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run black on soiling.py
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@martin-springer
martin-springer committed Aug 21, 2024
1 parent 8b3fa4a commit efa5042
Showing 1 changed file with 23 additions and 23 deletions.
46 changes: 23 additions & 23 deletions rdtools/soiling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,7 +216,7 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True,
if clean_criterion == "precip_and_shift":
# Detect which cleaning events are associated with rain
# within a 3 day window
precip_event = (
precip_event = (
precip_event.rolling(3, center=True, min_periods=1).apply(any).astype(bool))
df["clean_event"] = df["clean_event_detected"] & precip_event
elif clean_criterion == "precip_or_shift":
Expand Down Expand Up @@ -355,7 +355,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati
"inferred_end_loss": run.pi_norm.median(), # changed from mean/Matt
"slope_err": 10000, # added high dummy start value for later logic/Matt
"valid": False,
"clean_event": run.clean_event.iloc[0], # record of clean events to distiguisih
"clean_event": run.clean_event.iloc[0], # record of clean events to distiguisih
# from other breaks/Matt
"run_loss_baseline": 0.0, # loss from the polyfit over the soiling intercal/Matt
##############################################################
Expand Down Expand Up @@ -540,7 +540,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati
shift_perfect = shift # dont set to one 1 if correcting for a
# downshift (debateable alternative set to 1)
total_down = 0
elif (start_shift < 0) & (prev_shift >= 0):
elif (start_shift < 0) & (prev_shift >= 0):
# negative shift starts the interval, previous shift was cleaning
shift = 0
shift_perfect = 0
Expand Down Expand Up @@ -589,7 +589,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati
# filling the flat intervals may need to be recalculated
# for different assumptions
pm_frame_out.loss_perfect_clean = pm_frame_out.loss_perfect_clean.fillna(1)
# inferred_start_loss was set to the value from poly fit at the beginning of the
# inferred_start_loss was set to the value from poly fit at the beginning of the
# soiling interval
pm_frame_out['loss_inferred_clean'] = pm_frame_out.inferred_start_loss + \
pm_frame_out.days_since_clean * pm_frame_out.run_slope
Expand Down Expand Up @@ -810,7 +810,7 @@ def _calc_monte(self, monte, method="half_norm_clean"):
def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False,
method="half_norm_clean", clean_criterion="shift", precip_threshold=0.01,
min_interval_length=7, exceedance_prob=95.0, confidence_level=68.2, recenter=True,
max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5,
max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5,
neg_shift=False, piecewise=False):
"""
Run the SRR method from beginning to end. Perform the stochastic rate
Expand Down Expand Up @@ -960,21 +960,21 @@ def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False,
+------------------------+----------------------------------------------+
"""
self._calc_daily_df(day_scale=day_scale, clean_threshold=clean_threshold,
recenter=recenter, clean_criterion=clean_criterion,
self._calc_daily_df(day_scale=day_scale, clean_threshold=clean_threshold,
recenter=recenter, clean_criterion=clean_criterion,
precip_threshold=precip_threshold, outlier_factor=outlier_factor,
neg_shift=neg_shift, piecewise=piecewise)

self._calc_result_df(trim=trim, max_relative_slope_error=max_relative_slope_error,
max_negative_step=max_negative_step,
max_negative_step=max_negative_step,
min_interval_length=min_interval_length, neg_shift=neg_shift,
piecewise=piecewise)

self._calc_monte(reps, method=method)

# Calculate the P50 and confidence interval
half_ci = confidence_level / 2.0
result = np.percentile(self.monte_losses,
result = np.percentile(self.monte_losses,
[50, 50.0 - half_ci, 50.0 + half_ci, 100 - exceedance_prob])
P_level = result[3]

Expand All @@ -986,7 +986,7 @@ def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False,
["start", "end", "run_slope", "run_slope_low", "run_slope_high", "inferred_start_loss",
"inferred_end_loss", "inferred_recovery", "inferred_begin_shift", "length", "valid"]
].copy()
intervals_out.rename(columns={"run_slope": "soiling_rate",
intervals_out.rename(columns={"run_slope": "soiling_rate",
"run_slope_high": "soiling_rate_high",
"run_slope_low": "soiling_rate_low"}, inplace=True)

Expand Down Expand Up @@ -1175,7 +1175,7 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitat
reps=reps, day_scale=day_scale, clean_threshold=clean_threshold, trim=trim,
method=method, clean_criterion=clean_criterion, precip_threshold=precip_threshold,
min_interval_length=min_interval_length, exceedance_prob=exceedance_prob,
confidence_level=confidence_level, recenter=recenter,
confidence_level=confidence_level, recenter=recenter,
max_relative_slope_error=max_relative_slope_error, max_negative_step=max_negative_step,
outlier_factor=outlier_factor, neg_shift=neg_shift, piecewise=piecewise)
return sr, sr_ci, soiling_info
Expand Down Expand Up @@ -1269,7 +1269,7 @@ def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confide
return annual_soiling


def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14,
def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14,
max_relative_slope_error=500.0, reps=100000, confidence_level=68.2):
"""
Use Monte Carlo to calculate typical monthly soiling rates.
Expand Down Expand Up @@ -1703,9 +1703,9 @@ def iterative_signal_decomposition(

# Run Kalman Filter for obtaining soiling component
kdf, Ps = self._Kalman_filter_for_SR(
zs_series=soiling_dummy, clip_soiling=clip_soiling,
zs_series=soiling_dummy, clip_soiling=clip_soiling,
prescient_cleaning_events=pce, pruning_iterations=pruning_iterations,
clean_pruning_sensitivity=clean_pruning_sensitivity,
clean_pruning_sensitivity=clean_pruning_sensitivity,
perfect_cleaning=perfect_cleaning, process_noise=process_noise,
renormalize_SR=renormalize_SR)
soiling_ratio.append(kdf.soiling_ratio)
Expand Down Expand Up @@ -1774,7 +1774,7 @@ def iterative_signal_decomposition(
print("Now not assuming perfect cleaning")
elif not perfect_cleaning and (
ic >= max_iterations
or (ic >= change_point + n_steps
or (ic >= change_point + n_steps
and relative_improvement < convergence_criterion)):
if verbose:
if relative_improvement < convergence_criterion:
Expand Down Expand Up @@ -1988,7 +1988,7 @@ def run_bootstrap(
try:
df_out, result_dict = self.iterative_signal_decomposition(
max_iterations=18, order=order, clip_soiling=True, cleaning_sensitivity=dt,
pruning_iterations=1, clean_pruning_sensitivity=pt,
pruning_iterations=1, clean_pruning_sensitivity=pt,
process_noise=process_noise, ffill=ff, degradation_method=degradation_method,
**kwargs)

Expand Down Expand Up @@ -2022,7 +2022,7 @@ def run_bootstrap(

# Save sensitivities and weights for initial model fits
_parameters_n_weights = pd.concat(
[pd.DataFrame(combination_of_parameters), pd.Series(RMSEs),
[pd.DataFrame(combination_of_parameters), pd.Series(RMSEs),
pd.Series(SR_is_one_fraction), pd.Series(weights), pd.Series(small_soiling_signal)],
axis=1, ignore_index=True)

Expand Down Expand Up @@ -2071,7 +2071,7 @@ def run_bootstrap(
list_of_SCs = [
list_of_df_out[m].seasonal_component for m in range(nr_models) if weights[m] > 0]
seasonal_samples = _make_seasonal_samples(
list_of_SCs, sample_nr=sample_nr, min_multiplier=0.8, max_multiplier=1.75,
list_of_SCs, sample_nr=sample_nr, min_multiplier=0.8, max_multiplier=1.75,
max_shift=30)

# ###################### #
Expand Down Expand Up @@ -2105,7 +2105,7 @@ def run_bootstrap(
kdf, results_dict = temporary_cods_instance.iterative_signal_decomposition(
max_iterations=4, order=order, clip_soiling=True, cleaning_sensitivity=dt,
pruning_iterations=1, clean_pruning_sensitivity=pt, process_noise=pn,
renormalize_SR=renormalize_SR, ffill=ffill,
renormalize_SR=renormalize_SR, ffill=ffill,
degradation_method=degradation_method, **kwargs)

# If we can reject the null-hypothesis that there is a unit
Expand Down Expand Up @@ -2333,7 +2333,7 @@ def _Kalman_filter_for_SR(
index=zs_series.index,
dtype=float,
columns=[
"raw_pi", "raw_rates", "smooth_pi", "smooth_rates", "soiling_ratio",
"raw_pi", "raw_rates", "smooth_pi", "smooth_rates", "soiling_ratio",
"soiling_rates", "cleaning_events", "days_since_ce"])
dfk["cleaning_events"] = False

Expand Down Expand Up @@ -2374,7 +2374,7 @@ def _Kalman_filter_for_SR(
# Filter and smoother again
if not ce_0 == cleaning_events:
f = self._initialize_univariate_model(
zs_series, dt, process_noise, measurement_noise, rate_std, zs_std,
zs_series, dt, process_noise, measurement_noise, rate_std, zs_std,
initial_slope)
Xs, Ps, rate_std, zs_std = self._forward_pass(
f, zs_series, rolling_median_7, cleaning_events, soiling_events)
Expand Down Expand Up @@ -2527,7 +2527,7 @@ def _initialize_univariate_model(
def soiling_cods(
energy_normalized_daily, reps=512, confidence_level=68.2, degradation_method="YoY",
process_noise=1e-4, order_alternatives=(("SR", "SC", "Rd"), ("SC", "SR", "Rd")),
cleaning_sensitivity_alternatives=(0.25, 0.75),
cleaning_sensitivity_alternatives=(0.25, 0.75),
clean_pruning_sensitivity_alternatives=(1 / 1.5, 1.5), forward_fill_alternatives=(True, False),
verbose=False, **kwargs):
"""
Expand Down Expand Up @@ -2929,7 +2929,7 @@ def segmented_soiling_period(

R2_improve = R2_piecewise - R2_original
R2_percent_improve = (R2_piecewise / R2_original) - 1
R2_percent_of_possible_improve = R2_improve / (1 - R2_original)
R2_percent_of_possible_improve = R2_improve / (1 - R2_original)
# improvement relative to possible improvement

if len(y) < 45: # tighter requirements for shorter soiling periods
Expand Down

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