Features/make ready for release 022

doc/getting_started.rst

@@ -69,9 +69,9 @@ The basic usage of the windpowerlib is shown in the ModelChain example that is a
 To run the example you need the example weather and turbine data used:
 
  * :download:`Example weather data file <../example/weather.csv>`
- * :download:`Example power curve data file <../example/data/power_curves.csv>`
- * :download:`Example power coefficient curve data file <../example/data/power_coefficient_curves.csv>`
- * :download:`Example nominal power data file <../example/data/turbine_data.csv>`
+ * :download:`Example power curve data file <../windpowerlib/data/default_turbine_data/power_curves.csv>`
+ * :download:`Example power coefficient curve data file <../windpowerlib/data/default_turbine_data/power_coefficient_curves.csv>`
+ * :download:`Example nominal power data file <../windpowerlib/data/default_turbine_data/turbine_data.csv>`
 
 Furthermore, you have to install the windpowerlib and to run the notebook you also need to install `notebook` using pip3. To launch jupyter notebook type ``jupyter notebook`` in the terminal.
 This will open a browser window. Navigate to the directory containing the notebook to open it. See the jupyter notebook quick start guide for more information on `how to install <http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/install.html>`_ and

doc/whatsnew/v0-2-2.rst

@@ -1,4 +1,4 @@
-v0.2.2 ()
+v0.2.2 (February 20, 2024)
 ++++++++++++++++++++++++++++++
 
 * Updated the code basis to work for newer versions of python (support for python 3.6 to
@@ -8,4 +8,5 @@ v0.2.2 ()
 
 Contributors
 ############
- * Birgit Schachler
+ * Birgit Schachler
+ * Florian Maurer

setup.py

@@ -8,7 +8,7 @@ def read(fname):
 
 setup(
     name="windpowerlib",
-    version="0.2.2dev0",
+    version="0.2.2",
     description="Creating time series of wind power plants.",
     url="http://github.com/wind-python/windpowerlib",
     author="oemof developer group",
@@ -28,12 +28,14 @@ def read(fname):
     install_requires=["pandas", "requests"],
     extras_require={
         "dev": [
-            "pytest",
             "jupyter",
-            "sphinx_rtd_theme",
-            "numpy",
             "matplotlib",
+            "nbsphinx",
+            "numpy",
+            "pytest",
             "pytest-notebook",
+            "sphinx >= 1.4",
+            "sphinx_rtd_theme",
         ]
     },
 )

windpowerlib/__init__.py

@@ -1,6 +1,6 @@
 __copyright__ = "Copyright oemof developer group"
 __license__ = "MIT"
-__version__ = "0.2.2dev0"
+__version__ = "0.2.2"
 
 from .wind_turbine import WindTurbine  # noqa: F401
 from .data import get_turbine_types  # noqa: F401

windpowerlib/power_output.py

@@ -178,6 +178,7 @@ def power_curve_density_correction(
     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
@@ -190,25 +191,30 @@ 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)}
+
+    .. 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
@@ -217,17 +223,19 @@ def power_curve_density_correction(
     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
-            Power Curve Options in WindPRO". 1st edition, Aalborg,
-            EMD International A/S , 2010, p. 4
+           Power Curve Options in WindPRO". 1st edition, Aalborg,
+           EMD International A/S , 2010, p. 4
     .. [2] Svenningsen, L.: "Proposal of an Improved Power Curve Correction".
-            EMD International A/S , 2010
+           EMD International A/S , 2010
     .. [3] Biank, M.: "Methodology, Implementation and Validation of a
-            Variable Scale Simulation Model for Windpower based on the
-            Georeferenced Installation Register of Germany". Master's Thesis
-            at Reiner Lemoine Institute, 2014, p. 13
+           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(
@@ -269,6 +277,7 @@ def _get_power_output(
     wind_speed, power_curve_wind_speeds, density, power_curve_values
 ):
     """Get the power output at each timestep using only numpy to speed up performance
+
     Parameters
     ----------
     wind_speed : :numpy:`numpy.ndarray`
@@ -281,10 +290,12 @@ def _get_power_output(
     power_curve_values : :numpy:`numpy.ndarray`
         Power curve values corresponding to wind speeds in
         `power_curve_wind_speeds`.
+
     Returns
     -------
     :numpy:`numpy.array`
         Electrical power output of the wind turbine in W.
+
     """
     # Calculate the power curves for each timestep using vectors
     # NOTE: power_curves_per_ts.shape = [len(wind_speed), len(density)]

windpowerlib/wind_speed.py

@@ -36,7 +36,7 @@ def logarithmic_profile(
         Roughness length.
     obstacle_height : float
         Height of obstacles in the surrounding area of the wind turbine. Set
-        `obstacle_height` to zero for wide spread obstacles. Default: 0.
+        `obstacle_height` to zero for widespread obstacles. Default: 0.
 
     Returns
     -------
@@ -122,14 +122,14 @@ def hellman(
         Hub height of wind turbine.
     roughness_length : :pandas:`pandas.Series<series>` or numpy.array or float
         Roughness length. If given and `hellman_exponent` is None:
-        `hellman_exponent`=1 / ln(hub_height/roughness_length),
-        otherwise `hellman_exponent`=1/7. Default: None.
+        `hellman_exponent` = 1 / ln(hub_height/roughness_length),
+        otherwise `hellman_exponent` = 1/7. Default: None.
     hellman_exponent : None or float
         The Hellman exponent, which combines the increase in wind speed due to
         stability of atmospheric conditions and surface roughness into one
         constant. If None and roughness length is given
-        `hellman_exponent`=1 / ln(hub_height/roughness_length),
-        otherwise `hellman_exponent`=1/7. Default: None.
+        `hellman_exponent` = 1 / ln(hub_height/roughness_length),
+        otherwise `hellman_exponent` = 1/7. Default: None.
 
     Returns
     -------
@@ -152,7 +152,7 @@ def hellman(
 
     For the Hellman exponent :math:`\alpha` many studies use a value of 1/7 for
     onshore and a value of 1/9 for offshore. The Hellman exponent can also
-    be calulated by the following equation [2]_ [3]_:
+    be calculated by the following equation [2]_ [3]_:
 
     .. math:: \alpha=\frac{1}{\ln\left(\frac{h_{hub}}{z_0} \right)}
 
@@ -165,12 +165,12 @@ def hellman(
     References
     ----------
     .. [1] Sharp, E.: "Spatiotemporal disaggregation of GB scenarios depicting
-            increased wind capacity and electrified heat demand in dwellings".
-            UCL, Energy Institute, 2015, p. 83
+           increased wind capacity and electrified heat demand in dwellings".
+           UCL, Energy Institute, 2015, p. 83
     .. [2] Hau, E.: "Windkraftanlagen - Grundlagen, Technik, Einsatz,
-            Wirtschaftlichkeit". 4. Auflage, Springer-Verlag, 2008, p. 517
+           Wirtschaftlichkeit". 4. Auflage, Springer-Verlag, 2008, p. 517
     .. [3] Quaschning V.: "Regenerative Energiesysteme". München, Hanser
-            Verlag, 2011, p. 279
+           Verlag, 2011, p. 279
 
     """
     if hellman_exponent is None: