def test_soiling_srr_with_precip(normalized_daily, insolation, times):
precip = pd.Series(index=times, data=0)
precip['2019-01-18 00:00:00-07:00'] = 1
precip['2019-02-20 00:00:00-07:00'] = 1
kwargs = {'reps': 10, 'precipitation_daily': precip}
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
clean_criterion='precip_and_shift',
**kwargs)
assert 0.983270 == pytest.approx(sr, abs=1e-6),\
"Soiling ratio with clean_criterion='precip_and_shift' different from expected"
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
clean_criterion='precip_or_shift',
**kwargs)
assert 0.973228 == pytest.approx(sr, abs=1e-6),\
"Soiling ratio with clean_criterion='precip_or_shift' different from expected"
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
clean_criterion='precip',
**kwargs)
assert 0.976196 == pytest.approx(sr, abs=1e-6),\
"Soiling ratio with clean_criterion='precip' different from expected"
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
clean_criterion='shift',
**kwargs)
assert 0.963133 == pytest.approx(sr, abs=1e-6),\
"Soiling ratio with clean_criterion='shift' different from expected"
def test_soiling_srr_min_interval_length(normalized_daily, insolation):
'Test that a long minimum interval length prevents finding shorter intervals'
with pytest.raises(NoValidIntervalError):
np.random.seed(1977)
# normalized_daily intervals are 25 days long, so min=26 should fail:
_ = soiling_srr(normalized_daily, insolation, confidence_level=68.2,
reps=10, min_interval_length=26)
# but min=24 should be fine:
_ = soiling_srr(normalized_daily, insolation, confidence_level=68.2,
reps=10, min_interval_length=24)
def test_soiling_srr_method(normalized_daily, insolation):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=10,
method='random_clean')
assert 0.918767 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio with method="random_clean" different from expected value'
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=10,
method='perfect_clean')
assert 0.965653 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio with method="perfect_clean" different from expected value'
def test_soiling_srr_clean_threshold(normalized_daily, insolation):
'''Test that clean test_soiling_srr_clean_threshold works with a float and
can cause no soiling intervals to be found'''
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=10,
clean_threshold=0.01)
assert 0.963133 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio with specified clean_threshold different from expected value'
with pytest.raises(NoValidIntervalError):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=10,
clean_threshold=0.1)
def test_soiling_srr_outlier_factor(soiling_normalized_daily,
soiling_insolation):
_, _, info = soiling_srr(soiling_normalized_daily,
soiling_insolation,
reps=1,
outlier_factor=8)
assert len(info['soiling_interval_summary']) == 2,\
'Increasing the outlier_factor did not result in the expected number of soiling intervals'
def test_soiling_srr_recenter_false(normalized_daily, insolation):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=10,
recenter=False)
assert 1 == soiling_info['renormalizing_factor'],\
'Renormalizing factor != 1 with recenter=False'
assert 0.965158 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different than expected when recenter=False'
def test_soiling_srr_method(soiling_normalized_daily, soiling_insolation,
method, expected_sr):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily,
soiling_insolation,
reps=10,
method=method)
assert expected_sr == pytest.approx(sr, abs=1e-6),\
f'Soiling ratio with method="{method}" different from expected value'
def test_soiling_srr_dayscale(normalized_daily, insolation):
'Test that a long dayscale can prevent valid intervals from being found'
with pytest.raises(NoValidIntervalError):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
confidence_level=68.2,
reps=10,
day_scale=90)
def test_soiling_srr_trim(normalized_daily, insolation):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=10,
trim=True)
assert 0.978369 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio with trim=True different from expected value'
assert len(soiling_info['soiling_interval_summary']) == 1,\
'Wrong number of soiling intervals found with trim=True'
def test_soiling_srr_confidence_levels(normalized_daily, insolation):
'Tests SRR with different confidence level settingsf from above'
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, confidence_level=95, reps=10,
exceedance_prob=80.0)
assert np.array([0.957272, 0.964763]) == pytest.approx(sr_ci, abs=1e-6),\
'Confidence interval with confidence_level=95 different than expected'
assert 0.961285 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6),\
'soiling_info["exceedance_level"] different than expected when exceedance_prob=80'
def test_soiling_srr_max_negative_slope_error(normalized_daily, insolation):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=10,
max_relative_slope_error=50.0)
assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, True, False],\
'Soiling interval validity differs from expected when max_relative_slope_error=50.0'
assert 0.952995 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected when max_relative_slope_error=50.0'
def test_soiling_srr_argument_checks(soiling_normalized_daily,
soiling_insolation):
'''
Make sure various argument validation warnings and errors are raised
'''
kwargs = {
'energy_normalized_daily': soiling_normalized_daily,
'insolation_daily': soiling_insolation,
'reps': 10
}
with pytest.warns(UserWarning,
match='An even value of day_scale was passed'):
_ = soiling_srr(day_scale=12, **kwargs)
with pytest.raises(ValueError, match='clean_criterion must be one of'):
_ = soiling_srr(clean_criterion='bad', **kwargs)
with pytest.raises(ValueError, match='Invalid method specification'):
_ = soiling_srr(method='bad', **kwargs)
def test_soiling_srr_kwargs(monkeypatch, normalized_daily, insolation):
'''
Make sure that all soiling_srr parameters get passed on to SRRAnalysis and
SRRAnalysis.run(), i.e. all necessary inputs to SRRAnalysis are provided by
soiling_srr. Done by removing the SRRAnalysis default param values
and making sure everything still runs.
'''
# the __defaults__ attr is the tuple of default values in py3
monkeypatch.delattr(SRRAnalysis.__init__, "__defaults__")
monkeypatch.delattr(SRRAnalysis.run, "__defaults__")
_ = soiling_srr(normalized_daily, insolation, reps=10)
def test_soiling_srr_with_nan_interval(normalized_daily, insolation, times):
'''
Previous versions had a bug which would have raised an error when an entire interval
was NaN. See https://github.com/NREL/rdtools/issues/129
'''
reps = 10
normalized_corrupt = normalized_daily.copy()
normalized_corrupt[26:50] = np.nan
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt, insolation, reps=reps)
assert 0.947416 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected value when an entire interval was NaN'
def test_soiling_srr_consecutive_invalid(soiling_normalized_daily,
soiling_insolation, soiling_times,
method, expected_sr):
reps = 10
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily,
soiling_insolation,
reps=reps,
max_relative_slope_error=20.0,
method=method)
assert expected_sr == pytest.approx(sr, abs=1e-6),\
f'Soiling ratio different from expected value for {method} with consecutive invalid intervals' # noqa: E501
def test_soiling_srr_negative_step(normalized_daily, insolation):
stepped_daily = normalized_daily.copy()
stepped_daily.iloc[37:] = stepped_daily.iloc[25:] - 0.1
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(stepped_daily, insolation, reps=10)
assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, False, True],\
'Soiling interval validity differs from expected when a large negative step\
is incorporated into the data'
assert 0.934927 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected when a large negative step is incorporated into the data'
def test_soiling_srr(normalized_daily, insolation, times):
reps = 10
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=reps)
assert 0.963133 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected value'
assert np.array([0.961054, 0.964019]) == pytest.approx(sr_ci, abs=1e-6),\
'Confidence interval different from expected value'
assert 0.958292 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6),\
'Exceedance level different from expected value'
assert 0.984079 == pytest.approx(soiling_info['renormalizing_factor'], abs=1e-6),\
'Renormalizing factor different from expected value'
assert len(soiling_info['stochastic_soiling_profiles']) == reps,\
'Length of soiling_info["stochastic_soiling_profiles"] different than expected'
assert isinstance(soiling_info['stochastic_soiling_profiles'], list),\
'soiling_info["stochastic_soiling_profiles"] is not a list'
# Check soiling_info['soiling_interval_summary']
expected_summary_columns = ['start', 'end', 'soiling_rate', 'soiling_rate_low', 'soiling_rate_high',
'inferred_start_loss', 'inferred_end_loss', 'length', 'valid']
actual_summary_columns = soiling_info['soiling_interval_summary'].columns.values
for x in actual_summary_columns:
assert x in expected_summary_columns,\
"'{}' not an expected column in soiling_info['soiling_interval_summary']".format(x)
for x in expected_summary_columns:
assert x in actual_summary_columns,\
"'{}' was expected as a column, but not in soiling_info['soiling_interval_summary']".format(x)
assert isinstance(soiling_info['soiling_interval_summary'], pd.DataFrame),\
'soiling_info["soiling_interval_summary"] not a dataframe'
expected_means = pd.Series({'soiling_rate': -0.002617290,
'soiling_rate_low': -0.002828525,
'soiling_rate_high': -0.002396639,
'inferred_start_loss': 1.021514,
'inferred_end_loss': 0.9572880,
'length': 24.0,
'valid': 1.0})
expected_means = expected_means[['soiling_rate', 'soiling_rate_low', 'soiling_rate_high',
'inferred_start_loss', 'inferred_end_loss',
'length', 'valid']]
pd.testing.assert_series_equal(expected_means, soiling_info['soiling_interval_summary'].mean(),
check_exact=False, check_less_precise=6)
# Check soiling_info['soiling_ratio_perfect_clean']
pd.testing.assert_index_equal(soiling_info['soiling_ratio_perfect_clean'].index, times, check_names=False)
assert 0.967170 == pytest.approx(soiling_info['soiling_ratio_perfect_clean'].mean(), abs=1e-6),\
"The mean of soiling_info['soiling_ratio_perfect_clean'] differs from expected"
assert isinstance(soiling_info['soiling_ratio_perfect_clean'], pd.Series),\
'soiling_info["soiling_ratio_perfect_clean"] not a pandas series'
def test_soiling_srr_max_negative_slope_error(soiling_normalized_daily,
soiling_insolation):
np.random.seed(1977)
with pytest.warns(UserWarning, match='20% or more of the daily data'):
sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily,
soiling_insolation,
reps=10,
max_relative_slope_error=45.0)
assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, True, False],\
'Soiling interval validity differs from expected when max_relative_slope_error=45.0'
assert 0.958761 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected when max_relative_slope_error=45.0'
def test_soiling_srr_min_interval_length_default(soiling_normalized_daily,
soiling_insolation, start,
expected_sr):
'''
Make sure that the default value of min_interval_length is 7 days by testing
on a cropped version of the example data
'''
reps = 10
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily[start:],
soiling_insolation[start:],
reps=reps)
assert expected_sr == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected value'
def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation,
soiling_times, clean_criterion, expected_sr):
precip = pd.Series(index=soiling_times, data=0)
precip['2019-01-18 00:00:00-07:00'] = 1
precip['2019-02-20 00:00:00-07:00'] = 1
kwargs = {'reps': 10, 'precipitation_daily': precip}
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily,
soiling_insolation,
clean_criterion=clean_criterion,
**kwargs)
assert expected_sr == pytest.approx(sr, abs=1e-6),\
f"Soiling ratio with clean_criterion='{clean_criterion}' different from expected"
def test_soiling_srr_with_nan_interval(soiling_normalized_daily,
soiling_insolation):
'''
Previous versions had a bug which would have raised an error when an entire interval
was NaN. See https://github.com/NREL/rdtools/issues/129
'''
reps = 10
normalized_corrupt = soiling_normalized_daily.copy()
normalized_corrupt[26:50] = np.nan
np.random.seed(1977)
with pytest.warns(UserWarning, match='20% or more of the daily data'):
sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt,
soiling_insolation,
reps=reps)
assert 0.948792 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected value when an entire interval was NaN'
def test_soiling_srr_negative_step(soiling_normalized_daily,
soiling_insolation):
stepped_daily = soiling_normalized_daily.copy()
stepped_daily.iloc[37:] = stepped_daily.iloc[37:] - 0.1
np.random.seed(1977)
with pytest.warns(UserWarning, match='20% or more of the daily data'):
sr, sr_ci, soiling_info = soiling_srr(stepped_daily,
soiling_insolation,
reps=10)
assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, False, True],\
'Soiling interval validity differs from expected when a large negative step\
is incorporated into the data'
assert 0.936932 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected when a large negative step is incorporated into the data' # noqa: E501
def soiling_info(soiling_normalized_daily, soiling_insolation):
'''
Return results of running soiling_srr.
Returns
-------
calc_info : dict with keys:
['renormalizing_factor', 'exceedance_level',
'stochastic_soiling_profiles', 'soiling_interval_summary',
'soiling_ratio_perfect_clean']
'''
reps = 10
np.random.seed(1977)
sr, sr_ci, calc_info = soiling_srr(soiling_normalized_daily,
soiling_insolation,
reps=reps)
return calc_info
def _srr_soiling(self, energy_normalized_daily, insolation_daily,
**kwargs):
'''
Perform stochastic rate and recovery soiling analysis.
Parameters
---------
energy_normalized_daily : pandas.Series
Time Series of insolation-weighted aggregated normalized PV energy
insolation_daily : pandas.Series
Time Series of insolation, aggregated at same level as energy_normalized_daily
kwargs :
Extra parameters passed to soiling.soiling_srr()
Returns
-------
dict
Soiling results with keys:
'p50_sratio' : The median insolation-weighted soiling ratio
'sratio_confidence_interval' : list of lower and upper bounds of
insolation-weighted soiling ratio
confidence interval
'calc_info' : Dict of detailed results (see soiling.soiling_srr() docs)
'''
from rdtools import soiling
daily_freq = pd.tseries.offsets.Day()
if (energy_normalized_daily.index.freq != daily_freq
or insolation_daily.index.freq != daily_freq):
raise ValueError(
'Soiling SRR analysis requires daily aggregation.')
sr, sr_ci, soiling_info = soiling.soiling_srr(energy_normalized_daily,
insolation_daily,
**kwargs)
srr_results = {
'p50_sratio': sr,
'sratio_confidence_interval': sr_ci,
'calc_info': soiling_info
}
return srr_results
def test_soiling_srr(soiling_normalized_daily, soiling_insolation,
soiling_times):
reps = 10
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily,
soiling_insolation,
reps=reps)
assert 0.964369 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected value'
assert np.array([0.962540, 0.965295]) == pytest.approx(sr_ci, abs=1e-6),\
'Confidence interval different from expected value'
assert 0.960205 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6),\
'Exceedance level different from expected value'
assert 0.984079 == pytest.approx(soiling_info['renormalizing_factor'], abs=1e-6),\
'Renormalizing factor different from expected value'
assert len(soiling_info['stochastic_soiling_profiles']) == reps,\
'Length of soiling_info["stochastic_soiling_profiles"] different than expected'
assert isinstance(soiling_info['stochastic_soiling_profiles'], list),\
'soiling_info["stochastic_soiling_profiles"] is not a list'
# Check soiling_info['soiling_interval_summary']
expected_summary_columns = [
'start', 'end', 'soiling_rate', 'soiling_rate_low',
'soiling_rate_high', 'inferred_start_loss', 'inferred_end_loss',
'length', 'valid'
]
actual_summary_columns = soiling_info[
'soiling_interval_summary'].columns.values
for x in actual_summary_columns:
assert x in expected_summary_columns,\
f"'{x}' not an expected column in soiling_info['soiling_interval_summary']"
for x in expected_summary_columns:
assert x in actual_summary_columns,\
f"'{x}' was expected as a column, but not in soiling_info['soiling_interval_summary']"
assert isinstance(soiling_info['soiling_interval_summary'], pd.DataFrame),\
'soiling_info["soiling_interval_summary"] not a dataframe'
expected_means = pd.Series({
'soiling_rate': -0.002644544,
'soiling_rate_low': -0.002847504,
'soiling_rate_high': -0.002455915,
'inferred_start_loss': 1.020124,
'inferred_end_loss': 0.9566552,
'length': 24.0,
'valid': 1.0
})
expected_means = expected_means[[
'soiling_rate', 'soiling_rate_low', 'soiling_rate_high',
'inferred_start_loss', 'inferred_end_loss', 'length', 'valid'
]]
actual_means = soiling_info['soiling_interval_summary'][
expected_means.index].mean()
pd.testing.assert_series_equal(expected_means,
actual_means,
check_exact=False)
# Check soiling_info['soiling_ratio_perfect_clean']
pd.testing.assert_index_equal(
soiling_info['soiling_ratio_perfect_clean'].index,
soiling_times,
check_names=False)
sr_mean = soiling_info['soiling_ratio_perfect_clean'].mean()
assert 0.968265 == pytest.approx(sr_mean, abs=1e-6),\
"The mean of soiling_info['soiling_ratio_perfect_clean'] differs from expected"
assert isinstance(soiling_info['soiling_ratio_perfect_clean'], pd.Series),\
'soiling_info["soiling_ratio_perfect_clean"] not a pandas series'
