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fixing flake8 formatting
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nmoyer committed Aug 21, 2024
1 parent efa5042 commit 21da67d
Showing 1 changed file with 42 additions and 38 deletions.
80 changes: 42 additions & 38 deletions rdtools/soiling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -416,7 +416,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati
filt = ((results.run_slope > 0)
| (results.slope_err >= max_relative_slope_error / 100.0)
# |(results.max_neg_step <= -1.0 * max_negative_step)
)
)

results.loc[filt, "run_slope"] = 0
results.loc[filt, "run_slope_low"] = 0
Expand All @@ -434,7 +434,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati
# remove line 389, want to store data for inferred values
# for calculations below
# |results.loc[filt, 'valid'] = False
)
)
results.loc[filt, "run_slope"] = 0
results.loc[filt, "run_slope_low"] = 0
results.loc[filt, "run_slope_high"] = 0
Expand Down Expand Up @@ -505,9 +505,9 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati
begin_infer_shifts = [0]

for date, rs, d, start_shift, changepoint, forward_median in zip(
pm_frame_out.index, pm_frame_out.run_slope, pm_frame_out.days_since_clean,
pm_frame_out.inferred_begin_shift, pm_frame_out.slope_change_event,
pm_frame_out.forward_median):
pm_frame_out.index, pm_frame_out.run_slope, pm_frame_out.days_since_clean,
pm_frame_out.inferred_begin_shift, pm_frame_out.slope_change_event,
pm_frame_out.forward_median):
new_soil = d - day_start
day_start = d

Expand Down Expand Up @@ -641,7 +641,7 @@ def _calc_monte(self, monte, method="half_norm_clean"):

# Raise a warning if there is >20% invalid data
if ((method == "half_norm_clean") or (method == "random_clean")
or (method == "perfect_clean_complex") or (method == "inferred_clean_complex")):
or (method == "perfect_clean_complex") or (method == "inferred_clean_complex")):
valid_fraction = self.analyzed_daily_df["valid"].mean()
if valid_fraction <= 0.8:
warnings.warn('20% or more of the daily data is assigned to invalid soiling '
Expand Down Expand Up @@ -1262,7 +1262,7 @@ def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confide
{"soiling_ratio_median": all_annual_iwsr.quantile(0.5, axis=1),
"soiling_ratio_low": all_annual_iwsr.quantile(0.5 - confidence_level / 2 / 100, axis=1),
"soiling_ratio_high": all_annual_iwsr.quantile(0.5 + confidence_level / 2 / 100, axis=1),
})
})
annual_soiling.index.name = "year"
annual_soiling = annual_soiling.reset_index()

Expand Down Expand Up @@ -1507,10 +1507,10 @@ def __init__(self, energy_normalized_daily):
"represented by NaNs)")

def iterative_signal_decomposition(
self, order=("SR", "SC", "Rd"), degradation_method="YoY", max_iterations=18,
cleaning_sensitivity=0.5, convergence_criterion=5e-3, pruning_iterations=1,
clean_pruning_sensitivity=0.6, soiling_significance=0.75, process_noise=1e-4,
renormalize_SR=None, ffill=True, clip_soiling=True, verbose=False):
self, order=("SR", "SC", "Rd"), degradation_method="YoY", max_iterations=18,
cleaning_sensitivity=0.5, convergence_criterion=5e-3, pruning_iterations=1,
clean_pruning_sensitivity=0.6, soiling_significance=0.75, process_noise=1e-4,
renormalize_SR=None, ffill=True, clip_soiling=True, verbose=False):
"""
Estimates the soiling losses and the degradation rate of a PV system
based on its daily normalized energy, or daily Performance Index (PI).
Expand Down Expand Up @@ -1760,9 +1760,9 @@ def iterative_signal_decomposition(
# step to the next
if ic >= n_steps:
relative_improvement = (convergence_metric[-n_steps - 1] - convergence_metric[-1]
) / convergence_metric[-n_steps - 1]
if perfect_cleaning and (
ic >= max_iterations / 2 or relative_improvement < convergence_criterion):
) / convergence_metric[-n_steps - 1]
if perfect_cleaning and (ic >= max_iterations / 2 or
relative_improvement < convergence_criterion):
# From now on, do not assume perfect cleaning
perfect_cleaning = False
# Reorder to ensure SR first
Expand Down Expand Up @@ -1834,11 +1834,11 @@ def iterative_signal_decomposition(
return df_out, results_dict

def run_bootstrap(
self, 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),
clean_pruning_sensitivity_alternatives=(1 / 1.5, 1.5),
forward_fill_alternatives=(True, False), verbose=False, **kwargs):
self, 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),
clean_pruning_sensitivity_alternatives=(1 / 1.5, 1.5),
forward_fill_alternatives=(True, False), verbose=False, **kwargs):
"""
Bootstrapping of CODS algorithm for uncertainty analysis, inherently accounting
for model and parameter choices.
Expand Down Expand Up @@ -2206,9 +2206,10 @@ def run_bootstrap(
return self.result_df, self.degradation, self.soiling_loss

def _Kalman_filter_for_SR(
self, zs_series, process_noise=1e-4, zs_std=0.05, rate_std=0.005, max_soiling_rates=0.0005,
pruning_iterations=1, clean_pruning_sensitivity=0.6, renormalize_SR=None,
perfect_cleaning=False, prescient_cleaning_events=None, clip_soiling=True, ffill=True):
self, zs_series, process_noise=1e-4, zs_std=0.05, rate_std=0.005,
max_soiling_rates=0.0005, pruning_iterations=1, clean_pruning_sensitivity=0.6,
renormalize_SR=None, perfect_cleaning=False, prescient_cleaning_events=None,
clip_soiling=True, ffill=True):
"""
A function for estimating the underlying Soiling Ratio (SR) and the
rate of change of the SR (the soiling rate), based on a noisy time series
Expand Down Expand Up @@ -2293,7 +2294,7 @@ def _Kalman_filter_for_SR(
cleaning_events = prescient_cleaning_events
else:
if isinstance(prescient_cleaning_events, type(zs_series)) and (
prescient_cleaning_events.sum() > 4):
prescient_cleaning_events.sum() > 4):
if len(prescient_cleaning_events) == len(zs_series):
prescient_cleaning_events = prescient_cleaning_events.copy()
prescient_cleaning_events.index = zs_series.index
Expand Down Expand Up @@ -2475,15 +2476,14 @@ def _set_control_input(self, f, rolling_median_local, index, cleaning_events):
cleaning_events.remove(index)
else: # If the index with the maximum difference is not today...
cleaning_events.remove(index) # ...remove today from the list
if (
moving_diff[max_diff_index] > 0
and index + max_diff_index - HW + 1 not in cleaning_events):
if (moving_diff[max_diff_index] > 0
and index + max_diff_index - HW + 1 not in cleaning_events):
# ...and add the missing day
bisect.insort(cleaning_events, index + max_diff_index - HW + 1)
return u

def _smooth_results(
self, dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning):
self, dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning):
"""Smoother for Kalman Filter estimates. Smooths the Kalaman estimate
between given cleaning events and saves all in DataFrame dfk"""
# Save unsmoothed estimates
Expand All @@ -2509,7 +2509,7 @@ def _smooth_results(
return dfk, Xs, Ps

def _initialize_univariate_model(
self, zs_series, dt, process_noise, measurement_noise, rate_std, zs_std, initial_slope):
self, zs_series, dt, process_noise, measurement_noise, rate_std, zs_std, initial_slope):
"""Initializes the univariate Kalman Filter model, using the filterpy
package"""
f = KalmanFilter(dim_x=2, dim_z=1)
Expand All @@ -2526,10 +2526,11 @@ 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),
clean_pruning_sensitivity_alternatives=(1 / 1.5, 1.5), forward_fill_alternatives=(True, False),
verbose=False, **kwargs):
process_noise=1e-4, order_alternatives=(
("SR", "SC", "Rd"), ("SC", "SR", "Rd")),
cleaning_sensitivity_alternatives=(0.25, 0.75),
clean_pruning_sensitivity_alternatives=(1 / 1.5, 1.5),
forward_fill_alternatives=(True, False), verbose=False, **kwargs):
"""
Functional wrapper for :py:class:`~rdtools.soiling.CODSAnalysis` and its
subroutine :py:func:`~rdtools.soiling.CODSAnalysis.run_bootstrap`. Runs
Expand Down Expand Up @@ -2647,7 +2648,8 @@ def soiling_cods(

CODS = CODSAnalysis(energy_normalized_daily)

CODS.run_bootstrap(reps=reps, confidence_level=confidence_level, verbose=verbose,
CODS.run_bootstrap(
reps=reps, confidence_level=confidence_level, verbose=verbose,
degradation_method=degradation_method, process_noise=process_noise,
order_alternatives=order_alternatives,
cleaning_sensitivity_alternatives=cleaning_sensitivity_alternatives,
Expand Down Expand Up @@ -2745,7 +2747,7 @@ def _soiling_event_detection(x, y, ffill=True, tuner=5):


def _make_seasonal_samples(
list_of_SCs, sample_nr=10, min_multiplier=0.5, max_multiplier=2, max_shift=20):
list_of_SCs, sample_nr=10, min_multiplier=0.5, max_multiplier=2, max_shift=20):
"""Generate seasonal samples by perturbing the amplitude and the phase of
a seasonal components found with the fitted CODS model"""
samples = pd.DataFrame(
Expand All @@ -2769,7 +2771,8 @@ def _make_seasonal_samples(
shift = np.random.randint(-max_shift, max_shift)
# Set up the signal by shifting the orginal signal index, and
# constructing the new signal based on median_signal
shifted_signal = pd.Series(index=signal.index,
shifted_signal = pd.Series(
index=signal.index,
data=median_signal.reindex((signal.index.dayofyear - shift) % 365 + 1).values)
# Perturb amplitude by recentering to 0 multiplying by multiplier
samples.loc[:, i * sample_nr + j] = multiplier * (shifted_signal - signal_mean) + 1
Expand Down Expand Up @@ -2866,8 +2869,9 @@ def piecewise_linear(x, x0, b, k1, k2):


def segmented_soiling_period(
pr, fill_method="bfill", days_clean_vs_cp=7, initial_guesses=[13, 1, 0, 0],
bounds=None, min_r2=0.15): # note min_r2 was 0.6 and it could be worth testing 10 day forward median as b guess
pr, fill_method="bfill", days_clean_vs_cp=7, initial_guesses=[13, 1, 0, 0],
bounds=None, min_r2=0.15):
# note min_r2 was 0.6 and it could be worth testing 10 day forward median as b guess
"""
Applies segmented regression to a single deposition period
(data points in between two cleaning events).
Expand Down Expand Up @@ -2934,7 +2938,7 @@ def segmented_soiling_period(

if len(y) < 45: # tighter requirements for shorter soiling periods
if (R2_piecewise < min_r2) | (
(R2_percent_of_possible_improve < 0.5) & (R2_percent_improve < 0.5)):
(R2_percent_of_possible_improve < 0.5) & (R2_percent_improve < 0.5)):
z = [np.nan] * len(x)
cp_date = None
else:
Expand Down

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