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Atomize tests with a fixture
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* Upgrade docstring
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@echedey-ls
echedey-ls committed Nov 24, 2022
1 parent 7489f6e commit 0580bd7
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2 changes: 1 addition & 1 deletion pvlib/spectrum/mismatch.py
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Expand Up @@ -256,7 +256,6 @@ def martin_ruiz_spectral_modifier(clearness_index, airmass_absolute,
----------
clearness_index : numeric
Clearness index of the sky.
Should be obtained through :py:func:`~pvlib.irradiance.clearness_index`
airmass_absolute : numeric
Absolute airmass. Give attention to algorithm used (``kasten1966`` is
Expand Down Expand Up @@ -327,6 +326,7 @@ def martin_ruiz_spectral_modifier(clearness_index, airmass_absolute,
pvlib.atmosphere.get_absolute_airmass
pvlib.atmosphere.first_solar_spectral_correction
"""
# Note tests for this function are prefixed with test_martin_ruiz_mm_*

irrad_components = ('direct', 'sky_diffuse', 'ground_diffuse')
# Fitting parameters directly from [1]_
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208 changes: 133 additions & 75 deletions pvlib/tests/test_spectrum.py
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Expand Up @@ -174,115 +174,173 @@ def test_calc_spectral_mismatch_field(spectrl2_data):
assert_allclose(mm, expected, rtol=1e-6)


def test_martin_ruiz_spectral_modifier():
# tests with only cell_type given
# test with scalar values
clearness_index = 0.82
airmass_absolute = 1.2
# Expected values: Direct | Sky diffuse | Ground diffuse
# Do not change order in any 'expected' values list
expected = (1.00197741, 0.71632057, 0.94757498)

result = \
spectrum.martin_ruiz_spectral_modifier(clearness_index,
airmass_absolute,
cell_type='monosi')
assert_approx_equal(result['direct'], expected[0])
assert_approx_equal(result['sky_diffuse'], expected[1])
assert_approx_equal(result['ground_diffuse'], expected[2])

# test NaN in, NaN out
clearness_index = 0.82
airmass_absolute = np.nan
@pytest.fixture
def martin_ruiz_mismatch_data():
# Data to run tests of martin_ruiz_spectral_modifier
kwargs = {
'clearness_index': [0.56, 0.612, 0.664, 0.716, 0.768, 0.82],
'airmass_absolute': [2, 1.8, 1.6, 1.4, 1.2, 1],
'monosi_expected': {
'dir': [1.09149, 1.07275, 1.05432, 1.03622, 1.01842, 1.00093],
'sky': [0.88636, 0.85009, 0.81530, 0.78194, 0.74994, 0.71925],
'gnd': [1.02011, 1.00465, 0.98943, 0.97444, 0.95967, 0.94513]},
'polysi_expected': {
'dir': [1.09166, 1.07280, 1.05427, 1.03606, 1.01816, 1.00058],
'sky': [0.89443, 0.85553, 0.81832, 0.78273, 0.74868, 0.71612],
'gnd': [1.02638, 1.00888, 0.99168, 0.97476, 0.95814, 0.94180]},
'asi_expected': {
'dir': [1.07066, 1.05643, 1.04238, 1.02852, 1.01485, 1.00136],
'sky': [0.94889, 0.91699, 0.88616, 0.85637, 0.82758, 0.79976],
'gnd': [1.03801, 1.02259, 1.00740, 0.99243, 0.97769, 0.96316]},
'monosi_model_params_dict': {
'direct': {'c': 1.029, 'a': -3.13e-1, 'b': 5.24e-3},
'sky_diffuse': {'c': 0.764, 'a': -8.82e-1, 'b': -2.04e-2},
'ground_diffuse': {'c': 0.970, 'a': -2.44e-1, 'b': 1.29e-2}},
'monosi_custom_params_df': pd.DataFrame({
'direct': [1.029, -0.313, 0.00524],
'diffuse_sky': [0.764, -0.882, -0.0204]},
index=('c', 'a', 'b'))
}
return kwargs


def test_martin_ruiz_mm_scalar(martin_ruiz_mismatch_data):
# test scalar input ; only cell_type given
clearness_index = martin_ruiz_mismatch_data['clearness_index'][0]
airmass_absolute = martin_ruiz_mismatch_data['airmass_absolute'][0]
result = spectrum.martin_ruiz_spectral_modifier(clearness_index,
airmass_absolute,
cell_type='asi')

assert_approx_equal(result['direct'],
martin_ruiz_mismatch_data['asi_expected']['dir'][0],
significant=5)
assert_approx_equal(result['sky_diffuse'],
martin_ruiz_mismatch_data['asi_expected']['sky'][0],
significant=5)
assert_approx_equal(result['ground_diffuse'],
martin_ruiz_mismatch_data['asi_expected']['gnd'][0],
significant=5)


def test_martin_ruiz_mm_series(martin_ruiz_mismatch_data):
# test with Series input ; only cell_type given
clearness_index = pd.Series(martin_ruiz_mismatch_data['clearness_index'])
airmass_absolute = pd.Series(martin_ruiz_mismatch_data['airmass_absolute'])
expected = {
'dir': pd.Series(martin_ruiz_mismatch_data['polysi_expected']['dir']),
'sky': pd.Series(martin_ruiz_mismatch_data['polysi_expected']['sky']),
'gnd': pd.Series(martin_ruiz_mismatch_data['polysi_expected']['gnd'])}

result = spectrum.martin_ruiz_spectral_modifier(clearness_index,
airmass_absolute,
cell_type='polysi')
assert_series_equal(result['direct'], expected['dir'], atol=1e-5)
assert_series_equal(result['sky_diffuse'], expected['sky'], atol=1e-5)
assert_series_equal(result['ground_diffuse'], expected['gnd'], atol=1e-5)


def test_martin_ruiz_mm_nans(martin_ruiz_mismatch_data):
# test NaN in, NaN out ; only cell_type given
clearness_index = pd.Series(martin_ruiz_mismatch_data['clearness_index'])
airmass_absolute = pd.Series(martin_ruiz_mismatch_data['airmass_absolute'])
airmass_absolute[:5] = np.nan

result = spectrum.martin_ruiz_spectral_modifier(clearness_index,
airmass_absolute,
cell_type='monosi')
assert result.isna().all()

# test with Series input
clearness_index = pd.Series([0.56, 0.67, 0.80])
airmass_absolute = pd.Series([1.6, 1.4, 1.2])
expected = (
pd.Series([1.088928, 1.050989, 1.008082]), # Direct
pd.Series([0.901327, 0.816901, 0.726754]), # Sky diffuse
pd.Series([1.019917, 0.986947, 0.949899])) # Ground diffuse

result = \
spectrum.martin_ruiz_spectral_modifier(clearness_index,
airmass_absolute,
cell_type='polysi')
assert_series_equal(result['direct'], expected[0], atol=1e-5)
assert_series_equal(result['sky_diffuse'], expected[1], atol=1e-5)
assert_series_equal(result['ground_diffuse'], expected[2], atol=1e-5)
assert np.isnan(result['direct'][:5]).all()
assert not np.isnan(result['direct'][5:]).any()
assert np.isnan(result['sky_diffuse'][:5]).all()
assert not np.isnan(result['sky_diffuse'][5:]).any()
assert np.isnan(result['ground_diffuse'][:5]).all()
assert not np.isnan(result['ground_diffuse'][5:]).any()


def test_martin_ruiz_mm_model_dict(martin_ruiz_mismatch_data):
# test results when giving 'model_parameters' as dict
# test custom quantity of components and its names can be given
clearness_index = pd.Series(martin_ruiz_mismatch_data['clearness_index'])
airmass_absolute = pd.Series(martin_ruiz_mismatch_data['airmass_absolute'])
expected = {
'dir': pd.Series(martin_ruiz_mismatch_data['monosi_expected']['dir']),
'sky': pd.Series(martin_ruiz_mismatch_data['monosi_expected']['sky']),
'gnd': pd.Series(martin_ruiz_mismatch_data['monosi_expected']['gnd'])}
model_parameters = martin_ruiz_mismatch_data['monosi_model_params_dict']

result = spectrum.martin_ruiz_spectral_modifier(
clearness_index,
airmass_absolute,
model_parameters=model_parameters)
assert_allclose(result['direct'], expected['dir'], atol=1e-5)
assert_allclose(result['sky_diffuse'], expected['sky'], atol=1e-5)
assert_allclose(result['ground_diffuse'], expected['gnd'], atol=1e-5)


def test_martin_ruiz_mm_model_df(martin_ruiz_mismatch_data):
# test results when giving 'model_parameters' as DataFrame
# test custom quantity of components and its names can be given
clearness_index = np.array([0.56, 0.612, 0.664, 0.716, 0.768, 0.82])
airmass_absolute = np.array([2, 1.8, 1.6, 1.4, 1.2, 1])
model_parameters = pd.DataFrame({ # monosi values
'direct': [1.029, -0.313, 0.00524],
'diffuse_sky': [0.764, -0.882, -0.0204]},
index=('c', 'a', 'b'))
expected = ( # Direct / Sky diffuse / Ground diffuse
np.array([1.09149, 1.07274, 1.05432, 1.03621, 1.01841, 1.00092]),
np.array([0.88636, 0.85009, 0.81530, 0.78193, 0.74993, 0.71924]))
clearness_index = np.array(martin_ruiz_mismatch_data['clearness_index'])
airmass_absolute = np.array(martin_ruiz_mismatch_data['airmass_absolute'])
model_parameters = martin_ruiz_mismatch_data['monosi_custom_params_df']
expected = {
'dir': np.array(martin_ruiz_mismatch_data['monosi_expected']['dir']),
'sky': np.array(martin_ruiz_mismatch_data['monosi_expected']['sky'])}

result = spectrum.martin_ruiz_spectral_modifier(
clearness_index,
airmass_absolute,
model_parameters=model_parameters)
assert_allclose(result['direct'], expected[0], atol=1e-5)
assert_allclose(result['diffuse_sky'], expected[1], atol=1e-5)
assert_allclose(result['direct'], expected['dir'], atol=1e-5)
assert_allclose(result['diffuse_sky'], expected['sky'], atol=1e-5)


def test_martin_ruiz_mm_userwarning(martin_ruiz_mismatch_data):
# test warning is raised with both 'cell_type' and 'model_parameters'
# test results when giving 'model_parameters' as dict
clearness_index = np.array([0.56, 0.612, 0.664, 0.716, 0.768, 0.82])
airmass_absolute = np.array([2, 1.8, 1.6, 1.4, 1.2, 1])
model_parameters = { # Using 'monosi' values
'direct': {'c': 1.029, 'a': -3.13e-1, 'b': 5.24e-3},
'sky_diffuse': {'c': 0.764, 'a': -8.82e-1, 'b': -2.04e-2},
'ground_diffuse': {'c': 0.970, 'a': -2.44e-1, 'b': 1.29e-2}}
expected = ( # Direct / Sky diffuse / Ground diffuse
np.array([1.09149, 1.07274, 1.05432, 1.03621, 1.01841, 1.00092]),
np.array([0.88636, 0.85009, 0.81530, 0.78193, 0.74993, 0.71924]),
np.array([1.02011, 1.00465, 0.98943, 0.97443, 0.95967, 0.94513]))
clearness_index = pd.Series(martin_ruiz_mismatch_data['clearness_index'])
airmass_absolute = pd.Series(martin_ruiz_mismatch_data['airmass_absolute'])
model_parameters = martin_ruiz_mismatch_data['monosi_model_params_dict']

with pytest.warns(UserWarning,
match='Both "cell_type" and "model_parameters" given! '
'Using provided "model_parameters".'):
result = spectrum.martin_ruiz_spectral_modifier(
_ = spectrum.martin_ruiz_spectral_modifier(
clearness_index,
airmass_absolute,
cell_type='asi',
model_parameters=model_parameters)
assert_allclose(result['direct'], expected[0], atol=1e-5)
assert_allclose(result['sky_diffuse'], expected[1], atol=1e-5)
assert_allclose(result['ground_diffuse'], expected[2], atol=1e-5)


def test_martin_ruiz_spectral_modifier_errors():
# mock values to run errors
clearness_index = 0.75
airmass_absolute = 1.6
# test exception raised when cell_type does not exist in algorithm
def test_martin_ruiz_mm_error_notimplemented(martin_ruiz_mismatch_data):
# test exception is raised when cell_type does not exist in algorithm
clearness_index = np.array(martin_ruiz_mismatch_data['clearness_index'])
airmass_absolute = np.array(martin_ruiz_mismatch_data['airmass_absolute'])

with pytest.raises(NotImplementedError,
match='Cell type parameters not defined in algorithm!'):
_ = spectrum.martin_ruiz_spectral_modifier(clearness_index,
airmass_absolute,
cell_type='')
# test exception raised when missing cell_type and model_parameters


def test_martin_ruiz_mm_error_missing_params(martin_ruiz_mismatch_data):
# test exception is raised when missing cell_type and model_parameters
clearness_index = np.array(martin_ruiz_mismatch_data['clearness_index'])
airmass_absolute = np.array(martin_ruiz_mismatch_data['airmass_absolute'])

with pytest.raises(TypeError,
match='You must pass at least "cell_type" '
'or "model_parameters" as arguments!'):
_ = spectrum.martin_ruiz_spectral_modifier(clearness_index,
airmass_absolute)
# test for error in params keys
clearness_index = 0.74
airmass_absolute = 1.5


def test_martin_ruiz_mm_error_model_keys(martin_ruiz_mismatch_data):
# test exception is raised when in params keys
clearness_index = np.array(martin_ruiz_mismatch_data['clearness_index'])
airmass_absolute = np.array(martin_ruiz_mismatch_data['airmass_absolute'])
model_parameters = {
'direct': {'c': 1.029, 'a': -3.13e-1, 'b': 5.24e-3},
'sky_diffuse': {'c': 0.764, 'a': -8.82e-1, 'b': -2.04e-2},
'ground_diffuse': {'z': 0.970, 'x': -2.44e-1, 'y': 1.29e-2}}
'component_example': {'z': 0.970, 'x': -2.44e-1, 'y': 1.29e-2}}
with pytest.raises(ValueError,
match="You must specify model parameters with keys "
"'a','b','c' for each irradiation component."):
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