Merge branch 'release/v0.2.7'

.github/workflows/release.example

@@ -0,0 +1,35 @@
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+# GitHub recommends pinning actions to a commit SHA.
+# To get a newer version, you will need to update the SHA.
+# You can also reference a tag or branch, but the action may change without warning.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build
+      - name: Build package
+        run: python -m build
+      - name: Publish package
+        uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

.github/workflows/release.yml

@@ -46,10 +46,10 @@ jobs:
         run: |
           python3 setup.py sdist --formats=zip
           twine check dist/* --strict
-          python3 -m twine upload dist/*
+          python3 -m twine upload dist/* --verbose
         env:
           TWINE_USERNAME: __token__
-          TWINE_PASSWORD: ${{ secrets.TESTPYPI }}
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
           TWINE_REPOSITORY: testpypi
 
       # Upload to PyPI
@@ -58,10 +58,10 @@ jobs:
         run: |
           python3 setup.py sdist --formats=zip
           twine check dist/* --strict
-          python3 -m twine upload dist/*
+          python3 -m twine upload dist/* --verbose
         env:
           TWINE_USERNAME: __token__
-          TWINE_PASSWORD: ${{ secrets.PYPI }}
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
           TWINE_REPOSITORY: pypi
       # Make sure package is available on pypi
       - name: Sleep for 300s to make release available

pandahub/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.2.6"
+__version__ = "0.2.7"
 
 from pandahub.lib.PandaHub import PandaHub, PandaHubError
 from pandahub.client.PandaHubClient import PandaHubClient

pandahub/lib/PandaHub.py

@@ -1421,6 +1421,7 @@ def bulk_write_timeseries_to_db(self, timeseries, data_type,
                                              data_type,
                                              ts_format=ts_format,
                                              compress_ts_data=compress_ts_data,
+                                             element_index=col,
                                              **args)
             documents.append(doc)
         self.bulk_write_to_db(documents, collection_name=collection_name,

pandahub/lib/database_toolbox.py

@@ -184,11 +184,12 @@ def create_timeseries_document(timeseries,
                 "compressed_ts_data": compress_ts_data}
     document = add_timestamp_info_to_document(document, timeseries, ts_format)
     document = {**document, **kwargs}
+
     if not "_id" in document: # IDs set by users will not be overwritten
         document["_id"] = get_document_hash(document)
+
     if compress_ts_data:
-        document["timeseries_data"] = compress_timeseries_data(timeseries,
-                                                               ts_format)
+        document["timeseries_data"] = compress_timeseries_data(timeseries, ts_format)
     else:
         if ts_format == "timestamp_value":
             document["timeseries_data"] = convert_timeseries_to_subdocuments(timeseries)

pandahub/test/test_client.py

@@ -6,6 +6,7 @@
 
 def test_client_io(phc: PandaHubClient):
     phc.set_active_project("Manderbach")
+
     net = nw.mv_oberrhein()
     phc.write_network_to_db(net, name='mv_oberrhein', overwrite=True)
     net_loaded = phc.get_net_from_db(name='mv_oberrhein')

pandahub/test/test_networks.py

@@ -8,6 +8,24 @@
 from pandapipes.toolbox import nets_equal
 
 
+def test_additional_res_tables(ph):
+    import pandas as pd
+    ph.set_active_project("pytest")
+
+    # reset project aka delete everything
+    db = ph._get_project_database()
+    for cname in db.list_collection_names():
+        db.drop_collection(cname)
+
+    net1 = pp.create_empty_network()
+    net1['res_test'] = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
+    ph.write_network_to_db(net1, 'test')
+    net2 = ph.get_net_from_db('test')
+
+    assert('res_test' in net2)
+    assert(net1.res_test.shape == (2,2))
+
+
 def test_network_io(ph):
     ph.set_active_project("pytest")
     # reset project aka delete everything

pandahub/test/test_timeseries.py

@@ -13,8 +13,8 @@
 def test_from_tutorial(ph):
     ph.set_active_project(project)
     net = nw.simple_mv_open_ring_net()
-    p_mw_profiles = np.random.randint(low=0, high=100, size=(35040, len(net.load))) / 100 * net.load.p_mw.values
-    q_mvar_profiles = np.ones((35040, len(net.load)))
+    p_mw_profiles = np.random.randint(low=0, high=100, size=(35041, len(net.load))) / 100 * net.load.p_mw.values
+    q_mvar_profiles = np.ones((35041, len(net.load)))
     timestamps = pd.date_range(start="01/01/2020", end="31/12/2020", freq="15min")
     p_mw_profiles = pd.DataFrame(p_mw_profiles, index=timestamps)
     weekindex = p_mw_profiles.index[0:(7 * 96)]
@@ -45,10 +45,10 @@ def test_from_tutorial(ph):
                                                            datetime.datetime(2020, 1, 8, 0, 0)))
 
     assert (result.keys() == ['p_mw', 'q_mvar']).all()
-    assert result.size == 70080
+    assert result.size == 70082
     assert result.index.dtype == "<M8[ns]"
     assert np.isclose(result.p_mw.sum(), p_mw_profiles.sum()[0])
-    assert len(week) == 671
+    assert len(week) == 672
     # 17348.07
 
 def test_simbench_sinlge_ts(ph):
@@ -141,28 +141,27 @@ def test_del_single_ts_on_db(ph):
 def test_bulk_ts_on_db(ph):
     ph.set_active_project(project)
     # write bulk cts to db
-    p_mw_profiles = np.random.randint(low=0, high=100, size=(35041, 10))
+    p_mw_profiles = np.random.randint(low=0, high=100, size=(35041, 10)).astype(float)
     timestamps = pd.date_range(start="01/01/2020", end="31/12/2020", freq="15min")
     p_mw_profiles_no_time = pd.DataFrame(copy.deepcopy(p_mw_profiles))
-    p_mw_profiles = pd.DataFrame(p_mw_profiles, index=timestamps).convert_dtypes(int)
-    ph.bulk_write_timeseries_to_db(p_mw_profiles, element_type="load", data_type="p_mw",
-                            netname="bulk_write_net", collection_name="test_collection")
+    p_mw_profiles = pd.DataFrame(p_mw_profiles, index=timestamps)
 
+    ph.bulk_write_timeseries_to_db(p_mw_profiles,
+                                   element_type="load",
+                                   data_type="p_mw",
+                                   netname="bulk_write_net",
+                                   collection_name="test_collection")
 
-    result = ph.bulk_get_timeseries_from_db({"netname": 'bulk_write_net',
-                                                  "element_type": "load",
-                                                  },
-                                                 collection_name="test_collection",
-                                                 pivot_by_column="element_index"
-                                                 )
+    result = ph.bulk_get_timeseries_from_db({"netname": 'bulk_write_net', "element_type": "load",},
+                                            collection_name="test_collection",
+                                            pivot_by_column="element_index")
 
 
 
-    bulk_week = ph.bulk_get_timeseries_from_db({"netname": 'bulk_write_net',
-                                                  "element_type": "load",
-                                                  }, collection_name="test_collection"
-                                                 , pivot_by_column="element_index",
-                                                 timestamp_range=(datetime.datetime(2020, 1, 1, 0, 0),
+    bulk_week = ph.bulk_get_timeseries_from_db({"netname": 'bulk_write_net', "element_type": "load",},
+                                               collection_name="test_collection",
+                                               pivot_by_column="element_index",
+                                               timestamp_range=(datetime.datetime(2020, 1, 1, 0, 0),
                                                                 datetime.datetime(2020, 1, 8, 0, 0)))
 
 
@@ -176,8 +175,8 @@ def test_bulk_ts_on_db(ph):
                                                  pivot_by_column="element_index"
                                                  )
 
-    assert bulk_week.size == 6710
-    assert result_no_time.size == 350400
+    assert bulk_week.size == 6720
+    assert result_no_time.size == 350410
     assert result.sum().sum() == p_mw_profiles.sum().sum()
 
 
@@ -196,10 +195,10 @@ def test_bulk_ts_on_db(ph):
 
 def test_add_metadata(ph):
     ph.set_active_project(project)
-    p_mw_profiles = np.random.randint(low=0, high=100, size=(25, 10))
+    p_mw_profiles = np.random.randint(low=0, high=100, size=(25, 10)).astype(float)
     timestamps = pd.date_range(start="01/01/2020", end="01/02/2020",
                                freq="60min")
-    p_mw_profiles = pd.DataFrame(p_mw_profiles, index=timestamps).convert_dtypes(int)
+    p_mw_profiles = pd.DataFrame(p_mw_profiles, index=timestamps)
     ph.write_timeseries_to_db(p_mw_profiles[0],
                                     netname="test_add_metadata",
                                     element_index=0,
@@ -227,14 +226,14 @@ def test_add_metadata(ph):
     # check for new metadata
     meta_after = ph.get_timeseries_metadata(filter, collection_name="test_collection")
 
-    assert len(meta_after.columns) == 12
-    assert len(meta_before.columns) == 11
+    assert len(meta_after.columns) == 13
+    assert len(meta_before.columns) == 12
 
 def test_bulk_write_with_meta(ph):
     ph.set_active_project(project)
-    p_mw_profiles = np.random.randint(low=0, high=100, size=(97, 10))
+    p_mw_profiles = np.random.randint(low=0, high=100, size=(97, 10)).astype(float)
     timestamps = pd.date_range(start="01/01/2020", end="01/02/2020", freq="15min")
-    p_mw_profiles = pd.DataFrame(p_mw_profiles, index=timestamps).convert_dtypes(int)
+    p_mw_profiles = pd.DataFrame(p_mw_profiles, index=timestamps)
     meta = pd.DataFrame(p_mw_profiles.max(), columns=["max"], dtype=object)
 
     ph.bulk_write_timeseries_to_db(p_mw_profiles, element_type="meta_test_load", data_type="p_mw",

setup.py

@@ -37,7 +37,7 @@
     name='pandahub',
     packages=find_packages(),
     url='https://github.com/e2nIEE/pandahub',
-    version='0.2.6',
+    version='0.2.7',
     include_package_data=True,
     long_description_content_type='text/markdown',
     zip_safe=False,