From c8aae897143abfc2d343d2ea663e7096932b2abe Mon Sep 17 00:00:00 2001 From: nmoyer Date: Mon, 24 Jun 2024 11:57:49 -0600 Subject: [PATCH 01/58] =?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 02/58] 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 03/58] 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 04/58] 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 05/58] 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 06/58] 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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", + "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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", 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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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0IRms++67jwkTJvDll1/S3NwcZLAA5s6dyyuvvNIvAiOVorIGVu+qQhN6qG5PZfMJK6hHEyjojaRHE7jcgqKyhl4/eE12N41tLpyawOl2suTzw0OesHHJxGG8tqkMDdhxtAmLUcXpDf0JIMFqpKiskWizgYY2NyW1ejJPtNkYUihz5fZy1u2tprS+jViLkXNGp3LTeSODjFi81cieymaqmx1B381JiqahrQnh1WZUFQQCq9HAqp2VxFuNPZanyaDgcGuYDN0nI7302SE2Ha7jjS1mHvvWGQBBBqyn6/Q1qMsKy0iPt3KguoXRabG4PAIBlNXbMTY5qW91MiW34zx7Kpv1i/CiAIlRJlocbtwejTV7jh3Xg69qsuP2aFQ12Xso6dDoqbH3hbO/OFCDEJAUYyY+yoTDpWFQQVFgVGoMFqOB8oZWaludeDTB6l1V/vB3fZuLneWNTMqK99edE4XlLp6YzupdVYzPiOt1qHTl9nLW7DmG26Px6b4abj5/ZJdyXF5YSkVjOy4N0DTe2lbOlwdrsJiMtDvdJEWbKa1r67VhHUgKS+oprtQ7DFaTgfX7a2hzeXB7NOKtJt7fWcWFE9LDIrwZksHatGkTjz/+OBaLhZaWrj3K7OxsKisrT1pcJLNyezmHalopq29DURTqWp1kJUYd92ZPzk7gy4N1IAQGVWHMsJheN9pJ0SY+2VuNQCCE/oC3OYd+WsG4jHhSYs1UtzgRgFvTUFW9AUXoYVOjqtDu0ht7u8vDjqONON0aiwtyQvrNmhYndqdGm9PJx8XHAKhvc1JW347d5cHu8qAJQavDgwJ4hMBqMlCQl8QF41L5bJ/eaLo0QWKUiYY2F/uPtfC3Tw8xNj2u2/thMRqItxopqW3j6he+6DK2dKC6hZpmB8ea7Nzw0kbOyElgam7SCe+vr0Ftd3nYdLiOhnYXZfXttDnduNwCDfC4Ncoa7Pz2/T3srWzi8UVTgA5vEXTvpLrZgeatG5qwBzX2nRuixQU5fs+uPyksqWdvVQvNdqe/wQbd065uduB1CGl2tDMuXSXGYiTabCArMYr0eCtH6lqpb3X6vfMtR+r5/apixqXHUdVkJ8Fq4lBNW5Cn1tDm6tFrX1SQy9j0uD5lHVY3OyhvaNfrrAqPvL2TT/fVBHm4q3dVYVJVoKNzVtnkRFWcqAp4NIixGFm5vXxIDEDnMeRNh+uoaXGiaRotTg8mVSEtzoLRoBJtVrl/xVc43RqrdlZy76UThsxohWSwTCYTmtbzAPnRo0eJjY0NWdSpwN6qZkrr2/F4BGajQk2Lgze2lLG9tCGoYgfy80vyWbm9nC8P1uJ0a6iK0uvxp8O1bSiA2wMGRQ9jRZsNLC8s7VVobKAGiOOtRuKjTDS2O3F5dF0GRcHu1nB7W1SXRxBjUdA0aHa40TTB9tIGtpTU8+qGEh7+xuRe9ZZB77XvrWpm3d4aABrb3by/oxIUcHsEqiLwaKAqCh4h9AYccLg97K1q5nJbFjuONtHqcBNjMeDyaHg0QXWLE7cmeOmzQ92GyGaOTGZPZTMtdjdbjjTQ4nAHGazkGDMCcGnQ0O5ix9FGxqXH0eZ098qLbra7dO/IrdHu9CDQDY9Kh2GyuzRWFlVw9cw8FkzJ4suDtVQ12nG4PTg8AodL071HAe0uDU2IHhvqselxvL29nD+v2U9pXVu/hISKyhrYXtpAWX0bDW0ualuc3PXaNmpaHLQ5PH5jBWBQweEWzBqVwrbSBuKsJjYcqsWgqmQkWKhtdYHbQ22Lk02H67EYDcRZjZQ3tGNQFT7dV8Oiglwa25y8ua2cdqcbs9EQFOL0Ndq+5y05xsV3Zw4P0ttdfatvc2L3he41cDk8vLWtnI/3HGPu+GGcPzYVq8mA0aDQYbJ0fPXN5/7urWrmR//cfNLTV/oaCn/ps0N8vOcYqqpHM8ob7IEOOU6PoK7VidWkUtVkR0XvwB2pa+P+FV9xwzkjeHt7OSW1bZw9KplxGfGD4n2FlHQxa9Ysli1b1u2+1tZWXn75ZWbPnt3n87a0tPDQQw8xf/58kpOTURSFJUuW9Pr7DQ0N3HLLLaSlpRETE8PcuXPZsmVLn3X0B063RrzViNWkkhJrITHaTHlDO/+3o5L7V3zVZTDTV+EWTMli1qgUMhKsTM5O6HUFSI+3YjKqmAyK//+ZCVZW76rq1YB+XwaI+zIg22R3kxBlAhS/ZzUs3kpWghWTAX/4rcXuRlUVDApYTSptLg2HR1BU1sjj7+3msXd3sXJ7ub+3fLzfL6tvRw2IzDncGnaXbiCdHjCoCm5NN1YGRdfk8kBlk4OV28vJS4nCZFCIMhuwmgwB5/H0GCJLiDYTbzXh0gSeQNfGW151rU7MBjXggesIffquZ3lhaZfrWr2ritL6dvZUNNPu9OD2NnhC6I1fp58i1ttrBxidFkt6gpXUOCuJUSZM3vJXAE0TfLK3mi1HGthZ3tilY7RyezmbDtdT3tDOi58d6pekBF/YDnSDdLShnQPVrTS2u3F5w+FGVf+zGFUsRt3jHp8RR7PdhdmgUt/qpL7NxbA4C26PXncMit7wmwwqZqOKqihsK22gqKzB34mobnFS1+oIun8vfXaIf35Rwo6jTeyramFvVQv7qpqD9BZXNvPcx/uD7onTrWExBod+BdBod/PBzkp+/d5uPj9QCygYDYq/zJWAYzUhqGqys+lQHV8dbewxeaYvZdvgHYvzPe/HSyg5UN1Co91NfZubo52MlY92l0Z9mxu7S6PNpeH2CNpdGrvKm3hu7QG2lTZS2Wjn3aIK1hZXnzCRpz8IyWA98sgjbN68mcsuu4z/+7//A2D79u28+OKLFBQUUF1dzQMPPNDn89bU1PDoo4+ye/dupkyZ0qfvaprGZZddxr///W9uv/12fve733Hs2DHmzJnDvn37+qzlZFlckMPU3ETmjk/nm1OzOSM7AZdH81fUzgYkMFNswZQs5uQPY8GUrF7/3k3njWRu/jCSY83EW40kRZsprmrhWLOdjYdqT5jldqKkgUD6YtwK8pL8161p+sooOUlRRJmNTBuepI9PAB4BJoNKu9NDQ5sbVcFrdBR2VTTpmW1F5bxTpPeIiyubuzXChSX11LQ4MKgKBvTGz/cbPjzexV0Ught8IaCp3UVBXjJRZiO1LU5MBpUYiwFV0T0Yh9vTraGMtxqpa3N4x1sEyTFm/3GFJfU0tbtwaYL4KCOJUSbGDIuhutlBcWWT9//N/HnN/i4ZfEnRJvZXNWN3azg9ujH0eI2WqnRcl0EBq1GlrlUfu7zx5U3srmiizekhIcqI1WRgWEIUw5OjMBsUFK8Hc6zZwcGaVlZuL6eorMHfGdlb1YwQwv+bywrLTnivT0RBXhJJ0SbsLg9N7S4CZzEoQHyUkdFpsViMKu1OjX1VrXy6rwaryUB6vJWGNidtTg9tTg+VjXaizHpnQlUUDKpCbYuDNoebhjYnje0uXvrsEBdPTEcTgmiTilFRSI+3+u9LVZMdl0f3OjWgvtXJQ2/v5OnVxRSVNVDe0E5JbSuJ0eagTtLighzSE6JIiTHReciy3aVR1+qiutlBi91FnNWk3/Noo9/jQkCrw0NVkwOHW6Oi0Y4mxEll5fmyLMdnxFFc2US81RiUUNK5k5kcY+72PGaDgtkQ/Lz4Da53oybgSF0r7Q6X/myjRwAGg5BCgjNnzuS9997j1ltv5brrrgPg5z//OQCjR4/mvffew2az9fm8mZmZVFRUkJGRwebNm5kxY0avv7ts2TI+//xzXn/9dRYvXgzAVVddxbhx43jooYf497//3Wc9J8PY9DhvEoSTDYdqMRtV0uKsVDS009juYm9lU9DxgZlixxuEPx5VTXbirSbMRpVYi5G6VifFVboBOtbs6DHxw+cBtLs8PSYNdP4dt0f0akDelpPIrFEpON0eSmrb8GiCHUebGDMslsZ2FzmJViqbnCREGUmPt1DT4tB7ewImZ8dT1WSnvtVJk92NxaBQ1+Ikxmyg2e7udtJoQV4So9NiOFzTRpxVT+RweTQcQtM9KhX/GJ8m9EbftziZQYVvTs1iwZQsVhaVY1QVrCYDigLN3vGuvVUt3Y47NNndZCVEcbi2DVWB3RXNPP7ebu69dAIFeUm88vlhYi0GXG6NhBgTVU0Ov/dmNRnYWd5IdYuDNqc7yNupb3NhNqq4HB3jkQpgNChEm1TcmkBVdI/R4db8Bri21Ynd5ebMEcmMS4/zzsdqw2JU+da0bF7bXEplo17W7U4PSzceYXlhGSNSo4mzmqlrdRJtNuLW9FCo2RjaDJjAlVs2HKrjSF0b1c0OXJ7gPr0CRJkMNNlduufo3d7Q5uTLg7VMzU0ERQU8aAJyk6OwuwV1LQ4cHg23R6PdpRFlNmJ3641nVZOdRQW5lNa1scIbFvxkbzUbDtVy9yX5LC7I4UhdG0cb9HqsAW0ODy+sO8jh2jasJgMtDjclta043B7W7HH7Q4p7KpspLKnD7mrG7tKCwpm+fzo1QV5KFAV5yVQ3O/jqaCPN7S4a7S7cno6Oh8Wo8pV3HmKoE+f3VenzBlscbjIToli9q4qkaBOHvSG7zlmf49Lj2Haknka7NwMTiPbWz2iLiWFxFn1MURO0ON0ogEFRcHn0cVOXtzoaFP16XR6tTx3sUAl54vCFF15IcXEx27ZtY9++fWiaxujRoykoKAh52SaLxUJGRkZI3122bBnp6eksXLjQvy0tLY2rrrqKf/3rXzgcDiwWS0jnDgWfx1Rc2YzZaKCsvo2EKCNVTQoxJiNbSxu7xMh9FTWU8aTCknoc3nGhC0alEGM28Pf1h7A7PaiqQmWTnTV79ASEwHMWlTXw+Hu7aWzX57dYjCee/D1zZDLv76xi5sjkXuldMCWLrMQo/v7ZQaoaHTjdHhrbXUzNTSTOaqS0Tp/UanfpPefqFidGb0wvzmqipsWJEGB362GjNqeHGKvodtKoLSeRBVOyaWhzsfFQLXWtjbR7M82MCmgoKIqemBJjUnF69MYmO8nKXReN868QMmtkClVNdtLjrWw6XOfP3mxxePxTBgLHHArykihvaMd8sJay+nYa2pwcqBas3F7O/ZdN5CcXjuFvnx6i3amnpltMBvZWNZOZEEVStJlmuxurUSXabAx68C+emM6+Yy168g4KTo9+LWdkx/PwNyYDevjunaJy6lqcOAJaTpdHkBRt5v7LJgZNJG51eshMsFLb4sTlEZgMCs0OD+Ch7kgjw+LMTMiMZ3RaLJ/uq8bgvRehTDj2rWCy42gjLXY3Tm92YxcUXW9WopV4q4mSmlbcQmAyqCTHmEmLszA1N4GtJfUkxZi5YJwegfjJf7bS0OqgptlBtMVIu8tDlElFVfQQ7mPv7qK62YFRVWhzevR5ig6FZYVl/OeWs2my62OcW4/o3rrTI3Brgk/3VRNnNWExqphjVA7VttLU5qKysZ07l27l/LGplNa1UdvioqHNicPtwekRQR674r0H9182Eeh4TtYWH2PDwTpUoYHQPctos5Fgv6ZvLPn8MHsrm1FVfbw8MyGKxnYXCVEm/rL2AA6PQAUa2lwUlTX4n8l/fVnC4ZpWVFVB0wRWswHN20GJMhuwuzwkRplJiDJxtKEN/4Pgu0YF3G6NktpW9lU1D/gY1kmvdDF16lSmTp3aD1JOjq1btzJ9+vSghXkBzjrrLF544QX27t3LGWecMfBCHtZXRYgHNgDjx6ykNTaKJGEkrXQzqaPPZdPheoTQuOu1bTjcGiu3H2VYnJWLDyxj0c/uo/BgDQ3tbgp3H8WW2LtbVHBwG+V5+sD4gilZPPfxfswGFbuqYVDA7vRwpK6VNXuOBXlZhSX1OFweopRajJZ00qMMLP/yIE0epaNxqquDxES9dlZUkFBfzbxEDwn11bDfwMP/KuAr138QQmAxquRnxPkTJXg4AdvDjdhyEnljSxmNbcXEKnncfHYuTU59nI+dO6kePoaqJjvThifx2f5qXB5BeYOdPLNbD0UEeEEWk8qwOL3zUVTWQOHhuqCGtGB4IoUl9Syens2eiiZMBnBrgjiLgVan3kOPMumD9E6XRmK0ifR4K00Hj1DkzZrLS4khzfsbY4bFYndpNLQ58Qioa3Xy5KrioIxBW7IZ22UTWV5YyhOrirG7FHB1eKCLVk5m7JVFrPy/zXyZMoyqJjsej+4FHK5pJUpzYVCNXDFVN1Y+47BoahZNKyfzvvE89nIGKcbhpDOMgrgEbAkGMJmwXTyGGFWwYnsFHk0fazjW1oqqfMqB6o4OXGldG8e8K1/sq2pBoZXE6HisJgMVjR0p/vVtLhCCp6+awmPv7mJvVTNlta28+Ml+3ogx89jFI7FN7EhOOB7Vje2UVTfjcDtxejz+MSd9yoD+fwVItJiZMSKR88em0mR3s7eyic8P1pESbaTg2KUs+EYJtpxEfV7ayjWMzxiLLSeROy4cw59X7aGipZW6VgdR5hYSLem0O9wcq2ul0OWirtmBcNdgUFIxqKCqiu4xejwUrH4NLv42Z49K5vXCMv2+CEFdq4vaVhcGBRrbndS1uvxz2t7c9hWf7qnje+d+ndzkaL/3dLSh3Z+MAZBgOcDotPP9n205ieyrasbp1kiMNlLb4iLRXE2UMYcEi4qzvILxU0J8z6DHAzSjaVDfCg1tKskGM81ch8OjR5c09Lr7+1XFvLJ4ArayjcRPHMNzm9to8wiGJ0dRWfcsIxJuwWkx0tTuJjHaQE5SFFVNDuKtJupanf6fTIo2MTI1mj2VRzHRzuqN1gGfRtMvSzOFAxUVFVxwwQVdtmdmZgJQXl7eo8FyOBw4HB0PbFNTU7fH9QUN+BrA/gVcOeI7FJUupRhIOAh2/oc9jZOocejeTF2rk+hjl+lhkD/8hQIuoBhBPuWw3nqcX/H1yHZgA2wHgId1z81qMmA2qoxKjdHng3g8uDVwuDz+sZ/CknriLQZmVK/iav7I245Z5O7/ktIDUDn8HQBsahu8UABMhQlzYPcHxNOMkSaiiYdde7kauNpzNb9gJG7nY+wq18eXbC/m6fIeToCHG7mh9iU05Q1UYNGRuyF3Eq+/u5px/JsRe+CT2P9wpC2WtFgr1S0OYj1fMrv9SbLNs3jXcSsGxYSqKP5w4po9x/hqy1dMTFUxVcdiU/SJmTYFbCMAnOwa1sT6qjpyDBZqPS7qTE5cmiBJNXHTtFGAygeFpdgrXMRW/JzinVDwgxIKS+ppc7qJNhuZkz+Mn1+Sz/0rvmLHUX2eVk2zPsHZlhED1ceg8F8wchZN9uFkJUThcTRwOS8xfufZsOlsvRxetzEOcLTcwQpxDnFKJS6nHTMefiTuxwp8VPIerU6PPg+tuALbi3nEA4v5DPis49YfAH4PkAZkkI2FHxNPMxaOYmKC5W0Adte+BC1HKSypJzHa7J80nBxjYXHbt2nTYvgs6t80trn0xlYBo+KgpLSSov2lLMiP411nMyVlDbR5XHicTRT/9z5so+fA9x474XMw3H6IyvYqnE47CfhCfyoTLPEkRRtJijNz4cRh2IYler+hv5boR2u3kCLszHH8PzIBXsyDc/9My/qf8zWcNK2cAQUfsqggl5aVt3HEWEKiu4REBb5w3Aa04nJ7iKs3sYCXiQWe4xfUmWdgMaiI2mMU/W8yNoDVj1NMAtnMolqZjdOTCuhZzh4BVU3OIK/wCX4CTqhb/xCJLIL4y5iRPQKPJvzjc9cYvsM4QN2jQM1+SE0FOuZL1rW6iDF/xm3iWY6050JrKXkClqycw5LP7+aGc0b0qfG/IbGQjQ0l1GgO3G49XpdmeJ2ZGsB3+QV/BlIQ6Akq/EE/91jgR76T1EE+UNy4Ghr1DncRj1LWNhZjbAzxUSbivdM8Yi0G4qNMxB07zA/V3xJHKwlVM4E5vdYcCqeMwWpvb+825Ge1Wv37e+Lxxx/nkUce6Vc9+UCx9/8c1o0VQLoGC9TfkOUezZfiIioZg8OdyljvGGgRYGMdfR0BLPL93sMJrJzxBVuP1NNqdzMuPQ4Q7K9uxaQojB4WS7zVyOPv7cbh1ohTPIzmjwB8w/mlf/DAeeRyCgo2wws/B9qAz2H354B+iBXQCJ5r9wSH2MZ3edvwmj7RFljk2/lwQse/AXY+CTuHkc8xigEzcFHL1RhGvMie1IlUtzip2vMkWUAWX5JmaWYlt+NWktlb1eJN7W5irPEdjFUGbJYMli99j7KWZnJi41g0Rg9Xjq+tZjwaKCr5w2N550glHoNgGEa0wu3kZ8Wi0UI7r/rvm+3FmdgeLu4S6rzhnBHct+IrnG6BJgTVje3gaIZ178HuJRRt/ojyM37D8ORoLMfWMYH1aG27vfcUfx0Yw59IVCxMdm0hxuTGhIavW5JZdilrGt5kWLyVxZYGf3n78KVJdySZVwPV/vo2A92793EFwJPZFIx8EkbO9adtr3zxp+jB91aqGx6lxHAXuXExtDjcmDzHyKGepcuP8Z3zsij+aj8GRzspQKrhXWAHRQd2YFt3EVwwt0td9NPSwkWplRQf+Qoz+uC8AsSpJs5PyuLKicMABziboVNOxwRxEEGTbqy812Zb/xOmef8dzya/FzqNdUQrgEk/9ms8h0Y04CQwzPYtnmCleJZabRijW7fp5/Tfl0au5AMWax+wFwu7mcleJtOmpNMqMoFob+l7O7MG0GvYcsY1LYemOEZzMaVMJHnMFLK9+UgaAp4ZTZHlFgqnXsv4jDgO17aRkxTF1JZniVVgIqXe+CFc517LxtqtLFvzgj89HTj+EEFrK4vUXWhUckBtw6XoUYkEAO9Y0xP8hP9R7iXKPJVxyVao6ChXH/mdPs8EZvIgbg/salSp5TpmjJlPuSmFNaWt1Le5qOK3XM4RABbxXne1oF85ZQxWVFRUkJfkw263+/f3xL333svPfvYz/+empiZyc0N0bR9uBMD28I+x8S//Zn9l8AAKFBgPMM5zgO1iGhXKZNKEvt33EPWV4oD/K5vOplp7DQWoanIwITOBykYHdpeHTYfqqGxsp9WpgRDU26uZCOAmKIQ+0g1vrvgJ+/gi2NDQ0Wiq6IYycONUYKrj2/zy4OWkWPUeXOfr8RtXjvn3FQPjAdvhH0D27ykaO5+Vezq+M4GdqDzLO65v0irGITAhxF7GaJvJM5jgSBrV1KDh5kCjxj+3RTGFJG+5O8h3W7CVmQEnxTj0C3arFB9Rycfi71zoeirh9dexXXllUAPRZHczZlgcu8ubEAJW7TnC38zl3Lz7DaCcYsqJ2/sFWRdcSuOer7zfqvPfU18diALO5x/km5qwoGAISNadCKypX87WxksxG1axiODOT091w+b9K+pmO4Dt0N3YDp0JGX+CcZOw8SYArwMzKaSZDzkiLiM/L5mEg5tJYT8pjii2f7Qbo7OW4d7MlBS+8t8v25pb4axtYO0hClBbC1VbGUkFpcTq4UADWFBR7SrUd31efcwd7ibjiIt2b3l1vs7XgZLVa9gwegoXd/qubuDbupwzG7iYNRxN/Bbpddv8Rt9XviqgWSDf7iCfdbis6zhKPEddkygTmdjNY2nQSv1pc4GdBxvNvM4bJPIGHIoL7rACxY4XiN3wMsaE/+F3X/8OhbUuClZ33LNiBb01VuEsGsls/TZvrX6UN1KmE2sxkpcSo19/dwZr/344UgJUYiYHTLrlbvWtJmYCDPAbHuc/3E9JRSqvA1dCF50+AuePGQEbGrCE/P1LKCCdVM6giql85TVWAC8DBb1cmSdUThmD5csw7IxvW1ZWzxksFoul/xMyHn6Woof/FdTQFIO/FwgQZ4bzXFupEvtIcwInISGw4uUDbvV/WBPzFNNyE/TMNVXRB+Q9gn1VrSREm7CaVKZSTzZ01AQT4AIETOALKtAbh8DGUgv4v89QrjPCBd7vAfyWd8AOxVbYB0FGL9C42gL+/Kz/BcXrN9I5/SOfHViMrWxhAbVMocS1hTyT7uXtpJRoQHGAqoIwQDFlXEl8x7m1wIbdoRsyLNiwdDUEO38A8+dDXJx/U0FeEhsO1lLtXZXB4Wpn/Y5SbuZz/z3A8Raq+BqvsYXvB5wu0JDojUMP825ccLnnn7wqcig1rIOg8pkAHMXfy++Gzj3mYDbDv8+Bqfd10TSDJYxqzyA961Kmu+xQXkOxRwOiyDO3U2t36ann3p6Kfu4KePV/4fs9hAbLyig+WkE2TVRZUvzp0ZlEoXnMYO25E2nLjMKWmUzRhu4b1Hyggoexq8uDPFB0eYwNKIvAulvEej5vzCKFjikZgfVvOaBZAaE/CiNoYoTpCwBqiENVOxJ9NPRGP1CTrrW5a50GwIW18X+x/Xc9tgW3Bv1+MRBY4XNdsFA8yBvH5rFVfIeisij2VsVR3tDeNdP3wAFw7yKfViAHFRPF1GASBLU3AFc7HuOvwgIqvG4GFStXonfqXw84TiMDOkVQfFqhChtVwId6eXn3NcCAvxki5NXaw42pU6eyZcuWLitwbNiwgejoaMaNGzfomnZ7/x/44EBAoSuAGdJFiz8kECo29IfH96A8yCHWxn3AuIx44qx6rTV5f1gD2px6+msyh7vaSRPeBQ3BtzDP8RtCOKym8oxlQdcdDro0KPmd/t8VjXxeY3SnrSowgkPcyyfMZR0zTev8+wwaGB0Qr0CMpvfK83FRRC2vU0sRtRDwZ6OFK3Fho8W7rRt+H7yihS0nkdvmjuGc0SnEWw1EmZvID3iodS9qAxs+/JjOCffFBBvq4zECuFB5m+kcCPaYZn0Xpn6forgreJ1ZFDEFOpVSYNnqjUty1x/Y9usuOhKATJZyQ8JhKC2k2LOZfMrIp5XxtJNrrSXFVEWC2nENRQClz8Durd1fSG0t+dRi5RgxmBlBHOckZTM8Kp78zHj0XlLPf0W1TgKcbIrQG1VfiHUOTYxv3dWlEdPoeB6uxOr3YvQGuZIUz07gAMXebfo5k4FkFuGd3N1Nwl4qzSQTWMbT0ccQdQKfweOirYG3vtPDPvR2QIMoAde4PmCe+1E010F2VzTzxpajXec/Hj4MtHo/2BmLhzTcpHiNlc+TwntdP/I9lBps5+cw5R6InUc+Zu91XUr+vBch9f/p//Zfb1fGBvy7DQb8zRAR6WFVVFTQ2NjI6NGjMXnd38WLF7Ns2TLeeOMN/zysmpoaXn/9dRYsWDCoKe0+JjCLYr703+jAcEYQFvRwuwqN3Z5p4gl+qRY91qgCxzo2V/6FgiaFDZmLmDEiWV8uqq4Nj9AnjZbWtTGWbd2f0muwfL2+rpV1uHf7EW9DfQOFF1/LO6szuVx7Qb+eHtDLIZruwjaBx0BwKMwXStzHdsZQjuozhXZoQU9iVIAYi+8cibxOg/88NhJ7FuU9Lphq+NuDcPOjHbpyEnn6O9MoKmvg9y+WonGUf+AzkPrv5LC6WxPYJUzkv7Y8Xqek40ATzGInezQoVgMawEkXg+ak2L6Lpvpmip212MYYoeYItNVBVQ02yrEFhGm46FH48BkIavq7hg4BYjgMm5ZT7E3uKOYQV6JiA5bTSpuhIahz4w9fv3Y//HI5dA67V1djo55impliMEO0yqKxHR4r4vg9tOKqmqDf6vy7NsBm2gIEP1MqgVGBGMAe1FlQ2RfUidJjMJlejzsWjTr0GuXTid+A+eqiDeDchdBQBVUlULML2N/1IpKvgrqt/t/sMK6egNB4R91wqWAKiFQAnMchhisPs1K7hmbPRV3nPzbtCCgjjR20EIXV/wj6ykIFNDMQ8PUpl3wTxsaBrQJb6XxsDUcg/1IYbcM2ZSq01UPZ97Ed+BK+WgoEr5oReF+ymBIeae2qqoY0t8rj6fviq8888wwNDQ2Ul+uz/VeuXElZmT4i+5Of/ISEhATuvfdeXnnlFQ4dOsSIESMA3WDNmjWLG2+8kV27dpGamspzzz2Hx+Pp94SK3lBU1kCh+WoKnF926nHlkk8uxWwi3+tSFQPezo3e0MUvgqbl+oaYb8Kokcf/MUc74ISYZNj6IbDdv8vW9hy3NcRQOP0a5k1K59UNJXxV1oTRoOiTS5VD/t8OWvcs4CEFPawXaER0P8CAzZgFCGznnIvt3JEU5N3P4y9GsdD6R/3BUDs8zSAmXg+71gE7AzbGExju6hxW8T0cusZqRoF/UNmJPglYNel9zTgsgJF8YinGRT4mQuqfHf0j7FwIk6Z2Wa9tLyVMcFdSpsFYc6Ax+qingF8P12boGOP09ooVJ0wIiokaYNgI8LjIt0VTdLic/IwYSLNCe6tusJqPQcNRqD8KNRWQPgrGXwBGK7z/d+AL/9kCGxpfWKsIeP3YW/7tgZ2URcTwOh6CWjv0ENoi1sPKZ+Gqu4MvsrwcqNKvTTGTnxQN7t63I/lJcRQ3B2vpEh4sed2/3z8OFXBssbdsA8ddtU6NbjOQzkhU7BTTRD7JFFOHih2NY12MlZ/M8ZCUCxljoOkMqDoARw4CGzuOOesKaJvBok3vQ/tHQb/b2eNW0W9/pQIZBvRxZQAFhpud/JiX+Zg91FXexGPvxLNgarbXQBT79W3V0zyowuH3rfcCrtxnmFx6u36kiv58q7Bo1lgQGiQkQ9pIcLRCQioYTPqAoykdYpIpMuWy05PPJHEU2+6PgbVB5Q5QMKEHr7Ef6dUT/OCDD3YxWCtWrGDnzp3MmzeP/Hy9Cu3Zs4dVq1YxefJkrrjiipAEPfnkk5SUdPQ233jjDd544w0Arr32WhISErr9nsFg4L333uMXv/gFf/rTn2hvb2fGjBksWbLEr28wKSypp2HGORSvD/YO8snFdtbXsFVkQWkhcKir13XRNfBOHThb4OyvQ8wJFhIWGiAgJhOS8mDNo0BH79RW+wS2uhSKpl3NginZXDA2jaWbS6lucpAsOprWoNCdFXB4/0ygBcRd8kmiOHma3mAa6vVBo7zhoGnYchK59wc/4/EXTSy0PAken5IrwDvQD8AZl0NsGmxJAfdOiJoBE2yw5Xc9XGQG+VR2aZTwPtguYUQREKUYiCGWRd6gXPdjCd1RE/DvFILChK9/HbJ2UVjSQEObi1U7K3G4NdqoJMldQnunBq0YCApAByTUQHeJNfFBSRP+DozXGOshsPPAZAGzFVt+DLZxI0Bzg9sFbic4s8HjApcDWuuhvhQSciE2BcacC5cZYFUUuNb4tXZu/LsPVXYs4ZNPHMVU6x+83rf/Pux6Ag5fDiPGd3zV401AAmyTU/RtWu9eZgpgS47CdqTrdp9OvQzbOn6DbqIXZFMcEHXwJUkE1qMFDIepMyjeX0d+igdblB1baz20NkJdI3rcowmopQhXh2eclAuONmhPhOhESM2D3ClQNQH2F0JMGuSOB20URKfCnmw4/DY+b75zVp4+JjaN181QwS4yDQ7daAWMQ831fEGx6xirNn2DL/dewGNXTcfG4YDySKCIRpIw+5NVBDD5/Nns/vf3Ufm7fuHe57movAlbdoK+ikiMEaJidUOlGgADKBoYjGyphca4cbgMI7HdPA/K9sGnb2BreRMbApgJF36j+xvZj/TKYD388MNBn1944QWOHTvGjh07uhiD3bt3c+GFFx43yeF4HD58+ITHLFmypNtFcZOSknjxxRe7vJ9rKPAttZTPWGBfQGOVii01T+/1RsXD3gRgO/kIioGvA6QNh0tv1j2nvOlg7n7dryA0AZZoiE8Fx92w/n+C9xf+D0Vbamg4+yoSY8wssGWxameFP/Qd2IP1o6+Eo8fULR1jB7b4q7B9/ev6Ma01+qTF+HhABBitn/CbF6OYaHyL9FGPwTfOgKff9J74EsiZCHEJYE2AqnGQdyYkZMOhXVD/TtfrS5+GreoLbN6H3d+wo2uzexLRPAIXRmYwjJPKYMm8CCpeC9jQBi/+jIKrn6KwpB6zUWVvVQuJVJBsbfB3hH1GKKgh0jjhAgaBoSEbujergT8cqxu40YDwriuF3sCoZr0nbLKANQZcLhBuiE2E5CwwR4HJCnEKjD4H5htglQKOj3plyPXf7ejG2DB1XJevl6746oUd3ngUbv4rxMV7DzracbLm7oPdfeMMigOzFLs5ItAQFwN7SWYcSeRT3yXTsuPzRMjJwZaWAppLr8/2FnC2w7B6aKuFxhZorKOYTR2/H58GLqde3s50aGsBayzEJEFyOliTIDYB0MB4hr4tOQO2rAD2demk6KHMLPJzMqAsmUalmDhTWfAYnQvyOUCM589sqt/Pk/9p5W5aOrJBEy3sa4jGQIu/cVcB26gMuPJHbHh9OYnGBv/p/EkSiqIfqYqORQNB74wC00cOo7Ckjqm5CTAsDpKyYPhEOHYNRYUb2Bo9nWlt3SQv9TMhjWE98cQT3H777d16LhMmTPAvPnvzzTeftMBIxb/U0u6vU1TWsfhuftIEiE3XDZYlCowWKI7D5vnUe7O/A7FJIMaA0wFxqXqP50QoKqhGvfEaPwfa7oGtvwtuDMWTFG02YPverRTkJbHjaKN/GMlf4b3/L2IuxYaPdYMl9Ih+MdP0/SPH6MYW9J6YEOAfIxSgebDlJLLo8iv5cNf5XDQxAxKTIPdWKF0D834GMYlgtoI5Rg+txA8DazTMuBRW7QBvr1HXAsUteeTTio31gKdTeBKMhlg0A6RjxGZNPHF5dcY+HD1zLx8mnQ0VRwmaqNv6BravZmH7+g8pb2in2e6i8ZgeWur8EAVp83XjA9qAfIKNVLHXG9Qb4dyOkFXAGCLxufo99i186BvkUBQweBWoRtA8Xk8sGoxm7z4FYlUYPQsuMcJHRmj7oEOQ4QLwdCSw+NAbUAf5WLF5Z4r5jbHSETX2G4+md2HD63Dhjd6GLiDLLLpz6k0ITLue/K13d5s16CPQEH8KuEkAsrBR3214WdeeAJYkMEXpHqrm0o29y60bHNcwSGqBtgby97kpZiv5pIAlRi9rlwVMTv1zfDK0N+tlH50KcUngtOslZTTBxHkQlwUb3gT72iDNrwN200SKPSr5I0wUHzbSQDSJHAA8QUlZObjJUVdyoPUYXwVcM9GJaA1gJBpfuP1yANWALX8EtptWwUtn+etflySJHoZ+bLlJ2HKT9PonhF6vLDGQmMm2injqPNEUlrVgy0vr9vv9RUgGq6yszJ/s0B0mk8k/7nTac+Yiisv+1PE5LY8nN1ZjdbdywdgEbDlngNkC25qAehg3C6xx3vduaLrX1BuDBd7GS4WULJh4ETTUUXxI9zaL8WYwOZ+F/SNg9ndIj7dCD28EsJ05m+LN68DsAQ80CTio5JGfqmLLHQfR3hCPNcFrsKzeBtXbMGkemuxu8jPiO14vf+1DUHINjJmkN2jmKEjMgpgU/WEWHhh+JpyxCL76vV/LLqA5eRLFbg82kwla9LGAQG8kmmxAwYQZUrsPGx+XKhVcKZA3ETImw4xK2LQTqO84ZsMDMPZ8/zpsq1bXgIBmDeI63SJfaLAzvvEiX+iqGMhPSKe4MY58oiHVRn5NaVCaswqQne3NKgloUHzGy/v/ovImthypZ/rwJG+Yx3uswQCqFaIVGH2mXtYfAG2rgAI4czZsCDZYV/o1xlKMis0SrV+XQy/zFgFtnjhSjc3+68kHbJ+9DKNnwEgbgZ0OXKGvk+fn/EXYtr6CjZ10zT3tSgIT2cFwJuL0Hr87yJvxhQdJy9E9UY8JTDG6gTG7weMAjxUciWBth9gEbG1ubEdbgDS9DismvTzdZt3YuV16uWdN6HgmrDH6MUajt2M5VY8ufJkINevBG2bNB3YOH4Ma56G4uoL80UZoTKa4Jo0GNpBocAcOxIGA0coGIDDJxEW+1Uix3RngTcZ5fxcYlgsFT2IrfAwb06CvSRK+OiiELsBiZeqE0RSWdn1FzUAQksGaPHkyzz33HN/97nfJzs4O2ldWVsZzzz03OOv2RQJTp5L/ZgrF1JIPbPfEs69NwWD3MKwabOMzdG/L0QAt9TA6Xw/zRMV6ezKGHns93aKoes8nayxMv5z8Q/spZm1Aj7QFPnkQTEbSoifQ44jCiOmom+egqR+BCnYNDpHBqzUOcMRgi/F6WELTdZpM3spsAE1/mgqGJ+oNaJ53+NcSBWMnd1yPoug9VKNJ1615ICld9xD37aDI/gHF6CH86Jwx5I+Kg+Yk2NZOkfZ5kEGwMonoeCP5mXGQHN378vKR1AyeBsg9A1JzoXEy5M6D0qUBBzngX7/AdvsrkJfEWlr1aI9vPIDgMJXfG+kyeSSRfBo6PN+R2dj2naE3aLmTsNW8D3j81zcW/Ev7BBFYjkBhaRMN7RqFRxqx5SYH1xtF0Xv9MYkwchrM/yHsyYes8ZCcB9suIt/xYZD3ousfRr4lBn+OdPlINA5Rq0CNlkkczf7gq+6tfAUfvwTJD+JbZgmAhGHdFnufSE6GqZfAwTRoWtPDQQr6qhQejIwnJ20cZdWNFEWBrX13UOKOfw5VZp7uCbkc3rocpY8Hejzgsevek9uoG55UF9grIXZYR3RBCP2ZVQ1gdOkhfLvdP4EX0MteNep/ilGPLpxthIp82PxbwOtpXTyL/2zeS6vBQ7HTypXjkrGlJfD33YJydTtZFm8M30VHYrCXYsCmKdgSvR5xpRU9SWas18goeuf4wishbxxkjAr9XgQYLltuMra8burnABCSwXrqqaeYN28e48aN41vf+hZjxuivud63bx9vvvkmQgj+9a9/neAspw82kw2bawtggCn5FG2rpMYtMKQmQkycXtmzpkF7I2RlecMppo73YPQFRfH26uIhdwK2y2/G9k4DBKWvV1P04f/DzE1AD+GV5Fy0eJs3ZbYKjxpNgzOFdoPGnasP8WNLGoum53YYrEBU1T+WpcfHA56qztfjM3LQEWbIGA3nXEbxmi+BRorJ5745U6C5FsrMoLko3laFvqCezrQLZ2FLPIm3XDvsoLVB2giwxOoNeXM5lJYQmF0Hn8H//Z7C3Bu9mjtWYuhpXCWYYRA7A1vLux3HGo2QNkzvrWdNgt2zsdnXBIetkruZT9WJwFfUdFtvfPUqOhFG2PRwc0KKfu0F52P7/MMg/TbANia700lyUNFwYqbONJ56TysZBn2syl+PjrwMRWcGf80cx8nhvZ7ZP4ERW+DNSnTfuzNj0e+Im4nJ+Wx3uEmJTqPY7sJGcLagn+zRkJCpj1l57OBw6J0ntwtElDepxaqPDyZ7wDMC4tK8iQl0GC2h6QbJ44IYk16vO48HWWN142XSDVxRm0oxeeRTope9xcKkEbnsMpnJj3eBxQGmGGY0u/iyzAocAfbrvbhueptFRGFL8j4HlROBRkge2fH7mqZrGDddT6k9WTp7/QNMSAbrvPPOY8OGDTzwwAOsWLHCv05fVFQU8+bN45FHHpEeViAzzofPa0AxYRuVwaQGjcaWeNo8mh7zVg16emx8mh4mA72RD7UeqKr+VMYmw/DJcOHNsOYhoCYgWaGKEfwd6NzI5gJRFDWrlMWMJqepACc7OOoazjEtFqvmptwjWPL5ERYVeBe3VehaaX0PR/cvk+gZg1FvSEcW4OQi3OxC4Wv6Qx6XAhn54Gol/0glxXUV5NOGjVQYPbmPhdQJlxOcbZCQoXt9scmQYYOp5bBtK0Hp3Aeeo8CS64+mBoWXAt6/0G3WXdRoyJsEO9/1boiD5BFgMYExFtJGwegJsHNNcCZfYuIJL6FX71Hzvc0gOhGMZooqWtl2uImphuHYmIMvXblDb3zw5+xMFjmiKHIP47cNGZRqlcQbqojGHVyPPno1+Hvu3mcHdo+3B5+UBNaZkHselHZjsFLHQawZnA5s+WMhKo3iA27yo6Ip2mZFo+PdV35iY3RDExUHWrTuUbna9bEst3cQ1+3UDZrTA45MiAvoQPgbbe8Yo2qgy7yQwGONZj0xw2CiqLkSF2dQ7DNY1jhsI83YcnSPr+hgKcUVteRnjGKWIYrikhhUhuHmc7+9DOSDSjfFrRr5mbHYhg0DRxwMD5gWo6p6gpY5unt9YU7IE4cnT57MihUr0DSN6mo9BpuWltbl9R6nM753EFGcyYK0MdjSU8BkZnpeCltK6piSkwDR0Xq2lyL0FFnf5MuT7bWoKmCAxAwYcxa03wFfPNipET3WJQHAljMVjBa2VrRgSBlOQ9VYjmouSkUm7STiQEMT5oCQQEcD3b2GPqIoYDJDSh5nzF1A8Y6RXGY72xt2MetGPX0StjPbsO1wQsUhyBkD0fEnPvdxEeBJ0L0rRdE9vZRsyC2A2m9A6X+Djrbt0kOFxXQeTSkANgNd06fzSYUR4yBvMuzMRZ+EOU7v3ZuteigqLhUyJ8LOAmwUBgymhxDm7AnffbHEUFh+jEaHkR2eeGxnnQsb1wYcGA0pI4K/a04ERy22jImkfiaoIZ9KUUseB3hdCczC+zz4e46GkxSd0vFPgxEmzITSF7oeljde9xw8TkhIwZaRiS0tFppqeH3baHyJCEH1vlnD5nvcVIMe0jMa9aQLo0M3VkaT3sgbLOBp1qcLdEdvPA5foow1Dlv+WL4qmkq+fRtFeHhvtf6EXjouCdswC7uaNGqVZGg2cOXEFGyZCRQdOsw7VS3EOIvIgqCE2AOkkEEyxY0GbNnjwd0GWXmdfj9grDnCOOmVLlRVxWq1EhsbK41VJwpL6tlb1QLWbIqtZ2EbNw7MFmy5Jr0HpXm8CQsKkApWV3Dc+2RRVP3BSMqCMRdA3W3kFz/XJb3XlwCwkxRsI88Cxci0EcPYJjwcy5yA8aiZJrMZj8uKR3NjUE2YDApFZQ3Ysk7WUHSnWwFrDDbb2dhGjoEE3xQJb88wOQvsk2Bsu559mD1WD3OcDEJ4M+y8UwgMRn28J32MnkRQepigCaFs7+JBbQRs2aPg6Oag7WMZo6eHGy+AMV+DYRMgdTLUJEDmaH0+miVWN1pRcbqXMOYMfS6PXiBdV5E4WVQVhELBiFS2lNQyOWUEOFXgfPT8OoBzdIMdiL0F2mspckXRYmrHbB/DEa2UGE8l6UorxaYewqLO1u629oGAaTIGEwzrbjJ9NiTk6J0NzaHXk+hkvaOjmsk3TaLYtdNf9331fkuNG5vR6A0BoofyjEbvGJURHCZQPPq0AZMFopL0unGyGAzYRmVgu2IOfFzI61UN7K52Y9CcZCVGYctNZmLWMHYJQX5KJsS7QLFQvLcFN1kcNZhwaFsY6RJ6r8gAF2Zlo0SbyM+I1dPqPU49KzEQnzc4iKG8/iJkg7V582b+3//7f6xbtw6n08mqVau48MILqamp4fvf/z533XUXc+bM6UepkYfvLbQQS37+QshLCUhM8HQM2ppVfZkGk7ufDZZ3PMts1R9w26XYjpVjq38z6DCfJzCJ8yF1NKhmPY01O56i9Ch27Y8n1h3PhmoDJbUtaKg0trv1ORwDYbBAb1DjU3VD5AuTCo/eM4yK0zO7HC16eCUxT8/y6g8Cs6BMVr9Hxxkl8NXGoEM7T/xsAsjNh6OpQE3AGFQKtlgznHERjDtX90rHFkBiPGRM0FcW8Lj1azFbIDkNho2B/b5fmNG/9SLgWvX7nABuB9QImH4ubNkB1EPeuR1l70M1gNHE1j0tZGUk025yk1A3lUrqSTZsx0TX5BOdvq96E0xAurTBoIeHmUjQOFb8GL3OmOL0cF58CkQn6GVnicF29jnY1nV4yr7SnZ6Xoj+TJqNexzxu/CE91QRWkz4upRj1MHdUojek1g8oCqTnQe4k8qPr2Zuqj/VNzUsDkwXbyCxsmQm64XE7dcM70UlroaC1PZYaojjk/IyRBn3FjrjEYeRnxuvvF3M06t9JHtH970YgIRmszz//nAsvvJDs7GyuvfbaoIm6qampNDY28vzzz5/2Bqu7MYWgdyxlxeuhQE2fTa6nnvazl+obz4qO0VNtz7sKVpYAHQuW+ueuTDtTb5zN3owmTWNfo4cNlQKjWSMuKgpNUYkxGzkjO2Hg01hVg55ZqHh7g56OlHkShuljCpYofUJmv5WbN8SpKB2D5MkZkD0Njn0Tqt7yH9l54udFDIPMSRA9Fdo+7BiDyp0MCS7IytUzzYSApBF6OSeO0Bs/oemeg6LqUwXSxuoTM8sTYeRkvcc/UPjHVbwGNGO23hkYb9MnxQbi8YAWz7R8jfI99bjt8cTFjie/vI1adyOpHO4++UQNtUORATToY3+BWOPAPBqcAQYrM0sfB4xNATRv4oQ9INKQif5iRn2dQP3+jYIRqd4QmdfzMFm9yUSa902+Hv37vrR0j0ufM9hfWGMguwBbtoZtWsC6oZrmrYcGsLfq9dFgwjbKiC0jFRrLWLp6A/vNZ2JnN+0Mp01Nx9FswjYhT08g8Xh0o32KENJTcN999zFhwgS+/PJLmpubu6wsMXfuXF555ZV+EXiqUVhST0Obq2OGuab5wzOoDEzPxzfQGhsHI6bCxTfD6ocJWigXIHcyJKZ2eBmqypriWiqbDTQ622gTDuxONy6nm/PHjPR6V31MqugLgRmE4DWi3sF7zQNJ2Xqjb4qj2xHoUAlMFlG9SSDDxuheUdV+gtc/9GEEsiAuh6LoERS36T34KwHyz9B7/AnJesOnefRQY70VknP0MJP/Gg0QFa2vG5iRr4+dZI/u/Vy8UPCnPMfouvJngL2FIqeVrVuOMS038MWB3tVMEqxsPebmQGUDZQ0GdrhTGUYmwnWYNFPXV9JgjQlRWy6IxK5p/VGxMHkGbFnZsS1llJ4sExWjJ9GYzR31QjVAaib6ollbAk6UrBtr0O+Lonjvv9e7UoyAx2u4hDc5I1b3hPsLsxXS8rrPtlWtoLj0pdKcKtCmT2Y2mUA1YRrbQsu+JsyoqOQQlxSLLStJn8ivGfTn3tiPWoeYkLqlmzZt4sYbb8RisXS7KG52djaVlV3fpSLRw4SJ0aYO70RVOzyIgXTTFVV/aONTYdQMmHFr12NyR4PJQlFlGy+vP0RReQsXTszAKVTGpseSFmvGrYGqCD7cVcmAGqtur0HRGwzwpvwLfdLxcd6rFBI+b813T4wWSM7UJxRPuqDL4XroL4OvSIPoWIrVYUCad/tIfVHi2LSAJbYUSMyGpOEQl67fF1/4VlH0MZeYRP33siZD0riBD+Eoiu6txidD0ihIG8u2JgONLoVtla16o24062VhjgKjiWkjUhmdmYLRGkVSQg4lztHUecbhdAaWixdTVGh/mcMgPh3SOq2gYDTp4ddAolP1zkVMsv7/qDhdq2+JKmMsmDODv5OR7Z1YbdTPCd75V249TCrcgEH/vsG7cojB1LG6SH+gqvoE+sQelrMzmvRrsETrY50G7zyuxAw0UwqZjKeGXM6dYuPa8yZjG5Wtdz7MMbr33l/h8jAgpFI3mUxd3jsVyNGjR4mNPclB8FOUblOPfYOg/n8PAN7QQlFZA1sPOpmWeia20bfAAV+m1Rl6z1FRvRNQPRQeaeDGWSMYOyyeLUfqiYu28Nm+GqqaHFw0MfP486sGCr/X5fHaywEymr5MKl9IxhwFqSMgeyqUX0ZR/budXg0xkjPS88FiJn/yOIrXTCafI5A2CWKzwNUKvmdCUfR0cYNFDy0ZvHN3wJsWreoNU+oIvbFJHT4w1xh0vV4vy+pNrfe4mBqbQGG1g6l5egp2wMHe3r+RrOQYxqWP5dCRMorrcznkPEaMVk6OpyXohYSEslwWQFomRDfDsG4mHidno78PzDsfLy5LD9tZYjruXeD1WeP1ifm73+3YnpDm7SiogHcCsEF4Q59ubxja+zpug0E3FELTjVd/4ctKPV5d9q0M4+vcOI3gaCV/1EjQBPmJGdjyRutGzYfH7S2HUycZLiSDNWvWLJYtW8add97ZZV9raysvv/wys2fPPlltpxeD0eArCoWljTQ6FbZpCdimL4DmGjhWCFN+AKoFVAMFI1I6JqCqBmzDU/wrJyyantvhCQwB/jHA3HhsWXHo2U4D8EAGpuwr3nGN6Hg9TDeugOINHwKOjiWvzpyhr9SNAdvw0dgKzoaKGO9KEhn6PCSTCVAoOtrAlsPVTB+ejC03hqKyBrYcqmZ6bgK23BRvwxjlHXtQdU9hMFAU3YhGJ4DHgy09Ddv4nsNJenhbQLSCRzHjVNNpNORRrtUQ795CvIDXjd6wqBqiRxI9DMypXT0sVdW9a7KBMsDgnVtm8XrI3dTRqBhIy4XdvokG6BOnjcbgY4XQO0XCpBst4dHDhB434O776jO9wWg68TG+lWFUA6gqReWN7KxpY9KYEdhiNEhM6RpWPMUIqRY98sgjzJ49m8suu4yrr74agO3bt3Pw4EGefPJJqqureeCBB/pVaKQRlFwxwC816wsFI1LYcriGqdmp+hJEZ18JtWfC8AJ/JlpXL9A7iTkM5m74xwBLm7BlD1CGog/Vu1yUgt5IWWP1CaM5U8jf+02K6//bsbrDsAmQNFovQ2ucnq5uUWHYKP2z1tHQbTlST32bh62l9diGp1B4uI7Gdrf+OTcJhOr9vTj0d7WHOP7TVxRFbzijYgMMbM/4VtaYlhPLu3WtDItNxNEymjqtkmPOauI1fRHf14F8LQ5bUgj3yxIFKN3PQ7PGQV4+lJQDQh+/Ug0d2bedjYrJDCk56O9yO6hvix3W9bjA8LzBey5N867kfrIToHugLwbQYARLHFuPldDiNLOr1oEtI8O7rmfAM3oKGq+QDNbMmTN57733uPXWW7nuuusA+PnPfw7A6NGjee+997DZTrxIzalMl+SKMMGWk6inMWse/W/4FEgZrq+00VMvz/cwhUH9L8hLYuX2ctqcborKW3QvayBDHoGTLA0mfWwpKRvb9HOxfVwJ2jrgXMiY6H3xnVlvGK0pemZgfLp33KPjUZs+PIktJXVMy4kHISgYnsCWI/VMy03oaBz9BkvRw1CDhaJ4xz1OvCyYv2Ojaew71sTRqnj2trRg9+QRRSMpWilJHmgywPbGOGz5I/qux5mmZ8THd2PsrFbIyYe6vfq9scR0JFB0p11R9XtEJvrCvFrHIs7HQ/GGAw0GMIqOKSlDiaoybUwuWw6UMzEjCqKt3nBlwEOqeP9zuocEAS688EKKi4vZtm0b+/btQ9M0Ro8eTUFBQUhvJz6VKCpr8M6/gq9N6IdFP/sb37gM6B5DVFzvBmZ76rkOIracxI7OwJEGPcw5kJ5s0Goe3pBMTBKk5YNtBlQnQvYEfZDfFKc3EkaDnlrtsetGRzV615nTw1SdPVj/Z996dGiA3kAWVTvYUnaM6SPSdO9roPFdb1+SgFSVJrvG2Lx0Co9W0EYWZqqpUkaT4D5IlSGP/KQU3QPoK6YofTypuyxJ1QA546Fhpz+cfVzNigKWRD1EW5cNeLxjR30g8HUuQ4z/lR9Oh76UlNHU6fq9/z6F2uOTLvmpU6cyderUfpBy6lBYUk+02UhitCmsvKsgfFlpBu+M/t6OS4VB5Q9c5HVQPNnA0KBq1Ae2kzMg16anoKeP9U5QNXtfL2HU5zRpTn21Af/bW31lpxvA5VtK+XDXMS6alMmiglx9l9ulGy6PB1SVrWUNNLR5J2kPhsHyXa+X3oa2C0amsfVQFRfmj2LT3hJcprGUO2owYaKdYRTXaKGltntMHW8D6Iyi6NmXWVMATe90dfImuuiPToT04VC3BxAQldqn6wxLzJZujFUAYfDM9hchGSxVVUlPT+e1117jggu6pvm++uqrXHfddXg8Jzu7PTIJWjU7nPHNz1KIqLBBZw9lcMo6YEKxybuEUlKe7gGk5enGSjXoGWYmM7gMuifmTwIIPJW+ksaHu6qob3OzeldVh8HyGUc0EArTcpPYUtrA9BEnXq19IPB1CFZuLz9ug27LSdTn5WkjKdqfxurNcezZf4wKl4laMphgtIYW2nT7yqMbFEVPvIjLRB/rs4CiBBmfLh0as0V/o3eptzzjk4OuM9xC+L3mNFkWL2QPy263c9FFF/HEE0/w05/+tD81RTz7qprZcLCWeKsx/Ct/GIT5ToZerVDeH6gBDadq0ENJcSlgNFFUr1F48AjxViNN7S4KMq3YUowd7zPrgYsmpuse1sT0jo2Kip6yr88zs+Uk6p6VoRdZZH2g156Tt9Fvc7pP3KB7J8DbRuViS4/mtj9V0GKy4CKeBZPTvZNv+8iJEn2iY/U5bb6VSVRDkPHp0nk0W/Rko8xMfazQuwpExHQyT3NCNlhPP/00Gzdu5K677mLz5s387W9/w2o9dSaonQyrd1VR3+YK7jmHMxFqrAYd/9wsdAMSHa83kPuqadCMbDhYS35GPFuONmFLH+ZdWsdIkHcGXg8LFk3PZdH04cG9Y9/4ouah6EgtW0vrmTY8ud9fPd5bj8LXIQg0cMdFMYAqwBiDbdJwvtzh4fzx2dgyo/X18Pobk0lf3Bn8qfOBxqdLh0ZR9WzC5DH6vfSuMjJoHR/JSRGywTKZTDz77LPMnDmTW2+9lV27drFixQqGDx+ESY5hzsUT01m9q4qLA3vOksinu7lZKBSMNlJ4tI2LJ6bTZHczPTdeN2gm7ytRjhuu6Sb10utlbS3VjcqWI/X9brD66lH0ukFXVT0tPyqaFqESnZRKixpzciFnX5Zet7/nnW4QEDY8rlZV1b2q1BF6h8LU8zwzSfhx0kkX1113HTabjUWLFlFQUMDSpUtP/KVTnLHpcTTZ3YxNH6QJnyES0QPNQ4U/NOh9UZ/Zgi03BtuIgM6JEOCyB2djdiEg87DLLgXQx6+2ltYzLa//x6/606PoUo8MRnA7qWw1U9UkiI8bQMPgy9rzLR7dG6yJFLWrFFe0kZ/ZgG14L1LbJWFBv+RnTp06lcLCQr773e8yf/58zj///P44bcSycns5e6uaKW9oD2tDEOkDzUNmcH2hQd+KCp2NjqJ0rLzek8FS1eOPHaqGDqMS5gkx3dYj1UhKSgJ1DkFcSuLACujryiuWKIrrVVq1aN17DRODJTuQJ6bfnoTExETeffdd7rvvPj755JP+Om1EUt3soKy+nepmx1BLOS5dFuKNMAIbykHF6wEd/xi1Y7rAcc/TAxGU9dVtPVJVLrNlM3tiJgum5QysgG7KsaisQV/Auayh6/EmC/l5w4lKTGT6iNSu+4eIIavPEURIHtahQ4dI67y2F6AoCo888ghXXnkltbW1Jy0uUkmLs5CTFEVaXHjHxyN9oHlIM7sCswa7ozdG7YS/4X0VSZh5WJ0TMHryCmy5KfqqKkOgv7CknuJKPVv3trljgrWpKraxY7GNEV3f9zWEyEzFExOSwcrLyzvu/smTJ4ck5lRhwZQsWfEGgSE3uIHLNnXZ512B/2QyMNUBeKFnPxDoCZQ3tLO3qqX78LeqgtqPq5r3gYK8JDYcrCUx2tw15K2qHUs4DQE9hf76oz6f6mHFXhmsRx99FEVRuP/++1FVlUcfffSE31EU5bRdAHfIG1LJ4HAio3SKThcI9AT0JcjCYJHJTthyErlt7pieO47mqCFbHHYgx44jfVz6RChCnPiuqaqKoii0t7djNptRe9HrUxQlYle6aGpqIiEhgcbGRuK7W3RTIpEAp36PfiAYyDKLxPvRl/a2VwbrdEMaLEk4EYmN0GASqeUTqbr7m760t+EXIJdIJEGEc/bYcbPxBolwLp/j0R+6w6H8BxNpsAaA060ShSvhfh+WF5byo39uZnlh6XGPC+fpB+FgLMK5fI7H8XT3tu6GQ/kPJr1Kuhg5cmSf33GlKAoHDhwISVSkc6oPfEYK4X4ferPmZDiFjbrTEvRCzbKGIdF4oiSncCrDQI6nu7d193RLhe+VwZo9e/Zp/1LGvnC6VaJwJdzvQ2/WnAwno9udlqAXaoaBxu4Id33d0du6e7plJMuki26QSRfhS7j2lnviRHpPdv9g0pOWcNLYHeGu73RHZgmeJNJghS8vrz9EQ5uLxGgTN547cqjlnJAT6Y2065EMLaei8e1Le3tSi9+6XC727NlDY2MjmtZ1xn93byOWSE6GcA/zdeZEeiPteiRDSySGN/uTkDwsTdO49957ee6552hra+vxuNN94vCp2BsaTE6n8judrjVSGOh7Esr5T8V6MuDzsH7961/zxBNPcO211/KPf/wDIQS/+c1v+Otf/4rNZmPKlCl88MEHIYk/lQinlNNwT/HujnAqv95wMmUcadd6OjDQ9ySU89tyErnx3JGnjLHqKyEZrCVLlnDVVVfxl7/8hfnz5wNQUFDAzTffzIYNG1AUhTVr1vSr0EgknOaHRGKDGE7l1xtOpowj4VojsdNzMgz0PYmEe94TQ1UXQhrDKisr45577gHAYtFfoWG32wEwm81ce+21/OEPf+DXv/51P8mMTMIp5TQSx0rCqfx6w8mUcSRc6+k2fjLQ9yQS7nlPDFVdCMlgpaSk0NLSAkBsbCzx8fEcPHgw6Jj6+sjpyfc34RBn7qwhkh+OSCGUMg6HunIifBrjrXpzcSKDHAnXJDk5hqoDHJLBmjZtGps2bfJ/njt3Lk8//TTTpk1D0zT+9Kc/MWXKlH4TGWmEQ080HDRITsxQ3Ke+GhSfRqBXqfey7p36BHbOBrODEtIY1i233ILD4cDh0F8B/9hjj9HQ0MAFF1zA7NmzaWpq4ve//32/Co0kwiE2HQ4aJCdmKO5TX8fa+qKxqKyB8oZ22pxuWfdOEwZzfLzfJg43Njaydu1aDAYD55xzDsnJyf1x2iFBThyOLGQIqm8MRHn5zlne0E602SgnQp9GnGx9GrSJw4EkJCTwzW9+s79OJ5H0GhmC6hsDMZ4ZGDaUnv3pxWCOj5/0ShdHjx6lvr6e7hy16dOnn8zpJZJeEYkZkD0Rqd6i7x58bcKwiNItiSxCMlgNDQ3cfffdvPrqqzidzi77hRAoihKxK11IIotTKQMy3L3FngzqqXQPJOFLSAbrhhtuYOXKlXznO99h5syZJCQk9Jsgh8PBgw8+yD//+U/q6+ux2Wz86le/4uKLLz7u9x5++GEeeeSRLtstFot/jphEEu6Eu7cY7gb1dCJSvfGTISSDtWrVKu644w6eeuqp/tbDDTfcwLJly7jzzjsZO3YsS5Ys4dJLL+Xjjz/mvPPOO+H3//KXvxAbG+v/bDAY+l2jRNIX+tKwhLunEu4GNZBTvUE/HTsPIU8cHjNmTH9rYePGjSxdupQnnniCu+++G4DrrruOyZMnc8899/D555+f8ByLFy8mNTW137VJJKHSm4YlUhrXcDeogZzqDbqv8xBvNfLy+kNhX3f6g5DnYS1durTbV4qcDMuWLcNgMHDLLbf4t1mtVr7//e/zxRdfUFpaesJzCCFoamrqNglEIhkKejOPKRLXegx3TvW5iL6FcJvs7tOm7oTkYT3wwAM4HA7OPPNMvve975GTk9Nt6G3hwoV9Ou/WrVsZN25cl1z8s846C4Bt27aRm5t73HOMGjWKlpYWYmJiuOKKK/j9739PenrPryCXSAaa3nglkRRqixQiyRsMpK/e9ulUd0IyWEePHmXNmjVs27aNbdu2dXtMKFmCFRUVZGZmdtnu21ZeXt7jd5OSkrj99ts5++yzsVgsfPrppzz77LNs3LiRzZs3H3dCWuCqHaBPZJNI+oveNECR2rhK+p++hjJPp7oTksG66aab2LJlC/fee2+/Zgm2t7f7V38PxGq1+vf3xE9/+tOgz4sWLeKss87immuu4bnnnuN//ud/evzu448/3m2GoUTSH5zqYymS/uV08pj6SkhLM8XExHD33Xf3eyM/efJk0tPT+eijj4K279q1i0mTJvHXv/6VH/7wh306Z2ZmJpMmTeLDDz/s8ZjuPKzc3Fy5NJOkX4iUhAqJZCgY8KWZMjIyBmStwMzMTI4ePdple0VFBQBZWVl9Pmdubi51dXXHPcZisXTr2Ukk/cHpFLIZCmSH4PQhpCzBn//857z44ov+d2L1F1OnTmXv3r1dxpA2bNjg398XhBAcPnyYtLS0/pIokUjCjJXby1lbXM3K7T2PcYczp9ubnE+GkDwsu92OyWRizJgxXHXVVeTm5nbJElQUhbvuuqtP5128eDFPPvkkL7zwgn8elsPh4OWXX2bmzJn+DMEjR47Q1tbG+PHj/d+trq7uYpj+8pe/UF1dzfz580O5TIlEEjFE7jQWOcbZe0Iaw1LVEztmoa4leNVVV7FixQruuusuxowZwyuvvMLGjRv56KOPuOCCCwCYM2cOn3zySdBcq+joaL797W9zxhlnYLVa+eyzz1i6dClTpkxh/fr1REdH91qDfL2IRNJ/DHTILtJDgpGu/2QZ8DGsQ4cOhSSsN/zjH//ggQceCFpL8J133vEbq5645ppr+Pzzz1m+fDl2u528vDzuuece7r///j4ZK4lE0r8MtAcR6WOEka5/MOmzh9Xe3s7999/P3LlzWbBgwUDpGlKkhyWR9B+nuwchOT4D6mFFRUXx/PPPM3HixJAFSsIH2ZhIBhrpQUj6i5CyBAsKCtixY0d/a5EMAXINO4lEEimEZLCefvppli5dyosvvojb7e5vTZJB5FRfIFQikZw6hJQlaLPZqKmpoaqqCovFQnZ2NlFRUcEnVhS2b9/eb0IHEzmGJRlMZFg2fJD3YvAZ8CzB5ORkUlJSyM/PD0mgRCLpQM7DCR/kvQhvQjJYa9eu7WcZEsnpi1zsNHyQ9yK8CSkkeKojQ4ISiUQyOAx4SBDA4/Hwr3/9i3fffZeSkhIA8vLyuPzyy7nmmmu6faGjRCKRSCShEpKH1djYyLx589i0aRNxcXGMGjUK0FfAaGpq4qyzzuKDDz6IWO9EelgSiUQyOPSlvQ0prf3++++nsLCQP//5z1RXV7Nlyxa2bNnCsWPHeOaZZ9i8eTP3339/SOIlEolEIumOkDys7OxsFi9ezB//+Mdu999xxx0sW7bsuK+0D2dOdQ9Lpu5KTnfkMxA+DLiHVVtbe9yU9vHjx5/wpYmSoUOubiE53ZHPQGQSksEaM2YMb7/9do/73377bUaPHh2yKMnAEq6rW/T0Ijv5gjtJfxOuz4Dk+ISUJXjbbbdx++23c+mll3LnnXcybtw4AIqLi/nTn/7E6tWreeaZZ/pVqKT/CNfFSHuatCknc0r6m3B9BiTHJ2SDdezYMX7zm9/wwQcfBO0zmUw8+OCD3Hrrrf0iUHL60NOkzdNxMqccY5FIunJSE4dramr48MMPg+ZhXXTRRaSmpvabwKHgVE+6kIQ/L68/REObi8RoEzeeO3Ko5UQc0uBHDoMycRggNTWV73znOydzColE0g2R7FWGg7GQYeRTk5MyWM3NzZSUlFBfX093jtqJXmsvGXzCoTGRnJhIHmMJB2MRyQZf0jMhGaza2lpuv/12li9fjsfjAUAIgaIoQf/27ZOED+HQmPQX0viGJ+FgLCLZ4Et6JiSDdfPNN7Ny5UruuOMOzj//fJKSZC8mUgiHxqS/CGfjezobU2ksJANFSAZr1apV3HXXXfzud7/rbz2SAeZUakzC2fiGszGVSCKVkAxWdHQ0I0aM6GcpEknfCGfjG87GVCKJVEJa6eLaa69lxYoV/a1FIjllsOUkcuO5I8PWoIaCXHFEMtSE5GEtXryYTz75hPnz53PLLbeQm5vb7fuvpk+fftICJRJJeCDDnJKhJiSDdd555/n/vXr16i77ZZagRHLqIcOckqEmJIP18ssv97cOiUQS5oTzmKHk9CAkg3X99df3tw6JRCLpN07naQWnMiElXQRSUVHB9u3baW1t7Q89EolEctLI912dmoRssN566y3Gjx9PTk4O06dPZ8OGDYC+IO60adNkFmEEIrPAJKcK8n1XpyYhGayVK1eycOFCUlNTeeihh4LWEUxNTSU7O5slS5b0l0bJICF7pZJThVNxWoEkRIP16KOPcsEFF/DZZ5/x4x//uMv+s88+m61bt560OMngInulEokknAnJYO3YsYOrrrqqx/3p6ekcO3YsZFGSwcUXCgRkr1RyXGTYWDKUhGSwoqOjj5tkcfDgQVJSUkIWJRlcZChQ0ls615VINGCRqFmiE5LBmjt3Lq+88gput7vLvsrKSv72t79xySWXnLQ4yeAQqaFA2fAMPp3rSiR2diJRs0QnpHlYjz32GLNmzWLGjBlceeWVKIrCBx98wJo1a3j++ecRQvDQQw/1t1bJABGpE0LlUkGDT+e6EomrX4SbZjlnrPcoortXBfeCnTt38tOf/pSPP/44KEtwzpw5PPvss0yYMKHfRA42TU1NJCQk0NjYSHx8/FDLkfSAfNAlpwIvrz9EQ5uLxGgTN547cqjlDDp9aW9DNlg+6uvr2b9/P5qmMWrUKNLS0oDgNxBHGqeTwSoqa2Dl9nIAFkzJkg2/RDLInO4dr760tyGFBANJSkpixowZ/s9Op5MlS5bw5JNPsnfv3pM9vWSAKSypZ29VCyBkaK0PnO6NjKT/iNSQ/FDQJ4PldDp5++23OXDgAElJSVx++eVkZWUB0NbWxjPPPMPTTz9NZWUlo0ePHhDBkv6lIC+J8oZ2/78lvUOOn0kGE9lB0um1wSovL2fOnDkcOHDAP2YVFRXF22+/jdls5rvf/S5Hjx7lrLPO4s9//jMLFy4cMNGS/kP27kIj3AbuJac2soOk02uDdf/993Po0CHuuecezj//fA4dOsSjjz7KLbfcQk1NDZMmTeJf//oXs2fPHki9EklYIA19+NJbbyRcvJbe6OhLB2kwrmuoyq7XBmv16tXceOONPP744/5tGRkZXHnllVx22WW89dZbqOpJL/4ukUgkJ0VvvZFw8Vp6o6MvHaTBuK6hKrteW5iqqipmzZoVtM33+aabbpLGSiI5TQj3Cdu9nQgfLhPm+1vHYFzXUJVdrz0sj8eD1WoN2ub7nJCQ0L+qJBJJ2LJyezl7q5opb2gPy7Bob72RcAnr9reOwbiuoSq7PmUJHj58mC1btvg/NzY2ArBv3z4SExO7HD99+vSTUyeRSMKU8JhjGS7jUJLBodcTh1VV7XYicHcThH3bPB5P/6gcZE6nicPhzKnaGIX7dZ1IXzjpP91XiTgVGJCJwy+//PJJC5NI+kJ3A7vh1FiGSrgM9vfEifSFSygNTt3pBadCPR8Iem2wrr/++oHU4cfhcPDggw/yz3/+k/r6emw2G7/61a+4+OKLT/jdo0ePctddd7Fq1So0TWPu3Lk89dRTjBo1ahCUS/qb7hqjcG/se0O4NrK+RjLeqjcL4aavO8LJePYnp0I9HwhOei3B/ubqq69m2bJl3HnnnYwdO5YlS5awadMmPv74Y84777wev9fS0sL06dNpbGzk5z//OSaTiaeeegohBNu2bevT+7lkSDB8kT3PgUOG14aWwHU9x2fE0WR3nxb1fFDXEuxPNm7cyNKlS3niiSe4++67AbjuuuuYPHky99xzD59//nmP333uuefYt28fGzdu9K9t+PWvf53Jkyfz+9//nl//+teDcg2SgeVU61F3NsBDaZC78/w6L44MhFWHoS/ltbywlNW7qhifEUdCtDlsrsFH4LqeWYlRJ+w0hEvdGczfDavJU8uWLcNgMHDLLbf4t1mtVr7//e/zxRdfUFpaetzvzpgxI2gh3vHjx/O1r32N//73vwOqO9II93k0pxOdXyYY+Nl3n5YXlg7K/bLlJHLjuSNZs7uK+U+v4+nVxf5GdG9VM4Ul9WH38sOe9HRXx1fvqqK+zcWb28pZuf0oj7+3OyyeAZ/WeKuRcemxjEuP61U49nh1ZzAZzN8NK4O1detWxo0b18UtPOusswDYtm1bt9/TNI2ioiLOPPPMLvvOOussDhw4QHNzc7/rjVTCrdE5nek8ATPws+8+rd5VNaj36/2dVTTbXby/s4qCvKSgRjRcJtv66ElPd3X84onpJEWbyEuJprHdjcOthcUz4NPaZHdz/2UTuf+yib3yVI5XdwaTwfzdsAoJVlRUkJmZ2WW7b1t5eXm336urq8PhcJzwu/n5+d1+3+Fw4HA4/J+bmpr6rD2SCNdB/9ORziHOzp8LS+q5eGK6fzxjMJg/KZ33d1Yxf1J6tyHYcAqj9RQi7q6OLyrIZVFBblCYMxyegVCfxxPVncFiMH83rAxWe3s7Fouly3bfihrt7e09fg8I6bsAjz/+OI888kif9UYqp9o40KnKUN2nOy/O586Lu+/cRQrHK7twq//hpiecCauQYFRUVJCn48Nut/v39/Q9IKTvAtx77700Njb6/443ViaRSCSSoSGsPKzMzEyOHj3aZXtFRQWA/2WRnUlOTsZisfiP68t3QffMuvPOJBKJRBI+hJWHNXXqVPbu3dtlDGnDhg3+/d2hqipnnHEGmzdv7rJvw4YNjBo1iri4uH7XK5FIJJLBI6wM1uLFi/F4PLzwwgv+bQ6Hg5dffpmZM2eSm5sLwJEjR9izZ0+X727atCnIaBUXF7NmzRquvPLKwbkAiUQikQwYYbfSxVVXXcWKFSu46667GDNmDK+88gobN27ko48+4oILLgBgzpw5fPLJJwRKb25uZtq0aTQ3N3P33XdjMpn4wx/+gMfjYdu2baSlpfVag1zpQiKRSAaHiF3pAuAf//gHDzzwQNBagu+8847fWPVEXFwca9eu5a677uJXv/oVmqYxZ84cnnrqqT4ZK4lEIpGEJ2HnYYUDjY2NJCYmUlpaKj0siUQiGUCamprIzc2loaHhhC8DDjsPKxzwrYrhGzOTSCQSycDS3Nx8QoMlPaxu0DSN8vJy4uLiun1ppa9HEIkemNQ++ESqbpDah4JI1Q2haRdC0NzcTFZWFqp6/DxA6WF1g6qq5OTknPC4+Pj4iKtQPqT2wSdSdYPUPhREqm7ou/YTeVY+wiqtXSKRSCSSnpAGSyKRSCQRgTRYIWCxWHjooYcicjknqX3wiVTdILUPBZGqGwZeu0y6kEgkEklEID0siUQikUQE0mBJJBKJJCKQBksikUgkEYE0WBKJRCKJCKTBkkgkEkmvGOocPWmwJEPKUD8AEslg0djYONQSQua1114D6HapusFEGixg69atHDlyJKhCRUpD2tbWNtQSQuLgwYO0tbVht9uHWkqf2b59O/v27aOsrMy/LVLqy1tvvcVtt93GwYMHAX3dzEjgP//5D3Fxcaxfv36opfSZN954g0suuYSnnnqKw4cPD7WcPrF06VJGjx7N1VdfzWeffTbUck5vg7V7927OO+88vva1rzFlyhTOOussli9fjtvtRlGUsG6EiouLKSgo4Ac/+MFQS+kTRUVFXHbZZSxYsICRI0cyZ84c1q9fH9Zl7aOoqIiLL76Yyy+/nIKCAqZMmcKf/vQnf30Jd1avXs23vvUt/vnPf/LOO+8AnHCx0aFm69atzJw5k5tuuonLLrssotbWKy8v57LLLuO6667DbDYTHR1NdHT0UMvqFb5yv/7664mLi8NqteJwOIZaFojTlKqqKjFt2jRxzjnniJdeekm89NJLYtasWSIxMVE89NBDQgghNE0bWpHdoGmaWLZsmRg3bpxQFEUoiiLWrl071LJOiNvtFn/6059EWlqamD17tnjwwQfFbbfdJnJzc8X48ePD+hqcTqd47LHHRGJiopg9e7b485//LP7zn/+IOXPmiPj4ePHGG28MtcTj4qvHhYWFIiUlRURFRYmZM2eKbdu2CSGE8Hg8QymvW9ra2sSNN94oFEURs2fPFm+99Zaoqqoaall94qGHHhITJkwQr776qjhy5MhQy+kVjY2N4rrrrhOKoog5c+aIt956S7z77rvCarWKJ598UgihP8tDxWlrsJYuXSqMRqNYtmyZf1tZWZn49re/LRRFER9++OEQquuZAwcOiMmTJ4uUlBTxq1/9SkycOFHMmjVLuFyuoZZ2XN5//30xatQocdNNN4k9e/b4t69fv14oiiJ++ctfhu01vPvuu2L69OnizjvvFHv37vU/sPv27ROKoojf/e53Ydm56cyyZcvEJZdcIv76178KRVHEfffd57+WcNLvdrvFY489JhRFETfffLOorq7usW6Ek+5Ajhw5ItLT08Udd9zRZXsg4aS/tbVVjB07VowaNUr85S9/ESUlJUIIIQ4ePCiSkpLEwoULh7xzc9oarN/+9rciISHBfwOcTqcQQu+FnnXWWWLy5Mlh2aMrKSkR9913n793/OyzzwpFUcSLL744xMqOzx/+8AcxYcIEcezYMf82h8MhhBBi1qxZ4uKLLxZChNcD7OOzzz4Tv//974O0CyHEihUrxLBhw8Rrr70mhAhP7UJ06NqwYYNISEgQQghx0UUXiczMTLF69eqgY8KFzZs3i3PPPVeMHz/ev+2tt94S119/vbjnnnvESy+95K8/4ci6detEdHS02Lt3rxBCiH/84x9i4sSJYuLEieKKK64Q//73v4dYYTC+dvDzzz8XO3bs8LeHPmbMmCHmzJkj7Hb7kNaVU95g+W5E50J+6qmnRFxcnPj444+FECKop/naa68Ji8Uifv3rX3f73cGiJ+12u93/7+LiYnHJJZeInJwcUVNTM6j6eiJQd6D24uLioP1C6OU+Z84ccd5554n29vbBFdoNPZV5Zz799FMxefJkER8fLx5++GHx1Vdfifr6+qBzDDYn0r5s2TIxZswYIYQQW7duFYqiiOuvv17U1dUd93sDTU+6fZ7gz3/+c3HJJZcIRVHEmDFjRFxcnFAURSxcuFDs2LEj6ByDTU/aN2/eLIxGo1ixYoV46aWXhKqqYvHixeL6668Xw4YNE4qiiJdffnkIFHfQm7quaZrweDzixz/+sUhISPDX8aGqK6eswfKNO3T2PHwFvXr1amGxWMTDDz/s3+a7gZWVleKqq64SaWlpQ9KL60l7T7z22msiKipK3HPPPQOs7Pj0VbfPoE2bNk18+9vf9m8bCnqj3Vc/fvnLXwpFUcTcuXPF9ddfL77//e+LxMRE8Z3vfGew5AZxIu2+Mt24caOIi4sT5eXlQgghvv/97wuLxeLv7be2tg6OYC8nekZLSkrE4sWLhaIo4sILLxTvv/++KCkpEUePHhX/+7//K1RVFVdeeeWgavZxojLfvHmzSE1NFddee62YMmWKeOCBB0Rzc7MQQoiioiIxb948kZKSInbv3j2YsoUQfX9OhRDigQceEIqiiLfffnsAlZ2YU9JgrVu3TkyaNEkoiiIuueQSsWvXLiFE18Zw+vTpYtq0aeKrr77qsv/VV18VRqNR/OUvf+n2u0OtPXDbsWPHxE033SSsVqu/xznYDX9fdAdSWloqYmJixOOPPy6EGJoB3d5q931esWKFeO2110RNTY1/27333itUVRVPPPGEEGLwevx9Kff//ve/Yty4cf5Qd1NTk4iOjhZz584VN954o/je977nN2bhovvVV18VN9xwg1i/fn2Xfddcc41ISEjwN6Lh9oyee+65QlVVkZqaKj7//POgfatWrRLJycnipz/9qRAiPOtLoK5PP/1UKIoi/vvf/x73+IHmlDNYX3zxhRg/frwYMWKEuPLKK4WiKOK3v/1t0KCtr1F86623hKIo4le/+pU/HOXbV1xcLHJycsQtt9wyaJWpN9p74qOPPhLZ2dniW9/61iAoDeZkdK9bt04oiiI++OCDQVDalb5oP95Dum/fPjFmzBgxZcqUoJDtQNJb7T7dn376qYiOjhalpaX+fVdffbUwGAzCZDKJhx56SLS0tISFbp/mxsbGLmOHvuO+/PJLoShKUJQkHLT72pD333/fn8nr86R8EZtjx46J+fPni9zc3LCrL92xY8cOkZSUJH7yk58IIaTB6jd27dolLBaLeP3114UQQpx//vli7NixYv369d0ef+mll4qsrCyxcuVKIURwD3/SpEniuuuuE0IMzg3qq/ZAXS0tLX63/aOPPhJCCPHJJ5+It956K+i4cNHt47nnnhNGo9EfLnG73eLAgQNi8+bNA65biJPTLkRwz/jss88Ws2bNGrQGqLP2Cy644Ljaly5dKvLz80VDQ4P4+OOPxXnnnScMBoOIj48XY8aMEZ9++qkQInzLvHPovrq6WiQmJg5qKLyv2q+55hqhKIr44Q9/KIQQQcZh8eLFYuLEiaKxsXHghYuTq+vHjh0TeXl54mtf+5poamoaaKk9ckoZLJ+xCeyR+Xrwd9xxh79iBDYyJSUlIjY2VsyaNUts2bLFv/3LL78U8fHx4pFHHgkr7d01Jr7r2bNnj5g+fbo444wzxCOPPCJyc3NFSkrKgGY7noxuIYRYsGCBOOecc4QQenjwX//6l5g2bZqYPn26qK2tHTDdJ6u9s9f9wQcfCJPJJO68884BVNxBX7T79H/00UfCbDaLyy+/XBgMBnHuueeKdevWif/+97/+RnWgx2z7s8yfe+45oSiK+Nvf/jaAijsIpX0pLS0V8fHxXaIIO3fuFKNHjxbXXnvtoHSG+6PcFy5cKCZNmiRaWlqkh9VXli5dKn74wx+K3/zmN2LdunX+7YEF6Svo66+/XiQmJoo333wz6By+m7hkyRIxfPhwMXLkSPGnP/1JvPjii2LBggUiNzdXFBUVhaX27igpKRE33HCDPwzxzW9+Myj8E066NU0Tzc3NIjMzU3znO98RH374ofjGN74hFEUR8+fPF2VlZf2mu7+1B1JeXi5WrlwpZs+eLSZOnOgfDw1H7evXrxc2m01MmDBBPPPMM6K0tNT/DJx77rni5ptv7leDNVBlXllZKVasWCFsNpuYPXv2gGTH9mf7snTpUpGZmSmSk5PFzTffLH7961+Lr3/96yIpKWlAQuEDUe6apolf/epXQlEUf7bvUBitiDNYlZWVYt68eSImJkZMnz5dJCUlCYvFIh566CF/ymXnyZBlZWUiNjZWLFy40N+AezyeoAJfu3atOPfcc0VCQoJISUkRNptNfPbZZ2GrvTOffvqpmD9/vlBVVUybNq3XIa2h1L1//34RHR0tpk+fLmJjY0V+fr4/nBnu2teuXStuvvlmsXjxYhEXFyemTJkiNm3aFJbafWEop9Mp1q1bJ7766iu/YfJ9rz+nFAxkmf/oRz8SV199tYiNjRXTp0/3z0cMR+2B7cv69evFvHnzRGJiohg2bJiYNm1akDEJN+3d8dRTTwlFUYIWWxhsIs5gvfLKKyI5OVm8+uqrory8XNTW1oobbrhBxMXFidtuu63L8b4b89hjjwlVVcULL7wQVJEC/93e3i6qqqr6veEZKO2BfPjhh8JsNotnnnkmYnSvWbNGKIoihg0bNiC6B1L7ypUrxZgxY8ScOXPESy+9FDHaB6NXPFBlvmzZMhEbGytmzpw5YGHAgWxfHA6HqK+vF9u3b48I7T58BqyiokIsWbJkQLT3logzWLNnzxazZs0K2tba2iquv/56oSiKePfdd4UQXXsJTqdTjB49WsycOdM/+/zAgQNBMd2BzgYcSO1CDFxKeH/rDhxTe/7557vMqo8U7QcOHBjQOtOf2vfv39+lvkSC7s5lvn379gGd+iDbl+61h8tKKBFjsDwej7Db7WLevHni3HPP9W/3hTsKCwtFQUGBGDVqVJfC7ZzG/stf/lK8/PLLYvr06eKOO+4Y8AmTkap9IHUPdKbRQGof6NTvgdTe1tYWkbojucxl+9J/hKXB2r17t/jpT38qfvKTn4j777/fb/WFEOKKK64Q+fn5/sHtwN7CCy+8IBRFEU899ZQQoqvH4XK5xIwZM4TBYBCKoojMzEzx/vvvS+0RrFtqHxrtkapbah867f1BWBksh8Mh7r77bhEVFSXOPPNMMXbsWKEoihg1apR/7sCyZcuEoijipZde8t8QX+EfPnxYfO1rXxMjR47sMqi8ZcsWcf/994vY2FgRFxcnnn76aak9gnVL7bK+SO2Rob0/CRuD1dzcLO677z4xatQo8dvf/lYUFxcLj8cjPvzwQ5GVlSXOP/980dbWJtxut5gyZYq44IILxOHDh7uc5+GHHxaJiYn+eK0Q+o25/fbb/Yt9+iapnu7aI1W31D402iNVt9Q+dNr7m7AxWIcOHRIjR44UP/zhD0VDQ0PQvh/+8IciLS3Nv/rBP//5T6EoivjDH/7gj7H6eg1bt24VqqqKFStWCCE64rgbN270r5sltUe2bqld1hepPTK09zdhY7A0TRMvvPBC0DZf9th///tfYTQa/etxNTQ0iIULF4qMjIwuE942btwoFEURr7zyyuAIF5GrPVJ1CyG1CyHrS1+Q2odGe38TNgZLiA6L33lA8IknnhAGgyHoTbWlpaUiPT1dTJo0yT84ePToUXH77beLvLw8UVlZOXjCReRqj1TdQkjtsr70Dal9aLT3J2FlsDrjGzj86U9/KjIyMvy9Ct9N++CDD8T06dOFoihi6tSp4uyzzxYmk0k88sgjwu12D+ncgUjVHqm6pXZZX6T2yNB+MihCCEGYc+aZZzJixAiWLVuGx+PBYDD499XU1PD3v/+dAwcO0NTUxE9/+lPOPvvsIVQbTKRqj1TdILUPBZGqG6T2iGKoLeaJOHbsmIiKivK/GE8IvXfhe613OBOp2iNVtxBS+1AQqbqFkNojDXWoDeaJ2LFjB3a7nRkzZgBQWVnJv//9b+bNm0d1dfUQqzs+kao9UnWD1D4URKpukNojjbA1WMIbqdy0aRMJCQlkZWWxdu1abrvtNm666SaEEKiq6j8unIhU7ZGqG6T2oSBSdYPUHrEMnjMXGgsXLhSjR48WN998s4iLixNjx44Vq1atGmpZvSJStUeqbiGk9qEgUnULIbVHGmFtsNrb28XUqVOFoigiPj7evw5WJBCp2iNVtxBS+1AQqbqFkNojkbDPEvzlL3+Joig88sgjWCyWoZbTJyJVe6TqBql9KIhU3SC1Rxphb7A0TUNVw3ao7bhEqvZI1Q1S+1AQqbpBao80wt5gSSQSiUQCYZwlKJFIJBJJINJgSSQSiSQikAZLIpFIJBGBNFgSiUQiiQikwZJIJBJJRCANlkQikUgiAmmwJBKJRBIRSIMlkUgkkohAGiyJRCKRRATSYEkkEokkIpAGSyKRSCQRwf8HAAPqzgEGPYIAAAAASUVORK5CYII=", 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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 07/58] 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 08/58] 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 09/58] 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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0IRms++67jwkTJvDll1/S3NwcZLAA5s6dyyuvvNIvAiOVorIGVu+qQhN6qG5PZfMJK6hHEyjojaRHE7jcgqKyhl4/eE12N41tLpyawOl2suTzw0OesHHJxGG8tqkMDdhxtAmLUcXpDf0JIMFqpKiskWizgYY2NyW1ejJPtNkYUihz5fZy1u2tprS+jViLkXNGp3LTeSODjFi81cieymaqmx1B381JiqahrQnh1WZUFQQCq9HAqp2VxFuNPZanyaDgcGuYDN0nI7302SE2Ha7jjS1mHvvWGQBBBqyn6/Q1qMsKy0iPt3KguoXRabG4PAIBlNXbMTY5qW91MiW34zx7Kpv1i/CiAIlRJlocbtwejTV7jh3Xg69qsuP2aFQ12Xso6dDoqbH3hbO/OFCDEJAUYyY+yoTDpWFQQVFgVGoMFqOB8oZWaludeDTB6l1V/vB3fZuLneWNTMqK99edE4XlLp6YzupdVYzPiOt1qHTl9nLW7DmG26Px6b4abj5/ZJdyXF5YSkVjOy4N0DTe2lbOlwdrsJiMtDvdJEWbKa1r67VhHUgKS+oprtQ7DFaTgfX7a2hzeXB7NOKtJt7fWcWFE9LDIrwZksHatGkTjz/+OBaLhZaWrj3K7OxsKisrT1pcJLNyezmHalopq29DURTqWp1kJUYd92ZPzk7gy4N1IAQGVWHMsJheN9pJ0SY+2VuNQCCE/oC3OYd+WsG4jHhSYs1UtzgRgFvTUFW9AUXoYVOjqtDu0ht7u8vDjqONON0aiwtyQvrNmhYndqdGm9PJx8XHAKhvc1JW347d5cHu8qAJQavDgwJ4hMBqMlCQl8QF41L5bJ/eaLo0QWKUiYY2F/uPtfC3Tw8xNj2u2/thMRqItxopqW3j6he+6DK2dKC6hZpmB8ea7Nzw0kbOyElgam7SCe+vr0Ftd3nYdLiOhnYXZfXttDnduNwCDfC4Ncoa7Pz2/T3srWzi8UVTgA5vEXTvpLrZgeatG5qwBzX2nRuixQU5fs+uPyksqWdvVQvNdqe/wQbd065uduB1CGl2tDMuXSXGYiTabCArMYr0eCtH6lqpb3X6vfMtR+r5/apixqXHUdVkJ8Fq4lBNW5Cn1tDm6tFrX1SQy9j0uD5lHVY3OyhvaNfrrAqPvL2TT/fVBHm4q3dVYVJVoKNzVtnkRFWcqAp4NIixGFm5vXxIDEDnMeRNh+uoaXGiaRotTg8mVSEtzoLRoBJtVrl/xVc43RqrdlZy76UThsxohWSwTCYTmtbzAPnRo0eJjY0NWdSpwN6qZkrr2/F4BGajQk2Lgze2lLG9tCGoYgfy80vyWbm9nC8P1uJ0a6iK0uvxp8O1bSiA2wMGRQ9jRZsNLC8s7VVobKAGiOOtRuKjTDS2O3F5dF0GRcHu1nB7W1SXRxBjUdA0aHa40TTB9tIGtpTU8+qGEh7+xuRe9ZZB77XvrWpm3d4aABrb3by/oxIUcHsEqiLwaKAqCh4h9AYccLg97K1q5nJbFjuONtHqcBNjMeDyaHg0QXWLE7cmeOmzQ92GyGaOTGZPZTMtdjdbjjTQ4nAHGazkGDMCcGnQ0O5ix9FGxqXH0eZ098qLbra7dO/IrdHu9CDQDY9Kh2GyuzRWFlVw9cw8FkzJ4suDtVQ12nG4PTg8AodL071HAe0uDU2IHhvqselxvL29nD+v2U9pXVu/hISKyhrYXtpAWX0bDW0ualuc3PXaNmpaHLQ5PH5jBWBQweEWzBqVwrbSBuKsJjYcqsWgqmQkWKhtdYHbQ22Lk02H67EYDcRZjZQ3tGNQFT7dV8Oiglwa25y8ua2cdqcbs9EQFOL0Ndq+5y05xsV3Zw4P0ttdfatvc2L3he41cDk8vLWtnI/3HGPu+GGcPzYVq8mA0aDQYbJ0fPXN5/7urWrmR//cfNLTV/oaCn/ps0N8vOcYqqpHM8ob7IEOOU6PoK7VidWkUtVkR0XvwB2pa+P+FV9xwzkjeHt7OSW1bZw9KplxGfGD4n2FlHQxa9Ysli1b1u2+1tZWXn75ZWbPnt3n87a0tPDQQw8xf/58kpOTURSFJUuW9Pr7DQ0N3HLLLaSlpRETE8PcuXPZsmVLn3X0B063RrzViNWkkhJrITHaTHlDO/+3o5L7V3zVZTDTV+EWTMli1qgUMhKsTM5O6HUFSI+3YjKqmAyK//+ZCVZW76rq1YB+XwaI+zIg22R3kxBlAhS/ZzUs3kpWghWTAX/4rcXuRlUVDApYTSptLg2HR1BU1sjj7+3msXd3sXJ7ub+3fLzfL6tvRw2IzDncGnaXbiCdHjCoCm5NN1YGRdfk8kBlk4OV28vJS4nCZFCIMhuwmgwB5/H0GCJLiDYTbzXh0gSeQNfGW151rU7MBjXggesIffquZ3lhaZfrWr2ritL6dvZUNNPu9OD2NnhC6I1fp58i1ttrBxidFkt6gpXUOCuJUSZM3vJXAE0TfLK3mi1HGthZ3tilY7RyezmbDtdT3tDOi58d6pekBF/YDnSDdLShnQPVrTS2u3F5w+FGVf+zGFUsRt3jHp8RR7PdhdmgUt/qpL7NxbA4C26PXncMit7wmwwqZqOKqihsK22gqKzB34mobnFS1+oIun8vfXaIf35Rwo6jTeyramFvVQv7qpqD9BZXNvPcx/uD7onTrWExBod+BdBod/PBzkp+/d5uPj9QCygYDYq/zJWAYzUhqGqys+lQHV8dbewxeaYvZdvgHYvzPe/HSyg5UN1Co91NfZubo52MlY92l0Z9mxu7S6PNpeH2CNpdGrvKm3hu7QG2lTZS2Wjn3aIK1hZXnzCRpz8IyWA98sgjbN68mcsuu4z/+7//A2D79u28+OKLFBQUUF1dzQMPPNDn89bU1PDoo4+ye/dupkyZ0qfvaprGZZddxr///W9uv/12fve733Hs2DHmzJnDvn37+qzlZFlckMPU3ETmjk/nm1OzOSM7AZdH81fUzgYkMFNswZQs5uQPY8GUrF7/3k3njWRu/jCSY83EW40kRZsprmrhWLOdjYdqT5jldqKkgUD6YtwK8pL8161p+sooOUlRRJmNTBuepI9PAB4BJoNKu9NDQ5sbVcFrdBR2VTTpmW1F5bxTpPeIiyubuzXChSX11LQ4MKgKBvTGz/cbPjzexV0Ught8IaCp3UVBXjJRZiO1LU5MBpUYiwFV0T0Yh9vTraGMtxqpa3N4x1sEyTFm/3GFJfU0tbtwaYL4KCOJUSbGDIuhutlBcWWT9//N/HnN/i4ZfEnRJvZXNWN3azg9ujH0eI2WqnRcl0EBq1GlrlUfu7zx5U3srmiizekhIcqI1WRgWEIUw5OjMBsUFK8Hc6zZwcGaVlZuL6eorMHfGdlb1YwQwv+bywrLTnivT0RBXhJJ0SbsLg9N7S4CZzEoQHyUkdFpsViMKu1OjX1VrXy6rwaryUB6vJWGNidtTg9tTg+VjXaizHpnQlUUDKpCbYuDNoebhjYnje0uXvrsEBdPTEcTgmiTilFRSI+3+u9LVZMdl0f3OjWgvtXJQ2/v5OnVxRSVNVDe0E5JbSuJ0eagTtLighzSE6JIiTHReciy3aVR1+qiutlBi91FnNWk3/Noo9/jQkCrw0NVkwOHW6Oi0Y4mxEll5fmyLMdnxFFc2US81RiUUNK5k5kcY+72PGaDgtkQ/Lz4Da53oybgSF0r7Q6X/myjRwAGg5BCgjNnzuS9997j1ltv5brrrgPg5z//OQCjR4/mvffew2az9fm8mZmZVFRUkJGRwebNm5kxY0avv7ts2TI+//xzXn/9dRYvXgzAVVddxbhx43jooYf497//3Wc9J8PY9DhvEoSTDYdqMRtV0uKsVDS009juYm9lU9DxgZlixxuEPx5VTXbirSbMRpVYi5G6VifFVboBOtbs6DHxw+cBtLs8PSYNdP4dt0f0akDelpPIrFEpON0eSmrb8GiCHUebGDMslsZ2FzmJViqbnCREGUmPt1DT4tB7ewImZ8dT1WSnvtVJk92NxaBQ1+Ikxmyg2e7udtJoQV4So9NiOFzTRpxVT+RweTQcQtM9KhX/GJ8m9EbftziZQYVvTs1iwZQsVhaVY1QVrCYDigLN3vGuvVUt3Y47NNndZCVEcbi2DVWB3RXNPP7ebu69dAIFeUm88vlhYi0GXG6NhBgTVU0Ov/dmNRnYWd5IdYuDNqc7yNupb3NhNqq4HB3jkQpgNChEm1TcmkBVdI/R4db8Bri21Ynd5ebMEcmMS4/zzsdqw2JU+da0bF7bXEplo17W7U4PSzceYXlhGSNSo4mzmqlrdRJtNuLW9FCo2RjaDJjAlVs2HKrjSF0b1c0OXJ7gPr0CRJkMNNlduufo3d7Q5uTLg7VMzU0ERQU8aAJyk6OwuwV1LQ4cHg23R6PdpRFlNmJ3641nVZOdRQW5lNa1scIbFvxkbzUbDtVy9yX5LC7I4UhdG0cb9HqsAW0ODy+sO8jh2jasJgMtDjclta043B7W7HH7Q4p7KpspLKnD7mrG7tKCwpm+fzo1QV5KFAV5yVQ3O/jqaCPN7S4a7S7cno6Oh8Wo8pV3HmKoE+f3VenzBlscbjIToli9q4qkaBOHvSG7zlmf49Lj2Haknka7NwMTiPbWz2iLiWFxFn1MURO0ON0ogEFRcHn0cVOXtzoaFP16XR6tTx3sUAl54vCFF15IcXEx27ZtY9++fWiaxujRoykoKAh52SaLxUJGRkZI3122bBnp6eksXLjQvy0tLY2rrrqKf/3rXzgcDiwWS0jnDgWfx1Rc2YzZaKCsvo2EKCNVTQoxJiNbSxu7xMh9FTWU8aTCknoc3nGhC0alEGM28Pf1h7A7PaiqQmWTnTV79ASEwHMWlTXw+Hu7aWzX57dYjCee/D1zZDLv76xi5sjkXuldMCWLrMQo/v7ZQaoaHTjdHhrbXUzNTSTOaqS0Tp/UanfpPefqFidGb0wvzmqipsWJEGB362GjNqeHGKvodtKoLSeRBVOyaWhzsfFQLXWtjbR7M82MCmgoKIqemBJjUnF69MYmO8nKXReN868QMmtkClVNdtLjrWw6XOfP3mxxePxTBgLHHArykihvaMd8sJay+nYa2pwcqBas3F7O/ZdN5CcXjuFvnx6i3amnpltMBvZWNZOZEEVStJlmuxurUSXabAx68C+emM6+Yy168g4KTo9+LWdkx/PwNyYDevjunaJy6lqcOAJaTpdHkBRt5v7LJgZNJG51eshMsFLb4sTlEZgMCs0OD+Ch7kgjw+LMTMiMZ3RaLJ/uq8bgvRehTDj2rWCy42gjLXY3Tm92YxcUXW9WopV4q4mSmlbcQmAyqCTHmEmLszA1N4GtJfUkxZi5YJwegfjJf7bS0OqgptlBtMVIu8tDlElFVfQQ7mPv7qK62YFRVWhzevR5ig6FZYVl/OeWs2my62OcW4/o3rrTI3Brgk/3VRNnNWExqphjVA7VttLU5qKysZ07l27l/LGplNa1UdvioqHNicPtwekRQR674r0H9182Eeh4TtYWH2PDwTpUoYHQPctos5Fgv6ZvLPn8MHsrm1FVfbw8MyGKxnYXCVEm/rL2AA6PQAUa2lwUlTX4n8l/fVnC4ZpWVFVB0wRWswHN20GJMhuwuzwkRplJiDJxtKEN/4Pgu0YF3G6NktpW9lU1D/gY1kmvdDF16lSmTp3aD1JOjq1btzJ9+vSghXkBzjrrLF544QX27t3LGWecMfBCHtZXRYgHNgDjx6ykNTaKJGEkrXQzqaPPZdPheoTQuOu1bTjcGiu3H2VYnJWLDyxj0c/uo/BgDQ3tbgp3H8WW2LtbVHBwG+V5+sD4gilZPPfxfswGFbuqYVDA7vRwpK6VNXuOBXlZhSX1OFweopRajJZ00qMMLP/yIE0epaNxqquDxES9dlZUkFBfzbxEDwn11bDfwMP/KuAr138QQmAxquRnxPkTJXg4AdvDjdhyEnljSxmNbcXEKnncfHYuTU59nI+dO6kePoaqJjvThifx2f5qXB5BeYOdPLNbD0UEeEEWk8qwOL3zUVTWQOHhuqCGtGB4IoUl9Syens2eiiZMBnBrgjiLgVan3kOPMumD9E6XRmK0ifR4K00Hj1DkzZrLS4khzfsbY4bFYndpNLQ58Qioa3Xy5KrioIxBW7IZ22UTWV5YyhOrirG7FHB1eKCLVk5m7JVFrPy/zXyZMoyqJjsej+4FHK5pJUpzYVCNXDFVN1Y+47BoahZNKyfzvvE89nIGKcbhpDOMgrgEbAkGMJmwXTyGGFWwYnsFHk0fazjW1oqqfMqB6o4OXGldG8e8K1/sq2pBoZXE6HisJgMVjR0p/vVtLhCCp6+awmPv7mJvVTNlta28+Ml+3ogx89jFI7FN7EhOOB7Vje2UVTfjcDtxejz+MSd9yoD+fwVItJiZMSKR88em0mR3s7eyic8P1pESbaTg2KUs+EYJtpxEfV7ayjWMzxiLLSeROy4cw59X7aGipZW6VgdR5hYSLem0O9wcq2ul0OWirtmBcNdgUFIxqKCqiu4xejwUrH4NLv42Z49K5vXCMv2+CEFdq4vaVhcGBRrbndS1uvxz2t7c9hWf7qnje+d+ndzkaL/3dLSh3Z+MAZBgOcDotPP9n205ieyrasbp1kiMNlLb4iLRXE2UMYcEi4qzvILxU0J8z6DHAzSjaVDfCg1tKskGM81ch8OjR5c09Lr7+1XFvLJ4ArayjcRPHMNzm9to8wiGJ0dRWfcsIxJuwWkx0tTuJjHaQE5SFFVNDuKtJupanf6fTIo2MTI1mj2VRzHRzuqN1gGfRtMvSzOFAxUVFVxwwQVdtmdmZgJQXl7eo8FyOBw4HB0PbFNTU7fH9QUN+BrA/gVcOeI7FJUupRhIOAh2/oc9jZOocejeTF2rk+hjl+lhkD/8hQIuoBhBPuWw3nqcX/H1yHZgA2wHgId1z81qMmA2qoxKjdHng3g8uDVwuDz+sZ/CknriLQZmVK/iav7I245Z5O7/ktIDUDn8HQBsahu8UABMhQlzYPcHxNOMkSaiiYdde7kauNpzNb9gJG7nY+wq18eXbC/m6fIeToCHG7mh9iU05Q1UYNGRuyF3Eq+/u5px/JsRe+CT2P9wpC2WtFgr1S0OYj1fMrv9SbLNs3jXcSsGxYSqKP5w4po9x/hqy1dMTFUxVcdiU/SJmTYFbCMAnOwa1sT6qjpyDBZqPS7qTE5cmiBJNXHTtFGAygeFpdgrXMRW/JzinVDwgxIKS+ppc7qJNhuZkz+Mn1+Sz/0rvmLHUX2eVk2zPsHZlhED1ceg8F8wchZN9uFkJUThcTRwOS8xfufZsOlsvRxetzEOcLTcwQpxDnFKJS6nHTMefiTuxwp8VPIerU6PPg+tuALbi3nEA4v5DPis49YfAH4PkAZkkI2FHxNPMxaOYmKC5W0Adte+BC1HKSypJzHa7J80nBxjYXHbt2nTYvgs6t80trn0xlYBo+KgpLSSov2lLMiP411nMyVlDbR5XHicTRT/9z5so+fA9x474XMw3H6IyvYqnE47CfhCfyoTLPEkRRtJijNz4cRh2IYler+hv5boR2u3kCLszHH8PzIBXsyDc/9My/qf8zWcNK2cAQUfsqggl5aVt3HEWEKiu4REBb5w3Aa04nJ7iKs3sYCXiQWe4xfUmWdgMaiI2mMU/W8yNoDVj1NMAtnMolqZjdOTCuhZzh4BVU3OIK/wCX4CTqhb/xCJLIL4y5iRPQKPJvzjc9cYvsM4QN2jQM1+SE0FOuZL1rW6iDF/xm3iWY6050JrKXkClqycw5LP7+aGc0b0qfG/IbGQjQ0l1GgO3G49XpdmeJ2ZGsB3+QV/BlIQ6Akq/EE/91jgR76T1EE+UNy4Ghr1DncRj1LWNhZjbAzxUSbivdM8Yi0G4qNMxB07zA/V3xJHKwlVM4E5vdYcCqeMwWpvb+825Ge1Wv37e+Lxxx/nkUce6Vc9+UCx9/8c1o0VQLoGC9TfkOUezZfiIioZg8OdyljvGGgRYGMdfR0BLPL93sMJrJzxBVuP1NNqdzMuPQ4Q7K9uxaQojB4WS7zVyOPv7cbh1ohTPIzmjwB8w/mlf/DAeeRyCgo2wws/B9qAz2H354B+iBXQCJ5r9wSH2MZ3edvwmj7RFljk2/lwQse/AXY+CTuHkc8xigEzcFHL1RhGvMie1IlUtzip2vMkWUAWX5JmaWYlt+NWktlb1eJN7W5irPEdjFUGbJYMli99j7KWZnJi41g0Rg9Xjq+tZjwaKCr5w2N550glHoNgGEa0wu3kZ8Wi0UI7r/rvm+3FmdgeLu4S6rzhnBHct+IrnG6BJgTVje3gaIZ178HuJRRt/ojyM37D8ORoLMfWMYH1aG27vfcUfx0Yw59IVCxMdm0hxuTGhIavW5JZdilrGt5kWLyVxZYGf3n78KVJdySZVwPV/vo2A92793EFwJPZFIx8EkbO9adtr3zxp+jB91aqGx6lxHAXuXExtDjcmDzHyKGepcuP8Z3zsij+aj8GRzspQKrhXWAHRQd2YFt3EVwwt0td9NPSwkWplRQf+Qoz+uC8AsSpJs5PyuLKicMABziboVNOxwRxEEGTbqy812Zb/xOmef8dzya/FzqNdUQrgEk/9ms8h0Y04CQwzPYtnmCleJZabRijW7fp5/Tfl0au5AMWax+wFwu7mcleJtOmpNMqMoFob+l7O7MG0GvYcsY1LYemOEZzMaVMJHnMFLK9+UgaAp4ZTZHlFgqnXsv4jDgO17aRkxTF1JZniVVgIqXe+CFc517LxtqtLFvzgj89HTj+EEFrK4vUXWhUckBtw6XoUYkEAO9Y0xP8hP9R7iXKPJVxyVao6ChXH/mdPs8EZvIgbg/salSp5TpmjJlPuSmFNaWt1Le5qOK3XM4RABbxXne1oF85ZQxWVFRUkJfkw263+/f3xL333svPfvYz/+empiZyc0N0bR9uBMD28I+x8S//Zn9l8AAKFBgPMM5zgO1iGhXKZNKEvt33EPWV4oD/K5vOplp7DQWoanIwITOBykYHdpeHTYfqqGxsp9WpgRDU26uZCOAmKIQ+0g1vrvgJ+/gi2NDQ0Wiq6IYycONUYKrj2/zy4OWkWPUeXOfr8RtXjvn3FQPjAdvhH0D27ykaO5+Vezq+M4GdqDzLO65v0irGITAhxF7GaJvJM5jgSBrV1KDh5kCjxj+3RTGFJG+5O8h3W7CVmQEnxTj0C3arFB9Rycfi71zoeirh9dexXXllUAPRZHczZlgcu8ubEAJW7TnC38zl3Lz7DaCcYsqJ2/sFWRdcSuOer7zfqvPfU18diALO5x/km5qwoGAISNadCKypX87WxksxG1axiODOT091w+b9K+pmO4Dt0N3YDp0JGX+CcZOw8SYArwMzKaSZDzkiLiM/L5mEg5tJYT8pjii2f7Qbo7OW4d7MlBS+8t8v25pb4axtYO0hClBbC1VbGUkFpcTq4UADWFBR7SrUd31efcwd7ibjiIt2b3l1vs7XgZLVa9gwegoXd/qubuDbupwzG7iYNRxN/Bbpddv8Rt9XviqgWSDf7iCfdbis6zhKPEddkygTmdjNY2nQSv1pc4GdBxvNvM4bJPIGHIoL7rACxY4XiN3wMsaE/+F3X/8OhbUuClZ33LNiBb01VuEsGsls/TZvrX6UN1KmE2sxkpcSo19/dwZr/344UgJUYiYHTLrlbvWtJmYCDPAbHuc/3E9JRSqvA1dCF50+AuePGQEbGrCE/P1LKCCdVM6giql85TVWAC8DBb1cmSdUThmD5csw7IxvW1ZWzxksFoul/xMyHn6Woof/FdTQFIO/FwgQZ4bzXFupEvtIcwInISGw4uUDbvV/WBPzFNNyE/TMNVXRB+Q9gn1VrSREm7CaVKZSTzZ01AQT4AIETOALKtAbh8DGUgv4v89QrjPCBd7vAfyWd8AOxVbYB0FGL9C42gL+/Kz/BcXrN9I5/SOfHViMrWxhAbVMocS1hTyT7uXtpJRoQHGAqoIwQDFlXEl8x7m1wIbdoRsyLNiwdDUEO38A8+dDXJx/U0FeEhsO1lLtXZXB4Wpn/Y5SbuZz/z3A8Raq+BqvsYXvB5wu0JDojUMP825ccLnnn7wqcig1rIOg8pkAHMXfy++Gzj3mYDbDv8+Bqfd10TSDJYxqzyA961Kmu+xQXkOxRwOiyDO3U2t36ann3p6Kfu4KePV/4fs9hAbLyig+WkE2TVRZUvzp0ZlEoXnMYO25E2nLjMKWmUzRhu4b1Hyggoexq8uDPFB0eYwNKIvAulvEej5vzCKFjikZgfVvOaBZAaE/CiNoYoTpCwBqiENVOxJ9NPRGP1CTrrW5a50GwIW18X+x/Xc9tgW3Bv1+MRBY4XNdsFA8yBvH5rFVfIeisij2VsVR3tDeNdP3wAFw7yKfViAHFRPF1GASBLU3AFc7HuOvwgIqvG4GFStXonfqXw84TiMDOkVQfFqhChtVwId6eXn3NcCAvxki5NXaw42pU6eyZcuWLitwbNiwgejoaMaNGzfomnZ7/x/44EBAoSuAGdJFiz8kECo29IfH96A8yCHWxn3AuIx44qx6rTV5f1gD2px6+msyh7vaSRPeBQ3BtzDP8RtCOKym8oxlQdcdDro0KPmd/t8VjXxeY3SnrSowgkPcyyfMZR0zTev8+wwaGB0Qr0CMpvfK83FRRC2vU0sRtRDwZ6OFK3Fho8W7rRt+H7yihS0nkdvmjuGc0SnEWw1EmZvID3iodS9qAxs+/JjOCffFBBvq4zECuFB5m+kcCPaYZn0Xpn6forgreJ1ZFDEFOpVSYNnqjUty1x/Y9usuOhKATJZyQ8JhKC2k2LOZfMrIp5XxtJNrrSXFVEWC2nENRQClz8Durd1fSG0t+dRi5RgxmBlBHOckZTM8Kp78zHj0XlLPf0W1TgKcbIrQG1VfiHUOTYxv3dWlEdPoeB6uxOr3YvQGuZIUz07gAMXebfo5k4FkFuGd3N1Nwl4qzSQTWMbT0ccQdQKfweOirYG3vtPDPvR2QIMoAde4PmCe+1E010F2VzTzxpajXec/Hj4MtHo/2BmLhzTcpHiNlc+TwntdP/I9lBps5+cw5R6InUc+Zu91XUr+vBch9f/p//Zfb1fGBvy7DQb8zRAR6WFVVFTQ2NjI6NGjMXnd38WLF7Ns2TLeeOMN/zysmpoaXn/9dRYsWDCoKe0+JjCLYr703+jAcEYQFvRwuwqN3Z5p4gl+qRY91qgCxzo2V/6FgiaFDZmLmDEiWV8uqq4Nj9AnjZbWtTGWbd2f0muwfL2+rpV1uHf7EW9DfQOFF1/LO6szuVx7Qb+eHtDLIZruwjaBx0BwKMwXStzHdsZQjuozhXZoQU9iVIAYi+8cibxOg/88NhJ7FuU9Lphq+NuDcPOjHbpyEnn6O9MoKmvg9y+WonGUf+AzkPrv5LC6WxPYJUzkv7Y8Xqek40ATzGInezQoVgMawEkXg+ak2L6Lpvpmip212MYYoeYItNVBVQ02yrEFhGm46FH48BkIavq7hg4BYjgMm5ZT7E3uKOYQV6JiA5bTSpuhIahz4w9fv3Y//HI5dA67V1djo55impliMEO0yqKxHR4r4vg9tOKqmqDf6vy7NsBm2gIEP1MqgVGBGMAe1FlQ2RfUidJjMJlejzsWjTr0GuXTid+A+eqiDeDchdBQBVUlULML2N/1IpKvgrqt/t/sMK6egNB4R91wqWAKiFQAnMchhisPs1K7hmbPRV3nPzbtCCgjjR20EIXV/wj6ykIFNDMQ8PUpl3wTxsaBrQJb6XxsDUcg/1IYbcM2ZSq01UPZ97Ed+BK+WgoEr5oReF+ymBIeae2qqoY0t8rj6fviq8888wwNDQ2Ul+uz/VeuXElZmT4i+5Of/ISEhATuvfdeXnnlFQ4dOsSIESMA3WDNmjWLG2+8kV27dpGamspzzz2Hx+Pp94SK3lBU1kCh+WoKnF926nHlkk8uxWwi3+tSFQPezo3e0MUvgqbl+oaYb8Kokcf/MUc74ISYZNj6IbDdv8vW9hy3NcRQOP0a5k1K59UNJXxV1oTRoOiTS5VD/t8OWvcs4CEFPawXaER0P8CAzZgFCGznnIvt3JEU5N3P4y9GsdD6R/3BUDs8zSAmXg+71gE7AzbGExju6hxW8T0cusZqRoF/UNmJPglYNel9zTgsgJF8YinGRT4mQuqfHf0j7FwIk6Z2Wa9tLyVMcFdSpsFYc6Ax+qingF8P12boGOP09ooVJ0wIiokaYNgI8LjIt0VTdLic/IwYSLNCe6tusJqPQcNRqD8KNRWQPgrGXwBGK7z/d+AL/9kCGxpfWKsIeP3YW/7tgZ2URcTwOh6CWjv0ENoi1sPKZ+Gqu4MvsrwcqNKvTTGTnxQN7t63I/lJcRQ3B2vpEh4sed2/3z8OFXBssbdsA8ddtU6NbjOQzkhU7BTTRD7JFFOHih2NY12MlZ/M8ZCUCxljoOkMqDoARw4CGzuOOesKaJvBok3vQ/tHQb/b2eNW0W9/pQIZBvRxZQAFhpud/JiX+Zg91FXexGPvxLNgarbXQBT79W3V0zyowuH3rfcCrtxnmFx6u36kiv58q7Bo1lgQGiQkQ9pIcLRCQioYTPqAoykdYpIpMuWy05PPJHEU2+6PgbVB5Q5QMKEHr7Ef6dUT/OCDD3YxWCtWrGDnzp3MmzeP/Hy9Cu3Zs4dVq1YxefJkrrjiipAEPfnkk5SUdPQ233jjDd544w0Arr32WhISErr9nsFg4L333uMXv/gFf/rTn2hvb2fGjBksWbLEr28wKSypp2HGORSvD/YO8snFdtbXsFVkQWkhcKir13XRNfBOHThb4OyvQ8wJFhIWGiAgJhOS8mDNo0BH79RW+wS2uhSKpl3NginZXDA2jaWbS6lucpAsOprWoNCdFXB4/0ygBcRd8kmiOHma3mAa6vVBo7zhoGnYchK59wc/4/EXTSy0PAken5IrwDvQD8AZl0NsGmxJAfdOiJoBE2yw5Xc9XGQG+VR2aZTwPtguYUQREKUYiCGWRd6gXPdjCd1RE/DvFILChK9/HbJ2UVjSQEObi1U7K3G4NdqoJMldQnunBq0YCApAByTUQHeJNfFBSRP+DozXGOshsPPAZAGzFVt+DLZxI0Bzg9sFbic4s8HjApcDWuuhvhQSciE2BcacC5cZYFUUuNb4tXZu/LsPVXYs4ZNPHMVU6x+83rf/Pux6Ag5fDiPGd3zV401AAmyTU/RtWu9eZgpgS47CdqTrdp9OvQzbOn6DbqIXZFMcEHXwJUkE1qMFDIepMyjeX0d+igdblB1baz20NkJdI3rcowmopQhXh2eclAuONmhPhOhESM2D3ClQNQH2F0JMGuSOB20URKfCnmw4/DY+b75zVp4+JjaN181QwS4yDQ7daAWMQ831fEGx6xirNn2DL/dewGNXTcfG4YDySKCIRpIw+5NVBDD5/Nns/vf3Ufm7fuHe57movAlbdoK+ikiMEaJidUOlGgADKBoYjGyphca4cbgMI7HdPA/K9sGnb2BreRMbApgJF36j+xvZj/TKYD388MNBn1944QWOHTvGjh07uhiD3bt3c+GFFx43yeF4HD58+ITHLFmypNtFcZOSknjxxRe7vJ9rKPAttZTPWGBfQGOVii01T+/1RsXD3gRgO/kIioGvA6QNh0tv1j2nvOlg7n7dryA0AZZoiE8Fx92w/n+C9xf+D0Vbamg4+yoSY8wssGWxameFP/Qd2IP1o6+Eo8fULR1jB7b4q7B9/ev6Ma01+qTF+HhABBitn/CbF6OYaHyL9FGPwTfOgKff9J74EsiZCHEJYE2AqnGQdyYkZMOhXVD/TtfrS5+GreoLbN6H3d+wo2uzexLRPAIXRmYwjJPKYMm8CCpeC9jQBi/+jIKrn6KwpB6zUWVvVQuJVJBsbfB3hH1GKKgh0jjhAgaBoSEbujergT8cqxu40YDwriuF3sCoZr0nbLKANQZcLhBuiE2E5CwwR4HJCnEKjD4H5htglQKOj3plyPXf7ejG2DB1XJevl6746oUd3ngUbv4rxMV7DzracbLm7oPdfeMMigOzFLs5ItAQFwN7SWYcSeRT3yXTsuPzRMjJwZaWAppLr8/2FnC2w7B6aKuFxhZorKOYTR2/H58GLqde3s50aGsBayzEJEFyOliTIDYB0MB4hr4tOQO2rAD2demk6KHMLPJzMqAsmUalmDhTWfAYnQvyOUCM589sqt/Pk/9p5W5aOrJBEy3sa4jGQIu/cVcB26gMuPJHbHh9OYnGBv/p/EkSiqIfqYqORQNB74wC00cOo7Ckjqm5CTAsDpKyYPhEOHYNRYUb2Bo9nWlt3SQv9TMhjWE98cQT3H777d16LhMmTPAvPnvzzTeftMBIxb/U0u6vU1TWsfhuftIEiE3XDZYlCowWKI7D5vnUe7O/A7FJIMaA0wFxqXqP50QoKqhGvfEaPwfa7oGtvwtuDMWTFG02YPverRTkJbHjaKN/GMlf4b3/L2IuxYaPdYMl9Ih+MdP0/SPH6MYW9J6YEOAfIxSgebDlJLLo8iv5cNf5XDQxAxKTIPdWKF0D834GMYlgtoI5Rg+txA8DazTMuBRW7QBvr1HXAsUteeTTio31gKdTeBKMhlg0A6RjxGZNPHF5dcY+HD1zLx8mnQ0VRwmaqNv6BravZmH7+g8pb2in2e6i8ZgeWur8EAVp83XjA9qAfIKNVLHXG9Qb4dyOkFXAGCLxufo99i186BvkUBQweBWoRtA8Xk8sGoxm7z4FYlUYPQsuMcJHRmj7oEOQ4QLwdCSw+NAbUAf5WLF5Z4r5jbHSETX2G4+md2HD63Dhjd6GLiDLLLpz6k0ITLue/K13d5s16CPQEH8KuEkAsrBR3214WdeeAJYkMEXpHqrm0o29y60bHNcwSGqBtgby97kpZiv5pIAlRi9rlwVMTv1zfDK0N+tlH50KcUngtOslZTTBxHkQlwUb3gT72iDNrwN200SKPSr5I0wUHzbSQDSJHAA8QUlZObjJUVdyoPUYXwVcM9GJaA1gJBpfuP1yANWALX8EtptWwUtn+etflySJHoZ+bLlJ2HKT9PonhF6vLDGQmMm2injqPNEUlrVgy0vr9vv9RUgGq6yszJ/s0B0mk8k/7nTac+Yiisv+1PE5LY8nN1ZjdbdywdgEbDlngNkC25qAehg3C6xx3vduaLrX1BuDBd7GS4WULJh4ETTUUXxI9zaL8WYwOZ+F/SNg9ndIj7dCD28EsJ05m+LN68DsAQ80CTio5JGfqmLLHQfR3hCPNcFrsKzeBtXbMGkemuxu8jPiO14vf+1DUHINjJmkN2jmKEjMgpgU/WEWHhh+JpyxCL76vV/LLqA5eRLFbg82kwla9LGAQG8kmmxAwYQZUrsPGx+XKhVcKZA3ETImw4xK2LQTqO84ZsMDMPZ8/zpsq1bXgIBmDeI63SJfaLAzvvEiX+iqGMhPSKe4MY58oiHVRn5NaVCaswqQne3NKgloUHzGy/v/ovImthypZ/rwJG+Yx3uswQCqFaIVGH2mXtYfAG2rgAI4czZsCDZYV/o1xlKMis0SrV+XQy/zFgFtnjhSjc3+68kHbJ+9DKNnwEgbgZ0OXKGvk+fn/EXYtr6CjZ10zT3tSgIT2cFwJuL0Hr87yJvxhQdJy9E9UY8JTDG6gTG7weMAjxUciWBth9gEbG1ubEdbgDS9DismvTzdZt3YuV16uWdN6HgmrDH6MUajt2M5VY8ufJkINevBG2bNB3YOH4Ma56G4uoL80UZoTKa4Jo0GNpBocAcOxIGA0coGIDDJxEW+1Uix3RngTcZ5fxcYlgsFT2IrfAwb06CvSRK+OiiELsBiZeqE0RSWdn1FzUAQksGaPHkyzz33HN/97nfJzs4O2ldWVsZzzz03OOv2RQJTp5L/ZgrF1JIPbPfEs69NwWD3MKwabOMzdG/L0QAt9TA6Xw/zRMV6ezKGHns93aKoes8nayxMv5z8Q/spZm1Aj7QFPnkQTEbSoifQ44jCiOmom+egqR+BCnYNDpHBqzUOcMRgi/F6WELTdZpM3spsAE1/mgqGJ+oNaJ53+NcSBWMnd1yPoug9VKNJ1615ICld9xD37aDI/gHF6CH86Jwx5I+Kg+Yk2NZOkfZ5kEGwMonoeCP5mXGQHN378vKR1AyeBsg9A1JzoXEy5M6D0qUBBzngX7/AdvsrkJfEWlr1aI9vPIDgMJXfG+kyeSSRfBo6PN+R2dj2naE3aLmTsNW8D3j81zcW/Ev7BBFYjkBhaRMN7RqFRxqx5SYH1xtF0Xv9MYkwchrM/yHsyYes8ZCcB9suIt/xYZD3ousfRr4lBn+OdPlINA5Rq0CNlkkczf7gq+6tfAUfvwTJD+JbZgmAhGHdFnufSE6GqZfAwTRoWtPDQQr6qhQejIwnJ20cZdWNFEWBrX13UOKOfw5VZp7uCbkc3rocpY8Hejzgsevek9uoG55UF9grIXZYR3RBCP2ZVQ1gdOkhfLvdP4EX0MteNep/ilGPLpxthIp82PxbwOtpXTyL/2zeS6vBQ7HTypXjkrGlJfD33YJydTtZFm8M30VHYrCXYsCmKdgSvR5xpRU9SWas18goeuf4wishbxxkjAr9XgQYLltuMra8burnABCSwXrqqaeYN28e48aN41vf+hZjxuivud63bx9vvvkmQgj+9a9/neAspw82kw2bawtggCn5FG2rpMYtMKQmQkycXtmzpkF7I2RlecMppo73YPQFRfH26uIhdwK2y2/G9k4DBKWvV1P04f/DzE1AD+GV5Fy0eJs3ZbYKjxpNgzOFdoPGnasP8WNLGoum53YYrEBU1T+WpcfHA56qztfjM3LQEWbIGA3nXEbxmi+BRorJ5745U6C5FsrMoLko3laFvqCezrQLZ2FLPIm3XDvsoLVB2giwxOoNeXM5lJYQmF0Hn8H//Z7C3Bu9mjtWYuhpXCWYYRA7A1vLux3HGo2QNkzvrWdNgt2zsdnXBIetkruZT9WJwFfUdFtvfPUqOhFG2PRwc0KKfu0F52P7/MMg/TbANia700lyUNFwYqbONJ56TysZBn2syl+PjrwMRWcGf80cx8nhvZ7ZP4ERW+DNSnTfuzNj0e+Im4nJ+Wx3uEmJTqPY7sJGcLagn+zRkJCpj1l57OBw6J0ntwtElDepxaqPDyZ7wDMC4tK8iQl0GC2h6QbJ44IYk16vO48HWWN142XSDVxRm0oxeeRTope9xcKkEbnsMpnJj3eBxQGmGGY0u/iyzAocAfbrvbhueptFRGFL8j4HlROBRkge2fH7mqZrGDddT6k9WTp7/QNMSAbrvPPOY8OGDTzwwAOsWLHCv05fVFQU8+bN45FHHpEeViAzzofPa0AxYRuVwaQGjcaWeNo8mh7zVg16emx8mh4mA72RD7UeqKr+VMYmw/DJcOHNsOYhoCYgWaGKEfwd6NzI5gJRFDWrlMWMJqepACc7OOoazjEtFqvmptwjWPL5ERYVeBe3VehaaX0PR/cvk+gZg1FvSEcW4OQi3OxC4Wv6Qx6XAhn54Gol/0glxXUV5NOGjVQYPbmPhdQJlxOcbZCQoXt9scmQYYOp5bBtK0Hp3Aeeo8CS64+mBoWXAt6/0G3WXdRoyJsEO9/1boiD5BFgMYExFtJGwegJsHNNcCZfYuIJL6FX71Hzvc0gOhGMZooqWtl2uImphuHYmIMvXblDb3zw5+xMFjmiKHIP47cNGZRqlcQbqojGHVyPPno1+Hvu3mcHdo+3B5+UBNaZkHselHZjsFLHQawZnA5s+WMhKo3iA27yo6Ip2mZFo+PdV35iY3RDExUHWrTuUbna9bEst3cQ1+3UDZrTA45MiAvoQPgbbe8Yo2qgy7yQwGONZj0xw2CiqLkSF2dQ7DNY1jhsI83YcnSPr+hgKcUVteRnjGKWIYrikhhUhuHmc7+9DOSDSjfFrRr5mbHYhg0DRxwMD5gWo6p6gpY5unt9YU7IE4cnT57MihUr0DSN6mo9BpuWltbl9R6nM753EFGcyYK0MdjSU8BkZnpeCltK6piSkwDR0Xq2lyL0FFnf5MuT7bWoKmCAxAwYcxa03wFfPNipET3WJQHAljMVjBa2VrRgSBlOQ9VYjmouSkUm7STiQEMT5oCQQEcD3b2GPqIoYDJDSh5nzF1A8Y6RXGY72xt2MetGPX0StjPbsO1wQsUhyBkD0fEnPvdxEeBJ0L0rRdE9vZRsyC2A2m9A6X+Djrbt0kOFxXQeTSkANgNd06fzSYUR4yBvMuzMRZ+EOU7v3ZuteigqLhUyJ8LOAmwUBgymhxDm7AnffbHEUFh+jEaHkR2eeGxnnQsb1wYcGA0pI4K/a04ERy22jImkfiaoIZ9KUUseB3hdCczC+zz4e46GkxSd0vFPgxEmzITSF7oeljde9xw8TkhIwZaRiS0tFppqeH3baHyJCEH1vlnD5nvcVIMe0jMa9aQLo0M3VkaT3sgbLOBp1qcLdEdvPA5foow1Dlv+WL4qmkq+fRtFeHhvtf6EXjouCdswC7uaNGqVZGg2cOXEFGyZCRQdOsw7VS3EOIvIgqCE2AOkkEEyxY0GbNnjwd0GWXmdfj9grDnCOOmVLlRVxWq1EhsbK41VJwpL6tlb1QLWbIqtZ2EbNw7MFmy5Jr0HpXm8CQsKkApWV3Dc+2RRVP3BSMqCMRdA3W3kFz/XJb3XlwCwkxRsI88Cxci0EcPYJjwcy5yA8aiZJrMZj8uKR3NjUE2YDApFZQ3Ysk7WUHSnWwFrDDbb2dhGjoEE3xQJb88wOQvsk2Bsu559mD1WD3OcDEJ4M+y8UwgMRn28J32MnkRQepigCaFs7+JBbQRs2aPg6Oag7WMZo6eHGy+AMV+DYRMgdTLUJEDmaH0+miVWN1pRcbqXMOYMfS6PXiBdV5E4WVQVhELBiFS2lNQyOWUEOFXgfPT8OoBzdIMdiL0F2mspckXRYmrHbB/DEa2UGE8l6UorxaYewqLO1u629oGAaTIGEwzrbjJ9NiTk6J0NzaHXk+hkvaOjmsk3TaLYtdNf9331fkuNG5vR6A0BoofyjEbvGJURHCZQPPq0AZMFopL0unGyGAzYRmVgu2IOfFzI61UN7K52Y9CcZCVGYctNZmLWMHYJQX5KJsS7QLFQvLcFN1kcNZhwaFsY6RJ6r8gAF2Zlo0SbyM+I1dPqPU49KzEQnzc4iKG8/iJkg7V582b+3//7f6xbtw6n08mqVau48MILqamp4fvf/z533XUXc+bM6UepkYfvLbQQS37+QshLCUhM8HQM2ppVfZkGk7ufDZZ3PMts1R9w26XYjpVjq38z6DCfJzCJ8yF1NKhmPY01O56i9Ch27Y8n1h3PhmoDJbUtaKg0trv1ORwDYbBAb1DjU3VD5AuTCo/eM4yK0zO7HC16eCUxT8/y6g8Cs6BMVr9Hxxkl8NXGoEM7T/xsAsjNh6OpQE3AGFQKtlgznHERjDtX90rHFkBiPGRM0FcW8Lj1azFbIDkNho2B/b5fmNG/9SLgWvX7nABuB9QImH4ubNkB1EPeuR1l70M1gNHE1j0tZGUk025yk1A3lUrqSTZsx0TX5BOdvq96E0xAurTBoIeHmUjQOFb8GL3OmOL0cF58CkQn6GVnicF29jnY1nV4yr7SnZ6Xoj+TJqNexzxu/CE91QRWkz4upRj1MHdUojek1g8oCqTnQe4k8qPr2Zuqj/VNzUsDkwXbyCxsmQm64XE7dcM70UlroaC1PZYaojjk/IyRBn3FjrjEYeRnxuvvF3M06t9JHtH970YgIRmszz//nAsvvJDs7GyuvfbaoIm6qampNDY28vzzz5/2Bqu7MYWgdyxlxeuhQE2fTa6nnvazl+obz4qO0VNtz7sKVpYAHQuW+ueuTDtTb5zN3owmTWNfo4cNlQKjWSMuKgpNUYkxGzkjO2Hg01hVg55ZqHh7g56OlHkShuljCpYofUJmv5WbN8SpKB2D5MkZkD0Njn0Tqt7yH9l54udFDIPMSRA9Fdo+7BiDyp0MCS7IytUzzYSApBF6OSeO0Bs/oemeg6LqUwXSxuoTM8sTYeRkvcc/UPjHVbwGNGO23hkYb9MnxQbi8YAWz7R8jfI99bjt8cTFjie/vI1adyOpHO4++UQNtUORATToY3+BWOPAPBqcAQYrM0sfB4xNATRv4oQ9INKQif5iRn2dQP3+jYIRqd4QmdfzMFm9yUSa902+Hv37vrR0j0ufM9hfWGMguwBbtoZtWsC6oZrmrYcGsLfq9dFgwjbKiC0jFRrLWLp6A/vNZ2JnN+0Mp01Nx9FswjYhT08g8Xh0o32KENJTcN999zFhwgS+/PJLmpubu6wsMXfuXF555ZV+EXiqUVhST0Obq2OGuab5wzOoDEzPxzfQGhsHI6bCxTfD6ocJWigXIHcyJKZ2eBmqypriWiqbDTQ622gTDuxONy6nm/PHjPR6V31MqugLgRmE4DWi3sF7zQNJ2Xqjb4qj2xHoUAlMFlG9SSDDxuheUdV+gtc/9GEEsiAuh6LoERS36T34KwHyz9B7/AnJesOnefRQY70VknP0MJP/Gg0QFa2vG5iRr4+dZI/u/Vy8UPCnPMfouvJngL2FIqeVrVuOMS038MWB3tVMEqxsPebmQGUDZQ0GdrhTGUYmwnWYNFPXV9JgjQlRWy6IxK5p/VGxMHkGbFnZsS1llJ4sExWjJ9GYzR31QjVAaib6ollbAk6UrBtr0O+Lonjvv9e7UoyAx2u4hDc5I1b3hPsLsxXS8rrPtlWtoLj0pdKcKtCmT2Y2mUA1YRrbQsu+JsyoqOQQlxSLLStJn8ivGfTn3tiPWoeYkLqlmzZt4sYbb8RisXS7KG52djaVlV3fpSLRw4SJ0aYO70RVOzyIgXTTFVV/aONTYdQMmHFr12NyR4PJQlFlGy+vP0RReQsXTszAKVTGpseSFmvGrYGqCD7cVcmAGqtur0HRGwzwpvwLfdLxcd6rFBI+b813T4wWSM7UJxRPuqDL4XroL4OvSIPoWIrVYUCad/tIfVHi2LSAJbYUSMyGpOEQl67fF1/4VlH0MZeYRP33siZD0riBD+Eoiu6txidD0ihIG8u2JgONLoVtla16o24062VhjgKjiWkjUhmdmYLRGkVSQg4lztHUecbhdAaWixdTVGh/mcMgPh3SOq2gYDTp4ddAolP1zkVMsv7/qDhdq2+JKmMsmDODv5OR7Z1YbdTPCd75V249TCrcgEH/vsG7cojB1LG6SH+gqvoE+sQelrMzmvRrsETrY50G7zyuxAw0UwqZjKeGXM6dYuPa8yZjG5Wtdz7MMbr33l/h8jAgpFI3mUxd3jsVyNGjR4mNPclB8FOUblOPfYOg/n8PAN7QQlFZA1sPOpmWeia20bfAAV+m1Rl6z1FRvRNQPRQeaeDGWSMYOyyeLUfqiYu28Nm+GqqaHFw0MfP486sGCr/X5fHaywEymr5MKl9IxhwFqSMgeyqUX0ZR/budXg0xkjPS88FiJn/yOIrXTCafI5A2CWKzwNUKvmdCUfR0cYNFDy0ZvHN3wJsWreoNU+oIvbFJHT4w1xh0vV4vy+pNrfe4mBqbQGG1g6l5egp2wMHe3r+RrOQYxqWP5dCRMorrcznkPEaMVk6OpyXohYSEslwWQFomRDfDsG4mHidno78PzDsfLy5LD9tZYjruXeD1WeP1ifm73+3YnpDm7SiogHcCsEF4Q59ubxja+zpug0E3FELTjVd/4ctKPV5d9q0M4+vcOI3gaCV/1EjQBPmJGdjyRutGzYfH7S2HUycZLiSDNWvWLJYtW8add97ZZV9raysvv/wys2fPPlltpxeD0eArCoWljTQ6FbZpCdimL4DmGjhWCFN+AKoFVAMFI1I6JqCqBmzDU/wrJyyantvhCQwB/jHA3HhsWXHo2U4D8EAGpuwr3nGN6Hg9TDeugOINHwKOjiWvzpyhr9SNAdvw0dgKzoaKGO9KEhn6PCSTCVAoOtrAlsPVTB+ejC03hqKyBrYcqmZ6bgK23BRvwxjlHXtQdU9hMFAU3YhGJ4DHgy09Ddv4nsNJenhbQLSCRzHjVNNpNORRrtUQ795CvIDXjd6wqBqiRxI9DMypXT0sVdW9a7KBMsDgnVtm8XrI3dTRqBhIy4XdvokG6BOnjcbgY4XQO0XCpBst4dHDhB434O776jO9wWg68TG+lWFUA6gqReWN7KxpY9KYEdhiNEhM6RpWPMUIqRY98sgjzJ49m8suu4yrr74agO3bt3Pw4EGefPJJqqureeCBB/pVaKQRlFwxwC816wsFI1LYcriGqdmp+hJEZ18JtWfC8AJ/JlpXL9A7iTkM5m74xwBLm7BlD1CGog/Vu1yUgt5IWWP1CaM5U8jf+02K6//bsbrDsAmQNFovQ2ucnq5uUWHYKP2z1tHQbTlST32bh62l9diGp1B4uI7Gdrf+OTcJhOr9vTj0d7WHOP7TVxRFbzijYgMMbM/4VtaYlhPLu3WtDItNxNEymjqtkmPOauI1fRHf14F8LQ5bUgj3yxIFKN3PQ7PGQV4+lJQDQh+/Ug0d2bedjYrJDCk56O9yO6hvix3W9bjA8LzBey5N867kfrIToHugLwbQYARLHFuPldDiNLOr1oEtI8O7rmfAM3oKGq+QDNbMmTN57733uPXWW7nuuusA+PnPfw7A6NGjee+997DZTrxIzalMl+SKMMGWk6inMWse/W/4FEgZrq+00VMvz/cwhUH9L8hLYuX2ctqcborKW3QvayBDHoGTLA0mfWwpKRvb9HOxfVwJ2jrgXMiY6H3xnVlvGK0pemZgfLp33KPjUZs+PIktJXVMy4kHISgYnsCWI/VMy03oaBz9BkvRw1CDhaJ4xz1OvCyYv2Ojaew71sTRqnj2trRg9+QRRSMpWilJHmgywPbGOGz5I/qux5mmZ8THd2PsrFbIyYe6vfq9scR0JFB0p11R9XtEJvrCvFrHIs7HQ/GGAw0GMIqOKSlDiaoybUwuWw6UMzEjCqKt3nBlwEOqeP9zuocEAS688EKKi4vZtm0b+/btQ9M0Ro8eTUFBQUhvJz6VKCpr8M6/gq9N6IdFP/sb37gM6B5DVFzvBmZ76rkOIracxI7OwJEGPcw5kJ5s0Goe3pBMTBKk5YNtBlQnQvYEfZDfFKc3EkaDnlrtsetGRzV615nTw1SdPVj/Z996dGiA3kAWVTvYUnaM6SPSdO9roPFdb1+SgFSVJrvG2Lx0Co9W0EYWZqqpUkaT4D5IlSGP/KQU3QPoK6YofTypuyxJ1QA546Fhpz+cfVzNigKWRD1EW5cNeLxjR30g8HUuQ4z/lR9Oh76UlNHU6fq9/z6F2uOTLvmpU6cyderUfpBy6lBYUk+02UhitCmsvKsgfFlpBu+M/t6OS4VB5Q9c5HVQPNnA0KBq1Ae2kzMg16anoKeP9U5QNXtfL2HU5zRpTn21Af/bW31lpxvA5VtK+XDXMS6alMmiglx9l9ulGy6PB1SVrWUNNLR5J2kPhsHyXa+X3oa2C0amsfVQFRfmj2LT3hJcprGUO2owYaKdYRTXaKGltntMHW8D6Iyi6NmXWVMATe90dfImuuiPToT04VC3BxAQldqn6wxLzJZujFUAYfDM9hchGSxVVUlPT+e1117jggu6pvm++uqrXHfddXg8Jzu7PTIJWjU7nPHNz1KIqLBBZw9lcMo6YEKxybuEUlKe7gGk5enGSjXoGWYmM7gMuifmTwIIPJW+ksaHu6qob3OzeldVh8HyGUc0EArTcpPYUtrA9BEnXq19IPB1CFZuLz9ug27LSdTn5WkjKdqfxurNcezZf4wKl4laMphgtIYW2nT7yqMbFEVPvIjLRB/rs4CiBBmfLh0as0V/o3eptzzjk4OuM9xC+L3mNFkWL2QPy263c9FFF/HEE0/w05/+tD81RTz7qprZcLCWeKsx/Ct/GIT5ToZerVDeH6gBDadq0ENJcSlgNFFUr1F48AjxViNN7S4KMq3YUowd7zPrgYsmpuse1sT0jo2Kip6yr88zs+Uk6p6VoRdZZH2g156Tt9Fvc7pP3KB7J8DbRuViS4/mtj9V0GKy4CKeBZPTvZNv+8iJEn2iY/U5bb6VSVRDkPHp0nk0W/Rko8xMfazQuwpExHQyT3NCNlhPP/00Gzdu5K677mLz5s387W9/w2o9dSaonQyrd1VR3+YK7jmHMxFqrAYd/9wsdAMSHa83kPuqadCMbDhYS35GPFuONmFLH+ZdWsdIkHcGXg8LFk3PZdH04cG9Y9/4ouah6EgtW0vrmTY8ud9fPd5bj8LXIQg0cMdFMYAqwBiDbdJwvtzh4fzx2dgyo/X18Pobk0lf3Bn8qfOBxqdLh0ZR9WzC5DH6vfSuMjJoHR/JSRGywTKZTDz77LPMnDmTW2+9lV27drFixQqGDx+ESY5hzsUT01m9q4qLA3vOksinu7lZKBSMNlJ4tI2LJ6bTZHczPTdeN2gm7ytRjhuu6Sb10utlbS3VjcqWI/X9brD66lH0ukFXVT0tPyqaFqESnZRKixpzciFnX5Zet7/nnW4QEDY8rlZV1b2q1BF6h8LU8zwzSfhx0kkX1113HTabjUWLFlFQUMDSpUtP/KVTnLHpcTTZ3YxNH6QJnyES0QPNQ4U/NOh9UZ/Zgi03BtuIgM6JEOCyB2djdiEg87DLLgXQx6+2ltYzLa//x6/606PoUo8MRnA7qWw1U9UkiI8bQMPgy9rzLR7dG6yJFLWrFFe0kZ/ZgG14L1LbJWFBv+RnTp06lcLCQr773e8yf/58zj///P44bcSycns5e6uaKW9oD2tDEOkDzUNmcH2hQd+KCp2NjqJ0rLzek8FS1eOPHaqGDqMS5gkx3dYj1UhKSgJ1DkFcSuLACujryiuWKIrrVVq1aN17DRODJTuQJ6bfnoTExETeffdd7rvvPj755JP+Om1EUt3soKy+nepmx1BLOS5dFuKNMAIbykHF6wEd/xi1Y7rAcc/TAxGU9dVtPVJVLrNlM3tiJgum5QysgG7KsaisQV/Auayh6/EmC/l5w4lKTGT6iNSu+4eIIavPEURIHtahQ4dI67y2F6AoCo888ghXXnkltbW1Jy0uUkmLs5CTFEVaXHjHxyN9oHlIM7sCswa7ozdG7YS/4X0VSZh5WJ0TMHryCmy5KfqqKkOgv7CknuJKPVv3trljgrWpKraxY7GNEV3f9zWEyEzFExOSwcrLyzvu/smTJ4ck5lRhwZQsWfEGgSE3uIHLNnXZ512B/2QyMNUBeKFnPxDoCZQ3tLO3qqX78LeqgtqPq5r3gYK8JDYcrCUx2tw15K2qHUs4DQE9hf76oz6f6mHFXhmsRx99FEVRuP/++1FVlUcfffSE31EU5bRdAHfIG1LJ4HAio3SKThcI9AT0JcjCYJHJTthyErlt7pieO47mqCFbHHYgx44jfVz6RChCnPiuqaqKoii0t7djNptRe9HrUxQlYle6aGpqIiEhgcbGRuK7W3RTIpEAp36PfiAYyDKLxPvRl/a2VwbrdEMaLEk4EYmN0GASqeUTqbr7m760t+EXIJdIJEGEc/bYcbPxBolwLp/j0R+6w6H8BxNpsAaA060ShSvhfh+WF5byo39uZnlh6XGPC+fpB+FgLMK5fI7H8XT3tu6GQ/kPJr1Kuhg5cmSf33GlKAoHDhwISVSkc6oPfEYK4X4ferPmZDiFjbrTEvRCzbKGIdF4oiSncCrDQI6nu7d193RLhe+VwZo9e/Zp/1LGvnC6VaJwJdzvQ2/WnAwno9udlqAXaoaBxu4Id33d0du6e7plJMuki26QSRfhS7j2lnviRHpPdv9g0pOWcNLYHeGu73RHZgmeJNJghS8vrz9EQ5uLxGgTN547cqjlnJAT6Y2065EMLaei8e1Le3tSi9+6XC727NlDY2MjmtZ1xn93byOWSE6GcA/zdeZEeiPteiRDSySGN/uTkDwsTdO49957ee6552hra+vxuNN94vCp2BsaTE6n8judrjVSGOh7Esr5T8V6MuDzsH7961/zxBNPcO211/KPf/wDIQS/+c1v+Otf/4rNZmPKlCl88MEHIYk/lQinlNNwT/HujnAqv95wMmUcadd6OjDQ9ySU89tyErnx3JGnjLHqKyEZrCVLlnDVVVfxl7/8hfnz5wNQUFDAzTffzIYNG1AUhTVr1vSr0EgknOaHRGKDGE7l1xtOpowj4VojsdNzMgz0PYmEe94TQ1UXQhrDKisr45577gHAYtFfoWG32wEwm81ce+21/OEPf+DXv/51P8mMTMIp5TQSx0rCqfx6w8mUcSRc6+k2fjLQ9yQS7nlPDFVdCMlgpaSk0NLSAkBsbCzx8fEcPHgw6Jj6+sjpyfc34RBn7qwhkh+OSCGUMg6HunIifBrjrXpzcSKDHAnXJDk5hqoDHJLBmjZtGps2bfJ/njt3Lk8//TTTpk1D0zT+9Kc/MWXKlH4TGWmEQ080HDRITsxQ3Ke+GhSfRqBXqfey7p36BHbOBrODEtIY1i233ILD4cDh0F8B/9hjj9HQ0MAFF1zA7NmzaWpq4ve//32/Co0kwiE2HQ4aJCdmKO5TX8fa+qKxqKyB8oZ22pxuWfdOEwZzfLzfJg43Njaydu1aDAYD55xzDsnJyf1x2iFBThyOLGQIqm8MRHn5zlne0E602SgnQp9GnGx9GrSJw4EkJCTwzW9+s79OJ5H0GhmC6hsDMZ4ZGDaUnv3pxWCOj5/0ShdHjx6lvr6e7hy16dOnn8zpJZJeEYkZkD0Rqd6i7x58bcKwiNItiSxCMlgNDQ3cfffdvPrqqzidzi77hRAoihKxK11IIotTKQMy3L3FngzqqXQPJOFLSAbrhhtuYOXKlXznO99h5syZJCQk9Jsgh8PBgw8+yD//+U/q6+ux2Wz86le/4uKLLz7u9x5++GEeeeSRLtstFot/jphEEu6Eu7cY7gb1dCJSvfGTISSDtWrVKu644w6eeuqp/tbDDTfcwLJly7jzzjsZO3YsS5Ys4dJLL+Xjjz/mvPPOO+H3//KXvxAbG+v/bDAY+l2jRNIX+tKwhLunEu4GNZBTvUE/HTsPIU8cHjNmTH9rYePGjSxdupQnnniCu+++G4DrrruOyZMnc8899/D555+f8ByLFy8mNTW137VJJKHSm4YlUhrXcDeogZzqDbqv8xBvNfLy+kNhX3f6g5DnYS1durTbV4qcDMuWLcNgMHDLLbf4t1mtVr7//e/zxRdfUFpaesJzCCFoamrqNglEIhkKejOPKRLXegx3TvW5iL6FcJvs7tOm7oTkYT3wwAM4HA7OPPNMvve975GTk9Nt6G3hwoV9Ou/WrVsZN25cl1z8s846C4Bt27aRm5t73HOMGjWKlpYWYmJiuOKKK/j9739PenrPryCXSAaa3nglkRRqixQiyRsMpK/e9ulUd0IyWEePHmXNmjVs27aNbdu2dXtMKFmCFRUVZGZmdtnu21ZeXt7jd5OSkrj99ts5++yzsVgsfPrppzz77LNs3LiRzZs3H3dCWuCqHaBPZJNI+oveNECR2rhK+p++hjJPp7oTksG66aab2LJlC/fee2+/Zgm2t7f7V38PxGq1+vf3xE9/+tOgz4sWLeKss87immuu4bnnnuN//ud/evzu448/3m2GoUTSH5zqYymS/uV08pj6SkhLM8XExHD33Xf3eyM/efJk0tPT+eijj4K279q1i0mTJvHXv/6VH/7wh306Z2ZmJpMmTeLDDz/s8ZjuPKzc3Fy5NJOkX4iUhAqJZCgY8KWZMjIyBmStwMzMTI4ePdple0VFBQBZWVl9Pmdubi51dXXHPcZisXTr2Ukk/cHpFLIZCmSH4PQhpCzBn//857z44ov+d2L1F1OnTmXv3r1dxpA2bNjg398XhBAcPnyYtLS0/pIokUjCjJXby1lbXM3K7T2PcYczp9ubnE+GkDwsu92OyWRizJgxXHXVVeTm5nbJElQUhbvuuqtP5128eDFPPvkkL7zwgn8elsPh4OWXX2bmzJn+DMEjR47Q1tbG+PHj/d+trq7uYpj+8pe/UF1dzfz580O5TIlEEjFE7jQWOcbZe0Iaw1LVEztmoa4leNVVV7FixQruuusuxowZwyuvvMLGjRv56KOPuOCCCwCYM2cOn3zySdBcq+joaL797W9zxhlnYLVa+eyzz1i6dClTpkxh/fr1REdH91qDfL2IRNJ/DHTILtJDgpGu/2QZ8DGsQ4cOhSSsN/zjH//ggQceCFpL8J133vEbq5645ppr+Pzzz1m+fDl2u528vDzuuece7r///j4ZK4lE0r8MtAcR6WOEka5/MOmzh9Xe3s7999/P3LlzWbBgwUDpGlKkhyWR9B+nuwchOT4D6mFFRUXx/PPPM3HixJAFSsIH2ZhIBhrpQUj6i5CyBAsKCtixY0d/a5EMAXINO4lEEimEZLCefvppli5dyosvvojb7e5vTZJB5FRfIFQikZw6hJQlaLPZqKmpoaqqCovFQnZ2NlFRUcEnVhS2b9/eb0IHEzmGJRlMZFg2fJD3YvAZ8CzB5ORkUlJSyM/PD0mgRCLpQM7DCR/kvQhvQjJYa9eu7WcZEsnpi1zsNHyQ9yK8CSkkeKojQ4ISiUQyOAx4SBDA4/Hwr3/9i3fffZeSkhIA8vLyuPzyy7nmmmu6faGjRCKRSCShEpKH1djYyLx589i0aRNxcXGMGjUK0FfAaGpq4qyzzuKDDz6IWO9EelgSiUQyOPSlvQ0prf3++++nsLCQP//5z1RXV7Nlyxa2bNnCsWPHeOaZZ9i8eTP3339/SOIlEolEIumOkDys7OxsFi9ezB//+Mdu999xxx0sW7bsuK+0D2dOdQ9Lpu5KTnfkMxA+DLiHVVtbe9yU9vHjx5/wpYmSoUOubiE53ZHPQGQSksEaM2YMb7/9do/73377bUaPHh2yKMnAEq6rW/T0Ijv5gjtJfxOuz4Dk+ISUJXjbbbdx++23c+mll3LnnXcybtw4AIqLi/nTn/7E6tWreeaZZ/pVqKT/CNfFSHuatCknc0r6m3B9BiTHJ2SDdezYMX7zm9/wwQcfBO0zmUw8+OCD3Hrrrf0iUHL60NOkzdNxMqccY5FIunJSE4dramr48MMPg+ZhXXTRRaSmpvabwKHgVE+6kIQ/L68/REObi8RoEzeeO3Ko5UQc0uBHDoMycRggNTWV73znOydzColE0g2R7FWGg7GQYeRTk5MyWM3NzZSUlFBfX093jtqJXmsvGXzCoTGRnJhIHmMJB2MRyQZf0jMhGaza2lpuv/12li9fjsfjAUAIgaIoQf/27ZOED+HQmPQX0viGJ+FgLCLZ4Et6JiSDdfPNN7Ny5UruuOMOzj//fJKSZC8mUgiHxqS/CGfjezobU2ksJANFSAZr1apV3HXXXfzud7/rbz2SAeZUakzC2fiGszGVSCKVkAxWdHQ0I0aM6GcpEknfCGfjG87GVCKJVEJa6eLaa69lxYoV/a1FIjllsOUkcuO5I8PWoIaCXHFEMtSE5GEtXryYTz75hPnz53PLLbeQm5vb7fuvpk+fftICJRJJeCDDnJKhJiSDdd555/n/vXr16i77ZZagRHLqIcOckqEmJIP18ssv97cOiUQS5oTzmKHk9CAkg3X99df3tw6JRCLpN07naQWnMiElXQRSUVHB9u3baW1t7Q89EolEctLI912dmoRssN566y3Gjx9PTk4O06dPZ8OGDYC+IO60adNkFmEEIrPAJKcK8n1XpyYhGayVK1eycOFCUlNTeeihh4LWEUxNTSU7O5slS5b0l0bJICF7pZJThVNxWoEkRIP16KOPcsEFF/DZZ5/x4x//uMv+s88+m61bt560OMngInulEokknAnJYO3YsYOrrrqqx/3p6ekcO3YsZFGSwcUXCgRkr1RyXGTYWDKUhGSwoqOjj5tkcfDgQVJSUkIWJRlcZChQ0ls615VINGCRqFmiE5LBmjt3Lq+88gput7vLvsrKSv72t79xySWXnLQ4yeAQqaFA2fAMPp3rSiR2diJRs0QnpHlYjz32GLNmzWLGjBlceeWVKIrCBx98wJo1a3j++ecRQvDQQw/1t1bJABGpE0LlUkGDT+e6EomrX4SbZjlnrPcoortXBfeCnTt38tOf/pSPP/44KEtwzpw5PPvss0yYMKHfRA42TU1NJCQk0NjYSHx8/FDLkfSAfNAlpwIvrz9EQ5uLxGgTN547cqjlDDp9aW9DNlg+6uvr2b9/P5qmMWrUKNLS0oDgNxBHGqeTwSoqa2Dl9nIAFkzJkg2/RDLInO4dr760tyGFBANJSkpixowZ/s9Op5MlS5bw5JNPsnfv3pM9vWSAKSypZ29VCyBkaK0PnO6NjKT/iNSQ/FDQJ4PldDp5++23OXDgAElJSVx++eVkZWUB0NbWxjPPPMPTTz9NZWUlo0ePHhDBkv6lIC+J8oZ2/78lvUOOn0kGE9lB0um1wSovL2fOnDkcOHDAP2YVFRXF22+/jdls5rvf/S5Hjx7lrLPO4s9//jMLFy4cMNGS/kP27kIj3AbuJac2soOk02uDdf/993Po0CHuuecezj//fA4dOsSjjz7KLbfcQk1NDZMmTeJf//oXs2fPHki9EklYIA19+NJbbyRcvJbe6OhLB2kwrmuoyq7XBmv16tXceOONPP744/5tGRkZXHnllVx22WW89dZbqOpJL/4ukUgkJ0VvvZFw8Vp6o6MvHaTBuK6hKrteW5iqqipmzZoVtM33+aabbpLGSiI5TQj3Cdu9nQgfLhPm+1vHYFzXUJVdrz0sj8eD1WoN2ub7nJCQ0L+qJBJJ2LJyezl7q5opb2gPy7Bob72RcAnr9reOwbiuoSq7PmUJHj58mC1btvg/NzY2ArBv3z4SExO7HD99+vSTUyeRSMKU8JhjGS7jUJLBodcTh1VV7XYicHcThH3bPB5P/6gcZE6nicPhzKnaGIX7dZ1IXzjpP91XiTgVGJCJwy+//PJJC5NI+kJ3A7vh1FiGSrgM9vfEifSFSygNTt3pBadCPR8Iem2wrr/++oHU4cfhcPDggw/yz3/+k/r6emw2G7/61a+4+OKLT/jdo0ePctddd7Fq1So0TWPu3Lk89dRTjBo1ahCUS/qb7hqjcG/se0O4NrK+RjLeqjcL4aavO8LJePYnp0I9HwhOei3B/ubqq69m2bJl3HnnnYwdO5YlS5awadMmPv74Y84777wev9fS0sL06dNpbGzk5z//OSaTiaeeegohBNu2bevT+7lkSDB8kT3PgUOG14aWwHU9x2fE0WR3nxb1fFDXEuxPNm7cyNKlS3niiSe4++67AbjuuuuYPHky99xzD59//nmP333uuefYt28fGzdu9K9t+PWvf53Jkyfz+9//nl//+teDcg2SgeVU61F3NsBDaZC78/w6L44MhFWHoS/ltbywlNW7qhifEUdCtDlsrsFH4LqeWYlRJ+w0hEvdGczfDavJU8uWLcNgMHDLLbf4t1mtVr7//e/zxRdfUFpaetzvzpgxI2gh3vHjx/O1r32N//73vwOqO9II93k0pxOdXyYY+Nl3n5YXlg7K/bLlJHLjuSNZs7uK+U+v4+nVxf5GdG9VM4Ul9WH38sOe9HRXx1fvqqK+zcWb28pZuf0oj7+3OyyeAZ/WeKuRcemxjEuP61U49nh1ZzAZzN8NK4O1detWxo0b18UtPOusswDYtm1bt9/TNI2ioiLOPPPMLvvOOussDhw4QHNzc7/rjVTCrdE5nek8ATPws+8+rd5VNaj36/2dVTTbXby/s4qCvKSgRjRcJtv66ElPd3X84onpJEWbyEuJprHdjcOthcUz4NPaZHdz/2UTuf+yib3yVI5XdwaTwfzdsAoJVlRUkJmZ2WW7b1t5eXm336urq8PhcJzwu/n5+d1+3+Fw4HA4/J+bmpr6rD2SCNdB/9ORziHOzp8LS+q5eGK6fzxjMJg/KZ33d1Yxf1J6tyHYcAqj9RQi7q6OLyrIZVFBblCYMxyegVCfxxPVncFiMH83rAxWe3s7Fouly3bfihrt7e09fg8I6bsAjz/+OI888kif9UYqp9o40KnKUN2nOy/O586Lu+/cRQrHK7twq//hpiecCauQYFRUVJCn48Nut/v39/Q9IKTvAtx77700Njb6/443ViaRSCSSoSGsPKzMzEyOHj3aZXtFRQWA/2WRnUlOTsZisfiP68t3QffMuvPOJBKJRBI+hJWHNXXqVPbu3dtlDGnDhg3+/d2hqipnnHEGmzdv7rJvw4YNjBo1iri4uH7XK5FIJJLBI6wM1uLFi/F4PLzwwgv+bQ6Hg5dffpmZM2eSm5sLwJEjR9izZ0+X727atCnIaBUXF7NmzRquvPLKwbkAiUQikQwYYbfSxVVXXcWKFSu46667GDNmDK+88gobN27ko48+4oILLgBgzpw5fPLJJwRKb25uZtq0aTQ3N3P33XdjMpn4wx/+gMfjYdu2baSlpfVag1zpQiKRSAaHiF3pAuAf//gHDzzwQNBagu+8847fWPVEXFwca9eu5a677uJXv/oVmqYxZ84cnnrqqT4ZK4lEIpGEJ2HnYYUDjY2NJCYmUlpaKj0siUQiGUCamprIzc2loaHhhC8DDjsPKxzwrYrhGzOTSCQSycDS3Nx8QoMlPaxu0DSN8vJy4uLiun1ppa9HEIkemNQ++ESqbpDah4JI1Q2haRdC0NzcTFZWFqp6/DxA6WF1g6qq5OTknPC4+Pj4iKtQPqT2wSdSdYPUPhREqm7ou/YTeVY+wiqtXSKRSCSSnpAGSyKRSCQRgTRYIWCxWHjooYcicjknqX3wiVTdILUPBZGqGwZeu0y6kEgkEklEID0siUQikUQE0mBJJBKJJCKQBksikUgkEYE0WBKJRCKJCKTBkkgkEkmvGOocPWmwJEPKUD8AEslg0djYONQSQua1114D6HapusFEGixg69atHDlyJKhCRUpD2tbWNtQSQuLgwYO0tbVht9uHWkqf2b59O/v27aOsrMy/LVLqy1tvvcVtt93GwYMHAX3dzEjgP//5D3Fxcaxfv36opfSZN954g0suuYSnnnqKw4cPD7WcPrF06VJGjx7N1VdfzWeffTbUck5vg7V7927OO+88vva1rzFlyhTOOussli9fjtvtRlGUsG6EiouLKSgo4Ac/+MFQS+kTRUVFXHbZZSxYsICRI0cyZ84c1q9fH9Zl7aOoqIiLL76Yyy+/nIKCAqZMmcKf/vQnf30Jd1avXs23vvUt/vnPf/LOO+8AnHCx0aFm69atzJw5k5tuuonLLrssotbWKy8v57LLLuO6667DbDYTHR1NdHT0UMvqFb5yv/7664mLi8NqteJwOIZaFojTlKqqKjFt2jRxzjnniJdeekm89NJLYtasWSIxMVE89NBDQgghNE0bWpHdoGmaWLZsmRg3bpxQFEUoiiLWrl071LJOiNvtFn/6059EWlqamD17tnjwwQfFbbfdJnJzc8X48ePD+hqcTqd47LHHRGJiopg9e7b485//LP7zn/+IOXPmiPj4ePHGG28MtcTj4qvHhYWFIiUlRURFRYmZM2eKbdu2CSGE8Hg8QymvW9ra2sSNN94oFEURs2fPFm+99Zaoqqoaall94qGHHhITJkwQr776qjhy5MhQy+kVjY2N4rrrrhOKoog5c+aIt956S7z77rvCarWKJ598UgihP8tDxWlrsJYuXSqMRqNYtmyZf1tZWZn49re/LRRFER9++OEQquuZAwcOiMmTJ4uUlBTxq1/9SkycOFHMmjVLuFyuoZZ2XN5//30xatQocdNNN4k9e/b4t69fv14oiiJ++ctfhu01vPvuu2L69OnizjvvFHv37vU/sPv27ROKoojf/e53Ydm56cyyZcvEJZdcIv76178KRVHEfffd57+WcNLvdrvFY489JhRFETfffLOorq7usW6Ek+5Ajhw5ItLT08Udd9zRZXsg4aS/tbVVjB07VowaNUr85S9/ESUlJUIIIQ4ePCiSkpLEwoULh7xzc9oarN/+9rciISHBfwOcTqcQQu+FnnXWWWLy5Mlh2aMrKSkR9913n793/OyzzwpFUcSLL744xMqOzx/+8AcxYcIEcezYMf82h8MhhBBi1qxZ4uKLLxZChNcD7OOzzz4Tv//974O0CyHEihUrxLBhw8Rrr70mhAhP7UJ06NqwYYNISEgQQghx0UUXiczMTLF69eqgY8KFzZs3i3PPPVeMHz/ev+2tt94S119/vbjnnnvESy+95K8/4ci6detEdHS02Lt3rxBCiH/84x9i4sSJYuLEieKKK64Q//73v4dYYTC+dvDzzz8XO3bs8LeHPmbMmCHmzJkj7Hb7kNaVU95g+W5E50J+6qmnRFxcnPj444+FECKop/naa68Ji8Uifv3rX3f73cGiJ+12u93/7+LiYnHJJZeInJwcUVNTM6j6eiJQd6D24uLioP1C6OU+Z84ccd5554n29vbBFdoNPZV5Zz799FMxefJkER8fLx5++GHx1Vdfifr6+qBzDDYn0r5s2TIxZswYIYQQW7duFYqiiOuvv17U1dUd93sDTU+6fZ7gz3/+c3HJJZcIRVHEmDFjRFxcnFAURSxcuFDs2LEj6ByDTU/aN2/eLIxGo1ixYoV46aWXhKqqYvHixeL6668Xw4YNE4qiiJdffnkIFHfQm7quaZrweDzixz/+sUhISPDX8aGqK6eswfKNO3T2PHwFvXr1amGxWMTDDz/s3+a7gZWVleKqq64SaWlpQ9KL60l7T7z22msiKipK3HPPPQOs7Pj0VbfPoE2bNk18+9vf9m8bCnqj3Vc/fvnLXwpFUcTcuXPF9ddfL77//e+LxMRE8Z3vfGew5AZxIu2+Mt24caOIi4sT5eXlQgghvv/97wuLxeLv7be2tg6OYC8nekZLSkrE4sWLhaIo4sILLxTvv/++KCkpEUePHhX/+7//K1RVFVdeeeWgavZxojLfvHmzSE1NFddee62YMmWKeOCBB0Rzc7MQQoiioiIxb948kZKSInbv3j2YsoUQfX9OhRDigQceEIqiiLfffnsAlZ2YU9JgrVu3TkyaNEkoiiIuueQSsWvXLiFE18Zw+vTpYtq0aeKrr77qsv/VV18VRqNR/OUvf+n2u0OtPXDbsWPHxE033SSsVqu/xznYDX9fdAdSWloqYmJixOOPPy6EGJoB3d5q931esWKFeO2110RNTY1/27333itUVRVPPPGEEGLwevx9Kff//ve/Yty4cf5Qd1NTk4iOjhZz584VN954o/je977nN2bhovvVV18VN9xwg1i/fn2Xfddcc41ISEjwN6Lh9oyee+65QlVVkZqaKj7//POgfatWrRLJycnipz/9qRAiPOtLoK5PP/1UKIoi/vvf/x73+IHmlDNYX3zxhRg/frwYMWKEuPLKK4WiKOK3v/1t0KCtr1F86623hKIo4le/+pU/HOXbV1xcLHJycsQtt9wyaJWpN9p74qOPPhLZ2dniW9/61iAoDeZkdK9bt04oiiI++OCDQVDalb5oP95Dum/fPjFmzBgxZcqUoJDtQNJb7T7dn376qYiOjhalpaX+fVdffbUwGAzCZDKJhx56SLS0tISFbp/mxsbGLmOHvuO+/PJLoShKUJQkHLT72pD333/fn8nr86R8EZtjx46J+fPni9zc3LCrL92xY8cOkZSUJH7yk58IIaTB6jd27dolLBaLeP3114UQQpx//vli7NixYv369d0ef+mll4qsrCyxcuVKIURwD3/SpEniuuuuE0IMzg3qq/ZAXS0tLX63/aOPPhJCCPHJJ5+It956K+i4cNHt47nnnhNGo9EfLnG73eLAgQNi8+bNA65biJPTLkRwz/jss88Ws2bNGrQGqLP2Cy644Ljaly5dKvLz80VDQ4P4+OOPxXnnnScMBoOIj48XY8aMEZ9++qkQInzLvHPovrq6WiQmJg5qKLyv2q+55hqhKIr44Q9/KIQQQcZh8eLFYuLEiaKxsXHghYuTq+vHjh0TeXl54mtf+5poamoaaKk9ckoZLJ+xCeyR+Xrwd9xxh79iBDYyJSUlIjY2VsyaNUts2bLFv/3LL78U8fHx4pFHHgkr7d01Jr7r2bNnj5g+fbo444wzxCOPPCJyc3NFSkrKgGY7noxuIYRYsGCBOOecc4QQenjwX//6l5g2bZqYPn26qK2tHTDdJ6u9s9f9wQcfCJPJJO68884BVNxBX7T79H/00UfCbDaLyy+/XBgMBnHuueeKdevWif/+97/+RnWgx2z7s8yfe+45oSiK+Nvf/jaAijsIpX0pLS0V8fHxXaIIO3fuFKNHjxbXXnvtoHSG+6PcFy5cKCZNmiRaWlqkh9VXli5dKn74wx+K3/zmN2LdunX+7YEF6Svo66+/XiQmJoo333wz6By+m7hkyRIxfPhwMXLkSPGnP/1JvPjii2LBggUiNzdXFBUVhaX27igpKRE33HCDPwzxzW9+Myj8E066NU0Tzc3NIjMzU3znO98RH374ofjGN74hFEUR8+fPF2VlZf2mu7+1B1JeXi5WrlwpZs+eLSZOnOgfDw1H7evXrxc2m01MmDBBPPPMM6K0tNT/DJx77rni5ptv7leDNVBlXllZKVasWCFsNpuYPXv2gGTH9mf7snTpUpGZmSmSk5PFzTffLH7961+Lr3/96yIpKWlAQuEDUe6apolf/epXQlEUf7bvUBitiDNYlZWVYt68eSImJkZMnz5dJCUlCYvFIh566CF/ymXnyZBlZWUiNjZWLFy40N+AezyeoAJfu3atOPfcc0VCQoJISUkRNptNfPbZZ2GrvTOffvqpmD9/vlBVVUybNq3XIa2h1L1//34RHR0tpk+fLmJjY0V+fr4/nBnu2teuXStuvvlmsXjxYhEXFyemTJkiNm3aFJbafWEop9Mp1q1bJ7766iu/YfJ9rz+nFAxkmf/oRz8SV199tYiNjRXTp0/3z0cMR+2B7cv69evFvHnzRGJiohg2bJiYNm1akDEJN+3d8dRTTwlFUYIWWxhsIs5gvfLKKyI5OVm8+uqrory8XNTW1oobbrhBxMXFidtuu63L8b4b89hjjwlVVcULL7wQVJEC/93e3i6qqqr6veEZKO2BfPjhh8JsNotnnnkmYnSvWbNGKIoihg0bNiC6B1L7ypUrxZgxY8ScOXPESy+9FDHaB6NXPFBlvmzZMhEbGytmzpw5YGHAgWxfHA6HqK+vF9u3b48I7T58BqyiokIsWbJkQLT3logzWLNnzxazZs0K2tba2iquv/56oSiKePfdd4UQXXsJTqdTjB49WsycOdM/+/zAgQNBMd2BzgYcSO1CDFxKeH/rDhxTe/7557vMqo8U7QcOHBjQOtOf2vfv39+lvkSC7s5lvn379gGd+iDbl+61h8tKKBFjsDwej7Db7WLevHni3HPP9W/3hTsKCwtFQUGBGDVqVJfC7ZzG/stf/lK8/PLLYvr06eKOO+4Y8AmTkap9IHUPdKbRQGof6NTvgdTe1tYWkbojucxl+9J/hKXB2r17t/jpT38qfvKTn4j777/fb/WFEOKKK64Q+fn5/sHtwN7CCy+8IBRFEU899ZQQoqvH4XK5xIwZM4TBYBCKoojMzEzx/vvvS+0RrFtqHxrtkapbah867f1BWBksh8Mh7r77bhEVFSXOPPNMMXbsWKEoihg1apR/7sCyZcuEoijipZde8t8QX+EfPnxYfO1rXxMjR47sMqi8ZcsWcf/994vY2FgRFxcnnn76aak9gnVL7bK+SO2Rob0/CRuD1dzcLO677z4xatQo8dvf/lYUFxcLj8cjPvzwQ5GVlSXOP/980dbWJtxut5gyZYq44IILxOHDh7uc5+GHHxaJiYn+eK0Q+o25/fbb/Yt9+iapnu7aI1W31D402iNVt9Q+dNr7m7AxWIcOHRIjR44UP/zhD0VDQ0PQvh/+8IciLS3Nv/rBP//5T6EoivjDH/7gj7H6eg1bt24VqqqKFStWCCE64rgbN270r5sltUe2bqld1hepPTK09zdhY7A0TRMvvPBC0DZf9th///tfYTQa/etxNTQ0iIULF4qMjIwuE942btwoFEURr7zyyuAIF5GrPVJ1CyG1CyHrS1+Q2odGe38TNgZLiA6L33lA8IknnhAGgyHoTbWlpaUiPT1dTJo0yT84ePToUXH77beLvLw8UVlZOXjCReRqj1TdQkjtsr70Dal9aLT3J2FlsDrjGzj86U9/KjIyMvy9Ct9N++CDD8T06dOFoihi6tSp4uyzzxYmk0k88sgjwu12D+ncgUjVHqm6pXZZX6T2yNB+MihCCEGYc+aZZzJixAiWLVuGx+PBYDD499XU1PD3v/+dAwcO0NTUxE9/+lPOPvvsIVQbTKRqj1TdILUPBZGqG6T2iGKoLeaJOHbsmIiKivK/GE8IvXfhe613OBOp2iNVtxBS+1AQqbqFkNojDXWoDeaJ2LFjB3a7nRkzZgBQWVnJv//9b+bNm0d1dfUQqzs+kao9UnWD1D4URKpukNojjbA1WMIbqdy0aRMJCQlkZWWxdu1abrvtNm666SaEEKiq6j8unIhU7ZGqG6T2oSBSdYPUHrEMnjMXGgsXLhSjR48WN998s4iLixNjx44Vq1atGmpZvSJStUeqbiGk9qEgUnULIbVHGmFtsNrb28XUqVOFoigiPj7evw5WJBCp2iNVtxBS+1AQqbqFkNojkbDPEvzlL3+Joig88sgjWCyWoZbTJyJVe6TqBql9KIhU3SC1Rxphb7A0TUNVw3ao7bhEqvZI1Q1S+1AQqbpBao80wt5gSSQSiUQCYZwlKJFIJBJJINJgSSQSiSQikAZLIpFIJBGBNFgSiUQiiQikwZJIJBJJRCANlkQikUgiAmmwJBKJRBIRSIMlkUgkkohAGiyJRCKRRATSYEkkEokkIpAGSyKRSCQRwf8HAAPqzgEGPYIAAAAASUVORK5CYII=", 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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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", 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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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x8ssvc//993P//fej0+mYPXs2//73vyM5UA6HA4Ds7Oy4fYeNlHC7trQ/Z8OGDTFz6t27d4wxFc4J6ai/ttd64oknuPLKK0lNTeW4446jV69eWCwWBEHggw8+YOPGjVGCDVdffTUZGRk888wzPP744zz66KMIgsDkyZN56KGHOPzwwwHFIAD4+uuv+frrr+OOB8DlckWNaV9j7yoOhyNSk6wrfYXHu3Pnzk6fm/B49zVmrVZLenp6h/10NB+73c7o0aMpKytjzJgxnHvuuaSlpaHT6bDb7Tz++ONR92N/1i0YDDJlyhTWrFnD0KFDOeOMM8jMzIys1V133dWpSEdX6cqzX1FRgcPhiDKmuvNz2BU6u9/vvvtup+eG77dWq2XJkiX861//4r333uP6668HICkpifPPP5/77ruPhISEbo3rQJk7dy6ffPIJjzzyCC+99BLPPfccAKNGjWL+/PlMnTr1Vx2PiorKwUc1plRUVH4xtFotp59+Ops3b+aee+5hyZIlzJkzJ7IRO+yww1i/fv0vev0rr7ySK6+8kvr6er799lv++9//8u6777J161a2bNmCwWCIjKe2tjZuPzU1NUD8DWRbtTRQFNPaKnl1RF1dXdzj4TGErxUKhbjjjjvIzs5m/fr1MdLW4bf37Tn33HM599xzsdvtfP/99yxevJiXX36ZadOmsW3bNjIzMyPXePzxx7niiiv2OeZw+32NvaskJydjs9niikDE6yt8/ZNPPplFixZ16RpJSUmAMub2AiKiKNLU1NRh3aX29zbMf/7zH8rKyrjjjjtiPCarVq3i8ccfjzvu7qzbhx9+yJo1azjvvPN45ZVXoj6rqanp9kuIjmj77MfzNHX27B9M4q11+Joffvhhl2vUpaam8uijj/Loo4+ya9culi9fzvPPP88TTzyB3W7n1VdfBYh4hdt7eMM4HI6DNufZs2cze/Zs3G43P/zwA5988gnPPvsss2fP5qeffmLQoEEH5ToqKiqHBlUaXUVF5RcnHHYXDlGyWq0MGTKEn3/+GZvN9quMISsri7lz5/LOO+8wZcoUdu7cyZYtWwDFqANFwrg9oVCIb7/9FiDi0TkYfPvtt3FlvMNjCI+psbERu93OuHHjYgwpl8u1T2M0JSWFWbNm8eKLL3L++efT1NTEypUrASXMDYh8vy8SExPp27cve/bsoaSkpMOxd5XDDz8cSZIi67uvvgYOHEhKSgqrV68mGAx26RrhdYx3jdWrV3e4me6MXbt2AYoCXXviheu1HUM8j028uXb3GkAkhLA7XqHOnv1du3ZRVVVFYWFhlFfq16K7z2d7+vbty4UXXsjy5cuxWq0sXrw48llqaioAlZWVMeft2rULu93e5etotdourXlCQgJTpkzh3//+NzfffDN+v5/PP/+8y9dRUVH5baIaUyoqKgfMwoUL+frrr+MaB7W1tbz44osATJgwIXL86quvJhAIcMEFF8TduDQ3Nx+Q18rv97NkyZKYmjfBYDBiwJlMJgBOOukk0tLSWLhwIatXr45q/9hjj1FaWsqxxx5Lr1699ns87dm5cyfPPPNM1LEPP/yQ5cuX07dv34g0elZWFhaLhXXr1kWFrwWDQa688koaGxtj+v7iiy/iGgn19fXA3nmPGjWKY445hkWLFvHyyy/HHefmzZsj5wHMmzcPSZK44YYbou53WVkZTzzxRFenH+kL4JZbbomqZ2Sz2bjnnnti2ut0Oi6//HJqamq44oorYmotgeJJ2bp1a+T7c889F4B77703KnQyEAhw8803d2u8YcKy40uXLo06/tNPP3H//ffHtM/Ly+O4446jrKyMp556Kuqz8D3v6jVKS0u54YYb4o4rHLIYz0DoiAsuuACAe+65J5IjB4pBdu211yJJEhdeeGGX+zuYzJkzh6KiIp5++mk+++yzuG1WrVqFx+MBlGcwXt215uZm/H5/5LkHxTBPSkriww8/jHq+vV5vl7y0bUlPT6ehoSFuTa4lS5bEfU7DXsq2Y1JRUfl9oob5qaioHDA//PADjz/+eKQwbGFhIaBsbj799FO8Xi9z5szh1FNPjZxzwQUX8OOPP/LMM89QVFTE9OnT6dWrFzabjbKyMlasWMG8efMiOQbdxev1cuyxx1JQUMDYsWPp3bs3Pp+Pr7/+mm3btnH88cczePBgQPGUvfzyy5x22mlMnDiR0047jV69evHjjz/y1VdfkZ2dzfPPP3/gC9WGGTNmcM011/D5558zYsSISJ0pk8nESy+9FAlD0mg0XHHFFcyfP59hw4YxZ84cAoEAS5cuxWazMXny5JgN95lnnonJZOLoo4+moKAAWZZZuXIla9eu5fDDD48qoPzWW28xZcoULrzwQp544gnGjh1LSkoKVVVVbNq0iS1btrBq1SqysrIAuOaaa/jggw94//33Ofzww5k+fToOh4O3336bCRMm8NFHH3V5Dc466yzefvttPvroI4YOHcqcOXMIBoO89957jB49Oq7367bbbmPjxo0899xzfPzxx0yZMoWePXtSX1/Pzp07+e6777j33nsj93bixIlcdNFFvPDCCwwZMoRTTjkFvV7Pxx9/THJyMrm5uXGFQDrj3HPP5aGHHuKf//wny5Yto1+/fuzcuZNPPvmEuXPn8vbbb8ec8/TTT3PUUUdx1VVX8dVXX0Xu+eLFiznhhBP4+OOPo9qfcMIJ9O3bl0cffZQtW7Zw2GGHUVFRwSeffMLs2bOpqKiIucbkyZPRaDTcdNNNbN68OeJ9ufXWWzucy7hx47j++ut58MEHGTp0KKeeeioJCQl8/vnnbNmyhaOPPprrrruuW+tzsNDr9SxatIjp06cze/Zsxo0bx8iRI7FYLFRWVrJ27VpKS0upqanBYrGwceNGTj75ZI444giGDh1Kbm4uDQ0NfPjhhwSDwSgjVK/Xc/XVV3PnnXdy2GGHcfLJJxMKhfj666/Jzc0lNze3y+MM112bOXMmxxxzDAaDgREjRnDCCSdwzTXXUF5ezqRJkygoKMBgMPDjjz/yzTff0KtXr6hadyoqKr9TDqmWoIqKyh+CiooK+amnnpJPOukkuX///nJiYqKs1+vl7OxseebMmfLrr78eVwZclmX5448/lmfPni1nZmbKer1e7tGjhzx69Gj5lltukbdt2xbVljh1h8K0l30OBALyAw88IM+YMUPOz8+XjUajnJGRIY8dO1Z+9tlnZb/fH9PHmjVr5JNOOknOyMiQ9Xq9nJ+fL1988cXynj179nm9rtJWkvn777+Xp06dKicmJspWq1U+7rjj5DVr1sScEwwG5UceeUQeNGiQbDKZ5B49esjnnHOOXF5eHncczz77rHzSSSfJhYWFstlsllNTU+WRI0fKDzzwQFxZeafTKd97773y4YcfLickJMgmk0kuKCiQZ82aJT///PMxdbocDof8z3/+U87NzZWNRqM8YMAA+eGHH5ZLSkq6JY0uy0q9p7vuuksuLCyUDQaD3Lt3b/nmm2+WfT5fh/dbkiT5tddek6dMmSKnpqbKer1ezs3NlcePHy/fe++9ckVFRVR7URTlf//73/KAAQNkg8Eg5+TkyJdccolst9tlq9Uqjxw5Mqp9RxLYbfn555/lE044Qc7MzJQtFot8+OGHyy+++KJcVlbW4Rrs3LlTPuWUU+Tk5GTZYrHIRx55pPzJJ590eL2Kigr5L3/5i5ybmyubTCZ58ODB8gMPPCAHg8EO1+b111+P1AejtURBmM6e2YULF8rjx4+XrVarbDQa5cGDB8v33HNPTA04We5cCnxf8uztCUujd0ZdXZ18ww03yEOGDJHNZrOckJAg9+3bVz7llFPk119/XQ4Gg7IsKxL0N910kzxu3Di5R48essFgkHv27CnPmDFD/uyzz2L6lSRJfuCBB+Q+ffpEft6vu+462e12d0sa3eVyyRdffLHcs2dPWavVRt3/t99+Wz7zzDPlvn37ygkJCXJiYqI8ZMgQ+eabb5br6+u7tEYqKiq/bQRZbhcDo6KioqKi8idg586d9O/fnzPPPJOFCxce6uGoqKioqPwOUXOmVFRUVFT+0NTW1sbk83k8nohcfTyRBxUVFRUVla6g5kypqKioqPyheeyxx1i4cCGTJk0iJyeH2tpalixZQlVVFbNnz1aNKRUVFRWV/UY1plRUVFRU/tAcd9xxbNmyhSVLltDY2IhWq2XAgAGRGmQd1ZNSUVFRUVHZF2rOlIqKioqKioqKioqKyn6g5kypqKioqKioqKioqKjsB6oxpaKioqKioqKioqKish+oxpSKioqKioqKioqKisp+oBpTKioqKioqKioqKioq+4FqTKmoqKioqKioqKioqOwHqjGloqKioqKioqKioqKyH6jGlIqKioqKioqKioqKyn6gGlMqKioqKioqKioqKir7ge5QD+C3giRJVFdXk5iYiCAIh3o4KioqKn8aZFmmpaWF3NxcNBr1HV9b1L9NKioqKoeGrv5tUo2pVqqrq8nPzz/Uw1BRUVH501JZWUleXt6hHsZvCvVvk4qKisqhZV9/m1RjqpXExERAWbCkpKRDPJo/Fit3NvDOukoGZCVSXN/CGaN7cXTfjEM9rA4pbXTx1JKd1Dr9ZCcZuWxqP/pkWPerr0M999JGF69+V47NEyDNYuC88QX7PZdfgnfXVfD8ilJMOg2+kMTFE4s49Yh8Vu5s4NmlJZQ2tuALSiSbdNwzdzg9U82/6fmo7B9Op5P8/PzI72GVvah/m1Q6o7GxkaKioqhjJSUlZGT8dv/Gqqj8Xujq3ybVmGolHD6RlJR0QH+wShpcVNo85KdZKMrs+iYvfF6Yrp6/v9f7NRnYS0NOuZuylgA5GWkMyM8iKem3OdaSBhcfbGlmU30Qq1HP1qYQ25tCjOyzf8/ELzH37tzzkUlJ2IM6Nu9xMKxnMiP7ZB3QtQ82FmsS6M34EUAvY7EmkpSUxMBeGgKaSryY0BqhRYZvdrVwwshEXLKBEYXpbKttwRHSqxvMPxBqGFssB+tvk8ofE7/fH3MsMTFRfVZUVA4i+/rbpBpTB5GSBhcvriilyR0gPcHA3yb02edmt6TBxZqyJr7YUktDix+nL0SvNAu90iz7PD98vQqbB71WYN74QiYN+G1tlgGKMq38bUIfqpq95KWaf7NGX3g9N1bZcflD6LUCB7q1O9hzL2lw8a+Pf470d/sJQ/b5jHyxpZYmd4A9zd7fnNGdk2zCoNPS4guSaNKTnWQClHUb1zeDskY3IUlpu3mPnRNG5pKeYGBbbQvpCQbyUs2HcPQqKioqKioqf3ZUY+ogUmnz0OQOMCg7kW21LVQ1e7tkDG2ssrO7yYNZr8HtF+nfw0qTO7DP8yttHipsHhyeAPUtARZ8V/ab2SwvK65nU5WD4XnJEQNPluWYdr8Fz9qy4nqWFTfg8Aapc/rol2ml0uYhEJIYlJvE6IK0A+q/KNN60Ob26aYa1pY3I0syNQ4fb6zezcT+mTHrF17XGoevW8/koaBXmoVMq4EGVyDq7c//HVXA97sa2VXvRqcRaHQH2LzHQW6KCbNBS/8e1og397c2JxUVFRUVFZU/B6oxdRDJT7Og1wp8u6uR7GTTPt+ah40hSZLxB8WIF6TW4WN4Xso+z89PsxAMSey2eUg06mjxhX4Tm+VlxfXc+P4mnL4QSSYdVx7bj42VjhiP3f548n6Jsd66eAt1LT4AEoxaQqKMNyAiwwF7pg42ZY1ugqIUyTH6bmcjVc3eDtdVrxHQa4XfrCcnv9UL2+QO0CvNEjU+xTuVSVmjG1GSsbuD/HdNBUFRIhCSMOm19Mm0dsmLq6KiovJHJDk5maVLl8YcU1FR+fVQjamDjMsXwuENYjV2bWkrmjzUOr2IMngCIlaTlpnDcpg1LKdLm0NZkAmGJBqDfoKSTLXds89zfmk+3FBNndOPAHgDIp9srMFi1DEoO5F1u5v5bHMNs4blRIzJDKuBCpvnkBiCm6oc2D1BAERJxu0LEZJABtx+kdIGd6fj+jU9ayUNLppcfjQC+IISCSYdVpMuxuu0pszGjroW9FqBHXUtZCebmDUst8vP1K/JvsIg0xIMAIit31fbfZj0GuVeBUQG52q65MU9mHT1nv8WvK4qKip/bAwGA5MmTTrUw1BR+VOjGlMHkTVlTexqcGHSadjV4GJtuW2fm6hEs44Gl4aQJCGjGB82d6BLm69KmwdRghSLHoc3hC8o8sWWWsYUph/SzVuLL4iMYpAAePwh8tMsrNvdTEWTh2ZPgG3VTgbmJFLa4GJ7jUSKxRA3DPCXZnheMia9BlcghAaQ5L3jBmhy+Tsc16/tWau0eTDqtUwfnM3PNU4m9M/EGxCjvE4lDS5W7mhge40TT1BJNmr2uKhsKiHDavhNburDYZAlDS6WFddHjI+SBhc2tx+dVkNQUuYiA/6QhKb1/7vqXYwryvjVPG5dveclDS4e+aqYWoeP7GQT10wb8JtcexUVFRUVFZUDQzWmDiqKYIEsQ0iUKK5toaTB1eEmKj/NQkiU8Icz7IGQBN/tbOz0vLbn5ySZ2N3kBqBHkpGgKB/yUL/heSks215PqNUGafYGGZGfTCAksbHSjtMXoMbuZVe9i0BIwmrUkmLRHxIlr0kDsjh+RA5v/bCbkLjXAxLGpNd2OK5wjpxZp2FtuY2eqWYun9LvoI2tvWcjP81CeoKBJneA0QVpnHNkb4Aor86y4nrKGt14g1JUX66AyIJvyw+5od0R7Y2UGUOz+WJLLcW1LWjaL78MIZQQzBZfiBH5yfs9p+56j7qaF7mmzMbGSjsGrYY6p69LL1Z+K4RFcUBgTGHa72bcKioqKioqhwK11PxBZExhGiPyUzAbtBh0WoprW3hxRSklDa647cMCAW0RAK1WoKrZu8/rFWVauWb6AOaNL6RvDysGnZYUs/6Q58UMz0sm0awHlAes0eXnzR928+3OBtwBEadPxOEN0eIPkpNsosUvEhSlA/JMhb0aHa11Z+etK28mIIIU53ONRuhwXPlpFmocHj7bUkOFzcPbaytZVly/H6OPP64XV5Ty5g8VkWcoHBJ3zpG9mTE0OyK+MLF/ZtSGt9bpI96Imz1+3li9u9tr9GugCGvYMOuVsL3NexxU2Dy0+IKIEug1YNFrSDBoEATQCmDWC0iyTJMrAHT/GYi3xvsibNDuOwdNJijKeIMiQfHX97juLyUNLh75spgnv9nFU9/s5JGvin+Tz8vBZNmyZQiCEPdr9erVUW3Xr1/Psccei9VqJSUlhblz51JaWhq33yeffJKBAwdiNBopLCzkrrvuIhgM/hpTUlFRUVH5FVE9UweRokwr10wbwBurd7O6tImizARqnf4O314vK27AF4j2hZgNWrISjV02iIoyrcwalsOXW2qpbvbi8YtU2jyH/G1ydpIJtz9EQJQJhETKGtz4AnvNFRkw6rRIrfvMkCTzxZba/cov6U7oVXsvRKXNQ6M7tk6HBkWMIivR2KlnqqLJS0gCnUamxRdkyx7HQZGn7yifrKhVaXDBd2U4vSF0WjimXxazh+/Nh8pOMuH0Bmm/h7e5g3y0oZo6p+83FXa2rLied9ZWUtfio8LmoTDdwpSBmTi9Qcpbva6SBDISAkSeGV9IJsmsZWjPZJYV17PguzKCotxlQYr9ydnrqtR9TrIZAXB6QySadBHJ9996HlWlzUON04dJp8EfUrzrvyev2oFw3333MXny5KhjQ4cOjfx/+/btTJo0iZEjR/LOO+/g8/m4/fbbOeaYY9iwYQOZmZmRtvfeey+33XYbN954I9OmTWPt2rXceuut7NmzhxdeeOFXm5PK74eCGz/9xfounz/7F+tbRUVFNaZ+EeocPhzeIN9sr2dEfmeqfHKUByHBoCUz0cgRvVO7tXl5fVU5W6udSIDDF+K+T7ce0s1afpoFrVZAlGQElNBFUZKiPD8C0C/LSq3Th9D6/f6KULTdFG+stPP00l2cOCKX/DRLZOMKxDW48tMsJBp11BJtUOl1AhaDjoKMhA7v36YqBwBGrUBAlNEIAkN7HjwVpVqHj511Low6DRsr7ZFxLPiujJ8q7ARFiZAoU9rgYXutk2umDSA/zUKCaa+R2hZJBk8gRHlj56IavxQdGRLLihuwewNoBcVAqrB5efOH3VQ2e2kTAYsGSDLr8QYl8lNMeEISp4/KJz/Nwr8+/pmt1S1kJiqCFV2dX3iNE026bnlG99V2U5UDf0jEYtQSlCSW72gAiNT8OlTqle1pXyy8xuFFpwG7N4jLF0Sv1fLej5WMLvjjh/v169ePI488ssPPb7/9doxGI5988kmkIOoRRxxBv379ePjhh3nggQcAaGpq4p577uFvf/sb9913HwCTJk0iGAxy6623ctVVVzF48OBffkIqfwpkWULytkQd05gTEQQ18EhF5ddCNaYOIiUNLj7dVIPdG2TqgCy2tIoEdLQJGZCdRIpFTyAkEQiJCAI4vUE++Kma4XkpXfJwlDS4WF3aFGWoVDt+AzkaMggC6FotJUmKFnZINutINOvZY/eSlmCgoSVAZjc8cu2pdfjYsseBJxCi2uFlTamNoqwEDDot6QkGclPM7KhrYUhuUpS3sCjTygkjevLUkh2EHWcaFBW5vBQLp4/K73Adh+clk2E1Uuf0odfBnJG5B7VocnayiYJ0DRur7Ly7rpKVOxsYkptEVbMXX1Ak0Op6EiUpYiDlpZqpbvbGDfOTUTyACUZdp+v8S3hPOvIeljS4KG1w4Q2KiK3r7wtJbN3TEtNHSFK8axoBPEGJHklGmlwBPt1UQ1CUSTbr2NPsI8Go7fJzFF7jGqePWqdvn+3bFspuaPGh02jomWrmnCN7R+59WATEHRBBDiHJihG1ZY8DvVbDqN6pv4maX23nUuvwkWzW4/AGSTbrMes0OGVF4fLnPU4+21xzUPMBf2+EQiE++eQTzj333IghBdC7d28mT57M4sWLI8bUF198gc/nY968eVF9zJs3j1tuuYUPPvhANaZUDhqSt4WqJ8+OOpZ3+ZtoLao8uorKr4X66uIgEVbv+qI1f6ak0c2AHomdFnwdU5jGkJ7JGHQaNBoNvqBEutVAiy/Elj2OfV5Tqee0kfJGd9Rxb1Dk8801hyzXQVEZlNFqBIKS4hFpLyKg0QhsrrJT5/TR6PKTatEzY2j2fm8us5NNpFsNSDIYtRrs3gB7mr0Myk6kwuZh5c56ap0+vtlejz8kUm33sqy4nieW7GBTlR29ThvpSwYGZSeSkmDoVBRj0oAsTjosl8xEI/lpFnxB6aCtebj+Uo3Thzsg0tDi46fdzby7rpIqmydiSAE4vCFs7gDVdg+vryqn2R0/L8OoExiYncilk/t2KvXe3TyirtBWuCEsZR5++eDyh0g0RL/XkQCzIfbXk06jeDEd3iCb9zhY/FMV76ytJCiKOH0hdFrFo9gV8tMspJj1bK9rwekNsmJHwz7nW2nzUFzbwpY9dnbWu9lW28L/ttXzz7c3RPLlKm0eXP4QGgGCEogyNLT42FnXQlCUfjM1v8L3JKP1d45RJ9DsCSBJUqsSpIBRJyDL0OwOHNKx/hpceuml6HQ6kpKSmD59Ot9++23ks5KSErxeL8OHD485b/jw4ezatQufTzHGt2zZAsCwYcOi2uXk5JCRkRH5vCP8fj9OpzPqS0Xlj8xnn30Wlauo0+koKCjg6quvxuX6/eRsulwurrrqKnJzczGZTIwcOZL//ve/+9XXf/7zHwRBwGqN/lu9YcMGZs+eTa9evTCbzaSlpXHUUUfxxhtvxPRRWVnJrFmzSEpKYtCgQXz44Ycxbd59913S09NpaGjocCyiKJKVlcWjjz66X3P5M6B6pg4SYfUuULxLQrpln8ZBUaaVmUNzaGzx0+wOUNfip7zJQ48k0z7DxUoaXCz4rowdte7IG/0wFr32kBfwbWjx4Q+FPSexKnkObxC7J0iCQYsvJNHiD/H5llr2R0EsvCneVGVHkqHeFUAnQFCS+GRzDaIkI0ky+almGlwBvAGRjzdWs7XaQYsvhNxODl0GttW6OCw/hWq7t0NlxZIGFz/ubsbtF7EYtB2GKbb39HTF81OUaWXG0Gya3H4qGt34RLk1TE9C187GkIHGFj/PLy/FbNASkmKlNPRayLAa6Ztlpcbh63BOv1Ttr7Bww7rdzQRFkaXb66lv8VFj97G9xoE3FOtLC0kyKWYdBp0GpzdISJIjYX8BUSIQkjFoJTyBEKmWRESJGM9jZxRlWjmmfya1Tl+3zitrcuP0RT/RjlZjLOydavYECLSZU0hSlAdDooTbH8Rq1LKmzBYZx6EgfE8qbB40giIz7wmE2FbbgowSohuUICPRyIT+mb/5fK/9JTk5mSuvvJJJkyaRnp7Orl27eOihh5g0aRKffvop06dPp6mpCYC0tNiXY2lpaciyTHNzMzk5OTQ1NWE0GklISIjbNtxXR9x///3cddddB2dyKiq/A9avXw/A+++/T25uLm63mzfffJNHH30Uu93Oyy+/fIhH2DXmzp3L2rVrmT9/Pv379+ett97irLPOQpIk/vKXv3S5nz179nDttdeSm5uLwxH9Yt1ut5Ofn89ZZ51Fz549I2v1f//3f5SXl3PrrbdG2p533nn4/X7ee+89li1bxumnn87WrVspKioCwOFwcOWVV/Lwww9H5Xy2Z8WKFTQ0NDB37txursifB9WYOmjIBEUJpzeIPySzqcrOO+sq97nxGFOYxhdbamhoCdAzxQwCnD4qf5/hYpU2TyS0yeHb64nQAAadluxk0yF7813j8KFrv+NvRSsob+o1gkBQkvEERCQZnJ4Aa0qbaGzxs7HS3q18kqJMKwNzklhaXBc5FpKh3uEjJMlIkuLpqHX6SDDq8PhDyLKM3RvqsM9Usw4E+GZ7fYfjCd+DrEQD9R2EKXYk+d0VsYwvttTi9otKmCSKd0+SIY7dQUiG3TYPYwpSSDTrafEGCUrKegNY9DpqHD4+2lDN/7bVcXivVG4/YUjca1fYPGypdpBk0h+02l9FmVZG5CezfreNJneA0gbFm6oIHchoBGKM2h6JJk4dlU9mooEvttSyvaaFuhYlt80XVMQo3H6JJIsiQvHj7ma27HGSZFbyn9rnA8X7WRxTmMbGSju1Tn+XvEU1Di+h9m8vULxldm8w4tlKMOrQ4I96iSDKsLHSodxLYMWORsb2STukYiAj8pMxG7RsrXHS4gsiSaDTAbKAVgNZiUb+PrEP+WmWX7Wm2q/JYYcdxmGHHRb5/phjjuHkk09m2LBhXH/99UyfPj3yWWee6rafdbVdPG666SauvvrqyPdOp5P8/PxOz1FR+T2zfv16TCYTc+bMQatVokQmT57M0qVL+fjjjw/x6LrGZ599xtdffx0xoECZw+7du7nuuus444wzInPbFxdffDETJkwgLS2N9957L+qzSZMmxRRpPv744ykrK+OFF16IGFMej4dly5bx3XffcdRRRzFt2jTee+89vv7664gxdcMNNzBgwICYkOT2vPfee4waNYrevXt3afwd4fF4sFgsB9THb5Vuh/ldfvnlFBcX/xJj+V0zpjCdrEQTQUlGEEAU5UgeS2cUZVqZN76QwbmJ5KdZGF+UwaxhOfu8XjgMTK/VYNYLZCUayEzQc+zgLK6bMeCQbdDC+SLeQHtf1F40QEiUI8psMiiqf6JMkkkXCQPrDpmJRpQt7V7CkXDhra8oyrh8QWqdfupaYsOWBBSDxagV6Jlmodbhw6zXUFynKJq1J3wPki0GCjLMHNE79q31mjIbO+payE4yRiS/24e7xSPsIRIliUBIitQvk1v/jYsMFTYfQ3OTKcxIINGoxaTToNMKOHxK7o4oQ4tPZGOlPe6cahw+giEJs05DMCR1KY+oK5Q0uFj04x6q7F68QREZJf9pj8OPRGyxZAEQNEoo68qdjcwYmo3VpFVKB+ydrvKzJkl8vLGakgY3lTYPLb4Q76yt5JGvinlhRSn/+ngrL6wo7TBscUR+MsPzkhmRn9KFmQhoYwpfgUGnobzRzYsrSqlxeHH7QzHeWNj7LEpAICRS6/B1+1nvjK7Kw4eN/I831vDpxj3YPYoEvQwEQ0Tk3NMSDOSmWGKe44M55t8iKSkpHH/88WzatAmv10t6ejpAXK+SzWZDEARSUlIASE9Px+fz4fF44raN591qi9FoJCkpKepLReWPzI8//sigQYOijA2NRkNmZiY63e/jnf/ixYuxWq2cdtppUcfnzZtHdXU1P/zwQ5f6eeONN1i+fDnPPPNMt66fkZERtVaBQABZlqM85FarNRKO/P333/Paa6/x/PPPd9qvLMssXryYU045hZUrVyIIAgsXLoxp99prryEIAmvXrgXgzjvvRBAE1q9fz6mnnkpqamrEiPsj0u2n9LXXXuOZZ55hypQpXHbZZZx44omHpNjqb42iTCvThmRT1azks4hdSPQPM2lAFvlpln3KLbe/3oyh2fy420ZQlLG5g2RYDfxlbO+DKoLQXSptHoKSTE6SCYc3dkMnyspGObxx1gAIYNAKBCRocAUoyLB226uWk2zCYtBGFasNtBps4WvJKEIYHZFo0pJiMdAjyYjdE2BnXYtSeFWn4f0fq2IUzcIy2Z9trmHFjgZWlzZRbfdGiSus3NFAVbOX3U0e+mRakGXQa4Uu5c3UOnzUOnyIMqSYdTR7OvakAei0Akf1SaMgQyneOzrVwvqKZvy+2PPEeHJ/rauk1QqYdEr45cFiTVkTJY0uREkmEJIJhqQYkQytoMxBr2015Ow+QqJMVbOHhhY/Dk8QmeiQ0fQEPTZ3kNIGN3qdBkmSSbHoqWlVicxLNbOzzsWwnoaIERC+hwvX7ObN1RVIsqxImJt1rNzRwDXTO34RMaYwDaM29h2UPyRR7/Qhy9Az1YzFoEPAH1cIJExIkkk0de13xL4IF9pdubORoCjv03v06aYa/retlmBIxh2I/1z5Q4rc/8ZKOyt21rO7yc3uJjd9Mq0HzWP5WyY8R0EQKCoqwmw2s3nz5ph2mzdvpm/fvphMivx9OFdq8+bNjB07NtKutraWxsbGKLl1FZU/O01NTVRUVMR4W+rq6vj555+58MILD/gasiwjih2/4G3L/hpvW7ZsYdCgQTHnh/Mst2zZwrhx4zrto76+nquuuor58+eTl5fXaVtJkpAkiebmZt59912+/PJLnnrqqcjnKSkpDBw4kEceeYTHHnuM5cuXs3HjRsaNG0cwGOSiiy7ipptuon///p1e5/vvv6empoZTTjmFfv36cdhhh/H0009HvG9hnnrqKUaPHs3o0aOjjs+dO5czzzyTiy++GLc7Or//j0S3PVPV1dU8+eST1NTUcPLJJ1NQUMD8+fNpbGz8Jcb3u2L28BzGFWXQO93C8J7JzD28J5U2T5eS+IsyrTHFV/dFjcNLfYtf8ThIyqaou56E/S122xHhPIyAJMd9uNoaNwAZVj06AUKyjEGjwRsUMen3TxfFbNARz6zXaRRvhnYfNn+CUcexg3qQl5rAzjo3br+IKIM3KLGhws4bq3fHPW/FjgZ21LVQ5/RG8oxAMSzt3iBJJh3egMi26hY+3LAHlz/E1EFZ+wyVUkI1TYoAgCfU6cYcICPBiC8osW63jVqnj41VdkRJRm43bwHonZEQVxxlTGE6I/NSSLYYGJmX0qmASndoaAngC0pIrUacVhN7jzWC8hxLsuIRkVG+D0kytQ4f/jjGXb1LMbAMOg2iKCHJsLvJQ06SiexkE5U2L1oN7Kx3EQiJkQ3ysuJ6HvlqB1trnBTXtlDj8NLQ4mdDVXyPXZhKmyfuOCQZquw+fq520OTyo4vjvWqLXgM9U8wkdFEsozPCXqZ31lWxsdK+T+/RsuJ6Xv2+jEZXEIcvREc2s4ziqfxgwx62VDkIiDJOnyJ28sWW2j90Md/m5mY++eQTRo4ciclkQqfTccIJJ7Bo0SJaWvYqTVZUVLB06dKoXIIZM2ZgMpl45ZVXovp85ZVXEASBk0466VeahYrKb59wvtTgwYMJhUJ4vV5++OEH5syZw/Tp0yPlBTpi5syZvPXWW522Wb58OXq9vktf5eXl+zWPpqamDnMqw5/vi0suuYQBAwbwj3/8o0tt9Xo9WVlZ/POf/+SJJ57g73//e1Sbl156ia+++oq0tDROOeUUbr31VsaMGcMDDzyALMvccMMN+7zOe++9x7Bhw+jXT1FzveKKK/juu+/YsGFDpM3atWtZu3Ytl112Wcz55513HvPnz+fYY49lzpw5+7ze75Vu/yVPSEjgkksu4ZJLLuGbb77hqaee4rbbbuOuu+7ijDPO4LLLLmPUqFG/xFh/8xRlWrlm+gDWltuod/pZubMRuyeIXiswb3zhL+AxEhRPS6sMubabHsKuFrvtDmFvzcheKby0spTyRnckfEgANBqiNm9Ov4hOKxCSZAKSSI1d5O21lZQ1uiM5PV1JfN9UZWdPHElwRQ5c+b+wD2vEqNWyq8HFjtoWHN5gVF8hSea7nY0xwg1rymzsbnIjSjJ7mn2kJezNm8pPs6DXCjS5A4REkYAE3kY3dk+Q0QVpkXyeeHOKKM3VOhVjUCPgi5cs1YoGyEoyUtbkxqzXMnVAFusqmnF4g7j87QpD67VMG9wDUDbWbde17TN8MJ0PmYkGrEYdQZ0Gly9IklmHvyXQbo0Vg0oAdFoNqQkGUhMMZFqN7LZ5cAdEtOz1TCXoBYIypJj1eAIiIUnAatCRlmDg9NFKjsmC78rwBQ3Y3AFMem2kMPSy4nrs7kBUPS67J0CyWR/5Pp5wyNPf7MLl79hDGJRkvt/ViEGvQSvEz28DyLCamDUs56BIpIdV+YbmJLHE6ePnaif9eyQiy3LM/QWlBpbL17W3tB6/iMMTwB+SCYRCrSGZ8kEVJznU/OUvf6FXr16MGjWKjIwMdu7cySOPPEJdXV2UQXTXXXcxevRojj/+eG688cZI0d6MjAyuueaaSLu0tDRuvfVWbrvtNtLS0iJFe++8807++te/qrLoKipt+PHHHwG48cYbufHGGyPHjzvuON5++230en1HpwKwbt06/v3vf3fa5ogjjoiEn+2L3NzcTj8PhaJ//2u12kh01oHkSr7//vt8/PHH/PTTT12K9rr55pv561//Sn19PR9//DGXXXYZbreba6+9NtJm3LhxVFRUUFpaSnZ2NsnJyezcuZP77ruPr776Cp1Oxx133MGCBQsIBAKceuqpPPzwwxEvO8CiRYu44IILIt+fddZZ3HDDDTz99NO8+OKLADz55JNkZmZyxhlnxIzzlFNO2edc/ggc0GvRKVOmMGXKFKqqqjjvvPN4/fXXef311xk1ahS33norJ5xwwsEa5++KFTsaKK5twe4JkGDUYfcG8SzdFdcYODCFLBmtVjGkNIJAvx5WspNMcTdQ8WgrV30w696E6zfVO308+c3OSJ5IokmneBra5FPpNYIS9tVmb+cNSqyvaI54CLpi8JU1xuYntKcz28Cs19Av28raMhueQHwvkFYrxFkjGZ1Wg1EnExBD6NuEgIVDMbfVOCM1rERZ2bR/9XMtm6ocHc4pLKqxvqKZFIsiXd3ZDGSgvMlNolFPVqKR2hY/hekJNLh8ONwB3G3CHyVk3llbxY+7myN1uNqPYUOFnSZ3oNtiIB0xpjCdUb0b2bTHQYs3SEOrIaXTgE4jtHqgaC3yLBGSZPLTzAzrmUKqRU+Nw4vQKsBh0IZzrASSjFqyEk04vUGaPQEGZlvRaDQIgoAsy7T4QkiyjDcokp9qbuOxEWIKG4ckJVcoO8kU90VDpc1DkyeA0Ma92ta4AyWfrcEVICfZRH6qhTJb7HOpE8Co17BudzO90iwHHOYX9gbXtvgZmZfChAGZZCeZeGddJbUOH9nJpqgcyuF5yeg0Av4u2FMS0NQqtR9ervJGD05vkGq7JyIHX+NQ1rS7Spy/BYYPH87bb7/Nc889h8vlIi0tjaOPPprXX389KmRl4MCBLFu2jBtuuIFTTz0VnU7HlClT4iph3XLLLSQmJvL000/z8MMPk52dzY033sgtt9zya09PReU3zfr169FqtaxcuRK9Xk9TUxMPPvggX3/9NS+++CKXXHJJpG0oFOLOO+/k5ZdfxmKxcM899+DxeBgwYECn17BarYwcObJL4+kszK+8vJzCwsKoY0uXLo0ogXaUUwnxlUDDuFwuLr30Ui6//HJyc3Ox2+2AkvcEioKfXq+Pyn/q1asXvXr1AmDWrFmAIl5z3nnnRf0+0uv1Uetz8cUX83//938cffTRvPTSSyxYsIAlS5ZgtVqZMWNGlJromjVrqKioiDKIjEYjf//733nkkUd46KGHCAaDvPPOO1x99dUYjcaYueXk7FsD4I/AARlTXq+XN998k6effpqNGzcyePBgTj/9dD766CNOOukk7rzzTm677baDNdbfBe0l0pvdQbQa2F7j5I3VuznnyN6RzcaBeIbCam8CAllJRnSChvzUBN5ZV9mlnAlQNkA1di9NLj/9eyQedPU/mzuI3FpjSpaVOkeSLCjFTFvxBsW4oXmegEhxbQvZSaYuGXxjClP5YnN1XInt9mgEMOo0hEQJi0GLKMOgnCRkmY4V/gSwxsmBy0k2k2DQUu/0IUqwrtzGje9vYv4pSpz0xkpHjMcwHM7W2ZxKGlysK7fh8AYJtYavhffwGpTNeJpFT63Tj1YDoiSQZjFgMmgZVZDG8DxF1v2b7fXMPTyPz7bUKCIDMgRDEo1uP6ZmTVzvyC8hj67IkGewtcaJoFEkHTXQakCFQ/+U70MhmSa3H28ghMcvYnMrcvZJRh2+kERuiplUi57GlgB+UaShxc+Q3CS+L2lkQ5UDq1FHtV0xYnbWteANKIIXW6qdjC5IIy/VzIBsK2a9JsrIVEQ+5EiobNs1UDx1MhaDFo0goNcoxl+8CLkWb7B1PoJSX02ObheSocHpx2rSMSI/uVMPZVfX9m8T+kTlXC5cU6Hk+2k11Dmji3hPGpDFaaPzeO373XHHH1aN7AgZaHYHeeuHCnQaDZU2NzJg0msZkZ9ySNUJ94f2b8Q744gjjuB///tfl9peccUVXHHFFQcyNBWVPzzr169n8ODBHHXUUZFjY8eOJS8vj//85z9RxtQNN9zAzz//zE8//UQoFGL8+PGMGDECTZyw8bYsX76cyZMnd2k8ZWVlFBQUxP0sNzc3xsMVNlSGDRvGwoULCYVCUQZZOM+ys1zJxsZG6urqeOSRR3jkkUdiPk9NTWXOnDl88MEHHfYxZswYnnvuOUpLSzuUOX/llVfYunUr77//PgCff/45p512WiSE78ILL+T111+PGFPvv/8+/fv3jxn7P/7xD+bPn8/LL7+Mz+cjFApx8cUXx73mn0VTYb+MqZKSEp5++mleeeUVnE4nM2fO5KGHHuLYY48F4Pbbb+fmm2/mySef/FMZUyUNLoprWwiKMlajVnmTjiJ6EPSLLNlWhzcgRoyc9p6hteW2LnupwrLcuckmqh0+jDrYVuvE5gowJDeJ4toW3li9m/49EuO+LV64ZjePfFWMxy9iMeo4bVTeQd8ApSXo0WoE5FbViQSjHrNei8cfihg9YW9Ee2QZtuxx0L+HFbvHz9fbPBSmJ3Ro8J01RpHsfGdtJfUtfkRJptbpj9tWas2D0mlAEDQMyrZy6eS+vLCitOPJyDC0Z3LUGoUNWl9QMXY0GsWzsb3WydpyWxtDMIk6ZwNKGVRIMOlIMGpZuauRRKMubi2rSpuHFn+IBIMOhy8QU0sswajDaNCh1wZAEAiIEhU2DykWA9tqnMwalkNeqpmNlXZKGt2KJ6d1gyzKQFBCEIjrHalxeNlR52RTlUSKxXBAYgNt5cm/2FKLwxsEZEWVsLVNUCJSHDZ8NBCSCYVCZCfJuHyKpzDYavyOyEtBRqas0Y2utUDzT5VKfphGUIyZRev3kJdqIShK6LQCwVZRmHDtt0qbRynU3MaY0gpKWYEwFU0eft7jxKTX8PnmGgw6LWkWAwOyE3F4gjS5FGOufc6RDHj8ITISjaQl6Gnxi/iDEhr2GlV+UaKkroVHv95BoklPzxQzM4flHJBnp9ruodoezpOSI2qH8e5eWoIxrtGUYdWjQUO9K/7PThgJJTfNqNPQ7Ali0Gow6bURdcLfkzGloqJyaHA4HJSWlsZIc6ekpDB37lxef/11SktL6dOnD9XV1bz44ovs2rWLrCwlZWL8+PERFc3OOFhhfgaDocM0lpNPPpkXX3yR999/Pyrc7dVXXyU3NzdKjKY92dnZLF26NOb4/PnzWb58OZ9//jkZGRmdjnvp0qVoNBr69OkT9/PGxkauvfZannnmmciaybIcJQrhcrmi/t6///77nH766TF95eTkcNppp/HMM88QCAQ44YQTIl6yPyvdNqZmzpzJ119/TUJCAvPmzePyyy+PK3d4wgknMH/+/IMyyN8DYS9Thc2DQasBQUCv0xIUxUi+UKpFH6UoFg7P2Vbbgl4jsGJHQ5e9SvlpFlIseuyeAJlWI1aTjrwUM1821PJ9aSPIAhU2d9y3xSUNLhZ8W0aTO6jkdXiD7Kw7+Mnks4fnsq68mbJGN8lmPdOG9ODH3c3UOH14Q8GIpyXuVl2GJneAN3/YTaXNi4BS86YzzhrTm7PG9GZZcT2PL9lBnTO+mlr4unqtQEiSyE+zkJ9moX+PRFaXNsXUOwLlbX1agiHqWNgYPiw/hRq7N5LzY2gN9ctPs6DXCPy4x65cV1BU+XKSzYCAPxiiocXPxxurY8Lp8tMsBEWRJncg7hwm9MtkTJ803li9m7JGN0atQECU8YdClDUokvwT+2dG1Aab3QE8gVCkkLIM1Nh9CAJc1Oa6JQ0u3vyhgma3cn9cmuB+y6O39bwGQqKimGfU0ewJxrSVJJl0q5Emlz9i30hAuc2jyKQLAglGLZIM22udVNt9uPwhpYBxqwBLuH6TRgNVNg/1Th+BkHJcK4DZsDe2vcbhRUaOMnB0Og1mg5bsJBM1Di8BUcJs0OANSlQ1exmSm4TbH+LsIxXD/Y3VuymuccbMRZQVGX6nN8Rxg3uQYtGzZFsd1Q4vUqtTNlyA2NsSoMkVoMrmodHV/Rpr4XV+5MtiNlTZEYAR+SmcPiqfkXkp1Dh9DEwyRQmJlDS4+Orn2rj5XEERRhcks6q0Ebe/czVHb0CMGLoBUcIXlA5pfTsVFZXfF+vXr0eWZcaMGRPz2amnnsrrr7/O4sWLueaaa1iyZAljxoyJGFIADQ0NXfI4JSYm/uK5/DNnzuS4447jH//4B06nk759+7Jw4UK++OIL3njjjYjs+/Lly5k6dSq33347t99+OwAmkylGzRAUT5JWq4367KKLLiIpKYkxY8bQo0cPGhsbeffdd3n77be57rrrOvRKXX311YwdOzbKOJo+fTrXXHMNRx11FFarlSeeeIK//vWvAGzYsIGSkpIOc56uvPLKiIG4YMGCbq/XH41uG1MlJSU8+uijzJs3D6u14z/4Q4cOjWtp/1FpGxrlDYj0yUzgpwo7FUG34n0RFKnutnLYbcNzwiFZ3cpfksGo12IxKJvNLTVOBMCg1eIJhBAEHQatJuZtcaXNg9MXAlkJOdJr5H0qxUH387uKMq3cceIQ1pbb2F7TwsqdjYRaCw27fMEOVcQABI2APyjS5AoQFJU6Sw0t/n2uS0mDi3fWVlLZ5OlwTuHjvqCMICg1l15cUcqModn8XG2nqtmL1aijwuaJGB+aOOpsYWO4uLYlkkcjCNAnMyEioz4wJ4mVOxvRaQUkScYflPAEREx6LX2zrKwqsWHS7a1l1XZuLV4x7hz0OoExfdI4c3QvspNMPPJVMTvrWpABl19iR10LS7fXRdQhZw3LYVu1kwqbm7BpJgGugEhJvZvlOxrIT7NQafOwqcqhiIa0XrjFH6K4tiXOKPZNW8/rut3NJJl1rbk1sRi0GvLTLIREmSZ3AF1ryF9OkglBEGj2KM9BSJTxBkTc/iDa1sLPBq0Go07A41dUIXUIeIMS3qBEklmHOyCi12qwGrURLyAIikc0fN9QrmUyaKl1+mhoUQxArUZAQBnT8uJGEk06spNMTBqQhSzL3PPJNoId1FRzB0JUNnsYXdiTTzfVILfzwobvrSgDskx2silGvr2r61zj9KFBCZvdUuVgQv9Mrpk+IG65hUqbB1GW0WuJylUEcPmC/FDaxPCeKazf3YxfjH0CdRpl9QRBjhiiGgEm9s/g0in9VK+UiopKlwgr+bWX0wZlo5+YmMgHH3zANddcQ2NjY6TeGygy4itXrvxNvbRftGgRt9xyC7fffjs2m42BAweycOFCzjzzzEibsEy71Fmdlk446qijWLBgAa+++ip2ux2r1cqIESN4/fXXOeecc+Kes2TJEhYtWsTPP/8cdfzCCy+ktLSUG2+8kUAgwCmnnBLJ63z//ffp3bs3RxxxRNw+x4wZQ0FBAWazmalTp+7XXP5IdNuY2rFjR5faJSYmMnHixG4P6PdMrcPHzjoXiSYdowpSaXT5aXT5Meo0JJr0zB6Ww8xhOTG1isJKYRsr7V2qPwR76znlp5j5rqSRFIsBUZLQajRoWgUpQqJEQIx9W1zj8NLSRq3OoNNS5/TFhJq15UDyu1YUN7C6rAmPX6RnqgmLXodRp0EKSpHNZds9m14DyWYdmYlGnF6X4nmQFYMmXkhcW9aUNbFutw13q+KaFsVwiGeUhD2GmValBpEgCMw/ZQRVzV42Vtp5YUUJgZCy2zTpNK2FgfcSNobnf74NQRCw6JXaPKlmQ2R8NncASZLQagQkWRGrsBi02D3B1jwyDRur7IDA55trIkZYpc2DLxR/k947zRLxNEwakMWmKjs72hg8ogzvrati0oAsJg3Ias1XyqS8yU2zJ0CNwx/VdkOFHW9A8YLZPX7ChkbrHp915bZO17wzAiGRlTsbSTLrmDE0m3SrkUXr90S10QqQnGBgQv9MJvTP5M3Vu2n2BNCh3PO8VDPJZj2iJLGttoVquxdJhiSzjkBrvSqHN4RJr8VsUL5G5qewurSJQEgmwaAlM9GIxaDjm+31bKy0M2NoNnlpFrbXOCOeyMpmD+kJRt77sRKHJ4inNdfKrNciIGM2aAmIewsZ5ySbSbMYcAfiG4iSDLVOH2vLm3EHQhj1WnwBMe7zmGTU4wtK9ErrvmcnP81ColHHVpefoCjj9ouRZykv1cyasibWlNkiIYT5aRYK0xPY3egh2K72SpJJR4s/hC8o0ivdEvVCQSsoeVHZyUbcARG7JwiihFELep32TxMbr6KicnC45ppropQw22I0GnE693r+BwwYwP3338/u3btJSEjg3HPPRRRFhgwZ8msNd59YrVYef/xxHn/88Q7bTJo0qcuh86+88kpMiYV58+bFhEXui6lTp+JyxUYfabVa5s+fH9cgff/99ztV4tu0aRPl5eU8/fTTcT+/8847ufPOO7s1zt8zv4/S0r8TspNNDOtpoMGlbMyP6J2Kp/WteK80S4wh1ZZ4SeSdEQ4h+7akUcl7Mog4fSECokRAhASDltEFaQzPT2FWu+sW17oQNAJpFj12b5CsRAN2T7DTN+L7q/wXfmtu0WsJhCQaXQF6JBpbVe8EgpJEokFHU5vQr+xkM8kWPdV2L0FRQq/VkJdswqzX8M66yg4Lq4Zz1rwBiUCrwEFMYav2yLCz3s3YPsbIuhdlWqm2e7AYtMiSjD8kkWY1kp1kijm9KNPKsJ4prNzRgD+kePhqWg3TSpuHzzfX4A9JyDKYjRq0GoFEkw6jTmbqoCxKGlz8b1sdFr2WXfWuiHcqP81CsklPXbu8rx6JRm6ePTgqLG/lzkbal8D1BkVW7GiIyPGPKUxjY6WdNeW2mGKye5oVpbyj+2awbneIggwzJfVuvEEJrQDVdm+M12xfhPPJnN4Q5U0uTDodUMsRvVOxGrVRcu06rYa+WdbIcyrLMgu+KycoSrj9IY7oncr2mhZWlzUh0GpEiTKpFkU63eULYfcGaPGF6JFspFdaAt6gRK80C42uAFqNQIrFQFCUGZGnPL+CIHDttAHc+eHPVDYrOV0hCfwhka3Vyh9wnUbAoNMgt4bkaY0C2jbzq3F4MRs0UaGC7XH7QxSkWwhJMr7W+EUNincnKCn/N+k1jCpIZXh+CsPa5eV1haJMK30yE9hYaceokzHptVQ1e/lscw3bqp1R4X/hcN9rpg/A7g2yulRZ0/DLDLs3hACUNLiRZcXrl2zSEmxNrnL7Q1Q3+/C2cSsHRECQ2Frj5MUVpQdF/VFFRUWlLTNmzGDWrFkMGzaMvLw8pk6dSm1tbVwFOZUDZ+vWrXGPl5SUsHv3bm6++WZycnI4//zzf92B/UbptjFVWFjY4RtIjUZDSkoKo0eP5oorrmDQoEEHPMDfC/lpFnqlWWhyB0gx6yP5T5lWI4NyFaW4NWWKRGZnBlVXNyFtvQ2BkERDSyBS7DQkSoiSEpq0p13hzpIGF6UNLkRJxhlQDBjF+NN0+rYkbLyt3NVITpKpwxo28c7LSTJR5/RhMWjp3SoiUbGllmCrV0GrgQS9hoAkERLB5vbT4gthNelIt2ow67UM7ZnC6rImTLpYdbLwvMI5a1qtgCAIaDWK5Hq8WWlQcqbMei2pFj0T2hVMVqS809he10Jji49AUGLBd2Vx5zt7eA5fba2ltMFNeoKeRJOeqmYvy4obsHuDGHWKapzLLwESP5Q1Y9JrKMiwUNXswR+UCIVkLMa94gdFmVbmHV3AA18U4/QEkQCzXiAzKfoPx5oyxWsktLEaBUDfWqepbX9/m9CHgChR2eSJbI4FwOkPUmGTI2IUI/JzeGdtJZuqHAiCgNuvKOZ1h7DxbdQJygZdDlHv9PFThR2NIES8p6lmHRlJpnYGvzKbnGQTTa4AGkHY+6xbJBzeEPlpBkYXpNO/h5WVOxpZtzuATqMhEJTISjSRbjWwYkcDbr9IZqKBJlcASZb5NihGlCuLMq1MGljPwh8qCLRaE2E1x1SLHpc/RNAfQhCU8bj9IUbkp5CdZIo8aw5viASjFl9QbKtlESHRpAMEEgw6BEL4gxLJFj2O1pcHEuAJSqzY2RD5ee1umYRlxfX8b2s9nqBISJTxBCRESSnRUOvw4fWHMOiiw32LMq1cNKEPJQ0ubO4AekEgLUFPUIS0BB2lDR6l5pcgExCVlwEhSUanEfC1i88VUO6lAH+o+lMqKr8HNAYLGXNujDn2R0Oj0cT11Kj8utx99928/vrrDBo0iHfffReL5Y/3rO0P3TamJk6cyPLly6murmb8+PH06NGD2tpavv/+e3Jzc8nPz2fRokW89tprLF++/E9TwLetZ2ljpZ1lxfUMyU2ipMHNih317LH7Yt4OHyhhb0OFzUNqglLYbo/dhy8oEwpJeFrzXdrnSxn1Wo4qTGdVWROiJGPWazDpNfsM03EHQji9QXQaIhLseo2y0e1IhaxtEViA0QVprClr4pvtDciSjEYjkGg2kGKBimYvGpQ364GQhMMTwGLQkZtlxukL4vGHENDFVf8Lb95H9U7FGxCpd/podAfQaiS0GiU3JCzRrmn93i/KBKUQ/bIToxL024776aW7+HRjNb6Anx9KA3y2uYbLp/SLaXv22F689UMFoiSj1yo1jtISDGg1AsE4yWG+oMTHG6tJsSiKh4GQRALaKO/XmMJ0LhhfwA+lNipahRgqm7w8vXRnZMPd0OLDGxAjFYm1KPPLTzMza1h0fYeiTCujC1JZsq2OFm8oEm4mihL+oITVqCU3xczKnY04W40KSZbR7Ef4VjifbHOVPUrQo8UXIjfZSCAkkm410js9gV5twhZBKfAbFKWIWly61dDuWRexGHRUNXvxBkSykoy0ar5Q1uihxllJQXoCOo2GrEQD5U0eRdVPI+DwBpk0YK/hPCA7kQSTjqA7ukiz2x/Eoteh0QgYdRqCokSCUcfM1jVtcgfIsBrYWQeDc5PYUuVAkpWXA2GFPLNeQ4JBR7MngFGvQafR0SwpRpTQzmMaFGUMrQWeu2uMbKpy4A9JFKRbKKl3oxEgxaKjyRWg1qEIo8hASJTZWNnMxP5KgvKkAVk8cMpwVuxoIDXBwLCeyXyxpZaNVXYUx7GAL6hIxISHG4iTQwWKF6/W4SMz0agKUKio/IoIOj0JA48+1MNQ+ZOgGrTx6VycPw7Tp0/HaDSya9cuvvnmGxYuXMjSpUvZuXMnRqORk046iR07dtC/f3/uuOOOffbX0tLC9ddfz7Rp08jMzEQQhG7FWdbX13P++eeTkZGBxWLhqKOOYsmSJd2d1kGj2u5h3W4btU4f32yvJyhKuPwiJp0mSgziYBA24P4+sYj5p4xg7uF5FKRZ0GqUt93FdS6K61oiNXdg7ya32ad4TLQaAbsnhM0d6NQztabMxq56F5IkU9rgprzRTXaikQ1Vdt5dV8mLK0pbE/vjk92qKFaUaWVMYToj8pKxmnQkmfQMyE7k+BG5mPQaxNZCqwBarWLkeQMhNlXZ8QVF3P4QfbOsMcZPW2XEnGQTCUYdgqB4n7SCEmY5eWAWvTMS6JFoQkApnioAqWZ9h4agLEOwVSkuIEqUN7hj2in5bg50Gg02txJu9sWWWobnJTO6IJV0qwGTLtYgCUlKLa5gSMKo05DVKrYQ7vPFFaVs3uMk2awn2aKnrsWPKEvsbvJGjNPMRBNJJj0WvQ6dADqdgChDndPPmrLYAoJjCtPpn5WISa8hPKSgBJ5AiDXlzby7rpIfy5sj0v4Wg5Yksz4mX2xfhJ/NcX0z0LaZugzscSi5PakJBk4cmRsnLEwgM8nEoJwk+mYlkptiiXrW5x6ej0GnZVB2IhU25eetyRWg2RMkKCk5Q+WNbnRaAYNOmYcoKR4WhyfIoh/3RJ7VMYXpFGVY0Wqjhk9AVF4euAMhmt0B3H6xVV5epsbhRa8VaHQFSDTpcPpCJJh09EwxY9BqMGhpNajB6QvRv4eVvplWQpJMokmH2aBVVAjbYNRpYgRqusrwvGQSTTrqnQF0WoEEo446px+XLwSCEFl/myfIiytKWbhmd+TcSQOyuP2EIVw+pR/5aRZyU0wMyk5iWF6yYugL+46UTbHo0Wk19Ew1M298oeqVUlFRUVH5U9FtY+ree+/lzjvvJD8/P+p4r169uP3225k/fz7Jycn885//ZNWqVfvsr6mpiRdeeAG/389JJ53UrbH4/X6mTp3KkiVLePzxx/nwww/p0aMHM2bMYPny5d3q60AJb37fWVdFSb2LUb1TyUoyMaF/JoXpCfhC8cUgDpSiTCsT+2dSafPw9Dcl/Ljbjs29t/CsKMlRsufhTemUgVlKfaKQIo6QlmDYh2dqb90arUbZsIXVA4fkJkXeqMdbl0e+LObxJTt55MviiJDBBUcXMrF/FuP7ZnD6qHwyE40YtBpSzHoQZDwBEbcvSKXNy+Y9TnytCnhJZn3c3LPwvM45sjcDc5KwewNoCCv2QUaiAX9IwhcUW2sd7RWmCOc4xaMwIwGDToMgy2g1AkkWfUybsFcs1aLHExRJaZXAFwSBeeML6Z+dSGGGlRSzDrM+toBvklmPXqsYtmGDtm2OWlCS6d8jEatRR7JZj76NdTKmMI0jClLJTTFjMeoIikqtJU9A5K0fKmLmVZRp5dIpfSnKsmJos6MXZUXJzRtQ8o9KG93oNAIaQcPAnFjPXVcoylTqdxVlJqARiGzq9VqBFIseg1ZDTnJ0fmBJg4uVOxpw+UJUNXtJMuui1C8ntnpBIyUFtAKiFA6nU5Bk8IUk8lMtFGYm0CPJhE4rRIrsVtk9fLa5Jmo9BmQnxvwyFFAcfhajjpxkE05PgIe+LOY/K8tw+UKcODKX208YzHnjChhVkEa61YjVpCXJbMCk1zAyP5leaRZyU5R8yd7pCYzvm4EvKCIhRHKnijIsnDE6n8kDsyJ1sLqDYhAN5thBWSSZdEp4oigTrhsZlkDXapT6auvKm6POL2lwsXDNbu76+Gf+u7aSH8qaMBt0jOuTQbJFj7HNi4D2vyEEIDXBwPC8ZK6ZNiCSo6eioqKiovJnodthfrt27SI5OTnuZ6mpqZSXlwNQUFCAx+OJ264tvXv3prm5GUEQaGxs5D//+U+Xx/LSSy+xZcsWvv/++0j17MmTJzNixAiuv/56fvjhhy73daCEN79Dc5JY4lS8T0puEWQlmTiyMJ3CzIQYMYiDxbLiBupafIjS3jpCAKIksXmPPUqNrSjTSobVoNRE0mkQJZkEo65TI29MYTp9M2vZY/cyMDuJC44upNbp47PNNazb3YzVoIvr2VpT1sSGKntUrhMoxXXDifG+kMjA7CT0Wg0GgwZXQCQgS5GkeF9QIlzpKBCSqO+g7lFb8Yiwal5QkhnWMxm/qBR/HdUrlZ8q7Zj1Iq6AGJXjFO++hPOhdta1oNMIlDS4YpTtwvlkGysdBIIi63fbGZ6fTF6qmUqbB4NOy3GDe7ByZyNuf5AKmwdf6w5XkpQQL6Neg16r4YsttZG6V2GDIT3BwIyh2fiCIrUOH9nJe+sGFWVauWaaIoG9dHsdb/1Q0WpQKQZp+3mFhROO6J1GncOHJxiIfCa3Su0HJZlks56+mVZ21rvIsO5/gq+S+1XIA59vx9EaOhhqVZxLNMU+c5U2D3ZvkCSTjoYWpTZWvD73lhTwsGh9FdWtYg7hgEpRlNmyx0FGolEJ7WwTVxeSYHVJYyRcc9KALGocPh78YhvNnr3XC0qgFWRc/uDe2lh+EZs7SKPLz6zhORHjYXSBUvOrwuYBZIIhGacvxPC8FPJSzZECysV1LQiAWafBJYpIEtQ6/HyyqZrMRBPbapzdzpkKzwFga60TnVbJDwsEJcQ2YXlKtKkcZXiGX3YU17XQ5ApgNenQawRcvhCzhuXgC4psr2vB4Q605krJuAN7w1bD+YYT+2eqhpSKioqKyp+SbhtTvXv35pVXXmHmzJkxn7388suRKshNTU2kpe37bfaByOkuXryYAQMGRAwpAJ1OxznnnMPNN9/Mnj176Nmz53733x1MwRZ+fOthgoIWg8GIPy2JH9wiH7sl/LIWg9FIUXYqxuq+lPZIxWQyRX3l5eVhNu+/xyotwYBGEAjKe4UFBAFSLAb22GMFG0ARXzDrtbj9IfLT9p1EaDUpnhFr62ZMlmW8gRBVNg+SDE+1yeVpex0BZaMevtNhhT+TToM/JFFc28KgnCRG5KdQ6/Ch0wqUNriJl57hCUq8sKKUzEQjZ43pHXecYwrTGZnXSFmTG5MhhEajISdBjysQYl1FMwLQK8OCgBBRWuzMkPS3JvYLCGyvia0H1VYMJCgqYYoVTcqLhLZGUZJZh9MbxKDT4gspqmlajaLmlplo5Oi+GRGlxHDB3bbqjvlplrhqj2EjUpZl/re1njqnD0mWcftDUQZu2+KuIVEiJCmKh+HCyQKKRys/zUxagpEt1Q5cvhDf7WzEFxQPINdPwKTX0uIPIbYWNk5NiO9hzE+zoNcKOLwheqaa0Gu1cQ3d8Pefb65Br9WSn2ai2a2lzuFTTCYB6lt8jO2T1ir77sfbRiFij31vKYCSBhcNLb4ow6O1CyQZxDgK9S2+UJQoR1GmlVSLHrdfkbyXZEVtsK2n6W8T+rC23MYbq8vZuqclYvh5QyKeoKiEBXqD3VZObLt2hekJ1Dl9uH0hEOKrDH6/qyky9/DLDqVGVQhQXqyEDfbsJBMLvisj2aSnweUnJEr4Q36k1vsYkiT+t62OLXvs1Dh8HeZOqqioqKio/FHptjF17bXX8ve//52qqipOO+00evToQV1dHe+88w4//PADL7zwAgBLly79xcUntmzZwjHHHBNzfPjw4QD8/PPPHRpTfr8fv3/vZqhtPYP9wSK5+enLtzttswdY8Vz8z5YtWxa3LldDQwMjR46MMb7afwXQ4il30hIUEHQG9EYjOr0Bafgx+JNzYtTYRhekkhPYQ51bRCtpKSnz8bjLzoWTBzA4LwOdThdl6FbaPARFuVU+u5kF35VR2uCmutmLhGK4ba50xAg0jClMixhJbT0qOUkm9jR7cPlCJJr0bKtxcvqofARBoNru4YHPt0eU1drj8os8tWQXIMTdvIXFI6qavciyjCAIrfV2bLy7rpIhuUnUOv1MHZRFTrK5Uyn6NWU26lvXzh+ScPuDcZXtcpJN2D2BiAejwubhjdXl3HHC0CgvytPflOBu9baEU/t7JJtISzBElBLbhrXFM5riUdLgYlOVHVcghCTL6LWamNDNtsVdXa0y+uFxgBIO5mxVHxzVO409zV4MOi36VnGB7gojlDS4eOqbnXy3sxG7J0Dr5ZCBDKsxbuhgUaaVeeMLWfBdGUFR7tTQDRfKFiUJhyfE2MJ0tuyxU+3wkWrW4xdlfq52kmTWkZtsxhd04w9J6DVKLlg4LPXFFaVsrLLjbVfBtrM8ISnOhzZ3kJAoK7lVKDWmwp7G8L0ryrSyvaaF0no3oiQrRXFb+wqKMlpN1+qPxEMx6jPYWNmMRyOgE2gngq9Q1uRqY7AJEfXHZLOeY/pmMrpPWlS9M4NOyzH9UvhkUw2BkESKxYA/JJKRYKDc5sXl87Kn2YvNHWRjpV2VRldRUflNceutt/Laa69RXV2N1WrFbrczadIkQNl7dUZ5eTmFhYUsWLDgdy0DfqDzuO+++xg8eHC303EOFYIgcMcdd/xqta66bUz97W9/Q5Zl7rzzTq6++urI8ezsbJ577jkuvPBCAG655ZZfXP+/I+9X+FhTU2wCfpj777+fu+6666CNxeeLH3rWVTrySnm9Xqqrq/e7XyEll5SBWaxtV3i1V4qR9+84L9Jufeu/T7b+q9Fooow1rd6IRxRYrDWg0evJGHc6npzDIufLMoRkmWZ3gHvuuQdZliPn9gxCakhDtjGJraurMZlMTEiWCJmb2eb0MjwnE1t9DY5mC9NHFrLY7kWrUWr6xC9bC3UtPl5fVd7h5q0jw2NjpZ1ap5/0BENkw9g5MkadBq9G8VCIUmwR23BNpXDtJK2gbLbDsvThsSwrrqdXugWLQcPOekXIQhAg0ajD5grgDYroNAKVNk+3jZawQeDxh9BplQLB7UM3wzL1u1s9aAadBkmSIyGHoHgbvEERjSAwoEci63bbcIUkEkzxwzg7G9MN721k/W57lHdEg1IDbdqQjnODJg3I6tALF3OdehcNLsW4DZY08pexvfhxdzNBUSbFrGfCgExkWeaLLbW0+ELUOb1otRqcXsVrt6bMxo66FpJMOroxPfQaokQ5ShpcrCu3RXlTw8Wg2xuhkwZksmJHA40uP5IcwqDT4g+K+EMiZr02bj2zrhB+Dlv8IqIkE+xAeS8owpKtdZw5ulfUy45BuUlcMqVvTAhreoKBlTsbqXF4CYnKz4NOK1DfEoh4nCUZ9Jr9UyNUUVHZf0SPg6onz446lnf5m2gt8dMx/mx8+OGH3Hvvvdxyyy3MnDkzsi995plnDvHIfl1ycnJYtWoVRUVF+3X+fffdx6mnnvq7MaZ+bbplTImiSElJCaeffjp/+9vfKC4upqmpifT0dAYMGBD1FrxHjx4HfbDx6CxMsLPPbrrppihj0Ol0xohqdIcDNaZMpvgbqAPtVxT0+IISP+9xRIUP7atfSZLweDwd5r1pBh2HKVp5G1FSwpvm33cfXm/XFQu/bf33v8BZl91Ij6PPiBTAbUvd27cheZ0IOgManQF/cgLbDEZ+fCWVXlkpnXruDjvsMEaNGhW3MHJlZSVAVPuwZ25MYToDs5NYW25DECVESWLLHmfUWoY9JAkGLU5fCFFWQvcmD9ybQxLOVdJrBVpaDZ5Mq4GGFj8bKu2IkrJJbXYHOqxn1RHhfL2cJBOlDW6srfWq2tfOCnvs3li9m6Xb6rF7A3jaxbCFJDDrtUxolc+uaPawu9FNlc3TrXFV2jzssXtjwswkFC9ee9n2eHTFeDPoNOg1AnqtEDECbz9hSOQeg+J5CooyFoMGi1GHWa8lIEps3uNgW7WTWqePkChhNijFhNtfVd9aYLctyRZDlNFTafMgyjJmg5ZASEQQBJy+EAUZVvJSzZEizvlplkhu0YodDawpt1FW70KUQSsrYXO1HeQE7os1ZTYaWvwkGLW4/CG0GsXAkWRZKazbho1VjsgLgXDOXXi92taPC+enPb10F6UNLhIMiry8RqPFbNDiCYhoIqqQ+6dGqKKiovJLsWXLFgCuuOIKsrL2/k0ePHjwoRrSIcFoNHLkkUce6mFEIYoioVDoD1F4uVtqfrIsM3jwYFatWoUgCAwcOJDx48czcODAA8p92l/S09Pjep9sNkXkoLOcLaPRSFJSUtTXgdCjRw/+8Y9/MG/ePM466yxOPvlkZs6cyWFjx5NSMARjjyL06fnoknugtaahMVkRdHsfoF/KmNLoDEgykZCug9WvwRD78AsoXpsD6XtVeQvf7WpAQI7ZiAcbdxOoK8G/Zxve3Rup2vQ95euW8r9PFvHyyy/zzDPP8O9//5v77ruP22+/neuvv54rrriCiy66iPfffx/YqwjX1iCYM2cOvXr1Iisri6SkJAwGAzqdjoSEBMYM7M0nN82h9OkL2fOfS6h4+Uq2vnAld/z9DGbNmsWTTz4ZyfPxhyRSzTrSLHrOGtMLfcVannzySe7995NceffjPP3SG6xd/jUtJesJ7tlKXcnP+Bt2IzVXE3DU47Tb0Ile/P4glbZ9i7eECXsPfCGJzEQjPZJMjO2T3qHB4g2ImA1axZugjf4VYNZr6J+dGMmhCwuaeAOKsEZYBa8rY+qZEn9TXZCe0Lm3qdXT9uYPFZ1K7uenWSjMSECjUZT6Ek16hvZMjrrHbeuPabUaBIRWD5SMzR0gKMlMHZBFz1QLxw3OJrlVDhzApBMwaAWMei0ZCXoOy08m02pgQA8rvdIsUb/zwvlKiuKigZH5yZw7roC/TegDEDOfSQOymNA/E71Wg9mgVWqgRTLXuk9YBdHuDeIJiIocfKvwhlGnRd/uN71Jr4mEOYbXC4hR3gxTkG4h0axHlMFq0tMz1cLMIdmkJehJTTDQNzOBkw7rqYb4qaiodMj27ds566yz6NGjB0ajkV69enHuuedGpVxs2bKFOXPmkJqq5JaPHDmSV199NaqfZcuWIQgCCxcu5JZbbiE3N5ekpCSOPfZYiouLI+0KCgq49dZbAWWP1rb0zqRJkyKhfmGqq6s5/fTTSUxMJDk5mTPOOIPa2tq4c1m3bh0nnngiaWlpkRe277zzTlSbV155BUEQWLp0Kf/4xz/IyMggPT2duXPnxo04euuttzjqqKOwWq1YrVZGjhzJSy+9FNXmf//7H1OnTiUpKQmLxcL48eO7VA6ovLwcQRCiakTdeeedCILAzz//zFlnnUVycjI9evTgggsuwOFwRNoJgoDb7ebVV19VitgLQtTa1dbW8ve//528vDwMBgOFhYXcddddhEKhmOs/+OCD3HPPPRQWFmI0GnnnnXcwGAzcdtttMWPevn07giDwxBNPAErayyWXXMLgwYOxWq1kZWUxZcoUVq5cuc/5/9J0yzOl0+nIzs5GkuKlNf/6DBs2jM2bN8ccDx8bOnTorzaWAQMGdOg2fmLJTt76YTdNbj9t0zL0GmUTdsG4fPr27RP33IKCAr788kt8Pt8+vzZXNLBjjw2TRqTG1kIw4EcwJ4MAvdMSonJUAoEAWr0RMRib/9MVUpMsOOIcb27xdiscrD1GoxG9VoOkk/GFpKhQJTkU6OTMzunIWIX4huW+PHM7qmAHiiDL5ZlWZgzNpri2RTFU9Br697DyzI3PsHTp0m6NswJYCzTPOoFJn34Ut82dd97JypUrY3LmQujINRgxmkwYk60sqk6K8dDVBk0UN6fR6PLjDYhRMutywIfVqCfQaszlp1mwGrWKlLcMUkhkxY6GLilSFmVaOeWIPLbVOKPU3zRAz32InYQ9fRlWAxU2T4dhY0WZVu44cQifba6h2R1gQquiXFsvUFsBkEyrkWa3Ek6ZaNLTv4cVb0CktsXPgB6J5KaY0exQvF3BkIRGENBpBcx6DTqNhsIMKw5vSKkNJspRz3m84tThMS8rro/I3G9rU0Q7HHa5p9mDXquIoaQn6Kl3+mMUI/dFpc0TMQyX7WggFJIitdGsOh2DcpLYvMeJDBi0AoUZCTEepI6UN19cUUqTO0CfjAT6Zlnp18PKxkoHtS1+huQm4w2K6LWaSFirioqKSns2btzI0UcfTUZGBv/617/o168fNTU1fPTRRwQCAYxGI8XFxYwbN46srCyeeOIJ0tPTeeONNzj//POpq6vj+uuvj+rz5ptvZvz48fznP//B6XRyww03cMIJJ7Bt2za0Wi2LFy/m6aef5qWXXuKLL74gOTmZvLy8uOPzer0ce+yxVFdXc//999O/f38+/fRTzjjjjJi2S5cuZcaMGYwdO5bnnnuO5ORk/vvf/3LGGWfg8XhicpL++te/Mnv2bN566y0qKyu57rrrOOecc/jmm28ibW6//Xbuvvtu5s6dyzXXXENycjJbtmxh9+69dQHfeOMNzj33XObMmcOrr76KXq/n+eefZ/r06Xz55ZdMnTp1v+7NKaecwhlnnMGFF17I5s2buemmmwBFVA5g1apVTJkyhcmTJ0eMnrADora2ljFjxqDRaLj99tspKipi1apV3HPPPZSXl7NgwYKoaz3xxBP079+fhx9+mKSkJPr168fxxx/Pq6++yl133YVGs/fN34IFCzAYDJx9thLGGnaU3HHHHWRnZ+NyuVi8eDGTJk1iyZIlMcZxewoKCgAiquMHk27nTJ155pm89tprzJ49+6APprucfPLJXHLJJfzwww+MHTsWgFAoxBtvvMHYsWPJzc09xCNU3hhvr3GiUV49RxGUoMHl5/syB0f1i58nk5SUxLRp07p8rXDujFTvQhSVd92pZj3nHNU7qv+8vDz+t7mCa97+icYWL3IoCKEAWjnIgEwTl08sINOi6dBwq7b2Z+FWD/42+TYyUOPwUjB2Gv3TjWjlUJeMwLab0gBamlx+BEFAI4BRJyCKMiaDFlkMdmvt29JdY6q7/eYkm8lJNtPkVoqlfrGlFntLbIHfrtLSwVRLGlx88/1aVrb5JdwdRo4Zh3HOXRFBiGAbJQXH8gVUrP+UtcC7Gg1Gkwm0eoLoQKtHozNQbzKx/YVkslITo4y0wsJC7rnnnqhrFde6EAQBuaEEd105Gp2RBIsJf56dlSttHYZkhoJBauxedta5SNxHrlZRpjVK8GRZcT0Lviuj3ulHpxX4y9hebQRAvIQkWQmvdAXITbEwpjA9EuK2pswW8QvpWo1Ms15DkzuIViPwTXE9iUYdw/OS8AalGG98R3l67WXu2wqMhA2w4lpFJbLe6efttRVsr3V2Sz0xfI3aFj/ZSSYCIQlfSCQkShi0GqrsSohpglGLSadlVNx8wfjKm20NwYkDslprfSkiFttrWthR18Ko3qlRhqKKiopKW66++mp0Oh1r1qwhMzMzcjy8UQblRWEgEGDp0qWRtItZs2Zht9u56667+Pvf/x5Vmmfw4MG88cYbke+1Wi2nn346a9eu5cgjj+Swww6LGE9HHHEEGRkZHY7v1VdfZdu2bXz44YeceOKJAEybNg2v18uLL74Y1faSSy5hyJAhfPPNN+haC/pNnz6dxsZGbr75Zs4999woo2DGjBkR7wooRsH1119PbW0t2dnZlJWVcd9993H22WdHzee4446L/N/j8XDllVdy/PHHs3jx4sjxWbNmcfjhh3PzzTfvdzmgCy+8kOuuuw6AY489ll27dvHyyy/z0ksvIQgCRx55JBqNhszMzJhQwTvvvJPm5mZ+/vnniJr31KlTMZvNXHvttVx33XVRIZUmk4kvv/wSvX5vzc558+axePFilixZEpmzKIq88cYbnHDCCaSnpwOxTgtRFJk+fTrl5eU88cQT+zSmwvfql6DbPY8cOZK3336bKVOmMHfuXHJycmI2FXPnzu1Wn59//jlut5uWlhYAtm7dynvvvQcoD4rFYuHCCy/k1VdfpaSkhN69FUnsCy64gKeffprTTjuN+fPnk5WVxTPPPENxcTH/+9//uju1X4TwG+NRvVL58ufYECmTToPdEzxom5AR+ck0ufzsqtsrvewPSdQ7/SwrrgeI5ENMGpDFtTMG8vyyEmWjKVtINumwZFqx5hYxpn9mh9cpaXCxxbGR9RX2KGUzSaMn58TrufnU4ZHQoc6QZZlgMIjP52N7VROvr6tlWYmDYGsCvVmnoXdWAscPz2Fh9a2U1TVDKIBeEDmmTwoDM034fD5qbU42lDfg9njRyiHyk3RRxlxn+XBtQwy6S9iYyk+zkGTWUdXsJTvZRFCUcXn2/029MxAb7hU2lsvq7Pvdr8FoIifZjEGnocbuRZIgQa/BHZSQxL2eP0mS8MbxygWALVWx/Y4cOTLKmCppcFHa4CIQknBs/RbH6ncBaADuXwj372OcgqBBazBgSctGOHFN3DYff/wxr7zySsQz5/BDuT2I3Q+iRoegNbD5KzOnjilkREEW9gAESpvZIWpJS0wgL3VojAE0pGcyW6rsAFiMekRRKWqdm2yi2uFDFCW217YwIj+ly7lBbetidSRrv6y4ntWlTUiShE+C8kZ3t34nFLV6RzfvcSDLMu//WBVRoWz2BsmwGrAmmahx+giJxAjSQMfKm/EMQVByvsob3Th9SijHvkoMqKio/DnxeDwsX76cCy+8MMqQas8333zD1KlTY/5en3/++Xz++eesWrWKGTNmRI6HjZ4wYSXn3bt3dzs/aOnSpSQmJsb0+Ze//CXKmNq1axfbt2/n4YcfBogKZZs1axaffPIJxcXFDBo0qEvjzM7O5uuvv0YURS699NIOx/f9999js9k477zzoq4JirH24IMP4na7SUhI6Na8Oxqfz+ejvr5+n/oHn3zyCZMnTyY3NzdqXDNnzuTaa69l+fLlUcbUiSeeGGVIhdtmZ2ezYMGCiDH15ZdfUl1dzQUXXBDV9rnnnuOFF15g69atUXu3gQMH7nOeu3bt2meb/aXbxtS5554LwJ49e+JKSgqCgBivMEsn/OMf/4hyZb777ru8+66y+SorK6OgoABRFBFFMeottdFoZMmSJVx//fVcfvnleDweRo4cyeeffx5XZvxQEH5jXFzXgk6rJSDuTXDXCYpog14rHPAmJLzRbnIHsHuCGLRaQpJyLX9I5MMNe/jgpz1kJ5volWaJ5DbkJJtJTTDi8AWxe0I4fSEqbB6q7Z3n7ChhXPnsafZS44w2Rhpdvi6H+gmCgMFgwGAwMGZwEiUuLd/vbiEUEDHpBNITjVxwdCFnju5FZuL/seDbcrxBkexkExdN7sukAVksK65nU5WD4xIN5KZY9qkA156ysrJOPWc/7Kzlv6tKsDld+Px+Ug0wtX8qeUm6iEc0nqQ3o0aT1yMDe4ub6iYnHq8Pv9+HHAoihfxIwSCyGCCejFxKYmwoXNhLYNLsf5htstXCgOxEKmxajDolbyYoyRi1AgY5vhR9V2jv+au0eTDqtRxdlMFHS7rfryxLhPw+BDHQ4c/G9u3bWbRoUaf9NAGPfBJ73Ggy8eZt50cdK8q0cscJQ3jk6Rd57u5rMRqNaHRGQoKWCq0BtHoSLGYqNToaUxPZ/VZaXM/avffeG7MeRZlWrJKbNauXUhrnnPoaF3V19dh8MoLOgE4jdFs98YsttRTXtlDj8OJtVZxIMetw+UMYddpW77iMICiqlu3LGLQXo2hbH6v9sTVlNjZW2jFoNQRCIgOzEzn7yN6qV0pFRSWG5uZmRFHsMMQuTFNTEzk5sbm+4Sij9jnyYY9FmLCQQXcEsNpeO57hkJ2dHfV9XV0doJQJuvbaa+P21djY2K1xNjQ0AHS6PuHrnnrqqR22sdls+2VMHcg61tXV8fHHH8cYSGHar0W8+6vT6fi///s/nnzySex2OykpKbzyyivk5OQwffr0SLt///vfXHPNNVx88cXcfffdZGRkoNVque2229i2bds+x/pL0m1jqrs5IF2hK/GLr7zySlTiXJgePXrEJCceStrma7RVw/pscw0VTW7cbWS1BAF6ppqZN77wgDchbcNx1u1uJsWiw+dUEtFBaJUtDlKQrqG4LrrwbGWzB0drTSeNRsAXFPliSy1jCtM7HdeYwjQO65VK07baKLWwoATLdzREVMu6w5jCNIb0TObnPQ50Gg1DeyYzuiCNkgYXGysdJBh12Dx+bO4AL39XxrLiBv63rQ5/UCLRpOP2EwZ3ey3DxlxHIiQ9B7lYE9iEr9aJRZIRNAK1OUlcdcrwdpLX7SS9T9mbOFrSoIRFfba5hl31Ltz+EP6gRFqCHlkMcfKIHpTU2PixtA6LVmZYQY8Yz0HYMC+ceCq9D5vAEXlWEnRyl0Ipw1+D+vWJ2hyvKWtiXXkzvdMtPPKFRMfFBDqnvfEQHmuTO0C2Vbff/WYkd1xba3/+YIYxdxD2WZRpZWRuArIk4fN6gehrhH13jnLY8VP8vu+77764x9esWRPzBrAjygWBGfeYsJhN6A1GtHoD7376NeOH949pW1JSwuU33MauJj+ekAaPpEGnNxAU9Dh1BkwmI/lDetLsF3BXteDUGNDo9LzRXEaR1sbkMcMjpRnihSrGD19slcsQQKdVREtUQ0pFRSUeaWlpaLVaqqrihDW0IT09nZqa2AiesFhDZ2F6B0p6ejpr1sRGQbQXoAiP4aabbuowCmvAgAHdunbYW1dVVdVhFE34uk8++WSHXrdfS0W7LRkZGQwfPpx777037uft0206EqubN28eDz30UCT37KOPPuKqq65Cq9VG2rzxxhtMmjSJZ599NurccFTboaTbxtRvxePzW6Stdyg9wcCMocobjfw0C8N6JpOVaMLlC+EXZYxa0Go05CSbIsppB0LbvIwUs55AqhmHL4THLyIIMpU2DxqthrXlNiwGHSt2NDC6II0ah+JF0mkgIIIkyaRa9ARFOSbMKJ6heM30AZgMWj74aU9UuN/S7fWcsx9vqsPegbaJ/ACfbqqhwuYh1aJna42ILAeosXvZXOXA5Q9RkG7B7gmxZY9jv4y4fWE2apWaTEEJnQZKG9xRBmlbqu0equ17ldLC/xZlWhldkMbackXCel25jRZfCKtJR7lToj5kQpOYSd+eyTQHpZj1j4SLDTu329639rQd11ljlLDZwmdf4Ln/baayoQWNFMQghKhqdCIGA0pOnRjAJIj0STMwoSgFaxtDLpzYGTPWZi9JxYMw2A6La9ztyyCyWjr22B5Irps+jhplmAMJ+wTFOI9Ht8Yry/h9Xvy+veuzcE0V2Tm5Mfd9z549fP7+wk67eyOOd64WmP04bNiwgREjRsR8XlJSwpgxY+J632Stnj3OEEFBh9Vs5rV1qVhv/BeDendcP0xFReXPidlsZuLEibz77rvce++9HRpFU6dOZfHixVRXV0dtwl977TUsFssvKu09efJk3nnnHT766KOol15vvfVWVLsBAwbQr18/Nm7c2OGLs+4ybdo0tFotzz77LEcddVTcNuPHjyclJYWtW7dy2WWXHZTrdgej0Rj37/Xxxx/PZ599RlFREampqfvd/6BBgxg7diwLFixAFEX8fj/z5s2LaiMIQoyM+qZNm1i1atUBlTY6GOx3NpbD4WD16tU0NjYya9asA1rEPwrtvUMLvivDoNOSnmBgRH4yzZ4AoiQjAH4RdJLEpioHN763iUun9D0gI6Dt5rXa7uWb7fUUZchs2uNAksEdlNCJStJ8qkWI5GmBjFGvWP42dxCNIOAJSKRY9FHhVe0NxbDkc6XNw4kjcvmx3MZu294fNLcvtN95YG3fhIevW2HzUOvwEQxJSLKMPygSFCWMeg2iJFPe5KFHkomhPQ9+ocJKm4eQKGPSa/EGJUISuP0hGlqiN90lDS4e+bKYDVV2BGBEfkqMiED7ubVN4s9PMbOt2snqUhtpCYa4YV4diRyE+2tr7HaXE8b0x4WJd9dVMiQ3iXW7m2mxuvC11vxKMGg5fXQe5xxZ0KX+w2OdeO9dcG/8Atltc+bifXUUOgBKoqzJtDdnzuZ0Y29xU9XoxKKVaG5xk2nWYNKI+Hw+nG4PjXaXonJpSelQMe9AjDSj0djhm7cDLUfQEtLE/Zk6UOOvI3EWj8cTUU/aFzuBjKl/JXe3R5VHV1FRieHf//43Rx99NGPHjuXGG2+kb9++1NXV8dFHH/H888+TmJjIHXfcEcnBuf3220lLS+PNN9/k008/5cEHH4wSnzjYnHvuuTz66KOce+653HvvvfTr14/PPvuML7/8Mqbt888/z8yZM5k+fTrnn38+PXv2xGazsW3bNtavXx9JU+kqBQUF3Hzzzdx99914vd6ITPnWrVtpbGzkrrvuwmq18uSTT3Leeedhs9k49dRTycrKoqGhgY0bN9LQ0BDjsTmYDBs2jGXLlvHxxx+Tk5NDYmIiAwYM4F//+hdff/0148aN44orrmDAgAH4fD7Ky8v57LPPeO655/YZ3hnmggsu4O9//zvV1dWMGzcuxsN3/PHHc/fdd3PHHXcwceJEiouL+de//kVhYWFMHlk8+vbtC/wyuVP7ZUzdfffdzJ8/H6/XiyAIrF27ltTUVKZOncpxxx3HjTfeeLDH+bugrXdIrxUIijIj8hQVrEZXgJxkM4IA9Q4/EhCSoaFFCVl7eumu/d4EhwlvXpWQODvba5xRAoIhCYxasHmCJFuCyLLMmMJ0+mbWsrZcCcQSBAhJMoNykqLG0l7Va225jQ0V9ohxNXlgD95cXR4pbtrsDewz76ortK0TtHJnI81uPwLgC0oIGgFJhqxEI+6ASGZifI/AgRKuIxUMyWgAQaPkuWUmRr8hqbR5qHH6kCQZtz/ETxXNHXqvwmyosFNh81DR5GFHXQshScaqV6S5u1O8NZ6xuz/P0pjCNDZW2ql1+rEadBj1OiRCSJKMQachLcF4UDfKbXPmulvrbcqUKUyZMiXqWGfrsKy4njd/qIiRKG/PvHnzGDz6GF5eXkxxtQ05GKBXip6ZgzMI+HzUNrdg0UpRnrnwV2eEVQ87U7PsDFHQxs0fO1AjrdYVIl5QSnf7HZKXzq7mgKrop6KiEsOIESNYs2YNd9xxBzfddBMtLS1kZ2czZcqUiDd/wIABfP/999x8881ceumleL1eBg0axIIFC2Lkxg82FouFb775hiuvvJIbb7wRQRCYNm0a//3vfxk3blxU28mTJ7NmzRruvfderrrqKpqbm0lPT2fw4MGcfvrp+3X9sFz8k08+ydlnn41Op6Nfv35cccUVkTbnnHMOvXr14sEHH+Tvf/87LS0tZGVlMXLkyF98fR5//HEuvfRSzjzzTDweDxMnTmTZsmXk5OSwbt067r77bh566CGqqqpITEyksLCQGTNmdMvRcuaZZ3LVVVdRVVXFHXfcEfP5Lbfcgsfj4aWXXuLBBx9k8ODBPPfccyxevDiuhkN7umJw7S+C3M2iQM888wxXXHEFl1xyCTNnzmT27NmsW7eOww8/nEcffZRFixb9JgpodRen00lycjIOh+OACviWNLioalZqLX2xpTYq5O+LLbVsrLJTZfMiINPiVxKN9FqBvllWbpg5qEsKeF0dxxurd/Pqd+UxxW+TTDr6Zlnp3yORv03ow6ebanh++S68QQlkSDBqOXVUflSYXvtN6oj8FL7ZXh/ZmA7PS+aZpa19tDJtUBYvnDf6gOcR9ky1+JSCpP6QhDegbPAFjYDXH8IXktEIkJpg4OHTRhz0UL9lxfU8/NV2imsUlcRUs56HTx8ZdZ2SBhc3vLeR9bvtSIBGgCN6pzK/XW5V2z7f/KGC7EQjH22qxuMPEZLAbNBgNeq5elp/zhzdq8vja2sonHNk7/1+lto+wy9/V8ZPu5txB0T0GoHMJBP3nDT0FwmlPFiEx98+FLK7BmfYcwjRdaMOJrIss726mbs+2MDGsnrcHi+CGEQKBZCCAUQxiFEOIolBjpk6nRNG5jGmMHosmzdvZsGCBRHjbHe9nW1VTQT9frw+H3pCGAURxCDNTjdiKIAcUkI3ZTHAs5+u4eKZo2LGtnLlSiZMmNDluVy44AcyEk37ZcgfrN+/f0TUtflzUHDjp/t1nuhxUPXk2VHH8i5/E61lrxenfP6hL2WjovJ7pKu/f7vtmXrqqae4+uqrefDBB2NU+/r168fOnTu7P9o/EG3DsKLECFqLdIZFCDZW2iNeIwHITDQeVFnhokzFWNJrBfxitL0sI5OXao4IUWQmGkkw6hClIAFRUfsqrm3hxRWlkY1Re3lnUBTBwpLJGVYjRp02ypjaUGXvdvHRePOYMTSbBd+VodNo8AeDeIIhWrxBxNZLha8oytDkCvDxxuqDvtkPqwaW1Lv5//buPC6K+v8D+Gv2BpZrOQSUKzzQlExFLVNBzdu8NbuUUisttazEG7w1LM0ss0wsj2+eHZ552888sLzwFgVRQZF7WY49Pr8/cCeWBd3l2l14Px8PHuXM7PCe2dll3vP5fN4f/ZRhqTkFRl3rmtd3Qfz9HHAoHn+WmVf+k3p9S+aZO5koKNI+7h5WXBLe00liMMny05Q3l5GpEtKUOH07HWm5RfBwlPI37L4Ke8z8NR5n72SigatdtY5LqyrldYV8Uolyc/ZTlTiOQ9P6CvQPbYi0AgGUhRqoCjXILdRCCEDfyVEmBC6l5CIp44ZR99EWLVrgiy++4K9FANgcl4yTt9OhKtSivqsM9Zzs8EpLH/z4123cfKjkv3scpAI4u5WddIeEhODo0aMmFTZ5mKXEmy8GVnosHyGEEGJrzE6mbt26ZVCqsCRHR0dkZWVVNqZao/T4mOQMFUIDFPBykmHBrisoVKsgEXFwtpegVwvvargJYRCLBCgskfQKAGi0DMeup8FBWlyIYlgbXwR7O+FCchY4TgcGwF1eXImtZCJQ+uay5I1pcoYK9hIBskqMT8wr1D61m5upJCIhOjZywZmkTOQXaYrHgpV5xEBuvrrS44dKS0hT4vjNR8XdCx+XtL+WmmvQ1XFMp2cQ1sQDBy4/QJqyEEKhAPVd7cpNbPQ39+tPJiElKx+qxyURJSIB3BzKL5DwpH2ZmiiUPrboPy7hQnIWCtQ6ONuJ0TrAFZO7F3f+EgmKxwAlZ+TD3VFaLePSagpfwfJx0mEtN/5tAxX463oazt3NgkZn3FlAowUK1TrIpUBqdkGZxWFKtroFezshMT2veJ6vfA0auHLwcpLh2fpOSMrIQ9HjltwANznaBroZ/T4AcHZ2NqllSv9Zq02J1A8//IAxY8bAwcEBSqXSYN2///6Lzz77DCdPnoRIJEKXLl0QExODZ555xmg/K1aswMqVK3H79m34+Phg1KhRmDZt2hPHAhJCCLEtgqdvYsjZ2Zmvd19aYmIiPD2t94m1pehvdDacuoPvj91CSnYBHKQiCDlAWahFboEGXk5lDwKvjLaBbghwczB4k0smIK39XaHWMnAch17NveEoE0MiFKCgSIfjN9OfOv9VkIec70q2OS7ZoOw7AGi0OqMiDRVRstXFT2GPAHc5ysqkOABiAVBfYW9wvhPSlMYbm+n07Qw8zC0ExwE6BggfJxj6cWT6xDOsiSfmDWyOvi280aGhG1r7P7l1SX8O5TIxBAIOAgEHD7kEuY8LeJhDvy9zb2hP387ApXvZUBUVF/XQ6HT8Dbt+vqiXm9ZDfVc7DA/1tcpWqYQ0ZXHL4VPe65KfxaX7rmHT6TtVcn1Ulr4y5oi2fgiu52j0xcwAFGh0yMgrgqNMZPS5LDmmMT2vCJ5OUoQ0cIGnkwzNfBzRs7kXNscl4+j1NBQ+LigCrrhLaWWU/m6zhnNZWffu3cMnn3xiVNIXKJ7bLCwsDEVFRdi8eTN+/PFHXL9+HR07duTnitGbP38+Jk6ciEGDBmHfvn0YN24cFixY8MSJOQkhhNges1umunbtiiVLlqB///58FSiO46DRaPDtt9+W22pVl5Wu8mcnEUJZqIbqcStHfqEGF6uh61SQhxyf9GiCqN8u4U6Gis8/xEIODMDNh0qENHBBA1c73M9S4ZGyEPlqHQQAxCIOnUy8MdcXXrATC6Es0EDLirN0iUhgVKShosdRstVl14X7ZbZKSUQcXOwkcLWX4G5m9lMLDZiHQSjgwKE4aZOIBGhcT478Iq1R1zpfhT3S8gpx6V42LiRn42pqjlFVv9L8FPZQ2Itx8V4OktLzkZJVgPPJmVU2hs6UYxMLioumFKi1kJe4YdfPFxUaoEDvFsYT7llaWWOhkjNUuHA3GyENnA0+V/rPop1IgOMJj5CYnofzyVlWU4Huyv0cPFAWQiLiUKAxbqGyEwvhIDH+2i7dzTM0QIHQAIVBy3FKTgEYK75+H/coxYOcwkp9PkoXpqkNxSfee+89dOrUCQqFAlu3bjVYN2vWLEilUuzcuZPvP9+6dWs0atQIMTExWLx4MYDiCUDnzZuHMWPG8OWTw8LCoFarMWPGDEyaNAnNmjWr2QMjtRInlkHx8ntGywghNcfsx5Jz5sxBUlISmjVrhsmTJ4PjOHz99ddo27Ytbt68iZkzZ1ZHnDaNHxuTlInU7AJcS81Fkab4BtZeIgQ4Dpl5RU/fUQWENfHEoNYNYCcRQCQofsPzCrXQaHUQCjj0bF48L0xabhGEHAcRV9zooyrUmtxa5quwh7eTDEVaHT/XFAMgFQuqrMWtZCvYXzceoXTxaSEHtPJ1RZCnHJ5O0kqNHypL20A3BLnLIZMI4eUkRYCbA3xc7DGm0zN4o72/wc34rgspOJuUhbxCLfIK1Uh8lPfEViZfhT38FPZ4mFsIhuJzV6hlWPd3Io5ce1jp2E05tjb+CtRzsYOLnRheznaQS4tv2PWJbOljtCalW2V2X0zBnD8u4+cTSZjzx2WDc+irsIdYwOH/Eh4hr1CLQo0WdzJUZrcCVofkDBVup+ehSK2FgOMgE3EQC4qTHwGKW10b15MjK19tFG9Z71PJlkr9Z5R7PN5Px4p/ijQ6k6sJlqWyY/Wszfr163H06FF88803Rus0Gg127tyJwYMHGwxE9vf3R3h4OHbs2MEv27t3LwoKCozmSYmIiABjDL/++mu1HQOpWwRiKRxb9TX4EYgr/xCzJqnVakRHRyMgIABSqRTBwcFYsWKFWfv4v//7P36aHjs7OzRq1Ahz58412mb06NFo3bo1P41FYmKiyb9jwYIFNf7Z/ffff9GtWzfI5XK4uLhg0KBBuHXr1lNfl5OTg/nz5yMsLAxeXl6Qy+Vo0aIFFi9eXG6l1vj4eAwdOhQeHh6QSqUICAjAuHHjnvq7kpOT0bt3bzg5OaFp06b47bffjLbZsmUL3NzcjFrwS7pw4QI4jsPZs2ef+jutjdnJVMOGDXH8+HE0bdoU33zzDRhj+Omnn+Du7o6//voLfn6mVR+rS/Q3Ok28HCEVCxDk4QCFgwQu9qLibl2OUnSqxhaIkAbOkMvEBvPfyMRCZOQV8eW3PRwlEAg4aB8/uS7SavH7+fsmddvRd1Hq37I+vJylcLUTQeEghqejrNw5dypq14UU3HighKDUbp3sxOAEHPwU9ggNUFR5AhDkIcf4Lg3Rxt8VXs52cLITgTFm1LUuIU2Jv248RH6RBoUaHQo0DA7S4m3L64amL7IhFRt+HDPy1Fj657Vq7zqlf/8GPl8fTX2c0KeFNz9ps369vnXDGrtxlb6hT1cWIbdAgwA3O+QWFBfM0AvykKNjYw94OcnQwNUOOfnap3ZnrW76Loop2fnIzdcgO1+DAo0OEqHgcZfS4pZiiViI6w+USM0uu5z6k94n/Xsc0SEQPs5SSEQc3OTix9M1VPwzagvJtqkePnyISZMmYdGiRWXOi5KQkID8/HyEhIQYrQsJCcHNmzf5m5T4+HgAxcVBSvL29oa7uzu/viyFhYXIyckx+CGkNhs3bhwWLlyI8ePHY9++fRg4cCAmTpxo8qS4GzduROfOneHs7IyffvoJu3fvxpQpU4y+Jw8ePIgDBw7Az8/PqNy5KWo6mTKnW3Fpd+7cwbJly9CqVSusXr0av//+O4YMGYKoqCj07dvX6NwcPnwYbdu2RU5ODlatWoU///wTc+fOLXcewpJGjhyJ7OxsbN26FQMHDsSwYcOQkJDAr8/OzsbEiRMRExMDD4/y73W3bduGwMBAPP/880/9ndamQvNMNWvWDHv37kVhYSHS09P5JwHkyR5kFyA7X41DVx/iOV8XDAttgHRlEZrXd672cShBHnLUc1TjSkoOtAzIV2shFBTfvBfPS5UNZzsx8go1EHBAkYbh0JUHKFBrn9pFTb//N9r740F2AT9pbYC7Q5XepPKJiloLkZCDTsMgEwvg4SRFeJN6CPZ2NChhXdU3dvr3aOXhG0hIy8OCXVeQkp2PEW39+W2SM1RQFmj5FiYdK66cWLJMfnk3nfVd7JGuLIKy8L+xZ9mPWyGq+yY1yEOO3i28cS8z36iVoWR5erGwuDXT29muyop7VFbpbqDJGSocu56GxPR8OMpERgUz9HNp3clQob4rh4gOgRY7jpJdFIs0Wng6SSESFBf7yC0svo6EKJ77TQAGub0QLvbiMhOgp5V+D/KQo0V9Z5xwl8PVvggPc4vgZGc8/spcNVH1sCaMGzcOTZo0wfvvv1/m+vT04rn4FArjcZAKhQKMMWRmZsLb2xvp6emQSqVwcHAoc1v9vsqycOFCREeXPck1IbXNpUuXsGbNGsyfPx+ffvopgOIusfqusu+9916Znzm9e/fuYezYsXj33XcNWpTDw8ONtp05cyY/f1FMTIxJcxNZkqndissSGBiIxMREg++gLl26wMHBAZ9++imOHz+Ol156CUDxBO2vv/46unTpgj/++MPg78ubb775xBhVKhWOHDmC48eP44UXXkD37t2xdetW7N+/H0FBQQCAKVOmoEmTJkYt9aVt3boVgwcPfvJJMYFarQbHcRCJKpTiVEilRh9LpVL4+PhQImWC5AwV1DqGrk084ekkQ6fGHhjR1h8fdGlU7YmUr8IeLnZiJGWooNEBeNy9x9lOBMaKCxCk5xUhjC+EIIBMLICDVMQXIjBFyUH03Z/1wrA2vlV6k5WcoYJYKISvqx1EAiGc7EVwtBMjPbcIB68+wLHrT35SUxVSsvORkJaHlKx83HioxJK914y6kWl1Ouir0esYcPxGOu5kqAwKVZSm7+rn62oPsaB4jiqxkEO9xy0oNaG8VobkDBXuZKiQrSrChbvZWH7gBlYfu2VVBQdKthCGNfHErH7NMPJFf8zq18zo86U/zlda+qBnc2/4KuwtFLVhF0W1liG3QI3kzHxoUZyMcygu+c90gEwiRIZKjdwCNQ5ffWjUBbR0d8cnXWcSkRBezlK+m29dt23bNvzxxx/4/vvvn9pS96T1JdeZul1pU6dORXZ2Nv+TnJz8xHgIsWW//vorGGNldonNz8/H3r17n/j6H374AXl5eZgyZcpTf5dAUPFbXo7jkJeXh3Xr1oHjOHAch7CwMH59fHw8+vfvD1dXV8hkMrRs2RLr1q2r8O8zp1txWRwcHMp8mNO2bVsAMPhe2bJlC1JSUvDpp5+a3VOhqKgIjDGD3yWXy/lW+r///hs//fQTvvvuuyfu5+rVq7h8+TIGDx4MxhgaNWpUZg0GpVIJZ2dnvpDPkSNHwHEcfv75Z0yePBn169eHVCrFzZs3zTqOyqpQ2paYmIjNmzcjKSkJ+fmGf7A5jsOaNWuqJLjaRN8VKTW3EE3qOZo1h1Bl6bs2xd/LhlrLoNZoIRBw4MDh0NWHEAs4FGq0iE/Jga+rDMoCLXIL1QAAL2fzb+bvZeYjPa8Ie+NTq7T1Qn8jCAB2EiFURVrcz8pHvloHVUY+VFVYir18HArUWj5Zyi3Q4Nj1NP6GPchDjhcbeuBWWh709QPyCtVQa3VPHFdSsnXlfHImdp6/Dx0DPOQ12/e9rFYGX4U9xEIOKdkFEAkAZaEGOp2OH2tkjTfjYU08n/qQQl/W3pIFKEpXqgSAW2l5kAqBQi34+aAEHJCtUkMH4ObDPKRkF/APD0oeZ5FGizNJmfBT2Jd7nfVs7oWU7HyotQznk7PRNrByc8HZOqVSifHjx+PDDz+Ej48PP71HUVHxONasrCyIxWK4uRWXkC+rVSkjIwMcx8HFxQUA4ObmhoKCAqhUKtjb2xtt27p163LjkUqlkEpta8wLIRUVHx8PDw8PeHl5GSzXd6d9UpdYADh27BgUCgWuXr2K/v37Iz4+HgqFAoMGDcKSJUuqbKLrEydOoEuXLggPD+drA+j3fe3aNbz44ovw9PTEV199BTc3N6xfvx6jRo3CgwcP8Nlnn5n9+57WrXj//v0oKCgwqRteSYcOHQIAPPvss/yyY8eOAQC0Wi1eeuklnD59Gg4ODujZsyeWLl1aZmVTPRcXFwQHB2Pp0qVYtmwZjh49ivPnz+PFF1+EWq3G2LFjMXXqVDRu3PiJcW3btg3169dHu3btwHEcPvzwQ0yaNAk3btxAo0aN+O1++ukn5OTkGFVFnTp1Kl544QWsWrUKAoGgxiuLm51M7dq1C4MGDYJWq4Wnp6fRl35Vj5GpLSozD1BVKJ7Hxhl/3UhDIQNEHIfsfDW8nKRISMuDWqtDoVqLnAINPORSyO2EaFzPCa8852NWrNVZ3avkObyfpcJ3R2+h4PEkwToABWrtk3dQBdoGKlDfxQ43H+Y9bjlgKD165c0X/HH8ZhpupeVBLOQgl4nRqbEHXzmxvPOhT2QYYzh5KwPucgkeKcuf9LeyTJ2LS38Dfi01Fzn5ahRqdIi/nwNPR1mlihdYSkKaErsupOBOhgpt/F0tWoWu5DXNGMNv5+4BKE6kgOLCKhKRAIVqHV/BkqF4SgWhoJCfQDkhTYm98alQaxnfFfNJxyMRCfFcg9pTga8yHj16hAcPHmDp0qVYunSp0XpXV1f0798fW7duhZ2dHS5evGi0zcWLF9GwYUP+xkY/VurixYto164dv11qaioePXqE5s2bV9PREGJb0tPTy+zG5+DgAIlE8sQusUBxNz+VSoWhQ4di6tSpWLZsGeLi4jB79mzEx8fjr7/+qpL70vbt20MgEMDDwwPt27c3WBcVFYWioiIcPnwYvr6+AIDevXsjKysL0dHRePfdd+HsbN78jOZ0KzbVhQsXsGTJEgwcONAgSbt3r/jvzuDBgzF27FjMnTsX169fx/Tp09G5c2ecP3/e6KFQSWvWrMHgwYOhUCggEAgwY8YMtG3bFvPmzQNjzKRWw61bt2LQoEH8exUREYEZM2Zg5cqVWLZsGb/dypUrER4eblQNNSgoCFu2bDH5XFQ1s5Op6dOno0OHDvjf//5Hc0qZoaonka0ITycpf+PPAdDodLh0PweOMhEAARp6yvHXjUdgOh0e5RUhJ18DiVBgVszVXd1LH8eeiykQlPqCFIuqrnrgk37/2y8FYsGuK/zYpvh7WUhI++/pfpCHHNP7NMPKwzeRV6hBgLsDepsxKXNKdj5upSlxNUUHF3tJtSQsTxtfU/p69Xa2g7ezHbQ6hiJNEcTC4jLx+gImtqLk+K/U7IIntuLUFP15//7YLSSmq2AnEUKr1SFfwx6PbyxrIgCAMfDjwfQPMfTJ4ZNuHqrjM2oN328V5eXlhcOHDxstX7RoEY4ePYo9e/bA3d0dIpEI/fr1w/bt27FkyRI4OjoCKB7offjwYXz00Uf8a3v27AmZTIbY2FiDZCo2NhYcx2HAgAHVflykbtCqsnH/B8Nxfj6jv4XQ3romV9doNAb/FgqF/PdURbvEAoBOp0NBQQFmz56NyMhIAMVjriQSCSZNmoSDBw+iW7dulYz+yQ4dOoSuXbvyiZTeqFGjsGfPHpw4cQI9e/YsN36d7r/veI7jIBQKDf5dHnOSxMTERPTt2xe+vr744YcfjGIAgOHDh/PjsMLDw+Hl5YUBAwZg48aNGD16dLn7fvHFF3Hnzh3cunULXl5ecHZ2xo0bN7BgwQL8+eefEIlEmD17NtauXYuioiIMGTIEMTEx/MOnW7du4dy5cwZJk6OjIyIiIhAbG4v58+fDwcEBhw4dwuXLl42qNAKokrFWlWF2B9IbN25gypQplEiZwdITW+p//183HqFQrS1+0q3RwVEqxrBQX0R0CISfwh7JGflQFapxL7sQhRqGe5n5OH83y6yy0TVR3Ut/49i8vhPsxALYiwVwsRPBx7nqqweWjYODVASJiINQIMCttDzEJWYYbBHWxBOLBodgSq+mJhXw0NO3MBRpdJBLyy82UFlPGl9T1vXqq7CHk50IeYXFfxBz8jXIylfj2PU0qxk3ZYqSSYeXswwvBrlZRRU6/pr2doJEJAAn4CAVCSAScLATCyAs4xJoF6jgu/iZkyBV9WfU0t9vlSWTyRAWFmb04+XlBaFQiLCwML4lKTo6GiqVCn379sWePXuwY8cO9OnTB+7u7pg8eTK/T4VCgRkzZuC7777D9OnTcfToUcTExCAqKgqjR4+mOaZIldLl5xj8WJvExESIxWKDn6NHjwIo7hJbVutTXl4eioqKnlh8Qv96AEbja3r16gWguLR4dUtPTy+zhUjfPe5JrWtvv/22wXnp2rUrAJjVrfhpkpKSEB4eDpFIhIMHDxqd0/LOYY8ePcBxnEnnUCwWo0mTJnwL3HvvvYc333wTL730EtauXYu1a9fi4MGDOHv2LP766y8sXLiQf+3WrVvh6enJF8TQ+/DDD5Gbm4sNGzYAAL7++ms0aNAA/fv3N/r95rTQVQezkyl/f38olbb1x9LSTBkYXhO/39/VHlpd8ZNuxgC5nQihAcU3ZGM6PYMuTT0hE//XWKljQH6R1uwn16XLhVeUvmR06Zsz/Y3jI2URFA5SyGViOEhFVV49sLyY/rqehny1Fmotg4ADRKUGterjBmD2eUjOUEGtZfB2liFfzeAoq3y1tbI86ea7rOs1yEOOQHcHFGp00LDiogjucrFBCXVbUHqMUi8zWgxrIq7U3EI093HGMx5yOEiFkAiL56Jzd5TC/nHpfA6Ao1SIrHw1f52ZmyBV1WcUsPz3W00KDg7GkSNHIBaLMWTIEIwaNQoNGzbEsWPHjEr+Tp8+HcuWLcPWrVvRvXt3rFixApGRkVi5cqWFoifEMnx8fBAXF2fwox832KJFC6SlpSE1NdXgNfrutE/rElvWmCIAfI+OyhSdMJWbmxtSUlKMlt+/fx8A4O7uXu5ro6KiDM6LvlBDUFCQyd2KnyQpKQlhYWFgjOHw4cNlTvtQ3jnUM/ccxsbG4vLly3wr1549ezB06FA0atQI3t7eeOedd7B7925++23btmHAgAEGLXJA8VRMvXr1wsqVK5GcnIzff/8d7733ntF2gOWHGJndzW/atGmIiYlBr169ntiHkvzH0hNb6n//tQe5sJcKIeCKn3ZLhAL+RllfNllU6hG4j4tlZlJ/Uje00uNM9F3NSpZFry7JGSpk5asR7OWIy/dz4OogQfP6znxBkZJxiwUcgr2d4OEoRdtA02LTF9m4A8DdUVptZbufNIavvOs1O18DDsWTx6p1wJ2MfAR5OlrlRK1ldTvTL+vZ3Ascx1lk7GJ5Sr8fABCXmIG03EJ4OErh5SRDak4BTt/OwPnk4qkHEh+psPb4bf4YLVWi3NLfb9UlNjYWsbGxRstbt26NAwcOmLSPCRMmYMKECVUcGSG2RSKRoE2bNmWu69+/P2bMmIF169YZjK2JjY2FnZ1dud3j9AYPHozVq1djz549BvMT6W/WS49vqgypVGpUdA0Aunbtih07duD+/fsGxRp++ukn2NvbPzGGgIAABAQEGC03p1txee7cuYOwsDBotVocOXIE/v7+ZW43cOBATJ8+HXv27MHAgQP55Xv27AFjzKxz+OjRI3zyySf45ptv+JYzxhjy8vL4bZRKJZ/sJicnIy4ursyuewAwceJEdO/eHSNHjoRQKMSYMWNMjqUmmZ1MnT59Gg8fPkTDhg0RHh7ONw/qcRyH5cuXV1mAtYGli0/of39cYgZ2X0zB1ZQcaHQM8jJaPeo5yZBXqEGBpriwwr9JWZjzxyXM6vdsjcb9tEIWlrpx1I9nyi/SQijg0MTL0aAEvD5uL0cp9l1Oxanb6ZCJhXjO18Xk+bpq6lop7xyWF0PbQFfsjU9BvloHDoCDVIROVdS6UZXKSsQBGMyVZcm5pUoqnfSVvsZLbgcArzzng3RlIS7ezYZcKkRabiF2X0wxa0xeVbP09xshxHY9++yzeOeddzB79mwIhUKEhobizz//xOrVqzFv3jyDLmlz5szBnDlzcPDgQXTu3BkA0L17d/Tr1w9z5syBTqdD+/btcebMGURHR6Nv374GXcfS0tL47oX6Fp89e/bAw8MDHh4e/D7L06JFCxw5cgR//PEHvL294ejoiCZNmmD27NnYuXMnwsPDMWvWLCgUCmzYsAG7du3CkiVLzC4+oRcdHY3Q0FD07dsXkZGRKCgowKxZs4y6FQPFyVfnzp1x8OBBAMWTkIeHhyMlJQVr1qzBw4cP8fDhf9NpNGjQgG+lCg4Oxvjx4/HNN9/A0dERvXr1wvXr1zFjxgw8//zzGDZsmMkxf/zxx2jXrp3Ba3r06IHJkyfjhRdegFwux1dffcWPwdq2bRtcXFzKnBcMAF5++WU0a9YMhw8fxhtvvGG1Q4zMTqa+/vpr/v83bdpktJ6SqbJZemJL/e/3cpLxhRHkUuO3PyOvCEWP634LOEDHdLiXmW9Wxa+qGIxujU+79eOZVEVaqLU6aLQM/yZlQVyiSIc+7viUHBRqdBALi5vH9fN1mXI+LH2tlBfDiLb+OH07A7+fuw8GQFWktcpqfmUl4vez8nHhbhaKNDpk52sMWnQspbzW19Kfn4Q0JZbuu4aUnAI4SkWQiATQ6BgyVGpkqornnLqXmW/RsV/WcM0SQmzTN998g/r162PFihVITU1FQEAAli9fjg8//NBgO51OB63W+O/OL7/8gujoaKxevRrR0dHw8fHBRx99xE/Qq3fp0iUMHTrUYNm4ceMAAJ07d37qJL7Lly/H+PHj8eqrr0KlUvGvadKkCf7++29MmzYN48ePR35+Ppo2bYq1a9di1KhRFTsp+K9b8ZQpUzBkyBCIRCJ06dIFMTExRt2KtVottNr/KhpfvnwZt27dAgC88cYbRvuePXs2oqKi+H8vW7YMDRo0wA8//IAVK1bA3d0dr776KhYsWACJRGJSvAcPHsT27dtx6dIlg+XvvPMObt26hcjISBQVFWHw4MGYPn06gOJk6pVXXoFYLC53v8OGDUNUVBQ++OADk+KwBI5Z492QBeTk5MDZ2RnZ2dmVnpfAmitbHbn2EBtO3eFvNN9o74/OjT34dcsP3MDdzDykKdXFXbqEHJ7zdcGiwSEmHcvTqsSZIyFNaVVPu49ce4jVx27hboYK97LywXEc5BIhfFztMKVXU/48JqQpsftiCtafTEKWSg2hgENogGuNt+5Vh6jfL2HjqSSIBBw0OoY32vtjVr9nn/7CGlRWV8szSRmIv5cNVaEW9V1lqOdkh3c7B/HvmSWU9Vls4Gpn9Pk5fTsdKw7dhADAw9wCcJwAOp0OcpkYWh1DeBMPqNQ6g8+yranK79/ahs5N3RAQuatCr9OqsnF3xesGyxp8uMGgml/ioj6Vio2Q6pCamor69evj119/Rb9+/crdrk2bNuA4DnFxcTUYXTFTv38rNGkvKV9VJhPV4UktPr4KezTxKu6XKxHmI1+jg1QkgJ3YeLBfeapinqmSyag13RzqxzNl5hWB4wCNjiG7QAMuuwD3s1T8dvrxZ0EecshEAqTkFFhNoYPKUjiIIRRwAAcIBRxcHUx7YlWTSnZrPXY9DYevPcTDnAK0bOCC83ezIBUJLV4OHSj7s1jW5wcoLkOfr9aiSAtwj2ecyslXw0EqwvWHSgS6VX/xFUIIIaSqeHl5GbSmlZSTk4P4+Hjs3LkT//zzD3bs2FHD0ZmnwsnUvn37cOTIETx69AgzZ86En58f4uLiEBAQYNT8WJdU56S1VeFJ4xv0k7OmZOcju0CE9DwVBJwYV1NzEZeYYXIBhcp0zyvZpcnbSYbJPUwvK17d9Odu/ckk3M1UIb9ICy0DCjU67I1PRdtANz5WX4U9XOzESMkpQKCbA1+gwtb1CfHBsetpSErPg4u9BC3qW9dcJnpBHnK+MmJzbycczClAVr4a7QLd0KmJR40UKzElxrI+i6U/Pw1c7fCcrwv+TcoEAH6uuOI5qLQo0uiKS/wRQgghtcC///7L12WYPXu21c/NZ3YypVKp0L9/fxw8eJAvRfj+++/Dz88PMTEx8PX1RUxMTJUHaiuscaxPaU8b3yARCeEplyDxUR6yVUUQCAS4lppr8r4rMxj99O10nLubBZlIgAc5BSYncTUlyEOOxvUcIREJUKDWAgxwlon4EuEGsXKP73ErcKNrzV1F7aUi6FjxXFM/Hr+NlOwCk6sV1qSS5cZbNnCxmiSqpLKKTpT1+ZncvQlWHr6JP87dg34OXw6AVscgEgAp2db3WSGEWIeKdiE0FXUjJFVNX87dVpidTE2fPh1nzpzBtm3b8PLLLxv0IdTPpVGXWWNlK3NuzPU3oHczVY8nCuWgYwzx97KRkKasgQIKxV2aGLPmh+0MHAdwHCAScLCXioy6jSVnqJClUqOBqx0eKYvMLuBhrV1FkzNUyC3QwNlODGWhBmeTMvEotxDnk7OsKk7AOj+Lpij5+Sn52Q0NcMWRaw+Rk6+GRlfcQsUYkJCWB7lUjN0XU6wuWSSEEEJqO7OTqS1btmDu3LkYOHCgUV9HPz8/3Llzp8qCs1XWVNnK3Btz/Q3o7osp+DG7ANn5akiEAuQVamqky2LbQAWe83VBanYBvJxlVtc9Tl/Rr0jDIH48cZy9RIiezb2Mzk1qdgGupORAKhIYjKl6GmvuKuqrsIe3kwz3MlXIVqkBAIUaLe5kqKwqTj1r+iyaq/Rnt2dzL7QPdMPpxAxk5hVBLOQg5DgUaHQQCTncfKik1ilCCCGkhpk9NXRaWhqefbbs6l0CgaDMCc2I5ZS8MU/PK3o8oP3J9AUUFA4SiAQcijQ6PMwtrJEm1yAPOSZ3b4KJ3RqbNC9TTdOPw3GxE0Ot1cHNQQxHmbjM2bed7cRwlIlQ9HhMlX6uoKex5q6iQR5yTO7RBH1CfODpJIOTnQgPcgqh1mqtKs7aoPRnl+M4dGzsDjAGDkCRhkEHQCwUQCoSWHFLLiGEEFJ7md0yVb9+fVy8eLHMCbYuXLiAwMDAKgmMVI3K3Jhnq9RQa4tv3PILNdhwKgkAENakeidNs+bWBH1Fv/wiLYq0Org7ysqsDOersIeTnQh3M/Ph5Swre0xVOay9e1qQhxyMMTxSFqJIo4NIwMGaO2XaqrI+u+eTs6BlgFwmQm6BBo5SITyd7JCvLk5mra0llxBCCKntzE6mBg0ahPnz56Njx44ICQkBUDxRb1JSEr788ktERERUeZCk4ipzY24nFvLzCeWpdTh2PQ1XUnIxb0Dzak+orFXJ88kYA8dxZZ7XIA85IjoEYu3x21BrmdmluK05odx0Ogm/xCUj/3ElBLlECK3O9GSRmKasz25IA2dIRQJk5BVBKhKAASjSaCERCaAq1CI5Q0XvASGEEFKDzE6mZs+ejYMHD6Jt27Zo3rw5OI5DREQEEhIS0KRJE0RGRlZHnKQSKnJjfuFuFjLyiqDW/de1T61heJhTgGPX0+psMgWYfj7DmnjCV2FvtS1MFXX6diY0Wh3EQkCtBQrVOng5y6ymm581V0J8krLiLn2thTXxxMRujfD1wRtIzSlEgUaHLJUa3i52eJRbhLXHb9vccRNCKo4TSeHcYYTRMkJIzTE7mXJ0dMTff/+N5cuXY9euXQgKCoK9vT2mTp2KSZMmwc7OOm6oiPn0N3Mp2fnYeOoOVGrDAiM6FFcQs8aJWi3laTfu1tzCVFFtA13xZ3wKVGodOAD1nGQY1sbXKo7T1IIr1pZwmVMopm2gG4Ab0LLiQa9qBqRk58PFToKHOYXUQkhIHSKQyODy0uuWDoOQOq1Ck/ba2dkhMjKSWqFqkZI3c6nZ+cgv0kImFvBdufQ8HaXo3cLbQlFaF2suYV6dRrT1x7XUXOy79ABNveQQCoVlFuCwBFMqIVrj+2ZOBcfTtzOQXaABUPyAQz+VQJZKjQK11qzKkYQQQgipHLOr+ZHaqeTNnEgggJ1EaFRSQABqlSqpIpUSa4s3XwhA58YeEAqFVlVx0JSCK9b4vplXKIbBXiKETMRBwAFyqRASkRAejhIIBBzSlUU1FjchhBBS11WoZYrUPiVv5pp4OWJYaAPceKDE6cR03E7LQ6GGwdtZCieZmLoRPVbeDbC1dSGrDvriCHGJGXiYU4jTtzP45dYQ15PGqVlj6XlzCsW0DXRDG38FLtzLRk6+GnYSIdKVhUjL1UEmFsBNTg88CCGEkJpCyRQBUP7NXEKaErsvpuDY9TSIhQKzq9LVds/5OoPjOIQGKBDkIbfKLmTVads/ybiakguJSIh2zyisYm6wp41Ts9bS86aOrwvykGNYqC+SM1XIyVdDp9NBIhTAX2EPmUQEHxf7GoiWEEIIIQAlU6SEsm7mgjzk+LBLI7So74yL97LRor6z1dx8WlLppEk/v485Y19sWUKaEl8fuoFzd7KhZQyqIi2u2dDx1obCICKBAAIOyMxTQyDgAAGHJl6O9LCDEEIIqUGUTJEnSkhT4vTtdOyNT0VugQZXUnJqdfc1U5WXNFljF7Kqpk8kzyRmQqNjEAoALQOEj+fcItVPPym0RssgEHDgAGh1DD2be9X5zyYhdYk2PxcPNkwxWFbv9cUQ2jlaKCJC6h5Kpki59DfNcbczcDdTBRd7MR7kFCAuMaPO37CVlzRZaxeyqpScocKdDBV8XGR4kFMAcAz2QhH6PuddK4/XGgV5yNGzuRfOJGWgSMvAAXiYU4DUnAJLh0YIqUlMB3X6HaNlhJCaY1IyNWfOHJN3yHEcZs6cWeGAaiNbLUigv2lWFWlQpGXIVKnhKKP8G3hy0lQbupA9TWp2AXILNHCUiSATCyETC3E1JRcJacpaf+zWRH/PxADkFWmRllto0XgIIYSQusakO+OoqCiDf3McB8aY0TI9Sqb+Y6sFCRLSlEjJzodaq0NugQYcAI2u+Am4l5PM0uFZhbqQNJXHy1mGFvUluHg/B9mqIkhFApy7m0WtljWKg1gkgECtAwMgEwng4Si1dFCEEEJInWLSPFM6nY7/uXbtGgIDAzF//nzcvn0b+fn5uH37NubOnYvAwEBcvXq1umO2KdY4p83T6BPAQ1fTipPmx3myRCSAp5PMKiZoTUhT4si1h0hIU1o6lDrHV2EPP4U9VGod3OwlADjkFWqg1bKnvpZUnbaBCjzn6wInOxGc7URo5e/KF0IhhBBCSM0wu8/WxIkT8dZbb2Hq1Kn8Mn9/f0ybNg1qtRoTJkzAnj17qjRIW2aLBQlKJoD7r6jAAZCKBdDqGBykIosfg6229tUWJbs43s9SYeXhBGSpiiATC/EwpwBHrj20uS6ttijIQ47Z/Z5FXGLxHF/68vyEEEIIqTlmJ1N//fUXJk+eXOa6Dh06ICYmptJB1Sa2WJCgZAIol4ggEQkhAYNWB3Rq7GHxY6gr5cetmb6L45FrD+Ehl0LAAdkqNTaeuoOTtzLgp7CnJLcG1OWupoQQQog1MKmbX0lSqRRnzpwpc92ZM2cgkUgqHVRtoi8+YSuJFPBfAvhGe38Mal0fdhIh1DoGuUyEFvWdLR2eTbb26dW27on6Et3ZKjUABmWhBh5yic10aSXk3Llz6NOnD/z8/GBnZweFQoEXXngB69evN9r233//Rbdu3SCXy+Hi4oJBgwbh1q1bZe53xYoVCA4OhlQqRWBgIKKjo6FWq6v7cAghhNQws1umBg4ciOjoaMjlcrz22mtwdXVFZmYmNmzYgDlz5uD111+vjjhtki13RyvZ8uCnsIeHXII0ZZFVjJeyxdY+wHavh7KqUZZcFujugOM3HkHDACEHXH+oxHMNXGwuybXFipuk8rKysuDr64sRI0agfv36yMvLw4YNG/Dmm28iMTERM2bMAABcvXoVYWFhaNmyJTZv3oyCggLMmjULHTt2xLlz5+Dh4cHvc/78+Zg5cyYiIyPRvXt3xMXFYcaMGbh37x5Wr15tqUMlhBBSDcxOpr744gskJCTgww8/xIQJEyASiaDRaMAYQ6dOnfDFF19UR5w2qTZ0R9MXG0jPK4Kfwt5qbpBtsXuTLV4PZSWAAPhlYgGHc8mZ0DyuPaFlgMKGEkXAdpNcUjXCwsIQFhZmsKxv3764ffs2Vq9ezSdTs2bNglQqxc6dO+Hk5AQAaN26NRo1aoSYmBgsXrwYAJCeno558+ZhzJgxWLBgAf871Go1ZsyYgUmTJqFZs2Y1d4CEEEKqldnd/BwdHXHo0CHs3r0bn3zyCd566y18+umn2LNnDw4fPgxHR5p1W8+Wu6PplezyZ8mbzNrQPc4Wr4eyqlGWXJaSUwCVutQEkVZS1M/Ua8YWK26S6ufu7g6RqPh5o0ajwc6dOzF48GA+kQKKiy+Fh4djx44d/LK9e/eioKAAERERBvuLiIgAYwy//vprjcRPCCGkZlR4BtaePXuiZ8+eVRlLrWOr3dFKs3QrUG1pObDF66G8BFC/zNtJBmWBGlmq/8aCXLyXjeg/LmF2v2ctmnybes3YYpJLqp5++o/MzExs2bIF+/btw9dffw0ASEhIQH5+PkJCQoxeFxISgv3796OgoAAymQzx8fEAgBYtWhhs5+3tDXd3d359eQoLC1FY+N/kyzk5OZU9NEIIIdWowsnUvn37cOTIETx69AgzZ86En58f4uLiEBAQYNB3vK6zdCJSG9hi97jy2Nr1UF4CWHLZzyeSkJyhQpGGQQcgt0CD07cysPtiCj7s0sgicZtzzdhikkuq3rhx4/Ddd98BACQSCb766iu8++67AIq77gGAQmE8j5dCoQBjDJmZmfD29kZ6ejqkUikcHBzK3Fa/r/IsXLgQ0dHRlT0cQgghNcTsZEqlUqF///44ePAgX4zg/fffh5+fH2JiYuDr61vny6PTYPaqRS0HllVWAlhyWVgTD/x69h4KNMWtUwyARseQmVdU06HyzL1mbC3JJVVv2rRpGD16NB4+fIg//vgDH3zwAfLy8vDJJ5/w2zypAE/JdaZuV5apU6fi448/5v+dk5MDX19fUw6BEEKIBZidTE2fPh1nzpzBtm3b8PLLLxv0H+/evTtWrFhRpQHamtrSJc2aUMuB9bOXCJCd/99wKXuJAJ0aW66Fmq4ZYi4/Pz/4+fkBAHr37g2gOLEZOXIk3NzcAKDMVqWMjAxwHAcXFxcAgJubGwoKCqBSqWBvb2+0bevWrZ8Yh1QqhVQqrezhEEIIqSFmJ1NbtmzB3LlzMXDgQGi1WoN1fn5+uHPnTpUFZ4v03Yu8HKWIT8lBXGIG3chVAWo5sE4JaUqsPX4b+WodZCIO+RoGIQe4Olh+vjm6ZkhltG3bFqtWrcKtW7fQunVr2NnZ4eLFi0bbXbx4EQ0bNoRMJgPw31ipixcvol27dvx2qampePToEZo3b14zB0DqBE4ohvz5PkbLCCE1x+xkKi0tDc8++2yZ6wQCAfLz63YlLF+FPcQCDgevPQQH4Nj1NIQGKOimrgrou0/qWbobZV3vzpmQpsSuCynIydfAzUGCW4+LUGgZkJyRjwW7LtfZc0Ns3+HDhyEQCPDMM89AJBKhX79+2L59O5YsWcJXrb1z5w4OHz6Mjz76iH9dz549IZPJEBsba5BMxcbGguM4DBgwoKYPhdRiAqk93Lq/b+kwCKnTzE6m6tevj4sXLyI8PNxo3YULFxAYGFglgdki/c11sLcTUnMK8KyPE1JzCm26YIK10HefvJOhQmp2AbycZfBT2FusG2Vd785Z8v3IzldDrdWB48D389Mx4Ha6yqJFKAgxxdixY+Hk5IS2bduiXr16ePToEbZs2YJffvkFn376KV9QKTo6GqGhoejbty8iIyP5SXvd3d0xefJkfn8KhQIzZszAzJkzoVAo+El7o6KiMHr0aJpjihBCahmzk6lBgwZh/vz56NixI18mluM4JCUl4csvvzSaW6OuKHlzLRZw8HKWITWnkAomVBF990l3uQQ3HijRor6EnxPIEklMbaowWJantbrpj7+NvyvOJGVCLhUiU1WEvEItGADu8Y8li1AQYooXXngBa9euxbp165CVlQW5XI7nnnsOP//8M9544w1+u+DgYBw5cgRTpkzBkCFDIBKJ0KVLF8TExBhVsJ0+fTocHR2xcuVKxMTEwMvLC5GRkZg+fXpNHx4hhJBqZnYyNXv2bBw8eBBt27ZF8+bNwXEcIiIikJCQgCZNmiAyMtLsIJRKJWbMmIHNmzcjIyMDwcHBiIyMxKuvvvrU1x4+fBgLFizA+fPnoVKp8Mwzz2D06NEYP348hEKh2bFUVOmb665NPeHtbEeD36uIvjrbnQwVHGUipCmL4Kewt1iiWpsrDJrS6lby+P0U9vBxkeGv62l8AQoBB8jEQugYQ0Kakj4DxGpFRESY/BCwdevWOHDggEnbTpgwARMmTKhMaIQQQmyA2cmUo6Mj/v77byxfvhy7du1CUFAQ7O3tMXXqVEyaNAl2dubfVA4aNAhxcXFYtGgRGjdujI0bN2LEiBHQ6XR47bXXyn3dgQMH0KNHD3Tq1Anff/89HBwc8Pvvv2PixIlISEjA8uXLzY6lokrfXNM4qapVsjobYwwcx1k0Ua3N1eJMaXUrffw/n0iClgF2YgHy1TqAAaoiDf44n4KHuYWY3L1JrTpHhBBCCCFABSfttbOzQ2RkZIVaoUrbvXs39u/fzydQABAeHo6kpCR8+umnGD58eLktTLGxsRCLxdi5cyc/QWK3bt1w7do1xMbG1mgyBQDP+TqD4zhKpKqJtVVns7Z4qoqprW4lj1/hIIFIKIBGowMA6AAwHZBbUITER3m1rhskIYQQQggACMx9wZw5c7Bt27Yy1927dw9z5swxa387duyAXC7H0KFDDZZHRETg/v37OHXqVLmvFYvFkEgkRq1hLi4ufJnampCQpsTSP69hy5m7OHY9rcZ+LyHVQd/q9EZ7f5MLa/QJ8UZogCuc7Yqfz+i7+6m1gFDA1apukIQQYi10BUqkbow0+NEVKC0dFiF1itnJVFRUFIYNG4ZZs2YZrbt79y6io6PN2l98fDyaNm0KkciwkUxf3CI+Pr7c17733nsoKirChAkTcP/+fWRlZeHnn3/Gjh078Nlnnz3x9xYWFiInJ8fgp6JO387A+eQsZOYV4XxyFuISMyq8L0KsQZCHHJ0be5jcmhTkIcesfs+iYxNPyEQCCLji5SIB0IZaagkhpFownRaFyfEGP0ynffoLCSFVxuxkCgBef/11zJ8/HxEREUYT95orPT0dCoXCaLl+WVkzzuu1a9cOhw4dwo4dO1C/fn24uroiIiIC8+fPNyhVW5aFCxfC2dmZ//H19a3EUbDiCmbcf0/kCalrgjzkeOU5H7jLpRA+TqYEAgHi72UhIY2elBJCCCGk9qlQMvXBBx/gf//7H/73v/+hT58+UCord6PEcVyF1v3zzz8YOHAgWrdujT/++AOHDh3C1KlTMWPGDMydO/eJv3Pq1KnIzs7mf5KTkyscf9tAN7Rs4AKpWIj6znbwcqq5LoaEWJOwJp4Y3yUIfm72EHLF5dEv3cvB7osplg6NEEIIIaTKVagABQAMHToUnp6eGDhwIDp16oTdu3dXaD9ubm5ltj5lZBR3lSur1Upv/PjxqFevHnbs2MEXqQgPD4dAIEBUVBRef/11PPPMM2W+ViqVQiqVVijm0oI85BgW6ou1x29DrWXYG59a7vw8hNRmCWlKnE/ORl6RFjpWXCJdy2i+KUIIIYTUThVqmdLr3Lkz/vrrL6SlpeGFF17A5cuXzd5HixYtcOXKFWg0GoPlFy9eBAA0b9683NeeO3cOrVu3Nqr2FxoaCp1OhytXrpgdT2VIREK08XflJ5MlpK5JzlDhToYKEkHxV0uhhsHVXoxOjT2e8kpCCCGEENtTqWQKAJ599lmcOHECcrkcY8eONfv1AwcOhFKpNKoQuG7dOvj4+KBdu3blvtbHxwdnzpwxGrd14sQJAECDBg3MjqeiavMkrqR2SUhT4si1h08dx2TqdiX5KuwhFnLIV+vQwNUOPq4yvN7eH2FNPCsbNiGEEEKI1TG7m9/IkSPh4WH4lLlBgwY4fvw4RowYYXbrVK9evfDyyy/j/fffR05ODho2bIhNmzZh7969WL9+Pd/q9M4772DdunVISEiAv78/AOCjjz7ChAkT0K9fP7z77ruwt7fHwYMHsXTpUnTr1g3PPfecuYdXYbV5EldSeySkKfH9sVtIzyuCm4Ok3NLnpm5XWpCHHBEdAvkur34Ke/Ru4V0dh8LHmZyhom61hBBCCLEIs5OptWvXlrncyckJu3btqlAQ27dvx/Tp0zFr1ixkZGQgODgYmzZtwquvvspvo9VqodVqwdh/9fI+/PBD1K9fH19++SVGjx6N/Px8BAQEYPbs2fjoo48qFEtl1NZJXEntkZyhQnpeEZp6OeJKam65k+maul1Zwpp4wldhX+0PFiqa8BFCSEUERFbsHocQUrtVuABFVZLL5Vi+fDmWL19e7jaxsbGIjY01Wj5o0CAMGjSoGqMzDT0hr7ts6b03tTtqZbut6s9DcobK4N9VqTIJHyGEEEJIVTApmerSpQu++eYbBAcHo0uXLk/cluM4HDx4sEqCsxX0hLzusrX33tTuqJXttloT54XGKRJCCCHE0kxKpkp2rdPpdE+c+6nktnUFPSGvu2zxvTe1O2pluq3WxHmhcYqEEEIIsTSTkqnDhw/z/3/kyJHqisVm0RPyuqsy770tdQ+siCKNFmeSMuGnsK+2zwSNUySEEEKIJVnFmClbR0/I666Kvve21j3QHAlpSmyOS8bD3ELIJSL0bO5V48f2pETV1CS2tie7hBBCCKk8SqaqCD0hr7sq8t7bYvdAU52+nY5zd7MgEwmQna9Gak5Bjf7+JyWq1V0anhBCCCF1i0nJlEAgeOI4qZI4joNGo6lUULbIVp5i20qctV1t6Rpa9vXEgQPAGGDat0bVelKiWhOl4QkhpKZwQhHsm3QwWkYIqTkmfeJmzZplcjJVFyWkKbH0z2tIzS6Al7MMk7s3scobL3rabj2e1j3QFpLe8q6ntoEKNPSU425mPhq6yREaoKjRuJ6UqNZUaXhCCKkJAqkDPAZMtXQYhNRpJiVTUVFR1RyGbTt9OwPnk7MgEQrwIKcAcYkZVnkDTE/brUt53QNtJel90vXkIBHByU4MB0nNPyF9UqJaU6XhCSGEEFI3UFtwlWBgADgOsObC8PS03TbYStJb3vWUnKGCWsfQsaG7xeJ/0ji2migNTwghhJC6ocLJVHx8PK5cuYL8/HyjdW+99ValgrI1bQPd0LLBI6TkFCDYSVbj3ZpMRU/bbYOtJL3lXU+2Ej8hhBBCSGVxzMxZdlUqFV555RUcOnQIHMfxk/SWHFOl1WqrNsoakJOTA2dnZ2RnZ8PJycns1yekKSlJqSK2MF6oulnr9WROWXFrjN+alD6Xdfm6r+z3b21G58Z6BETusnQIVilxUR9Lh0BItTD1+9fslqm5c+ciMTERR48eRefOnbF9+3Y4Ojpi1apVuHjxIn755ZdKBW6rqEtQ1bCV8ULVzRqvJ3PeG2uM35qUPpc9m3thb3xqnb/uCSGEEFsjMPcFv/32G6ZMmYIXX3wRAODn54euXbtiy5YtaNWqFb799tsqD5LUHSXHC6XnFeFupnE3UmIZpd+buMQMHLn2EAlpSkuHZnNKn8uL97LpuieEmE1XmIe0Xxca/OgK8ywdFiF1itnJVGJiIoKDgyEUCsFxHFQqFb/u9ddfx6+//lqV8ZE6hsbbWK+S741YwOHY9TRsOHUH3x+7RQmVmUpf5y3qO9N1byGHDh3C22+/jeDgYDg4OKB+/fro378//vnnH6Nt//33X3Tr1g1yuRwuLi4YNGgQbt26VeZ+V6xYgeDgYEilUgQGBiI6Ohpqtbq6D4fUMUyrgeracYMfpq17c30SYklmd/NzcXFBXl7xUw9PT0/cuHEDL730EgBArVbz6wipCCqSYciaxtGUfG/uZ+Xj0NWHVl9x0FqVdZ37KuzpureAb7/9Funp6Zg4cSKaNWuGtLQ0LF26FO3bt8e+ffvQpUsXAMDVq1cRFhaGli1bYvPmzSgoKMCsWbPQsWNHnDt3Dh4eHvw+58+fj5kzZyIyMhLdu3dHXFwcZsyYgXv37mH16tWWOlRCCCHVwOxkqkWLFrh+/Tp69uyJ8PBwLFiwAI0aNYJEIsGcOXPw3HPPVUecpA6pC+NtTEmSrHH8mP69SUhT4nxyFrWkVELp67wuXPfWaOXKlfD09DRY1rNnTzRs2BALFizgk6lZs2ZBKpVi586d/EDk1q1bo1GjRoiJicHixYsBAOnp6Zg3bx7GjBmDBQsWAADCwsKgVqsxY8YMTJo0Cc2aNavBIySEEFKdzO7m98477yA3NxdA8dM3lUqFzp0744UXXkBSUhKWLl1a5UESUpvok6SndZGz5vFj+paVN9r7W0WSR0hFlU6kAEAul6NZs2ZITk4GAGg0GuzcuRODBw82qOjk7++P8PBw7Nixg1+2d+9eFBQUICIiwmCfERERYIxRV3hCCKllzG6ZGjZsGP//gYGBuH79Ol8m/cUXX4RCYZ1zLBFiLUydlNfax49RSwqprbKzs/Hvv//yrVIJCQnIz89HSEiI0bYhISHYv38/CgoKIJPJEB8fD6C4F0dJ3t7ecHd359eXp7CwEIWFhfy/c3JyKns4hBBCqlGFJ+3Vc3BwQL9+/aoiFkLqBFOTJBo/RohljB8/Hnl5eZg+fTqA4q57AMp8WKhQKMAYQ2ZmJry9vZGeng6pVAoHB4cyt9XvqzwLFy5EdHR0FRwFIYSQmlDhZEqpVOLOnTsoKCgwWteqVatKBUVIbWZOkkStP4TUrJkzZ2LDhg1YsWIFWrdubbCu5OT0pZVcZ+p2ZZk6dSo+/vhj/t85OTnw9fV9WtiEEEIsxOxkKi0tDWPGjMEff/xhtI4xBo7joNVqqyQ4QmorSpIIsT7R0dGYN28e5s+fjw8++IBf7ubmBgBltiplZGSA4zi4uLjw2xYUFEClUsHe3t5o29IJWmlSqRRSqbSSR0IIIaSmmJ1Mvfvuuzh06BAmTpyIpk2bQiKRVEdchBBCSI2Jjo5GVFQUoqKiMG3aNIN1QUFBsLOzw8WLF41ed/HiRTRs2BAymQzAf2OlLl68iHbt2vHbpaam4tGjR2jevHk1HgUhhJCaZnYydejQISxduhRjxoypjngIIYSQGjV37lxERUVhxowZmD17ttF6kUiEfv36Yfv27ViyZAkcHR0BAHfu3MHhw4fx0Ucf8dv27NkTMpkMsbGxBslUbGwsOI7DgAEDqv14CCGE1ByzkykHBwf4+/tXRyyEEEJIjVq6dClmzZqFnj17ok+fPjh58qTB+vbt2wMobrkKDQ1F3759ERkZyU/a6+7ujsmTJ/PbKxQKzJgxAzNnzoRCoeAn7Y2KisLo0aNpjilCCKllzE6m3nzzTWzZsgXdu3evjngIITbClImHCbF2+vG/e/fuxd69e43WM8YAAMHBwThy5AimTJmCIUOGQCQSoUuXLoiJiYGHh4fBa6ZPnw5HR0esXLkSMTEx8PLyQmRkJF8dkBBCSO3BMf1fChNpNBq88847yMnJQZ8+fcosFTto0KAqC7Cm5OTkwNnZGdnZ2QaTMhJCjOknHk7PK4Kbg4Qm7iWVQt+/5aNzYz0CIndZOgQjWlU27q543WBZgw83QGjvXGMxJC7qU2O/i5CaZOr3r9ktU7dv38apU6dw/fp1/Pbbb0br62o1P3pKT+oSUyceJoQQQgipzcxOpsaOHYvs7GwsW7aMqvk9Rk/pSV1j6sTDhBBCqg8nEELq29xoGSGk5pidTJ06dQpr1qzBiBEjqiMem0RP6UldY87Ew5ZGrcaEkNpKIJPD67VFlg6DkDrN7GSqXr16/OSEpBg9pSd1kT4xSc5QGfzbmlCrMSGEEEKqk9nJ1Pvvv4/vvvsOvXr1qo54bJItPaUnpKrYQqJCrcaEEEIIqU5mJ1MCgQAXLlxAq1at0Lt3b6NqfhzHGUxgWFcEecjpJo2YpLZ0O7OFRMVWW41ryzVCCCGE1HZmJ1OfffYZ///nzp0zWl9XkylCTGELrTmmsoVExRZbjWvTNUIIIYTUdhUqjU4IqRhbaM0xla0kKrbWalybrhFCCCGktjMrmcrPz8fUqVMxbtw4vPTSS9UVEyG1li205pjD1hIVW1DbrhFCSPXRFaqQeXSdwTLXziMhkNpbKCJC6h6zkik7Ozv89ttveO+996orHkJqNVtpzSGWQ9cIIcRUTKuG8uwug2UuL71moWgIqZvM7ubXsmVLxMfHo1OnTtURDyG1HrXmkKeha4QQQgixDQJzX7Bo0SIsWbIER48erY54CCGEEEIIIcQmmN0yNW7cOCiVSnTp0gWurq7w9vYGx3H8eo7jcP78+SoNkhBCCCGEWJ+AyF1P36iCEhf1qbZ9E1JVzE6m3Nzc4O7uXh2xEEIIMQPNR0UIIYRYltnJ1JEjR6ohDEKINaObdutD81ERQgghlmd2MkUIqVrWnqjQTbt1ovmoCCGEEMurUDKVkZGBL7/8EgcPHkR6ejrc3d3RrVs3TJo0Ca6urlUdIyG1li0kKnTTbp1oPipCCCHE8sxOpu7du4cOHTrgzp07aNq0Kfz8/HD//n3MnTsXP/30E44fPw4fH5/qiJWQWscWEhVbvWm39ha/yqL5qAghhBDLMzuZmjZtGvLz83Hq1CmEhobyy+Pi4tCvXz9MmzYNsbGxVRkjIbWWLSQqtnjTbgstflWB5qMihBBCLMvsZGrv3r2YN2+eQSIFAKGhoZgzZw5mzpxZZcERUtvZSqJiazftttDiRwghhBDbZ3YylZ2djYCAgDLXBQYGIjs7u7IxEVKn2FqiYgtsocWPEEIIIbbP7GQqMDAQu3btwssvv2y0bs+ePQgMDKySwAghpKJspcWPEEIIIbbN7GQqIiICkZGR0Ol0GDlyJLy9vZGSkoL169djxYoVWLRoUXXESQghZqEWP0IIIYRUN7OTqU8//RQJCQn4+uuvsXLlSn45Ywxjx47FJ598UqUBEkIIIYQ8TUDkLkuHUPM4AcRufkbLCCE1x+xkiuM4fPfdd/j4449x+PBhpKenw83NDV26dEHjxo2rI0ZCCCGEEFKK0M4RPqO/sXQYhNRpFX580aRJE7z33nuYPn063nvvPUqkCCGE2KTc3Fx89tln6N69Ozw8PMBxHKKiosrc9t9//0W3bt0gl8vh4uKCQYMG4datW2Vuu2LFCgQHB0MqlSIwMBDR0dFQq9XVeCSEEEJqmtktU3oPHz5EUlIS8vPzjdZ16tSpUkERQgghNSU9PR2rV6/Gc889hwEDBuCHH34oc7urV68iLCwMLVu2xObNm1FQUIBZs2ahY8eOOHfuHDw8PPht58+fj5kzZyIyMhLdu3dHXFwcZsyYgXv37mH16tU1dWiEEEKqmdnJVEpKCt58800cPnwYQPFYKaC4+x9jDBzHQavVVm2UhBBCSDXx9/dHZmYmOI7Do0ePyk2mZs2aBalUip07d8LJyQkA0Lp1azRq1AgxMTFYvHgxgOLkbN68eRgzZgwWLFgAAAgLC4NarcaMGTMwadIkNGvWrGYOjhBCSLUyO5n64IMPcPbsWSxevBghISGQSqXVERchhBBSIziOe+o2Go0GO3fuxFtvvcUnUkBxIhYeHo4dO3bwydTevXtRUFCAiIgIg31ERERg+vTp+PXXXymZIoSQWsLsZOro0aOIiYkx+iNRGUqlEjNmzMDmzZuRkZGB4OBgREZG4tVXXzXp9b/99hu++OILnD17FlqtFgEBAZg4cSLGjh1bZTESQkhVSkhTIjlDBV+FPZVwtwEJCQnIz89HSEiI0bqQkBDs378fBQUFkMlkiI+PBwC0aNHCYDtvb2+4u7vz68tSWFiIwsJC/t85OTlVdASEEEKqQ4Wq+fn6+lZpEIMGDUJcXBwWLVqExo0bY+PGjRgxYgR0Oh1ee+21J7520aJFfBGMqVOnQiwW4+rVqygqKqrSGAkhpKokpCnx/bFbSM8rgpuDBGM6PWNSQkUJmOWkp6cDABQKhdE6hUIBxhgyMzPh7e2N9PR0SKVSODg4lLmtfl9lWbhwIaKjo6sucFKr6YoKkHN6m8Eyp7aDIZDILBQRIXWP2cnU0KFDsXPnTnTr1q1KAti9ezf279/PJ1AAEB4ejqSkJHz66acYPnw4hEJhma/9559/MH36dCxcuBCfffYZv7xr165VEhshhFSH5AwV0vOK0NTLEVdSc3E3M/+pyVFFEzBStZ7UJbDkOlO3K23q1Kn4+OOP+X/n5ORU+QNMUnswTSGyj28yWObYqi9AyRQhNcbsZGrYsGEYM2YMdDod+vXrBzc3N6NtWrVqZfL+duzYAblcjqFDhxosj4iIwGuvvYZTp07hxRdfLPO1X3/9NaRSKT788EPzDoKQGkItCaQsvgp7uDlIcCU1F24OEjRwtXvqayqSgJGqo/9bV1arUkZGBjiOg4uLC79tQUEBVCoV7O3tjbZt3bp1ub9HKpXSWGRCCLEhZidTXbp0AVCcyKxcudJgXUWq+cXHx6Np06YQiQxD0fdLj4+PLzeZOnbsGJo2bYpt27Zh7ty5uHnzJry9vfHGG29gzpw5kEgk5f5e6pdOqhu1JJDyBHnIMabTM7ibmY8GrnYmXRcVScBI1QkKCoKdnR0uXrxotO7ixYto2LAhZLLi1gD9WKmLFy+iXbt2/Hapqal49OgRmjdvXjNBE0IIqXZmJ1Nr166t0gDS09PxzDPPGC3X90t/Ut/ye/fuIS0tDRMmTMDcuXPRrFkzHDx4EIsWLUJycjI2bNhQ7mupXzqpbpVpSaAWrdovyENu1ntbkQSMVB2RSIR+/fph+/btWLJkCRwdHQEAd+7cweHDh/HRRx/x2/bs2RMymQyxsbEGyVRsbCw4jsOAAQNqOnxCCCHVxOxkauTIkVUeREX7lut0OuTm5mLTpk185b/w8HDk5eVh2bJliI6ORsOGDct8LfVLJ9Wtoi0J1KJFymNuAkZMt2fPHuTl5SE3NxcAcPnyZWzduhUA0Lt3b9jb2yM6OhqhoaHo27cvIiMj+Ul73d3dMXnyZH5fCoUCM2bMwMyZM6FQKPhJe6OiojB69Ggqi04IIbWI2clUSdeuXcOjR4/QsmXLMqsWmcLNza3cPuhA2ZWTSr42NTUVPXr0MFjeq1cvLFu2DP/++2+5yRT1SyfVraItCdY6NoZay0ht9v777yMpKYn/95YtW7BlyxYAwO3btxEQEIDg4GAcOXIEU6ZMwZAhQyASidClSxfExMTAw8PDYH/Tp0+Ho6MjVq5ciZiYGHh5eSEyMhLTp0+v0eMihBBSvSqUTP3000+YNm0aUlJSAABxcXFo1aoVhg0bhpdffhljxowxeV8tWrTApk2boNFoDMZN6fulP6lveUhICFJTU42WM8YAAAKBwOQ4CKkOFWlJsMaxMdRaRmq7xMREk7Zr3bo1Dhw4YNK2EyZMwIQJEyoRFSGEEGtndraxZcsWjBo1Cq1atcLXX3/NJy5AcRW/zZs3m7W/gQMHQqlUYts2w3kS1q1bBx8fH4P+5qUNHjwYQHH3jJJ2794NgUCA0NBQs2IhxBroW7TeaO9vNUlLyday9Lwi3M3Mt3RIhBBCCCEWZ3bL1MKFCxEREYE1a9ZAq9Vi/Pjx/LqmTZtixYoVZu2vV69eePnll/H+++8jJycHDRs2xKZNm7B3716sX7+en2PqnXfewbp165CQkAB/f38AxeXTv/vuO4wbNw6PHj1Cs2bNcODAAaxcuRLjxo3jtyPE1ljb2BhrbC0jhBBCCLE0s5OpK1euYPHixWWue9rM7uXZvn07pk+fjlmzZiEjIwPBwcEGRSUAQKvVQqvVGrSEicVi7N+/H9OmTcOCBQuQkZGBwMBALFq0yKC4BCGkcqiSHCGEEEKIMbOTKXt7e2RnZ5e57t69e3B1dTU7CLlcjuXLl2P58uXlbhMbG4vY2Fij5QqFAqtWrcKqVavM/r2EWANbKexgba1lhBBCCCGWZvaYqQ4dOhiNldKLjY1FWFhYVcRFSJ2gL+yw4dQdfH/sFhLSlJYOiRBCCCGEmMjsZGrWrFk4efIk2rZti6+++gocx2H79u3o168fjh07RmVfCTEDFXYghBBCCLFdZidTbdq0wZ49e6BUKjF58mQwxrBgwQJcv34du3fvfmIpc0KIISrsQAghhBBiuyo0z1R4eDiuXLmChIQEPHjwAO7u7mjcuDGA4jmeOI6r0iAJqa2svbCDrYznIpZD1wghliWwc7J0CITUaRVKpvSCgoIQFBTE/3vjxo2YM2cOrl69WunACKkr9IUdEtKUOHLtodXclNJEveRp6BohxLKE9s7wnbDR0mEQUqeZnExlZ2fj119/xYMHD9C4cWO88sorEAiKewlu374ds2bNwuXLl2luJ0IqwBpvSkuO57qSmou4xAxqgSAGSl8jdzPz6doghNiMgMhd1bbvxEV9qm3fxLqYlEzdvHkTHTt2xMOHD/lufJ07d8avv/6KESNGYO/evXBxccGSJUvw4YcfVnfMhNQ61nhTWnI8l1jA4dj1NKi1zGqSPWJ5NOaPEEJIXWdSMjVz5kzk5OQgKioKbdq0wa1btzB//ny8+OKLuHz5MkaPHo0lS5bAxcWlmsMldVVtH5dhjTelJcdz3c/Kx6GrD60q2SOWpf9M9mzuBY7jrHLMHyHEtlVnyxEhVcWkZOro0aOYMWMGpk6dyi9r2LAhevXqhffeew/ffPNNtQVIiDV2gatq1lqIouR4rvPJWVaV7BHLqQufSUIIIcQUJiVTaWlp6NChg8Gyl156CQAwfPjwqo+KkBKssQtcddAnLtbIWpM9Yhl15TNJCCGEPI1JyZRWq4VMJjNYpv+3o6Nj1UdFSAnW2AWuKtha10VrTvZIzaqtn0lS/ajbVtXSqQuRd3G/wTKHFi9DIJZaKCJC6h6Tq/ldu3YNItF/m2u1WgAoswx6q1atqiA0QorVxlYR6iZFbFlt/EwSYouYugAZ+1cZLLMP7ghQMkVIjTE5mRo1alSZy998803+//WV/vSJFiFVpba1ilA3KWLrattnkhBCCKkIk5KptWvXVncchNQptbWblK11XSSEEEIIqQyTkqmRI0dWdxyE1Cm1sZsUdV2sWZS4EkIIIZZncjc/QkjVqm3dpKjrYs2hxJUQQgixDpRMEUKqTJFGizNJmfBT2NearovWiBJXQgixbtVZuTJxUZ9q2zcxn8DSARBCbF9CmhJ741Oh1jKIhRx6Nveim/tqVFvH3BFCCCG2hlqmCCGVpm8paePviiupueA4ztIh1Wq1ccwdIYQQYosomSKkDqmuogXUUlLzatuYO0IIIcQWUTJFSB1RnUULqKWEEEIIIXURjZkipI4oWbQgPa8IdzPzq3T/QR5ydG7sQYkUIQCUSiUmTZoEHx8fyGQytGzZEv/73/8sHRYhhJAqRi1ThNQR1BWPkJozaNAgxMXFYdGiRWjcuDE2btyIESNGQKfT4bXXXrN0eE9UnVXICCGktqFkipA6grriEVIzdu/ejf379/MJFACEh4cjKSkJn376KYYPHw6hUFjh/VOyQ0jdVt3fAVR63TyUTBGLq66iCMQYFS0gpPrt2LEDcrkcQ4cONVgeERGB1157DadOncKLL75o1j6bz94HgdS+KsMkhBBSBSiZeowxBgDIycmxcCR1y61HSqw7nogMVREU9hKM7BCAZ9zpZp+QukT/vav/HrZ18fHxaNq0KUQiwz+xISEh/PrykqnCwkIUFhby/87OzgYA6ApV1RQtsWW6IuPrQlekAicUWyAaUlv4fbSl2vYdH92j2vZd1Uz920TJ1GO5ubkAAF9fXwtHUrcts3QAhBCLyc3NhbOzs6XDqLT09HQ888wzRssVCgW/vjwLFy5EdHS00fJ7346qsvhI7Xb/uzGWDoGQcjkvs3QE5nva3yZKph7z8fFBcnIyHB0dy51wNCcnB76+vkhOToaTk1MNR1g5FLtlUOyWQbFbRkVjZ4whNzcXPj4+1RhdzXrSxNVPWjd16lR8/PHH/L91Oh0yMjLg5uZmU5Nh2/J1bMvovFsGnXfLqO7zburfJkqmHhMIBGjQoIFJ2zo5Odnsh4VitwyK3TIodsuoSOy1oUVKz83NrczWp4yMDAD/tVCVRSqVQiqVGixzcXGp0vhqki1fx7aMzrtl0Hm3jOo876b8baJ5pgghhJAq1KJFC1y5cgUajcZg+cWLFwEAzZs3t0RYhBBCqgElU4QQQkgVGjhwIJRKJbZt22awfN26dfDx8UG7du0sFBkhhJCqRt38zCCVSjF79myjLhi2gGK3DIrdMih2y7Dl2KtSr1698PLLL+P9999HTk4OGjZsiE2bNmHv3r1Yv359peaYshV0LVgGnXfLoPNuGdZy3jlWW2rREkIIIVZCqVRi+vTp2Lx5MzIyMhAcHIypU6fi1VdftXRohBBCqhAlU4QQQgghhBBSATRmihBCCCGEEEIqgJIpQgghhBBCCKkASqYIIYQQQgghpAIomSKEEEIIIYSQCqBkipA6Jjs7GwCg1WotHIn5kpKSAAC2WDfn8uXLuH//PgDbi/+XX37BihUrAAA6nc7C0RBS9zx69AgZGRmWDoMQUoY6Xc3v0qVLOHbsGBo0aIDQ0FB4eXkBKL7R4TjOwtE9WVJSEjQaDYKCgiwditkSEhJw/fp1eHh4IDg4GHK53NIhmezq1as4duwYXFxc0KRJE7Ro0QICgW08k7hz5w5effVVODk5Ye/evZYOxyz//vsvhg8fDrlcjtOnT0MsFls6JJOdPXsWH3/8MfLy8jB8+HB89NFHNnPN/PPPP/jwww9x8uRJ+Pv74+bNm3VijiRSvoKCAshkMgC28bfS1uXl5WHChAn4v//7P0gkErRp0wYjR45EWFiYpUOrE9RqNf/3hq73mnHo0CGIxWL+HtEW2MZf9CpWWFiId999F6GhoVixYgX69++PTp064YsvvgAAq/6w5Ofn48MPP0RgYCDWrFmD3NxcS4dkMqVSiVGjRiEsLAzjxo1D27Zt0b17d/z+++8ArPtpvVKpxFtvvYWOHTviiy++wKuvvorevXvju+++A2Ddset9/fXXOHnyJM6fP4/NmzcDsP7WqdzcXIwYMQJt2rRBu3btsG7dOptJpHQ6HRYtWoTOnTvD29sbkZGR6N69u00kUjk5ORgxYgRCQ0PRtGlTtG/fHjKZDHfv3rV0aMRCrl27huHDh2Pw4MEYMWIE/v77bxQUFACg1srqcuPGDXTu3BmXL1/GpEmT0KNHDxw7dgx9+vTBgQMHrP7725adOHECr7zyCgYPHoy33noL8fHx0Gg0AGzj770tOnfuHJ5//nmMGDECQ4YMQbNmzTBt2jQkJiYCsPLvGVYHLVu2jDVs2JD9+eef7O7du+zChQusV69ejOM4tmHDBqbRaCwdYpkuXbrEBg8ezHx9fZmfnx975pln2LFjxywdlkn++usv1rZtW/biiy+ynTt3shMnTrDffvuNubi4sJdeeomlpqZaOsRy7d69mzVp0oS98MILbPfu3ezq1avszJkzrGHDhqxNmzYsMzPT0iE+kU6nY4wxNnnyZObv789atmzJ2rVrx/Lz8xljjGm1WkuGV67Vq1czjuPYCy+8wA4cOMDy8vIsHZJZrly5wlq3bs2WLVvGsrKy+PfB2s2dO5eJxWLWvn17tnfvXqbVatns2bOZRCJh9+/fZ4wxmzkWUjW+//575ujoyAYMGMDefvtt1rhxYyaXy9nkyZMtHVqtpP98rVq1itWvX5+dO3eOXxcXF8c6dOjAGjduzI4ePWqpEGstnU7H5s2bxxwcHNjrr7/O3njjDVa/fn3m4eHB5s+fb+nwaq20tDQWGhrKBg0axC5cuMDOnDnDpk6dyhwdHVnPnj0tHd5T1alkSqfTsdzcXBYSEsKGDh3KCgsL+XXXrl1jr7zyCqtfvz47fvy4BaMsn/7mcv78+eyvv/5iLi4ubNSoUezhw4eWDu2J0tLS2LBhw1ifPn3Y+fPnDdbNmDGDOTg4sL///ttC0T1ZRkYGmzp1KhsxYgS7fv26wbrRo0ezpk2b2sxN/oABA9gXX3zB5syZw+zt7dmiRYsYY9aZTN27d4/17t2bCQQCdvbsWYOb9+zsbAtG9nT6WGfNmsXq1avHJyCMMXbu3Dl2/vx5lpGRYanwnmj79u2sRYsW7LvvvjM4zzExMYzjOPa///3PgtERS1AqlaxTp05s9OjRBt91w4cPZyKRiH3zzTeMMUqwq0OfPn1Yhw4djM7tuXPnmL29PXvttdcMvl9I5aWkpLDmzZuzmTNnsqKiIsYYY5mZmaxnz55MJBKxXbt2Mcboeq9qmzZtYjKZjJ04ccLgnmTWrFn8fa81q1PJFGPFHwAfHx82e/ZsxhgzSKj+/fdf5ubmxt5880326NEjC0VYvsuXL7NDhw7x/545cyaTyWRs27ZtVv/BHjFihEHs+ta//fv3M47j2L///mup0J7qyJEjfCJV8jy/8cYbbN68eSwvL4//8FtjYqI/171792YzZ85kWVlZLDQ0lDVs2JAlJCQwxqzzD8OePXuYq6sr++STTxhjjF29epUNGzaMderUiXXs2JF9++23LDk5mTFmnee9X79+rF+/fowxxi5evMg6derEPD09mUKhYA0bNmQbN260cIRlS09P5/9ff138/fffjOM49uOPPxosJ7XfuXPnGMdx7PDhw4wxxtRqNWOs+AFknz59mKOjI0tMTLRghLXXmDFjWEBAAP/vkp+7qKgoJpFI2C+//GKJ0GqtXbt2MY7j+L+N+r+fcXFxrG3btiwgIMDqH+jZAv09tv4efMWKFcze3p4VFBQYLE9OTmavvfYas7e3Zzdv3rRMsCaw/s77FVRe38qHDx8iICAABw8eBABIJBJ+25YtW2LChAnYunUrLl++XGOxllZe7E2bNkV4eDi/zXvvvQc/Pz988803fJ9SSysdu75Pd2xsLB87AH4Q+9WrVyGXy+Hi4lJjMZanvPPeuXNnNGrUCEDxeLr8/HyMHDkSGzZswIYNG9C8eXN89NFHAGCx8TBP6kssFApRVFSEhw8fwtvbG87Ozhg5ciTS09MRExMDoHiQs74/eE0rHTt73B+9Xbt2ePPNN/H111/jtddew3PPPYdHjx7B29sbeXl5GDduHN555x0A1nneFQoFzp49i+TkZLz33ntwcnLCd999h+nTp8PDwwPvvPMOfvvtN4v1Ay/v9yoUCv7/9eNHXV1d4erqirNnz9ZIbMR6pKenQyaT8ZU09Z+1xo0bY9y4cZBKpYiOjgZg5WMabFDLli3x4MED7Ny5E4Dh+Z04cSI8PDzw+++/o7Cw0FIh2jSlUmm0LDMzE1KpFAkJCQbL27RpgwkTJiAlJQXLli0DQNd7RTx48ACtWrVC+/btARTfgwPF74VAIMDRo0cNljdo0ACjRo2Cvb095s6dC8BKz7uls7nqsGbNGta0aVP+iU3pp9YRERHM29ub7d2712j95cuXmbe3N/vggw/KfG11e1rspcXGxjKO49jXX3/NZ/KWempsTuz6daNHj2bPPfccy83NrZEYy2Nq7Ddv3mSNGzdmISEhbPXq1WzLli3s7bffZhzH8eMHrO2a0T9Ze+mll/imcpVKxQYMGMDq1avHRo4cydq2bcuOHDlSo3Ez9vTYT548yUJCQljjxo3Z9u3bWU5ODr/NBx98wAQCAfv666/LfK2lY588eTKTy+WsV69erE2bNuzOnTv8ukuXLrEWLVqwbt26WeQpp7nfMw8ePGAeHh6sW7duTKlU1kSIpIZt3bqVHThwgMXFxfHjKRlj7M6dO0wikbDJkyczlUrFGPvvOyU7O5uNHz+ecRzHbt26xRijVsuq9OjRI+bt7c2GDRvGn/OS53fq1KnMxcWFf1+IaZRKJfv4449Zly5dWFhYGJs6dSo/BOH48eOM4zj2+eef8+dc//2YkpLChgwZwpycnGyme7+10Q9X4TiOffXVV/zyGzduMI7jWFRUFP/9U/J7ZvTo0czBwcFqW8FrVTKVnJzMxowZw0QiEeM4jvXp04e/4HU6Hf/G/Pvvv4zjODZmzBiWk5PDGPvvTcvMzGQDBw5kTZo04ZsbrSH28uTk5LCuXbuy4OBgi3WVq0jsarWa6XQ61rhxY/b222/XZLgGKhL7oUOHDG7qHz16xIYPH87s7Oxq9MbYnNjVajWrX78+27JlC79s2rRpTCKRMJFIxJYuXcqUSmWN3QiZGrtSqWTr1q1jmzZtMvo8XrlyhQUGBrIuXboYdNe1dOz66+L8+fOM4zgmkUjYu+++a7CPoqIitmTJEsZxXI12XajI9a4/np49e7LQ0NAnbktsz7p165i/vz9r1KgRc3JyYhzHsYiICIPP1JAhQ1hQUJDRmFfGGPvtt9+Ym5sbi46Orsmw64y5c+cyT09P9vPPPzPGmEGBrB9//JHZ2dmxU6dOWSo8m/Pzzz8zT09P9tJLL7GPP/6Y9enThwmFQta6dWv+frBt27asffv2/AOCkn744Qfm6OjI1qxZU9Oh1wpLlixh9erVY3369GFubm78PZNOp2ODBg0y+p7R/61Zu3Ytc3R0tNpxu7UmmSooKGCTJk1i3t7ebObMmWzkyJHMxcWFrVixgjH23xuivzF44403mKOjI1u7dq3Bcv26Vq1aGTyds4bYy3Pw4EEmFovZtGnTWGZmJktOTmZ//vknY6z6n9ZXJvarV68yiURicIOvUqnYxYsXn/paS8T+pHgmTZrE6tWrV2M3xubErtPpWE5ODmvZsiXbvXs3u3TpEgsLC2MikYg1bdqUOTk5sdjYWMZYzbTumHveS7eElFzfrl079vLLL1d7zHqmxq7/79ixYxnHcXw1Iv14E8aKK3XZ2dnVWEXOynxWCwsL2dixY5lEIjFoYSO2Kysri33yyScsMDCQzZ8/n507d44lJCSw0aNHMzs7O7Z48WJ+26NHjzKJRMKmT5/O33Dqr2WlUsl8fHz4sY2UaFetgoICFhQUxFq2bMmPEdVbvHgxc3BwYElJSRaKznbodDq2Y8cO9vzzz7PZs2eztLQ0vsBEdHQ0s7e354upbNq0iQkEAvbVV1/xD/H02yYlJTEHBwe+VYWud/N88sknbNy4cezHH39kYrGYjRs3jjFWfO9x9OhRJpPJ2KRJk/gxVfrz/uDBA8ZxHNu5c6fFYn+SWpNMMVY8IDMqKooxVlyFrXHjxqxVq1bs9u3bjLHiN0v/VOfRo0fM19eXPfvss+zkyZP8PtLT09mLL77I3nzzzRr9kJgSe2kl4xs9ejSrV68ei4qKYqGhoYzjOHb37l2rjZ2x4uZehULBrl27xhhj7NSpU6x79+7Mzc2txkqlV/a8a7Vadvv2bda6dWs2ePDgGu1qZk7sKSkpTC6Xs+eff56JRCLWpUsX9s8//7DTp0+z4OBg5ufnx98kWVvseiUTEcaKu2M4ODiwKVOmVHu8JZkSuz7+zMxM5u/vzziOY1u3buX3oVQqWUREBGvXrl2NtoBX9LPKWPENh0AgYAcPHqyJUEk127FjB2vRogVbvnw5y8/P57/XkpKSmL+/Pxs8eDB/bapUKvbuu+8yFxcXtm3bNoP9aDQa1qBBAzZ+/PgaP4a64siRI8zT05N16NCB3bx5k2VlZbHr16+zLl26sLffftvou5EY0+l0bNy4cWzgwIFGyeedO3cMuoxnZGSwfv36MX9/f3bgwAGDbdPT05lMJmNLly6tsdhrA/3flnfeeYeNGDGCqdVqNnToUCYSifjS/wUFBeyTTz5hYrGYfy8YK37vfvrpJyaXy9mJEycsEv/T2Gwypc9WS/9/SUuXLmVOTk7ss88+M1iuT6i2bNnCgoODma+vL/vqq6/Yrl272Pjx45mnpyfbt2+fVcZelry8PLZx40a+H+orr7xSbf1KqyJ2/fkfOnQoe/7551l8fDwbP348E4lErEePHtX2lK06zvuVK1fYqFGjWKNGjfgv3epIwisbu1arZa+++ipr0aIF27Bhg8HcWNOmTWNvv/02y83NtcrYS1OpVOzSpUts2LBhLCQkhF25cqXKYi2tKr5nfvvtNxYUFMQUCgX7+OOPWWxsLBszZgxzdXVlq1atYoxZ5zWjp4/tr7/+YgKBgP3++++MMeusoEhMt2XLFjZnzhyDZfqufa1bt2avvPKKwbqUlBT2zDPPsKZNm/LXgFqtZlu2bGH169e32uktaoutW7cyb29v5ujoyF566SXm4+PDmjdvzi5cuGDp0GxGSkpKmeOzb926xWQyGd9Kz1hxzxlnZ2fWvn17/ga+qKiIrVixggUGBlp1ZTlrpdPp2NChQ9mnn37KGCv+2+jl5cX3LsnJyWF5eXmsS5cuzNHRkUVGRrK///6bHTlyhLVr147179+/Rh8+msPmkqm///6bLzn85ptvsosXL/I3CvqbF/1TmqKiItahQwf2zDPP8HNHaTQagxuXuLg41rVrV1avXj3m7+/PmjdvzpeAtcbYS0tMTGTjxo1jrq6urEWLFtU2R1ZVx56fn89CQkKYj48PUygULDAwkO3fv98mYr99+zb74osv2EcffcTq1avHgoODrfa8l3xieffuXXbnzh3+Jlj/OSjvRtvSsZc+77du3WJffvkl++STT5inpyd79tlnq22sQFV/z/zzzz+sX79+zMvLiwUGBrKWLVsaTBVgbbGXZefOnYzjOLZw4cJqiZvUjLIS95KJcX5+PgsICGATJ0402u7UqVMsJCSEcRzHunXrxl599VXm6OjIIiIiqDBJDbhy5QpbvXo1i4yM5IcoEPOVnsrkwIEDjOM4vgiT/jtwx44drFGjRkwkErG+ffuyQYMGMTs7OxYZGcmP+yam0Z/TAQMGsNGjRzPGiv/+TJkyhXEcx0aMGMH8/PzYgQMHWHJyMvv000+ZWCxmDRo0YM7OzmzQoEFWOz8jYzaUTJU3K7Wnp2eZk3np37jt27czV1dX9tprrxntT6+oqIhlZGSws2fP2kTsJd24cYMJhUK2bNkym4r90qVLjOM45uHhwVauXGlTsR8/fpx169aNde7cma1evdqmYq8J1RX74cOHWYsWLVhoaCjfomPtsZf8nlGr1Sw3N5fFx8fbROyljyE/P99gfCOpnW7cuMFcXFz4AfalxwHeu3ePLVq0iL399tvslVde4VupCLFV8+bNY76+viwlJcVoXVJSEps+fTobNWoUGzx4MPu///s/C0RYe7Ru3ZrFxMTw//7888+ZTCZjAoGALVq0iGVlZfHrEhIS2IkTJ9ilS5csEapZbCaZqsys1EOHDmUeHh78jUBGRgZ78OABv768J7G2EHt1x1/VsZccC7V+/fpqaxWp7tgTEhKqtZtTdV8z1ak6z/uFCxds6nqvLd8z1KWv9tO/xz///DMTi8XUfYzUGX369GHdu3c3WFad9yZ1kf77JSwsjK1cuZLduHGDdenShYlEIta2bVsmFArZokWLGGPG46Ntgc0kUxWZlVr/hpw/f57Vr1+fdenShR04cICNGDGCvf766+z+/fsUuwViL12RyJZir6miHnTN0HmvS7GTmlO6C6qefllERAQLCQkxKI1++fJlfi4j6tpEapP79+8zNzc3NnfuXMZY8bjBkydPst69e7OHDx9aOLraRalUMn9/f+bv78/EYjELCwtjJ0+eZNeuXWPdunVjHMfZ7Dm3ymSqrAGC69evZzKZjC/5XfIp7/r165lUKuWrVJX1BPjdd9/lCzR4enpWW3lFip1ip9gpdoqdWJuS1WwZY2z37t1GXZZUKhV77rnn+HnRUlJS2Jw5cxjHcfzNJiG1gf6hwM6dO5lYLGZHjx5ld+/eZR988AGzt7dnzz33HEtLS6OHB1Vs8uTJLDg4mP38888GhbB++OEH9tZbb7GMjAybPOdWlUyVnJU6PDy8zFmpY2JiypyVevDgwQazUuvfjAcPHrANGzawhg0bMrlczpYvX06xU+wUO8VOsddw7MRySnabuXnzJuvRowfjOI5FR0cbJFhnz55lcrmcffPNN+zXX39lfn5+zNPTk/3000+WCJuQahcVFcV8fX3Z9OnTWf369VlgYCDbs2ePpcOqtfLy8gwKYelVdzf46mY1yVR5s1K3adOGn/8mNDTU7Fmpv/32W2Zvb8+GDx9e5tNcip1ip9gpdoq9emMnllEyiVKr1Wz8+PGM4zjWunVrtm7dOr77rD6x/uGHHxjHcczb25sJhcIan8ONkJqkVqv5BwtOTk5syZIllg6J2CiLJ1PVNSu1Puu9dOkSPyksxU6xU+wUO8Vec7ETy9BqtQZdZVauXMmcnJyYt7c3W7BgAbt69WqZRUU+/vhjxnEce+utt2q0aA0hljJlyhQ2ZcoUq52/iNgGq0imbHVWaoqdYqfYKXaKnVirI0eOsGeffZZJJBI2duxYduLECb6QREn6xOrChQt8t1FC6gKqVEqqgsWTKcZse1Zqip1iNxfFTrGby5ZjJzVPq9WyWbNmMY7jWO/evdkff/zB0tPTLR0WIYTUSlaRTOnZ8qzUFDvFTrFT7BQ7sRaHDh1ia9asMWrNJIQQUrVEsCICgcDgvydPnkSDBg3QpEkTAIBQKAQADBgwAK1atcLq1atx79495ObmYv/+/ejQoYNlAgfFbikUu2VQ7JZhy7GTmhUWFobOnTvz1wpjDBzHWTgqQgipfTjGGLN0EOXp27cv1Go19u3bxy9Tq9UQi8UWjMo0FLtlUOyWQbFbhi3HTgghhNQGAksHUJ6UlBScPHkSHTt2BAAUFRXh1KlTGDBgANLS0iwc3ZNR7JZBsVsGxW4Zthw7IYQQUltYXTKlbyj7999/kZOTg06dOuHevXuYPHkyunTpgnv37oHjOFhjgxrFbhkUu2VQ7JZhy7ETQgghtY1VjZkCwPfpPnPmDLy8vPDnn38iNjYWEokE27ZtQ8+ePS0cYfkodsug2C2DYrcMW46dEEIIqXVquOCFSWx5VmqK3TIodsug2C3DlmMnhBBCahOra5kCAJFIhJYtW6Jly5aIjo6GVCq1dEgmo9gtg2K3DIrdMmw5dkIIIaQ2sdpqfjqdji/pamsodsug2C2DYrcMW46dEEIIqS2sNpkihBBCCCGEEGtGjzUJIYQQQgghpAIomSKEEEIIIYSQCqBkihBCCCGEEEIqgJIpQgghhNRqX331FTiOQ/PmzS0dSqUcOXIEHMfhyJEjFXp9bGwsOI5DYmJilcZVkziOQ1RUlNmvu3//PqKionDu3DmjdVFRUfwcfpaQlZUFd3d3/O9//+OXxcfH46WXXoKjoyNat26N48ePG73u888/R+PGjVFQUGC0rlOnTpg0aVJ1hk0eo2SKEEIIIbXajz/+CAC4dOkSTp06ZeFoiCXcv38f0dHRZSZTo0ePxokTJ2o+qMeio6Ph4+OD4cOHAwA0Gg0GDRoEd3d3bN++HS1btkT//v2RlZXFvyYxMRHR0dFYtWoVZDKZ0T7nzp2Lb775BteuXaupw6izKJkihBBCSK115swZnD9/Hn369AEArFmzxsIR1T1arRaFhYWWDqNcDRo0QPv27S3yuzMyMvDdd99h/PjxfOvYjRs3cOPGDXz77bd4+eWXsWrVKhQUFODkyZP8695//30MGTIEXbp0KXO/nTt3RpMmTbB06dIaOY66jJIpQgghhNRa+uRp0aJFePHFF/G///0PKpXKYJvExERwHIeYmBh88cUXCAwMhFwuxwsvvGBwAwsAo0aNglwux82bN9G7d2/I5XL4+vpi8uTJBglDeV3y9L8rNjaWX3bmzBm8+uqrCAgIgJ2dHQICAjBixAgkJSVV+LhPnjyJDh06QCaTwcfHB1OnToVarS5z219++QUvvPACHBwcIJfL0aNHD5w9e9Zou++//x6NGzeGVCpFs2bNsHHjRowaNQoBAQFGx7dkyRLMmzcPgYGBkEqlOHz4MAoKCjB58mS0bNkSzs7OUCgUeOGFF/Dbb78Z/a6cnByMGTMGbm5ukMvl6NmzJ65fv2603c2bNxEREYFGjRrB3t4e9evXR79+/XDx4kV+myNHjiA0NBQAEBERAY7jDLoLltXNT6fTYcmSJQgODoZUKoWnpyfeeust3L1712C7sLAwNG/eHHFxcejYsSPs7e3xzDPPYNGiRdDpdGW/OSXExsZCo9HwrVIA+G57Dg4OAACxWAyJRMIv37RpE86cOfPUROnNN9/Exo0bkZub+9Q4SMVRMkWIhen7sOt/ZDIZvLy8EB4ejoULF+Lhw4cV2u/ly5cRFRVl033jCSGkMvLz87Fp0yaEhoaiefPmePvtt5Gbm4stW7aUuf3KlSuxf/9+LFu2DBs2bEBeXh569+6N7Oxsg+3UajVeeeUVdO3aFb/99hvefvttfPnll1i8eHGF4kxMTESTJk2wbNky7Nu3D4sXL0ZKSgpCQ0Px6NEjs/d3+fJldO3aFVlZWYiNjcWqVatw9uxZzJs3z2jbBQsWYMSIEWjWrBk2b96Mn3/+Gbm5uejYsSMuX77Mb7d69WqMHTsWISEh2L59O2bMmIHo6Ohyx2999dVXOHToEGJiYrBnzx4EBwejsLAQGRkZ+OSTT/Drr79i06ZNeOmllzBo0CD89NNP/GsZYxgwYAB+/vlnTJ48GTt27ED79u3Rq1cvo99z//59uLm5YdGiRdi7dy9WrlwJkUiEdu3a8V3cWrVqhbVr1wIAZsyYgRMnTuDEiRMYPXp0uefw/fffx5QpU/Dyyy/j999/x9y5c7F37168+OKLRu9JamoqXn/9dbzxxhv4/fff0atXL0ydOhXr168v/016bNeuXXj++efh4uLCLwsODoZCocDixYuRlZWFlStXIi8vD23atEFmZiY++ugjfPHFF3Bzc3vivsPCwpCXl1fhMXbERIwQYlFr165lANjatWvZiRMn2LFjx9jWrVvZpEmTmLOzM1MoFGz//v1m73fLli0MADt8+HDVB00IITbgp59+YgDYqlWrGGOM5ebmMrlczjp27Giw3e3btxkA1qJFC6bRaPjlp0+fZgDYpk2b+GUjR45kANjmzZsN9tG7d2/WpEkT/t+HDx8u8ztY/7vWrl1bbtwajYYplUrm4ODAli9f/tR9ljZ8+HBmZ2fHUlNTDfYZHBzMALDbt28zxhi7c+cOE4lE7MMPPzR4fW5uLvPy8mLDhg1jjDGm1WqZl5cXa9euncF2SUlJTCwWM39/f6PjCwoKYkVFRU+MU6PRMLVazd555x32/PPP88v37NnDABgcO2OMzZ8/nwFgs2fPfuI+i4qKWKNGjdhHH33EL4+Liyv3vM+ePZuVvCW+cuUKA8DGjRtnsN2pU6cYADZt2jR+WefOnRkAdurUKYNtmzVrxnr06PHE42eMMXt7e/bee+8ZLd+xYwdzcnJiAJhUKmXfffcdY4yxd955h3Xr1u2p+2WMsaKiIsZxHJsyZYpJ25OKoZYpQqxE8+bN0b59e3Ts2BGDBw/Gl19+iQsXLsDBwQGDBg3CgwcPLB0iIYTYlDVr1sDOzg6vvvoqAEAul2Po0KH466+/cOPGDaPt+/TpA6FQyP87JCQEAIy623Ech379+hksCwkJqXC3PKVSiSlTpqBhw4YQiUQQiUSQy+XIy8vDlStXzN7f4cOH0bVrV9SrV49fJhQKDbqSAcC+ffug0Wjw1ltvQaPR8D8ymQydO3fmWzSuXbuG1NRUDBs2zOD1fn5+6NChQ5kxvPLKKxCLxUbLt2zZgg4dOkAul0MkEkEsFmPNmjUGx3n48GEAwOuvv27w2tdee81ofxqNBgsWLECzZs0gkUggEokgkUhw48aNCp27kr9/1KhRBsvbtm2Lpk2b4uDBgwbLvby80LZtW4NlplwPWVlZUKlU8PT0NFo3YMAAPHz4EFeuXEF6ejrGjh2LY8eOYdOmTVi1ahXy8/PxwQcfwNvbG35+foiKigJjzGAfYrEYLi4uuHfvnqmHTiqAkilCrJifnx+WLl2K3NxcfPfddwBM61sfGxuLoUOHAgDCw8P5LoQl++gfOHAAXbt2hZOTE+zt7dGhQwejPxCEEGKrbt68iWPHjqFPnz5gjCErKwtZWVkYMmQIgP8q/JVUutuUVCoFUNxdsCR7e3ujCmpSqbTMEtWmeO211/D1119j9OjR2LdvH06fPo24uDh4eHgY/W5TpKenw8vLy2h56WX6h3ShoaEQi8UGP7/88gvfnS09PR0ADJIzvbKWAYC3t7fRsu3bt2PYsGGoX78+1q9fjxMnTiAuLg5vv/22wblLT0+HSCQyej/KOqaPP/4YM2fOxIABA/DHH3/g1KlTiIuLw3PPPVehc6f//eUdg4+PD79er6zudlKp9Km/X7++rGp8+n0EBwfDwcEBRUVFePfdPMkZ7QAACRRJREFUdzFjxgwEBQVhwYIF+Pvvv3H27FkcPHgQP/zwg8HfeD2ZTFbh80BMI7J0AISQJ+vduzeEQiGOHTsG4L++9a+++ioUCgVSUlLw7bffIjQ0FJcvX4a7uzv69OmDBQsWYNq0aVi5ciVatWoFAAgKCgIArF+/Hm+99Rb69++PdevWQSwW47vvvkOPHj2wb98+dO3a1WLHSwghVeHHH38EYwxbt27F1q1bjdavW7cO8+bNM2iJqkr6G+TSVexKj7fJzs7Gzp07MXv2bERGRvLL9eOLKsLNzQ2pqalGy0svc3d3BwBs3boV/v7+T9wfgDJ7SJT1ewCUOW/T+vXrERgYiF9++cVgfelz5ObmBo1Gg/T0dINEpazfpf97tmDBAoPljx49MhiHZA7970xJSUGDBg0M1t2/f58/b5Wl/z2mvM8LFiyASCTCJ598AgDYs2cPIiIi4OXlBS8vLwwbNgy7d+9GRESEwesyMzOrLF5SNkqmCLFyDg4OcHd3x/379wEAQ4YM4Z+sAsUlZ/v27Yt69eph48aNmDBhAjw8PNCoUSMAQLNmzQxKvqpUKkycOBF9+/bFjh07+OW9e/dGq1atMG3aNJqHhRBi07RaLdatW4egoCD88MMPRut37tyJpUuXYs+ePejbt2+1xKCvcHfhwgX06NGDX/77778bbMdxHBhjfCuY3g8//ACtVluh3x0eHo7ff/8dDx484FuOtFotfvnlF4PtevToAZFIhISEBAwePLjc/TVp0gReXl7YvHkzPv74Y375nTt38Pfff8PHx8ekuDiOg0QiMUikUlNTjar5hYeHY8mSJdiwYQMmTJjAL9+4cWOZ+yx97nbt2oV79+6hYcOG/LLyWhnLoi83vn79er4KIADExcXhypUrmD59+lP3YQqJRIJnnnkGCQkJT9zu2rVrWLJkCQ4dOsR3nWSMIS8vj99GqVQadfO7f/8+CgoK0KxZsyqJl5SNkilCbEDJL0ilUom5c+di27ZtSExMNPhja0r/8L///hsZGRkYOXIkNBqNwbqePXtiyZIlyMvL40uyEkKIrdmzZw/u37+PxYsXIywszGh98+bN8fXXX2PNmjXVlkx5eXmhW7duWLhwIVxdXeHv74+DBw9i+/btBts5OTmhU6dO+Pzzz+Hu7o6AgAAcPXoUa9asqXDLyowZM/D777+jS5cumDVrFuzt7fmKcCUFBARgzpw5mD59Om7duoWePXvC1dUVDx48wOnTp+Hg4IDo6GgIBAJER0fj3XffxZAhQ/D2228jKysL0dHR8Pb2hkBg2qiRvn37Yvv27Rg3bhyGDBmC5ORkzJ07F97e3gZj2Lp3745OnTrhs88+46vYHT9+HD///HOZ+4yNjUVwcDBCQkLwzz//4PPPPzdqUQoKCoKdnR02bNiApk2bQi6Xw8fHp8xEsEmTJhg7dixWrFgBgUCAXr16ITExETNnzoSvry8++ugjk47XFGFhYdizZ0+56xljGDt2LCIiIgwejPbo0QNfffUVGjVqBKVSiY0bN2LZsmUGr9WX9Q8PD6+yeEkZLFf7ghDC2H/V/OLi4spcr1QqmVAoZF27dmWMMdavXz9mb2/PFi5cyA4cOMBOnz7N4uLimIeHBxs5ciT/uvKq+a1fv54BeOLPnTt3qutwCSGk2g0YMIBJJBL28OHDcrd59dVXmUgkYqmpqXwFus8//9xoO5SqHjdy5Ejm4OBgtF3pinCMMZaSksKGDBnCFAoFc3Z2Zm+88QY7c+aMUVW5u3fvssGDBzNXV1fm6OjIevbsyeLj45m/v7/B97qp1fwYY+z48eOsffv2TCqVMi8vL/bpp5+y1atXG1Tz0/v1119ZeHg4c3JyYlKplPn7+7MhQ4awAwcOGGy3evVq1rBhQyaRSFjjxo3Zjz/+yPr3729Qie9J55IxxhYtWsQCAgKYVCplTZs2Zd9//32Z5y4rK4u9/fbbzMXFhdnb27OXX36ZXb161ej9yMzMZO+88w7z9PRk9vb27KWXXmJ//fUX69y5M+vcubPBPjdt2sSCg4OZWCw22E9Zv1+r1bLFixezxo0bM7FYzNzd3dkbb7zBkpOTDbbr3Lkze/bZZ42Oc+TIkQZVDstz8OBBBoCdPn26zPU//PAD8/HxYdnZ2QbLlUolGz16NHNzc2P16tVjkZGRTKvVGmzz5ptvshYtWjw1BlI5HGOl2gQJITUqNjYWERERiIuLQ5s2bYzWb968GcOHD8fcuXPx4YcfwtXVFbNnz8bs2bP5bQoLC+Hg4IA33niDH4C6detWDB06FIcPHzZ4Mrtv3z707NkTK1asKHfG95CQEEgkkio9TkIIIbVLVlYWGjdujAEDBmD16tWWDsdmhYSEoEOHDvj222+rbJ85OTnw8fHBl19+iTFjxlTZfokx6uZHiBW7c+cOPvnkEzg7O+Pdd981q299ef3DO3ToABcXF1y+fBkffPBB9R4AIYSQWiE1NRXz589HeHg43NzckJSUhC+//BK5ubmYOHGipcOzaUuWLMHAgQMxffp0o+6JFfXll1/Cz8/PqCAFqXqUTBFiJeLj4/k5Ph4+fIi//voLa9euhVAoxI4dO+Dh4QEAJvetb968OYDiWesdHR0hk8kQGBgINzc3rFixAiNHjkRGRgaGDBkCT09PpKWl4fz580hLS6vSp2OEEEJsn1QqRWJiIsaNG4eMjAzY29ujffv2WLVqFZ599llLh2fTevbsic8//xy3b9+usmTKyckJsbGxEInoVr+6UTc/QixM381PTyKRwMXFBU2bNkWPHj0wevRoPpECgHv37mHixIk4dOgQNBoNOnTogJiYGPTp0wdhYWEG80wsX74cy5cvx507d6DVarF27Vp+EsJjx45hyZIlOHHiBHJzc+Hp6YmWLVti1KhRBtUCCSGEEEJI2SiZIoQQQgghhJAKMK2WJSGEEEIIIYQQA5RMEUIIIYQQQkgFUDJFCCGEEEIIIRVAyRQhhBBCCCGEVAAlU4QQQgghhBBSAZRMEUIIIYQQQkgFUDJFCCGEEEIIIRVAyRQhhBBCCCGEVAAlU4QQQgghhBBSAZRMEUIIIYQQQkgFUDJFCCGEEEIIIRXw/+ceAs64zfDyAAAAAElFTkSuQmCC", + "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 10/58] 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 11/58] 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 12/58] 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 13/58] 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 14/58] 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 15/58] 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 16/58] 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 17/58] 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 18/58] 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 19/58] 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 20/58] 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 21/58] 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 22/58] 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 23/58] 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 24/58] 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 25/58] 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 26/58] 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 27/58] 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 28/58] 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 29/58] 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 30/58] 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 31/58] 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 32/58] 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 33/58] 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) From 693c570b1717b9498c701d25c50e1dd5db3dabe0 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Wed, 6 Nov 2024 09:42:25 -0500 Subject: [PATCH 34/58] remove soiling experimental warning label --- rdtools/soiling.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 92a3bfb8..738bf426 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1,10 +1,7 @@ -''' +""" 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 @@ -24,12 +21,6 @@ import statsmodels.api as sm lowess = sm.nonparametric.lowess -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 class NoValidIntervalError(Exception): From e5159d7a3bc4c3c6bbd095dd01458fb1e9ded375 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Wed, 6 Nov 2024 09:52:54 -0500 Subject: [PATCH 35/58] update changelog --- docs/sphinx/source/changelog/v3.0.0-beta.0.rst | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/docs/sphinx/source/changelog/v3.0.0-beta.0.rst b/docs/sphinx/source/changelog/v3.0.0-beta.0.rst index f69ffff4..f223b7a5 100644 --- a/docs/sphinx/source/changelog/v3.0.0-beta.0.rst +++ b/docs/sphinx/source/changelog/v3.0.0-beta.0.rst @@ -15,6 +15,8 @@ when compared with older versions of RdTools * Upgrade pvlib 0.9.0 to 0.11.0 (:pull:`428`) +* Upgrade soiling algorithms SRR and CODS. Remove experimental warning label. (:pull:`426`) + Enhancements ------------ * Added a new wrapper function for clearsky filters (:pull:`412`) @@ -22,7 +24,7 @@ Enhancements * Added codecov.yml configuration file (:pull:`420`) * Availability module no longer considered experimental (:pull:`429`) * Add capability to seed the CircularBlockBootstrap (:pull:`429`) -* Allow sub-daily aggregation in :py:func:`~rdtools.degradation.degradation_year_on_year` (:pull:`390`) +* Allow sub-daily aggregation in :py:func:`~rdtools.degradation.degradation_year_on_year` (:pull:`390`) Bug fixes --------- @@ -184,5 +186,8 @@ Contributors * Martin Springer (:ghuser:`martin-springer`) * Michael Deceglie (:ghuser:`mdeceglie`) * Kirsten Perry (:ghuser:`kperrynrel`) +* Matthew Muller (:ghuser:`mmuller`) +* Noah Moyer (:ghuser:`noromo01`) +* Quyen Nguyen (:ghuser:`qnguyen345`) * Dirk Jordan (:ghuser:`dirkjordan`) * Chris Deline (:ghuser:`cdeline`) From fcac6044565a072f9e3ebd9a86d02a156875390d Mon Sep 17 00:00:00 2001 From: martin-springer Date: Wed, 6 Nov 2024 09:53:57 -0500 Subject: [PATCH 36/58] rerun notebooks --- docs/TrendAnalysis_example.ipynb | 3916 +++++++++--------- docs/degradation_and_soiling_example.ipynb | 4240 ++++++++++---------- 2 files changed, 3959 insertions(+), 4197 deletions(-) diff --git a/docs/TrendAnalysis_example.ipynb b/docs/TrendAnalysis_example.ipynb index 12be8fb2..92f549c8 100644 --- a/docs/TrendAnalysis_example.ipynb +++ b/docs/TrendAnalysis_example.ipynb @@ -210,16 +210,7 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/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" - ] - } - ], + "outputs": [], "source": [ "ta.sensor_analysis(analyses=['yoy_degradation','srr_soiling'])" ] @@ -375,7 +366,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/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\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\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", " warnings.warn(\n" ] }, @@ -404,7 +395,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/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\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\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" ] }, @@ -433,7 +424,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/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\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\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" ] }, @@ -754,35 +745,6 @@ "execution_count": 22, "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "application/vnd.plotly.v1+json": { @@ -25504,28 +25466,28 @@ 0.7205152833333333, 0.7905545333333334, 0.8745185499999999, - 0.9732547833333334, + 0.9732547833333333, 1.1157427, 1.2492243833333334, 1.3228528333333331, - 1.138607233333333, + 1.1386072333333332, 0.7569224, 0.3741076333333333, 0.25149325, 0.33693615, - 0.42801210000000006, + 0.4280121, 0.38680276666666663, 0.35260566666666665, 0.2902100833333333, 0.18419575, 0.13504365000000002, - 0.11620035, + 0.11620035000000001, 0.10915488333333333, 0.10760953333333333, - 0.10870623333333335, + 0.10870623333333333, 0.10875608333333332, 0.1109661, - 0.1147547, + 0.11475469999999999, 0.11964, 0.12636975, 0.13580801666666667, @@ -25536,7 +25498,7 @@ 0.19164001666666666, 0.20388649999999997, 0.2135906333333333, - 0.22140046666666668, + 0.22140046666666666, 0.2301242166666667, 0.24567741666666668, 0.29389898333333336, @@ -25549,7 +25511,7 @@ 0.3802558, 0.3860384, 0.3931004833333333, - 0.4004284333333334, + 0.40042843333333333, 0.40775638333333336, 0.4156659166666667, 0.42387455, @@ -25563,10 +25525,10 @@ 0.49889880000000003, 0.51171025, 0.5259174999999999, - 0.5385960166666667, + 0.5385960166666666, 0.5488318833333334, 0.5604967833333333, - 0.5720786, + 0.5720785999999999, 0.58508945, 0.6000610666666667, 0.6166611166666666, @@ -25592,7 +25554,7 @@ 1.0244673499999999, 1.0322107166666665, 1.0522504166666669, - 1.10532405, + 1.1053240500000001, 1.1354002166666666, 1.1614717666666665, 1.2290351333333334, @@ -25600,9 +25562,9 @@ 1.2929926833333334, 1.3163557166666666, 1.3396855166666668, - 1.3611043999999999, + 1.3611044, 1.37959875, - 1.3766742166666663, + 1.3766742166666668, 1.3888874666666666, 1.4216223, 1.4108214666666665, @@ -25634,7 +25596,7 @@ 2.2616114166666668, 2.2745558, 2.267759583333333, - 2.3384302666666663, + 2.338430266666667, 2.3471374000000003, 2.4581533499999995, 2.5462050666666665, @@ -25642,16 +25604,16 @@ 2.6725249666666664, 2.72774215, 2.8356341666666665, - 2.9037791166666667, + 2.903779116666667, 2.8485120833333335, - 2.7826934666666667, + 2.7826934666666663, 2.6922821833333335, 2.62933825, 2.6098801333333332, 2.5976336499999997, 2.6016216500000002, 2.6026851166666662, - 2.5992787, + 2.5992786999999997, 2.590687883333333, 2.607769816666667, 2.6176899666666666, @@ -25734,7 +25696,7 @@ 2.776910866666667, 1.1961673666666668, 0.49072340000000003, - 0.29531140000000006, + 0.2953114, 0.28160265, 0.2745904166666666, 0.2675117166666666, @@ -25747,9 +25709,9 @@ 0.16415605, 0.14393356666666668, 0.1248244, - 0.11400695000000001, - 0.10569861666666668, - 0.09775584999999999, + 0.11400695, + 0.10569861666666666, + 0.09775585, 0.09110918333333333, 0.0821528, 0.07497439999999998, @@ -26669,8 +26631,8 @@ 0.2044788, 0.0817882, 0.0444382, - 0.046662600000000005, - 0.09674479999999999, + 0.0466626, + 0.0967448, 0.5151478, 0.8954869999999999, 0.4953274, @@ -26685,7 +26647,7 @@ 2.1442552, 2.3197836, 2.546523, - 2.6211732000000003, + 2.6211732, 2.8440945999999996, 3.0455356, 3.2174286, @@ -26699,19 +26661,19 @@ 4.7206914, 4.9761321999999995, 5.113049, - 5.1757804, + 5.175780399999999, 5.2860542, 5.3674108, 5.424049999999999, 5.5415282, - 5.638073800000001, + 5.6380738, 5.713554, 5.7689482, 5.881728600000001, - 5.968745800000001, + 5.9687458, 6.0242064, 6.143759599999999, - 6.249866800000002, + 6.2498667999999995, 6.3312068, 6.4174438, 6.500062, @@ -26733,7 +26695,7 @@ 3.4234965, 4.311019916666667, 1.9387574999999997, - 1.4387865833333333, + 1.438786583333333, 1.3199006666666666, 1.4006615, 1.743439, @@ -26742,7 +26704,7 @@ 7.718646166666667, 2.9087995833333333, 2.4608505833333334, - 2.1865788333333334, + 2.186578833333334, 1.825891333333333, 1.82089975, 2.7265985, @@ -26791,7 +26753,7 @@ 3.339302916666667, 3.272588166666667, 3.1977807499999997, - 2.739798833333334, + 2.7397988333333334, 2.501761666666667, 2.423388833333333, 2.373274, @@ -26802,14 +26764,14 @@ 2.1239104166666665, 2.24457075, 2.4053793333333333, - 2.4700709166666663, + 2.470070916666667, 2.3579344166666667, - 2.4243672499999995, + 2.4243672500000004, 2.568061833333333, 2.44416775, 2.3969384166666665, - 2.354485083333333, - 2.697229416666667, + 2.3544850833333335, + 2.6972294166666666, 4.024095083333333, 4.02004875, 2.586071333333333, @@ -26822,7 +26784,7 @@ 6.199330916666667, 4.74371225, 7.60961075, - 5.7480155, + 5.748015499999999, 5.951708583333333, 7.444755833333334, 7.56433825, @@ -26847,12 +26809,12 @@ 6.140261083333333, 6.070494999999999, 6.0147086666666665, - 5.938790166666667, + 5.938790166666666, 5.841429416666667, - 5.748396916666666, - 5.669393916666667, + 5.748396916666667, + 5.6693939166666665, 5.602330916666666, - 5.550226083333333, + 5.5502260833333334, 5.484854583333333, 5.438686583333333, 5.352702, @@ -26866,12 +26828,12 @@ 4.638822666666667, 4.358680416666667, 3.5052689166666666, - 2.3704714166666663, + 2.3704714166666667, 0.8832946666666667, 0.3964411666666666, 0.3117169166666667, 0.3005563333333333, - 0.29115358333333335, + 0.2911535833333333, 0.2824473333333334, 0.27427175, 0.26488558333333334, @@ -26883,7 +26845,7 @@ 0.15517024999999998, 0.1368125, 0.12644791666666666, - 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348.51199999999994, + 348.512, 393.519, 358.708, - 362.24300000000005, + 362.243, 282.288, 225.935, 461.588, @@ -8229,7 +8200,7 @@ 503.566, 583.565, 586.477, - 592.8580000000001, + 592.858, 596.8, 277.862, 576.716, @@ -8242,10 +8213,10 @@ 453.792, 505.763, 439.318, - 367.7480000000001, + 367.748, 639.158, 644.377, - 644.4209999999999, + 644.421, 653.186, 659.115, 659.291, @@ -8261,7 +8232,7 @@ 692.186, 475.109, 701.485, - 704.6010000000001, + 704.601, 460.25, 0.545, 129.013, @@ -8269,18 +8240,18 @@ 329.04, 208.373, 538.823, - 709.0160000000001, + 709.016, 571.761, 438.559, 741.944, - 741.5139999999999, + 741.514, 744.944, 744.168, 500.505, - 336.64300000000003, + 336.643, 371.861, 754.837, - 755.8889999999999, + 755.889, 234.914, 719.796, 298.507, @@ -8291,7 +8262,7 @@ 666.08, 547.873, 464.318, - 579.3969999999999, + 579.397, 736.488, 318.574, 651.231, @@ -8301,13 +8272,13 @@ 648.451, 735.86, 664.169, - 711.0360000000001, + 711.036, 729.925, 476.436, 352.586, 644.724, 625.967, - 160.21200000000002, + 160.212, 462.122, 507.976, 387.111, @@ -8319,17 +8290,17 @@ 339.963, 521.668, 386.445, - 689.1360000000001, + 689.136, 676.732, 136.17, - 269.85200000000003, + 269.852, 698.765, 357.431, - 680.1619999999999, + 680.162, 574.035, 116.235, 436.94, - 639.9069999999999, + 639.907, 215.271, 124.218, 226.243, @@ -8342,14 +8313,14 @@ 633.548, 458.389, 489.687, - 629.5459999999999, - 359.47900000000004, + 629.546, + 359.479, 604.034, 767.924, 547.163, 730.57, 21.019, - 491.0580000000001, + 491.058, 209.474, 468.106, 481.225, @@ -8370,7 +8341,7 @@ 749.596, 417.765, 606.737, - 422.86300000000006, + 422.863, 389.214, 93.872, 503.087, @@ -8381,10 +8352,10 @@ 701.204, 716.834, 593.689, - 667.9839999999999, + 667.984, 765.694, 670.247, - 527.7180000000001, + 527.718, 679.931, 764.466, 706.467, @@ -8394,16 +8365,16 @@ 560.002, 730.779, 514.555, - 613.4540000000001, + 613.454, 329.117, 243.106, 591.404, 348.76, 248.584, - 715.8760000000001, + 715.876, 734.539, - 729.8810000000001, - 790.2860000000001, + 729.881, + 790.286, 190.546, 783.807, 384.798, @@ -8414,7 +8385,7 @@ 638.118, 567.153, 284.909, - 718.8710000000001, + 718.871, 217.914, 642.109, 558.069, @@ -8443,31 +8414,31 @@ 504.75, 157.778, 731.698, - 790.0060000000001, + 790.006, 768.27, 352.619, 134.16, - 774.5139999999999, + 774.514, 780.465, 569.763, 773.721, - 674.1560000000001, + 674.156, 272.868, - 780.2339999999999, + 780.234, 379.656, 779.931, 776.903, - 776.0880000000001, + 776.088, 494.499, 0.363, 111.753, 765.253, - 534.6709999999999, - 560.0840000000001, + 534.671, + 560.084, 501.254, 438.922, - 284.47900000000004, - 745.3960000000001, + 284.479, + 745.396, 737.303, 468.216, 719.096, @@ -8477,11 +8448,11 @@ 717.318, 714.549, 503.77, - 705.5260000000001, + 705.526, 639.367, 228.897, 682.717, - 670.4889999999999, + 670.489, 413.283, 676.408, 671.805, @@ -8500,7 +8471,7 @@ 624.288, 269.295, 457.205, - 531.6990000000001, + 531.699, 421.101, 455.823, 494.146, @@ -8509,7 +8480,7 @@ 460.206, 5.428, 47.214, - 555.2669999999999, + 555.267, 483.389, 175.412, 360.575, @@ -8519,7 +8490,7 @@ 567.318, 569.686, 555.509, - 529.5740000000001, + 529.574, 443.607, 513.399, 454.607, @@ -8531,7 +8502,7 @@ 540.821, 108.858, 220.551, - 533.9830000000001, + 533.983, 218.101, 530.548, 504.128, @@ -8541,11 +8512,11 @@ 427.713, 282.073, 468.244, - 468.1830000000001, + 468.183, 266.46, 444.477, 401.573, - 350.48900000000003, + 350.489, 229.459, 141.884, 306.396, @@ -8562,11 +8533,11 @@ 220.033, 157.178, 222.604, - 236.21400000000003, + 236.214, 121.779, 195.259, 140.855, - 131.85399999999998, + 131.854, 234.105, 233.318, 198.48, @@ -8575,14 +8546,14 @@ 0.363, 109.094, 253.682, - 85.32799999999999, + 85.328, 244.648, - 178.15900000000002, + 178.159, 226.073, 215.012, 96.487, 136.467, - 179.21599999999998, + 179.216, 179.695, 171.856, 137.904, @@ -8603,11 +8574,11 @@ 41.918, 45.039, 57.878, - 54.211000000000006, + 54.211, 52.218, 50.187, 39.231, - 49.06399999999999, + 49.064, 40.376, 7.927, 26.128, @@ -8625,7 +8596,7 @@ 14.082, 26.712, 20.48, - 24.113000000000003, + 24.113, 13.46, 16.609, 15.211, @@ -8637,8 +8608,8 @@ 0.517, 9.838, 7.982, - 3.0610000000000004, - 5.577000000000001, + 3.061, + 5.577, 0.847, 4.668, -0.997, @@ -8661,14 +8632,14 @@ 38.653, 32.272, 41.136, - 53.138000000000005, + 53.138, 48.519, 49.807, 62.018, 35.262, 63.857, 72.005, - 65.29899999999999, + 65.299, 20.193, 90.684, 98.331, @@ -8681,10 +8652,10 @@ 141.092, 147.445, 158.329, - 168.61900000000003, + 168.619, 114.798, 114.457, - 176.40900000000002, + 176.409, 78.936, 104.398, 203.842, @@ -8705,13 +8676,13 @@ 255.515, 160.129, 104.707, - 210.61900000000003, + 210.619, 268.772, 243.194, 272.323, 277.091, 262.045, - 284.85400000000004, + 284.854, 262.072, 351.606, 355.945, @@ -8727,7 +8698,7 @@ 324.448, 340.921, 342.886, - 338.87800000000004, + 338.878, 327.449, 258.472, 333.797, @@ -8757,7 +8728,7 @@ 397.263, 503.583, 495.28, - 390.24300000000005, + 390.243, 263.988, 279.772, 193.305, @@ -8767,27 +8738,27 @@ 132.239, 448.072, 400.401, - 5.747000000000001, + 5.747, 0.55, 290.772, 333.362, 527.735, 43.129, - 0.5670000000000001, + 0.567, 365.497, 116.604, - 171.34400000000002, + 171.344, 183.186, 495.814, 485.035, 562.22, 384.275, - 368.9980000000001, + 368.998, 477.427, 268.453, 509.385, 425.384, - 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743.4580000000001, - 762.7819999999999, + 743.458, + 762.782, 770.941, - 87.28200000000001, + 87.282, 632.32, 531.236, 743.1, @@ -8924,10 +8895,10 @@ 502.245, 621.398, 115.954, - 568.7719999999999, + 568.772, 639.378, 149.454, - 679.2589999999999, + 679.259, 780.971, 731.544, 704.182, @@ -8938,12 +8909,12 @@ 659.423, 462.204, 758.311, - 781.6210000000001, + 781.621, 415.777, 788.079, 372.268, 415.518, - 547.0419999999999, + 547.042, 417.809, 635.42, 612.072, @@ -8953,23 +8924,23 @@ 373.138, 697.46, 397.918, - 697.1410000000001, - 620.7040000000001, + 697.141, + 620.704, 582.266, 688.393, 552.828, 550.274, 493.034, 145.32, - 628.9019999999999, + 628.902, 782.915, 620.418, 548.523, - 666.5419999999999, + 666.542, 683.427, 39.556, 393.915, - 581.0269999999999, + 581.027, 137.827, 377.652, 623.98, @@ -8979,9 +8950,9 @@ 598.991, 527.812, 697.24, - 637.2919999999999, + 637.292, 606.985, - 730.8610000000001, + 730.861, 108.213, 678.957, 585.448, @@ -8995,18 +8966,18 @@ 467.302, 627.118, 729.931, - 22.203000000000003, - 763.2660000000001, + 22.203, + 763.266, 500.522, 638.938, - 790.8810000000001, + 790.881, 496.035, 691.883, 621.205, - 790.1489999999999, + 790.149, 271.327, 145.391, - 689.1089999999999, + 689.109, 574.382, 779.914, 415.601, @@ -9017,10 +8988,10 @@ 515.711, 772.449, 769.955, - 763.7339999999999, - 667.6160000000001, + 763.734, + 667.616, 461.577, - 757.8710000000001, + 757.871, 749.673, 751.903, 633.009, @@ -9031,18 +9002,18 @@ 727.685, 125.165, 483.262, - 680.7289999999999, + 680.729, 596.139, - 725.7189999999999, + 725.719, 299.597, 172.417, - 714.8739999999999, + 714.874, 682.084, 434.44, 531.451, 0.379, 223.199, - 701.6610000000001, + 701.661, 442.566, 399.982, 538.085, @@ -9050,21 +9021,21 @@ 687.677, 672.135, 404.166, - 678.2739999999999, + 678.274, 674.706, - 673.2810000000001, - 650.4169999999999, + 673.281, + 650.417, 665.331, 661.317, 222.467, 316.895, - 626.7330000000001, + 626.733, 411.604, - 260.29400000000004, + 260.294, 426.447, 640.226, 602.988, - 626.1709999999999, + 626.171, 628.313, 627.806, 622.499, @@ -9073,15 +9044,15 @@ 543.656, 163.075, 117.573, - 132.22799999999998, + 132.228, 122.093, - 596.5409999999999, + 596.541, 398.199, 406.781, 25.49, 386.103, - 546.7280000000001, - 523.7819999999999, + 546.728, + 523.782, 482.871, 276.579, 354.144, @@ -9100,7 +9071,7 @@ 383.24, 293.002, 130.565, - 264.92400000000004, + 264.924, 229.673, 168.696, 271.377, @@ -9109,7 +9080,7 @@ 253.209, 256.198, 272.533, - 84.50200000000001, + 84.502, 318.541, 240.552, 50.649, @@ -9122,19 +9093,19 @@ 338.856, 214.517, 214.027, - 360.01300000000003, + 360.013, 209.155, 254.91, 392.082, 147.208, 280.405, - 303.41200000000003, + 303.412, 215.436, 315.728, 346.668, 343.37, 254.805, - 172.62099999999998, + 172.621, 87.287, 244.285, 191.455, @@ -9156,9 +9127,9 @@ 206.71, 304.849, 224.311, - 278.41200000000003, + 278.412, 187.067, - 290.66200000000003, + 290.662, 260.36, 263.878, 247.968, @@ -9173,20 +9144,20 @@ 44.637, 78.391, 6.909, - 68.78399999999999, + 68.784, 20.849, - 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585.5740000000001, - 610.2330000000001, + 585.574, + 610.233, 705.977, 628.797, - 513.3330000000001, + 513.333, 615.568, - 728.7689999999999, + 728.769, 773.181, 676.209, 715.171, @@ -10306,19 +10277,19 @@ 403.875, 785.888, 776.655, - 770.1419999999999, + 770.142, 767.747, - 766.3710000000001, - 762.9739999999999, - 764.6039999999999, - 760.6619999999999, - 754.8810000000001, + 766.371, + 762.974, + 764.604, + 760.662, + 754.881, 753.956, - 749.6460000000001, + 749.646, 738.365, - 739.2239999999999, - 744.2560000000001, - 574.4590000000001, + 739.224, + 744.256, + 574.459, 474.712, 323.199, 507.332, @@ -10326,7 +10297,7 @@ 560.607, 507.959, 705.817, - 680.2669999999999, + 680.267, 530.851, 415.689, 521.86, @@ -10337,23 +10308,23 @@ 444.961, 480.119, 570.082, - 601.2919999999999, - 681.5939999999999, - 680.2560000000001, - 700.2510000000001, + 601.292, + 681.594, + 680.256, + 700.251, 688.784, 685.684, 689.466, - 697.0139999999999, + 697.014, 678.813, 659.842, - 666.8610000000001, + 666.861, 622.268, 366.284, - 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335.82300000000004, + 335.823, 348.292, 312.86, 315.59, - 134.17700000000002, + 134.177, 205.422, 473.782, 479.629, @@ -51110,7 +51041,7 @@ 432.035, 302.988, 232.294, - 257.16200000000003, + 257.162, 425.555, 420.82, 421.519, @@ -51118,9 +51049,9 @@ 416.075, 216.835, 302.361, - 335.23900000000003, + 335.239, 406.512, - 307.29900000000004, + 307.299, 233.533, 277.9, 292.275, @@ -51128,27 +51059,27 @@ 106.76, 129.877, 221.459, - 201.74400000000003, + 201.744, 72.45, 109.474, 135.762, - 159.17700000000002, + 159.177, 244.885, - 173.49599999999998, + 173.496, 232.129, 263.961, 290.166, 189.974, 79.574, 141.356, - 26.398000000000003, + 26.398, 207.833, - 18.944000000000003, + 18.944, 152.334, 183.841, 157.734, - 163.96099999999998, - 165.94299999999998, + 163.961, + 165.943, 106.529, 151.018, 199.84, @@ -51174,18 +51105,18 @@ 20.788, 13.835, 26.381, - 7.673999999999999, + 7.674, 11.627, 9.161, 20.849, - 6.457000000000001, + 6.457, 0.446, 5.566, 14.479, 9.909, 15.453, 10.058, - 5.4110000000000005, + 5.411, 0.379, 5.502000000000001, 10.625, @@ -51193,10 +51124,10 @@ 6.155, 8.23, 7.388, - 5.0760000000000005, + 5.076, 5.874, - 2.8510000000000004, - 1.7009999999999998, + 2.851, + 1.701, 1.69, 2.939, 3.028, @@ -52003,11 +51934,11 @@ 16.108, 19.67, 26.673, - 12.595999999999998, + 12.596, 32.338, 37.612, 23.717, - 24.543000000000003, + 24.543, 38.774, 33.401, 36.897, @@ -52032,7 +51963,7 @@ 94.318, 172.984, 114.6, - 167.63299999999998, + 167.633, 26.767, 210.157, 180.466, @@ -52041,7 +51972,7 @@ 154.673, 233.863, 184.623, - 163.64700000000002, + 163.647, 191.356, 218.211, 157.52, @@ -52058,10 +51989,10 @@ 268.866, 150.599, 250.841, - 340.93699999999995, + 340.937, 111.687, 233.246, - 329.18300000000005, + 329.183, 90.822, 287.111, 247.852, @@ -52086,10 +52017,10 @@ 197.946, 272.841, 374.272, - 348.51199999999994, + 348.512, 393.519, 358.708, - 362.24300000000005, + 362.243, 282.288, 225.935, 461.588, @@ -52118,7 +52049,7 @@ 503.566, 583.565, 586.477, - 592.8580000000001, + 592.858, 596.8, 277.862, 576.716, @@ -52131,10 +52062,10 @@ 453.792, 505.763, 439.318, - 367.7480000000001, + 367.748, 639.158, 644.377, - 644.4209999999999, + 644.421, 653.186, 659.115, 659.291, @@ -52150,7 +52081,7 @@ 692.186, 475.109, 701.485, - 704.6010000000001, + 704.601, 460.25, 0.545, 129.013, @@ -52158,18 +52089,18 @@ 329.04, 208.373, 538.823, - 709.0160000000001, + 709.016, 571.761, 438.559, 741.944, - 741.5139999999999, + 741.514, 744.944, 744.168, 500.505, - 336.64300000000003, + 336.643, 371.861, 754.837, - 755.8889999999999, + 755.889, 234.914, 719.796, 298.507, @@ -52180,7 +52111,7 @@ 666.08, 547.873, 464.318, - 579.3969999999999, + 579.397, 736.488, 318.574, 651.231, @@ -52191,13 +52122,13 @@ 648.451, 735.86, 664.169, - 711.0360000000001, + 711.036, 729.925, 476.436, 352.586, 644.724, 625.967, - 160.21200000000002, + 160.212, 462.122, 507.976, 387.111, @@ -52210,17 +52141,17 @@ 795.054, 521.668, 386.445, - 689.1360000000001, + 689.136, 676.732, 136.17, - 269.85200000000003, + 269.852, 698.765, 357.431, - 680.1619999999999, + 680.162, 574.035, 116.235, 436.94, - 639.9069999999999, + 639.907, 215.271, 124.218, 226.243, @@ -52233,14 +52164,14 @@ 633.548, 458.389, 489.687, - 629.5459999999999, - 359.47900000000004, + 629.546, + 359.479, 604.034, 767.924, 547.163, 730.57, 21.019, - 491.0580000000001, + 491.058, 209.474, 468.106, 481.225, @@ -52262,7 +52193,7 @@ 796.227, 417.765, 606.737, - 422.86300000000006, + 422.863, 389.214, 93.872, 503.087, @@ -52274,10 +52205,10 @@ 701.204, 716.834, 593.689, - 667.9839999999999, + 667.984, 765.694, 670.247, - 527.7180000000001, + 527.718, 679.931, 764.466, 706.467, @@ -52287,16 +52218,16 @@ 560.002, 730.779, 514.555, - 613.4540000000001, + 613.454, 329.117, 243.106, 591.404, 348.76, 248.584, - 715.8760000000001, + 715.876, 734.539, - 729.8810000000001, - 790.2860000000001, + 729.881, + 790.286, 190.546, 783.807, 384.798, @@ -52307,7 +52238,7 @@ 638.118, 567.153, 284.909, - 718.8710000000001, + 718.871, 217.914, 642.109, 558.069, @@ -52336,31 +52267,31 @@ 504.75, 157.778, 731.698, - 790.0060000000001, + 790.006, 768.27, 352.619, 134.16, - 774.5139999999999, + 774.514, 780.465, 569.763, 773.721, - 674.1560000000001, + 674.156, 272.868, - 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132.22799999999998, + 132.228, 122.093, - 596.5409999999999, + 596.541, 398.199, 406.781, 25.49, 386.103, - 546.7280000000001, - 523.7819999999999, + 546.728, + 523.782, 482.871, 276.579, 354.144, @@ -53785,7 +53716,7 @@ 383.24, 293.002, 130.565, - 264.92400000000004, + 264.924, 229.673, 168.696, 271.377, @@ -53794,7 +53725,7 @@ 253.209, 256.198, 272.533, - 84.50200000000001, + 84.502, 318.541, 240.552, 50.649, @@ -53807,19 +53738,19 @@ 338.856, 214.517, 214.027, - 360.01300000000003, + 360.013, 209.155, 254.91, 392.082, 147.208, 280.405, - 303.41200000000003, + 303.412, 215.436, 315.728, 346.668, 343.37, 254.805, - 172.62099999999998, + 172.621, 87.287, 244.285, 191.455, @@ -53841,9 +53772,9 @@ 206.71, 304.849, 224.311, - 278.41200000000003, + 278.412, 187.067, - 290.66200000000003, + 290.662, 260.36, 263.878, 247.968, @@ -53858,20 +53789,20 @@ 44.637, 78.391, 6.909, - 68.78399999999999, + 68.784, 20.849, - 16.554000000000002, + 16.554, 39.49, 47.01, 21.751, 0.418, 48.909, - 48.998000000000005, + 48.998, 46.487, 39.633, 36.319, 33.654, - 16.102999999999998, + 16.103, 12.987, 31.788, 29.387, @@ -53888,8 +53819,8 @@ 13.719, 17.54, 11.913, - 7.702000000000001, - 6.287000000000001, + 7.702, + 6.287, 3.11, 6.678, 6.43, @@ -53899,7 +53830,7 @@ 4.894, -2.902, -5.131, - -0.7490000000000001, + -0.749, -2.698, 0.969, null, @@ -54689,7 +54620,7 @@ 6.7, 7.096, 8.929, - 13.279000000000002, + 13.279, 15.371, 7.454, 13.075, @@ -54705,7 +54636,7 @@ 41.725, 37.833, 52.328, - 57.388000000000005, + 57.388, 58.445, 66.752, 59.067, @@ -54714,7 +54645,7 @@ 84.414, 37.376, 28.099, - 75.23100000000001, + 75.231, 14.237, 39.985, 64.952, @@ -54729,12 +54660,12 @@ 64.016, 68.712, 36.445, - 0.5670000000000001, + 0.567, 0.561, 51.75, 39.958, 74.229, - 27.703000000000003, + 27.703, 14.11, 83.081, 56.54, @@ -54743,7 +54674,7 @@ 68.046, 85.427, 65.514, - 65.92699999999999, + 65.927, 98.766, 101.425, 95.001, @@ -54760,7 +54691,7 @@ 164.088, 155.477, 154.183, - 160.65200000000002, + 160.652, 166.708, 130.907, 203.545, @@ -54771,7 +54702,7 @@ 153.082, 160.118, 153.275, - 168.65200000000002, + 168.652, 130.472, 226.992, 130.07, @@ -54779,9 +54710,9 @@ 197.296, 174.95, 56.749, - 158.52700000000002, + 158.527, 20.76, - 164.75400000000002, + 164.754, 46.228, 116.995, 147.004, @@ -54789,21 +54720,21 @@ 175.203, 113.609, 120.49, - 85.81200000000001, + 85.812, 15.2, 139.22, - 172.78599999999997, + 172.786, 239.115, 225.093, 287.463, 289.561, 143.822, 213.432, - 273.80400000000003, + 273.804, 275.495, 258.89, 291.934, - 283.91200000000003, + 283.912, 284.121, 28.226, 267.842, @@ -54811,12 +54742,12 @@ 249.322, 97.99, 59.221, - 0.5720000000000001, + 0.572, 115.635, 335.432, 366.218, 89.258, - 49.851000000000006, + 49.851, 329.932, 357.569, 117.396, @@ -54835,7 +54766,7 @@ 213.598, 0.561, 116.317, - 355.31199999999995, + 355.312, 228.996, 179.778, 367.214, @@ -54846,8 +54777,8 @@ 192.451, 272.769, 477.206, - 337.35900000000004, - 340.50199999999995, + 337.359, + 340.502, 422.753, 446.068, 355.405, @@ -54860,12 +54791,12 @@ 430.174, 407.668, 428.137, - 434.73800000000006, + 434.738, 390.32, 346.36, 586.174, 402.052, - 555.7959999999999, + 555.796, 550.549, 500.026, 590.309, @@ -54878,13 +54809,13 @@ 510.806, 466.454, 228.864, - 680.1289999999999, - 684.2860000000001, - 665.6610000000001, + 680.129, + 684.286, + 665.661, 250.153, 416.388, 364.687, - 662.5889999999999, + 662.589, 375.296, 585.998, 309.986, @@ -54903,7 +54834,7 @@ 442.126, 473.931, 484.149, - 516.7130000000001, + 516.713, 517.197, 36.71, 19.648, @@ -54913,23 +54844,23 @@ 529.783, 388.773, 587.375, - 141.95600000000002, + 141.956, 527.905, - 669.8560000000001, - 670.4839999999999, + 669.856, + 670.484, 683.322, 184.755, 426.601, 623.424, 630.432, - 643.1990000000001, + 643.199, 639.015, 323.969, 187.375, 26.673, 563.944, 154.569, - 364.36800000000005, + 364.368, 654.331, 716.487, 693.188, @@ -54941,7 +54872,7 @@ 605.823, 532.668, 419.659, - 398.30300000000005, + 398.303, 276.838, 233.973, 317.258, @@ -54955,7 +54886,7 @@ 536.086, 621.431, 486.086, - 552.9490000000001, + 552.949, 454.943, 419.251, 371.767, @@ -54964,7 +54895,7 @@ 631.153, 614.885, 545.181, - 198.74400000000003, + 198.744, 275.021, 500.241, 568.309, @@ -54977,7 +54908,7 @@ 21.779, 396.206, 532.937, - 530.4209999999999, + 530.421, 298.876, 304.431, 508.202, @@ -54992,10 +54923,10 @@ 548.908, 194.318, 530.84, - 553.0319999999999, + 553.032, 575.725, 339.649, - 175.97400000000002, + 175.974, 396.371, 575.742, 539.687, @@ -55012,8 +54943,8 @@ 586.631, 671.789, 525.054, - 727.3710000000001, - 720.5880000000001, + 727.371, + 720.588, 550.659, 225.329, 618.095, @@ -55021,31 +54952,31 @@ 731.605, 470.903, 384.237, - 768.3919999999999, + 768.392, 491.542, 683.179, 570.115, - 691.8560000000001, + 691.856, 525.538, 636.015, 341.537, 48.722, 648.385, 676.27, - 288.66900000000004, + 288.669, 624.365, 131.077, 499.756, - 358.69199999999995, + 358.692, 705.03, 579.205, - 742.7810000000001, - 715.0939999999999, + 742.781, + 715.094, 690.821, 800, 748.77, 587.182, - 554.2819999999999, + 554.282, 761.961, 800, 800, @@ -55055,16 +54986,16 @@ 800, 504.419, 764.813, - 775.1080000000001, - 620.7869999999999, + 775.108, + 620.787, 800, 533.46, - 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495.6880000000001, + 495.688, 657.293, 507.745, 651.248, @@ -55189,8 +55120,8 @@ 640.865, 396.354, 432.321, - 348.62300000000005, - 630.6080000000001, + 348.623, + 630.608, 522.962, 0.368, 69.048, @@ -55203,9 +55134,9 @@ 597.741, 403.407, 588.404, - 583.9830000000001, + 583.983, 582.106, - 54.888000000000005, + 54.888, 455.911, 402.504, 565.375, @@ -55228,15 +55159,15 @@ 425.918, 325.588, 486.334, - 352.49800000000005, + 352.498, 324.647, 482.871, 378.886, 239.264, 395.082, - 319.98400000000004, + 319.984, 435.778, - 346.93800000000005, + 346.938, 454.48, 190.728, 200.17, @@ -55258,10 +55189,10 @@ 332.585, 234.826, 339.792, - 342.19800000000004, + 342.198, 352.163, 175.638, - 146.49200000000002, + 146.492, 0.374, 252.774, 311.88, @@ -55277,7 +55208,7 @@ 251.139, 267.187, 241.152, - 60.476000000000006, + 60.476, 245.143, 265.166, 273.391, @@ -55290,13 +55221,13 @@ 225.952, 217.198, 144.461, - 62.49100000000001, + 62.491, 118.387, 71.146, 195.925, 161.627, - 150.03799999999998, - 143.28799999999998, + 150.038, + 143.288, 137.408, 152.813, 82.167, @@ -55305,9 +55236,9 @@ 24.075, 29.899, 23.067, - 26.348000000000003, + 26.348, 23.073, - 25.363000000000003, + 25.363, 22.572, 23.838, 17.413, @@ -55324,21 +55255,21 @@ 6.16, 15.91, 8.913, - 11.109000000000002, + 11.109, 7.652, 8.115, 8.77, 7.718, 2.807, 9.271, - 5.422000000000001, + 5.422, 7.68, 2.174, 3.867, 5.56, - 3.1210000000000004, + 3.121, 3.661, - 0.7809999999999999, + 0.781, 3.275, 4.52, -4.977, @@ -56147,15 +56078,15 @@ 0.71, 1.822, 0.115, - 4.9430000000000005, + 4.943, 9.529, 2.516, 7.68, 11.489, - 12.380999999999998, + 12.381, 10.339, 11.682, - 12.265999999999998, + 12.266, 11.963, 23.1, 38.559, @@ -56170,13 +56101,13 @@ 15.91, 17.71, 18.745, - 25.633000000000003, + 25.633, 23.161, 28.628, 25.556, 27.725, 26.365, - 16.769000000000002, + 16.769, 40.536, 46.603, 71.465, @@ -56189,14 +56120,14 @@ 222.703, 176.706, 194.874, - 306.04900000000004, + 306.049, 194.609, - 264.48900000000003, + 264.489, 274.922, 183.687, - 132.05200000000002, + 132.052, 69.747, - 309.04400000000004, + 309.044, 177.614, 142.369, 303.203, @@ -56204,8 +56135,8 @@ 370.517, 378.759, 380.554, - 349.76800000000003, - 35.669000000000004, + 349.768, + 35.669, 192.523, 241.284, 250.847, @@ -56215,7 +56146,7 @@ 309.639, 320.897, 348.601, - 342.19800000000004, + 342.198, 251.876, 211.291, 438.135, @@ -56231,7 +56162,7 @@ 250.957, 0.611, 38.543, - 239.80900000000003, + 239.809, 308.83, 498.584, 304.893, @@ -56243,14 +56174,14 @@ 528.55, 185.036, 495.936, - 563.6080000000001, + 563.608, 400.081, - 582.8330000000001, - 591.8340000000001, + 582.833, + 591.834, 594.405, 485.954, 270.308, - 80.84100000000001, + 80.841, 121.63, 264.39, 243.057, @@ -56285,7 +56216,7 @@ 92.98, 472.896, 694.586, - 748.2310000000001, + 748.231, 713.09, 437.523, 764.94, @@ -56298,7 +56229,7 @@ 604.425, 459.595, 242.446, - 294.92900000000003, + 294.929, 506.919, 735.304, 743.893, @@ -56307,20 +56238,20 @@ 686.868, 449.244, 496.354, - 749.7230000000001, + 749.723, 343.789, 637.33, - 694.1289999999999, + 694.129, 493.321, 542.346, 652.674, 297.01, - 779.3910000000001, + 779.391, 522.323, 777.525, 710.893, 800, - 701.8639999999999, + 701.864, 800, 787.699, 772.801, @@ -56335,7 +56266,7 @@ 800, 800, 366.344, - 173.46900000000002, + 173.469, 117.848, 392.154, 244.229, @@ -56343,23 +56274,23 @@ 302.818, 287.271, 276.992, - 487.24300000000005, + 487.243, 345.826, 189.864, 171.68, 183.274, - 669.6360000000001, + 669.636, 481.616, - 322.54900000000004, + 322.549, 327.664, - 342.63800000000003, + 342.638, 760.75, 727.63, 464.263, - 342.20300000000003, + 342.203, 469.301, 245.286, - 337.93699999999995, + 337.937, 754.166, 549.674, 462.309, @@ -56392,7 +56323,7 @@ 199.74, 134.722, 194.565, - 197.71400000000003, + 197.714, 204.145, 195.788, 191.268, @@ -56405,15 +56336,15 @@ 127.747, 205.235, 142.116, - 159.92600000000002, - 170.49599999999998, + 159.926, + 170.496, 145.232, 234.617, 157.013, - 87.98100000000001, + 87.981, 201.458, 244.984, - 259.36400000000003, + 259.364, 293.249, 194.967, 315.832, @@ -56423,7 +56354,7 @@ 355.218, 116.609, 325.687, - 391.05300000000005, + 391.053, 342.644, 341.185, 382.062, @@ -56432,13 +56363,13 @@ 396.519, 245.22, 386.549, - 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671.3539999999999, + 671.354, 649.106, - 628.4169999999999, + 628.417, 630.014, 630.129, 608.818, @@ -88537,19 +88428,19 @@ 616.003, 620.891, 620.082, - 615.8919999999999, + 615.892, 584.633, 586.362, - 572.5930000000001, + 572.593, 555.085, 545.561, 554.111, - 573.8480000000001, + 573.848, 594.306, 596.937, 597.169, 593.364, - 613.4590000000001, + 613.459, 608.592, 602.993, 615.16, @@ -88569,7 +88460,7 @@ 581.831, 576.033, 574.2, - 517.9630000000001, + 517.963, 499.189, 507.425, 484.209, @@ -88580,7 +88471,7 @@ 514.406, 498.055, 466.174, - 441.8730000000001, + 441.873, 432.734, 410.426, 409.655, @@ -88589,10 +88480,10 @@ 400.412, 421.712, 450.23, - 458.48800000000006, + 458.488, 452.316, 439.483, - 446.1830000000001, + 446.183, 440.832, 451.667, 448.776, @@ -88708,24 +88599,24 @@ 124.135, 121.135, 124.344, - 135.05200000000002, + 135.052, 147.357, 153.363, 137.062, - 155.14700000000002, - 169.62599999999998, - 134.41899999999998, + 155.147, + 169.626, + 134.419, 107.514, 95.38, 90.662, 87.965, - 82.26100000000001, - 79.22800000000001, + 82.261, + 79.228, 74.796, 68.294, 62.337, - 56.25899999999999, - 50.891000000000005, + 56.259, + 50.891, 47.985, 46.085, 44.246, @@ -88748,8 +88639,8 @@ 16.692, 13.906, 12.618, - 12.029000000000002, - 11.324000000000002, + 12.029, + 11.324, 10.603, 9.882, 9.194, @@ -88758,7 +88649,7 @@ 5.307, 4.652, 3.848, - 2.8510000000000004, + 2.851, 2.741, 2.196, 0.836, @@ -89537,7 +89428,7 @@ -2.048, -1.509, -0.92, - -0.8320000000000001, + -0.832, -0.193, 0.765, 2.813, @@ -89563,7 +89454,7 @@ 56.347, 68.542, 82.652, - 83.73100000000001, + 83.731, 60.493, 101.486, 101.838, @@ -89591,9 +89482,9 @@ 179.106, 169.72, 172.313, - 178.53400000000002, + 178.534, 174.201, - 154.72299999999998, + 154.723, 169.951, 194.956, 227.719, @@ -89630,7 +89521,7 @@ 254.249, 317.335, 293.558, - 289.78700000000003, + 289.787, 238.647, 296.536, 333.059, @@ -89642,12 +89533,12 @@ 357.844, 310.228, 368.574, - 300.10900000000004, + 300.109, 273.518, 332.96, 394.785, 392.798, - 323.80400000000003, + 323.804, 474.619, 447.351, 481.627, @@ -89660,10 +89551,10 @@ 524.266, 536.494, 533.758, - 526.1709999999999, + 526.171, 530.955, 526.865, - 545.9580000000001, + 545.958, 558.736, 552.085, 551.133, @@ -89671,10 +89562,10 @@ 549.635, 585.161, 601.248, - 583.4159999999999, + 583.416, 557.37, - 588.2330000000001, - 594.5369999999999, + 588.233, + 594.537, 580.14, 565.359, 612.975, @@ -89684,7 +89575,7 @@ 391.086, 400.395, 461.593, - 477.11300000000006, + 477.113, 420.666, 325.726, 419.659, @@ -89692,32 +89583,32 @@ 308.428, 557.26, 662.633, - 668.7660000000001, + 668.766, 672.163, 710.447, - 701.6439999999999, + 701.644, 708.025, 703.5, - 699.6460000000001, + 699.646, 703.015, 720.919, 720.842, - 713.2719999999999, + 713.272, 716.927, 712.919, - 715.4739999999999, + 715.474, 715.584, - 722.5319999999999, + 722.532, 730.68, - 750.1080000000001, - 687.4789999999999, - 721.7439999999999, + 750.108, + 687.479, + 721.744, 712.457, 728.202, - 727.0239999999999, - 749.1389999999999, + 727.024, + 749.139, 775.18, - 757.0060000000001, + 757.006, 771.684, 772.488, 793.485, @@ -89734,30 +89625,30 @@ 753.367, 487.595, 521.519, - 797.5980000000001, + 797.598, 795.23, 584.82, 521.602, - 701.6110000000001, - 698.2919999999999, + 701.611, + 698.292, 755.96, - 753.4830000000001, + 753.483, 317.743, 684.148, 693.32, - 782.8380000000001, + 782.838, 720.451, 545.754, - 769.5310000000001, + 769.531, 523.011, 793.678, 617.654, 597.559, - 362.73800000000006, + 362.738, 797.878, 790.342, 748.567, - 767.2239999999999, + 767.224, 795.258, 701.98, 796.909, @@ -89767,25 +89658,25 @@ 699.635, 728.654, 586.571, - 794.8610000000001, + 794.861, 559.451, 632.535, - 673.1210000000001, + 673.121, 501.777, 787.908, - 544.1519999999999, + 544.152, 743.21, 539.637, 657.832, 679.628, 716.547, - 740.3969999999999, + 740.397, 556.115, 389.027, 621.354, 701.633, 693.188, - 601.1659999999999, + 601.166, 281.765, 389.379, 497.191, @@ -89794,12 +89685,12 @@ 577.993, 346.431, 630.206, - 664.8960000000001, + 664.896, 625.461, 525.246, 600.026, 436.577, - 547.5319999999999, + 547.532, 513.492, 214.682, 423.71, @@ -89813,7 +89704,7 @@ 399.151, 573.193, 622.378, - 634.7040000000001, + 634.704, 623.611, 607.695, 346.338, @@ -89831,7 +89722,7 @@ 516.465, 559.677, 561.697, - 388.11300000000006, + 388.113, 125.28, 104.624, 124.95, @@ -89852,7 +89743,7 @@ 131.87, 130.417, 284.358, - 177.03599999999997, + 177.036, 151.689, 163.565, 181.039, @@ -89864,15 +89755,15 @@ 95.001, 87.656, 93.195, - 84.78299999999999, + 84.783, 86.164, 91.642, 102.581, 113.339, 119.351, 133.918, - 170.30900000000003, - 143.05200000000002, + 170.309, + 143.052, 112.139, 103.264, 103.093, @@ -89884,7 +89775,7 @@ 98.64, 97.456, 88.311, - 86.79799999999999, + 86.798, 87.833, 92.958, 94.411, @@ -89901,7 +89792,7 @@ 175.054, 78.985, 70.342, - 71.34899999999999, + 71.349, 91.494, 117.952, 55.615, @@ -89910,7 +89801,7 @@ 47.621, 52.24, 67.749, - 141.30100000000002, + 141.301, 150.236, 109.816, 65.932, @@ -89920,8 +89811,8 @@ 54.949, 49.003, 45.7, - 41.38399999999999, - 37.513000000000005, + 41.384, + 37.513, 35.151, 30.824, 29.272, @@ -89938,29 +89829,29 @@ 18.503, 17.066, 15.712, - 14.154000000000002, + 14.154, 12.144, 10.284, 8.709, 8.34, 8.252, - 6.077999999999999, + 6.078, 5.114, 4.74, 4.938, 4.999, 5.362, 5.676, - 5.757999999999999, + 5.758, 5.604, 4.756, - 4.531000000000001, + 4.531, 3.248, 2.725, 1.348, - 0.0819999999999999, + 0.082, -1.663, - -1.3769999999999998, + -1.377, -1.558, null, null, @@ -91440,7 +91331,7 @@ 800, 800, 800, - 798.7539999999999, + 798.754, 800, 800, 800, @@ -92286,7 +92177,6 @@ } ], "layout": { - "autosize": true, "legend": { "title": { "text": "mask" @@ -93114,65 +93004,26 @@ }, "xaxis": { "anchor": "y", - "autorange": true, "domain": [ 0, 1 ], - "range": [ - "2010-02-24 22:07:32.2583", - "2010-03-08 16:23:27.7417" - ], "title": { "text": "datetime" - }, - "type": "date" + } }, "yaxis": { "anchor": "x", - "autorange": true, "domain": [ 0, 1 ], - "range": [ - -71.96125853018373, - 862.6622585301837 - ], "title": { "text": "ac_power" - }, - "type": "linear" + } } } - }, - "text/html": [ - "
" - ] + } }, "metadata": {}, "output_type": "display_data" @@ -93345,18 +93196,7 @@ "cell_type": "code", "execution_count": 17, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/soiling.py:27: UserWarning:\n", - "\n", - "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", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Calculate the daily insolation, required for the SRR calculation\n", "daily_insolation = filtered['insolation'].resample('D').sum()\n", @@ -93415,7 +93255,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:172: UserWarning:\n", + "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\rdtools\\plotting.py:172: UserWarning:\n", "\n", "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", "\n" @@ -93447,7 +93287,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:232: UserWarning:\n", + "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\rdtools\\plotting.py:232: UserWarning:\n", "\n", "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", "\n" @@ -93616,7 +93456,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:272: UserWarning:\n", + "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\rdtools\\plotting.py:272: UserWarning:\n", "\n", "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", "\n" @@ -94199,7 +94039,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "rdtools3-nb", "language": "python", "name": "python3" }, @@ -94213,7 +94053,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.12.7" } }, "nbformat": 4, From fd4dc471690c89668bcd73a3d2bd474be529a056 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Fri, 8 Nov 2024 12:13:37 -0500 Subject: [PATCH 37/58] fix double quotations left over from merge --- rdtools/soiling.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index a2742af3..7bbc6a2b 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1,5 +1,4 @@ """ -""" Functions for calculating soiling metrics from photovoltaic system data. """ From dc46cf61c28d982530182ef01148e754c5e56c22 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Fri, 8 Nov 2024 12:23:47 -0500 Subject: [PATCH 38/58] remove double import --- rdtools/soiling.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 7bbc6a2b..554f06b8 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -22,9 +22,6 @@ from statsmodels.tsa.seasonal import STL from statsmodels.tsa.stattools import adfuller -from rdtools import degradation as RdToolsDeg -from rdtools.bootstrap import _make_time_series_bootstrap_samples - lowess = sm.nonparametric.lowess # Used in CODSAnalysis/Matt From 849287335f34dec52a04d889a79b59552b03cbbb Mon Sep 17 00:00:00 2001 From: martin-springer Date: Fri, 8 Nov 2024 12:34:32 -0500 Subject: [PATCH 39/58] remove experimental soiling warnings from plotting --- rdtools/plotting.py | 44 ++++++-------------------------------------- 1 file changed, 6 insertions(+), 38 deletions(-) diff --git a/rdtools/plotting.py b/rdtools/plotting.py index dfd1c921..e8231a62 100644 --- a/rdtools/plotting.py +++ b/rdtools/plotting.py @@ -133,15 +133,10 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5, profile_alpha=0.05, ymin=None, ymax=None, profiles=None, point_color=None, profile_color='C1'): - ''' + """ Create figure to visualize Monte Carlo of soiling profiles used in the SRR analysis. - .. warning:: - 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. - Parameters ---------- soiling_info : dict @@ -168,13 +163,7 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5, Returns ------- fig : matplotlib.figure.Figure - ''' - 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.' - ) - + """ fig, ax = plt.subplots() renormalized = normalized_yield / soiling_info['renormalizing_factor'] ax.plot(renormalized.index, renormalized, 'o', alpha=point_alpha, @@ -197,14 +186,9 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5, def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5, profile_alpha=1, ymin=None, ymax=None, point_color=None, profile_color=None): - ''' + """ Create figure to visualize valid soiling profiles used in the SRR analysis. - .. warning:: - 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. - Parameters ---------- soiling_info : dict @@ -228,13 +212,7 @@ def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5, Returns ------- fig : matplotlib.figure.Figure - ''' - 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.' - ) - + """ sratio = soiling_info['soiling_ratio_perfect_clean'] fig, ax = plt.subplots() renormalized = normalized_yield / soiling_info['renormalizing_factor'] @@ -249,14 +227,9 @@ def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5, def soiling_rate_histogram(soiling_info, bins=None): - ''' + """ Create histogram of soiling rates found in the SRR analysis. - .. warning:: - 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. - Parameters ---------- soiling_info : dict @@ -268,12 +241,7 @@ def soiling_rate_histogram(soiling_info, bins=None): Returns ------- fig : matplotlib.figure.Figure - ''' - 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.' - ) + """ soiling_summary = soiling_info['soiling_interval_summary'] fig, ax = plt.subplots() From 7df691cc36dc2a80b2ea4f11b109bb0e65d7fa13 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Fri, 8 Nov 2024 15:03:50 -0500 Subject: [PATCH 40/58] remove warnings import --- rdtools/plotting.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rdtools/plotting.py b/rdtools/plotting.py index e8231a62..067d1a00 100644 --- a/rdtools/plotting.py +++ b/rdtools/plotting.py @@ -4,7 +4,6 @@ import pandas as pd import plotly.express as px import numpy as np -import warnings def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield, From b0c4510366276f33e176871f6076587f960b0edb Mon Sep 17 00:00:00 2001 From: martin-springer Date: Fri, 8 Nov 2024 16:15:27 -0500 Subject: [PATCH 41/58] quick fix mean runtimewarning --- rdtools/soiling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 554f06b8..8d1e7b47 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -341,8 +341,8 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati "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": np.nan if run.pi_norm.isna().any() else run.pi_norm.median(), # changed from mean/Matt + "inferred_end_loss": np.nan if run.pi_norm.isna().any() else 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 48f02dce6f5f3eb2233773a98b3c1d4ada6bb7c8 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Fri, 8 Nov 2024 16:27:11 -0500 Subject: [PATCH 42/58] re-run notebooks --- docs/TrendAnalysis_example.ipynb | 3999 ++++++++++--------- docs/degradation_and_soiling_example.ipynb | 4211 ++++++++++---------- 2 files changed, 4122 insertions(+), 4088 deletions(-) diff --git a/docs/TrendAnalysis_example.ipynb b/docs/TrendAnalysis_example.ipynb index 92f549c8..79bd74a7 100644 --- a/docs/TrendAnalysis_example.ipynb +++ b/docs/TrendAnalysis_example.ipynb @@ -56,6 +56,35 @@ "execution_count": 3, "metadata": {}, "outputs": [], + "source": [ + "empty = pd.Series([])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "empty.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ "# Set the random seed for numpy to ensure consistent results\n", "np.random.seed(0)" @@ -77,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -180,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -193,6 +222,26 @@ " temperature_model=meta['temp_model_params'])" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ta.results" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -208,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -224,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -234,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -254,15 +303,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.954\n", - "[0.95 0.957]\n" + "0.953\n", + "[0.949 0.957]\n" ] } ], @@ -282,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -306,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -330,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -359,20 +408,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\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", - " warnings.warn(\n" - ] - }, { "data": { - "image/png": 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", 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l3N/r4/PtNdz8ynqGpdh5c0M5VqN+xKy4qBTbxo0beemll/jXv/5FeXk5mZmZXHfddVxxxRVMmjQpYttbb70Vq9XKrbfe2i8CH2sU5CVR3tAe6t3UtboxG3RaAsH8b/c1dlE0hSX12M1GJg5JoLSuLdSj7c0DEzzftqpm6lrdofPazfJRSDzIeLgmp5dEu5nlm6sOOSuxM0VlDbR7fHh9fmwmA8NS7LS6feQmWdlV04oQ0OzysmFvPbFWE7quIWTCZOhlC9KbsXmNbW6anB7iLAZcHj9eAX6/TFYB+cJ6vH58fvn3/mYXugYuLzQ5PeypbeOOs8aFGtNxWQlsLm/EbDQQZ+2+g1BYUh9K1NA0rUt8M95qZENpA7FWIy0uHzlJtl5Z40sLS9m0r5HUWBNOjxeLpuMOWAGBpFJ0HewmA0JAYic3nNVkwGTQmTQkkWanB7fPT5Pw0Ob24/b6+M+WKmxmA0n2yKzPYMyloc2N1ycwGvR+HC+mUdfqASFlbHX5QrZLnMVAerwVu9nAZ9tqWL2rlpkjUjh3cnZoiMju/a2hBCuDrhNnNgBQ3tBOTpKNkto2EmzS0sxNtlPV5KK+zQMaXX47j0+gBVtyTbrIq5qcofXdKYsmp5e8lBianY2hGCZIe3h/ixu7xcjb6/exOsFKbrKNigYnVrOBdrcP4fFh0HV0XWdndQtPfLqT+lY3iQEL81AUW3fvRk/KriAvibwUGxv2uvEjCI/kW406PiHj9B6foLbVg0GDWKsRDWhx+Vi7p46tlc24PD5anF5qml0s+mL3Yc/0jMoVOXXqVB5//HFOOeUU3n//fUpLS3nooYe6KLUgEyZM4IQTDj7OqqWlhXvuuYczzzyT5ORkNE1j8eLFvZaroaGB66+/nrS0NGJiYpg7dy7r1q3rdtt33nmHadOmYbVaGTp0KPfccw9eb996x/1BeFA6K8GGxycYlR5HrNVIrNVITpKti6IpyEsi0W7i3MnZ3DB3FPmZcb2ObwTP5/b6KatvZ+2eOoDQ8a4+afgBH7hg0H1LRVO/Z1IGrVeQca3/bK0G4JQx6UzKSUDTZDzL44faFhdNTi9Wk47NLBVJi9PDL15YyyPLiylvaKfN7T3gfVmzuw6LUceg65ySn47dpKOHeX0E4BUCr9+Px+fHj1Rw8qWRwfK/fFTMY5/soLCkjiS7iexEG7oG26paQhZROEHFpSOVaGfFtbWyGbvZQHaijf+amMmpYyNjh0VlDdz8ynp+8vRXEW6/5Zur8AtpsXn9slFxeQVun0DXZIJQvMVIcqyFBLsJt0/wydZqbn5lfWgM5dBkO81ODxnxVsZkxJEWJxvc0rp2XF4/rW4fVU3OCHfSaeMzSLKbmDgkgdQ4Cwk2U7+4qcdmxmEz6cRYZKJIq8uHpoEfsJl19MAPVVbfxs6aVvbsb+WTrdUs21hOk1NW4gkm91iMBuxmA4bAPiW1bVQ1ubhgSjZJdnNoTGGc1YjZpBNn7dq5G5kWi81iwGzQMBs0NA1mDE8Ore/ORV+Ql0RBXlLIZS2fbPD5QdM12j0+6ts87N7fSozFxImjUjl1bAaxViPJdjOxFiMgqGtzs7umlUanh8pGZ8jCi5Zg+xF+jT2FGBw5icRZTYTnu+sa2Ew6M0Yk88eLJpGVaJMxQsAnoNnpxePz4/P7cfv8ZMRbSIuzMHFIPFVNziOS6RmVxfbcc88xf/58YmNje7X93LlzmTt37kG3279/P3/4wx8YOnQokydP5tNPP+21TH6/n7PPPpuNGzfyu9/9jtTUVJ544gnmzJlDYWEho0ePDm3773//mwsuuIA5c+bw6KOP8u2333LvvfdSXV3Nk08+2etz9ifhg6abnR6yE224vX7c3q5DKBw5iRHKJ5qeT0a8lb11bVhMBrZVNbNpXyPbKpt44P0tZMRbu7g/g2ytbMZi1GlxHTyDs6+DWoMD1Y0GHUOgy2U3GwO91GRaXV5cXsEJI5J5Y90+EODy+UmPs5AcY+atDeXYzQY2ljUQbzXh8voZm9mzVRmsYpKfaaPZ6cFg0DEZBbpfYDfpGA06Xp8fYZANqdmg0+72YjPLcVLXzBrOz19YS6vLS0Wjk0tn5PHnj4ppcXnRNEIWUThNTi9TchN5t6g8kDDkjFhf0+yiqsmJ1WRgZ00LewNu5uA1BOM2rS4vSwrLQj3308ZnsHxzFV6/wOfviNfoGoxKj8HpFRh1Da/PT22LC1+gc1BS28aQRCunT8gkO9FGeUN7wFXnYUpuIqt31+IXMqvBqkFVk5Prnl/LlNwEdE3jtPEZnDY+gyc+3UlDm7vbjlg0bK1spt3jo6LBKS1zn0AIAkpFI85qwm42UN7gx+sXtLh8xLi8bNrXSIzZEHhmBBOHJJBkN/Plzv20uX3srWulrN5JrMXAsqIKvH7BsBQ7P52RD8ixksEhG+GWxTWzhpMWZ+HdonLqA56OV9fKjkWC3Uxjm5vigFvxvvc2A/KZv+vs8dQ0u/h8+37a3F5cHtlBcnv9GDTpcvS4fJTVt3HpjKE0Ob04PT7WBioLye00hC6VYm6ynQT7gcdKHoztVc189F0lG0sbQu95eAJQZ+ttW1VzyE1qMmjYzQYSbSbcXj+jM+I4fXwGn22rZndNKx6/dOELIYfC1DS7aWyrJTXOQqzFGFHx6HASlWK76qqr+lkMSVZWFhUVFWRmZrJ27VqOP/74Xu+7ZMkSvvzyS15//XXmz58PwCWXXMKYMWO45557ePnll0Pb3nrrrTgcDj766COMRnkL4uPjuf/++/nNb37D2LFj+/fCeoEjJ5Eb5o4KVYpoavdS0yIN//4sdRWubM6alEVNs4sVW6vly7SnHrtZZ1tVS0TcJ5yxmXFsqWwmP9N+UBdZXwe1OnISmZybSJvbR2FJHeUN7Wworee3p+dHNJYxZgOxVhNN7Z5ArMhEeYMzEOMRxFkNVDe7Mekai7/cQ5PT263r45pZw0Mv8HOrdrOjugVdgxirEY/XT16KnZLaNjw+Hx6vD59fYDMbceQkMCc/HUdOImMy4ti0r4kxGXF8vn0/Te1e3F4/bW5ft0MwgtehkcUH31Xh8/u56V/r+fWpo5hXkBvKbt3f4kbXZAO4s7qZ1btquerEYcRbjXj9fkwGLaKs2LyCXOYV5HLlc2v4Zk89Rr9s8Ick2jhlTDrnTs5m2cZy3li3D49PEPKMCUFlk7QCgoOdN+1rJMFmIi3OQlqshVanVNTjshJYV1KPrsGKrTWMyYwLDR2obnLi9vopqe19vLczSwtLQwrSajIQazFi0DVcHQEnvD5BWpyJ/5qYydjMOP78UTHuZjdGXVpneSkxbK1s5hxHNol2E1efNJyisgbWlzagaz4qGp0k2WWnx+XxomsaVU2uiI5DY5ubqxd9jcvrpyAvieevmRFav3pXLbUtbnx+qGtx89aGcs6fMoSvd9dhNur8Z2s1bW4fCTZpvTtyEkmLs5CXYqOx3cv+FhdN7V6EAE9Y4NPp8YU6KUVlDVQ1OWl2erGapJvPatCItZrITLCGElyiaROKyhr480fF1LV6SLAZSYuzhCr8lDe082lxdZeB+WMy4qhv9chOthC4PD72e/34hOAvHxXT7JQdzok5idS2OKlr9eDxCzyBC3T5BBUNsgNnMujMHJFCk/PweseiUmz//Oc/D7he0zSsVis5OTlMmzYNi6V3qegWi4XMzMxoRGLJkiVkZGRw0UUXhZalpaVxySWX8OKLL+JyubBYLGzevJnNmzfz+OOPh5QawA033MB9993HkiVL+J//+Z+oZDhUgunMzU4v5Y1OcpNsJMeYD9gDPpQKCpNz5fYWk06r24fFqOH0+EkwGXocF9fq9pESY+rWkuxMvNWI2+sLVX3ojaxBy9Xl9eP1CUpq20IZm8F44ufb95OZYMUvRMhay0yw8vXuOjw+P60uHzJhQMbePi2u6TZo3dnylW4rOV7Oajaws6YVp9uHT4BB09ABn89Pkr3jNzlvcjY2k4HTxmewpLAMXeZt0ObysaK4mqWFpRHxkPBzxlo389raUmpb3SHr69zJ2Wza10hdq4z/yWvwsq2ymUc/2cFp4zOYMyadhjY318wa3uX+nTc5G7fXH0poCSYnBM8prYca2tw+3IFMySS7OdTQOHISQ3HDgrwkxmbGhdLc99a1yhhjQCvu3t/KaePSyU22s7GsgWanF4sxutJVweohZXUyOcRo8OH0yKxOLWguAAaDxtBkO3edPT607+Iv9wDwg7HpJNjNxFuNoc5M8JpuOnUUSwrL2FHdQmWTi8x4Mx6fRkObm4x4C0sLS2lyeom3Gnn1G/m33y/4tqwxpEQKS+pDWZe7alppd0tL692icnKSbASzFxNsRun6tRq5773N1DS7KMhLZmxmHIu/3MOO6hZpieng9oFBg1HpHd6v4G/w3KrdbChtQAhpkWbEW7GZDAdN8DoQhSX1+PzSw+X0+NhW1cy2qmbcXj+797dS2eRCRyZ/3Hiq9HL99vR8Hnh/S8iKNupaILHJF+j4yY5cs1NmA2cn2mhxealqchJsJjQdYi1GvH5BSW3r4LXYtECGkRCR+VbhyzVNIz4+njvuuIPbbrvtEEU9MOvXr2fatGnoemTYcPr06Tz99NNs27aNSZMmsX79egCOO+64iO2ys7PJyckJre8Ol8uFy9XR4Dc1NR2a0AsSKAKKgXzAceWnFLRVUfPt40zlx6QdP5WxmXEs21jOso3lHe68P/0e2vbCb/5I4eZKGirLKCwVOE7s2tDRKR+uYPmjlLMajn8lFAMIuiIT7WYSbEZK69pZs7u2S6NcVNZA7TeLKSMds22aHFtTvxPs8TBydORp21poqaliRqpGgl3giPfz8l+H0wAsO/6rri7KBbLBcCxo5Ia5o9i98zUSjNuwxdwCBDIYLQZs317LmeYb2Jo9HkdmDNWbX2No42rqc/4Xs0HD6QEvAotBIznWHGhwOuUEet2R37dvI23rO4whjzZzKiOykthZ1055cx1GyokxG7BjpB0fWVixbV0CW7fDgpU0NbeTv+cLmrJOC41tWl/agMvjp8Xl5R/L1jK6fQcOgwH2fwWF9wd++0YZKxGycozLK127jiEJ3JG+m0LvMOKHj+CdjeWs3f0f8vwbKW25mneLyhmTEde1wokQ4PPS1NLG9Nw4Eu0mvtlVS1bJlazeOZV5Ba/hyEnkkR9PDVntq3fVUl7fhq19GdvWng4nyecnXPk6chJpcnr5tLiG6mZX4P0WgMDn8/HBpkp+NlLnqbnJLKuVFmRjm5tfvLC2++y9xhow2qFTGb7CzWWMp5pWUx0tLh86GlafATMCAz6MBjkIeIQhhvnlP4EFABcw7/R5zDsrH0q3Q+u38MUdwGmwYAk8/wDE5sC8y5lXkEuT00tp/W5cnhZcdR/jNByPIA7QWLXsbYaaNZYnT8bVUM0c///wsbgCTTuOZevLcLz4awrIhpN/wU9nDOWWVzewq6YVnx9K69ooq2sjLd7CyaNS+XhLNftb3Hy9+13mWd6nnl8w+bTT2VrZTH2bhziLEWGRxZmtJjjP8Bgzqn8AzArdj+BvtbSwVFqlXh8Vje2cMjqV1r2ltJWuoyj5EhyvXgmshwWN9IaCFBMXeuaRaIZaUtm79xxOYjF7gDUu6dXyA7WBcnaOZx7BwcPcwS94k1o0w+vs4VKGG17C74cE7R+sSc6ltF6Oq02PM0sL02/AbNQx+gUW7XkuMjTzdeNF5LGFtGYzTROu75W80RKVYtuwYQNXXnklKSkp/OpXv2LUqFEAbN++nccff5yGhgYee+wxqqqqePTRR7njjjuIi4vjl7/8Zb8KH05FRQWnnHJKl+XBYs3l5eVMmjQpNJ6uuyLOWVlZlJf3nBDxwAMPsHDhwn6SWFIc/v/zV+OghGL8DOcztlW+yZJ9jWytbKLV5ePlNSXkp1j5SdvfpCL822cUjPsZxbu+IB8NSk0HPZ+D/+AA+OZqOHtNwO3hIjNBJj1UNMo6hnFWY+SgXI+Hjcs+5ASeYhzwx4aXef7LPYz55tfkMwTHPStl+lmQbSuZSjXFTS3k22Ph3Q8wATmA6+sTuMf9Z+wWeWxHU9g9X5CAY0EjD/GUVPjVe2DrUxQkp6Mtn8M0wOG+Gaa9zYsrVjENGRMVZeewhhdoRma+WUw6s8ek4/TInn8o4O7zgbsdRJjF+eqvOZcixpCDyz0Fh93Bdns8m7csJd5URxxG4jABfvbjIZUtFAOOBZkU8HOKeYT8VRtwLFgUcgd+tbMWj18wgjcp/rgOB59H/hALEmg6bQNmo8xe9PiElO0/H+DYcDcwlsKkPzMmI44Rex9kvAHe8pWy1buAqiYXWQk2Hnh/CxOHJMjOQbyAtv0UxDXyXXM9E+KSiC/5CD/V6HwIC66FBc+GTp+daOOqE4fxl2V3c4P2JnX1S2DLRzBuQoSYRWUNbCxtoK7VRXqchaZ2Dx6fH014Mek6FuGmYd19OJKycPxiMVgsnPvo55TVt7OvoT1SsdXVwXfvQUI6OM6KOE+BrRzNtw1oYr+xDb/PjwkDSZoNg0XDhE7B6FTmbQ/vIL9F0UdvUUwCIBv2iwFYDm88Brv/KDebPBNGjaYgL4lPN1fga3+Ecw0rWcdKVsc+gMWgMYsF+N0e2it/yWyeJNkCp4sH+aP/NYyFPwM24wAcy7+AS57AbjaEBrV7fAJNg+omF+9/W0ljwPq9VjzGaDdgvo385asoH3ZdqOFvDsTSLP7HOJ4v8PMF/L0Sfv2n0Hu0tLCUf3y+W/ZZBMRbTbS2OjGW3sIISih+tRAH60PPU2+Um6N9Aw5gKZDKfvJZDMB44Er9pyzySeXm9gnuf38ro3lYXjf/F2qrxvFS4D4DLT+jqCWDZfyGbdlTcKMzNNlKfZsc9O/0Cm5s/SBwji8AO2AiP+/2g8p6KESl2B5++GEyMjL44IMPIpZPmjSJCy+8kP/6r//i2Wef5ZlnnuG8887j5JNP5oknnjisiq29vb1bl6fVag2tD/+/p20PZIXdcccd/Pd//3foe1NTE7m5hzZmJ59w5bYbR9iy5NIL2clTtHri8fhl9YuttWtBl+sd1OLY8hQOAhlGrX0581bYsIFl+8wIId0KcVZjyD1lNuqcNj6jw32Y4GJylVQ2iYCdf9Hi+wmY9lPMfhxLFsHF13QcvnwTDp8bDF6Ki3ZDW0ejanHBH7mV25wXUtP8G3iv0/jGDz8MvEwAu+GVn+GYdmXge4A3foyDEaF7pwHzuJwXrM9iMSUwLiueOKuRb/bU4fL4sJoMspH1+2F/qVRshmAQfh0OoJg9WNhD8ZZ1wGjG8hUgU4dHB36TdGSPVqYatOPg7wG53gDP02Ay8dvT87ltSRGldW2M9bwDps6jyCQFeUlkJlipb3UzMi0WXG3w5TPAfopZRfnnz7Ep8wImBra/wPAdf/RtY2ruydS3ualr9fBJIHPUMcVP0berKa5oYUJ2Ig5fMw4eDjvbEng4GW55KJQBB/BD3gQdkmmBVy+D+U/DxI7YdrASSnKMGbfXT1aClcoGF2NNHpITzNiMThKb1kP9eor++TLLsmewt7YNZyDOGMHevbDnC7CmQuoUyO6I0ToMuylu3kwS7bT75X4WdNIxcVZmJo7UOKC2yz2Uv383DXrRXR1/v3gc/F667uZkmIndtxKA6Wxl7EnDKRgSh2OxvB9FLA49U2iQbfYzyrU57MDfwmsncxWX8xfzDOp9qSG3vEHXImJHQyHUwjp4B/a8Qx6/omTY6ZQkxFHk9fEDV0eHp6juaRwL98Htz4LNxvLNVTLG6PWTm2TDbNRZvWE3P6IEfIDh3ywF5gUP0BvlVvwJQEeWY7BmHTDeCKfwUz4LKLc2t5dCA6H3Tg/sp0Okp4kqHNzJ6+VpbORs9tWdijspjcwEmQAX3i7ptEl5D/NYtqjS/d966y3OP//8btdpmsZ5553HG2+8IU+g68ybN48dO3ZEL2UvsNlsEW7CIE6nM7Q+/P+etg2u7w6LxUJ8fHzE55BY0IhjQWOgkZQPSfCBAUgFzmAhBmow6bK4a5r+LRD+w/UtbbYIeD3wP2/NpqbJSYvLS5zVyMTsePwCshOsnOPIZl5BbqgRXLHuO1ZRGDrOpZZlGA0tHbJ892jkifash53rKd5ZCm21HY1FmBfwT4Y3Sdv6R2jtVFv0q0s6SV0C6x7rtKwdB9+F7h1AHnA1v2ZcrEZJbRufbatmb10bFU1O1uyW83HR1kbR9m95fcUnLF39Ja9/+rm8FxD2O+wln09Cx/UjX+6LkY3IxRCmZMMsv6cfAqQb6cwJGbi9PrJE90otuN19F07i2pNHyHhZczkEzpsPDOEZxjp3E94Fu56FjEmycMPcUSTHmEiwBaz0im8pXv81lOykeFMx1HVTkLbxafjHHRTkJoTSvScHVsnnaRcsuQ42fBbaJbzeodmo4/XD9HiNU1KbmWSs5KoJvlDD+vG+23l7Qzmtbh+6BnazoWNogN8PO7fCzi2wqwi2/ydStn07yTfWAy1YdCMmzYguDHjQKK5tlQqim254ftdF3fON7DwVJOqh/eYBVxdk4UjsOLCDdi6GkEVyaqI3ItU9yGheYB53keP9CCGayUywkmg3YTHKWGyCzYjFSkQL6wAu43HG7rmBnF1L+d20eO4LO6Z8R96DB0+Dkp2cNj6D3CQbV56Qx4+nD2XmiBRMNGLxE3rswmUrAhYt+PmBq3ts+7bj7zClRuCY55pgkvmvxFjkkJOCsF39Yf9HeJpC1DCZxfzA+QvOqniWjL0byIsxRGzhD8h5uInKYvP7/RQXF/e4fuvWrRGV/i0WS8hyOlwEMyo7E1yWHegdBl2QFRUVXaytiooKpk+ffljl7I4O60Qqnch15fhNi3jHewUJsXnEO/cAdPuy9YbwB9IBpBfPwxq3CJNBp77Nw/HDkimuaqam2cXSwlI2lsoMrakVW2gD0gL7jwTGm14LyfI6u8j/7BscpwR6+9UlgIv8lMkU1zo7LFMzUrnpgBHG8AGPA5mE9Ty7pa7bpcF7tzQgRy5ttNXfwg7uocJjByHb1GanV1aKmJ1J8eer2E4DlaQxmpiI43QoLBG6T71uPGseBO4EZJKN0aCDRwq2VO/m+gJuVwiM4dP3hs4v//ejNXxMeP5YMm5qPnue56ovYW9dG06PtLSL9m4g319BMTby7UBjD8Mx9j2BY6UBx4/vAZOJZcAYwp+nPfDWTSD+CpPnRMTbgmnsw5rLSanawh7RTmHdvpDMubhodTWgaXZMBo0J2QkdSQ7udtjyLfAdtMfBjqGQPwcyA+9gTTXbvZXUYqBNyLn27JqOASP5uRlg7n5qrMjf7AD8+0cwcgsOrTVi+6K3/k2hP4mCbo6jAxVVn9NdpdtiYCiNXKo9R6FpM9XOOZA4A5/fzLisOJweP5QBhq77+tnHOJ7A/5X0CATfjY7n7FtYdC7zzv878y49VY6sR7qF+aZZChamMMPbjFheofDlWThuu7z7++Df0/F350pdgWNeoa/la5aROPZX8E3H6s5ydn43gutttOFnCTP4D/7tM7uIEGx7DidRWWznnXceTzzxBI899ljIIgJp8Tz66KP83//9H+eee25o+VdffRWKwx0upkyZwrp167pMnbNmzRrsdjtjxowJbQd0mYGgvLycsrKy0Pojy3Whv4IPSvgPM4X1zPW/QEPTdrb7Nh7gOPaDfsKtQ4BzqOPU5vUU5CVx2vgMOUBZCJZvruL3b2/iy537qWt14xV7yOt0tpl8LFvEgFGy5ZP/lX84nUAFsB8MJgjYHMFretl4qXzh3YAT0j3RK+ogQUtqNDCavfyQxxhucpIeb8FilINjd9a0sPTfhVRRhJ2dWCijhu09vgRBK61PL+HGDqs2LjALAL4DX99zq3bzzOe7ePbDLyJ6sw7Azgd09guk8QTbtq6kukmmjm/a18Syqu9weKq52OjDkR5DaCBgd+x8FF68i6ItpXSfdL0b3v4NrFkK7g4TWw6aNjBWK8cgdmKmErOnIz6qA+n6pxh1Oc4szmrsyOh1tYJ3E/JONELpRti1Sia9AFTvws8OEqjBKHzYTX4SgXOGZeFItMtgVpdM3L7MA+mHFc/ArsgO+catD9JQWkl33XQ/EM/aTr+ddF8H358YM5yir+Y0nmZ8w2ucY61gTJKFxnZPt0otfN/8QHys++dsH7x9Dax8Bto7xjLelSIO2mgXtN0IpT3V7KyOkCGETAYOLZ/Oi6z75v94M2yTcDlDf8ecSrC729mrMY96LubfHfG4MA53zcioFNvf/vY3jjvuOG666SYSExMZPnw4w4cPJzExkd/85jdMmzaNv/3tb0CHey88NnWoVFRUsHXrVjweT2jZ/PnzqaqqCrlAQQ74fv311zn33HNDMbUJEyYwduxYnn76aXy+jl7tk08+iaZpoTFwR5QLOnpXwYej8yt8sraOOeINRnuJcOdFEn+QT1yXl8gB3MUC7jo+ldEZcVhNBmpaXLS6fbS6/aG089HU0jk1ZRQE5m6R38exUiYIVFYiXaSNFDcnIEijOOyaysSJPO/O6ljgAzxhLtIoCLpYgw3URNbzJ97kuvF28lJi8Ak5uHT53rVkUEqGew9WmkigtRdKNQ9powY/B+DNmwGZ7Xn+lCGyHezZGwnIQc9N7R4aqOumgW3vskQHTuAFNK0OP0JOXUQt0AheF9ji5edAlDxF8at3kBD42tUq3QsfLoRVz0OLdDk3Ob3kZ8bTVLeDfBrIpZ5JWR2eGD8wlxcCg8RFZA1TbyuwL+z4xVD8JdQEG+Ad6MAQ6hhv8jMKHxMQOKyt0Fbd8Qnn+L8c+Bo7890jsDtyqNJktpDYXt2jVZ4Y5n4P7gHpofcoSAJ1jOdfJDc/i2njm/xX/P7QOnnssQQdZL3vMDXCyt/Be3+Esl0gBEW1K7s+rz5oDuuhbAd4dhYcoJKSjOWndVpq65BJwFnO5xl/sPfyuNNgxJmgdbXMwom8v6mDs/JIcnIyX3zxBW+++SYffvghJSUlAJx++umcccYZXHDBBaG0e6vVyj/+8Y9eH/uxxx6joaEhlJ24bNkyyspkvODXv/41CQkJ3HHHHTz//PPs3r2bYcOGAVKxzZw5k6uvvprNmzeHKo/4fL4umYwPPfQQ5513Hqeffjo//vGP2bRpE4899hjXXXcd48aNi+aWHBpTpsBbkYvCk0oAMMFs1rDDQ8/o3aX7d8LfgNSMnVrbJ65g2XF/4dt9jViMOu1umUTi9cvxaLikSzcYQI6QL3AoB8BXb0DcmA6R2supo41kOhIwjIZ4avS7WeO6gRnBDX3yGru4KYyngPczDkZnhSCD2u/gqExmRevJNFoTKa5sZrNvPT8Q9RhNYPI5sRk6v3RDkdHusESFjGmRB69yAz31iIugrg5HTjKOnERe/4ZgJ797FtzJ/HN/SUWjk6GtLaEYRPAehN/ncBfQWHZwimklm7gQm9nI2LYmpJVsle7PLowBtkUsyWdZ6LjdN7Kl8NlfweWCEy7tqE5BNQ5KcBBPuOINyhdvqsftTYusYepqIdDkBvBCyTrYsQqSLwFaA65aC0WpSRTvbyM/1c4B+97/9SMofBn8Yc+HNgPEmh528ACRHg8H4Ihthq4zCHXxbgAwJh/KEuRwG7aF3ocgGazHwzZMe8+MtFTGz4UdaeDeAuynW3p61jf9DVpq4Lj5FLKc7uo9lRgh3wcmQzA00ED+vZfjWPCv7s8FMORO2HdL6Ks+YiHsui30zMWDfC+NsEoDh+kE8OwkaPUBMGwmxKRBah7sHQKVG4Gu+RTB50s+ywn9PsVRZ/qs2Nrb27nrrruYO3cuF110UcSA6P7gz3/+c0hRArzxxhshK+yyyy4jISGh2/0MBgPvv/8+v/vd7/j73/9Oe3s7xx9/PIsXLyY/P7LpOuecc3jjjTdYuHAhv/71r0lLS+POO+/k97//fb9eS9+IbHikuZ/GUmoiXpxRYWZTESfh4IuOBTnDDn6acj8YDZCYDZVLwlZ8hbb2GRLS52E3G4ixuOUkgsgqGMHedlCpOQg8pBqRT9E3z0LO1aGvfhpIxB9KwAD4+Q/GEWf08dKHj5HluZGhPkL+/nyygbD0/0lTYbMOrk8PeFn5/JBi6smnMHSeIqC4fDHnsp9nOINqTwp+EVATPrAbmzHgimzU7cOgrQ7p5torl2XmhuIcAHjbofYA07MsfQx+Jp+l8Eyy7nmc0Rm3MSU3EePWfehEKvfOMaTweMoM3mSXJ4Oy+lls0soCimE3NJbQhRHTYZcb2BNa1DU+NYRIqwqgHNY8Br5WHCdeheOk4bC8Eqn4a2F/x7xkDqTqOodNJJ1wFQlhA9lp6nxc5JXuWA2jTwpb5sKRlYaj62icrug6nHU7vLsFqJHLJp8EG3pSbD3Q8nm3i7uN38Wkw8h4qI6BKgt+tkFEaWAw0Uo+SyP3y54IMQlQkgTVxXTtigEnnAFrrdD+KV3cMntehtpaCrrfk/UuaNfGc7xvc6gjVcz78NhCHDfeE7FtMEFte9oUzPuGkOffBzq0pkyCxktx1L5EcbCwpZCi7LUAsy+Hqh1QXALuD4AYSBsO9iSITYf4FMgaBru/g4YvgMiSch1y5x72Cv99Vmw2m42nnnqK8ePHH3zjKNizZ89Bt1m8eHG3xZGTkpJ45plneOaZZw56jAsuuIALLrig7wIeLrLnQnlkjxpTPvM8NRGLwrMmV5CFAzvQBsTB8MgxSF0QAuJt4NUh9zio3E54D/YcniPLNoeC02azbGM5SwrLaHV5ZSBc7xgGEWx4O/dWZerxZih7N7QsPzGb4oa2UK/XAaGB5F9tq+Hfu+/j58jU7CbAcdrTsPycjoMOPwE0I6xrAtYFFsYHtu7AkT8Nh6kdNhmBNRH3Cd7lHOBZfQYNvgowAQbQcBETYbmOg7zRUN8Azc3QWi83TMwlItI+xA+1FXTu/YfY9xdo/S3ExDCPjsSW1+neOgqm1CcFMlx7m6ySiIcZfMJ7niwwh1l6rd2UtRo6GTwuKG0HqrquBzCNBI9OV2u0AtY+K69i2kXAdx2rPDsjtpwHjOZNCm0/i6ww09o1VR88sGsd7Fwdudjauxq0ABScBJvOgj0rgRiYcj5s+AoCQzV6R8/bRqa1A7Gp4GyCbDPYY8nfHUcxRXQ2+SJ/47GQMQYsCWCNkQpgVyL4i4GGwDY6JA6DGSYoskHdx3QZv9P8YSfLh5DzpQXY7D+eJONmwrMZivf/FcdnE+GUjtSl4L7CYGIP55Cpv0Erdk6YMArGXAMvyXFqr1uQOluHOIAJp0LuFMhvgJVesCVIl7fZIq/LngKxaRCTCGVpsOdbYEOXe+rq4gLtf6JyRRYUFLBp06b+luX7zcQLoPypyGXD82FbO4T5+UOWElDCWEh3QvV3kDoZ0g/W2RBgSwTNBCkjYepZsL6jcXYAjpI/Q9wcCNSB3LSvkZ01reCkS4A5pNQCJklH472y48U2BbRIOH4f6AaSkxNIqx3B0w0XM1+8ztcUcPzQiZGNwpDJ4HLCRBdsagYqIflCqFtOh2VnlQ1HjJDZd9taKSby+czkXeaZ/HxjQrpXgFiasRCWrRs3FZKHyoY1sQV2t4DQIKnTWMWYOHB6YdsBEnm2fk1RxtQuvevg9/BrLPjsXcodP4C9tQdwC0rCM9OKgWmm76jwrMJCmKVn7Oa1znbIsXvOdqhZSefeNADDhkCJCBgLnZVbFax9SsbwInDSmWIq2LZ2M2t2y3qOWyubOa1yUw9Zr1uguJNicXeNK/aIpsEJ18hHLD4D0kfCzMthdfgxJ9NjJ4Suyiv8e+csYvxOsNrBZYREE47JCTjKUqF2F6/TU5s4UTb8yZnSajPHgz0ZyhIDz7EfuAAyh0srtOBU+M4M5SuJcPsFCFqSi4EYHZn5K2x4jXnsNZzDKN6N2L7ok5txpI+BsXL2leB1nTslByb+lrXPO6nTMkE3QPZomPkgrA4MoDYDmowQEpcG9gRIcYO4QipqgwEMdjBYwGiF+CSwpUFMCiRlwPZ0aPkCaA2d1zH5cOdERqnYHnnkEc466ywmTpzIVVddFVFzURElY8fCR52WpQ+T8Y2SemBXaHHQUjrphCng9UBiDKSOkC/2wdAt8mFMzIDh06D0fNj/dtgGRfD2gzguvw9HTmKoMC1thPRTl/iPjgxfBNyk4a604poqwB8ZO9M08Hs5Z/IQshOsfLHxIt6qPQ6LnkLh3gYcnAYsl9va46UbR7igvV5aUSOOgx1uqA7GD46DtGEQY5BK0+cnf2czxUS65MbyvrS7AteRTAL5xBBqPEZNhsQ0MMdBTAO0VIHR3PW+uuNhpKFzyCqSZXdQOPv5yHiIT55b3osZBC3LQtf/MDbzApp6MSbRwdSOahPIY51t2hjqZBQBDmPnPG4geYhMJvB5oa0NWlfQJc6aOVk2rHs0cGlI5Ra+TT1seDbCGs7H3EUR5wNrGz5htzifL3fux242kuXe14Ni88LubyMX+XpZIFcI+SyNmQzmn8uG12KHySdCuBGYPxOKe1ZsnZVX+PcuqfiGGPC3SivFaAKfFYaYINHOxTtjkfGlTjG0+BFgT5TPktEIxjHS0olLhG/awV0DE6ZC4hDZ6awBJp4MJjOUrCDCNR9GDIRa8KGcRKWwk2Q8Ed3rxs9HspHQoFhrwvHKf8P1i4EwF2tuEkWl9awd9iMSY2wU7m3EkZMHk39I0epAaCTwKH0OzDMaZdthtMDQSdKTErovBoiJB4sNLDEQlwoxqZCcDaUZsO2FjvNmpvb4W/QXUdeK1HWdn//859x0000MGTKky8BmTdPYuPFAqemKCJKTiexZxkLKCHC3gXsyVFQRdE2EGoiTjoMtrWC2Q1y27DEdjIRM+XLZEyAlTwa1P9tKhOd+1+Pw1Qw48XyanF5ykuzddvAj3CJhyi08npRvs1Pc7uzqXtN0HNmxOHISmZaTwMufGdnXFJjV+fTfwEc7kDVOgOQM8E2QCQiuVkgfAfF2+GAjUAtjToT0DFlN1ucDjxOHqxFHmZMiqiKspshxN/E4wjM7hk6ARDs010KLBTJGg26WnYBw/F6wxNLRv++OLRTYGikmzGUbbrhefC+8fhrFQCyykO9x+Igl3HqIoYs7atx02LIZArFBB7A0LC4mG+du8szt8ZCWI2V3uOAbD3g7xZZSh8pqtehQZgicem+nAzVF3M9iUnBQGbGFvCuvsNZ7DgZdl+XCOsWhItka+dXUQ558Z4QfNINUbkMDv6zBALFJyFSlQLLK0ElQPBLY2e1hwi2zIrpRZuHEpYDTCM5mqVgtVtn4WyxgToB9CdC0GagklMUzJEt6LowJUllpTUhfuBkmzABXpQwjmKyQkCGPqxsg/0T5/O3+BDp10gAu5gSggiJaeZQcjFoipd5WEpiK8C9npFvIh88M8DW8dXeXYxTubSA+Npa6dg8Fw5IBDZKGUJzzMyj7XWi7kPNa0wL3OKXbe4nRBIYEMFmkdVqfJS27bS+E3cPoCt33haizIlNSUrokZSgOkeEzYHdQsU2SisfnBI8T6o4D18rI7S12yBwje48JWYEX+iAIAQaj3DcmEYY5oPFs2NjJafbR/0LmWAryMthY2hBarBMZKwr2cEt9kBvo3IfH3RzDMqUC3GIg5AM0mMDnAaTl5hiazIbhQ8hoF7Ik0QkngfMGMGdLJewXkDQEWvPB1QTpQ6HVBqNPhvb9MHoG2GJkA5OYBt58ed98PhwVH+DAGYpzhVOMCUdcUofSTs2GWJt0q5hjpfViMMlYQud7aLTDqDmwI1yxhV0j4PjmXzjoOugegKyhQEcj2lb7ltw14O6VCmo8ESNkAXImw5YpgEyQKOp0XTrIkvGd0Y0yyJ8B6EK6JNe30hG3nCk7UiLQLdF06ZZs1wlPOAmXWf6d2N3V4aCVU2PaqMkdAoBxqysi2zOSTimJvbXYQI6+17TIOqX2GEidAfsbgHhIzYe8U6Cke8UWTjGRqfivhy13ACSPgOYyqZScTeBpkwrJaJLvldkO9XGwdxuhTk96RkeVD2u8LOVmaASTEXzt0J4KyWnyeTNZIDlHTqSn6ZA/XS7fuYrwzqe0mtPJt8RQ7GomhxHswUJsgomiRi9WcR5Z+tvYjR3bO6ojE1qKyhoob2gnzmbipzPzOuKhZjP500+muOxUgpVw5jM78kYZDqA6NA3MNtBNYLZKa5UfAh9TBGzd3szYxIbDmkASlWLrywSgij6Qezzsfhn5og+XysrVDs4WyGuBbU0Q5oZCN0DqELDFgS22awPcE8ECwJYYiE+DYdMp2ngKxXwWFuPZDh/8GceVj8jpbQKd6mAjWkw2jjAXSY0+hVyxoeu5Qp3vVGS/L9DDDldu+JmSm0xhWZPMotN1mHMdIAIDeP1SEWePk/EXW5xswcedJK24rBHSPYIfLHGQkgXCC26PdOXW/buHsWpWMAStXDPEJYDZLJfZYuVLqWny/87oOoydCTv+HliQB2QTkYhQ+5zclG6Uj0UeM9g52MwzERZdPsgY6+5wxTYEkodDwSwo3AS0donh+UFaZZ0xGOXvbkuUHSavF9xO+K4JKIfs2RAfmK1b0wLWkCY94C4f4TG3iGzBpJhuq7o5AEfOToqmzeKuN78liT29rzhhMB9oJeGdB/mMdN5GhxnnwooySE2T78joyVDSjQUMPVr0we8R1pvRDHG5YEgCaxW0NUJbLfh1MNkh1iI7DubYjk5PXJK0LPGD3yM7TvFp4LRK5Wg0ymda+OVzpWmQnBtwA+ow+nip5HaYIBDHKwaaDWMp1veSPzyX0/SJVMQn097WzJ7Geox6PlsNBUwLxOe7i+0WltSHJvKNVDIajvGjcFhvh5ergH1wwq+6+zEOjNEIhljZqRp+POz+mGKg3tW/c0x2e+rDdmRFnymy5FNMBvnE4sgcIxvX5CEyYO9xgrcRdpUgy0tlyMbKZIdYg3xZOk3Z0yMi0HAZzTJRIn04xQnHQ+MXFOPrePirX4MV+RRMvrbrOLHYWdDyWui7yTiMNrEBe/BaCLxEWtBFnYJsAQNj7TopN0duEo6hqbLnCx3X4vdJ14fQpVVmtoDJJhVX8lAZY4wNZGb55TxsWOMgMRN8Y+V9a2oh3/t5qIHaTjDfJR5irdAwCrBKBWY0yPtqNIHRJv/uqXeaMSJwlSVgPxWGWGB71wy7zkrVD/KlT/4d1Mn6khdClyoPpI+C3WPpcNVNlQkI6ePBMB18K7qMd8wH6e4CZKGySvm/psnfGzfYkyjyJlDcPoz8pEk4TPmyAr4tjuCUNOgGaQ2hwW4BTgOdLTdAZvjVj0AmkfiIyLhc9wLLPCezt66N47okQdjobvA5IBMreiQbqWTtsrEXfrpoNl2HYZPghIvAmghWGySPhIz/gqolXY4YrrwcoRx3SZeUf3uCLFata/J5NFbIEICzQS436TI70GQNXKOA2ESZPOJuB+GTFqkx4AmIz5L722Kl8hM+eV2aBok5gC6f/9HHS7F2tgG7yCeL4uG55Ccn4Yg348h3UOQ0svD1NdSKOGL9XkoMx5HOt+QEhg5EdiwyImbN7nL/EDIR5+SfQeNemNZpLGdvCXYMxx0Hu3PIx8XWOAtjB9s4tiBNTU088cQTrFixgurqap566immT59OXV0dixcv5rzzzjvsZbSONQp9McSSRTEtOIYMC8zOlwSeTOnyaG+Cyhpo2wnJU+ROukGa/V0Kvx0ATZPKzRDY155A/swTKf5wB/m8Hbnt2udwDJ0e0XheDHDcCfCpVGz5QHXsJBpaVmMPxFtCPURbcN6wYUA7WIdEyqEbpYURfJm7yKp3WA8Go7xeQ0CRx6TIfc1WuS7c0ohJBPKkVeJ34VjnxBFw6wVl81vSIC0NfG3S4gtaaH5vwA0ZG4g5dXcPdRlvGXsS1GbDsAKIc8P2oXSOS3WrfDDAJZfB/wUKJ9NN3l7acEjMg4aAYrONlbG91OFQcAJ8vQ4HjV2toPigYshDKpzRgevwB0qcCTbWajQbMyF2Mo4RVsjKkfc19HuBdBULGG6Ul9SqAbs7nSsR4oeCxw/tLiKHEmxn77fr8PpzQG/tNJYvma5j5gKY7N0vlzcFqdiyOp7jzopN06SVNHSq/E11k0zMGHscVC3tun0EucgOWHPXVH+QXg6DDh6T9KQk5IA5EZw10Lof2hpAOAOJFUMAP8TEdrj/PU4Q7o7n1Born2ljoGMqwp4/NEjK7ogjjj4efG4oT8cxzIHjpOMp2rud1/ftJz+pDceEPCz2WGzNbtyaGx+jKOFcUlhKsHsZdMnnM6TLRLuR91CXz9GkU6ClEQ6l4LumQcZwSBiDQwgcMydCSg/n7SeiUmxlZWXMnj2b0tJSRo8ezdatW2kJlN1JTk7mqaeeoqSkJFRWS9E7CkakUrx6PPlxDZCZLXWVwSTHhrjaZY9waDU0GCFttNxJ1zuyw/qCrgfS7uUL5xg1Goc+H/5dA3wZtuE+eO/vXV0ymaOA44C1ONDZOnQoLZsLgPdCexYDDmPAekiKA1cWpHVKxNB1wChl6VaxaUBQuekd+ttskwOl/f4OK89glPdC+GWswhYPqSNlvG1ci0y0YXPHtUw9DpIDLkejTTbsweC4z9uzUgvKbbLC6BMgeyQkTQBfDcRMgNbOCReROIL72yLHa3VxeMYPgSEjoWE00A45Q+W1xiVD0mhIOBka3+28V0eMSosBMQSysgP3MRCL0oxMzktnI37yYywQH8hoC8ZfbfGELDcNQhZcSWDAVLhyyxwLeMCtyWzL0jURomTyOXGWy8Aj0zk6SESO4erqGkTv3nEsiUXmAwYUsK4HLMtOyspklpmyIJ/xmDhIHgaxp0LLdjANA8+3QH1kVmTcMGi2A/soDgRfIywdo1HeV90oP65m+d1kDqS822T2rtMp096FvyP+pumBDpMR3K6A6zGg8IzGjs5HqLOnIcdRpne4KEdNh5R0yBoPSWkUb9pBk55EYWUbjgkw7/g8/vOtxvi0TCr3VbChPB/dM5UTWA+m8FBC3IHdwsFOQ2KODImYD+Qe7gXWGBg+RT5jcYM0K/J3v/sdzc3NbNiwgfT0dNLT0yPWX3DBBbz7bjcvnOKAOHIScfzoR1C2FXKHBpYK+eAnZkp//BCHHCuSPa5jx74qtdB+ugz4mCyyJ5o1AmadBauKiAjou5Z3csnEQWymdPfsaQQtgbEjciguH0Nrw3sRpWlf31ZPfpYVR1KatDqTuxmSEHQ79nQd3fXMDQYZqPcFXJWh5Ua5ndcdSJDxQtpY2VB4nLCjAgf18lpGy3E9GM1hsbaw4wQTE3rCbIG0ETIhID0b2owwsQDW/Dtis85u3NeB/PImHJk2ZDZHFZ0pAhxJKZCVDxXbwOeCYSMCblKb7AGPmxoYr1UbaV2kBqqBtAFeE2TnIJWaDpoAgwHH0FQcuUly+ITLKe8VBJSbQSq3oLUsBBgD2ZJ7gNaqwMELpMWi6dIl3NIMpWmEqoAADr7mI8+FEffBAWBMk6717hRbTHrXZUH0DPCngz1sm5ByC0PT5TUFO0xmixxoP2EG1CRBwlBYbwQ+iey05Y6ExnjYl04+n3XNjhTyNmCwy4xFXQdnG4h22QE1x8mZCIw1kJIsXeZBpaDrUrMYLfK71yPjcaEOhyatM39Quflkx0IzyZicroHHJ63q9CFgiyE/dxjfVdQyYaQc9DyvIJd5U4eAz8OLnxnYWd5AmeF4SvRvyQsree1nRM/3OHQPtcCYyH6IWCUkQN40IBACOcxEJfFHH33ELbfcwvjx46mt7VpRYMSIEZT2WF1acUAyJ4E9E1Iz5QMuAi+mLR6ScmSyhNkiG5RDJeSSNAXGa6VC3nQoPgtqXjvAjkOkm2K4A9z7ITEDx7Ac2D+e91aPYZRnW2hM2wf7/ezcr+M4ZSS0mmWD3B0Hiw+G98yDysZs66p8gu5NQyDxxBYvlVraOBm4b2yAmm/BPrIji1TXZePRWYkdVKZA2rPZFoi3pENSHkFLNkhnVyTAur0NOLJiYMLt8N1/d9luI+AwxYI9S6aCezyQkBiwFM3S1ZY6BjLnQOXSSKsjbWzg/mRAWwpk53fcPz1gjRqM4PNLV5ixkyvXH8ycjZG99aBbDL/8b1MDsB9ixkNscsC480GsCxlD7VBs8yjnP2G1A0PWT0YgNZ4GumRFigPc95g4aI6H5E6JUp1/q6CFqgc6PZpJypqaL9258dlQ2wJ7P4nstGWOh5hKMNTg2PtZJ6vGHhi75pMKy2gAQ5y8f22N4GkPdAZSpCsyIT0QTwtrZsOVW9ADEfxdOm+jE4gbB5RBTAqke6G+RnYsdROOkbk4hg+RwznC9xcGHMOy2LSnmorScbQzD3g1tIlxci9iZuEdymg7z0FMJtk5D3oCDjNRKbb29nbS0noui9Lc3M2gJ0XvsFjkeCMIuMQCPWZdl2mzKcOhvlwWHu0Pgi5Js002TvFpMPVM+OhbYEs3O6SANUdaeekjwdsEegwYLKyohGqGM0psk3kEAuoMsezSrLKWnEWHhOxujtkHWcPR9EBadKd076B7EwEed6DaigDPaBjXDDEmORbOEmgMdFOgQkpf5TFIF4sxsL8pTg5LGDYR9nQoto6GU6coMFh9Wl6ybDTmnBlSbMFGtBiYTKascJGSDY2Z8rdJTJJK2+iXww3SRkDeeKj8knwqAtZFRkd2rB8w+2UZqOB98fsDSs0jr1vzBgbdmsJcr/4OF681NmC1EbAgDDB8ItSVw8TZkJArxynpAurr5DCGiq8jbtMMNofSTkLWT1w8mFPB3USkYrNwwDFvsSYQMfK+H4igC1sj4A40gs0uq2FYLLJjMGR812F6sRkB5Z/YdR0pAYtWB2GW8S6Q70JcslRs7Y1SqRkM8t6gRXoUoCOWpmkdVrHR1P02IqAI/F55nvh06V2wB6p+mCwdVnY4BiOOYWn80xxLKRZaGY2JKYwMlLj6ttxwkPkPw+ToDzQd0kYFMnYPUUn2gqgU2/jx4/nss8/4+c9/3u36t956i6lTpx6SYIoAeqARAhkLiksKxEH6UE/vYIRcklZp4eTmQ8GFUNiNYtNyYUi27EHGZsj6drZYMJvxmW20MYQqDTICz6/TaCM7OQkSUmVmXUw3rsio5dZ67knqBvnCmwBcYEuGFAGto2CoLtOpg/fQbIn+/AZz4P4ZAF1WWhg2Afak07Uc0hgc1Mr6njmJUnl0OneHEhwRSIJIllag1w12u7Su/T7ZUMfEQkY+ZM7EUflmIHb3Xx1uRU0Howb2sJhGyHILxHGMlsB3vUMZAFK5ydJnoSlwfIHeiq8dEpMhJUNaUKaA21aIgOs0mfCJYe18Rbh/4XUgv96PIzkBKhORBZWD9S0zZMmpnkjIBL0U4uN63iZI8NkQQTesEVKGgV4prbf0btygqSOgJRFs7aEhfh1uXiOO0IC0gGdAhHlVTEH3ZL38npAVsLh6kk0/+Hgwg7Ej3uv3yt/fHh9IAqJDsXW7v4GdtS4afUbcWjKN+lz24ETDx84jUK8xUhYt8FyK/lOWByAqxXbzzTdz5ZVX4nA4uPjiiwHw+/3s2LGDhQsX8tVXX7F06dKDHEXRK4J+d+ELuKFsYBMdfvr+OofQAiV/PHIgb95M+O40cC6P3DYlXcbJdIMsCdSeCTYbaAbystPZtW0odaZJZPi/pV5LJCs+h4vmjoGEQC+7N4PI+4tgo2HwAx6wJUlr2GqWAfng+mDPORqMxsDA6oB1YEmQSQr2ydDW6d5Zh1HkNFKMn/x9jTiy48FqBU4kMmEHyB4asCTtMoDv90pLQ9cDCssEHpscVD18KlTuAOpg3AQ5hgqkVWeJlenn4YRcXUbASxdXrmYIDojrqIIRTCgRSEsltingWQg0VEYjuK0wZCysG0a4YtMpDw3RK0baZ/+uascxOh4a0sDZSId5lB2piDsTnwaGVOmW7S1Bt6TRFBgAnSWvKS0H2QSGjfuzxgS27ygk3eEeNuEIt2q1gIvWLzq+a5osJaW3yjGViO5rdwbl0gwHTv7SAnIHE4KEP2CdaWHH6KlzpzM8PYG9De2YgGJyiPNPR9eNXHBcbvf7HE4OpMT7majOdNlll1FSUsL//M//cNddsjL7mWeeiRACXde5//77B1fl/KMdgwF8gWw/gykQnI7CdXYggi5Jk1XGotKy4JSL4KMiIpIbcsZCViB93GSDhLTAWC8DrR6dSeNG4tkygRrdTy2x6JZAxqHVLmMSpoO4kPobQyAIH3TnxiQBvg43JASsrSjdIwaTbJyC2am2GOluHTMVNnRSbCNHU/ydGzBRWNIgFRsGmHEVrOmk2HKHBSpZmOWwAp9HKrbgOYVffvfZ5bCAEZNkht7Q0XLcFEh5/N7u0+fDlVtwYHDEegP4NRlLCmZF2hKkZeb3y4lDY+IiEwFscdIizxgKVetCi/2EzXXtAa+AcrNXNvyxFnAm06HYcjqusztiY8GfJMcq9hZNkwrZYOzISgzGrRlPRFk0S1wg47HjuoKxz8kkBRQJIAwdlq4eiLv5vYHOgpCdvYTsgycgBeU7GEHLDQIGY++e16tOGU1VUztel4V99V7ayMeCxujUXhZz6C+Cv8ERImoVetddd3H55ZezdOlSduzYgd/vZ+TIkVx00UWMGNGLjBtF3zAYpTtK1wMv3WHwU2u6LOxgtMi4VOZYGH0hbP+/jm0yJ0JKYHyi0SwzEwM90il56WzytmIZPpZNu5tpwkJZC6yvduIYlh6oRNDPCrk3BM/p84I9Tn43mSMD9odCeFanbpCKKH0UMJWOSjGTIG0I+WOguM4v6/Jpmtz3+FOCFbI6GDKuwzqzJ0S6mwwG8AeUqcEqZ2rImwytDdIasQaUdmjoQw9ZaEHl1lOvPzQUI6DcNC3QMRCyhqnFGrA2ApaK2Q7xCZA1Dqo+ITi1UETyjEmOrLORwuvbmsm32XCY4zumH4vJ6KiM0x0mk1Rqlj4oNoDgwGtTYGC40SaLGcfnQ1OYYjNZZNxW71CuIfdwanqHi5tg4QAhLTajEXxamOvRIDuJwQ5Vf2AwBhJXfPT2/XfkJHL7WRN5b+Ne9B0eahoFWfFWCve14RjZP2INRg7JNhw6dCi33HJLf8miOBjBuAgcepZSdwRdkiarVKIJaTB2LmxfSyjLL2NMR7V7g0HGqQSAjiM3BUeGhUdeKaOOdOqEhezkWKYOC1QUCY4RO9LoOjLYJqTla7Z0jCsK0l/3U9PlPUnNg9xxUBpUbDMgPgdHTCoOg7kjxqbpsrZhZxIDA9mD1llnd5VukJaixQr+WBmHs6ZAQlLkPRYHsRh6k40aVG7B49gTpRVotoQpxYCStsRKq37DUILln8KzDpcia/KmaRngLqfYLXDE2zoUW2KKVOQ9EZMInniw9tGlHfRIGIygB121JhgyLHJqP1Pg2TAG7cywOFxMbKBaTuBeBIcYGAxIqzbQnPp9MlnJZO04d38RrMLTh+fVMTSZdSX7ac5Ow2TUGTskgYIRh38sWReidfdHwSE7PVtaWqivr0d0E8AcOnRoN3sookbX5UMdHKx8uM7hR/a+hR8yh8KJ8+DLJsAvKyFYAqWC0DteZj1QC08z4Y1JpI5sfJqN8ZlxOIYkdvRcD4dC7u11CVOgtx0Y4nC4OgfmgIt25FQolZX4yR8nxzl5nB1u5KBSMBogNCVpAHui/N8QUCqdFVvIxeqTrrCEIWB3SjdeRMzsENysQTorN12XClU3EBobF4xTWmLlOLS04VCzKXSIYAIGyJDkHuGhGQOTEmIgyQxNScg4aNyBrXprrPQmWKJxaXdKXTeZIWtiZPJvMGFDeJHlu7Z3rLPbw9b75fH8vg53o64hh40EM42tvXNF9vky+n68acPS+HpXLTnpKWSmxB32Gay75Qi++1EpNqfTycKFC3n22We7HccWxOfrISNIET3B6hqH8yEJBqy9JplNmDsVJuyTL7PVLhtSkC+y0DtcUQAmM6dPymPTnjravT6qm70d7ptgdtpAYTCAMILWe1dOnwkqK3uizBgdNUGmgI8cS1EjFFfUk58VH6ijGdhW6HDaj2B5ULGNBmNYw20wde/S0g2yY2G2BsbRmbpPVOiPZyU41s/nkb9hMA093CLUNNnpiU+C3EmBCU2lORQ+jq8eqBIaTs1CUmMjRTFmHMQDOsRYA9mXB5LFFJ1LW9O6ZhAmdcoODC/hRh4Rii0mpuM4wQ5D8JjBOC5I2XQd3Ea6m0FoIHDkJvGD8Vn8Z3M5sfbDP0B6oIlKsd1www08//zzXHDBBZx88skkJR3BTDfF4e/5BFORTTbZkKVkwbgfBMbLmDqsNOhQasF5vDSBIy+NyaNS2VbtJDXBHqnYjsAYlgNiNIHvcFuOgXhTUhbkTZWlv9Iy2bi1AqdTp7jGHTnw12CAcVNguR1oA/NcWbw3SE+xwKDVFiyGbTDL6+uv2GGXy9LCXHFdVgZkNcoYWNoIpMXTTZyNVPZ7zPg0KDdZKS534rBkyEPY4w9cmcJqA6ddjhmMRv5wvaYbIDMXGIEsE5bT0fEy6NLyDB8R0HnsXChGGehoBpVbMP3fbAEGsCPXiSa3YExmIs3OA8QwjxGiUmxvvPEG1113HU899VR/y6MYLARdLsEAeHJ2h2ILr4AenjhhMIDXBwYzPxifS1pKC45xhzAg+3BxuON8eiAdPzYZ4nNk5yA2nsl5sHmvl/zs8PT7gLK3WmHEj6B8B/xgXvdT5XR7roDVZrKA33T4xwgFE2QQ3ccodV0qtuQcGferj5xEVO6RjtUcj/A6KfG4sZtapDXk94PVcuDkEaNBJo5EW5YpNCNAAJMF7MOgTQdLVsfAaJBT3oQrtphu3J/BbFj8HWXYROAT7AAOlPu9E7KaP0w7zJX1BwNRKTZN05gW7TQGiqMHXQ8UG3bLcj4RhCk36LDaAvXlHMMycYwwdaSef9/QdBkPSgwoNpsNxzAbjqxOqfehOJsZTrkWGqpgxNjenycYgzOY5Xi9IzD49YDn0LSOAtRDJ0H9aqCxY0YFAIZjw8h+n500g5tmzGALxKPstgMb9boemM3hEAbVh6yowHjQ3Dyo8snOW2gbIDU3cuLqnqzE8ELdIcUYNj5tkCi2A1bzP8aI6i04//zz+fjjj/tblu8lRWUNLPpiN0VlDQMtSvdoeqCiQiA4HlrezcsackfqssGIpkzVsYKmyQY4NglikmXsy2iU7l2TLXK7YFZhShbkjOk5Pb8n9EAJp4EYStEdBhPEJED6GEAWKw4vJOwnlSZMxFqsuP1m9pPE6/uEtIhiE2WFip4+tjg55OCAU9schFDB4UAsOX2sLGKdOixyu+DEq0EsB8jWDB/kH3w3OrvtB4hB38YcBqJSbHfffTe7du3i+uuvp7CwkJqaGurq6rp8FAensKSehjYPhSX1Ay1K92iabGjNdvkJX965a63rHW4+XQ/UF/R3lFv6PhF02dkTAnEjo/werH4RuXHAfWnuqAHYp3OFJfAMButA12XWYmoOxA8DZLr/xUgFZzIkMyIthQRiaNXstHksrBU+Xt/hpajeKxMzevoE3eP9VcXCYISMsZA1CbI6WcrxnRJLbAeJ64XctFpH9uiRsKAPwqBvYw4DUT0do0fLmZXWr1/Ps88+2+N2KivywBSVNVDeIGcR/sG4A0zVMdDoekdjHJ4BGcoy65ReHsosgyNabmCwoQXum8kSqOThO0BsSOuoA9nXYRGBau7dVhAZCDRNKh9rPAwdD5u+JBiscgCOeSeytD6bZ5bX4cdMs89IvYhjndlIW0VLWNmqbtCNAbdrP1inwY5AyhDwuyGlU5mp2E41K3s7dq6nOeIGiB5nyj6GiUqx/f73v0cbDD3Do5zCknrsZiOJdtPg9313l2kXHNAdMXA4YD2EF4vtvM/3BV0nItYSrKrfmWADGxwXphv6fp+OYB2+XmE0Szds4nDknHO7Ais0iE/lk9V1aFgRwoBbj0FoZtowM3VYxkGUVuDZ6i8Xn26QQxTSRwTqdoYRF4+c/tUJxMtqJb0+rj5ovBTfp9hakKiejgULFvSzGN9PCvKSWLaxnDa3l6KyhqPz4etuHiyDQY7CDbpjvteExXOCBYW7bBJcz7Gj+INVSIYMB8tQcAUV23SwJXPqxHherG4gyZBEpdNLm4jHhCFsJumejiuAg2zTVwxm0N10icyYrUAycs64wLx7feFY+S2PQvrl6WhsbFRuxyhw5CSSnWjDbjYeY/7vQGzhcI2nOprodZxlEIzx60+CyTO2FBg+FkhA9qPHgMHMvGlDWThvGulp8cSbYmgjhRpvPItWl3bcim4//RxHDBbzNpq7WmQWK+iZQLac8bs/Z9RQHFaiVmxr167lzDPPxG63k5KSwsqVKwHYv38/559/Pp9++ml/yXhMU5CXRKLddGz5v0PuyGOooe4PtPAWupt14UMmjgUMJjnTeupoYJj85I2SMUdNwzEsjatnj2VIfDwuzYxmtFDZFihV1dMndI/6yWILZkbak2RVnQj5jZCQDMYEWU3FcOxX7DhWiOrp+PLLL5k1axbbt2/nsssuw+/vGPCYmppKY2Nj1IO3XS4Xt99+O9nZ2dhsNmbMmMHy5csPut+wYcPQNK3bTzDZJUhP2/3xj3+MSuZDwZGTyNUnDT863ZA9EYwnDUTB48FOTxZceAr6sYKuyymWMobBsBFywHZmTsfkrJqGY1gWJ07IYmhsEhp2Zo5KQfpke/rQ//dIN3TENyOWG+U8gomJkJjQv+5PxWElqhjbnXfeybhx41i9ejXNzc0888wzEevnzp3L888/H5VAV111FUuWLOHmm29m9OjRLF68mLPOOosVK1Ywa9asHvd75JFHaGlpiVgWnDPu9NNP77L9aaedxhVXXBGx7EjP+l1U1hDKVjqmFJtCAR3uyLg0yBgHMdWQnhPIDO0YRzZxSDrrt1UwND2e+PgYDmjpa8F/+tMdGYwFdx2+UuQyUbxfkG/RcfQleUQxoESl2L755hseeOABLBZLF2UCMGTIECorK/t83K+//ppXXnmFhx56iFtvvRWAK664gokTJ3Lbbbfx5Zdf9rhvdxOb3nvvvQBceumlXdaNGTOGyy67rM8y9ifh40uUYlOEigsfS1abwSQHVKeOkgouJVUWMdZ1OY+ZJsBsJztdTn9UMCzlwDHJUKZhP8fZuivjpWkUN8QA7RTX2HAMlgHwioMSlW1tMpki3I+d2bdvH7GxsX0+7pIlSzAYDFx//fWhZVarlWuvvZavvvqK0tLSPh3v5ZdfZvjw4Zx44ondrm9vb8fpdPZZzv6iIC+JNreX8ob271VVAEUPHItZpLouBzbHpUNslpyFIKhEArGy9RWtmGwJpKem4MgdgFhzMCbcjasxP380JGWSP+kYnpXzGCSqt2jmzJksWbKk23Wtra0sWrSI2bNn9/m469evZ8yYMcTHRw6MnD59OgAbNmzo07G2bNnCT3/6027XL168mJiYGGw2G+PHj+fll18+6DFdLhdNTU0Rn0Ph2M2KVCgCBAdrx6ZDbArYbISsrUAiyNRhqcTHJzBpWM7AyWkwdu1UGAw4Ro7i4hkFOIaNGhi5FFERlSty4cKFzJ49m7PPPpuf/OQnAGzcuJFdu3bx5z//mZqaGu6+++4+H7eiooKsrKwuy4PLysvLe32sl156CejeDXniiSdyySWXMHz4cMrLy3n88ce59NJLaWxs5Je//GWPx3zggQdYuHBhr2XoDd/HqgCK7xm6AWLi5SSoRmOkq1XTcOQm48iO75iR+gDeoCNOXLosQhCbisryPXrQRHdTX/eCTz75hF/+8pds3749YvnIkSN55plnorLYRo4cSX5+Pu+//37E8l27djFy5Egefvhhbr755oMex+/3M3ToUNLT01m3bt1Bt3e73RQUFFBWVkZ5eTk2W/dBYpfLhcvlCn1vamoiNzeXxsbGLlamQqEIIAS0NVJUsp91VR6mjUzHMTRstgi/X87xhuhDBZUj5LatKIb2ZlnvM33UsecqPspoamoiISHhoG1u1HVpTj31VIqLi9mwYQPbt2/H7/czcuRICgoKoi63ZbPZIhRHkGAcrCeF05mVK1eyb98+brnlll5tbzabufHGG/nFL35BYWFhj9mXFosFi6X/BmmqrEjF94KAO3J9dTsNLp11e+tx5CR1KAldB3+wzejlkIcjlWBjjQc0OcmoUmpHDYdccG3KlClMmTKlH0SRLsd9+/Z1WV5RUQFAdnbvJq186aWX0HU95CbtDbm5sgDqkZyVQGVFKr43GM1MHZ5F4d5Gpg1NpEuB4ODg9cE2ls8WK5NKejvxq2JQMKi6IFOmTGHbtm1dkjLWrFkTWn8wXC4XS5cuZc6cOb1WhCDdnQBpaWkH2bL/OCarjigU3RE+jUtQcYXH0gabQgsSLJI82IpMKw7IoFJs8+fPx+fz8fTTT4eWuVwuFi1axIwZM0JW1d69e9m6dWu3x3j//fdpaGjoNmkEoKampsuy5uZmHnnkEVJTUykoKOiHK+kdx2TVEYWiOzSNwr2N0kOxtzGwUAyaCvg9EqxKohTbUcWg+rVmzJjBxRdfzB133EF1dTWjRo3i+eefZ8+ePRHzvl1xxRWsXLmS7vJeXnrpJSwWC/Pmzev2HI8//jhvvfUW5557LkOHDqWiooLnnnuOvXv38sILL2A2q3pwCsXhIN5qZM2uWk4bn4HMMBSBefsGeek1VUrrqGNQKTaAf/7zn9x999288MIL1NfX43A4ePfddznllFMOum9TUxPvvfceZ599NgkJ3U/jftJJJ/Hll1/yzDPPUFtbS0xMDNOnT+e5557j1FNP7e/LOSAqeUTxfaLJ6SU/M54mpzeQMBKYEWRQW226rI4yuJxbioMQdbq/oveppz2x6IvdNLR5SLSbuPqk4YdBQoVi8NClIydE11JWg3F2A583uslfFf3OYU/3Vxw6anC24vtEl5mcgzOwd86QHGyo+NpRR69+MV3XoxqbpiYfPTDfxynbFYoIgjOLKxT9SK8U2+9///suiu3NN9/ku+++44wzziA/Px+ArVu38tFHHzFx4sRuq+0rFApFBJ2tNuXuU/QDvVJsCxYsiPj+9NNPU11dzaZNm0JKLciWLVs49dRT+zSGTKFQfI8JTyRRKPqBqFJ9HnroIW688cYuSg1g3Lhx3HjjjfzpT386ZOEUCsX3hOB4MYWiH4hKsZWVlWEy9TzpnslkoqysLGqhFAqFQqGIlqgU28SJE3niiSe6retYVlbGE088waRJkw5ZOIVCoVAo+kpUeawPP/wwZ5xxBmPGjOHCCy9k1Cg5Cd/27dt56623EELw4osv9qugCoVCoVD0hqgU26xZs1izZg133303b775Ju3t7YCcVuaMM85g4cKFymJTKBQKxYBwyJVH/H5/qLBwWloa+vdozqJDrTyiUCgUit5zxCqP6LqO1WolNjb2e6XUFAqFQjE4iVoTrV27ljPPPBO73U5KSgorV64EYP/+/Zx//vl8+umn/SWjQqFQKBS9JirF9uWXXzJr1iy2b9/OZZddhj9swsDU1FQaGxt56qmn+k1IhUKhUCh6S1SK7c4772TcuHFs3ryZ+++/v8v6uXPnhma9VigUCoXiSBKVYvvmm2+4+uqrsVgs3RZHHjJkCJWVlYcsnEKhUCgUfSUqxWYymSLcj53Zt28fsbGxUQulUCiOborKGlj0xW6KyhoGWhTF95CoFNvMmTNZsmRJt+taW1tZtGgRs2fPPiTBFArF0UthST0NbR4KS+oHWhTF95CoFNvChQtZu3YtZ599Nv/+978B2LhxI8888wwFBQXU1NRw991396ugCoXi6KEgL4lEu0lNoqsYEKIeoP3JJ5/wy1/+ku3bt0csHzlyJM8888z3wmJTA7QVCoXiyHHYB2ifeuqpFBcXs2HDBrZv347f72fkyJEUFBRENdu2QqFQKBT9wSFXHpkyZQpTpkzpB1EUCoVCoTh0ooqx6bpOVlYWn332WbfrX3rpJQwGNWmgQqFQKI48UZfUcjqd/PCHP+Rvf/tbf8qjUCgUCsUhEbVie+SRR/jZz37GLbfcwuWXX47T6exPuRQKhUKhiIqoFZvJZOLxxx9n8eLFvPHGG5x00kns3bu3P2VTKBQKhaLPHPI8M1dccQVffPEFDQ0NFBQU8J///Kc/5FIoFAqFIioOOSsSZGZkYWEhP/3pTznzzDM5+eST++OwCoVCoVD0mX6bGTQxMZH33nuPO++8MzQ3m0KhUCgUR5qoFNvu3bu54IILuizXNI2FCxeyceNGPvnkk6gEcrlc3H777WRnZ2Oz2ZgxYwbLly8/6H4LFixA07QuH6vV2u32zz77LOPGjcNqtTJ69GgeffTRqORVKBQKxeAiKldkXl7eAddPnDgxKmEArrrqKpYsWcLNN9/M6NGjWbx4MWeddRYrVqxg1qxZB93/ySefjJhZoLvxdE899RS/+MUvmDdvHv/93//N559/zk033URbWxu333571LIrFAqFYuDpVa3IP/zhD2iaxl133YWu6/zhD384+IE1rc+FkL/++mtmzJjBQw89xK233grI8XITJ04kPT2dL7/8ssd9FyxYwMKFC6mpqSE1NbXH7drb28nNzWXmzJm8++67oeWXXXYZb731FqWlpSQl9a5wq6oVqVAoFEeOXre5ohdomiZ0XRculyv0/WAfXdd7c+gIfve73wmDwSAaGxsjlt9///0CEHv37u1x33vuuUcAorq6WjQ2Ngq/39/tdu+9954AxHvvvRex/MsvvxSAeOGFF3otb2NjowC6yKtQKBSK/qe3bW6vYmx+vx+fz4fZbA59P9jH5/P1WRuvX7+eMWPGdNHE06dPB2DDhg0HPcaIESNISEggLi6Oyy67jKqqqi7nADjuuOMilhcUFKDremi9QqFQKI5O+iXdv7+oqKggKyury/LgsvLy8h73TUpK4sYbb+SEE07AYrHw+eef8/jjj/P111+zdu3akLKsqKjAYDCQnp4esb/ZbCYlJeWA53C5XLhcrtD3pqamPl2fQqFQKA4/g0qxtbe3Y7FYuiwPZja2t7f3uO9vfvObiO/z5s1j+vTpXHrppTzxxBP8v//3/0LHCFqe3Z3nQOd44IEHWLhw4UGvQ6FQKBQDR68U2/Dhw/s8x5qmaezcubNP+9hstgiLKEiwDqXNZuvT8X7605/y29/+lo8//jik2Gw2G263u9vtnU7nAc9xxx138N///d+h701NTeTm5vZJJoVCoVAcXnql2GbPnn1EJg/Nyspi3759XZZXVFQAkJ2d3edj5ubmUldXF3EOn89HdXV1hDvS7XZTW1t7wHNYLJZuLUqFQqFQDB56pdgWL158mMWQTJkyhRUrVtDU1BSRQLJmzZrQ+r4ghGDPnj1MnTo14hwAa9eu5ayzzgotX7t2LX6/X02aqlAoFEc5/VZSqz+YP38+Pp+Pp59+OrTM5XKxaNEiZsyYEXL77d27l61bt0bsW1NT0+V4Tz75JDU1NZx55pmhZaeeeirJyck8+eSTXba12+2cffbZ/XlJCoVCoTjCHFLyiMfjYevWrTQ2NuL3+7usP+WUU/p0vBkzZnDxxRdzxx13UF1dzahRo3j++efZs2cPzz77bGi7K664gpUrVyLCxpbn5eXxox/9iEmTJmG1Wlm1ahWvvPIKU6ZM4ec//3loO5vNxv/+7//yq1/9iosvvpgzzjiDzz//nBdffJH77ruP5OTkKO6EQqEY7BSVNVBYUk9BXhKOnMSBFkdxOIlmkJzP5xO33XabiI2NFbqu9/iJhvb2dnHrrbeKzMxMYbFYxPHHHy8++OCDiG1mz54tOot+3XXXifHjx4u4uDhhMpnEqFGjxO233y6ampq6Pc/TTz8t8vPzhdlsFiNHjhQPP/xwj4O6e0IN0FYojh6eW7VL/PWjYvHcql0DLUrUbCytF8+t2iU2ltYPtCgDQm/b3F6V1OrMvffey+9//3t+/vOfM2vWLC6//HIefPBBEhMTeeKJJ9A0jT/96U/88Ic/7HdFPJhQJbUUiqOHY8FiW/TFbhraPCTaTVx90vCBFueI09s2N6oY2+LFi7nkkkt48sknQ/GrgoICfvazn7FmzRo0TYu6ur9CoVAcDhw5iVx90vCjVqkBFOQlkWg3UZDXu3q231eiUmxlZWWceuqpAKH09+BYM7PZzGWXXcYLL7zQTyIqFAqFAo4N5XwkiEqxpaSk0NLSAkBsbCzx8fHs2rUrYpv6+vpDl06hUCgUij4SVVbk1KlT+eabb0Lf586dyyOPPMLUqVPx+/38/e9/Z/Lkyf0mpEKhUCgUvSUqi+3666+PKAh833330dDQwCmnnMLs2bNpamriL3/5S78KqlAoFApFb4gqK7I7Ghsb+fTTTzEYDJx44onfi/FgKitSoVAojhy9bXP7rbp/QkIC559/fn8dTqFQKBSKqDjkyiP79u2jvr6e7gy/adOmHcrhFQqFQqHoM1EptoaGBm699VZeeumlbqeAEUKgaVpUs2grFAqFQnEoRKXYrrrqKpYtW8aPf/xjZsyYQUJCQn/LpVAoFApFVESl2D766CNuuukmHn744f6WR6FQKBSKQyLqAdqjRo3qb1kUCoVCoThkoh7H9sorr3Q7VY1CoVAoFANJVK7Iu+++G5fLxXHHHcfll19OTk4OBoOhy3YXXXTRIQuoUCgUCkVfiEqx7du3j08++YQNGzawYcOGbrdRWZEKhUKhGAiiUmzXXHMN69at44477lBZkQqFQqEYVESl2FatWsXtt9/OwoUL+1sehUKhUCgOiaiSRzIzM78XtSAVCoVCcfQRlWL77W9/yzPPPBOak02hUCgUisFCVK5Ip9OJyWRi1KhRXHLJJeTm5nbJitQ0jVtuuaVfhFQoFIqBoqisgcKSegryktTM1UcJUU1bo+sHN/S+D1mRatoaheLYZ9EXu2lo85BoN3H1ScMHWpzvNYd12prdu3dHLZhCoVAcTRTkJYUsNsXRQZ8VW3t7O3/729+YO3cu55577uGQSaFQKAYNjpxE5YI8yuhz8ojNZuOpp56iqqrqcMijUCgUCsUhEVVWZEFBAZs2bepvWRQKhUKhOGSiUmyPPPIIr7zyCs888wxer7e/ZVIoFAqFImqiyop0OBzs37+fqqoqLBYLQ4YMwWazRR5Y09i4cWO/CToYUVmRCoVCceQ4rFmRycnJpKSkkJ+fH7WACoVCoVAcDqJSbJ9++mk/i6FQKBQKRf8QVYztcOJyubj99tvJzs7GZrMxY8YMli9fftD93njjDX70ox8xYsQI7HY7+fn5/Pa3v6WhoaHLtsOGDUPTtC6fX/ziF4fhihQKhUJxJInKYgPw+Xy8+OKLvPfee5SUlACQl5fHOeecw6WXXtrtxKO94aqrrmLJkiXcfPPNjB49msWLF3PWWWexYsUKZs2a1eN+119/PdnZ2Vx22WUMHTqUb7/9lscee4z333+fdevWdYkBTpkyhd/+9rcRy8aMGROVzAqFQqEYRIgoaGhoEDNmzBC6rouEhAQxdepUMXXqVJGYmCh0XRczZ84UjY2NfT7umjVrBCAeeuih0LL29nYxcuRIccIJJxxw3xUrVnRZ9vzzzwtA/OMf/4hYnpeXJ84+++w+y9eZxsZGAUR1rQqFQqHoG71tc6NyRd51110UFhby6KOPUlNTw7p161i3bh3V1dU89thjrF27lrvuuqvPx12yZAkGg4Hrr78+tMxqtXLttdfy1VdfUVpa2uO+c+bM6bLswgsvBGDLli3d7uN2u2ltbe2znAqFQqEYvESl2N58801uuOEGbrjhBkwmU2i5yWTil7/8Jb/85S9ZunRpn4+7fv16xowZ0yWNc/r06QBs2LChT8errKwEIDU1tcu6Tz75BLvdTmxsLMOGDeNvf/vbQY/ncrloamqK+CgUCoVicBFVjK22tvaAqf5jx46lrq6uz8etqKggKyury/LgsvLy8j4d78EHH8RgMDB//vyI5Q6Hg1mzZpGfn09tbS2LFy/m5ptvpry8nAcffLDH4z3wwANq1nCFQqEY5ERlsY0aNYp33nmnx/XvvPMOI0eO7PNx29vbsVgsXZZbrdbQ+t7y8ssv8+yzz/Lb3/6W0aNHd5Hvtttu4/zzz+eaa65h5cqVnHHGGfz1r3+lrKysx2PecccdNDY2hj4Hco0qFAqFYmCISrHdcMMNfPTRR5x11ll89NFH7Nmzhz179vDhhx9y9tlns3z5cm688cY+H9dms+FyubosdzqdofW94fPPP+faa6/ljDPO4L777jvo9sFJUb1e7wHH6FksFuLj4yM+CoVCoRhcROWKvOGGG6iuruaPf/wjH374YcQ6k8nE73//e375y1/2+bhZWVns27evy/KKigoAsrOzD3qMjRs3ct555zFx4kSWLFmC0di7S8zNzQWIyoWqUCgUisFD1OPYFixYwI033sjHH38cMY7thz/8YbfJGr1hypQprFixgqampghraM2aNaH1B2Lnzp2ceeaZpKen8/777xMbG9vrc+/atQuAtLS0vguuUCgUikFDVEWQDxdr1qxh5syZPPTQQ9x6662AzEScOHEiKSkprF69GoC9e/fS1tbG2LFjQ/tWVlZy0kkn4XQ6+eKLLxg2bFi356irqyMhISFiALnH42Hu3Ll88803lJSUkJmZ2St5VRFkhUKhOHIc1iLIQZqbmykpKaG+vp7u9OMpp5zSp+PNmDGDiy++mDvuuIPq6mpGjRrF888/z549e3j22WdD211xxRWsXLky4pxnnnkmu3bt4rbbbmPVqlWsWrUqtC4jI4PTTjsNkIkj9957L/Pnz2f48OHU1dXx8ssvs2nTJu6///5eKzWFQqFQDE6iTve/8cYbWbp0KT6fDwAhBJqmRfwdXNcX/vnPf3L33XfzwgsvUF9fj8Ph4N133z2okgxOkfOnP/2py7rZs2eHFNukSZMYP348L774IjU1NZjNZqZMmcJrr73GxRdf3Gd5FQqFQjG4iMoVedFFF7Fs2TJuuukmTj75ZJKSkrrdbvbs2Ycs4GBGuSIVCoXiyHFYXZEfffQRt9xyS7fWkUKhUCgUA0lU49jsdnuPyRkKhUKhUAwkUSm2yy67jDfffLO/ZVEoFAqF4pCJyhU5f/58Vq5cyZlnnsn1119Pbm5ut/OvTZs27ZAFVCgUCoWiL0SVPKLrHYZeMBMynEPJijyaUMkjCoVCceQ4rMkjixYtilowhUKhUCgOJ1EptiuvvLK/5VAoFAqFol+IKnkknIqKCjZu3KhmolYoFArFoCBqxfb2228zduxYcnJymDZtWqhQ8f79+5k6darKmlQoFArFgBCVYlu2bBkXXXQRqamp3HPPPRE1G1NTUxkyZAiLFy/uLxkVCoVCoeg1USm2P/zhD5xyyimsWrWKX/3qV13Wn3DCCaxfv/6QhVMoFAqFoq9Epdg2bdrEJZdc0uP6jIwMqquroxZKoVAoFIpoibqk1oGSRXbt2kVKSkrUQikUCoVCES1RKba5c+fy/PPP4/V6u6yrrKzkH//4B6effvohC6dQKBSK3lNU1sCiL3ZTVNYw0KIMKFEptvvuu4+ysjKOP/54nnrqKTRN48MPP+R//ud/mDRpEkII7rnnnv6WVaFQKBQHoLCknoY2D4Ul9QMtyoASVUktgO+++47f/OY3rFixIiIrcs6cOTz++OOMGzeu34QcrKiSWgqFYjBRVNZAYUk9BXlJOHISB1qcfqe3bW7Uii1IfX09O3bswO/3M2LECNLS0oDIGbWPVZRiUygUiiNHb9vcQ648kpSUxPHHH8+MGTNIS0vD7Xbz9NNPk5+ff6iHVigUigFHxa2OPvpUK9LtdvPOO++wc+dOkpKSOOecc8jOzgagra2Nxx57jEceeYTKykpGjhx5WARWKBSKI0l43OpYdO8di/RasZWXlzNnzhx27twZiqnZbDbeeecdzGYzP/3pT9m3bx/Tp0/n0Ucf5aKLLjpsQisUCsWRoiAvKRS3Uhwd9Fqx3XXXXezevZvbbruNk08+md27d/OHP/yB66+/nv379zNhwgRefPFFZs+efTjlVSgUiiOKIydRWWpHGb1WbMuXL+fqq6/mgQceCC3LzMzk4osv5uyzz+btt9+OmIBUoVAoFIqBoNeaqKqqipkzZ0YsC36/5pprlFJTKBQKxaCg19rI5/NhtVojlgW/JyQk9K9UCoXiqEBlDCoGI33KityzZw/r1q0LfW9sbARg+/btJCYmdtl+2rRphyadQqEY1KiMQcVgpNcDtHVd73bAdXcDsYPLfD5f/0g5SFEDtBXfd471SheKwUVv29xeW2yLFi3qF8EUCsWxg8oYVAxGeq3YrrzyysMph0KhUCgU/cKgS2V0uVzcfvvtZGdnY7PZmDFjBsuXL+/Vvvv27eOSSy4hMTGR+Ph4zj//fHbt2tXtts8++yzjxo3DarUyevRoHn300f68DIVCoVAMEINOsV111VX89a9/5dJLL+Vvf/sbBoOBs846i1WrVh1wv5aWFubOncvKlSu58847WbhwIevXr2f27NnU1tZGbPvUU09x3XXXMWHCBB599FFOOOEEbrrpJh588MHDeWkKhUKhOBKIQcSaNWsEIB566KHQsvb2djFy5EhxwgknHHDfBx98UADi66+/Di3bsmWLMBgM4o477ggta2trEykpKeLss8+O2P/SSy8VMTExoq6urtfyNjY2CkA0Njb2eh+FQnH0sLG0Xtz77nfi3ne/ExtL6wdanO89vW1zB5XFtmTJEgwGA9dff31omdVq5dprr+Wrr76itLT0gPsef/zxHH/88aFlY8eO5Qc/+AGvvfZaaNmKFSuora3lhhtuiNj/V7/6Fa2trbz33nv9eEUKheJoprCknm1VLWyrav7eT955NDGoFNv69esZM2ZMlzTO6dOnA7Bhw4Zu9/P7/RQVFXHcccd1WTd9+nR27txJc3Nz6BxAl20LCgrQdT20vjtcLhdNTU0RH4VCcexSkJfEmIxYxmTEqSLIRxF9GqB9uKmoqCArK6vL8uCy8vLybverq6vD5XIddN/8/HwqKiowGAykp6dHbGc2m0lJSenxHAAPPPAACxcu7PX1KBSKoxs1nOHoZFBZbO3t7Vgsli7Lg6W72tvbe9wP6NW+7e3tmM3mbo9jtVp7PAfAHXfcQWNjY+hzINeoQqFQKAaGQWWx2Ww2XC5Xl+VOpzO0vqf9gF7ta7PZcLvd3R7H6XT2eA6QirM75alQKBSKwcOgstiysrKoqKjosjy4LDhbd2eSk5OxWCy92jcrKwufz0d1dXXEdm63m9ra2h7PoVAoFIqjg0Gl2KZMmcK2bdu6JGWsWbMmtL47dF1n0qRJrF27tsu6NWvWMGLECOLi4iKO0XnbtWvX4vf7ezyHQqFQKI4OBpVimz9/Pj6fj6effjq0zOVysWjRImbMmEFubi4Ae/fuZevWrV32/eabbyIUVnFxMZ988gkXX3xxaNmpp55KcnIyTz75ZMT+Tz75JHa7nbPPPvtwXJpCoVAojhC9ru5/pLjkkkt48803ueWWWxg1ahTPP/88X3/9Nf/5z3845ZRTAJgzZw4rV64kXPTm5mamTp1Kc3Mzt956KyaTib/+9a/4fD42bNhAWlpaaNsnnniCX/3qV8yfP58zzjiDzz//nH/+85/cd9993Hnnnb2WVVX3VygUiiNHv1f3P1L885//5O677+aFF16gvr4eh8PBu+++G1JqPREXF8enn37KLbfcwr333ovf72fOnDk8/PDDEUoN4IYbbsBkMvGXv/yFd955h9zcXB5++GF+85vf9EnWoGJV49kUCoXi8BNsaw9mjw06i+1ooqysLOQeVSgUCsWRobS0lJycnB7XK8V2CPj9fsrLy4mLi+t2ElaQPYzc3FxKS0uPOnfl0Sr70So3KNkHgqNVbjh6ZY9WbiEEzc3NZGdno+s9p4gMOlfk0YSu6wfsNYQTHx9/VD144Rytsh+tcoOSfSA4WuWGo1f2aOROSEg46DaDKitSoVAoFIpDRSk2hUKhUBxTKMV2mLFYLNxzzz1HZSmuo1X2o1VuULIPBEer3HD0yn645VbJIwqFQqE4plAWm0KhUCiOKZRiUygUCsUxhVJsCoVCoTimUIpNoVAoFMcUSrEpFAqFol8Z6JxEpdgURw0D/bIoFEeCxsbGgRYhal599VWAHksMHimUYusj69evZ+/evREP39HQ4La1tQ20CFGza9cu2tracDqdAy1Kn9i4cSPbt2+nrKwstOxoeFYA3n77bW644QZ27doFyLqoRwv/+te/iIuL44svvhhoUfrEG2+8wemnn87DDz/Mnj17BlqcPvHKK68wcuRIfvKTn7Bq1aqBFkcptt6yZcsWZs2axQ9+8AMmT57M9OnTWbp0KV6vF03TBm2DVVxcTEFBAdddd91Ai9JnioqKOPvsszn33HMZPnw4c+bM4Ysvvhi09zpIUVERp512Gueccw4FBQVMnjyZv//976FnZbCzfPlyLrzwQl544QXeffddgAMWnB0srF+/nhkzZnDNNddw9tlnHzW1E8vLyzn77LO54oorMJvN2O127Hb7QIvVK4L3/MorryQuLg6r1YrL5RposUAoDkpVVZWYOnWqOPHEE8Vzzz0nnnvuOTFz5kyRmJgo7rnnHiGEEH6/f2CF7ITf7xdLliwRY8aMEZqmCU3TxKeffjrQYvUKr9cr/v73v4u0tDQxe/Zs8fvf/17ccMMNIjc3V4wdO3bQXofb7Rb33XefSExMFLNnzxaPPvqo+Ne//iXmzJkj4uPjxRtvvDHQIh6Q4DNcWFgoUlJShM1mEzNmzBAbNmwQQgjh8/kGUrweaWtrE1dffbXQNE3Mnj1bvP3226Kqqmqgxeo199xzjxg3bpx46aWXxN69ewdanF7R2NgorrjiCqFpmpgzZ454++23xXvvvSesVqv485//LISQ7/FAoRRbL3jllVeE0WgUS5YsCS0rKysTP/rRj4SmaeLjjz8eQOm6Z+fOnWLixIkiJSVF3HvvvWL8+PFi5syZwuPxDLRoB+WDDz4QI0aMENdcc43YunVraPkXX3whNE0Tt99++6C8jvfee09MmzZN3HzzzWLbtm2hF3v79u1C0zTxpz/9adB1gLpjyZIl4vTTTxf/93//JzRNE3feeWfoWgab/F6vV9x3331C0zTxs5/9TNTU1PT4bAw22YUQYu/evSIjI0PcdNNNXZaHM5hkb21tFaNHjxYjRowQTz75pCgpKRFCCLFr1y6RlJQkLrroogHvBCnF1gsefPBBkZCQEPqx3G63EEL2bKdPny4mTpw46HqIJSUl4s477wz1th9//HGhaZp45plnBliyg/PXv/5VjBs3TlRXV4eWuVwuIYQQM2fOFKeddpoQYnC97EIIsWrVKvGXv/wlQm4hhHjzzTdFenq6ePXVV4UQg0/uIEG51qxZIxISEoQQQvzwhz8UWVlZYvny5RHbDCbWrl0rTjrpJDF27NjQsrfffltceeWV4rbbbhPPPfdc6PkZbHz22WfCbreLbdu2CSGE+Oc//ynGjx8vxo8fLy644ALx8ssvD7CEkQTbwC+//FJs2rQp1BYGOf7448WcOXOE0+kc0GdFKbYwgj9a5x/k4YcfFnFxcWLFihVCCBHRe3311VeFxWIR999/f7f7Hgl6ktvpdIb+Li4uFqeffrrIyckR+/fvP6LyHYhw2cPlLy4ujlgvhLzvc+bMEbNmzRLt7e1HVtBO9HTPO/P555+LiRMnivj4eLFgwQLx7bffivr6+ohjHGkOJvuSJUvEqFGjhBBCrF+/XmiaJq688kpRV1d3wP2OBD3JHrQuf/vb34rTTz9daJomRo0aJeLi4oSmaeKiiy4SmzZtijjGYJB77dq1wmg0ijfffFM899xzQtd1MX/+fHHllVeK9PR0oWmaWLRo0RGXN5zePOt+v1/4fD7xq1/9SiQkJISe8YF6VpRiEx2xkc7WTPBHWb58ubBYLGLBggWhZcEfu7KyUlxyySUiLS3tiPcKe5K7J1599VVhs9nEbbfddpglOzh9lT2o+KZOnSp+9KMfhZYdaXojd/DZuP3224WmaWLu3LniyiuvFNdee61ITEwUP/7xj4+UuBEcTPbg/fz6669FXFycKC8vF0IIce211wqLxRKyHlpbW4+MwGEc7B0tKSkR8+fPF5qmiVNPPVV88MEHoqSkROzbt0/87//+r9B1XVx88cWDRu4ga9euFampqeKyyy4TkydPFnfffbdobm4WQghRVFQkzjjjDJGSkiK2bNlyJMUWQvT9HRVCiLvvvltomibeeeedwyjZwfneK7bPPvtMTJgwQWiaJk4//XSxefNmIUTXRnPatGli6tSp4ttvv+2y/qWXXhJGo1E8+eST3e47kHKHL6uurhbXXHONsFqtod7rQCiHvsgeTmlpqYiJiREPPPCAEOLIB6d7K3fw+5tvvileffVVsX///tCyO+64Q+i6Lh566CEhxJGzHvpyz1977TUxZsyYkHu9qalJ2O12MXfuXHH11VeLyy+/PKT0BpPsL730krjqqqvEF1980WXdpZdeKhISEkIN7mB6R0866SSh67pITU0VX375ZcS6jz76SCQnJ4vf/OY3QojB+byEy/X5558LTdPEa6+9dsDtDzffa8X21VdfibFjx4phw4aJiy++WGiaJh588MGI4HOw8Xz77beFpmni3nvvDbnBguuKi4tFTk6OuP7664/Ig9cbuXviP//5jxgyZIi48MILD7uc3XEosn/22WdC0zTx4YcfHgFJI+mL3Ad6mbdv3y5GjRolJk+eHOEqPpz0Vvag3J9//rmw2+2itLQ0tO4nP/mJMBgMwmQyiXvuuUe0tLQMGtmDcjc2NnaJbwa3W716tdA0LcLrMtByB9uPDz74IJS5HLTMgt6f6upqceaZZ4rc3NxB97x0x6ZNm0RSUpL49a9/LYRQim1A2Lx5s7BYLOL1118XQghx8skni9GjR4svvvii2+3POusskZ2dLZYtWyaEiLQYJkyYIK644gohxOH/Mfsqd7hMLS0tIXfBf/7zHyGEECtXrhRvv/32oJU9yBNPPCGMRmPIVeP1esXOnTvF2rVrhRCHV/ZDkVuIyJ72CSecIGbOnHnEGqrOsp9yyikHlP2VV14R+fn5oqGhQaxYsULMmjVLGAwGER8fL0aNGiU+//xzIcSRabSive+dQwY1NTUiMTHxiLnh+yr3pZdeKjRNEz//+c+FECJCicyfP1+MHz9eNDY2Hn7BxaE969XV1SIvL0/84Ac/EE1NTYdb1B753iq2oFIK7+EFLYKbbrop9BCFN0glJSUiNjZWzJw5U6xbty60fPXq1SI+Pl4sXLhw0MjdXaMTvJatW7eKadOmiUmTJomFCxeK3NxckZKSctgzOw9FdiGEOPfcc8WJJ54ohJBuyRdffFFMnTpVTJs2TdTW1g5KuTtb8B9++KEwmf5/e+cWElX3h+G1ZkqxZjxFpkIHNZESRp0Uk6EMuhiDTgxdFARKMBhi2UUg5EUJFUWQERJkZUoUIoKBBBVTiOWNUmEHIlAyNJtOJFmecub9LvrPznG+P1/Z3u75bX/PlewZ5WGt5XrXrNMsxKFDhzTznc6fuAf87927h4iICGzduhVmsxkOhwMdHR1obm5WOt+5WE9Ws9wvXLgAKSUuXbqkofFPZtO3DAwMIDo6OmRG4sWLF0hLS8PevXvnZCChRpm7XC5kZmbi27dv/IlNS5qamlBaWopTp06ho6NDeT690AOVUlxcjNjYWNy8eTPobwQqvKGhAStWrEBKSgrOnz+Py5cvY9u2bVi+fDmePn0adt7/xps3b1BSUqJMf+zYsSNo2inc3P1+P0ZGRpCUlITdu3fD4/Fg+/btkFKiqKgIg4ODYek9naGhIbS1taGwsBBr165V1mrVRC33zs5O2Gw2rFmzBrW1tRgYGFDav8PhgNvtVj3YtCp3r9eL1tZW2Gw2FBYWqr4jWM2+pampCUlJSYiPj4fb7cbJkyexZcsWxMXFaTL9rkWZ+/1+HD9+HFJKZWezHuFm6GDzer1wOp1YvHgx7HY74uLiEBkZiaNHjyrbUWcePB0cHITFYoHL5VI6e5/PF1Q57e3tcDgciImJwZIlS2Cz2fDw4cOw9J7JgwcPUFRUBJPJhJycnN+eStPbvbe3F4sWLYLdbofFYkFGRoYylRrO3u3t7XC73di1axesViuysrLQ3d2tmrea7oHpr8nJSXR0dODZs2dKgAV+T+1jFlqW+/79+7Fnzx5YLBbY7XblTGe4eU/vWzo7O+F0OhEbG4uEhATk5OQEhU64uf8bNTU1kFIGXWgx1xg62BobGxEfH4/r169jaGgInz9/RklJCaxWK8rKykLeH6jEEydOwGQyoa6uLqjRTf95bGwM79+/V72T0sJ7Oh6PBxEREaitrVXdW0v3+/fvQ0qJhIQETdy18m5ra8Pq1auxadMm1NfXq+6tlftcjbK1KveWlhZYLBbk5+drMv2oZd8yMTGBL1++oKenR3VvLdwDBILu3bt3aGho0MT9dzF0sBUWFmL9+vVBz75//47i4mJIKXHr1i0AoSOPyclJpKWlIT8/X7kRoK+vL2jeWcvdj1p6A9puk1fbffq638WLF0NuOqDg3dfXR6a99Pb2hrQXLdGy3Ht6ejRr61T7Fq3dw+VmGkMGm8/nw/j4OJxOJxwOh/I8MNXy6NEjrFu3DqmpqSEVMXN7f2VlJa5evQq73Y6DBw9qejiVqrfW7lrurtLSW+st8Vq6j46OknXXstz5f1Qf9z+FfLC9fPkSFRUVOHDgAKqqqpSRBADs3LkTGRkZykL99BFIXV0dpJSoqakBEPop5sePH8jLy4PZbIaUEklJSbh9+/a896bsTtWb3fVxp+pN3V0NyAbbxMQEDh8+jKioKOTm5iI9PR1SSqSmpirnL1paWiClRH19vVJ5gYrq7+/H5s2bkZKSErJA/vjxY1RVVcFiscBqteLcuXPz3puyO1Vvduf2Mp/c1YRksI2MjODIkSNITU3F6dOn8erVK/h8Png8HiQnJ2PDhg0YHR3F1NQUsrKysHHjRvT394f8nWPHjiE2NlaZUwZ+VmJ5ebly8WvgMPB89qbsTtWb3fVxp+pN3V1tSAbb69evkZKSgtLSUgwPDwe9VlpaiqVLlyq3UVy7dg1SSpw9e1aZBw6MRJ48eQKTyYTW1lYAv+aau7q6lLvR2Ju2O1Vvduf2Mp/c1YZksPn9ftTV1QU9C+yWa25uxoIFC5Q714aHh+FyuZCYmBhyuLCrqwtSSjQ2NrL3f0DVnao3wO4At5c/gbK72pAMNuDXKGLm4uaZM2dgNpuDvnl5YGAAy5YtQ2ZmprLQ+fbtW5SXl2PlypXwer3s/RtQdafqDbA7t5c/g7K7mpANtpkEFkErKiqQmJiojFQCFXznzh3Y7XZIKZGdnY2CggIsXLgQ1dXVmJqa0u38BVVvyu5Uvdmd28t8cv8bJAAIA5GbmytWrVolWlpahM/nE2azWXnt06dP4sqVK6Kvr098/fpVVFRUiIKCAh1tf0HVWwi67lS9hWB3PaDqLQRt91mhd7KqyYcPHxAVFaV8iSPwc8QS+Er7cIWqN0DXnao3wO56QNUboO0+W0x6B6uaPH/+XIyPj4u8vDwhhBBer1fcuHFDOJ1O8fHjR53t/j9UvYWg607VWwh21wOq3kLQdp8thgg2/G82tbu7W8TExIjk5GTR3t4uysrKxL59+wQAYTKZlPeFC1S9haDrTtVbCHbXA6reQtB2/2vm7sOh9rhcLqSlpcHtdsNqtSI9PR13797VW+s/oeoN0HWn6g2wux5Q9QZou88WwwTb2NgYsrOzIaVEdHS0ctdZuEPVG6DrTtUbYHc9oOoN0Hb/Gwy1K7KyslJIKUV1dbWIjIzUW+e3oeotBF13qt5CsLseUPUWgrb7bDFUsPn9fmEy0Vs2pOotBF13qt5CsLseUPUWgrb7bDFUsDEMwzDM/IpxhmEYxvBwsDEMwzCGgoONYRiGMRQcbAzDMIyh4GBjGIZhDAUHG8MwDGMoONgYhmEYQ8HBxjAMwxgKDjaGYRjGUHCwMQzDMIaCg41hGIYxFP8A+SZgmmJ7FdcAAAAASUVORK5CYII=", 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" ] @@ -388,20 +429,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\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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", + "image/png": 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", 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" ] @@ -417,17 +450,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\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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", @@ -453,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -484,6 +509,8 @@ " soiling_rate_high\n", " inferred_start_loss\n", " inferred_end_loss\n", + " inferred_recovery\n", + " inferred_begin_shift\n", " length\n", " valid\n", " \n", @@ -498,6 +525,8 @@ " 0.000000\n", " 1.063788\n", " 1.018062\n", + " -0.018062\n", + " NaN\n", " 29\n", " True\n", " \n", @@ -510,6 +539,8 @@ " -0.000640\n", " 1.024589\n", " 0.964412\n", + " 0.035588\n", + " 0.006526\n", " 63\n", " True\n", " \n", @@ -522,6 +553,8 @@ " 0.000000\n", " 1.072710\n", " 1.056087\n", + " -0.056087\n", + " 0.108299\n", " 28\n", " True\n", " \n", @@ -534,6 +567,8 @@ " -0.001000\n", " 1.057288\n", " 0.932740\n", + " 0.067260\n", + " 0.001202\n", " 109\n", " True\n", " \n", @@ -546,6 +581,8 @@ " -0.001307\n", " 1.020735\n", " 0.961439\n", + " -0.000840\n", + " 0.087995\n", " 31\n", " True\n", " \n", @@ -568,15 +605,15 @@ "12 -0.001301 -0.001000 1.057288 \n", "15 -0.002793 -0.001307 1.020735 \n", "\n", - " inferred_end_loss length valid \n", - "5 1.018062 29 True \n", - "6 0.964412 63 True \n", - "9 1.056087 28 True \n", - "12 0.932740 109 True \n", - "15 0.961439 31 True " + " inferred_end_loss inferred_recovery inferred_begin_shift length valid \n", + "5 1.018062 -0.018062 NaN 29 True \n", + "6 0.964412 0.035588 0.006526 63 True \n", + "9 1.056087 -0.056087 0.108299 28 True \n", + "12 0.932740 0.067260 0.001202 109 True \n", + "15 0.961439 -0.000840 0.087995 31 True " ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -596,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -618,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -704,7 +741,7 @@ "2010-02-25 14:20:00-07:00 True " ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -716,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -730,7 +767,7 @@ "Freq: min, dtype: bool" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -742,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -25466,28 +25503,28 @@ 0.7205152833333333, 0.7905545333333334, 0.8745185499999999, - 0.9732547833333333, + 0.9732547833333334, 1.1157427, 1.2492243833333334, 1.3228528333333331, - 1.1386072333333332, + 1.138607233333333, 0.7569224, 0.3741076333333333, 0.25149325, 0.33693615, - 0.4280121, + 0.42801210000000006, 0.38680276666666663, 0.35260566666666665, 0.2902100833333333, 0.18419575, 0.13504365000000002, - 0.11620035000000001, + 0.11620035, 0.10915488333333333, 0.10760953333333333, - 0.10870623333333333, + 0.10870623333333335, 0.10875608333333332, 0.1109661, - 0.11475469999999999, + 0.1147547, 0.11964, 0.12636975, 0.13580801666666667, @@ -25498,7 +25535,7 @@ 0.19164001666666666, 0.20388649999999997, 0.2135906333333333, - 0.22140046666666666, + 0.22140046666666668, 0.2301242166666667, 0.24567741666666668, 0.29389898333333336, @@ -25511,7 +25548,7 @@ 0.3802558, 0.3860384, 0.3931004833333333, - 0.40042843333333333, + 0.4004284333333334, 0.40775638333333336, 0.4156659166666667, 0.42387455, @@ -25525,10 +25562,10 @@ 0.49889880000000003, 0.51171025, 0.5259174999999999, - 0.5385960166666666, + 0.5385960166666667, 0.5488318833333334, 0.5604967833333333, - 0.5720785999999999, + 0.5720786, 0.58508945, 0.6000610666666667, 0.6166611166666666, @@ -25554,7 +25591,7 @@ 1.0244673499999999, 1.0322107166666665, 1.0522504166666669, - 1.1053240500000001, + 1.10532405, 1.1354002166666666, 1.1614717666666665, 1.2290351333333334, @@ -25562,9 +25599,9 @@ 1.2929926833333334, 1.3163557166666666, 1.3396855166666668, - 1.3611044, + 1.3611043999999999, 1.37959875, - 1.3766742166666668, + 1.3766742166666663, 1.3888874666666666, 1.4216223, 1.4108214666666665, @@ -25596,7 +25633,7 @@ 2.2616114166666668, 2.2745558, 2.267759583333333, - 2.338430266666667, + 2.3384302666666663, 2.3471374000000003, 2.4581533499999995, 2.5462050666666665, @@ -25604,16 +25641,16 @@ 2.6725249666666664, 2.72774215, 2.8356341666666665, - 2.903779116666667, + 2.9037791166666667, 2.8485120833333335, - 2.7826934666666663, + 2.7826934666666667, 2.6922821833333335, 2.62933825, 2.6098801333333332, 2.5976336499999997, 2.6016216500000002, 2.6026851166666662, - 2.5992786999999997, + 2.5992787, 2.590687883333333, 2.607769816666667, 2.6176899666666666, @@ -25696,7 +25733,7 @@ 2.776910866666667, 1.1961673666666668, 0.49072340000000003, - 0.2953114, + 0.29531140000000006, 0.28160265, 0.2745904166666666, 0.2675117166666666, @@ -25709,9 +25746,9 @@ 0.16415605, 0.14393356666666668, 0.1248244, - 0.11400695, - 0.10569861666666666, - 0.09775585, + 0.11400695000000001, + 0.10569861666666668, + 0.09775584999999999, 0.09110918333333333, 0.0821528, 0.07497439999999998, @@ -26631,8 +26668,8 @@ 0.2044788, 0.0817882, 0.0444382, - 0.0466626, - 0.0967448, + 0.046662600000000005, + 0.09674479999999999, 0.5151478, 0.8954869999999999, 0.4953274, @@ -26647,7 +26684,7 @@ 2.1442552, 2.3197836, 2.546523, - 2.6211732, + 2.6211732000000003, 2.8440945999999996, 3.0455356, 3.2174286, @@ -26661,19 +26698,19 @@ 4.7206914, 4.9761321999999995, 5.113049, - 5.175780399999999, + 5.1757804, 5.2860542, 5.3674108, 5.424049999999999, 5.5415282, - 5.6380738, + 5.638073800000001, 5.713554, 5.7689482, 5.881728600000001, - 5.9687458, + 5.968745800000001, 6.0242064, 6.143759599999999, - 6.2498667999999995, + 6.249866800000002, 6.3312068, 6.4174438, 6.500062, @@ -26695,7 +26732,7 @@ 3.4234965, 4.311019916666667, 1.9387574999999997, - 1.438786583333333, + 1.4387865833333333, 1.3199006666666666, 1.4006615, 1.743439, @@ -26704,7 +26741,7 @@ 7.718646166666667, 2.9087995833333333, 2.4608505833333334, - 2.186578833333334, + 2.1865788333333334, 1.825891333333333, 1.82089975, 2.7265985, @@ -26753,7 +26790,7 @@ 3.339302916666667, 3.272588166666667, 3.1977807499999997, - 2.7397988333333334, + 2.739798833333334, 2.501761666666667, 2.423388833333333, 2.373274, @@ -26764,14 +26801,14 @@ 2.1239104166666665, 2.24457075, 2.4053793333333333, - 2.470070916666667, + 2.4700709166666663, 2.3579344166666667, - 2.4243672500000004, + 2.4243672499999995, 2.568061833333333, 2.44416775, 2.3969384166666665, - 2.3544850833333335, - 2.6972294166666666, + 2.354485083333333, + 2.697229416666667, 4.024095083333333, 4.02004875, 2.586071333333333, @@ -26784,7 +26821,7 @@ 6.199330916666667, 4.74371225, 7.60961075, - 5.748015499999999, + 5.7480155, 5.951708583333333, 7.444755833333334, 7.56433825, @@ -26809,12 +26846,12 @@ 6.140261083333333, 6.070494999999999, 6.0147086666666665, - 5.938790166666666, + 5.938790166666667, 5.841429416666667, - 5.748396916666667, - 5.6693939166666665, + 5.748396916666666, + 5.669393916666667, 5.602330916666666, - 5.5502260833333334, + 5.550226083333333, 5.484854583333333, 5.438686583333333, 5.352702, @@ -26828,12 +26865,12 @@ 4.638822666666667, 4.358680416666667, 3.5052689166666666, - 2.3704714166666667, + 2.3704714166666663, 0.8832946666666667, 0.3964411666666666, 0.3117169166666667, 0.3005563333333333, - 0.2911535833333333, + 0.29115358333333335, 0.2824473333333334, 0.27427175, 0.26488558333333334, @@ -26845,7 +26882,7 @@ 0.15517024999999998, 0.1368125, 0.12644791666666666, - 0.11752608333333334, + 0.11752608333333335, 0.10969875, 0.10130758333333333, 0.09354658333333334, @@ -26853,7 +26890,7 @@ 0.07905275, 0.07192191666666667, 0.06500666666666666, - 0.05759391666666667, + 0.057593916666666675, 0.05052941666666667, 0.043630749999999996, null, @@ -27756,7 +27793,7 @@ 0.5753603333333333, 0.6237847, 0.6889745333333334, - 0.7542968999999999, + 0.7542969, 0.8222036666666667, 0.8893318, 0.9512414333333332, @@ -27788,7 +27825,7 @@ 4.3894543, 4.5023561333333335, 4.594483366666667, - 4.699615433333333, + 4.699615433333334, 4.7920739999999995, 4.882378900000001, 4.975152233333333, @@ -27796,11 +27833,11 @@ 5.1709868, 5.2737001333333335, 5.294541, - 5.452702966666666, + 5.452702966666665, 5.4771388000000005, - 5.562308033333333, - 5.719857033333333, - 5.804197933333333, + 5.562308033333334, + 5.719857033333334, + 5.804197933333334, 5.878781066666666, 5.965076833333333, 6.0640626666666675, @@ -27808,10 +27845,10 @@ 6.230507966666666, 6.3186592, 6.411531933333333, - 6.502217866666666, + 6.502217866666667, 6.5631335, 6.639257333333333, - 6.719738199999999, + 6.7197382, 6.804609233333333, 6.9014413999999995, 6.969762333333333, @@ -27841,9 +27878,9 @@ 5.897178749999999, 5.8440698, 5.7318608, - 5.6746308999999995, + 5.6746309, 5.60219155, - 5.531953349999999, + 5.531953349999998, 5.44490035, 5.359551999999999, 5.266011399999999, @@ -28779,11 +28816,11 @@ 0.6550443, 0.7525467, 0.8207284499999999, - 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13.72451175, 13.721098500000002, 13.7370159, - 13.730705550000001, + 13.730705549999998, 13.73017275, 13.726676249999999, 13.72864095, 13.731871049999999, 13.719933, 13.7571624, - 13.757079150000001, + 13.757079150000003, 13.721098500000002, 13.7559303, - 13.781587949999999, + 13.78158795, 13.7456073, 13.722164099999999, 13.7538324, 13.71565395, 13.697705249999999, - 13.732437149999997, - 13.752750149999999, - 13.7647881, - 13.759493399999998, - 13.7238624, - 13.738514399999998, + 13.73243715, + 13.75275015, + 13.764788099999999, + 13.7594934, + 13.723862400000002, + 13.7385144, 13.743659250000002, 13.7524338, 13.779323549999999, 13.7913282, - 13.80100185, + 13.801001849999999, 13.777875, 13.79893725, 13.7797065, @@ -28890,10 +28927,10 @@ 13.751567999999999, 13.754681549999999, 13.7438424, - 13.7379816, + 13.737981599999998, 13.731504750000001, 13.732903349999999, - 13.719949649999998, + 13.71994965, 13.71239055, 13.712024249999999, 13.701784499999999, @@ -28902,12 +28939,12 @@ 7.366309649999999, 7.29378225, 7.24656285, - 7.201091699999999, + 7.201091700000002, 7.13467485, 7.0508087999999995, 6.9790806, 6.888654450000001, - 6.7993272, + 6.7993272000000005, 6.728631300000001, 6.668774549999999, 6.585058350000001, @@ -28921,7 +28958,7 @@ 5.92585155, 5.838888600000001, 5.7718224000000005, - 5.657953050000001, + 5.65795305, 5.5887057, 5.52988125, 5.4436509, @@ -28952,7 +28989,7 @@ 0.11581740000000001, 0.1037628, 0.09538785000000001, - 0.08729595, + 0.08729594999999998, 0.0798534, 0.07355969999999999, 0.0663003, @@ -29866,9 +29903,9 @@ 0.8594443333333333, 0.9444073999999999, 0.9949228333333333, - 1.0603916333333332, + 1.0603916333333334, 1.1976, - 1.2869376333333333, + 1.2869376333333336, 1.4185904666666667, 1.5992284666666667, 1.7656449666666667, @@ -29905,9 +29942,9 @@ 5.361688466666666, 5.445503833333333, 5.534226033333333, - 5.6087101, + 5.608710099999999, 5.677538833333333, - 5.7635830666666665, + 5.763583066666667, 5.850641933333334, 5.9438385, 6.0200358, @@ 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0.1322682666666667, 0.12343596666666667, 0.11490306666666666, 0.10500623333333332, - 0.0944607, + 0.09446069999999998, 0.0855286, - 0.07677946666666666, + 0.07677946666666667, 0.06824656666666666, 0.05924793333333333, 0.0498501, @@ -30888,8 +30925,8 @@ 0.5973027, 0.7474841333333334, 0.8750835166666666, - 0.9618391333333334, - 1.0802661166666667, + 0.9618391333333333, + 1.080266116666667, 1.1452705166666668, 1.2259444333333334, 1.3219223, @@ -30899,9 +30936,9 @@ 1.70023395, 1.8831003666666666, 1.9952961, - 2.1574581499999996, + 2.15745815, 2.3268650666666666, - 2.4954411499999996, + 2.49544115, 2.6268125166666665, 2.7729893333333333, 2.85545785, @@ -30922,14 +30959,14 @@ 4.575897666666666, 4.666225866666666, 4.768983333333334, - 4.8611892166666655, + 4.861189216666666, 4.9746145833333335, 5.052513516666666, 5.1251782, 5.25643325, 5.317781983333333, - 5.428232966666666, - 5.515786183333333, + 5.428232966666667, + 5.515786183333334, 5.612777666666665, 5.68633965, 5.7792102, @@ -30957,9 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0.3277139, 0.29966496666666664, - 0.27454056666666665, + 0.2745405666666667, 0.2524404, 0.23092181666666667, 0.20958601666666665, @@ -31069,7 +31106,7 @@ 0.1532721333333333, 0.13266746666666665, 0.11189663333333333, - 0.08954721666666667, + 0.08954721666666665, 0.05199355, 0.03152181666666667, null, @@ -31989,17 +32026,17 @@ 0.0462476, 0.06027459999999999, 0.07642639999999999, - 0.0849256, + 0.08492560000000002, 0.090304, 0.1048456, - 0.1278698, + 0.12786979999999998, 0.16239779999999998, 0.1858536, 0.2849888, 0.3074652, 0.2836276, 0.3245632, - 0.3863318, + 0.38633180000000006, 0.4639036, 0.5510535999999999, 0.5508045999999999, @@ -32010,12 +32047,12 @@ 0.7123392000000001, 0.745091, 0.8278751999999999, - 0.8943415999999998, + 0.8943416000000001, 0.866769, 0.9710169999999999, 1.0692557999999999, - 0.9626506, - 0.9843136, + 0.9626506000000001, + 0.9843136000000001, 0.993178, 1.03003, 1.184659, @@ -32112,14 +32149,14 @@ 5.683093, 5.7226175999999995, 5.8417724, - 5.7862952, + 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14.116642799999997, 13.585364433333332, 13.186868833333333, 12.3885683, 11.596650766666666, 11.396568266666666, 11.338826666666666, - 11.626208966666667, - 10.722883533333334, + 11.626208966666665, + 10.72288353333333, 9.865041066666665, - 9.673911133333332, + 9.673911133333334, 10.344027933333335, - 10.975290266666665, - 11.884933233333333, + 10.975290266666667, + 11.884933233333332, 12.391530666666666, 11.959908933333335, 11.116076333333334, @@ -61092,50 +61129,50 @@ 9.674778566666665, 8.697393966666665, 8.5020087, - 8.126017266666667, + 8.126017266666668, 8.157915899999999, 8.545249433333334, 9.528165966666666, - 10.9742919, - 10.890396366666666, + 10.974291899999999, + 10.890396366666668, 11.0806916, 12.464853333333334, 13.257671033333335, 12.355867700000001, 12.380548633333332, 12.409468533333333, - 12.806933033333333, + 12.806933033333335, 13.8379021, 14.1485087, 14.021012366666668, 13.316361899999999, - 13.913319699999999, + 13.9133197, 13.724595666666668, - 13.866805633333334, + 13.86680563333333, 13.968393533333334, - 13.9877553, + 13.987755299999998, 13.734857566666667, 12.930746866666667, - 12.670025866666666, - 11.102230133333334, + 12.670025866666668, + 11.102230133333332, 10.6734071, 10.3227349, 9.899640199999999, - 11.821185066666667, + 11.821185066666665, 10.657924233333333, 10.171785133333334, 10.273913133333334, 9.356512366666667, 9.925450433333333, - 10.9432607, + 10.943260700000002, 11.521282266666667, 11.962560333333332, - 12.70426493333333, - 12.935116766666665, - 13.548015699999999, + 12.704264933333333, + 12.935116766666667, + 13.5480157, 13.685037433333333, - 13.662500533333331, - 13.588359533333332, + 13.662500533333334, + 13.588359533333334, 13.5390304, 13.253661200000002, 11.383147600000001, @@ -61145,9 +61182,9 @@ 13.5022054, 13.4084244, 13.327867666666666, - 13.289831533333333, - 13.283153933333331, - 13.2719264, + 13.289831533333334, + 13.283153933333333, + 13.271926399999998, 13.2538576, 13.208554666666664, 13.176279599999999, @@ -61156,57 +61193,57 @@ 13.078636066666666, 13.0297161, 13.0128257, - 12.9726128, + 12.972612799999999, 12.970206899999999, - 12.941516133333334, - 12.937833633333334, + 12.941516133333332, + 12.937833633333332, 12.881515933333333, - 12.8780953, + 12.878095299999998, 12.86911, 12.837816933333333, - 12.796081933333333, - 12.7700753, + 12.796081933333335, + 12.770075299999998, 12.724183166666668, 12.706114366666666, 12.668912933333333, 12.653724666666667, 12.515868233333332, - 12.6333809, + 12.633380899999999, 12.580631133333332, 12.544493533333334, 12.484607899999999, 12.434689566666666, 12.393952933333335, - 12.391383366666666, + 12.391383366666668, 12.372103433333333, 12.374001966666667, 12.3282244, 12.294378133333334, - 12.310466566666667, - 12.2689607, + 12.310466566666665, + 12.268960700000001, 12.167225499999999, 12.150940666666665, - 12.127929133333334, - 12.0723643, + 12.127929133333332, + 12.072364299999998, 12.065146599999999, 12.015228266666668, - 11.972036633333335, - 11.937961233333334, + 11.972036633333332, + 11.937961233333333, 11.8771427, 11.818582766666669, 11.7463894, 11.708795166666667, 11.636078066666666, - 11.579187533333334, + 11.579187533333336, 11.490365633333333, 11.4622477, - 11.376813699999998, + 11.3768137, 11.384653333333333, 11.337075433333332, 11.299432099999999, 11.246878733333332, 11.173965233333332, - 11.123130366666665, + 11.123130366666667, 11.097254666666664, 11.040135, 10.995797699999999, @@ -61214,11 +61251,11 @@ 10.920331000000001, 10.881542, 10.859856166666667, - 10.801639933333332, + 10.801639933333334, 10.6471386, 10.563799533333334, 9.9733393, - 10.431016766666666, + 10.431016766666668, 10.546107166666667, 10.506548933333333, 10.4544211, @@ -61237,7 +61274,7 @@ 9.7048114, 9.300456533333332, 9.3608659, - 9.449180433333334, + 9.449180433333336, 9.061290433333333, 7.7738066, 8.3716482, @@ -61245,7 +61282,7 @@ 8.039077533333334, 8.294053833333333, 8.353235699999999, - 8.805790400000001, + 8.8057904, 9.045856666666666, 9.018115166666666, 8.526149533333333, @@ -61259,7 +61296,7 @@ 7.07531, 8.000566766666667, 7.7237246, - 7.866196433333333, + 7.866196433333335, 7.610058100000001, 7.734853933333333, 7.390973899999999, @@ -62133,7 +62170,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -62165,7 +62202,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -62184,7 +62221,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -62199,7 +62236,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -62218,7 +62255,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -62253,7 +62290,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -62262,7 +62299,7 @@ "{'two_way_window_filter': {}, 'ad_hoc_filter': None}" ] }, - "execution_count": 28, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -62280,7 +62317,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -62316,7 +62353,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -62354,7 +62391,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -62372,7 +62409,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -62381,7 +62418,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -62492,7 +62529,7 @@ "2010-03-01 00:00:00-07:00 0.857710 " ] }, - "execution_count": 33, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -62512,7 +62549,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -62538,7 +62575,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 38, "metadata": {}, "outputs": [ { diff --git a/docs/degradation_and_soiling_example.ipynb b/docs/degradation_and_soiling_example.ipynb index a2ee58c5..22ff9c0e 100644 --- a/docs/degradation_and_soiling_example.ipynb +++ b/docs/degradation_and_soiling_example.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -315,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -5847,11 +5847,11 @@ 94.197, 27.059, 79.618, - 67.463, + 67.46300000000001, 40.778, - 169.499, - 151.932, - 150.478, + 169.49900000000002, + 151.93200000000002, + 150.47799999999998, 209.925, 42.622, 104.47, @@ -5879,7 +5879,7 @@ 114.572, 123.342, 125.693, - 152.427, + 152.42700000000002, 69.164, 13.053, 85.256, @@ -5906,24 +5906,24 @@ 58.313, 200.269, 49.003, - 174.212, - 170.689, + 174.21200000000002, + 170.68900000000002, 160.465, 140.541, 118.305, 96.531, 33.654, 58.676, - 45.171, + 45.17100000000001, 24.229, 39.969, 31.975, 28.143, 26.805, 15.784, - 17.556, - 7.839, - 15.051, + 17.555999999999997, + 7.838999999999999, + 15.050999999999998, 7.058, 5.285, 3.512, @@ -5954,26 +5954,26 @@ 3.941, 3.897, 5.4, - 7.283, - 7.861, + 7.282999999999999, + 7.861000000000001, 5.433, 5.472, - 5.2075, - 4.943, + 5.2075000000000005, + 4.9430000000000005, -0.815, -0.193, 5.307, - 4.536, + 4.5360000000000005, 7.146, 2.009, 7.058, 4.96, 6.265, - 5.786, + 5.7860000000000005, 2.02, 7.443, 2.824, - 5.054, + 5.053999999999999, 7.3, 4.96, 5.684, @@ -6005,20 +6005,20 @@ 2.736, -1.47, 0.754, - 4.608, - 1.299, + 4.6080000000000005, + 1.2990000000000002, 3.589, 3.033, 3.2645, - 3.496, + 3.4960000000000004, 3.49, 1.855, - 5.703, + 5.702999999999999, 3.567, 1.927, 6.683, 1.585, - 5.588, + 5.587999999999999, 2.516, 6.738, 1.249, @@ -6033,17 +6033,17 @@ 8.037, 6.54, 16.02, - 13.824, + 13.824000000000002, 16.202, 5.318, 15.442, 22.792, - 17.876, + 17.875999999999998, 20.931, 12.513, - 6.083, + 6.082999999999999, 20.882, - 12.029, + 12.029000000000002, 18.316, 0.589, 8.913, @@ -6054,10 +6054,10 @@ 28.545, 30.582, 25.886, - 23.078, + 23.078000000000003, 32.509, - 6.457, - 0.572, + 6.457000000000001, + 0.5720000000000001, 41.857, 33.819, 19.775, @@ -6067,7 +6067,7 @@ 31.375, 51.618, 53.6, - 59.171, + 59.17100000000001, 20.425, 39.649, 33.643, @@ -6075,7 +6075,7 @@ 42.82, 9.364, 86.836, - 73.353, + 73.35300000000001, 75.979, 83.478, 97.225, @@ -6088,7 +6088,7 @@ 3.011, 76.205, 150.632, - 15.971, + 15.970999999999998, 65.827, 220.914, 166.02, @@ -6101,11 +6101,11 @@ 497.169, 207.266, 596.558, - 640.199, + 640.1990000000001, 630.46, 360.399, 528.313, - 725.631, + 725.6310000000001, 451.617, 93.244, 718.199, @@ -6120,9 +6120,9 @@ 71.206, 540.865, 415.04, - 773.099, - 625.973, - 669.146, + 773.0989999999999, + 625.9730000000001, + 669.1460000000001, 721.442, 306.253, 482.728, @@ -6152,12 +6152,12 @@ 666.823, 225.555, 526.496, - 721.161, + 721.1610000000001, 425.957, 742.324, 789.455, 705.828, - 174.151, + 174.15099999999998, 610.145, 445.699, 87.992, @@ -6165,25 +6165,25 @@ 474.762, 674.541, 390.348, - 669.036, + 669.0360000000001, 227.02, 404.64, - 473.683, + 473.6830000000001, 731.186, 676.055, 233.626, - 734.792, + 734.7919999999999, 550.967, 412.386, 445.859, 587.132, 662.039, 559.451, - 668.739, - 772.141, - 342.688, + 668.7389999999999, + 772.1410000000001, + 342.68800000000005, 417.682, - 766.536, + 766.5360000000001, 463.872, 615.457, 565.045, @@ -6197,16 +6197,16 @@ 119.499, 505.757, 564.109, - 749.728, + 749.7280000000001, 494.086, - 342.368, + 342.36800000000005, 711.681, 607.965, 641.663, 236.472, 285.151, 610.508, - 762.589, + 762.5889999999999, 141.582, 653.434, 776.633, @@ -6230,10 +6230,10 @@ 122.533, 90.47, 561.808, - 633.829, + 633.8290000000001, 611.549, 629.689, - 690.766, + 690.7660000000001, 712.765, 766.602, 488.459, @@ -6243,23 +6243,23 @@ 184.48, 602.58, 438.465, - 454.678, + 454.67800000000005, 277.62, 594.394, 694.735, 282.844, 675.158, 562.215, - 578.907, + 578.9069999999999, 493.585, - 629.584, + 629.5840000000001, 750.345, - 767.527, + 767.5269999999999, 275.863, 759.847, 721.21, 380.961, - 0.468, + 0.4679999999999999, 377.454, 713.409, 204.596, @@ -6268,16 +6268,16 @@ 767.423, 765.419, 338.305, - 348.518, + 348.51800000000003, 289.588, 479.64, 440.32, 502.399, - 417.688, - 744.889, - 745.401, - 742.527, - 738.657, + 417.6880000000001, + 744.8889999999999, + 745.4010000000001, + 742.5269999999999, + 738.6569999999999, 599.448, 484.804, 306.132, @@ -6308,7 +6308,7 @@ 576.606, 376.48, 15.057, - 631.208, + 631.2080000000001, 439.17, 622.24, 498.628, @@ -6328,10 +6328,10 @@ 333.521, 299.559, 386.72, - 512.733, + 512.7330000000001, 555.614, 551.65, - 411.428, + 411.42800000000005, 431.099, 541.746, 535.927, @@ -6341,10 +6341,10 @@ 232.156, 463.569, 338.79, - 497.373, + 497.3730000000001, 333.819, 404.932, - 337.072, + 337.07199999999995, 322.307, 258.956, 470.126, @@ -6370,7 +6370,7 @@ 250.891, 269.648, 223.557, - 219.119, + 219.11900000000003, 224.762, 245.105, 202.466, @@ -6382,9 +6382,9 @@ 119.566, 90.673, 121.702, - 88.378, + 88.37799999999999, 116.037, - 88.763, + 88.76299999999999, 102.438, 107.85, 97.764, @@ -6395,19 +6395,19 @@ 44.213, 0.429, 39.936, - 51.134, + 51.13399999999999, 54.354, - 64.159, + 64.15899999999999, 26.227, 0.335, - 43.156, + 43.156000000000006, 41.059, 40.954, 48.133, 37.183, 25.748, 35.311, - 28.848, + 28.848000000000003, 20.342, 24.763, 29.96, @@ -6462,10 +6462,10 @@ 43.558, 40.36, 24.647, - 29.668, + 29.668000000000003, 46.983, - 43.696, - 0.666, + 43.696000000000005, + 0.6659999999999999, 0.677, 16.059, 31.441, @@ -6481,10 +6481,10 @@ 79.387, 41.852, 92.044, - 87.343, + 87.34299999999999, 63.438, 84.116, - 80.659, + 80.65899999999999, 117.98, 121.867, 153.159, @@ -6513,10 +6513,10 @@ 130.918, 202.504, 215.15, - 141.598, + 141.59799999999998, 164.55, 116.45, - 167.914, + 167.91400000000002, 160.74, 152.179, 9.469, @@ -6524,14 +6524,14 @@ 247.246, 227.889, 160.939, - 138.432, + 138.43200000000002, 138.493, 36.935, 107.619, 116.251, 92.897, 11.561, - 5.786, + 5.7860000000000005, 126.849, 134.314, 134.81, @@ -6544,12 +6544,12 @@ 145.16, 10.074, 60.41, - 148.177, + 148.17700000000002, 118.723, 157.079, 157.239, 83.5, - 171.432, + 171.43200000000002, 168.602, 171.861, 94.934, @@ -6563,7 +6563,7 @@ 259.777, 306.815, 29.987, - 12.359, + 12.359000000000002, 152.091, 86.324, 302.46, @@ -6580,11 +6580,11 @@ 171.856, 170.391, 160.482, - 264.544, + 264.54400000000004, 171.696, 281.435, - 312.667, - 355.758, + 312.66700000000003, + 355.75800000000004, 436.912, 318.183, 316.025, @@ -6614,13 +6614,13 @@ 400.401, 392.319, 455.46, - 485.558, + 485.5580000000001, 366.829, 378.236, 450.252, 408.224, 400.979, - 345.363, + 345.36300000000006, 357.222, 446.789, 129.376, @@ -6635,7 +6635,7 @@ 51.145, 336.059, 189.032, - 136.478, + 136.47799999999998, 245.11, 580.383, 432.117, @@ -6643,8 +6643,8 @@ 579.986, 598.44, 264.588, - 522.593, - 615.171, + 522.5930000000001, + 615.1709999999999, 339.324, 324.14, 107.206, @@ -6654,12 +6654,12 @@ 445.974, 305.466, 337.7, - 336.924, + 336.92400000000004, 403.566, 217.308, 156.782, 334.788, - 354.502, + 354.50199999999995, 382.552, 404.761, 437.21, @@ -6672,7 +6672,7 @@ 111.908, 191.482, 473.523, - 34.788, + 34.788000000000004, 250.263, 353.577, 457.86, @@ -6694,10 +6694,10 @@ 540.529, 518.645, 426.909, - 161.979, + 161.97899999999998, 67.997, 204.938, - 0.468, + 0.4679999999999999, 181.033, 44.153, 262.331, @@ -6710,7 +6710,7 @@ 682.689, 531.297, 731.153, - 747.994, + 747.9939999999999, 758.295, 465.739, 608.818, @@ -6720,7 +6720,7 @@ 746.965, 234.694, 767.659, - 672.917, + 672.9169999999999, 394.565, 516.14, 487.171, @@ -6734,13 +6734,13 @@ 443.833, 661.554, 511.45, - 748.969, + 748.9689999999999, 593.987, 650.653, 572.906, 515.48, 725.835, - 666.751, + 666.7510000000001, 436.164, 474.635, 670.187, @@ -6748,17 +6748,17 @@ 562.187, 354.684, 346.096, - 659.011, + 659.0110000000001, 339.528, - 529.348, + 529.3480000000001, 486.857, 493.469, 491.162, 662.936, 619.752, 428.853, - 545.963, - 757.001, + 545.9630000000001, + 757.0010000000001, 714.23, 666.663, 472.697, @@ -6772,7 +6772,7 @@ 212.827, 595.341, 662.903, - 572.829, + 572.8290000000001, 371.376, 741.388, 649.332, @@ -6781,15 +6781,15 @@ 764.285, 697.548, 697.201, - 680.504, + 680.5039999999999, 600.4304999999999, 520.357, 759.798, - 600.978, - 448.683, + 600.9780000000001, + 448.6830000000001, 730.454, - 720.484, - 635.167, + 720.4839999999999, + 635.1669999999999, 688.569, 419.229, 541.592, @@ -6808,7 +6808,7 @@ 619.476, 352.163, 563.663, - 549.343, + 549.3430000000001, 311.577, 516.405, 512.375, @@ -6839,7 +6839,7 @@ 161.715, 218.31, 317.715, - 177.466, + 177.46599999999998, 311.687, 223.782, 293.8, @@ -6850,12 +6850,12 @@ 94.99, 279.541, 229.761, - 155.538, + 155.53799999999998, 295.848, 303.952, 306.594, 301.133, - 129.288, + 129.28799999999998, 176.497, 300.384, 302.933, @@ -6877,17 +6877,17 @@ 127.433, 128.22, 57.646, - 177.939, + 177.93900000000002, 133.219, 273.232, - 196.619, + 196.61900000000003, 166.136, 219.642, - 175.038, + 175.03799999999998, 222.604, 181.551, 180.84, - 178.814, + 178.81400000000002, 200.594, 50.28, 184.061, @@ -6897,11 +6897,11 @@ 199.129, 106.644, 195.424, - 153.897, + 153.89700000000002, 204.095, 78.672, 159.1, - 141.163, + 141.16299999999998, 189.511, 183.841, 179.128, @@ -6950,13 +6950,13 @@ 80.626, 66.24, 77.604, - 78.864, + 78.86399999999999, 56.705, 76.882, 81.182, 51.112, 82.646, - 87.987, + 87.98700000000001, 85.069, 0.484, 0.517, @@ -6965,7 +6965,7 @@ 91.769, 68.999, 35.278, - 88.097, + 88.09700000000001, 98.227, 96.944, 90.431, @@ -6986,7 +6986,7 @@ 0.49, 168.101, 189.5, - 143.729, + 143.72899999999998, 194.191, 189.401, 195.947, @@ -6995,7 +6995,7 @@ 44.268, 288.944, 315.238, - 224.839, + 224.83900000000003, 296.426, 244.923, 69.544, @@ -7007,7 +7007,7 @@ 334.975, 329.546, 302.587, - 26.238, + 26.238000000000003, 290.634, 149.944, 233.835, @@ -7031,7 +7031,7 @@ 39.87, 44.676, 41.631, - 41.196, + 41.196000000000005, 36.043, 38.741, 18.377, @@ -7065,7 +7065,7 @@ 5.676, 0.143, 3.584, - 0.572, + 0.5720000000000001, -5.148, 0.016, -9.178, @@ -7097,7 +7097,7 @@ 29.2, 15.31, 45.078, - 43.366, + 43.36600000000001, 46.542, 29.261, 48.018, @@ -7110,18 +7110,18 @@ 58.863, 60.878, 39.589, - 0.567, + 0.5670000000000001, 42.386, 32.927, 34.529, 38.212, - 56.936, + 56.93600000000001, 43.503, 55.235, 50.467, 50.556, 16.747, - 20.898, + 20.898000000000003, 61.027, 46.658, 57.173, @@ -7131,18 +7131,18 @@ 71.421, 83.77, 26.783, - 41.461, + 41.461000000000006, 78.044, 74.124, - 59.634, + 59.63399999999999, 115.508, 118.156, 107.745, - 79.591, + 79.59100000000001, 126.271, 122.638, 188.741, - 166.059, + 166.05900000000003, 138.108, 126.805, 122.39, @@ -7158,11 +7158,11 @@ 172.555, 116.609, 119.758, - 82.701, + 82.70100000000001, 158.208, 125.814, 124.834, - 156.182, + 156.18200000000002, 89.572, 148.832, 146.68, @@ -7191,7 +7191,7 @@ 106.71, 136.847, 85.322, - 169.538, + 169.53799999999998, 120.303, 46.845, 134.782, @@ -7218,10 +7218,10 @@ 248.067, 235.443, 229.068, - 167.589, - 167.897, + 167.58900000000003, + 167.89700000000002, 225.467, - 144.087, + 144.08700000000002, 126.579, 203.523, 270.215, @@ -7233,8 +7233,8 @@ 150.809, 356.281, 501.089, - 540.166, - 598.176, + 540.1659999999999, + 598.1759999999999, 477.206, 603.368, 338.476, @@ -7249,7 +7249,7 @@ 538.3, 680.074, 689.962, - 594.333, + 594.3330000000001, 649.607, 619.317, 597.389, @@ -7262,7 +7262,7 @@ 377.162, 325.076, 300.252, - 294.169, + 294.16900000000004, 221.927, 138.68, 70.925, @@ -7277,10 +7277,10 @@ 470.374, 384.898, 347.764, - 341.873, + 341.87300000000005, 441.509, 522.923, - 507.123, + 507.1230000000001, 41.841, 446.007, 166.46, @@ -7291,18 +7291,18 @@ 578.258, 596.569, 105.125, - 712.226, + 712.2260000000001, 503.555, 469.895, 526.391, 528.66, - 756.037, - 756.142, - 168.288, + 756.0369999999999, + 756.1419999999999, + 168.28799999999998, 626.887, 589.665, - 739.521, - 659.792, + 739.5210000000001, + 659.7919999999999, 518.381, 412.386, 631.996, @@ -7322,33 +7322,33 @@ 504.931, 597.02, 179.035, - 628.412, + 628.4119999999999, 782.414, 749.073, 552.085, 628.77, 508.196, - 788.833, + 788.8330000000001, 734.456, 98.26, 784.115, 328.495, 471.029, - 5.637, + 5.6370000000000005, 515.656, - 310.977, + 310.97700000000003, 44.037, 74.565, 30.621, 455.251, 384.512, - 505.053, + 505.05300000000005, 717.059, - 377.553, + 377.55300000000005, 594.57, 652.944, 406.6, - 464.748, + 464.7480000000001, 697.658, 353.539, 314.654, @@ -7356,7 +7356,7 @@ 662.628, 445.253, 451.089, - 698.958, + 698.9580000000001, 392.572, 720.17, 434.044, @@ -7366,11 +7366,11 @@ 777.057, 721.205, 517.726, - 740.463, + 740.4630000000001, 693.293, 187.552, 397.009, - 728.874, + 728.8739999999999, 651.727, 752.712, 79.883, @@ -7392,7 +7392,7 @@ 303.88, 378.55, 364.753, - 520.473, + 520.4730000000001, 478.929, 460.096, 418.431, @@ -7400,8 +7400,8 @@ 296.844, 289.996, 258.472, - 302.174, - 356.192, + 302.17400000000004, + 356.19199999999995, 158.334, 355.807, 0.264, @@ -7433,7 +7433,7 @@ 16.912, 375.643, 205.725, - 311.549, + 311.54900000000004, 316.4, 222.588, 206.782, @@ -7448,7 +7448,7 @@ 255.593, 0.473, 121.558, - 152.383, + 152.38299999999998, 113.878, 128.418, 96.652, @@ -7462,7 +7462,7 @@ 97.599, 60.757, 128.374, - 135.427, + 135.42700000000002, 140.695, 144.813, 146.404, @@ -7519,7 +7519,7 @@ 5.626, 12.387, 12.1, - 12.266, + 12.265999999999998, 6.331, 12.007, 15.178, @@ -7534,11 +7534,11 @@ 6.023, 5.428, 5.318, - 5.208, + 5.207999999999999, 2.505, 6.237, 3.039, - 1.343, + 1.3430000000000002, 2.758, 0.644, 1.899, @@ -7550,14 +7550,14 @@ 0.616, 6.485, 1.123, - 5.802, + 5.8020000000000005, 9.975, 9.458, 8.693, 7.564, 5.312, 4.913, - 4.514, + 4.513999999999999, 1.315, 0, 3.033, @@ -7571,7 +7571,7 @@ -0.088, -0.36350000000000005, -0.639, - -7.179, + -7.178999999999999, -2.715, -1.872, -1.4455, @@ -7592,18 +7592,18 @@ 3.474, -3.623, 4.723, - 7.867, + 7.867000000000001, 3.297, - 7.162, + 7.162000000000001, 11.462, - 7.377, + 7.377000000000001, 10.146, 6.193, 6.903, 7.806, 3.523, 13.257, - 5.538, + 5.537999999999999, 1.888, 13.042, 14.528, @@ -7624,14 +7624,14 @@ 24.174, 24.581, 33.434, - 17.182, + 17.182000000000002, 8.379, 32.503, 31.782, 32.107, 59.562, 34.1, - 26.453, + 26.453000000000003, 36.869, 1.403, 25.688, @@ -7646,9 +7646,9 @@ 106.755, 71.972, 145.518, - 154.073, + 154.07299999999998, 172.362, - 178.402, + 178.40200000000002, 191.114, 208.637, 212.772, @@ -7675,18 +7675,18 @@ 615.067, 398.259, 502.746, - 669.399, + 669.3989999999999, 677.184, 437.65, 302.317, 402.746, 261.775, 718.887, - 716.619, + 716.6189999999999, 434.352, 479.232, 654.276, - 737.749, + 737.7489999999999, 360.811, 289.599, 715.727, @@ -7707,9 +7707,9 @@ 775.637, 787.325, 613.971, - 672.251, + 672.2510000000001, 498.055, - 385.558, + 385.5580000000001, 590.259, 525.279, 584.534, @@ -7719,16 +7719,16 @@ 220.556, 264.286, 274.603, - 510.563, + 510.5630000000001, 590.254, - 525.147, + 525.1469999999999, 406.06, 502.19, 754.056, 477.911, - 705.129, + 705.1289999999999, 601.369, - 779.496, + 779.4960000000001, 755.597, 323.991, 581.005, @@ -7740,31 +7740,31 @@ 536.4, 313.096, 143.806, - 725.119, + 725.1189999999999, 498.259, 142.033, 305.703, 535.976, 383.962, 494.466, - 546.392, + 546.3919999999999, 320.87, 229.982, 643.766, 721.012, 535.283, - 668.348, + 668.3480000000001, 705.922, 274.146, 762, 599.007, - 787.259, + 787.2589999999999, 504.854, - 785.486, - 146.949, + 785.4860000000001, + 146.94899999999998, 574.217, - 614.301, - 746.656, + 614.3009999999999, + 746.6560000000001, 721.095, 578.181, 600.554, @@ -7775,35 +7775,35 @@ 143.53, 496.64, 420.462, - 711.009, + 711.0089999999999, 275.214, 553.401, 748.176, 670.676, - 566.834, + 566.8340000000001, 113.906, - 312.854, + 312.85400000000004, 739.13, 343.101, - 711.389, + 711.3889999999999, 732.59, 680.658, 327.003, - 332.437, - 6.991, + 332.43699999999995, + 6.9910000000000005, 18.233, 316.956, - 520.468, + 520.4680000000001, 283.868, 293.123, 19.516, 528.175, - 770.792, + 770.7919999999999, 36.264, - 441.928, + 441.92800000000005, 704.623, - 705.751, - 772.719, + 705.7510000000001, + 772.7189999999999, 80.053, 604.265, 352.796, @@ -7814,15 +7814,15 @@ 400.847, 760.53, 509.528, - 678.896, + 678.8960000000001, 485.47, 721.585, 452.839, 382.233, 516.173, - 728.599, + 728.5989999999999, 289.654, - 83.026, + 83.02600000000001, 380.62, 486.951, 244.637, @@ -7843,13 +7843,13 @@ 222.946, 70.116, 482.431, - 404.563, + 404.5630000000001, 149.124, 432.282, 0.143, 183.241, 570.765, - 778.521, + 778.5210000000001, 437.826, 382.646, 218.172, @@ -7857,7 +7857,7 @@ 313.603, 359.017, 258.087, - 312.727, + 312.72700000000003, 491.003, 5.956, 616.432, @@ -7871,7 +7871,7 @@ 255.268, 212.183, 178.358, - 421.123, + 421.1230000000001, 627.211, 503.693, 534.627, @@ -7889,9 +7889,9 @@ 507.222, 653.379, 766.701, - 534.583, + 534.5830000000001, 547.433, - 684.886, + 684.8860000000001, 566.195, 662.985, 374.878, @@ -7900,10 +7900,10 @@ 627.878, 618.194, 766.25, - 466.113, + 466.11300000000006, 515.43, 789.18, - 596.084, + 596.0840000000001, 604.122, 588.09, 575.56, @@ -7911,7 +7911,7 @@ 738.437, 514.99, 564.747, - 512.204, + 512.2040000000001, 439.671, 486.989, 599.448, @@ -7922,7 +7922,7 @@ 211.654, 126.86, 178.82, - 264.924, + 264.92400000000004, 220.088, 77.218, 229.866, @@ -7932,15 +7932,15 @@ 278.055, 236.379, 531.01, - 550.588, - 142.677, + 550.5880000000001, + 142.67700000000002, 84.177, - 425.808, + 425.8080000000001, 577.636, 580.702, - 332.503, + 332.50300000000004, 583.763, - 570.407, + 570.4069999999999, 544.488, 543.893, 545.66, @@ -7948,11 +7948,11 @@ 430.372, 342.06, 503.715, - 335.823, + 335.82300000000004, 348.292, 312.86, 315.59, - 134.177, + 134.17700000000002, 205.422, 473.782, 479.629, @@ -7975,7 +7975,7 @@ 432.035, 302.988, 232.294, - 257.162, + 257.16200000000003, 425.555, 420.82, 421.519, @@ -7983,9 +7983,9 @@ 416.075, 216.835, 302.361, - 335.239, + 335.23900000000003, 406.512, - 307.299, + 307.29900000000004, 233.533, 277.9, 292.275, @@ -7993,27 +7993,27 @@ 106.76, 129.877, 221.459, - 201.744, + 201.74400000000003, 72.45, 109.474, 135.762, - 159.177, + 159.17700000000002, 244.885, - 173.496, + 173.49599999999998, 232.129, 263.961, 290.166, 189.974, 79.574, 141.356, - 26.398, + 26.398000000000003, 207.833, - 18.944, + 18.944000000000003, 152.334, 183.841, 157.734, - 163.961, - 165.943, + 163.96099999999998, + 165.94299999999998, 106.529, 151.018, 199.84, @@ -8039,18 +8039,18 @@ 20.788, 13.835, 26.381, - 7.674, + 7.673999999999999, 11.627, 9.161, 20.849, - 6.457, + 6.457000000000001, 0.446, 5.566, 14.479, 9.909, 15.453, 10.058, - 5.411, + 5.4110000000000005, 0.379, 5.502000000000001, 10.625, @@ -8058,10 +8058,10 @@ 6.155, 8.23, 7.388, - 5.076, + 5.0760000000000005, 5.874, - 2.851, - 1.701, + 2.8510000000000004, + 1.7009999999999998, 1.69, 2.939, 3.028, @@ -8085,11 +8085,11 @@ 16.108, 19.67, 26.673, - 12.596, + 12.595999999999998, 32.338, 37.612, 23.717, - 24.543, + 24.543000000000003, 38.774, 33.401, 36.897, @@ -8114,7 +8114,7 @@ 94.318, 172.984, 114.6, - 167.633, + 167.63299999999998, 26.767, 210.157, 180.466, @@ -8123,7 +8123,7 @@ 154.673, 233.863, 184.623, - 163.647, + 163.64700000000002, 191.356, 218.211, 157.52, @@ -8140,10 +8140,10 @@ 268.866, 150.599, 250.841, - 340.937, + 340.93699999999995, 111.687, 233.246, - 329.183, + 329.18300000000005, 90.822, 287.111, 247.852, @@ -8168,10 +8168,10 @@ 197.946, 272.841, 374.272, - 348.512, + 348.51199999999994, 393.519, 358.708, - 362.243, + 362.24300000000005, 282.288, 225.935, 461.588, @@ -8200,7 +8200,7 @@ 503.566, 583.565, 586.477, - 592.858, + 592.8580000000001, 596.8, 277.862, 576.716, @@ -8213,10 +8213,10 @@ 453.792, 505.763, 439.318, - 367.748, + 367.7480000000001, 639.158, 644.377, - 644.421, + 644.4209999999999, 653.186, 659.115, 659.291, @@ -8232,7 +8232,7 @@ 692.186, 475.109, 701.485, - 704.601, + 704.6010000000001, 460.25, 0.545, 129.013, @@ -8240,18 +8240,18 @@ 329.04, 208.373, 538.823, - 709.016, + 709.0160000000001, 571.761, 438.559, 741.944, - 741.514, + 741.5139999999999, 744.944, 744.168, 500.505, - 336.643, + 336.64300000000003, 371.861, 754.837, - 755.889, + 755.8889999999999, 234.914, 719.796, 298.507, @@ -8262,7 +8262,7 @@ 666.08, 547.873, 464.318, - 579.397, + 579.3969999999999, 736.488, 318.574, 651.231, @@ -8272,13 +8272,13 @@ 648.451, 735.86, 664.169, - 711.036, + 711.0360000000001, 729.925, 476.436, 352.586, 644.724, 625.967, - 160.212, + 160.21200000000002, 462.122, 507.976, 387.111, @@ -8290,17 +8290,17 @@ 339.963, 521.668, 386.445, - 689.136, + 689.1360000000001, 676.732, 136.17, - 269.852, + 269.85200000000003, 698.765, 357.431, - 680.162, + 680.1619999999999, 574.035, 116.235, 436.94, - 639.907, + 639.9069999999999, 215.271, 124.218, 226.243, @@ -8313,14 +8313,14 @@ 633.548, 458.389, 489.687, - 629.546, - 359.479, + 629.5459999999999, + 359.47900000000004, 604.034, 767.924, 547.163, 730.57, 21.019, - 491.058, + 491.0580000000001, 209.474, 468.106, 481.225, @@ -8341,7 +8341,7 @@ 749.596, 417.765, 606.737, - 422.863, + 422.86300000000006, 389.214, 93.872, 503.087, @@ -8352,10 +8352,10 @@ 701.204, 716.834, 593.689, - 667.984, + 667.9839999999999, 765.694, 670.247, - 527.718, + 527.7180000000001, 679.931, 764.466, 706.467, @@ -8365,16 +8365,16 @@ 560.002, 730.779, 514.555, - 613.454, + 613.4540000000001, 329.117, 243.106, 591.404, 348.76, 248.584, - 715.876, + 715.8760000000001, 734.539, - 729.881, - 790.286, + 729.8810000000001, + 790.2860000000001, 190.546, 783.807, 384.798, @@ -8385,7 +8385,7 @@ 638.118, 567.153, 284.909, - 718.871, + 718.8710000000001, 217.914, 642.109, 558.069, @@ -8414,31 +8414,31 @@ 504.75, 157.778, 731.698, - 790.006, + 790.0060000000001, 768.27, 352.619, 134.16, - 774.514, + 774.5139999999999, 780.465, 569.763, 773.721, - 674.156, + 674.1560000000001, 272.868, - 780.234, + 780.2339999999999, 379.656, 779.931, 776.903, - 776.088, + 776.0880000000001, 494.499, 0.363, 111.753, 765.253, - 534.671, - 560.084, + 534.6709999999999, + 560.0840000000001, 501.254, 438.922, - 284.479, - 745.396, + 284.47900000000004, + 745.3960000000001, 737.303, 468.216, 719.096, @@ -8448,11 +8448,11 @@ 717.318, 714.549, 503.77, - 705.526, + 705.5260000000001, 639.367, 228.897, 682.717, - 670.489, + 670.4889999999999, 413.283, 676.408, 671.805, @@ -8471,7 +8471,7 @@ 624.288, 269.295, 457.205, - 531.699, + 531.6990000000001, 421.101, 455.823, 494.146, @@ -8480,7 +8480,7 @@ 460.206, 5.428, 47.214, - 555.267, + 555.2669999999999, 483.389, 175.412, 360.575, @@ -8490,7 +8490,7 @@ 567.318, 569.686, 555.509, - 529.574, + 529.5740000000001, 443.607, 513.399, 454.607, @@ -8502,7 +8502,7 @@ 540.821, 108.858, 220.551, - 533.983, + 533.9830000000001, 218.101, 530.548, 504.128, @@ -8512,11 +8512,11 @@ 427.713, 282.073, 468.244, - 468.183, + 468.1830000000001, 266.46, 444.477, 401.573, - 350.489, + 350.48900000000003, 229.459, 141.884, 306.396, @@ -8533,11 +8533,11 @@ 220.033, 157.178, 222.604, - 236.214, + 236.21400000000003, 121.779, 195.259, 140.855, - 131.854, + 131.85399999999998, 234.105, 233.318, 198.48, @@ -8546,14 +8546,14 @@ 0.363, 109.094, 253.682, - 85.328, + 85.32799999999999, 244.648, - 178.159, + 178.15900000000002, 226.073, 215.012, 96.487, 136.467, - 179.216, + 179.21599999999998, 179.695, 171.856, 137.904, @@ -8574,11 +8574,11 @@ 41.918, 45.039, 57.878, - 54.211, + 54.211000000000006, 52.218, 50.187, 39.231, - 49.064, + 49.06399999999999, 40.376, 7.927, 26.128, @@ -8596,7 +8596,7 @@ 14.082, 26.712, 20.48, - 24.113, + 24.113000000000003, 13.46, 16.609, 15.211, @@ -8608,8 +8608,8 @@ 0.517, 9.838, 7.982, - 3.061, - 5.577, + 3.0610000000000004, + 5.577000000000001, 0.847, 4.668, -0.997, @@ -8632,14 +8632,14 @@ 38.653, 32.272, 41.136, - 53.138, + 53.138000000000005, 48.519, 49.807, 62.018, 35.262, 63.857, 72.005, - 65.299, + 65.29899999999999, 20.193, 90.684, 98.331, @@ -8652,10 +8652,10 @@ 141.092, 147.445, 158.329, - 168.619, + 168.61900000000003, 114.798, 114.457, - 176.409, + 176.40900000000002, 78.936, 104.398, 203.842, @@ -8676,13 +8676,13 @@ 255.515, 160.129, 104.707, - 210.619, + 210.61900000000003, 268.772, 243.194, 272.323, 277.091, 262.045, - 284.854, + 284.85400000000004, 262.072, 351.606, 355.945, @@ -8698,7 +8698,7 @@ 324.448, 340.921, 342.886, - 338.878, + 338.87800000000004, 327.449, 258.472, 333.797, @@ -8728,7 +8728,7 @@ 397.263, 503.583, 495.28, - 390.243, + 390.24300000000005, 263.988, 279.772, 193.305, @@ -8738,27 +8738,27 @@ 132.239, 448.072, 400.401, - 5.747, + 5.747000000000001, 0.55, 290.772, 333.362, 527.735, 43.129, - 0.567, + 0.5670000000000001, 365.497, 116.604, - 171.344, + 171.34400000000002, 183.186, 495.814, 485.035, 562.22, 384.275, - 368.998, + 368.9980000000001, 477.427, 268.453, 509.385, 425.384, - 502.498, + 502.4980000000001, 554.964, 513.294, 677.36, @@ -8774,10 +8774,10 @@ 714.345, 702.443, 717.885, - 724.134, + 724.1339999999999, 727.547, 706.307, - 539.142, + 539.1419999999999, 227.768, 250.484, 751.05, @@ -8796,7 +8796,7 @@ 181.804, 378.153, 591.482, - 541.718, + 541.7180000000001, 358.956, 150.275, 757.942, @@ -8804,7 +8804,7 @@ 274.316, 0.457, 204.205, - 610.277, + 610.2769999999999, 448.986, 611.918, 549.619, @@ -8815,7 +8815,7 @@ 100.253, 398.87, 190.613, - 0.468, + 0.4679999999999999, 105.714, 445.435, 0.473, @@ -8839,19 +8839,19 @@ 532.018, 494.895, 528.247, - 773.644, + 773.6439999999999, 621.304, 620.269, 730.977, - 755.531, + 755.5310000000001, 578.869, 491.603, 743.7, 343.772, - 716.536, + 716.5360000000001, 474.299, - 604.463, - 717.021, + 604.4630000000001, + 717.0210000000001, 383.422, 479.711, 609.187, @@ -8861,7 +8861,7 @@ 535.321, 754.832, 694.801, - 638.954, + 638.9540000000001, 629.722, 639.422, 586.4, @@ -8872,7 +8872,7 @@ 774.525, 428.737, 603.665, - 765.276, + 765.2760000000001, 644.735, 694.355, 629.992, @@ -8884,10 +8884,10 @@ 655.493, 562.65, 414.032, - 743.458, - 762.782, + 743.4580000000001, + 762.7819999999999, 770.941, - 87.282, + 87.28200000000001, 632.32, 531.236, 743.1, @@ -8895,10 +8895,10 @@ 502.245, 621.398, 115.954, - 568.772, + 568.7719999999999, 639.378, 149.454, - 679.259, + 679.2589999999999, 780.971, 731.544, 704.182, @@ -8909,12 +8909,12 @@ 659.423, 462.204, 758.311, - 781.621, + 781.6210000000001, 415.777, 788.079, 372.268, 415.518, - 547.042, + 547.0419999999999, 417.809, 635.42, 612.072, @@ -8924,23 +8924,23 @@ 373.138, 697.46, 397.918, - 697.141, - 620.704, + 697.1410000000001, + 620.7040000000001, 582.266, 688.393, 552.828, 550.274, 493.034, 145.32, - 628.902, + 628.9019999999999, 782.915, 620.418, 548.523, - 666.542, + 666.5419999999999, 683.427, 39.556, 393.915, - 581.027, + 581.0269999999999, 137.827, 377.652, 623.98, @@ -8950,9 +8950,9 @@ 598.991, 527.812, 697.24, - 637.292, + 637.2919999999999, 606.985, - 730.861, + 730.8610000000001, 108.213, 678.957, 585.448, @@ -8966,18 +8966,18 @@ 467.302, 627.118, 729.931, - 22.203, - 763.266, + 22.203000000000003, + 763.2660000000001, 500.522, 638.938, - 790.881, + 790.8810000000001, 496.035, 691.883, 621.205, - 790.149, + 790.1489999999999, 271.327, 145.391, - 689.109, + 689.1089999999999, 574.382, 779.914, 415.601, @@ -8988,10 +8988,10 @@ 515.711, 772.449, 769.955, - 763.734, - 667.616, + 763.7339999999999, + 667.6160000000001, 461.577, - 757.871, + 757.8710000000001, 749.673, 751.903, 633.009, @@ -9002,18 +9002,18 @@ 727.685, 125.165, 483.262, - 680.729, + 680.7289999999999, 596.139, - 725.719, + 725.7189999999999, 299.597, 172.417, - 714.874, + 714.8739999999999, 682.084, 434.44, 531.451, 0.379, 223.199, - 701.661, + 701.6610000000001, 442.566, 399.982, 538.085, @@ -9021,21 +9021,21 @@ 687.677, 672.135, 404.166, - 678.274, + 678.2739999999999, 674.706, - 673.281, - 650.417, + 673.2810000000001, + 650.4169999999999, 665.331, 661.317, 222.467, 316.895, - 626.733, + 626.7330000000001, 411.604, - 260.294, + 260.29400000000004, 426.447, 640.226, 602.988, - 626.171, + 626.1709999999999, 628.313, 627.806, 622.499, @@ -9044,15 +9044,15 @@ 543.656, 163.075, 117.573, - 132.228, + 132.22799999999998, 122.093, - 596.541, + 596.5409999999999, 398.199, 406.781, 25.49, 386.103, - 546.728, - 523.782, + 546.7280000000001, + 523.7819999999999, 482.871, 276.579, 354.144, @@ -9071,7 +9071,7 @@ 383.24, 293.002, 130.565, - 264.924, + 264.92400000000004, 229.673, 168.696, 271.377, @@ -9080,7 +9080,7 @@ 253.209, 256.198, 272.533, - 84.502, + 84.50200000000001, 318.541, 240.552, 50.649, @@ -9093,19 +9093,19 @@ 338.856, 214.517, 214.027, - 360.013, + 360.01300000000003, 209.155, 254.91, 392.082, 147.208, 280.405, - 303.412, + 303.41200000000003, 215.436, 315.728, 346.668, 343.37, 254.805, - 172.621, + 172.62099999999998, 87.287, 244.285, 191.455, @@ -9127,9 +9127,9 @@ 206.71, 304.849, 224.311, - 278.412, + 278.41200000000003, 187.067, - 290.662, + 290.66200000000003, 260.36, 263.878, 247.968, @@ -9144,20 +9144,20 @@ 44.637, 78.391, 6.909, - 68.784, + 68.78399999999999, 20.849, - 16.554, + 16.554000000000002, 39.49, 47.01, 21.751, 0.418, 48.909, - 48.998, + 48.998000000000005, 46.487, 39.633, 36.319, 33.654, - 16.103, + 16.102999999999998, 12.987, 31.788, 29.387, @@ -9174,8 +9174,8 @@ 13.719, 17.54, 11.913, - 7.702, - 6.287, + 7.702000000000001, + 6.287000000000001, 3.11, 6.678, 6.43, @@ -9185,7 +9185,7 @@ 4.894, -2.902, -5.131, - -0.749, + -0.7490000000000001, -2.698, 0.969, 1.668, @@ -9196,7 +9196,7 @@ 6.7, 7.096, 8.929, - 13.279, + 13.279000000000002, 15.371, 7.454, 13.075, @@ -9212,7 +9212,7 @@ 41.725, 37.833, 52.328, - 57.388, + 57.388000000000005, 58.445, 66.752, 59.067, @@ -9221,7 +9221,7 @@ 84.414, 37.376, 28.099, - 75.231, + 75.23100000000001, 14.237, 39.985, 64.952, @@ -9236,12 +9236,12 @@ 64.016, 68.712, 36.445, - 0.567, + 0.5670000000000001, 0.561, 51.75, 39.958, 74.229, - 27.703, + 27.703000000000003, 14.11, 83.081, 56.54, @@ -9250,7 +9250,7 @@ 68.046, 85.427, 65.514, - 65.927, + 65.92699999999999, 98.766, 101.425, 95.001, @@ -9267,7 +9267,7 @@ 164.088, 155.477, 154.183, - 160.652, + 160.65200000000002, 166.708, 130.907, 203.545, @@ -9278,7 +9278,7 @@ 153.082, 160.118, 153.275, - 168.652, + 168.65200000000002, 130.472, 226.992, 130.07, @@ -9286,9 +9286,9 @@ 197.296, 174.95, 56.749, - 158.527, + 158.52700000000002, 20.76, - 164.754, + 164.75400000000002, 46.228, 116.995, 147.004, @@ -9296,21 +9296,21 @@ 175.203, 113.609, 120.49, - 85.812, + 85.81200000000001, 15.2, 139.22, - 172.786, + 172.78599999999997, 239.115, 225.093, 287.463, 289.561, 143.822, 213.432, - 273.804, + 273.80400000000003, 275.495, 258.89, 291.934, - 283.912, + 283.91200000000003, 284.121, 28.226, 267.842, @@ -9318,12 +9318,12 @@ 249.322, 97.99, 59.221, - 0.572, + 0.5720000000000001, 115.635, 335.432, 366.218, 89.258, - 49.851, + 49.851000000000006, 329.932, 357.569, 117.396, @@ -9342,7 +9342,7 @@ 213.598, 0.561, 116.317, - 355.312, + 355.31199999999995, 228.996, 179.778, 367.214, @@ -9353,8 +9353,8 @@ 192.451, 272.769, 477.206, - 337.359, - 340.502, + 337.35900000000004, + 340.50199999999995, 422.753, 446.068, 355.405, @@ -9367,12 +9367,12 @@ 430.174, 407.668, 428.137, - 434.738, + 434.73800000000006, 390.32, 346.36, 586.174, 402.052, - 555.796, + 555.7959999999999, 550.549, 500.026, 590.309, @@ -9385,13 +9385,13 @@ 510.806, 466.454, 228.864, - 680.129, - 684.286, - 665.661, + 680.1289999999999, + 684.2860000000001, + 665.6610000000001, 250.153, 416.388, 364.687, - 662.589, + 662.5889999999999, 375.296, 585.998, 309.986, @@ -9410,7 +9410,7 @@ 442.126, 473.931, 484.149, - 516.713, + 516.7130000000001, 517.197, 36.71, 19.648, @@ -9420,23 +9420,23 @@ 529.783, 388.773, 587.375, - 141.956, + 141.95600000000002, 527.905, - 669.856, - 670.484, + 669.8560000000001, + 670.4839999999999, 683.322, 184.755, 426.601, 623.424, 630.432, - 643.199, + 643.1990000000001, 639.015, 323.969, 187.375, 26.673, 563.944, 154.569, - 364.368, + 364.36800000000005, 654.331, 716.487, 693.188, @@ -9448,7 +9448,7 @@ 605.823, 532.668, 419.659, - 398.303, + 398.30300000000005, 276.838, 233.973, 317.258, @@ -9462,7 +9462,7 @@ 536.086, 621.431, 486.086, - 552.949, + 552.9490000000001, 454.943, 419.251, 371.767, @@ -9471,7 +9471,7 @@ 631.153, 614.885, 545.181, - 198.744, + 198.74400000000003, 275.021, 500.241, 568.309, @@ -9484,7 +9484,7 @@ 21.779, 396.206, 532.937, - 530.421, + 530.4209999999999, 298.876, 304.431, 508.202, @@ -9499,10 +9499,10 @@ 548.908, 194.318, 530.84, - 553.032, + 553.0319999999999, 575.725, 339.649, - 175.974, + 175.97400000000002, 396.371, 575.742, 539.687, @@ -9519,8 +9519,8 @@ 586.631, 671.789, 525.054, - 727.371, - 720.588, + 727.3710000000001, + 720.5880000000001, 550.659, 225.329, 618.095, @@ -9528,43 +9528,43 @@ 731.605, 470.903, 384.237, - 768.392, + 768.3919999999999, 491.542, 683.179, 570.115, - 691.856, + 691.8560000000001, 525.538, 636.015, 341.537, 48.722, 648.385, 676.27, - 288.669, + 288.66900000000004, 624.365, 131.077, 499.756, - 358.692, + 358.69199999999995, 705.03, 579.205, - 742.781, - 715.094, + 742.7810000000001, + 715.0939999999999, 690.821, 748.77, 587.182, - 554.282, + 554.2819999999999, 761.961, 776.688, 336.329, 535.855, 504.419, 764.813, - 775.108, - 620.787, + 775.1080000000001, + 620.7869999999999, 533.46, - 620.032, - 442.313, + 620.0319999999999, + 442.3130000000001, 446.712, - 626.397, + 626.3969999999999, 730.421, 626.094, 611.075, @@ -9575,17 +9575,17 @@ 32.9, 480.328, 302.438, - 642.142, + 642.1419999999999, 624.982, - 709.286, + 709.2860000000001, 695.743, 769.873, 506.897, 316.768, 501.81, - 718.986, + 718.9860000000001, 703.252, - 721.464, + 721.4639999999999, 713.58, 684.627, 394.95, @@ -9593,12 +9593,12 @@ 595.693, 302.421, 124.163, - 675.026, + 675.0260000000001, 347.389, 402.818, 710.09, 781.137, - 761.119, + 761.1189999999999, 263.113, 21.366, 347.582, @@ -9630,20 +9630,20 @@ 781.797, 587.248, 696.288, - 572.917, + 572.9169999999999, 97.742, 572.516, 480.955, - 745.968, + 745.9680000000001, 370.55, 332.53, 743.727, 575.18, 315.629, - 743.843, - 743.458, + 743.8430000000001, + 743.4580000000001, 737.066, - 700.901, + 700.9010000000001, 134.535, 405.289, 524.943, @@ -9661,7 +9661,7 @@ 477.966, 584.98, 606.55, - 695.374, + 695.3739999999999, 533.124, 585.58, 685.563, @@ -9671,7 +9671,7 @@ 489.318, 477.454, 321.652, - 495.688, + 495.6880000000001, 657.293, 507.745, 651.248, @@ -9681,8 +9681,8 @@ 640.865, 396.354, 432.321, - 348.623, - 630.608, + 348.62300000000005, + 630.6080000000001, 522.962, 0.368, 69.048, @@ -9695,9 +9695,9 @@ 597.741, 403.407, 588.404, - 583.983, + 583.9830000000001, 582.106, - 54.888, + 54.888000000000005, 455.911, 402.504, 565.375, @@ -9720,15 +9720,15 @@ 425.918, 325.588, 486.334, - 352.498, + 352.49800000000005, 324.647, 482.871, 378.886, 239.264, 395.082, - 319.984, + 319.98400000000004, 435.778, - 346.938, + 346.93800000000005, 454.48, 190.728, 200.17, @@ -9750,10 +9750,10 @@ 332.585, 234.826, 339.792, - 342.198, + 342.19800000000004, 352.163, 175.638, - 146.492, + 146.49200000000002, 0.374, 252.774, 311.88, @@ -9769,7 +9769,7 @@ 251.139, 267.187, 241.152, - 60.476, + 60.476000000000006, 245.143, 265.166, 273.391, @@ -9782,13 +9782,13 @@ 225.952, 217.198, 144.461, - 62.491, + 62.49100000000001, 118.387, 71.146, 195.925, 161.627, - 150.038, - 143.288, + 150.03799999999998, + 143.28799999999998, 137.408, 152.813, 82.167, @@ -9797,9 +9797,9 @@ 24.075, 29.899, 23.067, - 26.348, + 26.348000000000003, 23.073, - 25.363, + 25.363000000000003, 22.572, 23.838, 17.413, @@ -9816,21 +9816,21 @@ 6.16, 15.91, 8.913, - 11.109, + 11.109000000000002, 7.652, 8.115, 8.77, 7.718, 2.807, 9.271, - 5.422, + 5.422000000000001, 7.68, 2.174, 3.867, 5.56, - 3.121, + 3.1210000000000004, 3.661, - 0.781, + 0.7809999999999999, 3.275, 4.52, -4.977, @@ -9845,15 +9845,15 @@ 0.71, 1.822, 0.115, - 4.943, + 4.9430000000000005, 9.529, 2.516, 7.68, 11.489, - 12.381, + 12.380999999999998, 10.339, 11.682, - 12.266, + 12.265999999999998, 11.963, 23.1, 38.559, @@ -9868,13 +9868,13 @@ 15.91, 17.71, 18.745, - 25.633, + 25.633000000000003, 23.161, 28.628, 25.556, 27.725, 26.365, - 16.769, + 16.769000000000002, 40.536, 46.603, 71.465, @@ -9887,14 +9887,14 @@ 222.703, 176.706, 194.874, - 306.049, + 306.04900000000004, 194.609, - 264.489, + 264.48900000000003, 274.922, 183.687, - 132.052, + 132.05200000000002, 69.747, - 309.044, + 309.04400000000004, 177.614, 142.369, 303.203, @@ -9902,8 +9902,8 @@ 370.517, 378.759, 380.554, - 349.768, - 35.669, + 349.76800000000003, + 35.669000000000004, 192.523, 241.284, 250.847, @@ -9913,7 +9913,7 @@ 309.639, 320.897, 348.601, - 342.198, + 342.19800000000004, 251.876, 211.291, 438.135, @@ -9929,7 +9929,7 @@ 250.957, 0.611, 38.543, - 239.809, + 239.80900000000003, 308.83, 498.584, 304.893, @@ -9941,14 +9941,14 @@ 528.55, 185.036, 495.936, - 563.608, + 563.6080000000001, 400.081, - 582.833, - 591.834, + 582.8330000000001, + 591.8340000000001, 594.405, 485.954, 270.308, - 80.841, + 80.84100000000001, 121.63, 264.39, 243.057, @@ -9983,7 +9983,7 @@ 92.98, 472.896, 694.586, - 748.231, + 748.2310000000001, 713.09, 437.523, 764.94, @@ -9996,7 +9996,7 @@ 604.425, 459.595, 242.446, - 294.929, + 294.92900000000003, 506.919, 735.304, 743.893, @@ -10005,19 +10005,19 @@ 686.868, 449.244, 496.354, - 749.723, + 749.7230000000001, 343.789, 637.33, - 694.129, + 694.1289999999999, 493.321, 542.346, 652.674, 297.01, - 779.391, + 779.3910000000001, 522.323, 777.525, 710.893, - 701.864, + 701.8639999999999, 787.699, 772.801, 209.441, @@ -10026,7 +10026,7 @@ 719.74, 780.085, 366.344, - 173.469, + 173.46900000000002, 117.848, 392.154, 244.229, @@ -10034,23 +10034,23 @@ 302.818, 287.271, 276.992, - 487.243, + 487.24300000000005, 345.826, 189.864, 171.68, 183.274, - 669.636, + 669.6360000000001, 481.616, - 322.549, + 322.54900000000004, 327.664, - 342.638, + 342.63800000000003, 760.75, 727.63, 464.263, - 342.203, + 342.20300000000003, 469.301, 245.286, - 337.937, + 337.93699999999995, 754.166, 549.674, 462.309, @@ -10082,7 +10082,7 @@ 199.74, 134.722, 194.565, - 197.714, + 197.71400000000003, 204.145, 195.788, 191.268, @@ -10095,15 +10095,15 @@ 127.747, 205.235, 142.116, - 159.926, - 170.496, + 159.92600000000002, + 170.49599999999998, 145.232, 234.617, 157.013, - 87.981, + 87.98100000000001, 201.458, 244.984, - 259.364, + 259.36400000000003, 293.249, 194.967, 315.832, @@ -10113,7 +10113,7 @@ 355.218, 116.609, 325.687, - 391.053, + 391.05300000000005, 342.644, 341.185, 382.062, @@ -10122,13 +10122,13 @@ 396.519, 245.22, 386.549, - 331.864, + 331.86400000000003, 300.291, 268.828, - 168.971, - 329.062, - 173.193, - 347.257, + 168.97099999999998, + 329.06199999999995, + 173.19299999999998, + 347.25699999999995, 271.795, 333.004, 252.493, @@ -10152,7 +10152,7 @@ 334.523, 337.414, 341.141, - 343.183, + 343.18300000000005, 151.491, 209.551, 358.461, @@ -10172,7 +10172,7 @@ 173.838, 165.359, 353.577, - 177.223, + 177.22299999999998, 79.707, 370.622, 373.562, @@ -10189,7 +10189,7 @@ 415.26, 514.406, 251.998, - 612.892, + 612.8919999999999, 648.126, 632.04, 379.249, @@ -10204,15 +10204,15 @@ 221.597, 637.259, 663.564, - 705.641, + 705.6410000000001, 522.433, 706.819, 620.214, 754.815, - 580.421, - 735.282, + 580.4209999999999, + 735.2819999999999, 571.739, - 87.877, + 87.87700000000001, 411.318, 659.908, 368.304, @@ -10224,15 +10224,15 @@ 451.854, 412.601, 734.682, - 630.801, + 630.8009999999999, 432.651, 561.719, - 512.353, - 656.214, - 750.042, + 512.3530000000001, + 656.2139999999999, + 750.0419999999999, 468.59, 485.894, - 544.333, + 544.3330000000001, 499.2, 517.511, 351.832, @@ -10248,13 +10248,13 @@ 657.832, 597.681, 619.245, - 585.574, - 610.233, + 585.5740000000001, + 610.2330000000001, 705.977, 628.797, - 513.333, + 513.3330000000001, 615.568, - 728.769, + 728.7689999999999, 773.181, 676.209, 715.171, @@ -10277,19 +10277,19 @@ 403.875, 785.888, 776.655, - 770.142, + 770.1419999999999, 767.747, - 766.371, - 762.974, - 764.604, - 760.662, - 754.881, + 766.3710000000001, + 762.9739999999999, + 764.6039999999999, + 760.6619999999999, + 754.8810000000001, 753.956, - 749.646, + 749.6460000000001, 738.365, - 739.224, - 744.256, - 574.459, + 739.2239999999999, + 744.2560000000001, + 574.4590000000001, 474.712, 323.199, 507.332, @@ -10297,7 +10297,7 @@ 560.607, 507.959, 705.817, - 680.267, + 680.2669999999999, 530.851, 415.689, 521.86, @@ -10308,23 +10308,23 @@ 444.961, 480.119, 570.082, - 601.292, - 681.594, - 680.256, - 700.251, + 601.2919999999999, + 681.5939999999999, + 680.2560000000001, + 700.2510000000001, 688.784, 685.684, 689.466, - 697.014, + 697.0139999999999, 678.813, 659.842, - 666.861, + 666.8610000000001, 622.268, 366.284, - 573.171, + 573.1709999999999, 577.112, 590.391, - 582.343, + 582.3430000000001, 558.493, 559.242, 282.018, 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12.475, 14.611, - 17.022, + 17.022000000000002, 18.718, 21.278, 23.199, @@ -10484,16 +10484,16 @@ 29.112, 31.011, 33.604, - 35.862, + 35.861999999999995, 37.926, 40.448, 42.782, 45.573, 55.153, 69.654, - 81.364, + 81.36399999999999, 87.095, - 54.211, + 54.211000000000006, 99.482, 100.423, 99.24, @@ -10506,7 +10506,7 @@ 133.351, 138.135, 142.82, - 146.867, + 146.86700000000002, 151.244, 157.178, 163.488, @@ -10537,7 +10537,7 @@ 287.827, 295.215, 299.856, - 302.234, + 302.23400000000004, 309.892, 314.621, 317.44, @@ -10545,7 +10545,7 @@ 330.642, 331.875, 339.731, - 342.627, + 342.62699999999995, 346.481, 350.742, 356.782, @@ -10583,7 +10583,7 @@ 504.92, 510.327, 512.226, - 518.343, + 518.3430000000001, 519.24, 519.95, 524.58, @@ -10603,17 +10603,17 @@ 579.172, 585.007, 588.85, - 592.291, + 592.2909999999999, 592.484, 599.475, 602.933, - 607.541, + 607.5409999999999, 611.576, 615.832, 614.643, 623.391, 624.674, - 629.843, + 629.8430000000001, 631.236, 637.975, 640.595, @@ -10628,9 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628.417, + 628.4169999999999, 630.014, 630.129, 608.818, @@ -10697,19 +10697,19 @@ 616.003, 620.891, 620.082, - 615.892, + 615.8919999999999, 584.633, 586.362, - 572.593, + 572.5930000000001, 555.085, 545.561, 554.111, - 573.848, + 573.8480000000001, 594.306, 596.937, 597.169, 593.364, - 613.459, + 613.4590000000001, 608.592, 602.993, 615.16, @@ -10729,7 +10729,7 @@ 581.831, 576.033, 574.2, - 517.963, + 517.9630000000001, 499.189, 507.425, 484.209, @@ -10740,7 +10740,7 @@ 514.406, 498.055, 466.174, - 441.873, + 441.8730000000001, 432.734, 410.426, 409.655, @@ -10749,10 +10749,10 @@ 400.412, 421.712, 450.23, - 458.488, + 458.48800000000006, 452.316, 439.483, - 446.183, + 446.1830000000001, 440.832, 451.667, 448.776, @@ -10868,24 +10868,24 @@ 124.135, 121.135, 124.344, - 135.052, + 135.05200000000002, 147.357, 153.363, 137.062, - 155.147, - 169.626, - 134.419, + 155.14700000000002, + 169.62599999999998, + 134.41899999999998, 107.514, 95.38, 90.662, 87.965, - 82.261, - 79.228, + 82.26100000000001, + 79.22800000000001, 74.796, 68.294, 62.337, - 56.259, - 50.891, + 56.25899999999999, + 50.891000000000005, 47.985, 46.085, 44.246, @@ -10908,8 +10908,8 @@ 16.692, 13.906, 12.618, - 12.029, - 11.324, + 12.029000000000002, + 11.324000000000002, 10.603, 9.882, 9.194, @@ -10918,7 +10918,7 @@ 5.307, 4.652, 3.848, - 2.851, + 2.8510000000000004, 2.741, 2.196, 0.836, @@ -10927,7 +10927,7 @@ -2.048, -1.509, -0.92, - -0.832, + -0.8320000000000001, -0.193, 0.765, 2.813, @@ -10953,7 +10953,7 @@ 56.347, 68.542, 82.652, - 83.731, + 83.73100000000001, 60.493, 101.486, 101.838, @@ -10981,9 +10981,9 @@ 179.106, 169.72, 172.313, - 178.534, + 178.53400000000002, 174.201, - 154.723, + 154.72299999999998, 169.951, 194.956, 227.719, @@ -11020,7 +11020,7 @@ 254.249, 317.335, 293.558, - 289.787, + 289.78700000000003, 238.647, 296.536, 333.059, @@ -11032,12 +11032,12 @@ 357.844, 310.228, 368.574, - 300.109, + 300.10900000000004, 273.518, 332.96, 394.785, 392.798, - 323.804, + 323.80400000000003, 474.619, 447.351, 481.627, @@ -11050,10 +11050,10 @@ 524.266, 536.494, 533.758, - 526.171, + 526.1709999999999, 530.955, 526.865, - 545.958, + 545.9580000000001, 558.736, 552.085, 551.133, @@ -11061,10 +11061,10 @@ 549.635, 585.161, 601.248, - 583.416, + 583.4159999999999, 557.37, - 588.233, - 594.537, + 588.2330000000001, + 594.5369999999999, 580.14, 565.359, 612.975, @@ -11074,7 +11074,7 @@ 391.086, 400.395, 461.593, - 477.113, + 477.11300000000006, 420.666, 325.726, 419.659, @@ -11082,34 +11082,34 @@ 308.428, 557.26, 662.633, - 668.766, + 668.7660000000001, 672.163, 710.447, - 701.644, + 701.6439999999999, 708.025, 703.5, - 699.646, + 699.6460000000001, 703.015, 720.919, 720.842, - 713.272, + 713.2719999999999, 716.927, 712.919, - 715.474, + 715.4739999999999, 715.584, - 722.532, + 722.5319999999999, 730.68, 745.39, - 750.108, + 750.1080000000001, 746.596, - 687.479, - 721.744, + 687.4789999999999, + 721.7439999999999, 712.457, 728.202, - 727.024, - 749.139, + 727.0239999999999, + 749.1389999999999, 775.18, - 757.006, + 757.0060000000001, 771.684, 772.488, 590.909, @@ -11124,25 +11124,25 @@ 521.519, 584.82, 521.602, - 701.611, - 698.292, + 701.6110000000001, + 698.2919999999999, 755.96, - 753.483, + 753.4830000000001, 317.743, 684.148, 693.32, - 782.838, + 782.8380000000001, 720.451, 545.754, - 769.531, + 769.5310000000001, 523.011, 617.654, 597.559, - 362.738, + 362.73800000000006, 790.342, 746.095, 748.567, - 767.224, + 767.2239999999999, 701.98, 791.784, 581.864, @@ -11152,22 +11152,22 @@ 586.571, 559.451, 632.535, - 673.121, + 673.1210000000001, 501.777, 787.908, - 544.152, + 544.1519999999999, 743.21, 539.637, 657.832, 679.628, 716.547, - 740.397, + 740.3969999999999, 556.115, 389.027, 621.354, 701.633, 693.188, - 601.166, + 601.1659999999999, 281.765, 389.379, 497.191, @@ -11176,12 +11176,12 @@ 577.993, 346.431, 630.206, - 664.896, + 664.8960000000001, 625.461, 525.246, 600.026, 436.577, - 547.532, + 547.5319999999999, 513.492, 214.682, 423.71, @@ -11195,7 +11195,7 @@ 399.151, 573.193, 622.378, - 634.704, + 634.7040000000001, 623.611, 607.695, 346.338, @@ -11213,7 +11213,7 @@ 516.465, 559.677, 561.697, - 388.113, + 388.11300000000006, 125.28, 104.624, 124.95, @@ -11234,7 +11234,7 @@ 131.87, 130.417, 284.358, - 177.036, + 177.03599999999997, 151.689, 163.565, 181.039, @@ -11246,15 +11246,15 @@ 95.001, 87.656, 93.195, - 84.783, + 84.78299999999999, 86.164, 91.642, 102.581, 113.339, 119.351, 133.918, - 170.309, - 143.052, + 170.30900000000003, + 143.05200000000002, 112.139, 103.264, 103.093, @@ -11266,7 +11266,7 @@ 98.64, 97.456, 88.311, - 86.798, + 86.79799999999999, 87.833, 92.958, 94.411, @@ -11283,7 +11283,7 @@ 175.054, 78.985, 70.342, - 71.349, + 71.34899999999999, 91.494, 117.952, 55.615, @@ -11292,7 +11292,7 @@ 47.621, 52.24, 67.749, - 141.301, + 141.30100000000002, 150.236, 109.816, 65.932, @@ -11302,8 +11302,8 @@ 54.949, 49.003, 45.7, - 41.384, - 37.513, + 41.38399999999999, + 37.513000000000005, 35.151, 30.824, 29.272, @@ -11320,29 +11320,29 @@ 18.503, 17.066, 15.712, - 14.154, + 14.154000000000002, 12.144, 10.284, 8.709, 8.34, 8.252, - 6.078, + 6.077999999999999, 5.114, 4.74, 4.938, 4.999, 5.362, 5.676, - 5.758, + 5.757999999999999, 5.604, 4.756, - 4.531, + 4.531000000000001, 3.248, 2.725, 1.348, - 0.082, + 0.0819999999999999, -1.663, - -1.377, + -1.3769999999999998, -1.558 ], "yaxis": "y" @@ -21887,7 +21887,7 @@ 800, 800, 800, - 798.754, + 798.7539999999999, 800, 800, 800, @@ -22704,7 +22704,7 @@ 800, 800, 796.073, - 792.417, + 792.4169999999999, null, null, null, @@ -23535,7 +23535,7 @@ 800, 800, 800, - 799.161, + 799.1610000000001, 800, 800, 800, @@ -23579,8 +23579,8 @@ 800, 800, 800, - 795.588, - 795.522, + 795.5880000000001, + 795.5219999999999, 800, 800, 800, @@ -24528,7 +24528,7 @@ 800, 800, 800, - 796.601, + 796.6010000000001, 800, 800, 800, @@ -26290,7 +26290,7 @@ 800, 800, 800, - 795.891, + 795.8910000000001, 800, 800, 800, @@ -27972,7 +27972,7 @@ 800, 800, 800, - 792.896, + 792.8960000000001, null, null, null, @@ -28743,7 +28743,7 @@ 800, 798.418, 800, - 799.866, + 799.8660000000001, 800, 800, 800, @@ -29768,7 +29768,7 @@ 800, 800, 800, - 797.598, + 797.5980000000001, 795.23, 800, 800, @@ -29961,7 +29961,7 @@ 800, 800, 796.909, - 794.861, + 794.8610000000001, 800, 800, 800, @@ -31222,7 +31222,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -45395,11 +45395,11 @@ 94.197, 27.059, 79.618, - 67.463, + 67.46300000000001, 40.778, - 169.499, - 151.932, - 150.478, + 169.49900000000002, + 151.93200000000002, + 150.47799999999998, 209.925, 42.622, null, @@ -45429,7 +45429,7 @@ 114.572, 123.342, 125.693, - 152.427, + 152.42700000000002, 69.164, 13.053, 85.256, @@ -45456,24 +45456,24 @@ 58.313, 200.269, 49.003, - 174.212, - 170.689, + 174.21200000000002, + 170.68900000000002, 160.465, 140.541, 118.305, 96.531, 33.654, 58.676, - 45.171, + 45.17100000000001, 24.229, 39.969, 31.975, 28.143, 26.805, 15.784, - 17.556, - 7.839, - 15.051, + 17.555999999999997, + 7.838999999999999, + 15.050999999999998, 7.058, 5.285, 3.512, @@ -45504,22 +45504,22 @@ 3.941, 3.897, 5.4, - 7.283, - 7.861, + 7.282999999999999, + 7.861000000000001, 5.433, 5.472, - 5.2075, - 4.943, + 5.2075000000000005, + 4.9430000000000005, -0.815, -0.193, 5.307, - 4.536, + 4.5360000000000005, 7.146, 2.009, 7.058, 4.96, 6.265, - 5.786, + 5.7860000000000005, 2.02, 7.443, 2.824, @@ -45527,7 +45527,7 @@ null, null, null, - 5.054, + 5.053999999999999, 7.3, 4.96, 5.684, @@ -46464,20 +46464,20 @@ 2.736, -1.47, 0.754, - 4.608, - 1.299, + 4.6080000000000005, + 1.2990000000000002, 3.589, 3.033, 3.2645, - 3.496, + 3.4960000000000004, 3.49, 1.855, - 5.703, + 5.702999999999999, 3.567, 1.927, 6.683, 1.585, - 5.588, + 5.587999999999999, 2.516, 6.738, 1.249, @@ -46492,19 +46492,19 @@ 8.037, 6.54, 16.02, - 13.824, + 13.824000000000002, 16.202, 5.318, null, null, 15.442, 22.792, - 17.876, + 17.875999999999998, 20.931, 12.513, - 6.083, + 6.082999999999999, 20.882, - 12.029, + 12.029000000000002, 18.316, 0.589, 8.913, @@ -46515,10 +46515,10 @@ 28.545, 30.582, 25.886, - 23.078, + 23.078000000000003, 32.509, - 6.457, - 0.572, + 6.457000000000001, + 0.5720000000000001, 41.857, 33.819, 19.775, @@ -46528,7 +46528,7 @@ 31.375, 51.618, 53.6, - 59.171, + 59.17100000000001, 20.425, 39.649, 33.643, @@ -46536,7 +46536,7 @@ 42.82, 9.364, 86.836, - 73.353, + 73.35300000000001, 75.979, 83.478, 97.225, @@ -46549,7 +46549,7 @@ 3.011, 76.205, 150.632, - 15.971, + 15.970999999999998, 65.827, 220.914, 166.02, @@ -46562,11 +46562,11 @@ 497.169, 207.266, 596.558, - 640.199, + 640.1990000000001, 630.46, 360.399, 528.313, - 725.631, + 725.6310000000001, 451.617, 93.244, 718.199, @@ -46581,9 +46581,9 @@ 71.206, 540.865, 415.04, - 773.099, - 625.973, - 669.146, + 773.0989999999999, + 625.9730000000001, + 669.1460000000001, 721.442, 306.253, 482.728, @@ -46613,12 +46613,12 @@ 666.823, 225.555, 526.496, - 721.161, + 721.1610000000001, 425.957, 742.324, 789.455, 705.828, - 174.151, + 174.15099999999998, 610.145, 445.699, 87.992, @@ -46626,25 +46626,25 @@ 474.762, 674.541, 390.348, - 669.036, + 669.0360000000001, 227.02, 404.64, - 473.683, + 473.6830000000001, 731.186, 676.055, 233.626, - 734.792, + 734.7919999999999, 550.967, 412.386, 445.859, 587.132, 662.039, 559.451, - 668.739, - 772.141, - 342.688, + 668.7389999999999, + 772.1410000000001, + 342.68800000000005, 417.682, - 766.536, + 766.5360000000001, 463.872, 615.457, 565.045, @@ -46658,16 +46658,16 @@ 119.499, 505.757, 564.109, - 749.728, + 749.7280000000001, 494.086, - 342.368, + 342.36800000000005, 711.681, 607.965, 641.663, 236.472, 285.151, 610.508, - 762.589, + 762.5889999999999, 141.582, 653.434, 776.633, @@ -46687,15 +46687,15 @@ 261.885, 567.307, 180.703, - 798.754, + 798.7539999999999, 489.973, 122.533, 90.47, 561.808, - 633.829, + 633.8290000000001, 611.549, 629.689, - 690.766, + 690.7660000000001, 712.765, 766.602, 488.459, @@ -46705,23 +46705,23 @@ 184.48, 602.58, 438.465, - 454.678, + 454.67800000000005, 277.62, 594.394, 694.735, 282.844, 675.158, 562.215, - 578.907, + 578.9069999999999, 493.585, - 629.584, + 629.5840000000001, 750.345, - 767.527, + 767.5269999999999, 275.863, 759.847, 721.21, 380.961, - 0.468, + 0.4679999999999999, 377.454, 713.409, 204.596, @@ -46730,16 +46730,16 @@ 767.423, 765.419, 338.305, - 348.518, + 348.51800000000003, 289.588, 479.64, 440.32, 502.399, - 417.688, - 744.889, - 745.401, - 742.527, - 738.657, + 417.6880000000001, + 744.8889999999999, + 745.4010000000001, + 742.5269999999999, + 738.6569999999999, 599.448, 484.804, 306.132, @@ -46770,7 +46770,7 @@ 576.606, 376.48, 15.057, - 631.208, + 631.2080000000001, 439.17, 622.24, 498.628, @@ -46790,10 +46790,10 @@ 333.521, 299.559, 386.72, - 512.733, + 512.7330000000001, 555.614, 551.65, - 411.428, + 411.42800000000005, 431.099, 541.746, 535.927, @@ -46803,10 +46803,10 @@ 232.156, 463.569, 338.79, - 497.373, + 497.3730000000001, 333.819, 404.932, - 337.072, + 337.07199999999995, 322.307, 258.956, 470.126, @@ -46832,7 +46832,7 @@ 250.891, 269.648, 223.557, - 219.119, + 219.11900000000003, 224.762, 245.105, 202.466, @@ -46844,9 +46844,9 @@ 119.566, 90.673, 121.702, - 88.378, + 88.37799999999999, 116.037, - 88.763, + 88.76299999999999, 102.438, 107.85, 97.764, @@ -46857,19 +46857,19 @@ 44.213, 0.429, 39.936, - 51.134, + 51.13399999999999, 54.354, - 64.159, + 64.15899999999999, 26.227, 0.335, - 43.156, + 43.156000000000006, 41.059, 40.954, 48.133, 37.183, 25.748, 35.311, - 28.848, + 28.848000000000003, 20.342, 24.763, 29.96, @@ -47717,10 +47717,10 @@ 43.558, 40.36, 24.647, - 29.668, + 29.668000000000003, 46.983, - 43.696, - 0.666, + 43.696000000000005, + 0.6659999999999999, 0.677, 16.059, 31.441, @@ -47736,10 +47736,10 @@ 79.387, 41.852, 92.044, - 87.343, + 87.34299999999999, 63.438, 84.116, - 80.659, + 80.65899999999999, 117.98, 121.867, 153.159, @@ -47768,10 +47768,10 @@ 130.918, 202.504, 215.15, - 141.598, + 141.59799999999998, 164.55, 116.45, - 167.914, + 167.91400000000002, 160.74, 152.179, 9.469, @@ -47779,14 +47779,14 @@ 247.246, 227.889, 160.939, - 138.432, + 138.43200000000002, 138.493, 36.935, 107.619, 116.251, 92.897, 11.561, - 5.786, + 5.7860000000000005, 126.849, 134.314, 134.81, @@ -47799,12 +47799,12 @@ 145.16, 10.074, 60.41, - 148.177, + 148.17700000000002, 118.723, 157.079, 157.239, 83.5, - 171.432, + 171.43200000000002, 168.602, 171.861, 94.934, @@ -47818,7 +47818,7 @@ 259.777, 306.815, 29.987, - 12.359, + 12.359000000000002, 152.091, 86.324, 302.46, @@ -47835,11 +47835,11 @@ 171.856, 170.391, 160.482, - 264.544, + 264.54400000000004, 171.696, 281.435, - 312.667, - 355.758, + 312.66700000000003, + 355.75800000000004, 436.912, 318.183, 316.025, @@ -47869,13 +47869,13 @@ 400.401, 392.319, 455.46, - 485.558, + 485.5580000000001, 366.829, 378.236, 450.252, 408.224, 400.979, - 345.363, + 345.36300000000006, 357.222, 446.789, 129.376, @@ -47890,7 +47890,7 @@ 51.145, 336.059, 189.032, - 136.478, + 136.47799999999998, 245.11, 580.383, 432.117, @@ -47898,8 +47898,8 @@ 579.986, 598.44, 264.588, - 522.593, - 615.171, + 522.5930000000001, + 615.1709999999999, 339.324, 324.14, 107.206, @@ -47909,12 +47909,12 @@ 445.974, 305.466, 337.7, - 336.924, + 336.92400000000004, 403.566, 217.308, 156.782, 334.788, - 354.502, + 354.50199999999995, 382.552, 404.761, 437.21, @@ -47927,7 +47927,7 @@ 111.908, 191.482, 473.523, - 34.788, + 34.788000000000004, 250.263, 353.577, 457.86, @@ -47949,10 +47949,10 @@ 540.529, 518.645, 426.909, - 161.979, + 161.97899999999998, 67.997, 204.938, - 0.468, + 0.4679999999999999, 181.033, 44.153, 262.331, @@ -47965,7 +47965,7 @@ 682.689, 531.297, 731.153, - 747.994, + 747.9939999999999, 758.295, 465.739, 608.818, @@ -47977,7 +47977,7 @@ 746.965, 234.694, 767.659, - 672.917, + 672.9169999999999, 394.565, 516.14, 487.171, @@ -47997,13 +47997,13 @@ 661.554, 800, 511.45, - 748.969, + 748.9689999999999, 593.987, 650.653, 572.906, 515.48, 725.835, - 666.751, + 666.7510000000001, 436.164, 474.635, 800, @@ -48014,9 +48014,9 @@ 562.187, 354.684, 346.096, - 659.011, + 659.0110000000001, 339.528, - 529.348, + 529.3480000000001, 486.857, 493.469, 796.073, @@ -48024,8 +48024,8 @@ 662.936, 619.752, 428.853, - 545.963, - 757.001, + 545.9630000000001, + 757.0010000000001, 714.23, 666.663, 472.697, @@ -48039,8 +48039,8 @@ 212.827, 595.341, 662.903, - 792.417, - 572.829, + 792.4169999999999, + 572.8290000000001, 371.376, 741.388, 649.332, @@ -48049,15 +48049,15 @@ 764.285, 697.548, 697.201, - 680.504, + 680.5039999999999, 600.4304999999999, 520.357, 759.798, - 600.978, - 448.683, + 600.9780000000001, + 448.6830000000001, 730.454, - 720.484, - 635.167, + 720.4839999999999, + 635.1669999999999, 688.569, 419.229, 541.592, @@ -48076,7 +48076,7 @@ 619.476, 352.163, 563.663, - 549.343, + 549.3430000000001, 311.577, 516.405, 512.375, @@ -48107,7 +48107,7 @@ 161.715, 218.31, 317.715, - 177.466, + 177.46599999999998, 311.687, 223.782, 293.8, @@ -48118,12 +48118,12 @@ 94.99, 279.541, 229.761, - 155.538, + 155.53799999999998, 295.848, 303.952, 306.594, 301.133, - 129.288, + 129.28799999999998, 176.497, 300.384, 302.933, @@ -48145,17 +48145,17 @@ 127.433, 128.22, 57.646, - 177.939, + 177.93900000000002, 133.219, 273.232, - 196.619, + 196.61900000000003, 166.136, 219.642, - 175.038, + 175.03799999999998, 222.604, 181.551, 180.84, - 178.814, + 178.81400000000002, 200.594, 50.28, 184.061, @@ -48165,11 +48165,11 @@ 199.129, 106.644, 195.424, - 153.897, + 153.89700000000002, 204.095, 78.672, 159.1, - 141.163, + 141.16299999999998, 189.511, 183.841, 179.128, @@ -48218,13 +48218,13 @@ 80.626, 66.24, 77.604, - 78.864, + 78.86399999999999, 56.705, 76.882, 81.182, 51.112, 82.646, - 87.987, + 87.98700000000001, 85.069, 0.484, 0.517, @@ -48233,7 +48233,7 @@ 91.769, 68.999, 35.278, - 88.097, + 88.09700000000001, 98.227, 96.944, 90.431, @@ -48254,7 +48254,7 @@ 0.49, 168.101, 189.5, - 143.729, + 143.72899999999998, 194.191, 189.401, 195.947, @@ -48263,7 +48263,7 @@ 44.268, 288.944, 315.238, - 224.839, + 224.83900000000003, 296.426, 244.923, 69.544, @@ -48275,7 +48275,7 @@ 334.975, 329.546, 302.587, - 26.238, + 26.238000000000003, 290.634, 149.944, 233.835, @@ -48299,7 +48299,7 @@ 39.87, 44.676, 41.631, - 41.196, + 41.196000000000005, 36.043, 38.741, 18.377, @@ -49157,7 +49157,7 @@ null, null, null, - 0.572, + 0.5720000000000001, -5.148, 0.016, null, @@ -49191,7 +49191,7 @@ 29.2, 15.31, 45.078, - 43.366, + 43.36600000000001, 46.542, 29.261, 48.018, @@ -49204,18 +49204,18 @@ 58.863, 60.878, 39.589, - 0.567, + 0.5670000000000001, 42.386, 32.927, 34.529, 38.212, - 56.936, + 56.93600000000001, 43.503, 55.235, 50.467, 50.556, 16.747, - 20.898, + 20.898000000000003, 61.027, 46.658, 57.173, @@ -49225,18 +49225,18 @@ 71.421, 83.77, 26.783, - 41.461, + 41.461000000000006, 78.044, 74.124, - 59.634, + 59.63399999999999, 115.508, 118.156, 107.745, - 79.591, + 79.59100000000001, 126.271, 122.638, 188.741, - 166.059, + 166.05900000000003, 138.108, 126.805, 122.39, @@ -49252,11 +49252,11 @@ 172.555, 116.609, 119.758, - 82.701, + 82.70100000000001, 158.208, 125.814, 124.834, - 156.182, + 156.18200000000002, 89.572, 148.832, 146.68, @@ -49285,7 +49285,7 @@ 106.71, 136.847, 85.322, - 169.538, + 169.53799999999998, 120.303, 46.845, 134.782, @@ -49312,10 +49312,10 @@ 248.067, 235.443, 229.068, - 167.589, - 167.897, + 167.58900000000003, + 167.89700000000002, 225.467, - 144.087, + 144.08700000000002, 126.579, 203.523, 270.215, @@ -49327,8 +49327,8 @@ 150.809, 356.281, 501.089, - 540.166, - 598.176, + 540.1659999999999, + 598.1759999999999, 477.206, 603.368, 338.476, @@ -49343,7 +49343,7 @@ 538.3, 680.074, 689.962, - 594.333, + 594.3330000000001, 649.607, 619.317, 597.389, @@ -49356,7 +49356,7 @@ 377.162, 325.076, 300.252, - 294.169, + 294.16900000000004, 221.927, 138.68, 70.925, @@ -49371,10 +49371,10 @@ 470.374, 384.898, 347.764, - 341.873, + 341.87300000000005, 441.509, 522.923, - 507.123, + 507.1230000000001, 41.841, 446.007, 166.46, @@ -49385,20 +49385,20 @@ 578.258, 596.569, 105.125, - 712.226, + 712.2260000000001, 503.555, 469.895, 526.391, 528.66, - 756.037, - 756.142, - 168.288, + 756.0369999999999, + 756.1419999999999, + 168.28799999999998, 800, 626.887, 800, 589.665, - 739.521, - 659.792, + 739.5210000000001, + 659.7919999999999, 518.381, 800, 412.386, @@ -49420,14 +49420,14 @@ 504.931, 597.02, 179.035, - 628.412, - 799.161, + 628.4119999999999, + 799.1610000000001, 782.414, 749.073, 552.085, 628.77, 508.196, - 788.833, + 788.8330000000001, 734.456, 98.26, 800, @@ -49436,24 +49436,24 @@ 471.029, 800, 800, - 5.637, + 5.6370000000000005, 515.656, 800, - 310.977, + 310.97700000000003, 44.037, 74.565, 30.621, 455.251, 384.512, - 505.053, + 505.05300000000005, 717.059, - 377.553, + 377.55300000000005, 594.57, 800, 800, 652.944, 406.6, - 464.748, + 464.7480000000001, 800, 800, 697.658, @@ -49472,7 +49472,7 @@ 662.628, 445.253, 451.089, - 698.958, + 698.9580000000001, 392.572, 800, 800, @@ -49494,7 +49494,7 @@ 800, 800, 800, - 740.463, + 740.4630000000001, 693.293, 800, 187.552, @@ -49502,7 +49502,7 @@ 800, 800, 800, - 728.874, + 728.8739999999999, 800, 651.727, 752.712, @@ -49524,8 +49524,8 @@ 800, 756.643, 508.648, - 795.588, - 795.522, + 795.5880000000001, + 795.5219999999999, 800, 800, 563.244, @@ -49542,7 +49542,7 @@ 303.88, 378.55, 364.753, - 520.473, + 520.4730000000001, 478.929, 460.096, 418.431, @@ -49550,8 +49550,8 @@ 296.844, 289.996, 258.472, - 302.174, - 356.192, + 302.17400000000004, + 356.19199999999995, 158.334, 355.807, 0.264, @@ -49583,7 +49583,7 @@ 16.912, 375.643, 205.725, - 311.549, + 311.54900000000004, 316.4, 222.588, 206.782, @@ -49598,7 +49598,7 @@ 255.593, 0.473, 121.558, - 152.383, + 152.38299999999998, 113.878, 128.418, 96.652, @@ -49612,7 +49612,7 @@ 97.599, 60.757, 128.374, - 135.427, + 135.42700000000002, 140.695, 144.813, 146.404, @@ -49669,7 +49669,7 @@ 5.626, 12.387, 12.1, - 12.266, + 12.265999999999998, 6.331, 12.007, 15.178, @@ -49684,11 +49684,11 @@ 6.023, 5.428, 5.318, - 5.208, + 5.207999999999999, 2.505, 6.237, 3.039, - 1.343, + 1.3430000000000002, 2.758, 0.644, 1.899, @@ -49700,14 +49700,14 @@ 0.616, 6.485, 1.123, - 5.802, + 5.8020000000000005, 9.975, 9.458, 8.693, 7.564, 5.312, 4.913, - 4.514, + 4.513999999999999, 1.315, 0, 3.033, @@ -49724,7 +49724,7 @@ null, null, null, - -7.179, + -7.178999999999999, null, null, null, @@ -50655,18 +50655,18 @@ 3.474, -3.623, 4.723, - 7.867, + 7.867000000000001, 3.297, - 7.162, + 7.162000000000001, 11.462, - 7.377, + 7.377000000000001, 10.146, 6.193, 6.903, 7.806, 3.523, 13.257, - 5.538, + 5.537999999999999, 1.888, 13.042, 14.528, @@ -50687,14 +50687,14 @@ 24.174, 24.581, 33.434, - 17.182, + 17.182000000000002, 8.379, 32.503, 31.782, 32.107, 59.562, 34.1, - 26.453, + 26.453000000000003, 36.869, 1.403, 25.688, @@ -50709,9 +50709,9 @@ 106.755, 71.972, 145.518, - 154.073, + 154.07299999999998, 172.362, - 178.402, + 178.40200000000002, 191.114, 208.637, 212.772, @@ -50738,18 +50738,18 @@ 615.067, 398.259, 502.746, - 669.399, + 669.3989999999999, 677.184, 437.65, 302.317, 402.746, 261.775, 718.887, - 716.619, + 716.6189999999999, 434.352, 479.232, 654.276, - 737.749, + 737.7489999999999, 360.811, 289.599, 715.727, @@ -50770,9 +50770,9 @@ 775.637, 787.325, 613.971, - 672.251, + 672.2510000000001, 498.055, - 385.558, + 385.5580000000001, 590.259, 525.279, 584.534, @@ -50782,16 +50782,16 @@ 220.556, 264.286, 274.603, - 510.563, + 510.5630000000001, 590.254, - 525.147, + 525.1469999999999, 406.06, 502.19, 754.056, 477.911, - 705.129, + 705.1289999999999, 601.369, - 779.496, + 779.4960000000001, 755.597, 323.991, 581.005, @@ -50803,32 +50803,32 @@ 536.4, 313.096, 143.806, - 725.119, + 725.1189999999999, 498.259, 142.033, 305.703, 535.976, 383.962, 494.466, - 546.392, + 546.3919999999999, 320.87, 229.982, 643.766, - 796.601, + 796.6010000000001, 721.012, 535.283, - 668.348, + 668.3480000000001, 705.922, 274.146, 762, 599.007, - 787.259, + 787.2589999999999, 504.854, - 785.486, - 146.949, + 785.4860000000001, + 146.94899999999998, 574.217, - 614.301, - 746.656, + 614.3009999999999, + 746.6560000000001, 721.095, 578.181, 600.554, @@ -50839,35 +50839,35 @@ 143.53, 496.64, 420.462, - 711.009, + 711.0089999999999, 275.214, 553.401, 748.176, 670.676, - 566.834, + 566.8340000000001, 113.906, - 312.854, + 312.85400000000004, 739.13, 343.101, - 711.389, + 711.3889999999999, 732.59, 680.658, 327.003, - 332.437, - 6.991, + 332.43699999999995, + 6.9910000000000005, 18.233, 316.956, - 520.468, + 520.4680000000001, 283.868, 293.123, 19.516, 528.175, - 770.792, + 770.7919999999999, 36.264, - 441.928, + 441.92800000000005, 704.623, - 705.751, - 772.719, + 705.7510000000001, + 772.7189999999999, 80.053, 604.265, 352.796, @@ -50878,15 +50878,15 @@ 400.847, 760.53, 509.528, - 678.896, + 678.8960000000001, 485.47, 721.585, 452.839, 382.233, 516.173, - 728.599, + 728.5989999999999, 289.654, - 83.026, + 83.02600000000001, 380.62, 486.951, 244.637, @@ -50907,13 +50907,13 @@ 222.946, 70.116, 482.431, - 404.563, + 404.5630000000001, 149.124, 432.282, 0.143, 183.241, 570.765, - 778.521, + 778.5210000000001, 437.826, 382.646, 218.172, @@ -50921,7 +50921,7 @@ 313.603, 359.017, 258.087, - 312.727, + 312.72700000000003, 491.003, 5.956, 616.432, @@ -50935,7 +50935,7 @@ 255.268, 212.183, 178.358, - 421.123, + 421.1230000000001, 627.211, 503.693, 534.627, @@ -50953,10 +50953,10 @@ 507.222, 653.379, 766.701, - 534.583, + 534.5830000000001, 547.433, 792.164, - 684.886, + 684.8860000000001, 566.195, 662.985, 374.878, @@ -50966,10 +50966,10 @@ 627.878, 618.194, 766.25, - 466.113, + 466.11300000000006, 515.43, 789.18, - 596.084, + 596.0840000000001, 604.122, 588.09, 575.56, @@ -50977,7 +50977,7 @@ 738.437, 514.99, 564.747, - 512.204, + 512.2040000000001, 439.671, 486.989, 599.448, @@ -50988,7 +50988,7 @@ 211.654, 126.86, 178.82, - 264.924, + 264.92400000000004, 220.088, 77.218, 229.866, @@ -50998,15 +50998,15 @@ 278.055, 236.379, 531.01, - 550.588, - 142.677, + 550.5880000000001, + 142.67700000000002, 84.177, - 425.808, + 425.8080000000001, 577.636, 580.702, - 332.503, + 332.50300000000004, 583.763, - 570.407, + 570.4069999999999, 544.488, 543.893, 545.66, @@ -51014,11 +51014,11 @@ 430.372, 342.06, 503.715, - 335.823, + 335.82300000000004, 348.292, 312.86, 315.59, - 134.177, + 134.17700000000002, 205.422, 473.782, 479.629, @@ -51041,7 +51041,7 @@ 432.035, 302.988, 232.294, - 257.162, + 257.16200000000003, 425.555, 420.82, 421.519, @@ -51049,9 +51049,9 @@ 416.075, 216.835, 302.361, - 335.239, + 335.23900000000003, 406.512, - 307.299, + 307.29900000000004, 233.533, 277.9, 292.275, @@ -51059,27 +51059,27 @@ 106.76, 129.877, 221.459, - 201.744, + 201.74400000000003, 72.45, 109.474, 135.762, - 159.177, + 159.17700000000002, 244.885, - 173.496, + 173.49599999999998, 232.129, 263.961, 290.166, 189.974, 79.574, 141.356, - 26.398, + 26.398000000000003, 207.833, - 18.944, + 18.944000000000003, 152.334, 183.841, 157.734, - 163.961, - 165.943, + 163.96099999999998, + 165.94299999999998, 106.529, 151.018, 199.84, @@ -51105,18 +51105,18 @@ 20.788, 13.835, 26.381, - 7.674, + 7.673999999999999, 11.627, 9.161, 20.849, - 6.457, + 6.457000000000001, 0.446, 5.566, 14.479, 9.909, 15.453, 10.058, - 5.411, + 5.4110000000000005, 0.379, 5.502000000000001, 10.625, @@ -51124,10 +51124,10 @@ 6.155, 8.23, 7.388, - 5.076, + 5.0760000000000005, 5.874, - 2.851, - 1.701, + 2.8510000000000004, + 1.7009999999999998, 1.69, 2.939, 3.028, @@ -51934,11 +51934,11 @@ 16.108, 19.67, 26.673, - 12.596, + 12.595999999999998, 32.338, 37.612, 23.717, - 24.543, + 24.543000000000003, 38.774, 33.401, 36.897, @@ -51963,7 +51963,7 @@ 94.318, 172.984, 114.6, - 167.633, + 167.63299999999998, 26.767, 210.157, 180.466, @@ -51972,7 +51972,7 @@ 154.673, 233.863, 184.623, - 163.647, + 163.64700000000002, 191.356, 218.211, 157.52, @@ -51989,10 +51989,10 @@ 268.866, 150.599, 250.841, - 340.937, + 340.93699999999995, 111.687, 233.246, - 329.183, + 329.18300000000005, 90.822, 287.111, 247.852, @@ -52017,10 +52017,10 @@ 197.946, 272.841, 374.272, - 348.512, + 348.51199999999994, 393.519, 358.708, - 362.243, + 362.24300000000005, 282.288, 225.935, 461.588, @@ -52049,7 +52049,7 @@ 503.566, 583.565, 586.477, - 592.858, + 592.8580000000001, 596.8, 277.862, 576.716, @@ -52062,10 +52062,10 @@ 453.792, 505.763, 439.318, - 367.748, + 367.7480000000001, 639.158, 644.377, - 644.421, + 644.4209999999999, 653.186, 659.115, 659.291, @@ -52081,7 +52081,7 @@ 692.186, 475.109, 701.485, - 704.601, + 704.6010000000001, 460.25, 0.545, 129.013, @@ -52089,18 +52089,18 @@ 329.04, 208.373, 538.823, - 709.016, + 709.0160000000001, 571.761, 438.559, 741.944, - 741.514, + 741.5139999999999, 744.944, 744.168, 500.505, - 336.643, + 336.64300000000003, 371.861, 754.837, - 755.889, + 755.8889999999999, 234.914, 719.796, 298.507, @@ -52111,7 +52111,7 @@ 666.08, 547.873, 464.318, - 579.397, + 579.3969999999999, 736.488, 318.574, 651.231, @@ -52122,13 +52122,13 @@ 648.451, 735.86, 664.169, - 711.036, + 711.0360000000001, 729.925, 476.436, 352.586, 644.724, 625.967, - 160.212, + 160.21200000000002, 462.122, 507.976, 387.111, @@ -52141,17 +52141,17 @@ 795.054, 521.668, 386.445, - 689.136, + 689.1360000000001, 676.732, 136.17, - 269.852, + 269.85200000000003, 698.765, 357.431, - 680.162, + 680.1619999999999, 574.035, 116.235, 436.94, - 639.907, + 639.9069999999999, 215.271, 124.218, 226.243, @@ -52164,14 +52164,14 @@ 633.548, 458.389, 489.687, - 629.546, - 359.479, + 629.5459999999999, + 359.47900000000004, 604.034, 767.924, 547.163, 730.57, 21.019, - 491.058, + 491.0580000000001, 209.474, 468.106, 481.225, @@ -52193,7 +52193,7 @@ 796.227, 417.765, 606.737, - 422.863, + 422.86300000000006, 389.214, 93.872, 503.087, @@ -52205,10 +52205,10 @@ 701.204, 716.834, 593.689, - 667.984, + 667.9839999999999, 765.694, 670.247, - 527.718, + 527.7180000000001, 679.931, 764.466, 706.467, @@ -52218,16 +52218,16 @@ 560.002, 730.779, 514.555, - 613.454, + 613.4540000000001, 329.117, 243.106, 591.404, 348.76, 248.584, - 715.876, + 715.8760000000001, 734.539, - 729.881, - 790.286, + 729.8810000000001, + 790.2860000000001, 190.546, 783.807, 384.798, @@ -52238,7 +52238,7 @@ 638.118, 567.153, 284.909, - 718.871, + 718.8710000000001, 217.914, 642.109, 558.069, @@ -52267,31 +52267,31 @@ 504.75, 157.778, 731.698, - 790.006, + 790.0060000000001, 768.27, 352.619, 134.16, - 774.514, + 774.5139999999999, 780.465, 569.763, 773.721, - 674.156, + 674.1560000000001, 272.868, - 780.234, + 780.2339999999999, 379.656, 779.931, 776.903, - 776.088, + 776.0880000000001, 494.499, 0.363, 111.753, 765.253, - 534.671, - 560.084, + 534.6709999999999, + 560.0840000000001, 501.254, 438.922, - 284.479, - 745.396, + 284.47900000000004, + 745.3960000000001, 737.303, 468.216, 719.096, @@ -52301,11 +52301,11 @@ 717.318, 714.549, 503.77, - 705.526, + 705.5260000000001, 639.367, 228.897, 682.717, - 670.489, + 670.4889999999999, 413.283, 676.408, 671.805, @@ -52324,7 +52324,7 @@ 624.288, 269.295, 457.205, - 531.699, + 531.6990000000001, 421.101, 455.823, 494.146, @@ -52333,7 +52333,7 @@ 460.206, 5.428, 47.214, - 555.267, + 555.2669999999999, 483.389, 175.412, 360.575, @@ -52343,7 +52343,7 @@ 567.318, 569.686, 555.509, - 529.574, + 529.5740000000001, 443.607, 513.399, 454.607, @@ -52355,7 +52355,7 @@ 540.821, 108.858, 220.551, - 533.983, + 533.9830000000001, 218.101, 530.548, 504.128, @@ -52365,11 +52365,11 @@ 427.713, 282.073, 468.244, - 468.183, + 468.1830000000001, 266.46, 444.477, 401.573, - 350.489, + 350.48900000000003, 229.459, 141.884, 306.396, @@ -52386,11 +52386,11 @@ 220.033, 157.178, 222.604, - 236.214, + 236.21400000000003, 121.779, 195.259, 140.855, - 131.854, + 131.85399999999998, 234.105, 233.318, 198.48, @@ -52399,14 +52399,14 @@ 0.363, 109.094, 253.682, - 85.328, + 85.32799999999999, 244.648, - 178.159, + 178.15900000000002, 226.073, 215.012, 96.487, 136.467, - 179.216, + 179.21599999999998, 179.695, 171.856, 137.904, @@ -52427,11 +52427,11 @@ 41.918, 45.039, 57.878, - 54.211, + 54.211000000000006, 52.218, 50.187, 39.231, - 49.064, + 49.06399999999999, 40.376, 7.927, 26.128, @@ -52449,7 +52449,7 @@ 14.082, 26.712, 20.48, - 24.113, + 24.113000000000003, 13.46, 16.609, 15.211, @@ -52461,8 +52461,8 @@ 0.517, 9.838, 7.982, - 3.061, - 5.577, + 3.0610000000000004, + 5.577000000000001, 0.847, 4.668, -0.997, @@ -53272,14 +53272,14 @@ 38.653, 32.272, 41.136, - 53.138, + 53.138000000000005, 48.519, 49.807, 62.018, 35.262, 63.857, 72.005, - 65.299, + 65.29899999999999, 20.193, 90.684, 98.331, @@ -53292,10 +53292,10 @@ 141.092, 147.445, 158.329, - 168.619, + 168.61900000000003, 114.798, 114.457, - 176.409, + 176.40900000000002, 78.936, 104.398, 203.842, @@ -53316,13 +53316,13 @@ 255.515, 160.129, 104.707, - 210.619, + 210.61900000000003, 268.772, 243.194, 272.323, 277.091, 262.045, - 284.854, + 284.85400000000004, 262.072, 351.606, 355.945, @@ -53338,7 +53338,7 @@ 324.448, 340.921, 342.886, - 338.878, + 338.87800000000004, 327.449, 258.472, 333.797, @@ -53368,7 +53368,7 @@ 397.263, 503.583, 495.28, - 390.243, + 390.24300000000005, 263.988, 279.772, 193.305, @@ -53378,27 +53378,27 @@ 132.239, 448.072, 400.401, - 5.747, + 5.747000000000001, 0.55, 290.772, 333.362, 527.735, 43.129, - 0.567, + 0.5670000000000001, 365.497, 116.604, - 171.344, + 171.34400000000002, 183.186, 495.814, 485.035, 562.22, 384.275, - 368.998, + 368.9980000000001, 477.427, 268.453, 509.385, 425.384, - 502.498, + 502.4980000000001, 554.964, 513.294, 677.36, @@ -53414,10 +53414,10 @@ 714.345, 702.443, 717.885, - 724.134, + 724.1339999999999, 727.547, 706.307, - 539.142, + 539.1419999999999, 227.768, 250.484, 751.05, @@ -53437,7 +53437,7 @@ 181.804, 378.153, 591.482, - 541.718, + 541.7180000000001, 358.956, 150.275, 757.942, @@ -53445,7 +53445,7 @@ 274.316, 0.457, 204.205, - 610.277, + 610.2769999999999, 448.986, 611.918, 549.619, @@ -53456,7 +53456,7 @@ 100.253, 398.87, 190.613, - 0.468, + 0.4679999999999999, 105.714, 445.435, 0.473, @@ -53480,19 +53480,19 @@ 532.018, 494.895, 528.247, - 773.644, + 773.6439999999999, 621.304, 620.269, 730.977, - 755.531, + 755.5310000000001, 578.869, 491.603, 743.7, 343.772, - 716.536, + 716.5360000000001, 474.299, - 604.463, - 717.021, + 604.4630000000001, + 717.0210000000001, 383.422, 479.711, 609.187, @@ -53502,7 +53502,7 @@ 535.321, 754.832, 694.801, - 638.954, + 638.9540000000001, 629.722, 639.422, 586.4, @@ -53513,8 +53513,8 @@ 774.525, 428.737, 603.665, - 795.891, - 765.276, + 795.8910000000001, + 765.2760000000001, 644.735, 694.355, 629.992, @@ -53526,10 +53526,10 @@ 655.493, 562.65, 414.032, - 743.458, - 762.782, + 743.4580000000001, + 762.7819999999999, 770.941, - 87.282, + 87.28200000000001, 632.32, 531.236, 743.1, @@ -53537,10 +53537,10 @@ 502.245, 621.398, 115.954, - 568.772, + 568.7719999999999, 639.378, 149.454, - 679.259, + 679.2589999999999, 780.971, 731.544, 704.182, @@ -53551,12 +53551,12 @@ 659.423, 462.204, 758.311, - 781.621, + 781.6210000000001, 415.777, 788.079, 372.268, 415.518, - 547.042, + 547.0419999999999, 417.809, 635.42, 612.072, @@ -53566,23 +53566,23 @@ 373.138, 697.46, 397.918, - 697.141, - 620.704, + 697.1410000000001, + 620.7040000000001, 582.266, 688.393, 552.828, 550.274, 493.034, 145.32, - 628.902, + 628.9019999999999, 782.915, 620.418, 548.523, - 666.542, + 666.5419999999999, 683.427, 39.556, 393.915, - 581.027, + 581.0269999999999, 137.827, 377.652, 794.52, @@ -53593,9 +53593,9 @@ 598.991, 527.812, 697.24, - 637.292, + 637.2919999999999, 606.985, - 730.861, + 730.8610000000001, 108.213, 678.957, 585.448, @@ -53610,19 +53610,19 @@ 797.196, 627.118, 729.931, - 22.203, - 763.266, + 22.203000000000003, + 763.2660000000001, 500.522, 638.938, - 790.881, + 790.8810000000001, 496.035, 691.883, 621.205, - 790.149, + 790.1489999999999, 796.238, 271.327, 145.391, - 689.109, + 689.1089999999999, 574.382, 779.914, 415.601, @@ -53633,10 +53633,10 @@ 515.711, 772.449, 769.955, - 763.734, - 667.616, + 763.7339999999999, + 667.6160000000001, 461.577, - 757.871, + 757.8710000000001, 749.673, 751.903, 633.009, @@ -53647,18 +53647,18 @@ 727.685, 125.165, 483.262, - 680.729, + 680.7289999999999, 596.139, - 725.719, + 725.7189999999999, 299.597, 172.417, - 714.874, + 714.8739999999999, 682.084, 434.44, 531.451, 0.379, 223.199, - 701.661, + 701.6610000000001, 442.566, 399.982, 538.085, @@ -53666,21 +53666,21 @@ 687.677, 672.135, 404.166, - 678.274, + 678.2739999999999, 674.706, - 673.281, - 650.417, + 673.2810000000001, + 650.4169999999999, 665.331, 661.317, 222.467, 316.895, - 626.733, + 626.7330000000001, 411.604, - 260.294, + 260.29400000000004, 426.447, 640.226, 602.988, - 626.171, + 626.1709999999999, 628.313, 627.806, 622.499, @@ -53689,15 +53689,15 @@ 543.656, 163.075, 117.573, - 132.228, + 132.22799999999998, 122.093, - 596.541, + 596.5409999999999, 398.199, 406.781, 25.49, 386.103, - 546.728, - 523.782, + 546.7280000000001, + 523.7819999999999, 482.871, 276.579, 354.144, @@ -53716,7 +53716,7 @@ 383.24, 293.002, 130.565, - 264.924, + 264.92400000000004, 229.673, 168.696, 271.377, @@ -53725,7 +53725,7 @@ 253.209, 256.198, 272.533, - 84.502, + 84.50200000000001, 318.541, 240.552, 50.649, @@ -53738,19 +53738,19 @@ 338.856, 214.517, 214.027, - 360.013, + 360.01300000000003, 209.155, 254.91, 392.082, 147.208, 280.405, - 303.412, + 303.41200000000003, 215.436, 315.728, 346.668, 343.37, 254.805, - 172.621, + 172.62099999999998, 87.287, 244.285, 191.455, @@ -53772,9 +53772,9 @@ 206.71, 304.849, 224.311, - 278.412, + 278.41200000000003, 187.067, - 290.662, + 290.66200000000003, 260.36, 263.878, 247.968, @@ -53789,20 +53789,20 @@ 44.637, 78.391, 6.909, - 68.784, + 68.78399999999999, 20.849, - 16.554, + 16.554000000000002, 39.49, 47.01, 21.751, 0.418, 48.909, - 48.998, + 48.998000000000005, 46.487, 39.633, 36.319, 33.654, - 16.103, + 16.102999999999998, 12.987, 31.788, 29.387, @@ -53819,8 +53819,8 @@ 13.719, 17.54, 11.913, - 7.702, - 6.287, + 7.702000000000001, + 6.287000000000001, 3.11, 6.678, 6.43, @@ -53830,7 +53830,7 @@ 4.894, -2.902, -5.131, - -0.749, + -0.7490000000000001, -2.698, 0.969, null, @@ -54620,7 +54620,7 @@ 6.7, 7.096, 8.929, - 13.279, + 13.279000000000002, 15.371, 7.454, 13.075, @@ -54636,7 +54636,7 @@ 41.725, 37.833, 52.328, - 57.388, + 57.388000000000005, 58.445, 66.752, 59.067, @@ -54645,7 +54645,7 @@ 84.414, 37.376, 28.099, - 75.231, + 75.23100000000001, 14.237, 39.985, 64.952, @@ -54660,12 +54660,12 @@ 64.016, 68.712, 36.445, - 0.567, + 0.5670000000000001, 0.561, 51.75, 39.958, 74.229, - 27.703, + 27.703000000000003, 14.11, 83.081, 56.54, @@ -54674,7 +54674,7 @@ 68.046, 85.427, 65.514, - 65.927, + 65.92699999999999, 98.766, 101.425, 95.001, @@ -54691,7 +54691,7 @@ 164.088, 155.477, 154.183, - 160.652, + 160.65200000000002, 166.708, 130.907, 203.545, @@ -54702,7 +54702,7 @@ 153.082, 160.118, 153.275, - 168.652, + 168.65200000000002, 130.472, 226.992, 130.07, @@ -54710,9 +54710,9 @@ 197.296, 174.95, 56.749, - 158.527, + 158.52700000000002, 20.76, - 164.754, + 164.75400000000002, 46.228, 116.995, 147.004, @@ -54720,21 +54720,21 @@ 175.203, 113.609, 120.49, - 85.812, + 85.81200000000001, 15.2, 139.22, - 172.786, + 172.78599999999997, 239.115, 225.093, 287.463, 289.561, 143.822, 213.432, - 273.804, + 273.80400000000003, 275.495, 258.89, 291.934, - 283.912, + 283.91200000000003, 284.121, 28.226, 267.842, @@ -54742,12 +54742,12 @@ 249.322, 97.99, 59.221, - 0.572, + 0.5720000000000001, 115.635, 335.432, 366.218, 89.258, - 49.851, + 49.851000000000006, 329.932, 357.569, 117.396, @@ -54766,7 +54766,7 @@ 213.598, 0.561, 116.317, - 355.312, + 355.31199999999995, 228.996, 179.778, 367.214, @@ -54777,8 +54777,8 @@ 192.451, 272.769, 477.206, - 337.359, - 340.502, + 337.35900000000004, + 340.50199999999995, 422.753, 446.068, 355.405, @@ -54791,12 +54791,12 @@ 430.174, 407.668, 428.137, - 434.738, + 434.73800000000006, 390.32, 346.36, 586.174, 402.052, - 555.796, + 555.7959999999999, 550.549, 500.026, 590.309, @@ -54809,13 +54809,13 @@ 510.806, 466.454, 228.864, - 680.129, - 684.286, - 665.661, + 680.1289999999999, + 684.2860000000001, + 665.6610000000001, 250.153, 416.388, 364.687, - 662.589, + 662.5889999999999, 375.296, 585.998, 309.986, @@ -54834,7 +54834,7 @@ 442.126, 473.931, 484.149, - 516.713, + 516.7130000000001, 517.197, 36.71, 19.648, @@ -54844,23 +54844,23 @@ 529.783, 388.773, 587.375, - 141.956, + 141.95600000000002, 527.905, - 669.856, - 670.484, + 669.8560000000001, + 670.4839999999999, 683.322, 184.755, 426.601, 623.424, 630.432, - 643.199, + 643.1990000000001, 639.015, 323.969, 187.375, 26.673, 563.944, 154.569, - 364.368, + 364.36800000000005, 654.331, 716.487, 693.188, @@ -54872,7 +54872,7 @@ 605.823, 532.668, 419.659, - 398.303, + 398.30300000000005, 276.838, 233.973, 317.258, @@ -54886,7 +54886,7 @@ 536.086, 621.431, 486.086, - 552.949, + 552.9490000000001, 454.943, 419.251, 371.767, @@ -54895,7 +54895,7 @@ 631.153, 614.885, 545.181, - 198.744, + 198.74400000000003, 275.021, 500.241, 568.309, @@ -54908,7 +54908,7 @@ 21.779, 396.206, 532.937, - 530.421, + 530.4209999999999, 298.876, 304.431, 508.202, @@ -54923,10 +54923,10 @@ 548.908, 194.318, 530.84, - 553.032, + 553.0319999999999, 575.725, 339.649, - 175.974, + 175.97400000000002, 396.371, 575.742, 539.687, @@ -54943,8 +54943,8 @@ 586.631, 671.789, 525.054, - 727.371, - 720.588, + 727.3710000000001, + 720.5880000000001, 550.659, 225.329, 618.095, @@ -54952,31 +54952,31 @@ 731.605, 470.903, 384.237, - 768.392, + 768.3919999999999, 491.542, 683.179, 570.115, - 691.856, + 691.8560000000001, 525.538, 636.015, 341.537, 48.722, 648.385, 676.27, - 288.669, + 288.66900000000004, 624.365, 131.077, 499.756, - 358.692, + 358.69199999999995, 705.03, 579.205, - 742.781, - 715.094, + 742.7810000000001, + 715.0939999999999, 690.821, 800, 748.77, 587.182, - 554.282, + 554.2819999999999, 761.961, 800, 800, @@ -54986,16 +54986,16 @@ 800, 504.419, 764.813, - 775.108, - 620.787, + 775.1080000000001, + 620.7869999999999, 800, 533.46, - 620.032, - 442.313, + 620.0319999999999, + 442.3130000000001, 796.909, 446.712, 800, - 626.397, + 626.3969999999999, 730.421, 626.094, 611.075, @@ -55006,9 +55006,9 @@ 32.9, 480.328, 302.438, - 642.142, + 642.1419999999999, 624.982, - 709.286, + 709.2860000000001, 695.743, 800, 793.27, @@ -55016,9 +55016,9 @@ 506.897, 316.768, 501.81, - 718.986, + 718.9860000000001, 703.252, - 721.464, + 721.4639999999999, 713.58, 684.627, 394.95, @@ -55026,12 +55026,12 @@ 595.693, 302.421, 124.163, - 675.026, + 675.0260000000001, 347.389, 402.818, 710.09, 781.137, - 761.119, + 761.1189999999999, 263.113, 21.366, 347.582, @@ -55069,20 +55069,20 @@ 781.797, 587.248, 696.288, - 572.917, + 572.9169999999999, 97.742, 572.516, 480.955, - 745.968, + 745.9680000000001, 370.55, 332.53, 743.727, 575.18, 315.629, - 743.843, - 743.458, + 743.8430000000001, + 743.4580000000001, 737.066, - 700.901, + 700.9010000000001, 134.535, 405.289, 524.943, @@ -55100,7 +55100,7 @@ 477.966, 584.98, 606.55, - 695.374, + 695.3739999999999, 533.124, 585.58, 685.563, @@ -55110,7 +55110,7 @@ 489.318, 477.454, 321.652, - 495.688, + 495.6880000000001, 657.293, 507.745, 651.248, @@ -55120,8 +55120,8 @@ 640.865, 396.354, 432.321, - 348.623, - 630.608, + 348.62300000000005, + 630.6080000000001, 522.962, 0.368, 69.048, @@ -55134,9 +55134,9 @@ 597.741, 403.407, 588.404, - 583.983, + 583.9830000000001, 582.106, - 54.888, + 54.888000000000005, 455.911, 402.504, 565.375, @@ -55159,15 +55159,15 @@ 425.918, 325.588, 486.334, - 352.498, + 352.49800000000005, 324.647, 482.871, 378.886, 239.264, 395.082, - 319.984, + 319.98400000000004, 435.778, - 346.938, + 346.93800000000005, 454.48, 190.728, 200.17, @@ -55189,10 +55189,10 @@ 332.585, 234.826, 339.792, - 342.198, + 342.19800000000004, 352.163, 175.638, - 146.492, + 146.49200000000002, 0.374, 252.774, 311.88, @@ -55208,7 +55208,7 @@ 251.139, 267.187, 241.152, - 60.476, + 60.476000000000006, 245.143, 265.166, 273.391, @@ -55221,13 +55221,13 @@ 225.952, 217.198, 144.461, - 62.491, + 62.49100000000001, 118.387, 71.146, 195.925, 161.627, - 150.038, - 143.288, + 150.03799999999998, + 143.28799999999998, 137.408, 152.813, 82.167, @@ -55236,9 +55236,9 @@ 24.075, 29.899, 23.067, - 26.348, + 26.348000000000003, 23.073, - 25.363, + 25.363000000000003, 22.572, 23.838, 17.413, @@ -55255,21 +55255,21 @@ 6.16, 15.91, 8.913, - 11.109, + 11.109000000000002, 7.652, 8.115, 8.77, 7.718, 2.807, 9.271, - 5.422, + 5.422000000000001, 7.68, 2.174, 3.867, 5.56, - 3.121, + 3.1210000000000004, 3.661, - 0.781, + 0.7809999999999999, 3.275, 4.52, -4.977, @@ -56078,15 +56078,15 @@ 0.71, 1.822, 0.115, - 4.943, + 4.9430000000000005, 9.529, 2.516, 7.68, 11.489, - 12.381, + 12.380999999999998, 10.339, 11.682, - 12.266, + 12.265999999999998, 11.963, 23.1, 38.559, @@ -56101,13 +56101,13 @@ 15.91, 17.71, 18.745, - 25.633, + 25.633000000000003, 23.161, 28.628, 25.556, 27.725, 26.365, - 16.769, + 16.769000000000002, 40.536, 46.603, 71.465, @@ -56120,14 +56120,14 @@ 222.703, 176.706, 194.874, - 306.049, + 306.04900000000004, 194.609, - 264.489, + 264.48900000000003, 274.922, 183.687, - 132.052, + 132.05200000000002, 69.747, - 309.044, + 309.04400000000004, 177.614, 142.369, 303.203, @@ -56135,8 +56135,8 @@ 370.517, 378.759, 380.554, - 349.768, - 35.669, + 349.76800000000003, + 35.669000000000004, 192.523, 241.284, 250.847, @@ -56146,7 +56146,7 @@ 309.639, 320.897, 348.601, - 342.198, + 342.19800000000004, 251.876, 211.291, 438.135, @@ -56162,7 +56162,7 @@ 250.957, 0.611, 38.543, - 239.809, + 239.80900000000003, 308.83, 498.584, 304.893, @@ -56174,14 +56174,14 @@ 528.55, 185.036, 495.936, - 563.608, + 563.6080000000001, 400.081, - 582.833, - 591.834, + 582.8330000000001, + 591.8340000000001, 594.405, 485.954, 270.308, - 80.841, + 80.84100000000001, 121.63, 264.39, 243.057, @@ -56216,7 +56216,7 @@ 92.98, 472.896, 694.586, - 748.231, + 748.2310000000001, 713.09, 437.523, 764.94, @@ -56229,7 +56229,7 @@ 604.425, 459.595, 242.446, - 294.929, + 294.92900000000003, 506.919, 735.304, 743.893, @@ -56238,20 +56238,20 @@ 686.868, 449.244, 496.354, - 749.723, + 749.7230000000001, 343.789, 637.33, - 694.129, + 694.1289999999999, 493.321, 542.346, 652.674, 297.01, - 779.391, + 779.3910000000001, 522.323, 777.525, 710.893, 800, - 701.864, + 701.8639999999999, 800, 787.699, 772.801, @@ -56266,7 +56266,7 @@ 800, 800, 366.344, - 173.469, + 173.46900000000002, 117.848, 392.154, 244.229, @@ -56274,23 +56274,23 @@ 302.818, 287.271, 276.992, - 487.243, + 487.24300000000005, 345.826, 189.864, 171.68, 183.274, - 669.636, + 669.6360000000001, 481.616, - 322.549, + 322.54900000000004, 327.664, - 342.638, + 342.63800000000003, 760.75, 727.63, 464.263, - 342.203, + 342.20300000000003, 469.301, 245.286, - 337.937, + 337.93699999999995, 754.166, 549.674, 462.309, @@ -56323,7 +56323,7 @@ 199.74, 134.722, 194.565, - 197.714, + 197.71400000000003, 204.145, 195.788, 191.268, @@ -56336,15 +56336,15 @@ 127.747, 205.235, 142.116, - 159.926, - 170.496, + 159.92600000000002, + 170.49599999999998, 145.232, 234.617, 157.013, - 87.981, + 87.98100000000001, 201.458, 244.984, - 259.364, + 259.36400000000003, 293.249, 194.967, 315.832, @@ -56354,7 +56354,7 @@ 355.218, 116.609, 325.687, - 391.053, + 391.05300000000005, 342.644, 341.185, 382.062, @@ -56363,13 +56363,13 @@ 396.519, 245.22, 386.549, - 331.864, + 331.86400000000003, 300.291, 268.828, - 168.971, - 329.062, - 173.193, - 347.257, + 168.97099999999998, + 329.06199999999995, + 173.19299999999998, + 347.25699999999995, 271.795, 333.004, 252.493, @@ -56393,7 +56393,7 @@ 334.523, 337.414, 341.141, - 343.183, + 343.18300000000005, 151.491, 209.551, 358.461, @@ -56413,7 +56413,7 @@ 173.838, 165.359, 353.577, - 177.223, + 177.22299999999998, 79.707, 370.622, 373.562, @@ -56430,7 +56430,7 @@ 415.26, 514.406, 251.998, - 612.892, + 612.8919999999999, 648.126, 632.04, 379.249, @@ -56452,21 +56452,21 @@ 637.259, 800, 663.564, - 705.641, + 705.6410000000001, 522.433, 706.819, 620.214, 754.815, 800, - 580.421, - 735.282, + 580.4209999999999, + 735.2819999999999, 800, 800, 571.739, 800, 800, 800, - 87.877, + 87.87700000000001, 411.318, 800, 800, @@ -56484,15 +56484,15 @@ 451.854, 412.601, 734.682, - 630.801, + 630.8009999999999, 432.651, 561.719, - 512.353, - 656.214, - 750.042, + 512.3530000000001, + 656.2139999999999, + 750.0419999999999, 468.59, 485.894, - 544.333, + 544.3330000000001, 499.2, 517.511, 351.832, @@ -56508,13 +56508,13 @@ 657.832, 597.681, 619.245, - 585.574, - 610.233, + 585.5740000000001, + 610.2330000000001, 705.977, 628.797, - 513.333, + 513.3330000000001, 615.568, - 728.769, + 728.7689999999999, 773.181, 676.209, 715.171, @@ -56538,23 +56538,23 @@ 45.21, 588.388, 566.977, - 792.896, + 792.8960000000001, 403.875, 785.888, 776.655, - 770.142, + 770.1419999999999, 767.747, - 766.371, - 762.974, - 764.604, - 760.662, - 754.881, + 766.3710000000001, + 762.9739999999999, + 764.6039999999999, + 760.6619999999999, + 754.8810000000001, 753.956, - 749.646, + 749.6460000000001, 738.365, - 739.224, - 744.256, - 574.459, + 739.2239999999999, + 744.2560000000001, + 574.4590000000001, 474.712, 323.199, 507.332, @@ -56562,7 +56562,7 @@ 560.607, 507.959, 705.817, - 680.267, + 680.2669999999999, 530.851, 415.689, 521.86, @@ -56573,23 +56573,23 @@ 444.961, 480.119, 570.082, - 601.292, - 681.594, - 680.256, - 700.251, + 601.2919999999999, + 681.5939999999999, + 680.2560000000001, + 700.2510000000001, 688.784, 685.684, 689.466, - 697.014, + 697.0139999999999, 678.813, 659.842, - 666.861, + 666.8610000000001, 622.268, 366.284, - 573.171, + 573.1709999999999, 577.112, 590.391, - 582.343, + 582.3430000000001, 558.493, 559.242, 282.018, @@ -56613,7 +56613,7 @@ 150.44, 160.151, 157.327, - 179.844, + 179.84400000000002, 197.891, 182.019, 173.672, @@ -56629,7 +56629,7 @@ 235.377, 216.697, 262.265, - 260.669, + 260.66900000000004, 309.672, 267.837, 229.062, @@ -56651,9 +56651,9 @@ 349.514, 329.546, 343.403, - 325.792, + 325.79200000000003, 307.327, - 334.683, + 334.68300000000005, 331.622, 327.074, 319.642, @@ -56664,7 +56664,7 @@ 300.819, 294.802, 291.185, - 287.287, + 287.28700000000003, 284.573, 282.497, 279.007, @@ -56689,21 +56689,21 @@ 66.565, 69.164, 69.786, - 69.511, + 69.51100000000001, 66.384, - 66.213, + 66.21300000000001, 65.756, 65.304, 67.028, 67.529, 64.578, - 61.016, + 61.016000000000005, 58.538, - 58.373, + 58.373000000000005, 55.989, 53.914, 54.927, - 55.009, + 55.00899999999999, 55.235, 53.363, 49.113, @@ -56722,7 +56722,7 @@ 13.521, 12.227, 10.333, - 7.542, + 7.542000000000001, 5.989, 3.749, 0.947, @@ -57496,13 +57496,13 @@ 3.072, 3.963, 5.263, - 6.122, + 6.122000000000001, 7.685, 9.425, 11.253, 12.475, 14.611, - 17.022, + 17.022000000000002, 18.718, 21.278, 23.199, @@ -57511,16 +57511,16 @@ 29.112, 31.011, 33.604, - 35.862, + 35.861999999999995, 37.926, 40.448, 42.782, 45.573, 55.153, 69.654, - 81.364, + 81.36399999999999, 87.095, - 54.211, + 54.211000000000006, 99.482, 100.423, 99.24, @@ -57533,7 +57533,7 @@ 133.351, 138.135, 142.82, - 146.867, + 146.86700000000002, 151.244, 157.178, 163.488, @@ -57564,7 +57564,7 @@ 287.827, 295.215, 299.856, - 302.234, + 302.23400000000004, 309.892, 314.621, 317.44, @@ -57572,7 +57572,7 @@ 330.642, 331.875, 339.731, - 342.627, + 342.62699999999995, 346.481, 350.742, 356.782, @@ -57610,7 +57610,7 @@ 504.92, 510.327, 512.226, - 518.343, + 518.3430000000001, 519.24, 519.95, 524.58, @@ -57630,17 +57630,17 @@ 579.172, 585.007, 588.85, - 592.291, + 592.2909999999999, 592.484, 599.475, 602.933, - 607.541, + 607.5409999999999, 611.576, 615.832, 614.643, 623.391, 624.674, - 629.843, + 629.8430000000001, 631.236, 637.975, 640.595, @@ -57655,9 +57655,9 @@ 669.058, 667.434, 668.821, - 675.004, + 675.0039999999999, 678.252, - 681.142, + 681.1419999999999, 684.534, 688.674, 696.695, @@ -57668,33 +57668,33 @@ 707.293, 708.262, 715.21, - 718.772, + 718.7719999999999, 718.799, - 722.631, - 725.901, + 722.6310000000001, + 725.9010000000001, 723.291, - 729.006, + 729.0060000000001, 734.077, 735.04, - 736.532, + 736.5319999999999, 745.318, 741.426, 738.029, 755.674, 753.345, - 754.848, + 754.8480000000001, 755.977, 760.513, 759.913, - 760.233, + 760.2330000000001, 764.301, - 764.631, + 764.6310000000001, 772.047, 774.007, 775.565, - 775.851, + 775.8510000000001, 779.59, - 781.511, + 781.5110000000001, 784.148, 788.178, 791.558, @@ -57702,22 +57702,22 @@ 794.3, 796.497, 786.967, - 782.744, - 733.366, + 782.7439999999999, + 733.3660000000001, 790.253, 783.405, - 773.611, - 741.729, - 730.636, - 744.261, + 773.6110000000001, + 741.7289999999999, + 730.6360000000001, + 744.2610000000001, 735.436, 725.538, 714.428, 706.302, 687.826, - 671.354, + 671.3539999999999, 649.106, - 628.417, + 628.4169999999999, 630.014, 630.129, 608.818, @@ -57727,19 +57727,19 @@ 616.003, 620.891, 620.082, - 615.892, + 615.8919999999999, 584.633, 586.362, - 572.593, + 572.5930000000001, 555.085, 545.561, 554.111, - 573.848, + 573.8480000000001, 594.306, 596.937, 597.169, 593.364, - 613.459, + 613.4590000000001, 608.592, 602.993, 615.16, @@ -57759,7 +57759,7 @@ 581.831, 576.033, 574.2, - 517.963, + 517.9630000000001, 499.189, 507.425, 484.209, @@ -57770,7 +57770,7 @@ 514.406, 498.055, 466.174, - 441.873, + 441.8730000000001, 432.734, 410.426, 409.655, @@ -57779,10 +57779,10 @@ 400.412, 421.712, 450.23, - 458.488, + 458.48800000000006, 452.316, 439.483, - 446.183, + 446.1830000000001, 440.832, 451.667, 448.776, @@ -57898,24 +57898,24 @@ 124.135, 121.135, 124.344, - 135.052, + 135.05200000000002, 147.357, 153.363, 137.062, - 155.147, - 169.626, - 134.419, + 155.14700000000002, + 169.62599999999998, + 134.41899999999998, 107.514, 95.38, 90.662, 87.965, - 82.261, - 79.228, + 82.26100000000001, + 79.22800000000001, 74.796, 68.294, 62.337, - 56.259, - 50.891, + 56.25899999999999, + 50.891000000000005, 47.985, 46.085, 44.246, @@ -57938,8 +57938,8 @@ 16.692, 13.906, 12.618, - 12.029, - 11.324, + 12.029000000000002, + 11.324000000000002, 10.603, 9.882, 9.194, @@ -57948,7 +57948,7 @@ 5.307, 4.652, 3.848, - 2.851, + 2.8510000000000004, 2.741, 2.196, 0.836, @@ -58727,7 +58727,7 @@ -2.048, -1.509, -0.92, - -0.832, + -0.8320000000000001, -0.193, 0.765, 2.813, @@ -58753,7 +58753,7 @@ 56.347, 68.542, 82.652, - 83.731, + 83.73100000000001, 60.493, 101.486, 101.838, @@ -58781,9 +58781,9 @@ 179.106, 169.72, 172.313, - 178.534, + 178.53400000000002, 174.201, - 154.723, + 154.72299999999998, 169.951, 194.956, 227.719, @@ -58820,7 +58820,7 @@ 254.249, 317.335, 293.558, - 289.787, + 289.78700000000003, 238.647, 296.536, 333.059, @@ -58832,12 +58832,12 @@ 357.844, 310.228, 368.574, - 300.109, + 300.10900000000004, 273.518, 332.96, 394.785, 392.798, - 323.804, + 323.80400000000003, 474.619, 447.351, 481.627, @@ -58850,10 +58850,10 @@ 524.266, 536.494, 533.758, - 526.171, + 526.1709999999999, 530.955, 526.865, - 545.958, + 545.9580000000001, 558.736, 552.085, 551.133, @@ -58861,10 +58861,10 @@ 549.635, 585.161, 601.248, - 583.416, + 583.4159999999999, 557.37, - 588.233, - 594.537, + 588.2330000000001, + 594.5369999999999, 580.14, 565.359, 612.975, @@ -58874,7 +58874,7 @@ 391.086, 400.395, 461.593, - 477.113, + 477.11300000000006, 420.666, 325.726, 419.659, @@ -58882,34 +58882,34 @@ 308.428, 557.26, 662.633, - 668.766, + 668.7660000000001, 672.163, 710.447, - 701.644, + 701.6439999999999, 708.025, 703.5, - 699.646, + 699.6460000000001, 703.015, 720.919, 720.842, - 713.272, + 713.2719999999999, 716.927, 712.919, - 715.474, + 715.4739999999999, 715.584, - 722.532, + 722.5319999999999, 730.68, 745.39, - 750.108, + 750.1080000000001, 746.596, - 687.479, - 721.744, + 687.4789999999999, + 721.7439999999999, 712.457, 728.202, - 727.024, - 749.139, + 727.0239999999999, + 749.1389999999999, 775.18, - 757.006, + 757.0060000000001, 771.684, 772.488, 590.909, @@ -58924,24 +58924,24 @@ 521.519, 584.82, 521.602, - 701.611, - 698.292, + 701.6110000000001, + 698.2919999999999, 755.96, - 753.483, + 753.4830000000001, 317.743, 684.148, 693.32, - 782.838, + 782.8380000000001, 720.451, 545.754, - 769.531, + 769.5310000000001, 523.011, 617.654, 597.559, - 362.738, + 362.73800000000006, 746.095, 748.567, - 767.224, + 767.2239999999999, 701.98, 581.864, 730.173, @@ -58950,22 +58950,22 @@ 586.571, 559.451, 632.535, - 673.121, + 673.1210000000001, 501.777, 787.908, - 544.152, + 544.1519999999999, 743.21, 539.637, 657.832, 679.628, 716.547, - 740.397, + 740.3969999999999, 556.115, 389.027, 621.354, 701.633, 693.188, - 601.166, + 601.1659999999999, 281.765, 389.379, 497.191, @@ -58974,12 +58974,12 @@ 577.993, 346.431, 630.206, - 664.896, + 664.8960000000001, 625.461, 525.246, 600.026, 436.577, - 547.532, + 547.5319999999999, 513.492, 214.682, 423.71, @@ -58993,7 +58993,7 @@ 399.151, 573.193, 622.378, - 634.704, + 634.7040000000001, 623.611, 607.695, 346.338, @@ -59011,7 +59011,7 @@ 516.465, 559.677, 561.697, - 388.113, + 388.11300000000006, 125.28, 104.624, 124.95, @@ -59032,7 +59032,7 @@ 131.87, 130.417, 284.358, - 177.036, + 177.03599999999997, 151.689, 163.565, 181.039, @@ -59044,15 +59044,15 @@ 95.001, 87.656, 93.195, - 84.783, + 84.78299999999999, 86.164, 91.642, 102.581, 113.339, 119.351, 133.918, - 170.309, - 143.052, + 170.30900000000003, + 143.05200000000002, 112.139, 103.264, 103.093, @@ -59064,7 +59064,7 @@ 98.64, 97.456, 88.311, - 86.798, + 86.79799999999999, 87.833, 92.958, 94.411, @@ -59081,7 +59081,7 @@ 175.054, 78.985, 70.342, - 71.349, + 71.34899999999999, 91.494, 117.952, 55.615, @@ -59090,7 +59090,7 @@ 47.621, 52.24, 67.749, - 141.301, + 141.30100000000002, 150.236, 109.816, 65.932, @@ -59100,8 +59100,8 @@ 54.949, 49.003, 45.7, - 41.384, - 37.513, + 41.38399999999999, + 37.513000000000005, 35.151, 30.824, 29.272, @@ -59118,29 +59118,29 @@ 18.503, 17.066, 15.712, - 14.154, + 14.154000000000002, 12.144, 10.284, 8.709, 8.34, 8.252, - 6.078, + 6.077999999999999, 5.114, 4.74, 4.938, 4.999, 5.362, 5.676, - 5.758, + 5.757999999999999, 5.604, 4.756, - 4.531, + 4.531000000000001, 3.248, 2.725, 1.348, - 0.082, + 0.0819999999999999, -1.663, - -1.377, + -1.3769999999999998, -1.558, null, null, @@ -60809,7 +60809,7 @@ 800, 798.418, 800, - 799.866, + 799.8660000000001, 800, 800, 800, @@ -61064,7 +61064,7 @@ 800, 800, 800, - 797.598, + 797.5980000000001, 795.23, 800, 800, @@ -61259,7 +61259,7 @@ 800, 796.909, 791.784, - 794.861, + 794.8610000000001, 800, 800, 800, @@ -62129,7 +62129,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -76204,11 +76204,11 @@ 94.197, 27.059, 79.618, - 67.463, + 67.46300000000001, 40.778, - 169.499, - 151.932, - 150.478, + 169.49900000000002, + 151.93200000000002, + 150.47799999999998, 209.925, 42.622, null, @@ -76238,7 +76238,7 @@ 114.572, 123.342, 125.693, - 152.427, + 152.42700000000002, 69.164, 13.053, 85.256, @@ -76265,24 +76265,24 @@ 58.313, 200.269, 49.003, - 174.212, - 170.689, + 174.21200000000002, + 170.68900000000002, 160.465, 140.541, 118.305, 96.531, 33.654, 58.676, - 45.171, + 45.17100000000001, 24.229, 39.969, 31.975, 28.143, 26.805, 15.784, - 17.556, - 7.839, - 15.051, + 17.555999999999997, + 7.838999999999999, + 15.050999999999998, 7.058, 5.285, 3.512, @@ -76313,22 +76313,22 @@ 3.941, 3.897, 5.4, - 7.283, - 7.861, + 7.282999999999999, + 7.861000000000001, 5.433, 5.472, - 5.2075, - 4.943, + 5.2075000000000005, + 4.9430000000000005, -0.815, -0.193, 5.307, - 4.536, + 4.5360000000000005, 7.146, 2.009, 7.058, 4.96, 6.265, - 5.786, + 5.7860000000000005, 2.02, 7.443, 2.824, @@ -76336,7 +76336,7 @@ null, null, null, - 5.054, + 5.053999999999999, 7.3, 4.96, 5.684, @@ -77273,20 +77273,20 @@ 2.736, -1.47, 0.754, - 4.608, - 1.299, + 4.6080000000000005, + 1.2990000000000002, 3.589, 3.033, 3.2645, - 3.496, + 3.4960000000000004, 3.49, 1.855, - 5.703, + 5.702999999999999, 3.567, 1.927, 6.683, 1.585, - 5.588, + 5.587999999999999, 2.516, 6.738, 1.249, @@ -77301,19 +77301,19 @@ 8.037, 6.54, 16.02, - 13.824, + 13.824000000000002, 16.202, 5.318, null, null, 15.442, 22.792, - 17.876, + 17.875999999999998, 20.931, 12.513, - 6.083, + 6.082999999999999, 20.882, - 12.029, + 12.029000000000002, 18.316, 0.589, 8.913, @@ -77324,10 +77324,10 @@ 28.545, 30.582, 25.886, - 23.078, + 23.078000000000003, 32.509, - 6.457, - 0.572, + 6.457000000000001, + 0.5720000000000001, 41.857, 33.819, 19.775, @@ -77337,7 +77337,7 @@ 31.375, 51.618, 53.6, - 59.171, + 59.17100000000001, 20.425, 39.649, 33.643, @@ -77345,7 +77345,7 @@ 42.82, 9.364, 86.836, - 73.353, + 73.35300000000001, 75.979, 83.478, 97.225, @@ -77358,7 +77358,7 @@ 3.011, 76.205, 150.632, - 15.971, + 15.970999999999998, 65.827, 220.914, 166.02, @@ -77371,11 +77371,11 @@ 497.169, 207.266, 596.558, - 640.199, + 640.1990000000001, 630.46, 360.399, 528.313, - 725.631, + 725.6310000000001, 451.617, 93.244, 718.199, @@ -77390,9 +77390,9 @@ 71.206, 540.865, 415.04, - 773.099, - 625.973, - 669.146, + 773.0989999999999, + 625.9730000000001, + 669.1460000000001, 721.442, 306.253, 482.728, @@ -77422,12 +77422,12 @@ 666.823, 225.555, 526.496, - 721.161, + 721.1610000000001, 425.957, 742.324, 789.455, 705.828, - 174.151, + 174.15099999999998, 610.145, 445.699, 87.992, @@ -77435,25 +77435,25 @@ 474.762, 674.541, 390.348, - 669.036, + 669.0360000000001, 227.02, 404.64, - 473.683, + 473.6830000000001, 731.186, 676.055, 233.626, - 734.792, + 734.7919999999999, 550.967, 412.386, 445.859, 587.132, 662.039, 559.451, - 668.739, - 772.141, - 342.688, + 668.7389999999999, + 772.1410000000001, + 342.68800000000005, 417.682, - 766.536, + 766.5360000000001, 463.872, 615.457, 565.045, @@ -77467,16 +77467,16 @@ 119.499, 505.757, 564.109, - 749.728, + 749.7280000000001, 494.086, - 342.368, + 342.36800000000005, 711.681, 607.965, 641.663, 236.472, 285.151, 610.508, - 762.589, + 762.5889999999999, 141.582, 653.434, 776.633, @@ -77500,10 +77500,10 @@ 122.533, 90.47, 561.808, - 633.829, + 633.8290000000001, 611.549, 629.689, - 690.766, + 690.7660000000001, 712.765, 766.602, 488.459, @@ -77513,23 +77513,23 @@ 184.48, 602.58, 438.465, - 454.678, + 454.67800000000005, 277.62, 594.394, 694.735, 282.844, 675.158, 562.215, - 578.907, + 578.9069999999999, 493.585, - 629.584, + 629.5840000000001, 750.345, - 767.527, + 767.5269999999999, 275.863, 759.847, 721.21, 380.961, - 0.468, + 0.4679999999999999, 377.454, 713.409, 204.596, @@ -77538,16 +77538,16 @@ 767.423, 765.419, 338.305, - 348.518, + 348.51800000000003, 289.588, 479.64, 440.32, 502.399, - 417.688, - 744.889, - 745.401, - 742.527, - 738.657, + 417.6880000000001, + 744.8889999999999, + 745.4010000000001, + 742.5269999999999, + 738.6569999999999, 599.448, 484.804, 306.132, @@ -77577,7 +77577,7 @@ 576.606, 376.48, 15.057, - 631.208, + 631.2080000000001, 439.17, 622.24, 498.628, @@ -77597,10 +77597,10 @@ 333.521, 299.559, 386.72, - 512.733, + 512.7330000000001, 555.614, 551.65, - 411.428, + 411.42800000000005, 431.099, 541.746, 535.927, @@ -77610,10 +77610,10 @@ 232.156, 463.569, 338.79, - 497.373, + 497.3730000000001, 333.819, 404.932, - 337.072, + 337.07199999999995, 322.307, 258.956, 470.126, @@ -77639,7 +77639,7 @@ 250.891, 269.648, 223.557, - 219.119, + 219.11900000000003, 224.762, 245.105, 202.466, @@ -77651,9 +77651,9 @@ 119.566, 90.673, 121.702, - 88.378, + 88.37799999999999, 116.037, - 88.763, + 88.76299999999999, 102.438, 107.85, 97.764, @@ -77664,19 +77664,19 @@ 44.213, 0.429, 39.936, - 51.134, + 51.13399999999999, 54.354, - 64.159, + 64.15899999999999, 26.227, 0.335, - 43.156, + 43.156000000000006, 41.059, 40.954, 48.133, 37.183, 25.748, 35.311, - 28.848, + 28.848000000000003, 20.342, 24.763, 29.96, @@ -78524,10 +78524,10 @@ 43.558, 40.36, 24.647, - 29.668, + 29.668000000000003, 46.983, - 43.696, - 0.666, + 43.696000000000005, + 0.6659999999999999, 0.677, 16.059, 31.441, @@ -78543,10 +78543,10 @@ 79.387, 41.852, 92.044, - 87.343, + 87.34299999999999, 63.438, 84.116, - 80.659, + 80.65899999999999, 117.98, 121.867, 153.159, @@ -78575,10 +78575,10 @@ 130.918, 202.504, 215.15, - 141.598, + 141.59799999999998, 164.55, 116.45, - 167.914, + 167.91400000000002, 160.74, 152.179, 9.469, @@ -78586,14 +78586,14 @@ 247.246, 227.889, 160.939, - 138.432, + 138.43200000000002, 138.493, 36.935, 107.619, 116.251, 92.897, 11.561, - 5.786, + 5.7860000000000005, 126.849, 134.314, 134.81, @@ -78606,12 +78606,12 @@ 145.16, 10.074, 60.41, - 148.177, + 148.17700000000002, 118.723, 157.079, 157.239, 83.5, - 171.432, + 171.43200000000002, 168.602, 171.861, 94.934, @@ -78625,7 +78625,7 @@ 259.777, 306.815, 29.987, - 12.359, + 12.359000000000002, 152.091, 86.324, 302.46, @@ -78642,11 +78642,11 @@ 171.856, 170.391, 160.482, - 264.544, + 264.54400000000004, 171.696, 281.435, - 312.667, - 355.758, + 312.66700000000003, + 355.75800000000004, 436.912, 318.183, 316.025, @@ -78676,13 +78676,13 @@ 400.401, 392.319, 455.46, - 485.558, + 485.5580000000001, 366.829, 378.236, 450.252, 408.224, 400.979, - 345.363, + 345.36300000000006, 357.222, 446.789, 129.376, @@ -78697,7 +78697,7 @@ 51.145, 336.059, 189.032, - 136.478, + 136.47799999999998, 245.11, 580.383, 432.117, @@ -78705,8 +78705,8 @@ 579.986, 598.44, 264.588, - 522.593, - 615.171, + 522.5930000000001, + 615.1709999999999, 339.324, 324.14, 107.206, @@ -78716,12 +78716,12 @@ 445.974, 305.466, 337.7, - 336.924, + 336.92400000000004, 403.566, 217.308, 156.782, 334.788, - 354.502, + 354.50199999999995, 382.552, 404.761, 437.21, @@ -78734,7 +78734,7 @@ 111.908, 191.482, 473.523, - 34.788, + 34.788000000000004, 250.263, 353.577, 457.86, @@ -78756,10 +78756,10 @@ 540.529, 518.645, 426.909, - 161.979, + 161.97899999999998, 67.997, 204.938, - 0.468, + 0.4679999999999999, 181.033, 44.153, 262.331, @@ -78772,7 +78772,7 @@ 682.689, 531.297, 731.153, - 747.994, + 747.9939999999999, 758.295, 465.739, 608.818, @@ -78782,7 +78782,7 @@ 746.965, 234.694, 767.659, - 672.917, + 672.9169999999999, 394.565, 516.14, 487.171, @@ -78796,13 +78796,13 @@ 443.833, 661.554, 511.45, - 748.969, + 748.9689999999999, 593.987, 650.653, 572.906, 515.48, 725.835, - 666.751, + 666.7510000000001, 436.164, 474.635, 670.187, @@ -78810,9 +78810,9 @@ 562.187, 354.684, 346.096, - 659.011, + 659.0110000000001, 339.528, - 529.348, + 529.3480000000001, 486.857, 493.469, 796.073, @@ -78820,8 +78820,8 @@ 662.936, 619.752, 428.853, - 545.963, - 757.001, + 545.9630000000001, + 757.0010000000001, 714.23, 666.663, 472.697, @@ -78835,8 +78835,8 @@ 212.827, 595.341, 662.903, - 792.417, - 572.829, + 792.4169999999999, + 572.8290000000001, 371.376, 741.388, 649.332, @@ -78845,15 +78845,15 @@ 764.285, 697.548, 697.201, - 680.504, + 680.5039999999999, 600.4304999999999, 520.357, 759.798, - 600.978, - 448.683, + 600.9780000000001, + 448.6830000000001, 730.454, - 720.484, - 635.167, + 720.4839999999999, + 635.1669999999999, 688.569, 419.229, 541.592, @@ -78872,7 +78872,7 @@ 619.476, 352.163, 563.663, - 549.343, + 549.3430000000001, 311.577, 516.405, 512.375, @@ -78903,7 +78903,7 @@ 161.715, 218.31, 317.715, - 177.466, + 177.46599999999998, 311.687, 223.782, 293.8, @@ -78914,12 +78914,12 @@ 94.99, 279.541, 229.761, - 155.538, + 155.53799999999998, 295.848, 303.952, 306.594, 301.133, - 129.288, + 129.28799999999998, 176.497, 300.384, 302.933, @@ -78941,17 +78941,17 @@ 127.433, 128.22, 57.646, - 177.939, + 177.93900000000002, 133.219, 273.232, - 196.619, + 196.61900000000003, 166.136, 219.642, - 175.038, + 175.03799999999998, 222.604, 181.551, 180.84, - 178.814, + 178.81400000000002, 200.594, 50.28, 184.061, @@ -78961,11 +78961,11 @@ 199.129, 106.644, 195.424, - 153.897, + 153.89700000000002, 204.095, 78.672, 159.1, - 141.163, + 141.16299999999998, 189.511, 183.841, 179.128, @@ -79014,13 +79014,13 @@ 80.626, 66.24, 77.604, - 78.864, + 78.86399999999999, 56.705, 76.882, 81.182, 51.112, 82.646, - 87.987, + 87.98700000000001, 85.069, 0.484, 0.517, @@ -79029,7 +79029,7 @@ 91.769, 68.999, 35.278, - 88.097, + 88.09700000000001, 98.227, 96.944, 90.431, @@ -79050,7 +79050,7 @@ 0.49, 168.101, 189.5, - 143.729, + 143.72899999999998, 194.191, 189.401, 195.947, @@ -79059,7 +79059,7 @@ 44.268, 288.944, 315.238, - 224.839, + 224.83900000000003, 296.426, 244.923, 69.544, @@ -79071,7 +79071,7 @@ 334.975, 329.546, 302.587, - 26.238, + 26.238000000000003, 290.634, 149.944, 233.835, @@ -79095,7 +79095,7 @@ 39.87, 44.676, 41.631, - 41.196, + 41.196000000000005, 36.043, 38.741, 18.377, @@ -79953,7 +79953,7 @@ null, null, null, - 0.572, + 0.5720000000000001, -5.148, 0.016, null, @@ -79987,7 +79987,7 @@ 29.2, 15.31, 45.078, - 43.366, + 43.36600000000001, 46.542, 29.261, 48.018, @@ -80000,18 +80000,18 @@ 58.863, 60.878, 39.589, - 0.567, + 0.5670000000000001, 42.386, 32.927, 34.529, 38.212, - 56.936, + 56.93600000000001, 43.503, 55.235, 50.467, 50.556, 16.747, - 20.898, + 20.898000000000003, 61.027, 46.658, 57.173, @@ -80021,18 +80021,18 @@ 71.421, 83.77, 26.783, - 41.461, + 41.461000000000006, 78.044, 74.124, - 59.634, + 59.63399999999999, 115.508, 118.156, 107.745, - 79.591, + 79.59100000000001, 126.271, 122.638, 188.741, - 166.059, + 166.05900000000003, 138.108, 126.805, 122.39, @@ -80048,11 +80048,11 @@ 172.555, 116.609, 119.758, - 82.701, + 82.70100000000001, 158.208, 125.814, 124.834, - 156.182, + 156.18200000000002, 89.572, 148.832, 146.68, @@ -80081,7 +80081,7 @@ 106.71, 136.847, 85.322, - 169.538, + 169.53799999999998, 120.303, 46.845, 134.782, @@ -80108,10 +80108,10 @@ 248.067, 235.443, 229.068, - 167.589, - 167.897, + 167.58900000000003, + 167.89700000000002, 225.467, - 144.087, + 144.08700000000002, 126.579, 203.523, 270.215, @@ -80123,8 +80123,8 @@ 150.809, 356.281, 501.089, - 540.166, - 598.176, + 540.1659999999999, + 598.1759999999999, 477.206, 603.368, 338.476, @@ -80139,7 +80139,7 @@ 538.3, 680.074, 689.962, - 594.333, + 594.3330000000001, 649.607, 619.317, 597.389, @@ -80152,7 +80152,7 @@ 377.162, 325.076, 300.252, - 294.169, + 294.16900000000004, 221.927, 138.68, 70.925, @@ -80167,10 +80167,10 @@ 470.374, 384.898, 347.764, - 341.873, + 341.87300000000005, 441.509, 522.923, - 507.123, + 507.1230000000001, 41.841, 446.007, 166.46, @@ -80181,18 +80181,18 @@ 578.258, 596.569, 105.125, - 712.226, + 712.2260000000001, 503.555, 469.895, 526.391, 528.66, - 756.037, - 756.142, - 168.288, + 756.0369999999999, + 756.1419999999999, + 168.28799999999998, 626.887, 589.665, - 739.521, - 659.792, + 739.5210000000001, + 659.7919999999999, 518.381, 412.386, 631.996, @@ -80212,34 +80212,34 @@ 504.931, 597.02, 179.035, - 628.412, - 799.161, + 628.4119999999999, + 799.1610000000001, 782.414, 749.073, 552.085, 628.77, 508.196, - 788.833, + 788.8330000000001, 734.456, 98.26, 784.115, 328.495, 471.029, - 5.637, + 5.6370000000000005, 515.656, - 310.977, + 310.97700000000003, 44.037, 74.565, 30.621, 455.251, 384.512, - 505.053, + 505.05300000000005, 717.059, - 377.553, + 377.55300000000005, 594.57, 652.944, 406.6, - 464.748, + 464.7480000000001, 697.658, 353.539, 314.654, @@ -80247,7 +80247,7 @@ 662.628, 445.253, 451.089, - 698.958, + 698.9580000000001, 392.572, 720.17, 434.044, @@ -80258,11 +80258,11 @@ 777.057, 721.205, 517.726, - 740.463, + 740.4630000000001, 693.293, 187.552, 397.009, - 728.874, + 728.8739999999999, 651.727, 752.712, 79.883, @@ -80274,8 +80274,8 @@ 493.706, 756.643, 508.648, - 795.588, - 795.522, + 795.5880000000001, + 795.5219999999999, 563.244, 328.407, 403.049, @@ -80286,7 +80286,7 @@ 303.88, 378.55, 364.753, - 520.473, + 520.4730000000001, 478.929, 460.096, 418.431, @@ -80294,8 +80294,8 @@ 296.844, 289.996, 258.472, - 302.174, - 356.192, + 302.17400000000004, + 356.19199999999995, 158.334, 355.807, 0.264, @@ -80327,7 +80327,7 @@ 16.912, 375.643, 205.725, - 311.549, + 311.54900000000004, 316.4, 222.588, 206.782, @@ -80342,7 +80342,7 @@ 255.593, 0.473, 121.558, - 152.383, + 152.38299999999998, 113.878, 128.418, 96.652, @@ -80356,7 +80356,7 @@ 97.599, 60.757, 128.374, - 135.427, + 135.42700000000002, 140.695, 144.813, 146.404, @@ -80413,7 +80413,7 @@ 5.626, 12.387, 12.1, - 12.266, + 12.265999999999998, 6.331, 12.007, 15.178, @@ -80428,11 +80428,11 @@ 6.023, 5.428, 5.318, - 5.208, + 5.207999999999999, 2.505, 6.237, 3.039, - 1.343, + 1.3430000000000002, 2.758, 0.644, 1.899, @@ -80444,14 +80444,14 @@ 0.616, 6.485, 1.123, - 5.802, + 5.8020000000000005, 9.975, 9.458, 8.693, 7.564, 5.312, 4.913, - 4.514, + 4.513999999999999, 1.315, 0, 3.033, @@ -80468,7 +80468,7 @@ null, null, null, - -7.179, + -7.178999999999999, null, null, null, @@ -81399,18 +81399,18 @@ 3.474, -3.623, 4.723, - 7.867, + 7.867000000000001, 3.297, - 7.162, + 7.162000000000001, 11.462, - 7.377, + 7.377000000000001, 10.146, 6.193, 6.903, 7.806, 3.523, 13.257, - 5.538, + 5.537999999999999, 1.888, 13.042, 14.528, @@ -81431,14 +81431,14 @@ 24.174, 24.581, 33.434, - 17.182, + 17.182000000000002, 8.379, 32.503, 31.782, 32.107, 59.562, 34.1, - 26.453, + 26.453000000000003, 36.869, 1.403, 25.688, @@ -81453,9 +81453,9 @@ 106.755, 71.972, 145.518, - 154.073, + 154.07299999999998, 172.362, - 178.402, + 178.40200000000002, 191.114, 208.637, 212.772, @@ -81482,18 +81482,18 @@ 615.067, 398.259, 502.746, - 669.399, + 669.3989999999999, 677.184, 437.65, 302.317, 402.746, 261.775, 718.887, - 716.619, + 716.6189999999999, 434.352, 479.232, 654.276, - 737.749, + 737.7489999999999, 360.811, 289.599, 715.727, @@ -81514,9 +81514,9 @@ 775.637, 787.325, 613.971, - 672.251, + 672.2510000000001, 498.055, - 385.558, + 385.5580000000001, 590.259, 525.279, 584.534, @@ -81526,16 +81526,16 @@ 220.556, 264.286, 274.603, - 510.563, + 510.5630000000001, 590.254, - 525.147, + 525.1469999999999, 406.06, 502.19, 754.056, 477.911, - 705.129, + 705.1289999999999, 601.369, - 779.496, + 779.4960000000001, 755.597, 323.991, 581.005, @@ -81547,32 +81547,32 @@ 536.4, 313.096, 143.806, - 725.119, + 725.1189999999999, 498.259, 142.033, 305.703, 535.976, 383.962, 494.466, - 546.392, + 546.3919999999999, 320.87, 229.982, 643.766, - 796.601, + 796.6010000000001, 721.012, 535.283, - 668.348, + 668.3480000000001, 705.922, 274.146, 762, 599.007, - 787.259, + 787.2589999999999, 504.854, - 785.486, - 146.949, + 785.4860000000001, + 146.94899999999998, 574.217, - 614.301, - 746.656, + 614.3009999999999, + 746.6560000000001, 721.095, 578.181, 600.554, @@ -81583,35 +81583,35 @@ 143.53, 496.64, 420.462, - 711.009, + 711.0089999999999, 275.214, 553.401, 748.176, 670.676, - 566.834, + 566.8340000000001, 113.906, - 312.854, + 312.85400000000004, 739.13, 343.101, - 711.389, + 711.3889999999999, 732.59, 680.658, 327.003, - 332.437, - 6.991, + 332.43699999999995, + 6.9910000000000005, 18.233, 316.956, - 520.468, + 520.4680000000001, 283.868, 293.123, 19.516, 528.175, - 770.792, + 770.7919999999999, 36.264, - 441.928, + 441.92800000000005, 704.623, - 705.751, - 772.719, + 705.7510000000001, + 772.7189999999999, 80.053, 604.265, 352.796, @@ -81622,15 +81622,15 @@ 400.847, 760.53, 509.528, - 678.896, + 678.8960000000001, 485.47, 721.585, 452.839, 382.233, 516.173, - 728.599, + 728.5989999999999, 289.654, - 83.026, + 83.02600000000001, 380.62, 486.951, 244.637, @@ -81651,13 +81651,13 @@ 222.946, 70.116, 482.431, - 404.563, + 404.5630000000001, 149.124, 432.282, 0.143, 183.241, 570.765, - 778.521, + 778.5210000000001, 437.826, 382.646, 218.172, @@ -81665,7 +81665,7 @@ 313.603, 359.017, 258.087, - 312.727, + 312.72700000000003, 491.003, 5.956, 616.432, @@ -81679,7 +81679,7 @@ 255.268, 212.183, 178.358, - 421.123, + 421.1230000000001, 627.211, 503.693, 534.627, @@ -81697,10 +81697,10 @@ 507.222, 653.379, 766.701, - 534.583, + 534.5830000000001, 547.433, 792.164, - 684.886, + 684.8860000000001, 566.195, 662.985, 374.878, @@ -81710,10 +81710,10 @@ 627.878, 618.194, 766.25, - 466.113, + 466.11300000000006, 515.43, 789.18, - 596.084, + 596.0840000000001, 604.122, 588.09, 575.56, @@ -81721,7 +81721,7 @@ 738.437, 514.99, 564.747, - 512.204, + 512.2040000000001, 439.671, 486.989, 599.448, @@ -81732,7 +81732,7 @@ 211.654, 126.86, 178.82, - 264.924, + 264.92400000000004, 220.088, 77.218, 229.866, @@ -81742,15 +81742,15 @@ 278.055, 236.379, 531.01, - 550.588, - 142.677, + 550.5880000000001, + 142.67700000000002, 84.177, - 425.808, + 425.8080000000001, 577.636, 580.702, - 332.503, + 332.50300000000004, 583.763, - 570.407, + 570.4069999999999, 544.488, 543.893, 545.66, @@ -81758,11 +81758,11 @@ 430.372, 342.06, 503.715, - 335.823, + 335.82300000000004, 348.292, 312.86, 315.59, - 134.177, + 134.17700000000002, 205.422, 473.782, 479.629, @@ -81785,7 +81785,7 @@ 432.035, 302.988, 232.294, - 257.162, + 257.16200000000003, 425.555, 420.82, 421.519, @@ -81793,9 +81793,9 @@ 416.075, 216.835, 302.361, - 335.239, + 335.23900000000003, 406.512, - 307.299, + 307.29900000000004, 233.533, 277.9, 292.275, @@ -81803,27 +81803,27 @@ 106.76, 129.877, 221.459, - 201.744, + 201.74400000000003, 72.45, 109.474, 135.762, - 159.177, + 159.17700000000002, 244.885, - 173.496, + 173.49599999999998, 232.129, 263.961, 290.166, 189.974, 79.574, 141.356, - 26.398, + 26.398000000000003, 207.833, - 18.944, + 18.944000000000003, 152.334, 183.841, 157.734, - 163.961, - 165.943, + 163.96099999999998, + 165.94299999999998, 106.529, 151.018, 199.84, @@ -81849,18 +81849,18 @@ 20.788, 13.835, 26.381, - 7.674, + 7.673999999999999, 11.627, 9.161, 20.849, - 6.457, + 6.457000000000001, 0.446, 5.566, 14.479, 9.909, 15.453, 10.058, - 5.411, + 5.4110000000000005, 0.379, 5.502000000000001, 10.625, @@ -81868,10 +81868,10 @@ 6.155, 8.23, 7.388, - 5.076, + 5.0760000000000005, 5.874, - 2.851, - 1.701, + 2.8510000000000004, + 1.7009999999999998, 1.69, 2.939, 3.028, @@ -82678,11 +82678,11 @@ 16.108, 19.67, 26.673, - 12.596, + 12.595999999999998, 32.338, 37.612, 23.717, - 24.543, + 24.543000000000003, 38.774, 33.401, 36.897, @@ -82707,7 +82707,7 @@ 94.318, 172.984, 114.6, - 167.633, + 167.63299999999998, 26.767, 210.157, 180.466, @@ -82716,7 +82716,7 @@ 154.673, 233.863, 184.623, - 163.647, + 163.64700000000002, 191.356, 218.211, 157.52, @@ -82733,10 +82733,10 @@ 268.866, 150.599, 250.841, - 340.937, + 340.93699999999995, 111.687, 233.246, - 329.183, + 329.18300000000005, 90.822, 287.111, 247.852, @@ -82761,10 +82761,10 @@ 197.946, 272.841, 374.272, - 348.512, + 348.51199999999994, 393.519, 358.708, - 362.243, + 362.24300000000005, 282.288, 225.935, 461.588, @@ -82793,7 +82793,7 @@ 503.566, 583.565, 586.477, - 592.858, + 592.8580000000001, 596.8, 277.862, 576.716, @@ -82806,10 +82806,10 @@ 453.792, 505.763, 439.318, - 367.748, + 367.7480000000001, 639.158, 644.377, - 644.421, + 644.4209999999999, 653.186, 659.115, 659.291, @@ -82825,7 +82825,7 @@ 692.186, 475.109, 701.485, - 704.601, + 704.6010000000001, 460.25, 0.545, 129.013, @@ -82833,18 +82833,18 @@ 329.04, 208.373, 538.823, - 709.016, + 709.0160000000001, 571.761, 438.559, 741.944, - 741.514, + 741.5139999999999, 744.944, 744.168, 500.505, - 336.643, + 336.64300000000003, 371.861, 754.837, - 755.889, + 755.8889999999999, 234.914, 719.796, 298.507, @@ -82855,7 +82855,7 @@ 666.08, 547.873, 464.318, - 579.397, + 579.3969999999999, 736.488, 318.574, 651.231, @@ -82866,13 +82866,13 @@ 648.451, 735.86, 664.169, - 711.036, + 711.0360000000001, 729.925, 476.436, 352.586, 644.724, 625.967, - 160.212, + 160.21200000000002, 462.122, 507.976, 387.111, @@ -82885,17 +82885,17 @@ 795.054, 521.668, 386.445, - 689.136, + 689.1360000000001, 676.732, 136.17, - 269.852, + 269.85200000000003, 698.765, 357.431, - 680.162, + 680.1619999999999, 574.035, 116.235, 436.94, - 639.907, + 639.9069999999999, 215.271, 124.218, 226.243, @@ -82908,14 +82908,14 @@ 633.548, 458.389, 489.687, - 629.546, - 359.479, + 629.5459999999999, + 359.47900000000004, 604.034, 767.924, 547.163, 730.57, 21.019, - 491.058, + 491.0580000000001, 209.474, 468.106, 481.225, @@ -82937,7 +82937,7 @@ 796.227, 417.765, 606.737, - 422.863, + 422.86300000000006, 389.214, 93.872, 503.087, @@ -82949,10 +82949,10 @@ 701.204, 716.834, 593.689, - 667.984, + 667.9839999999999, 765.694, 670.247, - 527.718, + 527.7180000000001, 679.931, 764.466, 706.467, @@ -82962,16 +82962,16 @@ 560.002, 730.779, 514.555, - 613.454, + 613.4540000000001, 329.117, 243.106, 591.404, 348.76, 248.584, - 715.876, + 715.8760000000001, 734.539, - 729.881, - 790.286, + 729.8810000000001, + 790.2860000000001, 190.546, 783.807, 384.798, @@ -82982,7 +82982,7 @@ 638.118, 567.153, 284.909, - 718.871, + 718.8710000000001, 217.914, 642.109, 558.069, @@ -83010,31 +83010,31 @@ 504.75, 157.778, 731.698, - 790.006, + 790.0060000000001, 768.27, 352.619, 134.16, - 774.514, + 774.5139999999999, 780.465, 569.763, 773.721, - 674.156, + 674.1560000000001, 272.868, - 780.234, + 780.2339999999999, 379.656, 779.931, 776.903, - 776.088, + 776.0880000000001, 494.499, 0.363, 111.753, 765.253, - 534.671, - 560.084, + 534.6709999999999, + 560.0840000000001, 501.254, 438.922, - 284.479, - 745.396, + 284.47900000000004, + 745.3960000000001, 737.303, 468.216, 719.096, @@ -83044,11 +83044,11 @@ 717.318, 714.549, 503.77, - 705.526, + 705.5260000000001, 639.367, 228.897, 682.717, - 670.489, + 670.4889999999999, 413.283, 676.408, 671.805, @@ -83067,7 +83067,7 @@ 624.288, 269.295, 457.205, - 531.699, + 531.6990000000001, 421.101, 455.823, 494.146, @@ -83076,7 +83076,7 @@ 460.206, 5.428, 47.214, - 555.267, + 555.2669999999999, 483.389, 175.412, 360.575, @@ -83086,7 +83086,7 @@ 567.318, 569.686, 555.509, - 529.574, + 529.5740000000001, 443.607, 513.399, 454.607, @@ -83098,7 +83098,7 @@ 540.821, 108.858, 220.551, - 533.983, + 533.9830000000001, 218.101, 530.548, 504.128, @@ -83108,11 +83108,11 @@ 427.713, 282.073, 468.244, - 468.183, + 468.1830000000001, 266.46, 444.477, 401.573, - 350.489, + 350.48900000000003, 229.459, 141.884, 306.396, @@ -83129,11 +83129,11 @@ 220.033, 157.178, 222.604, - 236.214, + 236.21400000000003, 121.779, 195.259, 140.855, - 131.854, + 131.85399999999998, 234.105, 233.318, 198.48, @@ -83141,14 +83141,14 @@ 140.921, 0.363, 109.094, - 85.328, + 85.32799999999999, 244.648, - 178.159, + 178.15900000000002, 226.073, 215.012, 96.487, 136.467, - 179.216, + 179.21599999999998, 179.695, 171.856, 137.904, @@ -83169,11 +83169,11 @@ 41.918, 45.039, 57.878, - 54.211, + 54.211000000000006, 52.218, 50.187, 39.231, - 49.064, + 49.06399999999999, 40.376, 7.927, 26.128, @@ -83191,7 +83191,7 @@ 14.082, 26.712, 20.48, - 24.113, + 24.113000000000003, 13.46, 16.609, 15.211, @@ -83203,8 +83203,8 @@ 0.517, 9.838, 7.982, - 3.061, - 5.577, + 3.0610000000000004, + 5.577000000000001, 0.847, 4.668, -0.997, @@ -84014,14 +84014,14 @@ 38.653, 32.272, 41.136, - 53.138, + 53.138000000000005, 48.519, 49.807, 62.018, 35.262, 63.857, 72.005, - 65.299, + 65.29899999999999, 20.193, 90.684, 98.331, @@ -84034,10 +84034,10 @@ 141.092, 147.445, 158.329, - 168.619, + 168.61900000000003, 114.798, 114.457, - 176.409, + 176.40900000000002, 78.936, 104.398, 203.842, @@ -84058,13 +84058,13 @@ 255.515, 160.129, 104.707, - 210.619, + 210.61900000000003, 268.772, 243.194, 272.323, 277.091, 262.045, - 284.854, + 284.85400000000004, 262.072, 351.606, 355.945, @@ -84080,7 +84080,7 @@ 324.448, 340.921, 342.886, - 338.878, + 338.87800000000004, 327.449, 258.472, 333.797, @@ -84110,7 +84110,7 @@ 397.263, 503.583, 495.28, - 390.243, + 390.24300000000005, 263.988, 279.772, 193.305, @@ -84120,27 +84120,27 @@ 132.239, 448.072, 400.401, - 5.747, + 5.747000000000001, 0.55, 290.772, 333.362, 527.735, 43.129, - 0.567, + 0.5670000000000001, 365.497, 116.604, - 171.344, + 171.34400000000002, 183.186, 495.814, 485.035, 562.22, 384.275, - 368.998, + 368.9980000000001, 477.427, 268.453, 509.385, 425.384, - 502.498, + 502.4980000000001, 554.964, 513.294, 677.36, @@ -84156,10 +84156,10 @@ 714.345, 702.443, 717.885, - 724.134, + 724.1339999999999, 727.547, 706.307, - 539.142, + 539.1419999999999, 227.768, 250.484, 751.05, @@ -84179,7 +84179,7 @@ 181.804, 378.153, 591.482, - 541.718, + 541.7180000000001, 358.956, 150.275, 757.942, @@ -84187,7 +84187,7 @@ 274.316, 0.457, 204.205, - 610.277, + 610.2769999999999, 448.986, 611.918, 549.619, @@ -84198,7 +84198,7 @@ 100.253, 398.87, 190.613, - 0.468, + 0.4679999999999999, 105.714, 445.435, 0.473, @@ -84222,19 +84222,19 @@ 532.018, 494.895, 528.247, - 773.644, + 773.6439999999999, 621.304, 620.269, 730.977, - 755.531, + 755.5310000000001, 578.869, 491.603, 743.7, 343.772, - 716.536, + 716.5360000000001, 474.299, - 604.463, - 717.021, + 604.4630000000001, + 717.0210000000001, 383.422, 479.711, 609.187, @@ -84244,7 +84244,7 @@ 535.321, 754.832, 694.801, - 638.954, + 638.9540000000001, 629.722, 639.422, 586.4, @@ -84255,8 +84255,8 @@ 774.525, 428.737, 603.665, - 795.891, - 765.276, + 795.8910000000001, + 765.2760000000001, 644.735, 694.355, 629.992, @@ -84268,10 +84268,10 @@ 655.493, 562.65, 414.032, - 743.458, - 762.782, + 743.4580000000001, + 762.7819999999999, 770.941, - 87.282, + 87.28200000000001, 632.32, 531.236, 743.1, @@ -84279,10 +84279,10 @@ 502.245, 621.398, 115.954, - 568.772, + 568.7719999999999, 639.378, 149.454, - 679.259, + 679.2589999999999, 780.971, 731.544, 704.182, @@ -84293,12 +84293,12 @@ 659.423, 462.204, 758.311, - 781.621, + 781.6210000000001, 415.777, 788.079, 372.268, 415.518, - 547.042, + 547.0419999999999, 417.809, 635.42, 612.072, @@ -84308,23 +84308,23 @@ 373.138, 697.46, 397.918, - 697.141, - 620.704, + 697.1410000000001, + 620.7040000000001, 582.266, 688.393, 552.828, 550.274, 493.034, 145.32, - 628.902, + 628.9019999999999, 782.915, 620.418, 548.523, - 666.542, + 666.5419999999999, 683.427, 39.556, 393.915, - 581.027, + 581.0269999999999, 137.827, 377.652, 794.52, @@ -84335,9 +84335,9 @@ 598.991, 527.812, 697.24, - 637.292, + 637.2919999999999, 606.985, - 730.861, + 730.8610000000001, 108.213, 678.957, 585.448, @@ -84352,19 +84352,19 @@ 797.196, 627.118, 729.931, - 22.203, - 763.266, + 22.203000000000003, + 763.2660000000001, 500.522, 638.938, - 790.881, + 790.8810000000001, 496.035, 691.883, 621.205, - 790.149, + 790.1489999999999, 796.238, 271.327, 145.391, - 689.109, + 689.1089999999999, 574.382, 779.914, 415.601, @@ -84375,10 +84375,10 @@ 515.711, 772.449, 769.955, - 763.734, - 667.616, + 763.7339999999999, + 667.6160000000001, 461.577, - 757.871, + 757.8710000000001, 749.673, 751.903, 633.009, @@ -84389,18 +84389,18 @@ 727.685, 125.165, 483.262, - 680.729, + 680.7289999999999, 596.139, - 725.719, + 725.7189999999999, 299.597, 172.417, - 714.874, + 714.8739999999999, 682.084, 434.44, 531.451, 0.379, 223.199, - 701.661, + 701.6610000000001, 442.566, 399.982, 538.085, @@ -84408,21 +84408,21 @@ 687.677, 672.135, 404.166, - 678.274, + 678.2739999999999, 674.706, - 673.281, - 650.417, + 673.2810000000001, + 650.4169999999999, 665.331, 661.317, 222.467, 316.895, - 626.733, + 626.7330000000001, 411.604, - 260.294, + 260.29400000000004, 426.447, 640.226, 602.988, - 626.171, + 626.1709999999999, 628.313, 627.806, 622.499, @@ -84431,15 +84431,15 @@ 543.656, 163.075, 117.573, - 132.228, + 132.22799999999998, 122.093, - 596.541, + 596.5409999999999, 398.199, 406.781, 25.49, 386.103, - 546.728, - 523.782, + 546.7280000000001, + 523.7819999999999, 482.871, 276.579, 354.144, @@ -84458,7 +84458,7 @@ 383.24, 293.002, 130.565, - 264.924, + 264.92400000000004, 229.673, 168.696, 271.377, @@ -84467,7 +84467,7 @@ 253.209, 256.198, 272.533, - 84.502, + 84.50200000000001, 318.541, 240.552, 50.649, @@ -84480,19 +84480,19 @@ 338.856, 214.517, 214.027, - 360.013, + 360.01300000000003, 209.155, 254.91, 392.082, 147.208, 280.405, - 303.412, + 303.41200000000003, 215.436, 315.728, 346.668, 343.37, 254.805, - 172.621, + 172.62099999999998, 87.287, 244.285, 191.455, @@ -84514,9 +84514,9 @@ 206.71, 304.849, 224.311, - 278.412, + 278.41200000000003, 187.067, - 290.662, + 290.66200000000003, 260.36, 263.878, 247.968, @@ -84531,20 +84531,20 @@ 44.637, 78.391, 6.909, - 68.784, + 68.78399999999999, 20.849, - 16.554, + 16.554000000000002, 39.49, 47.01, 21.751, 0.418, 48.909, - 48.998, + 48.998000000000005, 46.487, 39.633, 36.319, 33.654, - 16.103, + 16.102999999999998, 12.987, 31.788, 29.387, @@ -84561,8 +84561,8 @@ 13.719, 17.54, 11.913, - 7.702, - 6.287, + 7.702000000000001, + 6.287000000000001, 3.11, 6.678, 6.43, @@ -84572,7 +84572,7 @@ 4.894, -2.902, -5.131, - -0.749, + -0.7490000000000001, -2.698, 0.969, null, @@ -85362,7 +85362,7 @@ 6.7, 7.096, 8.929, - 13.279, + 13.279000000000002, 15.371, 7.454, 13.075, @@ -85378,7 +85378,7 @@ 41.725, 37.833, 52.328, - 57.388, + 57.388000000000005, 58.445, 66.752, 59.067, @@ -85387,7 +85387,7 @@ 84.414, 37.376, 28.099, - 75.231, + 75.23100000000001, 14.237, 39.985, 64.952, @@ -85402,12 +85402,12 @@ 64.016, 68.712, 36.445, - 0.567, + 0.5670000000000001, 0.561, 51.75, 39.958, 74.229, - 27.703, + 27.703000000000003, 14.11, 83.081, 56.54, @@ -85416,7 +85416,7 @@ 68.046, 85.427, 65.514, - 65.927, + 65.92699999999999, 98.766, 101.425, 95.001, @@ -85433,7 +85433,7 @@ 164.088, 155.477, 154.183, - 160.652, + 160.65200000000002, 166.708, 130.907, 203.545, @@ -85444,7 +85444,7 @@ 153.082, 160.118, 153.275, - 168.652, + 168.65200000000002, 130.472, 226.992, 130.07, @@ -85452,9 +85452,9 @@ 197.296, 174.95, 56.749, - 158.527, + 158.52700000000002, 20.76, - 164.754, + 164.75400000000002, 46.228, 116.995, 147.004, @@ -85462,21 +85462,21 @@ 175.203, 113.609, 120.49, - 85.812, + 85.81200000000001, 15.2, 139.22, - 172.786, + 172.78599999999997, 239.115, 225.093, 287.463, 289.561, 143.822, 213.432, - 273.804, + 273.80400000000003, 275.495, 258.89, 291.934, - 283.912, + 283.91200000000003, 284.121, 28.226, 267.842, @@ -85484,12 +85484,12 @@ 249.322, 97.99, 59.221, - 0.572, + 0.5720000000000001, 115.635, 335.432, 366.218, 89.258, - 49.851, + 49.851000000000006, 329.932, 357.569, 117.396, @@ -85508,7 +85508,7 @@ 213.598, 0.561, 116.317, - 355.312, + 355.31199999999995, 228.996, 179.778, 367.214, @@ -85519,8 +85519,8 @@ 192.451, 272.769, 477.206, - 337.359, - 340.502, + 337.35900000000004, + 340.50199999999995, 422.753, 446.068, 355.405, @@ -85533,12 +85533,12 @@ 430.174, 407.668, 428.137, - 434.738, + 434.73800000000006, 390.32, 346.36, 586.174, 402.052, - 555.796, + 555.7959999999999, 550.549, 500.026, 590.309, @@ -85551,13 +85551,13 @@ 510.806, 466.454, 228.864, - 680.129, - 684.286, - 665.661, + 680.1289999999999, + 684.2860000000001, + 665.6610000000001, 250.153, 416.388, 364.687, - 662.589, + 662.5889999999999, 375.296, 585.998, 309.986, @@ -85576,7 +85576,7 @@ 442.126, 473.931, 484.149, - 516.713, + 516.7130000000001, 517.197, 36.71, 19.648, @@ -85586,23 +85586,23 @@ 529.783, 388.773, 587.375, - 141.956, + 141.95600000000002, 527.905, - 669.856, - 670.484, + 669.8560000000001, + 670.4839999999999, 683.322, 184.755, 426.601, 623.424, 630.432, - 643.199, + 643.1990000000001, 639.015, 323.969, 187.375, 26.673, 563.944, 154.569, - 364.368, + 364.36800000000005, 654.331, 716.487, 693.188, @@ -85614,7 +85614,7 @@ 605.823, 532.668, 419.659, - 398.303, + 398.30300000000005, 276.838, 233.973, 317.258, @@ -85628,7 +85628,7 @@ 536.086, 621.431, 486.086, - 552.949, + 552.9490000000001, 454.943, 419.251, 371.767, @@ -85637,7 +85637,7 @@ 631.153, 614.885, 545.181, - 198.744, + 198.74400000000003, 275.021, 500.241, 568.309, @@ -85650,7 +85650,7 @@ 21.779, 396.206, 532.937, - 530.421, + 530.4209999999999, 298.876, 304.431, 508.202, @@ -85665,10 +85665,10 @@ 548.908, 194.318, 530.84, - 553.032, + 553.0319999999999, 575.725, 339.649, - 175.974, + 175.97400000000002, 396.371, 575.742, 539.687, @@ -85685,8 +85685,8 @@ 586.631, 671.789, 525.054, - 727.371, - 720.588, + 727.3710000000001, + 720.5880000000001, 550.659, 225.329, 618.095, @@ -85694,44 +85694,44 @@ 731.605, 470.903, 384.237, - 768.392, + 768.3919999999999, 491.542, 683.179, 570.115, - 691.856, + 691.8560000000001, 525.538, 636.015, 341.537, 48.722, 648.385, 676.27, - 288.669, + 288.66900000000004, 624.365, 131.077, 499.756, - 358.692, + 358.69199999999995, 705.03, 579.205, - 742.781, - 715.094, + 742.7810000000001, + 715.0939999999999, 690.821, 748.77, 587.182, - 554.282, + 554.2819999999999, 761.961, 776.688, 336.329, 535.855, 504.419, 764.813, - 775.108, - 620.787, + 775.1080000000001, + 620.7869999999999, 533.46, - 620.032, - 442.313, + 620.0319999999999, + 442.3130000000001, 796.909, 446.712, - 626.397, + 626.3969999999999, 730.421, 626.094, 611.075, @@ -85742,18 +85742,18 @@ 32.9, 480.328, 302.438, - 642.142, + 642.1419999999999, 624.982, - 709.286, + 709.2860000000001, 695.743, 793.27, 769.873, 506.897, 316.768, 501.81, - 718.986, + 718.9860000000001, 703.252, - 721.464, + 721.4639999999999, 713.58, 684.627, 394.95, @@ -85761,12 +85761,12 @@ 595.693, 302.421, 124.163, - 675.026, + 675.0260000000001, 347.389, 402.818, 710.09, 781.137, - 761.119, + 761.1189999999999, 263.113, 21.366, 347.582, @@ -85799,20 +85799,20 @@ 781.797, 587.248, 696.288, - 572.917, + 572.9169999999999, 97.742, 572.516, 480.955, - 745.968, + 745.9680000000001, 370.55, 332.53, 743.727, 575.18, 315.629, - 743.843, - 743.458, + 743.8430000000001, + 743.4580000000001, 737.066, - 700.901, + 700.9010000000001, 134.535, 405.289, 524.943, @@ -85830,7 +85830,7 @@ 477.966, 584.98, 606.55, - 695.374, + 695.3739999999999, 533.124, 585.58, 685.563, @@ -85840,7 +85840,7 @@ 489.318, 477.454, 321.652, - 495.688, + 495.6880000000001, 657.293, 507.745, 651.248, @@ -85850,8 +85850,8 @@ 640.865, 396.354, 432.321, - 348.623, - 630.608, + 348.62300000000005, + 630.6080000000001, 522.962, 0.368, 69.048, @@ -85864,9 +85864,9 @@ 597.741, 403.407, 588.404, - 583.983, + 583.9830000000001, 582.106, - 54.888, + 54.888000000000005, 455.911, 402.504, 565.375, @@ -85889,15 +85889,15 @@ 425.918, 325.588, 486.334, - 352.498, + 352.49800000000005, 324.647, 482.871, 378.886, 239.264, 395.082, - 319.984, + 319.98400000000004, 435.778, - 346.938, + 346.93800000000005, 454.48, 190.728, 200.17, @@ -85919,10 +85919,10 @@ 332.585, 234.826, 339.792, - 342.198, + 342.19800000000004, 352.163, 175.638, - 146.492, + 146.49200000000002, 0.374, 252.774, 311.88, @@ -85938,7 +85938,7 @@ 251.139, 267.187, 241.152, - 60.476, + 60.476000000000006, 245.143, 265.166, 273.391, @@ -85951,13 +85951,13 @@ 225.952, 217.198, 144.461, - 62.491, + 62.49100000000001, 118.387, 71.146, 195.925, 161.627, - 150.038, - 143.288, + 150.03799999999998, + 143.28799999999998, 137.408, 152.813, 82.167, @@ -85966,9 +85966,9 @@ 24.075, 29.899, 23.067, - 26.348, + 26.348000000000003, 23.073, - 25.363, + 25.363000000000003, 22.572, 23.838, 17.413, @@ -85985,21 +85985,21 @@ 6.16, 15.91, 8.913, - 11.109, + 11.109000000000002, 7.652, 8.115, 8.77, 7.718, 2.807, 9.271, - 5.422, + 5.422000000000001, 7.68, 2.174, 3.867, 5.56, - 3.121, + 3.1210000000000004, 3.661, - 0.781, + 0.7809999999999999, 3.275, 4.52, -4.977, @@ -86808,15 +86808,15 @@ 0.71, 1.822, 0.115, - 4.943, + 4.9430000000000005, 9.529, 2.516, 7.68, 11.489, - 12.381, + 12.380999999999998, 10.339, 11.682, - 12.266, + 12.265999999999998, 11.963, 23.1, 38.559, @@ -86831,13 +86831,13 @@ 15.91, 17.71, 18.745, - 25.633, + 25.633000000000003, 23.161, 28.628, 25.556, 27.725, 26.365, - 16.769, + 16.769000000000002, 40.536, 46.603, 71.465, @@ -86850,14 +86850,14 @@ 222.703, 176.706, 194.874, - 306.049, + 306.04900000000004, 194.609, - 264.489, + 264.48900000000003, 274.922, 183.687, - 132.052, + 132.05200000000002, 69.747, - 309.044, + 309.04400000000004, 177.614, 142.369, 303.203, @@ -86865,8 +86865,8 @@ 370.517, 378.759, 380.554, - 349.768, - 35.669, + 349.76800000000003, + 35.669000000000004, 192.523, 241.284, 250.847, @@ -86876,7 +86876,7 @@ 309.639, 320.897, 348.601, - 342.198, + 342.19800000000004, 251.876, 211.291, 438.135, @@ -86892,7 +86892,7 @@ 250.957, 0.611, 38.543, - 239.809, + 239.80900000000003, 308.83, 498.584, 304.893, @@ -86904,14 +86904,14 @@ 528.55, 185.036, 495.936, - 563.608, + 563.6080000000001, 400.081, - 582.833, - 591.834, + 582.8330000000001, + 591.8340000000001, 594.405, 485.954, 270.308, - 80.841, + 80.84100000000001, 121.63, 264.39, 243.057, @@ -86946,7 +86946,7 @@ 92.98, 472.896, 694.586, - 748.231, + 748.2310000000001, 713.09, 437.523, 764.94, @@ -86959,7 +86959,7 @@ 604.425, 459.595, 242.446, - 294.929, + 294.92900000000003, 506.919, 735.304, 743.893, @@ -86968,19 +86968,19 @@ 686.868, 449.244, 496.354, - 749.723, + 749.7230000000001, 343.789, 637.33, - 694.129, + 694.1289999999999, 493.321, 542.346, 652.674, 297.01, - 779.391, + 779.3910000000001, 522.323, 777.525, 710.893, - 701.864, + 701.8639999999999, 787.699, 772.801, 209.441, @@ -86989,7 +86989,7 @@ 719.74, 780.085, 366.344, - 173.469, + 173.46900000000002, 117.848, 392.154, 244.229, @@ -86997,23 +86997,23 @@ 302.818, 287.271, 276.992, - 487.243, + 487.24300000000005, 345.826, 189.864, 171.68, 183.274, - 669.636, + 669.6360000000001, 481.616, - 322.549, + 322.54900000000004, 327.664, - 342.638, + 342.63800000000003, 760.75, 727.63, 464.263, - 342.203, + 342.20300000000003, 469.301, 245.286, - 337.937, + 337.93699999999995, 754.166, 549.674, 462.309, @@ -87045,7 +87045,7 @@ 199.74, 134.722, 194.565, - 197.714, + 197.71400000000003, 204.145, 195.788, 191.268, @@ -87058,15 +87058,15 @@ 127.747, 205.235, 142.116, - 159.926, - 170.496, + 159.92600000000002, + 170.49599999999998, 145.232, 234.617, 157.013, - 87.981, + 87.98100000000001, 201.458, 244.984, - 259.364, + 259.36400000000003, 293.249, 194.967, 315.832, @@ -87076,7 +87076,7 @@ 355.218, 116.609, 325.687, - 391.053, + 391.05300000000005, 342.644, 341.185, 382.062, @@ -87085,13 +87085,13 @@ 396.519, 245.22, 386.549, - 331.864, + 331.86400000000003, 300.291, 268.828, - 168.971, - 329.062, - 173.193, - 347.257, + 168.97099999999998, + 329.06199999999995, + 173.19299999999998, + 347.25699999999995, 271.795, 333.004, 252.493, @@ -87115,7 +87115,7 @@ 334.523, 337.414, 341.141, - 343.183, + 343.18300000000005, 151.491, 209.551, 358.461, @@ -87135,7 +87135,7 @@ 173.838, 165.359, 353.577, - 177.223, + 177.22299999999998, 79.707, 370.622, 373.562, @@ -87152,7 +87152,7 @@ 415.26, 514.406, 251.998, - 612.892, + 612.8919999999999, 648.126, 632.04, 379.249, @@ -87167,15 +87167,15 @@ 221.597, 637.259, 663.564, - 705.641, + 705.6410000000001, 522.433, 706.819, 620.214, 754.815, - 580.421, - 735.282, + 580.4209999999999, + 735.2819999999999, 571.739, - 87.877, + 87.87700000000001, 411.318, 659.908, 368.304, @@ -87187,15 +87187,15 @@ 451.854, 412.601, 734.682, - 630.801, + 630.8009999999999, 432.651, 561.719, - 512.353, - 656.214, - 750.042, + 512.3530000000001, + 656.2139999999999, + 750.0419999999999, 468.59, 485.894, - 544.333, + 544.3330000000001, 499.2, 517.511, 351.832, @@ -87211,13 +87211,13 @@ 657.832, 597.681, 619.245, - 585.574, - 610.233, + 585.5740000000001, + 610.2330000000001, 705.977, 628.797, - 513.333, + 513.3330000000001, 615.568, - 728.769, + 728.7689999999999, 773.181, 676.209, 715.171, @@ -87237,23 +87237,23 @@ 45.21, 588.388, 566.977, - 792.896, + 792.8960000000001, 403.875, 785.888, 776.655, - 770.142, + 770.1419999999999, 767.747, - 766.371, - 762.974, - 764.604, - 760.662, - 754.881, + 766.3710000000001, + 762.9739999999999, + 764.6039999999999, + 760.6619999999999, + 754.8810000000001, 753.956, - 749.646, + 749.6460000000001, 738.365, - 739.224, - 744.256, - 574.459, + 739.2239999999999, + 744.2560000000001, + 574.4590000000001, 474.712, 323.199, 507.332, @@ -87261,7 +87261,7 @@ 560.607, 507.959, 705.817, - 680.267, + 680.2669999999999, 530.851, 415.689, 521.86, @@ -87272,23 +87272,23 @@ 444.961, 480.119, 570.082, - 601.292, - 681.594, - 680.256, - 700.251, + 601.2919999999999, + 681.5939999999999, + 680.2560000000001, + 700.2510000000001, 688.784, 685.684, 689.466, - 697.014, + 697.0139999999999, 678.813, 659.842, - 666.861, + 666.8610000000001, 622.268, 366.284, - 573.171, + 573.1709999999999, 577.112, 590.391, - 582.343, + 582.3430000000001, 558.493, 559.242, 282.018, @@ -87312,7 +87312,7 @@ 150.44, 160.151, 157.327, - 179.844, + 179.84400000000002, 197.891, 182.019, 173.672, @@ -87328,7 +87328,7 @@ 235.377, 216.697, 262.265, - 260.669, + 260.66900000000004, 309.672, 267.837, 229.062, @@ -87350,9 +87350,9 @@ 349.514, 329.546, 343.403, - 325.792, + 325.79200000000003, 307.327, - 334.683, + 334.68300000000005, 331.622, 327.074, 319.642, @@ -87363,7 +87363,7 @@ 300.819, 294.802, 291.185, - 287.287, + 287.28700000000003, 284.573, 282.497, 279.007, @@ -87388,21 +87388,21 @@ 66.565, 69.164, 69.786, - 69.511, + 69.51100000000001, 66.384, - 66.213, + 66.21300000000001, 65.756, 65.304, 67.028, 67.529, 64.578, - 61.016, + 61.016000000000005, 58.538, - 58.373, + 58.373000000000005, 55.989, 53.914, 54.927, - 55.009, + 55.00899999999999, 55.235, 53.363, 49.113, @@ -87421,7 +87421,7 @@ 13.521, 12.227, 10.333, - 7.542, + 7.542000000000001, 5.989, 3.749, 0.947, @@ -88195,13 +88195,13 @@ 3.072, 3.963, 5.263, - 6.122, + 6.122000000000001, 7.685, 9.425, 11.253, 12.475, 14.611, - 17.022, + 17.022000000000002, 18.718, 21.278, 23.199, @@ -88210,16 +88210,16 @@ 29.112, 31.011, 33.604, - 35.862, + 35.861999999999995, 37.926, 40.448, 42.782, 45.573, 55.153, 69.654, - 81.364, + 81.36399999999999, 87.095, - 54.211, + 54.211000000000006, 99.482, 100.423, 99.24, @@ -88232,7 +88232,7 @@ 133.351, 138.135, 142.82, - 146.867, + 146.86700000000002, 151.244, 157.178, 163.488, @@ -88263,7 +88263,7 @@ 287.827, 295.215, 299.856, - 302.234, + 302.23400000000004, 309.892, 314.621, 317.44, @@ -88271,7 +88271,7 @@ 330.642, 331.875, 339.731, - 342.627, + 342.62699999999995, 346.481, 350.742, 356.782, @@ -88309,7 +88309,7 @@ 504.92, 510.327, 512.226, - 518.343, + 518.3430000000001, 519.24, 519.95, 524.58, @@ -88329,17 +88329,17 @@ 579.172, 585.007, 588.85, - 592.291, + 592.2909999999999, 592.484, 599.475, 602.933, - 607.541, + 607.5409999999999, 611.576, 615.832, 614.643, 623.391, 624.674, - 629.843, + 629.8430000000001, 631.236, 637.975, 640.595, @@ -88354,9 +88354,9 @@ 669.058, 667.434, 668.821, - 675.004, + 675.0039999999999, 678.252, - 681.142, + 681.1419999999999, 684.534, 688.674, 696.695, @@ -88367,33 +88367,33 @@ 707.293, 708.262, 715.21, - 718.772, + 718.7719999999999, 718.799, - 722.631, - 725.901, + 722.6310000000001, + 725.9010000000001, 723.291, - 729.006, + 729.0060000000001, 734.077, 735.04, - 736.532, + 736.5319999999999, 745.318, 741.426, 738.029, 755.674, 753.345, - 754.848, + 754.8480000000001, 755.977, 760.513, 759.913, - 760.233, + 760.2330000000001, 764.301, - 764.631, + 764.6310000000001, 772.047, 774.007, 775.565, - 775.851, + 775.8510000000001, 779.59, - 781.511, + 781.5110000000001, 784.148, 788.178, 791.558, @@ -88401,24 +88401,24 @@ 794.3, 796.497, 798.418, - 799.866, + 799.8660000000001, 786.967, - 782.744, - 733.366, + 782.7439999999999, + 733.3660000000001, 790.253, 783.405, - 773.611, - 741.729, - 730.636, - 744.261, + 773.6110000000001, + 741.7289999999999, + 730.6360000000001, + 744.2610000000001, 735.436, 725.538, 714.428, 706.302, 687.826, - 671.354, + 671.3539999999999, 649.106, - 628.417, + 628.4169999999999, 630.014, 630.129, 608.818, @@ -88428,19 +88428,19 @@ 616.003, 620.891, 620.082, - 615.892, + 615.8919999999999, 584.633, 586.362, - 572.593, + 572.5930000000001, 555.085, 545.561, 554.111, - 573.848, + 573.8480000000001, 594.306, 596.937, 597.169, 593.364, - 613.459, + 613.4590000000001, 608.592, 602.993, 615.16, @@ -88460,7 +88460,7 @@ 581.831, 576.033, 574.2, - 517.963, + 517.9630000000001, 499.189, 507.425, 484.209, @@ -88471,7 +88471,7 @@ 514.406, 498.055, 466.174, - 441.873, + 441.8730000000001, 432.734, 410.426, 409.655, @@ -88480,10 +88480,10 @@ 400.412, 421.712, 450.23, - 458.488, + 458.48800000000006, 452.316, 439.483, - 446.183, + 446.1830000000001, 440.832, 451.667, 448.776, @@ -88599,24 +88599,24 @@ 124.135, 121.135, 124.344, - 135.052, + 135.05200000000002, 147.357, 153.363, 137.062, - 155.147, - 169.626, - 134.419, + 155.14700000000002, + 169.62599999999998, + 134.41899999999998, 107.514, 95.38, 90.662, 87.965, - 82.261, - 79.228, + 82.26100000000001, + 79.22800000000001, 74.796, 68.294, 62.337, - 56.259, - 50.891, + 56.25899999999999, + 50.891000000000005, 47.985, 46.085, 44.246, @@ -88639,8 +88639,8 @@ 16.692, 13.906, 12.618, - 12.029, - 11.324, + 12.029000000000002, + 11.324000000000002, 10.603, 9.882, 9.194, @@ -88649,7 +88649,7 @@ 5.307, 4.652, 3.848, - 2.851, + 2.8510000000000004, 2.741, 2.196, 0.836, @@ -89428,7 +89428,7 @@ -2.048, -1.509, -0.92, - -0.832, + -0.8320000000000001, -0.193, 0.765, 2.813, @@ -89454,7 +89454,7 @@ 56.347, 68.542, 82.652, - 83.731, + 83.73100000000001, 60.493, 101.486, 101.838, @@ -89482,9 +89482,9 @@ 179.106, 169.72, 172.313, - 178.534, + 178.53400000000002, 174.201, - 154.723, + 154.72299999999998, 169.951, 194.956, 227.719, @@ -89521,7 +89521,7 @@ 254.249, 317.335, 293.558, - 289.787, + 289.78700000000003, 238.647, 296.536, 333.059, @@ -89533,12 +89533,12 @@ 357.844, 310.228, 368.574, - 300.109, + 300.10900000000004, 273.518, 332.96, 394.785, 392.798, - 323.804, + 323.80400000000003, 474.619, 447.351, 481.627, @@ -89551,10 +89551,10 @@ 524.266, 536.494, 533.758, - 526.171, + 526.1709999999999, 530.955, 526.865, - 545.958, + 545.9580000000001, 558.736, 552.085, 551.133, @@ -89562,10 +89562,10 @@ 549.635, 585.161, 601.248, - 583.416, + 583.4159999999999, 557.37, - 588.233, - 594.537, + 588.2330000000001, + 594.5369999999999, 580.14, 565.359, 612.975, @@ -89575,7 +89575,7 @@ 391.086, 400.395, 461.593, - 477.113, + 477.11300000000006, 420.666, 325.726, 419.659, @@ -89583,32 +89583,32 @@ 308.428, 557.26, 662.633, - 668.766, + 668.7660000000001, 672.163, 710.447, - 701.644, + 701.6439999999999, 708.025, 703.5, - 699.646, + 699.6460000000001, 703.015, 720.919, 720.842, - 713.272, + 713.2719999999999, 716.927, 712.919, - 715.474, + 715.4739999999999, 715.584, - 722.532, + 722.5319999999999, 730.68, - 750.108, - 687.479, - 721.744, + 750.1080000000001, + 687.4789999999999, + 721.7439999999999, 712.457, 728.202, - 727.024, - 749.139, + 727.0239999999999, + 749.1389999999999, 775.18, - 757.006, + 757.0060000000001, 771.684, 772.488, 793.485, @@ -89625,30 +89625,30 @@ 753.367, 487.595, 521.519, - 797.598, + 797.5980000000001, 795.23, 584.82, 521.602, - 701.611, - 698.292, + 701.6110000000001, + 698.2919999999999, 755.96, - 753.483, + 753.4830000000001, 317.743, 684.148, 693.32, - 782.838, + 782.8380000000001, 720.451, 545.754, - 769.531, + 769.5310000000001, 523.011, 793.678, 617.654, 597.559, - 362.738, + 362.73800000000006, 797.878, 790.342, 748.567, - 767.224, + 767.2239999999999, 795.258, 701.98, 796.909, @@ -89658,25 +89658,25 @@ 699.635, 728.654, 586.571, - 794.861, + 794.8610000000001, 559.451, 632.535, - 673.121, + 673.1210000000001, 501.777, 787.908, - 544.152, + 544.1519999999999, 743.21, 539.637, 657.832, 679.628, 716.547, - 740.397, + 740.3969999999999, 556.115, 389.027, 621.354, 701.633, 693.188, - 601.166, + 601.1659999999999, 281.765, 389.379, 497.191, @@ -89685,12 +89685,12 @@ 577.993, 346.431, 630.206, - 664.896, + 664.8960000000001, 625.461, 525.246, 600.026, 436.577, - 547.532, + 547.5319999999999, 513.492, 214.682, 423.71, @@ -89704,7 +89704,7 @@ 399.151, 573.193, 622.378, - 634.704, + 634.7040000000001, 623.611, 607.695, 346.338, @@ -89722,7 +89722,7 @@ 516.465, 559.677, 561.697, - 388.113, + 388.11300000000006, 125.28, 104.624, 124.95, @@ -89743,7 +89743,7 @@ 131.87, 130.417, 284.358, - 177.036, + 177.03599999999997, 151.689, 163.565, 181.039, @@ -89755,15 +89755,15 @@ 95.001, 87.656, 93.195, - 84.783, + 84.78299999999999, 86.164, 91.642, 102.581, 113.339, 119.351, 133.918, - 170.309, - 143.052, + 170.30900000000003, + 143.05200000000002, 112.139, 103.264, 103.093, @@ -89775,7 +89775,7 @@ 98.64, 97.456, 88.311, - 86.798, + 86.79799999999999, 87.833, 92.958, 94.411, @@ -89792,7 +89792,7 @@ 175.054, 78.985, 70.342, - 71.349, + 71.34899999999999, 91.494, 117.952, 55.615, @@ -89801,7 +89801,7 @@ 47.621, 52.24, 67.749, - 141.301, + 141.30100000000002, 150.236, 109.816, 65.932, @@ -89811,8 +89811,8 @@ 54.949, 49.003, 45.7, - 41.384, - 37.513, + 41.38399999999999, + 37.513000000000005, 35.151, 30.824, 29.272, @@ -89829,29 +89829,29 @@ 18.503, 17.066, 15.712, - 14.154, + 14.154000000000002, 12.144, 10.284, 8.709, 8.34, 8.252, - 6.078, + 6.077999999999999, 5.114, 4.74, 4.938, 4.999, 5.362, 5.676, - 5.758, + 5.757999999999999, 5.604, 4.756, - 4.531, + 4.531000000000001, 3.248, 2.725, 1.348, - 0.082, + 0.0819999999999999, -1.663, - -1.377, + -1.3769999999999998, -1.558, null, null, @@ -91331,7 +91331,7 @@ 800, 800, 800, - 798.754, + 798.7539999999999, 800, 800, 800, @@ -93045,7 +93045,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -93082,7 +93082,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -93101,7 +93101,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 44, "metadata": { "tags": [ "nbsphinx-thumbnail" @@ -93147,7 +93147,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -93164,7 +93164,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -93194,9 +93194,24 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\Lib\\site-packages\\numpy\\lib\\_nanfunctions_impl.py:1241: RuntimeWarning:\n", + "\n", + "Mean of empty slice\n", + "\n", + "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\Lib\\site-packages\\numpy\\lib\\_nanfunctions_impl.py:1241: RuntimeWarning:\n", + "\n", + "Mean of empty slice\n", + "\n" + ] + } + ], "source": [ "# Calculate the daily insolation, required for the SRR calculation\n", "daily_insolation = filtered['insolation'].resample('D').sum()\n", @@ -93213,14 +93228,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The P50 insolation-weighted soiling ratio is 0.953\n" + "The P50 insolation-weighted soiling ratio is 0.952\n" ] } ], @@ -93230,14 +93245,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The 68.2% confidence interval for the insolation-weighted soiling ratio is 0.949–0.956\n" + "The 68.2% confidence interval for the insolation-weighted soiling ratio is 0.948–0.956\n" ] } ], @@ -93248,22 +93263,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 50, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\rdtools\\plotting.py:172: UserWarning:\n", - "\n", - "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", - "\n" - ] - }, { "data": { - "image/png": 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Zm5PEziob28qbaHZ6+evn+/jJzAJuPWPMAKtUicpg3X333YwbN45vv/0Wm80WYbAA5s2bx0svvdQrAuOVWpuLvTUtodeLi/KP2DiNOg2tLh9qHyOwu73d9jaWF5exs8pGs8ODABxuP/tqW464X18zJieJjYdUw3uowY5eo4SMFYBZrxoDIcDnF3xX1oTN6cFiGBRVKPOM8dksXXOQXVU2tlc2s/5gI6CGnJrsbnZW2Ribk0Sr20etzRWxb36ahSZ7M8HApEGnQQF0WoVV31eRbNJ1afzKGh3Y3V7KGh2dbn/x6wOsP9jAWxsNPPLjSQARBuzw56mwu9rGt/vrsbt9TMpLIT/NTJ3NhdXuodnppbrJidPjC3XUO6tsWAwaHJ62MKvXLzjU4ECrgf9uq2RsTlKX51Pd7MTr81Pd7DyMrp7TVWdfa3OxvaIZm9ODEKDXaTDqNOg14BSg1SqYdRpAoaHVjdvnp9nhYXlxGaOykyguaaTR7uH7iiYmDE4OtZ0jeQrnThnMyi0VbCtv6lYUBNRBxPvfVWJ3+/hoezW/nD+yw3VcuaWCmmYXapRbUNnk4m9f7mfF5gocbi9pFgOlDfYeGta+obikkd3VLYBgW3kTe2tasHt8eH1+kk16Pvi+mvnjsmPC44rKYK1fv55HH30Uo9FIS0vHTjEvL4+qqqqjFhfPNNrdNNo9IAROj593tlSERo5djayumDWUl74pwe/34/EJhqRbut1pG3Qa3IE5IHUkCukJhm7r7asQwAPnTeT6VzZQ1aSO9t0+gV6jei46Dbi8PvRaDakWPc0OD36NarQqm5xcfEL3Q3pBLigqYHlxGR6fQAANrW4eWrmdJLMeh9sHCD7bWYNBp8HjF+g1Cs6Ad1c0NI0Jeclsr2gOhNEUUi166lrUwcffvzrAqOykTq+PxaBFp1Gw2t088t72Dr/xvtoW6mwuqpqcXPLcN4zNTWJ0dvIRvYEEgzYQMnXRZPfgE0LtsL0+3F4/Hj/gF9S0uPlsZw0Oj4+Xrj0RAK9PXWgZPjMoAK8f6lvcfLS9OtTZt//dFxflhzzU3mTllgo+3VmDTqOEOuxPd1TzyY5qWt2+kEfo8flIMetJSjKRIwQJRi1NDm9oPktBodXtY2t5E7e9sZlJeSlUNztJMek5UGcPhXCLSxrZVWVj1fdVTMxLifhdglGJQw12mh2qsctJPvISklqbi1qbC7dP0GT3cPdb3/Ha2hKKhqZHHF+nUXCHjc6cHj+lDXbVc/dDglHHyi0VvXq/dfc+frO4lKVrDgJw+thB2JxuDtbZURSob/UE9IPL40MIP7e9sRmbw0N2iolHfjxpwIxWVAZLr9fj93c9QV5eXk5iYmKPj9vS0sJjjz3G2rVrWbduHY2NjSxZsoSrr766W/tbrVbuvPNOVqxYgd1uZ+bMmfzxj38ckDJSbq+fZJOO+lY3Wq+PbeVNbC1rotXl5dOdNTx58dSIH31rmZUUi4HfnDOO5cVlNLS60ShKt+ef0iwGNa6v06DRKCQYdLi9/lDyxZEacF/G8rMSjdQGwoAGrUJ6ogGDVkNpg+qNuIQfj89PVpKBWpsbrVZDnc3FM5/t5cs9tTxw3sRujZaDNLS6CHYTgoB3FQjpaRRQFPC4fYHvVt9zeHxsKmlk2tA0jHqtOro06/ALgdcnqG1x4/ULXvz6QKchstPHDuKlb0podXl55ZsSdlfbQoYD1MGDQDXUDo+fvdUtGHVaXF7/YTutYOJOeaMdv1A7Oqvdg+jksy6fauy3llkZm5OExajFLwQ+IUg26WlodRE4bdxeP41292HTw/fUtLClzEppg71XQkJby6xsK2/C6/NTa3Pj9Pi44dViqpqcCNrCsaD+RiMHJXL7mWNCHhlASb2dtfvrcHkFbi/YXT7KGuykmHUkmXRUWB1oNQpf7anjgqKCQKZuFS6Pj+2VzdTaXKHfb3lxGRsPNeLyioAnDa+vK6Ug3cIFRQVdtrdGuzsUuvcHrvvGQ03sqW5hW3kTi4vyqbW50GkVNJ7IAYNfqHNxBH7B3dU2/ueVDT2ec+3s2gbnNy0G3REHusuLy9hb04ICVFgdNLR2bFM+v6qytNGJEAKvz0+D3c3PX9nAxScUsKnUSkm9ndHZiWgU5ajPoTtEVeli1qxZLF++vNNtra2tLFmyhDlz5vT4uHV1dTz00EPs2LGDKVOm9Ghfv9/POeecw+uvv85NN93EH/7wB2pqapg7dy579uzpsZajZXFRPlMLUpmUl0yyWY/XL2h2ePD6BXUtLopLGiM+H5x4b3Z6mZiXQk6KiYl5Kd02HllJRpLMekw6DSadBq0Gkkw6PtpeHZrQPxy1NhdljY4OYbLO6EkVjuKSRnJTzCQatZh0ClnJRi4+oYBxuckkGLUEHARanF7sbj9evx+XR/UcfH7YWtbEo+/v4JH3trNySwVWu4eVWyq6/P6tZVZqbG50mrZ1guE3ol+ATlFQABEwViJgCGwuH1vLmhibk4hRp0GrUfD5BcGl9U2OrucVUywGjDoNTq/A7fNzMJA8E9TU0OrGoNWgVdTvTLboyU42kWTSheZPnvpoF//zygbeLC4N7Ztm0bO9ogmfX+BH9QgPR5JJHbU3O71MykvFbNAxKS+F4ZkJjMlJJsWsD53zugONfLG7lvUHGzoMjD7aXk2FVW0PL3x9IEJTtBSXNJJk0uPw+LEYdJRbHZRbnfiE+rsYtQomvQadRvVONh+y8sdVuwA1KtHi9PDJjmpaXD4URSG4FNSPoMnhRa/VqAM2RWFzqZWtZVZ2VtnUAYlXHRSFhzizk00EE1MF4PeDz+9neXFZSO+uKhvPfrY3oq25vX4s+o5dp82lDkx/+/4OPttVi8WgQ6tR0GsU1GBmUC/Y3T5KG+ysP9DAd+VNfLS9+qivbXB+M9Wip2hoGm8Wl3ZoT0EMOg1Ojx+7x099O2OlhP1rc/mwu304PX58ftU7r2528caGUraVN9PY6mbNvnoa7Z6jPofuEJWH9eCDDzJnzhzOOeccLr30UgC2bNnC/v37efzxx6mtreXee+/t8XFzc3OprKwkJyeHDRs2MGPGjG7vu3z5ctasWcO///1vFi9eDMBFF13E6NGjuf/++3n99dd7rOdouKCoIDRKe/azvRyoa6UaJ063D51GQ5PdHfH58In3w03Cd8W5UwZTa3OxudSqdpweL+sPNjIoyUC51cGIrMQu58O2llnZUNJAnc1NivnITaInE/JFQ9NY9X0VKBo0isDhVjvzgnQL04em8fXeOvx+8AqB2yfw+Gg30lPYXtnMgbpWUCDBoMNi0DJhcArQ0SsoLmlkcKqZ/bUt6LQKbq8/cuQO+NsVdwn3xnx+P0VD0ylrdFLf4iY9wUCCUYvN5UMIupxXTDbpaHJ41OQAARkJ+tDniksaaXZ48PgF6QkGdFrVAwaYO2YQFVYH3+yrZ82+OgYlGQFCI9XGQBjQHpiLCs5w6jTq2WgUdW5Kq6hVZyqsDpauOcigJCPpCQamFqSiUcCk19JQ2YROA2a9Bofbr851evyUNdojvJjikkbSLPrAsdW5xa6yEXtCsC1oFKhvcRG+ikGvBaNeQ5rFQF2Li1a3H5fPy5q99dgC3vGH31fjF+rvpChqx2y1e0g26bG7vXh9fuwuLy6vH7NBx4tfH+CM8dlsLrOSaNBi0GnITjaFfpdrTxlOo93NN3vrQ7201y8w6DRsLbNSYXVQUt/K0IwEXvz6AE6PjzPGZ7O4KJ+KJie6VhdNzshqPi0uH8KlvudwK8wbm8Wu6hZMOoWyBgdOn9rxu7x+nF43GqCyycmEwclHtdwgmGUZTEKBtgSkzkK/aRZDp166QauQFAin2t2+0Bxo8Joj1Pbd2OoBBB6/INWsp7rZyUmF6T3W3VOiMlgnnngi77//PjfccANXXnklALfffjsAI0aM4P3332fy5Mk9Pq7RaCQnJycaSSxfvpzs7GwWLVoUei8rK4uLLrqIV199FZfLhdFojOrYR8Pk/FRunDeSF78+gEGn4VCDHbfXxyc7ayLCLO0n3qNptNXNTow6dZTZ7BTYXV722D3otArNDg/PfubrdL5EXa/jRggRil8f6Xu8PtGtCfnJ+alMzEuhodXF3tpWnK1uPt5RzckjMjHrtUzJT2FfTSuGgO5WlwevH7QKpJj16LQKja1ump1ejFqFhhY3Rp2G8kYHOckdf8+ioWms3V/PpLwUnB4fX+2ppcXpxScE/kCIIywPAZ9oG1EadQrXzh5OQbqFZW4vfiFwenyYDVpaXT5QICPB0Gm4pdnpDYUtdRqFskYnj76/g7vOHkfR0DReWnOQRKMWl8eH169Q3+KmrNHOoYZWhqQn8F25Fa2ihvvC543OGJ/Nmr11ERmMCpBiNjAxL5mtZarH5/b5cYTmgATlVjUJY1ZhRiix4LtyGJqRQNHQND7dWcP+2lZ1TssnWLbuEG8WlzEs00KSyYDb6yMnxcyh+lZcXj8ub3Rl1oLLDPxCsKu6BbtLXRvoaTej4PWB0awm4Bh0WuwBg+oPzNnV2lwkmXTYXF6STXqumDWEnVU2dlQ2U9XswiIELo9qqJxetQ1XNzu5oKiAnVU2iksaKKm388XuWtYeqOeOM8dwQVEBt585hntav2N/XSt+v8AfSPx54J1tgBLKqKyxufD61AzLu84ex+AUEz6/QKdVz0cI1XMKNwIen8Bi0PHF/84D4NZlm1i9t45Gu1s1vIF9jDoN3wXWIUYbkn9nSwWbD1lZf7AxdA+kWfTsqLJh0mv446pdlDU6QoPMrCQjKSZtyOAqQIJRi8frR6tRyEoysb+2hQSDOrgJb38CcAZGGxpFbfuZiUZSLN2fM4+WqNdhzZ8/n127drF582b27NmD3+9nxIgRFBUVDUjZpk2bNjF9+vSIOocAM2fO5Pnnn2f37t1MmjSp74U8kBL5+or3mZwymFO8m9DU29nvHxka4UfEyLPM8PmfYNJitjqNbNpfz7SsViZntY2YQjEQwq+v+v/KZc/gajkdb1I6pxVmUGtz8f53lfgBnxA0trqpsbk6zJdsLbOy9rsShig7OMRYTi5MB68XmiogY0jkuQgBzhZOztPzSVMDJ+clwQML2co3FDOSop99EnmzORzw+xzOZRiDz/gPT360i4muVWz3juNQg4WpBamY9FpMNZvI8lax3TiPEVkJlDc0MUPzPkn+SXyfMJH6FjcIcHkD+ZPCT4JR1+mi0eAAobikkaKadxmk87FKn4MDEw6/BrvHT6qoxYgbRQkkJQjIMRm4PvV7zsw4myWOUZyao2VbdTPmxGyqmpwogRt2e4UNq2M/6Y5SfnTCGCjfCOmjmPFRETCR3AnPsGZfHVa7m321gpVbKrjnnPH88qRBLP9sCxUJOdQF5sOGiS3sqsijujkPt9dPktnACUPTIjyZC6bmongdvPqNWu2hye3FJ8pZ5FvLufOfBJ0ulMhQbnXg8qgdvYFaLE4btbasiN8kGPLNTzNT3+Ki1e1FoyjYXD7AR8OhJgYlGRiXncisFBu1Nj16rQaPT3TtATid4LFBUlaH3+Pdb3fQ0NBIlcOJPxD6S0GJmNdRgHO0v+QSL3zJELZqfsEBUyItJJBNLSe21jNs52ayiu6FhgbOTdnI5Pnz2Vpp49H3d9Ds9OLweElVqjD4fZj1Q9Ao4HJ7+PNLr5GYPg6n002ebzUWVxObXGeqHmNqBeUfr2BC2o+xOb00BjwmjeJj06EmTHoNSSYd+WlmnB4fNTYXda0uXnrhD4wiDQafAkCSSc83++tocfpCBktHA5O0u9lX/YPQeV57ynCcHh+Ndg8bSxoBNdys0ypoFAVzUyVKYuDK/P5UmLIIFt7W4Zp2RkNFKUs8P2WNB7buvw0T33Mhq1jDVXzVsEBNIkIdEG0ts3LutscYj57/pJ5OYvNbODR6iv1ncIV+Gf/1nEqtbSaJBh0Or49R2Ym0uLzUtbjw+ETIcM3gUzaKaeTSSFKTk9QWP9C3dU+jKn7bHwRDgt1NukhMTOTiiy/mH//4R8T777//Pueccw4ffPABCxYs6HRfl8uFy9U2d9Pc3ExBQUF0xW8DBqs+7K2M/IXUl30AwCvMYaXlFnR6LQdqWxEoDE418lv7YsbiJwPYpJuN8NpJpYHCxG6MWlr2UI86Wnt+0CuMPXEaX+2p46s9tYGQmMDj86OgkJ9uaUv48Pn45yffMGTN1Yygluc9s3nJ/wtuNf6d8yjGPvbnjLvkN23fU7YJDn7F1/tqcHj9mHUaTjnwZ/Vc/bDBB7/SvsqYnDQ1UeKFoW37/vA9/v3ub5jPJtxApeVMDhWez15fLlfv+DEKUEwun+hu5ETv35nDIQTwL07lSc+NeHxtobAUs57BqWZGZCVy6qhMNakk18Tk1DCPb9k9UPMe9YAdqCeBRgahoZUCVw06o9pRCh8oWjDRNqFbfvU+EpfOw4CLl0f8jV2ehFC83g8ks5fnTH9jBE4GEflUgjfP3cZjq3bRZPeQZNLxo6l53HPmcHjmLOqbDvJO8k95S3samsav+It4jGYFrvD9FgyjSDTpmVaQ2nZO+clM9n4Pfhfvbaui1emjuKSeG50PkQhkAFz4EUyYGcp4q7G5aHV6eLLlWrK0zXyrm83ld/6LrQ3e0KS8zenli9012N1+zHoNTQ4PDo8fDX60+DFo4Xr9J9xi+Yyv0n7Ia6YLqLE5qW52kZ5g6JgltvqvUL0LZl4B+UVt7zfXsfz5h6hrsdLq9uIL9HQGvRK4+KDRKGgVDZfwTsR1bMVAC4IMPKFRdQZQgpFEXNjJpuC+nWytaObR93dwsHQfL3pvwWDw83Pl9zRqh3OJ71ku5lN2k88exnORfxV44HzttYwe8iP+UfEj6gEfUJL2Q37ecDZWV2qEMdUqkGjU0hwwRkNYxQqWggnsGGkZ8QtWmBfwSYmTmmYnNpcPDYIVmsvIN0AVJiZctgJGnay2j8DvtLW8CafHj06BzCQj+c4d/I6H0JDBUAzAQVXAoB/DjUs5LJXbKXnuJIJpbi4vGBVAC/jhEvcl7OE8QL3sp5ne5yVeBSL7qeDibAAX8CGT+ZCLKNGMwJJgwenxUdfixuX180vu5GpTGa2o18GAoJXhTHpg3eG1dkKfF7+NRRwOR6chP5PJFNreFY8++igPPvhgr+rxEnZxyz4gA7VxXMEXpNhr+bPvZtz+ZEBQYXUx2tB2mwzxrm47UDeXUmUE/r2n5gqWlL7PjkovXp9gWGYCFVYHdrcasHB5fKEEjK07djPEvoOxqFUorvev5hXxE37g/IxEE5h3Pga2myEp0Ij2fA0V68hutlPR5CQ7xdImwA0nAJ/7Luf/VSyguOT3RASF3z2HC8Ne5tpXMX3besoThoZukiIqmeZ9DA3qAlwFuJivGKLbwe3+R/CIJISAoRkWmhxe1uytY+2BeuaMHkRq9TomZ1aBCFzHmvdC32UBLLRSwAH1jfBm0kk1sclLpwKq93Z3w2Nwy3/ZWmbltjc2s6+2lVNYR6G7Am0nYwn/yokMHvQ+vkA4aGx2AjSWQtMmAM5qfprEoRloG1/HrIAZeF57Nz93P40pcRiHGlr5+1c2phakonXUM5lvOFRfi6HagQE4K0khITwS++8zYO0lNI/8X2YOz8Du9rJjbzmjPeo1PIvV8Mwcdg3/P6wp6i9itbtJNhnQKB78QjAoyUh5o4NUrFiEA6MCp/I69XYPp9qfo3jKBXyx24bPL2hxeSO9dJ8Xdn4EzWXg98LZo8ESiAocXMc8zQZKaMEh2hZea7RasnVmvIFBSEaSjowOa67dJHS8vCTiCvym1fDPW5h86Z+46+xxvPPCGwzCCwrcyHP8yfgY59s/JQGYRhnDKVNHJAZ4WHmR952TqIfQfVnY+C4rlXdZqZnKv/w/Yj+jAXXNYPg81Y0sDZw3WLQuLPue4AqeYDpzeEOZwy7TGOy+avID7SoHJ7x2FvtT5vPV2F9T5k/GoNOSYNCiUxQyk4xYDFp+7HyBZJ8LtBVUqjLVe7pmBTxph9v+1cnVCLDtXYbSZnyMwa4koGEZy3iYalZwHQIoDBir9oTHbYzAeWzlPLZS51fYaJtGMafTlHMC25oNXOxWE1MSgITAb6JlV9cae4ljxmCZzeYILymI0+kMbe+Ku+66i1/96leh10EPKyoeUOcU9j4wjbHsD70dPpI5j+1kau7laX7JfqWQZLMRja/tcxn0nPDjn7X5bP7KX9BpB5Fo1JFqMagZRAqMGJRIsknHo+/vIKtyIzniv6H99Ea40/l7kgItVwvsffVGRt4QaOCVm6ByCzqHngxtMsJupz0a4H7th1R+tKob59JIXmtjhPYsmiNeA5yk1PGW8efc6vk1h5SpbCtvVrP3UOP/JfWt3JC8gYPffkO93UuC3kAaHRt3sHM6MmGhxsY1sKeYyaOKuHHuCO5e8R253mp0ncQl6oGTgNXpFow6DUkmPZ9sKWVKaykjA5/R46aw5O/4KQ/tNwz4o/YhHml6lL0kowA1zU4KR7WC4yvcdS1kaFMxaDRMTLMQGF+0cWgZCw59yKcTn2HyrFPwfL8+crt9L/O/v4ktaReT+cM7wTg4lGRRa3NxqMGOTqthfusakvzVtAKD8QTOyYd1y59weS7BDyQo8O3+ejVxYXAy1OyA0k1AixoCznkVTrlB/d6aXeCoweD30iBSQxP3BjQIrZ7RmaY2jV0XCemS+j0vU/feGCaffQOGtBIIJMKexF7+nWIiKax5pgb/o8AQYKTvQIfjmYALDZs5w7WZtWIs7zKf9cokfCIFv7orecEPhw10LMAMvmAKX3CIQkq0mR2OndL0KfPXfsra5OvRFFxAmiWTrCRjaH5xXP0BdZTrBUNgQBVqq00fsuuB00j82Tudz2+VF0e+DloeLyG36TfiMyYo5Txj+F8KOx6hU4L3SyaCM9nIGWzEWZdKGWPoLO4ztJP3eptjxmAFMwzbE3xv8ODBXe5rNBp7PSFj9i9WUf8XtZvqrJM8Waklw/Q7XnJfwtf2WWoj66U5Sz3wKr/gdt8/WFw0jtIGOy99U4Lb62P9gQaqmhw02r3kip2kEnnj/tB0CK0bcAImSKleCbWHIGuI2jm11GDBhMPnIUGvDZ1bI5BCIKzmglwEmDo3wBHh0k62d2ZYcoCX9b/nT97zeItFKIoRBYE2sPasaefnpFFCMlpcdh0afduxekKnn3/tarhvC81OLyMHJZFZURnIg4Z6TeQ+ZmDKzndpmn0RL31TQpa/mRLH1yGDBTCM/VjbfcVE6vgFj/GouJUadyp6reC7XTsgsZQ0nxefopCiN4G786SYwTRy+bbL2G37GYq9zVvPCP3rZH7jS/DBblh4H5MXnghaLVvLrDz6/g5SzHpOs60mg1aaUSI6hkt97/Ay56MoJvRaDXqtRk08yU6Ahv0QDIu6y2HzW5BXBMNngr0BPI2YNQIr2aCAVq+gRSFXmwC6MA+9i9+hfTto/76++B4+c2gY1/hp6DMGoCixMWLNTvh+iUBS0/YuvzvVCAu8OzlVt5P9FPIFJ7KdE6gyDSPDSfs01tDx64GR7Gdk2EA1HAswr/l5Fh/4HObdAlMuDFmn7PWovXEXC43GsIVDL5wOv1kLunbddmN55OtA28cTOF7g9Y/ZzRh+QxPdGxi3v/YKYMbKKNZ2+vkSzDQdRR3Q7hDVOqxYZOrUqWzcuLHDgua1a9disVgYPXp0/wrKyjpiNG+Mx87P/Uu5QrylTkAdOUGv22QAL/A/jDLYWXtA7VBa3eq6i13VrdicHvKo6TAHYwC0wWC2R20gxX+5iq2ljWCtBpz4sKPgp8ETNu9ngl9qLiSi4l4gdNU9r+bIGIA7dO/whP4PzDA3YDFoyUgw0NDqRscBLMKFyWlHL9qG1vXAIXKoD4wGwg2knyQghQxNKhkkH+YGPgSb3qJoaBoFaWZyTWo4pKvfay6P88H31WgUBTtWdHW7OlyD1E72O4Xd/ML3NyyiTs3aoh5a6shwWRmTbCDH6Aevu5M928goeYELebHrD9R+A69cBZ/+ke927qW4pFE1+HY3gykhk1pGmnwR4aFMLcxX3kerQKvLy5B0i7rkwuuF5vDO0gd122HTy9BUC9aDgJdUfAwxtTDM2EqOpplh2NE66qClpu0vgs6CgZ2TAmRsf7zDyPt853sR5xCOBpgUZlQy2v0BoFMNzET2cy3/5HYe5xbnC6qy0PqvyH3aH6ezwRgA9t3w3i/g5cth23tMzklUO2Jt27HxAG7UCbZAlzaEvWx+eAJbdxyKPF5Tm8vdPpeztV3Ie7yrmqlO9djBNtlepxNo0E2mlY5RqcOdm5WcI673PFri0mBVVlayc+dOPJ62HmPx4sVUV1fz1ltvhd6rq6vj3//+N+eee+6ApLRvZW7o/502XD0MNfg5X/tB6K1OM2CMgw//RxIZmMkgK6IxZePB8uY8lLoK/IG1OkHcXj/5VJJAx7AeRtQbJzAyG8ZGdq3+gGDMxYgbDV5MYdPTCuBMWsg1XE9r+LFcEQG2EN1JkvYC1nZdeybwQ7Zzt+8ezmUtrXYnuyqbSXV6wA16BXRo0NB2zfdlnElZ4qmQMg5IAhQyjCPImnYJGdMuUke6E8+HjMOs+1v5SyZn6rlx3kjSQI0ftfOIg9+XDKRYa0k06cjERiIdPf+uOF27ldu1S0n0V2OgmnpagBZoqQ391UPoL3JCTiWYFN+1Aa6B1Y8gVtyC5sA3eJ0O8lNNpNLAIBpRnO0NCPzS+B8MWi8JRh1ZSUaKSxrZWlIPte0X5bfAjs9g0+tQeTDUFkfoIRsXw/CShp0UnQs8LW1/4fzgt4Ah1KEGt4afd/i55VHfwTiNLF1BFm33Qtv1UkmnJnTM7UC9dgidDyNUr7mQSmbxXyLq96RNA30eh+9GBwOdr0+qL1tF2fL/ofSl69uOEBy4+tWk3H0enWq4XICAAqrQvnEq1B4MO5I19D8XcIh89YUGqoCMhPFtHw3Mm+JXp3u/4RTUG72tMbvJg5nXUp57IZ7MuWRosrs2vmE0G4b1+ZMhYi4k+Mwzz2C1WqmoUGPsK1eupKxMHdH+8pe/JCUlhbvuuouXXnqJAwcOMGzYMEA1WLNmzeKaa65h+/btZGZm8uyzz+Lz+Xo9oaK7DD3nbnjv89DrDABTLvXOsA5MA1lBt92jdu6Z2lzwBT5jHgO5Iw7/RS4beB2QPgrqdkHtxtCmNFr5jft/eTn7T3zvTWVnpQ1PoHqD8XATBwYi0oZO3vUE4UVmDGHuRQZQDSyeNZK1u83cWJ7GH9y/D3WcxeQxLGzOJgNgyFw4tB4izJsOAj6a2slk4U0aAY5KMrwlEZ3OYKz8jCdI8Z/J6/6zVZkChAIaQ5sX4gGa80+lILEZjDao2qmO6Ef9AEac0vbVfj9YK2H5pV1cEDt8+jSTz74LgjoC16YzD/IXXMtX47+hbL2b9HZebOga0HF/owJzdRvxuHU0exxt4R0R4bsCaiAuI2c6VH1PcBKoY6eSgNqNRe5fD+S5vsa4dw+jh1xMSepsKAtubWsXbcfzski7ni3JC6i1udTyP421TG7qpAKGpxK+fxccbZ5AhtlAhrk7MW8dnHwl1G2DzUsAL+UUokuZzIim/0R8sqtrqL62d/hMOIaw+IcNaCWPOmMWGcKKx12NjpYOZiioPnTMcadD/T5oLAFrDbgriCzEBIw+CYSPlj1b0bEfU+RWzDRD6Zttb2hQG61QB3XvcDY/5l2GKX7VcBkhByv8ZS5ctgxGzSIUygh8++u6m7neexdpWh/fMZOykddz0pbbMdCknkTg4z7Af+pvQHwKxlQyNrwOTQfImH4BnLqIUdNPg7o9UPk9VH4HB7eAu/NwJ4Ard1qf1xjslsHSaDRRra3y+Xq+2PDxxx+npKQk9Pqtt94KeU2XX345KSkpne6n1Wp5//33+d///V+efvppHA4HM2bMYOnSpYwZMzCl8SfPOBHeSyM0GwyQO5aMKiM4DkZ8th5Ar3ZCw8cthG1vAx4YeQqk5x/+i3xOcDshZTDkToD1DrDtAIIjzHp+1/r/2PqDF/jjRgObDjUiBCS6A7o660fcqAZLD2jAJHaHNqldnzbCixmPlvFT0phakMLTqxT+r+ZB7nHeT4YG3uVmLsj5GKo+ajv+tKvAlAy7v0QdIeph6A+gpC0JJGPYNDLyp0HVd1CTgGjeHjGSTgau1KximHJATYJQQGl3LjnAjxYuAFczWMsgKRsczZA1FnTtuo/MYZA8DZo3dXqZa9f9jg81cxgCTAi/GH71Gvpom4sfB7xa0UgyzSR35sVGkEr4KDlZgXmadVRErELtGH/UARSeCgYDlO4A0dEzIn08+KzQVElnKaeZVDPm0Auc1FLcwQsJJwO4ijd5cdD5bKtp4bvyJtJtJZxhPkjH1umD2u+JGIxoujtBmwIaDZz9MK6K/ThrdnAo+WymnTgTz6qP0XcRaO9qzqs74Wg/oDEaaFF0eHWpZCQPormuklQaaJ8NEmEAC2dD6nBo3K927PXp0FQDvrDC32MWQksVQ5MyoGYIlG2GDjOYkfgU0GrB44WdjONdfCw2vEd4SYV6GuG1C8g479kO+3tHnMBnuy5lDMV8w3kMcWWjH30fJ+2+Xb1/gs9q0cB5p58EjnGg6CB3IpRuhKkXqvemwQLJOZAzkV2miVT4xzDSe5CCmo1g30t74zysYEgHLb1NtwzWfffd18FgrVixgu+//54FCxaEDMLOnTtZtWoVEydO5Pzzz49K0MGDB4/4maVLl7J06dIO76elpfHCCy90eNzJgDJ6Iez+Z9vr3BMBI9RqoKWE9sExBdSRv6cZfA4YNgMSUo/8PUJAQibYrTDlIlj7InjCRr8tuxj0/vWcMelRzpsygaVrDpJcae0iBonaAbtREzAMREQ9dIAeDWa9JmweJwW0eiamu7n5jNE89ZHCn6rvxeBzos0dBRf8GJ4JzCOaxsKYeWBKAY0RKrZBajaMng8l64E6QA/jzoaMYWC0gM6EInR4bVsjGq0eON24J1CGXM1CS8ZERtioE2MSaPVgTIbkweBugcRBYOhkrmTOL2HltZ1eEg2Q/+0fIi+Zl4g5jUGonaQGmFx+E3uJDDMGL2c4GaNOgz0fQiA9uB5I16tzKMHXGZ42o9c2Z5IEedPAnAiGRDVbz1VBBMNng70WDDvAWgGezp6iYIeG1eGvQt8dTjoVGHZ8TLX+BJqdHrTaelyOrrz01siXvsPPvbWRBD4PGEwMvuwZqN5N4ZATwO+BA+dQv+cNAEpJo0BrAV/5EY53ZIaiwZ+TS6O1mQyznxxLJqRlg6MJbFXqInrU7N+gARRAZspQMKVB+mBIzYf0Q2oSSu1esB4CcxoMPQE8Tqjaora/pBwo3wTNuzo1shnAIUMCFlqp1ybQ5M3koH4qm9Axg7fJDM5p6QClhfp3ftZhDm3ykHRs6TewZG8lNky4m/wsmjuHDbZfM7Hy95g1hO7niDJj+dMgfTgkDVJvJK1e/TMk8I3Xjj0tGau/hoKJc6FiE2z5Lxm+MC86p/tPR4+WbhmsBx54IOL1888/T01NDdu2bevgvezYsYP58+cfNivvuKLo0kiDVTAlUDJcgNYATfsAT1ujU06BITPA3QBulzrqMXSdTRVC0YIlDVprAR+IS2D18wRvNAC9ewdFxb+imCeYVZhNYj0RBiti7RjQrINkN2i9RPSyGQApgWh+8PCmXPV8FIWJmQq3nT6c5z9qwevzUJCbCpnZMOFa2LsKTr9D3WfINPU5KKlfQmqeet5j5sChdZA0GAZPgrRhoDWDzkSG1gD1FqjaQX3YeYXQqLYjIzzuQbo6YjdY1I5Qo4GEdNW70uo7HmP0nMjX2iEQdlOO4zMiprzDnLQcAM1E8G8DYBH7+YOnQHW7Ah2EjcgsSB8waOiJ0FgJdZHp6KbwlZz+Th4gqR0MeVPAkgzGRDCmQMm30BIWthkxG1oqwJICpl3QYITWOjLaG5QwXHRusDKA81nGZ0zFoVHwCQc6xd69pRiWI8xtBCc6LSngdaoDMEsaDJmurrdQzDD9QsSet1DwUKaZTMHYPPi+5zVC22ehDmYYDJ9Evq0CXK3gbgWvC0xJ6qAmKU9NLmkuJ+hx+QAS0tQ/Rwok5ULGCKjNg5QcqNsHiZmqp5KYCZZU9TjlG9TfqyoXyrcQEX0BDgDmtEl4G3dRp+RiNljw6I00pCfzYZWLhR51TSc+1NGa1t3u+icH6go6EeZkEoHR2UlMHJ4HvkVs+tceTta1zfNHlBkzJasDn3bVggCmDx9EcYmeEQWjIUMDueNV4/vFg6FrWdfooq9jWVHNYT322GPcdNNNnYbaxo0bF6qWft111x21wLhn+EnUMghNYJI3I3OUOl8SfM4FImC0Ah3S1JPVDnXoaerILGuMGh84Iora0HQGtXMTAqb/hPqNSwiPcQ9iP2d8fzfVP/hzaLcgjegYGzbXUaqBBgyM8Kuj48gbo13TyciChAxwqjf0hEEKZ03KYfXeOkblBBYeL3ocWurAaG4rM5U3Sb2ZDSZ1BDruh2pHlTEM0oeBKYVtvhx2NwxhSrLCCK0JdAYyyrZRHz43FFjZ7wXVI3Vowe+AzLDVIVo9aHTg94G2i6afkA5DzoFDgYXHY2bDdtVEBc/9sIHuy58h4+W51KOmQ8zUf9WWXgy0YCQDMxFhofTRMGqeem2cYcsMwoMa2rCMLV8gLJaTD+ZkUApBn6B6q8Yk2PM52ErU/2eNhOSsgMGygDkV6ndDYwX4Ow+YKaQgaEZBRGSSAQzhAFfnlPCadQwmmwNTd1e2Z447/PbqwFqi9IBh87nUCxBsJxot5IzDlXkqvrp9JM1aDDmpULUL6tvWIXU1r3V4g5qsXkedWY1stNSBpxXszYDq7ZFZCIk5iIrPAj+LUdXk84AlQ81gMJjVEFr6MEjOBkOGei96/WBMUAcXyTlQuk4dYCRmQcUWMmy7Q3rrGAHGQoZmm/B680nUDWFiip3dNXU0eSbiw8m5fK4m/nSW66FPptnpZUxOMna3l8GpZjURQm9m4oh8Jv7iN/B2M/VlH+Mmq2OSRCfGCjrWOsWUpHqXX/wW8ODHwJ6qxtg0WGVlZej1nYxOA+j1+lCixPHO1ho7ZUxgCq1oMLCjzsXH32tJtyczO3E0wzJ1asNvDKwLGTwR9GZIHQwItWPtohF1it4MyXlqBYIRs9m3r5SCpnfbVs4DOLeTvfp/aZ/fZWAomAU41BF6K4ns1o6hwF+MwQ9owoxWSJMW8EFSgdq5mFNUza4WGj16Cgal0+IKxLq1WkjJVg2Gx6Gen84EaUMDxkQLOaMhKUPtbA0JoNWzocpLiyYXn1/HiMI00Jqo8iq4qr4jsV1Cgxsgc4w6Z+Wyw7BZkSepKF0bq+B5nXYDvF8BiRnqHFHJemjdG/pIh7y8wLUBIEuNLARH8fPwtyVOANOTTwJTK/U1qjclAMwJUDBVnWfabkMNibYjISw/rblBveZpw1Sv1pwKioYddW5KXE7GDdYwtHkTJOYGBgMWdSCjT1IHBZYktcOpO0S9q47wAQ2AMOaTqVipc9qwIUgOy/PMAE4uXcafxK8Y7LOhC6ygbm/YOhAW0uycwODNnK6eky84iRpG0iDy5l+vZkuOPFVtHzUL4et9tA0AlI77BehqTRcJqWqI2NMCDkUdHLjtYLCCqwUcjarhMSeSaS4ARw0k5oGiAZ1RNVpB7UYf6C2qQdLo1O0Abgfo9Oo8s+kM1XCZvqPUb8LpMpPu/h4Aa+YCUtJSOdhsYOSoKfzf2Mls3FfNt/vr8WsHsc4/HZPGztn+dSSGeeCh65+RG3raw+njBkUaGb0ZkgfBwnvJ2DyJjLxxFESbJKHVBc4lG5xW3CQyasgR5tp7gagM1sSJE3n22Wf5yU9+Ql5eXsS2srIynn322f4pNBsHFJc04sicTX5dCbkZ6WyuaGVvgw+9J520BAPDchPUTtJlU0dpWSPUxiBM6uueGKsgxkS1MeEjbfICqja1MqTls8jPNG4kPH0lA8jIK1Tj1ztVg+VlLE2mqZT6djPCbQstbKwHMkyBvYOjyLQwT8aQAIqWyUMz2VJqZeKwdkVRNVr1pvbYUY2WXg1pGixqaEXRgTGRrdUuikuqSDYbESiMzcuGZCfoTOyvdmKx+Gm17yA7rINXsMCwmer8g8sG+d1/RE2IvCnqXJbBDKkjYMQc2NpmsCJCep42+1cF5Gi0ULAYSpd3XlkjZyikJpFRdxD8terRDAlq2fgRc8BeQ8bBTwFP5L7h2SRKshpSTspVO3ZFA+YUNtdrsBuG4tPqGTpuMJjS1e16I+hy1A7LYFa9Ca0JDGm0HvgOaCQhzCglpGWD34yiS6TFkUyy7zvCjVoKG5mibKbJH1nurF1+XCS+I1X216OWeUgJdPJKwMsKQ2eAnAngqIPEHLUQ5Jh5amjtwMeo9ZJGgT1yUbALqCcx8L9OFs/l5atzNy116mJmp00dXOn16r1kSgVXEzgbISEFfK1qOEyf0KZRiLbfwpQE2mHq/42JaiNRNGqo0+cFvQEKZkFSHnvL34UUBa8jgXEZZs6Ydz7vb9iO36ih2mbm3IBn8+WeGsrKDmI2DmY9p2HwuFng26zOR4WTMrSjNxREo1UHiFkjYPa1avjvaNDpYMSpZFRvh/QRMG7q0R2vO18ZzU5PPvkkCxYsYPTo0fz4xz9m5Eh1Hf+ePXv4z3/+gxCCV1/tvF7V8UbR0DQ22U8nNbOerPwhTMrNYXtFM7VNAiU9A3Ky1VG/z6UaqMSAIdAdRdkLRVEnfP1eRo0VkKaHTUBpm9Gqp30ZPY0ayhg6FUq/BVcjmYWzGGEYR8v3+3EZPsXoVneq0UKDx8yo9CRwN6sluAeNitSgNzGhcCgThhe0jTIjvk6jGi2vo63+H4AlML+kM7Jy9W52V9sYnZ3EPeeMVzsFjwM0erLGe6jdbcZsT8Bat5NUAmt+hp0B+TPV0bLL3lFXdzAlwdCTVEOqM8CIU9XCvw0bO3zUqdeSEAgS+jCq3sGPfgPPLO88Qy1tEORPhLKNUKdAxmjQm9SOLGUQjJyvZpo1tstUTA8bEOh0IDRQeCKgqNdP+JlQmM+2AxqGZ6Wp3oHBqBqpYHjNmALpRvX7jKlgSkLrN+Co3E2Cu4KM4HxK4TTw2Mlwt5KRkAXft0JYZQgDcD4fs0qfHZEaqfadZqCTup3mztcitWEB/JASGADr1PlQ/F7VUwmSmq+G23RGdRF1+ggY/QPwtqp/WVNga0eD5dZnk6QBt2tfx6RYc1JgDjBJnbtqrYOWKjWBSWsEmtUQvjFdfc/Vqg4WNBrQmNXfzucCxahGD/xhRlHRgkHfNn/qcap/Gg+kFZBVtIiD29aQIJIhyQiDxjB8lODgvp0MG95WROnWhZN4/mMNuQY7FVV6vm0+jUSvm3lsD93HPmCvZVxEVZUOaPWqRkuaqu1oGXG6+ptkjFJDnH1MVAbrlFNOYe3atdx7772sWLEiVFjWbDazYMECHnzwQelhBVBHO9OhtRC0BiYazWwubSQ/rRWrXwvpuapb73OrDcl8lKOeIIoClkz1RvF5YNK5YLdSX98xZVsNjSSq82WDxsOoeVRX72OnfgITCvP4fM8odC0bmOBvBj/U+WBTwzBGTRmlNny/D9I6eY5ZZ0kN4QSNls/d1ikpihrvbzuRyHMyWEDRMGrSKYzKzYDKbVCVBuUJ4GyCkbPVUJ4vWe04zKk9u27B70nJVf/1uiB/KhSeBA2bae9H+MlEXYUGWpLVjtSUgBo47OTpzQmDIWci5E9Xzz17jBqO8nnA54Os0TByDnxXB86wLM9JP277f80+1Tqk5KremdcJfi+TBycyefBYcNvAmQIavWqcNMHRvVvtgFPy1X8NFvKMiZCWAXX7oXYboA0k/bSAx63ub62Ept2Er5Gbw1b2KWPVyxHo9zIwg3kQOCro4MmYDl+Fm6Q08LtVrzBIMEstHI1GnasD1aj5DKpH7QmsRbRkwdZEoCXk4XqAhCEzyLDoqftuX8fv1iptaw6Doa6kHNVwWUvV99x2cFrVBArhhfSCsP116p/XDbhVT0YJeFXBCElw/lRrUA2ezwUuGxPz0piYfx7UjFbz2A0JjBszgXEFOWBOjXgE0fU/mMDmfRVMLUjlg/V+PmuahwUnJ7EfBTUo+uqhVIYf6UGQehN4NarOo2XoDPXeTxwcXTSoh0S9cHjixImsWLECv99Pba1aGiQrK6vD86gkAcJS0yePGMrWvQeZMtgCJrM6QnHb1RCH5gidfE/QaCAxW+0MsyfClMU0f1pHMp0s9kwbrCZNJA+CEaexozmLJm0SH+5zs9udjU6MYwhrSQD2i/E06VPVEZUIGNrEjgU/u4WidO6BoT5FudMnL+tNkKAB7SS189Ia1c7B41STLIzJqjemiOgMVlAXqB1MQqa61KB8I1R+A7SFBessY0iyVwMaREKG+vtpdDDjdlj/23YHNUJWIG146Cmq5uyJgZBTi/r744MhRdBUTsbuBkLp4fqwpIusoWoHa0xWderNamfpcwF+NXnAiHpddUZVk6IFxakaYEUDCVmqwdSa1HlHvR6MgczJUT8Ah1X1Zu1WyG+AQ99AS3jdST8nsD2iB/kahTGmTLLcdvDVE2HcLUfwsMypqrakHlZK0BkhLV+9Zn4/+LxUm/LQOdXK4cFUbyafAUDmd/8lOEcYym6rcTPGaVV/N70l8DugtumkbHDUQ/1BaDWFDSw7ma3TGdTr5w0MVDTtutdgW9fo1XC4qyXgmWkgJZcdlU289d8doGg5b2I6k1KTKS4pwWr3UFzSyDWzhzM5L4XvDlay//P9CHc2/2UOeqOXEZRSzHg2N6eRFvj8YRfxHk0EJ5zkHHXZifEIA5Je4qgrXWg0GkwmE4mJidJYdYOtZVaKD1kpGjmM8RlCbdzmNDUUoijdzAjsAVqdGr4QPhgyDf+EC3B8vxQz1sgJ8rQh6mjfmARJ2eSN8FLbbKLBmUBmdh57q8Yz3FNJsr+FEs1w7KZMdnsSGJ0+XA1JGZN6VzedZCaFozOoczHZ4wLzDRY1PJkxMjKFugtj2G2CnUzOFBh+KlRuJXyN0V7/YGAQFnxU+UaTq9GoRu6UyyMMVgZA0hDVu1GUtnT05EBoKXGQ2hZ8PtVADj8Zmkqherv62hB2fQ1JarjSFOaN6wxqB+l1qN6XMUHtCKEtrV8bMFzeQMKLwazOZxgs6nyGIVE1/lq92m78etW4phfA4Kmwu4RwI1QIbTkOXijT6NC70shKtEOTk4iiXMaun5YAqAbU61LDcz1BqwefDrID5YeaKinT5DMs4lEXZjU0LIQ6V9UQmdSyqd7AGGOC+v0uW+D8jepJ+T1qKDVvuup1KmvVcGxGIZ2iKOqAym8Ie+BqOzSBuS2tQc2q9XtAq2dzpZ3d1QI0ChvKLUwapgklUIQGbYrChnIHuoRUal0eDvo1fOzxUa4pZa92CgWDErG7vZw+ru/XRAHqueqMXZ9rLxO1wdqwYQO/+c1v+PLLL3G73axatYr58+dTV1fHT3/6U2677Tbmzp3bi1KPDYpLGtUR0yErk/OHqRWtQe3IoG/car1RnaT2+xgxbT6YvbBhCRGLO8cshPQ80OrY7khml7WO8YX5jNcZ2Zbox58yjYZdNTRTRwVjaHRlsNGazOi8JMCnjur7G61enW/KHq924k5rYDI8bHHU4TICu4vOoKaFD58NB9dCxReAGiDTDBpB6aFq9HhpTpsS+E4D6AxUk0t2eB3BvCmQHMiCMyeBcQKhh2rpTaqH63WpcyKDRsKQE1XjkzYEcsZHSFIXdLZbLRWaF3SpnaCi6biPKRm8RjWL0u9TP5OUraZu602g6NVOO7juy5AAyQVqCPPgN+BuW3gcMpeK+ufWJHHQmUShJZMssw0ctrYPJOUe/honDAK/U11M3lO0xrZ50KRsUkdMge8+CftADpgz1U41Yww0RK53Gz92MN81mdi6v4FpuUbGD9KoHpDWoLYfnzswcDGo4dyEDEg+QkZcd+5jnUH14pzN4LEzJT+V/Q519XvQQHU2aCsalkGl1U5TUyLbDxjYbfdj1eZgMGczpiCX5CRzn5dIiqAfnzAf1d28Zs0a5s+fT15eHpdffnlEZYnMzEyampp47rnnpMHqhIgRk6KooZKg0YKOYYTewpgQmBT1w8i50GqFHf9EnapNV8vMBDrA4ho/zWTTWukg1ehgbamDDBLITB2DzZqKW5PNqPxBTBqeA5ZACLO3Qgw9JZhxmD4ERL76/74w+jqjahjHzFdr9/nryCaBi+bM5sBOPQesXgrHB2oTavWg0aPMvBXW/brtGENnBRYsGwKT8NrIm92UqnaUwqc+NnboVMANSfmBrM9ucKQRfnC71qAaeK9L7ZTNqaAZ0db+gg8i1+rU0mCpBZAzCQ6pBqtDBqQO3KRS5U9mp9tDliVDDSvSAiSpA4nDkTpYNQxHmuvqDK0ORKD9afSMGFsE36UQWtVuyVJDVyiQU0hwPUdosf7IQpaUO7B6E9hQ62f8kGTwelSj7/eoWasI1Yhrteq1MvTiAM2krgEbPyKD8WOOPCUQMmI+L4+9t4k126DObWPasNEkJyX1eQHagSSq3vHuu+9m3LhxfPvtt9hstg6lkObNm8dLL73UKwLjmfAJ0+CIp8OIKWi0nFb1dW9MhHaFOU3tKFMFTDgLXFao3AIj5qmdpaJOxE4rSGVTKUwpSGHpF7uoaHZx0O4j1Z2CFh0V/iR+NGowE4Zkq+tL8PdOxlG0aDSqJyBE3432tIEQ3PCTYP9aqNighh9Tc/BkjKTF5wp5dlvLm9i8v4KiwrlkrAs7RtoQ0BrYWt7ExgO1nJBnZuKQsMwqjUZNlHE7VC/LnAHZk6PLvjqS0dZo1HbnblXTuBVFnYfQaAI1JM1sK7OyoaKU6cOymJw+BAZPgUMbaF+dIYgLC+UePTuaLaSlZpFLGRm4AuHJI3Q1lkAKfrQh3OB+fr9qHJOzoDlgsDKz2u6rzBEd9zWYQwPJqUPT1Plmt0MNAfq9qvfmD2SXKI5A8k8vRxR0Ucxda3WMyM1gzb56huRmMWzwIK6aPbx3dcUYUQ1F169fzzXXXIPRaOy0KG5eXh5VVZ3VLDu+CIX/jvSMmGAaurkfRkaWTHXuJ2O4mnk27SKYdF6gJIs64p84JIsrZg1j0pAcThs3GMXvYVh2CoakdBr8KbRi5r+7W9R5K2NiYN1V/4UFuqSvNeiMkFYIY+fD6NNh1OmQOIjv67VYvUa2VKlrjYpLGml0wOZKO2QFFy4rqheo1arbnYL1FZ100HqT2nkbLKoXlBioUNFXGBLUOTKdIfD7a0OJmZvKmrA6vBQfskLqEMgeBdmjuziQQpM7EcUn2OhO5HurgRrSQJOgVoLwOQ//Z0xUU9+7U4bscAQNcfpYQt1bWlj4rrPkIG0Ck/NT1aSG4GDSYFaviyklMK+nUwctlkx1vrmfkgyORJNbYUJ+BopGy7ShR1o6EP9E5WHp9foOD0oMp7y8nMTEXkrPjmM6TJjGAFsrmik+4ODELBifM1pNjU4drHYYQYLZZcCPioYzMsPA5kNW0rUWNpRY2WtP5Izx2V18wzGMRqt6WQUz2Gfzs9NqYmh5E+Py0vmu2sH4ArXDKBqaRvFBwZTcRMi7Dz64BwaNhszRoOlkIr095jQ1Q8/vDRTt7eN7SatTy+x4A+unAlXspxQOpvhQk6rTkqxms+ZNhervaV/53UkKDbochFtB59eyyZdKtjaFcYl+dc4nfF6xMxIHqXNqvRFatqRBzlgo/VpNYkkLqyLeIbRq6XoBbXAZhc6kXhuPM7BS3NC3kZAeEGxDRUNSmFwgDVanzJo1i+XLl3Prrbd22Nba2sqSJUuYM2dOxx2PIzoLB8YCxSWNWJ1+1tXoGD8ocDPqD/OEV62BCflZTMhOAL+fs6Zr1Q7oSGus+oj217Xfr7PWCBnD2OirxarV0lBq5YrJgxk7XBtKGAiFfT0OcBrhor8FSvVoI7d3cU5qgd7MtpTn9o9B6QuCYdUwJhekR3aC6cPVrMzkYdC8LeKzzZo8GsjBrPWg9bup8qazT5jIdxoYmZuhJv0cjuDSBH0vhNr05oDOfPUapoQZLEMi6jPCgglHKYF1c4cheG20RvV4Ps/RZ5/2ArHax/QlUYUEH3zwQTZs2MA555zDf/+rPr9oy5YtvPDCCxQVFVFbW8u9997bq0LjiTeLS7lz+Va+2VfX54+M7ilFQ9NIteiZXpgNCdnqDXy4lHSNti1lWqMBhLp+JPgXXLPST7QPs3Y77NpbaFQDMm7MOCwpaUwrSA0UAk3u2NlqAgtfLSlq1Yku6PQc9CbVAAbThnuZrWVWlqw+wNYya/d3SsxWw2t5U2hfUbElYRCJ2mwc+hz0ekgUHvb589nrNvNtczLbGhW1rmFXf8k5qpHujbCuVh9YpjFWTWfPCpvXMSaCPswIJ2W3LUQ+4nED67SCj6sZYPq97ccAUXlYJ554Iu+//z433HADV155JQC33347ACNGjOD9999n8uTJvacyzlheXIbV7sbp8XHT/ChKA/UhHZI+DOYjdxJao/pgrNAjLgQdCoy2T6HuI4qGprFySwV2t5etZdaBCbvqjEwsyGTi0JxAvUedeg3bX0dtoLIB4rALwrs8B3N6oIBq73tY4Z1dt0fnWh1kjoLBE6B0LRktbfUVK1Jz0Vvyqa9pROszY8CB369jty+XLG8K3jIbE/MPE7JStIEnGPRCO1I06mM/hs5UvVxLatg56CEpExoCi+fTco68Rizi2MrAJhiFEYtTDn1N1DnU8+fPZ9euXWzevJk9e/bg9/sZMWIERUVFUT2d+FjCoNPg8QnG5Fhi31Xvzm+lCatK7fcEKiSEF2PtpLPuIybnp0Z0thET5f1FMDW8O2gCKdFddMSHDetoNGyth+KSyl4P+0Td2SXmqMkj2eMCz91S57KrEsYzPDuTr6vceMiikDJ8ig6XV8eOWh9TEpMPPxenaALeei+0I0VRjX1y4MkA4QZfo1HLYzUESpSlDokuQy8GOOzC+mOUo170M3XqVKZOndoLUo4dRmcnRfx7TKAzBlJ8w8ODA0NcjSy1+s4X8aKGjv/+1QGGpKsj/M46n6g8oW7Q3c6ug0E1mNR0/pzxUFYceMKxniFjplBltbBwwmC+3eHDLmyYXC6aSaQZIzurbEf4pq4efR0lxgSwZLOnuo41xdVMG6VX9SsaGDQMSi3qdyYPjonwnqR7RGWwNBoN2dnZvPHGG5x22mkdtr/22mtceeWV+Hz9O78RK3RZAy+eURR1pOr3DHiGVFyNLDVaNamlEw/0o+3VaDUKhxoc3DS/87Yy0MY5aDBXbqloM1wZ+WoljkGjocoNxkTGjJvKGBRQ4LsT8vmyOJGq79cgPD7cveE19RSNTk2O2eun0a9pM/iKopbDMqcCIvBYnOM7IhRPRD1Mdjqd/OAHP+BPf/pTb+qRxDJanZpYcJyHfNsTTGB4s7i080QGjabTa3bG+GwK0sxcd2rnYc1YyAILJukAbRP8pkRIG6GWKUovhKyxqoEwJAAKk/JS+cVZM9QUcn0iPiWNc6d0s1JHFHSaQKLRgjGRCYVDSE0wRBr89KHU61Mp8SWws1mDNFjxQ9Qhwaeeeop169Zx2223sWHDBv7+979jMvVD+m0csHJLBburW6iwOuLHE5BETdALWbu/njE5yd0O311QVMAFRQVdbu+rcGBPCHqz4cYTRVGfz5U1CvwO9dlaiFAGJV4H6IwMGz6CrbsFM0eP7r7+KAZDnV4nRU3gmJifysTCxMjjpuTR6Lfg05rYVK9hrCzaHTdE/Uvp9Xr+8pe/sHTpUt566y1mz57NoUOHelNbnNPLMXlJzBL0QsbmJLGrqplkU+/UgwweN9mk63kKei/ToRKEJQ3ShqtJC2kFbeWLgpmRikIjyWiSC2gUfVsVInidOoRNdWY1Qai9ETSnYcodjTOpgEnjRsqIQRxx1HfWlVdeyeTJk7ngggsoKipi2bJlvaErromXOaxYCDkdDbGiP+iFLFl9gDE5yTQ7vUfeqYfHHWhPqwMaLWQWgkawu7aVtd+WMGV4DpOHZAYSdHzUtvootXmxJLSqFeD7iK4fCd/FeNycTP7oWeQLPwzpp8dwSHqFXvGFp06dSnFxMTNmzGDhwoX84x//6I3DSvqYeF94GGv6uxzpx+hxj5qkbEgbwqbmJJrsHrYcrGmr8q43k5moY3CKicyk7pZb6qV1WEdCb1artQTrCg6Qh9V+7i2qxdzHGb32LIvU1FTee+89HnjgAR5++OHeOmxcEgtzD91hoDPQjpZY09/T7MXueoixlhUZoTu3gHFjknEfrGZafjJ4naHEnAVThrNtv45Jw7K7rtcXTi8ajsNeW60BEnJBeCIXFfcz7fuJeOk3BpKoDNaBAwfIyur4yANFUXjwwQe58MILqa+v72TP44NY60i7ItY6wp4S7/rjtYPqUB6rzEbR8BwmZhvUtXpeF+iMai3CXEugAnz/ejHFJY3sqrKxdn89N84bGXl9NRrIHKJ6gwO4RKN9PxEv/cZAEpXBGjp06GG3T5w4MSoxxwrx3pFK+od47aDCdUdkxOaNUj0snzuyfNEAhNyKhqaxdn89qRZDJwMCpX8KCndBuPd3Tdjzq3qj34iVed2+olsG66GHHkJRFO655x40Gg0PPfTQEfdRFOW4LYB7rDcaSe8QrwObcN0rt1QQyojV6tVsQZ9bNVwDWEFicn4qN84b2fmAYIDT2PvSs45Xr727KEKII+ZfazQaFEXB4XBgMBjQdOMHVxQlbitdNDc3k5KSQlNTE8nJPU/JDWZ1pVr0ESMoieRYo9PBmdseWcVfo+udx4b0Jj4vIA5rVPtq4NmXA9p4HCz3pL/tlofV/mGNh3t4oyR+Qz0SSU/p1EvUm8FjV72tGKDTTlx75K5voOs4xtqxYwG5xLsP6LDIUiI5AvGa0typbkVRF+3GSMmjaJc/xOxygjDitd1EizRYkmOWWL+Zw/XF2pqy7tKl7mCZJpTAI1YGjmgNz0AOPLvbduO13URLt1rS8OHDe/yMK0VR2LdvX1SiJJLeINYnoMP1dRVGjqU5ic60tH+gZoRGrQ603Vh/1cfEY5isu233eJt+6JbBmjNnznH/UEZJ/BHrN3O4vq461Vgyup1pkQte+4butt14NMZHQ7eyBI83jjZLUNJ3xJLH0R2OVm8snW9XWmJJoyT+6PUsQYkkVoi30fzR6o2lEXRXWmJJ47HO8T44OCqD5fF42LlzJ01NTZ2munf2NGKJ5GiI9TBfe+JNryS2ibcBW28TlcHy+/3cddddPPvss9jt9i4/F68LhyWxQWejyXgbzcebXkn/EY23dLwPgKJKa//tb3/LY489xuWXX87LL7+MEILf/e53/O1vf2Py5MlMmTKFDz/8sLe1So6CWE/x7ozjLWVXcnwRTfs+3td4RmWwli5dykUXXcRf//pXFi5cCEBRURHXXXcda9euRVEUPv30014VKjk64rHzj4eFm0dLPA0k4klrPBDP7Xug2kJUBqusrIz58+cDYDQaAXA6nQAYDAYuv/xyXnnllV6SKOkN4vHmOB5Gk+0HErFsFOJx0BPLxHP7Hqi2ENUcVkZGBi0tLQAkJiaSnJzM/v37Iz7T2Cgb9UDSPj4u51Jik/ZzErE4qR5sS8kmtbs40qDneM9kOx4YqLm0qAzWtGnTWL9+fej1vHnzeOqpp5g2bRp+v5+nn36aKVOm9JpISc+JxY5P0pH2A4n+6Ah6alCCbQno1tMHZNs79glvt/05QIkqJHj99dfjcrlwuVwAPPLII1itVk477TTmzJlDc3Mzf/zjH3tVqKRnxGMIUNI/YaKehnN60pa2llmpsDqwu72y7R0n9Gd4sNcqXTQ1NfH555+j1Wo5+eSTSU9P743DDgiy0kV8IUNQPaMvrlfwmBVWBxaDTj4L7jjiaNvTgFS6SElJ4Uc/+lFvHU4i6TYyBNUz+mI+MzxsKD3744v+nB8/6koX5eXlNDY20pmjNn369KM5vETSLY73xZSxQPA3OH3cIDlokPQZUYUErVYrd9xxB6+99hput7vDdiEEiqLEbaULGRKUSDpHhl8lvU2fhwSvvvpqVq5cySWXXMKJJ55ISkpKVEI7w+Vycd999/HKK6/Q2NjI5MmTefjhhznjjDMOu98DDzzAgw8+2OF9o9EYWiMmkUiODhl+jR2Ox8FDVAZr1apV3HzzzTz55JO9rYerr76a5cuXc+uttzJq1CiWLl3K2WefzWeffcYpp5xyxP3/+te/kpjY9tA4rVbb6xolkp5wLHUs8RR+PZaue2ccj4OHqBcOjxw5sre1sG7dOpYtW8Zjjz3GHXfcAcCVV17JxIkTufPOO1mzZs0Rj7F48WIyMzN7XZtEEi3d6VjipXONpwXox3qHHhw8JJt0LFl9IObbTm8Q9TqsZcuWdfpIkaNh+fLlaLVarr/++tB7JpOJn/70p3zzzTeUlpYe8RhCCJqbmztNApFIBoLurGOSZY96n2N9LWJwzV6z03vctJ2oPKx7770Xl8vFCSecwBVXXEF+fn6nobdFixb16LibNm1i9OjRHSbeZs6cCcDmzZspKCg47DEKCwtpaWkhISGB888/nz/+8Y9kZ2cfdp/wRdCgTgJKJL1Fd7ySeAq1xQvx5A2G01Nv+3hqO1EZrPLycj799FM2b97M5s2bO/1MNFmClZWV5Obmdng/+F5FRUWX+6alpXHTTTdx0kknYTQa+eqrr/jLX/7CunXr2LBhw2GzTx599NFOEzYkkt6gOx1QvHaukt6np6HM46ntRGWwrr32WjZu3Mhdd93Vq1mCDocjVP09HJPJFNreFbfcckvE6wsuuICZM2dy2WWX8eyzz/J///d/Xe5711138atf/Sr0urm5+YienETSXY71uRRJ73I8eUw9JSqD9fXXX/PrX/+6170Ss9kcEZoLEkxLN5vNPTreT37yE26//XY+/vjjwxoso9HYqaGUSHoD2QFJesLx5DH1lKgMVk5OTp/UCszNzaW8vLzD+5WVlQAMHjy4x8csKCigoaHhqLVJJNEiO6C+JV4yLCVHT1RZgrfffjsvvPBC6JlYvcXUqVPZvXt3h6SHtWvXhrb3BCEEBw8eJCsrq7ckSiSSGGPllgo+31XLyi1dz3HHMrH80M5YIyoPy+l0otfrGTlyJBdddBEFBQUdsgQVReG2227r0XEXL17M448/zvPPPx9ah+VyuViyZAknnnhiaF7p0KFD2O12xo4dG9q3tra2g2H661//Sm1tLQsXLozmNCUSSdwQv8tY5Bxn94mqlqBGc2THLNpaghdddBErVqzgtttuY+TIkbz00kusW7eOTz75hNNOOw2AuXPn8sUXX0SstbJYLFx88cVMmjQJk8nE119/zbJly5gyZQqrV6/GYrF0W4OsJSiR9B59HbKL95BgvOs/Wvq8luCBAweiEtYdXn75Ze69996IWoLvvvtuyFh1xWWXXcaaNWt48803cTqdDB06lDvvvJN77rmnR8ZKIpH0Ln3tQcT7HGG86+9PeuxhORwO7rnnHubNm8e5557bV7oGFOlhSSS9x/HuQUgOT596WGazmeeee47x48dHLVASO8jORNLXSA9C0ltElSVYVFTEtm3beluLZACQNewkEkm8EJXBeuqpp1i2bBkvvPACXq+3tzVJ+pFjvUCoRCI5dogqS3Dy5MnU1dVRXV2N0WgkLy+vQxUKRVHYsmVLrwntT+QclqQ/kWHZ2EH+Fv1Pn2cJpqenk5GRwZgxY6ISKJFI2pDrcGIH+VvENlEZrM8//7yXZUgkxy+y1mDsIH+L2CaqkOCxjgwJSiQSSf/Q5yFBAJ/Px6uvvsp7771HSUkJAEOHDuWHP/whl112WacPdJRIJBKJJFqi8rCamppYsGAB69evJykpicLCQkCtgNHc3MzMmTP58MMP49Y7kR6WRCKR9A896W+jSmu/5557KC4u5s9//jO1tbVs3LiRjRs3UlNTwzPPPMOGDRu45557ohIvkUgkEklnROVh5eXlsXjxYv70pz91uv3mm29m+fLlh32kfSxzrHtYMnVXcrwj74HYoc89rPr6+sOmtI8dO1Y+NDGGkdUtJMc78h6IT6IyWCNHjuSdd97pcvs777zDiBEjohYl6VtitbpFVw+ykw+4k/Q2sXoPSA5PVFmCN954IzfddBNnn302t956K6NHjwZg165dPP3003z00Uc888wzvSpU0nvEajHSrhZtysWckt4mVu8ByeGJ2mDV1NTwu9/9jg8//DBim16v57777uOGG27oFYGS44euFm0ej4s55RyLRNKRo1o4XFdXx8cffxyxDusHP/gBmZmZvSZwIDjWky4ksc+S1Qew2j2kWvRcM3v4QMuJO6TBjx/6ZeEwQGZmJpdccsnRHEIikXRCPHuVsWAsZBj52OSoDJbNZqOkpITGxkY6c9SO9Fh7Sf8TC52J5MjE8xxLLBiLeDb4kq6JymDV19dz00038eabb+Lz+QAQQqAoSsT/g9sksUMsdCa9hTS+sUksGIt4NviSronKYF133XWsXLmSm2++mVNPPZW0NDmKiRdioTPpLWLZ+B7PxlQaC0lfEZXBWrVqFbfddht/+MMfeluPpI85ljqTWDa+sWxMJZJ4JSqDZbFYGDZsWC9LkUh6Riwb31g2phJJvBJVpYvLL7+cFStW9LYWieSYYXJ+KtfMHh6zBjUaZMURyUATlYe1ePFivvjiCxYuXMj1119PQUFBp8+/mj59+lELlEgksYEMc0oGmqgM1imnnBL6/0cffdRhu8wSlEiOPWSYUzLQRGWwlixZ0ts6JBJJjBPLc4aS44OoDNZVV13V2zokEomk1zielxUcy0SVdBFOZWUlW7ZsobW1tTf0SCQSyVEjn3d1bBK1wXr77bcZO3Ys+fn5TJ8+nbVr1wJqQdxp06bJLMI4RGaBSY4V5POujk2iMlgrV65k0aJFZGZmcv/990fUEczMzCQvL4+lS5f2lkZJPyFHpZJjhWNxWYEkSoP10EMPcdppp/H111/zi1/8osP2k046iU2bNh21OEn/IkelEokklonKYG3bto2LLrqoy+3Z2dnU1NRELUrSvwRDgYAclUoOiwwbSwaSqAyWxWI5bJLF/v37ycjIiFqUpH+RoUBJd2nfVuLRgMWjZolKVAZr3rx5vPTSS3i93g7bqqqq+Pvf/86ZZ5551OIk/UO8hgJlx9P/tG8r8TjYiUfNEpWo1mE98sgjzJo1ixkzZnDhhReiKAoffvghn376Kc899xxCCO6///7e1irpI+J1QagsFdT/tG8r8Vj9ItY0yzVj3UcRnT0quBt8//333HLLLXz22WcRWYJz587lL3/5C+PGjes1kf1Nc3MzKSkpNDU1kZycPNByJF0gb3TJscCS1Qew2j2kWvRcM3v4QMvpd3rS30ZtsII0Njayd+9e/H4/hYWFZGVlAZFPII43jieDtbXMysotFQCcO2Ww7Pglkn7meB949aS/jSokGE5aWhozZswIvXa73SxdupTHH3+c3bt3H+3hJX1McUkju6tbACFDaz3geO9kJL1HvIbkB4IeGSy3280777zDvn37SEtL44c//CGDBw8GwG6388wzz/DUU09RVVXFiBEj+kSwpHcpGppGhdUR+r+ke8j5M0l/IgdIKt02WBUVFcydO5d9+/aF5qzMZjPvvPMOBoOBn/zkJ5SXlzNz5kz+/Oc/s2jRoj4TLek95OguOmJt4l5ybCMHSCrdNlj33HMPBw4c4M477+TUU0/lwIEDPPTQQ1x//fXU1dUxYcIEXn31VebMmdOXeiWSmEAa+tilu95IrHgt3dHRkwFSf5zXQF27bhusjz76iGuuuYZHH3009F5OTg4XXngh55xzDm+//TYazVEXf5dIJJKjorveSKx4Ld3R0ZMBUn+c10Bdu25bmOrqambNmhXxXvD1tddeK42VRHKcEOsLtru7ED5WFsz3to7+OK+Bunbd9rB8Ph8mkyniveDrlJSU3lUlkUhilpVbKthdbaPC6ojJsGh3vZFYCev2to7+OK+BunY9yhI8ePAgGzduDL1uamoCYM+ePaSmpnb4/PTp049OnUQiiVFiY41lrMxDSfqHbi8c1mg0nS4E7myBcPA9n8/XOyr7meNp4XAsc6x2RrF+XkfSF0v6j/cqEccCfbJweMmSJUctTCLpCZ1N7MZSZxktsTLZ3xVH0hcroTQ4dpcXHAvtvC/otsG66qqr+lJHCJfLxX333ccrr7xCY2MjkydP5uGHH+aMM8444r7l5eXcdtttrFq1Cr/fz7x583jyyScpLCzsB+WS3qazzijWO/vuEKudbLCTTDap3UKs6euMWDKevcmx0M77gqOuJdjbXHrppSxfvpxbb72VUaNGsXTpUtavX89nn33GKaec0uV+LS0tTJ8+naamJm6//Xb0ej1PPvkkQgg2b97co+dzyZBg7CJHnn2HDK8NLOF1PcfmJNHs9B4X7bxfawn2JuvWrWPZsmU89thj3HHHHQBceeWVTJw4kTvvvJM1a9Z0ue+zzz7Lnj17WLduXai24VlnncXEiRP54x//yG9/+9t+OQdJ33KsjajbG+CBNMideX7tiyMDMTVg6Mn1erO4lI+2VzM2J4kUiyFmziFIeF3PwanmIw4aYqXt9Of3xtTiqeXLl6PVarn++utD75lMJn7605/yzTffUFpaeth9Z8yYEVGId+zYsZx++un861//6lPd8Uasr6M5nmj/MMHw18Hf6c3i0n75vSbnp3LN7OF8uqOahU99yVMf7Qp1orurbRSXNMbcww+70tNZG/9oezWNdg//2VzByi3lPPr+jpi4B4Jak006RmcnMjo7qVvh2MO1nf6kP783pgzWpk2bGD16dAe3cObMmQBs3ry50/38fj9bt27lhBNO6LBt5syZ7Nu3D5vN1ut645VY63SOZ9ovwAx/HfydPtpe3a+/1wffV2Nzevjg+2qKhqZFdKKxstg2SFd6OmvjZ4zPJs2iZ2iGhSaHF5fXHxP3QFBrs9PLPeeM555zxnfLUzlc2+lP+vN7YyokWFlZSW5ubof3g+9VVFR0ul9DQwMul+uI+44ZM6bT/V0uFy6XK/S6ubm5x9rjiVid9D8eaR/ibP+6uKSRM8Znh+Yz+oOFE7L54PtqFk7I7jQEG0thtK5CxJ218QuKCrigqCAizBkL90C09+OR2k5/0Z/fG1MGy+FwYDQaO7wfrKjhcDi63A+Ial+ARx99lAcffLDHeuOVY20e6FhloH6nW88Yw61ndD64ixcOd+1irf3Hmp5YJqZCgmazOcLTCeJ0OkPbu9oPiGpfgLvuuoumpqbQ3+HmyiQSiUQyMMSUh5Wbm0t5eXmH9ysrKwFCD4tsT3p6OkajMfS5nuwLqmfWmXcmkUgkktghpjysqVOnsnv37g5zSGvXrg1t7wyNRsOkSZPYsGFDh21r166lsLCQpKSkXtcrkUgkkv4jpgzW4sWL8fl8PP/886H3XC4XS5Ys4cQTT6SgoACAQ4cOsXPnzg77rl+/PsJo7dq1i08//ZQLL7ywf05AIpFIJH1GzFW6uOiii1ixYgW33XYbI0eO5KWXXmLdunV88sknnHbaaQDMnTuXL774gnDpNpuNadOmYbPZuOOOO9Dr9TzxxBP4fD42b95MVlZWtzXIShcSiUTSP8RtpQuAl19+mXvvvTeiluC7774bMlZdkZSUxOeff85tt93Gww8/jN/vZ+7cuTz55JM9MlYSiUQiiU1izsOKBZqamkhNTaW0tFR6WBKJRNKHNDc3U1BQgNVqPeLDgGPOw4oFglUxgnNmEolEIulbbDbbEQ2W9LA6we/3U1FRQVJSUqcPrQyOCOLRA5Pa+5941Q1S+0AQr7ohOu1CCGw2G4MHD0ajOXweoPSwOkGj0ZCfn3/EzyUnJ8ddgwoitfc/8aobpPaBIF51Q8+1H8mzChJTae0SiUQikXSFNFgSiUQiiQukwYoCo9HI/fffH5flnKT2/idedYPUPhDEq27oe+0y6UIikUgkcYH0sCQSiUQSF0iDJZFIJJK4QBosiUQikcQF0mBJJBKJJC6QBksikUgk3WKgc/SkwZIMKAN9A0gk/UVTU9NAS4iaN954A6DTUnX9iTRYwKZNmzh06FBEg4qXjtRutw+0hKjYv38/drsdp9M50FJ6zJYtW9izZw9lZWWh9+Klvbz99tvceOON7N+/H1DrZsYD//znP0lKSmL16tUDLaXHvPXWW5x55pk8+eSTHDx4cKDl9Ihly5YxYsQILr30Ur7++uuBlnN8G6wdO3ZwyimncPrppzNlyhRmzpzJm2++idfrRVGUmO6Edu3aRVFRET/72c8GWkqP2Lp1K+eccw7nnnsuw4cPZ+7cuaxevTqmr3WQrVu3csYZZ/DDH/6QoqIipkyZwtNPPx1qL7HORx99xI9//GNeeeUV3n33XYAjFhsdaDZt2sSJJ57ItddeyznnnBNXtfUqKio455xzuPLKKzEYDFgsFiwWy0DL6hbB637VVVeRlJSEyWTC5XINtCwQxynV1dVi2rRp4uSTTxYvvviiePHFF8WsWbNEamqquP/++4UQQvj9/oEV2Ql+v18sX75cjB49WiiKIhRFEZ9//vlAyzoiXq9XPP300yIrK0vMmTNH3HfffeLGG28UBQUFYuzYsTF9Dm63WzzyyCMiNTVVzJkzR/z5z38W//znP8XcuXNFcnKyeOuttwZa4mEJtuPi4mKRkZEhzGazOPHEE8XmzZuFEEL4fL6BlNcpdrtdXHPNNUJRFDFnzhzx9ttvi+rq6oGW1SPuv/9+MW7cOPHaa6+JQ4cODbScbtHU1CSuvPJKoSiKmDt3rnj77bfFe++9J0wmk3j88ceFEOq9PFActwZr2bJlQqfTieXLl4feKysrExdffLFQFEV8/PHHA6iua/bt2ycmTpwoMjIyxMMPPyzGjx8vZs2aJTwez0BLOywffPCBKCwsFNdee63YuXNn6P3Vq1cLRVHEr3/965g9h/fee09Mnz5d3HrrrWL37t2hG3bPnj1CURTxhz/8ISYHN+1Zvny5OPPMM8Xf/vY3oSiKuPvuu0PnEkv6vV6veOSRR4SiKOK6664TtbW1XbaNWNIdzqFDh0R2dra4+eabO7wfTizpb21tFaNGjRKFhYXir3/9qygpKRFCCLF//36RlpYmFi1aNOCDm+PWYP3+978XKSkpoR/A7XYLIdRR6MyZM8XEiRNjckRXUlIi7r777tDo+C9/+YtQFEW88MILA6zs8DzxxBNi3LhxoqamJvSey+USQggxa9YsccYZZwghYusGDvL111+LP/7xjxHahRBixYoVYtCgQeKNN94QQsSmdiHadK1du1akpKQIIYT4wQ9+IHJzc8VHH30U8ZlYYcOGDWL27Nli7NixoffefvttcdVVV4k777xTvPjii6H2E4t8+eWXwmKxiN27dwshhHj55ZfF+PHjxfjx48X5558vXn/99QFWGEmwH1yzZo3Ytm1bqD8MMmPGDDF37lzhdDoHtK0c8wYr+EO0v8hPPvmkSEpKEp999pkQQkSMNN944w1hNBrFb3/720737S+60u50OkP/37VrlzjzzDNFfn6+qKur61d9XRGuO1z7rl27IrYLoV73uXPnilNOOUU4HI7+FdoJXV3z9nz11Vdi4sSJIjk5WTzwwAPiu+++E42NjRHH6G+OpH358uVi5MiRQgghNm3aJBRFEVdddZVoaGg47H59TVe6g57g7bffLs4880yhKIoYOXKkSEpKEoqiiEWLFolt27ZFHKO/6Ur7hg0bhE6nEytWrBAvvvii0Gg0YvHixeKqq64SgwYNEoqiiCVLlgyA4ja609b9fr/w+XziF7/4hUhJSQm18YFqK8eswQrOO7T3PIIX+qOPPhJGo1E88MADofeCP2BVVZW46KKLRFZW1oCM4rrS3hVvvPGGMJvN4s477+xjZYenp7qDBm3atGni4osvDr03EHRHe7B9/PrXvxaKooh58+aJq666Svz0pz8Vqamp4pJLLukvuREcSXvwmq5bt04kJSWJiooKIYQQP/3pT4XRaAyN9ltbW/tHcIAj3aMlJSVi8eLFQlEUMX/+fPHBBx+IkpISUV5eLv7f//t/QqPRiAsvvLBfNQc50jXfsGGDyMzMFJdffrmYMmWKuPfee4XNZhNCCLF161axYMECkZGRIXbs2NGfsoUQPb9PhRDi3nvvFYqiiHfeeacPlR2ZY9Jgffnll2LChAlCURRx5plniu3btwshOnaG06dPF9OmTRPfffddh+2vvfaa0Ol04q9//Wun+w609vD3ampqxLXXXitMJlNoxNnfHX9PdIdTWloqEhISxKOPPiqEGJgJ3e5qD75esWKFeOONN0RdXV3ovbvuuktoNBrx2GOPCSH6b8Tfk+v+r3/9S4wePToU6m5ubhYWi0XMmzdPXHPNNeKKK64IGbNY0f3aa6+Jq6++WqxevbrDtssuu0ykpKSEOtFYu0dnz54tNBqNyMzMFGvWrInYtmrVKpGeni5uueUWIURstpdwXV999ZVQFEX861//Ouzn+5pjzmB98803YuzYsWLYsGHiwgsvFIqiiN///vcRk7bBTvHtt98WiqKIhx9+OBSOCm7btWuXyM/PF9dff32/NabuaO+KTz75ROTl5Ykf//jH/aA0kqPR/eWXXwpFUcSHH37YD0o70hPth7tJ9+zZI0aOHCmmTJkSEbLtS7qrPaj7q6++EhaLRZSWloa2XXrppUKr1Qq9Xi/uv/9+0dLSEhO6g5qbmpo6zB0GP/ftt98KRVEioiSxoD3Yh3zwwQehTN6gJxWM2NTU1IiFCxeKgoKCmGsvnbFt2zaRlpYmfvnLXwohpMHqNbZv3y6MRqP497//LYQQ4tRTTxWjRo0Sq1ev7vTzZ599thg8eLBYuXKlECJyhD9hwgRx5ZVXCiH65wfqqfZwXS0tLSG3/ZNPPhFCCPHFF1+It99+O+JzsaI7yLPPPit0Ol0oXOL1esW+ffvEhg0b+ly3EEenXYjIkfFJJ50kZs2a1W8dUHvtp5122mG1L1u2TIwZM0ZYrVbx2WefiVNOOUVotVqRnJwsRo4cKb766ishROxe8/ah+9raWpGamtqvofCear/sssuEoiji5z//uRBCRBiHxYsXi/Hjx4umpqa+Fy6Orq3X1NSIoUOHitNPP100Nzf3tdQuOaYMVtDYhI/IgiP4m2++OdQwwjuZkpISkZiYKGbNmiU2btwYev/bb78VycnJ4sEHH4wp7Z11JsHz2blzp5g+fbqYNGmSePDBB0VBQYHIyMjo02zHo9EthBDnnnuuOPnkk4UQanjw1VdfFdOmTRPTp08X9fX1fab7aLW397o//PBDodfrxa233tqHitvoifag/k8++UQYDAbxwx/+UGi1WjF79mzx5Zdfin/961+hTrWv52x785o/++yzQlEU8fe//70PFbcRTf9SWloqkpOTO0QRvv/+ezFixAhx+eWX98tguDeu+6JFi8SECRNES0uL9LB6yrJly8TPf/5z8bvf/U58+eWXoffDL2TwQl911VUiNTVV/Oc//4k4RvBHXLp0qRgyZIgYPny4ePrpp8ULL7wgzj33XFFQUCC2bt0ak9o7o6SkRFx99dWhMMSPfvSjiPBPLOn2+/3CZrOJ3Nxccckll4iPP/5YnHfeeUJRFLFw4UJRVlbWa7p7W3s4FRUVYuXKlWLOnDli/PjxofnQWNS+evVqMXnyZDFu3DjxzDPPiNLS0tA9MHv2bHHdddf1qsHqq2teVVUlVqxYISZPnizmzJnTJ9mxvdm/LFu2TOTm5or09HRx3XXXid/+9rfirLPOEmlpaX0SCu+L6+73+8XDDz8sFEUJZfsOhNGKO4NVVVUlFixYIBISEsT06dNFWlqaMBqN4v777w+lXLZfDFlWViYSExPFokWLQh24z+eLuOCff/65mD17tkhJSREZGRli8uTJ4uuvv45Z7e356quvxMKFC4VGoxHTpk3rdkhrIHXv3btXWCwWMX36dJGYmCjGjBkTCmfGuvbPP/9cXHfddWLx4sUiKSlJTJkyRaxfvz4mtQfDUG63W3z55Zfiu+++Cxmm4H69uaSgL6/5//zP/4hLL71UJCYmiunTp4fWI8ai9vD+ZfXq1WLBggUiNTVVDBo0SEybNi3CmMSa9s548sknhaIoEcUW+pu4M1gvvfSSSE9PF6+99pqoqKgQ9fX14uqrrxZJSUnixhtv7PD54A/zyCOPCI1GI55//vmIhhT+f4fDIaqrq3u94+kr7eF8/PHHwmAwiGeeeSZudH/66adCURQxaNCgPtHdl9pXrlwpRo4cKebOnStefPHFuNHeH6Pivrrmy5cvF4mJieLEE0/sszBgX/YvLpdLNDY2ii1btsSF9iBBA1ZZWSmWLl3aJ9q7S9wZrDlz5ohZs2ZFvNfa2iquuuoqoSiKeO+994QQHUcJbrdbjBgxQpx44omh1ef79u2LiOn2dTZgX2oXou9Swntbd/ic2nPPPddhVX28aN+3b1+ftpne1L53794O7SUedLe/5lu2bOnTpQ+yf+lce6xUQokbg+Xz+YTT6RQLFiwQs2fPDr0fDHcUFxeLoqIiUVhY2OHitk9j//Wvfy2WLFkipk+fLm6++eY+XzAZr9r7UndfZxr1pfa+Tv3uS+12uz0udcfzNZf9S+8RkwZrx44d4pZbbhG//OUvxT333BOy+kIIcf7554sxY8aEJrfDRwvPP/+8UBRFPPnkk0KIjh6Hx+MRM2bMEFqtViiKInJzc8UHH3wgtcexbql9YLTHq26pfeC09wYxZbBcLpe44447hNlsFieccIIYNWqUUBRFFBYWhtYOLF++XCiKIl588cXQDxK8+AcPHhSnn366GD58eIdJ5Y0bN4p77rlHJCYmiqSkJPHUU09J7XGsW2qX7UVqjw/tvUnMGCybzSbuvvtuUVhYKH7/+9+LXbt2CZ/PJz7++GMxePBgceqppwq73S68Xq+YMmWKOO2008TBgwc7HOeBBx4QqampoXitEOoPc9NNN4WKfQYXqR7v2uNVt9Q+MNrjVbfUPnDae5uYMVgHDhwQw4cPFz//+c+F1WqN2Pbzn/9cZGVlhaofvPLKK0JRFPHEE0+EYqzBUcOmTZuERqMRK1asEEK0xXHXrVsXqpsltce3bqldthepPT609zYxY7D8fr94/vnnI94LZo/961//EjqdLlSPy2q1ikWLFomcnJwOC97WrVsnFEURL730Uv8IF/GrPV51CyG1CyHbS0+Q2gdGe28TMwZLiDaL335C8LHHHhNarTbiSbWlpaUiOztbTJgwITQ5WF5eLm666SYxdOhQUVVV1X/CRfxqj1fdQkjtsr30DKl9YLT3JjFlsNoTnDi85ZZbRE5OTmhUEfzRPvzwQzF9+nShKIqYOnWqOOmkk4RerxcPPvig8Hq9A7p2IF61x6tuqV22F6k9PrQfDYoQQhDjnHDCCQwbNozly5fj8/nQarWhbXV1dfzjH/9g3759NDc3c8stt3DSSScNoNpI4lV7vOoGqX0giFfdILXHFQNtMY9ETU2NMJvNoQfjCaGOLoKP9Y5l4lV7vOoWQmofCOJVtxBSe7yhGWiDeSS2bduG0+lkxowZAFRVVfH666+zYMECamtrB1jd4YlX7fGqG6T2gSBedYPUHm/ErMESgUjl+vXrSUlJYfDgwXz++efceOONXHvttQgh0Gg0oc/FEvGqPV51g9Q+EMSrbpDa45b+c+aiY9GiRWLEiBHiuuuuE0lJSWLUqFFi1apVAy2rW8Sr9njVLYTUPhDEq24hpPZ4I6YNlsPhEFOnThWKoojk5ORQHax4IF61x6tuIaT2gSBedQshtccjMZ8l+Otf/xpFUXjwwQcxGo0DLadHxKv2eNUNUvtAEK+6QWqPN2LeYPn9fjSamJ1qOyzxqj1edYPUPhDEq26Q2uONmDdYEolEIpFADGcJSiQSiUQSjjRYEolEIokLpMGSSCQSSVwgDZZEIpFI4gJpsCQSiUQSF0iDJZFIJJK4QBosiUQikcQF0mBJJBKJJC6QBksikUgkcYE0WBKJRCKJC6TBkkgkEklc8P8BoiuCae8jmu4AAAAASUVORK5CYII=", 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", 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" ] @@ -93280,22 +93285,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 51, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\rdtools\\plotting.py:232: UserWarning:\n", - "\n", - "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", - "\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -93313,7 +93308,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -93344,6 +93339,8 @@ " soiling_rate_high\n", " inferred_start_loss\n", " inferred_end_loss\n", + " inferred_recovery\n", + " inferred_begin_shift\n", " length\n", " valid\n", " \n", @@ -93356,8 +93353,10 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 0.756022\n", - " 0.756022\n", + " 0.757953\n", + " 0.757953\n", + " NaN\n", + " NaN\n", " 6\n", " False\n", " \n", @@ -93370,6 +93369,8 @@ " 0.0\n", " 0.793434\n", " 0.793434\n", + " NaN\n", + " NaN\n", " 0\n", " False\n", " \n", @@ -93382,6 +93383,8 @@ " 0.0\n", " 0.819566\n", " 0.819566\n", + " NaN\n", + " NaN\n", " 0\n", " False\n", " \n", @@ -93394,6 +93397,8 @@ " 0.0\n", " 1.053380\n", " 1.053380\n", + " NaN\n", + " NaN\n", " 1\n", " False\n", " \n", @@ -93406,6 +93411,8 @@ " 0.0\n", " 1.033119\n", " 1.033119\n", + " NaN\n", + " NaN\n", " 1\n", " False\n", " \n", @@ -93422,21 +93429,21 @@ "4 2010-03-08 00:00:00-07:00 2010-03-09 00:00:00-07:00 0.0 \n", "\n", " soiling_rate_low soiling_rate_high inferred_start_loss \\\n", - "0 0.0 0.0 0.756022 \n", + "0 0.0 0.0 0.757953 \n", "1 0.0 0.0 0.793434 \n", "2 0.0 0.0 0.819566 \n", "3 0.0 0.0 1.053380 \n", "4 0.0 0.0 1.033119 \n", "\n", - " inferred_end_loss length valid \n", - "0 0.756022 6 False \n", - "1 0.793434 0 False \n", - "2 0.819566 0 False \n", - "3 1.053380 1 False \n", - "4 1.033119 1 False " + " inferred_end_loss inferred_recovery inferred_begin_shift length valid \n", + "0 0.757953 NaN NaN 6 False \n", + "1 0.793434 NaN NaN 0 False \n", + "2 0.819566 NaN NaN 0 False \n", + "3 1.053380 NaN NaN 1 False \n", + "4 1.033119 NaN NaN 1 False " ] }, - "execution_count": 22, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -93449,19 +93456,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 53, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\rdtools\\rdtools\\plotting.py:272: UserWarning:\n", - "\n", - "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", - "\n" - ] - }, { "data": { "image/png": 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", @@ -93488,7 +93485,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -93650,7 +93647,7 @@ "11 8 " ] }, - "execution_count": 24, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -93664,7 +93661,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -93712,9 +93709,9 @@ " \n", " 2\n", " 2012\n", - " 0.950871\n", - " 0.943630\n", - " 0.956604\n", + " 0.947953\n", + " 0.938482\n", + " 0.954513\n", " \n", " \n", " 3\n", @@ -93752,14 +93749,14 @@ " year soiling_ratio_median soiling_ratio_low soiling_ratio_high\n", "0 2010 0.962952 0.954986 0.969507\n", "1 2011 0.957442 0.951042 0.962425\n", - "2 2012 0.950871 0.943630 0.956604\n", + "2 2012 0.947953 0.938482 0.954513\n", "3 2013 0.948067 0.938270 0.956403\n", "4 2014 0.934236 0.915437 0.947448\n", "5 2015 0.959483 0.945367 0.967091\n", "6 2016 0.966123 0.961269 0.970014" ] }, - "execution_count": 25, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -93795,7 +93792,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -93813,7 +93810,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -93822,7 +93819,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -93933,7 +93930,7 @@ "2010-03-01 00:00:00-07:00 0.857710 " ] }, - "execution_count": 28, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -93953,7 +93950,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -93979,7 +93976,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 60, "metadata": {}, "outputs": [ { From 0299804e2e05e347e23e0c97a0e6d0067090c226 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Fri, 8 Nov 2024 16:51:20 -0500 Subject: [PATCH 43/58] fix linting in soiling --- rdtools/soiling.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 8d1e7b47..600fdd03 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -341,12 +341,16 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati "run_slope_high": 0, "max_neg_step": min(run.delta), "start_loss": 1, - "inferred_start_loss": np.nan if run.pi_norm.isna().any() else run.pi_norm.median(), # changed from mean/Matt - "inferred_end_loss": np.nan if run.pi_norm.isna().any() else run.pi_norm.median(), # changed from mean/Matt + "inferred_start_loss": ( + np.nan if run.pi_norm.isna().any() else run.pi_norm.median() + ), + "inferred_end_loss": ( + np.nan if run.pi_norm.isna().any() else run.pi_norm.median() + ), "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 + # from other breaks/Matt "run_loss_baseline": 0.0, # loss from the polyfit over the soiling intercal/Matt ############################################################## } From 08e0090212efa6c7b94aa9ef21c4c3da00b29885 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 11 Nov 2024 11:01:03 -0500 Subject: [PATCH 44/58] update nbval and remove semicolons from nb's --- docs/TrendAnalysis_example.ipynb | 6 +++--- docs/TrendAnalysis_example_NSRDB.ipynb | 10 +++++----- docs/system_availability_example.ipynb | 12 ++++++------ setup.py | 4 +--- 4 files changed, 15 insertions(+), 17 deletions(-) diff --git a/docs/TrendAnalysis_example.ipynb b/docs/TrendAnalysis_example.ipynb index 79bd74a7..049e9e1d 100644 --- a/docs/TrendAnalysis_example.ipynb +++ b/docs/TrendAnalysis_example.ipynb @@ -187,7 +187,7 @@ "ax.plot(df.index, df.soiling, 'o', alpha=0.01)\n", "#ax.set_ylim(0,1500)\n", "fig.autofmt_xdate()\n", - "ax.set_ylabel('soiling signal');\n", + "ax.set_ylabel('soiling signal')\n", "plt.show()\n", "\n", "df['power'] = df['power_ac'] * df['soiling']" @@ -62188,7 +62188,7 @@ "# Visualize the results\n", "ta_new_filter.plot_degradation_summary('sensor', summary_title='Sensor-based degradation results',\n", " scatter_ymin=0.5, scatter_ymax=1.1,\n", - " hist_xmin=-30, hist_xmax=45);\n", + " hist_xmin=-30, hist_xmax=45)\n", "plt.show()" ] }, @@ -62275,7 +62275,7 @@ "# Visualize the results\n", "ta_stuck_filter.plot_degradation_summary('sensor', summary_title='Sensor-based degradation results',\n", " scatter_ymin=0.5, scatter_ymax=1.1,\n", - " hist_xmin=-30, hist_xmax=45);\n", + " hist_xmin=-30, hist_xmax=45)\n", "plt.show()" ] }, diff --git a/docs/TrendAnalysis_example_NSRDB.ipynb b/docs/TrendAnalysis_example_NSRDB.ipynb index 8cae4eec..c1e6cd75 100644 --- a/docs/TrendAnalysis_example_NSRDB.ipynb +++ b/docs/TrendAnalysis_example_NSRDB.ipynb @@ -143,7 +143,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -158,7 +158,7 @@ "ax.plot(df.index, df.soiling, 'o', alpha=0.01)\n", "#ax.set_ylim(0,1500)\n", "fig.autofmt_xdate()\n", - "ax.set_ylabel('soiling signal');\n", + "ax.set_ylabel('soiling signal')\n", "df['power'] = df['power_ac'] * df['soiling']\n", "\n", "plt.show()" @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "-0.394\n", - "[-0.939 0.102]\n" + "[-0.984 0.102]\n" ] } ], @@ -293,7 +293,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -314,7 +314,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "rdtools3-nb", "language": "python", "name": "python3" }, diff --git a/docs/system_availability_example.ipynb b/docs/system_availability_example.ipynb index 7a44ee09..be5d0cc0 100644 --- a/docs/system_availability_example.ipynb +++ b/docs/system_availability_example.ipynb @@ -139,7 +139,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -222,13 +222,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ls/3vjh_45x2nd120557szqfcm8g4gf61/T/ipykernel_18955/689292658.py:3: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_65036\\689292658.py:3: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " fig.axes[1].legend(loc='upper left')\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -608,7 +608,7 @@ "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -618,7 +618,7 @@ } ], "source": [ - "aa2.plot();" + "fig = aa2.plot()" ] }, { @@ -631,7 +631,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "rdtools3-nb", "language": "python", "name": "python3" }, diff --git a/setup.py b/setup.py index 3ddef155..c5f4a702 100755 --- a/setup.py +++ b/setup.py @@ -36,9 +36,7 @@ "pytest-cov", "coverage", "flake8", - # nbval greater than 0.9.6 has a bug with semicolon - # https://github.com/computationalmodelling/nbval/issues/194 - "nbval<=0.9.6", + "nbval", "pytest-mock", ] From ec105a32c49cbb2b2c000ec70cab8176e55e37e9 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 11 Nov 2024 11:26:15 -0500 Subject: [PATCH 45/58] re-run notebooks --- docs/TrendAnalysis_example_NSRDB.ipynb | 26 +++---- docs/degradation_and_soiling_example.ipynb | 85 +++++++++------------- 2 files changed, 48 insertions(+), 63 deletions(-) diff --git a/docs/TrendAnalysis_example_NSRDB.ipynb b/docs/TrendAnalysis_example_NSRDB.ipynb index c1e6cd75..c5318d56 100644 --- a/docs/TrendAnalysis_example_NSRDB.ipynb +++ b/docs/TrendAnalysis_example_NSRDB.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -233,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": {}, "outputs": [ { diff --git a/docs/degradation_and_soiling_example.ipynb b/docs/degradation_and_soiling_example.ipynb index 22ff9c0e..4dfcf46a 100644 --- a/docs/degradation_and_soiling_example.ipynb +++ b/docs/degradation_and_soiling_example.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -315,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -31222,7 +31222,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -62129,7 +62129,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -93045,7 +93045,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -93082,7 +93082,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -93101,7 +93101,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 33, "metadata": { "tags": [ "nbsphinx-thumbnail" @@ -93147,7 +93147,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -93164,7 +93164,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -93194,24 +93194,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\Lib\\site-packages\\numpy\\lib\\_nanfunctions_impl.py:1241: RuntimeWarning:\n", - "\n", - "Mean of empty slice\n", - "\n", - "c:\\Users\\mspringe\\.conda\\envs\\rdtools3-nb\\Lib\\site-packages\\numpy\\lib\\_nanfunctions_impl.py:1241: RuntimeWarning:\n", - "\n", - "Mean of empty slice\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Calculate the daily insolation, required for the SRR calculation\n", "daily_insolation = filtered['insolation'].resample('D').sum()\n", @@ -93228,7 +93213,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -93245,7 +93230,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -93263,7 +93248,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -93285,7 +93270,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -93308,7 +93293,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -93443,7 +93428,7 @@ "4 1.033119 NaN NaN 1 False " ] }, - "execution_count": 52, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -93456,7 +93441,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -93485,7 +93470,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -93647,7 +93632,7 @@ "11 8 " ] }, - "execution_count": 54, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -93661,7 +93646,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -93756,7 +93741,7 @@ "6 2016 0.966123 0.961269 0.970014" ] }, - "execution_count": 55, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -93792,7 +93777,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -93810,7 +93795,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -93819,7 +93804,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -93930,7 +93915,7 @@ "2010-03-01 00:00:00-07:00 0.857710 " ] }, - "execution_count": 58, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -93950,7 +93935,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -93976,7 +93961,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 49, "metadata": {}, "outputs": [ { From 5ded716c497dcbd2abb01545d3f3e0501b0867aa Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 11 Nov 2024 12:06:09 -0500 Subject: [PATCH 46/58] nbval update sanitize-with argument --- .github/workflows/nbval.yaml | 2 +- docs/TrendAnalysis_example_NSRDB.ipynb | 30 +++++++++++++------------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/.github/workflows/nbval.yaml b/.github/workflows/nbval.yaml index abc712ae..84d99b25 100644 --- a/.github/workflows/nbval.yaml +++ b/.github/workflows/nbval.yaml @@ -29,7 +29,7 @@ jobs: - name: Run notebook and check output run: | # --sanitize-with: pre-process text to remove irrelevant differences (e.g. warning filepaths) - pytest --nbval --sanitize-with docs/nbval_sanitization_rules.cfg docs/${{ matrix.notebook-file }} + pytest --nbval --nbval-sanitize-with docs/nbval_sanitization_rules.cfg docs/${{ matrix.notebook-file }} - name: Run notebooks again, save files run: | pip install nbconvert[webpdf] diff --git a/docs/TrendAnalysis_example_NSRDB.ipynb b/docs/TrendAnalysis_example_NSRDB.ipynb index c5318d56..a6863af5 100644 --- a/docs/TrendAnalysis_example_NSRDB.ipynb +++ b/docs/TrendAnalysis_example_NSRDB.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -233,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "-0.394\n", - "[-0.984 0.102]\n" + "[-0.939 0.102]\n" ] } ], @@ -288,12 +288,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] From 916fedda1cf7220368927959c2cc108ad1c12b5a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 23 Jan 2025 00:51:31 +0000 Subject: [PATCH 47/58] Bump notebook from 7.2.1 to 7.2.2 in /docs Bumps [notebook](https://github.com/jupyter/notebook) from 7.2.1 to 7.2.2. - [Release notes](https://github.com/jupyter/notebook/releases) - [Changelog](https://github.com/jupyter/notebook/blob/@jupyter-notebook/tree@7.2.2/CHANGELOG.md) - [Commits](https://github.com/jupyter/notebook/compare/@jupyter-notebook/tree@7.2.1...@jupyter-notebook/tree@7.2.2) --- updated-dependencies: - dependency-name: notebook dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- 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 f3c5dc95..4398ec1f 100644 --- a/docs/notebook_requirements.txt +++ b/docs/notebook_requirements.txt @@ -29,7 +29,7 @@ nbclient==0.10.0 nbconvert==7.16.4 nbformat==5.10.4 nest-asyncio==1.6.0 -notebook==7.2.1 +notebook==7.2.2 numexpr==2.10.1 pandocfilters==1.5.1 parso==0.8.4 From 3e4be1997d8a1017590bbfc9dbedec46bfca7178 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 23 May 2025 19:34:16 +0000 Subject: [PATCH 48/58] Bump tornado from 6.4.2 to 6.5.1 in /docs Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4.2 to 6.5.1. - [Changelog](https://github.com/tornadoweb/tornado/blob/master/docs/releases.rst) - [Commits](https://github.com/tornadoweb/tornado/compare/v6.4.2...v6.5.1) --- updated-dependencies: - dependency-name: tornado dependency-version: 6.5.1 dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- 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 4398ec1f..81c4b6e2 100644 --- a/docs/notebook_requirements.txt +++ b/docs/notebook_requirements.txt @@ -48,7 +48,7 @@ soupsieve==2.6 terminado==0.18.1 testpath==0.6.0 tinycss2==1.3.0 -tornado==6.4.2 +tornado==6.5.1 traitlets==5.14.3 wcwidth==0.2.13 webencodings==0.5.1 From 424fc7d991d4239dba202c812c1d26700ba1f8f4 Mon Sep 17 00:00:00 2001 From: cdeline Date: Mon, 23 Jun 2025 15:47:31 -0600 Subject: [PATCH 49/58] statsmodels 0.14.4 is not able to handle the latest scipy. --- setup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 441b16c0..ed56d3cc 100755 --- a/setup.py +++ b/setup.py @@ -47,7 +47,8 @@ "numpy >= 1.22.4", "pandas >= 1.4.4", "statsmodels >= 0.13.5", - "scipy >= 1.8.1", + # statsmodels 0.14.4 is not able to handle the latest scipy + "scipy >= 1.8.1, <1.16.0", "h5py >= 3.7.0", "plotly>=4.0.0", "xgboost >= 1.6.0", From ea8854e3c0ae33160c9307cb9e128825d52d58f1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 24 Jun 2025 20:48:39 +0000 Subject: [PATCH 50/58] Bump jinja2 from 3.1.5 to 3.1.6 in /docs Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.5 to 3.1.6. - [Release notes](https://github.com/pallets/jinja/releases) - [Changelog](https://github.com/pallets/jinja/blob/main/CHANGES.rst) - [Commits](https://github.com/pallets/jinja/compare/3.1.5...3.1.6) --- updated-dependencies: - dependency-name: jinja2 dependency-version: 3.1.6 dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- 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 4398ec1f..0cee77e6 100644 --- a/docs/notebook_requirements.txt +++ b/docs/notebook_requirements.txt @@ -15,7 +15,7 @@ ipython==8.26.0 ipython-genutils==0.2.0 ipywidgets==8.1.3 jedi==0.19.1 -Jinja2==3.1.5 +Jinja2==3.1.6 jsonschema==4.23.0 jupyter==1.0.0 jupyter-client==8.6.2 From f4b77bb04a1444f530c9d920e122430b7b377b32 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 24 Jun 2025 20:50:34 +0000 Subject: [PATCH 51/58] Bump requests from 2.32.3 to 2.32.4 Bumps [requests](https://github.com/psf/requests) from 2.32.3 to 2.32.4. - [Release notes](https://github.com/psf/requests/releases) - [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md) - [Commits](https://github.com/psf/requests/compare/v2.32.3...v2.32.4) --- updated-dependencies: - dependency-name: requests dependency-version: 2.32.4 dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 387589a4..d6f4a47b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,7 +20,7 @@ python-dateutil==2.9.0 pytz==2024.1 arch==7.0.0 filterpy==1.4.5 -requests==2.32.3 +requests==2.32.4 retrying==1.3.4 scikit-learn==1.5.1 scipy==1.13.1 From e2c387a95891c2f2d0058422024aad5f3c190a7a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 24 Jun 2025 20:51:19 +0000 Subject: [PATCH 52/58] Bump urllib3 from 2.2.2 to 2.5.0 Bumps [urllib3](https://github.com/urllib3/urllib3) from 2.2.2 to 2.5.0. - [Release notes](https://github.com/urllib3/urllib3/releases) - [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst) - [Commits](https://github.com/urllib3/urllib3/compare/2.2.2...2.5.0) --- updated-dependencies: - dependency-name: urllib3 dependency-version: 2.5.0 dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 387589a4..9a0ddd05 100644 --- a/requirements.txt +++ b/requirements.txt @@ -30,6 +30,6 @@ statsmodels==0.14.2 threadpoolctl==3.5.0 tomli==2.0.1 typing_extensions==4.12.2 -urllib3==2.2.2 +urllib3==2.5.0 xgboost==2.1.1 From 03e094e020bceb4f87aa1e26f5201f63ca59ef34 Mon Sep 17 00:00:00 2001 From: cdeline Date: Mon, 7 Jul 2025 13:01:19 -0600 Subject: [PATCH 53/58] try setup.py now that statsmodels has a new release. --- setup.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/setup.py b/setup.py index ed56d3cc..441b16c0 100755 --- a/setup.py +++ b/setup.py @@ -47,8 +47,7 @@ "numpy >= 1.22.4", "pandas >= 1.4.4", "statsmodels >= 0.13.5", - # statsmodels 0.14.4 is not able to handle the latest scipy - "scipy >= 1.8.1, <1.16.0", + "scipy >= 1.8.1", "h5py >= 3.7.0", "plotly>=4.0.0", "xgboost >= 1.6.0", From 3c43bdb7cbd6c3a5f7150bf4e91909dbf29496ef Mon Sep 17 00:00:00 2001 From: Michael Deceglie Date: Wed, 20 Aug 2025 19:28:16 -0600 Subject: [PATCH 54/58] Update changelog --- docs/sphinx/source/changelog.rst | 1 + docs/sphinx/source/changelog/v3.0.1.rst | 11 +++++++++++ 2 files changed, 12 insertions(+) create mode 100644 docs/sphinx/source/changelog/v3.0.1.rst diff --git a/docs/sphinx/source/changelog.rst b/docs/sphinx/source/changelog.rst index fc3d805a..371f6e00 100644 --- a/docs/sphinx/source/changelog.rst +++ b/docs/sphinx/source/changelog.rst @@ -1,5 +1,6 @@ RdTools Change Log ================== +.. include:: changelog/v3.0.1.rst .. include:: changelog/v3.0.0.rst .. include:: changelog/v2.1.8.rst .. include:: changelog/v2.1.7.rst diff --git a/docs/sphinx/source/changelog/v3.0.1.rst b/docs/sphinx/source/changelog/v3.0.1.rst new file mode 100644 index 00000000..cc941121 --- /dev/null +++ b/docs/sphinx/source/changelog/v3.0.1.rst @@ -0,0 +1,11 @@ +************************* +v3.0.1 (August 20, 2025) +************************* + +Requirements +------------ +* Updated Jinja2==3.1.6 in ``notebook_requirements.txt`` (:pull:`465`) +* Updated tornado==6.5.1 in ``notebook_requirements.txt`` (:pull:`465`) +* Updated requests==2.32.4 in ``requirements.txt`` (:pull:`465`) +* Updated urllib3==2.5.0 in ``requirements.txt`` (:pull:`465`) +* Removed constraint that scipy<1.16.0 (:pull:`465`) \ No newline at end of file From 8060f317773c9a7cc86c59a3ab1a14889064f112 Mon Sep 17 00:00:00 2001 From: Michael Deceglie Date: Wed, 20 Aug 2025 20:12:08 -0600 Subject: [PATCH 55/58] Update release date --- docs/sphinx/source/changelog/v3.0.1.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sphinx/source/changelog/v3.0.1.rst b/docs/sphinx/source/changelog/v3.0.1.rst index cc941121..9bfaaaa3 100644 --- a/docs/sphinx/source/changelog/v3.0.1.rst +++ b/docs/sphinx/source/changelog/v3.0.1.rst @@ -1,5 +1,5 @@ ************************* -v3.0.1 (August 20, 2025) +v3.0.1 (August 21, 2025) ************************* Requirements From af956a5619aa40ce94c4431ad38369b8a7a36297 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Wed, 4 Feb 2026 17:25:36 -0500 Subject: [PATCH 56/58] formatting and syntax changes --- rdtools/soiling.py | 84 +++++++++++++++++++++++++++++----------------- 1 file changed, 53 insertions(+), 31 deletions(-) diff --git a/rdtools/soiling.py b/rdtools/soiling.py index 600fdd03..c9d79406 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -2,7 +2,7 @@ Functions for calculating soiling metrics from photovoltaic system data. """ -from rdtools import degradation as RdToolsDeg +from rdtools import degradation from rdtools.bootstrap import _make_time_series_bootstrap_samples import bisect @@ -61,17 +61,15 @@ def __init__(self, energy_normalized_daily, insolation_daily, precipitation_dail 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") + 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") + 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): @@ -261,9 +259,9 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, 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 is not None: - cp_dates.append(pr.index[cp_date]) + sr, cp_index = _segmented_soiling_period(pr) + if cp_index is not None: + cp_dates.append(pr.index[cp_index]) # 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) @@ -1729,15 +1727,18 @@ def iterative_signal_decomposition( 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( + degradation.degradation_year_on_year( + degradation_trend[-1], uncertainty_method=None + ) + ) # Find degradation component if order[(ic - 1) % n_steps] == "Rd": # 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 = degradation.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))) @@ -2059,7 +2060,7 @@ def run_bootstrap(self, if np.sum(small_soiling_signal) > nr_models / 2: self.result_df = result_df self.residual_shift = results[np.argmax(weights)]["residual_shift"] - YOY = RdToolsDeg.degradation_year_on_year(pi) + YOY = degradation.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 @@ -2878,17 +2879,38 @@ def _progressBarWithETA(value, endvalue, time, bar_length=20): sys.stdout.flush() -############################################################################### -# all code below for new piecewise fitting in soiling intervals within srr/Matt -############################################################################### -def piecewise_linear(x, x0, b, k1, k2): +def _piecewise_linear(x, x0, b, k1, k2): + """ + Piecewise linear function with a single breakpoint. + + Parameters + ---------- + x : array-like + Independent variable + x0 : float + Breakpoint location where the slope changes + b : float + y-intercept of the first segment + k1 : float + Slope of the first segment (x < x0) + k2 : float + Change in slope for the second segment (x >= x0). + The slope of the second segment is k1 + k2. + + Returns + ------- + array-like + Piecewise linear values: + - For x < x0: k1 * x + b + - For x >= x0: k1 * x + b + k2 * (x - x0) + """ 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], +def _segmented_soiling_period( + pr, fill_method="bfill", days_clean_vs_cp=13, 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 """ @@ -2903,7 +2925,7 @@ def segmented_soiling_period( 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) + days_clean_vs_cp : numeric (default=13) 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 @@ -2918,8 +2940,8 @@ def segmented_soiling_period( 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. + cp_index: int + Integer index at which continuous change point occurred. None if segmentation was not possible. """ # Check if PR dataframe has datetime index @@ -2936,17 +2958,17 @@ def segmented_soiling_period( try: # Fit soiling profile with segmentation - p, e = curve_fit(piecewise_linear, x, y, p0=initial_guesses, bounds=bounds) + 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 use as an index. # 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]) + z = _piecewise_linear(x, *p) + cp_index = int(p[0]) else: z = [np.nan] * len(x) - cp_date = None + cp_index = None R2_original = st.linregress(y, x)[2] ** 2 R2_piecewise = st.linregress(y, z)[2] ** 2 @@ -2959,16 +2981,16 @@ def segmented_soiling_period( 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 + cp_index = None else: if (R2_percent_improve < 0.01) | (R2_piecewise < 0.4): z = [np.nan] * len(x) - cp_date = None + cp_index = None except ValueError as ex: print(f"Segmentation was not possible. Error: {ex}") z = [np.nan] * len(x) - cp_date = None + cp_index = None # Create Series from modelled profile sr = pd.Series(z, index=pr.index) - return sr, cp_date + return sr, cp_index From 3ce55728562f123350ad6e721b00a2101bd2fe19 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Wed, 4 Feb 2026 19:47:47 -0500 Subject: [PATCH 57/58] attempt refactor --- docs/soiling_options_comparison_v2.ipynb | 2923 ++++++++++++++++++++++ rdtools/soiling.py | 710 +++--- rdtools/test/soiling_test.py | 101 +- 3 files changed, 3361 insertions(+), 373 deletions(-) create mode 100644 docs/soiling_options_comparison_v2.ipynb diff --git a/docs/soiling_options_comparison_v2.ipynb b/docs/soiling_options_comparison_v2.ipynb new file mode 100644 index 00000000..d3b1567d --- /dev/null +++ b/docs/soiling_options_comparison_v2.ipynb @@ -0,0 +1,2923 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "48b5d224", + "metadata": {}, + "source": [ + "# Soiling Analysis Options Comparison\n", + "\n", + "This notebook demonstrates the impact of the new soiling analysis parameters introduced in rdtools.\n", + "\n", + "## New Parameters\n", + "\n", + "| Parameter | Default | Description |\n", + "|-----------|---------|-------------|\n", + "| `detect_neg_shifts` | `False` | Detect negative shifts in rolling median to subdivide intervals |\n", + "| `piecewise_fit` | `False` | Test for slope change points within intervals |\n", + "| `neg_shift_factor` | `2.5` | Multiplier for negative shift detection threshold |\n", + "| `min_piecewise_days` | `27` | Minimum interval length for piecewise fitting |\n", + "| `collapse_window_days` | `5` | Window for collapsing consecutive cleaning events |\n", + "| `forward_median_window` | `10` | Window for forward median validation |\n", + "\n", + "## Approach\n", + "\n", + "We build a master configuration DataFrame containing all parameter combinations, run each configuration, and then analyze the results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6b05044c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from itertools import product\n", + "import warnings\n", + "import rdtools\n", + "from rdtools import soiling" + ] + }, + { + "cell_type": "markdown", + "id": "f64db783", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "081e1c9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data range: 2010-02-25 00:00:00-07:00 to 2016-12-13 00:00:00-07:00\n", + "Days: 2484\n" + ] + } + ], + "source": [ + "# Load example data from PVDAQ system 4\n", + "file_url = ('https://datahub.duramat.org/dataset/'\n", + " 'a49bb656-7b36-437a-8089-1870a40c2a7d/'\n", + " 'resource/d2c3fcf4-4f5f-47ad-8743-fc29'\n", + " 'f1356835/download/pvdaq_system_4_2010'\n", + " '-2016_subset_soil_signal.csv')\n", + "cache_file = 'PVDAQ_system_4_2010-2016_subset_soilsignal.pickle'\n", + "\n", + "try:\n", + " df = pd.read_pickle(cache_file)\n", + "except FileNotFoundError:\n", + " df = pd.read_csv(file_url, index_col=0, parse_dates=True)\n", + " df.to_pickle(cache_file)\n", + "\n", + "# Rename columns and set up metadata\n", + "df = df.rename(columns={\n", + " 'ac_power': 'power_ac',\n", + " 'ambient_temp': 'Tamb',\n", + " 'poa_irradiance': 'poa',\n", + "})\n", + "\n", + "meta = {'latitude': 39.7406,\n", + " 'longitude': -105.1774,\n", + " 'timezone': 'Etc/GMT+7',\n", + " 'gamma_pdc': -0.005,\n", + " 'azimuth': 180,\n", + " 'tilt': 40,\n", + " 'power_dc_rated': 1000.0,\n", + " 'temp_model_params': 'open_rack_glass_polymer'}\n", + "\n", + "df.index = df.index.tz_localize(meta['timezone'])\n", + "freq = pd.infer_freq(df.index[:10])\n", + "\n", + "# Apply synthetic soiling signal\n", + "df['power'] = df['power_ac'] * df['soiling']\n", + "\n", + "# Create TrendAnalysis object for proper data preparation\n", + "ta = rdtools.TrendAnalysis(df['power'], df['poa'],\n", + " temperature_ambient=df['Tamb'],\n", + " gamma_pdc=meta['gamma_pdc'],\n", + " interp_freq=freq,\n", + " windspeed=df['wind_speed'],\n", + " power_dc_rated=meta['power_dc_rated'],\n", + " temperature_model=meta['temp_model_params'])\n", + "\n", + "# Run normalization and aggregation\n", + "ta.sensor_analysis(analyses=['yoy_degradation'])\n", + "\n", + "# Get aggregated daily data\n", + "energy_normalized_daily = ta.sensor_aggregated_performance\n", + "insolation_daily = ta.poa_global.resample('D').sum()\n", + "insolation_daily = insolation_daily.reindex(energy_normalized_daily.index)\n", + "\n", + "# Generate synthetic precipitation for demonstration\n", + "np.random.seed(42)\n", + "precip_probs = np.random.random(len(energy_normalized_daily))\n", + "precipitation_daily = pd.Series(\n", + " np.where(precip_probs > 0.85, np.random.uniform(1, 20, len(energy_normalized_daily)), 0),\n", + " index=energy_normalized_daily.index,\n", + " name='precipitation_daily'\n", + ")\n", + "\n", + "print(f\"Data range: {energy_normalized_daily.index[0]} to {energy_normalized_daily.index[-1]}\")\n", + "print(f\"Days: {len(energy_normalized_daily)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4e75bc9f", + "metadata": {}, + "source": [ + "## Define Master Configuration\n", + "\n", + "We define all the parameter combinations we want to explore. Each row represents one analysis configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "60a4cbe4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default configuration:\n", + " method: half_norm_clean\n", + " detect_neg_shifts: False\n", + " piecewise_fit: False\n", + " neg_shift_factor: 2.5\n", + " min_piecewise_days: 27\n", + " collapse_window_days: 5\n", + " forward_median_window: 10\n", + " clean_criterion: shift\n" + ] + } + ], + "source": [ + "# Define parameter values to explore\n", + "# Using dictionaries to define parameter space\n", + "\n", + "# Core boolean options\n", + "detect_neg_shifts_values = [False, True]\n", + "piecewise_fit_values = [False, True]\n", + "\n", + "# Method options (only those compatible with detect_neg_shifts)\n", + "method_values = ['half_norm_clean', 'perfect_clean', 'inferred_clean']\n", + "\n", + "# Numeric parameters with their test values\n", + "neg_shift_factor_values = [1.5, 2.5, 4.0] # default is 2.5\n", + "min_piecewise_days_values = [14, 27, 45] # default is 27\n", + "collapse_window_days_values = [1, 5, 10] # default is 5\n", + "forward_median_window_values = [5, 10, 20] # default is 10\n", + "\n", + "# Clean criterion options\n", + "clean_criterion_values = ['shift', 'precip', 'precip_and_shift', 'precip_or_shift']\n", + "\n", + "# Default values for reference\n", + "DEFAULTS = {\n", + " 'method': 'half_norm_clean',\n", + " 'detect_neg_shifts': False,\n", + " 'piecewise_fit': False,\n", + " 'neg_shift_factor': 2.5,\n", + " 'min_piecewise_days': 27,\n", + " 'collapse_window_days': 5,\n", + " 'forward_median_window': 10,\n", + " 'clean_criterion': 'shift',\n", + "}\n", + "\n", + "print(\"Default configuration:\")\n", + "for k, v in DEFAULTS.items():\n", + " print(f\" {k}: {v}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b2b1af8a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total unique configurations: 43\n", + "\n", + "Configurations by group:\n", + "config_group\n", + "core_matrix 10\n", + "clean_criterion 9\n", + "neg_shift_factor 6\n", + "min_piecewise_days 6\n", + "collapse_window_days 6\n", + "forward_median_window 6\n", + "Name: count, dtype: int64\n", + "\n", + "Configurations by method:\n", + "method\n", + "half_norm_clean 15\n", + "perfect_clean 15\n", + "inferred_clean 13\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "text/html": [ + "
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config_idconfig_nameconfig_groupmethoddetect_neg_shiftspiecewise_fitneg_shift_factormin_piecewise_dayscollapse_window_daysforward_median_windowclean_criterion
01half_norm_clean_dns=False_pw=Falsecore_matrixhalf_norm_cleanFalseFalse2.527510shift
12perfect_clean_dns=False_pw=Falsecore_matrixperfect_cleanFalseFalse2.527510shift
23half_norm_clean_dns=False_pw=Truecore_matrixhalf_norm_cleanFalseTrue2.527510shift
34perfect_clean_dns=False_pw=Truecore_matrixperfect_cleanFalseTrue2.527510shift
45half_norm_clean_dns=True_pw=Falsecore_matrixhalf_norm_cleanTrueFalse2.527510shift
56perfect_clean_dns=True_pw=Falsecore_matrixperfect_cleanTrueFalse2.527510shift
67inferred_clean_dns=True_pw=Falsecore_matrixinferred_cleanTrueFalse2.527510shift
78half_norm_clean_dns=True_pw=Truecore_matrixhalf_norm_cleanTrueTrue2.527510shift
89perfect_clean_dns=True_pw=Truecore_matrixperfect_cleanTrueTrue2.527510shift
910inferred_clean_dns=True_pw=Truecore_matrixinferred_cleanTrueTrue2.527510shift
1011half_norm_clean_nsf=1.5neg_shift_factorhalf_norm_cleanTrueFalse1.527510shift
1112perfect_clean_nsf=1.5neg_shift_factorperfect_cleanTrueFalse1.527510shift
1213inferred_clean_nsf=1.5neg_shift_factorinferred_cleanTrueFalse1.527510shift
1314half_norm_clean_nsf=4.0neg_shift_factorhalf_norm_cleanTrueFalse4.027510shift
1415perfect_clean_nsf=4.0neg_shift_factorperfect_cleanTrueFalse4.027510shift
1516inferred_clean_nsf=4.0neg_shift_factorinferred_cleanTrueFalse4.027510shift
1617half_norm_clean_mpd=14min_piecewise_dayshalf_norm_cleanFalseTrue2.514510shift
1718perfect_clean_mpd=14min_piecewise_daysperfect_cleanFalseTrue2.514510shift
1819inferred_clean_mpd=14min_piecewise_daysinferred_cleanTrueTrue2.514510shift
1920half_norm_clean_mpd=45min_piecewise_dayshalf_norm_cleanFalseTrue2.545510shift
2021perfect_clean_mpd=45min_piecewise_daysperfect_cleanFalseTrue2.545510shift
2122inferred_clean_mpd=45min_piecewise_daysinferred_cleanTrueTrue2.545510shift
2223half_norm_clean_cwd=1collapse_window_dayshalf_norm_cleanFalseFalse2.527110shift
2324perfect_clean_cwd=1collapse_window_daysperfect_cleanFalseFalse2.527110shift
2425inferred_clean_cwd=1collapse_window_daysinferred_cleanTrueFalse2.527110shift
2526half_norm_clean_cwd=10collapse_window_dayshalf_norm_cleanFalseFalse2.5271010shift
2627perfect_clean_cwd=10collapse_window_daysperfect_cleanFalseFalse2.5271010shift
2728inferred_clean_cwd=10collapse_window_daysinferred_cleanTrueFalse2.5271010shift
2829half_norm_clean_fmw=5forward_median_windowhalf_norm_cleanTrueFalse2.52755shift
2930perfect_clean_fmw=5forward_median_windowperfect_cleanTrueFalse2.52755shift
3031inferred_clean_fmw=5forward_median_windowinferred_cleanTrueFalse2.52755shift
3132half_norm_clean_fmw=20forward_median_windowhalf_norm_cleanTrueFalse2.527520shift
3233perfect_clean_fmw=20forward_median_windowperfect_cleanTrueFalse2.527520shift
3334inferred_clean_fmw=20forward_median_windowinferred_cleanTrueFalse2.527520shift
3435half_norm_clean_cc=precipclean_criterionhalf_norm_cleanFalseFalse2.527510precip
3536perfect_clean_cc=precipclean_criterionperfect_cleanFalseFalse2.527510precip
3637inferred_clean_cc=precipclean_criterioninferred_cleanTrueFalse2.527510precip
3738half_norm_clean_cc=precip_and_shiftclean_criterionhalf_norm_cleanFalseFalse2.527510precip_and_shift
3839perfect_clean_cc=precip_and_shiftclean_criterionperfect_cleanFalseFalse2.527510precip_and_shift
3940inferred_clean_cc=precip_and_shiftclean_criterioninferred_cleanTrueFalse2.527510precip_and_shift
4041half_norm_clean_cc=precip_or_shiftclean_criterionhalf_norm_cleanFalseFalse2.527510precip_or_shift
4142perfect_clean_cc=precip_or_shiftclean_criterionperfect_cleanFalseFalse2.527510precip_or_shift
4243inferred_clean_cc=precip_or_shiftclean_criterioninferred_cleanTrueFalse2.527510precip_or_shift
\n", + "
" + ], + "text/plain": [ + " config_id config_name config_group \\\n", + "0 1 half_norm_clean_dns=False_pw=False core_matrix \n", + "1 2 perfect_clean_dns=False_pw=False core_matrix \n", + "2 3 half_norm_clean_dns=False_pw=True core_matrix \n", + "3 4 perfect_clean_dns=False_pw=True core_matrix \n", + "4 5 half_norm_clean_dns=True_pw=False core_matrix \n", + "5 6 perfect_clean_dns=True_pw=False core_matrix \n", + "6 7 inferred_clean_dns=True_pw=False core_matrix \n", + "7 8 half_norm_clean_dns=True_pw=True core_matrix \n", + "8 9 perfect_clean_dns=True_pw=True core_matrix \n", + "9 10 inferred_clean_dns=True_pw=True core_matrix \n", + "10 11 half_norm_clean_nsf=1.5 neg_shift_factor \n", + "11 12 perfect_clean_nsf=1.5 neg_shift_factor \n", + "12 13 inferred_clean_nsf=1.5 neg_shift_factor \n", + "13 14 half_norm_clean_nsf=4.0 neg_shift_factor \n", + "14 15 perfect_clean_nsf=4.0 neg_shift_factor \n", + "15 16 inferred_clean_nsf=4.0 neg_shift_factor \n", + "16 17 half_norm_clean_mpd=14 min_piecewise_days \n", + "17 18 perfect_clean_mpd=14 min_piecewise_days \n", + "18 19 inferred_clean_mpd=14 min_piecewise_days \n", + "19 20 half_norm_clean_mpd=45 min_piecewise_days \n", + "20 21 perfect_clean_mpd=45 min_piecewise_days \n", + "21 22 inferred_clean_mpd=45 min_piecewise_days \n", + "22 23 half_norm_clean_cwd=1 collapse_window_days \n", + "23 24 perfect_clean_cwd=1 collapse_window_days \n", + "24 25 inferred_clean_cwd=1 collapse_window_days \n", + "25 26 half_norm_clean_cwd=10 collapse_window_days \n", + "26 27 perfect_clean_cwd=10 collapse_window_days \n", + "27 28 inferred_clean_cwd=10 collapse_window_days \n", + "28 29 half_norm_clean_fmw=5 forward_median_window \n", + "29 30 perfect_clean_fmw=5 forward_median_window \n", + "30 31 inferred_clean_fmw=5 forward_median_window \n", + "31 32 half_norm_clean_fmw=20 forward_median_window \n", + "32 33 perfect_clean_fmw=20 forward_median_window \n", + "33 34 inferred_clean_fmw=20 forward_median_window \n", + "34 35 half_norm_clean_cc=precip clean_criterion \n", + "35 36 perfect_clean_cc=precip clean_criterion \n", + "36 37 inferred_clean_cc=precip clean_criterion \n", + "37 38 half_norm_clean_cc=precip_and_shift clean_criterion \n", + "38 39 perfect_clean_cc=precip_and_shift clean_criterion \n", + "39 40 inferred_clean_cc=precip_and_shift clean_criterion \n", + "40 41 half_norm_clean_cc=precip_or_shift clean_criterion \n", + "41 42 perfect_clean_cc=precip_or_shift clean_criterion \n", + "42 43 inferred_clean_cc=precip_or_shift clean_criterion \n", + "\n", + " method detect_neg_shifts piecewise_fit neg_shift_factor \\\n", + "0 half_norm_clean False False 2.5 \n", + "1 perfect_clean False False 2.5 \n", + "2 half_norm_clean False True 2.5 \n", + "3 perfect_clean False True 2.5 \n", + "4 half_norm_clean True False 2.5 \n", + "5 perfect_clean True False 2.5 \n", + "6 inferred_clean True False 2.5 \n", + "7 half_norm_clean True True 2.5 \n", + "8 perfect_clean True True 2.5 \n", + "9 inferred_clean True True 2.5 \n", + "10 half_norm_clean True False 1.5 \n", + "11 perfect_clean True False 1.5 \n", + "12 inferred_clean True False 1.5 \n", + "13 half_norm_clean True False 4.0 \n", + "14 perfect_clean True False 4.0 \n", + "15 inferred_clean True False 4.0 \n", + "16 half_norm_clean False True 2.5 \n", + "17 perfect_clean False True 2.5 \n", + "18 inferred_clean True True 2.5 \n", + "19 half_norm_clean False True 2.5 \n", + "20 perfect_clean False True 2.5 \n", + "21 inferred_clean True True 2.5 \n", + "22 half_norm_clean False False 2.5 \n", + "23 perfect_clean False False 2.5 \n", + "24 inferred_clean True False 2.5 \n", + "25 half_norm_clean False False 2.5 \n", + "26 perfect_clean False False 2.5 \n", + "27 inferred_clean True False 2.5 \n", + "28 half_norm_clean True False 2.5 \n", + "29 perfect_clean True False 2.5 \n", + "30 inferred_clean True False 2.5 \n", + "31 half_norm_clean True False 2.5 \n", + "32 perfect_clean True False 2.5 \n", + "33 inferred_clean True False 2.5 \n", + "34 half_norm_clean False False 2.5 \n", + "35 perfect_clean False False 2.5 \n", + "36 inferred_clean True False 2.5 \n", + "37 half_norm_clean False False 2.5 \n", + "38 perfect_clean False False 2.5 \n", + "39 inferred_clean True False 2.5 \n", + "40 half_norm_clean False False 2.5 \n", + "41 perfect_clean False False 2.5 \n", + "42 inferred_clean True False 2.5 \n", + "\n", + " min_piecewise_days collapse_window_days forward_median_window \\\n", + "0 27 5 10 \n", + "1 27 5 10 \n", + "2 27 5 10 \n", + "3 27 5 10 \n", + "4 27 5 10 \n", + "5 27 5 10 \n", + "6 27 5 10 \n", + "7 27 5 10 \n", + "8 27 5 10 \n", + "9 27 5 10 \n", + "10 27 5 10 \n", + "11 27 5 10 \n", + "12 27 5 10 \n", + "13 27 5 10 \n", + "14 27 5 10 \n", + "15 27 5 10 \n", + "16 14 5 10 \n", + "17 14 5 10 \n", + "18 14 5 10 \n", + "19 45 5 10 \n", + "20 45 5 10 \n", + "21 45 5 10 \n", + "22 27 1 10 \n", + "23 27 1 10 \n", + "24 27 1 10 \n", + "25 27 10 10 \n", + "26 27 10 10 \n", + "27 27 10 10 \n", + "28 27 5 5 \n", + "29 27 5 5 \n", + "30 27 5 5 \n", + "31 27 5 20 \n", + "32 27 5 20 \n", + "33 27 5 20 \n", + "34 27 5 10 \n", + "35 27 5 10 \n", + "36 27 5 10 \n", + "37 27 5 10 \n", + "38 27 5 10 \n", + "39 27 5 10 \n", + "40 27 5 10 \n", + "41 27 5 10 \n", + "42 27 5 10 \n", + "\n", + " clean_criterion \n", + "0 shift \n", + "1 shift \n", + "2 shift \n", + "3 shift \n", + "4 shift \n", + "5 shift \n", + "6 shift \n", + "7 shift \n", + "8 shift \n", + "9 shift \n", + "10 shift \n", + "11 shift \n", + "12 shift \n", + "13 shift \n", + "14 shift \n", + "15 shift \n", + "16 shift \n", + "17 shift \n", + "18 shift \n", + "19 shift \n", + "20 shift \n", + "21 shift \n", + "22 shift \n", + "23 shift \n", + "24 shift \n", + "25 shift \n", + "26 shift \n", + "27 shift \n", + "28 shift \n", + "29 shift \n", + "30 shift \n", + "31 shift \n", + "32 shift \n", + "33 shift \n", + "34 precip \n", + "35 precip \n", + "36 precip \n", + "37 precip_and_shift \n", + "38 precip_and_shift \n", + "39 precip_and_shift \n", + "40 precip_or_shift \n", + "41 precip_or_shift \n", + "42 precip_or_shift " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Build master configuration DataFrame\n", + "# Strategy: Full factorial - all parameter combinations × all methods\n", + "\n", + "configs = []\n", + "config_counter = 0\n", + "\n", + "# Define all methods to test\n", + "# Note: random_clean does not support detect_neg_shifts\n", + "all_methods = ['half_norm_clean', 'perfect_clean', 'inferred_clean']\n", + "\n", + "# 1. Core options: detect_neg_shifts × piecewise_fit × method\n", + "for dns, pw in product([False, True], [False, True]):\n", + " for method in all_methods:\n", + " # inferred_clean requires detect_neg_shifts=True\n", + " if method == 'inferred_clean' and not dns:\n", + " continue\n", + " config_counter += 1\n", + " configs.append({\n", + " 'config_id': config_counter,\n", + " 'config_name': f'{method}_dns={dns}_pw={pw}',\n", + " 'config_group': 'core_matrix',\n", + " **{**DEFAULTS, 'method': method, 'detect_neg_shifts': dns, 'piecewise_fit': pw}\n", + " })\n", + "\n", + "# 2. neg_shift_factor variations (requires detect_neg_shifts=True) × method\n", + "for val in neg_shift_factor_values:\n", + " for method in all_methods:\n", + " if method == 'inferred_clean' or True: # all require dns=True for this param\n", + " config_counter += 1\n", + " configs.append({\n", + " 'config_id': config_counter,\n", + " 'config_name': f'{method}_nsf={val}',\n", + " 'config_group': 'neg_shift_factor',\n", + " **{**DEFAULTS, 'method': method, 'detect_neg_shifts': True,\n", + " 'neg_shift_factor': val}\n", + " })\n", + "\n", + "# 3. min_piecewise_days variations (requires piecewise_fit=True) × method\n", + "for val in min_piecewise_days_values:\n", + " for method in all_methods:\n", + " # inferred_clean requires detect_neg_shifts=True\n", + " dns = True if method == 'inferred_clean' else False\n", + " config_counter += 1\n", + " configs.append({\n", + " 'config_id': config_counter,\n", + " 'config_name': f'{method}_mpd={val}',\n", + " 'config_group': 'min_piecewise_days',\n", + " **{**DEFAULTS, 'method': method, 'detect_neg_shifts': dns,\n", + " 'piecewise_fit': True, 'min_piecewise_days': val}\n", + " })\n", + "\n", + "# 4. collapse_window_days variations × method\n", + "for val in collapse_window_days_values:\n", + " for method in all_methods:\n", + " # inferred_clean requires detect_neg_shifts=True\n", + " dns = True if method == 'inferred_clean' else False\n", + " config_counter += 1\n", + " configs.append({\n", + " 'config_id': config_counter,\n", + " 'config_name': f'{method}_cwd={val}',\n", + " 'config_group': 'collapse_window_days',\n", + " **{**DEFAULTS, 'method': method, 'detect_neg_shifts': dns,\n", + " 'collapse_window_days': val}\n", + " })\n", + "\n", + "# 5. forward_median_window variations (requires detect_neg_shifts=True) × method\n", + "for val in forward_median_window_values:\n", + " for method in all_methods:\n", + " config_counter += 1\n", + " configs.append({\n", + " 'config_id': config_counter,\n", + " 'config_name': f'{method}_fmw={val}',\n", + " 'config_group': 'forward_median_window',\n", + " **{**DEFAULTS, 'method': method, 'detect_neg_shifts': True,\n", + " 'forward_median_window': val}\n", + " })\n", + "\n", + "# 6. clean_criterion variations × method\n", + "for val in clean_criterion_values:\n", + " for method in all_methods:\n", + " # inferred_clean requires detect_neg_shifts=True\n", + " dns = True if method == 'inferred_clean' else False\n", + " config_counter += 1\n", + " configs.append({\n", + " 'config_id': config_counter,\n", + " 'config_name': f'{method}_cc={val}',\n", + " 'config_group': 'clean_criterion',\n", + " **{**DEFAULTS, 'method': method, 'detect_neg_shifts': dns,\n", + " 'clean_criterion': val}\n", + " })\n", + "\n", + "# Create DataFrame and remove duplicates\n", + "config_df = pd.DataFrame(configs)\n", + "\n", + "# Drop duplicates based on the actual parameter values\n", + "param_cols = ['method', 'detect_neg_shifts', 'piecewise_fit', 'neg_shift_factor',\n", + " 'min_piecewise_days', 'collapse_window_days', 'forward_median_window',\n", + " 'clean_criterion']\n", + "config_df = config_df.drop_duplicates(subset=param_cols, keep='first').reset_index(drop=True)\n", + "config_df['config_id'] = range(1, len(config_df) + 1)\n", + "\n", + "print(f\"Total unique configurations: {len(config_df)}\")\n", + "print(f\"\\nConfigurations by group:\")\n", + "print(config_df['config_group'].value_counts())\n", + "print(f\"\\nConfigurations by method:\")\n", + "print(config_df['method'].value_counts())\n", + "config_df" + ] + }, + { + "cell_type": "markdown", + "id": "0f76c8d4", + "metadata": {}, + "source": [ + "## Run All Configurations\n", + "\n", + "Execute the soiling analysis for each configuration and store the results." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "4a1aeae5", + "metadata": {}, + "outputs": [], + "source": [ + "def run_soiling_config(config, energy_norm, insolation, precipitation=None):\n", + " \"\"\"Run soiling analysis for a single configuration.\"\"\"\n", + " np.random.seed(1977) # reproducibility\n", + "\n", + " # Build kwargs, only including precipitation if needed\n", + " kwargs = {\n", + " 'reps': 200,\n", + " 'method': config['method'],\n", + " 'detect_neg_shifts': config['detect_neg_shifts'],\n", + " 'piecewise_fit': config['piecewise_fit'],\n", + " 'neg_shift_factor': config['neg_shift_factor'],\n", + " 'min_piecewise_days': config['min_piecewise_days'],\n", + " 'collapse_window_days': config['collapse_window_days'],\n", + " 'forward_median_window': config['forward_median_window'],\n", + " 'clean_criterion': config['clean_criterion'],\n", + " }\n", + "\n", + " try:\n", + " with warnings.catch_warnings():\n", + " warnings.simplefilter('ignore')\n", + "\n", + " # Use precipitation if criterion requires it\n", + " if 'precip' in config['clean_criterion']:\n", + " sr, sr_ci, info = soiling.soiling_srr(\n", + " energy_norm, insolation,\n", + " precipitation_daily=precipitation,\n", + " **kwargs\n", + " )\n", + " else:\n", + " sr, sr_ci, info = soiling.soiling_srr(\n", + " energy_norm, insolation, **kwargs\n", + " )\n", + "\n", + " summary = info['soiling_interval_summary']\n", + "\n", + " # Extract soiling profile - always the 'perfect_clean' deterministic profile\n", + " sr_profile = info.get('soiling_ratio_perfect_clean', pd.Series(dtype=float))\n", + "\n", + " # Extract stochastic profiles - these DIFFER by method!\n", + " # Take median of stochastic profiles for comparison\n", + " stochastic = info.get('stochastic_soiling_profiles', [])\n", + " if stochastic and len(stochastic) > 0:\n", + " stoch_df = pd.concat(stochastic, axis=1)\n", + " stoch_median = stoch_df.median(axis=1)\n", + " else:\n", + " stoch_median = pd.Series(dtype=float)\n", + "\n", + " return {\n", + " 'soiling_ratio': sr,\n", + " 'ci_lower': sr_ci[0],\n", + " 'ci_upper': sr_ci[1],\n", + " 'total_intervals': len(summary),\n", + " 'valid_intervals': summary['valid'].sum(),\n", + " 'sr_profile': sr_profile, # Deterministic perfect_clean profile\n", + " 'stoch_profile': stoch_median, # Median of stochastic profiles (method-specific)\n", + " 'error': None\n", + " }\n", + " except Exception as e:\n", + " return {\n", + " 'soiling_ratio': None,\n", + " 'ci_lower': None,\n", + " 'ci_upper': None,\n", + " 'total_intervals': None,\n", + " 'valid_intervals': None,\n", + " 'sr_profile': pd.Series(dtype=float),\n", + " 'stoch_profile': pd.Series(dtype=float),\n", + " 'error': str(e)[:50]\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "576af743", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1/43] half_norm_clean_dns=False_pw=False SR=0.9494 (6.4s)\n", + "[2/43] perfect_clean_dns=False_pw=False SR=0.9711 (1.8s)\n", + "[3/43] half_norm_clean_dns=False_pw=True Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "SR=0.9544 (5.7s)\n", + "[4/43] perfect_clean_dns=False_pw=True Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "SR=0.9747 (2.2s)\n", + "[5/43] half_norm_clean_dns=True_pw=False SR=0.9635 (5.2s)\n", + "[6/43] perfect_clean_dns=True_pw=False SR=0.9542 (2.5s)\n", + "[7/43] inferred_clean_dns=True_pw=False SR=0.9320 (2.3s)\n", + "[8/43] half_norm_clean_dns=True_pw=True Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "SR=0.9675 (5.7s)\n", + "[9/43] perfect_clean_dns=True_pw=True Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "SR=0.9516 (2.9s)\n", + "[10/43] inferred_clean_dns=True_pw=True Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "SR=0.9349 (3.0s)\n", + "[11/43] half_norm_clean_nsf=1.5 SR=0.9786 (8.1s)\n", + "[12/43] perfect_clean_nsf=1.5 SR=0.9597 (3.7s)\n", + "[13/43] inferred_clean_nsf=1.5 SR=0.9292 (4.1s)\n", + "[14/43] half_norm_clean_nsf=4.0 SR=0.9600 (6.1s)\n", + "[15/43] perfect_clean_nsf=4.0 SR=0.9515 (2.3s)\n", + "[16/43] inferred_clean_nsf=4.0 SR=0.9229 (2.3s)\n", + "[17/43] half_norm_clean_mpd=14 Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "SR=0.9544 (7.3s)\n", + "[18/43] perfect_clean_mpd=14 Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "SR=0.9747 (3.5s)\n", + "[19/43] inferred_clean_mpd=14 Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "Segmentation was not possible. Error: Each lower bound must be strictly less than each upper bound.\n", + "SR=0.9349 (5.1s)\n", + "[20/43] half_norm_clean_mpd=45 Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "SR=0.9546 (7.9s)\n", + "[21/43] perfect_clean_mpd=45 Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "SR=0.9746 (3.1s)\n", + "[22/43] inferred_clean_mpd=45 Segmentation was not possible. Error: Cannot calculate a linear regression if all x values are identical\n", + "SR=0.9447 (4.0s)\n", + "[23/43] half_norm_clean_cwd=1 SR=0.9529 (8.1s)\n", + "[24/43] perfect_clean_cwd=1 SR=0.9717 (2.9s)\n", + "[25/43] inferred_clean_cwd=1 SR=0.9359 (3.1s)\n", + "[26/43] half_norm_clean_cwd=10 SR=0.9470 (4.2s)\n", + "[27/43] perfect_clean_cwd=10 SR=0.9675 (1.8s)\n", + "[28/43] inferred_clean_cwd=10 SR=0.9347 (2.2s)\n", + "[29/43] half_norm_clean_fmw=5 SR=0.9635 (5.1s)\n", + "[30/43] perfect_clean_fmw=5 SR=0.9542 (2.3s)\n", + "[31/43] inferred_clean_fmw=5 SR=0.9310 (2.4s)\n", + "[32/43] half_norm_clean_fmw=20 SR=0.9635 (5.1s)\n", + "[33/43] perfect_clean_fmw=20 SR=0.9542 (2.4s)\n", + "[34/43] inferred_clean_fmw=20 SR=0.9339 (2.4s)\n", + "[35/43] half_norm_clean_cc=precip SR=0.9775 (6.5s)\n", + "[36/43] perfect_clean_cc=precip SR=0.9967 (4.7s)\n", + "[37/43] inferred_clean_cc=precip SR=0.9824 (6.9s)\n", + "[38/43] half_norm_clean_cc=precip_and_shift SR=0.9544 (3.6s)\n", + "[39/43] perfect_clean_cc=precip_and_shift SR=0.9786 (1.4s)\n", + "[40/43] inferred_clean_cc=precip_and_shift SR=0.9112 (1.9s)\n", + "[41/43] half_norm_clean_cc=precip_or_shift SR=0.9763 (6.7s)\n", + "[42/43] perfect_clean_cc=precip_or_shift SR=0.9964 (5.2s)\n", + "[43/43] inferred_clean_cc=precip_or_shift SR=0.9810 (7.6s)\n", + "\n", + "============================================================\n", + "Completed 43 configurations in 3.0 minutes\n", + "Successful: 43\n", + "Errors: 0\n" + ] + } + ], + "source": [ + "# Run all configurations\n", + "import time\n", + "results = []\n", + "total = len(config_df)\n", + "start_time = time.time()\n", + "\n", + "for idx, row in config_df.iterrows():\n", + " np.random.seed(1977) # Reproducibility\n", + " print(f\"[{idx+1}/{total}] {row['config_name'][:40]:<40}\", end=' ')\n", + " t0 = time.time()\n", + " result = run_soiling_config(row, energy_normalized_daily, insolation_daily, precipitation_daily)\n", + " results.append(result)\n", + " elapsed = time.time() - t0\n", + " if result['error']:\n", + " print(f\"ERROR: {result['error']}\")\n", + " else:\n", + " print(f\"SR={result['soiling_ratio']:.4f} ({elapsed:.1f}s)\")\n", + "\n", + "# Combine configuration and results\n", + "results_df = pd.DataFrame(results)\n", + "master_df = pd.concat([config_df, results_df], axis=1)\n", + "\n", + "total_time = time.time() - start_time\n", + "print(f\"\\n{'='*60}\")\n", + "print(f\"Completed {len(master_df)} configurations in {total_time/60:.1f} minutes\")\n", + "print(f\"Successful: {master_df['soiling_ratio'].notna().sum()}\")\n", + "print(f\"Errors: {master_df['error'].notna().sum()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2b7678ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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config_idconfig_namemethoddetect_neg_shiftspiecewise_fitsoiling_ratioci_lowerci_uppervalid_intervals
01half_norm_clean_dns=False_pw=Falsehalf_norm_cleanFalseFalse0.9494450.9450370.95330840
12perfect_clean_dns=False_pw=Falseperfect_cleanFalseFalse0.9711110.9703450.97192240
23half_norm_clean_dns=False_pw=Truehalf_norm_cleanFalseTrue0.9543780.9503700.95757244
34perfect_clean_dns=False_pw=Trueperfect_cleanFalseTrue0.9747200.9740310.97535144
45half_norm_clean_dns=True_pw=Falsehalf_norm_cleanTrueFalse0.9635050.9611780.96594354
56perfect_clean_dns=True_pw=Falseperfect_cleanTrueFalse0.9542310.9524590.95707754
67inferred_clean_dns=True_pw=Falseinferred_cleanTrueFalse0.9320120.9299350.93473254
78half_norm_clean_dns=True_pw=Truehalf_norm_cleanTrueTrue0.9674940.9654400.96933966
89perfect_clean_dns=True_pw=Trueperfect_cleanTrueTrue0.9516200.9489780.95399466
910inferred_clean_dns=True_pw=Trueinferred_cleanTrueTrue0.9348560.9321630.93751366
1011half_norm_clean_nsf=1.5half_norm_cleanTrueFalse0.9786270.9769940.98007272
1112perfect_clean_nsf=1.5perfect_cleanTrueFalse0.9597100.9560120.96252672
1213inferred_clean_nsf=1.5inferred_cleanTrueFalse0.9292080.9252250.93306772
1314half_norm_clean_nsf=4.0half_norm_cleanTrueFalse0.9600220.9576160.96240252
1415perfect_clean_nsf=4.0perfect_cleanTrueFalse0.9514710.9497340.95279952
1516inferred_clean_nsf=4.0inferred_cleanTrueFalse0.9228710.9209000.92479552
1617half_norm_clean_mpd=14half_norm_cleanFalseTrue0.9543780.9503700.95757244
1718perfect_clean_mpd=14perfect_cleanFalseTrue0.9747200.9740310.97535144
1819inferred_clean_mpd=14inferred_cleanTrueTrue0.9348560.9321630.93751366
1920half_norm_clean_mpd=45half_norm_cleanFalseTrue0.9546460.9506010.95832844
\n", + "
" + ], + "text/plain": [ + " config_id config_name method \\\n", + "0 1 half_norm_clean_dns=False_pw=False half_norm_clean \n", + "1 2 perfect_clean_dns=False_pw=False perfect_clean \n", + "2 3 half_norm_clean_dns=False_pw=True half_norm_clean \n", + "3 4 perfect_clean_dns=False_pw=True perfect_clean \n", + "4 5 half_norm_clean_dns=True_pw=False half_norm_clean \n", + "5 6 perfect_clean_dns=True_pw=False perfect_clean \n", + "6 7 inferred_clean_dns=True_pw=False inferred_clean \n", + "7 8 half_norm_clean_dns=True_pw=True half_norm_clean \n", + "8 9 perfect_clean_dns=True_pw=True perfect_clean \n", + "9 10 inferred_clean_dns=True_pw=True inferred_clean \n", + "10 11 half_norm_clean_nsf=1.5 half_norm_clean \n", + "11 12 perfect_clean_nsf=1.5 perfect_clean \n", + "12 13 inferred_clean_nsf=1.5 inferred_clean \n", + "13 14 half_norm_clean_nsf=4.0 half_norm_clean \n", + "14 15 perfect_clean_nsf=4.0 perfect_clean \n", + "15 16 inferred_clean_nsf=4.0 inferred_clean \n", + "16 17 half_norm_clean_mpd=14 half_norm_clean \n", + "17 18 perfect_clean_mpd=14 perfect_clean \n", + "18 19 inferred_clean_mpd=14 inferred_clean \n", + "19 20 half_norm_clean_mpd=45 half_norm_clean \n", + "\n", + " detect_neg_shifts piecewise_fit soiling_ratio ci_lower ci_upper \\\n", + "0 False False 0.949445 0.945037 0.953308 \n", + "1 False False 0.971111 0.970345 0.971922 \n", + "2 False True 0.954378 0.950370 0.957572 \n", + "3 False True 0.974720 0.974031 0.975351 \n", + "4 True False 0.963505 0.961178 0.965943 \n", + "5 True False 0.954231 0.952459 0.957077 \n", + "6 True False 0.932012 0.929935 0.934732 \n", + "7 True True 0.967494 0.965440 0.969339 \n", + "8 True True 0.951620 0.948978 0.953994 \n", + "9 True True 0.934856 0.932163 0.937513 \n", + "10 True False 0.978627 0.976994 0.980072 \n", + "11 True False 0.959710 0.956012 0.962526 \n", + "12 True False 0.929208 0.925225 0.933067 \n", + "13 True False 0.960022 0.957616 0.962402 \n", + "14 True False 0.951471 0.949734 0.952799 \n", + "15 True False 0.922871 0.920900 0.924795 \n", + "16 False True 0.954378 0.950370 0.957572 \n", + "17 False True 0.974720 0.974031 0.975351 \n", + "18 True True 0.934856 0.932163 0.937513 \n", + "19 False True 0.954646 0.950601 0.958328 \n", + "\n", + " valid_intervals \n", + "0 40 \n", + "1 40 \n", + "2 44 \n", + "3 44 \n", + "4 54 \n", + "5 54 \n", + "6 54 \n", + "7 66 \n", + "8 66 \n", + "9 66 \n", + "10 72 \n", + "11 72 \n", + "12 72 \n", + "13 52 \n", + "14 52 \n", + "15 52 \n", + "16 44 \n", + "17 44 \n", + "18 66 \n", + "19 44 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display master results table - filter to successful runs\n", + "display_cols = ['config_id', 'config_name', 'method', 'detect_neg_shifts', 'piecewise_fit',\n", + " 'soiling_ratio', 'ci_lower', 'ci_upper', 'valid_intervals']\n", + "master_df[master_df['soiling_ratio'].notna()][display_cols].head(20)" + ] + }, + { + "cell_type": "markdown", + "id": "601abbb9", + "metadata": {}, + "source": [ + "## Analysis: Compare Results by Configuration Group" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0d67acb1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results by Method:\n", + "============================================================\n", + " SR Mean SR Std SR Min SR Max Count Avg Valid Intervals\n", + "method \n", + "half_norm_clean 0.9612 0.0102 0.9470 0.9786 15 45.6667\n", + "inferred_clean 0.9391 0.0204 0.9112 0.9824 13 60.0769\n", + "perfect_clean 0.9688 0.0148 0.9515 0.9967 15 45.6667\n" + ] + } + ], + "source": [ + "# Summary statistics by method\n", + "print(\"Results by Method:\")\n", + "print(\"=\"*60)\n", + "method_summary = master_df[master_df['soiling_ratio'].notna()].groupby('method').agg({\n", + " 'soiling_ratio': ['mean', 'std', 'min', 'max', 'count'],\n", + " 'valid_intervals': 'mean'\n", + "}).round(4)\n", + "method_summary.columns = ['SR Mean', 'SR Std', 'SR Min', 'SR Max', 'Count', 'Avg Valid Intervals']\n", + "print(method_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "147b8c87", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Results by Configuration Group:\n", + "============================================================\n", + " SR Mean SR Std SR Min SR Max Count \\\n", + "config_group \n", + "clean_criterion 0.9727 0.0262 0.9112 0.9967 9 \n", + "collapse_window_days 0.9516 0.0156 0.9347 0.9717 6 \n", + "core_matrix 0.9553 0.0143 0.9320 0.9747 10 \n", + "forward_median_window 0.9501 0.0143 0.9310 0.9635 6 \n", + "min_piecewise_days 0.9563 0.0160 0.9349 0.9747 6 \n", + "neg_shift_factor 0.9503 0.0209 0.9229 0.9786 6 \n", + "\n", + " Avg Valid Intervals \n", + "config_group \n", + "clean_criterion 39.8889 \n", + "collapse_window_days 44.0000 \n", + "core_matrix 52.8000 \n", + "forward_median_window 54.0000 \n", + "min_piecewise_days 50.6667 \n", + "neg_shift_factor 62.0000 \n" + ] + } + ], + "source": [ + "# Summary statistics by configuration group\n", + "print(\"\\nResults by Configuration Group:\")\n", + "print(\"=\"*60)\n", + "group_summary = master_df[master_df['soiling_ratio'].notna()].groupby('config_group').agg({\n", + " 'soiling_ratio': ['mean', 'std', 'min', 'max', 'count'],\n", + " 'valid_intervals': 'mean'\n", + "}).round(4)\n", + "group_summary.columns = ['SR Mean', 'SR Std', 'SR Min', 'SR Max', 'Count', 'Avg Valid Intervals']\n", + "print(group_summary)" + ] + }, + { + "cell_type": "markdown", + "id": "b42e34eb", + "metadata": {}, + "source": [ + "## Visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "1bc0469a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize soiling ratio by method and core options\n", + "valid_df = master_df[master_df['soiling_ratio'].notna()].copy()\n", + "\n", + "# Focus on core_matrix group for main comparison\n", + "core_df = valid_df[valid_df['config_group'] == 'core_matrix'].copy()\n", + "\n", + "# Create pivot for heatmap-style visualization with extra space for colorbar\n", + "fig = plt.figure(figsize=(16, 4))\n", + "\n", + "# Create GridSpec with extra column for colorbar and more horizontal spacing\n", + "from matplotlib.gridspec import GridSpec\n", + "gs = GridSpec(1, 4, width_ratios=[1, 1, 1, 0.08], wspace=0.45)\n", + "\n", + "axes = [fig.add_subplot(gs[0, i]) for i in range(3)]\n", + "cax = fig.add_subplot(gs[0, 3])\n", + "\n", + "for idx, method in enumerate(['half_norm_clean', 'perfect_clean', 'inferred_clean']):\n", + " ax = axes[idx]\n", + " method_data = core_df[core_df['method'] == method]\n", + "\n", + " # Create matrix for dns × pw\n", + " pivot_data = []\n", + " for dns in [False, True]:\n", + " row = []\n", + " for pw in [False, True]:\n", + " if method == 'inferred_clean' and not dns:\n", + " row.append(np.nan)\n", + " else:\n", + " val = method_data[(method_data['detect_neg_shifts'] == dns) &\n", + " (method_data['piecewise_fit'] == pw)]['soiling_ratio']\n", + " row.append(val.values[0] if len(val) > 0 else np.nan)\n", + " pivot_data.append(row)\n", + "\n", + " pivot_arr = np.array(pivot_data)\n", + " im = ax.imshow(pivot_arr, cmap='RdYlGn', vmin=0.92, vmax=0.98, aspect='auto')\n", + "\n", + " # Add text annotations\n", + " for i in range(2):\n", + " for j in range(2):\n", + " if not np.isnan(pivot_arr[i, j]):\n", + " ax.text(j, i, f'{pivot_arr[i, j]:.4f}', ha='center', va='center', fontsize=12)\n", + " else:\n", + " ax.text(j, i, 'N/A', ha='center', va='center', fontsize=10, color='gray')\n", + "\n", + " ax.set_xticks([0, 1])\n", + " ax.set_xticklabels(['pw=False', 'pw=True'])\n", + " ax.set_yticks([0, 1])\n", + " ax.set_yticklabels(['dns=False', 'dns=True'])\n", + " ax.set_title(f'{method}')\n", + "\n", + "fig.colorbar(im, cax=cax, label='Soiling Ratio')\n", + "fig.suptitle('Soiling Ratio by Method × detect_neg_shifts × piecewise_fit', fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "956ab4bc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_79808\\703968651.py:8: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", + " bp1 = ax1.boxplot(data_by_method, labels=methods, patch_artist=True)\n", + "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_79808\\703968651.py:21: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", + " bp2 = ax2.boxplot(data_by_group, labels=groups, patch_artist=True)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Box plot of soiling ratio by method\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Box plot by method\n", + "ax1 = axes[0]\n", + "methods = ['half_norm_clean', 'perfect_clean', 'inferred_clean']\n", + "data_by_method = [valid_df[valid_df['method'] == m]['soiling_ratio'].values for m in methods]\n", + "bp1 = ax1.boxplot(data_by_method, labels=methods, patch_artist=True)\n", + "colors = ['steelblue', 'green', 'orange']\n", + "for patch, color in zip(bp1['boxes'], colors):\n", + " patch.set_facecolor(color)\n", + " patch.set_alpha(0.7)\n", + "ax1.set_ylabel('Soiling Ratio')\n", + "ax1.set_title('Soiling Ratio Distribution by Method')\n", + "ax1.axhline(1.0, color='k', linestyle='--', alpha=0.3)\n", + "\n", + "# Box plot by config group\n", + "ax2 = axes[1]\n", + "groups = valid_df['config_group'].unique()\n", + "data_by_group = [valid_df[valid_df['config_group'] == g]['soiling_ratio'].values for g in groups]\n", + "bp2 = ax2.boxplot(data_by_group, labels=groups, patch_artist=True)\n", + "ax2.set_ylabel('Soiling Ratio')\n", + "ax2.set_title('Soiling Ratio Distribution by Parameter Group')\n", + "ax2.tick_params(axis='x', rotation=45)\n", + "ax2.axhline(1.0, color='k', linestyle='--', alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7f921e80", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Scatter plot: Soiling Ratio vs Valid Intervals, colored by method\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "colors_map = {'half_norm_clean': 'steelblue', 'perfect_clean': 'green', 'inferred_clean': 'orange'}\n", + "for method, color in colors_map.items():\n", + " subset = valid_df[valid_df['method'] == method]\n", + " ax.scatter(subset['valid_intervals'], subset['soiling_ratio'],\n", + " label=method, alpha=0.6, s=50, c=color)\n", + "\n", + "ax.set_xlabel('Number of Valid Intervals')\n", + "ax.set_ylabel('Soiling Ratio (P50)')\n", + "ax.set_title('Soiling Ratio vs Valid Intervals by Method')\n", + "ax.legend()\n", + "ax.axhline(1.0, color='k', linestyle='--', alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3f544ffe", + "metadata": {}, + "source": [ + "## Soiling Profile Comparison\n", + "\n", + "Compare the daily soiling ratio profiles across different configurations." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f1c56a96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Valid profiles: 43 out of 43\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a profiles dictionary keyed by config_id for easy lookup\n", + "profiles = {i: results[i]['sr_profile'] for i in range(len(results))\n", + " if results[i]['sr_profile'] is not None and len(results[i]['sr_profile']) > 0}\n", + "\n", + "print(f\"Valid profiles: {len(profiles)} out of {len(results)}\")\n", + "\n", + "# Compare soiling profiles by method (using core matrix configurations)\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "\n", + "# Plot 1: Compare all 3 methods with default settings (dns=True, pw=True)\n", + "ax = axes[0, 0]\n", + "baseline_idx = master_df[(master_df['detect_neg_shifts'] == True) &\n", + " (master_df['piecewise_fit'] == True) &\n", + " (master_df['config_group'] == 'core_matrix')].index\n", + "\n", + "for idx in baseline_idx:\n", + " if idx in profiles:\n", + " profile = profiles[idx]\n", + " row = master_df.loc[idx]\n", + " label = f\"{row['method']} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Soiling Ratio')\n", + "ax.set_title('Method Comparison (dns=True, piecewise=True)')\n", + "ax.legend(loc='lower left', fontsize=8)\n", + "ax.set_ylim(0.85, 1.05)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.tick_params(axis='x', rotation=45)\n", + "\n", + "# Plot 2: Compare detect_neg_shifts effect (half_norm_clean)\n", + "ax = axes[0, 1]\n", + "hnc_idx = master_df[(master_df['method'] == 'half_norm_clean') &\n", + " (master_df['config_group'] == 'core_matrix')].index\n", + "\n", + "for idx in hnc_idx:\n", + " if idx in profiles:\n", + " profile = profiles[idx]\n", + " row = master_df.loc[idx]\n", + " dns_str = 'dns=T' if row['detect_neg_shifts'] else 'dns=F'\n", + " pw_str = 'pw=T' if row['piecewise_fit'] else 'pw=F'\n", + " label = f\"{dns_str}, {pw_str} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Soiling Ratio')\n", + "ax.set_title('half_norm_clean: dns/piecewise Effects')\n", + "ax.legend(loc='lower left', fontsize=8)\n", + "ax.set_ylim(0.85, 1.05)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.tick_params(axis='x', rotation=45)\n", + "\n", + "# Plot 3: Perfect clean with different settings\n", + "ax = axes[1, 0]\n", + "pc_idx = master_df[(master_df['method'] == 'perfect_clean') &\n", + " (master_df['config_group'] == 'core_matrix')].index\n", + "\n", + "for idx in pc_idx:\n", + " if idx in profiles:\n", + " profile = profiles[idx]\n", + " row = master_df.loc[idx]\n", + " dns_str = 'dns=T' if row['detect_neg_shifts'] else 'dns=F'\n", + " pw_str = 'pw=T' if row['piecewise_fit'] else 'pw=F'\n", + " label = f\"{dns_str}, {pw_str} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Soiling Ratio')\n", + "ax.set_title('perfect_clean: dns/piecewise Effects')\n", + "ax.legend(loc='lower left', fontsize=8)\n", + "ax.set_ylim(0.85, 1.05)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.tick_params(axis='x', rotation=45)\n", + "\n", + "# Plot 4: Inferred clean (only works with dns=True)\n", + "ax = axes[1, 1]\n", + "ic_idx = master_df[(master_df['method'] == 'inferred_clean') &\n", + " (master_df['soiling_ratio'].notna())].index\n", + "\n", + "for idx in ic_idx:\n", + " if idx in profiles:\n", + " profile = profiles[idx]\n", + " row = master_df.loc[idx]\n", + " config_short = row['config_name'].replace('inferred_clean_', '')[:20]\n", + " label = f\"{config_short} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label, alpha=0.6, linewidth=1)\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Soiling Ratio')\n", + "ax.set_title('inferred_clean: All Configurations')\n", + "ax.legend(loc='lower left', fontsize=6, ncol=2)\n", + "ax.set_ylim(0.85, 1.05)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "06e32cb2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Detailed profile comparison: overlay all methods with same core settings\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "\n", + "# Create a subplot for each (dns, pw) combination\n", + "settings = [(True, True), (True, False), (False, True), (False, False)]\n", + "colors_method = {'half_norm_clean': 'blue', 'perfect_clean': 'green', 'inferred_clean': 'red'}\n", + "\n", + "for ax, (dns, pw) in zip(axes.flatten(), settings):\n", + " subset_idx = master_df[(master_df['detect_neg_shifts'] == dns) &\n", + " (master_df['piecewise_fit'] == pw) &\n", + " (master_df['config_group'] == 'core_matrix')].index\n", + "\n", + " for idx in subset_idx:\n", + " if idx in profiles:\n", + " profile = profiles[idx]\n", + " row = master_df.loc[idx]\n", + " method = row['method']\n", + " color = colors_method.get(method, 'gray')\n", + " label = f\"{method} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label,\n", + " color=color, alpha=0.8, linewidth=1.5)\n", + "\n", + " ax.set_xlabel('Date')\n", + " ax.set_ylabel('Soiling Ratio')\n", + " dns_str = 'dns=True' if dns else 'dns=False'\n", + " pw_str = 'pw=True' if pw else 'pw=False'\n", + " ax.set_title(f'{dns_str}, {pw_str}')\n", + " ax.legend(loc='lower left', fontsize=8)\n", + " ax.set_ylim(0.85, 1.05)\n", + " ax.grid(True, alpha=0.3)\n", + " ax.tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1038a0ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Profile Statistics by Method:\n", + "============================================================\n", + " profile_mean profile_std profile_range n_points aggregated_sr\n", + " mean std mean mean mean mean\n", + "method \n", + "half_norm_clean 0.9649 0.0095 0.0371 0.2046 1840.4000 0.9612\n", + "inferred_clean 0.9602 0.0135 0.0501 0.2522 2077.4615 0.9391\n", + "perfect_clean 0.9649 0.0095 0.0371 0.2046 1840.4000 0.9688\n", + "\n", + "\n", + "Correlation: Profile Mean vs Aggregated SR\n", + "============================================================\n", + "half_norm_clean: r = 0.3354\n", + "perfect_clean: r = 0.9678\n", + "inferred_clean: r = 0.9282\n" + ] + } + ], + "source": [ + "# Profile statistics: compare profile characteristics across methods\n", + "profile_stats = []\n", + "for idx in profiles:\n", + " profile = profiles[idx]\n", + " row = master_df.loc[idx]\n", + " profile_stats.append({\n", + " 'method': row['method'],\n", + " 'config_name': row['config_name'],\n", + " 'dns': row['detect_neg_shifts'],\n", + " 'pw': row['piecewise_fit'],\n", + " 'profile_mean': profile.mean(),\n", + " 'profile_std': profile.std(),\n", + " 'profile_min': profile.min(),\n", + " 'profile_max': profile.max(),\n", + " 'profile_range': profile.max() - profile.min(),\n", + " 'n_points': len(profile),\n", + " 'aggregated_sr': row['soiling_ratio']\n", + " })\n", + "\n", + "profile_stats_df = pd.DataFrame(profile_stats)\n", + "\n", + "# Summary by method\n", + "print(\"Profile Statistics by Method:\")\n", + "print(\"=\"*60)\n", + "method_profile_summary = profile_stats_df.groupby('method').agg({\n", + " 'profile_mean': ['mean', 'std'],\n", + " 'profile_std': 'mean',\n", + " 'profile_range': 'mean',\n", + " 'n_points': 'mean',\n", + " 'aggregated_sr': 'mean'\n", + "}).round(4)\n", + "print(method_profile_summary.to_string())\n", + "\n", + "# Key insight: correlation between profile mean and aggregated SR\n", + "print(\"\\n\\nCorrelation: Profile Mean vs Aggregated SR\")\n", + "print(\"=\"*60)\n", + "for method in profile_stats_df['method'].unique():\n", + " subset = profile_stats_df[profile_stats_df['method'] == method]\n", + " corr = subset['profile_mean'].corr(subset['aggregated_sr'])\n", + " print(f\"{method}: r = {corr:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a2a5783c", + "metadata": {}, + "source": [ + "## Stochastic Profile Comparison (Method-Specific)\n", + "\n", + "The `soiling_ratio_perfect_clean` is the **same** for all methods - it's the deterministic profile. The actual method differences show up in the **stochastic Monte Carlo profiles**." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "d5958610", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Valid stochastic profiles: 43 out of 43\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create stochastic profiles dictionary\n", + "stoch_profiles = {i: results[i]['stoch_profile'] for i in range(len(results))\n", + " if results[i]['stoch_profile'] is not None and len(results[i]['stoch_profile']) > 0}\n", + "\n", + "print(f\"Valid stochastic profiles: {len(stoch_profiles)} out of {len(results)}\")\n", + "\n", + "# Compare STOCHASTIC profiles by method (these show method differences!)\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "\n", + "# Plot 1: Compare all 3 methods with default settings (dns=True, pw=True)\n", + "ax = axes[0, 0]\n", + "baseline_idx = master_df[(master_df['detect_neg_shifts'] == True) &\n", + " (master_df['piecewise_fit'] == True) &\n", + " (master_df['config_group'] == 'core_matrix')].index\n", + "\n", + "for idx in baseline_idx:\n", + " if idx in stoch_profiles:\n", + " profile = stoch_profiles[idx]\n", + " row = master_df.loc[idx]\n", + " label = f\"{row['method']} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Soiling Ratio (Stochastic Median)')\n", + "ax.set_title('Method Comparison - Stochastic Profiles (dns=True, pw=True)')\n", + "ax.legend(loc='lower left', fontsize=8)\n", + "ax.set_ylim(0.85, 1.05)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.tick_params(axis='x', rotation=45)\n", + "\n", + "# Plot 2: Compare detect_neg_shifts effect (half_norm_clean) - stochastic\n", + "ax = axes[0, 1]\n", + "hnc_idx = master_df[(master_df['method'] == 'half_norm_clean') &\n", + " (master_df['config_group'] == 'core_matrix')].index\n", + "\n", + "for idx in hnc_idx:\n", + " if idx in stoch_profiles:\n", + " profile = stoch_profiles[idx]\n", + " row = master_df.loc[idx]\n", + " dns_str = 'dns=T' if row['detect_neg_shifts'] else 'dns=F'\n", + " pw_str = 'pw=T' if row['piecewise_fit'] else 'pw=F'\n", + " label = f\"{dns_str}, {pw_str} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Soiling Ratio (Stochastic Median)')\n", + "ax.set_title('half_norm_clean: Stochastic Profiles')\n", + "ax.legend(loc='lower left', fontsize=8)\n", + "ax.set_ylim(0.85, 1.05)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.tick_params(axis='x', rotation=45)\n", + "\n", + "# Plot 3: Perfect clean with different settings - stochastic\n", + "ax = axes[1, 0]\n", + "pc_idx = master_df[(master_df['method'] == 'perfect_clean') &\n", + " (master_df['config_group'] == 'core_matrix')].index\n", + "\n", + "for idx in pc_idx:\n", + " if idx in stoch_profiles:\n", + " profile = stoch_profiles[idx]\n", + " row = master_df.loc[idx]\n", + " dns_str = 'dns=T' if row['detect_neg_shifts'] else 'dns=F'\n", + " pw_str = 'pw=T' if row['piecewise_fit'] else 'pw=F'\n", + " label = f\"{dns_str}, {pw_str} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Soiling Ratio (Stochastic Median)')\n", + "ax.set_title('perfect_clean: Stochastic Profiles')\n", + "ax.legend(loc='lower left', fontsize=8)\n", + "ax.set_ylim(0.85, 1.05)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.tick_params(axis='x', rotation=45)\n", + "\n", + "# Plot 4: Inferred clean - stochastic\n", + "ax = axes[1, 1]\n", + "ic_idx = master_df[(master_df['method'] == 'inferred_clean') &\n", + " (master_df['soiling_ratio'].notna())].index\n", + "\n", + "for idx in ic_idx:\n", + " if idx in stoch_profiles:\n", + " profile = stoch_profiles[idx]\n", + " row = master_df.loc[idx]\n", + " config_short = row['config_name'].replace('inferred_clean_', '')[:20]\n", + " label = f\"{config_short} (SR={row['soiling_ratio']:.4f})\"\n", + " ax.plot(profile.index, profile.values, label=label, alpha=0.6, linewidth=1)\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Soiling Ratio (Stochastic Median)')\n", + "ax.set_title('inferred_clean: Stochastic Profiles')\n", + "ax.legend(loc='lower left', fontsize=6, ncol=2)\n", + "ax.set_ylim(0.85, 1.05)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "faaadebe", + "metadata": {}, + "source": [ + "## Summary Table" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5d66c848", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All Results Sorted by Soiling Ratio (highest to lowest):\n", + "================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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config_namemethoddetect_neg_shiftspiecewise_fitsoiling_ratioci_lowerci_uppervalid_intervals
35perfect_clean_cc=precipperfect_cleanFalseFalse0.9966710.9963350.99703031
41perfect_clean_cc=precip_or_shiftperfect_cleanFalseFalse0.9964480.9961570.99681334
36inferred_clean_cc=precipinferred_cleanTrueFalse0.9823560.9799080.98450386
42inferred_clean_cc=precip_or_shiftinferred_cleanTrueFalse0.9810080.9775940.98389683
10half_norm_clean_nsf=1.5half_norm_cleanTrueFalse0.9786270.9769940.98007272
38perfect_clean_cc=precip_and_shiftperfect_cleanFalseFalse0.9786090.9773830.97988016
34half_norm_clean_cc=preciphalf_norm_cleanFalseFalse0.9774730.9731070.98106831
40half_norm_clean_cc=precip_or_shifthalf_norm_cleanFalseFalse0.9762940.9725420.97931034
17perfect_clean_mpd=14perfect_cleanFalseTrue0.9747200.9740310.97535144
3perfect_clean_dns=False_pw=Trueperfect_cleanFalseTrue0.9747200.9740310.97535144
20perfect_clean_mpd=45perfect_cleanFalseTrue0.9745510.9739160.97515844
23perfect_clean_cwd=1perfect_cleanFalseFalse0.9716850.9708820.97247540
1perfect_clean_dns=False_pw=Falseperfect_cleanFalseFalse0.9711110.9703450.97192240
7half_norm_clean_dns=True_pw=Truehalf_norm_cleanTrueTrue0.9674940.9654400.96933966
26perfect_clean_cwd=10perfect_cleanFalseFalse0.9674800.9659210.96902640
31half_norm_clean_fmw=20half_norm_cleanTrueFalse0.9635050.9611780.96594354
28half_norm_clean_fmw=5half_norm_cleanTrueFalse0.9635050.9611780.96594354
4half_norm_clean_dns=True_pw=Falsehalf_norm_cleanTrueFalse0.9635050.9611780.96594354
13half_norm_clean_nsf=4.0half_norm_cleanTrueFalse0.9600220.9576160.96240252
11perfect_clean_nsf=1.5perfect_cleanTrueFalse0.9597100.9560120.96252672
19half_norm_clean_mpd=45half_norm_cleanFalseTrue0.9546460.9506010.95832844
16half_norm_clean_mpd=14half_norm_cleanFalseTrue0.9543780.9503700.95757244
2half_norm_clean_dns=False_pw=Truehalf_norm_cleanFalseTrue0.9543780.9503700.95757244
37half_norm_clean_cc=precip_and_shifthalf_norm_cleanFalseFalse0.9543710.9469310.96019316
5perfect_clean_dns=True_pw=Falseperfect_cleanTrueFalse0.9542310.9524590.95707754
29perfect_clean_fmw=5perfect_cleanTrueFalse0.9542310.9524590.95707754
32perfect_clean_fmw=20perfect_cleanTrueFalse0.9542310.9524590.95707754
22half_norm_clean_cwd=1half_norm_cleanFalseFalse0.9528680.9481600.95623140
8perfect_clean_dns=True_pw=Trueperfect_cleanTrueTrue0.9516200.9489780.95399466
14perfect_clean_nsf=4.0perfect_cleanTrueFalse0.9514710.9497340.95279952
\n", + "
" + ], + "text/plain": [ + " config_name method detect_neg_shifts \\\n", + "35 perfect_clean_cc=precip perfect_clean False \n", + "41 perfect_clean_cc=precip_or_shift perfect_clean False \n", + "36 inferred_clean_cc=precip inferred_clean True \n", + "42 inferred_clean_cc=precip_or_shift inferred_clean True \n", + "10 half_norm_clean_nsf=1.5 half_norm_clean True \n", + "38 perfect_clean_cc=precip_and_shift perfect_clean False \n", + "34 half_norm_clean_cc=precip half_norm_clean False \n", + "40 half_norm_clean_cc=precip_or_shift half_norm_clean False \n", + "17 perfect_clean_mpd=14 perfect_clean False \n", + "3 perfect_clean_dns=False_pw=True perfect_clean False \n", + "20 perfect_clean_mpd=45 perfect_clean False \n", + "23 perfect_clean_cwd=1 perfect_clean False \n", + "1 perfect_clean_dns=False_pw=False perfect_clean False \n", + "7 half_norm_clean_dns=True_pw=True half_norm_clean True \n", + "26 perfect_clean_cwd=10 perfect_clean False \n", + "31 half_norm_clean_fmw=20 half_norm_clean True \n", + "28 half_norm_clean_fmw=5 half_norm_clean True \n", + "4 half_norm_clean_dns=True_pw=False half_norm_clean True \n", + "13 half_norm_clean_nsf=4.0 half_norm_clean True \n", + "11 perfect_clean_nsf=1.5 perfect_clean True \n", + "19 half_norm_clean_mpd=45 half_norm_clean False \n", + "16 half_norm_clean_mpd=14 half_norm_clean False \n", + "2 half_norm_clean_dns=False_pw=True half_norm_clean False \n", + "37 half_norm_clean_cc=precip_and_shift half_norm_clean False \n", + "5 perfect_clean_dns=True_pw=False perfect_clean True \n", + "29 perfect_clean_fmw=5 perfect_clean True \n", + "32 perfect_clean_fmw=20 perfect_clean True \n", + "22 half_norm_clean_cwd=1 half_norm_clean False \n", + "8 perfect_clean_dns=True_pw=True perfect_clean True \n", + "14 perfect_clean_nsf=4.0 perfect_clean True \n", + "\n", + " piecewise_fit soiling_ratio ci_lower ci_upper valid_intervals \n", + "35 False 0.996671 0.996335 0.997030 31 \n", + "41 False 0.996448 0.996157 0.996813 34 \n", + "36 False 0.982356 0.979908 0.984503 86 \n", + "42 False 0.981008 0.977594 0.983896 83 \n", + "10 False 0.978627 0.976994 0.980072 72 \n", + "38 False 0.978609 0.977383 0.979880 16 \n", + "34 False 0.977473 0.973107 0.981068 31 \n", + "40 False 0.976294 0.972542 0.979310 34 \n", + "17 True 0.974720 0.974031 0.975351 44 \n", + "3 True 0.974720 0.974031 0.975351 44 \n", + "20 True 0.974551 0.973916 0.975158 44 \n", + "23 False 0.971685 0.970882 0.972475 40 \n", + "1 False 0.971111 0.970345 0.971922 40 \n", + "7 True 0.967494 0.965440 0.969339 66 \n", + "26 False 0.967480 0.965921 0.969026 40 \n", + "31 False 0.963505 0.961178 0.965943 54 \n", + "28 False 0.963505 0.961178 0.965943 54 \n", + "4 False 0.963505 0.961178 0.965943 54 \n", + "13 False 0.960022 0.957616 0.962402 52 \n", + "11 False 0.959710 0.956012 0.962526 72 \n", + "19 True 0.954646 0.950601 0.958328 44 \n", + "16 True 0.954378 0.950370 0.957572 44 \n", + "2 True 0.954378 0.950370 0.957572 44 \n", + "37 False 0.954371 0.946931 0.960193 16 \n", + "5 False 0.954231 0.952459 0.957077 54 \n", + "29 False 0.954231 0.952459 0.957077 54 \n", + "32 False 0.954231 0.952459 0.957077 54 \n", + "22 False 0.952868 0.948160 0.956231 40 \n", + "8 True 0.951620 0.948978 0.953994 66 \n", + "14 False 0.951471 0.949734 0.952799 52 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Full results summary - sorted by soiling ratio\n", + "print(\"All Results Sorted by Soiling Ratio (highest to lowest):\")\n", + "print(\"=\"*80)\n", + "sort_cols = ['config_name', 'method', 'detect_neg_shifts', 'piecewise_fit',\n", + " 'soiling_ratio', 'ci_lower', 'ci_upper', 'valid_intervals']\n", + "valid_df[sort_cols].sort_values('soiling_ratio', ascending=False).head(30)" + ] + }, + { + "cell_type": "markdown", + "id": "028b014c", + "metadata": {}, + "source": [ + "## Recommendations\n", + "\n", + "Based on the analysis:\n", + "\n", + "1. **For sites with irregular/natural cleaning**: Enable `detect_neg_shifts=True`\n", + "2. **For sites with gradual soiling changes**: Enable `piecewise_fit=True`\n", + "3. **When precipitation data is available**: Use `clean_criterion='precip_or_shift'`\n", + "4. **For noisy data**: Increase `neg_shift_factor` to reduce false positive detections\n", + "5. **Using both options together** generally provides more accurate results by detecting more valid soiling intervals" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rdtools_313", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rdtools/soiling.py b/rdtools/soiling.py index c9d79406..a26ec8c4 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -70,9 +70,20 @@ def __init__(self, energy_normalized_daily, insolation_daily, precipitation_dail 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, - 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, + detect_neg_shifts=False, + piecewise_fit=False, + neg_shift_factor=2.5, + min_piecewise_days=27, + collapse_window_days=5, + ): """ Calculates self.daily_df, a pandas dataframe prepared for SRR analysis, and self.renorm_factor, the renormalization factor for the daily @@ -109,24 +120,30 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, 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. + detect_neg_shifts : bool, default False + If True, detects negative shifts in the rolling median and subdivides + intervals accordingly. + piecewise_fit : bool, default False + If True, tests for piecewise linear fits within intervals and + subdivides at detected change points. + neg_shift_factor : float, default 2.5 + Multiplier of ``clean_threshold`` to identify negative shift events. + Only used when ``detect_neg_shifts=True``. + min_piecewise_days : int, default 27 + Minimum interval length in days required to attempt piecewise fitting. + Only used when ``piecewise_fit=True``. + collapse_window_days : int, default 5 + Window size in days for collapsing multiple consecutive cleaning + events into a single event. """ 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", + stacklevel=3, + ) df = self.pm.to_frame() df.columns = ["pi"] @@ -187,17 +204,13 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, 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 - 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 + # Collapse multiple consecutive cleaning events into a single event + # to avoid fragmenting soiling intervals unnecessarily + reduced_cleaning_events = _collapse_cleaning_events( + df.clean_event_detected, df.delta.values, collapse_window_days + ) + df["clean_event_detected"] = reduced_cleaning_events - ########################################################################## precip_event = df["precip"] > precip_threshold if clean_criterion == "precip_and_shift": @@ -218,39 +231,23 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, 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 + # Add negative shifts which allows further segmentation of the soiling + # intervals and handles correction for data outages df.delta = df.delta.fillna(0) # to avoid NA corrupting calculation - if neg_shift is True: - df["drop_event"] = df.delta < -2.5 * clean_threshold + if detect_neg_shifts: + df["drop_event"] = df.delta < -neg_shift_factor * 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.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 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") - min_soil_length = 27 # min threshold of days necessary for piecewise fit + # When piecewise_fit is enabled, add a single breakpoint per soiling + # interval if statistical criteria are met with the piecewise linear fit + # compared to a single linear fit + if piecewise_fit: piecewise_loop = sorted(list(set(df["run"]))) cp_dates = [] for r in piecewise_loop: @@ -258,26 +255,30 @@ def _calc_daily_df(self, day_scale=13, clean_threshold="infer", recenter=True, 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_index = _segmented_soiling_period(pr) + if len(run) > min_piecewise_days and run.pi_norm.sum() > 0: + sr, cp_index = segmented_soiling_period(pr) if cp_index is not None: cp_dates.append(pr.index[cp_index]) - # 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, neg_shift=False, piecewise=False): + def _calc_result_df( + self, + trim=False, + max_relative_slope_error=500.0, + max_neg_step=0.05, + min_interval_days=7, + detect_neg_shifts=False, + forward_median_window=10, + ): """ Calculates self.result_df, a pandas dataframe summarizing the soiling intervals identified and self.analyzed_daily_df, a version of @@ -291,19 +292,19 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati max_relative_slope_error : float, default 500 the maximum relative size of the slope confidence interval for an interval to be considered valid (percentage). - max_negative_step : float, default 0.05 + max_neg_step : float, default 0.05 The maximum magnitude of negative discrete steps allowed in an interval for the interval to be considered valid (units of - normalized performance metric). - 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. + normalized performance metric). Only used when ``detect_neg_shifts=False``. + min_interval_days : int, default 7 + The minimum duration in days for an interval to be considered + valid. Cannot be less than 2 days. + detect_neg_shifts : bool, default False + If True, detects negative shifts in the rolling median and uses + shift corrections at segment boundaries. + forward_median_window : int, default 10 + Window size for forward median calculation used to validate shift + corrections. Only used when ``detect_neg_shifts=True``. """ daily_df = self.daily_df @@ -327,8 +328,6 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati # valid=False row if not run_filtered.empty: run = run_filtered - #################################################################### - # see commented changes result_dict = { "start": start, "end": end, @@ -345,14 +344,12 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati "inferred_end_loss": ( np.nan if run.pi_norm.isna().any() else run.pi_norm.median() ), - "slope_err": 10000, # added high dummy start value for later logic/Matt + "slope_err": 10000, # high dummy start value for later logic "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 - ############################################################## + "clean_event": run.clean_event.iloc[0], # record of clean events + "run_loss_baseline": 0.0, # loss from the polyfit over the soiling interval } - if len(run) > min_interval_length and run.pi_norm.sum() > 0: + if len(run) > min_interval_days 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] @@ -390,75 +387,51 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati 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 - 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 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) - ) - + # Filter results for each interval, setting invalid interval to slope of 0 + # When detect_neg_shifts=True, negative shifts are used as breaks for soiling + # intervals so we don't filter by max_neg_step + if detect_neg_shifts: + filt = (results.run_slope > 0) | ( + results.slope_err >= max_relative_slope_error / 100.0 + ) 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 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 - ) + else: + # Original logic: filter by max_neg_step as well + filt = ( + (results.run_slope > 0) + | (results.slope_err >= max_relative_slope_error / 100.0) + | (results.max_neg_step <= -1.0 * max_neg_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 + # Calculate beginning inferred shift (end of previous soiling period + # to start of current period) 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 is True) & (np.isnan(results.prev_end) & (results.valid is True)), - "prev_end"] = 1 # clean_event or clean_event_detected + "prev_end", + ] = 1 results["inferred_begin_shift"] = results.inferred_start_loss - results.prev_end - # if orginal shift detection was positive the shift should not be + # if original 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: + if not detect_neg_shifts: results.loc[filt, "valid"] = False if len(results[results.valid]) == 0: @@ -473,15 +446,15 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati 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 - if (piecewise) | (neg_shift): - ################################################################### + # Enhanced loss calculation with handling of negative shifts and changepoints + if detect_neg_shifts: pm_frame_out.inferred_begin_shift.bfill(inplace=True) + min_periods = forward_median_window // 2 pm_frame_out["forward_median"] = ( - pm_frame_out.pi.iloc[::-1].rolling(10, min_periods=5).median()) + pm_frame_out.pi.iloc[::-1] + .rolling(forward_median_window, min_periods=min_periods) + .median() + ) prev_shift = 1 soil_inferred_clean = [] soil_perfect_clean = [] @@ -592,7 +565,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, max_negati 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", detect_neg_shifts=False): """ Runs the Monte Carlo step of the SRR method. Calculates self.random_profiles, a list of the random soiling profiles realized in @@ -603,47 +576,43 @@ 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} \ + method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', 'inferred_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 - * 'perfect_clean' - each cleaning event returns the performance - 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' - 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. + * 'random_clean' - a random recovery between 0-100%. + * 'perfect_clean' - each cleaning event returns the performance + metric to 1. + * 'inferred_clean' - at each detected clean event the performance + metric increases based on fits to the data. + + When ``detect_neg_shifts=True``, 'perfect_clean' and 'inferred_clean' + use enhanced logic that handles negative shifts at segment boundaries. + detect_neg_shifts : bool, default False + If True, uses enhanced logic for 'perfect_clean' and 'inferred_clean' + methods that handles negative shifts at segment boundaries. """ # 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 in ("half_norm_clean", "random_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') + 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_neg_step" and/or decreasing ' + '"min_interval_days". Alternatively, consider using ' + 'method="perfect_clean". For more info see ' + "https://github.com/NREL/rdtools/issues/272", + stacklevel=3, + ) monte_losses = [] random_profiles = [] for _ in range(monte): @@ -737,48 +706,53 @@ def _calc_monte(self, monte, method="half_norm_clean"): 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 - ############################################### + if detect_neg_shifts: + # Enhanced logic with negative shift detection + 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 + ) + start_list.append(inter_start) + end = inter_start + row.run_loss + inter_start = end + results_rand["start_loss"] = start_list + else: + # Simple logic: each cleaning returns to 1 + 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 + + elif method == "inferred_clean": + if detect_neg_shifts: + # Enhanced logic with negative shift detection + 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 + ) + start_list.append(inter_start) + end = inter_start + row.run_loss + inter_start = end + results_rand["start_loss"] = start_list + else: + raise ValueError("method='inferred_clean' requires detect_neg_shifts=True") else: raise ValueError("Invalid method specification") @@ -801,13 +775,29 @@ 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): + 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_days=7, + exceedance_prob=95.0, + confidence_level=68.2, + recenter=True, + max_relative_slope_error=500.0, + max_neg_step=0.05, + outlier_factor=1.5, + detect_neg_shifts=False, + piecewise_fit=False, + neg_shift_factor=2.5, + min_piecewise_days=27, + collapse_window_days=5, + forward_median_window=10, + ): """ Run the SRR method from beginning to end. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented @@ -829,31 +819,24 @@ def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False, 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', 'inferred_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 - * 'perfect_clean' - each cleaning event returns the performance - 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' - 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. + * 'random_clean' - a random recovery between 0-100%. + * 'perfect_clean' - each cleaning event returns the performance + metric to 1. + * 'inferred_clean' - at each detected clean event the performance + metric increases based on fits to the data. + + When ``detect_neg_shifts=True``, 'perfect_clean' and 'inferred_clean' + use enhanced logic that handles negative shifts and tracks shift + corrections at segment boundaries. clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -868,9 +851,9 @@ def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False, The daily precipitation threshold for defining precipitation cleaning events. Units must be consistent with ``self.precipitation_daily`` - min_interval_length : int, default 7 - The minimum duration for an interval to be considered - valid. Cannot be less than 2 (days). + min_interval_days : int, default 7 + The minimum duration in days for an interval to be considered + valid. Cannot be less than 2 days. exceedance_prob : float, default 95.0 The probability level to use for exceedance value calculation in percent @@ -882,26 +865,37 @@ def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False, max_relative_slope_error : float, default 500 the maximum relative size of the slope confidence interval for an interval to be considered valid (percentage). - max_negative_step : float, default 0.05 + max_neg_step : float, default 0.05 The maximum magnitude of negative discrete steps allowed in an interval for the interval to be considered valid (units of - normalized performance metric). + normalized performance metric). Only used when ``detect_neg_shifts=False``. outlier_factor : float, default 1.5 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. + detect_neg_shifts : bool, default False + If True, detects negative shifts in the rolling median and subdivides + intervals accordingly. When a negative shift is detected followed by + a recovery, the algorithm tracks these transitions. When enabled with + ``method='perfect_clean'`` or ``method='inferred_clean'``, uses + enhanced logic that handles shift corrections at segment boundaries. + piecewise_fit : bool, default False + If True, tests for piecewise linear fits within soiling intervals. + Intervals that show a significant slope change are subdivided at the + detected change point. + neg_shift_factor : float, default 2.5 + Multiplier of ``clean_threshold`` to identify negative shift events. + Only used when ``detect_neg_shifts=True``. A value of 2.5 means a shift + of -2.5 * clean_threshold triggers interval subdivision. + min_piecewise_days : int, default 27 + Minimum interval length in days required to attempt piecewise fitting. + Only used when ``piecewise_fit=True``. + collapse_window_days : int, default 5 + Window size in days for collapsing multiple consecutive cleaning + events into a single event. + forward_median_window : int, default 10 + Window size for forward median calculation used to validate shift + corrections. Only used when ``detect_neg_shifts=True``. Returns ------- @@ -956,17 +950,41 @@ 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, - 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) + # Validate method and detect_neg_shifts compatibility + if detect_neg_shifts and method not in ( + "half_norm_clean", + "perfect_clean", + "inferred_clean", + ): + raise ValueError( + f"detect_neg_shifts=True requires method='half_norm_clean', 'perfect_clean' or " + f"'inferred_clean', got '{method}'" + ) - self._calc_monte(reps, method=method) + 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, + detect_neg_shifts=detect_neg_shifts, + piecewise_fit=piecewise_fit, + neg_shift_factor=neg_shift_factor, + min_piecewise_days=min_piecewise_days, + collapse_window_days=collapse_window_days, + ) + + self._calc_result_df( + trim=trim, + max_relative_slope_error=max_relative_slope_error, + max_neg_step=max_neg_step, + min_interval_days=min_interval_days, + detect_neg_shifts=detect_neg_shifts, + forward_median_window=forward_median_window, + ) + + self._calc_monte(reps, method=method, detect_neg_shifts=detect_neg_shifts) # Calculate the P50 and confidence interval half_ci = confidence_level / 2.0 @@ -987,8 +1005,7 @@ def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False, "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 + 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, @@ -998,14 +1015,31 @@ def run(self, reps=1000, day_scale=13, clean_threshold="infer", trim=False, 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_days=7, + exceedance_prob=95.0, + confidence_level=68.2, + recenter=True, + max_relative_slope_error=500.0, + max_neg_step=0.05, + outlier_factor=1.5, + detect_neg_shifts=False, + piecewise_fit=False, + neg_shift_factor=2.5, + min_piecewise_days=27, + collapse_window_days=5, + forward_median_window=10, +): """ Functional wrapper for :py:class:`~rdtools.soiling.SRRAnalysis`. Perform the stochastic rate and recovery soiling loss calculation. Based on the @@ -1038,31 +1072,23 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitat 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', 'inferred_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 - * 'perfect_clean' - each cleaning event returns the performance - 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' - 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. + * 'random_clean' - a random recovery between 0-100%. + * 'perfect_clean' - each cleaning event returns the performance + metric to 1. + * 'inferred_clean' - at each detected clean event the performance + metric increases based on fits to the data. + + When ``detect_neg_shifts=True``, 'perfect_clean' and 'inferred_clean' + use enhanced logic that handles negative shifts at segment boundaries. clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \ default 'shift' The method of partitioning the dataset into soiling intervals @@ -1076,9 +1102,9 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitat precip_threshold : float, default 0.01 The daily precipitation threshold for defining precipitation cleaning events. Units must be consistent with precip. - min_interval_length : int, default 7 - The minimum duration, in days, for an interval to be considered - valid. Cannot be less than 2 (days). + min_interval_days : int, default 7 + The minimum duration in days for an interval to be considered + valid. Cannot be less than 2 days. exceedance_prob : float, default 95.0 the probability level to use for exceedance value calculation in percent @@ -1090,26 +1116,37 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitat max_relative_slope_error : float, default 500.0 the maximum relative size of the slope confidence interval for an interval to be considered valid (percentage). - max_negative_step : float, default 0.05 + max_neg_step : float, default 0.05 The maximum magnitude of negative discrete steps allowed in an interval for the interval to be considered valid (units of normalized - performance metric). + performance metric). Only used when ``detect_neg_shifts=False``. outlier_factor : float, default 1.5 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. + detect_neg_shifts : bool, default False + If True, detects negative shifts in the rolling median and subdivides + intervals accordingly. When a negative shift is detected followed by + a recovery, the algorithm tracks these transitions. When enabled with + ``method='perfect_clean'`` or ``method='inferred_clean'``, uses + enhanced logic that handles shift corrections at segment boundaries. + piecewise_fit : bool, default False + If True, tests for piecewise linear fits within soiling intervals. + Intervals that show a significant slope change are subdivided at the + detected change point. + neg_shift_factor : float, default 2.5 + Multiplier of ``clean_threshold`` to identify negative shift events. + Only used when ``detect_neg_shifts=True``. A value of 2.5 means a shift + of -2.5 * clean_threshold triggers interval subdivision. + min_piecewise_days : int, default 27 + Minimum interval length in days required to attempt piecewise fitting. + Only used when ``piecewise_fit=True``. + collapse_window_days : int, default 5 + Window size in days for collapsing multiple consecutive cleaning + events into a single event. + forward_median_window : int, default 10 + Window size for forward median calculation used to validate shift + corrections. Only used when ``detect_neg_shifts=True``. Returns ------- @@ -1168,12 +1205,27 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, precipitat 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_days=min_interval_days, + exceedance_prob=exceedance_prob, + confidence_level=confidence_level, + recenter=recenter, + max_relative_slope_error=max_relative_slope_error, + max_neg_step=max_neg_step, + outlier_factor=outlier_factor, + detect_neg_shifts=detect_neg_shifts, + piecewise_fit=piecewise_fit, + neg_shift_factor=neg_shift_factor, + min_piecewise_days=min_piecewise_days, + collapse_window_days=collapse_window_days, + forward_median_window=forward_median_window, + ) return sr, sr_ci, soiling_info @@ -1265,8 +1317,13 @@ def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confide 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_days=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 @@ -1284,7 +1341,7 @@ def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14, ``soiling_rate_high``, ``soiling_rate_low``, ``soiling_rate``, ``length``, ``valid``,``start``, and ``end``. - min_interval_length : int, default 14 + min_interval_days : int, default 14 The minimum number of days a soiling interval must contain to be included in the calculation. Similar to the same parameter in :py:func:`soiling_srr` and :py:meth:`SRRAnalysis.run` but with a @@ -1340,8 +1397,10 @@ def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14, 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)].copy() + (soiling_interval_summary["length"] >= min_interval_days) + & (soiling_interval_summary["valid"]) + & (rel_error <= max_relative_slope_error) + ].copy() # count the overlap of each interval with each month month_counts = [] @@ -1652,8 +1711,7 @@ def iterative_signal_decomposition( 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']") + 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] @@ -2909,10 +2967,14 @@ def _piecewise_linear(x, x0, b, k1, k2): return np.piecewise(x, cond_list, func_list) -def _segmented_soiling_period( - pr, fill_method="bfill", days_clean_vs_cp=13, 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 +def segmented_soiling_period( + pr, + fill_method="bfill", + days_clean_vs_cp=13, + initial_guesses=[13, 1, 0, 0], + bounds=None, + min_r2=0.15, +): """ Applies segmented regression to a single deposition period (data points in between two cleaning events). @@ -2927,12 +2989,12 @@ def _segmented_soiling_period( Method to employ to fill any missing day. days_clean_vs_cp : numeric (default=13) Minimum number of days accepted between cleanings and change points. - bounds : numeric (default=None) + bounds : list (default=None) List of bounds for fitting function. If not specified, they are defined in the function. - initial_guesses : numeric (default=0.1) + initial_guesses : list (default=[13, 1, 0, 0]) List of initial guesses for fitting function - min_r2 : numeric (default=0.1) + min_r2 : numeric (default=0.15) Minimum R2 to consider valid the extracted soiling profile. Returns diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index 7f87cf2d..7b814ab3 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -70,21 +70,20 @@ 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)]) + "method,detect_neg_shifts,expected_sr", + [("random_clean", False, 0.936177), + ("half_norm_clean", False, 0.915093), + ("perfect_clean", False, 0.977116), + ("perfect_clean", True, 0.977116), + ("inferred_clean", True, 0.975805)]) def test_soiling_srr_consecutive_invalid( soiling_normalized_daily, soiling_insolation, soiling_times, - method, neg_shift, piecewise, expected_sr): + method, detect_neg_shifts, 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) + method=method, detect_neg_shifts=detect_neg_shifts) assert expected_sr == pytest.approx(sr, abs=1e-6), \ f"Soiling ratio different from expected value for {method} \ with consecutive invalid intervals" @@ -155,34 +154,34 @@ def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation): @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)]) + "method,detect_neg_shifts,expected_sr", + [("random_clean", False, 0.920444), + ("perfect_clean", False, 0.966912), + ("perfect_clean", True, 0.966912), + ("inferred_clean", True, 0.965565)]) def test_soiling_srr_method( - soiling_normalized_daily, soiling_insolation, method, neg_shift, piecewise, expected_sr + soiling_normalized_daily, soiling_insolation, method, detect_neg_shifts, expected_sr ): 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 + detect_neg_shifts=detect_neg_shifts ) 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): +def test_soiling_srr_min_interval_days(soiling_normalized_daily, soiling_insolation): "Test that a long minimum interval length prevents finding shorter intervals" with pytest.raises(NoValidIntervalError): np.random.seed(1977) # normalized_daily intervals are 25 days long, so min=26 should fail: _ = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=68.2, - reps=10, min_interval_length=26) + reps=10, min_interval_days=26) # but min=24 should be fine: _ = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=68.2, - reps=10, min_interval_length=24) + reps=10, min_interval_days=24) def test_soiling_srr_recenter_false(soiling_normalized_daily, soiling_insolation): @@ -241,11 +240,12 @@ def test_soiling_srr_with_nan_interval(soiling_normalized_daily, soiling_insolat 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), \ + # With perfect_clean method and detect_neg_shifts, the warning is not raised + np.random.seed(1977) + sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt, soiling_insolation, + reps=reps, method="perfect_clean", + detect_neg_shifts=True) + assert 0.936085 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio different from expected value when an entire interval was NaN" @@ -271,10 +271,10 @@ 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( +def test_soiling_srr_min_interval_days_default( soiling_normalized_daily, soiling_insolation, start, expected_sr): """ - Make sure that the default value of min_interval_length is 7 days by testing + Make sure that the default value of min_interval_days is 7 days by testing on a cropped version of the example data """ reps = 10 @@ -332,42 +332,45 @@ def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolatio # ########################### -# negetive shift and piecewise tests +# detect_neg_shifts and piecewise_fit tests # ########################### @pytest.mark.parametrize( - "method,neg_shift,expected_sr", + "method,detect_neg_shifts,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)]) + ("perfect_clean", True, 0.964117), + ("inferred_clean", True, 0.963585)]) def test_negative_shifts( soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times, - method, neg_shift, expected_sr): + method, detect_neg_shifts, 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) + method=method, detect_neg_shifts=detect_neg_shifts) assert expected_sr == pytest.approx(sr, abs=1e-6), \ - f'Soiling ratio with method="{method}" and neg_shift="{neg_shift}" \ + f'Soiling ratio with method="{method}" and detect_neg_shifts="{detect_neg_shifts}" \ different from expected value' @pytest.mark.parametrize( - "method,piecewise,expected_sr", + "method,piecewise_fit,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)]) + ("perfect_clean", True, 0.896936), + ("inferred_clean", True, 0.896214)]) def test_piecewise(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, - soiling_times, method, piecewise, expected_sr): + soiling_times, method, piecewise_fit, expected_sr): reps = 10 np.random.seed(1977) + # Note: piecewise methods require detect_neg_shifts=True for perfect_clean/inferred_clean + detect_neg_shifts = method in ("perfect_clean", "inferred_clean") sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope, soiling_insolation, reps=reps, method=method, - piecewise=piecewise) + detect_neg_shifts=detect_neg_shifts, + piecewise_fit=piecewise_fit) assert expected_sr == pytest.approx(sr, abs=1e-6), \ - f'Soiling ratio with method="{method}" and piecewise="{piecewise}" \ + f'Soiling ratio with method="{method}" and piecewise_fit="{piecewise_fit}" \ different from expected value' @@ -378,27 +381,27 @@ def test_piecewise_and_neg_shifts(soiling_normalized_daily_with_piecewise_slope, 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) + method="perfect_clean", detect_neg_shifts=True, + piecewise_fit=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) + method="perfect_clean", detect_neg_shifts=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 +def test_detect_neg_shifts_clean_threshold(soiling_normalized_daily_with_neg_shifts, + soiling_insolation): + """Test that clean_threshold works with detect_neg_shifts=True 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) + method="perfect_clean", detect_neg_shifts=True, + piecewise_fit=True) assert 0.934926 == pytest.approx(sr, abs=1e-6), \ "Soiling ratio with specified clean_threshold different from expected value" @@ -525,9 +528,9 @@ def test_monthly_soiling_rates(soiling_interval_summary): pd.testing.assert_frame_equal(result, expected, check_dtype=False) -def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary): +def test_monthly_soiling_rates_min_interval_days(soiling_interval_summary): np.random.seed(1977) - result = monthly_soiling_rates(soiling_interval_summary, min_interval_length=20) + result = monthly_soiling_rates(soiling_interval_summary, min_interval_days=20) expected = np.array( [[1.00000000e00, -1.24851539e-03, -2.10394564e-03, -3.98358211e-04, 1.00000000e00], From 54cf8fab47fadd640f4df9f1611fb19f12ca1ddd Mon Sep 17 00:00:00 2001 From: martin-springer Date: Wed, 4 Feb 2026 19:48:13 -0500 Subject: [PATCH 58/58] update soiling profiles --- docs/soiling_options_comparison_v2.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/soiling_options_comparison_v2.ipynb b/docs/soiling_options_comparison_v2.ipynb index d3b1567d..c160075a 100644 --- a/docs/soiling_options_comparison_v2.ipynb +++ b/docs/soiling_options_comparison_v2.ipynb @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "b2b1af8a", "metadata": {}, "outputs": [ @@ -1180,7 +1180,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "4a1aeae5", "metadata": {}, "outputs": [], @@ -1856,7 +1856,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "1bc0469a", "metadata": {}, "outputs": [ @@ -1987,7 +1987,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "7f921e80", "metadata": {}, "outputs": [ @@ -2033,7 +2033,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "f1c56a96", "metadata": {}, "outputs": [ @@ -2157,7 +2157,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "06e32cb2", "metadata": {}, "outputs": [ @@ -2292,7 +2292,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "d5958610", "metadata": {}, "outputs": [ @@ -2424,7 +2424,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "5d66c848", "metadata": {}, "outputs": [