Merge dev into new_data_structure_with_check

Author: uvchik

README.rst

@@ -151,7 +151,7 @@ you have basic knowledge of pandas it is easy to use any kind of data file.
         ),
     }
 
-    my_turbine = WindTurbine(**my_turbine2)
+    my_turbine = WindTurbine(**my_turbine_data)
 
 See the `modelchain_example` for more information.
 

tests/test_power_output.py

@@ -2,13 +2,13 @@
 SPDX-FileCopyrightText: 2019 oemof developer group <[email protected]>
 SPDX-License-Identifier: MIT
 """
+from typing import Dict
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 import pytest
 from numpy.testing import assert_allclose
 from pandas.util.testing import assert_series_equal
-
 from windpowerlib.power_output import (
     power_coefficient_curve,
     power_curve,
@@ -17,27 +17,31 @@
 
 
 class TestPowerOutput:
-    def test_power_coefficient_curve(self):
-        parameters = {
+    def setup_class(self):
+        self.parameters: Dict = {
             "wind_speed": pd.Series(data=[2.0, 5.5, 7.0]),
             "density": pd.Series(data=[1.3, 1.3, 1.3]),
             "rotor_diameter": 80,
             "power_coefficient_curve_wind_speeds": pd.Series([4.0, 5.0, 6.0]),
             "power_coefficient_curve_values": pd.Series([0.3, 0.4, 0.5]),
         }
 
+    def test_power_coefficient_curve_1(self):
         # Test wind_speed as pd.Series with density and power_coefficient_curve
         # as pd.Series and np.array
         power_output_exp = pd.Series(
             data=[0.0, 244615.399, 0.0], name="feedin_power_plant"
         )
         assert_series_equal(
-            power_coefficient_curve(**parameters), power_output_exp
+            power_coefficient_curve(**self.parameters), power_output_exp
         )
-        parameters["density"] = np.array(parameters["density"])
+
+        parameters = self.parameters
+        parameters["density"].to_numpy()
         assert_series_equal(
             power_coefficient_curve(**parameters), power_output_exp
         )
+
         parameters["power_coefficient_curve_values"] = np.array(
             parameters["power_coefficient_curve_values"]
         )
@@ -47,14 +51,22 @@ def test_power_coefficient_curve(self):
         assert_series_equal(
             power_coefficient_curve(**parameters), power_output_exp
         )
+
+    def test_power_coefficient_curve_output_types(self):
+        parameters = self.parameters
         # Test wind_speed as np.array with density and power_coefficient_curve
         # as np.array and pd.Series
+        assert isinstance(power_coefficient_curve(**parameters), pd.Series)
+        parameters["wind_speed"] = np.array(parameters["wind_speed"])
+        assert isinstance(power_coefficient_curve(**parameters), np.ndarray)
+
+    def test_power_coefficient_curve_2(self):
+        parameters = self.parameters
         power_output_exp = np.array([0.0, 244615.399, 0.0])
         parameters["wind_speed"] = np.array(parameters["wind_speed"])
         assert_allclose(
             power_coefficient_curve(**parameters), power_output_exp
         )
-        assert isinstance(power_coefficient_curve(**parameters), np.ndarray)
         parameters["density"] = pd.Series(data=parameters["density"])
         assert_allclose(
             power_coefficient_curve(**parameters), power_output_exp

tests/test_wind_turbine.py

@@ -38,6 +38,23 @@ def test_get_turbine_data_from_file(self):
         with pytest.raises(FileNotFoundError):
             get_turbine_data_from_file(turbine_type="...", path="not_existent")
 
+    def test_get_turbine_types(self, capsys):
+        get_turbine_types()
+        captured = capsys.readouterr()
+        assert "Enercon" in captured.out
+        get_turbine_types("oedb", print_out=False, filter_=False)
+        msg = "`turbine_library` is 'wrong' but must be 'local' or 'oedb'."
+        with pytest.raises(ValueError, match=msg):
+            get_turbine_types("wrong")
+
+    def test_wrong_url_load_turbine_data(self):
+        """Load turbine data from oedb."""
+
+        with pytest.raises(
+            ConnectionError, match="Database connection not successful"
+        ):
+            load_turbine_data_from_oedb("wrong_schema")
+
     @pytest.mark.filterwarnings("ignore:The WindTurbine")
     def test_string_representation_of_wind_turbine(self):
         assert "Wind turbine: ['hub height=120 m'" in repr(WindTurbine(120))

windpowerlib/power_output.py

@@ -176,10 +176,8 @@ def power_curve_density_correction(
 ):
     r"""
     Calculates the turbine power output using a density corrected power curve.
-
     This function is carried out when the parameter `density_correction` of an
     instance of the :class:`~.modelchain.ModelChain` class is True.
-
     Parameters
     ----------
     wind_speed : :pandas:`pandas.Series<series>` or numpy.array
@@ -192,41 +190,33 @@ def power_curve_density_correction(
         `power_curve_wind_speeds`.
     density : :pandas:`pandas.Series<series>` or numpy.array
         Density of air at hub height in kg/m³.
-
     Returns
     -------
     :pandas:`pandas.Series<series>` or numpy.array
         Electrical power output of the wind turbine in W.
         Data type depends on type of `wind_speed`.
-
     Notes
     -----
     The following equation is used for the site specific power curve wind
     speeds [1]_ [2]_ [3]_:
-
     .. math:: v_{site}=v_{std}\cdot\left(\frac{\rho_0}
                        {\rho_{site}}\right)^{p(v)}
-
     with:
         .. math:: p=\begin{cases}
                       \frac{1}{3} & v_{std} \leq 7.5\text{ m/s}\\
                       \frac{1}{15}\cdot v_{std}-\frac{1}{6} & 7.5
                       \text{ m/s}<v_{std}<12.5\text{ m/s}\\
                       \frac{2}{3} & \geq 12.5 \text{ m/s}
                     \end{cases},
-
         v: wind speed [m/s], :math:`\rho`: density [kg/m³]
-
     :math:`v_{std}` is the standard wind speed in the power curve
     (:math:`v_{std}`, :math:`P_{std}`),
     :math:`v_{site}` is the density corrected wind speed for the power curve
     (:math:`v_{site}`, :math:`P_{std}`),
     :math:`\rho_0` is the ambient density (1.225 kg/m³)
     and :math:`\rho_{site}` the density at site conditions (and hub height).
-
     It is assumed that the power output for wind speeds above the maximum
     and below the minimum wind speed given in the power curve is zero.
-
     References
     ----------
     .. [1] Svenningsen, L.: "Power Curve Air Density Correction And Other
@@ -238,14 +228,25 @@ def power_curve_density_correction(
             Variable Scale Simulation Model for Windpower based on the
             Georeferenced Installation Register of Germany". Master's Thesis
             at Reiner Lemoine Institute, 2014, p. 13
-
     """
     if density is None:
         raise TypeError(
             "`density` is None. For the calculation with a "
             + "density corrected power curve density at hub "
             + "height is needed."
         )
+
+    # Convert pd.Series to a numpy array to speed up the interpolation below.
+    if isinstance(wind_speed, pd.Series):
+        # save the indexes for later conversion to pd.Series
+        wind_speed_indexes = wind_speed.index
+        # change the wind speed Series to numpy array
+        wind_speed = wind_speed.values
+        # Set the panda series flag True
+        panda_series = True
+    else:
+        panda_series = False
+
     power_output = [
         (
             np.interp(
@@ -265,11 +266,11 @@ def power_curve_density_correction(
         for i in range(len(wind_speed))
     ]
 
-    # Power_output as pd.Series if wind_speed is pd.Series (else: np.array)
-    if isinstance(wind_speed, pd.Series):
+    # Convert results to the data type of the input data
+    if panda_series:
         power_output = pd.Series(
             data=power_output,
-            index=wind_speed.index,
+            index=wind_speed_indexes,  # Use previously saved wind speed index
             name="feedin_power_plant",
         )
     else:

windpowerlib/wind_turbine.py

@@ -351,7 +351,9 @@ def get_turbine_data_from_file(turbine_type, path):
     r"""
     Fetches turbine data from a csv file.
 
-    Make sure to provide wind speeds in m/s and power in W or
+    See `example_power_curves.csv', `example_power_coefficient_curves.csv` and
+    `example_turbine_data.csv` in example/data for the required format of
+    a csv file. Make sure to provide wind speeds in m/s and power in W or
     convert units after loading the data.
 
     Parameters
@@ -375,9 +377,9 @@ def get_turbine_data_from_file(turbine_type, path):
     --------
     >>> from windpowerlib import wind_turbine
     >>> import os
-    >>> path=os.path.join(os.path.dirname(__file__), '../tests/data',
+    >>> my_path = os.path.join(os.path.dirname(__file__), '../tests/data',
     ...     'power_curves.csv')
-    >>> d3=get_turbine_data_from_file('DUMMY 3', path)
+    >>> d3 = get_turbine_data_from_file('DUMMY 3', my_path)
     >>> print(d3['value'][7])
     18000.0
     >>> print(d3['value'].max())