02 20 12 38361.60 0.77 196.75 0.00 -2.37 -2.37 393.90 +2000 02 21 12 38361.60 0.12 292.70 0.00 -2.01 -2.01 381.55 +2000 02 22 12 38707.20 0.00 418.51 0.00 -1.24 -1.24 350.69 +2000 02 23 12 38707.20 0.00 439.57 0.00 1.45 1.45 401.86 +2000 02 24 12 39052.80 3.73 331.76 0.00 5.86 5.86 555.72 +2000 02 25 12 39052.80 1.10 339.54 0.00 7.30 7.30 594.72 +2000 02 26 12 39052.80 0.03 423.67 0.00 8.55 8.55 654.37 +2000 02 27 12 39398.40 13.61 178.53 0.00 6.18 6.18 686.13 +2000 02 28 12 39398.40 3.58 436.35 0.00 6.07 6.07 570.38 +2000 02 29 12 39744.00 3.63 395.03 0.00 4.67 4.67 497.04 +2000 03 01 12 39744.00 3.68 357.50 0.00 3.26 3.26 423.66 +2000 03 02 12 40089.60 0.97 320.14 0.00 3.38 3.38 469.62 +2000 03 03 12 40089.60 0.01 345.35 0.00 0.89 0.89 425.25 +2000 03 04 12 40435.20 0.00 457.70 0.00 0.76 0.76 373.79 +2000 03 05 12 40435.20 0.00 475.25 0.00 3.08 3.08 392.41 +2000 03 06 12 40780.80 0.00 473.52 0.00 4.77 4.77 428.97 +2000 03 07 12 40780.80 0.00 478.78 0.00 5.07 5.07 445.22 +2000 03 08 12 41126.40 0.00 484.85 0.00 8.74 8.74 559.13 +2000 03 09 12 41126.40 0.86 363.50 0.00 15.03 15.03 852.87 +2000 03 10 12 41472.00 0.23 469.39 0.00 14.51 14.51 797.47 +2000 03 11 12 41472.00 12.59 251.97 0.00 5.54 5.54 594.93 +2000 03 12 12 41817.60 3.41 194.70 0.00 2.94 2.94 553.43 +2000 03 13 12 41817.60 0.03 415.35 0.00 -1.39 -1.39 359.68 +2000 03 14 12 42163.20 0.00 483.79 0.00 0.57 0.57 358.26 +2000 03 15 12 42163.20 0.00 491.53 0.00 4.41 4.41 451.07 +2000 03 16 12 42508.80 14.39 385.29 0.00 8.30 8.30 533.66 +2000 03 17 12 42508.80 3.79 503.88 0.00 6.50 6.50 431.48 +2000 03 18 12 42854.40 0.00 436.35 0.00 -2.65 -2.65 270.63 +2000 03 19 12 42854.40 0.00 496.09 0.00 -2.41 -2.41 296.31 +2000 03 20 12 43200.00 0.99 311.42 0.00 1.36 1.36 454.84 +2000 03 21 12 43200.00 16.50 288.91 0.00 5.02 5.02 617.72 +2000 03 22 12 43200.00 4.27 182.93 0.00 4.11 4.11 650.57 +2000 03 23 12 43545.60 0.00 405.12 0.00 5.87 5.87 643.03 +2000 03 24 12 43545.60 0.20 380.31 0.00 6.73 6.73 599.79 +2000 03 25 12 43891.20 1.44 396.79 0.00 8.94 8.94 628.60 +2000 03 26 12 43891.20 0.37 521.96 0.00 11.85 11.85 655.05 +2000 03 27 12 44236.80 1.63 401.68 0.00 6.85 6.85 502.38 +2000 03 28 12 44236.80 1.54 385.62 0.00 6.19 6.19 538.79 +2000 03 29 12 44582.40 0.51 346.12 0.00 6.24 6.24 600.65 +2000 03 30 12 44582.40 0.06 231.41 0.00 4.29 4.29 579.57 +2000 03 31 12 44928.00 0.00 495.82 0.00 2.03 2.03 405.87 +2000 04 01 12 44928.00 0.00 518.65 0.00 4.82 4.82 440.68 +2000 04 02 12 45273.60 3.51 386.89 0.00 8.65 8.65 607.83 +2000 04 03 12 45273.60 21.27 258.76 0.00 10.68 10.68 899.30 +2000 04 04 12 45619.20 5.79 315.08 0.00 14.18 14.18 1005.23 +2000 04 05 12 45619.20 0.17 370.27 0.00 6.21 6.21 563.80 +2000 04 06 12 45964.80 0.29 329.91 0.00 3.99 3.99 533.62 +2000 04 07 12 45964.80 0.72 394.81 0.00 8.77 8.77 626.66 +2000 04 08 12 46310.40 13.61 326.42 0.00 7.30 7.30 606.00 +2000 04 09 12 46310.40 4.00 402.70 0.00 4.81 4.81 464.87 +2000 04 10 12 46656.00 0.12 460.98 0.00 3.74 3.74 478.94 +2000 04 11 12 46656.00 0.53 353.62 0.00 4.73 4.73 550.68 +2000 04 12 12 47001.60 0.14 406.32 0.00 4.86 4.86 537.18 +2000 04 13 12 47001.60 0.00 507.03 0.00 1.46 1.46 397.69 +2000 04 14 12 47347.20 0.00 525.87 0.00 3.47 3.47 457.18 +2000 04 15 12 47347.20 0.00 539.79 0.00 9.03 9.03 654.64 +2000 04 16 12 47347.20 7.40 400.91 0.00 14.27 14.27 870.10 +2000 04 17 12 47692.80 16.07 402.58 0.00 14.77 14.77 846.43 +2000 04 18 12 47692.80 14.42 192.86 0.00 7.36 7.36 725.26 +2000 04 19 12 48038.40 2.82 190.88 0.00 4.80 4.80 730.54 +2000 04 20 12 48038.40 7.89 351.06 0.00 9.33 9.33 852.41 +2000 04 21 12 48384.00 14.68 295.07 0.00 11.55 11.55 953.91 +2000 04 22 12 48384.00 12.79 171.26 0.00 8.99 8.99 883.25 +2000 04 23 12 48729.60 2.76 248.78 0.00 6.52 6.52 730.07 +2000 04 24 12 48729.60 0.07 453.95 0.00 5.43 5.43 628.47 +2000 04 25 12 48753.27 0.00 560.20 0.00 9.33 9.33 646.71 +2000 04 26 12 49075.20 0.00 557.67 0.00 8.70 8.70 607.49 +2000 04 27 12 49075.20 1.19 399.04 0.00 7.77 7.77 658.74 +2000 04 28 12 49420.80 0.31 469.38 0.00 8.70 8.70 720.77 +2000 04 29 12 49420.80 0.00 553.80 0.00 9.40 9.40 694.86 +2000 04 30 12 49766.40 0.00 552.52 0.00 11.69 11.69 713.13 +2000 05 01 12 49766.40 3.16 429.32 0.00 9.82 9.82 640.93 +2000 05 02 12 49766.40 0.83 570.80 0.00 12.07 12.07 699.80 +2000 05 03 12 50112.00 0.00 572.34 0.00 11.36 11.36 682.12 +2000 05 04 12 50112.00 1.96 428.01 0.00 13.17 13.17 815.06 +2000 05 05 12 50457.60 0.52 546.28 0.00 17.05 17.05 1079.86 +2000 05 06 12 50457.60 0.00 532.52 0.00 20.18 20.18 1321.76 +2000 05 07 12 50457.60 0.14 407.21 0.00 21.07 21.07 1419.00 +2000 05 08 12 50803.20 0.04 530.91 0.00 22.17 22.17 1454.33 +2000 05 09 12 50803.20 0.00 518.26 0.00 21.85 21.85 1491.26 +2000 05 10 12 51072.38 3.22 391.42 0.00 22.40 22.40 1499.29 +2000 05 11 12 51148.80 0.93 414.05 0.00 18.62 18.62 1154.53 +2000 05 12 12 51148.80 0.19 405.73 0.00 16.82 16.82 1125.92 +2000 05 13 12 51494.40 3.71 387.13 0.00 18.71 18.71 1224.89 +2000 05 14 12 51494.40 0.96 571.34 0.00 15.97 15.97 865.03 +2000 05 15 12 51494.40 0.00 571.72 0.00 10.98 10.98 637.47 +2000 05 16 12 51840.00 0.00 575.16 0.00 8.88 8.88 581.85 +2000 05 17 12 51840.00 0.00 578.07 0.00 10.50 10.50 693.25 +2000 05 18 12 51840.00 12.46 416.10 0.00 14.69 14.69 984.19 +2000 05 19 12 52185.60 6.00 402.17 0.00 18.33 18.33 1149.55 +2000 05 20 12 52185.60 4.53 187.55 0.00 10.95 10.95 975.37 +2000 05 21 12 52185.60 1.04 208.05 0.00 10.41 10.41 1024.51 +2000 05 22 12 52209.27 8.28 279.55 0.00 12.42 12.42 1064.23 +2000 05 23 12 52531.20 19.18 257.10 0.00 12.05 12.05 1074.38 +2000 05 24 12 52531.20 8.58 275.50 0.00 14.04 14.04 1191.03 +2000 05 25 12 52531.20 1.08 500.38 0.00 16.68 16.68 1148.21 +2000 05 26 12 52800.38 0.00 541.28 0.00 14.50 14.50 958.72 +2000 05 27 12 52876.80 0.82 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a/test/test_data/camels_us/basin_mean_forcing/maurer/02064000_lump_maurer_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/maurer/02064000_lump_maurer_forcing_leap.txt new file mode 100644 index 00000000..6cf9dd8f --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/maurer/02064000_lump_maurer_forcing_leap.txt @@ -0,0 +1,1100 @@ + 37.24 + 226.00 + 427165365 +Year Mnth Day Hr Dayl(s) PRCP(mm/day) SRAD(W/m2) SWE(mm) Tmax(C) Tmin(C) Vp(Pa) +2000 01 01 12 34214.40 0.00 282.52 0.00 7.12 7.12 604.96 +2000 01 02 12 34214.40 0.00 249.09 0.00 10.22 10.22 862.71 +2000 01 03 12 34214.40 3.14 177.90 0.00 14.12 14.12 1140.08 +2000 01 04 12 34214.40 13.35 175.67 0.00 14.24 14.24 1089.61 +2000 01 05 12 34214.40 3.30 285.34 0.00 8.13 8.13 657.59 +2000 01 06 12 34276.91 0.00 278.13 0.00 3.08 3.08 459.96 +2000 01 07 12 34557.21 0.00 292.58 0.00 3.16 3.16 448.89 +2000 01 08 12 34560.00 0.00 293.47 0.00 3.24 3.24 447.93 +2000 01 09 12 34560.00 11.75 200.97 0.00 3.50 3.50 532.06 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257.69 +2000 01 27 12 35942.40 0.00 366.24 0.00 -6.15 -6.15 214.49 +2000 01 28 12 36091.92 2.41 306.36 0.00 -6.51 -6.51 189.88 +2000 01 29 12 36288.00 5.54 310.37 0.00 -5.49 -5.49 203.35 +2000 01 30 12 36288.00 14.91 270.89 0.00 -3.70 -3.70 290.28 +2000 01 31 12 36292.47 3.58 250.08 0.00 -1.48 -1.48 388.36 +2000 02 01 12 36633.60 0.00 359.54 0.00 -2.31 -2.31 340.97 +2000 02 02 12 36633.60 0.00 361.42 0.00 -1.52 -1.52 347.95 +2000 02 03 12 36633.60 0.00 386.58 0.00 -0.11 -0.11 380.98 +2000 02 04 12 36979.20 0.00 361.95 0.00 1.62 1.62 438.34 +2000 02 05 12 36979.20 0.00 359.76 0.00 1.30 1.30 440.68 +2000 02 06 12 36979.20 0.00 340.20 0.00 1.39 1.39 459.95 +2000 02 07 12 37324.80 0.00 400.15 0.00 2.78 2.78 479.01 +2000 02 08 12 37324.80 0.00 388.55 0.00 4.22 4.22 503.93 +2000 02 09 12 37329.27 0.00 431.79 0.00 2.85 2.85 425.42 +2000 02 10 12 37670.40 0.00 422.04 0.00 7.07 7.07 585.53 +2000 02 11 12 37670.40 0.34 321.02 0.00 10.66 10.66 751.88 +2000 02 12 12 37933.29 1.56 324.72 0.00 8.77 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-1.05 -1.05 386.31 +2002 12 11 12 34214.40 5.48 181.77 0.00 0.08 0.08 464.98 +2002 12 12 12 34211.61 0.67 276.57 0.00 0.89 0.89 494.79 +2002 12 13 12 34131.69 13.82 206.27 0.00 3.54 3.54 593.59 +2002 12 14 12 34018.32 3.64 189.85 0.00 2.50 2.50 580.22 +2002 12 15 12 33931.31 0.00 309.62 0.00 2.92 2.92 543.38 +2002 12 16 12 33873.27 0.00 319.17 0.00 4.53 4.53 547.27 +2002 12 17 12 33868.80 0.10 272.49 0.00 3.33 3.33 489.84 +2002 12 18 12 33868.80 0.03 242.05 0.00 1.90 1.90 510.36 +2002 12 19 12 33868.80 8.14 198.11 0.00 2.69 2.69 588.88 +2002 12 20 12 33868.80 3.41 269.51 0.00 6.54 6.54 673.12 +2002 12 21 12 33868.80 0.33 317.55 0.00 7.09 7.09 643.10 +2002 12 22 12 33868.80 0.00 312.89 0.00 6.16 6.16 616.02 +2002 12 23 12 33868.80 1.63 212.71 0.00 8.34 8.34 744.73 +2002 12 24 12 33868.80 25.26 201.65 0.00 5.26 5.26 634.03 +2002 12 25 12 33868.80 7.20 180.15 0.00 2.18 2.18 539.44 +2002 12 26 12 33873.27 0.18 263.27 0.00 0.75 0.75 464.84 +2002 12 27 12 33931.31 0.00 291.76 0.00 0.13 0.13 411.51 +2002 12 28 12 34018.32 0.00 294.50 0.00 0.18 0.18 412.44 +2002 12 29 12 34131.69 0.00 298.69 0.00 2.48 2.48 490.71 +2002 12 30 12 34214.40 0.00 303.70 0.00 4.94 4.94 569.93 +2002 12 31 12 34214.40 3.09 233.34 0.00 6.07 6.07 638.21 diff --git a/test/test_data/camels_us/basin_mean_forcing/maurer/03015500_lump_maurer_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/maurer/03015500_lump_maurer_forcing_leap.txt new file mode 100644 index 00000000..ec464a7c --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/maurer/03015500_lump_maurer_forcing_leap.txt @@ -0,0 +1,1100 @@ + 41.91 + 477.00 + 831030801 +Year Mnth Day Hr Dayl(s) PRCP(mm/day) SRAD(W/m2) SWE(mm) Tmax(C) Tmin(C) Vp(Pa) +2000 01 01 12 32486.40 0.79 214.77 0.00 -2.22 -2.22 367.35 +2000 01 02 12 32486.40 2.99 234.68 0.00 1.63 1.63 457.09 +2000 01 03 12 32486.40 16.37 216.93 0.00 7.36 7.36 700.54 +2000 01 04 12 32486.40 7.30 182.41 0.00 8.98 8.98 803.13 +2000 01 05 12 32613.94 1.31 233.04 0.00 1.93 1.93 442.87 +2000 01 06 12 32815.44 0.31 171.84 0.00 -3.15 -3.15 347.26 +2000 01 07 12 32832.00 0.77 174.73 0.00 -1.22 -1.22 423.70 +2000 01 08 12 32832.00 0.20 226.27 0.00 -2.22 -2.22 379.37 +2000 01 09 12 32832.00 0.00 259.60 0.00 -1.04 -1.04 421.69 +2000 01 10 12 32890.35 6.93 163.98 0.00 2.33 2.33 572.96 +2000 01 11 12 33161.05 6.06 179.42 0.00 3.75 3.75 608.04 +2000 01 12 12 33177.60 2.56 146.44 0.00 0.70 0.70 489.76 +2000 01 13 12 33177.60 2.12 141.72 0.00 -1.91 -1.91 387.91 +2000 01 14 12 33179.52 0.92 229.89 0.00 -8.31 -8.31 210.60 +2000 01 15 12 33445.07 0.12 245.64 0.00 -8.49 -8.49 217.61 +2000 01 16 12 33523.20 0.17 246.76 0.00 -5.27 -5.27 242.86 +2000 01 17 12 33523.20 0.04 312.29 0.00 -8.49 -8.49 159.31 +2000 01 18 12 33581.55 1.03 202.93 0.00 -12.41 -12.41 142.67 +2000 01 19 12 33868.80 3.49 219.10 0.00 -10.33 -10.33 194.13 +2000 01 20 12 33868.80 4.77 155.01 0.00 -6.87 -6.87 258.09 +2000 01 21 12 33868.80 4.16 238.21 0.00 -10.07 -10.07 177.02 +2000 01 22 12 34197.84 0.95 232.11 0.00 -13.20 -13.20 135.70 +2000 01 23 12 34214.40 0.25 249.96 0.00 -12.12 -12.12 159.24 +2000 01 24 12 34214.40 0.51 237.60 0.00 -7.80 -7.80 213.56 +2000 01 25 12 34543.44 0.56 256.40 0.00 -11.14 -11.14 150.31 +2000 01 26 12 34560.00 1.62 235.09 0.00 -10.75 -10.75 155.80 +2000 01 27 12 34560.00 0.93 257.54 0.00 -12.73 -12.73 127.26 +2000 01 28 12 34905.60 0.14 291.76 0.00 -13.84 -13.84 121.17 +2000 01 29 12 34905.60 0.06 297.48 0.00 -13.29 -13.29 118.95 +2000 01 30 12 34907.52 1.43 275.93 0.00 -9.41 -9.41 163.74 +2000 01 31 12 35251.20 0.61 227.65 0.00 -3.95 -3.95 291.43 +2000 02 01 12 35251.20 2.15 192.46 0.00 -5.18 -5.18 289.40 +2000 02 02 12 35449.98 1.13 204.79 0.00 -6.84 -6.84 252.36 +2000 02 03 12 35596.80 2.79 208.16 0.00 -8.38 -8.38 236.88 +2000 02 04 12 35596.80 1.36 191.39 0.00 -5.06 -5.06 294.45 +2000 02 05 12 35942.40 0.63 299.38 0.00 -8.02 -8.02 189.69 +2000 02 06 12 35942.40 0.12 362.55 0.00 -7.27 -7.27 195.49 +2000 02 07 12 36209.87 0.06 294.07 0.00 -5.91 -5.91 221.42 +2000 02 08 12 36288.00 0.02 385.02 0.00 -10.85 -10.85 119.27 +2000 02 09 12 36289.92 0.00 389.92 0.00 -10.31 -10.31 138.88 +2000 02 10 12 36633.60 5.69 291.36 0.00 -0.78 -0.78 331.71 +2000 02 11 12 36633.60 1.59 314.25 0.00 0.51 0.51 343.81 +2000 02 12 12 36979.20 0.03 388.75 0.00 -6.81 -6.81 187.31 +2000 02 13 12 36979.20 5.47 297.24 0.00 -7.93 -7.93 193.18 +2000 02 14 12 37246.67 5.94 246.42 0.00 -4.26 -4.26 293.92 +2000 02 15 12 37324.80 1.55 250.69 0.00 -3.67 -3.67 321.17 +2000 02 16 12 37383.15 0.64 262.47 0.00 -2.93 -2.93 327.42 +2000 02 17 12 37670.40 0.21 316.29 0.00 -3.50 -3.50 285.91 +2000 02 18 12 37670.40 4.19 252.44 0.00 -4.33 -4.33 296.56 +2000 02 19 12 38016.00 1.39 140.70 0.00 -3.33 -3.33 370.94 +2000 02 20 12 38016.00 0.66 140.17 0.00 -3.85 -3.85 371.77 +2000 02 21 12 38361.60 0.15 168.90 0.00 -3.71 -3.71 379.60 +2000 02 22 12 38361.60 0.00 370.38 0.00 -0.67 -0.67 428.97 +2000 02 23 12 38707.20 0.00 388.54 0.00 4.51 4.51 562.57 +2000 02 24 12 38707.20 5.24 311.98 0.00 7.45 7.45 672.54 +2000 02 25 12 39052.80 1.38 411.38 0.00 9.01 9.01 701.04 +2000 02 26 12 39052.80 0.00 408.17 0.00 9.29 9.29 718.14 +2000 02 27 12 39398.40 5.30 325.87 0.00 10.22 10.22 722.05 +2000 02 28 12 39398.40 1.40 436.07 0.00 7.47 7.47 540.01 +2000 02 29 12 39727.44 3.48 386.97 0.00 4.97 4.97 490.36 +2000 03 01 12 39744.00 5.57 341.34 0.00 2.47 2.47 440.69 +2000 03 02 12 40011.47 1.56 349.43 0.00 4.22 4.22 487.94 +2000 03 03 12 40089.60 0.03 257.48 0.00 -1.34 -1.34 380.48 +2000 03 04 12 40288.38 0.00 431.74 0.00 -1.07 -1.07 354.80 +2000 03 05 12 40435.20 0.00 452.68 0.00 1.35 1.35 372.55 +2000 03 06 12 40562.74 0.00 455.43 0.00 1.87 1.87 388.71 +2000 03 07 12 40780.80 0.00 466.14 0.00 4.46 4.46 470.94 +2000 03 08 12 40908.34 0.02 414.73 0.00 9.30 9.30 632.30 +2000 03 09 12 41126.40 2.01 356.33 0.00 13.29 13.29 763.90 +2000 03 10 12 41184.75 0.87 399.65 0.00 9.01 9.01 566.26 +2000 03 11 12 41472.00 6.82 195.78 0.00 1.73 1.73 461.72 +2000 03 12 12 41473.92 2.28 227.49 0.00 -0.80 -0.80 417.57 +2000 03 13 12 41817.60 0.34 312.23 0.00 -2.61 -2.61 344.56 +2000 03 14 12 41819.52 0.23 369.00 0.00 -0.72 -0.72 376.43 +2000 03 15 12 42163.20 0.07 459.84 0.00 2.82 2.82 455.58 +2000 03 16 12 42165.12 7.97 373.04 0.00 7.46 7.46 551.67 +2000 03 17 12 42508.80 2.18 377.95 0.00 1.84 1.84 355.13 +2000 03 18 12 42508.80 0.02 460.49 0.00 -4.84 -4.84 234.04 +2000 03 19 12 42854.40 0.00 508.99 0.00 -2.33 -2.33 298.10 +2000 03 20 12 42854.40 0.03 402.87 0.00 4.47 4.47 533.09 +2000 03 21 12 43200.00 0.15 311.78 0.00 7.05 7.05 670.93 +2000 03 22 12 43543.68 0.04 337.70 0.00 6.46 6.46 668.82 +2000 03 23 12 43545.60 0.00 409.21 0.00 7.43 7.43 661.99 +2000 03 24 12 43889.28 1.22 366.02 0.00 7.42 7.42 606.48 +2000 03 25 12 43891.20 0.64 370.03 0.00 10.36 10.36 664.81 +2000 03 26 12 44234.88 0.08 485.83 0.00 10.02 10.02 597.09 +2000 03 27 12 44236.80 2.80 344.29 0.00 5.79 5.79 508.19 +2000 03 28 12 44580.48 4.55 359.61 0.00 4.10 4.10 463.29 +2000 03 29 12 44582.40 2.91 347.69 0.00 3.69 3.69 473.53 +2000 03 30 12 44926.08 0.50 218.09 0.00 2.56 2.56 507.73 +2000 03 31 12 44928.00 0.00 479.09 0.00 0.60 0.60 386.21 +2000 04 01 12 45215.25 0.73 381.21 0.00 3.54 3.54 432.79 +2000 04 02 12 45273.60 6.61 379.61 0.00 7.07 7.07 573.82 +2000 04 03 12 45491.66 21.21 234.19 0.00 9.65 9.65 820.23 +2000 04 04 12 45619.20 9.80 303.88 0.00 9.97 9.97 777.94 +2000 04 05 12 45837.26 1.87 323.01 0.00 3.25 3.25 488.42 +2000 04 06 12 45964.80 0.17 431.19 0.00 2.45 2.45 464.38 +2000 04 07 12 46111.62 16.29 379.42 0.00 5.38 5.38 518.68 +2000 04 08 12 46310.40 21.07 345.91 0.00 4.24 4.24 493.16 +2000 04 09 12 46388.53 5.99 382.70 0.00 1.84 1.84 421.95 +2000 04 10 12 46656.00 0.42 376.23 0.00 0.47 0.47 419.99 +2000 04 11 12 46672.56 1.98 276.48 0.00 1.18 1.18 468.51 +2000 04 12 12 47001.60 0.53 398.07 0.00 1.76 1.76 456.49 +2000 04 13 12 47001.60 0.00 469.08 0.00 1.53 1.53 427.68 +2000 04 14 12 47347.20 0.00 530.47 0.00 4.58 4.58 516.90 +2000 04 15 12 47347.20 0.00 529.42 0.00 12.07 12.07 782.55 +2000 04 16 12 47692.80 0.60 380.62 0.00 14.15 14.15 911.31 +2000 04 17 12 47692.80 1.22 366.72 0.00 12.37 12.37 855.70 +2000 04 18 12 48038.40 0.28 357.08 0.00 9.12 9.12 787.74 +2000 04 19 12 48038.40 0.00 366.63 0.00 8.51 8.51 809.79 +2000 04 20 12 48384.00 20.30 263.89 0.00 10.29 10.29 923.67 +2000 04 21 12 48384.00 9.66 283.42 0.00 10.63 10.63 915.24 +2000 04 22 12 48729.60 1.81 303.26 0.00 8.94 8.94 806.20 +2000 04 23 12 48729.60 0.18 274.64 0.00 6.62 6.62 708.51 +2000 04 24 12 49016.85 0.00 511.06 0.00 5.43 5.43 568.00 +2000 04 25 12 49075.20 0.00 559.41 0.00 7.25 7.25 527.55 +2000 04 26 12 49153.33 0.00 565.32 0.00 6.12 6.12 467.43 +2000 04 27 12 49420.80 0.00 554.08 0.00 5.56 5.56 477.66 +2000 04 28 12 49420.80 0.00 560.46 0.00 6.15 6.15 509.38 +2000 04 29 12 49766.40 0.00 565.72 0.00 8.52 8.52 567.55 +2000 04 30 12 49766.40 0.00 564.76 0.00 9.73 9.73 609.53 +2000 05 01 12 50110.08 9.14 415.37 0.00 9.36 9.36 654.70 +2000 05 02 12 50112.00 2.40 552.27 0.00 10.89 10.89 688.09 +2000 05 03 12 50190.13 0.02 516.65 0.00 8.65 8.65 627.03 +2000 05 04 12 50457.60 0.76 448.82 0.00 11.07 11.07 705.47 +2000 05 05 12 50457.60 0.20 549.53 0.00 13.76 13.76 899.43 +2000 05 06 12 50803.20 0.00 513.46 0.00 18.70 18.70 1257.78 +2000 05 07 12 50803.20 0.90 388.72 0.00 20.11 20.11 1384.44 +2000 05 08 12 50950.02 0.37 410.29 0.00 20.30 20.30 1411.94 +2000 05 09 12 51148.80 1.10 393.30 0.00 20.78 20.78 1486.50 +2000 05 10 12 51148.80 9.14 377.52 0.00 21.30 21.30 1412.78 +2000 05 11 12 51492.48 2.34 490.56 0.00 13.75 13.75 902.06 +2000 05 12 12 51494.40 0.00 537.87 0.00 13.38 13.38 1005.04 +2000 05 13 12 51494.40 2.21 335.75 0.00 20.37 20.37 1453.34 +2000 05 14 12 51840.00 0.62 479.03 0.00 14.30 14.30 877.09 +2000 05 15 12 51840.00 0.01 517.03 0.00 8.88 8.88 629.21 +2000 05 16 12 51856.56 0.00 563.98 0.00 7.57 7.57 574.70 +2000 05 17 12 52185.60 3.40 450.19 0.00 9.34 9.34 685.06 +2000 05 18 12 52185.60 49.29 417.03 0.00 14.71 14.71 963.49 +2000 05 19 12 52202.16 14.45 392.90 0.00 16.25 16.25 1119.14 +2000 05 20 12 52531.20 1.07 139.52 0.00 11.47 11.47 1045.39 +2000 05 21 12 52531.20 0.55 178.04 0.00 9.84 9.84 972.44 +2000 05 22 12 52531.20 0.10 369.14 0.00 9.50 9.50 888.60 +2000 05 23 12 52818.45 5.98 375.28 0.00 12.43 12.43 996.32 +2000 05 24 12 52876.80 6.07 240.36 0.00 13.52 13.52 1142.76 +2000 05 25 12 52876.80 1.23 448.45 0.00 15.06 15.06 1043.78 +2000 05 26 12 52954.93 0.01 526.83 0.00 12.00 12.00 854.23 +2000 05 27 12 53220.48 0.02 467.91 0.00 12.50 12.50 894.38 +2000 05 28 12 53222.40 0.01 522.35 0.00 11.85 11.85 844.24 +2000 05 29 12 53222.40 0.00 459.34 0.00 11.02 11.02 869.48 +2000 05 30 12 53238.95 0.00 518.55 0.00 13.05 13.05 971.29 +2000 05 31 12 53509.65 7.51 377.65 0.00 15.20 15.20 1156.30 +2000 06 01 12 53568.00 2.59 417.60 0.00 18.78 18.78 1400.75 +2000 06 02 12 53568.00 12.46 355.21 0.00 21.29 21.29 1541.58 +2000 06 03 12 53568.00 3.24 553.59 0.00 16.84 16.84 1061.41 +2000 06 04 12 53584.56 0.01 481.14 0.00 12.87 12.87 909.60 +2000 06 05 12 53786.06 13.73 373.57 0.00 13.09 13.09 1012.55 +2000 06 06 12 53913.60 5.12 186.16 0.00 11.97 11.97 1053.51 +2000 06 07 12 53913.60 0.40 463.23 0.00 10.09 10.09 884.19 +2000 06 08 12 53913.60 0.00 565.76 0.00 14.39 14.39 1036.86 +2000 06 09 12 53913.60 0.00 508.92 0.00 18.68 18.68 1398.29 +2000 06 10 12 53913.60 0.48 384.85 0.00 21.39 21.39 1659.12 +2000 06 11 12 53930.16 15.37 380.54 0.00 22.69 22.69 1770.73 +2000 06 12 12 54060.42 7.23 346.97 0.00 22.26 22.26 1803.69 +2000 06 13 12 54131.66 3.37 259.35 0.00 19.77 19.77 1769.92 +2000 06 14 12 54200.85 5.88 320.36 0.00 21.87 21.87 1938.82 +2000 06 15 12 54257.28 4.64 354.11 0.00 23.86 23.86 1998.45 +2000 06 16 12 54259.20 9.20 334.17 0.00 22.24 22.24 1871.68 +2000 06 17 12 54259.20 2.96 336.78 0.00 21.73 21.73 1759.28 +2000 06 18 12 54259.20 11.40 364.03 0.00 17.90 17.90 1333.29 +2000 06 19 12 54259.20 2.95 440.12 0.00 12.48 12.48 1040.51 +2000 06 20 12 54259.20 1.78 414.97 0.00 15.80 15.80 1211.10 +2000 06 21 12 54259.20 11.88 399.48 0.00 19.76 19.76 1482.08 +2000 06 22 12 54259.20 3.00 463.61 0.00 20.38 20.38 1539.57 +2000 06 23 12 54259.20 0.00 532.82 0.00 18.14 18.14 1319.85 +2000 06 24 12 54259.20 15.44 396.83 0.00 18.43 18.43 1389.05 +2000 06 25 12 54257.28 8.46 396.62 0.00 20.15 20.15 1558.60 +2000 06 26 12 54257.28 10.15 295.52 0.00 20.73 20.73 1771.71 +2000 06 27 12 54131.66 2.43 420.70 0.00 21.41 21.41 1663.72 +2000 06 28 12 54060.42 0.07 424.67 0.00 17.90 17.90 1324.15 +2000 06 29 12 53991.73 1.83 366.45 0.00 16.95 16.95 1280.35 +2000 06 30 12 53913.60 0.48 505.24 0.00 15.41 15.41 1143.52 +2000 07 01 12 53913.60 0.00 537.57 0.00 15.94 15.94 1165.71 +2000 07 02 12 53913.60 2.16 414.66 0.00 17.73 17.73 1289.83 +2000 07 03 12 53913.60 12.43 390.83 0.00 19.48 19.48 1506.33 +2000 07 04 12 53913.60 4.23 242.78 0.00 19.41 19.41 1644.91 +2000 07 05 12 53855.25 0.29 532.51 0.00 18.13 18.13 1317.51 +2000 07 06 12 53714.82 0.03 473.95 0.00 17.43 17.43 1192.40 +2000 07 07 12 53568.00 0.01 528.83 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32140.80 6.78 106.51 0.00 -3.18 -3.18 402.95 +2002 12 26 12 32142.72 1.38 134.68 0.00 -3.41 -3.41 387.27 +2002 12 27 12 32199.15 0.00 203.69 0.00 -4.83 -4.83 322.93 +2002 12 28 12 32268.34 0.75 185.80 0.00 -6.29 -6.29 280.49 +2002 12 29 12 32339.58 0.20 248.91 0.00 -4.64 -4.64 310.97 +2002 12 30 12 32469.84 3.52 210.00 0.00 -3.01 -3.01 350.34 +2002 12 31 12 32486.40 7.92 213.40 0.00 -0.06 -0.06 435.16 diff --git a/test/test_data/camels_us/basin_mean_forcing/maurer_extended/01022500_lump_maurer_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/maurer_extended/01022500_lump_maurer_forcing_leap.txt new file mode 100644 index 00000000..04edd705 --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/maurer_extended/01022500_lump_maurer_forcing_leap.txt @@ -0,0 +1,1100 @@ + 44.82 + 133.00 + 587675987 +Year Mnth Day Hr dayl(s) prcp(mm/day) srad(W/m2) swe(mm) tmax(C) tmin(C) vp(Pa) +2000 1 1 12 31185.94 0.0 233.66 0.0 -2.09 -14.99 192.89 +2000 1 2 12 31302.07 4.21 198.52 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32832.0 0.26 235.67 0.0 -10.0 -24.47 79.88 +2000 1 21 12 32841.88 6.93 230.21 0.0 -7.76 -20.69 103.2 +2000 1 22 12 33155.2 3.63 224.38 0.0 -10.41 -19.78 105.37 +2000 1 23 12 33177.6 0.68 229.28 0.0 -11.32 -23.31 86.5 +2000 1 24 12 33324.25 0.17 245.16 0.0 -5.51 -21.78 113.8 +2000 1 25 12 33523.2 14.59 235.82 0.0 -1.12 -13.31 214.54 +2000 1 26 12 33523.2 5.16 243.81 0.0 4.93 -8.86 300.25 +2000 1 27 12 33753.19 0.52 243.04 0.0 2.91 -9.38 268.92 +2000 1 28 12 33868.8 0.04 304.84 0.0 -4.17 -19.47 134.23 +2000 1 29 12 33950.74 0.0 298.35 0.0 -7.39 -18.8 129.21 +2000 1 30 12 34214.4 0.0 326.22 0.0 0.38 -16.43 166.16 +2000 1 31 12 34214.4 9.31 268.41 0.0 4.18 -13.87 231.93 +2000 2 1 12 34481.29 2.45 287.39 0.0 4.05 -5.94 344.9 +2000 2 2 12 34560.0 0.0 324.72 0.0 1.64 -12.03 233.47 +2000 2 3 12 34758.07 0.0 299.7 0.0 -4.12 -16.56 153.49 +2000 2 4 12 34905.6 0.06 270.4 0.0 -5.21 -19.09 122.24 +2000 2 5 12 34987.54 0.02 350.17 0.0 -4.5 -21.57 109.34 +2000 2 6 12 35251.2 0.0 346.97 0.0 -2.12 -18.02 139.42 +2000 2 7 12 35251.2 0.0 346.59 0.0 -1.67 -16.36 151.18 +2000 2 8 12 35596.8 0.0 364.06 0.0 1.12 -20.5 125.73 +2000 2 9 12 35596.8 0.0 355.12 0.0 -1.33 -16.96 164.45 +2000 2 10 12 35942.4 0.92 280.83 0.0 2.39 -11.44 241.35 +2000 2 11 12 35942.4 10.13 251.92 0.0 -0.26 -10.1 250.42 +2000 2 12 12 36288.0 2.6 376.05 0.0 2.73 -16.46 163.61 +2000 2 13 12 36288.0 1.48 303.54 0.0 -3.05 -21.71 117.93 +2000 2 14 12 36633.6 31.57 313.87 0.0 2.39 -16.69 187.56 +2000 2 15 12 36633.6 14.5 317.02 0.0 5.45 -7.26 317.35 +2000 2 16 12 36979.2 6.29 295.52 0.0 1.03 -11.52 244.74 +2000 2 17 12 36979.2 1.27 343.52 0.0 1.05 -14.51 179.33 +2000 2 18 12 37324.8 5.91 320.86 0.0 -6.73 -23.55 97.99 +2000 2 19 12 37324.8 3.99 324.7 0.0 -4.19 -19.87 126.85 +2000 2 20 12 37670.4 0.73 302.73 0.0 -1.76 -14.33 183.42 +2000 2 21 12 37670.4 0.02 403.57 0.0 -0.9 -15.53 167.57 +2000 2 22 12 38016.0 0.06 340.53 0.0 -1.55 -19.23 136.71 +2000 2 23 12 38016.0 0.16 346.86 0.0 3.94 -17.29 216.8 +2000 2 24 12 38361.6 0.04 348.0 0.0 8.75 -1.22 504.48 +2000 2 25 12 38361.6 0.0 419.43 0.0 11.59 -4.76 436.19 +2000 2 26 12 38707.2 0.06 315.02 0.0 5.47 -7.8 363.6 +2000 2 27 12 38707.2 0.08 303.45 0.0 7.07 -4.07 484.63 +2000 2 28 12 39052.8 5.21 228.89 0.0 9.04 2.59 682.16 +2000 2 29 12 39062.68 2.71 222.14 0.0 9.38 0.05 630.19 +2000 3 1 12 39398.4 0.22 213.62 0.0 6.26 -0.48 578.2 +2000 3 2 12 39628.39 6.55 247.41 0.0 6.45 -0.49 568.64 +2000 3 3 12 39744.0 8.88 167.55 0.0 4.35 -1.64 518.73 +2000 3 4 12 40089.6 2.58 190.48 0.0 2.58 -1.66 506.43 +2000 3 5 12 40089.6 0.45 267.88 0.0 4.24 -3.19 472.04 +2000 3 6 12 40435.2 0.07 331.55 0.0 4.47 -2.8 456.46 +2000 3 7 12 40435.2 0.0 406.97 0.0 4.69 -5.85 390.13 +2000 3 8 12 40780.8 0.0 450.61 0.0 9.18 -5.08 427.03 +2000 3 9 12 40780.8 3.3 319.48 0.0 7.61 -2.28 484.59 +2000 3 10 12 41126.4 1.21 348.83 0.0 7.34 -4.86 409.25 +2000 3 11 12 41126.4 9.16 284.76 0.0 2.35 -6.83 358.79 +2000 3 12 12 41472.0 21.46 241.45 0.0 1.42 -4.0 402.53 +2000 3 13 12 41817.6 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b/test/test_data/camels_us/basin_mean_forcing/maurer_extended/01547700_lump_maurer_forcing_leap.txt new file mode 100644 index 00000000..e9c88c2f --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/maurer_extended/01547700_lump_maurer_forcing_leap.txt @@ -0,0 +1,1100 @@ + 40.98 + 383.00 + 114169652 +Year Mnth Day Hr dayl(s) prcp(mm/day) srad(W/m2) swe(mm) tmax(C) tmin(C) vp(Pa) +2000 1 1 12 32832.0 0.48 224.65 0.0 7.39 -7.63 356.45 +2000 1 2 12 32832.0 0.77 224.57 0.0 7.5 -6.94 396.03 +2000 1 3 12 32832.0 6.45 195.57 0.0 12.28 -0.45 649.88 +2000 1 4 12 32832.0 3.18 132.94 0.0 15.46 8.27 955.96 +2000 1 5 12 32832.0 0.4 252.66 0.0 14.23 -2.91 551.57 +2000 1 6 12 33177.6 0.16 203.87 0.0 7.83 -8.16 339.35 +2000 1 7 12 33177.6 0.05 255.42 0.0 4.64 -7.35 345.07 +2000 1 8 12 33177.6 0.0 227.84 0.0 4.45 -5.1 392.21 +2000 1 9 12 33177.6 1.02 189.98 0.0 3.83 -5.76 404.1 +2000 1 10 12 33177.6 5.82 175.3 0.0 7.9 -2.25 513.77 +2000 1 11 12 33499.54 1.55 181.68 0.0 7.59 -0.3 579.96 +2000 1 12 12 33523.2 0.84 164.77 0.0 7.5 -1.11 554.92 +2000 1 13 12 33523.2 0.92 156.89 0.0 5.2 -2.55 461.76 +2000 1 14 12 33523.2 0.19 265.23 0.0 -0.16 -13.62 227.78 +2000 1 15 12 33599.62 0.0 235.71 0.0 -4.25 -13.69 193.42 +2000 1 16 12 33868.8 0.0 258.2 0.0 -1.19 -12.7 203.13 +2000 1 17 12 33868.8 0.0 284.07 0.0 6.31 -16.6 166.13 +2000 1 18 12 33868.8 0.01 221.78 0.0 -0.44 -17.31 141.22 +2000 1 19 12 34190.73 2.08 221.23 0.0 -7.19 -16.68 156.19 +2000 1 20 12 34214.4 3.52 231.68 0.0 -0.88 -11.61 211.58 +2000 1 21 12 34214.4 1.15 253.52 0.0 -2.33 -16.91 154.5 +2000 1 22 12 34214.4 0.52 228.54 0.0 -7.07 -18.91 122.87 +2000 1 23 12 34560.0 0.11 321.24 0.0 -5.88 -18.85 136.6 +2000 1 24 12 34560.0 0.0 234.64 0.0 -4.47 -10.85 223.6 +2000 1 25 12 34560.0 2.53 248.35 0.0 0.52 -11.07 245.73 +2000 1 26 12 34905.6 0.67 164.91 0.0 -2.26 -9.3 248.35 +2000 1 27 12 34905.6 0.01 298.48 0.0 -2.65 -15.9 170.36 +2000 1 28 12 34905.6 0.0 279.61 0.0 -5.69 -15.76 152.56 +2000 1 29 12 35251.2 0.0 342.86 0.0 -3.59 -17.73 138.7 +2000 1 30 12 35251.2 6.39 280.46 0.0 0.78 -18.04 157.12 +2000 1 31 12 35327.62 1.68 281.27 0.0 -1.31 -9.23 267.9 +2000 2 1 12 35596.8 0.57 155.13 0.0 -1.91 -6.26 328.3 +2000 2 2 12 35596.8 0.15 276.62 0.0 -1.52 -8.78 277.15 +2000 2 3 12 35942.4 2.19 257.4 0.0 -2.46 -12.72 221.52 +2000 2 4 12 35942.4 6.1 226.2 0.0 -2.71 -10.05 255.28 +2000 2 5 12 35942.4 1.63 272.17 0.0 2.57 -8.59 288.35 +2000 2 6 12 36288.0 0.05 325.82 0.0 -0.27 -9.68 274.94 +2000 2 7 12 36288.0 0.0 330.39 0.0 2.2 -7.11 297.2 +2000 2 8 12 36633.6 0.0 388.33 0.0 2.91 -15.79 182.97 +2000 2 9 12 36633.6 0.0 390.67 0.0 0.98 -16.1 170.14 +2000 2 10 12 36633.6 1.02 308.47 0.0 7.03 -12.71 242.03 +2000 2 11 12 36979.2 0.28 334.65 0.0 8.11 -5.17 360.36 +2000 2 12 12 36979.2 0.0 394.04 0.0 5.02 -10.87 265.26 +2000 2 13 12 37324.8 15.9 275.24 0.0 1.28 -11.04 245.49 +2000 2 14 12 37324.8 8.8 263.36 0.0 -0.3 -8.96 295.82 +2000 2 15 12 37646.73 2.35 245.8 0.0 2.73 -4.88 380.19 +2000 2 16 12 37670.4 0.3 344.53 0.0 2.68 -6.09 357.74 +2000 2 17 12 37670.4 0.55 324.59 0.0 8.36 -7.51 339.96 +2000 2 18 12 38016.0 9.11 271.2 0.0 4.25 -7.48 332.74 +2000 2 19 12 38016.0 3.52 189.8 0.0 -0.42 -4.95 381.46 +2000 2 20 12 38361.6 0.77 196.75 0.0 0.04 -4.65 393.9 +2000 2 21 12 38361.6 0.12 292.7 0.0 1.15 -4.8 381.55 +2000 2 22 12 38707.2 0.0 418.51 0.0 5.69 -6.93 350.69 +2000 2 23 12 38707.2 0.0 439.57 0.0 10.09 -6.13 401.86 +2000 2 24 12 39052.8 3.73 331.76 0.0 13.46 -0.82 555.72 +2000 2 25 12 39052.8 1.1 339.54 0.0 15.65 -0.52 594.72 +2000 2 26 12 39052.8 0.03 423.67 0.0 15.12 1.81 654.37 +2000 2 27 12 39398.4 13.61 178.53 0.0 11.99 3.05 686.13 +2000 2 28 12 39398.4 3.58 436.35 0.0 14.12 0.1 570.38 +2000 2 29 12 39744.0 3.63 395.03 0.0 9.92 -4.85 497.04 +2000 3 1 12 39744.0 3.68 357.5 0.0 12.2 -5.09 423.66 +2000 3 2 12 40089.6 0.97 320.14 0.0 8.44 -2.38 469.62 +2000 3 3 12 40089.6 0.01 345.35 0.0 5.18 -3.34 425.25 +2000 3 4 12 40435.2 0.0 457.7 0.0 8.6 -5.85 373.79 +2000 3 5 12 40435.2 0.0 475.25 0.0 12.59 -5.36 392.41 +2000 3 6 12 40780.8 0.0 473.52 0.0 13.78 -3.92 428.97 +2000 3 7 12 40780.8 0.0 478.78 0.0 14.12 -3.9 445.22 +2000 3 8 12 41126.4 0.0 484.85 0.0 22.45 -2.89 559.13 +2000 3 9 12 41126.4 0.86 363.5 0.0 25.82 5.18 852.87 +2000 3 10 12 41472.0 0.23 469.39 0.0 23.49 5.02 797.47 +2000 3 11 12 41472.0 12.59 251.97 0.0 17.35 -0.21 594.93 +2000 3 12 12 41817.6 3.41 194.7 0.0 5.29 0.53 553.43 +2000 3 13 12 41817.6 0.03 415.35 0.0 3.11 -6.46 359.68 +2000 3 14 12 42163.2 0.0 483.79 0.0 8.58 -6.0 358.26 +2000 3 15 12 42163.2 0.0 491.53 0.0 12.9 -2.93 451.07 +2000 3 16 12 42508.8 14.39 385.29 0.0 19.56 -1.04 533.66 +2000 3 17 12 42508.8 3.79 503.88 0.0 14.97 -3.05 431.48 +2000 3 18 12 42854.4 0.0 436.35 0.0 8.53 -9.85 270.63 +2000 3 19 12 42854.4 0.0 496.09 0.0 5.4 -9.02 296.31 +2000 3 20 12 43200.0 0.99 311.42 0.0 5.94 -3.2 454.84 +2000 3 21 12 43200.0 16.5 288.91 0.0 9.61 1.45 617.72 +2000 3 22 12 43200.0 4.27 182.93 0.0 7.61 1.91 650.57 +2000 3 23 12 43545.6 0.0 405.12 0.0 11.4 2.17 643.03 +2000 3 24 12 43545.6 0.2 380.31 0.0 14.39 -0.11 599.79 +2000 3 25 12 43891.2 1.44 396.79 0.0 19.03 0.08 628.6 +2000 3 26 12 43891.2 0.37 521.96 0.0 22.38 2.42 655.05 +2000 3 27 12 44236.8 1.63 401.68 0.0 15.73 -3.27 502.38 +2000 3 28 12 44236.8 1.54 385.62 0.0 13.27 -1.35 538.79 +2000 3 29 12 44582.4 0.51 346.12 0.0 11.6 0.46 600.65 +2000 3 30 12 44582.4 0.06 231.41 0.0 8.59 1.32 579.57 +2000 3 31 12 44928.0 0.0 495.82 0.0 10.15 -5.15 405.87 +2000 4 1 12 44928.0 0.0 518.65 0.0 15.11 -4.38 440.68 +2000 4 2 12 45273.6 3.51 386.89 0.0 19.59 -1.46 607.83 +2000 4 3 12 45273.6 21.27 258.76 0.0 15.61 5.94 899.3 +2000 4 4 12 45619.2 5.79 315.08 0.0 20.74 9.64 1005.23 +2000 4 5 12 45619.2 0.17 370.27 0.0 14.13 -1.92 563.8 +2000 4 6 12 45964.8 0.29 329.91 0.0 8.93 -1.78 533.62 +2000 4 7 12 45964.8 0.72 394.81 0.0 20.0 0.17 626.66 +2000 4 8 12 46310.4 13.61 326.42 0.0 14.54 1.26 606.0 +2000 4 9 12 46310.4 4.0 402.7 0.0 14.55 -4.24 464.87 +2000 4 10 12 46656.0 0.12 460.98 0.0 9.58 -2.51 478.94 +2000 4 11 12 46656.0 0.53 353.62 0.0 10.81 -0.83 550.68 +2000 4 12 12 47001.6 0.14 406.32 0.0 9.22 0.31 537.18 +2000 4 13 12 47001.6 0.0 507.03 0.0 7.87 -5.32 397.69 +2000 4 14 12 47347.2 0.0 525.87 0.0 11.4 -3.76 457.18 +2000 4 15 12 47347.2 0.0 539.79 0.0 19.03 0.73 654.64 +2000 4 16 12 47347.2 7.4 400.91 0.0 24.65 5.25 870.1 +2000 4 17 12 47692.8 16.07 402.58 0.0 24.96 4.71 846.43 +2000 4 18 12 47692.8 14.42 192.86 0.0 18.51 2.84 725.26 +2000 4 19 12 48038.4 2.82 190.88 0.0 6.59 2.84 730.54 +2000 4 20 12 48038.4 7.89 351.06 0.0 15.99 4.91 852.41 +2000 4 21 12 48384.0 14.68 295.07 0.0 15.78 7.31 953.91 +2000 4 22 12 48384.0 12.79 171.26 0.0 13.01 6.26 883.25 +2000 4 23 12 48729.6 2.76 248.78 0.0 9.66 3.24 730.07 +2000 4 24 12 48729.6 0.07 453.95 0.0 10.35 0.66 628.47 +2000 4 25 12 48753.27 0.0 560.2 0.0 19.85 1.14 646.71 +2000 4 26 12 49075.2 0.0 557.67 0.0 16.94 -0.27 607.49 +2000 4 27 12 49075.2 1.19 399.04 0.0 14.48 0.77 658.74 +2000 4 28 12 49420.8 0.31 469.38 0.0 13.75 3.5 720.77 +2000 4 29 12 49420.8 0.0 553.8 0.0 18.23 1.74 694.86 +2000 4 30 12 49766.4 0.0 552.52 0.0 21.51 2.81 713.13 +2000 5 1 12 49766.4 3.16 429.32 0.0 19.77 -0.65 640.93 +2000 5 2 12 49766.4 0.83 570.8 0.0 22.77 2.19 699.8 +2000 5 3 12 50112.0 0.0 572.34 0.0 21.59 0.79 682.12 +2000 5 4 12 50112.0 1.96 428.01 0.0 24.47 2.54 815.06 +2000 5 5 12 50457.6 0.52 546.28 0.0 26.31 8.22 1079.86 +2000 5 6 12 50457.6 0.0 532.52 0.0 29.29 11.86 1321.76 +2000 5 7 12 50457.6 0.14 407.21 0.0 30.19 12.17 1419.0 +2000 5 8 12 50803.2 0.04 530.91 0.0 31.46 13.21 1454.33 +2000 5 9 12 50803.2 0.0 518.26 0.0 29.47 13.68 1491.26 +2000 5 10 12 51072.38 3.22 391.42 0.0 31.41 14.02 1499.29 +2000 5 11 12 51148.8 0.93 414.05 0.0 27.82 8.46 1154.53 +2000 5 12 12 51148.8 0.19 405.73 0.0 24.63 8.09 1125.92 +2000 5 13 12 51494.4 3.71 387.13 0.0 26.62 11.46 1224.89 +2000 5 14 12 51494.4 0.96 571.34 0.0 26.81 5.25 865.03 +2000 5 15 12 51494.4 0.0 571.72 0.0 20.98 0.97 637.47 +2000 5 16 12 51840.0 0.0 575.16 0.0 17.81 -0.58 581.85 +2000 5 17 12 51840.0 0.0 578.07 0.0 20.18 0.89 693.25 +2000 5 18 12 51840.0 12.46 416.1 0.0 23.67 6.27 984.19 +2000 5 19 12 52185.6 6.0 402.17 0.0 27.3 10.21 1149.55 +2000 5 20 12 52185.6 4.53 187.55 0.0 20.56 6.87 975.37 +2000 5 21 12 52185.6 1.04 208.05 0.0 12.89 8.12 1024.51 +2000 5 22 12 52209.27 8.28 279.55 0.0 16.84 8.84 1064.23 +2000 5 23 12 52531.2 19.18 257.1 0.0 15.49 8.27 1074.38 +2000 5 24 12 52531.2 8.58 275.5 0.0 18.3 10.34 1191.03 +2000 5 25 12 52531.2 1.08 500.38 0.0 23.91 10.75 1148.21 +2000 5 26 12 52800.38 0.0 541.28 0.0 22.03 6.57 958.72 +2000 5 27 12 52876.8 0.82 397.21 0.0 21.64 7.06 981.73 +2000 5 28 12 52876.8 0.22 427.46 0.0 17.15 7.46 970.77 +2000 5 29 12 52876.8 0.0 369.15 0.0 16.02 8.36 983.25 +2000 5 30 12 52900.46 0.0 507.49 0.0 18.66 6.23 910.64 +2000 5 31 12 53222.4 2.32 399.54 0.0 21.4 7.04 1019.89 +2000 6 1 12 53222.4 0.61 535.2 0.0 26.23 10.78 1316.75 +2000 6 2 12 53222.4 4.6 361.18 0.0 29.5 16.63 1652.76 +2000 6 3 12 53222.4 1.21 556.65 0.0 29.46 10.9 1207.19 +2000 6 4 12 53222.4 3.43 416.85 0.0 21.56 5.53 976.69 +2000 6 5 12 53568.0 14.42 362.24 0.0 21.51 10.11 1160.63 +2000 6 6 12 53568.0 4.66 144.6 0.0 17.69 9.92 1114.94 +2000 6 7 12 53568.0 0.29 341.0 0.0 13.54 7.15 987.97 +2000 6 8 12 53568.0 0.0 539.22 0.0 23.61 9.33 1128.11 +2000 6 9 12 53568.0 0.0 528.3 0.0 25.46 11.63 1307.96 +2000 6 10 12 53568.0 0.0 537.83 0.0 29.68 13.92 1522.88 +2000 6 11 12 53568.0 10.57 395.45 0.0 31.41 15.68 1769.18 +2000 6 12 12 53591.66 6.04 375.44 0.0 31.51 17.71 1945.17 +2000 6 13 12 53837.18 23.8 246.98 0.0 27.09 18.1 1917.02 +2000 6 14 12 53913.6 6.23 241.93 0.0 22.6 16.5 1785.46 +2000 6 15 12 53913.6 6.48 206.04 0.0 21.44 16.53 1793.77 +2000 6 16 12 53913.6 4.89 302.26 0.0 25.44 17.46 1917.09 +2000 6 17 12 53913.6 4.29 350.63 0.0 29.68 18.92 1997.85 +2000 6 18 12 53913.6 5.13 327.21 0.0 25.18 16.04 1654.66 +2000 6 19 12 53913.6 1.11 448.51 0.0 20.46 11.89 1302.32 +2000 6 20 12 53913.6 0.0 524.66 0.0 23.59 11.44 1281.08 +2000 6 21 12 53913.6 20.01 412.62 0.0 27.87 12.03 1460.07 +2000 6 22 12 53913.6 5.26 488.9 0.0 28.18 16.65 1670.66 +2000 6 23 12 53913.6 0.0 532.2 0.0 27.21 13.22 1456.93 +2000 6 24 12 53913.6 0.0 529.15 0.0 26.91 13.25 1466.61 +2000 6 25 12 53913.6 0.37 398.92 0.0 28.71 14.03 1694.64 +2000 6 26 12 53913.6 5.51 325.18 0.0 29.05 19.5 2133.12 +2000 6 27 12 53913.6 9.42 286.05 0.0 27.7 19.94 2032.73 +2000 6 28 12 53591.66 2.1 507.04 0.0 24.38 12.76 1462.71 +2000 6 29 12 53568.0 1.3 373.57 0.0 25.03 13.72 1463.13 +2000 6 30 12 53568.0 0.34 523.76 0.0 23.97 11.56 1283.36 +2000 7 1 12 53568.0 0.0 548.78 0.0 24.72 10.32 1232.28 +2000 7 2 12 53568.0 0.0 546.65 0.0 26.69 11.72 1354.32 +2000 7 3 12 53568.0 4.38 399.09 0.0 27.63 13.52 1555.89 +2000 7 4 12 53568.0 1.16 287.79 0.0 23.99 16.62 1697.25 +2000 7 5 12 53568.0 0.0 524.79 0.0 27.83 14.34 1502.15 +2000 7 6 12 53491.57 0.01 485.01 0.0 27.8 11.43 1321.57 +2000 7 7 12 53222.4 0.0 539.06 0.0 24.92 11.05 1200.26 +2000 7 8 12 53222.4 0.0 561.88 0.0 23.42 7.86 1057.43 +2000 7 9 12 53222.4 4.55 414.24 0.0 24.54 9.32 1192.63 +2000 7 10 12 53222.4 1.2 534.08 0.0 26.69 12.45 1395.8 +2000 7 11 12 53222.4 0.0 505.84 0.0 27.74 15.16 1493.09 +2000 7 12 12 52876.8 0.0 560.02 0.0 26.48 9.83 1216.29 +2000 7 13 12 52876.8 0.09 477.26 0.0 26.15 11.06 1309.57 +2000 7 14 12 52876.8 1.69 368.05 0.0 24.79 13.26 1507.77 +2000 7 15 12 52876.8 11.44 360.94 0.0 27.49 15.7 1683.02 +2000 7 16 12 52531.2 9.23 308.45 0.0 23.54 14.99 1618.17 +2000 7 17 12 52531.2 1.67 452.5 0.0 25.08 15.04 1587.39 +2000 7 18 12 52531.2 0.0 443.91 0.0 25.46 15.61 1576.98 +2000 7 19 12 52531.2 0.72 391.75 0.0 26.79 12.52 1421.78 +2000 7 20 12 52185.6 0.19 339.38 0.0 22.24 12.99 1358.27 +2000 7 21 12 52185.6 2.25 393.9 0.0 25.07 11.26 1287.37 +2000 7 22 12 52185.6 0.59 519.93 0.0 25.15 11.75 1232.59 +2000 7 23 12 51840.0 0.0 515.98 0.0 22.52 10.04 1142.66 +2000 7 24 12 51840.0 0.0 523.23 0.0 24.01 10.69 1202.64 +2000 7 25 12 51840.0 0.0 447.02 0.0 22.2 12.64 1333.8 +2000 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0.0 3.45 -7.19 383.69 diff --git a/test/test_data/camels_us/basin_mean_forcing/maurer_extended/02064000_lump_maurer_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/maurer_extended/02064000_lump_maurer_forcing_leap.txt new file mode 100644 index 00000000..9910c9f8 --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/maurer_extended/02064000_lump_maurer_forcing_leap.txt @@ -0,0 +1,1100 @@ + 37.24 + 226.00 + 427165365 +Year Mnth Day Hr dayl(s) prcp(mm/day) srad(W/m2) swe(mm) tmax(C) tmin(C) vp(Pa) +2000 1 1 12 34214.4 0.0 282.52 0.0 14.83 -1.45 604.96 +2000 1 2 12 34214.4 0.0 249.09 0.0 16.17 4.65 862.71 +2000 1 3 12 34214.4 3.14 177.9 0.0 20.1 9.37 1140.08 +2000 1 4 12 34214.4 13.35 175.67 0.0 19.41 9.12 1089.61 +2000 1 5 12 34214.4 3.3 285.34 0.0 15.99 -0.25 657.59 +2000 1 6 12 34276.91 0.0 278.13 0.0 10.78 -4.38 459.96 +2000 1 7 12 34557.21 0.0 292.58 0.0 11.58 -4.22 448.89 +2000 1 8 12 34560.0 0.0 293.47 0.0 10.79 -4.62 447.93 +2000 1 9 12 34560.0 11.75 200.97 0.0 8.82 -2.56 532.06 +2000 1 10 12 34560.0 13.36 156.04 0.0 10.38 2.96 728.01 +2000 1 11 12 34560.0 2.7 279.58 0.0 16.34 3.35 769.94 +2000 1 12 12 34709.52 0.0 289.83 0.0 15.15 0.89 684.48 +2000 1 13 12 34905.6 0.0 296.36 0.0 16.48 0.47 634.71 +2000 1 14 12 34905.6 0.0 310.49 0.0 15.44 -4.88 458.42 +2000 1 15 12 34905.6 0.0 285.4 0.0 9.86 -6.9 375.2 +2000 1 16 12 34905.6 0.0 295.26 0.0 8.93 -4.33 436.09 +2000 1 17 12 35168.49 1.85 234.31 0.0 13.58 -4.17 452.01 +2000 1 18 12 35251.2 3.49 213.75 0.0 8.91 -6.57 360.35 +2000 1 19 12 35251.2 5.81 176.41 0.0 0.28 -7.08 336.89 +2000 1 20 12 35251.2 1.92 272.88 0.0 4.77 -5.8 356.87 +2000 1 21 12 35400.72 0.16 345.29 0.0 2.74 -10.33 264.55 +2000 1 22 12 35596.8 0.46 256.18 0.0 -1.09 -13.28 219.65 +2000 1 23 12 35596.8 0.63 209.16 0.0 -1.35 -8.6 280.57 +2000 1 24 12 35596.8 8.35 246.66 0.0 0.46 -8.97 297.62 +2000 1 25 12 35939.61 6.17 210.38 0.0 0.82 -6.15 335.31 +2000 1 26 12 35942.4 1.05 346.9 0.0 0.59 -10.56 257.69 +2000 1 27 12 35942.4 0.0 366.24 0.0 0.16 -12.53 214.49 +2000 1 28 12 36091.92 2.41 306.36 0.0 2.45 -14.75 189.88 +2000 1 29 12 36288.0 5.54 310.37 0.0 3.86 -14.51 203.35 +2000 1 30 12 36288.0 14.91 270.89 0.0 1.04 -9.32 290.28 +2000 1 31 12 36292.47 3.58 250.08 0.0 1.49 -4.17 388.36 +2000 2 1 12 36633.6 0.0 359.54 0.0 3.28 -7.34 340.97 +2000 2 2 12 36633.6 0.0 361.42 0.0 3.87 -6.75 347.95 +2000 2 3 12 36633.6 0.0 386.58 0.0 6.54 -5.98 380.98 +2000 2 4 12 36979.2 0.0 361.95 0.0 6.94 -3.58 438.34 +2000 2 5 12 36979.2 0.0 359.76 0.0 6.2 -3.86 440.68 +2000 2 6 12 36979.2 0.0 340.2 0.0 5.64 -3.03 459.95 +2000 2 7 12 37324.8 0.0 400.15 0.0 10.09 -3.16 479.01 +2000 2 8 12 37324.8 0.0 388.55 0.0 10.17 -1.59 503.93 +2000 2 9 12 37329.27 0.0 431.79 0.0 12.65 -6.3 425.42 +2000 2 10 12 37670.4 0.0 422.04 0.0 16.28 -1.1 585.53 +2000 2 11 12 37670.4 0.34 321.02 0.0 19.07 3.21 751.88 +2000 2 12 12 37933.29 1.56 324.72 0.0 16.7 0.39 646.16 +2000 2 13 12 38016.0 4.28 158.5 0.0 11.62 -2.15 518.82 +2000 2 14 12 38016.0 1.66 263.24 0.0 7.61 -0.42 573.01 +2000 2 15 12 38361.6 0.17 415.91 0.0 13.18 -0.11 596.51 +2000 2 16 12 38361.6 2.42 342.31 0.0 17.38 -1.5 589.5 +2000 2 17 12 38361.6 11.96 337.4 0.0 15.92 -0.24 614.53 +2000 2 18 12 38707.2 12.3 303.97 0.0 11.72 -0.65 597.47 +2000 2 19 12 38707.2 2.76 351.1 0.0 13.19 0.89 633.49 +2000 2 20 12 39050.01 0.08 450.24 0.0 16.84 -2.06 541.36 +2000 2 21 12 39052.8 0.0 446.46 0.0 12.11 -3.96 468.7 +2000 2 22 12 39052.8 0.0 450.38 0.0 12.96 -2.72 511.32 +2000 2 23 12 39398.4 0.0 452.69 0.0 16.37 -0.75 609.89 +2000 2 24 12 39398.4 0.0 453.83 0.0 20.7 2.43 771.12 +2000 2 25 12 39741.21 0.0 446.07 0.0 25.64 6.65 978.56 +2000 2 26 12 39744.0 0.73 323.53 0.0 24.24 7.86 1063.5 +2000 2 27 12 39744.0 3.0 304.64 0.0 22.39 6.8 994.88 +2000 2 28 12 40089.6 0.74 414.91 0.0 21.01 5.06 841.6 +2000 2 29 12 40089.6 0.39 407.99 0.0 18.28 -0.82 754.02 +2000 3 1 12 40435.2 0.03 397.64 0.0 19.95 0.43 666.37 +2000 3 2 12 40435.2 0.01 425.18 0.0 18.66 2.89 712.1 +2000 3 3 12 40435.2 0.0 436.58 0.0 14.89 -0.96 591.28 +2000 3 4 12 40780.8 0.0 426.22 0.0 14.8 0.16 613.77 +2000 3 5 12 40780.8 0.0 440.44 0.0 17.24 0.97 666.04 +2000 3 6 12 41126.4 0.0 448.48 0.0 21.72 3.06 750.04 +2000 3 7 12 41126.4 0.0 459.93 0.0 23.11 2.27 765.38 +2000 3 8 12 41188.91 0.0 454.42 0.0 25.09 5.36 960.53 +2000 3 9 12 41472.0 0.06 407.04 0.0 28.63 12.05 1306.42 +2000 3 10 12 41472.0 1.11 322.23 0.0 26.25 10.27 1249.78 +2000 3 11 12 41817.6 3.93 281.3 0.0 21.99 9.78 1133.84 +2000 3 12 12 41817.6 1.05 381.65 0.0 18.91 5.6 837.75 +2000 3 13 12 42163.2 0.02 442.75 0.0 13.19 -3.14 495.98 +2000 3 14 12 42163.2 0.0 450.81 0.0 12.51 -1.89 508.14 +2000 3 15 12 42163.2 0.0 480.53 0.0 18.39 0.43 644.52 +2000 3 16 12 42508.8 13.59 331.05 0.0 20.55 6.11 875.26 +2000 3 17 12 42508.8 3.65 394.7 0.0 18.72 5.29 796.83 +2000 3 18 12 42854.4 0.02 491.01 0.0 15.82 -2.78 518.96 +2000 3 19 12 42854.4 6.25 314.78 0.0 11.19 -2.06 525.36 +2000 3 20 12 43200.0 16.71 201.79 0.0 7.87 1.52 655.74 +2000 3 21 12 43200.0 4.7 230.52 0.0 10.0 3.63 740.72 +2000 3 22 12 43200.0 0.19 385.15 0.0 13.02 3.41 744.67 +2000 3 23 12 43545.6 0.0 432.17 0.0 16.78 4.95 800.38 +2000 3 24 12 43545.6 0.0 492.42 0.0 20.8 4.31 823.54 +2000 3 25 12 43891.2 2.08 363.77 0.0 23.17 6.73 968.34 +2000 3 26 12 43891.2 0.55 467.53 0.0 22.81 8.51 994.96 +2000 3 27 12 44236.8 2.4 361.3 0.0 18.79 4.16 833.62 +2000 3 28 12 44236.8 0.64 422.02 0.0 16.59 5.21 805.3 +2000 3 29 12 44236.8 0.25 366.73 0.0 16.33 2.29 726.71 +2000 3 30 12 44582.4 0.44 360.0 0.0 16.64 3.28 747.49 +2000 3 31 12 44582.4 0.1 501.2 0.0 18.27 3.53 719.62 +2000 4 1 12 44928.0 0.0 536.74 0.0 20.26 0.11 656.61 +2000 4 2 12 44928.0 3.61 370.58 0.0 19.82 4.81 918.81 +2000 4 3 12 45211.09 4.96 173.92 0.0 19.19 13.34 1352.5 +2000 4 4 12 45273.6 3.0 325.33 0.0 21.99 11.01 1143.9 +2000 4 5 12 45273.6 0.51 514.43 0.0 15.96 1.27 680.18 +2000 4 6 12 45619.2 0.0 533.69 0.0 19.3 2.19 727.12 +2000 4 7 12 45619.2 3.66 405.14 0.0 27.53 6.58 983.08 +2000 4 8 12 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201.65 0.0 9.96 -0.07 634.03 +2002 12 25 12 33868.8 7.2 180.15 0.0 6.19 -1.61 539.44 +2002 12 26 12 33873.27 0.18 263.27 0.0 5.42 -3.63 464.84 +2002 12 27 12 33931.31 0.0 291.76 0.0 5.94 -5.51 411.51 +2002 12 28 12 34018.32 0.0 294.5 0.0 6.07 -5.75 412.44 +2002 12 29 12 34131.69 0.0 298.69 0.0 9.35 -3.51 490.71 +2002 12 30 12 34214.4 0.0 303.7 0.0 12.55 -1.69 569.93 +2002 12 31 12 34214.4 3.09 233.34 0.0 13.31 -1.1 638.21 diff --git a/test/test_data/camels_us/basin_mean_forcing/maurer_extended/03015500_lump_maurer_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/maurer_extended/03015500_lump_maurer_forcing_leap.txt new file mode 100644 index 00000000..b78f0e76 --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/maurer_extended/03015500_lump_maurer_forcing_leap.txt @@ -0,0 +1,1100 @@ + 41.91 + 477.00 + 831030801 +Year Mnth Day Hr dayl(s) prcp(mm/day) srad(W/m2) swe(mm) tmax(C) tmin(C) vp(Pa) +2000 1 1 12 32486.4 0.79 214.77 0.0 3.12 -7.35 367.35 +2000 1 2 12 32486.4 2.99 234.68 0.0 10.84 -5.63 457.09 +2000 1 3 12 32486.4 16.37 216.93 0.0 14.25 1.31 700.54 +2000 1 4 12 32486.4 7.3 182.41 0.0 12.88 5.12 803.13 +2000 1 5 12 32613.94 1.31 233.04 0.0 9.16 -5.91 442.87 +2000 1 6 12 32815.44 0.31 171.84 0.0 3.98 -7.6 347.26 +2000 1 7 12 32832.0 0.77 174.73 0.0 2.58 -3.62 423.7 +2000 1 8 12 32832.0 0.2 226.27 0.0 1.42 -6.22 379.37 +2000 1 9 12 32832.0 0.0 259.6 0.0 3.75 -5.35 421.69 +2000 1 10 12 32890.35 6.93 163.98 0.0 5.47 -0.35 572.96 +2000 1 11 12 33161.05 6.06 179.42 0.0 7.95 0.4 608.04 +2000 1 12 12 33177.6 2.56 146.44 0.0 4.91 -2.71 489.76 +2000 1 13 12 33177.6 2.12 141.72 0.0 0.8 -4.75 387.91 +2000 1 14 12 33179.52 0.92 229.89 0.0 -3.26 -14.5 210.6 +2000 1 15 12 33445.07 0.12 245.64 0.0 -5.08 -12.22 217.61 +2000 1 16 12 33523.2 0.17 246.76 0.0 2.66 -10.59 242.86 +2000 1 17 12 33523.2 0.04 312.29 0.0 -0.31 -17.27 159.31 +2000 1 18 12 33581.55 1.03 202.93 0.0 -5.43 -17.51 142.67 +2000 1 19 12 33868.8 3.49 219.1 0.0 -5.5 -14.0 194.13 +2000 1 20 12 33868.8 4.77 155.01 0.0 -4.43 -8.74 258.09 +2000 1 21 12 33868.8 4.16 238.21 0.0 -4.88 -15.11 177.02 +2000 1 22 12 34197.84 0.95 232.11 0.0 -7.99 -18.06 135.7 +2000 1 23 12 34214.4 0.25 249.96 0.0 -6.93 -16.48 159.24 +2000 1 24 12 34214.4 0.51 237.6 0.0 -2.98 -11.19 213.56 +2000 1 25 12 34543.44 0.56 256.4 0.0 -5.12 -17.9 150.31 +2000 1 26 12 34560.0 1.62 235.09 0.0 -6.48 -15.37 155.8 +2000 1 27 12 34560.0 0.93 257.54 0.0 -6.58 -18.92 127.26 +2000 1 28 12 34905.6 0.14 291.76 0.0 -9.1 -18.56 121.17 +2000 1 29 12 34905.6 0.06 297.48 0.0 -4.97 -19.99 118.95 +2000 1 30 12 34907.52 1.43 275.93 0.0 0.75 -18.0 163.74 +2000 1 31 12 35251.2 0.61 227.65 0.0 -0.37 -7.86 291.43 +2000 2 1 12 35251.2 2.15 192.46 0.0 -2.22 -8.34 289.4 +2000 2 2 12 35449.98 1.13 204.79 0.0 -3.98 -10.07 252.36 +2000 2 3 12 35596.8 2.79 208.16 0.0 -5.6 -11.78 236.88 +2000 2 4 12 35596.8 1.36 191.39 0.0 -1.77 -6.92 294.45 +2000 2 5 12 35942.4 0.63 299.38 0.0 -0.1 -15.46 189.69 +2000 2 6 12 35942.4 0.12 362.55 0.0 -1.12 -13.82 195.49 +2000 2 7 12 36209.87 0.06 294.07 0.0 -0.9 -10.58 221.42 +2000 2 8 12 36288.0 0.02 385.02 0.0 -0.19 -21.28 119.27 +2000 2 9 12 36289.92 0.0 389.92 0.0 0.58 -21.31 138.88 +2000 2 10 12 36633.6 5.69 291.36 0.0 7.42 -6.82 331.71 +2000 2 11 12 36633.6 1.59 314.25 0.0 7.4 -6.15 343.81 +2000 2 12 12 36979.2 0.03 388.75 0.0 2.0 -15.87 187.31 +2000 2 13 12 36979.2 5.47 297.24 0.0 -1.79 -14.76 193.18 +2000 2 14 12 37246.67 5.94 246.42 0.0 -0.03 -7.95 293.92 +2000 2 15 12 37324.8 1.55 250.69 0.0 0.03 -7.36 321.17 +2000 2 16 12 37383.15 0.64 262.47 0.0 1.56 -6.87 327.42 +2000 2 17 12 37670.4 0.21 316.29 0.0 2.85 -9.46 285.91 +2000 2 18 12 37670.4 4.19 252.44 0.0 0.14 -8.82 296.56 +2000 2 19 12 38016.0 1.39 140.7 0.0 -1.86 -4.85 370.94 +2000 2 20 12 38016.0 0.66 140.17 0.0 -2.51 -5.34 371.77 +2000 2 21 12 38361.6 0.15 168.9 0.0 -2.33 -5.07 379.6 +2000 2 22 12 38361.6 0.0 370.38 0.0 4.99 -4.39 428.97 +2000 2 23 12 38707.2 0.0 388.54 0.0 11.7 -0.89 562.57 +2000 2 24 12 38707.2 5.24 311.98 0.0 13.87 1.61 672.54 +2000 2 25 12 39052.8 1.38 411.38 0.0 16.3 2.39 701.04 +2000 2 26 12 39052.8 0.0 408.17 0.0 15.69 2.71 718.14 +2000 2 27 12 39398.4 5.3 325.87 0.0 18.34 2.95 722.05 +2000 2 28 12 39398.4 1.4 436.07 0.0 15.43 -1.16 540.01 +2000 2 29 12 39727.44 3.48 386.97 0.0 9.79 -6.34 490.36 +2000 3 1 12 39744.0 5.57 341.34 0.0 10.79 -4.36 440.69 +2000 3 2 12 40011.47 1.56 349.43 0.0 10.11 -1.66 487.94 +2000 3 3 12 40089.6 0.03 257.48 0.0 6.04 -5.21 380.48 +2000 3 4 12 40288.38 0.0 431.74 0.0 5.91 -6.33 354.8 +2000 3 5 12 40435.2 0.0 452.68 0.0 9.29 -5.66 372.55 +2000 3 6 12 40562.74 0.0 455.43 0.0 9.11 -5.48 388.71 +2000 3 7 12 40780.8 0.0 466.14 0.0 13.69 -3.58 470.94 +2000 3 8 12 40908.34 0.02 414.73 0.0 20.87 -0.39 632.3 +2000 3 9 12 41126.4 2.01 356.33 0.0 23.64 3.86 763.9 +2000 3 10 12 41184.75 0.87 399.65 0.0 18.6 -1.27 566.26 +2000 3 11 12 41472.0 6.82 195.78 0.0 12.37 -2.77 461.72 +2000 3 12 12 41473.92 2.28 227.49 0.0 2.02 -3.49 417.57 +2000 3 13 12 41817.6 0.34 312.23 0.0 1.39 -6.83 344.56 +2000 3 14 12 41819.52 0.23 369.0 0.0 4.9 -5.42 376.43 +2000 3 15 12 42163.2 0.07 459.84 0.0 10.34 -3.28 455.58 +2000 3 16 12 42165.12 7.97 373.04 0.0 16.46 0.37 551.67 +2000 3 17 12 42508.8 2.18 377.95 0.0 10.54 -6.38 355.13 +2000 3 18 12 42508.8 0.02 460.49 0.0 2.93 -11.59 234.04 +2000 3 19 12 42854.4 0.0 508.99 0.0 7.11 -9.95 298.1 +2000 3 20 12 42854.4 0.03 402.87 0.0 10.18 -0.48 533.09 +2000 3 21 12 43200.0 0.15 311.78 0.0 11.91 2.68 670.93 +2000 3 22 12 43543.68 0.04 337.7 0.0 9.68 2.61 668.82 +2000 3 23 12 43545.6 0.0 409.21 0.0 12.74 3.06 661.99 +2000 3 24 12 43889.28 1.22 366.02 0.0 15.93 -0.26 606.48 +2000 3 25 12 43891.2 0.64 370.03 0.0 19.99 1.9 664.81 +2000 3 26 12 44234.88 0.08 485.83 0.0 19.0 0.85 597.09 +2000 3 27 12 44236.8 2.8 344.29 0.0 13.66 -1.62 508.19 +2000 3 28 12 44580.48 4.55 359.61 0.0 11.23 -2.98 463.29 +2000 3 29 12 44582.4 2.91 347.69 0.0 9.75 -2.85 473.53 +2000 3 30 12 44926.08 0.5 218.09 0.0 7.15 -0.28 507.73 +2000 3 31 12 44928.0 0.0 479.09 0.0 7.5 -5.34 386.21 +2000 4 1 12 45215.25 0.73 381.21 0.0 12.88 -4.37 432.79 +2000 4 2 12 45273.6 6.61 379.61 0.0 16.45 -1.51 573.82 +2000 4 3 12 45491.66 21.21 234.19 0.0 13.76 5.8 820.23 +2000 4 4 12 45619.2 9.8 303.88 0.0 15.24 5.61 777.94 +2000 4 5 12 45837.26 1.87 323.01 0.0 9.99 -2.74 488.42 +2000 4 6 12 45964.8 0.17 431.19 0.0 7.33 -2.47 464.38 +2000 4 7 12 46111.62 16.29 379.42 0.0 13.56 -1.3 518.68 +2000 4 8 12 46310.4 21.07 345.91 0.0 9.33 -1.67 493.16 +2000 4 9 12 46388.53 5.99 382.7 0.0 7.52 -4.25 421.95 +2000 4 10 12 46656.0 0.42 376.23 0.0 4.24 -4.05 419.99 +2000 4 11 12 46672.56 1.98 276.48 0.0 4.89 -2.34 468.51 +2000 4 12 12 47001.6 0.53 398.07 0.0 6.16 -2.29 456.49 +2000 4 13 12 47001.6 0.0 469.08 0.0 6.84 -3.66 427.68 +2000 4 14 12 47347.2 0.0 530.47 0.0 13.02 -2.59 516.9 +2000 4 15 12 47347.2 0.0 529.42 0.0 21.84 4.35 782.55 +2000 4 16 12 47692.8 0.6 380.62 0.0 21.68 6.62 911.31 +2000 4 17 12 47692.8 1.22 366.72 0.0 18.39 5.65 855.7 +2000 4 18 12 48038.4 0.28 357.08 0.0 14.42 4.9 787.74 +2000 4 19 12 48038.4 0.0 366.63 0.0 12.01 4.99 809.79 +2000 4 20 12 48384.0 20.3 263.89 0.0 13.93 7.17 923.67 +2000 4 21 12 48384.0 9.66 283.42 0.0 14.45 7.1 915.24 +2000 4 22 12 48729.6 1.81 303.26 0.0 12.75 4.95 806.2 +2000 4 23 12 48729.6 0.18 274.64 0.0 10.64 3.78 708.51 +2000 4 24 12 49016.85 0.0 511.06 0.0 11.66 0.06 568.0 +2000 4 25 12 49075.2 0.0 559.41 0.0 16.64 -0.78 527.55 +2000 4 26 12 49153.33 0.0 565.32 0.0 14.52 -2.86 467.43 +2000 4 27 12 49420.8 0.0 554.08 0.0 12.8 -2.15 477.66 +2000 4 28 12 49420.8 0.0 560.46 0.0 14.23 -1.56 509.38 +2000 4 29 12 49766.4 0.0 565.72 0.0 18.44 -0.28 567.55 +2000 4 30 12 49766.4 0.0 564.76 0.0 18.85 0.72 609.53 +2000 5 1 12 50110.08 9.14 415.37 0.0 17.11 1.11 654.7 +2000 5 2 12 50112.0 2.4 552.27 0.0 19.1 3.26 688.09 +2000 5 3 12 50190.13 0.02 516.65 0.0 15.57 0.77 627.03 +2000 5 4 12 50457.6 0.76 448.82 0.0 22.39 1.49 705.47 +2000 5 5 12 50457.6 0.2 549.53 0.0 21.62 5.56 899.43 +2000 5 6 12 50803.2 0.0 513.46 0.0 26.44 12.24 1257.78 +2000 5 7 12 50803.2 0.9 388.72 0.0 27.84 12.75 1384.44 +2000 5 8 12 50950.02 0.37 410.29 0.0 27.57 12.94 1411.94 +2000 5 9 12 51148.8 1.1 393.3 0.0 27.52 14.01 1486.5 +2000 5 10 12 51148.8 9.14 377.52 0.0 28.53 14.49 1412.78 +2000 5 11 12 51492.48 2.34 490.56 0.0 23.41 5.86 902.06 +2000 5 12 12 51494.4 0.0 537.87 0.0 20.88 6.19 1005.04 +2000 5 13 12 51494.4 2.21 335.75 0.0 26.61 15.85 1453.34 +2000 5 14 12 51840.0 0.62 479.03 0.0 21.91 5.66 877.09 +2000 5 15 12 51840.0 0.01 517.03 0.0 17.18 1.71 629.21 +2000 5 16 12 51856.56 0.0 563.98 0.0 15.52 -0.01 574.7 +2000 5 17 12 52185.6 3.4 450.19 0.0 18.43 0.91 685.06 +2000 5 18 12 52185.6 49.29 417.03 0.0 24.13 6.76 963.49 +2000 5 19 12 52202.16 14.45 392.9 0.0 22.34 9.77 1119.14 +2000 5 20 12 52531.2 1.07 139.52 0.0 19.13 8.38 1045.39 +2000 5 21 12 52531.2 0.55 178.04 0.0 12.1 7.68 972.44 +2000 5 22 12 52531.2 0.1 369.14 0.0 13.19 5.92 888.6 +2000 5 23 12 52818.45 5.98 375.28 0.0 19.02 6.89 996.32 +2000 5 24 12 52876.8 6.07 240.36 0.0 16.64 10.13 1142.76 +2000 5 25 12 52876.8 1.23 448.45 0.0 22.38 9.11 1043.78 +2000 5 26 12 52954.93 0.01 526.83 0.0 18.07 4.99 854.23 +2000 5 27 12 53220.48 0.02 467.91 0.0 18.39 6.71 894.38 +2000 5 28 12 53222.4 0.01 522.35 0.0 18.16 5.48 844.24 +2000 5 29 12 53222.4 0.0 459.34 0.0 15.59 5.9 869.48 +2000 5 30 12 53238.95 0.0 518.55 0.0 19.56 7.26 971.29 +2000 5 31 12 53509.65 7.51 377.65 0.0 21.23 9.43 1156.3 +2000 6 1 12 53568.0 2.59 417.6 0.0 26.57 11.93 1400.75 +2000 6 2 12 53568.0 12.46 355.21 0.0 27.15 15.81 1541.58 +2000 6 3 12 53568.0 3.24 553.59 0.0 24.61 8.67 1061.41 +2000 6 4 12 53584.56 0.01 481.14 0.0 18.97 5.76 909.6 +2000 6 5 12 53786.06 13.73 373.57 0.0 18.56 7.52 1012.55 +2000 6 6 12 53913.6 5.12 186.16 0.0 15.55 9.44 1053.51 +2000 6 7 12 53913.6 0.4 463.23 0.0 14.78 5.57 884.19 +2000 6 8 12 53913.6 0.0 565.76 0.0 23.32 6.97 1036.86 +2000 6 9 12 53913.6 0.0 508.92 0.0 24.79 12.78 1398.29 +2000 6 10 12 53913.6 0.48 384.85 0.0 28.43 15.06 1659.12 +2000 6 11 12 53930.16 15.37 380.54 0.0 29.49 16.08 1770.73 +2000 6 12 12 54060.42 7.23 346.97 0.0 27.28 16.81 1803.69 +2000 6 13 12 54131.66 3.37 259.35 0.0 23.88 16.01 1769.92 +2000 6 14 12 54200.85 5.88 320.36 0.0 26.81 17.77 1938.82 +2000 6 15 12 54257.28 4.64 354.11 0.0 29.81 18.57 1998.45 +2000 6 16 12 54259.2 9.2 334.17 0.0 26.59 17.23 1871.68 +2000 6 17 12 54259.2 2.96 336.78 0.0 26.48 17.12 1759.28 +2000 6 18 12 54259.2 11.4 364.03 0.0 22.75 12.52 1333.29 +2000 6 19 12 54259.2 2.95 440.12 0.0 18.1 7.67 1040.51 +2000 6 20 12 54259.2 1.78 414.97 0.0 24.0 9.11 1211.1 +2000 6 21 12 54259.2 11.88 399.48 0.0 27.28 12.81 1482.08 +2000 6 22 12 54259.2 3.0 463.61 0.0 25.2 15.28 1539.57 +2000 6 23 12 54259.2 0.0 532.82 0.0 24.86 11.33 1319.85 +2000 6 24 12 54259.2 15.44 396.83 0.0 25.11 11.75 1389.05 +2000 6 25 12 54257.28 8.46 396.62 0.0 27.64 13.04 1558.6 +2000 6 26 12 54257.28 10.15 295.52 0.0 24.69 16.49 1771.71 +2000 6 27 12 54131.66 2.43 420.7 0.0 27.68 16.02 1663.72 +2000 6 28 12 54060.42 0.07 424.67 0.0 24.21 10.85 1324.15 +2000 6 29 12 53991.73 1.83 366.45 0.0 22.06 11.49 1280.35 +2000 6 30 12 53913.6 0.48 505.24 0.0 21.36 9.31 1143.52 +2000 7 1 12 53913.6 0.0 537.57 0.0 22.69 9.43 1165.71 +2000 7 2 12 53913.6 2.16 414.66 0.0 25.97 10.05 1289.83 +2000 7 3 12 53913.6 12.43 390.83 0.0 26.09 12.76 1506.33 +2000 7 4 12 53913.6 4.23 242.78 0.0 22.88 16.29 1644.91 +2000 7 5 12 53855.25 0.29 532.51 0.0 25.43 11.63 1317.51 +2000 7 6 12 53714.82 0.03 473.95 0.0 25.07 9.73 1192.4 +2000 7 7 12 53568.0 0.01 528.83 0.0 22.42 9.57 1103.01 +2000 7 8 12 53568.0 0.0 548.24 0.0 21.12 7.19 1019.97 +2000 7 9 12 53568.0 16.58 398.67 0.0 22.85 9.57 1214.02 +2000 7 10 12 53568.0 4.37 451.7 0.0 23.38 13.36 1428.74 +2000 7 11 12 53369.21 0.0 507.6 0.0 26.13 13.7 1400.5 +2000 7 12 12 53222.4 0.0 553.09 0.0 24.84 9.13 1166.81 +2000 7 13 12 53222.4 0.0 541.34 0.0 25.4 10.43 1249.24 +2000 7 14 12 53222.4 9.44 401.59 0.0 27.25 11.84 1384.07 +2000 7 15 12 53094.86 4.6 370.26 0.0 24.17 12.34 1447.3 +2000 7 16 12 52876.8 6.11 281.99 0.0 22.31 14.78 1591.8 +2000 7 17 12 52876.8 1.46 381.88 0.0 22.59 14.93 1581.84 +2000 7 18 12 52876.8 0.0 470.2 0.0 25.34 14.45 1491.14 +2000 7 19 12 52609.33 0.0 444.73 0.0 20.66 11.1 1229.79 +2000 7 20 12 52531.2 0.0 505.72 0.0 20.58 8.89 1115.21 +2000 7 21 12 52531.2 4.69 390.61 0.0 23.26 9.98 1192.72 +2000 7 22 12 52403.66 1.23 493.81 0.0 21.42 10.09 1142.72 +2000 7 23 12 52185.6 0.0 525.59 0.0 20.96 8.02 1043.99 +2000 7 24 12 52185.6 0.0 538.66 0.0 22.96 8.42 1079.8 +2000 7 25 12 52127.25 0.0 535.68 0.0 24.54 9.66 1175.56 +2000 7 26 12 51840.0 0.0 528.66 0.0 25.9 11.19 1295.73 +2000 7 27 12 51840.0 0.0 517.08 0.0 27.02 12.71 1431.68 +2000 7 28 12 51712.46 6.93 381.65 0.0 28.15 13.88 1595.27 +2000 7 29 12 51494.4 7.07 357.51 0.0 27.77 15.6 1736.78 +2000 7 30 12 51494.4 14.73 330.68 0.0 26.61 16.24 1830.2 +2000 7 31 12 51226.93 12.0 307.56 0.0 27.49 18.15 1998.95 +2000 8 1 12 51148.8 7.26 333.4 0.0 28.04 18.19 2008.21 +2000 8 2 12 51148.8 15.42 317.44 0.0 27.3 17.54 1899.83 +2000 8 3 12 50803.2 4.8 347.76 0.0 25.9 14.55 1612.46 +2000 8 4 12 50803.2 0.29 467.32 0.0 23.09 12.16 1337.13 +2000 8 5 12 50604.42 6.33 396.98 0.0 22.82 8.06 1136.6 +2000 8 6 12 50457.6 16.3 394.72 0.0 24.13 8.97 1242.89 +2000 8 7 12 50457.6 3.88 383.98 0.0 22.72 14.33 1561.12 +2000 8 8 12 50112.0 0.1 370.64 0.0 26.77 14.93 1683.26 +2000 8 9 12 50112.0 5.62 360.92 0.0 27.82 14.86 1712.52 +2000 8 10 12 49782.95 2.0 358.07 0.0 26.95 15.72 1741.89 +2000 8 11 12 49766.4 2.13 325.12 0.0 24.69 14.72 1603.29 +2000 8 12 12 49708.05 0.69 382.4 0.0 23.44 11.22 1349.81 +2000 8 13 12 49420.8 0.04 463.79 0.0 21.8 11.13 1304.02 +2000 8 14 12 49420.8 0.02 459.79 0.0 24.61 11.79 1395.81 +2000 8 15 12 49075.2 8.83 354.06 0.0 25.45 13.42 1540.7 +2000 8 16 12 49075.2 2.32 472.71 0.0 26.7 14.15 1523.7 +2000 8 17 12 48729.6 0.58 353.01 0.0 23.03 10.44 1275.15 +2000 8 18 12 48729.6 0.17 347.01 0.0 19.49 10.67 1218.62 +2000 8 19 12 48400.56 0.0 485.59 0.0 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a/test/test_data/camels_us/basin_mean_forcing/nldas/01022500_lump_nldas_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/nldas/01022500_lump_nldas_forcing_leap.txt new file mode 100644 index 00000000..c37dc398 --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/nldas/01022500_lump_nldas_forcing_leap.txt @@ -0,0 +1,1100 @@ + 44.82 + 133.00 + 587675987 +Year Mnth Day Hr Dayl(s) PRCP(mm/day) SRAD(W/m2) SWE(mm) Tmax(C) Tmin(C) Vp(Pa) +2000 01 01 12 31185.94 0.00 189.60 0.00 -6.44 -6.44 310.74 +2000 01 02 12 31302.07 1.22 186.32 0.00 -0.60 -0.60 508.26 +2000 01 03 12 31370.89 2.79 211.95 0.00 3.53 3.53 684.96 +2000 01 04 12 31449.60 9.46 75.22 0.00 1.11 1.11 597.53 +2000 01 05 12 31449.60 22.91 228.00 0.00 0.80 0.80 580.11 +2000 01 06 12 31449.60 0.00 162.43 0.00 -9.46 -9.46 175.44 +2000 01 07 12 31596.25 0.05 161.48 0.00 0.93 0.93 576.52 +2000 01 08 12 31716.49 1.66 220.92 0.00 -1.77 -1.77 377.36 +2000 01 09 12 31795.20 0.00 153.51 0.00 -0.69 -0.69 423.49 +2000 01 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121.85 0.00 0.98 0.98 633.19 +2000 01 27 12 33753.19 0.31 123.03 0.00 -6.00 -6.00 354.12 +2000 01 28 12 33868.80 0.00 189.42 0.00 -14.29 -14.29 151.42 +2000 01 29 12 33950.74 0.00 293.01 0.00 -12.12 -12.12 196.03 +2000 01 30 12 34214.40 0.00 308.86 0.00 -10.28 -10.28 236.92 +2000 01 31 12 34214.40 8.00 106.77 0.00 -3.27 -3.27 455.64 +2000 02 01 12 34481.29 0.00 251.51 0.00 -1.80 -1.80 436.48 +2000 02 02 12 34560.00 0.00 263.80 0.00 -8.09 -8.09 260.33 +2000 02 03 12 34758.07 0.00 275.62 0.00 -12.27 -12.27 188.01 +2000 02 04 12 34905.60 0.00 344.39 0.00 -12.75 -12.75 189.19 +2000 02 05 12 34987.54 0.00 323.54 0.00 -12.07 -12.07 175.70 +2000 02 06 12 35251.20 0.00 322.48 0.00 -9.66 -9.66 205.79 +2000 02 07 12 35251.20 0.02 177.34 0.00 -7.95 -7.95 261.45 +2000 02 08 12 35596.80 0.04 366.02 0.00 -14.38 -14.38 137.01 +2000 02 09 12 35596.80 0.06 310.45 0.00 -5.93 -5.93 327.04 +2000 02 10 12 35942.40 0.00 309.41 0.00 -2.05 -2.05 403.92 +2000 02 11 12 35942.40 5.30 151.20 0.00 -0.96 -0.96 528.13 +2000 02 12 12 36288.00 11.01 380.78 0.00 -7.21 -7.21 292.71 +2000 02 13 12 36288.00 0.00 318.49 0.00 -9.05 -9.05 223.32 +2000 02 14 12 36633.60 38.37 107.31 0.00 -1.46 -1.46 498.68 +2000 02 15 12 36633.60 0.00 308.34 0.00 -2.54 -2.54 460.06 +2000 02 16 12 36979.20 1.43 172.18 0.00 -5.88 -5.88 348.61 +2000 02 17 12 36979.20 2.10 405.61 0.00 -7.29 -7.29 309.27 +2000 02 18 12 37324.80 0.61 338.60 0.00 -14.96 -14.96 136.74 +2000 02 19 12 37324.80 5.20 241.82 0.00 -5.17 -5.17 351.92 +2000 02 20 12 37670.40 0.03 351.86 0.00 -6.73 -6.73 301.87 +2000 02 21 12 37670.40 0.08 398.47 0.00 -6.10 -6.10 265.07 +2000 02 22 12 38016.00 0.00 313.51 0.00 -8.05 -8.05 228.36 +2000 02 23 12 38016.00 0.00 307.05 0.00 -0.15 -0.15 502.66 +2000 02 24 12 38361.60 0.00 401.95 0.00 1.78 1.78 572.19 +2000 02 25 12 38361.60 0.01 408.76 0.00 -0.69 -0.69 427.88 +2000 02 26 12 38707.20 0.00 373.18 0.00 -0.88 -0.88 451.80 +2000 02 27 12 38707.20 0.28 348.87 0.00 5.84 5.84 825.97 +2000 02 28 12 39052.80 8.60 243.57 0.00 7.42 7.42 952.12 +2000 02 29 12 39062.68 4.30 287.72 0.00 5.74 5.74 778.18 +2000 03 01 12 39398.40 0.01 329.10 0.00 4.05 4.05 604.00 +2000 03 02 12 39628.39 7.67 114.05 0.00 3.43 3.43 677.91 +2000 03 03 12 39744.00 8.51 197.68 0.00 2.51 2.51 623.16 +2000 03 04 12 40089.60 1.57 187.63 0.00 2.28 2.28 548.26 +2000 03 05 12 40089.60 0.01 150.16 0.00 0.53 0.53 459.80 +2000 03 06 12 40435.20 0.25 200.66 0.00 0.85 0.85 442.75 +2000 03 07 12 40435.20 0.00 208.50 0.00 1.17 1.17 403.20 +2000 03 08 12 40780.80 0.00 457.95 0.00 1.30 1.30 365.85 +2000 03 09 12 40780.80 0.36 216.81 0.00 3.24 3.24 587.24 +2000 03 10 12 41126.40 6.30 470.94 0.00 2.12 2.12 535.83 +2000 03 11 12 41126.40 0.12 315.76 0.00 -2.08 -2.08 286.16 +2000 03 12 12 41472.00 24.59 143.82 0.00 1.01 1.01 603.24 +2000 03 13 12 41817.60 0.93 476.77 0.00 -1.26 -1.26 420.96 +2000 03 14 12 41817.60 0.00 278.91 0.00 -3.12 -3.12 309.69 +2000 03 15 12 42163.20 0.00 464.18 0.00 4.31 4.31 621.05 +2000 03 16 12 42163.20 0.20 150.44 0.00 6.59 6.59 780.58 +2000 03 17 12 42508.80 11.26 180.13 0.00 -0.92 -0.92 451.21 +2000 03 18 12 42508.80 0.16 530.86 0.00 -6.99 -6.99 229.52 +2000 03 19 12 42854.40 0.03 527.79 0.00 -4.82 -4.82 279.92 +2000 03 20 12 42854.40 0.00 518.78 0.00 -1.45 -1.45 386.82 +2000 03 21 12 43200.00 0.00 518.02 0.00 1.86 1.86 460.39 +2000 03 22 12 43545.60 0.00 524.83 0.00 3.90 3.90 436.32 +2000 03 23 12 43545.60 0.00 516.03 0.00 5.96 5.96 460.16 +2000 03 24 12 43891.20 0.00 529.35 0.00 6.48 6.48 537.88 +2000 03 25 12 43891.20 0.01 515.68 0.00 5.67 5.67 468.51 +2000 03 26 12 44236.80 0.84 386.31 0.00 8.66 8.66 828.91 +2000 03 27 12 44236.80 0.21 472.39 0.00 7.49 7.49 591.17 +2000 03 28 12 44582.40 30.58 213.10 0.00 5.93 5.93 819.32 +2000 03 29 12 44582.40 3.56 231.24 0.00 6.29 6.29 856.57 +2000 03 30 12 44928.00 0.11 299.69 0.00 5.35 5.35 659.18 +2000 03 31 12 45273.60 0.08 254.29 0.00 5.74 5.74 627.03 +2000 04 01 12 45273.60 0.00 352.48 0.00 5.83 5.83 566.90 +2000 04 02 12 45619.20 4.91 359.00 0.00 8.24 8.24 700.97 +2000 04 03 12 45619.20 4.33 276.06 0.00 8.68 8.68 921.68 +2000 04 04 12 45964.80 16.19 191.12 0.00 8.98 8.98 1079.11 +2000 04 05 12 45964.80 3.83 329.51 0.00 9.94 9.94 981.71 +2000 04 06 12 46310.40 0.37 503.36 0.00 4.41 4.41 484.57 +2000 04 07 12 46310.40 0.41 306.48 0.00 5.45 5.45 639.67 +2000 04 08 12 46656.00 2.13 307.60 0.00 7.49 7.49 810.12 +2000 04 09 12 46771.61 19.42 237.65 0.00 11.02 11.02 1228.00 +2000 04 10 12 47001.60 1.32 236.03 0.00 2.47 2.47 501.02 +2000 04 11 12 47337.32 0.11 418.93 0.00 2.82 2.82 391.96 +2000 04 12 12 47347.20 9.36 202.51 0.00 4.41 4.41 684.90 +2000 04 13 12 47692.80 0.03 549.32 0.00 0.67 0.67 341.62 +2000 04 14 12 47692.80 0.00 553.59 0.00 2.17 2.17 355.86 +2000 04 15 12 48038.40 0.14 525.15 0.00 9.51 9.51 805.96 +2000 04 16 12 48038.40 0.06 222.47 0.00 10.09 10.09 1009.12 +2000 04 17 12 48384.00 0.00 553.22 0.00 2.96 2.96 390.73 +2000 04 18 12 48384.00 0.01 544.97 0.00 4.44 4.44 376.63 +2000 04 19 12 48729.60 0.16 139.16 0.00 4.12 4.12 621.73 +2000 04 20 12 48729.60 0.01 240.36 0.00 5.80 5.80 748.57 +2000 04 21 12 49075.20 0.16 250.69 0.00 6.49 6.49 705.74 +2000 04 22 12 49075.20 32.70 142.88 0.00 4.82 4.82 755.42 +2000 04 23 12 49420.80 24.75 211.86 0.00 8.26 8.26 1024.83 +2000 04 24 12 49420.80 10.79 104.34 0.00 8.44 8.44 1030.25 +2000 04 25 12 49766.40 1.13 333.89 0.00 4.25 4.25 562.99 +2000 04 26 12 49766.40 0.16 460.09 0.00 4.18 4.18 532.32 +2000 04 27 12 50112.00 2.47 156.54 0.00 3.31 3.31 628.41 +2000 04 28 12 50112.00 0.09 205.44 0.00 5.41 5.41 731.01 +2000 04 29 12 50457.60 0.00 562.50 0.00 9.35 9.35 762.96 +2000 04 30 12 50457.60 0.65 350.20 0.00 9.02 9.02 731.44 +2000 05 01 12 50803.20 0.00 511.64 0.00 6.23 6.23 490.70 +2000 05 02 12 50803.20 0.64 258.91 0.00 8.11 8.11 767.66 +2000 05 03 12 51148.80 0.00 526.23 0.00 7.36 7.36 530.77 +2000 05 04 12 51148.80 0.00 517.69 0.00 7.73 7.73 700.01 +2000 05 05 12 51412.46 0.07 369.73 0.00 11.70 11.70 989.69 +2000 05 06 12 51494.40 0.05 458.98 0.00 13.66 13.66 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12 28 12 31104.00 0.00 233.96 0.00 -7.00 -7.00 254.75 +2002 12 29 12 31104.00 0.00 140.70 0.00 -3.88 -3.88 396.01 +2002 12 30 12 31104.00 0.00 252.69 0.00 -7.95 -7.95 218.47 +2002 12 31 12 31104.00 1.01 79.80 0.00 -6.38 -6.38 353.06 diff --git a/test/test_data/camels_us/basin_mean_forcing/nldas/01547700_lump_nldas_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/nldas/01547700_lump_nldas_forcing_leap.txt new file mode 100644 index 00000000..b3cc7082 --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/nldas/01547700_lump_nldas_forcing_leap.txt @@ -0,0 +1,1100 @@ + 40.98 + 383.00 + 114169652 +Year Mnth Day Hr Dayl(s) PRCP(mm/day) SRAD(W/m2) SWE(mm) Tmax(C) Tmin(C) Vp(Pa) +2000 01 01 12 32832.00 0.15 261.88 0.00 0.62 0.62 499.95 +2000 01 02 12 32832.00 0.00 187.45 0.00 5.14 5.14 770.98 +2000 01 03 12 32832.00 0.92 96.68 0.00 10.83 10.83 1160.22 +2000 01 04 12 32832.00 11.50 110.01 0.00 11.91 11.91 1311.32 +2000 01 05 12 32832.00 0.00 169.23 0.00 -0.03 -0.03 456.86 +2000 01 06 12 33177.60 0.00 225.42 0.00 -1.67 -1.67 427.38 +2000 01 07 12 33177.60 0.08 125.44 0.00 -1.35 -1.35 443.58 +2000 01 08 12 33177.60 0.00 268.50 0.00 -1.89 -1.89 354.83 +2000 01 09 12 33177.60 0.70 160.20 0.00 1.19 1.19 466.03 +2000 01 10 12 33177.60 8.36 109.96 0.00 2.36 2.36 659.97 +2000 01 11 12 33499.54 0.62 175.25 0.00 3.56 3.56 581.48 +2000 01 12 12 33523.20 0.72 265.99 0.00 0.62 0.62 456.61 +2000 01 13 12 33523.20 1.77 77.11 0.00 -1.30 -1.30 432.90 +2000 01 14 12 33523.20 0.10 278.77 0.00 -10.48 -10.48 208.38 +2000 01 15 12 33599.62 0.02 244.56 0.00 -7.99 -7.99 284.35 +2000 01 16 12 33868.80 0.03 229.30 0.00 -2.73 -2.73 405.71 +2000 01 17 12 33868.80 0.04 300.30 0.00 -12.19 -12.19 137.46 +2000 01 18 12 33868.80 0.01 128.27 0.00 -11.34 -11.34 93.27 +2000 01 19 12 34190.73 0.04 177.54 0.00 -5.06 -5.06 241.01 +2000 01 20 12 34214.40 5.05 66.27 0.00 -3.69 -3.69 385.04 +2000 01 21 12 34214.40 0.59 221.48 0.00 -10.89 -10.89 196.85 +2000 01 22 12 34214.40 0.13 247.79 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34214.40 0.08 276.53 0.00 6.35 6.35 739.35 +2002 11 21 12 34214.40 0.58 128.93 0.00 4.85 4.85 782.72 +2002 11 22 12 33868.80 5.94 155.16 0.00 6.88 6.88 891.17 +2002 11 23 12 33868.80 0.56 225.36 0.00 1.22 1.22 531.37 +2002 11 24 12 33868.80 0.00 267.14 0.00 2.02 2.02 546.28 +2002 11 25 12 33868.80 0.00 86.32 0.00 2.86 2.86 588.02 +2002 11 26 12 33523.20 0.05 175.17 0.00 0.80 0.80 538.16 +2002 11 27 12 33523.20 5.96 172.91 0.00 -0.58 -0.58 495.57 +2002 11 28 12 33523.20 0.01 140.88 0.00 -2.61 -2.61 328.22 +2002 11 29 12 33523.20 0.04 180.46 0.00 -0.66 -0.66 415.38 +2002 11 30 12 33177.60 0.05 96.98 0.00 2.16 2.16 573.66 +2002 12 01 12 33177.60 0.60 159.53 0.00 -2.57 -2.57 315.64 +2002 12 02 12 33177.60 0.00 163.01 0.00 -2.54 -2.54 372.94 +2002 12 03 12 33177.60 0.63 272.29 0.00 -7.75 -7.75 269.47 +2002 12 04 12 33177.60 0.07 219.24 0.00 -9.57 -9.57 256.27 +2002 12 05 12 32908.43 9.00 120.11 0.00 -5.07 -5.07 380.69 +2002 12 06 12 32832.00 1.49 219.06 0.00 -4.12 -4.12 398.86 +2002 12 07 12 32832.00 0.00 258.23 0.00 -6.28 -6.28 341.55 +2002 12 08 12 32832.00 0.01 199.53 0.00 -3.42 -3.42 422.77 +2002 12 09 12 32832.00 0.00 262.33 0.00 -6.62 -6.62 284.06 +2002 12 10 12 32832.00 0.00 234.14 0.00 -6.69 -6.69 328.76 +2002 12 11 12 32832.00 12.92 91.95 0.00 -2.75 -2.75 408.99 +2002 12 12 12 32832.00 3.43 121.74 0.00 0.77 0.77 611.99 +2002 12 13 12 32808.34 2.68 205.49 0.00 0.80 0.80 612.80 +2002 12 14 12 32486.40 15.83 102.35 0.00 1.31 1.31 629.11 +2002 12 15 12 32486.40 0.04 214.52 0.00 0.85 0.85 592.09 +2002 12 16 12 32486.40 0.36 175.16 0.00 -0.41 -0.41 530.36 +2002 12 17 12 32486.40 0.00 257.54 0.00 -5.32 -5.32 355.50 +2002 12 18 12 32486.40 0.00 215.25 0.00 -5.67 -5.67 338.18 +2002 12 19 12 32486.40 0.00 76.99 0.00 -1.39 -1.39 504.75 +2002 12 20 12 32486.40 12.68 177.43 0.00 5.17 5.17 822.69 +2002 12 21 12 32486.40 0.79 106.83 0.00 1.25 1.25 531.05 +2002 12 22 12 32486.40 0.16 91.29 0.00 1.26 1.26 486.38 +2002 12 23 12 32486.40 0.02 152.24 0.00 -0.30 -0.30 461.24 +2002 12 24 12 32486.40 0.00 191.50 0.00 -1.23 -1.23 430.84 +2002 12 25 12 32486.40 18.81 134.36 0.00 -0.88 -0.88 523.07 +2002 12 26 12 32486.40 0.85 229.44 0.00 -1.99 -1.99 459.18 +2002 12 27 12 32486.40 0.02 241.71 0.00 -5.71 -5.71 366.86 +2002 12 28 12 32562.83 0.01 213.98 0.00 -3.17 -3.17 439.80 +2002 12 29 12 32808.34 0.43 239.67 0.00 -0.78 -0.78 528.32 +2002 12 30 12 32832.00 0.00 122.28 0.00 -1.88 -1.88 502.78 +2002 12 31 12 32832.00 1.65 145.34 0.00 3.30 3.30 692.27 diff --git a/test/test_data/camels_us/basin_mean_forcing/nldas/02064000_lump_nldas_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/nldas/02064000_lump_nldas_forcing_leap.txt new file mode 100644 index 00000000..1b9af002 --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/nldas/02064000_lump_nldas_forcing_leap.txt @@ -0,0 +1,1100 @@ + 37.24 + 226.00 + 427165365 +Year Mnth Day Hr Dayl(s) PRCP(mm/day) SRAD(W/m2) SWE(mm) Tmax(C) Tmin(C) Vp(Pa) +2000 01 01 12 34214.40 0.00 240.48 0.00 8.01 8.01 710.71 +2000 01 02 12 34214.40 0.00 293.75 0.00 11.42 11.42 1091.99 +2000 01 03 12 34214.40 0.00 266.71 0.00 14.24 14.24 1345.98 +2000 01 04 12 34214.40 16.28 85.76 0.00 15.38 15.38 1588.41 +2000 01 05 12 34214.40 0.00 301.10 0.00 5.13 5.13 598.44 +2000 01 06 12 34276.91 0.00 318.65 0.00 1.31 1.31 415.49 +2000 01 07 12 34557.21 0.00 317.27 0.00 3.29 3.29 502.68 +2000 01 08 12 34560.00 0.00 300.51 0.00 3.09 3.09 382.75 +2000 01 09 12 34560.00 9.06 90.55 0.00 4.42 4.42 679.83 +2000 01 10 12 34560.00 21.71 134.67 0.00 8.30 8.30 1020.67 +2000 01 11 12 34560.00 0.00 323.68 0.00 7.96 7.96 624.14 +2000 01 12 12 34709.52 0.00 324.31 0.00 4.70 4.70 411.07 +2000 01 13 12 34905.60 0.21 297.67 0.00 6.53 6.53 622.12 +2000 01 14 12 34905.60 0.00 340.86 0.00 -0.52 -0.52 275.03 +2000 01 15 12 34905.60 0.00 321.72 0.00 -1.84 -1.84 224.61 +2000 01 16 12 34905.60 0.00 206.26 0.00 2.46 2.46 458.94 +2000 01 17 12 35168.49 0.00 344.43 0.00 0.54 0.54 356.28 +2000 01 18 12 35251.20 1.68 106.28 0.00 -4.20 -4.20 283.58 +2000 01 19 12 35251.20 0.30 275.26 0.00 -1.25 -1.25 431.29 +2000 01 20 12 35251.20 8.09 262.38 0.00 -0.66 -0.66 481.94 +2000 01 21 12 35400.72 0.00 303.00 0.00 -5.69 -5.69 258.96 +2000 01 22 12 35596.80 0.00 270.87 0.00 -6.08 -6.08 173.41 +2000 01 23 12 35596.80 1.18 135.63 0.00 -1.87 -1.87 403.18 +2000 01 24 12 35596.80 0.00 274.36 0.00 -0.58 -0.58 481.52 +2000 01 25 12 35939.61 14.86 264.08 0.00 -1.08 -1.08 466.48 +2000 01 26 12 35942.40 0.00 203.74 0.00 -4.79 -4.79 301.98 +2000 01 27 12 35942.40 0.00 373.49 0.00 -5.17 -5.17 273.18 +2000 01 28 12 36091.92 0.00 371.97 0.00 -5.37 -5.37 220.23 +2000 01 29 12 36288.00 0.00 322.06 0.00 -3.53 -3.53 242.85 +2000 01 30 12 36288.00 20.43 112.65 0.00 -2.16 -2.16 454.79 +2000 01 31 12 36292.47 2.32 325.56 0.00 -2.79 -2.79 432.91 +2000 02 01 12 36633.60 0.00 285.00 0.00 -3.33 -3.33 394.81 +2000 02 02 12 36633.60 0.00 374.63 0.00 -2.28 -2.28 378.35 +2000 02 03 12 36633.60 0.00 257.66 0.00 -0.63 -0.63 422.90 +2000 02 04 12 36979.20 0.00 213.79 0.00 2.05 2.05 513.37 +2000 02 05 12 36979.20 0.00 359.37 0.00 0.75 0.75 426.91 +2000 02 06 12 36979.20 0.00 362.45 0.00 -0.19 -0.19 353.49 +2000 02 07 12 37324.80 0.00 377.65 0.00 2.77 2.77 451.51 +2000 02 08 12 37324.80 0.00 346.27 0.00 3.55 3.55 506.45 +2000 02 09 12 37329.27 0.00 387.01 0.00 3.20 3.20 535.67 +2000 02 10 12 37670.40 0.00 369.43 0.00 6.99 6.99 590.80 +2000 02 11 12 37670.40 0.53 255.13 0.00 9.58 9.58 805.16 +2000 02 12 12 37933.29 2.47 106.32 0.00 4.18 4.18 646.34 +2000 02 13 12 38016.00 0.00 215.55 0.00 1.43 1.43 585.87 +2000 02 14 12 38016.00 7.21 321.11 0.00 10.09 10.09 997.51 +2000 02 15 12 38361.60 0.00 401.58 0.00 6.59 6.59 577.29 +2000 02 16 12 38361.60 0.00 360.11 0.00 6.96 6.96 697.59 +2000 02 17 12 38361.60 0.00 375.72 0.00 5.37 5.37 539.64 +2000 02 18 12 38707.20 26.12 157.44 0.00 1.30 1.30 548.63 +2000 02 19 12 38707.20 2.92 277.36 0.00 7.17 7.17 773.42 +2000 02 20 12 39050.01 0.00 385.21 0.00 4.18 4.18 543.94 +2000 02 21 12 39052.80 0.00 411.38 0.00 3.36 3.36 446.85 +2000 02 22 12 39052.80 0.00 372.08 0.00 4.00 4.00 502.97 +2000 02 23 12 39398.40 0.00 208.12 0.00 7.03 7.03 683.45 +2000 02 24 12 39398.40 0.00 401.71 0.00 11.22 11.22 885.93 +2000 02 25 12 39741.21 0.00 409.46 0.00 15.35 15.35 1195.63 +2000 02 26 12 39744.00 0.00 416.61 0.00 15.55 15.55 1160.58 +2000 02 27 12 39744.00 1.04 284.29 0.00 14.31 14.31 1284.79 +2000 02 28 12 40089.60 3.49 443.01 0.00 11.37 11.37 906.05 +2000 02 29 12 40089.60 1.78 389.38 0.00 10.66 10.66 799.61 +2000 03 01 12 40435.20 0.07 332.87 0.00 9.94 9.94 693.09 +2000 03 02 12 40435.20 0.00 451.73 0.00 11.12 11.12 772.99 +2000 03 03 12 40435.20 0.00 476.84 0.00 5.70 5.70 467.00 +2000 03 04 12 40780.80 0.00 390.16 0.00 8.31 8.31 520.42 +2000 03 05 12 40780.80 0.00 475.54 0.00 9.22 9.22 560.53 +2000 03 06 12 41126.40 0.00 471.15 0.00 11.64 11.64 656.65 +2000 03 07 12 41126.40 0.00 474.30 0.00 12.60 12.60 797.97 +2000 03 08 12 41188.91 0.00 471.93 0.00 17.05 17.05 987.20 +2000 03 09 12 41472.00 0.00 454.55 0.00 18.55 18.55 1151.16 +2000 03 10 12 41472.00 0.00 481.31 0.00 17.36 17.36 1088.80 +2000 03 11 12 41817.60 2.57 276.80 0.00 15.65 15.65 1192.24 +2000 03 12 12 41817.60 4.96 343.52 0.00 12.03 12.03 1040.11 +2000 03 13 12 42163.20 0.00 428.91 0.00 3.14 3.14 415.74 +2000 03 14 12 42163.20 0.00 505.13 0.00 6.57 6.57 519.49 +2000 03 15 12 42163.20 0.00 373.00 0.00 9.79 9.79 662.83 +2000 03 16 12 42508.80 8.79 189.58 0.00 11.79 11.79 961.51 +2000 03 17 12 42508.80 11.16 376.15 0.00 14.22 14.22 1300.03 +2000 03 18 12 42854.40 0.00 514.58 0.00 2.08 2.08 399.81 +2000 03 19 12 42854.40 0.00 200.56 0.00 3.93 3.93 628.58 +2000 03 20 12 43200.00 9.82 130.39 0.00 7.44 7.44 924.22 +2000 03 21 12 43200.00 11.56 136.57 0.00 6.87 6.87 901.35 +2000 03 22 12 43200.00 0.50 260.19 0.00 7.24 7.24 860.47 +2000 03 23 12 43545.60 0.00 442.85 0.00 10.73 10.73 988.03 +2000 03 24 12 43545.60 0.00 431.59 0.00 13.11 13.11 1014.29 +2000 03 25 12 43891.20 0.00 429.19 0.00 15.02 15.02 1180.40 +2000 03 26 12 43891.20 1.94 537.29 0.00 15.51 15.51 990.90 +2000 03 27 12 44236.80 3.91 386.41 0.00 12.82 12.82 859.40 +2000 03 28 12 44236.80 0.17 440.81 0.00 12.34 12.34 938.58 +2000 03 29 12 44236.80 0.00 544.40 0.00 7.88 7.88 578.95 +2000 03 30 12 44582.40 0.79 388.56 0.00 10.06 10.06 782.31 +2000 03 31 12 44582.40 0.00 548.49 0.00 10.68 10.68 740.63 +2000 04 01 12 44928.00 0.00 509.88 0.00 12.23 12.23 748.81 +2000 04 02 12 44928.00 1.84 167.63 0.00 12.13 12.13 1117.10 +2000 04 03 12 45211.09 3.92 260.58 0.00 17.16 17.16 1707.98 +2000 04 04 12 45273.60 5.83 228.05 0.00 16.51 16.51 1572.29 +2000 04 05 12 45273.60 0.00 510.61 0.00 6.81 6.81 499.53 +2000 04 06 12 45619.20 0.00 510.74 0.00 11.85 11.85 786.24 +2000 04 07 12 45619.20 0.00 502.36 0.00 18.00 18.00 1173.16 +2000 04 08 12 45964.80 16.88 335.35 0.00 15.18 15.18 1292.54 +2000 04 09 12 45964.80 0.00 537.16 0.00 6.49 6.49 574.54 +2000 04 10 12 45964.80 0.00 520.54 0.00 9.78 9.78 628.69 +2000 04 11 12 46310.40 0.00 342.18 0.00 13.92 13.92 966.57 +2000 04 12 12 46310.40 0.00 362.90 0.00 15.31 15.31 1169.12 +2000 04 13 12 46656.00 0.99 300.15 0.00 7.65 7.65 724.77 +2000 04 14 12 46656.00 0.44 169.46 0.00 8.19 8.19 888.14 +2000 04 15 12 46658.79 3.18 134.04 0.00 12.27 12.27 1322.72 +2000 04 16 12 47001.60 0.00 357.39 0.00 17.40 17.40 1638.28 +2000 04 17 12 47001.60 13.18 272.72 0.00 18.77 18.77 1698.66 +2000 04 18 12 47347.20 33.87 145.21 0.00 12.41 12.41 1345.03 +2000 04 19 12 47347.20 3.00 481.54 0.00 11.65 11.65 1106.47 +2000 04 20 12 47349.99 0.00 346.33 0.00 14.32 14.32 1287.53 +2000 04 21 12 47692.80 0.89 405.91 0.00 17.46 17.46 1522.39 +2000 04 22 12 47692.80 0.00 265.88 0.00 11.90 11.90 966.28 +2000 04 23 12 48038.40 0.00 452.27 0.00 12.39 12.39 975.06 +2000 04 24 12 48038.40 0.00 193.32 0.00 11.05 11.05 968.10 +2000 04 25 12 48038.40 22.00 126.97 0.00 8.53 8.53 1052.09 +2000 04 26 12 48384.00 1.91 398.63 0.00 10.45 10.45 1036.67 +2000 04 27 12 48384.00 0.33 531.50 0.00 11.70 11.70 1025.54 +2000 04 28 12 48466.71 0.01 329.98 0.00 13.01 13.01 1159.42 +2000 04 29 12 48729.60 4.75 454.80 0.00 13.67 13.67 1130.05 +2000 04 30 12 48729.60 0.00 544.56 0.00 15.12 15.12 922.93 +2000 05 01 12 49070.73 0.00 559.05 0.00 16.38 16.38 1011.74 +2000 05 02 12 49075.20 0.00 394.38 0.00 18.15 18.15 1387.83 +2000 05 03 12 49075.20 0.00 557.77 0.00 16.07 16.07 1254.03 +2000 05 04 12 49420.80 0.00 549.68 0.00 17.25 17.25 1429.69 +2000 05 05 12 49420.80 0.00 542.53 0.00 20.55 20.55 1676.09 +2000 05 06 12 49420.80 0.15 537.94 0.00 22.48 22.48 1736.64 +2000 05 07 12 49766.40 0.00 549.48 0.00 23.73 23.73 1883.17 +2000 05 08 12 49766.40 0.00 548.73 0.00 22.83 22.83 1840.78 +2000 05 09 12 49766.40 0.00 548.51 0.00 23.17 23.17 2003.54 +2000 05 10 12 50107.53 0.00 527.26 0.00 22.61 22.61 1843.43 +2000 05 11 12 50112.00 0.00 566.11 0.00 17.34 17.34 1074.99 +2000 05 12 12 50112.00 0.00 549.59 0.00 21.13 21.13 1715.69 +2000 05 13 12 50308.08 0.00 479.57 0.00 24.98 24.98 2196.18 +2000 05 14 12 50457.60 1.57 572.52 0.00 19.82 19.82 1434.99 +2000 05 15 12 50457.60 0.00 576.98 0.00 13.97 13.97 847.17 +2000 05 16 12 50460.39 0.00 571.49 0.00 15.65 15.65 926.45 +2000 05 17 12 50803.20 0.13 535.32 0.00 18.47 18.47 1409.39 +2000 05 18 12 50803.20 0.00 529.61 0.00 22.09 22.09 1959.36 +2000 05 19 12 50803.20 0.24 427.20 0.00 23.15 23.15 2079.07 +2000 05 20 12 50999.28 3.53 410.40 0.00 23.48 23.48 2216.32 +2000 05 21 12 51148.80 10.48 419.82 0.00 20.12 20.12 1942.94 +2000 05 22 12 51148.80 27.71 376.66 0.00 20.46 20.46 1915.51 +2000 05 23 12 51148.80 0.20 445.99 0.00 19.68 19.68 1582.97 +2000 05 24 12 51231.51 0.00 537.03 0.00 21.96 21.96 1911.62 +2000 05 25 12 51494.40 1.34 371.49 0.00 22.79 22.79 1981.85 +2000 05 26 12 51494.40 0.20 570.28 0.00 19.20 19.20 1299.05 +2000 05 27 12 51494.40 3.82 298.98 0.00 19.77 19.77 1751.22 +2000 05 28 12 51494.40 13.77 344.08 0.00 20.78 20.78 2153.76 +2000 05 29 12 51690.48 10.71 162.18 0.00 15.12 15.12 1583.32 +2000 05 30 12 51840.00 0.70 485.87 0.00 13.56 13.56 1248.62 +2000 05 31 12 51840.00 0.00 558.88 0.00 16.16 16.16 1357.64 +2000 06 01 12 51840.00 0.00 528.92 0.00 22.40 22.40 1929.27 +2000 06 02 12 51840.00 0.03 548.06 0.00 27.17 27.17 2323.40 +2000 06 03 12 51842.79 2.72 457.73 0.00 25.45 25.45 2224.36 +2000 06 04 12 52123.09 0.00 331.71 0.00 20.37 20.37 1942.19 +2000 06 05 12 52185.60 1.50 321.07 0.00 20.30 20.30 2013.86 +2000 06 06 12 52185.60 17.83 497.52 0.00 19.20 19.20 1648.24 +2000 06 07 12 52185.60 0.45 573.22 0.00 15.98 15.98 1148.14 +2000 06 08 12 52185.60 0.00 558.36 0.00 18.45 18.45 1360.25 +2000 06 09 12 52185.60 0.00 561.52 0.00 21.45 21.45 1701.46 +2000 06 10 12 52185.60 0.00 557.78 0.00 23.80 23.80 2051.81 +2000 06 11 12 52185.60 0.00 556.54 0.00 24.87 24.87 2225.04 +2000 06 12 12 52268.31 0.01 519.50 0.00 26.68 26.68 2258.68 +2000 06 13 12 52381.68 0.00 473.82 0.00 28.28 28.28 2393.99 +2000 06 14 12 52468.69 14.58 316.41 0.00 26.08 26.08 2651.93 +2000 06 15 12 52526.73 1.92 440.48 0.00 26.22 26.22 2652.43 +2000 06 16 12 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a/test/test_data/camels_us/basin_mean_forcing/nldas/03015500_lump_nldas_forcing_leap.txt b/test/test_data/camels_us/basin_mean_forcing/nldas/03015500_lump_nldas_forcing_leap.txt new file mode 100644 index 00000000..f480d37a --- /dev/null +++ b/test/test_data/camels_us/basin_mean_forcing/nldas/03015500_lump_nldas_forcing_leap.txt @@ -0,0 +1,1100 @@ + 41.91 + 477.00 + 831030801 +Year Mnth Day Hr Dayl(s) PRCP(mm/day) SRAD(W/m2) SWE(mm) Tmax(C) Tmin(C) Vp(Pa) +2000 01 01 12 32486.40 0.15 264.31 0.00 -1.46 -1.46 477.43 +2000 01 02 12 32486.40 0.31 166.58 0.00 4.46 4.46 773.48 +2000 01 03 12 32486.40 3.90 115.79 0.00 8.16 8.16 1024.72 +2000 01 04 12 32486.40 18.56 141.44 0.00 8.39 8.39 1053.29 +2000 01 05 12 32613.94 0.00 174.05 0.00 -2.90 -2.90 391.68 +2000 01 06 12 32815.44 0.04 153.65 0.00 -2.86 -2.86 409.66 +2000 01 07 12 32832.00 0.27 133.06 0.00 -2.15 -2.15 421.47 +2000 01 08 12 32832.00 0.31 265.50 0.00 -3.28 -3.28 341.26 +2000 01 09 12 32832.00 0.37 118.99 0.00 0.63 0.63 482.51 +2000 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-8.93 -8.93 230.38 +2000 01 27 12 34560.00 0.33 322.77 0.00 -14.21 -14.21 135.41 +2000 01 28 12 34905.60 0.00 332.07 0.00 -13.69 -13.69 158.88 +2000 01 29 12 34905.60 0.18 332.45 0.00 -9.33 -9.33 205.78 +2000 01 30 12 34907.52 0.77 87.90 0.00 -4.46 -4.46 294.73 +2000 01 31 12 35251.20 0.07 115.67 0.00 -3.87 -3.87 409.81 +2000 02 01 12 35251.20 0.70 80.04 0.00 -6.09 -6.09 318.25 +2000 02 02 12 35449.98 0.28 303.36 0.00 -7.77 -7.77 211.28 +2000 02 03 12 35596.80 1.82 142.64 0.00 -4.54 -4.54 343.57 +2000 02 04 12 35596.80 0.87 150.83 0.00 -2.08 -2.08 484.52 +2000 02 05 12 35942.40 0.08 303.80 0.00 -5.24 -5.24 322.78 +2000 02 06 12 35942.40 0.00 315.32 0.00 -4.04 -4.04 289.91 +2000 02 07 12 36209.87 0.01 214.22 0.00 -2.26 -2.26 355.14 +2000 02 08 12 36288.00 0.00 325.50 0.00 -6.12 -6.12 179.03 +2000 02 09 12 36289.92 0.00 262.26 0.00 -0.55 -0.55 350.99 +2000 02 10 12 36633.60 1.23 131.29 0.00 1.21 1.21 530.91 +2000 02 11 12 36633.60 6.22 137.47 0.00 -0.11 -0.11 565.15 +2000 02 12 12 36979.20 0.18 332.92 0.00 -6.63 -6.63 211.35 +2000 02 13 12 36979.20 0.98 204.89 0.00 -2.86 -2.86 329.82 +2000 02 14 12 37246.67 11.17 105.22 0.00 -1.24 -1.24 528.19 +2000 02 15 12 37324.80 0.11 324.96 0.00 -4.66 -4.66 373.33 +2000 02 16 12 37383.15 0.61 154.09 0.00 -0.85 -0.85 510.07 +2000 02 17 12 37670.40 0.03 343.94 0.00 -6.13 -6.13 285.18 +2000 02 18 12 37670.40 1.23 132.26 0.00 -2.53 -2.53 380.22 +2000 02 19 12 38016.00 2.39 170.81 0.00 -2.97 -2.97 447.07 +2000 02 20 12 38016.00 0.19 185.29 0.00 -3.87 -3.87 409.12 +2000 02 21 12 38361.60 1.67 317.82 0.00 -2.31 -2.31 422.65 +2000 02 22 12 38361.60 0.03 193.35 0.00 1.77 1.77 478.30 +2000 02 23 12 38707.20 0.00 157.94 0.00 6.15 6.15 742.56 +2000 02 24 12 38707.20 0.01 186.18 0.00 8.90 8.90 865.09 +2000 02 25 12 39052.80 6.94 309.66 0.00 11.10 11.10 1129.22 +2000 02 26 12 39052.80 0.00 328.85 0.00 12.54 12.54 1109.26 +2000 02 27 12 39398.40 4.52 123.69 0.00 10.60 10.60 1054.23 +2000 02 28 12 39398.40 0.30 293.25 0.00 3.60 3.60 598.82 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+14400000;5.55607118412047;2.27966758384668;-1.01594616388723;0.0243303906906382;0.410302083666978;19.3;1.53784860557769;djf;237;6.90962099125364;jja diff --git a/test/test_data/camels_us/camels_attributes_v2.0/camels_geol.txt b/test/test_data/camels_us/camels_attributes_v2.0/camels_geol.txt new file mode 100644 index 00000000..9b2d645a --- /dev/null +++ b/test/test_data/camels_us/camels_attributes_v2.0/camels_geol.txt @@ -0,0 +1,672 @@ +gauge_id;geol_1st_class;glim_1st_class_frac;geol_2nd_class;glim_2nd_class_frac;carbonate_rocks_frac;geol_porostiy;geol_permeability +01013500;Siliciclastic sedimentary rocks;0.815904446815535;Basic volcanic rocks;0.179729452382194;0;0.1714;-14.7019 +01022500;Acid plutonic rocks;0.590658158506066;Siliciclastic sedimentary rocks;0.164618210344753;0;0.071;-14.2138 +01030500;Siliciclastic sedimentary rocks;0.573305400935238;Metamorphics;0.287010005606611;0.0521400935487284;0.1178;-14.4918 +01031500;Siliciclastic sedimentary rocks;0.448927859249061;Metamorphics;0.443862824494092;0.0262579789051996;0.0747;-14.841 +01047000;Metamorphics;0.308488043880523;Acid plutonic rocks;0.288612546329161;0;0.0522;-14.4819 +01052500;Siliciclastic sedimentary rocks;0.497458334479149;Metamorphics;0.374061696779951;0;0.0711;-15.1658 +01054200;Metamorphics;0.87144277772791;Siliciclastic sedimentary rocks;0.104295202824522;0;0.0288;-14.2147 +01055000;Siliciclastic sedimentary rocks;0.680843374255509;Metamorphics;0.168363152735523;0;0.1455;-14.3578 +01057000;Metamorphics;0.451110620382826;Acid plutonic rocks;0.409240381612252;0.0774334159306445;0.0251;-13.9903 +01073000;Metamorphics;0.943375194531556;Acid plutonic rocks;0.0566248054684436;0;0.01;-14.1 +01078000;Acid plutonic rocks;0.535649718608006;Metamorphics;0.458790179882329;0;0.011;-14.1061 +01118300;Acid plutonic rocks;0.759567143145735;Metamorphics;0.240432856854265;0;0.01;-14.1 +01121000;Metamorphics;1;NA;0;0;0.01;-14.1 +01123000;Metamorphics;1;NA;0;0;0.01;-14.1 +01134500;Metamorphics;0.622420641760885;Acid plutonic rocks;0.369333601870038;0;0.0109;-14.1198 +01137500;Acid plutonic rocks;0.791579905460923;Metamorphics;0.198146815659468;0;0.01;-14.1 +01139000;Carbonate sedimentary rocks;0.521678756766897;Acid plutonic rocks;0.286851760273367;0.521678756766897;0.0371;-12.922 +01139800;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +01142500;Carbonate sedimentary rocks;0.764501415297893;Metamorphics;0.0979583173139352;0.764501415297893;0.0585;-12.5655 +01144000;Metamorphics;0.663469277959176;Carbonate sedimentary rocks;0.323706719607693;0.323706719607693;0.0273;-13.3802 +01162500;Acid plutonic rocks;0.796052853540842;Metamorphics;0.203947146459158;0;0.01;-14.1 +01169000;Metamorphics;0.894390473275735;Siliciclastic sedimentary rocks;0.0859176613144848;0.0196918654097801;0.0204;-14.2609 +01170100;Metamorphics;0.538440036558264;Carbonate sedimentary rocks;0.318774098466062;0.318774098466062;0.0416;-13.7095 +01181000;Metamorphics;0.824255288283291;Acid plutonic rocks;0.165247556945932;0.00299517599801272;0.0112;-14.1061 +01187300;Siliciclastic sedimentary rocks;0.709479264050831;Acid plutonic rocks;0.143617007569745;0;0.1377;-14.8804 +01195100;Metamorphics;1;NA;0;0;0.01;-14.1 +01333000;Metamorphics;0.663358494099292;Carbonate sedimentary rocks;0.312362723516054;0.312362723516054;0.0302;-13.3765 +01350000;Siliciclastic sedimentary rocks;0.999449792702365;Water bodies;0.000550207297634583;0;0.1897;-15.2049 +01350080;Siliciclastic sedimentary rocks;0.997652206074361;Water bodies;0.00234779392563892;0;0.1802;-15.3774 +01350140;Siliciclastic sedimentary rocks;0.997893020338537;Water bodies;0.00210697966146313;0;0.1573;-15.8034 +01365000;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +01411300;Unconsolidated sediments;1;NA;0;0;0.22;-13 +01413500;Siliciclastic sedimentary rocks;0.999905874798265;Water bodies;9.41252017347397e-05;0;0.19;-15.1998 +01414500;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +01415000;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +01423000;Siliciclastic sedimentary rocks;0.999860585552546;Water bodies;0.000139414447453777;0;0.19;-15.1997 +01434025;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +01435000;Siliciclastic sedimentary rocks;0.999775793066617;Water bodies;0.000224206933382753;0;0.19;-15.1995 +01439500;Siliciclastic sedimentary rocks;0.998798736196798;Mixed sedimentary rocks;0.00120126380320172;0;0.1848;-15.2772 +01440000;Siliciclastic sedimentary rocks;0.844166670429744;Carbonate sedimentary rocks;0.149521985010988;0.149521985010988;0.1826;-14.227 +01440400;Siliciclastic sedimentary rocks;1;NA;0;0;0.1997;-14.8732 +01451800;Siliciclastic sedimentary rocks;0.997188070331833;Carbonate sedimentary rocks;0.00281192966816722;0.00281192966816722;0.134;-16.2182 +01466500;Unconsolidated sediments;1;NA;0;0;0.22;-13 +01484100;Unconsolidated sediments;1;NA;0;0;0.22;-13 +01485500;Unconsolidated sediments;1;NA;0;0;0.28;-10.9 +01486000;Unconsolidated sediments;1;NA;0;0;0.28;-10.9 +01487000;Unconsolidated sediments;1;NA;0;0;0.22;-13 +01491000;Unconsolidated sediments;1;NA;0;0;0.2365;-12.4235 +01510000;Mixed sedimentary rocks;0.836519363663835;Siliciclastic sedimentary rocks;0.0914729282084141;0.072007708127751;0.1221;-16.0426 +01516500;Siliciclastic sedimentary rocks;1;NA;0;0;0.1734;-15.4207 +01518862;Siliciclastic sedimentary rocks;1;NA;0;0;0.1615;-15.7286 +01532000;Siliciclastic sedimentary rocks;1;NA;0;0;0.1949;-14.736 +01539000;Siliciclastic sedimentary rocks;1;NA;0;0;0.1661;-15.5264 +01542810;Siliciclastic sedimentary rocks;1;NA;0;0;0.1953;-15.0206 +01543000;Siliciclastic sedimentary rocks;1;NA;0;0;0.2067;-14.6131 +01543500;Siliciclastic sedimentary rocks;1;NA;0;0;0.2146;-14.3537 +01544500;Siliciclastic sedimentary rocks;1;NA;0;0;0.1897;-15.1862 +01545600;Siliciclastic sedimentary rocks;1;NA;0;0;0.2206;-14.1559 +01547700;Siliciclastic sedimentary rocks;0.997024729715006;Carbonate sedimentary rocks;0.00297527028499441;0.00297527028499441;0.1826;-15.1697 +01548500;Siliciclastic sedimentary rocks;1;NA;0;0;0.2004;-14.8083 +01549500;Siliciclastic sedimentary rocks;1;NA;0;0;0.1958;-15.0026 +01550000;Siliciclastic sedimentary rocks;1;NA;0;0;0.2116;-14.3602 +01552000;Siliciclastic sedimentary rocks;1;NA;0;0;0.2093;-14.4365 +01552500;Siliciclastic sedimentary rocks;1;NA;0;0;0.1948;-15.0374 +01557500;Siliciclastic sedimentary rocks;0.926013295061592;Carbonate sedimentary rocks;0.0459601055434723;0.0459601055434723;0.1592;-15.272 +01567500;Siliciclastic sedimentary rocks;0.551590718676682;Carbonate sedimentary rocks;0.225621090995123;0.225621090995123;0.1141;-14.897 +01568000;Siliciclastic sedimentary rocks;0.531926570659407;Mixed sedimentary rocks;0.2126421097036;0.113782284235811;0.1027;-15.5296 +01580000;Metamorphics;0.999733839695157;Basic plutonic rocks;0.00026616030484326;0;0.01;-14.1 +01583500;Metamorphics;0.829405456786752;Carbonate sedimentary rocks;0.168434278878246;0.168434278878246;0.019;-13.7057 +01586610;Metamorphics;0.961900930450197;Basic volcanic rocks;0.0380990695498032;0;0.013;-14.039 +01591400;Metamorphics;0.993616014356905;Basic plutonic rocks;0.00638398564309513;0;0.01;-14.1 +01594950;Siliciclastic sedimentary rocks;1;NA;0;0;0.1321;-16.2759 +01596500;Siliciclastic sedimentary rocks;1;NA;0;0;0.1762;-15.2383 +01605500;Siliciclastic sedimentary rocks;0.642279561600673;Mixed sedimentary rocks;0.192518710553239;0.165201727846088;0.1845;-13.8824 +01606500;Siliciclastic sedimentary rocks;0.653422514096553;Carbonate sedimentary rocks;0.218157715931334;0.218157715931334;0.1651;-14.1648 +01613050;Siliciclastic sedimentary rocks;1;NA;0;0;0.1863;-15.2693 +01620500;Siliciclastic sedimentary rocks;1;NA;0;0;0.15;-15.9424 +01632000;Siliciclastic sedimentary rocks;0.904374610412785;Mixed sedimentary rocks;0.0815657883426893;0.0140596012445258;0.1627;-15.5394 +01632900;Carbonate sedimentary rocks;0.600787374828891;Siliciclastic sedimentary rocks;0.390739777455612;0.600787374828891;0.118;-12.9503 +01634500;Siliciclastic sedimentary rocks;0.763323565139575;Mixed sedimentary rocks;0.226186983282488;0.0104894515779372;0.1807;-15.0288 +01638480;Acid plutonic rocks;0.585641247292201;Metamorphics;0.215729590583884;0.00358403621985603;0.0249;-13.8061 +01639500;Metamorphics;0.625398343808927;Siliciclastic sedimentary rocks;0.27079113821372;0.0188350005556674;0.0692;-14.1274 +01644000;Acid plutonic rocks;0.551756051702972;Basic volcanic rocks;0.207084130301435;0.00186892239855705;0.0357;-13.7579 +01658500;Metamorphics;0.570399229177797;Siliciclastic sedimentary rocks;0.429600770822203;0;0.1217;-13.4126 +01664000;Metamorphics;0.526435602877036;Acid plutonic rocks;0.205444346041605;0.000526220239787649;0.036;-13.969 +01666500;Metamorphics;0.419864080188452;Siliciclastic sedimentary rocks;0.296386458255834;0.00471819521933519;0.0668;-14.2526 +01667500;Metamorphics;0.342850107287653;Siliciclastic sedimentary rocks;0.308524043821612;0.0018122148538136;0.0758;-14.1348 +01669000;Unconsolidated sediments;1;NA;0;0;0.22;-13 +01669520;Unconsolidated sediments;1;NA;0;0;0.2364;-12.4147 +02011400;Siliciclastic sedimentary rocks;0.602931221853005;Mixed sedimentary rocks;0.388764826510665;0.00749079206052457;0.1903;-14.6756 +02011460;Siliciclastic sedimentary rocks;0.907235232209663;Mixed sedimentary rocks;0.0885403388285152;0;0.1663;-15.5249 +02013000;Siliciclastic sedimentary rocks;0.845477711361184;Mixed sedimentary rocks;0.0952784034747456;0.0592438851640706;0.166;-15.1955 +02014000;Siliciclastic sedimentary rocks;0.806181306945497;Mixed sedimentary rocks;0.185444753536585;0.00837393951791807;0.1647;-15.4203 +02015700;Siliciclastic sedimentary rocks;0.552382367965338;Mixed sedimentary rocks;0.44625633066481;0.00129320601644648;0.1998;-14.4666 +02016000;Siliciclastic sedimentary rocks;0.74776708258271;Mixed sedimentary rocks;0.251907256519417;0.000309370607991626;0.1715;-15.2353 +02017500;Siliciclastic sedimentary rocks;0.691671969416604;Mixed sedimentary rocks;0.294438551481203;0.0138894791021928;0.1698;-15.1362 +02018000;Siliciclastic sedimentary rocks;0.757566645185594;Mixed sedimentary rocks;0.224710922823038;0.017722431991368;0.1626;-15.3324 +02027000;Metamorphics;0.52077053620051;Acid plutonic rocks;0.421060400475542;0;0.0128;-14.0448 +02027500;Acid plutonic rocks;0.621209557109168;Metamorphics;0.324502574527539;0;0.01;-14.1 +02028500;Metamorphics;0.604543414575887;Basic volcanic rocks;0.210707796101242;0;0.0271;-13.7687 +02038850;Siliciclastic sedimentary rocks;0.778358889973949;Metamorphics;0.154838599313989;0;0.0996;-15.8882 +02046000;Metamorphics;0.699590219483755;Acid plutonic rocks;0.255354173888654;0;0.0146;-14.046 +02051000;Metamorphics;0.891577846834585;Acid plutonic rocks;0.0929900485005312;0;0.01;-14.1 +02051500;Metamorphics;0.581439835258042;Acid plutonic rocks;0.346672892466263;0;0.0114;-14.0727 +02053200;Unconsolidated sediments;0.984255625085723;Siliciclastic sedimentary rocks;0.015744374914277;0;0.2195;-13.0346 +02053800;Metamorphics;0.480013578181848;Siliciclastic sedimentary rocks;0.326611715977813;0.0729889382588663;0.0496;-14.716 +02055100;Carbonate sedimentary rocks;0.711894666487338;Siliciclastic sedimentary rocks;0.233162934144311;0.711894666487338;0.0855;-12.9343 +02056900;Metamorphics;0.874492841440639;Acid plutonic rocks;0.0744660718315568;0;0.015;-14.1418 +02059500;Metamorphics;0.742918756453601;Siliciclastic sedimentary rocks;0.204644009130615;0.00410086073743985;0.0333;-14.5692 +02064000;Metamorphics;0.666461879225841;Siliciclastic sedimentary rocks;0.330859415166194;0;0.0533;-14.7619 +02065500;Acid volcanic rocks;0.341042015889036;Acid plutonic rocks;0.315779324696601;0;0.065;-14.1599 +02069700;Metamorphics;0.6446019839416;Acid plutonic rocks;0.179976130067979;0;0.0293;-14.521 +02070000;Metamorphics;0.750307914626826;Acid plutonic rocks;0.130250961945046;0;0.0154;-14.2184 +02074500;Metamorphics;0.849477169048237;Siliciclastic sedimentary rocks;0.0863988522272647;0;0.0312;-14.0036 +02077200;Metamorphics;1;NA;0;0;0.01;-14.1 +02081500;Metamorphics;0.814531673274444;Siliciclastic sedimentary rocks;0.178727035105163;0;0.0507;-14.0159 +02082950;Metamorphics;0.663425716451175;Acid plutonic rocks;0.25462301722777;0;0.0267;-14.0079 +02092500;Unconsolidated sediments;0.617161542361913;Carbonate sedimentary rocks;0.382838457638087;0.382838457638087;0.1587;-12.5406 +02096846;Metamorphics;0.810392128336641;Intermediate volcanic rocks;0.189607871663359;0;0.0252;-13.7966 +02102908;Unconsolidated sediments;0.984697843327091;Siliciclastic sedimentary rocks;0.0153021566729091;0;0.2786;-10.9658 +02108000;Unconsolidated sediments;0.931107645694035;Carbonate sedimentary rocks;0.0688923543059651;0.0688923543059651;0.209;-12.9173 +02111180;Metamorphics;1;NA;0;0;0.01;-14.1 +02111500;Metamorphics;1;NA;0;0;0.01;-14.1 +02112120;Metamorphics;0.829655125253756;Acid plutonic rocks;0.170344874746244;0;0.01;-14.1 +02112360;Metamorphics;1;NA;0;0;0.01;-14.1 +02118500;Metamorphics;1;NA;0;0;0.01;-14.1 +02125000;Siliciclastic sedimentary rocks;0.615394973786281;Metamorphics;0.384605026213719;0;0.1208;-14.7769 +02128000;Metamorphics;0.853690369881646;Siliciclastic sedimentary rocks;0.146309630118354;0;0.0363;-14.2609 +02137727;Metamorphics;0.997922127845432;Siliciclastic sedimentary rocks;0.00207787215456851;0;0.0104;-14.1023 +02140991;Metamorphics;0.958175752179893;Acid plutonic rocks;0.0418242478201066;0;0.01;-14.1 +02143000;Metamorphics;1;NA;0;0;0.01;-14.1 +02143040;Metamorphics;1;NA;0;0;0.01;-14.1 +02149000;Metamorphics;1;NA;0;0;0.01;-14.1 +02152100;Metamorphics;1;NA;0;0;0.01;-14.1 +02177000;Metamorphics;0.901321256484654;Acid plutonic rocks;0.0943378719034562;0;0.01;-14.1 +02178400;Metamorphics;0.957112234127722;Acid plutonic rocks;0.0362764598817192;0;0.0101;-14.0994 +02193340;Metamorphics;0.654942346952016;Acid plutonic rocks;0.345057653047984;0;0.01;-14.1 +02196000;Metamorphics;0.820199337841139;Acid plutonic rocks;0.137584164723251;0;0.0141;-14.0784 +02198100;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +02202600;Unconsolidated sediments;0.7578625468364;Siliciclastic sedimentary rocks;0.24122003638425;0;0.2323;-12.847 +02212600;Metamorphics;0.876013614794757;Basic plutonic rocks;0.121695451287377;0;0.01;-14.1 +02215100;Carbonate sedimentary rocks;0.717558452821849;Unconsolidated sediments;0.183771705631641;0.717558452821849;0.0894;-12.5398 +02216180;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +02221525;Metamorphics;0.998437365010984;Water bodies;0.00106099678432587;0;0.0102;-14.0988 +02231000;Unconsolidated sediments;0.939110290022701;Siliciclastic sedimentary rocks;0.0607075466921302;0;0.2217;-13.0101 +02231342;Unconsolidated sediments;0.98782094652533;Carbonate sedimentary rocks;0.0121790534746704;0.0121790534746704;0.2181;-12.9854 +02235200;Unconsolidated sediments;1;NA;0;0;0.22;-13 +02245500;Unconsolidated sediments;1;NA;0;0;0.22;-13 +02246000;Unconsolidated sediments;1;NA;0;0;0.22;-13 +02296500;Unconsolidated sediments;0.919545867808741;Siliciclastic sedimentary rocks;0.0804541321912587;0;0.2675;-11.431 +02297155;Unconsolidated sediments;0.967831721231695;Siliciclastic sedimentary rocks;0.0321682787683052;0;0.219;-13.0708 +02297310;Siliciclastic sedimentary rocks;0.596948710525639;Unconsolidated sediments;0.403051289474361;0;0.2021;-14.3133 +02298123;Carbonate sedimentary rocks;0.614796827160728;Unconsolidated sediments;0.356563630868354;0.614796827160728;0.1313;-11.9556 +02298608;Unconsolidated sediments;0.972752699866987;Siliciclastic sedimentary rocks;0.027247300133013;0;0.2192;-13.0599 +02299950;Unconsolidated sediments;1;NA;0;0;0.22;-13 +02300700;Unconsolidated sediments;0.701885393419089;Siliciclastic sedimentary rocks;0.290839797499045;0.00727480908186606;0.2101;-13.6311 +02310947;Unconsolidated sediments;0.858097778170796;Carbonate sedimentary rocks;0.141902221829204;0.141902221829204;0.2462;-11.1176 +02312200;Unconsolidated sediments;0.510069567308172;Carbonate sedimentary rocks;0.489930432691828;0.489930432691828;0.169;-11.4552 +02314500;Unconsolidated sediments;0.999496118859787;Water bodies;0.000503881140213435;0;0.2543;-11.7986 +02315500;Unconsolidated sediments;0.866708418434288;Siliciclastic sedimentary rocks;0.132503555405379;0;0.2452;-12.269 +02324400;Unconsolidated sediments;1;NA;0;0;0.22;-13 +02327100;Unconsolidated sediments;0.970085266905506;Carbonate sedimentary rocks;0.0299147330944936;0.0299147330944936;0.2152;-12.9641 +02342933;Unconsolidated sediments;0.987847403043524;Mixed sedimentary rocks;0.0121525969564764;0;0.2272;-12.7573 +02349900;Carbonate sedimentary rocks;0.866706259604174;Siliciclastic sedimentary rocks;0.132749353310773;0.866706259604174;0.0773;-12.2525 +02350900;Unconsolidated sediments;0.911789317394303;Siliciclastic sedimentary rocks;0.0767788755171916;0.0101653321950688;0.2325;-12.5837 +02361000;Unconsolidated sediments;0.724140833697359;Siliciclastic sedimentary rocks;0.219605919884016;0;0.1628;-14.2703 +02363000;Unconsolidated sediments;0.998605882041568;Siliciclastic sedimentary rocks;0.00139374370435393;0;0.2022;-13.169 +02369800;Unconsolidated sediments;1;NA;0;0;0.22;-13 +02371500;Unconsolidated sediments;0.980385220860512;Carbonate sedimentary rocks;0.0119474826491956;0.0119474826491956;0.2036;-13.1163 +02372250;Unconsolidated sediments;0.984187427355848;Carbonate sedimentary rocks;0.0158125726441524;0.0158125726441524;0.2051;-12.9898 +02374500;Unconsolidated sediments;0.715709380153751;Mixed sedimentary rocks;0.201239553930455;0;0.209;-13.6157 +02381600;Metamorphics;1;NA;0;0;0.01;-14.1 +02384540;Siliciclastic sedimentary rocks;1;NA;0;0;0.12;-16.5 +02395120;Carbonate sedimentary rocks;0.528965418071906;Mixed sedimentary rocks;0.296315100760036;0.528965418071906;0.1005;-13.7867 +02408540;Metamorphics;0.938780326828308;Acid plutonic rocks;0.0476663418452458;0;0.0114;-14.0771 +02415000;Metamorphics;0.899083116448072;Acid plutonic rocks;0.095760354416398;0;0.0104;-14.0917 +02422500;Unconsolidated sediments;0.955011810253244;Metamorphics;0.0262318414410296;0;0.2241;-12.7146 +02427250;Unconsolidated sediments;0.998631606319578;Siliciclastic sedimentary rocks;0.00135856971654971;9.82396387263089e-06;0.1718;-13.6538 +02430085;Unconsolidated sediments;1;NA;0;0;0.22;-13 +02430615;Unconsolidated sediments;1;NA;0;0;0.22;-13 +02450250;Siliciclastic sedimentary rocks;0.849873109911303;Carbonate sedimentary rocks;0.150117891116237;0.150117891116237;0.1705;-14.6896 +02464000;Siliciclastic sedimentary rocks;0.778522124494805;Unconsolidated sediments;0.221477875505195;0;0.1421;-15.7248 +02464146;Unconsolidated sediments;0.634121639656833;Siliciclastic sedimentary rocks;0.365878360343167;0;0.1834;-14.2806 +02464360;Unconsolidated sediments;0.745173159151308;Siliciclastic sedimentary rocks;0.254826840848692;0;0.1945;-13.8919 +02465493;Unconsolidated sediments;1;NA;0;0;0.232;-12.5809 +02469800;Unconsolidated sediments;0.865019346916069;Siliciclastic sedimentary rocks;0.134980653083931;0;0.209;-13.3145 +02472000;Unconsolidated sediments;0.903489166041712;Carbonate sedimentary rocks;0.0965108339582878;0.0965108339582878;0.2287;-12.0405 +02472500;Unconsolidated sediments;1;NA;0;0;0.2487;-11.9951 +02479155;Unconsolidated sediments;1;NA;0;0;0.2585;-11.6529 +02479300;Unconsolidated sediments;1;NA;0;0;0.2367;-12.4162 +02479560;Unconsolidated sediments;1;NA;0;0;0.2379;-12.3745 +02481000;Unconsolidated sediments;1;NA;0;0;0.2428;-12.2024 +02481510;Unconsolidated sediments;1;NA;0;0;0.2518;-11.8855 +03010655;Siliciclastic sedimentary rocks;1;NA;0;0;0.1893;-15.1986 +03011800;Siliciclastic sedimentary rocks;1;NA;0;0;0.2501;-13.1728 +03015500;Siliciclastic sedimentary rocks;1;NA;0;0;0.175;-15.466 +03021350;Siliciclastic sedimentary rocks;0.999982313960869;Water bodies;1.76860391311801e-05;0;0.1276;-16.3597 +03026500;Siliciclastic sedimentary rocks;1;NA;0;0;0.2212;-14.1475 +03028000;Siliciclastic sedimentary rocks;1;NA;0;0;0.2278;-13.925 +03049000;Siliciclastic sedimentary rocks;1;NA;0;0;0.1909;-15.1617 +03049800;Siliciclastic sedimentary rocks;1;NA;0;0;0.1451;-16.0333 +03050000;Siliciclastic sedimentary rocks;0.872262609579569;Unconsolidated sediments;0.072551286798189;0.0551861036222423;0.1797;-14.9513 +03066000;Siliciclastic sedimentary rocks;0.679580695049861;Carbonate sedimentary rocks;0.320419304950139;0.320419304950139;0.1388;-14.2876 +03069500;Siliciclastic sedimentary rocks;0.859052474111147;Carbonate sedimentary rocks;0.138111208510196;0.138111208510196;0.168;-14.8012 +03070500;Siliciclastic sedimentary rocks;0.998165405076187;Carbonate sedimentary rocks;0.00183459492381281;0.00183459492381281;0.1809;-15.2815 +03076600;Siliciclastic sedimentary rocks;1;NA;0;0;0.1923;-14.6633 +03078000;Siliciclastic sedimentary rocks;1;NA;0;0;0.148;-15.9667 +03140000;Siliciclastic sedimentary rocks;1;NA;0;0;0.12;-16.5 +03144000;Siliciclastic sedimentary rocks;1;NA;0;0;0.1337;-16.1342 +03159540;Siliciclastic sedimentary rocks;1;NA;0;0;0.12;-16.5 +03161000;Metamorphics;1;NA;0;0;0.01;-14.1 +03164000;Metamorphics;0.720980819190773;Acid plutonic rocks;0.200737153278384;0;0.0225;-14.0123 +03165000;Metamorphics;0.691696042096722;Siliciclastic sedimentary rocks;0.308303957903278;0;0.0439;-14.8399 +03170000;Metamorphics;0.820654484154169;Siliciclastic sedimentary rocks;0.174657729253726;0.00468778659210414;0.0363;-14.3792 +03173000;Siliciclastic sedimentary rocks;0.751739745108573;Carbonate sedimentary rocks;0.174389914067547;0.174389914067547;0.129;-15.3197 +03180500;Siliciclastic sedimentary rocks;0.978574042563195;Carbonate sedimentary rocks;0.0214259574368052;0.0214259574368052;0.187;-15.1304 +03182500;Siliciclastic sedimentary rocks;0.866232227997447;Carbonate sedimentary rocks;0.0968108325597525;0.0968108325597525;0.1711;-14.9304 +03186500;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +03187500;Siliciclastic sedimentary rocks;0.98259120857879;Unconsolidated sediments;0.0174087914212095;0;0.1905;-15.1617 +03213700;Siliciclastic sedimentary rocks;0.984140621186275;Mixed sedimentary rocks;0.0105901124401872;0.00526926637353809;0.1735;-15.4761 +03237280;Siliciclastic sedimentary rocks;0.991472145616181;Carbonate sedimentary rocks;0.00852785438381957;0.00852785438381957;0.122;-16.4139 +03237500;Mixed sedimentary rocks;0.646418702443683;Carbonate sedimentary rocks;0.215456675248387;0.215456675248387;0.1088;-15.4558 +03238500;Mixed sedimentary rocks;0.663552978362697;Carbonate sedimentary rocks;0.336447021637303;0.336447021637303;0.0998;-14.9187 +03241500;Carbonate sedimentary rocks;0.871500920547649;Mixed sedimentary rocks;0.128499079452351;0.871500920547649;0.0677;-12.4039 +03280700;Siliciclastic sedimentary rocks;1;NA;0;0;0.1743;-15.4907 +03281100;Siliciclastic sedimentary rocks;0.988201352473418;Unconsolidated sediments;0.0117986475265822;0;0.1317;-16.2624 +03281500;Siliciclastic sedimentary rocks;0.990075437682718;Unconsolidated sediments;0.00992456231728149;0;0.1378;-16.1537 +03285000;Carbonate sedimentary rocks;0.518657830264072;Siliciclastic sedimentary rocks;0.292415734854499;0.518657830264072;0.0949;-13.9498 +03291780;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +03300400;Mixed sedimentary rocks;0.796357744597813;Carbonate sedimentary rocks;0.195243528063585;0.195243528063585;0.1084;-15.5792 +03338780;Siliciclastic sedimentary rocks;0.674635433744648;Mixed sedimentary rocks;0.231254232545587;0.0941103337097648;0.1608;-15.1955 +03340800;Mixed sedimentary rocks;0.980283729316755;Carbonate sedimentary rocks;0.0167879562093645;0.0167879562093645;0.1192;-16.4173 +03346000;Siliciclastic sedimentary rocks;0.588651599921678;Carbonate sedimentary rocks;0.398747227600511;0.398747227600511;0.1373;-13.8606 +03357350;Mixed sedimentary rocks;0.918497810648034;Carbonate sedimentary rocks;0.0815021893519662;0.0815021893519662;0.1151;-16.1169 +03364500;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +03366500;Carbonate sedimentary rocks;0.857377770513553;Siliciclastic sedimentary rocks;0.142622229486447;0.857377770513553;0.0686;-12.4703 +03368000;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +03384450;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +03439000;Metamorphics;0.647692784590097;Acid plutonic rocks;0.352307215409903;0;0.01;-14.1 +03450000;Metamorphics;1;NA;0;0;0.01;-14.1 +03455500;Metamorphics;1;NA;0;0;0.01;-14.1 +03456500;Metamorphics;1;NA;0;0;0.01;-14.1 +03460000;Metamorphics;0.802664551627175;Siliciclastic sedimentary rocks;0.197335448372825;0;0.0607;-13.8009 +03463300;Metamorphics;1;NA;0;0;0.01;-14.1 +03471500;Siliciclastic sedimentary rocks;0.615496264945911;Carbonate sedimentary rocks;0.384503735054089;0.384503735054089;0.1484;-13.5244 +03473000;Siliciclastic sedimentary rocks;0.444259241426551;Carbonate sedimentary rocks;0.311902361703788;0.311902361703788;0.1169;-13.4257 +03479000;Metamorphics;0.950998809032443;Acid plutonic rocks;0.0490011909675571;0;0.01;-14.1 +03488000;Siliciclastic sedimentary rocks;0.698636811964852;Carbonate sedimentary rocks;0.181587266716014;0.181587266716014;0.1347;-15.1605 +03498500;Siliciclastic sedimentary rocks;0.589742111260871;Carbonate sedimentary rocks;0.205910183390597;0.205910183390597;0.1434;-14.6682 +03500000;Metamorphics;0.800032571439631;Acid plutonic rocks;0.195822063097363;0;0.01;-14.1 +03500240;Metamorphics;1;NA;0;0;0.01;-14.1 +03504000;Metamorphics;1;NA;0;0;0.01;-14.1 +03574500;Carbonate sedimentary rocks;0.529630566554591;Siliciclastic sedimentary rocks;0.33560839742396;0.529630566554591;0.1252;-13.0532 +03592718;Unconsolidated sediments;1;NA;0;0;0.2274;-12.7422 +03604000;Siliciclastic sedimentary rocks;0.752180992433543;Carbonate sedimentary rocks;0.219200660617508;0.219200660617508;0.1097;-15.3696 +04015330;Basic volcanic rocks;0.660215290815086;Basic plutonic rocks;0.339693693717424;0;0.0628;-13.0436 +04024430;Siliciclastic sedimentary rocks;0.640746401219068;Basic volcanic rocks;0.316238335445603;0;0.1771;-13.3583 +04027000;Siliciclastic sedimentary rocks;0.349075276574417;Basic volcanic rocks;0.22010073581504;0;0.0963;-13.9437 +04040500;Siliciclastic sedimentary rocks;0.812453268756799;Metamorphics;0.187546731243201;0;0.1442;-15.2173 +04043050;Basic volcanic rocks;0.629131441697012;Siliciclastic sedimentary rocks;0.370868558302987;0;0.1271;-13.5013 +04045500;Carbonate sedimentary rocks;0.69362956443155;Siliciclastic sedimentary rocks;0.253162856822983;0.69362956443155;0.0797;-13.215 +04056500;Carbonate sedimentary rocks;0.757852576005605;Siliciclastic sedimentary rocks;0.183082225285311;0.757852576005605;0.0767;-12.8981 +04057510;Carbonate sedimentary rocks;0.757626868237633;Siliciclastic sedimentary rocks;0.241890266845131;0.757626868237633;0.0745;-12.9392 +04057800;Metamorphics;0.768265978963594;Siliciclastic sedimentary rocks;0.231734021036406;0;0.0358;-14.6509 +04059500;Siliciclastic sedimentary rocks;0.461609619194202;Carbonate sedimentary rocks;0.410948191000787;0.410948191000787;0.1141;-13.6525 +04063700;Acid plutonic rocks;0.663717516554693;Basic volcanic rocks;0.336282483445307;0;0.0369;-13.5619 +04074950;Metamorphics;0.467018502255837;Basic volcanic rocks;0.318967144419434;0;0.0426;-13.4489 +04105700;Siliciclastic sedimentary rocks;0.760538193119705;Mixed sedimentary rocks;0.239461806880295;0;0.1634;-15.6936 +04115265;Siliciclastic sedimentary rocks;1;NA;0;0;0.1501;-15.9413 +04122200;Siliciclastic sedimentary rocks;0.908354932486883;Carbonate sedimentary rocks;0.0916450675131171;0.0916450675131171;0.1287;-15.806 +04122500;Siliciclastic sedimentary rocks;0.893355049028289;Carbonate sedimentary rocks;0.0896177522168549;0.0896177522168549;0.1479;-15.4606 +04124000;Siliciclastic sedimentary rocks;0.601540766858586;Mixed sedimentary rocks;0.379684625269857;0.0187746078715571;0.1338;-16.1341 +04127918;Carbonate sedimentary rocks;0.746355342696962;Siliciclastic sedimentary rocks;0.184819028166796;0.746355342696962;0.0752;-12.9921 +04127997;Mixed sedimentary rocks;0.702763628915581;Carbonate sedimentary rocks;0.173302870800827;0.173302870800827;0.1117;-15.6474 +04161580;Mixed sedimentary rocks;1;NA;0;0;0.12;-16.5 +04185000;Siliciclastic sedimentary rocks;0.54339835231288;Mixed sedimentary rocks;0.45660164768712;0;0.1254;-16.4001 +04196800;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +04197100;Siliciclastic sedimentary rocks;0.672442045613854;Carbonate sedimentary rocks;0.202901159447827;0.202901159447827;0.1161;-15.3924 +04197170;Carbonate sedimentary rocks;0.756871055421064;Mixed sedimentary rocks;0.127442330278603;0.756871055421064;0.0746;-12.9427 +04213000;Siliciclastic sedimentary rocks;1;NA;0;0;0.1779;-15.4256 +04213075;Siliciclastic sedimentary rocks;1;NA;0;0;0.1513;-15.9184 +04216418;Siliciclastic sedimentary rocks;0.989053228077678;Mixed sedimentary rocks;0.0059838397708922;0;0.1351;-16.2122 +04221000;Siliciclastic sedimentary rocks;1;NA;0;0;0.1431;-16.0641 +04224775;Siliciclastic sedimentary rocks;1;NA;0;0;0.1338;-16.2436 +04233000;Siliciclastic sedimentary rocks;0.833838325747043;Mixed sedimentary rocks;0.166161674252957;0;0.1204;-16.4933 +04256000;Metamorphics;0.808323502882156;Unconsolidated sediments;0.178433422116198;0;0.0475;-13.9037 +04296000;Carbonate sedimentary rocks;0.664550340287313;Metamorphics;0.194729912849098;0.664550340287313;0.0512;-12.7163 +05056000;Unconsolidated sediments;0.984864290467947;Siliciclastic sedimentary rocks;0.0109466967184578;0;0.1698;-13.5543 +05057000;Unconsolidated sediments;0.973592738478195;Siliciclastic sedimentary rocks;0.0231940232313623;0;0.1678;-13.6233 +05057200;Unconsolidated sediments;0.998473905116043;Water bodies;0.00152609488395686;0;0.1707;-13.5073 +05062500;Acid plutonic rocks;0.596955879668718;Basic volcanic rocks;0.252261129407413;0;0.0376;-13.7416 +05087500;Metamorphics;0.371331008537992;Intermediate plutonic rocks;0.255913434988446;0;0.0489;-13.7523 +05120500;Unconsolidated sediments;0.998402639784881;Water bodies;0.00159736021511921;0;0.1598;-13.7671 +05123400;Unconsolidated sediments;0.999838202473087;Mixed sedimentary rocks;0.000161797526913349;0;0.1617;-13.7206 +05129115;Metamorphics;0.665096749845499;Acid plutonic rocks;0.130206867330549;0;0.0345;-14.0593 +05131500;Metamorphics;0.411344669496105;Siliciclastic sedimentary rocks;0.269203869686815;0;0.0649;-14.2673 +05291000;Unconsolidated sediments;1;NA;0;0;0.1537;-13.9127 +05362000;Metamorphics;0.586033909882225;Basic volcanic rocks;0.280269298851978;0;0.0336;-13.6393 +05393500;Metamorphics;0.9999599510003;Basic plutonic rocks;4.0048999699735e-05;0;0.01;-14.1 +05399500;Acid plutonic rocks;0.429866036800679;Metamorphics;0.367219416810216;0;0.0365;-13.7795 +05408000;Mixed sedimentary rocks;0.555203199646282;Carbonate sedimentary rocks;0.444796800353718;0.444796800353718;0.1766;-12.1886 +05412500;Carbonate sedimentary rocks;0.701516318337579;Mixed sedimentary rocks;0.298483681662421;0.701516318337579;0.0779;-13.2029 +05413500;Carbonate sedimentary rocks;0.641304725089662;Mixed sedimentary rocks;0.358695274910338;0.641304725089662;0.1353;-12.0511 +05414000;Carbonate sedimentary rocks;0.691278595575542;Mixed sedimentary rocks;0.308721404424458;0.691278595575542;0.1248;-12.0161 +05444000;Carbonate sedimentary rocks;0.85335419257346;Mixed sedimentary rocks;0.14664580742654;0.85335419257346;0.0798;-12.1966 +05454000;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +05458000;Carbonate sedimentary rocks;0.985233705024963;Siliciclastic sedimentary rocks;0.0147662949750367;0.985233705024963;0.0609;-11.8694 +05466500;Siliciclastic sedimentary rocks;0.640842667534692;Mixed sedimentary rocks;0.326072191460081;0.0330851410052271;0.1614;-15.5393 +05487980;Siliciclastic sedimentary rocks;0.465504709804851;Mixed sedimentary rocks;0.405559762966487;0.128935527228662;0.1172;-15.8028 +05488200;Siliciclastic sedimentary rocks;0.979147259966688;Mixed sedimentary rocks;0.0208527400333122;0;0.12;-16.5 +05489000;Siliciclastic sedimentary rocks;0.971476002586052;Mixed sedimentary rocks;0.0241897972290126;0.00433420018493542;0.1198;-16.4793 +05495000;Siliciclastic sedimentary rocks;0.80010241029549;Carbonate sedimentary rocks;0.198942186259665;0.198942186259665;0.1081;-15.5626 +05495500;Carbonate sedimentary rocks;0.789407744611313;Mixed sedimentary rocks;0.171894982898906;0.789407744611313;0.0753;-12.7395 +05501000;Carbonate sedimentary rocks;0.904773216142982;Siliciclastic sedimentary rocks;0.0952267838570177;0.904773216142982;0.0725;-12.1192 +05503800;Siliciclastic sedimentary rocks;0.898008966921272;Carbonate sedimentary rocks;0.101991033078728;0.101991033078728;0.1767;-14.8532 +05507600;Carbonate sedimentary rocks;0.829122636378632;Siliciclastic sedimentary rocks;0.170877363621368;0.829122636378632;0.0822;-12.381 +05508805;Carbonate sedimentary rocks;0.66736607764904;Siliciclastic sedimentary rocks;0.33263392235096;0.66736607764904;0.1032;-12.931 +05514500;Carbonate sedimentary rocks;0.749627204373287;Siliciclastic sedimentary rocks;0.250372795626713;0.749627204373287;0.0925;-12.6513 +05525500;Carbonate sedimentary rocks;0.482991084749905;Siliciclastic sedimentary rocks;0.266622441269853;0.482991084749905;0.1047;-13.975 +05556500;Carbonate sedimentary rocks;0.52954243924008;Mixed sedimentary rocks;0.283591024443003;0.52954243924008;0.1214;-13.2329 +05584500;Carbonate sedimentary rocks;0.538811438135377;Siliciclastic sedimentary rocks;0.241071170017175;0.538811438135377;0.1045;-13.6542 +05585000;Carbonate sedimentary rocks;0.444550622703023;Mixed sedimentary rocks;0.304943044428201;0.444550622703023;0.1109;-14.085 +05591550;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +05592050;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +05592575;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +05593575;Carbonate sedimentary rocks;0.765769748641918;Siliciclastic sedimentary rocks;0.234230251358082;0.765769748641918;0.0904;-12.5964 +05593900;Carbonate sedimentary rocks;0.997612073644707;Siliciclastic sedimentary rocks;0.00238792635529275;0.997612073644707;0.0603;-11.8081 +05595730;Carbonate sedimentary rocks;0.749218139848958;Siliciclastic sedimentary rocks;0.250781860151042;0.749218139848958;0.0926;-12.6527 +06037500;Acid volcanic rocks;0.846379263831752;Unconsolidated sediments;0.148046386579409;0.000232933577629246;0.154;-12.7442 +06043500;Siliciclastic sedimentary rocks;0.296662378633003;Metamorphics;0.254057985485992;0.094635753332044;0.1012;-14.4232 +06154410;Carbonate sedimentary rocks;0.428846907163574;Intermediate plutonic rocks;0.319297842822457;0.428846907163574;0.0611;-13.3545 +06188000;Intermediate volcanic rocks;0.551447747638714;Unconsolidated sediments;0.191990404746454;0.0947513107460154;0.1079;-12.674 +06191500;Intermediate volcanic rocks;0.384368284543264;Unconsolidated sediments;0.204852645731489;0.0329991950256008;0.1396;-12.9291 +06221400;Metamorphics;0.379169732990129;Acid plutonic rocks;0.331293876672969;0.0941487402002535;NA;-14.1412 +06224000;Metamorphics;0.529861227536962;Acid plutonic rocks;0.369166122526313;0.0361954980279481;NA;-14.1064 +06278300;Acid plutonic rocks;0.946108017217568;Unconsolidated sediments;0.0405431344348935;0;0.0185;-14.0554 +06280300;Siliciclastic sedimentary rocks;0.786648963462242;Unconsolidated sediments;0.09129291223659;0;0.1387;-13.6275 +06289000;Carbonate sedimentary rocks;0.758824247736797;Unconsolidated sediments;0.113743703431666;0.758824247736797;0.0918;-12.1444 +06291500;Carbonate sedimentary rocks;0.40680958123534;Siliciclastic sedimentary rocks;0.3139700511903;0.40680958123534;0.1249;-13.8996 +06311000;Metamorphics;0.790209981180385;Siliciclastic sedimentary rocks;0.190001972961669;0.0197880458579458;0.0442;-14.2821 +06332515;Unconsolidated sediments;0.991565074716835;Siliciclastic sedimentary rocks;0.00843492528316537;0;0.2179;-13.0452 +06339100;Unconsolidated sediments;0.989231231686401;Siliciclastic sedimentary rocks;0.0107687683135985;0;0.2197;-13.0237 +06339500;Unconsolidated sediments;0.969931535925501;Siliciclastic sedimentary rocks;0.029973732844609;0;0.2074;-13.2317 +06344600;Unconsolidated sediments;0.983098076035488;Siliciclastic sedimentary rocks;0.0169019239645123;0;0.2195;-13.0372 +06350000;Unconsolidated sediments;0.986190484834622;Siliciclastic sedimentary rocks;0.0137780579893581;0;0.2194;-13.0304 +06352000;Unconsolidated sediments;0.998667780414871;Mixed sedimentary rocks;0.000827999900563756;0;0.2206;-12.9728 +06353000;Unconsolidated sediments;0.946747545203828;Siliciclastic sedimentary rocks;0.0528261804926513;0;0.2167;-13.1298 +06354000;Unconsolidated sediments;0.972454320906702;Siliciclastic sedimentary rocks;0.0272050875149156;0;0.2176;-13.0703 +06360500;Siliciclastic sedimentary rocks;0.876742855750927;Unconsolidated sediments;0.123257144249073;0;0.1657;-15.4093 +06404000;Metamorphics;0.656873992510644;Acid plutonic rocks;0.309576749787032;0;0.0133;-14.0612 +06406000;Metamorphics;0.452560595900876;Siliciclastic sedimentary rocks;0.263270070309326;0.0604696221401325;0.0619;-14.3357 +06408700;Carbonate sedimentary rocks;0.839549389754399;Metamorphics;0.102309854675168;0.839549389754399;0.0624;-12.233 +06409000;Carbonate sedimentary rocks;0.764987788852965;Siliciclastic sedimentary rocks;0.160183658516796;0.764987788852965;0.0789;-12.5115 +06431500;Carbonate sedimentary rocks;0.622672140343926;Siliciclastic sedimentary rocks;0.287305340119463;0.622672140343926;0.1;-12.9291 +06440200;Siliciclastic sedimentary rocks;0.826251057226108;Unconsolidated sediments;0.173748942773892;0;0.1299;-15.9866 +06441500;Siliciclastic sedimentary rocks;0.528819029224159;Unconsolidated sediments;0.471180970775841;0;0.1525;-15.0168 +06447000;Siliciclastic sedimentary rocks;0.622884602003789;Unconsolidated sediments;0.35952007899945;0.0142970757995729;0.166;-14.9252 +06447500;Siliciclastic sedimentary rocks;0.576928127359036;Unconsolidated sediments;0.423071872640964;0;0.1975;-14.3476 +06450500;Siliciclastic sedimentary rocks;0.571683431937556;Unconsolidated sediments;0.428316568062444;0;0.1862;-14.5376 +06452000;Siliciclastic sedimentary rocks;0.53489411297421;Unconsolidated sediments;0.452043970616004;0.00963280750384324;0.1592;-14.9098 +06453600;Siliciclastic sedimentary rocks;0.856611246522083;Unconsolidated sediments;0.139850145013297;0.00353860846462015;0.1554;-15.5719 +06464500;Siliciclastic sedimentary rocks;0.793660427179204;Unconsolidated sediments;0.206339572820796;0;0.1892;-14.8514 +06468170;Unconsolidated sediments;0.997758900704525;Water bodies;0.00224109929547499;0;0.1609;-13.7404 +06468250;Unconsolidated sediments;0.997372184792061;Water bodies;0.00262781520793915;0;0.1667;-13.6027 +06470800;Unconsolidated sediments;0.999914640153554;Siliciclastic sedimentary rocks;8.53598464455635e-05;0;0.1641;-13.6636 +06477500;Unconsolidated sediments;0.998987910995228;Siliciclastic sedimentary rocks;0.00101208900477196;0;0.1503;-13.9939 +06479215;Unconsolidated sediments;1;NA;0;0;0.1826;-13.2286 +06479438;Unconsolidated sediments;1;NA;0;0;0.1666;-13.6154 +06601000;Mixed sedimentary rocks;0.580802768434864;Siliciclastic sedimentary rocks;0.419197231565136;0;0.1493;-15.955 +06614800;Pyroclastics;0.41442176215181;Intermediate volcanic rocks;0.382188344554441;0;0.0799;-13.1665 +06622700;Siliciclastic sedimentary rocks;0.449417763541973;Unconsolidated sediments;0.313920977665326;0.00600117141259076;0.1571;-14.2348 +06623800;Metamorphics;0.508727749985555;Unconsolidated sediments;0.264267380890685;0;0.0689;-13.8248 +06632400;Unconsolidated sediments;0.570014457168209;Metamorphics;0.307428496657854;0;0.1383;-13.54 +06746095;Metamorphics;0.629691717846899;Intermediate volcanic rocks;0.273352085962271;0;0.0522;-13.556 +06784000;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +06803510;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +06803530;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +06814000;Mixed sedimentary rocks;0.576491052875378;Unconsolidated sediments;0.235154511003563;0.044336658278203;0.1515;-15.2504 +06847900;Unconsolidated sediments;0.885004735706505;Mixed sedimentary rocks;0.114995264293495;0;0.1723;-13.6252 +06853800;Unconsolidated sediments;0.847415210681318;Mixed sedimentary rocks;0.144516132418591;0.00404777987255088;0.1658;-13.8726 +06876700;Unconsolidated sediments;0.315040300353331;Carbonate sedimentary rocks;0.287128156506156;0.287128156506156;0.1256;-14.044 +06878000;Siliciclastic sedimentary rocks;0.476195556412305;Mixed sedimentary rocks;0.4044918535334;0;0.1474;-15.7131 +06879650;Carbonate sedimentary rocks;0.587737799498278;Mixed sedimentary rocks;0.412262200501722;0.587737799498278;0.0847;-13.7376 +06885500;Unconsolidated sediments;0.893184205303306;Mixed sedimentary rocks;0.0556634606464892;0.0511523340502047;0.2062;-13.0409 +06888500;Mixed sedimentary rocks;0.471248841915587;Carbonate sedimentary rocks;0.39967637546244;0.39967637546244;0.113;-14.0259 +06889200;Unconsolidated sediments;0.518782144013368;Mixed sedimentary rocks;0.287030592340962;0.164813591664893;0.1715;-13.6113 +06889500;Unconsolidated sediments;0.663276991331338;Mixed sedimentary rocks;0.191737497835091;0.0895963092517417;0.1933;-13.3897 +06892000;Unconsolidated sediments;0.644589259296337;Carbonate sedimentary rocks;0.252337963623747;0.252337963623747;0.1814;-12.7532 +06903400;Mixed sedimentary rocks;0.966432635899575;Siliciclastic sedimentary rocks;0.033567364100425;0;0.1207;-16.4877 +06906800;Carbonate sedimentary rocks;0.597091089573001;Siliciclastic sedimentary rocks;0.402908910426999;0.597091089573001;0.1224;-12.6822 +06910800;Mixed sedimentary rocks;0.497347355675493;Siliciclastic sedimentary rocks;0.260393634543184;0.172422728171331;0.1391;-14.96 +06911900;Siliciclastic sedimentary rocks;0.486761989453289;Mixed sedimentary rocks;0.358689334783396;0.0640141091377813;0.1647;-15.0593 +06917000;Carbonate sedimentary rocks;0.639892896974089;Mixed sedimentary rocks;0.171589942526184;0.639892896974089;0.0999;-13.0012 +06918460;Carbonate sedimentary rocks;0.829175357083337;Siliciclastic sedimentary rocks;0.170824642916663;0.829175357083337;0.0809;-12.3177 +06919500;Siliciclastic sedimentary rocks;0.560554501277656;Carbonate sedimentary rocks;0.439445498722343;0.439445498722343;0.096;-14.382 +06921070;Carbonate sedimentary rocks;0.991134087970009;Siliciclastic sedimentary rocks;0.00886591202999075;0.991134087970009;0.0619;-11.8062 +06921200;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +06934000;Carbonate sedimentary rocks;0.926072991120847;Siliciclastic sedimentary rocks;0.0609473099989462;0.926072991120847;0.0736;-11.9044 +07014500;Carbonate sedimentary rocks;0.532724007641709;Siliciclastic sedimentary rocks;0.446994683529878;0.532724007641709;0.1551;-12.2049 +07056000;Siliciclastic sedimentary rocks;0.656472311374056;Carbonate sedimentary rocks;0.282174134804815;0.282174134804815;0.158;-14.0789 +07057500;Carbonate sedimentary rocks;0.998044239181792;Siliciclastic sedimentary rocks;0.00195576081820847;0.998044239181792;0.0604;-11.8017 +07060710;Carbonate sedimentary rocks;0.857283608566464;Mixed sedimentary rocks;0.137040424154909;0.857283608566464;0.0892;-11.9212 +07066000;Carbonate sedimentary rocks;0.636085186630631;Siliciclastic sedimentary rocks;0.363914813369369;0.636085186630631;0.1364;-12.055 +07067000;Carbonate sedimentary rocks;0.52725531411042;Siliciclastic sedimentary rocks;0.462033271329778;0.52725531411042;0.1573;-12.131 +07068000;Siliciclastic sedimentary rocks;0.515031666781697;Carbonate sedimentary rocks;0.476526145008869;0.476526145008869;0.1684;-12.1665 +07071500;Siliciclastic sedimentary rocks;0.562480836325577;Carbonate sedimentary rocks;0.437519163674423;0.437519163674423;0.1781;-12.1937 +07083000;Metamorphics;0.503876589054494;Unconsolidated sediments;0.260678091675831;0;0.0668;-13.7463 +07142300;Unconsolidated sediments;1;NA;0;0;0.2413;-11.8217 +07145700;Unconsolidated sediments;0.785666985951064;Mixed sedimentary rocks;0.214333014048936;0;0.1679;-13.9559 +07148400;Siliciclastic sedimentary rocks;0.635407555896992;Unconsolidated sediments;0.352252050585736;0;0.205;-14.0603 +07149000;Unconsolidated sediments;0.510868516220127;Siliciclastic sedimentary rocks;0.489131483779873;0;0.2115;-13.5872 +07151500;Unconsolidated sediments;0.838132029254055;Mixed sedimentary rocks;0.0828472011753906;0;0.2333;-12.3308 +07167500;Siliciclastic sedimentary rocks;0.569290596666273;Mixed sedimentary rocks;0.329712541396791;0.0656573681434691;0.1616;-15.2534 +07180500;Mixed sedimentary rocks;0.938857852131091;Unconsolidated sediments;0.0611421478689094;0;0.1298;-16.1576 +07184000;Mixed sedimentary rocks;0.571479006701658;Siliciclastic sedimentary rocks;0.321433976833463;0;0.1596;-15.4824 +07195800;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +07196900;Siliciclastic sedimentary rocks;0.85539757297;Carbonate sedimentary rocks;0.14460242703;0.14460242703;0.1712;-14.7084 +07197000;Carbonate sedimentary rocks;0.694359968051739;Siliciclastic sedimentary rocks;0.237036629481106;0.694359968051739;0.1026;-12.6616 +07208500;Metamorphics;0.476946042126427;Basic volcanic rocks;0.395246095811802;0.0181393144368796;0.0624;-13.4318 +07226500;Unconsolidated sediments;0.516803975629448;Siliciclastic sedimentary rocks;0.347497629157586;6.47735365530563e-05;0.2054;-13.2545 +07261000;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +07263295;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +07290650;Unconsolidated sediments;1;NA;0;0;0.2756;-11.0545 +07291000;Unconsolidated sediments;1;NA;0;0;0.2522;-11.8728 +07292500;Unconsolidated sediments;1;NA;0;0;0.2429;-12.1976 +07295000;Unconsolidated sediments;1;NA;0;0;0.2365;-12.4216 +07299670;Siliciclastic sedimentary rocks;0.921041771402062;Unconsolidated sediments;0.0789582285979382;0;0.1303;-16.1798 +07301410;Unconsolidated sediments;0.99540480124875;Mixed sedimentary rocks;0.00459519875125002;0;0.2198;-13.0112 +07301500;Unconsolidated sediments;0.819016678290507;Siliciclastic sedimentary rocks;0.114868090958136;0;0.2127;-13.3382 +07315200;Siliciclastic sedimentary rocks;0.973307668988547;Unconsolidated sediments;0.0266923310114534;0;0.1908;-15.1413 +07315700;Siliciclastic sedimentary rocks;0.787573081813656;Unconsolidated sediments;0.212052895920422;0;0.2194;-13.9493 +07335700;Siliciclastic sedimentary rocks;0.863935517660851;Unconsolidated sediments;0.136064482339149;0;0.1991;-14.7247 +07340300;Siliciclastic sedimentary rocks;1;NA;0;0;0.1669;-15.6284 +07346045;Siliciclastic sedimentary rocks;0.758757597817185;Unconsolidated sediments;0.241242402182815;0;0.1971;-14.6716 +07359610;Siliciclastic sedimentary rocks;0.766005964022328;Mixed sedimentary rocks;0.233994035977672;0;0.1447;-16.042 +07362100;Unconsolidated sediments;1;NA;0;0;0.22;-13 +07362587;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +07373000;Unconsolidated sediments;0.999545109223863;Siliciclastic sedimentary rocks;0.000454890776136871;0;0.1582;-13.8823 +07375000;Unconsolidated sediments;1;NA;0;0;0.165;-13.7861 +07376000;Unconsolidated sediments;1;NA;0;0;0.1996;-12.9582 +08013000;Unconsolidated sediments;0.851996578915203;Siliciclastic sedimentary rocks;0.148003421084798;0;0.1728;-13.9362 +08014500;Unconsolidated sediments;0.87120703872165;Siliciclastic sedimentary rocks;0.12879296127835;0;0.1715;-13.9215 +08023080;Unconsolidated sediments;1;NA;0;0;0.2167;-13.0466 +08025500;Unconsolidated sediments;0.794952997794957;Siliciclastic sedimentary rocks;0.203210852609937;0;0.1929;-13.5444 +08029500;Unconsolidated sediments;1;NA;0;0;0.1819;-13.5446 +08050800;Siliciclastic sedimentary rocks;0.989332417836211;Unconsolidated sediments;0.0106675821637893;0;0.1903;-15.1765 +08066200;Unconsolidated sediments;0.92224252781773;Siliciclastic sedimentary rocks;0.0777574721822696;0;0.2104;-13.2746 +08066300;Unconsolidated sediments;1;NA;0;0;0.1861;-13.4839 +08070000;Unconsolidated sediments;1;NA;0;0;0.1825;-13.5354 +08070200;Unconsolidated sediments;1;NA;0;0;0.188;-13.4571 +08079600;Unconsolidated sediments;0.862654503989846;Mixed sedimentary rocks;0.131013798734627;0.00329213198315665;0.2054;-13.4623 +08082700;Siliciclastic sedimentary rocks;0.682641528521047;Unconsolidated sediments;0.317358471478953;0;0.208;-14.2036 +08086212;Siliciclastic sedimentary rocks;0.515138271081337;Mixed sedimentary rocks;0.348853576751712;0.00492913906644597;0.1434;-15.8201 +08086290;Siliciclastic sedimentary rocks;0.88856212399244;Unconsolidated sediments;0.103508794941006;0.00447971186625109;0.19;-14.9995 +08101000;Mixed sedimentary rocks;0.95254451736969;Unconsolidated sediments;0.0449993982673878;0.00245608436292188;0.1678;-15.173 +08103900;Mixed sedimentary rocks;1;NA;0;0;0.2289;-13.5955 +08104900;Mixed sedimentary rocks;0.626444369532105;Carbonate sedimentary rocks;0.340699752924152;0.340699752924152;0.1216;-14.2839 +08109700;Siliciclastic sedimentary rocks;0.544236219163692;Unconsolidated sediments;0.455504441494395;0;0.207;-14.084 +08150800;Carbonate sedimentary rocks;0.812705264135297;Unconsolidated sediments;0.0795433358506353;0.812705264135297;0.0837;-12.0426 +08155200;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +08158700;Carbonate sedimentary rocks;0.970592134154386;Unconsolidated sediments;0.0294078658456137;0.970592134154386;0.0647;-11.8353 +08158810;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +08164000;Unconsolidated sediments;0.865545353410271;Siliciclastic sedimentary rocks;0.134454646589729;0;0.2061;-13.4365 +08164300;Unconsolidated sediments;0.819987830117553;Siliciclastic sedimentary rocks;0.180012169882447;0;0.2141;-13.403 +08164600;Unconsolidated sediments;1;NA;0;0;0.2157;-13.0614 +08165300;Carbonate sedimentary rocks;1;NA;0;1;0.06;-11.8 +08171300;Carbonate sedimentary rocks;0.836617953957555;Unconsolidated sediments;0.142618823588868;0.836617953957555;0.0841;-12.0681 +08175000;Unconsolidated sediments;0.595371975222437;Siliciclastic sedimentary rocks;0.404628024777563;0;0.209;-13.7901 +08176900;Unconsolidated sediments;0.89177615554042;Siliciclastic sedimentary rocks;0.10822384445958;0;0.2165;-13.2419 +08178880;Carbonate sedimentary rocks;0.9847556936617;Unconsolidated sediments;0.0152443063383001;0.9847556936617;0.0624;-11.8183 +08189500;Unconsolidated sediments;0.985153092547404;Siliciclastic sedimentary rocks;0.0147662828003682;0;0.2195;-13.0338 +08190000;Carbonate sedimentary rocks;0.924281045407362;Unconsolidated sediments;0.0757189545926378;0.924281045407362;0.0721;-11.8909 +08190500;Carbonate sedimentary rocks;0.977785638581083;Unconsolidated sediments;0.0213402070768806;0.977785638581083;0.0635;-11.8297 +08194200;Unconsolidated sediments;0.986886616497362;Siliciclastic sedimentary rocks;0.0105866891086962;0;0.2195;-13.0266 +08195000;Carbonate sedimentary rocks;0.925547329334804;Unconsolidated sediments;0.0744526706651965;0.925547329334804;0.0719;-11.8893 +08196000;Carbonate sedimentary rocks;0.978856150741443;Unconsolidated sediments;0.0211438492585567;0.978856150741443;0.0634;-11.8254 +08198500;Carbonate sedimentary rocks;0.721963335508605;Unconsolidated sediments;0.274831199180652;0.721963335508605;0.1102;-11.9231 +08200000;Carbonate sedimentary rocks;0.933477049114124;Unconsolidated sediments;0.0665229508858755;0.933477049114124;0.0706;-11.8798 +08202700;Carbonate sedimentary rocks;0.799089852366373;Unconsolidated sediments;0.182647252102685;0.799089852366373;0.0973;-11.8593 +08267500;Intermediate plutonic rocks;0.450870606272811;Basic volcanic rocks;0.395061640925655;0;0.0676;-13.3318 +08269000;Mixed sedimentary rocks;0.821486045313246;Intermediate plutonic rocks;0.116105234407784;0;0.1655;-14.9532 +08271000;Intermediate plutonic rocks;0.69729156538123;Basic volcanic rocks;0.104056891494119;0;0.0425;-13.8495 +08324000;Acid volcanic rocks;0.392756875928801;Carbonate sedimentary rocks;0.123077032280925;0.123077032280925;0.1076;-12.8974 +08377900;Mixed sedimentary rocks;0.402551345099773;Intermediate plutonic rocks;0.223185057017132;0.201264136342777;0.0976;-14.0443 +08378500;Mixed sedimentary rocks;0.562972350108069;Carbonate sedimentary rocks;0.17613643919608;0.17613643919608;0.1226;-14.2476 +08380500;Carbonate sedimentary rocks;0.409077024627139;Intermediate plutonic rocks;0.266122762810631;0.409077024627139;0.07;-13.0611 +09034900;Acid plutonic rocks;1;NA;0;0;0.01;-14.1 +09035800;Metamorphics;0.721353320272577;Acid plutonic rocks;0.150496421341739;0;0.0369;-13.959 +09035900;Metamorphics;0.808648145952364;Acid plutonic rocks;0.173549963593142;0;0.0211;-14.0417 +09047700;Metamorphics;0.994983378610726;Unconsolidated sediments;0.0050166213892739;0;0.0111;-14.0945 +09065500;Acid plutonic rocks;0.975723134412589;Siliciclastic sedimentary rocks;0.023871044010617;0;0.0163;-14.0614 +09066000;Siliciclastic sedimentary rocks;0.674766017949802;Acid plutonic rocks;0.147768029216389;0;0.1965;-12.9834 +09066200;Acid plutonic rocks;0.592263526954021;Siliciclastic sedimentary rocks;0.295952871913625;0.0317182030297311;0.0948;-13.4797 +09066300;Siliciclastic sedimentary rocks;0.679595912313242;Acid plutonic rocks;0.265672490761214;0;0.1949;-13.0611 +09081600;Siliciclastic sedimentary rocks;0.761483061078774;Intermediate volcanic rocks;0.1417766744181;0.0185676703859297;0.1693;-14.5073 +09107000;Acid plutonic rocks;0.441886750839896;Unconsolidated sediments;0.359982506825244;0.0383622352430751;0.066;-13.6265 +09210500;Siliciclastic sedimentary rocks;0.543918098817221;Unconsolidated sediments;0.210531675048142;0.0460477871696853;0.1751;-14.7974 +09223000;Siliciclastic sedimentary rocks;0.579143382639512;Mixed sedimentary rocks;0.347384313620287;0.0188539336563506;0.1879;-14.9409 +09306242;Siliciclastic sedimentary rocks;0.999028023340524;Unconsolidated sediments;0.000971976659476112;0;0.1579;-15.7937 +09312600;Mixed sedimentary rocks;0.891046600752129;Siliciclastic sedimentary rocks;0.0664791092541508;0;0.1289;-16.2648 +09352900;Metamorphics;0.570075354932863;Acid plutonic rocks;0.339744457260488;0.0384493888402848;0.0242;-13.9404 +09378170;Siliciclastic sedimentary rocks;0.429207040393901;Intermediate plutonic rocks;0.289288388359826;0;0.1464;-14.2625 +09378630;Siliciclastic sedimentary rocks;0.810585092674077;Intermediate plutonic rocks;0.186890731195185;0;0.1564;-14.9889 +09386900;Siliciclastic sedimentary rocks;0.534335710591192;Mixed sedimentary rocks;0.205776200671653;0.164153092693113;0.1755;-13.7243 +09404450;Carbonate sedimentary rocks;0.522447659645459;Siliciclastic sedimentary rocks;0.456504085867337;0.522447659645459;0.1053;-13.6472 +09430500;Basic volcanic rocks;0.396461640599842;Siliciclastic sedimentary rocks;0.226634455876019;0;0.137;-12.6397 +09430600;Acid volcanic rocks;0.437927363518514;Pyroclastics;0.38318778899035;0;0.0917;-12.6916 +09447800;Acid volcanic rocks;0.463665046592738;Siliciclastic sedimentary rocks;0.460729745247677;0.0291895480441197;0.175;-12.4731 +09480000;Siliciclastic sedimentary rocks;0.594635115662451;Acid volcanic rocks;0.149261117865607;0.00154694965085734;0.2222;-12.3046 +09484000;Acid plutonic rocks;0.875276957830848;Metamorphics;0.10203725223132;0;0.0149;-14.0855 +09484600;Siliciclastic sedimentary rocks;0.705469102870204;Acid plutonic rocks;0.0964584204723188;0.057423150805567;0.2172;-12.6202 +09492400;Acid volcanic rocks;0.843137128562346;Siliciclastic sedimentary rocks;0.103926719047577;0;0.1087;-12.5 +09494000;Siliciclastic sedimentary rocks;0.41321108111038;Basic volcanic rocks;0.330815028793647;0;0.1492;-13.1224 +09497800;Siliciclastic sedimentary rocks;0.885671662373207;Carbonate sedimentary rocks;0.0907292588168322;0.0907292588168322;0.1898;-14.4479 +09497980;Mixed sedimentary rocks;0.468334752541027;Basic volcanic rocks;0.192886118021194;0;0.198;-12.8268 +09505200;Basic volcanic rocks;0.948838823916249;Carbonate sedimentary rocks;0.0372304828030229;0.0372304828030229;0.089;-12.4769 +09505350;Basic volcanic rocks;0.784032434789949;Siliciclastic sedimentary rocks;0.173257453748272;0.0427101114617792;0.1065;-12.9241 +09505800;Basic volcanic rocks;0.643723069247182;Carbonate sedimentary rocks;0.319065845670911;0.319065845670911;0.0871;-12.2767 +09508300;Acid plutonic rocks;0.58792910380522;Basic volcanic rocks;0.226174796357154;0;0.0366;-13.7643 +09510200;Acid plutonic rocks;0.562894269153209;Metamorphics;0.148393239489619;0;0.058;-13.6381 +09512280;Basic volcanic rocks;0.542420677378394;Metamorphics;0.366248027099376;0;0.0689;-13.1369 +09513780;Basic volcanic rocks;0.463837050158684;Acid plutonic rocks;0.255104346163524;0;0.0518;-13.2672 +10023000;Siliciclastic sedimentary rocks;0.837172079933476;Carbonate sedimentary rocks;0.0855090270915308;0.0855090270915308;0.2482;-12.4788 +10166430;Unconsolidated sediments;0.570636492683955;Water bodies;0.190016278580633;0.0732128931174254;0.2082;-12.9477 +10172700;Unconsolidated sediments;0.555234665351728;Siliciclastic sedimentary rocks;0.32336753435379;0.0439578519276792;0.2116;-12.9729 +10172800;Unconsolidated sediments;0.363505919863619;Carbonate sedimentary rocks;0.187684504909339;0.187684504909339;0.1938;-12.6177 +10173450;Acid volcanic rocks;0.439760440064625;Basic volcanic rocks;0.29807536276612;0.0242526491572531;0.0916;-12.4998 +10205030;Siliciclastic sedimentary rocks;0.524116930602061;Carbonate sedimentary rocks;0.460370318546233;0.460370318546233;0.1306;-13.6006 +10234500;Pyroclastics;0.709549585478298;Siliciclastic sedimentary rocks;0.163466478676177;0;0.1403;-12.904 +10242000;Siliciclastic sedimentary rocks;0.392344467765217;Pyroclastics;0.245880826594894;0.092511870618539;0.141;-13.3804 +10244950;Carbonate sedimentary rocks;0.801314362882168;Mixed sedimentary rocks;0.118881609300258;0.801314362882168;0.0787;-12.461 +10249300;Unconsolidated sediments;0.636768157816955;Acid volcanic rocks;0.25751814227509;0.00245417877367057;0.1677;-13.0643 +10258000;Acid plutonic rocks;0.608484443671625;Unconsolidated sediments;0.248758530687226;0;0.0622;-13.8264 +10258500;Metamorphics;0.395718316141743;Acid plutonic rocks;0.369689548053733;0;0.0228;-14.0221 +10259000;Acid plutonic rocks;0.665793734490064;Metamorphics;0.159732361637236;0;0.0221;-14.0366 +10259200;Intermediate plutonic rocks;0.492424217677858;Acid plutonic rocks;0.383673519679845;0;0.0236;-14.0277 +10263500;Unconsolidated sediments;0.458668037204093;Acid plutonic rocks;0.189574948718305;0;0.151;-13.4066 +10310500;Acid plutonic rocks;0.923871015714666;Unconsolidated sediments;0.0645911337715467;0;0.0245;-14.0105 +10316500;Carbonate sedimentary rocks;0.913144493851008;Unconsolidated sediments;0.0868555061489919;0.913144493851008;0.1125;-12.1935 +10329500;Acid volcanic rocks;0.601689190694653;Intermediate volcanic rocks;0.135196686367398;0;0.1037;-12.6509 +10336645;Acid plutonic rocks;0.627371039720803;Unconsolidated sediments;0.3725933604585;0;0.0883;-13.6901 +10336660;Unconsolidated sediments;0.412345225511582;Basic volcanic rocks;0.375326986554209;0;0.1395;-12.9785 +10336740;Acid plutonic rocks;0.843076603510652;Acid volcanic rocks;0.132965093918621;0;0.0226;-13.8489 +10343500;Acid volcanic rocks;0.584213358553792;Unconsolidated sediments;0.415786641446208;0;0.1441;-12.7079 +10348850;Unconsolidated sediments;0.47648621888967;Acid plutonic rocks;0.345897243988451;0;0.1243;-13.2917 +10396000;Basic volcanic rocks;0.857549287954332;Unconsolidated sediments;0.110758725990775;0;0.1071;-12.5654 +11098000;Acid plutonic rocks;0.780155008353107;Metamorphics;0.219844991646893;0;0.01;-14.1 +11124500;Siliciclastic sedimentary rocks;0.974892616918292;Metamorphics;0.0251073830817075;0;0.1937;-14.8934 +11141280;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +11143000;Intermediate plutonic rocks;0.590677556952723;Metamorphics;0.18300402395235;0.0158879610203631;0.0225;-14.1348 +11148900;Siliciclastic sedimentary rocks;0.889183485279982;Metamorphics;0.0640807345386816;0;0.2143;-13.5833 +11151300;Siliciclastic sedimentary rocks;0.852613317771706;Unconsolidated sediments;0.0667775022392132;0;0.1853;-14.7006 +11162500;Siliciclastic sedimentary rocks;0.938585783087827;Basic volcanic rocks;0.061414216912173;0;0.1839;-15.0342 +11176400;Metamorphics;0.608201576013396;Siliciclastic sedimentary rocks;0.391798423986604;0;0.0805;-14.531 +11180500;Siliciclastic sedimentary rocks;0.857542636911432;Unconsolidated sediments;0.122409806341684;0;0.1901;-14.9086 +11180960;Siliciclastic sedimentary rocks;1;NA;0;0;0.2342;-13.709 +11224500;Siliciclastic sedimentary rocks;0.940603584997702;Metamorphics;0.057798139122649;0;0.1793;-15.1345 +11230500;Acid plutonic rocks;0.829721832470696;Unconsolidated sediments;0.0699821618402604;0;0.0749;-13.5155 +11237500;Acid plutonic rocks;0.829271713672719;Unconsolidated sediments;0.154249534196046;0;0.0424;-13.9303 +11253310;Siliciclastic sedimentary rocks;0.929667810865659;Metamorphics;0.0599848199840308;0;0.1875;-14.8424 +11264500;Acid plutonic rocks;0.843699417033867;Unconsolidated sediments;0.116410671128763;0;0.0425;-13.8776 +11266500;Acid plutonic rocks;0.818612829103439;Unconsolidated sediments;0.146369450929911;0;0.1754;-13.0986 +11274500;Siliciclastic sedimentary rocks;0.490086348269721;Metamorphics;0.488096989471319;0;0.1028;-14.6138 +11274630;Metamorphics;0.615079983717025;Siliciclastic sedimentary rocks;0.369059589980934;0;0.0792;-14.4769 +11284400;Siliciclastic sedimentary rocks;0.935317548916698;Acid volcanic rocks;0.0644643010810125;0;0.118;-16.2416 +11299600;Basic volcanic rocks;0.680756202855115;Basic plutonic rocks;0.319243797144885;0;0.0645;-13.0108 +11381500;Acid volcanic rocks;0.562371618749279;Basic volcanic rocks;0.410628169354523;0;0.0936;-12.5354 +11383500;Acid volcanic rocks;0.495935685343198;Basic volcanic rocks;0.489476199237605;0;0.092;-12.5133 +11451100;Siliciclastic sedimentary rocks;0.971643036237749;Metamorphics;0.0283569637622516;0;0.1849;-15.1688 +11468500;Siliciclastic sedimentary rocks;0.984416976069011;Metamorphics;0.0155830239309893;0;0.1872;-15.1829 +11473900;Siliciclastic sedimentary rocks;0.575037875012922;Metamorphics;0.391772321056102;0;0.1206;-14.6914 +11475560;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +11476600;Siliciclastic sedimentary rocks;1;NA;0;0;0.19;-15.2 +11478500;Siliciclastic sedimentary rocks;0.958756582456278;Metamorphics;0.0326232853180604;0;0.1844;-15.1451 +11480390;Siliciclastic sedimentary rocks;0.962981466233055;Metamorphics;0.0370185337669447;0;0.1833;-15.1593 +11481200;Siliciclastic sedimentary rocks;0.728373095707469;Metamorphics;0.177443573043571;0;0.1609;-14.7976 +11482500;Metamorphics;0.535060107675517;Siliciclastic sedimentary rocks;0.462187933598356;0;0.0987;-14.4222 +11522500;Siliciclastic sedimentary rocks;0.479067101672585;Intermediate plutonic rocks;0.271722029943193;0.00243807117499689;0.0803;-14.935 +11523200;Basic plutonic rocks;0.822393371392538;Intermediate plutonic rocks;0.1548173503066;0;0.0148;-14.0749 +11528700;Siliciclastic sedimentary rocks;0.711542901274395;Metamorphics;0.142396471407674;0;0.0993;-15.5936 +11532500;Siliciclastic sedimentary rocks;0.427128498312215;Metamorphics;0.266985317119961;0;0.0715;-14.8472 +12010000;Basic volcanic rocks;0.512397763794633;Siliciclastic sedimentary rocks;0.481994899075887;0;0.1378;-13.8104 +12013500;Siliciclastic sedimentary rocks;0.563972915399654;Basic volcanic rocks;0.288717887151353;0;0.1655;-14.0964 +12020000;Basic volcanic rocks;0.633707026180392;Siliciclastic sedimentary rocks;0.312230447625972;0;0.1283;-13.3701 +12025000;Unconsolidated sediments;0.558434742913984;Basic volcanic rocks;0.269410785668739;0;0.1784;-13.2353 +12025700;Basic volcanic rocks;0.927643127830288;Siliciclastic sedimentary rocks;0.0723568721697116;0;0.0972;-12.6954 +12035000;Unconsolidated sediments;0.645786075592661;Basic volcanic rocks;0.184700046349863;0;0.1904;-13.274 +12040500;Siliciclastic sedimentary rocks;0.711322133153096;Unconsolidated sediments;0.285345252461474;0;0.1982;-14.5632 +12041200;Siliciclastic sedimentary rocks;0.891995709382866;Unconsolidated sediments;0.108004290617134;0;0.1932;-14.9632 +12043000;Siliciclastic sedimentary rocks;0.905165936534333;Unconsolidated sediments;0.0929797997341204;0;0.1926;-14.9904 +12048000;Siliciclastic sedimentary rocks;0.723004570598374;Basic volcanic rocks;0.276327977218593;0;0.1249;-13.4373 +12054000;Siliciclastic sedimentary rocks;0.556924516790145;Basic volcanic rocks;0.443075483209855;0;0.1457;-14.0037 +12056500;Siliciclastic sedimentary rocks;0.780961912045858;Basic volcanic rocks;0.219038087954142;0;0.1681;-14.6086 +12073500;Siliciclastic sedimentary rocks;0.736548963148387;Unconsolidated sediments;0.263451036851613;0;0.1979;-14.6204 +12082500;Intermediate volcanic rocks;0.34971411537898;Basic volcanic rocks;0.345537675676897;0;0.1187;-12.8654 +12092000;Basic volcanic rocks;0.524702625276192;Intermediate volcanic rocks;0.18094146322548;0;0.1234;-13.0903 +12095000;Basic volcanic rocks;0.518343133874812;Siliciclastic sedimentary rocks;0.257411973354303;0;0.1372;-13.3475 +12114500;Basic volcanic rocks;0.961666887294108;Acid plutonic rocks;0.0383331127058917;0;0.0869;-12.5613 +12115000;Basic volcanic rocks;0.776200424433409;Acid plutonic rocks;0.131007618645411;0;0.086;-12.7347 +12115500;Basic volcanic rocks;0.961099706997048;Unconsolidated sediments;0.0389002930029523;0;0.0951;-12.5195 +12117000;Basic volcanic rocks;0.716543671258721;Siliciclastic sedimentary rocks;0.283456328741279;0;0.1183;-13.2653 +12141300;Acid plutonic rocks;0.771978117894424;Unconsolidated sediments;0.0938602547192574;0;0.0458;-14.0366 +12143600;Acid plutonic rocks;0.469598770432686;Intermediate volcanic rocks;0.227105081761749;0;0.0826;-13.7298 +12144000;Acid plutonic rocks;0.364268296319447;Unconsolidated sediments;0.250573866548332;0;0.1078;-13.597 +12145500;Siliciclastic sedimentary rocks;0.629844463229811;Intermediate volcanic rocks;0.166397685780607;0;0.1602;-14.2285 +12147500;Basic volcanic rocks;0.990572771447746;Acid plutonic rocks;0.00935487491103681;0;0.0893;-12.5153 +12147600;Acid plutonic rocks;0.526342818672154;Basic volcanic rocks;0.335698092527188;0;0.0479;-13.3421 +12167000;Siliciclastic sedimentary rocks;0.38776638907617;Metamorphics;0.312447853407403;0;0.1195;-14.2882 +12175500;Metamorphics;0.872169630754137;Unconsolidated sediments;0.0814923791004794;0;0.0505;-13.8877 +12178100;Metamorphics;0.979336388802152;Unconsolidated sediments;0.0206636111978477;0;0.0143;-14.0773 +12186000;Metamorphics;0.54641523583567;Basic volcanic rocks;0.202529016252995;0;0.0685;-13.9296 +12189500;Metamorphics;0.617299181391871;Basic volcanic rocks;0.11016990012946;0;0.0614;-13.8887 +12358500;Siliciclastic sedimentary rocks;0.32022600276276;Metamorphics;0.310319524093197;0.153544319486106;0.1295;-14.155 +12374250;Siliciclastic sedimentary rocks;0.998381114136345;Unconsolidated sediments;0.0016188858636551;0;0.1202;-16.4943 +12375900;Siliciclastic sedimentary rocks;0.664125848691602;Carbonate sedimentary rocks;0.287532873097325;0.287532873097325;0.1209;-15.8662 +12377150;Carbonate sedimentary rocks;0.90536976825205;Siliciclastic sedimentary rocks;0.0946302317479503;0.90536976825205;0.0655;-12.2303 +12381400;Metamorphics;0.873094657953921;Carbonate sedimentary rocks;0.126699670129858;0.126699670129858;0.0503;-12.2586 +12383500;Carbonate sedimentary rocks;0.531704686597069;Metamorphics;0.46829531340293;0.531704686597069;0.0485;-12.3304 +12388400;Unconsolidated sediments;0.607998594932297;Basic plutonic rocks;0.392001405067703;0;0.1366;-13.6155 +12390700;Siliciclastic sedimentary rocks;0.913845607119166;Unconsolidated sediments;0.0854367987494303;0;0.1286;-16.1985 +12411000;Siliciclastic sedimentary rocks;0.967198091550656;Unconsolidated sediments;0.0324805990470812;0;0.1232;-16.3855 +12414500;Metamorphics;0.51329232764536;Siliciclastic sedimentary rocks;0.419676915761671;0;0.0598;-15.0861 +12447390;Acid plutonic rocks;1;NA;0;0;0.01;-14.1 +12451000;Metamorphics;0.64645516121772;Acid plutonic rocks;0.303747830907583;0;0.0294;-13.9986 +12488500;Intermediate volcanic rocks;0.391136993494885;Basic volcanic rocks;0.349854409374816;0;0.0846;-12.8344 +13011500;Siliciclastic sedimentary rocks;0.416881372515138;Unconsolidated sediments;0.305520249493637;0.0705337269306653;0.1988;-12.9643 +13011900;Siliciclastic sedimentary rocks;0.469588939887601;Unconsolidated sediments;0.311441731300985;0.118389408266658;0.1794;-13.6286 +13018300;Siliciclastic sedimentary rocks;0.357335678257657;Carbonate sedimentary rocks;0.303307717184428;0.303307717184428;0.1705;-13.0981 +13023000;Siliciclastic sedimentary rocks;0.411606970750814;Carbonate sedimentary rocks;0.216480807479054;0.216480807479054;0.1595;-14.127 +13083000;Siliciclastic sedimentary rocks;0.530252797880232;Acid volcanic rocks;0.408265313523444;0.0203038890646348;0.148;-13.5201 +13161500;Acid volcanic rocks;0.448205697629144;Mixed sedimentary rocks;0.273865964198378;0.0268271578290319;0.1164;-13.2545 +13235000;Acid plutonic rocks;0.96011377106886;Unconsolidated sediments;0.0263505483120769;0;0.0215;-14.0367 +13240000;Acid plutonic rocks;0.992831689534438;Siliciclastic sedimentary rocks;0.00577276406683095;0;0.0113;-14.1047 +13310700;Acid plutonic rocks;0.962844095602884;Unconsolidated sediments;0.0365111560108173;0;0.0255;-14.0186 +13313000;Acid plutonic rocks;0.841676653199672;Unconsolidated sediments;0.0829679004723138;0;0.0424;-13.9304 +13331500;Basic volcanic rocks;0.532892250655416;Acid plutonic rocks;0.445610170061072;0;0.0579;-13.1838 +13337000;Acid plutonic rocks;0.77578301799173;Metamorphics;0.215341279831447;0;0.0122;-14.0656 +13338500;Metamorphics;0.445057096505559;Acid plutonic rocks;0.289151534936054;0;0.0346;-13.7088 +13340000;Acid plutonic rocks;0.571494140772178;Metamorphics;0.249879840281328;0;0.0384;-13.7311 +13340600;Acid plutonic rocks;0.522783903266349;Metamorphics;0.475323525011783;0;0.0103;-14.101 +14020000;Basic volcanic rocks;1;NA;0;0;0.09;-12.5 +14092750;Unconsolidated sediments;0.512648683149388;Basic volcanic rocks;0.4764686334737;0;0.1754;-12.1067 +14096850;Basic volcanic rocks;0.487027050891787;Unconsolidated sediments;0.221286746290383;0;0.1374;-12.6426 +14137000;Basic volcanic rocks;0.725299906408341;Unconsolidated sediments;0.119013675265654;0;NA;-12.7777 +14138800;Basic volcanic rocks;0.740865631679338;Unconsolidated sediments;0.259134368320662;0;0.1392;-12.0854 +14138870;Basic volcanic rocks;0.9929602984639;Water bodies;0.00442829462668043;0;0.0911;-12.498 +14138900;Basic volcanic rocks;0.975387312254637;Water bodies;0.024612687745363;0;0.0932;-12.5123 +14139800;Basic volcanic rocks;0.740226101622159;Unconsolidated sediments;0.236597755189362;0;0.1373;-12.184 +14141500;Basic volcanic rocks;0.63549535218759;Siliciclastic sedimentary rocks;0.23659428545522;0;0.1377;-12.9441 +14154500;Mixed sedimentary rocks;0.385607126800943;Basic volcanic rocks;0.327603924337117;0;0.1317;-13.7224 +14158500;Basic volcanic rocks;0.926025671490152;Unconsolidated sediments;0.0550672472906692;0;0.1029;-12.4213 +14158790;Basic volcanic rocks;0.987830245936935;Acid volcanic rocks;0.012169754063065;0;0.09;-12.5 +14166500;Siliciclastic sedimentary rocks;0.711292413828124;Unconsolidated sediments;0.203838690763966;0;0.1909;-14.2774 +14182500;Basic volcanic rocks;0.851192250215564;Unconsolidated sediments;0.136540213656795;0;0.1092;-12.5041 +14185000;Basic volcanic rocks;0.802211622051385;Mixed sedimentary rocks;0.137407513645478;0;0.1081;-12.8911 +14185900;Basic volcanic rocks;0.956713966484082;Intermediate plutonic rocks;0.0191866894971637;0;0.0905;-12.5709 +14187000;Basic volcanic rocks;0.91441251124703;Mixed sedimentary rocks;0.0732858129137363;0;0.099;-12.7001 +14216500;Basic volcanic rocks;0.838252687689652;Intermediate volcanic rocks;0.161632836269863;0;0.09;-12.5002 +14222500;Basic volcanic rocks;0.75817133875236;Intermediate volcanic rocks;0.149271316675291;0;0.0871;-12.6245 +14236200;Basic volcanic rocks;0.619065082439889;Siliciclastic sedimentary rocks;0.221420443711768;0;0.1284;-13.1605 +14301000;Siliciclastic sedimentary rocks;0.65045953840262;Basic volcanic rocks;0.303668649324322;0;0.1384;-14.6236 +14303200;Siliciclastic sedimentary rocks;0.712490152693631;Basic plutonic rocks;0.263353124061706;0;0.1402;-14.8451 +14305500;Siliciclastic sedimentary rocks;0.579410772512109;Basic volcanic rocks;0.191016710838406;0;0.1421;-14.2961 +14306340;Siliciclastic sedimentary rocks;0.786551333703718;Basic plutonic rocks;0.213448666296282;0;0.1516;-14.9652 +14306500;Siliciclastic sedimentary rocks;0.799089682507401;Basic volcanic rocks;0.137016333546829;0;0.1701;-14.6983 +14308990;Acid plutonic rocks;0.550186356442864;Metamorphics;0.164955000677733;0;0.0252;-14.2313 +14309500;Siliciclastic sedimentary rocks;0.863563337763922;Basic volcanic rocks;0.0952252489278625;0;0.1731;-14.8976 +14316700;Basic volcanic rocks;0.44468021683718;Mixed sedimentary rocks;0.373505933259641;0;0.1338;-13.5958 +14325000;Siliciclastic sedimentary rocks;0.903702098986525;Basic plutonic rocks;0.0629955178768763;0;0.1708;-15.1799 +14362250;Basic volcanic rocks;0.983481199230568;Acid plutonic rocks;0.0165188007694322;0;0.0887;-12.5264 +14400000;Siliciclastic sedimentary rocks;0.606239199718127;Basic plutonic rocks;0.28897450891912;0;0.1216;-14.7163 diff --git a/test/test_data/camels_us/camels_attributes_v2.0/camels_hydro.txt b/test/test_data/camels_us/camels_attributes_v2.0/camels_hydro.txt new file mode 100644 index 00000000..028a5a2a --- /dev/null +++ b/test/test_data/camels_us/camels_attributes_v2.0/camels_hydro.txt @@ -0,0 +1,672 @@ +gauge_id;q_mean;runoff_ratio;slope_fdc;baseflow_index;stream_elas;q5;q95;high_q_freq;high_q_dur;low_q_freq;low_q_dur;zero_q_freq;hfd_mean +01013500;1.69915450753356;0.543437466590222;1.52821853538976;0.585225955779508;1.84532416164806;0.241106126475711;6.37302139711473;6.1;8.71428571428571;41.35;20.1707317073171;0;207.25 +01022500;2.17306214800791;0.602268929482991;1.77627980351081;0.554478447930409;1.70278237133413;0.20473435527605;7.12304944397925;3.9;2.29411764705882;65.15;17.1447368421053;0;166.25 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b/test/test_data/camels_us/camels_attributes_v2.0/camels_name.txt new file mode 100644 index 00000000..ad877e33 --- /dev/null +++ b/test/test_data/camels_us/camels_attributes_v2.0/camels_name.txt @@ -0,0 +1,672 @@ +gauge_id;huc_02;gauge_name +01013500;01;Fish River near Fort Kent, Maine +01022500;01;Narraguagus River at Cherryfield, Maine +01030500;01;Mattawamkeag River near Mattawamkeag, Maine +01031500;01;Piscataquis River near Dover-Foxcroft, Maine +01047000;01;Carrabassett River near North Anson, Maine +01052500;01;Diamond River near Wentworth Location, NH +01054200;01;Wild River at Gilead, Maine +01055000;01;Swift River near Roxbury, Maine +01057000;01;Little Androscoggin River near South Paris, Maine +01073000;01;OYSTER RIVER NEAR DURHAM, NH +01078000;01;SMITH RIVER NEAR BRISTOL, NH +01118300;01;PENDLETON HILL BROOK NEAR CLARKS FALLS, CT. +01121000;01;MOUNT HOPE RIVER NEAR WARRENVILLE, CT. +01123000;01;LITTLE RIVER NEAR HANOVER, CT. +01134500;01;MOOSE RIVER AT VICTORY, VT +01137500;01;AMMONOOSUC RIVER AT BETHLEHEM JUNCTION, NH +01139000;01;WELLS RIVER AT WELLS RIVER, VT +01139800;01;EAST ORANGE BRANCH AT EAST ORANGE, VT +01142500;01;AYERS BROOK AT RANDOLPH, VT +01144000;01;WHITE RIVER AT WEST HARTFORD, VT +01162500;01;PRIEST BROOK NEAR WINCHENDON, MA +01169000;01;NORTH RIVER AT SHATTUCKVILLE, MA +01170100;01;GREEN RIVER NEAR COLRAIN, MA +01181000;01;WEST BRANCH WESTFIELD RIVER AT HUNTINGTON, MA +01187300;01;HUBBARD RIVER NR. WEST HARTLAND, CT. +01195100;01;INDIAN RIVER NEAR CLINTON, CT. +01333000;02;GREEN RIVER AT WILLIAMSTOWN, MA +01350000;02;SCHOHARIE CREEK AT PRATTSVILLE NY +01350080;02;MANOR KILL AT WEST CONESVILLE NEAR GILBOA NY +01350140;02;MINE KILL NEAR NORTH BLENHEIM NY +01365000;02;RONDOUT CREEK NEAR LOWES CORNERS NY +01411300;02;TUCKAHOE RIVER AT HEAD OF RIVER NJ +01413500;02;EAST BR DELAWARE R AT MARGARETVILLE NY +01414500;02;MILL BROOK NEAR DUNRAVEN NY +01415000;02;TREMPER KILL NEAR ANDES NY +01423000;02;WEST BRANCH DELAWARE RIVER AT WALTON NY +01434025;02;BISCUIT BK ABOVE PIGEON BK AT FROST VALLEY NY +01435000;02;NEVERSINK RIVER NEAR CLARYVILLE NY +01439500;02;Bush Kill at Shoemakers, PA +01440000;02;FLAT BROOK NEAR FLATBROOKVILLE NJ +01440400;02;Brodhead Creek near Analomink, PA +01451800;02;Jordan Creek near Schnecksville, PA +01466500;02;MCDONALDS BRANCH IN LEBANON STATE FOREST NJ +01484100;02;BEAVERDAM BRANCH AT HOUSTON, DE +01485500;02;NASSAWANGO CREEK NEAR SNOW HILL, MD +01486000;02;MANOKIN BRANCH NEAR PRINCESS ANNE, MD +01487000;02;NANTICOKE RIVER NEAR BRIDGEVILLE, DE +01491000;02;CHOPTANK RIVER NEAR GREENSBORO, MD +01510000;02;OTSELIC RIVER AT CINCINNATUS NY +01516500;02;Corey Creek near Mainesburg, PA +01518862;02;Cowanesque River at Westfield, PA +01532000;02;Towanda Creek near Monroeton, PA +01539000;02;Fishing Creek near Bloomsburg, PA +01542810;02;Waldy Run near Emporium, PA +01543000;02;Driftwood Br Sinnemahoning Cr at Sterling Run, PA +01543500;02;Sinnemahoning Creek at Sinnemahoning, PA +01544500;02;Kettle Creek at Cross Fork, PA +01545600;02;Young Womans Creek near Renovo, PA +01547700;02;Marsh Creek at Blanchard, PA +01548500;02;Pine Creek at Cedar Run, PA +01549500;02;Blockhouse Creek near English Center, PA +01550000;02;Lycoming Creek near Trout Run, PA +01552000;02;Loyalsock Creek at Loyalsockville, PA +01552500;02;Muncy Creek near Sonestown, PA +01557500;02;Bald Eagle Creek at Tyrone, PA +01567500;02;Bixler Run near Loysville, PA +01568000;02;Sherman Creek at Shermans Dale, PA +01580000;02;DEER CREEK AT ROCKS, MD +01583500;02;WESTERN RUN AT WESTERN RUN, MD +01586610;02;MORGAN RUN NEAR LOUISVILLE, MD +01591400;02;CATTAIL CREEK NEAR GLENWOOD, MD +01594950;02;MCMILLAN F NEAR FORT PENDLETON, MD +01596500;02;SAVAGE RIVER NEAR BARTON, MD +01605500;02;SOUTH BRANCH POTOMAC RIVER AT FRANKLIN, WV +01606500;02;SO. BRANCH POTOMAC RIVER NR PETERSBURG, WV +01613050;02;Tonoloway Creek near Needmore, PA +01620500;02;NORTH RIVER NEAR STOKESVILLE, VA +01632000;02;N F SHENANDOAH RIVER AT COOTES STORE, VA +01632900;02;SMITH CREEK NEAR NEW MARKET, VA +01634500;02;CEDAR CREEK NEAR WINCHESTER, VA +01638480;02;CATOCTIN CREEK AT TAYLORSTOWN, VA +01639500;02;BIG PIPE CREEK AT BRUCEVILLE, MD +01644000;02;GOOSE CREEK NEAR LEESBURG, VA +01658500;02;S F QUANTICO CREEK NEAR INDEPENDENT HILL, VA +01664000;02;RAPPAHANNOCK RIVER AT REMINGTON, VA +01666500;02;ROBINSON RIVER NEAR LOCUST DALE, VA +01667500;02;RAPIDAN RIVER NEAR CULPEPER, VA +01669000;02;PISCATAWAY CREEK NEAR TAPPAHANNOCK, VA +01669520;02;DRAGON SWAMP AT MASCOT, VA +02011400;02;JACKSON RIVER NEAR BACOVA, VA +02011460;02;BACK CREEK NEAR SUNRISE, VA +02013000;02;DUNLAP CREEK NEAR COVINGTON, VA +02014000;02;POTTS CREEK NEAR COVINGTON, VA +02015700;02;BULLPASTURE RIVER AT WILLIAMSVILLE, VA +02016000;02;COWPASTURE RIVER NEAR CLIFTON FORGE, VA +02017500;02;JOHNS CREEK AT NEW CASTLE, VA +02018000;02;CRAIG CREEK AT PARR, VA +02027000;02;TYE RIVER NEAR LOVINGSTON, VA +02027500;02;PINEY RIVER AT PINEY RIVER, VA +02028500;02;ROCKFISH RIVER NEAR GREENFIELD, VA +02038850;02;HOLIDAY CREEK NEAR ANDERSONVILLE, VA +02046000;03;STONY CREEK NEAR DINWIDDIE, VA +02051000;03;NORTH MEHERRIN RIVER NEAR LUNENBURG, VA +02051500;03;MEHERRIN RIVER NEAR LAWRENCEVILLE, VA +02053200;03;POTECASI CREEK NEAR UNION, NC +02053800;03;S F ROANOKE RIVER NEAR SHAWSVILLE, VA +02055100;03;TINKER CREEK NEAR DALEVILLE, VA +02056900;03;BLACKWATER RIVER NEAR ROCKY MOUNT, VA +02059500;03;GOOSE CREEK NEAR HUDDLESTON, VA +02064000;03;FALLING RIVER NEAR NARUNA, VA +02065500;03;CUB CREEK AT PHENIX, VA +02069700;03;SOUTH MAYO RIVER NEAR NETTLERIDGE, VA +02070000;03;NORTH MAYO RIVER NEAR SPENCER, VA +02074500;03;SANDY RIVER NEAR DANVILLE, VA +02077200;03;HYCO CREEK NEAR LEASBURG, NC +02081500;03;TAR RIVER NEAR TAR RIVER, NC +02082950;03;LITTLE FISHING CREEK NEAR WHITE OAK, NC +02092500;03;TRENT RIVER NEAR TRENTON, NC +02096846;03;CANE CREEK NEAR ORANGE GROVE, NC +02102908;03;FLAT CREEK NEAR INVERNESS, NC +02108000;03;NORTHEAST CAPE FEAR RIVER NEAR CHINQUAPIN, NC +02111180;03;ELK CREEK AT ELKVILLE, NC +02111500;03;REDDIES RIVER AT NORTH WILKESBORO, NC +02112120;03;ROARING RIVER NEAR ROARING RIVER, NC +02112360;03;MITCHELL RIVER NEAR STATE ROAD, NC +02118500;03;HUNTING CREEK NEAR HARMONY, NC +02125000;03;BIG BEAR CR NR RICHFIELD, NC +02128000;03;LITTLE RIVER NEAR STAR, NC +02137727;03;CATAWBA R NR PLEASANT GARDENS, NC +02140991;03;JOHNS RIVER AT ARNEYS STORE, NC +02143000;03;HENRY FORK NEAR HENRY RIVER, NC +02143040;03;JACOB FORK AT RAMSEY, NC +02149000;03;COVE CREEK NEAR LAKE LURE, NC +02152100;03;FIRST BROAD RIVER NEAR CASAR, NC +02177000;03;CHATTOOGA RIVER NEAR CLAYTON, GA +02178400;03;TALLULAH RIVER NEAR CLAYTON, GA +02193340;03;KETTLE CREEK NEAR WASHINGTON, GA +02196000;03;STEVENS CREEK NEAR MODOC, SC +02198100;03;BEAVERDAM CREEK NEAR SARDIS, GA +02202600;03;BLACK CREEK NEAR BLITCHTON, GA +02212600;03;FALLING CREEK NEAR JULIETTE, GA +02215100;03;TUCSAWHATCHEE CREEK NEAR HAWKINSVILLE, GA +02216180;03;TURNPIKE CREEK NEAR MCRAE, GA +02221525;03;MURDER CREEK BELOW EATONTON, GA +02231000;03;ST. MARYS RIVER NR MACCLENNY, FLA. +02231342;03;FT DRUM CREEK AT SUNSHINE ST PKY NEAR FT DRUM, FL +02235200;03;BLACKWATER CREEK NEAR CASSIA, FL +02245500;03;SOUTH FORK BLACK CREEK NR PENNEY FARMS, FLA. +02246000;03;NORTH FORK BLACK CREEK NR MIDDLEBURG, FLA. +02296500;03;CHARLIE CREEK NEAR GARDNER FL +02297155;03;HORSE CREEK NEAR MYAKKA HEAD FL +02297310;03;HORSE CREEK NEAR ARCADIA FL +02298123;03;PRAIRIE CREEK NEAR FORT OGDEN FL +02298608;03;MYAKKA RIVER AT MYAKKA CITY FL +02299950;03;MANATEE RIVER NEAR MYAKKA HEAD FL +02300700;03;BULLFROG CREEK NEAR WIMAUMA FL +02310947;03;WITHLACOOCHEE RIVER NEAR CUMPRESSCO, FLA. +02312200;03;LITTLE WITHLACOOCHEE RIVER AT RERDELL, FLA. +02314500;03;SUWANNEE RIVER AT US 441, AT FARGO, GA +02315500;03;SUWANNEE RIVER AT WHITE SPRINGS, FLA. +02324400;03;FENHOLLOWAY RIVER NEAR FOLEY, FLA. +02327100;03;SOPCHOPPY RIVER NR SOPCHOPPY, FLA. +02342933;03;SOUTH FORK COWIKEE CREEK NEAR BATESVILLE AL +02349900;03;TURKEY CREEK AT BYROMVILLE, GA +02350900;03;KINCHAFOONEE CREEK NEAR DAWSON, GA +02361000;03;CHOCTAWHATCHEE RIVER NEAR NEWTON, AL. +02363000;03;PEA RIVER NEAR ARITON AL +02369800;03;BLACKWATER RIVER NEAR BRADLEY AL +02371500;03;CONECUH RIVER AT BRANTLEY AL +02372250;03;PATSALIGA CREEK NEAR BRANTLEY AL +02374500;03;MURDER CREEK NEAR EVERGREEN AL +02381600;03;FAUSETT CREEK NEAR TALKING ROCK, GA +02384540;03;MILL CREEK NEAR CRANDALL, GA +02395120;03;TWO RUN CREEK NEAR KINGSTON, GA +02408540;03;HATCHET CREEK BELOW ROCKFORD AL +02415000;03;HILLABEE CREEK NEAR HACKNEYVILLE AL +02422500;03;MULBERRY CREEK AT JONES AL +02427250;03;PINE BARREN CREEK NEAR SNOW HILL, AL. +02430085;03;RED BUD CREEK NR MOORES MILL, MS +02430615;03;MUD CREEK NR FAIRVIEW, MS +02450250;03;SIPSEY FORK NEAR GRAYSON AL +02464000;03;NORTH RIVER NEAR SAMANTHA AL +02464146;03;TURKEY CREEK NEAR TUSCALOOSA +02464360;03;BINION CREEK BELOW GIN CREEK NEAR SAMANTHA AL +02465493;03;ELLIOTTS CREEK AT MOUNDVILLE AL +02469800;03;SATILPA CREEK NEAR COFFEEVILLE AL +02472000;03;LEAF RIVER NR COLLINS, MS +02472500;03;BOUIE CREEK NR HATTIESBURG, MS +02479155;03;CYPRESS CREEK NR JANICE, MS +02479300;03;RED CREEK AT VESTRY, MS +02479560;03;ESCATAWPA RIVER NEAR AGRICOLA MS +02481000;03;BILOXI RIVER AT WORTHAM, MS +02481510;03;WOLF RIVER NR LANDON, MS +03010655;05;Oswayo Creek at Shinglehouse, PA +03011800;05;Kinzua Creek near Guffey, PA +03015500;05;Brokenstraw Creek at Youngsville, PA +03021350;05;French Creek near Wattsburg, PA +03026500;05;Sevenmile Run near Rasselas, PA +03028000;05;West Branch Clarion River at Wilcox, PA +03049000;05;Buffalo Creek near Freeport, PA +03049800;05;Little Pine Creek near Etna, PA +03050000;05;TYGART VALLEY RIVER NEAR DAILEY, WV +03066000;05;BLACKWATER R AT DAVIS, WV +03069500;05;CHEAT RIVER NEAR PARSONS, WV +03070500;05;BIG SANDY CREEK AT ROCKVILLE, WV +03076600;05;BEAR CREEK AT FRIENDSVILLE, MD +03078000;05;CASSELMAN RIVER AT GRANTSVILLE, MD +03140000;05;Mill Creek near Coshocton OH +03144000;05;Wakatomika Creek near Frazeysburg OH +03159540;05;Shade River near Chester OH +03161000;05;SOUTH FORK NEW RIVER NEAR JEFFERSON, NC +03164000;05;NEW RIVER NEAR GALAX, VA +03165000;05;CHESTNUT CREEK AT GALAX, VA +03170000;05;LITTLE RIVER AT GRAYSONTOWN, VA +03173000;05;WALKER CREEK AT BANE, VA +03180500;05;GREENBRIER RIVER AT DURBIN, WV +03182500;05;GREENBRIER RIVER AT BUCKEYE, WV +03186500;05;WILLIAMS RIVER AT DYER, WV +03187500;05;CRANBERRY RIVER NEAR RICHWOOD, WV +03213700;05;TUG FORK AT WILLIAMSON, WV +03237280;05;Upper Twin Creek at McGaw OH +03237500;05;Ohio Brush Creek near West Union OH +03238500;05;White Oak Creek near Georgetown OH +03241500;05;Massies Creek at Wilberforce OH +03280700;05;CUTSHIN CREEK AT WOOTON, KY +03281100;05;GOOSE CREEK AT MANCHESTER, KY +03281500;05;SOUTH FORK KENTUCKY RIVER AT BOONEVILLE, KY +03285000;05;DIX RIVER NEAR DANVILLE, KY +03291780;05;INDIAN-KENTUCK CREEK NR CANAAN, IND. +03300400;05;BEECH FORK AT MAUD, KY +03338780;05;NORTH FORK VERMILION RIVER NEAR BISMARCK, IL +03340800;05;BIG RACCOON CREEK NEAR FINCASTLE IND +03346000;05;NORTH FORK EMBARRAS RIVER NEAR OBLONG, IL +03357350;05;PLUM CREEK NEAR BAINBRIDGE, IND. +03364500;05;CLIFTY CREEK AT HARTSVILLE, IN +03366500;05;MUSCATATUCK RIVER NEAR DEPUTY, IN +03368000;05;BRUSH CREEK NEAR NEBRASKA, IN +03384450;05;LUSK CREEK NEAR EDDYVILLE, IL +03439000;06;FRENCH BROAD RIVER AT ROSMAN, NC +03450000;06;BEETREE CREEK NEAR SWANNANOA, NC +03455500;06;W F PIGEON R ABOVE LAKE LOGAN NR HAZELWOOD, NC +03456500;06;EAST FORK PIGEON RIVER NEAR CANTON, NC +03460000;06;CATALOOCHEE CREEK NEAR CATALOOCHEE, NC +03463300;06;SOUTH TOE RIVER NEAR CELO, NC +03471500;06;S F HOLSTON RIVER AT RIVERSIDE NR CHILHOWIE, VA +03473000;06;S F HOLSTON RIVER NEAR DAMASCUS, VA +03479000;06;WATAUGA RIVER NEAR SUGAR GROVE, NC +03488000;06;N F HOLSTON RIVER NEAR SALTVILLE, VA +03498500;06;LITTLE RIVER NEAR MARYVILLE, TN +03500000;06;LITTLE TENNESSEE RIVER NEAR PRENTISS, NC +03500240;06;CARTOOGECHAYE CREEK NEAR FRANKLIN, NC +03504000;06;NANTAHALA RIVER NEAR RAINBOW SPRINGS, NC +03574500;06;PAINT ROCK RIVER NEAR WOODVILLE AL +03592718;06;LITTLE YELLOW CREEK EAST NR BURNSVILLE, MS +03604000;06;BUFFALO RIVER NEAR FLAT WOODS, TN +04015330;04;KNIFE RIVER NEAR TWO HARBORS, MN +04024430;04;NEMADJI RIVER NEAR SOUTH SUPERIOR, WI +04027000;04;BAD RIVER NEAR ODANAH, WI +04040500;04;STURGEON RIVER NEAR SIDNAW, MI +04043050;04;TRAP ROCK RIVER NEAR LAKE LINDEN, MI +04045500;04;TAHQUAMENON RIVER NEAR PARADISE, MI +04056500;04;MANISTIQUE RIVER NEAR MANISTIQUE, MI +04057510;04;STURGEON RIVER NEAR NAHMA JUNCTION, MI +04057800;04;MIDDLE BRANCH ESCANABA RIVER AT HUMBOLDT, MI +04059500;04;FORD RIVER NEAR HYDE, MI +04063700;04;POPPLE RIVER NEAR FENCE, WI +04074950;04;WOLF RIVER AT LANGLADE, WI +04105700;04;AUGUSTA CREEK NEAR AUGUSTA, MI +04115265;04;FISH CREEK NEAR CRYSTAL, MI +04122200;04;WHITE RIVER NEAR WHITEHALL, MI +04122500;04;PERE MARQUETTE RIVER AT SCOTTVILLE, MI +04124000;04;MANISTEE RIVER NEAR SHERMAN, MI +04127918;04;PINE RIVER NEAR RUDYARD, MI +04127997;04;STURGEON RIVER AT WOLVERINE, MI +04161580;04;STONY CREEK NEAR ROMEO, MI +04185000;04;Tiffin River at Stryker OH +04196800;04;Tymochtee Creek at Crawford OH +04197100;04;Honey Creek at Melmore OH +04197170;04;Rock Creek at Tiffin OH +04213000;04;Conneaut Creek at Conneaut OH +04213075;04;Brandy Run near Girard, PA +04216418;04;TONAWANDA CREEK AT ATTICA NY +04221000;04;GENESEE RIVER AT WELLSVILLE NY +04224775;04;CANASERAGA CREEK ABOVE DANSVILLE NY +04233000;04;CAYUGA INLET NEAR ITHACA NY +04256000;04;INDEPENDENCE RIVER AT DONNATTSBURG NY +04296000;01;BLACK RIVER AT COVENTRY, VT +05056000;09;SHEYENNE RIVER NR WARWICK, ND +05057000;09;SHEYENNE RIVER NR COOPERSTOWN, ND +05057200;09;BALDHILL CREEK NR DAZEY, ND +05062500;09;WILD RICE RIVER AT TWIN VALLEY, MN +05087500;09;MIDDLE RIVER AT ARGYLE, MN +05120500;09;WINTERING RIVER NR KARLSRUHE, ND +05123400;09;WILLOW CREEK NR WILLOW CITY, ND +05129115;09;VERMILION RIVER NR CRANE LAKE, MN +05131500;09;LITTLE FORK RIVER AT LITTLEFORK, MN +05291000;07;WHETSTONE RIVER NEAR BIG STONE CITY, SD +05362000;07;JUMP RIVER AT SHELDON, WI +05393500;07;SPIRIT RIVER AT SPIRIT FALLS, WI +05399500;07;BIG EAU PLEINE RIVER AT STRATFORD, WI +05408000;07;KICKAPOO RIVER AT LA FARGE, WI +05412500;07;Turkey River at Garber, IA +05413500;07;GRANT RIVER AT BURTON, WI +05414000;07;PLATTE RIVER NEAR ROCKVILLE, WI +05444000;07;ELKHORN CREEK NEAR PENROSE, IL +05454000;07;Rapid Creek near Iowa City, IA +05458000;07;Little Cedar River near Ionia, IA +05466500;07;EDWARDS RIVER NEAR NEW BOSTON, IL +05487980;07;White Breast Creek near Dallas, IA +05488200;07;English Creek near Knoxville, IA +05489000;07;Cedar Creek near Bussey, IA +05495000;07;Fox River at Wayland, MO +05495500;07;BEAR CREEK NEAR MARCELLINE, IL +05501000;07;North River at Palmyra, MO +05503800;07;Crooked Creek near Paris, MO +05507600;07;Lick Creek at Perry, MO +05508805;07;Spencer Cr. bl Plum Cr. nr Frankford, MO +05514500;07;CUIVRE RIVER NEAR TROY, MO +05525500;07;SUGAR CREEK AT MILFORD, IL +05556500;07;BIG BUREAU CREEK AT PRINCETON, IL +05584500;07;LA MOINE RIVER AT COLMAR, IL +05585000;07;LA MOINE RIVER AT RIPLEY, IL +05591550;07;WHITLEY CREEK NEAR ALLENVILLE, IL +05592050;07;ROBINSON CREEK NEAR SHELBYVILLE, IL +05592575;07;HICKORY CREEK NEAR BROWNSTOWN, IL +05593575;07;LITTLE CROOKED CREEK NEAR NEW MINDEN, IL +05593900;07;EAST FORK SHOAL CREEK NEAR COFFEEN, IL +05595730;07;RAYSE CREEK NEAR WALTONVILLE, IL +06037500;10;Madison River near West Yellowstone MT +06043500;10;Gallatin River near Gallatin Gateway MT +06154410;10;Little Peoples Creek near Hays MT +06188000;10;LAMAR RIVER NR TOWER FALLS RANGER STATION, YNP +06191500;10;Yellowstone River at Corwin Springs MT +06221400;10;DINWOODY CREEK ABOVE LAKES, NEAR BURRIS, WYO. +06224000;10;BULL LAKE CREEK ABOVE BULL LAKE, WY +06278300;10;SHELL CREEK ABOVE SHELL CREEK RESERVOIR, WY +06280300;10;SOUTH FORK SHOSHONE RIVER NEAR VALLEY, WY +06289000;10;Little Bighorn River at State Line nr Wyola MT +06291500;10;Lodge Grass Cr ab Willow C Div nr Wyola MT +06311000;10;NORTH FORK POWDER RIVER NEAR HAZELTON, WY +06332515;10;BEAR DEN CREEK NR MANDAREE, ND +06339100;10;KNIFE RIVER AT MANNING, ND +06339500;10;KNIFE RIVER NR GOLDEN VALLEY, ND +06344600;10;GREEN RIVER NR NEW HRADEC, ND +06350000;10;CANNONBALL RIVER AT REGENT, ND +06352000;10;CEDAR CREEK NR HAYNES, ND +06353000;10;CEDAR CREEK NR RALEIGH, ND +06354000;10;CANNONBALL RIVER AT BREIEN, ND +06360500;10;MOREAU R NEAR WHITEHORSE SD +06404000;10;BATTLE CR NEAR KEYSTONE SD +06406000;10;BATTLE CR AT HERMOSA SD +06408700;10;RHOADS FORK NEAR ROCHFORD SD +06409000;10;CASTLE CR ABOVE DEERFIELD RES NEAR HILL CITY SD +06431500;10;SPEARFISH CR AT SPEARFISH SD +06440200;10;SOUTH FORK BAD R NEAR COTTONWOOD,SD +06441500;10;BAD R NEAR FORT PIERRE SD +06447000;10;WHITE R NEAR KADOKA SD +06447500;10;LITTLE WHITE R NEAR MARTIN SD +06450500;10;LITTLE WHITE R BELOW WHITE RIVER SD +06452000;10;WHITE R NEAR OACOMA SD +06453600;10;PONCA CREEK AT VERDEL, NEBR. +06464500;10;KEYA PAHA R AT WEWELA SD +06468170;10;JAMES RIVER NR GRACE CITY, ND +06468250;10;JAMES RIVER ABOVE ARROWWOOD LAKE NR KENSAL, ND +06470800;10;BEAR CREEK NR OAKES, ND +06477500;10;FIRESTEEL CR NEAR MOUNT VERNON SD +06479215;10;BIG SIOUX RIVER NR FLORENCE SD +06479438;10;BIG SIOUX R NEAR WATERTOWN SD +06601000;10;OMAHA CR AT HOMER, NEBR +06614800;10;MICHIGAN RIVER NEAR CAMERON PASS, CO. +06622700;10;NORTH BRUSH CREEK NEAR SARATOGA, WY +06623800;10;ENCAMPMENT RIVER AB HOG PARK CR, NR ENCAMPMENT, WY +06632400;10;ROCK CREEK AB KING CANYON CANAL, NR ARLINGTON, WY +06746095;10;JOE WRIGHT CREEK ABOVE JOE WRIGHT RESERVOIR, CO. +06784000;10;SOUTH LOUP R AT ST. MICHAEL, NEBR. +06803510;10;LITTLE SALT CREEK NEAR LINCOLN, NEBR. +06803530;10;ROCK CREEK NEAR CERESCO, NEBR. +06814000;10;TURKEY C NR SENECA, KS +06847900;10;PRAIRIE DOG C AB KEITH SEBELIUS LAKE, KS +06853800;10;WHITE ROCK C NR BURR OAK, KS +06876700;10;SALT C NR ADA, KS +06878000;10;CHAPMAN C NR CHAPMAN, KS +06879650;10;KINGS C NR MANHATTAN, KS +06885500;10;BLACK VERMILLION R NR FRANKFORT, KS +06888500;10;MILL C NR PAXICO, KS +06889200;10;SOLDIER C NR DELIA, KS +06889500;10;SOLDIER C NR TOPEKA, KS +06892000;10;STRANGER C NR TONGANOXIE, KS +06903400;10;Chariton River near Chariton, IA +06906800;10;Lamine River near Otterville, MO +06910800;10;MARAIS DES CYGNES R NR READING, KS +06911900;10;DRAGOON C NR BURLINGAME, KS +06917000;10;L OSAGE R AT FULTON, KS +06918460;10;Turnback Creek above Greenfield, MO +06919500;10;Cedar Creek near Pleasant View, MO +06921070;10;Pomme de Terre River near Polk, MO +06921200;10;Lindley Creek near Polk, MO +06934000;10;Gasconade River near Rich Fountain, MO +07014500;07;Meramec River near Sullivan, MO +07056000;11;Buffalo River near St. Joe, AR +07057500;11;North Fork River near Tecumseh, MO +07060710;11;North Sylamore Creek near Fifty Six, AR +07066000;11;Jacks Fork at Eminence, MO +07067000;11;Current River at Van Buren, MO +07068000;11;Current River at Doniphan, MO +07071500;11;Eleven Point River near Bardley, MO +07083000;11;HALFMOON CREEK NEAR MALTA, CO. +07142300;11;RATTLESNAKE C NR MACKSVILLE, KS +07145700;11;SLATE C AT WELLINGTON, KS +07148400;11;Salt Fork Arkansas River nr Alva, OK +07149000;11;MEDICINE LODGE R NR KIOWA, KS +07151500;11;CHIKASKIA R NR CORBIN, KS +07167500;11;OTTER C AT CLIMAX, KS +07180500;11;CEDAR C NR CEDAR POINT, KS +07184000;11;LIGHTNING C NR MCCUNE, KS +07195800;11;Flint Creek at Springtown, AR +07196900;11;Baron Fork at Dutch Mills, AR +07197000;11;Baron Fork at Eldon, OK +07208500;11;RAYADO CREEK NEAR CIMARRON, NM +07226500;11;UTE CREEK NEAR LOGAN, NM +07261000;11;Cadron Creek near Guy, AR +07263295;11;Maumelle River at Williams Junction, AR +07290650;08;BAYOU PIERRE NR WILLOWS, MS +07291000;08;HOMOCHITTO RIVER AT EDDICETON, MS +07292500;08;HOMOCHITTO RIVER AT ROSETTA, MS +07295000;08;BUFFALO RIVER NR WOODVILLE, MS +07299670;11;Groesbeck Ck at SH 6 nr Quanah, TX +07301410;11;Sweetwater Ck nr Kelton, TX +07301500;11;North Fork Red River near Carter, OK +07315200;11;E Fk Little Wichita Rv nr Henrietta, TX +07315700;11;Mud Creek near Courtney, OK +07335700;11;Kiamichi River near Big Cedar, OK +07340300;11;Cossatot River near Vandervoort, AR +07346045;11;Black Cypress Bayou at Jefferson, TX +07359610;08;Caddo River near Caddo Gap, AR +07362100;08;SMACKOVER CREEK NEAR SMACKOVER, ARK. +07362587;08;Alum Fork Saline River near Reform, AR +07373000;08;Big Creek at Pollock, LA +07375000;08;Tchefuncta River near Folsom, LA +07376000;08;Tickfaw River at Holden, LA +08013000;08;CALCASIEU RIVER NR GLENMORA, LA +08014500;08;Whiskey Chitto Creek Near Oberlin, LA +08023080;12;Bayou Grand Cane near Stanley, LA +08025500;12;Bayou Toro near Toro, LA +08029500;12;Big Cow Ck nr Newton, TX +08050800;12;Timber Ck nr Collinsville, TX +08066200;12;Long King Ck at Livingston, TX +08066300;12;Menard Ck nr Rye, TX +08070000;12;E Fk San Jacinto Rv nr Cleveland, TX +08070200;12;E Fk San Jacinto Rv nr New Caney, TX +08079600;12;DMF Brazos Rv at Justiceburg, TX +08082700;12;Millers Ck nr Munday, TX +08086212;12;Hubbard Ck bl Albany, TX +08086290;12;Big Sandy Ck abv Breckenridge, TX +08101000;12;Cowhouse Ck at Pidcoke, TX +08103900;12;S Fk Rocky Ck nr Briggs, TX +08104900;12;S Fk San Gabriel Rv at Georgetown, TX +08109700;12;Middle Yegua Ck nr Dime Box, TX +08150800;12;Beaver Ck nr Mason, TX +08155200;12;Barton Ck at SH 71 nr Oak Hill, TX +08158700;12;Onion Ck nr Driftwood, TX +08158810;12;Bear Ck bl FM 1826 nr Driftwood, TX +08164000;12;Lavaca Rv nr Edna, TX +08164300;12;Navidad Rv nr Hallettsville, TX +08164600;12;Garcitas Ck nr Inez, TX +08165300;12;N Fk Guadalupe Rv nr Hunt, TX +08171300;12;Blanco Rv nr Kyle, TX +08175000;12;Sandies Ck nr Westhoff, TX +08176900;12;Coleto Ck at Arnold Rd nr Schroeder, TX +08178880;12;Medina Rv at Bandera, TX +08189500;12;Mission Rv at Refugio, TX +08190000;12;Nueces Rv at Laguna, TX +08190500;12;W Nueces Rv nr Brackettville, TX +08194200;12;San Casimiro Ck nr Freer, TX +08195000;12;Frio Rv at Concan, TX +08196000;12;Dry Frio Rv nr Reagan Wells, TX +08198500;12;Sabinal Rv at Sabinal, TX +08200000;12;Hondo Ck nr Tarpley, TX +08202700;12;Seco Ck at Rowe Ranch nr D'Hanis, TX +08267500;13;RIO HONDO NEAR VALDEZ, NM +08269000;13;RIO PUEBLO DE TAOS NEAR TAOS, NM +08271000;13;RIO LUCERO NEAR ARROYO SECO, NM +08324000;13;JEMEZ RIVER NEAR JEMEZ, NM +08377900;13;RIO MORA NEAR TERRERO, NM +08378500;13;PECOS RIVER NEAR PECOS, NM +08380500;13;GALLINAS CREEK NEAR MONTEZUMA, NM +09034900;14;BOBTAIL CREEK NEAR JONES PASS, CO. +09035800;14;DARLING CREEK NEAR LEAL, CO. +09035900;14;SOUTH FORK OF WILLIAMS FORK NEAR LEAL, CO. +09047700;14;KEYSTONE GULCH NEAR DILLON, CO. +09065500;14;GORE CREEK AT UPPER STATION, NEAR MINTURN, CO. +09066000;14;BLACK GORE CREEK NEAR MINTURN, CO. +09066200;14;BOOTH CREEK NEAR MINTURN, CO. +09066300;14;MIDDLE CREEK NEAR MINTURN, CO. +09081600;14;CRYSTAL RIVER AB AVALANCHE C, NEAR REDSTONE, CO. +09107000;14;TAYLOR RIVER AT TAYLOR PARK, CO. +09210500;14;FONTENELLE C NR HERSCHLER RANCH, NR FONTENELLE, WY +09223000;14;HAMS FORK BELOW POLE CREEK, NEAR FRONTIER, WY +09306242;14;CORRAL GULCH NEAR RANGELY, CO. +09312600;14;WHITE RIVER BL TABBYUNE C NEAR SOLDIER SUMMIT, UT +09352900;14;VALLECITO CREEK NEAR BAYFIELD, CO. +09378170;14;SOUTH CREEK ABOVE RESERVOIR NEAR MONTICELLO, UT +09378630;14;RECAPTURE CREEK NEAR BLANDING, UT +09386900;15;RIO NUTRIA NEAR RAMAH, NM +09404450;15;EAST FORK VIRGIN RIVER NEAR GLENDALE, UT +09430500;15;GILA RIVER NEAR GILA, NM +09430600;15;MOGOLLON CREEK NEAR CLIFF, NM +09447800;15;BONITA CREEK NEAR MORENCI, AZ. +09480000;15;SANTA CRUZ RIVER NEAR LOCHIEL, AZ. +09484000;15;SABINO CREEK NEAR TUCSON, AZ. +09484600;15;PANTANO WASH NEAR VAIL, AZ. +09492400;15;EAST FORK WHITE RIVER NEAR FORT APACHE, AZ. +09494000;15;WHITE RIVER NEAR FORT APACHE, AZ. +09497800;15;CIBECUE CREEK NEAR CHYSOTILE, AZ. +09497980;15;CHERRY CREEK NEAR GLOBE, AZ. +09505200;15;WET BEAVER CREEK NEAR RIMROCK, AZ. +09505350;15;DRY BEAVER CREEK NEAR RIMROCK, AZ. +09505800;15;WEST CLEAR CREEK NEAR CAMP VERDE, AZ. +09508300;15;WET BOTTOM CREEK NEAR CHILDS, AZ. +09510200;15;SYCAMORE CREEK NEAR FORT MCDOWELL, AZ. +09512280;15;CAVE CREEK BLW COTTONWOOD CR NEAR CAVE CREEK, AZ. +09513780;15;NEW RIVER NEAR ROCK SPRINGS, AZ. +10023000;16;BIG CREEK NEAR RANDOLPH, UT +10166430;16;WEST CANYON CREEK NEAR CEDAR FORT, UT +10172700;16;VERNON CREEK NEAR VERNON, UT +10172800;16;SOUTH WILLOW CREEK NEAR GRANTSVILLE, UT +10173450;16;MAMMOTH CREEK ABV WEST HATCH DITCH, NEAR HATCH, UT +10205030;16;SALINA CREEK NEAR EMERY, UT +10234500;16;BEAVER RIVER NEAR BEAVER, UT +10242000;16;COAL CREEK NEAR CEDAR CITY, UT +10244950;16;STEPTOE C NR ELY, NV +10249300;16;S TWIN R NR ROUND MOUNTAIN, NV +10258000;18;TAHQUITZ C NR PALM SPRINGS CA +10258500;18;PALM CYN C NR PALM SPRINGS CA +10259000;18;ANDREAS C NR PALM SPRINGS CA +10259200;18;DEEP C NR PALM DESERT CA +10263500;18;BIG ROCK C NR VALYERMO CA +10310500;16;CLEAR CREEK NEAR CARSON CITY, NV +10316500;16;LAMOILLE C NR LAMOILLE, NV +10329500;16;MARTIN C NR PARADISE VALLEY, NV +10336645;16;GENERAL C NR MEEKS BAY CA +10336660;16;BLACKWOOD C NR TAHOE CITY CA +10336740;16;Logan House Ck nr Glenbrook, NV +10343500;16;SAGEHEN C NR TRUCKEE CA +10348850;16;GALENA C AT GALENA C STATE PARK +10396000;17;DONNER UND BLITZEN RIVER NR FRENCHGLEN OR +11098000;18;ARROYO SECO NR PASADENA CA +11124500;18;SANTA CRUZ C NR SANTA YNEZ CA +11141280;18;LOPEZ C NR ARROYO GRANDE CA +11143000;18;BIG SUR R NR BIG SUR CA +11148900;18;NACIMIENTO R BL SAPAQUE C NR BRYSON CA +11151300;18;SAN LORENZO C BL BITTERWATER C NR KING CITY CA +11162500;18;PESCADERO C NR PESCADERO CA +11176400;18;ARROYO VALLE BL LANG CN NR LIVERMORE CA +11180500;18;DRY C A UNION CITY CA +11180960;18;CULL C AB CULL C RES NR CASTRO VALLEY CA +11224500;18;LOS GATOS C AB NUNEZ CYN NR COALINGA CA +11230500;18;BEAR C NR LAKE THOMAS A EDISON CA +11237500;18;PITMAN C BL TAMARACK C CA +11253310;18;CANTUA C NR CANTUA CREEK CA +11264500;18;MERCED R A HAPPY ISLES BRIDGE NR YOSEMITE CA +11266500;18;MERCED R A POHONO BRIDGE NR YOSEMITE CA +11274500;18;ORESTIMBA C NR NEWMAN CA +11274630;18;DEL PUERTO C NR PATTERSON CA +11284400;18;BIG C AB WHITES GULCH NR GROVELAND CA +11299600;18;BLACK C NR COPPEROPOLIS CA +11381500;18;MILL C NR LOS MOLINOS CA +11383500;18;DEER C NR VINA CA +11451100;18;NF CACHE C A HOUGH SPRING NR CLEARLAKE OAKS CA +11468500;18;NOYO R NR FORT BRAGG CA +11473900;18;MF EEL R NR DOS RIOS CA +11475560;18;ELDER C NR BRANSCOMB CA +11476600;18;BULL C NR WEOTT CA +11478500;18;VAN DUZEN R NR BRIDGEVILLE CA +11480390;18;MAD R AB RUTH RES NR FOREST GLEN CA +11481200;18;LITTLE R NR TRINIDAD CA +11482500;18;REDWOOD C A ORICK CA +11522500;18;SALMON R A SOMES BAR CA +11523200;18;TRINITY R AB COFFEE C NR TRINITY CTR CA +11528700;18;SF TRINITY R BL HYAMPOM CA +11532500;18;SMITH R NR CRESCENT CITY CA +12010000;17;NASELLE RIVER NEAR NASELLE, WA +12013500;17;WILLAPA RIVER NEAR WILLAPA, WA +12020000;17;CHEHALIS RIVER NEAR DOTY, WA +12025000;17;NEWAUKUM RIVER NEAR CHEHALIS, WA +12025700;17;SKOOKUMCHUCK RIVER NEAR VAIL, WA +12035000;17;SATSOP RIVER NEAR SATSOP, WA +12040500;17;QUEETS RIVER NEAR CLEARWATER, WA +12041200;17;HOH RIVER AT US HIGHWAY 101 NEAR FORKS, WA +12043000;17;CALAWAH RIVER NEAR FORKS, WA +12048000;17;DUNGENESS RIVER NEAR SEQUIM, WA +12054000;17;DUCKABUSH RIVER NEAR BRINNON, WA +12056500;17;NF SKOKOMISH R BL STAIRCASE RPDS NR HOODSPORT, WA +12073500;17;HUGE CREEK NEAR WAUNA, WA +12082500;17;NISQUALLY RIVER NEAR NATIONAL, WA +12092000;17;PUYALLUP RIVER NEAR ELECTRON, WA +12095000;17;SOUTH PRAIRIE CREEK AT SOUTH PRAIRIE, WA +12114500;17;CEDAR RIVER BELOW BEAR CREEK NEAR CEDAR FALLS, WA +12115000;17;CEDAR RIVER NEAR CEDAR FALLS, WA +12115500;17;REX RIVER NEAR CEDAR FALLS, WA +12117000;17;TAYLOR CREEK NEAR SELLECK, WA +12141300;17;MIDDLE FORK SNOQUALMIE RIVER NEAR TANNER, WA +12143600;17;SF SNOQUALMIE RIVER AT EDGEWICK, WA +12144000;17;SF SNOQUALMIE RIVER AT NORTH BEND, WA +12145500;17;RAGING RIVER NEAR FALL CITY, WA +12147500;17;NORTH FORK TOLT RIVER NEAR CARNATION, WA +12147600;17;SOUTH FORK TOLT RIVER NEAR INDEX, WA +12167000;17;NF STILLAGUAMISH RIVER NEAR ARLINGTON, WA +12175500;17;THUNDER CREEK NEAR NEWHALEM, WA +12178100;17;NEWHALEM CREEK NEAR NEWHALEM, WA +12186000;17;SAUK RIVER AB WHITECHUCK RIVER NEAR DARRINGTON, WA +12189500;17;SAUK RIVER NEAR SAUK, WA +12358500;17;Middle Fork Flathead River nr West Glacier MT +12374250;17;Mill Cr ab Bassoo Cr nr Niarada MT +12375900;17;South Crow Creek near Ronan MT +12377150;17;Mission Cr ab Reservoir nr ST Ignatius MT +12381400;17;South Fork Jocko River near Arlee MT +12383500;17;Big Knife Creek near Arlee MT +12388400;17;Revais Cr bl West Fork nr Dixon MT +12390700;17;Prospect Creek at Thompson Falls MT +12411000;17;NF COEUR D ALENE R AB SHOSHONE CK NR PRICHARD ID +12414500;17;ST JOE RIVER AT CALDER ID +12447390;17;ANDREWS CREEK NEAR MAZAMA, WA +12451000;17;STEHEKIN RIVER AT STEHEKIN, WA +12488500;17;AMERICAN RIVER NEAR NILE, WA +13011500;17;PACIFIC CREEK AT MORAN WY +13011900;17;BUFFALO FORK AB LAVA CREEK NR MORAN WY +13018300;17;CACHE CREEK NEAR JACKSON, WY +13023000;17;GREYS RIVER AB RESERVOIR NR ALPINE WY +13083000;17;TRAPPER CREEK NR OAKLEY ID +13161500;17;BRUNEAU RIVER AT ROWLAND NV +13235000;17;SF PAYETTE RIVER AT LOWMAN ID +13240000;17;LAKE FORK PAYETTE RIVER AB JUMBO CR NR MCCALL ID +13310700;17;SF SALMON RIVER NR KRASSEL RANGER STATION ID +13313000;17;JOHNSON CREEK AT YELLOW PINE ID +13331500;17;MINAM RIVER NEAR MINAM, OR +13337000;17;LOCHSA RIVER NR LOWELL ID +13338500;17;SF CLEARWATER RIVER AT STITES ID +13340000;17;CLEARWATER RIVER AT OROFINO ID +13340600;17;NF CLEARWATER RIVER NR CANYON RANGER STATION ID +14020000;17;UMATILLA RIVER ABOVE MEACHAM CREEK, NR GIBBON, OR +14092750;17;SHITIKE CR, AT PETERS PASTURE, NR WARM SPRINGS, OR +14096850;17;BEAVER CREEK, BLW QUARTZ CR, NR SIMNASHO, OR. +14137000;17;SANDY RIVER NEAR MARMOT, OR +14138800;17;BLAZED ALDER CREEK NEAR RHODODENDRON, OREG. +14138870;17;FIR CREEK NEAR BRIGHTWOOD, OR +14138900;17;NORTH FORK BULL RUN RIVER NEAR MULTNOMAH FALLS, OR +14139800;17;SOUTH FORK BULL RUN RIVER NEAR BULL RUN, OR +14141500;17;LITTLE SANDY RIVER NEAR BULL RUN, OREG. +14154500;17;ROW RIVER ABOVE PITCHER CREEK NEAR, DORENA, OREG +14158500;17;MCKENZIE RIVER AT OUTLET OF CLEAR LAKE, OR +14158790;17;SMITH R AB SMITH R RES NR BELKNAP SPRGS,OREG. +14166500;17;LONG TOM RIVER NEAR NOTI, OREG. +14182500;17;LITTLE NORTH SANTIAM RIVER NEAR MEHAMA, OR +14185000;17;SOUTH SANTIAM RIVER BELOW CASCADIA, OR +14185900;17;QUARTZVILLE CREEK NEAR CASCADIA, OREG. +14187000;17;WILEY CREEK NEAR FOSTER, OR +14216500;17;MUDDY CREEK BELOW CLEAR CREEK NEAR COUGAR, WA +14222500;17;EAST FORK LEWIS RIVER NEAR HEISSON, WA +14236200;17;TILTON RIVER AB BEAR CANYON CREEK NEAR CINEBAR, WA +14301000;17;NEHALEM RIVER NEAR FOSS, OR +14303200;17;TUCCA CREEK NEAR BLAINE, OR. +14305500;17;SILETZ RIVER AT SILETZ, OR +14306340;17;EAST FORK LOBSTER CREEK NEAR ALSEA, OR. +14306500;17;ALSEA RIVER NEAR TIDEWATER, OR +14308990;17;COW CREEK ABV GALESVILLE RES, NR AZALEA, OR. +14309500;17;WEST FORK COW CREEK NEAR GLENDALE, OR +14316700;17;STEAMBOAT CREEK NEAR GLIDE, OR +14325000;17;SOUTH FORK COQUILLE RIVER AT POWERS, OR +14362250;17;STAR GULCH NEAR RUCH, OR. +14400000;17;CHETCO RIVER NEAR BROOKINGS, OR diff --git a/test/test_data/camels_us/camels_attributes_v2.0/camels_soil.txt b/test/test_data/camels_us/camels_attributes_v2.0/camels_soil.txt new file mode 100644 index 00000000..51cf0840 --- /dev/null +++ b/test/test_data/camels_us/camels_attributes_v2.0/camels_soil.txt @@ -0,0 +1,672 @@ 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+gauge_id;frac_forest;lai_max;lai_diff;gvf_max;gvf_diff;dom_land_cover_frac;dom_land_cover;root_depth_50;root_depth_99 +01013500;0.9063;4.1673039455613;3.34073185949204;0.804567420071824;0.37164816263051;0.883451918625571; Mixed Forests;NA;NA +01022500;0.9232;4.87139243857226;3.74669161645587;0.863935769102455;0.337712461949305;0.820493445115513; Mixed Forests;0.237434541158062;2.23844410060377 +01030500;0.8782;4.68519971859885;3.66554296378545;0.858502041122509;0.35139338964903;0.975257985818668; Mixed Forests;NA;NA +01031500;0.9548;4.90325937325786;3.99084340642749;0.870668492047483;0.398619402379116;0.999999999999894; Mixed Forests;0.249999999999973;2.39999999999975 +01047000;0.9906;5.08681076518099;4.30097838703039;0.891382765097595;0.445473208395092;0.850449984250475; Mixed Forests;0.241026999055001;2.3401799936999 +01052500;1;4.80082996154371;4.12431270006247;0.880034058274384;0.477327929873011;0.593588369118227; Mixed Forests;0.225615302147085;2.2374353476472 +01054200;1;5.01982728305181;4.28355132001824;0.886711470649713;0.455084304988135;0.580953863518831; Mixed Forests;0.224857231811114;2.23238154540737 +01055000;0.9916;5.03003298656618;4.31922587040795;0.886163454154676;0.471431451538885;0.524248820763417; Mixed Forests;0.221454929245781;2.20969952830511 +01057000;0.9415;5.36294869870711;4.58707712668376;0.905510480513665;0.458742009036353;0.532780644071474; Deciduous Broadleaf Forest;0.209186201297552;2.07314111305228 +01073000;0.9008;4.94394974332569;4.03508797625448;0.841420652113803;0.37914418264828;0.592432262113697; Mixed Forests;0.221470258347946;2.03318903590222 +01078000;0.9873;5.39168713701902;4.50529877309333;0.898650642821055;0.424911376148534;0.80744358110202; Mixed Forests;0.2384466148661;2.32297743244059 +01118300;0.933;5.58212061247045;4.80756858676214;0.911301525551912;0.469205884698835;0.999999999999802; Deciduous Broadleaf Forest;0.189999999999962;1.9999999999996 +01121000;0.9652;5.43468511944551;4.82319325251444;0.908561005365332;0.49762723161563;0.977811116624512; Deciduous Broadleaf Forest;0.191331333002476;2.00887555334968 +01123000;0.9141;5.40309665265931;4.80614370212756;0.907407231069;0.506890047828686;0.999999999999671; Deciduous Broadleaf Forest;0.189999999999938;1.99999999999934 +01134500;0.9963;4.93055038386039;4.27360672949305;0.886216248694368;0.476794082523046;0.603804054408798; Deciduous Broadleaf Forest;0.213488735177357;2.15483952963227 +01137500;0.9972;4.54146770171728;3.65822260825394;0.865791461025645;0.393542620689147;0.911610248242171; Mixed Forests;0.244696614894504;2.3646440992966 +01139000;0.9737;5.06947386160055;4.38950063332712;0.880091526597223;0.467340842967336;0.452346808933984; Deciduous Broadleaf Forest;0.212861029816346;2.09051348381424 +01139800;1;5.21312584249278;4.6331221706214;0.899032855945141;0.526026715530708;0.999999999999826; Deciduous Broadleaf Forest;0.189999999999967;1.99999999999965 +01142500;0.9799;4.88629199056056;4.23705313650813;0.884819536772351;0.493403982095888;0.604969248119383; cropland/natural vegetation mosaic;0.183950307518771;1.69751537593993 +01144000;0.9813;5.16403445079923;4.4950812735625;0.89722156778609;0.492946826571639;0.731155976555125; Deciduous Broadleaf Forest;0.196029823450306;1.97766994993881 +01162500;0.9663;5.40268168381068;4.1497119124084;0.884472664069785;0.332935260017412;0.999999999999901; Mixed Forests;0.249999999999975;2.39999999999976 +01169000;0.997;5.37812605705176;4.60685021277002;0.904142372090899;0.455684479740557;0.988783162334547; Mixed Forests;0.249326989740049;2.39551326493357 +01170100;0.997;5.38406386884322;4.61993664669675;0.899812262509083;0.456794566142046;0.858219381485414; Mixed Forests;0.241493162889092;2.34328775259382 +01181000;0.9944;5.38599001070863;4.70324312649302;0.903143492986125;0.481896140790201;0.799562720581036; Deciduous Broadleaf Forest;0.202026236765084;2.08017491176707 +01187300;0.9932;5.39719898479503;4.53536437201846;0.89126356656757;0.414442437904963;0.867247831785814; Deciduous Broadleaf Forest;0.197965130092812;2.0531008672853 +01195100;0.9171;5.46957947079099;4.8314648117427;0.907067964556654;0.497049296576576;0.979441556862029; Deciduous Broadleaf Forest;0.189794415568502;1.98972077843003 +01333000;0.9844;5.01056216819435;4.42414719479339;0.894909590269828;0.519146670417232;0.999999999999848; Deciduous Broadleaf Forest;0.189999999999971;1.9999999999997 +01350000;0.9966;5.05258398023143;4.3382137352647;0.886759764833532;0.465373102060762;0.99999999999988; Deciduous Broadleaf Forest;0.189999999999977;1.99999999999976 +01350080;0.994;5.01333725823479;4.14440211573058;0.87752431145346;0.429201486738433;0.999999999999911; Deciduous Broadleaf Forest;0.189999999999983;1.99999999999982 +01350140;0.991;5.04595847858574;4.26887893273398;0.886644694892424;0.459899742156523;0.990290671153929; Deciduous Broadleaf Forest;0.189902906711539;1.99514533557696 +01365000;1;5.24005393494721;4.63272668282463;0.902036826613734;0.502087051918074;1; Deciduous Broadleaf Forest;0.19;2 +01411300;0.8487;5.35399905495326;4.57430286657307;0.893805807629852;0.440481795904903;0.924639173552573; Mixed Forests;0.24547814820955;2.36984555924103 +01413500;0.9974;4.86205556802992;4.30875303638552;0.882832410627065;0.523567906976013;0.999999999999941; Deciduous Broadleaf Forest;0.189999999999989;1.99999999999988 +01414500;1;5.08983865346723;4.55250318020597;0.888611918388662;0.524892152234921;0.999999999999702; Deciduous Broadleaf Forest;0.189999999999943;1.9999999999994 +01415000;1;4.76484873051693;4.2208349203212;0.876677800872398;0.522300697654966;0.999999999999806; Deciduous Broadleaf Forest;0.189999999999963;1.99999999999961 +01423000;0.9854;4.3277191743789;3.79857578248262;0.859112741467978;0.525288841248747;0.749778722554289; Deciduous Broadleaf Forest;0.187497787225512;1.87488936127688 +01434025;1;5.14314734778216;4.64948301316971;0.894837677320544;0.541983526522803;0.999999999999865; Deciduous Broadleaf Forest;0.189999999999974;1.99999999999973 +01435000;1;5.20312035942056;4.64476553190449;0.898253635936545;0.519754010376718;0.999999999999926; Deciduous Broadleaf Forest;0.189999999999986;1.99999999999985 +01439500;0.997;5.1738784167396;4.65243877031221;0.890052775033654;0.5330223434058;0.999999999999806; Deciduous Broadleaf Forest;0.189999999999963;1.99999999999961 +01440000;0.908;5.28324889159735;4.75770179280648;0.896403758943618;0.53491092682513;0.995337981034132; Deciduous Broadleaf Forest;0.189953379810297;1.9976689905167 +01440400;0.9933;5.20499563465734;4.65007326477274;0.895056491515068;0.516361698143304;0.999999999999826; Deciduous Broadleaf Forest;0.189999999999967;1.99999999999965 +01451800;0.4915;2.05526000240721;1.71828991717763;0.69131267830246;0.421700868558926;0.993174449530736; cropland/natural vegetation mosaic;0.180068255504676;1.50341277523445 +01466500;1;4.19626522500114;2.75938120699037;0.823158650195399;0.236725953861312;0.999999999999999; Mixed Forests;0.25;2.4 +01484100;0.3605;2.1008648733162;1.62374975289874;0.706892873118885;0.355527723638529;0.999999999999867; cropland/natural vegetation mosaic;0.179999999999976;1.4999999999998 +01485500;0.867;4.69543317660803;3.27699692366798;0.834604916220643;0.258359551554251;0.856252477062077; Mixed Forests;0.239937673394324;2.27062722935569 +01486000;0.8888;5.07065859589908;3.37394965716796;0.844506039028114;0.236841856082778;0.959260937182051; Mixed Forests;0.247148265602743;2.36333484346384 +01487000;0.4165;2.15820157853317;1.67238301111612;0.70976547550281;0.354995129013951;0.999999999999887; cropland/natural vegetation mosaic;0.17999999999998;1.49999999999983 +01491000;0.2319;2.71163850102191;2.27644856574434;0.758678996356175;0.425617873070671;0.999999999999901; cropland/natural vegetation mosaic;0.179999999999982;1.49999999999985 +01510000;0.9804;4.50681500563141;3.90795854705555;0.867876568796167;0.503985761867626;0.573827994337115; Deciduous Broadleaf Forest;0.18573827994336;1.78691399716846 +01516500;0.9661;3.95898585044011;3.45923260358339;0.825131047893685;0.490608680334659;0.861260717456224; cropland/natural vegetation mosaic;0.181387392825386;1.56936964127135 +01518862;0.9837;3.9462546631322;3.44213455401123;0.829469641997281;0.503336971409249;0.814053709310645; cropland/natural vegetation mosaic;0.181859462906876;1.59297314534449 +01532000;0.9549;4.31637738507356;3.77238078146297;0.848866707788932;0.485315140301703;0.601677555784009; Deciduous Broadleaf Forest;0.186016775557839;1.80083877789199 +01539000;0.8311;4.66934873983734;4.0127895727749;0.86437506789668;0.462749305369928;0.538214252801332; Deciduous Broadleaf Forest;0.185382142527989;1.76910712640046 +01542810;1;5.26609017704681;4.74477721623558;0.900412061460897;0.550344057767211;1; Deciduous Broadleaf Forest;0.19;2 +01543000;0.9922;5.14140113479666;4.59833830638523;0.895337794742694;0.534380367902184;0.990163131627582; Deciduous Broadleaf Forest;0.189901631316254;1.99508156581361 +01543500;0.9878;5.1234237427107;4.57016349068812;0.894548372499437;0.524107205458267;0.99501174355862; Deciduous Broadleaf Forest;0.189950117435563;1.99750587177912 +01544500;1;5.06073178424241;4.53799803569008;0.892128243978807;0.530975320971718;0.999999999999927; Deciduous Broadleaf Forest;0.189999999999986;1.99999999999985 +01545600;1;5.19106669377622;4.61334465501053;0.897149346101821;0.503536544659278;0.999999999999948; Deciduous Broadleaf Forest;0.18999999999999;1.9999999999999 +01547700;0.9047;4.93350872254251;4.35365226047313;0.88039837334678;0.500770966483748;0.999999999999902; Deciduous Broadleaf Forest;0.189999999999981;1.9999999999998 +01548500;0.9866;4.78009863302879;4.23528190042025;0.876758810038773;0.508506796279639;0.86238249316826; Deciduous Broadleaf Forest;0.188623824931664;1.93119124658398 +01549500;0.9888;4.49686817711077;3.90673711880251;0.859754604483448;0.481439596917858;0.617998640360606; Deciduous Broadleaf Forest;0.186179986403587;1.80899932018014 +01550000;0.988;4.8363660280113;4.25841619080857;0.881260617826986;0.502453434481199;0.92298382871558; Deciduous Broadleaf Forest;0.189229838287131;1.96149191435759 +01552000;0.9911;4.96915477942079;4.3762242388389;0.885104673046246;0.486873809555214;0.945647482034736; Deciduous Broadleaf Forest;0.189456474820332;1.97282374101724 +01552500;0.9956;5.11032477385823;4.51848939233158;0.897540983804637;0.493856182053501;0.999999999999861; Deciduous Broadleaf Forest;0.189999999999974;1.99999999999972 +01557500;0.9012;4.95272838506501;4.36976719378894;0.88267435400829;0.495209331507821;0.963874309255788; Deciduous Broadleaf Forest;0.189638743092525;1.98193715462762 +01567500;0.7969;3.5345095740828;3.01220515432712;0.796606655206835;0.452560563008806;0.972529791795436; cropland/natural vegetation mosaic;0.180274702082025;1.51373510410207 +01568000;0.7062;4.36658247516699;3.83388413541656;0.842628992005503;0.487523050675634;0.594204504452499; Deciduous Broadleaf Forest;0.185942045044502;1.79710225222606 +01580000;0.4241;2.51160368259806;2.14675394806816;0.75273419286774;0.456907121095137;0.999999999999921; cropland/natural vegetation mosaic;0.179999999999986;1.49999999999988 +01583500;0.5155;2.86062418544735;2.41690568474656;0.763722723800089;0.43613413545485;0.996306438692287; cropland/natural vegetation mosaic;0.180036252195015;1.50181260975123 +01586610;0.3247;2.51629324989542;2.0915919074814;0.726847089521225;0.405830640710363;1; cropland/natural vegetation mosaic;0.18;1.5 +01591400;0.3311;2.26057046904825;1.80395608431239;0.717493569659222;0.380321038802715;0.999999999999563; cropland/natural vegetation mosaic;0.179999999999921;1.49999999999934 +01594950;0.9731;4.64588279116508;4.14429810821443;0.864965900841468;0.523308453207124;0.986386545740994; Deciduous Broadleaf Forest;0.18986386545741;1.9931932728705 +01596500;0.8759;4.46982718908188;3.91140941182847;0.851818190721852;0.476878614850464;0.999999999999935; Deciduous Broadleaf Forest;0.189999999999988;1.99999999999987 +01605500;0.9904;4.64915688671876;4.04463612200051;0.852298150918196;0.471469671944882;0.820488932813678; Deciduous Broadleaf Forest;0.188204889328115;1.91024446640666 +01606500;0.9336;4.7781702164762;4.19856691409865;0.868890746256199;0.489592692963541;0.939353305385887; Deciduous Broadleaf Forest;0.18939353305384;1.96967665269279 +01613050;0.9354;4.1872581265519;3.6668152161861;0.833965029771851;0.471154403986444;0.797045930052905; Deciduous Broadleaf Forest;0.187970459300513;1.89852296502632 +01620500;0.9722;4.88881116724834;4.18334326861728;0.871762825833652;0.433896124041833;0.99999999999992; Deciduous Broadleaf Forest;0.189999999999985;1.99999999999984 +01632000;0.708;4.93627861075352;4.27984082092486;0.882824186570665;0.469956503954019;0.952022532709412; Deciduous Broadleaf Forest;0.189520225327086;1.97601126635463 +01632900;0.5837;3.5517065926135;2.91834972749748;0.783806447679414;0.395233814948002;0.647345569852595; cropland/natural vegetation mosaic;0.183401973821402;1.67009869107113 +01634500;0.7781;4.94219718965402;4.34304389010814;0.883716170300379;0.492508282529291;0.954399261420887; Deciduous Broadleaf Forest;0.189543992614181;1.97719963071021 +01638480;0.4659;3.01334776910347;2.54095576161893;0.756075213807545;0.421985167588553;0.932091032791809; cropland/natural vegetation mosaic;0.18067908967206;1.53395448360386 +01639500;0.1173;2.0174467243133;1.61745223271913;0.692709729544412;0.379673786792657;0.777271138607882; cropland/natural vegetation mosaic;0.179999999999969;1.49999999999974 +01644000;0.6186;3.36772120701077;2.86663062368765;0.781567281658321;0.435170829608636;0.832589150886166; cropland/natural vegetation mosaic;0.181674108491111;1.58370542455663 +01658500;1;5.4006198349329;4.72887682162187;0.892101458830461;0.472730238965804;0.706957612753136; Deciduous Broadleaf Forest;0.207582543234702;2.11721695489769 +01664000;0.7519;4.43835402647343;3.83843969675652;0.854354475631049;0.473973206074874;0.542241246484979; cropland/natural vegetation mosaic;0.184577587535132;1.72887937675731 +01666500;0.8137;4.27082728641785;3.62681833508072;0.847894825157563;0.457157325149957;0.633615234052433; cropland/natural vegetation mosaic;0.183663847659454;1.68319238297356 +01667500;0.8033;4.11269399385096;3.4665615831167;0.83288555369401;0.443615626785547;0.681016911566322; cropland/natural vegetation mosaic;0.183189830884314;1.6594915442166 +01669000;0.9098;4.58690411526638;3.39562953751177;0.834544322120391;0.316450929139022;0.865924925520932; Mixed Forests;0.240614744786426;2.27933243296851 +01669520;0.9295;5.24181730267256;3.63264825945503;0.858528515451138;0.262273160874151;0.942779060011891; Mixed Forests;0.245994534200831;2.34850115401069 +02011400;0.9953;4.85527918631164;4.24929442749001;0.873318575617682;0.485857471618655;0.95949446195165; Deciduous Broadleaf Forest;0.189594944619501;1.97974723097569 +02011460;0.995;4.69844183612986;4.13344205538294;0.867337166511044;0.489582365761895;0.996692335461989; Deciduous Broadleaf Forest;0.189966923354597;1.9983461677308 +02013000;0.9852;5.04668484784626;4.45497798240239;0.887668936468779;0.502923232459737;0.999999999999883; Deciduous Broadleaf Forest;0.189999999999978;1.99999999999977 +02014000;0.9092;5.02834343771815;4.42779645331262;0.887158068880153;0.501687509496001;0.999999999999895; Deciduous Broadleaf Forest;0.18999999999998;1.99999999999979 +02015700;0.9911;4.88248247121394;4.23483303844214;0.871952261517807;0.469773421199717;0.94593002112049; Deciduous Broadleaf Forest;0.189459300211185;1.97296501056008 +02016000;0.9317;5.08865611310359;4.38544116609582;0.883890083471318;0.459359315461342;0.970437566611511; Deciduous Broadleaf Forest;0.190864348489429;2.00013271960562 +02017500;0.9349;4.91474675932603;4.30191091320888;0.874631453641945;0.484083896383084;0.983730939598011; Deciduous Broadleaf Forest;0.189837309395955;1.9918654697988 +02018000;0.8857;4.94866581910975;4.3210609777145;0.878832445827206;0.479377443145901;0.989967862089482; Deciduous Broadleaf Forest;0.189899948954525;1.99498740676292 +02027000;0.9407;4.69714945697209;4.06941646447907;0.876300780189812;0.48098056758126;0.854919175014026; Deciduous Broadleaf Forest;0.188549191750117;1.92745958750682 +02027500;0.9407;4.5929429373419;3.97700977921868;0.873203524525722;0.481617576815542;0.788713525778402; Deciduous Broadleaf Forest;0.187887135257784;1.8943567628892 +02028500;0.9644;4.79701177211998;4.17203937376531;0.882043953406286;0.492845279989145;0.847194342169234; Deciduous Broadleaf Forest;0.188471943421685;1.92359717108456 +02038850;0.93;5.39911956215813;4.33502453455268;0.884415059768877;0.379295616916169;0.661067261363841; Mixed Forests;0.229664035681803;2.26442690454525 +02046000;0.9359;5.26438545841656;3.83489455994279;0.869970589825404;0.300671494507184;0.897142303681014; Mixed Forests;0.242799961257646;2.30742807331271 +02051000;0.9387;5.02107202032299;4.02467197156703;0.869375921981836;0.38820652081184;0.732928425521236; Mixed Forests;0.231304989786471;2.15963558296898 +02051500;0.9081;4.9075760119925;3.67203923667903;0.854645475160068;0.32552022781499;0.616568024302389; Mixed Forests;0.223064565707884;2.07848407449621 +02053200;0.5848;3.23840871116945;2.4725213983593;0.772461165806653;0.345753417178203;0.720520142391216; cropland/natural vegetation mosaic;0.193190595143342;1.67422052291784 +02053800;0.8875;4.65899755582489;4.00261873772924;0.859656491866177;0.450491613080553;0.882382893734821; Deciduous Broadleaf Forest;0.188823828937322;1.94119144686719 +02055100;0.5089;3.83815511065226;3.18865229727275;0.803515950461627;0.409212776592198;0.79753578487204; cropland/natural vegetation mosaic;0.182024642151279;1.60123210756398 +02056900;0.9121;4.34172312357894;3.60235701269386;0.836008375763609;0.4138376345832;0.575353659512057; cropland/natural vegetation mosaic;0.184246463404865;1.71232317024382 +02059500;0.8421;4.27950354686228;3.60541985537108;0.82092827525927;0.42090023861122;0.602704130197199; cropland/natural vegetation mosaic;0.183972958698006;1.69864793490117 +02064000;0.909;4.34495821267114;3.33963570630212;0.811976265610765;0.338502392784494;0.645937066383965; cropland/natural vegetation mosaic;0.200980843396374;1.79107436126717 +02065500;0.9058;4.64200307644351;3.72183907052793;0.839236940213441;0.377732605070178;0.70950941517584; Mixed Forests;0.229665659062289;2.13855847365809 +02069700;0.9644;4.62359493720211;3.96450184133604;0.864540251701557;0.453502132707889;0.551536865565127; Deciduous Broadleaf Forest;0.189270530773955;1.8240490885894 +02070000;0.9642;4.90700972785846;4.16953832317611;0.873122617982759;0.440194087485727;0.808204714099991; Deciduous Broadleaf Forest;0.201507717153995;2.07671811435995 +02074500;0.8579;4.29436800025878;3.42217952540346;0.826504346686742;0.382980185305089;0.395286619245828; Mixed Forests;0.210357143331808;1.99011195655202 +02077200;0.7114;3.74045309270899;3.13653179297469;0.798286827775165;0.414105282966531;0.549414786012215; cropland/natural vegetation mosaic;0.196390002936987;1.80452027897422 +02081500;0.8481;3.92024223446729;3.14069002907884;0.804627352209743;0.379574896703892;0.668339419362045; cropland/natural vegetation mosaic;0.199762177105815;1.77546743231512 +02082950;0.7774;5.26206756916406;3.83804295028276;0.868648024220554;0.285769595563814;0.958847579263107; Mixed Forests;0.247119330548398;2.36296282133664 +02092500;0.7118;3.82676062304878;2.59782876758115;0.781631016042909;0.257270004783273;0.394503257724304; Woody Savannas;0.197606830149352;1.90671787688002 +02096846;0.9156;4.99332332092985;4.15626837830887;0.865021698917312;0.400102281812965;0.622104723406178; Deciduous Broadleaf Forest;0.212673716595458;2.15115811063589 +02102908;0.6906;3.02299760761192;1.70994071500244;0.682361794256493;0.160119280061686;0.788302948551575; Woody Savannas;0.179052734630216;1.84818793601253 +02108000;0.4232;2.63702849759526;1.98228425819537;0.718475186580843;0.315517396733031;0.644190679613565; cropland/natural vegetation mosaic;0.178309709481884;1.56601938784585 +02111180;1;5.03187822284585;4.25318829488287;0.892727998275567;0.436106107427534;0.656840636487286; Mixed Forests;0.229399488537255;2.26218877199659 +02111500;0.9831;4.92925238222501;4.05551787084901;0.881133066876777;0.39957481291272;0.724631344838321; Deciduous Broadleaf Forest;0.200126997418603;2.02794587716268 +02112120;0.901;4.90181182235316;4.01396924554183;0.877259310157319;0.394209014454276;0.717169377142482; Deciduous Broadleaf Forest;0.187194561361879;1.85887870044856 +02112360;0.8821;4.71433794550922;3.87999988911718;0.865150874873452;0.405740417567654;0.54191369538891; Deciduous Broadleaf Forest;0.18764613401649;1.79958966707081 +02118500;0.7055;3.85140129809756;3.21558857615898;0.815122726646121;0.413595251924537;0.69485076569058; cropland/natural vegetation mosaic;0.190177903775797;1.70008402670603 +02125000;0.3877;3.13798726622954;2.43450236945778;0.765736272306212;0.334612455464501;1; cropland/natural vegetation mosaic;0.18;1.5 +02128000;0.8588;4.7566050003026;3.90870651724432;0.853154451659852;0.388510223929043;0.529991017516312; cropland/natural vegetation mosaic;0.203721122084125;1.85755105476178 +02137727;0.9861;4.94385882074169;4.21224896255214;0.884545811342185;0.438529763154719;0.832261949630845; Mixed Forests;0.239935716977843;2.33290477985226 +02140991;0.9821;5.22739207116308;4.24349899513522;0.893465384016814;0.379211276568303;0.760817540707052; Mixed Forests;0.2356490524424;2.30432701628258 +02143000;0.6406;5.02399886715795;3.9857658813924;0.867145940721796;0.364828429324683;0.928442735066353; Mixed Forests;0.243559846155956;2.34990991454627 +02143040;0.9465;5.15514869581162;4.25010353619456;0.890847641475189;0.392197946656635;1; Mixed Forests;0.25;2.4 +02149000;0.9755;5.14733181143106;4.39656480552558;0.888101181287854;0.450501123310171;0.722414554692596; Mixed Forests;0.23334487328153;2.28896582187676 +02152100;0.9009;5.15563251354322;4.35214147636752;0.890226914921666;0.436464820168553;0.981943943680874; Mixed Forests;0.248374954931232;2.38736076057612 +02177000;0.9731;5.5136802990831;4.18115637620031;0.890163292936654;0.293348922009413;0.999814485416772; Mixed Forests;0.249988869124992;2.39992579416656 +02178400;0.9777;4.96105305591515;4.20076354834124;0.892712954785559;0.435319538015568;0.871478216538033; Mixed Forests;0.242288692992281;2.34859128661521 +02193340;0.8688;4.67935755669617;3.4315075071478;0.82947255627512;0.273614243592109;0.774148622235082; Woody Savannas;0.180326623998801;1.85809596443504 +02196000;0.8727;4.76769504611559;3.23309839543129;0.83135035252581;0.223176768809838;0.590810227162036; Mixed Forests;NA;NA +02198100;0.45;2.73026217962683;1.90125696474784;0.705479113766269;0.279467905751927;0.586844148195246; Woody Savannas;0.184765204426009;1.82953852465226 +02202600;0.6165;2.54187446492095;1.66103851258708;0.693048847818575;0.247333466450894;0.370211454753546; Woody Savannas;0.191687241605828;1.81950405710523 +02212600;0.927;5.51153795359543;4.21095971388185;0.87286350365816;0.292669299153987;0.905894082624215; Mixed Forests;0.244256308912554;2.36138427260067 +02215100;0.1516;2.40889726818858;1.77136740342861;0.683435986942373;0.294625683540903;0.536167549516255; Croplands;0.178698200720024;1.64027481832856 +02216180;0.4884;2.70554720673749;1.72315862216558;0.697486657726554;0.226169628256476;0.999999999999881; Woody Savannas;0.159999999999981;1.6999999999998 +02221525;0.8252;4.54554270289662;3.46411944980037;0.82050472179388;0.305972712680707;0.539267045240634; Mixed Forests;0.209686924229224;2.0890158332441 +02231000;0.9529;4.25283157719909;2.0681812525432;0.79254602167532;0.0918973629873362;0.523057750456032; Woody Savannas;0.180595722376445;2.30180853567097 +02231342;0.1387;1.94524322399072;1.04903253279207;0.609105742869899;0.161549277306079;0.539464124495951; Woody Savannas;0.169210717510085;1.60789282489922 +02235200;0.6142;2.87110471363333;1.54706680454205;0.663277915845662;0.13288863865315;0.924496121447088; Woody Savannas;0.166709746724395;1.75226008336525 +02245500;0.8235;3.57395982807049;1.65262660037896;0.71976486769677;0.0968132557224132;0.923771866364591; Woody Savannas;0.163049125345407;1.79909657372589 +02246000;0.8293;3.41958334943505;1.73047140969432;0.718484472492553;0.10906805213466;0.867539965002788; Woody Savannas;0.165493828334692;1.86985292227852 +02296500;0.1676;2.05790350985673;1.10712672522206;0.63194809125341;0.15627812229725;0.450248357693887; cropland/natural vegetation mosaic;0.173078941392801;1.57674306746028 +02297155;0.1264;1.36527463820637;0.744267636871703;0.519045045171521;0.146520462734127;0.638268270328162; Croplands;0.177715234411468;1.52284765588542 +02297310;0.1446;1.79762160881357;0.995954079343422;0.597264560820977;0.156687964480023;0.460053214041514; cropland/natural vegetation mosaic;0.174124905046572;1.55875094953408 +02298123;0.1369;1.75643977596403;0.883308803011696;0.604274338936125;0.149445477839439;0.536342401679411; Woody Savannas;0.175792199356789;1.7512345374008 +02298608;0.0756;1.6150768320766;0.851039394663679;0.574742130369203;0.145178650490269;0.413797221764155; Woody Savannas;0.171724055564717;1.58275944435283 +02299950;0.0572;1.58964988709343;0.775332745964247;0.554884635794472;0.120149458717096;0.470164458905992; Woody Savannas;0.171797964048069;1.65409555309042 +02300700;0.0836;1.41297864200571;0.72327591911984;0.54562303220961;0.134260037272919;1.00000000000003; cropland/natural vegetation mosaic;0.180000000000005;1.50000000000004 +02310947;0.3118;2.74560746000436;1.57813761478381;0.678994912180309;0.165802333035;0.995853382953654; Woody Savannas;0.160082932340926;1.69917067659072 +02312200;0.4082;2.93409556238531;1.70803861096808;0.696267406370364;0.172977903321378;0.880136116629287; Woody Savannas;0.162397277667408;1.6760272233258 +02314500;0.9741;4.75816059386786;2.42066748993061;0.82609323932724;0.0938290710834088;0.578845419834409; Evergreen Broadleaf Forest;NA;NA +02315500;0.9266;4.82020764900772;2.40055537568881;0.827409550313062;0.0887262783100483;0.523680645707772; Evergreen Broadleaf Forest;NA;NA +02324400;0.8795;4.42033464172447;2.11141974097776;0.822475802804867;0.0986818628077806;0.992038350687704; Woody Savannas;0.160318465972474;1.71035014410571 +02327100;1;4.77580582814947;2.18518846597743;0.857785189356194;0.0777254693304694;0.911987034771975; Evergreen Broadleaf Forest;0.196479481390883;2.88558314520361 +02342933;0.9083;4.91797538474336;3.51423174863121;0.832052509696639;0.256534853840066;0.748399609814207; Woody Savannas;0.182523008986056;1.87531343225886 +02349900;0.0989;1.8519289843841;1.4255002595712;0.655564809862036;0.316528696016026;0.99999999999991; Croplands;0.179999999999984;1.49999999999987 +02350900;0.3941;3.23322682360858;2.26498910054355;0.728975208853944;0.258408938930943;0.786239605875217; Woody Savannas;0.167882772858914;1.71075025660348 +02361000;0.4408;2.9741130556848;2.13347315135066;0.718239356657395;0.272116451075977;0.847737575918424; Woody Savannas;0.164080293756297;1.68285524014372 +02363000;0.635;3.79146086258454;2.76864162044451;0.774037908637042;0.283771536788565;0.87129164936872; Woody Savannas;0.171583751556802;1.79009584544177 +02369800;0.9643;4.7856085488563;2.6565141364343;0.834547531173437;0.11998486297589;0.527737453728973; Evergreen Broadleaf Forest;0.189086713542457;2.44810369846216 +02371500;0.6862;4.16548637373976;2.97215669849731;0.792318914072704;0.259565777126578;0.789594918264393; Woody Savannas;0.177476136373957;1.82850800172896 +02372250;0.7608;4.24444667347131;3.020283295074;0.799280964929441;0.257429146272682;0.884332139848143; Woody Savannas;0.170410107413662;1.78096750210625 +02374500;0.949;4.45438780766145;3.00479524130305;0.813844977009083;0.217179888468144;0.590988114870719; Mixed Forests;0.213188930338348;2.11369168040933 +02381600;0.933;4.97383158505133;4.24895904166669;0.877855269654297;0.428722596548418;1; Mixed Forests;0.25;2.4 +02384540;0.96;4.97381584841031;4.05505392059936;0.880692955270498;0.370491874698586;0.999999999999855; Mixed Forests;0.249999999999964;2.39999999999965 +02395120;0.7132;4.30597146088106;3.34684551633202;0.809084104463229;0.329762368634333;0.925797814458539; Mixed Forests;0.244780190738974;2.33347459574313 +02408540;0.8204;4.87018052400322;3.90035332229699;0.851980157332732;0.36119965235193;0.764379756657254; Mixed Forests;0.234726943592684;2.29439348459538 +02415000;0.9106;4.60681353727928;3.67096272614736;0.835316572739009;0.352650166141446;0.619938426677072; Mixed Forests;0.216884948013572;2.1238268708844 +02422500;0.6695;4.08143521385484;3.11273576509119;0.800324252762609;0.313533829873827;0.690686674051888; Woody Savannas;0.187838199335304;1.91651932816342 +02427250;0.9336;5.09858093663058;3.53894302152917;0.847363657094828;0.22758360390236;0.539183448712223; Mixed Forests;0.208526510384094;2.0774284140985 +02430085;0.8644;4.12181549576269;3.39998535626836;0.821247661156858;0.408144688372046;0.572027565076718; Deciduous Broadleaf Forest;0.210764675320566;2.1080132068645 +02430615;0.9889;4.77173319385168;3.88129280388728;0.85623781132562;0.384865655723673;0.699398926829295; Mixed Forests;0.231963935609707;2.27975957073118 +02450250;0.8413;5.15488483502046;4.45096560326245;0.884999661053539;0.450258331263426;0.75504416332525; Deciduous Broadleaf Forest;0.204187460904662;2.09142661515225 +02464000;0.9846;4.70913997284651;3.69292285638872;0.850792031512031;0.343898149892239;0.801704312623484; Mixed Forests;0.235024524956454;2.28990438703983 +02464146;0.9571;4.46645081988579;3.44433318523705;0.822112798591566;0.321957816083967;1; Mixed Forests;0.25;2.4 +02464360;0.9717;4.54810615517495;3.69264307765288;0.839518669380987;0.366609002902082;0.881219457184707; Mixed Forests;0.239309751146616;2.31685362002921 +02465493;0.8617;5.05457301661986;3.83426553998663;0.852160411980678;0.299785587158552;0.595506142589065; Mixed Forests;0.221089014986569;2.19178892134785 +02469800;0.9839;4.8243910327188;3.22398482484036;0.836938128432226;0.211624943262742;0.999999999999982; Mixed Forests;0.249999999999996;2.39999999999996 +02472000;0.6696;4.80920360163545;3.29996244817296;0.829551427193048;0.214407129448968;0.670563065071829; Mixed Forests;0.220350675856454;2.16939414555017 +02472500;0.5865;4.3527691154282;2.84438179519144;0.799800931331087;0.190561793931156;0.9024193621199; Woody Savannas;0.168782257409196;1.76830644651594 +02479155;0.9355;4.51258530895661;2.57358386537749;0.847353247206576;0.149711141501902;0.780826814545289; Evergreen Broadleaf Forest;0.210958659272726;2.86849608872708 +02479300;0.8895;4.63624885415172;2.77027365763764;0.812993083166148;0.148993865172104;0.63762627814095; Woody Savannas;0.176696023520461;2.14467294266341 +02479560;0.8849;4.44717266096067;2.67080037381341;0.803758744759213;0.156628670484991;0.791411100466532; Woody Savannas;0.174224699283905;1.90059184976252 +02481000;0.8845;4.72648308617008;2.70529170226504;0.815379377917311;0.135148907505314;0.580222881660147; Woody Savannas;0.176791084733596;2.24571025384184 +02481510;0.7816;4.81389314075691;2.86341006299748;0.818000907818996;0.146005154395507;0.752966005549184; Woody Savannas;0.169881359778027;2.02114419278598 +03010655;0.9918;4.94954183266703;4.38736859716443;0.88708771838425;0.525444263745692;0.937642883890603; Deciduous Broadleaf Forest;0.189376428838893;1.96882144194519 +03011800;1;4.99674038206774;4.46163990228695;0.885626193492327;0.526677052703512;0.999999999999892; Deciduous Broadleaf Forest;0.18999999999998;1.99999999999978 +03015500;0.9837;4.43618797915163;3.87080476446645;0.862184049596211;0.499168418497563;0.86590969897121; Deciduous Broadleaf Forest;0.188659096989695;1.93295484948546 +03021350;0.9747;3.8585515760454;3.30010891462518;0.840787111211881;0.497150506901601;0.724142077120881; cropland/natural vegetation mosaic;0.182758579228763;1.63792896143926 +03026500;1;4.89329291774893;4.3762500979124;0.876862318282506;0.531145729318732;1; Deciduous Broadleaf Forest;0.19;2 +03028000;0.9796;5.07509907990274;4.49930368784587;0.890177383790892;0.518974941929885;0.999999999999892; Deciduous Broadleaf Forest;0.189999999999979;1.99999999999978 +03049000;0.7165;3.91657576196527;3.39632422212796;0.816114906125586;0.484312861041795;0.522032272064431; cropland/natural vegetation mosaic;0.184779677279328;1.7389838639675 +03049800;0.2546;4.79489764979105;4.20785120564543;0.864868499264487;0.467993491184616;0.560304592912884; Deciduous Broadleaf Forest;0.185603045929129;1.78015229645644 +03050000;0.9766;4.70933237842244;4.14805448116218;0.888988997068325;0.528889130110991;0.993340269600995; Deciduous Broadleaf Forest;0.189937927611806;1.99672831228935 +03066000;0.9532;4.16452527356306;3.6224427006637;0.831258390513583;0.478638113939781;0.893425037900865; Deciduous Broadleaf Forest;0.188934250378984;1.94671251895023 +03069500;0.9408;4.72559086443894;4.12217042958076;0.875594118763101;0.491650008280562;0.963433053213144; Deciduous Broadleaf Forest;0.191284735022075;2.00293601290595 +03070500;0.717;4.49415330117219;3.93256887325326;0.857381159224643;0.494335264680772;0.984758961495206; Deciduous Broadleaf Forest;0.189847589614931;1.99237948074743 +03076600;0.8242;4.11844450795613;3.54889873856729;0.834435142764972;0.474658349985426;0.999999999999946; Deciduous Broadleaf Forest;0.18999999999999;1.99999999999989 +03078000;0.8405;3.95838547605953;3.43261629850989;0.825048498137183;0.470167106931949;0.999999999999719; Deciduous Broadleaf Forest;0.189999999999947;1.99999999999944 +03140000;0.7138;2.63864421119679;2.23446873559365;0.752420202723737;0.444178569609062;0.999999999999987; cropland/natural vegetation mosaic;0.179999999999998;1.49999999999998 +03144000;0.7303;3.78163071198284;3.28921874403365;0.81241393118542;0.476085966611129;0.871902602200929; cropland/natural vegetation mosaic;0.181280973977981;1.56404869889943 +03159540;0.8237;4.73081830516398;4.20754091052581;0.866127483167878;0.509861585921973;0.790314811605988; Deciduous Broadleaf Forest;0.187903148116039;1.89515740580282 +03161000;0.9955;4.75719310217404;3.95577364750185;0.859075183188891;0.414407151506024;0.874776118731227; Deciduous Broadleaf Forest;0.188747761187278;1.93738805936532 +03164000;0.9348;4.62254323382363;3.80942308653144;0.85271196998103;0.411461621039568;0.68422333225507; Deciduous Broadleaf Forest;0.187274020288859;1.84766321283734 +03165000;0.8448;4.52508552818155;3.50669519354201;0.839049846488229;0.364866874779998;0.756915513669197; cropland/natural vegetation mosaic;0.182430844863304;1.62154224316536 +03170000;0.8847;4.60649082229546;3.75740342752096;0.846196599836304;0.401959393737103;0.517929853240761; cropland/natural vegetation mosaic;0.184820701467575;1.74103507337944 +03173000;0.8116;4.59890307293148;3.9347367322623;0.857788370406377;0.444476012128365;0.941039283028062; Deciduous Broadleaf Forest;0.18941039283026;1.97051964151386 +03180500;0.9877;4.7204894704231;4.18294206949671;0.878130318929094;0.514514735308979;0.999999999999862; Deciduous Broadleaf Forest;0.189999999999974;1.99999999999972 +03182500;0.9922;4.78690396980826;4.16475595984205;0.874162207075278;0.484089689897908;0.996738322519331; Deciduous Broadleaf Forest;0.190195700648818;2.00130467099206 +03186500;0.9927;4.67225171748841;4.06581133262356;0.882180359336585;0.49564599819413;0.999999999999827; Deciduous Broadleaf Forest;0.189999999999967;1.99999999999965 +03187500;0.9918;4.64992809748902;4.0091413596824;0.877274803854362;0.474609118137714;0.999999999999899; Deciduous Broadleaf Forest;0.189999999999981;1.9999999999998 +03213700;0.9792;5.00692875116005;4.52457059679937;0.880496660789016;0.55014111454159;0.998970411557857; Deciduous Broadleaf Forest;0.189989704115561;1.99948520577878 +03237280;0.9863;5.07091481587603;4.57739736991113;0.885411083001504;0.542140307154602;0.999999999999902; Deciduous Broadleaf Forest;0.189999999999981;1.9999999999998 +03237500;0.5206;2.99664665053856;2.56978549410498;0.761121099713908;0.444266573370104;0.900924034764735; cropland/natural vegetation mosaic;0.180990759652328;1.54953798261737 +03238500;0.1747;2.1063281782519;1.78660228514153;0.706147831149303;0.431456003527489;0.713065547043022; cropland/natural vegetation mosaic;0.179999999999969;1.49999999999974 +03241500;0.0257;2.24324350933314;2.0392465022369;0.724295804892812;0.546764385536407;0.928623116033572; Croplands;0.17999999999996;1.49999999999966 +03280700;0.991;5.16521125192354;4.66888831249871;0.885982377812387;0.546180042105175;0.999999999999841; Deciduous Broadleaf Forest;0.18999999999997;1.99999999999968 +03281100;0.9463;5.18186853923429;4.68372819427339;0.890293526215611;0.546726722258909;0.994819556621489; Deciduous Broadleaf Forest;0.189948195566181;1.99740977831046 +03281500;0.9654;5.20447362051414;4.70080755334398;0.892178529614233;0.544715297281904;0.978481972290492; Deciduous Broadleaf Forest;0.189784819722882;1.98924098614506 +03285000;0.5302;3.26432528985945;2.70764145905298;0.768747376923992;0.40886530455857;0.944140159436694; cropland/natural vegetation mosaic;0.180415669898639;1.5207834949328 +03291780;0.7056;2.90564517916708;2.53487342347774;0.775579830273651;0.479546154788985;0.822977408505728; cropland/natural vegetation mosaic;0.181770225914943;1.58851129574714 +03300400;0.4901;3.20815408844817;2.66423595871674;0.765662216339916;0.411730711433684;0.857701636996266; cropland/natural vegetation mosaic;0.180083324756616;1.50416623783171 +03338780;0.0378;2.59064989694459;2.45716768306585;0.768584441290001;0.639405275104694;0.999967068669215; Croplands;0.179999999999978;1.49999999999981 +03340800;0.2735;2.32739943602337;2.11590589235822;0.727558093368342;0.544287111380029;0.95320429975528; Croplands;0.179999999999979;1.49999999999982 +03346000;0.0951;2.04947295020379;1.83078750387333;0.690632742470659;0.486963569457564;0.999999999999886; Croplands;0.17999999999998;1.49999999999983 +03357350;0.4476;2.29133092569116;2.01500060384415;0.734549613043997;0.496755225531061;0.636040478470611; Croplands;0.179999999999978;1.49999999999982 +03364500;0.2772;2.14798678158262;1.91588529814005;0.702462627000718;0.505472690404592;0.993801500383172; Croplands;0.179999999999977;1.49999999999981 +03366500;0.5748;3.05510201482997;2.67944651510158;0.778942167053368;0.483568403858434;0.832181206109489; cropland/natural vegetation mosaic;0.181678187938883;1.58390939694502 +03368000;0.6327;2.88044915005041;2.54639335539189;0.784873593652953;0.500461708273317;0.999999999999912; cropland/natural vegetation mosaic;0.179999999999984;1.49999999999987 +03384450;0.913;5.04300467690442;4.44709132051456;0.87881838804432;0.484392138563437;0.808300553413064; Deciduous Broadleaf Forest;0.18808300553412;1.90415027670644 +03439000;0.9948;4.92221460432667;4.17676619459093;0.882489602583882;0.420307201189919;0.639075079030488; Mixed Forests;0.228344504741812;2.25563003161201 +03450000;0.9721;4.72128807337547;4.10301747821503;0.865358502256865;0.45522173484809;0.999999999999999; Deciduous Broadleaf Forest;0.19;2 +03455500;0.9922;4.4319387389661;3.75747476520104;0.855383702922966;0.430051769679633;0.665872968008047; Deciduous Broadleaf Forest;0.210047621919469;2.13365081279632 +03456500;0.9658;4.48346687485418;3.82617616817567;0.864215150774727;0.442410152831385;0.938392612058662; Deciduous Broadleaf Forest;0.189383926120587;1.96919630602933 +03460000;0.9933;4.52498443754228;3.78777705640739;0.865025040678127;0.415000634540804;0.745638131018407; Mixed Forests;0.234738287861101;2.29825525240733 +03463300;1;4.62192046739631;3.8093538810753;0.878203045168123;0.402684599023233;0.664947702379634; Deciduous Broadleaf Forest;0.210103137857196;2.13402091904789 +03471500;0.8219;4.60330710362868;3.98693435190143;0.864491185268671;0.465434450634596;0.864577757810167; Deciduous Broadleaf Forest;0.188645777578041;1.93228887890458 +03473000;0.8628;4.69952965808126;4.01722630209039;0.865494478775092;0.446667342111976;0.79898008152073; Deciduous Broadleaf Forest;0.190203478096493;1.92795160580561 +03479000;0.9873;4.81713977168127;4.17742754235274;0.872686216005804;0.460275094637567;0.999999999999942; Deciduous Broadleaf Forest;0.189999999999989;1.99999999999988 +03488000;0.8315;4.4984705776486;3.93925404942136;0.855499167386807;0.478139660783776;0.884652750618312; Deciduous Broadleaf Forest;0.188846527506156;1.94232637530893 +03498500;0.8376;4.64545935091868;3.88411584459432;0.85795866099708;0.412352571394282;0.771385906335339; Mixed Forests;0.234543432095665;2.2248035909111 +03500000;0.9577;4.748509907415;4.01407930286087;0.87156240923298;0.43124245380727;0.511030611319021; Mixed Forests;0.219268324083719;2.13473661475734 +03500240;0.9592;4.64929669025411;4.0374247341675;0.873898501670182;0.48105592175301;0.647965404682653; Deciduous Broadleaf Forest;0.209192898448299;2.11601013036032 +03504000;0.9516;4.54515581685706;3.95886140578655;0.873042034631673;0.484601731699671;0.993376764388588; Deciduous Broadleaf Forest;0.190397394136668;2.00264929424441 +03574500;0.8794;4.76362267686753;4.2059100285027;0.869909978172896;0.48951724223341;0.918016891559683; Deciduous Broadleaf Forest;0.190239636840942;1.97263017624865 +03592718;0.9566;4.18322674489445;3.36137560022524;0.822929873572987;0.384212734812503;0.383190712352799; Deciduous Broadleaf Forest;0.202558327323587;1.93236361589155 +03604000;0.8342;3.98100759231407;3.37221909081378;0.809738429401209;0.424238291811288;0.541456499220633; Deciduous Broadleaf Forest;0.186930573885063;1.79021979251855 +04015330;0.9911;5.23756966014679;4.6162508137291;0.877813651845106;0.488353014311646;0.981064079062165; Mixed Forests;NA;NA +04024430;0.9961;4.8120789076139;4.31575506221106;0.871377271539348;0.536352905800742;0.441343739134549; cropland/natural vegetation mosaic;0.204755335012499;1.90711994645825 +04027000;0.9959;5.17113174788777;4.65947514146799;0.897480743867357;0.546740232094844;0.494047413098639; Mixed Forests;0.217832860844717;2.10711976818031 +04040500;1;4.69740736582492;4.0419929992906;0.865017252960934;0.439199154258158;0.927611085958414; Mixed Forests;0.245419478705267;2.35918511177249 +04043050;0.9769;4.8275769974587;4.30104483140362;0.877898995862202;0.525006337175029;0.574872156895534; cropland/natural vegetation mosaic;0.208446918203719;1.87386818670329 +04045500;0.982;4.70317769889167;3.91809303027979;0.867085121106002;0.41417489354279;0.965990117977217; Mixed Forests;0.247740656456637;2.37545851609202 +04056500;0.9888;4.39800251926104;3.72864217945137;0.826760401129849;0.429199310047094;0.786394560960003; Mixed Forests;NA;NA +04057510;0.9814;4.63669458785525;3.78182453092815;0.872936409191448;0.38699257180085;0.999854926472371; Mixed Forests;0.249991295588317;2.39994197058868 +04057800;1;4.63748498889994;3.9158502029003;0.867601957897585;0.419666726941433;0.999999999999907; Mixed Forests;0.249999999999977;2.39999999999978 +04059500;0.9954;4.8048535142403;4.11276029534763;0.876828158200188;0.449707172141686;0.91982388254828; Mixed Forests;0.244399966592872;2.32845623501897 +04063700;0.9981;5.04213474288441;4.36815042672385;0.886348979499516;0.466041128497796;0.999999999999784; Mixed Forests;0.249999999999946;2.39999999999948 +04074950;0.9667;4.80448882952551;4.25216137640408;0.850047377560946;0.493764059949608;0.75003475098612; Mixed Forests;0.234690628185567;2.28444105671537 +04105700;0.6706;2.55039781936257;2.18923831423583;0.718220746764391;0.457254841737131;0.996515726890118; cropland/natural vegetation mosaic;0.180034842731048;1.50174213655441 +04115265;0.1706;1.96798617681348;1.63659334400991;0.679642792447489;0.436318308608017;0.876627867453449; cropland/natural vegetation mosaic;0.179999999999968;1.49999999999974 +04122200;0.7431;3.69559025549975;3.20782518056797;0.793074576360232;0.475676368368808;0.501507490550934; cropland/natural vegetation mosaic;0.18899767398624;1.7757444448215 +04122500;0.7555;4.44761896096101;3.87003885952965;0.842021637443536;0.482854035992955;0.479109482837954; Deciduous Broadleaf Forest;0.205015568598124;1.99958368988722 +04124000;0.7036;3.91092897406717;3.36448937579536;0.807397723765373;0.459024489610168;0.405067202845371; Mixed Forests;0.210370770559049;1.96536380055536 +04127918;0.9563;4.01412877045209;3.22972545207096;0.832398907902695;0.38934315675667;0.756314490157454; Mixed Forests;0.232942014311002;2.18068304114155 +04127997;0.7608;3.96192966797981;3.50798680753509;0.820281473230937;0.504312395777837;0.389388457836036; cropland/natural vegetation mosaic;0.205729429949905;1.9316960837433 +04161580;0.3152;2.41978381304776;2.09102096700535;0.693710899392781;0.456337012794932;0.999999999999822; cropland/natural vegetation mosaic;0.179999999999968;1.49999999999973 +04185000;0.2041;1.84592525401715;1.60293849593544;0.680043417136934;0.486613921843887;0.732979105629534; Croplands;0.179999999999988;1.4999999999999 +04196800;0.0048;1.81411425207468;1.6041151959585;0.671311023705502;0.495105981139157;0.961429811610182; Croplands;0.179999999999983;1.49999999999986 +04197100;0.0067;1.83718404712037;1.60382308508412;0.68030032785027;0.488235116634404;0.9999999999999; Croplands;0.179999999999982;1.49999999999985 +04197170;0.0059;1.70878387943403;1.45224147051684;0.66064338318177;0.453758482598639;0.999999999999831; Croplands;0.179999999999969;1.49999999999975 +04213000;0.9317;4.25168277632418;3.72505336273642;0.847408704951671;0.518304691604992;0.595635576799432; cropland/natural vegetation mosaic;NA;NA +04213075;0.9686;3.27319431581858;2.80033890616057;0.783147064357307;0.474270176706988;0.999999999999656; cropland/natural vegetation mosaic;0.179999999999938;1.49999999999948 +04216418;0.9172;3.88824129368532;3.37878666188647;0.838975264945491;0.513022848564608;0.99999999999995; cropland/natural vegetation mosaic;0.179999999999991;1.49999999999993 +04221000;0.969;3.95608738968062;3.43920674799665;0.832442903389189;0.505112958942417;0.770251967946683; cropland/natural vegetation mosaic;0.182297480320516;1.61487401602648 +04224775;0.8681;4.3014540512561;3.75191908032576;0.852577026650596;0.497578419653706;0.521256014911527; Deciduous Broadleaf Forest;0.185212560149099;1.76062800745563 +04233000;0.9837;4.53690935911645;3.97650670919472;0.867171560925249;0.509071545232333;0.581980100641712; cropland/natural vegetation mosaic;0.184180198993533;1.70900994967862 +04256000;1;5.20271804347613;4.468289861097;0.892598470767604;0.472295430100911;0.822544572250814; Deciduous Broadleaf Forest;0.200647325664929;2.07098217109947 +04296000;1;4.60454466563404;3.94172213236666;0.872335954463139;0.472500209363317;0.589230119359614; cropland/natural vegetation mosaic;0.195212311293268;1.77941569023259 +05056000;0.0042;1.81981218842962;1.6144819854095;0.625342946330743;0.471967522564669;0.999999999999881; Croplands;0.179999999999979;1.49999999999982 +05057000;0.0063;1.90071941216296;1.69773558708916;0.638878562697772;0.488687916034312;0.999999999999879; Croplands;0.179999999999978;1.49999999999982 +05057200;0.0165;2.06940655556139;1.87929381514654;0.674540246084416;0.544824415991078;0.99999999999988; Croplands;0.179999999999978;1.49999999999982 +05062500;0.6171;3.02107125453087;2.76644142615203;0.744905145911643;0.557384988877901;0.453129010152885; cropland/natural vegetation mosaic;0.186073231281277;1.59308340662613 +05087500;0.1448;2.2419180151684;2.03320240208542;0.699764907289422;0.527982408758892;0.828238727748601; Croplands;0.179999999999978;1.49999999999982 +05120500;0.0019;1.82325472381611;1.61173030659595;0.622140438909572;0.469873901675634;0.999999999999897; Croplands;0.179999999999981;1.49999999999985 +05123400;0.0461;2.2342700483596;2.02126085674969;0.667085450303948;0.504552265673554;0.782852398935205; Croplands;0.17999999999998;1.49999999999983 +05129115;0.9664;3.60764066417868;3.00953838092709;0.725760327094818;0.350494083337378;0.880539810552875; Mixed Forests;NA;NA +05131500;0.9729;4.24841695055185;3.60315712559985;0.841512295703271;0.429902025813771;0.871843088643118; Mixed Forests;NA;NA +05291000;0.0059;2.15290652311635;1.91264892176577;0.69275448393126;0.513102972179959;0.999999999999895; Croplands;0.179999999999981;1.49999999999984 +05362000;0.9909;4.70499951859757;4.25412218425661;0.876083652593215;0.564946607106424;0.612158854344129; Deciduous Broadleaf Forest;0.192488009236505;1.88793340751156 +05393500;1;4.7766187170676;4.28948183933672;0.883505900905296;0.545893700866266;0.77692487824262; Deciduous Broadleaf Forest;0.187769248782413;1.8884624391212 +05399500;0.7338;2.27435942135575;1.96421098904217;0.736884081003536;0.502092967567429;0.524092058606631; Croplands;0.179999999999979;1.49999999999983 +05408000;0.9563;3.50121042431492;3.14098103919991;0.827816393429229;0.559201816081211;0.998333419775199; cropland/natural vegetation mosaic;0.17999999999997;1.49999999999975 +05412500;0.5276;2.55919788007918;2.34062083211968;0.768937628667029;0.583869954785008;0.794648730832891; Croplands;0.179999999999979;1.49999999999983 +05413500;0.599;2.64532222145083;2.32849782815829;0.762005270139825;0.510079291280595;0.539931214878236; Croplands;0.17999999999998;1.49999999999984 +05414000;0.4849;2.52018754479786;2.18590192139896;0.750584242860243;0.487467506597171;0.557714640873587; Croplands;0.179999999999977;1.49999999999981 +05444000;0.1186;2.6760152521279;2.50135257685574;0.792824094436423;0.637873294174967;0.999999999999851; Croplands;0.179999999999973;1.49999999999978 +05454000;0.0902;2.48120471078356;2.23894367417974;0.753595861001021;0.549114938910847;0.691333337409384; Croplands;0.179999999999976;1.4999999999998 +05458000;0.2524;2.54775992972658;2.39816468684933;0.78842119931427;0.652196614579621;0.999999999999828; Croplands;0.179999999999969;1.49999999999974 +05466500;0.0954;2.53229567437625;2.336498578874;0.759089343319279;0.589845885546495;0.959874083295987; Croplands;0.179999999999965;1.49999999999971 +05487980;0.2602;2.13140066738946;1.85135102498853;0.702141276493416;0.458897317764456;0.912178864230535; cropland/natural vegetation mosaic;0.179999999999974;1.49999999999979 +05488200;0.4427;2.07266022867746;1.81346230418321;0.715747574830875;0.492875267418361;0.99999999999988; cropland/natural vegetation mosaic;0.179999999999978;1.49999999999982 +05489000;0.3304;2.35262366617753;2.06792791433395;0.725155738377186;0.477643622621827;0.917528275071194; cropland/natural vegetation mosaic;0.179999999999985;1.49999999999987 +05495000;0.2856;2.04034320888399;1.76264186192161;0.70512027332582;0.468230404465751;0.602785033704367; cropland/natural vegetation mosaic;0.179999999999981;1.49999999999984 +05495500;0.1464;2.1243283379528;1.89536379240217;0.722141163130126;0.521633228620106;0.795595755645665; Croplands;0.179999999999976;1.4999999999998 +05501000;0.1158;2.01999665355484;1.74513794754518;0.691428766543143;0.452500454799662;0.566422259516081; cropland/natural vegetation mosaic;0.179999999999979;1.49999999999983 +05503800;0.076;1.82979657963044;1.56681745867443;0.668460894576613;0.43845940520777;0.980311335611387; Croplands;0.17999999999998;1.49999999999983 +05507600;0.0387;1.78656834702637;1.54644402178041;0.653289294468905;0.440595204879728;0.990571584255266; Croplands;0.179999999999982;1.49999999999985 +05508805;0.0858;2.0430553982642;1.7741971869677;0.681302455892271;0.447477486452302;0.552715012646662; Croplands;0.179999999999979;1.49999999999982 +05514500;0.2339;2.08120519217942;1.77538361188613;0.679528135747765;0.41806478849526;0.635537974788248; Croplands;0.180000195904538;1.50000979522785 +05525500;0.0268;2.46329095973191;2.34235200134787;0.755668632640496;0.636491624292006;0.99999999999991; Croplands;0.179999999999984;1.49999999999987 +05556500;0.1229;2.66723900294569;2.52020977704879;0.787726608288601;0.642965397801479;0.969207198164741; Croplands;0.179999999999987;1.49999999999989 +05584500;0.1497;2.43876788453189;2.24516435574703;0.751303362575985;0.575930425629986;0.786484400637138; Croplands;0.179999999999984;1.49999999999987 +05585000;0.202;2.35746054825502;2.14754288298977;0.741354839288628;0.554249466470184;0.586181022706473; Croplands;0.17999999999998;1.49999999999984 +05591550;0.0151;2.39005548499681;2.26764391366437;0.738904535289147;0.621045223474551;0.999999999999872; Croplands;0.179999999999977;1.49999999999981 +05592050;0.0709;2.20183009829153;2.03371170451892;0.712946723708932;0.55631010896392;0.88627914516875; Croplands;0.179999999999979;1.49999999999983 +05592575;0.0625;2.06497367283965;1.80048769931906;0.684111917146446;0.445652809814883;0.509790008147767; cropland/natural vegetation mosaic;0.179999999999969;1.49999999999974 +05593575;0.0737;1.84162459349327;1.51037067588845;0.685142911546648;0.397850194925885;0.999999999999887; Croplands;0.17999999999998;1.49999999999983 +05593900;0.0715;2.02069561266004;1.79242806049264;0.680991957004606;0.480127136210114;0.999999999999845; Croplands;0.179999999999972;1.49999999999977 +05595730;0.0879;2.13321198993484;1.81558162421787;0.705835326302162;0.433442495035369;0.674443926288172; Croplands;0.179999999999978;1.49999999999982 +06037500;0.8534;1.41387036725218;0.863027740983509;0.528578176479727;0.175220757301292;0.614774316938981; Grasslands;0.138674322375441;1.60973844510021 +06043500;0.7832;1.82321204631187;1.26700960634177;0.608127731901527;0.240699945159824;0.569777410354246; Evergreen Needleleaf Forest;0.149114996878696;1.6709332231061 +06154410;0.199;2.40820712965742;1.71484538589929;0.67267848033769;0.258549274589952;0.967485779681131; Grasslands;0.121625711015943;1.50975426609566 +06188000;0.782;1.3149742171691;1.02679094713805;0.495269283327226;0.275693952888312;0.866988586442248; Grasslands;0.126650570677869;1.53990342406713 +06191500;0.7861;1.32322097420128;0.97831040983476;0.482735406268428;0.237673948113263;0.723629282643493; Grasslands;NA;NA +06221400;0.297;0.571416671083167;0.422711459543106;0.247970307662283;0.122424104506462;0.999999999999872; Grasslands;0.119999999999985;1.49999999999981 +06224000;0.387;0.735980334341707;0.477690474438546;0.312042477192434;0.126745604810631;0.954286266592398; Grasslands;0.122285686670362;1.51371412002208 +06278300;0.5225;0.869292295992502;0.637294698983193;0.376741965657456;0.186161948489979;0.999999999999688; Grasslands;0.119999999999963;1.49999999999953 +06280300;0.5853;0.785631192811514;0.547076432699304;0.337862219862902;0.152621114130269;0.940016495318262; Grasslands;0.122999175234068;1.51799505140432 +06289000;0.6859;1.88384098292209;1.32089466911433;0.62537827113449;0.247782564481299;0.589597884489584; Grasslands;0.140520105775505;1.62312063465296 +06291500;0.5915;1.59392580262406;1.26085540486194;0.574551419647744;0.325020462146347;0.976329820144475; Grasslands;0.121183508992744;1.50710105395632 +06311000;0.3219;1.39235947175837;0.952058936292889;0.526202779148575;0.216614809409235;0.999999999999702; Grasslands;0.119999999999964;1.49999999999955 +06332515;0;1.3924555691975;1.19928054792769;0.540676946514819;0.408505304383603;0.642194693480465; Grasslands;0.141468318391145;1.49999999999978 +06339100;0;1.29832472269071;1.09585116617032;0.507897670000256;0.362451735094875;0.87621628835117; Grasslands;0.127427022698913;1.49999999999986 +06339500;2e-04;1.28384548245303;1.087549549492;0.503237315214142;0.362499277076841;0.557713950921958; Grasslands;0.146537162944666;1.49999999999987 +06344600;0;1.29171518246715;1.0902951829116;0.504651094366985;0.357330671801601;0.556778626787294; Grasslands;0.146593282392759;1.49999999999997 +06350000;0;1.37298757378111;1.19010954494035;0.503756353678352;0.370441094362271;0.589866320584699; Croplands;0.15539197923507;1.49999999999985 +06352000;0;1.32741386908539;1.14545032170026;0.499443683369832;0.367105673625094;0.667461296151084; Croplands;0.160047677769054;1.49999999999986 +06353000;1e-04;1.24410580922567;1.05717454139872;0.491669093062351;0.350440526218619;0.68987343026429; Grasslands;0.138607594184125;1.49999999999985 +06354000;1e-04;1.26567181394699;1.07856894102211;0.495896607873975;0.35450786103811;0.606339721666429; Grasslands;0.143619616699996;1.49999999999985 +06360500;1e-04;0.989619967595197;0.798461134012735;0.415567689301505;0.272846158485234;0.997485538703697; Grasslands;0.120150867677758;1.49999999999983 +06404000;0.7102;2.04627715806766;0.985466220927837;0.676336206317906;0.141667130999676;0.983912920656253; Evergreen Needleleaf Forest;0.169195646032802;1.79517387619675 +06406000;0.5327;1.68632774562847;0.942389005764146;0.589306094118542;0.178925809172646;0.534084206300004; Evergreen Needleleaf Forest;0.146704210314987;1.66022526188984 +06408700;0.9795;2.65234356314225;1.65479814401589;0.739318932462557;0.209748193172578;0.999999999999775; Evergreen Needleleaf Forest;0.169999999999962;1.79999999999959 +06409000;0.7952;2.39505712829344;1.57870065935215;0.690628900462691;0.231701221510647;0.825549192974724; Evergreen Needleleaf Forest;0.174104175782536;1.77800334276485 +06431500;0.9044;3.00761620898587;2.11134813374127;0.763443378363136;0.275231404650566;0.407361849038051; Evergreen Needleleaf Forest;0.19655622655769;1.92677180070762 +06440200;0;0.867418571780611;0.687038696790918;0.370097406391618;0.227159956917554;0.999999999999898; Grasslands;0.119999999999988;1.49999999999985 +06441500;5e-04;1.06473918516747;0.869384474417984;0.439278271138618;0.289506469528045;0.9913716613548; Grasslands;NA;NA +06447000;0.0358;1.01893092133378;0.798918071812832;0.416559935253665;0.247807625153193;0.967518216415255; Grasslands;0.121843672374132;1.50105234640917 +06447500;0;0.991240632188275;0.780478431999544;0.42320319612226;0.256293270237688;0.999999999999888; Grasslands;0.119999999999987;1.49999999999983 +06450500;1e-04;1.13912938878561;0.917527140320045;0.470693770001584;0.29350847482196;0.917764989233099; Grasslands;0.124934100645995;1.49999999999984 +06452000;0.0242;1.10201049688532;0.884343311756242;0.452366391054398;0.281297705466102;0.857849374954276; Grasslands;0.128476697689851;1.50052339812854 +06453600;0.0217;1.49718927559229;1.22962378247869;0.578954405058964;0.357184103997679;0.999999999999893; Croplands;0.179999999999981;1.49999999999984 +06464500;0.0109;1.29617759933298;1.05243268758376;0.530486156936392;0.337521488212213;0.646557354822828; Croplands;0.158793441289358;1.49999999999985 +06468170;0.003;1.86435021741836;1.6707502425575;0.64516244417717;0.507387065108272;0.999999999999871; Croplands;0.179999999999977;1.49999999999981 +06468250;0.0031;1.8859286776134;1.68746790070136;0.648196082953334;0.506212613106944;0.999999999999878; Croplands;0.179999999999978;1.49999999999982 +06470800;0.0061;2.20687526085596;2.0096650531317;0.700221525102677;0.558942578630896;0.999999999999873; Croplands;0.179999999999977;1.49999999999981 +06477500;0;1.47061689217256;1.21681166531563;0.578604954933992;0.370566196730059;0.999999999999889; Croplands;0.17999999999998;1.49999999999983 +06479215;0;1.98626675247076;1.72961794868463;0.668252395808743;0.478336979667499;0.999999999999872; Croplands;0.179999999999977;1.49999999999981 +06479438;0.0027;1.9240865044914;1.68283817433854;0.660582261746985;0.481337077357315;0.999999999999854; Croplands;0.179999999999974;1.49999999999978 +06601000;0.0365;2.34414157665115;2.15082471012727;0.741528525722756;0.573016110896769;0.999999999999882; Croplands;0.179999999999979;1.49999999999982 +06614800;0.821;1.03055560588837;0.744444489479065;0.408611088991165;0.199999988079071;1; Grasslands;0.12;1.5 +06622700;0.7496;2.1412589752985;1.355784647191;0.676669483792688;0.219318095949947;0.743937215366596; Evergreen Needleleaf Forest;0.157196860768328;1.72318116460996 +06623800;0.9211;1.9012036477088;1.29621824550357;0.639474433934156;0.228084554991603;0.7110427033298; Evergreen Needleleaf Forest;0.155552135166484;1.71331281099886 +06632400;0.7266;1.86095317923482;1.16036235007969;0.629400583687846;0.21480735498139;0.749181342567517; Grasslands;0.132540932871618;1.57524559722968 +06746095;0.821;1.6227939717298;1.11376940760019;0.576333230200438;0.223475679049099;0.829185841761724; Grasslands;0.128540707911914;1.55124424747148 +06784000;0.0143;1.42963616537242;1.19602276739748;0.566173065098834;0.369921948428106;0.65699608294954; Grasslands;0.140580235023004;1.4999999999998 +06803510;0;1.58808570110052;1.35500314906082;0.619456818532485;0.424149606744646;0.733866795972938; Croplands;0.164032007758364;1.49999999999984 +06803530;0;1.79077513285773;1.62238091802624;0.664271846984441;0.508359286692761;0.995765136415148; Croplands;0.179745908184895;1.49999999999982 +06814000;0.0044;1.53655726848347;1.28663522243118;0.607817628203384;0.395587606858712;0.885039676794084; Croplands;0.179999999999975;1.49999999999979 +06847900;0.001;0.922368767144321;0.659688667437801;0.418650602488718;0.213876991256613;0.564612419377989; Grasslands;0.146123254837301;1.49999999999983 +06853800;0;1.12923666727928;0.842923272986411;0.498530645293059;0.257662262851776;0.999999999999895; Croplands;0.179999999999981;1.49999999999984 +06876700;0;1.14847495528074;0.838265114689036;0.497010172279643;0.249108174617597;0.588381852300092; Grasslands;0.144697088861973;1.49999999999982 +06878000;0;1.42427377995873;1.11697599057737;0.552988961859571;0.303891819699653;0.874566058925174; Grasslands;0.127526036464464;1.49999999999979 +06879650;0.0311;2.34911578492211;2.07593821360133;0.698437934288608;0.517456713870101;0.999999999999716; Grasslands;0.119999999999966;1.49999999999957 +06885500;0.0076;1.50947202812709;1.29428756840972;0.604439141054782;0.411876672587273;0.964552266825597; Croplands;0.177873136009521;1.49999999999981 +06888500;0.0139;2.29176646117857;2.00991239674848;0.694714005677765;0.494829107304472;0.999999999999889; Grasslands;0.119999999999987;1.49999999999983 +06889200;0.0174;2.17623864050212;1.88267142933284;0.680795506728655;0.446230824727206;0.748328150857113; Grasslands;0.135100310948549;1.4999999999998 +06889500;0.0149;2.19793952795478;1.9079371897363;0.681450570509365;0.450941671394187;0.862050526765767; Grasslands;0.128276968394032;1.49999999999981 +06892000;0.067;1.76212127216237;1.49563910042382;0.653167003681937;0.412264662448835;0.55510488143676; Croplands;0.179805106605943;1.49999999999982 +06903400;0.222;1.99322483625339;1.7287309954836;0.694065576436255;0.467867883381994;0.518097242169555; cropland/natural vegetation mosaic;0.179999999999985;1.49999999999988 +06906800;0.3516;2.58040152472755;2.17476376213288;0.713011098998999;0.405741049849812;0.772637099142914; cropland/natural vegetation mosaic;0.180202270310869;1.51011351554409 +06910800;0.0074;2.3636460764871;2.07708476223741;0.699882225983353;0.496130723307075;0.999999999999898; Grasslands;0.119999999999988;1.49999999999985 +06911900;0.0178;2.21801290788113;1.92499318406645;0.682986901560195;0.461675621680232;0.999999999999879; Grasslands;0.119999999999986;1.49999999999982 +06917000;0.0592;1.95273742761646;1.63205431185369;0.654922465736711;0.383810724030357;0.68029729984583; Grasslands;0.139182162009225;1.49999999999979 +06918460;0.1134;3.04396052639321;2.5460219342134;0.743258546684765;0.400384818586843;0.975214647292108; cropland/natural vegetation mosaic;0.179156702999351;1.49999999999979 +06919500;0.243;2.89462683796565;2.45681434371279;0.7344234445602;0.419504069935262;0.839469622763087; cropland/natural vegetation mosaic;0.17999999999998;1.49999999999984 +06921070;0.2784;3.04052340776778;2.53423667672849;0.74548283959812;0.402675699659684;0.999999999999936; cropland/natural vegetation mosaic;0.179999999999988;1.4999999999999 +06921200;0.2526;3.24917820726933;2.75624567030735;0.771078998968568;0.430793570447503;0.999999999999987; cropland/natural vegetation mosaic;0.179999999999998;1.49999999999998 +06934000;0.5897;3.92357858384305;3.3984629455614;0.801395107840249;0.451681524287681;0.677428072279345; cropland/natural vegetation mosaic;0.183018202244873;1.65980369934359 +07014500;0.7777;4.61665236608421;4.10139325201242;0.846841320085243;0.493124473398287;0.667520813589652; Deciduous Broadleaf Forest;0.186675208135875;1.83376040679464 +07056000;0.8844;4.9581085853862;4.39844207916247;0.8709850141129;0.491920606681592;0.895609066252598; Deciduous Broadleaf Forest;0.188956090662512;1.94780453312618 +07057500;0.584;4.09228094379476;3.51462364469783;0.808381133293666;0.432314136422029;0.472710975480794; cropland/natural vegetation mosaic;0.17881836794751;1.71754078156178 +07060710;0.8541;5.45882179306809;4.81821306323152;0.899455276498995;0.48481994137688;0.99999999999989; Deciduous Broadleaf Forest;0.189999999999979;1.99999999999978 +07066000;0.693;4.61463130691326;4.07182736262562;0.844997812893784;0.478574461918826;0.747145082795381; Deciduous Broadleaf Forest;0.187471450827937;1.87357254139755 +07067000;0.7931;4.82952060726065;4.27578387287888;0.859879551642112;0.486571179353827;0.82485356585508; Deciduous Broadleaf Forest;0.188248535658531;1.91242678292737 +07068000;0.7417;4.91412916942315;4.35775791676582;0.864947960573778;0.489495021695475;0.854096897754082; Deciduous Broadleaf Forest;0.188540968977519;1.92704844887686 +07071500;0.5914;4.33099867251905;3.7685304739211;0.824529930625076;0.451512136302775;0.571042484762037; Deciduous Broadleaf Forest;0.18462649094537;1.78552124238082 +07083000;0.3563;0.788784878012727;0.525509900044236;0.327623934624092;0.153876644975375;0.999999999999996; Grasslands;0.12;1.49999999999999 +07142300;3e-04;1.16382103006498;0.848938456870195;0.48782477912583;0.24715153615008;0.521780795485334; Grasslands;0.148693152270853;1.49999999999977 +07145700;0.0346;1.22942820637912;0.829115688768598;0.51434044013458;0.221979011127496;0.551203140895782; Grasslands;0.146927811546242;1.49999999999991 +07148400;5e-04;0.963708627961121;0.700736160406498;0.425835705082562;0.232503105619712;0.997961861524936; Grasslands;0.120122288308483;1.49999999999983 +07149000;3e-04;1.06422124648142;0.767841912975682;0.455581505022561;0.238002705291896;0.921105233749057; Grasslands;0.124733685975035;1.49999999999982 +07151500;0.0101;1.22456562975733;0.818204106478568;0.502596359844986;0.215281718666136;0.620217090932165; Grasslands;0.142786974544053;1.49999999999986 +07167500;0.0167;2.15220110986121;1.84911266662024;0.670850766793714;0.462661949173918;0.999999999999934; Grasslands;0.119999999999992;1.4999999999999 +07180500;0.0015;1.8768305256217;1.59009707018444;0.631699297105892;0.43117848887169;0.99999999999989; Grasslands;0.119999999999987;1.49999999999983 +07184000;0.0078;1.71597588793418;1.38219988520904;0.616467737370962;0.340591654373604;0.73033348398206; Croplands;0.163820009038903;1.49999999999974 +07195800;0.2232;3.36641175992578;2.82112349254907;0.774488422528882;0.402198958248552;1.00000000000009; cropland/natural vegetation mosaic;0.180000000000016;1.50000000000013 +07196900;0.5407;3.59929599540988;3.08118311270273;0.780021905437418;0.419012719374475;0.975876510753336; cropland/natural vegetation mosaic;0.180241234892462;1.51206174462329 +07197000;0.6716;3.83361343012604;3.32131636934448;0.797898448104159;0.44410393025271;0.74253666251733; cropland/natural vegetation mosaic;0.175667161568296;1.57824330606991 +07208500;0.6805;1.65335497822553;0.913433783257069;0.598012502398732;0.188280394342668;0.660794679917564; Evergreen Needleleaf Forest;0.153039733995869;1.69823840397516 +07226500;0.0028;0.57729877316962;0.373908211117553;0.286549542775838;0.154655565548504;0.969301441916515; Grasslands;0.122455884646653;1.54911769293318 +07261000;0.2534;2.99298872543272;2.41469844469093;0.716195752715417;0.342285044153287;0.485625746748551; cropland/natural vegetation mosaic;NA;NA +07263295;0.8657;5.00082997287572;3.72864471659493;0.860619621828606;0.288205452807768;0.999999999999962; Mixed Forests;0.249999999999991;2.39999999999991 +07290650;0.7965;4.50496417589876;3.30367485407145;0.815306049793279;0.280783459405914;0.500505466899586; Woody Savannas;0.20465034718039;2.04761843451259 +07291000;0.9777;5.18817670710889;3.50972920311137;0.846542503064599;0.198569404747183;0.897761055218611; Mixed Forests;0.240798494969669;2.32843273865296 +07292500;0.9468;5.08576369378386;3.37341324963001;0.838933229317876;0.18525571829442;0.908405531888626; Mixed Forests;0.242263568279183;2.3523636606214 +07295000;0.94;5.06149285349947;3.27644922765419;0.839611750782864;0.180542519089726;0.774016000370215; Mixed Forests;0.229661440033311;2.24181120025907 +07299670;0.0012;0.833215154916569;0.552666652744231;0.374394954906342;0.168235660309998;0.999999999999889; Grasslands;0.119999999999987;1.49999999999983 +07301410;0;0.963149225549539;0.721125725484607;0.424016035425943;0.247578372277033;0.99999999999981; Grasslands;0.119999999999977;1.49999999999972 +07301500;8e-04;0.900953204538708;0.656207343780417;0.401804030994483;0.222052107525294;0.989357094393288; Grasslands;0.120638574336379;1.4999999999998 +07315200;0.0923;1.5062269354296;1.15002687028317;0.54717299115904;0.266567340450844;0.999999999999863; Grasslands;0.119999999999983;1.49999999999979 +07315700;0.0437;1.58936290160419;1.21458560315889;0.565856290669431;0.283918086188033;0.999999999999906; Grasslands;0.119999999999989;1.49999999999986 +07335700;0.9722;5.44559607897645;4.44515974879063;0.888598103089052;0.39321315802632;0.703286700688297; Deciduous Broadleaf Forest;0.207802797958678;2.11868531972445 +07340300;0.9591;5.47973402304278;4.38552547584363;0.884643900774579;0.356442307394382;0.574006632902014; Mixed Forests;0.224440397974111;2.2296026531607 +07346045;0.8518;3.81575919993679;2.97724272199964;0.771894053426273;0.321061571941672;0.920850574116832; Woody Savannas;0.167123448329464;1.75540459811802 +07359610;0.9453;4.80649629979972;3.96497189352464;0.84854284305384;0.379576312274333;0.546453959106729; Mixed Forests;0.2115558575315;2.10626778349365 +07362100;0.9703;5.18456735440731;3.79959471440346;0.861914255528427;0.267873188537989;0.999999999999909; Mixed Forests;0.249999999999977;2.39999999999978 +07362587;0.9966;5.30943302189054;3.96281643550199;0.874011373485945;0.292992308420367;0.999999999999734; Mixed Forests;0.249999999999934;2.39999999999936 +07373000;0.904;4.72230695487787;3.09047060497537;0.848567836384845;0.192403445222986;0.999463963434039; Mixed Forests;0.249951756709048;2.39962477440366 +07375000;0.7492;3.9967378508415;2.62312156237416;0.777066536199219;0.198863563157982;0.999080483788021; Woody Savannas;0.160018390324238;1.69981609675759 +07376000;0.9532;4.69876162316988;2.96879630183386;0.820968930358096;0.175539181067812;0.864431129431634; Woody Savannas;0.168889183518471;1.83464238739008 +08013000;0.9144;4.83560862597344;2.99419305654465;0.828604928185975;0.160985302859105;0.771939297807946; Mixed Forests;0.229474536802711;2.24035750846551 +08014500;0.8187;4.67051473252856;2.73525233165406;0.822381094170114;0.144744490811663;0.783430564648533; Mixed Forests;0.230508750818367;2.24840139525396 +08023080;0.8629;4.18630516918353;2.94563576946234;0.780328250341573;0.236945374423988;0.854248723489598; Woody Savannas;0.173117614885906;1.80202589355699 +08025500;0.932;4.80775979481762;3.00834693567701;0.828083653554347;0.156135526025938;0.738669542317031; Mixed Forests;0.226480258808521;2.2170686796218 +08029500;0.9705;4.20463188341052;2.59126678365461;0.784508844327435;0.166741370827285;0.737766126462068; Woody Savannas;0.183601048618408;1.8835637114765 +08050800;0.1279;1.89444751451393;1.50198672110604;0.610462838712088;0.3109194031271;0.999999999999823; Grasslands;0.119999999999979;1.49999999999973 +08066200;0.8456;4.24418412204036;2.66582934542741;0.789200259958581;0.177706653907655;0.89868963527253; Woody Savannas;0.169117932825436;1.77091725530888 +08066300;0.8251;5.07057720955688;3.00974291416069;0.834551991492677;0.136745309283975;0.616391359742145; Woody Savannas;0.190035160878904;2.0224014491113 +08070000;0.8302;4.20867048729618;2.6859104562771;0.7960914018796;0.18108335071141;0.52740133577521; Woody Savannas;0.202533879780227;2.03081906495731 +08070200;0.8129;4.2042554893669;2.69826220477451;0.794353898177783;0.187344426084686;0.556037796984995; Woody Savannas;0.199956598271344;2.01077354211044 +08079600;0;0.768546451054059;0.545950022423936;0.349582531724565;0.188481952250754;0.882353262930546; Grasslands;0.129306248618512;1.6797955515493 +08082700;0;0.839001609959874;0.47990030236406;0.38033921271764;0.121724044791997;0.987885412837265; Grasslands;0.120726875229736;1.49999999999976 +08086212;0.0038;1.25847854143659;0.933278038963336;0.491796077665384;0.239248988232265;0.999999999999848; Grasslands;0.119999999999982;1.49999999999977 +08086290;0.0066;1.29085255640854;0.954765924103444;0.498299436135718;0.233070144380114;0.999999999999873; Grasslands;0.119999999999985;1.49999999999981 +08101000;0.0067;1.23935478234518;0.821306553976997;0.488418364916188;0.19690570821472;0.953922338009696; Grasslands;0.122287713829571;1.54033804689561 +08103900;0.0201;1.09431110095987;0.683469312590824;0.443498383418616;0.154214789666331;0.795900552536619; Grasslands;0.130204972373151;1.68368950271678 +08104900;0.0263;1.33348236804569;0.827765487912811;0.503745935663548;0.175516397238595;0.555559891025212; Grasslands;0.137777604358982;1.58888802179486 +08109700;0.0282;1.46514675054897;1.04981878046603;0.562169924319256;0.246758765267091;0.315633774413334; cropland/natural vegetation mosaic;0.157455345493979;1.54235447912079 +08150800;0.0386;1.31840313138305;0.818088102780696;0.501699124800428;0.178338122625271;0.533637367818914; Grasslands;0.142895666776089;1.89015383065615 +08155200;0.088;1.34593437585115;0.739264585131771;0.501813649027629;0.133297548115374;0.548458747871507; Woody Savannas;0.141993648412057;1.61068712252385 +08158700;0.0414;1.34117542180576;0.794651986738496;0.506337626858737;0.158946957406712;0.582938429139933; Grasslands;0.138623663461192;1.71929635804788 +08158810;0.0548;1.33837641261247;0.807070184416084;0.497403457745784;0.155042114377036;0.813570284470072; Savannas;0.166640633482785;2.26202385231947 +08164000;0.074;1.55322983553728;1.02361735108593;0.582446894308869;0.213788702887233;0.62490056798203; cropland/natural vegetation mosaic;0.172929055672463;1.54333601372344 +08164300;0.0122;1.57092499362025;1.03843510761926;0.588425089090127;0.208063437691137;0.863809620391875; cropland/natural vegetation mosaic;0.179676975001694;1.50037606078181 +08164600;0.0573;1.5064877751827;0.977483837300251;0.572629452805666;0.212881107630395;0.787977468454422; cropland/natural vegetation mosaic;0.178350819944467;1.67279891323593 +08165300;0.1466;1.19440794786881;0.646378834889658;0.480704369008316;0.141313782500059;0.597384402946831; Grasslands;0.137337200337247;1.66680347126912 +08171300;0.0569;1.39837890845399;0.82213943355486;0.517311218607447;0.168631658794313;0.484060660687881; Grasslands;0.142028686130333;1.70056574691195 +08175000;0.0703;1.50328776538629;1.12426323765911;0.576422327104662;0.266871839937141;0.597012214494736; cropland/natural vegetation mosaic;0.165257484659476;1.55545141011294 +08176900;0.1949;1.51948289648685;1.03025132155715;0.583532288188548;0.233542001766257;0.563373740921656; cropland/natural vegetation mosaic;0.174023727304186;1.57918088462187 +08178880;0.4391;1.55079922702993;0.977153952314625;0.530201147673102;0.188921801762261;0.636206845225999; Woody Savannas;0.14544827380903;1.62724136904507 +08189500;0.1046;1.46356894052575;1.00323717014187;0.564346281122583;0.236393554177075;0.386727846065434; Closed Shrublands;0.177151920572622;2.05874660350245 +08190000;0.2878;1.28653667312889;0.669317140354942;0.492121795903854;0.12684093176896;0.506328301937754; Grasslands;0.139886182375236;1.60848635130506 +08190500;0.1388;1.03522336261494;0.528285841934137;0.443986963786039;0.11730797215066;0.859505200404885; Grasslands;0.126878888128056;1.61623569001696 +08194200;8e-04;1.0783347797166;0.661138004153719;0.446835555206807;0.175884842777964;0.912347106685082; Grasslands;0.125259173598886;1.49999999999992 +08195000;0.5043;1.68628715931265;0.94829477755453;0.555785114401505;0.154644087169344;0.671337552628395; Woody Savannas;0.146855836033324;1.63430952123308 +08196000;0.6396;1.76272275496437;0.907345183323834;0.582862069689777;0.138824703298379;0.811672170434494; Woody Savannas;0.157235090670683;1.74816210344641 +08198500;0.4132;1.72802171787476;1.01660784471567;0.555160779798099;0.166049187083442;0.620446996898917; Woody Savannas;0.148588915224734;1.64454006237441 +08200000;0.6625;1.61160065664638;0.924283147524083;0.543881619410579;0.167626210539206;0.67264735428689; Woody Savannas;0.146905894171461;1.63452947085719 +08202700;0.4827;1.59623942093614;0.914329026010688;0.551950067928857;0.161535753959016;0.785945113617326; Woody Savannas;0.161289122603495;1.78916286631507 +08267500;0.7348;1.64966779471623;0.95810094237629;0.587627751234368;0.203211590206167;0.609456811647502; Evergreen Needleleaf Forest;0.150472840582364;1.68283704349411 +08269000;0.7622;2.06732792022204;1.17097765682142;0.661587686863012;0.194897614285649;0.920753317446226; Evergreen Needleleaf Forest;0.166037665872296;1.77622599523367 +08271000;0.654;1.55378786276875;0.974419803106758;0.544135916021952;0.202262339000355;0.905041281817386; Grasslands;0.124747935909111;1.52848761545457 +08324000;0.5803;1.64669581725361;0.865648383043467;0.584625735957355;0.160920298700752;0.522832423718087; Evergreen Needleleaf Forest;0.160164625247913;1.76038854418825 +08377900;0.9747;1.91132929156545;1.13673501152197;0.641245683073591;0.214156211084936;0.968190665467538; Evergreen Needleleaf Forest;0.168409533273362;1.79045719964007 +08378500;0.965;1.94759292430995;1.14977731062135;0.646701237139998;0.208134199997404;0.903313809652401; Evergreen Needleleaf Forest;0.165165690482606;1.77099414289554 +08380500;0.8242;1.85416587874561;0.871586382108212;0.635941247569624;0.146555517595893;0.849580985822091; Evergreen Needleleaf Forest;0.168031080208166;1.78263445033183 +09034900;0.2933;0.990781316903521;0.736067247800084;0.39437462437118;0.209781365823118;0.999999999999881; Grasslands;0.119999999999986;1.49999999999982 +09035800;0.57;1.47794942890548;1.0156697631687;0.545968758822727;0.223787883484626;0.71470476318904; Grasslands;0.134264761840548;1.58558857104329 +09035900;0.6008;1.4526745817022;1.00181364768637;0.531989429411163;0.22374138668006;0.702390210416268; Grasslands;0.134880489479179;1.58928293687503 +09047700;0.7022;1.48608905865194;0.858300734316393;0.555913212348628;0.173284009165969;0.581672693440999; Grasslands;0.140916365327946;1.62549819196766 +09065500;0.8324;1.28613240291185;0.936785328574834;0.463035338065315;0.217116081398425;0.824512064646667; Grasslands;0.128774396767667;1.552646380606 +09066000;0.7706;1.66791963180343;1.15629728409431;0.582190914330547;0.242730295810776;0.849569718439843; Grasslands;0.127521514077988;1.54512908446784 +09066200;0.6686;1.60743488074119;1.22047082716605;0.528071072193403;0.261418620598234;0.991216375692508; Grasslands;0.120439181215375;1.50263508729225 +09066300;0.7632;2.24884633733839;1.6114073326392;0.662400263124846;0.271592550971937;0.643433719298986; Evergreen Needleleaf Forest;0.152171685964949;1.69303011578969 +09081600;0.6136;2.13435487032765;1.73034700294115;0.600361397381244;0.318935643665161;0.709604529869809; Grasslands;0.136351612350766;1.53331480860569 +09107000;0.6075;1.20076425149125;0.715420500326158;0.464051504201407;0.162126730746236;0.973363046820821; Grasslands;0.12133184765894;1.50799108595355 +09210500;0.2624;1.07824553884906;0.787071103323967;0.421651852902019;0.208727705446692;0.95195622658058; Grasslands;0.122402188670936;1.51441313202545 +09223000;0.433;1.69829447366005;1.23791288532326;0.592024539166712;0.266863108480598;0.68532611578062; Grasslands;0.136420290613439;1.72141281957424 +09306242;0;0.817796166654334;0.559897954810272;0.346300865438431;0.161054982390056;0.999999999999969; Grasslands;0.119999999999996;1.49999999999995 +09312600;0.0454;1.63132248834686;1.27845923334844;0.551638977779672;0.302182711987234;0.844737362585559; Grasslands;0.129315758244847;1.49999999999984 +09352900;0.5199;1.09729904790995;0.745756522214762;0.42690997318948;0.205398747592719;0.873660472903954; Grasslands;0.12631697635479;1.53790185812868 +09378170;0.2522;1.7696569588963;1.32412305616706;0.561753003967323;0.2846731387483;0.597383727480064; cropland/natural vegetation mosaic;0.155843023648754;1.49999999999938 +09378630;0.2678;1.74006416048949;1.1968197790416;0.578654106774012;0.254540226994485;0.76034392865949; Croplands;0.165620635719569;1.5 +09386900;0.3012;1.01451900413024;0.497994544953751;0.414352940877652;0.0999160403285754;0.428096937544555; Grasslands;0.149492370029029;1.84596348206989 +09404450;0.3323;1.19210412000389;0.710038819047667;0.448279768876601;0.150045992131507;0.661991729092218; Croplands;0.162695871247903;1.50462260524446 +09430500;0.4793;0.953463616041695;0.508049478146288;0.39922301007951;0.116776394785744;0.314497949836862; Woody Savannas;0.165478464121393;2.0112072885705 +09430600;0.7126;1.55294880047173;0.752624093190321;0.56034860690898;0.135674275315692;0.585494421784817; Woody Savannas;0.16819310388961;1.88251990428736 +09447800;0.1601;0.588618872377099;0.320768321168574;0.279558389342605;0.0827973582246185;0.712148810421495; Open Shrublands;0.182763914981703;2.71071606139806 +09480000;0.1254;0.859927188763967;0.611178689977028;0.37023915906174;0.200548239856346;0.71037741693002; Grasslands;0.141667823261453;1.91082671446752 +09484000;0.4456;0.931723479889018;0.456109733294195;0.403068471367726;0.106079266944095;0.631807423275088; Open Shrublands;0.188436635604687;2.61617377932196 +09484600;0.0621;0.649335259167385;0.410887939186568;0.306563001007221;0.140119512843138;0.67174068589611; Open Shrublands;0.175010504291693;2.5811413445337 +09492400;0.9911;2.26945193535119;1.19683470985945;0.725755839390612;0.212609329052377;0.999999999999883; Evergreen Needleleaf Forest;0.16999999999998;1.79999999999979 +09494000;0.8391;1.74472716044426;0.889040896516206;0.604638908142893;0.169272152754362;0.574200831321119; Evergreen Needleleaf Forest;0.17947063288372;2.01588848996011 +09497800;0.7996;0.948473839281472;0.490365032600617;0.393568822882453;0.0888557077129998;0.490505141942573; Closed Shrublands;0.186868634232982;2.35841567395118 +09497980;0.8725;1.17478214389889;0.538984863403833;0.460069784496169;0.0965081410216559;0.645823147125035; Closed Shrublands;0.188705696413066;2.31534604759781 +09505200;0.4805;1.10859069436476;0.505511034768385;0.463946177431202;0.114531288693541;0.541289780524391; Woody Savannas;0.164200519625035;2.02542370154732 +09505350;0.5015;1.23666753293872;0.508133436092429;0.491641925422204;0.0987600974717826;0.639522279781292; Woody Savannas;0.16892725748781;2.01012625450471 +09505800;0.531;1.30856416999328;0.576308821725487;0.513905227973341;0.119506913380289;0.669008820635472; Woody Savannas;0.167125525442433;1.88381593395476 +09508300;0.5022;0.847720812521106;0.386848691856764;0.361133615617032;0.0669897715566031;0.513274473679314; Closed Shrublands;0.185907227806648;2.29878828902533 +09510200;0.158;0.77259680499924;0.330580729994865;0.353340069492016;0.0668960148619856;0.654204087153728; Open Shrublands;0.186168163486139;2.61588572201511 +09512280;0.1822;0.6307001524792;0.323128409596528;0.312485668532912;0.08086273899196;0.986914945150973; Open Shrublands;0.199476597806032;3.08168092321129 +09513780;0.1114;0.595243332720995;0.289613757847605;0.297547159555981;0.0800408301344817;0.846505478893849; Open Shrublands;0.193860219155736;2.88510767045119 +10023000;0.1959;1.0289424091811;0.766396997212932;0.404784567443555;0.212440898580709;0.86316794815937; Grasslands;0.127780403386343;1.53865677516714 +10166430;0.0328;0.617262707269164;0.434249459832861;0.244527950061414;0.104249650871857;0.691510466033374; Grasslands;NA;NA +10172700;0.0017;0.679387483094161;0.40272808621891;0.312053011189098;0.112574447214789;0.999997433870469; Grasslands;0.120000102645165;1.50000051322573 +10172800;0.0366;1.16951704513406;0.807190128296021;0.442681684069278;0.185775681075303;0.999999999999846; Grasslands;0.119999999999982;1.49999999999977 +10173450;0.4463;1.03005848168764;0.594688891166896;0.405446032650896;0.128325498977479;0.655364169571458; Grasslands;0.138341123210711;1.70050602324648 +10205030;0.0326;1.46183892996959;1.15392945589386;0.526999810374846;0.290889750322732;0.852067375378165; Grasslands;0.131563580105695;1.71500981027189 +10234500;0.3513;1.54501278215626;0.86661014848995;0.557826857033383;0.158318701678768;0.564948045423182; Evergreen Needleleaf Forest;0.151903417718611;1.68551366775484 +10242000;0.1718;0.699187324168415;0.400489792151487;0.297948773916754;0.0908447463015904;0.731765300240481; Grasslands;0.136281380164026;1.64600238312449 +10244950;0.0622;0.761098185707974;0.440934095268483;0.33388803121918;0.114812372550121;0.999999999999837; Grasslands;0.11999999999998;1.49999999999976 +10249300;0.062;0.405882383884838;0.217840133365192;0.184261732208869;0.0501061689593908;0.857453799763176; Grasslands;0.1314027585456;1.72804110881232 +10258000;0.4365;0.796884699648813;0.305144585428993;0.376542704840187;0.0859741456384971;0.484057552061286; Open Shrublands;0.182858318294462;2.36248727006273 +10258500;0.2855;0.543802986776022;0.240569636459041;0.264162731645623;0.0394405756634727;0.983576298742777; Open Shrublands;0.199343051949704;3.07700681823981 +10259000;0.5081;0.824071840064609;0.324730423782019;0.367999884537156;0.0600560048781903;0.518736344720657; Woody Savannas;0.179250546211142;2.37376911739058 +10259200;0.1369;0.367125358665301;0.154409913025984;0.194934001574507;0.0289982929213293;0.999999999999836; Open Shrublands;0.199999999999967;3.09999999999949 +10263500;0.2769;0.649646537324547;0.257591173041664;0.300095188081063;0.04304937707926;0.757776042667653; Open Shrublands;0.189971061359727;2.76076467918826 +10310500;0.7131;1.10056526877372;0.52955624386324;0.444829054835264;0.11922662171317;0.43919648191564; Croplands;0.158465517569693;1.54914083938735 +10316500;0.0123;0.990930157222003;0.80425831401996;0.393197423405844;0.238643782052466;0.999999999999952; Grasslands;0.119999999999994;1.49999999999993 +10329500;0.002;0.619647411168595;0.472078569810909;0.281571461217218;0.162486716801161;0.999999999999985; Grasslands;0.119999999999998;1.49999999999998 +10336645;0.7136;1.67115153169321;0.754224367012205;0.573118225122399;0.140509562872745;0.716863468770617; Evergreen Needleleaf Forest;NA;NA +10336660;0.7908;1.97301910963347;1.07766027776433;0.592740232522128;0.155014561346345;0.633531725909811; cropland/natural vegetation mosaic;NA;NA +10336740;0.8173;1.90031835856871;0.822022317510461;0.616412847504874;0.134778226861356;0.991500424668114; Woody Savannas;0.160169991506637;1.69830008493362 +10343500;0.899;2.07229596195128;0.964737583860089;0.668533952573963;0.163249837070907;0.999999999999765; Evergreen Needleleaf Forest;0.16999999999996;1.79999999999958 +10348850;0.512;1.20195363235249;0.649418729360029;0.461687133235522;0.13557335547204;0.50973396025689; Grasslands;0.144477577732353;1.6467225693749 +10396000;0.0045;0.758705032204699;0.565572403594026;0.331167528702413;0.1778190074585;0.999126000912633; Grasslands;0.120052439945217;1.4999999999998 +11098000;0.2925;2.08845879298247;1.03780949273399;0.622953579466197;0.0693201317182293;0.99322678723593; Woody Savannas;0.160135464255253;1.69864535744695 +11124500;0.7047;1.81201730993914;0.924783749373159;0.569211658185333;0.12242526957835;0.810057557490203; Woody Savannas;0.167597697700392;1.96591941951371 +11141280;0.4501;2.76689060364182;1.67973843535795;0.682496786296927;0.146355928927575;0.970059933833893; Woody Savannas;0.160299400661634;1.7209580463159 +11143000;0.9458;3.2567347718764;1.52933065746459;0.739471333033719;0.054493405235871;0.805216561272284; Woody Savannas;NA;NA +11148900;0.425;1.83730318481868;1.05175625199061;0.551594639163174;0.117566782202974;0.811333145552234; Woody Savannas;0.160805275115361;1.79419474575924 +11151300;0.1373;1.24015195890477;0.841795173704703;0.479237051219196;0.219741737656454;0.493491928603501; Grasslands;0.152941649906851;2.08835824642425 +11162500;0.821;4.18934738665235;1.5899318139459;0.86307112439375;0.0906308069184338;0.880546433539844; Evergreen Needleleaf Forest;0.175916551923031;1.84336310236883 +11176400;0.353;1.41991360974801;0.876165143004999;0.464354024748625;0.126927011610772;0.719266145011686; Savannas;0.167192661450086;2.20348630150785 +11180500;0.0556;2.35431032986054;1.67566821182077;0.678677089011767;0.315978552771395;0.912436469457925; Woody Savannas;0.161751270610823;1.68248729389143 +11180960;0.2117;2.61805460281746;1.67301700609069;0.724395300567723;0.254996533629779;0.999999999998858; Woody Savannas;0.159999999999817;1.69999999999806 +11224500;0.1452;1.09376919081311;0.678320716994648;0.429112188513337;0.164172214132191;0.34954359267195; Open Shrublands;0.170860924456337;2.37880636379498 +11230500;0.2787;0.506763752775536;0.259213955775022;0.220773464750669;0.0664474277661366;0.36565617243596; Barren or Sparsely Vegetated;0.20059677092849;2.46831967429338 +11237500;0.8069;1.57688118000328;0.667834033866201;0.612876899015823;0.158618884284799;0.801102466128548; Woody Savannas;0.161988975338703;1.71988975338702 +11253310;0.1202;0.863089734240833;0.525380659134136;0.390518712005886;0.163800572348158;0.335355197677328; Grasslands;0.159428073647943;2.10788263214132 +11264500;0.5769;0.793275741147377;0.402805024494363;0.332696200910923;0.0897605994780551;0.528839851570256; Grasslands;0.144508813897989;1.67915777889158 +11266500;0.6741;1.02877872394747;0.510030255437849;0.405997878298848;0.10505524888062;0.468998489129567; Woody Savannas;0.147972572472778;1.67624129103814 +11274500;0.3051;1.27851850971171;0.829391046166388;0.472559799637672;0.190432144829606;0.718144426084802; Savannas;0.156597454835777;2.148212117445 +11274630;0.2803;1.15559163353592;0.706883681139151;0.45683550091126;0.183679585497422;0.633424321884727; Savannas;0.154066208493603;2.07997792737642 +11284400;0.7577;2.0719932884251;0.609740607364196;0.672270177843847;0.0766794623916319;0.590417840647218; Woody Savannas;0.164095821593528;1.74095821593528 +11299600;0.041;1.81443786469017;1.1131420230403;0.581647928093279;0.207342614505029;0.8437113658712; Savannas;0.168437113658696;2.29059795610967 +11381500;0.589;2.01059774080901;1.00777907462694;0.620595648687006;0.171160929357891;0.490515934223698; Evergreen Needleleaf Forest;0.165067195990695;1.85760163875667 +11383500;0.6998;2.36362406276608;1.15037021237244;0.700928504868258;0.174430121831192;0.629317344804107; Evergreen Needleleaf Forest;0.167718625584125;1.86271338400753 +11451100;0.8707;2.2141236488828;1.14949488901084;0.636207011399269;0.0945112691179305;0.984352112494652; Woody Savannas;0.160156478875038;1.70156478875037 +11468500;1;4.76305862680861;1.77657769280235;0.912422452106836;0.0804760627484359;0.999999999999882; Evergreen Needleleaf Forest;0.16999999999998;1.79999999999979 +11473900;0.9609;2.63348691057652;1.40846939169775;0.701223891288369;0.139687407428516;0.571273869493348; Evergreen Needleleaf Forest;0.165959065836095;1.77437028683157 +11475560;0.9922;5.34411087943821;2.12445282713393;0.909561799488599;0.0640448461398645;1; Evergreen Needleleaf Forest;0.17;1.8 +11476600;0.9966;4.94138737760664;2.01571370991028;0.888979936854681;0.0879924652543928;0.99999999999976; Evergreen Needleleaf Forest;0.169999999999959;1.79999999999957 +11478500;0.9953;3.81928713245715;2.05146290328687;0.806710851200292;0.126603662517281;0.887543856654097; Evergreen Needleleaf Forest;0.170994126517579;1.80523306972899 +11480390;0.9744;3.81689377949546;1.90300799613839;0.826250441964589;0.113354512959829;0.965143229628247; Evergreen Needleleaf Forest;0.169651432296249;1.79651432296246 +11481200;0.9378;4.8234575523288;1.493386892792;0.915706668280652;0.111697977175359;0.993617006410477; Evergreen Needleleaf Forest;0.170510639487117;1.80382979615329 +11482500;0.983;4.51387744006438;1.79710808415379;0.874256560693449;0.100323023349911;0.958935828777138; Evergreen Needleleaf Forest;NA;NA +11522500;0.9907;3.61095793717015;2.05640330483778;0.8122561100882;0.185545620643096;0.990774303124197; Evergreen Needleleaf Forest;0.169541377659541;1.79723229093707 +11523200;0.98;2.02664255784056;0.909522551530769;0.689249813645777;0.196088851743;0.663199786114606; Evergreen Needleleaf Forest;0.172788263607402;1.70144143340295 +11528700;0.9905;3.42524494871016;1.66872963867004;0.81336258475843;0.127174131416846;0.964375762613601; Evergreen Needleleaf Forest;0.16964375762612;1.79643757626119 +11532500;0.9993;3.62813886217083;1.57551621841601;0.814102720066942;0.116730442918013;0.999999999999942; Evergreen Needleleaf Forest;0.16999999999999;1.7999999999999 +12010000;0.9981;4.32923672056907;2.3695397252836;0.860577260873547;0.133434078670953;0.607243027914203; Evergreen Needleleaf Forest;0.201420557766845;2.0356541832513 +12013500;0.9896;4.98079631299641;2.74545792733346;0.880342963946733;0.126687736123556;0.783664271865468; Evergreen Needleleaf Forest;0.187306858250712;1.92980143688024 +12020000;0.9964;4.40155349175603;2.59235495336928;0.861865064532087;0.13950290773714;0.558828598635006; Evergreen Needleleaf Forest;0.20529371210914;2.06470284081842 +12025000;0.8617;4.97750583871132;3.11856414260785;0.871181521248055;0.177770161964783;0.680920790400709; Mixed Forests;0.225127747505937;2.18892994602341 +12025700;0.8684;4.30308971517653;2.62203191712528;0.863014588940553;0.163744057923923;0.616624088428145; Mixed Forests;0.21932992707424;2.16997445305677 +12035000;0.9705;4.93135156876329;2.72201936202378;0.882434092359161;0.132442871078532;0.606837928699957; Evergreen Needleleaf Forest;0.201452965703954;2.03589724277956 +12040500;0.9969;3.75000349855693;2.28235348495114;0.825804039691266;0.169795889872185;0.972205997963558; Evergreen Needleleaf Forest;NA;NA +12041200;0.9733;3.46832266282347;2.30598093480182;0.756026780118042;0.208556456816696;0.990856307375669; Evergreen Needleleaf Forest;NA;NA +12043000;1;3.96341825728875;2.4811342547291;0.8444731546655;0.160246292008158;0.999999999999911; Evergreen Needleleaf Forest;0.169999999999985;1.79999999999984 +12048000;0.9881;3.12567142570297;2.27428458818627;0.734066146217154;0.246331123846561;0.940292363935091; Evergreen Needleleaf Forest;0.169859798845701;1.80178511367315 +12054000;0.9947;3.43860108621479;2.47235776230438;0.785712524722857;0.297136932512806;0.999999999999919; Evergreen Needleleaf Forest;0.169999999999986;1.79999999999985 +12056500;1;3.68382625755259;2.7277407019415;0.83453280784627;0.310193882037524;0.999999999999847; Evergreen Needleleaf Forest;0.169999999999974;1.79999999999972 +12073500;1;3.87463487253021;2.43855628607308;0.859942939366415;0.180004382855299;0.943641545261968; Mixed Forests;0.245491323620957;2.36618492715718 +12082500;0.9312;3.45835137055083;2.21476841770041;0.775391810116081;0.225406411546188;0.834438822797545; Evergreen Needleleaf Forest;NA;NA +12092000;0.9239;3.14801874214801;2.10567902704306;0.723508140553561;0.212091888055932;0.825421297877065; Evergreen Needleleaf Forest;NA;NA +12095000;0.9717;4.37782292989303;2.62015549214034;0.867640445908271;0.174008595553055;0.515389209517009; Mixed Forests;0.211552417861487;2.09959509270597 +12114500;0.9922;4.35405533467192;2.90710813567905;0.884479127487193;0.288637268836896;0.99999999999977; Evergreen Needleleaf Forest;0.169999999999961;1.79999999999959 +12115000;0.9948;4.28254170174736;2.86210903629781;0.877436605309698;0.276965694678515;0.999999999999753; Evergreen Needleleaf Forest;0.169999999999958;1.79999999999956 +12115500;0.9922;3.51842223176625;2.03035334383886;0.822857479641182;0.20360915083611;0.927290232947036; Evergreen Needleleaf Forest;0.175816781364217;1.84362586023158 +12117000;1;3.53559729938611;1.87014538919296;0.840820697512864;0.140243336703091;0.999999999999892; Evergreen Needleleaf Forest;0.169999999999982;1.79999999999981 +12141300;0.9816;3.21860332360056;2.29006786151097;0.777077693249198;0.278885827267911;0.952313452446245; Evergreen Needleleaf Forest;0.169045608866921;1.79559359435028 +12143600;0.9978;3.76312617170109;2.6491217806556;0.836027813008218;0.280505384563135;0.762399367977812; Evergreen Needleleaf Forest;0.18900805056175;1.94256037921307 +12144000;0.9983;3.78615144491629;2.59443886318036;0.834496323975553;0.256613833630616;0.727997115201323; Evergreen Needleleaf Forest;0.191760230783874;1.96320173087901 +12145500;1;4.06688072109465;2.57800126922149;0.855451362584088;0.173084593703133;0.692919797956903; Mixed Forests;0.22543358383654;2.21575187877402 +12147500;1;3.76946054454278;2.3464851367504;0.849293083614083;0.230039274641707;0.875054416064896; Evergreen Needleleaf Forest;0.179995646714771;1.87496735036071 +12147600;1;3.90187784492826;2.8647238086607;0.856914171028348;0.318726767261297;0.999999999999606; Evergreen Needleleaf Forest;0.169999999999933;1.79999999999929 +12167000;0.9896;3.92151017806941;2.35963456273323;0.837400662326711;0.19620310072309;0.67321239502167; Evergreen Needleleaf Forest;0.193298030756572;1.97637656392905 +12175500;0.7897;1.99266346050159;1.49734419775058;0.538850128293707;0.231128383358204;0.459891467895559; Grasslands;NA;NA +12178100;0.9211;2.84425132904587;2.12109512651434;0.722009448606416;0.297828462080665;0.877084145629712; Evergreen Needleleaf Forest;0.163854207281474;1.76312524368877 +12186000;0.9801;3.15831410353106;2.36455633889689;0.770550822718146;0.30766410726754;0.918845389923327; Evergreen Needleleaf Forest;0.165942269496155;1.77565361697686 +12189500;0.9465;3.1796409320158;2.27343792699089;0.759429530502437;0.272276270130561;0.807519365153244; Evergreen Needleleaf Forest;0.170291228563415;1.81089991935497 +12358500;0.7376;2.46772009188513;1.85058932592079;0.681864939869034;0.290454496127149;0.708913901889856; Evergreen Needleleaf Forest;NA;NA +12374250;0.7973;2.87802118189041;1.93429655214955;0.757323039115623;0.203973889602829;0.986717450209424; Evergreen Needleleaf Forest;0.169335872510471;1.79601523506283 +12375900;0.6827;2.41165851908815;1.78735329323171;0.696930233355771;0.332312117106296;0.509261709635494; Evergreen Needleleaf Forest;0.145463085481745;1.65277851289028 +12377150;0.8676;1.5157380329565;1.12231312862952;0.467637915418463;0.228698973565882;0.83612205284418; Grasslands;0.128193897357769;1.54916338414651 +12381400;0.9605;2.88049301415744;2.06694364168083;0.758846167427078;0.266326282352591;0.999999999999927; Evergreen Needleleaf Forest;0.169999999999988;1.79999999999987 +12383500;0.9867;2.53491337045759;1.87643602239961;0.703465629479146;0.277997875352513;0.999999999999996; Evergreen Needleleaf Forest;0.169999999999999;1.79999999999999 +12388400;0.9585;2.26772863064327;1.59438105047204;0.673220736698775;0.227222424289311;0.973513896546497; Evergreen Needleleaf Forest;0.168675694827309;1.79205416896375 +12390700;0.9955;3.31098417774674;2.33647687792146;0.802053400675381;0.240649209875871;0.999999999999832; Evergreen Needleleaf Forest;0.169999999999971;1.7999999999997 +12411000;0.9917;4.10379461386996;2.83043153055181;0.868269478734231;0.256108158917151;0.996943164008947; Evergreen Needleleaf Forest;0.170244546879264;1.80183410159444 +12414500;0.9997;4.03768488914987;2.93014359029003;0.85749641762753;0.290844547862601;0.908507552683157; Evergreen Needleleaf Forest;0.177319395785322;1.85489546838986 +12447390;0.9643;1.19995288634616;0.789051457652392;0.489432534608229;0.180429428701463;0.86008012664727; Evergreen Needleleaf Forest;0.163004006332354;1.75802403799407 +12451000;0.8163;1.97791978452127;1.51326665416598;0.582522177404112;0.265615054870327;0.500664558463105; Grasslands;0.144966772076828;1.64980063246089 +12488500;0.9985;2.38121401061629;1.74426011764706;0.706262912058381;0.28009511834015;0.99999999999996; Evergreen Needleleaf Forest;0.169999999999993;1.79999999999993 +13011500;0.8122;1.81084588974842;1.37023863129182;0.605658451170447;0.308239819447317;0.691286367694072; Grasslands;0.135487500109256;1.59105953487235 +13011900;0.7746;1.57437320609269;1.15418711710618;0.539832883347134;0.247726855916539;0.631201277816054; Grasslands;0.138562133648414;1.60697369047793 +13018300;0.8095;2.30962539752425;1.83586790372427;0.6922286794575;0.347739287361605;0.563765570130796; Evergreen Needleleaf Forest;0.14818827850654;1.66912967103924 +13023000;0.6753;1.87364157511906;1.40980701215549;0.611180655898969;0.287211108638697;0.736901998443576; Grasslands;0.133519045536941;1.56800503669256 +13083000;0.0079;1.05500201306964;0.811150974298399;0.428496490773318;0.232974884452021;0.999999999999962; Grasslands;0.119999999999995;1.49999999999994 +13161500;0.0336;0.821447128498224;0.638787821054517;0.354673803722356;0.218202954671976;0.999999999999856; Grasslands;0.119999999999983;1.49999999999978 +13235000;0.8604;1.43593824094104;0.995221402904156;0.514688061224127;0.208678313868378;0.667812825298382; Grasslands;0.136881928051052;1.59654309397191 +13240000;0.8066;1.87788353746893;1.34529779485647;0.596303423041444;0.246180595677445;0.701138461484742; Grasslands;0.134943076925756;1.5896584615545 +13310700;0.9654;1.89139522226435;1.26368893082566;0.622701171133477;0.205221897284803;0.870885819973037; Evergreen Needleleaf Forest;0.165055218717673;1.7750784359278 +13313000;0.9807;1.71293556367413;1.12026992959473;0.600738448253882;0.20050150250705;0.878112517899477; Evergreen Needleleaf Forest;0.16608491649313;1.80266098613676 +13331500;0.6378;2.15693346265809;1.49431877864675;0.642674851310613;0.219379679939265;0.741723259861694; Evergreen Needleleaf Forest;0.158358892725162;1.72888062661878 +13337000;0.9726;3.46497736572959;2.54498084919373;0.799287303086475;0.297921204290153;0.759426241518438; Evergreen Needleleaf Forest;0.182163687778062;1.89218373772028 +13338500;0.8078;3.27154259451552;2.39396180377729;0.781083469477035;0.287994831510102;0.748573222584929; Evergreen Needleleaf Forest;0.169180664061539;1.7245719667753 +13340000;0.8441;3.20826048692301;2.32855320545126;0.768382832643968;0.285570662555941;0.745905561831354; Evergreen Needleleaf Forest;0.170979927140304;1.77676746130574 +13340600;0.998;3.97199590813066;2.93663144884478;0.852585517842381;0.316486241962141;0.854446775842601; Evergreen Needleleaf Forest;0.180096542459354;1.87661698121809 +14020000;0.8556;2.5025039117087;1.79096865948115;0.707012507856638;0.249825129638273;0.88198197263282; Evergreen Needleleaf Forest;0.164099098631629;1.7645945917897 +14092750;0.9807;2.93316803628849;1.9225441867823;0.757176130974677;0.226089300602374;0.999999999999828; Evergreen Needleleaf Forest;0.169999999999971;1.79999999999969 +14096850;0.4959;2.40480406755688;1.28567718656155;0.645244433413523;0.133585946353214;0.513025993589911; Evergreen Needleleaf Forest;0.145790154046557;1.65668488541836 +14137000;0.9887;4.2113687097293;2.6593185189321;0.860333011514749;0.210272764623219;0.894035284408142; Evergreen Needleleaf Forest;0.175087811008382;1.8401139861622 +14138800;1;4.076338433837;2.39381731270637;0.870552471703381;0.201933558813263;1; Evergreen Needleleaf Forest;0.17;1.8 +14138870;1;4.67447340654169;3.34427952300857;0.90179120429456;0.249967163532009;1; Evergreen Needleleaf Forest;0.17;1.8 +14138900;1;4.84831294803205;3.01983053381399;0.908449792744825;0.188340554272852;0.999999999999946; Evergreen Needleleaf Forest;0.169999999999991;1.7999999999999 +14139800;0.9977;4.25159788655508;2.68009533654077;0.875929113131245;0.184739894081358;0.999999999999838; Evergreen Needleleaf Forest;0.169999999999972;1.79999999999971 +14141500;1;4.21949746464748;2.55482202010079;0.872327644213352;0.171230371497622;0.975208697134138; Evergreen Needleleaf Forest;0.171983304229232;1.81487478171916 +14154500;1;4.49097945722547;2.49716695673762;0.894261162806781;0.153583409926847;0.959233328714638; Evergreen Needleleaf Forest;0.173261333702807;1.824460002771 +14158500;0.9963;3.74192877752494;2.34066390982961;0.812729140105172;0.218761149205906;0.999999999999948; Evergreen Needleleaf Forest;0.169999999999991;1.79999999999991 +14158790;1;4.72181362119179;3.19819075579034;0.895469721163015;0.286000497740234;0.999999999999596; Evergreen Needleleaf Forest;0.169999999999931;1.79999999999927 +14166500;0.903;4.51621585735164;2.60863295934061;0.867049380816263;0.135618777433955;0.699205046918288; Mixed Forests;0.225957086165659;2.21890255578443 +14182500;1;4.65830463494032;2.75858663603487;0.894381788142129;0.193598406512628;0.915275613842398; Evergreen Needleleaf Forest;0.176777950892573;1.85083463169423 +14185000;1;4.3036631804558;2.40596791928196;0.885869911735117;0.17277126322966;0.964118436719062; Evergreen Needleleaf Forest;0.172870525062449;1.82152893796831 +14185900;1;4.7311319814895;2.86470220799793;0.900979176587509;0.190576879948589;0.999999999999736; Evergreen Needleleaf Forest;0.169999999999955;1.79999999999953 +14187000;1;4.29081775077726;2.19770102790067;0.86648141837053;0.135645227910055;0.739719252786779; Evergreen Needleleaf Forest;0.190822459777058;1.95616844832793 +14216500;0.9311;3.43062578395127;2.06231561319795;0.802460890943066;0.240477625190657;0.854468648363977; Evergreen Needleleaf Forest;0.169772133708381;1.75634059450889 +14222500;0.9757;4.23563727704488;2.63372465828103;0.880547329703427;0.19388102050559;0.540211184307761; Evergreen Needleleaf Forest;0.206783105255322;2.07587328941479 +14236200;0.981;4.48146157330153;2.2754597465835;0.868109028322577;0.153470186739812;0.772942372654591; Evergreen Needleleaf Forest;0.188164610187595;1.93623457640688 +14301000;0.963;4.4742332737377;2.53866691857648;0.871688745461061;0.143892428782989;0.759989834748208; Evergreen Needleleaf Forest;0.189200813220102;1.94400609915068 +14303200;1;3.25878624168477;1.52092952217451;0.831343339615405;0.0773876667003325;0.999999999999569; Evergreen Needleleaf Forest;0.169999999999927;1.79999999999922 +14305500;0.9943;4.68396233467582;2.4424308168902;0.868955640354853;0.125302035540627;0.642023474735751; Evergreen Needleleaf Forest;0.198638122021126;2.01478591515842 +14306340;0.9933;3.37134542222173;1.69169992568094;0.816131621987596;0.0952809021474182;0.999999999999769; Evergreen Needleleaf Forest;0.169999999999961;1.79999999999958 +14306500;0.9993;3.7719180195097;1.97287401282357;0.859424594928914;0.133281623517335;0.950520591097222; Evergreen Needleleaf Forest;0.1739583527122;1.82968764534145 +14308990;1;4.60764550449176;2.27062086580749;0.901358084807924;0.110966581297728;0.999999999999916; Evergreen Needleleaf Forest;0.169999999999986;1.79999999999985 +14309500;1;4.22790223020142;1.98632519714406;0.883413539975636;0.115740864188229;0.999999999999936; Evergreen Needleleaf Forest;0.169999999999989;1.79999999999989 +14316700;1;4.85965245245316;2.82873460054803;0.914353686881862;0.171175529424384;0.999999999999874; Evergreen Needleleaf Forest;0.169999999999979;1.79999999999977 +14325000;1;4.15073036379494;1.86714822767883;0.873517081967616;0.115976631453161;0.997677467895268; Evergreen Needleleaf Forest;0.170185802568345;1.80139351926252 +14362250;1;4.4303377031505;2.45148883588938;0.868294274758778;0.117102269416005;0.999999999999999; Evergreen Needleleaf Forest;0.17;1.8 +14400000;1;3.21827145434417;1.43086321721779;0.743439833626179;0.0974050148342771;0.999999999999891; Evergreen Needleleaf Forest;0.169999999999981;1.7999999999998 diff --git a/test/test_data/camels_us/camels_attributes_v2.0/readme.txt b/test/test_data/camels_us/camels_attributes_v2.0/readme.txt new file mode 100644 index 00000000..bdbf95a3 --- /dev/null +++ b/test/test_data/camels_us/camels_attributes_v2.0/readme.txt @@ -0,0 +1,35 @@ +### CAMELS ATTRIBUTES HISTORY + +# VERSION 2.0 + +Data described in the published HESS paper - https://doi.org/10.5194/hess-2017-169 + +Major changes/additions: +- geological attributes added -> camels_geol + +Minor changes: +- camels_loc_topo split into camels_name and camels_topo +- all occurrences of "gage" changed to "gauge" +- the name of a few variables slightly changed to increase consistency throughout the data set + +# VERSION 1.0 + +Data described in the HESSD manuscript - https://doi.org/10.5194/hess-2017-169 + +### DATA USAGE AND CITATION + +By using the CAMELS attributes in your publication(s), you agree to cite: + +Addor, N., Newman, A. J., Mizukami, N. and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrology and Earth System Sciences, doi:10.5194/hess-2017-169, 2017. + +By using the CAMELS time series in your publication(s), you agree to cite: + +Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T. and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance, Hydrology and Earth System Sciences, 19, 209–223, doi:10.5194/hess-19-209-2015, 2015. + +### CONTRIBUTE TO CAMELS + +If you are working with a data set covering the CONUS and would like to produce catchment averages for CAMELS, please get in touch. + +We are doing our best to identify and correct errors. If you find unrealistic/suspicious values, please let us know ASAP. Thank you. + +Contacts: Nans Addor (n.addor@uea.ac.uk) & Andy Newman (anewman@ucar.edu) diff --git a/test/test_data/camels_us/hourly/usgs-streamflow-nldas_hourly.nc b/test/test_data/camels_us/hourly/usgs-streamflow-nldas_hourly.nc new file mode 100644 index 00000000..576eb95b Binary files /dev/null and b/test/test_data/camels_us/hourly/usgs-streamflow-nldas_hourly.nc differ diff --git a/test/test_data/camels_us/usgs_streamflow/01022500_streamflow_qc.txt b/test/test_data/camels_us/usgs_streamflow/01022500_streamflow_qc.txt new file mode 100644 index 00000000..9dfe6c64 --- /dev/null +++ b/test/test_data/camels_us/usgs_streamflow/01022500_streamflow_qc.txt @@ -0,0 +1,1096 @@ +01022500 2000 01 01 255.00 A:e +01022500 2000 01 02 272.00 A:e +01022500 2000 01 03 337.00 A +01022500 2000 01 04 359.00 A +01022500 2000 01 05 911.00 A +01022500 2000 01 06 1050.00 A +01022500 2000 01 07 960.00 A +01022500 2000 01 08 771.00 A +01022500 2000 01 09 593.00 A +01022500 2000 01 10 501.00 A +01022500 2000 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