Merge branch 'dev' into features/revise_example

Conflicts:
	example/basic_example.py
	example/weather.csv

Author: Birgit Schachler

doc/conf.py

@@ -14,6 +14,8 @@
 
 import sys
 import os
+import sphinx
+from sphinx.errors import VersionRequirementError
 
 # 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
@@ -23,7 +25,10 @@
 # -- General configuration ------------------------------------------------
 
 # If your documentation needs a minimal Sphinx version, state it here.
-#needs_sphinx = '1.0'
+needs_sphinx = '1.4.3'
+if needs_sphinx > sphinx.__display_version__:
+    message = 'This project needs at least Sphinx v%s' % needs_sphinx
+    raise VersionRequirementError(message)
 
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom

example/basic_example.py

@@ -24,7 +24,7 @@
 logging.getLogger().setLevel(logging.DEBUG)
 
 
-def read_weather_data(filename, datetime_column='Unnamed: 0',
+def read_weather_data(filename, datetime_column='time_index',
                       **kwargs):
     r"""
     Fetches weather data from a file.
@@ -61,7 +61,6 @@ def read_weather_data(filename, datetime_column='Unnamed: 0',
 
 # Read weather data from csv
 weather = read_weather_data('weather.csv')
-#weather.index.name = ''
 
 # Specification of the weather data set CoastDat2 (example data)
 data_height = {
@@ -75,14 +74,14 @@ def read_weather_data(filename, datetime_column='Unnamed: 0',
 enerconE126 = {
     'hub_height': 135,
     'd_rotor': 127,
-    'fetch_curve': 'P',  # 'P' for p-curve and 'cp' for cp-curve
+    'fetch_curve': 'p',  # 'p' for p-curve and 'cp' for cp-curve
     'turbine_name': 'ENERCON E 126 7500'}  # Turbine name as in register. Use
                                            # wind_turbine.get_turbine_types()
                                            # for a full list.
 vestasV90 = {
     'hub_height': 105,
     'd_rotor': 90,
-    'fetch_curve': 'P',
+    'fetch_curve': 'p',
     'turbine_name': 'VESTAS V 90 3000'}
 
 # Initialize WindTurbine objects

example/weather.csv

@@ -1,4 +1,4 @@
-,pressure,temp_air,v_wind,z0,temp_air_2,v_wind_2
+time_index,pressure,temp_air,v_wind,z0,temp_air_2,v_wind_2
 2010-01-01 00:00:00+01:00,98405.7,267.6,5.32697,0.15,267.57,7.80697
 2010-01-01 01:00:00+01:00,98382.7,267.6,5.46199,0.15,267.55,7.86199
 2010-01-01 02:00:00+01:00,98362.9,267.61,5.67899,0.15,267.54,8.59899

windpowerlib/data/cp_curves.csv

@@ -42,7 +42,9 @@ def temperature_gradient(temp_air, temp_height, hub_height):
     with:
         T: temperature [K], h: height [m]
 
-    :math:`h_{T,data}` is the height in which the temperature is measured.
+    :math:`h_{T,data}` is the height in which the temperature :math:`T_{air}`
+    is measured and :math:`T_{hub}` is the temperature at hub height
+    :math:`h_{hub}` of the wind turbine.
 
     Assumptions:
 
@@ -62,7 +64,7 @@ def temperature_interpol(temp_air_1, temp_air_2,
     r"""
     Calculates the temperature at hub height by inter- or extrapolation.
 
-    This fuction is carried out when the parameter `temperature_model` of an
+    This function is carried out when the parameter `temperature_model` of an
     instance of the :class:`~.modelchain.ModelChain` class
     is 'interpolation'.
 
@@ -109,19 +111,19 @@ def rho_barometric(pressure, pressure_height, hub_height, temp_hub):
     Calculates the density of air at hub height using the barometric height
     equation.
 
-    This fuction is carried out when the parameter `rho_model` of an instance
+    This function is carried out when the parameter `rho_model` of an instance
     of the :class:`~.modelchain.ModelChain` class is 'barometric'.
 
     Parameters
     ----------
     pressure : pandas.Series or array
-        Pressure in Pa.
+        Air pressure in Pa.
     pressure_height : float
         Height in m for which the parameter `pressure` applies.
     hub_height : float
         Hub height of wind turbine in m.
     temp_hub : pandas.Series or array
-        Temperature at hub height in K.
+        Air temperature at hub height in K.
 
     Returns
     -------
@@ -143,7 +145,7 @@ def rho_barometric(pressure, pressure_height, hub_height, temp_hub):
     :math:`h_{p,data}` is the height of the measurement or model data for
     pressure, :math:`p_0` the ambient air pressure, :math:`\rho_0` the ambient
     density of air, :math:`T_0` the ambient temperature and :math:`T_{hub}` the
-    temperature at hub height.
+    temperature at hub height :math:`h_{hub}`.
 
     Assumptions:
 
@@ -165,19 +167,19 @@ def rho_ideal_gas(pressure, pressure_height, hub_height, temp_hub):
     r"""
     Calculates the density of air at hub height using the ideal gas equation.
 
-    This fuction is carried out when the parameter `rho_model` of an instance
+    This function is carried out when the parameter `rho_model` of an instance
     of the :class:`~.modelchain.ModelChain` class is 'ideal_gas'.
 
     Parameters
     ----------
     pressure : pandas.Series or array
-        Pressure in Pa.
+        Air pressure in Pa.
     pressure_height : float
         Height in m for which the parameter `pressure` applies.
     hub_height : float
         Hub height of wind turbine in m.
     temp_hub : pandas.Series or array
-        Temperature at hub height in K.
+        Air temperature at hub height in K.
 
     Returns
     -------
@@ -198,8 +200,10 @@ def rho_ideal_gas(pressure, pressure_height, hub_height, temp_hub):
     with:
         T: temperature [K], :math:`\rho`: density [kg/m³], p: pressure [Pa]
 
-    :math:`R_s` is the specific gas constant of dry air (287.058 J/(kg*K)) and
-    :math:`p_{hub}` is the pressure at hub height.
+    :math:`h_{p,data}` is the height of the measurement or model data for
+    pressure, :math:`R_s` is the specific gas constant of dry air
+    (287.058 J/(kg*K)) and :math:`p_{hub}` is the pressure at hub height
+    :math:`h_{hub}`.
 
     References
     ----------

windpowerlib/data/P_curves.csv windpowerlib/data/p_curves.csvwindpowerlib/data/P_curves.csv renamed to windpowerlib/data/p_curves.csv

@@ -345,6 +345,8 @@ def run_model(self, weather, data_height):
 
         """
         v_wind = self.v_wind_hub(weather, data_height)
-        rho_hub = self.rho_hub(weather, data_height)
+        rho_hub = None if (self.power_output_model=='p_values' and
+                           self.density_corr==False) \
+                       else self.rho_hub(weather, data_height)
         self.power_output = self.turbine_power_output(v_wind, rho_hub)
         return self

windpowerlib/density.py

@@ -60,9 +60,6 @@ def cp_curve(v_wind, rho_hub, d_rotor, cp_values):
 
     """
     # cp time series
-    # TODO introduce new parameter v_cutout to allow for power output above
-    # maximum wind speed from power coefficient curve (maximum power output
-    # must be limited)
     cp_series = np.interp(v_wind, cp_values.index, cp_values.cp,
                           left=0, right=0)
     power_output = (1 / 8 * rho_hub * d_rotor ** 2 * np.pi
@@ -72,9 +69,8 @@ def cp_curve(v_wind, rho_hub, d_rotor, cp_values):
         series_index = v_wind.index
     except AttributeError:
         series_index = range(1, len(power_output)+1)
-    power_output = pd.Series(data=power_output, index=series_index,
-                             name='feedin_wind_turbine')
-    return power_output
+    return pd.Series(data=power_output, index=series_index,
+                     name='feedin_wind_turbine')
 
 
 def cp_curve_density_corr(v_wind, rho_hub, d_rotor, cp_values):
@@ -116,7 +112,7 @@ def cp_curve_density_corr(v_wind, rho_hub, d_rotor, cp_values):
     p_values = (1 / 8 * 1.225 * d_rotor ** 2 * np.pi *
                 np.power(cp_values.index, 3) * cp_values.cp)
     p_values = pd.DataFrame(data=np.array(p_values), index=cp_values.index,
-                            columns=['P'])
+                            columns=['p'])
     return p_curve_density_corr(v_wind, rho_hub, p_values)
 
 
@@ -133,33 +129,30 @@ def p_curve(p_values, v_wind):
     p_values : pandas.DataFrame
         Power curve of the wind turbine.
         Indices are the wind speeds of the power curve in m/s, the
-        corresponding power values in W are in the column 'P'.
+        corresponding power values in W are in the column 'p'.
     v_wind : pandas.Series or array
         Wind speed at hub height in m/s.
 
     Returns
     -------
     power_output : pandas.Series
-        Electrical power output of the wind turbine in W.
+        Electrical power output of the wind turbine in the unit of `p_values`.
 
     Notes
     -------
     It is assumed that the power output for wind speeds above the maximum
     wind speed given in the power curve is zero.
 
     """
-    # TODO introduce new parameter v_cutout to allow for power output above
-    # maximum wind speed from power curve
-    power_output = np.interp(v_wind, p_values.index, p_values.P,
+    power_output = np.interp(v_wind, p_values.index, p_values.p,
                              left=0, right=0)
     # Set index for time series
     try:
         series_index = v_wind.index
     except AttributeError:
         series_index = range(1, len(power_output)+1)
-    power_output = pd.Series(data=power_output, index=series_index,
-                             name='feedin_wind_turbine')
-    return power_output
+    return pd.Series(data=power_output, index=series_index,
+                     name='feedin_wind_turbine')
 
 
 def p_curve_density_corr(v_wind, rho_hub, p_values):
@@ -178,13 +171,13 @@ def p_curve_density_corr(v_wind, rho_hub, p_values):
         Density of air at hub height in kg/m³.
     p_values : pandas.DataFrame
         Power curve of the wind turbine.
-        The indices are the corresponding wind speeds of the power curve, the
-        power values containing column is called 'P'.
+        Indices are the wind speeds of the power curve in m/s, the
+        corresponding power values in W are in the column 'p'.
 
     Returns
     -------
     power_output : pandas.Series
-        Electrical power output of the wind turbine in W.
+        Electrical power output of the wind turbine in the unit of `p_values`.
 
     Notes
     -----
@@ -204,9 +197,11 @@ def p_curve_density_corr(v_wind, rho_hub, p_values):
         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_{std}`, :math:`P_{std}`),
     :math:`v_{site}` is the density corrected wind speed for the power curve
-    (:math:`v_{site}`, :math:`P_{std}`).
+    (: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
     wind speed given in the power curve is zero.
@@ -224,20 +219,17 @@ def p_curve_density_corr(v_wind, rho_hub, p_values):
             at Reiner Lemoine Institute, 2014, p. 13
 
     """
-    # TODO introduce new parameter v_cutout to allow for power output above
-    # maximum wind speed from power curve
     power_output = [(np.interp(v_wind[i],
                                p_values.index * (1.225 / rho_hub[i])**(
                                    np.interp(p_values.index,
                                              [7.5, 12.5], [1/3, 2/3])),
-                               p_values.P, left=0, right=0))
+                               p_values.p, left=0, right=0))
                     for i in range(len(v_wind))]
 
     # Set index for time series
     try:
         series_index = v_wind.index
     except AttributeError:
         series_index = range(1, len(power_output)+1)
-    power_output = pd.Series(data=power_output, index=series_index,
-                             name='feedin_wind_turbine')
-    return power_output
+    return pd.Series(data=power_output, index=series_index,
+                     name='feedin_wind_turbine')

windpowerlib/modelchain.py

@@ -29,7 +29,7 @@ def setUpClass(self):
         self.test_turbine = {'hub_height': 100,
                              'd_rotor': 80,
                              'turbine_name': 'ENERCON E 126 7500',
-                             'fetch_curve': 'P'}
+                             'fetch_curve': 'p'}
         self.test_wt = wt.WindTurbine(**self.test_turbine)
         self.test_modelchain = {'wind_model': 'hellman',
                                 'rho_model': 'barometric',
@@ -91,14 +91,14 @@ def test_rho_hub(self):
 
     def test_run_model(self):
         # Test with default parameters of modelchain (power curve)
-        power_output_exp = pd.Series(data=[1731887.39768, 3820152.27489])
+        power_output_exp = pd.Series(data=[1731.8874, 3820.15227])
         test_mc = mc.ModelChain(self.test_wt)
         test_mc.run_model(self.weather, self.data_height)
         assert_series_equal(test_mc.power_output, power_output_exp)
 
     def test_different_models(self):
         # Test with density corrected power curve
-        power_output_exp = pd.Series(data=[1430312.76771, 3746075.21279])
+        power_output_exp = pd.Series(data=[1430.31277, 3746.07521])
         self.test_modelchain['density_corr'] = True
         test_wt = wt.WindTurbine(**self.test_turbine)
         test_mc = mc.ModelChain(test_wt, **self.test_modelchain)

windpowerlib/power_output.py

@@ -15,7 +15,7 @@ def setUpClass(self):
         self.d_rotor = 80
         self.cp_values = pd.DataFrame(data={'cp': [0.3, 0.4, 0.5]},
                                       index=[4.0, 5.0, 6.0])
-        self.p_values = pd.DataFrame(data={'P': [300, 400, 500]},
+        self.p_values = pd.DataFrame(data={'p': [300, 400, 500]},
                                      index=[4.0, 5.0, 6.0])
 
     def test_cp_curve(self):