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fit_response_distributions.py
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fit_response_distributions.py
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#!/usr/bin/env python
# coding: utf-8
"""
Analyses the output from CoffeaJERCProcessor_L5 and fits the response distributions.
Author(s): Andris Potrebko (RTU)
"""
# import sys
# coffea_path = '/afs/cern.ch/user/a/anpotreb/top/JERC/coffea/'
# if coffea_path not in sys.path:
# sys.path.insert(0,coffea_path)
# ak_path = '/afs/cern.ch/user/a/anpotreb/top/JERC/local-packages/'
# if ak_path not in sys.path:
# sys.path.insert(0,ak_path)
from coffea import util
import numpy as np
# import inspect
import matplotlib.pyplot as plt
import hist
import warnings
from plotters.pltStyle import pltStyle
pltStyle(style='hep') #, font_frac=1.40
plt.rcParams['figure.subplot.left'] = plt.rcParams['figure.subplot.left']*1.4
# plt.rcParams['font.size'] = plt.rcParams['font.size']/0.98
plt.rcParams['figure.dpi'] = 150
import os
### import subpackages
import helpers as h
import plotters.plot_makers as plot_makers
# from common_binning import JERC_Constants
from fileNames.available_datasets import dataset_dictionary, dataset_labels
from plotters.plot_cutflow import plot_cutflow
def fit_response_distributions(data_tag='Pythia-TTBAR', config=None):
''' The script fits the response histograms (or calculates the medians) and creates the `txt` files with the fit results
(one file for each `Mean`, `MeanStd`, `Median`, `MedianStd`, `MeanRecoPt`)
'''
# Get the directory of the current script
script_dir = os.path.dirname(os.path.realpath(__file__))
if config is None:
config = {}
# Use the dictionary values, or the defaults if they are not in the dictionary
test_run = config.get('test_run', False)
load_fit_res = config.get('load_fit_res', False)
saveplots = config.get('saveplots', False)
combine_antiflavour = config.get('combine_antiflavour', False)
eta_binning = config.get('eta_binning', 'HCalPart')
pt_binning = config.get('pt_binning', 'MC_truth')
sum_neg_pos_eta_bool = config.get('sum_neg_pos_eta_bool', True)
tag_Lx = config.get('tag_Lx', '_L5')
add_tag = config.get('add_tag', '')
fit_tag = config.get('fit_tag', '')
flavors = config.get('flavors', ['b', 'ud', 'all', 'g', 'c', 's', 'q', 'u', 'd', 'unmatched'])
pt_to_fit = config.get('pt_to_fit', None)
eta_to_fit = config.get('eta_to_fit', None)
################ End of the parameters of the run and switches #########################
# ### Do some logic with the input partameters and the rest of parameters of the run
tag_full = tag_Lx+'_'+data_tag+add_tag
if test_run:
tag_full = tag_full+'_test'
outname = os.path.join(script_dir, 'out', 'CoffeaJERCOutputs'+tag_full+'.coffea')
tag_fit_res = tag_full
if eta_binning != "HCalPart":
tag_fit_res=tag_full+'_'+eta_binning
if pt_binning != "MC_truth":
tag_fit_res=tag_fit_res+'_pt-'+pt_binning
combine_antiflavour_txt = '_split_antiflav' if not combine_antiflavour else ''
tag_fit_res += combine_antiflavour_txt+fit_tag
# if not os.path.exists("out"):
# os.mkdir("out")
fig_path = os.path.join(script_dir, 'fig')
if not os.path.exists(fig_path):
os.mkdir(fig_path)
if test_run and not os.path.exists(script_dir+"/test"):
os.mkdir(script_dir+"/test/")
os.mkdir(script_dir+"/test/fig")
out_txt_path = script_dir+"/out_txt" if not test_run else script_dir+"/test/out_txt"
if not os.path.exists(out_txt_path):
os.mkdir(out_txt_path)
# ### End of do some logic with the input partameters and the rest of parameters of the run
################ Load the histograms and scale them according to their cross-sections #########################
output = util.load(outname)
print("Loaded histograms from: ", outname)
xsec_dict, legend_label = h.get_xsec_dict(data_tag, dataset_dictionary)
keys = output.keys()
try: ## in older files cutflow was not split into cutflow for jets and events. Should be removed in the future
Nev = {key: output[key]['cutflow_events']['all_events'].value for key in keys}
except KeyError:
Nev = {key: output[key]['cutflow']['all_events'].value for key in keys}
scale_factors = h.hist_div(xsec_dict, Nev)
all_histo_keys = output[next(iter(output.keys()))].keys()
hists_merged = {histo_key:h.sum_subhist(output, histo_key, scale_factors) for histo_key in all_histo_keys }
# ### Fit responses
# Define some global variables for the fit
from JetEtaBins import JetEtaBins, PtBins
jeteta_bins = JetEtaBins(eta_binning)
pt_bins = PtBins(pt_binning)
fiteta_bins = JetEtaBins(eta_binning, absolute=True) if sum_neg_pos_eta_bool else jeteta_bins
if pt_to_fit is None:
pt_bins_to_fit = range(pt_bins.nbins)
elif len(pt_to_fit)==1:
pt_bins_to_fit = [pt_bins.get_bin_idx(pt_to_fit[0])]
elif len(pt_to_fit)==2:
pt_bins_to_fit = range(pt_bins.get_bin_idx(pt_to_fit[0]), pt_bins.get_bin_idx(pt_to_fit[1])+1)
if eta_to_fit is None:
eta_bins_to_fit = range(fiteta_bins.nbins)
elif len(eta_to_fit)==1:
eta_bins_to_fit = [fiteta_bins.get_bin_idx(eta_to_fit[0])]
elif len(eta_to_fit)==2:
eta_bins_to_fit = range(fiteta_bins.get_bin_idx(eta_to_fit[0]), fiteta_bins.get_bin_idx(eta_to_fit[1])+1)
def fit_responses(hists, flavor='b', saveplots = None, scaled_hist=None):
''' Extract the jet flavor `flavor` from the histogram dictionary `hists` and fit in all the eta and pt bins.
Add `scaled_hist` if to produce the response distributions with all the samples stacked up.
Return a dictionary of ["Mean", "MeanStd", "Median", "MedianStd", "MeanRecoPt"] values.
'''
warnings.filterwarnings('ignore') ### filter out the many fit warnings
if saveplots==None:
saveplots = False if test_run or eta_binning != "HCalPart" else True
response_hists = {}
recopt_hists = {}
if not scaled_hist==None:
for sample in scaled_hist:
response_hist, recopt_hist = h.add_flavors(scaled_hist[sample], flavor, combine_antiflavour)
response_hist = h.rebin_hist(response_hist, 'jeteta' , jeteta_bins.edges)
recopt_hist = h.rebin_hist(recopt_hist, 'jeteta' , jeteta_bins.edges)
if sum_neg_pos_eta_bool==True:
response_hist = h.sum_neg_pos_eta(response_hist)
recopt_hist = h.sum_neg_pos_eta(recopt_hist)
response_hists[sample] = response_hist
recopt_hists[sample] = recopt_hist
response_hist, recopt_hist = h.add_flavors(hists, flavor, combine_antiflavour)
# breakpoint()
# print("response hist, values = ", response_hist.values()[5,45:55,jeteta_bins.get_bin_idx(4.8)])
# response_hist.values()[:,:,1] = 0
# recopt_hist.values()[:,1] = 0
# response_hist.values()[:,:,-1] = 0
# recopt_hist.values()[:,-1] = 0
response_hist = h.rebin_hist(response_hist, 'jeteta' , jeteta_bins.edges)
recopt_hist = h.rebin_hist(recopt_hist, 'jeteta' , jeteta_bins.edges)
response_hist = h.rebin_hist(response_hist, 'pt_gen' , pt_bins.edges)
recopt_hist = h.rebin_hist(recopt_hist, 'pt_gen' , pt_bins.edges)
if sum_neg_pos_eta_bool==True:
response_hist = h.sum_neg_pos_eta(response_hist)
recopt_hist = h.sum_neg_pos_eta(recopt_hist)
# print("response hist, values = ", response_hist[33j,45:55,1.305j:1.566j].values().flatten())
# print("response hist, values = ", response_hist[33j,45:55,1.566j:1.74j].values().flatten())
results = {key:np.zeros((pt_bins.nbins, fiteta_bins.nbins))
for key in ["Mean", "MeanStd", "Median", "MedianStd", "MeanRecoPt"] }
N_converged = 0
N_little_ev = 0
N_failed = 0
FitFigDir1 = fig_path+'/responses/responses'+tag_full
if saveplots and not os.path.exists(FitFigDir1):
os.mkdir(FitFigDir1)
FitFigDir = FitFigDir1+'/response_pt_eta_'+flavor+tag_full
if saveplots:
if not os.path.exists(FitFigDir):
os.mkdir(FitFigDir)
print("Response fit plots will be saved under ", FitFigDir)
elif not saveplots:
print("Response fit plots won't be saved")
for i in pt_bins_to_fit:
for k in eta_bins_to_fit:
if not scaled_hist==None:
histos = {sample: response_hists[sample][i, :, k] for sample in response_hists}
histos2plot = {key[10:]:histos[key] for key in histos.keys()}
h_stack = hist.Stack.from_dict(histos2plot)
histo = response_hist[i, :, k]
histopt = recopt_hist[i, k]
try:
Neff = histo.sum().value**2/(histo.sum().variance)
except ZeroDivisionError:
Neff = histo.sum().value**2/(histo.sum().variance+1e-20)
median, medianstd = h.get_median(histo, Neff) #, x_range=[0, 2]
##################### Mean of the pt_reco ######################
### (The mean includes events that potentially had ptresponse in the second peak at low pt)
### No way to distinguish it if only x*weights are saved instead of the whole histogram.
mean_reco_pt = histopt.value/np.sum(histo.values())
####################### Fitting ############################
p2, cov, chi2, Ndof, status, fitlims = h.fit_response(histo, Neff, Nfit=3, sigma_fit_window=1.5)
if status == 1:
N_converged += 1
elif status == -1:
N_little_ev += 1
else:
N_failed += 1
####################### Store the results ############################
results["Mean"][i,k] = p2[1]
results["MeanStd"][i,k] = np.sqrt(np.abs(cov[1,1]))
results["Median"][i,k] = median
results["MedianStd"][i,k] = medianstd
results["MeanRecoPt"][i,k] = mean_reco_pt
####################### Plotting ############################
if saveplots:
figName = FitFigDir+'/ptResponse'+pt_bins.idx2str(i)+fiteta_bins.idx2str(k)
hep_txt = pt_bins.idx2plot_str(i)+'\n'+fiteta_bins.idx2plot_str(k)+'\n'+f'{flav} jet'
txt2print = ('\n'+r'Mean = {0:0.3f}$\pm${1:0.3f}'.format(p2[1], np.sqrt(cov[1,1]))
+ '\nWidth = {0:0.3f}$\pm${1:0.3f}'.format(np.abs(p2[2]), np.sqrt(cov[2,2]))
+ '\n'+r'Median = {0:0.3f}$\pm${1:0.3f}'.format(median, medianstd)
+ '\n'+r'$\chi^2/ndof$ = {0:0.2g}/{1:0.0f}'.format(chi2, Ndof)
+ '\n'+r'Neff = {0:0.3g}'.format(Neff))
plot_makers.plot_response_dist(histo, p2, fitlims,
figName, dataset_name=legend_label, hep_txt=hep_txt, txt2print=txt2print, print_txt=True)
if not scaled_hist==None:
plot_makers.plot_response_dist_stack(h_stack, p2, fitlims,
figName+'stack', hep_txt=hep_txt, print_txt=False )
print("fit summary: ")
print(f"N bins converged = {N_converged}; N bins not fit because of too little data = {N_little_ev}; N bins not converged = {N_failed}")
warnings.filterwarnings('default')
return results
# ### Run fitting for each sample
medians = []
medianstds = []
if not combine_antiflavour:
flavors = np.concatenate([[flav, flav+'bar'] if flav in h.barable_flavors else [flav] for flav in flavors ])
print('-'*25)
print('-'*25)
print(f'Starting to fit each flavor in: {flavors}')
result_each_flav = {}
for flav in flavors:
print('-'*25)
print('-'*25)
print('Fitting flavor: ', flav)
if load_fit_res:
result = {}
keys = ["Mean", "MeanStd", "Median", "MedianStd", "MeanRecoPt"]
for key in keys:
result[key] = h.read_data(key, flav, tag_fit_res, out_txt_path)
else:
result = fit_responses(hists_merged, flav, saveplots=saveplots) #scaled_hist
result_each_flav[flav] = result
medians.append(result["Median"][0][0])
medianstds.append(result["MedianStd"][0][0])
for key in result:
h.save_data(result[key], key, flav, tag_fit_res, pt_bins.centres, fiteta_bins.edges, out_txt_path)
pass
# print("result = ", result)
# median = result["Median"]
# medianStd = result["MedianStd"]
# meanstd = np.sqrt(result["MeanStd"])
if eta_binning=="onebin": #or fine_etabins:
plot_makers.plot_corrections_eta(result["Median"], result["MedianStd"], pt_bins, fiteta_bins.centres, tag_fit_res, flav, plotptvals=[20, 35, 150, 400])
else:
plot_makers.plot_corrections(result, pt_bins.centres, fiteta_bins, tag_fit_res, flav, plotetavals=[0, 1.305, 2.5, 3.139], plotmean=True)
# plotters.plot_corrections_eta(result["Median"], result["MedianStd"], pt_bins, fiteta_bins.centres, tag_fit_res, flav, plotptvals=[20, 35, 150, 400])
from save_json import save_json
save_json(result_each_flav, pt_bins, fiteta_bins, out_txt_path+'/response_fit_results'+tag_fit_res+'.json')
print('-'*25)
print('-'*25)
print('Saving cutflow')
rc_bottom_def = plt.rcParams['figure.subplot.bottom']
plt.rcParams['figure.subplot.bottom'] = 0.39
tag_cutflow = tag_full[4:]
# hist1 = output[list(keys)[0]] ### plotting only the first
scale_cutflow = {key: 1 for key in output}
sum_cutflow = {histo_key:h.sum_subhist(output, histo_key, scale_cutflow) for histo_key in ['cutflow_events', 'cutflow_jets'] }
### normalize the cutflow histograms to the number of events
def normalize_cutflow(hists_cutflow):
for key in ['cutflow_events', 'cutflow_jets']:
hists_cutflow[key].variances()[:] = hists_cutflow[key].values()
return hists_cutflow
sum_cutflow = normalize_cutflow(sum_cutflow)
dataset_label = dataset_labels[tag_cutflow] if tag_cutflow in dataset_labels else tag_cutflow
print("dataset label: ", dataset_label)
# sum_cutflow['cutflow_events'].variances()[:] = sum_cutflow['cutflow_events'].values()
# sum_cutflow['cutflow_jets'].variances()[:] = sum_cutflow['cutflow_jets'].values()
jets_pet_ev = sum_cutflow['cutflow_jets']/sum_cutflow['cutflow_events']['all_events'].value
plot_cutflow(sum_cutflow['cutflow_events'], tag_cutflow, ylab='N events', fig_name='cutflow_Nevents', title_name=dataset_label, figdir=fig_path+'/cutflow/'+tag_cutflow)
plot_cutflow(sum_cutflow['cutflow_jets'], tag_cutflow, ylab='N jets', fig_name='cutflow_Njets', title_name=dataset_label, figdir=fig_path+'/cutflow/'+tag_cutflow)
plot_cutflow(jets_pet_ev, tag_cutflow, ylab='N jets/N events', fig_name='cutflow_Njets_per_ev', title_name=dataset_label, figdir=fig_path+'/cutflow/'+tag_cutflow)
# if 'cutflow_events' in hists_cutflow:
# drawing only for the first sample as in the hist_merged, the total number of events are normalized
if len(list(keys))>1:
for key in keys:
cutflow = normalize_cutflow(output[key])
dataset_label = dataset_labels[key] if key in dataset_labels else key
dataset_label.replace('_', ' ')
jets_pet_ev = cutflow['cutflow_jets']/cutflow['cutflow_events']['all_events'].value
plot_cutflow(cutflow['cutflow_events'], key, ylab='N events', fig_name='cutflow_Nevents', title_name=dataset_label, figdir=fig_path+'/cutflow/'+tag_cutflow)
plot_cutflow(cutflow['cutflow_jets'], key, ylab='N jets', fig_name='cutflow_Njets', title_name=dataset_label, figdir=fig_path+'/cutflow/'+tag_cutflow)
plot_cutflow(jets_pet_ev, key, ylab='N jets/N events', fig_name='cutflow_Njets_per_ev', title_name=dataset_label, figdir=fig_path+'/cutflow/'+tag_cutflow)
# else:
# print("cutflow histograms cannot be drawn because the cutflow isn't split into events and jets. Potentially not all events are selected")
# cutflow = hist1['cutflow'][['all_events', 'selected_events', 'events passing the lepton selection', 'events, alpha cut' ]]
# plot_cutflow(cutflow, list(keys)[0], ylab='N events', fig_name='cutflow_Nevents')
# cutflow = hist1['cutflow'][[ 'all_jets', 'gen_matched', 'jets, tight lepton id',
# 'jets, dR cut with leptons', 'jetpt cut',
# 'alpha cut; leading jets','iso jets']]
# plot_cutflow(cutflow, list(keys)[0], ylab='N jets', fig_name='cutflow_Njets')
plt.rcParams['figure.subplot.bottom'] = rc_bottom_def
print('-----'*10)
print("All done. Congrats!")
if __name__ == "__main__":
data_tags = ['Pythia-TTBAR', 'Herwig-TTBAR', 'QCD-MG-Py', 'QCD-MG-Her', 'QCD-Py', 'DY-MG-Py', 'DY-MG-Her']
# data_tags = ['Pythia-TTBAR_iso_dr_0p8','Pythia-TTBAR_iso_dr_1p2', 'Pythia-TTBAR_iso_dr_1p5'] #Pythia-semilep-TTBAR
# data_tags = ['Herwig-TTBAR'] #, 'scaled_pion', 'not_scaled_pion'] #Pythia-semilep-TTBAR
# data_tags = ['scaled_times2_pion', 'scaled_times5_pion', 'scaled_times10_pion', 'scaled_pion', 'not_scaled_pion'] #Pythia-semilep-TTBAR
# data_tags = ['QCD-Py_noiso'] # , 'Pythia-TTBAR_100files_noiso', 'DY-MG-Py_noiso', 'QCD-MG-Py_noiso'] # 'Pythia-non-semilep-TTBAR', 'DY-MG-Py', 'QCD-MG-Py' Pythia-semilep-TTBAR
# data_tags = ['QCD-Py' ] #Pythia-semilep-TTBAR
config = {
################ Parameters of the run and switches #########################
"test_run" : False, ### True check on a file that was created with a processor with `test_run=True` (maybe obsolete because this can be specified just in the data_tag)
"load_fit_res" : False, ### (also kind of obsolete because plotting scripts exist in `plotters` ) True if only replot the fit results without redoing histogram fits
"saveplots" : False, ### True if save all the response distributions. There are many eta/pt bins so it takes time and space
"combine_antiflavour" : True, ### True if combine the flavor and anti-flavour jets into one histogram
### Choose eta binning for the response fits.
### HCalPart: bin in HCal sectors, CaloTowers: the standard JERC binning,
### CoarseCalo: like 'CaloTowers' but many bins united; onebin: combine all eta bins
### Preprocessing always done in CaloTowers. For the reponse distributions, the bins can be merged.
"eta_binning" : "Summer20Flavor", ### HCalPart, CoarseCalo, JERC, CaloTowers, Summer20Flavor, onebin;
"pt_binning" : "MC_truth", ### MC_truth, Uncert, Coarse, onebin
"sum_neg_pos_eta_bool": True, ### if combining the positive and negative eta bins
"tag_Lx" : '_L5', ### L5 or L23, but L23 not supported since ages.
### Define the dataset either by using a `data_tag` available in `dataset_dictionary`
### Or manualy by defining `dataset` (below) with the path to the .txt file with the file names (without the redirectors).
### Or manually by defining `fileslist` as the list with file names.
### data_tag will be used to name output figures and histograms.
# data_tag = 'Herwig-TTBAR' # 'QCD-MG-Her' #'Herwig-TTBAR'
# data_tag = 'DY-FxFx'
### name of the specific run if parameters changed used for saving figures and output histograms.
"add_tag": '',
### if the fit strategy changed and the results need to be stored with a different name
"fit_tag": '', #_remove_bad_eta_bin
### Define which flavors should be fit
# "flavors": ['b', 'ud', 'all', 'g', 'c', 's', 'q', 'u', 'd', 'unmatched'],
"flavors": ['b_gluon_splitting', "b_prompt", 'ud', 'all', 'g', 'c_gluon_splitting', "c_prompt", 'b', 'c', 's', 'q', 'u', 'd', 'unmatched'],
# "flavors": ['b_gluon_splitting', "b_prompt", 'c_gluon_splitting', "c_prompt", 'b', 'c'],
### None if all the pt bins should be fit, otherwise a list of two numbers for the range of pt bins to fit, or just one number for a single pt bin
# "pt_to_fit": None,
# "pt_to_fit": [30],
# "eta_to_fit": [0],
}
for data_tag in data_tags:
fit_response_distributions(data_tag=data_tag, config=config)