def test_logarithmic_interpolation_extrapolation(self):
parameters = {'target_height': 80}
df = pd.DataFrame(data={10: [2.0, 2.0, 3.0],
80: [4.0, 5.0, 6.0],
200: [5.0, 8.0, 10.0]},
index=[0, 1, 2])
# target_height is equal to height given in a column of the DataFrame
exp_output = pd.Series(data=[4.0, 5.0, 6.0])
assert_series_equal(logarithmic_interpolation_extrapolation(
df, **parameters), exp_output)
# target_height is between heights given in the columns of the
# DataFrame
exp_output = pd.Series(
data=[4.61074042165, 6.83222126494, 8.44296168659])
parameters['target_height'] = 140
assert_series_equal(logarithmic_interpolation_extrapolation(
df, **parameters), exp_output)
exp_output = pd.Series(
data=[4.11328333429, 5.16992500144, 6.16992500144])
parameters['target_height'] = 90
assert_series_equal(logarithmic_interpolation_extrapolation(
df, **parameters), exp_output)
# target_height is greater than the heights given in the columns of the
# DataFrame
exp_output = pd.Series(
data=[5.19897784672, 8.59693354015, 10.7959113869])
parameters['target_height'] = 240
assert_series_equal(logarithmic_interpolation_extrapolation(
df, **parameters), exp_output)
# target_height is smaller than the heights given in the columns of the
# DataFrame
exp_output = pd.Series(data=[1.33333333333, 1.0, 2.0])
parameters['target_height'] = 5
assert_series_equal(logarithmic_interpolation_extrapolation(
df, **parameters), exp_output)
def wind_speed_hub(self, weather_df):
r"""
Calculates the wind speed at hub height.
The method specified by the parameter `wind_speed_model` is used.
Parameters
----------
weather_df : :pandas:`pandas.DataFrame<frame>`
DataFrame with time series for wind speed `wind_speed` in m/s and
roughness length `roughness_length` in m.
The columns of the DataFrame are a MultiIndex where the first level
contains the variable name (e.g. wind_speed) and the second level
contains the height at which it applies (e.g. 10, if it was
measured at a height of 10 m). See documentation of
:func:`ModelChain.run_model` for an example on how to create the
weather_df DataFrame.
Returns
-------
:pandas:`pandas.Series<series>` or numpy.array
Wind speed in m/s at hub height.
Notes
-----
If `weather_df` contains wind speeds at different heights the given
wind speed(s) closest to the hub height are used.
"""
if self.power_plant.hub_height in weather_df["wind_speed"]:
wind_speed_hub = weather_df["wind_speed"][
self.power_plant.hub_height]
elif self.wind_speed_model == "logarithmic":
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),
)]
wind_speed_hub = wind_speed.logarithmic_profile(
weather_df["wind_speed"][closest_height],
closest_height,
self.power_plant.hub_height,
weather_df["roughness_length"].iloc[:, 0],
self.obstacle_height,
)
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),
)]
wind_speed_hub = wind_speed.hellman(
weather_df["wind_speed"][closest_height],
closest_height,
self.power_plant.hub_height,
weather_df["roughness_length"].iloc[:, 0],
self.hellman_exp,
)
elif self.wind_speed_model == "interpolation_extrapolation":
logging.debug("Calculating wind speed using linear inter- or "
"extrapolation.")
wind_speed_hub = tools.linear_interpolation_extrapolation(
weather_df["wind_speed"], self.power_plant.hub_height)
elif self.wind_speed_model == "log_interpolation_extrapolation":
logging.debug("Calculating wind speed using logarithmic inter- or "
"extrapolation.")
wind_speed_hub = tools.logarithmic_interpolation_extrapolation(
weather_df["wind_speed"], self.power_plant.hub_height)
else:
raise ValueError(
"'{0}' is an invalid value. ".format(self.wind_speed_model) +
"`wind_speed_model` must be "
"'logarithmic', 'hellman', 'interpolation_extrapolation' " +
"or 'log_interpolation_extrapolation'.")
return wind_speed_hub