def test_disc_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix')
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.16046346e-02, 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.0, 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
max_zenith=100)
expected = pd.DataFrame(np.array(
[[6.68577449e+03, 1.16046346e-02, 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100)
expected = pd.DataFrame(np.array(
[[7.21238390e+03, 1.00000000e+00, 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_disc_keys():
clearsky_data = clearsky.ineichen(times, tus, linke_turbidity=3)
disc_data = irradiance.disc(clearsky_data['ghi'], ephem_data['zenith'],
ephem_data.index)
assert 'dni' in disc_data.columns
assert 'kt' in disc_data.columns
assert 'airmass' in disc_data.columns
def apply_decomposition_model(weather_df, model, location):
"""
Uses the specified decomposition model to calculate DNI and DHI.
Parameters
----------
weather_df : :pandas:`DataFrame`
Weather DataFrame containing all variables needed to apply the
decomposition model. See model functions for more information.
model : :obj:`str`
Decomposition model to use. Choose from 'reindl', 'erbs' or 'disc'.
location : :pvlib:`Location`
Returns
-------
:pandas:`DataFrame`
DataFrame with DNI and DHI.
"""
solar_position = location.get_solarposition(
weather_df.index, pressure=weather_df['pressure'].mean(),
temperature=weather_df['temp_air'].mean())
if model == 'reindl':
solar_position = location.get_solarposition(
weather_df.index, pressure=weather_df['pressure'].mean(),
temperature=weather_df['temp_air'].mean())
df = reindl(weather_df.ghi, weather_df.i0_h, solar_position.elevation)
df['dni_corrected'] = irradiance.dni(
weather_df['ghi'], df['dhi'], solar_position.zenith,
clearsky_dni=location.get_clearsky(
weather_df.index, solar_position=solar_position).dni,
clearsky_tolerance=1.1,
zenith_threshold_for_zero_dni=88.0,
zenith_threshold_for_clearsky_limit=80.0)
elif model == 'erbs':
df = irradiance.erbs(weather_df.ghi, solar_position.zenith,
weather_df.index)
df['dni_corrected'] = irradiance.dni(
weather_df['ghi'], df['dhi'], solar_position.zenith,
clearsky_dni=location.get_clearsky(
weather_df.index, solar_position=solar_position).dni,
clearsky_tolerance=1.1,
zenith_threshold_for_zero_dni=88.0,
zenith_threshold_for_clearsky_limit=80.0)
elif model == 'disc':
df = irradiance.disc(weather_df.ghi, solar_position.zenith,
weather_df.index, weather_df.pressure.mean())
df['dhi'] = weather_df.ghi - df.dni * pvlib.tools.cosd(
solar_position.zenith)
df['gni'] = df.dni + df.dhi
return df
def test_disc_keys():
clearsky_data = clearsky.ineichen(times, tus, linke_turbidity=3)
disc_data = irradiance.disc(clearsky_data['ghi'], ephem_data['zenith'],
ephem_data.index)
assert 'dni' in disc_data.columns
assert 'kt' in disc_data.columns
assert 'airmass' in disc_data.columns
def test_disc_value():
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
disc_data = irradiance.disc(ghi, zenith, times, pressure=pressure)
assert_almost_equal(disc_data['dni'].values, np.array([830.46, 676.09]), 1)
def test_disc_keys():
clearsky_data = clearsky.ineichen(times, tus, linke_turbidity=3)
disc_data = irradiance.disc(clearsky_data['GHI'], ephem_data['zenith'],
ephem_data.index)
assert 'DNI_gen_DISC' in disc_data.columns
assert 'Kt_gen_DISC' in disc_data.columns
assert 'AM' in disc_data.columns
def test_disc_value():
times = pd.DatetimeIndex(['2014-06-24T12-0700','2014-06-24T18-0700'])
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
disc_data = irradiance.disc(ghi, zenith, times, pressure=pressure)
assert_almost_equal(disc_data['dni'].values,
np.array([830.46, 676.09]), 1)
def test_disc_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix')
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.16046346e-02, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# max_zenith and/or max_airmass keep these results reasonable
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.0, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# still get reasonable values because of max_airmass=12 limit
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
max_zenith=100)
expected = pd.DataFrame(np.array(
[[0., 1.16046346e-02, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# still get reasonable values because of max_airmass=12 limit
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100)
expected = pd.DataFrame(np.array(
[[277.50185968, 1.0, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# max_zenith keeps this result reasonable
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_airmass=100)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.0, 36.39544757]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# allow zenith to be close to 90 and airmass to be infinite
# and we get crazy values
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
max_zenith=100, max_airmass=100)
expected = pd.DataFrame(np.array(
[[6.68577449e+03, 1.16046346e-02, 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# allow min cos zenith to be 0, zenith to be close to 90,
# and airmass to be very big and we get even higher DNI values
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100, max_airmass=100)
expected = pd.DataFrame(np.array(
[[7.21238390e+03, 1., 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def cloud_cover_to_irradiance_clearsky_scaling(cls, cloud_cover, location):
solar_position = location.get_solarposition(cloud_cover.index)
clearsky_irradiance = location.get_clearsky(
cloud_cover.index, model='ineichen', solar_position=solar_position)
ghi = cls.cloud_cover_to_ghi_linear(cloud_cover,
clearsky_irradiance['ghi'])
dni = disc(ghi, solar_position['zenith'], cloud_cover.index)['dni']
dhi = ghi - dni * np.cos(np.radians(solar_position['zenith']))
return pd.DataFrame({'ghi': ghi, 'dni': dni, 'dhi': dhi}).fillna(0)
def test_disc_overirradiance():
columns = ['dni', 'kt', 'airmass']
ghi = np.array([3000])
solar_zenith = np.full_like(ghi, 0)
times = pd.DatetimeIndex(start='2016-07-19 12:00:00', freq='1s',
periods=len(ghi), tz='America/Phoenix')
out = irradiance.disc(ghi=ghi, solar_zenith=solar_zenith,
datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[8.72544336e+02, 1.00000000e+00, 9.99493933e-01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_disc_overirradiance():
columns = ['dni', 'kt', 'airmass']
ghi = np.array([3000])
solar_zenith = np.full_like(ghi, 0)
times = pd.DatetimeIndex(start='2016-07-19 12:00:00', freq='1s',
periods=len(ghi), tz='America/Phoenix')
out = irradiance.disc(ghi=ghi, solar_zenith=solar_zenith,
datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[8.72544336e+02, 1.00000000e+00, 9.99493933e-01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_disc_value(pressure, expected):
# see GH 449 for pressure=None vs. 101325.
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2014-06-24T1200', '2014-06-24T1800'],
tz='America/Phoenix')
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
out = irradiance.disc(ghi, zenith, times, pressure=pressure)
expected_values = np.array(expected)
expected = pd.DataFrame(expected_values, columns=columns, index=times)
# check the pandas dataframe. check_less_precise is weird
assert_frame_equal(out, expected, check_less_precise=True)
# use np.assert_allclose to check values more clearly
assert_allclose(out.values, expected_values, atol=1e-5)
def test_disc_value(pressure, expected):
# see GH 449 for pressure=None vs. 101325.
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2014-06-24T1200', '2014-06-24T1800'],
tz='America/Phoenix')
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
out = irradiance.disc(ghi, zenith, times, pressure=pressure)
expected_values = np.array(expected)
expected = pd.DataFrame(expected_values, columns=columns, index=times)
# check the pandas dataframe. check_less_precise is weird
assert_frame_equal(out, expected, check_less_precise=True)
# use np.assert_allclose to check values more clearly
assert_allclose(out.values, expected_values, atol=1e-5)
def calc_dni_disc(self, time_range, ghi, mypressure=101325):
"""
Compute the dni-value based on given ghi value within a time range using
the DISC algortihm.
Parameter:
==========
time_range: pandas Series - time range.
ghi: pandas Series - with global horizontal irradiance (ghi) >> taken from DWD Forecast. [W/m2]
"""
dni = disc(ghi=ghi, solar_zenith=self.solpos.zenith, datetime_or_doy=time_range)
return dni
def dni(self, time, lat, lon, ghi, pressure):
dni = []
try:
ghi = self.ghi(ghi)
ps = self.pressure(pressure, 'Pa')
zenith = self.zenith(time, lat, lon)
dni = irradiance.disc(ghi, zenith, time, ps).dni.values
# irradiance.dirint(ghi, zenith, time, ps)
except Exception as e:
f = sys.exc_info()[2].tb_frame.f_back
print("error: %s, line %s, in %s" %
(f.f_code.co_filename, str(f.f_lineno), f.f_code.co_name))
print(e)
return (dni)
def test_disc_value():
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2014-06-24T1200', '2014-06-24T1800'],
tz='America/Phoenix')
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
out = irradiance.disc(ghi, zenith, times, pressure=pressure)
expected_values = np.array(
[[830.46567, 0.79742, 0.93505],
[676.09497, 0.63776, 3.02102]])
expected = pd.DataFrame(expected_values, columns=columns, index=times)
# check the pandas dataframe. check_less_precise is weird
assert_frame_equal(out, expected, check_less_precise=True)
# use np.assert_allclose to check values more clearly
assert_allclose(out.values, expected_values, atol=1e-5)
def cloud_cover_to_irradiance_clearsky_scaling(self,
cloud_cover,
method='linear',
**kwargs):
"""
Estimates irradiance from cloud cover in the following steps:
1. Determine clear sky GHI using Ineichen model and
climatological turbidity.
2. Estimate cloudy sky GHI using a function of
cloud_cover e.g.
:py:meth:`~ForecastModel.cloud_cover_to_ghi_linear`
3. Estimate cloudy sky DNI using the DISC model.
4. Calculate DHI from DNI and GHI.
Parameters
----------
cloud_cover : Series
Cloud cover in %.
method : str, default 'linear'
Method for converting cloud cover to GHI.
'linear' is currently the only option.
**kwargs
Passed to the method that does the conversion
Returns
-------
irrads : DataFrame
Estimated GHI, DNI, and DHI.
"""
solpos = self.location.get_solarposition(cloud_cover.index)
cs = self.location.get_clearsky(cloud_cover.index,
model='ineichen',
solar_position=solpos)
method = method.lower()
if method == 'linear':
ghi = self.cloud_cover_to_ghi_linear(cloud_cover, cs['ghi'],
**kwargs)
else:
raise ValueError('invalid method argument')
dni = disc(ghi, solpos['zenith'], cloud_cover.index)['dni']
dhi = ghi - dni * np.cos(np.radians(solpos['zenith']))
irrads = pd.DataFrame({'ghi': ghi, 'dni': dni, 'dhi': dhi}).fillna(0)
return irrads
def _make_pv_forecast(self, forecast) -> pd.DataFrame:
"""Compile the forecast required for PV generation prediction
Uses pvlib to generate solar irradiance predictions.
Arguments:
forecast {pandas.DataFrame} -- DarkSky originated forecast
"""
# Annoyingly, the PV & wind libraries want temperature named differently
pv_forecast = forecast.rename(columns={
'temperature': 'air_temp',
'windSpeed': 'wind_speed',
})
# Use PV lib to get insolation based on the cloud cover reported here
model = GFS()
# Next up, we get hourly solar irradiance using interpolated cloud cover
# We can get this from the clearsky GHI...
if tables in sys.modules:
# We can use Ineichen clear sky model (uses pytables for turbidity)
clearsky = self.pv_location.get_clearsky(pv_forecast.index)
else:
# We can't, so use 'Simplified Solis'
clearsky = self.pv_location.get_clearsky(pv_forecast.index,
model='simplified_solis')
# ... and by knowledge of where the sun is
solpos = self.pv_location.get_solarposition(pv_forecast.index)
ghi = model.cloud_cover_to_ghi_linear(pv_forecast['cloudCover'] * 100,
clearsky['ghi'])
dni = disc(ghi, solpos['zenith'], pv_forecast.index)['dni']
dhi = ghi - dni * np.cos(np.radians(solpos['zenith']))
# Whump it all together and we have our forecast!
pv_forecast['dni'] = dni
pv_forecast['dhi'] = dhi
pv_forecast['ghi'] = ghi
return pv_forecast
def cloud_cover_to_irradiance_clearsky_scaling(self, cloud_cover,
method='linear',
**kwargs):
"""
Estimates irradiance from cloud cover in the following steps:
1. Determine clear sky GHI using Ineichen model and
climatological turbidity.
2. Estimate cloudy sky GHI using a function of
cloud_cover e.g.
:py:meth:`~ForecastModel.cloud_cover_to_ghi_linear`
3. Estimate cloudy sky DNI using the DISC model.
4. Calculate DHI from DNI and DHI.
Parameters
----------
cloud_cover : Series
Cloud cover in %.
method : str
Method for converting cloud cover to GHI.
'linear' is currently the only option.
**kwargs
Passed to the method that does the conversion
Returns
-------
irrads : DataFrame
Estimated GHI, DNI, and DHI.
"""
solpos = self.location.get_solarposition(cloud_cover.index)
cs = self.location.get_clearsky(cloud_cover.index, model='ineichen',
solar_position=solpos)
method = method.lower()
if method == 'linear':
ghi = self.cloud_cover_to_ghi_linear(cloud_cover, cs['ghi'],
**kwargs)
else:
raise ValueError('invalid method argument')
dni = disc(ghi, solpos['zenith'], cloud_cover.index)['dni']
dhi = ghi - dni * np.cos(np.radians(solpos['zenith']))
irrads = pd.DataFrame({'ghi': ghi, 'dni': dni, 'dhi': dhi}).fillna(0)
return irrads
def test_disc_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix')
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times)
expected = pd.DataFrame(np.array([[0.00000000e+00, 1.16046346e-02, 12.0]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# max_zenith and/or max_airmass keep these results reasonable
out = irradiance.disc(ghi=1.0,
solar_zenith=89.99,
datetime_or_doy=times,
min_cos_zenith=0)
expected = pd.DataFrame(np.array([[0.00000000e+00, 1.0, 12.0]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# still get reasonable values because of max_airmass=12 limit
out = irradiance.disc(ghi=1.0,
solar_zenith=89.99,
datetime_or_doy=times,
max_zenith=100)
expected = pd.DataFrame(np.array([[0., 1.16046346e-02, 12.0]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# still get reasonable values because of max_airmass=12 limit
out = irradiance.disc(ghi=1.0,
solar_zenith=89.99,
datetime_or_doy=times,
min_cos_zenith=0,
max_zenith=100)
expected = pd.DataFrame(np.array([[277.50185968, 1.0, 12.0]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# max_zenith keeps this result reasonable
out = irradiance.disc(ghi=1.0,
solar_zenith=89.99,
datetime_or_doy=times,
min_cos_zenith=0,
max_airmass=100)
expected = pd.DataFrame(np.array([[0.00000000e+00, 1.0, 36.39544757]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# allow zenith to be close to 90 and airmass to be infinite
# and we get crazy values
out = irradiance.disc(ghi=1.0,
solar_zenith=89.99,
datetime_or_doy=times,
max_zenith=100,
max_airmass=100)
expected = pd.DataFrame(np.array(
[[6.68577449e+03, 1.16046346e-02, 3.63954476e+01]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
# allow min cos zenith to be 0, zenith to be close to 90,
# and airmass to be very big and we get even higher DNI values
out = irradiance.disc(ghi=1.0,
solar_zenith=89.99,
datetime_or_doy=times,
min_cos_zenith=0,
max_zenith=100,
max_airmass=100)
expected = pd.DataFrame(np.array([[7.21238390e+03, 1., 3.63954476e+01]]),
columns=columns,
index=times)
assert_frame_equal(out, expected)
def time_disc(self):
irradiance.disc(self.clearsky_irradiance.ghi,
self.solar_position.apparent_zenith,
self.times)
def simulate_power_by_station(station_index, surface_tilt, surface_azimuth,
pv_module, tcell_model_parameters, ghi, tamb,
wspd, albedo, days, lead_times, air_mass,
dni_extra, zenith, apparent_zenith, azimuth):
"""
This is the worker function for simulating power at a specified location. This function should be used inside
of `simulate_power` and direct usage is discouraged.
:param station_index: A station index
:param ghi: See `simulate_power`
:param tamb: See `simulate_power`
:param wspd: See `simulate_power`
:param albedo: See `simulate_power`
:param days: See `simulate_power`
:param lead_times: See `simulate_power`
:param air_mass: See `simulate_power`
:param dni_extra: See `simulate_power`
:param zenith: See `simulate_power`
:param apparent_zenith: See `simulate_power`
:param azimuth: See `simulate_power`
:param surface_tilt: See `simulate_power`
:param surface_azimuth: See `simulate_power`
:param pv_module: A PV module name
:param tcell_model_parameters: A cell module name
:return: A list with power, cell temperature, and the effective irradiance
"""
# Sanity check
assert 0 <= station_index < ghi.shape[3], 'Invalid station index'
# Determine the dimensions
num_analogs = ghi.shape[0]
num_lead_times = ghi.shape[1]
num_days = ghi.shape[2]
# Initialization
p_mp = np.zeros((num_analogs, num_lead_times, num_days))
tcell = np.zeros((num_analogs, num_lead_times, num_days))
effective_irradiance = np.zeros((num_analogs, num_lead_times, num_days))
pv_module = pvsystem.retrieve_sam("SandiaMod")[pv_module]
tcell_model_parameters = temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"][
tcell_model_parameters]
for day_index in range(num_days):
for lead_time_index in range(num_lead_times):
# Determine the current time
current_posix = days[day_index] + lead_times[lead_time_index]
current_time = pd.Timestamp(current_posix, tz="UTC", unit='s')
for analog_index in range(num_analogs):
ghi_ = ghi[analog_index, lead_time_index, day_index,
station_index]
if ghi_ == 0:
continue
albedo_ = albedo[analog_index, lead_time_index, day_index,
station_index]
wspd_ = wspd[analog_index, lead_time_index, day_index,
station_index]
tamb_ = tamb[analog_index, lead_time_index, day_index,
station_index]
air_mass_ = air_mass[lead_time_index, day_index, station_index]
dni_extra_ = dni_extra[lead_time_index, day_index,
station_index]
zenith_ = zenith[lead_time_index, day_index, station_index]
apparent_zenith_ = apparent_zenith[lead_time_index, day_index,
station_index]
azimuth_ = azimuth[lead_time_index, day_index, station_index]
##########################################################################################
# #
# Core procedures of simulating power at one location #
# #
##########################################################################################
# Decompose DNI from GHI
dni_dict = irradiance.disc(ghi_, zenith_, current_time)
# Calculate POA sky diffuse
poa_sky_diffuse = irradiance.haydavies(
surface_tilt, surface_azimuth, ghi_, dni_dict["dni"],
dni_extra_, apparent_zenith_, azimuth_)
# Calculate POA ground diffuse
poa_ground_diffuse = irradiance.get_ground_diffuse(
surface_tilt, ghi_, albedo_)
# Calculate angle of incidence
aoi = irradiance.aoi(surface_tilt, surface_azimuth,
apparent_zenith_, azimuth_)
# Calculate POA total
poa_irradiance = irradiance.poa_components(
aoi, dni_dict["dni"], poa_sky_diffuse, poa_ground_diffuse)
# Calculate cell temperature
tcell[analog_index, lead_time_index,
day_index] = pvsystem.temperature.sapm_cell(
poa_irradiance['poa_global'], tamb_, wspd_,
tcell_model_parameters['a'],
tcell_model_parameters['b'],
tcell_model_parameters["deltaT"])
# Calculate effective irradiance
effective_irradiance[
analog_index, lead_time_index,
day_index] = pvsystem.sapm_effective_irradiance(
poa_irradiance['poa_direct'],
poa_irradiance['poa_diffuse'], air_mass_, aoi,
pv_module)
# Calculate power
sapm_out = pvsystem.sapm(
effective_irradiance[analog_index, lead_time_index,
day_index],
tcell[analog_index, lead_time_index, day_index], pv_module)
# Save output to numpy
p_mp[analog_index, lead_time_index,
day_index] = sapm_out["p_mp"]
return [p_mp, tcell, effective_irradiance]
def test_disc_keys(irrad_data, ephem_data):
disc_data = irradiance.disc(irrad_data['ghi'], ephem_data['zenith'],
ephem_data.index)
assert 'dni' in disc_data.columns
assert 'kt' in disc_data.columns
assert 'airmass' in disc_data.columns