From 669ec756f571bb2a85bd7578c98a354e05832dd1 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 6 Aug 2024 14:02:42 -0400 Subject: [PATCH] lint soiling.py --- rdtools/soiling.py | 48 ++++++++++++++++++++++------------------------ 1 file changed, 23 insertions(+), 25 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index eac05474..6eb917b5 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -6,27 +6,25 @@ and PATCH releases) as the code matures. """ -from rdtools import degradation as RdToolsDeg -from rdtools.bootstrap import _make_time_series_bootstrap_samples - +import bisect +import itertools +import sys +import time import warnings -import pandas as pd import numpy as np -from scipy.stats.mstats import theilslopes -from filterpy.kalman import KalmanFilter +import pandas as pd +import scipy.stats as st +import statsmodels.api as sm from filterpy.common import Q_discrete_white_noise -import itertools -import bisect -import time -import sys +from filterpy.kalman import KalmanFilter +from scipy.optimize import curve_fit +from scipy.stats.mstats import theilslopes from statsmodels.tsa.seasonal import STL from statsmodels.tsa.stattools import adfuller -import statsmodels.api as sm -from scipy.optimize import curve_fit - -import scipy.stats as st +from rdtools import degradation as RdToolsDeg +from rdtools.bootstrap import _make_time_series_bootstrap_samples lowess = sm.nonparametric.lowess # Used in CODSAnalysis/Matt @@ -201,7 +199,7 @@ def _calc_daily_df( # Make a forward filled copy, just for use in # step, slope change detection - # 1/6/24 Note several errors in soiling fit due to ffill for rolling + # 1/6/24 Note several errors in soiling fit due to ffill for rolling # median change to day_scale/2 Matt df_ffill = df.copy() df_ffill = df.ffill(limit=int(round((day_scale / 2), 0))) @@ -220,8 +218,8 @@ def _calc_daily_df( df["clean_event_detected"] = df.delta > clean_threshold ########################################################################## - # Matt added these lines but the function "_collapse_cleaning_events" - # was written by Asmund, it reduces multiple days of cleaning events + # Matt added these lines but the function "_collapse_cleaning_events" + # was written by Asmund, it reduces multiple days of cleaning events # in a row to a single event reduced_cleaning_events = _collapse_cleaning_events( @@ -504,7 +502,7 @@ def _calc_result_df( "prev_end", ] = 1 # clean_event or clean_event_detected results["inferred_begin_shift"] = results.inferred_start_loss - results.prev_end - # if orginal shift detection was positive the shift should not be + # if orginal shift detection was positive the shift should not be # negative due to fitting results results.loc[results.clean_event == True, "inferred_begin_shift"] = np.clip( results.inferred_begin_shift, 0, 1 @@ -604,9 +602,9 @@ def _calc_result_df( shift = 0 shift_perfect = 0 total_down = start_shift - # check that shifts results in being at or above the median of + # check that shifts results in being at or above the median of # the next 10 days of data - # this catches places where start points of polyfits were + # this catches places where start points of polyfits were # skewed below where data start if (soil_infer + shift) < forward_median: shift = forward_median - soil_infer @@ -664,7 +662,7 @@ def _calc_monte(self, monte, method="half_norm_clean"): method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', perfect_clean_complex,inferred_clean_complex} \ default 'half_norm_clean' - + How to treat the recovery of each cleaning event * 'random_clean' - a random recovery between 0-100%, @@ -3364,9 +3362,9 @@ def segmented_soiling_period( 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 + Applies segmented regression to a single deposition period (data points in between two cleaning events). - Segmentation is neglected if change point occurs within a number of days + Segmentation is neglected if change point occurs within a number of days (days_clean_vs_cp) of the cleanings. Parameters @@ -3378,7 +3376,7 @@ def segmented_soiling_period( days_clean_vs_cp : numeric (default=7) Minimum number of days accepted between cleanings and change points. bounds : numeric (default=None) - List of bounds for fitting function. If not specified, they are + List of bounds for fitting function. If not specified, they are defined in the function. initial_guesses : numeric (default=0.1) List of initial guesses for fitting function @@ -3412,7 +3410,7 @@ def segmented_soiling_period( p, e = curve_fit(piecewise_linear, x, y, p0=initial_guesses, bounds=bounds) # Ignore change point if too close to a cleaning - # Change point p[0] converted to integer to extract a date. + # Change point p[0] converted to integer to extract a date. # None if no change point is found. if p[0] > days_clean_vs_cp and p[0] < len(y) - days_clean_vs_cp: z = piecewise_linear(x, *p)