From 198adad0308ff53be46076577cc1768e70973303 Mon Sep 17 00:00:00 2001 From: Fabian Riemer Date: Fri, 31 May 2024 15:12:03 +0200 Subject: [PATCH 01/62] bla --- scripts/submitViaHTCondor.py | 183 +++++++++++++++++++++++++++++++++++ 1 file changed, 183 insertions(+) create mode 100644 scripts/submitViaHTCondor.py diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py new file mode 100644 index 00000000..798f1ba3 --- /dev/null +++ b/scripts/submitViaHTCondor.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +""" +Script to submit training scripts to Topas via HTCondor +""" + +import sys +import os +import uuid +import subprocess + +UEXT = str(uuid.uuid4()) + + +class meta_option(object): + def __init__(self, id, default_val = None) -> None: + self.id = id + self.triggered = False + self.value = default_val + + def check(self, clo): + if self.triggered: + self.value = clo + self.triggered = False + return True + if clo == '---'+self.id: + self.triggered = True + return True + return False + + def valid(self): + return self.value is not None + + def __str__(self) -> str: + return self.id + ': '+self.value + + +opts = { + 'd' : meta_option('d'), + 't' : meta_option('t', '/work/friemer/hgcalml/trainingdata_split/'), + 'm' : meta_option('m', '/work/friemer/hgcalml/HGCalML/Train/paper_trainer_noRSU.py'), + 'h' : meta_option('h', 'HGCalML'), + 'n' : meta_option('n','TrainJob'), + 'cpu': meta_option('cpu', '1'), + 'memory': meta_option('memory', '15 GB'), + 'disk': meta_option('disk', '8 GB'), +} + +filtered_clo=[] + +for clo in sys.argv: + next = False + for _,o in opts.items(): + if o.check(clo): + next = True + break + if next: + continue + filtered_clo.append(clo) + +all_valid = True +for _,o in opts.items(): + all_valid = all_valid and o.valid() + +if '-h' in sys.argv or '--help' in sys.argv or (not all_valid): + print('script to submit commands within the container to sbatch.\n') + print('all commands are fully forwarded with one exception:') + print('\n ---d specifies a working directory that can be specified ' + 'that will contain the files. It is created if it does not exist.\n') + print('\n ---t specifies the location of the training-data to feed the model\n') + print('\n ---m specifies which model to execute\n') + print('\n ---h location of HGCalML-Folder with necessary modules\n') + print('\n ---n (opt) specifies a name for the scripts\n') + print('\n ---cpu (opt) number of cpus to request default: 1\n') + print('\n ---memory (opt) size of memory to request default: 15 GB\n') + print('\n ---disk (opt) size of memory to request default 8 GB\n') + sys.exit() + +if os.path.isdir(opts['d'].value): + var = input(\ + 'Working directory exists, are you sure you want to continue, please type "yes/y"\n') + var = var.lower() + if not var in ('yes', 'y'): + sys.exit() +else: + os.system('mkdir -p '+opts['d'].value) + + +filtered_clo = filtered_clo[1:] #remove +COMMANDS = " " +for clos in filtered_clo: + COMMANDS += clos + " " + +CWD = os.getcwd() + +#Create a tarball of the HGCalML folder +os.system(f'''tar -czf {opts['d'].value}/HGCalML.tar.gz {opts['h'].value}''') + +#Setup the sub file +sub_temp=f'''#!/bin/bash +Universe = docker +docker_image = cernml4reco/deepjetcore4:5ef28a3 +accounting_group = cms.jet + +requirements =(TARGET.CloudSite=="topas") ++RemoteJob = True ++RequestWalltime = 72 * 60 * 60 + +executable = {opts['n'].value+UEXT+'_run.sh'} + +should_transfer_files = YES +when_to_transfer_output = ON_EXIT + +transfer_input_files = {opts['m'].value}, {opts['t'].value}, {CWD+ '/' +opts['d'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['d'].value+ '/' + opts['n'].value+UEXT+'_run.sh'} +#transfer_output_files = {opts['n'].value+'_'+UEXT} + +output = {opts['n'].value+'_'+UEXT}.out +error = {opts['n'].value+'_'+UEXT}.err +log = {opts['n'].value+'_'+UEXT}.log + +request_cpus = {opts['cpu'].value} +request_memory = {opts['memory'].value} +request_disk = {opts['disk'].value} +''' + +## Path to your shell script +script_path = os.path.expanduser('~/private/wandb_api.sh') + +# Command to source the shell script and print the environment variable +command = f''' +echo "Checking for wandb API Key" +if [ -f {script_path} ]; then + source {script_path} + echo $WANDB_API_KEY +fi''' + +# Execute the command and capture the output +result = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, executable='/bin/bash') + +# Extract the WANDB_API_KEY from the command output +API_Key = result.stdout.strip().split('\n')[-1] +if API_Key is not None: + sub_temp += f''' +environment = "WANDB_API_KEY={API_Key}" +''' +else: + print('No WANDB_API_KEY found, please set it in your environment or in ~/private/wandb_api.sh') + +#End of the sub file +sub_temp += f''' +queue +''' + +with open(opts['d'].value+'/'+opts['n'].value+'_'+UEXT+'.sub','w', encoding='utf-8') as f: + f.write(sub_temp) + +#Setup Run script +runscript_temp=f''' +#!/bin/bash +tar -xzf HGCalML.tar.gz + +export HGCALML=$(readlink -f HGCalML) +echo $HGCALML + +export DEEPJETCORE_SUBPACKAGE=$HGCALML + +export PATH=$HGCALML/scripts:$PATH +export PYTHONPATH=$HGCALML/modules:$PYTHONPATH +export LD_LIBRARY_PATH=$HGCALML/modules:$LD_LIBRARY_PATH +export LC_ALL=C.UTF-8 # necessary for wandb +export LANG=C.UTF-8 # necessary for wandb + +ls -l + +python3 {os.path.basename(opts['m'].value)} dataCollection._n.djcdc {opts['n'].value+'_'+UEXT} +''' +with open(opts['d'].value+'/'+opts['n'].value+UEXT+'_run.sh','w', encoding='utf-8') as f: + f.write(runscript_temp) + +#Submit the job +COMMAND = ( + 'cd ' + opts['d'].value + '; pwd; condor_submit ' + opts['n'].value + '_' + UEXT + '.sub' +) +os.system(COMMAND) \ No newline at end of file From 36497dd23808be38439b128169742abb51c67b32 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Tue, 4 Jun 2024 12:48:12 +0200 Subject: [PATCH 02/62] created ProcessFeaturesCocoa --- modules/GravNetLayersRagged.py | 54 ++++++++++++++++++++++++++++++++++ modules/Layers.py | 3 ++ 2 files changed, 57 insertions(+) diff --git a/modules/GravNetLayersRagged.py b/modules/GravNetLayersRagged.py index cb62ff9c..4a7fbd0c 100644 --- a/modules/GravNetLayersRagged.py +++ b/modules/GravNetLayersRagged.py @@ -1580,6 +1580,60 @@ def __init__(self, 1.0, # recHitTime -> All zeros 1.0, # recHitHitR -> All zeros for tracks ]) +class ProcessFeaturesCocoa(ProcessFeatures): + def __init__(self, + **kwargs): + super().__init__(**kwargs) + self.mean_hit = tf.constant([ + 182, # recHitEnergy + 0.028, # recHitEta + 0.0, # recHitID -> don't normalize + 0.0074, # recHitTheta + 3012, # recHitR + 0.0, # recHitX -> centered around zero + 0.0, # recHitY -> centered around zero + 48, # recHitZ + 0.0, # recHitTime -> All zeros + 0.0, # recHitHitR + ]) + self.std_hit = tf.constant([ + 1087, # recHitEnergy + 1.46, # recHitEta + 1.0, # recHitID -> don't normalize + 1.78, # recHitTheta + 829, # recHitR + 1096, # recHitX + 1096, # recHitY + 2604, # recHitZ + 1.0, # recHitTime -> All zeros + 1.0, # recHitHitR + ]) + + self.mean_track = tf.constant([ + 0.0, # recHitEnergy + 0.0, # recHitEta + 0.0, # recHitID -> don't normalize + 1.57, # recHitTheta + 5607720, # recHitR + -4402849, # recHitX -> centered around zero + -3468792, # recHitY -> centered around zero + -1.1, # recHitZ -> All tracks are at z=315 + 0.0, # recHitTime -> All zeros + 0.0, # recHitHitR -> All zeros for tracks + ]) + self.std_track = tf.constant([ + 1.0, # recHitEnergy + 1.46, # recHitEta + 1.0, # recHitID -> don't normalize + 0.93, # recHitTheta + 2619667000, # recHitR + 2057752536, # recHitX + 1621205660, # recHitY + 2342, # recHitZ -> All tracks are at z=315 + 1.0, # recHitTime -> All zeros + 1.0, # recHitHitR -> All zeros for tracks + ]) + def get_config(self): diff --git a/modules/Layers.py b/modules/Layers.py index 1f7562cf..753b0a80 100644 --- a/modules/Layers.py +++ b/modules/Layers.py @@ -141,6 +141,9 @@ from GravNetLayersRagged import ProcessFeatures global_layers_list['ProcessFeatures']=ProcessFeatures +from GravNetLayersRagged import ProcessFeaturesCocoa +global_layers_list['ProcessFeaturesCocoa']=ProcessFeaturesCocoa + from GravNetLayersRagged import ManualCoordTransform global_layers_list['ManualCoordTransform']=ManualCoordTransform From fd5af926a914684cb8ae86c36afc92e21b606712 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Tue, 4 Jun 2024 12:51:17 +0200 Subject: [PATCH 03/62] created SubmitViaHTCondor --- scripts/submitViaHTCondor.py | 59 +++++++++++++++++++++++++----------- 1 file changed, 42 insertions(+), 17 deletions(-) diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 798f1ba3..3b8d191b 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -36,13 +36,12 @@ def __str__(self) -> str: opts = { 'd' : meta_option('d'), - 't' : meta_option('t', '/work/friemer/hgcalml/trainingdata_split/'), - 'm' : meta_option('m', '/work/friemer/hgcalml/HGCalML/Train/paper_trainer_noRSU.py'), - 'h' : meta_option('h', 'HGCalML'), + 'h' : meta_option('h', '/work/friemer/hgcalml/'), 'n' : meta_option('n','TrainJob'), 'cpu': meta_option('cpu', '1'), 'memory': meta_option('memory', '15 GB'), 'disk': meta_option('disk', '8 GB'), + 'gpu': meta_option('gpu', '1'), } filtered_clo=[] @@ -66,13 +65,12 @@ def __str__(self) -> str: print('all commands are fully forwarded with one exception:') print('\n ---d specifies a working directory that can be specified ' 'that will contain the files. It is created if it does not exist.\n') - print('\n ---t specifies the location of the training-data to feed the model\n') - print('\n ---m specifies which model to execute\n') print('\n ---h location of HGCalML-Folder with necessary modules\n') print('\n ---n (opt) specifies a name for the scripts\n') print('\n ---cpu (opt) number of cpus to request default: 1\n') print('\n ---memory (opt) size of memory to request default: 15 GB\n') print('\n ---disk (opt) size of memory to request default 8 GB\n') + print('\n ---gpu (opt) number of gpus to request default: 1\n') sys.exit() if os.path.isdir(opts['d'].value): @@ -92,8 +90,29 @@ def __str__(self) -> str: CWD = os.getcwd() + +#################################################################################################### +### Create Sub File ############################################################################# +#################################################################################################### + +#Get absolute filepath and transfer entire folder if there is a .djcdc file also replace filepaths with filenames +inputfileslocations ='' +NEWCOMMANDS = '' +for word in COMMANDS.split(): + if '.' in word: + if '.djcdc' in word: + inputfileslocations += os.path.join(CWD, os.path.dirname(word))+ '/, ' + else: + inputfileslocations += os.path.join(CWD, word) + ', ' + NEWCOMMANDS+=os.path.basename(word) + ' ' + else: + NEWCOMMANDS+=word + ' ' + #Create a tarball of the HGCalML folder -os.system(f'''tar -czf {opts['d'].value}/HGCalML.tar.gz {opts['h'].value}''') +os.system(f''' + cd {opts['h'].value} + tar -czf {opts['d'].value}/HGCalML.tar.gz HGCalML + cd {CWD}''') #Setup the sub file sub_temp=f'''#!/bin/bash @@ -105,13 +124,12 @@ def __str__(self) -> str: +RemoteJob = True +RequestWalltime = 72 * 60 * 60 -executable = {opts['n'].value+UEXT+'_run.sh'} +executable = {opts['n'].value+'_'+UEXT+'_run.sh'} should_transfer_files = YES when_to_transfer_output = ON_EXIT -transfer_input_files = {opts['m'].value}, {opts['t'].value}, {CWD+ '/' +opts['d'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['d'].value+ '/' + opts['n'].value+UEXT+'_run.sh'} -#transfer_output_files = {opts['n'].value+'_'+UEXT} +transfer_input_files = {inputfileslocations} {CWD+ '/' +opts['d'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['d'].value+ '/' + opts['n'].value+'_'+UEXT+'_run.sh'} output = {opts['n'].value+'_'+UEXT}.out error = {opts['n'].value+'_'+UEXT}.err @@ -120,20 +138,17 @@ def __str__(self) -> str: request_cpus = {opts['cpu'].value} request_memory = {opts['memory'].value} request_disk = {opts['disk'].value} +request_GPUs = {opts['gpu'].value} ''' -## Path to your shell script +# Command to get WANDB_API_KEY script_path = os.path.expanduser('~/private/wandb_api.sh') - -# Command to source the shell script and print the environment variable command = f''' echo "Checking for wandb API Key" if [ -f {script_path} ]; then source {script_path} echo $WANDB_API_KEY fi''' - -# Execute the command and capture the output result = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, executable='/bin/bash') # Extract the WANDB_API_KEY from the command output @@ -153,6 +168,11 @@ def __str__(self) -> str: with open(opts['d'].value+'/'+opts['n'].value+'_'+UEXT+'.sub','w', encoding='utf-8') as f: f.write(sub_temp) + +#################################################################################################### +### Create Run File ############################################################################# +#################################################################################################### + #Setup Run script runscript_temp=f''' #!/bin/bash @@ -171,12 +191,17 @@ def __str__(self) -> str: ls -l -python3 {os.path.basename(opts['m'].value)} dataCollection._n.djcdc {opts['n'].value+'_'+UEXT} +{NEWCOMMANDS} ''' -with open(opts['d'].value+'/'+opts['n'].value+UEXT+'_run.sh','w', encoding='utf-8') as f: +with open(opts['d'].value+'/'+opts['n'].value+ '_' +UEXT+'_run.sh','w', encoding='utf-8') as f: f.write(runscript_temp) -#Submit the job + + +#################################################################################################### +### Submit the Job ############################################################################# +#################################################################################################### + COMMAND = ( 'cd ' + opts['d'].value + '; pwd; condor_submit ' + opts['n'].value + '_' + UEXT + '.sub' ) From c40b93f6f996d18b03fb5c98f6b822c92e2c6c69 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Tue, 4 Jun 2024 12:51:32 +0200 Subject: [PATCH 04/62] created plotWindow --- scripts/plotWindow.py | 230 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 230 insertions(+) create mode 100644 scripts/plotWindow.py diff --git a/scripts/plotWindow.py b/scripts/plotWindow.py new file mode 100644 index 00000000..384ef4bb --- /dev/null +++ b/scripts/plotWindow.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python3 + +from DeepJetCore import DataCollection +import os +from argparse import ArgumentParser +import numpy as np +import matplotlib.pyplot as plt +import math +from multiprocessing import Process +import random +from datastructures import TrainData_NanoML, TrainData_NanoMLPF, TrainData_Cocoa +import plotly.express as px +import pandas as pd +import tqdm +from djcdata.dataPipeline import TrainDataGenerator + + +parser = ArgumentParser('') +parser.add_argument('inputFile') +parser.add_argument('outputDir') +parser.add_argument('--hipsearch',action='store_true') +parser.add_argument('--plots',action='store_true') +parser.add_argument('--pf',action='store_true') +args = parser.parse_args() + +outdir = args.outputDir+'/' +### rewrite! +os.system('mkdir -p '+outdir) +td_class = TrainData_Cocoa +#td_class = TrainData_NanoML +if args.pf: + td_class = TrainData_NanoMLPF + +#read a file +def invokeGen(infile): + if infile[-6:] == '.djcdc': + dc = DataCollection(infile) + td = dc.dataclass() + tdclass = dc.dataclass + dc.setBatchSize(1) + gen = dc.invokeGenerator() + elif infile[-6:] == '.djctd': + td = td_class() + tdclass = td_class + td.readFromFile(infile) + gen = TrainDataGenerator() + gen.setBatchSize(1) + gen.setBuffer(td) + elif infile[-5:] == '.root': + print('reading from root file, converting...') + td = td_class() + tdclass = td_class + td.readFromSourceFile(infile,{},True) + td.writeToFile(infile+'.djctd') + td.readFromFile(infile+'.djctd') + print('conversion done') + gen = TrainDataGenerator() + gen.setBatchSize(1) + gen.setBuffer(td) + + gen.setSkipTooLargeBatches(False) + nevents = gen.getNBatches() + #gen.cast_to = tdclass + gen.cast_to = None + return gen.feedTrainData,nevents,td + +gen,nevents,td = invokeGen(args.inputFile) + +def shuffle_truth_colors(df, qualifier="truthHitAssignementIdx"): + ta = df[qualifier] + unta = np.unique(ta) + unta = unta[unta>-0.1] + np.random.seed(42) + np.random.shuffle(unta) + out = ta.copy() + dfo = df.copy() + for i in range(len(unta)): + out[ta ==unta[i]]=i + dfo[qualifier] = out + return dfo + +def toDataFrame(thegen, thetd): + data = next(thegen())#this is a dict, row splits can be ignored, this is per event + return TrainData_NanoML.createPandasDataFrame(data, 0) + + +def compressShowerFeatures(df): + dfout = df.drop_duplicates(subset = ["truthHitAssignementIdx"]) + return dfout[dfout["truthHitAssignementIdx"]>=0] + +def quickplotshower(df,out): + fig = px.scatter_3d(df, x="recHitX", y="recHitZ", z="recHitY", + color="hitratio", size="recHitLogEnergy", + symbol = "marker", + hover_data=['rel_std','totruthHitAssignedEnergies_ratio','marker','hitratio','nhits','corratio'], + template='plotly_dark', + color_continuous_scale=px.colors.sequential.Rainbow) + fig.update_traces(marker=dict(line=dict(width=0))) + fig.write_html(out) + +def hipsearch(df3d, i, outdir, makeplots=False): + truthHitAssignementIdx = df3d['truthHitAssignementIdx'] + utidx = np.unique(truthHitAssignementIdx) + counter=0 + Er_dep=[] + Er_corr_dep=[] + E =[] + for t in utidx: + if t < 0: + continue + seldf = df3d[df3d['truthHitAssignementIdx']==t] + + depsum = np.ones_like(seldf['recHitEnergy'])*np.sum(seldf['recHitEnergy']) + nhits = float(len(seldf['recHitEnergy'])) + seldf['energy_ratio'] = seldf['recHitEnergy']/seldf['truthHitAssignedEnergies'] + + seldf['totruthHitAssignedEnergies_ratio'] = depsum/seldf['truthHitAssignedEnergies'] + E.append(np.mean(seldf['truthHitAssignedEnergies'])) + + Er_dep.append(np.mean(seldf['totruthHitAssignedEnergies_ratio'])) + + hitratio = seldf['recHitEnergy']/depsum + seldf['nhits'] = nhits + hitratio *= nhits #1. on average for uniform etc. + seldf['hitratio'] = hitratio + + m = np.mean(seldf['hitratio']) + s = np.std(seldf['hitratio']-m) + + seldf['rel_std']= (hitratio-m)/s + seldf['marker'] = np.array(seldf['rel_std'] > 5.,dtype='int32') + ewithout = np.sum((1.-seldf['marker'])*seldf['recHitEnergy']) + seldf['corratio'] = ewithout/seldf['truthHitAssignedEnergies'] + + Er_corr_dep.append(np.mean(seldf['corratio'])) + if makeplots and np.all(depsum < seldf['truthHitAssignedEnergies']*1.1): + quickplotshower(seldf,outdir+str(i)+'_'+str(counter)+'.html') + counter+=1 + + return Er_dep, Er_corr_dep , E + + + +hitdf = pd.DataFrame() +showerdf = pd.DataFrame() +eventdf = pd.DataFrame() + +print(nevents,'events') +#3D plots +Er_dep, Er_corr_dep, E = [], [], [] +for i in tqdm.tqdm(range(nevents)): + + df = toDataFrame(gen,td) + + #print(df.columns) + + dfshowers = compressShowerFeatures(df) + showerhits = df[df["truthHitAssignementIdx"]>=0] + #depvstruthenergy.append(np.sum(showerhits['recHitEnergy'])/(np.sum(dfshowers['truthHitAssignedEnergies'])+1.)) + + from globals import pu + #df["recHitLogEnergy"]*= (1. - (1.-1e-2)*(df["truthHitAssignementIdx"]>=pu.t_idx_offset)) + df3d = shuffle_truth_colors(df) + df3d['orig_truthHitAssignementIdx']=df['truthHitAssignementIdx'] + df3d['t_inv_spec'] = np.where(df3d['truthHitAssignementIdx']<0, + 0.,np.ones_like(df3d['truthHitSpectatorFlag'])) * 1./(df3d['truthHitSpectatorFlag']+1e-1) + #plot the first 20 as 3D plots + if i < 20 and args.plots: + #makes a copy + + hover_data=['recHitEnergy', + 'recHitHitR', + 'truthHitAssignedEnergies', + 'truthHitAssignedT', + 'truthHitAssignedX', + 'truthHitAssignedY', + 'truthHitAssignedZ', + 'truthHitAssignementIdx', + 'orig_truthHitAssignementIdx', + 'truthHitAssignedPIDs', + 'truthHitSpectatorFlag'] + + print('N hits', len(df3d)) + + fig = px.scatter_3d(df3d, x="recHitX", y="recHitZ", z="recHitY", + color="truthHitAssignementIdx", size="recHitLogEnergy", + symbol = "recHitID", + hover_data=hover_data, + template='plotly_dark', + color_continuous_scale=px.colors.sequential.Rainbow) + fig.update_traces(marker=dict(line=dict(width=0))) + ccfile = outdir + str(i) + "_event.html" + fig.write_html(ccfile) + + continue + + fig = px.scatter_3d(df3d, x="recHitX", y="recHitZ", z="recHitY", + color="truthHitAssignementIdx", size="recHitHitR", + symbol = "recHitID", + hover_data=hover_data, + template='plotly_dark', + color_continuous_scale=px.colors.sequential.Rainbow) + fig.update_traces(marker=dict(line=dict(width=0))) + ccfile = outdir + str(i) + "_event_hitsize.html" + fig.write_html(ccfile) + + fig = px.scatter_3d(df3d, x="recHitX", y="recHitZ", z="recHitY", + color="truthHitAssignementIdx", size="t_inv_spec", + hover_data=hover_data, + symbol = "recHitID", + template='plotly_dark', + color_continuous_scale=px.colors.sequential.Rainbow) + fig.update_traces(marker=dict(line=dict(width=0))) + ccfile = outdir + str(i) + "_spect.html" + fig.write_html(ccfile) + + if args.hipsearch: + iEr_dep, iEr_corr_dep, iE = hipsearch(df3d, i, outdir, args.plots) + Er_dep+=iEr_dep + Er_corr_dep+=iEr_corr_dep + E+=iE + +plt.hist(Er_dep,bins=31,label='uncorr',alpha=0.5) +plt.hist(Er_corr_dep,bins=31,label='corr',alpha=0.5) +plt.legend() +plt.savefig(outdir +'hipcorr.pdf') + + +dfout = pd.DataFrame(zip(E,Er_dep,Er_corr_dep), columns = ['E','Er','Er_corr']) +dfout.to_pickle("df.pkl") \ No newline at end of file From 0b3faa3b49b984d6a614f255234898b217593ffa Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Tue, 4 Jun 2024 12:51:51 +0200 Subject: [PATCH 05/62] created TrainData_Cocoa --- modules/datastructures/TrainData_Cocoa.py | 129 ++++++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 modules/datastructures/TrainData_Cocoa.py diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py new file mode 100644 index 00000000..b9746477 --- /dev/null +++ b/modules/datastructures/TrainData_Cocoa.py @@ -0,0 +1,129 @@ +from datastructures.TrainData_NanoML import TrainData_NanoML +import uproot +import pandas as pd +import awkward as ak +import numpy as np +from DeepJetCore import SimpleArray + + +class TrainData_Cocoa(TrainData_NanoML): + + def convertFromSourceFile(self, filename, weighterobjects, istraining): + #open File + with uproot.open(filename) as file: + events = file[file.keys()[0]] + data = events.arrays(library='ak')#[0:100]#JUST FOR TESTING REMOVE SLICING LATER!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + + #convert data to training data + trainData,rs = self.converttotrainingdfvec(data) + + #make simpleArray features + features = trainData[['recHitEnergy', 'recHitEta', 'istrack','recHitTheta', 'recHitR', 'recHitX', 'recHitY', 'recHitZ', 'recHitTime', 'recHitHitR']] + farr = SimpleArray(np.float32(features.to_numpy()),rs,name="recHitFeatures") + + #make simpleArray truth + t={} + #First the float32 fields + for field in ['t_energy','t_spectator', 't_time','t_rec_energy']: + t[field] = SimpleArray(np.float32(trainData[field].to_numpy().reshape(-1, 1)),rs,name=field) + #Then the int32 fields + for field in ['t_idx', 't_pid', 't_fully_contained', 't_is_unique']: + t[field] = SimpleArray(np.int32(trainData[field].to_numpy().reshape(-1, 1)),rs,name=field) + #Then the pos field + t['t_pos'] = SimpleArray(np.float32(np.array(trainData['t_pos'].tolist())),rs,name='t_pos') + + #return result + return [farr, + t['t_idx'], t['t_energy'], t['t_pos'], t['t_time'], + t['t_pid'], t['t_spectator'], t['t_fully_contained'], + t['t_rec_energy'], t['t_is_unique'] ],[], [] + + def convertevent(self, event): + #convert cells to df + df_cell = pd.DataFrame() + df_cell['recHitEnergy'] = ak.to_dataframe(event['cell_e'], how='outer') + df_cell['recHitEta'] = ak.to_dataframe(event['cell_eta'], how='outer') + df_cell['istrack'] = 0 + df_cell['recHitTheta'] =ak.to_dataframe(event['cell_phi'], how='outer') + df_cell['recHitR'] = 0 #will be calculated later + df_cell['recHitX']= ak.to_dataframe(event['cell_x'], how='outer') + df_cell['recHitY']= ak.to_dataframe(event['cell_y'], how='outer') + df_cell['recHitZ']= ak.to_dataframe(event['cell_z'], how='outer') + df_cell['recHitTime'] = 0 + df_cell['recHitHitR'] = 0 + + df_cell['t_idx'] = ak.to_dataframe(event['cell_parent_idx'], how='outer') + + #Calculate Distance from 0,0,0 + df_cell['recHitR']= np.sqrt(df_cell['recHitX']**2+df_cell['recHitY']**2+df_cell['recHitZ']**2) + + + #convert particle information to df + df_particle= pd.DataFrame() + + df_particle['particle_idx'] = np.arange(len(event['particle_e'])) + df_particle['t_energy'] = ak.to_dataframe(event['particle_e'], how='outer') + df_particle['t_pos'] = 0 #will be calculated later + df_particle['t_time'] = 0 + df_particle['t_pid'] = ak.to_dataframe(event['particle_pdgid'], how='outer') + df_particle['t_spectator'] = 0 + df_particle['t_fully_contained'] = 1 + df_particle['t_rec_energy'] = ak.to_dataframe(event['particle_dep_energy'], how='outer') + df_particle['particle_prod_x'] = ak.to_dataframe(event['particle_prod_x'], how='outer') + df_particle['particle_prod_y'] = ak.to_dataframe(event['particle_prod_y'], how='outer') + df_particle['particle_prod_z'] = ak.to_dataframe(event['particle_prod_z'], how='outer') + + #convert track information to df + df_track = pd.DataFrame() + df_track['t_idx'] = ak.to_dataframe(event['track_parent_idx'], how='outer') + df_track['recHitX'] = ak.to_dataframe(event['track_x_layer_0'], how='outer') + df_track['recHitY'] = ak.to_dataframe(event['track_y_layer_0'], how='outer') + df_track['recHitZ'] = ak.to_dataframe(event['track_z_layer_0'], how='outer') + df_track['recHitR']= np.sqrt(df_track['recHitX']**2+df_track['recHitY']**2+df_track['recHitZ']**2) + + df_track['recHitTheta'] =ak.to_dataframe(event['track_theta'], how='outer') + df_track['recHitEta'] = -np.log(np.tan(df_track['recHitTheta']/2)) + + df_track['isTrack'] = 1 + df_track['recHitEnergy'] = 0 + + df_track['recHitTime'] = 0 + df_track['recHitHitR'] = 0 + + #join track information to particle information + df_track = df_track.join(df_particle, on='t_idx') + + #join cell information to truth information + df_cell = df_cell.join(df_particle, on='t_idx') + + df_training = pd.concat([df_cell, df_track]) + + #set NaN values (particle information for hits made from background noise to -1) + df_training.fillna(-1, inplace=True) + + #set t_is_unique + df_training['t_is_unique'] = 0 + first_occurrence_mask = (df_training['t_idx'] != -1) & (df_training.groupby('t_idx').cumcount() == 0) + df_training.loc[first_occurrence_mask, 't_is_unique'] = 1 + + + df_training['t_pos'] = df_training.apply(lambda row: [row['particle_prod_x'], row['particle_prod_y'], row['particle_prod_z']], axis=1) + + return df_training + + def converttotrainingdfvec(self, data): + traindata = np.array([self.convertevent(data[eventnumber]) for eventnumber in np.arange(len(data))]) + + #Remove all events with an energy lower than 15GeV + E_cutoff = 15000 + E = np.sqrt(data['true_jet_pt']**2 * np.cos(data['true_jet_phi'])**2 + data['true_jet_m']**2) + mask = ak.to_numpy(ak.all(E >= E_cutoff, axis=1)) + traindata = traindata[mask] + + #find the row splits + rs = np.cumsum([len(df) for df in traindata]) + rs = np.insert(rs, 0, 0) + + #concat all events + traindata = pd.concat(traindata) + return traindata, rs From 3bd321d3768a04ce174b3d81676b8818d66e83e5 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Fri, 7 Jun 2024 11:27:59 +0200 Subject: [PATCH 06/62] submitViaHTCondor input overhaul --- scripts/submitViaHTCondor.py | 28 +++++++++++++--------------- 1 file changed, 13 insertions(+), 15 deletions(-) diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 3b8d191b..0f860352 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -8,6 +8,8 @@ import uuid import subprocess +import re + UEXT = str(uuid.uuid4()) @@ -35,9 +37,8 @@ def __str__(self) -> str: opts = { - 'd' : meta_option('d'), 'h' : meta_option('h', '/work/friemer/hgcalml/'), - 'n' : meta_option('n','TrainJob'), + 'n' : meta_option('n','TrainJob_'+UEXT), 'cpu': meta_option('cpu', '1'), 'memory': meta_option('memory', '15 GB'), 'disk': meta_option('disk', '8 GB'), @@ -63,8 +64,6 @@ def __str__(self) -> str: if '-h' in sys.argv or '--help' in sys.argv or (not all_valid): print('script to submit commands within the container to sbatch.\n') print('all commands are fully forwarded with one exception:') - print('\n ---d specifies a working directory that can be specified ' - 'that will contain the files. It is created if it does not exist.\n') print('\n ---h location of HGCalML-Folder with necessary modules\n') print('\n ---n (opt) specifies a name for the scripts\n') print('\n ---cpu (opt) number of cpus to request default: 1\n') @@ -73,14 +72,14 @@ def __str__(self) -> str: print('\n ---gpu (opt) number of gpus to request default: 1\n') sys.exit() -if os.path.isdir(opts['d'].value): +if os.path.isdir(opts['n'].value): var = input(\ 'Working directory exists, are you sure you want to continue, please type "yes/y"\n') var = var.lower() if not var in ('yes', 'y'): sys.exit() else: - os.system('mkdir -p '+opts['d'].value) + os.system('mkdir -p '+opts['n'].value) filtered_clo = filtered_clo[1:] #remove @@ -98,8 +97,9 @@ def __str__(self) -> str: #Get absolute filepath and transfer entire folder if there is a .djcdc file also replace filepaths with filenames inputfileslocations ='' NEWCOMMANDS = '' +pattern = r'\.[a-zA-Z]' for word in COMMANDS.split(): - if '.' in word: + if re.search(pattern, word): if '.djcdc' in word: inputfileslocations += os.path.join(CWD, os.path.dirname(word))+ '/, ' else: @@ -111,13 +111,13 @@ def __str__(self) -> str: #Create a tarball of the HGCalML folder os.system(f''' cd {opts['h'].value} - tar -czf {opts['d'].value}/HGCalML.tar.gz HGCalML + tar -czf {opts['n'].value}/HGCalML.tar.gz HGCalML cd {CWD}''') #Setup the sub file sub_temp=f'''#!/bin/bash Universe = docker -docker_image = cernml4reco/deepjetcore4:5ef28a3 +docker_image = cernml4reco/deepjetcore4:06bc79b accounting_group = cms.jet requirements =(TARGET.CloudSite=="topas") @@ -129,7 +129,7 @@ def __str__(self) -> str: should_transfer_files = YES when_to_transfer_output = ON_EXIT -transfer_input_files = {inputfileslocations} {CWD+ '/' +opts['d'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['d'].value+ '/' + opts['n'].value+'_'+UEXT+'_run.sh'} +transfer_input_files = {inputfileslocations} {CWD+ '/' +opts['n'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['n'].value+ '/' + opts['n'].value+'_'+UEXT+'_run.sh'} output = {opts['n'].value+'_'+UEXT}.out error = {opts['n'].value+'_'+UEXT}.err @@ -165,7 +165,7 @@ def __str__(self) -> str: queue ''' -with open(opts['d'].value+'/'+opts['n'].value+'_'+UEXT+'.sub','w', encoding='utf-8') as f: +with open(opts['n'].value+'/'+opts['n'].value+'_'+UEXT+'.sub','w', encoding='utf-8') as f: f.write(sub_temp) @@ -179,8 +179,6 @@ def __str__(self) -> str: tar -xzf HGCalML.tar.gz export HGCALML=$(readlink -f HGCalML) -echo $HGCALML - export DEEPJETCORE_SUBPACKAGE=$HGCALML export PATH=$HGCALML/scripts:$PATH @@ -193,7 +191,7 @@ def __str__(self) -> str: {NEWCOMMANDS} ''' -with open(opts['d'].value+'/'+opts['n'].value+ '_' +UEXT+'_run.sh','w', encoding='utf-8') as f: +with open(opts['n'].value+'/'+opts['n'].value+ '_' +UEXT+'_run.sh','w', encoding='utf-8') as f: f.write(runscript_temp) @@ -203,6 +201,6 @@ def __str__(self) -> str: #################################################################################################### COMMAND = ( - 'cd ' + opts['d'].value + '; pwd; condor_submit ' + opts['n'].value + '_' + UEXT + '.sub' + 'cd ' + opts['n'].value + '; pwd; condor_submit ' + opts['n'].value + '_' + UEXT + '.sub' ) os.system(COMMAND) \ No newline at end of file From 83ce2636b0357eee88d49dba54b9e987693c58fd Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Mon, 10 Jun 2024 11:34:03 +0200 Subject: [PATCH 07/62] better filechecking and outputtransfer for submitViaHTCondor --- scripts/submitViaHTCondor.py | 34 ++++++++++++++++++++++++---------- 1 file changed, 24 insertions(+), 10 deletions(-) diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 0f860352..bb96c95d 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -37,8 +37,8 @@ def __str__(self) -> str: opts = { - 'h' : meta_option('h', '/work/friemer/hgcalml/'), 'n' : meta_option('n','TrainJob_'+UEXT), + 'f' : meta_option('f', '/work/friemer/hgcalml/HGCalML'), 'cpu': meta_option('cpu', '1'), 'memory': meta_option('memory', '15 GB'), 'disk': meta_option('disk', '8 GB'), @@ -64,8 +64,8 @@ def __str__(self) -> str: if '-h' in sys.argv or '--help' in sys.argv or (not all_valid): print('script to submit commands within the container to sbatch.\n') print('all commands are fully forwarded with one exception:') - print('\n ---h location of HGCalML-Folder with necessary modules\n') print('\n ---n (opt) specifies a name for the scripts\n') + print('\n ---f location of other files to transfer like the HGCalML-Folder with necessary modules\n') print('\n ---cpu (opt) number of cpus to request default: 1\n') print('\n ---memory (opt) size of memory to request default: 15 GB\n') print('\n ---disk (opt) size of memory to request default 8 GB\n') @@ -97,9 +97,8 @@ def __str__(self) -> str: #Get absolute filepath and transfer entire folder if there is a .djcdc file also replace filepaths with filenames inputfileslocations ='' NEWCOMMANDS = '' -pattern = r'\.[a-zA-Z]' for word in COMMANDS.split(): - if re.search(pattern, word): + if os.path.exists(word): if '.djcdc' in word: inputfileslocations += os.path.join(CWD, os.path.dirname(word))+ '/, ' else: @@ -109,10 +108,17 @@ def __str__(self) -> str: NEWCOMMANDS+=word + ' ' #Create a tarball of the HGCalML folder -os.system(f''' - cd {opts['h'].value} - tar -czf {opts['n'].value}/HGCalML.tar.gz HGCalML - cd {CWD}''') +if os.path.isdir(opts['f'].value): + os.system(f''' + cd {opts['f'].value} + cd ../ + tar -czf {opts['n'].value}/HGCalML.tar.gz {os.path.basename(opts['f'].value)} + cd {CWD}''') +elif os.path.isfile(opts['f'].value) and opts['f'].value.endswith('.tar.gz'): + os.system(f''' + cp {opts['f'].value} {opts['n'].value}/HGCalML.tar.gz''') +else: + raise Exception('Folder not found, please specify the correct path to the HGCalML-folder with ---f ') #Setup the sub file sub_temp=f'''#!/bin/bash @@ -130,6 +136,7 @@ def __str__(self) -> str: when_to_transfer_output = ON_EXIT transfer_input_files = {inputfileslocations} {CWD+ '/' +opts['n'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['n'].value+ '/' + opts['n'].value+'_'+UEXT+'_run.sh'} +transfer_output_files = . output = {opts['n'].value+'_'+UEXT}.out error = {opts['n'].value+'_'+UEXT}.err @@ -190,6 +197,13 @@ def __str__(self) -> str: ls -l {NEWCOMMANDS} + +ls -l + +rm HGCalML.tar.gz +rm -r HGCalML + +ls -l ''' with open(opts['n'].value+'/'+opts['n'].value+ '_' +UEXT+'_run.sh','w', encoding='utf-8') as f: f.write(runscript_temp) @@ -200,7 +214,7 @@ def __str__(self) -> str: ### Submit the Job ############################################################################# #################################################################################################### -COMMAND = ( +SUBMITCOMMAND = ( 'cd ' + opts['n'].value + '; pwd; condor_submit ' + opts['n'].value + '_' + UEXT + '.sub' ) -os.system(COMMAND) \ No newline at end of file +os.system(SUBMITCOMMAND) \ No newline at end of file From d30301c7b00488ba9b8171a19b2560deb829114d Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Mon, 10 Jun 2024 11:54:04 +0200 Subject: [PATCH 08/62] created trainingscript --- Train/dummy.py | 4 +- Train/paper_trainer_Cocoa.py | 438 +++++++++++++++++++++++++++++++++++ 2 files changed, 440 insertions(+), 2 deletions(-) create mode 100644 Train/paper_trainer_Cocoa.py diff --git a/Train/dummy.py b/Train/dummy.py index a32264ae..80b97e1a 100644 --- a/Train/dummy.py +++ b/Train/dummy.py @@ -66,10 +66,10 @@ if not train.args.no_wandb: wandb.init( project=train.args.wandb_project, - config=wandb_config, + #config=wandb_config, ) wandb.save(sys.argv[0]) # Save python file - wandb.save(train.args.configFile) # Save config file + #wandb.save(train.args.configFile) # Save config file else: wandb.active=False diff --git a/Train/paper_trainer_Cocoa.py b/Train/paper_trainer_Cocoa.py new file mode 100644 index 00000000..bddbda95 --- /dev/null +++ b/Train/paper_trainer_Cocoa.py @@ -0,0 +1,438 @@ +""" +Training script based on paper_trainer_noRSU_fixed_jk.py +""" + +import os +import pdb +import sys +import yaml +import shutil +from argparse import ArgumentParser + +# import wandb +from DeepJetCore.wandb_interface import wandb_wrapper as wandb +import tensorflow as tf +from tensorflow.keras.layers import Concatenate, Dense, Dropout +from tensorflow.keras import Model + +from DeepJetCore.training.DeepJet_callbacks import simpleMetricsCallback +from DeepJetCore.DJCLayers import StopGradient, ScalarMultiply + +import training_base_hgcal +from Layers import LLRegulariseGravNetSpace, DummyLayer +from Layers import ScaledGooeyBatchNorm2 +from Layers import MixWhere +from Layers import ProcessFeaturesCocoa +from Layers import RaggedGravNet +from Layers import PlotCoordinates +from Layers import DistanceWeightedMessagePassing, TranslationInvariantMP +from Layers import LLFillSpace +from Layers import LLExtendedObjectCondensation +from Layers import LLExtendedObjectCondensation2 +from Layers import LLExtendedObjectCondensation3 +from Layers import LLExtendedObjectCondensation4 +from Layers import DictModel +from Layers import RaggedGlobalExchange +from Layers import SphereActivation +from Layers import Multi +from Layers import ShiftDistance +# from Layers import LLRegulariseGravNetSpace +from Layers import SplitOffTracks, ConcatRaggedTensors +from Regularizers import AverageDistanceRegularizer +from Initializers import EyeInitializer +from model_blocks import tiny_pc_pool, condition_input +from model_blocks import extent_coords_if_needed +from model_blocks import create_outputs +from model_tools import apply_weights_from_path +from model_blocks import random_sampling_unit, random_sampling_block2 +from noise_filter import noise_filter +from callbacks import plotClusteringDuringTraining +from callbacks import plotClusterSummary +from callbacks import NanSweeper, DebugPlotRunner +from tensorflow.keras.layers import BatchNormalization, LayerNormalization + +#################################################################################################### +### Load Configuration ############################################################################# +#################################################################################################### + +parser = ArgumentParser('training') +parser.add_argument('--run_name', help="wandb run name", default="test") +parser.add_argument('--no_wandb', help="Don't use wandb", action='store_true') +parser.add_argument('--wandb_project', help="wandb_project", default="Paper_Models") +#parser.add_argument('--lr', help="learning rate", default="0.0001") + +train = training_base_hgcal.HGCalTraining(parser=parser) + + +N_CLUSTER_SPACE_COORDINATES = 3 +N_GRAVNET_SPACE_COORDINATES = 4 +d_shape = 64 +NEIGHBOURS = [32, 64] +LOSS_IMPLEMENTATION = "hinge" +GRAVNET_ITERATIONS = len(NEIGHBOURS) +LOSS_OPTIONS = { + "beta_loss_scale": 1.0, + "too_much_beta_scale": 0.0, + "energy_loss_weight": 0.01, + "classification_loss_weight": 0.01, + "position_loss_weight": 0.0, + "timing_loss_weight": 0.0, + "q_min": 1.0, + "use_average_cc_pos": 0.9999, +} +BATCHNORM_OPTIONS = { + "max_viscosity": 0.9999, + "learn": False +} +DENSE_ACTIVATION = "elu" +DENSE_INIT = "he_normal" +DENSE_REGULARIZER_RATE = 1e-9 +DENSE_REGULARIZER = tf.keras.regularizers.l2(DENSE_REGULARIZER_RATE) +DROPOUT = 1e-2 +DISTANCE_SCALE = True +loss_layer = LLExtendedObjectCondensation3 + +TRAINING = { + "stage_1": { + "batch_size": 100000, + "learning_rate": 0.002, + "epochs": 5, + }, + + "stage_2": { + "batch_size": 100000, + "learning_rate": 0.0002, + "epochs": 10, + }, + + "stage_3": { + "batch_size": 100000, + "learning_rate": 0.00002, + "epochs": 20, + }, +} + +wandb_config = { + "gravnet_iterations" : GRAVNET_ITERATIONS, + "gravnet_space_coordinates" : N_GRAVNET_SPACE_COORDINATES, + "cluster_space_coordinates" : N_CLUSTER_SPACE_COORDINATES, + "loss_energy_weight" : LOSS_OPTIONS['energy_loss_weight'], + "loss_classification_weight" : LOSS_OPTIONS['classification_loss_weight'], + "loss_qmin" : LOSS_OPTIONS['q_min'], + "loss_use_average_cc_pos" : LOSS_OPTIONS['use_average_cc_pos'], + "loss_too_much_beta_scale" : LOSS_OPTIONS['too_much_beta_scale'], + "loss_beta_scale" : LOSS_OPTIONS['beta_loss_scale'], + "dense_activation" : DENSE_ACTIVATION, + "dense_kernel_reg" : DENSE_REGULARIZER_RATE, + "dense_dropout" : DROPOUT, + "distance_scale" : DISTANCE_SCALE, +} + +for i in range(GRAVNET_ITERATIONS): + wandb_config[f"gravnet_{i}_neighbours"] = NEIGHBOURS[i] +for i in range(1, len(TRAINING)+1): + wandb_config[f"train_{i}_lr"] = TRAINING[f"stage_{i}"]['learning_rate'] + wandb_config[f"train_{i}_epochs"] = TRAINING[f"stage_{i}"]['epochs'] + wandb_config[f"train_{i}_batchsize"] = TRAINING[f"stage_{i}"]['batch_size'] + if i == 1: + wandb_config[f"train_{i}+_max_visc"] = 0.999 + wandb_config[f"train_{i}+_fluidity_decay"] = 0.1 + +if not train.args.no_wandb: + wandb.init( + project=train.args.wandb_project, + config=wandb_config, + ) + wandb.wandb().save(sys.argv[0]) # Save python file +else: + wandb.active=False + +RECORD_FREQUENCY = 10 +PLOT_FREQUENCY = 40 + +############################################################################### +### Define Model ############################################################## +############################################################################### + +def GravNet_plus_TEQMP(name, + x, cprime, hit_energy, t_idx, + rs, + d_shape, + n_neighbours, + debug_outdir, + plot_debug_every, + space_reg_strength=1e-2, + ): + + xgn, gncoords, gnnidx, gndist = RaggedGravNet( + name = "GravNet_"+name, # 76929, 42625, 42625 + n_neighbours=n_neighbours, + n_dimensions=N_GRAVNET_SPACE_COORDINATES, + n_filters=d_shape, + n_propagate=d_shape, + coord_initialiser_noise=1e-3, + feature_activation='elu', + )([x, rs]) + + if space_reg_strength > 0: + gndist = LLRegulariseGravNetSpace(name=f'gravnet_coords_reg_{name}' , + record_metrics=True, + scale=space_reg_strength)([gndist, cprime, gnnidx]) + + gncoords = PlotCoordinates( + plot_every=plot_debug_every, + outdir = debug_outdir, + name=f'gncoords_{name}' + )([gncoords, hit_energy, t_idx, rs]) + + x = DummyLayer()([x, gncoords]) #just so the branch is not optimised away, anyway used further down + x = Concatenate()([xgn, x]) + + x = TranslationInvariantMP([64, 32, 16, 8], + layer_norm = True, + activation = None, #layer norm takes care + sum_weight = True)([x, gnnidx, gndist]) + + return Concatenate()([xgn, x]) + + + + +def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): + """ + Function that defines the model to train + """ + + ########################################################################### + ### Pre-processing step ################################################### + ########################################################################### + + orig_input = td.interpretAllModelInputs(Inputs) + pre_processed = condition_input(orig_input, no_scaling=True, no_prime=False, new_prime=True) + + + prime_coords = pre_processed['prime_coords'] + + c_coords = prime_coords + is_track = pre_processed['is_track'] + rs = pre_processed['row_splits'] + energy = pre_processed['rechit_energy'] + t_idx = pre_processed['t_idx'] + x = pre_processed['features'] + + x = ProcessFeaturesCocoa()(x) + + c_coords = PlotCoordinates( + plot_every=plot_debug_every, + outdir=debug_outdir, + name='input_c_coords', + )([c_coords, energy, t_idx, rs]) + + [cprime_hit], [cprime_track], rs_hit, rs_track = SplitOffTracks()([is_track, [prime_coords], rs]) + + c_coords = extent_coords_if_needed(prime_coords, x, N_CLUSTER_SPACE_COORDINATES) + + x = Concatenate()([x, c_coords, is_track]) + x = Dense(64, name='dense_pre_loop', activation=DENSE_ACTIVATION)(x) + + allfeat = [] + print("Available keys: ", pre_processed.keys()) + + ########################################################################### + ### Loop over GravNet Layers ############################################## + ########################################################################### + + + for i in range(GRAVNET_ITERATIONS): + + x = Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + + #x = LayerNormalization()(x) + x_pre = x + x = Concatenate()([c_coords, x]) + + [x_hit, energy_hit, t_idx_hit], [x_track, energy_track, t_idx_track], rs_hit, rs_track = SplitOffTracks()( + [is_track, [x, energy, t_idx], rs]) + + + x_hit = GravNet_plus_TEQMP(f'gnhit_{i}', x_hit, cprime_hit, energy_hit, t_idx_hit, rs_hit, + d_shape, NEIGHBOURS[i], debug_outdir, plot_debug_every) + x_track = GravNet_plus_TEQMP(f'gntrack_{i}', x_track, cprime_track, energy_track, t_idx_track, rs_track, + d_shape, 16, debug_outdir, plot_debug_every) + + [xgn], rs = ConcatRaggedTensors()([ + [x_track], + [x_hit], + rs_track, rs_hit]) + + x = Concatenate()([xgn, x_pre]) + #x = LayerNormalization()(x) + + x = GravNet_plus_TEQMP(f'gncomb_{i}', x, prime_coords, energy, t_idx, rs, + d_shape, NEIGHBOURS[i], debug_outdir, plot_debug_every, space_reg_strength=1e-2) + + x = Dense(d_shape, + name=f"dense_post_gravnet_1_iteration_{i}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = Dense(d_shape, + name=f"dense_post_gravnet_2_iteration_{i}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + + #x = LayerNormalization()(x) + allfeat.append(x) + if len(allfeat) > 1: + x = Concatenate()(allfeat) + else: + x = allfeat[0] + + + ########################################################################### + ### Create output of model and define loss ################################ + ########################################################################### + + + x = Dense(128, + name=f"dense_final_{1}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = Dense(64, + name=f"dense_final_{2}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = Dense(64, + name=f"dense_final_{3}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + #x = BatchNormalization()(x) + + + pred_beta, pred_ccoords, pred_dist, \ + pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, \ + pred_pos, pred_time, pred_time_unc, pred_id = \ + create_outputs(x, + n_ccoords=N_CLUSTER_SPACE_COORDINATES, + fix_distance_scale=not DISTANCE_SCALE, + is_track=is_track, + set_track_betas_to_one=True) + + + pred_beta = loss_layer( + scale=1., + use_energy_weights=True, + record_metrics=True, + print_loss=train.args.no_wandb, + name="ExtendedOCLoss", + implementation = LOSS_IMPLEMENTATION, + **LOSS_OPTIONS)( + [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, + pred_energy_high_quantile, pred_pos, pred_time, pred_time_unc, pred_id, energy, + pre_processed['t_idx'] , pre_processed['t_energy'] , pre_processed['t_pos'] , + pre_processed['t_time'] , pre_processed['t_pid'] , pre_processed['t_spectator_weight'], + pre_processed['t_fully_contained'], pre_processed['t_rec_energy'], + pre_processed['t_is_unique'], pre_processed['row_splits']]) + + pred_ccoords = PlotCoordinates( + plot_every=plot_debug_every, + outdir = debug_outdir, + name='condensation' + )([pred_ccoords, pred_beta, pre_processed['t_idx'], rs]) + + model_outputs = { + 'pred_beta': pred_beta, + 'pred_ccoords': pred_ccoords, + 'pred_energy_corr_factor': pred_energy_corr, + 'pred_energy_low_quantile': pred_energy_low_quantile, + 'pred_energy_high_quantile': pred_energy_high_quantile, + 'pred_pos': pred_pos, + 'pred_time': pred_time, + 'pred_id': pred_id, + 'pred_dist': pred_dist, + 'rechit_energy': energy, + 'row_splits': pre_processed['row_splits'], + # 'no_noise_sel': pre_processed['no_noise_sel'], + # 'no_noise_rs': pre_processed['no_noise_rs'], + } + + # return DictModel(inputs=Inputs, outputs=model_outputs) + return Model(inputs=Inputs, outputs=model_outputs) + + +############################################################################### +### Set up training ########################################################### +############################################################################### + + + +if not train.modelSet(): + train.setModel( + config_model, + td=train.train_data.dataclass(), + debug_outdir=train.outputDir+'/intplots', + plot_debug_every=RECORD_FREQUENCY*PLOT_FREQUENCY, + ) + train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space + train.setCustomOptimizer(tf.keras.optimizers.Adam())#clipnorm=1.)) + train.compileModel(learningrate=1e-4) + train.keras_model.summary() + +############################################################################### +### Callbacks ################################################################# +############################################################################### + + +samplepath = train.val_data.getSamplePath(train.val_data.samples[0]) +PUBLISHPATH = "" +PUBLISHPATH += [d for d in train.outputDir.split('/') if len(d)][-1] + + +cb = []#[NanSweeper()] #this takes a bit of time checking each batch but could be worth it + +cb += [ + plotClusterSummary( + outputfile=train.outputDir + "/clustering/", + samplefile=train.val_data.getSamplePath(train.val_data.samples[0]), + after_n_batches=1000 + ) + ] + + +# cb += [wandbCallback()] + +############################################################################### +### Actual Training ########################################################### +############################################################################### + + +N_TRAINING_STAGES = len(TRAINING) +for i in range(1, N_TRAINING_STAGES+1): + print(f"Starting training stage {i}") + learning_rate = TRAINING[f"stage_{i}"]['learning_rate'] + epochs = TRAINING[f"stage_{i}"]['epochs'] + batch_size = TRAINING[f"stage_{i}"]['batch_size'] + train.change_learning_rate(learning_rate) + print(f"Training for {epochs} epochs") + print(f"Learning rate set to {learning_rate}") + print(f"Batch size: {batch_size}") + + if i == 2: + # fix the keras batch norm + def fix_batchnorm(model): + for layer in model.layers: + if isinstance(layer, BatchNormalization): + layer.trainable = False + train.applyFunctionToAllModels(fix_batchnorm) + train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, True) + #train.distributeModelToGPUs() #re-distribute changes + train.compileModel(learningrate=learning_rate) # recompile models + + train.trainModel( + nepochs=epochs, + batchsize=batch_size, + add_progbar=True, + additional_callbacks=cb + ) From 0f549559897a175457e5908bad659b0d084f5d2e Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Tue, 11 Jun 2024 14:22:34 +0200 Subject: [PATCH 09/62] energy smearing for tracks --- modules/datastructures/TrainData_Cocoa.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index b9746477..18fdc7f3 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -85,13 +85,15 @@ def convertevent(self, event): df_track['recHitEta'] = -np.log(np.tan(df_track['recHitTheta']/2)) df_track['isTrack'] = 1 - df_track['recHitEnergy'] = 0 df_track['recHitTime'] = 0 df_track['recHitHitR'] = 0 #join track information to particle information df_track = df_track.join(df_particle, on='t_idx') + + #Smear true energy for track + df_track['recHitEnergy'] = df_track['t_energy'].apply(lambda x: np.random.normal(x, 0.01 * x)) #join cell information to truth information df_cell = df_cell.join(df_particle, on='t_idx') From 6a3f03c2f9ceab339e39dcae332fbb2401f9cec4 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Tue, 11 Jun 2024 14:23:13 +0200 Subject: [PATCH 10/62] minor changes --- Train/paper_trainer_Cocoa.py | 18 ++++++++++++------ scripts/analyse_hgcal_predictions.py | 2 +- scripts/submitViaHTCondor.py | 4 +++- 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/Train/paper_trainer_Cocoa.py b/Train/paper_trainer_Cocoa.py index bddbda95..3c7a5a78 100644 --- a/Train/paper_trainer_Cocoa.py +++ b/Train/paper_trainer_Cocoa.py @@ -77,7 +77,7 @@ "classification_loss_weight": 0.01, "position_loss_weight": 0.0, "timing_loss_weight": 0.0, - "q_min": 1.0, + "q_min": 0.8, "use_average_cc_pos": 0.9999, } BATCHNORM_OPTIONS = { @@ -89,27 +89,33 @@ DENSE_REGULARIZER_RATE = 1e-9 DENSE_REGULARIZER = tf.keras.regularizers.l2(DENSE_REGULARIZER_RATE) DROPOUT = 1e-2 -DISTANCE_SCALE = True +DISTANCE_SCALE = False loss_layer = LLExtendedObjectCondensation3 TRAINING = { "stage_1": { - "batch_size": 100000, + "batch_size": 5000, "learning_rate": 0.002, "epochs": 5, }, "stage_2": { - "batch_size": 100000, + "batch_size": 5000, "learning_rate": 0.0002, "epochs": 10, }, "stage_3": { - "batch_size": 100000, + "batch_size": 5000, "learning_rate": 0.00002, "epochs": 20, }, + + "stage_4": { + "batch_size": 5000, + "learning_rate": 0.00001, + "epochs": 40, + }, } wandb_config = { @@ -230,7 +236,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): [cprime_hit], [cprime_track], rs_hit, rs_track = SplitOffTracks()([is_track, [prime_coords], rs]) - c_coords = extent_coords_if_needed(prime_coords, x, N_CLUSTER_SPACE_COORDINATES) + c_coords = extent_coords_if_needed(c_coords, x, N_CLUSTER_SPACE_COORDINATES) x = Concatenate()([x, c_coords, is_track]) x = Dense(64, name='dense_pre_loop', activation=DENSE_ACTIVATION)(x) diff --git a/scripts/analyse_hgcal_predictions.py b/scripts/analyse_hgcal_predictions.py index 27fd06cb..819ef557 100755 --- a/scripts/analyse_hgcal_predictions.py +++ b/scripts/analyse_hgcal_predictions.py @@ -149,7 +149,7 @@ def analyse(preddir, 'pred_pos', 'pred_time', 'pred_id', 'pred_dist', 'rechit_energy', 'no_noise_sel'] for key in pred_keys: - if key is 'no_noise_sel': + if key == 'no_noise_sel': try: predictions_dict[key] = predictions_dict[key][pred_mask] except KeyError: diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index bb96c95d..54fe3053 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -65,7 +65,7 @@ def __str__(self) -> str: print('script to submit commands within the container to sbatch.\n') print('all commands are fully forwarded with one exception:') print('\n ---n (opt) specifies a name for the scripts\n') - print('\n ---f location of other files to transfer like the HGCalML-Folder with necessary modules\n') + print('\n ---f location of the HGCalML-Folder with necessary modules\n') print('\n ---cpu (opt) number of cpus to request default: 1\n') print('\n ---memory (opt) size of memory to request default: 15 GB\n') print('\n ---disk (opt) size of memory to request default 8 GB\n') @@ -202,6 +202,8 @@ def __str__(self) -> str: rm HGCalML.tar.gz rm -r HGCalML +rm *.djctd +rm *.djcdc ls -l ''' From 7fd7a453c5a7b1b467d371b791a6c4aa7aef8ea5 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Fri, 14 Jun 2024 12:01:58 +0200 Subject: [PATCH 11/62] update submitscript --- Train/paper_trainer_Cocoa.py | 11 +++++---- modules/GravNetLayersRagged.py | 44 ++++++++++++++++------------------ scripts/submitViaHTCondor.py | 6 ++++- 3 files changed, 32 insertions(+), 29 deletions(-) diff --git a/Train/paper_trainer_Cocoa.py b/Train/paper_trainer_Cocoa.py index 3c7a5a78..0fb6e1b5 100644 --- a/Train/paper_trainer_Cocoa.py +++ b/Train/paper_trainer_Cocoa.py @@ -77,7 +77,7 @@ "classification_loss_weight": 0.01, "position_loss_weight": 0.0, "timing_loss_weight": 0.0, - "q_min": 0.8, + "q_min": 0.95, "use_average_cc_pos": 0.9999, } BATCHNORM_OPTIONS = { @@ -94,25 +94,25 @@ TRAINING = { "stage_1": { - "batch_size": 5000, + "batch_size": 10000, "learning_rate": 0.002, "epochs": 5, }, "stage_2": { - "batch_size": 5000, + "batch_size": 10000, "learning_rate": 0.0002, "epochs": 10, }, "stage_3": { - "batch_size": 5000, + "batch_size": 10000, "learning_rate": 0.00002, "epochs": 20, }, "stage_4": { - "batch_size": 5000, + "batch_size": 10000, "learning_rate": 0.00001, "epochs": 40, }, @@ -385,6 +385,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): train.setCustomOptimizer(tf.keras.optimizers.Adam())#clipnorm=1.)) train.compileModel(learningrate=1e-4) train.keras_model.summary() + train.saveModel(train.outputDir+'/model_init.h5') ############################################################################### ### Callbacks ################################################################# diff --git a/modules/GravNetLayersRagged.py b/modules/GravNetLayersRagged.py index 4a7fbd0c..54bd430a 100644 --- a/modules/GravNetLayersRagged.py +++ b/modules/GravNetLayersRagged.py @@ -1580,6 +1580,27 @@ def __init__(self, 1.0, # recHitTime -> All zeros 1.0, # recHitHitR -> All zeros for tracks ]) + + def get_config(self): + config = {} + base_config = super(ProcessFeatures, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + + + def compute_output_shape(self, input_shape): + return input_shape + + + def call(self, inputs): + features = inputs + is_track = tf.cast(features[:,2:3], bool) # is True if feature is != 0 (also true for -1) + + normalized_hits = (features - self.mean_hit) / self.std_hit + normalized_tracks = (features - self.mean_track) / self.std_track + normalized = tf.where(is_track, normalized_tracks, normalized_hits) + + return normalized + class ProcessFeaturesCocoa(ProcessFeatures): def __init__(self, **kwargs): @@ -1635,29 +1656,6 @@ def __init__(self, ]) - - def get_config(self): - config = {} - base_config = super(ProcessFeatures, self).get_config() - return dict(list(base_config.items()) + list(config.items())) - - - def compute_output_shape(self, input_shape): - return input_shape - - - def call(self, inputs): - features = inputs - is_track = tf.cast(features[:,2:3], bool) # is True if feature is != 0 (also true for -1) - - normalized_hits = (features - self.mean_hit) / self.std_hit - normalized_tracks = (features - self.mean_track) / self.std_track - normalized = tf.where(is_track, normalized_tracks, normalized_hits) - - return normalized - - - diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 54fe3053..58415e22 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -123,7 +123,7 @@ def __str__(self) -> str: #Setup the sub file sub_temp=f'''#!/bin/bash Universe = docker -docker_image = cernml4reco/deepjetcore4:06bc79b +docker_image = cernml4reco/deepjetcore4:abc9aee accounting_group = cms.jet requirements =(TARGET.CloudSite=="topas") @@ -205,6 +205,10 @@ def __str__(self) -> str: rm *.djctd rm *.djcdc +rm -r temp +rm -r var +rm -r wandb + ls -l ''' with open(opts['n'].value+'/'+opts['n'].value+ '_' +UEXT+'_run.sh','w', encoding='utf-8') as f: From 825d7875a6a0d51fb3cad34e127ba27c66e5599d Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Mon, 17 Jun 2024 11:16:25 +0200 Subject: [PATCH 12/62] created LExtendedObjectCondensation5 (total energy prediction) --- modules/Layers.py | 2 ++ modules/LossLayers.py | 48 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+) diff --git a/modules/Layers.py b/modules/Layers.py index 753b0a80..9367b4e4 100644 --- a/modules/Layers.py +++ b/modules/Layers.py @@ -312,6 +312,7 @@ from LossLayers import LLFullObjectCondensationUncertainty, LLFullObjectCondensationID from LossLayers import LLExtendedObjectCondensation, LLExtendedObjectCondensation2 from LossLayers import LLExtendedObjectCondensation3, LLExtendedObjectCondensation4 +from LossLayers import LLExtendedObjectCondensation5 from LossLayers import LLEdgeClassifier, AmbiguousTruthToNoiseSpectator, LLGoodNeighbourHood, LLKnnPushPullObjectCondensation from LossLayers import LLEnergySums,LLKnnSimpleObjectCondensation, LLPushTracks, LLFullOCThresholds, LLLocalEnergyConservation from LossLayers import LLRegulariseGravNetSpace, LLSpectatorPenalty @@ -354,6 +355,7 @@ global_layers_list['LLExtendedObjectCondensation2']=LLExtendedObjectCondensation2 global_layers_list['LLExtendedObjectCondensation3']=LLExtendedObjectCondensation3 global_layers_list['LLExtendedObjectCondensation4']=LLExtendedObjectCondensation4 +global_layers_list['LLExtendedObjectCondensation5']=LLExtendedObjectCondensation5 global_layers_list['LLFullObjectCondensationID']=LLFullObjectCondensationID global_layers_list['LLGraphCondOCLoss']=LLGraphCondOCLoss global_layers_list['LLPFCondensates']=LLPFCondensates diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 3c5621d2..40e2aec4 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3096,6 +3096,54 @@ def calc_energy_correction_factor_loss(self, else: return prediction_loss, uncertainty_loss + matching_loss +class LLExtendedObjectCondensation5(LLExtendedObjectCondensation): + ''' + Same as `LLExtendedObjecCondensation3` but uses total energy instead of correction as prediction: + ''' + + + def __init__(self, *args, **kwargs): + super(LLExtendedObjectCondensation5, self).__init__(*args, **kwargs) + + + def calc_energy_correction_factor_loss(self, + t_energy, t_dep_energies, + pred_energy, pred_uncertainty_low, pred_uncertainty_high, + return_concat=False): + """ + Wrong name for parity, but calculates the total energy instead of the correction factor. + * t_energy -> Truth energy of shower + * t_dep_energies -> Sum of deposited energy IF clustered perfectly + * pred_energy -> predicted energz + * pred_uncertainty_low -> predicted uncertainty + * pred_uncertainty_high -> predicted uncertainty (should be equal to ...low) + """ + t_energy = tf.clip_by_value(t_energy,0.,1e12) + t_dep_energies = tf.clip_by_value(t_dep_energies,0.,1e12) + t_dep_energies = tf.where(t_dep_energies / t_energy > 2.0, 2.0 * t_energy, t_dep_energies) + t_dep_energies = tf.where(t_dep_energies / t_energy < 0.5, 0.5 * t_energy, t_dep_energies) + + epred = pred_energy + sigma = pred_uncertainty_high * t_dep_energies + 1.0 + + # Uncertainty 'sigma' must minimize this term: + # ln(2*pi*sigma^2) + (E_true - E-pred)^2/sigma^2 + matching_loss = (pred_uncertainty_low)**2 + prediction_loss = tf.math.divide_no_nan((t_energy - epred)**2, sigma**2) + + uncertainty_loss = tf.math.log(sigma**2) + + matching_loss = tf.debugging.check_numerics(matching_loss, "matching_loss") + prediction_loss = tf.debugging.check_numerics(prediction_loss, "matching_loss") + uncertainty_loss = tf.debugging.check_numerics(uncertainty_loss, "matching_loss") + prediction_loss = tf.clip_by_value(prediction_loss, 0, 10) + uncertainty_loss = tf.clip_by_value(uncertainty_loss, 0, 10) + + if return_concat: + return tf.concat([prediction_loss, matching_loss + uncertainty_loss], axis=-1) + else: + return prediction_loss, uncertainty_loss + matching_loss + class LLFullObjectCondensationUncertainty(LLFullObjectCondensation): From 921970229b4b1335c983990350991db735b8cf55 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Mon, 17 Jun 2024 11:19:35 +0200 Subject: [PATCH 13/62] overhauled training script --- Train/paper_trainer_Cocoa.py | 242 ++++++++++------------------------- 1 file changed, 65 insertions(+), 177 deletions(-) diff --git a/Train/paper_trainer_Cocoa.py b/Train/paper_trainer_Cocoa.py index 0fb6e1b5..044c9611 100644 --- a/Train/paper_trainer_Cocoa.py +++ b/Train/paper_trainer_Cocoa.py @@ -2,11 +2,7 @@ Training script based on paper_trainer_noRSU_fixed_jk.py """ -import os -import pdb import sys -import yaml -import shutil from argparse import ArgumentParser # import wandb @@ -15,41 +11,20 @@ from tensorflow.keras.layers import Concatenate, Dense, Dropout from tensorflow.keras import Model -from DeepJetCore.training.DeepJet_callbacks import simpleMetricsCallback -from DeepJetCore.DJCLayers import StopGradient, ScalarMultiply import training_base_hgcal from Layers import LLRegulariseGravNetSpace, DummyLayer -from Layers import ScaledGooeyBatchNorm2 -from Layers import MixWhere from Layers import ProcessFeaturesCocoa from Layers import RaggedGravNet from Layers import PlotCoordinates -from Layers import DistanceWeightedMessagePassing, TranslationInvariantMP -from Layers import LLFillSpace -from Layers import LLExtendedObjectCondensation -from Layers import LLExtendedObjectCondensation2 +from Layers import TranslationInvariantMP +from Layers import LLExtendedObjectCondensation5 from Layers import LLExtendedObjectCondensation3 -from Layers import LLExtendedObjectCondensation4 -from Layers import DictModel -from Layers import RaggedGlobalExchange -from Layers import SphereActivation -from Layers import Multi -from Layers import ShiftDistance -# from Layers import LLRegulariseGravNetSpace -from Layers import SplitOffTracks, ConcatRaggedTensors -from Regularizers import AverageDistanceRegularizer -from Initializers import EyeInitializer -from model_blocks import tiny_pc_pool, condition_input +from model_blocks import condition_input from model_blocks import extent_coords_if_needed from model_blocks import create_outputs -from model_tools import apply_weights_from_path -from model_blocks import random_sampling_unit, random_sampling_block2 -from noise_filter import noise_filter -from callbacks import plotClusteringDuringTraining from callbacks import plotClusterSummary -from callbacks import NanSweeper, DebugPlotRunner -from tensorflow.keras.layers import BatchNormalization, LayerNormalization +from tensorflow.keras.layers import BatchNormalization #################################################################################################### ### Load Configuration ############################################################################# @@ -61,100 +36,30 @@ parser.add_argument('--wandb_project', help="wandb_project", default="Paper_Models") #parser.add_argument('--lr', help="learning rate", default="0.0001") -train = training_base_hgcal.HGCalTraining(parser=parser) - +# get the args so far but ignore the other args to pass them on +# this prevents errors with wandb in layers +pre_args,_ = parser.parse_known_args() -N_CLUSTER_SPACE_COORDINATES = 3 -N_GRAVNET_SPACE_COORDINATES = 4 -d_shape = 64 -NEIGHBOURS = [32, 64] -LOSS_IMPLEMENTATION = "hinge" -GRAVNET_ITERATIONS = len(NEIGHBOURS) -LOSS_OPTIONS = { - "beta_loss_scale": 1.0, - "too_much_beta_scale": 0.0, - "energy_loss_weight": 0.01, - "classification_loss_weight": 0.01, - "position_loss_weight": 0.0, - "timing_loss_weight": 0.0, - "q_min": 0.95, - "use_average_cc_pos": 0.9999, -} -BATCHNORM_OPTIONS = { - "max_viscosity": 0.9999, - "learn": False -} -DENSE_ACTIVATION = "elu" -DENSE_INIT = "he_normal" -DENSE_REGULARIZER_RATE = 1e-9 -DENSE_REGULARIZER = tf.keras.regularizers.l2(DENSE_REGULARIZER_RATE) -DROPOUT = 1e-2 -DISTANCE_SCALE = False -loss_layer = LLExtendedObjectCondensation3 - -TRAINING = { - "stage_1": { - "batch_size": 10000, - "learning_rate": 0.002, - "epochs": 5, - }, - - "stage_2": { - "batch_size": 10000, - "learning_rate": 0.0002, - "epochs": 10, - }, - - "stage_3": { - "batch_size": 10000, - "learning_rate": 0.00002, - "epochs": 20, - }, - - "stage_4": { - "batch_size": 10000, - "learning_rate": 0.00001, - "epochs": 40, - }, -} - -wandb_config = { - "gravnet_iterations" : GRAVNET_ITERATIONS, - "gravnet_space_coordinates" : N_GRAVNET_SPACE_COORDINATES, - "cluster_space_coordinates" : N_CLUSTER_SPACE_COORDINATES, - "loss_energy_weight" : LOSS_OPTIONS['energy_loss_weight'], - "loss_classification_weight" : LOSS_OPTIONS['classification_loss_weight'], - "loss_qmin" : LOSS_OPTIONS['q_min'], - "loss_use_average_cc_pos" : LOSS_OPTIONS['use_average_cc_pos'], - "loss_too_much_beta_scale" : LOSS_OPTIONS['too_much_beta_scale'], - "loss_beta_scale" : LOSS_OPTIONS['beta_loss_scale'], - "dense_activation" : DENSE_ACTIVATION, - "dense_kernel_reg" : DENSE_REGULARIZER_RATE, - "dense_dropout" : DROPOUT, - "distance_scale" : DISTANCE_SCALE, -} - -for i in range(GRAVNET_ITERATIONS): - wandb_config[f"gravnet_{i}_neighbours"] = NEIGHBOURS[i] -for i in range(1, len(TRAINING)+1): - wandb_config[f"train_{i}_lr"] = TRAINING[f"stage_{i}"]['learning_rate'] - wandb_config[f"train_{i}_epochs"] = TRAINING[f"stage_{i}"]['epochs'] - wandb_config[f"train_{i}_batchsize"] = TRAINING[f"stage_{i}"]['batch_size'] - if i == 1: - wandb_config[f"train_{i}+_max_visc"] = 0.999 - wandb_config[f"train_{i}+_fluidity_decay"] = 0.1 - -if not train.args.no_wandb: +if not pre_args.no_wandb: wandb.init( - project=train.args.wandb_project, - config=wandb_config, + project=pre_args.wandb_project, + config={}, ) wandb.wandb().save(sys.argv[0]) # Save python file else: wandb.active=False + +#parses the rest of the arguments +train = training_base_hgcal.HGCalTraining(parser=parser) -RECORD_FREQUENCY = 10 -PLOT_FREQUENCY = 40 + +PLOT_FREQUENCY = 600 + +DENSE_ACTIVATION = 'elu' +DENSE_INIT = "he_normal" +d_shape = 64 +DENSE_REGULARIZER = tf.keras.regularizers.l2(1e-9) +NEIGHBOURS = [64,32,32,16] ############################################################################### ### Define Model ############################################################## @@ -173,7 +78,7 @@ def GravNet_plus_TEQMP(name, xgn, gncoords, gnnidx, gndist = RaggedGravNet( name = "GravNet_"+name, # 76929, 42625, 42625 n_neighbours=n_neighbours, - n_dimensions=N_GRAVNET_SPACE_COORDINATES, + n_dimensions=3, n_filters=d_shape, n_propagate=d_shape, coord_initialiser_noise=1e-3, @@ -215,7 +120,6 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): orig_input = td.interpretAllModelInputs(Inputs) pre_processed = condition_input(orig_input, no_scaling=True, no_prime=False, new_prime=True) - prime_coords = pre_processed['prime_coords'] @@ -234,9 +138,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): name='input_c_coords', )([c_coords, energy, t_idx, rs]) - [cprime_hit], [cprime_track], rs_hit, rs_track = SplitOffTracks()([is_track, [prime_coords], rs]) - - c_coords = extent_coords_if_needed(c_coords, x, N_CLUSTER_SPACE_COORDINATES) + c_coords = extent_coords_if_needed(c_coords, x, 3) x = Concatenate()([x, c_coords, is_track]) x = Dense(64, name='dense_pre_loop', activation=DENSE_ACTIVATION)(x) @@ -249,34 +151,15 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): ########################################################################### - for i in range(GRAVNET_ITERATIONS): + for i in range(len(NEIGHBOURS)): x = Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, kernel_regularizer=DENSE_REGULARIZER)(x) x = Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, kernel_regularizer=DENSE_REGULARIZER)(x) - #x = LayerNormalization()(x) - x_pre = x x = Concatenate()([c_coords, x]) - - [x_hit, energy_hit, t_idx_hit], [x_track, energy_track, t_idx_track], rs_hit, rs_track = SplitOffTracks()( - [is_track, [x, energy, t_idx], rs]) - - - x_hit = GravNet_plus_TEQMP(f'gnhit_{i}', x_hit, cprime_hit, energy_hit, t_idx_hit, rs_hit, - d_shape, NEIGHBOURS[i], debug_outdir, plot_debug_every) - x_track = GravNet_plus_TEQMP(f'gntrack_{i}', x_track, cprime_track, energy_track, t_idx_track, rs_track, - d_shape, 16, debug_outdir, plot_debug_every) - - [xgn], rs = ConcatRaggedTensors()([ - [x_track], - [x_hit], - rs_track, rs_hit]) - x = Concatenate()([xgn, x_pre]) - #x = LayerNormalization()(x) - x = GravNet_plus_TEQMP(f'gncomb_{i}', x, prime_coords, energy, t_idx, rs, d_shape, NEIGHBOURS[i], debug_outdir, plot_debug_every, space_reg_strength=1e-2) @@ -313,28 +196,33 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): x = Dense(64, name=f"dense_final_{3}", activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, - kernel_regularizer=DENSE_REGULARIZER)(x) - #x = BatchNormalization()(x) - + kernel_regularizer=DENSE_REGULARIZER)(x) pred_beta, pred_ccoords, pred_dist, \ pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, \ pred_pos, pred_time, pred_time_unc, pred_id = \ create_outputs(x, - n_ccoords=N_CLUSTER_SPACE_COORDINATES, - fix_distance_scale=not DISTANCE_SCALE, + n_ccoords=3, + fix_distance_scale=True, is_track=is_track, set_track_betas_to_one=True) - pred_beta = loss_layer( + pred_beta = LLExtendedObjectCondensation3( scale=1., use_energy_weights=True, record_metrics=True, print_loss=train.args.no_wandb, name="ExtendedOCLoss", - implementation = LOSS_IMPLEMENTATION, - **LOSS_OPTIONS)( + implementation = "hinge", + beta_loss_scale = 1.0, + too_much_beta_scale = 0.0, + energy_loss_weight = 0.4, + classification_loss_weight = 0.4, + position_loss_weight = 0.0, + timing_loss_weight = 0.0, + q_min = 1.0, + use_average_cc_pos = 0.9999)( [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, pred_pos, pred_time, pred_time_unc, pred_id, energy, pre_processed['t_idx'] , pre_processed['t_energy'] , pre_processed['t_pos'] , @@ -379,7 +267,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): config_model, td=train.train_data.dataclass(), debug_outdir=train.outputDir+'/intplots', - plot_debug_every=RECORD_FREQUENCY*PLOT_FREQUENCY, + plot_debug_every=PLOT_FREQUENCY, ) train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space train.setCustomOptimizer(tf.keras.optimizers.Adam())#clipnorm=1.)) @@ -414,32 +302,32 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): ### Actual Training ########################################################### ############################################################################### +train.change_learning_rate(2e-3) +train.trainModel( + nepochs=2, + batchsize=10000, + add_progbar=pre_args.no_wandb, + additional_callbacks=cb, + collect_gradients = 4 #average out more gradients + ) -N_TRAINING_STAGES = len(TRAINING) -for i in range(1, N_TRAINING_STAGES+1): - print(f"Starting training stage {i}") - learning_rate = TRAINING[f"stage_{i}"]['learning_rate'] - epochs = TRAINING[f"stage_{i}"]['epochs'] - batch_size = TRAINING[f"stage_{i}"]['batch_size'] - train.change_learning_rate(learning_rate) - print(f"Training for {epochs} epochs") - print(f"Learning rate set to {learning_rate}") - print(f"Batch size: {batch_size}") - - if i == 2: - # fix the keras batch norm - def fix_batchnorm(model): - for layer in model.layers: - if isinstance(layer, BatchNormalization): - layer.trainable = False - train.applyFunctionToAllModels(fix_batchnorm) - train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, True) - #train.distributeModelToGPUs() #re-distribute changes - train.compileModel(learningrate=learning_rate) # recompile models - - train.trainModel( - nepochs=epochs, - batchsize=batch_size, - add_progbar=True, - additional_callbacks=cb +# loop through model layers and turn batch normalisation to fixed +def fix_batchnorm(m): + for layer in m.layers: + if isinstance(layer, BatchNormalization): + layer.trainable = False + +#apply to all models +train.applyFunctionToAllModels(fix_batchnorm) + + +#recompile +train.compileModel(learningrate=2e-4) +print('entering second training phase') +train.trainModel( + nepochs=10, + batchsize=1000, + add_progbar=False, + additional_callbacks=cb, + collect_gradients = 4 ) From e6d4c4b0e1efe1c08df96122e200e4e62130a1da Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Mon, 17 Jun 2024 11:20:44 +0200 Subject: [PATCH 14/62] small typo --- scripts/submitViaHTCondor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 58415e22..ebd1e688 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -205,7 +205,7 @@ def __str__(self) -> str: rm *.djctd rm *.djcdc -rm -r temp +rm -r tmp rm -r var rm -r wandb From 8298cbae1b422e2be0ce901ff4fa7067a1387de3 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Wed, 19 Jun 2024 10:27:42 +0200 Subject: [PATCH 15/62] optimised o_helper_m_indicies kernal with rs and new m_not definition --- .../compiled/oc_helper_m_indices_kernel.cc | 67 +++++++++++++++---- .../compiled/oc_helper_m_indices_kernel.cu.cc | 51 +++++++++++--- modules/compiled/oc_helper_m_indices_kernel.h | 5 +- .../compiled/oc_helper_m_indices_module.cc | 4 +- modules/object_condensation.py | 48 ++++++------- modules/oc_helper_ops.py | 18 +++-- 6 files changed, 140 insertions(+), 53 deletions(-) diff --git a/modules/compiled/oc_helper_m_indices_kernel.cc b/modules/compiled/oc_helper_m_indices_kernel.cc index b6672df2..9eabc7e4 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.cc +++ b/modules/compiled/oc_helper_m_indices_kernel.cc @@ -39,13 +39,17 @@ struct MIndicesOpFunctor { const int *d_truthidx, const int *d_unique_idx, + const int *rs, int * out_idx, - float * m_not, + int * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, + + const int n_rs, + const int n_max_in_rs, bool calc_m_not) { //main axis: n_unique == K_obj @@ -53,7 +57,7 @@ struct MIndicesOpFunctor { for (int k = 0; k < n_unique; k++) { - // + int uqidx = d_unique_idx[k]; int puqcounter=0; @@ -66,15 +70,38 @@ struct MIndicesOpFunctor { for(int prem = puqcounter; prem < n_max_per_unique; prem++){ out_idx[I2D(k, prem, n_max_per_unique)] = -1; } + //m_not + if(calc_m_not){ + //find row for uqidx + int rowForUqidx = 0; + while(rowForUqidx rs[rowForUqidx]){ + rowForUqidx++; + } + rowForUqidx--; - // - if(calc_m_not ){ - //m_not + + int placedelementscounter=0; for(int i_v = 0; i_v < n_vert; i_v++ ){ - if(uqidx>=0 && d_truthidx[i_v] == uqidx) - m_not [I2D(k, i_v, n_vert)] = 0.; - else - m_not [I2D(k, i_v, n_vert)] = 1.; + //find row for i_v + int rowFori_v= 0; + while(rowFori_v rs[rowFori_v]){ + rowFori_v++; + } + rowFori_v--; + //compare rows + + if(rowFori_v==rowForUqidx){ + + //if same row, check if uqidx + if(uqidx>=0 && d_truthidx[i_v] != uqidx){ + m_not [I2D(k, placedelementscounter, n_max_in_rs)] = i_v; + placedelementscounter++; + } + } + } + //fill rest with -1 + for(int m_not_idx = placedelementscounter; m_not_idx < n_max_in_rs; m_not_idx++){ + m_not [I2D(k, m_not_idx, n_max_in_rs)] = -1; } } @@ -103,10 +130,17 @@ class MIndicesOp : public OpKernel { const Tensor &t_tidx = context->input(0); const Tensor &t_uqtixs = context->input(1); const Tensor &t_nmax_puq = context->input(2);//needs to be evaluated in kernel + const Tensor &t_rs = context->input(3); + const Tensor &t_max_in_rs = context->input(4); + const int n_vert = t_tidx.dim_size(0); const int n_unique = t_uqtixs.dim_size(0); int n_max_per_unique=0; + const int n_rs = t_rs.dim_size(0); + + int n_max_in_rs = 0; + MIndicesMaxUqOpFunctor()( context->eigen_device(), @@ -114,6 +148,13 @@ class MIndicesOp : public OpKernel { &n_max_per_unique ); + MIndicesMaxUqOpFunctor()( + context->eigen_device(), + t_max_in_rs.flat().data(), + &n_max_in_rs + ); + + std::cout << "n_max_in_rs:" << n_max_in_rs << std::endl; Tensor *out_idx = NULL; OP_REQUIRES_OK(context, context->allocate_output(0, {n_unique, n_max_per_unique}, &out_idx)); @@ -121,8 +162,7 @@ class MIndicesOp : public OpKernel { Tensor *m_not = NULL; TensorShape m_not_shape={1, 1}; if(calc_m_not_) - m_not_shape={n_unique, n_vert}; - + m_not_shape={n_unique, n_max_in_rs}; OP_REQUIRES_OK(context, context->allocate_output(1, m_not_shape, &m_not)); @@ -130,13 +170,16 @@ class MIndicesOp : public OpKernel { context->eigen_device(), t_tidx.flat().data(), t_uqtixs.flat().data(), + t_rs.flat().data(), out_idx->flat().data(), - m_not->flat().data(), + m_not->flat().data(), n_vert, n_unique, n_max_per_unique, + n_rs, + n_max_in_rs, calc_m_not_ ); diff --git a/modules/compiled/oc_helper_m_indices_kernel.cu.cc b/modules/compiled/oc_helper_m_indices_kernel.cu.cc index eb303f6e..e5affe01 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.cu.cc +++ b/modules/compiled/oc_helper_m_indices_kernel.cu.cc @@ -22,13 +22,17 @@ __global__ static void calc( const int *d_truthidx, const int *d_unique_idx, + const int *rs, int * out_idx, - float * m_not, + int * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, + const int n_rs, + const int n_max_in_rs, + bool calc_m_not){ const int k = blockIdx.x * blockDim.x + threadIdx.x; @@ -49,15 +53,38 @@ static void calc( for(int prem = puqcounter; prem < n_max_per_unique; prem++){ out_idx[I2D(k, prem, n_max_per_unique)] = -1; } + //m_not + if(calc_m_not){ + //find row for uqidx + int rowForUqidx = 0; + while(rowForUqidx rs[rowForUqidx]){ + rowForUqidx++; + } + rowForUqidx--; - // - if(calc_m_not ){ - //m_not + + int placedelementscounter=0; for(int i_v = 0; i_v < n_vert; i_v++ ){ - if(uqidx>=0 && d_truthidx[i_v] == uqidx) - m_not [I2D(k, i_v, n_vert)] = 0.; - else - m_not [I2D(k, i_v, n_vert)] = 1.; + //find row for i_v + int rowFori_v= 0; + while(rowFori_v rs[rowFori_v]){ + rowFori_v++; + } + rowFori_v--; + //compare rows + + if(rowFori_v==rowForUqidx){ + + //if same row, check if uqidx + if(uqidx>=0 && d_truthidx[i_v] != uqidx){ + m_not [I2D(k, placedelementscounter, n_max_in_rs)] = i_v; + placedelementscounter++; + } + } + } + //fill rest with -1 + for(int m_not_idx = placedelementscounter; m_not_idx < n_max_in_rs; m_not_idx++){ + m_not [I2D(k, m_not_idx, n_max_in_rs)] = -1; } } @@ -86,13 +113,16 @@ struct MIndicesOpFunctor { const int *d_truthidx, const int *d_unique_idx, + const int *rs, int * out_idx, - float * m_not, + int * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, + const int n_rs, + const int n_max_in_rs, bool calc_m_not @@ -102,6 +132,7 @@ struct MIndicesOpFunctor { calc<<>>( d_truthidx, d_unique_idx, + rs, out_idx, m_not, @@ -109,6 +140,8 @@ struct MIndicesOpFunctor { n_vert, n_unique, n_max_per_unique, + n_rs, + n_max_in_rs, calc_m_not ); diff --git a/modules/compiled/oc_helper_m_indices_kernel.h b/modules/compiled/oc_helper_m_indices_kernel.h index dda3275d..e9893630 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.h +++ b/modules/compiled/oc_helper_m_indices_kernel.h @@ -21,13 +21,16 @@ struct MIndicesOpFunctor { const int *d_truthidx, const int *d_unique_idx, + const int *rs, int * out_idx, - float * m_not, + int * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, + const int n_rs, + const int n_max_in_rs, bool calc_m_not diff --git a/modules/compiled/oc_helper_m_indices_module.cc b/modules/compiled/oc_helper_m_indices_module.cc index f20af945..5218f49d 100644 --- a/modules/compiled/oc_helper_m_indices_module.cc +++ b/modules/compiled/oc_helper_m_indices_module.cc @@ -18,7 +18,9 @@ REGISTER_OP("MIndices") .Input("asso_idxs: int32") .Input("unique_idxs: int32") .Input("nmax_per_unique: int32") + .Input("rs: int32") + .Input("nmax_in_rs: int32") .Output("sel_idxs: int32") - .Output("m_not: float32"); + .Output("m_not: int32"); diff --git a/modules/object_condensation.py b/modules/object_condensation.py index d8b23534..ed9b294f 100644 --- a/modules/object_condensation.py +++ b/modules/object_condensation.py @@ -3,7 +3,7 @@ import tensorflow as tf from oc_helper_ops import CreateMidx, SelectWithDefault from binned_select_knn_op import BinnedSelectKnn - +import time def huber(x, d): @@ -118,8 +118,8 @@ def _weighted_alpha_k(self, v_k_m, w_k_m, fraction_wrt_alpha : float): return ak * (1. - fraction_wrt_alpha) + fraction_wrt_alpha * wvk - def create_Ms(self, truth_idx): - self.Msel, self.Mnot, _ = CreateMidx(truth_idx, calc_m_not=True) + def create_Ms(self, truth_idx, rs): + self.Msel, self.Mnot, _ = CreateMidx(truth_idx, rs, calc_m_not=True) def set_input(self, @@ -128,6 +128,7 @@ def set_input(self, d, pll, truth_idx, + rs, object_weight, is_spectator_weight, calc_Ms=True, @@ -153,7 +154,7 @@ def set_input(self, self.q_v = tf.where(truth_idx < 0, noise_qmin, self.q_v) # set noise qmin if calc_Ms: - self.create_Ms(truth_idx) + self.create_Ms(truth_idx, rs=rs) if self.Msel is None: self.valid=False return @@ -677,7 +678,7 @@ def set_input(self, beta, ): #replace beta with per-object normalised value - self.create_Ms(truth_idx) + self.create_Ms(truth_idx, rs=rs) beta_max = tf.reduce_max(self.Mnot * tf.expand_dims(beta,axis=0),axis=1, keepdims=True) #K x 1 x 1 beta_max *= self.Mnot #K x V x 1 @@ -710,10 +711,9 @@ def __call__(self, beta, truth_idx, object_weight, is_spectator_weight, - rs, energies = None): #rs last - + starttime = time.time() tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen = 6*[tf.constant(0., tf.float32)] #batch loop @@ -721,29 +721,25 @@ def __call__(self, beta, return tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen batch_size = rs.shape[0] - 1 mdict = {} - - for b in tf.range(batch_size): - - self.loss_impl.set_input( - beta[rs[b]:rs[b + 1]], - x[rs[b]:rs[b + 1]], - d[rs[b]:rs[b + 1]], - pll[rs[b]:rs[b + 1]], - truth_idx[rs[b]:rs[b + 1]], - object_weight[rs[b]:rs[b + 1]], - is_spectator_weight[rs[b]:rs[b + 1]] + self.loss_impl.set_input( + beta, + x, + d, + pll, + truth_idx, + rs, + object_weight, + is_spectator_weight ) - tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen = self.loss_impl.add_to_terms( - tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen - ) - - #just last row split is good enough for metric - if energies is not None and b == batch_size-1: - mdict = self.loss_impl.calc_metrics(energies[rs[b]:rs[b + 1]]) - + tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen = self.loss_impl.add_to_terms( + tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen + ) + + mdict = self.loss_impl.calc_metrics(energies) bs = tf.cast(batch_size, dtype='float32') + 1e-3 out = [a/bs for a in [tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen]] + print('OC loss time total:', time.time()-starttime) return out, mdict diff --git a/modules/oc_helper_ops.py b/modules/oc_helper_ops.py index d9685614..6d7c0e4c 100644 --- a/modules/oc_helper_ops.py +++ b/modules/oc_helper_ops.py @@ -28,7 +28,7 @@ _op = tf.load_op_library('oc_helper_m_indices.so') -def CreateMidx(truth_idxs, calc_m_not=False): +def CreateMidx(truth_idxs, rs, calc_m_not=False): ''' /* * Takes as helper input @@ -50,8 +50,14 @@ def CreateMidx(truth_idxs, calc_m_not=False): ''' - #only consider non-noise - c_truth_idxs = truth_idxs[truth_idxs>=0] + # Repeat each rs according to the differences to offset the indices + differences = tf.concat([tf.math.abs(rs[1:] - rs[:-1]), [0]], axis=0) + repeated_rs = tf.reshape(tf.repeat(rs, differences),(-1,1)) + offset_truth_idxs = truth_idxs + repeated_rs + + offset_truth_idxs = tf.where(truth_idxs<0, -1, offset_truth_idxs) + + c_truth_idxs= offset_truth_idxs[offset_truth_idxs>=0] unique_idxs, _, cperunique = tf.unique_with_counts(c_truth_idxs) nmax_per_unique = tf.reduce_max(cperunique) @@ -60,11 +66,15 @@ def CreateMidx(truth_idxs, calc_m_not=False): if nmax_per_unique.numpy() < 1: return None, None, None + nmax_in_rs = tf.reduce_max(rs[1:]-rs[:-1]) + sel_dxs, m_not = _op.MIndices( calc_m_not=calc_m_not, asso_idxs = truth_idxs, unique_idxs = unique_idxs, - nmax_per_unique = nmax_per_unique + nmax_per_unique = nmax_per_unique, + rs = rs, + nmax_in_rs = nmax_in_rs ) return sel_dxs, tf.expand_dims(m_not,axis=2), tf.expand_dims(cperunique,axis=1) #just some conventions From 520570e6125664af31941de114aef72985ead834 Mon Sep 17 00:00:00 2001 From: FantasticFabian Date: Wed, 19 Jun 2024 10:44:38 +0200 Subject: [PATCH 16/62] went back to old m_not definition --- .../compiled/oc_helper_m_indices_kernel.cc | 39 +++++-------------- .../compiled/oc_helper_m_indices_kernel.cu.cc | 29 ++++---------- modules/compiled/oc_helper_m_indices_kernel.h | 3 +- .../compiled/oc_helper_m_indices_module.cc | 3 +- modules/object_condensation.py | 6 +-- modules/oc_helper_ops.py | 8 ++-- 6 files changed, 23 insertions(+), 65 deletions(-) diff --git a/modules/compiled/oc_helper_m_indices_kernel.cc b/modules/compiled/oc_helper_m_indices_kernel.cc index 9eabc7e4..b1b1c30d 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.cc +++ b/modules/compiled/oc_helper_m_indices_kernel.cc @@ -42,14 +42,13 @@ struct MIndicesOpFunctor { const int *rs, int * out_idx, - int * m_not, + float * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, const int n_rs, - const int n_max_in_rs, bool calc_m_not) { //main axis: n_unique == K_obj @@ -74,35 +73,25 @@ struct MIndicesOpFunctor { if(calc_m_not){ //find row for uqidx int rowForUqidx = 0; - while(rowForUqidx rs[rowForUqidx]){ + while(rowForUqidx+1= rs[rowForUqidx+1]){ rowForUqidx++; } - rowForUqidx--; - int placedelementscounter=0; for(int i_v = 0; i_v < n_vert; i_v++ ){ //find row for i_v int rowFori_v= 0; - while(rowFori_v rs[rowFori_v]){ + while(rowFori_v+1= rs[rowFori_v+1]){ rowFori_v++; } - rowFori_v--; //compare rows - if(rowFori_v==rowForUqidx){ - - //if same row, check if uqidx - if(uqidx>=0 && d_truthidx[i_v] != uqidx){ - m_not [I2D(k, placedelementscounter, n_max_in_rs)] = i_v; - placedelementscounter++; - } + if(rowFori_v!=rowForUqidx || (uqidx>=0 && d_truthidx[i_v] == uqidx)){ + m_not [I2D(k, i_v, n_vert)] = 0.0; + }else{ + m_not [I2D(k, i_v, n_vert)] = 1.0; } } - //fill rest with -1 - for(int m_not_idx = placedelementscounter; m_not_idx < n_max_in_rs; m_not_idx++){ - m_not [I2D(k, m_not_idx, n_max_in_rs)] = -1; - } } } @@ -131,7 +120,6 @@ class MIndicesOp : public OpKernel { const Tensor &t_uqtixs = context->input(1); const Tensor &t_nmax_puq = context->input(2);//needs to be evaluated in kernel const Tensor &t_rs = context->input(3); - const Tensor &t_max_in_rs = context->input(4); const int n_vert = t_tidx.dim_size(0); @@ -139,8 +127,6 @@ class MIndicesOp : public OpKernel { int n_max_per_unique=0; const int n_rs = t_rs.dim_size(0); - int n_max_in_rs = 0; - MIndicesMaxUqOpFunctor()( context->eigen_device(), @@ -148,13 +134,7 @@ class MIndicesOp : public OpKernel { &n_max_per_unique ); - MIndicesMaxUqOpFunctor()( - context->eigen_device(), - t_max_in_rs.flat().data(), - &n_max_in_rs - ); - std::cout << "n_max_in_rs:" << n_max_in_rs << std::endl; Tensor *out_idx = NULL; OP_REQUIRES_OK(context, context->allocate_output(0, {n_unique, n_max_per_unique}, &out_idx)); @@ -162,7 +142,7 @@ class MIndicesOp : public OpKernel { Tensor *m_not = NULL; TensorShape m_not_shape={1, 1}; if(calc_m_not_) - m_not_shape={n_unique, n_max_in_rs}; + m_not_shape={n_unique, n_vert}; OP_REQUIRES_OK(context, context->allocate_output(1, m_not_shape, &m_not)); @@ -173,13 +153,12 @@ class MIndicesOp : public OpKernel { t_rs.flat().data(), out_idx->flat().data(), - m_not->flat().data(), + m_not->flat().data(), n_vert, n_unique, n_max_per_unique, n_rs, - n_max_in_rs, calc_m_not_ ); diff --git a/modules/compiled/oc_helper_m_indices_kernel.cu.cc b/modules/compiled/oc_helper_m_indices_kernel.cu.cc index e5affe01..9b861492 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.cu.cc +++ b/modules/compiled/oc_helper_m_indices_kernel.cu.cc @@ -25,13 +25,12 @@ static void calc( const int *rs, int * out_idx, - int * m_not, + float * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, const int n_rs, - const int n_max_in_rs, bool calc_m_not){ @@ -57,35 +56,25 @@ static void calc( if(calc_m_not){ //find row for uqidx int rowForUqidx = 0; - while(rowForUqidx rs[rowForUqidx]){ + while(rowForUqidx+1= rs[rowForUqidx+1]){ rowForUqidx++; } - rowForUqidx--; - int placedelementscounter=0; for(int i_v = 0; i_v < n_vert; i_v++ ){ //find row for i_v int rowFori_v= 0; - while(rowFori_v rs[rowFori_v]){ + while(rowFori_v+1= rs[rowFori_v+1]){ rowFori_v++; } - rowFori_v--; //compare rows - if(rowFori_v==rowForUqidx){ - - //if same row, check if uqidx - if(uqidx>=0 && d_truthidx[i_v] != uqidx){ - m_not [I2D(k, placedelementscounter, n_max_in_rs)] = i_v; - placedelementscounter++; - } + if(rowFori_v!=rowForUqidx || (uqidx>=0 && d_truthidx[i_v] == uqidx)){ + m_not [I2D(k, i_v, n_vert)] = 0.0; + }else{ + m_not [I2D(k, i_v, n_vert)] = 1.0; } } - //fill rest with -1 - for(int m_not_idx = placedelementscounter; m_not_idx < n_max_in_rs; m_not_idx++){ - m_not [I2D(k, m_not_idx, n_max_in_rs)] = -1; - } } @@ -116,13 +105,12 @@ struct MIndicesOpFunctor { const int *rs, int * out_idx, - int * m_not, + float * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, const int n_rs, - const int n_max_in_rs, bool calc_m_not @@ -141,7 +129,6 @@ struct MIndicesOpFunctor { n_unique, n_max_per_unique, n_rs, - n_max_in_rs, calc_m_not ); diff --git a/modules/compiled/oc_helper_m_indices_kernel.h b/modules/compiled/oc_helper_m_indices_kernel.h index e9893630..ebb2d4de 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.h +++ b/modules/compiled/oc_helper_m_indices_kernel.h @@ -24,13 +24,12 @@ struct MIndicesOpFunctor { const int *rs, int * out_idx, - int * m_not, + float * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, const int n_rs, - const int n_max_in_rs, bool calc_m_not diff --git a/modules/compiled/oc_helper_m_indices_module.cc b/modules/compiled/oc_helper_m_indices_module.cc index 5218f49d..7a1159ef 100644 --- a/modules/compiled/oc_helper_m_indices_module.cc +++ b/modules/compiled/oc_helper_m_indices_module.cc @@ -19,8 +19,7 @@ REGISTER_OP("MIndices") .Input("unique_idxs: int32") .Input("nmax_per_unique: int32") .Input("rs: int32") - .Input("nmax_in_rs: int32") .Output("sel_idxs: int32") - .Output("m_not: int32"); + .Output("m_not: float32"); diff --git a/modules/object_condensation.py b/modules/object_condensation.py index ed9b294f..97e7991a 100644 --- a/modules/object_condensation.py +++ b/modules/object_condensation.py @@ -3,7 +3,6 @@ import tensorflow as tf from oc_helper_ops import CreateMidx, SelectWithDefault from binned_select_knn_op import BinnedSelectKnn -import time def huber(x, d): @@ -713,7 +712,6 @@ def __call__(self, beta, is_spectator_weight, rs, energies = None): #rs last - starttime = time.time() tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen = 6*[tf.constant(0., tf.float32)] #batch loop @@ -737,9 +735,7 @@ def __call__(self, beta, ) mdict = self.loss_impl.calc_metrics(energies) - bs = tf.cast(batch_size, dtype='float32') + 1e-3 - out = [a/bs for a in [tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen]] - print('OC loss time total:', time.time()-starttime) + out = [tot_V_att, tot_V_rep, tot_Noise_pen, tot_B_pen, tot_pll,tot_too_much_B_pen] return out, mdict diff --git a/modules/oc_helper_ops.py b/modules/oc_helper_ops.py index 6d7c0e4c..b85b352c 100644 --- a/modules/oc_helper_ops.py +++ b/modules/oc_helper_ops.py @@ -65,18 +65,16 @@ def CreateMidx(truth_idxs, rs, calc_m_not=False): #eager if nmax_per_unique.numpy() < 1: return None, None, None - - nmax_in_rs = tf.reduce_max(rs[1:]-rs[:-1]) sel_dxs, m_not = _op.MIndices( calc_m_not=calc_m_not, - asso_idxs = truth_idxs, + asso_idxs = offset_truth_idxs, unique_idxs = unique_idxs, nmax_per_unique = nmax_per_unique, - rs = rs, - nmax_in_rs = nmax_in_rs + rs = rs ) + return sel_dxs, tf.expand_dims(m_not,axis=2), tf.expand_dims(cperunique,axis=1) #just some conventions @ops.RegisterGradient("CreateMidx") From 4dae37d1df60a4397f1a08cc7fb1620f5fd0ab2a Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 21 Jun 2024 10:28:16 +0200 Subject: [PATCH 17/62] improved submitscript a bit --- scripts/submitViaHTCondor.py | 122 ++++++++++++++++++++--------------- 1 file changed, 70 insertions(+), 52 deletions(-) diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index ebd1e688..330c8d70 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -2,17 +2,20 @@ """ Script to submit training scripts to Topas via HTCondor """ +# Usage: +# python3 submitViaHTCondor.py ---n JobName +# +# Example usage for a pytohn script with 3 arguments +# python3 submitViaHTCondor python3 mypythonscript.py --arg1 --arg2 --arg3 ---n JobName + +#Imports import sys import os import uuid import subprocess -import re - -UEXT = str(uuid.uuid4()) - - +#Usefull class to handle meta options class meta_option(object): def __init__(self, id, default_val = None) -> None: self.id = id @@ -34,8 +37,9 @@ def valid(self): def __str__(self) -> str: return self.id + ': '+self.value - - + +UEXT = str(uuid.uuid4()) +#Define possible meta parameters opts = { 'n' : meta_option('n','TrainJob_'+UEXT), 'f' : meta_option('f', '/work/friemer/hgcalml/HGCalML'), @@ -43,8 +47,11 @@ def __str__(self) -> str: 'memory': meta_option('memory', '15 GB'), 'disk': meta_option('disk', '8 GB'), 'gpu': meta_option('gpu', '1'), + 'wandb': meta_option('wandb', 'True'), + 'submit': meta_option('submit', 'True') } +#Filter out meta parameters filtered_clo=[] for clo in sys.argv: @@ -60,9 +67,19 @@ def __str__(self) -> str: all_valid = True for _,o in opts.items(): all_valid = all_valid and o.valid() + +filtered_clo = filtered_clo[1:] +COMMANDS = " " +for clos in filtered_clo: + COMMANDS += clos + " " + +#convert strings to bool +opts['wandb'].value = opts['wandb'].value.lower() in ('true', 't', 'yes', 'y','1') +opts['submit'].value = opts['submit'].value.lower() in ('true', 't', 'yes', 'y', '1') +#Help messages if '-h' in sys.argv or '--help' in sys.argv or (not all_valid): - print('script to submit commands within the container to sbatch.\n') + print('script to create a submission and script file and submit them via HTCondor to topas\n') print('all commands are fully forwarded with one exception:') print('\n ---n (opt) specifies a name for the scripts\n') print('\n ---f location of the HGCalML-Folder with necessary modules\n') @@ -70,8 +87,11 @@ def __str__(self) -> str: print('\n ---memory (opt) size of memory to request default: 15 GB\n') print('\n ---disk (opt) size of memory to request default 8 GB\n') print('\n ---gpu (opt) number of gpus to request default: 1\n') + print('\n ---wandb (opt) search for a Wandb API Key default: True\n') + print('\n ---submit (opt) submit the files in the end default: True\n') sys.exit() +#Create dictionary for files if os.path.isdir(opts['n'].value): var = input(\ 'Working directory exists, are you sure you want to continue, please type "yes/y"\n') @@ -82,16 +102,12 @@ def __str__(self) -> str: os.system('mkdir -p '+opts['n'].value) -filtered_clo = filtered_clo[1:] #remove -COMMANDS = " " -for clos in filtered_clo: - COMMANDS += clos + " " - +#Get current working directory CWD = os.getcwd() #################################################################################################### -### Create Sub File ############################################################################# +### Create Sub File ################################################################################ #################################################################################################### #Get absolute filepath and transfer entire folder if there is a .djcdc file also replace filepaths with filenames @@ -130,17 +146,17 @@ def __str__(self) -> str: +RemoteJob = True +RequestWalltime = 72 * 60 * 60 -executable = {opts['n'].value+'_'+UEXT+'_run.sh'} +executable = {opts['n'].value+'_run.sh'} should_transfer_files = YES when_to_transfer_output = ON_EXIT -transfer_input_files = {inputfileslocations} {CWD+ '/' +opts['n'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['n'].value+ '/' + opts['n'].value+'_'+UEXT+'_run.sh'} +transfer_input_files = {inputfileslocations} {CWD+ '/' +opts['n'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['n'].value+ '/' + opts['n'].value+'_run.sh'} transfer_output_files = . -output = {opts['n'].value+'_'+UEXT}.out -error = {opts['n'].value+'_'+UEXT}.err -log = {opts['n'].value+'_'+UEXT}.log +output = {opts['n'].value}.out +error = {opts['n'].value}.err +log = {opts['n'].value}.log request_cpus = {opts['cpu'].value} request_memory = {opts['memory'].value} @@ -148,79 +164,81 @@ def __str__(self) -> str: request_GPUs = {opts['gpu'].value} ''' -# Command to get WANDB_API_KEY -script_path = os.path.expanduser('~/private/wandb_api.sh') -command = f''' -echo "Checking for wandb API Key" -if [ -f {script_path} ]; then - source {script_path} - echo $WANDB_API_KEY -fi''' -result = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, executable='/bin/bash') - -# Extract the WANDB_API_KEY from the command output -API_Key = result.stdout.strip().split('\n')[-1] -if API_Key is not None: - sub_temp += f''' -environment = "WANDB_API_KEY={API_Key}" -''' -else: - print('No WANDB_API_KEY found, please set it in your environment or in ~/private/wandb_api.sh') +#Get Wandb API Key +if opts['wandb'].value: + # Command to get WANDB_API_KEY + script_path = os.path.expanduser('~/private/wandb_api.sh') + command = f''' + echo "Checking for wandb API Key" + if [ -f {script_path} ]; then + source {script_path} + echo $WANDB_API_KEY + fi''' + result = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, executable='/bin/bash') + + # Extract the WANDB_API_KEY from the command output + API_Key = result.stdout.strip().split('\n')[-1] + if API_Key is not None: + sub_temp += f'''environment = "WANDB_API_KEY={API_Key}" + ''' + else: + print('No WANDB_API_KEY found, please set it in your environment or in ~/private/wandb_api.sh or use the --no_wandb flag.') + #End of the sub file sub_temp += f''' queue ''' -with open(opts['n'].value+'/'+opts['n'].value+'_'+UEXT+'.sub','w', encoding='utf-8') as f: +with open(opts['n'].value+'/'+opts['n'].value+'.sub','w', encoding='utf-8') as f: f.write(sub_temp) #################################################################################################### -### Create Run File ############################################################################# +### Create Run File ################################################################################ #################################################################################################### #Setup Run script runscript_temp=f''' #!/bin/bash -tar -xzf HGCalML.tar.gz +#Unpack the HGCalML.tar.gz and set the necessary environment variables +tar -xzf HGCalML.tar.gz export HGCALML=$(readlink -f HGCalML) export DEEPJETCORE_SUBPACKAGE=$HGCALML - export PATH=$HGCALML/scripts:$PATH export PYTHONPATH=$HGCALML/modules:$PYTHONPATH export LD_LIBRARY_PATH=$HGCALML/modules:$LD_LIBRARY_PATH export LC_ALL=C.UTF-8 # necessary for wandb export LANG=C.UTF-8 # necessary for wandb +#Display the content of the directory for debugging ls -l +#Run the command {NEWCOMMANDS} -ls -l - +#Clean up rm HGCalML.tar.gz rm -r HGCalML rm *.djctd rm *.djcdc - rm -r tmp rm -r var rm -r wandb +#Display the content of the directory for debugging ls -l ''' -with open(opts['n'].value+'/'+opts['n'].value+ '_' +UEXT+'_run.sh','w', encoding='utf-8') as f: +with open(opts['n'].value+'/'+opts['n'].value+'_run.sh','w', encoding='utf-8') as f: f.write(runscript_temp) - #################################################################################################### -### Submit the Job ############################################################################# +### Submit the Job ################################################################################# #################################################################################################### - -SUBMITCOMMAND = ( - 'cd ' + opts['n'].value + '; pwd; condor_submit ' + opts['n'].value + '_' + UEXT + '.sub' -) -os.system(SUBMITCOMMAND) \ No newline at end of file +if opts['submit'].value: + SUBMITCOMMAND = ( + 'cd ' + opts['n'].value + '; pwd; condor_submit ' + opts['n'].value + '.sub' + ) + os.system(SUBMITCOMMAND) \ No newline at end of file From bfbdc3efb116d139967a533db3a4d159fce208eb Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 21 Jun 2024 10:35:13 +0200 Subject: [PATCH 18/62] testfile for Loss --- tests/testOC.py | 83 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 tests/testOC.py diff --git a/tests/testOC.py b/tests/testOC.py new file mode 100644 index 00000000..deb71db6 --- /dev/null +++ b/tests/testOC.py @@ -0,0 +1,83 @@ +import tensorflow as tf +import numpy as np +from LossLayers import LLFullObjectCondensation +import random + +#Small test to check if the loss function is working as expected afer I changed the oc-Kernals + +def random_loss(seed): + tf.random.set_seed(seed) + random.seed(seed) + np.random.seed(seed) + + llFOBC = LLFullObjectCondensation(scale=1., + use_energy_weights=True, + record_metrics=True, + print_loss=True, + name="ExtendedOCLoss", + implementation = "hinge", + beta_loss_scale = 1.0, + too_much_beta_scale = 0.0, + energy_loss_weight = 0.4, + classification_loss_weight = -0.4, + position_loss_weight = 0.0, + timing_loss_weight = 0.0, + q_min = 1.0, + use_average_cc_pos = 0.9999) + + # Adjusting the inputs to have random values but similar to the provided examples + length = np.random.randint(1000, 1100) + + + pred_beta = tf.random.uniform((length, 1), minval=0.996, maxval=1.0, dtype=tf.float32) + pred_ccoords = tf.random.uniform((length, 3), minval=-76.6, maxval=292.2, dtype=tf.float32) + pred_distscale = tf.ones((length, 1), dtype=tf.float32) + pred_energy = tf.random.uniform((length, 1), minval=1.009, maxval=1.194, dtype=tf.float32) + pred_energy_low_quantile = tf.zeros((length, 1), dtype=tf.float32) + pred_energy_high_quantile = tf.random.uniform((length, 1), minval=0, maxval=165, dtype=tf.float32) + pred_pos = tf.random.uniform((length, 2), minval=-29.99, maxval=166.66, dtype=tf.float32) + pred_time = tf.random.uniform((length, 1), minval=7.211, maxval=10.004, dtype=tf.float32) + pred_time_unc = tf.random.uniform((length, 1), minval=0.974, maxval=3.322, dtype=tf.float32) + pred_id = tf.random.uniform((length, 6), minval=0, maxval=8, dtype=tf.float32) + rechit_energy = tf.random.uniform((length, 1), minval=13.72, maxval=4103.71, dtype=tf.float32) + t_idx = tf.random.uniform((length, 1), minval=1, maxval=8, dtype=tf.int32) + t_energy = tf.random.uniform((length, 1), minval=2302.9, maxval=6639.13, dtype=tf.float32) + t_pos = tf.random.uniform((length, 3), minval=-0.0027, maxval=0.0011, dtype=tf.float32) + t_time = tf.zeros((length, 1), dtype=tf.float32) + t_pid = tf.random.uniform((length, 1), minval=-211, maxval=211, dtype=tf.int32) + t_spectator_weights = tf.zeros((length, 1), dtype=tf.float32) + t_fully_contained = tf.ones((length, 1), dtype=tf.int32) + t_rec_energy = tf.random.uniform((length, 1), minval=812.48, maxval=2587.7, dtype=tf.float32) + t_is_unique = tf.random.uniform((length, 1), minval=0, maxval=1, dtype=tf.int32) + + #randomly generate rowsplits for testing + # Generate three random integers between 1 and length-1, ensuring they are sorted + num_splits = np.random.randint(1, 3) + random_splits = sorted(np.random.randint(8, length, size=num_splits)) + + # Append 0 at the beginning and 'length' at the end to form valid row splits + rowsplits = [0] + random_splits + [length] + + #rowsplits = [0] + [length] + + # Convert to a TensorFlow constant + rowsplits = tf.constant(rowsplits, dtype=tf.int32) + + + # Grouping inputs + inputs = [pred_beta, pred_ccoords, pred_distscale, + pred_energy, pred_energy_low_quantile, pred_energy_high_quantile, + pred_pos, pred_time, pred_time_unc, pred_id, + rechit_energy, + t_idx, t_energy, t_pos, t_time, t_pid, t_spectator_weights, t_fully_contained, t_rec_energy, + t_is_unique, + rowsplits] + + + loss = llFOBC.loss(inputs) # Adjust this call based on how the loss function is actually used + print(loss) + return loss + + +for i in [42,300,500,1,565656,400432,342342,3242342]: + random_loss(i) From 3efa3dc405c588f1878eedf38160504c9df5055d Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 21 Jun 2024 13:49:32 +0200 Subject: [PATCH 19/62] created simpler training script --- Train/paper_trainer_Cocoa.py | 20 +- Train/paper_trainer_Cocoa_simple.py | 338 ++++++++++++++++++++++++++++ 2 files changed, 354 insertions(+), 4 deletions(-) create mode 100644 Train/paper_trainer_Cocoa_simple.py diff --git a/Train/paper_trainer_Cocoa.py b/Train/paper_trainer_Cocoa.py index 044c9611..a568ea34 100644 --- a/Train/paper_trainer_Cocoa.py +++ b/Train/paper_trainer_Cocoa.py @@ -302,10 +302,10 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): ### Actual Training ########################################################### ############################################################################### -train.change_learning_rate(2e-3) +train.change_learning_rate(5e-3) train.trainModel( nepochs=2, - batchsize=10000, + batchsize=50000, add_progbar=pre_args.no_wandb, additional_callbacks=cb, collect_gradients = 4 #average out more gradients @@ -325,9 +325,21 @@ def fix_batchnorm(m): train.compileModel(learningrate=2e-4) print('entering second training phase') train.trainModel( - nepochs=10, - batchsize=1000, + nepochs=5, + batchsize=50000, add_progbar=False, additional_callbacks=cb, collect_gradients = 4 ) + + +#recompile +train.compileModel(learningrate=1e-4) +print('entering third training phase') +train.trainModel( + nepochs=10, + batchsize=50000, + add_progbar=False, + additional_callbacks=cb, + collect_gradients = 4 + ) \ No newline at end of file diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py new file mode 100644 index 00000000..b76f99ad --- /dev/null +++ b/Train/paper_trainer_Cocoa_simple.py @@ -0,0 +1,338 @@ +""" +Training script based on paper_trainer_noRSU_fixed_jk.py +""" + +import sys +from argparse import ArgumentParser + +# import wandb +from DeepJetCore.wandb_interface import wandb_wrapper as wandb +import tensorflow as tf +from tensorflow.keras.layers import Concatenate, Dense, Dropout +from tensorflow.keras import Model + + +import training_base_hgcal +from Layers import LLRegulariseGravNetSpace, DummyLayer +from Layers import ProcessFeaturesCocoa +from Layers import RaggedGravNet +from Layers import PlotCoordinates +from Layers import TranslationInvariantMP +from Layers import LLExtendedObjectCondensation5 +from Layers import LLExtendedObjectCondensation3 +from model_blocks import condition_input +from model_blocks import extent_coords_if_needed +from model_blocks import create_outputs +from callbacks import plotClusterSummary +from tensorflow.keras.layers import BatchNormalization + +#################################################################################################### +### Load Configuration ############################################################################# +#################################################################################################### + +parser = ArgumentParser('training') +parser.add_argument('--run_name', help="wandb run name", default="test") +parser.add_argument('--no_wandb', help="Don't use wandb", action='store_true') +parser.add_argument('--wandb_project', help="wandb_project", default="Paper_Models") +#parser.add_argument('--lr', help="learning rate", default="0.0001") + +# get the args so far but ignore the other args to pass them on +# this prevents errors with wandb in layers +pre_args,_ = parser.parse_known_args() + +if not pre_args.no_wandb: + wandb.init( + project=pre_args.wandb_project, + config={}, + ) + wandb.wandb().save(sys.argv[0]) # Save python file +else: + wandb.active=False + +#parses the rest of the arguments +train = training_base_hgcal.HGCalTraining(parser=parser) + + +PLOT_FREQUENCY = 600 + +DENSE_ACTIVATION = 'elu' +DENSE_INIT = "he_normal" +d_shape = 64 +DENSE_REGULARIZER = tf.keras.regularizers.l2(1e-9) +NEIGHBOURS = [64,64,64,64] + +############################################################################### +### Define Model ############################################################## +############################################################################### + +def GravNet(name, + x, cprime, hit_energy, t_idx, + rs, + d_shape, + n_neighbours, + debug_outdir, + plot_debug_every, + space_reg_strength=-1e-2, + ): + + x= Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x= Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x= Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + + x = BatchNormalization()(x) + + x = Concatenate()([cprime, x]) + + xgn, gncoords, gnnidx, gndist = RaggedGravNet( + name = "GravNet_"+name, # 76929, 42625, 42625 + n_neighbours=n_neighbours, + n_dimensions=4, + n_filters=d_shape, + n_propagate=d_shape, + coord_initialiser_noise=1e-3, + feature_activation='elu', + )([x, rs]) + + if space_reg_strength > 0: + gndist = LLRegulariseGravNetSpace(name=f'gravnet_coords_reg_{name}' , + record_metrics=True, + scale=space_reg_strength)([gndist, cprime, gnnidx]) + + gncoords = PlotCoordinates( + plot_every=plot_debug_every, + outdir = debug_outdir, + name=f'gncoords_{name}' + )([gncoords, hit_energy, t_idx, rs]) + + x = DummyLayer()([x, gncoords]) #just so the branch is not optimised away, anyway used further down + x = BatchNormalization()(x) + + return Concatenate()([xgn, x]) + + + + +def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): + """ + Function that defines the model to train + """ + + ########################################################################### + ### Pre-processing step ################################################### + ########################################################################### + + orig_input = td.interpretAllModelInputs(Inputs) + pre_processed = condition_input(orig_input, no_scaling=True, no_prime=False, new_prime=True) + + prime_coords = pre_processed['prime_coords'] + + c_coords = prime_coords + is_track = pre_processed['is_track'] + rs = pre_processed['row_splits'] + energy = pre_processed['rechit_energy'] + t_idx = pre_processed['t_idx'] + x = pre_processed['features'] + + x = ProcessFeaturesCocoa()(x) + + c_coords = PlotCoordinates( + plot_every=plot_debug_every, + outdir=debug_outdir, + name='input_c_coords', + )([c_coords, energy, t_idx, rs]) + + c_coords = extent_coords_if_needed(c_coords, x, 3) + + x = Concatenate()([x, c_coords, is_track]) + x = Dense(64, name='dense_pre_loop', activation=DENSE_ACTIVATION)(x) + + allfeat = [] + print("Available keys: ", pre_processed.keys()) + + ########################################################################### + ### Loop over GravNet Layers ############################################## + ########################################################################### + + + for i in range(len(NEIGHBOURS)): + + + x = GravNet(f'gncomb_{i}', x, prime_coords, energy, t_idx, rs, + d_shape, NEIGHBOURS[i], debug_outdir, plot_debug_every, space_reg_strength=-1e-2) + + #x = LayerNormalization()(x) + allfeat.append(x) + if len(allfeat) > 1: + x = Concatenate()(allfeat) + else: + x = allfeat[0] + + + ########################################################################### + ### Create output of model and define loss ################################ + ########################################################################### + + + x = Dense(128, + name=f"dense_final_{1}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = BatchNormalization()(x) + x = Dense(64, + name=f"dense_final_{2}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = Dense(64, + name=f"dense_final_{3}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = BatchNormalization()(x) + + pred_beta, pred_ccoords, pred_dist, \ + pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, \ + pred_pos, pred_time, pred_time_unc, pred_id = \ + create_outputs(x, + n_ccoords=3, + fix_distance_scale=True, + is_track=is_track, + set_track_betas_to_one=True) + + + pred_beta = LLExtendedObjectCondensation3( + scale=1., + use_energy_weights=True, + record_metrics=True, + print_loss=train.args.no_wandb, + name="ExtendedOCLoss", + implementation = "hinge", + beta_loss_scale = 1.0, + too_much_beta_scale = 0.0, + energy_loss_weight = 0.4, + classification_loss_weight = 0.4, + position_loss_weight = 0.0, + timing_loss_weight = 0.0, + q_min = 1.0, + use_average_cc_pos = 0.9999)( + [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, + pred_energy_high_quantile, pred_pos, pred_time, pred_time_unc, pred_id, energy, + pre_processed['t_idx'] , pre_processed['t_energy'] , pre_processed['t_pos'] , + pre_processed['t_time'] , pre_processed['t_pid'] , pre_processed['t_spectator_weight'], + pre_processed['t_fully_contained'], pre_processed['t_rec_energy'], + pre_processed['t_is_unique'], pre_processed['row_splits']]) + + pred_ccoords = PlotCoordinates( + plot_every=plot_debug_every, + outdir = debug_outdir, + name='condensation' + )([pred_ccoords, pred_beta, pre_processed['t_idx'], rs]) + + model_outputs = { + 'pred_beta': pred_beta, + 'pred_ccoords': pred_ccoords, + 'pred_energy_corr_factor': pred_energy_corr, + 'pred_energy_low_quantile': pred_energy_low_quantile, + 'pred_energy_high_quantile': pred_energy_high_quantile, + 'pred_pos': pred_pos, + 'pred_time': pred_time, + 'pred_id': pred_id, + 'pred_dist': pred_dist, + 'rechit_energy': energy, + 'row_splits': pre_processed['row_splits'], + # 'no_noise_sel': pre_processed['no_noise_sel'], + # 'no_noise_rs': pre_processed['no_noise_rs'], + } + + # return DictModel(inputs=Inputs, outputs=model_outputs) + return Model(inputs=Inputs, outputs=model_outputs) + + +############################################################################### +### Set up training ########################################################### +############################################################################### + + + +if not train.modelSet(): + train.setModel( + config_model, + td=train.train_data.dataclass(), + debug_outdir=train.outputDir+'/intplots', + plot_debug_every=PLOT_FREQUENCY, + ) + train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space + train.setCustomOptimizer(tf.keras.optimizers.Adam())#clipnorm=1.)) + train.compileModel(learningrate=1e-4) + train.keras_model.summary() + train.saveModel(train.outputDir+'/model_init.h5') + +############################################################################### +### Callbacks ################################################################# +############################################################################### + + +samplepath = train.val_data.getSamplePath(train.val_data.samples[0]) +PUBLISHPATH = "" +PUBLISHPATH += [d for d in train.outputDir.split('/') if len(d)][-1] + + +cb = []#[NanSweeper()] #this takes a bit of time checking each batch but could be worth it + +cb += [ + plotClusterSummary( + outputfile=train.outputDir + "/clustering/", + samplefile=train.val_data.getSamplePath(train.val_data.samples[0]), + after_n_batches=1000 + ) + ] + + +# cb += [wandbCallback()] + +############################################################################### +### Actual Training ########################################################### +############################################################################### + +train.change_learning_rate(5e-3) +train.trainModel( + nepochs=2, + batchsize=50000, + add_progbar=pre_args.no_wandb, + additional_callbacks=cb, + collect_gradients = 4 #average out more gradients + ) + +# loop through model layers and turn batch normalisation to fixed +def fix_batchnorm(m): + for layer in m.layers: + if isinstance(layer, BatchNormalization): + layer.trainable = False + +#apply to all models +train.applyFunctionToAllModels(fix_batchnorm) + + +#recompile +train.compileModel(learningrate=2e-4) +print('entering second training phase') +train.trainModel( + nepochs=5, + batchsize=50000, + add_progbar=pre_args.no_wandb, + additional_callbacks=cb, + collect_gradients = 4 + ) + + +#recompile +train.compileModel(learningrate=1e-4) +print('entering third training phase') +train.trainModel( + nepochs=10, + batchsize=50000, + add_progbar=args.no_wandb, + additional_callbacks=cb, + collect_gradients = 4 + ) \ No newline at end of file From c8a088ab63c3e6079e41754e1b6c7297dd6683bf Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 21 Jun 2024 14:13:43 +0200 Subject: [PATCH 20/62] changed submissionscript to be more flexible --- scripts/submitViaHTCondor.py | 52 +++++++++++++++++++++++++++--------- 1 file changed, 39 insertions(+), 13 deletions(-) diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 330c8d70..d2e61f85 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -82,7 +82,7 @@ def __str__(self) -> str: print('script to create a submission and script file and submit them via HTCondor to topas\n') print('all commands are fully forwarded with one exception:') print('\n ---n (opt) specifies a name for the scripts\n') - print('\n ---f location of the HGCalML-Folder with necessary modules\n') + print('\n ---f location of folder with other necessary files\n') print('\n ---cpu (opt) number of cpus to request default: 1\n') print('\n ---memory (opt) size of memory to request default: 15 GB\n') print('\n ---disk (opt) size of memory to request default 8 GB\n') @@ -110,7 +110,7 @@ def __str__(self) -> str: ### Create Sub File ################################################################################ #################################################################################################### -#Get absolute filepath and transfer entire folder if there is a .djcdc file also replace filepaths with filenames +#Get absolute filepath and transfer entire folder if there is a .djcdc file also replace filepaths with filenames in command inputfileslocations ='' NEWCOMMANDS = '' for word in COMMANDS.split(): @@ -123,20 +123,28 @@ def __str__(self) -> str: else: NEWCOMMANDS+=word + ' ' -#Create a tarball of the HGCalML folder +#Create a tarball of the folder +hasfolder = True if os.path.isdir(opts['f'].value): + print('Folder found, creating a tarball of the folder...') os.system(f''' cd {opts['f'].value} cd ../ - tar -czf {opts['n'].value}/HGCalML.tar.gz {os.path.basename(opts['f'].value)} + tar -czf {opts['n'].value}/ZipFolder.tar.gz {os.path.basename(opts['f'].value)} cd {CWD}''') + inputfileslocations += CWD+ '/' +opts['n'].value + '/ZipFolder.tar.gz, ' elif os.path.isfile(opts['f'].value) and opts['f'].value.endswith('.tar.gz'): + print('File found, copying the file to the submission folder...') os.system(f''' - cp {opts['f'].value} {opts['n'].value}/HGCalML.tar.gz''') + cp {opts['f'].value} {opts['n'].value}/ZipFolder.tar.gz''') + inputfileslocations += CWD+ '/' +opts['n'].value + '/ZipFolder.tar.gz, ' else: - raise Exception('Folder not found, please specify the correct path to the HGCalML-folder with ---f ') + print('Folder not found, no additional files will be transferred.') + hasfolder = False + #Setup the sub file +print('Creating the submission file...') sub_temp=f'''#!/bin/bash Universe = docker docker_image = cernml4reco/deepjetcore4:abc9aee @@ -151,7 +159,7 @@ def __str__(self) -> str: should_transfer_files = YES when_to_transfer_output = ON_EXIT -transfer_input_files = {inputfileslocations} {CWD+ '/' +opts['n'].value}/HGCalML.tar.gz, {CWD+ '/' +opts['n'].value+ '/' + opts['n'].value+'_run.sh'} +transfer_input_files = {inputfileslocations} {CWD+ '/' +opts['n'].value+ '/' + opts['n'].value+'_run.sh'} transfer_output_files = . output = {opts['n'].value}.out @@ -198,12 +206,26 @@ def __str__(self) -> str: ### Create Run File ################################################################################ #################################################################################################### -#Setup Run script +print('Creating the run file...') +#Unpack the ZipFolder.tar.gz runscript_temp=f''' #!/bin/bash +''' -#Unpack the HGCalML.tar.gz and set the necessary environment variables -tar -xzf HGCalML.tar.gz +if hasfolder: + runscript_temp += f''' +#Unpack the ZipFolder.tar.gz and set the necessary environment variables +tar -xzf ZipFolder.tar.gz + +#Display the content of the directory for debugging +ls -l + +''' + +#if the folder is my HGCalML, set the necessary environment variables +if os.path.basename(opts['f'].value)=='HGCalML': + runscript_temp += f''' +#Set some environment variables export HGCALML=$(readlink -f HGCalML) export DEEPJETCORE_SUBPACKAGE=$HGCALML export PATH=$HGCALML/scripts:$PATH @@ -211,13 +233,17 @@ def __str__(self) -> str: export LD_LIBRARY_PATH=$HGCALML/modules:$LD_LIBRARY_PATH export LC_ALL=C.UTF-8 # necessary for wandb export LANG=C.UTF-8 # necessary for wandb +''' -#Display the content of the directory for debugging -ls -l - +#Run the commands +runscript_temp += f''' #Run the command {NEWCOMMANDS} +''' +#if the folder is my HGCalML, clean up unnecessary files +if os.path.basename(opts['f'].value)=='HGCalML': + runscript_temp += f''' #Clean up rm HGCalML.tar.gz rm -r HGCalML From 4d04372feb0bf2b605a5eda29a982931aa2703d8 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 24 Jun 2024 11:14:47 +0200 Subject: [PATCH 21/62] disabled energy training uncertainty --- Train/paper_trainer_Cocoa_simple.py | 35 ++++++++++++++++------------- modules/LossLayers.py | 13 +++++++++-- 2 files changed, 31 insertions(+), 17 deletions(-) diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index b76f99ad..1eb27376 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -82,9 +82,9 @@ def GravNet(name, x= Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, kernel_regularizer=DENSE_REGULARIZER)(x) - x = BatchNormalization()(x) - x = Concatenate()([cprime, x]) + + x = BatchNormalization()(x) xgn, gncoords, gnnidx, gndist = RaggedGravNet( name = "GravNet_"+name, # 76929, 42625, 42625 @@ -93,7 +93,7 @@ def GravNet(name, n_filters=d_shape, n_propagate=d_shape, coord_initialiser_noise=1e-3, - feature_activation='elu', + feature_activation='tanh', )([x, rs]) if space_reg_strength > 0: @@ -104,7 +104,8 @@ def GravNet(name, gncoords = PlotCoordinates( plot_every=plot_debug_every, outdir = debug_outdir, - name=f'gncoords_{name}' + name=f'gncoords_{name}', + publish='wandb' )([gncoords, hit_energy, t_idx, rs]) x = DummyLayer()([x, gncoords]) #just so the branch is not optimised away, anyway used further down @@ -142,6 +143,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): plot_every=plot_debug_every, outdir=debug_outdir, name='input_c_coords', + publish='wandb' )([c_coords, energy, t_idx, rs]) c_coords = extent_coords_if_needed(c_coords, x, 3) @@ -214,6 +216,8 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): classification_loss_weight = 0.4, position_loss_weight = 0.0, timing_loss_weight = 0.0, + downweight_low_energy=False, + train_energy_unc=False, q_min = 1.0, use_average_cc_pos = 0.9999)( [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, @@ -226,7 +230,8 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): pred_ccoords = PlotCoordinates( plot_every=plot_debug_every, outdir = debug_outdir, - name='condensation' + name='condensation', + publish='wandb' )([pred_ccoords, pred_beta, pre_processed['t_idx'], rs]) model_outputs = { @@ -284,7 +289,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): plotClusterSummary( outputfile=train.outputDir + "/clustering/", samplefile=train.val_data.getSamplePath(train.val_data.samples[0]), - after_n_batches=1000 + after_n_batches=500 ) ] @@ -295,12 +300,12 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): ### Actual Training ########################################################### ############################################################################### -train.change_learning_rate(5e-3) +train.change_learning_rate(1e-3) train.trainModel( nepochs=2, batchsize=50000, add_progbar=pre_args.no_wandb, - additional_callbacks=cb, + #additional_callbacks=cb, collect_gradients = 4 #average out more gradients ) @@ -315,24 +320,24 @@ def fix_batchnorm(m): #recompile -train.compileModel(learningrate=2e-4) +train.compileModel(learningrate=1e-4) print('entering second training phase') train.trainModel( - nepochs=5, + nepochs=15, batchsize=50000, add_progbar=pre_args.no_wandb, - additional_callbacks=cb, + #additional_callbacks=cb, collect_gradients = 4 ) #recompile -train.compileModel(learningrate=1e-4) +train.compileModel(learningrate=1e-5) print('entering third training phase') train.trainModel( - nepochs=10, + nepochs=25, batchsize=50000, - add_progbar=args.no_wandb, - additional_callbacks=cb, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, collect_gradients = 4 ) \ No newline at end of file diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 40e2aec4..af523ca6 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -2178,6 +2178,7 @@ def __init__(self, *, energy_loss_weight=1., beta_push=0., implementation = '', global_weight=False, + train_energy_unc=True, **kwargs): """ Read carefully before changing parameters @@ -2318,6 +2319,7 @@ def __init__(self, *, energy_loss_weight=1., self.loc_time=time.time() self.call_count=0 self.beta_push = beta_push + self.train_energy_unc=train_energy_unc assert kalpha_damping_strength >= 0. and kalpha_damping_strength <= 1. @@ -2727,7 +2729,8 @@ def get_config(self): 'div_repulsion' : self.div_repulsion, 'dynamic_payload_scaling_onset': self.dynamic_payload_scaling_onset, 'beta_push': self.beta_push, - 'implementation': self.implementation + 'implementation': self.implementation, + 'train_energy_unc': self.train_energy_unc } base_config = super(LLFullObjectCondensation, self).get_config() return dict(list(base_config.items()) + list(config.items())) @@ -3026,6 +3029,10 @@ def calc_energy_correction_factor_loss(self, epred = pred_energy * t_dep_energies sigma = pred_uncertainty_high * t_dep_energies + 1.0 + + if not self.train_energy_unc: + sigma = tf.ones_like(sigma) + # Uncertainty 'sigma' must minimize this term: # ln(2*pi*sigma^2) + (E_true - E-pred)^2/sigma^2 @@ -3033,7 +3040,9 @@ def calc_energy_correction_factor_loss(self, prediction_loss = tf.math.divide_no_nan((t_energy - epred)**2, sigma**2) uncertainty_loss = tf.math.log(sigma**2) - + if not self.train_energy_unc: + uncertainty_loss = pred_uncertainty_high**2 + matching_loss = tf.debugging.check_numerics(matching_loss, "matching_loss") prediction_loss = tf.debugging.check_numerics(prediction_loss, "matching_loss") uncertainty_loss = tf.debugging.check_numerics(uncertainty_loss, "matching_loss") From 7123550392a174659acf71bedb4193ba414153d2 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 24 Jun 2024 14:34:23 +0200 Subject: [PATCH 22/62] finished LLExtendedObjectCondensation5 --- Train/paper_trainer_Cocoa_simple.py | 4 ++-- modules/LossLayers.py | 11 ++--------- scripts/submitViaHTCondor.py | 6 +++--- 3 files changed, 7 insertions(+), 14 deletions(-) diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index 1eb27376..096ed691 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -84,10 +84,10 @@ def GravNet(name, x = Concatenate()([cprime, x]) - x = BatchNormalization()(x) + x = BatchNormalization()(x) xgn, gncoords, gnnidx, gndist = RaggedGravNet( - name = "GravNet_"+name, # 76929, 42625, 42625 + name = "GravNet_"+name, n_neighbours=n_neighbours, n_dimensions=4, n_filters=d_shape, diff --git a/modules/LossLayers.py b/modules/LossLayers.py index af523ca6..de73e6e1 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3129,18 +3129,11 @@ def calc_energy_correction_factor_loss(self, """ t_energy = tf.clip_by_value(t_energy,0.,1e12) t_dep_energies = tf.clip_by_value(t_dep_energies,0.,1e12) - t_dep_energies = tf.where(t_dep_energies / t_energy > 2.0, 2.0 * t_energy, t_dep_energies) - t_dep_energies = tf.where(t_dep_energies / t_energy < 0.5, 0.5 * t_energy, t_dep_energies) - epred = pred_energy - sigma = pred_uncertainty_high * t_dep_energies + 1.0 + prediction_loss = (t_energy - pred_energy)**2 - # Uncertainty 'sigma' must minimize this term: - # ln(2*pi*sigma^2) + (E_true - E-pred)^2/sigma^2 matching_loss = (pred_uncertainty_low)**2 - prediction_loss = tf.math.divide_no_nan((t_energy - epred)**2, sigma**2) - - uncertainty_loss = tf.math.log(sigma**2) + uncertainty_loss = pred_uncertainty_high**2 matching_loss = tf.debugging.check_numerics(matching_loss, "matching_loss") prediction_loss = tf.debugging.check_numerics(prediction_loss, "matching_loss") diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index d2e61f85..08ff73ad 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -44,8 +44,8 @@ def __str__(self) -> str: 'n' : meta_option('n','TrainJob_'+UEXT), 'f' : meta_option('f', '/work/friemer/hgcalml/HGCalML'), 'cpu': meta_option('cpu', '1'), - 'memory': meta_option('memory', '15 GB'), - 'disk': meta_option('disk', '8 GB'), + 'memory': meta_option('memory', '5 GB'), + 'disk': meta_option('disk', '2 GB'), 'gpu': meta_option('gpu', '1'), 'wandb': meta_option('wandb', 'True'), 'submit': meta_option('submit', 'True') @@ -152,7 +152,7 @@ def __str__(self) -> str: requirements =(TARGET.CloudSite=="topas") +RemoteJob = True -+RequestWalltime = 72 * 60 * 60 ++RequestWalltime = 24 * 60 * 60 executable = {opts['n'].value+'_run.sh'} From 71726b2223548009616b5df41ae2d40bd0094bb9 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 24 Jun 2024 16:22:19 +0200 Subject: [PATCH 23/62] created testLoadModel --- tests/testloadmodel.py | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 tests/testloadmodel.py diff --git a/tests/testloadmodel.py b/tests/testloadmodel.py new file mode 100644 index 00000000..03c59817 --- /dev/null +++ b/tests/testloadmodel.py @@ -0,0 +1,37 @@ +from tensorflow.keras.models import load_model + +from DeepJetCore.DJCLosses import * +from DeepJetCore.DJCLayers import * +import imp +from Layers import * +from Losses import * +from Metrics import * + + +def get_custom_objects(): + imp.find_module('Layers') + imp.find_module('Losses') + imp.find_module('Metrics') + custom_objs = {} + custom_objs.update(djc_global_loss_list) + custom_objs.update(djc_global_layers_list) + custom_objs.update(global_loss_list) + custom_objs.update(global_layers_list) + custom_objs.update(global_metrics_list) + return custom_objs + + +custom_objs = get_custom_objects() + +model_path = '/work/friemer/hgcalml/paper_BS100000__noDS_qm01_lr0005/model_results/KERAS_model.h5' + + +model_path = '/work/friemer/hgcalml/simplerGravNet5/results/KERAS_model.h5' + +# import h5py + +# with h5py.File(model_path, 'r') as f: +# f.visititems(lambda name, obj: print(name, obj.shape if hasattr(obj, 'shape') else '')) + + +model = load_model(model_path, custom_objects=custom_objs) From fd19819e33f4ac99522200898f35acf966a76fdb Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 24 Jun 2024 16:22:19 +0200 Subject: [PATCH 24/62] created testLoadModel --- tests/testloadmodel.py | 40 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 tests/testloadmodel.py diff --git a/tests/testloadmodel.py b/tests/testloadmodel.py new file mode 100644 index 00000000..6b130563 --- /dev/null +++ b/tests/testloadmodel.py @@ -0,0 +1,40 @@ +from DeepJetCore.modeltools import load_model + +from DeepJetCore.DJCLosses import * +from DeepJetCore.DJCLayers import * +import imp +from Layers import * +from Losses import * +from Metrics import * + + +def get_custom_objects(): + imp.find_module('Layers') + imp.find_module('Losses') + imp.find_module('Metrics') + custom_objs = {} + custom_objs.update(djc_global_loss_list) + custom_objs.update(djc_global_layers_list) + custom_objs.update(global_loss_list) + custom_objs.update(global_layers_list) + custom_objs.update(global_metrics_list) + return custom_objs + + +custom_objs = get_custom_objects() + +model_path = '/work/friemer/hgcalml/paper_BS100000__noDS_qm01_lr0005/model_results/KERAS_model.h5' + + +model_path = '/work/friemer/hgcalml/simplerGravNet5/results/KERAS_model.h5' + + +# import h5py + +# with h5py.File(model_path, 'r') as f: +# f.visititems(lambda name, obj: print(name, obj.shape if hasattr(obj, 'shape') else '')) + + +model = load_model(model_path) +model.save('testsavedMOdel.h5') +model2 = load_model('testsavedMOdel.h5') \ No newline at end of file From cfdb1fa5d969d140f5fd3a724996315d4612337f Mon Sep 17 00:00:00 2001 From: Fabian Date: Wed, 26 Jun 2024 16:53:09 +0200 Subject: [PATCH 25/62] fixed inconsistant saving resulting in loading error --- Train/paper_trainer_Cocoa.py | 2 +- Train/paper_trainer_Cocoa_simple.py | 8 ++++---- tests/testloadmodel.py | 19 ++++++++++++++++++- 3 files changed, 23 insertions(+), 6 deletions(-) diff --git a/Train/paper_trainer_Cocoa.py b/Train/paper_trainer_Cocoa.py index a568ea34..a3f11f4e 100644 --- a/Train/paper_trainer_Cocoa.py +++ b/Train/paper_trainer_Cocoa.py @@ -269,7 +269,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): debug_outdir=train.outputDir+'/intplots', plot_debug_every=PLOT_FREQUENCY, ) - train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space + #train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space train.setCustomOptimizer(tf.keras.optimizers.Adam())#clipnorm=1.)) train.compileModel(learningrate=1e-4) train.keras_model.summary() diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index 096ed691..9a8d0f73 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -267,7 +267,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): debug_outdir=train.outputDir+'/intplots', plot_debug_every=PLOT_FREQUENCY, ) - train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space + #train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space train.setCustomOptimizer(tf.keras.optimizers.Adam())#clipnorm=1.)) train.compileModel(learningrate=1e-4) train.keras_model.summary() @@ -320,10 +320,10 @@ def fix_batchnorm(m): #recompile -train.compileModel(learningrate=1e-4) +train.compileModel(learningrate=5e-4) print('entering second training phase') train.trainModel( - nepochs=15, + nepochs=10, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, @@ -332,7 +332,7 @@ def fix_batchnorm(m): #recompile -train.compileModel(learningrate=1e-5) +train.compileModel(learningrate=1e-4) print('entering third training phase') train.trainModel( nepochs=25, diff --git a/tests/testloadmodel.py b/tests/testloadmodel.py index 6b130563..372c185a 100644 --- a/tests/testloadmodel.py +++ b/tests/testloadmodel.py @@ -6,6 +6,7 @@ from Layers import * from Losses import * from Metrics import * +import h5py def get_custom_objects(): @@ -26,7 +27,12 @@ def get_custom_objects(): model_path = '/work/friemer/hgcalml/paper_BS100000__noDS_qm01_lr0005/model_results/KERAS_model.h5' -model_path = '/work/friemer/hgcalml/simplerGravNet5/results/KERAS_model.h5' +model_path = '/work/friemer/hgcalml/testCocoaLoad2/KERAS_model.h5' +# #Print shapes of all layers in model +# print('Model Path:', model_path) +# with h5py.File(model_path, 'r') as f: +# f.visititems(lambda name, obj: print(name, obj.shape if hasattr(obj, 'shape') else '')) + # import h5py @@ -36,5 +42,16 @@ def get_custom_objects(): model = load_model(model_path) + + + +# #print shapes of all layers in model and shapes of all weights +# for layer in model.layers: +# print(layer.name) +# print('input_shape:', layer.input_shape) +# print('output_shape',layer.output_shape) +# #print shape of weights +# print('shape of weights: ',[w.shape for w in layer.get_weights()]) +# print('') model.save('testsavedMOdel.h5') model2 = load_model('testsavedMOdel.h5') \ No newline at end of file From f7b1843c9f5f8d2571e50f673ad2d5580947b86d Mon Sep 17 00:00:00 2001 From: Fabian Date: Thu, 4 Jul 2024 15:06:36 +0200 Subject: [PATCH 26/62] added simple pretrained model --- models/simple_Cocoa/KERAS_model.h5 | Bin 0 -> 3353296 bytes models/simple_Cocoa/description.txt | 4 + .../paper_trainer_Cocoa_simple.py | 343 ++++++++++++++++++ 3 files changed, 347 insertions(+) create mode 100644 models/simple_Cocoa/KERAS_model.h5 create mode 100644 models/simple_Cocoa/description.txt create mode 100644 models/simple_Cocoa/paper_trainer_Cocoa_simple.py diff --git a/models/simple_Cocoa/KERAS_model.h5 b/models/simple_Cocoa/KERAS_model.h5 new file mode 100644 index 0000000000000000000000000000000000000000..89b3dac53c86c2db75f6c6fab950369b74c0ca78 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but it showed great improvement, so future models can be faster \ No newline at end of file diff --git a/models/simple_Cocoa/paper_trainer_Cocoa_simple.py b/models/simple_Cocoa/paper_trainer_Cocoa_simple.py new file mode 100644 index 00000000..e034f027 --- /dev/null +++ b/models/simple_Cocoa/paper_trainer_Cocoa_simple.py @@ -0,0 +1,343 @@ +""" +Training script based on paper_trainer_noRSU_fixed_jk.py +""" + +import sys +from argparse import ArgumentParser + +# import wandb +from DeepJetCore.wandb_interface import wandb_wrapper as wandb +import tensorflow as tf +from tensorflow.keras.layers import Concatenate, Dense, Dropout +from tensorflow.keras import Model + + +import training_base_hgcal +from Layers import LLRegulariseGravNetSpace, DummyLayer +from Layers import ProcessFeaturesCocoa +from Layers import RaggedGravNet +from Layers import PlotCoordinates +from Layers import TranslationInvariantMP +from Layers import LLExtendedObjectCondensation5 +from Layers import LLExtendedObjectCondensation3 +from model_blocks import condition_input +from model_blocks import extent_coords_if_needed +from model_blocks import create_outputs +from callbacks import plotClusterSummary +from tensorflow.keras.layers import BatchNormalization + +#################################################################################################### +### Load Configuration ############################################################################# +#################################################################################################### + +parser = ArgumentParser('training') +parser.add_argument('--run_name', help="wandb run name", default="test") +parser.add_argument('--no_wandb', help="Don't use wandb", action='store_true') +parser.add_argument('--wandb_project', help="wandb_project", default="Paper_Models") +#parser.add_argument('--lr', help="learning rate", default="0.0001") + +# get the args so far but ignore the other args to pass them on +# this prevents errors with wandb in layers +pre_args,_ = parser.parse_known_args() + +if not pre_args.no_wandb: + wandb.init( + project=pre_args.wandb_project, + config={}, + ) + wandb.wandb().save(sys.argv[0]) # Save python file +else: + wandb.active=False + +#parses the rest of the arguments +train = training_base_hgcal.HGCalTraining(parser=parser) + + +PLOT_FREQUENCY = 600 + +DENSE_ACTIVATION = 'elu' +DENSE_INIT = "he_normal" +d_shape = 64 +DENSE_REGULARIZER = tf.keras.regularizers.l2(1e-9) +NEIGHBOURS = [64,64,64,64] + +############################################################################### +### Define Model ############################################################## +############################################################################### + +def GravNet(name, + x, cprime, hit_energy, t_idx, + rs, + d_shape, + n_neighbours, + debug_outdir, + plot_debug_every, + space_reg_strength=-1e-2, + ): + + x= Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x= Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x= Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + + x = Concatenate()([cprime, x]) + + x = BatchNormalization()(x) + + xgn, gncoords, gnnidx, gndist = RaggedGravNet( + name = "GravNet_"+name, + n_neighbours=n_neighbours, + n_dimensions=4, + n_filters=d_shape, + n_propagate=d_shape, + coord_initialiser_noise=1e-3, + feature_activation='tanh', + )([x, rs]) + + if space_reg_strength > 0: + gndist = LLRegulariseGravNetSpace(name=f'gravnet_coords_reg_{name}' , + record_metrics=True, + scale=space_reg_strength)([gndist, cprime, gnnidx]) + + gncoords = PlotCoordinates( + plot_every=plot_debug_every, + outdir = debug_outdir, + name=f'gncoords_{name}', + #publish='wandb' + )([gncoords, hit_energy, t_idx, rs]) + + x = DummyLayer()([x, gncoords]) #just so the branch is not optimised away, anyway used further down + x = BatchNormalization()(x) + + return Concatenate()([xgn, x]) + + + + +def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): + """ + Function that defines the model to train + """ + + ########################################################################### + ### Pre-processing step ################################################### + ########################################################################### + + orig_input = td.interpretAllModelInputs(Inputs) + pre_processed = condition_input(orig_input, no_scaling=True, no_prime=False, new_prime=True) + + prime_coords = pre_processed['prime_coords'] + + c_coords = prime_coords + is_track = pre_processed['is_track'] + rs = pre_processed['row_splits'] + energy = pre_processed['rechit_energy'] + t_idx = pre_processed['t_idx'] + x = pre_processed['features'] + + x = ProcessFeaturesCocoa()(x) + + c_coords = PlotCoordinates( + plot_every=plot_debug_every, + outdir=debug_outdir, + name='input_c_coords', + # publish='wandb' + )([c_coords, energy, t_idx, rs]) + + c_coords = extent_coords_if_needed(c_coords, x, 3) + + x = Concatenate()([x, c_coords, is_track]) + x = Dense(64, name='dense_pre_loop', activation=DENSE_ACTIVATION)(x) + + allfeat = [] + print("Available keys: ", pre_processed.keys()) + + ########################################################################### + ### Loop over GravNet Layers ############################################## + ########################################################################### + + + for i in range(len(NEIGHBOURS)): + + + x = GravNet(f'gncomb_{i}', x, prime_coords, energy, t_idx, rs, + d_shape, NEIGHBOURS[i], debug_outdir, plot_debug_every, space_reg_strength=-1e-2) + + #x = LayerNormalization()(x) + allfeat.append(x) + if len(allfeat) > 1: + x = Concatenate()(allfeat) + else: + x = allfeat[0] + + + ########################################################################### + ### Create output of model and define loss ################################ + ########################################################################### + + + x = Dense(128, + name=f"dense_final_{1}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = BatchNormalization()(x) + x = Dense(64, + name=f"dense_final_{2}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = Dense(64, + name=f"dense_final_{3}", + activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, + kernel_regularizer=DENSE_REGULARIZER)(x) + x = BatchNormalization()(x) + + pred_beta, pred_ccoords, pred_dist, \ + pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, \ + pred_pos, pred_time, pred_time_unc, pred_id = \ + create_outputs(x, + n_ccoords=3, + fix_distance_scale=True, + is_track=is_track, + set_track_betas_to_one=True) + + + pred_beta = LLExtendedObjectCondensation3( + scale=1., + use_energy_weights=True, + record_metrics=True, + print_loss=train.args.no_wandb, + name="ExtendedOCLoss", + implementation = "hinge", + beta_loss_scale = 1.0, + too_much_beta_scale = 0.0, + energy_loss_weight = 0.4, + classification_loss_weight = 0.4, + position_loss_weight = 0.0, + timing_loss_weight = 0.0, + downweight_low_energy=False, + train_energy_unc=False, + q_min = 1.0, + use_average_cc_pos = 0.9999)( + [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, + pred_energy_high_quantile, pred_pos, pred_time, pred_time_unc, pred_id, energy, + pre_processed['t_idx'] , pre_processed['t_energy'] , pre_processed['t_pos'] , + pre_processed['t_time'] , pre_processed['t_pid'] , pre_processed['t_spectator_weight'], + pre_processed['t_fully_contained'], pre_processed['t_rec_energy'], + pre_processed['t_is_unique'], pre_processed['row_splits']]) + + pred_ccoords = PlotCoordinates( + plot_every=plot_debug_every, + outdir = debug_outdir, + name='condensation', + #publish='wandb' + )([pred_ccoords, pred_beta, pre_processed['t_idx'], rs]) + + model_outputs = { + 'pred_beta': pred_beta, + 'pred_ccoords': pred_ccoords, + 'pred_energy_corr_factor': pred_energy_corr, + 'pred_energy_low_quantile': pred_energy_low_quantile, + 'pred_energy_high_quantile': pred_energy_high_quantile, + 'pred_pos': pred_pos, + 'pred_time': pred_time, + 'pred_id': pred_id, + 'pred_dist': pred_dist, + 'rechit_energy': energy, + 'row_splits': pre_processed['row_splits'], + # 'no_noise_sel': pre_processed['no_noise_sel'], + # 'no_noise_rs': pre_processed['no_noise_rs'], + } + + # return DictModel(inputs=Inputs, outputs=model_outputs) + return Model(inputs=Inputs, outputs=model_outputs) + + +############################################################################### +### Set up training ########################################################### +############################################################################### + + + +if not train.modelSet(): + train.setModel( + config_model, + td=train.train_data.dataclass(), + debug_outdir=train.outputDir+'/intplots', + plot_debug_every=PLOT_FREQUENCY, + ) + #train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space + train.setCustomOptimizer(tf.keras.optimizers.Adam())#clipnorm=1.)) + train.compileModel(learningrate=1e-4) + train.keras_model.summary() + train.saveModel(train.outputDir+'/model_init.h5') + +############################################################################### +### Callbacks ################################################################# +############################################################################### + + +samplepath = train.val_data.getSamplePath(train.val_data.samples[0]) +PUBLISHPATH = "" +PUBLISHPATH += [d for d in train.outputDir.split('/') if len(d)][-1] + + +cb = []#[NanSweeper()] #this takes a bit of time checking each batch but could be worth it + +cb += [ + plotClusterSummary( + outputfile=train.outputDir + "/clustering/", + samplefile=train.val_data.getSamplePath(train.val_data.samples[0]), + after_n_batches=500 + ) + ] + + +# cb += [wandbCallback()] + +############################################################################### +### Actual Training ########################################################### +############################################################################### + +train.change_learning_rate(1e-3) +train.trainModel( + nepochs=2, + batchsize=50000, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, + collect_gradients = 4 #average out more gradients + ) + +# loop through model layers and turn batch normalisation to fixed +def fix_batchnorm(m): + for layer in m.layers: + if isinstance(layer, BatchNormalization): + layer.trainable = False + +#apply to all models +train.applyFunctionToAllModels(fix_batchnorm) + + +#recompile +train.compileModel(learningrate=5e-4) +print('entering second training phase') +train.trainModel( + nepochs=10, + batchsize=50000, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, + collect_gradients = 4 + ) + + +#recompile +train.compileModel(learningrate=1e-4) +print('entering third training phase') +train.trainModel( + nepochs=25, + batchsize=50000, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, + collect_gradients = 4 + ) \ No newline at end of file From 6548d49496a71d7f8660fa0c2a2d11ce140d0066 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 9 Jul 2024 11:07:00 +0200 Subject: [PATCH 27/62] added plotting notebook --- Train/paper_trainer_Cocoa_simple.py | 12 +- modules/extra_plots.py | 3 + scripts/generatePlots.ipynb | 518 ++++++++++++++++++++++++++++ scripts/submitViaHTCondor.py | 2 +- 4 files changed, 528 insertions(+), 7 deletions(-) create mode 100644 scripts/generatePlots.ipynb diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index 9a8d0f73..dc7f69bb 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -105,7 +105,7 @@ def GravNet(name, plot_every=plot_debug_every, outdir = debug_outdir, name=f'gncoords_{name}', - publish='wandb' + #publish='wandb' )([gncoords, hit_energy, t_idx, rs]) x = DummyLayer()([x, gncoords]) #just so the branch is not optimised away, anyway used further down @@ -143,7 +143,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): plot_every=plot_debug_every, outdir=debug_outdir, name='input_c_coords', - publish='wandb' + # publish='wandb' )([c_coords, energy, t_idx, rs]) c_coords = extent_coords_if_needed(c_coords, x, 3) @@ -203,7 +203,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): set_track_betas_to_one=True) - pred_beta = LLExtendedObjectCondensation3( + pred_beta = LLExtendedObjectCondensation5( scale=1., use_energy_weights=True, record_metrics=True, @@ -218,7 +218,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): timing_loss_weight = 0.0, downweight_low_energy=False, train_energy_unc=False, - q_min = 1.0, + q_min = 0.1, use_average_cc_pos = 0.9999)( [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, pred_pos, pred_time, pred_time_unc, pred_id, energy, @@ -231,7 +231,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): plot_every=plot_debug_every, outdir = debug_outdir, name='condensation', - publish='wandb' + #publish='wandb' )([pred_ccoords, pred_beta, pre_processed['t_idx'], rs]) model_outputs = { @@ -335,7 +335,7 @@ def fix_batchnorm(m): train.compileModel(learningrate=1e-4) print('entering third training phase') train.trainModel( - nepochs=25, + nepochs=35, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, diff --git a/modules/extra_plots.py b/modules/extra_plots.py index 2acc337e..588e48f8 100644 --- a/modules/extra_plots.py +++ b/modules/extra_plots.py @@ -236,6 +236,7 @@ def energy_resolution(df, bins=None, binwidth=10., addfit=False): # ax2.hist(bins[1:], weights=entries, histtype='stepfilled', color='grey', alpha=0.2, zorder=1) ax2.hist(bins[1:], weights=entries, color='grey', alpha=0.2, zorder=1, label='Counts') yticks = ax2.get_yticks() + ax2.set_yticks(yticks) ax2.set_yticklabels(yticks, fontsize=20) ax2.set_title("Respone and Counts", fontsize=40) ax2.set_ylabel("Counts", fontsize=40) @@ -254,6 +255,7 @@ def energy_resolution(df, bins=None, binwidth=10., addfit=False): ax3.set_xticks(xticks) ax3.set_xticklabels(xticks, fontsize=20) yticks = ax3.get_yticks() + ax3.set_yticks(yticks) ax3.set_yticklabels(np.round(yticks, 3), fontsize=20) ax3.grid(alpha=0.5, linestyle='--') # fit resolution function to sigmaOverE @@ -572,6 +574,7 @@ def noise_performance(noise_df): return fig MAP_DICT = { + -1: 5, # Noise 0: 5, # Whatever 13: 0, # Muon -13: 0, diff --git a/scripts/generatePlots.ipynb b/scripts/generatePlots.ipynb new file mode 100644 index 00000000..cb6cfb0d --- /dev/null +++ b/scripts/generatePlots.ipynb @@ -0,0 +1,518 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "import gzip\n", + "import pickle\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import sys\n", + "\n", + "sys.path.append('../modules')\n", + "tf.config.set_visible_devices(tf.config.list_physical_devices('GPU')[1], 'GPU')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analysis data keys: dict_keys(['showers_dataframe', 'events_dataframe', 'scalar_variables', 'alpha_ids', 'noise_masks', 'matched', 'matched_showers', 'processed_dataframe', 'features', 'truth', 'prediction'])\n", + "shower keys Index(['pred_energy_tracks_raw', 'truthHitSpectatorFlag', 'pred_sid',\n", + " 'pred_ccoords', 'pred_no_minbias_fraction', 'pred_energy_corr_factor',\n", + " 'truthHitFullyContainedFlag', 'truthHitAssignedEnergies',\n", + " 'truthHitAssignedZ', 'pred_energy_low_quantile', 'pred_energy',\n", + " 'truthHitAssignedPhi', 'pred_energy_hits_corrected', 'pred_time',\n", + " 'truthHitAssignementIdx', 'truthHitAssignedEta',\n", + " 'pred_energy_tracks_corrected', 'pred_energy_hits_raw',\n", + " 'pred_energy_high_quantile', 'no_noise_sel', 'truthHitAssignedT',\n", + " 'pred_dist', 'truthHitAssignedY', 'pred_energy_unc', 'pred_id',\n", + " 'truthHitAssignedX', 't_rec_energy', 'truthHitAssignedPIDs', 'pred_pos',\n", + " 'event_id'],\n", + " dtype='object')\n", + "events None\n", + "scalar keys dict_keys(['beta_threshold', 'distance_threshold', 'iou_threshold', 'matching_mode', 'is_soft', 'de_e_cut', 'angle_cut'])\n", + "matched keys Index(['truth_sid', 'pred_sid', 'n_truth', 'e_truth', 'n_pred', 'e_pred',\n", + " 'n_pred_and_truth', 'e_pred_and_truth', 'n_pred_not_truth',\n", + " 'e_pred_not_truth', 'event_id'],\n", + " dtype='object')\n", + "processed keys Index(['pred_sid', 'pred_no_minbias_fraction', 'pred_energy',\n", + " 'pred_energy_hits_raw', 'pred_energy_hits_corrected',\n", + " 'pred_energy_tracks_raw', 'pred_energy_tracks_corrected',\n", + " 'pred_energy_unc', 'pred_ccoords_0', 'pred_ccoords_1', 'pred_ccoords_2',\n", + " 'pred_energy_corr_factor', 'pred_energy_low_quantile',\n", + " 'pred_energy_high_quantile', 'pred_pos_0', 'pred_pos_1', 'pred_time',\n", + " 'pred_dist', 'rechit_energy', 'no_noise_sel', 'event_id'],\n", + " dtype='object')\n", + "features list keys dict_keys(['recHitEnergy', 'recHitEta', 'recHitID', 'recHitTheta', 'recHitR', 'recHitX', 'recHitY', 'recHitZ', 'recHitTime', 'recHitHitR', 'recHitXY'])\n", + "truth list keys dict_keys(['truthHitAssignementIdx', 'truthHitAssignedEnergies', 'truthHitAssignedX', 'truthHitAssignedY', 'truthHitAssignedZ', 'truthHitAssignedEta', 'truthHitAssignedPhi', 'truthHitAssignedT', 'truthHitAssignedPIDs', 'truthHitSpectatorFlag', 'truthHitFullyContainedFlag', 't_rec_energy'])\n", + "prediction list keys dict_keys(['pred_beta', 'pred_ccoords', 'pred_energy_corr_factor', 'pred_energy_low_quantile', 'pred_energy_high_quantile', 'pred_pos', 'pred_time', 'pred_id', 'pred_dist', 'rechit_energy', 'row_splits', 'no_noise_sel'])\n" + ] + } + ], + "source": [ + "#Open file\n", + "filepath = '/work/friemer/hgcalml/SimpleCocoa/analyseSimpleCocoa/analysisoutput'\n", + "\n", + "# Open the file and load the data\n", + "with gzip.open(filepath, 'rb') as input_file:\n", + " analysis_data = pickle.load(input_file)\n", + " \n", + "# Print the keys of the data\n", + "print('Analysis data keys:', analysis_data.keys())\n", + "print('shower keys ',analysis_data['showers_dataframe'].keys())\n", + "print('events',analysis_data['events_dataframe'])\n", + "print('scalar keys',analysis_data['scalar_variables'].keys())\n", + "#print(analysis_data['alpha_ids'])\n", + "#print(analysis_data['noise_masks'])\n", + "#print(analysis_data['matched'])\n", + "print('matched keys',analysis_data['matched_showers'].keys())\n", + "print('processed keys',analysis_data['processed_dataframe'].keys())\n", + "print('features list keys', analysis_data['features'][0].keys())\n", + "print('truth list keys', analysis_data['truth'][0].keys())\n", + "print('prediction list keys', analysis_data['prediction'][0].keys())\n", + "\n", + "df = analysis_data['showers_dataframe']\n", + "t_list = analysis_data['truth']\n", + "pred_list = analysis_data['prediction']\n", + "proc_df = analysis_data['processed_dataframe']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neutral Efficiency, Fakerate, p" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_375296/2368924373.py:40: RuntimeWarning: invalid value encountered in long_scalars\n", + " fake_rate.append(np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]))\n", + "/tmp/ipykernel_375296/2368924373.py:42: RuntimeWarning: invalid value encountered in long_scalars\n", + " corr_class_prob.append(np.sum(matched[mask_PID_match]) / np.sum(mask_bintruth[mask_PID_match]))\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def help_calc_efficiencies(df, bins, mask=None):\n", + " n_trues = []\n", + " n_pred = []\n", + " n_fakes = []\n", + " n_matched = []\n", + " efficiencies = []\n", + " fake_rate = []\n", + " corr_class_prob = []\n", + " mask_predicted = np.isnan(df['pred_energy']) == False\n", + " mask_truth = np.isnan(df['truthHitAssignedEnergies']) == False\n", + " \n", + " if mask == None:\n", + " mask_PID_truth = np.ones(len(df), dtype=bool)\n", + " mask_PID_pred = np.ones(len(df), dtype=bool)\n", + " else:\n", + " mask_PID_truth = df['truthHitAssignedPIDs'].isin(mask)\n", + " mask_PID_pred = df['pred_id'].isin(mask) #TODO: change pred_id is not PID!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n", + " mask_PID_match = np.logical_and(mask_PID_truth, mask_PID_pred)\n", + " \n", + " for i in range(len(bins) - 1):\n", + " mask_bintruth = np.logical_and(\n", + " df['truthHitAssignedEnergies'] >= bins[i],\n", + " df['truthHitAssignedEnergies'] < bins[i + 1])\n", + " n_trues.append(np.sum(mask_bintruth))\n", + " mask_binpred = np.logical_and(\n", + " df['pred_energy'] >= bins[i],\n", + " df['pred_energy'] < bins[i + 1])\n", + " n_pred.append(np.sum(mask_binpred))\n", + "\n", + " matched = np.logical_and(\n", + " mask_predicted,\n", + " mask_bintruth)\n", + " n_matched.append(np.sum(matched))\n", + " faked = np.logical_and(\n", + " mask_binpred,\n", + " np.logical_not(mask_truth))\n", + " n_fakes.append(np.sum(faked))\n", + "\n", + " efficiencies.append(np.sum(matched[mask_PID_truth]) / np.sum(mask_bintruth[mask_PID_truth]))\n", + " fake_rate.append(np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]))\n", + " \n", + " corr_class_prob.append(np.sum(matched[mask_PID_match]) / np.sum(mask_bintruth[mask_PID_match]))\n", + " return np.array(n_trues), np.array(n_pred), np.array(n_matched), np.array(n_fakes), np.array(efficiencies), np.array(fake_rate), np.array(corr_class_prob)\n", + "def efficiency_plots(df, bins):\n", + " # Calculate the bin positions and widths\n", + " binwidth = bins[1:] - bins[:-1]\n", + " x_pos = bins[:-1] + binwidth / 2\n", + " x_err = binwidth / 2\n", + " \n", + " # Calculate the efficiencies and fake rates for the total\n", + " n_trues, n_pred, n_matched, n_fakes, efficiencies, fake_rate, corr_class_prob= help_calc_efficiencies(df, bins)\n", + " \n", + " y_eff = efficiencies *100\n", + " yerr_eff = np.sqrt(efficiencies * (1 - efficiencies) / n_trues)\n", + " \n", + " y_fake = fake_rate *100\n", + " yerr_fake = np.sqrt(fake_rate * (1 - fake_rate) / n_pred)\n", + " \n", + " y_corr = corr_class_prob*100\n", + " yerr_corr = np.sqrt(corr_class_prob * (1 - corr_class_prob) / n_trues)\n", + " \n", + " # Calculate the efficiencies and fake rates for the photons\n", + " n_trues_photon, n_pred_photon, n_matched_photon, n_fakes_photon, efficiencies_photon, \\\n", + " fake_rate_photon, corr_class_prob_photon = help_calc_efficiencies(df, bins,[22])\n", + " \n", + " \n", + " y_eff_photon = efficiencies_photon*100\n", + " yerr_eff_photon = np.sqrt(efficiencies_photon * (1 - efficiencies_photon) / n_trues_photon)\n", + " \n", + " y_fake_photon = fake_rate_photon*100\n", + " yerr_fake_photon = np.sqrt(fake_rate_photon * (1 - fake_rate_photon) / n_pred_photon)\n", + " \n", + " y_corr_photon = corr_class_prob_photon*100\n", + " yerr_corr_photon = np.sqrt(corr_class_prob_photon * (1 - corr_class_prob_photon) / n_trues_photon)\n", + " \n", + " # Calculate the efficiencies and fake rates for the neutral hadrons\n", + " #130: \"K0L\", 310: \"K0S\", 311: \"K0\", 2112: \"neutron\",-2112: \"antineutron\",3122: \"Lambda\",-3122: \"antilambda\"3322: \"Xi0\",-3322: \"antixi0\"\n", + " mask_n_h = [130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]\n", + " n_trues_neutral_hadrons, n_pred_neutral_hadrons, n_matched_neutral_hadrons, \\\n", + " n_fakes_neutral_hadrons, efficiencies_neutral_hadrons, fake_rate_neutral_hadrons, corr_class_prob_neutral_hadrons \\\n", + " = help_calc_efficiencies(df, bins, mask_n_h)\n", + " \n", + " y_eff_neutral_hadrons = efficiencies_neutral_hadrons*100\n", + " yerr_eff_neutral_hadrons = np.sqrt(efficiencies_neutral_hadrons * (1 - efficiencies_neutral_hadrons) / n_trues_neutral_hadrons)\n", + " \n", + " y_fake_neutral_hadrons = fake_rate_neutral_hadrons*100\n", + " yerr_fake_neutral_hadrons = np.sqrt(fake_rate_neutral_hadrons * (1 - fake_rate_neutral_hadrons) / n_pred_neutral_hadrons)\n", + " \n", + " y_corr_neutral_hadrons = corr_class_prob_neutral_hadrons*100\n", + " yerr_corr_neutral_hadrons = np.sqrt(corr_class_prob_neutral_hadrons * (1 - corr_class_prob_neutral_hadrons) / n_trues_neutral_hadrons)\n", + " \n", + " # Create the plots\n", + " fig, ((ax1, ax2, ax3), (ax4, ax5,ax6), (ax7,ax8,ax9)) = plt.subplots(nrows=3, ncols=3, figsize=(20, 10))\n", + " ax1.errorbar(x_pos, y_eff, xerr=x_err, yerr=yerr_eff, fmt='o', color='red', label='Efficiency')\n", + " ax1.set_ylabel('efficiency total', fontsize=10)\n", + " ax2.errorbar(x_pos, y_eff_photon, xerr=x_err, yerr=yerr_eff_photon, fmt='o', color='red', label='Efficiency')\n", + " ax2.set_ylabel('efficiency gamma', fontsize=10)\n", + " ax3.errorbar(x_pos, y_eff_neutral_hadrons, xerr=x_err, yerr=yerr_eff_neutral_hadrons, fmt='o', color='red', label='Efficiency')\n", + " ax3.set_ylabel('efficiency n.H.', fontsize=10)\n", + " \n", + " ax4.errorbar(x_pos, y_fake, xerr=x_err, yerr=yerr_fake, fmt='o', color='red', label='Fake rate')\n", + " ax4.set_ylabel('fake rate total', fontsize=10)\n", + " ax5.errorbar(x_pos, y_fake_photon, xerr=x_err, yerr=yerr_fake_photon, fmt='o', color='red', label='Fake rate')\n", + " ax5.set_ylabel('fake rate gamma', fontsize=10)\n", + " ax6.errorbar(x_pos, y_fake_neutral_hadrons, xerr=x_err, yerr=yerr_fake_neutral_hadrons, fmt='o', color='red', label='Fake rate')\n", + " ax6.set_ylabel('fake rate n.H.', fontsize=10)\n", + " \n", + " ax7.errorbar(x_pos, y_corr, xerr=x_err, yerr=yerr_corr, fmt='o', color='red', label='probability of correct class')\n", + " ax7.set_ylabel('p corr total', fontsize=10)\n", + " ax8.errorbar(x_pos, y_corr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', color='red', label='probability of correct class')\n", + " ax8.set_ylabel('p corr gamma', fontsize=10)\n", + " ax9.errorbar(x_pos, y_corr_neutral_hadrons, xerr=x_err, yerr=yerr_corr_neutral_hadrons, fmt='o', color='red', label='probability of correct class')\n", + " ax9.set_ylabel('p corr n.H.', fontsize=10)\n", + "\n", + " for ax in [ax1, ax2,ax3, ax4, ax5, ax6, ax7, ax8, ax9]: \n", + " ax.set_xticks(bins)\n", + " ax.set_xticklabels(bins, fontsize=10)\n", + " ax.set_xlim(bins[0], bins[-1])\n", + " ax.grid(True, which='major', axis='y', linestyle='--', alpha=0.5)\n", + " ax.grid(True, which='major', axis='x', linestyle='--', alpha=0.5)\n", + " ax.set_xlabel('Energy [GeV]', fontsize=10)\n", + " \n", + " yticks1 = np.round(np.arange(40, 101, 20), 1)\n", + " ax.set_yticks(yticks1)\n", + " ax.set_yticklabels([f\"{y}%\" for y in yticks1], fontsize=10)\n", + " ax.set_ylim(20, 101)\n", + "\n", + " return fig\n", + "energy_bins = np.array([1,2,3,4,5,10,20,30,50])*1000\n", + "fig=efficiency_plots(df, bins=energy_bins)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Jet nParticles, deltaE, deltaphi, deltaEta" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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enaeElak6nX+BIgSgXCpX+47OnDkjSQoODpYkpaSkqLCwUD179rQt07hxY9WqVUvJycnq2LGjkpOT1aJFC7tdZbGxsXrssce0b98+tW7d+or1FBQUqKCgwPY4NzfXVS8JcJn6oX5qXiPQ3TFKJT07z90RSoXdeID5lJsiVFxcrISEBHXq1EnNmzeXJGVlZcnT01NBQUF2y4aFhSkrK8u2zO9L0OX5l+eVZPbs2Zo+fbqTXwGAP6rq6ylvq4cSVqa6O0qpsBsPMJ9yU4Ti4uL03Xffadu2bS5f1+TJk5WYmGh7nJubq6ioKJevFzCbGkHe2ji+W4U4sJvdeIA5lYsiFB8fr7Vr12rr1q2qWbOmbXp4eLguXLignJwcu61Cx48fV3h4uG2ZXbt22Y13/Phx27ySeHl5ycvLy8mvAkBJagR5UywAlFtuvY6QYRiKj4/X6tWr9cUXX6hu3bp289u0aSOr1apNmzbZpqWlpSkzM1MxMTGSpJiYGO3du1fZ2dm2ZTZs2KCAgAA1bdq0bF4IAACokNy6RSguLk7Lly/XRx99JH9/f9sxPYGBgfL29lZgYKBGjBihxMREBQcHKyAgQI8//rhiYmLUsWNHSVKvXr3UtGlTPfjgg5o3b56ysrL0zDPPKC4ujq0+AADgmtxahBYvXixJ6t69u930JUuWaPjw4ZKkl19+WZUqVdKAAQPsLqh4mYeHh9auXavHHntMMTEx8vX11bBhwzRjxoyyehkAAKCCcmsRMgzjustUqVJFSUlJSkpKuuoytWvX1qeffurMaAAAwAS41xgAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAth4vQsmXL9Mknn9geT5w4UUFBQbrjjjv0n//8x6nhAAAAXMnhIvT888/L29tbkpScnKykpCTNmzdP1atX17hx45weEAAAwFUqO/qEI0eOqH79+pKkNWvWaMCAARo9erQ6deqk7t27OzsfAACAyzi8RcjPz08nT56UJK1fv1533323JKlKlSo6d+6cc9MBAAC4kMNbhO6++26NHDlSrVu31qFDh9S3b19J0r59+1SnTh1n5wMAAHAZh7cIJSUlKSYmRr/88os+/PBDVatWTZKUkpKiwYMHOz0gAACAqzi8RSg3N1cLFy5UpUr2HWratGk6cuSI04IBAAC4msNbhOrWrasTJ05cMf3UqVOqW7euU0IBAACUBYeLkGEYJU7Py8tTlSpVbjoQAABAWSn1rrHExERJksVi0bPPPisfHx/bvKKiIu3cuVPR0dFODwgAAOAqpS5C33zzjaRLW4T27t0rT09P2zxPT0+1atVKEyZMcH5CAAAAFyl1Efryyy8lSQ899JBeeeUVBQQEuCwUAABAWXD4rLElS5a4IgcAAECZc7gI5efna86cOdq0aZOys7NVXFxsN/+HH35wWjgAAABXcrgIjRw5Ulu2bNGDDz6oiIgIWSwWV+QCAABwOYeL0GeffaZPPvlEnTp1ckUeAACAMuPwdYSqVq2q4OBgV2QBAAAoUw4XoZkzZ+rZZ5/V2bNnXZEHAACgzDi8a2z+/PnKyMhQWFiY6tSpI6vVajf/66+/dlo4AAAAV3K4CPXr188FMQAAAMqew0Vo6tSprsgBAABQ5hw+RkiScnJy9MYbb2jy5Mk6deqUpEu7xH7++WenhgMAAHAlh7cI7dmzRz179lRgYKB+/PFHjRo1SsHBwVq1apUyMzP19ttvuyInAJSJ9Ow8d0colaq+nqoR5O3uGECF53ARSkxM1PDhwzVv3jz5+/vbpvft21cPPPCAU8MBQFmp6uspb6uHElamujtKqXhbPbRxfDfKEHCTHC5CX331lf72t79dMb1GjRrKyspySigAKGs1gry1cXw3nc6/4O4o15WenaeElak6nX+BIgTcJIeLkJeXl3Jzc6+YfujQIYWEhDglFAC4Q40gb4oFYDIOHyz9v//7v5oxY4YKCwslSRaLRZmZmZo0aZIGDBjg9IAAAACu4nARmj9/vvLy8hQaGqpz586pW7duql+/vvz9/fXcc8+5IiMAAIBLOLxrLDAwUBs2bNC2bdu0Z88e5eXl6fbbb1fPnj1dkQ8AAMBlHC5CR44cUVRUlDp37qzOnTu7IhMAAECZcHjXWJ06ddStWze9/vrrOn36tCsyAQAAlAmHi9Du3bvVvn17zZgxQxEREerXr58++OADFRQUuCIfAACAyzhchFq3bq0XXnhBmZmZ+uyzzxQSEqLRo0crLCxMDz/8sCsyAgAAuMQN3WtMunTa/J133qnXX39dGzduVN26dbVs2TJnZgMAAHCpGy5CP/30k+bNm6fo6Gi1b99efn5+SkpKcmY2AAAAl3L4rLG//e1vWr58ubZv367GjRtryJAh+uijj1S7dm1X5AMAAHAZh4vQrFmzNHjwYC1cuFCtWrVyRSYAAIAy4XARyszMlMVicUUWAACAMuXwMUIWi0X/+te/9Oc//1kxMTH6+eefJUl///vftW3bNqcHBAAAcBWHi9CHH36o2NhYeXt765tvvrFdP+jMmTN6/vnnnR4QAADAVRwuQrNmzdJrr72m119/XVar1Ta9U6dO+vrrr50aDgAAwJUcLkJpaWnq2rXrFdMDAwOVk5Pj0Fhbt27Vvffeq8jISFksFq1Zs8Zu/vDhw2WxWOx+evfubbfMqVOnNGTIEAUEBCgoKEgjRoxQXl6eoy8LAACYkMNFKDw8XOnp6VdM37Ztm2677TaHxsrPz1erVq2uef2h3r1769ixY7afFStW2M0fMmSI9u3bpw0bNmjt2rXaunWrRo8e7VAOAABgTg6fNTZq1CiNHTtWb731liwWi44ePark5GRNmDBBU6ZMcWisPn36qE+fPtdcxsvLS+Hh4SXOO3DggNatW6evvvpKbdu2lSQtWrRIffv21YsvvqjIyEiH8gAAAHNxuAg9+eSTKi4uVo8ePXT27Fl17dpVXl5emjBhgh5//HGnB9y8ebNCQ0NVtWpV3XXXXZo1a5aqVasmSUpOTlZQUJCtBElSz549ValSJe3cuVP3339/iWMWFBTY3SQ2NzfX6bkBAED553ARslgsevrpp/XEE08oPT1deXl5atq0qfz8/Jwernfv3urfv7/q1q2rjIwMPfXUU+rTp4+Sk5Pl4eGhrKwshYaG2j2ncuXKCg4OVlZW1lXHnT17tqZPn+70vAAAoGJxuAhd5unpqaZNmzozyxUGDRpk+71FixZq2bKl6tWrp82bN6tHjx43PO7kyZOVmJhoe5ybm6uoqKibygoAACqeG77pqjvcdtttql69uu1g7fDwcGVnZ9stc/HiRZ06deqqxxVJl447CggIsPsBAADmU6GK0E8//aSTJ08qIiJCkhQTE6OcnBylpKTYlvniiy9UXFysDh06uCsmAACoIG5415gz5OXl2Z2Kf/jwYaWmpio4OFjBwcGaPn26BgwYoPDwcGVkZGjixImqX7++YmNjJUlNmjRR7969NWrUKL322msqLCxUfHy8Bg0axBljcNjPOed0Ov+Cu2NcVZUTeaov6cipc+6OAgC3jFIVodtvv12bNm1S1apVNWPGDE2YMEE+Pj43vfLdu3frzjvvtD2+fNzOsGHDtHjxYu3Zs0fLli1TTk6OIiMj1atXL82cOVNeXl6257z77ruKj49Xjx49VKlSJQ0YMEALFy686Wwwl59zzqnn/C06V1jk7ihX1cxyWJ94SS+uT5O3tb6q+nq6OxIAVHilKkIHDhxQfn6+qlatqunTp+vRRx91ShHq3r27DMO46vzPP//8umMEBwdr+fLlN50F5nY6/4LOFRZpwcBo1Q91/hmQzlDlRKC0WnplULS8a7dRjSBvd0cCgAqvVEUoOjpaDz30kDp37izDMPTiiy9e9XT5Z5991qkBgbJUP9RPzWsEujtGySyXvnP1Q/yk8lSCco5IZ0+6O4U9n2pSEGeCAri+UhWhpUuXaurUqVq7dq0sFos+++wzVa585VMtFgtFCDCTnCNSUnup8Ky7k9iz+khxuyhDAK6rVEWoUaNGeu+99yRJlSpV0qZNm664kCEAEzp78lIJ6v+6VL2hu9NccuKQtGrUpWwUIQDX4fBZY8XFxa7IAaAiq95Qiox2dwoAcNgNnT6fkZGhBQsW6MCBA5Kkpk2bauzYsapXr55TwwEAALiSwxdU/Pzzz9W0aVPt2rVLLVu2VMuWLbVz5041a9ZMGzZscEVGAAAAl7ihu8+PGzdOc+bMuWL6pEmTdPfddzstHAAAgCs5vEXowIEDGjFixBXTH374Ye3fv98poQAAAMqCw0UoJCREqampV0xPTU3lTDIAAFChOLxrbNSoURo9erR++OEH3XHHHZKk7du3a+7cubZbZAAAAFQEDhehKVOmyN/fX/Pnz9fkyZMlSZGRkZo2bZrGjBnj9IAAAACu4nARslgsGjdunMaNG6dff/1VkuTv7+/0YAAAAK52Q9cRuowCBAAAKjKHD5YGAAC4VVCEAACAaVGEAACAaTlUhAoLC9WjRw99//33rsoDAABQZhwqQlarVXv27HFVFgAAgDLl8K6xP//5z3rzzTddkQUAAKBMOXz6/MWLF/XWW29p48aNatOmjXx9fe3mv/TSS04LBwAA4EoOF6HvvvtOt99+uyTp0KFDdvMsFotzUgEAAJQBh4vQl19+6YocAAAAZe6GT59PT0/X559/rnPnzkmSDMNwWigAAICy4HAROnnypHr06KGGDRuqb9++OnbsmCRpxIgRGj9+vNMDAgAAuIrDRWjcuHGyWq3KzMyUj4+PbfrAgQO1bt06p4YDAABwJYePEVq/fr0+//xz1axZ0256gwYN9J///MdpweBCOUeksyfdneI3Jw5dfxkAAFzA4SKUn59vtyXoslOnTsnLy8spoeBCOUekpPZS4Vl3J7Fn9ZF8qrk7BQDAZBwuQl26dNHbb7+tmTNnSrp0ynxxcbHmzZunO++80+kB4WRnT14qQf1fl6o3dHea3/hUk4Ki3J0CAGAyDhehefPmqUePHtq9e7cuXLigiRMnat++fTp16pS2b9/uioxwheoNpchod6cAAMCtHD5Yunnz5jp06JA6d+6s++67T/n5+erfv7+++eYb1atXzxUZAQAAXMLhLUKSFBgYqKefftrZWQAAAMrUDRWh06dP680339SBAwckSU2bNtVDDz2k4OBgp4YDAABwJYd3jW3dulV16tTRwoULdfr0aZ0+fVoLFy5U3bp1tXXrVldkBAAAcAmHtwjFxcVp4MCBWrx4sTw8PCRJRUVF+stf/qK4uDjt3bvX6SEBAABcweEtQunp6Ro/frytBEmSh4eHEhMTlZ6e7tRwAAAAruRwEbr99tttxwb93oEDB9SqVSunhAIAACgLpdo1tmfPHtvvY8aM0dixY5Wenq6OHTtKknbs2KGkpCTNmTPHNSkBAABcoFRFKDo6WhaLRYZh2KZNnDjxiuUeeOABDRw40HnpAAAAXKhURejw4cOuzgEAAFDmSlWEateu7eocAAAAZe6GLqh49OhRbdu2TdnZ2SouLrabN2bMGKcEAwAAcDWHi9DSpUv1yCOPyNPTU9WqVZPFYrHNs1gsFCEAAFBhOFyEpkyZomeffVaTJ09WpUoOn30PAABQbjjcZM6ePatBgwZRggAAQIXncJsZMWKE3n//fVdkAQAAKFMO7xqbPXu2/ud//kfr1q1TixYtZLVa7ea/9NJLTgsHoAQnDrk7wW/KUxYAuAE3VIQ+//xzNWrUSJKuOFgagIv4VJOsPtKqUe5OYs/qcykbAFRADheh+fPn66233tLw4cNdEAfAVQVFSXG7pLMn3Z3Enk+1S9kAoAJyuAh5eXmpU6dOrsgC4HqCoigdAOBEDh8sPXbsWC1atMgVWQAAAMqUw1uEdu3apS+++EJr165Vs2bNrjhYetWqVU4LBwAA4EoOF6GgoCD179/fFVkAAADKlMNFaMmSJa7IAQAAUOZu6KarAAD3S8/Oc3eEUqnq66kaQd7ujgGUyOEiVLdu3WteL+iHH364qUAAgGur6uspb6uHElamujtKqXhbPbRxfDfKEMolh4tQQkKC3ePCwkJ98803WrdunZ544gln5QIAXEWNIG9tHN9Np/MvuDvKdaVn5ylhZapO51+gCKFccrgIjR07tsTpSUlJ2r17t0Njbd26VS+88IJSUlJ07NgxrV69Wv369bPNNwxDU6dO1euvv66cnBx16tRJixcvVoMGDWzLnDp1So8//rg+/vhjVapUSQMGDNArr7wiPz8/R18aAFQYNYK8KRaAEzjtFvJ9+vTRhx9+6NBz8vPz1apVKyUlJZU4f968eVq4cKFee+017dy5U76+voqNjdX58+dtywwZMkT79u3Thg0btHbtWm3dulWjR4++qdcCAADMwWkHS3/wwQcKDg526Dl9+vRRnz59SpxnGIYWLFigZ555Rvfdd58k6e2331ZYWJjWrFmjQYMG6cCBA1q3bp2++uortW3bVpK0aNEi9e3bVy+++KIiIyNLHLugoEAFBQW2x7m5uQ7lBgAAtwaHi1Dr1q3tDpY2DENZWVn65Zdf9Oqrrzot2OHDh5WVlaWePXvapgUGBqpDhw5KTk7WoEGDlJycrKCgIFsJkqSePXuqUqVK2rlzp+6///4Sx549e7amT5/utKwAAKBicrgI/f4YHkmqVKmSQkJC1L17dzVu3NhZuZSVlSVJCgsLs5seFhZmm5eVlaXQ0FC7+ZUrV1ZwcLBtmZJMnjxZiYmJtse5ubmKiuL+TQAAmI3DRWjq1KmuyFGmvLy85OXl5e4YAADAzZx2sLSzhYeHS5KOHz9uN/348eO2eeHh4crOzrabf/HiRZ06dcq2DAAAwNWUughVqlRJHh4e1/ypXNl5F6quW7euwsPDtWnTJtu03Nxc7dy5UzExMZKkmJgY5eTkKCUlxbbMF198oeLiYnXo0MFpWQAAwK2p1M1l9erVV52XnJyshQsXqri42KGV5+XlKT093fb48OHDSk1NVXBwsGrVqqWEhATNmjVLDRo0UN26dTVlyhRFRkbajlNq0qSJevfurVGjRum1115TYWGh4uPjNWjQoKueMQYAAHBZqYvQ5VPYfy8tLU1PPvmkPv74Yw0ZMkQzZsxwaOW7d+/WnXfeaXt8+QDmYcOGaenSpZo4caLy8/M1evRo5eTkqHPnzlq3bp2qVKlie867776r+Ph49ejRw3ZBxYULFzqUAwAAmNMN7cs6evSopk6dqmXLlik2Nlapqalq3ry5w+N0795dhmFcdb7FYtGMGTOuWbCCg4O1fPlyh9cNAADg0MHSZ86c0aRJk1S/fn3t27dPmzZt0scff3xDJQgAAMDdSr1FaN68eZo7d67Cw8O1YsWKEneVAQAAVCSlLkJPPvmkvL29Vb9+fS1btkzLli0rcblVq1Y5LRwAAIArlboIDR061O7WGgAAABVdqYvQ0qVLXRgDAACg7JXbK0sDAAC4GkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYVmV3B8Ct7eecczqdf8HdMa4rPTvP3REAAG5AEYLL/JxzTj3nb9G5wiJ3RykVb6uHqvp6ujsGAKAMUYTgMqfzL+hcYZEWDIxW/VA/d8e5rqq+nqoR5O3uGACAMkQRgsvVD/VT8xqB7o4BAMAVOFgaAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFkUIAACYFjddBXBrOnHI3Ql+41NNCopydwoAJaAIAbi1+FSTrD7SqlHuTvIbq48Ut4syBJRDFCEAt5agqEul4+xJdye55MShS6Xs7EmKEFAOUYQA3HqCoigdAEqFg6UBAIBpUYQAAIBpUYQAAIBpUYQAAIBpUYQAAIBpUYQAAIBpUYQAAIBpUYQAAIBpcUFFAIDLpWfnuTtCqVT19VSNIG93x0AZoggBAFymqq+nvK0eSliZ6u4opeJt9dDG8d0oQyZSrovQtGnTNH36dLtpjRo10sGDByVJ58+f1/jx4/Xee++poKBAsbGxevXVVxUWFuaOuACAP6gR5K2N47vpdP4Fd0e5rvTsPCWsTNXp/AsUIRMp10VIkpo1a6aNGzfaHleu/FvkcePG6ZNPPtH777+vwMBAxcfHq3///tq+fbs7ogIASlAjyJtigXKr3BehypUrKzw8/IrpZ86c0Ztvvqnly5frrrvukiQtWbJETZo00Y4dO9SxY8eyjgoAACqYcn/W2Pfff6/IyEjddtttGjJkiDIzMyVJKSkpKiwsVM+ePW3LNm7cWLVq1VJycvI1xywoKFBubq7dDwAAMJ9yXYQ6dOigpUuXat26dVq8eLEOHz6sLl266Ndff1VWVpY8PT0VFBRk95ywsDBlZWVdc9zZs2crMDDQ9hMVFeXCVwEAAMqrcr1rrE+fPrbfW7ZsqQ4dOqh27dr6xz/+IW/vG9/fPHnyZCUmJtoe5+bmUoYAADChcr1F6I+CgoLUsGFDpaenKzw8XBcuXFBOTo7dMsePHy/xmKLf8/LyUkBAgN0PAAAwnwpVhPLy8pSRkaGIiAi1adNGVqtVmzZtss1PS0tTZmamYmJi3JgSAABUFOV619iECRN07733qnbt2jp69KimTp0qDw8PDR48WIGBgRoxYoQSExMVHBysgIAAPf7444qJieGMMQAAUCrlugj99NNPGjx4sE6ePKmQkBB17txZO3bsUEhIiCTp5ZdfVqVKlTRgwAC7CyoCAACURrkuQu+9994151epUkVJSUlKSkoqo0QAAOBWUqGOEQIAAHAmihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADAtihAAADCtyu4OAMf9nHNOp/Mv3NBzq5zIU31J6b/k6bxxxrnB/iA9O8+l4wOAK1SUf7uq+nqqRpC3u2NUeBShCubnnHPqOX+LzhUW3dDzm1kO6xMvaex7qdrn4iIkSd5WD1X19XT5egDgZlX19ZS31UMJK1PdHaVUvK0e2ji+G2XoJlGEKpjT+Rd0rrBICwZGq36on8PPr3IiUFotvTIoWuert3BBQnv8PxYAFUWNIG9tHN/thre4l6X07DwlrEzV6fwL/Bt7kyhCFVT9UD81rxHo+BMtl8pT/RA/KfIGng8At7AaQd4UC5PhYGkAAGBaFCEAAGBaFCEAAGBaFCEAAGBaFCEAAGBaFCEAAGBaFCEAAGBaXEcIAMrCiUPuTmDPp5oUFOXuFIDbUYQAwJV8qklWH2nVKHcnsWf1keJ2UYZgehQhAHCloKhLhePsSXcn+c2JQ5eK2dmTFCGYHkUIAFwtKIrCAZRTHCwNAABMiyIEAABMiyIEAABMiyIEAABMiyIEAABMi7PGXC3niFNPm61yIk/NLIdV5USgZPFzfIDydlE3AADciCLkSjlHpKT2UuFZpw1ZX9InXpJW38QgVp9LF3kDAMDkKEKudPbkpRLU/3WpekOnDJn+S57GvpeqVwZFq37IDWwRkri0PgAA/0URKgvVG0qR0U4Z6rxxRvuMMzpfvYUUGeiUMQEAMCsOlgYAAKZFEQIAAKZFEQIAAKbFMUIAYFbl7XIanMgBN6AIAYDZ+FS7dBmNVaPcncSe1UeK20UZQpmiCAGA2QRFXSocTrzY6007cehSMTt7kiKEMkURAgAzCoqicADiYGkAAGBibBECAKCCSs/Oc3eEUqnq66kaQd7ujlEiihAAABVMVV9PeVs9lLAy1d1RSsXb6qGN47uVyzJEEQIAoIKpEeStjeO76XT+BXdHua707DwlrEzV6fwLFCEAAOAcNYK8y2WxqGhumSKUlJSkF154QVlZWWrVqpUWLVqk9u3buzsWAMARXOQRZeyWKEIrV65UYmKiXnvtNXXo0EELFixQbGys0tLSFBoa6u54AIDr4SKPcJNbogi99NJLGjVqlB566CFJ0muvvaZPPvlEb731lp588kk3pwMAXBcXeYSbVPgidOHCBaWkpGjy5Mm2aZUqVVLPnj2VnJxc4nMKCgpUUFBge3zmzBlJUm5urnPD/ZonFRiX/tdJY+f9mqvigrPK+zVXubkWp4wJAOVCpUDJL9DdKX7jgn/DzciVf7cu/902DOOGx6jwRejEiRMqKipSWFiY3fSwsDAdPHiwxOfMnj1b06dPv2J6VJSLGv+cLk4fMmaB04cEAJTEBf+Gm5Er/279+uuvCgy8sRJd4YvQjZg8ebISExNtj4uLi3Xq1ClVq1ZNFgtbWW5Wbm6uoqKidOTIEQUEBLg7Dv6Lz6X84rMpn/hcyq/Ln01mZqYsFosiIyNveKwKX4SqV68uDw8PHT9+3G768ePHFR4eXuJzvLy85OXlZTctKCjIVRFNKyAggH88yiE+l/KLz6Z84nMpvwIDA2/6s6nw9xrz9PRUmzZttGnTJtu04uJibdq0STExMW5MBgAAyrsKv0VIkhITEzVs2DC1bdtW7du314IFC5Sfn287iwwAAKAkt0QRGjhwoH755Rc9++yzysrKUnR0tNatW3fFAdQoG15eXpo6deoVux/hXnwu5RefTfnE51J+OfOzsRg3c84ZAABABVbhjxECAAC4URQhAABgWhQhAABgWhQhAABgWhQhOMW0adNksVjsfho3buzuWKa0detW3XvvvYqMjJTFYtGaNWvs5huGoWeffVYRERHy9vZWz5499f3337snrIlc73MZPnz4Fd+h3r17uyesicyePVvt2rWTv7+/QkND1a9fP6Wlpdktc/78ecXFxalatWry8/PTgAEDrriIL5yvNJ9N9+7dr/jePProow6thyIEp2nWrJmOHTtm+9m2bZu7I5lSfn6+WrVqpaSkpBLnz5s3TwsXLtRrr72mnTt3ytfXV7GxsTp//nwZJzWX630uktS7d2+779CKFSvKMKE5bdmyRXFxcdqxY4c2bNigwsJC9erVS/n5+bZlxo0bp48//ljvv/++tmzZoqNHj6p///5uTG0OpflsJGnUqFF235t58+Y5tiIDcIKpU6carVq1cncM/IEkY/Xq1bbHxcXFRnh4uPHCCy/YpuXk5BheXl7GihUr3JDQnP74uRiGYQwbNsy477773JIHv8nOzjYkGVu2bDEM49L3w2q1Gu+//75tmQMHDhiSjOTkZHfFNKU/fjaGYRjdunUzxo4de1PjskUITvP9998rMjJSt912m4YMGaLMzEx3R8IfHD58WFlZWerZs6dtWmBgoDp06KDk5GQ3JoMkbd68WaGhoWrUqJEee+wxnTx50t2RTOfMmTOSpODgYElSSkqKCgsL7b4zjRs3Vq1atfjOlLE/fjaXvfvuu6pevbqaN2+uyZMn6+zZsw6Ne0tcWRru16FDBy1dulSNGjXSsWPHNH36dHXp0kXfffed/P393R0P/5WVlSVJV1x1PSwszDYP7tG7d2/1799fdevWVUZGhp566in16dNHycnJ8vDwcHc8UyguLlZCQoI6deqk5s2bS7r0nfH09Lzixtx8Z8pWSZ+NJD3wwAOqXbu2IiMjtWfPHk2aNElpaWlatWpVqcemCMEp+vTpY/u9ZcuW6tChg2rXrq1//OMfGjFihBuTARXDoEGDbL+3aNFCLVu2VL169bR582b16NHDjcnMIy4uTt999x3HN5ZDV/tsRo8ebfu9RYsWioiIUI8ePZSRkaF69eqVamx2jcElgoKC1LBhQ6Wnp7s7Cn4nPDxckq444+X48eO2eSgfbrvtNlWvXp3vUBmJj4/X2rVr9eWXX6pmzZq26eHh4bpw4YJycnLsluc7U3au9tmUpEOHDpLk0PeGIgSXyMvLU0ZGhiIiItwdBb9Tt25dhYeHa9OmTbZpubm52rlzp2JiYtyYDH/0008/6eTJk3yHXMwwDMXHx2v16tX64osvVLduXbv5bdq0kdVqtfvOpKWlKTMzk++Mi13vsylJamqqJDn0vWHXGJxiwoQJuvfee1W7dm0dPXpUU6dOlYeHhwYPHuzuaKaTl5dn9/+GDh8+rNTUVAUHB6tWrVpKSEjQrFmz1KBBA9WtW1dTpkxRZGSk+vXr577QJnCtzyU4OFjTp0/XgAEDFB4eroyMDE2cOFH169dXbGysG1Pf+uLi4rR8+XJ99NFH8vf3tx33ExgYKG9vbwUGBmrEiBFKTExUcHCwAgIC9PjjjysmJkYdO3Z0c/pb2/U+m4yMDC1fvlx9+/ZVtWrVtGfPHo0bN05du3ZVy5YtS7+imzrnDPivgQMHGhEREYanp6dRo0YNY+DAgUZ6erq7Y5nSl19+aUi64mfYsGGGYVw6hX7KlClGWFiY4eXlZfTo0cNIS0tzb2gTuNbncvbsWaNXr15GSEiIYbVajdq1axujRo0ysrKy3B37llfSZyLJWLJkiW2Zc+fOGX/5y1+MqlWrGj4+Psb9999vHDt2zH2hTeJ6n01mZqbRtWtXIzg42PDy8jLq169vPPHEE8aZM2ccWo/lvysDAAAwHY4RAgAApkURAgAApkURAgAApkURAgAApkURAgAApkURAgAApkURAgAApkURAgAApkURAm5RP/74oywWi+3eO+XBwYMH1bFjR1WpUkXR0dHujmNjsVi0Zs2aUi07bdo0l2YfPnw4tzsByhBFCHCR4cOHy2KxaM6cOXbT16xZI4vF4qZU7jV16lT5+voqLS3N7iaWZeVqJebYsWPq06dPmecpySuvvKKlS5e6O4ZTUOpQEVCEABeqUqWK5s6dq9OnT7s7itNcuHDhhp+bkZGhzp07q3bt2qpWrZoTU12bYRi6ePHiVeeHh4fLy8urzPJcS2BgoIKCgtwdAzANihDgQj179lR4eLhmz5591WVK2kqxYMEC1alTx/b48v+zfv755xUWFqagoCDNmDFDFy9e1BNPPKHg4GDVrFlTS5YsuWL8gwcP6o477lCVKlXUvHlzbdmyxW7+d999pz59+sjPz09hYWF68MEHdeLECdv87t27Kz4+XgkJCapevfpV74ZeXFysGTNmqGbNmvLy8lJ0dLTWrVtnm2+xWJSSkqIZM2bIYrFo2rRpJY5zeX3x8fEKDAxU9erVNWXKFP3+toh///vf1bZtW/n7+ys8PFwPPPCAsrOzbfM3b94si8Wizz77TG3atJGXl5feeecdTZ8+Xd9++60sFossFotty8sfd4399NNPGjx4sIKDg+Xr66u2bdtq586dJeaVpDfeeENNmjRRlSpV1LhxY7366qu2eRcuXFB8fLwiIiJUpUoV1a5d+5r/PfxxK0r37t01ZswYTZw4UcHBwQoPD7/qe1faTHfccYcmTZpkt/wvv/wiq9WqrVu3SpIKCgo0YcIE1ahRQ76+vurQoYM2b95sW37p0qUKCgrS559/riZNmsjPz0+9e/fWsWPHJF3673rZsmX66KOPbO/3758PlBtOvlksgP8aNmyYcd999xmrVq0yqlSpYhw5csQwDMNYvXq18fuv3tSpU41WrVrZPffll182ateubTeWv7+/ERcXZxw8eNB48803DUlGbGys8dxzzxmHDh0yZs6caVitVtt6Dh8+bEgyatasaXzwwQfG/v37jZEjRxr+/v7GiRMnDMMwjNOnTxshISHG5MmTjQMHDhhff/21cffddxt33nmnbd3dunUz/Pz8jCeeeMI4ePCgcfDgwRJf70svvWQEBAQYK1asMA4ePGhMnDjRsFqtxqFDhwzDMIxjx44ZzZo1M8aPH28cO3bM+PXXX0sc5/L6xo4daxw8eNB45513DB8fH+P//u//bMu8+eabxqeffmpkZGQYycnJRkxMjNGnTx/b/Mt3em/ZsqWxfv16Iz093fjpp5+M8ePHG82aNTOOHTtmHDt2zDh79qxhGJfucr169WrDMAzj119/NW677TajS5cuxr/+9S/j+++/N1auXGn8+9//LvHzeuedd4yIiAjjww8/NH744Qfjww8/NIKDg42lS5cahmEYL7zwghEVFWVs3brV+PHHH41//etfxvLly0t87Zc/6/vuu8/u/QgICDCmTZtmHDp0yFi2bJlhsViM9evXX3WM62X661//atSqVcsoLi62PWfRokV200aOHGnccccdxtatW4309HTjhRdeMLy8vGyf55IlSwyr1Wr07NnT+Oqrr4yUlBSjSZMmxgMPPGB7H//0pz8ZvXv3tr3fBQUFV80MuAtFCHCR3/9B69ixo/Hwww8bhnHjRah27dpGUVGRbVqjRo2MLl262B5fvHjR8PX1NVasWGEYxm9FaM6cObZlCgsLjZo1axpz5841DMMwZs6cafTq1ctu3UeOHDEkGWlpaYZhXPpD3Lp16+u+3sjISOO5556zm9auXTvjL3/5i+1xq1atjKlTp15znG7duhlNmjSx+yM9adIko0mTJld9zldffWVIspWry0VozZo1dsuV9F4bhn0R+tvf/mb4+/sbJ0+eLHFdfxyjXr16VxSbmTNnGjExMYZhGMbjjz9u3HXXXXav51pKKkKdO3e2W6Zdu3bGpEmTrjrG9TJlZ2cblStXNrZu3WqbHxMTYxvzP//5j+Hh4WH8/PPPdmP06NHDmDx5smEYl4qQJCM9Pd02PykpyQgLC7vqawHKI3aNAWVg7ty5WrZsmQ4cOHDDYzRr1kyVKv32lQ0LC1OLFi1sjz08PFStWjW7XUSSFBMTY/u9cuXKatu2rS3Ht99+qy+//FJ+fn62n8aNG0u6dDzPZW3atLlmttzcXB09elSdOnWym96pU6cbes0dO3a0O6A8JiZG33//vYqKiiRJKSkpuvfee1WrVi35+/urW7dukqTMzEy7cdq2bevwulNTU9W6dWsFBwdfd9n8/HxlZGRoxIgRdu/hrFmzbO/f8OHDlZqaqkaNGmnMmDFav369w5latmxp9zgiIuKKz9mRTCEhIerVq5feffddSdLhw4eVnJysIUOGSJL27t2roqIiNWzY0G6MLVu22P134ePjo3r16pUqF1BeVXZ3AMAMunbtqtjYWE2ePFnDhw+3m1epUiW7418kqbCw8IoxrFar3WOLxVLitOLi4lLnysvL07333qu5c+deMS8iIsL2u6+vb6nHdLX8/HzFxsYqNjZW7777rkJCQpSZmanY2NgrDuS+kdze3t6lXjYvL0+S9Prrr6tDhw528zw8PCRJt99+uw4fPqzPPvtMGzdu1J/+9Cf17NlTH3zwQanX48jnXJpMkjRkyBCNGTNGixYt0vLly9WiRQtbsc7Ly5OHh4dSUlLsniNJfn5+18z1x/+WgfKOIgSUkTlz5ig6OlqNGjWymx4SEqKsrCwZhmHbCuLMa//s2LFDXbt2lSRdvHhRKSkpio+Pl3Tpj/SHH36oOnXqqHLlG//nICAgQJGRkdq+fbtt64wkbd++Xe3bt3d4vD8emLxjxw41aNBAHh4eOnjwoE6ePKk5c+YoKipKkrR79+5Sjevp6WnbqnQ1LVu21BtvvKFTp05dd6tQWFiYIiMj9cMPP9i2ppQkICBAAwcO1MCBA/X//t//U+/evUs1/o0obab77rtPo0eP1rp167R8+XINHTrUNq9169YqKipSdna2unTpcsNZSvN+A+7GrjGgjLRo0UJDhgzRwoUL7aZ3795dv/zyi+bNm6eMjAwlJSXps88+c9p6k5KStHr1ah08eFBxcXE6ffq0Hn74YUlSXFycTp06pcGDB+urr75SRkaGPv/8cz300EMO/wF74oknNHfuXK1cuVJpaWl68sknlZqaqrFjxzqcOTMzU4mJiUpLS9OKFSu0aNEi2zi1atWSp6enFi1apB9++EH//Oc/NXPmzFKNW6dOHR0+fFipqak6ceKECgoKrlhm8ODBCg8PV79+/bR9+3b98MMP+vDDD5WcnFzimNOnT9fs2bO1cOFCHTp0SHv37tWSJUv00ksvSZJeeuklrVixQgcPHtShQ4f0/vvvKzw83KWnyF8vk3Rpa1m/fv00ZcoUHThwQIMHD7bNa9iwoYYMGaKhQ4dq1apVOnz4sHbt2qXZs2frk08+KXWOOnXqaM+ePUpLS9OJEydK3NIJuBtFCChDM2bMuGKXRpMmTfTqq68qKSlJrVq10q5duzRhwgSnrXPOnDmaM2eOWrVqpW3btumf//ynqlevLkm2rThFRUXq1auXWrRooYSEBAUFBdkdj1QaY8aMUWJiosaPH68WLVpo3bp1+uc//6kGDRo4nHno0KE6d+6c2rdvr7i4OI0dO1ajR4+WdGkL2tKlS/X++++radOmmjNnjl588cVSjTtgwAD17t1bd955p0JCQrRixYorlvH09NT69esVGhqqvn37qkWLFpozZ84Vu4guGzlypN544w0tWbJELVq0ULdu3bR06VLVrVtXkuTv76958+apbdu2ateunX788Ud9+umnDr+/jrhepsuGDBmib7/9Vl26dFGtWrXs5i1ZskRDhw7V+PHj1ahRI/Xr109fffXVFctdy6hRo9SoUSO1bdtWISEh2r59u1NeH+BMFoMdugDKke7duys6OloLFixwdxQAJsAWIQAAYFoUIQAAYFrsGgMAAKbFFiEAAGBaFCEAAGBaFCEAAGBaFCEAAGBaFCEAAGBaFCEAAGBaFCEAAGBaFCEAAGBa/x8PZ7fu1bFTOAAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Jet metrics\n", + "reseta = []\n", + "resphi = []\n", + "relresE = []\n", + "nParicles_pred = []\n", + "nParicles_truth = []\n", + "\n", + "for i in range(len(t_list)):\n", + " # reseta.append(np.sum(t_list[i]['truthHitAssignedEta'])-np.sum(pred_list[i]['pred_eta']))\n", + " # resphi.append(np.sum(t_list[i]['truthHitAssignedPhi'])-np.sum(pred_list[i]['pred_phi']))\n", + " jet_E_truth = np.sum(np.unique(t_list[i]['truthHitAssignedEnergies']))\n", + " jet_E_pred = np.sum(np.unique(df[df['event_id']==i]['pred_energy']))\n", + " relresE.append((jet_E_truth-jet_E_pred)/jet_E_truth)\n", + " nParicles_pred.append(len(np.unique(df[df['event_id']==i]['pred_energy'])))\n", + " nParicles_truth.append(len(np.unique(t_list[i]['truthHitAssignementIdx'])))\n", + " \n", + "# plt.hist(reseta)\n", + "# plt.hist(resphi)\n", + "\n", + "plt.hist(nParicles_pred, label='pred', histtype='step')\n", + "plt.hist(nParicles_truth, label='truth', histtype='step')\n", + "plt.ylabel('Number of events')\n", + "plt.xlabel('Number of particles in event')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "plt.hist(relresE)\n", + "plt.ylabel('Number of events')\n", + "plt.xlabel('Relative Jet energy residual')\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neutral Particles E, phi, eta" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "vscode": { + "languageId": "ruby" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322])\n", + "\n", + "pt_pred = df[mask_neutral]['pred_energy']\n", + "pt_truth = df[mask_neutral]['truthHitAssignedEnergies']\n", + "\n", + "# pred_ccoords = df[mask_neutral]['pred_ccoords']\n", + "# #calculate eta and phi from ccoords\n", + "# theta_pred = df[mask_neutral]['truthHitAssignedX']/np.sqrt(df[mask_neutral]['truthHitAssignedX']**2 + df[mask_neutral]['truthHitAssignedY']**2 + df[mask_neutral]['truthHitAssignedZ']**2)\n", + "# eta_pred = -np.log(np.tan(theta_pred/2))\n", + "# phi_pred = np.arctan2(df[mask_neutral]['truthHitAssignedY'], df[mask_neutral]['truthHitAssignedX'])\n", + "\n", + "\n", + "# eta_truth = df[mask_neutral]['truthHitAssignedEta']\n", + "# phi_truth = df[mask_neutral]['truthHitAssignedPhi']\n", + "\n", + "relrespt = (pt_pred - pt_truth) / pt_truth\n", + "relreseta = (eta_pred - eta_truth) / eta_truth\n", + "relresphi = (phi_pred - phi_truth) / phi_truth\n", + "\n", + "plt.hist(relrespt)\n", + "plt.xlabel('relres Energy')\n", + "plt.ylabel('Number of neutral particles')\n", + "plt.show()\n", + "\n", + "# plt.hist(relreseta)\n", + "# plt.xlabel('relres Eta')\n", + "# plt.ylabel('Number of neutral particles')\n", + "# plt.show()\n", + "\n", + "# plt.hist(relresphi)\n", + "# plt.xlabel('relres Phi')\n", + "# plt.ylabel('Number of neutral particles')\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Energy Resolution" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def energy_resolution(df, bins=None):\n", + " data, ratios = calc_resolution(df, bins)\n", + " means = data['means']\n", + " stddevs = data['stddevs']\n", + " sigma_over_e = data['sigma_over_e']\n", + " std_error = data['std_error']\n", + " entries = data['entries']\n", + "\n", + " # make boxplots of the ratios for each bin\n", + " fig, ax1 = plt.subplots(figsize=(20, 10))\n", + " # ax1 and ax2 for histogram + mean\n", + " # ax3 and ax4 for boxplot and stdddev\n", + " binwidth = bins[1:] - bins[:-1]\n", + " x = bins[:-1] + binwidth / 2\n", + " xerr = binwidth / 2\n", + " y = means\n", + " yerr = std_error\n", + " # plot x, y with error bars\n", + " ax1.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='o', color='blue', zorder=2, label='Mean Response')\n", + " # set xticks\n", + " xticks = np.round(bins, 0).astype(int)\n", + " ax1.set_xticks(xticks)\n", + " ax1.set_xticklabels(xticks, fontsize=20)\n", + " ymin = np.round(ax1.get_ylim()[0], 1)\n", + " ymax = np.round(ax1.get_ylim()[1], 1)\n", + " ydelta = max(1 - ymin, ymax - 1)\n", + " ax1.set_ylim(1 - ydelta, 1 + ydelta)\n", + " # set yticks to be evenlyt spaced around 1\n", + " yticks = np.round(np.arange(1 - ydelta, 1 + ydelta, 0.02), 2)\n", + " yticks = np.round(np.linspace(1 - ydelta, 1 + ydelta, 11), 2)\n", + " ax1.set_yticks(yticks)\n", + " ax1.set_yticklabels(yticks, fontsize=20)\n", + " ax1.grid(alpha=0.5, linestyle='--')\n", + " ax1.set_ylabel('Mean Response', fontsize=40)\n", + " ax1.set_xlabel('Energy [GeV]', fontsize=40)\n", + " \n", + " return fig\n", + "\n", + "def calc_resolution(showers, bins, predstring='pred_energy'):\n", + " \"\"\"Calculate resolution for a given dataframe and energy bins.\"\"\"\n", + " has_truth = np.isnan(showers['truthHitAssignedEnergies']) == False\n", + " has_pred = np.isnan(showers[predstring]) == False\n", + " matched = np.logical_and(has_truth, has_pred)\n", + "\n", + " all_ratios = []\n", + " means = []\n", + " stddevs = []\n", + " sigma_over_e = []\n", + " std_error = []\n", + " entries = []\n", + "\n", + " for i in range(len(bins) - 1):\n", + " mask_truth_bin = np.logical_and(\n", + " showers['truthHitAssignedEnergies'] >= bins[i],\n", + " showers['truthHitAssignedEnergies'] < bins[i + 1])\n", + " mask_bin = np.logical_and(mask_truth_bin, matched)\n", + " ratios = showers[predstring][mask_bin] / showers['truthHitAssignedEnergies'][mask_bin]\n", + " all_ratios.append(ratios)\n", + " means.append(np.mean(all_ratios[-1]))\n", + " stddevs.append(np.std(all_ratios[-1]))\n", + " sigma_over_e.append(stddevs[-1] / means[-1])\n", + " std_error.append(stddevs[-1] / np.sqrt(len(all_ratios[-1])))\n", + " entries.append(len(all_ratios[-1]))\n", + "\n", + " data = pd.DataFrame({\n", + " \"means\": np.array(means),\n", + " \"stddevs\": np.array(stddevs),\n", + " \"sigma_over_e\": np.array(sigma_over_e),\n", + " \"std_error\": np.array(std_error),\n", + " \"entries\": np.array(entries),\n", + " })\n", + "\n", + " return data, all_ratios\n", + "\n", + "energy_bins = np.array([15,20,30,50,200])*1000\n", + "fig=energy_resolution(df, bins=energy_bins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 08ff73ad..767fa1ce 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -245,7 +245,7 @@ def __str__(self) -> str: if os.path.basename(opts['f'].value)=='HGCalML': runscript_temp += f''' #Clean up -rm HGCalML.tar.gz +rm ZipFolder.tar.gz rm -r HGCalML rm *.djctd rm *.djcdc From b9939fb4ab154ffe9b38338d510523ff2e77b762 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 9 Jul 2024 14:36:59 +0200 Subject: [PATCH 28/62] train phi and eta --- Train/paper_trainer_Cocoa_simple.py | 3 +- modules/LossLayers.py | 10 +++ modules/datastructures/TrainData_Cocoa.py | 14 ++-- scripts/generatePlots.ipynb | 96 +++++++++++++++-------- 4 files changed, 80 insertions(+), 43 deletions(-) diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index dc7f69bb..2bd21088 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -198,6 +198,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): pred_pos, pred_time, pred_time_unc, pred_id = \ create_outputs(x, n_ccoords=3, + n_pos = 3, fix_distance_scale=True, is_track=is_track, set_track_betas_to_one=True) @@ -214,7 +215,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): too_much_beta_scale = 0.0, energy_loss_weight = 0.4, classification_loss_weight = 0.4, - position_loss_weight = 0.0, + position_loss_weight = 1.0, timing_loss_weight = 0.0, downweight_low_energy=False, train_energy_unc=False, diff --git a/modules/LossLayers.py b/modules/LossLayers.py index de73e6e1..4c6eccc5 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3145,6 +3145,16 @@ def calc_energy_correction_factor_loss(self, return tf.concat([prediction_loss, matching_loss + uncertainty_loss], axis=-1) else: return prediction_loss, uncertainty_loss + matching_loss + + def calc_position_loss(self, t_pos, pred_pos): + if tf.shape(pred_pos)[-1] == 2:#also has z component, but don't use it here + raise ValueError("Position loss is implemented for cosphi, sinphi, eta") + if not self.position_loss_weight: + return 0.*tf.reduce_sum((pred_pos-t_pos)**2,axis=-1, keepdims=True) + + ploss = tf.reduce_sum(tf.math.abs(t_pos - pred_pos)**2, axis=1) + ploss = tf.debugging.check_numerics(ploss, "ploss loss") + return ploss class LLFullObjectCondensationUncertainty(LLFullObjectCondensation): diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index 18fdc7f3..3e1c026e 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -12,7 +12,7 @@ def convertFromSourceFile(self, filename, weighterobjects, istraining): #open File with uproot.open(filename) as file: events = file[file.keys()[0]] - data = events.arrays(library='ak')#[0:100]#JUST FOR TESTING REMOVE SLICING LATER!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + data = events.arrays(library='ak')#[0:10]#JUST FOR TESTING REMOVE SLICING LATER!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! #convert data to training data trainData,rs = self.converttotrainingdfvec(data) @@ -63,15 +63,13 @@ def convertevent(self, event): df_particle['particle_idx'] = np.arange(len(event['particle_e'])) df_particle['t_energy'] = ak.to_dataframe(event['particle_e'], how='outer') - df_particle['t_pos'] = 0 #will be calculated later df_particle['t_time'] = 0 df_particle['t_pid'] = ak.to_dataframe(event['particle_pdgid'], how='outer') df_particle['t_spectator'] = 0 df_particle['t_fully_contained'] = 1 df_particle['t_rec_energy'] = ak.to_dataframe(event['particle_dep_energy'], how='outer') - df_particle['particle_prod_x'] = ak.to_dataframe(event['particle_prod_x'], how='outer') - df_particle['particle_prod_y'] = ak.to_dataframe(event['particle_prod_y'], how='outer') - df_particle['particle_prod_z'] = ak.to_dataframe(event['particle_prod_z'], how='outer') + df_particle['particle_eta'] = ak.to_dataframe(event['particle_eta_lay0'], how='outer') + df_particle['particle_phi'] = ak.to_dataframe(event['particle_phi_lay0'], how='outer') #convert track information to df df_track = pd.DataFrame() @@ -102,15 +100,15 @@ def convertevent(self, event): #set NaN values (particle information for hits made from background noise to -1) df_training.fillna(-1, inplace=True) + + #encode pos as cos(phi), sin(phi), eta + df_training['t_pos'] = df_training.apply(lambda row: [np.cos(row['particle_phi']), np.sin(row['particle_phi']), row['particle_eta']], axis=1) #set t_is_unique df_training['t_is_unique'] = 0 first_occurrence_mask = (df_training['t_idx'] != -1) & (df_training.groupby('t_idx').cumcount() == 0) df_training.loc[first_occurrence_mask, 't_is_unique'] = 1 - - df_training['t_pos'] = df_training.apply(lambda row: [row['particle_prod_x'], row['particle_prod_y'], row['particle_prod_z']], axis=1) - return df_training def converttotrainingdfvec(self, data): diff --git a/scripts/generatePlots.ipynb b/scripts/generatePlots.ipynb index cb6cfb0d..bb338a31 100644 --- a/scripts/generatePlots.ipynb +++ b/scripts/generatePlots.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 52, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (3.0.4) doesn't match a supported version!\n", + " warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n" + ] + } + ], "source": [ "import gzip\n", "import pickle\n", @@ -20,9 +29,18 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-09 13:30:52.998924: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-07-09 13:31:03.615007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11236 MB memory: -> device: 1, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:03:00.0, compute capability: 5.2\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -97,17 +115,19 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_375296/2368924373.py:40: RuntimeWarning: invalid value encountered in long_scalars\n", + "/tmp/ipykernel_378932/2368924373.py:40: RuntimeWarning: invalid value encountered in long_scalars\n", " fake_rate.append(np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]))\n", - "/tmp/ipykernel_375296/2368924373.py:42: RuntimeWarning: invalid value encountered in long_scalars\n", - " corr_class_prob.append(np.sum(matched[mask_PID_match]) / np.sum(mask_bintruth[mask_PID_match]))\n" + "/tmp/ipykernel_378932/2368924373.py:42: RuntimeWarning: invalid value encountered in long_scalars\n", + " corr_class_prob.append(np.sum(matched[mask_PID_match]) / np.sum(mask_bintruth[mask_PID_match]))\n", + "/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n", + " xys = np.asarray(xys)\n" ] }, { @@ -263,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -330,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 9, "metadata": { "vscode": { "languageId": "ruby" @@ -338,14 +358,24 @@ }, "outputs": [ { - "data": { - "image/png": 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crODgYLVp00YxMTE6fPiwzZyKigrFxcXJ19dXfn5+GjduXIMbR+7bt08DBw5U69atFRoaqtTUVHs3GwAAXMXsLkQdO3a87MseZ86cUa9evbR06dJLLk9NTVVaWpqWL1+unTt3ytvbW7Gxsfrxxx+tc+Li4nTgwAFlZWUpIyNDOTk5mjBhgnV5VVWVBg8erI4dOyo/P19/+ctfNGfOHK1YscLeTQcAAFcpk8VisVzJBw8ePKiSkhKdO3fOZvzee++9siAmkzZs2KARI0ZIurB3KCQkRNOmTdNTTz0lSaqsrFRgYKDS09M1evRoHTp0SBEREcrLy1P//v0lSZmZmRo6dKi+/vprhYSEaNmyZXr22WdVWloqT09PSdKMGTO0ceNGff75543KVlVVJbPZrMrKSvn6+l7R9l1O2Iz3HL5OZ/ty/jBXRwAA4LLs+ftt9zlEx44d0/3336/CwkLruUOSrHeudtQ5RMXFxSotLVVMTIx1zGw2KyoqSrm5uRo9erRyc3Pl5+dnLUOSFBMTIzc3N+3cuVP333+/cnNzdccdd1jLkCTFxsZqwYIFOnXqlNq1a9fgu2tqalRTU2N9X1VV5ZBtAgAAzZPdh8wmT56s8PBwlZeX65prrtGBAweUk5Oj/v37a+vWrQ4LVlpaKkkKDAy0GQ8MDLQuKy0tVUBAgM1yDw8P+fv728y51Dp++h0/l5KSIrPZbH1xE0oAAK5udhei3NxczZs3T9dee63c3Nzk5uamAQMGKCUlxebKs5Zs5syZqqystL6OHz/u6kgAAMCJ7C5EdXV18vHxkSRde+21OnHihKQLJ1sXFRU5LFhQUJAkqayszGa8rKzMuiwoKEjl5eU2y8+fP6+KigqbOZdax0+/4+e8vLzk6+tr8wIAAFcvuwtRjx499Nlnn0mSoqKilJqaqu3bt2vevHnq1KmTw4KFh4crKChI2dnZ1rGqqirt3LnT+hDZ6OhonT59Wvn5+dY5mzdvVn19vaKioqxzcnJyVFtba52TlZWlLl26XPL8IQAAYDx2F6LnnnvO+iyzefPmqbi4WAMHDtT777+vtLQ0u9ZVXV2tgoICFRQUSLpwInVBQYFKSkpkMpk0ZcoUvfDCC3rnnXdUWFioMWPGKCQkxHolWrdu3TRkyBCNHz9eu3bt0vbt25WYmKjRo0crJCREkvT73/9enp6eGjdunA4cOKA333xTL7/8spKSkuzddAAAcJW64svuf6qiokLt2rWzXmnWWFu3btWvf/3rBuPx8fFKT0+XxWLR7NmztWLFCp0+fVoDBgzQq6++qs6dO9t8d2Jiot599125ublp5MiRSktLU9u2ba1z9u3bp4SEBOXl5enaa6/VxIkTNX369Ebn5LL7hrjsHgDQ3Nnz9/t/KkQXTza+2q/CohA1RCECADR39vz9tvuQ2fnz5zVr1iyZzWaFhYUpLCxMZrNZzz33nM15OgAAAC2F3TdmnDhxotavX6/U1FTryc25ubmaM2eO/vOf/2jZsmUODwkAAOBMdheitWvXat26dbrnnnusY5GRkQoNDdXDDz9MIQIAAC2O3YfMvLy8FBYW1mA8PDzc5vEYAAAALYXdhSgxMVHPP/+8zbO+ampq9OKLLyoxMdGh4QAAAJqC3YfM9u7dq+zsbN1www3q1auXJOmzzz7TuXPnNGjQID3wwAPWuevXr3dcUgAAACexuxD5+flp5MiRNmNX+2X3AADg6mZ3IVq5cqUzcgAAALiM3ecQAQAAXG0oRAAAwPAoRAAAwPAoRAAAwPAoRAAAwPAadZVZWlpao1c4adKkKw4DAADgCo0qRIsXL27UykwmE4UIAAC0OI0qRMXFxc7OAQAA4DKcQwQAAAzP7jtVS9LXX3+td955RyUlJTp37pzNskWLFjkkGAAAQFOxuxBlZ2fr3nvvVadOnfT555+rR48e+vLLL2WxWNS3b19nZAQAAHAquw+ZzZw5U0899ZQKCwvVunVrvf322zp+/LjuvPNOjRo1yhkZAQAAnMruQnTo0CGNGTNGkuTh4aEffvhBbdu21bx587RgwQKHBwQAAHA2uwuRt7e39byh4OBgHT161Lrsu+++c1wyAACAJmL3OUS33nqrPvnkE3Xr1k1Dhw7VtGnTVFhYqPXr1+vWW291RkYAAACnsrsQLVq0SNXV1ZKkuXPnqrq6Wm+++aZuuukmrjADAAAtkl2FqK6uTl9//bUiIyMlXTh8tnz5cqcEAwAAaCp2FSJ3d3cNHjxYhw4dkp+fn5MioSUIm/GeqyPY7cv5w1wdAQDQTNl9UnWPHj107NgxZ2QBAABwCbsL0QsvvKCnnnpKGRkZOnnypKqqqmxeAAAALY3dJ1UPHTpUknTvvffKZDJZxy0Wi0wmk+rq6hyXDgAAoAnYXYi2bNnijBwAAAAuY3chCg8PV2hoqM3eIenCHqLjx487LBgAAEBTsfscovDwcH377bcNxisqKhQeHu6QUAAAAE3J7kJ08Vyhn6uurlbr1q0dEgoAAKApNfqQWVJSkiTJZDJp1qxZuuaaa6zL6urqtHPnTvXu3dvhAQEAAJyt0YVo7969ki7sISosLJSnp6d1maenp3r16qWnnnrK8QkBAACcrNGF6OLVZWPHjtXLL78sX19fp4UCAABoSnafQ7Ry5comK0N1dXWaNWuWwsPD1aZNG/3qV7/S888/L4vFYp1jsViUnJys4OBgtWnTRjExMTp8+LDNeioqKhQXFydfX1/5+flp3Lhx1gfUAgAA2H3Z/d13333Z5Zs3b77iMD+3YMECLVu2TKtWrVL37t21e/dujR07VmazWZMmTZIkpaamKi0tTatWrVJ4eLhmzZql2NhYHTx40HqSd1xcnE6ePKmsrCzV1tZq7NixmjBhgtauXeuwrAAAoOWyuxD16tXL5n1tba0KCgq0f/9+xcfHOyyYJO3YsUP33Xefhg278FDOsLAwvfHGG9q1a5ekC3uHlixZoueee0733XefJGn16tUKDAzUxo0bNXr0aB06dEiZmZnKy8tT//79JUmvvPKKhg4dqoULFyokJMShmQEAQMtjdyFavHjxJcfnzJnj8MNQt912m1asWKEvvvhCnTt31meffaZPPvlEixYtkiQVFxertLRUMTEx1s+YzWZFRUUpNzdXo0ePVm5urvz8/KxlSJJiYmLk5uamnTt36v7772/wvTU1NaqpqbG+5xltAABc3ew+h+iX/OEPf9Drr7/uqNVJkmbMmKHRo0era9euatWqlfr06aMpU6YoLi5OklRaWipJCgwMtPlcYGCgdVlpaakCAgJslnt4eMjf39865+dSUlJkNputr9DQUIduFwAAaF4cVohyc3MdfmPGf/7zn1qzZo3Wrl2rPXv2aNWqVVq4cKFWrVrl0O/5uZkzZ6qystL64pEkAABc3ew+ZPbAAw/YvLdYLDp58qR2796tWbNmOSyYJD399NPWvUSS1LNnT3311VdKSUlRfHy8goKCJEllZWUKDg62fq6srMx6k8igoCCVl5fbrPf8+fOqqKiwfv7nvLy85OXl5dBtAQAAzZfde4h+eijJbDbL399fd911l95//33Nnj3boeHOnj0rNzfbiO7u7qqvr5d04blqQUFBys7Oti6vqqrSzp07FR0dLUmKjo7W6dOnlZ+fb52zefNm1dfXKyoqyqF5AQBAy2T3HqKVK1c6I8clDR8+XC+++KI6dOig7t27a+/evVq0aJEee+wxSRceIzJlyhS98MILuummm6yX3YeEhGjEiBGSpG7dumnIkCEaP368li9frtraWiUmJmr06NFcYQYAACRdQSGSpNOnT+utt97S0aNH9fTTT8vf31979uxRYGCgrr/+eoeFe+WVVzRr1iz96U9/Unl5uUJCQvTEE08oOTnZOueZZ57RmTNnNGHCBJ0+fVoDBgxQZmamzflMa9asUWJiogYNGiQ3NzeNHDlSaWlpDssJAABaNpPlp7d9boR9+/Zp0KBB8vPz05dffqmioiJ16tRJzz33nEpKSrR69WpnZXWZqqoqmc1mVVZWOuUu3WEz3nP4OtHQl/OHuToCAKAJ2fP32+5ziJKSkjR27FgdPnzYZi/M0KFDlZOTY39aAAAAF7O7EOXl5emJJ55oMH799df/4n19AAAAmjO7C5GXl9cl79z8xRdf6LrrrnNIKAAAgKZkdyG69957NW/ePNXW1kq6cKVXSUmJpk+frpEjRzo8IAAAgLPZXYheeuklVVdXKyAgQD/88IPuvPNO3XjjjfLx8dGLL77ojIwAAABOZfdl92azWVlZWfrkk0+0b98+VVdXq2/fvjYPWAUAAGhJrug+RJI0YMAADRgwwJFZAAAAXOKKClF2drays7NVXl5ufYzGRY5+4j0AAICz2V2I5s6dq3nz5ql///4KDg6WyWRyRi4AAIAmY3chWr58udLT0/XII484Iw8AAECTs/sqs3Pnzum2225zRhYAAACXsLsQPf7441q7dq0zsgAAALiE3YfMfvzxR61YsUIfffSRIiMj1apVK5vlixYtclg4AACApmB3Idq3b5969+4tSdq/f7/NMk6wBgAALZHdhWjLli3OyAEAAOAydp9DBAAAcLWhEAEAAMOjEAEAAMOjEAEAAMNrVCHq27evTp06JUmaN2+ezp4969RQAAAATalRhejQoUM6c+aMpAvPMquurnZqKAAAgKbUqMvue/furbFjx2rAgAGyWCxauHCh2rZte8m5ycnJDg0IAADgbI0qROnp6Zo9e7YyMjJkMpn0wQcfyMOj4UdNJhOFCAAAtDiNKkRdunTRunXrJElubm7Kzs5WQECAU4MBAAA0FbvvVF1fX++MHAAAAC5jdyGSpKNHj2rJkiU6dOiQJCkiIkKTJ0/Wr371K4eGAwAAaAp234do06ZNioiI0K5duxQZGanIyEjt3LlT3bt3V1ZWljMyAgAAOJXde4hmzJihqVOnav78+Q3Gp0+frt/85jcOCwcAANAU7N5DdOjQIY0bN67B+GOPPaaDBw86JBQAAEBTsrsQXXfddSooKGgwXlBQwJVnAACgRbL7kNn48eM1YcIEHTt2TLfddpskafv27VqwYIGSkpIcHhAAAMDZ7C5Es2bNko+Pj1566SXNnDlTkhQSEqI5c+Zo0qRJDg8IAADgbHYXIpPJpKlTp2rq1Kn6/vvvJUk+Pj4ODwYAANBUrug+RBdRhAAAwNXA7pOqAQAArjYUIgAAYHjNvhB98803+sMf/qD27durTZs26tmzp3bv3m1dbrFYlJycrODgYLVp00YxMTE6fPiwzToqKioUFxcnX19f+fn5ady4caqurm7qTQEAAM2UXYWotrZWgwYNalA4nOXUqVO6/fbb1apVK33wwQc6ePCgXnrpJbVr1846JzU1VWlpaVq+fLl27twpb29vxcbG6scff7TOiYuL04EDB5SVlaWMjAzl5ORowoQJTbINAACg+bPrpOpWrVpp3759zsrSwIIFCxQaGqqVK1dax8LDw63/bLFYtGTJEj333HO67777JEmrV69WYGCgNm7cqNGjR+vQoUPKzMxUXl6e+vfvL0l65ZVXNHToUC1cuFAhISENvrempkY1NTXW91VVVc7aRAAA0AzYfcjsD3/4g1577TVnZGngnXfeUf/+/TVq1CgFBASoT58++vvf/25dXlxcrNLSUsXExFjHzGazoqKilJubK0nKzc2Vn5+ftQxJUkxMjNzc3LRz585Lfm9KSorMZrP1FRoa6qQtBAAAzYHdl92fP39er7/+uj766CP169dP3t7eNssXLVrksHDHjh3TsmXLlJSUpP/7v/9TXl6eJk2aJE9PT8XHx6u0tFSSFBgYaPO5wMBA67LS0tIGjxTx8PCQv7+/dc7PzZw50+au21VVVZQiAACuYnYXov3796tv376SpC+++MJmmclkckyq/6++vl79+/fXn//8Z0lSnz59tH//fi1fvlzx8fEO/a6f8vLykpeXl9PWDwAAmhe7C9GWLVuckeOSgoODFRERYTPWrVs3vf3225KkoKAgSVJZWZmCg4Otc8rKytS7d2/rnPLycpt1nD9/XhUVFdbPAwAAY7viy+6PHDmiTZs26YcffpB04QRnR7v99ttVVFRkM/bFF1+oY8eOki6cYB0UFKTs7Gzr8qqqKu3cuVPR0dGSpOjoaJ0+fVr5+fnWOZs3b1Z9fb2ioqIcnhkAALQ8dhei//znPxo0aJA6d+6soUOH6uTJk5KkcePGadq0aQ4NN3XqVH366af685//rCNHjmjt2rVasWKFEhISJF04RDdlyhS98MILeuedd1RYWKgxY8YoJCREI0aMkHRhj9KQIUM0fvx47dq1S9u3b1diYqJGjx59ySvMAACA8dhdiKZOnapWrVqppKRE11xzjXX8oYceUmZmpkPD3XzzzdqwYYPeeOMN9ejRQ88//7yWLFmiuLg465xnnnlGEydO1IQJE3TzzTerurpamZmZat26tXXOmjVr1LVrVw0aNEhDhw7VgAEDtGLFCodmBQAALZfJYuexrqCgIG3atEm9evWSj4+PPvvsM3Xq1EnHjh1TZGTkVXkH6KqqKpnNZlVWVsrX19fh6w+b8Z7D14mGvpw/zNURAABNyJ6/33bvITpz5ozNnqGLKioquDILAAC0SHYXooEDB2r16tXW9yaTSfX19UpNTdWvf/1rh4YDAABoCnZfdp+amqpBgwZp9+7dOnfunJ555hkdOHBAFRUV2r59uzMyAgAAOJXde4h69OihL774QgMGDNB9992nM2fO6IEHHtDevXv1q1/9yhkZAQAAnMruPUTSheeFPfvss47OAgAA4BJXVIhOnTql1157TYcOHZIkRUREaOzYsfL393doOAAAgKZg9yGznJwchYWFKS0tTadOndKpU6eUlpam8PBw5eTkOCMjAACAU9m9hyghIUEPPfSQli1bJnd3d0lSXV2d/vSnPykhIUGFhYUODwkAAOBMdu8hOnLkiKZNm2YtQ5Lk7u6upKQkHTlyxKHhAAAAmoLdhahv377Wc4d+6tChQ+rVq5dDQgEAADSlRh0y27dvn/WfJ02apMmTJ+vIkSO69dZbJUmffvqpli5dqvnz5zsnJQAAgBM16llmbm5uMplM+m9TTSaT6urqHBauueBZZlcHnmUGAMZiz9/vRu0hKi4udkgwAACA5qhRhahjx47OzgEAAOAyV3RjxhMnTuiTTz5ReXm56uvrbZZNmjTJIcEAAACait2FKD09XU888YQ8PT3Vvn17mUwm6zKTyUQhAgAALY7dhWjWrFlKTk7WzJkz5eZm91X7AAAAzY7djebs2bMaPXo0ZQgAAFw17G4148aN07/+9S9nZAEAAHAJuw+ZpaSk6Le//a0yMzPVs2dPtWrVymb5okWLHBYOAACgKVxRIdq0aZO6dOkiSQ1OqgYAAGhp7C5EL730kl5//XU9+uijTogDAADQ9Ow+h8jLy0u33367M7IAAAC4hN2FaPLkyXrllVeckQUAAMAl7D5ktmvXLm3evFkZGRnq3r17g5Oq169f77BwAAAATcHuQuTn56cHHnjAGVkAAABcwu5CtHLlSmfkAAAAcBluNw0AAAzP7j1E4eHhl73f0LFjx/6nQAAAAE3N7kI0ZcoUm/e1tbXau3evMjMz9fTTTzsqFwAAQJOxuxBNnjz5kuNLly7V7t27/+dAAAAATc1h5xDdc889evvttx21OgAAgCbjsEL01ltvyd/f31GrAwAAaDJ2HzLr06ePzUnVFotFpaWl+vbbb/Xqq686NBwAAEBTsLsQjRgxwua9m5ubrrvuOt11113q2rWro3IBAAA0GbsL0ezZs52RAwAAwGVa1I0Z58+fL5PJZHPp/48//qiEhAS1b99ebdu21ciRI1VWVmbzuZKSEg0bNkzXXHONAgIC9PTTT+v8+fNNnB4AADRXjS5Ebm5ucnd3v+zLw8PuHU6NlpeXp7/97W+KjIy0GZ86dareffdd/etf/9K2bdt04sQJm2et1dXVadiwYTp37px27NihVatWKT09XcnJyU7LCgAAWpZGN5gNGzb84rLc3FylpaWpvr7eIaF+rrq6WnFxcfr73/+uF154wTpeWVmp1157TWvXrtXdd98t6cKz1rp166ZPP/1Ut956qz788EMdPHhQH330kQIDA9W7d289//zzmj59uubMmSNPT88G31dTU6Oamhrr+6qqKqdsFwAAaB4avYfovvvua/Dq2rWr0tPTtXDhQo0aNUpFRUVOCZmQkKBhw4YpJibGZjw/P1+1tbU24127dlWHDh2Um5sr6UJZ69mzpwIDA61zYmNjVVVVpQMHDlzy+1JSUmQ2m62v0NBQJ2wVAABoLq7oHKITJ05o/Pjx6tmzp86fP6+CggKtWrVKHTt2dHQ+rVu3Tnv27FFKSkqDZaWlpfL09JSfn5/NeGBgoEpLS61zflqGLi6/uOxSZs6cqcrKSuvr+PHjDtgSAADQXNl10k9lZaX+/Oc/65VXXlHv3r2VnZ2tgQMHOiubjh8/rsmTJysrK0utW7d22vf8nJeXl7y8vJrs+wAAgGs1eg9RamqqOnXqpIyMDL3xxhvasWOHU8uQdOGQWHl5ufr27SsPDw95eHho27ZtSktLk4eHhwIDA3Xu3DmdPn3a5nNlZWUKCgqSJAUFBTW46uzi+4tzAACAsTV6D9GMGTPUpk0b3XjjjVq1apVWrVp1yXnr1693WLhBgwapsLDQZmzs2LHq2rWrpk+frtDQULVq1UrZ2dkaOXKkJKmoqEglJSWKjo6WJEVHR+vFF19UeXm5AgICJElZWVny9fVVRESEw7ICAICWq9GFaMyYMTaP7GgKPj4+6tGjh82Yt7e32rdvbx0fN26ckpKS5O/vL19fX02cOFHR0dG69dZbJUmDBw9WRESEHnnkEaWmpqq0tFTPPfecEhISOCwGAAAk2VGI0tPTnRjjyi1evFhubm4aOXKkampqFBsba/NMNXd3d2VkZOjJJ59UdHS0vL29FR8fr3nz5rkwNQAAaE5MFovF4uoQzV1VVZXMZrMqKyvl6+vr8PWHzXjP4etEQ1/OH+bqCACAJmTP3+8W9egOAAAAZ6AQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw6MQAQAAw2vWhSglJUU333yzfHx8FBAQoBEjRqioqMhmzo8//qiEhAS1b99ebdu21ciRI1VWVmYzp6SkRMOGDdM111yjgIAAPf300zp//nxTbgoAAGjGmnUh2rZtmxISEvTpp58qKytLtbW1Gjx4sM6cOWOdM3XqVL377rv617/+pW3btunEiRN64IEHrMvr6uo0bNgwnTt3Tjt27NCqVauUnp6u5ORkV2wSAABohkwWi8Xi6hCN9e233yogIEDbtm3THXfcocrKSl133XVau3atHnzwQUnS559/rm7duik3N1e33nqrPvjgA/32t7/ViRMnFBgYKElavny5pk+frm+//Vaenp7/9XurqqpkNptVWVkpX19fh29X2Iz3HL5OXB2+nD/M1REAoMWy5+93s95D9HOVlZWSJH9/f0lSfn6+amtrFRMTY53TtWtXdejQQbm5uZKk3Nxc9ezZ01qGJCk2NlZVVVU6cODAJb+npqZGVVVVNi8AAHD1ajGFqL6+XlOmTNHtt9+uHj16SJJKS0vl6ekpPz8/m7mBgYEqLS21zvlpGbq4/OKyS0lJSZHZbLa+QkNDHbw1AACgOWkxhSghIUH79+/XunXrnP5dM2fOVGVlpfV1/Phxp38nAABwHQ9XB2iMxMREZWRkKCcnRzfccIN1PCgoSOfOndPp06dt9hKVlZUpKCjIOmfXrl0267t4FdrFOT/n5eUlLy8vB28FAABorpr1HiKLxaLExERt2LBBmzdvVnh4uM3yfv36qVWrVsrOzraOFRUVqaSkRNHR0ZKk6OhoFRYWqry83DonKytLvr6+ioiIaJoNAQAAzVqz3kOUkJCgtWvX6t///rd8fHys5/yYzWa1adNGZrNZ48aNU1JSkvz9/eXr66uJEycqOjpat956qyRp8ODBioiI0COPPKLU1FSVlpbqueeeU0JCAnuBAACApGZeiJYtWyZJuuuuu2zGV65cqUcffVSStHjxYrm5uWnkyJGqqalRbGysXn31Vetcd3d3ZWRk6Mknn1R0dLS8vb0VHx+vefPmNdVmAACAZq5F3YfIVbgPEVyF+xABwJW7au9DBAAA4AwUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgUIgAAYHgerg4A4JeFzXjP1RHs9uX8Ya6OAAB2Yw8RAAAwPAoRAAAwPAoRAAAwPAoRAAAwPAoRAAAwPAoRAAAwPAoRAAAwPAoRAAAwPAoRAAAwPAoRAAAwPAoRAAAwPJ5lBsCheP4agJaIPUQAAMDwKEQAAMDwDFWIli5dqrCwMLVu3VpRUVHatWuXqyMBAIBmwDDnEL355ptKSkrS8uXLFRUVpSVLlig2NlZFRUUKCAhwdTwALsR5TwBMFovF4uoQTSEqKko333yz/vrXv0qS6uvrFRoaqokTJ2rGjBmX/WxVVZXMZrMqKyvl6+vr8Gwt8V/GAGAUlM+Wy56/34bYQ3Tu3Dnl5+dr5syZ1jE3NzfFxMQoNze3wfyamhrV1NRY31dWVkq68MM6Q33NWaesFwDwv+sw9V+ujmAI++fGOnydF/9uN2bfjyEK0Xfffae6ujoFBgbajAcGBurzzz9vMD8lJUVz585tMB4aGuq0jAAAGJl5ifPW/f3338tsNl92jiEKkb1mzpyppKQk6/v6+npVVFSoffv2MplMDvueqqoqhYaG6vjx4045FNcS8Zs0xG/SEL9JQ/wmDfGbNGS038Risej7779XSEjIf51riEJ07bXXyt3dXWVlZTbjZWVlCgoKajDfy8tLXl5eNmN+fn5Oy+fr62uI/2Lag9+kIX6ThvhNGuI3aYjfpCEj/Sb/bc/QRYa47N7T01P9+vVTdna2day+vl7Z2dmKjo52YTIAANAcGGIPkSQlJSUpPj5e/fv31y233KIlS5bozJkzGjt2rKujAQAAFzNMIXrooYf07bffKjk5WaWlperdu7cyMzMbnGjdlLy8vDR79uwGh+eMjN+kIX6ThvhNGuI3aYjfpCF+k19mmPsQAQAA/BJDnEMEAABwORQiAABgeBQiAABgeBQiAABgeBSiZuLFF1/UbbfdpmuuucapN4FszpYuXaqwsDC1bt1aUVFR2rVrl6sjuVROTo6GDx+ukJAQmUwmbdy40dWRXC4lJUU333yzfHx8FBAQoBEjRqioqMjVsVxq2bJlioyMtN5oLzo6Wh988IGrYzUr8+fPl8lk0pQpU1wdxWXmzJkjk8lk8+rataurYzUrFKJm4ty5cxo1apSefPJJV0dxiTfffFNJSUmaPXu29uzZo169eik2Nlbl5eWujuYyZ86cUa9evbR06VJXR2k2tm3bpoSEBH366afKyspSbW2tBg8erDNnzrg6msvccMMNmj9/vvLz87V7927dfffduu+++3TgwAFXR2sW8vLy9Le//U2RkZGujuJy3bt318mTJ62vTz75xNWRmhcLmpWVK1dazGazq2M0uVtuucWSkJBgfV9XV2cJCQmxpKSkuDBV8yHJsmHDBlfHaHbKy8stkizbtm1zdZRmpV27dpZ//OMfro7hct9//73lpptusmRlZVnuvPNOy+TJk10dyWVmz55t6dWrl6tjNGvsIYLLnTt3Tvn5+YqJibGOubm5KSYmRrm5uS5MhuausrJSkuTv7+/iJM1DXV2d1q1bpzNnzvBYIkkJCQkaNmyYzb9bjOzw4cMKCQlRp06dFBcXp5KSEldHalYMc6dqNF/fffed6urqGtw1PDAwUJ9//rmLUqG5q6+v15QpU3T77berR48ero7jUoWFhYqOjtaPP/6otm3basOGDYqIiHB1LJdat26d9uzZo7y8PFdHaRaioqKUnp6uLl266OTJk5o7d64GDhyo/fv3y8fHx9XxmgX2EDnRjBkzGpzE9vMXf/CBK5OQkKD9+/dr3bp1ro7icl26dFFBQYF27typJ598UvHx8Tp48KCrY7nM8ePHNXnyZK1Zs0atW7d2dZxm4Z577tGoUaMUGRmp2NhYvf/++zp9+rT++c9/ujpas8EeIieaNm2aHn300cvO6dSpU9OEacauvfZaubu7q6yszGa8rKxMQUFBLkqF5iwxMVEZGRnKycnRDTfc4Oo4Lufp6akbb7xRktSvXz/l5eXp5Zdf1t/+9jcXJ3ON/Px8lZeXq2/fvtaxuro65eTk6K9//atqamrk7u7uwoSu5+fnp86dO+vIkSOujtJsUIic6LrrrtN1113n6hjNnqenp/r166fs7GyNGDFC0oXDIdnZ2UpMTHRtODQrFotFEydO1IYNG7R161aFh4e7OlKzVF9fr5qaGlfHcJlBgwapsLDQZmzs2LHq2rWrpk+fbvgyJEnV1dU6evSoHnnkEVdHaTYoRM1ESUmJKioqVFJSorq6OhUUFEiSbrzxRrVt29a14ZpAUlKS4uPj1b9/f91yyy1asmSJzpw5o7Fjx7o6mstUV1fb/L+34uJiFRQUyN/fXx06dHBhMtdJSEjQ2rVr9e9//1s+Pj4qLS2VJJnNZrVp08bF6Vxj5syZuueee9ShQwd9//33Wrt2rbZu3apNmza5OprL+Pj4NDivzNvbW+3btzfs+WZPPfWUhg8fro4dO+rEiROaPXu23N3d9fDDD7s6WvPh6svccEF8fLxFUoPXli1bXB2tybzyyiuWDh06WDw9PS233HKL5dNPP3V1JJfasmXLJf87ER8f7+poLnOp30OSZeXKla6O5jKPPfaYpWPHjhZPT0/LddddZxk0aJDlww8/dHWsZsfol90/9NBDluDgYIunp6fl+uuvtzz00EOWI0eOuDpWs2KyWCyWpq9hAAAAzQdXmQEAAMOjEAEAAMOjEAEAAMOjEAEAAMOjEAEAAMOjEAEAAMOjEAEAAMOjEAEAAMOjEAFoFu666y5NmTLF1TEAGBSFCIBhbd26VSaT6ZKvi89JA2AMPNwVgFOdO3dOnp6ezWY9l1JUVCRfX1+bsYCAAKd8lyTV1dXJZDLJzY3/Two0F/yvEYBD3XXXXUpMTNSUKVN07bXXKjY2VpK0f/9+3XPPPWrbtq0CAwP1yCOP6LvvvvvF9YSFhen555/XmDFj5OvrqwkTJkiSPvnkEw0cOFBt2rRRaGioJk2apDNnzlg/9+qrr+qmm25S69atFRgYqAcffPC/Zg4ICFBQUJDN62JZefTRRzVixAgtXLhQwcHBat++vRISElRbW2v9fE1NjZ566ildf/318vb2VlRUlLZu3Wpdnp6eLj8/P73zzjuKiIiQl5eXSkpKdPLkSQ0bNkxt2rRReHi41q5dq7CwMC1ZskSS9Nhjj+m3v/2tTdba2loFBATotdde+6/bBaDxKEQAHG7VqlXy9PTU9u3btXz5cp0+fVp33323+vTpo927dyszM1NlZWX63e9+d9n1LFy4UL169dLevXs1a9YsHT16VEOGDNHIkSO1b98+vfnmm/rkk0+UmJgoSdq9e7cmTZqkefPmqaioSJmZmbrjjjv+5+3ZsmWLjh49qi1btmjVqlVKT09Xenq6dXliYqJyc3O1bt067du3T6NGjdKQIUN0+PBh65yzZ89qwYIF+sc//qEDBw4oICBAY8aM0YkTJ7R161a9/fbbWrFihcrLy62fefzxx5WZmamTJ09axzIyMnT27Fk99NBD//N2AfgJCwA40J133mnp06ePzdjzzz9vGTx4sM3Y8ePHLZIsRUVF1s9NnjzZurxjx46WESNG2Hxm3LhxlgkTJtiMffzxxxY3NzfLDz/8YHn77bctvr6+lqqqqkZl3bJli0WSxdvb2+YVERFhnRMfH2/p2LGj5fz589axUaNGWR566CGLxWKxfPXVVxZ3d3fLN998Y7PuQYMGWWbOnGmxWCyWlStXWiRZCgoKrMsPHTpkkWTJy8uzjh0+fNgiybJ48WLrWEREhGXBggXW98OHD7c8+uijjdo+AI3HOUQAHK5fv3427z/77DNt2bJFbdu2bTD36NGj6ty58yXX079//wbr2bdvn9asWWMds1gsqq+vV3FxsX7zm9+oY8eO6tSpk4YMGaIhQ4bo/vvv1zXXXHPZvB9//LF8fHys71u1amWzvHv37nJ3d7e+Dw4OVmFhoSSpsLBQdXV1DbahpqZG7du3t7739PRUZGSk9X1RUZE8PDzUt29f69iNN96odu3a2azn8ccf14oVK/TMM8+orKxMH3zwgTZv3nzZ7QFgPwoRAIfz9va2eV9dXa3hw4drwYIFDeYGBwfbtZ4nnnhCkyZNajC3Q4cO8vT01J49e7R161Z9+OGHSk5O1pw5c5SXlyc/P79f/J7w8PDLLv95QTKZTKqvr7dmcnd3V35+vk1pkmRTANu0aSOTyfSL3/FLxowZoxkzZig3N1c7duxQeHi4Bg4caPd6AFwehQiA0/Xt21dvv/22wsLC5OFx5f/a6du3rw4ePKgbb7zxF+d4eHgoJiZGMTExmj17tvz8/LR582Y98MADV/y9l9OnTx/V1dWpvLzcrqLSpUsXnT9/Xnv37rXuUTty5IhOnTplM699+/YaMWKEVq5cqdzcXI0dO9ah+QFcwEnVAJwuISFBFRUVevjhh5WXl6ejR49q06ZNGjt2rOrq6hq9nunTp2vHjh1KTExUQUGBDh8+rH//+9/Wk6ozMjKUlpamgoICffXVV1q9erXq6+vVpUuXy663vLxcpaWlNq+fXkV2OZ07d1ZcXJzGjBmj9evXq7i4WLt27VJKSoree++9X/xc165dFRMTowkTJmjXrl3au3evJkyYcMk9SY8//rhWrVqlQ4cOKT4+vlG5ANiHQgTA6UJCQrR9+3bV1dVp8ODB6tmzp6ZMmSI/Pz+77sUTGRmpbdu26YsvvtDAgQPVp08fJScnKyQkRJLk5+en9evX6+6771a3bt20fPlyvfHGG+revftl19ulSxcFBwfbvPLz8xuda+XKlRozZoymTZumLl26aMSIEcrLy1OHDh0u+7nVq1crMDBQd9xxh+6//36NHz9ePj4+at26tc28mJgYBQcHKzY21rqtABzLZLFYLK4OAQCQvv76a4WGhuqjjz7SoEGDrOPV1dW6/vrrtXLlSqcd+gOMjnOIAMBFNm/erOrqavXs2VMnT57UM888o7CwMOu9k+rr6/Xdd9/ppZdekp+fn+69914XJwauXhQiAHCR2tpa/d///Z+OHTsmHx8f3XbbbVqzZo31qraSkhKFh4frhhtuUHp6+v90QjqAy+OQGQAAMDxOqgYAAIZHIQIAAIZHIQIAAIZHIQIAAIZHIQIAAIZHIQIAAIZHIQIAAIZHIQIAAIb3/wBs391wOs6PugAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Error encountered: Shape of passed values is (3349, 1), indices imply (3349, 3)\n", + "Verifying the structure of 'pred_pos'...\n", + "First item in 'pred_pos': -1.194890022277832\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'pos_pred_df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 19\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst item in \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpred_pos\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfirst_item\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# If the structure is incorrect, you might need to adjust how 'pred_pos' is being processed\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# For example, if 'pred_pos' does not contain lists or tuples of three elements, you need to correct this\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m phi_pred \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marctan2(\u001b[43mpos_pred_df\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msinphi\u001b[39m\u001b[38;5;124m'\u001b[39m], pos_pred_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcosphi\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 20\u001b[0m eta_pred \u001b[38;5;241m=\u001b[39m pos_pred_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meta\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 22\u001b[0m phi_truth \u001b[38;5;241m=\u001b[39m df[mask_neutral][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtruthHitAssignedPhi\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'pos_pred_df' is not defined" + ] } ], "source": [ @@ -354,15 +384,13 @@ "pt_pred = df[mask_neutral]['pred_energy']\n", "pt_truth = df[mask_neutral]['truthHitAssignedEnergies']\n", "\n", - "# pred_ccoords = df[mask_neutral]['pred_ccoords']\n", - "# #calculate eta and phi from ccoords\n", - "# theta_pred = df[mask_neutral]['truthHitAssignedX']/np.sqrt(df[mask_neutral]['truthHitAssignedX']**2 + df[mask_neutral]['truthHitAssignedY']**2 + df[mask_neutral]['truthHitAssignedZ']**2)\n", - "# eta_pred = -np.log(np.tan(theta_pred/2))\n", - "# phi_pred = np.arctan2(df[mask_neutral]['truthHitAssignedY'], df[mask_neutral]['truthHitAssignedX'])\n", + "pos_pred_df = pd.DataFrame(df[mask_neutral]['pred_pos'].tolist(), columns=['cosphi', 'sinphi', 'eta'])\n", "\n", + "phi_pred = np.arctan2(pos_pred_df['sinphi'], pos_pred_df['cosphi'])\n", + "eta_pred = pos_pred_df['eta']\n", "\n", - "# eta_truth = df[mask_neutral]['truthHitAssignedEta']\n", - "# phi_truth = df[mask_neutral]['truthHitAssignedPhi']\n", + "phi_truth = df[mask_neutral]['truthHitAssignedPhi']\n", + "eta_truth = df[mask_neutral]['truthHitAssignedEta']\n", "\n", "relrespt = (pt_pred - pt_truth) / pt_truth\n", "relreseta = (eta_pred - eta_truth) / eta_truth\n", @@ -373,15 +401,15 @@ "plt.ylabel('Number of neutral particles')\n", "plt.show()\n", "\n", - "# plt.hist(relreseta)\n", - "# plt.xlabel('relres Eta')\n", - "# plt.ylabel('Number of neutral particles')\n", - "# plt.show()\n", + "plt.hist(relreseta)\n", + "plt.xlabel('relres Eta')\n", + "plt.ylabel('Number of neutral particles')\n", + "plt.show()\n", "\n", - "# plt.hist(relresphi)\n", - "# plt.xlabel('relres Phi')\n", - "# plt.ylabel('Number of neutral particles')\n", - "# plt.show()" + "plt.hist(relresphi)\n", + "plt.xlabel('relres Phi')\n", + "plt.ylabel('Number of neutral particles')\n", + "plt.show()" ] }, { @@ -393,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -510,7 +538,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, From 8153a6772f68ed80f694f28b414cc5389835b396 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 9 Jul 2024 14:36:59 +0200 Subject: [PATCH 29/62] train phi and eta --- Train/paper_trainer_Cocoa_simple.py | 3 +- modules/LossLayers.py | 10 +++ modules/datastructures/TrainData_Cocoa.py | 14 ++-- scripts/generatePlots.ipynb | 96 +++++++++++++++-------- 4 files changed, 80 insertions(+), 43 deletions(-) diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index dc7f69bb..2bd21088 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -198,6 +198,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): pred_pos, pred_time, pred_time_unc, pred_id = \ create_outputs(x, n_ccoords=3, + n_pos = 3, fix_distance_scale=True, is_track=is_track, set_track_betas_to_one=True) @@ -214,7 +215,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): too_much_beta_scale = 0.0, energy_loss_weight = 0.4, classification_loss_weight = 0.4, - position_loss_weight = 0.0, + position_loss_weight = 1.0, timing_loss_weight = 0.0, downweight_low_energy=False, train_energy_unc=False, diff --git a/modules/LossLayers.py b/modules/LossLayers.py index de73e6e1..dc694f2c 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3145,6 +3145,16 @@ def calc_energy_correction_factor_loss(self, return tf.concat([prediction_loss, matching_loss + uncertainty_loss], axis=-1) else: return prediction_loss, uncertainty_loss + matching_loss + + def calc_position_loss(self, t_pos, pred_pos): + if tf.shape(pred_pos)[-1] == 2:#also has z component, but don't use it here + raise ValueError("Position loss is implemented for cosphi, sinphi, eta") + if not self.position_loss_weight: + return 0.*tf.reduce_sum((pred_pos-t_pos)**2,axis=-1, keepdims=True) + + ploss = tf.reduce_sum(tf.math.abs(t_pos - pred_pos)**2, axis=-1, keepdims=True) + ploss = tf.debugging.check_numerics(ploss, "ploss loss") + return ploss class LLFullObjectCondensationUncertainty(LLFullObjectCondensation): diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index 18fdc7f3..3e1c026e 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -12,7 +12,7 @@ def convertFromSourceFile(self, filename, weighterobjects, istraining): #open File with uproot.open(filename) as file: events = file[file.keys()[0]] - data = events.arrays(library='ak')#[0:100]#JUST FOR TESTING REMOVE SLICING LATER!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + data = events.arrays(library='ak')#[0:10]#JUST FOR TESTING REMOVE SLICING LATER!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! #convert data to training data trainData,rs = self.converttotrainingdfvec(data) @@ -63,15 +63,13 @@ def convertevent(self, event): df_particle['particle_idx'] = np.arange(len(event['particle_e'])) df_particle['t_energy'] = ak.to_dataframe(event['particle_e'], how='outer') - df_particle['t_pos'] = 0 #will be calculated later df_particle['t_time'] = 0 df_particle['t_pid'] = ak.to_dataframe(event['particle_pdgid'], how='outer') df_particle['t_spectator'] = 0 df_particle['t_fully_contained'] = 1 df_particle['t_rec_energy'] = ak.to_dataframe(event['particle_dep_energy'], how='outer') - df_particle['particle_prod_x'] = ak.to_dataframe(event['particle_prod_x'], how='outer') - df_particle['particle_prod_y'] = ak.to_dataframe(event['particle_prod_y'], how='outer') - df_particle['particle_prod_z'] = ak.to_dataframe(event['particle_prod_z'], how='outer') + df_particle['particle_eta'] = ak.to_dataframe(event['particle_eta_lay0'], how='outer') + df_particle['particle_phi'] = ak.to_dataframe(event['particle_phi_lay0'], how='outer') #convert track information to df df_track = pd.DataFrame() @@ -102,15 +100,15 @@ def convertevent(self, event): #set NaN values (particle information for hits made from background noise to -1) df_training.fillna(-1, inplace=True) + + #encode pos as cos(phi), sin(phi), eta + df_training['t_pos'] = df_training.apply(lambda row: [np.cos(row['particle_phi']), np.sin(row['particle_phi']), row['particle_eta']], axis=1) #set t_is_unique df_training['t_is_unique'] = 0 first_occurrence_mask = (df_training['t_idx'] != -1) & (df_training.groupby('t_idx').cumcount() == 0) df_training.loc[first_occurrence_mask, 't_is_unique'] = 1 - - df_training['t_pos'] = df_training.apply(lambda row: [row['particle_prod_x'], row['particle_prod_y'], row['particle_prod_z']], axis=1) - return df_training def converttotrainingdfvec(self, data): diff --git a/scripts/generatePlots.ipynb b/scripts/generatePlots.ipynb index cb6cfb0d..bb338a31 100644 --- a/scripts/generatePlots.ipynb +++ b/scripts/generatePlots.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 52, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (3.0.4) doesn't match a supported version!\n", + " warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n" + ] + } + ], "source": [ "import gzip\n", "import pickle\n", @@ -20,9 +29,18 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-09 13:30:52.998924: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-07-09 13:31:03.615007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11236 MB memory: -> device: 1, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:03:00.0, compute capability: 5.2\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -97,17 +115,19 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_375296/2368924373.py:40: RuntimeWarning: invalid value encountered in long_scalars\n", + "/tmp/ipykernel_378932/2368924373.py:40: RuntimeWarning: invalid value encountered in long_scalars\n", " fake_rate.append(np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]))\n", - "/tmp/ipykernel_375296/2368924373.py:42: RuntimeWarning: invalid value encountered in long_scalars\n", - " corr_class_prob.append(np.sum(matched[mask_PID_match]) / np.sum(mask_bintruth[mask_PID_match]))\n" + "/tmp/ipykernel_378932/2368924373.py:42: RuntimeWarning: invalid value encountered in long_scalars\n", + " corr_class_prob.append(np.sum(matched[mask_PID_match]) / np.sum(mask_bintruth[mask_PID_match]))\n", + "/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n", + " xys = np.asarray(xys)\n" ] }, { @@ -263,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -330,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 9, "metadata": { "vscode": { "languageId": "ruby" @@ -338,14 +358,24 @@ }, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Error encountered: Shape of passed values is (3349, 1), indices imply (3349, 3)\n", + "Verifying the structure of 'pred_pos'...\n", + "First item in 'pred_pos': -1.194890022277832\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'pos_pred_df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 19\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst item in \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpred_pos\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfirst_item\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# If the structure is incorrect, you might need to adjust how 'pred_pos' is being processed\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# For example, if 'pred_pos' does not contain lists or tuples of three elements, you need to correct this\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m phi_pred \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marctan2(\u001b[43mpos_pred_df\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msinphi\u001b[39m\u001b[38;5;124m'\u001b[39m], pos_pred_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcosphi\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 20\u001b[0m eta_pred \u001b[38;5;241m=\u001b[39m pos_pred_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meta\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 22\u001b[0m phi_truth \u001b[38;5;241m=\u001b[39m df[mask_neutral][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtruthHitAssignedPhi\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'pos_pred_df' is not defined" + ] } ], "source": [ @@ -354,15 +384,13 @@ "pt_pred = df[mask_neutral]['pred_energy']\n", "pt_truth = df[mask_neutral]['truthHitAssignedEnergies']\n", "\n", - "# pred_ccoords = df[mask_neutral]['pred_ccoords']\n", - "# #calculate eta and phi from ccoords\n", - "# theta_pred = df[mask_neutral]['truthHitAssignedX']/np.sqrt(df[mask_neutral]['truthHitAssignedX']**2 + df[mask_neutral]['truthHitAssignedY']**2 + df[mask_neutral]['truthHitAssignedZ']**2)\n", - "# eta_pred = -np.log(np.tan(theta_pred/2))\n", - "# phi_pred = np.arctan2(df[mask_neutral]['truthHitAssignedY'], df[mask_neutral]['truthHitAssignedX'])\n", + "pos_pred_df = pd.DataFrame(df[mask_neutral]['pred_pos'].tolist(), columns=['cosphi', 'sinphi', 'eta'])\n", "\n", + "phi_pred = np.arctan2(pos_pred_df['sinphi'], pos_pred_df['cosphi'])\n", + "eta_pred = pos_pred_df['eta']\n", "\n", - "# eta_truth = df[mask_neutral]['truthHitAssignedEta']\n", - "# phi_truth = df[mask_neutral]['truthHitAssignedPhi']\n", + "phi_truth = df[mask_neutral]['truthHitAssignedPhi']\n", + "eta_truth = df[mask_neutral]['truthHitAssignedEta']\n", "\n", "relrespt = (pt_pred - pt_truth) / pt_truth\n", "relreseta = (eta_pred - eta_truth) / eta_truth\n", @@ -373,15 +401,15 @@ "plt.ylabel('Number of neutral particles')\n", "plt.show()\n", "\n", - "# plt.hist(relreseta)\n", - "# plt.xlabel('relres Eta')\n", - "# plt.ylabel('Number of neutral particles')\n", - "# plt.show()\n", + "plt.hist(relreseta)\n", + "plt.xlabel('relres Eta')\n", + "plt.ylabel('Number of neutral particles')\n", + "plt.show()\n", "\n", - "# plt.hist(relresphi)\n", - "# plt.xlabel('relres Phi')\n", - "# plt.ylabel('Number of neutral particles')\n", - "# plt.show()" + "plt.hist(relresphi)\n", + "plt.xlabel('relres Phi')\n", + "plt.ylabel('Number of neutral particles')\n", + "plt.show()" ] }, { @@ -393,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -510,7 +538,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, From 2d6358f021838dbffd3285e81b2594765d5fbd11 Mon Sep 17 00:00:00 2001 From: Fabian Date: Wed, 10 Jul 2024 11:43:24 +0200 Subject: [PATCH 30/62] started working on new Mnot --- .../compiled/oc_helper_m_indices_kernel.cc | 36 ++++++++++++------ .../compiled/oc_helper_m_indices_kernel.cu.cc | 24 ++++++++---- modules/compiled/oc_helper_m_indices_kernel.h | 3 +- .../compiled/oc_helper_m_indices_module.cc | 3 +- modules/object_condensation.py | 37 ++++++++++++------- modules/oc_helper_ops.py | 23 +++++++++++- tests/testOC.py | 33 +++++++++++++++++ 7 files changed, 122 insertions(+), 37 deletions(-) diff --git a/modules/compiled/oc_helper_m_indices_kernel.cc b/modules/compiled/oc_helper_m_indices_kernel.cc index b1b1c30d..f4d9bfa0 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.cc +++ b/modules/compiled/oc_helper_m_indices_kernel.cc @@ -42,11 +42,12 @@ struct MIndicesOpFunctor { const int *rs, int * out_idx, - float * m_not, + int * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, + const int n_max_in_rs, const int n_rs, bool calc_m_not) { @@ -78,20 +79,25 @@ struct MIndicesOpFunctor { } + int mnot_index = 0; for(int i_v = 0; i_v < n_vert; i_v++ ){ //find row for i_v int rowFori_v= 0; while(rowFori_v+1= rs[rowFori_v+1]){ rowFori_v++; } - //compare rows - - if(rowFori_v!=rowForUqidx || (uqidx>=0 && d_truthidx[i_v] == uqidx)){ - m_not [I2D(k, i_v, n_vert)] = 0.0; - }else{ - m_not [I2D(k, i_v, n_vert)] = 1.0; + + //compare rows and index + if (rowFori_v == rowForUqidx && (uqidx < 0 || d_truthidx[i_v] != uqidx)){ + m_not [I2D(k, mnot_index, n_max_in_rs)] = i_v; + mnot_index++; } } + //fill rest with -1 + while(mnot_index < n_max_in_rs){ + m_not [I2D(k, mnot_index, n_max_in_rs)] = -1; + mnot_index++; + } } } @@ -120,20 +126,25 @@ class MIndicesOp : public OpKernel { const Tensor &t_uqtixs = context->input(1); const Tensor &t_nmax_puq = context->input(2);//needs to be evaluated in kernel const Tensor &t_rs = context->input(3); - + const Tensor &t_max_in_rs = context->input(4); const int n_vert = t_tidx.dim_size(0); const int n_unique = t_uqtixs.dim_size(0); - int n_max_per_unique=0; const int n_rs = t_rs.dim_size(0); - + int n_max_per_unique=0; MIndicesMaxUqOpFunctor()( context->eigen_device(), t_nmax_puq.flat().data(), &n_max_per_unique ); + int n_max_in_rs = 0; + MIndicesMaxUqOpFunctor()( + context->eigen_device(), + t_max_in_rs.flat().data(), + &n_max_in_rs + ); Tensor *out_idx = NULL; OP_REQUIRES_OK(context, context->allocate_output(0, @@ -142,7 +153,7 @@ class MIndicesOp : public OpKernel { Tensor *m_not = NULL; TensorShape m_not_shape={1, 1}; if(calc_m_not_) - m_not_shape={n_unique, n_vert}; + m_not_shape={n_unique, n_max_in_rs}; OP_REQUIRES_OK(context, context->allocate_output(1, m_not_shape, &m_not)); @@ -153,12 +164,13 @@ class MIndicesOp : public OpKernel { t_rs.flat().data(), out_idx->flat().data(), - m_not->flat().data(), + m_not->flat().data(), n_vert, n_unique, n_max_per_unique, n_rs, + n_max_in_rs, calc_m_not_ ); diff --git a/modules/compiled/oc_helper_m_indices_kernel.cu.cc b/modules/compiled/oc_helper_m_indices_kernel.cu.cc index 9b861492..f91536ca 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.cu.cc +++ b/modules/compiled/oc_helper_m_indices_kernel.cu.cc @@ -25,12 +25,13 @@ static void calc( const int *rs, int * out_idx, - float * m_not, + int * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, const int n_rs, + const int n_max_in_rs, bool calc_m_not){ @@ -61,20 +62,25 @@ static void calc( } + int mnot_index = 0; for(int i_v = 0; i_v < n_vert; i_v++ ){ //find row for i_v int rowFori_v= 0; while(rowFori_v+1= rs[rowFori_v+1]){ rowFori_v++; } - //compare rows - - if(rowFori_v!=rowForUqidx || (uqidx>=0 && d_truthidx[i_v] == uqidx)){ - m_not [I2D(k, i_v, n_vert)] = 0.0; - }else{ - m_not [I2D(k, i_v, n_vert)] = 1.0; + + //compare rows and index + if (rowFori_v == rowForUqidx && (uqidx < 0 || d_truthidx[i_v] != uqidx)){ + m_not [I2D(k, mnot_index, n_max_in_rs)] = i_v; + mnot_index++; } } + //fill rest with -1 + while(mnot_index < n_max_in_rs){ + m_not [I2D(k, mnot_index, n_max_in_rs)] = -1; + mnot_index++; + } } @@ -105,12 +111,13 @@ struct MIndicesOpFunctor { const int *rs, int * out_idx, - float * m_not, + int * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, const int n_rs, + const int n_max_in_rs, bool calc_m_not @@ -129,6 +136,7 @@ struct MIndicesOpFunctor { n_unique, n_max_per_unique, n_rs, + n_max_in_rs, calc_m_not ); diff --git a/modules/compiled/oc_helper_m_indices_kernel.h b/modules/compiled/oc_helper_m_indices_kernel.h index ebb2d4de..e9893630 100644 --- a/modules/compiled/oc_helper_m_indices_kernel.h +++ b/modules/compiled/oc_helper_m_indices_kernel.h @@ -24,12 +24,13 @@ struct MIndicesOpFunctor { const int *rs, int * out_idx, - float * m_not, + int * m_not, const int n_vert, const int n_unique, const int n_max_per_unique, const int n_rs, + const int n_max_in_rs, bool calc_m_not diff --git a/modules/compiled/oc_helper_m_indices_module.cc b/modules/compiled/oc_helper_m_indices_module.cc index 7a1159ef..5218f49d 100644 --- a/modules/compiled/oc_helper_m_indices_module.cc +++ b/modules/compiled/oc_helper_m_indices_module.cc @@ -19,7 +19,8 @@ REGISTER_OP("MIndices") .Input("unique_idxs: int32") .Input("nmax_per_unique: int32") .Input("rs: int32") + .Input("nmax_in_rs: int32") .Output("sel_idxs: int32") - .Output("m_not: float32"); + .Output("m_not: int32"); diff --git a/modules/object_condensation.py b/modules/object_condensation.py index 6c42e4ae..9eb9cf4c 100644 --- a/modules/object_condensation.py +++ b/modules/object_condensation.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- import tensorflow as tf -from oc_helper_ops import CreateMidx, SelectWithDefault +from oc_helper_ops import CreateMidx, SelectWithDefault, SelectWithDefaultMnot from binned_select_knn_op import BinnedSelectKnn @@ -226,25 +226,32 @@ def calc_dsq_rep(self): dsq = tf.expand_dims(self.x_k, axis=1) - tf.expand_dims(self.x_v, axis=0) #K x V x C dsq = tf.reduce_sum(dsq**2, axis=-1, keepdims=True) #K x V x 1 return dsq - + #@tf.function def V_rep_k(self): K = tf.reduce_sum(tf.ones_like(self.q_k)) - N_notk = tf.reduce_sum(self.Mnot, axis=1) + #future remark: if this gets too large, one could use a kNN/radiusNN here dsq = self.calc_dsq_rep() + + dsq_mnot = SelectWithDefaultMnot(self.Mnot, dsq, 0.) #K x V x 1 + # nogradbeta = tf.stop_gradient(self.beta_k_m) #weight. tf.reduce_sum( tf.exp(-dsq) * d_v_e, , axis=1) / tf.reduce_sum( tf.exp(-dsq) ) - sigma = self.weighted_d_k_m(dsq) #create gradients for all, but prefer k vertex - dsq = tf.math.divide_no_nan(dsq, sigma**2 + 1e-4) #K x V x 1 + sigma = self.weighted_d_k_m(dsq_mnot) #create gradients for all, but prefer k vertex + dsq_mnot = tf.math.divide_no_nan(dsq_mnot, sigma**2 + 1e-4) + + - V_rep = self.rep_func(dsq) * self.Mnot * tf.expand_dims(self.q_v,axis=0) #K x V x 1 + V_rep = self.rep_func(dsq_mnot) * tf.expand_dims(self.q_v,axis=0) if self.rep_range > 0: - N_notk = tf.reduce_sum(tf.where(dsq < self.rep_range**2 , 1., tf.zeros_like(dsq)) * self.Mnot, axis=1) + N_notk = tf.reduce_sum(tf.where(dsq_mnot < self.rep_range**2 , 1., tf.zeros_like(dsq_mnot)), axis=1) + else: + N_notk = tf.reduce_sum(tf.ones_like(dsq_mnot), axis=1) V_rep = self.q_k * tf.reduce_sum(V_rep, axis=1) #K x 1 #if self.global_weight: @@ -393,16 +400,20 @@ def _calc_contamination(self, energies, rel_metrics_radius, energies_k): x_k_alpha = tf.gather_nd(self.x_k_m,self.alpha_k, batch_dims=1) dsq = tf.expand_dims(x_k_alpha, axis=1) - tf.expand_dims(self.x_v, axis=0) #K x V x C + dsq_mnot = SelectWithDefaultMnot(self.Mnot, dsq, 0.) #K x V x C dsq = tf.reduce_sum(dsq**2, axis=-1) #K x V - in_radius = rel_metrics_radius > tf.sqrt(dsq) / self.d_k# K x V + dsq_mnot = tf.reduce_sum(dsq_mnot**2, axis=-1) #K x V + in_radius = rel_metrics_radius > tf.sqrt(dsq) / self.d_k # K x V + in_radius_mnot = rel_metrics_radius > tf.sqrt(dsq_mnot) / self.d_k # K x V energies_k_v = tf.expand_dims(energies, axis=0) # K x V x 1 energies_ir_all_k_v = tf.where(in_radius[...,tf.newaxis], energies_k_v , 0.) - energies_ir_not_k_v = tf.where(in_radius[...,tf.newaxis], self.Mnot * energies_k_v , 0.) + + energies_ir_not_k_v = tf.where(in_radius_mnot[...,tf.newaxis], energies_k_v , 0.) rel_cont_k = tf.reduce_sum(energies_ir_not_k_v, axis=1)/tf.reduce_sum(energies_ir_all_k_v, axis=1) - - beta_cont_k = tf.reduce_sum(tf.where(in_radius[...,tf.newaxis], self.Mnot * self.beta_v[tf.newaxis,...], 0.), axis=1) # K x 1? + + beta_cont_k = tf.reduce_sum(tf.where(in_radius_mnot[...,tf.newaxis], self.beta_v[tf.newaxis,...], 0.), axis=1) # K x 1? beta_cont_k /= tf.reduce_sum(tf.where(in_radius[...,tf.newaxis], self.beta_v[tf.newaxis,...], 0.), axis=1) # the same for deposited energy > 20 GeV @@ -678,8 +689,8 @@ def set_input(self, beta, #replace beta with per-object normalised value self.create_Ms(truth_idx, rs=rs) - beta_max = tf.reduce_max(self.Mnot * tf.expand_dims(beta,axis=0),axis=1, keepdims=True) #K x 1 x 1 - beta_max *= self.Mnot #K x V x 1 + beta_max = tf.reduce_max(SelectWithDefault(self.Mnot, tf.expand_dims(beta,axis=0),0),axis=1, keepdims=True) #K x 1 x 1 + beta_max = SelectWithDefault(self.Msel, beta_max, 0.) # this is the same reduced in K given the others are zero with exception of noise (filtered later) beta_max = tf.reduce_max(beta_max, axis=0) # V x 1 diff --git a/modules/oc_helper_ops.py b/modules/oc_helper_ops.py index b85b352c..6130c73b 100644 --- a/modules/oc_helper_ops.py +++ b/modules/oc_helper_ops.py @@ -66,16 +66,19 @@ def CreateMidx(truth_idxs, rs, calc_m_not=False): if nmax_per_unique.numpy() < 1: return None, None, None + nmax_in_rs = tf.reduce_max(rs[1:]-rs[:-1]) + sel_dxs, m_not = _op.MIndices( calc_m_not=calc_m_not, asso_idxs = offset_truth_idxs, unique_idxs = unique_idxs, nmax_per_unique = nmax_per_unique, - rs = rs + rs = rs, + nmax_in_rs = nmax_in_rs ) - return sel_dxs, tf.expand_dims(m_not,axis=2), tf.expand_dims(cperunique,axis=1) #just some conventions + return sel_dxs, m_not, tf.expand_dims(cperunique,axis=1) #just some conventions @ops.RegisterGradient("CreateMidx") def _CreateMidxGrad(op, sel_dxs, m_not): @@ -98,6 +101,22 @@ def SelectWithDefault(indices, tensor, default=0, no_check=False): tf.assert_equal(tf.shape(tensor)[1], tf.shape(out)[2])]): return out +def SelectWithDefaultMnot(Mnot, tensor, default=0, no_check=False): + K, V, _ = tensor.shape + _, X = Mnot.shape + + # Step 1: Create a range tensor of shape (V,) and expand dimensions to match dsq for broadcasting + v_range = tf.range(V) + v_range_expanded = tf.expand_dims(tf.expand_dims(v_range, axis=0), axis=-1) # Shape (1, V, 1) + + # Step 2: Expand Mnot dimensions and compare against v_range to create a mask + Mnot_expanded = tf.expand_dims(Mnot, axis=1) # Shape (K, 1, X) + mask = tf.reduce_any(tf.equal(Mnot_expanded, v_range_expanded), axis=-1, keepdims=True) # Shape (K, V, 1) + + # Step 3: Apply the mask to dsq, setting unselected elements to 0 + tensor_mnot = tf.where(mask, tensor, tf.zeros_like(tensor)) + + return tensor_mnot def per_rs_segids_to_unique(pred_sid, rs, return_nseg=False, strict_check=True): diff --git a/tests/testOC.py b/tests/testOC.py index deb71db6..a342ef5a 100644 --- a/tests/testOC.py +++ b/tests/testOC.py @@ -81,3 +81,36 @@ def random_loss(seed): for i in [42,300,500,1,565656,400432,342342,3242342]: random_loss(i) + +# corrtruth tf.Tensor(2.93983, shape=(), dtype=float32) +# ExtendedOCLoss loss 642.5552 +# ExtendedOCLoss loss 642.5552 +# tf.Tensor(642.5552, shape=(), dtype=float32) +# corrtruth tf.Tensor(2.8827302, shape=(), dtype=float32) +# ExtendedOCLoss loss 644.0271 +# ExtendedOCLoss loss 644.0271 +# tf.Tensor(644.0271, shape=(), dtype=float32) +# corrtruth tf.Tensor(2.8496196, shape=(), dtype=float32) +# ExtendedOCLoss loss 643.1459 +# ExtendedOCLoss loss 643.1459 +# tf.Tensor(643.1459, shape=(), dtype=float32) +# corrtruth tf.Tensor(2.903933, shape=(), dtype=float32) +# ExtendedOCLoss loss 640.93335 +# ExtendedOCLoss loss 640.93335 +# tf.Tensor(640.93335, shape=(), dtype=float32) +# corrtruth tf.Tensor(2.935431, shape=(), dtype=float32) +# ExtendedOCLoss loss 644.8252 +# ExtendedOCLoss loss 644.8252 +# tf.Tensor(644.8252, shape=(), dtype=float32) +# corrtruth tf.Tensor(2.9261627, shape=(), dtype=float32) +# ExtendedOCLoss loss 645.67664 +# ExtendedOCLoss loss 645.67664 +# tf.Tensor(645.67664, shape=(), dtype=float32) +# corrtruth tf.Tensor(3.0330386, shape=(), dtype=float32) +# ExtendedOCLoss loss 640.2022 +# ExtendedOCLoss loss 640.2022 +# tf.Tensor(640.2022, shape=(), dtype=float32) +# corrtruth tf.Tensor(2.9854856, shape=(), dtype=float32) +# ExtendedOCLoss loss 647.0576 +# ExtendedOCLoss loss 647.0576 +# tf.Tensor(647.0576, shape=(), dtype=float32) From a56d68ed75416522fa81270f52178d82439419c9 Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 12 Jul 2024 13:40:03 +0200 Subject: [PATCH 31/62] fixed isTrack and ClassLoss --- Train/paper_trainer_Cocoa_simple.py | 1 + models/simple_Cocoa/KERAS_model.h5 | Bin 3353296 -> 3353696 bytes models/simple_Cocoa/description.txt | 3 +- .../paper_trainer_Cocoa_simple.py | 9 ++-- modules/LossLayers.py | 50 ++++++++++++++++++ modules/datastructures/TrainData_Cocoa.py | 36 +++++++++++-- 6 files changed, 90 insertions(+), 9 deletions(-) diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index 2bd21088..8d8420eb 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -198,6 +198,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): pred_pos, pred_time, pred_time_unc, pred_id = \ create_outputs(x, n_ccoords=3, + n_classes=4, n_pos = 3, fix_distance_scale=True, is_track=is_track, diff --git a/models/simple_Cocoa/KERAS_model.h5 b/models/simple_Cocoa/KERAS_model.h5 index 89b3dac53c86c2db75f6c6fab950369b74c0ca78..be7094ce30da1d5a750c72c8cd3b4792e4920db6 100644 GIT binary patch literal 3353696 zcmeD^1wd3w*NdQHqhJ?;g+WX0odE?@48U$zVhIHnSx_*su>-NYy93!fV|VAX*(++B7N)bIb^_n!Cro_A-?nYnXf&N*}Da%Xf?pT@-sS12r_m#L}DL}teQ6#w}q zTs`VJaaCejg!d4B7zy_j;l3gt|HDXDKt^HZgnBcfe@kP&B2Z}9wzaRXj9`lWi+7=! zEN~nDOYBFi|9$^=aG;eJTaGodJpR)s2~6n&ME;Sup_QmKlKkBpA& zr;L--QCv}9Yl{sH+)E1js;k#UWlgyjMzZ2E3z?~`mohF$t>~kSQ%9;|2&BA`5!aH= zCjMEb0zP+><)=5kih~!Gd2s+EQc3#@4hrd|j0yd2KitF>`k}1I8dou{AD$l}s+h3I zaQcAqWH5A0hQ{@3E&&St(J7kMpBdL*d|XguOk_;BLg?~){naPW3tUDzA{XU@5uu#k z#d$2+gBjZ?hD;V)kiYZxpu9nQPnr`7ZDw`pc554Eh?}c(ilLFkYj`ZQ= z3T(%h7YJpy5Waj5Igz`nVB3!>0g|aM}a+q9#ojWzI zBvv__C!5*Gx9)p@1)xO-q;J0H!>dvT~hx6CNv+lYM#k 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great improvement, so future models can be faster \ No newline at end of file +Model has been trained for ~2h, but it showed great improvement, so future models can be faster +now with 3D position (cosphi, sinphi, eta) \ No newline at end of file diff --git a/models/simple_Cocoa/paper_trainer_Cocoa_simple.py b/models/simple_Cocoa/paper_trainer_Cocoa_simple.py index e034f027..2bd21088 100644 --- a/models/simple_Cocoa/paper_trainer_Cocoa_simple.py +++ b/models/simple_Cocoa/paper_trainer_Cocoa_simple.py @@ -198,12 +198,13 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): pred_pos, pred_time, pred_time_unc, pred_id = \ create_outputs(x, n_ccoords=3, + n_pos = 3, fix_distance_scale=True, is_track=is_track, set_track_betas_to_one=True) - pred_beta = LLExtendedObjectCondensation3( + pred_beta = LLExtendedObjectCondensation5( scale=1., use_energy_weights=True, record_metrics=True, @@ -214,11 +215,11 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): too_much_beta_scale = 0.0, energy_loss_weight = 0.4, classification_loss_weight = 0.4, - position_loss_weight = 0.0, + position_loss_weight = 1.0, timing_loss_weight = 0.0, downweight_low_energy=False, train_energy_unc=False, - q_min = 1.0, + q_min = 0.1, use_average_cc_pos = 0.9999)( [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, pred_pos, pred_time, pred_time_unc, pred_id, energy, @@ -335,7 +336,7 @@ def fix_batchnorm(m): train.compileModel(learningrate=1e-4) print('entering third training phase') train.trainModel( - nepochs=25, + nepochs=35, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 24135528..3d47c64a 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3155,6 +3155,56 @@ def calc_position_loss(self, t_pos, pred_pos): ploss = tf.reduce_sum(tf.math.abs(t_pos - pred_pos)**2, axis=-1, keepdims=True) ploss = tf.debugging.check_numerics(ploss, "ploss loss") return ploss + + def calc_classification_loss(self, orig_t_pid, pred_id, t_is_unique=None, hasunique=None): + """ + Truth PID is not yet one-hot encoded + Encoding: + 0: Photon + 1: Neutral Hadron + 2: Charged + 3: Ambiguous + """ + if self.classification_loss_weight <= 0: + return tf.reduce_mean(pred_id,axis=1, keepdims=True) + + charged_conditions = [ + tf.abs(orig_t_pid) == 11, # Electrons + tf.abs(orig_t_pid) == 13, # Muons + tf.abs(orig_t_pid) == 211, # Charged Pions + tf.abs(orig_t_pid) == 321, # Charged Kaons + tf.abs(orig_t_pid) == 2212, # Protons + tf.abs(orig_t_pid) == 3112, # Sigma- + tf.abs(orig_t_pid) == 3222, # Sigma+ + tf.abs(orig_t_pid) == 3312, # Xi- + ] + neutral_hadronic_conditions = [ + tf.abs(orig_t_pid) == 130, # Klong + tf.abs(orig_t_pid) == 310, # Kshort + tf.abs(orig_t_pid) == 311, # K0 + tf.abs(orig_t_pid) == 2112, # Neutrons + tf.abs(orig_t_pid) == 3122, # Lambda + tf.abs(orig_t_pid) == 3322, # Xi0 + ] + charged_true = tf.reduce_any(charged_conditions, axis=0) + neutral_hadronic_true = tf.reduce_any(neutral_hadronic_conditions, axis=0) + + truth_pid_tmp = tf.zeros_like(orig_t_pid) - 1 # Initialize with -1 + truth_pid_tmp = tf.where(tf.abs(orig_t_pid) == 22, 0, truth_pid_tmp) # Photons + truth_pid_tmp = tf.where(neutral_hadronic_true, 1, truth_pid_tmp) # Neutral Had. + truth_pid_tmp = tf.where(charged_true, 2, truth_pid_tmp) # Charged + truth_pid_tmp = tf.where(truth_pid_tmp == -1, 3, truth_pid_tmp) # Catch rest + truth_pid_tmp = tf.cast(truth_pid_tmp, tf.int32) + + truth_pid_onehot = tf.one_hot(truth_pid_tmp, depth=4) + truth_pid_onehot = tf.reshape(truth_pid_onehot, (-1, 4)) + + pred_id = tf.clip_by_value(pred_id, 1e-9, 1. - 1e-9) + classloss = tf.keras.losses.categorical_crossentropy(truth_pid_onehot, pred_id) + + classloss = tf.debugging.check_numerics(classloss, "classloss") + + return classloss[...,tf.newaxis] class LLFullObjectCondensationUncertainty(LLFullObjectCondensation): diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index 3e1c026e..99176965 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -18,7 +18,7 @@ def convertFromSourceFile(self, filename, weighterobjects, istraining): trainData,rs = self.converttotrainingdfvec(data) #make simpleArray features - features = trainData[['recHitEnergy', 'recHitEta', 'istrack','recHitTheta', 'recHitR', 'recHitX', 'recHitY', 'recHitZ', 'recHitTime', 'recHitHitR']] + features = trainData[['recHitEnergy', 'recHitEta', 'isTrack','recHitTheta', 'recHitR', 'recHitX', 'recHitY', 'recHitZ', 'recHitTime', 'recHitHitR']] farr = SimpleArray(np.float32(features.to_numpy()),rs,name="recHitFeatures") #make simpleArray truth @@ -43,7 +43,7 @@ def convertevent(self, event): df_cell = pd.DataFrame() df_cell['recHitEnergy'] = ak.to_dataframe(event['cell_e'], how='outer') df_cell['recHitEta'] = ak.to_dataframe(event['cell_eta'], how='outer') - df_cell['istrack'] = 0 + df_cell['isTrack'] = 0 df_cell['recHitTheta'] =ak.to_dataframe(event['cell_phi'], how='outer') df_cell['recHitR'] = 0 #will be calculated later df_cell['recHitX']= ak.to_dataframe(event['cell_x'], how='outer') @@ -82,14 +82,19 @@ def convertevent(self, event): df_track['recHitTheta'] =ak.to_dataframe(event['track_theta'], how='outer') df_track['recHitEta'] = -np.log(np.tan(df_track['recHitTheta']/2)) - df_track['isTrack'] = 1 - df_track['recHitTime'] = 0 df_track['recHitHitR'] = 0 #join track information to particle information df_track = df_track.join(df_particle, on='t_idx') + #set is Track to -1 or 1 depending on sign of t_pid + df_track['isTrack'] = pdgid_to_charge(df_track['t_pid']) + #raise Error if any istrack is zero + if 0 in df_track['isTrack'].values: + raise ValueError("Error in isTrack") + + #Smear true energy for track df_track['recHitEnergy'] = df_track['t_energy'].apply(lambda x: np.random.normal(x, 0.01 * x)) @@ -127,3 +132,26 @@ def converttotrainingdfvec(self, data): #concat all events traindata = pd.concat(traindata) return traindata, rs + +#HelpFunction to convert PDGIDs to the charge of the particle +def pdgid_to_charge(pdgid): + charge_dict = { + 11: -1, # electron + -11: 1, # positron + 13: -1, # muon + -13: 1, # antimuon + 211: 1, # pi+ + -211: -1, # pi- + 321: 1, # K+ + -321: -1, # K- + 2212: 1, # proton + -2212: -1, # antiproton + 3112: -1, # Sigma- + -3112: 1, # antilambda_c+ + 3222: 1, # Sigma+ + -3222: -1, # antilambda_c- + 3312: -1, # Xi- + -3312: 1, # antixi+ + } + # Default to 0, check if valid later + return np.vectorize(charge_dict.get)(pdgid, 0) From 575345fbc2ce164295f7019d29e8de8daeefddea Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 12 Jul 2024 13:41:17 +0200 Subject: [PATCH 32/62] tried new matching --- modules/ShowersMatcher2.py | 87 +++++++++++++++++++- scripts/generatePlots.ipynb | 157 ++++++++++++++++++++++-------------- 2 files changed, 182 insertions(+), 62 deletions(-) diff --git a/modules/ShowersMatcher2.py b/modules/ShowersMatcher2.py index aa6cc10c..0fe8c8ee 100644 --- a/modules/ShowersMatcher2.py +++ b/modules/ShowersMatcher2.py @@ -150,6 +150,19 @@ def _assign_additional_attr(self): # attr['assigned_to_noise_frac'] = assigned_to_noise_frac # print("A2N", assigned_to_noise_frac) + def _assign_hasTrack(self): + id = self.features_dict['recHitID'] + print(id) + truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0].astype(np.int32) * 1 + pred_sid = self.pred_sid * 1 + + for n, attr in self.graph.nodes(data=True): + if attr['type'] == ShowersMatcher._NODE_TYPE_TRUTH_SHOWER: + filt = truth_sid == n + else: + filt = pred_sid == n + + attr['hasTrack'] = np.any(id[filt]) def _build_data_graph(self): """ @@ -201,7 +214,7 @@ def _build_data_graph(self): for k in keys: if k in skip: continue - elif k == 'pred_id': + elif k == 'pred_id' or k == 'pred_pos': # print("SHAPE pred_id: ", self.predictions_dict[k].shape) # print("UNIQUE pred_id: ", np.unique(self.predictions_dict[k])) # node_attributes[k] = np.argmax( @@ -245,13 +258,43 @@ def _cost_matrix_angle_based(self, truth_shower_sid, pred_shower_sid): y['energy']) return C + + def _cost_matrix_Cocoa(self, truth_shower_sid, pred_shower_sid): + + n = max(len(truth_shower_sid), len(pred_shower_sid)) + C = np.zeros((n, n)) + + for a, j in enumerate(pred_shower_sid): + x = self.graph.nodes(data=True)[j] + for b, i in enumerate(truth_shower_sid): + y = self.graph.nodes(data=True)[i] + if (x['hasTrack'] != y['hasTrack']): + C[a, b] =0 + else: + if y['hasTrack']: + reldeltaEsq = 0 + else: + reldeltaEsq = (np.abs(x['pred_energy'] - y['truthHitAssignedEnergies'])/ y['truthHitAssignedEnergies'])**2 + + pred_phi = np.arctan2(x['pred_pos'][1], x['pred_pos'][0]) + deltaPhi = np.abs(pred_phi - y['truthHitAssignedPhi']) + deltaEta = np.abs(x['pred_pos'][2] - y['truthHitAssignedEta']) + + deltaRsq = deltaPhi**2 + deltaEta**2 + + d= np.sqrt(reldeltaEsq + 5*deltaRsq) + + C[a, b] = 1/(d+1e-3) + + return C def _cost_matrix_intersection_based(self, truth_shower_sid, pred_shower_sid, return_raw=False): pred_shower_energy = np.array([self.graph.nodes[x]['pred_energy'] for x in pred_shower_sid]) truth_shower_energy = np.array([self.graph.nodes[x]['truthHitAssignedEnergies'] for x in truth_shower_sid]) weight = self.features_dict['recHitEnergy'][:, 0] - is_track = np.abs(self.features_dict['recHitZ'][:,0]) == 315 # TODO: This works for now, but is very hacky + is_track = np.abs(self.features_dict['recHitID'][:,0]) + #is_track = np.abs(self.features_dict['recHitZ'][:,0]) == 315 # TODO: This works for now, but is very hacky weight[is_track] = 0 @@ -329,11 +372,51 @@ def _match_single_pass(self): self.calculated_graph = matched_full_graph + #based on _match_single_pass + def _match_cocoa(self): + truth_shower_sid = [ + x[0] + for x in self.graph.nodes(data=True) + if x[1]['type']==ShowersMatcher._NODE_TYPE_TRUTH_SHOWER + ] + pred_shower_sid = [ + x[0] + for x in self.graph.nodes(data=True) + if x[1]['type']==ShowersMatcher._NODE_TYPE_PRED_SHOWER + ] + + if not len(truth_shower_sid) > 0: + print("No truth showers") + return + if not len(pred_shower_sid) > 0: + print("No predicted showers") + return + + self._assign_hasTrack() + + C = self._cost_matrix_Cocoa(truth_shower_sid, pred_shower_sid) + + row_id, col_id = linear_sum_assignment(C, maximize=True) + + self.truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0] + matched_full_graph = nx.Graph() + matched_full_graph.add_nodes_from(self.graph.nodes(data=True)) + + for p, t in zip(row_id, col_id): + if C[p, t] > 0: + matched_full_graph.add_edge( + truth_shower_sid[t], + pred_shower_sid[p], + attached_in_pass=0) + + self.calculated_graph = matched_full_graph def process(self, extra=False): self._build_data_graph() if self.match_mode == 'iou_max': self._match_single_pass() + elif self.match_mode == 'cocoa': + self._match_cocoa() else: raise NotImplementedError('Match mode not found') if extra: diff --git a/scripts/generatePlots.ipynb b/scripts/generatePlots.ipynb index 6399e7fe..3c8e96e6 100644 --- a/scripts/generatePlots.ipynb +++ b/scripts/generatePlots.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -29,16 +29,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-07-09 13:30:52.998924: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "2024-07-12 12:37:33.447415: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-07-09 13:31:03.615007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11236 MB memory: -> device: 1, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:03:00.0, compute capability: 5.2\n" + "2024-07-12 12:37:35.195685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11473 MB memory: -> device: 1, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:03:00.0, compute capability: 5.2\n" ] }, { @@ -46,17 +46,16 @@ "output_type": "stream", "text": [ "Analysis data keys: dict_keys(['showers_dataframe', 'events_dataframe', 'scalar_variables', 'alpha_ids', 'noise_masks', 'matched', 'matched_showers', 'processed_dataframe', 'features', 'truth', 'prediction'])\n", - "shower keys Index(['pred_energy_tracks_raw', 'truthHitSpectatorFlag', 'pred_sid',\n", - " 'pred_ccoords', 'pred_no_minbias_fraction', 'pred_energy_corr_factor',\n", - " 'truthHitFullyContainedFlag', 'truthHitAssignedEnergies',\n", - " 'truthHitAssignedZ', 'pred_energy_low_quantile', 'pred_energy',\n", - " 'truthHitAssignedPhi', 'pred_energy_hits_corrected', 'pred_time',\n", - " 'truthHitAssignementIdx', 'truthHitAssignedEta',\n", - " 'pred_energy_tracks_corrected', 'pred_energy_hits_raw',\n", - " 'pred_energy_high_quantile', 'no_noise_sel', 'truthHitAssignedT',\n", - " 'pred_dist', 'truthHitAssignedY', 'pred_energy_unc', 'pred_id',\n", - " 'truthHitAssignedX', 't_rec_energy', 'truthHitAssignedPIDs', 'pred_pos',\n", - " 'event_id'],\n", + "shower keys Index(['t_rec_energy', 'pred_energy_unc', 'pred_sid', 'pred_energy_tracks_raw',\n", + " 'pred_no_minbias_fraction', 'truthHitAssignedEnergies', 'pred_energy',\n", + " 'pred_energy_hits_raw', 'pred_energy_hits_corrected',\n", + " 'pred_energy_high_quantile', 'pred_time', 'truthHitAssignedY',\n", + " 'pred_energy_tracks_corrected', 'truthHitAssignedZ', 'pred_ccoords',\n", + " 'truthHitAssignedEta', 'truthHitAssignedPIDs', 'truthHitAssignementIdx',\n", + " 'truthHitAssignedX', 'truthHitSpectatorFlag', 'pred_pos',\n", + " 'truthHitAssignedT', 'pred_energy_low_quantile',\n", + " 'truthHitFullyContainedFlag', 'truthHitAssignedPhi', 'pred_id',\n", + " 'pred_dist', 'no_noise_sel', 'pred_energy_corr_factor', 'event_id'],\n", " dtype='object')\n", "events None\n", "scalar keys dict_keys(['beta_threshold', 'distance_threshold', 'iou_threshold', 'matching_mode', 'is_soft', 'de_e_cut', 'angle_cut'])\n", @@ -69,8 +68,8 @@ " 'pred_energy_tracks_raw', 'pred_energy_tracks_corrected',\n", " 'pred_energy_unc', 'pred_ccoords_0', 'pred_ccoords_1', 'pred_ccoords_2',\n", " 'pred_energy_corr_factor', 'pred_energy_low_quantile',\n", - " 'pred_energy_high_quantile', 'pred_pos_0', 'pred_pos_1', 'pred_time',\n", - " 'pred_dist', 'rechit_energy', 'no_noise_sel', 'event_id'],\n", + " 'pred_energy_high_quantile', 'pred_pos_0', 'pred_pos_1', 'pred_pos_2',\n", + " 'pred_time', 'pred_dist', 'rechit_energy', 'no_noise_sel', 'event_id'],\n", " dtype='object')\n", "features list keys dict_keys(['recHitEnergy', 'recHitEta', 'recHitID', 'recHitTheta', 'recHitR', 'recHitX', 'recHitY', 'recHitZ', 'recHitTime', 'recHitHitR', 'recHitXY'])\n", "truth list keys dict_keys(['truthHitAssignementIdx', 'truthHitAssignedEnergies', 'truthHitAssignedX', 'truthHitAssignedY', 'truthHitAssignedZ', 'truthHitAssignedEta', 'truthHitAssignedPhi', 'truthHitAssignedT', 'truthHitAssignedPIDs', 'truthHitSpectatorFlag', 'truthHitFullyContainedFlag', 't_rec_energy'])\n", @@ -80,7 +79,9 @@ ], "source": [ "#Open file\n", - "filepath = '/work/friemer/hgcalml/SimpleCocoa/analyseSimpleCocoa/analysisoutput'\n", + "#filepath = '/work/friemer/hgcalml/SimpleCocoa/analyseSimpleCocoa/analysisoutput'\n", + "filepath = '/work/friemer/hgcalml/CocoaTotalEnergyQmin01PhiEta/analysisoutput'\n", + "filepath = '/work/friemer/hgcalml/analysisoutputoldmatching'\n", "\n", "# Open the file and load the data\n", "with gzip.open(filepath, 'rb') as input_file:\n", @@ -115,24 +116,26 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_378932/2368924373.py:40: RuntimeWarning: invalid value encountered in long_scalars\n", + "/tmp/ipykernel_420976/3249749180.py:47: RuntimeWarning: invalid value encountered in long_scalars\n", " fake_rate.append(np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]))\n", - "/tmp/ipykernel_378932/2368924373.py:42: RuntimeWarning: invalid value encountered in long_scalars\n", - " corr_class_prob.append(np.sum(matched[mask_PID_match]) / np.sum(mask_bintruth[mask_PID_match]))\n", + "/tmp/ipykernel_420976/3249749180.py:49: RuntimeWarning: invalid value encountered in long_scalars\n", + " corr_class_prob.append(np.sum(matched[mask_PID_matched]) / np.sum(mask_bintruth[mask_PID_matched]))\n", + "/tmp/ipykernel_420976/3249749180.py:46: RuntimeWarning: invalid value encountered in long_scalars\n", + " efficiencies.append(np.sum(matched[mask_PID_truth]) / np.sum(mask_bintruth[mask_PID_truth]))\n", "/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n", " xys = np.asarray(xys)\n" ] }, { "data": { - "image/png": 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", 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      " ] @@ -155,11 +158,18 @@ " \n", " if mask == None:\n", " mask_PID_truth = np.ones(len(df), dtype=bool)\n", - " mask_PID_pred = np.ones(len(df), dtype=bool)\n", + " mask_PID_pred = mask_PID_truth\n", " else:\n", - " mask_PID_truth = df['truthHitAssignedPIDs'].isin(mask)\n", - " mask_PID_pred = df['pred_id'].isin(mask) #TODO: change pred_id is not PID!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n", - " mask_PID_match = np.logical_and(mask_PID_truth, mask_PID_pred)\n", + " if(mask == 0):\n", + " mask_PID_truth = df['truthHitAssignedPIDs'].isin([22])\n", + " elif(mask == 1):\n", + " mask_PID_truth = df['truthHitAssignedPIDs'].isin([130,310,311,2112,-2112,3122,-3122,3322,-3322])\n", + " else:\n", + " raise ValueError(\"mask must be 0 or 1\")\n", + " \n", + " mask_PID_pred = df['pred_id'].isin([mask])\n", + " mask_PID_matched = np.logical_and(mask_PID_truth, mask_PID_pred)\n", + " \n", " \n", " for i in range(len(bins) - 1):\n", " mask_bintruth = np.logical_and(\n", @@ -183,7 +193,7 @@ " efficiencies.append(np.sum(matched[mask_PID_truth]) / np.sum(mask_bintruth[mask_PID_truth]))\n", " fake_rate.append(np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]))\n", " \n", - " corr_class_prob.append(np.sum(matched[mask_PID_match]) / np.sum(mask_bintruth[mask_PID_match]))\n", + " corr_class_prob.append(np.sum(matched[mask_PID_matched]) / np.sum(mask_bintruth[mask_PID_matched]))\n", " return np.array(n_trues), np.array(n_pred), np.array(n_matched), np.array(n_fakes), np.array(efficiencies), np.array(fake_rate), np.array(corr_class_prob)\n", "def efficiency_plots(df, bins):\n", " # Calculate the bin positions and widths\n", @@ -205,7 +215,7 @@ " \n", " # Calculate the efficiencies and fake rates for the photons\n", " n_trues_photon, n_pred_photon, n_matched_photon, n_fakes_photon, efficiencies_photon, \\\n", - " fake_rate_photon, corr_class_prob_photon = help_calc_efficiencies(df, bins,[22])\n", + " fake_rate_photon, corr_class_prob_photon = help_calc_efficiencies(df, bins,0)\n", " \n", " \n", " y_eff_photon = efficiencies_photon*100\n", @@ -219,10 +229,10 @@ " \n", " # Calculate the efficiencies and fake rates for the neutral hadrons\n", " #130: \"K0L\", 310: \"K0S\", 311: \"K0\", 2112: \"neutron\",-2112: \"antineutron\",3122: \"Lambda\",-3122: \"antilambda\"3322: \"Xi0\",-3322: \"antixi0\"\n", - " mask_n_h = [130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]\n", + " \n", " n_trues_neutral_hadrons, n_pred_neutral_hadrons, n_matched_neutral_hadrons, \\\n", " n_fakes_neutral_hadrons, efficiencies_neutral_hadrons, fake_rate_neutral_hadrons, corr_class_prob_neutral_hadrons \\\n", - " = help_calc_efficiencies(df, bins, mask_n_h)\n", + " = help_calc_efficiencies(df, bins, 1)\n", " \n", " y_eff_neutral_hadrons = efficiencies_neutral_hadrons*100\n", " yerr_eff_neutral_hadrons = np.sqrt(efficiencies_neutral_hadrons * (1 - efficiencies_neutral_hadrons) / n_trues_neutral_hadrons)\n", @@ -267,7 +277,7 @@ " yticks1 = np.round(np.arange(40, 101, 20), 1)\n", " ax.set_yticks(yticks1)\n", " ax.set_yticklabels([f\"{y}%\" for y in yticks1], fontsize=10)\n", - " ax.set_ylim(20, 101)\n", + " #ax.set_ylim(20, 101)\n", "\n", " return fig\n", "energy_bins = np.array([1,2,3,4,5,10,20,30,50])*1000\n", @@ -283,12 +293,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
      " ] @@ -319,7 +329,9 @@ " # reseta.append(np.sum(t_list[i]['truthHitAssignedEta'])-np.sum(pred_list[i]['pred_eta']))\n", " # resphi.append(np.sum(t_list[i]['truthHitAssignedPhi'])-np.sum(pred_list[i]['pred_phi']))\n", " jet_E_truth = np.sum(np.unique(t_list[i]['truthHitAssignedEnergies']))\n", - " jet_E_pred = np.sum(np.unique(df[df['event_id']==i]['pred_energy']))\n", + " #remove nan values\n", + " help = np.unique(df[df['event_id']==i]['pred_energy'])\n", + " jet_E_pred = np.sum(help[np.isnan(help)==False])\n", " relresE.append((jet_E_truth-jet_E_pred)/jet_E_truth)\n", " nParicles_pred.append(len(np.unique(df[df['event_id']==i]['pred_energy'])))\n", " nParicles_truth.append(len(np.unique(t_list[i]['truthHitAssignementIdx'])))\n", @@ -350,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "vscode": { "languageId": "ruby" @@ -361,21 +373,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "Error encountered: Shape of passed values is (3349, 1), indices imply (3349, 3)\n", - "Verifying the structure of 'pred_pos'...\n", - "First item in 'pred_pos': -1.194890022277832\n" + "3.1108510494232178\n", + "-2.6898539066314697\n", + "3.127699375152588\n", + "-3.1045305728912354\n", + "3.9383437037467957\n" ] }, { - "ename": "NameError", - "evalue": "name 'pos_pred_df' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[9], line 19\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst item in \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpred_pos\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfirst_item\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# If the structure is incorrect, you might need to adjust how 'pred_pos' is being processed\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# For example, if 'pred_pos' does not contain lists or tuples of three elements, you need to correct this\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m phi_pred \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marctan2(\u001b[43mpos_pred_df\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msinphi\u001b[39m\u001b[38;5;124m'\u001b[39m], pos_pred_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcosphi\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 20\u001b[0m eta_pred \u001b[38;5;241m=\u001b[39m pos_pred_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meta\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 22\u001b[0m phi_truth \u001b[38;5;241m=\u001b[39m df[mask_neutral][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtruthHitAssignedPhi\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "\u001b[0;31mNameError\u001b[0m: name 'pos_pred_df' is not defined" - ] + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -384,7 +417,10 @@ "pt_pred = df[mask_neutral]['pred_energy']\n", "pt_truth = df[mask_neutral]['truthHitAssignedEnergies']\n", "\n", - "pos_pred_df = pd.DataFrame(df[mask_neutral]['pred_pos'].tolist(), columns=['cosphi', 'sinphi', 'eta'])\n", + "help = df[mask_neutral]['pred_pos'].tolist()\n", + "#remove Nan\n", + "help = [x for x in help if str(x) != 'nan']\n", + "pos_pred_df = pd.DataFrame(help, columns=['cosphi', 'sinphi', 'eta'])\n", "\n", "phi_pred = np.arctan2(pos_pred_df['sinphi'], pos_pred_df['cosphi'])\n", "eta_pred = pos_pred_df['eta']\n", @@ -393,21 +429,21 @@ "eta_truth = df[mask_neutral]['truthHitAssignedEta']\n", "\n", "relrespt = (pt_pred - pt_truth) / pt_truth\n", - "relreseta = (eta_pred - eta_truth) / eta_truth\n", - "relresphi = (phi_pred - phi_truth) / phi_truth\n", + "reseta = (eta_pred - eta_truth)\n", + "resphi = (phi_pred - phi_truth)\n", "\n", "plt.hist(relrespt)\n", "plt.xlabel('relres Energy')\n", "plt.ylabel('Number of neutral particles')\n", "plt.show()\n", "\n", - "plt.hist(relreseta)\n", - "plt.xlabel('relres Eta')\n", + "plt.hist(reseta)\n", + "plt.xlabel('Delta Eta')\n", "plt.ylabel('Number of neutral particles')\n", "plt.show()\n", "\n", - "plt.hist(relresphi)\n", - "plt.xlabel('relres Phi')\n", + "plt.hist(resphi)\n", + "plt.xlabel('Delta Phi')\n", "plt.ylabel('Number of neutral particles')\n", "plt.show()" ] @@ -416,7 +452,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Energy Resolution" + "## Charged Energy Resolution" ] }, { @@ -426,7 +462,7 @@ "outputs": [ { "data": { - "image/png": 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", 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      " ] @@ -469,7 +505,7 @@ " ax1.set_yticks(yticks)\n", " ax1.set_yticklabels(yticks, fontsize=20)\n", " ax1.grid(alpha=0.5, linestyle='--')\n", - " ax1.set_ylabel('Mean Response', fontsize=40)\n", + " ax1.set_ylabel('Mean Response Charged', fontsize=40)\n", " ax1.set_xlabel('Energy [GeV]', fontsize=40)\n", " \n", " return fig\n", @@ -511,7 +547,8 @@ " return data, all_ratios\n", "\n", "energy_bins = np.array([15,20,30,50,200])*1000\n", - "fig=energy_resolution(df, bins=energy_bins)" + "charged_mask = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312])\n", + "fig=energy_resolution(df[charged_mask], bins=energy_bins)" ] }, { From 3999a8da60c1b799234bc9ab3d0086da12db0aa0 Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 12 Jul 2024 13:50:13 +0200 Subject: [PATCH 33/62] added detailed prints --- modules/LossLayers.py | 6 ++++++ tests/testOC.py | 48 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 54 insertions(+) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 24135528..327c14ec 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -2619,6 +2619,12 @@ def loss(self, inputs): is_spectator_weight=is_spectator, rs=rowsplits, energies = rechit_energy) + print("Att: ", att) + print("Rep: ", rep) + print("Min: ", min_b) + print("Noise: ", noise) + print("Excee: ", exceed_beta) + print("Payload: ", payload) #log the OC metrics dict, if any self.wandb_log(mdict) diff --git a/tests/testOC.py b/tests/testOC.py index a342ef5a..f18b495c 100644 --- a/tests/testOC.py +++ b/tests/testOC.py @@ -83,34 +83,82 @@ def random_loss(seed): random_loss(i) # corrtruth tf.Tensor(2.93983, shape=(), dtype=float32) +# Att: tf.Tensor(636.08734, shape=(), dtype=float32) +# Rep: tf.Tensor(0.0, shape=(), dtype=float32) +# Min: tf.Tensor(-0.0024269393, shape=(), dtype=float32) +# Noise: tf.Tensor(0.0, shape=(), dtype=float32) +# Excee: tf.Tensor(0.0, shape=(), dtype=float32) +# Payload: tf.Tensor([ 2.7801 0. 0. -1.6151048 5.305293 ], shape=(5,), dtype=float32) # ExtendedOCLoss loss 642.5552 # ExtendedOCLoss loss 642.5552 # tf.Tensor(642.5552, shape=(), dtype=float32) # corrtruth tf.Tensor(2.8827302, shape=(), dtype=float32) +# Att: tf.Tensor(637.69476, shape=(), dtype=float32) +# Rep: tf.Tensor(0.0, shape=(), dtype=float32) +# Min: tf.Tensor(-0.0018903641, shape=(), dtype=float32) +# Noise: tf.Tensor(0.0, shape=(), dtype=float32) +# Excee: tf.Tensor(0.0, shape=(), dtype=float32) +# Payload: tf.Tensor([ 2.7772715 0. 0. -1.6486813 5.2056036], shape=(5,), dtype=float32) # ExtendedOCLoss loss 644.0271 # ExtendedOCLoss loss 644.0271 # tf.Tensor(644.0271, shape=(), dtype=float32) # corrtruth tf.Tensor(2.8496196, shape=(), dtype=float32) +# Att: tf.Tensor(636.7689, shape=(), dtype=float32) +# Rep: tf.Tensor(0.0, shape=(), dtype=float32) +# Min: tf.Tensor(-0.0022501661, shape=(), dtype=float32) +# Noise: tf.Tensor(0.0, shape=(), dtype=float32) +# Excee: tf.Tensor(0.0, shape=(), dtype=float32) +# Payload: tf.Tensor([ 2.7283812 0. 0. -1.5966831 5.247504 ], shape=(5,), dtype=float32) # ExtendedOCLoss loss 643.1459 # ExtendedOCLoss loss 643.1459 # tf.Tensor(643.1459, shape=(), dtype=float32) # corrtruth tf.Tensor(2.903933, shape=(), dtype=float32) +# Att: tf.Tensor(634.7787, shape=(), dtype=float32) +# Rep: tf.Tensor(0.0, shape=(), dtype=float32) +# Min: tf.Tensor(-0.002124406, shape=(), dtype=float32) +# Noise: tf.Tensor(0.0, shape=(), dtype=float32) +# Excee: tf.Tensor(0.0, shape=(), dtype=float32) +# Payload: tf.Tensor([ 2.7691891 0. 0. -1.6075772 4.9952393], shape=(5,), dtype=float32) # ExtendedOCLoss loss 640.93335 # ExtendedOCLoss loss 640.93335 # tf.Tensor(640.93335, shape=(), dtype=float32) # corrtruth tf.Tensor(2.935431, shape=(), dtype=float32) +# Att: tf.Tensor(638.36597, shape=(), dtype=float32) +# Rep: tf.Tensor(0.0, shape=(), dtype=float32) +# Min: tf.Tensor(-0.0021555822, shape=(), dtype=float32) +# Noise: tf.Tensor(0.0, shape=(), dtype=float32) +# Excee: tf.Tensor(0.0, shape=(), dtype=float32) +# Payload: tf.Tensor([ 2.7554877 0. 0. -1.5892332 5.2951083], shape=(5,), dtype=float32) # ExtendedOCLoss loss 644.8252 # ExtendedOCLoss loss 644.8252 # tf.Tensor(644.8252, shape=(), dtype=float32) # corrtruth tf.Tensor(2.9261627, shape=(), dtype=float32) +# Att: tf.Tensor(639.1542, shape=(), dtype=float32) +# Rep: tf.Tensor(0.0, shape=(), dtype=float32) +# Min: tf.Tensor(-0.0028582471, shape=(), dtype=float32) +# Noise: tf.Tensor(0.0, shape=(), dtype=float32) +# Excee: tf.Tensor(0.0, shape=(), dtype=float32) +# Payload: tf.Tensor([ 2.7812486 0. 0. -1.5922239 5.336297 ], shape=(5,), dtype=float32) # ExtendedOCLoss loss 645.67664 # ExtendedOCLoss loss 645.67664 # tf.Tensor(645.67664, shape=(), dtype=float32) # corrtruth tf.Tensor(3.0330386, shape=(), dtype=float32) +# Att: tf.Tensor(633.7874, shape=(), dtype=float32) +# Rep: tf.Tensor(0.0, shape=(), dtype=float32) +# Min: tf.Tensor(-0.001937026, shape=(), dtype=float32) +# Noise: tf.Tensor(0.0, shape=(), dtype=float32) +# Excee: tf.Tensor(0.0, shape=(), dtype=float32) +# Payload: tf.Tensor([ 2.7607439 0. 0. -1.6043333 5.2602963], shape=(5,), dtype=float32) # ExtendedOCLoss loss 640.2022 # ExtendedOCLoss loss 640.2022 # tf.Tensor(640.2022, shape=(), dtype=float32) # corrtruth tf.Tensor(2.9854856, shape=(), dtype=float32) +# Att: tf.Tensor(640.62555, shape=(), dtype=float32) +# Rep: tf.Tensor(0.0, shape=(), dtype=float32) +# Min: tf.Tensor(-0.0024138745, shape=(), dtype=float32) +# Noise: tf.Tensor(0.0, shape=(), dtype=float32) +# Excee: tf.Tensor(0.0, shape=(), dtype=float32) +# Payload: tf.Tensor([ 2.7953646 0. 0. -1.5740322 5.2131767], shape=(5,), dtype=float32) # ExtendedOCLoss loss 647.0576 # ExtendedOCLoss loss 647.0576 # tf.Tensor(647.0576, shape=(), dtype=float32) From 90277ce89db5cdfd302da79c168129eae67859d8 Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 12 Jul 2024 14:38:10 +0200 Subject: [PATCH 34/62] fixed V_rep mnot selection --- modules/object_condensation.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/modules/object_condensation.py b/modules/object_condensation.py index 9eb9cf4c..5adf662f 100644 --- a/modules/object_condensation.py +++ b/modules/object_condensation.py @@ -237,17 +237,17 @@ def V_rep_k(self): dsq = self.calc_dsq_rep() - - dsq_mnot = SelectWithDefaultMnot(self.Mnot, dsq, 0.) #K x V x 1 - # nogradbeta = tf.stop_gradient(self.beta_k_m) #weight. tf.reduce_sum( tf.exp(-dsq) * d_v_e, , axis=1) / tf.reduce_sum( tf.exp(-dsq) ) - sigma = self.weighted_d_k_m(dsq_mnot) #create gradients for all, but prefer k vertex - dsq_mnot = tf.math.divide_no_nan(dsq_mnot, sigma**2 + 1e-4) - + sigma = self.weighted_d_k_m(dsq) #create gradients for all, but prefer k vertex + dsq = tf.math.divide_no_nan(dsq, sigma**2 + 1e-4) - V_rep = self.rep_func(dsq_mnot) * tf.expand_dims(self.q_v,axis=0) + V_rep = self.rep_func(dsq) * tf.expand_dims(self.q_v,axis=0) + V_rep = SelectWithDefaultMnot(self.Mnot, V_rep, 0.) #K x V x 1 + + + dsq_mnot = SelectWithDefaultMnot(self.Mnot, dsq, 0.) #K x V x 1 if self.rep_range > 0: N_notk = tf.reduce_sum(tf.where(dsq_mnot < self.rep_range**2 , 1., tf.zeros_like(dsq_mnot)), axis=1) else: From d7d3a1af3ab31b5bfc5f4034f826845b01f2a545 Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 12 Jul 2024 14:57:32 +0200 Subject: [PATCH 35/62] fixed contamination --- modules/LossLayers.py | 7 +---- modules/object_condensation.py | 4 +-- modules/oc_helper_ops.py | 7 +++++ tests/testOC.py | 56 +++++----------------------------- 4 files changed, 17 insertions(+), 57 deletions(-) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 327c14ec..763a42e8 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -2619,12 +2619,7 @@ def loss(self, inputs): is_spectator_weight=is_spectator, rs=rowsplits, energies = rechit_energy) - print("Att: ", att) - print("Rep: ", rep) - print("Min: ", min_b) - print("Noise: ", noise) - print("Excee: ", exceed_beta) - print("Payload: ", payload) + print(mdict) #log the OC metrics dict, if any self.wandb_log(mdict) diff --git a/modules/object_condensation.py b/modules/object_condensation.py index 5adf662f..4bcd72ae 100644 --- a/modules/object_condensation.py +++ b/modules/object_condensation.py @@ -400,11 +400,9 @@ def _calc_contamination(self, energies, rel_metrics_radius, energies_k): x_k_alpha = tf.gather_nd(self.x_k_m,self.alpha_k, batch_dims=1) dsq = tf.expand_dims(x_k_alpha, axis=1) - tf.expand_dims(self.x_v, axis=0) #K x V x C - dsq_mnot = SelectWithDefaultMnot(self.Mnot, dsq, 0.) #K x V x C dsq = tf.reduce_sum(dsq**2, axis=-1) #K x V - dsq_mnot = tf.reduce_sum(dsq_mnot**2, axis=-1) #K x V in_radius = rel_metrics_radius > tf.sqrt(dsq) / self.d_k # K x V - in_radius_mnot = rel_metrics_radius > tf.sqrt(dsq_mnot) / self.d_k # K x V + in_radius_mnot = SelectWithDefaultMnot(self.Mnot, in_radius, 0.) #K x V energies_k_v = tf.expand_dims(energies, axis=0) # K x V x 1 energies_ir_all_k_v = tf.where(in_radius[...,tf.newaxis], energies_k_v , 0.) diff --git a/modules/oc_helper_ops.py b/modules/oc_helper_ops.py index 6130c73b..04dc5425 100644 --- a/modules/oc_helper_ops.py +++ b/modules/oc_helper_ops.py @@ -102,6 +102,10 @@ def SelectWithDefault(indices, tensor, default=0, no_check=False): return out def SelectWithDefaultMnot(Mnot, tensor, default=0, no_check=False): + #tensor dim = K x V x 1 or K x V + no_extra_dim = (len(tf.shape(tensor)) == 2) + if no_extra_dim: + tensor = tf.expand_dims(tensor, axis=-1) K, V, _ = tensor.shape _, X = Mnot.shape @@ -116,6 +120,9 @@ def SelectWithDefaultMnot(Mnot, tensor, default=0, no_check=False): # Step 3: Apply the mask to dsq, setting unselected elements to 0 tensor_mnot = tf.where(mask, tensor, tf.zeros_like(tensor)) + if no_extra_dim: + tensor_mnot = tf.squeeze(tensor_mnot, axis=-1) + return tensor_mnot diff --git a/tests/testOC.py b/tests/testOC.py index f18b495c..bfe73303 100644 --- a/tests/testOC.py +++ b/tests/testOC.py @@ -83,82 +83,42 @@ def random_loss(seed): random_loss(i) # corrtruth tf.Tensor(2.93983, shape=(), dtype=float32) -# Att: tf.Tensor(636.08734, shape=(), dtype=float32) -# Rep: tf.Tensor(0.0, shape=(), dtype=float32) -# Min: tf.Tensor(-0.0024269393, shape=(), dtype=float32) -# Noise: tf.Tensor(0.0, shape=(), dtype=float32) -# Excee: tf.Tensor(0.0, shape=(), dtype=float32) -# Payload: tf.Tensor([ 2.7801 0. 0. -1.6151048 5.305293 ], shape=(5,), dtype=float32) +# {'containment': , 'beta_containment': , 'containment_E20': , 'beta_containment_E20': , 'contamination': , 'beta_contamination': , 'contamination_E20': , 'beta_contamination_E20': } # ExtendedOCLoss loss 642.5552 # ExtendedOCLoss loss 642.5552 # tf.Tensor(642.5552, shape=(), dtype=float32) # corrtruth tf.Tensor(2.8827302, shape=(), dtype=float32) -# Att: tf.Tensor(637.69476, shape=(), dtype=float32) -# Rep: tf.Tensor(0.0, shape=(), dtype=float32) -# Min: tf.Tensor(-0.0018903641, shape=(), dtype=float32) -# Noise: tf.Tensor(0.0, shape=(), dtype=float32) -# Excee: tf.Tensor(0.0, shape=(), dtype=float32) -# Payload: tf.Tensor([ 2.7772715 0. 0. -1.6486813 5.2056036], shape=(5,), dtype=float32) +# {'containment': , 'beta_containment': , 'containment_E20': , 'beta_containment_E20': , 'contamination': , 'beta_contamination': , 'contamination_E20': , 'beta_contamination_E20': } # ExtendedOCLoss loss 644.0271 # ExtendedOCLoss loss 644.0271 # tf.Tensor(644.0271, shape=(), dtype=float32) # corrtruth tf.Tensor(2.8496196, shape=(), dtype=float32) -# Att: tf.Tensor(636.7689, shape=(), dtype=float32) -# Rep: tf.Tensor(0.0, shape=(), dtype=float32) -# Min: tf.Tensor(-0.0022501661, shape=(), dtype=float32) -# Noise: tf.Tensor(0.0, shape=(), dtype=float32) -# Excee: tf.Tensor(0.0, shape=(), dtype=float32) -# Payload: tf.Tensor([ 2.7283812 0. 0. -1.5966831 5.247504 ], shape=(5,), dtype=float32) +# {'containment': , 'beta_containment': , 'containment_E20': , 'beta_containment_E20': , 'contamination': , 'beta_contamination': , 'contamination_E20': , 'beta_contamination_E20': } # ExtendedOCLoss loss 643.1459 # ExtendedOCLoss loss 643.1459 # tf.Tensor(643.1459, shape=(), dtype=float32) # corrtruth tf.Tensor(2.903933, shape=(), dtype=float32) -# Att: tf.Tensor(634.7787, shape=(), dtype=float32) -# Rep: tf.Tensor(0.0, shape=(), dtype=float32) -# Min: tf.Tensor(-0.002124406, shape=(), dtype=float32) -# Noise: tf.Tensor(0.0, shape=(), dtype=float32) -# Excee: tf.Tensor(0.0, shape=(), dtype=float32) -# Payload: tf.Tensor([ 2.7691891 0. 0. -1.6075772 4.9952393], shape=(5,), dtype=float32) +# {'containment': , 'beta_containment': , 'containment_E20': , 'beta_containment_E20': , 'contamination': , 'beta_contamination': , 'contamination_E20': , 'beta_contamination_E20': } # ExtendedOCLoss loss 640.93335 # ExtendedOCLoss loss 640.93335 # tf.Tensor(640.93335, shape=(), dtype=float32) # corrtruth tf.Tensor(2.935431, shape=(), dtype=float32) -# Att: tf.Tensor(638.36597, shape=(), dtype=float32) -# Rep: tf.Tensor(0.0, shape=(), dtype=float32) -# Min: tf.Tensor(-0.0021555822, shape=(), dtype=float32) -# Noise: tf.Tensor(0.0, shape=(), dtype=float32) -# Excee: tf.Tensor(0.0, shape=(), dtype=float32) -# Payload: tf.Tensor([ 2.7554877 0. 0. -1.5892332 5.2951083], shape=(5,), dtype=float32) +# {'containment': , 'beta_containment': , 'containment_E20': , 'beta_containment_E20': , 'contamination': , 'beta_contamination': , 'contamination_E20': , 'beta_contamination_E20': } # ExtendedOCLoss loss 644.8252 # ExtendedOCLoss loss 644.8252 # tf.Tensor(644.8252, shape=(), dtype=float32) # corrtruth tf.Tensor(2.9261627, shape=(), dtype=float32) -# Att: tf.Tensor(639.1542, shape=(), dtype=float32) -# Rep: tf.Tensor(0.0, shape=(), dtype=float32) -# Min: tf.Tensor(-0.0028582471, shape=(), dtype=float32) -# Noise: tf.Tensor(0.0, shape=(), dtype=float32) -# Excee: tf.Tensor(0.0, shape=(), dtype=float32) -# Payload: tf.Tensor([ 2.7812486 0. 0. -1.5922239 5.336297 ], shape=(5,), dtype=float32) +# {'containment': , 'beta_containment': , 'containment_E20': , 'beta_containment_E20': , 'contamination': , 'beta_contamination': , 'contamination_E20': , 'beta_contamination_E20': } # ExtendedOCLoss loss 645.67664 # ExtendedOCLoss loss 645.67664 # tf.Tensor(645.67664, shape=(), dtype=float32) # corrtruth tf.Tensor(3.0330386, shape=(), dtype=float32) -# Att: tf.Tensor(633.7874, shape=(), dtype=float32) -# Rep: tf.Tensor(0.0, shape=(), dtype=float32) -# Min: tf.Tensor(-0.001937026, shape=(), dtype=float32) -# Noise: tf.Tensor(0.0, shape=(), dtype=float32) -# Excee: tf.Tensor(0.0, shape=(), dtype=float32) -# Payload: tf.Tensor([ 2.7607439 0. 0. -1.6043333 5.2602963], shape=(5,), dtype=float32) +# {'containment': , 'beta_containment': , 'containment_E20': , 'beta_containment_E20': , 'contamination': , 'beta_contamination': , 'contamination_E20': , 'beta_contamination_E20': } # ExtendedOCLoss loss 640.2022 # ExtendedOCLoss loss 640.2022 # tf.Tensor(640.2022, shape=(), dtype=float32) # corrtruth tf.Tensor(2.9854856, shape=(), dtype=float32) -# Att: tf.Tensor(640.62555, shape=(), dtype=float32) -# Rep: tf.Tensor(0.0, shape=(), dtype=float32) -# Min: tf.Tensor(-0.0024138745, shape=(), dtype=float32) -# Noise: tf.Tensor(0.0, shape=(), dtype=float32) -# Excee: tf.Tensor(0.0, shape=(), dtype=float32) -# Payload: tf.Tensor([ 2.7953646 0. 0. -1.5740322 5.2131767], shape=(5,), dtype=float32) +# {'containment': , 'beta_containment': , 'containment_E20': , 'beta_containment_E20': , 'contamination': , 'beta_contamination': , 'contamination_E20': , 'beta_contamination_E20': } # ExtendedOCLoss loss 647.0576 # ExtendedOCLoss loss 647.0576 # tf.Tensor(647.0576, shape=(), dtype=float32) From 56808b163eb3988b0d1a3a8ccb4d7071d03cba27 Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 12 Jul 2024 14:57:53 +0200 Subject: [PATCH 36/62] removed print --- modules/LossLayers.py | 1 - 1 file changed, 1 deletion(-) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 763a42e8..24135528 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -2619,7 +2619,6 @@ def loss(self, inputs): is_spectator_weight=is_spectator, rs=rowsplits, energies = rechit_energy) - print(mdict) #log the OC metrics dict, if any self.wandb_log(mdict) From b8def35b60b05abec721929662f514d71339f474 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 15 Jul 2024 15:13:18 +0200 Subject: [PATCH 37/62] optimised selectMnot for large batches --- modules/object_condensation.py | 6 ++--- modules/oc_helper_ops.py | 48 ++++++++++++++++++++++------------ 2 files changed, 34 insertions(+), 20 deletions(-) diff --git a/modules/object_condensation.py b/modules/object_condensation.py index 4bcd72ae..0b6deed3 100644 --- a/modules/object_condensation.py +++ b/modules/object_condensation.py @@ -244,10 +244,10 @@ def V_rep_k(self): V_rep = self.rep_func(dsq) * tf.expand_dims(self.q_v,axis=0) - V_rep = SelectWithDefaultMnot(self.Mnot, V_rep, 0.) #K x V x 1 + V_rep = SelectWithDefaultMnot(self.Mnot, V_rep) #K x V x 1 - dsq_mnot = SelectWithDefaultMnot(self.Mnot, dsq, 0.) #K x V x 1 + dsq_mnot = SelectWithDefaultMnot(self.Mnot, dsq) #K x V x 1 if self.rep_range > 0: N_notk = tf.reduce_sum(tf.where(dsq_mnot < self.rep_range**2 , 1., tf.zeros_like(dsq_mnot)), axis=1) else: @@ -402,7 +402,7 @@ def _calc_contamination(self, energies, rel_metrics_radius, energies_k): dsq = tf.expand_dims(x_k_alpha, axis=1) - tf.expand_dims(self.x_v, axis=0) #K x V x C dsq = tf.reduce_sum(dsq**2, axis=-1) #K x V in_radius = rel_metrics_radius > tf.sqrt(dsq) / self.d_k # K x V - in_radius_mnot = SelectWithDefaultMnot(self.Mnot, in_radius, 0.) #K x V + in_radius_mnot = SelectWithDefaultMnot(self.Mnot, in_radius) #K x V energies_k_v = tf.expand_dims(energies, axis=0) # K x V x 1 energies_ir_all_k_v = tf.where(in_radius[...,tf.newaxis], energies_k_v , 0.) diff --git a/modules/oc_helper_ops.py b/modules/oc_helper_ops.py index 04dc5425..e24f39ca 100644 --- a/modules/oc_helper_ops.py +++ b/modules/oc_helper_ops.py @@ -101,29 +101,43 @@ def SelectWithDefault(indices, tensor, default=0, no_check=False): tf.assert_equal(tf.shape(tensor)[1], tf.shape(out)[2])]): return out -def SelectWithDefaultMnot(Mnot, tensor, default=0, no_check=False): - #tensor dim = K x V x 1 or K x V +def SelectWithDefaultMnot(Mnot, tensor): + # Check if tensor has a third dimension, if not, expand it no_extra_dim = (len(tf.shape(tensor)) == 2) if no_extra_dim: - tensor = tf.expand_dims(tensor, axis=-1) - K, V, _ = tensor.shape - _, X = Mnot.shape - - # Step 1: Create a range tensor of shape (V,) and expand dimensions to match dsq for broadcasting - v_range = tf.range(V) - v_range_expanded = tf.expand_dims(tf.expand_dims(v_range, axis=0), axis=-1) # Shape (1, V, 1) - - # Step 2: Expand Mnot dimensions and compare against v_range to create a mask - Mnot_expanded = tf.expand_dims(Mnot, axis=1) # Shape (K, 1, X) - mask = tf.reduce_any(tf.equal(Mnot_expanded, v_range_expanded), axis=-1, keepdims=True) # Shape (K, V, 1) + tensor = tf.expand_dims(tensor, axis=-1) - # Step 3: Apply the mask to dsq, setting unselected elements to 0 - tensor_mnot = tf.where(mask, tensor, tf.zeros_like(tensor)) + K, V, _ = tensor.shape + + # Create a base mask of shape (K, V) initialized to False + mask = tf.zeros((K, V), dtype=tf.bool) + + # Flatten Mnot and create row indices + row_indices = tf.repeat(tf.range(K), repeats=Mnot.shape[1]) + col_indices = tf.reshape(Mnot, [-1]) + + # Filter out invalid indices (-1) + valid_mask = col_indices >= 0 + row_indices = tf.boolean_mask(row_indices, valid_mask) + col_indices = tf.boolean_mask(col_indices, valid_mask) + + # Stack indices together + indices = tf.stack([row_indices, col_indices], axis=1) + + # Scatter update to create the mask + updates = tf.ones(indices.shape[0], dtype=tf.bool) + mask = tf.tensor_scatter_nd_update(mask, indices, updates) + + # Expand the mask to match the last dimension of the tensor + mask_expanded = tf.expand_dims(mask, -1) + + # Apply the mask to set the elements to 0 + tensor = tf.where(mask_expanded, tensor, tf.zeros_like(tensor)) if no_extra_dim: - tensor_mnot = tf.squeeze(tensor_mnot, axis=-1) + tensor = tf.squeeze(tensor, axis=-1) - return tensor_mnot + return tensor def per_rs_segids_to_unique(pred_sid, rs, return_nseg=False, strict_check=True): From dc317461ef1523bf3061b2867a4a1a51947c2168 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 16 Jul 2024 15:02:35 +0200 Subject: [PATCH 38/62] added tidx mathcing for testing --- modules/OCHits2Showers.py | 6 +-- modules/ShowersMatcher2.py | 89 ++++++++++++++++++++++++++++++++++++-- 2 files changed, 89 insertions(+), 6 deletions(-) diff --git a/modules/OCHits2Showers.py b/modules/OCHits2Showers.py index b2f12591..017b6e99 100644 --- a/modules/OCHits2Showers.py +++ b/modules/OCHits2Showers.py @@ -753,12 +753,12 @@ def process_endcap2(hits2showers_layer, energy_gather_layer, features_dict, if not isinstance(alpha_idx, np.ndarray): alpha_idx = alpha_idx.numpy() alpha_idx = np.reshape(alpha_idx, newshape=(-1,)) - print("Made showers") + # print("Made showers") processed_pred_dict = dict() processed_pred_dict['pred_sid'] = pred_sid - print("Get energy") + # print("Get energy") energy_data = energy_gather_layer( pred_sid, predictions_dict['pred_energy_corr_factor'], @@ -770,7 +770,7 @@ def process_endcap2(hits2showers_layer, energy_gather_layer, features_dict, alpha_idx_tracks = alpha_idx_tracks, t_is_minbias = is_minbias, ) - print("Got energy") + # print("Got energy") try: diff --git a/modules/ShowersMatcher2.py b/modules/ShowersMatcher2.py index 0fe8c8ee..80d08414 100644 --- a/modules/ShowersMatcher2.py +++ b/modules/ShowersMatcher2.py @@ -152,7 +152,6 @@ def _assign_additional_attr(self): def _assign_hasTrack(self): id = self.features_dict['recHitID'] - print(id) truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0].astype(np.int32) * 1 pred_sid = self.pred_sid * 1 @@ -164,6 +163,22 @@ def _assign_hasTrack(self): attr['hasTrack'] = np.any(id[filt]) + #JUST FOR TESTING + def _assign_tidx(self): + truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0].astype(np.int32) * 1 + pred_sid = self.pred_sid * 1 + print(pred_sid) + + for n, attr in self.graph.nodes(data=True): + if attr['type'] == ShowersMatcher._NODE_TYPE_TRUTH_SHOWER: + filt = truth_sid == n + tidx = self.truth_dict['truthHitAssignementIdx'] + attr['tidx'] = np.argmax(np.bincount(tidx[filt].flatten())) + else: + filt = pred_sid == n + tidx = self.predictions_dict['t_idx'] + print("TIDX", tidx[filt]) + attr['tidx'] = tidx[filt] def _build_data_graph(self): """ Builds a graph in which all truth showers and all predicted showers are nodes @@ -285,7 +300,24 @@ def _cost_matrix_Cocoa(self, truth_shower_sid, pred_shower_sid): d= np.sqrt(reldeltaEsq + 5*deltaRsq) C[a, b] = 1/(d+1e-3) - + print(C) + return C + + #JUST FOR TESTING + def _cost_matrix_tidx(self, truth_shower_sid, pred_shower_sid): + + n = max(len(truth_shower_sid), len(pred_shower_sid)) + C = np.zeros((n, n)) + + for a, j in enumerate(pred_shower_sid): + x = self.graph.nodes(data=True)[j] + for b, i in enumerate(truth_shower_sid): + y = self.graph.nodes(data=True)[i] + if (x['t_idx'] != y['truthHitAssignementIdx']): + C[a, b] =0 + else: + C[a, b] = 1 + print(C) return C @@ -293,7 +325,7 @@ def _cost_matrix_intersection_based(self, truth_shower_sid, pred_shower_sid, ret pred_shower_energy = np.array([self.graph.nodes[x]['pred_energy'] for x in pred_shower_sid]) truth_shower_energy = np.array([self.graph.nodes[x]['truthHitAssignedEnergies'] for x in truth_shower_sid]) weight = self.features_dict['recHitEnergy'][:, 0] - is_track = np.abs(self.features_dict['recHitID'][:,0]) + is_track = np.bool8(np.abs(self.features_dict['recHitID'][:,0])) #is_track = np.abs(self.features_dict['recHitZ'][:,0]) == 315 # TODO: This works for now, but is very hacky weight[is_track] = 0 @@ -356,8 +388,11 @@ def _match_single_pass(self): C = self._cost_matrix_intersection_based(truth_shower_sid, pred_shower_sid) + print(C) row_id, col_id = linear_sum_assignment(C, maximize=True) + print("Row ID", row_id) + print("Col ID", col_id) self.truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0] matched_full_graph = nx.Graph() @@ -371,6 +406,8 @@ def _match_single_pass(self): attached_in_pass=0) self.calculated_graph = matched_full_graph + #[3 4 2 6 1 5 0] + # [6 1 5 4 3 2 7 0 8] #based on _match_single_pass def _match_cocoa(self): @@ -397,6 +434,49 @@ def _match_cocoa(self): C = self._cost_matrix_Cocoa(truth_shower_sid, pred_shower_sid) row_id, col_id = linear_sum_assignment(C, maximize=True) + print("Row ID", row_id) + print("Col ID", col_id) + + self.truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0] + matched_full_graph = nx.Graph() + matched_full_graph.add_nodes_from(self.graph.nodes(data=True)) + + for p, t in zip(row_id, col_id): + if C[p, t] > 0: + matched_full_graph.add_edge( + truth_shower_sid[t], + pred_shower_sid[p], + attached_in_pass=0) + + self.calculated_graph = matched_full_graph + + #JUST FOR TESTING + def _match_tidx(self): + truth_shower_sid = [ + x[0] + for x in self.graph.nodes(data=True) + if x[1]['type']==ShowersMatcher._NODE_TYPE_TRUTH_SHOWER + ] + pred_shower_sid = [ + x[0] + for x in self.graph.nodes(data=True) + if x[1]['type']==ShowersMatcher._NODE_TYPE_PRED_SHOWER + ] + + if not len(truth_shower_sid) > 0: + print("No truth showers") + return + if not len(pred_shower_sid) > 0: + print("No predicted showers") + return + + #self._assign_tidx() + + C = self._cost_matrix_tidx(truth_shower_sid, pred_shower_sid) + + row_id, col_id = linear_sum_assignment(C, maximize=True) + print("Row ID", row_id) + print("Col ID", col_id) self.truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0] matched_full_graph = nx.Graph() @@ -417,6 +497,9 @@ def process(self, extra=False): self._match_single_pass() elif self.match_mode == 'cocoa': self._match_cocoa() + elif self.match_mode == 'tidx': + #JUST FOR TESTING + self._match_tidx() else: raise NotImplementedError('Match mode not found') if extra: From 2b3f7e20c0cd902a8c7b35814d4ea57b012e8d09 Mon Sep 17 00:00:00 2001 From: Fabian Date: Wed, 17 Jul 2024 10:49:52 +0200 Subject: [PATCH 39/62] fixed wrong normalisation --- modules/GravNetLayersRagged.py | 22 +++++++++++----------- modules/LossLayers.py | 2 +- modules/datastructures/TrainData_Cocoa.py | 4 ++++ 3 files changed, 16 insertions(+), 12 deletions(-) diff --git a/modules/GravNetLayersRagged.py b/modules/GravNetLayersRagged.py index fe7b22c9..a546c8a4 100644 --- a/modules/GravNetLayersRagged.py +++ b/modules/GravNetLayersRagged.py @@ -1652,26 +1652,26 @@ def __init__(self, ]) self.mean_track = tf.constant([ - 0.0, # recHitEnergy + 15173, # recHitEnergy 0.0, # recHitEta 0.0, # recHitID -> don't normalize 1.57, # recHitTheta - 5607720, # recHitR - -4402849, # recHitX -> centered around zero - -3468792, # recHitY -> centered around zero - -1.1, # recHitZ -> All tracks are at z=315 + 2574, # recHitR + 0.0, # recHitX -> centered around zero + 0.0, # recHitY -> centered around zero + -2.1, # recHitZ -> 0.0, # recHitTime -> All zeros 0.0, # recHitHitR -> All zeros for tracks ]) self.std_track = tf.constant([ - 1.0, # recHitEnergy - 1.46, # recHitEta + 19126, # recHitEnergy + 1.31, # recHitEta 1.0, # recHitID -> don't normalize 0.93, # recHitTheta - 2619667000, # recHitR - 2057752536, # recHitX - 1621205660, # recHitY - 2342, # recHitZ -> All tracks are at z=315 + 780, # recHitR + 990, # recHitX + 991, # recHitY + 2297, # recHitZ -> 1.0, # recHitTime -> All zeros 1.0, # recHitHitR -> All zeros for tracks ]) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 3d47c64a..3e40d357 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3152,7 +3152,7 @@ def calc_position_loss(self, t_pos, pred_pos): if not self.position_loss_weight: return 0.*tf.reduce_sum((pred_pos-t_pos)**2,axis=-1, keepdims=True) - ploss = tf.reduce_sum(tf.math.abs(t_pos - pred_pos)**2, axis=-1, keepdims=True) + ploss = huber(tf.sqrt(tf.reduce_sum((t_pos-pred_pos) ** 2, axis=-1, keepdims=True) + 1e-4), 0.1) ploss = tf.debugging.check_numerics(ploss, "ploss loss") return ploss diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index 99176965..9f0b40f6 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -117,6 +117,10 @@ def convertevent(self, event): return df_training def converttotrainingdfvec(self, data): + #For some reason, there is an outlier event with a track at -1e12, which is not physical + data = data[ak.all(data.track_x_layer_0 > -2000, axis=1)] + + #Convert events one by one traindata = np.array([self.convertevent(data[eventnumber]) for eventnumber in np.arange(len(data))]) #Remove all events with an energy lower than 15GeV From 8b0ecb68d2d7f09ea3cc2d1b178aaa1b481c27ef Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 22 Jul 2024 16:04:12 +0200 Subject: [PATCH 40/62] fixed Energyloss --- modules/LossLayers.py | 8 +++----- modules/model_blocks.py | 5 +++-- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 3e40d357..76259e81 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3123,14 +3123,13 @@ def calc_energy_correction_factor_loss(self, Wrong name for parity, but calculates the total energy instead of the correction factor. * t_energy -> Truth energy of shower * t_dep_energies -> Sum of deposited energy IF clustered perfectly - * pred_energy -> predicted energz + * pred_energy -> predicted energy * pred_uncertainty_low -> predicted uncertainty * pred_uncertainty_high -> predicted uncertainty (should be equal to ...low) """ t_energy = tf.clip_by_value(t_energy,0.,1e12) - t_dep_energies = tf.clip_by_value(t_dep_energies,0.,1e12) - - prediction_loss = (t_energy - pred_energy)**2 + + prediction_loss = huber((t_energy - pred_energy)**2/1e8, 10) matching_loss = (pred_uncertainty_low)**2 uncertainty_loss = pred_uncertainty_high**2 @@ -3138,7 +3137,6 @@ def calc_energy_correction_factor_loss(self, matching_loss = tf.debugging.check_numerics(matching_loss, "matching_loss") prediction_loss = tf.debugging.check_numerics(prediction_loss, "matching_loss") uncertainty_loss = tf.debugging.check_numerics(uncertainty_loss, "matching_loss") - prediction_loss = tf.clip_by_value(prediction_loss, 0, 10) uncertainty_loss = tf.clip_by_value(uncertainty_loss, 0, 10) if return_concat: diff --git a/modules/model_blocks.py b/modules/model_blocks.py index 08370110..610a3889 100644 --- a/modules/model_blocks.py +++ b/modules/model_blocks.py @@ -426,7 +426,8 @@ def create_outputs(x, n_ccoords=3, trainable=True, is_track=None, set_track_betas_to_one=False, - predict_spectator_weights=False): + predict_spectator_weights=False, + pred_e_factor = 0.01): ''' returns * pred_beta Dense(1) @@ -477,7 +478,7 @@ def create_outputs(x, n_ccoords=3, name = name_prefix+'_energy', kernel_initializer='zeros', activation=energy_act, - trainable=trainable)(ScalarMultiply(0.01)(x)) + trainable=trainable)(ScalarMultiply(pred_e_factor)(x)) if energy_factor: pred_energy = Add(name= name_prefix+'_one_plus_energy')([OnesLike()(pred_energy),pred_energy]) From 1b5d7f2db6a4970d0f28ec589bfc2e1dd72e2c94 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 22 Jul 2024 16:04:43 +0200 Subject: [PATCH 41/62] restructured TrainData_Cocoa --- modules/datastructures/TrainData_Cocoa.py | 36 ++++++++++++++++------- 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index 9f0b40f6..22d1bd7b 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -44,7 +44,7 @@ def convertevent(self, event): df_cell['recHitEnergy'] = ak.to_dataframe(event['cell_e'], how='outer') df_cell['recHitEta'] = ak.to_dataframe(event['cell_eta'], how='outer') df_cell['isTrack'] = 0 - df_cell['recHitTheta'] =ak.to_dataframe(event['cell_phi'], how='outer') + df_cell['recHitTheta'] = 0 #will be calculated later df_cell['recHitR'] = 0 #will be calculated later df_cell['recHitX']= ak.to_dataframe(event['cell_x'], how='outer') df_cell['recHitY']= ak.to_dataframe(event['cell_y'], how='outer') @@ -56,6 +56,7 @@ def convertevent(self, event): #Calculate Distance from 0,0,0 df_cell['recHitR']= np.sqrt(df_cell['recHitX']**2+df_cell['recHitY']**2+df_cell['recHitZ']**2) + df_cell['recHitTheta'] = np.arctan2(df_cell['recHitR'], df_cell['recHitZ']) #convert particle information to df @@ -63,10 +64,7 @@ def convertevent(self, event): df_particle['particle_idx'] = np.arange(len(event['particle_e'])) df_particle['t_energy'] = ak.to_dataframe(event['particle_e'], how='outer') - df_particle['t_time'] = 0 df_particle['t_pid'] = ak.to_dataframe(event['particle_pdgid'], how='outer') - df_particle['t_spectator'] = 0 - df_particle['t_fully_contained'] = 1 df_particle['t_rec_energy'] = ak.to_dataframe(event['particle_dep_energy'], how='outer') df_particle['particle_eta'] = ak.to_dataframe(event['particle_eta_lay0'], how='outer') df_particle['particle_phi'] = ak.to_dataframe(event['particle_phi_lay0'], how='outer') @@ -79,8 +77,8 @@ def convertevent(self, event): df_track['recHitZ'] = ak.to_dataframe(event['track_z_layer_0'], how='outer') df_track['recHitR']= np.sqrt(df_track['recHitX']**2+df_track['recHitY']**2+df_track['recHitZ']**2) - df_track['recHitTheta'] =ak.to_dataframe(event['track_theta'], how='outer') - df_track['recHitEta'] = -np.log(np.tan(df_track['recHitTheta']/2)) + df_track['recHitTheta'] = np.arctan2(df_track['recHitR'], df_track['recHitZ']) + df_track['recHitEta'] = ak.to_dataframe(event['track_eta_layer_0'], how='outer') df_track['recHitTime'] = 0 df_track['recHitHitR'] = 0 @@ -106,29 +104,45 @@ def convertevent(self, event): #set NaN values (particle information for hits made from background noise to -1) df_training.fillna(-1, inplace=True) - #encode pos as cos(phi), sin(phi), eta - df_training['t_pos'] = df_training.apply(lambda row: [np.cos(row['particle_phi']), np.sin(row['particle_phi']), row['particle_eta']], axis=1) - + #Set other default values + df_training['t_time'] = 0 + df_training['t_spectator'] = 0 + df_training['t_fully_contained'] = 1 + #set t_is_unique df_training['t_is_unique'] = 0 first_occurrence_mask = (df_training['t_idx'] != -1) & (df_training.groupby('t_idx').cumcount() == 0) df_training.loc[first_occurrence_mask, 't_is_unique'] = 1 + + #encode pos as cos(phi), sin(phi), eta + df_training['t_pos'] = df_training.apply(lambda row: [np.cos(row['particle_phi']), np.sin(row['particle_phi']), row['particle_eta']], axis=1) + + #set t_pos to [cos(phi), sin(phi), eta] based on RecHitX,Y,Z if t_idx is -1 + df_training['t_pos'] = df_training['t_pos'].where(df_training['t_idx'] != -1, df_training.apply(lambda row: [np.cos(np.arctan2(row['recHitY'], row['recHitX'])), np.sin(np.arctan2(row['recHitY'], row['recHitX'])), row['recHitEta']], axis=1)) + + #replace -1 in t_energy and t_rec_energy with recHitEnergy + df_training['t_energy'] = df_training['t_energy'].where(df_training['t_energy'] != -1, df_training['recHitEnergy']) + df_training['t_rec_energy'] = df_training['t_rec_energy'].where(df_training['t_rec_energy'] != -1, df_training['recHitEnergy']) return df_training def converttotrainingdfvec(self, data): #For some reason, there is an outlier event with a track at -1e12, which is not physical + print("Number of events before removing outlier: ", len(data)) data = data[ak.all(data.track_x_layer_0 > -2000, axis=1)] + print("Number of events after removing outlier: ", len(data)) #Convert events one by one - traindata = np.array([self.convertevent(data[eventnumber]) for eventnumber in np.arange(len(data))]) + traindata = np.array([self.convertevent(data[eventnumber]) for eventnumber in np.arange(len(data))], dtype=object) #Remove all events with an energy lower than 15GeV E_cutoff = 15000 + print("Number of events before removing low energy events: ", len(traindata)) E = np.sqrt(data['true_jet_pt']**2 * np.cos(data['true_jet_phi'])**2 + data['true_jet_m']**2) mask = ak.to_numpy(ak.all(E >= E_cutoff, axis=1)) traindata = traindata[mask] - + print("Number of events after removing low energy events: ", len(traindata)) + #find the row splits rs = np.cumsum([len(df) for df in traindata]) rs = np.insert(rs, 0, 0) From cec9b9dc8076286787f8181baf7f2cc47c335228 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 23 Jul 2024 14:56:05 +0200 Subject: [PATCH 42/62] fixed matching --- modules/OCHits2Showers.py | 5 ++-- modules/ShowersMatcher2.py | 31 +++++++-------------- tests/checkDJC.py | 55 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 68 insertions(+), 23 deletions(-) create mode 100644 tests/checkDJC.py diff --git a/modules/OCHits2Showers.py b/modules/OCHits2Showers.py index 017b6e99..a09ac1b8 100644 --- a/modules/OCHits2Showers.py +++ b/modules/OCHits2Showers.py @@ -725,8 +725,9 @@ def process_endcap2(hits2showers_layer, energy_gather_layer, features_dict, """ if not 'no_noise_sel' in predictions_dict.keys(): N_pred = len(predictions_dict['pred_beta']) - predictions_dict['no_noise_sel'] = np.arange(N_pred).reshape((N_pred,1)).astype(int) - is_track = np.abs(features_dict['recHitZ']) == 315 + predictions_dict['no_noise_sel'] = np.arange(N_pred).reshape((N_pred,1)).astype(int) + is_track = np.abs(features_dict['recHitID']) + # is_track = np.abs(features_dict['recHitZ']) == 315 print("Make showers") if not hdbscan: diff --git a/modules/ShowersMatcher2.py b/modules/ShowersMatcher2.py index 80d08414..5e80ee4b 100644 --- a/modules/ShowersMatcher2.py +++ b/modules/ShowersMatcher2.py @@ -167,18 +167,17 @@ def _assign_hasTrack(self): def _assign_tidx(self): truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0].astype(np.int32) * 1 pred_sid = self.pred_sid * 1 - print(pred_sid) for n, attr in self.graph.nodes(data=True): if attr['type'] == ShowersMatcher._NODE_TYPE_TRUTH_SHOWER: filt = truth_sid == n - tidx = self.truth_dict['truthHitAssignementIdx'] - attr['tidx'] = np.argmax(np.bincount(tidx[filt].flatten())) + tidx = self.truth_dict['truthHitAssignementIdx'][filt].flatten() + attr['tidx'] = np.argmax(np.bincount(tidx)) else: filt = pred_sid == n - tidx = self.predictions_dict['t_idx'] - print("TIDX", tidx[filt]) - attr['tidx'] = tidx[filt] + tidx = self.predictions_dict['t_idx'][filt].flatten() + hist, _ = np.histogram(tidx, bins=range(-2, np.max(tidx)+1)) + attr['tidx'] = np.argmax(hist)-1 def _build_data_graph(self): """ Builds a graph in which all truth showers and all predicted showers are nodes @@ -292,15 +291,15 @@ def _cost_matrix_Cocoa(self, truth_shower_sid, pred_shower_sid): reldeltaEsq = (np.abs(x['pred_energy'] - y['truthHitAssignedEnergies'])/ y['truthHitAssignedEnergies'])**2 pred_phi = np.arctan2(x['pred_pos'][1], x['pred_pos'][0]) - deltaPhi = np.abs(pred_phi - y['truthHitAssignedPhi']) - deltaEta = np.abs(x['pred_pos'][2] - y['truthHitAssignedEta']) + truth_phi = np.arctan2(y['truthHitAssignedY'], y['truthHitAssignedX']) + deltaPhi = np.abs(pred_phi - truth_phi) + deltaEta = np.abs(x['pred_pos'][2] - y['truthHitAssignedZ']) deltaRsq = deltaPhi**2 + deltaEta**2 d= np.sqrt(reldeltaEsq + 5*deltaRsq) C[a, b] = 1/(d+1e-3) - print(C) return C #JUST FOR TESTING @@ -313,11 +312,10 @@ def _cost_matrix_tidx(self, truth_shower_sid, pred_shower_sid): x = self.graph.nodes(data=True)[j] for b, i in enumerate(truth_shower_sid): y = self.graph.nodes(data=True)[i] - if (x['t_idx'] != y['truthHitAssignementIdx']): + if (x['tidx'] != y['tidx']): C[a, b] =0 else: C[a, b] = 1 - print(C) return C @@ -388,11 +386,8 @@ def _match_single_pass(self): C = self._cost_matrix_intersection_based(truth_shower_sid, pred_shower_sid) - print(C) row_id, col_id = linear_sum_assignment(C, maximize=True) - print("Row ID", row_id) - print("Col ID", col_id) self.truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0] matched_full_graph = nx.Graph() @@ -406,8 +401,6 @@ def _match_single_pass(self): attached_in_pass=0) self.calculated_graph = matched_full_graph - #[3 4 2 6 1 5 0] - # [6 1 5 4 3 2 7 0 8] #based on _match_single_pass def _match_cocoa(self): @@ -434,8 +427,6 @@ def _match_cocoa(self): C = self._cost_matrix_Cocoa(truth_shower_sid, pred_shower_sid) row_id, col_id = linear_sum_assignment(C, maximize=True) - print("Row ID", row_id) - print("Col ID", col_id) self.truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0] matched_full_graph = nx.Graph() @@ -470,13 +461,11 @@ def _match_tidx(self): print("No predicted showers") return - #self._assign_tidx() + self._assign_tidx() C = self._cost_matrix_tidx(truth_shower_sid, pred_shower_sid) row_id, col_id = linear_sum_assignment(C, maximize=True) - print("Row ID", row_id) - print("Col ID", col_id) self.truth_sid = self.truth_dict['truthHitAssignementIdx'][:, 0] matched_full_graph = nx.Graph() diff --git a/tests/checkDJC.py b/tests/checkDJC.py new file mode 100644 index 00000000..e7f4422f --- /dev/null +++ b/tests/checkDJC.py @@ -0,0 +1,55 @@ +from datastructures import TrainData_Cocoa +from djcdata.dataPipeline import TrainDataGenerator +import numpy as np +import matplotlib.pyplot as plt + +td = TrainData_Cocoa() + +td.readFromFile('/work/friemer/hgcalml/trainingdata/singleQuarkJet_train.djctd') +gen = TrainDataGenerator() +gen.setBatchSize(1) +gen.setBuffer(td) +gen.setSkipTooLargeBatches(False) + +data = next(gen.feedTrainData())#this is a dict, row splits can be ignored, this is per event +df= TrainData_Cocoa.createPandasDataFrame(data, 0) +print(df.keys()) +# Index(['recHitEnergy', 'recHitEta', 'recHitHitR', 'recHitID', +# 'recHitLogEnergy', 'recHitR', 'recHitTheta', 'recHitTime', 'recHitX', +# 'recHitY', 'recHitZ', 't_rec_energy', 'truthHitAssignedEnergies', +# 'truthHitAssignedEta', 'truthHitAssignedPIDs', 'truthHitAssignedPhi', +# 'truthHitAssignedT', 'truthHitAssignedX', 'truthHitAssignedY', +# 'truthHitAssignedZ', 'truthHitAssignementIdx', +# 'truthHitFullyContainedFlag', 'truthHitSpectatorFlag'], +# dtype='object') + + +phi_truth = np.arctan2(df['truthHitAssignedY'], df['truthHitAssignedX']) +eta_truth = df['truthHitAssignedZ'] + +# phi_truth = df['truthHitAssignedPhi'] +# eta_truth = df['truthHitAssignedEta'] #Wrong definition of eta in the nanoML file vs cococa + +# print(max(abs(phi_pred - phi_truth))) +# print(max(abs(eta_pred - eta_truth))) + +phi_hit = np.arctan2(df['recHitY'], df['recHitX']) +#Calculate Eta from X,Y,Z +eta_hit = np.arcsinh(df['recHitZ']/np.sqrt(df['recHitX']**2 + df['recHitY']**2)) + +print(np.std(phi_hit - phi_truth)) +print(max(abs(phi_hit - phi_truth))) +plt.hist(phi_hit - phi_truth, bins=25) +plt.ylabel('number of hits') +plt.xlabel('phi_hit - phi_truth') +plt.savefig('phi_hit.png') + +print(np.std(eta_hit - eta_truth)) +print(max(abs(eta_hit - eta_truth))) +plt.hist(eta_hit - eta_truth, bins=25) +plt.ylabel('number of hits') +plt.xlabel('eta_hit - eta_truth') +plt.savefig('eta_hit.png') + + + From 8a771754fb8a49d84f77adc7afff923c2e2d0f10 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 23 Jul 2024 14:57:41 +0200 Subject: [PATCH 43/62] created analyse_cocoa --- Train/paper_trainer_Cocoa_simple.py | 34 +- scripts/analyse_cocoa_predictions.py | 199 +++++++++++ scripts/generatePlots.ipynb | 476 +++++++++++++++------------ scripts/plot_cocoa.py | 409 +++++++++++++++++++++++ 4 files changed, 898 insertions(+), 220 deletions(-) create mode 100755 scripts/analyse_cocoa_predictions.py create mode 100644 scripts/plot_cocoa.py diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index 8d8420eb..57bff269 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -1,7 +1,3 @@ -""" -Training script based on paper_trainer_noRSU_fixed_jk.py -""" - import sys from argparse import ArgumentParser @@ -148,7 +144,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): c_coords = extent_coords_if_needed(c_coords, x, 3) - x = Concatenate()([x, c_coords, is_track]) + x = Concatenate()([x, c_coords, is_track])#, tf.cast(t_idx, tf.float32)]) #JUST FOR TESTING FIXME x = Dense(64, name='dense_pre_loop', activation=DENSE_ACTIVATION)(x) allfeat = [] @@ -194,15 +190,17 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): x = BatchNormalization()(x) pred_beta, pred_ccoords, pred_dist, \ - pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, \ + pred_energy, pred_energy_low_quantile, pred_energy_high_quantile, \ pred_pos, pred_time, pred_time_unc, pred_id = \ create_outputs(x, n_ccoords=3, n_classes=4, n_pos = 3, fix_distance_scale=True, + energy_factor=False, is_track=is_track, - set_track_betas_to_one=True) + set_track_betas_to_one=True, + pred_e_factor=10000) pred_beta = LLExtendedObjectCondensation5( @@ -214,15 +212,16 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): implementation = "hinge", beta_loss_scale = 1.0, too_much_beta_scale = 0.0, - energy_loss_weight = 0.4, + energy_loss_weight =1.0, classification_loss_weight = 0.4, - position_loss_weight = 1.0, + position_loss_weight = 10.0, timing_loss_weight = 0.0, downweight_low_energy=False, + potential_scaling = 1.0, train_energy_unc=False, q_min = 0.1, use_average_cc_pos = 0.9999)( - [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, + [pred_beta, pred_ccoords, pred_dist, pred_energy, pred_energy_low_quantile, pred_energy_high_quantile, pred_pos, pred_time, pred_time_unc, pred_id, energy, pre_processed['t_idx'] , pre_processed['t_energy'] , pre_processed['t_pos'] , pre_processed['t_time'] , pre_processed['t_pid'] , pre_processed['t_spectator_weight'], @@ -239,15 +238,16 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): model_outputs = { 'pred_beta': pred_beta, 'pred_ccoords': pred_ccoords, - 'pred_energy_corr_factor': pred_energy_corr, - 'pred_energy_low_quantile': pred_energy_low_quantile, - 'pred_energy_high_quantile': pred_energy_high_quantile, + 'pred_energy': pred_energy, + #'pred_energy_low_quantile': pred_energy_low_quantile, + # 'pred_energy_high_quantile': pred_energy_high_quantile, 'pred_pos': pred_pos, 'pred_time': pred_time, 'pred_id': pred_id, 'pred_dist': pred_dist, 'rechit_energy': energy, 'row_splits': pre_processed['row_splits'], + #'t_idx': pre_processed['t_idx'], #JUST FOR TESTING FIXME # 'no_noise_sel': pre_processed['no_noise_sel'], # 'no_noise_rs': pre_processed['no_noise_rs'], } @@ -302,7 +302,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): ### Actual Training ########################################################### ############################################################################### -train.change_learning_rate(1e-3) +train.change_learning_rate(1e-4) train.trainModel( nepochs=2, batchsize=50000, @@ -322,10 +322,10 @@ def fix_batchnorm(m): #recompile -train.compileModel(learningrate=5e-4) +train.compileModel(learningrate=2e-5) print('entering second training phase') train.trainModel( - nepochs=10, + nepochs=15, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, @@ -334,7 +334,7 @@ def fix_batchnorm(m): #recompile -train.compileModel(learningrate=1e-4) +train.compileModel(learningrate=5e-6) print('entering third training phase') train.trainModel( nepochs=35, diff --git a/scripts/analyse_cocoa_predictions.py b/scripts/analyse_cocoa_predictions.py new file mode 100755 index 00000000..e7fd2e9e --- /dev/null +++ b/scripts/analyse_cocoa_predictions.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +""" +Analysis script to run of the predictions of the model. +""" + +import pdb +import os +import argparse +import pickle +import gzip +import mgzip +import pandas as pd +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt + +from OCHits2Showers import OCHits2ShowersLayer, OCHits2ShowersLayer_HDBSCAN +from OCHits2Showers import process_endcap2, OCGatherEnergyCorrFac2, OCGatherEnergyCorrFac_new +from ShowersMatcher2 import ShowersMatcher +from hplots.hgcal_analysis_plotter import HGCalAnalysisPlotter +import extra_plots as ep +from visualize_event import dataframe_to_plot, matched_plot +from plot_cocoa import plot_everything + + +def analyse(preddir, + pdfpath, + beta_threshold, + distance_threshold, + iou_threshold, + matching_mode, + analysisoutpath, + nfiles, + nevents, + local_distance_scaling, + de_e_cut, + angle_cut, + extra=False, + shower0=False, + ): + + hits2showers = OCHits2ShowersLayer( + beta_threshold, + distance_threshold, + local_distance_scaling) + showers_matcher = ShowersMatcher(matching_mode, iou_threshold, de_e_cut, angle_cut, shower0) + + + files_to_be_tested = [ + os.path.join(preddir, x) + for x in os.listdir(preddir) + if (x.endswith('.bin.gz') and x.startswith('pred'))] + if nfiles!=-1: + files_to_be_tested = files_to_be_tested[0:min(nfiles, len(files_to_be_tested))] + + showers_dataframe = pd.DataFrame() + features = [] + truth = [] + prediction = [] + alpha_ids = [] + matched = [] + event_id = 0 + + ############################################################################################### + ### Loop over all events ###################################################################### + ############################################################################################### + + for i, file in enumerate(files_to_be_tested): + print(f"Analysing file {i+1}/{len(files_to_be_tested)}") + with mgzip.open(file, 'rb') as analysis_file: + file_data = pickle.load(analysis_file) + for j, endcap_data in enumerate(file_data): + if (nevents != -1) and (j > nevents): + continue + # print(f"Analysing endcap {j+1}/{len(file_data)}") + features_dict, truth_dict, predictions_dict = endcap_data + features.append(features_dict) + truth.append(truth_dict) + + print(f"Analyzing event {event_id}") + + pred_sid, _, alpha_idx, _, _ = hits2showers( + predictions_dict['pred_ccoords'], + predictions_dict['pred_beta'], + predictions_dict['pred_dist']) + + predictions_dict['pred_sid'] = pred_sid + prediction.append(predictions_dict) + + + if not isinstance(alpha_idx, np.ndarray): + alpha_idx = alpha_idx.numpy() + alpha_idx = np.reshape(alpha_idx, newshape=(-1,)) + + alpha_ids.append(alpha_idx) + + showers_matcher.set_inputs( + features_dict=features_dict, + truth_dict=truth_dict, + predictions_dict=predictions_dict, + pred_alpha_idx=alpha_idx + ) + showers_matcher.process(extra=extra) + dataframe = showers_matcher.get_result_as_dataframe() + matched_truth_sid, matched_pred_sid = showers_matcher.get_matched_hit_sids() + matched.append((matched_truth_sid, matched_pred_sid)) + + dataframe['event_id'] = event_id + dataframe['pred_id_value'] = dataframe['pred_id'].apply(lambda x: np.argmax(x)) + showers_dataframe = pd.concat((showers_dataframe, dataframe)) + + event_id += 1 + + ############################################################################################### + ### Write to file ############################################################################# + ############################################################################################### + scalar_variables = { + 'beta_threshold': str(beta_threshold), + 'distance_threshold': str(distance_threshold), + 'iou_threshold': str(iou_threshold), + 'matching_mode': str(matching_mode), + 'de_e_cut': str(de_e_cut), + 'angle_cut': str(angle_cut), + } + + if len(analysisoutpath) > 0: + analysis_data = { + 'showers_dataframe' : showers_dataframe, + 'scalar_variables' : scalar_variables, + 'alpha_ids' : alpha_ids, + 'matched': matched, + } + + analysis_data['features'] = features + analysis_data['truth'] = truth + analysis_data['prediction'] = prediction + + with gzip.open(analysisoutpath, 'wb') as output_file: + print("Writing dataframes to pickled file",analysisoutpath) + pickle.dump(analysis_data, output_file) + + ############################################################################################### + ### Make Plots ################################################################################ + ############################################################################################### + + if(len(pdfpath) > 0): + plot_everything(showers_dataframe, pdfpath) + print("DONE") + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + 'Analyse predictions from object condensation and plot relevant results') + parser.add_argument('preddir', + help='Directory with .bin.gz files or a txt file with full paths of the \ + bin-gz files from the prediction.') + parser.add_argument('-p', + help="Output directory for the final analysis pdf file (otherwise, it won't be produced)", + default='') + parser.add_argument('-b', help='Beta threshold (default 0.1)', default='0.1') + parser.add_argument('-d', help='Distance threshold (default 0.5)', default='0.5') + parser.add_argument('-i', help='IOU threshold (default 0.1)', default='0.1') + parser.add_argument('-m', help='Matching mode', default='iou_max') + parser.add_argument('--analysisoutpath', + help='Will dump analysis data to a file to remake plots without re-running everything.', + default='') + parser.add_argument('--nfiles', + help='Maximum number of files. -1 for everything in the preddir', + default=-1) + parser.add_argument('--nevents', help='Maximum number of events (per file)', default=-1) + parser.add_argument('--no_local_distance_scaling', help='With local distance scaling', + action='store_true') + parser.add_argument('--de_e_cut', help='dE/E threshold to allow match.', default=-1) + parser.add_argument('--angle_cut', help='Angle cut for angle based matching', default=-1) + parser.add_argument('--extra', + help="Calculate more information for showers", + action='store_true') + parser.add_argument('--shower0', + help="Only match with truth shower ID=0", + action='store_true') + + args = parser.parse_args() + + analyse(preddir=args.preddir, + pdfpath=args.p, + beta_threshold=float(args.b), + distance_threshold=float(args.d), + iou_threshold=float(args.i), + matching_mode=args.m, + analysisoutpath=args.analysisoutpath, + nfiles=int(args.nfiles), + local_distance_scaling=not args.no_local_distance_scaling, + de_e_cut=float(args.de_e_cut), + angle_cut=float(args.angle_cut), + nevents=int(args.nevents), + extra=args.extra, + shower0=args.shower0 + ) + diff --git a/scripts/generatePlots.ipynb b/scripts/generatePlots.ipynb index 3c8e96e6..98a09edb 100644 --- a/scripts/generatePlots.ipynb +++ b/scripts/generatePlots.ipynb @@ -22,6 +22,7 @@ "import pandas as pd\n", "import tensorflow as tf\n", "import sys\n", + "import plotly.express as px\n", "\n", "sys.path.append('../modules')\n", "tf.config.set_visible_devices(tf.config.list_physical_devices('GPU')[1], 'GPU')" @@ -36,44 +37,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-07-12 12:37:33.447415: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "2024-07-23 13:50:08.153579: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-07-12 12:37:35.195685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11473 MB memory: -> device: 1, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:03:00.0, compute capability: 5.2\n" + "2024-07-23 13:50:08.906419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 44 MB memory: -> device: 1, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:03:00.0, compute capability: 5.2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Analysis data keys: dict_keys(['showers_dataframe', 'events_dataframe', 'scalar_variables', 'alpha_ids', 'noise_masks', 'matched', 'matched_showers', 'processed_dataframe', 'features', 'truth', 'prediction'])\n", - "shower keys Index(['t_rec_energy', 'pred_energy_unc', 'pred_sid', 'pred_energy_tracks_raw',\n", - " 'pred_no_minbias_fraction', 'truthHitAssignedEnergies', 'pred_energy',\n", - " 'pred_energy_hits_raw', 'pred_energy_hits_corrected',\n", - " 'pred_energy_high_quantile', 'pred_time', 'truthHitAssignedY',\n", - " 'pred_energy_tracks_corrected', 'truthHitAssignedZ', 'pred_ccoords',\n", - " 'truthHitAssignedEta', 'truthHitAssignedPIDs', 'truthHitAssignementIdx',\n", - " 'truthHitAssignedX', 'truthHitSpectatorFlag', 'pred_pos',\n", - " 'truthHitAssignedT', 'pred_energy_low_quantile',\n", - " 'truthHitFullyContainedFlag', 'truthHitAssignedPhi', 'pred_id',\n", - " 'pred_dist', 'no_noise_sel', 'pred_energy_corr_factor', 'event_id'],\n", - " dtype='object')\n", - "events None\n", - "scalar keys dict_keys(['beta_threshold', 'distance_threshold', 'iou_threshold', 'matching_mode', 'is_soft', 'de_e_cut', 'angle_cut'])\n", - "matched keys Index(['truth_sid', 'pred_sid', 'n_truth', 'e_truth', 'n_pred', 'e_pred',\n", - " 'n_pred_and_truth', 'e_pred_and_truth', 'n_pred_not_truth',\n", - " 'e_pred_not_truth', 'event_id'],\n", - " dtype='object')\n", - "processed keys Index(['pred_sid', 'pred_no_minbias_fraction', 'pred_energy',\n", - " 'pred_energy_hits_raw', 'pred_energy_hits_corrected',\n", - " 'pred_energy_tracks_raw', 'pred_energy_tracks_corrected',\n", - " 'pred_energy_unc', 'pred_ccoords_0', 'pred_ccoords_1', 'pred_ccoords_2',\n", - " 'pred_energy_corr_factor', 'pred_energy_low_quantile',\n", - " 'pred_energy_high_quantile', 'pred_pos_0', 'pred_pos_1', 'pred_pos_2',\n", - " 'pred_time', 'pred_dist', 'rechit_energy', 'no_noise_sel', 'event_id'],\n", + "Analysis data keys: dict_keys(['showers_dataframe', 'scalar_variables', 'alpha_ids', 'matched', 'features', 'truth', 'prediction'])\n", + "shower keys Index(['truthHitAssignedPhi', 'pred_ccoords', 'pred_beta', 'pred_sid',\n", + " 't_rec_energy', 'truthHitAssignementIdx', 'hasTrack', 'pred_time',\n", + " 'truthHitSpectatorFlag', 'pred_energy', 'truthHitAssignedY', 't_idx',\n", + " 'truthHitAssignedPIDs', 'truthHitAssignedEnergies', 'truthHitAssignedT',\n", + " 'pred_id', 'pred_dist', 'truthHitAssignedX', 'pred_pos',\n", + " 'truthHitFullyContainedFlag', 'truthHitAssignedEta',\n", + " 'truthHitAssignedZ', 'event_id'],\n", " dtype='object')\n", + "scalar keys dict_keys(['beta_threshold', 'distance_threshold', 'iou_threshold', 'matching_mode', 'de_e_cut', 'angle_cut'])\n", "features list keys dict_keys(['recHitEnergy', 'recHitEta', 'recHitID', 'recHitTheta', 'recHitR', 'recHitX', 'recHitY', 'recHitZ', 'recHitTime', 'recHitHitR', 'recHitXY'])\n", "truth list keys dict_keys(['truthHitAssignementIdx', 'truthHitAssignedEnergies', 'truthHitAssignedX', 'truthHitAssignedY', 'truthHitAssignedZ', 'truthHitAssignedEta', 'truthHitAssignedPhi', 'truthHitAssignedT', 'truthHitAssignedPIDs', 'truthHitSpectatorFlag', 'truthHitFullyContainedFlag', 't_rec_energy'])\n", - "prediction list keys dict_keys(['pred_beta', 'pred_ccoords', 'pred_energy_corr_factor', 'pred_energy_low_quantile', 'pred_energy_high_quantile', 'pred_pos', 'pred_time', 'pred_id', 'pred_dist', 'rechit_energy', 'row_splits', 'no_noise_sel'])\n" + "prediction list keys dict_keys(['pred_beta', 'pred_ccoords', 'pred_energy', 'pred_pos', 'pred_time', 'pred_id', 'pred_dist', 'rechit_energy', 'row_splits', 't_idx', 'pred_sid'])\n" ] } ], @@ -81,7 +66,16 @@ "#Open file\n", "#filepath = '/work/friemer/hgcalml/SimpleCocoa/analyseSimpleCocoa/analysisoutput'\n", "filepath = '/work/friemer/hgcalml/CocoaTotalEnergyQmin01PhiEta/analysisoutput'\n", - "filepath = '/work/friemer/hgcalml/analysisoutputoldmatching'\n", + "filepath = '/work/friemer/hgcalml/analysisoutputoldmatchingfixedID'\n", + "filepath = '/work/friemer/hgcalml/analysisoutputfixedID'\n", + "filepath = '/work/friemer/hgcalml/analysisoutputtidx2'\n", + "filepath = '/work/friemer/hgcalml/analysisoutputtidx2cocoaa'\n", + "filepath = '/work/friemer/hgcalml/analysisoutputtidx5cocoa'\n", + "filepath = '/work/friemer/hgcalml/analysisoutputfixedNormtidx'\n", + "filepath = '/work/friemer/hgcalml/analysisoutputfixedNormcontinue'\n", + "filepath = '/work/friemer/hgcalml/analysistidx6'\n", + "filepath = '/work/friemer/hgcalml/analysisTidx9'\n", + "filepath = '/work/friemer/hgcalml/analysisTidx9cocoamatch'\n", "\n", "# Open the file and load the data\n", "with gzip.open(filepath, 'rb') as input_file:\n", @@ -90,21 +84,23 @@ "# Print the keys of the data\n", "print('Analysis data keys:', analysis_data.keys())\n", "print('shower keys ',analysis_data['showers_dataframe'].keys())\n", - "print('events',analysis_data['events_dataframe'])\n", "print('scalar keys',analysis_data['scalar_variables'].keys())\n", "#print(analysis_data['alpha_ids'])\n", - "#print(analysis_data['noise_masks'])\n", "#print(analysis_data['matched'])\n", - "print('matched keys',analysis_data['matched_showers'].keys())\n", - "print('processed keys',analysis_data['processed_dataframe'].keys())\n", "print('features list keys', analysis_data['features'][0].keys())\n", "print('truth list keys', analysis_data['truth'][0].keys())\n", "print('prediction list keys', analysis_data['prediction'][0].keys())\n", "\n", "df = analysis_data['showers_dataframe']\n", + "feature_list = analysis_data['features']\n", "t_list = analysis_data['truth']\n", "pred_list = analysis_data['prediction']\n", - "proc_df = analysis_data['processed_dataframe']" + "\n", + "df['pred_id_value'] = df['pred_id'].apply(lambda x: np.argmax(x))\n", + "\n", + "has_truth = np.isnan(df['truthHitAssignedEnergies']) == False\n", + "has_pred = np.isnan(df['pred_energy']) == False\n", + "matched = np.logical_and(has_truth, has_pred)" ] }, { @@ -116,26 +112,22 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_420976/3249749180.py:47: RuntimeWarning: invalid value encountered in long_scalars\n", - " fake_rate.append(np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]))\n", - "/tmp/ipykernel_420976/3249749180.py:49: RuntimeWarning: invalid value encountered in long_scalars\n", - " corr_class_prob.append(np.sum(matched[mask_PID_matched]) / np.sum(mask_bintruth[mask_PID_matched]))\n", - "/tmp/ipykernel_420976/3249749180.py:46: RuntimeWarning: invalid value encountered in long_scalars\n", - " efficiencies.append(np.sum(matched[mask_PID_truth]) / np.sum(mask_bintruth[mask_PID_truth]))\n", - "/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n", - " xys = np.asarray(xys)\n" + "/tmp/ipykernel_536938/840209094.py:48: RuntimeWarning: invalid value encountered in double_scalars\n", + " fake_err = np.sqrt(fake * (1 - fake) / np.sum(faked[mask_PID_pred]))\n", + "/tmp/ipykernel_536938/840209094.py:47: RuntimeWarning: invalid value encountered in long_scalars\n", + " fake = np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred])\n" ] }, { "data": { - "image/png": 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nRbjgQ0RERFQFx+PLcnJynB47kJOTg3bt2il1zp075/R7paWlyMvLU36/oqlTp6JHjx645ZZbMGTIEEyZMgW+vr7o27cvtm/fXu2CT7169VCvXr1K5dVOJSUJcvPmuHzrrcqJsOquopMkCWazWTnp7lDxZ0mSlLpVcSyUlG+ruvrVlVcXo1rltYm9uvoi9cnVcm/1ydWxdy3l/JyqjkVu2BCmwMCq61cZSfXl1b2BpjblUi3Laxuj5n3q0QOIjASysqp+j48kAZGRMN9zj/IOH933CWJ9TuYKi0FERERERsJ754iIiIiqEBMTg/DwcGzbtk0pKygowL59+9C5c2cAQOfOnZGfn48DBw4odbZv3w673Y5OnTpVavPYsWNYs2YNXn31VQBld82UlJQAAEpKSipdseyyii+F/PNn++zZfCk4EZGWzGZg7tyy/6/m2Iw5c3hsJiIiIiKP4IKPm6SKk/gK20JDQ92uo3ZbeiRy7KJizrXHnJMRiDa+CwsLcejQIRw6dAgAkJmZiUOHDuH06dOQJAkpKSmYMmUKNm/ejCNHjuCJJ55As2bN0KdPHwDA9ddfj3vuuQdDhgzBd999h6+//hrDhw/HgAED0KyZ85syZFnG008/jdmzZ8Pf3x8AkJSUhKVLl+LYsWN49913kZSUVOs+yCtXAs2bOxdGRgLr10Pq25dzEReJHLuomHPtMeca6dsXWL++2mMz+vb1TlwGwfGtPT3NM0Q+zokcu6iYc+0x52QE3h7fkuzth8oJqqCgABaLBVarFUFBQd4Oh4iISBdE+37cuXMnunfvXql80KBBWLFiBWRZxqRJk7BkyRLk5+fjjjvuwIIFC5CYmKjUzcvLw/Dhw/Hxxx/DZDLh4Ycfxrx58xAQEODU5uLFi/Hll19i/fr1Stm5c+fw2GOP4bvvvsM999yDFStWoGHDhi7F7pRrf39gzx7g7FkgIgLo0oVXjxMReZvNxmOzF4g2FxEZc01ERFSZt78fueBzjRwf3IULFxAcHFxlHbvdjqysLDRv3rzaZ3m7UkfttvRI5NhFxZxrjzknI8jPz0ejRo34D38NcC6iLpFjFxVzrj3mnIyAcxHtqDUXcbUe5yKkNuZce8w5GYG35yL8m+WmmtbLZFlGUVGR23XUbkuPRI5dVMy59phzMgKOb+1xLqIOkWMXFXOuPeacjIDjW3t6mmeIfJwTOXZRMefaY87JCLw9vrngQ0REREREREREREREJDgu+BAREREREREREREREQmOCz5uqul5kyaTCeHh4W7XUbstPRI5dlEx59pjzskIOL61x7mIOkSOXVTMufaYczICjm/t6WmeIfJxTuTYRcWca485JyPw9viWZG8/VE5QjpcT8kWQRERE/8PvR+0w10RERJXx+1E7zDUREVFl3v5+5HKqm+x2e43bTp486XYdtdvSI5FjFxVzrj3mnIyA41t7nIuoQ+TYRcWca485JyPg+NaenuYZIh/nRI5dVMy59phzMgJvj28u+LipphukZFnGlStX3K6jdlt6JHLsomLOtceckxFwfGuPcxF1iBy7qJhz7THnZAQc39rT0zxD5OOcyLGLijnXHnNORuDt8c0FHyIiIiIiIiIiIiIiIsFxwYeIiIiIiIiIiIiIiEhwXPBxk8lUfQpNJhMiIyPdrqN2W3okcuyiYs61x5yTEXB8a49zEXWIHLuomHPtMedkBBzf2tPTPEPk45zIsYuKOdcec05G4O3x7ePVvdcBkiTVuC0gIOCqv3+1Omq3pUcixy4q5lx7zDkZQU3fi+QZnIuoQ+TYRcWca485JyPgXER77s5FXK3HuQipjTnXHnNORuDtuQiXU91ks9lq3HbixAm366jdlh6JHLuomHPtMedkBBzf2uNcRB0ixy4q5lx7zDkZAce39vQ0zxD5OCdy7KJizrXHnJMReHt8c8HHw+x2uyp11G5Lj0SOXVTMufaYcyLSGucirhM5dlEx59pjzolIa1rPM0Q+zokcu6iYc+0x50SexQUfIiIiIiIiIiIiIiIiwXHBh4iIiIiIiIiIiIiISHCSLMuyt4MQUUFBASwWC/Lz82GxWKqsI8syrly5Aj8/v2pf1uRKHbXb0iORYxcVc6495pyMwGq1Ijg4GFarFUFBQd4Op07jXERdIscuKuZce8w5GQHnItpRay7iaj3ORUhtzLn2mHMyAm/PRXiHj4f5+PioUkfttvRI5NhFxZxrjzknIq1xLuI6kWMXFXOuPeaciLSm9TxD5OOcyLGLijnXHnNO5Flc8HFTTS8as9vtSEtLc7uO2m3pkcixi4o51x5zTkbA8a09zkXUIXLsomLOtceckxFwfGtPT/MMkY9zIscuKuZce8w5GYG3xzcXfIiIiIiIiIiIiIiIiATHBR8iIiIiIiIiIiIiIiLBccGHiIiIiIiIiIiIiIhIcJIsy7K3gxBRQUEBLBYL8vPzYbFYqqwjyzLsdjtMJhMkSbrmOmq3pUcixy4q5lx7zDkZgdVqRXBwMKxWK4KCgrwdTp3GuYi6RI5dVMy59phzMgLORbSj1lzE1Xqci5DamHPtMedkBN6ei/AOHw8rLS1VpY7abemRyLGLijnXHnNORFrjXMR1IscuKuZce8w5EWlN63mGyMc5kWMXFXOuPeacyLO44OMmu91e47bMzEy366jdlh6JHLuomHPtMecas9mAnTuB994r+6/N5u2IDIHjW3uci6hD5NhFxZxrjzknI+D41p6e5hkiH+dEjl1UzLn2mHMyAm+Pby74EBFR3bNxIxAdDXTvDjz2WNl/o6PLyomIiIiIBGGz2TBhwgTExMSgQYMGiIuLw6uvvoryT+eXZRkTJ05EREQEGjRogOTkZKSlpSnbi4uL8de//hVBQUFITEzEV1995bSPmTNnYsSIEZr1iYiIiDyHCz5ERFS3bNwI9OsHnDnjXJ6VVVbORR8iIiIiEsT06dOxcOFCzJ8/H8eOHcP06dMxY8YMvPnmm0qdGTNmYN68eVi0aBH27dsHf39/9OzZE5cvXwYALFmyBAcOHEBqaiqefvppPPbYY8qCUWZmJpYuXYrXXnvNK/0jIiIidfl4O4C6zmS6+pqaK3XUbkuPRI5dVMy59syXLwNFRYDZ7O1Q6iabDRg5Eih3xaNClgFJAkaNApKT+Rl4SlGRtyOgCjgXcZ3IsYuKOdcec04klm+++Qa9e/fGfffdBwCIjo7Ge++9h++++w5A2d09c+bMwfjx49G7d28AwLvvvouwsDB8+OGHGDBgAI4dO4YHH3wQbdq0QWxsLMaMGYPc3Fw0adIEQ4cOxfTp0z36Ummt5xkiH+dEjl1UzLn2mHMiz5JkuaqzYnQ1BQUFsFgssFqtHp0YEVEdI0nejoDIowoAWAB+P2qAcxEiIqLK6tr349SpU7FkyRJ88cUXSExMxOHDh9GjRw/MmjULAwcOxMmTJxEXF4eDBw+iXbt2yu/deeedaNeuHebOnYvFixdj1apV+PLLL7F161Y899xzyMrKwpo1a/DBBx/gww8/dCmW4uJiFBcXKz8XFBQgKioKeXl5Sq4lSYLJZILdbnd67Fx15SaTCZIkVVtuq/AeTseJ4orvR6iu3Gw2Q5Zlp3JHLNWVuxo7+8Q+sU/sE/vEPlUVe0FBAUJCQrw2F+EdPm6qab1MlmUUFRXB398fUjUneV2po3ZbeiRy7KJizrUnyzKYaSJSG+ci6hA5dlEx59pjzskI6to1rWPHjkVBQQFatWoFs9kMm82G1157DQMHDgQAZGdnAwDCwsKcfi8sLEzZ9uSTT+LHH39E69atERoainXr1uHChQuYOHEidu7cifHjx2Pt2rWIi4vDsmXL0Lx58ypjmTZtGiZPnlypPD09HYGBgQAAi8WCiIgI5OTkwGq1QpZllJSUICIiAk2aNEFWVhaKyt0RHh4ejuDgYJw6dQqFhYXw9fWFJEmIjIxEQEAAMjIylBN5siwjLCwMQUFBSE9Pd4ohISEBpaWlOHnyJEpKSuDr6wuz2YzExEQUFRXhTLnHPfv5+SE2NhZWq1XJEQD4+/srC1i5ublKecU+OYSGhiI0NLTGPl25ckUpr6pPABATEwMfHx+cOHFCiV2SJKVPmZmZSl2TySRUn8q/S6r856SXPlksFhw/fhwAlO9F0fskwucUGBiIixcv1qk+1cXPiX269j5dvHgR3sQ7fK6R46qhvLw8NGrUqMo6NpsNaWlpSEhIgLmaRwe5UkfttvRI5NhFxZxrz2azIf3wYcTHxzPnnrJ7N9Cr19XrffYZ0LWr5+MxoAsXLiAkKqrOXFWrZ5yLqEvk2EXFnGuPOScjuHDhglevqlXb2rVrMWbMGMycORNt2rTBoUOHkJKSglmzZmHQoEH45ptvkJSUhN9++w0RERHK7/Xv3x+SJOH999+vst3BgwejXbt2iImJwT//+U/s27cPM2bMwNGjR7Fhw4Yqf6e6O3zOnz+vzEUqXilts9mQnp6OhIQE+Pr6VnuldElJCdLS0pR/K1V1BbXNZkNGRgYSEhIqLVo7rqwuKSlBenq60o5IV4VXjL2uXOnuSrm3+mS323HixAnExcUp34ui90nvn5PdbkdGRgbi4uKcHu0mcp/q4ufEPrnXpwsXLqBJkya8w4eIyAjkhg0Bf3++P8ZTevQAIiOBrKyq3+MjSWXbe/TgZ+Ap5a58qQtsNhv+9a9/4T//+Q+ys7PRrFkz/O1vf8P48eOVEw2yLGPSpElYunQp8vPzkZSUhIULFyIhIQFA2cmRv//97/joo48QHh6OBQsWIDk5WdnHzJkzcfr0aaeXLxMREREBwJgxYzB27FgMGDAAAHDjjTfil19+wbRp0zBo0CCEh4cDAHJycpwWfHJycpwe8Vbejh078NNPP+Htt9/GmDFj0KtXL/j7+6N///6YP39+tbHUq1cP9erVq1TuWFgpr/yJXJPJpPxcvrxifZPJVKmtiu1KkgRJkqpdtHYslJRvp7r61ZXXFKMa5bWJvbr6IvXJ1XJv9cnRfsV9iNwnUT4nNXKgtz6pUc4+id8nb19YxbdkERFR3WE2A3Pnlv1/xUfVOH6eM4eLPeSy6dOnY+HChZg/fz6OHTuG6dOnY8aMGU6LMzNmzMC8efOwaNEi7Nu3D/7+/ujZsycuX74MAFiyZAkOHDiA1NRUPP3003jssceUK4QyMzOxdOlSvPbaa17pHxEREenbpUuXKp1sMpvNytXMMTExCA8Px7Zt25TtBQUF2LdvHzp37lypvcuXL2PYsGFYvHix8oi4kpISAGV3mFS8YpmIiIjEwgUfN9X07GtJkuDn5+d2HbXb0iORYxcVc6495lwjffsC69cDFZ89HhlZVt63r3fiMoi6Nr6/+eYb9O7dG/fddx+io6PRr18/9OjRA9999x2Asrt75syZg/Hjx6N379646aab8O677+K3335TXoB87NgxPPjgg2jTpg2GDRuG8+fPK88SHjp0KKZPn+7Wbd6ci6hD5NhFxZxrjzknI6hr4/uBBx7Aa6+9hk8//RSnTp3Cpk2bMGvWLDz00EMAyvqbkpKCKVOmYPPmzThy5AieeOIJNGvWDH369KnU3quvvopevXqhffv2AICkpCRs3LgRP/74I+bPn4+kpKRax6ineYbIxzmRYxcVc6495pyMwNvjm+/wuUaO5+bXlecCExHVOTYbsGcPcPYsEBEBdOnCO3s0UNe+H6dOnYolS5bgiy++QGJiIg4fPowePXpg1qxZGDhwIE6ePIm4uDgcPHjQ6bEpd955J9q1a4e5c+di8eLFWLVqFb788kts3boVzz33HLKysrBmzRp88MEHysLQ1VT33Py8vDwl13zWMvvEPrFP7BP7ZPQ+FRQU1Kl3+Fy8eBETJkzApk2bcO7cOTRr1gx/+ctfMHHiRPj5+QH43+NllyxZgvz8fNxxxx1YsGABEhMTndo6evQoHnroIRw6dAj+/v4AynI2fPhwrF69Gtdddx3WrFmD+Ph4l2Kra/M+IiIiNXj7+5ELPtfI8cHl5+fDYrFUWUeWZVitVlgslmpX9lypo3ZbeiRy7KJizrXHnJMRWK1WBAcH15l/+Nvtdvzzn//EjBkzlMeevPbaaxg3bhwAuPSi5JKSEqSkpOCzzz5DaGgoZs+ejdatW+PWW2/Fzp07sXjxYqxduxZxcXFYtmwZmle8O+1P//rXvzB58uRK5d999x0CAwMBABaLBRERETh79iysVitkWUZxcTGaN2+OJk2a4Ndff0VRUZHyu+Hh4bBYLPj555+Vk2gAEBkZiYCAAJw4cUI5kSfLMho3boyQkBCkp6c7xZCQkIDS0lKcPHkSxcXFqFevHsxmMxITE1FYWIgzZ84odf38/BAbG4v8/HxkZ2cr5f7+/oiKikJubq5yB1RVfXIIDQ1FaGholX0KDg7GyZMncaXcO6Wq6hNQ9igcHx8fnDhxQoldkiSlT5mZmUpdk8mkrz5FRCDg4EGc/eEHlISG4tIttwBms9KntLS0Kj8nvfTJ1bFX/nPSe5+uZexp3acGDRrgjz/+qFN9qoufE/t07X26ePEiOnbsWGfmInqm1nkRV+vxvAipjTnXHnNORuDt8yJc8LlGjolNXl4eGjVqVGUdm82GtLQ0JCQkVPuyJlfqqN2WHokcu6iYc+0x52QEFy5cqFNX1a5duxZjxozBzJkz0aZNGxw6dAgpKSmYNWsWBg0a5NKCT1UGDx6Mdu3aISYmBv/85z+xb98+zJgxA0ePHsWGDRuq/J3q7vA5f/68MhepeKW0zWZDeno6EhIS4OvrW+WV0na7HSdOnEBcXJxybKrqCmqbzYaMjAwkJCRU+seZ48rqkpISpKenIz4+XnlZpShXhVeMXfdXum/aBNPzz0MqdwJWjoyEffZsmB5+uMrY9dYnV8deTbHrrU96v8vCbrcjIyMDcXFxTu8EEblPdfFzYp/c69OFCxfQpEmTOjMX0TO1zou4Wo/nRUhtzLn2mHMyAm+fF/HRfI9EREREKrh06RJOnz7tdOUvANx0002q7WPMmDEYO3YsBgwYAAC48cYb8csvv2DatGkYNGgQwsPDAQA5OTlOCz45OTlOj3grb8eOHfjpp5/w9ttvY8yYMejVqxf8/f3Rv39/zJ8/v9pY6tWrh3r16lUqdyyslFf+RK7JZFJ+Ll9eniRJVbZT8WdJkpS6VXEslJRvq7r61ZVXF6Na5bWJvbr6Xu/Txo1A//5Aheu2pKwsmPv3V95XVlNfXY3d031ydexdS7nXPycXYqxtuZp9UiMHeuuTGuXsk/h94glEIiIiMjIu+BAREZFQzp8/j8GDB2PLli1Vbq941a87Ll26VOlkk9lsVq5mjomJQXh4OLZt26Ys8BQUFGDfvn0YOnRopfYuX76MYcOGYfXq1coj4hxXLpeUlKgaO9WedOkSUFSk7/d92WzAyJGVFnsAlJVJEjBqFJCcrO9+AGV9ISIiIiIiItVwwcdNNT1vUpIk+Pv7u11H7bb0SMjYBX8hvJA5FxxzTkagxfhOSUlBfn4+9u3bh27dumHTpk3IycnBlClT8MYbb6i6rwceeACvvfYaWrRogTZt2uDgwYOYNWsWnnzySQBl/U1JScGUKVOQkJCAmJgYTJgwAc2aNUOfPn0qtffqq6+iV69eaN++PQAgKSkJY8aMweDBgzF//nwkJSXVOkbORdQhSRKuu+UWb4fhPlkGzpwBqnmXgp6YAfifPi3keBGVyH9HiVzF8a09Pc0zRD7OiRy7qJhz7THnZATeHt98h881cjyrls8FNqiNG8uuni333HxERgJz5wJ9+3ovLiIiL9Pi+zEiIgIfffQROnbsiKCgIHz//fdITEzE5s2bMWPGDOzdu1e1fV28eBETJkzApk2bcO7cOTRr1gx/+ctfMHHiRPj5+QEoe/HopEmTsGTJEuTn5+OOO+7AggULkJiY6NTW0aNH8dBDD+HQoUPw9/cHUPaugeHDh2P16tW47rrrsGbNGsTHx7sUG+ciHsB/eGqP/xQhIpXx+1E7zDUREVFl3v5+5ILPNXJ8cBcuXEBwcHCVdex2O/Ly8hASElLts4ddqaN2W3okVOwbNwL9+lU+QeE4SfTnc/P1Tqic1xHMORlBfn4+GjVq5NGJTVBQEH788UdER0ejZcuWWLNmDZKSkpCZmYk2bdrg0qVLHtmv3nAuoi673Y68X3/Vf+y7dwO9el293mefAV27ej4eN9jtduQVF+s/53WIyH9HiVylxVyEyqg1F3G1niHmIoLGLirmXHvMORmBt+cifKSbm2paL5NlGbm5uWjUqJFbddRuS49kWcbvp0+jkZ+fvh+LVoeemy/bbMKOF1GJ/HeUyFVaXEdy3XXX4fjx44iOjkbbtm2xePFiREdHY9GiRYiIiPD4/vWGcxF1yLKM3D/+QKOGDfX9Hd6jR9ldxVlZVc9HJKlse48e+u4H/pyLZGUJOV5EJfLfUSJX8ZpW7bk7F3G1niHmIoLGLirmXHvMORmBt+ciXPAh3eBz87VlBoBjx7wdBhFRrY0aNQpnz54FAEyaNAn33HMPVq9eDT8/P6xYscK7wRF5mtlc9gjZfv3KFnfK/2PCcbfxnDm6X+whIiIiIiIi9XHBh4iIiITy+OOPK/9/yy234JdffsF///tftGjRAqGhoV6MjEgjffuWPUK2qvcJzpkjxKNliYiIiIiISH1c8HGTVMPLfSVJgsVicbuO2m3pkSRJyE5PR9OmTfX9DM869tx8S2GhkONFVCL/HSVylTfGd8OGDXHzzTdrvl+94FxEHcLF3rcv0Ls3sGcPcPYsEBEBdOki1J09wuW8DmDOyQg4vrWnp3mGyMc5kWMXFXOuPeacjMDb41uSvf1QOUE5Xk7IF0EajM0GREdf/bn5mZlCnXAhIlKLFt+Psixj/fr12LFjB86dOwe73e60fePGjR7Zr95wLkJERFQZvx+1w1wTERFV5u3vR5dupSgoKHD5j9FUPMlUcdvZs2fdrqN2W3okTOyO5+YD/3tOvoNgz80XJud1CHNORqDF+E5JScFf//pXZGZmIiAgABaLxemP0XAuog6RYxcVc6495pyMgONbe3qaZ4h8nBM5dlEx59pjzskIvD2+XXqkW3Bw8FVvRZJlGZIkwWazqRKYKGq6QUqWZVitVjRt2tStOmq3pUdCxV5HnpsvVM7rCOacjECLG4dXrVqFjRs3opcrj9g0AM5F1CFy7KJizrXHnJMR8CEm2nN3LuJqPc5FSG3MufaYczICb89FXFrw2bFjh6fjIBJLHXhuPhGRqCwWC2JjY70dBhERERERERGRrri04HPnnXd6Og4i8ZjNQLdu3o6CiMhw/vWvf2Hy5MlYtmwZGjRo4O1wiIiIiAAACxYsQG5uLiZOnOjtUIiIiMigXFrwqcqlS5dw+vRpXLlyxan8pptucjsokdT0qDtJkhAaGup2HbXbqjWbzeN3sngsdqoWc6495pyMQIvx3b9/f7z33nto2rQpoqOj4evr67T9hx9+8HgMemKIuYgGRI5dVMy59phzMgJvju8NGzYgMzPTcAs+eppniHycEzl2UTHn2mPOyQi8Pb4luZYPlTt//jwGDx6MLVu2VLldzXf42Gw2/Otf/8J//vMfZGdno1mzZvjb3/6G8ePHK4mTZRmTJk3C0qVLkZ+fj6SkJCxcuBAJCQkAgOLiYvz973/HRx99hPDwcCxYsADJycnKPmbOnInTp0/jzTffrFVsBQUFsFgssFqtCAoKUq3PurNxY9Xvqpk7V5h31RARkXa0+H7s378/duzYgX79+iEsLKzSZGrSpEke2a/eGGYuAmhy8QkREdUNhvp+9DLmmoiIqDJvfz/W+g6flJQU5OfnY9++fejWrRs2bdqEnJwcTJkyBW+88YaqwU2fPh0LFy7EypUr0aZNG3z//fcYPHgwLBYLRo4cCQCYMWMG5s2bh5UrVyImJgYTJkxAz5498fPPP6N+/fpYsmQJDhw4gNTUVGzZsgWPPfYYcnJyIEkSMjMzsXTpUnz//ffXHKPdbq9xW1ZWFpo3bw6TyXTNddRuy2UbNwL9+gEV1wSzssrK169XbdFH9djpqphz7THnZAQ1fS+q5dNPP8XWrVtxxx13eHxfIqjTcxFAs4tPeIzWHnOuPeacjECLuQg5c3cu4mo9r81FNCJy7KJizrXHnJMReHsuUusFn+3bt+Ojjz5Chw4dYDKZ0LJlS/zf//0fgoKCMG3aNNx3332qBffNN9+gd+/eSpvR0dF477338N133wEou7tnzpw5GD9+PHr37g0AePfddxEWFoYPP/wQAwYMwLFjx/Dggw+iTZs2iI2NxZgxY5Cbm4smTZpg6NChmD59ulsrbTXdICXLMoqKityuU5u2Lp0/D9licf+qV5sNGDmy8mJP2Y4ASSo7+ZKcrMoVtrLN5lIeSD2ujj1SD3NORqDF+I6KiuJVpOXobS6i6nFOw4tPeIzWHnOuPeacjMCT43v06NEu1Zs1a5bHYtAjPc0zRD7OiRy7qJhz7THnZATeHt+1XvApKipC06ZNAQCNGjXC+fPnkZiYiBtvvFH1Z+bffvvtWLJkCU6cOIHExEQcPnwYe/fuVSZPmZmZyM7OdnpEm8ViQadOnZCamooBAwagbdu2WLVqFf744w9s3boVERERCA0NxerVq1G/fn089NBDLsVSXFyM4uJi5eeCggIAZY+dczzGTpIkmEwm2O12yLIMm80Gu90Ou90Os9mslDs4VrIddcuXS5LkVGaz2SDLcqW65dux2Wy47pZbXOqP22S57Epbi0WV5swA7D/9pPTN0aeKK6JmsxmyLDuVO/JeXXnFvNe23PF5VFde3edRMXa99QlwbeyJ1Ce9f06OOhXbFrlPdfFzYp/c65Oaj3atzhtvvIF//OMfWLRoEaKjoz2+P6od6dIloKhIuItPoMHYJSIisR08eNDp57179+KWW25BgwYNlDJvP7efiIiIjK3WCz7XXXcdjh8/jujoaLRt2xaLFy9GdHQ0Fi1ahIiICFWDGzt2LAoKCtCqVSuYzWbYbDa89tprGDhwIAAgOzsbABAWFub0e2FhYcq2J598Ej/++CNat26N0NBQrFu3DhcuXMDEiROxc+dOjB8/HmvXrkVcXByWLVuG5s2bVxnLtGnTMHny5ErlJ0+eVK4ytlgsiIiIQE5ODqxWK+x2O/Ly8pCXl4ewsDBkZWWhqKhI+d3w8HAEBgYiPz8f6enpygmzyMhIBAQEICMjw+kEsePPyZMnnWJISEhAaWkpMjIy0LrWWdaPvLw8JQ+OPmVmZirbTSYTEhMTUVRUhDPlHuvi5+eH2NhYWK1W5XMHAH9/f0RFRSEvLw+5ublKecXPySE0NBShoaFVfk7BwcE4deoUrly5opRX9TkBQExMDHx8fJCWlubUP731ydWxJ1Kf9P45BQYGAgDOnTuHixcv1ok+1cXPiX1yr0+OCyI86fHHH8elS5cQFxeHhg0bwtfX12l7Xl6ex2Og6ol88QmOHVOlLSIiqpt27Njh9HNgYCDWrFmD2NhYL0VERERE5EySa3mP0X/+8x+Ulpbib3/7Gw4cOIB77rkHeXl58PPzw4oVK/Doo4+qFtzatWsxZswYzJw5E23atMGhQ4eQkpKCWbNmYdCgQfjmm2+QlJSE3377zWmxqX///pAkCe+//36V7Q4ePBjt2rVDTEwM/vnPf2Lfvn2YMWMGjh49ig0bNlT5O1Xd4RMVFYXff/8dlj9PNFS8UlqWZeUlTTXd4XPhwgUEBQUpVwJVdQW1LMu4ePEiLBZLpdvCyt/hU3D2rNKWW1eF79kD8/331/DplLF/+inkcu9QuNarwm02GwpsNiX2unKluyvl3rzDx5WxJ1Kf9P45AcDFixeVhZ+60Ke6+DmxT+71yWq1onHjxh59OeHKlStr3D5o0CCP7FdvHHOM/Px8ZS5SkSzLsFqtsFgs1V5x7Eqd2rQlmcR9Fnf+hQtXzQOpx9WxR+phzskIrFYrgoODNXlRcmBgIA4fPmzYBR+15iKu1lNzXqNHIscuKuZce8w5GYGWc5Gq1HrBp6JLly7hv//9L1q0aIHQ0FC14gJQ9oz+sWPHYtiwYUrZlClT8J///Af//e9/cfLkScTFxeHgwYNo166dUufOO+9Eu3btMHfu3Ept7tixAy+99BJSU1MxZswY+Pj4YMaMGfjpp5/QtWtX/P777y7F5pjYeOuD8zibDYiOLntGflVDRJLKXpicmanOY1SIiKhOqPPfjzqi21yXu1vMLbt3A716Xb3eZ58BXbuqs09/f3XaISIir9Hy+5ELPjqdixAREXmRt78fa30J5iuvvIJLly4pPzds2BA333wz/P398corr6ga3KVLl5Srhh0cd8oAZY+QCQ8Px7Zt25TtBQUF2LdvHzp37lypvcuXL2PYsGFYvHix8oi4kpISAEBJSck1vXeg4pXYFbedPHnS7Tpqt+USsxlwLJhVXHF3/DxnjmqLParGTi5hzrXHnJMRaDm+z507h6NHj+LHH390+mM0upuL5OTA3qBB2eKJO3969Ci7uKS6K/8kCYiKKqvn7r78/WFv0IDHaI3xe1F7zDkZAce39vR0zkPk45zIsYuKOdcec05G4O3xXet3+EyePBnPPvssGjZs6FR+6dIlTJ48GRMnTlQtuAceeACvvfYaWrRogTZt2uDgwYOYNWsWnnzySQBlj6JJSUnBlClTkJCQgJiYGEyYMAHNmjVDnz59KrX36quvolevXmjfvj0AICkpCWPGjMHgwYMxf/58JCUl1TrGmm6QkmUZV65ccbuO2m25rG9fYP36shcil3t3BCIjyxZ7+vZVZz/wQOx0Vcy59phzMgItxveBAwcwaNAgHDt2rNL+qnrkXF1XZ+cijotP+vUrW9wp36YHLj7hMVp7zLn2mHMyAk+O74oXlsiyjP/+978oLCx0Kr/ppps8FoMe6WmeIfJxTuTYRcWca485JyPw9viu9YKPLMtVPmPx8OHDCAkJUSUohzfffBMTJkzAc889h3PnzqFZs2Z45plnnBaV/vGPf6CoqAhPP/008vPzcccdd+Dzzz9H/fr1ndo6evQo1q1bh0OHDill/fr1w86dO9GlSxdcd911WLNmjarx1wl9+wK9ewN79gBnzwIREUCXLnyMGxERec2TTz6JxMREvPPOOwgLC+Ozn+syDS8+ISIiupp27dpBkiSnEzn3//nuW0e5ES8+ISIiIv1wecGnUaNGkCQJkiQhMTHR6eSKzWZDYWEhnn32WVWDCwwMxJw5czBnzpxq60iShFdeeeWqj5O74YYbkJaW5lRmMpmwYMECLFiwQI1w6y6zGejWzdtREBERAQBOnjyJDRs2ID4+3tuhkBZ48QkREelEZmamt0MgIiIiqpHLCz5z5syBLMt48sknMXnyZFgsFmWbn58foqOjq3xvTl1X8R1DFbdFRka6XUfttvRI5NhFxZxrjzknI9BifN999904fPgwF3z+ZIi5iAYXn/AYrT3mXHvMORmBJ8d3y5YtPda2yPQ0zxD5OCdy7KJizrXHnJMReHt8S3ItHyq3a9cu3H777fD19fVUTEIoKCiAxWKB1WpFUFCQt8MhIiLSBS2+H3NzczFo0CB07NgRN9xwQ6U5yYMPPuiR/eoN5yJERESV8ftRO8w1ERFRZd7+fqz1ctOdd94Jk8mEDRs2YMqUKZgyZQo2bdpk2GfU1tRvm82GEydOuF1H7bb0SOTYRcWca485JyPQYnynpqbi66+/xuTJk/HII4+gT58+yp+HHnrI4/vXG85F1CFy7KJizrXHnJMReHN8X3/99TAb8JGjeppniHycEzl2UTHn2mPOyQi8Pb5dfqSbQ3p6Onr16oWsrCxcd911AIBp06YhKioKn376KeLi4lQPUmR2u12VOmq3pUcixy4q5lx7zDmR+0aMGIHHH38cEyZMQFhYmLfD0T3ORVwncuyiYs61x5wTec60adNgtVq9HYbuaD3PEPk4J3LsomLOtcecE3lWrRd8Ro4cibi4OHz77bcICQkBAPz+++94/PHHMXLkSHz66aeqB0lERETk8Pvvv+P555/nYg8RERHpSp8+fbwdAhERERlcrRd8du3a5bTYAwCNGzfG66+/jqSkJFWDIyIiIqqob9++2LFjB+8qJiIiIiIiIiIqp9YLPvXq1cPFixcrlRcWFsLPz0+VoERiMlX/GiSTyYSYmBi366jdlh6JHLuomHPtMedkBFqM78TERIwbNw579+7FjTfeCF9fX6ftI0eO9HgMesK5iDpEjl1UzLn2mHMyAi3Gd05ODl588UVs27YN586dgyzLTtu9/ex+relpniHycU7k2EXFnGuPOScj8Pb4rvWCz/3334+nn34a77zzDjp27AgA2LdvH5599lk8+OCDqgcoOh+fq6fYlTpqt6VHIscuKuZce8w5kfvefvttBAQEYNeuXdi1a5fTNkmSDLfgczWci7hO5NhFxZxrjzknct/f/vY3nD59GhMmTEBERAQkSfJ2SLqm9TxD5OOcyLGLijnXHnNO5Fm1Xm6aN28e4uLi0LlzZ9SvXx/169dHUlIS4uPjMWfOHA+EqG81vWjMbrcjLS3N7Tpqt6VHIscuKuZce8w5GYEW4zszM7PaPydPnvT4/vWGcxF1iBy7qJhz7THnZARajO+9e/di9erVGDp0KPr06YPevXs7/VFbVlYWHn/8cTRu3BgNGjTAjTfeiO+//17ZLssyJk6ciIiICDRo0ADJyclIS0tTthcXF+Ovf/0rgoKCkJiYiK+++sqp/ZkzZ2LEiBHXHJ+e5hkiH+dEjl1UzLn2mHMyAm+P71ovqQYHB+Ojjz5Ceno6jh07BgC4/vrrER8fr3pwRERERERERER6EhUVVekxbp5y4cIFJCUloXv37tiyZQuaNGmCtLQ0NGrUSKkzY8YMzJs3DytXrkRMTAwmTJiAnj174ueff0b9+vWxZMkSHDhwAKmpqdiyZQsee+wx5OTkQJIkZGZmYunSpU4LSERERCSuWi/4vPLKK3jxxRcRHx/vtMjzxx9/YObMmZg4caKqARIRERFVdObMGWzevBmnT5/GlStXnLbNmjVL1X1lZWXhpZdewpYtW3Dp0iXEx8dj+fLl6NChA4Cyq2onTZqEpUuXIj8/H0lJSVi4cCESEhIAlF1V+/e//x0fffQRwsPDsWDBAiQnJyvtz5w5E6dPn8abb76patxERETkGXPmzMHYsWOxePFiREdHe3Rf06dPR1RUFJYvX66UxcTEKP8vyzLmzJmD8ePHK3cXvfvuuwgLC8OHH36IAQMG4NixY3jwwQfRpk0bxMbGYsyYMcjNzUWTJk0wdOhQTJ8+HUFBQVeNpbi4GMXFxcrPBQUFAMreWeR4b5EkSTCZTLDb7ZBlGTabDXa7HXa7HWazWSl3MJlMkCRJqeNox1Fe/n1INpsNsiwr7ZbneF+CY3+O7WazGbIsO11t7YixuvKKMda2vHyfqip3NXZHecUrxUXqU8XY9dYnAJXGk+h90vvn5KhTsW2R+1QXPyf2yb0+eftdfrVe8Jk8eTKeffZZNGzY0Kn80qVLmDx5Mhd8iIiIyKO2bduGBx98ELGxsfjvf/+LG264AadOnYIsy7j55ptV3RevqiUiIqKKHn30UVy6dAlxcXFo2LAhfH19nbbn5eWptq/NmzejZ8+eeOSRR7Br1y40b94czz33HIYMGQKg7FG32dnZTheTWCwWdOrUCampqRgwYADatm2LVatW4Y8//sDWrVsRERGB0NBQrF69GvXr18dDDz3kUizTpk3D5MmTK5WfPHlSWTCyWCyIiIhATk4OrFYr7HY78vLykJeXh7CwMGRlZaGoqEj53fDwcAQHB+P06dPIy8tDeno6TCYTIiMjERAQgIyMDKcTxI4/FR/jm5CQgNLSUmRkZCjt+Pj4IDExEUVFRThz5oxS18/PD7GxsbBarcjOzlbK/f39ERUVhby8POTm5jrls3yfHEJDQxEaGlptn06dOuV0YVJVfQLKFvB8fHyQnp7ulANHnzIzM5W6JpNJqD6Vf7Rg+c9JL30KDAxEfn6+kvO60Ce9f06BgYEAgHPnzuHixYt1ok918XNin9zrk+OCCG+R5Freh2wymZCTk4MmTZo4lW/fvh2PPvoozp8/r2qAelVQUACLxYL8/HxYLJYq6zhWGB0rf9daR+229Ejk2EXFnGuPOScjsFqtCA4OhtVqdelK0WvRsWNH3HvvvZg8eTICAwNx+PBhNG3aFAMHDsQ999yDoUOHqravsWPH4uuvv8aePXuq3C7LMpo1a4YXXngBL774IoCyHISFhWHFihUYMGAAnnvuOQQFBeH111/HH3/8gYYNG+LcuXNo0qQJ7rnnHjzzzDMunWip6qraqKgo/P7778pcpOLVSY7jjtlsrvaqWgAoLS2FJEnKsamqq5Ycv+e4iqq8ilemOn5fpCuxKsZeV64uc6Xcm1fVujL2ROqT3j+n6ojcp7r4ObFP7vXJarWicePGHp2LrFy5ssbtgwYNUm1f9evXBwCMHj0ajzzyCPbv349Ro0Zh0aJFGDRoEL755hskJSXht99+Q0REhPJ7/fv3hyRJeP/991FSUoKUlBR89tlnCA0NxezZs9G6dWvceuut2LlzJxYvXoy1a9ciLi4Oy5YtQ/PmzauMxVNzEcdnarPZnL6HORepG38nXSnnXMQ4n1N1RO5TXfyc2Cf9z0Vq4vIdPo0aNVIOgImJiU4nK202GwoLC/Hss896JEiRlZaWws/Pz+06arelRyLHLirmXHvMOZH7jh07hvfeew8A4OPjgz/++AMBAQF45ZVX0Lt3b1UXfES4qjYjI0O5Uq7iVUuyXPaIirCwMDRp0qTKq5YsFgsyMzNhs9mU+V1VVy3JsozmzZujYcOGSE9Pd4rBcSXWyZMnYbPZlJM6ol2J5YhdkqQ6c3VZVZ+TXvrk6tgTqU8ifE4hISHIy8urU32qi58T+3TtfSp/xbinqLmgczV2ux0dOnTA1KlTAQDt27fH0aNHlQUfV/j6+uKtt95yKhs8eDBGjhyJgwcP4sMPP8Thw4cxY8YMjBw5Ehs2bKiynXr16qFevXqVyh3f++U5Tnw55iKOnx3/rchkMqG0tFT5Hi7ftoMsy7hy5QrMZnO17ZjNZqfvcwDKwk9F1ZXXFKMa5VXts7rYq6svUp9cLfdGnxwniv38/CpdlClqn2oq10OfHH+Pq8p5TbHruU9ql7NP4vepum1acfkOn5UrV0KWZTz55JOYM2eO010tfn5+iI6ORufOnT0WqN447vDJy8tzeqxLeTabDWlpaUhISKj2g3aljtpt6ZHIsYuKOdcec05GcOHCBYSEhHj0Spbw8HDs2LED119/PVq3bo3XX38dDz74IA4fPoykpCQUFhaqti8Rrqo9f/68MhepeHWSzWZDeno6EhIS4OvrW+XVSXa7HSdOnEBcXJxybKrqqiWbzYaMjAwkJCRU+seZYxJcUlKC9PR0xMfHKxNdUa7Eqhh7Xbm6zJVyb/XJ1bEnUp/0/jnZ7XZkZGQgLi7O6R+vIvepLn5O7JN7fbpw4QKaNGnitatq1dayZUv83//9H95++22lbOHChZgyZQqysrJw8uRJxMXF4eDBg2jXrp1S584770S7du0wd+7cSm3u2LEDL730ElJTUzFmzBj4+PhgxowZ+Omnn9C1a1f8/vvvLsWm1nkRV+vxvAipjTnXHnNORqDFeZGauHyHj+PKkZiYGCQlJcHHp9av/yEiIiJy22233Ya9e/fi+uuvR69evfDCCy/gyJEj2LhxI2677TZV9yX6VbWO/7/aVbWOq6IqtlPxZ8fd3jVdzWQymZzaEulKrIqxV1dfpD65Wu6tPrk69q6lnJ9TzTGqkQO99UmNcvZJ/D7VtROISUlJOH78uFPZiRMn0LJlSwBl52jCw8Oxbds2ZcGnoKAA+/btq/Ku58uXL2PYsGFYvXq1ckeJY1GtpKTE6y+aJiIiIvdUPXuqwZ133snFHiIiIvKaWbNmoVOnTgCAyZMn4+6778b777+P6OhovPPOO6ruKyIiAq1bt3Yqu/7663H69GkAZXcbAUBOTo5TnZycHGVbRTt27MBPP/2E4cOHY+fOnejVqxf8/f3Rv39/7Ny5U9X4iYiISGzPP/88vv32W0ydOhXp6elYs2YNlixZgmHDhgEoW3RLSUnBlClTsHnzZhw5cgRPPPEEmjVrhj59+lRq79VXX0WvXr3Qvn17AGULShs3bsSPP/6I+fPnIykpScvuERERkcq4cuNh1V2RVNs6arelRyLHLirmXHvMOZH7YmNjlf/39/fHokWLPLavunBVLecirhM5dlEx59pjzonEcuutt2LTpk0YN24cXnnlFcTExGDOnDkYOHCgUucf//gHioqK8PTTTyM/Px933HEHPv/8c+XRtA5Hjx7FunXrcOjQIaWsX79+2LlzJ7p06YLrrrsOa9asUb0PWs8zRD7OiRy7qJhz7THnRJ7l8jt8yJnjWbV15bnAREREaqhr34/79+/H7bffjsmTJ6N///747rvvMGTIECxZskQ50TJ9+nS8/vrrWLlyJWJiYjBhwgT8+OOP+PnnnyudaHn55ZdRXFyMf//73wCAdevWYcyYMfj4448xb948nD17Fp9++qlLsdW1XBMREalBi+/H5cuX49FHH0XDhg090r4oOBchIiKqzNvfj1xSdVNN62WyLKOwsNDtOmq3pUcixy4q5lx7zDkZgRbju1GjRggJCan0p3HjxmjevDnuvPNOLF++XJV9Oa6qfe+993DDDTfg1VdfrfKq2hEjRuDpp5/GrbfeisLCwhqvqp08ebJS1q9fP9x3333o0qULfvzxxypfrHw1nIuoQ+TYRcWca485JyPQYnyPHTsW4eHheOqpp/DNN994fH96p6d5hsjHOZFjFxVzrj3mnIzA2+O71gs+y5cvx6VLlzwRi5DsdnuN286cOeN2HbXb0iORYxcVc6495pyMQIvxPXHiRJhMJtx3332YPHkyJk+ejPvuuw8mkwnDhg1DYmIihg4diqVLl6qyv/vvvx9HjhzB5cuXcezYMQwZMsRpuyRJeOWVV5CdnY3Lly/jq6++QmJiYqV2brjhBqSlpcHf318pM5lMWLBgAaxWK7777jvEx8fXOj7ORdQhcuyiYs61x5yTEWgxvrOysrBy5Urk5uaiW7duaNWqFaZPn47s7GyP71uP9DTPEPk4J3LsomLOtceckxF4e3zXesGHV7IQERGRN+3duxdTpkzBqlWrMGLECIwYMQKrVq3ClClTcODAASxduhQzZ87EvHnzvB0qERER1UE+Pj546KGH8NFHH+HXX3/FkCFDsHr1arRo0QIPPvggPvroI6+f7CEiIiJjqvWCD69kISIiIm/aunUrkpOTK5Xffffd2Lp1KwCgV69eOHnypNahERERkcGEhYXhjjvuQOfOnWEymXDkyBEMGjQIcXFx2Llzp7fDIyIiIoOp9YIPr2RxJklSjdv8/PzcrqN2W3okcuyiYs61x5yTEWgxvkNCQvDxxx9XKv/4448REhICACgqKkJgYKDHY9EDzkXUIXLsomLOtceckxFoNb5zcnLw73//G23atEG3bt1QUFCATz75BJmZmcjKykL//v0xaNAgTWLxNj3NM0Q+zokcu6iYc+0x52QE3h7fkuzmW4T27duHZcuWYeXKlYiIiMCFCxfQqFEjLF++HN26dVMpTP0pKCiAxWKB1WpFUFCQt8MhIiLSBS2+H5cuXYqhQ4eiV69e6NixIwBg//79+Oyzz7Bo0SI89dRTeOONN/Ddd9/h/fff90gMesC5CBERUWVafD8+8MAD2Lp1KxITE/H3v/8dTzzxhHLRicO5c+cQHh5epy+I5VyEiIioMm9/P9b6Dh+AV7KUV9N6mSzLyM/Pd7uO2m3pkcixi4o51x5zTkagxfgeMmQIdu3aBX9/f2zcuBEbN25Ew4YNsWvXLjz11FMAgBdeeKFOL/aUx7mIOkSOXVTMufaYczICLcZ306ZNsWvXLhw9ehQpKSmVFnsAoEmTJsjMzPR4LHqgp3mGyMc5kWMXFXOuPeacjMDb49untr9Q/kqWIUOGVLqSxd/fHy+88AJmzpypaqB6VdPVOna7HdnZ2QgMDITZbL7mOmq3pUcixy4q5lx7zDkZgVZXsSYlJSEpKUmTfekd5yLqEDl2UTHn2mPOyQi0mIu88847V60jSRJatmzp8Vj0wN25iKv1OBchtTHn2mPOyQi8fXdvrRd8HFeydO7cudo6RrqShYiIiIiIiIiMY+TIkYiPj8fIkSOdyufPn4/09HTMmTPHO4ERERGR4dX6kW7vvPNOjYs9gLGuZCEiIiIiIiIi49iwYUOVdxrffvvtWL9+vRciIiIiIipT6wWfkSNHYt68eZXK58+fj5SUFDViEookSTVu8/f3d7uO2m3pkcixi4o51x5zTkbA8a091eciNhuwcyfw3ntl/7XZrr0tgYgcu6iYc+0x52QEWozv33//HRaLpVJ5UFAQcnNzPb5/vdHTOQ+Rj3Mixy4q5lx7zDkZgbfHtyTX8i1CzZs3x+bNm3HLLbc4lf/www948MEHcebMGVUD1KuCggJYLBZYrVYEBQV5OxwiIiJd4PejdjyS640bgVGjgPLzuchIYO5coG9fdfZBRETkQVrMRW644QY8++yzGD58uFP5m2++iYULF+Lnn3/2yH71hvM+IiKiyrz9/VjrO3x4JYuzq72cMDc31+06arelRyLHLirmXHvMORmBFuN7+fLluHTpksf3IwrV5iLr1wP9+jkv9gBAVhbQrx/s69dzLkKqYs61x5yTEWgxvkePHo1//OMfmDRpEnbt2oVdu3Zh4sSJGDt2LJ5//nmP719v9HTOQ+TjnMixi4o51x5zTkbg7fFd6wWf+Ph4fP7555XKt2zZgtjYWFWCEklNN0jJsozc3Fy366jdlh6JHLuomHPtMedkBFqM77FjxyI8PBxPPfUUvvnmG4/vT+/UmIv8npkJadQooKp6f5ZJKSnIzcnhXIRUw5xrjzknI9BifD/55JN444038M4776B79+7o3r07/vOf/2DhwoUYMmSIx/evN3o65yHycU7k2EXFnGuPOScj8Pb49qntL4wePRrDhw/H+fPncddddwEAtm3bhjfeeANz5sxROz4iIiIiJ1lZWfj444+xYsUKdOvWDbGxsRg8eDAGDRqE8PBwb4cnpOs6dqy5gixDyspCwwMHgFattAmKiIhIx4YOHYqhQ4fi/PnzaNCgAQICArwdEhEREVHt7/DhlSxERETkTT4+PnjooYfw0Ucf4ddff8WQIUOwevVqtGjRAg8++CA++ugjr99CXVeZz5/3dghERES60qRJEy72EBERkW7UesEHKLuS5cyZM8jJyUFBQQFOnjyJJ554Qu3YhCBJUo3bLBaL23XUbkuPRI5dVMy59phzMgKtx3dYWBjuuOMOdO7cGSaTCUeOHMGgQYMQFxeHnTt3ahqLt6gxF8n7z39c2lf96GjORUg1zLn2mHMyAi3Gd05ODv7617+iWbNm8PHxgdlsdvpjNHo65yHycU7k2EXFnGuPOScj8Pb4lmRvP1ROUAUFBbBYLLBarQgKCvJ2OERERLqg1fdjTk4OVq1aheXLl+PkyZPo06cPnnrqKSQnJ6OoqAivvPIK1q5di19++cVjMXibqrm22YDoaCArq+r3+EgSEBkJZGYCBjyRRURE4tBiLnLvvffi9OnTGD58OCIiIiqd2Ondu7dH9qs3PC9CRERUmbe/H2t9hw+vZHFW0yNj7HY7zp4963YdtdvSI5FjFxVzrj3mnIxAi/H9wAMPICoqCitWrMCQIUOQlZWF9957D8nJyQAAf39/vPDCC/j11189HoseqDIXOXcO9tmzywoqXo3058/2WbPK6nEuQiphzrXHnJMRaDG+9+7di9WrV2Po0KHo06cPevfu7fTHaPR0zkPk45zIsYuKOdcec05G4O3xXesFn7/97W/44YcfMGHCBKxfvx4bN250+mM0Nd0gJcsyrFar23XUbkuPRI5dVMy59phzMgItxnfTpk2xa9cuHD16FCkpKQgJCalUp0mTJsjMzPR4LHqg2lzkoYeA9euB5s2dK0RGAuvXQ37oIc5FSFXMufaYczICLcZ3VFQU/x6Vo6dzHiIf50SOXVTMufaYczICb49vn9r+wt69e7Fnzx60a9fOA+EQERER1eydd965ah1JktCyZUsNoqlj+vYFevcG9uwBzp4FIiKALl3KHuNms3k7OiIiIl2YM2cOxo4di8WLFyM6Otrb4RAREREpar3gwytZiIiIyJtGjhyJ+Ph4jBw50ql8/vz5SE9Px5w5c7wTWF1hNgPdunk7CiIiIt169NFHcenSJcTFxaFhw4bw9fV12p6Xl+elyIiIiMjoar3gwytZnFV8OWPFbaGhoW7XUbstPRI5dlEx59pjzskItBjfGzZswObNmyuV33777Xj99dcNt+DDuYg6RI5dVMy59phzMgItxrfR5hpXo6d5hsjHOZFjFxVzrj3mnIzA2+Nbkmt5u06jRo1w6dIllJaWGvpKloKCAlgsFlitVgQFBXk7HCIiIl3Q4vuxfv36OHr0KOLj453K09PTccMNN+Dy5cse2a/ecC5CRERUGb8ftcNcExERVebt78drusOH/sdut9e4LSsrC82bN4fJZLrmOmq3pUcixy4q5lx7zDkZQU3fi2qJj4/H559/juHDhzuVb9myBbGxsR7fv95wLqIOkWMXFXOuPeacjECLuQgAZGRkYPny5cjIyMDcuXPRtGlTbNmyBS1atECbNm00iUEv3J2LuFqPcxFSG3OuPeacjECruUh1ar3gM2jQIE/EIayabpCSZRlFRUVu11G7LT0SOXZRMefaY87JCLQY36NHj8bw4cNx/vx53HXXXQCAbdu24Y033jDkhSm1mhvYbMCePcDZs0BEBNClC2SAcxGIHbuomHPtMedkBFqM7127duHee+9FUlISdu/ejddeew1NmzbF4cOH8c4772D9+vUej0FP9HTOQ+TjnMixi4o51x5zTkbg7fFd6wUfgFeyEJGXVHGiEmazt6MiIo09+eSTKC4uxmuvvYZXX30VABAdHY2FCxfiiSee8HJ0OrZxIzBqFHDmzP/KIiOB2bOBG27wXlxERESCGTt2LKZMmYLRo0cjMDBQKb/rrrswf/58L0ZGRERERlfre+d27dqFG2+8Efv27cPGjRtRWFgIADh8+DAmTZqkeoBERADKTlRGRwPduwOPPVb23+josnIiMpyhQ4fizJkzyMnJQUFBAU6ePMnFnpps2gT06+e82AMAWVkw9e+PgC++8E5cREREAjpy5AgeeuihSuVNmzZFbm6uFyIiIiIiKlPrBR/HlSxffvkl/Pz8lPK77roL3377rarBiaCm502aTCaEh4e7XUfttvRI5NhFJVTON26s9kQl+vUTZtFHqJwTXSOtx3eTJk0QEBCg6T715qpzg4YNYUpJAaq6rfzPsmYzZsB0ldvOORchtTHn2mPOyQi0GN/BwcE4e/ZspfKDBw+iefPmHt+/3ujpnIfIxzmRYxcVc6495pyMwNvju9aPdDty5AjWrFlTqdyoV7JIklTjtuDg4Kv+/tXqqN2WHokcu6gkSUKwry9w6ZK3Q6mZzQaMHFn9iUpJKntEUXKy7h/vJgEc51Tn1fS9qJacnBy8+OKL2LZtG86dO1fp+bg2m83jMejJVeciLVvW/PuyDCkrC9i7F+jWrea2OBchFTHn2mPOyQi0mIsMGDAAL730Ej744ANIkgS73Y6vv/4aL774oiHvOHb3vIir9TgXIbUx59pjzskItJiL1KTWCz6OK1liYmKcyo16JYvdbq9x26lTpxAdHV3typ4rddRuS49Ejl1UdrsdprpwVbwsl935Y7F4OxKXnMzI4DinOq2m70W1/O1vf8Pp06cxYcIEREREeH0y5W1Xm4u4erSxZ2XVWJdzEVIbc6495pyMQIu5yNSpUzFs2DBERUXBZrOhdevWsNlseOyxxzB+/HiP719v3D0v4mo9zkVIbcy59phzMgIt5iI1qfWCD69kcVbxquKK265cueJ2HbXb0iORYxcVc+0dHOdU12kxvvfu3Ys9e/agXbt2Ht+XCK42Nzi9eDFaPPPM1dsJD7/qfjgXITUx59pjzskItBjffn5+WLp0KSZMmICjR4+isLAQ7du3R0JCgsf3rUd6Ouch8nFO5NhFxZxrjzknI/D2+K71gg+vZCGqO44fOID4+HiY9fwotN27gV69rl7vs8+Arl09H48bbDYb8Ntv3g6DSHhRUVFem0C9/vrrGDduHEaNGoU5c+YAAC5fvowXXngBa9euRXFxMXr27IkFCxYgLCwMAJCXl4dBgwZhx44dSEhIwLJly9C+fXulzWHDhiE2NhYvvPCCR2K+lJQEOTKy7LFtVeRNliSUhoXB1KWLR/ZPRERUV7Vo0QItWrTwdhhEREREilov+PBKFqK6Q27YEPD31/e7b3r0ACIjgWpOVEKSyrb36KHvfgBl7yMiIrfNmTMHY8eOxeLFixEdHa3Zfvfv34/Fixfjpptucip//vnn8emnn+KDDz6AxWLB8OHD0bdvX3z99dcAgNdeew0XL17EDz/8gIULF2LIkCH4/vvvAQDffvst9u3bh3nz5nkucLMZ9tmzYe7fv+yYWf5Y+ufj8HLGjUOE3o+hREREXjR69Gi8+uqr8Pf3x+jRo2usO2vWLI2iIiIiInJ2zQ9LbNGiBXr16oX+/fsberGnpudNmkwmREZGul1H7bb0SOTYRSVMzs1mYO7csv+v+J4Ox89z5uh/sQcC5ZzIDVqM70cffRQ7d+5EXFwcAgMDERIS4vTHEwoLCzFw4EAsXboUjRo1UsqtViveeecdzJo1C3fddRduueUWLF++HN988w2+/fZbAMCxY8cwYMAAJCYm4umnn8axY8cAACUlJXj22WexaNEit+60dGlu8PDDwPr1QMX3LUZGAh98gOAnn+RcRODYRcWca485JyPw1Pg+ePAgSkpKlP+v7s+hQ4c8sn+H119/HZIkISUlRSm7fPkyhg0bhsaNGyMgIAAPP/wwcnJylO15eXl44IEHEBAQgPbt2+PgwYNObQ4bNgxvvPHGNcekp3MeIh/nRI5dVMy59phzMgJvj2+X7vDhlSzVq+lF0ZIkISAg4Kq/f7U6arelRyLHLiqhct63b9mJylGjgDNn/lceGVm22NO3r9dCqw2hck50jWr6XlSL41FqWho2bBjuu+8+JCcnY8qUKUr5gQMHUFJSguTkZKWsVatWaNGiBVJTU3Hbbbehbdu22L59O/7+979j69atyh1CM2bMQLdu3dChQweXYiguLkZxcbHyc0FBAYCyF0La/ryDUJIkmEwm2O125bF3DRo0gCzLkPr2hf2BByDv3g0pOxtyeDhMd94JyccHDe12pxdLmkwmSJKktOvg7+8PAJXKHRNau92OBg0aKG2ZzWbIsuzUtiPG6srLx34t5Y7Yqyt3Nfby5eWJ1idXyr3Zp4YNG7o09kTqk94/p4CAANgr/J0XvU918XNin669T556UfKOHTuq/H8t6fVuY3fPi7haj+dFSG3MufaYczICLc6L1MSlBZ+KV7JUx9ud8YaKE82K2zIyMhAXF1ftlbuu1FG7LT0SOXZRCZfzvn2B3r2BPXuAs2eBiAigSxch7uxxEC7nRNegpu9FtQwaNMjj+yhv7dq1+OGHH7B///5K27Kzs+Hn54fg4GCn8rCwMGRnZwMAxo4di6FDhyIuLg7R0dF45513kJaWhpUrVyI1NRXPPvssvvjiC3To0AFLly6FxWKpMo5p06Zh8uTJlcrT0tIQFBQEALBYLIiIiEBOTg6sVivsdjsuXLiA+Ph4hIWFISs7G0XNmyt3+oQXFiIwMBD79+9HUFCQcsIsMjISAQEByMjIUE6cOf4bHx+PkydPOsWQkJCA0tJSZGRk4MKFC2jUqBF8fHyQmJiIoqIinCm3WO/n54fY2FhYrVYlR0DZYlJUVBTy8vKQm5urlFfsk0NoaChCQ0ORlZWFoqIipTw8PBzBwcE4deoUrly5opRX1ScAiImJgY+PD44fP67EbjKZlD5lZmYqdU0mk1B9SktLq/Jz0kufXB17IvVJ759TYGAgioqK4O/vj4sXL9aJPtXFz4l9cq9PjgsiPMlqtcJms1W6szgvLw8+Pj7K97Kayt9tXP7iE8fdxmvWrMFdd90FAFi+fDmuv/56fPvtt7jtttsq3W28ZMkSAP+72/jtt992698n7p4XcbUez4uQ2phz7THnZARanBepiSR7663HgisoKIDFYkFeXp7To13Ks9lsSEtLQ0JCQo2TkavVUbstPRI5dlEx59pjzskILly4gJCQEFitVo+c7HDIyMjA8uXLkZGRgblz56Jp06bYsmULWrRogTZt2qi2n19//RUdOnTAl19+qVxN261bN7Rr1w5z5szBmjVrMHjwYKc7bwCgY8eO6N69O6ZPn15lu3fddRdGjRqFX375BZ988gk+/fRTDBkyBI0bN672kSpV3eETFRWF8+fPK3ORildK22w2pKenIyEhAb6+vlVeKW2323HixAmnf3RVdQW14x9nCQkJlS7ycZysLykpQXp6OuLj42E2m4W6Krxi7HXlSndXyr3VJ1fHnkh90vvnZLfblZMs5R81IXKf6uLnxD6516cLFy6gSZMmHp2L3HvvvXjggQfw3HPPOZUvWrQImzdvxmeffab6PgcNGoSQkBDMnj3baS6yfft23H333bhw4YLTBSgtW7ZESkoKnn/+eYwbNw4ZGRlYs2YN5s+fj/fffx+pqal47bXXcP78eZfvnvbUXESSJJSUlCAtLc3pe5hzkbrxd9KVcs5FjPM5cS7CPhmhT1rMRWri0h0+5XnjShYiIiIih127duHee+9FUlISdu/ejddeew1NmzbF4cOH8c4772D9+vWq7evAgQM4d+4cbr75ZqXMZrNh9+7dmD9/PrZu3YorV64gPz/f6SRLTk4OwsPDq2xz+fLlCA4ORu/evdG3b1/06dMHvr6+eOSRRzBx4sRqY6lXrx7q1atXqdxxMqO88v94MplMys/ly8uTJKnKdir+LEmSUrcqjpMT5duqrn515dXFqFZ5bWKvrr5IfXK13Ft9cnXsXUs5P6eaY1QjB3rrkxrl7JP4fdLiwqp9+/ZV+Tj7bt264eWXX1Z9f3q/2/jkyZM13m2cl5eHvLy8sruNq7mb6/Tp08jLy0N6ejpMJlO1dxs7/tR0t7GjHdHuNk5PT3fKQV25666qz0kvfQoMDER+fr6S87rQJ71/ToGBgQCAc+fO8W5j9qnO9kmLu41rUus7fLxxJYse8Q4fdYkcu6iYc+0x52QEWtzh07lzZzzyyCMYPXo0AgMDcfjwYcTGxuK7775D3759nSaB7rp48SJ++eUXp7LBgwejVatWeOmllxAVFYUmTZrgvffew8MPPwwAOH78OFq1aqW8w6e88+fPo2PHjti7dy+aN2+O3r17K3f7fPjhh/jXv/7l8sueORdRl8ixi4o51x5zTkagxVzE398f3377LW688Uan8iNHjqBTp064dOmSavsywt3GvMOHd/jwDh/jfE68w4d9MkKfhLvDR+srWfSuuiuOHNtiYmLcrqN2W3okcuyiYs61x5yTEWgxvo8cOYI1a9ZUKm/atKnTFTxqCAwMxA033OBU5u/vj8aNGyvlTz31FEaPHo2QkBAEBQVhxIgR6Ny5c6XFHgBISUnBCy+8gOZ/vkMnKSkJq1atQo8ePbBkyRIkJSXVOkbORdQhcuyiYs61x5yTEWgxvjt27IglS5bgzTffdCpftGgRbrnlFlX3JcLdxr6+vtXebWwymRAXFwcfHx+n8op8fHwQFxcHX19fp8Wc8u2aTCbExsbCbDZXWvApH0vFdkS5Q62q2KurL0qf9H4noWNMVcx5bWOvrpyfU+VYHHMRHx+fKv8ei9gntcvZJ/H75OvrW+U2rdR6wae4uBilpaWVyktKSvDHH3+oEpRwbDZU9yJ5x6SmJk511GxLMCLHLirmXHvMOZH7goODcfbsWcTExDiVHzx4UFlI0dLs2bNhMpnw8MMPo7i4GD179sSCBQsq1du6dSvS09OxatUqpWz48OH4/vvv0alTJ3Ts2BGTJk1SPT415w+ci5DamHPtMedE7psyZQqSk5Nx+PBh3H333QCAbdu2Yf/+/fjiiy9U3dfdd9+NI0eOOJVVvNvY19cX27Ztc7rb+PTp0+jcuXOl9s6fP49XXnkFe/fuBVC2eFRSUgKg7LyOJ140rfU8Q+TjnMixi4o51x5zTuRZtb70xXElS0WeuJJFBPaPPgKio4Hu3YHHHiv7b3Q0sHEj7HY70tLSKt2G5vT75ets3KheW4IROXZRMefaY87JCLQY3wMGDMBLL72E7Oxs5Rbrr7/+Gi+++CKeeOIJj+9/586dTi83rl+/Pt566y3k5eWhqKgIGzdurPKK2p49e2Lfvn1OVwg1bNgQ69atQ0FBAb766is0bdq01vG4Ozdw9djEuQipjTnXHnNORqDF+E5KSkJqaiqioqKwbt06fPzxx4iPj8ePP/6ILl26qLovx93G5f+Uv9vYYrEodxvv2LEDBw4cwODBg2t9t/GxY8eu+W5jPc0zRD7OiRy7qJhz7THnZATeHt+1XvCZMmUK3n77bXTt2hWTJ0/G5MmT0bVrVyxbtgxTp05VPcCsrCw8/vjjaNy4MRo0aIAbb7wR33//vbJdlmVMnDgRERERaNCgAZKTk51e+FRcXIy//vWvCAoKQmJiIr766iun9mfOnIkRI0Zcc3ymQYOAiu8KyMoC+vUDNm1yvaFNm8p+R422iIiI6rCpU6eiVatWiIqKQmFhIVq3bo2uXbvi9ttvx/jx470dHhERERlAu3btsHr1avz000/4/vvvsWzZMiQkJHglltmzZ+P+++/Hww8/jK5duyI8PBwbN26sVM9xt3H5dzIPHz4csbGx6NSpE65cueKRu42JiIhIO7W+h85xJcvMmTOxbt06NGjQADfddBPeeecd1Sc3Fy5cQFJSErp3744tW7agSZMmSEtLc3ox8YwZMzBv3jysXLkSMTExmDBhAnr27Imff/4Z9evXx5IlS3DgwAGkpqZiy5YteOyxx5CTkwNJkpCZmYmlS5c6LSCpQpYBSYIpJQXYuvWq1aWLF2EaNars99xsi4iIqK7z8/PD0qVLMWHCBBw9ehSFhYVo3769106yEBERUd1XUFCgvHi5oKCgxrqefkHzzp07nX523G381ltv1fh7PXv2RM+ePZ3KHHcbExERUd1wTQ9NdFzJ4mnTp09HVFQUli9frpSVf16/LMuYM2cOxo8fj969ewMA3n33XYSFheHDDz/EgAEDcOzYMTz44INo06YNYmNjMWbMGOTm5qJJkyYYOnQopk+f7tJkrLi4GMXFxcrPjgle1a8JBCDLkLKyUH//ftgTE2E2m2G32yGXW9RxPNLluo4da975n201+P57yNddV+mZuo52bDYb7Ha7st1sNkOWZafbyCRJgslkqra8Yoy1LTeZTMrjdaoqdzV2R3nFW+BE6lPF2PXWJ6Ds71D5+EXvk94/J0edim2L3Ke6+DmxT+71yRPPfa9OixYt0KJFC832R0RERMbVqFEjnD17Fk2bNkVwcHCVLxuXZbnKuRIRERGRVlxa8PHWlSybN29Gz5498cgjj2DXrl1o3rw5nnvuOQwZMgQAkJmZiezsbCQnJyu/Y7FY0KlTJ6SmpmLAgAFo27YtVq1ahT/++ANbt25FREQEQkNDsXr1atSvXx8PPfSQS7FMmzYNkydPrnUffM6fR35+Ppo0aYKsrCwUFRUp28LDw2GxWFxuK/zPk27lH1kHAAkJCSgtLcXJkycBABkZGTCbzUhMTERRURHOlHtMnJ+fH2JjY2G1WpGdna2U+/v7IyoqCnl5ecjNzVXKLRYLIiIikJOTA6vVqpSHhoYiNDS0yj4FBwfj1KlTuHLlilIeGRmJgIAAZGRkOJ2cjImJgY+PDzIyMpTYJUlS+pSZmanUNZlMQvWpus9JL32yWCzw8/NTcl4X+iTC55SQkIBz587VqT7Vxc+Jfbr2Pl28eBGeMHr0aLz66qvw9/fH6NGja6w7a9Ysj8SgV+XfCVTVtoSEBLfrqN2WHokcu6iYc+0x52QEnhrf27dvR0hICABgx44dHtmHqPQ0zxD5OCdy7KJizrXHnJMReHt8S3L5S3erYTablStZHFf3VuSJK1nq168PoOwkzyOPPIL9+/dj1KhRWLRoEQYNGoRvvvkGSUlJ+O233xAREaH8Xv/+/SFJEt5//32UlJQgJSUFn332GUJDQzF79my0bt0at956K3bu3InFixdj7dq1iIuLw7Jly5QXF1ZU1R0+UVFRsAKoaYmr+PPP4ZOcXOMdPlc2b0a9Pn2umo8rW7fCJzkZFT+y8nfJlJSUwNfXF5IkCXVVeMXY68qV7q6Ue/MOn8uXLys5rwt90vvnBAClpaXw8XFeaxe5T3Xxc2Kf3OuT1WpF48aNYbVaVb0IpHv37ti0aROCg4PRvXv3autJkoTt27ertl89KygogMViQX5+frUXkMiyjCtXrsDPz6/K+ZurddRuS49Ejl1UzLn2mHMyAqvViuDgYNXnIn379sWKFSsQFBSEd999F48++ijq1aunWvsiUmsu4mo9zkVIbcy59phzMgJPzUVc5dIdPt66ksVut6NDhw6YOnUqAKB9+/Y4evSosuDjCl9f30rPsR08eDBGjhyJgwcP4sMPP8Thw4cxY8YMjBw5Ehs2bKiynXr16lU5mat2tUySIDdvjszISDjeKFDV6p7NZkNmfDyui4yElJVV9Xt8/mzrZPPmSJBlmM3manYp4ZdffkFCQoJSx7HwU1XdqsqrW4FUq7w2sVdXX5Q+1abcG32y2WxV5ry2sVdXzs+pciw2mw2ZmZlV5rym2PXcJ7XL2Sfx++SpSXv5+QevqnVWcZGw4raajjuu1lG7LT0SOXZRMefaY87JCGr6XnTHJ598gqKiIgQFBWHw4MG455570LRpU4/sSzTuzkVcrce5CKmNOdcec05G4Km5iKtcWvCZO3cu2rdvj6CgIPzyyy+aXckSERGB1q1bO5Vdf/31yqJMeHg4ACAnJ8fpDp+cnBy0a9euyjZ37NiBn376CW+//TbGjBmDXr16wd/fH/3798f8+fOvLVBJcl6o+fNkl332bMCVg5fZDPvs2TD37+9+W0RERHWc1WqFzWZTLkZxyMvLg4+Pj1euoCEiIqK6rVWrVhg3bhy6d+8OWZaxbt26auccTzzxhMbREREREZVx6YFyjitZgLK7Y8o//9+TkpKScPz4caeyEydOoGXLlgDK3hkQHh6Obdu2KdsLCgqwb98+dO7cuVJ7ly9fxrBhw7B48WKYzWblMWIAUFJSck2Po7OvXAlUfAxcZCSwfj3g4vuBAJTVXb9enbaIiIjqsAEDBmDt2rWVytetW4cBAwZ4ISIiIiKq6xYuXIj//ve/ePHFFyFJEsaPH49Ro0ZV+pOSkuLtUImIiMjAXLrDx1tXsjz//PO4/fbbMXXqVPTv3x/fffcdlixZgiVLlgAoe5RNSkoKpkyZgoSEBMTExGDChAlo1qwZ+lTxTpxXX30VvXr1Qvv27QGULSiNGTMGgwcPxvz585GUlFT7IB94ABg4ENizBzh7FoiIALp0Kbsbx2Zz6SVNSp2+fYHevdVpS0Aixy4q5lx7zDmR+/bt24dZs2ZVKu/WrRtefvllL0Skb2rOHzgXIbUx59pjzomuTVJSEr799lsAZX+PTpw4wUe6uUjreYbIxzmRYxcVc6495pzIsyS54hvFq/D111/jhRdeQEZGBvLy8hAYGFjlM/olSUJeXp6qAX7yyScYN24c0tLSEBMTg9GjR2PIkCHKdlmWMWnSJCxZsgT5+fm44447sGDBAiQmJjq1c/ToUTz00EM4dOgQ/P39AZQ9T2/48OFYvXo1rrvuOqxZswbx8fEuxeV4OaG3Xr5ERESkR1p8P/r7++Pbb7/FjTfe6FR+5MgRdOrUCZcuXfLIfvWGcxEiIqLKPPX92LdvX6xYsQJBQUFYuXIl+vfvjwYNGqjWvog4FyEiIqrM29+PLi34lGcymZCdnW34K1kcH1x+fj4sFkuVdWRZRlFREfz9/at9ibUrddRuS49Ejl1UzLn2mHMyAqvViuDgYI9ObLp3744bbrgBb775plP5sGHD8OOPP2LPnj0e2a/ecC6iLpFjFxVzrj3mnIzAU3MRPz8//PLLL4iIiIDZbMbZs2d5XkSluYir9TgXIbUx59pjzskItDgvUhOX7qHr27cvCgoKAADLly9HYGCgR4MSid1ur3HbmTNn3K6jdlt6JHLsomLOtceckxFoMb6nTJmCt99+G127dsXkyZMxefJkdO3aFcuWLcPUqVM9vn+94VxEHSLHLirmXHvMORmBp8a341H3K1euVB51/+6771b5x2j0NM8Q+TgncuyiYs61x5yTEXh7fLv0Dp9PPvkERUVFCAoKwpNPPol7773X8LcuExERkXckJSUhNTUVM2fOxLp169CgQQPcdNNNeOedd5CQkODt8IiIiKgOWrRoEUaPHo1PP/0UkiRh/Pjx1T7qXs13GxMRERHVhksLPo4rWbp3765cyVLd7Uic2BAREZGntWvXDqtXr/Z2GERERGQQt99+O7799lsAZY+6P3HihOEf6UZERET649KCD69kqV5Nz5uUJAl+fn5u11G7LT0SOXZRMefaY87JCDw1vgsKCpSLTRyPma2O0V4azLmIOkSOXVTMufaYczICLcZ3ZmYmmjRp4vH9iEJP8wyRj3Mixy4q5lx7zDkZgbfHtyTLslybXzCZTMjOzjb8lSyOlxN66+VLREREeuSp78fyL0c2mUxVTqBkWYYkSbDZbKrtV884FyEiIqpMq+/HPXv2YPHixcjIyMD69evRvHlzrFq1CjExMbjjjjs8tl894VyEiIioMm9/P5pq+wu8ksVZTetlsiwjPz/f7Tpqt6VHIscuKuZce8w5GYGnxvf27dsREhICANixYwe2b99e6Y+j3Gg4F1GHyLGLijnXHnNORqDF+N6wYQN69uyJBg0a4ODBgyguLgYAWK1WTJ061eP71xs9zTNEPs6JHLuomHPtMedkBN4e37Ve8GnZsiX27t2Lxx9/HJ07d0ZWVhYAYNWqVdi7d6/qAeqd3W6vcVt2drbbddRuS49Ejl1UzLn2mHMyAk+N77lz5+LSpUsAgF9++QW33XYb7rzzzir/GA3nIuoQOXZRMefaY87JCLQY31OmTMGiRYuwdOlS+Pr6KuVJSUn44YcfPL5/vdHTPEPk45zIsYuKOdcec05G4O3xXesFH17JQkRERFr75JNPUFRUBAAYPHgwrFarlyMiIkOy2YCdO4H33iv7r0EeIUlEzo4fP46uXbtWKrdYLMjPz9c+ICIiIqI/+dT2FxxXsjzxxBNYu3atUp6UlIQpU6aoGhwRERERALRq1Qrjxo1D9+7dIcsy1q1bV+2zcJ944gmNoyMiQ9i4ERg1Cjhz5n9lkZHA3LlA377ei4uINBceHo709HRER0c7le/duxexsbHeCYqIiIgI17DgwytZnFX10ujy2/z9/d2uo3ZbeiRy7KJizrXHnJMReGp8L1y4EC+88AI+/fRTSJKE8ePHV7kvSZIMt+DDuYg6RI5dVELlfONGoF8/oOLzuLOyysrXrxdi0UeonBNdIy3G95AhQzBq1CgsW7YMkiTht99+Q2pqKl588UVMmDDB4/vXGz3NM0Q+zokcu6iYc+0x52QE3h7fklzLtwjFxsZiyZIlSE5ORmBgIA4fPozY2Fi8++67eP311/Hzzz97KlZdKSgogMVigdVqrfYKYyIiIqPR4vvRZDIhOzsbTZs29Uj7ouBchOqEPx/VqGs2G9C6ddniTlUkCWjeHPjpJ8Bs1ja2a+Hv7+0IiDxKi+9HWZYxdepUTJs2TXnHYL169fDiiy/i1Vdf9cg+9YhzESIiosq8/f1Y6zt8eCWLs6u9LDAvLw8hISEwmap+XZIrddRuS49Ejl1UzLn2mHMyAk+9nLBv375YsWIFgoKCsHz5cgQGBnpkPyLiXEQdIscuKrvdDlNAgLfDcJ8slz3mzWLxdiQuyT1/nuOc6jQtXpQsSRJefvlljBkzBunp6SgsLETr1q0RUBeOadfA3bmIq/U4FyG1MefaY87JCLSYi9Sk1n+zxo4di8ceewx33303CgsL0bVrV/z973/HM888gxEjRngiRl2r6QYpWZaRm5vrdh2129IjkWMXFXOuPeacjMBT4/uTTz5B0Z93Ajz55JO4ePGiR/YjIs5F1CFy7KJirr2D45zqOi3Ht5+fH1q3bo2OHTsadrEHcH8u4mo9zkVIbcy59phzMgJvj+9a3+HDK1mIiIhIa61atcK4cePQvXt3yLKMdevWVXtrtNHe4UMksuMHDiA+Ph5mPT8KbfduoFevq9f77DOgined6onNZgN++83bYRARERERkYfUesHHwXElCxEREZGnLVq0CKNHj8ann34KSZIwfvz4Kl+EKEkSF3yIBCI3bFj2Thk9L/j06AFERpa9w6eqq/UkqWx7jx767gdQ9j4iIiIiIiKqs655wYfKVHWyqfw2i8Xidh2129IjkWMXFXOuPeacjMBT4/v222/Ht99+CwAwmUw4ceIEmjZt6pF9iYZzEXWIHLuohMm52QzMnQv061e2uFN+0ccR+5w5+l/sgUA5J3IDx7f29DTPEPk4J3LsomLOtceckxF4e3xLsrcfKieogoICWCwWWK3Wah8pQ0REZDRafD/+8ssvaNGihdcnUd7GuQiRxjZuBEaNAs6c+V9ZVFTZYk/fvl4Li4ic8ftRO8w1ERFRZd7+fjRpvsc6xm6317jt7NmzbtdRuy09Ejl2UTHn2mPOyQi0GN8tW7bE3r178fjjj6Nz587IysoCAKxatQp79+71+P71hnMRdYgcu6iEy3nfvsCpU8COHcCaNWX/zcwUarFHuJwTXQOOb+3paZ4h8nFO5NhFxZxrjzknI/D2+OaCj5tqukFKlmVYrVa366jdlh6JHLuomHPtMedkBFqM7w0bNqBnz55o0KABDh48iOLiYgCA1WrF1KlTPb5/veFcRB0ixy4qIXNuNgPdugF/+UvZfwV4jFt5QuacqJY4vrWnp3mGyMc5kWMXFXOuPeacjMDb45sLPkRERCSUKVOmYNGiRVi6dCl8fX2V8qSkJPzwww9ejIyIiIiIiIiIyHu44ENERERCOX78OLp27Vqp3GKxID8/X/uAiIiIiIiIiIh0gAs+bqrphdGSJCE0NNTtOmq3pUcixy4q5lx7zDkZgRbjOzw8HOnp6ZXK9+7di9jYWI/vX284F1GHyLGLijnXHnNORsDxrT09zTNEPs6JHLuomHPtMedkBN4e3z5e3XsdYDJVv2ZmMpkQGhp61d+/Wh2129IjkWMXFXOuPeacjKCm70W1DBkyBKNGjcKyZcsgSRJ+++03pKam4sUXX8SECRM8vn+94VxEHSLHLirmXHvMORmBFnMRcubuXMTVepyLkNqYc+0x52QE3p6LcCbkJrvdXuO2X3/91e06arelRyLHLirmXHvMORmBFuN77NixeOyxx3D33XejsLAQXbt2xd///nc888wzGDFihMf3rzeci6hD5NhFxZxrjzknI+D41p6e5hkiH+dEjl1UzLn2mHMyAm+Pb97h4yZZlmvcVlRU9L86NhuwZw9w9iwQEQF06QIZcK7jalvXWEevRI5dVMy59phzMgItxrckSXj55ZcxZswYpKeno7CwEK1bt0ZAQIDH961H7s4NXD02cS5CamPOtceckxFwfGtPT/MMkY9zIscuKuZce8w5GYG3xzcXfLSycSMwahRw5sz/yiIjgdmzgRtu8F5cREREgvLz80Pr1q29HQYRERERERERkS7wkW5a2LQJ6NfPebEHALKyYOrfHwFffOGduIiIiIiIiIiIiIiIqE7ggo+brvZywvCGDWFKSQGqupXrz7JmM2bAdJVbvUwmE8LDw6++v6vU0SuRYxcVc6495pyMgONbe+7ODVw9NnEuQmpjzrXHnJMRcHxrT0/zDJGPcyLHLirmXHvMORmBt8e3JHv7oXKCKigogMVigdVqRVBQUPUVJcm1BnfsALp1UyU2IiIib3H5+5HcxlwTERFVxu9H7TDXRERElXn7+5HLqW6y2+3XtK1S3aysq+7n5MmTV93f1erolcixi4o51x5zTkbA8a09d+cGrh6bOBchtTHn2mPOyQg4vrWnp3mGyMc5kWMXFXOuPeacjMDb45sLPm6q6QYpWZZxevFi19oJD7/qfq5cuXLV/V2tjl6JHLuomHPtMedkBBzf2nN3buDqsYlzEVIbc6495pyMgONbe3qaZ4h8nBM5dlEx59pjzskIvD2+ueDjYZeSkiBHRlb7aDdZklASHg506aJxZEREREREREREREREVFdwwcfTzGbYZ88u+/+Kiz5//pwzbhxgNmscGBERERERERERERER1RVc8HGTyVR9Ck0mEyIjI2F6+GFg/XqgeXPnCpGRwAcfIPjJJ2tsx6ktV/Z3lbb0SOTYRcWca485JyPg+Naeu3MDV49NnIuQ2phz7THnZAQc39rT0zxD5OOcyLGLijnXHnNORuDt8S3J3n6onKAKCgpgsVhgtVoRFBTk2i/ZbMCePcDZs0BERNlj3HhnDxER1SHX9P1I14S5JiIiqozfj9phromIiCrz9vcjl1PdZLPZatx24sSJ/9Uxm4Fu3YC//KXsv2Zz5TqutnWNdfRK5NhFxZxrjzknI+D41p67cwPORcqIHLuomHPtMedkBBzf2tPTPEPk45zIsYuKOdcec05G4O3xzQUfD7Pb7arUUbstPRI5dlEx59pjzolIa5yLuE7k2EXFnGuPOScirWk9zxD5OCdy7KJizrXHnBN5Fhd8iIiIiIiIiIiIiIiIBMcFHyIiIiIiIiIiIiIiIsFJsizL3g5CRI6XL+Xn58NisVRZR5ZlXLlyBX5+fpAk6ZrrqN2WHokcu6iYc+0x52QEVqsVwcHBfHmvBjgXUZfIsYuKOdcec05GwLmIdtSai7haj3MRUhtzrj3mnIzA23MR3uHjYT4+PqrUUbstPRI5dlEx59pjzolIa5yLuE7k2EXFnGuPOScirWk9zxD5OCdy7KJizrXHnBN5Fhd83FTTi8bsdjvS0tLcrqN2W3okcuyiYs61x5yTEXB8a49zEXWIHLuomHPtMedkBBzf2tPTPEPk45zIsYuKOdcec05G4O3xzQUfIiIiIiIiIiIiIiIiwXHBh4iIiIiIiIiIiIiISHBc8CEiIiIiIiIiIiIiIhKcJMuy7O0gRFRQUACLxYL8/HxYLJYq68iyDLvdDpPJBEmSrrmO2m3pkcixi4o51x5zTkZgtVoRHBwMq9WKoKAgb4dTp3Euoi6RYxcVc6495pyMgHMR7ag1F3G1HucipDbmXHvMORmBt+civMPHw0pLS1Wpo3ZbeiRy7KJizrXHnBOR1jgXcZ3IsYuKOdcec05EWtN6niHycU7k2EXFnGuPOSfyLC74uMlut9e4LTMz0+06arelRyLHLirmXHvMORkBx7f2OBdRh8ixi4o51x5zTkbA8a09Pc0zRD7OiRy7qJhz7THnZATeHt9c8CEiIiIiIiIiIiIiIhIcF3yIiIiIiIiIiIiIiIgExwUfDzOZrp5iV+qo3ZYeiRy7qJhz7THnRKQ1zkVcJ3LsomLOtcecE5HWtJ5niHycEzl2UTHn2mPOiTxLkmVZ9nYQIiooKIDFYoHVakVQUJC3wyEiItIFfj9qh7kmIiKqjN+P2mGuiYiIKvP29yOXVN1U03qZLMsoLCx0u47abemRyLGLijnXHnNORsDxrT3ORdQhcuyiYs61x5yTEXB8a09P8wyRj3Mixy4q5lx7zDkZgbfHNxd83GS322vcdubMGbfrqN2WHokcu6iYc+0x52QEHN/a41xEHSLHLirmXHvMORkBx7f29DTPEPk4J3LsomLOtceckxF4e3xzwYeIiIiIiIiIiIiIiEhwXPAhIiIiIiIiIiIiIiISHBd83CRJUo3b/Pz83K6jdlt6JHLsomLOtceckxFwfGuPcxF1iBy7qJhz7THnZAQc39rT0zxD5OOcyLGLijnXHnNORuDt8S3J3n6LkKAKCgpgsVhgtVoRFBTk7XCIiIh0gd+P2mGuiYiIKuP3o3aYayIiosq8/f3IO3zcVNN6mSzLyM/Pd7uO2m3pkcixi4o51x5zTkbA8a09zkXUIXLsomLOtceckxFwfGtPT/MMkY9zIscuKuZce8w5GYG3xzcXfNxkt9tr3Jadne12HbXb0iORYxcVc6495pyMgONbe16Zi9hswM6dwHvvlf3XZqtVW3okcuyiYs61x5yTEXB8a09P5zxEPs6JHLuomHPtMedkBN4e3z5e3TtdG5sN2LMHOHsWiIgAunQBzGZvR0VERERGsGkT8PzzwJkz/yuLjATmzgV69/ZeXEREREREREQGJ9QdPq+//jokSUJKSopSdvnyZQwbNgyNGzdGQEAAHn74YeTk5Cjb8/Ly8MADDyAgIADt27fHwYMHndocNmwY3njjDa264L5Nm4DoaKB7d+Cxx8r+Gx0NbNzo7ciIiIiojgv44guY+vd3XuwBgKwsoF+/snkKEREREREREXmFMAs++/fvx+LFi3HTTTc5lT///PP4+OOP8cEHH2DXrl347bff0LdvX2X7a6+9hosXL+KHH35At27dMGTIEGXbt99+i3379jktINWWJEk1bvP393e7jqNe6O7dNZ5kkTZtcqktPXI1D6Qe5lx7zDkZAce39jSbi9jtCJ82DajqecR/lplSUuBfv76Q44DHaO0x59pjzskIOL61p9Y5DzXmLCIf50SOXVTMufaYczICb49vSfb2W4RcUFhYiJtvvhkLFizAlClT0K5dO8yZMwdWqxVNmjTBmjVr0K9fPwDAf//7X1x//fVITU3Fbbfdhl69euHBBx/Es88+i2PHjqFDhw4oKipCSUkJbr31Vrz99tvo0KFDrWMqKCiAxWKB1WpFUFCQ2l2uzGYDWrYsW9ypiiQBzZsDp07x8W5EROQ1mn8/Gpjmud65s+zO4qvZsQPo1s3T0RAREVWJcxHtMNdERESVefv7UYg7fIYNG4b77rsPycnJTuUHDhxASUmJU3mrVq3QokULpKamAgDatm2L7du3o7S0FFu3blXuEJoxYwa6devm8mJPcXExCgoKnP4AQElJCWw2G2w2m/JCJrvdDpvNhpKSEpw7dw6lpaVO5Y4/sizDbrfj3LlzTu041uDK1y3dsaP6xR6g7MraM2dwYfNmpa2yYtmpHUeM1ZVXjLG25Y7YqysvX+Yol2VZyZUjdkd5xfoi9cnVcm/1ydWxJ1Kf9P45lZaWIjc3F6WlpXWmT3Xxc2Kf3OtTSUkJSFtXe3Fxbm6u23UAwF7TPKScghMnvP6Symvhah5IPcy59phzMgKOb+2pMs9Qac4i8nFO5NhFxZxrjzknI/D2+Pbx6t5dsHbtWvzwww/Yv39/pW3Z2dnw8/NDcHCwU3lYWBiys7MBAGPHjsXQoUMRFxeH6OhovPPOO0hLS8PKlSuRmpqKZ599Fl988QU6dOiApUuXwmKxVBnHtGnTMHny5ErlGRkZykqdxWJBREQEcnJyYLVaYbfbkZeXh4SEBISFhSErKwtFRUXK74aHhyMwMBAZGRmwWCwwmcrW3yIjIxEQEICMjAxlgAQcPIhIF/KVc/gwzl53HXx8fJCYmIiioiKcKfcIOD8/P8TGxsJqtSo5AgB/f39ERUUhLy8Pubm5SnnFPjmEhoYiNDS0yj4FBwfj1KlTuHLlilJeVZ8AICYmBj4+PkhLS0NeXh5CQkJgMpmQkJCA0tJSZGZmKnVNJpNwfSpPb31ydeyJ1Ce9f06BgYG4ePEiiouLcfHixTrRp7r4ObFP7vXJcUEEaaemm7VlWUZubi4aNWrkVh0AkMPDXYonv359+Ov/BvJKXM0DqYc51x5zTkYgwENM6hx35yKu1lNzXqNHIscuKuZce8w5GYG35yK6fqTbr7/+ig4dOuDLL79U7szp1q2b8ki3NWvWYPDgwSguLnb6vY4dO6J79+6YPn16le3eddddGDVqFH755Rd88skn+PTTTzFkyBA0btwYb7zxRpW/U1xc7LSfgoICREVF4fz588pBSpIkmEwm2O125Urn9PR0JCQkwNfXVyl3cNQ9ceIE4uLiYP7zUWwmkwmSJClXdgOAfft2+PbocdWcnVq+HM0eewxmsxlms1m5k8PBEWN15RVjrG25I/bqysv3yVEOlN0plZ6ejvj4eJjNZqW84oqoSH2qGLve+uTq2BOpT3r/nOx2OzIyMhAXF6fsR/Q+1cXPiX1yr08XLlxAkyZN+GgPDThuE8/Ly6v2H0w2mw1paWlISEhQjvXXUgcAbFeuwN6yJXxyciBVNX2UJMjNm+P4558joVWrGtvSI1fzQOphzrXHnJMRXLhwASEhIZyLaECtuYir9dSc1+iRyLGLijnXHnNORuDtuYiu7/A5cOAAzp07h5tvvlkps9ls2L17N+bPn4+tW7fiypUryM/Pd7rLJycnB+HVXIW6fPlyBAcHo3fv3ujbty/69OkDX19fPPLII5g4cWK1sdSrVw/16tWrVO5YWCmv/Ilck8mk/Fy+vDxJkqpsx+nnO+9ESXj4VU+yXL71Vqe2HG1Xt8+KqotRrfLqDuaORZ6KeahN7Hrsk6vl3uqTS2PvGsv5OdUcoxo50Fuf1Chnn8TvEyftdZjZjJxx49A8JaXs3YHl5yN/vpTSPns23yVIRERERERE5CW6fofP3XffjSNHjuDQoUPKnw4dOmDgwIHK//v6+mLbtm3K7xw/fhynT59G586dK7V3/vx5vPLKK3jzzTcBwOldA+Xfe1Mb0p8nOKrbZrFY3K4DAJKPDwqnTHH8UsVGAADy7NmwhIRctS09cjUPpB7mXHvMORkBx7f2NJuLSBLMjzwCed06oHlz542RkcD69ZD69hX2OMdjtPaYc+0x52QEHN/aU2ueocacReTjnMixi4o51x5zTkbg7fGt60e6VaX8I90AYOjQofjss8+wYsUKBAUFYcSIEQCAb775ptLvDhw4EJ07d8bw4cMBADNmzMC6deuwatUqvPDCC4iJicFbb73lUhyOW5c1vzVr40Zg1Cig3LscEBUFzJkD9O2rXRxERERV8Nr3owF5Ndc2G7BnD3D2LBARAXTpwjt7iIhIFzgX0U6lXHN+YDz8zImIKvH2XETXd/i4Yvbs2bj//vvx8MMPo2vXrggPD8fGjRsr1du6dSvS09Px3HPPKWXDhw9HbGwsOnXqhCtXrmDSpEm13n/Fdy1U3Hb27Fm36zjV69MHOHUK2LEDWLOm7L+ZmUDfvi63pUcixy4q5lx7zDkZAce39jSfi9jtZf+Q79YN+Mtfyv775z/sRT7OiRy7qJhz7THnZAQc39qz2+1lF6dGRwPduwOPPVb23+ho2Nevr/08w8N19Eq42Kv5zFHFOTm9Ei7ndQBzTkbg7fEt3ILPzp07lbt7AKB+/fp46623kJeXh6KiImzcuLHK9/f07NkT+/btc3ofQcOGDbFu3ToUFBTgq6++QtOmTWsdT003SMmyDKvV6nadSvWqOcnialt6JHLsomLOtceckxFwfGvPK3MRN9vSI5FjFxVzrj3mnIyA41t78ubNQL9+zk8iAYCsLEj9+8P2wQeazTNEPs4JFfvGjdV+5ujXT5hFH6FyXkcw52QE3h7fPl7dOxERERERERERCcv00ktAVSe3ZBmQJIRNmwY8+ywf9eUC6dIloKhI37my2YCRI2v8zDFqFJCcrO9+AGV9ISKqY7jgc40cK3UFBQUwV/MFZrPZUFhY6HYdtdvSI5FjFxVzrj3mnIygoKAAgPevaDECzkXUJXLsomLOtceckxFwLqIdR44vZmej2tdTyzKQnQ3b55/D3K1btW2pNc8Q+Thns9kQccstKPJ2IO6S5bI7fywWb0fiksL9+4UcL6IS+e8okau8PRfhgs81+v333wEA0dHR3g2EiIhIh37//XdYBPlHnqg4FyEiIqoe5yKe55iLRLlSuXdvj8ZCdM1uvdXbERBRHeWtuQgXfK5RSEgIAOD06dPVfnAFBQWIiorCr7/+iqCgoGuuo3ZbeiRy7KJizrXHnJMRWK1WtGjRQvmeJM/hXERdIscuKuZce8w5GQHnItpRay7iaj3ORUhtzLn2mHMyAm/PRbjgc41MJhMAwGKxXPUAFRQUpEodtdvSI5FjFxVzrj3mnIzA8T1JnsO5iGeIHLuomHPtMedkBJyLeJ7acxFX63EuQmpjzrXHnJMReGsuwhkQERERERERERERERGR4LjgQ0REREREREREREREJDgu+FyjevXqYdKkSahXr57H66jdlh6JHLuomHPtMedkBBzn2uFcRF0ixy4q5lx7zDkZAce5dvQ4zxD58xc5dlEx59pjzskIvD3OJVmWZa/smYiIiIiIiIiIiIiIiFTBO3yIiIiIiIiIiIiIiIgExwUfIiIiIiIiIiIiIiIiwXHBh4iIiIiIiIiIiIiISHBc8CEiIiIiIiIiIiIiIhIcF3yIiIiIiIiIiIiIiIgEZ+gFn927d+OBBx5As2bNIEkSPvzwQ6ftsizjrrvugq+vLyRJgp+fH5KTk3H8+HGlTl5eHjp06ABJkpQ/ISEhWLNmjVJn//79aNq0qbLdx8cHt912GyRJQkpKCgDg9OnT6Nixo1M7jj+tWrUCAOzcuROtW7d22paYmIisrCynuN9//31YLBZIkgSz2Yx7770XpaWlTnV27tyJm2++GfXq1UN8fDxWrFiheu4mTpyIiIgINGjQAMnJyUhLS3Oqk5eXh4EDByIoKAjBwcF46qmnUFhY6FTnxx9/RJcuXVC/fn1ERUVhxowZlWL54IMP0KpVK9SvXx833ngjPvvss1r1RRTTpk3DrbfeisDAQDRt2hR9+vRxGosAcPnyZQwbNgyNGzdGQEAAHn74YeTk5DjVOX36NO677z40bNgQTZs2xZgxY65pfLz11luIjo5G/fr10alTJ3z33Xeq99nbFi5ciJtuuglBQUEICgpC586dsWXLFmU78011wb/+9a9qv3cAjnNP41yEcxGRcC6iPc5FyAg4F/EuzkU4FxEJ5yLa41yEjKDOzUVkA/vss8/kl19+Wd64caMMQN60aZPT9tdff1328fGRR4wYIW/YsEG+88475QYNGshRUVFyYWGhLMuyfM8998iBgYFycHCw/PLLL8thYWFygwYNZEmS5KNHj8pWq1X5+frrr5eHDx8uA5AByE2bNpVHjRoll5aWyomJiTIA2cfHR27cuLFcv359GYCcmJgonz9/Xj558qTcoEEDuV69erKfn588YMAAWZIk2Ww2y82bN1diTktLkyVJklu0aCFv2LBBfvbZZ2UA8qOPPqrUOXnypNywYUN59OjR8s8//yy/+eabstlslj///HNVc2exWOQPP/xQPnz4sPzggw/KMTEx8h9//KHUueeee+S2bdvK3377rbxnzx45Pj5e/stf/qJst1qtclhYmDxw4ED56NGj8nvvvSc3aNBAXrx4sVLn66+/ls1mszxjxgz5559/lsePHy/7+vrKR44ccbkvoujZs6e8fPly+ejRo/KhQ4fkXr16yS1atFDGoizL8rPPPitHRUXJ27Ztk7///nv5tttuk2+//XZle2lpqXzDDTfIycnJ8sGDB+XPPvtMDg0NlceNG6fUcWV8rF27Vvbz85OXLVsm//TTT/KQIUPk4OBgOScnR5tkaGTz5s3yp59+Kp84cUI+fvy4/M9//lP29fWVjx49Kssy8011w6RJk+Q2bdrIZ8+eVf6cP39e2c5x7lmci3AuIhLORbTHuQgZAeci3sW5COciIuFcRHuci5AR1LW5iKEXfMqr+OVst9vl8PBweebMmUpZfn6+7OfnJwOQd+3aJf/888/KZOSDDz6QZVmWt2zZokxcxo0bJ7/xxhsyAFmSJDk7O1u+ePGiHBQUJAOQTSaTPHz4cPmzzz5TfueRRx6R27ZtKy9cuFD28fGRAcipqanyP/7xDzkyMlIGIC9dulSWZVl+9NFH5bi4OBmAvHv3blmWZblfv34yADk7O1uJ+5ZbbpF9fHzk4uJiWZZl+R//+Ifcpk0bp/4/+uijcs+ePT2au3r16snvvfeeLMuykrv9+/crdbZs2SJLkiRnZWXJsizLCxYskBs1aqTELcuy/NJLL8nXXXed8nP//v3l++67zymeTp06yc8888w19UUk586dU8aiLJfl2NfXVxmLsizLx44dU8aQLJdNSE0mk9P4WLhwoRwUFFSr8dGxY0d52LBhys82m01u1qyZPG3aNPU7qjONGjWS3377beab6oxJkybJbdu2rXIbx7m2OBfhXEQ0nIt4B+ciVNdwLqIfnItwLiIazkW8g3MRqmvq2lzE0I90q0lmZiays7ORnJyslFksFrRt2xYAEBISgtTUVPj7+6O0tFSp1717d0iSBACw2WzKbY5t2rRBWFgYhg0bhv/7v/8DANjtdvz+++9ITU1F/fr1AQCxsbFIS0vDpEmTlNu+PvvsM6SmpiIgIAAA0K9fPwBAz549ldvHHLcO79u3D6GhoQgLC1Pi7t+/P0pLS/HTTz8BAFJTU5365WgrNTVVjdRVm7tOnTop+0hNTUVwcDA6dOig1ElOTobJZMK+ffuUOl27doWfn59TnMePH8eFCxc06YueWa1WAGVjEQAOHDiAkpISp3y0atUKLVq0cMr7jTfe6DQ+evbsiYKCApfHx5UrV3DgwAGnOiaTCcnJyXU67zabDWvXrkVRURE6d+7MfFOdkpaWhmbNmiE2NhYDBw7E6dOnAfC44m2ci1w7zkW0wbmItjgXobqMcxF94lzk2nEuog3ORbTFuQjVZXVpLsIFn2pkZ2cDgNMHZbfbcebMGYSGhuKGG25AdnY2AgMD4efnh19//RUBAQHw9/cHADRt2lRpR5IkNGvWDGvXrsUPP/yAl19+WWnz0qVLyM7Oht1uh9lsxp133okVK1Zg8+bNSp05c+bgt99+w5UrVyBJEoKDg5XYHM92dQxCq9WKRo0aOfUlPj4eAPDLL78oMZXvl6OtgoIC/PHHH+4lDlXnzvGzY1t2draSIwcfHx+EhIQ41amqjfL7qK6OY3tdZbfbkZKSgqSkJNxwww0AynLh5+enjA+Hinm/1pw6xkdubi5sNpth8n7kyBEEBASgXr16ePbZZ7Fp0ya0bt2a+aY6o1OnTlixYgU+//xzLFy4EJmZmejSpQsuXrzIce5lnItcO85FPI9zEe1wLkJ1Heci+sW5yLXjXMTzOBfRDuciVNfVtbmIT61qG9ywYcNgtVpx1113Vdp23XXX4dChQ7BarejSpQtyc3ORm5urbL98+TJGjRqFL7/8Ej4+1af93nvvBVA24XEoLi7GxYsXlStZiIYNG4ajR49i79693g6lziv/d3v9+vUYNGgQdu3a5e2wiFTj+N4BgJtuugmdOnVCy5YtsW7dOjRo0MCLkVFVOBchveBcRDuci1Bdx7mIWDgXIb3gXEQ7nItQXVfX5iK8w6ca4eHhAKDcGjx8+HB88sknaNOmDWJjY5U6Fy9exJUrV3Dp0iXEx8ejbdu2uHLlCsxmM37++WeEh4dDlmWcOnUK586dw80334wbb7xR2c/mzZvx9ttvw2QywWazIT8/32m/kiQhLCwMfn5+8PPzgyzLTnUck50WLVoAKLtF2HFbr0N6ejoAoGXLlkrcjvYdcnJyEBQUpMogrpi78vtwbAsPD8e5c+ectpeWliIvL8+pTlVtlN9HdXUc2+six1jcsWMHIiMjlfLw8HBcuXJFGR8OFfN+rTl1jI/Q0FCYzWbD5N3Pzw/x8fG45ZZbMG3aNLRt2xZz585lvqnOCg4ORmJiItLT0znOvYxzEfVyV34fnIu4j3MRbXEuQkbDuYh+cC6iXu7K74NzEfdxLqItzkXIaESfi3DBpxoxMTEIDw/HV199heHDh2PTpk3YvHkzfvzxR3Tu3BkA0LlzZxQVFcHHxwfb/p+9Ow+PokrfBvxUJ+kQknQHDCRhT0gA2REREUG2AQYXVlkE5Qd8KAoaFBl1RBRcUGYEZFwAUUAGRYbFQQUUWWQRERgQ2QIkQRYTMIR0IAhJus/3R6Z7aLLQoaur6qSe+7r6klSdnHrP24fuV04tGzYAADZu3Ain04mCggJUrVrVs0J48uRJbN68Gfv27UO/fv0AFN2Hb/DgwXjvvfdw5coVAPD04z7jxV3INGzYEHl5eQCAlStXetq4L/Pq3bs3gKJL0LKysryKhuXLlyM4OBiNGzf2xO0+jtv69es941Ird9ceIzc3Fzt37vTKXU5ODvbs2eNps3HjRrhcLrRt29bTZsuWLSgoKPCKs2HDhp7LswM9FiMRQnjm4saNGxEfH++1v3Xr1ggJCfHKR0pKCk6ePOmV919++cVrfqxfvx42m83n+WG1WtG6dWuvNi6XCxs2bKiQeb+ey+XC1atXmW+qsC5duoTU1FTExcVxnuuMtYj/uWMtoi7WIsbAWoQqOtYixsFaxP/csRZRF2sRY2AtQhWd9LWIMLGLFy+KvXv3ir179woAYsaMGWLv3r3i119/FUII8eabbwqr1SrCw8PFxx9/LHr06CFq164t0tPTxeXLl4UQQvTs2VOEhYWJqlWripdfflnExMSIsLAwAUB8++23IicnR4SFhQlFUUSTJk3Ek08+KRRFERaLRdSpU0ckJyeLwsJC0aBBAwFABAcHi4EDB4rQ0FABQISHh4vo6Gixa9cuERYWJipVqiRCQ0PFQw89JBRFEUFBQaJmzZqeMR07dkwoiiLq1asnVq1aJcaOHSsAiEGDBnnapKWlicqVK4uJEyeKw4cPi/fee08EBQWJdevWqZq7qKgo8e9//1vs379f9O7dW8THx4s//vjD00fPnj1Fq1atxM6dO8W2bdtEUlKSGDJkiGd/Tk6OiImJEQ8//LA4cOCAWLp0qahcubKYO3eup8327dtFcHCw+Pvf/y4OHz4sXn75ZRESEiJ++eWXcs4G43v88ceF3W4XmzdvFhkZGZ6Xey4KIcSYMWNEnTp1xMaNG8Xu3btFu3btRLt27Tz7CwsLRdOmTUX37t3Fvn37xLp160S1atXECy+84Gnjy/xYunSpCA0NFQsXLhSHDh0Sjz76qIiKihKZmZnaJEMjzz//vPj+++9Fenq62L9/v3j++eeFoiji22+/FUIw31QxTJgwQWzevFmkp6eL7du3i27duono6Ghx7tw5IQTneaCxFmEtIhPWItpjLUJmwFpEX6xFWIvIhLWI9liLkBlUtFrE1As+mzZtEgCKvYYPHy6EEMLlcpW4H4BYsGCBEEKI8+fPi/j4eK990dHRYunSpZ7j/PTTT6JatWqe/UFBQWLMmDGiY8eOIjk5WQghxIkTJ0SbNm28+gkJCRG9e/cWx48f98TbqFEjrzaJiYni1KlTXuP67LPPRGRkpAAgLBaL6NGjhygoKCg29pYtWwqr1SoSEhI841Ezdy+99JKIiYkRoaGhomvXriIlJcWrj/Pnz4shQ4aIiIgIYbPZxIgRI8TFixe92vz888/i7rvvFqGhoaJmzZrizTffLBbLsmXLRIMGDYTVahVNmjQRX3/9dbnGIosbzUUhhPjjjz/EE088IapUqSIqV64s+vbtKzIyMrz6OXHihPjzn/8swsLCRHR0tJgwYcJNzY9//OMfok6dOsJqtYo77rhD/Pjjj4EYtq5Gjhwp6tatK6xWq6hWrZro2rWrp6gRgvmmimHQoEEiLi5OWK1WUbNmTTFo0CDP944QnOeBxlqEtYhMWItoj7UImQFrEX2xFmEtIhPWItpjLUJmUNFqEUUIIUBERERERERERERERETS4jN8iIiIiIiIiIiIiIiIJMcFHyIiIiIiIiIiIiIiIslxwYeIiIiIiIiIiIiIiEhyXPAhIiIiIiIiIiIiIiKSHBd8iIiIiIiIiIiIiIiIJMcFHyIiIiIiIiIiIiIiIslxwYeIiIiIiIiIiIiIiEhyXPAhogqvU6dOUBQFiqJg3759usWxcOFCTxzjx4/XLQ4iIiLSFmsRIiIi0hNrESLz4IIPEXn5v//7P8+X77Wvnj176h2aX0aPHo2MjAw0bdrUa/uKFSvQpUsXVKlSBWFhYWjYsCFGjhyJvXv3+tRvfn4+oqOj8eabb5a4/9VXX0VMTAwKCgowaNAgZGRkoF27dn6Ph4iIqKJiLcJahIiISE+sRViLEMmMCz5EVEzPnj2RkZHh9frss88Cesz8/PyA9l+5cmXExsYiODjYs+25557DoEGD0LJlS6xevRopKSn49NNPkZCQgBdeeMGnfq1WK4YNG4YFCxYU2yeEwMKFC/HII48gJCQEYWFhiI2NhdVqVW1cREREFRFrEdYiREREemItwlqESFZc8CGiYkJDQxEbG+v1qlKlime/oiiYP38++vbti8qVKyMpKQmrV6/26uPAgQP485//jIiICMTExODhhx9GVlaWZ3+nTp0wbtw4jB8/HtHR0ejRowcAYPXq1UhKSkKlSpXQuXNnLFq0CIqiICcnB3l5ebDZbFi+fLnXsb744guEh4fj4sWLPo/xxx9/xPTp0zFjxgzMmDEDHTp0QJ06ddC6dWtMmjQJa9eu9Wr/73//G7fddhsqVaqEhIQETJkyBYWFhQCAUaNG4ejRo9i2bZvX73z//fdIS0vDqFGjfI6LiIiIWIuwFiEiItIXaxHWIkSy4oIPEd2UKVOmYODAgdi/fz969eqFoUOHIjs7GwCQk5ODLl26oFWrVti9ezfWrVuHs2fPYuDAgV59LFq0CFarFdu3b8ecOXOQnp6OAQMGoE+fPvj555/x2GOP4cUXX/S0Dw8Px+DBg4udNbJgwQIMGDAAkZGRPsf/2WefISIiAk888USJ+xVF8fx569ateOSRR5CcnIxDhw5h7ty5WLhwIV5//XUAQLNmzdCmTRt8/PHHxeK666670KhRI5/jIiIiIt+wFmEtQkREpCfWIqxFiAxJEBFdY/jw4SIoKEiEh4d7vV5//XVPGwBi0qRJnp8vXbokAIi1a9cKIYR49dVXRffu3b36PXXqlAAgUlJShBBC3HPPPaJVq1ZebZ577jnRtGlTr20vvviiACAuXLgghBBi586dIigoSPz2229CCCHOnj0rgoODxebNm0sd0z333COSk5O9tvXs2VM0b97ca9vbb7/tNeacnBwhhBBdu3YVb7zxhlfbxYsXi7i4OM/Pc+bMEREREeLixYtCCCFyc3NF5cqVxfz5832Kh4iIiIqwFmEtQkREpCfWIqxFiGTGK3yIqJjOnTtj3759Xq8xY8Z4tWnevLnnz+Hh4bDZbDh37hwA4Oeff8amTZsQERHhebnP5khNTfX8XuvWrb36TElJQZs2bby23XHHHcV+btKkCRYtWgQA+Oc//4m6deuiY8eOfo4aGDlyJPbt24e5c+ciLy8PQgjPeKZOneo1HvfDDi9fvgwAGDJkCJxOJ5YtWwYA+Pzzz2GxWDBo0CC/4yIiIjIb1iKsRYiIiPTEWoS1CJGsgm/chIjMJjw8HImJiWW2CQkJ8fpZURS4XC4AwKVLl3D//ffjrbfeKvZ7cXFxXse5Gf/v//0/vPfee3j++eexYMECjBgxwutSY18kJSVh27ZtKCgo8IwlKioKUVFROH36tFfbS5cuYcqUKejXr1+xfipVqgQAsNlsGDBgABYsWICRI0diwYIFGDhwICIiIm5qjERERGbGWoS1CBERkZ5Yi7AWIZIVr/AhItXddtttOHjwIOrVq4fExESvV1nFTMOGDbF7926vbbt27SrWbtiwYfj1118xe/ZsHDp0CMOHDy93jEOGDMGlS5fw/vvv+zSelJSUYmNJTEyExfK/j9FRo0Zh27Zt+Oqrr/DDDz/woYREREQ6YS3CWoSIiEhPrEVYixDphQs+RFTM1atXkZmZ6fXKysry+ffHjh2L7OxsDBkyBLt27UJqaiq++eYbjBgxAk6ns9Tfe+yxx3DkyBE899xzOHr0KJYtW4aFCxcC8H5YYJUqVdCvXz9MnDgR3bt3R61atco9xnbt2mHChAmYMGECnnnmGWzbtg2//vorfvzxR3z00UdQFMVTtEyePBmffPIJpkyZgoMHD+Lw4cNYunQpJk2a5NVnx44dkZiYiEceeQSNGjXCXXfdVe64iIiIiLUIaxEiIiJ9sRZhLUIkKy74EFEx69atQ1xcnNfr7rvv9vn3a9Soge3bt8PpdKJ79+5o1qwZxo8fj6ioKK8zP64XHx+P5cuXY+XKlWjevDk++OADvPjiiwCA0NBQr7ajRo1Cfn4+Ro4ceXODBPD3v/8dn376Kfbu3Yv77rsPSUlJePDBB+FyubBjxw7YbDYAQI8ePfDVV1/h22+/RZs2bXDnnXdi5syZqFu3rld/iqJg5MiRuHDhgl9xERERmR1rEdYiREREemItwlqESFaKcD99i4jIgF5//XXMmTMHp06d8tq+ePFiPP300/jtt99gtVrL7KNTp05o2bIlZs2aFcBIfWe0eIiIiKh0rEWIiIhIT6xFiKg8dL3CZ8uWLbj//vtRo0YNKIqCL774wmu/EAKTJ09GXFwcwsLC0K1bNxw7dsyrTXZ2NoYOHQqbzYaoqCiMGjUKly5d8uw/ceIEOnbsiPDwcHTs2BEnTpzw+v377rsPK1asCNQQiaic3n//fezatQtpaWlYvHgx/va3v3ndi/by5ctITU3Fm2++iccee+yGRc21/UZEROCXX34JVOg3tGTJEkRERGDr1q26xUBE3liLENH1WIsQkZZYixDR9ViLEJE/dF3wycvLQ4sWLfDee++VuH/69OmYPXs25syZg507dyI8PBw9evTAlStXPG2GDh2KgwcPYv369fjqq6+wZcsWPProo579EyZMQM2aNbFv3z7ExcXh2Wef9ez7/PPPYbFY0L9//8ANkojK5dixY+jduzcaN26MV199FRMmTMArr7zi2T99+nQ0atQIsbGxeOGFF3zqc8mSJTh06BD27duHhg0bBijyG3vggQewb98+pKSkFLvPLRHpg7UIEV2PtQgRaYm1CBFdj7UIEfnDMLd0UxQFq1atQp8+fQAUncVSo0YNTJgwwVOMOBwOxMTEYOHChRg8eDAOHz6Mxo0bY9euXbj99tsBFN1js1evXjh9+jRq1KiBxo0bY8aMGejZsyfWrl2LZ599FgcPHkROTg7atGmDjRs3onbt2jeM7+rVq7h69arnZ5fLhezsbNxyyy1eD00jIiIyMyEELl68iBo1apR5b2ojYi1CREQkP9YirEWIiIj0pHctEqz5EX2Unp6OzMxMdOvWzbPNbrejbdu22LFjBwYPHowdO3YgKirKU9QAQLdu3WCxWLBz50707dsXLVq0wHfffYfu3bvj22+/RfPmzQEAEydOxNixY30qagBg2rRpmDJlirqDJCIiqqBOnTqFWrVq6R2GX1iLEBERyYu1CGsRIiIiPelVixh2wSczMxMAEBMT47U9JibGsy8zMxPVq1f32h8cHIyqVat62vz973/HY489hnr16qF58+aYO3cutmzZgn379uGtt97CwIEDsXv3bnTv3h2zZ88u9b6XL7zwAp555hnPzw6HA3Xq1EFaWhqioqIAFJ2NY7FY4HK5IISA0+lEWloa6tevj5CQEM92N3fb48ePIz4+HkFBQZ7tiqLA6XR62jqdTqSnp6N+/frFzpxxrxQWFBQgLS0NCQkJCAoKQlBQEIQQcLlcnrbuGEvbfn2M5d3ujr207deOqazY3duvjRGAVGO6PnajjcnXuSfTmIz+PrlcLqSnpyM+Pt5rhV/mMVXE94lj8m9MOTk5SEhIQGRkJGTHWoS1CCD/30lftrMWMc/7xFqEYzLDmFiLyFeLKIqCgoICpKamen0PsxapGH8nfdnOWsQ87xNrEY7JDGPSuxYx7IKPWmrWrImvvvrK8/PVq1fRo0cPLFq0CK+99hoiIyORkpKCnj17Yu7cuXjyySdL7Cc0NBShoaHFtkdFRaFKlSol/o7T6fQ8NNH9xVFSm8jISFSpUqXUNu52WVlZsNvtZfZls9lu2JcRyRy7rHyde6Qep9OJiIiIMj8TiCqK6/8n3MxYi8hB5thlxVpEe6xFyExYi/yP0WuRa9uV9Z3AWoTUxlpEe6xFyEz0qkUMe0Pb2NhYAMDZs2e9tp89e9azLzY2FufOnfPaX1hYiOzsbE+b673xxhvo3r07Wrdujc2bN6N///4ICQlBv379sHnz5nLHWdYbpygKwsPD/W6jdl9GJHPssmLOtceckxlUpPnNWiRwfRmRzLHLijnXHnNOZlCR5rdZahFf27EWIbUx59pjzskM9J7fhl3wiY+PR2xsLDZs2ODZlpubi507d6Jdu3YAgHbt2iEnJwd79uzxtNm4cSNcLhfatm1brM/Dhw/j008/xauvvgqgaFW5oKAAQNGls9dfiuWLay8/LGlf7dq1/W6jdl9GJHPssmLOtceckxlUpPnNWiRwfRmRzLHLijnXHnNOZlCR5rdZahFf27EWIbUx59pjzskM9J7fuh790qVL2LdvH/bt2weg6IGE+/btw8mTJ6EoCsaPH4/XXnsNq1evxi+//IJHHnkENWrUQJ8+fQAAt956K3r27InRo0fjp59+wvbt2zFu3DgMHjwYNWrU8DqWEAKPPvooZs6cifDwcABA+/bt8eGHH+Lw4cP45JNP0L59+3KP4fp7Cl6/Lysry+82avdlRDLHLivmXHvMOZmBbPObtQhrETeZY5cVc6495pzMQLb5zVrE93asRUhtzLn2mHMyA73nt64LPrt370arVq3QqlUrAMAzzzyDVq1aYfLkyQCAv/zlL3jyySfx6KOPok2bNrh06RLWrVuHSpUqefpYsmQJGjVqhK5du6JXr164++67MW/evGLHmjdvHmJiYnDfffd5tr3yyiu4cuUK2rZti8TERIwdO7bcY7j2oU0l7cvKyvK7jdp9GZHMscuKOdcec05mINv8Zi3CWsRN5thlxZxrjzknM5BtfrMW8b0daxFSG3OuPeaczEDv+a0IvSOQVG5uLux2O7Kzs8t8OOGxY8eQlJRU5gMFb9RG7b6MSObYZcWca485JzO4cOECqlatCofDAZvNpnc4FRprEXXJHLusmHPtMedkBqxFtKNWLeJrO9YipDbmXHvMOZmB3rUIb5hIREREREREREREREQkOS74+ElRlDL32e12v9uo3ZcRyRy7rJhz7THnZAac39pjLaIOmWOXFXOuPeaczIDzW3tGqjNk/pyTOXZZMefaY87JDPSe37yl201yX7rMy8SJiIj+h9+P2mGuiYiIiuP3o3aYayIiouL0/n7kFT5+crlcZe7LyMjwu43afRmRzLHLijnXHnNOZsD5rT3WIuqQOXZZMefaY87JDDi/tWekOkPmzzmZY5cVc6495pzMQO/5zQUfP5V1gZQQAg6Hw+82avdlRDLHLivmXHvMOZkB57f2WIuoQ+bYZcWca485JzPg/NaekeoMmT/nZI5dVsy59phzMgO95zcXfIiIiIiIiIiIiIiIiCTHBR8iIiIiIiIiIiIiIiLJccHHT4qilLkvOjra7zZq92VEMscuK+Zce8w5mQHnt/ZYi6hD5thlxZxrjzknM+D81p6R6gyZP+dkjl1WzLn2mHMyA73ntyL0vqmcpHJzc2G32+FwOGCz2fQOh4iIyBD4/agd5pqIiKg4fj9qh7kmIiIqTu/vR17h4yeXy1XmvlOnTvndRu2+jEjm2GXFnGuPOScz4PzWHmsRdcgcu6yYc+0x52QGnN/aM1KdIfPnnMyxy4o51x5zTmag9/zmgo+fyrpASgiBvLw8v9uo3ZcRyRy7rJhz7THnZAac39pjLaIOmWOXFXOuPeaczIDzW3tGqjNk/pyTOXZZMefaY87JDPSe31zwISIiIiIiIiIiIiIikhwXfIiIiIiIiIiIiIiIiCTHBR8/WSylp9BisSA2NtbvNmr3ZUQyxy4r5lx7zDmZAee39liLqEPm2GXFnGuPOScz4PzWnpHqDJk/52SOXVbMufaYczIDvee3IvS+qZykcnNzYbfb4XA4YLPZ9A6HiIjIEPj9qB3mmoiIqDh+P2qHuSYiIipO7+9HLqf6yeVylbkvLS3N7zZq92VEMscuK+Zce8w5mQHnt/ZYi6hD5thlxZxrjzknM+D81p6R6gyZP+dkjl1WzLn2mHMyA73nNxd8/FTWBVJCCOTn5/vdRu2+jEjm2GXFnGuPOScz4PzWHmsRdcgcu6yYc+0x52QGnN/aM1KdIfPnnMyxy4o51x5zTmag9/zmgg8REREREREREREREZHkuOBDREREREREREREREQkOS74+MliKT2FFosFtWrV8ruN2n0Zkcyxy4o51x5zTmbA+a091iLqkDl2WTHn2mPOyQw4v7VnpDpD5s85mWOXFXOuPeaczEDv+R2s69ErAEVRytwXERFxw9+/URu1+zIimWOXFXOuPeaczKCs70UKDNYi6pA5dlkx59pjzskMWItoz99axNd2rEVIbcy59phzMgO9axEup/rJ6XSWue/o0aN+t1G7LyOSOXZZMefaY87JDDi/tcdaRB0yxy4r5lx7zDmZAee39oxUZ8j8OSdz7LJizrXHnJMZ6D2/ueATYC6XS5U2avdlRDLHLivmXHvMORFpjbWI72SOXVbMufaYcyLSmtZ1hsyfczLHLivmXHvMOVFgccGHiIiIiIiIiIiIiIhIclzwISIiIiIiIiIiIiIikpwihBB6ByGj3Nxc2O125OTkwG63l9hGCIH8/HxYrdZSH9bkSxu1+zIimWOXFXOuPeaczMDhcCAqKgoOhwM2m03vcCo01iLqkjl2WTHn2mPOyQxYi2hHrVrE13asRUhtzLn2mHMyA71rEV7hE2DBwcGqtFG7LyOSOXZZMefaY86JSGusRXwnc+yyYs61x5wTkda0rjNk/pyTOXZZMefaY86JAosLPn4q60FjLpcLx44d87uN2n0Zkcyxy4o51x5zTmbA+a091iLqkDl2WTHn2mPOyQw4v7VnpDpD5s85mWOXFXOuPeaczEDv+c0FHyIiIiIiIiIiIiIiIslxwYeIiIiIiIiIiIiIiEhyXPAhIiIiIiIiIiIiIiKSnCKEEHoHIaPc3FzY7Xbk5OTAbreX2EYIAZfLBYvFAkVRbrqN2n0Zkcyxy4o51x5zTmbgcDgQFRUFh8MBm82mdzgVGmsRdckcu6yYc+0x52QGrEW0o1Yt4ms71iKkNuZce8w5mYHetQiv8AmwwsJCVdqo3ZcRyRy7rJhz7THnRKQ11iK+kzl2WTHn2mPOiUhrWtcZMn/OyRy7rJhz7THnRIFl6AUfp9OJl156CfHx8QgLC0P9+vXx6quv4tqLkoQQmDx5MuLi4hAWFoZu3brh2LFjnv1Xr17Fww8/DJvNhgYNGuC7777zOsbf/vY3PPnkkzcdo8vlKnNfenq6323U7suIZI5dVsy59phzMgPOb+2xFlGHzLHLijnXHnNOZsD5rT0j1Rkyf87JHLusmHPtMedkBnrPb0Mv+Lz11lv44IMP8O677+Lw4cN46623MH36dPzjH//wtJk+fTpmz56NOXPmYOfOnQgPD0ePHj1w5coVAMC8efOwZ88e7NixA48++igeeughz4JReno6PvzwQ7z++uu6jI+IiIiMTYaTT4iIiKjiYi1CRERE5RGsdwBl+eGHH9C7d2/ce++9AIB69erhs88+w08//QSgqKiZNWsWJk2ahN69ewMAPvnkE8TExOCLL77A4MGDcfjwYTzwwANo0qQJEhISMHHiRGRlZaFatWp4/PHH8dZbb/G+vkRERFQi98knixYtQpMmTbB7926MGDECdrsdTz31FID/nXyyaNEixMfH46WXXkKPHj1w6NAhVKpUyevkk7Vr1+Khhx7C2bNnoSiK5+ST3bt36zxSIiIiMiLWIkRERFQehl7wueuuuzBv3jwcPXoUDRo0wM8//4xt27ZhxowZAIqu0MnMzES3bt08v2O329G2bVvs2LEDgwcPRosWLbB48WL88ccf+OabbxAXF4fo6GgsWbIElSpVQt++fX2K5erVq7h69arn59zcXABFZ9s4nU4AgKIosFgscLlcEEJ4trtcLgQFBXm2u1ksFs/vudu6t1+/zel0QlEUr36v78e93f3foKAgz8PQ3Nwxlrb9+hjLu90de2nbfY3dvf36S+BkGtP1sRttTO79N5p7Mo3J6O+T+8GE1/ct85gq4vvEMfk3puv3ya4inHzifn/8baN2X0Ykc+yyYs61x5wTycUstYiv7ViLkNqYc+0x50SBZegFn+effx65ublo1KgRgoKC4HQ68frrr2Po0KEAgMzMTABATEyM1+/FxMR49o0cORL79+9H48aNER0djWXLluHChQuYPHkyNm/ejEmTJmHp0qWoX78+Pv74Y9SsWbPEWKZNm4YpU6YU237ixAlkZWUBKFpsiouLw9mzZ+FwODxtHA4HoqOjcebMGeTl5Xm2x8bGIioqCiEhIUhLS/Nsr1WrFiIiIpCamur1D3nx8fFQFMXr0mwASEpKQmFhIdLT0wEAaWlpsFgsaNCgAfLy8nD69GlPW6vVioSEBDgcDk+OACA8PBy1a9dGdna2ZzxljSk6OrrMMZ04cQL5+fk+jSk4ONgzfvd/rx8TAOnGdKP3yQhjKs/ck2VMRn+fGjRogIyMjAo1por4PnFMNz+mS5cuoSKR4eQTAKWefAIA9evXh6IoAFDiIl5QUBASExO9TiopbXEvKSnJ63jX9uNWv359TxuZFiyvj72iLML6sl2vMZVn7skyJhnepwYNGhRboJd9TBXxfeKY1BlTRWCGWkRRFCiKUux7mLVIxfs7abQxsRZhLcL3iWOqiLWIIq6N2GCWLl2KiRMn4m9/+xuaNGmCffv2Yfz48ZgxYwaGDx+OH374Ae3bt8dvv/2GuLg4z+8NHDgQiqLg888/L7HfESNGoGXLloiPj8df//pX7Ny5E9OnT8eBAwewYsWKEn+npMKmdu3aOH/+POx2O4DiE0QIgcuXLyM8PLzMK3wuXryIypUrewqgkiaOEAJ//PEHwsPDcf1b5u7H6XTi8uXLnr5k+stwfewV8S+40cYE+Db3ZBqT0d8nAPjjjz8QFhbmtU3mMVXE94lj8m9MDocDt9xyCxwOR4W4ZarL5cJf//pXTJ8+3evkkxdeeAEAfKpFCgoKMH78eKxZswbR0dGYOXMmGjdujDZt2mDz5s2YO3euTyefvPLKKyWefPLTTz8hMjISwP8W99wLy0IIFBQUIC4uDtWqVcOpU6eKLe7Z7XYcOXIEADzfB+7FvaNHj3rmmBACMTExsNlsOH78uFcM7gXLtLQ0FBQUICQkBEFBQWjQoAEuXbpU4oJlTk5OiQuWWVlZJS5YlrZYXtKYoqKikJaWVuKC5bVjAv63YHn06FFP7IqilLkIK8uYyrOwrMeYfJ17Mo1JhvcpMjISFy9erFBjqojvE8d082O6ePEi7rjjDtYiEtUi7vf60qVLnu9h1iKsRViLVNz3ibUIx1TRx6R3LWLoBZ/atWvj+eefx9ixYz3bXnvtNfzzn//EkSNHkJaWhvr162Pv3r1o2bKlp80999yDli1b4p133inW56ZNm/Dcc89hx44dmDhxIoKDgzF9+nQcPHgQHTt2xPnz532KLTc3F3a7HdnZ2ahSpUqJbZxOJ44dO4akpCQEBQXddBu1+zIimWOXFXOuPeaczODChQuoWrVqhflHFhlOPvn99989tcj1i3hOpxPHjx9HUlISQkJCSlzEc7lcOHr0KOrXr+/5bCppcc/pdCI1NRVJSUme/yG+th8AKCgowPHjx5GYmIigoCCpFiyvj72iLML6sl2vMfk692Qak9HfJ5fLhdTUVNSvX99zHNnHVBHfJ47JvzFduHAB1apVYy0iUS2iKAoKCgpw7Ngxr+9h1iIV4++kL9tZi5jnfWItwjGZYUx61yKGvqXb5cuXvf7yA/BcKQMUrZzFxsZiw4YNngWf3Nxc7Ny5E48//nix/q5cuYKxY8diyZIlnjNj3G9YQUFBhXvuABEREfln4sSJeP755zF48GAAQLNmzfDrr79i2rRpGD58OGJjYwEAZ8+e9fpHlrNnz3qdjHKtTZs24eDBg5g/fz4mTpyIXr16ITw8HAMHDsS7775baiyhoaEIDQ0ttt39jxnXurZ+slgsnp+vr6vcFEUpsZ/rf1YUxdO2JO5/nLi2r9Lal7a9tBjV2l6e2EtrL9OYfN2u15h8nXs3s53vU9kxqpEDo41Jje0ck/xjqmgnVpmlFnG3udH3MGuRIjKNydftrEXM9z6xFuGYKuqY9K5FSo7YIO6//368/vrr+Prrr3HixAmsWrUKM2bM8NxfVlEUjB8/Hq+99hpWr16NX375BY888ghq1KiBPn36FOvv1VdfRa9evdCqVSsAQPv27bFy5Urs378f7777Ltq3b6/l8IiIiMjgynPyiZv75JN27doV68998sncuXM9J58UFBQA4MknREREVBxrESIiIioPQ1/h849//AMvvfQSnnjiCZw7dw41atTAY489hsmTJ3va/OUvf0FeXh4effRR5OTk4O6778a6detQqVIlr74OHDiAZcuWYd++fZ5tAwYMwObNm9GhQwc0bNgQn376abljvP4y4uv3Wa1Wv9uo3ZcRyRy7rJhz7THnZAYVbX67Tz6pU6cOmjRpgr1792LGjBkYOXIkAO+TT5KSkhAfH4+XXnqpXCefTJw4ESNGjLjpk09Yi6hD5thlxZxrjzknM6ho89sMtYiv7ViLkNqYc+0x52QGes9vQz/Dx8jcz/CpKPcFJiIiUkNF+368ePEiXnrpJaxatcpz8smQIUMwefJkWK1WAEUPEH755Zcxb948z8kn77//Pho0aODV14EDB9C3b1/s27cP4eHhAIru7ztu3DgsWbLEc/JJYmKiT7FVtFwTERGpoaJ9P7IWISIikove349c8LlJ7jcuJycHdru9xDZCCDgcDtjt9lJX9nxpo3ZfRiRz7LJizrXHnJMZOBwOREVF8X/8NcBaRF0yxy4r5lx7zDmZAWsR7ahVi/jajrUIqY051x5zTmagdy1i6Gf4yMB939zS9mVmZvrdRu2+jEjm2GXFnGuPOScz4PzWHmsRdcgcu6yYc+0x52QGnN/aM1KdIfPnnMyxy4o51x5zTmag9/zmgg8REREREREREREREZHkuOBDREREREREREREREQkOS74+Kms+00qioLw8HC/26jdlxHJHLusmHPtMedkBpzf2mMtog6ZY5cVc6495pzMgPNbe0aqM2T+nJM5dlkx59pjzskM9J7fihBC6BqBpNwPJ+SDIImIiP5Hq+/H3bt3Y9myZTh58iTy8/O99q1cuTJgxzUS1iJERETF8ftRO8w1ERFRcXp/P/IKHz/d6GGBWVlZfrdRuy8jkjl2WTHn2mPOyQy0mN9Lly7FXXfdhcOHD2PVqlUoKCjAwYMHsXHjRtjt9oAf32hYi6hD5thlxZxrjzknM+D81p6R6gyZP+dkjl1WzLn2mHMyA73nNxd8/FTWBVJCCGRlZfndRu2+jEjm2GXFnGuPOScz0GJ+v/HGG5g5cya+/PJLWK1WvPPOOzhy5AgGDhyIOnXqBPz4RsNaRB0yxy4r5lx7zDmZAee39oxUZ8j8OSdz7LJizrXHnJMZ6D2/ueBDREREUklNTcW9994LALBarcjLy4OiKHj66acxb948naMjIiIiIiIiItIHF3yIiIhIKlWqVMHFixcBADVr1sSBAwcAADk5Obh8+bKeoRERERERERER6SZY7wBkpyhKmfvsdrvfbdTuy4hkjl1WzLn2mHMyAy3md8eOHbF+/Xo0a9YMDz74IJKTk7Fx40asX78eXbt2DfjxjYa1iDpkjl1WzLn2mHMyA85v7RmpzpD5c07m2GXFnGuPOScz0Ht+K0Lvm8pJKjc3F3a7HQ6HAzabTe9wiIiIDEGL78fs7GxcuXIFNWrUgMvlwvTp0/HDDz8gKSkJkyZNQpUqVQJyXKNhLUJERFQcvx+1w1wTEREVp/f3I2/p5ieXy1XmvoyMDL/bqN2XEckcu6yYc+0x52QGWszvqlWrokaNGgAAi8WC559/HqtXr8bbb79tmsWea7EWUYfMscuKOdcec05mwPmtPSPVGTJ/zskcu6yYc+0x52QGes9vLvj4qawLpIQQcDgcfrdRuy8jkjl2WTHn2mPOyQy0nN/nzp3DgQMHsH//fq+X2bAWUYfMscuKOdcec05mwPmtPSPVGTJ/zskcu6yYc+0x52QGes9vPsOHiIiIpLJnzx4MHz4chw8fLlZIKYoCp9OpU2RERERERERERPrxacHnmWee8bnDGTNm3HQwRERERDcycuRINGjQAB999BFiYmJ0fyAiEREREREREZER+LTgs3fvXp86M+M/uJQ1ZkVREB0d7XcbtfsyIpljlxVzrj3mnMxAi/mdlpaGFStWIDExMeDHkgFrEXXIHLusmHPtMedkBpzf2jNSnSHz55zMscuKOdcec05moPf8VoTeN5WTVG5uLux2OxwOB2w2m97hEBERGYIW3499+vTBww8/jP79+wekf1mwFiEiIiqO34/aYa6JiIiK0/v70aL5ESsYl8tV5r5Tp0753UbtvoxI5thlxZxrjzknM9Bifs+fPx8ff/wxpkyZghUrVmD16tVeL7NhLaIOmWOXFXOuPeaczEDP+X3rrbciKChIt+PrxUh1hsyfczLHLivmXHvMOZmB3vPbp1u6XW/37t1YtmwZTp48ifz8fK99K1euVCUwWZR1gZQQAnl5eX63UbsvI5I5dlkx59pjzskMtJjfO3bswPbt27F27dpi+xRFgdPpDHgMRsJaRB0yxy4r5lx7zDmZgZ7ze9q0aXA4HLodXy9GqjNk/pyTOXZZMefaY87JDPSe3+W+wmfp0qW46667cPjwYaxatQoFBQU4ePAgNm7cCLvdHogYiYiIiDyefPJJDBs2DBkZGXC5XF4vsy32EBERkXH06dMHw4cP1zsMIiIiMrFyX+HzxhtvYObMmRg7diwiIyPxzjvvID4+Ho899hji4uICESMRERGRx/nz5/H0008jJiZG71CIiIjIRHJzc31qx+fZEBERkV7KveCTmpqKe++9FwBgtVqRl5cHRVHw9NNPo0uXLpgyZYrqQRqZxVL6RVIWiwWxsbF+t1G7LyOSOXZZMefaY87JDLSY3/369cOmTZtQv379gB9LBqxF1CFz7LJizrXHnJMZBHJ+R0VFQVGUUvcLIUx5e1kj1Rkyf87JHLusmHPtMedkBnrP73Iv+FSpUgUXL14EANSsWRMHDhxAs2bNkJOTg8uXL6seoNGVVewpioKoqKgb/v6N2qjdlxHJHLusmHPtMedkBmV9L6qlQYMGeOGFF7Bt2zY0a9YMISEhXvufeuqpgMdgJKxF1CFz7LJizrXHnJMZBLIW2bRpk+fPQgj06tUL8+fPR82aNQN2TBn4W4v42o61CKmNOdcec05moMW/i5Sl3As+HTt2xPr169GsWTM8+OCDSE5OxsaNG7F+/Xp07do1EDEamsvlKnPfiRMnUK9evVJX9nxpo3ZfRiRz7LJizrXHnJMZlPW9qJb58+cjIiIC33//Pb7//nuvfYqimG7Bh7WIOmSOXVbMufaYczKDQNYi99xzj9fPQUFBuPPOO5GQkBCwY8rA31rE13asRUhtzLn2mHMyAy3+XaQs5V7weffdd3HlyhUAwIsvvoiQkBD88MMP6N+/PyZNmqR6gEYnhChzX35+vt9t1O7LiGSOXVbMufaYczIDLeZ3enp6wI8hE9Yi6pA5dlkx59pjzskMOL+1Z6Q6Q+bPOZljlxVzrj3mnMxA7/ld7gWfqlWrev5ssVjw/PPPqxoQERERERERERERERERlU+5F3yCgoKQkZGB6tWre20/f/48qlevbrqHExIREZG2hBBYvnw5Nm3ahHPnzhW7XHrlypU6RUZERERmo/d9+omIiIiuVe4Fn9IuSbp69SqsVqvfAcmmrPtNWiwW1KpVy+82avdlRDLHLivmXHvMOZmBFvN7/PjxmDt3Ljp37oyYmBjT/0MLaxF1yBy7rJhz7THnZAaBnN/9+vXz+vnKlSsYM2YMwsPDvbab7eQTI9UZMn/OyRy7rJhz7THnZAZ6z2+fF3xmz54NoOjsFffDkt2cTie2bNmCRo0aqR+hwZX1j0yKonjl6WbbqN2XEckcu6yYc+0x52QGWiy+LF68GCtXrkSvXr0CfiwZsBZRh8yxy4o51x5zTmYQyFrEbrd7/Txs2LCAHUsm/tYivrZjLUJqY861x5yTGeh9UqrPCz4zZ84EUHSFz5w5cxAUFOTZZ7VaUa9ePcyZM0f9CA2urFvYOZ1OpKamon79+l75Km8btfsyIpljlxVzrj3mnMxAi1u72u12JCQkBPw4smAtog6ZY5cVc6495pzMIJC1yIIFCwLWt8z8rUV8bcdahNTGnGuPOScz0PuRNz4v+KSnpwMAOnfujJUrV6JKlSoBC6oiuf65AjfbRu2+jEjm2GXFnGuPOSfy3yuvvIIpU6bg448/RlhYmN7hGB5rEd/JHLusmHPtMedEpDWt6wyZP+dkjl1WzLn2mHOiwCr3M3w2bdrk+bP7eT56X6ZERERE5jFw4EB89tlnqF69OurVq4eQkBCv/f/5z390ioyIiIjM7P3330dWVhYmT56sdyhERERkUjf1BKFPPvkEzZo1Q1hYGMLCwtC8eXMsXrxY7dgAAGfOnMGwYcNwyy23ICwsDM2aNcPu3bs9+4UQmDx5MuLi4hAWFoZu3brh2LFjnv1Xr17Fww8/DJvNhgYNGuC7777z6v9vf/sbnnzyyYDETkREROobPnw49uzZg2HDhqF///7o3bu314uIiIhIDytWrMDChQv1DoOIiIhMrNxX+MyYMQMvvfQSxo0bh/bt2wMAtm3bhjFjxiArKwtPP/20asFduHAB7du3R+fOnbF27VpUq1YNx44d87qd3PTp0zF79mwsWrQI8fHxeOmll9CjRw8cOnQIlSpVwrx587Bnzx7s2LEDa9euxUMPPYSzZ89CURSkp6fjww8/9FpAKi+LpfQ1M4vFgvj4eL/bqN2XEckcu6yYc+0x52QGWszvr7/+Gt988w3uvvvugB9LBqxF1CFz7LJizrXHnJMZ6Dm/N2zYoNux9WSkOkPmzzmZY5cVc6495pzMQO/5Xe6j/+Mf/8AHH3yAt956Cw888AAeeOABTJ8+He+//z5mz56tanBvvfUWateujQULFuCOO+5AfHw8unfvjvr16wMourpn1qxZmDRpEnr37o3mzZvjk08+wW+//YYvvvgCAHD48GE88MADaNKkCcaOHYvff/8dWVlZAIDHH38cb731Fmw2m6pxXys4+MZrar60UbsvI5I5dlkx59pjzon8V7t27YB+d19P9quNWYv4TubYZcWca485J5KPGWoRX9uxFiG1MefaY86JAqvcCz4ZGRm46667im2/6667kJGRoUpQbqtXr8btt9+OBx98ENWrV0erVq3w4Ycfevanp6cjMzMT3bp182yz2+1o27YtduzYAQBo0aIFtm3bhj/++APffPMN4uLiEB0djSVLlqBSpUro27evT7FcvXoVubm5Xi8AKCgogNPphNPp9Dx0zOVywel0oqCgACkpKSgsLPTa7n4JIeByuXD06FGvftzPRrq2bUFBAY4ePeq17dr2QgjP8dx9AUWF37Vt3TGWtv36GMu73R17adt9jd29/fr2Mo3J1+16jcnXuSfTmIz+PhUWFuLYsWMoLCysMGOqiO8Tx+TfmAoKChBob7/9Nv7yl7/gxIkTAT+W+2rjkJAQrF27FocOHcLbb79d4tXGc+bMwc6dOxEeHo4ePXrgypUrAOB1tfGjjz6Khx56yJNL99XGr7/++k3HWNZDT10uF44dO+Z3G7X7MiKZY5cVc6495pzMQKv5fezYMcybNw+vvfYapk6d6vVSkxlqEV/bsRYhtTHn2mPOyQz0nt/lXlJNTEzEsmXL8Ne//tVr++eff46kpCTVAgOAtLQ0fPDBB3jmmWfw17/+Fbt27cJTTz0Fq9WK4cOHIzMzEwAQExPj9XsxMTGefSNHjsT+/fvRuHFjREdHY9myZbhw4QImT56MzZs3Y9KkSVi6dCnq16+Pjz/+GDVr1iwxlmnTpmHKlCklxug+y9hutyMuLg5nz56Fw+GAy+VCdnY2srOzERMTgzNnziAvL8/zu7GxsYiMjEROTg6OHz/uudyrVq1aiIiIQGpqqtc/xrlfaWlpXjEkJSWhsLAQqampyM7OxvHjxxEcHIwGDRogLy8Pp0+f9rS1Wq1ISEiAw+Hw5AgAwsPDUbt2bWRnZ3uugCppTG7R0dGIjo4ucUxRUVE4ceIE8vPzPdtLGhMAxMfHIzg4GMePH/fEbrFYPGNKT0/3tLVYLFKN6dozqq59n4wyJl/nnkxjMvr7FBkZCQA4d+4cLl68WCHGVBHfJ47JvzG5T4gIpGHDhuHy5cuoX78+KleujJCQEK/92dnZqh3r2quN3eLj4z1/vv5qY6DoWYcxMTH44osvMHjwYK+rjRMSEjBx4kRkZWWhWrVqmlxtTEREROr68MMP8fjjjyM6OhqxsbFQFMWzT1EUTJ48WbVjsRYhIiKi8lCE+7QOH61YsQKDBg1Ct27dPM/w2b59OzZs2IBly5b5fMWML6xWK26//Xb88MMPnm1PPfUUdu3ahR07duCHH35A+/bt8dtvvyEuLs7TZuDAgVAUBZ9//nmJ/Y4YMQItW7ZEfHw8/vrXv2Lnzp2YPn06Dhw4gBUrVpT4O1evXsXVq1c9P+fm5qJ27dr4/fffPWfWKIoCi8UCl8vlOdP5+PHjSEpKQkhIiGe7m7vt0aNHUb9+fQQFBXm2K4riObMbKDqbOjU1FUlJSV7FpLs9UHS10fHjx5GYmIigoCAEBQV5ruRwc8dY2vbrYyzvdnfspW2/dkxlxe7efv2KqExjuj52o43J17kn05iM/j65XC6kpqaifv36XvfzlHlMFfF94pj8G9OFCxdQrVo1OByOgP3DwaJFi8rcP3z4cNWO1bhxY/To0QOnT5/G999/j5o1a+KJJ57A6NGjARSd+FG/fn3s3bsXLVu29PzePffcg5YtW+Kdd97B3LlzsXjxYqxfvx7ffPMNnnjiCZw5cwaffvop/vWvf3luQ3sjrEVYi1S0zxnWIqxF+D5xTLLWInXr1sUTTzyB5557LiD9X8sMtYiiKCgoKMCxY8e8vodZi1SMv5O+bGctYp73ibUIx2SGMWlRi5Sl3Ff49O/fHzt37sTMmTM9RcGtt96Kn376Ca1atVI1uLi4ODRu3Nhr26233upZlImNjQUAnD171mvB5+zZs16FzrU2bdqEgwcPYv78+Zg4cSJ69eqF8PBwDBw4EO+++26psYSGhiI0NLTYdncBca1rP7AsFovn52u3X0tRlBL7uf5nRVE8bUviLgiu7au09qVtLy1GtbaXJ/bS2ss0Jl+36zUmX+fezWzn+1R2jGrkwGhjUmM7xyT/mErbpyY1F3RuhFcb82pj2a+6K+l9MsqYeLUxrzbm+8QxyXq18YULF/Dggw8G/DiAOWqRqKgonDx50ut7mLUIaxHWIhXzfWItwjGZYUxa1CJlKfcVPlp66KGHcOrUKWzdutWz7emnn8bOnTvxww8/QAiBGjVq4Nlnn8WECRMAFJ1hUr16dSxcuBCDBw/26u/KlSu47bbbsGTJErRq1QrJycmwWCyYOXMm9u3bh06dOiEnJ8en2HJzc2G325GTkwO73V5iG/cKo3vl72bbqN2XEckcu6yYc+0x52QGDocDUVFRmp3JcuXKFa/iC4Cqx5XhauPz5897apHrz05yf+64F+NKOjsJAAoLCz0nlri3X3/Wkvv33GdRXcvdj/vZUO7fl+lMrOtjryhnl/myXa8xAb7NPZnGZPT3qTQyj6kivk8ck39jcjgcuOWWWwJai4waNQpt2rTBmDFjAtL/tcxQi7jfU6fT6fU9zFqkYvyd9GU7axHzvE+lkXlMFfF94piMX4uUpdxX+AQFBSEjIwPVq1f32n7+/HlUr1692GD98fTTT+Ouu+7CG2+8gYEDB+Knn37CvHnzMG/ePABFb8j48ePx2muvISkpCfHx8XjppZdQo0YN9OnTp1h/r776Knr16uW5Eql9+/aYOHEiRowYgXfffddzizo1FRYWwmq1+t1G7b6MSObYZcWca485J/JfXl4ennvuOSxbtgznz58vtl/NWkT2q42FEJ5/PLl2+7XcBbLVai22GH1tv0II5Ofne91ipKRYnE4ngoKCPH3JdIXa9bGX1l6mMfm6XY8x+Tr3bnY736fisbj/HpeU87JiN/KY1N7OMck/Ji2uNk5MTMRLL72EH3/8Ec2aNSv2PMGnnnpKtWOZoRZxby8sLCzze5i1yP/INCZft7MWMcf7xFrkxts5JvnHpEUtUpaSIy5DaauyV69eVf0fMdu0aYNVq1bhs88+Q9OmTfHqq69i1qxZGDp0qKfNX/7yFzz55JN49NFH0aZNG1y6dAnr1q1DpUqVvPo6cOAAli1b5nX58YABA3DvvfeiQ4cO2L9/P955551yx3j9iuP1+9LT0/1uo3ZfRiRz7LJizrXHnJMZaDG///KXv2Djxo344IMPEBoaivnz52PKlCmoUaMGPvnkE1WP1b59e6SkpHhtO3r0KOrWrQug6LLt2NhYbNiwwbM/NzcXO3fuRLt27Yr1d+XKFYwdOxZz5871/E99QUEBgKL7tt/MYhVrEXXIHLusmHPtMedkBlrM73nz5iEiIgLff/893n33XcycOdPzmjVrlqrHMkMt4ms71iKkNuZce8w5mYHe89vnK3xmz54NoGjVbP78+YiIiPDsczqd2LJlCxo1aqR6gPfddx/uu+++UvcrioKpU6di6tSpZfbTtGnTYvcFtFgseP/99/H++++rEisREREF3pdffolPPvkEnTp1wogRI9ChQwckJiaibt26WLJkideJIf6qCFcbExERkbqufbZAoLEWISIiovLwecFn5syZAIqu8JkzZ47XpUlWqxX16tXDnDlz1I+QiIiI6BrZ2dlISEgAUPS8nuzsbADA3Xffjccff1zVY7mvNn7hhRcwdepUxMfHl3i1cV5eHh599FHk5OTg7rvvLvNq43379nm2DRgwAJs3b0aHDh3QsGFDfPrpp6rGT0RERHJjLUJERETl4fOCj/sMls6dO2PlypWoUqVKwIKqSEq7z19526jdlxHJHLusmHPtMedE/ktISEB6ejrq1KmDRo0aYdmyZbjjjjvw5ZdfIioqSvXjyX61MWsR38kcu6yYc+0x50TyMUMt4ms71iKkNuZce8w5UWAporSH8lCZcnNzYbfb4XA4YLPZ9A6HiIjIELT4fpw5cyaCgoLw1FNP4bvvvsP9998PIQQKCgowY8YMJCcnB+S4RsNahIiIqDh+P2qHuSYiIipO7+9Hn6/woZKVtV4mhEBeXh7Cw8OhKMpNt1G7LyOSOXZZMefaY87JDLQ4j+Tpp5/2/Llbt244cuQI9uzZg8TERDRv3jzgxzca1iLqkDl2WTHn2mPOyQx4Tqv2/K1FfG3HWoTUxpxrjzknM9C7FuE1dH5yuVxl7jt9+rTfbdTuy4hkjl1WzLn2mHMyAz3md926ddGvXz9TLvYArEXUInPssmLOtceckxlwfmvPSHWGzJ9zMscuK+Zce8w5mYHe85tX+BAREZFUZs+eXeJ2RVFQqVIlJCYmomPHjggKCtI4MiIiIjKLrVu3Yu7cuUhNTcXy5ctRs2ZNLF68GPHx8bj77rv1Do+IiIhMigs+REREJJWZM2fi999/x+XLl1GlShUAwIULF1C5cmVERETg3LlzSEhIwKZNm1C7dm2doyUiIqKKZsWKFXj44YcxdOhQ7N27F1evXgUAOBwOvPHGG1izZo3OERIREZFZ3dQt3bZu3Yphw4ahXbt2OHPmDABg8eLF2LZtm6rByaCs+00qigKr1ep3G7X7MiKZY5cVc6495pzMQIv5/cYbb6BNmzY4duwYzp8/j/Pnz+Po0aNo27Yt3nnnHZw8eRKxsbFez/qpyFiLqEPm2GXFnGuPOScz0GJ+v/baa5gzZw4+/PBDhISEeLa3b98e//nPfwJ+fKMxUp0h8+eczLHLijnXHnNOZqD3/FZEOZ8idO2ZLIsXL8ahQ4eQkJCAd999F2vWrDHNmSy5ubmw2+1wOByw2Wx6h0NERGQIWnw/1q9fHytWrEDLli29tu/duxf9+/dHWloafvjhB/Tv3x8ZGRkBicEIWIsQEREVp8X3Y+XKlXHo0CHUq1cPkZGR+Pnnn5GQkIC0tDQ0btwYV65cCchxjYa1CBERUXF6fz+W+wofnsniraz1MiEEcnJy/G6jdl9GJHPssmLOtceckxloMb8zMjJQWFhYbHthYSEyMzMBADVq1MDFixcDHosRsBZRh8yxy4o51x5zTmagxfyOjY3F8ePHi23ftm0bEhISAn58ozFSnSHz55zMscuKOdcec05moPf8LveCT0pKCjp27Fhsu91uR05OjhoxScXlcgFOJ7B5M/DZZ0X/dTo9+zIzM4valPH7N2qjdl9GJHPssmLOtceckxloMb87d+6Mxx57DHv37vVs27t3Lx5//HF06dIFAPDLL78gPj4+4LEYgb+1AWuRIjLHLivmXHvMOZmBFvN79OjRSE5Oxs6dO6EoCn777TcsWbIEzz77LB5//PGAH99ojFRnyPw5J3PssmLOtceckxnoPb+Dy/sL7jNZ6tWr57XdrGey4MsvgRdfBE6f/t+2WrWAd94BevfWLy4iIqIK6qOPPsLDDz+M1q1be642LiwsRNeuXfHRRx8BACIiIvD222/rGSYRERFVUM8//zxcLhe6du2Ky5cvo2PHjggNDcWzzz6LJ598Uu/wiIiIyMTKveDjPpPl448/9pzJsmPHDjz77LN46aWXAhGjoVmGDy++8cwZYMAAYNkyoGlT7YMiIiKqwGJjY7F+/XqkpKQgJSUFANCwYUM0bNjQ06Zz5856hUdEREQVnKIoePHFFzFx4kQcP34cly5dQuPGjREREaF3aERERGRy5V7w4ZksPhACUBRYxo9H+LZtUBSl1KaKoiA8PLzMNr6287UvI5I5dlkx59pjzskMtJzf1y/ymJW/tQFrkSIyxy4r5lx7zDmZgRbze+TIkXjnnXcQGRmJxo0be7bn5eXhySefxMcffxzwGIzESHWGzJ9zMscuK+Zce8w5mYHe81sRN/kUofz8fFOfyZKbmwu73Q4HAFtZDTdtAjp10iYoIiIinXm+Hx0O2GxlfkOSn5hrIiKi4rT4fgwKCkJGRgaqV6/utT0rKwuxsbEoLCwMyHGNhrUIERFRcXp/P1rK+wsjR47ExYsXYbVa0bhxY9xxxx2IiIhAXl4eRo4cGYgYpZZ79OgNHyiYlZXl0wMMb9TO176MSObYZcWca485JzPg/Naev7UBa5EiMscuK+Zce8w5mUEg53dubi4cDgeEELh48SJyc3M9rwsXLmDNmjXFFoHMwEh1hsyfczLHLivmXHvMOZmB3vO73As+ixYtwh9//FFs+x9//IFPPvlElaAqkpxKlVDWRVRCCGRlZZXZxtd2vvZlRDLHLivmXHvMOZkB57f2/K0NWIsUkTl2WTHn2mPOyQwCOb+joqJQtWpVKIqCBg0aoEqVKp5XdHQ0Ro4cibFjxwbs+EZlpDpD5s85mWOXFXOuPeaczEDv+e3zM3xyc3MhhPCcyVKpUiXPPqfTadozWUp9+xQFomZNXG7dWstwiIiIiIiIiCgANm3aBCEEunTpghUrVqBq1aqefVarFXXr1kWNGjV0jJCIiIjMzucFn6ioKCiK4jmT5XqKomDKlCmqBicNRQGuXbn774OZXDNnAkFBOgVFRERUcW3duhVz585Famoqli9fjpo1a2Lx4sWIj4/H3XffrXd4REREVAHdc889AID09HTUrl0bFku5b5pCREREFFA+L/jwTJaSiUWLgBdfBE6f/t/GWrWAWbOg9OkD+9mzUP67AFQSRVFgt9vLbONrO1/7MiKZY5cVc6495pzMQIv5vWLFCjz88MMYOnQo9u7di6tXrwIAHA4H3njjDaxZsybgMRiJv7UBa5EiMscuK+Zce8w5mYEW87tu3boAgMuXL+PkyZPIz8/32t+8efOAx2AkRqozZP6ckzl2WTHn2mPOyQz0nt+KKOdN5X799VeeyYKiW9zZ7XY4HA7YwsOBrVuBjAwgLg7o0IFX9hARkSl5fT/abAE5RqtWrfD000/jkUceQWRkJH7++WckJCRg7969+POf/4zMzMyAHNdotMg1ERGRbLT4fvz9998xYsQIrF27tsT9TqczIMc1GtYiRERExen9/VjuVZu6devCYrHg8uXLOHLkCPbv3+/1MhuXy1W0uNOpEzBkSNF//7vY43K5kJGRUdSmjN+/URu1+zIimWOXFXOuPeaczECL+Z2SkoKOHTsW226325GTkxPw4xuNv7UBa5EiMscuK+Zce8w5mYEW83v8+PHIycnBzp07ERYWhnXr1mHRokVISkrC6tWrA358ozFSnSHz55zMscuKOdcec05moPf8LveCz++//4777rsPkZGRaNKkCVq1auX1MpuyLpASQsDhcPjdRu2+jEjm2GXFnGuPOScz0GJ+x8bG4vjx48W2b9u2DQkJCQE/vtGwFlGHzLHLijnXHnNOZqDF/N64cSNmzJiB22+/HRaLBXXr1sWwYcMwffp0TJs2LeDHNxoj1Rkyf87JHLusmHPtMedkBnrP73Iv+PBMFiIiItLT6NGjkZycjJ07d0JRFPz2229YsmQJnn32WTz++ON6h0dEREQVXF5eHqpXrw4AqFKlCn7//XcAQLNmzfCf//xHz9CIiIjI5ILL+wsbN27Ev//9b68zWf70pz/BZrNh2rRpuPfeewMRJxEREREA4Pnnn4fL5ULXrl1x+fJldOzYEaGhoXj22Wfx5JNP6h0eERERVXANGzZESkoK6tWrhxYtWmDu3LmoV68e5syZg7i4OL3DIyIiIhMr94JPSWeyNGjQwLRnsiiKUua+6Ohov9uo3ZcRyRy7rJhz7THnZAZazG9FUfDiiy9i4sSJOH78OC5duoTGjRsjIiIi4Mc2ItYi6pA5dlkx59pjzskMtJjfycnJyMjIAAC8/PLL6NmzJ5YsWQKr1YqFCxcG/PhGY6Q6Q+bPOZljlxVzrj3mnMxA7/mtiHLeVK5NmzZ47bXX0KNHDzzwwAOIiorCtGnTMHv2bCxfvhypqamBitVQcnNzYbfb4XA4YLPZ9A6HiIjIELT4fhw5ciTeeecdREZGem3Py8vDk08+iY8//jggxzUa1iJERETF6fH9ePnyZRw5cgR16tRBdHS0Jsc0AtYiRERExen9/VjuZ/hcfybL2rVrUadOHcyePRtvvPGG6gEancvlKnPfqVOn/G6jdl9GJHPssmLOtceckxloMb8XLVqEP/74o9j2P/74A5988knAj280rEXUIXPssmLOtceckxkEen4XFBSgfv36OHz4sGdb5cqVcdttt5lqsedaRqozZP6ckzl2WTHn2mPOyQz0nt/lvqXbsGHDPH9u3bo1fv31V1OeyeJW1gVSQgjk5eX53UbtvoxI5thlxZxrjzknMwjk/M7NzYUQAkIIXLx4EZUqVfLsczqdWLNmjee2s2bCWkQdMscuK+Zce8w5mUGg53dISAiuXLkS0GPIxkh1hsyfczLHLivmXHvMOZmB3vO7XAs+BQUFaNSoEb766ivceuutAP53JgsRERFRIEVFRUFRFCiKggYNGhTbrygKpkyZokNkREREZCZjx47FW2+9hfnz5yM4uNzn0RIREREFTLkqE57JQkRERHrZtGkThBDo0qULVqxYgapVq3r2Wa1W1K1bFzVq1NAxQiIiIjKDXbt2YcOGDfj222/RrFkzhIeHe+1fuXKlTpERERGR2ZX7VBSeyeLNYin9MUgWiwWxsbF+t1G7LyOSOXZZMefaY87JDAI5v++55x4AQHp6OmrXrs2/S//FWkQdMscuK+Zce8w5mYEW8zsqKgr9+/cP+HFkYaQ6Q+bPOZljlxVzrj3mnMxA7/mtiHLeVK5v377YsGEDIiIiTH0mS25uLux2OxwOB2w2m97hEBERGYKW34+XL1/GyZMnkZ+f77W9efPmAT2uUbAWISIiKo7fj9phromIiIrT+/ux3MtN7jNZevTogRo1asBut3u9AunNN9+EoigYP368Z9uVK1cwduxY3HLLLYiIiED//v1x9uxZz/7s7Gzcf//9iIiIQKtWrbB3716vPseOHYu33377pmNyuVxl7ktLS/O7jdp9GZHMscuKOdcec05moMX8/v3333HfffchMjISTZo0QatWrbxeZsNaRB0yxy4r5lx7zDmZAee39oxUZ8j8OSdz7LJizrXHnJMZ6D2/y31PtgULFgQijhvatWsX5s6dW+ys3aeffhpff/01/vWvf8Fut2PcuHHo168ftm/fDgB4/fXXcfHiRfznP//BBx98gNGjR2P37t0AgB9//BE7d+7E7Nmzbzqusi6QEkIgPz/f7zZq92VEMscuK+Zce8w5mYEW83v8+PHIycnBzp070alTJ6xatQpnz57Fa6+95tdJHLJiLaIOmWOXFXOuPeaczIDzW3tGqjNk/pyTOXZZMefaY87JDPSe31LcMPHSpUsYOnQoPvzwQ1SpUsWz3eFw4KOPPsKMGTPQpUsXtG7dGgsWLMAPP/yAH3/8EQBw+PBhDB48GA0aNMCjjz6Kw4cPAwAKCgowZswYzJkzB0FBQbqMi4iIiMpv48aNmDFjBm6//XZYLBbUrVsXw4YNw/Tp0zFt2jS9wyMiIiIiIiIi0kW5r/DRw9ixY3HvvfeiW7dueO211zzb9+zZg4KCAnTr1s2zrVGjRqhTpw527NiBO++8Ey1atMDGjRvx//7f/8M333zjuUJo+vTp6NSpE26//XafYrh69SquXr3q+Tk3NxcA4HQ64XQ6AQCKosBiscDlckEIAafTCZfLBZfLhaCgIM92N/cDnNxtr92uKIrXNqfTCSFEsbbX9uM+nnt/UFAQhBBel5G5Yyxt+/Uxlne7O/bStvsau3v79ZfAyTSm62M32pgA3+aeTGMy+vvkbnN93zKPqSK+TxyTf2O6fl8g5OXloXr16gCAKlWq4Pfff0eDBg3QrFkz/Oc//wn48YmIiIiIiIiIjMjwCz5Lly7Ff/7zH+zatavYvszMTFitVkRFRXltj4mJQWZmJgDg+eefx+OPP4769eujXr16+Oijj3Ds2DEsWrQIO3bswJgxY/Dtt9/i9ttvx4cffljqc4imTZuGKVOmFNuenp6OrKwsAIDdbkdcXBzOnj0Lh8MBIQQKCwuRk5ODatWq4cyZM8jLy/P8bmxsrOd4qampUBQFAFCrVi1EREQgNTXV8w95QgjExMQAAI4dO+YVQ1JSEgoLC5GWlobCwkKkpqYiKCgIDRo0QF5eHk6fPu1pa7VakZCQAIfD4ckRAISHh6N27drIzs72jKekMblFR0cjOjq6xDFFRUXhxIkTXg/RLmlMABAfH4/g4GCkpqZ6YlcUxTOm9PR0T1uLxSLVmEp7n4wyJl/nnkxjkuF9qlWrFrKysirUmCri+8Qx3fyYLl68iEBr2LAhUlJSUK9ePbRo0QJz585FvXr1MGfOHMTFxQX02G+++SZeeOEFJCcnY9asWQCKnic4YcIELF26FFevXkWPHj3w/vvve763s7OzMXz4cGzatAlJSUn4+OOPvZ41NHbsWCQkJGDChAk3FZN7wa20fbVq1fK7jdp9GZHMscuKOdcec05moPX8vnLlCipVqqTZ8SpiLeJrO9YipDbmXHvMOZmB3vNbEXrfVK4Mp06dwu23347169d7rszp1KkTWrZsiVmzZuHTTz/FiBEjvK68AYA77rgDnTt3xltvvVViv126dEFycjJ+/fVXfPXVV/j6668xevRo3HLLLaXe+7+kK3zc/6hms9kA8Kxwjolj4pg4Jo6JY8rNzUXVqlXhcDg8349q++c//4nCwkL83//9H/bs2YOePXsiOzsbVqsVCxcuxKBBgwJy3F27dmHgwIGw2Wzo3Lmz5x9ZHn/8cXz99ddYuHCh53mCFovF8zzBCRMmYM+ePZg3bx4++OADbN261et5guPGjcPOnTvLfYvZ3Nxc2O32gOaaiIhINlp8P7pcLrz++uuYM2cOzp49i6NHjyIhIQEvvfQS6tWrh1GjRgXkuKxFiIiIjE/v70e/rvAJ9Jkse/bswblz53Dbbbd5tjmdTmzZsgXvvvsuvvnmG+Tn5yMnJ8frKp+zZ88iNja2xD4XLFiAqKgo9O7dG/369UOfPn0QEhKCBx98EJMnTy41ltDQUISGhpa47/qiyP0PX06nE6mpqahfv77X9mtd2+b6fq792el04tixYyW2u9b1fSmKUmL70raXtgKp1vbyxF5ae1nGVJ7teozJ17l3s9v5PhWPxel04ujRo6X+PZZxTGpv55gq5pjUNmzYMM+fW7dujV9//RVHjhxBnTp1EB0dHZBjXvs8wWtvL+t+nuCnn36KLl26ACiqNW699Vb8+OOPuPPOO4s9T3DevHkA/vc8wfnz5/uUu9JuL5ufn1/m7WXT0tJQv359hISElLiI53K5cPz4ccTHx3viKGlxz+l0Ij09HfXr1/dcGXptP+4xpaWlISEhAUFBQVItWF4fe0VZhPVlu15j8nXuyTQmo79PLpcL6enpiI+P9/qMl3lMFfF94pj8G9O1VycHymuvvYZFixZh+vTpGD16tGd706ZNMWvWrIAs+FTkWkRRFBQUFCA1NdXre5i1SMX4O+nLdtYi5nmfWItwTGYYkxa1SFnKveDjcml3JkvXrl3xyy+/eG0bMWIEGjVqhOeeew61a9dGSEgINmzYgP79+wMAUlJScPLkSbRr165Yf7///jumTp2Kbdu2ASgqFgoKCgAUfbEG4rkD10/QG7ZxOoGtW4GMDCAuDujQAfjvl065+5KMzLHLijnXHnNO5J+CggI0atQIX331FW699VYAQOXKlb1ODgkEIzxPsLTby6alpXnOGrr+9n0ulwvZ2dmoUqUKYmJiSrx9X2RkJLKzs+F0Oj1Fakm373O5XJ5XWlqaVwzuWxKmpqYiOzsbLpcLwcHBUt2S8Pjx457YLRZLhbnNYknvk1HG5Ovck2lMRn+fIiMj4XK5cO7cOa9bcMo8por4PnFM/o3JvQgRSJ988gnmzZuHrl27YsyYMZ7tLVq0wJEjRwJyzIpci0RFReHkyZPIysryfA+zFmEtwlqkYr5PrEU4JjOMSYtapCzlvqXb1KlTsWjRIkydOhWjR4/GgQMHkJCQgM8//xyzZs3Cjh07AhUrAO9bugFFly6vWbMGCxcuhM1mw5NPPgkA+OGHH4r97tChQ9GuXTuMGzcOQFFxs2zZMixevBgTJkxAfHw83nvvPZ/icF+a5S5cSuK+KicpKanUs2W82vz730ByMnDN5EWtWsA778DZu3f5+tLoLGu1yBy7rJhz7THnZAYXLlwI+C3datasie+++86z4BNoS5cuxeuvv45du3ahUqVKXrWIL7eXdTgcePzxx7F9+3bUq1cPH3zwAUJCQnDvvfdix44dePHFF316nmBpt5f9/fffPbVISWfVHj9+HElJSWVe4XP91YelnVWbmpqKpKSkMs+qPX78OBITE6U8q/ba2CvK2WW+bNfzrFpf5p5MYzL6++RyuTxXePOsWo6poo7pwoULqFatWkBrkbCwMBw5cgR169ZFZGQkfv75ZyQkJODQoUO44447cOnSJVWPV9FrEfcVPseOHfP6HmYtUjH+TvqynbWIed4n1iIckxnGpEUtUpZyX+Gjx5ksZZk5cyYsFgv69+/v9XDC633zzTc4fvw4Fi9e7Nk2btw47N69G23btsUdd9yBl19+WcvQva1aBQwcCFy//nbmDDBgALBsGdC0qT6xERERGcjYsWPx1ltvYf78+QgO9uvutDd06tQpJCcnY/369Td9G1u73Y5PP/3Ua1uXLl3wt7/9DUuWLEFaWhpSUlIwevRoTJ06tdTnCZZ2e1n3P2Zc69r/ebJYLJ6fr91+LUVRSuzn+p8VRfG0LYn7Hyeu7au09qVtLy1GtbaXJ/bS2ss0Jl+36zUmX+fezWzn+1R2jLy9LMdUUcekxYlVjRs3xtatW1G3bl2v7cuXL0erVq1UPZZZahF3mxt9D7MWKSLTmHzdzlrEfO8TaxGOqaKOSYtapCzl/leSM2fOIDExsdh2l8vluT1aIG3evNnr50qVKuG999674ZU5PXr0QI8ePby2Va5cGcuWLfMrntLedPe+6+9JWWKbW26BZfz44os9QNE2RYFl/HjEHz3q9/GMSubYZcWca485JzPQYn7v2rULGzZswLfffotmzZohPDzca//KlStVO5aRnidYGlVqER8+m9Tsy4hkjl1WzLn2mHMyAy3m9+TJkzF8+HCcOXMGLpcLK1euREpKCj755BN89dVXqh7LDLWIr+1Yi5DamHPtMedkBnrP73Iv+Gh5JktF4MuZx6HVq5fdQAgoZ84geMcO4L8PYfTneEYlc+yyYs61x5wT+S8qKsrz7L5AqwjPE/Tlc8fXzyY1+zIimWOXFXOuPeacyH+9e/fGl19+ialTpyI8PByTJ0/Gbbfdhi+//BJ/+tOfVD2WWWoRX9uxFiG1MefaY86JAqvcf8O0PJNFBtffU/D6fTd6XofL5YKvF3ll7tuH2E6dyuxL1ueDyBy7rJhz7THnZAZlfS+qZcGCBQE/hltkZCSaXndL1fDwcNxyyy2e7aNGjcIzzzyDqlWrep4n2K5dO9x5553F+hs/fjwmTJiAmjVrAgDat2+PxYsXo3v37pg3bx7at29f7hjVqEV8+WxSsy8jkjl2WTHn2mPOyQy0qEVOnz6NDh06YP369cX2/fjjjyXWADfLDLWIr+1Yi5DamHPtMedkBlrUImUp9/VF7jNZvvvuO8+ZLIcPHw7ImSxmcXLuXJ/aOatVC3AkREREVF4zZ87Efffdh/79+6Njx46IjY0t8bZy7ucJPvHEE55t48aNQ0JCAtq2bYv8/Hx9nydIREREPunevTuys7OLbd++fTt69uypeTysRYiIiMit3Ff4aHkmi1lcbt8eolYtKGfOlPwcH0WBqFkTl1u3LvrZ6QS2bgUyMoC4OKBDB4Cr4kRERJow2vMEiYiISFt33nknunfvjk2bNiEyMhIAsGXLFtx///145ZVXAn581iJERERUmnJf4WO0M1kqhKAguGbOLPqzonjv++/PrpkzixZ1Vq0C6tUDOncGHnqo6L/16gEqPqCaiIiIiIiIiEo2f/581KlTB/fffz+uXr2KTZs24d5778XUqVPx9NNP6x0eERERmZgiREmXlJRu5MiR2L9/f6lnspiluMnNzYXdbkdOTg7sdnuJbYQQcLlcsFgsUK5fyCmpzapVQHIycPr0/xrUrg3MmgXRty9cK1bAMnAglOvfsv/2Lf71L7j69CnzeEblS65IXcy59phzMgOHw4GoqCg4HA7YbDa9w6nQAlKLlPHZpGZfRiRz7LJizrXHnJMZaFWL5Ofn495778Xly5exf/9+TJs2DePGjQvY8YxIrVrE13asRUhtzLn2mHMyA73/XaTcV/jwTJbyKSws9L1Nv37AiRPApk3Ap58W/Tc9vWi70wlLcnLJt3xzb0tORuHVq+oFrzFfckXqYs61x5wTqevKlSt6h2B45apFNOzLiGSOXVbMufaYc6Kbs3//fq/XkSNH8Morr+DUqVMYNmwYOnbs6NlH3rSuM2T+nJM5dlkx59pjzokCq9wLPhaLBUuXLkVISAi6dOmCBx54ANOmTUNycnIg4jM8l8tV5r709PTytQkKAjp1AoYMKfrvf5/N4/r+eyi//YZS176FgHLmDM4uX17m8YzKl1yRuphz7THnZAZazG+Xy4VXX30VNWvWREREBNLS0gAAL730Ej766KOAH99oVK9FNOjLiGSOXVbMufaYczKDQM3vli1bolWrVmjZsqXn1bFjR5w+fRpz58717GvVqlVAjm9kRqozZP6ckzl2WTHn2mPOyQz0nt/BvjQq6QyVV155BUOGDPE6kwUAmjdvrm6EBABQMjN9ahf0++8BjoSIiEhfr732GhYtWoTp06dj9OjRnu1NmzbFrFmzMGrUKB2jIyIiooooPT1d7xCIiIiIbsinBZ+WLVtCURRc+7gf989z587FvHnzIISAoihwOp0BC9bMRGysT+2c1aoFOBIiIiJ9ffLJJ5g3bx66du2KMWPGeLa3aNECR44c0TEyIiIiqqjq1q2rdwhEREREN+TTgg/PZLl5FsuN75rnSxt06ICC2FgEnz0LpaTn+CgKRM2auNKmzU1EaQw+5YFUxZxrjzkn8t+ZM2eQmJhYbLvL5UJBQYEOERmbarWIyn0Zkcyxy4o51x5zTqSeQ4cO4eTJk8jPz/fa/sADD+gUkTFpXWfI/Dknc+yyYs61x5wTBZYiREmrB3Qjubm5sNvtcDgcsNls2hx05UpgwICiP1/7tin/fbLP8uVAv37axEJERFQCLb4fW7dujaeffhrDhg1DZGQkfv75ZyQkJGDq1KlYv349tm7dGpDjGo0utQgREZHBafH9mJaWhr59++KXX37xuhuK8t//NzfLnU9YixARERWn9/ejT1f4lIRnshQpa71MCIG8vDyEh4d7Cr+baeNp1707wv/1LyjjxwOnT/9vZ61awKxZEH37Iu/SpRv2ZUS+5oHUw5xrjzknM9DiPJLJkydj+PDhOHPmDFwuF1auXImUlBR88skn+OqrrwJ+fKPRtBZRqS8jkjl2WTHn2mPOyQy0qEWSk5MRHx+PDRs2ID4+Hj/99BPOnz+PCRMm4O9//3vAj280/tYivrZjLUJqY861x5yTGeh9fU25r6FLS0tDixYt0LRpU9x7773o06cP+vTpg759+6Jv376BiNHQXC5XmftOnz79vzZOJ7B5M/DZZ0X/dTqLt7lRX336ACdOAJs2AZ9+WvTf9HSgXz+f+zIimWOXFXOuPeaczECL+d27d298+eWX+O677xAeHo7Jkyfj8OHD+PLLL/GnP/0p4Mc3mnLVIjfZRu2+jEjm2GXFnGuPOScz0GJ+79ixA1OnTkV0dDQsFgssFgvuvvtuTJs2DU899VTAj280RqozZP6ckzl2WTHn2mPOyQz0nt/lvsKHZ7LcpJUrgeTk4lfmzJwJNG1avr6CgoBOnVQNj4iISBanT59Ghw4dsH79+mL7fvzxR9x55506REVERERm4XQ6ERkZCQCIjo7Gb7/9hoYNG6Ju3bpISUnROToiIiIys3Jf4cMzWW7CqlVFz965drEHAM6cgWXgQER8+60+cREREUmoe/fuyM7OLrZ9+/bt6Nmzpw4RERERkZk0bdoUP//8MwCgbdu2mD59OrZv346pU6ciISFB5+iIiIjIzMq94FPSmSwATHsmS1n3m1QUBaFXr8KSnAyUdO++/26LffNNKDe41EtRFFit1hse70ZtjErm2GXFnGuPOScz0GJ+33nnnejevTsuXrzo2bZlyxb06tULL7/8csCPbzT+1ga+fjaxFiG1MefaY87JDLSY35MmTfLcrmXq1KlIT09Hhw4dsGbNGsyePTvgxzcaI9UZMn/OyRy7rJhz7THnZAZ6z29FlPMpQh06dMCECRPQp08fPPTQQ7hw4QImTZqEefPmYc+ePThw4ECgYjWU3Nxc2O12OBwO2Gy20hv6+gZv2sTbtBERkfR8/n70g8vlwoABA5CdnY1vvvkGP/zwAx544AG89tprSE5ODsgxjUiLXBMREckmUN+P+/fvR9OmTWGxlHzebHZ2NqpUqaL7P/JoibUIERFRcXp/P5b7Ch+eyeKtrPWy8qylif9eKVVWXzk5OTc83o3aGJXMscuKOdcec05moMX8tlgsWLp0KUJCQtClSxc88MADmDZtmqkWe67lb23g62cTaxFSG3OuPeaczCBQ87tVq1bIysoCACQkJOD8+fNe+6tWrWqqxZ5rGanOkPlzTubYZcWca485JzPQe377tOCzf/9+zyJPjx490K9fPwBAYmIijhw5gqysLJw7dw5dunQJXKQG5SrjVmwulwsn5871rZ+YmBseJzMz84bHu1Ebo5I5dlkx59pjzskMAjW/9+/f7/U6cuQIXnnlFZw6dQrDhg1Dx44dPfvMxt/awNfPJtYipDbmXHvMOZlBoOZ3VFQU0tPTAQAnTpzg36NrGKnOkPlzTubYZcWca485JzPQe34H+9KoVatWyMjIQPXq1ZGQkIBdu3bhlltu8eyvWrVqwAKU3eX27SFq1YJy5kyJz/ERioLCmBhYOnTQIToiIiI5tGzZEoqieJ0p4/557ty5mDdvHoQQUBQFTqdTx0iJiIioIurfvz/uuecexMXFQVEU3H777QgKCiqxbVpamsbRERERERXxacHHfSZL9erVeSZLeQUFwTVzJoIGDix6ns+1iz7/vdz77AsvIK6UQtEQnE5g61YgIwOIiwM6dACMHC8REVU47jNqiYiIiPQwb9489OvXD8ePH8dTTz2F0aNHIzIyUu+wiIiIiLz4tODDM1lKV9Y9ehVFQXh4OJR+/YDly4HkZOD06f81qFULYsYMiLZtb3ivX09fvhxPzfsGr1xZYtx45x3gv7f2U0NAYqcyMefaY87JDAI1v+vWrRuQfisCf2sDXz+bdKtFNCJz7LJizrXHnJMZBHJ+9+zZEwCwZ88eJCcnc8Hnv4xUZ8j8OSdz7LJizrXHnJMZ6D2/FeHjU4TWrVvnOZNl6tSppRY2Znlgcm5uLux2OxwOB2w2m2+/JNuVMitXAgMGFL8VnXvSLl+u6qIPERHJ76a+H2/SoUOHcPLkSeTn53ttf+CBBwJ6XKPQMtdERESy4PejdphrIiKi4vT+fvTpCh+AZ7KU5kYPC8zOzkbVqlVhsViKFnc6dSq7ja99ldbm1Kkb9uUTpxN46qkSnzsEIYoWfZKTgW7dVFm0crlcyL56VZ3YySe+zj1SD3NOZqDFbV/T0tLQt29f/PLLL17P9XGfRWO2Z/iUqxa5yTZq92VEMscuK+Zce8w5mQFvQa89f2sRX9uxFiG1MefaY87JDPSuRcr9N2vBggVc7LlGWRdICSGQlZXld5vy9BVdrx4sNhsQEeHfy24HzpwpK6Ci27zZ7f4fKyICFpvNpzyQenyde6Qe5pzMQIv5nZycjPj4eJw7dw6VK1fGwYMHsWXLFtx+++3YvHlzwI9vNEarRWT9nJM5dlkx59pjzskMOL+1Z6Q6Q+bPOZljlxVzrj3mnMxA7/nt8xU+REREREawY8cObNy4EdHR0bBYLLBYLLj77rsxbdo0PPXUU9i7d6/eIRIRERERERERaY4LPhVMyp49SExMRJC/t1nbsgXo1evG7dasATp29O9Y+O/td377ze9+iIio4nM6nZ6rjaOjo/Hbb7+hYcOGqFu3LlJSUnSOjoiIiIiIiIhIH1zw8ZP7eQGl7bPb7X63KU9ftrg4KBERgL/3wezeHahVq+i2biVdhqYoRfu7d1flGT6Ky+VTHkg9vs49Ug9zTmagxfxu2rQpfv75Z8THx6Nt27aYPn06rFYr5s2bh4SEhIAf32iMVovI+jknc+yyYs61x5yTGXB+a89IdYbMn3Myxy4r5lx7zDmZgd7zWxF631ROUrm5ubDb7XA4HLDZbHqHExgrVwIDBhT9+dpp4p60y5cD/fppHxcRERmWFt+P33zzDfLy8tCvXz8cP34c9913H44ePYpbbrkFn3/+Obp06RKQ4xqNKWoRIiKicuL3o3aYayIiouL0/n708zIQcrlcZe7LyMjwu43affmsX7+iRZ2aNb2316ql+mKP6rHTDTHn2mPOyQwCNb/379/v6btHjx7o99/voMTERBw5cgRZWVk4d+6caRZ7rlWhaxENyRy7rJhz7THnZAac39ozUp0h8+eczLHLijnXHnNOZqD3/OaCj5/KukBKCAGHw+F3G7X7Kpd+/YATJ4BNm4BPPy36b3q66lf2BCR2KhNzrj3mnMwgUPO7VatWyMrKAgAkJCTg/PnzXvurVq2q+2XTeqnwtYhGZI5dVsy59phzMgPOb+0Zqc6Q+XNO5thlxZxrjzknM9B7fvMZPnRjQUFAp056R0FERCYWFRWF9PR0VK9eHSdOnND9jBkiIiIilOdc8wAArJJJREFUIiIiIqPhgg8REREZXv/+/XHPPfcgLi4OiqLg9ttvR1BQUIlt09LSNI6OiIiIiIiIiEh/XPDxU1m3j1EUBdHR0X63UbsvI5I5dlkx59pjzskMAjW/582bh379+uH48eN46qmnMHr0aERGRgbkWLJhLaIOmWOXFXOuPeaczIDzW3tGqjNk/pyTOXZZMefaY87JDPSe34rQ+6ZyksrNzYXdbofD4YDNZtM7HCIiIkPQ4vtxxIgRmD17tukXfFiLEBERFcfvR+0w10RERMXp/f1o0fyIFUxZzxBwuVw4deqU323U7suIZI5dVsy59phzMgMt5veCBQtMv9hzLdYi6pA5dlkx59pjzskMOL+1Z6Q6Q+bPOZljlxVzrj3mnMxA7/nNBR8/lXWBlBACeXl5frdRuy8jkjl2WTHn2mPOyQw4v7XHWkQdMscuK+Zce8w5mQHnt/aMVGfI/Dknc+yyYs61x5yTGeg9v7ngQ0REREREREREREREJDku+BAREREREREREREREUkuWO8AZGexlL5mZrFYEBsb63cbtfsyIpljl5WUOXc6ga1bgYwMIC4O6NABCArSOyqfSZlzonLi/NYeaxF1yBy7rJhz7THnZAac39ozUp0h8+eczLHLijnXHnNOZqD3/FaE3jeVk1Rubi7sdjscDgdsNpve4RBVfCtXAsnJwOnT/9tWqxbwzjtAv376xUVEXvj9qB3mmoiIqDh+P2qHuSYiIipO7+9HLqf6yeVylbkvLS3N7zZq92VEMscuK6lyvnIlMGCA92IPAJw5U7R95Up94ionqXJOdJM4v7XHWkQdMscuK+Zce8w5mQHnt/aMVGfI/Dknc+yyYs61x5yTGeg9v3lLNz+VdYGUEAL5+fl+t1G7LyOSOXZZCSFQkJMDcemSsW+L5nQCTz0FlDQ3hAAUpejKn27djD0OAMLp5DynCo/zW3usRdQhc+yyYs61x5yTGXB+a89IdYbMn3Myxy4r5lx7zDmZgd7zmws+RCbWsHVrvUPwnxBFV/7Y7XpHckNBAHD4sN5hEBERERERERERUQXEW7oRERERERERERERERFJjlf4+MliKX3NzGKxoFatWn63UbsvI5I5dllZLBZcysxEeHg4FEXRO5zSbdkC9Op143Zr1gAdOwY+Hj8IIVALZX9uEMmO81t7rEXUIXPssmLOtceckxlwfmvPSHWGzJ9zMscuK+Zce8w5mYHe85sLPn4q6x/KFUVBRETEDX//Rm3U7suIZI5dVoqiICImRu8wbqx7d6BWLeDMmZKf46MoRfu7dzf8M3wUAJzlVNEZegG5gmItog6ZY5cVc6495pzMgLWI9vytRXxtx1qE1Maca485JzPQuxbhcqqfnE5nmfuOHj3qdxu1+zIimWOXlTQ5DwoC3nmn6M/Xf2C6f541y/CLPYBEOSfyA+e39liLqEPm2GXFnGuPOScz4PzWnpHqDJk/52SOXVbMufaYczIDvec3F3wCzOVyqdJG7b6MSObYZSVNzvv1A5YvB2rW9N5eq1bR9n799InrJkiTcyKqMFiL+E7m2GXFnGuPOScirWldZ8j8OSdz7LJizrXHnBMFFm/pRkRy6NcP6N0b2LoVyMgA4uKADh2kuLKHiIiIiIiIiIiIKNC44ENE8ggKAjp10jsKIiIiIiIiIiIiIsNRhCjpKeh0I7m5ubDb7cjJyYHdbi+xjRAC+fn5sFqtpT6syZc2avdlRDLHLivmXHvMOZmBw+FAVFQUHA4HbDab3uFUaKxF1CVz7LJizrXHnJMZsBbRjlq1iK/tWIuQ2phz7THnZAZ61yJ8hk+ABQff+CIqX9qo3ZcRyRy7rJhz7THnRKQ11iK+kzl2WTHn2mPOiUhrWtcZMn/OyRy7rJhz7THnRIHFBR8/lfWgMZfLhWPHjvndRu2+jEjm2GXFnGuPOScz4PzWHmsRdcgcu6yYc+0x52QGnN/aM1KdIfPnnMyxy4o51x5zTmag9/zmgg8REREREREREREREZHkuOBDREREREREREREREQkOS74EBERERERERERERERSU4RQgi9g5BRbm4u7HY7cnJyYLfbS2wjhIDL5YLFYoGiKDfdRu2+jEjK2J1OYOtWICMDiIsDOnQAgoL0jspnUuZccsw5mYHD4UBUVBQcDgdsNpve4VRorEXUJXPssmLOtceckxmwFtGOWrWIr+1Yi5DamHPtMedkBnrXIrzCJ8AKCwtVaaN2X0YkVewrVwL16gGdOwMPPVT033r1irZLRKqcVxDMORFpjbWI72SOXVbMufaYcyLSmtZ1hsyfczLHLivmXHvMOVFgccHHTy6Xq8x96enpfrdRuy8jkir2lSuBAQOA06e9t585U7RdkkUfqXJeQTDnZAac39pjLaIOmWOXFXOuPeaczIDzW3tGqjNk/pyTOXZZMefaY87JDPSe38G6Hp3oGsrly0BenrFvi+Z0Ak89BZR0J0QhAEUBkpOBbt2MPQ6gaCxEREREREREREREVCFwwYcMo2Hr1nqH4D8hiq78KeX+xUYSBACHD+sdBhERERERERERERGpgLd0CzCL5cYp9qWN2n0RAZwvemDOiUhrrEV8J3PssmLOtcecE5HWtK4zZP6ckzl2WTHn2mPOiQJLEaKke1PRjeTm5sJut8PhcMBms+kdTsWQl6d3BDe2ZQvQq9eN261ZA3TsGPh4/BUerncERFTB8PtRO8w1ERFRcfx+1A5zTUREVJze34+8pZufylovE0IgLy8P4eHhUBTlptuo3ZcRCSGQJ4TxY+/eHahVCzhzpuTn+ChK0f7u3Q3/DB8hBPIuXTJ+zisQmf+OEvmK55Foj7WIOmSOXVbMufaYczID1iLa87cW8bUdaxFSG3OuPeaczEDvWoTX0PnJ5XKVue/06dN+t1G7LyOSJvagIOCdd4r+fP0Xk/vnWbMMv9gDSJTzCoQ5JzPg/NYeaxF1yBy7rJhz7THnZAac39ozUp0h8+eczLHLijnXHnNOZqD3/OaCD1F59esHLF8O1Kzpvb1WraLt/frpExcRERERERERERERmRZv6UZ0M/r1A3r3BrZuBTIygLg4oEMHKa7sISIiIiIiIiIiIqKKhws+firrfpOKosBqtfrdRu2+jEjK2IOCgE6d9I7ipkmZc8kx52QGnN/aYy2iDpljlxVzrj3mnMyA81t7RqozZP6ckzl2WTHn2mPOyQz0nt+K0PspQpLKzc2F3W6Hw+GAzWbTOxwiIiJD4PejdphrIiKi4vj9qB3mmoiIqDi9vx/5DB8/lbVeJoRATk6O323U7suIZI5dVsy59phzMgPOb+2xFlGHzLHLijnXHnNOZsD5rT0j1Rkyf87JHLusmHPtMedkBnrPby74+MnlcpW5LzMz0+82avdlRDLHLivmXHvMOZkB57f2WIuoQ+bYZcWca485JzPg/NaekeoMmT/nZI5dVsy59phzMgO95zcXfIiIiIiIiIiIiIiIiCTHBR8iIiIiIiIiIiIiIiLJccHHT4qilLkvPDzc7zZq92VEMscuK+Zce8w5mQHnt/ZYi6hD5thlxZxrjzknM+D81p6R6gyZP+dkjl1WzLn2mHMyA73ntyL0foqQpHJzc2G32+FwOGCz2fQOh4iIrud0Alu3AhkZQFwc0KEDEBSkd1QVHr8ftcNcExERFcfvR+0w10RERMXp/f3IK3z8dKOHBWZlZfndRu2+jEjm2GXFnGuPOdfQypVAvXpA587AQw8V/bdevaLtFFCc39pjLaIOmWOXFXOuPeaczIDzW3tGqjNk/pyTOXZZMefaY87JDPSe31zw8VNZF0gJIZCVleV3G7X7MiKZY5cVc6495lwjK1cCAwYAp097bz9zpmg7F30CivNbe6xF1CFz7LJizrXHnJMZcH5rz0h1hsyfczLHLivmXHvMOZmB3vM7WNejExGZjHL5MpCXx1uLBYrTCTz1FFDSl6sQgKIAyclAt258DwIlL0/vCIiIiIiIiIiITIkLPkREGmrYurXeIZibEEVX/tjtekdSYXEZjYiIiIiIiIhIH7ylm58URSlzn91u97uN2n0Zkcyxy4o51x5zTUSBwFpEHTLHLivmXHvMOZkB57f2jFRnyPw5J3PssmLOtceckxnoPb8VofdN5SSVm5sLu90Oh8MBm82mdzhEJAve7iqwtmwBevW6cbs1a4COHQMfjwnl5ubCXqMGvx81wFqEiIioOH4/aoe5JiIiKk7v70fe0s1PLperzH1nz55FTEwMLJaSL6bypY3afRmRzLHLijnXnsvlwtncXOY8kLp3B2rVAs6cKfk5PopStL97dz7DJ0BcBQV6h2A6rEXUIXPssmLOtceckxmU9b1IgeFvLeJrO9YipDbmXHvMOZmB3rUI/2b5qawLpIQQcDgcfrdRuy8jkjl2WTHn2mPONRAUBLzzTtGfr7+E1v3zrFlc7Akgzm/tsRZRh8yxy4o51x5zTmbA+a09I9UZMn/OyRy7rJhz7THnZAZ6z28u+BARUcXSrx+wfDlQs6b39lq1irb366dPXERERERERERERAHEW7rdJPdKXW5uLoJKOVPc6XTi0qVLfrdRuy8jkjl2WTHn2mPONdStG7B/P/DDD0BmJhAbC9x1V9GVPbm5ekdXoeX+N796n9FiBqxF1CVz7LJizrXHnJMZsBbRjlq1iK/tWIuQ2phz7THnZAZ61yJc8LlJ58+fBwDUq1dP30CIiIgM6Pz587Db7XqHUaGxFiEiIioda5HAYy1CRERUOr1qES743KSqVasCAE6ePFnqG5ebm4vatWvj1KlTsNlsN91G7b6MSObYZcWca485JzNwOByoU6eO53uSAoe1iLpkjl1WzLn2mHMyA9Yi2lGrFvG1HWsRUhtzrj3mnMxA71qECz43yWIpevyR3W6/4QeUzWZTpY3afRmRzLHLijnXHnNOZuD+nqTAYS0SGDLHLivmXHvMOZkBa5HAU7sW8bUdaxFSG3OuPeaczECvWoQVEBERERERERERERERkeS44ENERERERERERERERCQ5LvjcpNDQULz88ssIDQ0NeBu1+zIimWOXFXOuPeaczIDzXDusRdQlc+yyYs61x5yTGXCea8eIdYbM77/MscuKOdcec05moPc8V4QQQpcjExERERERERERERERkSp4hQ8REREREREREREREZHkuOBDREREREREREREREQkOS74EBERERERERERERERSY4LPkRERERERERERERERJIz9YLPli1bcP/996NGjRpQFAVffPGF134hBLp06YKQkBAoigKr1Ypu3bohJSXF0yY7Oxu33347FEXxvKpWrYpPP/3U02bXrl2oXr26Z39wcDDuvPNOKIqC8ePHAwBOnjyJO+64w6sf96tRo0YAgM2bN6Nx48Ze+xo0aIAzZ854xf3555/DbrdDURQEBQXhz3/+MwoLC73abN68GbfddhtCQ0ORmJiIhQsXqp67yZMnIy4uDmFhYejWrRuOHTvm1SY7OxtDhw6FzWZDVFQURo0ahUuXLnm12b9/Pzp06IBKlSqhdu3amD59erFY/vWvf6FRo0aoVKkSmjVrhjVr1pRrLLKYNm0a2rRpg8jISFSvXh19+vTxmosAcOXKFYwdOxa33HILIiIi0L9/f5w9e9arzcmTJ3HvvfeicuXKqF69OiZOnHhT8+O9995DvXr1UKlSJbRt2xY//fST6mPW2wcffIDmzZvDZrPBZrOhXbt2WLt2rWc/800VwSuvvFLq9w7AeR5orEVYi8iEtYj2WIuQGbAW0RdrEdYiMmEtoj3WImQGFa4WESa2Zs0a8eKLL4qVK1cKAGLVqlVe+998800RHBwsnnzySbFixQpxzz33iLCwMFG7dm1x6dIlIYQQPXv2FJGRkSIqKkq8+OKLIiYmRoSFhQlFUcSBAweEw+Hw/HzrrbeKcePGCQACgKhevbpITk4WhYWFokGDBgKACA4OFrfccouoVKmSACAaNGggfv/9d5GWlibCwsJEaGiosFqtYvDgwUJRFBEUFCRq1qzpifnYsWNCURRRp04dsWLFCjFmzBgBQAwaNMjTJi0tTVSuXFk888wz4tChQ+If//iHCAoKEuvWrVM1d3a7XXzxxRfi559/Fg888ICIj48Xf/zxh6dNz549RYsWLcSPP/4otm7dKhITE8WQIUM8+x0Oh4iJiRFDhw4VBw4cEJ999pkICwsTc+fO9bTZvn27CAoKEtOnTxeHDh0SkyZNEiEhIeKXX37xeSyy6NGjh1iwYIE4cOCA2Ldvn+jVq5eoU6eOZy4KIcSYMWNE7dq1xYYNG8Tu3bvFnXfeKe666y7P/sLCQtG0aVPRrVs3sXfvXrFmzRoRHR0tXnjhBU8bX+bH0qVLhdVqFR9//LE4ePCgGD16tIiKihJnz57VJhkaWb16tfj666/F0aNHRUpKivjrX/8qQkJCxIEDB4QQzDdVDC+//LJo0qSJyMjI8Lx+//13z37O88BiLcJaRCasRbTHWoTMgLWIvliLsBaRCWsR7bEWITOoaLWIqRd8rnX9l7PL5RKxsbHib3/7m2dbTk6OsFqtAoD4/vvvxaFDhzzFyL/+9S8hhBBr1671FC4vvPCCePvttwUAoSiKyMzMFBcvXhQ2m00AEBaLRYwbN06sWbPG8zsPPvigaNGihfjggw9EcHCwACB27Ngh/vKXv4hatWoJAOLDDz8UQggxaNAgUb9+fQFAbNmyRQghxIABAwQAkZmZ6Ym7devWIjg4WFy9elUIIcRf/vIX0aRJE6/xDxo0SPTo0SOguQsNDRWfffaZEEJ4crdr1y5Pm7Vr1wpFUcSZM2eEEEK8//77okqVKp64hRDiueeeEw0bNvT8PHDgQHHvvfd6xdO2bVvx2GOP3dRYZHLu3DnPXBSiKMchISGeuSiEEIcPH/bMISGKClKLxeI1Pz744ANhs9nKNT/uuOMOMXbsWM/PTqdT1KhRQ0ybNk39gRpMlSpVxPz585lvqjBefvll0aJFixL3cZ5ri7UIaxHZsBbRB2sRqmhYixgHaxHWIrJhLaIP1iJU0VS0WsTUt3QrS3p6OjIzM9GtWzfPNrvdjhYtWgAAqlatih07diA8PByFhYWedp07d4aiKAAAp9PpucyxSZMmiImJwdixY/GnP/0JAOByuXD+/Hns2LEDlSpVAgAkJCTg2LFjePnllz2Xfa1ZswY7duxAREQEAGDAgAEAgB49enguH3NfOrxz505ER0cjJibGE/fAgQNRWFiIgwcPAgB27NjhNS53Xzt27FAjdaXmrm3btp5j7NixA1FRUbj99ts9bbp16waLxYKdO3d62nTs2BFWq9UrzpSUFFy4cEGTsRiZw+EAUDQXAWDPnj0oKCjwykejRo1Qp04dr7w3a9bMa3706NEDubm5Ps+P/Px87Nmzx6uNxWJBt27dKnTenU4nli5diry8PLRr1475pgrl2LFjqFGjBhISEjB06FCcPHkSAD9X9MZa5OaxFtEGaxFtsRahioy1iDGxFrl5rEW0wVpEW6xFqCKrSLUIF3xKkZmZCQBeb5TL5cLp06cRHR2Npk2bIjMzE5GRkbBarTh16hQiIiIQHh4OAKhevbqnH0VRUKNGDSxduhT/+c9/8OKLL3r6vHz5MjIzM+FyuRAUFIR77rkHCxcuxOrVqz1tZs2ahd9++w35+flQFAVRUVGe2Nz3dnVPQofDgSpVqniNJTExEQDw66+/emK6dlzuvnJzc/HHH3/4lziUnDv3z+59mZmZnhy5BQcHo2rVql5tSurj2mOU1sa9v6JyuVwYP3482rdvj6ZNmwIoyoXVavXMD7fr836zOXXPj6ysLDidTtPk/ZdffkFERARCQ0MxZswYrFq1Co0bN2a+qcJo27YtFi5ciHXr1uGDDz5Aeno6OnTogIsXL3Ke64y1yM1jLRJ4rEW0w1qEKjrWIsbFWuTmsRYJPNYi2mEtQhVdRatFgsvV2uTGjh0Lh8OBLl26FNvXsGFD7Nu3Dw6HAx06dEBWVhaysrI8+69cuYLk5GSsX78ewcGlp/3Pf/4zgKKCx+3q1au4ePGi50wWorFjx+LAgQPYtm2b3qFUeNf+3V6+fDmGDx+O77//Xu+wiFTj/t4BgObNm6Nt27aoW7culi1bhrCwMB0jo5KwFiGjYC2iHdYiVNGxFpELaxEyCtYi2mEtQhVdRatFeIVPKWJjYwHAc2nwuHHj8NVXX6FJkyZISEjwtLl48SLy8/Nx+fJlJCYmokWLFsjPz0dQUBAOHTqE2NhYCCFw4sQJnDt3DrfddhuaNWvmOc7q1asxf/58WCwWOJ1O5OTkeB1XURTExMTAarXCarVCCOHVxl3s1KlTB0DRJcLuy3rdjh8/DgCoW7euJ253/25nz56FzWZTZRJfn7trj+HeFxsbi3PnznntLywsRHZ2tlebkvq49hiltXHvr4jcc3HTpk2oVauWZ3tsbCzy8/M988Pt+rzfbE7d8yM6OhpBQUGmybvVakViYiJat26NadOmoUWLFnjnnXeYb6qwoqKi0KBBAxw/fpzzXGesRdTL3bXHYC3iP9Yi2mItQmbDWsQ4WIuol7trj8FaxH+sRbTFWoTMRvZahAs+pYiPj0dsbCy+++47jBs3DqtWrcLq1auxf/9+tGvXDgDQrl075OXlITg4GBs2bAAAbNy4EU6nEwUFBahatapnhfDkyZPYvHkz9u3bh379+gEoug/f4MGD8d577+HKlSsA4OnHfcaLu5Bp2LAh8vLyAAArV670tHFf5tW7d28ARZegZWVleRUNy5cvR3BwMBo3buyJ230ct/Xr13vGpVburj1Gbm4udu7c6ZW7nJwc7Nmzx9Nm48aNcLlcaNu2rafNli1bUFBQ4BVnw4YNPZdnB3osRiKE8MzFjRs3Ij4+3mt/69atERIS4pWPlJQUnDx50ivvv/zyi9f8WL9+PWw2m8/zw2q1onXr1l5tXC4XNmzYUCHzfj2Xy4WrV68y31RhXbp0CampqYiLi+M81xlrEf9zx1pEXaxFjIG1CFV0rEWMg7WI/7ljLaIu1iLGwFqEKjrpaxFhYhcvXhR79+4Ve/fuFQDEjBkzxN69e8Wvv/4qhBDizTffFFarVYSHh4uPP/5Y9OjRQ9SuXVukp6eLy5cvCyGE6NmzpwgLCxNVq1YVL7/8soiJiRFhYWECgPj2229FTk6OCAsLE4qiiCZNmognn3xSKIoiLBaLqFOnjkhOThaFhYWiQYMGAoAIDg4WAwcOFKGhoQKACA8PF9HR0WLXrl0iLCxMVKpUSYSGhoqHHnpIKIoigoKCRM2aNT1jOnbsmFAURdSrV0+sWrVKjB07VgAQgwYN8rRJS0sTlStXFhMnThSHDx8W7733nggKChLr1q1TNXdRUVHi3//+t9i/f7/o3bu3iI+PF3/88Yenj549e4pWrVqJnTt3im3btomkpCQxZMgQz/6cnBwRExMjHn74YXHgwAGxdOlSUblyZTF37lxPm+3bt4vg4GDx97//XRw+fFi8/PLLIiQkRPzyyy/lnA3G9/jjjwu73S42b94sMjIyPC/3XBRCiDFjxog6deqIjRs3it27d4t27dqJdu3aefYXFhaKpk2biu7du4t9+/aJdevWiWrVqokXXnjB08aX+bF06VIRGhoqFi5cKA4dOiQeffRRERUVJTIzM7VJhkaef/558f3334v09HSxf/9+8fzzzwtFUcS3334rhGC+qWKYMGGC2Lx5s0hPTxfbt28X3bp1E9HR0eLcuXNCCM7zQGMtwlpEJqxFtMdahMyAtYi+WIuwFpEJaxHtsRYhM6hotYipF3w2bdokABR7DR8+XAghhMvlKnE/ALFgwQIhhBDnz58X8fHxXvuio6PF0qVLPcf56aefRLVq1Tz7g4KCxJgxY0THjh1FcnKyEEKIEydOiDZt2nj1ExISInr37i2OHz/uibdRo0ZebRITE8WpU6e8xvXZZ5+JyMhIAUBYLBbRo0cPUVBQUGzsLVu2FFarVSQkJHjGo2buXnrpJRETEyNCQ0NF165dRUpKilcf58+fF0OGDBERERHCZrOJESNGiIsXL3q1+fnnn8Xdd98tQkNDRc2aNcWbb75ZLJZly5aJBg0aCKvVKpo0aSK+/vrrco1FFjeai0II8ccff4gnnnhCVKlSRVSuXFn07dtXZGRkePVz4sQJ8ec//1mEhYWJ6OhoMWHChJuaH//4xz9EnTp1hNVqFXfccYf48ccfAzFsXY0cOVLUrVtXWK1WUa1aNdG1a1dPUSME800Vw6BBg0RcXJywWq2iZs2aYtCgQZ7vHSE4zwONtQhrEZmwFtEeaxEyA9Yi+mItwlpEJqxFtMdahMygotUiihBCgIiIiIiIiIiIiIiIiKTFZ/gQERERERERERERERFJjgs+REREREREREREREREkuOCDxERERERERERERERkeS44ENERERERERERERERCQ5LvgQERERERERERERERFJjgs+REREREREREREREREkuOCDxERERERERERERERkeS44ENERERERERERERERCQ5LvgQUYXXqVMnKIoCRVGwb98+3eJYuHChJ47x48frFgcRERFpi7UIERER6Ym1CJF5cMGHiLz83//9n+fL99pXz5499Q7NL6NHj0ZGRgaaNm3qtX3FihXo0qULqlSpgrCwMDRs2BAjR47E3r17feo3Pz8f0dHRePPNN0vc/+qrryImJgYFBQUYNGgQMjIy0K5dO7/HQ0REVFGxFmEtQkREpCfWIqxFiGTGBR8iKqZnz57IyMjwen322WcBPWZ+fn5A+69cuTJiY2MRHBzs2fbcc89h0KBBaNmyJVavXo2UlBR8+umnSEhIwAsvvOBTv1arFcOGDcOCBQuK7RNCYOHChXjkkUcQEhKCsLAwxMbGwmq1qjYuIiKiioi1CGsRIiIiPbEWYS1CJCsu+BBRMaGhoYiNjfV6ValSxbNfURTMnz8fffv2ReXKlZGUlITVq1d79XHgwAH8+c9/RkREBGJiYvDwww8jKyvLs79Tp04YN24cxo8fj+joaPTo0QMAsHr1aiQlJaFSpUro3LkzFi1aBEVRkJOTg7y8PNhsNixfvtzrWF988QXCw8Nx8eJFn8f4448/Yvr06ZgxYwZmzJiBDh06oE6dOmjdujUmTZqEtWvXerX/97//jdtuuw2VKlVCQkICpkyZgsLCQgDAqFGjcPToUWzbts3rd77//nukpaVh1KhRPsdFRERErEVYixAREemLtQhrESJZccGHiG7KlClTMHDgQOzfvx+9evXC0KFDkZ2dDQDIyclBly5d0KpVK+zevRvr1q3D2bNnMXDgQK8+Fi1aBKvViu3bt2POnDlIT0/HgAED0KdPH/z888947LHH8OKLL3rah4eHY/DgwcXOGlmwYAEGDBiAyMhIn+P/7LPPEBERgSeeeKLE/YqieP68detWPPLII0hOTsahQ4cwd+5cLFy4EK+//joAoFmzZmjTpg0+/vjjYnHdddddaNSokc9xERERkW9Yi7AWISIi0hNrEdYiRIYkiIiuMXz4cBEUFCTCw8O9Xq+//rqnDQAxadIkz8+XLl0SAMTatWuFEEK8+uqronv37l79njp1SgAQKSkpQggh7rnnHtGqVSuvNs8995xo2rSp17YXX3xRABAXLlwQQgixc+dOERQUJH777TchhBBnz54VwcHBYvPmzaWO6Z577hHJycle23r27CmaN2/ute3tt9/2GnNOTo4QQoiuXbuKN954w6vt4sWLRVxcnOfnOXPmiIiICHHx4kUhhBC5ubmicuXKYv78+T7FQ0REREVYi7AWISIi0hNrEdYiRDLjFT5EVEznzp2xb98+r9eYMWO82jRv3tzz5/DwcNhsNpw7dw4A8PPPP2PTpk2IiIjwvNxnc6Smpnp+r3Xr1l59pqSkoE2bNl7b7rjjjmI/N2nSBIsWLQIA/POf/0TdunXRsWNHP0cNjBw5Evv27cPcuXORl5cHIYRnPFOnTvUaj/thh5cvXwYADBkyBE6nE8uWLQMAfP7557BYLBg0aJDfcREREZkNaxHWIkRERHpiLcJahEhWwTduQkRmEx4ejsTExDLbhISEeP2sKApcLhcA4NKlS7j//vvx1ltvFfu9uLg4r+PcjP/3//4f3nvvPTz//PNYsGABRowY4XWpsS+SkpKwbds2FBQUeMYSFRWFqKgonD592qvtpUuXMGXKFPTr169YP5UqVQIA2Gw2DBgwAAsWLMDIkSOxYMECDBw4EBERETc1RiIiIjNjLcJahIiISE+sRViLEMmKV/gQkepuu+02HDx4EPXq1UNiYqLXq6xipmHDhti9e7fXtl27dhVrN2zYMPz666+YPXs2Dh06hOHDh5c7xiFDhuDSpUt4//33fRpPSkpKsbEkJibCYvnfx+ioUaOwbds2fPXVV/jhhx/4UEIiIiKdsBZhLUJERKQn1iKsRYj0wgUfIirm6tWryMzM9HplZWX5/Ptjx45FdnY2hgwZgl27diE1NRXffPMNRowYAafTWervPfbYYzhy5Aiee+45HD16FMuWLcPChQsBeD8ssEqVKujXrx8mTpyI7t27o1atWuUeY7t27TBhwgRMmDABzzzzDLZt24Zff/0VP/74Iz766CMoiuIpWiZPnoxPPvkEU6ZMwcGDB3H48GEsXboUkyZN8uqzY8eOSExMxCOPPIJGjRrhrrvuKndcRERExFqEtQgREZG+WIuwFiGSFRd8iKiYdevWIS4uzut19913+/z7NWrUwPbt2+F0OtG9e3c0a9YM48ePR1RUlNeZH9eLj4/H8uXLsXLlSjRv3hwffPABXnzxRQBAaGioV9tRo0YhPz8fI0eOvLlBAvj73/+OTz/9FHv37sV9992HpKQkPPjgg3C5XNixYwdsNhsAoEePHvjqq6/w7bffok2bNrjzzjsxc+ZM1K1b16s/RVEwcuRIXLhwwa+4iIiIzI61CGsRIiIiPbEWYS1CJCtFuJ++RURkQK+//jrmzJmDU6dOeW1fvHgxnn76afz222+wWq1l9tGpUye0bNkSs2bNCmCkvjNaPERERFQ61iJERESkJ9YiRFQeul7hs2XLFtx///2oUaMGFEXBF1984bVfCIHJkycjLi4OYWFh6NatG44dO+bVJjs7G0OHDoXNZkNUVBRGjRqFS5cuefafOHECHTt2RHh4ODp27IgTJ054/f59992HFStWBGqIRFRO77//Pnbt2oW0tDQsXrwYf/vb37zuRXv58mWkpqbizTffxGOPPXbDoubafiMiIvDLL78EKvQbWrJkCSIiIrB161bdYiAib6xFiOh6rEWISEusRYjoeqxFiMgfui745OXloUWLFnjvvfdK3D99+nTMnj0bc+bMwc6dOxEeHo4ePXrgypUrnjZDhw7FwYMHsX79enz11VfYsmULHn30Uc/+CRMmoGbNmti3bx/i4uLw7LPPevZ9/vnnsFgs6N+/f+AGSUTlcuzYMfTu3RuNGzfGq6++igkTJuCVV17x7J8+fToaNWqE2NhYvPDCCz71uWTJEhw6dAj79u1Dw4YNAxT5jT3wwAPYt28fUlJSit3nloj0wVqEiK7HWoSItMRahIiux1qEiPxhmFu6KYqCVatWoU+fPgCKzmKpUaMGJkyY4ClGHA4HYmJisHDhQgwePBiHDx9G48aNsWvXLtx+++0Aiu6x2atXL5w+fRo1atRA48aNMWPGDPTs2RNr167Fs88+i4MHDyInJwdt2rTBxo0bUbt27RvGd/XqVVy9etXzs8vlQnZ2Nm655Ravh6YRERGZmRACFy9eRI0aNcq8N7URsRYhIiKSH2sR1iJERER60rsWCdb8iD5KT09HZmYmunXr5tlmt9vRtm1b7NixA4MHD8aOHTsQFRXlKWoAoFu3brBYLNi5cyf69u2LFi1a4LvvvkP37t3x7bffonnz5gCAiRMnYuzYsT4VNQAwbdo0TJkyRd1BEhERVVCnTp1CrVq19A7DL6xFiIiI5MVahLUIERGRnvSqRQy74JOZmQkA+P/t3XucTdX/P/DXPmfmzEwz5gyGmWFGBoNyj0hSKt8UhSTp6kf35NpXd4pI9E3yqYRE9Q1JyPfTRSW6DkmE8sFcCpMZMeYME2bmnPX7Yz5nf+bMzRlnn3X2mv16Ph7zYPZes/Z7vc+yz9usvc9OSEjw2Z6QkKDvy83NRePGjX32h4WFoUGDBnqb//mf/8H999+P5s2bo2PHjliwYAG++eYb7NixA7NmzcKwYcPw008/4ZprrsG8efOq/dzLJ554AhMnTtS/d7lcaNasGbKyshAXFweg7Gocm80Gj8cDIQTcbjeysrLQsmVLhIeH69u9vG0zMjKQmpoKu92ub9c0DW63W2/rdruRnZ2Nli1bVrpyxrtSWFJSgqysLLRo0QJ2ux12ux1CCHg8Hr2tN8bqtleMsbbbvbFXt738mGqK3bu9fIwAlBpTxdjNNiZ/555KYzL76+TxeJCdnY3U1FSfFX6Vx1QXXyeOKbAxFRQUoEWLFqhXrx5Ux1qEtQig/r9Jf7azFrHO68RahGOywphYi6hXi2iahpKSEmRmZvq8D7MWqRv/Jv3ZzlrEOq8TaxGOyQpjCnUtYtoFH6M0bdoU//znP/Xvz5w5g379+uHtt9/G9OnTUa9ePezduxfXXnstFixYgDFjxlTZT0REBCIiIiptj4uLQ/369av8GbfbrT800fvGUVWbevXqoX79+tW28bY7evQonE5njX3FxsaetS8zUjl2Vfk798g4brcbMTExNZ4TiOqKiv8JtzLWImpQOXZVsRaRj7UIWQlrkf8wey1Svl1N7wmsRchorEXkYy1CVhKqWsS0H2ibmJgIAMjLy/PZnpeXp+9LTEzEkSNHfPaXlpYiPz9fb1PR888/j2uuuQZdu3bFpk2bcNNNNyE8PBxDhgzBpk2bah1nTS+cpmmIjo4OuI3RfZmRyrGrijmXjzknK6hL85u1SPD6MiOVY1cVcy4fc05WUJfmt1VqEX/bsRYhozHn8jHnZAWhnt+mXfBJTU1FYmIiNmzYoG8rLCzEli1b0LNnTwBAz549UVBQgG3btultvvrqK3g8HvTo0aNSn3v27MGyZcvw3HPPAShbVS4pKQFQdutsxVux/FH+9sOq9qWkpATcxui+zEjl2FXFnMvHnJMV1KX5zVokeH2Zkcqxq4o5l485JyuoS/PbKrWIv+1Yi5DRmHP5mHOyglDP75Ae/eTJk9ixYwd27NgBoOyBhDt27MCBAwegaRrGjx+P6dOnY926ddi1axfuuusuNGnSBIMHDwYAXHDBBbj22mtx77334scff8T333+Phx9+GMOHD0eTJk18jiWEwH333YeXX34Z0dHRAIBevXph0aJF2LNnD9555x306tWr1mOo+JmCFfcdPXo04DZG92VGKseuKuZcPuacrEC1+c1ahLWIl8qxq4o5l485JytQbX6zFvG/HWsRMhpzLh9zTlYQ6vkd0gWfn376CV26dEGXLl0AABMnTkSXLl0wZcoUAMCjjz6KMWPG4L777sPFF1+MkydP4rPPPkNkZKTex3vvvYe2bdvi6quvRv/+/XHZZZdh4cKFlY61cOFCJCQk4Prrr9e3Pfvsszh9+jR69OiBVq1aYfTo0bUeQ/mHNlW17+jRowG3MbovM1I5dlUx5/Ix52QFqs1v1iKsRbxUjl1VzLl8zDlZgWrzm7WI/+1Yi5DRmHP5mHOyglDPb02EOgJFFRYWwul0Ij8/v8aHE+7fvx9paWk1PlDwbG2M7suMVI5dVcy5fMw5WcHx48fRoEEDuFwuxMbGhjqcOo21iLFUjl1VzLl8zDlZAWsReYyqRfxtx1qEjMacy8eckxWEuhbhByYSEREREREREREREREpjgs+AdI0rcZ9Tqcz4DZG92VGKseuKuZcPuacrIDzWz7WIsZQOXZVMefyMedkBZzf8pmpzlD5PKdy7KpizuVjzskKQj2/+ZFu58h76zJvEyciIvoPvj/Kw1wTERFVxvdHeZhrIiKiykL9/sg7fALk8Xhq3Hf48OGA2xjdlxmpHLuqmHP5mHOyAs5v+ViLGEPl2FXFnMvHnJMVcH7LZ6Y6Q+XznMqxq4o5l485JysI9fzmgk+AarpBSggBl8sVcBuj+zIjlWNXFXMuH3NOVsD5LR9rEWOoHLuqmHP5mHOyAs5v+cxUZ6h8nlM5dlUx5/Ix52QFoZ7fXPAhIiIiIiIiIiIiIiJSHBd8iIiIiIiIiIiIiIiIFMcFnwBpmlbjvvj4+IDbGN2XGakcu6qYc/mYc7ICzm/5WIsYQ+XYVcWcy8eckxVwfstnpjpD5fOcyrGrijmXjzknKwj1/NZEqD9UTlGFhYVwOp1wuVyIjY0NdThERESmwPdHeZhrIiKiyvj+KA9zTUREVFmo3x95h0+APB5PjfsOHjwYcBuj+zIjlWNXFXMuH3NOVsD5LR9rEWOoHLuqmHP5mHOyAs5v+cxUZ6h8nlM5dlUx5/Ix52QFoZ7fXPAJUE03SAkhUFRUFHAbo/syI5VjVxVzLh9zTlbA+S0faxFjqBy7qphz+ZhzsgLOb/nMVGeofJ5TOXZVMefyMedkBaGe31zwISIiIiIiIiIiIiIiUhwXfIiIiIiIiIiIiIiIiBTHBZ8A2WzVp9BmsyExMTHgNkb3ZUYqx64q5lw+5pysgPNbPtYixlA5dlUx5/Ix52QFnN/ymanOUPk8p3LsqmLO5WPOyQpCPb81EeoPlVNUYWEhnE4nXC4XYmNjQx0OERGRKfD9UR7mmoiIqDK+P8rDXBMREVUW6vdHLqcGyOPx1LgvKysr4DZG92VGKseuKuZcPuacrIDzWz7WIsZQOXZVMefyMedkBZzf8pmpzlD5PKdy7KpizuVjzskKQj2/ueAToJpukBJCoLi4OOA2RvdlRirHrirmXD7mnKyA81s+1iLGUDl2VTHn8jHnZAWc3/KZqc5Q+TyncuyqYs7lY87JCkI9v7ngQ0REREREREREREREpDgu+BARERERERERERERESmOCz4BstmqT6HNZkNycnLAbYzuy4xUjl1VzLl8zDlZAee3fKxFjKFy7KpizuVjzskKOL/lM1OdofJ5TuXYVcWcy8eckxWEen6HhfTodYCmaTXui4mJOevPn62N0X2Zkcqxq4o5l485Jyuo6X2RgoO1iDFUjl1VzLl8zDlZAWsR+QKtRfxtx1qEjMacy8eckxWEuhbhcmqA3G53jfv27dsXcBuj+zIjlWNXFXMuH3NOVsD5LR9rEWOoHLuqmHP5mHOyAs5v+cxUZ6h8nlM5dlUx5/Ix52QFoZ7fXPAJMo/HY0gbo/syI5VjVxVzLh9zTkSysRbxn8qxq4o5l485JyLZZNcZKp/nVI5dVcy5fMw5UXBxwYeIiIiIiIiIiIiIiEhxXPAhIiIiIiIiIiIiIiJSnCaEEKEOQkWFhYVwOp0oKCiA0+msso0QAsXFxXA4HNU+rMmfNkb3ZUYqx64q5lw+5pyswOVyIS4uDi6XC7GxsaEOp05jLWIslWNXFXMuH3NOVsBaRB6jahF/27EWIaMx5/Ix52QFoa5FeIdPkIWFhRnSxui+zEjl2FXFnMvHnBORbKxF/Kdy7KpizuVjzolINtl1hsrnOZVjVxVzLh9zThRcXPAJUE0PGvN4PNi/f3/AbYzuy4xUjl1VzLl8zDlZAee3fKxFjKFy7KpizuVjzskKOL/lM1OdofJ5TuXYVcWcy8eckxWEen5zwYeIiIiIiIiIiIiIiEhxXPAhIiIiIiIiIiIiIiJSHBd8iIiIiIiIiIiIiIiIFKcJIUSog1BRYWEhnE4nCgoK4HQ6q2wjhIDH44HNZoOmaefcxui+zEjl2FXFnMvHnJMVuFwuxMXFweVyITY2NtTh1GmsRYylcuyqYs7lY87JCliLyGNULeJvO9YiZDTmXD7mnKwg1LUI7/AJstLSUkPaGN2XGakcu6qYc/mYc4ncbmDTJmD58rI/3e5QR0QUEqxF/Kdy7KpizuVjzolINtl1hsrnOZVjVxVzLh9zThRcXPAJkMfjqXFfdnZ2wG2M7suMVI5dVcy5fMy5RKtXA82bA1deCdx2W9mfzZuXbaeg4vyWj7WIMVSOXVXMuXzMOVkB57d8ZqozVD7PqRy7qphz+ZhzsoJQz28u+BARUd2yejUwdChw6JDv9pycsu1c9KFacLvdmDx5MlJTUxEVFYWWLVviueeeQ/lPxBVCYMqUKUhKSkJUVBT69u2L/fv36/vPnDmDO++8E7GxsWjdujW+/PJLn2O8+OKLGDNmjLQxERERkTpYixAREVFthIU6ACIiK9H+/hsoKgLs9lCHUje53cDYsUBVj6cTAtA0YNw4oG9fvgbBUlQU6ggMNWvWLMyfPx9vv/022rVrh59++gkjR46E0+nE2LFjAQCzZ8/GvHnz8PbbbyM1NRWTJ09Gv3798NtvvyEyMhILFy7Etm3bkJ6ejk8//RS33XYb8vLyoGkasrOzsWjRIvz0008hHikRERGZEWsRIiIiqg0u+ASZzXb2m6j8aWN0X2akcuyqYs7la9O1a6hDsDYhyu78qeahshS4uraM9sMPP2DQoEEYMGAAAKB58+ZYvnw5fvzxRwBlV9TOnTsXTz/9NAYNGgQAeOedd5CQkIC1a9di+PDh2LNnDwYOHIh27dqhRYsWmDRpEo4ePYpGjRrhwQcfxKxZs4L6IEfWIv5TOXZVMefyMedEarFKLeJvO9YiZDTmXD7mnCi4uOATIHsNV4jb7Xa0bt36rD9/tjZG92VGKseuKuZcvprOF0RkTpdeeikWLlyIffv2oXXr1vjll1/w3XffYc6cOQCA7Oxs5Obmom/fvvrPOJ1O9OjRA+np6Rg+fDg6deqEd999F6dOncL69euRlJSE+Ph4vPfee4iMjMSNN97oVyxnzpzBmTNn9O8LCwv1v7vdbgCApmmw2WzweDz6R720bNkSmqYBgM92oOw/W3a7Ha1atYIQQu/HZrNB0zT9e6+0tDSf45Xvx6tly5Z6G7vdDiGEz2cYe2OsbnvFGGu73Rt7ddv9jd27veLnL6s0poqxm21MtZl7qoxJhdepdevW8Hg8PvGoPqa6+DpxTMaMqS6wQi2iaRo0Tav0PsxapO79mzTbmFiLsBbh68Qx1cVahAs+ASr/gle1r6ioCNHR0Xpxcy5tjO7LjFSOXVXMuXxCCBTl5jLnwfTNN0D//mdv98knwOWXBz8eCxIuF9C0aajDMMzjjz+OwsJCtG3bFna7HW63GzNmzMDtt98OAMjNzQUAJCQk+PxcQkKCvm/UqFHYuXMnLrzwQsTHx2PlypU4fvw4pkyZgk2bNuHpp5/GihUr0LJlS7z11ltoWk3+Zs6cialTp1banpGRgXr16gEo+wVPUlIS8vLy4HK5IIRASUkJkpKS0KhRI+Tk5KCo3MfuJSYmwul0Yu/evQCgn5uSk5MRExODzMxMvWAVQiAhIQGxsbHIyMjwiSEtLQ2lpaXIyspCSUkJwsPD9QsLioqKcKjcM7UcDgdatGgBl8ul5wgAoqOjkZKSgvz8fBw9elTfXnFMXvHx8YiPj69yTHFxcfj9999RXFysb69qTACQmpqKsLAw7Nu3T49d0zR9TNnZ2Xpbm82m1JjKP7+h/OtkljH5O/dUGpMKr1O9evVw4sSJOjWmuvg6cUznPqYTJ06gLrFCLeJ9rU+ePKm/D7MWYS3CWqTuvk6sRTimuj6mUNcimqhpxYKqVVhYCKfTifz8fNSvX7/KNm63G/v370daWlq1V/b708bovsxI5dhVxZzLx5xL4HYDzZsDOTlVP8dH04DkZCA7m8/wCZLjx4+jQYMGcLlcQf1oEFlWrFiBSZMm4cUXX0S7du2wY8cOjB8/HnPmzMGIESPwww8/oFevXvjzzz+RlJSk/9ywYcOgaRref//9KvsdOXIkOnfujNTUVDz55JPYsmULZs+ejd27d+PDDz+s8mequqo2JSUFf/31l16LVLw6ye12IyMjA2lpaQgPD6/y6iSPx4N9+/ahZcuW+rmpqquW3G43MjMzkZaWVmnR2ns1U0lJCTIyMtCqVSvY7XalrsSqGHtdvLrMbGPyd+6pNCazv04ejweZmZlo2bKlzxXlKo+pLr5OHFNgYzp+/DgaNWrEWkShWkTTNJSUlGD//v0+78OsRerGv0l/trMWsc7rxFqEY7LCmEJdi/AOHyIiqjvsduCVV4ChQ8sWd8ov+nj/Uzh3Lhd7yG+TJk3C448/juHDhwMAOnTogD/++AMzZ87EiBEjkJiYCADIy8vz+SVLXl4eOnfuXGWfGzduxK+//oo333wTkyZNQv/+/REdHY1hw4bh1VdfrTaWiIgIREREVNru/WVGeeX/82Sz2fTvy28vT9O0Kvup+L2maXrbqnh/OVG+r+raV7e9uhiN2l6b2Ktrr9KY/N0eqjH5O/fOZTtfp5pjNCIHZhuTEds5JvXHVNcurLJKLeJtc7b3YdYiZVQak7/bWYtY73ViLcIx1dUxhboWqTpiIiIiVQ0ZAqxaVfljxZKTy7YPGRKauEhJf//9d6UCz26361cQpaamIjExERs2bND3FxYWYsuWLejZs2el/k6fPo3Ro0djwYIFsNvLPpalpKQEQNlVnRWvEiIiIiJrYy1CREREtcE7fAJU8TbiivscDkfAbYzuy4xUjl1VzLl8zLlEQ4YAgwYB334LHD4MJCUBvXvzzh4J6tr8vuGGGzBjxgw0a9YM7dq1w/bt2zFnzhyMGjUKQNl4x48fj+nTpyMtLQ2pqamYPHkymjRpgsGDB1fq77nnnkP//v3RpUsXAECvXr0wadIkjBw5Eq+++ip69epV6xhZixhD5dhVxZzLx5yTFdS1+W2FWsTfdqxFyGjMuXzMOVlBqOc3n+FzjrzP8KkrnwtMRERkhLr2/njixAlMnjwZa9aswZEjR9CkSRPceuutmDJlChwOB4CyBwg/88wzWLhwIQoKCnDZZZfh9ddfR+vWrX362r17N2688Ubs2LED0dHRAMo+3/fhhx/Ge++9hzZt2mDZsmVo1aqVX7HVtVwTEREZoa69P7IWISIiUkuo3x+54HOOvC9cQUEBnE5nlW2EEHC5XHA6ndWu7PnTxui+zEjl2FXFnMvHnJMVuFwuxMXF8T/+ErAWMZbKsauKOZePOScrYC0ij1G1iL/tWIuQ0Zhz+ZhzsoJQ1yJ8hk+AvJ+bW92+3NzcgNsY3ZcZqRy7qphz+ZhzsgLOb/lYixhD5dhVxZzLx5yTFXB+y2emOkPl85zKsauKOZePOScrCPX85oIPERERERERERERERGR4rjgQ0REREREREREREREpLiwUAegupo+b1LTNERHRwfcxui+zEjJ2N1u4NtvgcOHgaQkoHdvwG4PdVR+UzLnimPOyQo4v+VjLWIMlWNXFXMuH3NOVsD5LZ+Z6gyVz3Mqx64q5lw+5pysINTzWxNCiJBGoCjvwwn5IEiLWr0aGDcOOHToP9uSk4FXXgGGDAldXEREIcb3R3mYayIiosr4/igPc01ERFRZqN8f+ZFuATrbwwKPHj0acBuj+zIjpWJfvRoYOtR3sQcAcnLKtq9eHZq4akmpnNcRzDlZAee3fKxFjKFy7KpizuVjzskKOL/lM1OdofJ5TuXYVcWcy8eckxWEen7zI90CVNMNUkIIHD16FPXr1w+ojdF9mZEQAscOHEB9h8PcH4vmdgNjxwJVve5CAJpWdudP377mHgcA4XYrO19UpfK/USJ/8cZh+ViLGEPl2FXFnMvHnJMVsBaRL9BaxN92rEXIaMy5fMw5WUGoaxEu+JBptOnaNdQhBE6Isjt/nM5QR3JWdgDYsyfUYRARERERERERERGRAfiRbkRERERERERERERERIrjHT4B0jStxn1OpzPgNkb3ZUaapiE3IwONGzeGzWbidchvvgH69z97u08+AS6/PPjxBMDj8cB58qSS80VVKv8bJfIX57d8rEWMoXLsqmLO5WPOyQo4v+UzU52h8nlO5dhVxZzLx5yTFYR6fmsi1B8qp6jCwkI4nU64XC7ExsaGOhySxe0GmjcHcnKqfo6PpgHJyUB2tumf4UNEFAx8f5SHuSYiIqqM74/yMNdERESVhfr90cS3UqjB4/HUuO/w4cMBtzG6LzNSJna7HXjllbK/V1yt9X4/d64Siz3K5LwOYc7JCji/5WMtYgyVY1cVcy4fc05WwPktn5nqDJXPcyrHrirmXD7mnKwg1PObCz4BqukGKSEEXC5XwG2M7suMlIp9yBBg1SqgaVPf7cnJZduHDAlNXLWkVM7rCOacrIDzWz7WIsZQOXZVMefyMedkBZzf8pmpzlD5PKdy7KpizuVjzskKQj2/+QwfonMxZAgwaBDw7bfA4cNAUhLQu7cSd/YQERERERERERERUd3DBR+ic2W3A336hDoKIiJLWrVqFVauXIkDBw6guLjYZ9/PP/8coqiIiIiIiIiIiEKHH+kWIK3ic1wq7IuPjw+4jdF91ZrbDWzaBCxfXvan221s/whi7FQt5lw+5pysQMb8njdvHkaOHImEhARs374d3bt3R8OGDZGVlYXrrrsu6Mc3G0vUIhKoHLuqmHP5mHOyAs5v+cxUZ6h8nlM5dlUx5/Ix52QFoZ7fpl7wcbvdmDx5MlJTUxEVFYWWLVviueee8/kcPCEEpkyZgqSkJERFRaFv377Yv3+/vv/MmTO48847ERsbi9atW+PLL7/0OcaLL76IMWPGnHOMNlv1KbTZbIiPjw+4jdF91crq1UDz5sCVVwK33Vb2Z/PmZdsNFJTYqUbMuXzMOVmBjPn9+uuvY+HChfjHP/4Bh8OBRx99FF988QXGjh0Ll8sV9OObTZ2vRSRROXZVMefyMedkBZzf8pmpzlD5PKdy7KpizuVjzskKQj2/Tf2va9asWZg/fz5effVV7NmzB7NmzcLs2bPxj3/8Q28ze/ZszJs3D2+88Qa2bNmC6Oho9OvXD6dPnwYALFy4ENu2bUN6ejruu+8+3HbbbfqCUXZ2NhYtWoQZM2acc4wej6fGfQcPHgy4jdF9+W31amDoUODQId/tOTll2w1c9DE8djor5lw+5pysQMb8PnDgAC699FIAQFRUFE6cOAEAuPPOO7F8+fKgH99s6nQtIpHKsauKOZePOScr4PyWz0x1hsrnOZVjVxVzLh9zTlYQ6vlt6mf4/PDDDxg0aBAGDBgAAGjevDmWL1+OH3/8EUDZ3T1z587F008/jUGDBgEA3nnnHSQkJGDt2rUYPnw49uzZg4EDB6Jdu3Zo0aIFJk2ahKNHj6JRo0Z48MEHMWvWLMTGxp5zjOXvNqpqX1FRUcBtatPX33/9BeF0lj1fJhBuNzB2LFDV8YQANA0YNw7o2zfwYwEQbrdfeSDj+Dv3yDjMOVmBjPmdmJiI/Px8nH/++WjWrBk2b96MTp06ITs725L/vsxWi6h6nlM5dlUx5/Ix52QFnN/ymanOUPk8p3LsqmLO5WPOyQpCPb9NveBz6aWXYuHChdi3bx9at26NX375Bd999x3mzJkDoOwOndzcXPTt21f/GafTiR49eiA9PR3Dhw9Hp06d8O677+LUqVNYv349kpKSEB8fj/feew+RkZG48cYb/YrlzJkzOHPmjP59YWEhgLKPnXP/+5k2mqbBZrPB4/FACAG32w2PxwOPxwO73a5v9/Le3uVtW367pmk+29xuN4QQldqW78ftdqNN165+jSdgQpTd+eN0GtKdHYDn11/1sXnHVHFF1G63Qwjhs92b9+q2V8x7bbd7X4/qtlf3elSM3WxjAvybeyqNyeyvk7dNxb5VHlNdfJ04psDGVHFfMFx11VVYt24dunTpgpEjR2LChAlYtWoVfvrpJwwZMiToxyciIiIiIiIiMiNTL/g8/vjjKCwsRNu2bWG32+F2uzFjxgzcfvvtAIDc3FwAQEJCgs/PJSQk6PtGjRqFnTt34sILL0R8fDxWrlyJ48ePY8qUKdi0aROefvpprFixAi1btsRbb72Fpk2bVhnLzJkzMXXq1Erbs7Ky9DuEnE4nkpKSkJeXB5fLBY/Hg/z8fOTn5yMhIQE5OTkoKirSfzYxMRH16tVDQUEBMjIy9F+YJScnIyYmBpmZmT6/IPZ+ZWVl+cSQlpaG0tJSZGZm4sJaZ9k88vPz9Tx4x5Sdna3vt9lsaN26NYqKinCo3MfMORwOtGjRAi6XS3/dASA6OhopKSnIz8/H0aNH9e0VXyev+Ph4xMfHV/k6xcXF4ffff0dxcbG+varXCQBSU1MRFhbm8ywpAKYbk79zT6Uxmf11qlevHgDgyJEj+kdQqT6muvg6cUyBjcl7QUQwLVy4UD/+6NGj0bBhQ/zwww8YOHAg7r///qAfn4iIiIiIiIjIjDQR6nuMarBixQpMmjQJL774Itq1a4cdO3Zg/PjxmDNnDkaMGIEffvgBvXr1wp9//omkpCT954YNGwZN0/D+++9X2e/IkSPRuXNnpKam4sknn8SWLVswe/Zs7N69Gx9++GGVP1PVHT4pKSk4duwYnP++y6XildJCCBQWFsLpdNZ4h8/x48cRGxsLTdP07RWvoBZC4MSJE3A6nZVuCyt/h0/h4cN6XwFdFf7tt7Bff30Nr04Zz8cfQ1x2mU8s53JVuNvtRqHbrcdeV65092d7KO/w8WfuqTQms79OAHDixAl94acujKkuvk4cU2BjcrlcaNiwIVwuV0AfmUpn560xCgoK9FqkIiEEXC4XnE6nfq4/lzZG92VGKseuKuZcPuacrMDlciEuLo61iARG1SL+tmMtQkZjzuVjzskKQl2LmHrBJyUlBY8//jhGjx6tb5s+fTr+93//F//617+QlZWFli1bYvv27ejcubPe5oorrkDnzp3xyiuvVOpz48aNeOyxx5Ceno5JkyYhLCwMs2fPxq+//orLL78cx44d8ys2b2FTZ4tItxto3hzIyan6OT6aBiQnA9nZhjzDh4iI6gZZ74+nT5/Gzp07ceTIkUqLZAMHDgzacc2kztciRERE54Dvj/Iw10RERJWF+v3RJv2ItfD333/rVw17ee+UAco+QiYxMREbNmzQ9xcWFmLLli3o2bNnpf5Onz6N0aNHY8GCBfpHxJWUlAAASkpKzum5AxV/yVRxX1ZWVsBtjO7LL3Y74F0wq7ji7v1+7lzDFnsMjZ38wpzLx5yTFciY35999hmaNWuGSy65BAMHDsTgwYP1L3+fzVeX1NlaRDKVY1cVcy4fc05WwPktn5nqDJXPcyrHrirmXD7mnKwg1PPb1As+N9xwA2bMmIGPP/4Yv//+O9asWYM5c+bov8zRNA3jx4/H9OnTsW7dOuzatQt33XUXmjRpgsGDB1fq77nnnkP//v3RpUsXAECvXr2wevVq7Ny5E6+++ip69epV6xhrukFKCIHi4uKA2xjdl9+GDAFWrQIqPtcoOblsu4EPxjY8djor5lw+5pysQMb8HjNmDG6++WYcPnzY5xl3Ho/nnC7eUF2drkUkUjl2VTHn8jHnZAWc3/KZqc5Q+TyncuyqYs7lY87JCkI9v8P8aTSkFr/YX7169TkHU9E//vEPTJ48GQ899BCOHDmCJk2a4P7778eUKVP0No8++iiKiopw3333oaCgAJdddhk+++wzREZG+vS1e/durFy5Ejt27NC3DR06FJs2bULv3r3Rpk0bLFu2zLDY64whQ4BBg4BvvwUOHwaSkoDevfkxbkREFDJ5eXmYOHEiEhISQh0KEREREREREZFp+LXgU93D94KtXr16mDt3LubOnVttG03TMG3aNEybNq3Gvtq3b4/9+/f7bLPZbHj99dfx+uuvGxFu3WW3A336hDoKIiIiAP+5YKNly5ahDoWIiIiIiIiIyDQ0Eep7jBTlffhSQUFBtQtiQggUFRUhOjoaWsXn4NSijdF9mZHKsauKOZePOScrcLlciIuLC+rDCf/++2/cfPPNaNSoETp06IDw8HCf/WPHjg3Kcc2GtYixVI5dVcy5fMw5WYGMWqQ6hw8fRklJCZo1ayb1uKFiVC3ibzvWImQ05lw+5pysIJS1CMAFn3PmLWxC9cIRERGZkYz3x8WLF+OBBx5AZGQkGjZs6PMfBU3TkJWVFZTjmg1rESIiospC+f54wQUXYN++fZZ5piBrESIiospC/f5oO5cfWrVqFYYNG4ZLLrkEF110kc+X1dRUyLnd7rMWe/60MbovM1I5dlUx5/Ix52QFMub3U089halTp8LlcuH3339Hdna2/mWVxZ7yWIsYQ+XYVcWcy8eckxWEcn6/8847+Oqrr0J2/FAxU52h8nlO5dhVxZzLx5yTFYR6ftd6wWfevHkYOXIkEhISsH37dnTv3h0NGzZEVlYWrrvuumDEqDSPx2NIG6P7MiOVY1cVcy4fc04UuOLiYtxyyy2w2c7puhXLYS3iP5VjVxVzLh9zThQ8F198Ma644opQh2E6susMlc9zKseuKuZcPuacKLjCavsDr7/+OhYuXIhbb70VS5cuxaOPPooWLVpgypQpyM/PD0aMRERERLoRI0bg/fffx5NPPhnqUIiIiMhCdu7c6Ve7jh07BjkSIiIioqrVesHnwIEDuPTSSwEAUVFROHHiBADgzjvvxCWXXIJXX33V2AiJiIiIynG73Zg9ezbWr1+Pjh07Ijw83Gf/nDlzQhQZERER1WWdO3eGpmnwPgrZ+xxBIYS+XdO0kH+UCxEREVlXrRd8EhMTkZ+fj/PPPx/NmjXD5s2b0alTJ2RnZ+tFj5XU9HEyNpsNqampAbcxui8zUjl2VTHn8jHnZAUy5veuXbvQpUsXAMDu3bt99nl/8WIlrEWMoXLsqmLO5WPOyQqCOb+zs7P1vwsh0L59e3zyySc4//zzg3ZMFZipzlD5PKdy7KpizuVjzskKQj2/a330q666CuvWrQMAjBw5EhMmTMB//dd/4ZZbbsGNN95oeICqCws7+5qaP22M7suMVI5dVcy5fMw5UeA2btxY7VcwHpSck5ODO+64Aw0bNkRUVBQ6dOiAn376Sd8vhMCUKVOQlJSEqKgo9O3bF/v379f3nzlzBnfeeSdiY2PRunVrfPnllz79v/jiixgzZozhcXuxFvGfyrGrijmXjzknOnfnn3++/tW8eXNomobk5GSf7cFY/LFCLeJvO9YiZDTmXD7mnCi4ar3gs3DhQjz11FMAgNGjR+Ott97CBRdcgGnTpmH+/PmGB2h2NT1ozOPxYP/+/QG3MbovM1I5dlUx5/Ix52QFdW1+Hz9+HL169UJ4eDg+/fRT/Pbbb3jppZdQv359vc3s2bMxb948vPHGG9iyZQuio6PRr18/nD59GkBZ7bRt2zakp6fjvvvuw2233abfFZ2dnY1FixZhxowZ5xwjaxFjqBy7qphz+ZhzsoK6Nr+tUIv42461CBmNOZePOScrCPX8rvWS6qFDh5CSkqJ/P3z4cAwfPhxCCBw8eBDNmjUzNEAiIiKi8k6fPo1//OMf2LhxI44cOVKpmPr5558NO9asWbOQkpKCJUuW6NtSU1P1vwshMHfuXDz99NMYNGgQAOCdd95BQkIC1q5di+HDh2PPnj0YOHAg2rVrhxYtWmDSpEk4evQoGjVqhAcffBCzZs1CbGysYTETERFR3cFahIiIiGqj1gs+qampOHz4MBo3buyzPT8/H6mpqXw4IREREQXV3Xffjc8//xxDhw5F9+7dg/rcnnXr1qFfv364+eab8fXXX6Np06Z46KGHcO+99wIouyo2NzcXffv21X/G6XSiR48eSE9Px/Dhw9GpUye8++67OHXqFNavX4+kpCTEx8fjvffeQ2RkpN8fiXvmzBmcOXNG/76wsBAA4Ha79fpL0zTYbDZ4PB4IIeB2u+HxeODxeGC32/XtXt7PFva2Lb+94kOn3W43hBCV2pbvx3s873673Q4hhM+inDfG6rZXjLG2272xV7fd39i92ysuKKo0poqxm21MgH9zT6Uxmf118rap2LfKY6qLrxPHFNiYZP9OItjPD7RCLeJ9rSu+D7MWqRv/Jv3ZzlrEOq8TaxGOyQpjCvX6SK0XfIQQVRY0J0+eRGRkpCFBEREREVXnn//8Jz755BP06tUr6MfKysrC/PnzMXHiRDz55JPYunUrxo4dC4fDgREjRiA3NxcAkJCQ4PNzCQkJ+r5Ro0Zh586duPDCCxEfH4+VK1fi+PHjmDJlCjZt2oSnn34aK1asQMuWLfHWW2+hadOmVcYyc+ZMTJ06tcoYvVflOp1OJCUlIS8vDy6XCx6PB/n5+cjPz0dCQgJycnJQVFSk/2xiYiLq1auHgoICZGRk6EVqcnIyYmJikJmZ6fOfMu9XVlaWTwxpaWkoLS1FZmYm8vPzkZGRgbCwMLRu3RpFRUU4dOiQ3tbhcKBFixZwuVx6jgAgOjoaKSkpyM/Px9GjR/XtFcfkFR8fj/j4+CrHFBcXh99//x3FxcX69qrGBJRdzBQWFoaMjAw9dpvNpo+p/AO6bTabUmMq//yG8q+TWcbk79xTaUxmf53q1asHADhy5AhOnDhRJ8ZUF18njimwMXkXIYKhS5cuPr8POXXqFG644QY4HA6fdkbebWyFWiQuLg4HDhzweR9mLcJahLVI3XydWItwTFYYUzBrEX9oovwSVQ0mTpwIAHjllVdw77334rzzztP3ud1ubNmyBXa7Hd9//31wIjWZwsJCOJ1OFBQUwOl0VtnGu8LoXfk71zZG92VGKseuKuZcPuacrMDlciEuLg4ulytoHw1y4YUXYsWKFejYsWNQ+i/P4XCgW7du+OGHH/RtY8eOxdatW5Geno4ffvgBvXr1wp9//omkpCS9zbBhw6BpGt5///0q+x05ciQ6d+6M1NRUPPnkk9iyZQtmz56N3bt348MPP6zyZ6q6qjYlJQXHjh3Ta5GKVyd5zzt2u73GO3xKS0uhaZp+bqrqqiXvz3mvoiqv4pWp3p9X6UqsirHXlavL/Nkeyqtq/Zl7Ko3J7K9TdVQeU118nTimwMbkcrnQsGHDoNQiVS12VOWZZ54x7JhWqEW8r6nb7fZ5H2YtUjf+TfqznbWIdV6n6qg8prr4OnFM5q1F/OH3HT7bt28HUPYGu2vXLp8rWBwOBzp16oT//u//Nj5CxZWWlla62udc2hjdlxmpHLuqmHP5mHOiwL300kt47LHH8MYbb+D8888P6rGSkpJw4YUX+my74IIL9F+EJCYmAgDy8vJ8fsmSl5eHzp07V9nnxo0b8euvv+LNN9/EpEmT0L9/f0RHR2PYsGF49dVXq40lIiICERERlbZ7f4FSnrfYFELovzwpv708b4HscDgqLUaX71cIgeLiYtjt9ir78bZ3u92w2+16X95ftlRU3fbq+jZqe1XHrC726tqrNCZ/t4diTP7OvXPdztepcizef8dV5bym2M08JqO3c0zqj6m6fUYwciHHX1aoRbzbS0tLa3wfZi3yHyqNyd/trEWs8TqxFjn7do5J/TEFsxbxR9URV2Hjxo3YuHEjRowYgU8//VT/fuPGjVi/fj0WLFiAtLS0YMZqShVXHCvuy87ODriN0X2Zkcqxq4o5l485JyuQMb+7deuG06dPo0WLFqhXrx4aNGjg82WkXr16Ye/evT7b9u3bpy80paamIjExERs2bND3FxYWYsuWLejZs2el/k6fPo3Ro0djwYIF+n/qS0pKAAAlJSXn9Fm/rEWMoXLsqmLO5WPOyQrq2vy2Qi3ibzvWImQ05lw+5pysINTzu9bP8FmyZIn+d+/n5iUnJxsXEREREVENbr31VuTk5OD5559HQkJClVeGGWXChAm49NJL8fzzz2PYsGH48ccfsXDhQixcuBBA2dVE48ePx/Tp05GWlobU1FRMnjwZTZo0weDBgyv199xzz6F///7o0qULgLJf4kyaNAkjR47Eq6++KuW5RERERBQcTz75JHJzc/HWW28Z1idrESIiIqqNWi/4eDweTJ8+HS+99BJOnjwJoOyBW4888gieeuqpam9zIiIiIjLCDz/8gPT0dHTq1Cnox7r44ouxZs0aPPHEE5g2bRpSU1Mxd+5c3H777XqbRx99FEVFRbjvvvtQUFCAyy67DJ999hkiIyN9+tq9ezdWrlyJHTt26NuGDh2KTZs2oXfv3mjTpg2WLVsW9DERERFRcOTk5ODgwYOG9slahIiIiGqj1gs+Tz31FBYvXowXXnhBv/Lju+++w7PPPovTp09jxowZhgepMn8WwPxdJDOyLzNSOXZVMefyMedEgWvbti1OnTol7XjXX389rr/++mr3a5qGadOmYdq0aTX20759e+zfv99nm81mw+uvv47XX3/dkFirwlrEfyrHrirmXD7mnCh43n777aD0a4VaxN92rEXIaMy5fMw5UXBpQghRmx9o0qQJ3njjDQwcONBn+0cffYSHHnoIOTk5hgZoVoWFhXA6nXC5XIiNjQ11OERERKYg4/3x888/x9SpUzFjxgx06NAB4eHhPvut8r7MWoSIiKgyvj/Kw1wTERFVFur3x1rf4ZOfn4+2bdtW2t62bVvk5+cbEpRKalovE0KgqKgI0dHR1T5fwJ82RvdlRirHrirmXD7mnKyglteRnJNrr70WAHD11VdXOramaef0sGGVsRYxhsqxq4o5l485JyuQUYsAwIYNG7BhwwYcOXKk0sOZjXyGjwoCrUX8bcdahIzGnMvHnJMVyKpFqlPre+g6deqEV199tdL2V199Vcpn6ZtNxcKu4r5Dhw4F3MbovsxI5dhVxZzLx5yTFciY3xs3bsTGjRvx1Vdf+Xx5t1kNaxFjqBy7qphz+ZhzsgIZ83vq1Km45pprsGHDBhw9ehTHjx/3+bIaM9UZKp/nVI5dVcy5fMw5WUGo53et7/CZPXs2BgwYgC+//BI9e/YEAKSnp+PgwYP45JNPDA+QiIiIqLwrrrgi1CEQERGRhb3xxhtYunQp7rzzzlCHQkREROSj1gs+V1xxBfbt24fXXnsN//rXvwAAQ4YMwUMPPYQmTZoYHiARERFRVf7++28cOHAAxcXFPts7duwYooiIiIjICoqLi3HppZeGOgwiIiKiSmq94HPgwAGkpKRgxowZVe5r1qyZIYGpoqbPm9Q0DQ6HI+A2RvdlRirHrirmXD7mnKxAxvz+66+/MHLkSHz66adV7rfaM3xYixhD5dhVxZzLx5yTFciY3/fccw+WLVuGyZMnB/1YKjBTnaHyeU7l2FXFnMvHnJMVhHp+a6KWTxGy2+04fPgwGjdu7LP92LFjaNy4sWV+yVJYWAin0wmXy4XY2NhQh0NERGQKMt4fb7/9dvzxxx+YO3cu+vTpgzVr1iAvLw/Tp0/HSy+9hAEDBgTluGbDWoSIiKgyGe+P48aNwzvvvIOOHTuiY8eOCA8P99k/Z86coBzXbFiLEBERVRbq90dbbX9ACFHlKtXJkycRGRlpSFAqqWm9TAiBgoKCgNsY3ZcZqRy7qphz+ZhzsgIZ8/urr77CnDlz0K1bN9hsNpx//vm44447MHv2bMycOTPoxzcb1iLGUDl2VTHn8jHnZAUy5vfOnTvRuXNn2Gw27N69G9u3b9e/duzYEfTjm42Z6gyVz3Mqx64q5lw+5pysINTz2++PdJs4cSKAsluSJk+ejPPOO0/f53a7sWXLFnTu3NnwAM3O4/HUuC83Nxf16tWD3W4/5zZG92VGKseuKuZcPuacrKCm90WjFBUV6Xca169fH3/99Rdat26NDh064Oeffw768c2GtYgxVI5dVcy5fMw5WYGMWmTjxo1BP4ZKAq1F/G3HWoSMxpzLx5yTFcioRWri94LP9u3bAZStUO3atQsOh0Pf53A40KlTJ/z3f/+38RESERERldOmTRvs3bsXzZs3R6dOnbBgwQI0b94cb7zxBpKSkkIdHhERERERERFRSPi94OO9gmXkyJF45ZVX+PmsREREFBLjxo3D4cOHAQDPPPMMrr32Wrz33ntwOBxYunRpaIMjIiIiIiIiIgoRvxd8vJYsWRKMOJRV1fOMyu+Ljo4OuI3RfZmRyrGrijmXjzknK5Axv++44w797127dsUff/yBf/3rX2jWrBni4+ODfnyzYS1iDJVjVxVzLh9zTlbA+S2fmeoMlc9zKseuKuZcPuacrCDU81sToX6KkKIKCwvhdDrhcrl4txMREdG/8f1RHuaaiIioMr4/ysNcExERVRbq98da3+FDvs72cML8/Hw0aNAANpvtnNsY3ZcZqRy7qphz+ZhzsgIZDyecOHFilds1TUNkZCRatWqFQYMGoUGDBkGPxQxYixhD5dhVxZzLx5yTFQS7FikpKcH999+PyZMnIzU1NajHUkWgtYi/7ViLkNGYc/mYc7ICGb8XqQn/ZQWophukhBA4evRowG2M7suMVI5dVcy5fMw5WYGM+b19+3YsXrwYCxcuxNdff42vv/4aixYtwuLFi7FhwwZMnDgRrVq1wm+//Rb0WMyAtYgxVI5dVcy5fMw5WUGw53d4eDg+/PDDoB5DNWaqM1Q+z6kcu6qYc/mYc7KCUM/vWi34lJSUYNSoUcjOzg5WPEREREQ1GjRoEPr27Ys///wT27Ztw7Zt23Do0CH813/9F2699Vbk5OTg8ssvx4QJE0IdKhEREdVBgwcPxtq1a0MdBhEREVEltfpIN++VLJMnTw5WPEREREQ1evHFF/HFF1/4fBau0+nEs88+i2uuuQbjxo3DlClTcM0114QwSiIiIqqr0tLSMG3aNHz//ffo2rUroqOjffaPHTs2RJERERGR1dX6GT7eK1l41WwZTdNq3Od0OgNuY3RfZqRy7KpizuVjzskKZMxvl8uFI0eO4MILL/TZ/tdff6GwsBAAEBcXh+Li4qDHYgasRYyhcuyqYs7lY87JCmTM78WLFyMuLk6/07ji8a224GOmOkPl85zKsauKOZePOScrCPX8rvWCD69k8VXTA8ZsNhuSkpLO+vNna2N0X2akcuyqYs7lY87JCmQ8eHPQoEEYNWoUXnrpJVx88cUAgK1bt+K///u/MXjwYADAjz/+iNatWwc9FjNgLWIMlWNXFXMuH3NOVhDsWkQIgU2bNqFx48aIiooK6rFUEWgt4m871iJkNOZcPuacrEDG70VqoolaPkUoNTW1+s40DVlZWQEHpYLCwkI4nU4cP34ccXFxVbbxeDzIy8tDQkJCtS+0P22M7suMVI5dVcy5fMw5WUFBQQHq168Pl8vl85FrRjp58iQmTJiAd955B6WlpQCAsLAwjBgxAi+//DKio6OxY8cOAEDnzp2DEoMZsBYxlsqxq4o5l485JysIdi3i8XgQGRmJX3/9FWlpaYb3rxKjahF/27EWIaMx5/Ix52QFMn4vUpNa/cvyXsny22+/ITs7u9KXVRZ7yqtpvUwIAZfLFXAbo/syI5VjVxVzLh9zTlYgY37HxMRg0aJFOHbsGLZv347t27fj2LFjWLhwoX7ncefOnev0Yk95rEWMoXLsqmLO5WPOyQqCPb9tNhvS0tJw7NixoB5HJWaqM1Q+z6kcu6qYc/mYc7KCUM/vWi/4pKWl4dChQ8GKh4iIiMgvMTEx6NixIzp27IiYmJhQh0NEREQW8cILL2DSpEnYvXt3qEMhIiIi8lGrZ/iUv5LF6rcuExEREREREZH13HXXXfj777/RqVMnOByOSs/yyc/PD1FkREREZHW1WvAB/nMly/z589G+fftgxKQUTdNq3BcfHx9wG6P7MiOVY1cVcy4fc05WwPktH2sRY6gcu6qYc/mYc7ICGfN77ty5QT+GSsxUZ6h8nlM5dlUx5/Ix52QFoZ7fmqjlh8rVr18ff//9N0pLSy19JYv34YShevgSERGRGfH9UR7mmoiIqDK+P8rDXBMREVUW6vfHWt/hwytZfHk8nhr35eTkoGnTprDZqn5ckj9tjO7LjFSOXVXMuXzMOVlBTe+LRigpKcH999+PyZMnIzU1NajHUgVrEWOoHLuqmHP5mHOygmDXIl5utxtr167Fnj17AADt2rXDwIEDYbfbpRzfTAKtRfxtx1qEjMacy8eckxXIqkWqU+sFnxEjRgQjDmXVdIOUEAJFRUUBtzG6LzNSOXZVKZlztxv49lvg8GEgKQno3RtQ6D9USuacqJaCPb/Dw8Px4YcfYvLkyUE9jkpYixhD5dhVxZzLx5yTFciY3xkZGejfvz9ycnLQpk0bAMDMmTORkpKCjz/+GC1btgx6DGZipjpD5fOcyrGrijmXjzknKwj1/D6npVS3240PP/wQ06dPx/Tp07FmzRq43W6jYyMi+o/Vq4HmzYErrwRuu63sz+bNy7YTkaUMHjwYa9euDXUYREREZFFjx45Fy5YtcfDgQfz888/4+eefceDAAaSmpmLs2LGhDo+IiIgsrNZ3+PBKFiKSbvVqYOhQoOIKeU5O2fZVq4AhQ0ITGxFJl5aWhmnTpuH7779H165dER0d7bOfv2ghIiKiYPr666+xefNmNGjQQN/WsGFDvPDCC+jVq1cIIyMiIiKrq/WCj/dKlvLFzbFjx3DHHXdg7Nix+Pjjjw0P0sxq+rxJm82GxMTEgNsY3ZcZqRy7qmw2GxLr1YPt1ClA00IdTvXcbmDs2MqLPUDZNk0Dxo0D+vY1/ce72YTgPKc6T8b8Xrx4MeLi4rBt2zZs27bNZ5+maZZb8GEtYgyVY1cVcy4fc05WIGN+R0RE4MSJE5W2nzx5Eg6HI+jHNxsz1Rkqn+dUjl1VzLl8zDlZQajntyZq+aFy0dHR2Lx5Mzp06OCz/ZdffkGvXr1w8uRJQwM0q8LCQjidTrhcLsTGxoY6HKJzY+aFnrqKn1NLdVyw3x+FEDhw4AAaN26MqKgow/tXCWsRIiKiymS8P9511134+eefsXjxYnTv3h0AsGXLFtx7773o2rUrli5dGpTjmg1rESIiospC/f5Y6+UmXsniy+Px1LgvKysr4DZG92VGKseuKuY6NDjPqa4L9vwWQiAtLQ2HDh0K6nFUwlrEGCrHrirmXD7mnKxAxvyeN28eWrZsiZ49eyIyMhKRkZHo1asXWrVqhVdeeSXoxzcbM9UZKp/nVI5dVcy5fMw5WUGo53etP9Lt+uuvx3333VfpSpYHHngAAwcONDxAs6vpBikhBIqLiwNuY3RfZqRy7KoSQmDvtm1o1aoV7Gb+KLRvvgH69z97u08+AS6/PPjxBMDtdqP4zz85z6lOC/b8ttlsSEtLw7Fjx5CWlhbUY6mCtYgxVI5dVcy5fMw5WYGM+R0XF4ePPvoIGRkZ2LNnDwDgggsuQKtWrYJ+bDMyU52h8nlO5dhVxZzLx5yTFYR6ftd6wWfevHkYMWIEevbsifDwcABAaWkpBg4caMkrWYhUJs47D4iONvezb665BkhOBnJyqv44NE0r23/NNeYeB1D2PCIiCtgLL7yASZMmYf78+Wjfvn2owyEiIiKLatWqlWUXeYiIiMicav2Rbt4rWfbt24dVq1Zh1apV2Lt3L9asWQOn0xmMGInIyux2wLuYXPGZQ97v5841/2IPERnmrrvuwo8//ohOnTohKioKDRo08PkKphdeeAGapmH8+PH6ttOnT2P06NFo2LAhYmJicNNNNyEvL0/fn5+fjxtuuAExMTHo0qULtm/f7tPn6NGj8dJLLwU1biIiIjLOTTfdhFmzZlXaPnv2bNx8881BPTZrESIiIqpJre/w8eKVLGVsturXzGw2G5KTkwNuY3RfZqRy7KpSKudDhgCrVgHjxgHln9uRnFy22DNkSMhCqw2lck50jmTM77lz5wb9GFXZunUrFixYgI4dO/psnzBhAj7++GN88MEHcDqdePjhhzFkyBB8//33AIAZM2bgxIkT+PnnnzF//nzce++9+OmnnwAAmzdvxpYtWzBv3rxzjou1iDFUjl1VzLl8zDlZgYz5/c033+DZZ5+ttP26664L6sJJXa1F/G3HWoSMxpzLx5yTFYR6ftd6weemm25C9+7d8dhjj/lsnz17NrZu3YoPPvjAsOBUoFW846DCvpiYmLP+/NnaGN2XGakcu6qUy/mQIcCgQcC33wKHDwNJSUDv3krd2aNczonOQU3vi0YZMWJE0I9R0cmTJ3H77bdj0aJFmD59ur7d5XJh8eLFWLZsGa666ioAwJIlS3DBBRdg8+bNuOSSS7Bnzx4MHz4crVu3xn333YeFCxcCAEpKSvDAAw/gzTffDOhZaqxFjKFy7KpizuVjzskKZNQiJ0+ehMPhqLQ9PDwchYWFQTtmXa1F/G3HWoSMxpzLx5yTFcioRWpS6wWfUF3JYlbuGp7J4Xa7kZmZiZYtW1ZbPPnTxui+zEjl2FWlZM7tdqBPn1BHcc6UzDlRLdX0vmj0cdauXas/KLldu3YYOHBg0P5tjR49GgMGDEDfvn19fsmybds2lJSUoG/fvvq2tm3bolmzZkhPT8cll1yCTp064auvvsI999yD9evX61flzp49G3369EG3bt38iuHMmTM4c+aM/r33F0rFxcV63jVNg81mg8fjgRACbrcbWVlZaNmyJcLDw/XtXt62GRkZSE1N1fNns9mgaZrP6+l2u5GdnY2WLVtWKmC9VzCVlJQgKysLLVq0gN1uh91uhxACHo9Hb+uNsbrtFWOs7XZv7NVtrzhHq4vdu718jACUGlPF2M02Jn/nnkpjMvvr5PF4kJ2djdTUVJ8rD1UeU118nTimwMZUXFyMYOvQoQPef/99TJkyxWf7ihUrcOGFFwblmHW5FtE0DSUlJcjMzPR5H2YtUjf+TfqznbWIdV4n1iIckxXGJKMWqUmtF3xCcSWLyipO0HNtY3RfZqRy7KpizuVjzokCl5GRgf79+yMnJwdt2rQBAMycORMpKSn4+OOP0bJlS0OPt2LFCvz888/YunVrpX25ublwOByIi4vz2Z6QkIDc3FwAwOOPP44HH3wQLVu2RPPmzbF48WLs378fb7/9NtLT0/HAAw/g888/R7du3bBo0aJqn4k4c+ZMTJ06tdL2rKwsxMbGAgCcTieSkpKQl5cHl8sFj8eD/Px81K9fHwkJCcjJyUFRUZH+s4mJiahXrx7y8/Phdrv1IjU5ORkxMTHIzMzUz1sej0f/ysrK8okhLS0NpaWlyMzMRH5+PjweD8LCwtC6dWsUFRXhULmP43Q4HGjRogVcLpeeIwCIjo5GSkoK8vPzcfToUX17xTF5xcfHIz4+vsoxxcXF4ffff/cptKsaEwCkpqYiLCwMGRkZeuw2m00fU3Z2tt7WZrMpNab9+/dX+TqZZUz+zj2VxmT216levXrweDw4cuQITpw4USfGVBdfJ44psDHJ+L3E5MmTMWTIEGRmZup31WzYsAHLly8Pyqee1PVaJC4uDgcOHMDRo0f192HWIqxFWIvUzdeJtQjHZIUxhXqNRBPll6j80L17d1x//fWVrmR59tln8X//93/Ytm2boQGaVWFhIZxOp164VMXtdmP//v1IS0ur8a6cs7Uxui8zUjl2VTHn8jHnZAXHjx9HgwYN4HK59P/4G61///4QQuC9995DgwYNAADHjh3DHXfcAZvNho8//tiwYx08eBDdunXDF198oV8N26dPH3Tu3Blz587FsmXLMHLkSJ+rXYGyeunKK6+s8oHOAHDVVVdh3Lhx+OOPP/DPf/4TH3/8Me699140bNiw2jumq7qqNiUlBX/99Zdei1R1VW1GRgbS0tJqvMNn3759PncfVndVbWZmJtLS0mq8qjYjIwOtWrVS8qra8rHXlavL/Nkeyqtq/Zl7Ko3J7K+Tx+PR7zbmVbUcU10d0/Hjx9GoUaOg1iIA8PHHH+P555/Hjh07EBUVhY4dO+KZZ57BFVdcYehxrFCLeO/w2b9/v8/7MGuRuvFv0p/trEWs8zqxFuGYrDAmWbVIdWp9h4/sK1mIiIiIyvv666+xefNmfbEHABo2bIgXXngBvXr1MvRY27Ztw5EjR3DRRRfp29xuN7755hu8+uqrWL9+PYqLi1FQUOBzZW1eXh4SExOr7HPJkiWIi4vDoEGDMGTIEAwePBjh4eG4+eabK11QU15ERAQiIiIqbff+MqO88v95stls+vflt5enaVqV/VT8XtM0vW1VvL+cKN9Xde2r215djEZtr03s1bVXaUz+bg/VmPyde+eyna9TzTEakQOzjcmI7RyT+mOSdWHVgAEDMGDAgKAfxyq1iLfN2d6HWYuUUWlM/m5nLWK914m1CMdUV8ckqxapTq0XfG644QasXbsWzz//PFatWqVfyfLll18afiWLCqp70b37Kn4m5bm0MbovM1I5dlUx5/Ix52QFMuZ3RESEz+3/XtV97Gwgrr76auzatctn28iRI9G2bVs89thjSElJQXh4ODZs2ICbbroJALB3714cOHAAPXv2rNTfX3/9hWnTpuG7774DUPYLm5KSEgBlV3WeyzOQWIsYQ+XYVcWcy8eckxXUtflthVrE33asRchozLl8zDlZQajnd60XfAB5V7Iow+0Gvv0WOHwYSEoCevcue7g8gLCws6fYp42RfSlG5dhVxZzLx5wTBe7666/Hfffdh8WLF6N79+4AgC1btuCBBx7AwIEDDT1WvXr10L59e59t0dHRaNiwob797rvvxsSJE9GgQQPExsZizJgx6NmzJy655JJK/Y0fPx6PPPIImjZtCgDo1asX3n33XVxzzTVYuHCh4XcoAcbWD6xFyGjMuXzMOZFarFKL+NuOtQgZjTmXjzknCi7TL6fm5OTgjjvuQMOGDREVFYUOHTrgp59+0vcLITBlyhQkJSUhKioKffv29Xng05kzZ3DnnXciNjYWrVu3xpdffunT/4svvogxY8acc3yejz4CmjcHrrwSuO22sj+bNwdWr4bH48H+/fsrfe6gz8+Xb7N6tXF9KUbl2FXFnMvHnJMVyJjf8+bNQ8uWLdGzZ09ERkYiMjISvXr1QqtWrfDKK68E/fgVvfzyy7j++utx00034fLLL0diYiJWr15dqd369euRkZGBhx56SN/28MMPo0WLFujRoweKi4vxzDPP1Pr4gdYG/p6bWIuQ0Zhz+ZhzsgIrzm/VaxF/27EWIaMx5/Ix52QFoZ7fpl5SPX78OHr16oUrr7wSn376KRo1aoT9+/frDwMEgNmzZ2PevHl4++23kZqaismTJ6Nfv3747bffEBkZiYULF2Lbtm1IT0/Hp59+ittuuw15eXnQNA3Z2dlYtGiRzwJSbdlGjKi8MScHGDoUWLkSqHAlTrXWrAGGDQPKPQTqnPsiIiKqw+Li4vDRRx8hIyMDe/bsAQBccMEFaNWqlZTjb9q0yef7yMhIvPbaa3jttddq/Ll+/fqhX79+PtvOO+88rFy50ugQiYiIqA5jLUJERETVMfWCz6xZs5CSkoIlS5bo21JTU/W/CyEwd+5cPP300xg0aBAA4J133kFCQgLWrl2L4cOHY8+ePRg4cCDatWuHFi1aYNKkSTh69CgaNWqEBx98ELNmzUJsbKyxgQsBaBps48cD69eftbl24gRs48ZVXuw5h76IiIisolWrVtIWeYiIiIiqIv79/3hN00IcCREREZHJP9Jt3bp16NatG26++WY0btwYXbp0waJFi/T92dnZyM3NRd++ffVtTqcTPXr0QHp6OgCgU6dO+O6773Dq1CmsX78eSUlJiI+Px3vvvYfIyEjceOONfsVy5swZFBYW+nwBQLUlnRDQcnIQuXWrfhuXx+OB2+3Wv7yFYZvu3aH9+Wf1B/93X1E//QQhhE8f3n6828sfo+xHfdt7Y6lue8UYa7vdO6bqtvsbe/nt5b9UG5M/20M5por914Ux1cXXiWPimFQbU7DddNNNmDVrVqXts2fPxs033xz04xMREREtXrwY7du31z9etn379njzzTdDHRYRERFZXEB3+AT7SpasrCzMnz8fEydOxJNPPomtW7di7NixcDgcGDFiBHJzcwEACQkJPj+XkJCg7xs1ahR27tyJCy+8EPHx8Vi5ciWOHz+OKVOmYNOmTXj66aexYsUKtGzZEm+99Zb+4MKKZs6cialTp9Z6DGF//YWCggI0atQIOTk5KCoq0vclJibC6XT63Vfiv/Nd/hlFAJCWlobS0lJkZWUBADIzM2G329G6dWsUFRXh0KFDeluHw4EWLVrA5XLpOQLKHvqYkpKC/Px8HD16VN/udDqRlJSEvLw8uFwufXt8fDzi4+OrHFNcXBx+//13FBcX69uTk5MRExODzMxMn88xTE1NRVhYGDIzM/XYNU3Tx5Sdna23tdlsSo2putfJLGNyOp1wOBx6zuvCmFR4ndLS0nDkyJE6Naa6+DpxTOc+phMnTiDYvvnmGzz77LOVtl933XV46aWXgn58s7HZqr9+x2azIS0tLeA2RvdlRirHrirmXD7mnKxAxvyeMmUK5syZgzFjxqBnz54AgPT0dEyYMAEHDhzAtGnTgh6DmZipzlD5PKdy7KpizuVjzskKQj2/NeFdtamFxYsX4+WXX9Z/WZWWlobx48fjnnvuMTQ4h8OBbt264YcfftC3jR07Flu3bkV6ejp++OEH9OrVC3/++SeSkpL0NsOGDYOmaXj//fer7HfkyJHo3LkzUlNT8eSTT2LLli2YPXs2du/ejQ8//LDKnzlz5gzOnDmjf19YWIiUlBS4ANT0gXBnPvsMYX37wm63w+PxoHy6vS9+8bp1iBg8+Kz5KF6/HmF9+6LiS+btx+12o6SkBOHh4dA0DXa7HUIIn1/KaZoGm81W7faKMdZ2u81mg6Zp1W6veOV3dbF7t1d8yJVKY6oYu9nGBACnT5/Wc14XxmT21wkASktLERbmu9au8pjq4uvEMQU2JpfLhYYNG8Llchn/kan/FhUVhR07dqBNmzY+2//1r3+hS5cuOHXqVFCOazaFhYVwOp0oKCio9gISIQSKi4vhcDiqvUDHnzZG92VGKseuKuZcPuacrMDlciEuLi6otUijRo0wb9483HrrrT7bly9fjjFjxvhceFOXGVWL+NuOtQgZjTmXjzknK5BRi9Sk1stNU6ZMwbhx43DDDTfggw8+wAcffIAbbrgBEyZMwJQpUwwNLikpCRdeeKHPtgsuuAAHDhwAUHYFMgDk5eX5tMnLy9P3VbRx40b8+uuvePjhh7Fp0yb0798f0dHRGDZsWKUHH5YXERGB2NhYny8AqHa1TNMgkpORnZysb7LZbLDb7fqX95dp2a1aQSQnA9Wd6P7dV1bTphBC+PTh7cf79ccff+iLPWU/qvm09f5SrrrtFWOs7fbyCwdVbfc3du+2iu1VGpO/20M1Jo/H45PzujAms79OAPS7N+rKmOri68QxBTYmGUV7hw4dqryoY8WKFZXqBiuouEhYcV92dnbAbYzuy4xUjl1VzLl8zDlZgYz5XVJSgm7dulXa3rVrV5SWlgb9+GZjpjpD5fOcyrGrijmXjzknKwj1/K71R7rNnz8fixYt8rmSZeDAgejYsSPGjBlj6K3LvXr1wt69e3227du3D+effz6Aso+QSUxMxIYNG9C5c2cAZVeYbNmyBQ8++GCl/k6fPo3Ro0fjvffeg91u93keQUlJybk/d0DTgPJX8f/7l12el18G/v3LuhrZ7fC8/DLsw4YF3hcREVEdN3nyZAwZMgSZmZm46qqrAAAbNmzA8uXL8cEHH4Q4OiIiIqrr7rzzTsyfPx9z5szx2b5w4ULcfvvtIYqKiIiI6Bzu8JF5JcuECROwefNmPP/888jIyMCyZcuwcOFCjB49GkDZlc3jx4/H9OnTsW7dOuzatQt33XUXmjRpgsFVfETac889h/79+6NLly4AyhaUVq9ejZ07d+LVV19Fr169ah2j5+23gYrP/UlOBlatAm680f+Obryx7GeM6IuIiKgOu+GGG7B27VpkZGTgoYcewiOPPIJDhw7hyy+/rPL9n4iIiMhoixcvRvv27XHPPffgnnvuQYcOHbBo0SLYbDZMnDhR/yIiIiKSqdZ3+Mi8kuXiiy/GmjVr8MQTT2DatGlITU3F3LlzfY7z6KOPoqioCPfddx8KCgpw2WWX4bPPPkNkZKRPX7t378bKlSuxY8cOfdvQoUOxadMm9O7dG23atMGyZctqH+QNNwC33w58+y1w+DCQlAT07l12N47b7ddDmvQ2Q4YAgwYZ05eCVI5dVcy5fMw5kTEGDBiAAQMGhDoMJRhZP7AWIaMx5/Ix50SB2717Ny666CIAQGZmJgAgPj4e8fHx2L17t96Oz6coI7vOUPk8p3LsqmLO5WPOiYJLExWfKH4WY8aMwTvvvIOUlBRccsklAIAtW7bgwIEDuOuuuxAeHq63rbgoVJd4H04YqocvERERmRHfH+VhromIiCrj+6M8zDUREVFloX5/rPUdPrySxVdN62VCCBQVFSE6OrrafPjTxui+zEjl2FXFnMvHnJMV1PI6EjIAaxFjqBy7qphz+ZhzsgLWIvIFWov42461CBmNOZePOScrCHUtUut76DZu3OjX11dffRWMeE3H4/HUuO/QoUMBtzG6LzNSOXZVMefyMedkBZzf8rEWMYbKsauKOZePOScr4PyWz0x1hsrnOZVjVxVzLh9zTlYQ6vnND00kIiIiIiIiIiIiIiJSHBd8iIiISFlCiJDfLk1EREREREREZAZc8AlQTZ83qWkaHA5HwG2M7suMVI5dVcy5fMw5WYGs+b148WK0b98ekZGRiIyMRPv27fHmm29KObbZsBYxhsqxq4o5l485Jyvg/JbPTHWGyuc5lWNXFXMuH3NOVhDq+a0JXhZ7TgoLC+F0OuFyuRAbGxvqcIiIiExBxvvjlClTMGfOHIwZMwY9e/YEAKSnp+PVV1/FhAkTMG3atKAc12xYixAREVXG90d5mGsiIqLKQv3+yDt8AlTTepkQAgUFBQG3MbovM1I5dlUx5/Ix52QFMub3/PnzsWjRIsycORMDBw7EwIEDMXPmTCxcuBCvv/560I9vNqxFjKFy7KpizuVjzskKOL/lM1OdofJ5TuXYVcWcy8eckxWEen5zwSdAHo+nxn25ubkBtzG6LzNSOXZVMefyMedkBTLmd0lJCbp161Zpe9euXVFaWhr045sNaxFjqBy7qphz+ZhzsgLOb/nMVGeofJ5TOXZVMefyMedkBaGe31zwISIiIqXceeedmD9/fqXtCxcuxO233x6CiIiIiIiIiIiIQi8s1AEQERER1dbixYvx+eef45JLLgEAbNmyBQcOHMBdd92FiRMn6u3mzJkTqhCJiIiIiIiIiKTigk+ANE2rcV90dHTAbYzuy4xUjl1VzLl8zDlZgYz5vXv3blx00UUAgMzMTABAfHw84uPjsXv3bqmxmAFrEWOoHLuqmHP5mHOyAs5v+cxUZ6h8nlM5dlUx5/Ix52QFoZ7fmgj1U4QUVVhYCKfTCZfLhdjY2FCHQ0REZAp8f5SHuSYiIqqM74/yMNdERESVhfr9kc/wCdDZHhZ49OjRgNsY3ZcZqRy7qphz+ZhzsgLOb/lYixhD5dhVxZzLx5yTFXB+y2emOkPl85zKsauKOZePOScrCPX85oJPgGq6QUoIgaNHjwbcxui+zEjl2FXFnMvHnJMVcH7Lx1rEGCrHrirmXD7mnKyA81s+M9UZKp/nVI5dVcy5fMw5WUGo5zcXfIiIiIiIiIiIiIiIiBTHBR8iIiIiIiIiIiIiIiLFccEnQJqm1bjP6XQG3MbovsxI5dhVxZzLx5yTFXB+y8daxBgqx64q5lw+5pysgPNbPjPVGSqf51SOXVXMuXzMOVlBqOe3JkL9oXKKKiwshNPphMvlQmxsbKjDISIiMgW+P8rDXBMREVXG90d5mGsiIqLKQv3+yDt8AuTxeGrcd/jw4YDbGN2XGakcu6qYc/mYc7ICzm/5WIsYQ+XYVcWcy8eckxVwfstnpjpD5fOcyrGrijmXjzknKwj1/OaCT4BqukFKCAGXyxVwG6P7MiOVY1cVcy4fc05WwPktH2sRY6gcu6qYc/mYc7ICzm/5zFRnqHyeUzl2VTHn8jHnZAWhnt9c8CEiIiIiIiIiIiIiIlIcF3yIiIiIiIiIiIiIiIgUxwWfAGmaVuO++Pj4gNsY3ZcZqRy7qphz+ZhzsgLOb/lYixhD5dhVxZzLx5yTFXB+y2emOkPl85zKsauKOZePOScrCPX81kSoP1ROUYWFhXA6nXC5XIiNjQ11OERERKbA90d5mGsiIqLK+P4oD3NNRERUWajfH3mHT4A8Hk+N+w4ePPifNm43sGkTsHx52Z9ud+U2/vZ1jm3MSuXYVcWcy8eckxVwfssXaG3AWqSMyrGrijmXjzknK+D8ls9MdYbK5zmVY1cVcy4fc05WEOr5HRbSo9cBNd0gJYRAUVFRWZvVq4Fx44BDh/7TIDkZ4uWXUdS+fY39VOorgDZmpXLsqmLO5WPOyQo4v+ULtDbw99zEWoSMxpzLx5yTFXB+y2emOkPl85zKsauKOZePOScrCPX85h0+MqxZAwwd6rvYAwA5ObANG4aYzz8PTVxERERERERERERERFQncMEnyLQTJ2AbNw6oamXv39sSZs4s+7g3IiIiIiIiIiIiIiKic8AFnwDZbNWn0GazoU337tD+/LPaNpoQCM/Nhe377896nMTExLMe72xtzErl2FXFnMvHnJMVcH7LF2ht4O+5ibUIGY05l485Jyvg/JbPTHWGyuc5lWNXFXMuH3NOVhDq+c1n+ARI07Rz2lepbW7uWY8TFxcXcBuzUjl2VTHn8jHnZAW1ee8jY5ytFjGqfmAtQkZjzuVjzskKWIvIF2gt4m871iJkNOZcPuacrCDUtQiXUwPk8Xhq3Hf4rbf86ych4azHycrKOuvxztbGrFSOXVXMuXzMOVkB57d8gdYG/p6bWIuQ0Zhz+ZhzsgLOb/nMVGeofJ5TOXZVMefyMedkBaGe31zwCZCo6tk85fa5uneHSE4GqlnZE5qGksREiMsuO+txiouLz3q8s7UxK5VjVxVzLh9zTlbA+S1foLWBv+cm1iJkNOZcPuacrIDzWz4z1Rkqn+dUjl1VzLl8zDlZQajnNxd8gs1uh+fll8v+XnHR59/f5z3xBGC3Sw6MiIiIiIiIiIiIiIjqCi74yHDjjcCqVUDTpr7bk5PhWbkSJ6+5JjRxERERERERERERERFRnRAW6gBUZ7NVv2Zms9mQnJxc1mbIEGDQIODbb4HDh4GkJKB377I2RUU19lOprwDamJXKsauKOZePOScr4PyWL9DawN9zE2sRMhpzLh9zTlbA+S2fmeoMlc9zKseuKuZcPuacrCDU85sLPgHSqnk2j3dfTEzMfzbY7UCfPr5tAN82/vZ1jm3MSuXYVcWcy8eckxXU9L5IwVGrWuQc2xjdlxmpHLuqmHP5mHOyAtYi8gVai/jbjrUIGY05l485JysIdS3C5dQAud3uGvft27cv4DZG92VGKseuKuZcPuacrIDzWz7WIsZQOXZVMefyMedkBZzf8pmpzlD5PKdy7KpizuVjzskKQj2/ueATZB6Px5A2RvdlRirHrirmXD7mnIhkYy3iP5VjVxVzLh9zTkSyya4zVD7PqRy7qphz+ZhzouDigg8REREREREREREREZHiuOBDRERERERERERERESkOE0IIUIdhIoKCwvhdDpRUFAAp9NZZRshBIqLi+FwOKp9WJM/bYzuy4xUjl1VzLl8zDlZgcvlQlxcHFwuF2JjY0MdTp3GWsRYKseuKuZcPuacrIC1iDxG1SL+tmMtQkZjzuVjzskKQl2L8A6fIAsLCzOkjdF9mZHKsauKOZePOSci2ViL+E/l2FXFnMvHnBORbLLrDJXPcyrHrirmXD7mnCi4uOAToJoeNObxeLB///6A2xjdlxmpHLuqmHP5mHOyAs5v+ViLGEPl2FXFnMvHnJMVcH7LZ6Y6Q+XznMqxq4o5l485JysI9fzmgg8REREREREREREREZHiuOBDRERERERERERERESkOC74EBERERERERERERERKU4TQohQB6GiwsJCOJ1OFBQUwOl0VtlGCAGPxwObzQZN0865jdF9mZHKsauKOZePOScrcLlciIuLg8vlQmxsbKjDqdNYixhL5dhVxZzLx5yTFbAWkceoWsTfdqxFyGjMuXzMOVlBqGsR3uETZKWlpYa0MbovM1I5dlUx5/Ix50QkG2sR/6kcu6qYc/mYcyKSTXadofJ5TuXYVcWcy8ecEwUXF3wC5PF4atyXnZ0dcBuj+zIjlWNXFXMuH3NOVsD5LR9rEWOoHLuqmHP5mHOyAs5v+cxUZ6h8nlM5dlUx5/Ix52QFoZ7fXPAhIiIiIiIiIiIiIiJSHBd8iIiIiIiIiIiIiIiIFMcFnyCz2c6eYn/aGN2XGakcu6qYc/mYcyKSjbWI/1SOXVXMuXzMORHJJrvOUPk8p3LsqmLO5WPOiYJLE0KIUAehosLCQjidTrhcLsTGxoY6HCIiIlPg+6M8zDUREVFlfH+Uh7kmIiKqLNTvj1xSDVBN62VCCJw8eTLgNkb3ZUYqx64q5lw+5pysgPNbPtYixlA5dlUx5/Ix52QFnN/ymanOUPk8p3LsqmLO5WPOyQpCPb+54BMgj8dT475Dhw4F3MbovsxI5dhVxZzLx5yTFXB+y8daxBgqx64q5lw+5pysgPNbPjPVGSqf51SOXVXMuXzMOVlBqOc3F3yIiIiIiIiIiIiIiIgUxwUfIiIiIiIiIiIiIiIixXHBJ0CaptW4z+FwBNzG6L7MSOXYVcWcy8eckxVwfsvHWsQYKseuKuZcPuacrIDzWz4z1Rkqn+dUjl1VzLl8zDlZQajntyZC/RQhRRUWFsLpdMLlciE2NjbU4RAREZkC3x/lYa6JiIgq4/ujPMw1ERFRZaF+f+QdPgGqab1MCIGCgoKA2xjdlxmpHLuqmHP5mHOyAs5v+ViLGEPl2FXFnMvHnJMVcH7LZ6Y6Q+XznMqxq4o5l485JysI9fzmgk+APB5Pjftyc3MDblOpndsNbNoELF9e9qfbXau+zEjl2FXFnMvHnJMVcH7LF5JaJMC+zEjl2FXFnMvHnJMVcH7LZ6Y6Q+XznMqxq4o5l485JysI9fwOC+nRqfbWrAEmTAAOHfrPtuRk4JVXgEGDQhcXERERERERERERERGFjFJ3+LzwwgvQNA3jx4/Xt50+fRqjR49Gw4YNERMTg5tuugl5eXn6/vz8fNxwww2IiYlBly5dsH37dp8+R48ejZdeeknWEAIS8/nnsA0b5rvYAwA5OcDQoWWLQUREREREREREREREZDnKLPhs3boVCxYsQMeOHX22T5gwAf/3f/+HDz74AF9//TX+/PNPDBkyRN8/Y8YMnDhxAj///DP69OmDe++9V9+3efNmbNmyxWcBqbY0TatxX3R0dMBtAEDzeJA4cyZQ1WcA/nubbfx4REdGnrUvM/I3D2Qc5lw+5pysgPNbPmm1iIF9mZHKsauKOZePOScr4PyWz0x1hsrnOZVjVxVzLh9zTlYQ6vmtiVA/RcgPJ0+exEUXXYTXX38d06dPR+fOnTF37ly4XC40atQIy5Ytw9ChQwEA//rXv3DBBRcgPT0dl1xyCfr374+BAwfigQcewJ49e9CtWzcUFRWhpKQEF198Md58801069btrDGcOXMGZ86c0b8vLCxESkoK8vPzERsbC6DsxbTZbPB4PD4PZ6puu81mg6Zp1W53//vZPACATZtg79v3rHG6v/wS6NMHAGC32yGE8PncQG8s1W33N3ZDxvTv7UDlzzasbjvHxDFxTBwTx2TuMRUWFqJBgwZwuVz6+yMFR2FhIZxOJ3NNRERUDt8f5WGuiYiIKgv1+6MSz/AZPXo0BgwYgL59+2L69On69m3btqGkpAR9yy2EtG3bFs2aNdMXfDp16oSvvvoK99xzD9avX6/fITR79mz06dPHr8UeAJg5cyamTp1aaXtGRgbq1asHAHA6nUhKSkJeXh5cLheEEDh16hSSk5PRuHFj5OTkoKioSP/ZxMRExMbG4tdff0VYWJi++pecnIyYmBhkZmbqv8irt307mvoR54EtW3C6SRPY7Xa0bt0aRUVFOFTuI+AcDgdatGgBl8uF3NxcfXt0dLS+gHX06FF9e8UxecXHxyM+Pr7KMcXFxeH3339HcXGxvr2qMQFAamoqwsLCsG/fPpw6dQpRUVHQNA1paWkoLS1Fdna23tZmsyk1pv379/u8NmYbk79zT6Uxmf11io2NhcPhQHFxMQoLC+vEmOri68QxBTamEydOgOQ624OL8/Pz0aBBA31h7lzaGN2XGakcu6qYc/mYc7KCUD8o2YoCrUX8bcdahIzGnMvHnJMVhLoWMf0dPitWrMCMGTOwdetWREZGok+fPvodPsuWLcPIkSN97rwBgO7du+PKK6/ErFmz4HK58OCDD+L7779H8+bNMX/+fISHh2PAgAFIT0/HU089hc8//xzdunXDokWL4HQ6q4yjujt8/vrrL9SvXx9A5Sul3W43MjIykJaWhvDw8CqvlPZ4PNi3bx9atmwJu92ub694BbXnq68Qfs01Z83X70uWoMltt8Futyt1VXhJSQkyMjLQqlUr2O32OnOluz/bQzUmf+eeSmMy++vk8XiQmZmJli1b+hQ2Ko+pLr5OHFNgYzp+/DgaNWrEKz0l8F41lJ+fr9ciFbndbuzfvx9paWn6uf5c2hjdlxmpHLuqmHP5mHOyguPHj/NuY0mMqkX8bcdahIzGnMvHnJMVhLoWMfUdPgcPHsS4cePwxRdfIDIy8pz6cDqdWLZsmc+2q666Ci+++CLee+89ZGVlYe/evbj33nsxbdo0vPTSS1X2ExERgYiIiErbvQsr5ZX/Ra7NZtO/r27lWtO0Kvvx+f6KK1CSmIiwvDxoVa3RaRpE06Y4ffHFPn15+67umBVVF6NR26s7mXsXeSrmoTaxm3FM/m4P1Zj8mnvnuJ2vU80xGpEDs43JiO0ck/pjYtFORERERERERBQapr53btu2bThy5AguuugihIWFISwsDF9//TXmzZuHsLAwJCQkoLi4GAUFBT4/l5eXh8TExCr7XLJkCeLi4jBo0CBs2rQJgwcPRnh4OG6++WZs2rQp+IM6V3Y78p54ouzvFR/89O/vPS+/DPAXbURERERERERERERElmPqO3yuvvpq7Nq1y2fbyJEj0bZtWzz22GNISUlBeHg4NmzYgJtuugkAsHfvXhw4cAA9e/as1N9ff/2FadOm4bvvvgNQdhthSUkJgLKPFKv4ETX+0CouvlTY53Q6A27jbWe/+WaIJk2gTZgAlHuWA5KTgblzoQ0eDGde3ln7MiN/80DGYc7lY87JCji/5ZNZixjVlxmpHLuqmHP5mHOyAs5v+cxUZ6h8nlM5dlUx5/Ix52QFoZ7fpn+GT0Xln+EDAA8++CA++eQTLF26FLGxsRgzZgwA4Icffqj0s7fffjt69uyJhx9+GAAwe/ZsrFy5Eu+++y4eeeQRpKam4rXXXvMrDu9n1Ybks/jcbuDbb4HDh4GkJKB3b97ZQ0REphDS90eLYa6JiIgq4/ujPMw1ERFRZaF+fzT1R7r54+WXX8b111+Pm266CZdffjkSExOxevXqSu3Wr1+PjIwMPPTQQ/q2hx9+GC1atECPHj1QXFyMZ555ptbHr/hw7Yr7Dh8+HHCbSu3sdqBPH+DWW8v+/Pdij799mZHKsauKOZePOScr4PyWLyS1SIB9mZHKsauKOZePOScr4PyWz0x1hsrnOZVjVxVzLh9zTlYQ6vmt3ILPpk2b9Lt7ACAyMhKvvfYa8vPzUVRUhNWrV1f5/J5+/fphy5YtPg+gPu+887By5UoUFhbiyy+/ROPGjWsdT003SAkh4HK5Am5jdF9mpHLsqmLO5WPOyQo4v+VjLWIMlWNXFXMuH3NOVsD5LZ+Z6gyVz3Mqx64q5lw+5pysINTzW7kFHyIiIiIiIiIiIiIiIvIVFuoAVOVdqSssLIS9mufnuN1unDx5MuA2RvdlRirHrirmXD7mnKygsLAQQOivaLEC1iLGUjl2VTHn8jHnZAWsReQxqhbxtx1rETIacy4fc05WEOpahAs+5+jYsWMAgObNm4c2ECIiIhM6duwYnE5nqMOo01iLEBERVY+1SPCxFiEiIqpeqGoRLvicowYNGgAADhw4UO0LV1hYiJSUFBw8eBCxsbHn3MbovsxI5dhVxZzLx5yTFbhcLjRr1kx/n6TgYS1iLJVjVxVzLh9zTlbAWkQeo2oRf9uxFiGjMefyMedkBaGuRbjgc45strLHHzmdzrOeoGJjYw1pY3RfZqRy7KpizuVjzskKvO+TFDysRYJD5dhVxZzLx5yTFbAWCT6jaxF/27EWIaMx5/Ix52QFoapFWAEREREREREREREREREpjgs+REREREREREREREREiuOCzzmKiIjAM888g4iIiKC3MbovM1I5dlUx5/Ix52QFnOfysBYxlsqxq4o5l485JyvgPJfHjHWGyq+/yrGrijmXjzknKwj1PNeEECIkRyYiIiIiIiIiIiIiIiJD8A4fIiIiIiIiIiIiIiIixXHBh4iIiIiIiIiIiIiISHFc8CEiIiIiIiIiIiIiIlIcF3yIiIiIiIiIiIiIiIgUZ+kFn2+++QY33HADmjRpAk3TsHbtWp/9QghcddVVCA8Ph6ZpcDgc6Nu3L/bu3au3yc/PR7du3aBpmv7VoEEDLFu2TG+zdetWNG7cWN8fFhaGSy65BJqmYfz48QCAAwcOoHv37j79eL/atm0LANi0aRMuvPBCn32tW7dGTk6OT9zvv/8+nE4nNE2D3W7Hddddh9LSUp82mzZtwkUXXYSIiAi0atUKS5cuNTx3U6ZMQVJSEqKiotC3b1/s37/fp01+fj5uv/12xMbGIi4uDnfffTdOnjzp02bnzp3o3bs3IiMjkZKSgtmzZ1eK5YMPPkDbtm0RGRmJDh064JNPPqnVWFQxc+ZMXHzxxahXrx4aN26MwYMH+8xFADh9+jRGjx6Nhg0bIiYmBjfddBPy8vJ82hw4cAADBgzAeeedh8aNG2PSpEnnND9ee+01NG/eHJGRkejRowd+/PFHw8ccavPnz0fHjh0RGxuL2NhY9OzZE59++qm+n/mmuuDZZ5+t9n0H4DwPNtYirEVUwlpEPtYiZAWsRUKLtQhrEZWwFpGPtQhZQZ2rRYSFffLJJ+Kpp54Sq1evFgDEmjVrfPa/8MILIiwsTIwZM0Z8+OGH4oorrhBRUVEiJSVFnDx5UgghxLXXXivq1asn4uLixFNPPSUSEhJEVFSU0DRN7N69W7hcLv37Cy64QDz88MMCgAAgGjduLMaNGydKS0tF69atBQARFhYmGjZsKCIjIwUA0bp1a/HXX3+JrKwsERUVJSIiIoTD4RDDhw8XmqYJu90umjZtqse8f/9+oWmaaNasmfjwww/FAw88IACIW265RW+TlZUlzjvvPDFx4kTx22+/iX/84x/CbreLzz77zNDcOZ1OsXbtWvHLL7+IgQMHitTUVHHq1Cm9zbXXXis6deokNm/eLL799lvRqlUrceutt+r7XS6XSEhIELfffrvYvXu3WL58uYiKihILFizQ23z//ffCbreL2bNni99++008/fTTIjw8XOzatcvvsaiiX79+YsmSJWL37t1ix44don///qJZs2b6XBRCiAceeECkpKSIDRs2iJ9++klccskl4tJLL9X3l5aWivbt24u+ffuK7du3i08++UTEx8eLJ554Qm/jz/xYsWKFcDgc4q233hK//vqruPfee0VcXJzIy8uTkwxJ1q1bJz7++GOxb98+sXfvXvHkk0+K8PBwsXv3biEE8011wzPPPCPatWsnDh8+rH/99ddf+n7O8+BiLcJaRCWsReRjLUJWwFoktFiLsBZRCWsR+ViLkBXUtVrE0gs+5VV8c/Z4PCIxMVG8+OKL+raCggLhcDgEAPH111+L3377TS9GPvjgAyGEEJ9++qleuDzxxBPipZdeEgCEpmkiNzdXnDhxQsTGxgoAwmaziYcfflh88skn+s/cfPPNolOnTmL+/PkiLCxMABDp6eni0UcfFcnJyQKAWLRokRBCiFtuuUW0bNlSABDffPONEEKIoUOHCgAiNzdXj7tr164iLCxMnDlzRgghxKOPPiratWvnM/5bbrlF9OvXL6i5i4iIEMuXLxdCCD13W7du1dt8+umnQtM0kZOTI4QQ4vXXXxf169fX4xZCiMcee0y0adNG/37YsGFiwIABPvH06NFD3H///ec0FpUcOXJEn4tClOU4PDxcn4tCCLFnzx59DglRVpDabDaf+TF//nwRGxtbq/nRvXt3MXr0aP17t9stmjRpImbOnGn8QE2mfv364s0332S+qc545plnRKdOnarcx3kuF2sR1iKqYS0SGqxFqK5hLWIerEVYi6iGtUhosBahuqau1SKW/ki3mmRnZyM3Nxd9+/bVtzmdTnTq1AkA0KBBA6SnpyM6OhqlpaV6uyuvvBKapgEA3G63fptju3btkJCQgNGjR+O//uu/AAAejwfHjh1Deno6IiMjAQAtWrTA/v378cwzz+i3fX3yySdIT09HTEwMAGDo0KEAgH79+um3j3lvHd6yZQvi4+ORkJCgxz1s2DCUlpbi119/BQCkp6f7jMvbV3p6uhGpqzZ3PXr00I+Rnp6OuLg4dOvWTW/Tt29f2Gw2bNmyRW9z+eWXw+Fw+MS5d+9eHD9+XMpYzMzlcgEom4sAsG3bNpSUlPjko23btmjWrJlP3jt06OAzP/r164fCwkK/50dxcTG2bdvm08Zms6Fv3751Ou9utxsrVqxAUVERevbsyXxTnbJ//340adIELVq0wO23344DBw4A4Hkl1FiLnDvWInKwFpGLtQjVZaxFzIm1yLljLSIHaxG5WItQXVaXahEu+FQjNzcXAHxeKI/Hg0OHDiE+Ph7t27dHbm4u6tWrB4fDgYMHDyImJgbR0dEAgMaNG+v9aJqGJk2aYMWKFfj555/x1FNP6X3+/fffyM3Nhcfjgd1uxxVXXIGlS5di3bp1epu5c+fizz//RHFxMTRNQ1xcnB6b97NdvZPQ5XKhfv36PmNp1aoVAOCPP/7QYyo/Lm9fhYWFOHXqVGCJQ9W5837v3Zebm6vnyCssLAwNGjTwaVNVH+WPUV0b7/66yuPxYPz48ejVqxfat28PoCwXDodDnx9eFfN+rjn1zo+jR4/C7XZbJu+7du1CTEwMIiIi8MADD2DNmjW48MILmW+qM3r06IGlS5fis88+w/z585GdnY3evXvjxIkTnOchxlrk3LEWCT7WIvKwFqG6jrWIebEWOXesRYKPtYg8rEWorqtrtUhYrVpb3OjRo+FyuXDVVVdV2temTRvs2LEDLpcLvXv3xtGjR3H06FF9/+nTpzFu3Dh88cUXCAurPu3XXXcdgLKCx+vMmTM4ceKEfiUL0ejRo7F792589913oQ6lziv/b3vVqlUYMWIEvv7661CHRWQY7/sOAHTs2BE9evTA+eefj5UrVyIqKiqEkVFVWIuQWbAWkYe1CNV1rEXUwlqEzIK1iDysRaiuq2u1CO/wqUZiYiIA6LcGP/zww/jnP/+Jdu3aoUWLFnqbEydOoLi4GH///TdatWqFTp06obi4GHa7Hb/99hsSExMhhMDvv/+OI0eO4KKLLkKHDh3046xbtw5vvvkmbDYb3G43CgoKfI6raRoSEhLgcDjgcDgghPBp4y12mjVrBqDsFmHvbb1eGRkZAIDzzz9fj9vbv1deXh5iY2MNmcQVc1f+GN59iYmJOHLkiM/+0tJS5Ofn+7Spqo/yx6iujXd/XeSdixs3bkRycrK+PTExEcXFxfr88KqY93PNqXd+xMfHw263WybvDocDrVq1QteuXTFz5kx06tQJr7zyCvNNdVZcXBxat26NjIwMzvMQYy1iXO7KH4O1SOBYi8jFWoSshrWIebAWMS535Y/BWiRwrEXkYi1CVqN6LcIFn2qkpqYiMTERX375JR5++GGsWbMG69atw86dO9GzZ08AQM+ePVFUVISwsDBs2LABAPDVV1/B7XajpKQEDRo00FcIDxw4gE2bNmHHjh0YMmQIgLLP4Rs+fDhee+01nD59GgD0frxXvHgLmTZt2qCoqAgAsHr1ar2N9zavQYMGASi7Be3o0aM+RcOqVasQFhaGCy+8UI/bexyvL774Qh+XUbkrf4zCwkJs2bLFJ3cFBQXYtm2b3uarr76Cx+NBjx499DbffPMNSkpKfOJs06aNfnt2sMdiJkIIfS5+9dVXSE1N9dnftWtXhIeH++Rj7969OHDggE/ed+3a5TM/vvjiC8TGxvo9PxwOB7p27erTxuPxYMOGDXUy7xV5PB6cOXOG+aY66+TJk8jMzERSUhLneYixFgk8d6xFjMVaxBxYi1Bdx1rEPFiLBJ471iLGYi1iDqxFqK5TvhYRFnbixAmxfft2sX37dgFAzJkzR2zfvl388ccfQgghXnjhBeFwOER0dLR46623RL9+/URKSorIzs4Wf//9txBCiGuvvVZERUWJBg0aiGeeeUYkJCSIqKgoAUB8/vnnoqCgQERFRQlN00S7du3EmDFjhKZpwmaziWbNmolx48aJ0tJS0bp1awFAhIWFiWHDhomIiAgBQERHR4v4+HixdetWERUVJSIjI0VERIS47bbbhKZpwm63i6ZNm+pj2r9/v9A0TTRv3lysWbNGjB49WgAQt9xyi94mKytLnHfeeWLSpEliz5494rXXXhN2u1189tlnhuYuLi5OfPTRR2Lnzp1i0KBBIjU1VZw6dUrv49prrxVdunQRW7ZsEd99951IS0sTt956q76/oKBAJCQkiDvvvFPs3r1brFixQpx33nliwYIFepvvv/9ehIWFif/5n/8Re/bsEc8884wIDw8Xu3btquVsML8HH3xQOJ1OsWnTJnH48GH9yzsXhRDigQceEM2aNRNfffWV+Omnn0TPnj1Fz5499f2lpaWiffv24pprrhE7duwQn332mWjUqJF44okn9Db+zI8VK1aIiIgIsXTpUvHbb7+J++67T8TFxYnc3Fw5yZDk8ccfF19//bXIzs4WO3fuFI8//rjQNE18/vnnQgjmm+qGRx55RGzatElkZ2eL77//XvTt21fEx8eLI0eOCCE4z4ONtQhrEZWwFpGPtQhZAWuR0GItwlpEJaxF5GMtQlZQ12oRSy/4bNy4UQCo9DVixAghhBAej6fK/QDEkiVLhBBCHDt2TKSmpvrsi4+PFytWrNCP8+OPP4pGjRrp++12u3jggQfE5ZdfLsaNGyeEEOL3338XF198sU8/4eHhYtCgQSIjI0OPt23btj5tWrVqJQ4ePOgzruXLl4t69eoJAMJms4l+/fqJkpKSSmPv3LmzcDgcokWLFvp4jMzd5MmTRUJCgoiIiBBXX3212Lt3r08fx44dE7feequIiYkRsbGxYuTIkeLEiRM+bX755Rdx2WWXiYiICNG0aVPxwgsvVIpl5cqVonXr1sLhcIh27dqJjz/+uFZjUcXZ5qIQQpw6dUo89NBDon79+uK8884TN954ozh8+LBPP7///ru47rrrRFRUlIiPjxePPPLIOc2Pf/zjH6JZs2bC4XCI7t27i82bNwdj2CE1atQocf755wuHwyEaNWokrr76ar2oEYL5prrhlltuEUlJScLhcIimTZuKW265RX/fEYLzPNhYi7AWUQlrEflYi5AVsBYJLdYirEVUwlpEPtYiZAV1rRbRhBACREREREREREREREREpCw+w4eIiIiIiIiIiIiIiEhxXPAhIiIiIiIiIiIiIiJSHBd8iIiIiIiIiIiIiIiIFMcFHyIiIiIiIiIiIiIiIsVxwYeIiIiIiIiIiIiIiEhxXPAhIiIiIiIiIiIiIiJSHBd8iIiIiIiIiIiIiIiIFMcFHyIiIiIiIiIiIiIiIsVxwYeI6rw+ffpA0zRomoYdO3aELI6lS5fqcYwfPz5kcRAREZFcrEWIiIgolFiLEFkHF3yIyMf/+3//T3/zLf917bXXhjq0gNx77704fPgw2rdv77P9ww8/xFVXXYX69esjKioKbdq0wahRo7B9+3a/+i0uLkZ8fDxeeOGFKvc/99xzSEhIQElJCW655RYcPnwYPXv2DHg8REREdRVrEdYiREREocRahLUIkcq44ENElVx77bU4fPiwz9fy5cuDeszi4uKg9n/eeechMTERYWFh+rbHHnsMt9xyCzp37ox169Zh7969WLZsGVq0aIEnnnjCr34dDgfuuOMOLFmypNI+IQSWLl2Ku+66C+Hh4YiKikJiYiIcDodh4yIiIqqLWIuwFiEiIgol1iKsRYhUxQUfIqokIiICiYmJPl/169fX92uahjfffBM33ngjzjvvPKSlpWHdunU+fezevRvXXXcdYmJikJCQgDvvvBNHjx7V9/fp0wcPP/wwxo8fj/j4ePTr1w8AsG7dOqSlpSEyMhJXXnkl3n77bWiahoKCAhQVFSE2NharVq3yOdbatWsRHR2NEydO+D3GzZs3Y/bs2ZgzZw7mzJmD3r17o1mzZujatSuefvppfPrppz7tP/roI1x00UWIjIxEixYtMHXqVJSWlgIA7r77buzbtw/fffedz898/fXXyMrKwt133+13XERERMRahLUIERFRaLEWYS1CpCou+BDROZk6dSqGDRuGnTt3on///rj99tuRn58PACgoKMBVV12FLl264KeffsJnn32GvLw8DBs2zKePt99+Gw6HA99//z3eeOMNZGdnY+jQoRg8eDB++eUX3H///Xjqqaf09tHR0Rg+fHilq0aWLFmCoUOHol69en7Hv3z5csTExOChhx6qcr+mafrfv/32W9x1110YN24cfvvtNyxYsABLly7FjBkzAAAdOnTAxRdfjLfeeqtSXJdeeinatm3rd1xERETkH9YirEWIiIhCibUIaxEiUxJEROWMGDFC2O12ER0d7fM1Y8YMvQ0A8fTTT+vfnzx5UgAQn376qRBCiOeee05cc801Pv0ePHhQABB79+4VQghxxRVXiC5duvi0eeyxx0T79u19tj311FMCgDh+/LgQQogtW7YIu90u/vzzTyGEEHl5eSIsLExs2rSp2jFdccUVYty4cT7brr32WtGxY0efbS+99JLPmAsKCoQQQlx99dXi+eef92n77rvviqSkJP37N954Q8TExIgTJ04IIYQoLCwU5513nnjzzTf9ioeIiIjKsBZhLUJERBRKrEVYixCpjHf4EFElV155JXbs2OHz9cADD/i06dixo/736OhoxMbG4siRIwCAX375BRs3bkRMTIz+5b2aIzMzU/+5rl27+vS5d+9eXHzxxT7bunfvXun7du3a4e233wYA/O///i/OP/98XH755QGOGhg1ahR27NiBBQsWoKioCEIIfTzTpk3zGY/3YYd///03AODWW2+F2+3GypUrAQDvv/8+bDYbbrnlloDjIiIishrWIqxFiIiIQom1CGsRIlWFnb0JEVlNdHQ0WrVqVWOb8PBwn+81TYPH4wEAnDx5EjfccANmzZpV6eeSkpJ8jnMu7rnnHrz22mt4/PHHsWTJEowcOdLnVmN/pKWl4bvvvkNJSYk+lri4OMTFxeHQoUM+bU+ePImpU6diyJAhlfqJjIwEAMTGxmLo0KFYsmQJRo0ahSVLlmDYsGGIiYk5pzESERFZGWsR1iJEREShxFqEtQiRqniHDxEZ7qKLLsKvv/6K5s2bo1WrVj5fNRUzbdq0wU8//eSzbevWrZXa3XHHHfjjjz8wb948/PbbbxgxYkStY7z11ltx8uRJvP76636NZ+/evZXG0qpVK9hs/zmN3n333fjuu+/wz3/+Ez/88AMfSkhERBQirEVYixAREYUSaxHWIkShwgUfIqrkzJkzyM3N9fk6evSo3z8/evRo5Ofn49Zbb8XWrVuRmZmJ9evXY+TIkXC73dX+3P33349//etfeOyxx7Bv3z6sXLkSS5cuBeD7sMD69etjyJAhmDRpEq655hokJyfXeow9e/bEI488gkceeQQTJ07Ed999hz/++AObN2/G4sWLoWmaXrRMmTIF77zzDqZOnYpff/0Ve/bswYoVK/D000/79Hn55ZejVatWuOuuu9C2bVtceumltY6LiIiIWIuwFiEiIgot1iKsRYhUxQUfIqrks88+Q1JSks/XZZdd5vfPN2nSBN9//z3cbjeuueYadOjQAePHj0dcXJzPlR8VpaamYtWqVVi9ejU6duyI+fPn46mnngIARERE+LS9++67UVxcjFGjRp3bIAH8z//8D5YtW4bt27fj+uuvR1paGm6++WZ4PB6kp6cjNjYWANCvXz/885//xOeff46LL74Yl1xyCV5++WWcf/75Pv1pmoZRo0bh+PHjAcVFRERkdaxFWIsQERGFEmsR1iJEqtKE9+lbREQmNGPGDLzxxhs4ePCgz/Z3330XEyZMwJ9//gmHw1FjH3369EHnzp0xd+7cIEbqP7PFQ0RERNVjLUJEREShxFqEiGqDd/gQkam8/vrr2Lp1K7KysvDuu+/ixRdf9Pks2r///huZmZl44YUXcP/995+1qCnfb0xMDHbt2hWs0M/qvffeQ0xMDL799tuQxUBEREQ1Yy1CREREocRahIgCwTt8iMhUJkyYgPfffx/5+flo1qwZ7rzzTjzxxBMICwsDADz77LOYMWMGLr/8cnz00UeIiYk5a585OTk4deoUAKBZs2Z+F0NGO3HiBPLy8gAAcXFxiI+PD0kcREREVD3WIkRERBRKrEWIKBBc8CEiIiIiIiIiIiIiIlIcP9KNiIiIiIiIiIiIiIhIcVzwISIiIiIiIiIiIiIiUhwXfIiIiIiIiIiIiIiIiBTHBR8iIiIiIiIiIiIiIiLFccGHiIiIiIiIiIiIiIhIcVzwISIiIiIiIiIiIiIiUhwXfIiIiIiIiIiIiIiIiBTHBR8iIiIiIiIiIiIiIiLF/X+Q+nXBPdYMcwAAAABJRU5ErkJggg==", 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", "text/plain": [ "
      " ] @@ -146,13 +138,13 @@ ], "source": [ "def help_calc_efficiencies(df, bins, mask=None):\n", - " n_trues = []\n", - " n_pred = []\n", - " n_fakes = []\n", - " n_matched = []\n", " efficiencies = []\n", + " efficiencies_err = []\n", " fake_rate = []\n", + " fake_rate_err = []\n", " corr_class_prob = []\n", + " corr_class_prob_err = [] \n", + " \n", " mask_predicted = np.isnan(df['pred_energy']) == False\n", " mask_truth = np.isnan(df['truthHitAssignedEnergies']) == False\n", " \n", @@ -166,8 +158,8 @@ " mask_PID_truth = df['truthHitAssignedPIDs'].isin([130,310,311,2112,-2112,3122,-3122,3322,-3322])\n", " else:\n", " raise ValueError(\"mask must be 0 or 1\")\n", + " mask_PID_pred = df['pred_id_value'].isin([mask])\n", " \n", - " mask_PID_pred = df['pred_id'].isin([mask])\n", " mask_PID_matched = np.logical_and(mask_PID_truth, mask_PID_pred)\n", " \n", " \n", @@ -175,26 +167,34 @@ " mask_bintruth = np.logical_and(\n", " df['truthHitAssignedEnergies'] >= bins[i],\n", " df['truthHitAssignedEnergies'] < bins[i + 1])\n", - " n_trues.append(np.sum(mask_bintruth))\n", " mask_binpred = np.logical_and(\n", " df['pred_energy'] >= bins[i],\n", " df['pred_energy'] < bins[i + 1])\n", - " n_pred.append(np.sum(mask_binpred))\n", "\n", " matched = np.logical_and(\n", " mask_predicted,\n", " mask_bintruth)\n", - " n_matched.append(np.sum(matched))\n", " faked = np.logical_and(\n", " mask_binpred,\n", " np.logical_not(mask_truth))\n", - " n_fakes.append(np.sum(faked))\n", - "\n", - " efficiencies.append(np.sum(matched[mask_PID_truth]) / np.sum(mask_bintruth[mask_PID_truth]))\n", - " fake_rate.append(np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]))\n", " \n", - " corr_class_prob.append(np.sum(matched[mask_PID_matched]) / np.sum(mask_bintruth[mask_PID_matched]))\n", - " return np.array(n_trues), np.array(n_pred), np.array(n_matched), np.array(n_fakes), np.array(efficiencies), np.array(fake_rate), np.array(corr_class_prob)\n", + " eff = np.sum(matched[mask_PID_truth]) / np.sum(mask_bintruth[mask_PID_truth])\n", + " eff_err = np.sqrt(eff * (1 - eff) / np.sum(matched[mask_PID_truth])) \n", + " efficiencies.append(eff)\n", + " efficiencies_err.append(eff_err)\n", + " \n", + " fake = np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred])\n", + " fake_err = np.sqrt(fake * (1 - fake) / np.sum(faked[mask_PID_pred]))\n", + " fake_rate.append(fake)\n", + " fake_rate_err.append(fake_err)\n", + " \n", + " cc_prob = np.sum(matched[mask_PID_matched]) / np.sum(matched[mask_PID_truth])\n", + " cc_prob_err = np.sqrt(cc_prob * (1 - cc_prob) / np.sum(matched[mask_PID_truth]))\n", + " corr_class_prob.append(cc_prob)\n", + " corr_class_prob_err.append(cc_prob_err)\n", + " \n", + " return np.array(efficiencies), np.array(efficiencies_err), np.array(fake_rate), np.array(fake_rate_err), np.array(corr_class_prob), np.array(corr_class_prob_err)\n", + "\n", "def efficiency_plots(df, bins):\n", " # Calculate the bin positions and widths\n", " binwidth = bins[1:] - bins[:-1]\n", @@ -202,68 +202,42 @@ " x_err = binwidth / 2\n", " \n", " # Calculate the efficiencies and fake rates for the total\n", - " n_trues, n_pred, n_matched, n_fakes, efficiencies, fake_rate, corr_class_prob= help_calc_efficiencies(df, bins)\n", - " \n", - " y_eff = efficiencies *100\n", - " yerr_eff = np.sqrt(efficiencies * (1 - efficiencies) / n_trues)\n", - " \n", - " y_fake = fake_rate *100\n", - " yerr_fake = np.sqrt(fake_rate * (1 - fake_rate) / n_pred)\n", - " \n", - " y_corr = corr_class_prob*100\n", - " yerr_corr = np.sqrt(corr_class_prob * (1 - corr_class_prob) / n_trues)\n", + " yeff, yerr_eff, yfake, yerr_fake, ycorr, yerr_corr = help_calc_efficiencies(df, bins)\n", + " yeff, yerr_eff, yfake, yerr_fake, ycorr, yerr_corr = \\\n", + " yeff*100, yerr_eff*100, yfake*100, yerr_fake*100, ycorr*100, yerr_corr*100\n", " \n", " # Calculate the efficiencies and fake rates for the photons\n", - " n_trues_photon, n_pred_photon, n_matched_photon, n_fakes_photon, efficiencies_photon, \\\n", - " fake_rate_photon, corr_class_prob_photon = help_calc_efficiencies(df, bins,0)\n", - " \n", - " \n", - " y_eff_photon = efficiencies_photon*100\n", - " yerr_eff_photon = np.sqrt(efficiencies_photon * (1 - efficiencies_photon) / n_trues_photon)\n", - " \n", - " y_fake_photon = fake_rate_photon*100\n", - " yerr_fake_photon = np.sqrt(fake_rate_photon * (1 - fake_rate_photon) / n_pred_photon)\n", - " \n", - " y_corr_photon = corr_class_prob_photon*100\n", - " yerr_corr_photon = np.sqrt(corr_class_prob_photon * (1 - corr_class_prob_photon) / n_trues_photon)\n", + " yeff_photon, yerr_eff_photon, yfake_photon, yerr_fake_photon, ycorr_photon, yerr_corr_photon = help_calc_efficiencies(df, bins, 0)\n", + " yeff_photon, yerr_eff_photon, yfake_photon, yerr_fake_photon, ycorr_photon, yerr_corr_photon = \\\n", + " yeff_photon*100, yerr_eff_photon*100, yfake_photon*100, yerr_fake_photon*100, ycorr_photon*100, yerr_corr_photon*100\n", " \n", " # Calculate the efficiencies and fake rates for the neutral hadrons\n", " #130: \"K0L\", 310: \"K0S\", 311: \"K0\", 2112: \"neutron\",-2112: \"antineutron\",3122: \"Lambda\",-3122: \"antilambda\"3322: \"Xi0\",-3322: \"antixi0\"\n", - " \n", - " n_trues_neutral_hadrons, n_pred_neutral_hadrons, n_matched_neutral_hadrons, \\\n", - " n_fakes_neutral_hadrons, efficiencies_neutral_hadrons, fake_rate_neutral_hadrons, corr_class_prob_neutral_hadrons \\\n", - " = help_calc_efficiencies(df, bins, 1)\n", - " \n", - " y_eff_neutral_hadrons = efficiencies_neutral_hadrons*100\n", - " yerr_eff_neutral_hadrons = np.sqrt(efficiencies_neutral_hadrons * (1 - efficiencies_neutral_hadrons) / n_trues_neutral_hadrons)\n", - " \n", - " y_fake_neutral_hadrons = fake_rate_neutral_hadrons*100\n", - " yerr_fake_neutral_hadrons = np.sqrt(fake_rate_neutral_hadrons * (1 - fake_rate_neutral_hadrons) / n_pred_neutral_hadrons)\n", - " \n", - " y_corr_neutral_hadrons = corr_class_prob_neutral_hadrons*100\n", - " yerr_corr_neutral_hadrons = np.sqrt(corr_class_prob_neutral_hadrons * (1 - corr_class_prob_neutral_hadrons) / n_trues_neutral_hadrons)\n", + " yeff_nh, yerr_eff_nh, yfake_nh, yerr_fake_nh, ycorr_nh, yerr_corr_nh = help_calc_efficiencies(df, bins, 1)\n", + " yeff_nh, yerr_eff_nh, yfake_nh, yerr_fake_nh, ycorr_nh, yerr_corr_nh = \\\n", + " yeff_nh*100, yerr_eff_nh*100, yfake_nh*100, yerr_fake_nh*100, ycorr_nh*100, yerr_corr_nh*100\n", " \n", " # Create the plots\n", " fig, ((ax1, ax2, ax3), (ax4, ax5,ax6), (ax7,ax8,ax9)) = plt.subplots(nrows=3, ncols=3, figsize=(20, 10))\n", - " ax1.errorbar(x_pos, y_eff, xerr=x_err, yerr=yerr_eff, fmt='o', color='red', label='Efficiency')\n", + " ax1.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o', color='red', label='Efficiency')\n", " ax1.set_ylabel('efficiency total', fontsize=10)\n", - " ax2.errorbar(x_pos, y_eff_photon, xerr=x_err, yerr=yerr_eff_photon, fmt='o', color='red', label='Efficiency')\n", + " ax2.errorbar(x_pos, yeff_photon, xerr=x_err, yerr=yerr_eff_photon, fmt='o', color='red', label='Efficiency')\n", " ax2.set_ylabel('efficiency gamma', fontsize=10)\n", - " ax3.errorbar(x_pos, y_eff_neutral_hadrons, xerr=x_err, yerr=yerr_eff_neutral_hadrons, fmt='o', color='red', label='Efficiency')\n", + " ax3.errorbar(x_pos, yeff_nh, xerr=x_err, yerr=yerr_eff_nh, fmt='o', color='red', label='Efficiency')\n", " ax3.set_ylabel('efficiency n.H.', fontsize=10)\n", " \n", - " ax4.errorbar(x_pos, y_fake, xerr=x_err, yerr=yerr_fake, fmt='o', color='red', label='Fake rate')\n", + " ax4.errorbar(x_pos, yfake, xerr=x_err, yerr=yerr_fake, fmt='o', color='red', label='Fake rate')\n", " ax4.set_ylabel('fake rate total', fontsize=10)\n", - " ax5.errorbar(x_pos, y_fake_photon, xerr=x_err, yerr=yerr_fake_photon, fmt='o', color='red', label='Fake rate')\n", + " ax5.errorbar(x_pos, yfake_photon, xerr=x_err, yerr=yerr_fake_photon, fmt='o', color='red', label='Fake rate')\n", " ax5.set_ylabel('fake rate gamma', fontsize=10)\n", - " ax6.errorbar(x_pos, y_fake_neutral_hadrons, xerr=x_err, yerr=yerr_fake_neutral_hadrons, fmt='o', color='red', label='Fake rate')\n", + " ax6.errorbar(x_pos, yfake_nh, xerr=x_err, yerr=yerr_fake_nh, fmt='o', color='red', label='Fake rate')\n", " ax6.set_ylabel('fake rate n.H.', fontsize=10)\n", " \n", - " ax7.errorbar(x_pos, y_corr, xerr=x_err, yerr=yerr_corr, fmt='o', color='red', label='probability of correct class')\n", + " ax7.errorbar(x_pos, ycorr, xerr=x_err, yerr=yerr_corr, fmt='o', color='red', label='probability of correct class')\n", " ax7.set_ylabel('p corr total', fontsize=10)\n", - " ax8.errorbar(x_pos, y_corr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', color='red', label='probability of correct class')\n", + " ax8.errorbar(x_pos, ycorr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', color='red', label='probability of correct class')\n", " ax8.set_ylabel('p corr gamma', fontsize=10)\n", - " ax9.errorbar(x_pos, y_corr_neutral_hadrons, xerr=x_err, yerr=yerr_corr_neutral_hadrons, fmt='o', color='red', label='probability of correct class')\n", + " ax9.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', color='red', label='probability of correct class')\n", " ax9.set_ylabel('p corr n.H.', fontsize=10)\n", "\n", " for ax in [ax1, ax2,ax3, ax4, ax5, ax6, ax7, ax8, ax9]: \n", @@ -293,12 +267,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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      " ] @@ -308,7 +302,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -326,21 +320,29 @@ "nParicles_truth = []\n", "\n", "for i in range(len(t_list)):\n", - " # reseta.append(np.sum(t_list[i]['truthHitAssignedEta'])-np.sum(pred_list[i]['pred_eta']))\n", - " # resphi.append(np.sum(t_list[i]['truthHitAssignedPhi'])-np.sum(pred_list[i]['pred_phi']))\n", - " jet_E_truth = np.sum(np.unique(t_list[i]['truthHitAssignedEnergies']))\n", - " #remove nan values\n", - " help = np.unique(df[df['event_id']==i]['pred_energy'])\n", - " jet_E_pred = np.sum(help[np.isnan(help)==False])\n", + " mask = np.logical_and(df['event_id'] == i, matched)\n", + " jet_E_truth = np.sum(df[mask]['truthHitAssignedEnergies'])\n", + " jet_E_pred = np.sum(df[mask]['pred_energy'])\n", " relresE.append((jet_E_truth-jet_E_pred)/jet_E_truth)\n", - " nParicles_pred.append(len(np.unique(df[df['event_id']==i]['pred_energy'])))\n", - " nParicles_truth.append(len(np.unique(t_list[i]['truthHitAssignementIdx'])))\n", " \n", - "# plt.hist(reseta)\n", - "# plt.hist(resphi)\n", + " nParicles_pred.append(len(df[mask]['pred_sid']))\n", + " nParicles_truth.append(len(df[mask]['truthHitAssignementIdx']))\n", + " \n", + " t_eta = np.mean(df[mask]['truthHitAssignedZ'])\n", + " pred_eta = df[mask]['pred_pos'].apply(lambda x: x[2]).mean()\n", + " reseta.append(t_eta-pred_eta)\n", + " \n", + " t_phi = np.mean(np.arctan2(df[mask]['truthHitAssignedY'], df[mask]['truthHitAssignedX']))\n", + " pred_phi = df[mask]['pred_pos'].apply(lambda x: np.arctan2(x[1], x[0])).mean()\n", + " delta_phi = t_phi-pred_phi\n", + " delta_phi = np.where(delta_phi > np.pi, delta_phi-2*np.pi, delta_phi)\n", + " delta_phi = np.where(delta_phi < -np.pi, delta_phi+2*np.pi, delta_phi)\n", + " resphi.append(delta_phi)\n", "\n", - "plt.hist(nParicles_pred, label='pred', histtype='step')\n", - "plt.hist(nParicles_truth, label='truth', histtype='step')\n", + "bins = np.linspace(1, 18, 9)\n", + "plt.hist(nParicles_pred, bins=bins, label='pred', histtype='step')\n", + "plt.hist(nParicles_truth, bins=bins, label='truth', histtype='step')\n", + "plt.text(0.95, 0.95, 'Quark Jet', horizontalalignment='right', verticalalignment='top', transform=plt.gca().transAxes)\n", "plt.ylabel('Number of events')\n", "plt.xlabel('Number of particles in event')\n", "plt.legend()\n", @@ -350,7 +352,44 @@ "plt.ylabel('Number of events')\n", "plt.xlabel('Relative Jet energy residual')\n", "plt.show()\n", - "\n" + "\n", + "plt.hist(reseta)\n", + "plt.ylabel('Number of events')\n", + "plt.xlabel('Jet Eta residual')\n", + "plt.show()\n", + "\n", + "plt.hist(resphi)\n", + "plt.ylabel('Number of events')\n", + "plt.xlabel('Jet Phi residual')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.linspace(1, 18, 9)\n", + "#set color to #67c4ce\n", + "plt.hist(nParicles_pred, bins=bins, label='pred', histtype='step', color='#67c4ce')\n", + "#plt.hist(nParicles_truth, bins=bins, label='truth', histtype='step')\n", + "plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes)\n", + "plt.ylabel('Number of events')\n", + "plt.xlabel(\"$\\\\Delta \\\\eta$\")\n", + "plt.legend()\n", + "plt.show()" ] }, { @@ -362,27 +401,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "vscode": { "languageId": "ruby" } }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.1108510494232178\n", - "-2.6898539066314697\n", - "3.127699375152588\n", - "-3.1045305728912354\n", - "3.9383437037467957\n" - ] - }, { "data": { - "image/png": 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", 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5ealx48Z8NxgAAKiUyhyIYmNjdfHiRdlsNnXr1k0hISGurAsAAKDcmFpDVK1aNT355JMqLi52VT0AAADlzvSi6jvuuEPffvutK2oBAABwC9PfZfb3v/9dY8aM0bFjx9SuXTvVqlXLYX/r1q2dVhwAAEB5MB2I+vfvL8nxO8tsNpv9cvyioiLnVQcAAFAOTAcivroDAABUNaYDUaNGjVxRBwAAgNuYDkSXfffdd8rIyFBBQYFD+wMPPPCniwIAAChPpgPRoUOH9Je//EW7d++2rx2SZL9zNWuIAABAZWP6svunn35aoaGhOnnypGrWrKk9e/Zo06ZNat++vTZu3OiCEgEAAFzL9AxRamqq1q9fr3r16snDw0MeHh7q2LGjZs2apZEjR3KPIgAAUOmYniEqKiqSr6+vJKlevXo6fvy4pEuLrffv3+/c6gAAAMqB6Rmili1baufOnQoNDVVERIQSExPl5eWlt956SzfddJMragQAAHAp04Fo0qRJysvLkyTNmDFD9913nzp16qTrrrtOS5YscXqBAAAArmY6EMXExNh/btq0qfbt26fTp0+rTp069ivNAAAAKpNrvg+RJB09elSSFBIS4pRiAAAA3MH0ouqLFy9q8uTJ8vf3V+PGjdW4cWP5+/tr0qRJKiwsdEWNAAAALmV6huipp57S0qVLlZiYqMjISEmXLsWfNm2aTp06pTfffNPpRQIAALiS6UC0ePFivf/+++rZs6e9rXXr1goJCdGAAQMIRAAAoNIxfcrM29tbjRs3LtEeGhoqLy8vZ9QEAABQrkwHovj4eM2cOVP5+fn2tvz8fD333HOKj493anEAAADlwfQps2+//VYpKSm64YYb1KZNG0nSzp07VVBQoO7du6tPnz72vkuXLnVepQAAAC5iOhAFBASob9++Dm1cdg8AACoz04Fo4cKFrqgDAADAbUyvIQIAAKhqCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyynSV2bx588o84MiRI6+5GAAAAHcoUyCaO3dumQaz2WwEIgAAUOmUKRAdPnzY1XUAAAC4DWuIAACA5Zm+U7UkHTt2TJ9++qkyMjJUUFDgsO/ll192SmEAAADlxXQgSklJ0QMPPKCbbrpJ+/btU8uWLXXkyBEZhqHw8HBX1AgAAOBSpk+ZTZw4UWPHjtXu3bvl4+Ojjz/+WEePHtXdd9+tfv36uaJGAAAAlzIdiPbu3avBgwdLkqpVq6bz58+rdu3amjFjhl544QWnFwgAAOBqpgNRrVq17OuGGjZsqIMHD9r3/fLLL86rDAAAoJyYXkPUoUMHffnll2rRooXuvfdejRkzRrt379bSpUvVoUMHV9QIAADgUqYD0csvv6zc3FxJ0vTp05Wbm6slS5bo5ptv5gozAABQKZkKREVFRTp27Jhat24t6dLps/nz57ukMAAAgPJiag2Rp6enoqOjdebMGVfVAwAAUO5ML6pu2bKlDh065IpaAAAA3MJ0IPrnP/+psWPHasWKFTpx4oRycnIcHgAAAJWN6UXV9957ryTpgQcekM1ms7cbhiGbzaaioiLnVQcAAFAOTM8Qbdiwwf5Yv369/XF524xNmzbp/vvvV3BwsGw2mz755BOH/Y899phsNpvDo0ePHg59Tp8+rYEDB8rPz08BAQEaOnSo/Sq4y3bt2qVOnTrJx8dHISEhSkxMNHvYAACgCjM9QxQaGqqQkBCH2SHp0gzR0aNHTY2Vl5enNm3a6PHHH1efPn1K7dOjRw8tXLjQvu3t7e2wf+DAgTpx4oSSk5NVWFioIUOGaMSIEVq8eLEkKScnR9HR0YqKitL8+fO1e/duPf744woICNCIESNM1QsAAKqmawpEJ06cUIMGDRzaT58+rdDQUFOnzHr27KmePXtetY+3t7eCgoJK3bd3716tXr1a27ZtU/v27SVJr732mu6991699NJLCg4O1nvvvaeCggItWLBAXl5euvXWW5WWlqaXX36ZQAQAACRdwymzy2uFfi83N1c+Pj5OKeq3Nm7cqAYNGqh58+Z68sknderUKfu+1NRUBQQE2MOQJEVFRcnDw0Nbt2619+ncubO8vLzsfWJiYrR///4r3j4gPz+fxeIAAFhImWeIEhISJEk2m02TJ09WzZo17fuKioq0detWtW3b1qnF9ejRQ3369FFoaKgOHjyof/zjH+rZs6dSU1Pl6empzMzMEjNV1apVU926dZWZmSlJyszMVGhoqEOfwMBA+746deqUeN1Zs2Zp+vTpTj0WAABQcZU5EH377beSLs0Q7d6922HGxcvLS23atNHYsWOdWlz//v3tP7dq1UqtW7dWkyZNtHHjRnXv3t2pr/VbEydOtAdA6dI6pJCQEJe9HgAAcK8yB6INGzZIkoYMGaJXX31Vfn5+LivqSm666SbVq1dPBw4cUPfu3RUUFKSTJ0869Ll48aJOnz5tX3cUFBSkrKwshz6Xt6+0Nsnb27vE4m0AAFB1mV5DtHDhQreEIUk6duyYTp06pYYNG0qSIiMjdfbsWe3YscPeZ/369SouLlZERIS9z6ZNm1RYWGjvk5ycrObNm5d6ugwAAFiP6avMunXrdtX9Zu5FlJubqwMHDti3Dx8+rLS0NNWtW1d169bV9OnT1bdvXwUFBengwYMaN26cmjZtqpiYGElSixYt1KNHDw0fPlzz589XYWGh4uPj1b9/fwUHB0uSHnnkEU2fPl1Dhw7V+PHjlZ6erldffVVz5841e+gAAKCKMh2I2rRp47BdWFiotLQ0paenKzY21tRY27dvV9euXe3bl9ftxMbG6s0339SuXbu0aNEinT17VsHBwYqOjtbMmTMdTme99957io+PV/fu3eXh4aG+fftq3rx59v3+/v5au3at4uLi1K5dO9WrV09TpkzhknsAAGBnMwzDcMZA06ZNU25url566SVnDFeh5OTkyN/fX9nZ2W47XQgAQFk1nrDS3SWYdmR2L6ePaebz2/Qaoit59NFHtWDBAmcNBwAAUG6cFohSU1NdcmNGAAAAVzO9huj33zlmGIZOnDih7du3a/LkyU4rDAAAoLyYDkT+/v4O2x4eHmrevLlmzJih6OhopxUGAABQXkwHot9+8zwAAEBVcE1riM6ePau3335bEydO1OnTpyVJ33zzjX766SenFgcAAFAeTM8Q7dq1S927d1dAQICOHDmi4cOHq27dulq6dKkyMjL07rvvuqJOAAAAlzE9Q5SQkKAhQ4bohx9+cLiq7N5779WmTZucWhwAAEB5MB2Itm3bpr/97W8l2q+//nplZmY6pSgAAIDyZDoQeXt7Kycnp0T7999/r/r16zulKAAAgPJkOhA98MADmjFjhv3b4202mzIyMjR+/Hj17dvX6QUCAAC4mulANGfOHOXm5qpBgwY6f/687r77bjVt2lS+vr567rnnXFEjAACAS13TjRmTk5P15ZdfateuXcrNzVV4eLiioqJcUR8AAIDLmQ5El3Xs2FEdO3Z0Zi0AAABucU2BKCUlRSkpKTp58qSKi4sd9vGN9wAAoLIxHYimT5+uGTNmqH379mrYsKFsNpsr6gIAACg3pgPR/PnzlZSUpEGDBrmiHgAAgHJn+iqzgoIC3Xnnna6oBQAAwC1MB6Jhw4Zp8eLFrqgFAADALUyfMrtw4YLeeustrVu3Tq1bt1b16tUd9r/88stOKw4AAKA8XNO33bdt21aSlJ6e7rCPBdYAAKAyMh2INmzY4Io6AAAA3Mb0GiIAAICqhkAEAAAsj0AEAAAsj0AEAAAsr0yBKDw8XGfOnJEkzZgxQ+fOnXNpUQAAAOWpTIFo7969ysvLk3Tpu8xyc3NdWhQAAEB5KtNl923bttWQIUPUsWNHGYahl156SbVr1y6175QpU5xaIAAAgKuVKRAlJSVp6tSpWrFihWw2m1atWqVq1Uo+1WazEYgAAEClU6ZA1Lx5c73//vuSJA8PD6WkpKhBgwYuLQwAAKC8mL5TdXFxsSvqAAAAcBvTgUiSDh48qFdeeUV79+6VJIWFhenpp59WkyZNnFocAABAeTB9H6I1a9YoLCxMX3/9tVq3bq3WrVtr69atuvXWW5WcnOyKGgEAAFzK9AzRhAkTNHr0aM2ePbtE+/jx43XPPfc4rTgAAIDyYHqGaO/evRo6dGiJ9scff1zfffedU4oCAAAoT6YDUf369ZWWllaiPS0tjSvPAABApWT6lNnw4cM1YsQIHTp0SHfeeackafPmzXrhhReUkJDg9AIBAABczXQgmjx5snx9fTVnzhxNnDhRkhQcHKxp06Zp5MiRTi8QAADA1UwHIpvNptGjR2v06NH69ddfJUm+vr5OLwwAAKC8XNN9iC4jCAEAgKrA9KJqAACAqoZABAAALI9ABAAALM9UICosLFT37t31ww8/uKoeAACAcmcqEFWvXl27du1yVS0AAABuYfqU2aOPPqp33nnHFbUAAAC4henL7i9evKgFCxZo3bp1ateunWrVquWw/+WXX3ZacQAAAOXBdCBKT09XeHi4JOn777932Gez2ZxTFQAAQDkyHYg2bNjgijoAAADc5povuz9w4IDWrFmj8+fPS5IMw3BaUQAAAOXJdCA6deqUunfvrmbNmunee+/ViRMnJElDhw7VmDFjnF4gAACAq5kORKNHj1b16tWVkZGhmjVr2tsffvhhrV692qnFAQAAlAfTa4jWrl2rNWvW6IYbbnBov/nmm/Xjjz86rTAAAIDyYnqGKC8vz2Fm6LLTp0/L29vb1FibNm3S/fffr+DgYNlsNn3yyScO+w3D0JQpU9SwYUPVqFFDUVFRJe6Sffr0aQ0cOFB+fn4KCAjQ0KFDlZub69Bn165d6tSpk3x8fBQSEqLExERTdQIAgKrNdCDq1KmT3n33Xfu2zWZTcXGxEhMT1bVrV1Nj5eXlqU2bNnr99ddL3Z+YmKh58+Zp/vz52rp1q2rVqqWYmBhduHDB3mfgwIHas2ePkpOTtWLFCm3atEkjRoyw78/JyVF0dLQaNWqkHTt26MUXX9S0adP01ltvmTxyAABQVdkMk5eHpaenq3v37goPD9f69ev1wAMPaM+ePTp9+rQ2b96sJk2aXFshNpuWLVum3r17S7o0OxQcHKwxY8Zo7NixkqTs7GwFBgYqKSlJ/fv31969exUWFqZt27apffv2kqTVq1fr3nvv1bFjxxQcHKw333xTzz77rDIzM+Xl5SVJmjBhgj755BPt27ev1Fry8/OVn59v387JyVFISIiys7Pl5+d3TccHAEB5aTxhpbtLMO3I7F5OHzMnJ0f+/v5l+vw2PUPUsmVLff/99+rYsaMefPBB5eXlqU+fPvr222+vOQyV5vDhw8rMzFRUVJS9zd/fXxEREUpNTZUkpaamKiAgwB6GJCkqKkoeHh7aunWrvU/nzp3tYUiSYmJitH//fp05c6bU1541a5b8/f3tj5CQEKcdFwAAqHhML6qWLgWTZ5991tm1OMjMzJQkBQYGOrQHBgba92VmZqpBgwYO+6tVq6a6des69AkNDS0xxuV9derUKfHaEydOVEJCgn378gwRAAComq4pEJ05c0bvvPOO9u7dK0kKCwvTkCFDVLduXacW5y7e3t6mF4gDAIDKy/Qps02bNqlx48aaN2+ezpw5ozNnzmjevHkKDQ3Vpk2bnFZYUFCQJCkrK8uhPSsry74vKChIJ0+edNh/8eJFnT592qFPaWP89jUAAIC1mQ5EcXFxevjhh3X48GEtXbpUS5cu1aFDh9S/f3/FxcU5rbDQ0FAFBQUpJSXF3paTk6OtW7cqMjJSkhQZGamzZ89qx44d9j7r169XcXGxIiIi7H02bdqkwsJCe5/k5GQ1b9681NNlAADAekwHogMHDmjMmDHy9PS0t3l6eiohIUEHDhwwNVZubq7S0tKUlpYm6dJC6rS0NGVkZMhms2nUqFH65z//qU8//VS7d+/W4MGDFRwcbL8SrUWLFurRo4eGDx+ur7/+Wps3b1Z8fLz69++v4OBgSdIjjzwiLy8vDR06VHv27NGSJUv06quvOqwRAgAA1mZ6DVF4eLj27t2r5s2bO7Tv3btXbdq0MTXW9u3bHe5ddDmkxMbGKikpSePGjVNeXp5GjBihs2fPqmPHjlq9erV8fHzsz3nvvfcUHx+v7t27y8PDQ3379tW8efPs+/39/bV27VrFxcWpXbt2qlevnqZMmeJwryIAAGBtZboP0a5du+w/7927V+PGjdNTTz2lDh06SJK++uorvf7665o9e7Yefvhh11XrJmbuYwAAgLtxH6JLzHx+lykQeXh4yGaz6Y+62mw2FRUVmau2EiAQAQAqEwLRJWY+v8t0yuzw4cNOKQwAAKAiKlMgatSokavrAAAAcJtrujHj8ePH9eWXX+rkyZMqLi522Ddy5EinFAYAAFBeTAeipKQk/e1vf5OXl5euu+462Ww2+z6bzUYgAgAAlY7pQDR58mRNmTJFEydOlIeH6dsYAQAAVDimE825c+fUv39/whAAAKgyTKeaoUOH6sMPP3RFLQAAAG5h+pTZrFmzdN9992n16tVq1aqVqlev7rD/5ZdfdlpxAAAA5eGaAtGaNWvsX93x+0XVAAAAlY3pQDRnzhwtWLBAjz32mAvKAQAAKH+m1xB5e3vrrrvuckUtAAAAbmE6ED399NN67bXXXFELAACAW5g+Zfb1119r/fr1WrFihW699dYSi6qXLl3qtOIAAADKg+lAFBAQoD59+riiFgAAALcwHYgWLlzoijoAAADchttNAwAAyzM9QxQaGnrV+w0dOnToTxUEAABQ3kwHolGjRjlsFxYW6ttvv9Xq1av1zDPPOKsuAACAcmM6ED399NOltr/++uvavn37ny4IAACgvDltDVHPnj318ccfO2s4AACAcuO0QPTRRx+pbt26zhoOAACg3Jg+ZXbbbbc5LKo2DEOZmZn6+eef9cYbbzi1OAAAgPJgOhD17t3bYdvDw0P169dXly5ddMsttzirLgAAgHJjOhBNnTrVFXUAAAC4DTdmBAAAllfmGSIPD4+r3pBRkmw2my5evPiniwIAAChPZQ5Ey5Ytu+K+1NRUzZs3T8XFxU4pCgAAoDyVORA9+OCDJdr279+vCRMmaPny5Ro4cKBmzJjh1OIAAADKwzWtITp+/LiGDx+uVq1a6eLFi0pLS9OiRYvUqFEjZ9cHAADgcqYCUXZ2tsaPH6+mTZtqz549SklJ0fLly9WyZUtX1QcAAOByZT5llpiYqBdeeEFBQUH63//+V+opNAAAgMrIZhiGUZaOHh4eqlGjhqKiouTp6XnFfkuXLnVacRVFTk6O/P39lZ2dLT8/P3eXAwDAVTWesNLdJZh2ZHYvp49p5vO7zDNEgwcP/sPL7gEAACqjMgeipKQkF5YBAADgPtypGgAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWF6FDkTTpk2TzWZzeNxyyy32/RcuXFBcXJyuu+461a5dW3379lVWVpbDGBkZGerVq5dq1qypBg0a6JlnntHFixfL+1AAAEAFVs3dBfyRW2+9VevWrbNvV6v2/0sePXq0Vq5cqQ8//FD+/v6Kj49Xnz59tHnzZklSUVGRevXqpaCgIG3ZskUnTpzQ4MGDVb16dT3//PPlfiwAAKBiqvCBqFq1agoKCirRnp2drXfeeUeLFy9Wt27dJEkLFy5UixYt9NVXX6lDhw5au3atvvvuO61bt06BgYFq27atZs6cqfHjx2vatGny8vIq78MBAAAVUIU+ZSZJP/zwg4KDg3XTTTdp4MCBysjIkCTt2LFDhYWFioqKsve95ZZbdOONNyo1NVWSlJqaqlatWikwMNDeJyYmRjk5OdqzZ88VXzM/P185OTkODwAAUHVV6EAUERGhpKQkrV69Wm+++aYOHz6sTp066ddff1VmZqa8vLwUEBDg8JzAwEBlZmZKkjIzMx3C0OX9l/ddyaxZs+Tv729/hISEOPfAAABAhVKhT5n17NnT/nPr1q0VERGhRo0a6YMPPlCNGjVc9roTJ05UQkKCfTsnJ4dQBABAFVahZ4h+LyAgQM2aNdOBAwcUFBSkgoICnT171qFPVlaWfc1RUFBQiavOLm+Xti7pMm9vb/n5+Tk8AABA1VWpAlFubq4OHjyohg0bql27dqpevbpSUlLs+/fv36+MjAxFRkZKkiIjI7V7926dPHnS3ic5OVl+fn4KCwsr9/oBAEDFVKFPmY0dO1b333+/GjVqpOPHj2vq1Kny9PTUgAED5O/vr6FDhyohIUF169aVn5+fnnrqKUVGRqpDhw6SpOjoaIWFhWnQoEFKTExUZmamJk2apLi4OHl7e7v56AAAQEVRoQPRsWPHNGDAAJ06dUr169dXx44d9dVXX6l+/fqSpLlz58rDw0N9+/ZVfn6+YmJi9MYbb9if7+npqRUrVujJJ59UZGSkatWqpdjYWM2YMcNdhwQAACogm2EYhruLqOhycnLk7++v7Oxs1hMBACq8xhNWursE047M7uX0Mc18fleqNUQAAACuUKFPmQEA4G6VcbYF5jFDBAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALK+auwsAAFhH4wkr3V0CUCpmiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOXxXWYAUAnxnWCAc1lqhuj1119X48aN5ePjo4iICH399dfuLgkAAFQAlglES5YsUUJCgqZOnapvvvlGbdq0UUxMjE6ePOnu0gAAgJvZDMMw3F1EeYiIiNDtt9+uf/3rX5Kk4uJihYSE6KmnntKECROu+tycnBz5+/srOztbfn5+5VEugHLE6SfA/Y7M7uX0Mc18fltiDVFBQYF27NihiRMn2ts8PDwUFRWl1NTUEv3z8/OVn59v387OzpZ06Y11hZZT17hkXFdKnx7j7hJMq4zvMwBYhSs+Yy+PWZa5H0sEol9++UVFRUUKDAx0aA8MDNS+fftK9J81a5amT59eoj0kJMRlNVY2/q+4uwIAQFXiys+VX3/9Vf7+/lftY4lAZNbEiROVkJBg3y4uLtbp06d13XXXyWazubGyyiknJ0chISE6evQopxzLEe+7+/Deuwfvu3tU5PfdMAz9+uuvCg4O/sO+lghE9erVk6enp7Kyshzas7KyFBQUVKK/t7e3vL29HdoCAgJcWaIl+Pn5Vbi/LFbA++4+vPfuwfvuHhX1ff+jmaHLLHGVmZeXl9q1a6eUlBR7W3FxsVJSUhQZGenGygAAQEVgiRkiSUpISFBsbKzat2+vO+64Q6+88ory8vI0ZMgQd5cGAADczDKB6OGHH9bPP/+sKVOmKDMzU23bttXq1atLLLSG83l7e2vq1KklTkPCtXjf3Yf33j14392jqrzvlrkPEQAAwJVYYg0RAADA1RCIAACA5RGIAACA5RGIAACA5RGI4DJHjhzR0KFDFRoaqho1aqhJkyaaOnWqCgoKHPrt2rVLnTp1ko+Pj0JCQpSYmOimiquO5557Tnfeeadq1qx5xZuK2my2Eo/333+/fAutYsryvmdkZKhXr16qWbOmGjRooGeeeUYXL14s30ItoHHjxiV+v2fPnu3usqqc119/XY0bN5aPj48iIiL09ddfu7uka2aZy+5R/vbt26fi4mL9+9//VtOmTZWenq7hw4crLy9PL730kqRLt3yPjo5WVFSU5s+fr927d+vxxx9XQECARowY4eYjqLwKCgrUr18/RUZG6p133rliv4ULF6pHjx72be7I/uf80fteVFSkXr16KSgoSFu2bNGJEyc0ePBgVa9eXc8//7wbKq7aZsyYoeHDh9u3fX193VhN1bNkyRIlJCRo/vz5ioiI0CuvvKKYmBjt379fDRo0cHd55hlAOUpMTDRCQ0Pt22+88YZRp04dIz8/3942fvx4o3nz5u4or8pZuHCh4e/vX+o+ScayZcvKtR6ruNL7/tlnnxkeHh5GZmamve3NN980/Pz8HP4O4M9r1KiRMXfuXHeXUaXdcccdRlxcnH27qKjICA4ONmbNmuXGqq4dp8xQrrKzs1W3bl37dmpqqjp37iwvLy972+X/YZw5c8YdJVpKXFyc6tWrpzvuuEMLFiyQwW3JXCo1NVWtWrVyuCFsTEyMcnJytGfPHjdWVjXNnj1b1113nW677Ta9+OKLnJp0ooKCAu3YsUNRUVH2Ng8PD0VFRSk1NdWNlV07Tpmh3Bw4cECvvfaa/XSZJGVmZio0NNSh3+UPi8zMTNWpU6dca7SSGTNmqFu3bqpZs6bWrl2rv//978rNzdXIkSPdXVqVlZmZWeLu+L/9fYfzjBw5UuHh4apbt662bNmiiRMn6sSJE3r55ZfdXVqV8Msvv6ioqKjU3+d9+/a5qao/hxkimDZhwoRSF+T+9vH7vxA//fSTevTooX79+jmc00fZXcv7fjWTJ0/WXXfdpdtuu03jx4/XuHHj9OKLL7rwCConZ7/vuHZm/iwSEhLUpUsXtW7dWk888YTmzJmj1157Tfn5+W4+ClRUzBDBtDFjxuixxx67ap+bbrrJ/vPx48fVtWtX3XnnnXrrrbcc+gUFBSkrK8uh7fJ2UFCQcwquIsy+72ZFRERo5syZys/Pr/TfSeRMznzfg4KCSlyFw+972f2ZP4uIiAhdvHhRR44cUfPmzV1QnbXUq1dPnp6epf77XVl/lwlEMK1+/fqqX79+mfr+9NNP6tq1q9q1a6eFCxfKw8NxUjIyMlLPPvusCgsLVb16dUlScnKymjdvzumy3zHzvl+LtLQ01alThzD0O8583yMjI/Xcc8/p5MmT9qtwkpOT5efnp7CwMKe8RlX2Z/4s0tLS5OHhUTmvfqqAvLy81K5dO6WkpKh3796SpOLiYqWkpCg+Pt69xV0jAhFc5qefflKXLl3UqFEjvfTSS/r555/t+y7/D+KRRx7R9OnTNXToUI0fP17p6el69dVXNXfuXHeVXSVkZGTo9OnTysjIUFFRkdLS0iRJTZs2Ve3atbV8+XJlZWWpQ4cO8vHxUXJysp5//nmNHTvWvYVXcn/0vkdHRyssLEyDBg1SYmKiMjMzNWnSJMXFxRFEnSg1NVVbt25V165d5evrq9TUVI0ePVqPPvoo/9FyooSEBMXGxqp9+/a644479MorrygvL09Dhgxxd2nXxt2XuaHqWrhwoSGp1Mdv7dy50+jYsaPh7e1tXH/99cbs2bPdVHHVERsbW+r7vmHDBsMwDGPVqlVG27Ztjdq1axu1atUy2rRpY8yfP98oKipyb+GV3B+974ZhGEeOHDF69uxp1KhRw6hXr54xZswYo7Cw0H1FV0E7duwwIiIiDH9/f8PHx8do0aKF8fzzzxsXLlxwd2lVzmuvvWbceOONhpeXl3HHHXcYX331lbtLumY2w+A6WwAAYG1cZQYAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQARY2MaNG2Wz2XT27NkKMQ7+vKSkJAUEBFy1z7Rp09S2bVunvi6/A6jsCERAJfXYY4/JZrPJZrOpevXqCg0N1bhx43ThwgWXvm6XLl00atQoh7Y777xTJ06ckL+/v0tfG3/s4Ycf1vfff+/uMoBKhy93BSqxHj16aOHChSosLNSOHTsUGxsrm82mF154oVzr8PLysn9hb1VTUFAgLy+vSvNaNWrUUI0aNZxUEWAdzBABlZi3t7eCgoIUEhKi3r17KyoqSsnJyfb9xcXFmjVrlkJDQ1WjRg21adNGH3300RXHO3XqlAYMGKDrr79eNWvWVKtWrfS///3Pvv+xxx7T559/rldffdU+O3XkyBGH0yU5OTmqUaOGVq1a5TD2smXL5Ovrq3PnzkmSjh49qoceekgBAQGqW7euHnzwQR05cuSqx5uenq6ePXuqdu3aCgwM1KBBg/TLL7/Y93fp0kUjR47UuHHjVLduXQUFBWnatGkOY5w9e1bDhg1T/fr15efnp27dumnnzp32/ZdPJ7399tsKDQ2Vj4+PJGnfvn3q2LGjfHx8FBYWpnXr1slms+mTTz6RJHXr1k3x8fEOr/Xzzz/Ly8tLKSkppR7PlV7rj2rcuXOn/Zvc/fz81K5dO23fvl1S6afMZs+ercDAQPn6+mro0KElZhFLm/Xr3bu3HnvsMfv2f/7zH7Vv316+vr4KCgrSI488opMnT5Z6XEBlRCACqoj09HRt2bLFYYZh1qxZevfddzV//nzt2bNHo0eP1qOPPqrPP/+81DEuXLigdu3aaeXKlUpPT9eIESM0aNAgff3115KkV199VZGRkRo+fLhOnDihEydOKCQkxGEMPz8/3XfffVq8eLFD+3vvvafevXurZs2aKiwsVExMjHx9ffXFF19o8+bNql27tnr06KGCgoJSazt79qy6deum2267Tdu3b9fq1auVlZWlhx56yKHfokWLVKtWLW3dulWJiYmaMWOGQ0js16+fTp48qVWrVmnHjh0KDw9X9+7ddfr0aXufAwcO6OOPP9bSpUuVlpamoqIie+1bt27VW2+9pWeffdbhdYcNG6bFixcrPz/f3vbf//5X119/vbp161bqMZX2WmWpceDAgbrhhhu0bds27dixQxMmTFD16tVLHf+DDz7QtGnT9Pzzz2v79u1q2LCh3njjjSvWcyWFhYWaOXOmdu7cqU8++URHjhxxCExApWcAqJRiY2MNT09Po1atWoa3t7chyfDw8DA++ugjwzAM48KFC0bNmjWNLVu2ODxv6NChxoABAwzDMIwNGzYYkowzZ85c8XV69epljBkzxr599913G08//bRDn9+Ps2zZMqN27dpGXl6eYRiGkZ2dbfj4+BirVq0yDMMw/vOf/xjNmzc3iouL7WPk5+cbNWrUMNasWVNqHTNnzjSio6Md2o4ePWpIMvbv32+vrWPHjg59br/9dmP8+PGGYRjGF198Yfj5+RkXLlxw6NOkSRPj3//+t2EYhjF16lSjevXqxsmTJ+37V61aZVSrVs04ceKEvS05OdmQZCxbtswwDMM4f/68UadOHWPJkiX2Pq1btzamTZtW6vFc6bXKUqOvr6+RlJRU6pgLFy40/P397duRkZHG3//+d4c+ERERRps2bezbpf2ZPvjgg0ZsbOwVa9+2bZshyfj1118Nwyjb7xJQkbGGCKjEunbtqjfffFN5eXmaO3euqlWrpr59+0q6NPNw7tw53XPPPQ7PKSgo0G233VbqeEVFRXr++ef1wQcf6KefflJBQYHy8/NVs2ZNU3Xde++9ql69uj799FP1799fH3/8sfz8/BQVFSXp0imfAwcOyNfX1+F5Fy5c0MGDB0sdc+fOndqwYYNq165dYt/BgwfVrFkzSVLr1q0d9jVs2NB+amfnzp3Kzc3Vdddd59Dn/PnzDq/bqFEj1a9f3769f/9+hYSEOKyTuuOOOxzG8PHx0aBBg7RgwQI99NBD+uabb5Senq5PP/209DfpCq9VlhoTEhI0bNgw/ec//1FUVJT69eunJk2alDr+3r179cQTTzi0RUZGasOGDVet6/d27NihadOmaefOnTpz5oyKi4slSRkZGQoLCzM1FlAREYiASqxWrVpq2rSpJGnBggVq06aN3nnnHQ0dOlS5ubmSpJUrV+r66693eJ63t3ep47344ot69dVX9corr6hVq1aqVauWRo0adcXTWFfi5eWlv/71r1q8eLH69++vxYsX6+GHH1a1apf+ycnNzVW7du303nvvlXjub8PBb+Xm5ur+++8vdcF4w4YN7T///tSRzWazf3jn5uaqYcOG2rhxY4kxfrvuplatWn94jKUZNmyY2rZtq2PHjmnhwoXq1q2bGjVqdNXn/P61ylLjtGnT9Mgjj2jlypVatWqVpk6dqvfff19/+ctfrqluDw8PGYbh0FZYWGj/OS8vTzExMYqJidF7772n+vXrKyMjQzExMaZ/N4CKikAEVBEeHh76xz/+oYSEBD3yyCMKCwuTt7e3MjIydPfdd5dpjM2bN+vBBx/Uo48+KunSouzvv//eYQbAy8tLRUVFfzjWwIEDdc8992jPnj1av369/vnPf9r3hYeHa8mSJWrQoIH8/PzKVFt4eLg+/vhjNW7c2B6szAoPD1dmZqaqVaumxo0bl/l5zZs319GjR5WVlaXAwEBJ0rZt20r0a9Wqldq3b6//+7//0+LFi/Wvf/3LZTU2a9ZMzZo10+jRozVgwAAtXLiw1EDUokULbd26VYMHD7a3ffXVVw596tevrxMnTti3i4qKlJ6erq5du0q6tKD81KlTmj17tn3N2OVF3EBVwaJqoArp16+fPD099frrr8vX11djx47V6NGjtWjRIh08eFDffPONXnvtNS1atKjU5998881KTk7Wli1btHfvXv3tb39TVlaWQ5/GjRtr69atOnLkiH755Rf77Mvvde7cWUFBQRo4cKBCQ0MVERFh3zdw4EDVq1dPDz74oL744gsdPnxYGzdu1MiRI3Xs2LFSx4uLi9Pp06c1YMAAbdu2TQcPHtSaNWs0ZMiQMgU0SYqKilJkZKR69+6ttWvX6siRI9qyZYueffbZq37A33PPPWrSpIliY2O1a9cubd68WZMmTZJ0aQbqt4YNG6bZs2fLMIxrmrH5oxrPnz+v+Ph4bdy4UT/++KM2b96sbdu2qUWLFqWO9/TTT2vBggVauHChvv/+e02dOlV79uxx6NOtWzetXLlSK1eu1L59+/Tkk0863GDxxhtvlJeXl1577TUdOnRIn376qWbOnGn62ICKjEAEVCHVqlVTfHy8EhMTlZeXp5kzZ2ry5MmaNWuWWrRooR49emjlypUKDQ0t9fmTJk1SeHi4YmJi1KVLFwUFBal3794OfcaOHStPT0+FhYXZT52UxmazacCAAdq5c6cGDhzosK9mzZratGmTbrzxRvXp00ctWrSwXw5+pRmj4OBgbd68WUVFRYqOjlarVq00atQoBQQEyMOjbP+U2Ww2ffbZZ+rcubOGDBmiZs2aqX///vrxxx/tMz+l8fT01CeffKLc3FzdfvvtGjZsmP0qs8uXyl82YMAAVatWTQMGDCixzxk1enp66tSpUxo8eLCaNWumhx56SD179tT06dNLHe/hhx/W5MmTNW7cOLVr104//vijnnzySYc+jz/+uGJjYzV48GDdfffduummm+yzQ9KlGaSkpCR9+OGHCgsL0+zZs/XSSy+ZPjagIrMZvz9xDAD4Q5s3b1bHjh114MABhwXNR44cUZMmTbRt2zaFh4e7sUIAZhCIAKAMli1bptq1a+vmm2/WgQMH9PTTT6tOnTr68ssvJV1ahHzq1CmNHTtWhw8f1ubNm91cMQAzWFQNAGXw66+/avz48crIyFC9evUUFRWlOXPm2Pdv3rxZXbt2VbNmza56N3AAFRMzRAAAwPJYVA0AACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACzv/wE4IJZTKJD1yQAAAABJRU5ErkJggg==", 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", 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", 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frWeeeUaTJk3SkCFDdNttt1U4yOTJk2UYhkaOHFnhbQAAgKrF9GTaqVOnqqSkRJKUlZWlkpISffzxx2rZsmWF7/jZuHGj3nrrLSUlJVXo8wAAoGoyVVRcLpeOHDniLRS1a9fWm2++eUUBSkpKNGjQIL3zzjv685//fEXbAgAAVYupSz/h4eHq06ePTp8+HbAA6enpuueee5SSkvKr6zqdThUXF/u8AABA1WX60k9iYqIOHjyohISEK975nDlztGXLFm3cuNGv9bOzs5WVlXXF+wUAXN3ix/7D6ggVcnjyPVZHuOqYnkz75z//WaNHj9bnn3+u48ePV/gMR2FhoUaMGKGPPvpINWrU8OszmZmZKioq8r4KCwvNxgcAAFcR02dU7r77bklS//79ZRiGd9zj8cgwDLlcLr+2s3nzZp08edLnabYul0urV6/Wf/3Xf8npdCo8PNznMw6HQw6Hw2xkAABwlTJdVFasWBGQHffu3Vs7d+70GUtLS1ObNm00ZsyYciUFAACEHtNFJSEhQXFxcT5nU6Qfz6iYuRQTGRmpxMREn7HatWurQYMG5cYBAEBoMj1HJSEhQd9991258VOnTgVkgi0AAMBPTJ9R+Wkuyv+vpKTE70mxl7Ny5cor+jwAAKha/C4qGRkZkiTDMDRu3DjVqlXL+57L5dKGDRvUrl27gAcEAAChy++isnXrVkk/nlHZuXOnIiIivO9FREQoOTlZo0ePDnxCAAAQsvwuKj/d7ZOWlqbXX39dUVFRQQsFAAAgVWCOSk5OTjByAAAAlGO6qPTq1esX31++fHmFwwAAAPyc6aKSnJzss3zhwgVt27ZNu3btUmpqasCCAQAAmC4q06ZNu+T4hAkTVFJScsWBAAAAfmL6gW+X88gjj+j9998P1OYAAAACV1TWrVt3xQ98AwAA+DnTl34eeOABn2WPx6Pjx49r06ZNGjduXMCCAQAAmC4q0dHRPsthYWFq3bq1Jk6cqD59+gQsGAAAAM9RAQAAtlWhOSpnzpzRu+++q8zMTJ06dUqStGXLFh09ejSg4QAAQGgzfUZlx44d6t27t+rWravDhw9r6NChql+/vubNm6eCggJ9+OGHwcgJAABCkOkzKhkZGUpLS9M333zjc5fP3XffrdWrVwc0HAAACG2mi8rGjRv1xBNPlBu/9tprdeLEiYCEAgAAkCpQVBwOh4qLi8uNf/3114qJiQlIKAAAAKkCRaV///6aOHGiLly4IEkyDEMFBQUaM2aMfvOb3wQ8IAAACF2mi8prr72mkpISNWzYUOfPn1f37t3VokULRUZGatKkScHICAAAQlSFHvi2dOlSrVmzRjt27FBJSYnat2+vlJSUYOQDAAAhzHRR+UnXrl3VtWvXQGYBAADwUaGikpeXp7y8PJ08eVJut9vnPb5BGQAABIrpopKVlaWJEyeqY8eOaty4sQzDCEYuAAAA80XlzTffVG5urn7/+98HIw8AAICX6bt+ysrK1Llz52BkAQAA8GG6qPzhD3/Q7Nmzg5EFAADAh+lLP//617/09ttva9myZUpKSlL16tV93p86dWrAwgEAgNBWoW9PbteunSRp165dPu8xsRYAAASS6aKyYsWKYOQAAAAox/QcFQAAgMpCUQEAALZFUQEAALZFUQEAALblV1Fp3769Tp8+LUmaOHGizp07F9RQAAAAkp9FZe/evSotLZX043f9lJSUBDUUAACA5Oftye3atVNaWpq6du0qj8ejV199VXXq1Lnkui+++GJAAwIAgNDlV1HJzc3V+PHj9fnnn8swDC1cuFDVqpX/qGEYFBUAABAwfhWV1q1ba86cOZKksLAw5eXlqWHDhkENBgAAYPrJtG63Oxg5AAAAyqnQ7ckHDhzQ008/rZSUFKWkpGj48OE6cOCA6e3MmjVLSUlJioqKUlRUlDp16qSFCxdWJBIAAKiCTBeVxYsXq23btvrqq6+UlJSkpKQkbdiwQTfccIOWLl1qaltNmzbV5MmTtXnzZm3atEm9evXSgAEDtHv3brOxAABAFWT60s/YsWM1atQoTZ48udz4mDFjdMcdd/i9rXvvvddnedKkSZo1a5bWr1+vG264wWw0AABQxZg+o7J371499thj5caHDBmiPXv2VDiIy+XSnDlzVFpaqk6dOl1yHafTqeLiYp8XAACoukwXlZiYGG3btq3c+LZt2yp0J9DOnTtVp04dORwOPfnkk5o/f77atm17yXWzs7MVHR3tfcXFxZneHwAAuHqYvvQzdOhQPf744zp48KA6d+4sSfryyy/1yiuvKCMjw3SA1q1ba9u2bSoqKtLcuXOVmpqqVatWXbKsZGZm+uyjuLiYsgIAQBVmuqiMGzdOkZGReu2115SZmSlJatKkiSZMmKDhw4ebDhAREaEWLVpIkjp06KCNGzfq9ddf11tvvVVuXYfDIYfDYXofAADg6mS6qBiGoVGjRmnUqFE6e/asJCkyMjJggdxut5xOZ8C2BwAArl6mi8rPXWlByczMVN++fdWsWTOdPXtWs2fP1sqVK7V48eIr2i4AAKgarqioXKmTJ09q8ODBOn78uKKjo5WUlKTFixebusUZAABUXZYWlffee8/K3QMAAJur0CP0AQAAKoOponLhwgX17t1b33zzTbDyAAAAeJkqKtWrV9eOHTuClQUAAMCH6Us/jzzyCHNLAABApTA9mfbixYt6//33tWzZMnXo0EG1a9f2eX/q1KkBCwcAAEKb6aKya9cutW/fXpL09ddf+7xnGEZgUgEAAKgCRWXFihXByAEAAFBOhW9Pzs/P1+LFi3X+/HlJksfjCVgoAAAAqQJF5YcfflDv3r3VqlUr3X333Tp+/Lgk6bHHHtMzzzwT8IAAACB0mS4qo0aNUvXq1VVQUKBatWp5xx988EEtWrQooOEAAEBoMz1HZcmSJVq8eLGaNm3qM96yZUt9++23AQsGAABg+oxKaWmpz5mUn5w6dUoOhyMgoQAAAKQKFJVu3brpww8/9C4bhiG3260pU6aoZ8+eAQ0HAABCm+lLP1OmTFHv3r21adMmlZWV6dlnn9Xu3bt16tQpffnll8HICAAAQpTpMyqJiYn6+uuv1bVrVw0YMEClpaV64IEHtHXrVl1//fXByAgAAEKU6TMqkhQdHa3nn38+0FkAAAB8VKionD59Wu+995727t0rSWrbtq3S0tJUv379gIYDAAChzfSln9WrVys+Pl4zZszQ6dOndfr0ac2YMUMJCQlavXp1MDICAIAQZfqMSnp6uh588EHNmjVL4eHhkiSXy6U//vGPSk9P186dOwMeEgAAhCbTZ1Ty8/P1zDPPeEuKJIWHhysjI0P5+fkBDQcAAEKb6aLSvn1779yUn9u7d6+Sk5MDEgoAAEDy89LPjh07vD8PHz5cI0aMUH5+vm677TZJ0vr16zVz5kxNnjw5OCkBAEBI8quotGvXToZhyOPxeMeeffbZcuv953/+px588MHApQMAACHNr6Jy6NChYOcAAAAox6+i0rx582DnAAAAKKdCD3w7duyY1qxZo5MnT8rtdvu8N3z48IAEAwAAMF1UcnNz9cQTTygiIkINGjSQYRje9wzDoKgAAICAMV1Uxo0bpxdffFGZmZkKCzN9dzMAAIDfTDeNc+fO6aGHHqKkAACAoDPdNh577DH99a9/DUYWAAAAH6Yv/WRnZ6tfv35atGiRbrzxRlWvXt3n/alTpwYsHAAACG0VKiqLFy9W69atJancZFoAAIBAMV1UXnvtNb3//vt69NFHgxAHAADg30zPUXE4HOrSpUswsgAAAPgwXVRGjBihN954IxhZAAAAfJi+9PPVV19p+fLl+vzzz3XDDTeUm0w7b968gIUDAAChzXRRqVu3rh544IFgZAEAAPBhuqjk5OQEIwcAAEA5PF4WAADYlukzKgkJCb/4vJSDBw9eUSAAAICfmC4qI0eO9Fm+cOGCtm7dqkWLFulPf/qTqW1lZ2dr3rx52rdvn2rWrKnOnTvrlVde8T5MDgAAhDbTRWXEiBGXHJ85c6Y2bdpkalurVq1Senq6br75Zl28eFHPPfec+vTpoz179qh27dpmowEAgCrGdFG5nL59+yozM9PUZNtFixb5LOfm5qphw4bavHmzbr/99nLrO51OOZ1O73JxcXHFAwMAANsLWFGZO3eu6tevf0XbKCoqkqTLbic7O1tZWVlXtA8z4sf+o9L2Fco4zgBCxdX4/93hyfdYun/TReWmm27ymUzr8Xh04sQJfffdd/rv//7vCgdxu90aOXKkunTposTExEuuk5mZqYyMDO9ycXGx4uLiKrxPAABgb6aLyn333eezHBYWppiYGPXo0UNt2rSpcJD09HTt2rVLa9asuew6DodDDoejwvsAAABXF9NFZfz48QEPMWzYMH3++edavXq1mjZtGvDtAwCAq1PA5qhUhMfj0dNPP6358+dr5cqVSkhIsDIOAACwGb+LSlhY2C8+6E2SDMPQxYsX/d55enq6Zs+erQULFigyMlInTpyQJEVHR6tmzZp+bwcAAFRNfheV+fPnX/a9devWacaMGXK73aZ2PmvWLElSjx49fMZzcnL06KOPmtoWAACoevwuKgMGDCg3tn//fo0dO1afffaZBg0apIkTJ5raucfjMbU+AAAILRX6UsJjx45p6NChuvHGG3Xx4kVt27ZNH3zwgZo3bx7ofAAAIISZKipFRUUaM2aMWrRood27dysvL0+fffbZZZ97AgAAcCX8vvQzZcoUvfLKK4qNjdVf/vKXS14KAgAACCTD4+dEkbCwMNWsWVMpKSkKDw+/7Hrz5s0LWLhfU1xcrOjoaBUVFSkqKirg278aH3UMAEAgBeMR+mb+fvt9RmXw4MG/ensyAABAIPldVHJzc4MYAwAAoLwK3fUDAABQGSgqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtigqAADAtiwtKqtXr9a9996rJk2ayDAMffrpp1bGAQAANmNpUSktLVVycrJmzpxpZQwAAGBT1azced++fdW3b1+/13c6nXI6nd7l4uLiYMQCAAA2cVXNUcnOzlZ0dLT3FRcXZ3UkAAAQRFdVUcnMzFRRUZH3VVhYaHUkAAAQRJZe+jHL4XDI4XBYHQMAAFSSq+qMCgAACC0UFQAAYFuWXvopKSlRfn6+d/nQoUPatm2b6tevr2bNmlmYDAAA2IGlRWXTpk3q2bOndzkjI0OSlJqaqtzcXItSAQAAu7C0qPTo0UMej8fKCAAAwMaYowIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGyLogIAAGzLFkVl5syZio+PV40aNXTrrbfqq6++sjoSAACwAcuLyscff6yMjAyNHz9eW7ZsUXJysu68806dPHnS6mgAAMBilheVqVOnaujQoUpLS1Pbtm315ptvqlatWnr//fetjgYAACxWzcqdl5WVafPmzcrMzPSOhYWFKSUlRevWrSu3vtPplNPp9C4XFRVJkoqLi4OSz+08F5TtAgBwtQjG39iftunxeH51XUuLyvfffy+Xy6VGjRr5jDdq1Ej79u0rt352draysrLKjcfFxQUtIwAAoSx6evC2ffbsWUVHR//iOpYWFbMyMzOVkZHhXXa73Tp16pQaNGggwzBUXFysuLg4FRYWKioqysKk1uNY+OJ4+OJ4+OJ4/BvHwhfHw1egjofH49HZs2fVpEmTX13X0qJyzTXXKDw8XP/85z99xv/5z38qNja23PoOh0MOh8NnrG7duuXWi4qK4hfq/3AsfHE8fHE8fHE8/o1j4Yvj4SsQx+PXzqT8xNLJtBEREerQoYPy8vK8Y263W3l5eerUqZOFyQAAgB1YfuknIyNDqamp6tixo2655RZNnz5dpaWlSktLszoaAACwmOVF5cEHH9R3332nF198USdOnFC7du20aNGichNs/eFwODR+/Phyl4dCEcfCF8fDF8fDF8fj3zgWvjgevqw4HobHn3uDAAAALGD5A98AAAAuh6ICAABsi6ICAABsi6ICAABsq8oWlf79+6tZs2aqUaOGGjdurN///vc6duyY1bEscfjwYT322GNKSEhQzZo1df3112v8+PEqKyuzOpolJk2apM6dO6tWrVqXfGBgVTdz5kzFx8erRo0auvXWW/XVV19ZHckyq1ev1r333qsmTZrIMAx9+umnVkeyTHZ2tm6++WZFRkaqYcOGuu+++7R//36rY1lm1qxZSkpK8j7YrFOnTlq4cKHVsWxh8uTJMgxDI0eOrJT9Vdmi0rNnT33yySfav3+//va3v+nAgQP67W9/a3UsS+zbt09ut1tvvfWWdu/erWnTpunNN9/Uc889Z3U0S5SVlWngwIF66qmnrI5S6T7++GNlZGRo/Pjx2rJli5KTk3XnnXfq5MmTVkezRGlpqZKTkzVz5kyro1hu1apVSk9P1/r167V06VJduHBBffr0UWlpqdXRLNG0aVNNnjxZmzdv1qZNm9SrVy8NGDBAu3fvtjqapTZu3Ki33npLSUlJlbdTT4hYsGCBxzAMT1lZmdVRbGHKlCmehIQEq2NYKicnxxMdHW11jEp1yy23eNLT073LLpfL06RJE092draFqexBkmf+/PlWx7CNkydPeiR5Vq1aZXUU26hXr57n3XfftTqGZc6ePetp2bKlZ+nSpZ7u3bt7RowYUSn7rbJnVH7u1KlT+uijj9S5c2dVr17d6ji2UFRUpPr161sdA5WorKxMmzdvVkpKincsLCxMKSkpWrdunYXJYEdFRUWSxP8Tklwul+bMmaPS0tKQ/nqX9PR03XPPPT7/h1SGKl1UxowZo9q1a6tBgwYqKCjQggULrI5kC/n5+XrjjTf0xBNPWB0Flej777+Xy+Uq99TnRo0a6cSJExalgh253W6NHDlSXbp0UWJiotVxLLNz507VqVNHDodDTz75pObPn6+2bdtaHcsSc+bM0ZYtW5SdnV3p+76qisrYsWNlGMYvvvbt2+dd/09/+pO2bt2qJUuWKDw8XIMHD5anCj2I1+zxkKSjR4/qrrvu0sCBAzV06FCLkgdeRY4FgEtLT0/Xrl27NGfOHKujWKp169batm2bNmzYoKeeekqpqanas2eP1bEqXWFhoUaMGKGPPvpINWrUqPT9X1WP0P/uu+/0ww8//OI61113nSIiIsqNHzlyRHFxcVq7dm2VOXVn9ngcO3ZMPXr00G233abc3FyFhV1VPfUXVeR3Izc3VyNHjtSZM2eCnM4eysrKVKtWLc2dO1f33Xefdzw1NVVnzpwJ+TOOhmFo/vz5PscmFA0bNkwLFizQ6tWrlZCQYHUcW0lJSdH111+vt956y+oolerTTz/V/fffr/DwcO+Yy+WSYRgKCwuT0+n0eS/QLP9SQjNiYmIUExNToc+63W5JktPpDGQkS5k5HkePHlXPnj3VoUMH5eTkVKmSIl3Z70aoiIiIUIcOHZSXl+f9Y+x2u5WXl6dhw4ZZGw6W83g8evrppzV//nytXLmSknIJbre7Sv0N8Vfv3r21c+dOn7G0tDS1adNGY8aMCWpJka6youKvDRs2aOPGjeratavq1aunAwcOaNy4cbr++uurzNkUM44ePaoePXqoefPmevXVV/Xdd99534uNjbUwmTUKCgp06tQpFRQUyOVyadu2bZKkFi1aqE6dOtaGC7KMjAylpqaqY8eOuuWWWzR9+nSVlpYqLS3N6miWKCkpUX5+vnf50KFD2rZtm+rXr69mzZpZmKzypaena/bs2VqwYIEiIyO985aio6NVs2ZNi9NVvszMTPXt21fNmjXT2bNnNXv2bK1cuVKLFy+2Olqli4yMLDdX6af5n5Uyh6lS7i2qZDt27PD07NnTU79+fY/D4fDEx8d7nnzySc+RI0esjmaJnJwcj6RLvkJRamrqJY/FihUrrI5WKd544w1Ps2bNPBEREZ5bbrnFs379eqsjWWbFihWX/F1ITU21Olqlu9z/ETk5OVZHs8SQIUM8zZs390RERHhiYmI8vXv39ixZssTqWLZRmbcnX1VzVAAAQGipWhMVAABAlUJRAQAAtkVRAQAAtkVRAQAAtkVRAQAAtkVRAQAAtkVRAQAAtkVRAQAAtkVRAVCpJkyYoHbt2lmy7x49emjkyJG/uI5hGPr0008rJQ+AX0dRAfCrHn30URmGIcMwVL16dTVq1Eh33HGH3n//fe8Xfl7JtgP1jcU/ZTQMQ9HR0erSpYuWL19uahvHjx9X3759A5IHwJWjqADwy1133aXjx4/r8OHDWrhwoXr27KkRI0aoX79+unjxotXxvHJycnT8+HF9+eWXuuaaa9SvXz8dPHjQ78/HxsbK4XAEMSEAMygqAPzicDgUGxura6+9Vu3bt9dzzz2nBQsWaOHChcrNzfWud+bMGf3hD39QTEyMoqKi1KtXL23fvv2S25wwYYI++OADLViwwHsmZOXKlZKkMWPGqFWrVqpVq5auu+46jRs3ThcuXPjVnHXr1lVsbKwSExM1a9YsnT9/XkuXLvW+73a79eyzz6p+/fqKjY3VhAkTfD7PpR/AXigqACqsV69eSk5O1rx587xjAwcO1MmTJ7Vw4UJt3rxZ7du3V+/evXXq1Klynx89erR+97vfec/WHD9+XJ07d5b041fL5+bmas+ePXr99df1zjvvaNq0aaby1axZU5JUVlbmHfvggw9Uu3ZtbdiwQVOmTNHEiRN9igwAe6GoALgibdq00eHDhyVJa9as0VdffaW//vWv6tixo1q2bKlXX31VdevW1dy5c8t9tk6dOqpZs6b3bE1sbKwiIiIkSS+88II6d+6s+Ph43XvvvRo9erQ++eQTv3OdO3dOL7zwgsLDw9W9e3fveFJSksaPH6+WLVtq8ODB6tixo/Ly8q7sIAAImmpWBwBwdfN4PDIMQ5K0fft2lZSUqEGDBj7rnD9/XgcOHDC13Y8//lgzZszQgQMHVFJSoosXLyoqKupXP/fwww8rPDxc58+fV0xMjN577z0lJSV53//5z5LUuHFjnTx50lQ2AJWHogLgiuzdu1cJCQmSpJKSEjVu3Ng7z+Tn6tat6/c2161bp0GDBikrK0t33nmnoqOjNWfOHL322mu/+tlp06YpJSVF0dHRiomJKfd+9erVfZYNw7jiO5cABA9FBUCFLV++XDt37tSoUaMkSe3bt9eJEydUrVo1xcfH+7WNiIgIuVwun7G1a9eqefPmev75571j3377rV/bi42NVYsWLfz7BwCwPeaoAPCL0+nUiRMndPToUW3ZskUvv/yyBgwYoH79+mnw4MGSpJSUFHXq1En33XeflixZosOHD2vt2rV6/vnntWnTpktuNz4+Xjt27ND+/fv1/fff68KFC2rZsqUKCgo0Z84cHThwQDNmzND8+fMr858LwCYoKgD8smjRIjVu3Fjx8fG66667tGLFCs2YMUMLFixQeHi4pB8vo3zxxRe6/fbblZaWplatWumhhx7St99+q0aNGl1yu0OHDlXr1q3VsWNHxcTE6Msvv1T//v01atQoDRs2TO3atdPatWs1bty4yvznArAJw+PxeKwOAQAAcCmcUQEAALZFUQEAALZFUQEAALZFUQEAALZFUQEAALZFUQEAALZFUQEAALZFUQEAALZFUQEAALZFUQEAALZFUQEAALb1/wC1SfCaEDJa5AAAAABJRU5ErkJggg==", + "image/png": 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djj6bNm1ScXGxoqKiHH22bt3q9AmS9evXq0WLFqXeOgMAANcm06HIZrPp6NGjJdoPHz6s2rVrm9pWbm6u0tLSlJaWJkk6duyY0tLSlJmZqdzcXD3zzDPasWOHjh8/ro0bN+ree+9Vs2bNHLfuWrVqpR49emjo0KH64osv9Pnnn2v48OF68MEHFRISIkkaMGCArFar4uLitG/fPi1btkyzZ892uj0GAABQrjdajxo1SkeOHHG0HT58WGPGjNE999xjalu7du1S+/btHd9MS0hIUPv27TVp0iR5e3trz549uueee9S8eXPFxcWpY8eO+uyzz+Tj4+PYxuLFi9WyZUt169ZNvXr10m233eb0DiKbzaZ169bp2LFj6tixo8aMGaNJkybxOD4AAHBiMQzDMLNCTk6OevTooV27dun666+X9Ottrc6dO2v58uXy9/d3R50Vym63y2azKScnh/FFwJ9oPH5VRZdg2vFpvSu6BABuYPbvt+mnz2w2m7Zv367169frm2++Uc2aNdWuXTt16dKlXAUDAABUBqZDkSRZLBbFxMSU63tnAAAAlVGZQtGcOXM0bNgw1ahRQ3PmzPnDviNHjnRJYQAAAJ5UplD06quvauDAgapRo4ZeffXVy/azWCyEIgAAUCWVKRQdO3as1J8BAACuFqYfyU9KStLFixdLtP/8889KSkpySVEAAACeZjoUTZ06Vbm5uSXaL168yEdUAQBAlWU6FBmGIYvFUqL9m2++Ub169VxSFAAAgKeV+ZH8unXrymKxyGKxqHnz5k7BqKioSLm5uXriiSfcUiQAAIC7lTkUvfbaazIMQ48++qimTp0qm83mWGa1WtW4cWNFR0e7pUgAAAB3K3MoGjJkiAoLC2WxWHTnnXcqNDTUnXUBAAB4lKkxRdWqVdOTTz6p4uJid9UDAABQIUwPtL7lllv09ddfu6MWAACACmP622dPPfWUxowZoxMnTqhjx46qXbu20/J27dq5rDgAAABPMR2KHnzwQUnO3zizWCyOR/WLiopcVx0AAICHmA5FfOYDAABcjUyHorCwMHfUAQAAUKFMh6JL9u/fr8zMTOXn5zu133PPPVdcFAAAgKeZDkVHjx7Vfffdp/T0dMdYIkmON1wzpggAAFRFph/Jf/rppxUeHq7Tp0+rVq1a2rdvn7Zu3aqbbrpJW7ZscUOJAAAA7mf6SlFqaqo2bdqkBg0ayMvLS15eXrrtttuUnJyskSNH8g4jAABQJZm+UlRUVCRfX19JUoMGDXTy5ElJvw7AzsjIcG11AAAAHmL6SlFERIS++eYbhYeHKyoqSjNmzJDVatX8+fPVpEkTd9QIAADgdqZD0YQJE3ThwgVJUlJSku666y517txZ9evX17Jly1xeIAAAgCeYDkWxsbGOn5s1a6aDBw/qzJkzqlu3ruMJNAAAgKqm3O8pkqTvvvtOkhQaGuqSYgAAACqK6YHWhYWFmjhxomw2mxo3bqzGjRvLZrNpwoQJKigocEeNAAAAbmf6StGIESO0fPlyzZgxQ9HR0ZJ+fUx/ypQp+umnnzRv3jyXFwkAAOBupkPRkiVLtHTpUvXs2dPR1q5dO4WGhqp///6EIgAAUCWZvn3m4+Ojxo0bl2gPDw+X1Wp1RU0AAAAeZzoUDR8+XM8//7zy8vIcbXl5eXrxxRc1fPhwlxYHAADgKaZvn3399dfauHGjrr/+ekVGRkqSvvnmG+Xn56tbt27q27evo+/y5ctdVykAAIAbmQ5F/v7+6tevn1Mbj+QDAICqznQoWrhwoct2vnXrVs2cOVO7d+/WqVOntGLFCvXp00eSVFBQoAkTJmj16tU6evSobDabunfvrmnTpikkJMSxjcaNG+vbb7912m5ycrLGjx/vmN+zZ4/i4+P15ZdfqmHDhhoxYoSeffZZlx0HAACo+kyPKXKlCxcuKDIyUnPnzi2x7OLFi/rqq680ceJEffXVV1q+fLkyMjJ0zz33lOiblJSkU6dOOaYRI0Y4ltntdsXExCgsLEy7d+/WzJkzNWXKFM2fP9+txwYAAKqWK3qj9ZXq2bOn06P9v2Wz2bR+/XqntjfeeEO33HKLMjMz1ahRI0e7r6+vgoKCSt3O4sWLlZ+frwULFshqtapNmzZKS0vTrFmzNGzYsFLXycvLcxpIbrfbzR4aAACoYir0SpFZOTk5slgs8vf3d2qfNm2a6tevr/bt22vmzJkqLCx0LEtNTVWXLl2cXhcQGxurjIwMnT17ttT9JCcny2azOSbGTAEAcPWrMqHol19+0bhx49S/f3/5+fk52keOHKmlS5dq8+bNevzxx/XSSy85jRfKyspSYGCg07YuzWdlZZW6r8TEROXk5DimS994AwAAV68KvX1WVgUFBfr73/8uwzBKvDE7ISHB8XO7du1ktVr1+OOPKzk5WT4+PuXan4+PT7nXBQAAVVOZQtGcOXPKvMGRI0eWu5jSXApE3377rTZt2uR0lag0UVFRKiws1PHjx9WiRQsFBQUpOzvbqc+l+cuNQwIAANeeMoWiV199tUwbs1gsLg1FlwLRoUOHtHnzZtWvX/9P10lLS5OXl5cCAgIkSdHR0XruuedUUFCg6tWrS5LWr1+vFi1aqG7dui6rFQAAVG1lCkXHjh1zy85zc3N1+PBhp/2kpaWpXr16Cg4O1t/+9jd99dVXSklJUVFRkWMMUL169WS1WpWamqqdO3eqa9eu8vX1VWpqqkaPHq2HHnrIEXgGDBigqVOnKi4uTuPGjdPevXs1e/bsMgc9AABwbbAYhmFU1M63bNmirl27lmgfMmSIpkyZovDw8FLX27x5s+644w599dVXeuqpp3Tw4EHl5eUpPDxcgwYNUkJCgtOYoN++vLFBgwYaMWKExo0bV+Y67Xa7bDabcnJy/vT2HXCtazx+VUWXYNrxab0rugQAbmD273e5QtGJEyf0ySefKDMzU/n5+U7LZs2aZXZzlR6hCCg7QhGAysLs32/TT59t3LhR99xzj5o0aaKDBw8qIiJCx48fl2EY6tChQ7mKBgAAqGim31OUmJiosWPHKj09XTVq1NC///1vfffdd7r99tt1//33u6NGAAAAtzMdig4cOKDBgwdLkqpVq6aff/5ZderUUVJSkqZPn+7yAgEAADzBdCiqXbu2YxxRcHCwjhw54lj2448/uq4yAAAADzI9pqhTp07atm2bWrVqpV69emnMmDFKT0/X8uXL1alTJ3fUCAAA4HamQ9GsWbOUm5srSZo6dapyc3O1bNky3XDDDVflk2cAAODaYCoUFRUV6cSJE2rXrp2kX2+lvfXWW24pDAAAwJNMjSny9vZWTEyMzp496656AAAAKoTpgdYRERE6evSoO2oBAACoMKZD0QsvvKCxY8cqJSVFp06dkt1ud5oAAACqItMDrXv16iVJuueee2SxWBzthmHIYrGoqKjIddUBAAB4iOlQtHnzZnfUAQAAUKFMh6Lw8HCFhoY6XSWSfr1S9N1337msMAAAAE8yPaYoPDxcP/zwQ4n2M2fOKDw83CVFAQAAeJrpUHRp7NDv5ebmqkaNGi4pCgAAwNPKfPssISFBkmSxWDRx4kTVqlXLsayoqEg7d+7UjTfe6PICAQAAPKHMoejrr7+W9OuVovT0dFmtVscyq9WqyMhIjR071vUVAgAAeECZQ9Glp84eeeQRzZ49W35+fm4rCgAAwNNMP322cOFCd9QBAABQoUyHojvvvPMPl2/atKncxQAAAFQU06EoMjLSab6goEBpaWnau3evhgwZ4rLCAAAAPMl0KHr11VdLbZ8yZYpyc3OvuCAAAICKYPo9RZfz0EMPacGCBa7aHAAAgEe5LBSlpqby8kYAAFBlmb591rdvX6d5wzB06tQp7dq1SxMnTnRZYQAAAJ5kOhTZbDaneS8vL7Vo0UJJSUmKiYlxWWEAAACexHuKAAAAVM4xRefOndM777yjxMREnTlzRpL01Vdf6fvvv3dpcQAAAJ5i+krRnj171K1bN/n7++v48eMaOnSo6tWrp+XLlyszM1Pvv/++O+oEAABwK9NXihISEvTII4/o0KFDTk+b9erVS1u3bnVpcQAAAJ5iOhR9+eWXevzxx0u0X3fddcrKynJJUQAAAJ5mOhT5+PjIbreXaP/f//6nhg0buqQoAAAATzMdiu655x4lJSWpoKBAkmSxWJSZmalx48apX79+pra1detW3X333QoJCZHFYtHHH3/stNwwDE2aNEnBwcGqWbOmunfvrkOHDjn1OXPmjAYOHCg/Pz/5+/srLi6uxOdG9uzZo86dO6tGjRoKDQ3VjBkzzB42AAC4ypkORa+88opyc3MVEBCgn3/+WbfffruaNWsmX19fvfjii6a2deHCBUVGRmru3LmlLp8xY4bmzJmjt956Szt37lTt2rUVGxurX375xdFn4MCB2rdvn9avX6+UlBRt3bpVw4YNcyy32+2KiYlRWFiYdu/erZkzZ2rKlCmaP3++2UMHAABXMYthGEZ5Vty2bZv27Nmj3NxcdejQQd27d7+yQiwWrVixQn369JH061WikJAQjRkzRmPHjpUk5eTkKDAwUIsWLdKDDz6oAwcOqHXr1vryyy910003SZLWrFmjXr166cSJEwoJCdG8efP03HPPKSsrS1arVZI0fvx4ffzxxzp48GCpteTl5SkvL88xb7fbFRoaqpycHPn5+V3RcQJXu8bjV1V0CaYdn9a7oksA4AZ2u102m63Mf7/L/e2z2267TU899ZSeffbZKw5EpTl27JiysrKctm2z2RQVFaXU1FRJv35vzd/f3xGIJKl79+7y8vLSzp07HX26dOniCESSFBsbq4yMDJ09e7bUfScnJ8tmszmm0NBQlx8fAACoXEy/p0iSNm7cqI0bN+r06dMqLi52WrZgwQKXFHbpSbbAwECn9sDAQMeyrKwsBQQEOC2vVq2a6tWr59QnPDy8xDYuLatbt26JfScmJiohIcExf+lKEQAAuHqZDkVTp05VUlKSbrrpJgUHB8tisbijrgrl4+MjHx+fii4DgIdwyw+AVI5Q9NZbb2nRokUaNGiQO+pxCAoKkiRlZ2crODjY0Z6dna0bb7zR0ef06dNO6xUWFurMmTOO9YOCgpSdne3U59L8pT4AAACmxxTl5+fr1ltvdUctTsLDwxUUFKSNGzc62ux2u3bu3Kno6GhJUnR0tM6dO6fdu3c7+mzatEnFxcWKiopy9Nm6davjFQKStH79erVo0aLUW2cAAODaZDoUPfbYY1qyZIlLdp6bm6u0tDSlpaVJ+nVwdVpamjIzM2WxWDRq1Ci98MIL+uSTT5Senq7BgwcrJCTE8YRaq1at1KNHDw0dOlRffPGFPv/8cw0fPlwPPvigQkJCJEkDBgyQ1WpVXFyc9u3bp2XLlmn27NlOY4YAAABM3z775ZdfNH/+fG3YsEHt2rVT9erVnZbPmjWrzNvatWuXunbt6pi/FFSGDBmiRYsW6dlnn9WFCxc0bNgwnTt3TrfddpvWrFnj9M21xYsXa/jw4erWrZu8vLzUr18/zZkzx7HcZrNp3bp1io+PV8eOHdWgQQNNmjTJ6V1GAAAApt9T9NsQU2JjFos2bdp0xUVVNmbfcwBcy6rioOWqiIHWwJ8z+/fb9JWizZs3l6swAACAyqzcL28EAAC4mhCKAAAARCgCAACQRCgCAACQVMZQ1KFDB8fHU5OSknTx4kW3FgUAAOBpZQpFBw4c0IULFyT9+u2z3NxctxYFAADgaWV6JP/GG2/UI488ottuu02GYejll19WnTp1Su07adIklxYIAADgCWUKRYsWLdLkyZOVkpIii8Wi//73v6pWreSqFouFUAQAAKqkMoWiFi1aaOnSpZIkLy8vbdy4UQEBAW4tDAAAwJNMv9G6uLjYHXUAAABUKNOhSJKOHDmi1157TQcOHJAktW7dWk8//bSaNm3q0uIAAAA8xfR7itauXavWrVvriy++ULt27dSuXTvt3LlTbdq00fr1691RIwAAgNuZvlI0fvx4jR49WtOmTSvRPm7cOP31r391WXEAAACeYvpK0YEDBxQXF1ei/dFHH9X+/ftdUhQAAICnmQ5FDRs2VFpaWon2tLQ0nkgDAABVlunbZ0OHDtWwYcN09OhR3XrrrZKkzz//XNOnT1dCQoLLCwQAAPAE06Fo4sSJ8vX11SuvvKLExERJUkhIiKZMmaKRI0e6vEAAAABPMB2KLBaLRo8erdGjR+v8+fOSJF9fX5cXBgAA4Enlek/RJYQhAABwtTA90BoAAOBqRCgCAAAQoQgAAECSyVBUUFCgbt266dChQ+6qBwAAoEKYCkXVq1fXnj173FULAABAhTF9++yhhx7Su+++645aAAAAKozpR/ILCwu1YMECbdiwQR07dlTt2rWdls+aNctlxQEAAHiK6VC0d+9edejQQZL0v//9z2mZxWJxTVUAAAAeZjoUbd682R11AAAAVKhyP5J/+PBhrV27Vj///LMkyTAMlxUFAADgaaZD0U8//aRu3bqpefPm6tWrl06dOiVJiouL05gxY1xeIAAAgCeYDkWjR49W9erVlZmZqVq1ajnaH3jgAa1Zs8alxQEAAHiK6VC0bt06TZ8+Xddff71T+w033KBvv/3WZYVd0rhxY1kslhJTfHy8JOmOO+4oseyJJ55w2kZmZqZ69+6tWrVqKSAgQM8884wKCwtdXisAAKi6TA+0vnDhgtMVokvOnDkjHx8flxT1W19++aWKiooc83v37tVf//pX3X///Y62oUOHKikpyTH/2/qKiorUu3dvBQUFafv27Tp16pQGDx6s6tWr66WXXnJ5vQAAoGoyfaWoc+fOev/99x3zFotFxcXFmjFjhrp27erS4iSpYcOGCgoKckwpKSlq2rSpbr/9dkefWrVqOfXx8/NzLFu3bp3279+vf/7zn7rxxhvVs2dPPf/885o7d67y8/NdXi8AAKiaTIeiGTNmaP78+erZs6fy8/P17LPPKiIiQlu3btX06dPdUaNDfn6+/vnPf+rRRx91eifS4sWL1aBBA0VERCgxMVEXL150LEtNTVXbtm0VGBjoaIuNjZXdbte+fftK3U9eXp7sdrvTBAAArm6mb59FRETof//7n9544w35+voqNzdXffv2VXx8vIKDg91Ro8PHH3+sc+fO6eGHH3a0DRgwQGFhYQoJCdGePXs0btw4ZWRkaPny5ZKkrKwsp0AkyTGflZVV6n6Sk5M1depU9xwEAAColEyHIkmy2Wx67rnnXF3Ln3r33XfVs2dPhYSEONqGDRvm+Llt27YKDg5Wt27ddOTIETVt2rRc+0lMTFRCQoJj3m63KzQ0tPyFAwCASq9coejs2bN69913deDAAUlS69at9cgjj6hevXouLe63vv32W23YsMFxBehyoqKiJP36csmmTZsqKChIX3zxhVOf7OxsSVJQUFCp2/Dx8XHLoHEAAFB5mR5TtHXrVjVu3Fhz5szR2bNndfbsWc2ZM0fh4eHaunWrO2qUJC1cuFABAQHq3bv3H/ZLS0uTJMetvOjoaKWnp+v06dOOPuvXr5efn59at27ttnoBAEDVYvpKUXx8vB544AHNmzdP3t7ekn597P2pp55SfHy80tPTXV5kcXGxFi5cqCFDhqhatf8r+ciRI1qyZIl69eql+vXra8+ePRo9erS6dOmidu3aSZJiYmLUunVrDRo0SDNmzFBWVpYmTJig+Ph4rgYBAAAH01eKDh8+rDFjxjgCkSR5e3srISFBhw8fdmlxl2zYsEGZmZl69NFHndqtVqs2bNigmJgYtWzZUmPGjFG/fv20cuVKp9pSUlLk7e2t6OhoPfTQQxo8eLDTe40AAABMXynq0KGDDhw4oBYtWji1HzhwQJGRkS4r7LdiYmJK/eBsaGioPv300z9dPywsTKtXr3ZHaQAA4CpRplC0Z88ex88jR47U008/rcOHD6tTp06SpB07dmju3LmaNm2ae6oEAABwM4tR2iWY3/Hy8pLFYin1ao3TxiwWp09yXC3sdrtsNptycnKc3pYNoKTG41dVdAnXhOPT/vihEwDm/36X6UrRsWPHrrgwAACAyqxMoSgsLMzddQAAAFSocr288eTJk9q2bZtOnz6t4uJip2UjR450SWEAAACeZDoULVq0SI8//risVqvq16/v9GFWi8VCKAIAAFWS6VA0ceJETZo0SYmJifLyMv2aIwAAgErJdKq5ePGiHnzwQQIRAAC4qphONnFxcfrwww/dUQsAAECFMX37LDk5WXfddZfWrFmjtm3bqnr16k7LZ82a5bLiAAAAPKVcoWjt2rWOz3z8fqA1AABAVWQ6FL3yyitasGCBHn74YTeUAwAAUDFMjyny8fHRX/7yF3fUAgAAUGFMh6Knn35ar7/+ujtqAQAAqDCmb5998cUX2rRpk1JSUtSmTZsSA62XL1/usuIAAAA8xXQo8vf3V9++fd1RCwAAQIUxHYoWLlzojjoAAAAqFK+lBgAAUDmuFIWHh//h+4iOHj16RQUBAABUBNOhaNSoUU7zBQUF+vrrr7VmzRo988wzrqoLAADAo0yHoqeffrrU9rlz52rXrl1XXBAAAEBFcNmYop49e+rf//63qzYHAADgUS4LRR999JHq1avnqs0BAAB4lOnbZ+3bt3caaG0YhrKysvTDDz/ozTffdGlxAAAAnmI6FPXp08dp3svLSw0bNtQdd9yhli1buqouAAAAjzIdiiZPnuyOOgAAACoUL28EAACQiStFXl5ef/jSRkmyWCwqLCy84qIAAAA8rcyhaMWKFZddlpqaqjlz5qi4uNglRQEAAHhamUPRvffeW6ItIyND48eP18qVKzVw4EAlJSW5tDgAAABPKdeYopMnT2ro0KFq27atCgsLlZaWpvfee09hYWGurg8AAMAjTIWinJwcjRs3Ts2aNdO+ffu0ceNGrVy5UhEREe6qDwAAwCPKHIpmzJihJk2aKCUlRf/617+0fft2de7c2Z21acqUKbJYLE7Tb9+F9Msvvyg+Pl7169dXnTp11K9fP2VnZzttIzMzU71791atWrUUEBCgZ555hsHgAACghDKPKRo/frxq1qypZs2a6b333tN7771Xar/ly5e7rDhJatOmjTZs2OCYr1bt/0oePXq0Vq1apQ8//FA2m03Dhw9X37599fnnn0uSioqK1Lt3bwUFBWn79u06deqUBg8erOrVq+ull15yaZ0AAKBqK3MoGjx48J8+ku8O1apVU1BQUIn2nJwcvfvuu1qyZInuvPNOSdLChQvVqlUr7dixQ506ddK6deu0f/9+bdiwQYGBgbrxxhv1/PPPa9y4cZoyZYqsVqunDwcAAFRSZQ5FixYtcmMZl3fo0CGFhISoRo0aio6OVnJysho1aqTdu3eroKBA3bt3d/Rt2bKlGjVqpNTUVHXq1Empqalq27atAgMDHX1iY2P15JNPat++fWrfvn2p+8zLy1NeXp5j3m63u+8AAQBApVCp32gdFRWlRYsWac2aNZo3b56OHTumzp076/z588rKypLVapW/v7/TOoGBgcrKypIkZWVlOQWiS8svLbuc5ORk2Ww2xxQaGuraAwMAAJWO6W+feVLPnj0dP7dr105RUVEKCwvTBx98oJo1a7ptv4mJiUpISHDM2+12ghEAAFe5Sn2l6Pf8/f3VvHlzHT58WEFBQcrPz9e5c+ec+mRnZzvGIAUFBZV4Gu3SfGnjlC7x8fGRn5+f0wQAAK5uVSoU5ebm6siRIwoODlbHjh1VvXp1bdy40bE8IyNDmZmZio6OliRFR0crPT1dp0+fdvRZv369/Pz81Lp1a4/XDwAAKq9Kffts7NixuvvuuxUWFqaTJ09q8uTJ8vb2Vv/+/WWz2RQXF6eEhATVq1dPfn5+GjFihKKjo9WpUydJUkxMjFq3bq1BgwZpxowZysrK0oQJExQfHy8fH58KPjoAAFCZVOpQdOLECfXv318//fSTGjZsqNtuu007duxQw4YNJUmvvvqqvLy81K9fP+Xl5Sk2NlZvvvmmY31vb2+lpKToySefVHR0tGrXrq0hQ4bwjTYAAFCCxTAMo6KLqOzsdrtsNptycnIYXwT8icbjV1V0CdeE49N6V3QJQKVn9u93lRpTBAAA4C6EIgAAABGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJFXyUJScnKybb75Zvr6+CggIUJ8+fZSRkeHU54477pDFYnGannjiCac+mZmZ6t27t2rVqqWAgAA988wzKiws9OShAACASq5aRRfwRz799FPFx8fr5ptvVmFhof7xj38oJiZG+/fvV+3atR39hg4dqqSkJMd8rVq1HD8XFRWpd+/eCgoK0vbt23Xq1CkNHjxY1atX10svveTR4wEAAJVXpQ5Fa9ascZpftGiRAgICtHv3bnXp0sXRXqtWLQUFBZW6jXXr1mn//v3asGGDAgMDdeONN+r555/XuHHjNGXKFFmt1hLr5OXlKS8vzzFvt9tddEQAAKCyqtS3z34vJydHklSvXj2n9sWLF6tBgwaKiIhQYmKiLl686FiWmpqqtm3bKjAw0NEWGxsru92uffv2lbqf5ORk2Ww2xxQaGuqGowEAAJVJpb5S9FvFxcUaNWqU/vKXvygiIsLRPmDAAIWFhSkkJER79uzRuHHjlJGRoeXLl0uSsrKynAKRJMd8VlZWqftKTExUQkKCY95utxOMAAC4ylWZUBQfH6+9e/dq27ZtTu3Dhg1z/Ny2bVsFBwerW7duOnLkiJo2bVquffn4+MjHx+eK6gUAAFVLlbh9Nnz4cKWkpGjz5s26/vrr/7BvVFSUJOnw4cOSpKCgIGVnZzv1uTR/uXFIAADg2lOpQ5FhGBo+fLhWrFihTZs2KTw8/E/XSUtLkyQFBwdLkqKjo5Wenq7Tp087+qxfv15+fn5q3bq1W+oGAABVT6W+fRYfH68lS5boP//5j3x9fR1jgGw2m2rWrKkjR45oyZIl6tWrl+rXr689e/Zo9OjR6tKli9q1aydJiomJUevWrTVo0CDNmDFDWVlZmjBhguLj47lFBgAAHCr1laJ58+YpJydHd9xxh4KDgx3TsmXLJElWq1UbNmxQTEyMWrZsqTFjxqhfv35auXKlYxve3t5KSUmRt7e3oqOj9dBDD2nw4MFO7zUCAACo1FeKDMP4w+WhoaH69NNP/3Q7YWFhWr16tavKAgAAV6FKfaUIAADAUwhFAAAAIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIquQvbwSudY3Hr6roEgDgmsGVIgAAABGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJPGZDwCokqrqJ2COT+td0SUAl8WVIgAAABGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJPHtM1xDquq3ogAAnnFNhaK5c+dq5syZysrKUmRkpF5//XXdcsstFV0WAFwzquL/nPAR22vHNXP7bNmyZUpISNDkyZP11VdfKTIyUrGxsTp9+nRFlwYAACoBi2EYRkUX4QlRUVG6+eab9cYbb0iSiouLFRoaqhEjRmj8+PF/uK7dbpfNZlNOTo78/Pw8UW6lVxX/bw8AyoMrRVWX2b/f18Tts/z8fO3evVuJiYmONi8vL3Xv3l2pqakl+ufl5SkvL88xn5OTI+nXk+sOEZPXumW7AIAr12j0hxVdwjVh79RYl2/z0t/tsl7/uSZC0Y8//qiioiIFBgY6tQcGBurgwYMl+icnJ2vq1Kkl2kNDQ91WIwAA1zLba+7b9vnz52Wz2f603zURisxKTExUQkKCY764uFhnzpxR/fr1ZbFYKrCyqslutys0NFTfffcdtx/LiXN45TiHrsF5vHKcQ9coy3k0DEPnz59XSEhImbZ5TYSiBg0ayNvbW9nZ2U7t2dnZCgoKKtHfx8dHPj4+Tm3+/v7uLPGa4Ofnxy+AK8Q5vHKcQ9fgPF45zqFr/Nl5LMsVokuuiafPrFarOnbsqI0bNzraiouLtXHjRkVHR1dgZQAAoLK4Jq4USVJCQoKGDBmim266Sbfccotee+01XbhwQY888khFlwYAACqBayYUPfDAA/rhhx80adIkZWVl6cYbb9SaNWtKDL6G6/n4+Gjy5Mklbkmi7DiHV45z6BqcxyvHOXQNd5zHa+Y9RQAAAH/kmhhTBAAA8GcIRQAAACIUAQAASCIUAQAASCIUwQ3OnDmjgQMHys/PT/7+/oqLi1Nubm6Z1jUMQz179pTFYtHHH3/s3kIrObPn8cyZMxoxYoRatGihmjVrqlGjRho5cqTj233Xgrlz56px48aqUaOGoqKi9MUXX/xh/w8//FAtW7ZUjRo11LZtW61evdpDlVZuZs7j22+/rc6dO6tu3bqqW7euunfv/qfn/Vpg9t/iJUuXLpXFYlGfPn3cW2AVYfY8njt3TvHx8QoODpaPj4+aN29u7r9rA3CxHj16GJGRkcaOHTuMzz77zGjWrJnRv3//Mq07a9Yso2fPnoYkY8WKFe4ttJIzex7T09ONvn37Gp988olx+PBhY+PGjcYNN9xg9OvXz4NVV5ylS5caVqvVWLBggbFv3z5j6NChhr+/v5GdnV1q/88//9zw9vY2ZsyYYezfv9+YMGGCUb16dSM9Pd3DlVcuZs/jgAEDjLlz5xpff/21ceDAAePhhx82bDabceLECQ9XXnmYPYeXHDt2zLjuuuuMzp07G/fee69niq3EzJ7HvLw846abbjJ69eplbNu2zTh27JixZcsWIy0trcz7JBTBpfbv329IMr788ktH23//+1/DYrEY33///R+u+/XXXxvXXXedcerUqWs+FF3JefytDz74wLBarUZBQYE7yqxUbrnlFiM+Pt4xX1RUZISEhBjJycml9v/73/9u9O7d26ktKirKePzxx91aZ2Vn9jz+XmFhoeHr62u899577iqx0ivPOSwsLDRuvfVW45133jGGDBlCKDLMn8d58+YZTZo0MfLz88u9T26fwaVSU1Pl7++vm266ydHWvXt3eXl5aefOnZdd7+LFixowYIDmzp1b6vforjXlPY+/l5OTIz8/P1WrdnW/pzU/P1+7d+9W9+7dHW1eXl7q3r27UlNTS10nNTXVqb8kxcbGXrb/taA85/H3Ll68qIKCAtWrV89dZVZq5T2HSUlJCggIUFxcnCfKrPTKcx4/+eQTRUdHKz4+XoGBgYqIiNBLL72koqKiMu/36v5NCY/LyspSQECAU1u1atVUr149ZWVlXXa90aNH69Zbb9W9997r7hKrhPKex9/68ccf9fzzz2vYsGHuKLFS+fHHH1VUVFTiDfWBgYE6ePBgqetkZWWV2r+s5/dqVJ7z+Hvjxo1TSEhIicB5rSjPOdy2bZveffddpaWleaDCqqE85/Ho0aPatGmTBg4cqNWrV+vw4cN66qmnVFBQoMmTJ5dpv1wpQpmMHz9eFovlD6ey/tL8vU8++USbNm3Sa6+95tqiKyF3nsffstvt6t27t1q3bq0pU6ZceeFAGUybNk1Lly7VihUrVKNGjYoup0o4f/68Bg0apLffflsNGjSo6HKqtOLiYgUEBGj+/Pnq2LGjHnjgAT333HN66623yrwNrhShTMaMGaOHH374D/s0adJEQUFBOn36tFN7YWGhzpw5c9nbYps2bdKRI0fk7+/v1N6vXz917txZW7ZsuYLKKxd3nsdLzp8/rx49esjX11crVqxQ9erVr7TsSq9Bgwby9vZWdna2U3t2dvZlz1dQUJCp/teC8pzHS15++WVNmzZNGzZsULt27dxZZqVm9hweOXJEx48f19133+1oKy4ulvTr1eGMjAw1bdrUvUVXQuX5txgcHKzq1avL29vb0daqVStlZWUpPz9fVqv1z3dc7tFIQCkuDRDetWuXo23t2rV/OED41KlTRnp6utMkyZg9e7Zx9OhRT5VeqZTnPBqGYeTk5BidOnUybr/9duPChQueKLXSuOWWW4zhw4c75ouKiozrrrvuDwda33XXXU5t0dHRDLQ2eR4NwzCmT59u+Pn5GampqZ4osdIzcw5//vnnEr//7r33XuPOO+800tPTjby8PE+WXqmY/beYmJhohIWFGUVFRY621157zQgODi7zPglFcLkePXoY7du3N3bu3Gls27bNuOGGG5weJT9x4oTRokULY+fOnZfdhq7xp88Mw/x5zMnJMaKiooy2bdsahw8fNk6dOuWYCgsLK+owPGbp0qWGj4+PsWjRImP//v3GsGHDDH9/fyMrK8swDMMYNGiQMX78eEf/zz//3KhWrZrx8ssvGwcOHDAmT57MI/mG+fM4bdo0w2q1Gh999JHTv7nz589X1CFUOLPn8Pd4+uxXZs9jZmam4evrawwfPtzIyMgwUlJSjICAAOOFF14o8z4JRXC5n376yejfv79Rp04dw8/Pz3jkkUecfkEeO3bMkGRs3rz5stsgFJk/j5s3bzYklTodO3asYg7Cw15//XWjUaNGhtVqNW655RZjx44djmW33367MWTIEKf+H3zwgdG8eXPDarUabdq0MVatWuXhiisnM+cxLCys1H9zkydP9nzhlYjZf4u/RSj6P2bP4/bt242oqCjDx8fHaNKkifHiiy+a+p9Ci2EYhonbfAAAAFclnj4DAAAQoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQhAJbFo0aISHwX+vYcfflh9+vTxSD2XbNmyRRaLRefOnbtsn7LUbtbx48dlsViUlpbm0u0CuDxCEQCPePjhh2WxWGSxWGS1WtWsWTMlJSWpsLCwzNuYPXu2Fi1a5L4iS3Hrrbfq1KlTstlsHt0vAM+rVtEFALh29OjRQwsXLlReXp5Wr16t+Ph4Va9eXYmJiWVa32wwKSoqksVikZdX+f//z2q1KigoqNzrA6g6uFIEwGN8fHwUFBSksLAwPfnkk+revbs++eQTpz5r165Vq1atVKdOHfXo0UOnTp1yLPuz22eXbmN98sknat26tXx8fJSZmam8vDyNHTtW1113nWrXrq2oqCht2bLFsd63336ru+++W3Xr1lXt2rXVpk0brV69WlLpt88WLVqkRo0aqVatWrrvvvv0008/OdVRWp2jRo3SHXfc4Zhfs2aNbrvtNvn7+6t+/fq66667dOTIkbKdSABuQSgCUGFq1qyp/Px8x/zFixf18ssv6//9v/+nrVu3KjMzU2PHjjW1zYsXL2r69Ol65513tG/fPgUEBGj48OFKTU3V0qVLtWfPHt1///3q0aOHDh06JEmKj49XXl6etm7dqvT0dE2fPl116tQpdfs7d+5UXFychg8frrS0NHXt2lUvvPCC6WO/cOGCEhIStGvXLm3cuFFeXl667777VFxcbHpbAFyD22cAPM4wDG3cuFFr167ViBEjHO0FBQV666231LRpU0nS8OHDlZSUZGrbBQUFevPNNxUZGSlJyszM1MKFC5WZmamQkBBJ0tixY7VmzRotXLhQL730kjIzM9WvXz+1bdtWktSkSZPLbn/27Nnq0aOHnn32WUlS8+bNtX37dq1Zs8ZUnf369XOaX7BggRo2bKj9+/crIiLC1LYAuAZXigB4TEpKiurUqaMaNWqoZ8+eeuCBBzRlyhTH8lq1ajkCkSQFBwfr9OnTpvZhtVrVrl07x3x6erqKiorUvHlz1alTxzF9+umnjttVI0eO1AsvvKC//OUvmjx5svbs2XPZ7R84cEBRUVFObdHR0aZqlKRDhw6pf//+atKkifz8/NS4cWNJv4Y4ABWDK0UAPKZr166aN2+erFarQkJCVK2a86+g6tWrO81bLBYZhmFqHzVr1pTFYnHM5+bmytvbW7t375a3t7dT30u3yB577DHFxsZq1apVWrdunZKTk/XKK684XcUyw8vLq0TdBQUFTvN33323wsLC9PbbbyskJETFxcWKiIhwup0IwLMIRQA8pnbt2mrWrJlH99m+fXsVFRXp9OnT6ty582X7hYaG6oknntATTzyhxMREvf3226WGolatWmnnzp1ObTt27HCab9iwofbu3evUlpaW5gh9P/30kzIyMvT22287atq2bVu5jg+A63D7DMBVrXnz5ho4cKAGDx6s5cuX69ixY/riiy+UnJysVatWSfr1ybC1a9fq2LFj+uqrr7R582a1atWq1O2NHDlSa9as0csvv6xDhw7pjTfeKDGe6M4779SuXbv0/vvv69ChQ5o8ebJTSKpbt67q16+v+fPn6/Dhw9q0aZMSEhLcdxIAlAmhCMBVb+HChRo8eLDGjBmjFi1aqE+fPvryyy/VqFEjSb++zyg+Pl6tWrVSjx491Lx5c7355pulbqtTp056++23NXv2bEVGRmrdunWaMGGCU5/Y2FhNnDhRzz77rG6++WadP39egwcPdiz38vLS0qVLtXv3bkVERGj06NGaOXOm+04AgDKxGGZv2AMAAFyFuFIEAAAgQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAk6f8DVJcXBu3aqxgAAAAASUVORK5CYII=", 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      " ] @@ -413,39 +441,49 @@ ], "source": [ "mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322])\n", + "mask_neutral_match = np.logical_and(mask_neutral, matched)\n", "\n", - "pt_pred = df[mask_neutral]['pred_energy']\n", - "pt_truth = df[mask_neutral]['truthHitAssignedEnergies']\n", - "\n", - "help = df[mask_neutral]['pred_pos'].tolist()\n", - "#remove Nan\n", - "help = [x for x in help if str(x) != 'nan']\n", - "pos_pred_df = pd.DataFrame(help, columns=['cosphi', 'sinphi', 'eta'])\n", - "\n", - "phi_pred = np.arctan2(pos_pred_df['sinphi'], pos_pred_df['cosphi'])\n", - "eta_pred = pos_pred_df['eta']\n", + "#neutral particles \n", + "reseta = []\n", + "resphi = []\n", + "relresE = []\n", "\n", - "phi_truth = df[mask_neutral]['truthHitAssignedPhi']\n", - "eta_truth = df[mask_neutral]['truthHitAssignedEta']\n", + "for i in range(len(t_list)):\n", + " mask = np.logical_and(df['event_id'] == i, mask_neutral_match)\n", + " truth_e = df[mask]['truthHitAssignedEnergies']\n", + " pred_e = df[mask]['pred_energy']\n", + " newrelresE = ((truth_e-pred_e)/truth_e).to_numpy()\n", + " relresE = np.concatenate((relresE, newrelresE))\n", + " \n", + " t_eta = df[mask]['truthHitAssignedZ']\n", + " pred_eta = df[mask]['pred_pos'].apply(lambda x: x[2])\n", + " delta_eta = (t_eta-pred_eta).to_numpy()\n", + " reseta = np.concatenate((reseta, delta_eta))\n", + " \n", + " t_phi = np.arctan2(df[mask]['truthHitAssignedY'], df[mask]['truthHitAssignedX'])\n", + " pred_phi = df[mask]['pred_pos'].apply(lambda x: np.arctan2(x[1], x[0]))\n", + " delta_phi = (t_phi-pred_phi).to_numpy()\n", + " delta_phi = np.where(delta_phi > np.pi, delta_phi-2*np.pi, delta_phi)\n", + " delta_phi = np.where(delta_phi < -np.pi, delta_phi+2*np.pi, delta_phi)\n", "\n", - "relrespt = (pt_pred - pt_truth) / pt_truth\n", - "reseta = (eta_pred - eta_truth)\n", - "resphi = (phi_pred - phi_truth)\n", + " resphi = np.concatenate((resphi, delta_phi))\n", "\n", - "plt.hist(relrespt)\n", - "plt.xlabel('relres Energy')\n", + "plt.hist(relresE)\n", "plt.ylabel('Number of neutral particles')\n", + "plt.xlabel('Relative energy residual')\n", "plt.show()\n", "\n", "plt.hist(reseta)\n", - "plt.xlabel('Delta Eta')\n", + "plt.xlabel('Eta residual')\n", "plt.ylabel('Number of neutral particles')\n", "plt.show()\n", "\n", + "\n", "plt.hist(resphi)\n", - "plt.xlabel('Delta Phi')\n", + "plt.xlabel('Phi residual')\n", "plt.ylabel('Number of neutral particles')\n", - "plt.show()" + "plt.show()\n", + "\n" ] }, { @@ -457,14 +495,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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      " + "
      " ] }, "metadata": {}, @@ -473,24 +511,34 @@ ], "source": [ "def energy_resolution(df, bins=None):\n", - " data, ratios = calc_resolution(df, bins)\n", - " means = data['means']\n", - " stddevs = data['stddevs']\n", - " sigma_over_e = data['sigma_over_e']\n", - " std_error = data['std_error']\n", - " entries = data['entries']\n", + " has_truth = np.isnan(df['truthHitAssignedEnergies']) == False\n", + " has_pred = np.isnan(df['pred_energy']) == False\n", + " matched = np.logical_and(has_truth, has_pred)\n", + "\n", + " means = []\n", + " std_error = []\n", + "\n", + " for i in range(len(bins) - 1):\n", + " mask_truth_bin = np.logical_and(\n", + " df['truthHitAssignedEnergies'] >= bins[i],\n", + " df['truthHitAssignedEnergies'] < bins[i + 1])\n", + " mask_bin = np.logical_and(mask_truth_bin, matched)\n", + " ratios = df['pred_energy'][mask_bin] / df['truthHitAssignedEnergies'][mask_bin]\n", + " means.append(np.mean(ratios))\n", + " std_error.append(np.std(ratios) / np.sqrt(len(ratios)))\n", "\n", " # make boxplots of the ratios for each bin\n", - " fig, ax1 = plt.subplots(figsize=(20, 10))\n", - " # ax1 and ax2 for histogram + mean\n", - " # ax3 and ax4 for boxplot and stdddev\n", + " fig, ax1 = plt.subplots(figsize=(12, 6))\n", + " \n", " binwidth = bins[1:] - bins[:-1]\n", " x = bins[:-1] + binwidth / 2\n", - " xerr = binwidth / 2\n", + " xerr = binwidth / 2 \n", " y = means\n", " yerr = std_error\n", + " \n", " # plot x, y with error bars\n", " ax1.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='o', color='blue', zorder=2, label='Mean Response')\n", + " \n", " # set xticks\n", " xticks = np.round(bins, 0).astype(int)\n", " ax1.set_xticks(xticks)\n", @@ -498,59 +546,81 @@ " ymin = np.round(ax1.get_ylim()[0], 1)\n", " ymax = np.round(ax1.get_ylim()[1], 1)\n", " ydelta = max(1 - ymin, ymax - 1)\n", - " ax1.set_ylim(1 - ydelta, 1 + ydelta)\n", - " # set yticks to be evenlyt spaced around 1\n", + " ax1.set_ylim(1 - ydelta, 1 + ydelta) \n", + " # set yticks to be evenly spaced around 1\n", " yticks = np.round(np.arange(1 - ydelta, 1 + ydelta, 0.02), 2)\n", " yticks = np.round(np.linspace(1 - ydelta, 1 + ydelta, 11), 2)\n", " ax1.set_yticks(yticks)\n", " ax1.set_yticklabels(yticks, fontsize=20)\n", + " \n", " ax1.grid(alpha=0.5, linestyle='--')\n", " ax1.set_ylabel('Mean Response Charged', fontsize=40)\n", " ax1.set_xlabel('Energy [GeV]', fontsize=40)\n", " \n", " return fig\n", "\n", - "def calc_resolution(showers, bins, predstring='pred_energy'):\n", - " \"\"\"Calculate resolution for a given dataframe and energy bins.\"\"\"\n", - " has_truth = np.isnan(showers['truthHitAssignedEnergies']) == False\n", - " has_pred = np.isnan(showers[predstring]) == False\n", - " matched = np.logical_and(has_truth, has_pred)\n", - "\n", - " all_ratios = []\n", - " means = []\n", - " stddevs = []\n", - " sigma_over_e = []\n", - " std_error = []\n", - " entries = []\n", - "\n", - " for i in range(len(bins) - 1):\n", - " mask_truth_bin = np.logical_and(\n", - " showers['truthHitAssignedEnergies'] >= bins[i],\n", - " showers['truthHitAssignedEnergies'] < bins[i + 1])\n", - " mask_bin = np.logical_and(mask_truth_bin, matched)\n", - " ratios = showers[predstring][mask_bin] / showers['truthHitAssignedEnergies'][mask_bin]\n", - " all_ratios.append(ratios)\n", - " means.append(np.mean(all_ratios[-1]))\n", - " stddevs.append(np.std(all_ratios[-1]))\n", - " sigma_over_e.append(stddevs[-1] / means[-1])\n", - " std_error.append(stddevs[-1] / np.sqrt(len(all_ratios[-1])))\n", - " entries.append(len(all_ratios[-1]))\n", - "\n", - " data = pd.DataFrame({\n", - " \"means\": np.array(means),\n", - " \"stddevs\": np.array(stddevs),\n", - " \"sigma_over_e\": np.array(sigma_over_e),\n", - " \"std_error\": np.array(std_error),\n", - " \"entries\": np.array(entries),\n", - " })\n", - "\n", - " return data, all_ratios\n", "\n", "energy_bins = np.array([15,20,30,50,200])*1000\n", "charged_mask = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312])\n", "fig=energy_resolution(df[charged_mask], bins=energy_bins)" ] }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_condensation(event_id, pred_list, t_list, feature_list, location='/work/friemer/hgcalml/condensationplots/condensation'):\n", + " coords = pred_list[event_id]['pred_ccoords']\n", + "\n", + " if coords.shape[1] < 3: #add a zero\n", + " coords = tf.concat([coords, tf.zeros_like(coords)[:,0:1]], axis=-1)\n", + "\n", + " data={\n", + " 'X': coords[:,0],\n", + " 'Y': coords[:,1],\n", + " 'Z': coords[:,2],\n", + " 'hyper_idx': t_list[event_id]['truthHitAssignementIdx'][:,0],\n", + " 'features': pred_list[event_id]['pred_beta'][:,0],\n", + " 'pdgid': t_list[event_id]['truthHitAssignedPIDs'][:,0],\n", + " 'recHitID': feature_list[event_id]['recHitID'][:,0],\n", + " }\n", + " eventdf = pd.DataFrame(data)\n", + "\n", + " hover_data = {'X': True, 'Y': True, 'Z': True, 'hyper_idx': True,'features': True, 'pdgid': True,'recHitID': True}\n", + "\n", + " fig = px.scatter_3d(eventdf, x=\"X\", y=\"Y\", z=\"Z\", \n", + " color=\"hyper_idx\",\n", + " size='features',\n", + " template='plotly_dark',\n", + " hover_data=hover_data,\n", + " color_continuous_scale=px.colors.sequential.Rainbow)\n", + " fig.update_traces(marker=dict(line=dict(width=0)))\n", + "\n", + " fig.write_html(location+str(event_id)+\".html\")\n", + " return fig\n", + "\n", + "#plot_condensation(0, pred_list, t_list, feature_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1001 1001\n" + ] + } + ], + "source": [ + "print(len(df['event_id'].unique()), len(t_list))" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py new file mode 100644 index 00000000..21ed5c5c --- /dev/null +++ b/scripts/plot_cocoa.py @@ -0,0 +1,409 @@ +import gzip +import pickle +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import tensorflow as tf +import sys +import os +import plotly.express as px +import argparse + + +def calc_efficiencies(df, bins, mask=None): + efficiencies = [] + efficiencies_err = [] + fake_rate = [] + fake_rate_err = [] + corr_class_prob = [] + corr_class_prob_err = [] + + mask_predicted = np.isnan(df['pred_energy']) == False + mask_truth = np.isnan(df['truthHitAssignedEnergies']) == False + + if mask == None: + mask_PID_truth = np.ones(len(df), dtype=bool) + mask_PID_pred = mask_PID_truth + else: + if(mask == 0): + mask_PID_truth = df['truthHitAssignedPIDs'].isin([22]) + elif(mask == 1): + mask_PID_truth = df['truthHitAssignedPIDs'].isin([130,310,311,2112,-2112,3122,-3122,3322,-3322]) + else: + raise ValueError("mask must be 0 or 1") + mask_PID_pred = df['pred_id_value'].isin([mask]) + + mask_PID_matched = np.logical_and(mask_PID_truth, mask_PID_pred) + + + for i in range(len(bins) - 1): + mask_bintruth = np.logical_and( + df['truthHitAssignedEnergies'] >= bins[i], + df['truthHitAssignedEnergies'] < bins[i + 1]) + mask_binpred = np.logical_and( + df['pred_energy'] >= bins[i], + df['pred_energy'] < bins[i + 1]) + + matched = np.logical_and( + mask_predicted, + mask_bintruth) + faked = np.logical_and( + mask_binpred, + np.logical_not(mask_truth)) + + eff = np.sum(matched[mask_PID_truth]) / np.sum(mask_bintruth[mask_PID_truth]) + eff_err = np.sqrt(eff * (1 - eff) / np.sum(matched[mask_PID_truth])) + efficiencies.append(eff) + efficiencies_err.append(eff_err) + + fake = np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred]) + fake_err = np.sqrt(fake * (1 - fake) / np.sum(faked[mask_PID_pred])) + fake_rate.append(fake) + fake_rate_err.append(fake_err) + + cc_prob = np.sum(matched[mask_PID_matched]) / np.sum(matched[mask_PID_truth]) + cc_prob_err = np.sqrt(cc_prob * (1 - cc_prob) / np.sum(matched[mask_PID_truth])) + corr_class_prob.append(cc_prob) + corr_class_prob_err.append(cc_prob_err) + + return np.array(efficiencies), np.array(efficiencies_err), np.array(fake_rate), np.array(fake_rate_err), np.array(corr_class_prob), np.array(corr_class_prob_err) + +def plot_efficencies(df, bins): + # Calculate the bin positions and widths + binwidth = bins[1:] - bins[:-1] + x_pos = bins[:-1] + binwidth / 2 + x_err = binwidth / 2 + + # Calculate the efficiencies and fake rates for the total + yeff, yerr_eff, yfake, yerr_fake, ycorr, yerr_corr = calc_efficiencies(df, bins) + yeff, yerr_eff, yfake, yerr_fake, ycorr, yerr_corr = \ + yeff*100, yerr_eff*100, yfake*100, yerr_fake*100, ycorr*100, yerr_corr*100 + + # Calculate the efficiencies and fake rates for the photons + yeff_photon, yerr_eff_photon, yfake_photon, yerr_fake_photon, ycorr_photon, yerr_corr_photon = calc_efficiencies(df, bins, 0) + yeff_photon, yerr_eff_photon, yfake_photon, yerr_fake_photon, ycorr_photon, yerr_corr_photon = \ + yeff_photon*100, yerr_eff_photon*100, yfake_photon*100, yerr_fake_photon*100, ycorr_photon*100, yerr_corr_photon*100 + + # Calculate the efficiencies and fake rates for the neutral hadrons + #130: "K0L", 310: "K0S", 311: "K0", 2112: "neutron",-2112: "antineutron",3122: "Lambda",-3122: "antilambda"3322: "Xi0",-3322: "antixi0" + yeff_nh, yerr_eff_nh, yfake_nh, yerr_fake_nh, ycorr_nh, yerr_corr_nh = calc_efficiencies(df, bins, 1) + yeff_nh, yerr_eff_nh, yfake_nh, yerr_fake_nh, ycorr_nh, yerr_corr_nh = \ + yeff_nh*100, yerr_eff_nh*100, yfake_nh*100, yerr_fake_nh*100, ycorr_nh*100, yerr_corr_nh*100 + + # Create the plots + fig, ((ax1, ax2, ax3), (ax4, ax5,ax6), (ax7,ax8,ax9)) = plt.subplots(nrows=3, ncols=3, figsize=(20, 10)) + ax1.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o', color='red', label='Efficiency') + ax1.set_ylabel('efficiency total', fontsize=10) + ax2.errorbar(x_pos, yeff_photon, xerr=x_err, yerr=yerr_eff_photon, fmt='o', color='red', label='Efficiency') + ax2.set_ylabel('efficiency gamma', fontsize=10) + ax3.errorbar(x_pos, yeff_nh, xerr=x_err, yerr=yerr_eff_nh, fmt='o', color='red', label='Efficiency') + ax3.set_ylabel('efficiency n.H.', fontsize=10) + + ax4.errorbar(x_pos, yfake, xerr=x_err, yerr=yerr_fake, fmt='o', color='red', label='Fake rate') + ax4.set_ylabel('fake rate total', fontsize=10) + ax5.errorbar(x_pos, yfake_photon, xerr=x_err, yerr=yerr_fake_photon, fmt='o', color='red', label='Fake rate') + ax5.set_ylabel('fake rate gamma', fontsize=10) + ax6.errorbar(x_pos, yfake_nh, xerr=x_err, yerr=yerr_fake_nh, fmt='o', color='red', label='Fake rate') + ax6.set_ylabel('fake rate n.H.', fontsize=10) + + ax7.errorbar(x_pos, ycorr, xerr=x_err, yerr=yerr_corr, fmt='o', color='red', label='probability of correct class') + ax7.set_ylabel('p corr total', fontsize=10) + ax8.errorbar(x_pos, ycorr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', color='red', label='probability of correct class') + ax8.set_ylabel('p corr gamma', fontsize=10) + ax9.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', color='red', label='probability of correct class') + ax9.set_ylabel('p corr n.H.', fontsize=10) + + for ax in [ax1, ax2,ax3, ax4, ax5, ax6, ax7, ax8, ax9]: + ax.set_xticks(bins) + ax.set_xticklabels(bins, fontsize=10) + ax.set_xlim(bins[0], bins[-1]) + ax.grid(True, which='major', axis='y', linestyle='--', alpha=0.5) + ax.grid(True, which='major', axis='x', linestyle='--', alpha=0.5) + ax.set_xlabel('Energy [GeV]', fontsize=10) + + yticks1 = np.round(np.arange(40, 101, 20), 1) + ax.set_yticks(yticks1) + ax.set_yticklabels([f"{y}%" for y in yticks1], fontsize=10) + #ax.set_ylim(20, 101) + + return fig +def plot_jet_metrics(df): + #Jet metrics + reseta = [] + resphi = [] + relresE = [] + nParicles_pred = [] + nParicles_truth = [] + + matched = calc_matched_mask(df) + + for i in range(len(df['event_id'].unique())): + mask = np.logical_and(df['event_id'] == i, matched) + + jet_E_truth = np.sum(df[mask]['truthHitAssignedEnergies']) + jet_E_pred = np.sum(df[mask]['pred_energy']) + relresE.append((jet_E_truth-jet_E_pred)/jet_E_truth) + + nParicles_pred.append(len(df[mask]['pred_sid'])) + nParicles_truth.append(len(df[mask]['truthHitAssignementIdx'])) + + t_eta = np.mean(df[mask]['truthHitAssignedZ']) + pred_eta = df[mask]['pred_pos'].apply(lambda x: x[2]).mean() + reseta.append(t_eta-pred_eta) + + t_phi = np.mean(np.arctan2(df[mask]['truthHitAssignedY'], df[mask]['truthHitAssignedX'])) + pred_phi = df[mask]['pred_pos'].apply(lambda x: np.arctan2(x[1], x[0])).mean() + delta_phi = calc_deltaphi(t_phi, pred_phi) + resphi.append(delta_phi) + + nParticle_bins = np.linspace(0, 30, 31) + fig_jet_nparticle = plt.figure() + plt.hist(nParicles_truth, bins=nParticle_bins, label='Truth', color='#fee7ae') + plt.grid(alpha=0.5) + plt.hist(nParicles_pred, bins=nParticle_bins, label='Prediction', histtype='step', color='#67c4ce', linestyle='--') + plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes) + plt.ylabel('Events') + plt.xlabel('Jet nConstituents') + plt.legend() + plt.close() + + relres_bins = np.linspace(-1, 1, 51) + fig_jet_relresE = plt.figure() + plt.hist(relresE, bins=relres_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.5) + plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes) + plt.ylabel('Number of events') + plt.xlabel('rel. res. Cal. Jet Energy') + plt.close() + + + res_bins = np.linspace(-0.2, 0.2, 51) + fig_jet_reseta = plt.figure() + plt.hist(reseta, bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.5) + plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes) + plt.ylabel('Number of events') + plt.xlabel('Jet $\\Delta \\eta$') + plt.close() + + fig_jet_resphi = plt.figure() + plt.hist(resphi, bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.5) + plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes) + plt.ylabel('Number of events') + plt.xlabel('Jet $\\Delta \\phi$') + plt.close() + + return fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi + +def calc_matched_mask(df): + has_truth = np.isnan(df['truthHitAssignedEnergies']) == False + has_pred = np.isnan(df['pred_energy']) == False + matched = np.logical_and(has_truth, has_pred) + return matched + +def calc_deltaphi(t_phi, pred_phi): + delta_phi = t_phi-pred_phi + delta_phi = np.where(delta_phi > np.pi, delta_phi-2*np.pi, delta_phi) + delta_phi = np.where(delta_phi < -np.pi, delta_phi+2*np.pi, delta_phi) + return delta_phi + +def plot_particle_metrics(df): + + matched = calc_matched_mask(df) + + reseta = [] + resphi = [] + relresE = [] + + for i in range(len(df['event_id'].unique())): + mask = np.logical_and(df['event_id'] == i, matched) + + truth_e = df[mask]['truthHitAssignedEnergies'] + pred_e = df[mask]['pred_energy'] + newrelresE = ((truth_e-pred_e)/truth_e).to_numpy() + relresE = np.concatenate((relresE, newrelresE)) + + t_eta = df[mask]['truthHitAssignedZ'] + pred_eta = df[mask]['pred_pos'].apply(lambda x: x[2]) + delta_eta = (t_eta-pred_eta).to_numpy() + reseta = np.concatenate((reseta, delta_eta)) + + t_phi = np.arctan2(df[mask]['truthHitAssignedY'], df[mask]['truthHitAssignedX']) + pred_phi = df[mask]['pred_pos'].apply(lambda x: np.arctan2(x[1], x[0])) + delta_phi = calc_deltaphi(t_phi, pred_phi) + resphi = np.concatenate((resphi, delta_phi)) + + relres_bins = np.linspace(-1, 1, 51) + fig_relresE = plt.figure() + plt.hist(relresE, bins=relres_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.5) + plt.xlabel('res. res. Neutral Particle Energy') + plt.ylabel('Number of neutral particles') + plt.close() + + res_bins = np.linspace(-0.2, 0.2, 51) + fig_reseta = plt.figure() + plt.hist(reseta, bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.5) + plt.xlabel('Neutral Particle $\\Delta \\eta$') + plt.ylabel('Number of neutral particles') + plt.close() + + fig_resphi = plt.figure() + plt.hist(resphi, bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.5) + plt.xlabel('Neutral Particle $\\Delta \\phi$') + plt.ylabel('Number of neutral particles') + plt.close() + + return fig_relresE, fig_reseta, fig_resphi + +def plt_energy_resolution(df, bins=None): + has_truth = np.isnan(df['truthHitAssignedEnergies']) == False + has_pred = np.isnan(df['pred_energy']) == False + matched = np.logical_and(has_truth, has_pred) + + means = [] + std_error = [] + + for i in range(len(bins) - 1): + mask_truth_bin = np.logical_and( + df['truthHitAssignedEnergies'] >= bins[i], + df['truthHitAssignedEnergies'] < bins[i + 1]) + mask_bin = np.logical_and(mask_truth_bin, matched) + diff = np.abs(df['pred_energy'][mask_bin] - df['truthHitAssignedEnergies'][mask_bin]) + #ratios = df['pred_energy'][mask_bin] / df['truthHitAssignedEnergies'][mask_bin] + means.append(np.mean(diff)) + std_error.append(np.std(diff) / np.sqrt(len(diff))) + + means = np.array(means) + std_error = np.array(std_error) + + # make boxplots of the ratios for each bin + fig, ax1 = plt.subplots(figsize=(12, 6)) + + binwidth = bins[1:] - bins[:-1] + x = bins[:-1] + binwidth / 2 + xerr = binwidth / 2 + y = means + yerr = std_error + + #Convert MeV to GeV + x = x/1000 + xerr = xerr/1000 + y = y/1000 + yerr = yerr/1000 + bins = bins/1000 + + # plot x, y with error bars + ax1.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='o', color='#67c4ce') + + # set xticks + xticks = np.round(bins, 0).astype(int) + ax1.set_xticks(xticks) + ax1.set_xticklabels(xticks, fontsize=10) + + # ymin = np.round(ax1.get_ylim()[0], 1) + # ymax = np.round(ax1.get_ylim()[1], 1) + # ydelta = max(1 - ymin, ymax - 1) + # ax1.set_ylim(1 - ydelta, 1 + ydelta) + # # set yticks to be evenly spaced around 1 + # yticks = np.round(np.arange(1 - ydelta, 1 + ydelta, 0.02), 2) + # yticks = np.round(np.linspace(1 - ydelta, 1 + ydelta, 11), 2) + # ax1.set_yticks(yticks) + # ax1.set_yticklabels(yticks, fontsize=20) + + ax1.grid(alpha=0.5, linestyle='--') + ax1.set_ylabel('$\sigma E [GeV]$', fontsize=20) + ax1.set_xlabel('Energy [GeV]', fontsize=20) + + return fig + +def plot_condensation(event_id, pred_list, t_list, feature_list, location='/work/friemer/hgcalml/condensationplots/condensation'): + coords = pred_list[event_id]['pred_ccoords'] + + if coords.shape[1] < 3: #add a zero + coords = tf.concat([coords, tf.zeros_like(coords)[:,0:1]], axis=-1) + + data={ + 'X': coords[:,0], + 'Y': coords[:,1], + 'Z': coords[:,2], + 'hyper_idx': t_list[event_id]['truthHitAssignementIdx'][:,0], + 'features': pred_list[event_id]['pred_beta'][:,0], + 'pdgid': t_list[event_id]['truthHitAssignedPIDs'][:,0], + 'recHitID': feature_list[event_id]['recHitID'][:,0], + } + eventdf = pd.DataFrame(data) + + hover_data = {'X': True, 'Y': True, 'Z': True, 'hyper_idx': True,'features': True, 'pdgid': True,'recHitID': True} + + fig = px.scatter_3d(eventdf, x="X", y="Y", z="Z", + color="hyper_idx", + size='features', + template='plotly_dark', + hover_data=hover_data, + color_continuous_scale=px.colors.sequential.Rainbow) + fig.update_traces(marker=dict(line=dict(width=0))) + + fig.write_html(location+str(event_id)+".html") + return fig + +def plot_everything(df, outputpath='/work/friemer/hgcalml/testplots/'): + + #Create Output directory + if not os.path.exists(outputpath): + print("Creating output directory", outputpath) + os.mkdir(outputpath) + else: + var = input(\ + 'Working directory exists, are you sure you want to continue, please type "yes/y"\n') + var = var.lower() + if not var in ('yes', 'y'): + sys.exit() + + #Create everything + energy_bins_neutral = np.array([1,2,3,4,5,10,20,30,50])*1000 + fig_eff=plot_efficencies(df, bins=energy_bins_neutral) + + fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi = plot_jet_metrics(df) + + mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]) + fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi = plot_particle_metrics(df[mask_neutral]) + + energy_bins_charged = np.array([15,20,30,50,200])*1000 + mask_charged = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) + fig_charged_res = plt_energy_resolution(df[mask_charged], bins=energy_bins_charged) + + #Save everything + fig_eff.savefig(os.path.join(outputpath,'efficiencies.png')) + fig_jet_nparticle.savefig(os.path.join(outputpath,'jet_nparticle.png')) + fig_jet_relresE.savefig(os.path.join(outputpath,'jet_relresE.png')) + fig_jet_reseta.savefig(os.path.join(outputpath,'jet_reseta.png')) + fig_jet_resphi.savefig(os.path.join(outputpath,'jet_resphi.png')) + fig_neutral_relresE.savefig(os.path.join(outputpath,'neutral_relresE.png')) + fig_neutral_reseta.savefig(os.path.join(outputpath,'neutral_reseta.png')) + fig_neutral_resphi.savefig(os.path.join(outputpath,'neutral_resphi.png')) + fig_charged_res.savefig(os.path.join(outputpath,'charged_res.png')) + + + +def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalml/testplots/'): + with gzip.open(analysisfilepath, 'rb') as input_file: + analysis_data = pickle.load(input_file) + df = analysis_data['showers_dataframe'] + plot_everything(df, outputpath) + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + 'Create Plots for the COCOA analysis') + parser.add_argument('analysisfile', + help='Filepath to analysis file created by analyse_cocoa_predictions.py containing shower dataframe') + parser.add_argument('outputlocation', + help="Output directory for the plots", + default='') + + args = parser.parse_args() + + plot_everything_from_file(args.analysisfile, args.outputlocation) From 88fb6f9847207b991cd3fff7c0e4396549b66ef3 Mon Sep 17 00:00:00 2001 From: Fabian Date: Fri, 26 Jul 2024 10:50:17 +0200 Subject: [PATCH 44/62] minor changes to plotting routine --- scripts/analyse_cocoa_predictions.py | 11 +-- scripts/plot_cocoa.py | 104 +++++++++++++++------------ scripts/submitViaHTCondor.py | 2 +- 3 files changed, 68 insertions(+), 49 deletions(-) diff --git a/scripts/analyse_cocoa_predictions.py b/scripts/analyse_cocoa_predictions.py index e7fd2e9e..03253599 100755 --- a/scripts/analyse_cocoa_predictions.py +++ b/scripts/analyse_cocoa_predictions.py @@ -114,16 +114,18 @@ def analyse(preddir, ############################################################################################### ### Write to file ############################################################################# ############################################################################################### - scalar_variables = { + if len(analysisoutpath) > 0: + print("Writing dataframes to pickled file", analysisoutpath) + + scalar_variables = { 'beta_threshold': str(beta_threshold), 'distance_threshold': str(distance_threshold), 'iou_threshold': str(iou_threshold), 'matching_mode': str(matching_mode), 'de_e_cut': str(de_e_cut), 'angle_cut': str(angle_cut), - } - - if len(analysisoutpath) > 0: + } + analysis_data = { 'showers_dataframe' : showers_dataframe, 'scalar_variables' : scalar_variables, @@ -144,6 +146,7 @@ def analyse(preddir, ############################################################################################### if(len(pdfpath) > 0): + print('Starting to plot', pdfpath) plot_everything(showers_dataframe, pdfpath) print("DONE") diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py index 21ed5c5c..c9f6e33f 100644 --- a/scripts/plot_cocoa.py +++ b/scripts/plot_cocoa.py @@ -69,10 +69,6 @@ def calc_efficiencies(df, bins, mask=None): return np.array(efficiencies), np.array(efficiencies_err), np.array(fake_rate), np.array(fake_rate_err), np.array(corr_class_prob), np.array(corr_class_prob_err) def plot_efficencies(df, bins): - # Calculate the bin positions and widths - binwidth = bins[1:] - bins[:-1] - x_pos = bins[:-1] + binwidth / 2 - x_err = binwidth / 2 # Calculate the efficiencies and fake rates for the total yeff, yerr_eff, yfake, yerr_fake, ycorr, yerr_corr = calc_efficiencies(df, bins) @@ -90,6 +86,15 @@ def plot_efficencies(df, bins): yeff_nh, yerr_eff_nh, yfake_nh, yerr_fake_nh, ycorr_nh, yerr_corr_nh = \ yeff_nh*100, yerr_eff_nh*100, yfake_nh*100, yerr_fake_nh*100, ycorr_nh*100, yerr_corr_nh*100 + + #Convert to GeV + bins = bins/1000 + + # Calculate the bin positions and widths + binwidth = bins[1:] - bins[:-1] + x_pos = bins[:-1] + binwidth / 2 + x_err = binwidth / 2 + # Create the plots fig, ((ax1, ax2, ax3), (ax4, ax5,ax6), (ax7,ax8,ax9)) = plt.subplots(nrows=3, ncols=3, figsize=(20, 10)) ax1.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o', color='red', label='Efficiency') @@ -117,8 +122,7 @@ def plot_efficencies(df, bins): ax.set_xticks(bins) ax.set_xticklabels(bins, fontsize=10) ax.set_xlim(bins[0], bins[-1]) - ax.grid(True, which='major', axis='y', linestyle='--', alpha=0.5) - ax.grid(True, which='major', axis='x', linestyle='--', alpha=0.5) + ax.grid(alpha=0.4) ax.set_xlabel('Energy [GeV]', fontsize=10) yticks1 = np.round(np.arange(40, 101, 20), 1) @@ -136,16 +140,21 @@ def plot_jet_metrics(df): nParicles_truth = [] matched = calc_matched_mask(df) + has_truth = np.isnan(df['truthHitAssignedEnergies']) == False + has_pred = np.isnan(df['pred_energy']) == False for i in range(len(df['event_id'].unique())): - mask = np.logical_and(df['event_id'] == i, matched) + event_mask = df['event_id'] == i + mask = np.logical_and(event_mask, matched) jet_E_truth = np.sum(df[mask]['truthHitAssignedEnergies']) jet_E_pred = np.sum(df[mask]['pred_energy']) relresE.append((jet_E_truth-jet_E_pred)/jet_E_truth) - nParicles_pred.append(len(df[mask]['pred_sid'])) - nParicles_truth.append(len(df[mask]['truthHitAssignementIdx'])) + pred_event_mask = np.logical_and(event_mask, has_pred) + truth_event_mask = np.logical_and(event_mask, has_truth) + nParicles_pred.append(np.sum(pred_event_mask)) + nParicles_truth.append(np.sum(truth_event_mask)) t_eta = np.mean(df[mask]['truthHitAssignedZ']) pred_eta = df[mask]['pred_pos'].apply(lambda x: x[2]).mean() @@ -159,39 +168,43 @@ def plot_jet_metrics(df): nParticle_bins = np.linspace(0, 30, 31) fig_jet_nparticle = plt.figure() plt.hist(nParicles_truth, bins=nParticle_bins, label='Truth', color='#fee7ae') - plt.grid(alpha=0.5) + plt.grid(alpha=0.4) plt.hist(nParicles_pred, bins=nParticle_bins, label='Prediction', histtype='step', color='#67c4ce', linestyle='--') - plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes) - plt.ylabel('Events') - plt.xlabel('Jet nConstituents') - plt.legend() + plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.ylabel('Events', fontsize=20) + plt.xlabel('Jet nConstituents', fontsize=20) + plt.legend(fontsize=10) + plt.xticks(np.linspace(0, 30, 7)) plt.close() relres_bins = np.linspace(-1, 1, 51) fig_jet_relresE = plt.figure() - plt.hist(relresE, bins=relres_bins, histtype='step', color='#67c4ce', linestyle='--') - plt.grid(alpha=0.5) - plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes) - plt.ylabel('Number of events') - plt.xlabel('rel. res. Cal. Jet Energy') + plt.hist(np.clip(relresE, -1, 1), bins=relres_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.ylabel('Number of events', fontsize=20) + plt.xlabel('rel. res. Cal. Jet Energy', fontsize=20) + plt.xticks(np.linspace(-1, 1, 5)) plt.close() res_bins = np.linspace(-0.2, 0.2, 51) fig_jet_reseta = plt.figure() - plt.hist(reseta, bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') - plt.grid(alpha=0.5) - plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes) - plt.ylabel('Number of events') - plt.xlabel('Jet $\\Delta \\eta$') + plt.hist(np.clip(reseta,-0.2,0.2), bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.ylabel('Number of events', fontsize=20) + plt.xlabel('Jet $\\Delta \\eta$', fontsize=20) + plt.xticks(np.linspace(-0.2, 0.2, 5)) plt.close() fig_jet_resphi = plt.figure() - plt.hist(resphi, bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') - plt.grid(alpha=0.5) - plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes) - plt.ylabel('Number of events') - plt.xlabel('Jet $\\Delta \\phi$') + plt.hist(np.clip(resphi, -0.2, 0.2), bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.ylabel('Number of events', fontsize=20) + plt.xlabel('Jet $\\Delta \\phi$', fontsize=20) + plt.xticks(np.linspace(-0.2, 0.2, 5)) plt.close() return fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi @@ -236,25 +249,28 @@ def plot_particle_metrics(df): relres_bins = np.linspace(-1, 1, 51) fig_relresE = plt.figure() - plt.hist(relresE, bins=relres_bins, histtype='step', color='#67c4ce', linestyle='--') - plt.grid(alpha=0.5) - plt.xlabel('res. res. Neutral Particle Energy') - plt.ylabel('Number of neutral particles') + plt.hist(np.clip(relresE, -1,1), bins=relres_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.xlabel('rel. res. Neutral Particle Energy', fontsize=20) + plt.ylabel('Number of neutral particles', fontsize=20) + plt.xticks(np.linspace(-1, 1, 5)) plt.close() - res_bins = np.linspace(-0.2, 0.2, 51) + res_bins = np.linspace(-0.4, 0.4, 51) fig_reseta = plt.figure() - plt.hist(reseta, bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') - plt.grid(alpha=0.5) - plt.xlabel('Neutral Particle $\\Delta \\eta$') - plt.ylabel('Number of neutral particles') + plt.hist(np.clip(reseta, -0.4, 0.4), bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.xlabel('Neutral Particle $\\Delta \\eta$', fontsize=20) + plt.ylabel('Number of neutral particles', fontsize=20) + plt.xticks(np.linspace(-0.4, 0.4, 5)) plt.close() fig_resphi = plt.figure() - plt.hist(resphi, bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') - plt.grid(alpha=0.5) - plt.xlabel('Neutral Particle $\\Delta \\phi$') - plt.ylabel('Number of neutral particles') + plt.hist(np.clip(resphi, -0.4, 0.4), bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.xlabel('Neutral Particle $\\Delta \\phi$', fontsize=20) + plt.ylabel('Number of neutral particles', fontsize=20) + plt.xticks(np.linspace(-0.4, 0.4, 5)) plt.close() return fig_relresE, fig_reseta, fig_resphi @@ -314,7 +330,7 @@ def plt_energy_resolution(df, bins=None): # ax1.set_yticks(yticks) # ax1.set_yticklabels(yticks, fontsize=20) - ax1.grid(alpha=0.5, linestyle='--') + ax1.grid(alpha=0.4) ax1.set_ylabel('$\sigma E [GeV]$', fontsize=20) ax1.set_xlabel('Energy [GeV]', fontsize=20) @@ -363,7 +379,7 @@ def plot_everything(df, outputpath='/work/friemer/hgcalml/testplots/'): if not var in ('yes', 'y'): sys.exit() - #Create everything + #Create everything energy_bins_neutral = np.array([1,2,3,4,5,10,20,30,50])*1000 fig_eff=plot_efficencies(df, bins=energy_bins_neutral) diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 13f23858..4f2c266b 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -44,7 +44,7 @@ def __str__(self) -> str: 'n' : meta_option('n','TrainJob_'+UEXT), 'f' : meta_option('f', '/work/friemer/hgcalml/HGCalML'), 'cpu': meta_option('cpu', '1'), - 'memory': meta_option('memory', '5 GB'), + 'memory': meta_option('memory', '10 GB'), 'disk': meta_option('disk', '2 GB'), 'gpu': meta_option('gpu', '1'), 'wandb': meta_option('wandb', 'True'), From e78d834c7ab82528514c3ed5b80c97ef6a80df5c Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 29 Jul 2024 14:35:37 +0200 Subject: [PATCH 45/62] created input coords plotter --- modules/ShowersMatcher2.py | 4 +- modules/compiled/bin_by_coordinates_kernel.cc | 6 +- scripts/analyse_cocoa_predictions.py | 10 +-- scripts/plot_cocoa.py | 89 ++++++++++++------- 4 files changed, 69 insertions(+), 40 deletions(-) diff --git a/modules/ShowersMatcher2.py b/modules/ShowersMatcher2.py index 5e80ee4b..13cbe416 100644 --- a/modules/ShowersMatcher2.py +++ b/modules/ShowersMatcher2.py @@ -176,8 +176,10 @@ def _assign_tidx(self): else: filt = pred_sid == n tidx = self.predictions_dict['t_idx'][filt].flatten() - hist, _ = np.histogram(tidx, bins=range(-2, np.max(tidx)+1)) + beta = self.predictions_dict['pred_beta'][filt].flatten() + hist, _ = np.histogram(tidx, bins=range(-2, np.max(tidx)+1), weights=beta) attr['tidx'] = np.argmax(hist)-1 + def _build_data_graph(self): """ Builds a graph in which all truth showers and all predicted showers are nodes diff --git a/modules/compiled/bin_by_coordinates_kernel.cc b/modules/compiled/bin_by_coordinates_kernel.cc index f0c0cb1e..80c13348 100644 --- a/modules/compiled/bin_by_coordinates_kernel.cc +++ b/modules/compiled/bin_by_coordinates_kernel.cc @@ -23,12 +23,12 @@ struct BinByCoordinatesNbinsHelperOpFunctor { //just because a int n_nbins, int nrs){ int n=1; - printf("n bins:"); + //printf("n bins:"); for(int i=0;i 0): - print('Starting to plot', pdfpath) - plot_everything(showers_dataframe, pdfpath) + if(len(outputpath) > 0): + print('Starting to plot', outputpath) + plot_everything(showers_dataframe, prediction, truth, features, outputpath) print("DONE") @@ -185,7 +185,7 @@ def analyse(preddir, args = parser.parse_args() analyse(preddir=args.preddir, - pdfpath=args.p, + outputpath=args.p, beta_threshold=float(args.b), distance_threshold=float(args.d), iou_threshold=float(args.i), diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py index c9f6e33f..67553f5e 100644 --- a/scripts/plot_cocoa.py +++ b/scripts/plot_cocoa.py @@ -336,37 +336,50 @@ def plt_energy_resolution(df, bins=None): return fig -def plot_condensation(event_id, pred_list, t_list, feature_list, location='/work/friemer/hgcalml/condensationplots/condensation'): - coords = pred_list[event_id]['pred_ccoords'] +def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation'): + if(coordspace == 'condensation'): + coords = pred_dict['pred_ccoords'] - if coords.shape[1] < 3: #add a zero - coords = tf.concat([coords, tf.zeros_like(coords)[:,0:1]], axis=-1) + if coords.shape[1] < 3: #add a zero + coords = tf.concat([coords, tf.zeros_like(coords)[:,0:1]], axis=-1) + X = coords[:,0] + Y = coords[:,1] + Z = coords[:,2] + sizefield = 'features' + elif(coordspace == 'input'): + X = feature_dict['recHitX'][:,0] + Y = feature_dict['recHitY'][:,0] + Z = feature_dict['recHitZ'][:,0] + sizefield = 'rechit_energy_scaled' + else: + raise ValueError('coordspace must be "condensation" or "input"') data={ - 'X': coords[:,0], - 'Y': coords[:,1], - 'Z': coords[:,2], - 'hyper_idx': t_list[event_id]['truthHitAssignementIdx'][:,0], - 'features': pred_list[event_id]['pred_beta'][:,0], - 'pdgid': t_list[event_id]['truthHitAssignedPIDs'][:,0], - 'recHitID': feature_list[event_id]['recHitID'][:,0], + 'X': X, + 'Y': Y, + 'Z': Z, + 't_idx': t_dict['truthHitAssignementIdx'][:,0], + 'features': pred_dict['pred_beta'][:,0], + 'pdgid': t_dict['truthHitAssignedPIDs'][:,0], + 'recHitID': feature_dict['recHitID'][:,0], + 'rechit_energy': feature_dict['recHitEnergy'][:,0], + 'rechit_energy_scaled': np.log(feature_dict['recHitEnergy'][:,0]+1), } eventdf = pd.DataFrame(data) - hover_data = {'X': True, 'Y': True, 'Z': True, 'hyper_idx': True,'features': True, 'pdgid': True,'recHitID': True} + hover_data = {'X': True, 'Y': True, 'Z': True, 't_idx': True,'features': True, 'pdgid': True,'recHitID': True, 'rechit_energy': True} fig = px.scatter_3d(eventdf, x="X", y="Y", z="Z", - color="hyper_idx", - size='features', + color="t_idx", + size=sizefield, template='plotly_dark', hover_data=hover_data, color_continuous_scale=px.colors.sequential.Rainbow) fig.update_traces(marker=dict(line=dict(width=0))) - - fig.write_html(location+str(event_id)+".html") + return fig -def plot_everything(df, outputpath='/work/friemer/hgcalml/testplots/'): +def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friemer/hgcalml/testplots/'): #Create Output directory if not os.path.exists(outputpath): @@ -378,30 +391,41 @@ def plot_everything(df, outputpath='/work/friemer/hgcalml/testplots/'): var = var.lower() if not var in ('yes', 'y'): sys.exit() + os.makedirs(os.path.join(outputpath, 'condensation'), exist_ok=True) - #Create everything + #Create everything + + print('Plotting efficiencies') energy_bins_neutral = np.array([1,2,3,4,5,10,20,30,50])*1000 fig_eff=plot_efficencies(df, bins=energy_bins_neutral) + fig_eff.savefig(os.path.join(outputpath,'efficiencies.png')) + print('Plotting jet metrics') fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi = plot_jet_metrics(df) - - mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]) - fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi = plot_particle_metrics(df[mask_neutral]) - - energy_bins_charged = np.array([15,20,30,50,200])*1000 - mask_charged = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) - fig_charged_res = plt_energy_resolution(df[mask_charged], bins=energy_bins_charged) - - #Save everything - fig_eff.savefig(os.path.join(outputpath,'efficiencies.png')) fig_jet_nparticle.savefig(os.path.join(outputpath,'jet_nparticle.png')) fig_jet_relresE.savefig(os.path.join(outputpath,'jet_relresE.png')) fig_jet_reseta.savefig(os.path.join(outputpath,'jet_reseta.png')) fig_jet_resphi.savefig(os.path.join(outputpath,'jet_resphi.png')) + + print('Plotting particle metrics') + mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]) + fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi = plot_particle_metrics(df[mask_neutral]) fig_neutral_relresE.savefig(os.path.join(outputpath,'neutral_relresE.png')) fig_neutral_reseta.savefig(os.path.join(outputpath,'neutral_reseta.png')) fig_neutral_resphi.savefig(os.path.join(outputpath,'neutral_resphi.png')) + + print('Plotting energy resolution') + energy_bins_charged = np.array([15,20,30,50,200])*1000 + mask_charged = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) + fig_charged_res = plt_energy_resolution(df[mask_charged], bins=energy_bins_charged) fig_charged_res.savefig(os.path.join(outputpath,'charged_res.png')) + + print('Plotting condensation and input') + for event_id in range(10): + fig = plot_condensation(pred_list[event_id], t_list[event_id], feature_list[event_id], 'condensation') + fig.write_html(os.path.join(outputpath, 'condensation' ,'condensation'+str(event_id)+".html")) + fig = plot_condensation(pred_list[event_id], t_list[event_id], feature_list[event_id], 'input') + fig.write_html(os.path.join(outputpath, 'condensation' ,'input'+str(event_id)+".html")) @@ -409,7 +433,11 @@ def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalm with gzip.open(analysisfilepath, 'rb') as input_file: analysis_data = pickle.load(input_file) df = analysis_data['showers_dataframe'] - plot_everything(df, outputpath) + pred_list = analysis_data['prediction'] + t_list = analysis_data['truth'] + feature_list = analysis_data['features'] + + plot_everything(df, pred_list, t_list, feature_list, outputpath) if __name__ == '__main__': parser = argparse.ArgumentParser( @@ -421,5 +449,4 @@ def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalm default='') args = parser.parse_args() - - plot_everything_from_file(args.analysisfile, args.outputlocation) + plot_everything_from_file(args.analysisfile, args.outputlocation) From 6e2aaf54de170796d2b14159317c8c931886cbd6 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 30 Jul 2024 15:30:09 +0200 Subject: [PATCH 46/62] new Energy def for noise and pfjetmask --- modules/datastructures/TrainData_Cocoa.py | 28 +++++++++++++++-------- 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index 22d1bd7b..a24fce9e 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -104,6 +104,8 @@ def convertevent(self, event): #set NaN values (particle information for hits made from background noise to -1) df_training.fillna(-1, inplace=True) + noisemask = df_training['t_idx']==-1 + #Set other default values df_training['t_time'] = 0 df_training['t_spectator'] = 0 @@ -118,12 +120,13 @@ def convertevent(self, event): df_training['t_pos'] = df_training.apply(lambda row: [np.cos(row['particle_phi']), np.sin(row['particle_phi']), row['particle_eta']], axis=1) #set t_pos to [cos(phi), sin(phi), eta] based on RecHitX,Y,Z if t_idx is -1 - df_training['t_pos'] = df_training['t_pos'].where(df_training['t_idx'] != -1, df_training.apply(lambda row: [np.cos(np.arctan2(row['recHitY'], row['recHitX'])), np.sin(np.arctan2(row['recHitY'], row['recHitX'])), row['recHitEta']], axis=1)) + df_training.loc[noisemask, 't_pos'] = df_training[noisemask].apply(lambda row: [np.cos(np.arctan2(row['recHitY'], row['recHitX'])), np.sin(np.arctan2(row['recHitY'], row['recHitX'])), row['recHitEta']], axis=1) + + #replace -1 in t_energy and t_rec_energy with the sum of all noise hits + noiseEnergy = np.sum(df_training[noisemask]['recHitEnergy']) + df_training.loc[noisemask, 't_energy'] = noiseEnergy + df_training.loc[noisemask, 't_rec_energy'] = noiseEnergy - #replace -1 in t_energy and t_rec_energy with recHitEnergy - df_training['t_energy'] = df_training['t_energy'].where(df_training['t_energy'] != -1, df_training['recHitEnergy']) - df_training['t_rec_energy'] = df_training['t_rec_energy'].where(df_training['t_rec_energy'] != -1, df_training['recHitEnergy']) - return df_training def converttotrainingdfvec(self, data): @@ -134,14 +137,21 @@ def converttotrainingdfvec(self, data): #Convert events one by one traindata = np.array([self.convertevent(data[eventnumber]) for eventnumber in np.arange(len(data))], dtype=object) - + #Remove all events with an energy lower than 15GeV E_cutoff = 15000 - print("Number of events before removing low energy events: ", len(traindata)) E = np.sqrt(data['true_jet_pt']**2 * np.cos(data['true_jet_phi'])**2 + data['true_jet_m']**2) - mask = ak.to_numpy(ak.all(E >= E_cutoff, axis=1)) + mask_e = ak.to_numpy(ak.all(E >= E_cutoff, axis=1)) + + #Remove all events with more than one jet + n_pflow_jets = ak.num(data['pflow_jet_pt']) + mask_jets = ak.to_numpy(n_pflow_jets == 1) + + mask = np.logical_and(mask_e, mask_jets) + + print("Number of events before removing low energy events and multiple jets: ", len(traindata)) traindata = traindata[mask] - print("Number of events after removing low energy events: ", len(traindata)) + print("Number of events after removing low energy events and multiple jets: ", len(traindata)) #find the row splits rs = np.cumsum([len(df) for df in traindata]) From c171b4c16922dc073730e5e58c65b717d52af045 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 30 Jul 2024 15:30:49 +0200 Subject: [PATCH 47/62] log(Eloss) --- modules/LossLayers.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 76259e81..7e7b0e21 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3129,20 +3129,18 @@ def calc_energy_correction_factor_loss(self, """ t_energy = tf.clip_by_value(t_energy,0.,1e12) - prediction_loss = huber((t_energy - pred_energy)**2/1e8, 10) + prediction_loss = tf.math.log((t_energy - pred_energy)**2+1) - matching_loss = (pred_uncertainty_low)**2 - uncertainty_loss = pred_uncertainty_high**2 + uncertainty_loss = pred_uncertainty_high**2 + pred_uncertainty_low**2 - matching_loss = tf.debugging.check_numerics(matching_loss, "matching_loss") prediction_loss = tf.debugging.check_numerics(prediction_loss, "matching_loss") uncertainty_loss = tf.debugging.check_numerics(uncertainty_loss, "matching_loss") uncertainty_loss = tf.clip_by_value(uncertainty_loss, 0, 10) if return_concat: - return tf.concat([prediction_loss, matching_loss + uncertainty_loss], axis=-1) + return tf.concat([prediction_loss, uncertainty_loss], axis=-1) else: - return prediction_loss, uncertainty_loss + matching_loss + return prediction_loss, uncertainty_loss def calc_position_loss(self, t_pos, pred_pos): if tf.shape(pred_pos)[-1] == 2:#also has z component, but don't use it here From 1aa9ac2d7c347ec414df1931a732a4e6fcdc4f62 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 30 Jul 2024 15:31:27 +0200 Subject: [PATCH 48/62] distributions boxplots --- scripts/plot_cocoa.py | 71 +++++++++++++++++++++++++++++++++++++------ 1 file changed, 62 insertions(+), 9 deletions(-) diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py index 67553f5e..47a43923 100644 --- a/scripts/plot_cocoa.py +++ b/scripts/plot_cocoa.py @@ -1,6 +1,7 @@ import gzip import pickle import matplotlib.pyplot as plt +import matplotlib import numpy as np import pandas as pd import tensorflow as tf @@ -379,6 +380,52 @@ def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation' return fig +def plot_distribution(truth_list, feature_list): + print('Plotting distribution') + n_tracks = [] + n_hits = [] + n_particles = [] + n_charged = [] + n_photon = [] + n_neutral_had = [] + for i in range(len(feature_list)): + n_tracks.append(np.sum(np.abs(feature_list[i]['recHitID']))) + n_hits.append(len(feature_list[i]['recHitID'])-n_tracks[-1]) + truth_data ={ + 'truthHitAssignementIdx': truth_list[i]['truthHitAssignementIdx'][:,0], + 'truthHitAssignedPIDs': truth_list[i]['truthHitAssignedPIDs'][:,0], + } + truth_df = pd.DataFrame(truth_data) + n_particles.append(len(truth_df['truthHitAssignementIdx'].unique())) + + charged_mask = truth_df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) + photon_mask = truth_df['truthHitAssignedPIDs'].isin([22]) + neutral_mask = truth_df['truthHitAssignedPIDs'].isin([130,310,311,2112,-2112,3122,-3122,3322,-3322]) + n_charged.append(len(truth_df[charged_mask]['truthHitAssignementIdx'].unique())) + n_photon.append(len(truth_df[photon_mask]['truthHitAssignementIdx'].unique())) + n_neutral_had.append(len(truth_df[neutral_mask]['truthHitAssignementIdx'].unique())) + #convert to numpy + n_tracks, n_hits, n_particles, n_charged, n_photon, n_neutral_had = np.array(n_tracks), np.array(n_hits), np.array(n_particles), np.array(n_charged), np.array(n_photon), np.array(n_neutral_had) + + #create horizontal boxplot + fig = plt.figure() + values = [n_charged, n_neutral_had, n_photon, n_particles, n_tracks,n_hits/100] + labels = ['ch. hadrons','nu. hadrons','photons','total particles', 'tracks','cells [$10^2$]'] + + plt.boxplot(values, labels=labels, vert=False, patch_artist=True, + boxprops=dict(facecolor="limegreen"), medianprops=dict(color="black"), + whis = (0, 100)) + + plt.grid(alpha=0.4, axis='x') + # Add mean values as text + for pos, data in zip(np.arange(len(values)), values): + mean = np.mean(data) + plt.text(21.5, pos+1, f'({mean:.1f})', va='center', ha='left', fontsize=10) + + plt.close() + return fig + + def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friemer/hgcalml/testplots/'): #Create Output directory @@ -395,30 +442,32 @@ def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friem #Create everything + matplotlib.rcParams.update({'font.size': 20}) + print('Plotting efficiencies') energy_bins_neutral = np.array([1,2,3,4,5,10,20,30,50])*1000 fig_eff=plot_efficencies(df, bins=energy_bins_neutral) - fig_eff.savefig(os.path.join(outputpath,'efficiencies.png')) + fig_eff.savefig(os.path.join(outputpath,'efficiencies.png'), bbox_inches='tight') print('Plotting jet metrics') fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi = plot_jet_metrics(df) - fig_jet_nparticle.savefig(os.path.join(outputpath,'jet_nparticle.png')) - fig_jet_relresE.savefig(os.path.join(outputpath,'jet_relresE.png')) - fig_jet_reseta.savefig(os.path.join(outputpath,'jet_reseta.png')) - fig_jet_resphi.savefig(os.path.join(outputpath,'jet_resphi.png')) + fig_jet_nparticle.savefig(os.path.join(outputpath,'jet_nparticle.png'), bbox_inches='tight') + fig_jet_relresE.savefig(os.path.join(outputpath,'jet_relresE.png'), bbox_inches='tight') + fig_jet_reseta.savefig(os.path.join(outputpath,'jet_reseta.png'), bbox_inches='tight') + fig_jet_resphi.savefig(os.path.join(outputpath,'jet_resphi.png'), bbox_inches='tight') print('Plotting particle metrics') mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]) fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi = plot_particle_metrics(df[mask_neutral]) - fig_neutral_relresE.savefig(os.path.join(outputpath,'neutral_relresE.png')) - fig_neutral_reseta.savefig(os.path.join(outputpath,'neutral_reseta.png')) - fig_neutral_resphi.savefig(os.path.join(outputpath,'neutral_resphi.png')) + fig_neutral_relresE.savefig(os.path.join(outputpath,'neutral_relresE.png'), bbox_inches='tight') + fig_neutral_reseta.savefig(os.path.join(outputpath,'neutral_reseta.png'), bbox_inches='tight') + fig_neutral_resphi.savefig(os.path.join(outputpath,'neutral_resphi.png'), bbox_inches='tight') print('Plotting energy resolution') energy_bins_charged = np.array([15,20,30,50,200])*1000 mask_charged = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) fig_charged_res = plt_energy_resolution(df[mask_charged], bins=energy_bins_charged) - fig_charged_res.savefig(os.path.join(outputpath,'charged_res.png')) + fig_charged_res.savefig(os.path.join(outputpath,'charged_res.png'), bbox_inches='tight') print('Plotting condensation and input') for event_id in range(10): @@ -427,6 +476,10 @@ def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friem fig = plot_condensation(pred_list[event_id], t_list[event_id], feature_list[event_id], 'input') fig.write_html(os.path.join(outputpath, 'condensation' ,'input'+str(event_id)+".html")) + print('Plotting distribution') + fig_dist = plot_distribution(t_list, feature_list) + fig_dist.savefig(os.path.join(args.outputlocation, 'distribution.png'), bbox_inches='tight') + def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalml/testplots/'): From d4414fa7465a4935cb30fcd3817e137e3305b3db Mon Sep 17 00:00:00 2001 From: Fabian Date: Wed, 7 Aug 2024 15:35:57 +0200 Subject: [PATCH 49/62] small fix --- scripts/plot_cocoa.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py index 47a43923..9573bd1d 100644 --- a/scripts/plot_cocoa.py +++ b/scripts/plot_cocoa.py @@ -478,7 +478,7 @@ def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friem print('Plotting distribution') fig_dist = plot_distribution(t_list, feature_list) - fig_dist.savefig(os.path.join(args.outputlocation, 'distribution.png'), bbox_inches='tight') + fig_dist.savefig(os.path.join(outputpath, 'distribution.png'), bbox_inches='tight') From e5dbaa876810b540cd57a3cb0d08886d797761e3 Mon Sep 17 00:00:00 2001 From: Fabian Date: Tue, 13 Aug 2024 10:51:41 +0200 Subject: [PATCH 50/62] Energy in GeV and better energy cut --- modules/datastructures/TrainData_Cocoa.py | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index a24fce9e..f6e3770b 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -41,7 +41,7 @@ def convertFromSourceFile(self, filename, weighterobjects, istraining): def convertevent(self, event): #convert cells to df df_cell = pd.DataFrame() - df_cell['recHitEnergy'] = ak.to_dataframe(event['cell_e'], how='outer') + df_cell['recHitEnergy'] = ak.to_dataframe(event['cell_e'], how='outer')/1000 df_cell['recHitEta'] = ak.to_dataframe(event['cell_eta'], how='outer') df_cell['isTrack'] = 0 df_cell['recHitTheta'] = 0 #will be calculated later @@ -63,9 +63,9 @@ def convertevent(self, event): df_particle= pd.DataFrame() df_particle['particle_idx'] = np.arange(len(event['particle_e'])) - df_particle['t_energy'] = ak.to_dataframe(event['particle_e'], how='outer') + df_particle['t_energy'] = ak.to_dataframe(event['particle_e'], how='outer')/1000 df_particle['t_pid'] = ak.to_dataframe(event['particle_pdgid'], how='outer') - df_particle['t_rec_energy'] = ak.to_dataframe(event['particle_dep_energy'], how='outer') + df_particle['t_rec_energy'] = ak.to_dataframe(event['particle_dep_energy'], how='outer')/1000 df_particle['particle_eta'] = ak.to_dataframe(event['particle_eta_lay0'], how='outer') df_particle['particle_phi'] = ak.to_dataframe(event['particle_phi_lay0'], how='outer') @@ -140,18 +140,13 @@ def converttotrainingdfvec(self, data): #Remove all events with an energy lower than 15GeV E_cutoff = 15000 - E = np.sqrt(data['true_jet_pt']**2 * np.cos(data['true_jet_phi'])**2 + data['true_jet_m']**2) - mask_e = ak.to_numpy(ak.all(E >= E_cutoff, axis=1)) + E_sum = ak.sum(data['particle_e'], axis=1) + mask = ak.to_numpy(E_sum >= E_cutoff) + - #Remove all events with more than one jet - n_pflow_jets = ak.num(data['pflow_jet_pt']) - mask_jets = ak.to_numpy(n_pflow_jets == 1) - - mask = np.logical_and(mask_e, mask_jets) - - print("Number of events before removing low energy events and multiple jets: ", len(traindata)) + print("Number of events before removing low energy events: ", len(traindata)) traindata = traindata[mask] - print("Number of events after removing low energy events and multiple jets: ", len(traindata)) + print("Number of events after removing low energy events: ", len(traindata)) #find the row splits rs = np.cumsum([len(df) for df in traindata]) From a607c82eedfed9d8071e2f7af2127ccd05b16e28 Mon Sep 17 00:00:00 2001 From: Fabian Date: Thu, 15 Aug 2024 14:33:50 +0200 Subject: [PATCH 51/62] changed MeV to GeV and added new plotting functions --- modules/GravNetLayersRagged.py | 12 +-- scripts/plot_cocoa.py | 186 +++++++++++++++++++++++++++++---- 2 files changed, 173 insertions(+), 25 deletions(-) diff --git a/modules/GravNetLayersRagged.py b/modules/GravNetLayersRagged.py index a546c8a4..23bc5321 100644 --- a/modules/GravNetLayersRagged.py +++ b/modules/GravNetLayersRagged.py @@ -1627,19 +1627,19 @@ def __init__(self, **kwargs): super().__init__(**kwargs) self.mean_hit = tf.constant([ - 182, # recHitEnergy + 0.182, # recHitEnergy 0.028, # recHitEta 0.0, # recHitID -> don't normalize 0.0074, # recHitTheta 3012, # recHitR 0.0, # recHitX -> centered around zero 0.0, # recHitY -> centered around zero - 48, # recHitZ + 0.0, # recHitZ 0.0, # recHitTime -> All zeros 0.0, # recHitHitR ]) self.std_hit = tf.constant([ - 1087, # recHitEnergy + 1.087, # recHitEnergy 1.46, # recHitEta 1.0, # recHitID -> don't normalize 1.78, # recHitTheta @@ -1652,19 +1652,19 @@ def __init__(self, ]) self.mean_track = tf.constant([ - 15173, # recHitEnergy + 15.173, # recHitEnergy 0.0, # recHitEta 0.0, # recHitID -> don't normalize 1.57, # recHitTheta 2574, # recHitR 0.0, # recHitX -> centered around zero 0.0, # recHitY -> centered around zero - -2.1, # recHitZ -> + 0.0, # recHitZ -> 0.0, # recHitTime -> All zeros 0.0, # recHitHitR -> All zeros for tracks ]) self.std_track = tf.constant([ - 19126, # recHitEnergy + 19.126, # recHitEnergy 1.31, # recHitEta 1.0, # recHitID -> don't normalize 0.93, # recHitTheta diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py index 9573bd1d..0dd88a89 100644 --- a/scripts/plot_cocoa.py +++ b/scripts/plot_cocoa.py @@ -88,8 +88,6 @@ def plot_efficencies(df, bins): yeff_nh*100, yerr_eff_nh*100, yfake_nh*100, yerr_fake_nh*100, ycorr_nh*100, yerr_corr_nh*100 - #Convert to GeV - bins = bins/1000 # Calculate the bin positions and widths binwidth = bins[1:] - bins[:-1] @@ -132,6 +130,160 @@ def plot_efficencies(df, bins): #ax.set_ylim(20, 101) return fig +def plot_efficency_and_fakerate(df, bins): + # Calculate the efficiencies and fake rates for the total + efficiencies, efficiencies_err, fake_rate, fake_rate_err = [], [], [], [] + + mask_predicted = np.isnan(df['pred_energy']) == False + mask_truth = np.isnan(df['truthHitAssignedEnergies']) == False + + + for i in range(len(bins) - 1): + mask_bintruth = np.logical_and( + df['truthHitAssignedEnergies'] >= bins[i], + df['truthHitAssignedEnergies'] < bins[i + 1]) + mask_binpred = np.logical_and( + df['pred_energy'] >= bins[i], + df['pred_energy'] < bins[i + 1]) + + matched = np.logical_and( + mask_predicted, + mask_bintruth) + faked = np.logical_and( + mask_binpred, + np.logical_not(mask_truth)) + + eff = np.sum(matched) / np.sum(mask_bintruth) + eff_err = np.sqrt(eff * (1 - eff) / np.sum(matched)) + efficiencies.append(eff) + efficiencies_err.append(eff_err) + + fake = np.sum(faked) / np.sum(mask_binpred) + fake_err = np.sqrt(fake * (1 - fake) / np.sum(faked)) + fake_rate.append(fake) + fake_rate_err.append(fake_err) + + yeff, yerr_eff, yfake, yerr_fake, = np.array(efficiencies)*100, np.array(efficiencies_err)*100, np.array(fake_rate)*100, np.array(fake_rate_err)*100 + + # Calculate the bin positions and widths + binwidth = bins[1:] - bins[:-1] + x_pos = bins[:-1] + binwidth / 2 + x_err = binwidth / 2 + + fig_eff = plt.figure() + plt.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o', color='red') + plt.xticks(bins) + plt.xticklabels(bins, fontsize=10) + plt.xlim(bins[0], bins[-1]) + plt.xlabel('Energy [GeV]', fontsize=10) + + yticks1 = np.round(np.arange(40, 101, 20), 1) + plt.yticks(yticks1) + plt.yticklabels([f"{y}%" for y in yticks1], fontsize=10) + plt.ylabel('Efficiency') + + plt.grid(alpha=0.4) + + plt.close() + + fig_fake = plt.figure() + plt.errorbar(x_pos, yfake, xerr=x_err, yerr=yerr_fake, fmt='o', color='red') + plt.xticks(bins) + plt.xticklabels(bins, fontsize=10) + plt.xlim(bins[0], bins[-1]) + plt.xlabel('Energy [GeV]', fontsize=10) + + yticks1 = np.round(np.arange(0, 21, 5), 1) + plt.yticks(yticks1) + plt.yticklabels([f"{y}%" for y in yticks1], fontsize=10) + plt.ylabel('Fake rate') + + plt.grid(alpha=0.4) + + plt.close() + + return fig_eff, fig_fake + +def calc_classification_p(df, bins, mask): + corr_class_prob = [] + corr_class_prob_err = [] + + mask_predicted = np.isnan(df['pred_energy']) == False + mask_truth = np.isnan(df['truthHitAssignedEnergies']) == False + + if(mask == 0): + mask_PID_truth = df['truthHitAssignedPIDs'].isin([22]) + elif(mask == 1): + mask_PID_truth = df['truthHitAssignedPIDs'].isin([130,310,311,2112,-2112,3122,-3122,3322,-3322]) + elif(mask == 2): + mask_PID_truth = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) + else: + raise ValueError("mask must be 0, 1 or 2") + mask_PID_pred = df['pred_id_value'].isin([mask]) + + mask_PID_matched = np.logical_and(mask_PID_truth, mask_PID_pred) + + + for i in range(len(bins) - 1): + mask_bintruth = np.logical_and( + df['truthHitAssignedEnergies'] >= bins[i], + df['truthHitAssignedEnergies'] < bins[i + 1]) + mask_binpred = np.logical_and( + df['pred_energy'] >= bins[i], + df['pred_energy'] < bins[i + 1]) + + matched = np.logical_and( + mask_predicted, + mask_bintruth) + + + cc_prob = np.sum(matched[mask_PID_matched]) / np.sum(matched[mask_PID_truth]) + cc_prob_err = np.sqrt(cc_prob * (1 - cc_prob) / np.sum(matched[mask_PID_truth])) + corr_class_prob.append(cc_prob) + corr_class_prob_err.append(cc_prob_err) + + return np.array(corr_class_prob), np.array(corr_class_prob_err) + +def plot_classification_p(df, bins): + ycorr_photon, yerr_corr_photon = calc_classification_p(df, bins, 0) + ycorr_photon, yerr_corr_photon = ycorr_photon*100, yerr_corr_photon*100 + + ycorr_nh, yerr_corr_nh = calc_classification_p(df, bins, 1) + ycorr_nh, yerr_corr_nh = ycorr_nh*100, yerr_corr_nh*100 + + ycorr_ch, yerr_corr_ch = calc_classification_p(df, bins, 2) + ycorr_ch, yerr_corr_ch = ycorr_ch*100, yerr_corr_ch*100 + + # Calculate the bin positions and widths + binwidth = bins[1:] - bins[:-1] + x_pos = bins[:-1] + binwidth / 2 + x_err = binwidth / 2 + + # Create the plots + fig_classification = plt.figure() + plt.errorbar(x_pos, ycorr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', color='red', label='Photon') + plt.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', color='blue', label='Neutral Hadron') + plt.errorbar(x_pos, ycorr_ch, xerr=x_err, yerr=yerr_corr_ch, fmt='o', color='green', label='Charged Hadron') + + plt.legend() + plt.grid(alpha=0.4) + + plt.xticks(bins) + plt.xticklabels(bins, fontsize=10) + plt.xlim(bins[0], bins[-1]) + plt.xlabel('Energy [GeV]', fontsize=10) + + yticks1 = np.round(np.arange(40, 101, 20), 1) + plt.yticks(yticks1) + plt.yticklabels([f"{y}%" for y in yticks1], fontsize=10) + plt.ylabel('Probability of predicting correct class') + + plt.close() + + return fig_classification + + + def plot_jet_metrics(df): #Jet metrics reseta = [] @@ -306,13 +458,6 @@ def plt_energy_resolution(df, bins=None): y = means yerr = std_error - #Convert MeV to GeV - x = x/1000 - xerr = xerr/1000 - y = y/1000 - yerr = yerr/1000 - bins = bins/1000 - # plot x, y with error bars ax1.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='o', color='#67c4ce') @@ -381,11 +526,11 @@ def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation' return fig def plot_distribution(truth_list, feature_list): - print('Plotting distribution') n_tracks = [] n_hits = [] n_particles = [] - n_charged = [] + n_charged_had = [] + n_lepton = [] n_photon = [] n_neutral_had = [] for i in range(len(feature_list)): @@ -398,19 +543,22 @@ def plot_distribution(truth_list, feature_list): truth_df = pd.DataFrame(truth_data) n_particles.append(len(truth_df['truthHitAssignementIdx'].unique())) - charged_mask = truth_df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) + charged_hd_mask = truth_df['truthHitAssignedPIDs'].isin([211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) + lepton_mask = truth_df['truthHitAssignedPIDs'].isin([11,-11,13,-13]) photon_mask = truth_df['truthHitAssignedPIDs'].isin([22]) neutral_mask = truth_df['truthHitAssignedPIDs'].isin([130,310,311,2112,-2112,3122,-3122,3322,-3322]) - n_charged.append(len(truth_df[charged_mask]['truthHitAssignementIdx'].unique())) + n_charged_had.append(len(truth_df[charged_hd_mask]['truthHitAssignementIdx'].unique())) + n_lepton.append(len(truth_df[lepton_mask]['truthHitAssignementIdx'].unique())) n_photon.append(len(truth_df[photon_mask]['truthHitAssignementIdx'].unique())) n_neutral_had.append(len(truth_df[neutral_mask]['truthHitAssignementIdx'].unique())) #convert to numpy - n_tracks, n_hits, n_particles, n_charged, n_photon, n_neutral_had = np.array(n_tracks), np.array(n_hits), np.array(n_particles), np.array(n_charged), np.array(n_photon), np.array(n_neutral_had) + n_tracks, n_hits, n_particles, n_charged_had, n_lepton, n_photon, n_neutral_had = \ + np.array(n_tracks), np.array(n_hits), np.array(n_particles), np.array(n_charged_had), np.array(n_lepton), np.array(n_photon), np.array(n_neutral_had) #create horizontal boxplot fig = plt.figure() - values = [n_charged, n_neutral_had, n_photon, n_particles, n_tracks,n_hits/100] - labels = ['ch. hadrons','nu. hadrons','photons','total particles', 'tracks','cells [$10^2$]'] + values = [n_lepton, n_charged_had, n_neutral_had, n_photon, n_particles, n_tracks,n_hits/100] + labels = ['leptons', 'ch. hadrons','nu. hadrons','photons','total particles', 'tracks','cells [$10^2$]'] plt.boxplot(values, labels=labels, vert=False, patch_artist=True, boxprops=dict(facecolor="limegreen"), medianprops=dict(color="black"), @@ -420,7 +568,7 @@ def plot_distribution(truth_list, feature_list): # Add mean values as text for pos, data in zip(np.arange(len(values)), values): mean = np.mean(data) - plt.text(21.5, pos+1, f'({mean:.1f})', va='center', ha='left', fontsize=10) + plt.text(21.2, pos+1, f'({mean:.1f})', va='center', ha='left') plt.close() return fig @@ -445,7 +593,7 @@ def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friem matplotlib.rcParams.update({'font.size': 20}) print('Plotting efficiencies') - energy_bins_neutral = np.array([1,2,3,4,5,10,20,30,50])*1000 + energy_bins_neutral = np.array([1,2,3,4,5,10,20,30,50]) fig_eff=plot_efficencies(df, bins=energy_bins_neutral) fig_eff.savefig(os.path.join(outputpath,'efficiencies.png'), bbox_inches='tight') @@ -464,7 +612,7 @@ def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friem fig_neutral_resphi.savefig(os.path.join(outputpath,'neutral_resphi.png'), bbox_inches='tight') print('Plotting energy resolution') - energy_bins_charged = np.array([15,20,30,50,200])*1000 + energy_bins_charged = np.array([15,20,30,50,200]) mask_charged = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) fig_charged_res = plt_energy_resolution(df[mask_charged], bins=energy_bins_charged) fig_charged_res.savefig(os.path.join(outputpath,'charged_res.png'), bbox_inches='tight') From 934ecc13d9f60f2134998efb1ed70db16e51b45e Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 26 Aug 2024 16:57:05 +0200 Subject: [PATCH 52/62] added eta phi cut --- modules/LossLayers.py | 1 + modules/datastructures/TrainData_Cocoa.py | 40 ++++++++++++++++++----- scripts/submitViaHTCondor.py | 2 +- 3 files changed, 34 insertions(+), 9 deletions(-) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 7e7b0e21..3d342f41 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3130,6 +3130,7 @@ def calc_energy_correction_factor_loss(self, t_energy = tf.clip_by_value(t_energy,0.,1e12) prediction_loss = tf.math.log((t_energy - pred_energy)**2+1) + #prediction_loss = ((t_energy - pred_energy)/t_energy)**2 uncertainty_loss = pred_uncertainty_high**2 + pred_uncertainty_low**2 diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index f6e3770b..71a94314 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -99,7 +99,7 @@ def convertevent(self, event): #join cell information to truth information df_cell = df_cell.join(df_particle, on='t_idx') - df_training = pd.concat([df_cell, df_track]) + df_training = pd.concat([df_cell, df_track], ignore_index=True) #set NaN values (particle information for hits made from background noise to -1) df_training.fillna(-1, inplace=True) @@ -127,6 +127,16 @@ def convertevent(self, event): df_training.loc[noisemask, 't_energy'] = noiseEnergy df_training.loc[noisemask, 't_rec_energy'] = noiseEnergy + #set is_energymax to one if the hit has the maximum energy for each particle (t_idx) + # df_training['is_energymax'] = 0 + # for i in np.unique(df_training['t_idx']): + # mask = np.logical_and(df_training['t_idx'] == i, df_training['t_idx'] != -1) + # max_energy_idx = df_training.loc[mask, 'recHitEnergy'].idxmax() + # df_training.loc[mask, 'is_energymax'][max_energy_idx] = 1 + + # #JUST FOR TESTING FIXME + # df_training['isTrack']=df_training['is_energymax'] + return df_training def converttotrainingdfvec(self, data): @@ -138,15 +148,29 @@ def converttotrainingdfvec(self, data): #Convert events one by one traindata = np.array([self.convertevent(data[eventnumber]) for eventnumber in np.arange(len(data))], dtype=object) - #Remove all events with an energy lower than 15GeV - E_cutoff = 15000 - E_sum = ak.sum(data['particle_e'], axis=1) - mask = ak.to_numpy(E_sum >= E_cutoff) + if True: + #Remove all events with an energy lower than 15GeV + # E_cutoff = 15000 + # E_sum = ak.sum(data['particle_e'], axis=1) + # mask = ak.to_numpy(E_sum >= E_cutoff) + mask = np.ones(len(traindata), dtype=bool) + + #Remove all events with a particle that has multiple clusters + for i in range(len(data)): + for j in range(len(data.particle_pdgid[i])): + particlemask = data.cell_parent_idx[i] == j + mean_phi = ak.mean(data.cell_phi[i][particlemask]) + mean_eta = ak.mean(data.cell_eta[i][particlemask]) + phi_mask = abs(data.cell_phi[i][particlemask] - mean_phi) < 0.5 + eta_mask = abs(data.cell_eta[i][particlemask] - mean_eta) < 0.5 + energy_outside = ak.sum(data.cell_e[i][particlemask][~phi_mask | ~eta_mask]) + energy_total = ak.sum(data.cell_e[i][particlemask]) + mask[i] = mask[i] and energy_outside < 0.1 * energy_total - print("Number of events before removing low energy events: ", len(traindata)) - traindata = traindata[mask] - print("Number of events after removing low energy events: ", len(traindata)) + print("Number of events before removing low energy events: ", len(traindata)) + traindata = traindata[mask] + print("Number of events after removing low energy events: ", len(traindata)) #find the row splits rs = np.cumsum([len(df) for df in traindata]) diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index 4f2c266b..f5506717 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -45,7 +45,7 @@ def __str__(self) -> str: 'f' : meta_option('f', '/work/friemer/hgcalml/HGCalML'), 'cpu': meta_option('cpu', '1'), 'memory': meta_option('memory', '10 GB'), - 'disk': meta_option('disk', '2 GB'), + 'disk': meta_option('disk', '5 GB'), 'gpu': meta_option('gpu', '1'), 'wandb': meta_option('wandb', 'True'), 'submit': meta_option('submit', 'True') From fe6b2579ae41266d97339f0f95b740981fff2ae3 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 26 Aug 2024 16:57:34 +0200 Subject: [PATCH 53/62] added jet clustering --- Train/paper_trainer_Cocoa_simple.py | 45 ++- scripts/analyse_cocoa_predictions.py | 145 +++++++- scripts/generatePlots.ipynb | 350 +++++++++----------- scripts/plot_cocoa.py | 476 ++++++++++++++++++++++----- 4 files changed, 709 insertions(+), 307 deletions(-) diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index 57bff269..8c707d43 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -200,7 +200,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): energy_factor=False, is_track=is_track, set_track_betas_to_one=True, - pred_e_factor=10000) + pred_e_factor=10) pred_beta = LLExtendedObjectCondensation5( @@ -212,9 +212,9 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): implementation = "hinge", beta_loss_scale = 1.0, too_much_beta_scale = 0.0, - energy_loss_weight =1.0, - classification_loss_weight = 0.4, - position_loss_weight = 10.0, + energy_loss_weight = 1.0, + classification_loss_weight = 1.0, + position_loss_weight = 1.0, timing_loss_weight = 0.0, downweight_low_energy=False, potential_scaling = 1.0, @@ -247,7 +247,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): 'pred_dist': pred_dist, 'rechit_energy': energy, 'row_splits': pre_processed['row_splits'], - #'t_idx': pre_processed['t_idx'], #JUST FOR TESTING FIXME + 't_idx': pre_processed['t_idx'], #JUST FOR TESTING FIXME # 'no_noise_sel': pre_processed['no_noise_sel'], # 'no_noise_rs': pre_processed['no_noise_rs'], } @@ -302,15 +302,26 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): ### Actual Training ########################################################### ############################################################################### -train.change_learning_rate(1e-4) +train.change_learning_rate(1e-3) train.trainModel( - nepochs=2, + nepochs=100, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, collect_gradients = 4 #average out more gradients ) + +#recompile +train.compileModel(learningrate=5e-4) +print('entering second training phase') +train.trainModel( + nepochs=350, + batchsize=50000, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, + collect_gradients = 4 + ) # loop through model layers and turn batch normalisation to fixed def fix_batchnorm(m): for layer in m.layers: @@ -320,24 +331,32 @@ def fix_batchnorm(m): #apply to all models train.applyFunctionToAllModels(fix_batchnorm) - #recompile -train.compileModel(learningrate=2e-5) -print('entering second training phase') +train.compileModel(learningrate=5e-4) +print('entering third training phase') train.trainModel( - nepochs=15, + nepochs=600, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, collect_gradients = 4 ) +train.compileModel(learningrate=1e-4) +print('entering third training phase') +train.trainModel( + nepochs=800, + batchsize=50000, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, + collect_gradients = 4 + ) #recompile -train.compileModel(learningrate=5e-6) +train.compileModel(learningrate=1e-5) print('entering third training phase') train.trainModel( - nepochs=35, + nepochs=1000, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, diff --git a/scripts/analyse_cocoa_predictions.py b/scripts/analyse_cocoa_predictions.py index 40040cd0..bd0bc1d3 100755 --- a/scripts/analyse_cocoa_predictions.py +++ b/scripts/analyse_cocoa_predictions.py @@ -13,6 +13,8 @@ import numpy as np import tensorflow as tf import matplotlib.pyplot as plt +import fastjet as fj +from scipy.optimize import linear_sum_assignment from OCHits2Showers import OCHits2ShowersLayer, OCHits2ShowersLayer_HDBSCAN from OCHits2Showers import process_endcap2, OCGatherEnergyCorrFac2, OCGatherEnergyCorrFac_new @@ -37,6 +39,7 @@ def analyse(preddir, angle_cut, extra=False, shower0=False, + datasetname='Quark Jet' ): hits2showers = OCHits2ShowersLayer( @@ -54,6 +57,7 @@ def analyse(preddir, files_to_be_tested = files_to_be_tested[0:min(nfiles, len(files_to_be_tested))] showers_dataframe = pd.DataFrame() + jets_dataframe = pd.DataFrame() features = [] truth = [] prediction = [] @@ -87,7 +91,7 @@ def analyse(preddir, predictions_dict['pred_sid'] = pred_sid prediction.append(predictions_dict) - + #Match showers if not isinstance(alpha_idx, np.ndarray): alpha_idx = alpha_idx.numpy() alpha_idx = np.reshape(alpha_idx, newshape=(-1,)) @@ -109,14 +113,128 @@ def analyse(preddir, dataframe['pred_id_value'] = dataframe['pred_id'].apply(lambda x: np.argmax(x)) showers_dataframe = pd.concat((showers_dataframe, dataframe)) + #Cluster to jets + has_truth = np.isnan(dataframe['truthHitAssignedEnergies']) == False + has_pred = np.isnan(dataframe['pred_energy']) == False + + E_truth = np.asarray(dataframe[has_truth]['truthHitAssignedEnergies'], dtype=np.double) + eta_truth = dataframe[has_truth]['truthHitAssignedZ'] + phi_truth = np.arctan2(dataframe[has_truth]['truthHitAssignedY'], dataframe[has_truth]['truthHitAssignedX']) + pt_truth = E_truth / np.cosh(eta_truth) + px_truth = np.asarray(pt_truth * np.cos(phi_truth), dtype=np.double) + py_truth = np.asarray(pt_truth * np.sin(phi_truth), dtype=np.double) + pz_truth = np.asarray(pt_truth * np.sinh(eta_truth), dtype=np.double) + + particles_truth = np.array([fj.PseudoJet(px_truth[i], py_truth[i], pz_truth[i], E_truth[i]) for i in range(len(dataframe[has_truth]['truthHitAssignedX']))]) + + E_pred = np.asarray(dataframe[has_pred]['pred_energy'], dtype=np.double) + eta_pred = dataframe[has_pred]['pred_pos'].apply(lambda x: x[2]) + phi_pred = dataframe[has_pred]['pred_pos'].apply(lambda x: np.arctan2(x[1], x[0])) + pt_pred = E_pred / np.cosh(eta_pred) + px_pred = np.asarray(pt_pred * np.cos(phi_pred), dtype=np.double) + py_pred = np.asarray(pt_pred * np.sin(phi_pred), dtype=np.double) + pz_pred = np.asarray(pt_pred * np.sinh(eta_pred), dtype=np.double) + particles_pred = np.array([fj.PseudoJet(px_pred[i], py_pred[i], pz_pred[i], E_pred[i]) for i in range(len(dataframe[has_pred]['pred_pos']))]) + + # Choose jet clustering algorithm and parameters + R = 0.4 # Jet radius parameter + jet_def = fj.JetDefinition(fj.antikt_algorithm, R) + + # Cluster jets + cluster_sequence_truth = fj.ClusterSequence(particles_truth.tolist(), jet_def) + cluster_sequence_pred = fj.ClusterSequence(particles_pred.tolist(), jet_def) + jets_truth = [jet for jet in cluster_sequence_truth.inclusive_jets() if len(jet.constituents()) >= 2] + jets_pred = [jet for jet in cluster_sequence_pred.inclusive_jets() if len(jet.constituents()) >= 2] + + jet_truth_data = { + 'true_pt': [jet.pt() for jet in jets_truth], + 'true_eta': [jet.eta() for jet in jets_truth], + 'true_phi': [jet.phi() for jet in jets_truth], + 'true_mass': [jet.m() for jet in jets_truth], + 'true_n_constituents': [len(jet.constituents()) for jet in jets_truth] + } + jet_pred_dta = { + 'pred_pt': [jet.pt() for jet in jets_pred], + 'pred_eta': [jet.eta() for jet in jets_pred], + 'pred_phi': [jet.phi() for jet in jets_pred], + 'pred_mass': [jet.m() for jet in jets_pred], + 'pred_n_constituents': [len(jet.constituents()) for jet in jets_pred] + } + jet_truth_df = pd.DataFrame(jet_truth_data) + jet_pred_df = pd.DataFrame(jet_pred_dta) + + jet_truth_df['event_id'] = event_id + jet_pred_df['event_id'] = event_id + + # Match jets + n = max(len(jet_truth_df), len(jet_pred_df)) + C = np.zeros((n, n)) + + for a in range(len(jet_truth_df)): + for b in range(len(jet_pred_df)): + delta_eta = jet_truth_df['true_eta'].iloc[a] - jet_pred_df['pred_eta'].iloc[b] + delta_phi = jet_truth_df['true_phi'].iloc[a] - jet_pred_df['pred_phi'].iloc[b] + C[a, b] = 1/np.sqrt(delta_eta**2 + delta_phi**2) + row_id, col_id = linear_sum_assignment(C, maximize=True) + + matched_jets = [] + for a, b in zip(row_id, col_id): + if C[a, b] > 0: + matched_jets.append({ + 'matched': True, + 'true_pt': jet_truth_df['true_pt'].iloc[a], + 'true_eta': jet_truth_df['true_eta'].iloc[a], + 'true_phi': jet_truth_df['true_phi'].iloc[a], + 'true_mass': jet_truth_df['true_mass'].iloc[a], + 'true_n_constituents': jet_truth_df['true_n_constituents'].iloc[a], + 'pred_pt': jet_pred_df['pred_pt'].iloc[b], + 'pred_eta': jet_pred_df['pred_eta'].iloc[b], + 'pred_phi': jet_pred_df['pred_phi'].iloc[b], + 'pred_mass': jet_pred_df['pred_mass'].iloc[b], + 'pred_n_constituents': jet_pred_df['pred_n_constituents'].iloc[b] + }) + else: + if C[a, :].max() == 0 and a 0: - print("Writing dataframes to pickled file", analysisoutpath) - + scalar_variables = { 'beta_threshold': str(beta_threshold), 'distance_threshold': str(distance_threshold), @@ -128,14 +246,14 @@ def analyse(preddir, analysis_data = { 'showers_dataframe' : showers_dataframe, + 'jets_dataframe' : jets_dataframe, 'scalar_variables' : scalar_variables, 'alpha_ids' : alpha_ids, 'matched': matched, + 'features': features, + 'truth': truth, + 'prediction': prediction } - - analysis_data['features'] = features - analysis_data['truth'] = truth - analysis_data['prediction'] = prediction with gzip.open(analysisoutpath, 'wb') as output_file: print("Writing dataframes to pickled file",analysisoutpath) @@ -144,10 +262,10 @@ def analyse(preddir, ############################################################################################### ### Make Plots ################################################################################ ############################################################################################### - + if(len(outputpath) > 0): print('Starting to plot', outputpath) - plot_everything(showers_dataframe, prediction, truth, features, outputpath) + plot_everything(showers_dataframe, prediction, truth, features, jets_dataframe, outputpath, datasetname) print("DONE") @@ -181,8 +299,14 @@ def analyse(preddir, parser.add_argument('--shower0', help="Only match with truth shower ID=0", action='store_true') + parser.add_argument('--gluon', action='store_true', help='Writing Gluon dataset') args = parser.parse_args() + + if args.gluon: + datasetname='Gluon Jet' + else: + datasetname='Quark Jet' analyse(preddir=args.preddir, outputpath=args.p, @@ -197,6 +321,7 @@ def analyse(preddir, angle_cut=float(args.angle_cut), nevents=int(args.nevents), extra=args.extra, - shower0=args.shower0 + shower0=args.shower0, + datasetname=datasetname, ) diff --git a/scripts/generatePlots.ipynb b/scripts/generatePlots.ipynb index 98a09edb..0617868a 100644 --- a/scripts/generatePlots.ipynb +++ b/scripts/generatePlots.ipynb @@ -12,20 +12,30 @@ "/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (3.0.4) doesn't match a supported version!\n", " warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hi\n" + ] } ], "source": [ + "\n", + "\n", + "import sys\n", + "sys.path.append('../modules')\n", "import gzip\n", "import pickle\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", - "import sys\n", "import plotly.express as px\n", "\n", - "sys.path.append('../modules')\n", - "tf.config.set_visible_devices(tf.config.list_physical_devices('GPU')[1], 'GPU')" + "#tf.config.set_visible_devices(tf.config.list_physical_devices('GPU')[1], 'GPU')\n", + "print('hi')" ] }, { @@ -34,73 +44,66 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2024-07-23 13:50:08.153579: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-07-23 13:50:08.906419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 44 MB memory: -> device: 1, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:03:00.0, compute capability: 5.2\n" + "hi\n" ] - }, + } + ], + "source": [ + "print('hi')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis data keys: dict_keys(['showers_dataframe', 'scalar_variables', 'alpha_ids', 'matched', 'features', 'truth', 'prediction'])\n", - "shower keys Index(['truthHitAssignedPhi', 'pred_ccoords', 'pred_beta', 'pred_sid',\n", - " 't_rec_energy', 'truthHitAssignementIdx', 'hasTrack', 'pred_time',\n", - " 'truthHitSpectatorFlag', 'pred_energy', 'truthHitAssignedY', 't_idx',\n", - " 'truthHitAssignedPIDs', 'truthHitAssignedEnergies', 'truthHitAssignedT',\n", - " 'pred_id', 'pred_dist', 'truthHitAssignedX', 'pred_pos',\n", - " 'truthHitFullyContainedFlag', 'truthHitAssignedEta',\n", - " 'truthHitAssignedZ', 'event_id'],\n", - " dtype='object')\n", - "scalar keys dict_keys(['beta_threshold', 'distance_threshold', 'iou_threshold', 'matching_mode', 'de_e_cut', 'angle_cut'])\n", - "features list keys dict_keys(['recHitEnergy', 'recHitEta', 'recHitID', 'recHitTheta', 'recHitR', 'recHitX', 'recHitY', 'recHitZ', 'recHitTime', 'recHitHitR', 'recHitXY'])\n", - "truth list keys dict_keys(['truthHitAssignementIdx', 'truthHitAssignedEnergies', 'truthHitAssignedX', 'truthHitAssignedY', 'truthHitAssignedZ', 'truthHitAssignedEta', 'truthHitAssignedPhi', 'truthHitAssignedT', 'truthHitAssignedPIDs', 'truthHitSpectatorFlag', 'truthHitFullyContainedFlag', 't_rec_energy'])\n", - "prediction list keys dict_keys(['pred_beta', 'pred_ccoords', 'pred_energy', 'pred_pos', 'pred_time', 'pred_id', 'pred_dist', 'rechit_energy', 'row_splits', 't_idx', 'pred_sid'])\n" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." ] } ], "source": [ "#Open file\n", - "#filepath = '/work/friemer/hgcalml/SimpleCocoa/analyseSimpleCocoa/analysisoutput'\n", - "filepath = '/work/friemer/hgcalml/CocoaTotalEnergyQmin01PhiEta/analysisoutput'\n", - "filepath = '/work/friemer/hgcalml/analysisoutputoldmatchingfixedID'\n", - "filepath = '/work/friemer/hgcalml/analysisoutputfixedID'\n", - "filepath = '/work/friemer/hgcalml/analysisoutputtidx2'\n", - "filepath = '/work/friemer/hgcalml/analysisoutputtidx2cocoaa'\n", - "filepath = '/work/friemer/hgcalml/analysisoutputtidx5cocoa'\n", - "filepath = '/work/friemer/hgcalml/analysisoutputfixedNormtidx'\n", - "filepath = '/work/friemer/hgcalml/analysisoutputfixedNormcontinue'\n", - "filepath = '/work/friemer/hgcalml/analysistidx6'\n", "filepath = '/work/friemer/hgcalml/analysisTidx9'\n", "filepath = '/work/friemer/hgcalml/analysisTidx9cocoamatch'\n", + "filepath = '/work/friemer/hgcalml/analysisFIxedEloss6cocoamatch'\n", + "filepath = '/work/friemer/hgcalml/analysisJustELossTidxmatch'\n", + "print('hi')\n", "\n", - "# Open the file and load the data\n", - "with gzip.open(filepath, 'rb') as input_file:\n", - " analysis_data = pickle.load(input_file)\n", + "# # Open the file and load the data\n", + "# with gzip.open(filepath, 'rb') as input_file:\n", + "# analysis_data = pickle.load(input_file)\n", " \n", - "# Print the keys of the data\n", - "print('Analysis data keys:', analysis_data.keys())\n", - "print('shower keys ',analysis_data['showers_dataframe'].keys())\n", - "print('scalar keys',analysis_data['scalar_variables'].keys())\n", - "#print(analysis_data['alpha_ids'])\n", - "#print(analysis_data['matched'])\n", - "print('features list keys', analysis_data['features'][0].keys())\n", - "print('truth list keys', analysis_data['truth'][0].keys())\n", - "print('prediction list keys', analysis_data['prediction'][0].keys())\n", - "\n", - "df = analysis_data['showers_dataframe']\n", - "feature_list = analysis_data['features']\n", - "t_list = analysis_data['truth']\n", - "pred_list = analysis_data['prediction']\n", - "\n", - "df['pred_id_value'] = df['pred_id'].apply(lambda x: np.argmax(x))\n", - "\n", - "has_truth = np.isnan(df['truthHitAssignedEnergies']) == False\n", - "has_pred = np.isnan(df['pred_energy']) == False\n", - "matched = np.logical_and(has_truth, has_pred)" + "# # Print the keys of the data\n", + "# print('Analysis data keys:', analysis_data.keys())\n", + "# print('shower keys ',analysis_data['showers_dataframe'].keys())\n", + "# print('scalar keys',analysis_data['scalar_variables'].keys())\n", + "# #print(analysis_data['alpha_ids'])\n", + "# #print(analysis_data['matched'])\n", + "# print('features list keys', analysis_data['features'][0].keys())\n", + "# print('truth list keys', analysis_data['truth'][0].keys())\n", + "# print('prediction list keys', analysis_data['prediction'][0].keys())\n", + "\n", + "# df = analysis_data['showers_dataframe']\n", + "# feature_list = analysis_data['features']\n", + "# t_list = analysis_data['truth']\n", + "# pred_list = analysis_data['prediction']\n", + "\n", + "# df['pred_id_value'] = df['pred_id'].apply(lambda x: np.argmax(x))\n", + "\n", + "# has_truth = np.isnan(df['truthHitAssignedEnergies']) == False\n", + "# has_pred = np.isnan(df['pred_energy']) == False\n", + "# matched = np.logical_and(has_truth, has_pred)" ] }, { @@ -116,24 +119,15 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_536938/840209094.py:48: RuntimeWarning: invalid value encountered in double_scalars\n", - " fake_err = np.sqrt(fake * (1 - fake) / np.sum(faked[mask_PID_pred]))\n", - "/tmp/ipykernel_536938/840209094.py:47: RuntimeWarning: invalid value encountered in long_scalars\n", - " fake = np.sum(faked[mask_PID_pred]) / np.sum(mask_binpred[mask_PID_pred])\n" + "ename": "NameError", + "evalue": "name 'df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 120\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fig\n\u001b[1;32m 119\u001b[0m energy_bins \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m20\u001b[39m,\u001b[38;5;241m30\u001b[39m,\u001b[38;5;241m50\u001b[39m])\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m1000\u001b[39m\n\u001b[0;32m--> 120\u001b[0m fig\u001b[38;5;241m=\u001b[39mefficiency_plots(\u001b[43mdf\u001b[49m, bins\u001b[38;5;241m=\u001b[39menergy_bins)\n", + "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -158,6 +152,7 @@ " mask_PID_truth = df['truthHitAssignedPIDs'].isin([130,310,311,2112,-2112,3122,-3122,3322,-3322])\n", " else:\n", " raise ValueError(\"mask must be 0 or 1\")\n", + " print(df[df['pred_id_value']==1]['truthHitAssignedPIDs'])\n", " mask_PID_pred = df['pred_id_value'].isin([mask])\n", " \n", " mask_PID_matched = np.logical_and(mask_PID_truth, mask_PID_pred)\n", @@ -267,48 +262,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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f2rZtq8cee0zz589XZmam/vSnPyk2NpZTVQAAVNHVt4KRJA8PDxmGYbfs6qe1X8/Vd2Uuu3v01U93dwW3juwsWrRI586dU+/evdWwYUPb1yeffCLpynNC1q5dq379+ql169aaPHmyhgwZoi+//NLWh6enp1asWCFPT09FR0frd7/7nUaOHGl3Xx4AAHBt3t7eKikpuW67Bg0aKCMjw25Z2VkXR/uqLm4d2fllMvylxo0ba8OGDdftJzIyUl9//bWzygIA4LYTFRWlrVu36scff5S/v3+loy0PPvigFixYoA8++EDR0dH68MMPtXfvXrtbvfyyr1/eUqa61YgJygAA3BZOH6yxn/Pcc8/p8ccfV9u2bXXhwgUlJCRU2C4mJkbTpk3TCy+8oIsXL+qJJ57QyJEjtWfPnkr7uvrSc3cg7AAA4Gp+9a7c0fjzsdX3mV5+Vz73BrVs2VJJSUl2y0aNGlVh21mzZl3z5rwV9RUVFVXujE6nTp2ue5bHGQg7AAC4WnDjK49uuI2fjeVOhB0AAKpDcGPCh5u49WosAAAAVyPsAAAAUyPsAADgZNUx6fZ24Yx9SdgBAMBJyu4OXFhY6OZKzKNsX15952VHMUEZAAAn8fT0VHBwsO1ZT35+frZHIsAxhmGosLBQ2dnZCg4Olqen5033RdgBAMCJyp707eqHW94ugoODK3x6uiMIOwCUe2yv0txdhAv51w2TNaKFu8vAbcJisahhw4YKDQ116AGZKM/Ly6tKIzplCDvAbcy/bpgKDR912zFF2uHualyn0PBR5pjNBB5UK09PT6f8oUbVEXaA25g1ooUyx2zWyZwsd5fiMrnH9qrbjilXtpGwA9yWCDvAbc4a0cLUISBNMvWoFYDr49JzAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgam4NO3PmzNFdd92lgIAAhYaGavDgwUpNTbVrc/HiRcXGxqpevXry9/fXkCFDlJWVZdcmPT1dDz/8sPz8/BQaGqrnn39ely9frs5NAQAANZRbw86GDRsUGxurLVu2aM2aNSouLla/fv1UUFBga/Pss8/qyy+/1KeffqoNGzbo5MmTevTRR23rS0pK9PDDD+vSpUv697//rSVLligxMVHTp093xyYBAIAappY7P3zlypV2rxMTExUaGqrk5GTdf//9OnfunBYvXqylS5fqwQcflCQlJCSoTZs22rJli+655x6tXr1a+/fv19q1axUWFqZOnTrppZde0pQpUzRz5kx5e3u7Y9MAAEANUaPm7Jw7d06SFBISIklKTk5WcXGx+vbta2vTunVrRUREKCkpSZKUlJSk9u3bKywszNYmJiZGeXl52rdvX4WfU1RUpLy8PLsvAABgTjUm7JSWlmrSpEnq0aOH2rVrJ0nKzMyUt7e3goOD7dqGhYUpMzPT1ubqoFO2vmxdRebMmaOgoCDbV+PGjZ28NQAAoKaoMWEnNjZWe/fu1d///neXf9bUqVN17tw529fx48dd/pkAAMA93Dpnp8z48eO1YsUKbdy4UY0aNbItt1qtunTpknJzc+1Gd7KysmS1Wm1ttm3bZtdf2dVaZW1+ycfHRz4+Pk7eittXZvoh5edkXb/hLSj32F53lwAAqCK3hh3DMPTMM89o2bJlWr9+vZo0aWK3vmvXrvLy8tK6des0ZMgQSVJqaqrS09MVHR0tSYqOjtYrr7yi7OxshYaGSpLWrFmjwMBAtW3btno36DaUmX5IgYt7yGopcncpLlNo+Mi/btj1GwIAaiS3hp3Y2FgtXbpUX3zxhQICAmxzbIKCglS7dm0FBQVpzJgxiouLU0hIiAIDA/XMM88oOjpa99xzjySpX79+atu2rR577DHNnz9fmZmZ+tOf/qTY2FhGb6pBfk6WrJYibe8yT8GR7dxdjkv41w2TNaKFu8sAANwkt4adRYsWSZJ69+5ttzwhIUGjRo2SJL3xxhvy8PDQkCFDVFRUpJiYGL3zzju2tp6enlqxYoWefvppRUdHq06dOnr88cc1e/bs6toMSAqObKfmHXu6uwwAAMpx+2ms6/H19VV8fLzi4+MrbRMZGamvv/7amaUBAACTqDFXYwEAALgCYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJiaw2FnyZIl+uqrr2yvX3jhBQUHB+vee+/VsWPHnFocAABAVTkcdv785z+rdu3akqSkpCTFx8dr/vz5ql+/vp599lmnFwgAAFAVtRx9w/Hjx9W8eXNJ0vLlyzVkyBCNGzdOPXr0UO/evZ1dHwAAQJU4PLLj7++vM2fOSJJWr16thx56SJLk6+urCxcuOLc6AACAKnJ4ZOehhx7Sk08+qc6dO+vgwYMaOHCgJGnfvn2Kiopydn0AAABV4vDITnx8vKKjo3Xq1Cl99tlnqlevniQpOTlZw4cPd3qBAAAAVeHwyE5eXp4WLlwoDw/7nDRz5kwdP37caYUBAAA4g8MjO02aNNHp06fLLT979qyaNGnilKIAAACcxeGwYxhGhcvz8/Pl6+tb5YIAAACc6YZPY8XFxUmSLBaLpk+fLj8/P9u6kpISbd26VZ06dXJ6gQAAAFVxw2Fn586dkq6M7OzZs0fe3t62dd7e3urYsaOee+4551cIAABQBTccdr777jtJ0ujRo/XWW28pMDDQZUUBAAA4i8NXYyUkJLiiDgAAAJdwOOwUFBRo7ty5WrdunbKzs1VaWmq3/siRI04rDgAAoKocDjtPPvmkNmzYoMcee0wNGzaUxWJxRV0AAABO4XDY+eabb/TVV1+pR48erqgHAADAqRy+z07dunUVEhLiiloAAACczuGw89JLL2n69OkqLCx0RT0AAABO5fBprNdee02HDx9WWFiYoqKi5OXlZbd+x44dTisOAACgqhwOO4MHD3ZBGQAAAK7hcNiZMWOGK+oAAABwCYfn7EhSbm6u/vrXv2rq1Kk6e/aspCunr3766SenFgcAAFBVDo/s7N69W3379lVQUJB+/PFHjR07ViEhIfr888+Vnp6uDz74wBV1AgAA3BSHR3bi4uI0atQoHTp0SL6+vrblAwcO1MaNG51aHAAAQFU5HHZ++OEHPfXUU+WW33HHHcrMzHRKUQAAAM7icNjx8fFRXl5eueUHDx5UgwYNnFIUAACAszgcdn71q19p9uzZKi4uliRZLBalp6drypQpGjJkiNMLBAAAqAqHw85rr72m/Px8hYaG6sKFC+rVq5eaN2+ugIAAvfLKK66oEQAA4KY5fDVWUFCQ1qxZo02bNmn37t3Kz89Xly5d1LdvX1fUBwAAUCUOh53jx4+rcePG6tmzp3r27OmKmgAAAJzG4dNYUVFR6tWrl9577z3l5OS4oiYAAACncTjsbN++XXfffbdmz56thg0bavDgwfrnP/+poqIiV9QHAABQJQ6Hnc6dO2vBggVKT0/XN998owYNGmjcuHEKCwvTE0884YoaAQAAbtpNPRtLunLJ+QMPPKD33ntPa9euVZMmTbRkyRJn1gYAAFBlNx12Tpw4ofnz56tTp066++675e/vr/j4eGfWBgAAUGUOX431l7/8RUuXLtXmzZvVunVrjRgxQl988YUiIyNdUR8AAECVOBx2Xn75ZQ0fPlwLFy5Ux44dXVETAACA0zgcdtLT02WxWFxRCwAAgNM5PGfHYrHo+++/1+9+9ztFR0frp59+kiT97W9/06ZNm5xeIAAAQFU4HHY+++wzxcTEqHbt2tq5c6ft/jrnzp3Tn//8Z6cXCAAAUBUOh52XX35Z7777rt577z15eXnZlvfo0UM7duxwanEAAABV5fCcndTUVN1///3llgcFBSk3N9ehvjZu3KgFCxYoOTlZGRkZWrZsmQYPHmxbP2rUqHL37omJidHKlSttr8+ePatnnnlGX375pTw8PDRkyBC99dZb8vf3d6gWAOaWe2yv0txdhIv41w2TNaKFu8sAaiyHw47ValVaWpqioqLslm/atElNmzZ1qK+CggJ17NhRTzzxhB599NEK2/Tv318JCQm21z4+PnbrR4wYoYyMDK1Zs0bFxcUaPXq0xo0bp6VLlzpUCwBz8q8bpkLDR912TJFMOvhcaPgoc8xmAg9QCYfDztixYzVx4kS9//77slgsOnnypJKSkvTcc89p2rRpDvU1YMAADRgw4JptfHx8ZLVaK1x34MABrVy5Uj/88IO6desmSXr77bc1cOBAvfrqqwoPD3eoHgDmY41oocwxm3UyJ8vdpbhE7rG96rZjypXtI+wAFXI47Lz44osqLS1Vnz59VFhYqPvvv18+Pj567rnn9Mwzzzi9wPXr1ys0NFR169bVgw8+qJdffln16tWTJCUlJSk4ONgWdCSpb9++8vDw0NatW/XrX/+6wj6LiorsHlyal5fn9LoB1BzWiBamDQJpkmlHrABncTjsWCwW/fGPf9Tzzz+vtLQ05efnq23bti6ZI9O/f389+uijatKkiQ4fPqz/+Z//0YABA5SUlCRPT09lZmYqNDTU7j21atVSSEiIMjMzK+13zpw5mjVrltPrBQAANY/DYaeMt7e32rZt68xayhk2bJjt+/bt26tDhw5q1qyZ1q9frz59+tx0v1OnTlVcXJztdV5enho3blylWgEAQM100w8CdYemTZuqfv36Sku7ck2F1WpVdna2XZvLly/r7Nmzlc7zka7MAwoMDLT7AgAA5nRLhZ0TJ07ozJkzatiwoSQpOjpaubm5Sk5OtrX59ttvVVpaqu7du7urTAAAUIPc9GksZ8jPz7eN0kjS0aNHtWvXLoWEhCgkJESzZs3SkCFDZLVadfjwYb3wwgtq3ry5YmJiJElt2rRR//79NXbsWL377rsqLi7W+PHjNWzYMK7EAgAAkm5wZKdLly7KycmRJM2ePVuFhYVO+fDt27erc+fO6ty5syQpLi5OnTt31vTp0+Xp6andu3frV7/6lVq2bKkxY8aoa9eu+v777+3utfPRRx+pdevW6tOnjwYOHKiePXvqf//3f51SHwAAuPXd0MjOgQMHVFBQoLp162rWrFn6/e9/Lz8/vyp/eO/evWUYRqXrV61add0+QkJCuIEgAACo1A2FnU6dOmn06NHq2bOnDMPQq6++Wuml5tOnT3dqgQAAAFVxQ2EnMTFRM2bM0IoVK2SxWPTNN9+oVq3yb7VYLIQdAABQo9xQ2GnVqpX+/ve/S5I8PDy0bt26cjfzAwAAqIkcvhqrtLTUFXUAAAC4xE1den748GG9+eabOnDggCSpbdu2mjhxopo1a+bU4gAAAKrK4ZsKrlq1Sm3bttW2bdvUoUMHdejQQVu3btWdd96pNWvWuKJGAACAm3ZTTz1/9tlnNXfu3HLLp0yZooceeshpxQEAAFSVwyM7Bw4c0JgxY8otf+KJJ7R//36nFAUAAOAsDoedBg0aaNeuXeWW79q1iyu0AABAjePwaayxY8dq3LhxOnLkiO69915J0ubNmzVv3jzFxcU5vUAAAICqcDjsTJs2TQEBAXrttdc0depUSVJ4eLhmzpypCRMmOL1AAACAqnA47FgsFj377LN69tlndf78eUlSQECA0wsDAABwhpu6z04ZQg4AAKjpHJ6gDAAAcCsh7AAAAFMj7AAAAFNzKOwUFxerT58+OnTokKvqAQAAcCqHwo6Xl5d2797tqloAAACczuHTWL/73e+0ePFiV9QCAADgdA5fen758mW9//77Wrt2rbp27ao6derYrX/99dedVhwAAEBVORx29u7dqy5dukiSDh48aLfOYrE4pyoAAAAncTjsfPfdd66oAwAAwCVu+tLztLQ0rVq1ShcuXJAkGYbhtKIAAACcxeGwc+bMGfXp00ctW7bUwIEDlZGRIUkaM2aMJk+e7PQCAQAAqsLhsPPss8/Ky8tL6enp8vPzsy0fOnSoVq5c6dTiAAAAqsrhOTurV6/WqlWr1KhRI7vlLVq00LFjx5xWGAAAgDM4PLJTUFBgN6JT5uzZs/Lx8XFKUQAAAM7icNi577779MEHH9heWywWlZaWav78+XrggQecWhwAAEBVOXwaa/78+erTp4+2b9+uS5cu6YUXXtC+fft09uxZbd682RU1AgAA3DSHR3batWungwcPqmfPnho0aJAKCgr06KOPaufOnWrWrJkragQAALhpDo/sSFJQUJD++Mc/OrsWAAAAp7upsJOTk6PFixfrwIEDkqS2bdtq9OjRCgkJcWpxAAAAVeXwaayNGzcqKipKCxcuVE5OjnJycrRw4UI1adJEGzdudEWNAAAAN83hkZ3Y2FgNHTpUixYtkqenpySppKREf/jDHxQbG6s9e/Y4vUgAAICb5fDITlpamiZPnmwLOpLk6empuLg4paWlObU4AACAqnI47HTp0sU2V+dqBw4cUMeOHZ1SFAAAgLPc0Gms3bt3276fMGGCJk6cqLS0NN1zzz2SpC1btig+Pl5z5851TZUAAAA36YbCTqdOnWSxWGQYhm3ZCy+8UK7db3/7Ww0dOtR51QEAAFTRDYWdo0ePuroOAAAAl7ihsBMZGenqOgAAAFzipm4qePLkSW3atEnZ2dkqLS21WzdhwgSnFAYAAOAMDoedxMREPfXUU/L29la9evVksVhs6ywWC2EHAADUKA6HnWnTpmn69OmaOnWqPDwcvnIdAACgWjmcVgoLCzVs2DCCDgAAuCU4nFjGjBmjTz/91BW1AAAAOJ3Dp7HmzJmj//qv/9LKlSvVvn17eXl52a1//fXXnVYcAABAVd1U2Fm1apVatWolSeUmKAMAANQkDoed1157Te+//75GjRrlgnIAAACcy+E5Oz4+PurRo4cragEAAHA6h8POxIkT9fbbb7uiFgAAAKdz+DTWtm3b9O2332rFihW68847y01Q/vzzz51WHAAAQFU5HHaCg4P16KOPuqIWAAAAp3M47CQkJLiiDgAAAJfgNsgAAMDUHB7ZadKkyTXvp3PkyJEqFQQAAOBMDoedSZMm2b0uLi7Wzp07tXLlSj3//PPOqgsAAMApHA47EydOrHB5fHy8tm/f7lBfGzdu1IIFC5ScnKyMjAwtW7ZMgwcPtq03DEMzZszQe++9p9zcXPXo0UOLFi1SixYtbG3Onj2rZ555Rl9++aU8PDw0ZMgQvfXWW/L393d00wAAgAk5bc7OgAED9Nlnnzn0noKCAnXs2FHx8fEVrp8/f74WLlyod999V1u3blWdOnUUExOjixcv2tqMGDFC+/bt05o1a7RixQpt3LhR48aNq9K2AAAA83B4ZKcy//znPxUSEuLQewYMGKABAwZUuM4wDL355pv605/+pEGDBkmSPvjgA4WFhWn58uUaNmyYDhw4oJUrV+qHH35Qt27dJElvv/22Bg4cqFdffVXh4eEV9l1UVKSioiLb67y8PIfqBgAAtw6Hw07nzp3tJigbhqHMzEydOnVK77zzjtMKO3r0qDIzM9W3b1/bsqCgIHXv3l1JSUkaNmyYkpKSFBwcbAs6ktS3b195eHho69at+vWvf11h33PmzNGsWbOcVisAAKi5HA47V8+pkSQPDw81aNBAvXv3VuvWrZ1VlzIzMyVJYWFhdsvDwsJs6zIzMxUaGmq3vlatWgoJCbG1qcjUqVMVFxdne52Xl6fGjRs7q3QAAFCDOBx2ZsyY4Yo6qpWPj498fHzcXQYAAKgGNfamglarVZKUlZVltzwrK8u2zmq1Kjs722795cuXdfbsWVsbAABwe7vhsOPh4SFPT89rftWq5bT5zmrSpImsVqvWrVtnW5aXl6etW7cqOjpakhQdHa3c3FwlJyfb2nz77bcqLS1V9+7dnVYLAAC4dd1wOlm2bFml65KSkrRw4UKVlpY69OH5+flKS0uzvT569Kh27dqlkJAQRUREaNKkSXr55ZfVokULNWnSRNOmTVN4eLht3lCbNm3Uv39/jR07Vu+++66Ki4s1fvx4DRs2rNIrsQAAwO3lhsNO2eXfV0tNTdWLL76oL7/8UiNGjNDs2bMd+vDt27frgQcesL0umzT8+OOPKzExUS+88IIKCgo0btw45ebmqmfPnlq5cqV8fX1t7/noo480fvx49enTx3ZTwYULFzpUBwAAMK+bOu908uRJzZgxQ0uWLFFMTIx27dqldu3aOdxP7969ZRhGpestFotmz559zRAVEhKipUuXOvzZAADg9uDQBOVz585pypQpat68ufbt26d169bpyy+/vKmgAwAAUB1ueGRn/vz5mjdvnqxWqz7++OMKT2sBAADUNDccdl588UXVrl1bzZs315IlS7RkyZIK233++edOKw4AAKCqbjjsjBw50u4xEQAAALeCGw47iYmJLiwDAADANWrsHZQBAACcgbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMrZa7CwAAVF3usb1Kc3cRLuJfN0zWiBbuLgO3MMIOANzC/OuGqdDwUbcdU6Qd7q7GNQoNH2WO2UzgwU0j7ADALcwa0UKZYzbrZE6Wu0txidxje9Vtx5Qr20fYwU0i7ADALc4a0cK0QSBNMu2IFaoPE5QBAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICp8WwsF8tMP6R8kz6gT7rykD4AAGoywo4LZaYfUuDiHrJaitxdiksVGj7yrxvm7jIAAKgQYceF8nOyZLUUaXuXeQqObOfuclzGv27YlacuAwBQAxF2qkFwZDs179jT3WUAAHBbYoIyAAAwNcIOAAAwtRoddmbOnCmLxWL31bp1a9v6ixcvKjY2VvXq1ZO/v7+GDBmirCzzXvkEAAAcV6PDjiTdeeedysjIsH1t2rTJtu7ZZ5/Vl19+qU8//VQbNmzQyZMn9eijj7qxWgAAUNPU+AnKtWrVktVqLbf83LlzWrx4sZYuXaoHH3xQkpSQkKA2bdpoy5Ytuueee6q7VAAAUAPV+JGdQ4cOKTw8XE2bNtWIESOUnp4uSUpOTlZxcbH69u1ra9u6dWtFREQoKSnpmn0WFRUpLy/P7gsAAJhTjQ473bt3V2JiolauXKlFixbp6NGjuu+++3T+/HllZmbK29tbwcHBdu8JCwtTZmbmNfudM2eOgoKCbF+NGzd24VYAAAB3qtGnsQYMGGD7vkOHDurevbsiIyP1j3/8Q7Vr177pfqdOnaq4uDjb67y8PAIPAAAmVaNHdn4pODhYLVu2VFpamqxWqy5duqTc3Fy7NllZWRXO8bmaj4+PAgMD7b4AAIA53VJhJz8/X4cPH1bDhg3VtWtXeXl5ad26dbb1qampSk9PV3R0tBurBAAANUmNPo313HPP6ZFHHlFkZKROnjypGTNmyNPTU8OHD1dQUJDGjBmjuLg4hYSEKDAwUM8884yio6O5EgsAANjU6LBz4sQJDR8+XGfOnFGDBg3Us2dPbdmyRQ0aNJAkvfHGG/Lw8NCQIUNUVFSkmJgYvfPOO26uGgAA1CQ1Ouz8/e9/v+Z6X19fxcfHKz4+vpoqAgAAt5pbas4OAACAowg7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Gq5uwAAAK4n99hepbm7CBfyrxsma0QLd5dhWoQdAECN5V83TIWGj7rtmCLtcHc1rlNo+ChzzGYCj4sQdgAANZY1ooUyx2zWyZwsd5fiMrnH9qrbjilXtpGw4xKEHQBAjWaNaGHqEJAmmXrUqiZggjIAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1HhcBAEANYOYnu7v7qe6EHQAA3Oh2eLK7u5/qTtgBAMCNzP5k95rwVHfCDgAAbmbmJ7vXhKe6M0EZAACYGmEHAACYGmEHAACYmmnCTnx8vKKiouTr66vu3btr27Zt7i4JAADUAKYIO5988oni4uI0Y8YM7dixQx07dlRMTIyys7PdXRoAAHAzU4Sd119/XWPHjtXo0aPVtm1bvfvuu/Lz89P777/v7tIAAICb3fKXnl+6dEnJycmaOnWqbZmHh4f69u2rpKSkCt9TVFSkoqIi2+tz585JkvLy8pxa2/n8AuUVGVf+6+S+AQC4Fbjyb2FZf4ZhXLPdLR92Tp8+rZKSEoWFhdktDwsLU0pKSoXvmTNnjmbNmlVueePGjV1So+b2d02/AADcKlz4t/D8+fMKCgqqdP0tH3ZuxtSpUxUXF2d7XVpaqrNnz6pevXqyWCxurKx65eXlqXHjxjp+/LgCAwPdXY7bsB9+xr64gv1wBfvhZ+yLK2rafjAMQ+fPn1d4ePg1293yYad+/fry9PRUVpb9bbazsrJktVorfI+Pj498fHzslgUHB7uqxBovMDCwRhy07sZ++Bn74gr2wxXsh5+xL66oSfvhWiM6ZW75Ccre3t7q2rWr1q1bZ1tWWlqqdevWKTo62o2VAQCAmuCWH9mRpLi4OD3++OPq1q2b7r77br355psqKCjQ6NGj3V0aAABwM1OEnaFDh+rUqVOaPn26MjMz1alTJ61cubLcpGXY8/Hx0YwZM8qd0rvdsB9+xr64gv1wBfvhZ+yLK27V/WAxrne9FgAAwC3slp+zAwAAcC2EHQAAYGqEHQAAYGqEHQAAYGqEHZOaM2eO7rrrLgUEBCg0NFSDBw9WamrqNd+TmJgoi8Vi9+Xr61tNFbvGzJkzy21T69atr/meTz/9VK1bt5avr6/at2+vr7/+upqqda2oqKhy+8JisSg2NrbC9mY5HjZu3KhHHnlE4eHhslgsWr58ud16wzA0ffp0NWzYULVr11bfvn116NCh6/YbHx+vqKgo+fr6qnv37tq2bZuLtsA5rrUfiouLNWXKFLVv31516tRReHi4Ro4cqZMnT16zz5v5/aoJrndMjBo1qtx29e9//UcdmOmYkFThvxcWi0ULFiyotM+aekwQdkxqw4YNio2N1ZYtW7RmzRoVFxerX79+KigouOb7AgMDlZGRYfs6duxYNVXsOnfeeafdNm3atKnStv/+9781fPhwjRkzRjt37tTgwYM1ePBg7d27txordo0ffvjBbj+sWbNGkvR//s//qfQ9ZjgeCgoK1LFjR8XHx1e4fv78+Vq4cKHeffddbd26VXXq1FFMTIwuXrxYaZ+ffPKJ4uLiNGPGDO3YsUMdO3ZUTEyMsrOzXbUZVXat/VBYWKgdO3Zo2rRp2rFjhz7//HOlpqbqV7/61XX7deT3q6a43jEhSf3797fbro8//viafZrtmJBkt/0ZGRl6//33ZbFYNGTIkGv2WyOPCQO3hezsbEOSsWHDhkrbJCQkGEFBQdVXVDWYMWOG0bFjxxtu/5vf/MZ4+OGH7ZZ1797deOqpp5xcmftNnDjRaNasmVFaWlrhejMeD5KMZcuW2V6XlpYaVqvVWLBggW1Zbm6u4ePjY3z88ceV9nP33XcbsbGxttclJSVGeHi4MWfOHJfU7Wy/3A8V2bZtmyHJOHbsWKVtHP39qokq2hePP/64MWjQIIf6uR2OiUGDBhkPPvjgNdvU1GOCkZ3bxLlz5yRJISEh12yXn5+vyMhINW7cWIMGDdK+ffuqozyXOnTokMLDw9W0aVONGDFC6enplbZNSkpS37597ZbFxMQoKSnJ1WVWq0uXLunDDz/UE088cc2H35rxeLja0aNHlZmZafczDwoKUvfu3Sv9mV+6dEnJycl27/Hw8FDfvn1NdZycO3dOFovlus8NdOT361ayfv16hYaGqlWrVnr66ad15syZStveDsdEVlaWvvrqK40ZM+a6bWviMUHYuQ2UlpZq0qRJ6tGjh9q1a1dpu1atWun999/XF198oQ8//FClpaW69957deLEiWqs1rm6d++uxMRErVy5UosWLdLRo0d133336fz58xW2z8zMLHfn7bCwMGVmZlZHudVm+fLlys3N1ahRoyptY8bj4ZfKfq6O/MxPnz6tkpISUx8nFy9e1JQpUzR8+PBrPuzR0d+vW0X//v31wQcfaN26dZo3b542bNigAQMGqKSkpML2t8MxsWTJEgUEBOjRRx+9ZruaekyY4nERuLbY2Fjt3bv3uudNo6Oj7R6eeu+996pNmzb6y1/+opdeesnVZbrEgAEDbN936NBB3bt3V2RkpP7xj3/c0P+hmNXixYs1YMAAhYeHV9rGjMcDrq+4uFi/+c1vZBiGFi1adM22Zv39GjZsmO379u3bq0OHDmrWrJnWr1+vPn36uLEy93n//fc1YsSI616kUFOPCUZ2TG78+PFasWKFvvvuOzVq1Mih93p5ealz585KS0tzUXXVLzg4WC1btqx0m6xWq7KysuyWZWVlyWq1Vkd51eLYsWNau3atnnzySYfeZ8bjoezn6sjPvH79+vL09DTlcVIWdI4dO6Y1a9Zcc1SnItf7/bpVNW3aVPXr1690u8x8TEjS999/r9TUVIf/zZBqzjFB2DEpwzA0fvx4LVu2TN9++62aNGnicB8lJSXas2ePGjZs6IIK3SM/P1+HDx+udJuio6O1bt06u2Vr1qyxG+G41SUkJCg0NFQPP/ywQ+8z4/HQpEkTWa1Wu595Xl6etm7dWunP3NvbW127drV7T2lpqdatW3dLHydlQefQoUNau3at6tWr53Af1/v9ulWdOHFCZ86cqXS7zHpMlFm8eLG6du2qjh07OvzeGnNMuHuGNFzj6aefNoKCgoz169cbGRkZtq/CwkJbm8cee8x48cUXba9nzZplrFq1yjh8+LCRnJxsDBs2zPD19TX27dvnjk1wismTJxvr1683jh49amzevNno27evUb9+fSM7O9swjPL7YPPmzUatWrWMV1991Thw4IAxY8YMw8vLy9izZ4+7NsGpSkpKjIiICGPKlCnl1pn1eDh//ryxc+dOY+fOnYYk4/XXXzd27txpu8po7ty5RnBwsPHFF18Yu3fvNgYNGmQ0adLEuHDhgq2PBx980Hj77bdtr//+978bPj4+RmJiorF//35j3LhxRnBwsJGZmVnt23ejrrUfLl26ZPzqV78yGjVqZOzatcvu34yioiJbH7/cD9f7/aqprrUvzp8/bzz33HNGUlKScfToUWPt2rVGly5djBYtWhgXL1609WH2Y6LMuXPnDD8/P2PRokUV9nGrHBOEHZOSVOFXQkKCrU2vXr2Mxx9/3PZ60qRJRkREhOHt7W2EhYUZAwcONHbs2FH9xTvR0KFDjYYNGxre3t7GHXfcYQwdOtRIS0uzrf/lPjAMw/jHP/5htGzZ0vD29jbuvPNO46uvvqrmql1n1apVhiQjNTW13DqzHg/fffddhb8LZdtaWlpqTJs2zQgLCzN8fHyMPn36lNs/kZGRxowZM+yWvf3227b9c/fddxtbtmyppi26OdfaD0ePHq3034zvvvvO1scv98P1fr9qqmvti8LCQqNfv35GgwYNDC8vLyMyMtIYO3ZsudBi9mOizF/+8hejdu3aRm5uboV93CrHhMUwDMOlQ0cAAABuxJwdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdwKR+/PFHWSwW7dq1y92l2KSkpOiee+6Rr6+vOnXq5O5ybCwWi5YvX35DbWfOnOnS2keNGqXBgwe7rH/gdkTYAVxk1KhRslgsmjt3rt3y5cuXy2KxuKkq95oxY4bq1Kmj1NTUcg9crQ6VBZWMjAwNGDCg2uupyFtvvaXExER3l+EUBDfUFIQdwIV8fX01b9485eTkuLsUp7l06dJNv/fw4cPq2bOnIiMjb+qp2jfLMAxdvny50vVWq1U+Pj7VVs+1BAUFKTg42N1lAKZC2AFcqG/fvrJarZozZ06lbSoabXjzzTcVFRVle132f8h//vOfFRYWpuDgYM2ePVuXL1/W888/r5CQEDVq1EgJCQnl+k9JSdG9994rX19ftWvXThs2bLBbv3fvXg0YMED+/v4KCwvTY489ptOnT9vW9+7dW+PHj9ekSZNUv359xcTEVLgdpaWlmj17tho1aiQfHx916tRJK1eutK23WCxKTk7W7NmzZbFYNHPmzAr7Kfu88ePHKygoSPXr19e0adN09WP8/va3v6lbt24KCAiQ1WrVb3/7W2VnZ9vWr1+/XhaLRd988426du0qHx8fffjhh5o1a5b+85//yGKxyGKx2EZQfnka68SJExo+fLhCQkJUp04ddevWTVu3bq2wXkn661//qjZt2sjX11etW7fWO++8Y1t36dIljR8/Xg0bNpSvr68iIyOveTz8cjSkd+/emjBhgl544QWFhITIarVWuu9utKZ7771XU6ZMsWt/6tQpeXl5aePGjZKkoqIiPffcc7rjjjtUp04dde/eXevXr7e1T0xMVHBwsFatWqU2bdrI399f/fv3V0ZGhqQrx/WSJUv0xRdf2Pb31e8HqpV7n0MKmNfjjz9uDBo0yPj8888NX19f4/jx44ZhGMayZcuMq3/1ZsyYYXTs2NHuvW+88YYRGRlp11dAQIARGxtrpKSkGIsXLzYkGTExMcYrr7xiHDx40HjppZcMLy8v2+eUPcm6UaNGxj//+U9j//79xpNPPmkEBAQYp0+fNgzDMHJycowGDRoYU6dONQ4cOGDs2LHDeOihh4wHHnjA9tm9evUy/P39jeeff95ISUkxUlJSKtze119/3QgMDDQ+/vhjIyUlxXjhhRcMLy8v4+DBg4ZhGEZGRoZx5513GpMnTzYyMjKM8+fPV9hP2edNnDjRSElJMT788EPDz8/P+N///V9bm8WLFxtff/21cfjwYSMpKcmIjo42BgwYYFtf9jTnDh06GKtXrzbS0tKMEydOGJMnTzbuvPNOIyMjw8jIyDAKCwsNwzAMScayZcsMwzCM8+fPG02bNjXuu+8+4/vvvzcOHTpkfPLJJ8a///3vCn9eH374odGwYUPjs88+M44cOWJ89tlnRkhIiJGYmGgYhmEsWLDAaNy4sbFx40bjxx9/NL7//ntj6dKlFW572c960KBBdvsjMDDQmDlzpnHw4EFjyZIlhsViMVavXl1pH9er6f/+3/9rREREGKWlpbb3lD2xu2zZk08+adx7773Gxo0bjbS0NGPBggWGj4+P7eeZkJBgeHl5GX379jV++OEHIzk52WjTpo3x29/+1rYff/Ob3xj9+/e37e+ioqJKawZcibADuMjVf7Tuuece44knnjAM4+bDTmRkpFFSUmJb1qpVK+O+++6zvb58+bJRp04d4+OPPzYM4+ewM3fuXFub4uJio1GjRsa8efMMwzCMl156yejXr5/dZx8/ftyQZKSmphqGceWPbefOna+7veHh4cYrr7xit+yuu+4y/vCHP9hed+zY0ZgxY8Y1++nVq5fRpk0buz/EU6ZMMdq0aVPpe3744QdDki1AlYWd5cuX27WraF8bhn3Y+ctf/mIEBAQYZ86cqfCzftlHs2bNyoWXl156yYiOjjYMwzCeeeYZ48EHH7TbnmupKOz07NnTrs1dd91lTJkypdI+rldTdna2UatWLWPjxo229dHR0bY+jx07Znh6eho//fSTXR99+vQxpk6dahjGlbAjyUhLS7Otj4+PN8LCwirdFsBdOI0FVIN58+ZpyZIlOnDgwE33ceedd8rD4+df2bCwMLVv39722tPTU/Xq1bM7nSNJ0dHRtu9r1aqlbt262er4z3/+o++++07+/v62r9atW0u6Mr+mTNeuXa9ZW15enk6ePKkePXrYLe/Ro8dNbfM999xjN4k7Ojpahw4dUklJiSQpOTlZjzzyiCIiIhQQEKBevXpJktLT0+366datm8OfvWvXLnXu3FkhISHXbVtQUKDDhw9rzJgxdvvw5Zdftu2/UaNGadeuXWrVqpUmTJig1atXO1xThw4d7F43bNiw3M/ZkZoaNGigfv366aOPPpIkHT16VElJSRoxYoQkac+ePSopKVHLli3t+tiwYYPdceHn56dmzZrdUF2AO9VydwHA7eD+++9XTEyMpk6dqlGjRtmt8/DwsJuPIknFxcXl+vDy8rJ7bbFYKlxWWlp6w3Xl5+frkUce0bx588qta9iwoe37OnXq3HCfrlZQUKCYmBjFxMToo48+UoMGDZSenq6YmJhyk6dvpu7atWvfcNv8/HxJ0nvvvafu3bvbrfP09JQkdenSRUePHtU333yjtWvX6je/+Y369u2rf/7znzf8OY78nG+kJkkaMWKEJkyYoLfffltLly5V+/btbeE5Pz9fnp6eSk5OtnuPJPn7+1+zrl8ey0BNQNgBqsncuXPVqVMntWrVym55gwYNlJmZKcMwbKMZzrw3zpYtW3T//fdLki5fvqzk5GSNHz9e0pU/xJ999pmioqJUq9bN/3MQGBio8PBwbd682TbKIkmbN2/W3Xff7XB/v5wMvGXLFrVo0UKenp5KSUnRmTNnNHfuXDVu3FiStH379hvq19vb2zY6VJkOHTror3/9q86ePXvd0Z2wsDCFh4fryJEjtlGRigQGBmro0KEaOnSo/vu//1v9+/e/of5vxo3WNGjQII0bN04rV67U0qVLNXLkSNu6zp07q6SkRNnZ2brvvvtuupYb2d9AdeA0FlBN2rdvrxEjRmjhwoV2y3v37q1Tp05p/vz5Onz4sOLj4/XNN9847XPj4+O1bNkypaSkKDY2Vjk5OXriiSckSbGxsTp79qyGDx+uH374QYcPH9aqVas0evRoh/9IPf/885o3b54++eQTpaam6sUXX9SuXbs0ceJEh2tOT09XXFycUlNT9fHHH+vtt9+29RMRESFvb2+9/fbbOnLkiP71r3/ppZdeuqF+o6KidPToUe3atUunT59WUVFRuTbDhw+X1WrV4MGDtXnzZh05ckSfffaZkpKSKuxz1qxZmjNnjhYuXKiDBw9qz549SkhI0Ouvvy5Jev311/Xxxx8rJSVFBw8e1Keffiqr1erSy8uvV5N0ZdRr8ODBmjZtmg4cOKDhw4fb1rVs2VIjRozQyJEj9fnnn+vo0aPatm2b5syZo6+++uqG64iKitLu3buVmpqq06dPVzhiCVQHwg5QjWbPnl3u9EObNm30zjvvKD4+Xh07dtS2bdv03HPPOe0z586dq7lz56pjx47atGmT/vWvf6l+/fqSZBuNKSkpUb9+/dS+fXtNmjRJwcHBdvODbsSECRMUFxenyZMnq3379lq5cqX+9a9/qUWLFg7XPHLkSF24cEF33323YmNjNXHiRI0bN07SlZGwxMREffrpp2rbtq3mzp2rV1999Yb6HTJkiPr3768HHnhADRo00Mcff1yujbe3t1avXq3Q0FANHDhQ7du319y5c8udzinz5JNP6q9//asSEhLUvn179erVS4mJiWrSpIkkKSAgQPPnz1e3bt1011136ccff9TXX3/t8P51xPVqKjNixAj95z//0X333aeIiAi7dQkJCRo5cqQmT56sVq1aafDgwfrhhx/KtbuWsWPHqlWrVurWrZsaNGigzZs3O2X7AEdZDE6wAqhBevfurU6dOunNN990dykATIKRHQAAYGqEHQAAYGqcxgIAAKbGyA4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADC1/weA7Ol3SRnHNAAAAABJRU5ErkJggg==", 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", 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.9.18' requires the ipykernel package.\n", + "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n", + "\u001b[1;31mCommand: '/bin/python3 -m pip install ipykernel -U --user --force-reinstall'" + ] } ], "source": [ @@ -320,13 +285,16 @@ "nParicles_truth = []\n", "\n", "for i in range(len(t_list)):\n", - " mask = np.logical_and(df['event_id'] == i, matched)\n", + " eventmask = df['event_id'] == i\n", + " mask = np.logical_and(eventmask, matched)\n", " jet_E_truth = np.sum(df[mask]['truthHitAssignedEnergies'])\n", " jet_E_pred = np.sum(df[mask]['pred_energy'])\n", " relresE.append((jet_E_truth-jet_E_pred)/jet_E_truth)\n", " \n", - " nParicles_pred.append(len(df[mask]['pred_sid']))\n", - " nParicles_truth.append(len(df[mask]['truthHitAssignementIdx']))\n", + " pred_mask = np.logical_and(eventmask, has_pred)\n", + " nParicles_pred.append(np.sum(pred_mask))\n", + " truth_mask = np.logical_and(eventmask, has_truth)\n", + " nParicles_truth.append(len(np.unique(df[truth_mask]['truthHitAssignementIdx'])))\n", " \n", " t_eta = np.mean(df[mask]['truthHitAssignedZ'])\n", " pred_eta = df[mask]['pred_pos'].apply(lambda x: x[2]).mean()\n", @@ -364,34 +332,6 @@ "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bins = np.linspace(1, 18, 9)\n", - "#set color to #67c4ce\n", - "plt.hist(nParicles_pred, bins=bins, label='pred', histtype='step', color='#67c4ce')\n", - "#plt.hist(nParicles_truth, bins=bins, label='truth', histtype='step')\n", - "plt.text(0.02, 0.98, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes)\n", - "plt.ylabel('Number of events')\n", - "plt.xlabel(\"$\\\\Delta \\\\eta$\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -401,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "vscode": { "languageId": "ruby" @@ -409,34 +349,14 @@ }, "outputs": [ { - "data": { - "image/png": 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", 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q27evatSooQYNGuipp57ShQsXPPqsW7dOnTp1kt1uV7NmzZSammqqVgAAcPUqdyCqU6eOwsPDZbPZ1Lx5c4WHh7sXh8OhO+64Q/fff7+pD//iiy+UlJSkTZs2KS0tTcXFxerZs6fOnDnj7jNu3DgtX75c77//vr744gsdOXJEAwYMcG8vKSlR3759VVRUpC+//FJvvfWWUlNTNW3aNHefnJwc9e3bV927d1dWVpbGjh2rRx55RKtXrzZVLwAAuDrZDMMwytPxrbfekmEYGjFihBYsWCCHw+HeFhISoiZNmiguLu53FfPDDz+oQYMG+uKLL9StWzfl5+erfv36Wrp0qe69915J0r59+9SqVStlZGSoS5cu+vTTT3XnnXfqyJEjioiIkCQtWrRIEydO1A8//KCQkBBNnDhRK1eu1O7du92fNWjQIJ06dUqrVq0qU0dhYaEKCwvd6y6XS9HR0crPz1doaOjvOkcA8IYmk1YGuoQrcnhu30CXAAtxuVxyOBzl+vtd7hczDh06VBcuXJDNZtPtt9+u6Ojo313oL+Xn50uS+9ZbZmamiouLFR8f7+7TsmVLXXPNNe5AlJGRobZt27rDkCQlJCRo9OjRys7OVseOHZWRkeFxjIt9xo4de8k65syZw3woAAAsxNQcoipVqmj06NEqLS31eiGlpaUaO3as/vjHP6pNmzaSJKfTqZCQEIWFhXn0jYiIkNPpdPf5eRi6uP3itsv1cblcOnfuXJlaJk+erPz8fPfy7bffeuUcAQBAxWT6qztuuukmbd++XY0bN/ZqIUlJSdq9e7c2bNjg1eNeCbvdLrvdHugyAACAn5gORH/5y180fvx4fffdd4qNjVXNmjU9trdr1850EWPGjNGKFSu0fv16NWrUyN0eGRmpoqIinTp1ymOUKC8vT5GRke4+W7Zs8TjexafQft7nl0+m5eXlKTQ0VNWrVzddLwAAuLqYDkSDBg2S5PmdZTabzf04fklJSbmPZRiGHnvsMX344Ydat26dYmJiPLbHxsaqatWqSk9P18CBAyVJ+/fvV25urnsCd1xcnJ599lkdO3ZMDRo0kCSlpaUpNDRUrVu3dvf55JNPPI6dlpb2uyeBAwCAq4PpQOTNr+5ISkrS0qVL9e9//1u1a9d2z/lxOByqXr26HA6HEhMTlZycrPDwcIWGhuqxxx5TXFycunTpIknq2bOnWrdurYcfflgpKSlyOp2aMmWKkpKS3Le9Hn30Uf3jH//QhAkTNGLECK1du1bvvfeeVq6snE9pAAAA7zIdiLw5d+jVV1+VJN12220e7YsXL9awYcMk/fTFskFBQRo4cKAKCwuVkJCgV155xd03ODhYK1as0OjRoxUXF6eaNWtq6NChHl8jEhMTo5UrV2rcuHF68cUX1ahRI73++utKSEjw2rkAAIDKq9zvIfqlPXv2KDc3V0VFRR7td999t1cKq0jMvMcAAPyB9xABv80n7yG66NChQ7rnnnu0a9cu99whSe6v8zAzhwgAAKAiMP1dZk888YRiYmJ07Ngx1ahRQ9nZ2Vq/fr1uuOEGrVu3zgclAgAA+JbpEaKMjAytXbtW9erVU1BQkIKCgtS1a1fNmTNHjz/+uLZv3+6LOgEAAHzG9AhRSUmJateuLUmqV6+ejhw5Iumnyda//KZ6AACAysD0CFGbNm20Y8cOxcTEqHPnzkpJSVFISIhee+01NW3a1Bc1AgAA+JTpQDRlyhSdOXNGkjRr1izdeeeduuWWW1S3bl29++67Xi8QAADA10wHop+/u6dZs2bat2+fTpw4oTp16rifNAMAAKhMTAein7v4LfDR0dFeKQYAACAQTE+qvnDhgqZOnSqHw6EmTZqoSZMmcjgcmjJlioqLi31RIwAAgE+ZHiF67LHHtGzZMqWkpLi/HDUjI0MzZszQ8ePH3V/HAQAAUFmYDkRLly7VO++8o969e7vb2rVrp+joaD344IMEIgAAUOmYvmVmt9vVpEmTMu0xMTEKCQnxRk0AAAB+ZToQjRkzRrNnz1ZhYaG7rbCwUM8++6zGjBnj1eIAAAD8wfQts+3btys9PV2NGjVS+/btJUk7duxQUVGRevTooQEDBrj7Llu2zHuVAgAA+IjpQBQWFqaBAwd6tPHYPQAAqMxMB6LFixf7og4AAICAMT2HCAAA4GpDIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZXrqfMFi5cWO4DPv7441dcDAAAQCCUKxC98MIL5TqYzWYjEAEAgEqnXIEoJyfH13UAAAAEDHOIAACA5Zl+U7Ukfffdd/r444+Vm5uroqIij23z58/3SmEAAAD+YjoQpaen6+6771bTpk21b98+tWnTRocPH5ZhGOrUqZMvagQAAPAp07fMJk+erCeffFK7du1StWrV9K9//Uvffvutbr31Vt13332+qBEAAMCnTAeivXv3asiQIZKkKlWq6Ny5c6pVq5ZmzZql5557zusFAgAA+JrpQFSzZk33vKGoqCgdPHjQve3HH3/0XmUAAAB+YnoOUZcuXbRhwwa1atVKffr00fjx47Vr1y4tW7ZMXbp08UWNAAAAPmU6EM2fP18FBQWSpJkzZ6qgoEDvvvuurrvuOp4wAwAAlZKpQFRSUqLvvvtO7dq1k/TT7bNFixb5pDAAAAB/MTWHKDg4WD179tTJkyd9VQ8AAIDfmZ5U3aZNGx06dMgXtQAAAASE6UD0zDPP6Mknn9SKFSt09OhRuVwujwUAAKCyMT2puk+fPpKku+++Wzabzd1uGIZsNptKSkq8Vx0AAIAfmA5En3/+uS/qAAAACBjTgSgmJkbR0dEeo0PSTyNE3377rdcKAwAA8BfTc4hiYmL0ww8/lGk/ceKEYmJivFIUAACAP5kORBfnCv1SQUGBqlWr5pWiAAAA/Knct8ySk5MlSTabTVOnTlWNGjXc20pKSrR582Z16NDB6wUCAAD4WrkD0fbt2yX9NEK0a9cuhYSEuLeFhISoffv2evLJJ71fIQAAgI+VOxBdfLps+PDhevHFFxUaGuqzogAAAPzJ9FNmixcv9kUdAAAAAWM6EN1+++2X3b527dorLgYAACAQTAei9u3be6wXFxcrKytLu3fv1tChQ71WGAAAgL+YDkQvvPDCJdtnzJihgoKC310QAACAv5l+D9Gv+dOf/qQ333zTW4cDAADwG68FooyMDNMvZly/fr3uuusuNWzYUDabTR999JHH9mHDhslms3ksvXr18uhz4sQJDR48WKGhoQoLC1NiYmKZkaqdO3fqlltuUbVq1RQdHa2UlJQrOkcAAHB1Mn3LbMCAAR7rhmHo6NGj2rZtm6ZOnWrqWGfOnFH79u01YsSIMse9qFevXh5Pttntdo/tgwcP1tGjR5WWlqbi4mINHz5co0aN0tKlSyVJLpdLPXv2VHx8vBYtWqRdu3ZpxIgRCgsL06hRo0zVCwAArk6mA5HD4fBYDwoKUosWLTRr1iz17NnT1LF69+6t3r17X7aP3W5XZGTkJbft3btXq1at0tatW3XDDTdIkl566SX16dNH8+bNU8OGDfX222+rqKhIb775pkJCQnT99dcrKytL8+fPJxABAABJleA9ROvWrVODBg1Up04d3X777XrmmWdUt25dST/dpgsLC3OHIUmKj49XUFCQNm/erHvuuUcZGRnq1q2bx5u1ExIS9Nxzz+nkyZOqU6dOmc8sLCxUYWGhe93lcvnwDAEAQKBd0RyiU6dO6fXXX9fkyZN14sQJSdJ///tfff/9914trlevXlqyZInS09P13HPP6YsvvlDv3r1VUlIiSXI6nWrQoIHHPlWqVFF4eLicTqe7T0REhEefi+sX+/zSnDlz5HA43Et0dLRXzwsAAFQspkeIdu7cqR49eigsLEyHDx/WyJEjFR4ermXLlik3N1dLlizxWnGDBg1y/9y2bVu1a9dO1157rdatW6cePXp47XN+afLkye4vs5V+GiEiFAEAcPUyPUKUnJys4cOH6+uvv/Z4qqxPnz5av369V4v7paZNm6pevXo6cOCAJCkyMlLHjh3z6HPhwgWdOHHCPe8oMjJSeXl5Hn0urv/a3CS73a7Q0FCPBQAAXL1MB6KtW7fqz3/+c5n2P/zhD796C8pbvvvuOx0/flxRUVGSpLi4OJ06dUqZmZnuPmvXrlVpaak6d+7s7rN+/XoVFxe7+6SlpalFixaXnD8EAACsx3Qgstvtl5xk/NVXX6l+/fqmjlVQUKCsrCxlZWVJknJycpSVlaXc3FwVFBToqaee0qZNm3T48GGlp6erX79+atasmRISEiRJrVq1Uq9evTRy5Eht2bJFGzdu1JgxYzRo0CA1bNhQkvTQQw8pJCREiYmJys7O1rvvvqsXX3zR45YYAACwNtOB6O6779asWbPcIy42m025ubmaOHGiBg4caOpY27ZtU8eOHdWxY0dJP92O69ixo6ZNm6bg4GDt3LlTd999t5o3b67ExETFxsbqP//5j8e7iN5++221bNlSPXr0UJ8+fdS1a1e99tpr7u0Oh0OfffaZcnJyFBsbq/Hjx2vatGk8cg8AANxshmEYZnbIz8/Xvffeq23btun06dNq2LChnE6n4uLi9Mknn6hmzZq+qjVgXC6XHA6H8vPzmU8EoEJoMmlloEu4Iofn9g10CbAQM3+/r+jFjGlpadqwYYN27typgoICderUSfHx8VdcMAAAQCCZDkQXde3aVV27dvVmLQAAAAFxRYEoPT1d6enpOnbsmEpLSz228Y33AACgsjEdiGbOnKlZs2bphhtuUFRUlGw2my/qAgAA8BvTgWjRokVKTU3Vww8/7It6AAAA/M70Y/dFRUW6+eabfVELAABAQJgORI888oiWLl3qi1oAAAACwvQts/Pnz+u1117TmjVr1K5dO1WtWtVj+/z5871WHAAAgD9c0bfdd+jQQZK0e/duj21MsAYAAJWR6UD0+eef+6IOAACAgDE9hwgAAOBqQyACAACWRyACAACWRyACAACWV65A1KlTJ508eVKSNGvWLJ09e9anRQEAAPhTuQLR3r17debMGUk/fZdZQUGBT4sCAADwp3I9dt+hQwcNHz5cXbt2lWEYmjdvnmrVqnXJvtOmTfNqgQAAAL5WrkCUmpqq6dOna8WKFbLZbPr0009VpUrZXW02G4EIAABUOuUKRC1atNA777wjSQoKClJ6eroaNGjg08IAAAD8xfSbqktLS31RBwAAQMCYDkSSdPDgQS1YsEB79+6VJLVu3VpPPPGErr32Wq8WBwAA4A+m30O0evVqtW7dWlu2bFG7du3Url07bd68Wddff73S0tJ8USMAAIBPmR4hmjRpksaNG6e5c+eWaZ84caLuuOMOrxUHAADgD6ZHiPbu3avExMQy7SNGjNCePXu8UhQAAIA/mQ5E9evXV1ZWVpn2rKwsnjwDAACVkulbZiNHjtSoUaN06NAh3XzzzZKkjRs36rnnnlNycrLXCwQAAPA104Fo6tSpql27tp5//nlNnjxZktSwYUPNmDFDjz/+uNcLBAAA8DXTgchms2ncuHEaN26cTp8+LUmqXbu21wsDAADwlyt6D9FFBCEAAHA1MD2pGgAA4GpDIAIAAJZHIAIAAJZnKhAVFxerR48e+vrrr31VDwAAgN+ZCkRVq1bVzp07fVULAABAQJi+ZfanP/1Jb7zxhi9qAQAACAjTj91fuHBBb775ptasWaPY2FjVrFnTY/v8+fO9VhwAAIA/mA5Eu3fvVqdOnSRJX331lcc2m83mnaoAAAD8yHQg+vzzz31RBwAAQMBc8WP3Bw4c0OrVq3Xu3DlJkmEYXisKAADAn0wHouPHj6tHjx5q3ry5+vTpo6NHj0qSEhMTNX78eK8XCAAA4GumA9G4ceNUtWpV5ebmqkaNGu72Bx54QKtWrfJqcQAAAP5geg7RZ599ptWrV6tRo0Ye7dddd52++eYbrxUGAADgL6ZHiM6cOeMxMnTRiRMnZLfbvVIUAACAP5kORLfccouWLFniXrfZbCotLVVKSoq6d+/u1eIAAAD8wfQts5SUFPXo0UPbtm1TUVGRJkyYoOzsbJ04cUIbN270RY0AAAA+ZXqEqE2bNvrqq6/UtWtX9evXT2fOnNGAAQO0fft2XXvttb6oEQAAwKdMjxBJksPh0NNPP+3tWgAAAALiigLRyZMn9cYbb2jv3r2SpNatW2v48OEKDw/3anEAAAD+YPqW2fr169WkSRMtXLhQJ0+e1MmTJ7Vw4ULFxMRo/fr1vqgRAADAp0yPECUlJemBBx7Qq6++quDgYElSSUmJ/vKXvygpKUm7du3yepEAAAC+ZHqE6MCBAxo/frw7DElScHCwkpOTdeDAAVPHWr9+ve666y41bNhQNptNH330kcd2wzA0bdo0RUVFqXr16oqPj9fXX3/t0efEiRMaPHiwQkNDFRYWpsTERBUUFHj02blzp2655RZVq1ZN0dHRSklJMXfSAADgqmY6EHXq1Mk9d+jn9u7dq/bt25s61pkzZ9S+fXu9/PLLl9yekpKihQsXatGiRdq8ebNq1qyphIQEnT9/3t1n8ODBys7OVlpamlasWKH169dr1KhR7u0ul0s9e/ZU48aNlZmZqb///e+aMWOGXnvtNVO1AgCAq1e5bpnt3LnT/fPjjz+uJ554QgcOHFCXLl0kSZs2bdLLL7+suXPnmvrw3r17q3fv3pfcZhiGFixYoClTpqhfv36SpCVLligiIkIfffSRBg0apL1792rVqlXaunWrbrjhBknSSy+9pD59+mjevHlq2LCh3n77bRUVFenNN99USEiIrr/+emVlZWn+/PkewQkAAFhXuQJRhw4dZLPZZBiGu23ChAll+j300EN64IEHvFJYTk6OnE6n4uPj3W0Oh0OdO3dWRkaGBg0apIyMDIWFhbnDkCTFx8crKChImzdv1j333KOMjAx169ZNISEh7j4JCQl67rnndPLkSdWpU6fMZxcWFqqwsNC97nK5vHJOAACgYipXIMrJyfF1HWU4nU5JUkREhEd7RESEe5vT6VSDBg08tlepUkXh4eEefWJiYsoc4+K2SwWiOXPmaObMmd45EQAAUOGVKxA1btzY13VUKJMnT1ZycrJ73eVyKTo6OoAVAQAAX7qiFzMeOXJEGzZs0LFjx1RaWuqx7fHHH/dKYZGRkZKkvLw8RUVFudvz8vLUoUMHd59jx4557HfhwgWdOHHCvX9kZKTy8vI8+lxcv9jnl+x2u+x2u1fOAwAAVHymA1Fqaqr+/Oc/KyQkRHXr1pXNZnNvs9lsXgtEMTExioyMVHp6ujsAuVwubd68WaNHj5YkxcXF6dSpU8rMzFRsbKwkae3atSotLVXnzp3dfZ5++mkVFxeratWqkqS0tDS1aNHikrfLAACA9Zh+7H7q1KmaNm2a8vPzdfjwYeXk5LiXQ4cOmTpWQUGBsrKylJWVJemnuUpZWVnKzc2VzWbT2LFj9cwzz+jjjz/Wrl27NGTIEDVs2FD9+/eXJLVq1Uq9evXSyJEjtWXLFm3cuFFjxozRoEGD1LBhQ0k/TfQOCQlRYmKisrOz9e677+rFF1/0uCUGAACszfQI0dmzZzVo0CAFBZnOUmVs27ZN3bt3d69fDClDhw5VamqqJkyYoDNnzmjUqFE6deqUunbtqlWrVqlatWrufd5++22NGTNGPXr0UFBQkAYOHKiFCxe6tzscDn322WdKSkpSbGys6tWrp2nTpvHIPQAAcLMZP3+WvhwmTJig8PBwTZo0yVc1VTgul0sOh0P5+fkKDQ0NdDkAoCaTVga6hCtyeG7fQJcACzHz99v0CNGcOXN05513atWqVWrbtq17Xs5F8+fPN3tIAACAgLqiQLR69Wq1aNFCkspMqgYAAKhsTAei559/Xm+++aaGDRvmg3IAAAD8z/TMaLvdrj/+8Y++qAUAACAgTAeiJ554Qi+99JIvagEAAAgI07fMtmzZorVr12rFihW6/vrry0yqXrZsmdeKAwAA8AfTgSgsLEwDBgzwRS0AAAABYToQLV682Bd1AAAABMzvf900AABAJWd6hCgmJuay7xsy+31mAAAAgWY6EI0dO9Zjvbi4WNu3b9eqVav01FNPeasuAAAAvzEdiJ544olLtr/88svatm3b7y4IAADA37w2h6h3797617/+5a3DAQAA+I3XAtEHH3yg8PBwbx0OAADAb0zfMuvYsaPHpGrDMOR0OvXDDz/olVde8WpxAAAA/mA6EPXv399jPSgoSPXr19dtt92mli1beqsuAAAAvzEdiKZPn+6LOgAAAAKGFzMCAADLK/cIUVBQ0GVfyChJNptNFy5c+N1FAQAA+FO5A9GHH374q9syMjK0cOFClZaWeqUoAAAAfyp3IOrXr1+Ztv3792vSpElavny5Bg8erFmzZnm1OAAAAH+4ojlER44c0ciRI9W2bVtduHBBWVlZeuutt9S4cWNv1wcAAOBzpgJRfn6+Jk6cqGbNmik7O1vp6elavny52rRp46v6AAAAfK7ct8xSUlL03HPPKTIyUv/85z8veQsNAACgMrIZhmGUp2NQUJCqV6+u+Ph4BQcH/2q/ZcuWea24isLlcsnhcCg/P1+hoaGBLgcA1GTSykCXcEUOz+0b6BJgIWb+fpd7hGjIkCG/+dg9AABAZVTuQJSamurDMgAAAAKHN1UDAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLq9CBaMaMGbLZbB5Ly5Yt3dvPnz+vpKQk1a1bV7Vq1dLAgQOVl5fncYzc3Fz17dtXNWrUUIMGDfTUU0/pwoUL/j4VAABQgVUJdAG/5frrr9eaNWvc61Wq/F/J48aN08qVK/X+++/L4XBozJgxGjBggDZu3ChJKikpUd++fRUZGakvv/xSR48e1ZAhQ1S1alX97W9/8/u5AACAiqnCB6IqVaooMjKyTHt+fr7eeOMNLV26VLfffrskafHixWrVqpU2bdqkLl266LPPPtOePXu0Zs0aRUREqEOHDpo9e7YmTpyoGTNmKCQkxN+nAwAAKqAKfctMkr7++ms1bNhQTZs21eDBg5WbmytJyszMVHFxseLj4919W7ZsqWuuuUYZGRmSpIyMDLVt21YRERHuPgkJCXK5XMrOzv7VzywsLJTL5fJYAADA1atCB6LOnTsrNTVVq1at0quvvqqcnBzdcsstOn36tJxOp0JCQhQWFuaxT0REhJxOpyTJ6XR6hKGL2y9u+zVz5syRw+FwL9HR0d49MQAAUKFU6FtmvXv3dv/crl07de7cWY0bN9Z7772n6tWr++xzJ0+erOTkZPe6y+UiFAEAcBWr0CNEvxQWFqbmzZvrwIEDioyMVFFRkU6dOuXRJy8vzz3nKDIyssxTZxfXLzUv6SK73a7Q0FCPBQAAXL0qVSAqKCjQwYMHFRUVpdjYWFWtWlXp6enu7fv371dubq7i4uIkSXFxcdq1a5eOHTvm7pOWlqbQ0FC1bt3a7/UDAICKqULfMnvyySd11113qXHjxjpy5IimT5+u4OBgPfjgg3I4HEpMTFRycrLCw8MVGhqqxx57THFxcerSpYskqWfPnmrdurUefvhhpaSkyOl0asqUKUpKSpLdbg/w2QEAgIqiQgei7777Tg8++KCOHz+u+vXrq2vXrtq0aZPq168vSXrhhRcUFBSkgQMHqrCwUAkJCXrllVfc+wcHB2vFihUaPXq04uLiVLNmTQ0dOlSzZs0K1CkBAIAKyGYYhhHoIio6l8slh8Oh/Px85hMBqBCaTFoZ6BKuyOG5fQNdAizEzN/vSjWHCAAAwBcIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIq9GP3AICrS2V8Oo4n46yBESIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5lgpEL7/8spo0aaJq1aqpc+fO2rJlS6BLAgAAFUCVQBfgL++++66Sk5O1aNEide7cWQsWLFBCQoL279+vBg0aBLo8AAHUZNLKQJcAIMBshmEYgS7CHzp37qwbb7xR//jHPyRJpaWlio6O1mOPPaZJkyZddl+XyyWHw6H8/HyFhob6o1wAfkQgwtXm8Ny+gS6hQjDz99sSI0RFRUXKzMzU5MmT3W1BQUGKj49XRkZGmf6FhYUqLCx0r+fn50v66cICuPqUFp4NdAmAV/H36icXr0N5xn4sEYh+/PFHlZSUKCIiwqM9IiJC+/btK9N/zpw5mjlzZpn26Ohon9UIAIC3OBYEuoKK5fTp03I4HJftY4lAZNbkyZOVnJzsXi8tLdWJEydUt25d2Wy2AFbmGy6XS9HR0fr222+5JehjXGv/4Vr7D9faf7jW5hiGodOnT6thw4a/2dcSgahevXoKDg5WXl6eR3teXp4iIyPL9Lfb7bLb7R5tYWFhviyxQggNDeUfmJ9wrf2Ha+0/XGv/4VqX32+NDF1kicfuQ0JCFBsbq/T0dHdbaWmp0tPTFRcXF8DKAABARWCJESJJSk5O1tChQ3XDDTfopptu0oIFC3TmzBkNHz480KUBAIAAs0wgeuCBB/TDDz9o2rRpcjqd6tChg1atWlVmorUV2e12TZ8+vcxtQngf19p/uNb+w7X2H66171jmPUQAAAC/xhJziAAAAC6HQAQAACyPQAQAACyPQAQAACyPQGRRzz77rG6++WbVqFGj3C+dHDZsmGw2m8fSq1cv3xZ6FbiSa20YhqZNm6aoqChVr15d8fHx+vrrr31b6FXgxIkTGjx4sEJDQxUWFqbExEQVFBRcdp/bbrutzO/1o48+6qeKK4+XX35ZTZo0UbVq1dS5c2dt2bLlsv3ff/99tWzZUtWqVVPbtm31ySef+KnSys/MtU5NTS3z+1utWjU/Vnv1IBBZVFFRke677z6NHj3a1H69evXS0aNH3cs///lPH1V49biSa52SkqKFCxdq0aJF2rx5s2rWrKmEhASdP3/eh5VWfoMHD1Z2drbS0tK0YsUKrV+/XqNGjfrN/UaOHOnxe52SkuKHaiuPd999V8nJyZo+fbr++9//qn379kpISNCxY8cu2f/LL7/Ugw8+qMTERG3fvl39+/dX//79tXv3bj9XXvmYvdbST2+t/vnv7zfffOPHiq8iBixt8eLFhsPhKFffoUOHGv369fNpPVez8l7r0tJSIzIy0vj73//ubjt16pRht9uNf/7znz6ssHLbs2ePIcnYunWru+3TTz81bDab8f333//qfrfeeqvxxBNP+KHCyuumm24ykpKS3OslJSVGw4YNjTlz5lyy//3332/07dvXo61z587Gn//8Z5/WeTUwe63N/D8cl8cIEUxZt26dGjRooBYtWmj06NE6fvx4oEu66uTk5MjpdCo+Pt7d5nA41LlzZ2VkZASwsootIyNDYWFhuuGGG9xt8fHxCgoK0ubNmy+779tvv6169eqpTZs2mjx5ss6ePevrciuNoqIiZWZmevw+BgUFKT4+/ld/HzMyMjz6S1JCQgK/v7/hSq61JBUUFKhx48aKjo5Wv379lJ2d7Y9yrzqWeVM1fr9evXppwIABiomJ0cGDB/XXv/5VvXv3VkZGhoKDgwNd3lXD6XRKUpm3qEdERLi3oSyn06kGDRp4tFWpUkXh4eGXvW4PPfSQGjdurIYNG2rnzp2aOHGi9u/fr2XLlvm65Erhxx9/VElJySV/H/ft23fJfZxOJ7+/V+BKrnWLFi305ptvql27dsrPz9e8efN08803Kzs7W40aNfJH2VcNRoiuIpMmTSozue6Xy6/9oyqPQYMG6e6771bbtm3Vv39/rVixQlu3btW6deu8dxKVhK+vNf6Pr6/1qFGjlJCQoLZt22rw4MFasmSJPvzwQx08eNCLZwH4RlxcnIYMGaIOHTro1ltv1bJly1S/fn39z//8T6BLq3QYIbqKjB8/XsOGDbtsn6ZNm3rt85o2bap69erpwIED6tGjh9eOWxn48lpHRkZKkvLy8hQVFeVuz8vLU4cOHa7omJVZea91ZGRkmYmnFy5c0IkTJ9zXtDw6d+4sSTpw4ICuvfZa0/VeberVq6fg4GDl5eV5tOfl5f3qdY2MjDTVHz+5kmv9S1WrVlXHjh114MABX5R4VSMQXUXq16+v+vXr++3zvvvuOx0/ftzjj7ZV+PJax8TEKDIyUunp6e4A5HK5tHnzZtNPBV4Nynut4+LidOrUKWVmZio2NlaStHbtWpWWlrpDTnlkZWVJkiV/ry8lJCREsbGxSk9PV//+/SVJpaWlSk9P15gxYy65T1xcnNLT0zV27Fh3W1pamuLi4vxQceV1Jdf6l0pKSrRr1y716dPHh5VepQI9qxuB8c033xjbt283Zs6cadSqVcvYvn27sX37duP06dPuPi1atDCWLVtmGIZhnD592njyySeNjIwMIycnx1izZo3RqVMn47rrrjPOnz8fqNOoFMxea8MwjLlz5xphYWHGv//9b2Pnzp1Gv379jJiYGOPcuXOBOIVKo1evXkbHjh2NzZs3Gxs2bDCuu+4648EHH3Rv/+6774wWLVoYmzdvNgzDMA4cOGDMmjXL2LZtm5GTk2P8+9//Npo2bWp069YtUKdQIb3zzjuG3W43UlNTjT179hijRo0ywsLCDKfTaRiGYTz88MPGpEmT3P03btxoVKlSxZg3b56xd+9eY/r06UbVqlWNXbt2BeoUKg2z13rmzJnG6tWrjYMHDxqZmZnGoEGDjGrVqhnZ2dmBOoVKi0BkUUOHDjUklVk+//xzdx9JxuLFiw3DMIyzZ88aPXv2NOrXr29UrVrVaNy4sTFy5Ej3P1L8OrPX2jB+evR+6tSpRkREhGG3240ePXoY+/fv93/xlczx48eNBx980KhVq5YRGhpqDB8+3CN45uTkeFz73Nxco1u3bkZ4eLhht9uNZs2aGU899ZSRn58foDOouF566SXjmmuuMUJCQoybbrrJ2LRpk3vbrbfeagwdOtSj/3vvvWc0b97cCAkJMa6//npj5cqVfq648jJzrceOHevuGxERYfTp08f473//G4CqKz+bYRhGQIamAAAAKgieMgMAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAKA35CamqqwsLDL9pkxY4bXv3x33bp1stlsOnXqlFePC6AsAhEAnxs2bJhsNluZpVevXu4+NptNH330UeCKvIwHHnhAX331VaDLAOBDfNs9AL/o1auXFi9e7NFmt9t9/rlFRUUKCQn5XceoXr26qlev7qWKAFREjBAB8Au73a7IyEiPpU6dOpKkJk2aSJLuuece2Ww29/rBgwfVr18/RUREqFatWrrxxhu1Zs2ay37OxVtXr7/+umJiYlStWjVJ0qlTp/TII4+ofv36Cg0N1e23364dO3a499uxY4e6d++u2rVrKzQ0VLGxsdq2bZukS98ymzt3riIiIlS7dm0lJibq/PnzHttvu+02jR071qOtf//+GjZsmHv9f//3f3XDDTeodu3aioyM1EMPPaRjx46V53IC8DICEYCA27p1qyRp8eLFOnr0qHu9oKBAffr0UXp6urZv365evXrprrvuUm5u7mWPd+DAAf3rX//SsmXLlJWVJUm67777dOzYMX366afKzMxUp06d1KNHD504cUKSNHjwYDVq1Ehbt25VZmamJk2apKpVq17y+O+9955mzJihv/3tb9q2bZuioqL0yiuvmD7v4uJizZ49Wzt27NBHH32kw4cPewQmAP7DLTMAfrFixQrVqlXLo+2vf/2r/vrXv6p+/fqSpLCwMEVGRrq3t2/fXu3bt3evz549Wx9++KE+/vhjjRkz5lc/q6ioSEuWLHEfd8OGDdqyZYuOHTvmvk03b948ffTRR/rggw80atQo5ebm6qmnnlLLli0lSdddd92vHn/BggVKTExUYmKiJOmZZ57RmjVryowS/ZYRI0a4f27atKkWLlyoG2+8UQUFBWWuFQDfYoQIgF90795dWVlZHsujjz562X0KCgr05JNPqlWrVgoLC1OtWrW0d+/e3xwhaty4sTsMST/dDisoKFDdunVVq1Yt95KTk6ODBw9KkpKTk/XII48oPj5ec+fOdbdfyt69e9W5c2ePtri4uN+6BGVkZmbqrrvu0jXXXKPatWvr1ltvlaTfPD8A3scIEQC/qFmzppo1a2ZqnyeffFJpaWmaN2+emjVrpurVq+vee+9VUVHRb37WzxUUFCgqKkrr1q0r0/fi3KAZM2booYce0sqVK/Xpp59q+vTpeuedd3TPPfeYqvmioKAgGYbh0VZcXOz++cyZM0pISFBCQoLefvtt1a9fX7m5uUpISPjN8wPgfQQiABVC1apVVVJS4tG2ceNGDRs2zB1KCgoKdPjwYdPH7tSpk5xOp6pUqeKesH0pzZs3V/PmzTVu3Dg9+OCDWrx48SUDUatWrbR582YNGTLE3bZp0yaPPvXr19fRo0fd6yUlJdq9e7e6d+8uSdq3b5+OHz+uuXPnKjo6WpLck7gB+B+3zAD4RWFhoZxOp8fy448/urc3adJE6enpcjqdOnnypKSf5vFcnBi9Y8cOPfTQQyotLTX92fHx8YqLi1P//v312Wef6fDhw/ryyy/19NNPa9u2bTp37pzGjBmjdevW6ZtvvtHGjRu1detWtWrV6pLHe+KJJ/Tmm29q8eLF+uqrrzR9+nRlZ2d79Ln99tu1cuVKrVy5Uvv27dPo0aM9XrB4zTXXKCQkRC+99JIOHTqkjz/+WLNnzzZ9bgC8g0AEwC9WrVqlqKgoj6Vr167u7c8//7zS0tIUHR2tjh07SpLmz5+vOnXq6Oabb9Zdd92lhIQEderUyfRn22w2ffLJJ+rWrZuGDx+u5s2ba9CgQfrmm28UERGh4OBgHT9+XEOGDFHz5s11//33q3fv3po5c+Ylj/fAAw9o6tSpmjBhgmJjY/XNN99o9OjRHn1GjBihoUOHasiQIbr11lvVtGlT9+iQ9NMIUmpqqt5//321bt1ac+fO1bx580yfGwDvsBm/vMkNAABgMYwQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy/v/29DulK71QyoAAAAASUVORK5CYII=", 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.9.18' requires the ipykernel package.\n", + "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n", + "\u001b[1;31mCommand: '/bin/python3 -m pip install ipykernel -U --user --force-reinstall'" + ] } ], "source": [ @@ -468,7 +388,7 @@ "\n", " resphi = np.concatenate((resphi, delta_phi))\n", "\n", - "plt.hist(relresE)\n", + "plt.hist(relresE, histtype='step', color='#67c4ce', linestyle='--')\n", "plt.ylabel('Number of neutral particles')\n", "plt.xlabel('Relative energy residual')\n", "plt.show()\n", @@ -495,18 +415,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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      " - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.9.18' requires the ipykernel package.\n", + "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n", + "\u001b[1;31mCommand: '/bin/python3 -m pip install ipykernel -U --user --force-reinstall'" + ] } ], "source": [ @@ -567,9 +487,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.9.18' requires the ipykernel package.\n", + "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n", + "\u001b[1;31mCommand: '/bin/python3 -m pip install ipykernel -U --user --force-reinstall'" + ] + } + ], "source": [ "def plot_condensation(event_id, pred_list, t_list, feature_list, location='/work/friemer/hgcalml/condensationplots/condensation'):\n", " coords = pred_list[event_id]['pred_ccoords']\n", @@ -606,26 +537,59 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "1001 1001\n" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.9.18' requires the ipykernel package.\n", + "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n", + "\u001b[1;31mCommand: '/bin/python3 -m pip install ipykernel -U --user --force-reinstall'" ] } ], "source": [ - "print(len(df['event_id'].unique()), len(t_list))" + "def plotDistributions(t_list, feature_list):\n", + " n_cells = []\n", + " n_tracks = []\n", + " photons = []\n", + " neutral_hadrons = []\n", + " charged_hadrons = []\n", + " for i in range(len(t_list)):\n", + " n_tracks.append(np.sum(np.abs(feature_list[i]['recHitID'])))\n", + " n_cells.append(len(t_list[i]['truthHitAssignementIdx'])-n_tracks[-1])\n", + " photons.append(np.sum(t_list[i]['truthHitAssignedPIDs']==22))\n", + " neutral_hadrons.append(np.sum(t_list[i]['truthHitAssignedPIDs'].isin([130,310,311,2112,-2112,3122,-3122,3322,-3322])))\n", + " charged_hadrons.append(np.sum(t_list[i]['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312])))\n", + " #create boxplots for the distributions\n", + " fig = plt.figure(figsize=(12, 12))\n", + " plt.boxplot([n_cells, n_tracks, photons, neutral_hadrons, charged_hadrons])\n", + " plt.set_xticklabels(['n_cells', 'n_tracks', 'photons', 'neutral_hadrons', 'charged_hadrons'])\n", + " plt.set_ylabel('Number of particles')\n", + " plt.set_title('Number of particles per event')\n", + "\n", + "plotDistributions(t_list, feature_list) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.9.18' requires the ipykernel package.\n", + "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n", + "\u001b[1;31mCommand: '/bin/python3 -m pip install ipykernel -U --user --force-reinstall'" + ] + } + ], "source": [] } ], @@ -645,7 +609,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py index 0dd88a89..5537c9b7 100644 --- a/scripts/plot_cocoa.py +++ b/scripts/plot_cocoa.py @@ -9,6 +9,7 @@ import os import plotly.express as px import argparse +from scipy.stats import norm def calc_efficiencies(df, bins, mask=None): @@ -163,7 +164,7 @@ def plot_efficency_and_fakerate(df, bins): fake_rate.append(fake) fake_rate_err.append(fake_err) - yeff, yerr_eff, yfake, yerr_fake, = np.array(efficiencies)*100, np.array(efficiencies_err)*100, np.array(fake_rate)*100, np.array(fake_rate_err)*100 + yeff, yerr_eff, yfake, yerr_fake, = np.array(efficiencies), np.array(efficiencies_err), np.array(fake_rate), np.array(fake_rate_err) # Calculate the bin positions and widths binwidth = bins[1:] - bins[:-1] @@ -171,15 +172,15 @@ def plot_efficency_and_fakerate(df, bins): x_err = binwidth / 2 fig_eff = plt.figure() - plt.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o', color='red') - plt.xticks(bins) - plt.xticklabels(bins, fontsize=10) + plt.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o') plt.xlim(bins[0], bins[-1]) - plt.xlabel('Energy [GeV]', fontsize=10) + #plt.xscale('log') + #plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + plt.xticks([1,5,10,20,30,50]) + plt.xlabel('Energy [GeV]') - yticks1 = np.round(np.arange(40, 101, 20), 1) - plt.yticks(yticks1) - plt.yticklabels([f"{y}%" for y in yticks1], fontsize=10) + # yticks1 = np.round(np.arange(40, 101, 20), 1) + # plt.yticks(yticks1) plt.ylabel('Efficiency') plt.grid(alpha=0.4) @@ -187,15 +188,16 @@ def plot_efficency_and_fakerate(df, bins): plt.close() fig_fake = plt.figure() - plt.errorbar(x_pos, yfake, xerr=x_err, yerr=yerr_fake, fmt='o', color='red') - plt.xticks(bins) - plt.xticklabels(bins, fontsize=10) + plt.errorbar(x_pos, yfake, xerr=x_err, yerr=yerr_fake, fmt='o') plt.xlim(bins[0], bins[-1]) - plt.xlabel('Energy [GeV]', fontsize=10) - - yticks1 = np.round(np.arange(0, 21, 5), 1) - plt.yticks(yticks1) - plt.yticklabels([f"{y}%" for y in yticks1], fontsize=10) + #plt.xscale('log') + #plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + plt.xticks([1,5,10,20,30,50]) + plt.xlabel('Energy [GeV]') + + # yticks2 = np.round(np.arange(0, 21, 5), 1) + # plt.yticks(yticks2) + plt.ylim(bottom=0) plt.ylabel('Fake rate') plt.grid(alpha=0.4) @@ -246,13 +248,13 @@ def calc_classification_p(df, bins, mask): def plot_classification_p(df, bins): ycorr_photon, yerr_corr_photon = calc_classification_p(df, bins, 0) - ycorr_photon, yerr_corr_photon = ycorr_photon*100, yerr_corr_photon*100 + #ycorr_photon, yerr_corr_photon = ycorr_photon*100, yerr_corr_photon*100 ycorr_nh, yerr_corr_nh = calc_classification_p(df, bins, 1) - ycorr_nh, yerr_corr_nh = ycorr_nh*100, yerr_corr_nh*100 + #ycorr_nh, yerr_corr_nh = ycorr_nh*100, yerr_corr_nh*100 ycorr_ch, yerr_corr_ch = calc_classification_p(df, bins, 2) - ycorr_ch, yerr_corr_ch = ycorr_ch*100, yerr_corr_ch*100 + #ycorr_ch, yerr_corr_ch = ycorr_ch*100, yerr_corr_ch*100 # Calculate the bin positions and widths binwidth = bins[1:] - bins[:-1] @@ -261,22 +263,22 @@ def plot_classification_p(df, bins): # Create the plots fig_classification = plt.figure() - plt.errorbar(x_pos, ycorr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', color='red', label='Photon') - plt.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', color='blue', label='Neutral Hadron') - plt.errorbar(x_pos, ycorr_ch, xerr=x_err, yerr=yerr_corr_ch, fmt='o', color='green', label='Charged Hadron') + plt.errorbar(x_pos, ycorr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', label='Photon') + plt.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', label='Neutral Hadron') + plt.errorbar(x_pos, ycorr_ch, xerr=x_err, yerr=yerr_corr_ch, fmt='o', label='Charged Particle') plt.legend() plt.grid(alpha=0.4) - plt.xticks(bins) - plt.xticklabels(bins, fontsize=10) plt.xlim(bins[0], bins[-1]) - plt.xlabel('Energy [GeV]', fontsize=10) + #plt.xscale('log') + #plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + plt.xticks([1,5,10,20,30,50]) + plt.xlabel('Energy [GeV]') - yticks1 = np.round(np.arange(40, 101, 20), 1) - plt.yticks(yticks1) - plt.yticklabels([f"{y}%" for y in yticks1], fontsize=10) - plt.ylabel('Probability of predicting correct class') + # yticks = np.round(np.arange(0.4, 101, 20), 1) + # plt.yticks(yticks) + plt.ylabel('Probability of correct class') plt.close() @@ -284,11 +286,12 @@ def plot_classification_p(df, bins): -def plot_jet_metrics(df): +def plot_jet_metrics_based_on_particles(df, datasetname='QuarkJet'): #Jet metrics reseta = [] resphi = [] relresE = [] + relrespt = [] nParicles_pred = [] nParicles_truth = [] @@ -314,17 +317,22 @@ def plot_jet_metrics(df): reseta.append(t_eta-pred_eta) t_phi = np.mean(np.arctan2(df[mask]['truthHitAssignedY'], df[mask]['truthHitAssignedX'])) - pred_phi = df[mask]['pred_pos'].apply(lambda x: np.arctan2(x[1], x[0])).mean() + pred_phi = df[mask]['pred_pos'].mean() delta_phi = calc_deltaphi(t_phi, pred_phi) resphi.append(delta_phi) - + + jet_pt_truth = jet_E_truth/np.cosh(t_eta) + jet_pt_pred = jet_E_pred/np.cosh(pred_eta) + relrespt.append((jet_pt_truth-jet_pt_pred)/jet_pt_truth) + + print(datasetname) nParticle_bins = np.linspace(0, 30, 31) fig_jet_nparticle = plt.figure() - plt.hist(nParicles_truth, bins=nParticle_bins, label='Truth', color='#fee7ae') + plt.hist(nParicles_truth, bins=nParticle_bins, label='Truth', color='orange', alpha=0.5)#color='#fee7ae') plt.grid(alpha=0.4) - plt.hist(nParicles_pred, bins=nParticle_bins, label='Prediction', histtype='step', color='#67c4ce', linestyle='--') - plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - plt.ylabel('Events', fontsize=20) + plt.hist(nParicles_pred, bins=nParticle_bins, label='Prediction', histtype='step')#color='#67c4ce', linestyle='--') + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.ylabel('Number of events', fontsize=20) plt.xlabel('Jet nConstituents', fontsize=20) plt.legend(fontsize=10) plt.xticks(np.linspace(0, 30, 7)) @@ -332,9 +340,10 @@ def plot_jet_metrics(df): relres_bins = np.linspace(-1, 1, 51) fig_jet_relresE = plt.figure() - plt.hist(np.clip(relresE, -1, 1), bins=relres_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.hist(np.clip(relresE, -1, 1), bins=relres_bins, histtype='step')#color='#67c4ce', linestyle='--') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(relresE, relres_bins) plt.ylabel('Number of events', fontsize=20) plt.xlabel('rel. res. Cal. Jet Energy', fontsize=20) plt.xticks(np.linspace(-1, 1, 5)) @@ -343,24 +352,188 @@ def plot_jet_metrics(df): res_bins = np.linspace(-0.2, 0.2, 51) fig_jet_reseta = plt.figure() - plt.hist(np.clip(reseta,-0.2,0.2), bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.hist(np.clip(reseta,-0.2,0.2), bins=res_bins, histtype='step')# color='#67c4ce', linestyle='--') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(reseta, res_bins) plt.ylabel('Number of events', fontsize=20) plt.xlabel('Jet $\\Delta \\eta$', fontsize=20) plt.xticks(np.linspace(-0.2, 0.2, 5)) plt.close() fig_jet_resphi = plt.figure() - plt.hist(np.clip(resphi, -0.2, 0.2), bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.hist(np.clip(resphi, -0.2, 0.2), bins=res_bins, histtype='step')#color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(resphi, res_bins) + plt.ylabel('Number of events', fontsize=20) + plt.xlabel('Jet $\\Delta \\phi$', fontsize=20) + plt.xticks(np.linspace(-0.2, 0.2, 5)) + plt.close() + + fig_jet_pt = plt.figure() + plt.hist(relrespt, bins=relres_bins, histtype='step') plt.grid(alpha=0.4) plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(relrespt, relres_bins) plt.ylabel('Number of events', fontsize=20) + plt.xlabel('rel. res. Cal. Jet $p_T$', fontsize=20) + plt.xticks(np.linspace(-1, 1, 5)) + plt.close() + + return fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi, fig_jet_pt + +def correct_jet_pt(jets_df, bins): + + corrected_jets_df = jets_df + for i in range(len(bins) - 1): + mask_bintruth = np.logical_and( + jets_df['true_pt'] >= bins[i], + jets_df['true_pt'] < bins[i + 1]) + matched = np.logical_and( + jets_df['matched'], + mask_bintruth) + relrespt = [] + for j in range(len(jets_df['event_id'].unique())): + event_id_mask = jets_df['event_id'] == j + mask = np.logical_and(event_id_mask, matched) + + jet_pt_truth = jets_df[mask]['true_pt'] + jet_pt_pred = jets_df[mask]['pred_pt'] + relrespt.append((jet_pt_truth-jet_pt_pred)/jet_pt_truth) + + relres_bins = np.linspace(-1, 1, 51) + relrespt = np.concatenate(relrespt) + fig_jet_pt = plt.figure() + plt.hist(np.clip(relrespt, -1,1), bins=relres_bins, histtype='step') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(relrespt, relres_bins) + plt.ylabel('Number of jets', fontsize=20) + plt.xlabel('rel. res. Cal. Jet $p_T$', fontsize=20) + plt.xticks(np.round(np.linspace(-1, 1, 9), 2), fontsize=12) + plt.savefig(f'/work/friemer/hgcalml/testplots/jet_pt_{bins[i]}.png') + plt.close() + + corr_factor = np.mean(relrespt) + print('Correction factor:', corr_factor) + corrected_jets_df.loc[mask_bintruth, 'pred_pt'] = corrected_jets_df.loc[mask_bintruth, 'pred_pt']*(1+corr_factor) + return corrected_jets_df + + +def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): + #Jet metrics + reseta = [] + resphi = [] + relrespt = [] + nParicles_pred = [] + nParicles_truth = [] + + jets_df = correct_jet_pt(jets_df, [0,5,10,20,30,50,100, 1000]) + + for i in range(len(jets_df['event_id'].unique())): + matched = jets_df['matched'] + event_id_mask = jets_df['event_id'] == i + mask = np.logical_and(event_id_mask, matched) + + nParicles_truth.append(jets_df[mask]['true_n_constituents']) + nParicles_pred.append(jets_df[mask]['pred_n_constituents']) + + t_eta = jets_df[mask]['true_eta'] + pred_eta = jets_df[mask]['pred_eta'] + reseta.append(t_eta-pred_eta) + + t_phi = jets_df[mask]['true_phi'] + pred_phi = jets_df[mask]['pred_phi'] + delta_phi = calc_deltaphi(t_phi, pred_phi) + resphi.append(delta_phi) + + jet_pt_truth = jets_df[mask]['true_pt'] + jet_pt_pred = jets_df[mask]['pred_pt'] + relrespt.append((jet_pt_truth-jet_pt_pred)/jet_pt_truth) + + + print('Total unmatched jets:', np.sum([jets_df['matched'] == False])) + + nParicles_truth = np.concatenate(nParicles_truth) + nParicles_pred = np.concatenate(nParicles_pred) + reseta = np.concatenate(reseta) + resphi = np.concatenate(resphi) + relrespt = np.concatenate(relrespt) + + nParticle_bins = np.linspace(0, 30, 31) + fig_jet_nparticle = plt.figure() + plt.hist(nParicles_truth, bins=nParticle_bins, label='Truth', color='orange', alpha=0.5)#color='#fee7ae') + plt.grid(alpha=0.4) + plt.hist(nParicles_pred, bins=nParticle_bins, label='Prediction', histtype='step')#color='#67c4ce', linestyle='--') + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.ylabel('Number of jets', fontsize=20) + plt.xlabel('Jet nConstituents', fontsize=20) + plt.legend(fontsize=10) + plt.xticks(np.linspace(0, 30, 7)) + plt.close() + + res_bins = np.linspace(-0.2, 0.2, 51) + fig_jet_reseta = plt.figure() + plt.hist(np.clip(reseta,-0.2,0.2), bins=res_bins, histtype='step')# color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(reseta, res_bins) + plt.ylabel('Number of jets', fontsize=20) + plt.xlabel('Jet $\\Delta \\eta$', fontsize=20) + plt.xticks(np.linspace(-0.2, 0.2, 5)) + plt.close() + + fig_jet_resphi = plt.figure() + plt.hist(np.clip(resphi, -0.2, 0.2), bins=res_bins, histtype='step')#color='#67c4ce', linestyle='--') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(resphi, res_bins) + plt.ylabel('Number of jets', fontsize=20) plt.xlabel('Jet $\\Delta \\phi$', fontsize=20) plt.xticks(np.linspace(-0.2, 0.2, 5)) plt.close() - return fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi + relres_bins = np.linspace(-1, 1, 51) + fig_jet_pt = plt.figure() + plt.hist(np.clip(relrespt, -1,1), bins=relres_bins, histtype='step') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(relrespt, relres_bins) + plt.ylabel('Number of jets', fontsize=20) + plt.xlabel('rel. res. Cal. Jet $p_T$', fontsize=20) + plt.xticks(np.round(np.linspace(-1, 1, 9), 2), fontsize=12) + plt.close() + + return fig_jet_nparticle, fig_jet_reseta, fig_jet_resphi, fig_jet_pt + + +def write_mean_std_displayed(data, bins): + #remove inf and Nan + data = np.array(data, dtype=np.float64) + data = data[np.isfinite(data)] + data = data[np.isnan(data) == False] + + displayed = np.sum(np.logical_and(data >= bins[0], data < bins[-1]))/len(data) + mean= np.mean(data) + std = np.std(data) + + #Fit a normal distribution to the data + data = data[np.logical_and(data >= bins[0], data < bins[-1])] + mu, sigma = norm.fit(data) + + textstr = '\n'.join(( + r'$\mathrm{Mean}=%.2f$' % (mean, ), + r'$\mathrm{Std\ }=%.2f$' % (std, ), + r'$\mu=%.2f$' % (mu, ), + r'$\sigma=%.2f$' % (sigma, ), + r'$\mathrm{Displayed}=%.2f$' % (displayed, ))) + + # Add the text box + plt.text(0.95, 0.95, textstr, transform=plt.gca().transAxes, + fontsize=12, verticalalignment='top', horizontalalignment='right', + bbox=dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')) + def calc_matched_mask(df): has_truth = np.isnan(df['truthHitAssignedEnergies']) == False @@ -381,6 +554,7 @@ def plot_particle_metrics(df): reseta = [] resphi = [] relresE = [] + relrespt = [] for i in range(len(df['event_id'].unique())): mask = np.logical_and(df['event_id'] == i, matched) @@ -399,11 +573,17 @@ def plot_particle_metrics(df): pred_phi = df[mask]['pred_pos'].apply(lambda x: np.arctan2(x[1], x[0])) delta_phi = calc_deltaphi(t_phi, pred_phi) resphi = np.concatenate((resphi, delta_phi)) + + truth_pt = truth_e/np.cosh(t_eta) + pred_pt = pred_e/np.cosh(pred_eta) + newrelrespt = ((truth_pt-pred_pt)/truth_pt).to_numpy() + relrespt = np.concatenate((relrespt, newrelrespt)) relres_bins = np.linspace(-1, 1, 51) fig_relresE = plt.figure() - plt.hist(np.clip(relresE, -1,1), bins=relres_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.hist(np.clip(relresE, -1,1), bins=relres_bins, histtype='step')#color='#67c4ce', linestyle='--') plt.grid(alpha=0.4) + write_mean_std_displayed(relresE, relres_bins) plt.xlabel('rel. res. Neutral Particle Energy', fontsize=20) plt.ylabel('Number of neutral particles', fontsize=20) plt.xticks(np.linspace(-1, 1, 5)) @@ -411,27 +591,102 @@ def plot_particle_metrics(df): res_bins = np.linspace(-0.4, 0.4, 51) fig_reseta = plt.figure() - plt.hist(np.clip(reseta, -0.4, 0.4), bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.hist(np.clip(reseta, -0.4, 0.4), bins=res_bins, histtype='step')#color='#67c4ce', linestyle='--') plt.grid(alpha=0.4) + write_mean_std_displayed(reseta, res_bins) plt.xlabel('Neutral Particle $\\Delta \\eta$', fontsize=20) plt.ylabel('Number of neutral particles', fontsize=20) plt.xticks(np.linspace(-0.4, 0.4, 5)) plt.close() fig_resphi = plt.figure() - plt.hist(np.clip(resphi, -0.4, 0.4), bins=res_bins, histtype='step', color='#67c4ce', linestyle='--') + plt.hist(np.clip(resphi, -0.4, 0.4), bins=res_bins, histtype='step')#color='#67c4ce', linestyle='--') plt.grid(alpha=0.4) + write_mean_std_displayed(resphi, res_bins) plt.xlabel('Neutral Particle $\\Delta \\phi$', fontsize=20) plt.ylabel('Number of neutral particles', fontsize=20) plt.xticks(np.linspace(-0.4, 0.4, 5)) plt.close() + + fig_pt = plt.figure() + plt.hist(np.clip(relrespt, -1, 1), bins=relres_bins, histtype='step') + plt.grid(alpha=0.4) + write_mean_std_displayed(relrespt, relres_bins) + plt.xlabel('rel. res. Neutral Particle $p_T$', fontsize=20) + plt.ylabel('Number of neutral particles', fontsize=20) + plt.xticks(np.linspace(-1, 1, 5)) + plt.close() + + return fig_relresE, fig_reseta, fig_resphi, fig_pt + +def calc_energy_resolution(df, bins=None): + matched = calc_matched_mask(df) + + ratios = [] + ratios_err= [] + diffs = [] + diffs_err = [] - return fig_relresE, fig_reseta, fig_resphi + for i in range(len(bins) - 1): + mask_truth_bin = np.logical_and( + df['truthHitAssignedEnergies'] >= bins[i], + df['truthHitAssignedEnergies'] < bins[i + 1]) + mask_bin = np.logical_and(mask_truth_bin, matched) + diff = np.abs(df['pred_energy'][mask_bin] - df['truthHitAssignedEnergies'][mask_bin]) + diffs.append(np.mean(diff)) + diffs_err.append(np.std(diff) / np.sqrt(len(diff))) + + ratio = df['pred_energy'][mask_bin] / df['truthHitAssignedEnergies'][mask_bin] + ratios.append(np.mean(ratio)) + ratios_err.append(np.std(ratio) / np.sqrt(len(ratio))) + + diffs, diffs_err, ratios, ratios_err = np.array(diffs), np.array(diffs_err), np.array(ratios), np.array(ratios_err) + return diffs, diffs_err, ratios, ratios_err +def plot_energy_resolution(df, bins=None): + diffs, diffs_err, ratios, ratios_err = calc_energy_resolution(df, bins) -def plt_energy_resolution(df, bins=None): - has_truth = np.isnan(df['truthHitAssignedEnergies']) == False - has_pred = np.isnan(df['pred_energy']) == False - matched = np.logical_and(has_truth, has_pred) + neutral_mask = df['truthHitAssignedPIDs'].isin([22,130,310,311,2112,-2112,3122,-3122,3322,-3322]) + diffs_neutral, diffs_err_neutral, ratios_neutral, ratios_err_neutral = calc_energy_resolution(df[neutral_mask], bins) + + charged_mask = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) + diffs_charged, diffs_err_charged, ratios_charged, ratios_err_charged = calc_energy_resolution(df[charged_mask], bins) + + # Calculate the bin positions and widths + binwidth = bins[1:] - bins[:-1] + x_pos = bins[:-1] + binwidth / 2 + x_err = binwidth / 2 + # Create the stacked plots of diff and ratio with one errorbar for total, neutral and charged each + fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(12, 8), sharex=True, gridspec_kw={'height_ratios': [3, 1]}) + + ax1.errorbar(x_pos, diffs, xerr=x_err, yerr=diffs_err, fmt='o', label='Total') + ax1.errorbar(x_pos, diffs_neutral, xerr=x_err, yerr=diffs_err_neutral, fmt='o', label='Neutral') + ax1.errorbar(x_pos, diffs_charged, xerr=x_err, yerr=diffs_err_charged, fmt='o', label='Charged') + ax1.set_ylabel('$\sigma E [GeV]$', fontsize=20) + ax1.grid(alpha=0.4) + ax1.legend() + + ax2.errorbar(x_pos, ratios, xerr=x_err, yerr=ratios_err, fmt='o', label='Total') + ax2.errorbar(x_pos, ratios_neutral, xerr=x_err, yerr=ratios_err_neutral, fmt='o', label='Neutral') + ax2.errorbar(x_pos, ratios_charged, xerr=x_err, yerr=ratios_err_charged, fmt='o', label='Charged') + ax2.set_ylabel('$E_{pred} / E_{truth}$', fontsize=20) + ax2.grid(alpha=0.4) + + ydelta = np.round(max(1 - ax2.get_ylim()[0], ax2.get_ylim()[1] - 1),1) + ax2.set_ylim(1 - ydelta, 1 + ydelta) + + ax2.set_xlim(bins[0], bins[-1]) + ax2.set_xscale('log') + ax2.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + xticks = np.round(bins, 0).astype(int) + ax2.set_xticks(xticks) + ax2.set_xticklabels(xticks) + + fig.subplots_adjust(hspace=0.1) + + return fig + +def plot_pt_resolution(df, bins=None): + matched = calc_matched_mask(df) means = [] std_error = [] @@ -441,10 +696,21 @@ def plt_energy_resolution(df, bins=None): df['truthHitAssignedEnergies'] >= bins[i], df['truthHitAssignedEnergies'] < bins[i + 1]) mask_bin = np.logical_and(mask_truth_bin, matched) - diff = np.abs(df['pred_energy'][mask_bin] - df['truthHitAssignedEnergies'][mask_bin]) - #ratios = df['pred_energy'][mask_bin] / df['truthHitAssignedEnergies'][mask_bin] - means.append(np.mean(diff)) - std_error.append(np.std(diff) / np.sqrt(len(diff))) + # diff = np.abs(df['pred_energy'][mask_bin] - df['truthHitAssignedEnergies'][mask_bin]) + # means.append(np.mean(diff)) + # std_error.append(np.std(diff) / np.sqrt(len(diff))) + + + t_eta = df[mask_bin]['truthHitAssignedZ'] + pred_eta = df[mask_bin]['pred_pos'].apply(lambda x: x[2]) + t_energy = df[mask_bin]['truthHitAssignedEnergies'] + pred_energy = df[mask_bin]['pred_energy'] + t_pt = t_energy/np.cosh(t_eta) + pred_pt = pred_energy/np.cosh(pred_eta) + ratios = pred_pt / t_pt + means.append(np.mean(ratios)) + std_error.append(np.std(ratios) / np.sqrt(len(ratios))) + means = np.array(means) std_error = np.array(std_error) @@ -459,25 +725,18 @@ def plt_energy_resolution(df, bins=None): yerr = std_error # plot x, y with error bars - ax1.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='o', color='#67c4ce') + ax1.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='o')#, color='#67c4ce') # set xticks + ax1.set_xlim(bins[0], bins[-1]) + ax1.set_xscale('log') + ax1.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) xticks = np.round(bins, 0).astype(int) ax1.set_xticks(xticks) - ax1.set_xticklabels(xticks, fontsize=10) - - # ymin = np.round(ax1.get_ylim()[0], 1) - # ymax = np.round(ax1.get_ylim()[1], 1) - # ydelta = max(1 - ymin, ymax - 1) - # ax1.set_ylim(1 - ydelta, 1 + ydelta) - # # set yticks to be evenly spaced around 1 - # yticks = np.round(np.arange(1 - ydelta, 1 + ydelta, 0.02), 2) - # yticks = np.round(np.linspace(1 - ydelta, 1 + ydelta, 11), 2) - # ax1.set_yticks(yticks) - # ax1.set_yticklabels(yticks, fontsize=20) + ax1.set_xticklabels(xticks) ax1.grid(alpha=0.4) - ax1.set_ylabel('$\sigma E [GeV]$', fontsize=20) + ax1.set_ylabel('$\sigma p_T [GeV]$', fontsize=20) ax1.set_xlabel('Energy [GeV]', fontsize=20) return fig @@ -572,9 +831,21 @@ def plot_distribution(truth_list, feature_list): plt.close() return fig + +def plot_energydistribution(df): + fig, ax = plt.subplots(figsize=(12, 6)) + ax.hist(df['truthHitAssignedEnergies'], histtype='step', color='blue', label='Truth') + ax.hist(df['pred_energy'], histtype='step', label='Prediction') + ax.set_yscale('log') + ax.set_xscale('log') + ax.set_xlabel('Energy [GeV]') + ax.set_ylabel('Number of showers') + ax.legend() + ax.grid(alpha=0.4) + return fig -def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friemer/hgcalml/testplots/'): +def plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath='/work/friemer/hgcalml/testplots/', datasetname='Quark Jet'): #Create Output directory if not os.path.exists(outputpath): @@ -594,51 +865,68 @@ def plot_everything(df, pred_list, t_list, feature_list, outputpath='/work/friem print('Plotting efficiencies') energy_bins_neutral = np.array([1,2,3,4,5,10,20,30,50]) - fig_eff=plot_efficencies(df, bins=energy_bins_neutral) - fig_eff.savefig(os.path.join(outputpath,'efficiencies.png'), bbox_inches='tight') + fig_eff_overview=plot_efficencies(df, bins=energy_bins_neutral) + fig_eff_overview.savefig(os.path.join(outputpath,'efficienciesOverview_{}.png'.format(datasetname)), bbox_inches='tight') + + fig_eff, fig_fake = plot_efficency_and_fakerate(df, bins=energy_bins_neutral) + fig_eff.savefig(os.path.join(outputpath,'efficiencies_{}.png'.format(datasetname)), bbox_inches='tight') + fig_fake.savefig(os.path.join(outputpath,'fake_rate_{}.png'.format(datasetname)), bbox_inches='tight') print('Plotting jet metrics') - fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi = plot_jet_metrics(df) - fig_jet_nparticle.savefig(os.path.join(outputpath,'jet_nparticle.png'), bbox_inches='tight') - fig_jet_relresE.savefig(os.path.join(outputpath,'jet_relresE.png'), bbox_inches='tight') - fig_jet_reseta.savefig(os.path.join(outputpath,'jet_reseta.png'), bbox_inches='tight') - fig_jet_resphi.savefig(os.path.join(outputpath,'jet_resphi.png'), bbox_inches='tight') + fig_jet_nparticle, fig_jet_reseta, fig_jet_resphi, fig_jet_pt = plot_jet_metrics_from_jets(jets_df, datasetname=datasetname) + fig_jet_nparticle.savefig(os.path.join(outputpath,'jet_nparticle_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_reseta.savefig(os.path.join(outputpath,'jet_reseta_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_resphi.savefig(os.path.join(outputpath,'jet_resphi_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_pt.savefig(os.path.join(outputpath,'jet_pt_{}.png'.format(datasetname)), bbox_inches='tight') print('Plotting particle metrics') mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]) - fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi = plot_particle_metrics(df[mask_neutral]) - fig_neutral_relresE.savefig(os.path.join(outputpath,'neutral_relresE.png'), bbox_inches='tight') - fig_neutral_reseta.savefig(os.path.join(outputpath,'neutral_reseta.png'), bbox_inches='tight') - fig_neutral_resphi.savefig(os.path.join(outputpath,'neutral_resphi.png'), bbox_inches='tight') + fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi, fig_neutral_pt = plot_particle_metrics(df[mask_neutral]) + fig_neutral_relresE.savefig(os.path.join(outputpath,'neutral_relresE_{}.png'.format(datasetname)), bbox_inches='tight') + fig_neutral_reseta.savefig(os.path.join(outputpath,'neutral_reseta_{}.png'.format(datasetname)), bbox_inches='tight') + fig_neutral_resphi.savefig(os.path.join(outputpath,'neutral_resphi_{}.png'.format(datasetname)), bbox_inches='tight') + fig_neutral_pt.savefig(os.path.join(outputpath,'neutral_pt_{}.png'.format(datasetname)), bbox_inches='tight') + + fig_class = plot_classification_p(df, energy_bins_neutral) + fig_class.savefig(os.path.join(outputpath,'classification_p_{}.png'.format(datasetname)), bbox_inches='tight') - print('Plotting energy resolution') - energy_bins_charged = np.array([15,20,30,50,200]) - mask_charged = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) - fig_charged_res = plt_energy_resolution(df[mask_charged], bins=energy_bins_charged) - fig_charged_res.savefig(os.path.join(outputpath,'charged_res.png'), bbox_inches='tight') + print('Plotting energy resolution') + energy_bins_combined = np.array([1,5,10,20,30,50,200]) + fig_new_res = plot_energy_resolution(df, bins=energy_bins_combined) + fig_new_res.savefig(os.path.join(outputpath,'energy_resolution_{}.png'.format(datasetname)), bbox_inches='tight') print('Plotting condensation and input') for event_id in range(10): fig = plot_condensation(pred_list[event_id], t_list[event_id], feature_list[event_id], 'condensation') - fig.write_html(os.path.join(outputpath, 'condensation' ,'condensation'+str(event_id)+".html")) + fig.write_html(os.path.join(outputpath, 'condensation' ,'condensation{}_{}.html'.format(event_id, datasetname))) fig = plot_condensation(pred_list[event_id], t_list[event_id], feature_list[event_id], 'input') - fig.write_html(os.path.join(outputpath, 'condensation' ,'input'+str(event_id)+".html")) + fig.write_html(os.path.join(outputpath, 'condensation' ,'input{}_{}.html'.format(event_id, datasetname))) print('Plotting distribution') fig_dist = plot_distribution(t_list, feature_list) - fig_dist.savefig(os.path.join(outputpath, 'distribution.png'), bbox_inches='tight') + fig_dist.savefig(os.path.join(outputpath, 'distribution_{}.png'.format(datasetname)), bbox_inches='tight') + # fig_edist = plot_energydistribution(df) + # fig_edist.savefig(os.path.join(outputpath, 'energydistribution.png'), bbox_inches='tight') + # fig_edist_neutral = plot_energydistribution(df[mask_neutral]) + # fig_edist_neutral.savefig(os.path.join(outputpath, 'energydistribution_neutral.png'), bbox_inches='tight') -def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalml/testplots/'): + # fig_edist_charged = plot_energydistribution(df[mask_charged]) + # fig_edist_charged.savefig(os.path.join(outputpath, 'energydistribution_charged.png'), bbox_inches='tight') + + + + +def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalml/testplots/', datasetname='Quark Jet'): with gzip.open(analysisfilepath, 'rb') as input_file: analysis_data = pickle.load(input_file) df = analysis_data['showers_dataframe'] pred_list = analysis_data['prediction'] t_list = analysis_data['truth'] feature_list = analysis_data['features'] - - plot_everything(df, pred_list, t_list, feature_list, outputpath) + jets_df = analysis_data['jets_dataframe'] + plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath, datasetname=datasetname) if __name__ == '__main__': parser = argparse.ArgumentParser( @@ -648,6 +936,12 @@ def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalm parser.add_argument('outputlocation', help="Output directory for the plots", default='') + parser.add_argument('--gluon', action='store_true', help='Writing Gluon dataset') args = parser.parse_args() - plot_everything_from_file(args.analysisfile, args.outputlocation) + + if args.gluon: + datasetname='Gluon Jet' + else: + datasetname='Quark Jet' + plot_everything_from_file(args.analysisfile, args.outputlocation, datasetname=datasetname) From 052a226a832ccff37ff3c27ab2bcc6914fe2b750 Mon Sep 17 00:00:00 2001 From: Fabian Date: Wed, 28 Aug 2024 16:14:52 +0200 Subject: [PATCH 54/62] Added some comments and changed jet metrics plots --- modules/LossLayers.py | 5 +- modules/datastructures/TrainData_Cocoa.py | 20 +- scripts/analyse_cocoa_predictions.py | 13 +- scripts/plot_cocoa.py | 352 +++++++++++----------- scripts/submitViaHTCondor.py | 3 +- 5 files changed, 206 insertions(+), 187 deletions(-) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 9368249e..ccf8be2c 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3289,7 +3289,8 @@ def calc_energy_correction_factor_loss(self, class LLExtendedObjectCondensation5(LLExtendedObjectCondensation): ''' - Same as `LLExtendedObjecCondensation3` but uses total energy instead of correction as prediction: + Same as `LLExtendedObjecCondensation3` but uses total energy instead of correction as prediction, + position loss for [cos(phi), sin(phi), eta] and classification loss into categories for COCOA ''' @@ -3342,7 +3343,7 @@ def calc_classification_loss(self, orig_t_pid, pred_id, t_is_unique=None, hasuni 0: Photon 1: Neutral Hadron 2: Charged - 3: Ambiguous + 3: Ambiguous """ if self.classification_loss_weight <= 0: return tf.reduce_mean(pred_id,axis=1, keepdims=True) diff --git a/modules/datastructures/TrainData_Cocoa.py b/modules/datastructures/TrainData_Cocoa.py index 71a94314..9f6e6e30 100644 --- a/modules/datastructures/TrainData_Cocoa.py +++ b/modules/datastructures/TrainData_Cocoa.py @@ -1,3 +1,7 @@ +""" +Class for the conversion of the COCOA data to DJC format +start conversion with 'convertDJCFromSource -i /path/to/input -o /path/to/output -c TrainData_Cocoa --gpu' +""" from datastructures.TrainData_NanoML import TrainData_NanoML import uproot import pandas as pd @@ -61,7 +65,6 @@ def convertevent(self, event): #convert particle information to df df_particle= pd.DataFrame() - df_particle['particle_idx'] = np.arange(len(event['particle_e'])) df_particle['t_energy'] = ak.to_dataframe(event['particle_e'], how='outer')/1000 df_particle['t_pid'] = ak.to_dataframe(event['particle_pdgid'], how='outer') @@ -86,12 +89,11 @@ def convertevent(self, event): #join track information to particle information df_track = df_track.join(df_particle, on='t_idx') - #set is Track to -1 or 1 depending on sign of t_pid + #set is Track to -1 or 1 depending on charge of particle df_track['isTrack'] = pdgid_to_charge(df_track['t_pid']) - #raise Error if any istrack is zero + #raise Error if any isTrack is zero (should not happen) if 0 in df_track['isTrack'].values: - raise ValueError("Error in isTrack") - + raise ValueError("Error in isTrack. Unknown PDGID for particle with track.") #Smear true energy for track df_track['recHitEnergy'] = df_track['t_energy'].apply(lambda x: np.random.normal(x, 0.01 * x)) @@ -99,6 +101,7 @@ def convertevent(self, event): #join cell information to truth information df_cell = df_cell.join(df_particle, on='t_idx') + #concat cell and track information df_training = pd.concat([df_cell, df_track], ignore_index=True) #set NaN values (particle information for hits made from background noise to -1) @@ -148,12 +151,13 @@ def converttotrainingdfvec(self, data): #Convert events one by one traindata = np.array([self.convertevent(data[eventnumber]) for eventnumber in np.arange(len(data))], dtype=object) + #Remove broken events (set to False for Testdata) if True: + mask = np.ones(len(traindata), dtype=bool) #Remove all events with an energy lower than 15GeV # E_cutoff = 15000 # E_sum = ak.sum(data['particle_e'], axis=1) # mask = ak.to_numpy(E_sum >= E_cutoff) - mask = np.ones(len(traindata), dtype=bool) #Remove all events with a particle that has multiple clusters for i in range(len(data)): @@ -168,9 +172,9 @@ def converttotrainingdfvec(self, data): mask[i] = mask[i] and energy_outside < 0.1 * energy_total - print("Number of events before removing low energy events: ", len(traindata)) + print("Number of events before cuts: ", len(traindata)) traindata = traindata[mask] - print("Number of events after removing low energy events: ", len(traindata)) + print("Number of events after cuts: ", len(traindata)) #find the row splits rs = np.cumsum([len(df) for df in traindata]) diff --git a/scripts/analyse_cocoa_predictions.py b/scripts/analyse_cocoa_predictions.py index bd0bc1d3..7177084b 100755 --- a/scripts/analyse_cocoa_predictions.py +++ b/scripts/analyse_cocoa_predictions.py @@ -1,9 +1,9 @@ #!/usr/bin/env python3 """ -Analysis script to run of the predictions of the model. +Analysis script adapted from analyse_hgcal_predictions to run of the predictions of the model for the COCOA dataset. +Call with python analyse_cocoa_predictions.py -h for help. """ -import pdb import os import argparse import pickle @@ -16,12 +16,8 @@ import fastjet as fj from scipy.optimize import linear_sum_assignment -from OCHits2Showers import OCHits2ShowersLayer, OCHits2ShowersLayer_HDBSCAN -from OCHits2Showers import process_endcap2, OCGatherEnergyCorrFac2, OCGatherEnergyCorrFac_new +from OCHits2Showers import OCHits2ShowersLayer from ShowersMatcher2 import ShowersMatcher -from hplots.hgcal_analysis_plotter import HGCalAnalysisPlotter -import extra_plots as ep -from visualize_event import dataframe_to_plot, matched_plot from plot_cocoa import plot_everything @@ -83,6 +79,7 @@ def analyse(preddir, print(f"Analyzing event {event_id}") + #Create showers pred_sid, _, alpha_idx, _, _ = hits2showers( predictions_dict['pred_ccoords'], predictions_dict['pred_beta'], @@ -281,7 +278,7 @@ def analyse(preddir, parser.add_argument('-b', help='Beta threshold (default 0.1)', default='0.1') parser.add_argument('-d', help='Distance threshold (default 0.5)', default='0.5') parser.add_argument('-i', help='IOU threshold (default 0.1)', default='0.1') - parser.add_argument('-m', help='Matching mode', default='iou_max') + parser.add_argument('-m', help='Matching mode', default='cocoa') parser.add_argument('--analysisoutpath', help='Will dump analysis data to a file to remake plots without re-running everything.', default='') diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py index 5537c9b7..08d3ea0a 100644 --- a/scripts/plot_cocoa.py +++ b/scripts/plot_cocoa.py @@ -1,3 +1,10 @@ +""" +Script to create evaluation plots for COCOA models +either call plot_everything function from different script like analyse_cocoa_predictions.py +or run this script directly with the following command if analysisfile is already created: +python plot_cocoa.py /path/to/analysisfile /path/to/outputdir +""" + import gzip import pickle import matplotlib.pyplot as plt @@ -10,9 +17,13 @@ import plotly.express as px import argparse from scipy.stats import norm +from scipy.optimize import curve_fit def calc_efficiencies(df, bins, mask=None): + """ + Helper function to calculate the efficiencies, fake rates and classification accuracy for the photons and neutral hadrons + """ efficiencies = [] efficiencies_err = [] fake_rate = [] @@ -71,12 +82,9 @@ def calc_efficiencies(df, bins, mask=None): return np.array(efficiencies), np.array(efficiencies_err), np.array(fake_rate), np.array(fake_rate_err), np.array(corr_class_prob), np.array(corr_class_prob_err) def plot_efficencies(df, bins): - - # Calculate the efficiencies and fake rates for the total - yeff, yerr_eff, yfake, yerr_fake, ycorr, yerr_corr = calc_efficiencies(df, bins) - yeff, yerr_eff, yfake, yerr_fake, ycorr, yerr_corr = \ - yeff*100, yerr_eff*100, yfake*100, yerr_fake*100, ycorr*100, yerr_corr*100 - + """ + Function to plot the efficiencies, fake rates and classification accuracy for the photons and neutral hadrons similar to the HGPFlow paper + """ # Calculate the efficiencies and fake rates for the photons yeff_photon, yerr_eff_photon, yfake_photon, yerr_fake_photon, ycorr_photon, yerr_corr_photon = calc_efficiencies(df, bins, 0) yeff_photon, yerr_eff_photon, yfake_photon, yerr_fake_photon, ycorr_photon, yerr_corr_photon = \ @@ -88,50 +96,54 @@ def plot_efficencies(df, bins): yeff_nh, yerr_eff_nh, yfake_nh, yerr_fake_nh, ycorr_nh, yerr_corr_nh = \ yeff_nh*100, yerr_eff_nh*100, yfake_nh*100, yerr_fake_nh*100, ycorr_nh*100, yerr_corr_nh*100 - - # Calculate the bin positions and widths binwidth = bins[1:] - bins[:-1] x_pos = bins[:-1] + binwidth / 2 x_err = binwidth / 2 # Create the plots - fig, ((ax1, ax2, ax3), (ax4, ax5,ax6), (ax7,ax8,ax9)) = plt.subplots(nrows=3, ncols=3, figsize=(20, 10)) - ax1.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o', color='red', label='Efficiency') - ax1.set_ylabel('efficiency total', fontsize=10) + fig, ((ax2, ax3), (ax5,ax6), (ax8,ax9)) = plt.subplots(nrows=3, ncols=2, figsize=(12, 10)) ax2.errorbar(x_pos, yeff_photon, xerr=x_err, yerr=yerr_eff_photon, fmt='o', color='red', label='Efficiency') ax2.set_ylabel('efficiency gamma', fontsize=10) ax3.errorbar(x_pos, yeff_nh, xerr=x_err, yerr=yerr_eff_nh, fmt='o', color='red', label='Efficiency') ax3.set_ylabel('efficiency n.H.', fontsize=10) + ytickseff = np.round(np.arange(40, 101, 20), 1) + ax2.set_yticks(ytickseff) + ax2.set_yticklabels([f"{y}%" for y in ytickseff], fontsize=10) + ax3.set_yticks(ytickseff) + ax3.set_yticklabels([f"{y}%" for y in ytickseff], fontsize=10) - ax4.errorbar(x_pos, yfake, xerr=x_err, yerr=yerr_fake, fmt='o', color='red', label='Fake rate') - ax4.set_ylabel('fake rate total', fontsize=10) ax5.errorbar(x_pos, yfake_photon, xerr=x_err, yerr=yerr_fake_photon, fmt='o', color='red', label='Fake rate') ax5.set_ylabel('fake rate gamma', fontsize=10) ax6.errorbar(x_pos, yfake_nh, xerr=x_err, yerr=yerr_fake_nh, fmt='o', color='red', label='Fake rate') ax6.set_ylabel('fake rate n.H.', fontsize=10) + yticksfake1 = np.round(np.arange(0, 50, 20), 1) + ax5.set_yticks(yticksfake1) + ax5.set_yticklabels([f"{y}%" for y in yticksfake1], fontsize=10) + yticksfake2 = np.round(np.arange(0, 70, 20), 1) + ax6.set_yticks(yticksfake2) + ax6.set_yticklabels([f"{y}%" for y in yticksfake2], fontsize=10) - ax7.errorbar(x_pos, ycorr, xerr=x_err, yerr=yerr_corr, fmt='o', color='red', label='probability of correct class') - ax7.set_ylabel('p corr total', fontsize=10) ax8.errorbar(x_pos, ycorr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', color='red', label='probability of correct class') ax8.set_ylabel('p corr gamma', fontsize=10) ax9.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', color='red', label='probability of correct class') ax9.set_ylabel('p corr n.H.', fontsize=10) + yticksp = np.round(np.arange(0, 101, 20), 1) + ax8.set_yticks(yticksp) + ax8.set_yticklabels([f"{y}%" for y in yticksp], fontsize=10) + ax9.set_yticks(yticksp) + ax9.set_yticklabels([f"{y}%" for y in yticksp], fontsize=10) - for ax in [ax1, ax2,ax3, ax4, ax5, ax6, ax7, ax8, ax9]: + for ax in [ax2,ax3, ax5, ax6, ax8, ax9]: ax.set_xticks(bins) ax.set_xticklabels(bins, fontsize=10) ax.set_xlim(bins[0], bins[-1]) ax.grid(alpha=0.4) ax.set_xlabel('Energy [GeV]', fontsize=10) - - yticks1 = np.round(np.arange(40, 101, 20), 1) - ax.set_yticks(yticks1) - ax.set_yticklabels([f"{y}%" for y in yticks1], fontsize=10) #ax.set_ylim(20, 101) return fig -def plot_efficency_and_fakerate(df, bins): +def plot_efficency_and_fakerate(df, bins): # Calculate the efficiencies and fake rates for the total efficiencies, efficiencies_err, fake_rate, fake_rate_err = [], [], [], [] @@ -174,39 +186,28 @@ def plot_efficency_and_fakerate(df, bins): fig_eff = plt.figure() plt.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o') plt.xlim(bins[0], bins[-1]) - #plt.xscale('log') - #plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) - plt.xticks([1,5,10,20,30,50]) - plt.xlabel('Energy [GeV]') - - # yticks1 = np.round(np.arange(40, 101, 20), 1) - # plt.yticks(yticks1) - plt.ylabel('Efficiency') - - plt.grid(alpha=0.4) - + plt.xticks(bins) + plt.xlabel('Energy [GeV]') + plt.ylabel('Efficiency') + plt.grid(alpha=0.4) plt.close() fig_fake = plt.figure() plt.errorbar(x_pos, yfake, xerr=x_err, yerr=yerr_fake, fmt='o') plt.xlim(bins[0], bins[-1]) - #plt.xscale('log') - #plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) - plt.xticks([1,5,10,20,30,50]) - plt.xlabel('Energy [GeV]') - - # yticks2 = np.round(np.arange(0, 21, 5), 1) - # plt.yticks(yticks2) - plt.ylim(bottom=0) - plt.ylabel('Fake rate') - - plt.grid(alpha=0.4) - + plt.xticks(bins) + plt.xlabel('Energy [GeV]') + plt.ylim(bottom=0, top=0.1) + plt.ylabel('Fake rate') + plt.grid(alpha=0.4) plt.close() return fig_eff, fig_fake def calc_classification_p(df, bins, mask): + """ + Helper function to calculate the classification accuracy for the photons, neutral hadrons and charged particles + """ corr_class_prob = [] corr_class_prob_err = [] @@ -221,10 +222,8 @@ def calc_classification_p(df, bins, mask): mask_PID_truth = df['truthHitAssignedPIDs'].isin([11,-11,13,-13,211,-211,321,-321,2212,-2212,3112,-3112,3222,-3222,3312,-3312]) else: raise ValueError("mask must be 0, 1 or 2") - mask_PID_pred = df['pred_id_value'].isin([mask]) - - mask_PID_matched = np.logical_and(mask_PID_truth, mask_PID_pred) - + mask_PID_pred = df['pred_id_value'].isin([mask]) + mask_PID_matched = np.logical_and(mask_PID_truth, mask_PID_pred) for i in range(len(bins) - 1): mask_bintruth = np.logical_and( @@ -236,8 +235,7 @@ def calc_classification_p(df, bins, mask): matched = np.logical_and( mask_predicted, - mask_bintruth) - + mask_bintruth) cc_prob = np.sum(matched[mask_PID_matched]) / np.sum(matched[mask_PID_truth]) cc_prob_err = np.sqrt(cc_prob * (1 - cc_prob) / np.sum(matched[mask_PID_truth])) @@ -247,14 +245,9 @@ def calc_classification_p(df, bins, mask): return np.array(corr_class_prob), np.array(corr_class_prob_err) def plot_classification_p(df, bins): - ycorr_photon, yerr_corr_photon = calc_classification_p(df, bins, 0) - #ycorr_photon, yerr_corr_photon = ycorr_photon*100, yerr_corr_photon*100 - - ycorr_nh, yerr_corr_nh = calc_classification_p(df, bins, 1) - #ycorr_nh, yerr_corr_nh = ycorr_nh*100, yerr_corr_nh*100 - + ycorr_photon, yerr_corr_photon = calc_classification_p(df, bins, 0) + ycorr_nh, yerr_corr_nh = calc_classification_p(df, bins, 1) ycorr_ch, yerr_corr_ch = calc_classification_p(df, bins, 2) - #ycorr_ch, yerr_corr_ch = ycorr_ch*100, yerr_corr_ch*100 # Calculate the bin positions and widths binwidth = bins[1:] - bins[:-1] @@ -267,65 +260,65 @@ def plot_classification_p(df, bins): plt.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', label='Neutral Hadron') plt.errorbar(x_pos, ycorr_ch, xerr=x_err, yerr=yerr_corr_ch, fmt='o', label='Charged Particle') - plt.legend() - plt.grid(alpha=0.4) - + plt.legend() + plt.grid(alpha=0.4) plt.xlim(bins[0], bins[-1]) - #plt.xscale('log') - #plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) plt.xticks([1,5,10,20,30,50]) plt.xlabel('Energy [GeV]') - - # yticks = np.round(np.arange(0.4, 101, 20), 1) - # plt.yticks(yticks) - plt.ylabel('Probability of correct class') - + plt.ylabel('Accuracy of classification') plt.close() return fig_classification - - def plot_jet_metrics_based_on_particles(df, datasetname='QuarkJet'): - #Jet metrics + """ + plots the jet metrics under the assumption, that one event is one jet + """ + reseta = [] resphi = [] relresE = [] - relrespt = [] nParicles_pred = [] nParicles_truth = [] + true_pt = [] + pred_pt = [] - matched = calc_matched_mask(df) has_truth = np.isnan(df['truthHitAssignedEnergies']) == False has_pred = np.isnan(df['pred_energy']) == False for i in range(len(df['event_id'].unique())): event_mask = df['event_id'] == i - mask = np.logical_and(event_mask, matched) + truth_mask = np.logical_and(event_mask, has_truth) + pred_mask = np.logical_and(event_mask, has_pred) - jet_E_truth = np.sum(df[mask]['truthHitAssignedEnergies']) - jet_E_pred = np.sum(df[mask]['pred_energy']) + jet_E_truth = np.sum(df[truth_mask]['truthHitAssignedEnergies']) + jet_E_pred = np.sum(df[pred_mask]['pred_energy']) relresE.append((jet_E_truth-jet_E_pred)/jet_E_truth) - pred_event_mask = np.logical_and(event_mask, has_pred) - truth_event_mask = np.logical_and(event_mask, has_truth) - nParicles_pred.append(np.sum(pred_event_mask)) - nParicles_truth.append(np.sum(truth_event_mask)) + nParicles_pred.append(np.sum(pred_mask)) + nParicles_truth.append(np.sum(truth_mask)) - t_eta = np.mean(df[mask]['truthHitAssignedZ']) - pred_eta = df[mask]['pred_pos'].apply(lambda x: x[2]).mean() - reseta.append(t_eta-pred_eta) + t_eta = np.average(df[truth_mask]['truthHitAssignedZ'], weights=df[truth_mask]['truthHitAssignedEnergies']) + pred_eta = np.average(df[pred_mask]['pred_pos'].apply(lambda x: x[2]), weights=df[pred_mask]['pred_energy']) + reseta.append(t_eta - pred_eta) - t_phi = np.mean(np.arctan2(df[mask]['truthHitAssignedY'], df[mask]['truthHitAssignedX'])) - pred_phi = df[mask]['pred_pos'].mean() + t_phi = np.average(np.arctan2(df[truth_mask]['truthHitAssignedY'], df[truth_mask]['truthHitAssignedX']), weights=df[truth_mask]['truthHitAssignedEnergies']) + pred_phi = np.average(df[pred_mask]['pred_pos'].apply(lambda x: np.arctan2(x[1], x[0])), weights=df[pred_mask]['pred_energy']) delta_phi = calc_deltaphi(t_phi, pred_phi) resphi.append(delta_phi) - jet_pt_truth = jet_E_truth/np.cosh(t_eta) - jet_pt_pred = jet_E_pred/np.cosh(pred_eta) - relrespt.append((jet_pt_truth-jet_pt_pred)/jet_pt_truth) + true_pt.append(np.sum(df[truth_mask]['truthHitAssignedEnergies'] / np.cosh(df[truth_mask]['truthHitAssignedZ']))) + pred_pt.append(np.sum(df[pred_mask]['pred_energy'] / np.cosh(df[pred_mask]['pred_pos'].apply(lambda x: x[2])))) - print(datasetname) + #Correct jet pt + jets_df = pd.DataFrame() + jets_df['true_pt'] = true_pt + jets_df['pred_pt'] = pred_pt + jets_df['matched'] = 1 + jets_df = correct_jet_pt(jets_df, [0,5,10,20,30,50,100, 1000]) + relrespt = np.array((jets_df['true_pt'] - jets_df['pred_pt'])/ jets_df['true_pt']) + + #Start plotting nParticle_bins = np.linspace(0, 30, 31) fig_jet_nparticle = plt.figure() plt.hist(nParicles_truth, bins=nParticle_bins, label='Truth', color='orange', alpha=0.5)#color='#fee7ae') @@ -345,10 +338,9 @@ def plot_jet_metrics_based_on_particles(df, datasetname='QuarkJet'): plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) write_mean_std_displayed(relresE, relres_bins) plt.ylabel('Number of events', fontsize=20) - plt.xlabel('rel. res. Cal. Jet Energy', fontsize=20) + plt.xlabel('rel. res. Jet Energy', fontsize=20) plt.xticks(np.linspace(-1, 1, 5)) - plt.close() - + plt.close() res_bins = np.linspace(-0.2, 0.2, 51) fig_jet_reseta = plt.figure() @@ -372,57 +364,73 @@ def plot_jet_metrics_based_on_particles(df, datasetname='QuarkJet'): plt.close() fig_jet_pt = plt.figure() - plt.hist(relrespt, bins=relres_bins, histtype='step') + plt.hist(np.clip(relrespt, -1, 1), bins=relres_bins, histtype='step') plt.grid(alpha=0.4) plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) write_mean_std_displayed(relrespt, relres_bins) plt.ylabel('Number of events', fontsize=20) - plt.xlabel('rel. res. Cal. Jet $p_T$', fontsize=20) + plt.xlabel('rel. res. Jet $p_T$', fontsize=20) plt.xticks(np.linspace(-1, 1, 5)) plt.close() return fig_jet_nparticle, fig_jet_relresE, fig_jet_reseta, fig_jet_resphi, fig_jet_pt -def correct_jet_pt(jets_df, bins): +def correct_jet_pt(jets_df, bins, plotEachBin=False, outputDir='/work/friemer/hgcalml/testplots/'): + """ + Function to calculate a correction factor for the jet pt in bins of pt, fit a correction function and apply it to the jets_df + """ - corrected_jets_df = jets_df + corr_factors = [] for i in range(len(bins) - 1): - mask_bintruth = np.logical_and( - jets_df['true_pt'] >= bins[i], - jets_df['true_pt'] < bins[i + 1]) - matched = np.logical_and( + mask_binpred = np.logical_and( + jets_df['pred_pt'] >= bins[i], + jets_df['pred_pt'] < bins[i + 1]) + mask = np.logical_and( jets_df['matched'], - mask_bintruth) - relrespt = [] - for j in range(len(jets_df['event_id'].unique())): - event_id_mask = jets_df['event_id'] == j - mask = np.logical_and(event_id_mask, matched) - - jet_pt_truth = jets_df[mask]['true_pt'] - jet_pt_pred = jets_df[mask]['pred_pt'] - relrespt.append((jet_pt_truth-jet_pt_pred)/jet_pt_truth) - - relres_bins = np.linspace(-1, 1, 51) - relrespt = np.concatenate(relrespt) - fig_jet_pt = plt.figure() - plt.hist(np.clip(relrespt, -1,1), bins=relres_bins, histtype='step') - plt.grid(alpha=0.4) - plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - write_mean_std_displayed(relrespt, relres_bins) - plt.ylabel('Number of jets', fontsize=20) - plt.xlabel('rel. res. Cal. Jet $p_T$', fontsize=20) - plt.xticks(np.round(np.linspace(-1, 1, 9), 2), fontsize=12) - plt.savefig(f'/work/friemer/hgcalml/testplots/jet_pt_{bins[i]}.png') - plt.close() + mask_binpred) + jet_pt_truth = jets_df[mask]['true_pt'] + jet_pt_pred = jets_df[mask]['pred_pt'] + respt = np.array((jet_pt_truth-jet_pt_pred)) + relrespt = np.array((jet_pt_truth-jet_pt_pred)/jet_pt_truth) + + if plotEachBin: + relres_bins = np.linspace(-1, 1, 51) + fig_jet_pt = plt.figure() + plt.hist(np.clip(relrespt, -1,1), bins=relres_bins, histtype='step') + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + write_mean_std_displayed(relrespt, relres_bins) + plt.ylabel('Number of jets', fontsize=20) + plt.xlabel('rel. res. Cal. Jet $p_T$', fontsize=20) + plt.xticks(np.round(np.linspace(-1, 1, 9), 2), fontsize=12) + plt.savefig(os.path.join(outputDir, f'/jet_pt_{bins[i]}.png')) + plt.close() corr_factor = np.mean(relrespt) - print('Correction factor:', corr_factor) - corrected_jets_df.loc[mask_bintruth, 'pred_pt'] = corrected_jets_df.loc[mask_bintruth, 'pred_pt']*(1+corr_factor) + corr_factors.append(corr_factor) + + #fit function to correction factor over bin middle + corr_factors = np.array(corr_factors) + + bins = np.array(bins) + bin_middle = bins[:-1] + (bins[1:] - bins[:-1])/2 + + mask = np.logical_and(~np.isnan(corr_factors), ~np.isinf(corr_factors)) #Should not be necessary, unless evaluation is performed on very small dataset + + def linearfunc(x, a, b): + return a*x+b + + popt, pcov = curve_fit(linearfunc, bin_middle[mask], corr_factors[mask]) + + corrected_jets_df = jets_df + corrected_jets_df['pred_pt'] = corrected_jets_df['pred_pt']*(1 + linearfunc(corrected_jets_df['pred_pt'], *popt)) return corrected_jets_df def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): - #Jet metrics + """ + plots the jet metrics from the results of jet clustering algorithm + """ reseta = [] resphi = [] relrespt = [] @@ -451,8 +459,7 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): jet_pt_truth = jets_df[mask]['true_pt'] jet_pt_pred = jets_df[mask]['pred_pt'] relrespt.append((jet_pt_truth-jet_pt_pred)/jet_pt_truth) - - + print('Total unmatched jets:', np.sum([jets_df['matched'] == False])) nParicles_truth = np.concatenate(nParicles_truth) @@ -463,9 +470,9 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): nParticle_bins = np.linspace(0, 30, 31) fig_jet_nparticle = plt.figure() - plt.hist(nParicles_truth, bins=nParticle_bins, label='Truth', color='orange', alpha=0.5)#color='#fee7ae') + plt.hist(nParicles_truth, bins=nParticle_bins, label='Truth', color='orange', alpha=0.5) plt.grid(alpha=0.4) - plt.hist(nParicles_pred, bins=nParticle_bins, label='Prediction', histtype='step')#color='#67c4ce', linestyle='--') + plt.hist(nParicles_pred, bins=nParticle_bins, label='Prediction', histtype='step') plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) plt.ylabel('Number of jets', fontsize=20) plt.xlabel('Jet nConstituents', fontsize=20) @@ -475,7 +482,7 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): res_bins = np.linspace(-0.2, 0.2, 51) fig_jet_reseta = plt.figure() - plt.hist(np.clip(reseta,-0.2,0.2), bins=res_bins, histtype='step')# color='#67c4ce', linestyle='--') + plt.hist(np.clip(reseta,-0.2,0.2), bins=res_bins, histtype='step') plt.grid(alpha=0.4) plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) write_mean_std_displayed(reseta, res_bins) @@ -485,7 +492,7 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): plt.close() fig_jet_resphi = plt.figure() - plt.hist(np.clip(resphi, -0.2, 0.2), bins=res_bins, histtype='step')#color='#67c4ce', linestyle='--') + plt.hist(np.clip(resphi, -0.2, 0.2), bins=res_bins, histtype='step') plt.grid(alpha=0.4) plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) write_mean_std_displayed(resphi, res_bins) @@ -509,6 +516,9 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): def write_mean_std_displayed(data, bins): + """ + Helper function to write the mean, std and displayed fraction of the data into the plot + """ #remove inf and Nan data = np.array(data, dtype=np.float64) data = data[np.isfinite(data)] @@ -523,8 +533,8 @@ def write_mean_std_displayed(data, bins): mu, sigma = norm.fit(data) textstr = '\n'.join(( - r'$\mathrm{Mean}=%.2f$' % (mean, ), - r'$\mathrm{Std\ }=%.2f$' % (std, ), + # r'$\mathrm{Mean}=%.2f$' % (mean, ), + # r'$\mathrm{Std\ }=%.2f$' % (std, ), r'$\mu=%.2f$' % (mu, ), r'$\sigma=%.2f$' % (sigma, ), r'$\mathrm{Displayed}=%.2f$' % (displayed, ))) @@ -548,7 +558,7 @@ def calc_deltaphi(t_phi, pred_phi): return delta_phi def plot_particle_metrics(df): - + matched = calc_matched_mask(df) reseta = [] @@ -581,7 +591,7 @@ def plot_particle_metrics(df): relres_bins = np.linspace(-1, 1, 51) fig_relresE = plt.figure() - plt.hist(np.clip(relresE, -1,1), bins=relres_bins, histtype='step')#color='#67c4ce', linestyle='--') + plt.hist(np.clip(relresE, -1,1), bins=relres_bins, histtype='step') plt.grid(alpha=0.4) write_mean_std_displayed(relresE, relres_bins) plt.xlabel('rel. res. Neutral Particle Energy', fontsize=20) @@ -591,7 +601,7 @@ def plot_particle_metrics(df): res_bins = np.linspace(-0.4, 0.4, 51) fig_reseta = plt.figure() - plt.hist(np.clip(reseta, -0.4, 0.4), bins=res_bins, histtype='step')#color='#67c4ce', linestyle='--') + plt.hist(np.clip(reseta, -0.4, 0.4), bins=res_bins, histtype='step') plt.grid(alpha=0.4) write_mean_std_displayed(reseta, res_bins) plt.xlabel('Neutral Particle $\\Delta \\eta$', fontsize=20) @@ -600,7 +610,7 @@ def plot_particle_metrics(df): plt.close() fig_resphi = plt.figure() - plt.hist(np.clip(resphi, -0.4, 0.4), bins=res_bins, histtype='step')#color='#67c4ce', linestyle='--') + plt.hist(np.clip(resphi, -0.4, 0.4), bins=res_bins, histtype='step') plt.grid(alpha=0.4) write_mean_std_displayed(resphi, res_bins) plt.xlabel('Neutral Particle $\\Delta \\phi$', fontsize=20) @@ -622,9 +632,9 @@ def plot_particle_metrics(df): def calc_energy_resolution(df, bins=None): matched = calc_matched_mask(df) - ratios = [] + ratios = [] #Relative deviation of predicted energy from truth energy ratios_err= [] - diffs = [] + diffs = [] #Absolute deviation of predicted energy from truth energy diffs_err = [] for i in range(len(bins) - 1): @@ -696,26 +706,21 @@ def plot_pt_resolution(df, bins=None): df['truthHitAssignedEnergies'] >= bins[i], df['truthHitAssignedEnergies'] < bins[i + 1]) mask_bin = np.logical_and(mask_truth_bin, matched) - # diff = np.abs(df['pred_energy'][mask_bin] - df['truthHitAssignedEnergies'][mask_bin]) - # means.append(np.mean(diff)) - # std_error.append(np.std(diff) / np.sqrt(len(diff))) - - + t_eta = df[mask_bin]['truthHitAssignedZ'] pred_eta = df[mask_bin]['pred_pos'].apply(lambda x: x[2]) t_energy = df[mask_bin]['truthHitAssignedEnergies'] pred_energy = df[mask_bin]['pred_energy'] t_pt = t_energy/np.cosh(t_eta) pred_pt = pred_energy/np.cosh(pred_eta) + ratios = pred_pt / t_pt means.append(np.mean(ratios)) std_error.append(np.std(ratios) / np.sqrt(len(ratios))) - means = np.array(means) std_error = np.array(std_error) - # make boxplots of the ratios for each bin fig, ax1 = plt.subplots(figsize=(12, 6)) binwidth = bins[1:] - bins[:-1] @@ -742,6 +747,10 @@ def plot_pt_resolution(df, bins=None): return fig def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation'): + """ + Plots the condensation space of a event (coordspace='condensation") or the input space (coordspace='input') + """ + if(coordspace == 'condensation'): coords = pred_dict['pred_ccoords'] @@ -750,7 +759,7 @@ def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation' X = coords[:,0] Y = coords[:,1] Z = coords[:,2] - sizefield = 'features' + sizefield = 'beta' elif(coordspace == 'input'): X = feature_dict['recHitX'][:,0] Y = feature_dict['recHitY'][:,0] @@ -764,7 +773,7 @@ def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation' 'Y': Y, 'Z': Z, 't_idx': t_dict['truthHitAssignementIdx'][:,0], - 'features': pred_dict['pred_beta'][:,0], + 'beta': pred_dict['pred_beta'][:,0], 'pdgid': t_dict['truthHitAssignedPIDs'][:,0], 'recHitID': feature_dict['recHitID'][:,0], 'rechit_energy': feature_dict['recHitEnergy'][:,0], @@ -772,7 +781,7 @@ def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation' } eventdf = pd.DataFrame(data) - hover_data = {'X': True, 'Y': True, 'Z': True, 't_idx': True,'features': True, 'pdgid': True,'recHitID': True, 'rechit_energy': True} + hover_data = {'X': True, 'Y': True, 'Z': True, 't_idx': True,'beta': True, 'pdgid': True,'recHitID': True, 'rechit_energy': True} fig = px.scatter_3d(eventdf, x="X", y="Y", z="Z", color="t_idx", @@ -785,6 +794,9 @@ def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation' return fig def plot_distribution(truth_list, feature_list): + """ + Makes boxplots of the number of particles, tracks, hits, etc. in the events + """ n_tracks = [] n_hits = [] n_particles = [] @@ -858,9 +870,7 @@ def plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath='/w if not var in ('yes', 'y'): sys.exit() os.makedirs(os.path.join(outputpath, 'condensation'), exist_ok=True) - - #Create everything - + matplotlib.rcParams.update({'font.size': 20}) print('Plotting efficiencies') @@ -873,12 +883,19 @@ def plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath='/w fig_fake.savefig(os.path.join(outputpath,'fake_rate_{}.png'.format(datasetname)), bbox_inches='tight') print('Plotting jet metrics') - fig_jet_nparticle, fig_jet_reseta, fig_jet_resphi, fig_jet_pt = plot_jet_metrics_from_jets(jets_df, datasetname=datasetname) - fig_jet_nparticle.savefig(os.path.join(outputpath,'jet_nparticle_{}.png'.format(datasetname)), bbox_inches='tight') - fig_jet_reseta.savefig(os.path.join(outputpath,'jet_reseta_{}.png'.format(datasetname)), bbox_inches='tight') - fig_jet_resphi.savefig(os.path.join(outputpath,'jet_resphi_{}.png'.format(datasetname)), bbox_inches='tight') - fig_jet_pt.savefig(os.path.join(outputpath,'jet_pt_{}.png'.format(datasetname)), bbox_inches='tight') - + fig_jet_nparticle_all, fig_jet_relresE_all, fig_jet_reseta_all, fig_jet_resphi_all, fig_jet_pt_all = plot_jet_metrics_based_on_particles(df, datasetname=datasetname) + fig_jet_nparticle_all.savefig(os.path.join(outputpath,'jet_nparticle_all_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_relresE_all.savefig(os.path.join(outputpath,'jet_relresE_all_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_reseta_all.savefig(os.path.join(outputpath,'jet_reseta_all_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_resphi_all.savefig(os.path.join(outputpath,'jet_resphi_all_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_pt_all.savefig(os.path.join(outputpath,'jet_pt_all_{}.png'.format(datasetname)), bbox_inches='tight') + + fig_jet_nparticle_clustered, fig_jet_reseta_clustered, fig_jet_resphi_clustered, fig_jet_pt_clustered = plot_jet_metrics_from_jets(jets_df, datasetname=datasetname) + fig_jet_nparticle_clustered.savefig(os.path.join(outputpath,'jet_nparticle_clustered_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_reseta_clustered.savefig(os.path.join(outputpath,'jet_reseta_clustered_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_resphi_clustered.savefig(os.path.join(outputpath,'jet_resphi_clustered_{}.png'.format(datasetname)), bbox_inches='tight') + fig_jet_pt_clustered.savefig(os.path.join(outputpath,'jet_pt_clustered_{}.png'.format(datasetname)), bbox_inches='tight') + print('Plotting particle metrics') mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]) fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi, fig_neutral_pt = plot_particle_metrics(df[mask_neutral]) @@ -902,9 +919,9 @@ def plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath='/w fig = plot_condensation(pred_list[event_id], t_list[event_id], feature_list[event_id], 'input') fig.write_html(os.path.join(outputpath, 'condensation' ,'input{}_{}.html'.format(event_id, datasetname))) - print('Plotting distribution') - fig_dist = plot_distribution(t_list, feature_list) - fig_dist.savefig(os.path.join(outputpath, 'distribution_{}.png'.format(datasetname)), bbox_inches='tight') + # print('Plotting distribution') + # fig_dist = plot_distribution(t_list, feature_list) + # fig_dist.savefig(os.path.join(outputpath, 'distribution_{}.png'.format(datasetname)), bbox_inches='tight') # fig_edist = plot_energydistribution(df) # fig_edist.savefig(os.path.join(outputpath, 'energydistribution.png'), bbox_inches='tight') @@ -913,35 +930,34 @@ def plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath='/w # fig_edist_neutral.savefig(os.path.join(outputpath, 'energydistribution_neutral.png'), bbox_inches='tight') # fig_edist_charged = plot_energydistribution(df[mask_charged]) - # fig_edist_charged.savefig(os.path.join(outputpath, 'energydistribution_charged.png'), bbox_inches='tight') - - - + # fig_edist_charged.savefig(os.path.join(outputpath, 'energydistribution_charged.png'), bbox_inches='tight') def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalml/testplots/', datasetname='Quark Jet'): + #Load analysis file created by analyse_cocoa_predictions.py with gzip.open(analysisfilepath, 'rb') as input_file: analysis_data = pickle.load(input_file) + df = analysis_data['showers_dataframe'] pred_list = analysis_data['prediction'] t_list = analysis_data['truth'] feature_list = analysis_data['features'] jets_df = analysis_data['jets_dataframe'] + plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath, datasetname=datasetname) if __name__ == '__main__': parser = argparse.ArgumentParser( - 'Create Plots for the COCOA analysis') + 'Create plots for the COCOA analysis') parser.add_argument('analysisfile', help='Filepath to analysis file created by analyse_cocoa_predictions.py containing shower dataframe') parser.add_argument('outputlocation', help="Output directory for the plots", default='') - parser.add_argument('--gluon', action='store_true', help='Writing Gluon dataset') - + parser.add_argument('--gluon', action='store_true', help='Write Gluon dataset instead of Quark dataset in top left corner') args = parser.parse_args() if args.gluon: datasetname='Gluon Jet' else: datasetname='Quark Jet' - plot_everything_from_file(args.analysisfile, args.outputlocation, datasetname=datasetname) + plot_everything_from_file(args.analysisfile, args.outputlocation, datasetname=datasetname) \ No newline at end of file diff --git a/scripts/submitViaHTCondor.py b/scripts/submitViaHTCondor.py index f5506717..117aa81a 100644 --- a/scripts/submitViaHTCondor.py +++ b/scripts/submitViaHTCondor.py @@ -44,7 +44,7 @@ def __str__(self) -> str: 'n' : meta_option('n','TrainJob_'+UEXT), 'f' : meta_option('f', '/work/friemer/hgcalml/HGCalML'), 'cpu': meta_option('cpu', '1'), - 'memory': meta_option('memory', '10 GB'), + 'memory': meta_option('memory', '20 GB'), 'disk': meta_option('disk', '5 GB'), 'gpu': meta_option('gpu', '1'), 'wandb': meta_option('wandb', 'True'), @@ -230,6 +230,7 @@ def __str__(self) -> str: export DEEPJETCORE_SUBPACKAGE=$HGCALML export PATH=$HGCALML/scripts:$PATH export PYTHONPATH=$HGCALML/modules:$PYTHONPATH +export PYTHONPATH=$HGCALML/scripts:$PYTHONPATH export LD_LIBRARY_PATH=$HGCALML/modules:$LD_LIBRARY_PATH export LC_ALL=C.UTF-8 # necessary for wandb export LANG=C.UTF-8 # necessary for wandb From 23d5e5f7f33c7d5595c5cd69c5192037dc920b5e Mon Sep 17 00:00:00 2001 From: Fabian Date: Wed, 28 Aug 2024 16:15:44 +0200 Subject: [PATCH 55/62] changed matching to pt instead of E --- modules/ShowersMatcher2.py | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/modules/ShowersMatcher2.py b/modules/ShowersMatcher2.py index 13cbe416..b2ee7a68 100644 --- a/modules/ShowersMatcher2.py +++ b/modules/ShowersMatcher2.py @@ -287,19 +287,23 @@ def _cost_matrix_Cocoa(self, truth_shower_sid, pred_shower_sid): if (x['hasTrack'] != y['hasTrack']): C[a, b] =0 else: - if y['hasTrack']: - reldeltaEsq = 0 - else: - reldeltaEsq = (np.abs(x['pred_energy'] - y['truthHitAssignedEnergies'])/ y['truthHitAssignedEnergies'])**2 - pred_phi = np.arctan2(x['pred_pos'][1], x['pred_pos'][0]) truth_phi = np.arctan2(y['truthHitAssignedY'], y['truthHitAssignedX']) deltaPhi = np.abs(pred_phi - truth_phi) - deltaEta = np.abs(x['pred_pos'][2] - y['truthHitAssignedZ']) + pred_eta = x['pred_pos'][2] + truth_eta = y['truthHitAssignedZ'] + deltaEta = np.abs(pred_eta - truth_eta) deltaRsq = deltaPhi**2 + deltaEta**2 - d= np.sqrt(reldeltaEsq + 5*deltaRsq) + if y['hasTrack']: + reldeltaptsq = 0 + else: + pt_truth = y['truthHitAssignedEnergies']/np.cosh(truth_eta) + pt_pred = x['pred_energy']/np.cosh(pred_eta) + reldeltaptsq = (np.abs(pt_pred - pt_truth)/ pt_truth)**2 + + d= np.sqrt(reldeltaptsq + 5*deltaRsq) C[a, b] = 1/(d+1e-3) return C From 4d6f86a49d5a6b41abd1016f817d42a6cc04677e Mon Sep 17 00:00:00 2001 From: Fabian Date: Wed, 28 Aug 2024 16:16:16 +0200 Subject: [PATCH 56/62] created script to predict and analyse both testingsets at once --- scripts/predict_analyse_plot_cocoa.py | 87 +++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 scripts/predict_analyse_plot_cocoa.py diff --git a/scripts/predict_analyse_plot_cocoa.py b/scripts/predict_analyse_plot_cocoa.py new file mode 100644 index 00000000..b48e876b --- /dev/null +++ b/scripts/predict_analyse_plot_cocoa.py @@ -0,0 +1,87 @@ +""" +Helper script to run prediction and analysis for a cocoa model on both test sets. +""" + +from DeepJetCore.wandb_interface import wandb_wrapper as wandb +wandb.active = False + +from argparse import ArgumentParser +from hgcal_predictor import HGCalPredictor +import os +from analyse_cocoa_predictions import analyse + +if __name__ == '__main__': + parser = ArgumentParser( + 'Helper script to run prediction and analysis for a cocoa model on both test sets.') + parser.add_argument('modellocation', + help='location of the KERAS_model.h5 file') + parser.add_argument('outputdir', + help="Output directory", + default='') + parser.add_argument('-testfiles', + help="Location of the test files", + default='/work/friemer/hgcalml/testdata/') + parser.add_argument("--max_files", help="Limit number of files", default=-1) + parser.add_argument("--max_steps", help="Limit number of steps per file", default=-1) + #Arguments for analyse + parser.add_argument('-b', help='Beta threshold (default 0.1)', default='0.1') + parser.add_argument('-d', help='Distance threshold (default 0.5)', default='0.5') + parser.add_argument('-i', help='IOU threshold (default 0.1)', default='0.1') + parser.add_argument('-m', help='Matching mode', default='cocoa') + parser.add_argument('--nfiles', + help='Maximum number of files to analyse. -1 for everything in the preddir', + default=-1) + parser.add_argument('--nevents', help='Maximum number of events (per file) to analyse', default=-1) + + args = parser.parse_args() + + +HGCalPredictor( + os.path.join(args.testfiles, 'quarkDC._n.djcdc'), + os.path.join(args.testfiles, 'quarkDC._n.djcdc'), + os.path.join(args.outputdir, 'predictionQuark'), + inputdir=None, + unbuffered=False, + max_files=int(args.max_files), + toydata=False, + ).predict(model_path=args.modellocation, max_steps=int(args.max_steps)) + +HGCalPredictor( + os.path.join(args.testfiles, 'gluonDC._n.djcdc'), + os.path.join(args.testfiles, 'gluonDC._n.djcdc'), + os.path.join(args.outputdir, 'predictionGluon'), + inputdir=None, + unbuffered=False, + max_files=int(args.max_files), + toydata=False, + ).predict(model_path=args.modellocation, max_steps=int(args.max_steps)) + +analyse(preddir=os.path.join(args.outputdir, 'predictionQuark'), + outputpath=os.path.join(args.outputdir, 'plotsQuark'), + beta_threshold=float(args.b), + distance_threshold=float(args.d), + iou_threshold=float(args.i), + matching_mode=args.m, + analysisoutpath=os.path.join(args.outputdir, 'analysisQuark'), + nfiles=int(args.nfiles), + local_distance_scaling=True, + de_e_cut=-1.0, + angle_cut=-1.0, + nevents=int(args.nevents), + datasetname='Quark Jet', + ) + +analyse(preddir=os.path.join(args.outputdir, 'predictionGluon'), + outputpath=os.path.join(args.outputdir, 'plotsGluon'), + beta_threshold=float(args.b), + distance_threshold=float(args.d), + iou_threshold=float(args.i), + matching_mode=args.m, + analysisoutpath=os.path.join(args.outputdir, 'analysisGluon'), + nfiles=int(args.nfiles), + local_distance_scaling=True, + de_e_cut=-1.0, + angle_cut=-1.0, + nevents=int(args.nevents), + datasetname='Gluon Jet', + ) \ No newline at end of file From 7a3d31a7e747962b7b77d714ecb96640a4df45a6 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 2 Sep 2024 14:06:28 +0200 Subject: [PATCH 57/62] Added Classification weights --- modules/LossLayers.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/modules/LossLayers.py b/modules/LossLayers.py index ccf8be2c..fe1a1674 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3384,7 +3384,14 @@ def calc_classification_loss(self, orig_t_pid, pred_id, t_is_unique=None, hasuni classloss = tf.debugging.check_numerics(classloss, "classloss") - return classloss[...,tf.newaxis] + # Weight the loss by the class weights to account for class imbalance + class_weights = tf.constant([1/2.5, 1/0.4, 1/3.75, 1.0], dtype=tf.float32) #Inverse of frequency of classes in COCOA dataset + class_weights = tf.reshape(class_weights, (1, 4)) + weighted_classloss = classloss * tf.reduce_sum(truth_pid_onehot * class_weights, axis=-1) + + weighted_classloss = tf.debugging.check_numerics(weighted_classloss, "weighted_classloss") + + return weighted_classloss[..., tf.newaxis] class LLFullObjectCondensationUncertainty(LLFullObjectCondensation): From 6febf7b2633f26dc0447f60b7ad2321ee1181005 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 2 Sep 2024 14:06:47 +0200 Subject: [PATCH 58/62] renamed eval script --- scripts/{predict_analyse_plot_cocoa.py => eval_cocoa.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename scripts/{predict_analyse_plot_cocoa.py => eval_cocoa.py} (100%) diff --git a/scripts/predict_analyse_plot_cocoa.py b/scripts/eval_cocoa.py similarity index 100% rename from scripts/predict_analyse_plot_cocoa.py rename to scripts/eval_cocoa.py From 0b354237ff7620c812760203baf3cffdd5f35012 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 2 Sep 2024 14:07:24 +0200 Subject: [PATCH 59/62] addded datasetname --- scripts/plot_cocoa.py | 192 +++++++++++++++++++----------------------- 1 file changed, 85 insertions(+), 107 deletions(-) diff --git a/scripts/plot_cocoa.py b/scripts/plot_cocoa.py index 08d3ea0a..057e316a 100644 --- a/scripts/plot_cocoa.py +++ b/scripts/plot_cocoa.py @@ -103,9 +103,9 @@ def plot_efficencies(df, bins): # Create the plots fig, ((ax2, ax3), (ax5,ax6), (ax8,ax9)) = plt.subplots(nrows=3, ncols=2, figsize=(12, 10)) - ax2.errorbar(x_pos, yeff_photon, xerr=x_err, yerr=yerr_eff_photon, fmt='o', color='red', label='Efficiency') + ax2.errorbar(x_pos, yeff_photon, xerr=x_err, yerr=yerr_eff_photon, fmt='o', label='Efficiency') ax2.set_ylabel('efficiency gamma', fontsize=10) - ax3.errorbar(x_pos, yeff_nh, xerr=x_err, yerr=yerr_eff_nh, fmt='o', color='red', label='Efficiency') + ax3.errorbar(x_pos, yeff_nh, xerr=x_err, yerr=yerr_eff_nh, fmt='o', label='Efficiency') ax3.set_ylabel('efficiency n.H.', fontsize=10) ytickseff = np.round(np.arange(40, 101, 20), 1) ax2.set_yticks(ytickseff) @@ -113,9 +113,9 @@ def plot_efficencies(df, bins): ax3.set_yticks(ytickseff) ax3.set_yticklabels([f"{y}%" for y in ytickseff], fontsize=10) - ax5.errorbar(x_pos, yfake_photon, xerr=x_err, yerr=yerr_fake_photon, fmt='o', color='red', label='Fake rate') + ax5.errorbar(x_pos, yfake_photon, xerr=x_err, yerr=yerr_fake_photon, fmt='o', label='Fake rate') ax5.set_ylabel('fake rate gamma', fontsize=10) - ax6.errorbar(x_pos, yfake_nh, xerr=x_err, yerr=yerr_fake_nh, fmt='o', color='red', label='Fake rate') + ax6.errorbar(x_pos, yfake_nh, xerr=x_err, yerr=yerr_fake_nh, fmt='o', label='Fake rate') ax6.set_ylabel('fake rate n.H.', fontsize=10) yticksfake1 = np.round(np.arange(0, 50, 20), 1) ax5.set_yticks(yticksfake1) @@ -124,9 +124,9 @@ def plot_efficencies(df, bins): ax6.set_yticks(yticksfake2) ax6.set_yticklabels([f"{y}%" for y in yticksfake2], fontsize=10) - ax8.errorbar(x_pos, ycorr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', color='red', label='probability of correct class') + ax8.errorbar(x_pos, ycorr_photon, xerr=x_err, yerr=yerr_corr_photon, fmt='o', label='probability of correct class') ax8.set_ylabel('p corr gamma', fontsize=10) - ax9.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', color='red', label='probability of correct class') + ax9.errorbar(x_pos, ycorr_nh, xerr=x_err, yerr=yerr_corr_nh, fmt='o', label='probability of correct class') ax9.set_ylabel('p corr n.H.', fontsize=10) yticksp = np.round(np.arange(0, 101, 20), 1) ax8.set_yticks(yticksp) @@ -143,7 +143,7 @@ def plot_efficencies(df, bins): #ax.set_ylim(20, 101) return fig -def plot_efficency_and_fakerate(df, bins): +def plot_efficency_and_fakerate(df, bins, datasetname='Quark Jet'): # Calculate the efficiencies and fake rates for the total efficiencies, efficiencies_err, fake_rate, fake_rate_err = [], [], [], [] @@ -186,20 +186,22 @@ def plot_efficency_and_fakerate(df, bins): fig_eff = plt.figure() plt.errorbar(x_pos, yeff, xerr=x_err, yerr=yerr_eff, fmt='o') plt.xlim(bins[0], bins[-1]) - plt.xticks(bins) - plt.xlabel('Energy [GeV]') + plt.xticks(np.array([1,5,10,20,30,50])) + plt.xlabel('Truth Energy [GeV]') plt.ylabel('Efficiency') - plt.grid(alpha=0.4) + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) plt.close() fig_fake = plt.figure() plt.errorbar(x_pos, yfake, xerr=x_err, yerr=yerr_fake, fmt='o') plt.xlim(bins[0], bins[-1]) - plt.xticks(bins) - plt.xlabel('Energy [GeV]') - plt.ylim(bottom=0, top=0.1) + plt.xticks(np.array([1,5,10,20,30,50])) + plt.xlabel('Predicted Energy [GeV]') + plt.ylim(bottom=0, top=max(0.1, np.max(yfake) + 0.01)) plt.ylabel('Fake rate') - plt.grid(alpha=0.4) + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) plt.close() return fig_eff, fig_fake @@ -244,7 +246,7 @@ def calc_classification_p(df, bins, mask): return np.array(corr_class_prob), np.array(corr_class_prob_err) -def plot_classification_p(df, bins): +def plot_classification_p(df, bins, datasetname='Quark Jet'): ycorr_photon, yerr_corr_photon = calc_classification_p(df, bins, 0) ycorr_nh, yerr_corr_nh = calc_classification_p(df, bins, 1) ycorr_ch, yerr_corr_ch = calc_classification_p(df, bins, 2) @@ -265,7 +267,9 @@ def plot_classification_p(df, bins): plt.xlim(bins[0], bins[-1]) plt.xticks([1,5,10,20,30,50]) plt.xlabel('Energy [GeV]') - plt.ylabel('Accuracy of classification') + plt.ylabel('Accuracy of classification') + plt.ylim(0, 1) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) plt.close() return fig_classification @@ -335,8 +339,7 @@ def plot_jet_metrics_based_on_particles(df, datasetname='QuarkJet'): fig_jet_relresE = plt.figure() plt.hist(np.clip(relresE, -1, 1), bins=relres_bins, histtype='step')#color='#67c4ce', linestyle='--') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - write_mean_std_displayed(relresE, relres_bins) + write_mean_std_displayed(relresE, relres_bins, datasetname) plt.ylabel('Number of events', fontsize=20) plt.xlabel('rel. res. Jet Energy', fontsize=20) plt.xticks(np.linspace(-1, 1, 5)) @@ -346,8 +349,7 @@ def plot_jet_metrics_based_on_particles(df, datasetname='QuarkJet'): fig_jet_reseta = plt.figure() plt.hist(np.clip(reseta,-0.2,0.2), bins=res_bins, histtype='step')# color='#67c4ce', linestyle='--') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - write_mean_std_displayed(reseta, res_bins) + write_mean_std_displayed(reseta, res_bins, datasetname) plt.ylabel('Number of events', fontsize=20) plt.xlabel('Jet $\\Delta \\eta$', fontsize=20) plt.xticks(np.linspace(-0.2, 0.2, 5)) @@ -356,8 +358,7 @@ def plot_jet_metrics_based_on_particles(df, datasetname='QuarkJet'): fig_jet_resphi = plt.figure() plt.hist(np.clip(resphi, -0.2, 0.2), bins=res_bins, histtype='step')#color='#67c4ce', linestyle='--') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - write_mean_std_displayed(resphi, res_bins) + write_mean_std_displayed(resphi, res_bins, datasetname) plt.ylabel('Number of events', fontsize=20) plt.xlabel('Jet $\\Delta \\phi$', fontsize=20) plt.xticks(np.linspace(-0.2, 0.2, 5)) @@ -366,8 +367,7 @@ def plot_jet_metrics_based_on_particles(df, datasetname='QuarkJet'): fig_jet_pt = plt.figure() plt.hist(np.clip(relrespt, -1, 1), bins=relres_bins, histtype='step') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - write_mean_std_displayed(relrespt, relres_bins) + write_mean_std_displayed(relrespt, relres_bins, datasetname) plt.ylabel('Number of events', fontsize=20) plt.xlabel('rel. res. Jet $p_T$', fontsize=20) plt.xticks(np.linspace(-1, 1, 5)) @@ -427,7 +427,7 @@ def linearfunc(x, a, b): return corrected_jets_df -def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): +def plot_jet_metrics_from_jets(jets_df, datasetname='Quark Jet'): """ plots the jet metrics from the results of jet clustering algorithm """ @@ -484,8 +484,7 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): fig_jet_reseta = plt.figure() plt.hist(np.clip(reseta,-0.2,0.2), bins=res_bins, histtype='step') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - write_mean_std_displayed(reseta, res_bins) + write_mean_std_displayed(reseta, res_bins, datasetname) plt.ylabel('Number of jets', fontsize=20) plt.xlabel('Jet $\\Delta \\eta$', fontsize=20) plt.xticks(np.linspace(-0.2, 0.2, 5)) @@ -494,8 +493,7 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): fig_jet_resphi = plt.figure() plt.hist(np.clip(resphi, -0.2, 0.2), bins=res_bins, histtype='step') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - write_mean_std_displayed(resphi, res_bins) + write_mean_std_displayed(resphi, res_bins, datasetname) plt.ylabel('Number of jets', fontsize=20) plt.xlabel('Jet $\\Delta \\phi$', fontsize=20) plt.xticks(np.linspace(-0.2, 0.2, 5)) @@ -505,8 +503,7 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): fig_jet_pt = plt.figure() plt.hist(np.clip(relrespt, -1,1), bins=relres_bins, histtype='step') plt.grid(alpha=0.4) - plt.text(0.05, 0.95, 'Quark Jet', horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) - write_mean_std_displayed(relrespt, relres_bins) + write_mean_std_displayed(relrespt, relres_bins, datasetname) plt.ylabel('Number of jets', fontsize=20) plt.xlabel('rel. res. Cal. Jet $p_T$', fontsize=20) plt.xticks(np.round(np.linspace(-1, 1, 9), 2), fontsize=12) @@ -515,7 +512,7 @@ def plot_jet_metrics_from_jets(jets_df, datasetname='QuarkJet'): return fig_jet_nparticle, fig_jet_reseta, fig_jet_resphi, fig_jet_pt -def write_mean_std_displayed(data, bins): +def write_mean_std_displayed(data, bins, datasetname='Quark Jet'): """ Helper function to write the mean, std and displayed fraction of the data into the plot """ @@ -543,6 +540,7 @@ def write_mean_std_displayed(data, bins): plt.text(0.95, 0.95, textstr, transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='right', bbox=dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) def calc_matched_mask(df): @@ -557,7 +555,7 @@ def calc_deltaphi(t_phi, pred_phi): delta_phi = np.where(delta_phi < -np.pi, delta_phi+2*np.pi, delta_phi) return delta_phi -def plot_particle_metrics(df): +def plot_particle_metrics(df, datasetname='Quark Jet'): matched = calc_matched_mask(df) @@ -593,7 +591,7 @@ def plot_particle_metrics(df): fig_relresE = plt.figure() plt.hist(np.clip(relresE, -1,1), bins=relres_bins, histtype='step') plt.grid(alpha=0.4) - write_mean_std_displayed(relresE, relres_bins) + write_mean_std_displayed(relresE, relres_bins, datasetname) plt.xlabel('rel. res. Neutral Particle Energy', fontsize=20) plt.ylabel('Number of neutral particles', fontsize=20) plt.xticks(np.linspace(-1, 1, 5)) @@ -603,7 +601,7 @@ def plot_particle_metrics(df): fig_reseta = plt.figure() plt.hist(np.clip(reseta, -0.4, 0.4), bins=res_bins, histtype='step') plt.grid(alpha=0.4) - write_mean_std_displayed(reseta, res_bins) + write_mean_std_displayed(reseta, res_bins, datasetname) plt.xlabel('Neutral Particle $\\Delta \\eta$', fontsize=20) plt.ylabel('Number of neutral particles', fontsize=20) plt.xticks(np.linspace(-0.4, 0.4, 5)) @@ -612,7 +610,7 @@ def plot_particle_metrics(df): fig_resphi = plt.figure() plt.hist(np.clip(resphi, -0.4, 0.4), bins=res_bins, histtype='step') plt.grid(alpha=0.4) - write_mean_std_displayed(resphi, res_bins) + write_mean_std_displayed(resphi, res_bins, datasetname) plt.xlabel('Neutral Particle $\\Delta \\phi$', fontsize=20) plt.ylabel('Number of neutral particles', fontsize=20) plt.xticks(np.linspace(-0.4, 0.4, 5)) @@ -621,7 +619,7 @@ def plot_particle_metrics(df): fig_pt = plt.figure() plt.hist(np.clip(relrespt, -1, 1), bins=relres_bins, histtype='step') plt.grid(alpha=0.4) - write_mean_std_displayed(relrespt, relres_bins) + write_mean_std_displayed(relrespt, relres_bins, datasetname) plt.xlabel('rel. res. Neutral Particle $p_T$', fontsize=20) plt.ylabel('Number of neutral particles', fontsize=20) plt.xticks(np.linspace(-1, 1, 5)) @@ -652,7 +650,7 @@ def calc_energy_resolution(df, bins=None): diffs, diffs_err, ratios, ratios_err = np.array(diffs), np.array(diffs_err), np.array(ratios), np.array(ratios_err) return diffs, diffs_err, ratios, ratios_err -def plot_energy_resolution(df, bins=None): +def plot_energy_resolution(df, bins=None, datasetname='Quark Jet'): diffs, diffs_err, ratios, ratios_err = calc_energy_resolution(df, bins) neutral_mask = df['truthHitAssignedPIDs'].isin([22,130,310,311,2112,-2112,3122,-3122,3322,-3322]) @@ -671,14 +669,16 @@ def plot_energy_resolution(df, bins=None): ax1.errorbar(x_pos, diffs, xerr=x_err, yerr=diffs_err, fmt='o', label='Total') ax1.errorbar(x_pos, diffs_neutral, xerr=x_err, yerr=diffs_err_neutral, fmt='o', label='Neutral') ax1.errorbar(x_pos, diffs_charged, xerr=x_err, yerr=diffs_err_charged, fmt='o', label='Charged') - ax1.set_ylabel('$\sigma E [GeV]$', fontsize=20) - ax1.grid(alpha=0.4) + ax1.set_ylabel('$\sigma E [GeV]$') + ax1.grid(alpha=0.4) + #write datasetname in top right corner + ax2.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) ax1.legend() ax2.errorbar(x_pos, ratios, xerr=x_err, yerr=ratios_err, fmt='o', label='Total') ax2.errorbar(x_pos, ratios_neutral, xerr=x_err, yerr=ratios_err_neutral, fmt='o', label='Neutral') ax2.errorbar(x_pos, ratios_charged, xerr=x_err, yerr=ratios_err_charged, fmt='o', label='Charged') - ax2.set_ylabel('$E_{pred} / E_{truth}$', fontsize=20) + ax2.set_ylabel('$E_{pred} / E_{truth}$') ax2.grid(alpha=0.4) ydelta = np.round(max(1 - ax2.get_ylim()[0], ax2.get_ylim()[1] - 1),1) @@ -686,6 +686,7 @@ def plot_energy_resolution(df, bins=None): ax2.set_xlim(bins[0], bins[-1]) ax2.set_xscale('log') + ax2.set_xlabel('Truth Energy [GeV]') ax2.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) xticks = np.round(bins, 0).astype(int) ax2.set_xticks(xticks) @@ -695,57 +696,6 @@ def plot_energy_resolution(df, bins=None): return fig -def plot_pt_resolution(df, bins=None): - matched = calc_matched_mask(df) - - means = [] - std_error = [] - - for i in range(len(bins) - 1): - mask_truth_bin = np.logical_and( - df['truthHitAssignedEnergies'] >= bins[i], - df['truthHitAssignedEnergies'] < bins[i + 1]) - mask_bin = np.logical_and(mask_truth_bin, matched) - - t_eta = df[mask_bin]['truthHitAssignedZ'] - pred_eta = df[mask_bin]['pred_pos'].apply(lambda x: x[2]) - t_energy = df[mask_bin]['truthHitAssignedEnergies'] - pred_energy = df[mask_bin]['pred_energy'] - t_pt = t_energy/np.cosh(t_eta) - pred_pt = pred_energy/np.cosh(pred_eta) - - ratios = pred_pt / t_pt - means.append(np.mean(ratios)) - std_error.append(np.std(ratios) / np.sqrt(len(ratios))) - - means = np.array(means) - std_error = np.array(std_error) - - fig, ax1 = plt.subplots(figsize=(12, 6)) - - binwidth = bins[1:] - bins[:-1] - x = bins[:-1] + binwidth / 2 - xerr = binwidth / 2 - y = means - yerr = std_error - - # plot x, y with error bars - ax1.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='o')#, color='#67c4ce') - - # set xticks - ax1.set_xlim(bins[0], bins[-1]) - ax1.set_xscale('log') - ax1.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) - xticks = np.round(bins, 0).astype(int) - ax1.set_xticks(xticks) - ax1.set_xticklabels(xticks) - - ax1.grid(alpha=0.4) - ax1.set_ylabel('$\sigma p_T [GeV]$', fontsize=20) - ax1.set_xlabel('Energy [GeV]', fontsize=20) - - return fig - def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation'): """ Plots the condensation space of a event (coordspace='condensation") or the input space (coordspace='input') @@ -786,7 +736,7 @@ def plot_condensation(pred_dict, t_dict, feature_dict, coordspace='condensation' fig = px.scatter_3d(eventdf, x="X", y="Y", z="Z", color="t_idx", size=sizefield, - template='plotly_dark', + template='plotly_white', hover_data=hover_data, color_continuous_scale=px.colors.sequential.Rainbow) fig.update_traces(marker=dict(line=dict(width=0))) @@ -843,6 +793,39 @@ def plot_distribution(truth_list, feature_list): plt.close() return fig +def plot_truth_jet_nconstrituents(jets_df, df, datasetname='Quark Jet'): + nParicles_truth_jet = [] + + jets_df = correct_jet_pt(jets_df, [0,5,10,20,30,50,100, 1000]) + matched = jets_df['matched'] + for i in range(len(jets_df['event_id'].unique())): + event_id_mask = jets_df['event_id'] == i + mask = np.logical_and(event_id_mask, matched) + + nParicles_truth_jet.append(jets_df[mask]['true_n_constituents']) + nParicles_truth_jet = np.concatenate(nParicles_truth_jet) + + nParicles_truth_particle = [] + has_truth = np.isnan(df['truthHitAssignedEnergies']) == False + + for i in range(len(df['event_id'].unique())): + event_mask = df['event_id'] == i + truth_mask = np.logical_and(event_mask, has_truth) + nParicles_truth_particle.append(np.sum(truth_mask)) + nParicles_truth_particle = np.array(nParicles_truth_particle) + + nParticle_bins = np.linspace(0, 30, 31) + fig_nparticle_truth_compare = plt.figure() + plt.hist(nParicles_truth_jet, bins=nParticle_bins, label='Anti-$k_T$ based', color='blue', alpha=0.5) + plt.hist(nParicles_truth_particle, bins=nParticle_bins, label='Event based', color='orange', alpha=0.5) + plt.grid(alpha=0.4) + plt.text(0.05, 0.95, datasetname, horizontalalignment='left', verticalalignment='top', transform=plt.gca().transAxes, fontsize=10) + plt.ylabel('Number of jets', fontsize=20) + plt.xlabel('Jet nConstituents', fontsize=20) + plt.legend(fontsize=12) + plt.xticks(np.linspace(0, 30, 7)) + plt.close() + return fig_nparticle_truth_compare def plot_energydistribution(df): fig, ax = plt.subplots(figsize=(12, 6)) @@ -878,7 +861,7 @@ def plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath='/w fig_eff_overview=plot_efficencies(df, bins=energy_bins_neutral) fig_eff_overview.savefig(os.path.join(outputpath,'efficienciesOverview_{}.png'.format(datasetname)), bbox_inches='tight') - fig_eff, fig_fake = plot_efficency_and_fakerate(df, bins=energy_bins_neutral) + fig_eff, fig_fake = plot_efficency_and_fakerate(df, bins=energy_bins_neutral, datasetname=datasetname) fig_eff.savefig(os.path.join(outputpath,'efficiencies_{}.png'.format(datasetname)), bbox_inches='tight') fig_fake.savefig(os.path.join(outputpath,'fake_rate_{}.png'.format(datasetname)), bbox_inches='tight') @@ -898,18 +881,18 @@ def plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath='/w print('Plotting particle metrics') mask_neutral = df['truthHitAssignedPIDs'].isin([22,130, 310, 311, 2112, -2112, 3122, -3122, 3322, -3322]) - fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi, fig_neutral_pt = plot_particle_metrics(df[mask_neutral]) + fig_neutral_relresE, fig_neutral_reseta, fig_neutral_resphi, fig_neutral_pt = plot_particle_metrics(df[mask_neutral], datasetname=datasetname) fig_neutral_relresE.savefig(os.path.join(outputpath,'neutral_relresE_{}.png'.format(datasetname)), bbox_inches='tight') fig_neutral_reseta.savefig(os.path.join(outputpath,'neutral_reseta_{}.png'.format(datasetname)), bbox_inches='tight') fig_neutral_resphi.savefig(os.path.join(outputpath,'neutral_resphi_{}.png'.format(datasetname)), bbox_inches='tight') fig_neutral_pt.savefig(os.path.join(outputpath,'neutral_pt_{}.png'.format(datasetname)), bbox_inches='tight') - fig_class = plot_classification_p(df, energy_bins_neutral) + fig_class = plot_classification_p(df, energy_bins_neutral, datasetname=datasetname) fig_class.savefig(os.path.join(outputpath,'classification_p_{}.png'.format(datasetname)), bbox_inches='tight') print('Plotting energy resolution') energy_bins_combined = np.array([1,5,10,20,30,50,200]) - fig_new_res = plot_energy_resolution(df, bins=energy_bins_combined) + fig_new_res = plot_energy_resolution(df, bins=energy_bins_combined, datasetname=datasetname) fig_new_res.savefig(os.path.join(outputpath,'energy_resolution_{}.png'.format(datasetname)), bbox_inches='tight') print('Plotting condensation and input') @@ -918,19 +901,14 @@ def plot_everything(df, pred_list, t_list, feature_list, jets_df, outputpath='/w fig.write_html(os.path.join(outputpath, 'condensation' ,'condensation{}_{}.html'.format(event_id, datasetname))) fig = plot_condensation(pred_list[event_id], t_list[event_id], feature_list[event_id], 'input') fig.write_html(os.path.join(outputpath, 'condensation' ,'input{}_{}.html'.format(event_id, datasetname))) + + # print('Plotting truth compare') + # fig_nparticle_truth_compare = plot_truth_jet_nconstrituents(jets_df, df, datasetname=datasetname) + # fig_nparticle_truth_compare.savefig(os.path.join(outputpath,'nparticle_truth_compare_{}.png'.format(datasetname)), bbox_inches='tight') # print('Plotting distribution') # fig_dist = plot_distribution(t_list, feature_list) - # fig_dist.savefig(os.path.join(outputpath, 'distribution_{}.png'.format(datasetname)), bbox_inches='tight') - - # fig_edist = plot_energydistribution(df) - # fig_edist.savefig(os.path.join(outputpath, 'energydistribution.png'), bbox_inches='tight') - - # fig_edist_neutral = plot_energydistribution(df[mask_neutral]) - # fig_edist_neutral.savefig(os.path.join(outputpath, 'energydistribution_neutral.png'), bbox_inches='tight') - - # fig_edist_charged = plot_energydistribution(df[mask_charged]) - # fig_edist_charged.savefig(os.path.join(outputpath, 'energydistribution_charged.png'), bbox_inches='tight') + # fig_dist.savefig(os.path.join(outputpath, 'distribution_{}.png'.format(datasetname)), bbox_inches='tight') def plot_everything_from_file(analysisfilepath, outputpath='/work/friemer/hgcalml/testplots/', datasetname='Quark Jet'): #Load analysis file created by analyse_cocoa_predictions.py From 29b66cf4aa3acdf1897ddc64adf704dd421dbf85 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 2 Sep 2024 14:21:31 +0200 Subject: [PATCH 60/62] Added AtanhWeighing Loss --- Train/paper_trainer_Cocoa_simple.py | 50 ++++++++++++++++++++--------- modules/LossLayers.py | 4 ++- modules/object_condensation.py | 24 ++++++++++++++ 3 files changed, 62 insertions(+), 16 deletions(-) diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index 8c707d43..b9dd8fec 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -49,7 +49,7 @@ train = training_base_hgcal.HGCalTraining(parser=parser) -PLOT_FREQUENCY = 600 +PLOT_FREQUENCY = 6000 DENSE_ACTIVATION = 'elu' DENSE_INIT = "he_normal" @@ -110,8 +110,6 @@ def GravNet(name, return Concatenate()([xgn, x]) - - def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): """ Function that defines the model to train @@ -199,7 +197,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): fix_distance_scale=True, energy_factor=False, is_track=is_track, - set_track_betas_to_one=True, + set_track_betas_to_one=False, pred_e_factor=10) @@ -209,7 +207,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): record_metrics=True, print_loss=train.args.no_wandb, name="ExtendedOCLoss", - implementation = "hinge", + implementation = "atanh", beta_loss_scale = 1.0, too_much_beta_scale = 0.0, energy_loss_weight = 1.0, @@ -240,7 +238,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): 'pred_ccoords': pred_ccoords, 'pred_energy': pred_energy, #'pred_energy_low_quantile': pred_energy_low_quantile, - # 'pred_energy_high_quantile': pred_energy_high_quantile, + #'pred_energy_high_quantile': pred_energy_high_quantile, 'pred_pos': pred_pos, 'pred_time': pred_time, 'pred_id': pred_id, @@ -261,7 +259,6 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): ############################################################################### - if not train.modelSet(): train.setModel( config_model, @@ -322,7 +319,19 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): #additional_callbacks=cb, collect_gradients = 4 ) -# loop through model layers and turn batch normalisation to fixed + +#recompile +train.compileModel(learningrate=1e-4) +print('entering third training phase') +train.trainModel( + nepochs=500, + batchsize=50000, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, + collect_gradients = 4 + ) + +# loop through model layers and turn batch normalisation to fixed def fix_batchnorm(m): for layer in m.layers: if isinstance(layer, BatchNormalization): @@ -335,30 +344,41 @@ def fix_batchnorm(m): train.compileModel(learningrate=5e-4) print('entering third training phase') train.trainModel( - nepochs=600, + nepochs=750, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, collect_gradients = 4 ) - +#recompile train.compileModel(learningrate=1e-4) print('entering third training phase') train.trainModel( - nepochs=800, + nepochs=1000, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, collect_gradients = 4 ) - -#recompile train.compileModel(learningrate=1e-5) print('entering third training phase') train.trainModel( - nepochs=1000, + nepochs=1100, + batchsize=50000, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, + collect_gradients = 4 + ) +train.compileModel(learningrate=1e-6) +print('entering third training phase') +train.trainModel( + nepochs=1200, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, collect_gradients = 4 - ) \ No newline at end of file + ) +try: + wandb.wandb().save(train.outputDir + "/KERAS_model.h5") +except: + print("Failed to save model") \ No newline at end of file diff --git a/modules/LossLayers.py b/modules/LossLayers.py index fe1a1674..09a3888f 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -2232,7 +2232,7 @@ def __init__(self, *, energy_loss_weight=1., raise ValueError("huber_energy_scale>0 and alt_energy_loss exclude each other") - from object_condensation import Basic_OC_per_sample, PushPull_OC_per_sample, PreCond_OC_per_sample + from object_condensation import Basic_OC_per_sample, PushPull_OC_per_sample, PreCond_OC_per_sample, Hinge_OC_per_sample_atanhweighing from object_condensation import Hinge_OC_per_sample_damped, Hinge_OC_per_sample, Hinge_Manhatten_OC_per_sample from object_condensation import Dead_Zone_Hinge_OC_per_sample,Hinge_OC_per_sample_learnable_qmin, Hinge_OC_per_sample_learnable_qmin_betascale_position impl = Basic_OC_per_sample @@ -2256,6 +2256,8 @@ def __init__(self, *, energy_loss_weight=1., impl = Hinge_OC_per_sample_learnable_qmin if implementation == 'hinge_qmin_betascale_pos': impl = Hinge_OC_per_sample_learnable_qmin_betascale_position + if implementation == 'atanh': + impl = Hinge_OC_per_sample_atanhweighing self.implementation = implementation diff --git a/modules/object_condensation.py b/modules/object_condensation.py index 0b6deed3..e4900a91 100644 --- a/modules/object_condensation.py +++ b/modules/object_condensation.py @@ -268,6 +268,7 @@ def V_rep_k(self): def Pll_k(self): tanhsqbeta = self.beta_v**2 #softer here + #tanhsqbeta = tf.math.atanh(self.beta_v*(1-1e-3))**2 tanhsqbeta = tf.debugging.check_numerics(tanhsqbeta, "OC: pw b**2") pw = tanhsqbeta * tf.clip_by_value((1.-tf.clip_by_value(self.isn_v+self.sw_v,0.,1.)),0.,1.) + 1e-6 @@ -454,6 +455,29 @@ def __init__(self, **kwargs): super(Hinge_OC_per_sample, self).__init__(**kwargs) self.condensation_damping = 0.0 # Don't stop any gradients #self.rep_range = 2. +class Hinge_OC_per_sample_atanhweighing(Hinge_OC_per_sample): + ''' + Same as Hinge_OC_per_sample but with atanh(beta)^2 weighting for the payloads + ''' + def __init__(self, **kwargs): + super(Hinge_OC_per_sample_atanhweighing, self).__init__(**kwargs) + #@tf.function + def Pll_k(self): + #tanhsqbeta = self.beta_v**2 #softer here + tanhsqbeta = tf.math.atanh(self.beta_v*(1-1e-3))**2 + tanhsqbeta = tf.debugging.check_numerics(tanhsqbeta, "OC: pw b**2") + pw = tanhsqbeta * tf.clip_by_value((1.-tf.clip_by_value(self.isn_v+self.sw_v,0.,1.)),0.,1.) + 1e-6 + + pw = tf.debugging.check_numerics(pw, "OC: pw") + + pll_k_m = SelectWithDefault(self.Msel, self.pll_v, 0.) #K x V_perobj x P + pw_k_m = SelectWithDefault(self.Msel, pw, 0.) #K x V-obj x P + pw_k_sum = tf.reduce_sum(pw_k_m, axis=1) + pw_k_sum = tf.where(pw_k_sum <= 0., 1e-2, pw_k_sum) + + pll_k = tf.math.divide_no_nan(tf.reduce_sum(pll_k_m * pw_k_m, axis=1), + pw_k_sum )#K x P + return pll_k From 4287bc2ad00ab22f575064fa71a475a79f2b8862 Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 2 Sep 2024 14:27:32 +0200 Subject: [PATCH 61/62] renamed Loss to avoid MergeConfict --- Train/paper_trainer_Cocoa_simple.py | 5 ++--- modules/Layers.py | 4 ++-- modules/LossLayers.py | 4 ++-- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index b9dd8fec..e2662b79 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -14,8 +14,7 @@ from Layers import RaggedGravNet from Layers import PlotCoordinates from Layers import TranslationInvariantMP -from Layers import LLExtendedObjectCondensation5 -from Layers import LLExtendedObjectCondensation3 +from Layers import LLExtendedObjectCondensationCocoa from model_blocks import condition_input from model_blocks import extent_coords_if_needed from model_blocks import create_outputs @@ -201,7 +200,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): pred_e_factor=10) - pred_beta = LLExtendedObjectCondensation5( + pred_beta = LLExtendedObjectCondensationCocoa( scale=1., use_energy_weights=True, record_metrics=True, diff --git a/modules/Layers.py b/modules/Layers.py index 8e7d0ff7..c30187b1 100644 --- a/modules/Layers.py +++ b/modules/Layers.py @@ -315,7 +315,7 @@ from LossLayers import LLFullObjectCondensationUncertainty, LLFullObjectCondensationID from LossLayers import LLExtendedObjectCondensation, LLExtendedObjectCondensation2 from LossLayers import LLExtendedObjectCondensation3, LLExtendedObjectCondensation4 -from LossLayers import LLExtendedObjectCondensation5 +from LossLayers import LLExtendedObjectCondensationCocoa from LossLayers import LLEdgeClassifier, AmbiguousTruthToNoiseSpectator, LLGoodNeighbourHood, LLKnnPushPullObjectCondensation from LossLayers import LLEnergySums,LLKnnSimpleObjectCondensation, LLPushTracks, LLFullOCThresholds, LLLocalEnergyConservation from LossLayers import LLRegulariseGravNetSpace, LLSpectatorPenalty @@ -358,7 +358,7 @@ global_layers_list['LLExtendedObjectCondensation2']=LLExtendedObjectCondensation2 global_layers_list['LLExtendedObjectCondensation3']=LLExtendedObjectCondensation3 global_layers_list['LLExtendedObjectCondensation4']=LLExtendedObjectCondensation4 -global_layers_list['LLExtendedObjectCondensation5']=LLExtendedObjectCondensation5 +global_layers_list['LLExtendedObjectCondensationCocoa']=LLExtendedObjectCondensationCocoa global_layers_list['LLFullObjectCondensationID']=LLFullObjectCondensationID global_layers_list['LLGraphCondOCLoss']=LLGraphCondOCLoss global_layers_list['LLPFCondensates']=LLPFCondensates diff --git a/modules/LossLayers.py b/modules/LossLayers.py index 09a3888f..d03f6068 100644 --- a/modules/LossLayers.py +++ b/modules/LossLayers.py @@ -3289,7 +3289,7 @@ def calc_energy_correction_factor_loss(self, else: return prediction_loss, uncertainty_loss + matching_loss -class LLExtendedObjectCondensation5(LLExtendedObjectCondensation): +class LLExtendedObjectCondensationCocoa(LLExtendedObjectCondensation): ''' Same as `LLExtendedObjecCondensation3` but uses total energy instead of correction as prediction, position loss for [cos(phi), sin(phi), eta] and classification loss into categories for COCOA @@ -3297,7 +3297,7 @@ class LLExtendedObjectCondensation5(LLExtendedObjectCondensation): def __init__(self, *args, **kwargs): - super(LLExtendedObjectCondensation5, self).__init__(*args, **kwargs) + super(LLExtendedObjectCondensationCocoa, self).__init__(*args, **kwargs) def calc_energy_correction_factor_loss(self, From 0f608b991fa0d760df628f5f024bc4199659b2ec Mon Sep 17 00:00:00 2001 From: Fabian Date: Mon, 16 Sep 2024 10:16:44 +0200 Subject: [PATCH 62/62] cleaned up for pull request --- Train/paper_trainer_Cocoa.py | 345 ---------- Train/paper_trainer_Cocoa_simple.py | 30 +- models/simple_Cocoa/KERAS_model.h5 | Bin 3353696 -> 3346740 bytes models/simple_Cocoa/description.txt | 6 +- .../paper_trainer_Cocoa_simple.py | 94 ++- modules/LossLayers.py | 2 +- modules/object_condensation.py | 2 +- scripts/analyse_cocoa_predictions.py | 5 +- scripts/eval_cocoa.py | 2 +- scripts/generatePlots.ipynb | 617 ------------------ scripts/plot_cocoa.py | 6 +- scripts/submitViaHTCondor.py | 2 +- tests/testOC.py | 43 +- 13 files changed, 98 insertions(+), 1056 deletions(-) delete mode 100644 Train/paper_trainer_Cocoa.py delete mode 100644 scripts/generatePlots.ipynb diff --git a/Train/paper_trainer_Cocoa.py b/Train/paper_trainer_Cocoa.py deleted file mode 100644 index a3f11f4e..00000000 --- a/Train/paper_trainer_Cocoa.py +++ /dev/null @@ -1,345 +0,0 @@ -""" -Training script based on paper_trainer_noRSU_fixed_jk.py -""" - -import sys -from argparse import ArgumentParser - -# import wandb -from DeepJetCore.wandb_interface import wandb_wrapper as wandb -import tensorflow as tf -from tensorflow.keras.layers import Concatenate, Dense, Dropout -from tensorflow.keras import Model - - -import training_base_hgcal -from Layers import LLRegulariseGravNetSpace, DummyLayer -from Layers import ProcessFeaturesCocoa -from Layers import RaggedGravNet -from Layers import PlotCoordinates -from Layers import TranslationInvariantMP -from Layers import LLExtendedObjectCondensation5 -from Layers import LLExtendedObjectCondensation3 -from model_blocks import condition_input -from model_blocks import extent_coords_if_needed -from model_blocks import create_outputs -from callbacks import plotClusterSummary -from tensorflow.keras.layers import BatchNormalization - -#################################################################################################### -### Load Configuration ############################################################################# -#################################################################################################### - -parser = ArgumentParser('training') -parser.add_argument('--run_name', help="wandb run name", default="test") -parser.add_argument('--no_wandb', help="Don't use wandb", action='store_true') -parser.add_argument('--wandb_project', help="wandb_project", default="Paper_Models") -#parser.add_argument('--lr', help="learning rate", default="0.0001") - -# get the args so far but ignore the other args to pass them on -# this prevents errors with wandb in layers -pre_args,_ = parser.parse_known_args() - -if not pre_args.no_wandb: - wandb.init( - project=pre_args.wandb_project, - config={}, - ) - wandb.wandb().save(sys.argv[0]) # Save python file -else: - wandb.active=False - -#parses the rest of the arguments -train = training_base_hgcal.HGCalTraining(parser=parser) - - -PLOT_FREQUENCY = 600 - -DENSE_ACTIVATION = 'elu' -DENSE_INIT = "he_normal" -d_shape = 64 -DENSE_REGULARIZER = tf.keras.regularizers.l2(1e-9) -NEIGHBOURS = [64,32,32,16] - -############################################################################### -### Define Model ############################################################## -############################################################################### - -def GravNet_plus_TEQMP(name, - x, cprime, hit_energy, t_idx, - rs, - d_shape, - n_neighbours, - debug_outdir, - plot_debug_every, - space_reg_strength=1e-2, - ): - - xgn, gncoords, gnnidx, gndist = RaggedGravNet( - name = "GravNet_"+name, # 76929, 42625, 42625 - n_neighbours=n_neighbours, - n_dimensions=3, - n_filters=d_shape, - n_propagate=d_shape, - coord_initialiser_noise=1e-3, - feature_activation='elu', - )([x, rs]) - - if space_reg_strength > 0: - gndist = LLRegulariseGravNetSpace(name=f'gravnet_coords_reg_{name}' , - record_metrics=True, - scale=space_reg_strength)([gndist, cprime, gnnidx]) - - gncoords = PlotCoordinates( - plot_every=plot_debug_every, - outdir = debug_outdir, - name=f'gncoords_{name}' - )([gncoords, hit_energy, t_idx, rs]) - - x = DummyLayer()([x, gncoords]) #just so the branch is not optimised away, anyway used further down - x = Concatenate()([xgn, x]) - - x = TranslationInvariantMP([64, 32, 16, 8], - layer_norm = True, - activation = None, #layer norm takes care - sum_weight = True)([x, gnnidx, gndist]) - - return Concatenate()([xgn, x]) - - - - -def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): - """ - Function that defines the model to train - """ - - ########################################################################### - ### Pre-processing step ################################################### - ########################################################################### - - orig_input = td.interpretAllModelInputs(Inputs) - pre_processed = condition_input(orig_input, no_scaling=True, no_prime=False, new_prime=True) - - prime_coords = pre_processed['prime_coords'] - - c_coords = prime_coords - is_track = pre_processed['is_track'] - rs = pre_processed['row_splits'] - energy = pre_processed['rechit_energy'] - t_idx = pre_processed['t_idx'] - x = pre_processed['features'] - - x = ProcessFeaturesCocoa()(x) - - c_coords = PlotCoordinates( - plot_every=plot_debug_every, - outdir=debug_outdir, - name='input_c_coords', - )([c_coords, energy, t_idx, rs]) - - c_coords = extent_coords_if_needed(c_coords, x, 3) - - x = Concatenate()([x, c_coords, is_track]) - x = Dense(64, name='dense_pre_loop', activation=DENSE_ACTIVATION)(x) - - allfeat = [] - print("Available keys: ", pre_processed.keys()) - - ########################################################################### - ### Loop over GravNet Layers ############################################## - ########################################################################### - - - for i in range(len(NEIGHBOURS)): - - x = Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, - kernel_regularizer=DENSE_REGULARIZER)(x) - x = Dense(d_shape,activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, - kernel_regularizer=DENSE_REGULARIZER)(x) - - x = Concatenate()([c_coords, x]) - - x = GravNet_plus_TEQMP(f'gncomb_{i}', x, prime_coords, energy, t_idx, rs, - d_shape, NEIGHBOURS[i], debug_outdir, plot_debug_every, space_reg_strength=1e-2) - - x = Dense(d_shape, - name=f"dense_post_gravnet_1_iteration_{i}", - activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, - kernel_regularizer=DENSE_REGULARIZER)(x) - x = Dense(d_shape, - name=f"dense_post_gravnet_2_iteration_{i}", - activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, - kernel_regularizer=DENSE_REGULARIZER)(x) - - #x = LayerNormalization()(x) - allfeat.append(x) - if len(allfeat) > 1: - x = Concatenate()(allfeat) - else: - x = allfeat[0] - - - ########################################################################### - ### Create output of model and define loss ################################ - ########################################################################### - - - x = Dense(128, - name=f"dense_final_{1}", - activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, - kernel_regularizer=DENSE_REGULARIZER)(x) - x = Dense(64, - name=f"dense_final_{2}", - activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, - kernel_regularizer=DENSE_REGULARIZER)(x) - x = Dense(64, - name=f"dense_final_{3}", - activation=DENSE_ACTIVATION, kernel_initializer=DENSE_INIT, - kernel_regularizer=DENSE_REGULARIZER)(x) - - pred_beta, pred_ccoords, pred_dist, \ - pred_energy_corr, pred_energy_low_quantile, pred_energy_high_quantile, \ - pred_pos, pred_time, pred_time_unc, pred_id = \ - create_outputs(x, - n_ccoords=3, - fix_distance_scale=True, - is_track=is_track, - set_track_betas_to_one=True) - - - pred_beta = LLExtendedObjectCondensation3( - scale=1., - use_energy_weights=True, - record_metrics=True, - print_loss=train.args.no_wandb, - name="ExtendedOCLoss", - implementation = "hinge", - beta_loss_scale = 1.0, - too_much_beta_scale = 0.0, - energy_loss_weight = 0.4, - classification_loss_weight = 0.4, - position_loss_weight = 0.0, - timing_loss_weight = 0.0, - q_min = 1.0, - use_average_cc_pos = 0.9999)( - [pred_beta, pred_ccoords, pred_dist, pred_energy_corr, pred_energy_low_quantile, - pred_energy_high_quantile, pred_pos, pred_time, pred_time_unc, pred_id, energy, - pre_processed['t_idx'] , pre_processed['t_energy'] , pre_processed['t_pos'] , - pre_processed['t_time'] , pre_processed['t_pid'] , pre_processed['t_spectator_weight'], - pre_processed['t_fully_contained'], pre_processed['t_rec_energy'], - pre_processed['t_is_unique'], pre_processed['row_splits']]) - - pred_ccoords = PlotCoordinates( - plot_every=plot_debug_every, - outdir = debug_outdir, - name='condensation' - )([pred_ccoords, pred_beta, pre_processed['t_idx'], rs]) - - model_outputs = { - 'pred_beta': pred_beta, - 'pred_ccoords': pred_ccoords, - 'pred_energy_corr_factor': pred_energy_corr, - 'pred_energy_low_quantile': pred_energy_low_quantile, - 'pred_energy_high_quantile': pred_energy_high_quantile, - 'pred_pos': pred_pos, - 'pred_time': pred_time, - 'pred_id': pred_id, - 'pred_dist': pred_dist, - 'rechit_energy': energy, - 'row_splits': pre_processed['row_splits'], - # 'no_noise_sel': pre_processed['no_noise_sel'], - # 'no_noise_rs': pre_processed['no_noise_rs'], - } - - # return DictModel(inputs=Inputs, outputs=model_outputs) - return Model(inputs=Inputs, outputs=model_outputs) - - -############################################################################### -### Set up training ########################################################### -############################################################################### - - - -if not train.modelSet(): - train.setModel( - config_model, - td=train.train_data.dataclass(), - debug_outdir=train.outputDir+'/intplots', - plot_debug_every=PLOT_FREQUENCY, - ) - #train.applyFunctionToAllModels(RaggedGravNet.set_all_gn_space_trainable, False) #start with fixed GN space - train.setCustomOptimizer(tf.keras.optimizers.Adam())#clipnorm=1.)) - train.compileModel(learningrate=1e-4) - train.keras_model.summary() - train.saveModel(train.outputDir+'/model_init.h5') - -############################################################################### -### Callbacks ################################################################# -############################################################################### - - -samplepath = train.val_data.getSamplePath(train.val_data.samples[0]) -PUBLISHPATH = "" -PUBLISHPATH += [d for d in train.outputDir.split('/') if len(d)][-1] - - -cb = []#[NanSweeper()] #this takes a bit of time checking each batch but could be worth it - -cb += [ - plotClusterSummary( - outputfile=train.outputDir + "/clustering/", - samplefile=train.val_data.getSamplePath(train.val_data.samples[0]), - after_n_batches=1000 - ) - ] - - -# cb += [wandbCallback()] - -############################################################################### -### Actual Training ########################################################### -############################################################################### - -train.change_learning_rate(5e-3) -train.trainModel( - nepochs=2, - batchsize=50000, - add_progbar=pre_args.no_wandb, - additional_callbacks=cb, - collect_gradients = 4 #average out more gradients - ) - -# loop through model layers and turn batch normalisation to fixed -def fix_batchnorm(m): - for layer in m.layers: - if isinstance(layer, BatchNormalization): - layer.trainable = False - -#apply to all models -train.applyFunctionToAllModels(fix_batchnorm) - - -#recompile -train.compileModel(learningrate=2e-4) -print('entering second training phase') -train.trainModel( - nepochs=5, - batchsize=50000, - add_progbar=False, - additional_callbacks=cb, - collect_gradients = 4 - ) - - -#recompile -train.compileModel(learningrate=1e-4) -print('entering third training phase') -train.trainModel( - nepochs=10, - batchsize=50000, - add_progbar=False, - additional_callbacks=cb, - collect_gradients = 4 - ) \ No newline at end of file diff --git a/Train/paper_trainer_Cocoa_simple.py b/Train/paper_trainer_Cocoa_simple.py index e2662b79..d632934d 100644 --- a/Train/paper_trainer_Cocoa_simple.py +++ b/Train/paper_trainer_Cocoa_simple.py @@ -300,7 +300,7 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): train.change_learning_rate(1e-3) train.trainModel( - nepochs=100, + nepochs=150, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, @@ -310,9 +310,8 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): #recompile train.compileModel(learningrate=5e-4) -print('entering second training phase') train.trainModel( - nepochs=350, + nepochs=400, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, @@ -321,9 +320,8 @@ def config_model(Inputs, td, debug_outdir=None, plot_debug_every=2000): #recompile train.compileModel(learningrate=1e-4) -print('entering third training phase') train.trainModel( - nepochs=500, + nepochs=600, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, @@ -340,38 +338,42 @@ def fix_batchnorm(m): train.applyFunctionToAllModels(fix_batchnorm) #recompile -train.compileModel(learningrate=5e-4) -print('entering third training phase') +train.compileModel(learningrate=1e-3) train.trainModel( - nepochs=750, + nepochs=650, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, collect_gradients = 4 ) #recompile +train.compileModel(learningrate=5e-4) +train.trainModel( + nepochs=800, + batchsize=50000, + add_progbar=pre_args.no_wandb, + #additional_callbacks=cb, + collect_gradients = 4 + ) train.compileModel(learningrate=1e-4) -print('entering third training phase') train.trainModel( - nepochs=1000, + nepochs=1200, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, collect_gradients = 4 ) train.compileModel(learningrate=1e-5) -print('entering third training phase') train.trainModel( - nepochs=1100, + nepochs=1400, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, collect_gradients = 4 ) train.compileModel(learningrate=1e-6) -print('entering third training phase') train.trainModel( - nepochs=1200, + nepochs=1500, batchsize=50000, add_progbar=pre_args.no_wandb, #additional_callbacks=cb, diff --git a/models/simple_Cocoa/KERAS_model.h5 b/models/simple_Cocoa/KERAS_model.h5 index 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