def test_dirindex_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
# times don't have any physical relevance
times = pd.DatetimeIndex(['2014-06-24T12-0700','2014-06-24T18-0700'])
ghi = pd.Series([0, 1], index=times)
ghi_clearsky = pd.Series([0, 1], index=times)
dni_clearsky = pd.Series([0, 5], index=times)
solar_zenith = pd.Series([90, 89.99], index=times)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, min_cos_zenith=0)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, max_zenith=90)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, min_cos_zenith=0, max_zenith=100)
expected = pd.Series([nan, 5.], index=times)
assert_series_equal(out, expected)
def test_dirindex_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
# times don't have any physical relevance
times = pd.DatetimeIndex(['2014-06-24T12-0700','2014-06-24T18-0700'])
ghi = pd.Series([0, 1], index=times)
ghi_clearsky = pd.Series([0, 1], index=times)
dni_clearsky = pd.Series([0, 5], index=times)
solar_zenith = pd.Series([90, 89.99], index=times)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, min_cos_zenith=0)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, max_zenith=100)
expected = pd.Series([0., 5.], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, min_cos_zenith=0, max_zenith=100)
expected = pd.Series([0., 5.], index=times)
assert_series_equal(out, expected)
def test_dirindex(times):
ghi = pd.Series([0, 0, 1038.62, 254.53], index=times)
ghi_clearsky = pd.Series(
np.array([0., 79.73860422, 1042.48031487, 257.20751138]),
index=times
)
dni_clearsky = pd.Series(
np.array([0., 316.1949056, 939.95469881, 646.22886049]),
index=times
)
zenith = pd.Series(
np.array([124.0390863, 82.85457044, 10.56413562, 72.41687122]),
index=times
)
pressure = 93193.
tdew = 10.
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky,
zenith, times, pressure=pressure,
temp_dew=tdew)
dirint_close_values = irradiance.dirint(ghi, zenith, times,
pressure=pressure,
use_delta_kt_prime=True,
temp_dew=tdew).values
expected_out = np.array([np.nan, 0., 748.31562753, 630.72592644])
tolerance = 1e-8
assert np.allclose(out, expected_out, rtol=tolerance, atol=0,
equal_nan=True)
tol_dirint = 0.2
assert np.allclose(out.values, dirint_close_values, rtol=tol_dirint, atol=0,
equal_nan=True)
def test_dirindex(times):
ghi = pd.Series([0, 0, 1038.62, 254.53], index=times)
ghi_clearsky = pd.Series(
np.array([0., 79.73860422, 1042.48031487, 257.20751138]),
index=times
)
dni_clearsky = pd.Series(
np.array([0., 316.1949056, 939.95469881, 646.22886049]),
index=times
)
zenith = pd.Series(
np.array([124.0390863, 82.85457044, 10.56413562, 72.41687122]),
index=times
)
pressure = 93193.
tdew = 10.
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky,
zenith, times, pressure=pressure,
temp_dew=tdew)
dirint_close_values = irradiance.dirint(ghi, zenith, times,
pressure=pressure,
use_delta_kt_prime=True,
temp_dew=tdew).values
expected_out = np.array([np.nan, 0., 748.31562753, 630.72592644])
tolerance = 1e-8
assert np.allclose(out, expected_out, rtol=tolerance, atol=0,
equal_nan=True)
tol_dirint = 0.2
assert np.allclose(out.values, dirint_close_values, rtol=tol_dirint, atol=0,
equal_nan=True)
def calc_dni_dirindex(self, time_range, ghi, mypressure=101325, dew_point=None):
"""
Compute the dni-value based on given ghi value within a time range using
the DIRINDEX algortihm.
Parameter:
==========
time_range: pandas Series - time range.
ghi: pandas Series - with global horizontal irradiance (ghi) >> taken from DWD Forecast. [W/m2]
mypressure: reference pressure [Pa]
dew_point: pandas Series of Dew Point [degC]
"""
dni = dirindex(dni_clearsky=self.clearsky.dni, ghi_clearsky=self.clearsky.ghi, ghi=ghi,
times=time_range, pressure=mypressure, zenith=self.solpos.zenith, temp_dew=dew_point)
return dni
def time_dirindex(self):
irradiance.dirindex(self.clearsky_irradiance.ghi,
self.clearsky_irradiance.ghi,
self.clearsky_irradiance.dni,
self.solar_position.apparent_zenith,
self.times)
