From c8aae897143abfc2d343d2ea663e7096932b2abe Mon Sep 17 00:00:00 2001 From: nmoyer Date: Mon, 24 Jun 2024 11:57:49 -0600 Subject: [PATCH 01/33] =?UTF-8?q?Matt=E2=80=99s=20updates=20to=20SRR=20alg?= =?UTF-8?q?orithm=20(detect=20negative=20shifts=20in=20soiling=20ratio=20a?= =?UTF-8?q?nd=20fit=20multiple=20soiling=20rates=20per=20soiling=20interva?= =?UTF-8?q?l=20(piecewise))=20as=20well=20as=20CODS=20algorithm=20being=20?= =?UTF-8?q?added?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- rdtools/soiling.py | 560 ++++++++++++++++++++++++++++++++++++++------- 1 file changed, 475 insertions(+), 85 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 5e713a03..f0030050 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1,3 +1,5 @@ + + ''' Functions for calculating soiling metrics from photovoltaic system data. @@ -5,6 +7,7 @@ and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures. ''' + from rdtools import degradation as RdToolsDeg from rdtools.bootstrap import _make_time_series_bootstrap_samples @@ -22,7 +25,12 @@ from statsmodels.tsa.seasonal import STL from statsmodels.tsa.stattools import adfuller import statsmodels.api as sm -lowess = sm.nonparametric.lowess + +from scipy.optimize import curve_fit + +import scipy.stats as st + +lowess = sm.nonparametric.lowess #Used in CODSAnalysis/Matt warnings.warn( 'The soiling module is currently experimental. The API, results, ' @@ -78,10 +86,11 @@ def __init__(self, energy_normalized_daily, insolation_daily, if pd.infer_freq(self.precipitation_daily.index) != 'D': raise ValueError('Precipitation series must have ' 'daily frequency') - + ############################################################################### + #add neg_shift and piecewise into parameters/Matt def _calc_daily_df(self, day_scale=13, clean_threshold='infer', recenter=True, clean_criterion='shift', precip_threshold=0.01, - outlier_factor=1.5): + outlier_factor=1.5,neg_shift=True,piecewise=True): ''' Calculates self.daily_df, a pandas dataframe prepared for SRR analysis, and self.renorm_factor, the renormalization factor for the daily @@ -124,14 +133,17 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', 'recommended, otherwise, consecutive days may be erroneously ' 'flagged as cleaning events. ' 'See https://github.com/NREL/rdtools/issues/189') + df = self.pm.to_frame() df.columns = ['pi'] - df_insol = self.insolation_daily.to_frame() + df_insol = self.insolation_daily.to_frame() df_insol.columns = ['insol'] + df = df.join(df_insol) precip = self.precipitation_daily + if precip is not None: df_precip = precip.to_frame() df_precip.columns = ['precip'] @@ -157,8 +169,9 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', df['pi_norm'] = df['pi'] / renorm # Find the beginning and ends of outages longer than dayscale - bfill = df['pi_norm'].fillna(method='bfill', limit=day_scale) - ffill = df['pi_norm'].fillna(method='ffill', limit=day_scale) + #THIS CODE TRIGGERES DEPRECATION WARNING hance minor changes/Matt + bfill = df['pi_norm'].bfill(limit=day_scale) + ffill = df['pi_norm'].ffill(limit=day_scale) out_start = (~df['pi_norm'].isnull() & bfill.shift(-1).isnull()) out_end = (~df['pi_norm'].isnull() & ffill.shift(1).isnull()) @@ -168,7 +181,9 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', # Make a forward filled copy, just for use in # step, slope change detection - df_ffill = df.fillna(method='ffill', limit=day_scale).copy() + #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))) # Calculate rolling median df['pi_roll_med'] = \ @@ -180,8 +195,15 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', deltas = abs(df.delta) clean_threshold = deltas.quantile(0.75) + \ outlier_factor * (deltas.quantile(0.75) - deltas.quantile(0.25)) - + 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 in a row to a single event + reduced_cleaning_events = \ + _collapse_cleaning_events(df.clean_event_detected, df.delta.values, 5) + df['clean_event_detected']=reduced_cleaning_events + ########################################################################## precip_event = (df['precip'] > precip_threshold) if clean_criterion == 'precip_and_shift': @@ -202,18 +224,64 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', '"precip", "shift"}') df['clean_event'] = df.clean_event | out_start | out_end - - df = df.fillna(0) - + + ####################################################################### + #add negative shifts which allows further segmentation of the soiling + #intervals and handles correction for data outages/Matt + if neg_shift==True: + df['drop_event'] = (df.delta < -2.5*clean_threshold) + df['break_event'] = df.clean_event | df.drop_event + else: + df['break_event'] = df.clean_event.copy() + ####################################################################### + #This happens earlier than in the original code but is necessary + #for adding piecewise breakpoints/Matt # Give an index to each soiling interval/run - df['run'] = df.clean_event.cumsum() - df.index.name = 'date' # this gets used by name - + df['run'] = df.break_event.cumsum() + df.index.name = 'date' # this gets used by name + + ####################################################################### + #df.fillna(0) /remove as the zeros introduced in pi_nome negatively + #impact various fits in the code, I havent yet found the original purpose + #or a failure due to removing/Matt + + ##################################################################### + #piecewise=True enables adding a single breakpoint per soiling intervals + # if statistical criteria are met with the piecewise linear fit + #compared to a single linear fit. Intervals <45 days reqire more + #stringent statistical improvements/Matt + if piecewise==True: + warnings.warn('Piecewise = True was passed, for both Piecewise=True' + 'and neg_shift=True cleaning_method choices should' + 'be perfect_clean_complex or inferred_clean_complex') + min_soil_length=27 # min threshold of days necessary for piecewise fit + piecewise_loop = sorted(list(set(df['run']))) + cp_dates=[] + for r in piecewise_loop: + run = df[df['run'] == r] + pr=run.pi_norm.copy() + pr=pr.ffill()#linear fitting cant handle nans + pr=pr.bfill()#catch first position nan + if len(run) > min_soil_length and run.pi_norm.sum() > 0: + sr,cp_date=segmented_soiling_period(pr,days_clean_vs_cp=13) + if cp_date!=None: + cp_dates.append(pr.index[cp_date]) + #save changes to df, note I would like to rename "clean_event" from + #original code to something like "break_event + df['slope_change_event'] = df.index.isin(cp_dates) + df['break_event'] = df.break_event | df.slope_change_event + df['run'] = df.break_event.cumsum() + else: + df['slope_change_event']=False + + ###################################################################### self.renorm_factor = renorm self.daily_df = df + ###################################################################### + #added neg_shift into parameters in the following def/Matt def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, - max_negative_step=0.05, min_interval_length=7): + max_negative_step=0.05, min_interval_length=7,neg_shift=True): ''' Calculates self.result_df, a pandas dataframe summarizing the soiling intervals identified and self.analyzed_daily_df, a version of @@ -244,11 +312,11 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, else: res_loop = sorted(list(set(daily_df['run']))) - for r in res_loop: + for r in res_loop: #Matt added .iloc due to deprecation warning run = daily_df[daily_df['run'] == r] - length = (run.day[-1] - run.day[0]) - start_day = run.day[0] - end_day = run.day[-1] + length = (run.day.iloc[-1] - run.day.iloc[0]) + start_day = run.day.iloc[0] + end_day = run.day.iloc[-1] start = run.index[0] end = run.index[-1] run_filtered = run[run.pi_norm > 0] @@ -257,6 +325,8 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, # valid=False row if not run_filtered.empty: run = run_filtered + #################################################################### + #see commented changes result_dict = { 'start': start, 'end': end, @@ -267,9 +337,13 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, 'run_slope_high': 0, 'max_neg_step': min(run.delta), 'start_loss': 1, - 'inferred_start_loss': run.pi_norm.mean(), - 'inferred_end_loss': run.pi_norm.mean(), - 'valid': False + 'inferred_start_loss': run.pi_norm.median(),#changed from mean/Matt + '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 from other breaks/Matt + 'run_loss_baseline':0.0# loss from the polyfit over the soiling intercal/Matt + ############################################################## } if len(run) > min_interval_length and run.pi_norm.sum() > 0: fit = theilslopes(run.pi_norm, run.day) @@ -277,9 +351,27 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, result_dict['run_slope'] = fit[0] result_dict['run_slope_low'] = fit[2] result_dict['run_slope_high'] = min([0.0, fit[3]]) - result_dict['inferred_start_loss'] = fit_poly(start_day) - result_dict['inferred_end_loss'] = fit_poly(end_day) result_dict['valid'] = True + ######################################################## + #moved the following 2 line to the next section within conditional statement/Matt + #result_dict['inferred_start_loss'] = fit_poly(start_day) + #result_dict['inferred_end_loss'] = fit_poly(end_day) + + #################################################### + #the following is moved here so median values are retained/Matt + # for soiling inferrences when rejected fits occur + result_dict['slope_err'] = (result_dict['run_slope_high'] - result_dict['run_slope_low'])\ + / abs(result_dict['run_slope']) + + if (result_dict['slope_err'] <= (max_relative_slope_error / 100.0))&(result_dict['run_slope']<0): + result_dict['inferred_start_loss'] = fit_poly(start_day) + result_dict['inferred_end_loss'] = fit_poly(end_day) + ############################################# + #calculate loss over soiling interval per polyfit/matt + result_dict['run_loss_baseline']=result_dict['inferred_start_loss']-result_dict['inferred_end_loss'] + + ############################################### + result_list.append(result_dict) results = pd.DataFrame(result_list) @@ -287,31 +379,73 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, if results.empty: raise NoValidIntervalError('No valid soiling intervals were found') + """ # Filter results for each interval, - # setting invalid interval to slope of 0 + # setting invalid interval to slope of 0 + #moved above to line 356/Matt results['slope_err'] = ( results.run_slope_high - results.run_slope_low)\ / abs(results.run_slope) - # critera for exclusions - 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 - results.loc[filt, 'run_slope_high'] = 0 - results.loc[filt, 'valid'] = False - + """ + ############################################################### + # negative shifts are now used as breaks for soiling intervals/Matt + #so new criteria for final filter to modify dataframe + if neg_shift==True: + warnings.warn('neg_shift = True was passed, for both Piecewise=True' + 'and neg_shift=True cleaning_method choices should' + 'be perfect_clean_complex or inferred_clean_complex') + 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 + results.loc[filt, 'run_slope_high'] = 0 + #only intervals that are now not valid are those that dont meet + #the minimum inteval length or have no data + #results.loc[filt, 'valid'] = False + ################################################################## + #original code below setting soiling intervals with extreme negative + #shift to zero slopes, /Matt + if neg_shift==False: + filt = ( + (results.run_slope > 0) | + (results.slope_err >= max_relative_slope_error / 100.0) | + (results.max_neg_step <= -1.0 * max_negative_step) + #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 + #results.loc[filt, 'valid'] = False # Calculate the next inferred start loss from next valid interval results['next_inferred_start_loss'] = np.clip( results[results.valid].inferred_start_loss.shift(-1), 0, 1) + # Calculate the inferred recovery at the end of each interval - results['inferred_recovery'] = np.clip( - results.next_inferred_start_loss - results.inferred_end_loss, - 0, 1) + ######################################################################## + #remove clipping on 'inferred_recovery' so absolute recovery can be + #used in later step where clipping can be considered/Matt + results['inferred_recovery'] = results.next_inferred_start_loss - results.inferred_end_loss + + ######################################################################## + #calculate beginning inferred shift (end of previous soiling period + #to start of current period)/Matt + results['prev_end'] = results[results.valid].inferred_end_loss.shift(1) + #if the current interval starts with a clean event, the previous end + #is a nan, and the current interval is valid then set prev_end=1 + results.loc[(results.clean_event==True)&(np.isnan(results.prev_end)&(results.valid==True)),'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 negative due to fitting results + results.loc[results.clean_event==True,'inferred_begin_shift']=np.clip(results.inferred_begin_shift,0,1) + ####################################################################### + if len(results[results.valid]) == 0: raise NoValidIntervalError('No valid soiling intervals were found') @@ -326,24 +460,107 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, pm_frame_out['loss_inferred_clean'] = np.nan pm_frame_out['days_since_clean'] = \ (pm_frame_out.index - pm_frame_out.start).dt.days - - # Calculate the daily derate - pm_frame_out['loss_perfect_clean'] = \ - pm_frame_out.start_loss + \ - pm_frame_out.days_since_clean * pm_frame_out.run_slope - # 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) + + ####################################################################### + #new code for perfect and inferred clean with handling of/Matt + #negative shifts and changepoints within soiling intervals + #goes to line 563 + ####################################################################### + pm_frame_out.inferred_begin_shift.bfill(inplace=True) + pm_frame_out['forward_median']=pm_frame_out.pi.iloc[::-1].rolling(10,min_periods=5).median() + prev_shift=1 + soil_inferred_clean=[] + soil_perfect_clean=[] + day_start=-1 + start_infer=1 + start_perfect=1 + soil_infer=1 + soil_perfect=1 + total_down=0 + shift=0 + shift_perfect=0 + begin_perfect_shifts=[0] + 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): + new_soil=d-day_start + day_start=d + + if new_soil<=0:#begin new soil period + if (start_shift==prev_shift)|(changepoint==True):#no shift at + #a slope changepoint + shift=0 + shift_perfect=0 + else: + if (start_shift<0)&(prev_shift<0):#(both negative) or + #downward shifts to start last 2 intervals + shift=0 + shift_perfect=0 + total_down=total_down+start_shift #adding total downshifts + #to subtract from an eventual cleaning event + elif(start_shift>0)&(prev_shift>=0):#(both positive) or + #cleanings start the last 2 intervals + shift=start_shift + shift_perfect=1 + total_down=0 + #add #####################3/27/24 + elif(start_shift==0)&(prev_shift>=0):#( + shift=start_shift + shift_perfect=start_shift + total_down=0 + ############################################################# + elif (start_shift>=0)&(prev_shift<0):#cleaning starts the current + #interval but there was a previous downshift + shift=start_shift+total_down #correct for the negative shifts + 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):#negative shift starts the interval, + #previous shift was cleaning + shift=0 + shift_perfect=0 + total_down=start_shift + #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 skewed below where data start + if (soil_infer+shift)0:#within soiling period + #append the daily soiling ratio to each modeled fit + soil_infer=start_infer+rs*d + soil_inferred_clean.append(soil_infer) + + soil_perfect=start_perfect+rs*d + soil_perfect_clean.append(soil_perfect) pm_frame_out['loss_inferred_clean'] = \ - pm_frame_out.inferred_start_loss + \ - pm_frame_out.days_since_clean * pm_frame_out.run_slope - # filling the flat intervals may need to be recalculated - # for different assumptions - pm_frame_out.loss_inferred_clean = \ - pm_frame_out.loss_inferred_clean.fillna(1) + pd.Series(soil_inferred_clean,index=pm_frame_out.index) + pm_frame_out['loss_perfect_clean'] = \ + pd.Series(soil_perfect_clean,index=pm_frame_out.index) + results['begin_perfect_shift']=pd.Series(begin_perfect_shifts) + results['begin_infer_shift']=pd.Series(begin_infer_shifts) + ####################################################################### self.result_df = results self.analyzed_daily_df = pm_frame_out @@ -413,6 +630,7 @@ def _calc_monte(self, monte, method='half_norm_clean'): # randomize the extent of the cleaning inter_start = 1.0 + delta_previous_run_loss=0 start_list = [] if (method == 'half_norm_clean') or (method == 'random_clean'): # Randomize recovery of valid intervals only @@ -444,9 +662,9 @@ def _calc_monte(self, monte, method='half_norm_clean'): # forward and back fill to note the limits of random constant # derate for invalid intervals results_rand['previous_end'] = \ - results_rand.end_loss.fillna(method='ffill') + results_rand.end_loss.ffill() results_rand['next_start'] = \ - results_rand.start_loss.fillna(method='bfill') + results_rand.start_loss.bfill() # Randomly select random constant derate for invalid intervals # based on previous end and next beginning @@ -472,13 +690,46 @@ def _calc_monte(self, monte, method='half_norm_clean'): invalid_update['start_loss'] = replace_levels invalid_update.index = invalid_intervals.index results_rand.update(invalid_update) - elif method == 'perfect_clean': for i, row in results_rand.iterrows(): start_list.append(inter_start) end = inter_start + row.run_loss inter_start = 1 results_rand['start_loss'] = start_list + ################################################################## + #matt additions + + elif method == 'perfect_clean_complex': + for i, row in results_rand.iterrows(): + if row.begin_perfect_shift>0: + inter_start=np.clip((inter_start+row.begin_perfect_shift+delta_previous_run_loss),end,1) + delta_previous_run_loss=-1*row.run_loss-row.run_loss_baseline + else: + delta_previous_run_loss=delta_previous_run_loss-1*row.run_loss-row.run_loss_baseline + #inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) + start_list.append(inter_start) + end = inter_start + row.run_loss + + inter_start = end + results_rand['start_loss'] = start_list + + elif method == 'inferred_clean_complex': + for i, row in results_rand.iterrows(): + if row.begin_infer_shift>0: + inter_start=np.clip((inter_start+row.begin_infer_shift+delta_previous_run_loss),end,1) + delta_previous_run_loss=-1*row.run_loss-row.run_loss_baseline + else: + delta_previous_run_loss=delta_previous_run_loss-1*row.run_loss-row.run_loss_baseline + #inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) + start_list.append(inter_start) + end = inter_start + row.run_loss + + inter_start = end + results_rand['start_loss'] = start_list + """ + + """ + ############################################### else: raise ValueError("Invalid method specification") @@ -490,8 +741,8 @@ def _calc_monte(self, monte, method='half_norm_clean'): df_rand['days_since_clean'] = \ (df_rand.index - df_rand.start).dt.days df_rand['loss'] = df_rand.start_loss + \ - df_rand.days_since_clean * df_rand.run_slope - + df_rand.days_since_clean * df_rand.run_slope + df_rand['soil_insol'] = df_rand.loss * df_rand.insol soiling_ratio = ( @@ -505,12 +756,13 @@ def _calc_monte(self, monte, method='half_norm_clean'): self.random_profiles = random_profiles self.monte_losses = monte_losses - + ####################################################################### + #add neg_shift and piecewise to the following def/Matt def run(self, reps=1000, day_scale=13, clean_threshold='infer', - trim=False, method='half_norm_clean', + trim=False, method='perfect_clean_complex', 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=True,piecewise=True): ''' Run the SRR method from beginning to end. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented @@ -532,17 +784,28 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', trim : bool, default False Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ - default 'half_norm_clean' + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', + perfect_clean_complex,inferred_clean_complex} \ + default 'perfect_clean_complex' + How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval. + the interval.pair with piecewise=False and neg_shift=False + *'perfect_clean_complex', pair with piecewise=True and neg_shift=True + each detected clean event returns the performance metric to 1 while + negative shifts in the data or piecewise linear fits result in no + cleaning + *'inferred_clean_complex', pair with piecewise=True and neg_shift=True + at each detected clean event the performance metric increases based on + fits to the data while negative shifts in the data or piecewise + linear fits result in no cleaning clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -579,6 +842,18 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. + neg_shift : boolean where True results in additional subdividing of + soiling intervals when negative shifts are found in the rolling + median of the performance metric. Inferred corrections in the + soilign fit are made at these negative shifts. False result in no + additional subdivides of the data where excessive negative shifts + can invalidate a soiling interval + piecewise : boolean where True results in each soiling interval of + sufficient length being tested for significant fit improvement with + 2 piecewise linear fits. If the criteria of significance is met the + soiling interval is subdivided into the 2 seperate intervals. False + result in no piecewise fit being tested. + Returns ------- @@ -638,11 +913,14 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', recenter=recenter, clean_criterion=clean_criterion, precip_threshold=precip_threshold, - outlier_factor=outlier_factor) + 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, - min_interval_length=min_interval_length) + min_interval_length=min_interval_length, + neg_shift=neg_shift) self._calc_monte(reps, method=method) # Calculate the P50 and confidence interval @@ -655,10 +933,12 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', P_level = result[3] # Construct calc_info output - + ############################################### + #add inferred_recovery, inferred_begin_shift /Matt + ############################################### intervals_out = self.result_df[ ['start', 'end', 'run_slope', 'run_slope_low', - 'run_slope_high', 'inferred_start_loss', 'inferred_end_loss', + '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', 'run_slope_high': 'soiling_rate_high', @@ -666,24 +946,46 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', }, inplace=True) df_d = self.analyzed_daily_df - sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] + #sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] + sr_perfect = df_d.loss_perfect_clean + ###################################################### + #enable addtional items to be output//Matt + sr_inferred = df_d.loss_inferred_clean + sr_days_since_clean=df_d.days_since_clean + sr_run_slope=df_d.run_slope + sr_infer_rec=df_d.inferred_recovery + sr_infer_begin_rec=df_d.inferred_begin_shift + sr_changepoints=df_d.slope_change_event + ###################################################### + calc_info = { 'exceedance_level': P_level, 'renormalizing_factor': self.renorm_factor, 'stochastic_soiling_profiles': self.random_profiles, 'soiling_interval_summary': intervals_out, - 'soiling_ratio_perfect_clean': sr_perfect + 'soiling_ratio_perfect_clean': sr_perfect, + ########################################## + #add these lines to output//Matt + 'soiling_ratio_inferred_clean':sr_inferred, + 'days_since_clean':sr_days_since_clean, + 'run_slope':sr_run_slope, + 'inferred_recovery':sr_infer_rec, + 'inferred_begin_shift':sr_infer_begin_rec, + 'change_points':sr_changepoints + ############################################# } return (result[0], result[1:3], calc_info) - +#more updates are needed for documentation but added additional inputs +#that are in srr.run /Matt def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitation_daily=None, day_scale=13, clean_threshold='infer', - trim=False, method='half_norm_clean', + trim=False, method='perfect_clean_complex', 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=True,piecewise=True): ''' Functional wrapper for :py:class:`~rdtools.soiling.SRRAnalysis`. Perform the stochastic rate and recovery soiling loss calculation. Based on the @@ -834,7 +1136,9 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, recenter=recenter, max_relative_slope_error=max_relative_slope_error, max_negative_step=max_negative_step, - outlier_factor=outlier_factor) + outlier_factor=outlier_factor, + neg_shift=neg_shift, + piecewise=piecewise) return sr, sr_ci, soiling_info @@ -1762,11 +2066,11 @@ def run_bootstrap(self, self.soiling_loss = [0, 0, (1 - result_df.soiling_ratio).mean()] self.small_soiling_signal = True self.errors = ( - 'Soiling signal is small relative to the noise. ' - 'Iterative decomposition not possible. ' - 'Degradation found by RdTools YoY.') - warnings.warn(self.errors) - return self.result_df, self.degradation, self.soiling_loss + 'Soiling signal is small relative to the noise.' + 'Iterative decomposition not possible.\n' + 'Degradation found by RdTools YoY') + print(self.errors) + return self.small_soiling_signal = False # Aggregate all bootstrap samples @@ -2507,8 +2811,7 @@ def _make_seasonal_samples(list_of_SCs, sample_nr=10, min_multiplier=0.5, ''' Generate seasonal samples by perturbing the amplitude and the phase of a seasonal components found with the fitted CODS model ''' samples = pd.DataFrame(index=list_of_SCs[0].index, - columns=range(int(sample_nr*len(list_of_SCs))), - dtype=float) + columns=range(int(sample_nr*len(list_of_SCs)))) # From each fitted signal, we will generate new seaonal components for i, signal in enumerate(list_of_SCs): # Remove beginning and end of signal @@ -2621,3 +2924,90 @@ def _progressBarWithETA(value, endvalue, time, bar_length=20): "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left) + " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent)) sys.stdout.flush() +############################################################################### +#all code below for new piecewise fitting in soiling intervals within srr/Matt +############################################################################### +def piecewise_linear(x, x0, b, k1, k2): + cond_list=[x=x0] + func_list=[lambda x: k1*x+b, lambda x: k1*x+b+k2*(x-x0)] + return np.piecewise(x, cond_list, func_list) + +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 + """ + 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 (days_clean_vs_cp) of the cleanings. + + Parameters + ---------- + pr : + Series of daily performance ratios measured during the given deposition period. + fill_method : str (default='bfill') + Method to employ to fill any missing day. + 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 defined in the function. + initial_guesses : numeric (default=0.1) + List of initial guesses for fitting function + min_r2 : numeric (default=0.1) + Minimum R2 to consider valid the extracted soiling profile. + + Returns + ------- + sr: numeric + Series containing the daily soiling ratio values after segmentation. + List of nan if segmentation was not possible. + cp_date: datetime + Datetime in which continuous change points occurred. + None if segmentation was not possible. + """ + + #Check if PR dataframe has datetime index + if not isinstance(pr.index, pd.DatetimeIndex): + raise ValueError('The time series does not have DatetimeIndex') + + #Define bounds if not provided + if bounds==None: + #bounds are neg in first 4 and pos in second 4 + #ordered as x0,b,k1,k2 where x0 is the breakpoint k1 and k2 are slopes + bounds=[(13,-5,-np.inf, -np.inf),((len(pr)-13),5,+np.inf,+np.inf)] + y=pr.values + x=np.arange(0.,len(y)) + + try: + #Fit soiling profile with segmentation + 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. None if no change point is found. + if p[0]>days_clean_vs_cp and p[0] Date: Mon, 22 Jul 2024 11:42:36 -0600 Subject: [PATCH 02/33] committing updates to merge with aggregated_filters_for_trials --- docs/TrendAnalysis_example_pvdaq4.ipynb | 4 +- docs/notebook_requirements.txt | 2 +- rdtools/soiling.py | 153 +++++++++++++++++------- rdtools/test/conftest.py | 28 +++++ rdtools/test/soiling_test.py | 107 +++++++++++++++-- setup.py | 2 +- 6 files changed, 233 insertions(+), 63 deletions(-) diff --git a/docs/TrendAnalysis_example_pvdaq4.ipynb b/docs/TrendAnalysis_example_pvdaq4.ipynb index 991b2a9f..7298028a 100644 --- a/docs/TrendAnalysis_example_pvdaq4.ipynb +++ b/docs/TrendAnalysis_example_pvdaq4.ipynb @@ -62349,7 +62349,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -62363,7 +62363,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/notebook_requirements.txt b/docs/notebook_requirements.txt index 8afe0f87..458fea78 100644 --- a/docs/notebook_requirements.txt +++ b/docs/notebook_requirements.txt @@ -31,7 +31,7 @@ nbformat==5.1.0 nest-asyncio==1.5.5 notebook==6.4.12 numexpr==2.8.0 -pandocfilters==1.4.2 +pandocfilters==1.5.1 parso==0.5.2 pexpect==4.6.0 pickleshare==0.7.5 diff --git a/rdtools/soiling.py b/rdtools/soiling.py index f0030050..5e737bbc 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -90,7 +90,7 @@ def __init__(self, energy_normalized_daily, insolation_daily, #add neg_shift and piecewise into parameters/Matt def _calc_daily_df(self, day_scale=13, clean_threshold='infer', recenter=True, clean_criterion='shift', precip_threshold=0.01, - outlier_factor=1.5,neg_shift=True,piecewise=True): + outlier_factor=1.5,neg_shift=False,piecewise=False): ''' Calculates self.daily_df, a pandas dataframe prepared for SRR analysis, and self.renorm_factor, the renormalization factor for the daily @@ -127,6 +127,18 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. ''' if (day_scale % 2 == 0) and ('shift' in clean_criterion): warnings.warn('An even value of day_scale was passed. An odd value is ' @@ -200,9 +212,11 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', ########################################################################## #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(df.clean_event_detected, df.delta.values, 5) df['clean_event_detected']=reduced_cleaning_events + ########################################################################## precip_event = (df['precip'] > precip_threshold) @@ -281,7 +295,7 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', ###################################################################### #added neg_shift into parameters in the following def/Matt def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, - max_negative_step=0.05, min_interval_length=7,neg_shift=True): + max_negative_step=0.05, min_interval_length=7,neg_shift=False): ''' Calculates self.result_df, a pandas dataframe summarizing the soiling intervals identified and self.analyzed_daily_df, a version of @@ -337,8 +351,8 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, 'run_slope_high': 0, 'max_neg_step': min(run.delta), 'start_loss': 1, - 'inferred_start_loss': run.pi_norm.median(),#changed from mean/Matt - 'inferred_end_loss': run.pi_norm.median(),#changed from mean/Matt + 'inferred_start_loss': run.pi_norm.mean(),#changed from mean/Matt + 'inferred_end_loss': run.pi_norm.mean(),#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 from other breaks/Matt @@ -362,7 +376,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, # for soiling inferrences when rejected fits occur result_dict['slope_err'] = (result_dict['run_slope_high'] - result_dict['run_slope_low'])\ / abs(result_dict['run_slope']) - + if (result_dict['slope_err'] <= (max_relative_slope_error / 100.0))&(result_dict['run_slope']<0): result_dict['inferred_start_loss'] = fit_poly(start_day) result_dict['inferred_end_loss'] = fit_poly(end_day) @@ -371,7 +385,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, result_dict['run_loss_baseline']=result_dict['inferred_start_loss']-result_dict['inferred_end_loss'] ############################################### - + result_list.append(result_dict) results = pd.DataFrame(result_list) @@ -416,13 +430,13 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, (results.max_neg_step <= -1.0 * max_negative_step) #remove line 389, want to store data for inferred values #for calculations below - #|results.loc[filt, 'valid'] = False + # |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 - #results.loc[filt, 'valid'] = False + results.loc[filt, 'valid'] = False # Calculate the next inferred start loss from next valid interval results['next_inferred_start_loss'] = np.clip( results[results.valid].inferred_start_loss.shift(-1), @@ -575,18 +589,31 @@ def _calc_monte(self, monte, method='half_norm_clean'): ---------- monte : int number of Monte Carlo simulations to run - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ - default '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% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken - randomly from a half normal distribution with its - mode (mu) at 1 and - its sigma equal to 1/3 * (1-b) where b is the intercept - of the fit to the interval. + randomly from a half normal distribution with its mode (mu) at 1 and + its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to + the interval, + pair with piecewise=False and neg_shift=False + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True ''' # Raise a warning if there is >20% invalid data @@ -635,6 +662,9 @@ def _calc_monte(self, monte, method='half_norm_clean'): if (method == 'half_norm_clean') or (method == 'random_clean'): # Randomize recovery of valid intervals only valid_intervals = results_rand[results_rand.valid].copy() + valid_intervals['inferred_recovery'] = np.clip( + valid_intervals.inferred_recovery, + 0, 1) valid_intervals['inferred_recovery'] = \ valid_intervals.inferred_recovery.fillna(1.0) @@ -759,10 +789,11 @@ def _calc_monte(self, monte, method='half_norm_clean'): ####################################################################### #add neg_shift and piecewise to the following def/Matt def run(self, reps=1000, day_scale=13, clean_threshold='infer', - trim=False, method='perfect_clean_complex', + 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,neg_shift=True,piecewise=True): + 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 and recovery soiling loss calculation. Based on the methods presented @@ -793,19 +824,22 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', * 'random_clean' - a random recovery between 0-100%, pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1, pair with piecewise=False and neg_shift=False + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval.pair with piecewise=False and neg_shift=False - *'perfect_clean_complex', pair with piecewise=True and neg_shift=True - each detected clean event returns the performance metric to 1 while - negative shifts in the data or piecewise linear fits result in no - cleaning - *'inferred_clean_complex', pair with piecewise=True and neg_shift=True - at each detected clean event the performance metric increases based on - fits to the data while negative shifts in the data or piecewise - linear fits result in no cleaning + the interval, + pair with piecewise=False and neg_shift=False + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -842,17 +876,18 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. - neg_shift : boolean where True results in additional subdividing of - soiling intervals when negative shifts are found in the rolling - median of the performance metric. Inferred corrections in the - soilign fit are made at these negative shifts. False result in no - additional subdivides of the data where excessive negative shifts - can invalidate a soiling interval - piecewise : boolean where True results in each soiling interval of - sufficient length being tested for significant fit improvement with - 2 piecewise linear fits. If the criteria of significance is met the - soiling interval is subdivided into the 2 seperate intervals. False - result in no piecewise fit being tested. + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. Returns @@ -981,11 +1016,11 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', #that are in srr.run /Matt def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitation_daily=None, day_scale=13, clean_threshold='infer', - trim=False, method='perfect_clean_complex', + 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,neg_shift=True,piecewise=True): + outlier_factor=1.5,neg_shift=False,piecewise=False): ''' Functional wrapper for :py:class:`~rdtools.soiling.SRRAnalysis`. Perform the stochastic rate and recovery soiling loss calculation. Based on the @@ -1018,17 +1053,31 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, trim : bool, default False Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals - method : str, {'half_norm_clean', 'random_clean', 'perfect_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% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval. + the interval, + pair with piecewise=False and neg_shift=False + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -1064,6 +1113,18 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. Returns ------- @@ -2903,7 +2964,7 @@ def _find_numeric_outliers(x, multiplier=1.5, where='both', verbose=False): def _RMSE(y_true, y_pred): '''Calculates the Root Mean Squared Error for y_true and y_pred, where y_pred is the "prediction", and y_true is the truth.''' - mask = ~np.isnan(y_pred) + mask = ~pd.isnull(y_pred) return np.sqrt(np.mean((y_pred[mask]-y_true[mask])**2)) diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py index f22a05f5..7318d91d 100644 --- a/rdtools/test/conftest.py +++ b/rdtools/test/conftest.py @@ -85,6 +85,34 @@ def soiling_normalized_daily(soiling_times): return normalized_daily +@pytest.fixture() +def soiling_normalized_daily_with_neg_shifts(soiling_times): + interval_1_v1 = 1 - 0.005 * np.arange(0, 15, 1) + interval_1_v2 = (0.9 - 0.005 * 15) - 0.005 * np.arange(0, 10, 1) + interval_2 = 1 - 0.002 * np.arange(0, 25, 1) + interval_3_v1 = 1 - 0.001 * np.arange(0, 10, 1) + interval_3_v2 = (0.95 - 0.001 * 10) - 0.001 * np.arange(0, 15, 1) + profile = np.concatenate((interval_1_v1, interval_1_v2, interval_2, interval_3_v1, interval_3_v2)) + np.random.seed(1977) + noise = 0.01 * np.random.rand(75) + normalized_daily = pd.Series(data=profile, index=soiling_times) + normalized_daily = normalized_daily + noise + + return normalized_daily + +@pytest.fixture() +def soiling_normalized_daily_with_piecewise_slope(soiling_times): + interval_1_v1 = 1 - 0.002 * np.arange(0, 20, 1) + interval_1_v2 = (1 - 0.002 * 20) - 0.007 * np.arange(0, 20, 1) + interval_2_v1 = 1 - 0.01 * np.arange(0, 20, 1) + interval_2_v2 = (1 - 0.01 * 20) - 0.001 * np.arange(0, 15, 1) + profile = np.concatenate((interval_1_v1, interval_1_v2, interval_2_v1, interval_2_v2)) + np.random.seed(1977) + noise = 0.01 * np.random.rand(75) + normalized_daily = pd.Series(data=profile, index=soiling_times) + normalized_daily = normalized_daily + noise + + return normalized_daily @pytest.fixture() def soiling_insolation(soiling_times): diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index a1a67837..673d4277 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -25,11 +25,23 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times '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' + #wait to see which tests matt wants to keep + #assert len(soiling_info['change_points']) == len(soiling_normalized_daily), \ + # 'length of soiling_info["change_points"] different than expected' + #assert isinstance(soiling_info['change_points'], pd.Series), \ + # 'soiling_info["change_points"] not a pandas series' + #assert (soiling_info['change_points'] == False).all(), \ + # 'not all values in soiling_inf["change_points"] are False' + #assert len(soiling_info['days_since_clean']) == len(soiling_normalized_daily), \ + # 'length of soiling_info["days_since_clean"] different than expected' + #assert isinstance(soiling_info['days_since_clean'], pd.Series), \ + # 'soiling_info["days_since_clean"] not a pandas series' + # 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'] + 'soiling_rate_high', 'inferred_start_loss', 'inferred_end_loss','inferred_recovery','inferred_begin_shift', + 'length', 'valid'] actual_summary_columns = soiling_info['soiling_interval_summary'].columns.values for x in actual_summary_columns: @@ -45,10 +57,12 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times 'soiling_rate_high': -0.002455915, 'inferred_start_loss': 1.020124, 'inferred_end_loss': 0.9566552, + 'inferred_recovery': 0.065416, #Matt might not keep + 'inferred_begin_shift': 0.084814, #Matt might not keep 'length': 24.0, 'valid': 1.0}) expected_means = expected_means[['soiling_rate', 'soiling_rate_low', 'soiling_rate_high', - 'inferred_start_loss', 'inferred_end_loss', + 'inferred_start_loss', 'inferred_end_loss', 'inferred_recovery', 'inferred_begin_shift', '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) @@ -64,16 +78,18 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times @pytest.mark.filterwarnings("ignore:.*20% or more of the daily data.*:UserWarning") -@pytest.mark.parametrize('method,expected_sr', - [('random_clean', 0.936177), - ('half_norm_clean', 0.915093), - ('perfect_clean', 0.977116)]) +@pytest.mark.parametrize('method,neg_shift,piecewise,expected_sr', + [('random_clean', False, False, 0.936177), + ('half_norm_clean', False, False, 0.915093), + ('perfect_clean', False, False, 0.977116), + ('perfect_clean_complex', True, True, 0.977116), + ('inferred_clean_complex', True, True, 0.975805)]) def test_soiling_srr_consecutive_invalid(soiling_normalized_daily, soiling_insolation, - soiling_times, method, expected_sr): + soiling_times, method, neg_shift, piecewise, 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) + max_relative_slope_error=20.0, method=method, piecewise=piecewise, neg_shift=neg_shift) assert expected_sr == pytest.approx(sr, abs=1e-6), \ f'Soiling ratio different from expected value for {method} with consecutive invalid intervals' # noqa: E501 @@ -101,7 +117,7 @@ def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation, s def test_soiling_srr_confidence_levels(soiling_normalized_daily, soiling_insolation): - 'Tests SRR with different confidence level settingsf from above' + 'Tests SRR with different confidence level settings from above' np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=95, reps=10, exceedance_prob=80.0) @@ -147,8 +163,9 @@ def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation): @pytest.mark.parametrize('method,expected_sr', [('random_clean', 0.920444), - ('perfect_clean', 0.966912) - ]) + ('perfect_clean', 0.966912), + ('perfect_clean_complex', 0.966912), + ('inferred_clean_complex', 0.965565)]) 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, @@ -222,7 +239,12 @@ def test_soiling_srr_with_nan_interval(soiling_normalized_daily, soiling_insolat 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' - + ''' + 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, method="perfect_clean_complex", piecewise=True, neg_shift=True) + assert 0.974297 == pytest.approx(sr, abs=1e-6), \ + 'Soiling ratio different from expected value when an entire interval was NaN' + ''' def test_soiling_srr_outlier_factor(soiling_normalized_daily, soiling_insolation): _, _, info = soiling_srr(soiling_normalized_daily, soiling_insolation, @@ -305,7 +327,66 @@ def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolatio with pytest.raises(ValueError, match='Invalid method specification'): _ = soiling_srr(method='bad', **kwargs) +# ########################### +# negetive shift and piecewise tests +# ########################### +@pytest.mark.parametrize('method,neg_shift,expected_sr', + [('half_norm_clean', False, 0.940237), + ('half_norm_clean', True, 0.975057), + ('perfect_clean_complex', False, 0.941591), + ('perfect_clean_complex', True, 0.964117), + ('inferred_clean_complex', False, 0.939747), + ('inferred_clean_complex', True, 0.963585)]) +def test_negative_shifts(soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times, method, neg_shift, expected_sr): + reps = 10 + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=reps, + method=method, neg_shift=neg_shift) + assert expected_sr == pytest.approx(sr, abs=1e-6), \ + f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" different from expected value' + +@pytest.mark.parametrize('method,piecewise,expected_sr', + [('half_norm_clean', False, 0.8670264), + ('half_norm_clean', True, 0.927017), + ('perfect_clean_complex', False, 0.891499), + ('perfect_clean_complex', True, 0.896936), + ('inferred_clean_complex', False, 0.874486), + ('inferred_clean_complex', True, 0.896214)]) +def test_piecewise(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, soiling_times, method, piecewise, expected_sr): + reps = 10 + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, reps=reps, + method=method, piecewise=piecewise) + assert expected_sr == pytest.approx(sr, abs=1e-6), \ + f'Soiling ratio with method="{method}" and piecewise="{piecewise}" different from expected value' + +def test_piecewise_and_neg_shifts(soiling_normalized_daily_with_piecewise_slope, soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times): + reps = 10 + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, reps=reps, + method='perfect_clean_complex', piecewise=True, neg_shift=True) + assert 0.896936 == pytest.approx(sr, abs=1e-6), \ + 'Soiling ratio different from expected value for data with piecewise slopes' + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=reps, + method='perfect_clean_complex', piecewise=True, neg_shift=True) + assert 0.964117 == pytest.approx(sr, abs=1e-6), \ + 'Soiling ratio different from expected value for data with negative shifts' +def test_complex_sr_clean_threshold(soiling_normalized_daily_with_neg_shifts, soiling_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(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=10, + clean_threshold=0.1, method='perfect_clean_complex', piecewise=True, neg_shift=True) + assert 0.934926 == 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(soiling_normalized_daily_with_neg_shifts, soiling_insolation, + reps=10, clean_threshold=1) + ''' # ########################### # annual_soiling_ratios tests # ########################### diff --git a/setup.py b/setup.py index 4e0fc9b3..74e389d0 100755 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ INSTALL_REQUIRES = [ 'matplotlib >= 3.0.0', - 'numpy >= 1.17.3', + 'numpy >= 1.17.3, <2.0', # pandas restricted to <2.1 until # https://github.com/pandas-dev/pandas/issues/55794 # is resolved From f23a497924de29bb544db9a21d073b06ea41c40a Mon Sep 17 00:00:00 2001 From: nmoyer Date: Mon, 22 Jul 2024 12:04:01 -0600 Subject: [PATCH 03/33] Making sure there will be no merge conflicts --- docs/notebook_requirements.txt | 24 +- rdtools/soiling.py | 645 +++++---------------------------- 2 files changed, 109 insertions(+), 560 deletions(-) diff --git a/docs/notebook_requirements.txt b/docs/notebook_requirements.txt index 458fea78..fc83aa5d 100644 --- a/docs/notebook_requirements.txt +++ b/docs/notebook_requirements.txt @@ -10,18 +10,18 @@ decorator==4.3.0 defusedxml==0.7.1 entrypoints==0.2.3 html5lib==1.0.1 -ipykernel==4.8.2 -ipython==8.10.0 +ipykernel==6.29.4 +ipython==8.23.0 ipython-genutils==0.2.0 ipywidgets==7.3.0 jedi==0.16.0 -Jinja2==3.0.0 +Jinja2==3.1.3 jsonschema==2.6.0 jupyter==1.0.0 -jupyter-client==6.1.7 -jupyter-console==6.4.0 -jupyter-core==4.11.2 -jupyterlab-pygments==0.2.2 +jupyter-client==8.6.1 +jupyter-console==6.6.3 +jupyter-core==5.7.2 +jupyterlab-pygments==0.3.0 lxml==4.9.1 MarkupSafe==2.0.0 mistune==2.0.3 @@ -30,17 +30,17 @@ nbconvert==7.0.0 nbformat==5.1.0 nest-asyncio==1.5.5 notebook==6.4.12 -numexpr==2.8.0 +numexpr==2.10.0 pandocfilters==1.5.1 parso==0.5.2 pexpect==4.6.0 pickleshare==0.7.5 prometheus-client==0.3.0 -prompt-toolkit==3.0.30 +prompt-toolkit==3.0.43 ptyprocess==0.6.0 pycparser==2.20 Pygments==2.15.0 -pyzmq==22.2.1 +pyzmq==26.0.2 qtconsole==4.3.1 Send2Trash==1.8.0 simplegeneric==0.8.1 @@ -49,7 +49,7 @@ terminado==0.8.3 testpath==0.3.1 tinycss2==1.1.1 tornado==6.3.3 -traitlets==5.0.0 +traitlets==5.14.3 wcwidth==0.1.7 webencodings==0.5.1 -widgetsnbextension==3.3.0 +widgetsnbextension==3.3.0 \ No newline at end of file diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 5e737bbc..ce318021 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1,5 +1,3 @@ - - ''' Functions for calculating soiling metrics from photovoltaic system data. @@ -7,7 +5,6 @@ and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures. ''' - from rdtools import degradation as RdToolsDeg from rdtools.bootstrap import _make_time_series_bootstrap_samples @@ -25,12 +22,7 @@ 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 - -lowess = sm.nonparametric.lowess #Used in CODSAnalysis/Matt +lowess = sm.nonparametric.lowess warnings.warn( 'The soiling module is currently experimental. The API, results, ' @@ -86,11 +78,10 @@ def __init__(self, energy_normalized_daily, insolation_daily, if pd.infer_freq(self.precipitation_daily.index) != 'D': raise ValueError('Precipitation series must have ' 'daily frequency') - ############################################################################### - #add neg_shift and piecewise into parameters/Matt + def _calc_daily_df(self, day_scale=13, clean_threshold='infer', recenter=True, clean_criterion='shift', precip_threshold=0.01, - outlier_factor=1.5,neg_shift=False,piecewise=False): + outlier_factor=1.5): ''' Calculates self.daily_df, a pandas dataframe prepared for SRR analysis, and self.renorm_factor, the renormalization factor for the daily @@ -127,35 +118,20 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. - neg_shift : bool, default True - where True results in additional subdividing of soiling intervals - when negative shifts are found in the rolling median of the performance - metric. Inferred corrections in the soiling fit are made at these - negative shifts. False results in no additional subdivides of the - data where excessive negative shifts can invalidate a soiling interval. - piecewise : bool, default True - where True results in each soiling interval of sufficient length - being tested for significant fit improvement with 2 piecewise linear - fits. If the criteria of significance is met the soiling interval is - subdivided into the 2 separate intervals. False results in no - piecewise fit being tested. ''' if (day_scale % 2 == 0) and ('shift' in clean_criterion): warnings.warn('An even value of day_scale was passed. An odd value is ' 'recommended, otherwise, consecutive days may be erroneously ' 'flagged as cleaning events. ' 'See https://github.com/NREL/rdtools/issues/189') - df = self.pm.to_frame() df.columns = ['pi'] - df_insol = self.insolation_daily.to_frame() + df_insol = self.insolation_daily.to_frame() df_insol.columns = ['insol'] - df = df.join(df_insol) precip = self.precipitation_daily - if precip is not None: df_precip = precip.to_frame() df_precip.columns = ['precip'] @@ -181,9 +157,8 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', df['pi_norm'] = df['pi'] / renorm # Find the beginning and ends of outages longer than dayscale - #THIS CODE TRIGGERES DEPRECATION WARNING hance minor changes/Matt - bfill = df['pi_norm'].bfill(limit=day_scale) - ffill = df['pi_norm'].ffill(limit=day_scale) + bfill = df['pi_norm'].fillna(method='bfill', limit=day_scale) + ffill = df['pi_norm'].fillna(method='ffill', limit=day_scale) out_start = (~df['pi_norm'].isnull() & bfill.shift(-1).isnull()) out_end = (~df['pi_norm'].isnull() & ffill.shift(1).isnull()) @@ -193,9 +168,7 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', # 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 median change to day_scale/2 Matt - df_ffill=df.copy() - df_ffill = df.ffill(limit=int(round((day_scale/2),0))) + df_ffill = df.fillna(method='ffill', limit=day_scale).copy() # Calculate rolling median df['pi_roll_med'] = \ @@ -207,17 +180,8 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', deltas = abs(df.delta) clean_threshold = deltas.quantile(0.75) + \ outlier_factor * (deltas.quantile(0.75) - deltas.quantile(0.25)) - + 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 in a row to a single event - - reduced_cleaning_events = \ - _collapse_cleaning_events(df.clean_event_detected, df.delta.values, 5) - df['clean_event_detected']=reduced_cleaning_events - - ########################################################################## precip_event = (df['precip'] > precip_threshold) if clean_criterion == 'precip_and_shift': @@ -238,64 +202,18 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', '"precip", "shift"}') df['clean_event'] = df.clean_event | out_start | out_end - - ####################################################################### - #add negative shifts which allows further segmentation of the soiling - #intervals and handles correction for data outages/Matt - if neg_shift==True: - df['drop_event'] = (df.delta < -2.5*clean_threshold) - df['break_event'] = df.clean_event | df.drop_event - else: - df['break_event'] = df.clean_event.copy() - ####################################################################### - #This happens earlier than in the original code but is necessary - #for adding piecewise breakpoints/Matt + + df = df.fillna(0) + # Give an index to each soiling interval/run - df['run'] = df.break_event.cumsum() - df.index.name = 'date' # this gets used by name - - ####################################################################### - #df.fillna(0) /remove as the zeros introduced in pi_nome negatively - #impact various fits in the code, I havent yet found the original purpose - #or a failure due to removing/Matt - - ##################################################################### - #piecewise=True enables adding a single breakpoint per soiling intervals - # if statistical criteria are met with the piecewise linear fit - #compared to a single linear fit. Intervals <45 days reqire more - #stringent statistical improvements/Matt - if piecewise==True: - warnings.warn('Piecewise = True was passed, for both Piecewise=True' - 'and neg_shift=True cleaning_method choices should' - 'be perfect_clean_complex or inferred_clean_complex') - min_soil_length=27 # min threshold of days necessary for piecewise fit - piecewise_loop = sorted(list(set(df['run']))) - cp_dates=[] - for r in piecewise_loop: - run = df[df['run'] == r] - pr=run.pi_norm.copy() - pr=pr.ffill()#linear fitting cant handle nans - pr=pr.bfill()#catch first position nan - if len(run) > min_soil_length and run.pi_norm.sum() > 0: - sr,cp_date=segmented_soiling_period(pr,days_clean_vs_cp=13) - if cp_date!=None: - cp_dates.append(pr.index[cp_date]) - #save changes to df, note I would like to rename "clean_event" from - #original code to something like "break_event - df['slope_change_event'] = df.index.isin(cp_dates) - df['break_event'] = df.break_event | df.slope_change_event - df['run'] = df.break_event.cumsum() - else: - df['slope_change_event']=False - - ###################################################################### + df['run'] = df.clean_event.cumsum() + df.index.name = 'date' # this gets used by name + self.renorm_factor = renorm self.daily_df = df - ###################################################################### - #added neg_shift into parameters in the following def/Matt def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, - max_negative_step=0.05, min_interval_length=7,neg_shift=False): + max_negative_step=0.05, min_interval_length=7): ''' Calculates self.result_df, a pandas dataframe summarizing the soiling intervals identified and self.analyzed_daily_df, a version of @@ -326,11 +244,11 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, else: res_loop = sorted(list(set(daily_df['run']))) - for r in res_loop: #Matt added .iloc due to deprecation warning + for r in res_loop: run = daily_df[daily_df['run'] == r] - length = (run.day.iloc[-1] - run.day.iloc[0]) - start_day = run.day.iloc[0] - end_day = run.day.iloc[-1] + length = (run.day[-1] - run.day[0]) + start_day = run.day[0] + end_day = run.day[-1] start = run.index[0] end = run.index[-1] run_filtered = run[run.pi_norm > 0] @@ -339,8 +257,6 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, # valid=False row if not run_filtered.empty: run = run_filtered - #################################################################### - #see commented changes result_dict = { 'start': start, 'end': end, @@ -351,13 +267,9 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, 'run_slope_high': 0, 'max_neg_step': min(run.delta), 'start_loss': 1, - 'inferred_start_loss': run.pi_norm.mean(),#changed from mean/Matt - 'inferred_end_loss': run.pi_norm.mean(),#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 from other breaks/Matt - 'run_loss_baseline':0.0# loss from the polyfit over the soiling intercal/Matt - ############################################################## + 'inferred_start_loss': run.pi_norm.mean(), + 'inferred_end_loss': run.pi_norm.mean(), + 'valid': False } if len(run) > min_interval_length and run.pi_norm.sum() > 0: fit = theilslopes(run.pi_norm, run.day) @@ -365,27 +277,9 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, result_dict['run_slope'] = fit[0] result_dict['run_slope_low'] = fit[2] result_dict['run_slope_high'] = min([0.0, fit[3]]) + result_dict['inferred_start_loss'] = fit_poly(start_day) + result_dict['inferred_end_loss'] = fit_poly(end_day) result_dict['valid'] = True - ######################################################## - #moved the following 2 line to the next section within conditional statement/Matt - #result_dict['inferred_start_loss'] = fit_poly(start_day) - #result_dict['inferred_end_loss'] = fit_poly(end_day) - - #################################################### - #the following is moved here so median values are retained/Matt - # for soiling inferrences when rejected fits occur - result_dict['slope_err'] = (result_dict['run_slope_high'] - result_dict['run_slope_low'])\ - / abs(result_dict['run_slope']) - - if (result_dict['slope_err'] <= (max_relative_slope_error / 100.0))&(result_dict['run_slope']<0): - result_dict['inferred_start_loss'] = fit_poly(start_day) - result_dict['inferred_end_loss'] = fit_poly(end_day) - ############################################# - #calculate loss over soiling interval per polyfit/matt - result_dict['run_loss_baseline']=result_dict['inferred_start_loss']-result_dict['inferred_end_loss'] - - ############################################### - result_list.append(result_dict) results = pd.DataFrame(result_list) @@ -393,73 +287,31 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, if results.empty: raise NoValidIntervalError('No valid soiling intervals were found') - """ # Filter results for each interval, - # setting invalid interval to slope of 0 - #moved above to line 356/Matt + # setting invalid interval to slope of 0 results['slope_err'] = ( results.run_slope_high - results.run_slope_low)\ / abs(results.run_slope) - """ - ############################################################### - # negative shifts are now used as breaks for soiling intervals/Matt - #so new criteria for final filter to modify dataframe - if neg_shift==True: - warnings.warn('neg_shift = True was passed, for both Piecewise=True' - 'and neg_shift=True cleaning_method choices should' - 'be perfect_clean_complex or inferred_clean_complex') - 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 - results.loc[filt, 'run_slope_high'] = 0 - #only intervals that are now not valid are those that dont meet - #the minimum inteval length or have no data - #results.loc[filt, 'valid'] = False - ################################################################## - #original code below setting soiling intervals with extreme negative - #shift to zero slopes, /Matt - if neg_shift==False: - filt = ( - (results.run_slope > 0) | - (results.slope_err >= max_relative_slope_error / 100.0) | - (results.max_neg_step <= -1.0 * max_negative_step) - #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 - results.loc[filt, 'valid'] = False + # critera for exclusions + 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 + results.loc[filt, 'run_slope_high'] = 0 + results.loc[filt, 'valid'] = False + # Calculate the next inferred start loss from next valid interval results['next_inferred_start_loss'] = np.clip( results[results.valid].inferred_start_loss.shift(-1), 0, 1) - # Calculate the inferred recovery at the end of each interval - ######################################################################## - #remove clipping on 'inferred_recovery' so absolute recovery can be - #used in later step where clipping can be considered/Matt - results['inferred_recovery'] = results.next_inferred_start_loss - results.inferred_end_loss - - ######################################################################## - #calculate beginning inferred shift (end of previous soiling period - #to start of current period)/Matt - results['prev_end'] = results[results.valid].inferred_end_loss.shift(1) - #if the current interval starts with a clean event, the previous end - #is a nan, and the current interval is valid then set prev_end=1 - results.loc[(results.clean_event==True)&(np.isnan(results.prev_end)&(results.valid==True)),'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 negative due to fitting results - results.loc[results.clean_event==True,'inferred_begin_shift']=np.clip(results.inferred_begin_shift,0,1) - ####################################################################### - + results['inferred_recovery'] = np.clip( + results.next_inferred_start_loss - results.inferred_end_loss, + 0, 1) if len(results[results.valid]) == 0: raise NoValidIntervalError('No valid soiling intervals were found') @@ -474,107 +326,24 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, pm_frame_out['loss_inferred_clean'] = np.nan pm_frame_out['days_since_clean'] = \ (pm_frame_out.index - pm_frame_out.start).dt.days - - ####################################################################### - #new code for perfect and inferred clean with handling of/Matt - #negative shifts and changepoints within soiling intervals - #goes to line 563 - ####################################################################### - pm_frame_out.inferred_begin_shift.bfill(inplace=True) - pm_frame_out['forward_median']=pm_frame_out.pi.iloc[::-1].rolling(10,min_periods=5).median() - prev_shift=1 - soil_inferred_clean=[] - soil_perfect_clean=[] - day_start=-1 - start_infer=1 - start_perfect=1 - soil_infer=1 - soil_perfect=1 - total_down=0 - shift=0 - shift_perfect=0 - begin_perfect_shifts=[0] - 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): - new_soil=d-day_start - day_start=d - - if new_soil<=0:#begin new soil period - if (start_shift==prev_shift)|(changepoint==True):#no shift at - #a slope changepoint - shift=0 - shift_perfect=0 - else: - if (start_shift<0)&(prev_shift<0):#(both negative) or - #downward shifts to start last 2 intervals - shift=0 - shift_perfect=0 - total_down=total_down+start_shift #adding total downshifts - #to subtract from an eventual cleaning event - elif(start_shift>0)&(prev_shift>=0):#(both positive) or - #cleanings start the last 2 intervals - shift=start_shift - shift_perfect=1 - total_down=0 - #add #####################3/27/24 - elif(start_shift==0)&(prev_shift>=0):#( - shift=start_shift - shift_perfect=start_shift - total_down=0 - ############################################################# - elif (start_shift>=0)&(prev_shift<0):#cleaning starts the current - #interval but there was a previous downshift - shift=start_shift+total_down #correct for the negative shifts - 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):#negative shift starts the interval, - #previous shift was cleaning - shift=0 - shift_perfect=0 - total_down=start_shift - #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 skewed below where data start - if (soil_infer+shift)0:#within soiling period - #append the daily soiling ratio to each modeled fit - soil_infer=start_infer+rs*d - soil_inferred_clean.append(soil_infer) - - soil_perfect=start_perfect+rs*d - soil_perfect_clean.append(soil_perfect) - pm_frame_out['loss_inferred_clean'] = \ - pd.Series(soil_inferred_clean,index=pm_frame_out.index) + # Calculate the daily derate pm_frame_out['loss_perfect_clean'] = \ - pd.Series(soil_perfect_clean,index=pm_frame_out.index) + pm_frame_out.start_loss + \ + pm_frame_out.days_since_clean * pm_frame_out.run_slope + # 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) + + pm_frame_out['loss_inferred_clean'] = \ + pm_frame_out.inferred_start_loss + \ + pm_frame_out.days_since_clean * pm_frame_out.run_slope + # filling the flat intervals may need to be recalculated + # for different assumptions + pm_frame_out.loss_inferred_clean = \ + pm_frame_out.loss_inferred_clean.fillna(1) - results['begin_perfect_shift']=pd.Series(begin_perfect_shifts) - results['begin_infer_shift']=pd.Series(begin_infer_shifts) - ####################################################################### self.result_df = results self.analyzed_daily_df = pm_frame_out @@ -589,31 +358,18 @@ def _calc_monte(self, monte, method='half_norm_clean'): ---------- monte : int number of Monte Carlo simulations to run - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', - perfect_clean_complex,inferred_clean_complex} \ - default 'half_norm_clean' - + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ + default 'half_norm_clean' How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100%, - pair with piecewise=False and neg_shift=False + * 'random_clean' - a random recovery between 0-100% * 'perfect_clean' - each cleaning event returns the performance - metric to 1, - pair with piecewise=False and neg_shift=False + metric to 1 * 'half_norm_clean' - The starting point of each interval is taken - randomly from a half normal distribution with its mode (mu) at 1 and - its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval, - pair with piecewise=False and neg_shift=False - *'perfect_clean_complex' - each detected clean event returns the - performance metric to 1 while negative shifts in the data or - piecewise linear fits result in no cleaning, - pair with piecewise=True and neg_shift=True - *'inferred_clean_complex' - at each detected clean event the - performance metric increases based on fits to the data while - negative shifts in the data or piecewise linear fits result in no - cleaning, - pair with piecewise=True and neg_shift=True + randomly from a half normal distribution with its + mode (mu) at 1 and + its sigma equal to 1/3 * (1-b) where b is the intercept + of the fit to the interval. ''' # Raise a warning if there is >20% invalid data @@ -657,14 +413,10 @@ def _calc_monte(self, monte, method='half_norm_clean'): # randomize the extent of the cleaning inter_start = 1.0 - delta_previous_run_loss=0 start_list = [] if (method == 'half_norm_clean') or (method == 'random_clean'): # Randomize recovery of valid intervals only valid_intervals = results_rand[results_rand.valid].copy() - valid_intervals['inferred_recovery'] = np.clip( - valid_intervals.inferred_recovery, - 0, 1) valid_intervals['inferred_recovery'] = \ valid_intervals.inferred_recovery.fillna(1.0) @@ -692,9 +444,9 @@ def _calc_monte(self, monte, method='half_norm_clean'): # forward and back fill to note the limits of random constant # derate for invalid intervals results_rand['previous_end'] = \ - results_rand.end_loss.ffill() + results_rand.end_loss.fillna(method='ffill') results_rand['next_start'] = \ - results_rand.start_loss.bfill() + results_rand.start_loss.fillna(method='bfill') # Randomly select random constant derate for invalid intervals # based on previous end and next beginning @@ -720,46 +472,13 @@ def _calc_monte(self, monte, method='half_norm_clean'): invalid_update['start_loss'] = replace_levels invalid_update.index = invalid_intervals.index results_rand.update(invalid_update) + elif method == 'perfect_clean': for i, row in results_rand.iterrows(): start_list.append(inter_start) end = inter_start + row.run_loss inter_start = 1 results_rand['start_loss'] = start_list - ################################################################## - #matt additions - - elif method == 'perfect_clean_complex': - for i, row in results_rand.iterrows(): - if row.begin_perfect_shift>0: - inter_start=np.clip((inter_start+row.begin_perfect_shift+delta_previous_run_loss),end,1) - delta_previous_run_loss=-1*row.run_loss-row.run_loss_baseline - else: - delta_previous_run_loss=delta_previous_run_loss-1*row.run_loss-row.run_loss_baseline - #inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) - start_list.append(inter_start) - end = inter_start + row.run_loss - - inter_start = end - results_rand['start_loss'] = start_list - - elif method == 'inferred_clean_complex': - for i, row in results_rand.iterrows(): - if row.begin_infer_shift>0: - inter_start=np.clip((inter_start+row.begin_infer_shift+delta_previous_run_loss),end,1) - delta_previous_run_loss=-1*row.run_loss-row.run_loss_baseline - else: - delta_previous_run_loss=delta_previous_run_loss-1*row.run_loss-row.run_loss_baseline - #inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) - start_list.append(inter_start) - end = inter_start + row.run_loss - - inter_start = end - results_rand['start_loss'] = start_list - """ - - """ - ############################################### else: raise ValueError("Invalid method specification") @@ -771,8 +490,8 @@ def _calc_monte(self, monte, method='half_norm_clean'): df_rand['days_since_clean'] = \ (df_rand.index - df_rand.start).dt.days df_rand['loss'] = df_rand.start_loss + \ - df_rand.days_since_clean * df_rand.run_slope - + df_rand.days_since_clean * df_rand.run_slope + df_rand['soil_insol'] = df_rand.loss * df_rand.insol soiling_ratio = ( @@ -786,14 +505,12 @@ def _calc_monte(self, monte, method='half_norm_clean'): self.random_profiles = random_profiles self.monte_losses = monte_losses - ####################################################################### - #add neg_shift and piecewise to the following def/Matt + 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,neg_shift=False,piecewise=False): + max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5): ''' Run the SRR method from beginning to end. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented @@ -815,31 +532,17 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', trim : bool, default False Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', - perfect_clean_complex,inferred_clean_complex} \ - default 'perfect_clean_complex' - + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ + default 'half_norm_clean' How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100%, - pair with piecewise=False and neg_shift=False + * 'random_clean' - a random recovery between 0-100% * 'perfect_clean' - each cleaning event returns the performance - metric to 1, - pair with piecewise=False and neg_shift=False + metric to 1 * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval, - pair with piecewise=False and neg_shift=False - *'perfect_clean_complex' - each detected clean event returns the - performance metric to 1 while negative shifts in the data or - piecewise linear fits result in no cleaning, - pair with piecewise=True and neg_shift=True - *'inferred_clean_complex' - at each detected clean event the - performance metric increases based on fits to the data while - negative shifts in the data or piecewise linear fits result in no - cleaning, - pair with piecewise=True and neg_shift=True + the interval. clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -876,19 +579,6 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. - neg_shift : bool, default True - where True results in additional subdividing of soiling intervals - when negative shifts are found in the rolling median of the performance - metric. Inferred corrections in the soiling fit are made at these - negative shifts. False results in no additional subdivides of the - data where excessive negative shifts can invalidate a soiling interval. - piecewise : bool, default True - where True results in each soiling interval of sufficient length - being tested for significant fit improvement with 2 piecewise linear - fits. If the criteria of significance is met the soiling interval is - subdivided into the 2 separate intervals. False results in no - piecewise fit being tested. - Returns ------- @@ -948,14 +638,11 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', recenter=recenter, clean_criterion=clean_criterion, precip_threshold=precip_threshold, - outlier_factor=outlier_factor, - neg_shift=neg_shift, - piecewise=piecewise) + outlier_factor=outlier_factor) self._calc_result_df(trim=trim, max_relative_slope_error=max_relative_slope_error, max_negative_step=max_negative_step, - min_interval_length=min_interval_length, - neg_shift=neg_shift) + min_interval_length=min_interval_length) self._calc_monte(reps, method=method) # Calculate the P50 and confidence interval @@ -968,12 +655,10 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', P_level = result[3] # Construct calc_info output - ############################################### - #add inferred_recovery, inferred_begin_shift /Matt - ############################################### + intervals_out = self.result_df[ ['start', 'end', 'run_slope', 'run_slope_low', - 'run_slope_high', 'inferred_start_loss', 'inferred_end_loss','inferred_recovery','inferred_begin_shift', + 'run_slope_high', 'inferred_start_loss', 'inferred_end_loss', 'length', 'valid']].copy() intervals_out.rename(columns={'run_slope': 'soiling_rate', 'run_slope_high': 'soiling_rate_high', @@ -981,46 +666,24 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', }, inplace=True) df_d = self.analyzed_daily_df - #sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] - sr_perfect = df_d.loss_perfect_clean - ###################################################### - #enable addtional items to be output//Matt - sr_inferred = df_d.loss_inferred_clean - sr_days_since_clean=df_d.days_since_clean - sr_run_slope=df_d.run_slope - sr_infer_rec=df_d.inferred_recovery - sr_infer_begin_rec=df_d.inferred_begin_shift - sr_changepoints=df_d.slope_change_event - ###################################################### - + sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] calc_info = { 'exceedance_level': P_level, 'renormalizing_factor': self.renorm_factor, 'stochastic_soiling_profiles': self.random_profiles, 'soiling_interval_summary': intervals_out, - 'soiling_ratio_perfect_clean': sr_perfect, - ########################################## - #add these lines to output//Matt - 'soiling_ratio_inferred_clean':sr_inferred, - 'days_since_clean':sr_days_since_clean, - 'run_slope':sr_run_slope, - 'inferred_recovery':sr_infer_rec, - 'inferred_begin_shift':sr_infer_begin_rec, - 'change_points':sr_changepoints - ############################################# + 'soiling_ratio_perfect_clean': sr_perfect } return (result[0], result[1:3], calc_info) -#more updates are needed for documentation but added additional inputs -#that are in srr.run /Matt + def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitation_daily=None, 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,neg_shift=False,piecewise=False): + max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5): ''' Functional wrapper for :py:class:`~rdtools.soiling.SRRAnalysis`. Perform the stochastic rate and recovery soiling loss calculation. Based on the @@ -1053,31 +716,17 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, trim : bool, default False Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', - perfect_clean_complex,inferred_clean_complex} \ + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ default 'half_norm_clean' - How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100%, - pair with piecewise=False and neg_shift=False + * 'random_clean' - a random recovery between 0-100% * 'perfect_clean' - each cleaning event returns the performance - metric to 1, - pair with piecewise=False and neg_shift=False + metric to 1 * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval, - pair with piecewise=False and neg_shift=False - *'perfect_clean_complex' - each detected clean event returns the - performance metric to 1 while negative shifts in the data or - piecewise linear fits result in no cleaning, - pair with piecewise=True and neg_shift=True - *'inferred_clean_complex' - at each detected clean event the - performance metric increases based on fits to the data while - negative shifts in the data or piecewise linear fits result in no - cleaning, - pair with piecewise=True and neg_shift=True + the interval. clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -1113,18 +762,6 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. - neg_shift : bool, default True - where True results in additional subdividing of soiling intervals - when negative shifts are found in the rolling median of the performance - metric. Inferred corrections in the soiling fit are made at these - negative shifts. False results in no additional subdivides of the - data where excessive negative shifts can invalidate a soiling interval. - piecewise : bool, default True - where True results in each soiling interval of sufficient length - being tested for significant fit improvement with 2 piecewise linear - fits. If the criteria of significance is met the soiling interval is - subdivided into the 2 separate intervals. False results in no - piecewise fit being tested. Returns ------- @@ -1197,9 +834,7 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, 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) + outlier_factor=outlier_factor) return sr, sr_ci, soiling_info @@ -2127,11 +1762,11 @@ def run_bootstrap(self, self.soiling_loss = [0, 0, (1 - result_df.soiling_ratio).mean()] self.small_soiling_signal = True self.errors = ( - 'Soiling signal is small relative to the noise.' - 'Iterative decomposition not possible.\n' - 'Degradation found by RdTools YoY') - print(self.errors) - return + 'Soiling signal is small relative to the noise. ' + 'Iterative decomposition not possible. ' + 'Degradation found by RdTools YoY.') + warnings.warn(self.errors) + return self.result_df, self.degradation, self.soiling_loss self.small_soiling_signal = False # Aggregate all bootstrap samples @@ -2872,7 +2507,8 @@ def _make_seasonal_samples(list_of_SCs, sample_nr=10, min_multiplier=0.5, ''' Generate seasonal samples by perturbing the amplitude and the phase of a seasonal components found with the fitted CODS model ''' samples = pd.DataFrame(index=list_of_SCs[0].index, - columns=range(int(sample_nr*len(list_of_SCs)))) + columns=range(int(sample_nr*len(list_of_SCs))), + dtype=float) # From each fitted signal, we will generate new seaonal components for i, signal in enumerate(list_of_SCs): # Remove beginning and end of signal @@ -2964,7 +2600,7 @@ def _find_numeric_outliers(x, multiplier=1.5, where='both', verbose=False): def _RMSE(y_true, y_pred): '''Calculates the Root Mean Squared Error for y_true and y_pred, where y_pred is the "prediction", and y_true is the truth.''' - mask = ~pd.isnull(y_pred) + mask = ~np.isnan(y_pred) return np.sqrt(np.mean((y_pred[mask]-y_true[mask])**2)) @@ -2984,91 +2620,4 @@ def _progressBarWithETA(value, endvalue, time, bar_length=20): sys.stdout.write( "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left) + " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent)) - sys.stdout.flush() -############################################################################### -#all code below for new piecewise fitting in soiling intervals within srr/Matt -############################################################################### -def piecewise_linear(x, x0, b, k1, k2): - cond_list=[x=x0] - func_list=[lambda x: k1*x+b, lambda x: k1*x+b+k2*(x-x0)] - return np.piecewise(x, cond_list, func_list) - -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 - """ - 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 (days_clean_vs_cp) of the cleanings. - - Parameters - ---------- - pr : - Series of daily performance ratios measured during the given deposition period. - fill_method : str (default='bfill') - Method to employ to fill any missing day. - 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 defined in the function. - initial_guesses : numeric (default=0.1) - List of initial guesses for fitting function - min_r2 : numeric (default=0.1) - Minimum R2 to consider valid the extracted soiling profile. - - Returns - ------- - sr: numeric - Series containing the daily soiling ratio values after segmentation. - List of nan if segmentation was not possible. - cp_date: datetime - Datetime in which continuous change points occurred. - None if segmentation was not possible. - """ - - #Check if PR dataframe has datetime index - if not isinstance(pr.index, pd.DatetimeIndex): - raise ValueError('The time series does not have DatetimeIndex') - - #Define bounds if not provided - if bounds==None: - #bounds are neg in first 4 and pos in second 4 - #ordered as x0,b,k1,k2 where x0 is the breakpoint k1 and k2 are slopes - bounds=[(13,-5,-np.inf, -np.inf),((len(pr)-13),5,+np.inf,+np.inf)] - y=pr.values - x=np.arange(0.,len(y)) - - try: - #Fit soiling profile with segmentation - 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. None if no change point is found. - if p[0]>days_clean_vs_cp and p[0] Date: Wed, 31 Jul 2024 11:28:07 -0600 Subject: [PATCH 04/33] Improvements in order to pass checks and pytesting Signed-off-by: nmoyer --- docs/notebook_requirements.txt | 4 - rdtools/soiling.py | 2218 ++++++++++++++++++++++---------- rdtools/test/soiling_test.py | 16 +- 3 files changed, 1525 insertions(+), 713 deletions(-) diff --git a/docs/notebook_requirements.txt b/docs/notebook_requirements.txt index b6577309..fc83aa5d 100644 --- a/docs/notebook_requirements.txt +++ b/docs/notebook_requirements.txt @@ -31,11 +31,7 @@ nbformat==5.1.0 nest-asyncio==1.5.5 notebook==6.4.12 numexpr==2.10.0 -<<<<<<< HEAD pandocfilters==1.5.1 -======= -pandocfilters==1.4.2 ->>>>>>> remotes/origin/aggregated_filters_for_trials parso==0.5.2 pexpect==4.6.0 pickleshare==0.7.5 diff --git a/rdtools/soiling.py b/rdtools/soiling.py index ce318021..2860d427 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1,10 +1,11 @@ -''' +""" Functions for calculating soiling metrics from photovoltaic system data. The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures. -''' +""" + from rdtools import degradation as RdToolsDeg from rdtools.bootstrap import _make_time_series_bootstrap_samples @@ -22,23 +23,29 @@ from statsmodels.tsa.seasonal import STL from statsmodels.tsa.stattools import adfuller import statsmodels.api as sm -lowess = sm.nonparametric.lowess + +from scipy.optimize import curve_fit + +import scipy.stats as st + +lowess = sm.nonparametric.lowess # Used in CODSAnalysis/Matt warnings.warn( - 'The soiling module is currently experimental. The API, results, ' - 'and default behaviors may change in future releases (including MINOR ' - 'and PATCH releases) as the code matures.' + "The soiling module is currently experimental. The API, results, " + "and default behaviors may change in future releases (including MINOR " + "and PATCH releases) as the code matures." ) # Custom exception class NoValidIntervalError(Exception): - '''raised when no valid rows appear in the result dataframe''' + """raised when no valid rows appear in the result dataframe""" + pass -class SRRAnalysis(): - ''' +class SRRAnalysis: + """ Class for running the stochastic rate and recovery (SRR) photovoltaic soiling loss analysis presented in Deceglie et al. JPV 8(2) p547 2018 @@ -55,10 +62,11 @@ class SRRAnalysis(): precipitation_daily : pandas.Series, default None Daily total precipitation. (Ignored if ``clean_criterion='shift'`` in subsequent calculations.) - ''' + """ - def __init__(self, energy_normalized_daily, insolation_daily, - precipitation_daily=None): + def __init__( + self, energy_normalized_daily, insolation_daily, precipitation_daily=None + ): self.pm = energy_normalized_daily # daily performance metric self.insolation_daily = insolation_daily self.precipitation_daily = precipitation_daily # daily precipitation @@ -66,23 +74,32 @@ def __init__(self, energy_normalized_daily, insolation_daily, # insolation-weighted soiling ratios in _calc_monte: self.monte_losses = [] - if pd.infer_freq(self.pm.index) != 'D': - raise ValueError('Daily performance metric series must have ' - 'daily frequency') + if pd.infer_freq(self.pm.index) != "D": + raise ValueError( + "Daily performance metric series must have " "daily frequency" + ) - if pd.infer_freq(self.insolation_daily.index) != 'D': - raise ValueError('Daily insolation series must have ' - 'daily frequency') + if pd.infer_freq(self.insolation_daily.index) != "D": + raise ValueError("Daily insolation series must have " "daily frequency") if self.precipitation_daily is not None: - if pd.infer_freq(self.precipitation_daily.index) != 'D': - raise ValueError('Precipitation series must have ' - 'daily frequency') - - def _calc_daily_df(self, day_scale=13, clean_threshold='infer', - recenter=True, clean_criterion='shift', precip_threshold=0.01, - outlier_factor=1.5): - ''' + if pd.infer_freq(self.precipitation_daily.index) != "D": + raise ValueError("Precipitation series must have " "daily frequency") + + ############################################################################### + # add neg_shift and piecewise into parameters/Matt + def _calc_daily_df( + self, + day_scale=13, + clean_threshold="infer", + recenter=True, + clean_criterion="shift", + precip_threshold=0.01, + outlier_factor=1.5, + neg_shift=False, + piecewise=False, + ): + """ Calculates self.daily_df, a pandas dataframe prepared for SRR analysis, and self.renorm_factor, the renormalization factor for the daily performance @@ -118,26 +135,41 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. - ''' - if (day_scale % 2 == 0) and ('shift' in clean_criterion): - warnings.warn('An even value of day_scale was passed. An odd value is ' - 'recommended, otherwise, consecutive days may be erroneously ' - 'flagged as cleaning events. ' - 'See https://github.com/NREL/rdtools/issues/189') + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. + """ + if (day_scale % 2 == 0) and ("shift" in clean_criterion): + warnings.warn( + "An even value of day_scale was passed. An odd value is " + "recommended, otherwise, consecutive days may be erroneously " + "flagged as cleaning events. " + "See https://github.com/NREL/rdtools/issues/189" + ) df = self.pm.to_frame() - df.columns = ['pi'] + df.columns = ["pi"] df_insol = self.insolation_daily.to_frame() - df_insol.columns = ['insol'] + df_insol.columns = ["insol"] df = df.join(df_insol) precip = self.precipitation_daily + if precip is not None: df_precip = precip.to_frame() - df_precip.columns = ['precip'] + df_precip.columns = ["precip"] df = df.join(df_precip) else: - df['precip'] = 0 + df["precip"] = 0 # find first and last valid data point start = df[~df.pi.isnull()].index[0] @@ -145,22 +177,23 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', df = df[start:end] # create a day count column - df['day'] = range(len(df)) + df["day"] = range(len(df)) # Recenter to median of first year, as in YoY degradation if recenter: - oneyear = start + pd.Timedelta('364d') - renorm = df.loc[start:oneyear, 'pi'].median() + oneyear = start + pd.Timedelta("364d") + renorm = df.loc[start:oneyear, "pi"].median() else: renorm = 1 - df['pi_norm'] = df['pi'] / renorm + df["pi_norm"] = df["pi"] / renorm # Find the beginning and ends of outages longer than dayscale - bfill = df['pi_norm'].fillna(method='bfill', limit=day_scale) - ffill = df['pi_norm'].fillna(method='ffill', limit=day_scale) - out_start = (~df['pi_norm'].isnull() & bfill.shift(-1).isnull()) - out_end = (~df['pi_norm'].isnull() & ffill.shift(1).isnull()) + # THIS CODE TRIGGERES DEPRECATION WARNING hance minor changes/Matt + bfill = df["pi_norm"].bfill(limit=day_scale) + ffill = df["pi_norm"].ffill(limit=day_scale) + out_start = ~df["pi_norm"].isnull() & bfill.shift(-1).isnull() + out_end = ~df["pi_norm"].isnull() & ffill.shift(1).isnull() # clean up the first and last elements out_start.iloc[-1] = False @@ -168,53 +201,127 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer', # Make a forward filled copy, just for use in # step, slope change detection - df_ffill = df.fillna(method='ffill', limit=day_scale).copy() + # 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))) # Calculate rolling median - df['pi_roll_med'] = \ - df_ffill.pi_norm.rolling(day_scale, center=True).median() + df["pi_roll_med"] = df_ffill.pi_norm.rolling(day_scale, center=True).median() # Detect steps in rolling median - df['delta'] = df.pi_roll_med.diff() - if clean_threshold == 'infer': + df["delta"] = df.pi_roll_med.diff() + if clean_threshold == "infer": deltas = abs(df.delta) - clean_threshold = deltas.quantile(0.75) + \ - outlier_factor * (deltas.quantile(0.75) - deltas.quantile(0.25)) + clean_threshold = deltas.quantile(0.75) + outlier_factor * ( + deltas.quantile(0.75) - deltas.quantile(0.25) + ) + + df["clean_event_detected"] = df.delta > clean_threshold - df['clean_event_detected'] = (df.delta > clean_threshold) - precip_event = (df['precip'] > precip_threshold) + ########################################################################## + # 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( + df.clean_event_detected, df.delta.values, 5 + ) + df["clean_event_detected"] = reduced_cleaning_events - if clean_criterion == 'precip_and_shift': + ########################################################################## + precip_event = df["precip"] > precip_threshold + + if clean_criterion == "precip_and_shift": # Detect which cleaning events are associated with rain # within a 3 day window - 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': - df['clean_event'] = (df['clean_event_detected'] | precip_event) - elif clean_criterion == 'precip': - df['clean_event'] = precip_event - elif clean_criterion == 'shift': - df['clean_event'] = df['clean_event_detected'] + 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": + df["clean_event"] = df["clean_event_detected"] | precip_event + elif clean_criterion == "precip": + df["clean_event"] = precip_event + elif clean_criterion == "shift": + df["clean_event"] = df["clean_event_detected"] else: - raise ValueError('clean_criterion must be one of ' - '{"precip_and_shift", "precip_or_shift", ' - '"precip", "shift"}') + raise ValueError( + "clean_criterion must be one of " + '{"precip_and_shift", "precip_or_shift", ' + '"precip", "shift"}' + ) - df['clean_event'] = df.clean_event | out_start | out_end + df["clean_event"] = df.clean_event | out_start | out_end - df = df.fillna(0) + ####################################################################### + # add negative shifts which allows further segmentation of the soiling + # intervals and handles correction for data outages/Matt + df.delta = df.delta.fillna(0) # to avoid NA corrupting calculation + if neg_shift == True: + df["drop_event"] = df.delta < -2.5 * clean_threshold + df["break_event"] = df.clean_event | df.drop_event + else: + df["break_event"] = df.clean_event.copy() + + ####################################################################### + # This happens earlier than in the original code but is necessary + # for adding piecewise breakpoints/Matt # Give an index to each soiling interval/run - df['run'] = df.clean_event.cumsum() - df.index.name = 'date' # this gets used by name + df["run"] = df.break_event.cumsum() + df.index.name = "date" # this gets used by name + + ####################################################################### + # df.fillna(0) /remove as the zeros introduced in pi_norm negatively + # impact various fits in the code, I havent yet found the original purpose + # or a failure due to removing/Matt + + ##################################################################### + # piecewise=True enables adding a single breakpoint per soiling intervals + # if statistical criteria are met with the piecewise linear fit + # compared to a single linear fit. Intervals <45 days reqire more + # stringent statistical improvements/Matt + if piecewise == True: + warnings.warn( + "Piecewise = True was passed, for both Piecewise=True" + "and neg_shift=True cleaning_method choices should" + "be perfect_clean_complex or inferred_clean_complex" + ) + min_soil_length = 27 # min threshold of days necessary for piecewise fit + piecewise_loop = sorted(list(set(df["run"]))) + cp_dates = [] + for r in piecewise_loop: + run = df[df["run"] == r] + pr = run.pi_norm.copy() + pr = pr.ffill() # linear fitting cant handle nans + pr = pr.bfill() # catch first position nan + if len(run) > min_soil_length and run.pi_norm.sum() > 0: + sr, cp_date = segmented_soiling_period(pr, days_clean_vs_cp=13) + if cp_date != None: + cp_dates.append(pr.index[cp_date]) + # save changes to df, note I would like to rename "clean_event" from + # original code to something like "break_event + df["slope_change_event"] = df.index.isin(cp_dates) + df["break_event"] = df.break_event | df.slope_change_event + df["run"] = df.break_event.cumsum() + else: + df["slope_change_event"] = False + ###################################################################### self.renorm_factor = renorm self.daily_df = df - def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, - max_negative_step=0.05, min_interval_length=7): - ''' + ###################################################################### + # added neg_shift into parameters in the following def/Matt + def _calc_result_df( + self, + trim=False, + max_relative_slope_error=500.0, + max_negative_step=0.05, + min_interval_length=7, + neg_shift=False, + ): + """ Calculates self.result_df, a pandas dataframe summarizing the soiling intervals identified and self.analyzed_daily_df, a version of self.daily_df with additional columns calculated during analysis. @@ -234,21 +341,26 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, min_interval_length : int, default 7 The minimum duration for an interval to be considered valid. Cannot be less than 2 (days). - ''' + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + """ daily_df = self.daily_df result_list = [] if trim: # ignore first and last interval - res_loop = sorted(list(set(daily_df['run'])))[1:-1] + res_loop = sorted(list(set(daily_df["run"])))[1:-1] else: - res_loop = sorted(list(set(daily_df['run']))) - - for r in res_loop: - run = daily_df[daily_df['run'] == r] - length = (run.day[-1] - run.day[0]) - start_day = run.day[0] - end_day = run.day[-1] + res_loop = sorted(list(set(daily_df["run"]))) + for r in res_loop: # Matt added .iloc due to deprecation warning + run = daily_df[daily_df["run"] == r] + length = run.day.iloc[-1] - run.day.iloc[0] + start_day = run.day.iloc[0] + end_day = run.day.iloc[-1] start = run.index[0] end = run.index[-1] run_filtered = run[run.pi_norm > 0] @@ -257,98 +369,284 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, # valid=False row if not run_filtered.empty: run = run_filtered + #################################################################### + # see commented changes result_dict = { - 'start': start, - 'end': end, - 'length': length, - 'run': r, - 'run_slope': 0, - 'run_slope_low': 0, - 'run_slope_high': 0, - 'max_neg_step': min(run.delta), - 'start_loss': 1, - 'inferred_start_loss': run.pi_norm.mean(), - 'inferred_end_loss': run.pi_norm.mean(), - 'valid': False + "start": start, + "end": end, + "length": length, + "run": r, + "run_slope": 0, + "run_slope_low": 0, + "run_slope_high": 0, + "max_neg_step": min(run.delta), + "start_loss": 1, + "inferred_start_loss": run.pi_norm.median(), # changed from mean/Matt + "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 from other breaks/Matt + "run_loss_baseline": 0.0, # loss from the polyfit over the soiling intercal/Matt + ############################################################## } if len(run) > min_interval_length and run.pi_norm.sum() > 0: fit = theilslopes(run.pi_norm, run.day) fit_poly = np.poly1d(fit[0:2]) - result_dict['run_slope'] = fit[0] - result_dict['run_slope_low'] = fit[2] - result_dict['run_slope_high'] = min([0.0, fit[3]]) - result_dict['inferred_start_loss'] = fit_poly(start_day) - result_dict['inferred_end_loss'] = fit_poly(end_day) - result_dict['valid'] = True + result_dict["run_slope"] = fit[0] + result_dict["run_slope_low"] = fit[2] + result_dict["run_slope_high"] = min([0.0, fit[3]]) + result_dict["valid"] = True + ######################################################## + # moved the following 2 line to the next section within conditional statement/Matt + # result_dict['inferred_start_loss'] = fit_poly(start_day) + # result_dict['inferred_end_loss'] = fit_poly(end_day) + + #################################################### + # the following is moved here so median values are retained/Matt + # for soiling inferrences when rejected fits occur + result_dict["slope_err"] = ( + result_dict["run_slope_high"] - result_dict["run_slope_low"] + ) / abs(result_dict["run_slope"]) + + if (result_dict["slope_err"] <= (max_relative_slope_error / 100.0)) & ( + result_dict["run_slope"] < 0 + ): + result_dict["inferred_start_loss"] = fit_poly(start_day) + result_dict["inferred_end_loss"] = fit_poly(end_day) + ############################################# + # calculate loss over soiling interval per polyfit/matt + result_dict["run_loss_baseline"] = ( + result_dict["inferred_start_loss"] + - result_dict["inferred_end_loss"] + ) + + ############################################### + result_list.append(result_dict) results = pd.DataFrame(result_list) if results.empty: - raise NoValidIntervalError('No valid soiling intervals were found') + raise NoValidIntervalError("No valid soiling intervals were found") + """ # Filter results for each interval, - # setting invalid interval to slope of 0 + # setting invalid interval to slope of 0 + #moved above to line 356/Matt results['slope_err'] = ( results.run_slope_high - results.run_slope_low)\ / abs(results.run_slope) - # critera for exclusions - filt = ( - (results.run_slope > 0) | - (results.slope_err >= max_relative_slope_error / 100.0) | - (results.max_neg_step <= -1.0 * max_negative_step) - ) + """ + ############################################################### + # negative shifts are now used as breaks for soiling intervals/Matt + # so new criteria for final filter to modify dataframe + if neg_shift == True: + warnings.warn( + "neg_shift = True was passed, for both Piecewise=True" + "and neg_shift=True cleaning_method choices should" + "be perfect_clean_complex or inferred_clean_complex" + ) + 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 - results.loc[filt, 'run_slope_high'] = 0 - 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 + # only intervals that are now not valid are those that dont meet + # the minimum inteval length or have no data + # results.loc[filt, 'valid'] = False + ################################################################## + # original code below setting soiling intervals with extreme negative + # shift to zero slopes, /Matt + if neg_shift == False: + filt = ( + (results.run_slope > 0) + | (results.slope_err >= max_relative_slope_error / 100.0) + | (results.max_neg_step <= -1.0 * max_negative_step) + # remove line 389, want to store data for inferred values + # for calculations below + # |results.loc[filt, 'valid'] = False + ) + print(results.slope_err) + results.loc[filt, "run_slope"] = 0 + results.loc[filt, "run_slope_low"] = 0 + results.loc[filt, "run_slope_high"] = 0 # Calculate the next inferred start loss from next valid interval - results['next_inferred_start_loss'] = np.clip( - results[results.valid].inferred_start_loss.shift(-1), - 0, 1) + results["next_inferred_start_loss"] = np.clip( + results[results.valid].inferred_start_loss.shift(-1), 0, 1 + ) + # Calculate the inferred recovery at the end of each interval - results['inferred_recovery'] = np.clip( - results.next_inferred_start_loss - results.inferred_end_loss, - 0, 1) + ######################################################################## + # remove clipping on 'inferred_recovery' so absolute recovery can be + # used in later step where clipping can be considered/Matt + results["inferred_recovery"] = ( + results.next_inferred_start_loss - results.inferred_end_loss + ) + + ######################################################################## + # calculate beginning inferred shift (end of previous soiling period + # to start of current period)/Matt + results["prev_end"] = results[results.valid].inferred_end_loss.shift(1) + # if the current interval starts with a clean event, the previous end + # is a nan, and the current interval is valid then set prev_end=1 + results.loc[ + (results.clean_event == True) + & (np.isnan(results.prev_end) & (results.valid == True)), + "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 negative due to fitting results + results.loc[results.clean_event == True, "inferred_begin_shift"] = np.clip( + results.inferred_begin_shift, 0, 1 + ) + ####################################################################### + if neg_shift == False: + results.loc[filt, "valid"] = False if len(results[results.valid]) == 0: - raise NoValidIntervalError('No valid soiling intervals were found') + raise NoValidIntervalError("No valid soiling intervals were found") new_start = results.start.iloc[0] new_end = results.end.iloc[-1] pm_frame_out = daily_df[new_start:new_end] - pm_frame_out = pm_frame_out.reset_index() \ - .merge(results, how='left', on='run') \ - .set_index('date') - - pm_frame_out['loss_perfect_clean'] = np.nan - pm_frame_out['loss_inferred_clean'] = np.nan - pm_frame_out['days_since_clean'] = \ - (pm_frame_out.index - pm_frame_out.start).dt.days - - # Calculate the daily derate - pm_frame_out['loss_perfect_clean'] = \ - pm_frame_out.start_loss + \ - pm_frame_out.days_since_clean * pm_frame_out.run_slope - # 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) - - pm_frame_out['loss_inferred_clean'] = \ - pm_frame_out.inferred_start_loss + \ - pm_frame_out.days_since_clean * pm_frame_out.run_slope - # filling the flat intervals may need to be recalculated - # for different assumptions - pm_frame_out.loss_inferred_clean = \ - pm_frame_out.loss_inferred_clean.fillna(1) + pm_frame_out = ( + pm_frame_out.reset_index() + .merge(results, how="left", on="run") + .set_index("date") + ) + + pm_frame_out["loss_perfect_clean"] = np.nan + pm_frame_out["loss_inferred_clean"] = np.nan + pm_frame_out["days_since_clean"] = ( + pm_frame_out.index - pm_frame_out.start + ).dt.days + + ####################################################################### + # new code for perfect and inferred clean with handling of/Matt + # negative shifts and changepoints within soiling intervals + # goes to line 563 + ####################################################################### + pm_frame_out.inferred_begin_shift.bfill(inplace=True) + pm_frame_out["forward_median"] = ( + pm_frame_out.pi.iloc[::-1].rolling(10, min_periods=5).median() + ) + prev_shift = 1 + soil_inferred_clean = [] + soil_perfect_clean = [] + day_start = -1 + start_infer = 1 + start_perfect = 1 + soil_infer = 1 + soil_perfect = 1 + total_down = 0 + shift = 0 + shift_perfect = 0 + begin_perfect_shifts = [0] + 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, + ): + new_soil = d - day_start + day_start = d + + if new_soil <= 0: # begin new soil period + if (start_shift == prev_shift) | (changepoint == True): # no shift at + # a slope changepoint + shift = 0 + shift_perfect = 0 + else: + if (start_shift < 0) & (prev_shift < 0): # (both negative) or + # downward shifts to start last 2 intervals + shift = 0 + shift_perfect = 0 + total_down = total_down + start_shift # adding total downshifts + # to subtract from an eventual cleaning event + elif (start_shift > 0) & (prev_shift >= 0): # (both positive) or + # cleanings start the last 2 intervals + shift = start_shift + shift_perfect = 1 + total_down = 0 + # add #####################3/27/24 + elif (start_shift == 0) & (prev_shift >= 0): # ( + shift = start_shift + shift_perfect = start_shift + total_down = 0 + ############################################################# + elif (start_shift >= 0) & ( + prev_shift < 0 + ): # cleaning starts the current + # interval but there was a previous downshift + shift = ( + start_shift + total_down + ) # correct for the negative shifts + 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 + ): # negative shift starts the interval, + # previous shift was cleaning + shift = 0 + shift_perfect = 0 + total_down = start_shift + # 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 skewed below where data start + if (soil_infer + shift) < forward_median: + shift = forward_median - soil_infer + if (soil_perfect + shift_perfect) < forward_median: + shift_perfect = forward_median - soil_perfect + + # append the daily soiling ratio to each modeled fit + begin_perfect_shifts.append(shift_perfect) + begin_infer_shifts.append(shift) + # clip to last value in case shift ends up negative + soil_infer = np.clip((soil_infer + shift), soil_infer, 1) + start_infer = ( + soil_infer # make next start value the last inferred value + ) + soil_inferred_clean.append(soil_infer) + # clip to last value in case shift ends up negative + soil_perfect = np.clip((soil_perfect + shift_perfect), soil_perfect, 1) + start_perfect = soil_perfect + soil_perfect_clean.append(soil_perfect) + if changepoint == False: + prev_shift = start_shift # assigned at new soil period + + elif new_soil > 0: # within soiling period + # append the daily soiling ratio to each modeled fit + soil_infer = start_infer + rs * d + soil_inferred_clean.append(soil_infer) + + soil_perfect = start_perfect + rs * d + soil_perfect_clean.append(soil_perfect) + + pm_frame_out["loss_inferred_clean"] = pd.Series( + soil_inferred_clean, index=pm_frame_out.index + ) + pm_frame_out["loss_perfect_clean"] = pd.Series( + soil_perfect_clean, index=pm_frame_out.index + ) + results["begin_perfect_shift"] = pd.Series(begin_perfect_shifts) + results["begin_infer_shift"] = pd.Series(begin_infer_shifts) + ####################################################################### self.result_df = results self.analyzed_daily_df = pm_frame_out - def _calc_monte(self, monte, method='half_norm_clean'): - ''' + def _calc_monte(self, monte, method="half_norm_clean"): + """ Runs the Monte Carlo step of the SRR method. Calculates self.random_profiles, a list of the random soiling profiles realized in the calculation, and self.monte_losses, a list of the @@ -358,47 +656,66 @@ def _calc_monte(self, monte, method='half_norm_clean'): ---------- monte : int number of Monte Carlo simulations to run - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ - default '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% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and - its sigma equal to 1/3 * (1-b) where b is the intercept - of the fit to the interval. - ''' + its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to + the interval, + pair with piecewise=False and neg_shift=False + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True + """ # Raise a warning if there is >20% invalid data - if (method == 'half_norm_clean') or (method == 'random_clean'): - valid_fraction = self.analyzed_daily_df['valid'].mean() + if ( + (method == "half_norm_clean") + or (method == "random_clean") + 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 ' - 'intervals. This can be problematic with the "half_norm_clean" ' - 'and "random_clean" cleaning assumptions. Consider more permissive ' - 'validity criteria such as increasing "max_relative_slope_error" ' - 'and/or "max_negative_step" and/or decreasing "min_interval_length".' - ' Alternatively, consider using method="perfect_clean". For more' - ' info see https://github.com/NREL/rdtools/issues/272' - ) + warnings.warn( + "20% or more of the daily data is assigned to invalid soiling " + 'intervals. This can be problematic with the "half_norm_clean" ' + 'and "random_clean" cleaning assumptions. Consider more permissive ' + 'validity criteria such as increasing "max_relative_slope_error" ' + 'and/or "max_negative_step" and/or decreasing "min_interval_length".' + ' Alternatively, consider using method="perfect_clean". For more' + " info see https://github.com/NREL/rdtools/issues/272" + ) monte_losses = [] random_profiles = [] for _ in range(monte): results_rand = self.result_df.copy() df_rand = self.analyzed_daily_df.copy() # only really need this column from the original frame: - df_rand = df_rand[['insol', 'run']] - results_rand['run_slope'] = \ - np.random.uniform(results_rand.run_slope_low, - results_rand.run_slope_high) - results_rand['run_loss'] = \ - results_rand.run_slope * results_rand.length + df_rand = df_rand[["insol", "run"]] + results_rand["run_slope"] = np.random.uniform( + results_rand.run_slope_low, results_rand.run_slope_high + ) + results_rand["run_loss"] = results_rand.run_slope * results_rand.length - results_rand['end_loss'] = np.nan - results_rand['start_loss'] = np.nan + results_rand["end_loss"] = np.nan + results_rand["start_loss"] = np.nan # Make groups that start with a valid interval and contain # subsequent invalid intervals @@ -409,16 +726,21 @@ def _calc_monte(self, monte, method='half_norm_clean'): group += 1 group_list.append(group) - results_rand['group'] = group_list + results_rand["group"] = group_list # randomize the extent of the cleaning inter_start = 1.0 + delta_previous_run_loss = 0 start_list = [] - if (method == 'half_norm_clean') or (method == 'random_clean'): + if (method == "half_norm_clean") or (method == "random_clean"): # Randomize recovery of valid intervals only valid_intervals = results_rand[results_rand.valid].copy() - valid_intervals['inferred_recovery'] = \ + valid_intervals["inferred_recovery"] = np.clip( + valid_intervals.inferred_recovery, 0, 1 + ) + valid_intervals["inferred_recovery"] = ( valid_intervals.inferred_recovery.fillna(1.0) + ) end_list = [] for i, row in valid_intervals.iterrows(): @@ -426,27 +748,25 @@ def _calc_monte(self, monte, method='half_norm_clean'): end = inter_start + row.run_loss end_list.append(end) - if method == 'half_norm_clean': + if method == "half_norm_clean": # Use a half normal with the inferred clean at the # 3sigma point x = np.clip(end + row.inferred_recovery, 0, 1) inter_start = 1 - abs(np.random.normal(0.0, (1 - x) / 3)) - elif method == 'random_clean': + elif method == "random_clean": inter_start = np.random.uniform(end, 1) # Update the valid rows in results_rand valid_update = pd.DataFrame() - valid_update['start_loss'] = start_list - valid_update['end_loss'] = end_list + valid_update["start_loss"] = start_list + valid_update["end_loss"] = end_list valid_update.index = valid_intervals.index results_rand.update(valid_update) # forward and back fill to note the limits of random constant # derate for invalid intervals - results_rand['previous_end'] = \ - results_rand.end_loss.fillna(method='ffill') - results_rand['next_start'] = \ - results_rand.start_loss.fillna(method='bfill') + results_rand["previous_end"] = results_rand.end_loss.ffill() + results_rand["next_start"] = results_rand.start_loss.bfill() # Randomly select random constant derate for invalid intervals # based on previous end and next beginning @@ -469,49 +789,129 @@ def _calc_monte(self, monte, method='half_norm_clean'): # Update results rand with the invalid rows replace_levels = np.concatenate(replace_levels) invalid_update = pd.DataFrame() - invalid_update['start_loss'] = replace_levels + invalid_update["start_loss"] = replace_levels invalid_update.index = invalid_intervals.index results_rand.update(invalid_update) - elif method == 'perfect_clean': + elif method == "perfect_clean": for i, row in results_rand.iterrows(): start_list.append(inter_start) end = inter_start + row.run_loss inter_start = 1 - results_rand['start_loss'] = start_list + results_rand["start_loss"] = start_list + ################################################################## + # matt additions + + elif method == "perfect_clean_complex": + for i, row in results_rand.iterrows(): + if row.begin_perfect_shift > 0: + inter_start = np.clip( + ( + inter_start + + row.begin_perfect_shift + + delta_previous_run_loss + ), + end, + 1, + ) + delta_previous_run_loss = ( + -1 * row.run_loss - row.run_loss_baseline + ) + else: + delta_previous_run_loss = ( + delta_previous_run_loss + - 1 * row.run_loss + - row.run_loss_baseline + ) + # inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) + start_list.append(inter_start) + end = inter_start + row.run_loss + + inter_start = end + results_rand["start_loss"] = start_list + + elif method == "inferred_clean_complex": + for i, row in results_rand.iterrows(): + if row.begin_infer_shift > 0: + inter_start = np.clip( + ( + inter_start + + row.begin_infer_shift + + delta_previous_run_loss + ), + end, + 1, + ) + delta_previous_run_loss = ( + -1 * row.run_loss - row.run_loss_baseline + ) + else: + delta_previous_run_loss = ( + delta_previous_run_loss + - 1 * row.run_loss + - row.run_loss_baseline + ) + # inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) + start_list.append(inter_start) + end = inter_start + row.run_loss + + inter_start = end + results_rand["start_loss"] = start_list + """ + + """ + ############################################### else: raise ValueError("Invalid method specification") - df_rand = df_rand.reset_index() \ - .merge(results_rand, how='left', on='run') \ - .set_index('date') - df_rand['loss'] = np.nan - df_rand['days_since_clean'] = \ - (df_rand.index - df_rand.start).dt.days - df_rand['loss'] = df_rand.start_loss + \ - df_rand.days_since_clean * df_rand.run_slope + df_rand = ( + df_rand.reset_index() + .merge(results_rand, how="left", on="run") + .set_index("date") + ) + df_rand["loss"] = np.nan + df_rand["days_since_clean"] = (df_rand.index - df_rand.start).dt.days + df_rand["loss"] = ( + df_rand.start_loss + df_rand.days_since_clean * df_rand.run_slope + ) - df_rand['soil_insol'] = df_rand.loss * df_rand.insol + df_rand["soil_insol"] = df_rand.loss * df_rand.insol soiling_ratio = ( - df_rand.soil_insol.sum() / df_rand.insol[ - ~df_rand.soil_insol.isnull()].sum() + df_rand.soil_insol.sum() + / df_rand.insol[~df_rand.soil_insol.isnull()].sum() ) monte_losses.append(soiling_ratio) - random_profile = df_rand['loss'].copy() - random_profile.name = 'stochastic_soiling_profile' + random_profile = df_rand["loss"].copy() + random_profile.name = "stochastic_soiling_profile" random_profiles.append(random_profile) self.random_profiles = random_profiles self.monte_losses = monte_losses - 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): - ''' + ####################################################################### + # add neg_shift and piecewise to the following def/Matt + 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, + neg_shift=False, + piecewise=False, + ): + """ Run the SRR method from beginning to end. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented in Deceglie et al. "Quantifying Soiling Loss Directly From PV Yield" @@ -532,17 +932,31 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', trim : bool, default False Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals - method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \ - default 'half_norm_clean' + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', + perfect_clean_complex,inferred_clean_complex} \ + default 'perfect_clean_complex' + How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval. + the interval, + pair with piecewise=False and neg_shift=False + * 'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + * 'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -579,6 +993,18 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. Returns ------- @@ -632,59 +1058,101 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer', | | be treated as a valid soiling interval | +------------------------+----------------------------------------------+ - ''' - 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) - self._calc_result_df(trim=trim, - max_relative_slope_error=max_relative_slope_error, - max_negative_step=max_negative_step, - min_interval_length=min_interval_length) + """ + 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, + min_interval_length=min_interval_length, + neg_shift=neg_shift, + ) self._calc_monte(reps, method=method) # Calculate the P50 and confidence interval half_ci = confidence_level / 2.0 - result = np.percentile(self.monte_losses, - [50, - 50.0 - half_ci, - 50.0 + half_ci, - 100 - exceedance_prob]) + result = np.percentile( + self.monte_losses, + [50, 50.0 - half_ci, 50.0 + half_ci, 100 - exceedance_prob], + ) P_level = result[3] # Construct calc_info output - + ############################################### + # add inferred_recovery, inferred_begin_shift /Matt + ############################################### intervals_out = self.result_df[ - ['start', 'end', 'run_slope', 'run_slope_low', - 'run_slope_high', 'inferred_start_loss', 'inferred_end_loss', - 'length', 'valid']].copy() - intervals_out.rename(columns={'run_slope': 'soiling_rate', - 'run_slope_high': 'soiling_rate_high', - 'run_slope_low': 'soiling_rate_low', - }, inplace=True) + [ + "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", + "run_slope_high": "soiling_rate_high", + "run_slope_low": "soiling_rate_low", + }, + inplace=True, + ) df_d = self.analyzed_daily_df - sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] + # sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] + sr_perfect = df_d.loss_perfect_clean + calc_info = { - 'exceedance_level': P_level, - 'renormalizing_factor': self.renorm_factor, - 'stochastic_soiling_profiles': self.random_profiles, - 'soiling_interval_summary': intervals_out, - 'soiling_ratio_perfect_clean': sr_perfect + "exceedance_level": P_level, + "renormalizing_factor": self.renorm_factor, + "stochastic_soiling_profiles": self.random_profiles, + "soiling_interval_summary": intervals_out, + "soiling_ratio_perfect_clean": sr_perfect, } return (result[0], result[1:3], calc_info) -def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, - precipitation_daily=None, 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): - ''' +# more updates are needed for documentation but added additional inputs +# that are in srr.run /Matt +def soiling_srr( + energy_normalized_daily, + insolation_daily, + reps=1000, + precipitation_daily=None, + 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, + neg_shift=False, + piecewise=False, +): + """ Functional wrapper for :py:class:`~rdtools.soiling.SRRAnalysis`. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented in Deceglie et al. JPV 8(2) p547 2018. @@ -716,17 +1184,31 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, trim : bool, default False Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals - method : str, {'half_norm_clean', 'random_clean', 'perfect_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% + * 'random_clean' - a random recovery between 0-100%, + pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1 + metric to 1, + pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval. + the interval, + pair with piecewise=False and neg_shift=False + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or + piecewise linear fits result in no cleaning, + pair with piecewise=True and neg_shift=True + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no + cleaning, + pair with piecewise=True and neg_shift=True clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -762,6 +1244,18 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. + neg_shift : bool, default True + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the + data where excessive negative shifts can invalidate a soiling interval. + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no + piecewise fit being tested. Returns ------- @@ -814,11 +1308,13 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, | 'valid' | Whether the interval meets the criteria to | | | be treated as a valid soiling interval | +------------------------+----------------------------------------------+ - ''' + """ - srr = SRRAnalysis(energy_normalized_daily, - insolation_daily, - precipitation_daily=precipitation_daily) + srr = SRRAnalysis( + energy_normalized_daily, + insolation_daily, + precipitation_daily=precipitation_daily, + ) sr, sr_ci, soiling_info = srr.run( reps=reps, @@ -834,14 +1330,17 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, recenter=recenter, max_relative_slope_error=max_relative_slope_error, max_negative_step=max_negative_step, - outlier_factor=outlier_factor) + outlier_factor=outlier_factor, + neg_shift=neg_shift, + piecewise=piecewise, + ) return sr, sr_ci, soiling_info def _count_month_days(start, end): - '''Return a dict of number of days between start and end - (inclusive) in each month''' + """Return a dict of number of days between start and end + (inclusive) in each month""" days = pd.date_range(start, end) months = [x.month for x in days] out_dict = {} @@ -851,9 +1350,10 @@ def _count_month_days(start, end): return out_dict -def annual_soiling_ratios(stochastic_soiling_profiles, - insolation_daily, confidence_level=68.2): - ''' +def annual_soiling_ratios( + stochastic_soiling_profiles, insolation_daily, confidence_level=68.2 +): + """ Return annualized soiling ratios and associated confidence intervals based on stochastic soiling profiles from SRR. Note that each year may be affected by previous years' profiles for all SRR cleaning @@ -893,7 +1393,7 @@ def annual_soiling_ratios(stochastic_soiling_profiles, | | for insolation-weighted soiling ratio for | | | the year | +------------------------+-------------------------------------------+ - ''' + """ # Create a df with each realization as a column all_profiles = pd.concat(stochastic_soiling_profiles, axis=1) @@ -901,10 +1401,11 @@ def annual_soiling_ratios(stochastic_soiling_profiles, if not all_profiles.index.isin(insolation_daily.index).all(): warnings.warn( - 'The indexes of stochastic_soiling_profiles are not entirely ' - 'contained within the index of insolation_daily. Every day in ' - 'stochastic_soiling_profiles should be represented in ' - 'insolation_daily. This may cause erroneous results.') + "The indexes of stochastic_soiling_profiles are not entirely " + "contained within the index of insolation_daily. Every day in " + "stochastic_soiling_profiles should be represented in " + "insolation_daily. This may cause erroneous results." + ) insolation_daily = insolation_daily.reindex(all_profiles.index) @@ -912,30 +1413,37 @@ def annual_soiling_ratios(stochastic_soiling_profiles, all_profiles_weighted = all_profiles.multiply(insolation_daily, axis=0) # Compute the insolation-weighted soiling ratio (IWSR) for each realization - annual_insolation = insolation_daily.groupby( - insolation_daily.index.year).sum() + annual_insolation = insolation_daily.groupby(insolation_daily.index.year).sum() all_annual_weighted_sums = all_profiles_weighted.groupby( - all_profiles_weighted.index.year).sum() - all_annual_iwsr = all_annual_weighted_sums.multiply( - 1/annual_insolation, axis=0) - - annual_soiling = pd.DataFrame({ - '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' + all_profiles_weighted.index.year + ).sum() + all_annual_iwsr = all_annual_weighted_sums.multiply(1 / annual_insolation, axis=0) + + annual_soiling = pd.DataFrame( + { + "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() return annual_soiling -def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14, - max_relative_slope_error=500.0, reps=100000, - confidence_level=68.2): - ''' +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. Samples possible soiling rates from soiling rate confidence intervals associated with soiling intervals assuming a uniform @@ -1000,75 +1508,75 @@ def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14, | | intervals contribute, the confidence interval | | | is likely to underestimate the true uncertainty. | +-----------------------+--------------------------------------------------+ - ''' + """ # filter to intervals of interest - high = soiling_interval_summary['soiling_rate_high'] - low = soiling_interval_summary['soiling_rate_low'] - rate = soiling_interval_summary['soiling_rate'] + high = soiling_interval_summary["soiling_rate_high"] + low = soiling_interval_summary["soiling_rate_low"] + rate = soiling_interval_summary["soiling_rate"] rel_error = 100 * abs((high - low) / rate) intervals = soiling_interval_summary[ - (soiling_interval_summary['length'] >= min_interval_length) & - (soiling_interval_summary['valid']) & - (rel_error <= max_relative_slope_error) + (soiling_interval_summary["length"] >= min_interval_length) + & (soiling_interval_summary["valid"]) + & (rel_error <= max_relative_slope_error) ].copy() # count the overlap of each interval with each month month_counts = [] for _, row in intervals.iterrows(): - month_counts.append(_count_month_days(row['start'], row['end'])) + month_counts.append(_count_month_days(row["start"], row["end"])) # divy up the monte carlo reps based on overlap for month in range(1, 13): days_in_month = np.array([x[month] for x in month_counts]) - sample_col = f'samples_for_month_{month}' + sample_col = f"samples_for_month_{month}" if days_in_month.sum() > 0: - intervals[sample_col] = np.ceil( - days_in_month / days_in_month.sum() * reps) + intervals[sample_col] = np.ceil(days_in_month / days_in_month.sum() * reps) else: intervals[sample_col] = 0 intervals[sample_col] = intervals[sample_col].astype(int) # perform the monte carlo month by month - ci_quantiles = [0.5 - confidence_level/2/100, 0.5 + confidence_level/2/100] + ci_quantiles = [0.5 - confidence_level / 2 / 100, 0.5 + confidence_level / 2 / 100] monthly_rate_data = [] relevant_interval_count = [] for month in range(1, 13): rates = [] - sample_col = f'samples_for_month_{month}' + sample_col = f"samples_for_month_{month}" relevant_intervals = intervals[intervals[sample_col] > 0] for _, row in relevant_intervals.iterrows(): - rates.append(np.random.uniform( - row['soiling_rate_low'], - row['soiling_rate_high'], - row[sample_col])) + rates.append( + np.random.uniform( + row["soiling_rate_low"], row["soiling_rate_high"], row[sample_col] + ) + ) rates = [x for sublist in rates for x in sublist] if rates: - monthly_rate_data.append(np.quantile(rates, - [0.5, ci_quantiles[0], - ci_quantiles[1]])) + monthly_rate_data.append( + np.quantile(rates, [0.5, ci_quantiles[0], ci_quantiles[1]]) + ) else: - monthly_rate_data.append(np.array([np.nan]*3)) + monthly_rate_data.append(np.array([np.nan] * 3)) relevant_interval_count.append(len(relevant_intervals)) monthly_rate_data = np.array(monthly_rate_data) # make a dataframe out of the results - monthly_soiling_df = pd.DataFrame(data=monthly_rate_data, - columns=['soiling_rate_median', - 'soiling_rate_low', - 'soiling_rate_high']) - monthly_soiling_df.insert(0, 'month', range(1, 13)) - monthly_soiling_df['interval_count'] = relevant_interval_count + monthly_soiling_df = pd.DataFrame( + data=monthly_rate_data, + columns=["soiling_rate_median", "soiling_rate_low", "soiling_rate_high"], + ) + monthly_soiling_df.insert(0, "month", range(1, 13)) + monthly_soiling_df["interval_count"] = relevant_interval_count return monthly_soiling_df -class CODSAnalysis(): - ''' +class CODSAnalysis: + """ Container for the Combined Degradation and Soiling (CODS) algorithm for degradation and soiling loss analysis. Based on the method presented in [1]_. @@ -1164,7 +1672,7 @@ class CODSAnalysis(): ---------- .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219 - ''' + """ def __init__(self, energy_normalized_daily): self.pm = energy_normalized_daily # daily performance metric @@ -1173,18 +1681,30 @@ def __init__(self, energy_normalized_daily): first_keeper = self.pm.isna().idxmin() self.pm = self.pm.loc[first_keeper:] - if self.pm.index.freq != 'D': - raise ValueError('Daily performance metric series must have ' - 'daily frequency (missing dates should be ' - 'represented by NaNs)') + if self.pm.index.freq != "D": + raise ValueError( + "Daily performance metric series must have " + "daily frequency (missing dates should be " + "represented by NaNs)" + ) def iterative_signal_decomposition( - self, order=('SR', 'SC', 'Rd'), degradation_method='YoY', - max_iterations=18, cleaning_sensitivity=.5, convergence_criterion=5e-3, - pruning_iterations=1, clean_pruning_sensitivity=.6, soiling_significance=.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). The underlying assumption is that the PI @@ -1323,14 +1843,15 @@ def iterative_signal_decomposition( .. [3] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219 - ''' + """ pi = self.pm.copy() - if degradation_method == 'STL' and 'Rd' in order: - order = tuple([c for c in order if c != 'Rd']) + if degradation_method == "STL" and "Rd" in order: + order = tuple([c for c in order if c != "Rd"]) - if 'SR' not in order: - raise ValueError('\'SR\' must be in argument \'order\' ' + - '(e.g. order=[\'SR\', \'SC\', \'Rd\']') + if "SR" not in order: + raise ValueError( + "'SR' must be in argument 'order' " + "(e.g. order=['SR', 'SC', 'Rd']" + ) n_steps = len(order) day = np.arange(len(pi)) degradation_trend = [1] @@ -1343,39 +1864,39 @@ def iterative_signal_decomposition( convergence_metric = [_RMSE(pi, np.ones((len(pi),)))] # Find possible cleaning events based on the performance index - ce, rm9 = _rolling_median_ce_detection(pi.index, pi, ffill=ffill, - tuner=cleaning_sensitivity) + ce, rm9 = _rolling_median_ce_detection( + pi.index, pi, ffill=ffill, tuner=cleaning_sensitivity + ) pce = _collapse_cleaning_events(ce, rm9.diff().values, 5) small_soiling_signal, perfect_cleaning = False, True ic = 0 # iteration counter if verbose: - print('It. nr\tstep\tRMSE\ttimer') + print("It. nr\tstep\tRMSE\ttimer") if verbose: - print('{:}\t- \t{:.5f}'.format(ic, convergence_metric[ic])) + print("{:}\t- \t{:.5f}".format(ic, convergence_metric[ic])) while ic < max_iterations: t0 = time.time() ic += 1 # Find soiling component - if order[(ic-1) % n_steps] == 'SR': + if order[(ic - 1) % n_steps] == "SR": if ic > 2: # Add possible cleaning events found by considering # the residuals pce = soiling_dfs[-1].cleaning_events.copy() cleaning_sensitivity *= 1.2 # decrease sensitivity ce, rm9 = _rolling_median_ce_detection( - pi.index, residuals, ffill=ffill, - tuner=cleaning_sensitivity) + pi.index, residuals, ffill=ffill, tuner=cleaning_sensitivity + ) ce = _collapse_cleaning_events(ce, rm9.diff().values, 5) pce[ce] = True clean_pruning_sensitivity /= 1.1 # increase pruning sensitivity # Decompose input signal - soiling_dummy = (pi / - degradation_trend[-1] / - seasonal_component[-1] / - residual_shift) + soiling_dummy = ( + pi / degradation_trend[-1] / seasonal_component[-1] / residual_shift + ) # Run Kalman Filter for obtaining soiling component kdf, Ps = self._Kalman_filter_for_SR( @@ -1386,100 +1907,136 @@ def iterative_signal_decomposition( clean_pruning_sensitivity=clean_pruning_sensitivity, perfect_cleaning=perfect_cleaning, process_noise=process_noise, - renormalize_SR=renormalize_SR) + renormalize_SR=renormalize_SR, + ) soiling_ratio.append(kdf.soiling_ratio) soiling_dfs.append(kdf) # Find seasonal component - if order[(ic-1) % n_steps] == 'SC': + if order[(ic - 1) % n_steps] == "SC": season_dummy = pi / soiling_ratio[-1] # Decompose signal if season_dummy.isna().sum() > 0: - season_dummy.interpolate('linear', inplace=True) + season_dummy.interpolate("linear", inplace=True) season_dummy = season_dummy.apply(np.log) # Log transform # Run STL model - STL_res = STL(season_dummy, period=365, seasonal=999999, - seasonal_deg=0, trend_deg=0, - robust=True, low_pass_jump=30, seasonal_jump=30, - trend_jump=365).fit() + STL_res = STL( + season_dummy, + period=365, + seasonal=999999, + seasonal_deg=0, + trend_deg=0, + robust=True, + low_pass_jump=30, + seasonal_jump=30, + trend_jump=365, + ).fit() # Smooth result - smooth_season = lowess(STL_res.seasonal.apply(np.exp), - pi.index, is_sorted=True, delta=30, - frac=180/len(pi), return_sorted=False) + smooth_season = lowess( + STL_res.seasonal.apply(np.exp), + pi.index, + is_sorted=True, + delta=30, + frac=180 / len(pi), + return_sorted=False, + ) # Ensure periodic seaonal component - seasonal_comp = _force_periodicity(smooth_season, - season_dummy.index, - pi.index) + seasonal_comp = _force_periodicity( + smooth_season, season_dummy.index, pi.index + ) seasonal_component.append(seasonal_comp) - if degradation_method == 'STL': # If not YoY - deg_trend = pd.Series(index=pi.index, - data=STL_res.trend.apply(np.exp)) + if degradation_method == "STL": # If not YoY + deg_trend = pd.Series( + index=pi.index, data=STL_res.trend.apply(np.exp) + ) degradation_trend.append(deg_trend / deg_trend.iloc[0]) - yoy_save.append(RdToolsDeg.degradation_year_on_year( - degradation_trend[-1], uncertainty_method=None)) + yoy_save.append( + RdToolsDeg.degradation_year_on_year( + degradation_trend[-1], uncertainty_method=None + ) + ) # Find degradation component - if order[(ic-1) % n_steps] == 'Rd': + if order[(ic - 1) % n_steps] == "Rd": # Decompose signal - trend_dummy = (pi / - seasonal_component[-1] / - soiling_ratio[-1]) + trend_dummy = pi / seasonal_component[-1] / soiling_ratio[-1] # Run YoY yoy = RdToolsDeg.degradation_year_on_year( - trend_dummy, uncertainty_method=None) + trend_dummy, uncertainty_method=None + ) # Convert degradation rate to trend - degradation_trend.append(pd.Series( - index=pi.index, data=(1 + day * yoy / 100 / 365.0))) + degradation_trend.append( + pd.Series(index=pi.index, data=(1 + day * yoy / 100 / 365.0)) + ) yoy_save.append(yoy) # Combine and calculate residual flatness - total_model = (degradation_trend[-1] * - seasonal_component[-1] * - soiling_ratio[-1]) + total_model = ( + degradation_trend[-1] * seasonal_component[-1] * soiling_ratio[-1] + ) residuals = pi / total_model residual_shift = residuals.mean() total_model *= residual_shift convergence_metric.append(_RMSE(pi, total_model)) if verbose: - print('{:}\t{:}\t{:.5f}\t\t\t{:.1f} s'.format( - ic, order[(ic-1) % n_steps], convergence_metric[-1], - time.time()-t0)) + print( + "{:}\t{:}\t{:.5f}\t\t\t{:.1f} s".format( + ic, + order[(ic - 1) % n_steps], + convergence_metric[-1], + time.time() - t0, + ) + ) # Convergence happens if there is no improvement in RMSE from one # step to the next if ic >= n_steps: - relative_improvement = ((convergence_metric[-n_steps-1] - - convergence_metric[-1]) / - convergence_metric[-n_steps-1]) + 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): + 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 - order = tuple([order[(i+n_steps-1-(ic-1) % n_steps) % n_steps] - for i in range(n_steps)]) + order = tuple( + [ + order[(i + n_steps - 1 - (ic - 1) % n_steps) % n_steps] + for i in range(n_steps) + ] + ) change_point = ic if verbose: - print('Now not assuming perfect cleaning') - elif (not perfect_cleaning and - (ic >= max_iterations or - (ic >= change_point + n_steps and - relative_improvement < - convergence_criterion))): + print("Now not assuming perfect cleaning") + elif not perfect_cleaning and ( + ic >= max_iterations + or ( + ic >= change_point + n_steps + and relative_improvement < convergence_criterion + ) + ): if verbose: if relative_improvement < convergence_criterion: - print('Convergence reached.') + print("Convergence reached.") else: - print('Max iterations reached.') + print("Max iterations reached.") ic = max_iterations # Initialize output DataFrame - df_out = pd.DataFrame(index=pi.index, - columns=['soiling_ratio', 'soiling_rates', - 'cleaning_events', 'seasonal_component', - 'degradation_trend', 'total_model', - 'residuals']) + df_out = pd.DataFrame( + index=pi.index, + columns=[ + "soiling_ratio", + "soiling_rates", + "cleaning_events", + "seasonal_component", + "degradation_trend", + "total_model", + "residuals", + ], + ) # Save values df_out.seasonal_component = seasonal_component[-1] @@ -1494,26 +2051,28 @@ def iterative_signal_decomposition( soiling_loss = (1 - df_out.soiling_ratio).mean() * 100 # Total model - df_out.total_model = (df_out.soiling_ratio * - df_out.seasonal_component * - df_out.degradation_trend) + df_out.total_model = ( + df_out.soiling_ratio * df_out.seasonal_component * df_out.degradation_trend + ) df_out.residuals = pi / df_out.total_model residual_shift = df_out.residuals.mean() df_out.total_model *= residual_shift RMSE = _RMSE(pi, df_out.total_model) - adf_res = adfuller(df_out.residuals.dropna(), regression='ctt', autolag=None) + adf_res = adfuller(df_out.residuals.dropna(), regression="ctt", autolag=None) if verbose: - print('p-value for the H0 that there is a unit root in the' + - 'residuals (using the Augmented Dickey-fuller test):' + - '{:.3e}'.format(adf_res[1])) + print( + "p-value for the H0 that there is a unit root in the" + + "residuals (using the Augmented Dickey-fuller test):" + + "{:.3e}".format(adf_res[1]) + ) # Check size of soiling signal vs residuals - SR_amp = float(np.diff(df_out.soiling_ratio.quantile([.1, .9]))) - residuals_amp = float(np.diff(df_out.residuals.quantile([.1, .9]))) + SR_amp = float(np.diff(df_out.soiling_ratio.quantile([0.1, 0.9]))) + residuals_amp = float(np.diff(df_out.residuals.quantile([0.1, 0.9]))) soiling_signal_strength = SR_amp / residuals_amp if soiling_signal_strength < soiling_significance: if verbose: - print('Soiling signal is small relative to the noise') + print("Soiling signal is small relative to the noise") small_soiling_signal = True df_out.SR_high = 1.0 df_out.SR_low = 1.0 - SR_amp @@ -1525,24 +2084,25 @@ def iterative_signal_decomposition( residual_shift=residual_shift, RMSE=RMSE, small_soiling_signal=small_soiling_signal, - adf_res=adf_res + adf_res=adf_res, ) 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=(.25, .75), - clean_pruning_sensitivity_alternatives=(1/1.5, 1.5), - forward_fill_alternatives=(True, False), - verbose=False, - **kwargs): - ''' + 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, + ): + """ Bootstrapping of CODS algorithm for uncertainty analysis, inherently accounting for model and parameter choices. @@ -1661,7 +2221,7 @@ def run_bootstrap(self, ---------- .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219 - ''' + """ pi = self.pm.copy() # ###################### # @@ -1669,14 +2229,20 @@ def run_bootstrap(self, # ###################### # # Generate combinations of model parameter alternatives - parameter_alternatives = [order_alternatives, - cleaning_sensitivity_alternatives, - clean_pruning_sensitivity_alternatives, - forward_fill_alternatives] + parameter_alternatives = [ + order_alternatives, + cleaning_sensitivity_alternatives, + clean_pruning_sensitivity_alternatives, + forward_fill_alternatives, + ] index_list = list(itertools.product([0, 1], repeat=len(parameter_alternatives))) - combination_of_parameters = [[parameter_alternatives[j][indexes[j]] - for j in range(len(parameter_alternatives))] - for indexes in index_list] + combination_of_parameters = [ + [ + parameter_alternatives[j][indexes[j]] + for j in range(len(parameter_alternatives)) + ] + for indexes in index_list + ] nr_models = len(index_list) bootstrap_samples_list, list_of_df_out, results = [], [], [] @@ -1685,68 +2251,94 @@ def run_bootstrap(self, reps += nr_models - reps % nr_models if verbose: - print('Initially fitting {:} models'.format(nr_models)) + print("Initially fitting {:} models".format(nr_models)) t00 = time.time() # For each combination of model parameter alternatives, fit one model: for c, (order, dt, pt, ff) in enumerate(combination_of_parameters): 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, process_noise=process_noise, ffill=ff, - degradation_method=degradation_method, **kwargs) + max_iterations=18, + order=order, + clip_soiling=True, + cleaning_sensitivity=dt, + pruning_iterations=1, + clean_pruning_sensitivity=pt, + process_noise=process_noise, + ffill=ff, + degradation_method=degradation_method, + **kwargs, + ) # Save results list_of_df_out.append(df_out) results.append(result_dict) - adf = result_dict['adf_res'] + adf = result_dict["adf_res"] # If we can reject the null-hypothesis that there is a unit # root in the residuals: - if adf[1] < .05: + if adf[1] < 0.05: # ... generate bootstrap samples based on the fit: bootstrap_samples_list.append( _make_time_series_bootstrap_samples( - pi, df_out.total_model, - sample_nr=int(reps / nr_models))) + pi, df_out.total_model, sample_nr=int(reps / nr_models) + ) + ) # Print progress if verbose: - _progressBarWithETA(c+1, nr_models, time.time()-t00, - bar_length=30) + _progressBarWithETA( + c + 1, nr_models, time.time() - t00, bar_length=30 + ) except ValueError as ex: print(ex) # Revive results - adfs = np.array([(r['adf_res'][0] if r['adf_res'][1] < 0.05 else 0) for r in results]) - RMSEs = np.array([r['RMSE'] for r in results]) + adfs = np.array( + [(r["adf_res"][0] if r["adf_res"][1] < 0.05 else 0) for r in results] + ) + RMSEs = np.array([r["RMSE"] for r in results]) SR_is_one_fraction = np.array( - [(df.soiling_ratio == 1).mean() for df in list_of_df_out]) - small_soiling_signal = [r['small_soiling_signal'] for r in results] + [(df.soiling_ratio == 1).mean() for df in list_of_df_out] + ) + small_soiling_signal = [r["small_soiling_signal"] for r in results] # Calculate weights weights = 1 / RMSEs / (1 + SR_is_one_fraction) weights /= np.sum(weights) # Save sensitivities and weights for initial model fits - _parameters_n_weights = pd.concat([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) + _parameters_n_weights = pd.concat( + [ + 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, + ) if verbose: # Print summary - _parameters_n_weights.columns = ['order', 'dt', 'pt', 'ff', 'RMSE', - 'SR==1', 'weights', 'small_soiling_signal'] + _parameters_n_weights.columns = [ + "order", + "dt", + "pt", + "ff", + "RMSE", + "SR==1", + "weights", + "small_soiling_signal", + ] if verbose: - print('\n', _parameters_n_weights) + print("\n", _parameters_n_weights) # Check if data is decomposable if np.sum(adfs == 0) > nr_models / 2: raise RuntimeError( - 'Test for stationary residuals (Augmented Dickey-Fuller' - + ' test) not passed in half of the instances:\nData not' - + ' decomposable.') + "Test for stationary residuals (Augmented Dickey-Fuller" + + " test) not passed in half of the instances:\nData not" + + " decomposable." + ) # Save best model self.initial_fits = [df for df in list_of_df_out] @@ -1756,83 +2348,110 @@ def run_bootstrap(self, # don't do bootstrapping if np.sum(small_soiling_signal) > nr_models / 2: self.result_df = result_df - self.residual_shift = results[np.argmax(weights)]['residual_shift'] + self.residual_shift = results[np.argmax(weights)]["residual_shift"] YOY = RdToolsDeg.degradation_year_on_year(pi) self.degradation = [YOY[0], YOY[1][0], YOY[1][1]] self.soiling_loss = [0, 0, (1 - result_df.soiling_ratio).mean()] self.small_soiling_signal = True self.errors = ( - 'Soiling signal is small relative to the noise. ' - 'Iterative decomposition not possible. ' - 'Degradation found by RdTools YoY.') + "Soiling signal is small relative to the noise. " + "Iterative decomposition not possible. " + "Degradation found by RdTools YoY." + ) warnings.warn(self.errors) return self.result_df, self.degradation, self.soiling_loss self.small_soiling_signal = False # Aggregate all bootstrap samples - all_bootstrap_samples = pd.concat(bootstrap_samples_list, axis=1, - ignore_index=True) + all_bootstrap_samples = pd.concat( + bootstrap_samples_list, axis=1, ignore_index=True + ) # Seasonal samples are generated from previously fitted seasonal # components, by perturbing amplitude and phase shift # Number of samples per fit: sample_nr = int(reps / nr_models) - 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=.8, - max_multiplier=1.75, - max_shift=30) + 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, + max_shift=30, + ) # ###################### # # ###### STAGE 2 ####### # # ###################### # if verbose and reps > 0: - print('\nBootstrapping for uncertainty analysis', - '({:} realizations):'.format(reps)) - order = ('SR', 'SC' if degradation_method == 'STL' else 'Rd') + print( + "\nBootstrapping for uncertainty analysis", + "({:} realizations):".format(reps), + ) + order = ("SR", "SC" if degradation_method == "STL" else "Rd") t0 = time.time() - bt_kdfs, bt_SL, bt_deg, parameters, adfs, RMSEs, SR_is_1, rss, errors = \ - [], [], [], [], [], [], [], [], ['Bootstrapping errors'] + bt_kdfs, bt_SL, bt_deg, parameters, adfs, RMSEs, SR_is_1, rss, errors = ( + [], + [], + [], + [], + [], + [], + [], + [], + ["Bootstrapping errors"], + ) for b in range(reps): try: # randomly choose model sensitivities - dt = np.random.uniform(parameter_alternatives[1][0], - parameter_alternatives[1][-1]) - pt = np.random.uniform(parameter_alternatives[2][0], - parameter_alternatives[2][-1]) + dt = np.random.uniform( + parameter_alternatives[1][0], parameter_alternatives[1][-1] + ) + pt = np.random.uniform( + parameter_alternatives[2][0], parameter_alternatives[2][-1] + ) pn = np.random.uniform(process_noise / 1.5, process_noise * 1.5) - renormalize_SR = np.random.choice([None, - np.random.uniform(.5, .95)]) + renormalize_SR = np.random.choice([None, np.random.uniform(0.5, 0.95)]) ffill = np.random.choice([True, False]) parameters.append([dt, pt, pn, renormalize_SR, ffill]) # Sample to infer soiling from - bootstrap_sample = \ - all_bootstrap_samples[b] / seasonal_samples[b] + bootstrap_sample = all_bootstrap_samples[b] / seasonal_samples[b] # Set up a temprary instance of the CODSAnalysis object temporary_cods_instance = CODSAnalysis(bootstrap_sample) # Do Signal decomposition for soiling and degradation component - 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, - degradation_method=degradation_method, **kwargs) + 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, + degradation_method=degradation_method, + **kwargs, + ) + ) # If we can reject the null-hypothesis that there is a unit # root in the residuals: - if results_dict['adf_res'][1] < .05: # Save the results + if results_dict["adf_res"][1] < 0.05: # Save the results bt_kdfs.append(kdf) - adfs.append(results_dict['adf_res'][0]) - RMSEs.append(results_dict['RMSE']) - bt_deg.append(results_dict['degradation']) - bt_SL.append(results_dict['soiling_loss']) - rss.append(results_dict['residual_shift']) + adfs.append(results_dict["adf_res"][0]) + RMSEs.append(results_dict["RMSE"]) + bt_deg.append(results_dict["degradation"]) + bt_SL.append(results_dict["soiling_loss"]) + rss.append(results_dict["residual_shift"]) SR_is_1.append((kdf.soiling_ratio == 1).mean()) else: seasonal_samples.drop(columns=[b], inplace=True) @@ -1843,20 +2462,33 @@ def run_bootstrap(self, # Print progress if verbose: - _progressBarWithETA(b+1, reps, time.time()-t0, bar_length=30) + _progressBarWithETA(b + 1, reps, time.time() - t0, bar_length=30) # Reweight and save weights weights = 1 / np.array(RMSEs) / (1 + np.array(SR_is_1)) weights /= np.sum(weights) self._parameters_n_weights = pd.concat( - [pd.DataFrame(parameters), - pd.Series(RMSEs), - pd.Series(adfs), - pd.Series(SR_is_1), - pd.Series(weights)], - axis=1, ignore_index=True) - self._parameters_n_weights.columns = ['dt', 'pt', 'pn', 'RSR', 'ffill', - 'RMSE', 'ADF', 'SR==1', 'weights'] + [ + pd.DataFrame(parameters), + pd.Series(RMSEs), + pd.Series(adfs), + pd.Series(SR_is_1), + pd.Series(weights), + ], + axis=1, + ignore_index=True, + ) + self._parameters_n_weights.columns = [ + "dt", + "pt", + "pn", + "RSR", + "ffill", + "RMSE", + "ADF", + "SR==1", + "weights", + ] # ###################### # # ###### STAGE 3 ####### # @@ -1873,68 +2505,83 @@ def run_bootstrap(self, concat_ce = pd.concat([kdf.cleaning_events for kdf in bt_kdfs], axis=1) # Find confidence intervals for SR and soiling rates - df_out['SR_low'] = concat_SR.quantile(ci_low_edge, 1) - df_out['SR_high'] = concat_SR.quantile(ci_high_edge, 1) - df_out['rates_low'] = concat_r_s.quantile(ci_low_edge, 1) - df_out['rates_high'] = concat_r_s.quantile(ci_high_edge, 1) + df_out["SR_low"] = concat_SR.quantile(ci_low_edge, 1) + df_out["SR_high"] = concat_SR.quantile(ci_high_edge, 1) + df_out["rates_low"] = concat_r_s.quantile(ci_low_edge, 1) + df_out["rates_high"] = concat_r_s.quantile(ci_high_edge, 1) # Save best estimate and bootstrapped estimates of SR and soiling rates df_out.soiling_ratio = df_out.soiling_ratio.clip(lower=0, upper=1) - df_out.loc[df_out.soiling_ratio.diff() == 0, 'soiling_rates'] = 0 - df_out['bt_soiling_ratio'] = (concat_SR * weights).sum(1) - df_out['bt_soiling_rates'] = (concat_r_s * weights).sum(1) + df_out.loc[df_out.soiling_ratio.diff() == 0, "soiling_rates"] = 0 + df_out["bt_soiling_ratio"] = (concat_SR * weights).sum(1) + df_out["bt_soiling_rates"] = (concat_r_s * weights).sum(1) # Set probability of cleaning events df_out.cleaning_events = (concat_ce * weights).sum(1) # Find degradation rates - self.degradation = [np.dot(bt_deg, weights), - np.quantile(bt_deg, ci_low_edge), - np.quantile(bt_deg, ci_high_edge)] - df_out.degradation_trend = 1 + np.arange(len(pi)) * \ - self.degradation[0] / 100 / 365.0 + self.degradation = [ + np.dot(bt_deg, weights), + np.quantile(bt_deg, ci_low_edge), + np.quantile(bt_deg, ci_high_edge), + ] + df_out.degradation_trend = ( + 1 + np.arange(len(pi)) * self.degradation[0] / 100 / 365.0 + ) # Soiling losses - self.soiling_loss = [np.dot(bt_SL, weights), - np.quantile(bt_SL, ci_low_edge), - np.quantile(bt_SL, ci_high_edge)] + self.soiling_loss = [ + np.dot(bt_SL, weights), + np.quantile(bt_SL, ci_low_edge), + np.quantile(bt_SL, ci_high_edge), + ] # Save "confidence intervals" for seasonal component df_out.seasonal_component = (seasonal_samples * weights).sum(1) - df_out['seasonal_low'] = seasonal_samples.quantile(ci_low_edge, 1) - df_out['seasonal_high'] = seasonal_samples.quantile(ci_high_edge, 1) + df_out["seasonal_low"] = seasonal_samples.quantile(ci_low_edge, 1) + df_out["seasonal_high"] = seasonal_samples.quantile(ci_high_edge, 1) # Total model with confidence intervals - df_out.total_model = (df_out.degradation_trend * - df_out.seasonal_component * - df_out.soiling_ratio) - df_out['model_low'] = concat_tot_mod.quantile(ci_low_edge, 1) - df_out['model_high'] = concat_tot_mod.quantile(ci_high_edge, 1) + df_out.total_model = ( + df_out.degradation_trend * df_out.seasonal_component * df_out.soiling_ratio + ) + df_out["model_low"] = concat_tot_mod.quantile(ci_low_edge, 1) + df_out["model_high"] = concat_tot_mod.quantile(ci_high_edge, 1) # Residuals and residual shift df_out.residuals = pi / df_out.total_model self.residual_shift = df_out.residuals.mean() df_out.total_model *= self.residual_shift self.RMSE = _RMSE(pi, df_out.total_model) - self.adf_results = adfuller(df_out.residuals.dropna(), - regression='ctt', autolag=None) + self.adf_results = adfuller( + df_out.residuals.dropna(), regression="ctt", autolag=None + ) self.result_df = df_out self.errors = errors if verbose: - print('\nFinal RMSE: {:.5f}'.format(self.RMSE)) + print("\nFinal RMSE: {:.5f}".format(self.RMSE)) if len(self.errors) > 1: print(self.errors) return self.result_df, self.degradation, self.soiling_loss - def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, - rate_std=.005, max_soiling_rates=.0005, - pruning_iterations=1, clean_pruning_sensitivity=.6, - renormalize_SR=None, perfect_cleaning=False, - prescient_cleaning_events=None, - clip_soiling=True, ffill=True): - ''' + 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, + ): + """ 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 of daily (corrected) normalized energy using a Kalman Filter (KF). See @@ -2005,47 +2652,62 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, References ---------- .. [1] R. R. Labbe, Kalman and Bayesian Filters in Python. 2016. - ''' + """ # Ensure numeric index zs_series = zs_series.copy() # Make copy, so as not to change input original_index = zs_series.index.copy() - if (original_index.dtype not in [int, 'int64']): + if original_index.dtype not in [int, "int64"]: zs_series.index = range(len(zs_series)) # Check prescient_cleaning_events. If not present, find cleaning events if isinstance(prescient_cleaning_events, list): cleaning_events = prescient_cleaning_events else: - if (isinstance(prescient_cleaning_events, type(zs_series)) and - (prescient_cleaning_events.sum() > 4)): + if isinstance(prescient_cleaning_events, type(zs_series)) and ( + 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 else: raise ValueError( - "The indices of prescient_cleaning_events must correspond to the" + - " indices of zs_series; they must be of the same length") + "The indices of prescient_cleaning_events must correspond to the" + + " indices of zs_series; they must be of the same length" + ) else: # If no prescient cleaning events, detect cleaning events ce, rm9 = _rolling_median_ce_detection( - zs_series.index, zs_series, tuner=0.5) - prescient_cleaning_events = \ - _collapse_cleaning_events(ce, rm9.diff().values, 5) + zs_series.index, zs_series, tuner=0.5 + ) + prescient_cleaning_events = _collapse_cleaning_events( + ce, rm9.diff().values, 5 + ) - cleaning_events = prescient_cleaning_events[prescient_cleaning_events].index.tolist() + cleaning_events = prescient_cleaning_events[ + prescient_cleaning_events + ].index.tolist() # Find soiling events (e.g. dust storms) soiling_events = _soiling_event_detection( - zs_series.index, zs_series, ffill=ffill, tuner=5) + zs_series.index, zs_series, ffill=ffill, tuner=5 + ) soiling_events = soiling_events[soiling_events].index.tolist() # Initialize various parameters if ffill: - rolling_median_13 = zs_series.ffill().rolling(13, center=True).median().ffill().bfill() - rolling_median_7 = zs_series.ffill().rolling(7, center=True).median().ffill().bfill() + rolling_median_13 = ( + zs_series.ffill().rolling(13, center=True).median().ffill().bfill() + ) + rolling_median_7 = ( + zs_series.ffill().rolling(7, center=True).median().ffill().bfill() + ) else: - rolling_median_13 = zs_series.bfill().rolling(13, center=True).median().ffill().bfill() - rolling_median_7 = zs_series.bfill().rolling(7, center=True).median().ffill().bfill() + rolling_median_13 = ( + zs_series.bfill().rolling(13, center=True).median().ffill().bfill() + ) + rolling_median_7 = ( + zs_series.bfill().rolling(7, center=True).median().ffill().bfill() + ) # A rough estimate of the measurement noise measurement_noise = (rolling_median_13 - zs_series).var() # An initial guess of the slope @@ -2053,28 +2715,44 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, dt = 1 # All time stemps are one day # Initialize Kalman filter - f = self._initialize_univariate_model(zs_series, dt, process_noise, - measurement_noise, rate_std, - zs_std, initial_slope) + f = self._initialize_univariate_model( + zs_series, + dt, + process_noise, + measurement_noise, + rate_std, + zs_std, + initial_slope, + ) # Initialize miscallenous variables - dfk = pd.DataFrame(index=zs_series.index, dtype=float, - columns=['raw_pi', 'raw_rates', 'smooth_pi', - 'smooth_rates', 'soiling_ratio', - 'soiling_rates', 'cleaning_events', - 'days_since_ce']) - dfk['cleaning_events'] = False + dfk = pd.DataFrame( + index=zs_series.index, + dtype=float, + columns=[ + "raw_pi", + "raw_rates", + "smooth_pi", + "smooth_rates", + "soiling_ratio", + "soiling_rates", + "cleaning_events", + "days_since_ce", + ], + ) + dfk["cleaning_events"] = False # Kalman Filter part: ####################################################################### # Call the forward pass function (the actual KF procedure) Xs, Ps, rate_std, zs_std = self._forward_pass( - f, zs_series, rolling_median_7, cleaning_events, soiling_events) + f, zs_series, rolling_median_7, cleaning_events, soiling_events + ) # Save results and smooth with rts smoother dfk, Xs, Ps = self._smooth_results( - dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, - perfect_cleaning) + dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning + ) ####################################################################### # Some steps to clean up the soiling data: @@ -2087,34 +2765,45 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, rm_smooth_pi = dfk.smooth_pi.rolling(7).median().shift(-6) pi_after_cleaning = rm_smooth_pi.loc[cleaning_events] # Detect outiers/false positives - false_positives = _find_numeric_outliers(pi_after_cleaning, - clean_pruning_sensitivity, 'lower') - cleaning_events = \ - false_positives[~false_positives].index.tolist() + false_positives = _find_numeric_outliers( + pi_after_cleaning, clean_pruning_sensitivity, "lower" + ) + cleaning_events = false_positives[~false_positives].index.tolist() # 2: Remove longer periods with positive (soiling) rates if (dfk.smooth_rates > max_soiling_rates).sum() > 1: exceeding_rates = dfk.smooth_rates > max_soiling_rates new_cleaning_events = _collapse_cleaning_events( - exceeding_rates, dfk.smooth_rates, 4) - cleaning_events.extend( - new_cleaning_events[new_cleaning_events].index) + exceeding_rates, dfk.smooth_rates, 4 + ) + cleaning_events.extend(new_cleaning_events[new_cleaning_events].index) cleaning_events.sort() # 3: If the list of cleaning events has changed, run the Kalman # 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, - initial_slope) + f = self._initialize_univariate_model( + 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) + f, zs_series, rolling_median_7, cleaning_events, soiling_events + ) dfk, Xs, Ps = self._smooth_results( - dfk, f, Xs, Ps, zs_series, cleaning_events, - soiling_events, perfect_cleaning) + dfk, + f, + Xs, + Ps, + zs_series, + cleaning_events, + soiling_events, + perfect_cleaning, + ) else: counter = 100 # Make sure the while loop stops @@ -2123,14 +2812,14 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, if perfect_cleaning: # SR = 1 after cleaning events if len(cleaning_events) > 0: pi_dummy = pd.Series(index=dfk.index, data=np.nan) - pi_dummy.loc[cleaning_events] = \ - dfk.smooth_pi.loc[cleaning_events] + pi_dummy.loc[cleaning_events] = dfk.smooth_pi.loc[cleaning_events] dfk.soiling_ratio = 1 / pi_dummy.ffill() * dfk.smooth_pi # Set the SR in the first soiling period based on the mean # ratio of the Kalman estimate (smooth_pi) and the SR - dfk.loc[:cleaning_events[0], 'soiling_ratio'] = \ - dfk.loc[:cleaning_events[0], 'smooth_pi'] \ + dfk.loc[: cleaning_events[0], "soiling_ratio"] = ( + dfk.loc[: cleaning_events[0], "smooth_pi"] * (dfk.soiling_ratio / dfk.smooth_pi).mean() + ) else: # If no cleaning events dfk.soiling_ratio = 1 else: # Otherwise, if the inut signal has been decomposed, and @@ -2138,40 +2827,42 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05, dfk.soiling_ratio = dfk.smooth_pi # 5: Renormalize Soiling Ratio if renormalize_SR is not None: - dfk.soiling_ratio /= dfk.loc[cleaning_events, 'soiling_ratio' - ].quantile(renormalize_SR) + dfk.soiling_ratio /= dfk.loc[cleaning_events, "soiling_ratio"].quantile( + renormalize_SR + ) # 6: Force soiling ratio to not exceed 1: if clip_soiling: dfk.soiling_ratio.clip(upper=1, inplace=True) dfk.soiling_rates = dfk.smooth_rates - dfk.loc[dfk.soiling_ratio.diff() == 0, 'soiling_rates'] = 0 + dfk.loc[dfk.soiling_ratio.diff() == 0, "soiling_rates"] = 0 # Set number of days since cleaning event nr_days_dummy = pd.Series(index=dfk.index, data=np.nan) - nr_days_dummy.loc[cleaning_events] = [int(date-dfk.index[0]) - for date in cleaning_events] + nr_days_dummy.loc[cleaning_events] = [ + int(date - dfk.index[0]) for date in cleaning_events + ] nr_days_dummy.iloc[0] = 0 dfk.days_since_ce = range(len(zs_series)) - nr_days_dummy.ffill() # Save cleaning events and soiling events - dfk.loc[cleaning_events, 'cleaning_events'] = True + dfk.loc[cleaning_events, "cleaning_events"] = True dfk.index = original_index # Set index back to orignial index return dfk, Ps - def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events, - soiling_events): - ''' Run the forward pass of the Kalman Filter algortihm ''' + def _forward_pass( + self, f, zs_series, rolling_median_7, cleaning_events, soiling_events + ): + """Run the forward pass of the Kalman Filter algortihm""" zs = zs_series.values N = len(zs) Xs, Ps = np.zeros((N, 2)), np.zeros((N, 2, 2)) # Enter forward pass of filtering algorithm for i, z in enumerate(zs): - if 7 < i < N-7 and (i in cleaning_events or i in soiling_events): - rolling_median_local = rolling_median_7.loc[i-5:i+5].values - u = self._set_control_input(f, rolling_median_local, i, - cleaning_events) + if 7 < i < N - 7 and (i in cleaning_events or i in soiling_events): + rolling_median_local = rolling_median_7.loc[i - 5 : i + 5].values + u = self._set_control_input(f, rolling_median_local, i, cleaning_events) f.predict(u=u) # Predict wth control input u else: # If no cleaning detection, predict without control input f.predict() @@ -2183,49 +2874,61 @@ def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events, rate_std, zs_std = Ps[-1, 1, 1], Ps[-1, 0, 0] return Xs, Ps, rate_std, zs_std # Convert to numpy and return - def _set_control_input(self, f, rolling_median_local, index, - cleaning_events): - ''' + def _set_control_input(self, f, rolling_median_local, index, cleaning_events): + """ For each cleaning event, sets control input u based on current Kalman Filter state estimate (f.x), and the median value for the following week. If the cleaning event seems to be misplaced, moves the cleaning event to a more sensible location. If the cleaning event seems to be correct, removes other cleaning events in the 10 days surrounding this day - ''' + """ u = np.zeros(f.x.shape) # u is the control input window_size = 11 # len of rolling_median_local HW = 5 # Half window moving_diff = np.diff(rolling_median_local) # Index of maximum change in rolling median max_diff_index = moving_diff.argmax() - if max_diff_index == HW-1 or index not in cleaning_events: + if max_diff_index == HW - 1 or index not in cleaning_events: # The median zs of the week after the cleaning event - z_med = rolling_median_local[HW+3] + z_med = rolling_median_local[HW + 3] # Set control input this future median u[0] = z_med - np.dot(f.H, np.dot(f.F, f.x)) # If the change is bigger than the measurement noise: - if np.abs(u[0]) > np.sqrt(f.R)/2: - index_dummy = [n+3 for n in range(window_size-HW-1) - if n+3 != HW] - cleaning_events = [ce for ce in cleaning_events - if ce-index+HW not in index_dummy] + if np.abs(u[0]) > np.sqrt(f.R) / 2: + index_dummy = [ + n + 3 for n in range(window_size - HW - 1) if n + 3 != HW + ] + cleaning_events = [ + ce for ce in cleaning_events if ce - index + HW not in index_dummy + ] else: # If the cleaning event is insignificant u[0] = 0 if index in 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) + 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): - ''' Smoother for Kalman Filter estimates. Smooths the Kalaman estimate - between given cleaning events and saves all in DataFrame dfk''' + def _smooth_results( + 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 dfk.raw_pi = Xs[:, 0] dfk.raw_rates = Xs[:, 1] @@ -2240,8 +2943,7 @@ def _smooth_results(self, dfk, f, Xs, Ps, zs_series, cleaning_events, # Smooth between cleaning events for start, end in zip(ce_dummy[:-1], ce_dummy[1:]): num_ind = df_num_ind.loc[start:end].iloc[:-1] - Xs[num_ind], Ps[num_ind], _, _ = f.rts_smoother(Xs[num_ind], - Ps[num_ind]) + Xs[num_ind], Ps[num_ind], _, _ = f.rts_smoother(Xs[num_ind], Ps[num_ind]) # Save smoothed estimates dfk.smooth_pi = Xs[:, 0] @@ -2249,17 +2951,22 @@ def _smooth_results(self, dfk, f, Xs, Ps, zs_series, cleaning_events, return dfk, Xs, Ps - def _initialize_univariate_model(self, zs_series, dt, process_noise, - measurement_noise, rate_std, zs_std, - initial_slope): - ''' Initializes the univariate Kalman Filter model, using the filterpy - package ''' + def _initialize_univariate_model( + 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) - f.F = np.array([[1., dt], - [0., 1.]]) - f.H = np.array([[1., 0.]]) - f.P = np.array([[zs_std**2, 0], - [0, rate_std**2]]) + f.F = np.array([[1.0, dt], [0.0, 1.0]]) + f.H = np.array([[1.0, 0.0]]) + f.P = np.array([[zs_std**2, 0], [0, rate_std**2]]) f.Q = Q_discrete_white_noise(dim=2, dt=dt, var=process_noise**2) f.x = np.array([initial_slope[1], initial_slope[0]]) f.B = np.zeros(f.F.shape) @@ -2268,19 +2975,20 @@ def _initialize_univariate_model(self, zs_series, dt, process_noise, return f -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=(.25, .75), - clean_pruning_sensitivity_alternatives=(1/1.5, 1.5), - forward_fill_alternatives=(True, False), - verbose=False, - **kwargs): - ''' +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, +): + """ Functional wrapper for :py:class:`~rdtools.soiling.CODSAnalysis` and its subroutine :py:func:`~rdtools.soiling.CODSAnalysis.run_bootstrap`. Runs the combined degradation and soiling (CODS) algorithm with bootstrapping. @@ -2393,7 +3101,7 @@ def soiling_cods(energy_normalized_daily, ---------- .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219 - ''' + """ CODS = CODSAnalysis(energy_normalized_daily) @@ -2407,17 +3115,23 @@ def soiling_cods(energy_normalized_daily, cleaning_sensitivity_alternatives=cleaning_sensitivity_alternatives, clean_pruning_sensitivity_alternatives=clean_pruning_sensitivity_alternatives, forward_fill_alternatives=forward_fill_alternatives, - **kwargs) + **kwargs, + ) sr = 1 - CODS.soiling_loss[0] / 100 sr_ci = 1 - np.array(CODS.soiling_loss[1:3]) / 100 - return sr, sr_ci, CODS.degradation[0], np.array(CODS.degradation[1:3]), \ - CODS.result_df + return ( + sr, + sr_ci, + CODS.degradation[0], + np.array(CODS.degradation[1:3]), + CODS.result_df, + ) def _collapse_cleaning_events(inferred_ce_in, metric, f=4): - ''' A function for replacing quick successive cleaning events with one + """A function for replacing quick successive cleaning events with one (most probable) cleaning event. Parameters @@ -2434,10 +3148,9 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4): ------- inferred_ce : pandas.Series boolean values for cleaning events - ''' + """ # Ensure numeric index - if isinstance(inferred_ce_in.index, - pd.core.indexes.datetimes.DatetimeIndex): + if isinstance(inferred_ce_in.index, pd.core.indexes.datetimes.DatetimeIndex): saveindex = inferred_ce_in.copy().index inferred_ce_in.index = range(len(saveindex)) else: @@ -2457,11 +3170,10 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4): end_true_vals = collapsed_ce_dummy.loc[start_true_vals:].idxmin() - 1 if end_true_vals >= start_true_vals: # If the island ends # Find the day with mac probability of being a cleaning event - max_diff_day = \ - metric.loc[start_true_vals-f:end_true_vals+f].idxmax() + max_diff_day = metric.loc[start_true_vals - f : end_true_vals + f].idxmax() # Set all days in this period as false - collapsed_ce.loc[start_true_vals-f:end_true_vals+f] = False - collapsed_ce_dummy.loc[start_true_vals-f:end_true_vals+f] = False + collapsed_ce.loc[start_true_vals - f : end_true_vals + f] = False + collapsed_ce_dummy.loc[start_true_vals - f : end_true_vals + f] = False # Set the max probability day as True (cleaning event) collapsed_ce.loc[max_diff_day] = True # Find the next island of true values @@ -2475,49 +3187,54 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4): def _rolling_median_ce_detection(x, y, ffill=True, rolling_window=9, tuner=1.5): - ''' Finds cleaning events in a time series of performance index (y) ''' + """Finds cleaning events in a time series of performance index (y)""" y = pd.Series(index=x, data=y) if ffill: # forward fill NaNs in y before running mean rm = y.ffill().rolling(rolling_window, center=True).median() else: # ... or backfill instead rm = y.bfill().rolling(rolling_window, center=True).median() - Q3 = rm.diff().abs().quantile(.75) - Q1 = rm.diff().abs().quantile(.25) + Q3 = rm.diff().abs().quantile(0.75) + Q1 = rm.diff().abs().quantile(0.25) limit = Q3 + tuner * (Q3 - Q1) cleaning_events = rm.diff() > limit return cleaning_events, rm def _soiling_event_detection(x, y, ffill=True, tuner=5): - ''' Finds cleaning events in a time series of performance index (y) ''' + """Finds cleaning events in a time series of performance index (y)""" y = pd.Series(index=x, data=y) if ffill: # forward fill NaNs in y before running mean rm = y.ffill().rolling(9, center=True).median() else: # ... or backfill instead rm = y.bfill().rolling(9, center=True).median() - Q3 = rm.diff().abs().quantile(.99) - Q1 = rm.diff().abs().quantile(.01) + Q3 = rm.diff().abs().quantile(0.99) + Q1 = rm.diff().abs().quantile(0.01) limit = Q1 - tuner * (Q3 - Q1) soiling_events = rm.diff() < limit return soiling_events -def _make_seasonal_samples(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(index=list_of_SCs[0].index, - columns=range(int(sample_nr*len(list_of_SCs))), - dtype=float) +def _make_seasonal_samples( + 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( + index=list_of_SCs[0].index, + columns=range(int(sample_nr * len(list_of_SCs))), + dtype=float, + ) # From each fitted signal, we will generate new seaonal components for i, signal in enumerate(list_of_SCs): # Remove beginning and end of signal signal_mean = signal.mean() # Make a signal matrix where each column is a year and each row a date - year_matrix = signal.rename('values').to_frame().assign( - doy=signal.index.dayofyear, - year=signal.index.year - ).pivot(index='doy', columns='year', values='values') + year_matrix = ( + signal.rename("values") + .to_frame() + .assign(doy=signal.index.dayofyear, year=signal.index.year) + .pivot(index="doy", columns="year", values="values") + ) # We will use the median signal through all the years... median_signal = year_matrix.median(1) for j in range(sample_nr): @@ -2529,24 +3246,27 @@ def _make_seasonal_samples(list_of_SCs, sample_nr=10, min_multiplier=0.5, shifted_signal = pd.Series( index=signal.index, data=median_signal.reindex( - (signal.index.dayofyear-shift) % 365 + 1).values) + (signal.index.dayofyear - shift) % 365 + 1 + ).values, + ) # Perturb amplitude by recentering to 0 multiplying by multiplier - samples.loc[:, i*sample_nr + j] = \ + samples.loc[:, i * sample_nr + j] = ( multiplier * (shifted_signal - signal_mean) + 1 + ) return samples def _force_periodicity(in_signal, signal_index, out_index): - ''' Function for forcing periodicity in a seasonal component signal ''' + """Function for forcing periodicity in a seasonal component signal""" # Make sure the in_signal is a Series if isinstance(in_signal, np.ndarray): - signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), - data=in_signal) + signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), data=in_signal) elif isinstance(in_signal, pd.Series): - signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), - data=in_signal.values) + signal = pd.Series( + index=pd.DatetimeIndex(signal_index.date), data=in_signal.values + ) else: - raise ValueError('in_signal must be numpy array or pandas Series') + raise ValueError("in_signal must be numpy array or pandas Series") # Make sure that we don't remove too much of the data: remove_length = np.min([180, int((len(signal) - 365) / 2)]) @@ -2558,66 +3278,162 @@ def _force_periodicity(in_signal, signal_index, out_index): # Make a signal matrix where each column is a year and each row is a date year_matrix = pd.DataFrame(index=np.arange(0, 365), columns=unique_years) for year in unique_years: - dates_in_year = pd.date_range(str(year)+'-01-01', str(year)+'-12-31') + dates_in_year = pd.date_range(str(year) + "-01-01", str(year) + "-12-31") # We cut off the extra day(s) of leap years - year_matrix[year] = \ - signal.loc[str(year)].reindex(dates_in_year).values[:365] + year_matrix[year] = signal.loc[str(year)].reindex(dates_in_year).values[:365] # We will use the median signal through all the years... median_signal = year_matrix.median(1) # The output is the median signal broadcasted to the whole time series output = pd.Series( - index=out_index, - data=median_signal.reindex(out_index.dayofyear - 1).values) + index=out_index, data=median_signal.reindex(out_index.dayofyear - 1).values + ) return output -def _find_numeric_outliers(x, multiplier=1.5, where='both', verbose=False): - ''' Function for finding numeric outliers ''' +def _find_numeric_outliers(x, multiplier=1.5, where="both", verbose=False): + """Function for finding numeric outliers""" try: # Calulate third and first quartile - Q3 = np.quantile(x, .75) - Q1 = np.quantile(x, .25) + Q3 = np.quantile(x, 0.75) + Q1 = np.quantile(x, 0.25) except IndexError as ie: print(ie, x) except RuntimeWarning as rw: print(rw, x) IQR = Q3 - Q1 # Interquartile range - if where == 'upper': # If detecting upper outliers + if where == "upper": # If detecting upper outliers if verbose: - print('Upper limit', Q3 + multiplier * IQR) - return (x > Q3 + multiplier * IQR) - elif where == 'lower': # If detecting lower outliers + print("Upper limit", Q3 + multiplier * IQR) + return x > Q3 + multiplier * IQR + elif where == "lower": # If detecting lower outliers if verbose: - print('Lower limit', Q1 - multiplier * IQR) - return (x < Q1 - multiplier * IQR) - elif where == 'both': # If detecting both lower and upper outliers + print("Lower limit", Q1 - multiplier * IQR) + return x < Q1 - multiplier * IQR + elif where == "both": # If detecting both lower and upper outliers if verbose: - print('Upper, lower limit', - Q3 + multiplier * IQR, - Q1 - multiplier * IQR) + print("Upper, lower limit", Q3 + multiplier * IQR, Q1 - multiplier * IQR) return (x > Q3 + multiplier * IQR), (x < Q1 - multiplier * IQR) def _RMSE(y_true, y_pred): - '''Calculates the Root Mean Squared Error for y_true and y_pred, where - y_pred is the "prediction", and y_true is the truth.''' - mask = ~np.isnan(y_pred) - return np.sqrt(np.mean((y_pred[mask]-y_true[mask])**2)) + """Calculates the Root Mean Squared Error for y_true and y_pred, where + y_pred is the "prediction", and y_true is the truth.""" + mask = ~pd.isnull(y_pred) + return np.sqrt(np.mean((y_pred[mask] - y_true[mask]) ** 2)) def _MSD(y_true, y_pred): - '''Calculates the Mean Signed Deviation for y_true and y_pred, where y_pred - is the "prediction", and y_true is the truth.''' + """Calculates the Mean Signed Deviation for y_true and y_pred, where y_pred + is the "prediction", and y_true is the truth.""" return np.mean(y_pred - y_true) def _progressBarWithETA(value, endvalue, time, bar_length=20): - ''' Prints a progressbar with an estimated time of "arrival" ''' + """Prints a progressbar with an estimated time of "arrival" """ percent = float(value) / endvalue * 100 - arrow = '-' * int(round(percent/100 * bar_length)-1) + '>' - spaces = ' ' * (bar_length - len(arrow)) + arrow = "-" * int(round(percent / 100 * bar_length) - 1) + ">" + spaces = " " * (bar_length - len(arrow)) used = time / 60 # Time Used - left = used / percent*(100-percent) # Estimated time left + left = used / percent * (100 - percent) # Estimated time left sys.stdout.write( - "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left) + - " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent)) - sys.stdout.flush() \ No newline at end of file + "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left) + + " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent) + ) + sys.stdout.flush() + + +############################################################################### +# all code below for new piecewise fitting in soiling intervals within srr/Matt +############################################################################### +def piecewise_linear(x, x0, b, k1, k2): + cond_list = [x < x0, x >= x0] + func_list = [lambda x: k1 * x + b, lambda x: k1 * x + b + k2 * (x - x0)] + return np.piecewise(x, cond_list, func_list) + + +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 + """ + 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 (days_clean_vs_cp) of the cleanings. + + Parameters + ---------- + pr : + Series of daily performance ratios measured during the given deposition period. + fill_method : str (default='bfill') + Method to employ to fill any missing day. + 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 defined in the function. + initial_guesses : numeric (default=0.1) + List of initial guesses for fitting function + min_r2 : numeric (default=0.1) + Minimum R2 to consider valid the extracted soiling profile. + + Returns + ------- + sr: numeric + Series containing the daily soiling ratio values after segmentation. + List of nan if segmentation was not possible. + cp_date: datetime + Datetime in which continuous change points occurred. + None if segmentation was not possible. + """ + + # Check if PR dataframe has datetime index + if not isinstance(pr.index, pd.DatetimeIndex): + raise ValueError("The time series does not have DatetimeIndex") + + # Define bounds if not provided + if bounds == None: + # bounds are neg in first 4 and pos in second 4 + # ordered as x0,b,k1,k2 where x0 is the breakpoint k1 and k2 are slopes + bounds = [(13, -5, -np.inf, -np.inf), ((len(pr) - 13), 5, +np.inf, +np.inf)] + y = pr.values + x = np.arange(0.0, len(y)) + + try: + # Fit soiling profile with segmentation + 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. 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) + cp_date = int(p[0]) + else: + z = [np.nan] * len(x) + cp_date = None + R2_original = st.linregress(y, x)[2] ** 2 + R2_piecewise = st.linregress(y, z)[2] ** 2 + + R2_improve = R2_piecewise - R2_original + R2_percent_improve = (R2_piecewise / R2_original) - 1 + 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 + if (R2_piecewise < min_r2) | ( + (R2_percent_of_possible_improve < 0.5) & (R2_percent_improve < 0.5) + ): + z = [np.nan] * len(x) + cp_date = None + else: + if (R2_percent_improve < 0.01) | (R2_piecewise < 0.4): + z = [np.nan] * len(x) + cp_date = None + except: + z = [np.nan] * len(x) + cp_date = None + # Create Series from modelled profile + sr = pd.Series(z, index=pr.index) + + return sr, cp_date diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 673d4277..4ae6c6b9 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -239,12 +239,12 @@ def test_soiling_srr_with_nan_interval(soiling_normalized_daily, soiling_insolat 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' - ''' + 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, method="perfect_clean_complex", piecewise=True, neg_shift=True) - assert 0.974297 == pytest.approx(sr, abs=1e-6), \ + assert 0.974225 == pytest.approx(sr, abs=1e-6), \ 'Soiling ratio different from expected value when an entire interval was NaN' - ''' + def test_soiling_srr_outlier_factor(soiling_normalized_daily, soiling_insolation): _, _, info = soiling_srr(soiling_normalized_daily, soiling_insolation, @@ -331,11 +331,11 @@ def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolatio # negetive shift and piecewise tests # ########################### @pytest.mark.parametrize('method,neg_shift,expected_sr', - [('half_norm_clean', False, 0.940237), + [('half_norm_clean', False, 0.980143), ('half_norm_clean', True, 0.975057), - ('perfect_clean_complex', False, 0.941591), + ('perfect_clean_complex', False, 0.983797), ('perfect_clean_complex', True, 0.964117), - ('inferred_clean_complex', False, 0.939747), + ('inferred_clean_complex', False, 0.983265), ('inferred_clean_complex', True, 0.963585)]) def test_negative_shifts(soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times, method, neg_shift, expected_sr): reps = 10 @@ -381,12 +381,12 @@ def test_complex_sr_clean_threshold(soiling_normalized_daily_with_neg_shifts, so clean_threshold=0.1, method='perfect_clean_complex', piecewise=True, neg_shift=True) assert 0.934926 == 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(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=10, clean_threshold=1) - ''' + # ########################### # annual_soiling_ratios tests # ########################### From 35a3ec991f360a805f5489444434218828ee74c8 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Fri, 2 Aug 2024 12:51:05 -0600 Subject: [PATCH 05/33] formatting conftest.py and soiling_test.py --- rdtools/test/conftest.py | 118 +++-- rdtools/test/soiling_test.py | 999 ++++++++++++++++++++++++----------- 2 files changed, 751 insertions(+), 366 deletions(-) diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py index 7318d91d..72de0246 100644 --- a/rdtools/test/conftest.py +++ b/rdtools/test/conftest.py @@ -9,8 +9,7 @@ import rdtools -rdtools_base_version = \ - parse_version(parse_version(rdtools.__version__).base_version) +rdtools_base_version = parse_version(parse_version(rdtools.__version__).base_version) # decorator takes one argument: the base version for which it should fail @@ -26,17 +25,20 @@ def wrapper(func): def inner(*args, **kwargs): # fail if the version is too high if rdtools_base_version >= parse_version(version): - pytest.fail('the tested function is scheduled to be ' - 'removed in %s' % version) + pytest.fail( + "the tested function is scheduled to be " "removed in %s" % version + ) # otherwise return the function to be executed else: return func(*args, **kwargs) + return inner + return wrapper def assert_isinstance(obj, klass): - assert isinstance(obj, klass), f'got {type(obj)}, expected {klass}' + assert isinstance(obj, klass), f"got {type(obj)}, expected {klass}" def assert_warnings(messages, record): @@ -58,17 +60,19 @@ def assert_warnings(messages, record): assert found_match, f"warning '{pattern}' not in {warning_messages}" -requires_pvlib_below_090 = \ - pytest.mark.skipif(parse_version(pvlib.__version__) > parse_version('0.8.1'), - reason='requires pvlib <= 0.8.1') +requires_pvlib_below_090 = pytest.mark.skipif( + parse_version(pvlib.__version__) > parse_version("0.8.1"), + reason="requires pvlib <= 0.8.1", +) # %% Soiling fixtures + @pytest.fixture() def soiling_times(): - tz = 'Etc/GMT+7' - times = pd.date_range('2019/01/01', '2019/03/16', freq='D', tz=tz) + tz = "Etc/GMT+7" + times = pd.date_range("2019/01/01", "2019/03/16", freq="D", tz=tz) return times @@ -85,6 +89,7 @@ def soiling_normalized_daily(soiling_times): return normalized_daily + @pytest.fixture() def soiling_normalized_daily_with_neg_shifts(soiling_times): interval_1_v1 = 1 - 0.005 * np.arange(0, 15, 1) @@ -92,7 +97,9 @@ def soiling_normalized_daily_with_neg_shifts(soiling_times): interval_2 = 1 - 0.002 * np.arange(0, 25, 1) interval_3_v1 = 1 - 0.001 * np.arange(0, 10, 1) interval_3_v2 = (0.95 - 0.001 * 10) - 0.001 * np.arange(0, 15, 1) - profile = np.concatenate((interval_1_v1, interval_1_v2, interval_2, interval_3_v1, interval_3_v2)) + profile = np.concatenate( + (interval_1_v1, interval_1_v2, interval_2, interval_3_v1, interval_3_v2) + ) np.random.seed(1977) noise = 0.01 * np.random.rand(75) normalized_daily = pd.Series(data=profile, index=soiling_times) @@ -100,13 +107,16 @@ def soiling_normalized_daily_with_neg_shifts(soiling_times): return normalized_daily + @pytest.fixture() def soiling_normalized_daily_with_piecewise_slope(soiling_times): interval_1_v1 = 1 - 0.002 * np.arange(0, 20, 1) interval_1_v2 = (1 - 0.002 * 20) - 0.007 * np.arange(0, 20, 1) interval_2_v1 = 1 - 0.01 * np.arange(0, 20, 1) interval_2_v2 = (1 - 0.01 * 20) - 0.001 * np.arange(0, 15, 1) - profile = np.concatenate((interval_1_v1, interval_1_v2, interval_2_v1, interval_2_v2)) + profile = np.concatenate( + (interval_1_v1, interval_1_v2, interval_2_v1, interval_2_v2) + ) np.random.seed(1977) noise = 0.01 * np.random.rand(75) normalized_daily = pd.Series(data=profile, index=soiling_times) @@ -114,6 +124,7 @@ def soiling_normalized_daily_with_piecewise_slope(soiling_times): return normalized_daily + @pytest.fixture() def soiling_insolation(soiling_times): insolation = np.empty((75,)) @@ -128,8 +139,8 @@ def soiling_insolation(soiling_times): @pytest.fixture() def cods_times(): - tz = 'Etc/GMT+7' - cods_times = pd.date_range('2019/01/01', '2021/01/01', freq='D', tz=tz) + tz = "Etc/GMT+7" + cods_times = pd.date_range("2019/01/01", "2021/01/01", freq="D", tz=tz) return cods_times @@ -141,7 +152,9 @@ def cods_normalized_daily_wo_noise(cods_times): interval_3 = 1 - 0.001 * np.arange(0, 25, 1) profile = np.concatenate((interval_1, interval_2, interval_3)) repeated_profile = np.concatenate([profile for _ in range(int(np.ceil(N / 75)))]) - cods_normalized_daily_wo_noise = pd.Series(data=repeated_profile[:N], index=cods_times) + cods_normalized_daily_wo_noise = pd.Series( + data=repeated_profile[:N], index=cods_times + ) return cods_normalized_daily_wo_noise @@ -159,18 +172,21 @@ def cods_normalized_daily_small_soiling(cods_normalized_daily_wo_noise): N = len(cods_normalized_daily_wo_noise) np.random.seed(1977) noise = 1 + 0.02 * (np.random.rand(N) - 0.5) - cods_normalized_daily_small_soiling = cods_normalized_daily_wo_noise.apply( - lambda row: 1-(1-row)*0.1) * noise + cods_normalized_daily_small_soiling = ( + cods_normalized_daily_wo_noise.apply(lambda row: 1 - (1 - row) * 0.1) * noise + ) return cods_normalized_daily_small_soiling # %% Availability fixtures -ENERGY_PARAMETER_SPACE = list(itertools.product( - [0, np.nan], # outage value for power - [0, np.nan, None], # value for cumulative energy (None means real value) - [0, 0.25, 0.5, 0.75, 1.0], # fraction of comms outage that is power outage -)) +ENERGY_PARAMETER_SPACE = list( + itertools.product( + [0, np.nan], # outage value for power + [0, np.nan, None], # value for cumulative energy (None means real value) + [0, 0.25, 0.5, 0.75, 1.0], # fraction of comms outage that is power outage + ) +) # display names for the test cases. default is just 0..N ENERGY_PARAMETER_IDS = ["_".join(map(str, p)) for p in ENERGY_PARAMETER_SPACE] @@ -180,20 +196,23 @@ def _generate_energy_data(power_value, energy_value, outage_fraction): Generate an artificial mixed communication/power outage. """ # a few days of clearsky irradiance for creating a plausible power signal - times = pd.date_range('2019-01-01', '2019-01-15 23:59', freq='15min', - tz='US/Eastern') + times = pd.date_range( + "2019-01-01", "2019-01-15 23:59", freq="15min", tz="US/Eastern" + ) location = pvlib.location.Location(40, -80) # use haurwitz to avoid dependency on `tables` - clearsky = location.get_clearsky(times, model='haurwitz') + clearsky = location.get_clearsky(times, model="haurwitz") # just set base inverter power = ghi+clipping for simplicity - base_power = clearsky['ghi'].clip(upper=0.8*clearsky['ghi'].max()) - - inverter_power = pd.DataFrame({ - 'inv0': base_power, - 'inv1': base_power*0.7, - 'inv2': base_power*1.3, - }) + base_power = clearsky["ghi"].clip(upper=0.8 * clearsky["ghi"].max()) + + inverter_power = pd.DataFrame( + { + "inv0": base_power, + "inv1": base_power * 0.7, + "inv2": base_power * 1.3, + } + ) expected_power = inverter_power.sum(axis=1) # dawn/dusk points expected_power[expected_power < 10] = 0 @@ -202,10 +221,10 @@ def _generate_energy_data(power_value, energy_value, outage_fraction): expected_power *= 1.05 + np.random.normal(0, scale=0.05, size=len(times)) # calculate what part of the comms outage is a power outage - comms_outage = slice('2019-01-03 00:00', '2019-01-06 00:00') + comms_outage = slice("2019-01-03 00:00", "2019-01-06 00:00") start = times.get_loc(comms_outage.start) stop = times.get_loc(comms_outage.stop) - power_outage = slice(start, int(start + outage_fraction * (stop-start))) + power_outage = slice(start, int(start + outage_fraction * (stop - start))) expected_loss = inverter_power.iloc[power_outage, :].sum().sum() / 4 inverter_power.iloc[power_outage, :] = 0 meter_power = inverter_power.sum(axis=1) @@ -219,14 +238,16 @@ def _generate_energy_data(power_value, energy_value, outage_fraction): meter_energy[comms_outage] = energy_value inverter_power.loc[comms_outage, :] = power_value - expected_type = 'real' if outage_fraction > 0 else 'comms' + expected_type = "real" if outage_fraction > 0 else "comms" - return (meter_power, - meter_energy, - inverter_power, - expected_power, - expected_loss, - expected_type) + return ( + meter_power, + meter_energy, + inverter_power, + expected_power, + expected_loss, + expected_type, + ) @pytest.fixture(params=ENERGY_PARAMETER_SPACE, ids=ENERGY_PARAMETER_IDS) @@ -254,13 +275,12 @@ def energy_data_comms_single(): @pytest.fixture def availability_analysis_object(energy_data_outage_single): - (meter_power, - meter_energy, - inverter_power, - expected_power, - _, _) = energy_data_outage_single - - aa = rdtools.availability.AvailabilityAnalysis(meter_power, inverter_power, meter_energy, - expected_power) + (meter_power, meter_energy, inverter_power, expected_power, _, _) = ( + energy_data_outage_single + ) + + aa = rdtools.availability.AvailabilityAnalysis( + meter_power, inverter_power, meter_energy, expected_power + ) aa.run() return aa diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 4ae6c6b9..20691e45 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -12,189 +12,297 @@ 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' - #wait to see which tests matt wants to keep - #assert len(soiling_info['change_points']) == len(soiling_normalized_daily), \ - # 'length of soiling_info["change_points"] different than expected' - #assert isinstance(soiling_info['change_points'], pd.Series), \ - # 'soiling_info["change_points"] not a pandas series' - #assert (soiling_info['change_points'] == False).all(), \ - # 'not all values in soiling_inf["change_points"] are False' - #assert len(soiling_info['days_since_clean']) == len(soiling_normalized_daily), \ - # 'length of soiling_info["days_since_clean"] different than expected' - #assert isinstance(soiling_info['days_since_clean'], pd.Series), \ - # 'soiling_info["days_since_clean"] not a pandas series' - + 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' + # wait to see which tests matt wants to keep + # assert len(soiling_info['change_points']) == len(soiling_normalized_daily), \ + # 'length of soiling_info["change_points"] different than expected' + # assert isinstance(soiling_info['change_points'], pd.Series), \ + # 'soiling_info["change_points"] not a pandas series' + # assert (soiling_info['change_points'] == False).all(), \ + # 'not all values in soiling_inf["change_points"] are False' + # assert len(soiling_info['days_since_clean']) == len(soiling_normalized_daily), \ + # 'length of soiling_info["days_since_clean"] different than expected' + # assert isinstance(soiling_info['days_since_clean'], pd.Series), \ + # 'soiling_info["days_since_clean"] not a pandas series' # 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','inferred_recovery','inferred_begin_shift', - 'length', 'valid'] - actual_summary_columns = soiling_info['soiling_interval_summary'].columns.values + expected_summary_columns = [ + "start", + "end", + "soiling_rate", + "soiling_rate_low", + "soiling_rate_high", + "inferred_start_loss", + "inferred_end_loss", + "inferred_recovery", + "inferred_begin_shift", + "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']" + 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, - 'inferred_recovery': 0.065416, #Matt might not keep - 'inferred_begin_shift': 0.084814, #Matt might not keep - 'length': 24.0, - 'valid': 1.0}) - expected_means = expected_means[['soiling_rate', 'soiling_rate_low', 'soiling_rate_high', - 'inferred_start_loss', 'inferred_end_loss', 'inferred_recovery', 'inferred_begin_shift', - 'length', 'valid']] - actual_means = soiling_info['soiling_interval_summary'][expected_means.index].mean() + 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, + "inferred_recovery": 0.065416, # Matt might not keep + "inferred_begin_shift": 0.084814, # Matt might not keep + "length": 24.0, + "valid": 1.0, + } + ) + expected_means = expected_means[ + [ + "soiling_rate", + "soiling_rate_low", + "soiling_rate_high", + "inferred_start_loss", + "inferred_end_loss", + "inferred_recovery", + "inferred_begin_shift", + "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' + 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' @pytest.mark.filterwarnings("ignore:.*20% or more of the daily data.*:UserWarning") -@pytest.mark.parametrize('method,neg_shift,piecewise,expected_sr', - [('random_clean', False, False, 0.936177), - ('half_norm_clean', False, False, 0.915093), - ('perfect_clean', False, False, 0.977116), - ('perfect_clean_complex', True, True, 0.977116), - ('inferred_clean_complex', True, True, 0.975805)]) -def test_soiling_srr_consecutive_invalid(soiling_normalized_daily, soiling_insolation, - soiling_times, method, neg_shift, piecewise, expected_sr): +@pytest.mark.parametrize( + "method,neg_shift,piecewise,expected_sr", + [ + ("random_clean", False, False, 0.936177), + ("half_norm_clean", False, False, 0.915093), + ("perfect_clean", False, False, 0.977116), + ("perfect_clean_complex", True, True, 0.977116), + ("inferred_clean_complex", True, True, 0.975805), + ], +) +def test_soiling_srr_consecutive_invalid( + soiling_normalized_daily, + soiling_insolation, + soiling_times, + method, + neg_shift, + piecewise, + 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, piecewise=piecewise, neg_shift=neg_shift) - assert expected_sr == pytest.approx(sr, abs=1e-6), \ - f'Soiling ratio different from expected value for {method} with consecutive invalid intervals' # noqa: E501 - - -@pytest.mark.parametrize('clean_criterion,expected_sr', - [('precip_and_shift', 0.982546), - ('precip_or_shift', 0.973433), - ('precip', 0.976196), - ('shift', 0.964369)]) -def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation, soiling_times, - clean_criterion, expected_sr): + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, + soiling_insolation, + reps=reps, + max_relative_slope_error=20.0, + method=method, + piecewise=piecewise, + neg_shift=neg_shift, + ) + assert expected_sr == pytest.approx( + sr, abs=1e-6 + ), f"Soiling ratio different from expected value for {method} with consecutive invalid intervals" # noqa: E501 + + +@pytest.mark.parametrize( + "clean_criterion,expected_sr", + [ + ("precip_and_shift", 0.982546), + ("precip_or_shift", 0.973433), + ("precip", 0.976196), + ("shift", 0.964369), + ], +) +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 + 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 - } + 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" + 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_confidence_levels(soiling_normalized_daily, soiling_insolation): - 'Tests SRR with different confidence level settings from above' + "Tests SRR with different confidence level settings from above" np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, - confidence_level=95, reps=10, exceedance_prob=80.0) - assert np.array([0.959322, 0.966066]) == pytest.approx(sr_ci, abs=1e-6), \ - 'Confidence interval with confidence_level=95 different than expected' - assert 0.962691 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6), \ - 'soiling_info["exceedance_level"] different than expected when exceedance_prob=80' + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, + soiling_insolation, + confidence_level=95, + reps=10, + exceedance_prob=80.0, + ) + assert np.array([0.959322, 0.966066]) == pytest.approx( + sr_ci, abs=1e-6 + ), "Confidence interval with confidence_level=95 different than expected" + assert 0.962691 == pytest.approx( + soiling_info["exceedance_level"], abs=1e-6 + ), 'soiling_info["exceedance_level"] different than expected when exceedance_prob=80' def test_soiling_srr_dayscale(soiling_normalized_daily, soiling_insolation): - 'Test that a long dayscale can prevent valid intervals from being found' + "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(soiling_normalized_daily, soiling_insolation, - confidence_level=68.2, reps=10, day_scale=91) + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, + soiling_insolation, + confidence_level=68.2, + reps=10, + day_scale=91, + ) def test_soiling_srr_clean_threshold(soiling_normalized_daily, soiling_insolation): - '''Test that clean test_soiling_srr_clean_threshold works with a float and - can cause no soiling intervals to be found''' + """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(soiling_normalized_daily, soiling_insolation, reps=10, - clean_threshold=0.01) - assert 0.964369 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio with specified clean_threshold different from expected value' + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.01 + ) + assert 0.964369 == 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(soiling_normalized_daily, soiling_insolation, - reps=10, clean_threshold=0.1) + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.1 + ) def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation): np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10, - trim=True) - - assert 0.978093 == 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' - - -@pytest.mark.parametrize('method,expected_sr', - [('random_clean', 0.920444), - ('perfect_clean', 0.966912), - ('perfect_clean_complex', 0.966912), - ('inferred_clean_complex', 0.965565)]) -def test_soiling_srr_method(soiling_normalized_daily, soiling_insolation, method, expected_sr): + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, trim=True + ) + + assert 0.978093 == 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" + + +@pytest.mark.parametrize( + "method,expected_sr", + [ + ("random_clean", 0.920444), + ("perfect_clean", 0.966912), + ("perfect_clean_complex", 0.966912), + ("inferred_clean_complex", 0.965565), + ], +) +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' + 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_min_interval_length(soiling_normalized_daily, soiling_insolation): - 'Test that a long minimum interval length prevents finding shorter intervals' + "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(soiling_normalized_daily, soiling_insolation, confidence_level=68.2, - reps=10, min_interval_length=26) + _ = soiling_srr( + soiling_normalized_daily, + soiling_insolation, + confidence_level=68.2, + reps=10, + min_interval_length=26, + ) # but min=24 should be fine: - _ = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=68.2, - reps=10, min_interval_length=24) + _ = soiling_srr( + soiling_normalized_daily, + soiling_insolation, + confidence_level=68.2, + reps=10, + min_interval_length=24, + ) def test_soiling_srr_recenter_false(soiling_normalized_daily, soiling_insolation): np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10, - recenter=False) - assert 1 == soiling_info['renormalizing_factor'], \ - 'Renormalizing factor != 1 with recenter=False' - assert 0.966387 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different than expected when recenter=False' + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily, soiling_insolation, reps=10, recenter=False + ) + assert ( + 1 == soiling_info["renormalizing_factor"] + ), "Renormalizing factor != 1 with recenter=False" + assert 0.966387 == pytest.approx( + sr, abs=1e-6 + ), "Soiling ratio different than expected when recenter=False" def test_soiling_srr_negative_step(soiling_normalized_daily, soiling_insolation): @@ -202,102 +310,137 @@ def test_soiling_srr_negative_step(soiling_normalized_daily, soiling_insolation) 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 test_soiling_srr_max_negative_slope_error(soiling_normalized_daily, soiling_insolation): + 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 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) + 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 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' + 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_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' - - 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, method="perfect_clean_complex", piecewise=True, neg_shift=True) - assert 0.974225 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different from expected value when an entire interval was NaN' - + 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" + + 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, + method="perfect_clean_complex", + piecewise=True, + neg_shift=True, + ) + assert 0.974225 == pytest.approx( + sr, abs=1e-6 + ), "Soiling ratio different from expected value when an entire interval was NaN" + 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' + _, _, 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_kwargs(monkeypatch, soiling_normalized_daily, soiling_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(soiling_normalized_daily, soiling_insolation, reps=10) -@pytest.mark.parametrize(('start,expected_sr'), - [(18, 0.984779), (17, 0.981258)]) -def test_soiling_srr_min_interval_length_default(soiling_normalized_daily, soiling_insolation, - start, expected_sr): - ''' +@pytest.mark.parametrize(("start,expected_sr"), [(18, 0.984779), (17, 0.981258)]) +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' + 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" -@pytest.mark.parametrize('test_param', ['energy_normalized_daily', - 'insolation_daily', - 'precipitation_daily']) +@pytest.mark.parametrize( + "test_param", ["energy_normalized_daily", "insolation_daily", "precipitation_daily"] +) def test_soiling_srr_non_daily_inputs(test_param): - ''' + """ Validate the frequency check for input time series - ''' - dummy_daily_explicit = pd.Series(0, index=pd.date_range('2019-01-01', periods=10, freq='d')) - dummy_daily_implicit = pd.Series(0, index=pd.date_range('2019-01-01', periods=10, freq='d')) + """ + dummy_daily_explicit = pd.Series( + 0, index=pd.date_range("2019-01-01", periods=10, freq="d") + ) + dummy_daily_implicit = pd.Series( + 0, index=pd.date_range("2019-01-01", periods=10, freq="d") + ) dummy_daily_implicit.index.freq = None dummy_nondaily = pd.Series(0, index=dummy_daily_explicit.index[::2]) kwargs = { - 'energy_normalized_daily': dummy_daily_explicit, - 'insolation_daily': dummy_daily_explicit, - 'precipitation_daily': dummy_daily_explicit, + "energy_normalized_daily": dummy_daily_explicit, + "insolation_daily": dummy_daily_explicit, + "precipitation_daily": dummy_daily_explicit, } # no error for implicit daily inputs kwargs[test_param] = dummy_daily_implicit @@ -305,88 +448,160 @@ def test_soiling_srr_non_daily_inputs(test_param): # yes error for non-daily inputs kwargs[test_param] = dummy_nondaily - with pytest.raises(ValueError, match='must have daily frequency'): + with pytest.raises(ValueError, match="must have daily frequency"): _ = SRRAnalysis(**kwargs) 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 + "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'): + 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="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) - with pytest.raises(ValueError, match='Invalid method specification'): - _ = soiling_srr(method='bad', **kwargs) # ########################### # negetive shift and piecewise tests # ########################### -@pytest.mark.parametrize('method,neg_shift,expected_sr', - [('half_norm_clean', False, 0.980143), - ('half_norm_clean', True, 0.975057), - ('perfect_clean_complex', False, 0.983797), - ('perfect_clean_complex', True, 0.964117), - ('inferred_clean_complex', False, 0.983265), - ('inferred_clean_complex', True, 0.963585)]) -def test_negative_shifts(soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times, method, neg_shift, expected_sr): +@pytest.mark.parametrize( + "method,neg_shift,expected_sr", + [ + ("half_norm_clean", False, 0.980143), + ("half_norm_clean", True, 0.975057), + ("perfect_clean_complex", False, 0.983797), + ("perfect_clean_complex", True, 0.964117), + ("inferred_clean_complex", False, 0.983265), + ("inferred_clean_complex", True, 0.963585), + ], +) +def test_negative_shifts( + soiling_normalized_daily_with_neg_shifts, + soiling_insolation, + soiling_times, + method, + neg_shift, + expected_sr, +): reps = 10 np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=reps, - method=method, neg_shift=neg_shift) - assert expected_sr == pytest.approx(sr, abs=1e-6), \ - f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" different from expected value' - -@pytest.mark.parametrize('method,piecewise,expected_sr', - [('half_norm_clean', False, 0.8670264), - ('half_norm_clean', True, 0.927017), - ('perfect_clean_complex', False, 0.891499), - ('perfect_clean_complex', True, 0.896936), - ('inferred_clean_complex', False, 0.874486), - ('inferred_clean_complex', True, 0.896214)]) -def test_piecewise(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, soiling_times, method, piecewise, expected_sr): + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily_with_neg_shifts, + soiling_insolation, + reps=reps, + method=method, + neg_shift=neg_shift, + ) + assert expected_sr == pytest.approx( + sr, abs=1e-6 + ), f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" different from expected value' + + +@pytest.mark.parametrize( + "method,piecewise,expected_sr", + [ + ("half_norm_clean", False, 0.8670264), + ("half_norm_clean", True, 0.927017), + ("perfect_clean_complex", False, 0.891499), + ("perfect_clean_complex", True, 0.896936), + ("inferred_clean_complex", False, 0.874486), + ("inferred_clean_complex", True, 0.896214), + ], +) +def test_piecewise( + soiling_normalized_daily_with_piecewise_slope, + soiling_insolation, + soiling_times, + method, + piecewise, + expected_sr, +): reps = 10 np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, reps=reps, - method=method, piecewise=piecewise) - assert expected_sr == pytest.approx(sr, abs=1e-6), \ - f'Soiling ratio with method="{method}" and piecewise="{piecewise}" different from expected value' - -def test_piecewise_and_neg_shifts(soiling_normalized_daily_with_piecewise_slope, soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times): + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily_with_piecewise_slope, + soiling_insolation, + reps=reps, + method=method, + piecewise=piecewise, + ) + assert expected_sr == pytest.approx( + sr, abs=1e-6 + ), f'Soiling ratio with method="{method}" and piecewise="{piecewise}" different from expected value' + + +def test_piecewise_and_neg_shifts( + soiling_normalized_daily_with_piecewise_slope, + soiling_normalized_daily_with_neg_shifts, + soiling_insolation, + soiling_times, +): reps = 10 np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, reps=reps, - method='perfect_clean_complex', piecewise=True, neg_shift=True) - assert 0.896936 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different from expected value for data with piecewise slopes' + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily_with_piecewise_slope, + soiling_insolation, + reps=reps, + method="perfect_clean_complex", + piecewise=True, + neg_shift=True, + ) + assert 0.896936 == pytest.approx( + sr, abs=1e-6 + ), "Soiling ratio different from expected value for data with piecewise slopes" np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=reps, - method='perfect_clean_complex', piecewise=True, neg_shift=True) - assert 0.964117 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio different from expected value for data with negative shifts' - -def test_complex_sr_clean_threshold(soiling_normalized_daily_with_neg_shifts, soiling_insolation): - '''Test that clean test_soiling_srr_clean_threshold works with a float and - can cause no soiling intervals to be found''' + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily_with_neg_shifts, + soiling_insolation, + reps=reps, + method="perfect_clean_complex", + piecewise=True, + neg_shift=True, + ) + assert 0.964117 == pytest.approx( + sr, abs=1e-6 + ), "Soiling ratio different from expected value for data with negative shifts" + + +def test_complex_sr_clean_threshold( + soiling_normalized_daily_with_neg_shifts, soiling_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(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=10, - clean_threshold=0.1, method='perfect_clean_complex', piecewise=True, neg_shift=True) - assert 0.934926 == pytest.approx(sr, abs=1e-6), \ - 'Soiling ratio with specified clean_threshold different from expected value' - + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily_with_neg_shifts, + soiling_insolation, + reps=10, + clean_threshold=0.1, + method="perfect_clean_complex", + piecewise=True, + neg_shift=True, + ) + assert 0.934926 == 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(soiling_normalized_daily_with_neg_shifts, soiling_insolation, - reps=10, clean_threshold=1) - + sr, sr_ci, soiling_info = soiling_srr( + soiling_normalized_daily_with_neg_shifts, + soiling_insolation, + reps=10, + clean_threshold=1, + ) + + # ########################### # annual_soiling_ratios tests # ########################### @@ -394,25 +609,30 @@ def test_complex_sr_clean_threshold(soiling_normalized_daily_with_neg_shifts, so @pytest.fixture() def multi_year_profiles(): - times = pd.date_range('01-01-2018', '11-30-2019', freq='D') - data = np.array([0]*365 + [10]*334) + times = pd.date_range("01-01-2018", "11-30-2019", freq="D") + data = np.array([0] * 365 + [10] * 334) profiles = [pd.Series(x + data, times) for x in range(10)] # make insolation slighly longer to test for proper normalization - times = pd.date_range('01-01-2018', '12-31-2019', freq='D') - insolation = 350*[0.8] + (len(times)-350)*[1] + times = pd.date_range("01-01-2018", "12-31-2019", freq="D") + insolation = 350 * [0.8] + (len(times) - 350) * [1] insolation = pd.Series(insolation, index=times) return profiles, insolation def test_annual_soiling_ratios(multi_year_profiles): - expected_data = np.array([[2018, 4.5, 1.431, 7.569], - [2019, 14.5, 11.431, 17.569]]) - expected = pd.DataFrame(data=expected_data, - columns=['year', 'soiling_ratio_median', 'soiling_ratio_low', - 'soiling_ratio_high']) - expected['year'] = expected['year'].astype(int) + expected_data = np.array([[2018, 4.5, 1.431, 7.569], [2019, 14.5, 11.431, 17.569]]) + expected = pd.DataFrame( + data=expected_data, + columns=[ + "year", + "soiling_ratio_median", + "soiling_ratio_low", + "soiling_ratio_high", + ], + ) + expected["year"] = expected["year"].astype(int) srr_profiles, insolation = multi_year_profiles result = annual_soiling_ratios(srr_profiles, insolation) @@ -421,12 +641,17 @@ def test_annual_soiling_ratios(multi_year_profiles): def test_annual_soiling_ratios_confidence_interval(multi_year_profiles): - expected_data = np.array([[2018, 4.5, 0.225, 8.775], - [2019, 14.5, 10.225, 18.775]]) - expected = pd.DataFrame(data=expected_data, - columns=['year', 'soiling_ratio_median', 'soiling_ratio_low', - 'soiling_ratio_high']) - expected['year'] = expected['year'].astype(int) + expected_data = np.array([[2018, 4.5, 0.225, 8.775], [2019, 14.5, 10.225, 18.775]]) + expected = pd.DataFrame( + data=expected_data, + columns=[ + "year", + "soiling_ratio_median", + "soiling_ratio_low", + "soiling_ratio_high", + ], + ) + expected["year"] = expected["year"].astype(int) srr_profiles, insolation = multi_year_profiles result = annual_soiling_ratios(srr_profiles, insolation, confidence_level=95) @@ -437,9 +662,11 @@ def test_annual_soiling_ratios_confidence_interval(multi_year_profiles): def test_annual_soiling_ratios_warning(multi_year_profiles): srr_profiles, insolation = multi_year_profiles insolation = insolation.iloc[:-200] - match = ('The indexes of stochastic_soiling_profiles are not entirely contained ' - 'within the index of insolation_daily. Every day in stochastic_soiling_profiles ' - 'should be represented in insolation_daily. This may cause erroneous results.') + match = ( + "The indexes of stochastic_soiling_profiles are not entirely contained " + "within the index of insolation_daily. Every day in stochastic_soiling_profiles " + "should be represented in insolation_daily. This may cause erroneous results." + ) with pytest.warns(UserWarning, match=match): _ = annual_soiling_ratios(srr_profiles, insolation) @@ -451,41 +678,48 @@ def test_annual_soiling_ratios_warning(multi_year_profiles): @pytest.fixture() def soiling_interval_summary(): - starts = ['2019/01/01', '2019/01/16', '2019/02/08', '2019/03/06'] - starts = pd.to_datetime(starts).tz_localize('America/Denver') - ends = ['2019/01/15', '2019/02/07', '2019/03/05', '2019/04/07'] - ends = pd.to_datetime(ends).tz_localize('America/Denver') + starts = ["2019/01/01", "2019/01/16", "2019/02/08", "2019/03/06"] + starts = pd.to_datetime(starts).tz_localize("America/Denver") + ends = ["2019/01/15", "2019/02/07", "2019/03/05", "2019/04/07"] + ends = pd.to_datetime(ends).tz_localize("America/Denver") slopes = [-0.005, -0.002, -0.001, -0.002] slopes_low = [-0.0055, -0.0025, -0.0015, -0.003] slopes_high = [-0.004, 0, 0, -0.001] valids = [True, True, False, True] soiling_interval_summary = pd.DataFrame() - soiling_interval_summary['start'] = starts - soiling_interval_summary['end'] = ends - soiling_interval_summary['soiling_rate'] = slopes - soiling_interval_summary['soiling_rate_low'] = slopes_low - soiling_interval_summary['soiling_rate_high'] = slopes_high - soiling_interval_summary['inferred_start_loss'] = np.nan - soiling_interval_summary['inferred_end_loss'] = np.nan - soiling_interval_summary['length'] = (ends - starts).days - soiling_interval_summary['valid'] = valids + soiling_interval_summary["start"] = starts + soiling_interval_summary["end"] = ends + soiling_interval_summary["soiling_rate"] = slopes + soiling_interval_summary["soiling_rate_low"] = slopes_low + soiling_interval_summary["soiling_rate_high"] = slopes_high + soiling_interval_summary["inferred_start_loss"] = np.nan + soiling_interval_summary["inferred_end_loss"] = np.nan + soiling_interval_summary["length"] = (ends - starts).days + soiling_interval_summary["valid"] = valids return soiling_interval_summary def _build_monthly_summary(top_rows): - ''' + """ Convienience function to build a full monthly soiling summary dataframe from the expected_top_rows which summarize Jan-April - ''' - - all_rows = np.vstack((top_rows, [[1, np.nan, np.nan, np.nan, 0]]*8)) - - df = pd.DataFrame(data=all_rows, - columns=['month', 'soiling_rate_median', 'soiling_rate_low', - 'soiling_rate_high', 'interval_count']) - df['month'] = range(1, 13) + """ + + all_rows = np.vstack((top_rows, [[1, np.nan, np.nan, np.nan, 0]] * 8)) + + df = pd.DataFrame( + data=all_rows, + columns=[ + "month", + "soiling_rate_median", + "soiling_rate_low", + "soiling_rate_high", + "interval_count", + ], + ) + df["month"] = range(1, 13) return df @@ -494,11 +728,38 @@ def test_monthly_soiling_rates(soiling_interval_summary): np.random.seed(1977) result = monthly_soiling_rates(soiling_interval_summary) - expected = np.array([ - [1.00000000e+00, -2.42103810e-03, -5.00912766e-03, -7.68551806e-04, 2.00000000e+00], - [2.00000000e+00, -1.25092837e-03, -2.10091842e-03, -3.97354321e-04, 1.00000000e+00], - [3.00000000e+00, -2.00313359e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00], - [4.00000000e+00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e+00]]) + expected = np.array( + [ + [ + 1.00000000e00, + -2.42103810e-03, + -5.00912766e-03, + -7.68551806e-04, + 2.00000000e00, + ], + [ + 2.00000000e00, + -1.25092837e-03, + -2.10091842e-03, + -3.97354321e-04, + 1.00000000e00, + ], + [ + 3.00000000e00, + -2.00313359e-03, + -2.68359541e-03, + -1.31927678e-03, + 1.00000000e00, + ], + [ + 4.00000000e00, + -1.99729563e-03, + -2.68067699e-03, + -1.31667446e-03, + 1.00000000e00, + ], + ] + ) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -508,11 +769,38 @@ def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary): np.random.seed(1977) result = monthly_soiling_rates(soiling_interval_summary, min_interval_length=20) - expected = np.array([ - [1.00000000e+00, -1.24851539e-03, -2.10394564e-03, -3.98358211e-04, 1.00000000e+00], - [2.00000000e+00, -1.25092837e-03, -2.10091842e-03, -3.97330424e-04, 1.00000000e+00], - [3.00000000e+00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00], - [4.00000000e+00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e+00]]) + expected = np.array( + [ + [ + 1.00000000e00, + -1.24851539e-03, + -2.10394564e-03, + -3.98358211e-04, + 1.00000000e00, + ], + [ + 2.00000000e00, + -1.25092837e-03, + -2.10091842e-03, + -3.97330424e-04, + 1.00000000e00, + ], + [ + 3.00000000e00, + -2.00309454e-03, + -2.68359541e-03, + -1.31927678e-03, + 1.00000000e00, + ], + [ + 4.00000000e00, + -1.99729563e-03, + -2.68067699e-03, + -1.31667446e-03, + 1.00000000e00, + ], + ] + ) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -520,13 +808,36 @@ def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary): def test_monthly_soiling_rates_max_slope_err(soiling_interval_summary): np.random.seed(1977) - result = monthly_soiling_rates(soiling_interval_summary, max_relative_slope_error=120) - - expected = np.array([ - [1.00000000e+00, -4.74910923e-03, -5.26236739e-03, -4.23901493e-03, 1.00000000e+00], - [2.00000000e+00, np.nan, np.nan, np.nan, 0.00000000e+00], - [3.00000000e+00, -2.00074270e-03, -2.68073474e-03, -1.31786434e-03, 1.00000000e+00], - [4.00000000e+00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00]]) + result = monthly_soiling_rates( + soiling_interval_summary, max_relative_slope_error=120 + ) + + expected = np.array( + [ + [ + 1.00000000e00, + -4.74910923e-03, + -5.26236739e-03, + -4.23901493e-03, + 1.00000000e00, + ], + [2.00000000e00, np.nan, np.nan, np.nan, 0.00000000e00], + [ + 3.00000000e00, + -2.00074270e-03, + -2.68073474e-03, + -1.31786434e-03, + 1.00000000e00, + ], + [ + 4.00000000e00, + -2.00309454e-03, + -2.68359541e-03, + -1.31927678e-03, + 1.00000000e00, + ], + ] + ) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -536,11 +847,38 @@ def test_monthly_soiling_rates_confidence_level(soiling_interval_summary): np.random.seed(1977) result = monthly_soiling_rates(soiling_interval_summary, confidence_level=95) - expected = np.array([ - [1.00000000e+00, -2.42103810e-03, -5.42313113e-03, -1.21156562e-04, 2.00000000e+00], - [2.00000000e+00, -1.25092837e-03, -2.43731574e-03, -6.23842627e-05, 1.00000000e+00], - [3.00000000e+00, -2.00313359e-03, -2.94998476e-03, -1.04988760e-03, 1.00000000e+00], - [4.00000000e+00, -1.99729563e-03, -2.95063841e-03, -1.04869949e-03, 1.00000000e+00]]) + expected = np.array( + [ + [ + 1.00000000e00, + -2.42103810e-03, + -5.42313113e-03, + -1.21156562e-04, + 2.00000000e00, + ], + [ + 2.00000000e00, + -1.25092837e-03, + -2.43731574e-03, + -6.23842627e-05, + 1.00000000e00, + ], + [ + 3.00000000e00, + -2.00313359e-03, + -2.94998476e-03, + -1.04988760e-03, + 1.00000000e00, + ], + [ + 4.00000000e00, + -1.99729563e-03, + -2.95063841e-03, + -1.04869949e-03, + 1.00000000e00, + ], + ] + ) expected = _build_monthly_summary(expected) @@ -551,11 +889,38 @@ def test_monthly_soiling_rates_reps(soiling_interval_summary): np.random.seed(1977) result = monthly_soiling_rates(soiling_interval_summary, reps=3) - expected = np.array([ - [1.00000000e+00, -2.88594088e-03, -5.03736679e-03, -6.47391131e-04, 2.00000000e+00], - [2.00000000e+00, -1.67359565e-03, -2.00504171e-03, -1.33240044e-03, 1.00000000e+00], - [3.00000000e+00, -1.22306993e-03, -2.19274892e-03, -1.11793240e-03, 1.00000000e+00], - [4.00000000e+00, -1.94675549e-03, -2.42574164e-03, -1.54850795e-03, 1.00000000e+00]]) + expected = np.array( + [ + [ + 1.00000000e00, + -2.88594088e-03, + -5.03736679e-03, + -6.47391131e-04, + 2.00000000e00, + ], + [ + 2.00000000e00, + -1.67359565e-03, + -2.00504171e-03, + -1.33240044e-03, + 1.00000000e00, + ], + [ + 3.00000000e00, + -1.22306993e-03, + -2.19274892e-03, + -1.11793240e-03, + 1.00000000e00, + ], + [ + 4.00000000e00, + -1.94675549e-03, + -2.42574164e-03, + -1.54850795e-03, + 1.00000000e00, + ], + ] + ) expected = _build_monthly_summary(expected) From 3fdf0b08f6c16574145152916c0edc14da7d6eb6 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Mon, 5 Aug 2024 14:30:33 -0600 Subject: [PATCH 06/33] fixing formatting --- docs/TrendAnalysis_example_pvdaq4.ipynb | 195 +++++++++++++++++------- rdtools/soiling.py | 169 ++++++++++---------- rdtools/test/soiling_test.py | 14 +- 3 files changed, 233 insertions(+), 145 deletions(-) diff --git a/docs/TrendAnalysis_example_pvdaq4.ipynb b/docs/TrendAnalysis_example_pvdaq4.ipynb index cc1c9ccc..3bf6883c 100644 --- a/docs/TrendAnalysis_example_pvdaq4.ipynb +++ b/docs/TrendAnalysis_example_pvdaq4.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -135,12 +135,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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cAO3mHZV9+sXxylgexfdwLcXc5pbZs5V5ToRalbb8Sp8R9dpr2tw7VVUB0el0YnNUUfrjFMaaqPRWr16Nfv36YerUqWjatCkCAgLQrl078cyqmjVLP/PEzs4OEydOxP3793HmzJlShwsPD0dmZqb4qqynkqmBnFeja/3URWOazPyq0uY1yQ1o8Ne/Sgks9i+rmlR1Fpa7uztu3rxZont6ejoAwMOj9MOQzs7O2LlzJ1JSUpCcnIyGDRuiYcOG6Nq1K9zc3ODi4lLmvPV7E3/+WdolV08Pnln6AFplkPPsmMqixcyW8q9/BeFfCmeIUXgDgIgMTpgpiyW+O/x9fMqkAvL666+bPWFBEBAVFWXWOB06dMChQ4eQlZVlcCA9MTFR7F+eBg0aoEGDBgAg7lEMHTq03PGuXr0KAHBzU3CzzogdO/Zi0CBlfqz8IymbVt4fJXNuHuyKr9KAl2U4Ba3ocjQO32d1e7FaZFIB+e6770yu9nrmDg8AwcHB+PDDD/HZZ5+J14Hk5ubiiy++gJ+fn7iXkJKSgsePH6Nly5ZlTi88PBz5+fn45z//KXa7fft2iSLx4MEDfPzxx3B1dUWnTp3Mzm1Jb58EBg1SOoX5Gs3Ya9EfuKWnXxmePHkiHqNTkiXfSz8/P/hZZMrq0n/GXuzR+PdRCpMKSHJysoVjPOXn54eQkBCEh4cjIyMDXl5eWLt2LZKTkw32ZkJDQ3HkyBEQkdhtwYIFuHDhAvz8/FCtWjXs2LED33zzDebOnYvnnntOHG7FihXYsWMHBgwYgAYNGiA9PR3R0dFISUlBbGws7OzsKmVZmXY8N2OveHKDnB4/AZwVqB9a2GvSmgtKB1CIqo6BAMC6desQERGB2NhY3Lt3D97e3tizZw8CAgLKHK9du3bYvn07du3ahYKCAnh7e2PLli0ICQkxGK5bt244fvw41qxZg7t376JGjRrw9fVFdHQ0evbsaclFM5lWmkaKexHAtxacvlLvy20LTbf9+98othd1cVZPtIr8TpF5K0Hre6tqpaqzsICnp+wuWrQI6enpyMnJwalTp9CnTx+DYQ4fPmyw9wEAQUFBSExMRFZWFh49eoQTJ06UKB7A03tgffPNN0hPT0deXh7u3buH/fv3q6Z4KEGulXJUJf9ILVlMLLXCUWpF1rnY32Wd0chMw0WpAgXkq6++Qq9evVC3bl1Uq1YNtra2JV6MMXWI55UdswBJBWTr1q3o378//vjjD7z66qsoLCzEiBEj8Oqrr0Kn08Hb2xv/+c9/5M7KZLRhgIvSEVgls9ZrcKyRVpqwJRWQ+fPnw9fXF0lJSYiMjATw9FTfDRs24MKFC0hPT0fjxo1lDVqVWeLL1K1bN9mnWRl45cf0JqlsJauVlb6cJBWQX375Ba+++ipsbW1RrdrT4/BPnjwBADRq1AgTJkzAwoUL5UvJVGHzZvX9QEx92BizPruVDiAzLW4cSSogjo6O4umuLi4usLe3F68WB4D69evj2rVr8iRkqjE9SekEJY39Nk/pCEwhanj8m6/SARQmqYC0aNECv/zyi/h3hw4dEBsbi/z8fOTk5GDjxo3i1eBMmsreGrHE7ndV3KU3F79H5kleEIStIc/gzW7PICzkGaXjYIsG9xrkJKmADB48GDt37hRvfPjuu+/i8OHDcHFxgZubG44dO4YZM2bIGpQpQ4271WrMBAAff8zFoDIcSDP8lylHUgGZOnUqUlJSxBsL9u/fH4cPH8bYsWMxfvx4fPvttwgLC5MzJ2Oq9/EtpRNUDa3pFn67eQutSX1veFXbo5TtQsIXXngBS5YswYcffogePXrINVn2F0t8MS2xJa/E3oGSP1opy6vGPSgtrfgmHwe+TX76L1OW6q5EZ0yNfvrpJ6UjyK6e0gGY5kkqIESE1atXw9fXF66urkavQtef3suYJcj/LMiyDdiQUslztLxTKtwT0iI17lFWFklr+WnTpmHx4sXo0KEDRo0ahdq1a8udi0G7N1WsDMf4vWFMcZIKyNq1azF06FBs2bJF7jxMQVp+xoYlsnMBVz81fmfVmMlSJDVhZWdn46WXXpI7C2OMsb9oYeNFUgF58cUX8cMPP8idhWmAFr7UWlP80QSsbP/1UToB05NUQFauXImTJ0/igw8+wN27d+XOxEqhlVN5i9LiMzsqw7Zh7uL/G4fvUzCJ9gwbpr7PXa7vota+05JvZXL16lVERESgXr16qFGjBpycnAxezs7OcmdlrExa2jvy8eHNaKZ9kg6iDx06FIIgyJ2FqZS5B5MXdABmnLVYHMY0t6VurSQVkJiYGJljsNJo8UygV18NwoyzlZNZi+8Ps04XZvdB29n7AVSdM7H4SnTGGABtNQGqkUMVvHZa0iKvW7euzP6CIMDBwQHPPvssfHx8xJsuMvXT+paT0vmVnj9TTlW8+4akJQ4LCxOPgRQ/BbFod0EQ4OTkhPDwcEybNq2CURmzLj/8yxeXbwMt3JTLwE2ArCIkNWGdPXsW3t7e6NGjB7Zu3Ypz587h3LlziI+PR2BgIDp06ICEhARs3boVPj4+CA8Px6pVq+TOXiVp8VRea1Haey/1/XNzc8Oodafw3EeneCXONElSAVmyZAnq16+PgwcPYvDgwWjXrh3atWuHIUOG4ODBg3Bzc0NUVBQGDRqEAwcOwN/fHytXrpQ7O2MAuAAydaoKGwWSCsiOHTswcOBAo/0EQcArr7yCbdu2PZ2BjQ2GDh2K33//XXpKxhTSROkAjKmYpAJSWFiIy5cvl9r/0qVLKCwsFP+2t7eHg4ODSdPOzc3F9OnT4eHhAZ1OBz8/Pxw4cMCkcePi4uDj4wMHBwe4ublhzJgxuHPnjtFho6Ki0KpVKzg4OKBZs2ZYtmyZSfNQQvKCIIOX0szdstLylth3Kni/mXbI/fsMU/lvR1IBeeWVV7By5UosX74cOTk5YvecnBwsW7YMn376KQYMGCB2P3HiBLy8vEyadlhYGBYvXoyRI0di6dKlsLW1Rb9+/fD999+XOd6qVaswYsQI1KlTB4sXL8bYsWMRFxeHF1980SAjAKxevRpvvPEG2rRpg2XLlqFLly6YPHkyFi5caMa7wNRETQWWMbkcVjpAOSSdhbV06VJcuXIFkydPxtSpU+Hu/vS+Punp6cjLy4Ovry+WLl0K4GlR0el0eOedd8qd7qlTpxAXF4dFixZh6tSpAIDQ0FC0bdsW06ZNw/Hjxp9hmZeXh5kzZyIgIAAHDhwQzwTr2rUrBgwYgM8//xyTJk0C8PROwu+++y6CgoIQHx8PABg7diwKCwsxZ84cjBs3rso/38TYqahH/tEW3VddMHkaHQCclTUVY1VPoNIByiFpD6ROnTpISEhAfHw8Ro8ejRYtWqBFixYYPXo04uPjcfz4cdSpUwcA4ODggM8//xwjRowod7rx8fGwtbXFuHHjxG4ODg4YM2YMTpw4gdTUVKPjXbhwAffv38fw4cMNbrHSv39/1KxZE3FxcWK3Q4cO4e7du5gwYYLBNN566y08evQIe/eqe5dRKQ0bNjRr+B28J2CSvxX7l1kXKc23RfemY1T+O5J85YsgCBgyZAiGDBkiW5ikpCQ0b94cTk5OBt19fX0BPD192NOz5MNMc3NzAQA6na5EP51Oh6SkJBQWFsLGxgZJSUkAgM6dOxsM16lTJ7H/qFGjjObLzc0V5wUAWVlZZiyduvUCYNqRJianBJWvIBgri6puZZKeni42hxWl75aWlmZ0vGbNmkEQBCQkJBh0v3z5Mm7fvo3s7Gzcu3dPnIetrS3q1atnMKydnR3q1q1b6jwAYP78+XB2dhZfxoqZVn3OKzIG4Ny5c0pHYBpi0h5I48aNYWNjg0uXLqF69epo3LhxuXfjFQQBV65cMStMdna20due6M/gys7ONjqeq6srhg0bhrVr16JVq1YYPHgwbt68iUmTJqF69ep48uSJOG52djbs7OyMTsfBwaHUeQBAeHi4wbGcrKwsqyoijA3cdAPJ7dsrHUPTqtLV/SYVkO7du0MQBNjY2Bj8LTedTmfQRKSnP4vKWBOV3urVq5GdnY2pU6eKB+BHjRqFpk2bYtu2bahZs6Y4jby8PKPT0B/wL429vT3f14sxxv5iUgEpfvt2S93O3d3dHTdv3izRPT09HQDg4eFR6rjOzs7YuXMnUlJSkJycjIYNG6Jhw4bo2rUr3Nzc4OLiIs6joKAAGRkZBs1YeXl5uHv3bpnzYMwUX365FyEh2mkSrEpbzEp4e8ZefGylTcSqOgbSoUMH/PrrryUOTicmJor9y9OgQQMEBASgYcOGuH//Ps6cOYOXXnrJYB4AcPr0aYPxTp8+jcLCQpPmURWUt0LZv3+/WdOLiLCOFdTMmeUvx7/PVEIQphk7lA5gQZJvprhp0yaDbvv370dAQAD8/PzEa0DMFRwcjIKCAnz22Wdit9zcXHzxxRfw8/MTjzekpKTg0qVL5U4vPDwc+fn5+Oc//yl269mzJ+rUqVPi5o6rVq2Co6MjgoKsc0tBbuMP5Zs1fOwTCwWpZBsLjXf/6LnKzcGYGkg6jXfatGlwdHQUr+24du0aBg8ejLp168LDwwPvvPMOdDqdwfUcpvDz80NISAjCw8ORkZEBLy8vrF27FsnJyYiKihKHCw0NxZEjRwxuJb9gwQJcuHABfn5+qFatGnbs2IFvvvkGc+fOxXPP/e/XrdPpMGfOHLz11lsICQlBnz59cOzYMaxfvx7z5s0Tr19hzBxDhwbhXz9Yx14Wq7iq0iwoqYCcO3cO//73v8W/161bB1tbWyQlJcHV1RXDhw/Hp59+anYB0U8rIiICsbGxuHfvHry9vbFnzx4EBASUOV67du2wfft27Nq1CwUFBfD29saWLVsQEhJSYtgJEyagevXq+Oijj7Br1y54enpiyZIlmDJlitl5rUl5twEx90dhLT8ia1kOxuQmqYBkZmaibt264t/79u1Dr1694OrqCgDo1asXvvrqK0mBHBwcsGjRIixatKjUYQ4fPlyiW1BQkFnNT2PHjsXYsWOlRGSMMbNY65MqJR0DcXd3x8WLFwE8PUPqzJkz6N27t9j/4cOH4im/jDHGrJOkPZCBAwdi2bJlyMnJQWJiIuzt7TF48GCx/7lz59CkCT9JgTHGrJmk3YS5c+diyJAhiI2NRUZGBmJiYlC/fn0AT6/Ojo+PN9gjYYxpBx/vkYc1NlkVJ2kPpGbNmtiwYUOp/W7cuAFHR8cKBWOMMaZuku/GWxobGxs4OzvLPVnGGGMqw0e6WaWx9qaRpV2Ang2f/qs1VaG5RWnW+P2XfQ+EVR0xMXsRFsYrHr2BA4MwUOkQjFUi3gNhks0u/24yjFVp1r55xQWEmeV5M4e3xqYRa2yKYJaxwgq//0VxAWFmWW/lPwjGmOkkHQM5evRomf0FQYCDgwOeffZZo4+oZYyxqsjabmkiqYAEBgaa/ETCZs2aITIyEsOHD5cyK2YFrOEHwzdUZKwkSQXk66+/xvTp05Gbm4uxY8fCy8sLAPDbb79hzZo10Ol0eO+993D9+nWsXr0ar732GmxtbREcHCxreMYYY8qRXEAcHByQmJgIOzs7g34TJkxAYGAgTp48iYULF+LNN99E586dsXDhQi4gjLEqx5r3XiUdRN+wYQNee+21EsUDeHo79pEjR2Lt2rXi36NGjcIvv/xSsaSMsUpjrSs8Ji9JBeTRo0f4448/Su2fnp6Ohw8fin+7uLjA1tZWyqwYY8yqTLKi4iypgPTs2RMff/wx9uzZU6Lf7t27sXTpUvTs2VPsdvbsWTRq1EhySMaY5VnDyQ5asFvpADKSdAxk+fLl6NGjBwYOHIi//e1vaNq0KQDgypUruHnzJho2bIhly5YBAHJycpCSkoI33nhDvtRMNXrN2IsDvOJhrEqSVEAaNGiAn376CZ9++in279+P69evAwBatWqFt99+G+PHj0eNGjUAPD0Gsm/fPvkSM1X5TekAjGmAtR5Il3wzRUdHR7zzzjt455135MzDNOADb2DmeaVTMMaUxrcyYWZ77TVusmLMXHtGNcS0ng2xZ1RDpaPIRvIeyP79+xEVFYWrV6/i3r17ICKD/oIg4MqVKxUOyJgaWdstKZjltW3bFm3bKp1CXpIKyKJFizBjxgzUr18fvr6+aNeundy5GGOMqZykAqI/TXffvn2oXr263JkYY4xpgKRjIPfu3UNwcLBFikdubi6mT58ODw8P6HQ6+Pn54cCBAyaNe/DgQfTo0QOurq5wcXGBr68vYmNjSwwnCILR14IFC+ReHGZFuMmKMUOS9kB8fX1x+fJlubMAAMLCwhAfH4+3334bzZo1Q0xMDPr164dDhw7h+edLf5zRrl27MGjQIHTp0gWzZ8+GIAjYsmULQkNDcefOHfzzn/80GL5Xr14IDQ016NaxY0eLLBNjWsTHeVh5JBWQlStX4uWXX0bnzp3x2muvyRbm1KlTiIuLw6JFizB16lQAQGhoKNq2bYtp06bh+PHjpY67fPlyuLu747vvvoO9vT0AYPz48WjZsiViYmJKFJDmzZtj1KhRsmVnjLGqRlIT1vDhw5Gfn4//+7//g7OzM9q0aQNvb2+DV/v27c2ebnx8PGxtbTFu3Dixm4ODA8aMGYMTJ04gNTW11HGzsrJQu3ZtsXgAQLVq1eDq6gqdTmd0nOzsbOTk5Jidkxmyxgukqire42DmkFRA6tSpg2bNmiEgIAA+Pj6oV68e6tata/CqU6eO2dNNSkpC8+bN4eTkZNDd19cXwNN7apUmMDAQP//8MyIiIvD777/jypUrmDNnDk6fPo1p06aVGD4mJgY1atSATqdD69atsXHjxnLz5ebmIisry+DFGGNVlaQmrMOHD8sc46n09HSjj8DVd0tLSyt13IiICFy7dg3z5s3D3LlzATy9Wn7r1q0YOHCgwbBdu3bFsGHD0LhxY6SlpWHFihUYOXIkMjMz8Y9//KPUecyfPx+RkZFSFo0xxqyOqq5Ez87ONmiC0nNwcBD7l8be3h7NmzdHcHAwNm3ahPXr16Nz584YNWoUTp48aTBsQkICpkyZgldeeQVvvvkmzpw5g7Zt22LmzJllziM8PByZmZniq6wmNWvHTR2MMZP2QI4ePQoACAgIMPi7PPrhTaXT6ZCbm1uiu/44RWnHMgBg4sSJOHnyJH788UfY2Dyti8OGDUObNm0wZcoUJCYmljqunZ0dJk6cKBaT0s72sre3N1rgGGOsKjKpgAQGBkIQBGRnZ8POzk78uzREBEEQUFBQYFYYd3d33Lx5s0T39PR0AICHh4fR8fLy8hAVFYVp06aJxQMAqlevjpdffhnLly9HXl6e0Sco6nl6egIA/vzzT7MyM8ZYVWVSATl06BAAiCtg/d9y69ChAw4dOoSsrCyDA+n6vYcOHToYHe/u3bvIz883WrCePHmCwsLCcovZ1atXAQBubm4S0zPGWNUiUPG7ICooMTER/v7+BteB5Obmom3btqhbt654LCMlJQWPHz9Gy5YtAQAFBQVwdXVFvXr18NNPP4mF7uHDh2jVqhVq1qyJixcvAgBu375dokg8ePAAHTt2RGZmJm7evFnmnkpRWVlZcHZ2RmZmZokzx6qCoqfv8jER68Gfa9VmznpN8t14LcHPzw8hISEIDw9HRkYGvLy8sHbtWiQnJyMqKkocLjQ0FEeOHBHvAGxra4upU6fivffeg7+/P0JDQ1FQUICoqCjcuHED69evF8ddsWIFduzYgQEDBqBBgwZIT09HdHQ0UlJSEBsba3LxYIyxqs6kAvL666+bPWFBEAxW+qZat24dIiIiEBsbi3v37sHb2xt79uwp94D8u+++i8aNG2Pp0qWIjIxEbm4uvL29ER8fj6FDh4rDdevWDcePH8eaNWtw9+5d1KhRA76+voiOjjZ4jjsrH2+dMla1mdSE1ahRozIPmhudsCCIxxWsVVVvwmLWqfidBXhDoWqRvQkrOTlZjlyMMcasiKouJGSMKY/3OJipuIAwxhiTxKQmLBsbG9jY2ODx48ews7ODjY1NucdEBEFAfn6+LCEZY4ypj0kF5D//+Q8EQUC1atUM/maMMVZ1mVRAZs+eXebfjDHGqh4+BsIYY0wSyQUkKysLkZGR8PX1Rf369VG/fn34+vri/fff5wctMcZYFSCpgKSlpaFjx46IjIzEw4cP0a1bN3Tr1g2PHj3C7Nmz4ePjI95BlzHGmHWSdC+s6dOn49atW9izZw/69etn0O+rr75CSEgIZsyYgbVr18oSkjHGmPpI2gP5+uuv8fbbb5coHgDw8ssvY/Lkydi3b1+FwzHGlHf27FmlIzCVklRAHj16hPr165fa/5lnnsGjR48kh2KMqceguJIPeWMMkFhAWrdujU2bNiEvL69EvydPnmDTpk1o3bp1hcMxxpTxaaCt0hGYBkg+BjJ8+HD4+vpiwoQJaN68OQDg8uXL+PTTT3H+/Hls3rxZ1qCMscrTt29f4PDe8gdkVZqkAhISEoJHjx5hxowZePPNN8Wr0okI9erVQ3R0NIKDg2UNyhhjTF0kP5EwLCwMo0aNwunTp3H9+nUAQMOGDdG5c2fxlieMMcasV4XW9NWqVYO/vz/8/f3lysMYY0wjJB1EP3v2LDZt2mTQbf/+/QgICICfnx+WLl0qSzjGGGPqJamATJs2zeAg+bVr1zB48GBcu3YNAPDOO+/gs88+kychY4wxVZJUQM6dO4fnn39e/HvdunWwtbVFUlISEhMTERwcjE8//VS2kIwxxtRHUgHJzMxE3bp1xb/37duHXr16wdXVFQDQq1cv/P777/IkZIwxpkqSCoi7uzsuXrwIAEhPT8eZM2fQu3dvsf/Dhw9hY8N3imfMWty+fVvpCEyFJJ2FNXDgQCxbtgw5OTlITEyEvb09Bg8eLPY/d+4cmjRpIltIxpiynvvoFJIXBCkdg6mMpAIyd+5c3L59G7GxsXBxcUFMTIx4b6ysrCzEx8fjrbfekjUoY4wxdZHUzlSzZk1s2LAB9+7dw7Vr1xASEmLQ78aNG5gzZ46kQLm5uZg+fTo8PDyg0+ng5+eHAwcOmDTuwYMH0aNHD7i6usLFxQW+vr6IjY01OmxUVBRatWoFBwcHNGvWDMuWLZOUlzFrxXscrDyyH6iwsbGBs7MzqlevLmn8sLAwLF68GCNHjsTSpUtha2uLfv364fvvvy9zvF27dqF3797Iy8vD7NmzMW/ePOh0OoSGhmLJkiUGw65evRpvvPEG2rRpg2XLlqFLly6YPHkyFi5cKCkzY4xVRQIRkdIh9E6dOgU/Pz8sWrQIU6dOBQDk5OSgbdu2qFevHo4fP17quL1798bPP/+Mq1evwt7eHgCQn5+Pli1bokaNGjh37hwAIDs7G56envD398eePXvE8UeNGoUdO3YgNTUVtWvXNilvVlYWnJ2dkZmZCScnJ6mLzZhqNZrxvxsq8h5J1WDOek1Vp0rFx8fD1tYW48aNE7s5ODhgzJgxOHHiBFJTU0sdNysrC7Vr1xaLB/D0Viuurq7Q6XRit0OHDuHu3buYMGGCwfhvvfUWHj16hL17+Q6kjDFmClUVkKSkJDRv3rxE1fP19QVQ9pPRAgMD8fPPPyMiIgK///47rly5gjlz5uD06dOYNm2awTwAoHPnzgbjd+rUCTY2NmJ/Y3Jzc5GVlWXwYoyxqkpVt81NT0+Hu7t7ie76bmlpaaWOGxERgWvXrmHevHmYO3cuAMDR0RFbt27FwIEDDeZha2uLevXqGYxvZ2eHunXrljmP+fPnIzIy0qxlYowxa6WqPZDs7GyDJig9BwcHsX9p7O3t0bx5cwQHB2PTpk1Yv349OnfujFGjRuHkyZMG87CzszM6DQcHhzLnER4ejszMTPFVVpMaY4xZO1Xtgeh0OuTm5pbonpOTI/YvzcSJE3Hy5En8+OOP4lXww4YNQ5s2bTBlyhQkJiaK0zD2KF79fMqah729vdECxxhjVZGq9kDc3d2Rnp5eoru+m4eHh9Hx8vLyEBUVhaCgIINbqFSvXh0vv/wyTp8+LRYNd3d3FBQUICMjo8Q07t69W+o8GKvqPvqITzBhhlRVQDp06IBff/21xMFp/d5Dhw4djI539+5d5Ofno6CgoES/J0+eoLCwUOynn8bp06cNhjt9+jQKCwtLnQdjVd0yvh0WK0ZVBSQ4OBgFBQUGzxLJzc3FF198AT8/P3h6egIAUlJScOnSJXGYevXqwcXFBdu3bzdonnr48CF2796Nli1bik1TPXv2RJ06dbBq1SqDea9atQqOjo4ICuJz3RnTe4d3yFkZVHUMxM/PDyEhIQgPD0dGRga8vLywdu1aJCcnIyoqShwuNDQUR44cgf4aSFtbW0ydOhXvvfce/P39ERoaioKCAkRFReHGjRtYv369OK5Op8OcOXPw1ltvISQkBH369MGxY8ewfv16zJs3D3Xq1Kn05WZMrSZPDsLiGdx0xYxTVQEBnj6cKiIiArGxsbh37x68vb2xZ88eBAQElDneu+++i8aNG2Pp0qWIjIxEbm4uvL29ER8fj6FDhxoMO2HCBFSvXh0fffQRdu3aBU9PTyxZsgRTpkyx5KIxxphVUdWtTLSGb2XCqgK+nUnVotlbmTDGGNMOLiCMMcYk4QLCGGNMEi4gjDHGJOECwhhjTBIuIIwxxiThAsIYM1kjvqiQFcEFhDHGmCRcQBhjZdo82FXpCEyluIAwxsrk5+endASmUlxAGGOMScIFhDHGmCRcQBhjjEnCBYQxxpgkXEAYY4xJwgWEMcaYJFxAGGOMScIFhDHGmCRcQBhjZuH7YTE9LiCMMcYk4QLCGCtX8oIgpSMwFeICwhhjTBIuIIwxxiThAsIYY0wS1RWQ3NxcTJ8+HR4eHtDpdPDz88OBAwfKHa9Ro0YQBMHoq1mzZgbDljbcggULLLVYjDFmdaopHaC4sLAwxMfH4+2330azZs0QExODfv364dChQ3j++edLHe/jjz/Gw4cPDbpdv34d7733Hnr37l1i+F69eiE0NNSgW8eOHeVZCMas0NqXa2JnGjDQQ+kkTDVIRRITEwkALVq0SOyWnZ1NTZs2pS5dupg9vTlz5hAASkhIMOgOgN56660K583MzCQAlJmZWeFpMcaYGpizXlNVE1Z8fDxsbW0xbtw4sZuDgwPGjBmDEydOIDU11azpbdy4EY0bN0bXrl2N9s/OzkZOTk6FMjPGWFWlqgKSlJSE5s2bw8nJyaC7r68vAODs2bNmTevixYt47bXXjPaPiYlBjRo1oNPp0Lp1a2zcuFFybsYYq4pUdQwkPT0d7u7uJbrru6WlpZk8rQ0bNgAARo4cWaJf165dMWzYMDRu3BhpaWlYsWIFRo4ciczMTPzjH/8odZq5ubnIzc0V/87KyjI5D2OMWRtVFZDs7GzY29uX6O7g4CD2N0VhYSHi4uLQsWNHtGrVqkT/hIQEg79ff/11dOrUCTNnzkRYWBh0Op3R6c6fPx+RkZEmZWCMMWunqiYsnU5nsIWvpz9OUdqKvbgjR47g5s2bRvc+jLGzs8PEiRNx//59nDlzptThwsPDkZmZKb7MPSbDGGPWRFV7IO7u7rh582aJ7unp6QAADw/Tzh/csGEDbGxsMGLECJPn7enpCQD4888/Sx3G3t7e6B4SY4xVRaoqIB06dMChQ4eQlZVlcCA9MTFR7F+e3NxcbN26FYGBgSYXHAC4evUqAMDNzc3kcYgIAB8LYYxZD/36TL9+K5PFTyo2w8mTJ0tcB5KTk0NeXl7k5+cndrt+/TpdvHjR6DS2bdtGACgqKspo/4yMjBLdsrKyqGnTpuTq6kq5ubkm501NTSUA/OIXv/hlda/U1NRy14Gq2gPx8/NDSEgIwsPDkZGRAS8vL6xduxbJycmIiooShwsNDcWRI0eMVsgNGzbA3t4eQ4cONTqPFStWYMeOHRgwYAAaNGiA9PR0REdHIyUlBbGxsbCzszM5r4eHB1JTU1GrVi0IglCif1ZWFjw9PZGamlri1GS10EJGQBs5tZAR0EZOLWQEtJHT3IxEhAcPHpjUgqOqAgIA69atQ0REBGJjY3Hv3j14e3tjz549CAgIKHfcrKws7N27F0FBQXB2djY6TLdu3XD8+HGsWbMGd+/eRY0aNeDr64vo6Gj07NnTrKw2NjZ49tlnyx3OyclJtV8uPS1kBLSRUwsZAW3k1EJGQBs5zclY2vqzOIGMbcYzWWRlZcHZ2RmZmZmq/XJpISOgjZxayAhoI6cWMgLayGnJjKo6jZcxxph2cAGxIHt7e8yaNUvVp/5qISOgjZxayAhoI6cWMgLayGnJjNyExRhjTBLeA2GMMSYJFxDGGGOScAFhjDEmCRcQxhhjknABYYwxDVPyPCguIEwxfAIgU6PMzEylI5hk8+bNAGD0NkqVhQuIGZKSkpCSkmLwBVPbSvDx48dKRyjX1atX8fjxY9U/j/7cuXP47bffcOPGDbGb2j7vnTt3YsKECeLdpAsLCxVOZNymTZtQq1atEg9zU5Nt27ahd+/eWLJkCZKTk5WOU6q4uDg0bdoUI0aMwPfff69oFi4gJrh48SKef/55vPjii2jfvj18fX2xdetW5OfnQxAEVaxULl++jE6dOuGNN95QOkqpzp8/j6CgIAwYMACNGzdGYGAgEhISVPH+FXX+/Hn06tUL/fv3R6dOndC+fXt88skn4uetFgcOHMDgwYMRGxuLPXv2AHh6fzY1SUpKgp+fH15//XUEBQWp8nYfaWlpCAoKQmhoKOzs7ODo6AhHR0elY5Wgfy9Hjx6NWrVqwcHBwegD+CqVyfcur6L++OMP6tixI3Xt2pWio6MpOjqa/P39ycXFhWbNmkVERIWFhYrlKywspPj4eGrevDkJgkCCINDhw4cVy2NMfn4+ffLJJ+Tm5kbdu3en//znPzRhwgTy9PSkli1bqiZvXl4ezZs3j1xcXKh79+60bNky2rRpEwUGBpKTkxNt27ZN6YhE9L/v25kzZ6hu3bqk0+nIz8+Pzp49S0REBQUFSsYjIqLHjx/T3//+dxIEgbp37047d+6kP/74Q+lYRs2aNYtatWpFGzZsoJSUFKXjlJCZmUmhoaEkCAIFBgbSzp07ae/eveTg4EAffvghET39jSmBC0g54uLiqFq1ahQfHy92u3HjBg0fPpwEQaCDBw8qmI7oypUr1LZtW6pbty7NnTuXWrduTf7+/vTkyRNFcxX19ddfU5MmTej111+nS5cuid0TEhJIEASaPn26KvLu3buXfHx86O2336Zff/1V/FH+9ttvJAgC/fe//1V0Y6G4+Ph46t27N3366ackCALNnDlTzKxkzvz8fJo3bx4JgkBjx46l27dvl/r5Kv1+pqSkUP369Wny5MkluhelVM5Hjx5Rs2bNqEmTJrRq1Sq6fv06ERFdvXqVateuTUOGDFF0g4ELSDkWLlxIzs7O4oeUl5dHRE+3/nx9falt27aKblldv36dZs6cKW59rlixggRBoDVr1iiWqbjFixdTq1atDB7mpX9wl7+/P/Xq1YuIlF+ZfP/99/TRRx+VeOjY9u3bqV69erR582YiUj6nfv6JiYnk7OxMREQvvfQSubu704EDBwyGUcrp06epW7du1LJlS7Hbzp07afTo0TRt2jSKjo426+FtlnL06FFydHSkX3/9lYiI1q1bR61bt6bWrVvToEGDaOPGjYpl069zjh8/ThcuXBDXPXrPPfccBQYGUk5OjmKfNxeQv+g/rOIfxJIlS6hWrVp06NAhIiKDLbzNmzeTvb09ffDBB0bHrayMOTk54v8vX75MvXv3pmeffZbu3Llj0TzGFM1YNOfly5cN+hM9fS8DAwPp+eefp+zsbMVyluXYsWPUtm1bcnJyotmzZ9NPP/1E9+7dM5iGUhnj4+PJy8uLiIiSkpJIEAQaPXo0/fnnn2WOV1k59XtG//rXv6h3794kCAJ5eXlRrVq1SBAEGjJkCF24cMFgGpWd8fTp01StWjXavn07RUdHk42NDQUHB9Po0aOpXr16JAgCffHFFxbNZkrOogoLC6mgoIDeeustcnZ2Fr+PShSRKl9A9O3exbfY9R/GgQMHyN7enmbPni1203/It27domHDhpGbm5tFt6ZKy1iazZs3k06no2nTplksU3HmZtQXmI4dO9Lw4cPFbpZmSk795zt9+nQSBIF69OhBo0ePpjFjxpCLiwu9+uqrimbUv0+nTp2iWrVqUVpaGhERjRkzhuzt7cWt5kePHimSU5/v+vXrFBwcTIIgUM+ePenrr7+m69ev082bN2nOnDlkY2NDISEhimTUO336NLm6utKoUaOoffv2FBERQQ8ePCAiovPnz1OfPn2obt26pT5Cu7JyGhMREUGCINCuXbssmKxsVbqAHD16lNq0aUOCIFDv3r3pl19+IaKSKzIfHx/q2LEj/fTTTyX6b9iwgapVq0arVq0yOm5lZSzaLSMjg15//XVycHAQt/AsuXI2J2NRqampVKNGDZo/fz4RWf5AoKk59X9v376dNm/eTHfu3BG7hYeHk42NDS1atIiI5N9yNue93LJlCzVv3lxsQs3KyiJHR0fq0aMH/f3vf6f/+7//E4uL3EzNuWHDBgoLC6OEhIQS/UaOHEnOzs7iClCp3063bt3IxsaGXF1d6fjx4wb9vvnmG6pTpw5NmTKFiCyzp2Tu70ef4dixYyQIAm3ZsqXM4S2pyhaQEydOUMuWLalRo0YUEhJCgiDQwoULDQ726VdoO3fuJEEQaO7cuWJTi77f5cuX6dlnn6Vx48bJ/uUyJWNpvv32W/rb3/5GgwcPljWTnBmPHj1KgiDQ/v37LZrR3Jxl/RB/++038vLyovbt2xs0HVZmRn2+Y8eOkaOjI6Wmpor9RowYQba2tlS9enWaNWsWPXz4UNaMpubUZ8zMzCxxTEk/3MmTJ0kQBIO9+8rMqP8Nf/311+IZjPo9DX2LQkZGBvXt25c8PT1l/7xNzVmaCxcuUO3atWnSpElExAWkUv3yyy9kb29PX375JRERvfDCC9SsWTNKSEgwOny/fv3Iw8ODdu/eTUSGW8tt2rSh0NBQIpL3QzQ3Y9H5P3z4UNzF/fbbb4mI6MiRI7Rz505Zc0rJqLdy5UqqVq2a2GSQn59PV65codOnT8uasaI5iQy3PLt06UL+/v6yr1CKZwwICCgzY1xcHLVo0YLu379Phw4doueff55sbW3JycmJvLy86NixY0Qk/4pF6ntZvAn49u3b5OLiYpGmVnMzjhw5kgRBoPHjxxMRGazAg4ODqXXr1pSZmal4zqIyMjKoYcOG9OKLL1JWVpbs2UxRJQuIfuVfdMtIvzU8efJk8YtSdKVx/fp1qlmzJvn7+9OPP/4odj958iQ5OTlRZGSkIhmNrRz0uS9dukQ+Pj7Url07ioyMJE9PT6pbt65sZ41VJCMR0YABA6hr165E9LQ5a/369dSxY0fy8fGhu3fvypKxojmL71Xu37+fqlevTm+//bZs+czNqM/57bffkp2dHfXv359sbW2pW7dudPToUdqyZYu4MpT72Jyc7+XKlStJEAT6/PPPFclYNE9qaio5OTmV2CP++eefqWnTpjRq1CjZC7Ec7+WQIUOoTZs29PDhQ94DsYS4uDgaP348LViwgI4ePSp2L/pm6z+M0aNHk4uLC+3YscNgGvoPOiYmhho0aECNGzemTz75hNasWUMDBgwgT09POn/+vKIZjbl+/TqFhYWJu+cDBw40aO5QKmNhYSE9ePCA3N3d6dVXX6WDBw/SK6+8QoIgUN++fenGjRuSMsqds6i0tDTavXs3de/enVq3bi0eD1MyY0JCAnl7e1OrVq1o+fLllJqaKn5Xu3XrRmPHjq1QAbHUe3nr1i3avn07eXt7U/fu3St0tqCcv++4uDhyd3enOnXq0NixY+mDDz6gl19+mWrXrl3hZlZLvJeFhYU0d+5cEgRBPMuxsouI1RaQW7duUZ8+fahGjRrk4+NDtWvXJnt7e5o1a5Z42lvxi65u3LhBNWvWpCFDhogr2oKCAoMP5fDhw9StWzdydnamunXrkre3N33//feKZyzu2LFj1LdvX7KxsaGOHTua3FRTWRl///13cnR0JB8fH6pZsya1aNFCbGpTU87Dhw/T2LFjKTg4mGrVqkXt27enH374QdGM+uaVvLw8Onr0KP30009iodCPV5HToi35Xr755ps0YsQIqlmzJvn4+IjXLymZsejvOyEhgfr06UMuLi5Ur1496tixo8EKX8mcxixZsoQEQTC40LkyWW0BWbt2LdWpU4c2bNhAaWlpdPfuXQoLC6NatWrRhAkTSgyv//DmzZtHNjY29Nlnnxl8sYr+Pzs7m/744w/JKxJLZSzq4MGDZGdnR8uXL1dlxu+++44EQaB69epVOKMlc+7evZu8vLwoMDCQoqOjVZfREluclnov4+PjqWbNmuTn51fhZitL/r5zc3Pp3r17dO7cuQpltEROPX1BSU9Pp5iYmArnlMpqC0j37t3J39/foNujR49o9OjRJAgC7d27l4hKVva8vDxq2rQp+fn5iVenXrlyxaCdUq6zrSyZkUie02Llzlj0+Mvq1atLXF2rxpxXrlyR5TOXM+Pvv/9e4vOWiyXfy3Pnzqnye2mJ37elcyp9twEiKywgBQUFlJOTQ3369KFu3bqJ3fW7/WfOnKFOnTpRkyZNSnwAxU/bnT59On3xxRfk4+NDkydPlu3CrKqeUc4zRiyZU65TYC2Z8fHjx7JktHROLbyXcl54qZWcFaXpAnLx4kWaMmUKTZo0id59912xUhMRDRo0iFq0aCEe7Cxa4T/77DMSBIGWLFlCRCW31J88eULPPfcc2drakiAI5O7uTl9//TVnVDCjVnJqIaNWcmoho5ZyWoImC0hubi5NnTqVdDodde7cmZo1a0aCIFCTJk3E86nj4+NJEASKjo4WPzT9B5ScnEwvvvgiNW7cuMTBxx9//JHeffddqlmzJtWqVYs+/vhjzqhgRq3k1EJGreTUQkYt5bQkzRWQBw8e0MyZM6lJkya0cOFCunz5MhUUFNDBgwfJw8ODXnjhBXr8+DHl5+dT+/btKSAggJKTk0tMZ/bs2eTi4iK2QRI9/fAmTpwo3pROf4EbZ1Qmo1ZyaiGjVnJqIaOWclqa5grItWvXqHHjxjR+/Hi6f/++Qb/x48eTm5ubeCVzbGwsCYJAixcvFtsN9ZU+KSmJbGxsaPv27UT0v7bJU6dOifei4YzKZtRKTi1k1EpOLWTUUk5L01wBKSwspM8++8ygm/5Mni1btlC1atXE+9ncv3+fhgwZQs8880yJi3JOnTpFgiDQ2rVrOaNKM2olpxYyaiWnFjJqKaelaa6AEP2vShc/6LRo0SKytbU1eOpdamoq1a9fn9q0aSMegLp58yZNnDiRGjZsSLdu3eKMKs6olZxayKiVnFrIqKWclqTJAlKc/uDUlClT6JlnnhG3BPQf7P79+8nHx4cEQaAOHTpQly5dqHr16hQZGUn5+fmVcj41Z6xaObWQUSs5tZBRSznlJBARwUp07twZjRo1Qnx8PAoKCmBrayv2u3PnDqKionDlyhVkZWVhypQp6NKlC2fUaEat5NRCRq3k1EJGLeWUhdIVTC4ZGRmk0+nEB/0QPd0i0D/eUw04o3y0kFMLGYm0kVMLGYm0k1MuNkoXMLlcuHABOTk5eO655wAAt27dwsaNG9GnTx/cvn1b4XRPcUb5aCGnFjIC2siphYyAdnLKRfMFhP5qgfvhhx/g7OwMDw8PHD58GBMmTMDrr78OIoKNjY04HGfUbkat5NRCRq3k1EJGLeWUXeXt7FjWkCFDqGnTpjR27FiqVasWNWvWjL755hulYxngjPLRQk4tZCTSRk4tZCTSTk65WEUByc7Opg4dOpAgCOTk5CTeW0ZNOKN8tJBTCxmJtJFTCxmJtJNTTlZzFtb06dMhCAIiIyNhb2+vdByjOKN8tJBTCxkBbeTUQkZAOznlYjUFpLCwEDY26j6kwxnlo4WcWsgIaCOnFjIC2skpF6spIIwxxipX1SmVjDHGZMUFhDHGmCRcQBhjjEnCBYQxxpgkXEAYY4xJwgWEMcaYJFxAGGOMScIFhDHGmCRcQBhjjEnCBYQxxpgkXEAYY4xJ8v+OWqwGhi5FyAAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -220,16 +220,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\soiling.py:27: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n", - "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\soiling.py:379: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_negative_step\" and/or decreasing \"min_interval_length\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n", + "C:\\Users\\nmoyer\\.conda\\envs\\soilpytest\\lib\\site-packages\\rdtools\\soiling.py:366: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_negative_step\" and/or decreasing \"min_interval_length\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n", " warnings.warn('20% or more of the daily data is assigned to invalid soiling '\n" ] } @@ -248,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -258,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -278,15 +276,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-1.273\n", - "[-1.607 -0.959]\n" + "-0.509\n", + "[-0.761 -0.295]\n" ] } ], @@ -299,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -332,7 +330,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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" ] @@ -378,7 +376,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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gqioul6vPuRWZBC6A5KS4a/L/I488wpAhQ3j22We5//77uf/++zEajcyePZu//OUvyRwor9cLQElJSca+E0ZKol1Xdj/mm2++SbumQYMGpRlTiZyQ7vrreq7HH3+cq6++mry8PE444QQGDhyI3W5HEATeeOMN1q1blyLYcO2111JYWMiTTz7JY489xiOPPIIgCEyfPp0///nPHHLIIYBmEAB89NFHfPTRRxnHAxAIBFLGtKex9xav15usSdabvhLj3bZtW4/vTWK8exqzwWCgoKCg2366ux6Px8Phhx9OTU0NkyZN4oILLiA/Px+j0YjH4+Gxxx5LeR57c9/i8TgzZsxg1apVjB07lp///OcUFRUl79Xdd9/do0hHb+nNu19XV4fX600xpvryOewNPT3vV199tcdjE8/bYDCwZMkS/vCHP/Daa69xww03AJCdnc1FF13Efffdh8Ph6NO49pU5c+bwzjvv8PDDD/PMM8/w1FNPAXDYYYfxwAMPcNxxx32v49HR0dn/6MaUjo7Od4bBYOCcc85hw4YN3HPPPSxZsoTTTz89ORE7+OCDWbt27Xd6/quvvpqrr76a1tZWPv30U/7zn//w6quvsmnTJiorKzGbzcnxNDc3Z+ynqakJyDyB7KqWBppiWlclr+5oaWnJuD0xhsS5JEnizjvvpKSkhLVr16ZJWydW73fnggsu4IILLsDj8fD555+zaNEinn32WU488UQ2b95MUVFR8hyPPfYYV1111R7HnGi/p7H3lpycHNxud0YRiEx9Jc7/s5/9jIULF/bqHNnZ2YA25t0FRGRZpr29vdu6S7s/2wT//Oc/qamp4c4770zzmKxcuZLHHnss47j7ct/efPNNVq1axYUXXsj8+fNT9jU1NfV5EaI7ur77mTxNPb37+5NM9zpxzjfffLPXNery8vJ45JFHeOSRR9i+fTvLly/n73//O48//jgej4fnn38eIOkV3t3Dm8Dr9e63a549ezazZ88mGAzy5Zdf8s477/C3v/2N2bNn8/XXXzNq1Kj9ch4dHZ0Dgy6NrqOj852TCLtLhCg5nU7GjBnDxo0bcbvd38sYiouLmTNnDq+88gozZsxg27ZtVFZWAppRB5qE8e5IksSnn34KkPTo7A8+/fTTjDLeiTEkxtTW1obH4+Goo45KM6QCgcAejdHc3FxOPvlknn76aS666CLa29v55JNPAC3MDUj+vieysrIYOnQoDQ0NVFdXdzv23nLIIYegKEry/u6pr5EjR5Kbm8sXX3xBPB7v1TkS9zHTOb744otuJ9M9sX37dkBToNudTOF6XceQyWOT6Vr7eg4gGULYF69QT+/+9u3b2bVrF4MHD07xSn1f9PX93J2hQ4dyySWXsHz5cpxOJ4sWLUruy8vLA6C+vj7tuO3bt+PxeHp9HoPB0Kt77nA4mDFjBn/5y1+45ZZbiEajLF68uNfn0dHR+WGiG1M6Ojr7zIIFC/joo48yGgfNzc08/fTTAEyZMiW5/dprryUWi3HxxRdnnLh0dHTsk9cqGo2yZMmStJo38Xg8acBZrVYAzjjjDPLz81mwYAFffPFFSvtHH32UHTt2cPzxxzNw4MC9Hs/ubNu2jSeffDJl25tvvsny5csZOnRoUhq9uLgYu93OmjVrUsLX4vE4V199NW1tbWl9v//++xmNhNbWVuDb6z7ssMM49thjWbhwIc8++2zGcW7YsCF5HMDcuXNRFIUbb7wx5XnX1NTw+OOP9/byk30B3HrrrSn1jNxuN/fcc09ae6PRyJVXXklTUxNXXXVVWq0l0DwpmzZtSv5+wQUXAHDvvfemhE7GYjFuueWWPo03QUJ2fOnSpSnbv/76a+6///609gMGDOCEE06gpqaGv/71ryn7Es+8t+fYsWMHN954Y8ZxJUIWMxkI3XHxxRcDcM899yRz5EAzyK6//noUReGSSy7pdX/7k9NPP50hQ4Ywb9483nvvvYxtVq5cSSgUArR3MFPdtY6ODqLRaPK9B80wz87O5s0330x5v8PhcK+8tF0pKCjA5XJlrMm1ZMmSjO9pwkvZdUw6Ojo/TvQwPx0dnX3myy+/5LHHHksWhh08eDCgTW7effddwuEwp59+OmeddVbymIsvvpivvvqKJ598kiFDhnDSSScxcOBA3G43NTU1rFixgrlz5yZzDPpKOBzm+OOPp6KigsmTJzNo0CAikQgfffQRmzdv5pRTTmH06NGA5il79tlnOfvss5k6dSpnn302AwcO5KuvvuLDDz+kpKSEv//97/t+o7owc+ZMrrvuOhYvXsyECROSdaasVivPPPNMMgxJFEWuuuoqHnjgAcaNG8fpp59OLBZj6dKluN1upk+fnjbhPvfcc7FarRxzzDFUVFSgqiqffPIJq1ev5pBDDkkpoPzSSy8xY8YMLrnkEh5//HEmT55Mbm4uu3btYv369VRWVrJy5UqKi4sBuO6663jjjTd4/fXXOeSQQzjppJPwer28/PLLTJkyhbfeeqvX9+AXv/gFL7/8Mm+99RZjx47l9NNPJx6P89prr3H44Ydn9H7dfvvtrFu3jqeeeoq3336bGTNm0L9/f1pbW9m2bRufffYZ9957b/LZTp06lV//+tf84x//YMyYMZx55pmYTCbefvttcnJyKCsryygE0hMXXHABf/7zn/nd737HsmXLGDZsGNu2beOdd95hzpw5vPzyy2nHzJs3jyOPPJJrrrmGDz/8MPnMFy1axKmnnsrbb7+d0v7UU09l6NChPPLII1RWVnLwwQdTV1fHO++8w+zZs6mrq0s7x/Tp0xFFkZtvvpkNGzYkvS+33XZbt9dy1FFHccMNN/CnP/2JsWPHctZZZ+FwOFi8eDGVlZUcc8wx/P73v+/T/dlfmEwmFi5cyEknncTs2bM56qijmDhxIna7nfr6elavXs2OHTtoamrCbrezbt06fvazn3HooYcyduxYysrKcLlcvPnmm8Tj8RQj1GQyce2113LXXXdx8MEH87Of/QxJkvjoo48oKyujrKys1+NM1F2bNWsWxx57LGazmQkTJnDqqady3XXXUVtby7Rp06ioqMBsNvPVV1/x8ccfM3DgwJRadzo6Oj9SDqiWoI6Ozk+Curo69a9//at6xhlnqMOHD1ezsrJUk8mklpSUqLNmzVJfeOGFjDLgqqqqb7/9tjp79my1qKhINZlMar9+/dTDDz9cvfXWW9XNmzentCVD3aEEu8s+x2Ix9cEHH1RnzpyplpeXqxaLRS0sLFQnT56s/u1vf1Oj0WhaH6tWrVLPOOMMtbCwUDWZTGp5ebl62WWXqQ0NDXs8X2/pKsn8+eefq8cdd5yalZWlOp1O9YQTTlBXrVqVdkw8HlcffvhhddSoUarValX79eun/vKXv1Rra2szjuNvf/ubesYZZ6iDBw9WbTabmpeXp06cOFF98MEHM8rK+3w+9d5771UPOeQQ1eFwqFarVa2oqFBPPvlk9e9//3tanS6v16v+7ne/U8vKylSLxaKOGDFCfeihh9Tq6uo+SaOrqlbv6e6771YHDx6sms1mddCgQeott9yiRiKRbp+3oijqv/71L3XGjBlqXl6eajKZ1LKyMvXoo49W7733XrWuri6lvSzL6l/+8hd1xIgRqtlsVktLS9XLL79c9Xg8qtPpVCdOnJjSvjsJ7K5s3LhRPfXUU9WioiLVbrerhxxyiPr000+rNTU13d6Dbdu2qWeeeaaak5Oj2u129YgjjlDfeeedbs9XV1ennnfeeWpZWZlqtVrV0aNHqw8++KAaj8e7vTcvvPBCsj4YnSUKEvT0zi5YsEA9+uijVafTqVosFnX06NHqPffck1YDTlV7lgLfkzz77iSk0XuipaVFvfHGG9UxY8aoNptNdTgc6tChQ9UzzzxTfeGFF9R4PK6qqiZBf/PNN6tHHXWU2q9fP9VsNqv9+/dXZ86cqb733ntp/SqKoj744IPqQQcdlPy8//73v1eDwWCfpNEDgYB62WWXqf3791cNBkPK83/55ZfVc889Vx06dKjqcDjUrKwsdcyYMeott9yitra29uoe6ejo/LARVHW3GBgdHR0dHZ3/AbZt28bw4cM599xzWbBgwYEejo6Ojo7OjxA9Z0pHR0dH5ydNc3NzWj5fKBRKytVnEnnQ0dHR0dHpDXrOlI6Ojo7OT5pHH32UBQsWMG3aNEpLS2lubmbJkiXs2rWL2bNn68aUjo6Ojs5eoxtTOjo6Ojo/aU444QQqKytZsmQJbW1tGAwGRowYkaxB1l09KR0dHR0dnT2h50zp6Ojo6Ojo6Ojo6OjsBXrOlI6Ojo6Ojo6Ojo6Ozl6gG1M6Ojo6Ojo6Ojo6Ojp7gW5M6ejo6Ojo6Ojo6Ojo7AW6MaWjo6Ojo6Ojo6Ojo7MX6MaUjo6Ojo6Ojo6Ojo7OXqAbUzo6Ojo6Ojo6Ojo6OnuBbkzp6Ojo6Ojo6Ojo6OjsBboxpaOjo6Ojo6Ojo6OjsxcYD/QAfigoikJjYyNZWVkIgnCgh6Ojo6PzP4Oqqvj9fsrKyhBFfY2vK/rfJh0dHZ0DQ2//NunGVCeNjY2Ul5cf6GHo6Ojo/M9SX1/PgAEDDvQwflDof5t0dHR0Dix7+tukG1OdZGVlAdoNy87O3qs+drQFeP6zWtyhGPl2MxceXcFBhc7v7PgdbQH+umQb6xu8CEBpjg2TQeTg8lyqWv38/PCBHDO0cK+u5fvgk20u5n9WS4HDRHswztxjBh/w8Saewa6OMEaDwC+PGMSxw4oO6Jj2hU+2uXhlTT0jirN+sO/EjrYADR1h+ufZUt73HW0BPqxsYcVWF4FonBZfFKMBREFAAEaW5XDTrJF9+ozp/DDx+XyUl5cnv4d1vmV//G3S+enS1tbGkCFDUrZVV1dTWPjD+p7X0fkx0tu/Tbox1UkifCI7O3uv/2BNzM4mKyubXR1hBuTZGFLUt0leX4/3NEVoCAmIZjsGUSAmmshxWqjxq5QW5jOivJjs7B/uRNPhjNAeM1Dri5FlNWJ3OA/IZKHaFaDeHaI8344nbqTWp9IeEQhEJF5Z18bIgf36/Cy/S7qOd0/jcjgjYLJR2RZn4A/wnah2BfjP1220B2MUOIJcOiWbIUXO5PY6d5SdfpVgDMwmG7IAiiCQ5zBT51fZ0i4x8SB9gvlTQQ9jS2d//G3S+ekSjUbTtmVlZenvio7OfmRPf5t0Y2o/M6TI2aeJd2JinKA8387U4b33hPjDEh2hOAZRYGixk7lHD0YQhL0y5vYnC1btZFVNB5MG5/GLSYMytmnyRrCaRAYXZhOOKwdkIlXtCvDwB1XUtAdxmo0cO7yQ+o4QHaE4NpOIPyKxqyPc473si3Gzr8clxtvki1CabeW6k0akHVvtCrCqph2XP8Yn21zUu0NYTQZOnVD6gzIKAerdIdqDMUaVZLG52Z+814nthQ4z4biEokJUUrCaRMwGAZMoEJPVAz18HR0dHR0dnf9xdGNqP9OXCXLXibw/IjEw387AfDuXTjmo15PegQV2hvdzUusOcVCRs88T+u+CBat28sd3NhOTFD7Y2AyQNKiWVbWyrMoFaGFcnnAcbzjOhPJcBuTZvvexrqppZ81ON+GYTExWaQ1ECUclUFXCMZm4rPQ4rmpXgKdX7Oj0rJh7/ez29rh31zfxZU07NrORFl+E1bXulOMS79Q3uzx4QjFCMQUBEAR47tMaQGDS4PwD/o4kKM+3U+Aws7nZT4HDnLzXie2ra90IgEmEuKIlgxZlWYjEFfIdZkqyrQf2Anpgb41sHR0dHR0dnR8PujG1H+nrBHlVTTvf7PIQikr4I1Jn7lBsj56QBOWdxledO0QsrvDFjnZavJGM3orvkyWbW4nEZYwCROIyS7e4mDS4gHfXN/LsZzX4wxKiANk2M0cPLaC+I8yU4UUHaMwCsqISlRRUVaXNHyUUV0j4PNyBWI9Hv7u+idW1bsaUZffp2XXnkemJaleAT7a6CERlonEZh8WUsd8mXwQRiMQUAFRAVaHBE+HVNfWsq/f0yWD/LhlS5OTSKQelhbYOKXIyc2wJMUmhxRcmGFUQ0fKlXP4oKhCTFF5ZU79Pxsp3ZfDsrbGso6Oj0xdycnJYunRp2jYdHZ3vD92Y2o/0fYIsEInL+CMSsgobG32YjWKvPTSJieiLX+xke2uAaFzmm12eNG/F982APBuqCrFOi0RVVR7+oIqVO9rxhCQAZBVCMYntrQEcFgOtvijVrsD3Pu5Jg/PJd5hxB+OogBST6Ro85gnHu72fy6paeWV1Pa5AlCZvhMMr8nr97LrzyGQiMeFv8kYwGUXK82y0BqIMLLBzeEV+Wr+l2Vaqmnwou/WjqCq5NmOfjL7vg0yhsdWuAO9XNvNNvYdw9Fvj1mExEI4rOC1GbCYDzd4IuzrCAHsVMvldGTz17hBVzX4sRoE2f/QHdb97g+5V09H5cWA2m5k2bdqBHoaOzv80ujG1H+nLBBm0iXyhw0JHMA6ApMCO1iD17lCvJzBDipwM75fFuzTiDsaIyQpVzf59vpZ9YURJFsbOsCwB2NYSwB+NE4zGU9rZTAYkWWFrS5i69jBbmn1cd+L371UTEJKTdTVlO5gM3dcVWL/LS1RSGJhvo6EjQqHD0qfnlskj05VE7tMn29qIyyomUSDXbiIck7GZDcw5pH/acUOKnJxzeDmfbW/THkCXa5EUlS9qOvZo9P0QJtL17hDr6j3sbA8id24TBTAaRHKNRmKyQkxWKMmxoqrqXhlF9e4Qde4QhU4zde7QfjV4mrxhtrf6iUoKFqNIo+fbvMgfwv3tiYSRWecOYTIIzD16MNNGFB/oYeno6Ojo6Pwg0Y2p/UgiNGlDg5dx/XP2OFEaUuRkTP9strYGktvaQ3Hue3dzryda1a4ALn+EmKTgDUsIAize0MTU4UUHbALk8sewmAwYFZWYpNDqjxCTVZQurhKTCCNLs6lq9hGTFGRZobYtuNcT2r2doNa7Q0hKupCBUQSz0YDTauw2L2f8gBwsRpE6dxiDKNAW7Jt3rSexksSEtqrFT6svwoyRxTT7oowfkEMwKhGXVT7Z2kamHKj1u7yE4lJKf6IA/XOsdISkXp33hxCeVucO0VVjwmIUOW/yIAqdZra2BMh3mDl5XGnSI1ySZaGyydcnz2yzN8K2lgBZViOq2jtBi2VVrazf5WX8gJxuP2MufwxBEHBaDPgjEu+sa2LS4AKAH8z97Y5VNW7W7/LQEYzhDUu0B6M/WMNPR0dHR0fnQKMbU/uRRGhSezBGQ0d4jxOQaleAjQ2+tO0NnnCvJoRdJ9xRSUEUwWIQCMdlKhu8B8yYGj8gB4fZSItfk2yNxJWUkDOjAIMLnfTLtrCpUUUQBOKKiigKNHrCfQ732xcDoDzfjsNiSNtuFEXG989GFMVuVQanjSjmnMPLeeubBkaXaYqEmYzBvTH0EgbC2NJslvgibGz0MbxfFoVOC3FZRVYUVmxz8XVdBwPy7FwxY2jyede0BZF3i/EzGgR8EYkCp5kpPahFHojwtIQHrqth2OSNYBBT7/vBA3M5eVxpiprhyeNKKc+3E5Vk3qtswiiKrNjq4vCK3olslORYGdffjCsQ65Wa5LKqVv7w9ib8EYksq/b1mflzphKOS8Q73Wqra93c9Po6xpTlUucOcdigvF7nyn0ffBtKGmbh2l3UtoUIxWVEAba3BHlvQxNXzhh2oIepo6Ojo6Pzg0M3pvYjfV0hX1XTTlswighJY0MUwGrqPrRs9/PVuUOYDQJxSUFWIaKqZNsMjO1/4BJQp40oZta4El76sk7z+nQu+Ato/1RU6AhF+WSrC0lRMQgC/fOtFDjMfLyltc8CCbvnqq2udffaeBlS5OTQQXlUNvhSQvzissKWZj9HDS3sMSRu9vhSGj3hpCG3e9tlVa0891kNcVntk1Jjeb4dk0FgY5OPocVODq/Ip9BpAVTq2kM0esMoKoSiMu3BGPOWbqc83955P4J0dbaJAhQ6LBwxpIBTJ5T1aGT3FJ72XdBVfVCSFYqzrRw2KJ8dbQGMBq04ryhAnsPM5IMKuP+9zayudWMzGdjZHuTFL3YydXgR4ahMXFHJtRnwhOK9MlISAi7twRgD8+29yndbv8uLPyJRUWCjtj2ccdGi2hXgk21tKTGjcVmlssFHMCoTiSus2dnR63N+13RVFXX5oyiKqt37OJgNmoHZEYz94MMTdXR+rFTc9O5+77P2gdn7vU8dHZ3M6MbUfqQ8345JFFhS1YokK7z+1S5Ksq09TF4FTKJIts1IMCZh7QwrG1aclSYs0B3N3gjN3jCyChYjgMhRQwp/ADkOmkqe0sWQEgQ0w1GFUFwhHJPJ7lzdL8qyEpdVJgzovbpdgkSu2pqdHcRlmcUbmjAbDd16qXav7QUCNpNIXFaSaUaiALKiYjUaeHd9U7chXT2Fdla7Ajz3WQ3f1HkwGwU8oVjfxBISdqgKm5t8xGWVmCQjAAZBu48Jvb5gVKuHpaoqwZiM3SQS6rwYUQBvJE6dO8j6XV6avJFu5dFd/hgmg0hpjpW2QIz2PagZ7iv17hA17UFicRlvOE6rP8aWJh9Oi5Epw4uobPQxtMjJ2P7ZvPF1Iw2eMFFJIRDRwhhXbHWxwxVAUlSKnRZaA1HynRYG5Nn2OPnvTd7a7owfkEOW1UhtexiLUURRSfOkrqpxs8MVoGvUoIrmoTWKAjk2E0cNKWDWuB9G3a+EqqiiqHjDcQqdZqJhCaMIoiiS7zAzrJ/zBx+eqKPzv4iqKijh1Dxp0ZZ1gEajo/O/iW5M7WeKsy3YTAYEk4HathDPfVbT7WSuNMeKySgSkWTyHRYG5tuRFRWntfePJcdmoiMUIxRXiElgNKjUd4QOiDJegmpXgDW17pR8F5FOz5Sg/V9Ek+uOyypxWaGmLYBB1DxyfV2xTxg085ZuY1trkEhMZkK5llv03oYmxnV66RKem8QqfEcwRp7DjMNiIMtmoj0QTXrPBAGissLSqlaM28RuQ7qqXQFeWVNPszfC5iZfyrOud4do9UUJxWV8EZVAVGZdfQeLNzTtcVJa7w4RV1SOHVrIR5tbqGkLMqyfE19YQjSkCmWYDAYqCh0MyLN1njOSNKQAZAUkWaGywUdlgw+LUWRCeS53nDom7dzjB+SQazfTEZLItZv3q4czUzgfaIWn3aF40vCWFPCGJb6u83BQkYPzJg9k/S4vHaEYRgGiaNdvEAUGFdg1I8UA7lAMVQFQqXeHkiG3Pd3nrs+q6+/dMW1EMU3eMEu3tNIRirOhwUujJ5zSv8sfJRiV2D0VTwWqWvzk2c0oqtrrc373aB5Agyhg6CxnYBAF+uXYyLObOG/yQEpzbHy8xdUnKX8dHZ3vHiXsZ9cT56dsG3Dlvw/QaHR0/jfpXTyZzh5J5O1sbQkQjEq4gzGKsszEZTXpjcjEwHw7hw7Mw2wQCUYljhlauMdjEpTn24nLCoFOlTwVzWMhK707/rui3h1CVlW6piKpaF4Us0HUDBYVjAaISjJxWSuQG45JjCzJ2qtV7yZvmK0tATyhOBFJ4cuaDiobvCze0MQf3t7EY//dxk2vr+O2RRv4ZJuLOneIFl+UWleAGleI8f1zsBgNJNJmrCYDAhCIxDEZoCMUo7LBm3beVTVu1tVryfrr6jVZ+gTl+XaMBgFVBafFgMkgsrP925DEhER5Jrp621p9EZp9ET7b1kadOwiqgEkUENG8kQ6LgZikJHNeJEXLS0sgABFJJRxXCMc1oZLdx5pg2ohi7jh1NBceNYg7Th293zyciVCyJz7eziMfVXHT6+tZVtUKaIWny3JSRT60cFCVuKzyfmUzRVlmLEZRyw1EywFzWgw0eSPk2k2MKctBQCDbZqTRE2H5VlfyPte5Q7y3oYlqVyDjuJ5esYN/f1nH0yt2ZGyze/t19V7qO8Jsa/HjC8eSSoAJirLMWEwGMglBSrKKOxTjuc9qePD9LTz8YdUez/ldM2lwPhPKc8mxmbCaDIRiCuG4jMUokGU1UZarLULEJJk1Ozt6pVSqo6Oj01vee+89BEFI/hiNRioqKrj22msJBA7s92NfCAQCXHPNNZSVlWG1Wpk4cSL/+c9/9qqvf/7zn5qIkTN1LrRs2bKUe9X154svvkhpW19fz8knn0x2djajRo3izTffTDvPq6++SkFBAS6Xq9uxyLJMcXExjzzyyF5dy/8CumdqP5HI2zlsUB7hmIykKGRZTT16Wcrz7eTaTMl8ETVCn3IpEpPnRJK7NsnUvCgHcrJTnm9ncIGDeneIqKwNrtNhQLjTYxKJyyBo0uOSohCMSgiCJlG+dyveAjEpVXUhEpcJRDTDti3wrbegq8MgLKlEpBjVbUFisoLYGT4XjMqgggw0eqPYTCIFTnOG82qeNS2kMdUVMaTIyXmTB/LYf7cRiGhKi95wHLNR3KN8ftcaYpsafRgEAVlR8IXjBCISqqqiADEJWv0xPt7SSmWDl5ljS7AYBaJGESmupOTjfTtizeDujmkjivd7mGjXYsKhmMzWFj/PfVbD3KMHa4Wn21NzswwiZNtMSaGGslw7500eyHOf1RKXFU22XhSJSwqokO+wYDGJWIwiEUkh32EmHNMm/83eCCur22noCKcZ6n2tDZcQ6Gj2hvGGJb7a6UnKsyeYNLiAYcVO1tV70o6XVZAllbgkIQoRfD3UMfu+GFLk5JzDyvn3lzupd4dQVRVZUWnyRhmQZ0dVVV5ZXU99RxijIHDqhB9GeKKOjs5Pg7Vr1wLw+uuvU1ZWRjAY5N///jePPPIIHo+HZ5999gCPsHfMmTOH1atX88ADDzB8+HBeeuklfvGLX6AoCuedd16v+2loaOD666+nrKwMrzd9ERfgvvvuY/r06Snbxo4dm/L7hRdeSDQa5bXXXmPZsmWcc845bNq0iSFDhgDg9Xq5+uqreeihhygq6l6YasWKFbhcLubMmdPra/hfQzem9hOJfKmPNrdgEASG9csi125i6vCibiceQ4qcHDu8iGZfhAF5Nra1BhhZksX5Rwzq1WQlUefIYhSISioGEcpyrcw9evABn5xdd9IIrCYDH21qJiIpxOXUybukgpDM80rkBql8tq2NZVWtfZ7MTxqcT3GWhZ3ubz0EVpOIKxAlLilIPaheq0B7INo5idS2CZ1GlUHQ9ufazMkV+q6U5tgwGw10hLRcoy1NvpQQy19MGkRVs5//rKojGlNZsdXF8JIsfnnEoF4pzqkqaNGPapeyUd9eTOJfiqqFl/nDEodV5LNhl5f2QJS4rKRI0oPmqRpU6KAk28qyqtbvRVCgPN9OlsXI9nCcuKTQL99GXNaUHK0mkWZvqofOahCRZZVPtrWRbdNky4uyrJTkWDGJApub/ITjClFJwWwUsZoMOMwGInGFoUVOTh5XCsB7G5pYuqW1x1pSCY9LXxYxOjqLT0uKSkcwxlvrGpP3cUiRkzMPLafFF6WhI5xmzEIilFSgh9fyO6VryGVpjpVX1tRT2eAlGNWKVquAURQ4dFAey6pcfFHTTlxSiMkqC9c2MGlwgW5Q6ejo7BfWrl2L1Wrl9NNPx2DQQlqmT5/O0qVLefvttw/w6HrHe++9x0cffZQ0oEC7hp07d/L73/+en//858lr2xOXXXYZU6ZMIT8/n9deey1jm2HDhnHEEUd020coFGLZsmV89tlnHHnkkZx44om89tprfPTRR0lj6sYbb2TEiBHMnTu3x/G89tprHHbYYQwaNKhX4+9pTHZ7+jzqp0Cfw/yuvPJKqqqqvoux/OgJxCR2uUNsafbx3vpG3lvfxCtr6nsM45k0OJ+SbCtrdnbgC8dp9kV6fb6iLDOqohLttBTMJgOlOTbK8+1UuwIsq2o9YCFEQ4qcnDaxTMs1UlXMhvSXTQUQBLKtBoyigCgK1LqD3PDqOu56qzI59t5cy5AiJ8P6ZSXzsUDzgMiKiiBoRlF3iGgFhO1mI6Kg1cCymYyYjEIy72tAvjXjRLvJG8FsFDEbBAKROG983ZASulXtCvD59jYikooKxGSV6s66YnuSzdfCRv2YRBEELa9lT1QUOTjnsHIOKnIwqMCBzWxMO85pMXDi6H68X9ncbXjbsqpWHl+yLRmKtz8QBHBYjFhMIjFJ8+gt3dLCa1/tSjN2Y4pKOC7T5A3j8keZt3QbC9fW4w3HqWkLoqoqVqNIoFN4Y9nWVna2h2gLRGn2RpKFrwudZho9YZZXaUIVuxfPfb+yGV9Ywh+JM6F8z7XhAHY3gYIxmbe+buCS+atZsGonoH2uc2ymbvtwmA0UZVkYWuRE7RSx2B/05rNS7Qpw99sbeXDxFu57dxO3LlzPJ1vbcPmiKGifSxEQRYFPtrXxZU07/ohEOC6jKAq7OkLJENED/T2jo6Pz4+err75i1KhRKcaGKIoUFRVhNP441vwXLVqE0+nk7LPPTtk+d+5cGhsb+fLLL3vVz4svvsjy5ct58skn92k8sVgMVVVxOBzJbU6nk0hEm2N+/vnn/Otf/+Lvf/97j/2oqsqiRYs488wz+eSTTxAEgQULFqS1+9e//oUgCKxevRqAu+66C0EQWLt2LWeddRZ5eXlJI+6nSJ+NqX/961+MHj2aE044gTfffLPXhS5/6tS7Q7j8USRF827EFRVJUWj2RnrMX0p4p0qyrcwYWdzrfCkNgZJcGwNyrdjNBiYPzsdsNLC61t3rPJDvajJU7Qrw7Kc1tAWiSArIspbnYux84xI5PWaDSJ7djCho3rWYpNIaiPHCF3X84e2NLKtq7dW1VLsCNHu1L4mEJ0BSOgUuFFLEMAQBHGYRq0nEYhSwmQxYTAb659mYOryI8gIHBQ4TUudBWh5VuilT7QrwyVYX7YEoHSEJWYFgVKKqM1wMOosC7/YRicsKVc3+Hu97QvbeahQRRMiyGLAaezanSnKsSY+M2WjghNH9GFLkpCzHkmJMSorKh5taqHOHMuZuLatq5bY3Knnm0x3c9kblfjGoVtW42d4awCAKhGMyrf4IW5r8/PuLnQSickpbAc2rOKyfU1Pui0psbQ6ywxViZL8sLEYRURSISppQgsNiQETLR4rEFWrag8xbup0Fq3by0pd1BCISkqISism8X9mcvOeJ4rT+SJxmbzRlX09EpXRfk6TCzvYQf3q/iseXbGVVTTv+qJRiyCfKZpmNAkcOKdCelQCvrKnn4Q/2LXeq2hVgwaqdPPxh1R4/K4k8P19Ywh+VafBG8UellPdUATyhOF935uzJsqp9nhRwB2Is3tDU68+mjo6OTne0t7dTV1fHuHHjUra3tLSwcePGNONkb1BVFUmSevWzt1RWVjJq1Kg042/8+PHJ/XuitbWVa665hgceeIABAwb02PaKK67AaDSSnZ3NSSedxKeffpqyPzc3l5EjR/Lwww/T0dHBG2+8wbp16zjqqKOIx+P8+te/5uabb2b48OE9nufzzz+nqamJM888k2OPPZaDDz6YefPmpbX761//yuGHH87hhx+esn3OnDkMHTqUV199laeeemqP9+DHSp+NqcbGRp544gmampr42c9+RkVFBQ888ABtbW3fxfh+NJTn2zEIQjKfQ1UhJin4O70VPU1IJw3OZ3i/LKpdQWKS3CsDNTGRD8dl/FEJoyjQ5I1gEgW2NPm7nSjv3sd3NRlaVeNmW6sfURBxmEWMRoECp5lDB+VhMgionZNMq0mkPRgjrqgp5oqsqGxtDrChwdsrwYZ6dwiTQcS8mxdd5Vv1QKfFQI7NqOURKZp3oDTbhiiCOxClIxgjIimM7JeF2hmCZRS1Y7uuxnc9Z1xRqch3JM8VV8AdjCWfYXm+nXy7KXltApoXrLLBu8f7Xtce4rPqdjyhOJKihYUZhW/NusSH1ySAw2Rg8kEFyXMmxCuMBjhhTCmTD8rH1Fm3KSIpVLf4afNHMwoKvPjFTpo8YeKSTJs/yoqt3Sem9h7NMxeNy0iKFpYYjkldQhc7r8UgMLTYwSED84jEtVpXgYhEts1IVJJZXesmKqmYDCJ5DhN2i5Esi5G4oiZVGEUBdrlDPPdpLbs6wsiqSiQu4zCLycWKaleAxRuaqG0PUtMWBBT8ESnl/epuoaEwy0qmYA0V8IXjPPdZLf9cUUOLTys8rKpgNYqYjZrxrqrwdZ2HDzY2s6XJjzcU45tdmQVBekPic/zKml2sq/dQkm2hPRhjda27G4O99wtgkgrtwXineIyAURQwG0Vc/ihvrWvks+1ttHrDrNuH8evo6PzvksiXGj16NJIkEQ6H+fLLLzn99NM56aSTuO+++3o8ftasWbz00ks9tlm+fDkmk6lXP7W1tXt1He3t7eTnp5e0SWxrb2/fYx+XX345I0aM4De/+U23bXJycrj66qv5+9//ztKlS3nssceor69n2rRpfPDBByltn3nmGT788EPy8/M588wzue2225g0aRIPPvggqqpy44037nFMr732GuPGjWPYMK1o+1VXXcVnn33GN998k2yzevVqVq9ezW9/+9u04y+88EIeeOABjj/+eE4//fQ9nu/HSp+NKYfDweWXX05lZSX//e9/OfTQQ7n99tspLy/noosuYs2aNd/FOH8U5DvNOC1GHBYjA/NtFDjM7GwP8c66Rq575RseX7ItbWKTqIUzoTwHk0FIqpftybBJTORHFmcR6lTCq2vXvBlbW/w0eyN7VN5KeD9sJjFNkWxfSBh6oZhMTFKISAo2s5EBeXZikorVJGphfQK4fFpej9o5Ge6KxSQyrn8OBQ7zHgUbyvPtSIpCBqdBUkkwKimdxiqYjKKW7xKKdqrcacVUS7OtFGdbsZsM2IxicjU+HJNZsdWV8lwSRktMUTCJWnig2SCQ7zAjCN+ahoKg1bHSvGBaDSeTQdyjgZhlM5JlNWI1ihgFcFqMCKJmEAmAwaCd02gUsFlEmr0Rnl6xA4CZY0uIyzI728N8tKkZX0RKFmBVVU14o9kXwReJM3NsSTK8bVlVa1LWPhRXkRVlv+T1TBpcwMQBudjMRkwGAYMopHnsTAaBESVZXHzMYGaOLeG0iWVcffwwhhQ7CMYk4pJCKCYDmpfJ5Y/jC8dxB2McN7IfQ4sd2M0GBATCcYX2YBSrUSQWV0CAmAy5dhMD8mysqmlnbV0H4ZhW7Lo9GKfOHUqGASbUBx9bsi2j10jtxkmoqpp3MhDVcsMSoiQqKoqiqROKnVsaOsL4I3FNfGUf7m1CRGNsaTYqsLHRh0kUWLHVldFgnzS4gAkDcpOest6iKJowRTAmU98R4uPNLezqCLOuwcf2Fs3LuD/DQnV0dH76fPXVVwDcdNNNmEwm7HY7RxxxBNnZ2bz88stpana7s2bNGg4++OAe2xx66KHJCf+efsrKynrsa3dPVtfF765/93enp32giW+8/fbbPP300z22Pfjgg3n00Uc544wzOPbYY5k7dy6ff/45paWl3HDDDSltjzrqKOrq6tiyZQtut5u7776bbdu2cd999/H3v/8do9HInXfeycCBAykpKeG3v/1tMgwwwcKFCznzzDOTv//iF7+guLg4xTv1xBNPUFRUxM9//vO08XY99qfMPkmjz5gxg4ULF1JTU8NRRx3FCy+8wOTJk5k8efKPJmlwf1HvDmExGjh1fBkD8m0ML8kiIn2r8uYOxnntq/qUiU1Xz9D7lc3EZZXDBuX1OMFOkJjIb3MFkGTNEAnGZGraggwpclCSY+WoIQV7lBnXlM7cNHsj+y1ks94dwhOOU+S0JOWhs6xGbCYDwaiEqmpudxUt/E6SVawmA0UOE1ajgEmEbKuBaSOKWL/Ly4TyHH55xCBmji2h3h3KaGjWu0M0esLIPVyCLGv5ZSoQiEgEIxLeiIykdIYwSTIbGjws2dyCyx9B7JQfNwja+D2heMpzSSjunTyulIpCB3aLkTy7mRElWUmjb1WNm2BUoizXht1sZEixk7nHaAp2m5v9mESBRk847ZrK8+0UOS34I3G8YQl3MI6vU8UvIRAQlzU1REWBcEyh0GlOeiTW7/ISiMo4O/Xp2/xRLCZN+j3xNR2Oy+xo1bx/Cdbv8iIIAg6T9uAEQaDaFdhnr2VClOSKGUMZX56T0bMjAI2eMP/+so5X1uzivfVNgMCYslwURVMvlFUtFy7hhbIaBSKSgiDArbNHc3ZnvtiU4YUYRAFPOA6CQL7NRHG2hSmdgjCratwEOsUWQDOC/JE4C9c2JMUZvtnlyeg1KsmxJotN746ClhfX4o9p77bSWbNMUpNFrGOyijcUxxOKE5UUOkJxynJtvS7UvTuJ74Jmf5SJA3I55/Byjh1ehCcUz7hQMqTIyUFFjh4/K5kwdNY3UzvVLoNRGVPCQAfq3FpdPT3cT0dHp7esXbsWg8HA559/zurVq3n//feZMWMGH330EU8//XRKW0mSuO222ygrK2Po0KH85z//IRQKMWLEiB7P4XQ6mThxYq9+zOZMqr0atbW1aZ6s5cuXA1BQUJDR++R2a387MnmtEgQCAa644gquvPJKysrK8Hg8eDweYrEYAB6Ph2Aw2O3xubm5nHLKKaxfv55wOHXuaDKZGDFiBDk5Ws3Iyy67jP/3//4fxxxzDM899xzPPfccS5Ys4euvv+aTTz7h/vvvTx67atUq6urqUgwii8XC//3f//HSSy/h8XhwuVy88sor/OpXv8JisaSNrbS0tNtx/5TYp8y+cDjMv//9b+bNm8e6desYPXo055xzDm+99RZnnHEGd911F7fffvv+GusPmsSEprotSDSu0OaPEYkrnfVytIlint2UNJSGFDlTZJnX7OzAZBD26IFJkJjIxySFZm8jsqqteIuCtjI9vF8Ws8btWcI4x2aiOMtMVFL3uHKS8KLtSf2tPN+OySDQ6tcS2hUF2v1RzAZNutpmFFPyZJwWA3FZRQb6ZVtRVO063q9sRlY0Q+bSKYNZV+/ttgjrsioXvoiUlDYXAbMRIl1CoBPy7In/ZSqqWucOIwhakVun1URRthF/OE57ME5JjpLxuTR0hDGKIk6LkcmD87lixjCGFDmToWQtnaIi/fNsnDC6hEmDC5g0uIDVtW5WbHXx9rpG3q9sYu7Rg5MqhkOKnMwaV8rmJi8dwTiSCnIGt1soKX+u8Nn2dsb0z2bFVhdNngguXwRJ0ULcQOj0CGlFfNXOWbGkQEcwluxv/IAc8uxmmr1hRDR58u2tgf0i351Qumv1Rdnc6E/bL6B5ALe1+DGKIpKs0BaIInaGz9Jp7CeMAKXTe2Y2KGxq8mEyiMwcW0I4JtMejFGcZSUcCyKYBHxRmQqLMWmwJPLrEqhoBs+mRh/vbWii0GlJhut2/VQkyhlIPUjLJ0isVCWOT7zXBgEcFhPecByDoC0mmEVhr+9v4rtgV0eYAXk2hhQ5WVbVSrM3wraWAFlWY8pCiRa+6MrocdSK92ohsBFJQVVVYp0f1WjnZylhUNEpz584stD5bV09XelPR0enN6xdu5bRo0dz5JFHJrdNnjyZAQMG8M9//pPLL788uf3GG29k48aNfP3110iSxNFHH82ECRMQxZ79AsuXL0+TEe+OmpoaKioqMu4rKytLCiwkSBhy48aNY8GCBUiSlJI3tWHDBiBdtrwrbW1ttLS08PDDD/Pwww+n7c/Ly+P000/njTfe6LaPxHd8T/O4+fPns2nTJl5//XUAFi9ezNlnn50M4bvkkkt44YUXuPvuuwHNWzZ8+PC0sf/mN7/hgQce4NlnnyUSiSBJEpdddlnGc+5pXvlTYa+MqerqaubNm8f8+fPx+XzMmjWLP//5zxx//PEA3HHHHdxyyy088cQT/zPGVGJC896GJlZWt3PYoDwAGjpCdIS1orrN3ij9876VX04YYJub/QzMtzNzbAmCICQnRL055xUzhrK11Ud1axCLUWR8eS4njyulJNtKvTuUbNcd3nAcf0RKm3DtTsKL1p0xs/u45h49mO0t6wlEJAyCFmInywoVJVlpXrdwXCYuQySu4AnG6ZdjwRuSiCsKgwvstPhiLN3SiorQbT2gfIcmYhFPhlVpNZh6omsNJgFN5ry+I4Sx88OfYzXhi8aJywpWkxHb7glZaJ6n1bVumj1horLCiq0uTpvYv9P70c52VwCbyaCJEQAbGrw0esLMHFtCqy9KjSuIJxzDH5EIxeQUQ7U0x0pcoUdZ98S12owikbjMpkYvRoOI02xEVrVJsckgdk6ANe+IxSAQiMnEFTCJKsP6fXsfE8bc0yt2sH6XB4txn5zXKSQ8Ph9uaiYipYpOGAUwd4ZeRiWVKNp+bzhGOK6gdHoPE1/LRlEz0hU0z0+bL8Lyra30z7MlDYt19R5eXl0HaDXHpnQpUzCiJIsvd7jTZMtDMZkPNzZz3YkjmFCeS7M3QkmONWmEDSlyMrI0my9r2pEVNVk3bXcENEl7q8mAJCsokmZ4qJ0GVTAWT+bYAWxo9PH4km3MHr93NZwShmpXSnKsjOtvxhWIpfxBq3eHkuIqXcebQFFhZGkW9R0R4pJCWyCW2fASwGkSMRlF7GYjOTZzr+XldXR0dLxeLzt27EiT5s7NzWXOnDm88MIL7Nixg4MOOojGxkaefvpptm/fTnGx9nfq6KOPJjc3d4/nSYT59YaewvzMZjOHHXZYxn0/+9nPePrpp3n99ddTwt2ef/55ysrKmDx5crf9lpSUsHTp0rTtDzzwAMuXL2fx4sUUFhZ2e3xHRwfvvPMOEydOxGq1ZmzT1tbG9ddfz5NPPpm8Z6qqpni8AoFAyjzw9ddf55xzzknrq7S0lLPPPpsnn3ySWCzGqaeeysCBA7sd3/8CfTamZs2axUcffYTD4WDu3LlceeWVGeUOTz31VB544IH9MsgfC0M669s0dITZ3OzvnAwrRGSFHIsRp82UMqHLtKK8NwzMcyAr4DQbOWxQHlua/Cze0ITZaOjR8GnyRrCYRCoKs4nElR5XEBLFSi1GgTZ/dI+rz+X5dkrzbLT6NTU/k0HAaTMxsiSbGlcAX3NAW/kH6PyXKGiGgzsYJ8tiwB2U2e4KYhBgY4OPPIeZYFTKOGGbPb6UT7a1UtngIy5rBZMDUQmlh1imxDRYFGBIkYNTJ5Tx5NJqIpKC2SAybWQRy6pcBKMSVpOAPyylXHciN6zJEyYUVzAI4AnHefubxk6jRMtvMhm0HLFQTMZmEqlq9tPkDeMLS+xwBYjJ2rXvcAVTPEDrd3mJ72Z0ZEIFfJ2evmCnG8EgRKkotGM2GHD5I/ijMoJAMtQygazA2+uakpL6CQPc0JlQ4wnFGV2WvdchaAkSxvjqGjc1bcEUI0YUoCjbQpbFRIMn1dBu9kYxGUUsRgNhScLY6XmUFC0/LRFG6wrGEYBnPtnBunoP4wfkMn5ADluavzWIxvXPSdbV+n9HVrBmZwdVzb5k0esEnlCcDQ1ezjmsPLm4ASSPLcqyYDUZejSmRAGcVhNnTCzjq50d7HAFicvaooEgaOqWCQS0BYW3vmmg0ZNeWHhvKM+3MzDfTnswxsB8rfBuYvzl+XbyHCaaPOFkPqHZIKAARU4zbYEYTZ4o+Q4TsqzSFoil9J1YhJBVCMQUnIJAjs1EltWYkn+no6Oj0xNr165FVVUmTZqUtu+ss87ihRdeYNGiRVx33XUsWbKESZMmJQ0pAJfL1SuPU1ZWVrdG0P5i1qxZnHDCCfzmN7/B5/MxdOhQFixYwPvvv8+LL76YlH1fvnw5xx13HHfccQd33HEHAFarlWnTpqX1OX/+fAwGQ8q+8847j4EDB3LYYYdRWFjItm3bePjhh2lpaWH+/Pndju/aa69l8uTJKcbRSSedxHXXXceRRx6J0+nk8ccf51e/+hUA33zzDdXV1d3mPF199dVJA/G5557ry636SdJnY6q6uppHHnmEuXPn9pgYOHbs2IyW9k+drgZSoyfM2+saKXZaaPZGsZkNlGRb09rvy+RjVY2bZl+EwwblUdng06SgoxLRuMxBRQ52dYgZQ7SqXQEWVzbR5AnT5AkzoTy3xxXlJm+Y7a3+ziLBYkq9nkzUu0Pk2S0cOiiP9Q0+hhU5cAVjLKtqpSjLSpM3QjimYDYKOCxGWnzRpAfGYTZo+UF8u5Lf1CmWMLI0O+OEbUiRkwfOnMB7G5pYsdWFJKvUuYN4Q1qIXGISjqD12dXEMooCpbk2QMBuMWBTtdyiBk+Y9kCMuKzSFogjKcGUVZuECMiw4izWNXiT4ZxbW/1UuwJMGpzP0GInO1xBsixGglGJldVuDCIIgo3yfBtVzT6MqgqCgNylum61K8Db6xoIxdIn6yKQYzehKCqiIBCWNPGMrmhRaALF2drEP89uoskXYVRJNu9XNhHrNDIVYHVtO5UNXvplW3BaTQQicZq8WoigLGv1nvaVhNiJPxpP8waZjSKjSrKpdYe0cL4uGERQFZWAJCGiTeBzbUYkRRMyiUra+ERBxSiCJyyxdEsrn1e3c3hFHjPHltAWiFHoNPN+ZXOKZ/X8yYP429LtNHjDKcWaw3GZldXtNHRohg3Awx9WJY2ycw4rZ0J5Lht2eTXZ9Qz2ekKtUBAErjtxBA9/WEVdewiTQSEc01xsopqqNjm6LDslDLivJDx/Ln+MoiwLM8eW0OyL0OqL8sqaeuKymrz28yYP5L73tuDvjINVVO1dCsZk8uxmrCaRRk+EYOfnMOXadvs93nkDEkWYfwr885//5NJLL8XhcBAIpOaArV27lhtuuIEvvvgCo9HIjBkzeOihhzjooIPS+nniiSeYN28eNTU1lJWVcdFFF3HLLbdgMnVfg0xH53+FhJLf7nLaoE30s7KyeOONN7juuutoa2ujoKAgub+1tZVPPvnkB7Vov3DhQm699VbuuOMO3G43I0eOZMGCBZx77rnJNqqqIstazb69Yfz48bz88ss89dRTBAIB8vPzOeaYY3jhhRcy3keAJUuWsHDhQjZu3Jiy/ZJLLmHHjh3cdNNNxGIxzjzzTG699VZA80oNGjSIQw89NGOfkyZNoqKiApvNxnHHHbdX1/JTos/G1NatW3vVLisri6lTp/Z5QD8VVFWlNMdKrs3ElmYfcVmmPRjnlTX1e8w56i0Jz0izL0KLL4LDbCQck1EVrbZSVUsQq1Fk8YYmDq/ITznnu+ubWF/vJRKXkRVoD8Z6OBO4/DFMBk2Jri0Qoz3Qc/tECGMwKlGSbSXWKX0+IM9GfUeYnx0ygAKHmQKnmYVfNeDyR5E7DSdZ0YQ7DJ2eKlkhGYcUjEo9TtjG9c+h0GlmdU0H9R0hzEYRQVFwWkxE4jIiEFOUFG+E3WwgEJHoCMWwmQyYDSLBmMS6ug78Xbw4MUlJKarc9Rrz7CZCMQm72YAnFOfFL3YydXgRqgqhmNQ5WVfpl20lFNXqI7UFYtjMBmJhBUFVsZi+NbZX1bTT6o928d5pCEC2zci4AVoy6ZYmH/5I+peyQYSJ5bmcOqEsaURMGJDLhPJcNjf5qG0PJr0qsgK+iIQ/IlHg0HJ5Eg49Y2ddqH2Z4L+7vpGV1e1sbPARiKbHXkbjCqtr3ZiNIiZRRJLl5IRdUTWvpohmZIdiMvlOC/1zbYTjMoGIRJM3TDimCYloNaoMKIpKjSvI+5XNmI0Gmjxh2oJRKvLtBDsL/U4anM+zn4p0td8SxXQPG5THJ9vamLd0O9lWI1/VdpBQ4JsyvIhzDiunX7aVygYP21o1o13gW0MjkYP1xtcNTB1exHUnjtDEGVqDhGPhZL6exShQmmOjKMtCOK70Kl8yE8uqWpm3dBvVriCRmEyW1cSI0iycFiNN3gitvggzRhbT7It2XnsBWRZD0piKK1DsNHHKhDJy7WaWVrVq70Snx1hTwBTIshhpD8ZTzp3vNCUNuJ9CiF9DQwPXX389ZWVleL3elH1btmxh2rRpTJw4kVdeeYVIJMIdd9zBscceyzfffENRUVGy7b333svtt9/OTTfdxIknnsjq1au57bbbaGho4B//+Mf3fVk6Oj84rrvuOq677rqM+ywWCz6fL/n7iBEjuP/++9m5cycOh4MLLrgAWZYZM2bM9zXcPeJ0Onnsscd47LHHum0zbdq0Xot9zZ8/P83bdNNNN3HTTTf1aVzHHXdc2qIQgMFg4IEHHshokL7++us9KvGtX7+e2trajDWnQCvae9ddd/VpnD9mfhylpX8kJFaGP9nWhicUJy7L5NjMWpFRmwmLUUwW8d0fxlRXr1R9R5iJ5bm8/U0D3i6Ta4MIrt3C8qpdAT7Z1kowKqGoKmajiKL0nDg+fkAOuXYzHSGJXLuZsf1zehxbqocuxOqaDjY0ePhihxuTQaDFF2HqcG3iMaZ/DpuavNrkvlOtTeniPVI7/xOKybT6oxm9Yokwsjp3iGZvhLis4AvHKc2x0h6IYTIIBCJKWl0jETCIIiU5VqYOL6LFF6HZG8FoEKhtS1XPkWQFlz+a8RrX1Xfwr89r8Ybj+MISb33TSGWDl0BUwmYyEIhKBKISwbYgBlHAZhY5elgRvkiciKSQbTFSkmPrYigKWIwiYQNJAQCrUeCQgXmcdnD/ZNjdvI+3s7iyCTWu0NV/ZDGKVBQ6mDaimPJ8ezKUFOCTrS78UQmXP0JUUpMTexXNKyMIAk6zNtFWVC3fbW+UHqtdAe5+ayNf1rQT61RRTHoIu6ACobjM4EI7VS2B5HMXAFEQGD8gm5q2ENlWE7l2E+MG5JJnN/PFjnaG9XNiNRk4qNCBPxJnbV0H/k5jNcduIi6r5FhFqlq0cL5mb5Rc27f5gRajAaMooHRK81uNBqJxhY82tbCrI8TO9iCKqhKOyRgMWo2oqmY/39R5aA/GUFUwiyIGkYyy/JG4TGWDl9/OGEZ5vp15S7fz/oYmLYew05M5bUQRI0qyaAvEGNc/p8/fDdWuAPOWbmdjow9ZUTuDSzXDL8dmYmxpNu+1B/nv5laG93NqCxruUFrStqSo1HV6EI1ip7cKAURwmg2ddfPSjeFIXKF/ro25Rw/+SYT4XXbZZUyZMoX8/Hxee+21lH133HEHFouFd955h+zsbEDLxxg2bBgPPfQQDz74IKDVlLnnnnu49NJLk3Vypk2bRjwe57bbbuOaa65h9OjR3++F6ej8iJk5cyYnn3wy48aNY8CAARx33HE0NzdnVJDT2Xc2bdqUcXt1dTU7d+7klltuobS0lIsuuuj7HdgPlD4bU4MHD+7WMyCKIrm5uRx++OFcddVVjBo1ap8H+GMhMZmvavHT0BHCajTQ0qlgF1e0BHqjQWRosXO/rN7u7pWaUJ7L1OFFVDX78NV2aAn7Kp31dmJp4Wkmg4HSHC3cLjHx7mlcCWGCygYvY/vnJH/vicTE6q8fb2VTox9JUTAbRY44qJi2QIznPqvBbDQQjcvk2M1EfRHMJhFREBElraCromrqZwmvVSgqsXDtLiYNLkiZuCWUEQudZra1BCjNsdDoCdPgiWAyCFiMBnJsJjpC8WSYX67diNNs4oQx/Tj/iEEMKXImDY83vm6gti2EwLf1r4yGdDGGRJimqqoUZVuJSQphSQFUglEJg6h5/SRZ85qYRQFJUWn0RlhT68YkilTk22n1x8i2GZPPYNLgfA6ryGd1jRt3MIbDYiAck2n0RijJtiavvaLQkZRK70pcVllT66baFUi2TYSAuQJRZEXBbDAgCkpK3o9BFMmyGnCYjagq9M+1YjMb9yp8q94dosETTtZagu7FNBQFmrxRrCYDFqNmhFiMIgZRIC7DYYPyGVWWzeYmn1Z0tzVAnTvEtpYAFqPI9JHFjB+Qw9gBOVTu8uK0magosPPVzg5W17rpmnrmj0hJOfhsm4kip5kmn2Yk+6Nxsuwm8h1mWnwRhhQ52Nzsx2gQcFpMxBWFXR0hglGZIYUOvtrpJiYp3daYMBnE5MLDkCInp00oY+mWVsJxBVPne/3x5lZWbG2jJMdKQ0e4z57rVTVudrYHQdW8YQZBJRSXGVhgpyjLytIqFx3BOAZRQlHUpCJnaY6Fxo5w0pvmDsVZsrkVQYB8u4lTJ/YHYHWtm3q3JrCiZjAYg1EJVyDK+l3e/eZ1P1C8+OKLLF++nE2bNnHbbbel7JMkiXfeeYcLLrggaUgBDBo0iOnTp7No0aKkMfX+++8TiUTSEuvnzp3LrbfeyhtvvKEbUzr7BdFsp/D0m9K2/dQQRTGjp0bn++WPf/wjL7zwAqNGjeLVV1/Fbv/pvWt7Q5+luqZOnYqqqjQ0NFBRUcHkyZMZNGgQDQ0NyLJMeXk5Cxcu5LDDDvufKuDbtXCmpKi0BaOYDAJmo4BRFBhdks2gAjuHVeR3WytJkyxu7VWdlkS+znEjiinOtjKqNJtXVtezpdmv5WGImhGSY9UmxbuHpw3MtzMg387EgTlccuxBXHfiiD1Ogsrz7Yztn0N5vr3XY313fRPf1HsJxmQtvyUms701kCxQXJJlSSreOa1GcmwmKgpt2C2mpIWQWMGPd6pj72wPp9T9SYytwKElz2dZjQSjMnazgfI8G4UOCwaBTk+cFooldioMluRakoZUAlVVmTQ4j3ynma72k6KqrO40UDLdmyKnhaishSgGozJFWRZOHF3K4EIHhwzKxW4xEpMVYrKKJCsEYzLZNiM5djOjy7JSVvaHFDm57sQRnDKhDIfFSCiqyZt3hGIptXyKssxkW004LN8qDQqArdOztKsjnCxA+8TH23nm0x1s2OUlKimEYxLR3dwpsqpSnm9jyogicuwm2oNxvOH4XtcgEwWtHlZPRwto6nwHD9TCEBNFne1mI+PLtbpJ1500gnH9c4jLKqNKsvBHJbKtRsb1zyYUl3h1TT03vb6eV9bsYm29hy1NPt74upFWX1Tztu12wlpXMPk5MBpEDIJWADkqqXQEY0w+KJ9cu5na9jC5NjOjy3JwWo2IgkBDR1gril3XgVEUyLeb07xSAprk/6+OHZyy8DBtRDFzj64g127CYBQRgTyHCX9EoqizTljfi2drmVeGzoLOoggGQcRmNlKcZdEMWiCuqLgCUVZsdWlKoNOHMTDfhrGz4DRoOWOCCoGYzNZmH6oKhw3Ko9BpxmIQyZQ9ZzYIuAMxFqyq4+EP0wsc/1hobW3lmmuu4YEHHmDAgAFp+6urqwmHw4wfPz5t3/jx49m+fXuy4GVlZSWgySV3pbS0lMLCwuT+TESjUXw+X8qPjk53CEYTjpHHpPwIRj0nT+e7Yf78+ciyTGVlJUcfffSBHs4Phj57pk466SS++OILtm/fTnl5eXJ7XV0dJ554ImeccQbz589n2rRp3Hnnnbz77rs99uf3+/njH//IN998w9dff01bWxt33nlnr2MtW1tbueGGG3jnnXcIhUJMmDCBe+6553tPiOtaOHNsWQ7uUIxWXxRQybWbsVuNlNlMbG7ysX6XN01lry/S47ufb0S/LAqdFmrag8STq+QiVouA3WIkEpfTwtNmji1h+VYXeXYzJ/eiHlXX8ZlErfJr14T27o53B2OonbksKtpK/REHFTBleBHvVzazpq6DaFxhZL8sOkIxJFnBH5E1AyImEZW+lToHsJjEZKHQruweVvjspzVIisqujhBmgwFJUZIeEqfZQCQuo3Z67lbVfGuYdX0G1xw/jKWbW1m6tZW4rBlfGxu8GQU9EnWhGjxhjJ21nGaNK+XwinwaPWHagzGGF2dR3xHCG44jySqhqMShg/IQBIFx3Xj7wjGZ/rmaZDtA/1xbSi2fSYMLOGxQG1UtfvBFicma0Rrq9IolQrqafBGsRpFoXCAgKwQimsctUZcrQVzSirHmOzSZ6yKnmTp3mA0NffM6VLsCvF/ZjNEgYjOJKKqK0pnL1zUPTARsZpHRZdncOEvzZK+udePyRynKsqTl+iVKCZRmW0GAmrYgoahmFIaisnbvVTrD3WB4PydtAQNDiy1sbw2CACZRpC2ofR4unXIQ/fNsPP95Le5ADKOoCWJ0hOJMGV6EKMCU4UU0ecP8dcl2QjGZqKSF7x48MJdmX4SqJj8GUQthjMtabtGQQgezxpUxe3x6wcKrjhtOUZaF5z6toT0Y61TVNOAKxPZKWrw0x4aAFgYLYBREHBYtD3DFtrYUQ1ZRtTw70Ay7u04fy7yl22jyRmn1R4h3hmPKisKa2g6+2ukhx26iOMuCwSBiktNDZUMxpbMUgcy6es9+qUl2ILj88ssZMWIEv/nNbzLuTxTkzFR8Mz8/H1VV6ejooLS0lPb2diwWCw6HI2PbTMU9E9x///3JOi86Ojo6Oj98+mxM3Xvvvdx1110phhTAwIEDueOOO/jjH//IhRdeyO9+9zuuueaaPfbX3t7OP/7xDyZMmMAZZ5zBP//5z16PJRqNctxxx+HxeHjssccoLi5m3rx5zJw5k//+97/fqwBGwkDZ0OBlXKf3JuE9Kcm2IggCjZ4wH29pzVgrqWsB30x1lDKdr6usOsDCtfXEZBWTUcRsFMm2mHAFoogiKSFf1a4Ar6yu55tdHgRgS7Nvj56pruP7ZHsbAnDM0EI+2tTC7W9UcsRB+YwfkJs24Z42oojFG5poD0QRBIGx/bNTPEHVLj/tosBXdR2EYzKiAB3BOEaDQGy3mDBRAJvZyITy3IxS3YmQu8eXbGVXR5i4rCApIAoKUVnFZhKRFIWYLCOKAnazgR2tAV5YWcu6eg8TynNTnkFZrp2yPFtSpU9RtRCxroZpKiresFasOdduTobjJZ6Tqqo891kN63d5sdpFjILIJ9vayLWbM4Z3Je75YYPyiMoKRkEg155ay2dIkZPrThrB6lo3r3+1i63NflBlEEgq/JXn2ynNttLii2gGlKhJiluMWghqV+IyxGWFcf1zaOgIU+cO4QpEeW9DE5ub9vyepI19YB6NnjCBzvwrAU10ISqpZFkMCKLA8aP6cfn0oSleuUxkeufnfbydGleQaEzuNAKATkW+XJtJO4/ViMNioiTHQjAqU1FoTxqkU4cXceWMYbQHorz0ZR2SrNLqi/LCyloEQaAs18qwfk6e/bSGhs5CvzVtIcaUGTn/iEHJMbQFoyiKSkyWiUkqW1sCIDSlSZ0nCl+7/DGcVhMjS7PZ1hpgQK6NAfl2pnYpndAX8p1mZEUhENXqs3lCMRxmA63edC9XZaOPZVWtTBtRnDTgNzR4UVWVnW0hmn0RdrQF8IbjmESBYFTCmGNFFIVE3WTtOZpEcu0mbEYDuzrCyXpmP0Zef/113n77bb7++us9hrT2tL/rvt62252bb76Za6+9Nvm7z+dL+3uro6Ojo/PDoc/G1Pbt28nJySw+kJeXR21tLQAVFRWEQj3LZ4MWb97R0YEgCLS1tfXJmHrmmWeorKzk888/T1bPnj59OhMmTOCGG27gyy+/7HVf+0piJb49GEtKKp97+MDkvnp3iNIca3Jl3SRqxlXCwOlawLe3il67y6pfMX0Y85Zuoz0YxyBAeyBGJC5jMghsafInV4xTPBWSQlWzf4+ryV3Hl/AKvLO+qTNBH1ZWtzMg38bRQwpTJo/TRhTz4FnjWbHVRZ4j3QuWa7cwsl82y7a6iEnaRFRVSQoWdMVpMXLe5IGcPE5b7U/Uzdl93O6glhdl7MxPErUcemKSgsNswGYWicsqJoNIQJUpybHSHowhCKQ9HxC0nK3OviVFZcVWV9p1VLsCLFy7C29YwiAKhGJSMrRy9+c0b+k2draH8cdiuEMxxvbPoi0QTXsG5fl2TKLAkqpWBCA/18qIkqyME+6SbCtzDunPc5/WUNsewiwKtPq0Pg+vyOfY4YWMKsvGHYyxYqsLXzhOKCZrAgO73emEyMClUw7ixS92Ut0awBeO8+WOdt7b0MSVM4Z1+57s/r5UtwU7BRE6Zc5VOgsBq2TZtFCUwwfnp11P4jPT9flm2lZR6MAgQEJfTkHzECXelQnleTR6Qixcu4tgTCYQldjS5Kco25oSupjvsGAUE3WrEmISKjvbw9z91kYiXQx7RYXatiCratqZNLggmd/V0amIaewUo4jEpBSp82pXgIc/rKK2LUirL0JcUdnhCoAg0OqLsKnJR4sv0ue8o/J8O4MLHHjDcexo6oiKqtLQEclYBysmK1Q2eJk2ojjle6vAYebyGUMBuPutjayudROOKxgNIr6IBCrkOswoqqa0OXFgHiaD2Cm2o5JlMzK4wLHPNcm+bwKBAFdccQVXXnklZWVleDweAGIx7Xl6PB5MJlNSljmTV8ntdiMIQrIYZkFBAZFIhFAolJZT4Ha7u5UbBk3FTE+q19HR0fnx0GdjatCgQcyfP59Zs2al7Xv22WeTVZDb29szhkPszr7UJVm0aBEjRoxIGlIARqORX/7yl9xyyy00NDTQv3//ve6/L6yrquWtv/2R4hwna+Iirk+KGTWggIAk8mWdD29MAIOJwSX5eKIqdV6J1Wtt9C/I4twjh/Kzo8fucwHf8nw7xw4r4sNNLdS2BQl0FnJVZZVolwz8hKeioSNEIKJN/p/47za2NPmS6m+Zwti6jq/eHeLKl9YmJbQBGjvCbG1J96p1XQFPUO0KsH6XB08oRjAqMa5/DhubYJc7nMxTshsNBGJavovZKHDmoQO4csawPYZEThtRxH83t+AJxbAKKoVOC7KieakKnGYGFzgIxDQPk9EgEokrDMy3cnhFPodX5LO61s2KrS4+3tKKSRSoKHBQ7Qomw+G2taQbn/XuEIGojFnUcsG6KyFRnm9nQJ6d2rZgsujr6hoPeQ4zK7a6ktLoiWdw7PAimn0RBuTZWLOzA0XxEI7Jyf2734sx/XNo8kZwWIyIooDLH03ZX5ZrJRiViEmaGEiW1YgnFCPaabyW5liIyyQn28P7OXlTUfBFZORuDMlMJDy1/1ixA1VRMRoETYRDgDyHhaIsC7KiUpJjTZt8J4yORF2n604cAZD2zEHzuMZkzQAyiWAwiIzol4XDYqQ4aTAJSIpWsDgaV7CaRLKtu4tqqETiSloNJSDFkErgj8o89t9tnDc5RlxROWl0CW9vaNLqmnV2IqukLIysqnGzrt5DJC7TEYpjMQjEFBWjqIWuWk0GatuCvLehqVf3uOu9Tngnl2xu4bPtbZqXTFWSday6XkEkrtAW0LyrmTziU4cXcedpY3hvQxMbdnnY1RGmJNtKR0gTsjGKAmajgWBUxigqjCjJ4tQJpZTl2vep+PiBoq2tjZaWFh5++GEefvjhtP15eXmcfvrpvPbaa9hsNjZs2JDWZsOGDQwdOhSrVfv8JnKlNmzYkCxsCdDc3ExbWxtjx479jq5GR0dHR+f7ps/G1PXXX8///d//sWvXLs4++2z69etHS0sLr7zyCl9++WWyfsbSpUu/84rTlZWVHHvssWnbEwnCGzdu7NaYikajRKPfhmvta5KvRQqweclrbO78/Ztu2q3c7fc1wJvAv99YzHmnz0ybiLS1tTF58mRsNhtWqxWr1Zr8d9f/R1UDW1rDeOIC/piAYjATw4C14mBMuSUUZ1koybYmvTnXnjicJ974lKXbOvDFBbwBM//6PMSXNW4mDMjNmAfV1cPywsqd+KOp6eiKCoGItEevWkIQ4ZtdHiRZYVCBg2OHFxKISXhDcS0EzSRiEEWisoLJIJLvMDO8n5O73qrkm3oPUUnhhFH9MoZEThtRzD1njKWywYuiqnyyzcXO9jCg4rQYOefw8mQYZqsvSnF2am5OvTuUFDrY3Ozn6GFFNHgiBGNanlUwJqeF+iUEKGpcQVQBrCYxpUBzQjb/9bW72N4S0IoSq2AxaF6MAXlWPKF4UuGwwGFm5tgSQDM46jvCCMCYsuxkraCElzExGV6zs4O4LGMzG4jLCmNKcih0Wli/y5vcv7HRS0xSyLEZMRkNGDpD4gS02k6RuILdbERRSQoJSJJCTFKwmQzIe5DQ73q9z35aw1c73cm8N4MAZTlWynJtnDaxjNIcW8bJ97vrm/h8ezsGEXZ1hFhd66Yk25o26VdVFX9UIt9pxhuKIQoC2TYTDosRoyjw+le7MBk0MYu4rHTmyakYxHT1SncwjpDupOuRQFTCE4olPXBOsxFZVpAUleIsCzNG9dvNi5jIR9KMv7iiGd12q4FgVMIdjBKKySzd0pr0bvfFoAJ48YudRONKcpHDIJA0ZOUuIXqNHs1rurtHXFXV5HfElTOG8fiSrayu6aDBEybLoilfgiYPX+gw81l1G22BKOGYnKaw+WOhpKQkY4H5Bx54gOXLl7N48WIKCwsxGo2ceuqpLFy4kD/96U9kZWUBWr7w0qVL+d3vfpc8dubMmVitVubPn59iTM2fPx9BEDjjjDO+8+vS0dHRuO222/jXv/5FY2MjTqcTj8fDtGnTAFi2bFmPx9bW1jJ48GCee+65H7UM+L5ex3333cfo0aN/NN9dgiD0SX9hX+mzMXXppZeiqip33XVXSlx3SUkJTz31FJdccgkAt95663ceqtCd9yux7ftM8i2y91kYMQVfPLOHLhQKsWPHjr3ut+Ss23D268/Rw4pSwnkuPGIAj//fyWntaw1Glpgt/MNuI8thTzHYVIMJVTSRm+XAMPYkECpSjhVFOHpYIUvf/A/LBCGjAWi1WtnQHGJ7TROiJGAxmIlFjbj8USxGA6eOL+OzHe0osoqsqmRbTUwoz6EjGOfZT2vY0ekhEjtv14QBuRmNt4SH7d31TQSiMk6Lgaik4A7GaO4MpUrUCirwmFO8I7tPMIf3yyLfYdaKrQJkULZLCFC0BaKU5FiJxJWk5yPhPVpV66a+XatdlZBnlxSwmAwYOr0TcVllwgDN8EkYViZRYMbIYjY3+Wj2RVO8HV3HqglzGDhpdAmVTT5O7hTAWFfvSe43ilrh5UZvBIuskmc3YzIIFDrt2MwiOVYzMVlhQ4OXLU0+XJ25bmajgIKaFLXYEwlZdEHQwu6kzuK7JqOBXJspTVgiQbUrwIebmvBFtCK4JoOIyx/l8Ir8jGGwiVywbJuJQQUO5hzSH0HQDKnathDZNiP+iESWVTOw+mVbybaZGFmSnXJOb1grSo2sGSIJOf6eUBSVYf00EZD3NjRp70FhEWvqOnBajOzqCPN+ZXPSizhpcAETB7SxocFLKKapTSZCBCNxgZisEI3HqXcHUVX6XI9uVY2bVl9EC1eUVZxmEQQBm0kkHJMJxDSXmYoWBgmpuZ6FTnPKd8SE8hxe+rIOf1TCbBDIyjUybUQxqqpS2eBhaVUrEUnBYY5T5w7tt/p53zdWqzU5serK/PnzMRgMKfvuvvtuDj/8cE455RRuuummZNHewsLClOKj+fn53Hbbbdx+++3k5+cni/bedddd/OpXv9Jl0XX2G3LIy64nzk/ZNuDKfx+g0fzwePPNN7n33nu59dZbmTVrVnJe+uSTTx7gkX2/lJaWsnLlSoYMGbJXx993332cddZZPxpj6vumT8aULMtUV1dzzjnncOmll1JVVUV7ezsFBQWMGDEiJWymX79++32wmfihJPmGw32VM06lvDBzHtq+9pvrtHPIwDyG98tKEb9YubUxY3tVloiHJTrCQTq6t0U5fdhksKZusxoNTB1exMk/v6rP435bEDCYLIw74/8wTziFmKSQ5zATlRR2toeIxCQ2vfowsVAA0WgCoxkhL4uBYwfwwoacNKPNG4Pl1V7cEZW2sIrqLECxF5JlNbFiqwtVpVvBj0xCB//+0kRDRxiDAFaTgaKs9IWCSYM1w6U9GGNgvjV5bH1nIdRwTEoqoQlAttVAjt3M8aP6MaIkiy1NfjY2elmzsyPFsNrc7Gf8gFxOHleaFga6u8DFK6vrqWzyUZptTRosXfe/X9lMnTuEKAqYDCK5NhMN3jCFTjP1HWHCMc2DM7Iki8pGH9G4JvFe5w5hMWly5b2hPN+uqRC6Q5pxgqb+6AtL0ENkb707hKxo91iSFEwGAXdnLlKmMNhEeBuQvN5lVa2YDCLFWWZ2dWgFmAcV2NkQlhha7GRLi59lVa00esLMHFvC+5XNtPiiOC0GPGGtcHQibFBSundW5djMrKv3MmlwASePK2Vzo4/KJh9OsxGTQUx5txLXds7h5Ywqy+aDymYUVcVuMdDmi+GSoknjrS0QRyXUZzl6lz+KrKg4rUaUcBwELU/QKAqE40pnEWRNNn/SQdriQdecqZgkE5dVDhuUx+ZmP6trO4hKCrk2I76IhCwrrKv3sKXZhzsQJxLXiiM3+6JYTca9ls//MTFy5EiWLVvGjTfeyFlnnYXRaGTGjBk89NBDFBUVpbS99dZbycrKYt68eTz00EOUlJRw0003ceuttx6g0evo/O+RKENw1VVXUVz8bbrB/9qChsVi4YgjjjjQw0hBlmUkSfpJ5Ij2yZhSVZXRo0fz9ttvM2vWLEaOHPldjatXFBQUdJsMDJklbBPs7yTffv368Zvf/IZwOEwkEqHN42dTfTsd/iBKPIoqx1DjMRQpjipFUWXt/4nkGqvNmrHfRN2SvUUWzAgCaeIXi1bX7FO/JrMFixGimlYBAlCSY93rMauqihSLMLjQiZJjIybJtPpj5NqNuAIxgpE4/m2rkAPfypgHgOplvet/wuwLKDvhEg4dlEezL5oiNvHuPRezoKkWuy09hNJqtaIazFS5wgQkEcFoxmi28ML2UjZUFDN58mROO+20pDjCzLElCILAgDwbwaYdrNwexBdWCLbU422PYVJFooIZo9GMUTQxrn8OU4cXJdUVE2GPp07oz7p6b4onZnchiwSJ7dWuAAidtoqQvh9IFiVOGF5rdrqRJIWqZq347ZhB2Xyxo53Pq9vpn2vDKAq0+oOYDAIHFTho9acLZWRiSJGTO08bw7yPt/PuhkYt1E8FdzBKTVuwWy9GQkyhPRAjZtDym6qa/Ty9YgeXTjmIqcOL0s6zez+J+lF1gN1iwG42EokrZFmNNPkiKeGSGxq8ScXEHa2BZL6TilbXTORbY0oAHGaRuKJiNoocP7o4GXI5IM+WvPc2iwGnxZgSOrd7WYFsmwmTQeDQQfksrmzCaBCQu4RD5thMKZ7N3YU3dqfaFWBLkw+DKGhCK1Yj4Zis1TQLSyiAxSBgMoqMKs1OemJ3DxM1GYTkuCeU5/BNnYeOUIxsq1YPbWlVK62dOXy7PGGsJpFQTEbuNNR/7EV7u9JdgdBDDz2U//73v73q46qrruKqq67azyPT0dkzFTf1XJZmb6l9YPZeH7tlyxbuvvtuPv74YzweD/369WPatGk8/fTTyflYZWUlt956KytWrCAcDjNy5Eh+97vfceGFFyb7WbZsGdOnT+ell16isrKS5557jkAgwKRJk5g3bx4jRmh5thUVFezcuRP4doE/EfqVKcyvsbGRa665hsWLFyOKIjNnzkwJ3+3KmjVr+MMf/sCnn35KKBRi1KhR3HzzzZxzzjnJNvPnz2fu3Ll8/PHHvPLKK7z66quoqsrUqVP561//SllZWUqfL730Ek888UQyL3Po0KFceeWVyYgvgP/+97/cf//9rF69GkmSOPjgg/nDH/6wx3JAmcL87rrrLu6++24qKyu55557eO+997BarcyePZtHHnkkKTaX+Fv0/PPP8/zzzwNazdnEvWtubk6WQmptbaV///5cdNFF3HrrrRiNxpTzP/jgg8RiMZ555hnq6+t57rnnuOSSS7jxxhv54x//mDLmLVu2MGrUKB577DGuuuoqXC4Xd955J8uWLaOurg673c7YsWO5++67M6b8fJ/0yZgyGo2UlJSgdJdd/z0zbty4bpOBge81yXfkyJFpbuMFq+p4fMlWglFN8tvXOakBbYXYYTFw6IBsguEIQnZJxn4HDRrEokWLiEQiRCKRpLG2qb6Nr3a0kGOG1g4/OWaVepePUChENBoBOY6oxLFn57CrQ6sTVJZrxWY2oKoqK7727NP1Gk1WVPXbJBODKGAzG6hv8+3TCvUhBxXjL8mizh2iMMtCvsPCx5tbsZoMqFJsr/s9eHA/+g3IpdkXxdQp8ZwwfJYKMZoCfoIBf6/7e+8reA+47LLLGHPkjBRxhJljS6h3h7jlt1ex8pPl3XciCGw0W3jRaiWGEVU0IZrMVFmsWE6eyd1/vCejIMmCBQvYsmVLWt5ctTtK5XYv+dl2ttaI/NfSTmhEaUqbMqedIZ0r6Ot3ealq8TO2LIdadwijIFDvDmM2GrAYRUJxie0tAfwRCVmFjU0+zEYDizc0dRum15UhRU5Om1jG0qpWopKmtyerWu2s7kIFu4oprKpxs7nJx5AiR0qeGPSs9gck3/Wpw4tSDMgNDV5WbHVR7QoyMN+elIDf3OxPKdCcQOXbkD+DCEaDSGGWmaIsS0rIZSLP7pihhWxu9jNleFEyJ6y7sgKbm/0UZ1sYXOCgxRdBluMgCOTYTPTPs9HoCbOsqjUl9K67PKp6dwhPOM7Yshw2NfuIJsNMVRS0wrpWk4GBBXau6CJD3zVMdGC+PWUxYEiRk9IcW2fuoSafXpJlYYkv0llk2IKkat9oY8qyU5QLdXR0dLqybt06jjnmGAoLC/nDH/7AsGHDaGpq4q233iIWi2GxWKiqquKoo46iuLiYxx9/nIKCAl588UUuuugiWlpauOGGG1L6vOWWWzj66KP55z//ic/n48Ybb+TUU09l8+bNGAwGFi1axLx583jmmWd4//33ycnJyViQG7QooOOPP57Gxkbuv/9+hg8fzrvvvsvPf/7ztLZLly5l5syZTJ48maeeeoqcnBz+85//8POf/5xQKJSWk/SrX/2K2bNn89JLL1FfX8/vf/97fvnLX/Lxxx8n2yRKC82ZM4frrruOnJwcKisrk8YgwIsvvsgFF1zA6aefzvPPP4/JZOLvf/87J510Eh988MFe11c988wz+fnPf84ll1zChg0buPnmmwFNVA5g5cqVzJgxg+nTp3P77bcDkJ2thco3NzczadIkRFHkjjvuYMiQIaxcuZJ77rmH2tpannvuuZRzPf744wwfPpyHHnqI7Oxshg0bximnnMLzzz/P3XffjSh++4f4ueeew2w2c/75WhhrwlFy5513UlJSQiAQYNGiRUybNo0lS5ZkDNfuSkVFBUBSdXx/0uecqXPPPZd//etfzJ6996sT+4uf/exnXH755Xz55ZfJJF9JknjxxReZPHlymtX/fVOaY8UgiESlOIqiIopgNYiE4gpmA8TiCl83+ClwWFJeoK7k5uZmjFHtquI2uXMV+eEPttIeiiF0TvzMBpGwotXNeebTmmSIWp7DjCmnmOE3LiIciaJKMVQpRpZRJhaLMX1oLr84tDTNgKtv89Lq9mEVFcoPHkfV5iiBiERMVjGIKjWuAE/8dwv9RhxMiUMzfhLHajLBYSLRCLIkdXvPLBYrB5XnMHGgVkuq3h3i8+1tuPzxfTKmRg4o4KwpB6Uo9SUmp3Ksu7pRe8Zqtaat7idynXa2eHo+WFWJRSPEoqmevAiwY8fwbj1Rr732GgsXLtzj2JZk2DZt2jSWLl2a9GSE4zKVjV7MRgMtn71C4/pPMJotFOY4aQkqRDEiGE0IRgui0YTNZmX1104equrPmPLCpKFWVFTEiSeemHa+Jm+YSDCIEosjGMwIBiNDi/dshAGsqHLhDcf5eEsrE8q/zY3bXcFw5tgSmrxhPtnWhicUp649REzWQgRbfBGuO3EEU4cXJUPaTAYtP23m2JJkbt2ujjBLt1h5/vOdKZ4oowjmThXA8lwrrf4oUUnGZjJw3KjiFKOya07XnooNd22XUJB0+aO4gzG8oTj1HSFeWFmLJGthewnjqydjpdkboSMUIxaXUVCJxNWkg9JkEBheksWcQ/on72Hi/do9pLXeHUr+lOfbkxLqjZ4wzf4oEwfkMmVEEaqqsnDtLna2h/lqZ0fKM9LR0dHpyrXXXovRaGTVqlUpIbGJiTJonpJYLMbSpUuTaRcnn3wyHo+Hu+++m//7v/9LKc0zevRoXnzxxeTvBoOBc845h9WrV3PEEUdw8MEHJ42nQw89lMLCwm7H9/zzz7N582befPNNTjvtNABOPPFEwuEwTz/9dErbyy+/nDFjxvDxxx8nPS8nnXQSbW1t3HLLLVxwwQUpc7qZM2fy+OOPJ393u93ccMMNNDc3U1JSQk1NDffddx/nn39+yvWccMIJyX+HQiGuvvpqTjnlFBYtWpTcfvLJJ3PIIYdwyy237HU5oEsuuYTf//73ABx//PFs376dZ599lmeeeQZBEDjiiCMQRZGioqK0UMG77rqLjo4ONm7cmFTzPu6447DZbFx//fX8/ve/TwmptFqtfPDBB5hMpuS2uXPnsmjRIpYsWZK8ZlmWefHFFzn11FOTZSlGjBiR4rSQZZmTTjqJ2tpaHn/88T0aU4ln9V3Q554nTpzIyy+/zIwZM5gzZw6lpaVpuUlz5szpU5+LFy8mGAzi92uegU2bNvHaa68B2otit9u55JJLeP7556murmbQIK1Y5sUXX8y8efM4++yzeeCBByguLubJJ5+kqqqq12EY3zUDC+z0z7NS7QogKxCJa1LfsgKCAKNKshFFoc8S8ZmkyrPtJoIxTSXOZBCwmYz4IjEicQkp+u3qtKKoDC12Ut9hQDRZyHeYaPRE0aoqwU6jg4rxh/VY+6feHeLjpioaOsIIMQlZVolKCkEEss+8h0uOG5ZSj6jrBDjPKvLLw8soyzKmGFvbGttZ1gBVW1wUOLSit03eMAML7MRkhbxjfoEgxYjHoiDHEeQYeRbo8AeR4jGUeBSrqFDm1Ay5roZgVlZWUv2uq1Lfro7wPoVSWq3WNBGIVr/m/fIF91xnrTsaA3Jywrs7+5JHl5BurneHiCsqx40o5vMd7ViMIrGoC8/OTQC0dXO8D2gBNr+Tun3ChAlpxlS1K8DCrxpo/exl3J+93LlVYL7JzL8tFnKcDhz29LBKm82GPy7Q4JcpLCqi34mXMqWLKl7CeM2PtfLJp2v5cpmFGEa8MRjcL49WVxS7zYLBZmdnQ4Stu/KpyP/WQ5TICUp85hJGhaqqfLnDjT8SJxCTGVOaRbUriD8qYex8rmFJQQ3H2dLs5+Tx38qX7/557PrcMhksmXLfEp+Rzc0+atqCiGgFlG2deWrD+2X1aKyU5FgpzjKzucmP2SgQjWsKhUVZZvIcFqYML2JxZTMNHWH659m489QxyWvvev46dygpS5/wVgFpXqtlVa3JOnGVTb6UZ6Sjo6OTIBQKsXz5ci655JK03MKufPzxxxx33HFp+esXXXQRixcvZuXKlcycOTO5PWH0JEgoOe/cubPP+UFLly4lKysrrc/zzjsvxZjavn07W7Zs4aGHHgK0BfwEJ598Mu+88w5VVVWMGjWqV+MsKSnho48+QpZlrrjiim7H9/nnn+N2u7nwwgtTzgmasfanP/2JYDCIw+Ho03V3N75IJEJra+se9Q/eeecdpk+fTllZWcq4Zs2axfXXX8/y5ctTjKnTTjstxZBKtC0pKeG5555LGlMffPABjY2NXHzxxSltn3rqKf7xj3+wadOmFEXu3qQdbd++fY9t9pY+G1MXXHABAA0NDRklJQVBQJbltO098Zvf/CbFlfnqq6/y6quvAlBTU0NFRQWyLCPLckoImcViYcmSJdxwww1ceeWVhEIhJk6cyOLFi5k6dWpfL22/U55vJ9dm4ptdHixGA3kOE63eGHFZy9lRVZVGT5ix/XP2akV3d8/FyH5ZBCKatHhprg1ZVvFGYknRg6is4gnFGVqsKXgtXLsLRdHksM0GgZisSTfXtgV58Yud3HnqmGTfXY0hkygQiEm0+qJEYnJS+jpBTFbZUO9J2bZ7PZuOuIHxu60StZtbCbfUpXh4fBGJVl+EY4cV8oXhl0TjMv6ohN0kEpNVynJsOIJRAlEZY2dy/a+nDum2sGym4sgffPABgUAgzROX+PfGujY+q2qkxe0nHI1Apyevf7aRgw8+OGWyvK6+g78vqyYUV4gLJoxWO0ixtC+/PRGWDd3mJu2L8WezpaoANvujjOiXBQJs9QX3ut+EkdaVVTXt7HQHUeNdPX8qcjyKHI8SCey5HEFzYSlT/9+1GdUWP3jrPb55fV5K+40Z+njvRu3/RqMR0WRBNJqxObO4aV1q6/J8OxPKc1nz5efs+vRtKp0OgrJIXpaD9rBKXDAQF0yERRMhm5UvjdvJai1PMQSLbTYGF4xIG8Pun9Xdn+uyqlbe/KaRne1BSrOtbGvxJ0UjJVkhLivMHFvSrbGSzBNzh7CZDcQkheJsC/6IhMNiYnCBg3X1HlbtcCMI0OQJpxVgTnxGC51mtrUEGNffTJ07lCLX3zXMsOs7VJptRVXpdgFAR0fnf5eOjg5kWe42xC5Be3s7paWladsTUUa758gnPBYJEnlXe7Pg2N7entFwKClJTcFoaWkBtDJB119/fca+2tpSlyP3NE6XywXQ4/1JnPess87qto3b7d4rY2pf7mNLSwtvv/12moGUYPd7ken5Go1G/t//+3888cQTeDwecnNzmT9/PqWlpZx00knJdn/5y1+47rrruOyyy/jjH/9IYWEhBoOB22+/nc2bN6f1+33SZ2MqUz2OfaU38YvdJQP369cvmRD3Q2D3XI5E0dUxZdms2dlBRJIR0KSLFaAtGMUV2PswswRdc01AM9Se+7QmzdBRVZX2QAyXP5ZcVf5sR7vmWZIlBDQFs6WbWxneL4tJg/PT6hl9sr0NXziO2SAgdZMftbHRlzKxymTE7E6izZqdHfgjcUwGkbGl2SzxRdjVEeawQXlYTQaWbmklrihk24zYLQZyFBOBqIzcWQPqw43N3RY9zehBKBrf472tdgXIWbGDpVtaaemsLyUA5x41iHNOG5vsd0iRk3X1HgRRwG42MOiCP1Oeb+f2U8dw9EF5RKNRwuEw/1lZzatfVhMIhnB5ApiRkaUI4VAEWYphVOLklg3qdjzTpk2joKAgafSFw2FcngAujx9kzSNnUCXkWDTN8Opq9Ewoz0EQhKShsmG+SHWPd6J7EkZaKprnR5Xje9kr5Gc70/KEEnLeH/SxX0mSQJKAIEZByVhHbebYEj5aVMPOle/tsb9HX4dHM2wPh8NpxuWyqlZeXvQOr/7lFrKdqSUHworITo+EJBjBYMJhtxGSRRSDGcFgxm63stFi47Ws82jyRpKfyQTBYJBg0w5OGKDgLrTiHmTh/U1t7PLJOCwGbGYDwZjEDleQiKQgonnFO4KpYbOJz1+dO0SWVRN+2V1VMpPqZdfQ2XX1nj7Vx9LR0fnpk5+fj8FgYNeuXT22KygooKmpKW17Y6OmPtxTmN6+UlBQwKpVq9K2Nzc3p/yeGMPNN9/cbRRWQgCjtyS8dbt27epWVTpx3ieeeKJbr9v3paLdlcLCQsaPH8+9996bcf/u6TbdRWHNnTuXP//5z8ncs7feeotrrrkGg8GQbPPiiy8ybdo0/va3v6Ucm4hqO5D02Zj6IXh8fqjsnstx6ZSDknLZ1a4grZ2J213Nj1BMYX29J22VeG/YPVwnHE8XConJCttaAyA0MzDfTnVbEFTId1oIxjShAatRxB+N8+qa+uTkqKsxVJptJctqpLLBi6pkNqYavRHmfbydv/x8YnJs3YVBdR3/zLElPPdZDUZRxBOKU90WTOZolGRbeb+ymYpCB5KiMLTYySdbXXSEvp1Umw3CHgvLdpeL1NN9vXTKQcRkhXfXN2I2iMiKSqEzXQ1y/IAccm1mXIEoBoNI/zxNhMBoNGI0GmkOqWzyGok7+2GwKYw/yMrw4iw+2tQMURkVreDv2P7ZKd6Yrtxxxx1p2zK9e4nQtVjs29w1o9GY1jaR33P3zdez4+dziEQiVNa5WFPdgiDH2d7UjhyPI8hxcswqI4usiEo8xXs3dOjQtDFNGpzPoAIH2/fJmHJ0+6z2JYdOFU3delHi0X1b3NhdJXRZVSt/eHsTO7+uod3VQrur5+O93fz+7pAjWd0Q5tCKPK47cURy7GvXrmXKlCkZ+xINRkSjKemRk0QTgkHLgXv8NRv/faSQ55/5B4MHD06T0fd7Pbz32ktsd0dZtNJAbpaDSmkggW25qQXDXUHcze0cNnoYVa4fb70pHR2d7wabzcbUqVN59dVXuffee7s1io477jgWLVpE4/9n777jm6reP4B/bnbTdKWDFuiyQAsCIlBAZsueioAILqiAAxQRVPYoS0ScgArKF1CGgoCDKaviDxlFZUOFQmmhLd0jbdNmnN8fJbFp0pK0SZO0z/v14qW99+bmuTe36X3uOec5qakGN+HffvstpFKpTUt7R0dHY8eOHfjll18Mur1t27bNYLvw8HA0b94cFy5cwPLly63y3v379wefz8eXX36JJ554wuQ23bp1g6enJ65evYo33njDKu9rCbFYbLKlaujQodi/fz/CwsLg5eVV4/23bNkSnTt3xsaNG6HRaFBaWoqYmBiDbTiOM/r7evHiRZw6dapWUxtZQ41HY+Xn5+P06dPIysrC4MGDa3US64vKXdnu5pagVwtfTOr5CPZfSkNKTjGKSjUo02hRMQdRaZnRU2JrxNHcT4Z7uSUwTKk48DmGgmIVWj5aXo3lVGI2OgZ74fDV+yh5MHeMUqXRl4/WHYducs82TTwQKJdiy+k7+PVCKvKL/+tKqMMA/HY1HXEJGYgKL5/bwdwkRiTgo0dzT5y7k4uuYd4Y9KCVKS4hA8k5xQiUuyAlpwRX7hVAUVZeKZHHlc8NJBHyEeLjavWB8GG+MkSGeOHwlXSUqrUQC3jwlomMtguUSzE6siku382HzEWIJx9rrE9wU3KKkZav1I9VupxWgGcjA3HmVg6Ky/7rGssDMKJ90xolfJWTVd2XT8UvoMsJGf+1Mt7IwtrjN/HkY43Ru3dv9O7dG8B/ydmFu3nIu19ecl3I4xDi44qZg1oalSivKqYp0c0g0MxESdEU+Ek5DG/jB18pz6grpanulUqlEgKZF+ISMoxKbgfKpZB7e8OzcSigUUHA1CgtVUJVVgqlGV0TeEKRyZv+QLkUQmZZl8yKxGKx0ZO3i3fzUahUw1vCUPPptwENJ4Raq0V6vtIg9uq6fWo1amg1aqDU+JxkA/jjJnAjLQehoaEADH9HL126j68+NJzYfG/lnVRQtmofWoQ0pSIUhBAjH3/8Mbp3747OnTtj1qxZaNasGe7fv49ffvkF69atg5ubGxYuXKgfg7NgwQLI5XJs3boV+/btw8qVKw2KT1jbSy+9hE8++QQvvfQSli1bhubNm2P//v04dOiQ0bbr1q3DoEGDMGDAAIwfPx5NmjRBTk4Orl27hr///ls/TMVcISEhmDNnDpYsWYKSkhKMHTsWHh4euHr1KrKyshAbGwuZTIbVq1dj3LhxyMnJwahRo+Dn54fMzExcuHABmZmZRi021tSmTRvExcXh119/RUBAANzc3BAeHo7Fixfj8OHD6Nq1K6ZOnYrw8HAolUokJSVh//79+Oqrrx7avVPn5ZdfxquvvorU1FR07drVqIVv6NChWLJkCRYuXIhevXohISEBixcvRmhoqFlDKXQPfW0xdqpGydSSJUuwYsUKlJSUgOM4xMfHw8vLC3369EG/fv0wa9Ysa8fpFKrqyhbmK8PgNgGIv52DjEIlKjfmCPkcmjey3pPcQLkUQh6Hu7lKuLsI9KWtAYDjGLSMh1K1FtfSCjC6YyDu5ZZXQhPwOYxo0wR+7hKc+DfToPRzxck97+WW6Of8SUgvhEKpwqV7BUaTm2q0DJfv5euTKXNjr1iqeVCl7nrp+UrcuK8AYwylag00Gi3UWkAkALwkInRv7oMpvZvb5Ml4gIcLmjdy048va+wpNVgfl5BRPs6rRI38EhU8XITYePI20vJLcCElXz/eTMjn9GOV/B+Mj9Gy/+YzkooFFhckAcxPVnXn+I8bWbiZUYg72UU4n5wHAAaJ76Sej2DL6Tu4X6CESq1FmYbBVSyw6Ga5vFpep2pbJKuiS+i2nkk2Gq8T5ivD1x8uwt3cmeVzZp1L0RdNmN6vBQI9REYJ2r+p2dh5+hZyCovgKRWDMYbtZ+8A4Ay6zslDW8K7ywioy0rBacqgUZdBCA20qlI0duPDTcCqHF9nqrtj26YecJMIcCuv5mPSAIATiPXzZVX8DGo7F11CVimM6zBavl+JRFLtuC5CSMP12GOP4ezZs1i4cCFmz56NwsJC+Pv7o3fv3hCJyh9MhoeH488//8ScOXMwZcoUlJSUoGXLlgZzI9mKVCrFsWPH8NZbb2HWrFngOA79+/fH999/j65duxpsGx0djbNnz2LZsmWYNm0acnNz4e3tjVatWhnMM2UJXbn41atX4/nnn4dAIEDz5s0N5ql74YUXEBQUhJUrV+LVV19FYWEh/Pz80K5dO5ufn88++wxTpkzBmDFjUFxcrJ9nKiAgAOfOncOSJUvw4Ycf4u7du3Bzc0NoaCgGDhxoUUPLmDFjMG3aNNy9excLFy40Wj937lwUFxdjw4YNWLlyJVq1aoWvvvoKe/bsMVnDoTJLx65bgmMWTgr0xRdfYOrUqZg8eTIGDRqEIUOG4Ny5c2jfvj0++eQT7N69G3/88Yet4rWZgoICeHh4ID8/X18/vyYSMxVV3jjGJWRg/YlbuJ1VBHeJAElZRfB1E8PPXYK3+rYw62m/uTF89FsC0vOVKNNocS+3GHnFav2cOX5uYvRt1QjpBaV4oUswUvOK8dmRGygp00DI5zD0scaICvczqty19UyyvtXthS7BaOrlgq9P3ELC/UIkZxdDIuSQllcKLR5MXiriY/7QlhjbqerxP5acQ93585WJ8FdyHu7nl0CjBbQob5mSCPmQu4qwdHhrixI4S+Iy1ZVOt27xr1dwNbUQLkJeeTIlFaKkTAt/DzHcJEJ9Fbk+Lf0M5iBaf+IWbqQXIKtIBR4H+MjERl25KsbwsAlczT2WtcdvIu56BsJ8XZGUXYJxXYMxqE2Awf4TMxX46FACbmcXQSYSYESHJgjwcLH55KyJmQrsu5iG07ey9efthS7BJn9Htp9NxppjNyDi81Cm0eLNPs0xJjLIYF+6YwLKK+ml5hXry3oL+RweC/TEjP7hSMkpxmdHbyAtrwT5JeXdE5VqLaRCHmRiId7u38Jg35VpNBqDPt46cQkZ+L/zCZAUpaOFj9hwyoHMPBy5dBe37+dCqVTqpyqApuxBsRMVmKYMwc8vQ2NPKVo0csOMAf9dG7t27ap2UPLDfHngHF4b2MFo+YkTJyzq1v3yhlN4qXtYjb7HrPX9Wx/Ruan/ajPBrqY4H3dXP2+wrOmbW8GX2qYVpzaT9hLibMz9/rW4ZWrNmjWYPn06Vq5caVS1r3nz5rhx44bl0dYjVbUO6Fp2AMBFyIdUJICvmwRuLgL4u0us2jWm4gSif9zIQolKC4byBIex8glIEzOL4OkiRGpeCRLSFShVa6FlDNlFauw8dxcZhaUGN/OmWt0qDkDffykN19MKwOMBTAu4inkI9JIatd6Yo7oWFiGfQ6aiDC5CHrQAGAfwWPlxgWmRVViKE/9m6su3W/Omv7pxX7pz7ucmQkpuCbSMIaeoDHKpEBotg5DPVTkHkaeLEByPB4mQB7VWC6mYj7Q8pVE3tOqSuZocy5OPNcb55DwkZZfATSKAt0xkcv8zBoTrx9EcvJyOYw9K15vz/jVJ/iqX6D53J1d/rZoe51RehZLjYNQ6auqcNfVywf/+7xb+TS+Chmnh4SLUd50LlEsR4C7BvdxiaLRa6HpfFpdp4SLSwt/duGphRaYSKaC8ha66BP/Rs8n4/Mi/uF9Qqu+Wy+fKj0mrBWQSAYR8DkHeUuSVqAyujZEjR+LYtXR8dzIRYV5CnLuVgXyFAuk5heBp1GjhK8aItn6QSzhsOpGA36/eg1qlAlOXwksE9HzU9MMOd3d3DBw40GT3y4r/r1KpwPH4EIuF1MWPEEJIg2NxMnXr1i2DUoUVubm5IS8vr7Yx1UsV57g5dycXEf5uSMxUILOwvJpfSk6x1W76KyY+7i4C+LiKkZpXPnaKoXyuq8xCJYR8DseuZ6BUpYFGy1BYWn7nqFRpcOluvsnKXabmxykvdADcSC+vqMJQfvPJ53NWu7mq2IVOUapCck4xNA/uOnU30BotypM5wGpJR2W6yob7LqahbVMP/Q2yrjR1QpkGUhEfQj4PWYpS5CvVEPD5GNjaH409pSbnIOrRwhdJ2UVgWi0yi7S4nVUMsUCJCym5Bk/5TY3Jq8lxVUxwFgxrhcv38tG6SflTzGPXM432r3uPfRfTkJxTrG8petj71zT5M/W7kl6grLJaXKdQb7RrmoW0AiUi3CUGhTtMnTPGGFQaBg8XAdLylSjiqSGTCJCaV35t66pi/nI+FX8n54IPQKVhkApr1v3SHJ1C5WjqJUV2URm0WgbNg4ceuou7SFk+19Vfd3Lh7SpG5Q4FQd6u8POUIUlRhgB/f7iXqSHxKoWrWIAp0c30E+8WJ8rgghb614X5uUJsoqw9AP00E9VJzFTgwwNXcS+7wDiTJYQQQhoA3sM3MeTh4aGvd19ZUlIS/Pys372qPqg8Fqh5IzeoNAwcgKSs8rlcEjMVVnkvXeLzQpdgxHQLRdumHpCK/3tirihVIymrvOpWS383qLUMsgrrNQzIKFQiNa/YaL+9qpiYM8BDghJV+fgloLylqMxENcGaSMxUYOPJ27iaWogCpQopuSVGJd/FAg4iAQ+PNnFHi0Zu+hvo7KIy3M2t+SS3lekqs208eRuzdl18MObmv3Peu6UfHvGV4fEgT0iEfLTyd4e/hwSNPaVVnrtOoXL4uolRVKYFh/IWRJVai70XUg2uCXPKyz9MxXFIX5+4hUC5FG/0bv5gbJPp/etec/pWtr6lyJz3r5jIWPI5VPW7UtV+dK1n0/q2wIwB5QNW4xIykJipMHlMuvnflGotXMUC+LmJwQE4dj0DX58oLxExJjIIr/R8BD6uYqgZIBDwEOpr3cImiZkKfZxhvjJM6d0Mwd5SXUV5AxqUFxNp6e8Gfw+JUVKnu/76tPSDn7sEhUo1mvnJIOTz9NumPHgAIeZz4FBe+VLA59Xq9yMlpxga8BD9aCBUD6poEkIIIQ2JxS1Tffr0wcqVK/HUU0/p51LhOA5qtRpffvllla1WDUVV3Zoqt+wAwMHLacgoLIOvmwgqTfXlvC1VsUUhLV+JhPuFYAVKFJVqyosdMAZlmQbX0gsh5HNo5O6CEpUWucUqiPmAkM/DuaRcdAr1NhgXVF2XLW9XEYpKVVBrASEfEPA5qxxTxS50d3OV4Ew8AW/sIYFEJMCoDoGIDCkvR1+bpKMqF+/mI7e4DHwOyCkqw8b/u42KBQwGtwnAvdwSJOcUw0sqAsfjECSXVhtDmK8MMd1CkV1UiuuphVCz8u5d2UVlBhP3mlNe/mGqa92qav+mWoqaN3J76HvVNPkz9bvysM+z8rQAyTnFEPI5xHQL1XdFrdiY4+cuho9MhA7BXricWoBCpRrdm3kanJOocD8sfbo1TvybCS9XUZVzl9WEqVa7qHA//Hw+FbezisDngIrPC/gcIBbykJJbgjA/WZVdHs8n5yEhvRA3MwqRmKGAkM/h+PX7+iQy1NsVaXkl0JSqIRXVvvKlNRJ8QkjNcUIJ5P1eM1pGCKk7FrdMLV68GHfu3EGrVq0wY8YMcByHNWvWoFOnTrh58ybmz59vizidQuWn/pVbmiq27OhuoEN8XMBQPm7GVjcinULlCG/kBrHA8OMuVWvQtqkHYrqFItzfDV5SEUR8DhxXPlfT1bQC/XE87NgC5VKE+7vBVSwAjytPxnzcxFY5Jl0XOg+pCIHeEjSVu0Aq4hk8wE/JLZ/Dy99dYtAyZ80ufuXHzMDncQ+KEzDcy1Piu1NJ+nOie+9Xe4VhwbBWeLVXmFkxRIX74Z3+EfBzF4PPPWg14Bn/elbXOmiO6m5+dcly5USt4mTKKo0Gl+7lY+e5FHz0W0K1ram1+Rwq/66Yu5+zt3Nw8W4eMgqUuJpaiI0nbyMlpxjnk/Nw7HoGPjqUgI9+S8C/9xVQqrRIzCxCgLsE/h4Sk+ckKtwPC4Y9ijetXCGyYlKbnFOM/ZfSyieHdhGAsf8SKSEPEAvK5yt7sm1juLsIUVKm0beiVTz/un0Gyl3AcRz4PA4FSjV+vZCGj35LQEpOMSIC3ODrJoaPTIymXlKM7hhYq+Oy1e8aIcQ8PKEYbu2HGvzjCY3nQHRkKpUKsbGxCAkJgVgsRkREBFavXm3RPv7v//5PP02Pi4sLmjdvjiVLlhht9/fff6Nv376QyWTw9PTEiBEjcOuWeZNWLF++HD/99JNFcdVWTeMtKCjAsmXLEBUVBX9/f8hkMrRp0wYffPCBUaXWpKQkcBxn8t/333//0PdKSUnB4MGD4e7ujpYtW+Lnn3822mbnzp3w9vZGZmbVEy1evHgRHMfhn3/+eeh7OhqLW6aaNWuGkydPYvr06fjiiy/AGMO3336L6OhobN26FUFBVVe6qu8sHdMSKJfC100CjVZpsmuPtei6QcUn5WDzn0m4eV8BT6kAGm352wbKpWjsKYGrmA8vVxHyS1RgAMrUGiTnFOvHmVR3bGG+MoyODERybhHuZBWDz+OsdkgVC12c+DcTecUqiIU85BapcL9ACa22vJqfWPBflyZzy4Sbq2JLgr+7GMWlaihVWqi1GiiUKv15AsqvAx1dYmJuIYYQHxmkIgEyFKUI8pZWOXFvTVXV+qQbk6bSMATJpUZlyHWTKafklCKjQAlPqRD3C5QGLWdVvZ81Pgdz9pOYqcAf/2YivUCJ4lINmnhJoNIwXLqX/9+8WjezwAHo3szHYB4zALVq8bNUxQQ1PV+JU4nZuJdbgsaeLvCUCqFSMxSr1PCUihHi4wJfNwnSC0vB5zikFygR4CHRd3msXCQmOacYIgEPitLycVYSIQ9JWUXYePI2MgpLcS+3BG4uQqTml+CSmVMXVHf9Wvt3jRDSsEyePBnfffcdlixZgsjISBw6dAhvvfUWCgsLMWfOnIe+ftu2bXjxxRcxevRofPvtt5DJZEhMTERqaqrBdtevX0dUVBTatWuHHTt2QKlUYsGCBejRowfOnz8PX9/qK5EuX74co0aNwvDhw2tzuGarTbzJycn49NNP8eKLL2L69OmQyWT4448/sGjRIhw+fBiHDx826i7+5ptv4rnnnjNY1rx584fGOW7cOJSWluLHH39EXFwcRo8ejatXryIsLAxA+by0b731FlatWlVtzLt27UJoaCgef/zxh76no6nRPFOtWrXCwYMHUVpaiuzsbP2TgIau8lN/xpjJCUd1Klbdq01BAXPo9rvlVBI0WoYshQouIj60jOknZ72TXd41qlSlhYjP4V6uEnLX/1qXzOnOI+Tz4esmBmNAoVJttWPSFX5QaRjCfFxxOU2DVo1dyidBVpd3XZTbsJvR2ds5+Pd+IR5t7I67ucXgKkwUnFWkQqgvB8aYvox4oVKNoActagNb++vn6KquEIOuBQ4AvFyF6NHcOqXyK6t885uYqcCqQwm4lVUEb1chAJj83HTzZ5VqGPKKVfCQGk9abGvV3dSn5BRDpWXoHuaDk4lZEAv4CJJL0aaJB+7lluBaeiEC3CUAB5PzmNVlQqBLavdfStNPmn0tvRDtgjzROdQbt7OLkFNUBlexAGAcejT3QZaiDBfv5iGrsBSpuSV4pNIYrooPHX78KwU3H7S+qR/MD6bSMAS4S3AzQ4HcolIwxuHQlfSHdl+0ZhVJQgip6MqVK9iwYQOWLVuGd999FwAQFRWF7OxsLF26FK+99hrk8qofKt67dw+vvPIKXn31VXzxxRf65dHR0UbbLliwAGKxGHv37tWXue7QoQOaN2+OVatW4YMPPrDy0dVObeINDQ1FUlISXF1d9ct69+4NV1dXvPvuuzh58iS6d+9u8JqgoCB06dLFohiLi4sRFxeHkydP4oknnkD//v3x448/4vDhw/pkaubMmQgPD0dMTEy1+/rxxx8xcuRIi97fFJVKBY7jIBDUKMWpEYu7+VUkFovRuHFjSqQeqNjlRXcDXVW3OKDuxxucvZ2N1HwlBAJAwCtPjjiOQ3ZRGQLcJdBoGcrUmvKiXBwgFvLR04KuVrqy0kq1FmUaLfw9rFvyXTcZ8dGEDGQUKFGq1qJ5I1d4y8Ro1kiGKdHNbHKTV7HF49CVdOSXqKDVMmjx3xxXA1v7Iy2/BOfv5iG3qAzp+SVIyy9BQnqhQctIdYUYdOf4iTBvABxO38qu8tqxpn0XU3EjoxClKg3u5iqRmldiVC0OAFJyilCgLJ/0Ts0Y/NzFVm85q445XU29XUUoUWvROdQbL3UN0Y9F0l27MwaEY0b/8Bp1S6tYMMIadGPsguRSg7L5MwaEo8sj3hDyeSgpUyMxU4Hdf9/FqcRs5BaV6iv9JWUV4eztbKN9+rtL4CUVY1jbxgj1dcWQtgGYEt0MQXKpvuiGlgFCAYeMglLEJ+VUG2dNC4kQQsjD/PTTT2CMGd1ox8TEoKSkBAcPHqz29d988w2Kioowc+bMardTq9XYu3cvRo4caTBfUHBwMKKjo7Fnz55qX89xHIqKirB582Z9F7ioqCj9+suXL+Opp56Cl5cXJBIJ2rVrh82bN1e7T1vG6+rqapBI6XTq1AlAedc8aygrKwNjzOC9ZDKZvivhn3/+iW+//Rbr1q2rdj/Xr1/H1atXMXLkSDDG0Lx5c5M1GBQKBTw8PDBlyhQAQFxcHDiOw3fffYcZM2agSZMmEIvFuHnzplWOz1w1StuSkpKwY8cO3LlzByUlhn9YOY7Dhg0brBKcM9IlHnEJGQ/t8meNggIPYzhhKQcBj4OIz4caDP4eEv1T++ScYsjEfOQVl09UWqpmEPO18JH91/rwsO48uu6E+y+lIaeorFZje6raf48WvkgvUOLRxu64fK8ARaVqiAQ8uAhNz+9jDboWj45BXjhxIxMlZRqotAyM6ca08MFxHBgr7zZZpimvangnqxipgpIH80cxnLuT+9BiFABwLikHSVnF8HUrP/e2bLEEgJwiFThwEAuAEhVDqUqLg5fTjVp/PFxEKCrTQK3RQiIUYMCj/nXaQvGwbrSmClfoulxWvnYtjdtWrTOmvgMSMxVIzFAgo0AJtZZBIuDhxn0FVOr/5r0CyhOq49czjCbF1iWViVlF8HMTo2cLX321xru5JTh+/T5+vZAGidC8Z2lUZIIQYiuXL1+Gr68v/P39DZa3bdtWv746J06cgFwux/Xr1/HUU0/h8uXLkMvlGDFiBFauXKlPRBITE1FSUqLfb+X3Onz4MJRKpb6wWmWnTp1C7969ER0dra8NoNt3QkICunbtCj8/P3z++efw9vbGli1bMH78eNy/fx/vvfeeZSfFCvFW5dixYwCARx991GjdihUrMGfOHAgEArRv3x7vvfcennzyyWr35+npiYiICHz00Uf49NNP8fvvv+PChQvo2rUrVCoVXnnlFcyePRstWrSodj+7du1CkyZN0LlzZ3AchzfffBPTpk3DjRs3DLoafvvttygoKNAnUzqzZ8/GE088ga+++go8Hq/OK4tbnEzt27cPI0aMgEajgZ+fH8Riw4GOtpqHxdmYewNiy/EGlW8AB7b2R8cQORLSC8HncRjRvonBTdZP/9zDoStp0GjKb9pK1eU31RUr+pnjXm4JsovKTN6Q11an0PJKfYmZRUjLL0FJmQYcB+QWlWHjydtWfz/gv88y4X75eZNJhOArVShUquEhFUH0oLBHp1A5Hgv0xD/JuRDwyotwlKi0iE/KgYeLEI88GHtUXXwpOcUoKFFDwAeSc4rhKubb/OY1KtwXR67dR05RKcQCDpGhXibH44T7u6GwVI0ytRaBche0eTA3VV0x53eq4sOMqsaA1WQiYWvN8WVK5e+AlJxiFJaq4eUqQm5RaXnirtaAoXwcoq7NkAOQW6wyqupXcYybSsMMfg/DfGVo6uWCjMJSpOcr4e8heWjrojkPfWpyTgkhJDs722Q3PldXV4hEImRnZ5t41X/u3buH4uJiPPPMM5g9ezY+/fRTxMfHY+HChbh8+TL++OOP8h44D/Zj6r3kcjkYY8jNzUVAQIDJ9+nSpQt4PB58fX2NusItWrQIZWVlOH78OAIDAwEAgwcPRl5eHmJjY/Hqq6/Cw8Oyv5e1jdeUixcvYuXKlXj66acNkjSxWIxJkyahX79+CAgIQHJyMlavXo2nnnoKX3/9NSZOnFjtfjds2ICRI0dCLpeDx+Nh3rx56NSpE5YuXQrG2ENbDYHyLn4jRozQ5xAxMTGYN28e1q5di08//VS/3dq1axEdHY1WrVoZvD4sLAw7d+40+1xYm8XJ1Ny5c9GtWzd8//33NKdUNeqi1elhKt8AchyH0R0D9TdZF1Ly0SlUob/JYozhz5tZuF9YCgDQMobMwlKLbhxtedMJ/Hdet5y+g/QCJTgOyCoshadUiIIS643RMvWeurEotzOLwOdx8HETQ+4qQqi3KyJDykujz+hf3jK3+c8k5BWXgccBfK68RVCjZUYPGyrfhKbllyAlpwj5JWrwOMCmlUkeiAr3w9Lh5SXAEzMVKFFpjZKViq2OJ/7NhJDPs0myXB1zf6cqzktWuXWvpi1Mddk6o+suey+3GDyOg5DPg5YxlKi0+kSKxwF+bmKoNQz7L6UZjHtKzFTg4t18qDTM5ATLuuvUku+m6h760JgqQuxHU5yP1G9eN1jWeOKX4Evr9mHXw6jVaoOf+Xy+/u9hdQ/hH/aAXqvVQqlUYuHChZg1axaA8jFXIpEI06ZNw9GjR9G3b1+z9lfTxoBjx46hT58++kRKZ/z48Thw4ABOnTqFgQMHVhm/VvvfnJwcx4HP5xv8bI14k5KSMHToUAQGBuKbb74xWBcQEID169cbLHvmmWfQuXNnzJo1C+PHj692/FHXrl2RnJyMW7duwd/fHx4eHrhx4waWL1+O3377DQKBAAsXLsTGjRtRVlaGUaNGYdWqVfpWtVu3buH8+fMGSZObmxtiYmKwadMmLFu2DK6urjh27BiuXr1qskqjNcZa1YbFY6Zu3LiBmTNnUiJlhtqWsa6NxEwF0vJLIORzRjeAIgEfHYO9jMY/RIX7YVAbf+h6zKm1QEZhqcnxM1Wpq5vO+/lKKFUaFJdpwONxKNOw8iqEFsRqiTBfGSJD5PBzk8BTKoKbWIiuYd4Y1zUEMwaEG9yovtm7OV56IhiN3CWQivjg88qn4q08hqzyGKC4hAwcvJwOpVoLAY+Dp1QITR1NhKorAb5g2KNVjicK85WhTRMPeEpFCPNxRcL9woeOt7E2c36nKs5LlllYBiGfM+j2V5PxP3VRAlw3JgsAZgwIx5C2jfGIrwzDH2+CRh4uEAt44D/4xhY8KM+fkluMU4nZRlMYPGyCZWt+N9GYKkLsS1tSYPDP0SQlJUEoFBr8+/333wEA3t7eJlufioqKUFZWVm3xCd3rARiNrxk0aBCA8tLiFbcz9V45OTngOA6enp6WHdgD2dnZJluIGjduXOV76rz88ssG56VPnz5Wj/fOnTuIjo6GQCDA0aNHH3pOAUAoFOLZZ59FdnY2bty4Ydb24eHh+ha41157DS+++CK6d++OjRs3YuPGjTh69Cj++ecf/PHHH3j//ff1r/3xxx/h5+dnVBDjzTffRGFhIbZu3QoAWLNmDZo2bYqnnnrK6P0taaGzBYtbpoKDg6FQ2HZAPKmdik+KhTwOfVr66VtOAEDI4/DHzSwEuBsXiJC7isEDB+7BM3Ahj2fR0w9dF6NL9/LRpolHrW7Wquo6pBvD1CfcD3/eKv+iad3YHZmKMpt2M03JKUZesQocgLQCJfKuZ+Jubol+bquKcafmKeHnJoGriI/WTTzQ6RFvg89At7+KrXiX7pW3Jvi7S5CSWwK1FlYv4vEwD+t2GiiXolStwf7LaRDweDjxb6bRcdmbripiMgAfNzFiuoUalQ+vSbJfl11ydYlbSZkG6QWliGjkBo1Wi5TsEnAAVBoGjgMYA8J8XZFeUGowhYFugmVd6Xdbfj40pooQUp3GjRsjPj7eYFl4eDgAoE2bNvj++++Rnp5uMG7q0qVLAIDWrVtXu++2bdvi9OnTRst1D1Z5D+ZrDAsLg4uLi36/FV26dAnNmjWzePyRjre3N9LS0oyW60qz+/j4VPnaRYsW4Y033tD/7ObmZtV479y5g6ioqPLq0nFxaNq06UNfo1P5HJpr06ZNuHr1Knbt2gUAOHDgAJ555hn92KcJEybgu+++Q2xsLIDy8VLDhw83aJEDyqdiGjRoENauXYtBgwbhl19+QWxsrNF2gP2HGFncMjVnzhysWrUKxcXFD9+4AbN25S9LVLxJV2kZAjwqdeXhHnQeq+LaE/C48hs1APklZbiQkmv2eydmKnDwcjou3s3HwcvpNT7+6iq36W7e0gtL0cTTBXJXETIVZWYVd6iNQLkUQj6HzMIy8Lnyebj+vV8+MWzlyVOTc4qRrSjF3Vwl/s0oNJlwVL4JbdPEA0FyKdwkQgTLXfBkuwDM6B9eJ4lK5eu1uus3p6gMJSotRAIOecUqh2uJ0LUilU+c/KjBPEp10cJUE6ZadyrG2qOFDziG8pL8vPJufhGN3CAW8nAltUCfxFS8piqXfrcVRz2nhBDHIBKJ0LFjR4N/uqThqaeeAsdxRpXvNm3aBBcXlyq7x+nouncdOHDAYPn+/fsBQD++SSAQYNiwYdi9ezcKCwv12yUnJ+P48eMYMWLEQ49DLBYbFV0DgD59+uDYsWNG81p9++23kEql1ZYbDwkJMTgvuiTTGvEmJycjKioKGo0Gx44dQ3Bw8ENfo6NSqfDDDz/Ax8cHzZo1M/t1WVlZeOedd/DZZ5/pW84YYygqKtJvo1Ao9IlaSkoK4uPjq+ym99Zbb+HixYsYN24c+Hw+Jk2aZHYsdcnilqmzZ88iIyMDzZo1Q3R0tL4pUofjOHz22WdWC9AZ2XsMQXVPinWtK029XJClMCwykJipwPW0AggEPEClBY8DSjUM284ko21TT7Mm97TWmKnq9lNxjp4T/2ZCrWGQiriHFneorTBfGWK6hWJt2Q1cSytEqVoLb0n5/D2VizUUKMtwN6+8FeHm/SLsv5SGN3s3N2ptMzUGKC2/BC4iPkrKtNVEY6g2BQBMFSqpal6ss7dzkJZXAq1Wi/R8JVyEApt1rayN6lqRbNnCVFNV/c7q4lx16DpSC5RgAJgWEPI58Pk8tGvqiZ7hvgbJuj3GajriOSWEOL5HH30UEyZMwMKFC8Hn8xEZGYnffvsN69evx9KlSw26pC1evBiLFy/G0aNH0atXLwBA//79MWzYMCxevBharRZdunTBuXPnEBsbi6FDhxp0HYuNjUVkZCSGDh2KWbNm6SfB9fHxwYwZMx4aa5s2bRAXF4dff/0VAQEBcHNzQ3h4OBYuXIi9e/ciOjoaCxYsgFwux9atW7Fv3z6sXLnS4uITNYlXIBCgV69eOHr0KAAgIyMD0dHRSEtLw4YNG5CRkYGMjAz99k2bNtW3Uk2fPh0qlQrdunWDv78/UlJSsHr1apw/fx4bN2402RJUlenTp6Nz584YPXq0ftmAAQMwY8YMPPHEE5DJZPj888/1RS127doFT09Pk/OCAUC/fv3QqlUrHD9+HC+88ILDDjGyOJlas2aN/v+3b99utJ6SKdsXYXiYhw3UT89X4sZ9BdwkhjfCuu5zvZr74vC1+yhTa+Em5kNRqsaJfzPNSqas1eXHnP1ULCGu0vDrpJlXdw6W77uKe3lKFJdp4SkVGhVraO7njoR0BcR8DmptebXBqpLsyp+PSMDHY03Nv3Zqm7yb6m5Y1fWbWViKkjINNIxBowUKS1XYePK2wblxBFUll45ada6639l9F9NwI0MBlZqBAeBz5eX4fWQi9GjhA393idG+HOnYCCGkOl988QWaNGmC1atXIz09HSEhIfjss8/w5ptvGmyn1Wqh0WiMHuD98MMPiI2Nxfr16xEbG4vGjRvj7bffxsKFCw22i4iIQFxcHGbOnIlRo0ZBIBCgd+/eWLVqFXx9fR8a52effYYpU6ZgzJgxKC4uRq9evRAXF4fw8HD8+eefmDNnDqZMmYKSkhK0bNkSGzduxPjx42t8XiyJV6PRQKP5b+6Mq1ev4tatWwCAF154wWjfCxcuxKJFiwCUd6Vct24dtm3bhoKCAri5uaFTp044dOgQ+vfvb3a8R48exe7du3HlyhWD5RMmTMCtW7cwa9YslJWVYeTIkZg7dy6A8mTqySefhFAorHK/o0ePNuoO6Wg45oiPle2goKAAHh4eyM/PN5ggrSZMPelPyy8BwKFTqH3Hl8QlZGD9iVvwlZV3jXu1Vxh6tfA1iju3qBQ3M4tQXKqGgM9DZIgXFgx71KzYEzMVVnkyXt1+dMeRX1yGjMIytGrsZnZ8tbX9bDLWHCsfkKlUaTC+Wyje7N3cYJu4hAzM2nURBUo13CUCrBhZXoZ065lkfZLyQpdg/bnXqUliFJeQ8dD9VseSlqnPj97A1ycSyyd41mjh6yaGWoM6Pf8PU9U5tHeLcU0t+uUKtp9NBsBQqmaQCnlgAHxkYqg0WshdRfB9MDbMkRJaS1jz+7e+oXNT/4XM2lfj12qK83F39fMGy5q+udVm1fySVgyxyX5Jw5Oeno4mTZrgp59+wrBhw6rcrmPHjuA4zmjcXV0w9/u3RpP2kupVfMrMGMOO+BScv5sHDsBjgZ51Ng7GFN3g/Owi4zFGFUuAMwacvZ2N07dy0NJfBj6fb3YLm7WejJvaj65lAUCVRQZsLbOwFIpSNYR8Hvg8HnzdxEbbBMqlaNHIDbeziuDhItQvM2eeJEu7adW2NdDUe+rmHqsYQ2KmAueSclCq1kCtLW8hKS7ToImni1FXR3uq2NJ27k6uvnS4vVuMzVW59Swq3BdHrt4vL34i0MJVLICiVI1sRSlK1VoUKlVIz1fabJ41QgghxNr8/f0NWtMqKigowOXLl7F371789ddf2LNnTx1HZ5kaJ1OHDh1CXFwcsrKyMH/+fAQFBSE+Ph4hISFmNZfWd7pEIC4hA2kFSkgEPDBW3sXOnjdx5tysn0/OQ3JOMZKzi6HRMiTcV+CxQE+7V+ky1YLCcVydjg3RJRRKlQbFpRrIZSKjblZA+Q29WsvgLhHgfkEpNp68jQXDHjUrUbI0Ga1JAvaw9zQVQ0pOMTIVpRDw+eBQXr690YNS8bYu/mEJXXJ57k4u0vOVOJWYjXu5JRjY2t/hq86Zaj2LCvfD0qdb4/K9fGgZw66/7iJTUaZ/TUmZFlIR32bzrBFCCCF16e+//9bXZVi4cCGGDx9u75CqZXEyVVxcjKeeegpHjx7Vj1F5/fXXERQUhFWrViEwMBCrVq2yeqDOSjcB5/0CJTjUfalrU6q7Wdc9vfeRiXAtrQCeEgEKyzRoVKn8tz2YmoTYku5s1oqhsFQNX5kYKg2DTCzApXv5Ri0Cusp/GYVlD8Z0lbfcmDu3j6Vje+pinEygXAqZSACNlkHI5yAW6sq+yx2qPHrFAiWnErP1E9dyHGf3ibQfpqrWs6hwPwTKpfjuVBKyKiRSQHl1P1vPs0YIIYTUFV05d2dhcWn0uXPn4ty5c9i1axfy8/MNDrZ///44cuSIVQN0dmG+MswYEI6pfZpjTKcg9Gju2K12uqf6KTklUJapkZRTgpyiMhy4lKafUNTWqirL7Qjz2eiSYy0ABoZStdZg0lQdXeW/EB8XMACeLkKz462uLLw9hfnKMKV3M7Rt6gGZRAilSov4pBycSMi0d2hGwnxlGNwmAEFyqcH1Ys3Jaq2h8rVe1TWuuybO3M6BSqMB/0GtFamQhwBPF7T0d4OnVGj3uTYIIYSQhsbilqmdO3diyZIlePrpp436OgYFBSE5OdlqwdUXuhu3r0/cwsW7+biQkme3we8Pa/Go+FQ/JbcYqmIVRHxAUarGrxdSbT4mo7oiAdbozlZbuuQ4PikHCemFSEgv1Ld86FoRKo7r8nWTQKNVVjmnlymOPLYnKtwPafkleH//dRSXaVCqVqK4TI34pByHiVHHEa6X6lR1rZuKWXdNdAzyQraiFHyOg1jAR7NGMlxPL0RiZrFRdU5CCCGE2J7FLVOZmZl49NFHTe+MxzM5oRkxPSlnXTO3xSPMV4Y2TTzA5zhoGUOxiqGkTIPf/83E4l+v2LSlRDfhrYuQh+ScYqPz5AgtC2G+MoyJDMILXYKNWj4qnuONJ28jr1iF7s189N38zOEILXDVySwsg5aVd/XTVfVzVBWvF3tOpG1KVd8Jpq7xihNVdwyWY2CbAMgkAiSkF6KkTI0wXynEQh7SC5T2Ohyndf78eQwZMgRBQUFwcXGBXC7HE088gS1bthht+/fff6Nv376QyWTw9PTEiBEj9OWHK1u9ejUiIiIgFosRGhqK2NhYqFQqWx8OIYSQOmZxy1STJk1w6dIlkxNsXbx4EaGhoVYJrL5xhBtkc1o8dK0qaflKeMtEUKo1yC9RQ8OALEUZTidm6yegtZWq5sFyNKZaEeISMgwqyQn5nMWfeV21qNR0zqW2TT3g6SJCRqESIj6HCH93RIbIH/5CO3LEsuiWfCdUrhC66lACEu4Xgs8BWgZcupsPHo/DltNJYAx2n4LBmeTl5SEwMBBjx45FkyZNUFRUhK1bt+LFF19EUlIS5s2bBwC4fv06oqKi0K5dO+zYsUM/gWaPHj1w/vx5g8JLy5Ytw/z58zFr1iz0798f8fHxmDdvHu7du4f169fb61AJIYTYgMXJ1IgRI7Bs2TL06NEDbduWz53DcRzu3LmDTz75BDExMVYPsj6wd5ejxEwF0vJLqr25N55nSoEytWEyU6ZhSMossmms/h4StGlSPg+Wo48BqVz4oeINcpBcWuOKg7YuKFGb5EJXXe7Ev5nwchVhcJsAh79xd8Suk+Z+J1RMenu18MX2s3eQlF0MjYZBwwF8HqBUM/B5HK6lFuKbP27ZtSuxs4mKikJUVJTBsqFDh+L27dtYv369PplasGABxGIx9u7dq59vpEOHDmjevDlWrVqFDz74AACQnZ2NpUuXYtKkSVi+fLn+PVQqFebNm4dp06ahVatWdXeAhBBCbMriZGrhwoU4evQoOnXqhNatW4PjOMTExCAxMRHh4eGYNWuWLeKsF+qi4popFW+chTwOfVr6may+VvGGc98lBUw1CvF5QIivq81irW4eLGdg76TZXLVNLgLlUvRs4etU8xqVqTU4dyfXoa6rh30nmEp6E9ILUapSQwsADHAV8FGm1oLP46DWMsjEfH23QWf5bByRj48PMjLKi+6o1Wrs3bsXL730ksHEjcHBwYiOjsaePXv0ydTBgwehVCqNHizGxMRg7ty5+OmnnyiZIlbDCcTw6DbWaBkhpO5YnEy5ubnhzz//xGeffYZ9+/YhLCwMUqkUs2fPxrRp0+Di4hg3KfaSmKnA2dvZADiH6WpT+cY5wMPFoFCC7oa4YqtKE08XZCtKkVei1u+HAxAR4I7BbQJsFqszJCMVC0wAMEoo7JU0W6I23U5Nzfel26cjHndipgIHL6dDpSkf5zWwtb9DxgkYd72s/Lsbn5SDK6n5AMoHvHI8wE0iQEmZFqVqDSRCPvg8nkOOtXN0Wq0WWq0Wubm52LlzJw4dOoQ1a9YAABITE1FSUqLvjVFR27ZtcfjwYSiVSkgkEly+fBkA0KZNG4PtAgIC4OPjo19fldLSUpSWlup/LigoqO2hkXqMJ5LAs/vz9g6DkAatRpP2uri4YNasWdQKVUlipgIfHUrA+bt54AA8FuiJGf3D7X7jZurG2ZxKYvsupuGbP26hqLQ8oZIIeRjwqO1vRB05GdGdt+ScYqTnK+HvIUGQXOp0Xapqk7TqioT4yET4vxtZOHw1HVKxAG2aeDjE9V6ZvhJehfmmajpezJbiEjKw8eRtqDRMf01V/t1lDBDy+fBxEyOzsAxeLgKotYBcJoJUxEe/Vo3wWKCXwz6IcGSTJ0/GunXrAAAikQiff/45Xn31VQDlXfcAQC43Hhsol8vBGENubi4CAgKQnZ0NsVgMV1fjFny5XK7fV1Xef/99xMbG1vZwCCGE1JEaJVPEtJScYqQVKCER8MBYeSEFR+hq87BCCRW7een+JWYq4OsmQpC3FDfuKyDkAa5iIXzdGnb3gYqTGt+4r0CINw8J9wsNSoM74o26KbVJWpNzivH3nRwoH4ypyy5SobBEhZ4ONIeTjnFCwhyuGEVipgIbT97G1dRC+LqJAEA/yXPF310AuJCSh5IyDVRqLRjHoVipglwqhIDHw2OBXnU+kXV9MWfOHEycOBEZGRn49ddf8cYbb6CoqAjvvPOOfpvqxnBWXGfudqbMnj0b06dP1/9cUFCAwMBAcw6BEEKIHVAyZUW6CV3vFyjBobyQgqN0tamuUELlLkEVW63kUhFaNJJBo2UI8XF1+KpttqY7b8k5xRALeLhwNw8AhwOX0vTnxtFu1K0tLV9ZfiNfaXmp2jFLpFd+mOCIxShScoqh0jD4uYmQUVgGXzex/ney8u/upJ6PID4pB7v+SsHVewUoUmlx/b6i/HpMyaVkqoaCgoIQFBQEABg8eDCA8sRm3Lhx8Pb2BgCTrUo5OTngOA6enp4AAG9vbyiVShQXF0MqlRpt26FDh2rjEIvFEIsb9kMrQghxJhbPM0WqppvQdWqf5nizT3OH7PKko7vBfKFLsNENf8WbTbGQj+e7BKPzI94oU2sfjAdruHTn7dVeYejbyg8AB4mQh5sZCsQn5TjEfGK2x8Dnc5CJ/3sWwwMQ5idz2GS74txNjjBNQWW6wiseUhFaNXZDTLfQKr87wnxl8HeXQK0FREI+OJSPZ1RrtDh89b7DzKPl7Dp16gS1Wo1bt24hLCwMLi4uuHTpktF2ly5dQrNmzSCRSAD8N1aq8rbp6enIyspC69atbR88IYSQOkMtU1bmyON9Kqsq1so3mxkFSvx4LgWlai3O3CpPpsZ2Cq7rcB2G7ryl5hXjt6v3IRbwoHzQKuOIN+rW1inUG+2aZiGtQIkADw2EfD5cRDx0ecTH3qGZxRGLnFgak64V/FaFxMlFxIeQz3OIlrb64Pjx4+DxeHjkkUcgEAgwbNgw7N69GytXroSbmxsAIDk5GcePH8fbb7+tf93AgQMhkUiwadMmdO7cWb9806ZN4DgOw4cPr+tDIYQQYkOUTBEjlScIXX/iFpQqLXxkQmQXqXAuKbdBJ1M6FZOKCHeJvty8o92oW5uuBVZ3feyIT8H5u3m4k52M6+kFDt0iq+OIDz0siSnMV4bRkYHIVCiRnFOCkjINmni5oEUjt3qZwNvSK6+8And3d3Tq1AmNGjVCVlYWdu7ciR9++AHvvvuufjLe2NhYREZGYujQoZg1a5Z+0l4fHx/MmDFDvz+5XI558+Zh/vz5kMvl+kl7Fy1ahIkTJ1JZdGJVmpJC3N8602BZo+c/AN/FzU4REdLwUDJFTNLd1H194hZyi8rAwJClUEEi5KFjiJedo3MMFZOKiomTI96oW5vuGOMSMhyy6EpD4SkV44lHfHDuTi4i/N3QvBHdQFnqiSeewMaNG7F582bk5eVBJpPhsccew3fffYcXXnhBv11ERATi4uIwc+ZMjBo1CgKBAL1798aqVav0CZfO3Llz4ebmhrVr12LVqlXw9/fHrFmzMHfu3Lo+PFLfMS1U2clGywghdcesZGrx4sVm75DjOMyfP7/GARHHkJipwL6LaUhIL0SAhwQ5xWVo4umCZzo2pVapChpC4lSdQLkUbhIBkrOLIODx0LKx4xRdqe90XUrP3cmFSqMpryKZW4ILKXn1svCJrcTExBhNsFuVDh064MiRI2ZtO3XqVEydOrU2oRFCCHECZiVTixYtMviZ4zgwxoyW6VAyZcxZymUD/82Xdf1+IVLzSqDVMgj4PLhJBOgU6m3v8IiDkYkE8JCKIOA49Gju4/DXd30R5ivDwNb+2HjyNnKLNUjNU6J3hB/SC0qpdZAQQgipI2ZV89PNDK/VapGQkIDQ0FAsW7YMt2/fRklJCW7fvo0lS5YgNDQU169ft3XMTkdXanzrmWR8feKWw1fbOns7G+fv5qG4VA21RguJkAcvqRCFSnU9rU5nHYmZCsQlZDj852sNiZkKbD97B9+duoO0fCV4AO4XlGL333cbxPE7EpGAj45BXmAArqQW1NvCJ4QQQogjsnjM1FtvvYWXXnoJs2fP1i8LDg7GnDlzoFKpMHXqVBw4cMCqQTo7R5zXpnocOAB8HgcBjwNjQFGpBiqN1qhFkpSrODdXfZ1fSkfXcnn+bh7UGi3KNAxFSjUEPA63MosMJjAmtlem1iAxqwjtmnqiZ7ivvhAKIYQQQmzP4nmm/vjjD3Tr1s3kum7duuH//u//ah1UfeNs5bI7hcrxWKAnPFyECJRL4S4RQKXRIjVPiR3nUqjl4YGKLVH1bX6p6lrZUnKK9UUnXEUCCHgctABUWi2KSjXILCyt+4AboMRMBQ5eTodKwyDkcxgdGYgxkUGUSBFCCCF1yOJkSiwW49y5cybXnTt3DiKRqNZB1TfVTZDriMJ8ZRjdMRC+bmJoGJClKINGy6BUqZGUVeT0iYI1VO66CcCpEubqPKxbqm6OI6VaizKNFq4iPsAYGCufOPZUYjbiEjLsFH3DoUvgOwZ7QSTgG4xbJYQQQkjdsLib39NPP43Y2FjIZDI899xz8PLyQm5uLrZu3YrFixfj+eeft0WcTs8Zq76JBHyEyKVIySkGHwwaLQdXscCpEwVrqdx1k+O4ejO/1MO6pepKwscn5SCzsBS/nL8HLQN0HUD/Sc7F4l+vAgCiwv3scAQNg7O1eBNCCCH1kcXJ1Mcff4zExES8+eabmDp1KgQCAdRqNRhj6NmzJz7++GNbxElsrHK1Qd2NWnJOMRq5SyAW8CB3FWFKdDOnThSsxdSNrDMmzKaYc5NecZ6p/ZfSIBbyoFSVz22i1WqRXVSKy/fyKZmyoYYwQTQhhBDi6CxOptzc3HDs2DEcPHgQx48fR05ODry9vREdHY3+/ftTVxMnVFXxBN2NGmMMHMfRDVsF9flG1pxj0yXfABDq7YrUPCXKVFpoAZRqAG2pGt4y6vJra/UlgSeEEEKclcXJlM7AgQMxcOBAa8ZC7KSqbl10o2ZaxVa8Xi187R2OTVT32VdOvkdHBqJlY3d8dyoJGYVl4ACA45ClKKvTmAkhhBBC6lqNk6lDhw4hLi4OWVlZmD9/PoKCghAfH4+QkBD4+tbPG0xnVt2kwTT2wnwNqQR6VSon3+kFSjDGoNYwcAB4XHllm9wiSqYIIfVLyKx99g6BEOJgLE6miouL8dRTT+Ho0aP6Ln2vv/46goKCsGrVKgQGBmLVqlVWD5TU3MMSgPrcZc3aajtnWHVJrbOomHwLeRx2/ZWC62mFKCnTgPegl6+bixA962mrHSGEEEKIjsXJ1Ny5c3Hu3Dns2rUL/fr1g7u7u35d//79sXr1aqsGSGrPnASAuvSZpzateKaSWgBOl1xVTL4vpOThzJ/ZKNOUF58Q8jl4y8To16oRAuVSO0dKCCGEEGJbFidTO3fuxJIlS/D0009Do9EYrAsKCkJycrLVgiPWUR+68TlKi05tWvEqJ7XxSTk4n5znlF0Gdcl3al4x+DwOIj4PSqaFt0wMBmDvxTRcSc3HipGPOc0xORtH+Z0ghNgPxxdC9vgQo2WEkLpjcTKVmZmJRx991OQ6Ho+HkhKa0NXROHs3Pkcbp1TTVrzKSS1jqFWXQUfQKdQbHYPluJ1dBD7HIae4FKl5pQCAnKIybDl9BwuHmf6+IDXnaL8ThBD74Iml8O7/ur3DIKRBsziZatKkCS5duoTo6GijdRcvXkRoaKhVAiPW5czd+Go7TslRVE5qAeBCSp5TtxjqJvDVdfnb8Eeifp2WlR9fYqbCKT8vR1ZfficIIYQQZ2dxMjVixAgsW7YMPXr0QNu2bQEAHMfhzp07+OSTTxATE2P1IJ0FdbuxDV2Lzrk7uRDyOTDG7B1SjVVOap25xVBHF/eG/7uFMrXWYF1+iQpfn7hFLSdWVh+67hJCCCH1Ac/SFyxcuBCNGzdGp06d0LFjR3Ach5iYGLRu3Rp+fn6YNWuWxUEoFApMmzYNjRs3hkQiQbt27fD999+b9drjx4+jX79+8PPzg0wmQ9u2bfH5558bjeeyNV23m61nkvH1iVtIzFTU6fvXZ2G+Mgxs7Q8hn4NKw3Dwcnq9Ob9hvjL0auHr9InGvotpuJiSBx6PAwdAyANEfA7B3lJkF5Xhbi51/7UmXSvnC12CKVElhBBC7MjiZMrNzQ1//vknlixZAplMhrCwMEilUsyePRsnTpyAi4vlT0hHjBiBzZs3Y+HChThw4AAiIyMxduxYbNu2rdrXHTlyBH379oVarcbXX3+Nn376CVFRUXjrrbcwffp0i+OojYrdbujm0TZEAj46BnvR+XUwiZkK/HY1DfklahSVacEAqLRAmYYh/nYOhHyOWk5soL4k4oQQQogzq9GkvS4uLpg1a1aNWqEq279/Pw4fPoxt27Zh7NixAIDo6GjcuXMH7777Lp599lnw+XyTr920aROEQiH27t0LV1dXAEDfvn2RkJCATZs24bPPPqt1fOaibje2RefXcaXkFKO4TAseB2ge9MDkADAASpUWjdwldMNPCCGEkHrJ4papxYsXY9euXSbX3bt3D4sXL7Zof3v27IFMJsMzzzxjsDwmJgapqak4c+ZMla8VCoUQiURGrWGenp6QSCQWxVFbuq5obZt6YGBrf7p5tLLK3ZoAIC4ho95093NmgXIpvKUi8HkchA++UXSj2tRahnNJOfQ5EUKIDWiVCqRvm2XwT6uk71tC6pLFydSiRYswevRoLFiwwGjd3bt3ERsba9H+Ll++jJYtW0IgMGwk0xW3uHz5cpWvfe2111BWVoapU6ciNTUVeXl5+O6777Bnzx6899571b5vaWkpCgoKDP7VRmKmAgcvp+Pi3fx6NabHkei6NQGo9+PTEjMVTpMshvnKMKV3MzwW6Ikgb1e4iv77WhHyOWi0jLplEkKIDTCtBqUplw3+MW3djhknpKGzOJkCgOeffx7Lli1DTExMrQs9ZGdnQy6XGy3XLcvOzq7ytZ07d8axY8ewZ88eNGnSBF5eXoiJicGyZcswY8aMat/3/fffh4eHh/5fYGBgrY6DxkzVnfp+rp2xmElUuB9WjGyL4Y83QZDcFa4Pmqg0DHAVCxy+W6YzJa+EEEIIcRw1SqbeeOMNfP/99/j+++8xZMgQKBS1uwHhOK5G6/766y88/fTT6NChA3799VccO3YMs2fPxrx587BkyZJq33P27NnIz8/X/0tJSalx/ACN6alLzn6uH3bj7qzJYpivDIPbBMDPXQw+nwexgIOAA0pUanuHVi1nTF4JIYQQ4hhqVIACAJ555hn4+fnh6aefRs+ePbF///4a7cfb29tk61NOTg4AmGy10pkyZQoaNWqEPXv26ItUREdHg8fjYdGiRXj++efxyCOPmHytWCyGWCyuUcymVJ6QlcZM2Y4zn+vETAU+OpSAtAIlAtwlmDEg3Ch+R0kWazJvWpivDDHdQpGWdxVJ2cUQCXjIKChDfFJOnX9O5sZPE+ASQgghpKZq1DKl06tXL/zxxx/IzMzEE088gatXr1q8jzZt2uDatWtQqw2fXl+6dAkA0Lp16ypfe/78eXTo0MGo2l9kZCS0Wi2uXbtmcTy1QaWK646znuuzt7Nx/m4e8ovLcP5uHuKTcoy2cYQ5hGrTWhMV7oeuzXwgFvLgKuJDyK+6ddlWLInfUZJXQgghhDifWiVTAPDoo4/i1KlTkMlkeOWVVyx+/dNPPw2FQmFUIXDz5s1o3LgxOnfuXOVrGzdujHPnzhmN2zp16hQAoGnTphbHQ4htlU9qy1h5+fCq2DtZrE1Xw8RMBTIKSiHi81Cs0qCxpwsiQ6puYba2xEwF9l1MQ3JOsVnxO0LySgghhBDnZHE3v3HjxsHX19dgWdOmTXHy5EmMHTvW4tapQYMGoV+/fnj99ddRUFCAZs2aYfv27Th48CC2bNmib3WaMGECNm/ejMTERAQHBwMA3n77bUydOhXDhg3Dq6++CqlUiqNHj+Kjjz5C37598dhjj1l6eITYVKdQOR4L9ER6vhL+HpI6TTIsUbG1RsjjkJpXgsRMhVmJRkpOMfJKVPCSipBZWIb/CqXbnq5FKjmnGOn5Spy7k4sgufShrU1hvjJKogghhBBiMYuTqY0bN5pc7u7ujn379tUoiN27d2Pu3LlYsGABcnJyEBERge3bt2PMmDH6bTQaDTQaDRj778bszTffRJMmTfDJJ59g4sSJKCkpQUhICBYuXIi33367RrEQYkthvjLM6B/u8OO9dK018Uk5OPFvJo5dz8CFlDyzWm4C5VII+RzyS9Ro4iWBkM+vs3FIuha1jsFeOHcnF13DvDGoTYDDnmdCCCGEOLcaF6CwJplMhs8++wyfffZZldts2rQJmzZtMlo+YsQIjBgxwobREVI9Sws1OEsrSJivDCk5xVBpmEXFGXRFKDaevA2VhpnVMmQtFVvUguRSSqQIIYQQYlNmJVO9e/fGF198gYiICPTu3bvabTmOw9GjR60SnDNJzFTg7O1sABw6hcrpBq4B0H3mf9zIgkrD4O0qstqYm5pU0rPFvmtanCFQLsXA1v7gOA6RIXX3++DMlR4JIYQQ4nzMKkBRsWudVqsFY6zKf1qt1mbBOipduevVx25izbEb+Oi3BJqrpp7Tjc3Zce4uLqTkwd9dbLU5oWw575Gl+65JcQbdexy7nonzyXlWitx89i7eQZzLsWPH8PLLLyMiIgKurq5o0qQJnnrqKfz1119G2/7999/o27cvZDIZPD09MWLECNy6dcvkflevXo2IiAiIxWKEhoYiNjYWKpXK1odDCCGkjpnVMnX8+HH9/8fFxdkqFqeVklOMtAIlJAIeGAPS85U0V009pxub0zrAHUcLlLiSWoAWjdys0p3NlvMe1WTflnZLpHmbiDP58ssvkZ2djbfeegutWrVCZmYmPvroI3Tp0gWHDh3S98a4fv06oqKi0K5dO+zYsQNKpRILFixAjx49cP78eYPCTMuWLcP8+fMxa9Ys9O/fH/Hx8Zg3bx7u3buH9evX2+tQCSGE2IBDjJlydoFyKQLcJbhfoAQHwN9DQnPV1HO67m/phaVo19QTPcN9rdadzZbzHtXFnEo0bxNxJmvXroWfn5/BsoEDB6JZs2ZYvny5PplasGABxGIx9u7dC3d3dwBAhw4d0Lx5c6xatQoffPABACA7OxtLly7FpEmTsHz5cgBAVFQUVCoV5s2bh2nTpqFVq1Z1eISEEEJsiZIpKwjzlWHGgHD9BKx1OUaE2Ictx+Y4677r8j0IsZbKiRRQXhSpVatWSElJAQCo1Wrs3bsXL730kj6RAoDg4GBER0djz549+mTq4MGDUCqViImJMdhnTEwM5s6di59++omSKUIIqUfMSqZ4PB44rropRv/DcRzUanWtgnJGzlKhjViPLT9zZ913RRXHWhLiTPLz8/H333/rW6USExNRUlKCtm3bGm3btm1bHD58GEqlEhKJBJcvXwYAtGnTxmC7gIAA+Pj46NcTYg0cXwBpeDejZYSQumPWb9yCBQvMTqYIIQ1bYqYCH/2WoJ+YeEb/cHrQQJzKlClTUFRUhLlz5wIo77oHAHK58STbcrkcjDHk5uYiICAA2dnZEIvFcHV1Nbmtbl9VKS0tRWlpqf7ngoKC2hwKqed4Ylf4Dp9t7zAIadDMSqYWLVpk4zAIIfXF2ds5uJCSBxGfh/sFSsQn5ThUMmXLsvPE+c2fPx9bt27F6tWr0aFDB4N11T1UrLjO3O1Mef/99xEbG2tmtIQQQuzNrNLo5OESMxWIS8hw+JLozhIncQ6mrycGBoDjAEfr6GfLsvPE+cXGxmLp0qVYtmwZ3njjDf1yb29vADDZqpSTkwOO4+Dp6anfVqlUori42OS2plq3Kpo9ezby8/P1/3TjtgghhDimGnesvXz5Mq5du4aSEuN5dV566aVaBeVsdDdo2UVlVp241dqcJc6HoZYFx1DV9dQp1BvtmmYhrUCJCHcJIkOqv3msS45ctp2ua/uKjY3FokWLsGjRIsyZM8dgXVhYGFxcXHDp0iWj1126dAnNmjWDRCIB8N9YqUuXLqFz58767dLT05GVlYXWrVtXG4dYLIZYLK7t4RBCCKkjFidTxcXFePLJJ3Hs2DFwHKcfZF6x60JDS6ZScoqRnFMMH5kIyTnFDnWDVpEj30iaq74khPVBVdeTrrqlI1bzc9Sy7XRd29eSJUuwaNEizJs3DwsXLjRaLxAIMGzYMOzevRsrV66Em5sbACA5ORnHjx/H22+/rd924MCBkEgk2LRpk0EytWnTJnAch+HDh9v8eAghhNQdi5OpJUuWICkpCb///jt69eqF3bt3w83NDV999RUuXbqEH374wRZxOrz0fCVu3FfATSJw2CpmjnojaYn6kBDWF9VdT45a3dJRy7bTdW0/H330ERYsWICBAwdiyJAhOH36tMH6Ll26AChvuYqMjMTQoUMxa9Ys/aS9Pj4+mDFjhn57uVyOefPmYf78+ZDL5fpJexctWoSJEydSWXRCCKlnLE6mfv75Z8ycORNdu3YFAAQFBaF9+/bo06cPnnvuOXz55Zf46quvrB6oo/NwEcLPTYRSNXPYyoeOeiNpifqQENYXzno9OWKiR9e1/fz6668AyueHOnjwoNF63cOxiIgIxMXFYebMmRg1ahQEAgF69+6NVatWwdfX1+A1c+fOhZubG9auXYtVq1bB398fs2bN0lcHJMRatKVFyD7wucEy70FTwRMbV5MkhNgGxyxsRpFKpTh06BB69OgBPp+P33//Hd27dwcA7Nu3DxMmTEB6erpNgrWlgoICeHh4ID8/32BSRnPEJWRg8a9XUahUw00iwIJhrRAVbjwRJLGOxEyF093Ak7rljOOPGvJ1XZvv3/qOzo1jCZm1z94hGNAU5+Pu6ucNljV9cyv4Ug87RWS5pBVD7B0CISaZ+/1rccuUp6cnioqKAJTPHH/jxg19MqVSqfTrGhp/DwnaNBEhU1HmsC1T9YUjtiw0RI6asOjGHyXnFEPI5xDTLdQpHm7QdU0IIYQ4H4uTqTZt2uDff//FwIEDER0djeXLl6N58+YQiURYvHgxHnvsMVvE6dAC5VIEyaXILipDkFxKXXRIvaVLoADg4OV0hyyYoCsIk19chozCMmw8edvhEj5CCCGE1A8WJ1MTJkzAjRs3AADLli1D9+7d0atXLwDlrVb79++3boROwFnHjhBiiYoV58rUGqg0DB2DvRyuYEKgXAohn0NGYRl83URQaZhDxUcIIYSQ+sPiZGr06NH6/w8NDcW///6rL5PetWvXh05IWF9RFx1S31WsOHfuTi6EfM4hCyaE+coQ0y0UG0/ehkrDqLWYEEIIITZT40l7dVxdXTFs2DBrxEIIcWAVK84FyaUY2Nof6QVKOOJMAFHhfgiUSxtka7GjjmUjhBBC6qMaJ1MKhQLJyclQKpVG69q3b1+roAghjqdyd1bgv3FTF1LyHGrcFOC4rcW2THZo8l9CCCGkblmcTGVmZmLSpEn6uTkqYqx8jiWNRmOV4AghjqVighKXkEETzVrI1skOTf5LCCGE1C2Lk6lXX30Vx44dw1tvvYWWLVtCJBLZIi5CiIOjiWYtZ+tkhz4TQgghpG5ZnEwdO3YMH330ESZNmmSLeAghToKqWFrO1skOfSaEEEJI3bI4mXJ1dUVwcLAtYiGEOBlHHZfkqOoi2aHPhBBCCKk7PEtf8OKLL2Lnzp22iIUQQuq9MF8ZerXwpYSHEEIIqQcsbplaunQpJkyYgKeffhpDhgwxOa/UiBEjrBIcIYTUV1TCnBBCCHF+FidTt2/fxpkzZ/Dvv//i559/NlpP1fwIIXXFWRMSKmFOCCGE1A8WJ1OvvPIK8vPz8emnn1I1P0JszFmThbrgzAkJlTAnhFgDx+NDHNjaaBkhpO5YnEydOXMGGzZswNixY20RDyHkAUdPFuyd6DlzQkIlzAkh1sCTyOD/3Ap7h0FIg2ZxMtWoUSN4enraIBRCSEWOnCw4QqLnzAkJlTAnhBBC6geLk6nXX38d69atw6BBg2wRDyHkAUdOFhwh0XP2hMQaJczt3TpICCGENHQWJ1M8Hg8XL15E+/btMXjwYKNqfhzH4e2337ZagM6CbmqItTlysuAoiV5DnlPJEVoHCSGEkIbO4mTqvffe0///+fPnjdY3xGSKbmqIpeISMnDxbj7aNvVAVLhflds5arLgyIlefWDOwxlHaB0khBBCGroalUYnhuimxrE4eithXEIGFv96FYVKNdwk5b+C1SVUjspREz1nZ+7DGUdpHSSEEEIaMouSqZKSEsyePRuTJ09G9+7dbRWT06GbGsfhDK2EF+/mo1CpRoi3C5KyS3D5Xr5TJlOA4yeugHPEWJG5D2eodZAQoi0tRu7vmw2WefUaB55YaqeICGl4LEqmXFxc8PPPP+O1116zVTxOiW5qHIcztBK2beoBN4kASdklcJMI0LqJh71DqhFnSFydIcbKLHk4Q62DhDRsTKOC4p99Bss8uz9np2gIaZgs7ubXrl07XL58GT179rRFPE6LbmocgzO0EupaoS7fy0frJqbHTDlaa4qpeJwhcXWGGCujhzOEEEKI87A4mVqxYgVefPFFPProo+jVq5ctYiKkxpzlRjQq3K/Krn2O1ppSVTzOkLg6Q4ym0MMZQgghxDlYnExNnjwZCoUCvXv3hpeXFwICAsBxnH49x3G4cOGCVYMkxBLOfiPqaK0pVcXjDImrM8RICCGEEOdlcTLl7e0NHx8fW8RCSJUcrdubLTlaa0p18ThD4uoMMRJCCCHEOVmcTMXFxdkgDEKq5mjd3sxRm+TP0VpTHC0eQgh5mJBZ+x6+ESGEWIHFyRQhdc3Rur09jLnJX3UJl6O1pjhaPIQQQgghjqBGyVROTg4++eQTHD16FNnZ2fDx8UHfvn0xbdo0eHl5WTtG0sA5Wre3hzEn+XPG1jZCCCGEEGKIZ+kL7t27h/bt22PZsmXIz89HUFAQ8vLysGTJErRv3x6pqam2iJM0YLpuZi90CXaKpMOc5K9iwpVdVIa7uSV2iJQQQgghhNSGxS1Tc+bMQUlJCc6cOYPIyEj98vj4eAwbNgxz5szBpk2brBkjIU7VzcycMUbO1tpGHENDKsRCCCGEOAOLW6YOHjyIpUuXGiRSABAZGYnFixfjwIEDVguOEGcV5itDrxa+Vd7wOltrG7E/XdfQrWeS8fWJW0jMVNg7pHqjsLAQ7733Hvr37w9fX19wHIdFixaZ3Pbvv/9G3759IZPJ4OnpiREjRuDWrVsmt129ejUiIiIgFosRGhqK2NhYqFQqGx4JIYSQumZxMpWfn4+QkBCT60JDQ5Gfn1/bmAhpEB6WcBFSEXUNtZ3s7GysX78epaWlGD58eJXbXb9+HVFRUSgrK8OOHTvwv//9D//++y969OiBzMxMg22XLVuGt956CyNGjMChQ4cwefJkLF++HFOmTLHx0RBCCKlLFnfzCw0Nxb59+9CvXz+jdQcOHEBoaKhVAiOEEPIf6hpqO8HBwcjNzQXHccjKysI333xjcrsFCxZALBZj7969cHd3BwB06NABzZs3x6pVq/DBBx8AKE/Oli5dikmTJmH58uUAgKioKKhUKsybNw/Tpk1Dq1at6ubgCCGE2JTFLVMxMTH4/PPPMXXqVPz1119ITU3FX3/9hbfffhuff/45JkyYYIs4nUpipgJxCRnUDaeGGtr5a2jHS8pZ+rlT11Db4TgOHMdVu41arcbevXsxcuRIfSIFlCdi0dHR2LNnj37ZwYMHoVQqERMTY7CPmJgYMMbw008/WTV+Qggh9mNxy9S7776LxMRErFmzBmvXrtUvZ4zhlVdewTvvvGPVAJ0NlbyunYZ2/hra8ToTWxZ7qOnn7kyFWOqbxMRElJSUoG3btkbr2rZti8OHD0OpVEIikeDy5csAgDZt2hhsFxAQAB8fH/16QmqN40HoHWS0jBBSdyxOpjiOw7p16zB9+nQcP34c2dnZ8Pb2Ru/evdGiRQtbxOhUnG2CWUfT0M5fQzteZ2HrJJc+d+eTnZ0NAJDL5Ubr5HI5GGPIzc1FQEAAsrOzIRaL4erqanJb3b5MKS0tRWlpqf7ngoICK0RP6iu+ixsaT/zC3mEQ0qDVaNJeAAgPD0d4eLg1Y6kXHG1cg7OVUna082drDe14nYWtkx363J1Xdd0BK64zd7vK3n//fcTGxtYsOEIIIXWuxslURkYG7ty5g5IS44pSPXv2rFVQzsycOYbqijN2IXOk81cXGtrxOgtbJzv0uTsfb29vADDZqpSTkwOO4+Dp6anfVqlUori4GFKp1GjbDh06VPk+s2fPxvTp0/U/FxQUIDAw0ApHQAghxBYsTqbS0tLw4osv4vjx4wDKx0oB5U/aGGPgOA4ajca6UToZRxnX4KxdiRzl/Jmrtq1/zna8DUFdJDv0uTuXsLAwuLi44NKlS0brLl26hGbNmkEikQD4b6zUpUuX0LlzZ/126enpyMrKQuvWrat8H7FYDLFYbOXoCSGE2IrFydQbb7yBf/75Bx988AHatm1LX/oOjLoS2Z4ztv7VlLN1Ga0tSnZIRQKBAMOGDcPu3buxcuVKuLm5AQCSk5Nx/PhxvP322/ptBw4cCIlEgk2bNhkkU5s2bQLHcdXOZUUIIcS5WJxM/f7771i1apVRydfaUCgUmDdvHnbs2IGcnBxERERg1qxZGDNmjFmv//nnn/Hxxx/jn3/+gUajQUhICN566y288sorVovRGVFXIttz1tY/SzWkpJGYp74l1wcOHEBRUREKCwsBAFevXsWPP/4IABg8eDCkUiliY2MRGRmJoUOHYtasWVAqlViwYAF8fHwwY8YM/b7kcjnmzZuH+fPnQy6Xo3///oiPj8eiRYswceJEmmOKEELqkRpV87N2/+0RI0YgPj4eK1asQIsWLbBt2zaMHTsWWq0Wzz33XLWvXbFiBebOnYvXXnsNs2fPhlAoxPXr11FWVmbVGJ0VPV23rYbS+tdQkkZinvqYXL/++uu4c+eO/uedO3di586dAIDbt28jJCQEERERiIuLw8yZMzFq1CgIBAL07t0bq1atgq+vr8H+5s6dCzc3N6xduxarVq2Cv78/Zs2ahblz59bpcZH6TVumRMHZXQbL3DuNBE8ksVNEhDQ8HNMNejLT5MmTIRKJ8Omnn1olgP3792PIkCH6BEqnf//+uHLlCpKTk8Hn802+9q+//kKnTp3w/vvv47333qtVHAUFBfDw8EB+fr7BhIyEPExipqLet/7Vx5tnUnNxCRnYeiZZn1y/0CUYvVr4PvyFVaDv36rRuamZkFn77B1CndAU5+Pu6ucNljV9cyv4Ug87RWS5pBVD7B0CISaZ+/1rccvU6NGjMWnSJGi1WgwbNkxf4aii9u3bm72/PXv2QCaT4ZlnnjFYHhMTg+eeew5nzpxB165dTb52zZo1EIvFePPNNy07CEKsqCG0/lGXUVJRQ2mRJYQQQh7G4mSqd+/eAMoTmbVr1xqsq0k1v8uXL6Nly5YQCAxD0c0yf/ny5SqTqRMnTqBly5bYtWsXlixZgps3byIgIAAvvPACFi9eDJFIVOX7WntiREcYP+AIMRDzOcPnVTnGh8XpDMdEao+Sa0KItdiqFZFavEhdsTiZ2rhxo1UDyM7OxiOPPGK0XDfLfHUzxd+7dw+ZmZmYOnUqlixZglatWuHo0aNYsWIFUlJSsHXr1ipfa82JER2hC5QjxEDM5wyfl6UxOsMxEetpCC2yhBBCyMNYnEyNGzfO6kHUdKZ4rVaLwsJCbN++XV/5Lzo6GkVFRfj0008RGxuLZs2amXytNSdGdITB+Y4QAzGfM3xelsboDMdECCGEEGJNvNq8OCEhASdPnkRRUVGN9+Ht7V3ljPLAfy1UVb0WAAYMGGCwfNCgQQCAv//+u8rXisViuLu7G/yrKUcYP+AIMRDz2evzSsxUIC4hA4mZiodua2mMdA02TJZcU4QQQkh9Y3HLFAB8++23mDNnDtLS0gAA8fHxaN++PUaPHo1+/fph0qRJZu+rTZs22L59O9RqtcG4Kd0s89XNFN+2bVukp6cbLdcVKOTxapUrms0Rxg84QgzEfPb4vCzthmdpjHQNNjzUtZMQQkhDZ3G2sXPnTowfPx7t27fHmjVrULGyevv27bFjxw6L9vf0009DoVBg1y7DeRI2b96Mxo0bG8weX9nIkSMBlE+2WNH+/fvB4/EQGRlpUSy1EeYrQ68Wvna9kXCEGIj56vrzqtgNL7uoDHdzSx76GktjpGuwYanJNUUIIYTUJxa3TL3//vuIiYnBhg0boNFoMGXKFP26li1bYvXq1Rbtb9CgQejXrx9ef/11FBQUoFmzZti+fTsOHjyILVu26OeYmjBhAjZv3ozExEQEBwcDKC+fvm7dOkyePBlZWVlo1aoVjhw5grVr12Ly5Mn67Qgh1A2PWB9dU4QQQho6i5Opa9eu4YMPPjC5Ti6XV1t9ryq7d+/G3LlzsWDBAuTk5CAiIsKgqAQAaDQaaDQag5YwoVCIw4cPY86cOVi+fDlycnIQGhqKFStWGBSXIIRQNzxifXRNEUIIaegsTqakUiny8/NNrrt37x68vLwsDkImk+Gzzz7DZ599VuU2mzZtwqZNm4yWy+VyfPXVV/jqq68sfl9CGhpblrOmOaYaJiqRTghxRLaYv4rmriKmWDxmqlu3bkZjpXQ2bdqEqKgoa8RFiFmokphj0BUi2HomGV+fuEWfByGEEEIaBItbphYsWIDu3bujU6dOeO6558BxHHbv3o2FCxfixIkTOHv2rC3iJMQIVRJzDImZCuy7mIbknGJ0DPaiOaYIIYQQ0mBYnEx17NgRBw4cwOTJkzFjxgwAwPLly9G8eXPs37+/2lLmhFgTTRJrf7qENjmnGOn5Spy7k4sguZQKERBCzGaL7liEEFJXajTPVHR0NK5du4bExETcv38fPj4+aNGiBYDyOZ44jrNqkISYQpXE7E+X0HYM9sK5O7noGuaNQW0CKKklhJA6wnNxt3cIhDRoNUqmdMLCwhAWFqb/edu2bVi8eDGuX79e68AIeRiqJGZ/FRPaILmUEilCCKlDfKkHAqdus3cYhDRoZidT+fn5+Omnn3D//n20aNECTz75JHi88voVu3fvxoIFC3D16lWa24nUqfpUScwZq+FRQksIIYSQhsysZOrmzZvo0aMHMjIy9N34evXqhZ9++gljx47FwYMH4enpiZUrV+LNN9+0dcyE1DvOXEyjPiW0hBBCCCGWMCuZmj9/PgoKCrBo0SJ07NgRt27dwrJly9C1a1dcvXoVEydOxMqVK+Hp6WnjcAmpn6iYBiGEEEKI8zErmfr9998xb948zJ49W7+sWbNmGDRoEF577TV88cUXNguQkIaAimkQQgghhDgfs5KpzMxMdOvWzWBZ9+7dAQDPPvus9aMipIGhsUeEEEIIIc7HrGRKo9FAIpEYLNP97ObmZv2oCGmAHH3skTMWyCCEkPpMqypF0aXDBstc2/QDTyi2U0SENDxmV/NLSEiAQPDf5hqNBgBMlkFv3769FUIjhDgKZy6QUV9RcksIYSolcg5/ZbBMGtEDoGSKkDpjdjI1fvx4k8tffPFF/f/rKv3pEi1CSP1ABTIcCyW3hBBS90Jm7bP6PpNWDLH6PkndMiuZ2rhxo63jIIQ4MCqQ4VgouSWEEEIcg1nJ1Lhx42wdByH1Un3piuWMBTLqy7k3hZJbQgghxDGY3c2PEGKZ+tYVy9ELZFRU3859Zc6Y3BJCCCH1Ec/eARBSX1XsipVdVIa7uSUmt0vMVCAuIQOJmYo6jrD+MvfcO7MwXxl6tfClRIoQQgixI2qZIsRGzOmKVd9bUOyFusERQgghpC5QMkWIjZjTFYsKCdhGQ+oGV5/HhhFCCCGOjpIpQmzoYeOMqAXFdpxpjFdNUcsmqUu2KAtNSENnq98rKrledyiZIsSOGlILCrE+atkkhBBC7IuSKULsrCG0oBDboJZNx6VQKDBv3jzs2LEDOTk5iIiIwKxZszBmzJga7a/1wkPgiaVWjpIQUl/RBMN1h5IpQghxUtSy6bhGjBiB+Ph4rFixAi1atMC2bdswduxYaLVaPPfcc/YOjxBCiJVQMkUIIQ7E0oIS1LLpePbv34/Dhw/rEygAiI6Oxp07d/Duu+/i2WefBZ/Pt3OUhBBif/VhzBglU4QQ4iCooET9sGfPHshkMjzzzDMGy2NiYvDcc8/hzJkz6Nq1q52iI4SQ+s8aSZq2tNis7SiZeoAxBgAoKCiwcySkvriVpcC93BI08XLBIz50Q0we7npyJtKychDu54aEjBwkpMjgK9baOyyb033v6r6Hnd3ly5fRsmVLCASGf2Lbtm2rX19VMlVaWorS0lL9z/n5+QDM/6NOGhZtmfF1oS0rBscX2iEaUt8Fvb3T3iHUKd337sP+NlEy9UBhYSEAIDAw0M6REEJIuW/tHUAdKywshIeHh73DqLXs7Gw88sgjRsvlcrl+fVXef/99xMbGGi2/9+V4q8VH6rfUdZPsHQIh9crD/jZRMvVA48aNkZKSAjc3N3AcZ3KbgoICBAYGIiUlBe7u7nUcYe1Q7PZBsdsHxW4fNY2dMYbCwkI0btzYhtHVrar+jjxs3ezZszF9+nT9z1qtFjk5OfD29q72dY7Gma9jZ0bn3T7ovNuHrc+7uX+bKJl6gMfjoWnTpmZt6+7u7rS/LBS7fVDs9kGx20dNYq8PLVI63t7eJlufcnJyAPzXQmWKWCyGWCw2WObp6WnV+OqSM1/HzozOu33QebcPW553c/428WzyzoQQQkgD1aZNG1y7dg1qtdpg+aVLlwAArVu3tkdYhBBCbICSKUIIIcSKnn76aSgUCuzatctg+ebNm9G4cWN07tzZTpERQgixNurmZwGxWIyFCxcadcFwBhS7fVDs9kGx24czx25NgwYNQr9+/fD666+joKAAzZo1w/bt23Hw4EFs2bKlQcwxRdeCfdB5tw867/bhKOedY/WlFi0hhBDiIBQKBebOnYsdO3YgJycHERERmD17NsaMGWPv0AghhFgRJVOEEEIIIYQQUgM0ZooQQgghhBBCaoCSKUIIIYQQQgipAUqmCCGEEEIIIaQGKJkihBBCCCGEkBqgZIqQBiY/Px8AoNFo7ByJ5e7cuQMAcMa6OVevXkVqaioA54v/hx9+wOrVqwEAWq3WztEQ0vBkZWUhJyfH3mEQQkxo0NX8rly5ghMnTqBp06aIjIyEv78/gPIbHY7j7Bxd9e7cuQO1Wo2wsDB7h2KxxMRE/Pvvv/D19UVERARkMpm9QzLb9evXceLECXh6eiI8PBxt2rQBj+cczySSk5MxZswYuLu74+DBg/YOxyJ///03nn32WchkMpw9exZCodDeIZntn3/+wfTp01FUVIRnn30Wb7/9ttNcM3/99RfefPNNnD59GsHBwbh582aDmCOJVE2pVEIikQBwjr+Vzq6oqAhTp07F//3f/0EkEqFjx44YN24coqKi7B1ag6BSqfR/b+h6rxvHjh2DUCjU3yM6A+f4i25lpaWlePXVVxEZGYnVq1fjqaeeQs+ePfHxxx8DgEP/spSUlODNN99EaGgoNmzYgMLCQnuHZDaFQoHx48cjKioKkydPRqdOndC/f3/88ssvABz7ab1CocBLL72EHj164OOPP8aYMWMwePBgrFu3DoBjx66zZs0anD59GhcuXMCOHTsAOH7rVGFhIcaOHYuOHTuic+fO2Lx5s9MkUlqtFitWrECvXr0QEBCAWbNmoX///k6RSBUUFGDs2LGIjIxEy5Yt0aVLF0gkEty9e9feoRE7SUhIwLPPPouRI0di7Nix+PPPP6FUKgFQa6Wt3LhxA7169cLVq1cxbdo0DBgwACdOnMCQIUNw5MgRh//+dmanTp3Ck08+iZEjR+Kll17C5cuXoVarATjH33tndP78eTz++OMYO3YsRo0ahVatWmHOnDlISkoC4ODfM6wB+vTTT1mzZs3Yb7/9xu7evcsuXrzIBg0axDiOY1u3bmVqtdreIZp05coVNnLkSBYYGMiCgoLYI488wk6cOGHvsMzyxx9/sE6dOrGuXbuyvXv3slOnTrGff/6ZeXp6su7du7P09HR7h1il/fv3s/DwcPbEE0+w/fv3s+vXr7Nz586xZs2asY4dO7Lc3Fx7h1gtrVbLGGNsxowZLDg4mLVr14517tyZlZSUMMYY02g09gyvSuvXr2ccx7EnnniCHTlyhBUVFdk7JItcu3aNdejQgX366acsLy9P/zk4uiVLljChUMi6dOnCDh48yDQaDVu4cCETiUQsNTWVMcac5liIdXz99dfMzc2NDR8+nL388susRYsWTCaTsRkzZtg7tHpJ9/v11VdfsSZNmrDz58/r18XHx7Nu3bqxFi1asN9//91eIdZbWq2WLV26lLm6urLnn3+evfDCC6xJkybM19eXLVu2zN7h1VuZmZksMjKSjRgxgl28eJGdO3eOzZ49m7m5ubGBAwfaO7yHalDJlFarZYWFhaxt27bsmWeeYaWlpfp1CQkJ7Mknn2RNmjRhJ0+etGOUVdPdXC5btoz98ccfzNPTk40fP55lZGTYO7RqZWZmstGjR7MhQ4awCxcuGKybN28ec3V1ZX/++aedoqteTk4Omz17Nhs7diz7999/DdZNnDiRtWzZ0mlu8ocPH84+/vhjtnjxYiaVStmKFSsYY46ZTN27d48NHjyY8Xg89s8//xjcvOfn59sxsofTxbpgwQLWqFEjfQLCGGPnz59nFy5cYDk5OfYKr1q7d+9mbdq0YevWrTM4z6tWrWIcx7Hvv//ejtERe1AoFKxnz55s4sSJBt91zz77LBMIBOyLL75gjFGCbQtDhgxh3bp1Mzq358+fZ1KplD333HMG3y+k9tLS0ljr1q3Z/PnzWVlZGWOMsdzcXDZw4EAmEAjYvn37GGN0vVvb9u3bmUQiYadOnTK4J1mwYIH+vteRNahkirHyX4DGjRuzhQsXMsaYQUL1999/M29vb/biiy+yrKwsO0VYtatXr7Jjx47pf54/fz6TSCRs165dDv+LPXbsWIPYda1/hw8fZhzHsb///tteoT1UXFycPpGqeJ5feOEFtnTpUlZUVKT/5XfExER3rgcPHszmz5/P8vLyWGRkJGvWrBlLTExkjDnmH4YDBw4wLy8v9s477zDGGLt+/TobPXo069mzJ+vRowf78ssvWUpKCmPMMc/7sGHD2LBhwxhjjF26dIn17NmT+fn5Mblczpo1a8a2bdtm5whNy87O1v+/7rr4888/Gcdx7H//+5/BclL/nT9/nnEcx44fP84YY0ylUjHGyh9ADhkyhLm5ubGkpCQ7Rlh/TZo0iYWEhOh/rvh7t2jRIiYSidgPP/xgj9DqrX379jGO4/R/G3V/P+Pj41mnTp1YSEiIwz/Qcwa6e2zdPfjq1auZVCplSqXSYHlKSgp77rnnmFQqZTdv3rRPsGZw/M77NVRV38qMjAyEhITg6NGjAACRSKTftl27dpg6dSp+/PFHXL16tc5irayq2Fu2bIno6Gj9Nq+99hqCgoLwxRdf6PuU2lvl2HV9ujdt2qSPHYB+EPv169chk8ng6elZZzFWparz3qtXLzRv3hxA+Xi6kpISjBs3Dlu3bsXWrVvRunVrvP322wBgt/Ew1fUl5vP5KCsrQ0ZGBgICAuDh4YFx48YhOzsbq1atAlA+yFnXH7yuVY6dPeiP3rlzZ7z44otYs2YNnnvuOTz22GPIyspCQEAAioqKMHnyZEyYMAGAY553uVyOf/75BykpKXjttdfg7u6OdevWYe7cufD19cWECRPw888/260feFXvK5fL9f+vGz/q5eUFLy8v/PPPP3USG3Ec2dnZkEgk+kqaut+1Fi1aYPLkyRCLxYiNjQXg4GManFC7du1w//597N27F4Dh+X3rrbfg6+uLX375BaWlpfYK0akpFAqjZbm5uRCLxUhMTDRY3rFjR0ydOhVpaWn49NNPAdD1XhP3799H+/bt0aVLFwDl9+BA+WfB4/Hw+++/Gyxv2rQpxo8fD6lUiiVLlgBw0PNu72zOFjZs2MBatmypf2JT+al1TEwMCwgIYAcPHjRaf/XqVRYQEMDeeOMNk6+1tYfFXtmmTZsYx3FszZo1+kzeXk+NLYldt27ixInsscceY4WFhXUSY1XMjf3mzZusRYsWrG3btmz9+vVs586d7OWXX2Ycx+nHDzjaNaN7sta9e3d9U3lxcTEbPnw4a9SoERs3bhzr1KkTi4uLq9O4GXt47KdPn2Zt27ZlLVq0YLt372YFBQX6bd544w3G4/HYmjVrTL7W3rHPmDGDyWQyNmjQINaxY0eWnJysX3flyhXWpk0b1rdvX7s85bT0e+b+/fvM19eX9e3blykUiroIkdSxH3/8kR05coTFx8frx1MyxlhycjITiURsxowZrLi4mDH233dKfn4+mzJlCuM4jt26dYsxRq2W1pSVlcUCAgLY6NGj9ee84vmdPXs28/T01H8uxDwKhYJNnz6d9e7dm0VFRbHZs2frhyCcPHmScRzHPvzwQ/05130/pqWlsVGjRjF3d3en6d7vaHTDVTiOY59//rl++Y0bNxjHcWzRokX675+K3zMTJ05krq6uDtsKXq+SqZSUFDZp0iQmEAgYx3FsyJAh+gteq9XqP5i///6bcRzHJk2axAoKChhj/31oubm57Omnn2bh4eH65kZHiL0qBQUFrE+fPiwiIsJuXeVqErtKpWJarZa1aNGCvfzyy3UZroGaxH7s2DGDm/qsrCz27LPPMhcXlzq9MbYkdpVKxZo0acJ27typXzZnzhwmEomYQCBgH330EVMoFHV2I2Ru7AqFgm3evJlt377d6Pfx2rVrLDQ0lPXu3dugu669Y9ddFxcuXGAcxzGRSMReffVVg32UlZWxlStXMo7j6rTrQk2ud93xDBw4kEVGRla7LXE+mzdvZsHBwax58+bM3d2dcRzHYmJiDH6nRo0axcLCwozGvDLG2M8//8y8vb1ZbGxsXYbdYCxZsoT5+fmx7777jjHGDApk/e9//2MuLi7szJkz9grP6Xz33XfMz8+Pde/enU2fPp0NGTKE8fl81qFDB/39YKdOnViXLl30Dwgq+uabb5ibmxvbsGFDXYdeL6xcuZI1atSIDRkyhHl7e+vvmbRaLRsxYoTR94zub83GjRuZm5ubw47brTfJlFKpZNOmTWMBAQFs/vz5bNy4cczT05OtXr2aMfbfB6K7MXjhhReYm5sb27hxo8Fy3br27dsbPJ1zhNircvToUSYUCtmcOXNYbm4uS0lJYb/99htjzPZP62sT+/Xr15lIJDK4wS8uLmaXLl166GvtEXt18UybNo01atSozm6MLYldq9WygoIC1q5dO7Z//3525coVFhUVxQQCAWvZsiVzd3dnmzZtYozVTeuOpee9cktIxfWdO3dm/fr1s3nMOubGrvvvK6+8wjiO01cj0o03Yay8UpeLi0udVeSsze9qaWkpe+WVV5hIJDJoYSPOKy8vj73zzjssNDSULVu2jJ0/f54lJiayiRMnMhcXF/bBBx/ot/3999+ZSCRic+fO1d9w6q5lhULBGjdurB/bSIm2dSmVShYWFsbatWunHyOq88EHHzBXV1d2584dO0XnPLRaLduzZw97/PHH2cKFC1lmZqa+wERsbCyTSqX6Yirbt29nPB6Pff755/qHeLpt79y5w1xdXfWtKnS9W+add95hkydPZv/73/+YUChkkydPZoyV33v8/vvvTCKRsGnTpunHVOnO+/379xnHcWzv3r12i7069SaZYqx8QOaiRYsYY+VV2Fq0aMHat2/Pbt++zRgr/7B0T3WysrJYYGAge/TRR9np06f1+8jOzmZdu3ZlL774Yp3+kpgTe2UV45s4cSJr1KgRW7RoEYuMjGQcx7G7d+86bOyMlTf3yuVylpCQwBhj7MyZM6x///7M29u7zkql1/a8azQadvv2bdahQwc2cuTIOu1qZknsaWlpTCaTsccff5wJBALWu3dv9tdff7GzZ8+yiIgIFhQUpL9JcrTYdSomIoyVd8dwdXVlM2fOtHm8FZkTuy7+3NxcFhwczDiOYz/++KN+HwqFgsXExLDOnTvXaQt4TX9XGSu/4eDxeOzo0aN1ESqxsT179rA2bdqwzz77jJWUlOi/1+7cucOCg4PZyJEj9ddmcXExe/XVV5mnpyfbtWuXwX7UajVr2rQpmzJlSp0fQ0MRFxfH/Pz8WLdu3djNmzdZXl4e+/fff1nv3r3Zyy+/bPTdSIxptVo2efJk9vTTTxsln8nJyQZdxnNyctiwYcNYcHAwO3LkiMG22dnZTCKRsI8++qjOYq8PdH9bJkyYwMaOHctUKhV75plnmEAg0Jf+VyqV7J133mFCoVD/WTBW/tl9++23TCaTsVOnTtkl/odx2mRKl61W/v+KPvroI+bu7s7ee+89g+W6hGrnzp0sIiKCBQYGss8//5zt27ePTZkyhfn5+bFDhw45ZOymFBUVsW3btun7oT755JM261dqjdh15/+ZZ55hjz/+OLt8+TKbMmUKEwgEbMCAATZ7ymaL837t2jU2fvx41rx5c/2Xri2S8NrGrtFo2JgxY1ibNm3Y1q1bDebGmjNnDnv55ZdZYWGhQ8ZeWXFxMbty5QobPXo0a9u2Lbt27ZrVYq3MGt8zP//8MwsLC2NyuZxNnz6dbdq0iU2aNIl5eXmxr776ijHmmNeMji62P/74g/F4PPbLL78wxhyzgiIx386dO9nixYsNlum69nXo0IE9+eSTBuvS0tLYI488wlq2bKm/BlQqFdu5cydr0qSJw05vUV/8+OOPLCAggLm5ubHu3buzxo0bs9atW7OLFy/aOzSnkZaWZnJ89q1bt5hEItG30jNW3nPGw8ODdenSRX8DX1ZWxlavXs1CQ0MdurKco9JqteyZZ55h7777LmOs/G+jv7+/vndJQUEBKyoqYr1792Zubm5s1qxZ7M8//2RxcXGsc+fO7KmnnqrTh4+WcLpk6s8//9SXHH7xxRfZpUuX9DcKupsX3VOasrIy1q1bN/bII4/o545Sq9UGNy7x8fGsT58+rFGjRiw4OJi1bt1aXwLWEWOvLCkpiU2ePJl5eXmxNm3a2GyOLGvHXlJSwtq2bcsaN27M5HI5Cw0NZYcPH3aK2G/fvs0+/vhj9vbbb7NGjRqxiIgIhz3vFZ9Y3r17lyUnJ+tvgnW/B1XdaNs79srn/datW+yTTz5h77zzDvPz82OPPvqozcYKWPt75q+//mLDhg1j/v7+LDQ0lLVr185gqgBHi92UvXv3Mo7j2Pvvv2+TuEndMJW4V0yMS0pKWEhICHvrrbeMtjtz5gxr27Yt4ziO9e3bl40ZM4a5ubmxmJgYKkxSB65du8bWr1/PZs2apR+iQCxXeSqTI0eOMI7j9EWYdN+Be/bsYc2bN2cCgYANHTqUjRgxgrm4uLBZs2bpx30T8+jO6fDhw9nEiRMZY+V/f2bOnMk4jmNjx45lQUFB7MiRIywlJYW9++67TCgUsqZNmzIPDw82YsQIh52fkTEnSqaqmpXaz8/P5GReug9u9+7dzMvLiz333HNG+9MpKytjOTk57J9//nGK2Cu6ceMG4/P57NNPP3Wq2K9cucI4jmO+vr5s7dq1ThX7yZMnWd++fVmvXr3Y+vXrnSr2umCr2I8fP87atGnDIiMj9S06jh57xe8ZlUrFCgsL2eXLl50i9srHUFJSYjC+kdRPN27cYJ6envoB9pXHAd67d4+tWLGCvfzyy+zJJ5/Ut1IR4qyWLl3KAgMDWVpamtG6O3fusLlz57Lx48ezkSNHsv/7v/+zQ4T1R4cOHdiqVav0P3/44YdMIpEwHo/HVqxYwfLy8vTrEhMT2alTp9iVK1fsEapFnCaZqs2s1M888wzz9fXV3wjk5OSw+/fv69dX9STWGWK3dfzWjr3iWKgtW7bYrFXE1rEnJibatJuTra8ZW7Lleb948aJTXe/15XuGuvTVf7rP+LvvvmNCoZC6j5EGY8iQIax///4Gy2x5b9IQ6b5foqKi2Nq1a9mNGzdY7969mUAgYJ06dWJ8Pp+tWLGCMWY8PtoZOE0yVZNZqXUfyIULF1iTJk1Y79692ZEjR9jYsWPZ888/z1JTUyl2O8ReuSKRM8VeV0U96Jqh896QYid1p3IXVB3dspiYGNa2bVuD0uhXr17Vz2VEXZtIfZKamsq8vb3ZkiVLGGPl4wZPnz7NBg8ezDIyMuwcXf2iUChYcHAwCw4OZkKhkEVFRbHTp0+zhIQE1rdvX8ZxnNOec4dMpkwNENyyZQuTSCT6kt8Vn/Ju2bKFicVifZUqU0+AX331VX2BBj8/P5uVV6TYKXaKnWKn2ImjqVjNljHG9u/fb9Rlqbi4mD322GP6edHS0tLY4sWLGcdx+ptNQuoD3UOBvXv3MqFQyH7//Xd29+5d9sYbbzCpVMoee+wxlpmZSQ8PrGzGjBksIiKCfffddwaFsL755hv20ksvsZycHKc85w6VTFWclTo6OtrkrNSrVq0yOSv1yJEjDWal1n0Y9+/fZ1u3bmXNmjVjMpmMffbZZxQ7xU6xU+wUex3HTuynYreZmzdvsgEDBjCO41hsbKxBgvXPZaS4sgAAFdBJREFUP/8wmUzGvvjiC/bTTz+xoKAg5ufnx7799lt7hE2IzS1atIgFBgayuXPnsiZNmrDQ0FB24MABe4dVbxUVFRkUwtKxdTd4W3OYZKqqWak7duyon/8mMjLS4lmpv/zySyaVStmzzz5r8mkuxU6xU+wUO8Vu29iJfVRMolQqFZsyZQrjOI516NCBbd68Wd99VpdYf/PNN4zjOBYQEMD4fH6dz+FGSF1SqVT6Bwvu7u5s5cqV9g6JOCm7J1O2mpVal/VeuXJFPyksxU6xU+wUO8Ved7ET+9BoNAZdZdauXcvc3d1ZQEAAW758Obt+/brJoiLTp09nHMexl156qU6L1hBiLzNnzmQzZ8502PmLiHNwiGTKWWelptgpdoqdYqfYiaOKi4tjjz76KBOJROyVV15hp06d0heSqEiXWF28eFHfbZSQhoAqlRJrsHsyxZhzz0pNsVPslqLYKXZLOXPspO5pNBq2YMECxnEcGzx4MPv1119Zdna2vcMihJB6ySGSKR1nnpWaYqfYKXaKnWInjuLYsWNsw4YNRq2ZhBBCrEsAB8Lj8Qz+e/r0aTRt2hTh4eEAAD6fDwAYPnw42rdvj/Xr1+PevXsoLCzE4cOH0a1bN/sEDordXih2+6DY7cOZYyd1KyoqCr169dJfK4wxcBxn56gIIaT+4RhjzN5BVGXo0KFQqVQ4dOiQfplKpYJQKLRjVOah2O2DYrcPit0+nDl2QgghpD7g2TuAqqSlpeH06dPo0aMHAKCsrAxnzpzB8OHDkZmZaefoqkex2wfFbh8Uu304c+yEEEJIfeFwyZSuoezvv/9GQUEBevbsiXv37mHGjBno3bs37t27B47j4IgNahS7fVDs9kGx24czx04IIYTUNw41ZgqAvk/3uXPn4O/vj99++w2bNm2CSCTCrl27MHDgQDtHWDWK3T4odvug2O3DmWMnhBBC6p06LnhhFmeelZpitw+K3T4odvtw5tgJIYSQ+sThWqYAQCAQoF27dmjXrh1iY2MhFovtHZLZKHb7oNjtg2K3D2eOnRBCCKlPHLaan1ar1Zd0dTYUu31Q7PZBsduHM8dOCCGE1BcOm0wRQgghhBBCiCOjx5qEEEIIIYQQUgOUTBFCCCGEEEJIDVAyRQghhBBCCCE1QMkUIYQQQuq1zz//HBzHoXXr1vYOpVbi4uLAcRzi4uJq9PpNmzaB4zgkJSVZNa66xHEcFi1aZPHrUlNTsWjRIpw/f95o3aJFi/Rz+NlDXl4efHx88P333+uXXb58Gd27d4ebmxs6dOiAkydPGr3uww8/RIsWLaBUKo3W9ezZE9OmTbNl2OQBSqYIIYQQUq/973//AwBcuXIFZ86csXM0xB5SU1MRGxtrMpmaOHEiTp06VfdBPRAbG4vGjRvj2WefBQCo1WqMGDECPj4+2L17N9q1a4ennnoKeXl5+tckJSUhNjYWX331FSQSidE+lyxZgi+++AIJCQl1dRgNFiVThBBCCKm3zp07hwsXLmDIkCEAgA0bNtg5ooZHo9GgtLTU3mFUqWnTpujSpYtd3jsnJwfr1q3DlClT9K1jN27cwI0bN/Dll1+iX79++Oqrr6BUKnH69Gn9615//XWMGjUKvXv3NrnfXr16ITw8HB999FGdHEdDRskUIYQQQuotXfK0YsUKdO3aFd9//z2Ki4sNtklKSgLHcVi1ahU+/vhjhIaGQiaT4YknnjC4gQWA8ePHQyaT4ebNmxg8eDBkMhkCAwMxY8YMg4Shqi55uvfatGmTftm5c+cwZswYhISEwMXFBSEhIRg7dizu3LlT4+M+ffo0unXrBolEgsaNG2P27NlQqVQmt/3hhx/wxBNPwNXVFTKZDAMGDMA///xjtN3XX3+NFi1aQCwWo1WrVti2bRvGjx+PkJAQo+NbuXIlli5ditDQUIjFYhw/fhxKpRIzZsxAu3bt4OHhAblcjieeeAI///yz0XsVFBRg0qRJ8Pb2hkwmw8CBA/Hvv/8abXfz5k3ExMSgefPmkEqlaNKkCYYNG4ZLly7pt4mLi0NkZCQAICYmBhzHGXQXNNXNT6vVYuXKlYiIiIBYLIafnx9eeukl3L1712C7qKgotG7dGvHx8ejRowekUikeeeQRrFixAlqt1vSHU8GmTZugVqv1rVIA9N32XF1dAQBCoRAikUi/fPv27Th37txDE6UXX3wR27ZtQ2Fh4UPjIDVHyRQhdqbrw677J5FI4O/vj+joaLz//vvIyMio0X6vXr2KRYsWOXXfeEIIqY2SkhJs374dkZGRaN26NV5++WUUFhZi586dJrdfu3YtDh8+jE8//RRbt25FUVERBg8ejPz8fIPtVCoVnnzySfTp0wc///wzXn75ZXzyySf44IMPahRnUlISwsPD8emnn+LQoUP44IMPkJaWhsjISGRlZVm8v6tXr6JPnz7Iy8vDpk2b8NVXX+Gff/7B0qVLjbZdvnw5xo4di1atWmHHjh347rvvUFhYiB49euDq1av67davX49XXnkFbdu2xe7duzFv3jzExsZWOX7r888/x7Fjx7Bq1SocOHAAERERKC0tRU5ODt555x389NNP2L59O7p3744RI0bg22+/1b+WMYbhw4fju+++w4wZM7Bnzx506dIFgwYNMnqf1NRUeHt7Y8WKFTh48CDWrl0LgUCAzp0767u4tW/fHhs3bgQAzJs3D6dOncKpU6cwceLEKs/h66+/jpkzZ6Jfv3745ZdfsGTJEhw8eBBdu3Y1+kzS09Px/PPP44UXXsAvv/yCQYMGYfbs2diyZUvVH9ID+/btw+OPPw5PT0/9soiICMjlcnzwwQfIy8vD2rVrUVRUhI4dOyI3Nxdvv/02Pv74Y3h7e1e776ioKBQVFdV4jB0xEyOE2NXGjRsZALZx40Z26tQpduLECfbjjz+yadOmMQ8PDyaXy9nhw4ct3u/OnTsZAHb8+HHrB00IIU7g22+/ZQDYV199xRhjrLCwkMlkMtajRw+D7W7fvs0AsDZt2jC1Wq1ffvbsWQaAbd++Xb9s3LhxDADbsWOHwT4GDx7MwsPD9T8fP37c5Hew7r02btxYZdxqtZopFArm6urKPvvss4fus7Jnn32Wubi4sPT0dIN9RkREMADs9u3bjDHGkpOTmUAgYG+++abB6wsLC5m/vz8bPXo0Y4wxjUbD/P39WefOnQ22u3PnDhMKhSw4ONjo+MLCwlhZWVm1carVaqZSqdiECRPY448/rl9+4MABBsDg2BljbNmyZQwAW7hwYbX7LCsrY82bN2dvv/22fnl8fHyV533hwoWs4i3xtWvXGAA2efJkg+3OnDnDALA5c+bol/Xq1YsBYGfOnDHYtlWrVmzAgAHVHj9jjEmlUvbaa68ZLd+zZw9zd3dnAJhYLGbr1q1jjDE2YcIE1rdv34fulzHGysrKGMdxbObMmWZtT2qGWqYIcRCtW7dGly5d0KNHD4wcORKffPIJLl68CFdXV4wYMQL379+3d4iEEOJUNmzYABcXF4wZMwYAIJPJ8Mwzz+CPP/7AjRs3jLYfMmQI+Hy+/ue2bdsCgFF3O47jMGzYMINlbdu2rXG3PIVCgZkzZ6JZs2YQCAQQCASQyWQoKirCtWvXLN7f8ePH0adPHzRq1Ei/jM/nG3QlA4BDhw5BrVbjpZdeglqt1v+TSCTo1auXvkUjISEB6enpGD16tMHrg4KC0K1bN5MxPPnkkxAKhUbLd+7ciW7dukEmk0EgEEAoFGLDhg0Gx3n8+HEAwPPPP2/w2ueee85of2q1GsuXL0erVq0gEokgEAggEolw48aNGp27iu8/fvx4g+WdOnVCy5YtcfToUYPl/v7+6NSpk8Eyc66HvLw8FBcXw8/Pz2jd8OHDkZGRgWvXriE7OxuvvPIKTpw4ge3bt+Orr75CSUkJ3njjDQQEBCAoKAiLFi0CY8xgH0KhEJ6enrh37565h05qgJIpQhxYUFAQPvroIxQWFmLdunUAzOtbv2nTJjzzzDMAgOjoaH0Xwop99I8cOYI+ffrA3d0dUqkU3bp1M/oDQQghzurmzZs4ceIEhgwZAsYY8vLykJeXh1GjRgH4r8JfRZW7TYnFYgDl3QUrkkqlRhXUxGKxyRLV5njuueewZs0aTJw4EYcOHcLZs2cRHx8PX19fo/c2R3Z2Nvz9/Y2WV16me0gXGRkJoVBo8O+HH37Qd2fLzs4GAIPkTMfUMgAICAgwWrZ7926MHj0aTZo0wZYtW3Dq1CnEx8fj5ZdfNjh32dnZEAgERp+HqWOaPn065s+fj+HDh+PXX3/FmTNnEB8fj8cee6xG5073/lUdQ+PGjfXrdUx1txOLxQ99f916U9X4dPuIiIiAq6srysrK8Oqrr2LevHkICwvD8uXL8eeff+Kff/7B0aNH8c033xj8jdeRSCQ1Pg/EPAJ7B0AIqd7gwYPB5/Nx4sQJAP/1rR8zZgzkcjnS0tLw5ZdfIjIyElevXoWPjw+GDBmC5cuXY86cOVi7di3at28PAAgLCwMAbNmyBS+99BKeeuopbN68GUKhEOvWrcOAAQNw6NAh9OnTx27HSwgh1vC///0PjDH8+OOP+PHHH43Wb968GUuXLjVoibIm3Q1y5Sp2lcfb5OfnY+/evVi4cCFmzZqlX64bX1QT3t7eSE9PN1peeZmPjw8A4Mcff0RwcHC1+wNgsoeEqfcBYHLepi1btiA0NBQ//PCDwfrK58jb2xtqtRrZ2dkGiYqp99L9PVu+fLnB8qysLINxSJbQvWdaWhqaNm1qsC41NVV/3mpL9z7mfM7Lly+HQCDAO++8AwA4cOAAYmJi4O/vD39/f4wePRr79+9HTEyMwetyc3OtFi8xjZIpQhycq6srfHx8kJqaCgAYNWqU/skqUF5ydujQoWjUqBG2bduGqVOnwtfXF82bNwcAtGrVyqDka3FxMd566y0MHToUe/bs0S8fPHgw2rdvjzlz5tA8LIQQp6bRaLB582aEhYXhm2++MVq/d+9efPTRRzhw4ACGDh1qkxh0Fe4uXryIAQMG6Jf/8ssvBttxHAfGmL4VTOebb76BRqOp0XtHR0fjl19+wf379/UtRxqNBj/88IPBdgMGDIBAIEBiYiJGjhxZ5f7Cw8Ph7++PHTt2YPr06frlycnJ+PPPP9G4cWOz4uI4DiKRyCCRSk9PN6rmFx0djZUrV2Lr1q2YOnWqfvm2bdtM7rPyudu3bx/u3buHZs2a6ZdV1cpoiq7c+JYtW/RVAAEgPj4e165dw9y5cx+6D3OIRCI88sgjSExMrHa7hIQErFy5EseOHdN3nWSMoaioSL+NQqEw6uaXmpoKpVKJVq1aWSVeYholU4Q4gYpfkAqFAkuWLMGuXbuQlJRk8MfWnP7hf/75J3JycjBu3Dio1WqDdQMHDsTKlStRVFSkL8lKCCHO5sCBA0hNTcUHH3yAqKgoo/WtW7fGmjVrsGHDBpslU/7+/ujbty/ef/99eHl5ITg4GEePHsXu3bsNtnN3d0fPnj3x4YcfwsfHByEhIfj999+xYcOGGreszJs3D7/88gt69+6NBQsWQCqV6ivCVRQSEoLFixdj7ty5uHXrFgYOHAgvLy/cv38fZ8+ehaurK2JjY8Hj8RAbG4tXX30Vo0aNwssvv4y8vDzExsYiICAAPJ55o0aGDh2K3bt3Y/LkyRg1ahRSUlKwZMkSBAQEGIxh69+/P3r27In33ntPX8Xu5MmT+O6770zuc9OmTYiIiEDbtm3x119/4cMPPzRqUQoLC4OLiwu2bt2Kli1bQiaToXHjxiYTwfDwcLzyyitYvXo1eDweBg0ahKSkJMyfPx+BgYF4++23zTpec0RFReHAgQNVrmeM4ZVXXkFMTIzBg9EBAwbg888/R/PmzaFQKLBt2zZ8+umnBq/VlfWPjo62WrzEBPvVviCEMPZfNb/4+HiT6xUKBePz+axPnz6MMcaGDRvGpFIpe//999mRI0fY2bNnWXx8PPP19WXjxo3Tv66qan5btmxhAKr9l5ycbKvDJYQQmxs+fDgTiUQsIyOjym3GjBnDBAIBS09P11eg+/DDD422Q6XqcePGjWOurq5G21WuCMcYY2lpaWzUqFFMLpczDw8P9sILL7Bz584ZVZW7e/cuGzlyJPPy8mJubm5s4MCB7PLlyyw4ONjge93can6MMXby5EnWpUsXJhaLmb+/P3v33XfZ+vXrDar56fz0008sOjqaubu7M7FYzIKDg9moUaPYkSNHDLZbv349a9asGROJRKxFixbsf//7H3vqqacMKvFVdy4ZY2zFihUsJCSEicVi1rJlS/b111+bPHd5eXns5ZdfZp6enkwqlbJ+/fqx69evG30eubm5bMKECczPz49JpVLWvXt39scff7BevXqxXr16Gexz+/btLCIiggmFQoP9mHp/jUbDPvjgA9aixf+3d8cgjWxhGIa/i2sURY2MRkgh2AgSDRYWwhRqZcBGQbGJRCEiiJLGQrBIITZRGEIKRYSkFsHaQgUtBK0EsVUjghgIamwEw9zisoHcrOzu3Ox1Xd6nnDkM53Tznfnn/O12ZWWl3dTUZAeDQfv29rZoXF9fn+3z+UrWGQqFik45fM/+/r4tyT49Pf3m/a2tLdvr9dpPT09F119eXuxwOGwbhmG3tLTYi4uLdj6fLxozMTFhd3V1fXcO+G/+su1/fRME8L9KpVKamprS2dmZenp6Su5vb29rfHxcy8vLmp+fV2Njo6LRqKLRaGHM6+uramtrFQwGCz+g7uzsaGxsTIeHh0U7s3t7ewoEAkokEu92fPf7/XK5XGVdJwDgz/L4+Kj29nYNDw9rc3Pzo6fzafn9fpmmqfX19bI98/n5WV6vV5ZlaXp6umzPRSnK/IDfWDqd1sLCghoaGjQzM/NTtfXv1Yebpim3263Ly0vNzc392gUAAP4I9/f3WllZ0cDAgAzD0M3NjSzLUi6XUyQS+ejpfWqxWEwjIyNaWloqKU90yrIstba2lhxIgfIjTAG/iYuLi0KPj4eHBx0fHyuZTKqiokK7u7tqbm6WpB+ure/s7JT0T9f6uro6VVdXq62tTYZhKJFIKBQKKZvNanR0VB6PR5lMRufn58pkMmXdHQMAfH5VVVW6vr7W7Oysstmsampq1Nvbq42NDfl8vo+e3qcWCAS0urqqq6ursoWp+vp6pVIpffnCq/6vRpkf8MG+lvl95XK55Ha71dHRocHBQYXD4UKQkqS7uztFIhEdHBzo7e1NpmlqbW1NQ0ND6u/vL+ozEY/HFY/HlU6nlc/nlUwmC00Ij46OFIvFdHJyolwuJ4/Ho+7ubk1OThadFggAAIBvI0wBAAAAgAM/dpYlAAAAAKAIYQoAAAAAHCBMAQAAAIADhCkAAAAAcIAwBQAAAAAOEKYAAAAAwAHCFAAAAAA4QJgCAAAAAAcIUwAAAADgAGEKAAAAABwgTAEAAACAA38Dgx89iwTIQrwAAAAASUVORK5CYII=", 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", 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", 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" ] @@ -427,14 +425,15 @@ "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "AttributeError", + "evalue": "'TrendAnalysis' object has no attribute 'plot_degradation_timeseries'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[18], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Plot a time-dependent median (plus confidence interval) of sensor-based degradation results\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_degradation_timeseries\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msensor\u001b[39m\u001b[38;5;124m'\u001b[39m, rolling_days\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m365\u001b[39m)\n", + "\u001b[1;31mAttributeError\u001b[0m: 'TrendAnalysis' object has no attribute 'plot_degradation_timeseries'" + ] } ], "source": [ @@ -458,13 +457,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\plotting.py:172: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + "C:\\Users\\nmoyer\\.conda\\envs\\soilpytest\\lib\\site-packages\\rdtools\\plotting.py:165: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -486,13 +485,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\plotting.py:232: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + "C:\\Users\\nmoyer\\.conda\\envs\\soilpytest\\lib\\site-packages\\rdtools\\plotting.py:225: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -514,13 +513,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\plotting.py:272: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + "C:\\Users\\nmoyer\\.conda\\envs\\soilpytest\\lib\\site-packages\\rdtools\\plotting.py:265: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", + "image/png": 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Use at your own risk!\n", + " warnings.warn(\"The XGBoost filter is an experimental clipping filter \"\n", + "C:\\Users\\nmoyer\\.conda\\envs\\soilpytest\\lib\\site-packages\\xgboost\\core.py:158: UserWarning: [21:44:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-06abd128ca6c1688d-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:872: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.\n", + " warnings.warn(smsg, UserWarning)\n" ] } ], @@ -843,6 +844,35 @@ "execution_count": 26, "metadata": {}, "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "application/vnd.plotly.v1+json": { @@ -61368,6 +61398,7 @@ } ], "layout": { + "autosize": true, "legend": { "title": { "text": "mask" @@ -61391,6 +61422,11 @@ "line": { "color": "#E5ECF6", "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 } }, "type": "bar" @@ -61402,6 +61438,11 @@ "line": { "color": "#E5ECF6", "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 } }, "type": "barpolar" @@ -61600,9 +61641,10 @@ "histogram": [ { "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 } }, "type": "histogram" @@ -61738,11 +61780,10 @@ ], "scatter": [ { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -61920,6 +61961,7 @@ "arrowhead": 0, "arrowwidth": 1 }, + "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, @@ -62184,26 +62226,66 @@ }, "xaxis": { "anchor": "y", + "autorange": true, "domain": [ 0, 1 ], + "range": [ + "2012-12-30 17:25:38.9338", + "2013-01-23 06:33:21.0662" + ], "title": { "text": "datetime" - } + }, + "type": "date" }, "yaxis": { "anchor": "x", + "autorange": true, "domain": [ 0, 1 ], + "range": [ + -1.3697493381233599, + 19.223241354790026 + ], "title": { "text": "energy_Wh" - } + }, + "type": "linear" } } - } + }, + "image/png": 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", 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", 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", 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", + "image/png": 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", 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" ] @@ -62326,6 +62408,13 @@ " scatter_ymin=0.5, scatter_ymax=1.1,\n", " hist_xmin=-30, hist_xmax=45);" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -62345,7 +62434,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 2860d427..eac05474 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -136,16 +136,16 @@ def _calc_daily_df( in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. neg_shift : bool, default True - where True results in additional subdividing of soiling intervals - when negative shifts are found in the rolling median of the performance - metric. Inferred corrections in the soiling fit are made at these - negative shifts. False results in no additional subdivides of the + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the data where excessive negative shifts can invalidate a soiling interval. - piecewise : bool, default True - where True results in each soiling interval of sufficient length - being tested for significant fit improvement with 2 piecewise linear - fits. If the criteria of significance is met the soiling interval is - subdivided into the 2 separate intervals. False results in no + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no piecewise fit being tested. """ if (day_scale % 2 == 0) and ("shift" in clean_criterion): @@ -201,7 +201,8 @@ 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 median change to day_scale/2 Matt + # 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))) @@ -219,7 +220,9 @@ 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 in a row to a single event + # 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( df.clean_event_detected, df.delta.values, 5 @@ -257,7 +260,7 @@ def _calc_daily_df( # add negative shifts which allows further segmentation of the soiling # intervals and handles correction for data outages/Matt df.delta = df.delta.fillna(0) # to avoid NA corrupting calculation - if neg_shift == True: + if neg_shift is True: df["drop_event"] = df.delta < -2.5 * clean_threshold df["break_event"] = df.clean_event | df.drop_event else: @@ -281,7 +284,7 @@ def _calc_daily_df( # if statistical criteria are met with the piecewise linear fit # compared to a single linear fit. Intervals <45 days reqire more # stringent statistical improvements/Matt - if piecewise == True: + if piecewise is True: warnings.warn( "Piecewise = True was passed, for both Piecewise=True" "and neg_shift=True cleaning_method choices should" @@ -297,7 +300,7 @@ def _calc_daily_df( pr = pr.bfill() # catch first position nan if len(run) > min_soil_length and run.pi_norm.sum() > 0: sr, cp_date = segmented_soiling_period(pr, days_clean_vs_cp=13) - if cp_date != None: + if cp_date is not None: cp_dates.append(pr.index[cp_date]) # save changes to df, note I would like to rename "clean_event" from # original code to something like "break_event @@ -433,7 +436,7 @@ def _calc_result_df( """ # Filter results for each interval, - # setting invalid interval to slope of 0 + # setting invalid interval to slope of 0 #moved above to line 356/Matt results['slope_err'] = ( results.run_slope_high - results.run_slope_low)\ @@ -442,7 +445,7 @@ def _calc_result_df( ############################################################### # negative shifts are now used as breaks for soiling intervals/Matt # so new criteria for final filter to modify dataframe - if neg_shift == True: + if neg_shift is True: warnings.warn( "neg_shift = True was passed, for both Piecewise=True" "and neg_shift=True cleaning_method choices should" @@ -463,7 +466,7 @@ def _calc_result_df( ################################################################## # original code below setting soiling intervals with extreme negative # shift to zero slopes, /Matt - if neg_shift == False: + if neg_shift is False: filt = ( (results.run_slope > 0) | (results.slope_err >= max_relative_slope_error / 100.0) @@ -472,7 +475,6 @@ def _calc_result_df( # for calculations below # |results.loc[filt, 'valid'] = False ) - print(results.slope_err) results.loc[filt, "run_slope"] = 0 results.loc[filt, "run_slope_low"] = 0 results.loc[filt, "run_slope_high"] = 0 @@ -497,17 +499,18 @@ def _calc_result_df( # if the current interval starts with a clean event, the previous end # is a nan, and the current interval is valid then set prev_end=1 results.loc[ - (results.clean_event == True) - & (np.isnan(results.prev_end) & (results.valid == True)), + (results.clean_event is True) + & (np.isnan(results.prev_end) & (results.valid is True)), "prev_end", - ] = 1 ##############################clean_event or clean_event_detected + ] = 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 negative due to fitting results + # 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 ) ####################################################################### - if neg_shift == False: + if neg_shift is False: results.loc[filt, "valid"] = False if len(results[results.valid]) == 0: @@ -601,8 +604,10 @@ 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 the next 10 days of data - # this catches places where start points of polyfits were skewed below where data start + # 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 + # skewed below where data start if (soil_infer + shift) < forward_median: shift = forward_median - soil_infer if (soil_perfect + shift_perfect) < forward_median: @@ -621,7 +626,7 @@ def _calc_result_df( soil_perfect = np.clip((soil_perfect + shift_perfect), soil_perfect, 1) start_perfect = soil_perfect soil_perfect_clean.append(soil_perfect) - if changepoint == False: + if changepoint is False: prev_shift = start_shift # assigned at new soil period elif new_soil > 0: # within soiling period @@ -662,24 +667,24 @@ def _calc_monte(self, monte, method="half_norm_clean"): How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100%, + * 'random_clean' - a random recovery between 0-100%, pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1, + metric to 1, pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval, + the interval, pair with piecewise=False and neg_shift=False - *'perfect_clean_complex' - each detected clean event returns the - performance metric to 1 while negative shifts in the data or + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or piecewise linear fits result in no cleaning, pair with piecewise=True and neg_shift=True - *'inferred_clean_complex' - at each detected clean event the - performance metric increases based on fits to the data while - negative shifts in the data or piecewise linear fits result in no + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no cleaning, pair with piecewise=True and neg_shift=True """ @@ -935,28 +940,28 @@ def run( method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', perfect_clean_complex,inferred_clean_complex} \ default 'perfect_clean_complex' - + How to treat the recovery of each cleaning event - * 'random_clean' - a random recovery between 0-100%, + * 'random_clean' - a random recovery between 0-100%, pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1, + metric to 1, pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval, + the interval, pair with piecewise=False and neg_shift=False - * 'perfect_clean_complex' - each detected clean event returns the - performance metric to 1 while negative shifts in the data or + * 'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or piecewise linear fits result in no cleaning, pair with piecewise=True and neg_shift=True - * 'inferred_clean_complex' - at each detected clean event the - performance metric increases based on fits to the data while - negative shifts in the data or piecewise linear fits result in no + * 'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no cleaning, - pair with piecewise=True and neg_shift=True + pair with piecewise=True and neg_shift=True clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -994,16 +999,16 @@ def run( in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. neg_shift : bool, default True - where True results in additional subdividing of soiling intervals - when negative shifts are found in the rolling median of the performance - metric. Inferred corrections in the soiling fit are made at these - negative shifts. False results in no additional subdivides of the + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the data where excessive negative shifts can invalidate a soiling interval. - piecewise : bool, default True - where True results in each soiling interval of sufficient length - being tested for significant fit improvement with 2 piecewise linear - fits. If the criteria of significance is met the soiling interval is - subdivided into the 2 separate intervals. False results in no + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no piecewise fit being tested. Returns @@ -1187,26 +1192,26 @@ def soiling_srr( 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%, + * 'random_clean' - a random recovery between 0-100%, pair with piecewise=False and neg_shift=False * 'perfect_clean' - each cleaning event returns the performance - metric to 1, + metric to 1, pair with piecewise=False and neg_shift=False * 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to - the interval, + the interval, pair with piecewise=False and neg_shift=False - *'perfect_clean_complex' - each detected clean event returns the - performance metric to 1 while negative shifts in the data or + *'perfect_clean_complex' - each detected clean event returns the + performance metric to 1 while negative shifts in the data or piecewise linear fits result in no cleaning, pair with piecewise=True and neg_shift=True - *'inferred_clean_complex' - at each detected clean event the - performance metric increases based on fits to the data while - negative shifts in the data or piecewise linear fits result in no + *'inferred_clean_complex' - at each detected clean event the + performance metric increases based on fits to the data while + negative shifts in the data or piecewise linear fits result in no cleaning, pair with piecewise=True and neg_shift=True clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ @@ -1245,16 +1250,16 @@ def soiling_srr( in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events. neg_shift : bool, default True - where True results in additional subdividing of soiling intervals - when negative shifts are found in the rolling median of the performance - metric. Inferred corrections in the soiling fit are made at these - negative shifts. False results in no additional subdivides of the + where True results in additional subdividing of soiling intervals + when negative shifts are found in the rolling median of the performance + metric. Inferred corrections in the soiling fit are made at these + negative shifts. False results in no additional subdivides of the data where excessive negative shifts can invalidate a soiling interval. - piecewise : bool, default True - where True results in each soiling interval of sufficient length - being tested for significant fit improvement with 2 piecewise linear - fits. If the criteria of significance is met the soiling interval is - subdivided into the 2 separate intervals. False results in no + piecewise : bool, default True + where True results in each soiling interval of sufficient length + being tested for significant fit improvement with 2 piecewise linear + fits. If the criteria of significance is met the soiling interval is + subdivided into the 2 separate intervals. False results in no piecewise fit being tested. Returns @@ -2861,7 +2866,7 @@ def _forward_pass( # Enter forward pass of filtering algorithm for i, z in enumerate(zs): if 7 < i < N - 7 and (i in cleaning_events or i in soiling_events): - rolling_median_local = rolling_median_7.loc[i - 5 : i + 5].values + rolling_median_local = rolling_median_7.loc[i - 5: i + 5].values u = self._set_control_input(f, rolling_median_local, i, cleaning_events) f.predict(u=u) # Predict wth control input u else: # If no cleaning detection, predict without control input @@ -3170,10 +3175,10 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4): end_true_vals = collapsed_ce_dummy.loc[start_true_vals:].idxmin() - 1 if end_true_vals >= start_true_vals: # If the island ends # Find the day with mac probability of being a cleaning event - max_diff_day = metric.loc[start_true_vals - f : end_true_vals + f].idxmax() + max_diff_day = metric.loc[start_true_vals - f: end_true_vals + f].idxmax() # Set all days in this period as false - collapsed_ce.loc[start_true_vals - f : end_true_vals + f] = False - collapsed_ce_dummy.loc[start_true_vals - f : end_true_vals + f] = False + collapsed_ce.loc[start_true_vals - f: end_true_vals + f] = False + collapsed_ce_dummy.loc[start_true_vals - f: end_true_vals + f] = False # Set the max probability day as True (cleaning event) collapsed_ce.loc[max_diff_day] = True # Find the next island of true values @@ -3359,8 +3364,10 @@ 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 (data points in between two cleaning events). - Segmentation is neglected if change point occurs within a number of days (days_clean_vs_cp) of the cleanings. + 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 + (days_clean_vs_cp) of the cleanings. Parameters ---------- @@ -3371,7 +3378,8 @@ 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 defined in the function. + 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 min_r2 : numeric (default=0.1) @@ -3392,7 +3400,7 @@ def segmented_soiling_period( raise ValueError("The time series does not have DatetimeIndex") # Define bounds if not provided - if bounds == None: + if bounds is None: # bounds are neg in first 4 and pos in second 4 # ordered as x0,b,k1,k2 where x0 is the breakpoint k1 and k2 are slopes bounds = [(13, -5, -np.inf, -np.inf), ((len(pr) - 13), 5, +np.inf, +np.inf)] @@ -3404,7 +3412,8 @@ 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. None if no change point is found. + # 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) cp_date = int(p[0]) diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 20691e45..605e3e91 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -33,17 +33,6 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times assert isinstance( soiling_info["stochastic_soiling_profiles"], list ), 'soiling_info["stochastic_soiling_profiles"] is not a list' - # wait to see which tests matt wants to keep - # assert len(soiling_info['change_points']) == len(soiling_normalized_daily), \ - # 'length of soiling_info["change_points"] different than expected' - # assert isinstance(soiling_info['change_points'], pd.Series), \ - # 'soiling_info["change_points"] not a pandas series' - # assert (soiling_info['change_points'] == False).all(), \ - # 'not all values in soiling_inf["change_points"] are False' - # assert len(soiling_info['days_since_clean']) == len(soiling_normalized_daily), \ - # 'length of soiling_info["days_since_clean"] different than expected' - # assert isinstance(soiling_info['days_since_clean'], pd.Series), \ - # 'soiling_info["days_since_clean"] not a pandas series' # Check soiling_info['soiling_interval_summary'] expected_summary_columns = [ @@ -68,7 +57,8 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times 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']" + ), 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' From 669ec756f571bb2a85bd7578c98a354e05832dd1 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 6 Aug 2024 14:02:42 -0400 Subject: [PATCH 07/33] 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) From 23710a050ff9c5fd9791948dfe69d95533a0ddd6 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 6 Aug 2024 14:11:13 -0400 Subject: [PATCH 08/33] lint line length --- rdtools/soiling.py | 247 +++++++++++++-------------------------------- 1 file changed, 70 insertions(+), 177 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 6eb917b5..48413d15 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -62,9 +62,7 @@ class SRRAnalysis: subsequent calculations.) """ - def __init__( - self, energy_normalized_daily, insolation_daily, precipitation_daily=None - ): + def __init__(self, energy_normalized_daily, insolation_daily, precipitation_daily=None): self.pm = energy_normalized_daily # daily performance metric self.insolation_daily = insolation_daily self.precipitation_daily = precipitation_daily # daily precipitation @@ -73,9 +71,7 @@ def __init__( self.monte_losses = [] if pd.infer_freq(self.pm.index) != "D": - raise ValueError( - "Daily performance metric series must have " "daily frequency" - ) + raise ValueError("Daily performance metric series must have " "daily frequency") if pd.infer_freq(self.insolation_daily.index) != "D": raise ValueError("Daily insolation series must have " "daily frequency") @@ -234,9 +230,7 @@ def _calc_daily_df( # Detect which cleaning events are associated with rain # within a 3 day window precip_event = ( - precip_event.rolling(3, center=True, min_periods=1) - .apply(any) - .astype(bool) + 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": @@ -419,8 +413,7 @@ def _calc_result_df( ############################################# # calculate loss over soiling interval per polyfit/matt result_dict["run_loss_baseline"] = ( - result_dict["inferred_start_loss"] - - result_dict["inferred_end_loss"] + result_dict["inferred_start_loss"] - result_dict["inferred_end_loss"] ) ############################################### @@ -486,9 +479,7 @@ def _calc_result_df( ######################################################################## # remove clipping on 'inferred_recovery' so absolute recovery can be # used in later step where clipping can be considered/Matt - results["inferred_recovery"] = ( - results.next_inferred_start_loss - results.inferred_end_loss - ) + results["inferred_recovery"] = results.next_inferred_start_loss - results.inferred_end_loss ######################################################################## # calculate beginning inferred shift (end of previous soiling period @@ -497,8 +488,7 @@ def _calc_result_df( # if the current interval starts with a clean event, the previous end # is a nan, and the current interval is valid then set prev_end=1 results.loc[ - (results.clean_event is True) - & (np.isnan(results.prev_end) & (results.valid is True)), + (results.clean_event is True) & (np.isnan(results.prev_end) & (results.valid is True)), "prev_end", ] = 1 # clean_event or clean_event_detected results["inferred_begin_shift"] = results.inferred_start_loss - results.prev_end @@ -517,16 +507,12 @@ def _calc_result_df( new_end = results.end.iloc[-1] pm_frame_out = daily_df[new_start:new_end] pm_frame_out = ( - pm_frame_out.reset_index() - .merge(results, how="left", on="run") - .set_index("date") + pm_frame_out.reset_index().merge(results, how="left", on="run").set_index("date") ) pm_frame_out["loss_perfect_clean"] = np.nan pm_frame_out["loss_inferred_clean"] = np.nan - pm_frame_out["days_since_clean"] = ( - pm_frame_out.index - pm_frame_out.start - ).dt.days + pm_frame_out["days_since_clean"] = (pm_frame_out.index - pm_frame_out.start).dt.days ####################################################################### # new code for perfect and inferred clean with handling of/Matt @@ -585,13 +571,9 @@ def _calc_result_df( shift_perfect = start_shift total_down = 0 ############################################################# - elif (start_shift >= 0) & ( - prev_shift < 0 - ): # cleaning starts the current + elif (start_shift >= 0) & (prev_shift < 0): # cleaning starts the current # interval but there was a previous downshift - shift = ( - start_shift + total_down - ) # correct for the negative shifts + shift = start_shift + total_down # correct for the negative shifts shift_perfect = shift # dont set to one 1 if correcting for a # downshift (debateable alternative set to 1) total_down = 0 @@ -616,9 +598,7 @@ def _calc_result_df( begin_infer_shifts.append(shift) # clip to last value in case shift ends up negative soil_infer = np.clip((soil_infer + shift), soil_infer, 1) - start_infer = ( - soil_infer # make next start value the last inferred value - ) + start_infer = soil_infer # make next start value the last inferred value soil_inferred_clean.append(soil_infer) # clip to last value in case shift ends up negative soil_perfect = np.clip((soil_perfect + shift_perfect), soil_perfect, 1) @@ -741,8 +721,8 @@ def _calc_monte(self, monte, method="half_norm_clean"): valid_intervals["inferred_recovery"] = np.clip( valid_intervals.inferred_recovery, 0, 1 ) - valid_intervals["inferred_recovery"] = ( - valid_intervals.inferred_recovery.fillna(1.0) + valid_intervals["inferred_recovery"] = valid_intervals.inferred_recovery.fillna( + 1.0 ) end_list = [] @@ -809,22 +789,14 @@ def _calc_monte(self, monte, method="half_norm_clean"): for i, row in results_rand.iterrows(): if row.begin_perfect_shift > 0: inter_start = np.clip( - ( - inter_start - + row.begin_perfect_shift - + delta_previous_run_loss - ), + (inter_start + row.begin_perfect_shift + delta_previous_run_loss), end, 1, ) - delta_previous_run_loss = ( - -1 * row.run_loss - row.run_loss_baseline - ) + delta_previous_run_loss = -1 * row.run_loss - row.run_loss_baseline else: delta_previous_run_loss = ( - delta_previous_run_loss - - 1 * row.run_loss - - row.run_loss_baseline + delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline ) # inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) start_list.append(inter_start) @@ -837,22 +809,14 @@ def _calc_monte(self, monte, method="half_norm_clean"): for i, row in results_rand.iterrows(): if row.begin_infer_shift > 0: inter_start = np.clip( - ( - inter_start - + row.begin_infer_shift - + delta_previous_run_loss - ), + (inter_start + row.begin_infer_shift + delta_previous_run_loss), end, 1, ) - delta_previous_run_loss = ( - -1 * row.run_loss - row.run_loss_baseline - ) + delta_previous_run_loss = -1 * row.run_loss - row.run_loss_baseline else: delta_previous_run_loss = ( - delta_previous_run_loss - - 1 * row.run_loss - - row.run_loss_baseline + delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline ) # inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) start_list.append(inter_start) @@ -869,21 +833,16 @@ def _calc_monte(self, monte, method="half_norm_clean"): raise ValueError("Invalid method specification") df_rand = ( - df_rand.reset_index() - .merge(results_rand, how="left", on="run") - .set_index("date") + df_rand.reset_index().merge(results_rand, how="left", on="run").set_index("date") ) df_rand["loss"] = np.nan df_rand["days_since_clean"] = (df_rand.index - df_rand.start).dt.days - df_rand["loss"] = ( - df_rand.start_loss + df_rand.days_since_clean * df_rand.run_slope - ) + df_rand["loss"] = df_rand.start_loss + df_rand.days_since_clean * df_rand.run_slope df_rand["soil_insol"] = df_rand.loss * df_rand.insol soiling_ratio = ( - df_rand.soil_insol.sum() - / df_rand.insol[~df_rand.soil_insol.isnull()].sum() + df_rand.soil_insol.sum() / df_rand.insol[~df_rand.soil_insol.isnull()].sum() ) monte_losses.append(soiling_ratio) random_profile = df_rand["loss"].copy() @@ -1353,9 +1312,7 @@ def _count_month_days(start, end): return out_dict -def annual_soiling_ratios( - stochastic_soiling_profiles, insolation_daily, confidence_level=68.2 -): +def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confidence_level=68.2): """ Return annualized soiling ratios and associated confidence intervals based on stochastic soiling profiles from SRR. Note that each year @@ -1558,9 +1515,7 @@ def monthly_soiling_rates( rates = [x for sublist in rates for x in sublist] if rates: - monthly_rate_data.append( - np.quantile(rates, [0.5, ci_quantiles[0], ci_quantiles[1]]) - ) + monthly_rate_data.append(np.quantile(rates, [0.5, ci_quantiles[0], ci_quantiles[1]])) else: monthly_rate_data.append(np.array([np.nan] * 3)) @@ -1943,14 +1898,10 @@ def iterative_signal_decomposition( return_sorted=False, ) # Ensure periodic seaonal component - seasonal_comp = _force_periodicity( - smooth_season, season_dummy.index, pi.index - ) + seasonal_comp = _force_periodicity(smooth_season, season_dummy.index, pi.index) seasonal_component.append(seasonal_comp) if degradation_method == "STL": # If not YoY - deg_trend = pd.Series( - index=pi.index, data=STL_res.trend.apply(np.exp) - ) + deg_trend = pd.Series(index=pi.index, data=STL_res.trend.apply(np.exp)) degradation_trend.append(deg_trend / deg_trend.iloc[0]) yoy_save.append( RdToolsDeg.degradation_year_on_year( @@ -1963,9 +1914,7 @@ def iterative_signal_decomposition( # Decompose signal trend_dummy = pi / seasonal_component[-1] / soiling_ratio[-1] # Run YoY - yoy = RdToolsDeg.degradation_year_on_year( - trend_dummy, uncertainty_method=None - ) + yoy = RdToolsDeg.degradation_year_on_year(trend_dummy, uncertainty_method=None) # Convert degradation rate to trend degradation_trend.append( pd.Series(index=pi.index, data=(1 + day * yoy / 100 / 365.0)) @@ -1973,9 +1922,7 @@ def iterative_signal_decomposition( yoy_save.append(yoy) # Combine and calculate residual flatness - total_model = ( - degradation_trend[-1] * seasonal_component[-1] * soiling_ratio[-1] - ) + total_model = degradation_trend[-1] * seasonal_component[-1] * soiling_ratio[-1] residuals = pi / total_model residual_shift = residuals.mean() total_model *= residual_shift @@ -1998,8 +1945,7 @@ def iterative_signal_decomposition( 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 + ic >= max_iterations / 2 or relative_improvement < convergence_criterion ): # From now on, do not assume perfect cleaning perfect_cleaning = False @@ -2240,10 +2186,7 @@ def run_bootstrap( ] index_list = list(itertools.product([0, 1], repeat=len(parameter_alternatives))) combination_of_parameters = [ - [ - parameter_alternatives[j][indexes[j]] - for j in range(len(parameter_alternatives)) - ] + [parameter_alternatives[j][indexes[j]] for j in range(len(parameter_alternatives))] for indexes in index_list ] nr_models = len(index_list) @@ -2288,20 +2231,14 @@ def run_bootstrap( # Print progress if verbose: - _progressBarWithETA( - c + 1, nr_models, time.time() - t00, bar_length=30 - ) + _progressBarWithETA(c + 1, nr_models, time.time() - t00, bar_length=30) except ValueError as ex: print(ex) # Revive results - adfs = np.array( - [(r["adf_res"][0] if r["adf_res"][1] < 0.05 else 0) for r in results] - ) + adfs = np.array([(r["adf_res"][0] if r["adf_res"][1] < 0.05 else 0) for r in results]) RMSEs = np.array([r["RMSE"] for r in results]) - SR_is_one_fraction = np.array( - [(df.soiling_ratio == 1).mean() for df in list_of_df_out] - ) + SR_is_one_fraction = np.array([(df.soiling_ratio == 1).mean() for df in list_of_df_out]) small_soiling_signal = [r["small_soiling_signal"] for r in results] # Calculate weights @@ -2366,18 +2303,14 @@ def run_bootstrap( self.small_soiling_signal = False # Aggregate all bootstrap samples - all_bootstrap_samples = pd.concat( - bootstrap_samples_list, axis=1, ignore_index=True - ) + all_bootstrap_samples = pd.concat(bootstrap_samples_list, axis=1, ignore_index=True) # Seasonal samples are generated from previously fitted seasonal # components, by perturbing amplitude and phase shift # Number of samples per fit: sample_nr = int(reps / nr_models) list_of_SCs = [ - list_of_df_out[m].seasonal_component - for m in range(nr_models) - if weights[m] > 0 + 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, @@ -2412,12 +2345,8 @@ def run_bootstrap( for b in range(reps): try: # randomly choose model sensitivities - dt = np.random.uniform( - parameter_alternatives[1][0], parameter_alternatives[1][-1] - ) - pt = np.random.uniform( - parameter_alternatives[2][0], parameter_alternatives[2][-1] - ) + dt = np.random.uniform(parameter_alternatives[1][0], parameter_alternatives[1][-1]) + pt = np.random.uniform(parameter_alternatives[2][0], parameter_alternatives[2][-1]) pn = np.random.uniform(process_noise / 1.5, process_noise * 1.5) renormalize_SR = np.random.choice([None, np.random.uniform(0.5, 0.95)]) ffill = np.random.choice([True, False]) @@ -2430,20 +2359,18 @@ def run_bootstrap( temporary_cods_instance = CODSAnalysis(bootstrap_sample) # Do Signal decomposition for soiling and degradation component - 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, - degradation_method=degradation_method, - **kwargs, - ) + 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, + degradation_method=degradation_method, + **kwargs, ) # If we can reject the null-hypothesis that there is a unit @@ -2528,9 +2455,7 @@ def run_bootstrap( np.quantile(bt_deg, ci_low_edge), np.quantile(bt_deg, ci_high_edge), ] - df_out.degradation_trend = ( - 1 + np.arange(len(pi)) * self.degradation[0] / 100 / 365.0 - ) + df_out.degradation_trend = 1 + np.arange(len(pi)) * self.degradation[0] / 100 / 365.0 # Soiling losses self.soiling_loss = [ @@ -2556,9 +2481,7 @@ def run_bootstrap( self.residual_shift = df_out.residuals.mean() df_out.total_model *= self.residual_shift self.RMSE = _RMSE(pi, df_out.total_model) - self.adf_results = adfuller( - df_out.residuals.dropna(), regression="ctt", autolag=None - ) + self.adf_results = adfuller(df_out.residuals.dropna(), regression="ctt", autolag=None) self.result_df = df_out self.errors = errors @@ -2679,38 +2602,22 @@ def _Kalman_filter_for_SR( + " indices of zs_series; they must be of the same length" ) else: # If no prescient cleaning events, detect cleaning events - ce, rm9 = _rolling_median_ce_detection( - zs_series.index, zs_series, tuner=0.5 - ) - prescient_cleaning_events = _collapse_cleaning_events( - ce, rm9.diff().values, 5 - ) + ce, rm9 = _rolling_median_ce_detection(zs_series.index, zs_series, tuner=0.5) + prescient_cleaning_events = _collapse_cleaning_events(ce, rm9.diff().values, 5) - cleaning_events = prescient_cleaning_events[ - prescient_cleaning_events - ].index.tolist() + cleaning_events = prescient_cleaning_events[prescient_cleaning_events].index.tolist() # Find soiling events (e.g. dust storms) - soiling_events = _soiling_event_detection( - zs_series.index, zs_series, ffill=ffill, tuner=5 - ) + soiling_events = _soiling_event_detection(zs_series.index, zs_series, ffill=ffill, tuner=5) soiling_events = soiling_events[soiling_events].index.tolist() # Initialize various parameters if ffill: - rolling_median_13 = ( - zs_series.ffill().rolling(13, center=True).median().ffill().bfill() - ) - rolling_median_7 = ( - zs_series.ffill().rolling(7, center=True).median().ffill().bfill() - ) + rolling_median_13 = zs_series.ffill().rolling(13, center=True).median().ffill().bfill() + rolling_median_7 = zs_series.ffill().rolling(7, center=True).median().ffill().bfill() else: - rolling_median_13 = ( - zs_series.bfill().rolling(13, center=True).median().ffill().bfill() - ) - rolling_median_7 = ( - zs_series.bfill().rolling(7, center=True).median().ffill().bfill() - ) + rolling_median_13 = zs_series.bfill().rolling(13, center=True).median().ffill().bfill() + rolling_median_7 = zs_series.bfill().rolling(7, center=True).median().ffill().bfill() # A rough estimate of the measurement noise measurement_noise = (rolling_median_13 - zs_series).var() # An initial guess of the slope @@ -2842,9 +2749,7 @@ def _Kalman_filter_for_SR( # Set number of days since cleaning event nr_days_dummy = pd.Series(index=dfk.index, data=np.nan) - nr_days_dummy.loc[cleaning_events] = [ - int(date - dfk.index[0]) for date in cleaning_events - ] + nr_days_dummy.loc[cleaning_events] = [int(date - dfk.index[0]) for date in cleaning_events] nr_days_dummy.iloc[0] = 0 dfk.days_since_ce = range(len(zs_series)) - nr_days_dummy.ffill() @@ -2854,9 +2759,7 @@ def _Kalman_filter_for_SR( return dfk, Ps - def _forward_pass( - self, f, zs_series, rolling_median_7, cleaning_events, soiling_events - ): + def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events, soiling_events): """Run the forward pass of the Kalman Filter algortihm""" zs = zs_series.values N = len(zs) @@ -2864,7 +2767,7 @@ def _forward_pass( # Enter forward pass of filtering algorithm for i, z in enumerate(zs): if 7 < i < N - 7 and (i in cleaning_events or i in soiling_events): - rolling_median_local = rolling_median_7.loc[i - 5: i + 5].values + rolling_median_local = rolling_median_7.loc[i - 5 : i + 5].values u = self._set_control_input(f, rolling_median_local, i, cleaning_events) f.predict(u=u) # Predict wth control input u else: # If no cleaning detection, predict without control input @@ -2899,9 +2802,7 @@ def _set_control_input(self, f, rolling_median_local, index, cleaning_events): u[0] = z_med - np.dot(f.H, np.dot(f.F, f.x)) # If the change is bigger than the measurement noise: if np.abs(u[0]) > np.sqrt(f.R) / 2: - index_dummy = [ - n + 3 for n in range(window_size - HW - 1) if n + 3 != HW - ] + index_dummy = [n + 3 for n in range(window_size - HW - 1) if n + 3 != HW] cleaning_events = [ ce for ce in cleaning_events if ce - index + HW not in index_dummy ] @@ -3173,10 +3074,10 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4): end_true_vals = collapsed_ce_dummy.loc[start_true_vals:].idxmin() - 1 if end_true_vals >= start_true_vals: # If the island ends # Find the day with mac probability of being a cleaning event - max_diff_day = metric.loc[start_true_vals - f: end_true_vals + f].idxmax() + max_diff_day = metric.loc[start_true_vals - f : end_true_vals + f].idxmax() # Set all days in this period as false - collapsed_ce.loc[start_true_vals - f: end_true_vals + f] = False - collapsed_ce_dummy.loc[start_true_vals - f: end_true_vals + f] = False + collapsed_ce.loc[start_true_vals - f : end_true_vals + f] = False + collapsed_ce_dummy.loc[start_true_vals - f : end_true_vals + f] = False # Set the max probability day as True (cleaning event) collapsed_ce.loc[max_diff_day] = True # Find the next island of true values @@ -3248,14 +3149,10 @@ def _make_seasonal_samples( # constructing the new signal based on median_signal shifted_signal = pd.Series( index=signal.index, - data=median_signal.reindex( - (signal.index.dayofyear - shift) % 365 + 1 - ).values, + 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 - ) + samples.loc[:, i * sample_nr + j] = multiplier * (shifted_signal - signal_mean) + 1 return samples @@ -3265,9 +3162,7 @@ def _force_periodicity(in_signal, signal_index, out_index): if isinstance(in_signal, np.ndarray): signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), data=in_signal) elif isinstance(in_signal, pd.Series): - signal = pd.Series( - index=pd.DatetimeIndex(signal_index.date), data=in_signal.values - ) + signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), data=in_signal.values) else: raise ValueError("in_signal must be numpy array or pandas Series") @@ -3287,9 +3182,7 @@ def _force_periodicity(in_signal, signal_index, out_index): # We will use the median signal through all the years... median_signal = year_matrix.median(1) # The output is the median signal broadcasted to the whole time series - output = pd.Series( - index=out_index, data=median_signal.reindex(out_index.dayofyear - 1).values - ) + output = pd.Series(index=out_index, data=median_signal.reindex(out_index.dayofyear - 1).values) return output From fa1d79bb8442997d325f2a646df48ca4c80bff07 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 6 Aug 2024 14:18:43 -0400 Subject: [PATCH 09/33] revert TrendAnalysis notebook changes --- docs/TrendAnalysis_example_pvdaq4.ipynb | 197 +++++++----------------- 1 file changed, 54 insertions(+), 143 deletions(-) diff --git a/docs/TrendAnalysis_example_pvdaq4.ipynb b/docs/TrendAnalysis_example_pvdaq4.ipynb index 3bf6883c..08baff10 100644 --- a/docs/TrendAnalysis_example_pvdaq4.ipynb +++ b/docs/TrendAnalysis_example_pvdaq4.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -135,12 +135,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -220,14 +220,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nmoyer\\.conda\\envs\\soilpytest\\lib\\site-packages\\rdtools\\soiling.py:366: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_negative_step\" and/or decreasing \"min_interval_length\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n", + "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\soiling.py:27: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " warnings.warn(\n", + "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\soiling.py:379: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_negative_step\" and/or decreasing \"min_interval_length\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n", " warnings.warn('20% or more of the daily data is assigned to invalid soiling '\n" ] } @@ -246,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -256,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -276,15 +278,15 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-0.509\n", - "[-0.761 -0.295]\n" + "-1.273\n", + "[-1.607 -0.959]\n" ] } ], @@ -297,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -330,7 +332,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -376,7 +378,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -403,7 +405,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -425,15 +427,14 @@ "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'TrendAnalysis' object has no attribute 'plot_degradation_timeseries'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[18], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Plot a time-dependent median (plus confidence interval) of sensor-based degradation results\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_degradation_timeseries\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msensor\u001b[39m\u001b[38;5;124m'\u001b[39m, rolling_days\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m365\u001b[39m)\n", - "\u001b[1;31mAttributeError\u001b[0m: 'TrendAnalysis' object has no attribute 'plot_degradation_timeseries'" - ] + "data": { + "image/png": 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", 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The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -485,13 +486,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nmoyer\\.conda\\envs\\soilpytest\\lib\\site-packages\\rdtools\\plotting.py:225: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\plotting.py:232: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -513,13 +514,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\nmoyer\\.conda\\envs\\soilpytest\\lib\\site-packages\\rdtools\\plotting.py:265: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\plotting.py:272: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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dunVzLgcA+AavT9/l5eWpQ4cOFdpDQkKcyytTXFys4uJi5/vCwkJJUlFRkcu1OIp/qLTdnXVaTVX7WB1P7r8r269KVXVZ9efoSl21HS9X1lXbcXR1+1Xx5M+rPvbRFfWxfVe24c6/ibLPGmNcXkeljJdde+21Jj4+vkJ7bm6ukWQWLFhQ6eeSk5ONJF68ePHi5cXXsWPHPJoJXj9SCg0NrfRoKD8/X9L/HzH9UlJSkh5//HHne4fDofz8fIWGhspms0m6lOSRkZE6duyYmjVrVgfV+x7GtG4wrp7HmNaNsnE9evSobDabIiIiPLp+r4dS165dlZaWptLS0nLnlf75z39Kkrp06VLp5+x2u+x2e7m24ODgSvs2a9aMX0oPY0zrBuPqeYxp3bjqqqvqZFy9fqHDsGHDdO7cOW3YsKFc+6pVqxQREaHevXt7qTIAQH3z+pHSPffco7i4OE2cOFFFRUW65pprlJaWpu3bt2vNmjV8RwkAfIjXQ0mSNm7cqGeeeUZz5sxRfn6+YmJilJaWpv/4j/9wa712u13JyckVpvngOsa0bjCunseY1o26HlebMZ6+ng8AANd4/ZwSAABlCCUAgGUQSgAAy7isQsmTz2V69tlnZbPZqvyelC9xZ1z/+te/Ki4uThEREbLb7QoPD9eAAQO0devWOq7a2twZ040bNyohIUHXXHONrrjiCkVHR+v3v/+9Dh06VMdVW5874/qvf/1Ljz32mPr166fg4GDZbDa9/fbbdVuwhbgzdidPntQjjzyisLAwBQYGKjY2VhkZGa4V4tH7Q3hZXFycCQ4ONitWrDA7d+40Y8eONZLM2rVra7WeTz/91NjtdtOyZUvTuXPnOqq24XBnXNetW2emTZtm1q1bZ3bt2mU2btxo7r77biPJrF69uh6qtyZ3xrRXr15myJAh5q233jK7du0yq1evNp06dTJBQUEmKyurHqq3LnfGNTMz04SFhZm77rrLJCQkGElm5cqVdV+0Rbg6dhcvXjRdunQxbdu2NWvWrDEffPCBGTp0qPHz8zO7du2qdR2XTSht2bLFSDKpqanl2uPi4kxERIQpLS2t0XpKSkrMjTfeaKZOnWr69evn86HkqXH9uR9//NG0adPG3HbbbZ4qs0Fxd0z//e9/V2g7fvy48ff3N2PGjPForQ2Ju+P6008/Of97//79PhVK7ozdsmXLjCTz8ccfO9tKSkrM9ddfb3r16lXrWi6b6TtPPZdp4cKFys/P13PPPVcXZTY4dfG8K39/fwUHB1d4XImvcHdMw8PDK7RFRESobdu2OnbsmEdrbUjcHddGjS6bP4e15s7Ybdq0Sdddd51iY2OdbX5+fnrwwQe1b98+HT9+vFa1XDY/BU88lyk7O1vz58/X8uXLFRQUVCd1NjSeet6Vw+FQaWmpcnNzlZycrIMHD+qJJ57weL0NQV08Q+y7777TkSNH1LlzZ4/U2BDxbDbXuTN2WVlZzn6VffbAgQO1quWyCaW8vLxK7yj+a89lKuNwODR69GgNHz5cgwYNqpMaGyJ3x7XMoEGD5O/vrzZt2ujll1/W+vXrNXjwYI/W2lB4akzLlJaWasyYMQoKCtL06dM9UmND5Olx9SXujJ2nx92SobRr1y7ZbLYavT777DPn58oeWVGZ6pZJ0ksvvaRDhw7p5Zdf9tBeWI83xrXM0qVLtW/fPr377rsaOHCgRowYobS0NHd3yeu8OaaSZIzRmDFj9OGHH+qdd95RZGSkO7tjGd4eV1/kzth5ctwtOal/3XXX6Y033qhR36ioKEmuP5dJko4ePao5c+Zo4cKFatKkiQoKCiRd+j9Qh8OhgoIC2e12XXHFFbXcE2up73H9uWuvvdb530OGDNE999yjyZMna8SIEQ16Lt+bY2qM0dixY7VmzRqtWrVKQ4cOrWHV1ufNcfVF7oydx8e91pdGWNS4ceNMUFCQKSkpKdeelpZmJJmPPvqoys9mZmb+6tMVp02bVsd7YE3ujGt15syZYySZEydOeKLMBsUTY+pwOMzo0aONzWYzb731Vl2V2qB48nfV166+c2fs4uLiTExMTIX2BQsWGEnm+PHjtarlsgmlrVu3Gklm3bp15drj4+N/9ZLGM2fOmMzMzAqvG264wURHR5vMzExz6NChut4FS3JnXKvicDhMv379THBwcIV/BL7A3TF1OBxmzJgxxmazmddff70uS21QPPm76muh5M7Yvfrqq0aS2bNnj7OtpKTEdO7c2fTu3bvWtVw2oWTMpcRu3ry5ef31183OnTvNuHHjjCSzZs2acv1Gjx5tGjdubHJycqpdH99TusSdcR0yZIiZPXu22bBhg9m1a5dJTU11fnl22bJl9b0rluHOmE6ZMsVIMqNHjza7d+8u9/rHP/5R37tiKe7+DUhPTzfp6enmhRdeMJLM5MmTnW2Xu5qMXWXjdvHiRdO5c2cTGRlp1q5da3bs2GGGDRvGl2eNMebs2bNm6tSpplWrVqZJkyamW7duJi0trUK/UaNGGUnm8OHD1a6PULrEnXF94YUXTM+ePU3z5s1N48aNTWhoqBk4cKB5//3363EPrMedMW3Xrl2V08zt2rWrv52wIHf/BlQ3hX+5q8nYVTVuJ06cMA8//LAJCQkxAQEBpk+fPmbHjh0u1cHzlAAAltFwL3sCAFx2CCUAgGUQSgAAyyCUAACWQSgBACyDUAIAWAahBACwDEIJbtu7d6+GDRumqKgo2e12tWzZUrGxsS4/L+mRRx5RdHR0ubbo6Gg98sgjzvc5OTmy2Wx6++23XS/ci3744QfNnTtXu3btqpP1FxQUKCwsTOvWrXO2ZWVl6dZbb1XTpk3Vo0cPffTRRxU+9+KLL6pjx466ePFilet+77335Ofnp1OnTlXZp+wu33W1f5I0e/Zsde/eXQ6Ho862gfpHKMEtW7ZsUd++fVVUVKRFixbpgw8+0JIlS3TLLbdo/fr1Lq1z9uzZ2rRpU7V9Wrdurd27dzfYZzL98MMPSklJqbM/2ikpKYqIiNCIESMkXbrj/fDhwxUWFqaNGzfqxhtv1NChQ513xJcuBX1KSopWrFihgICAKte9YcMG3X777WrRokWd1F5TM2bM0OHDh7Vq1Sqv1gEPc+2GFMAlt99+u7n66qsrvbHqTz/95LHttGvXzowaNcpj6/O0H3/8sVY3lz116pSRZJKTkz1eS15enrniiivMihUrnG3Z2dlGksnNzTXGXKr3yiuvNNu2bXP2iY+P/9Ux/vHHH01wcLB55ZVXqu1Xduf9zMxMl/ejJqZMmWI6duxoHA5HnW4H9YcjJbglLy9PYWFhFR6jLKnCc5IcDocWLVqkmJgY2e12hYeH6+GHH9a//vWvcv0qm777pcqm7+bOnSubzaYDBw4oISFBV111lVq2bKnRo0ersLCw3OcLCgo0ZswYhYSEKCgoSIMHD9Z3330nm82muXPnVrvtsqmp1atX64knnlCbNm1kt9v1zTff6NSpU5o0aZKuv/56BQUFKTw8XAMGDNCHH35Yrvayo4yUlBTnw+p+Pj156NAhjRw5UuHh4bLb7erUqZOWLVtWbV1l3n77bZWWljqPkiQ5p+OuvPJKSZK/v7+aNGnibE9LS9P//u//avHixdWuOyMjQ4WFhRo2bJiz7auvvlJ8fLwCAwMVFhamCRMm6OzZsxU+u2PHDg0dOlRt27ZVQECArrnmGj366KM6ffq0s8+HH34om81W6QMg33nnHdlsNu3fv9/Z9tBDD+ngwYPKzMysydCgASCU4JbY2Fjt3btXU6dO1d69e1VSUlJl34kTJ+rpp59WXFyc3nvvPc2bN0/bt29X3759y/1hctf999+vjh07asOGDZo5c6ZSU1PLPSbc4XDo3nvvVWpqqp5++mlt2rRJvXv3Vnx8fK22k5SUpKNHj2rFihXavHmzwsPDnQ82S05O1pYtW7Ry5Up16NBBd9xxh3OqrnXr1tq+fbskacyYMdq9e7d2796t2bNnS5Kys7PVs2dPZWVlafHixXr//fc1ePBgTZ06VSkpKb9a15YtW3TTTTcpODjY2RYTE6OQkBC98MILKigo0LJly3T+/HndfPPNOnPmjKZPn66XXnpJoaGh1a57w4YNio2NVUREhCTp3//+t/r166esrCy9+uqrWr16tc6dO6cpU6ZU+Oy3336r2NhYLV++XB988IHmzJmjvXv36tZbb3X+3tx222266aabKg3gV155RT179lTPnj2dbT169FBQUJC2bNnyq+OCBsLbh2po2E6fPm1uvfVW552U/f39Td++fc2CBQvM2bNnnf2+/PJLI8lMmjSp3Of37t1rJJlZs2Y520aNGlXhbte/nL47fPhwhefdJCcnG0lm0aJF5T47adIkExAQ4Jzi2bJli5Fkli9fXq5f2UPJfm1KrWxq6vbbb6+2nzHGlJaWmpKSEnPnnXeaYcOGOdurm74bOHCgadu2rSksLCzXPmXKFBMQEGDy8/Or3WZgYKCZMGFChfZNmzaZZs2aGUnGbreb1157zRhjzJgxY8xdd91Vo30JCwszixcvdrY9/fTTxmazmc8++6xc37i4uGqn7xwOhykpKTFHjhwxksy7777rXLZy5UojyXz66afOtn379hlJZtWqVRXWdcstt7j03B5YE0dKcEtoaKg+/PBD7d+/XwsXLtTQoUN18OBBJSUlqWvXrs4joLLplZ9PUUlSr1691KlTJ2VkZHispiFDhpR7361bN128eFEnT56UJP3tb3+TJP3ud78r1y8hIaFW27n//vsrbV+xYoW6d++ugIAA+fn5yd/fXxkZGfryyy9/dZ0XL15URkaGhg0bpsDAQJWWljpfgwYN0sWLF7Vnz54qP19QUKAffvhB4eHhFZbdd999OnnypL788kvl5eVp/Pjx+vvf/660tDStWLFCFy5c0JQpU9S6dWtFRUVp7ty5Mj97iMDf/vY3nT59WsOHD3e2ZWZmqnPnzrrhhhvKbWvkyJEVtn/y5ElNmDBBkZGRznFp166dJJUbm4SEBIWHh5c7Wlq6dKlatGhRbkqyTHh4uI4fP17lmKBhIZTgETfffLOefvpppaenKzc3V9OnT1dOTo4WLVok6dK5J+nS1NUvRUREOJd7wi+noOx2uyTpwoULzlr8/PwUEhJSrl/Lli1rtZ3K9uWll17SxIkT1bt3b23YsEF79uzR/v37FR8f79x+dfLy8lRaWqqlS5fK39+/3GvQoEGSVO1UZ9k2qrp6zm63KyYmRldeeaV+/PFHPfroo3r22Wd19dVX6/nnn9fHH3+sTz/9VBkZGXrzzTfLnbP785//rB49epQ735eXl6dWrVpV2M4v2xwOh+6++25t3LhRTz31lDIyMrRv3z5nwP58bOx2ux599FGlpqaqoKBAp06d0n//939r7Nixzp/lzwUEBNRobNEwVDw7DbjJ399fycnJ+uMf/6isrCxJ/x8U33//vdq2bVuuf25ursLCwuqtvtDQUJWWlio/P79cMJ04caJW67HZbBXa1qxZozvuuEPLly8v117Zif/KNG/eXI0bN9ZDDz2kyZMnV9qnffv2VX6+bJzLzm1V5/nnn5efn59mzJghSdq2bZsSExPVqlUrtWrVSr/73e+0detWJSYmyuFwaNOmTZo6dWqF7VU2br9sy8rK0ueff663335bo0aNcrZ/8803ldY2ceJELVy4UG+99ZYuXryo0tJSTZgwodK++fn59fr7g7rFkRLc8v3331faXjYdU3ZCfMCAAZIu/dH+uf379+vLL7/UnXfeWYdVltevXz9JqvA9qp9/0dRVNputwv/Nf/HFF9q9e3e5tl8evZUJDAxU//799emnn6pbt266+eabK7yquxihSZMm6tChg7799ttq6/z666+1aNEivfHGG/L395ckGWN0/vx5Z59z5845p+8+/vhjnThxosKUZf/+/XXgwAF9/vnn5dpTU1PLvS8L8F+OzWuvvVZpfa1bt9YDDzygV199VStWrNC9996rqKioSvt+9913uv7666vdXzQcHCnBLQMHDlTbtm117733KiYmRg6HQ5999pkWL16soKAgTZs2TZJ03XXXafz48Vq6dKkaNWqke+65Rzk5OZo9e7YiIyPLXR1X1+Lj43XLLbfoiSeeUFFRkXr06KHdu3frnXfekVTxUvba+M1vfqN58+YpOTlZ/fr109dff60//OEPat++vUpLS539mjZtqnbt2undd9/VnXfeqZCQEIWFhSk6OlpLlizRrbfeqttuu00TJ05UdHS0zp49q2+++UabN2/Wzp07q63hjjvu0LZt26pcbozR+PHjlZiYqD59+jjbBw4cqD/96U+69tprde7cOaWmpurll1+WdGnqrkuXLurYsWO5dT322GN66623NHjwYM2fP18tW7bU2rVr9dVXX5XrFxMTo6uvvlozZ86UMUYhISHavHmzduzYUWWd06ZNU+/evSVJK1eurLRPXl6eDh06pP/8z/+sdkzQgHj1Mgs0eOvXrzcjR4401157rQkKCjL+/v4mKirKPPTQQyY7O7tc359++sm88MILpmPHjsbf39+EhYWZBx980Bw7dqxcP3evvjt16lS5z5ZdzXX48GFnW35+vklMTDTBwcEmMDDQxMXFmT179hhJZsmSJdXuc9nVd+np6RWWFRcXmxkzZpg2bdqYgIAA0717d/OXv/yl0n3661//am666SZjt9uNpAr7N3r0aNOmTRvj7+9vWrRoYfr27Wvmz59fbW3GGJORkWEkmX379lW6/M033zQREREVru47d+6cGTt2rAkNDTUtW7Y0M2fOdH4BOjIyssqrErOzs01cXJwJCAgwISEhZsyYMebdd9+tcPVdWb+mTZua5s2bmwceeMAcPXq02iseo6OjTadOnarc1//6r/8y/v7+5sSJE1UPCBoUmzE/u7wG8GGpqan6/e9/r48++kh9+/b1djlu6datm2655ZYK57ZcsW/fPvXu3VtffPGFunbt6oHqauaLL77QDTfcoGXLlmnSpEmV9rntttsUFRWltWvX1ltdqFuEEnxSWlqajh8/rq5du6pRo0bas2ePXnzxRd10003OS8Ybsu3bt2vYsGE6dOhQhQtLrO7bb7/VkSNHNGvWLB09elTffPONAgMDK/T7+9//rrvvvlvZ2dnq0KGDFypFXeBCB/ikpk2bat26dRoxYoQGDRqkN954Q4888og2b97s7dI8Ij4+Xi+++KIOHz7s7VJqbd68eYqLi9O5c+eUnp5eaSBJl84nvfPOOwTSZYYjJQCAZXCkBACwDEIJAGAZhBIAwDIIJQCAZRBKAADLIJQAAJZBKAEALINQAgBYBqEEALCM/wPIB3nz33CubAAAAABJRU5ErkJggg==", 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", 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The support for old JSON model will be discontinued in XGBoost 2.3.\n", - " warnings.warn(smsg, UserWarning)\n" + "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\lib\\site-packages\\rdtools\\filtering.py:826: UserWarning: The XGBoost filter is an experimental clipping filter that is still under development. The API, results, and default behaviors may change in future releases (including MINOR and PATCH). Use at your own risk!\n", + " warnings.warn(\n" ] } ], @@ -844,35 +843,6 @@ "execution_count": 26, "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "application/vnd.plotly.v1+json": { @@ -61398,7 +61368,6 @@ } ], "layout": { - "autosize": true, "legend": { "title": { "text": "mask" @@ -61422,11 +61391,6 @@ "line": { "color": "#E5ECF6", "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 } }, "type": "bar" @@ -61438,11 +61402,6 @@ "line": { "color": "#E5ECF6", "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 } }, "type": "barpolar" @@ -61641,10 +61600,9 @@ "histogram": [ { "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "histogram" @@ -61780,10 +61738,11 @@ ], "scatter": [ { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } }, "type": "scatter" } @@ -61961,7 +61920,6 @@ "arrowhead": 0, "arrowwidth": 1 }, - "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, @@ -62226,66 +62184,26 @@ }, "xaxis": { "anchor": "y", - "autorange": true, "domain": [ 0, 1 ], - "range": [ - "2012-12-30 17:25:38.9338", - "2013-01-23 06:33:21.0662" - ], "title": { "text": "datetime" - }, - "type": "date" + } }, "yaxis": { "anchor": "x", - "autorange": true, "domain": [ 0, 1 ], - "range": [ - -1.3697493381233599, - 19.223241354790026 - ], "title": { "text": "energy_Wh" - }, - "type": "linear" + } } } - }, - "image/png": 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", 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", + "image/png": 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", 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", 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", 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" ] @@ -62408,19 +62326,12 @@ " scatter_ymin=0.5, scatter_ymax=1.1,\n", " hist_xmin=-30, hist_xmax=45);" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -62434,7 +62345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.13" } }, "nbformat": 4, From d642210bc8b8293c42b840b25f442ba8d673ad60 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 6 Aug 2024 14:45:44 -0400 Subject: [PATCH 10/33] revert conftest.py --- rdtools/test/conftest.py | 142 +++++++++++++-------------------------- 1 file changed, 47 insertions(+), 95 deletions(-) diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py index 72de0246..f22a05f5 100644 --- a/rdtools/test/conftest.py +++ b/rdtools/test/conftest.py @@ -9,7 +9,8 @@ import rdtools -rdtools_base_version = parse_version(parse_version(rdtools.__version__).base_version) +rdtools_base_version = \ + parse_version(parse_version(rdtools.__version__).base_version) # decorator takes one argument: the base version for which it should fail @@ -25,20 +26,17 @@ def wrapper(func): def inner(*args, **kwargs): # fail if the version is too high if rdtools_base_version >= parse_version(version): - pytest.fail( - "the tested function is scheduled to be " "removed in %s" % version - ) + pytest.fail('the tested function is scheduled to be ' + 'removed in %s' % version) # otherwise return the function to be executed else: return func(*args, **kwargs) - return inner - return wrapper def assert_isinstance(obj, klass): - assert isinstance(obj, klass), f"got {type(obj)}, expected {klass}" + assert isinstance(obj, klass), f'got {type(obj)}, expected {klass}' def assert_warnings(messages, record): @@ -60,19 +58,17 @@ def assert_warnings(messages, record): assert found_match, f"warning '{pattern}' not in {warning_messages}" -requires_pvlib_below_090 = pytest.mark.skipif( - parse_version(pvlib.__version__) > parse_version("0.8.1"), - reason="requires pvlib <= 0.8.1", -) +requires_pvlib_below_090 = \ + pytest.mark.skipif(parse_version(pvlib.__version__) > parse_version('0.8.1'), + reason='requires pvlib <= 0.8.1') # %% Soiling fixtures - @pytest.fixture() def soiling_times(): - tz = "Etc/GMT+7" - times = pd.date_range("2019/01/01", "2019/03/16", freq="D", tz=tz) + tz = 'Etc/GMT+7' + times = pd.date_range('2019/01/01', '2019/03/16', freq='D', tz=tz) return times @@ -90,41 +86,6 @@ def soiling_normalized_daily(soiling_times): return normalized_daily -@pytest.fixture() -def soiling_normalized_daily_with_neg_shifts(soiling_times): - interval_1_v1 = 1 - 0.005 * np.arange(0, 15, 1) - interval_1_v2 = (0.9 - 0.005 * 15) - 0.005 * np.arange(0, 10, 1) - interval_2 = 1 - 0.002 * np.arange(0, 25, 1) - interval_3_v1 = 1 - 0.001 * np.arange(0, 10, 1) - interval_3_v2 = (0.95 - 0.001 * 10) - 0.001 * np.arange(0, 15, 1) - profile = np.concatenate( - (interval_1_v1, interval_1_v2, interval_2, interval_3_v1, interval_3_v2) - ) - np.random.seed(1977) - noise = 0.01 * np.random.rand(75) - normalized_daily = pd.Series(data=profile, index=soiling_times) - normalized_daily = normalized_daily + noise - - return normalized_daily - - -@pytest.fixture() -def soiling_normalized_daily_with_piecewise_slope(soiling_times): - interval_1_v1 = 1 - 0.002 * np.arange(0, 20, 1) - interval_1_v2 = (1 - 0.002 * 20) - 0.007 * np.arange(0, 20, 1) - interval_2_v1 = 1 - 0.01 * np.arange(0, 20, 1) - interval_2_v2 = (1 - 0.01 * 20) - 0.001 * np.arange(0, 15, 1) - profile = np.concatenate( - (interval_1_v1, interval_1_v2, interval_2_v1, interval_2_v2) - ) - np.random.seed(1977) - noise = 0.01 * np.random.rand(75) - normalized_daily = pd.Series(data=profile, index=soiling_times) - normalized_daily = normalized_daily + noise - - return normalized_daily - - @pytest.fixture() def soiling_insolation(soiling_times): insolation = np.empty((75,)) @@ -139,8 +100,8 @@ def soiling_insolation(soiling_times): @pytest.fixture() def cods_times(): - tz = "Etc/GMT+7" - cods_times = pd.date_range("2019/01/01", "2021/01/01", freq="D", tz=tz) + tz = 'Etc/GMT+7' + cods_times = pd.date_range('2019/01/01', '2021/01/01', freq='D', tz=tz) return cods_times @@ -152,9 +113,7 @@ def cods_normalized_daily_wo_noise(cods_times): interval_3 = 1 - 0.001 * np.arange(0, 25, 1) profile = np.concatenate((interval_1, interval_2, interval_3)) repeated_profile = np.concatenate([profile for _ in range(int(np.ceil(N / 75)))]) - cods_normalized_daily_wo_noise = pd.Series( - data=repeated_profile[:N], index=cods_times - ) + cods_normalized_daily_wo_noise = pd.Series(data=repeated_profile[:N], index=cods_times) return cods_normalized_daily_wo_noise @@ -172,21 +131,18 @@ def cods_normalized_daily_small_soiling(cods_normalized_daily_wo_noise): N = len(cods_normalized_daily_wo_noise) np.random.seed(1977) noise = 1 + 0.02 * (np.random.rand(N) - 0.5) - cods_normalized_daily_small_soiling = ( - cods_normalized_daily_wo_noise.apply(lambda row: 1 - (1 - row) * 0.1) * noise - ) + cods_normalized_daily_small_soiling = cods_normalized_daily_wo_noise.apply( + lambda row: 1-(1-row)*0.1) * noise return cods_normalized_daily_small_soiling # %% Availability fixtures -ENERGY_PARAMETER_SPACE = list( - itertools.product( - [0, np.nan], # outage value for power - [0, np.nan, None], # value for cumulative energy (None means real value) - [0, 0.25, 0.5, 0.75, 1.0], # fraction of comms outage that is power outage - ) -) +ENERGY_PARAMETER_SPACE = list(itertools.product( + [0, np.nan], # outage value for power + [0, np.nan, None], # value for cumulative energy (None means real value) + [0, 0.25, 0.5, 0.75, 1.0], # fraction of comms outage that is power outage +)) # display names for the test cases. default is just 0..N ENERGY_PARAMETER_IDS = ["_".join(map(str, p)) for p in ENERGY_PARAMETER_SPACE] @@ -196,23 +152,20 @@ def _generate_energy_data(power_value, energy_value, outage_fraction): Generate an artificial mixed communication/power outage. """ # a few days of clearsky irradiance for creating a plausible power signal - times = pd.date_range( - "2019-01-01", "2019-01-15 23:59", freq="15min", tz="US/Eastern" - ) + times = pd.date_range('2019-01-01', '2019-01-15 23:59', freq='15min', + tz='US/Eastern') location = pvlib.location.Location(40, -80) # use haurwitz to avoid dependency on `tables` - clearsky = location.get_clearsky(times, model="haurwitz") + clearsky = location.get_clearsky(times, model='haurwitz') # just set base inverter power = ghi+clipping for simplicity - base_power = clearsky["ghi"].clip(upper=0.8 * clearsky["ghi"].max()) - - inverter_power = pd.DataFrame( - { - "inv0": base_power, - "inv1": base_power * 0.7, - "inv2": base_power * 1.3, - } - ) + base_power = clearsky['ghi'].clip(upper=0.8*clearsky['ghi'].max()) + + inverter_power = pd.DataFrame({ + 'inv0': base_power, + 'inv1': base_power*0.7, + 'inv2': base_power*1.3, + }) expected_power = inverter_power.sum(axis=1) # dawn/dusk points expected_power[expected_power < 10] = 0 @@ -221,10 +174,10 @@ def _generate_energy_data(power_value, energy_value, outage_fraction): expected_power *= 1.05 + np.random.normal(0, scale=0.05, size=len(times)) # calculate what part of the comms outage is a power outage - comms_outage = slice("2019-01-03 00:00", "2019-01-06 00:00") + comms_outage = slice('2019-01-03 00:00', '2019-01-06 00:00') start = times.get_loc(comms_outage.start) stop = times.get_loc(comms_outage.stop) - power_outage = slice(start, int(start + outage_fraction * (stop - start))) + power_outage = slice(start, int(start + outage_fraction * (stop-start))) expected_loss = inverter_power.iloc[power_outage, :].sum().sum() / 4 inverter_power.iloc[power_outage, :] = 0 meter_power = inverter_power.sum(axis=1) @@ -238,16 +191,14 @@ def _generate_energy_data(power_value, energy_value, outage_fraction): meter_energy[comms_outage] = energy_value inverter_power.loc[comms_outage, :] = power_value - expected_type = "real" if outage_fraction > 0 else "comms" + expected_type = 'real' if outage_fraction > 0 else 'comms' - return ( - meter_power, - meter_energy, - inverter_power, - expected_power, - expected_loss, - expected_type, - ) + return (meter_power, + meter_energy, + inverter_power, + expected_power, + expected_loss, + expected_type) @pytest.fixture(params=ENERGY_PARAMETER_SPACE, ids=ENERGY_PARAMETER_IDS) @@ -275,12 +226,13 @@ def energy_data_comms_single(): @pytest.fixture def availability_analysis_object(energy_data_outage_single): - (meter_power, meter_energy, inverter_power, expected_power, _, _) = ( - energy_data_outage_single - ) - - aa = rdtools.availability.AvailabilityAnalysis( - meter_power, inverter_power, meter_energy, expected_power - ) + (meter_power, + meter_energy, + inverter_power, + expected_power, + _, _) = energy_data_outage_single + + aa = rdtools.availability.AvailabilityAnalysis(meter_power, inverter_power, meter_energy, + expected_power) aa.run() return aa From 9122b5638319db1f9af3e6cfa8399e5293e2e8db Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 6 Aug 2024 14:46:18 -0400 Subject: [PATCH 11/33] revert notebook requirements --- docs/notebook_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebook_requirements.txt b/docs/notebook_requirements.txt index fc83aa5d..ec068272 100644 --- a/docs/notebook_requirements.txt +++ b/docs/notebook_requirements.txt @@ -52,4 +52,4 @@ tornado==6.3.3 traitlets==5.14.3 wcwidth==0.1.7 webencodings==0.5.1 -widgetsnbextension==3.3.0 \ No newline at end of file +widgetsnbextension==3.3.0 From cd4fbb6b5dc0aadb6dc35b973bd561804628dddd Mon Sep 17 00:00:00 2001 From: nmoyer Date: Wed, 7 Aug 2024 12:59:57 -0600 Subject: [PATCH 12/33] added piecewise and neg_shift PI data back to conftest.py --- rdtools/test/conftest.py | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py index f22a05f5..69d25423 100644 --- a/rdtools/test/conftest.py +++ b/rdtools/test/conftest.py @@ -85,6 +85,39 @@ def soiling_normalized_daily(soiling_times): return normalized_daily +@pytest.fixture() +def soiling_normalized_daily_with_neg_shifts(soiling_times): + interval_1_v1 = 1 - 0.005 * np.arange(0, 15, 1) + interval_1_v2 = (0.9 - 0.005 * 15) - 0.005 * np.arange(0, 10, 1) + interval_2 = 1 - 0.002 * np.arange(0, 25, 1) + interval_3_v1 = 1 - 0.001 * np.arange(0, 10, 1) + interval_3_v2 = (0.95 - 0.001 * 10) - 0.001 * np.arange(0, 15, 1) + profile = np.concatenate( + (interval_1_v1, interval_1_v2, interval_2, interval_3_v1, interval_3_v2) + ) + np.random.seed(1977) + noise = 0.01 * np.random.rand(75) + normalized_daily = pd.Series(data=profile, index=soiling_times) + normalized_daily = normalized_daily + noise + + return normalized_daily + + +@pytest.fixture() +def soiling_normalized_daily_with_piecewise_slope(soiling_times): + interval_1_v1 = 1 - 0.002 * np.arange(0, 20, 1) + interval_1_v2 = (1 - 0.002 * 20) - 0.007 * np.arange(0, 20, 1) + interval_2_v1 = 1 - 0.01 * np.arange(0, 20, 1) + interval_2_v2 = (1 - 0.01 * 20) - 0.001 * np.arange(0, 15, 1) + profile = np.concatenate( + (interval_1_v1, interval_1_v2, interval_2_v1, interval_2_v2) + ) + np.random.seed(1977) + noise = 0.01 * np.random.rand(75) + normalized_daily = pd.Series(data=profile, index=soiling_times) + normalized_daily = normalized_daily + noise + + return normalized_daily @pytest.fixture() def soiling_insolation(soiling_times): From e9a2552b906bc12252d3c403e38d6e8bfbee6b86 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Thu, 8 Aug 2024 12:55:33 -0600 Subject: [PATCH 13/33] formatting fixes --- rdtools/soiling.py | 12 +++++------- rdtools/test/conftest.py | 2 ++ rdtools/test/soiling_test.py | 6 ++++-- 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 48413d15..cb5cfc56 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -494,7 +494,7 @@ def _calc_result_df( results["inferred_begin_shift"] = results.inferred_start_loss - results.prev_end # 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.loc[results.clean_event, "inferred_begin_shift"] = np.clip( results.inferred_begin_shift, 0, 1 ) ####################################################################### @@ -549,7 +549,7 @@ def _calc_result_df( day_start = d if new_soil <= 0: # begin new soil period - if (start_shift == prev_shift) | (changepoint == True): # no shift at + if (start_shift == prev_shift) | (changepoint): # no shift at # a slope changepoint shift = 0 shift_perfect = 0 @@ -676,15 +676,13 @@ def _calc_monte(self, monte, method="half_norm_clean"): ): 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 " + warnings.warn('20% or more of the daily data is assigned to invalid soiling ' 'intervals. This can be problematic with the "half_norm_clean" ' 'and "random_clean" cleaning assumptions. Consider more permissive ' 'validity criteria such as increasing "max_relative_slope_error" ' 'and/or "max_negative_step" and/or decreasing "min_interval_length".' ' Alternatively, consider using method="perfect_clean". For more' - " info see https://github.com/NREL/rdtools/issues/272" - ) + ' info see https://github.com/NREL/rdtools/issues/272') monte_losses = [] random_profiles = [] for _ in range(monte): @@ -3330,7 +3328,7 @@ def segmented_soiling_period( if (R2_percent_improve < 0.01) | (R2_piecewise < 0.4): z = [np.nan] * len(x) cp_date = None - except: + except IndexError as x: z = [np.nan] * len(x) cp_date = None # Create Series from modelled profile diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py index 69d25423..8f272e8c 100644 --- a/rdtools/test/conftest.py +++ b/rdtools/test/conftest.py @@ -85,6 +85,7 @@ def soiling_normalized_daily(soiling_times): return normalized_daily + @pytest.fixture() def soiling_normalized_daily_with_neg_shifts(soiling_times): interval_1_v1 = 1 - 0.005 * np.arange(0, 15, 1) @@ -119,6 +120,7 @@ def soiling_normalized_daily_with_piecewise_slope(soiling_times): return normalized_daily + @pytest.fixture() def soiling_insolation(soiling_times): insolation = np.empty((75,)) diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 605e3e91..2b2b2dc7 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -494,7 +494,8 @@ def test_negative_shifts( ) assert expected_sr == pytest.approx( sr, abs=1e-6 - ), f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" different from expected value' + ), f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" \ + different from expected value' @pytest.mark.parametrize( @@ -527,7 +528,8 @@ def test_piecewise( ) assert expected_sr == pytest.approx( sr, abs=1e-6 - ), f'Soiling ratio with method="{method}" and piecewise="{piecewise}" different from expected value' + ), f'Soiling ratio with method="{method}" and piecewise="{piecewise}" \ + different from expected value' def test_piecewise_and_neg_shifts( From 612c9f1813495bf643fbccaabdb538eba8e5fbf1 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Sat, 10 Aug 2024 11:50:55 -0600 Subject: [PATCH 14/33] minor formatting issue in soiling.py --- rdtools/soiling.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index cb5cfc56..31db5b5e 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -677,12 +677,13 @@ def _calc_monte(self, monte, method="half_norm_clean"): 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 ' - 'intervals. This can be problematic with the "half_norm_clean" ' - 'and "random_clean" cleaning assumptions. Consider more permissive ' - 'validity criteria such as increasing "max_relative_slope_error" ' - 'and/or "max_negative_step" and/or decreasing "min_interval_length".' - ' Alternatively, consider using method="perfect_clean". For more' - ' info see https://github.com/NREL/rdtools/issues/272') + 'intervals. This can be problematic with the "half_norm_clean" ' + 'and "random_clean" cleaning assumptions. Consider more permissive ' + 'validity criteria such as increasing "max_relative_slope_error" ' + 'and/or "max_negative_step" and/or decreasing ' + '"min_interval_length". Alternatively, consider using ' + 'method="perfect_clean". For more info see ' + 'https://github.com/NREL/rdtools/issues/272') monte_losses = [] random_profiles = [] for _ in range(monte): From 0f020b5e818aff3a4c19fe0e95c636af46df1f58 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Tue, 13 Aug 2024 09:32:33 -0600 Subject: [PATCH 15/33] testing some changes to pass notebook checks --- rdtools/soiling.py | 231 ++++++++++++++++++++++++--------------------- 1 file changed, 124 insertions(+), 107 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 31db5b5e..aed262fe 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -199,7 +199,7 @@ def _calc_daily_df( # median change to day_scale/2 Matt df_ffill = df.copy() df_ffill = df.ffill(limit=int(round((day_scale / 2), 0))) - + #df_ffill = df.ffill(limit=day_scale) # Calculate rolling median df["pi_roll_med"] = df_ffill.pi_norm.rolling(day_scale, center=True).median() @@ -217,12 +217,12 @@ def _calc_daily_df( # 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( df.clean_event_detected, df.delta.values, 5 ) df["clean_event_detected"] = reduced_cleaning_events - + ########################################################################## precip_event = df["precip"] > precip_threshold @@ -315,6 +315,7 @@ def _calc_result_df( max_negative_step=0.05, min_interval_length=7, neg_shift=False, + piecewise=False ): """ Calculates self.result_df, a pandas dataframe summarizing the soiling @@ -518,112 +519,129 @@ def _calc_result_df( # new code for perfect and inferred clean with handling of/Matt # negative shifts and changepoints within soiling intervals # goes to line 563 + if (piecewise==True)|(neg_shift==True): ####################################################################### - pm_frame_out.inferred_begin_shift.bfill(inplace=True) - pm_frame_out["forward_median"] = ( - pm_frame_out.pi.iloc[::-1].rolling(10, min_periods=5).median() - ) - prev_shift = 1 - soil_inferred_clean = [] - soil_perfect_clean = [] - day_start = -1 - start_infer = 1 - start_perfect = 1 - soil_infer = 1 - soil_perfect = 1 - total_down = 0 - shift = 0 - shift_perfect = 0 - begin_perfect_shifts = [0] - 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, - ): - new_soil = d - day_start - day_start = d - - if new_soil <= 0: # begin new soil period - if (start_shift == prev_shift) | (changepoint): # no shift at - # a slope changepoint - shift = 0 - shift_perfect = 0 - else: - if (start_shift < 0) & (prev_shift < 0): # (both negative) or - # downward shifts to start last 2 intervals - shift = 0 - shift_perfect = 0 - total_down = total_down + start_shift # adding total downshifts - # to subtract from an eventual cleaning event - elif (start_shift > 0) & (prev_shift >= 0): # (both positive) or - # cleanings start the last 2 intervals - shift = start_shift - shift_perfect = 1 - total_down = 0 - # add #####################3/27/24 - elif (start_shift == 0) & (prev_shift >= 0): # ( - shift = start_shift - shift_perfect = start_shift - total_down = 0 - ############################################################# - elif (start_shift >= 0) & (prev_shift < 0): # cleaning starts the current - # interval but there was a previous downshift - shift = start_shift + total_down # correct for the negative shifts - 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 - ): # negative shift starts the interval, - # previous shift was cleaning + pm_frame_out.inferred_begin_shift.bfill(inplace=True) + pm_frame_out["forward_median"] = ( + pm_frame_out.pi.iloc[::-1].rolling(10, min_periods=5).median() + ) + prev_shift = 1 + soil_inferred_clean = [] + soil_perfect_clean = [] + day_start = -1 + start_infer = 1 + start_perfect = 1 + soil_infer = 1 + soil_perfect = 1 + total_down = 0 + shift = 0 + shift_perfect = 0 + begin_perfect_shifts = [0] + 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, + ): + new_soil = d - day_start + day_start = d + + if new_soil <= 0: # begin new soil period + if (start_shift == prev_shift) | (changepoint): # no shift at + # a slope changepoint shift = 0 shift_perfect = 0 - total_down = start_shift - # 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 - # skewed below where data start - if (soil_infer + shift) < forward_median: - shift = forward_median - soil_infer - if (soil_perfect + shift_perfect) < forward_median: - shift_perfect = forward_median - soil_perfect - - # append the daily soiling ratio to each modeled fit - begin_perfect_shifts.append(shift_perfect) - begin_infer_shifts.append(shift) - # clip to last value in case shift ends up negative - soil_infer = np.clip((soil_infer + shift), soil_infer, 1) - start_infer = soil_infer # make next start value the last inferred value - soil_inferred_clean.append(soil_infer) - # clip to last value in case shift ends up negative - soil_perfect = np.clip((soil_perfect + shift_perfect), soil_perfect, 1) - start_perfect = soil_perfect - soil_perfect_clean.append(soil_perfect) - if changepoint is False: - prev_shift = start_shift # assigned at new soil period - - elif new_soil > 0: # within soiling period - # append the daily soiling ratio to each modeled fit - soil_infer = start_infer + rs * d - soil_inferred_clean.append(soil_infer) - - soil_perfect = start_perfect + rs * d - soil_perfect_clean.append(soil_perfect) - - pm_frame_out["loss_inferred_clean"] = pd.Series( - soil_inferred_clean, index=pm_frame_out.index - ) - pm_frame_out["loss_perfect_clean"] = pd.Series( - soil_perfect_clean, index=pm_frame_out.index - ) - - results["begin_perfect_shift"] = pd.Series(begin_perfect_shifts) - results["begin_infer_shift"] = pd.Series(begin_infer_shifts) + else: + if (start_shift < 0) & (prev_shift < 0): # (both negative) or + # downward shifts to start last 2 intervals + shift = 0 + shift_perfect = 0 + total_down = total_down + start_shift # adding total downshifts + # to subtract from an eventual cleaning event + elif (start_shift > 0) & (prev_shift >= 0): # (both positive) or + # cleanings start the last 2 intervals + shift = start_shift + shift_perfect = 1 + total_down = 0 + # add #####################3/27/24 + elif (start_shift == 0) & (prev_shift >= 0): # ( + shift = start_shift + shift_perfect = start_shift + total_down = 0 + ############################################################# + elif (start_shift >= 0) & (prev_shift < 0): # cleaning starts the current + # interval but there was a previous downshift + shift = start_shift + total_down # correct for the negative shifts + 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 + ): # negative shift starts the interval, + # previous shift was cleaning + shift = 0 + shift_perfect = 0 + total_down = start_shift + # 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 + # skewed below where data start + if (soil_infer + shift) < forward_median: + shift = forward_median - soil_infer + if (soil_perfect + shift_perfect) < forward_median: + shift_perfect = forward_median - soil_perfect + + # append the daily soiling ratio to each modeled fit + begin_perfect_shifts.append(shift_perfect) + begin_infer_shifts.append(shift) + # clip to last value in case shift ends up negative + soil_infer = np.clip((soil_infer + shift), soil_infer, 1) + start_infer = soil_infer # make next start value the last inferred value + soil_inferred_clean.append(soil_infer) + # clip to last value in case shift ends up negative + soil_perfect = np.clip((soil_perfect + shift_perfect), soil_perfect, 1) + start_perfect = soil_perfect + soil_perfect_clean.append(soil_perfect) + if changepoint is False: + prev_shift = start_shift # assigned at new soil period + + elif new_soil > 0: # within soiling period + # append the daily soiling ratio to each modeled fit + soil_infer = start_infer + rs * d + soil_inferred_clean.append(soil_infer) + + soil_perfect = start_perfect + rs * d + soil_perfect_clean.append(soil_perfect) + + pm_frame_out["loss_inferred_clean"] = pd.Series( + soil_inferred_clean, index=pm_frame_out.index + ) + pm_frame_out["loss_perfect_clean"] = pd.Series( + soil_perfect_clean, index=pm_frame_out.index + ) + + results["begin_perfect_shift"] = pd.Series(begin_perfect_shifts) + results["begin_infer_shift"] = pd.Series(begin_infer_shifts) + else: + pm_frame_out['loss_perfect_clean'] = \ + pm_frame_out.start_loss + \ + pm_frame_out.days_since_clean * pm_frame_out.run_slope + # 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 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 + # filling the flat intervals may need to be recalculated + # for different assumptions + pm_frame_out.loss_inferred_clean = \ + pm_frame_out.loss_inferred_clean.fillna(1) ####################################################################### self.result_df = results self.analyzed_daily_df = pm_frame_out @@ -1295,7 +1313,6 @@ def soiling_srr( neg_shift=neg_shift, piecewise=piecewise, ) - return sr, sr_ci, soiling_info From 6d5ce23a8ae5c5f0bb7eb3c95bf036a2b0c600e5 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Tue, 13 Aug 2024 10:33:18 -0600 Subject: [PATCH 16/33] trying another minor change for notebook checks --- rdtools/soiling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index aed262fe..a4c2b798 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -217,12 +217,12 @@ def _calc_daily_df( # 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( df.clean_event_detected, df.delta.values, 5 ) df["clean_event_detected"] = reduced_cleaning_events - + ''' ########################################################################## precip_event = df["precip"] > precip_threshold From b99c2de8e60e51bcac3079335875f07dc5900108 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Tue, 13 Aug 2024 11:28:34 -0600 Subject: [PATCH 17/33] soiling.py change to pass notebook checks --- rdtools/soiling.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index a4c2b798..85a50893 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -402,6 +402,7 @@ def _calc_result_df( #################################################### # the following is moved here so median values are retained/Matt # for soiling inferrences when rejected fits occur + result_dict["slope_err"] = ( result_dict["run_slope_high"] - result_dict["run_slope_low"] ) / abs(result_dict["run_slope"]) @@ -416,7 +417,7 @@ def _calc_result_df( result_dict["run_loss_baseline"] = ( result_dict["inferred_start_loss"] - result_dict["inferred_end_loss"] ) - + ############################################### result_list.append(result_dict) @@ -470,6 +471,7 @@ def _calc_result_df( results.loc[filt, "run_slope"] = 0 results.loc[filt, "run_slope_low"] = 0 results.loc[filt, "run_slope_high"] = 0 + results.loc[filt, "valid"] = False # Calculate the next inferred start loss from next valid interval results["next_inferred_start_loss"] = np.clip( @@ -499,8 +501,8 @@ def _calc_result_df( results.inferred_begin_shift, 0, 1 ) ####################################################################### - if neg_shift is False: - results.loc[filt, "valid"] = False + #if neg_shift is False: + # results.loc[filt, "valid"] = False if len(results[results.valid]) == 0: raise NoValidIntervalError("No valid soiling intervals were found") From ab286087696b062c9b50a257566f0462b8b26c79 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Fri, 16 Aug 2024 13:33:14 -0600 Subject: [PATCH 18/33] Trying some changes in the notebooks to pass tests --- docs/TrendAnalysis_example_pvdaq4.ipynb | 6 +++--- docs/degradation_and_soiling_example_pvdaq_4.ipynb | 6 +++--- docs/system_availability_example.ipynb | 4 ++-- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/TrendAnalysis_example_pvdaq4.ipynb b/docs/TrendAnalysis_example_pvdaq4.ipynb index 08baff10..a4001fc5 100644 --- a/docs/TrendAnalysis_example_pvdaq4.ipynb +++ b/docs/TrendAnalysis_example_pvdaq4.ipynb @@ -28,7 +28,7 @@ "import numpy as np\n", "import pvlib\n", "import rdtools\n", - "%matplotlib inline" + "#%matplotlib inline" ] }, { @@ -62331,7 +62331,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -62345,7 +62345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/degradation_and_soiling_example_pvdaq_4.ipynb b/docs/degradation_and_soiling_example_pvdaq_4.ipynb index f7325ce1..e67e4969 100644 --- a/docs/degradation_and_soiling_example_pvdaq_4.ipynb +++ b/docs/degradation_and_soiling_example_pvdaq_4.ipynb @@ -35,7 +35,7 @@ "import numpy as np\n", "import pvlib\n", "import rdtools\n", - "%matplotlib inline" + "#%matplotlib inline" ] }, { @@ -93961,7 +93961,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -93975,7 +93975,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/system_availability_example.ipynb b/docs/system_availability_example.ipynb index bd860b68..9a36859e 100644 --- a/docs/system_availability_example.ipynb +++ b/docs/system_availability_example.ipynb @@ -649,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -663,7 +663,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.5" } }, "nbformat": 4, From 2dbbeae8ade107a84945eed8c69f8fb74a231b1e Mon Sep 17 00:00:00 2001 From: nmoyer Date: Fri, 16 Aug 2024 22:05:33 -0600 Subject: [PATCH 19/33] Fixing pytests and reverting notebooks --- docs/TrendAnalysis_example_pvdaq4.ipynb | 6 +++--- ...gradation_and_soiling_example_pvdaq_4.ipynb | 6 +++--- rdtools/soiling.py | 1 + rdtools/test/soiling_test.py | 18 +++++++----------- 4 files changed, 14 insertions(+), 17 deletions(-) diff --git a/docs/TrendAnalysis_example_pvdaq4.ipynb b/docs/TrendAnalysis_example_pvdaq4.ipynb index a4001fc5..08baff10 100644 --- a/docs/TrendAnalysis_example_pvdaq4.ipynb +++ b/docs/TrendAnalysis_example_pvdaq4.ipynb @@ -28,7 +28,7 @@ "import numpy as np\n", "import pvlib\n", "import rdtools\n", - "#%matplotlib inline" + "%matplotlib inline" ] }, { @@ -62331,7 +62331,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -62345,7 +62345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/degradation_and_soiling_example_pvdaq_4.ipynb b/docs/degradation_and_soiling_example_pvdaq_4.ipynb index e67e4969..f7325ce1 100644 --- a/docs/degradation_and_soiling_example_pvdaq_4.ipynb +++ b/docs/degradation_and_soiling_example_pvdaq_4.ipynb @@ -35,7 +35,7 @@ "import numpy as np\n", "import pvlib\n", "import rdtools\n", - "#%matplotlib inline" + "%matplotlib inline" ] }, { @@ -93961,7 +93961,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -93975,7 +93975,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 85a50893..35ce5902 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1056,6 +1056,7 @@ def run( max_negative_step=max_negative_step, min_interval_length=min_interval_length, neg_shift=neg_shift, + piecewise=piecewise ) self._calc_monte(reps, method=method) diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 2b2b2dc7..6edbfca7 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -239,20 +239,20 @@ def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation): @pytest.mark.parametrize( - "method,expected_sr", + "method,neg_shift,piecewise,expected_sr", [ - ("random_clean", 0.920444), - ("perfect_clean", 0.966912), - ("perfect_clean_complex", 0.966912), - ("inferred_clean_complex", 0.965565), + ("random_clean", False, False, 0.920444), + ("perfect_clean", False, False, 0.966912), + ("perfect_clean_complex", True, True, 0.966912), + ("inferred_clean_complex", True, True, 0.965565), ], ) def test_soiling_srr_method( - soiling_normalized_daily, soiling_insolation, method, expected_sr + soiling_normalized_daily, soiling_insolation, method, neg_shift, piecewise, expected_sr ): np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily, soiling_insolation, reps=10, method=method + soiling_normalized_daily, soiling_insolation, reps=10, method=method, neg_shift=neg_shift, piecewise=piecewise ) assert expected_sr == pytest.approx( sr, abs=1e-6 @@ -469,9 +469,7 @@ def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolatio [ ("half_norm_clean", False, 0.980143), ("half_norm_clean", True, 0.975057), - ("perfect_clean_complex", False, 0.983797), ("perfect_clean_complex", True, 0.964117), - ("inferred_clean_complex", False, 0.983265), ("inferred_clean_complex", True, 0.963585), ], ) @@ -503,9 +501,7 @@ def test_negative_shifts( [ ("half_norm_clean", False, 0.8670264), ("half_norm_clean", True, 0.927017), - ("perfect_clean_complex", False, 0.891499), ("perfect_clean_complex", True, 0.896936), - ("inferred_clean_complex", False, 0.874486), ("inferred_clean_complex", True, 0.896214), ], ) From febe693b5980185a21955cd5804a53102088dbaa Mon Sep 17 00:00:00 2001 From: nmoyer Date: Mon, 19 Aug 2024 10:40:58 -0600 Subject: [PATCH 20/33] undoing some black formatting --- rdtools/soiling.py | 36 ++++++++++++++++++------------------ rdtools/test/soiling_test.py | 35 ++++++++++------------------------- 2 files changed, 28 insertions(+), 43 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 35ce5902..f431aeb2 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -199,7 +199,6 @@ def _calc_daily_df( # median change to day_scale/2 Matt df_ffill = df.copy() df_ffill = df.ffill(limit=int(round((day_scale / 2), 0))) - #df_ffill = df.ffill(limit=day_scale) # Calculate rolling median df["pi_roll_med"] = df_ffill.pi_norm.rolling(day_scale, center=True).median() @@ -402,7 +401,7 @@ def _calc_result_df( #################################################### # the following is moved here so median values are retained/Matt # for soiling inferrences when rejected fits occur - + result_dict["slope_err"] = ( result_dict["run_slope_high"] - result_dict["run_slope_low"] ) / abs(result_dict["run_slope"]) @@ -417,7 +416,7 @@ def _calc_result_df( result_dict["run_loss_baseline"] = ( result_dict["inferred_start_loss"] - result_dict["inferred_end_loss"] ) - + ############################################### result_list.append(result_dict) @@ -501,9 +500,10 @@ def _calc_result_df( results.inferred_begin_shift, 0, 1 ) ####################################################################### - #if neg_shift is False: - # results.loc[filt, "valid"] = False - + ''' + if neg_shift is False: + results.loc[filt, "valid"] = False + ''' if len(results[results.valid]) == 0: raise NoValidIntervalError("No valid soiling intervals were found") new_start = results.start.iloc[0] @@ -521,8 +521,8 @@ def _calc_result_df( # new code for perfect and inferred clean with handling of/Matt # negative shifts and changepoints within soiling intervals # goes to line 563 - if (piecewise==True)|(neg_shift==True): - ####################################################################### + if (piecewise) | (neg_shift): + ################################################################### pm_frame_out.inferred_begin_shift.bfill(inplace=True) pm_frame_out["forward_median"] = ( pm_frame_out.pi.iloc[::-1].rolling(10, min_periods=5).median() @@ -540,7 +540,7 @@ def _calc_result_df( shift_perfect = 0 begin_perfect_shifts = [0] begin_infer_shifts = [0] - + for date, rs, d, start_shift, changepoint, forward_median in zip( pm_frame_out.index, pm_frame_out.run_slope, @@ -551,7 +551,7 @@ def _calc_result_df( ): new_soil = d - day_start day_start = d - + if new_soil <= 0: # begin new soil period if (start_shift == prev_shift) | (changepoint): # no shift at # a slope changepoint @@ -596,7 +596,7 @@ def _calc_result_df( shift = forward_median - soil_infer if (soil_perfect + shift_perfect) < forward_median: shift_perfect = forward_median - soil_perfect - + # append the daily soiling ratio to each modeled fit begin_perfect_shifts.append(shift_perfect) begin_infer_shifts.append(shift) @@ -610,22 +610,22 @@ def _calc_result_df( soil_perfect_clean.append(soil_perfect) if changepoint is False: prev_shift = start_shift # assigned at new soil period - + elif new_soil > 0: # within soiling period # append the daily soiling ratio to each modeled fit soil_infer = start_infer + rs * d soil_inferred_clean.append(soil_infer) - + soil_perfect = start_perfect + rs * d soil_perfect_clean.append(soil_perfect) - + pm_frame_out["loss_inferred_clean"] = pd.Series( soil_inferred_clean, index=pm_frame_out.index ) pm_frame_out["loss_perfect_clean"] = pd.Series( soil_perfect_clean, index=pm_frame_out.index ) - + results["begin_perfect_shift"] = pd.Series(begin_perfect_shifts) results["begin_infer_shift"] = pd.Series(begin_infer_shifts) else: @@ -636,7 +636,8 @@ def _calc_result_df( # 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 soiling interval + # 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 @@ -3304,7 +3305,6 @@ def segmented_soiling_period( Datetime in which continuous change points occurred. None if segmentation was not possible. """ - # Check if PR dataframe has datetime index if not isinstance(pr.index, pd.DatetimeIndex): raise ValueError("The time series does not have DatetimeIndex") @@ -3349,7 +3349,7 @@ def segmented_soiling_period( if (R2_percent_improve < 0.01) | (R2_piecewise < 0.4): z = [np.nan] * len(x) cp_date = None - except IndexError as x: + except: z = [np.nan] * len(x) cp_date = None # Create Series from modelled profile diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 6edbfca7..42c52236 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -36,18 +36,9 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times # 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", - "inferred_recovery", - "inferred_begin_shift", - "length", - "valid", - ] + "start", "end", "soiling_rate", "soiling_rate_low", "soiling_rate_high", + "inferred_start_loss", "inferred_end_loss", "inferred_recovery", + "inferred_begin_shift", "length", "valid"] actual_summary_columns = soiling_info["soiling_interval_summary"].columns.values for x in actual_summary_columns: @@ -63,18 +54,11 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times 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, - "inferred_recovery": 0.065416, # Matt might not keep - "inferred_begin_shift": 0.084814, # Matt might not keep - "length": 24.0, - "valid": 1.0, - } - ) + { + "soiling_rate": -0.002644544, "soiling_rate_low": -0.002847504, + "soiling_rate_high": -0.002455915, "inferred_start_loss": 1.020124, + "inferred_end_loss": 0.9566552, "inferred_recovery": 0.065416, + "inferred_begin_shift": 0.084814, "length": 24.0, "valid": 1.0}) expected_means = expected_means[ [ "soiling_rate", @@ -252,7 +236,8 @@ def test_soiling_srr_method( ): np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily, soiling_insolation, reps=10, method=method, neg_shift=neg_shift, piecewise=piecewise + soiling_normalized_daily, soiling_insolation, reps=10, method=method, + neg_shift=neg_shift, piecewise=piecewise ) assert expected_sr == pytest.approx( sr, abs=1e-6 From ca7627bd491c37a96c398f09516d817a8b4131c2 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Mon, 19 Aug 2024 11:23:48 -0600 Subject: [PATCH 21/33] cleaning up formatting redundancies in soiling_test.py --- rdtools/test/soiling_test.py | 700 ++++++++++------------------------- 1 file changed, 195 insertions(+), 505 deletions(-) diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 42c52236..4c78459f 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -12,27 +12,20 @@ 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' + 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 = [ @@ -42,45 +35,29 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times 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']" + 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' + 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, "inferred_recovery": 0.065416, - "inferred_begin_shift": 0.084814, "length": 24.0, "valid": 1.0}) + {"soiling_rate": -0.002644544, "soiling_rate_low": -0.002847504, + "soiling_rate_high": -0.002455915, "inferred_start_loss": 1.020124, + "inferred_end_loss": 0.9566552, "inferred_recovery": 0.065416, + "inferred_begin_shift": 0.084814, "length": 24.0, "valid": 1.0}) expected_means = expected_means[ - [ - "soiling_rate", - "soiling_rate_low", - "soiling_rate_high", - "inferred_start_loss", - "inferred_end_loss", - "inferred_recovery", - "inferred_begin_shift", - "length", - "valid", - ] - ] + ["soiling_rate", "soiling_rate_low", "soiling_rate_high", "inferred_start_loss", + "inferred_end_loss", "inferred_recovery", "inferred_begin_shift", "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, - ) + 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 @@ -93,101 +70,62 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times @pytest.mark.filterwarnings("ignore:.*20% or more of the daily data.*:UserWarning") @pytest.mark.parametrize( "method,neg_shift,piecewise,expected_sr", - [ - ("random_clean", False, False, 0.936177), - ("half_norm_clean", False, False, 0.915093), - ("perfect_clean", False, False, 0.977116), - ("perfect_clean_complex", True, True, 0.977116), - ("inferred_clean_complex", True, True, 0.975805), - ], -) + [("random_clean", False, False, 0.936177), + ("half_norm_clean", False, False, 0.915093), + ("perfect_clean", False, False, 0.977116), + ("perfect_clean_complex", True, True, 0.977116), + ("inferred_clean_complex", True, True, 0.975805)]) + def test_soiling_srr_consecutive_invalid( - soiling_normalized_daily, - soiling_insolation, - soiling_times, - method, - neg_shift, - piecewise, - expected_sr, -): + soiling_normalized_daily, soiling_insolation, soiling_times, + method, neg_shift, piecewise, 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, - piecewise=piecewise, - neg_shift=neg_shift, - ) - assert expected_sr == pytest.approx( - sr, abs=1e-6 - ), f"Soiling ratio different from expected value for {method} with consecutive invalid intervals" # noqa: E501 - - -@pytest.mark.parametrize( - "clean_criterion,expected_sr", - [ - ("precip_and_shift", 0.982546), - ("precip_or_shift", 0.973433), - ("precip", 0.976196), - ("shift", 0.964369), - ], -) -def test_soiling_srr_with_precip( - soiling_normalized_daily, - soiling_insolation, - soiling_times, - clean_criterion, - expected_sr, -): + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, + reps=reps, max_relative_slope_error=20.0, + method=method, piecewise=piecewise, + neg_shift=neg_shift) + assert expected_sr == pytest.approx(sr, abs=1e-6),\ + f"Soiling ratio different from expected value for {method} with consecutive invalid intervals" + + +@pytest.mark.parametrize("clean_criterion,expected_sr", + [("precip_and_shift", 0.982546), + ("precip_or_shift", 0.973433), + ("precip", 0.976196), + ("shift", 0.964369)]) + +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" + 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_confidence_levels(soiling_normalized_daily, soiling_insolation): "Tests SRR with different confidence level settings from above" np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily, - soiling_insolation, - confidence_level=95, - reps=10, - exceedance_prob=80.0, - ) - assert np.array([0.959322, 0.966066]) == pytest.approx( - sr_ci, abs=1e-6 - ), "Confidence interval with confidence_level=95 different than expected" - assert 0.962691 == pytest.approx( - soiling_info["exceedance_level"], abs=1e-6 - ), 'soiling_info["exceedance_level"] different than expected when exceedance_prob=80' + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, + confidence_level=95,reps=10, exceedance_prob=80.0) + assert np.array([0.959322, 0.966066]) == pytest.approx(sr_ci, abs=1e-6),\ + "Confidence interval with confidence_level=95 different than expected" + assert 0.962691 == pytest.approx(soiling_info["exceedance_level"], abs=1e-6),\ + 'soiling_info["exceedance_level"] different than expected when exceedance_prob=80' def test_soiling_srr_dayscale(soiling_normalized_daily, soiling_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( - soiling_normalized_daily, - soiling_insolation, - confidence_level=68.2, - reps=10, - day_scale=91, - ) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, + confidence_level=68.2, reps=10, day_scale=91) def test_soiling_srr_clean_threshold(soiling_normalized_daily, soiling_insolation): @@ -195,53 +133,44 @@ def test_soiling_srr_clean_threshold(soiling_normalized_daily, soiling_insolatio can cause no soiling intervals to be found""" np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.01 - ) - assert 0.964369 == pytest.approx( - sr, abs=1e-6 - ), "Soiling ratio with specified clean_threshold different from expected value" + soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.01) + assert 0.964369 == 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( - soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.1 - ) + soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.1) def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation): np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily, soiling_insolation, reps=10, trim=True - ) + soiling_normalized_daily, soiling_insolation, reps=10, trim=True) - assert 0.978093 == 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" + assert 0.978093 == 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" @pytest.mark.parametrize( "method,neg_shift,piecewise,expected_sr", - [ - ("random_clean", False, False, 0.920444), - ("perfect_clean", False, False, 0.966912), - ("perfect_clean_complex", True, True, 0.966912), - ("inferred_clean_complex", True, True, 0.965565), - ], -) + [("random_clean", False, False, 0.920444), + ("perfect_clean", False, False, 0.966912), + ("perfect_clean_complex", True, True, 0.966912), + ("inferred_clean_complex", True, True, 0.965565)]) + def test_soiling_srr_method( soiling_normalized_daily, soiling_insolation, method, neg_shift, piecewise, expected_sr ): np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily, soiling_insolation, reps=10, method=method, + soiling_normalized_daily, soiling_insolation, reps=10, method=method, neg_shift=neg_shift, piecewise=piecewise ) - assert expected_sr == pytest.approx( - sr, abs=1e-6 - ), f'Soiling ratio with method="{method}" different from expected value' + assert expected_sr == pytest.approx(sr, abs=1e-6),\ + f'Soiling ratio with method="{method}" different from expected value' def test_soiling_srr_min_interval_length(soiling_normalized_daily, soiling_insolation): @@ -249,35 +178,22 @@ def test_soiling_srr_min_interval_length(soiling_normalized_daily, soiling_insol with pytest.raises(NoValidIntervalError): np.random.seed(1977) # normalized_daily intervals are 25 days long, so min=26 should fail: - _ = soiling_srr( - soiling_normalized_daily, - soiling_insolation, - confidence_level=68.2, - reps=10, - min_interval_length=26, - ) + _ = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=68.2, + reps=10, min_interval_length=26) # but min=24 should be fine: - _ = soiling_srr( - soiling_normalized_daily, - soiling_insolation, - confidence_level=68.2, - reps=10, - min_interval_length=24, - ) + _ = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=68.2, + reps=10, min_interval_length=24) def test_soiling_srr_recenter_false(soiling_normalized_daily, soiling_insolation): np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily, soiling_insolation, reps=10, recenter=False - ) - assert ( - 1 == soiling_info["renormalizing_factor"] - ), "Renormalizing factor != 1 with recenter=False" - assert 0.966387 == pytest.approx( - sr, abs=1e-6 - ), "Soiling ratio different than expected when recenter=False" + soiling_normalized_daily, soiling_insolation, reps=10, recenter=False) + assert (1 == soiling_info["renormalizing_factor"]),\ + "Renormalizing factor != 1 with recenter=False" + assert 0.966387 == pytest.approx(sr, abs=1e-6),\ + "Soiling ratio different than expected when recenter=False" def test_soiling_srr_negative_step(soiling_normalized_daily, soiling_insolation): @@ -286,43 +202,30 @@ def test_soiling_srr_negative_step(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( - stepped_daily, soiling_insolation, reps=10 - ) + 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\ + 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 + assert 0.936932 == 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_max_negative_slope_error( - soiling_normalized_daily, soiling_insolation -): +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, - ) + 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" + 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" + 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_with_nan_interval(soiling_normalized_daily, soiling_insolation): @@ -335,34 +238,24 @@ def test_soiling_srr_with_nan_interval(soiling_normalized_daily, soiling_insolat 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" + 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" 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, - method="perfect_clean_complex", - piecewise=True, - neg_shift=True, - ) - assert 0.974225 == pytest.approx( - sr, abs=1e-6 - ), "Soiling ratio different from expected value when an entire interval was NaN" + sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt, soiling_insolation, + reps=reps, method="perfect_clean_complex", + piecewise=True, neg_shift=True) + assert 0.974225 == pytest.approx(sr, abs=1e-6),\ + "Soiling ratio different from expected value when an entire interval was NaN" 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" + 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_kwargs(monkeypatch, soiling_normalized_daily, soiling_insolation): @@ -379,9 +272,9 @@ def test_soiling_srr_kwargs(monkeypatch, soiling_normalized_daily, soiling_insol @pytest.mark.parametrize(("start,expected_sr"), [(18, 0.984779), (17, 0.981258)]) + def test_soiling_srr_min_interval_length_default( - soiling_normalized_daily, soiling_insolation, start, expected_sr -): + 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 @@ -391,24 +284,19 @@ def test_soiling_srr_min_interval_length_default( 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" + assert expected_sr == pytest.approx(sr, abs=1e-6),\ + "Soiling ratio different from expected value" @pytest.mark.parametrize( - "test_param", ["energy_normalized_daily", "insolation_daily", "precipitation_daily"] -) + "test_param", ["energy_normalized_daily", "insolation_daily", "precipitation_daily"]) + def test_soiling_srr_non_daily_inputs(test_param): """ Validate the frequency check for input time series """ - dummy_daily_explicit = pd.Series( - 0, index=pd.date_range("2019-01-01", periods=10, freq="d") - ) - dummy_daily_implicit = pd.Series( - 0, index=pd.date_range("2019-01-01", periods=10, freq="d") - ) + dummy_daily_explicit = pd.Series(0, index=pd.date_range("2019-01-01", periods=10, freq="d")) + dummy_daily_implicit = pd.Series(0, index=pd.date_range("2019-01-01", periods=10, freq="d")) dummy_daily_implicit.index.freq = None dummy_nondaily = pd.Series(0, index=dummy_daily_explicit.index[::2]) @@ -451,128 +339,78 @@ def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolatio # ########################### @pytest.mark.parametrize( "method,neg_shift,expected_sr", - [ - ("half_norm_clean", False, 0.980143), - ("half_norm_clean", True, 0.975057), - ("perfect_clean_complex", True, 0.964117), - ("inferred_clean_complex", True, 0.963585), - ], -) + [("half_norm_clean", False, 0.980143), + ("half_norm_clean", True, 0.975057), + ("perfect_clean_complex", True, 0.964117), + ("inferred_clean_complex", True, 0.963585)]) + def test_negative_shifts( - soiling_normalized_daily_with_neg_shifts, - soiling_insolation, - soiling_times, - method, - neg_shift, - expected_sr, -): + soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times, + method, neg_shift, expected_sr): reps = 10 np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily_with_neg_shifts, - soiling_insolation, - reps=reps, - method=method, - neg_shift=neg_shift, - ) - assert expected_sr == pytest.approx( - sr, abs=1e-6 - ), f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" \ + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, + soiling_insolation, reps=reps, + method=method, neg_shift=neg_shift) + assert expected_sr == pytest.approx(sr, abs=1e-6),\ + f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" \ different from expected value' @pytest.mark.parametrize( "method,piecewise,expected_sr", - [ - ("half_norm_clean", False, 0.8670264), - ("half_norm_clean", True, 0.927017), - ("perfect_clean_complex", True, 0.896936), - ("inferred_clean_complex", True, 0.896214), - ], -) -def test_piecewise( - soiling_normalized_daily_with_piecewise_slope, - soiling_insolation, - soiling_times, - method, - piecewise, - expected_sr, -): + [("half_norm_clean", False, 0.8670264), + ("half_norm_clean", True, 0.927017), + ("perfect_clean_complex", True, 0.896936), + ("inferred_clean_complex", True, 0.896214)]) + +def test_piecewise(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, + soiling_times, method, piecewise, expected_sr): reps = 10 np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily_with_piecewise_slope, - soiling_insolation, - reps=reps, - method=method, - piecewise=piecewise, - ) - assert expected_sr == pytest.approx( - sr, abs=1e-6 - ), f'Soiling ratio with method="{method}" and piecewise="{piecewise}" \ + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, + soiling_insolation, reps=reps, method=method, + piecewise=piecewise) + assert expected_sr == pytest.approx(sr, abs=1e-6),\ + f'Soiling ratio with method="{method}" and piecewise="{piecewise}" \ different from expected value' -def test_piecewise_and_neg_shifts( - soiling_normalized_daily_with_piecewise_slope, - soiling_normalized_daily_with_neg_shifts, - soiling_insolation, - soiling_times, -): +def test_piecewise_and_neg_shifts(soiling_normalized_daily_with_piecewise_slope, + soiling_normalized_daily_with_neg_shifts, + soiling_insolation, soiling_times): reps = 10 np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily_with_piecewise_slope, - soiling_insolation, - reps=reps, - method="perfect_clean_complex", - piecewise=True, - neg_shift=True, - ) - assert 0.896936 == pytest.approx( - sr, abs=1e-6 - ), "Soiling ratio different from expected value for data with piecewise slopes" + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, + soiling_insolation, reps=reps, + method="perfect_clean_complex", piecewise=True, + neg_shift=True) + assert 0.896936 == pytest.approx(sr, abs=1e-6),\ + "Soiling ratio different from expected value for data with piecewise slopes" np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr( - soiling_normalized_daily_with_neg_shifts, - soiling_insolation, - reps=reps, - method="perfect_clean_complex", - piecewise=True, - neg_shift=True, - ) - assert 0.964117 == pytest.approx( - sr, abs=1e-6 - ), "Soiling ratio different from expected value for data with negative shifts" + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, + soiling_insolation, reps=reps, + method="perfect_clean_complex", piecewise=True, + neg_shift=True) + assert 0.964117 == pytest.approx(sr, abs=1e-6),\ + "Soiling ratio different from expected value for data with negative shifts" -def test_complex_sr_clean_threshold( - soiling_normalized_daily_with_neg_shifts, soiling_insolation -): +def test_complex_sr_clean_threshold(soiling_normalized_daily_with_neg_shifts, soiling_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( - soiling_normalized_daily_with_neg_shifts, - soiling_insolation, - reps=10, - clean_threshold=0.1, - method="perfect_clean_complex", - piecewise=True, - neg_shift=True, - ) - assert 0.934926 == pytest.approx( - sr, abs=1e-6 - ), "Soiling ratio with specified clean_threshold different from expected value" + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, + soiling_insolation, reps=10, clean_threshold=0.1, + method="perfect_clean_complex", piecewise=True, + neg_shift=True) + assert 0.934926 == 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( - soiling_normalized_daily_with_neg_shifts, - soiling_insolation, - reps=10, - clean_threshold=1, - ) + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, + soiling_insolation, reps=10, clean_threshold=1) # ########################### @@ -598,13 +436,7 @@ def test_annual_soiling_ratios(multi_year_profiles): expected_data = np.array([[2018, 4.5, 1.431, 7.569], [2019, 14.5, 11.431, 17.569]]) expected = pd.DataFrame( data=expected_data, - columns=[ - "year", - "soiling_ratio_median", - "soiling_ratio_low", - "soiling_ratio_high", - ], - ) + columns=["year", "soiling_ratio_median", "soiling_ratio_low", "soiling_ratio_high"]) expected["year"] = expected["year"].astype(int) srr_profiles, insolation = multi_year_profiles @@ -617,13 +449,7 @@ def test_annual_soiling_ratios_confidence_interval(multi_year_profiles): expected_data = np.array([[2018, 4.5, 0.225, 8.775], [2019, 14.5, 10.225, 18.775]]) expected = pd.DataFrame( data=expected_data, - columns=[ - "year", - "soiling_ratio_median", - "soiling_ratio_low", - "soiling_ratio_high", - ], - ) + columns=["year", "soiling_ratio_median", "soiling_ratio_low", "soiling_ratio_high"]) expected["year"] = expected["year"].astype(int) srr_profiles, insolation = multi_year_profiles @@ -638,8 +464,7 @@ def test_annual_soiling_ratios_warning(multi_year_profiles): match = ( "The indexes of stochastic_soiling_profiles are not entirely contained " "within the index of insolation_daily. Every day in stochastic_soiling_profiles " - "should be represented in insolation_daily. This may cause erroneous results." - ) + "should be represented in insolation_daily. This may cause erroneous results.") with pytest.warns(UserWarning, match=match): _ = annual_soiling_ratios(srr_profiles, insolation) @@ -684,14 +509,8 @@ def _build_monthly_summary(top_rows): df = pd.DataFrame( data=all_rows, - columns=[ - "month", - "soiling_rate_median", - "soiling_rate_low", - "soiling_rate_high", - "interval_count", - ], - ) + columns=["month", "soiling_rate_median", "soiling_rate_low", + "soiling_rate_high", "interval_count"]) df["month"] = range(1, 13) return df @@ -702,37 +521,10 @@ def test_monthly_soiling_rates(soiling_interval_summary): result = monthly_soiling_rates(soiling_interval_summary) expected = np.array( - [ - [ - 1.00000000e00, - -2.42103810e-03, - -5.00912766e-03, - -7.68551806e-04, - 2.00000000e00, - ], - [ - 2.00000000e00, - -1.25092837e-03, - -2.10091842e-03, - -3.97354321e-04, - 1.00000000e00, - ], - [ - 3.00000000e00, - -2.00313359e-03, - -2.68359541e-03, - -1.31927678e-03, - 1.00000000e00, - ], - [ - 4.00000000e00, - -1.99729563e-03, - -2.68067699e-03, - -1.31667446e-03, - 1.00000000e00, - ], - ] - ) + [[1.00000000e00, -2.42103810e-03, -5.00912766e-03, -7.68551806e-04, 2.00000000e00], + [2.00000000e00, -1.25092837e-03, -2.10091842e-03, -3.97354321e-04, 1.00000000e00], + [3.00000000e00, -2.00313359e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00], + [4.00000000e00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -743,37 +535,10 @@ def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary): result = monthly_soiling_rates(soiling_interval_summary, min_interval_length=20) expected = np.array( - [ - [ - 1.00000000e00, - -1.24851539e-03, - -2.10394564e-03, - -3.98358211e-04, - 1.00000000e00, - ], - [ - 2.00000000e00, - -1.25092837e-03, - -2.10091842e-03, - -3.97330424e-04, - 1.00000000e00, - ], - [ - 3.00000000e00, - -2.00309454e-03, - -2.68359541e-03, - -1.31927678e-03, - 1.00000000e00, - ], - [ - 4.00000000e00, - -1.99729563e-03, - -2.68067699e-03, - -1.31667446e-03, - 1.00000000e00, - ], - ] - ) + [[1.00000000e00, -1.24851539e-03, -2.10394564e-03, -3.98358211e-04, 1.00000000e00], + [2.00000000e00, -1.25092837e-03, -2.10091842e-03, -3.97330424e-04, 1.00000000e00], + [3.00000000e00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00], + [4.00000000e00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -786,31 +551,10 @@ def test_monthly_soiling_rates_max_slope_err(soiling_interval_summary): ) expected = np.array( - [ - [ - 1.00000000e00, - -4.74910923e-03, - -5.26236739e-03, - -4.23901493e-03, - 1.00000000e00, - ], - [2.00000000e00, np.nan, np.nan, np.nan, 0.00000000e00], - [ - 3.00000000e00, - -2.00074270e-03, - -2.68073474e-03, - -1.31786434e-03, - 1.00000000e00, - ], - [ - 4.00000000e00, - -2.00309454e-03, - -2.68359541e-03, - -1.31927678e-03, - 1.00000000e00, - ], - ] - ) + [[1.00000000e00, -4.74910923e-03, -5.26236739e-03, -4.23901493e-03, 1.00000000e00], + [2.00000000e00, np.nan, np.nan, np.nan, 0.00000000e00], + [3.00000000e00, -2.00074270e-03, -2.68073474e-03, -1.31786434e-03, 1.00000000e00], + [4.00000000e00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) @@ -821,37 +565,10 @@ def test_monthly_soiling_rates_confidence_level(soiling_interval_summary): result = monthly_soiling_rates(soiling_interval_summary, confidence_level=95) expected = np.array( - [ - [ - 1.00000000e00, - -2.42103810e-03, - -5.42313113e-03, - -1.21156562e-04, - 2.00000000e00, - ], - [ - 2.00000000e00, - -1.25092837e-03, - -2.43731574e-03, - -6.23842627e-05, - 1.00000000e00, - ], - [ - 3.00000000e00, - -2.00313359e-03, - -2.94998476e-03, - -1.04988760e-03, - 1.00000000e00, - ], - [ - 4.00000000e00, - -1.99729563e-03, - -2.95063841e-03, - -1.04869949e-03, - 1.00000000e00, - ], - ] - ) + [[1.00000000e00, -2.42103810e-03, -5.42313113e-03, -1.21156562e-04, 2.00000000e00], + [2.00000000e00, -1.25092837e-03, -2.43731574e-03, -6.23842627e-05, 1.00000000e00], + [3.00000000e00, -2.00313359e-03, -2.94998476e-03, -1.04988760e-03, 1.00000000e00], + [4.00000000e00, -1.99729563e-03, -2.95063841e-03, -1.04869949e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) @@ -863,37 +580,10 @@ def test_monthly_soiling_rates_reps(soiling_interval_summary): result = monthly_soiling_rates(soiling_interval_summary, reps=3) expected = np.array( - [ - [ - 1.00000000e00, - -2.88594088e-03, - -5.03736679e-03, - -6.47391131e-04, - 2.00000000e00, - ], - [ - 2.00000000e00, - -1.67359565e-03, - -2.00504171e-03, - -1.33240044e-03, - 1.00000000e00, - ], - [ - 3.00000000e00, - -1.22306993e-03, - -2.19274892e-03, - -1.11793240e-03, - 1.00000000e00, - ], - [ - 4.00000000e00, - -1.94675549e-03, - -2.42574164e-03, - -1.54850795e-03, - 1.00000000e00, - ], - ] - ) + [[1.00000000e00, -2.88594088e-03, -5.03736679e-03, -6.47391131e-04, 2.00000000e00], + [2.00000000e00, -1.67359565e-03, -2.00504171e-03, -1.33240044e-03, 1.00000000e00], + [3.00000000e00, -1.22306993e-03, -2.19274892e-03, -1.11793240e-03, 1.00000000e00], + [4.00000000e00, -1.94675549e-03, -2.42574164e-03, -1.54850795e-03, 1.00000000e00]]) expected = _build_monthly_summary(expected) From 8b3fa4accde975fb7c11ae022f1878376a0c4303 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Mon, 19 Aug 2024 13:05:57 -0600 Subject: [PATCH 22/33] reformatting soiling.py and minor reformatting to soiling_test.py --- rdtools/soiling.py | 876 ++++++++++------------------------- rdtools/test/soiling_test.py | 98 ++-- 2 files changed, 280 insertions(+), 694 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index f431aeb2..e8140048 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -28,11 +28,9 @@ lowess = sm.nonparametric.lowess # Used in CODSAnalysis/Matt -warnings.warn( - "The soiling module is currently experimental. The API, results, " - "and default behaviors may change in future releases (including MINOR " - "and PATCH releases) as the code matures." -) +warnings.warn("The soiling module is currently experimental. The API, results, " + "and default behaviors may change in future releases (including MINOR " + "and PATCH releases) as the code matures.") # Custom exception @@ -82,17 +80,9 @@ def __init__(self, energy_normalized_daily, insolation_daily, precipitation_dail ############################################################################### # add neg_shift and piecewise into parameters/Matt - def _calc_daily_df( - self, - day_scale=13, - clean_threshold="infer", - recenter=True, - clean_criterion="shift", - precip_threshold=0.01, - outlier_factor=1.5, - neg_shift=False, - piecewise=False, - ): + def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, + clean_criterion="shift", precip_threshold=0.01, outlier_factor=1.5, + neg_shift=False, piecewise=False): """ Calculates self.daily_df, a pandas dataframe prepared for SRR analysis, and self.renorm_factor, the renormalization factor for the daily @@ -143,12 +133,10 @@ def _calc_daily_df( piecewise fit being tested. """ if (day_scale % 2 == 0) and ("shift" in clean_criterion): - warnings.warn( - "An even value of day_scale was passed. An odd value is " - "recommended, otherwise, consecutive days may be erroneously " - "flagged as cleaning events. " - "See https://github.com/NREL/rdtools/issues/189" - ) + warnings.warn("An even value of day_scale was passed. An odd value is " + "recommended, otherwise, consecutive days may be erroneously " + "flagged as cleaning events. " + "See https://github.com/NREL/rdtools/issues/189") df = self.pm.to_frame() df.columns = ["pi"] @@ -228,9 +216,8 @@ def _calc_daily_df( if clean_criterion == "precip_and_shift": # Detect which cleaning events are associated with rain # within a 3 day window - precip_event = ( - precip_event.rolling(3, center=True, min_periods=1).apply(any).astype(bool) - ) + 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": df["clean_event"] = df["clean_event_detected"] | precip_event @@ -239,11 +226,8 @@ def _calc_daily_df( elif clean_criterion == "shift": df["clean_event"] = df["clean_event_detected"] else: - raise ValueError( - "clean_criterion must be one of " - '{"precip_and_shift", "precip_or_shift", ' - '"precip", "shift"}' - ) + raise ValueError("clean_criterion must be one of" + '{"precip_and_shift", "precip_or_shift", "precip", "shift"}') df["clean_event"] = df.clean_event | out_start | out_end @@ -276,11 +260,9 @@ def _calc_daily_df( # compared to a single linear fit. Intervals <45 days reqire more # stringent statistical improvements/Matt if piecewise is True: - warnings.warn( - "Piecewise = True was passed, for both Piecewise=True" - "and neg_shift=True cleaning_method choices should" - "be perfect_clean_complex or inferred_clean_complex" - ) + warnings.warn("Piecewise = True was passed, for both Piecewise=True" + "and neg_shift=True cleaning_method choices should" + "be perfect_clean_complex or inferred_clean_complex") min_soil_length = 27 # min threshold of days necessary for piecewise fit piecewise_loop = sorted(list(set(df["run"]))) cp_dates = [] @@ -307,15 +289,8 @@ def _calc_daily_df( ###################################################################### # added neg_shift into parameters in the following def/Matt - def _calc_result_df( - self, - trim=False, - max_relative_slope_error=500.0, - max_negative_step=0.05, - min_interval_length=7, - neg_shift=False, - piecewise=False - ): + def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negative_step=0.05, + min_interval_length=7, neg_shift=False, piecewise=False): """ Calculates self.result_df, a pandas dataframe summarizing the soiling intervals identified and self.analyzed_daily_df, a version of @@ -380,9 +355,8 @@ def _calc_result_df( "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 from other breaks/Matt + "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 ############################################################## } @@ -407,15 +381,13 @@ def _calc_result_df( ) / abs(result_dict["run_slope"]) if (result_dict["slope_err"] <= (max_relative_slope_error / 100.0)) & ( - result_dict["run_slope"] < 0 - ): + result_dict["run_slope"] < 0): result_dict["inferred_start_loss"] = fit_poly(start_day) result_dict["inferred_end_loss"] = fit_poly(end_day) ############################################# # calculate loss over soiling interval per polyfit/matt result_dict["run_loss_baseline"] = ( - result_dict["inferred_start_loss"] - result_dict["inferred_end_loss"] - ) + result_dict["inferred_start_loss"] - result_dict["inferred_end_loss"]) ############################################### @@ -438,16 +410,13 @@ def _calc_result_df( # negative shifts are now used as breaks for soiling intervals/Matt # so new criteria for final filter to modify dataframe if neg_shift is True: - warnings.warn( - "neg_shift = True was passed, for both Piecewise=True" - "and neg_shift=True cleaning_method choices should" - "be perfect_clean_complex or inferred_clean_complex" - ) - filt = ( - (results.run_slope > 0) - | (results.slope_err >= max_relative_slope_error / 100.0) - # |(results.max_neg_step <= -1.0 * max_negative_step) - ) + warnings.warn("neg_shift = True was passed, for both Piecewise=True" + "and neg_shift=True cleaning_method choices should" + "be perfect_clean_complex or inferred_clean_complex") + 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 @@ -459,13 +428,12 @@ def _calc_result_df( # original code below setting soiling intervals with extreme negative # shift to zero slopes, /Matt if neg_shift is False: - filt = ( - (results.run_slope > 0) - | (results.slope_err >= max_relative_slope_error / 100.0) - | (results.max_neg_step <= -1.0 * max_negative_step) - # remove line 389, want to store data for inferred values - # for calculations below - # |results.loc[filt, 'valid'] = False + filt = ((results.run_slope > 0) + | (results.slope_err >= max_relative_slope_error / 100.0) + | (results.max_neg_step <= -1.0 * max_negative_step) + # 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 @@ -474,8 +442,7 @@ def _calc_result_df( # Calculate the next inferred start loss from next valid interval results["next_inferred_start_loss"] = np.clip( - results[results.valid].inferred_start_loss.shift(-1), 0, 1 - ) + results[results.valid].inferred_start_loss.shift(-1), 0, 1) # Calculate the inferred recovery at the end of each interval ######################################################################## @@ -491,14 +458,12 @@ def _calc_result_df( # is a nan, and the current interval is valid then set prev_end=1 results.loc[ (results.clean_event is True) & (np.isnan(results.prev_end) & (results.valid is True)), - "prev_end", - ] = 1 # clean_event or clean_event_detected + "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 # negative due to fitting results results.loc[results.clean_event, "inferred_begin_shift"] = np.clip( - results.inferred_begin_shift, 0, 1 - ) + results.inferred_begin_shift, 0, 1) ####################################################################### ''' if neg_shift is False: @@ -510,8 +475,7 @@ def _calc_result_df( new_end = results.end.iloc[-1] pm_frame_out = daily_df[new_start:new_end] pm_frame_out = ( - pm_frame_out.reset_index().merge(results, how="left", on="run").set_index("date") - ) + pm_frame_out.reset_index().merge(results, how="left", on="run").set_index("date")) pm_frame_out["loss_perfect_clean"] = np.nan pm_frame_out["loss_inferred_clean"] = np.nan @@ -525,8 +489,7 @@ def _calc_result_df( ################################################################### pm_frame_out.inferred_begin_shift.bfill(inplace=True) pm_frame_out["forward_median"] = ( - pm_frame_out.pi.iloc[::-1].rolling(10, min_periods=5).median() - ) + pm_frame_out.pi.iloc[::-1].rolling(10, min_periods=5).median()) prev_shift = 1 soil_inferred_clean = [] soil_perfect_clean = [] @@ -542,13 +505,9 @@ def _calc_result_df( 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 @@ -570,7 +529,7 @@ def _calc_result_df( shift_perfect = 1 total_down = 0 # add #####################3/27/24 - elif (start_shift == 0) & (prev_shift >= 0): # ( + elif (start_shift == 0) & (prev_shift >= 0): shift = start_shift shift_perfect = start_shift total_down = 0 @@ -581,10 +540,8 @@ def _calc_result_df( 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 - ): # negative shift starts the interval, - # previous shift was cleaning + elif (start_shift < 0) & (prev_shift >= 0): + # negative shift starts the interval, previous shift was cleaning shift = 0 shift_perfect = 0 total_down = start_shift @@ -620,31 +577,25 @@ def _calc_result_df( soil_perfect_clean.append(soil_perfect) pm_frame_out["loss_inferred_clean"] = pd.Series( - soil_inferred_clean, index=pm_frame_out.index - ) + soil_inferred_clean, index=pm_frame_out.index) pm_frame_out["loss_perfect_clean"] = pd.Series( - soil_perfect_clean, index=pm_frame_out.index - ) + soil_perfect_clean, index=pm_frame_out.index) results["begin_perfect_shift"] = pd.Series(begin_perfect_shifts) results["begin_infer_shift"] = pd.Series(begin_infer_shifts) else: - pm_frame_out['loss_perfect_clean'] = \ - pm_frame_out.start_loss + \ + pm_frame_out['loss_perfect_clean'] = pm_frame_out.start_loss + \ pm_frame_out.days_since_clean * pm_frame_out.run_slope # 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) + 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 # soiling interval - pm_frame_out['loss_inferred_clean'] = \ - pm_frame_out.inferred_start_loss + \ + pm_frame_out['loss_inferred_clean'] = pm_frame_out.inferred_start_loss + \ pm_frame_out.days_since_clean * pm_frame_out.run_slope # filling the flat intervals may need to be recalculated # for different assumptions - pm_frame_out.loss_inferred_clean = \ - pm_frame_out.loss_inferred_clean.fillna(1) + pm_frame_out.loss_inferred_clean = pm_frame_out.loss_inferred_clean.fillna(1) ####################################################################### self.result_df = results self.analyzed_daily_df = pm_frame_out @@ -689,12 +640,8 @@ 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") - ): + if ((method == "half_norm_clean") or (method == "random_clean") + 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 ' @@ -713,8 +660,7 @@ def _calc_monte(self, monte, method="half_norm_clean"): # only really need this column from the original frame: df_rand = df_rand[["insol", "run"]] results_rand["run_slope"] = np.random.uniform( - results_rand.run_slope_low, results_rand.run_slope_high - ) + results_rand.run_slope_low, results_rand.run_slope_high) results_rand["run_loss"] = results_rand.run_slope * results_rand.length results_rand["end_loss"] = np.nan @@ -739,11 +685,9 @@ def _calc_monte(self, monte, method="half_norm_clean"): # Randomize recovery of valid intervals only valid_intervals = results_rand[results_rand.valid].copy() valid_intervals["inferred_recovery"] = np.clip( - valid_intervals.inferred_recovery, 0, 1 - ) + valid_intervals.inferred_recovery, 0, 1) valid_intervals["inferred_recovery"] = valid_intervals.inferred_recovery.fillna( - 1.0 - ) + 1.0) end_list = [] for i, row in valid_intervals.iterrows(): @@ -810,14 +754,11 @@ def _calc_monte(self, monte, method="half_norm_clean"): if row.begin_perfect_shift > 0: inter_start = np.clip( (inter_start + row.begin_perfect_shift + delta_previous_run_loss), - end, - 1, - ) + end, 1) delta_previous_run_loss = -1 * row.run_loss - row.run_loss_baseline else: delta_previous_run_loss = ( - delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline - ) + delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline) # inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) start_list.append(inter_start) end = inter_start + row.run_loss @@ -830,31 +771,24 @@ def _calc_monte(self, monte, method="half_norm_clean"): if row.begin_infer_shift > 0: inter_start = np.clip( (inter_start + row.begin_infer_shift + delta_previous_run_loss), - end, - 1, - ) + end, 1) delta_previous_run_loss = -1 * row.run_loss - row.run_loss_baseline else: delta_previous_run_loss = ( - delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline - ) + delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline) # inter_start=np.clip((inter_start+row.begin_shift+delta_previous_run_loss),0,1) start_list.append(inter_start) end = inter_start + row.run_loss inter_start = end results_rand["start_loss"] = start_list - """ - - """ ############################################### else: raise ValueError("Invalid method specification") df_rand = ( - df_rand.reset_index().merge(results_rand, how="left", on="run").set_index("date") - ) + df_rand.reset_index().merge(results_rand, how="left", on="run").set_index("date")) df_rand["loss"] = np.nan df_rand["days_since_clean"] = (df_rand.index - df_rand.start).dt.days df_rand["loss"] = df_rand.start_loss + df_rand.days_since_clean * df_rand.run_slope @@ -862,8 +796,7 @@ def _calc_monte(self, monte, method="half_norm_clean"): df_rand["soil_insol"] = df_rand.loss * df_rand.insol soiling_ratio = ( - df_rand.soil_insol.sum() / df_rand.insol[~df_rand.soil_insol.isnull()].sum() - ) + df_rand.soil_insol.sum() / df_rand.insol[~df_rand.soil_insol.isnull()].sum()) monte_losses.append(soiling_ratio) random_profile = df_rand["loss"].copy() random_profile.name = "stochastic_soiling_profile" @@ -874,25 +807,11 @@ def _calc_monte(self, monte, method="half_norm_clean"): ####################################################################### # add neg_shift and piecewise to the following def/Matt - 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, - neg_shift=False, - piecewise=False, - ): + 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, + neg_shift=False, piecewise=False): """ Run the SRR method from beginning to end. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented @@ -1041,32 +960,22 @@ def run( +------------------------+----------------------------------------------+ """ - 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, - min_interval_length=min_interval_length, - neg_shift=neg_shift, - piecewise=piecewise - ) + 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, + 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, - [50, 50.0 - half_ci, 50.0 + half_ci, 100 - exceedance_prob], - ) + result = np.percentile(self.monte_losses, + [50, 50.0 - half_ci, 50.0 + half_ci, 100 - exceedance_prob]) P_level = result[3] # Construct calc_info output @@ -1074,67 +983,33 @@ def run( # add inferred_recovery, inferred_begin_shift /Matt ############################################### intervals_out = self.result_df[ - [ - "start", - "end", - "run_slope", - "run_slope_low", - "run_slope_high", - "inferred_start_loss", - "inferred_end_loss", - "inferred_recovery", - "inferred_begin_shift", - "length", - "valid", - ] + ["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", - "run_slope_high": "soiling_rate_high", - "run_slope_low": "soiling_rate_low", - }, - inplace=True, - ) + intervals_out.rename(columns={"run_slope": "soiling_rate", + "run_slope_high": "soiling_rate_high", + "run_slope_low": "soiling_rate_low"}, inplace=True) df_d = self.analyzed_daily_df # sr_perfect = df_d[df_d['valid']]['loss_perfect_clean'] sr_perfect = df_d.loss_perfect_clean - calc_info = { - "exceedance_level": P_level, - "renormalizing_factor": self.renorm_factor, - "stochastic_soiling_profiles": self.random_profiles, - "soiling_interval_summary": intervals_out, - "soiling_ratio_perfect_clean": sr_perfect, - } + calc_info = {"exceedance_level": P_level, "renormalizing_factor": self.renorm_factor, + "stochastic_soiling_profiles": self.random_profiles, + "soiling_interval_summary": intervals_out, + "soiling_ratio_perfect_clean": sr_perfect} return (result[0], result[1:3], calc_info) # more updates are needed for documentation but added additional inputs # that are in srr.run /Matt -def soiling_srr( - energy_normalized_daily, - insolation_daily, - reps=1000, - precipitation_daily=None, - 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, - neg_shift=False, - piecewise=False, -): +def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitation_daily=None, + 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, + neg_shift=False, piecewise=False): """ Functional wrapper for :py:class:`~rdtools.soiling.SRRAnalysis`. Perform the stochastic rate and recovery soiling loss calculation. Based on the @@ -1293,30 +1168,16 @@ def soiling_srr( +------------------------+----------------------------------------------+ """ - srr = SRRAnalysis( - energy_normalized_daily, - insolation_daily, - precipitation_daily=precipitation_daily, - ) + srr = SRRAnalysis(energy_normalized_daily, insolation_daily, + precipitation_daily=precipitation_daily) sr, sr_ci, soiling_info = srr.run( - 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, - max_relative_slope_error=max_relative_slope_error, - max_negative_step=max_negative_step, - outlier_factor=outlier_factor, - neg_shift=neg_shift, - piecewise=piecewise, - ) + 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, + 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 @@ -1380,12 +1241,10 @@ def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confide all_profiles = all_profiles.dropna() if not all_profiles.index.isin(insolation_daily.index).all(): - warnings.warn( - "The indexes of stochastic_soiling_profiles are not entirely " - "contained within the index of insolation_daily. Every day in " - "stochastic_soiling_profiles should be represented in " - "insolation_daily. This may cause erroneous results." - ) + warnings.warn("The indexes of stochastic_soiling_profiles are not entirely " + "contained within the index of insolation_daily. Every day in " + "stochastic_soiling_profiles should be represented in " + "insolation_daily. This may cause erroneous results.") insolation_daily = insolation_daily.reindex(all_profiles.index) @@ -1400,29 +1259,18 @@ def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confide all_annual_iwsr = all_annual_weighted_sums.multiply(1 / annual_insolation, axis=0) annual_soiling = pd.DataFrame( - { - "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 - ), - } - ) + {"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() return annual_soiling -def monthly_soiling_rates( - soiling_interval_summary, - min_interval_length=14, - max_relative_slope_error=500.0, - reps=100000, - confidence_level=68.2, -): +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. Samples possible soiling rates from soiling rate confidence @@ -1497,9 +1345,7 @@ def monthly_soiling_rates( rel_error = 100 * abs((high - low) / rate) intervals = soiling_interval_summary[ (soiling_interval_summary["length"] >= min_interval_length) - & (soiling_interval_summary["valid"]) - & (rel_error <= max_relative_slope_error) - ].copy() + & (soiling_interval_summary["valid"]) & (rel_error <= max_relative_slope_error)].copy() # count the overlap of each interval with each month month_counts = [] @@ -1526,11 +1372,8 @@ def monthly_soiling_rates( relevant_intervals = intervals[intervals[sample_col] > 0] for _, row in relevant_intervals.iterrows(): - rates.append( - np.random.uniform( - row["soiling_rate_low"], row["soiling_rate_high"], row[sample_col] - ) - ) + rates.append(np.random.uniform( + row["soiling_rate_low"], row["soiling_rate_high"], row[sample_col])) rates = [x for sublist in rates for x in sublist] @@ -1545,8 +1388,7 @@ def monthly_soiling_rates( # make a dataframe out of the results monthly_soiling_df = pd.DataFrame( data=monthly_rate_data, - columns=["soiling_rate_median", "soiling_rate_low", "soiling_rate_high"], - ) + columns=["soiling_rate_median", "soiling_rate_low", "soiling_rate_high"]) monthly_soiling_df.insert(0, "month", range(1, 13)) monthly_soiling_df["interval_count"] = relevant_interval_count @@ -1660,28 +1502,15 @@ def __init__(self, energy_normalized_daily): self.pm = self.pm.loc[first_keeper:] if self.pm.index.freq != "D": - raise ValueError( - "Daily performance metric series must have " - "daily frequency (missing dates should be " - "represented by NaNs)" - ) + raise ValueError("Daily performance metric series must have " + "daily frequency (missing dates should be " + "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). @@ -1828,8 +1657,7 @@ def iterative_signal_decomposition( if "SR" not in order: raise ValueError( - "'SR' must be in argument 'order' " + "(e.g. order=['SR', 'SC', 'Rd']" - ) + "'SR' must be in argument 'order' " + "(e.g. order=['SR', 'SC', 'Rd']") n_steps = len(order) day = np.arange(len(pi)) degradation_trend = [1] @@ -1843,8 +1671,7 @@ def iterative_signal_decomposition( # Find possible cleaning events based on the performance index ce, rm9 = _rolling_median_ce_detection( - pi.index, pi, ffill=ffill, tuner=cleaning_sensitivity - ) + pi.index, pi, ffill=ffill, tuner=cleaning_sensitivity) pce = _collapse_cleaning_events(ce, rm9.diff().values, 5) small_soiling_signal, perfect_cleaning = False, True @@ -1865,28 +1692,22 @@ def iterative_signal_decomposition( pce = soiling_dfs[-1].cleaning_events.copy() cleaning_sensitivity *= 1.2 # decrease sensitivity ce, rm9 = _rolling_median_ce_detection( - pi.index, residuals, ffill=ffill, tuner=cleaning_sensitivity - ) + pi.index, residuals, ffill=ffill, tuner=cleaning_sensitivity) ce = _collapse_cleaning_events(ce, rm9.diff().values, 5) pce[ce] = True clean_pruning_sensitivity /= 1.1 # increase pruning sensitivity # Decompose input signal soiling_dummy = ( - pi / degradation_trend[-1] / seasonal_component[-1] / residual_shift - ) + pi / degradation_trend[-1] / seasonal_component[-1] / residual_shift) # Run Kalman Filter for obtaining soiling component kdf, Ps = self._Kalman_filter_for_SR( - zs_series=soiling_dummy, - clip_soiling=clip_soiling, - prescient_cleaning_events=pce, - pruning_iterations=pruning_iterations, - clean_pruning_sensitivity=clean_pruning_sensitivity, - perfect_cleaning=perfect_cleaning, - process_noise=process_noise, - renormalize_SR=renormalize_SR, - ) + zs_series=soiling_dummy, clip_soiling=clip_soiling, + prescient_cleaning_events=pce, pruning_iterations=pruning_iterations, + clean_pruning_sensitivity=clean_pruning_sensitivity, + perfect_cleaning=perfect_cleaning, process_noise=process_noise, + renormalize_SR=renormalize_SR) soiling_ratio.append(kdf.soiling_ratio) soiling_dfs.append(kdf) @@ -1898,36 +1719,20 @@ def iterative_signal_decomposition( season_dummy = season_dummy.apply(np.log) # Log transform # Run STL model STL_res = STL( - season_dummy, - period=365, - seasonal=999999, - seasonal_deg=0, - trend_deg=0, - robust=True, - low_pass_jump=30, - seasonal_jump=30, - trend_jump=365, - ).fit() + season_dummy, period=365, seasonal=999999, seasonal_deg=0, trend_deg=0, + robust=True, low_pass_jump=30, seasonal_jump=30, trend_jump=365).fit() # Smooth result smooth_season = lowess( - STL_res.seasonal.apply(np.exp), - pi.index, - is_sorted=True, - delta=30, - frac=180 / len(pi), - return_sorted=False, - ) + STL_res.seasonal.apply(np.exp), pi.index, is_sorted=True, delta=30, + frac=180 / len(pi), return_sorted=False) # Ensure periodic seaonal component seasonal_comp = _force_periodicity(smooth_season, season_dummy.index, pi.index) seasonal_component.append(seasonal_comp) if degradation_method == "STL": # If not YoY deg_trend = pd.Series(index=pi.index, data=STL_res.trend.apply(np.exp)) degradation_trend.append(deg_trend / deg_trend.iloc[0]) - yoy_save.append( - RdToolsDeg.degradation_year_on_year( - degradation_trend[-1], uncertainty_method=None - ) - ) + yoy_save.append(RdToolsDeg.degradation_year_on_year( + degradation_trend[-1], uncertainty_method=None)) # Find degradation component if order[(ic - 1) % n_steps] == "Rd": @@ -1937,8 +1742,7 @@ def iterative_signal_decomposition( yoy = RdToolsDeg.degradation_year_on_year(trend_dummy, uncertainty_method=None) # Convert degradation rate to trend degradation_trend.append( - pd.Series(index=pi.index, data=(1 + day * yoy / 100 / 365.0)) - ) + pd.Series(index=pi.index, data=(1 + day * yoy / 100 / 365.0))) yoy_save.append(yoy) # Combine and calculate residual flatness @@ -1949,43 +1753,29 @@ def iterative_signal_decomposition( convergence_metric.append(_RMSE(pi, total_model)) if verbose: - print( - "{:}\t{:}\t{:.5f}\t\t\t{:.1f} s".format( - ic, - order[(ic - 1) % n_steps], - convergence_metric[-1], - time.time() - t0, - ) - ) + print("{:}\t{:}\t{:.5f}\t\t\t{:.1f} s".format( + ic, order[(ic - 1) % n_steps], convergence_metric[-1], time.time() - t0)) # Convergence happens if there is no improvement in RMSE from one # step to the next if ic >= n_steps: - relative_improvement = ( - convergence_metric[-n_steps - 1] - convergence_metric[-1] + 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 - ): + 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 order = tuple( - [ - order[(i + n_steps - 1 - (ic - 1) % n_steps) % n_steps] - for i in range(n_steps) - ] - ) + [order[(i + n_steps - 1 - (ic - 1) % n_steps) % n_steps] + for i in range(n_steps)]) change_point = ic if verbose: print("Now not assuming perfect cleaning") elif not perfect_cleaning and ( ic >= max_iterations - or ( - ic >= change_point + n_steps - and relative_improvement < convergence_criterion - ) - ): + or (ic >= change_point + n_steps + and relative_improvement < convergence_criterion)): if verbose: if relative_improvement < convergence_criterion: print("Convergence reached.") @@ -1997,15 +1787,8 @@ def iterative_signal_decomposition( df_out = pd.DataFrame( index=pi.index, columns=[ - "soiling_ratio", - "soiling_rates", - "cleaning_events", - "seasonal_component", - "degradation_trend", - "total_model", - "residuals", - ], - ) + "soiling_ratio", "soiling_rates", "cleaning_events", "seasonal_component", + "degradation_trend", "total_model", "residuals"]) # Save values df_out.seasonal_component = seasonal_component[-1] @@ -2021,19 +1804,16 @@ def iterative_signal_decomposition( # Total model df_out.total_model = ( - df_out.soiling_ratio * df_out.seasonal_component * df_out.degradation_trend - ) + df_out.soiling_ratio * df_out.seasonal_component * df_out.degradation_trend) df_out.residuals = pi / df_out.total_model residual_shift = df_out.residuals.mean() df_out.total_model *= residual_shift RMSE = _RMSE(pi, df_out.total_model) adf_res = adfuller(df_out.residuals.dropna(), regression="ctt", autolag=None) if verbose: - print( - "p-value for the H0 that there is a unit root in the" - + "residuals (using the Augmented Dickey-fuller test):" - + "{:.3e}".format(adf_res[1]) - ) + print("p-value for the H0 that there is a unit root in the" + + "residuals (using the Augmented Dickey-fuller test):" + + "{:.3e}".format(adf_res[1])) # Check size of soiling signal vs residuals SR_amp = float(np.diff(df_out.soiling_ratio.quantile([0.1, 0.9]))) @@ -2047,30 +1827,18 @@ def iterative_signal_decomposition( df_out.SR_low = 1.0 - SR_amp # Set up results dictionary - results_dict = dict( - degradation=degradation, - soiling_loss=soiling_loss, - residual_shift=residual_shift, - RMSE=RMSE, - small_soiling_signal=small_soiling_signal, - adf_res=adf_res, - ) + results_dict = dict(degradation=degradation, soiling_loss=soiling_loss, + residual_shift=residual_shift, RMSE=RMSE, + small_soiling_signal=small_soiling_signal, adf_res=adf_res) return df_out, results_dict def run_bootstrap( - self, - reps=512, - confidence_level=68.2, - degradation_method="YoY", - process_noise=1e-4, + 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, - ): + forward_fill_alternatives=(True, False), verbose=False, **kwargs): """ Bootstrapping of CODS algorithm for uncertainty analysis, inherently accounting for model and parameter choices. @@ -2199,16 +1967,12 @@ def run_bootstrap( # Generate combinations of model parameter alternatives parameter_alternatives = [ - order_alternatives, - cleaning_sensitivity_alternatives, - clean_pruning_sensitivity_alternatives, - forward_fill_alternatives, - ] + order_alternatives, cleaning_sensitivity_alternatives, + clean_pruning_sensitivity_alternatives, forward_fill_alternatives] index_list = list(itertools.product([0, 1], repeat=len(parameter_alternatives))) combination_of_parameters = [ [parameter_alternatives[j][indexes[j]] for j in range(len(parameter_alternatives))] - for indexes in index_list - ] + for indexes in index_list] nr_models = len(index_list) bootstrap_samples_list, list_of_df_out, results = [], [], [] @@ -2223,17 +1987,10 @@ def run_bootstrap( for c, (order, dt, pt, ff) in enumerate(combination_of_parameters): 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, - process_noise=process_noise, - ffill=ff, - degradation_method=degradation_method, - **kwargs, - ) + max_iterations=18, order=order, clip_soiling=True, cleaning_sensitivity=dt, + pruning_iterations=1, clean_pruning_sensitivity=pt, + process_noise=process_noise, ffill=ff, degradation_method=degradation_method, + **kwargs) # Save results list_of_df_out.append(df_out) @@ -2245,9 +2002,7 @@ def run_bootstrap( # ... generate bootstrap samples based on the fit: bootstrap_samples_list.append( _make_time_series_bootstrap_samples( - pi, df_out.total_model, sample_nr=int(reps / nr_models) - ) - ) + pi, df_out.total_model, sample_nr=int(reps / nr_models))) # Print progress if verbose: @@ -2267,28 +2022,13 @@ 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.Series(SR_is_one_fraction), - pd.Series(weights), - pd.Series(small_soiling_signal), - ], - axis=1, - ignore_index=True, - ) + [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) if verbose: # Print summary _parameters_n_weights.columns = [ - "order", - "dt", - "pt", - "ff", - "RMSE", - "SR==1", - "weights", - "small_soiling_signal", - ] + "order", "dt", "pt", "ff", "RMSE", "SR==1", "weights", "small_soiling_signal"] if verbose: print("\n", _parameters_n_weights) @@ -2297,8 +2037,7 @@ def run_bootstrap( raise RuntimeError( "Test for stationary residuals (Augmented Dickey-Fuller" + " test) not passed in half of the instances:\nData not" - + " decomposable." - ) + + " decomposable.") # Save best model self.initial_fits = [df for df in list_of_df_out] @@ -2330,38 +2069,22 @@ def run_bootstrap( # Number of samples per fit: sample_nr = int(reps / nr_models) list_of_SCs = [ - list_of_df_out[m].seasonal_component for m in range(nr_models) if weights[m] > 0 - ] + 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, - max_shift=30, - ) + list_of_SCs, sample_nr=sample_nr, min_multiplier=0.8, max_multiplier=1.75, + max_shift=30) # ###################### # # ###### STAGE 2 ####### # # ###################### # if verbose and reps > 0: - print( - "\nBootstrapping for uncertainty analysis", - "({:} realizations):".format(reps), - ) + print("\nBootstrapping for uncertainty analysis", + "({:} realizations):".format(reps)) order = ("SR", "SC" if degradation_method == "STL" else "Rd") t0 = time.time() bt_kdfs, bt_SL, bt_deg, parameters, adfs, RMSEs, SR_is_1, rss, errors = ( - [], - [], - [], - [], - [], - [], - [], - [], - ["Bootstrapping errors"], - ) + [], [], [], [], [], [], [], [], ["Bootstrapping errors"]) for b in range(reps): try: # randomly choose model sensitivities @@ -2380,18 +2103,10 @@ def run_bootstrap( # Do Signal decomposition for soiling and degradation component 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, - degradation_method=degradation_method, - **kwargs, - ) + 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, + degradation_method=degradation_method, **kwargs) # If we can reject the null-hypothesis that there is a unit # root in the residuals: @@ -2418,27 +2133,10 @@ def run_bootstrap( weights = 1 / np.array(RMSEs) / (1 + np.array(SR_is_1)) weights /= np.sum(weights) self._parameters_n_weights = pd.concat( - [ - pd.DataFrame(parameters), - pd.Series(RMSEs), - pd.Series(adfs), - pd.Series(SR_is_1), - pd.Series(weights), - ], - axis=1, - ignore_index=True, - ) + [pd.DataFrame(parameters), pd.Series(RMSEs), pd.Series(adfs), + pd.Series(SR_is_1), pd.Series(weights)], axis=1, ignore_index=True) self._parameters_n_weights.columns = [ - "dt", - "pt", - "pn", - "RSR", - "ffill", - "RMSE", - "ADF", - "SR==1", - "weights", - ] + "dt", "pt", "pn", "RSR", "ffill", "RMSE", "ADF", "SR==1", "weights"] # ###################### # # ###### STAGE 3 ####### # @@ -2471,18 +2169,14 @@ def run_bootstrap( # Find degradation rates self.degradation = [ - np.dot(bt_deg, weights), - np.quantile(bt_deg, ci_low_edge), - np.quantile(bt_deg, ci_high_edge), - ] + np.dot(bt_deg, weights), np.quantile(bt_deg, ci_low_edge), + np.quantile(bt_deg, ci_high_edge)] df_out.degradation_trend = 1 + np.arange(len(pi)) * self.degradation[0] / 100 / 365.0 # Soiling losses self.soiling_loss = [ - np.dot(bt_SL, weights), - np.quantile(bt_SL, ci_low_edge), - np.quantile(bt_SL, ci_high_edge), - ] + np.dot(bt_SL, weights), np.quantile(bt_SL, ci_low_edge), + np.quantile(bt_SL, ci_high_edge)] # Save "confidence intervals" for seasonal component df_out.seasonal_component = (seasonal_samples * weights).sum(1) @@ -2491,8 +2185,7 @@ def run_bootstrap( # Total model with confidence intervals df_out.total_model = ( - df_out.degradation_trend * df_out.seasonal_component * df_out.soiling_ratio - ) + df_out.degradation_trend * df_out.seasonal_component * df_out.soiling_ratio) df_out["model_low"] = concat_tot_mod.quantile(ci_low_edge, 1) df_out["model_high"] = concat_tot_mod.quantile(ci_high_edge, 1) @@ -2513,20 +2206,9 @@ 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 @@ -2611,16 +2293,14 @@ 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 else: raise ValueError( "The indices of prescient_cleaning_events must correspond to the" - + " indices of zs_series; they must be of the same length" - ) + + " indices of zs_series; they must be of the same length") else: # If no prescient cleaning events, detect cleaning events ce, rm9 = _rolling_median_ce_detection(zs_series.index, zs_series, tuner=0.5) prescient_cleaning_events = _collapse_cleaning_events(ce, rm9.diff().values, 5) @@ -2646,43 +2326,26 @@ def _Kalman_filter_for_SR( # Initialize Kalman filter f = self._initialize_univariate_model( - zs_series, - dt, - process_noise, - measurement_noise, - rate_std, - zs_std, - initial_slope, - ) + zs_series, dt, process_noise, measurement_noise, rate_std, zs_std, initial_slope) # Initialize miscallenous variables dfk = pd.DataFrame( index=zs_series.index, dtype=float, columns=[ - "raw_pi", - "raw_rates", - "smooth_pi", - "smooth_rates", - "soiling_ratio", - "soiling_rates", - "cleaning_events", - "days_since_ce", - ], - ) + "raw_pi", "raw_rates", "smooth_pi", "smooth_rates", "soiling_ratio", + "soiling_rates", "cleaning_events", "days_since_ce"]) dfk["cleaning_events"] = False # Kalman Filter part: ####################################################################### # Call the forward pass function (the actual KF procedure) Xs, Ps, rate_std, zs_std = self._forward_pass( - f, zs_series, rolling_median_7, cleaning_events, soiling_events - ) + f, zs_series, rolling_median_7, cleaning_events, soiling_events) # Save results and smooth with rts smoother dfk, Xs, Ps = self._smooth_results( - dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning - ) + dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning) ####################################################################### # Some steps to clean up the soiling data: @@ -2696,16 +2359,14 @@ def _Kalman_filter_for_SR( pi_after_cleaning = rm_smooth_pi.loc[cleaning_events] # Detect outiers/false positives false_positives = _find_numeric_outliers( - pi_after_cleaning, clean_pruning_sensitivity, "lower" - ) + pi_after_cleaning, clean_pruning_sensitivity, "lower") cleaning_events = false_positives[~false_positives].index.tolist() # 2: Remove longer periods with positive (soiling) rates if (dfk.smooth_rates > max_soiling_rates).sum() > 1: exceeding_rates = dfk.smooth_rates > max_soiling_rates new_cleaning_events = _collapse_cleaning_events( - exceeding_rates, dfk.smooth_rates, 4 - ) + exceeding_rates, dfk.smooth_rates, 4) cleaning_events.extend(new_cleaning_events[new_cleaning_events].index) cleaning_events.sort() @@ -2713,27 +2374,12 @@ 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, - initial_slope, - ) + 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 - ) + f, zs_series, rolling_median_7, cleaning_events, soiling_events) dfk, Xs, Ps = self._smooth_results( - dfk, - f, - Xs, - Ps, - zs_series, - cleaning_events, - soiling_events, - perfect_cleaning, - ) + dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning) else: counter = 100 # Make sure the while loop stops @@ -2748,8 +2394,7 @@ def _Kalman_filter_for_SR( # ratio of the Kalman estimate (smooth_pi) and the SR dfk.loc[: cleaning_events[0], "soiling_ratio"] = ( dfk.loc[: cleaning_events[0], "smooth_pi"] - * (dfk.soiling_ratio / dfk.smooth_pi).mean() - ) + * (dfk.soiling_ratio / dfk.smooth_pi).mean()) else: # If no cleaning events dfk.soiling_ratio = 1 else: # Otherwise, if the inut signal has been decomposed, and @@ -2758,8 +2403,7 @@ def _Kalman_filter_for_SR( # 5: Renormalize Soiling Ratio if renormalize_SR is not None: dfk.soiling_ratio /= dfk.loc[cleaning_events, "soiling_ratio"].quantile( - renormalize_SR - ) + renormalize_SR) # 6: Force soiling ratio to not exceed 1: if clip_soiling: @@ -2824,8 +2468,7 @@ def _set_control_input(self, f, rolling_median_local, index, cleaning_events): if np.abs(u[0]) > np.sqrt(f.R) / 2: index_dummy = [n + 3 for n in range(window_size - HW - 1) if n + 3 != HW] cleaning_events = [ - ce for ce in cleaning_events if ce - index + HW not in index_dummy - ] + ce for ce in cleaning_events if ce - index + HW not in index_dummy] else: # If the cleaning event is insignificant u[0] = 0 if index in cleaning_events: @@ -2834,23 +2477,13 @@ def _set_control_input(self, f, rolling_median_local, index, cleaning_events): 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 - ): + 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 @@ -2876,15 +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) @@ -2900,18 +2525,11 @@ 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, -): + 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): """ Functional wrapper for :py:class:`~rdtools.soiling.CODSAnalysis` and its subroutine :py:func:`~rdtools.soiling.CODSAnalysis.run_bootstrap`. Runs @@ -3029,29 +2647,17 @@ def soiling_cods( CODS = CODSAnalysis(energy_normalized_daily) - CODS.run_bootstrap( - reps=reps, - confidence_level=confidence_level, - verbose=verbose, - degradation_method=degradation_method, - process_noise=process_noise, + 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, clean_pruning_sensitivity_alternatives=clean_pruning_sensitivity_alternatives, - forward_fill_alternatives=forward_fill_alternatives, - **kwargs, - ) + forward_fill_alternatives=forward_fill_alternatives, **kwargs) sr = 1 - CODS.soiling_loss[0] / 100 sr_ci = 1 - np.array(CODS.soiling_loss[1:3]) / 100 - return ( - sr, - sr_ci, - CODS.degradation[0], - np.array(CODS.degradation[1:3]), - CODS.result_df, - ) + return (sr, sr_ci, CODS.degradation[0], np.array(CODS.degradation[1:3]), CODS.result_df) def _collapse_cleaning_events(inferred_ce_in, metric, f=4): @@ -3139,26 +2745,22 @@ 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( index=list_of_SCs[0].index, columns=range(int(sample_nr * len(list_of_SCs))), - dtype=float, - ) + dtype=float) # From each fitted signal, we will generate new seaonal components for i, signal in enumerate(list_of_SCs): # Remove beginning and end of signal signal_mean = signal.mean() # Make a signal matrix where each column is a year and each row a date year_matrix = ( - signal.rename("values") - .to_frame() + signal.rename("values").to_frame() .assign(doy=signal.index.dayofyear, year=signal.index.year) - .pivot(index="doy", columns="year", values="values") - ) + .pivot(index="doy", columns="year", values="values")) # We will use the median signal through all the years... median_signal = year_matrix.median(1) for j in range(sample_nr): @@ -3167,10 +2769,8 @@ 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, - data=median_signal.reindex((signal.index.dayofyear - shift) % 365 + 1).values, - ) + 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 return samples @@ -3252,8 +2852,7 @@ def _progressBarWithETA(value, endvalue, time, bar_length=20): left = used / percent * (100 - percent) # Estimated time left sys.stdout.write( "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left) - + " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent) - ) + + " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent)) sys.stdout.flush() @@ -3267,13 +2866,8 @@ 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). @@ -3335,14 +2929,12 @@ 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 - ) # improvement relative to possible improvement + 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 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: diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 4c78459f..8939e5e0 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -14,17 +14,17 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times 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),\ + 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),\ + 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),\ + 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),\ + 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),\ + 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),\ + assert isinstance(soiling_info["stochastic_soiling_profiles"], list), \ 'soiling_info["stochastic_soiling_profiles"] is not a list' # Check soiling_info['soiling_interval_summary'] @@ -35,13 +35,13 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times actual_summary_columns = soiling_info["soiling_interval_summary"].columns.values for x in actual_summary_columns: - assert (x in expected_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),\ + 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),\ + assert isinstance(soiling_info["soiling_interval_summary"], pd.DataFrame), \ 'soiling_info["soiling_interval_summary"] not a dataframe' expected_means = pd.Series( @@ -75,26 +75,25 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times ("perfect_clean", False, False, 0.977116), ("perfect_clean_complex", True, True, 0.977116), ("inferred_clean_complex", True, True, 0.975805)]) - def test_soiling_srr_consecutive_invalid( - soiling_normalized_daily, soiling_insolation, soiling_times, - method, neg_shift, piecewise, expected_sr): + soiling_normalized_daily, soiling_insolation, soiling_times, + method, neg_shift, piecewise, 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, piecewise=piecewise, neg_shift=neg_shift) - assert expected_sr == pytest.approx(sr, abs=1e-6),\ - f"Soiling ratio different from expected value for {method} with consecutive invalid intervals" + assert expected_sr == pytest.approx(sr, abs=1e-6), \ + f"Soiling ratio different from expected value for {method} \ + with consecutive invalid intervals" @pytest.mark.parametrize("clean_criterion,expected_sr", - [("precip_and_shift", 0.982546), - ("precip_or_shift", 0.973433), - ("precip", 0.976196), - ("shift", 0.964369)]) - + [("precip_and_shift", 0.982546), + ("precip_or_shift", 0.973433), + ("precip", 0.976196), + ("shift", 0.964369)]) 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) @@ -105,18 +104,18 @@ def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation, 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),\ + assert expected_sr == pytest.approx(sr, abs=1e-6), \ f"Soiling ratio with clean_criterion='{clean_criterion}' different from expected" def test_soiling_srr_confidence_levels(soiling_normalized_daily, soiling_insolation): "Tests SRR with different confidence level settings from above" np.random.seed(1977) - sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, - confidence_level=95,reps=10, exceedance_prob=80.0) - assert np.array([0.959322, 0.966066]) == pytest.approx(sr_ci, abs=1e-6),\ + sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, + confidence_level=95, reps=10, exceedance_prob=80.0) + assert np.array([0.959322, 0.966066]) == pytest.approx(sr_ci, abs=1e-6), \ "Confidence interval with confidence_level=95 different than expected" - assert 0.962691 == pytest.approx(soiling_info["exceedance_level"], abs=1e-6),\ + assert 0.962691 == pytest.approx(soiling_info["exceedance_level"], abs=1e-6), \ 'soiling_info["exceedance_level"] different than expected when exceedance_prob=80' @@ -134,7 +133,7 @@ def test_soiling_srr_clean_threshold(soiling_normalized_daily, soiling_insolatio np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr( soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.01) - assert 0.964369 == pytest.approx(sr, abs=1e-6),\ + assert 0.964369 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio with specified clean_threshold different from expected value" with pytest.raises(NoValidIntervalError): @@ -148,9 +147,9 @@ def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation): sr, sr_ci, soiling_info = soiling_srr( soiling_normalized_daily, soiling_insolation, reps=10, trim=True) - assert 0.978093 == pytest.approx(sr, abs=1e-6),\ + assert 0.978093 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio with trim=True different from expected value" - assert (len(soiling_info["soiling_interval_summary"]) == 1),\ + assert (len(soiling_info["soiling_interval_summary"]) == 1), \ "Wrong number of soiling intervals found with trim=True" @@ -160,7 +159,6 @@ def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation): ("perfect_clean", False, False, 0.966912), ("perfect_clean_complex", True, True, 0.966912), ("inferred_clean_complex", True, True, 0.965565)]) - def test_soiling_srr_method( soiling_normalized_daily, soiling_insolation, method, neg_shift, piecewise, expected_sr ): @@ -169,7 +167,7 @@ def test_soiling_srr_method( soiling_normalized_daily, soiling_insolation, reps=10, method=method, neg_shift=neg_shift, piecewise=piecewise ) - assert expected_sr == pytest.approx(sr, abs=1e-6),\ + assert expected_sr == pytest.approx(sr, abs=1e-6), \ f'Soiling ratio with method="{method}" different from expected value' @@ -190,9 +188,9 @@ def test_soiling_srr_recenter_false(soiling_normalized_daily, soiling_insolation np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr( soiling_normalized_daily, soiling_insolation, reps=10, recenter=False) - assert (1 == soiling_info["renormalizing_factor"]),\ + assert (1 == soiling_info["renormalizing_factor"]), \ "Renormalizing factor != 1 with recenter=False" - assert 0.966387 == pytest.approx(sr, abs=1e-6),\ + assert 0.966387 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different than expected when recenter=False" @@ -205,11 +203,11 @@ def test_soiling_srr_negative_step(soiling_normalized_daily, soiling_insolation) 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],\ + 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),\ + assert 0.936932 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different from expected when a large negative step is\ incorporated into the data" @@ -221,10 +219,10 @@ def test_soiling_srr_max_negative_slope_error(soiling_normalized_daily, soiling_ reps=10, max_relative_slope_error=45.0) assert list(soiling_info["soiling_interval_summary"]["valid"].values) == [ - True, True, False],\ + 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),\ + assert 0.958761 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different from expected when max_relative_slope_error=45.0" @@ -239,14 +237,14 @@ def test_soiling_srr_with_nan_interval(soiling_normalized_daily, soiling_insolat 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),\ + assert 0.948792 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different from expected value when an entire interval was NaN" 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, method="perfect_clean_complex", piecewise=True, neg_shift=True) - assert 0.974225 == pytest.approx(sr, abs=1e-6),\ + assert 0.974225 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different from expected value when an entire interval was NaN" @@ -254,7 +252,7 @@ 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),\ + assert (len(info["soiling_interval_summary"]) == 2), \ "Increasing the outlier_factor did not result in the expected number of soiling intervals" @@ -272,9 +270,8 @@ def test_soiling_srr_kwargs(monkeypatch, soiling_normalized_daily, soiling_insol @pytest.mark.parametrize(("start,expected_sr"), [(18, 0.984779), (17, 0.981258)]) - def test_soiling_srr_min_interval_length_default( - soiling_normalized_daily, soiling_insolation, start, expected_sr): + 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 @@ -284,13 +281,12 @@ def test_soiling_srr_min_interval_length_default( 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),\ + assert expected_sr == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different from expected value" @pytest.mark.parametrize( "test_param", ["energy_normalized_daily", "insolation_daily", "precipitation_daily"]) - def test_soiling_srr_non_daily_inputs(test_param): """ Validate the frequency check for input time series @@ -343,16 +339,15 @@ def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolatio ("half_norm_clean", True, 0.975057), ("perfect_clean_complex", True, 0.964117), ("inferred_clean_complex", True, 0.963585)]) - def test_negative_shifts( soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times, - method, neg_shift, expected_sr): + method, neg_shift, expected_sr): reps = 10 np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=reps, method=method, neg_shift=neg_shift) - assert expected_sr == pytest.approx(sr, abs=1e-6),\ + assert expected_sr == pytest.approx(sr, abs=1e-6), \ f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" \ different from expected value' @@ -363,7 +358,6 @@ def test_negative_shifts( ("half_norm_clean", True, 0.927017), ("perfect_clean_complex", True, 0.896936), ("inferred_clean_complex", True, 0.896214)]) - def test_piecewise(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, soiling_times, method, piecewise, expected_sr): reps = 10 @@ -371,7 +365,7 @@ def test_piecewise(soiling_normalized_daily_with_piecewise_slope, soiling_insola sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, reps=reps, method=method, piecewise=piecewise) - assert expected_sr == pytest.approx(sr, abs=1e-6),\ + assert expected_sr == pytest.approx(sr, abs=1e-6), \ f'Soiling ratio with method="{method}" and piecewise="{piecewise}" \ different from expected value' @@ -382,17 +376,17 @@ def test_piecewise_and_neg_shifts(soiling_normalized_daily_with_piecewise_slope, reps = 10 np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, - soiling_insolation, reps=reps, + soiling_insolation, reps=reps, method="perfect_clean_complex", piecewise=True, neg_shift=True) - assert 0.896936 == pytest.approx(sr, abs=1e-6),\ + assert 0.896936 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different from expected value for data with piecewise slopes" np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts, soiling_insolation, reps=reps, method="perfect_clean_complex", piecewise=True, neg_shift=True) - assert 0.964117 == pytest.approx(sr, abs=1e-6),\ + assert 0.964117 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different from expected value for data with negative shifts" @@ -404,7 +398,7 @@ def test_complex_sr_clean_threshold(soiling_normalized_daily_with_neg_shifts, so soiling_insolation, reps=10, clean_threshold=0.1, method="perfect_clean_complex", piecewise=True, neg_shift=True) - assert 0.934926 == pytest.approx(sr, abs=1e-6),\ + assert 0.934926 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio with specified clean_threshold different from expected value" with pytest.raises(NoValidIntervalError): @@ -509,7 +503,7 @@ def _build_monthly_summary(top_rows): df = pd.DataFrame( data=all_rows, - columns=["month", "soiling_rate_median", "soiling_rate_low", + columns=["month", "soiling_rate_median", "soiling_rate_low", "soiling_rate_high", "interval_count"]) df["month"] = range(1, 13) From efa5042df50eae3a83af390d823cdb9cd37a2fd3 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Wed, 21 Aug 2024 09:50:33 -0400 Subject: [PATCH 23/33] run black on soiling.py --- rdtools/soiling.py | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index e8140048..143d1ec7 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -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": @@ -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 ############################################################## @@ -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 @@ -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 @@ -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 @@ -960,13 +960,13 @@ 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) @@ -974,7 +974,7 @@ def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False, # 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] @@ -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) @@ -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 @@ -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. @@ -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) @@ -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: @@ -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) @@ -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) @@ -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) # ###################### # @@ -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 @@ -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 @@ -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) @@ -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): """ @@ -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 From 21da67dfe932342742ffb54b1db1802c80d83a61 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Wed, 21 Aug 2024 10:26:17 -0600 Subject: [PATCH 24/33] fixing flake8 formatting --- rdtools/soiling.py | 80 ++++++++++++++++++++++++---------------------- 1 file changed, 42 insertions(+), 38 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 143d1ec7..9b8ef69e 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -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 @@ -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 @@ -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 @@ -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 ' @@ -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() @@ -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). @@ -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 @@ -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. @@ -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 @@ -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 @@ -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 @@ -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) @@ -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 @@ -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, @@ -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( @@ -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 @@ -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). @@ -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: From 5ef6c817b1d22ca1424ed51d021ae56ad3306140 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Wed, 21 Aug 2024 10:31:11 -0600 Subject: [PATCH 25/33] fixing flake8 formatting --- rdtools/soiling.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 9b8ef69e..6164bbaf 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1761,7 +1761,7 @@ def iterative_signal_decomposition( 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 + if perfect_cleaning and (ic >= max_iterations / 2 or relative_improvement < convergence_criterion): # From now on, do not assume perfect cleaning perfect_cleaning = False @@ -2206,9 +2206,9 @@ 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, + 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 @@ -2476,7 +2476,7 @@ 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 + 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) @@ -2509,7 +2509,8 @@ 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) @@ -2529,7 +2530,7 @@ def soiling_cods( 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), + 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 @@ -2772,7 +2773,7 @@ def _make_seasonal_samples( # 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, + 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 @@ -2870,7 +2871,7 @@ 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): + 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 From e66c29536c25524e316527a75949fc235a847bbe Mon Sep 17 00:00:00 2001 From: nmoyer Date: Wed, 21 Aug 2024 15:59:31 -0600 Subject: [PATCH 26/33] removing _collapse_cleaning_events so half_norm_clean results are not affected --- rdtools/soiling.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 6164bbaf..002d17f2 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -438,7 +438,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati results.loc[filt, "run_slope"] = 0 results.loc[filt, "run_slope_low"] = 0 results.loc[filt, "run_slope_high"] = 0 - results.loc[filt, "valid"] = False + # results.loc[filt, "valid"] = False # Calculate the next inferred start loss from next valid interval results["next_inferred_start_loss"] = np.clip( @@ -465,10 +465,10 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati results.loc[results.clean_event, "inferred_begin_shift"] = np.clip( results.inferred_begin_shift, 0, 1) ####################################################################### - ''' + if neg_shift is False: results.loc[filt, "valid"] = False - ''' + if len(results[results.valid]) == 0: raise NoValidIntervalError("No valid soiling intervals were found") new_start = results.start.iloc[0] From 628cfe83e63922030b3e51478ffc6eaac7949995 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Thu, 22 Aug 2024 11:15:10 -0600 Subject: [PATCH 27/33] fixing notebook failures --- rdtools/soiling.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 002d17f2..7adc1c4a 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -438,7 +438,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati results.loc[filt, "run_slope"] = 0 results.loc[filt, "run_slope_low"] = 0 results.loc[filt, "run_slope_high"] = 0 - # results.loc[filt, "valid"] = False + results.loc[filt, "valid"] = False # Calculate the next inferred start loss from next valid interval results["next_inferred_start_loss"] = np.clip( @@ -465,10 +465,10 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati results.loc[results.clean_event, "inferred_begin_shift"] = np.clip( results.inferred_begin_shift, 0, 1) ####################################################################### - + ''' if neg_shift is False: results.loc[filt, "valid"] = False - + ''' if len(results[results.valid]) == 0: raise NoValidIntervalError("No valid soiling intervals were found") new_start = results.start.iloc[0] @@ -2952,4 +2952,4 @@ def segmented_soiling_period( # Create Series from modelled profile sr = pd.Series(z, index=pr.index) - return sr, cp_date + return sr, cp_date \ No newline at end of file From 3860ada9bc64f9d4e0ca0c8551643324f750dff6 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Tue, 27 Aug 2024 10:25:08 -0600 Subject: [PATCH 28/33] Switching back code towards final review version --- docs/sphinx/source/changelog/pending.rst | 1 + docs/system_availability_example.ipynb | 4 ++-- rdtools/soiling.py | 23 ++++++++++++----------- 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst index 6fb1eaef..c8943e82 100644 --- a/docs/sphinx/source/changelog/pending.rst +++ b/docs/sphinx/source/changelog/pending.rst @@ -14,6 +14,7 @@ Enhancements * Added a new wrapper function for clearsky filters (:pull:`412`) * Improve test coverage, especially for the newly added filter capabilities (:pull:`413`) * Added codecov.yml configuration file (:pull:`420`) +* Added new methods perfect_clean_complex and inferred_clean_complex which detects negative shifts and piecewise changes in the slope for soiling detection in :py:func:`~rdtools.soiling.soiling_srr`(:pull:`426`) Bug fixes --------- diff --git a/docs/system_availability_example.ipynb b/docs/system_availability_example.ipynb index 9a36859e..bd860b68 100644 --- a/docs/system_availability_example.ipynb +++ b/docs/system_availability_example.ipynb @@ -649,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -663,7 +663,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 7adc1c4a..04c1d012 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -185,8 +185,9 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, # step, slope change detection # 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))) + #df_ffill = df.copy() + #df_ffill = df.ffill(limit=int(round((day_scale / 2), 0))) + df_ffill = df.fillna(method='ffill', limit=day_scale).copy() # Calculate rolling median df["pi_roll_med"] = df_ffill.pi_norm.rolling(day_scale, center=True).median() @@ -204,12 +205,12 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, # 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( - df.clean_event_detected, df.delta.values, 5 - ) - df["clean_event_detected"] = reduced_cleaning_events - ''' + if piecewise is True: + reduced_cleaning_events = _collapse_cleaning_events( + df.clean_event_detected, df.delta.values, 5 + ) + df["clean_event_detected"] = reduced_cleaning_events + ########################################################################## precip_event = df["precip"] > precip_threshold @@ -438,7 +439,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati results.loc[filt, "run_slope"] = 0 results.loc[filt, "run_slope_low"] = 0 results.loc[filt, "run_slope_high"] = 0 - results.loc[filt, "valid"] = False + # results.loc[filt, "valid"] = False # Calculate the next inferred start loss from next valid interval results["next_inferred_start_loss"] = np.clip( @@ -465,10 +466,10 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati results.loc[results.clean_event, "inferred_begin_shift"] = np.clip( results.inferred_begin_shift, 0, 1) ####################################################################### - ''' + if neg_shift is False: results.loc[filt, "valid"] = False - ''' + if len(results[results.valid]) == 0: raise NoValidIntervalError("No valid soiling intervals were found") new_start = results.start.iloc[0] From f64077ad64500010630c8319d205167dcdf841f3 Mon Sep 17 00:00:00 2001 From: nmoyer Date: Tue, 27 Aug 2024 17:03:35 -0600 Subject: [PATCH 29/33] fixing minor formatting --- rdtools/soiling.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 04c1d012..56f05c29 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -185,9 +185,8 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, # step, slope change detection # 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))) - df_ffill = df.fillna(method='ffill', limit=day_scale).copy() + df_ffill = df.copy() + df_ffill = df.ffill(limit=int(round((day_scale / 2), 0))) # Calculate rolling median df["pi_roll_med"] = df_ffill.pi_norm.rolling(day_scale, center=True).median() @@ -2953,4 +2952,4 @@ def segmented_soiling_period( # Create Series from modelled profile sr = pd.Series(z, index=pr.index) - return sr, cp_date \ No newline at end of file + return sr, cp_date From 7517ddcdd49ac659a0fd89920a8d2cda23e9656e Mon Sep 17 00:00:00 2001 From: "Nguyen, Quyen" Date: Thu, 24 Oct 2024 13:37:41 -0600 Subject: [PATCH 30/33] added fix for bare except and pytests for segmented_soiling_period functoin --- rdtools/soiling.py | 3 +- rdtools/test/soiling_test.py | 84 ++++++++++++++++++++++++++++++++++++ 2 files changed, 86 insertions(+), 1 deletion(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 56f05c29..e1bcf8dd 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -2946,7 +2946,8 @@ def segmented_soiling_period( if (R2_percent_improve < 0.01) | (R2_piecewise < 0.4): z = [np.nan] * len(x) cp_date = None - except: + except ValueError as ex: + print(f"Segmentation was not possible. Error: {ex}") z = [np.nan] * len(x) cp_date = None # Create Series from modelled profile diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 8939e5e0..6954866b 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -5,6 +5,7 @@ from rdtools.soiling import annual_soiling_ratios from rdtools.soiling import monthly_soiling_rates from rdtools.soiling import NoValidIntervalError +from rdtools.soiling import segmented_soiling_period import pytest @@ -582,3 +583,86 @@ def test_monthly_soiling_rates_reps(soiling_interval_summary): expected = _build_monthly_summary(expected) pd.testing.assert_frame_equal(result, expected, check_dtype=False) + + +# ###################################### +# invalid segmented_soiling_period tests +# ###################################### + + +@pytest.fixture +def pr_series(): + """ + Panda series of daily performance ratios measured during the given deposition period + with datetime index and is length 10. + """ + pr_idx = pd.date_range(start="2022-01-01", periods=10, freq="D") + pr_series = pd.Series(np.random.rand(10), index=pr_idx) + return pr_series + + +def test_no_datetime_index_pr(pr_series): + """ + Tests if ValueError is raised when pr_series does not have datetime index. + """ + pr = pr_series.reset_index() + with pytest.raises(ValueError, match = "The time series does not have DatetimeIndex"): + _ = segmented_soiling_period(pr) + + +def test_no_change_point(pr_series): + """ + Tests if no change point was found when fitting soiling profile with segmentation. + """ + days_clean_vs_cp = 7 + result_sr, result_cp_date = segmented_soiling_period(pr_series, + days_clean_vs_cp=days_clean_vs_cp) + expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index) + expected_cp_date = None + + pd.testing.assert_series_equal(result_sr, expected_sr) + assert result_cp_date == expected_cp_date + + +def test_except_block(): + """ + Tests except block for when all segementation methods did not work. + """ + pr_idx = pd.date_range(start="2022-01-01", periods=5, freq="D") + pr_series = pd.Series(np.array([1,2,3,4,5]), index=pr_idx) + result_sr, result_cp_date = segmented_soiling_period(pr_series) + + expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index) + expected_cp_date = None + + pd.testing.assert_series_equal(result_sr, expected_sr) + assert result_cp_date == expected_cp_date + + +def test_short_segmentation_periods(): + """ + Tests if segmentation fails for short soiling periods. + """ + pr_idx = pd.date_range(start="2022-01-01", periods=35, freq="D") + pr_series = pd.Series(np.random.normal(loc=5, scale=2, size=35), index=pr_idx) + result_sr, result_cp_date = segmented_soiling_period(pr_series) + + expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index) + expected_cp_date = None + + pd.testing.assert_series_equal(result_sr, expected_sr) + assert result_cp_date == expected_cp_date + + +def test_long_segmentation_periods(): + "Tests if segmentation fails for longer soiling periods." + pr_idx = pd.date_range(start="2022-01-01", periods=47, freq="D") + testing_list = list(np.arange(46)) + [50] + pr_series = pd.Series(testing_list, index=pr_idx) + result_sr, result_cp_date = segmented_soiling_period(pr_series) + + expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index) + expected_cp_date = None + + pd.testing.assert_series_equal(result_sr, expected_sr) + assert result_cp_date == expected_cp_date From cee6105e2892873f779b029d64c92a087286705e Mon Sep 17 00:00:00 2001 From: "Nguyen, Quyen" Date: Thu, 24 Oct 2024 14:08:02 -0600 Subject: [PATCH 31/33] updated changelogs --- docs/sphinx/source/changelog/v2.2.0-beta.2.rst | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/docs/sphinx/source/changelog/v2.2.0-beta.2.rst b/docs/sphinx/source/changelog/v2.2.0-beta.2.rst index 32fa3da2..0b9c94d6 100644 --- a/docs/sphinx/source/changelog/v2.2.0-beta.2.rst +++ b/docs/sphinx/source/changelog/v2.2.0-beta.2.rst @@ -12,6 +12,13 @@ Bug fixes * Fix flake8 missing whitespaces ``bootstrap_test.py``, ``soiling_cods_test.py`` (:pull:`400`) * Specify dtype for seasonal samples ``soiling.py`` (:pull:`400`) * Update deprecated `check_less_precise` to `rtol` ``soiling_cods_test.py`` (:pull:`400`) +* Fixed pylint bare except error for :py:func:`~rdtools.soiling.segmented_soiling_period` +in ``soiling.py`` (:pull:`432`) + +Testing +------- +* Added pytests to cover invalid segementations for +:py:func:`~rdtools.soiling.segmented_soiling_period` in ``soiling_cods_test.py`` (:pull:`432`) Requirements ------------ @@ -20,4 +27,5 @@ Requirements Contributors ------------ * Martin Springer (:ghuser:`martin-springer`) -* Michael Deceglie (:ghuser:`mdeceglie`) \ No newline at end of file +* Michael Deceglie (:ghuser:`mdeceglie`) +* Quyen Nguyen (:ghuser:`qnguyen345`) From cfd58581170e0e064341ec8438556d25ef014dba Mon Sep 17 00:00:00 2001 From: "Nguyen, Quyen" Date: Thu, 24 Oct 2024 15:00:28 -0600 Subject: [PATCH 32/33] deleted updates in wrong changelogs --- docs/sphinx/source/changelog/v2.2.0-beta.2.rst | 8 -------- 1 file changed, 8 deletions(-) diff --git a/docs/sphinx/source/changelog/v2.2.0-beta.2.rst b/docs/sphinx/source/changelog/v2.2.0-beta.2.rst index 0b9c94d6..6f0dab31 100644 --- a/docs/sphinx/source/changelog/v2.2.0-beta.2.rst +++ b/docs/sphinx/source/changelog/v2.2.0-beta.2.rst @@ -12,13 +12,6 @@ Bug fixes * Fix flake8 missing whitespaces ``bootstrap_test.py``, ``soiling_cods_test.py`` (:pull:`400`) * Specify dtype for seasonal samples ``soiling.py`` (:pull:`400`) * Update deprecated `check_less_precise` to `rtol` ``soiling_cods_test.py`` (:pull:`400`) -* Fixed pylint bare except error for :py:func:`~rdtools.soiling.segmented_soiling_period` -in ``soiling.py`` (:pull:`432`) - -Testing -------- -* Added pytests to cover invalid segementations for -:py:func:`~rdtools.soiling.segmented_soiling_period` in ``soiling_cods_test.py`` (:pull:`432`) Requirements ------------ @@ -28,4 +21,3 @@ Contributors ------------ * Martin Springer (:ghuser:`martin-springer`) * Michael Deceglie (:ghuser:`mdeceglie`) -* Quyen Nguyen (:ghuser:`qnguyen345`) From 6172ea0424efebfd1bd062852fde2cd1d354caf4 Mon Sep 17 00:00:00 2001 From: "Nguyen, Quyen" Date: Thu, 24 Oct 2024 15:11:22 -0600 Subject: [PATCH 33/33] fixed lint error --- rdtools/test/soiling_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 6954866b..7f87cf2d 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -606,7 +606,7 @@ def test_no_datetime_index_pr(pr_series): Tests if ValueError is raised when pr_series does not have datetime index. """ pr = pr_series.reset_index() - with pytest.raises(ValueError, match = "The time series does not have DatetimeIndex"): + with pytest.raises(ValueError, match="The time series does not have DatetimeIndex"): _ = segmented_soiling_period(pr) @@ -629,7 +629,7 @@ def test_except_block(): Tests except block for when all segementation methods did not work. """ pr_idx = pd.date_range(start="2022-01-01", periods=5, freq="D") - pr_series = pd.Series(np.array([1,2,3,4,5]), index=pr_idx) + pr_series = pd.Series(np.array([1, 2, 3, 4, 5]), index=pr_idx) result_sr, result_cp_date = segmented_soiling_period(pr_series) expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index)