minor refactoring and CI update

.github/workflows/tests-coverage.yml

@@ -33,16 +33,16 @@ jobs:
 
     steps:
       - name: Checkout repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v7
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Set up Conda
         if: runner.os == 'Windows'
-        uses: conda-incubator/setup-miniconda@v2
+        uses: conda-incubator/setup-miniconda@v4
         with:
           miniconda-version: "latest"
           python-version: ${{ matrix.python-version }}

pytest.ini

@@ -1,2 +1,3 @@
 [pytest]
-addopts = --doctest-modules
+addopts = --doctest-modules
+testpaths = tests windpowerlib

setup.py

@@ -33,7 +33,6 @@ def read(fname):
             "nbsphinx",
             "numpy",
             "pytest",
-            "pytest-notebook",
             "sphinx >= 1.4",
             "sphinx_rtd_theme",
         ]

tests/test_data_handling.py

@@ -93,7 +93,7 @@ def test_store_turbine_data_from_oedb(self, caplog):
         for fn in os.listdir(self.orig_path):
             t[fn] = os.path.getmtime(os.path.join(self.orig_path, fn))
         with caplog.at_level(logging.WARNING):
-            store_turbine_data_from_oedb()
+            store_turbine_data_from_oedb(threshold=1.0)
         for fn in os.listdir(self.orig_path):
             assert t[fn] < os.path.getmtime(os.path.join(self.orig_path, fn))
         assert "The turbine library data contains too many faulty" not in caplog.text
@@ -151,7 +151,7 @@ def test_wrong_url_load_turbine_data(self):
             ConnectionError,
             match=r"Database \(oep\) connection not successful*",
         ):
-            store_turbine_data_from_oedb("wrong_schema")
+            store_turbine_data_from_oedb(table="wrong_table")
 
     @pytest.mark.skip(reason="Use it to check a persistent ssl error")
     def test_wrong_ssl_connection(self):

tests/test_modelchain.py

@@ -59,6 +59,17 @@ def setup_class(cls):
             ],
         )
 
+    def test_get_closest_height(self):
+        """Test the _get_closest_height helper method."""
+        test_mc = mc.ModelChain(wt.WindTurbine(**self.test_turbine))
+        # Hub height is 100, so 110 is the closest height
+        heights = np.array([10, 80, 110, 150])
+        assert test_mc._get_closest_height(heights) == 110
+
+        # Test when there is an exact match
+        heights = np.array([10, 100, 150])
+        assert test_mc._get_closest_height(heights) == 100
+
     def test_temperature_hub(self):
         # Test modelchain with temperature_model='linear_gradient'
         test_mc = mc.ModelChain(wt.WindTurbine(**self.test_turbine))

windpowerlib/data.py

@@ -133,7 +133,7 @@ def fetch_turbine_data_from_oedb(
 
     """
     # url of OpenEnergy Platform that contains the oedb
-    oep_url = "https://oep.iks.cs.ovgu.de/"
+    oep_url = "https://openenergyplatform.org/"
     url = oep_url + "/api/v0/schema/{}/tables/{}/rows/?".format(schema, table)
 
     # load data

windpowerlib/modelchain.py

@@ -17,7 +17,7 @@
 )
 
 
-class ModelChain(object):
+class ModelChain:
     r"""Model to determine the output of a wind turbine
 
     The ModelChain class provides a standardized, high-level
@@ -163,6 +163,14 @@ def __init__(
         self.hellman_exp = hellman_exp
         self.power_output = None
 
+    def _get_closest_height(self, heights):
+        return heights[
+            min(
+                range(len(heights)),
+                key=lambda i: abs(heights[i] - self.power_plant.hub_height),
+            )
+        ]
+
     def temperature_hub(self, weather_df):
         r"""
         Calculates the temperature of air at hub height.
@@ -200,15 +208,9 @@ def temperature_hub(self, weather_df):
             logging.debug(
                 "Calculating temperature using temperature " "gradient."
             )
-            closest_height = weather_df["temperature"].columns[
-                min(
-                    range(len(weather_df["temperature"].columns)),
-                    key=lambda i: abs(
-                        weather_df["temperature"].columns[i]
-                        - self.power_plant.hub_height
-                    ),
-                )
-            ]
+            closest_height = self._get_closest_height(
+                weather_df["temperature"].columns
+            )
             temperature_hub = temperature.linear_gradient(
                 weather_df["temperature"][closest_height],
                 closest_height,
@@ -273,15 +275,9 @@ def density_hub(self, weather_df):
             logging.debug(
                 "Calculating density using barometric height " "equation."
             )
-            closest_height = weather_df["pressure"].columns[
-                min(
-                    range(len(weather_df["pressure"].columns)),
-                    key=lambda i: abs(
-                        weather_df["pressure"].columns[i]
-                        - self.power_plant.hub_height
-                    ),
-                )
-            ]
+            closest_height = self._get_closest_height(
+                weather_df["pressure"].columns
+            )
             density_hub = density.barometric(
                 weather_df["pressure"][closest_height],
                 closest_height,
@@ -290,15 +286,9 @@ def density_hub(self, weather_df):
             )
         elif self.density_model == "ideal_gas":
             logging.debug("Calculating density using ideal gas equation.")
-            closest_height = weather_df["pressure"].columns[
-                min(
-                    range(len(weather_df["pressure"].columns)),
-                    key=lambda i: abs(
-                        weather_df["pressure"].columns[i]
-                        - self.power_plant.hub_height
-                    ),
-                )
-            ]
+            closest_height = self._get_closest_height(
+                weather_df["pressure"].columns
+            )
             density_hub = density.ideal_gas(
                 weather_df["pressure"][closest_height],
                 closest_height,
@@ -358,15 +348,9 @@ def wind_speed_hub(self, weather_df):
             logging.debug(
                 "Calculating wind speed using logarithmic wind " "profile."
             )
-            closest_height = weather_df["wind_speed"].columns[
-                min(
-                    range(len(weather_df["wind_speed"].columns)),
-                    key=lambda i: abs(
-                        weather_df["wind_speed"].columns[i]
-                        - self.power_plant.hub_height
-                    ),
-                )
-            ]
+            closest_height = self._get_closest_height(
+                weather_df["wind_speed"].columns
+            )
             wind_speed_hub = wind_speed.logarithmic_profile(
                 weather_df["wind_speed"][closest_height],
                 closest_height,
@@ -376,15 +360,9 @@ def wind_speed_hub(self, weather_df):
             )
         elif self.wind_speed_model == "hellman":
             logging.debug("Calculating wind speed using hellman equation.")
-            closest_height = weather_df["wind_speed"].columns[
-                min(
-                    range(len(weather_df["wind_speed"].columns)),
-                    key=lambda i: abs(
-                        weather_df["wind_speed"].columns[i]
-                        - self.power_plant.hub_height
-                    ),
-                )
-            ]
+            closest_height = self._get_closest_height(
+                weather_df["wind_speed"].columns
+            )
             wind_speed_hub = wind_speed.hellman(
                 weather_df["wind_speed"][closest_height],
                 closest_height,
@@ -503,7 +481,7 @@ def run_model(self, weather_df):
         >>> my_weather_df = pd.DataFrame(np.random.rand(2,6),
         ...                           index=pd.date_range('1/1/2012',
         ...                                               periods=2,
-        ...                                               freq='H'),
+        ...                                               freq='h'),
         ...                           columns=[np.array(['wind_speed',
         ...                                              'wind_speed',
         ...                                              'temperature',

windpowerlib/tools.py

@@ -69,12 +69,12 @@ def linear_interpolation_extrapolation(df, target_height):
     ...                                      wind_speed_80m)),
     ...                           index=pd.date_range('1/1/2012',
     ...                                               periods=2,
-    ...                                               freq='H'),
+    ...                                               freq='h'),
     ...                           columns=[np.array(['wind_speed',
     ...                                              'wind_speed']),
     ...                                    np.array([10, 80])])
     >>> value=linear_interpolation_extrapolation(
-    ...     weather_df['wind_speed'], 100)[0]
+    ...     weather_df['wind_speed'], 100).iloc[0]
 
     """
     # find closest heights

windpowerlib/turbine_cluster_modelchain.py

@@ -275,7 +275,7 @@ def run_model(self, weather_df):
         >>> my_weather_df = pd.DataFrame(np.random.rand(2,6),
         ...                           index=pd.date_range('1/1/2012',
         ...                                               periods=2,
-        ...                                               freq='H'),
+        ...                                               freq='h'),
         ...                           columns=[np.array(['wind_speed',
         ...                                              'wind_speed',
         ...                                              'temperature',

windpowerlib/wind_farm.py

@@ -12,7 +12,7 @@
 import warnings
 
 
-class WindFarm(object):
+class WindFarm:
     r"""
     Defines a standard set of wind farm attributes.
 

windpowerlib/wind_turbine.py

@@ -14,7 +14,7 @@
 from typing import NamedTuple
 
 
-class WindTurbine(object):
+class WindTurbine:
     r"""
     Defines a standard set of wind turbine attributes.
 

windpowerlib/wind_turbine_cluster.py

@@ -12,7 +12,7 @@
 import pandas as pd
 
 
-class WindTurbineCluster(object):
+class WindTurbineCluster:
     r"""
     Defines a standard set of wind turbine cluster attributes.