def test_aoi_gt_90(spectrl2_data):
# test that returned irradiance values are non-negative when aoi > 90
# see GH #1348
kwargs, _ = spectrl2_data
kwargs['apparent_zenith'] = 70
kwargs['aoi'] = 130
kwargs['surface_tilt'] = 60
spectra = spectrum.spectrl2(**kwargs)
for key in ['poa_direct', 'poa_global']:
message = f'{key} contains negative values for aoi>90'
assert np.all(spectra[key] >= 0), message
def test_spectrl2_array(spectrl2_data):
# test that supplying arrays instead of scalars works
kwargs, expected = spectrl2_data
kwargs = {k: np.array([v, v, v]) for k, v in kwargs.items()}
actual = spectrum.spectrl2(**kwargs)
assert actual['wavelength'].shape == (122, )
keys = [
'dni_extra', 'dhi', 'dni', 'poa_sky_diffuse', 'poa_ground_diffuse',
'poa_direct', 'poa_global'
]
for key in keys:
assert actual[key].shape == (122, 3)
def test_spectrl2_series(spectrl2_data):
# test that supplying Series instead of scalars works
kwargs, expected = spectrl2_data
kwargs.pop('dayofyear')
index = pd.to_datetime(['2020-03-15 10:45:59'] * 3)
kwargs = {k: pd.Series([v, v, v], index=index) for k, v in kwargs.items()}
actual = spectrum.spectrl2(**kwargs)
assert actual['wavelength'].shape == (122, )
keys = [
'dni_extra', 'dhi', 'dni', 'poa_sky_diffuse', 'poa_ground_diffuse',
'poa_direct', 'poa_global'
]
for key in keys:
assert actual[key].shape == (122, 3)
def test_spectrl2(spectrl2_data):
# compare against output from solar_utils wrapper around NREL spectrl2_2.c
kwargs, expected = spectrl2_data
actual = spectrum.spectrl2(**kwargs)
assert_allclose(expected['wavelength'].values, actual['wavelength'])
assert_allclose(expected['specdif'].values,
actual['dhi'].ravel(),
atol=7e-5)
assert_allclose(expected['specdir'].values,
actual['dni'].ravel(),
atol=1.5e-4)
assert_allclose(expected['specetr'],
actual['dni_extra'].ravel(),
atol=2e-4)
assert_allclose(expected['specglo'],
actual['poa_global'].ravel(),
atol=1e-4)
# for consistency with the technical report.
relative_airmass = atmosphere.get_relative_airmass(solpos.apparent_zenith,
model='kasten1966')
# %%
# With all the necessary inputs in hand we can model spectral irradiance using
# :py:func:`pvlib.spectrum.spectrl2`. Note that because we are calculating
# the spectra for more than one set of conditions, we will get back 2-D
# arrays (one dimension for wavelength, one for time).
spectra = spectrum.spectrl2(
apparent_zenith=solpos.apparent_zenith,
aoi=aoi,
surface_tilt=tilt,
ground_albedo=albedo,
surface_pressure=pressure,
relative_airmass=relative_airmass,
precipitable_water=water_vapor_content,
ozone=ozone,
aerosol_turbidity_500nm=tau500,
)
# %%
# The ``poa_global`` array represents the total spectral irradiance on our
# hypothetical solar panel. Let's plot it against wavelength to recreate
# Figure 5-1A:
plt.figure()
plt.plot(spectra['wavelength'], spectra['poa_global'])
plt.xlim(200, 2700)
plt.ylim(0, 1.8)
def test_dayofyear_missing(spectrl2_data):
# test that not specifying dayofyear with non-pandas inputs raises error
kwargs, expected = spectrl2_data
kwargs.pop('dayofyear')
with pytest.raises(ValueError, match='dayofyear must be specified'):
_ = spectrum.spectrl2(**kwargs)