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plot_efficiencies.py
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import os
import numpy as np
import json
import math
import pandas as pd
from tqdm import tqdm
import pickle
from glob import glob
from root_plotting import *
import ROOT
ROOT.gErrorIgnoreLevel = ROOT.kWarning
get_ipython().run_cell_magic('cpp', '', '#include "RooDoubleCB.cc"\n')
def getCountedEvents(h, xrange=None):
nEvents = 0
err = np.double(0.)
if xrange is not None:
bin_low = h.GetXaxis().FindBin(xrange[0])
bin_high = h.GetXaxis().FindBin(xrange[1])
nEvents = h.IntegralAndError(bin_low, bin_high, err)
else:
nEvents = h.GetEntries()
err = 1 / np.sqrt(nEvents)
return nEvents, err
def estimateBins(h,nbins=5):
evts = [h.GetBinContent(i) for i in range(h.GetXaxis().GetNbins())]
tot = np.sum(evts)
frac = 1
n_evts = 0
bins = f'{h.GetBinLowEdge(1)}'
for i in range(1, h.GetXaxis().GetNbins()):
n_evts += h.GetBinContent(i)
if n_evts > frac * tot / nbins:
bins += ', '+str(round(h.GetBinLowEdge(i),2))
frac += 1
bins += ', '+str(round(h.GetBinLowEdge(h.GetXaxis().GetNbins()+1),2))
return bins
def rejectFit(counts, err, chisqr=None, isMC=False):
out = False
if isMC:
if counts < 1: out = True
if chisqr is not None:
if chisqr>10: out = True
else:
if counts < 10: out = True
if counts < err: out = True
if chisqr is not None:
if chisqr>10: out = True
return out
def print_badfits(list):
print(f'{"Trigger Path" : <25}{"Binning": <10}{"Bin" : <5}{"Fraction" : <10}{"Chi-Square Value"}')
print(f'{"~"*24} {"~"*9} {"~"*4} {"~"*9} {"~"*16}')
for fit_dict in list:
frac = 'Num' if fit_dict['frac']=='num' else 'Denom'
print(f'{fit_dict["trigger"]: <25}{fit_dict["key"]: <10}{fit_dict["bin"]: <5}{frac: <10}{round(fit_dict["chi square"],3)}')
def fit_jpsi_simple(h, signal_model='gauss', bkg_model='exp', signal_params=None, bkg_params=None, get_params=False, savename=None, show=False, verbose=False, get_chisqr=True, polypars=2):
m_JPsi = 3.096900
data_yield = h.GetEntries()
data_min = h.GetXaxis().GetXmin()
data_max = h.GetXaxis().GetXmax()
mass = ROOT.RooRealVar('mass', 'mass', data_min, data_max)
data = ROOT.RooDataHist('data', 'data', mass, h)
if signal_model:
if 'gauss' in signal_model:
if signal_params:
mean = ROOT.RooRealVar('mean', 'mean', signal_params[0])
sigma = ROOT.RooRealVar('sigma', 'sigma', signal_params[1])
else:
mean = ROOT.RooRealVar('mean', 'mean', m_JPsi, 3.0 , 3.15)
sigma = ROOT.RooRealVar('sigma', 'sigma', .03 , .02, .07)
signal = ROOT.RooGaussian('signal', 'signal', mass, mean, sigma)
elif 'dcb' in signal_model:
mean = ROOT.RooRealVar('mean', 'mean', m_JPsi, m_JPsi-.05 , m_JPsi+.05)
if signal_params:
sigma = ROOT.RooRealVar('sigma', 'sigma', signal_params[1])
al = ROOT.RooRealVar('al', 'al', signal_params[2])
ar = ROOT.RooRealVar('ar', 'ar', signal_params[3])
nl = ROOT.RooRealVar('nl', 'nl', signal_params[4])
nr = ROOT.RooRealVar('nr', 'nr', signal_params[5])
dcb = ROOT.RooDoubleCB('dcb', 'dcb', mass, mean, sigma, al, ar, nl, nr)
else:
sigma = ROOT.RooRealVar('sigma', 'sigma', .03 , .01, .07)
al = ROOT.RooRealVar('al', 'al', .2, 10.)
ar = ROOT.RooRealVar('ar', 'ar', .2, 10.)
nl = ROOT.RooRealVar('nl', 'nl', .2, 10.)
nr = ROOT.RooRealVar('nr', 'nr', .2, 10.)
signal = ROOT.RooDoubleCB('signal', 'signal', mass, mean, sigma, al, ar, nl, nr)
else:
raise ValueError('Input Valid Signal Model')
if bkg_model:
if 'exp' in bkg_model:
if bkg_params:
alpha = ROOT.RooRealVar('alpha', 'alpha', bkg_params[0], .5 * bkg_params, 2 * bkg_params)
else:
alpha = ROOT.RooRealVar('alpha', 'alpha', -1, -10, 0)
bkg = ROOT.RooExponential('bkg','bkg',mass,alpha)
elif 'poly' in bkg_model:
if bkg_params:
pars = [ROOT.RooRealVar(f'a{i}', f'a{i}', par, .5 * par, 2 * par) for i, par in enumerate(bkg_params[:-1])]
offset = ROOT.RooRealVar('offset','offset', bkg_params[-2], .5 * bkg_params[-2], 2 * bkg_params[-2])
else:
offset = ROOT.RooRealVar('offset','offset', m_JPsi, -10, 10)
par_range = [0, -50,50]
pars = [ROOT.RooRealVar(f'a{i}', f'a{i}', par_range[0], par_range[1], par_range[2]) for i in range(polypars)]
diff = ROOT.RooFormulaVar('diff','mass-offset', ROOT.RooArgList(mass, offset))
bkg = ROOT.RooPolynomial('bkg', 'bkg', diff, ROOT.RooArgList(*pars))
else:
raise ValueError('Input Valid Background Model')
if signal_model and bkg_model:
sigyield = ROOT.RooRealVar('sigyield', 'signal yield in model', .01 * data_yield, 0, data_yield)
bkgyield = ROOT.RooRealVar('bkgyield', 'background yield in model', .8 * data_yield, 0, data_yield)
model = ROOT.RooAddPdf('model', 'model', ROOT.RooArgList(signal, bkg), ROOT.RooArgList(sigyield, bkgyield))
r = model.fitTo(data, ROOT.RooFit.Save(True), ROOT.RooFit.Verbose(False), ROOT.RooFit.PrintEvalErrors(-1), ROOT.RooFit.PrintLevel(-1))
elif signal_model:
sigyield = ROOT.RooRealVar('sigyield', 'signal yield in model', 0, data_yield)
model = ROOT.RooAddPdf('model', 'model', ROOT.RooArgList(signal), ROOT.RooArgList(sigyield))
r = model.fitTo(data, ROOT.RooFit.Save(True), ROOT.RooFit.Verbose(False), ROOT.RooFit.PrintEvalErrors(-1), ROOT.RooFit.PrintLevel(-1))
elif bkg_model:
bkgyield = ROOT.RooRealVar('bkgyield', 'background yield in model', data_yield, 0, data_yield)
model = ROOT.RooAddPdf('model', 'model', ROOT.RooArgList(bkg), ROOT.RooArgList(bkgyield))
mass.setRange("sb1", data_min, m_JPsi - .35)
mass.setRange("sb2", m_JPsi + .35, data_max)
# sb_data = data.reduce(ROOT.RooFit.CutRange("sb_1, sb_2"))
r = model.fitTo(data, ROOT.RooFit.Range("sb1"), ROOT.RooFit.Save(True))#, ROOT.RooFit.Verbose(True), ROOT.RooFit.PrintEvalErrors(-1), ROOT.RooFit.PrintLevel(-1))
# r1 = model.fitTo(data, ROOT.RooFit.Save(True))#, ROOT.RooFit.Verbose(True), ROOT.RooFit.PrintEvalErrors(-1), ROOT.RooFit.PrintLevel(-1))
# r1.Print(0)
else: raise ValueError('Input Some Type of Model')
if signal_model and bkg_model:
out = [sigyield.getVal(), sigyield.getError()]
if 'gauss' in signal_model:
out_params = (mean.getVal(), sigma.getVal())
elif 'dcb' in signal_model:
out_params = (mean.getVal(), sigma.getVal(), al.getVal(), ar.getVal(), nl.getVal(), nr.getVal())
elif signal_model:
out = [sigyield.getVal(), sigyield.getError()]
if 'gauss' in signal_model:
out_params = (mean.getVal(), sigma.getVal())
elif 'dcb' in signal_model:
out_params = (mean.getVal(), sigma.getVal(), al.getVal(), ar.getVal(), nl.getVal(), nr.getVal())
elif bkg_model:
out = [bkgyield.getVal(), bkgyield.getError()]
if 'exp' in bkg_model:
out_params = (alpha.getVal())
elif 'poly' in bkg_model:
out_params = tuple(par.getVal() for par in pars)
out_params = out_params + (offset.getVal(),)
else: raise ValueError('Input Some Type of Model')
if get_params: out.append(out_params)
if show or savename:
c = ROOT.TCanvas()
frame = mass.frame(ROOT.RooFit.Title('Fit' if not savename else savename))
data.plotOn(frame, ROOT.RooFit.Name('data'), ROOT.RooFit.CutRange("sb_1, sb_2"))
if signal_model and bkg_model:
model.plotOn(frame, ROOT.RooFit.Name('model'), ROOT.RooFit.LineColor(38), ROOT.RooFit.Normalization(sigyield.getVal()+bkgyield.getVal(),ROOT.RooAbsReal.NumEvent))
model.plotOn(frame, ROOT.RooFit.Components('signal'), ROOT.RooFit.LineColor(32))
model.plotOn(frame, ROOT.RooFit.Components('bkg'), ROOT.RooFit.LineColor(46))
elif signal_model:
model.plotOn(frame, ROOT.RooFit.Name('model'), ROOT.RooFit.LineColor(38), ROOT.RooFit.Normalization(sigyield.getVal(),ROOT.RooAbsReal.NumEvent))
elif bkg_model:
model.plotOn(frame, ROOT.RooFit.Name('model'), ROOT.RooFit.LineColor(38), ROOT.RooFit.Normalization(bkgyield.getVal(),ROOT.RooAbsReal.NumEvent),ROOT.RooFit.Range("sb_1, sb_2"))
else: raise ValueError('Input Some Type of Model')
model.paramOn(frame, ROOT.RooFit.Layout(0.12, 0.3, 0.88), ROOT.RooFit.Format('NE', ROOT.RooFit.FixedPrecision(3)))
frame.getAttText().SetTextSize(.025)
frame.Draw()
text = ROOT.TText()
text.SetTextFont(43)
text.SetTextSize(20)
chisqr = frame.chiSquare('model', 'data')
# chisqr = ROOT.RooChi2Var('chi2', 'chi2', model, data).getVal()
text.DrawTextNDC(.65, .85, f'Chi-Square = {round(chisqr,2)}')
if get_chisqr: out.append(chisqr)
# if verbose:
# print(h.GetTitle(), chisqr)
if savename:
# os.makedirs()
# print(os.path.abspath(savename))
c.SaveAs(savename)
if show: out.append(c)
# Recurse polynomial fit to improve convergence
if bkg_model:
if 'poly' in bkg_model:
if verbose: print(f'Background Fit Order: {polypars} - Chi-Square: {chisqr}')
if chisqr > 1. and polypars<9:
new_out = fit_jpsi_simple(h, signal_model, bkg_model, signal_params, bkg_params, get_params, savename, show, verbose, get_chisqr=True, polypars=polypars+1)
if new_out[3] < out[3]: out = new_out
if show: c.Draw()
return out
def processMCFitParams(filename, trigger_dict):
print('Generating Fit Parameters...')
ROOT.RooMsgService.instance().setGlobalKillBelow(ROOT.RooFit.MsgLevel.ERROR)
f = ROOT.TFile(filename)
outputs_params = {}
for j, (num_name, (num_path, denom_path)) in enumerate(pbar := tqdm(trigger_dict.items(), ncols=100, unit=' Triggers')):
pbar.set_description(num_name)
outputs_params_var = {}
for name, opts in options.items():
nbins = len(opts['bins']) - 1
eff_output = np.zeros(nbins, np.dtype('<U99,float,float,float,float'))
# TODO Remove Excl/Incl naming for MC in DiElectronEfficiencies.py script (lxplus)
if False:
if 'Excl' in num_path:
num_path = num_path.replace('Excl','Incl')
denom_path = denom_path.replace('Excl','Incl')
elif 'Incl' in num_path:
num_path = num_path.replace('Incl','Excl')
denom_path = denom_path.replace('Incl','Excl')
num_h = f.Get(''.join([num_path,opts['suffix']]))
denom_h = f.Get(''.join([denom_path,opts['suffix']]))
outputs_params_num_bin = []
outputs_params_denom_bin = []
for i in range(1, nbins + 1):
# print(num_name, name, i)
num_bin = num_h.ProjectionX('h1',i,i+1)
denom_bin = denom_h.ProjectionX('h2',i,i+1)
_, _, num_params = fit_jpsi_simple(num_bin, signal_model='dcb', bkg_model=None, signal_params=None, get_params=True, savename=None, show=False)
_, _, denom_params = fit_jpsi_simple(denom_bin, signal_model='dcb', bkg_model=None, signal_params=None, get_params=True, savename=None, show=False)
outputs_params_num_bin.append(num_params)
outputs_params_denom_bin.append(denom_params)
outputs_params_var[name] = {'num' : outputs_params_num_bin, 'denom' : outputs_params_denom_bin}
outputs_params[num_name] = outputs_params_var
return outputs_params
def processMCEffs(filename, trigger_dict, verbose=False, paramset=None, savefits=False):
print('Calculating Efficiencies for MC...')
ROOT.RooMsgService.instance().setGlobalKillBelow(ROOT.RooFit.MsgLevel.ERROR)
f = ROOT.TFile(filename)
outputs_mc = []
badfits_mc = []
for j, (num_name, (num_path, denom_path)) in enumerate(pbar := tqdm(trigger_dict.items(), ncols=100, unit=' Triggers')):
pbar.set_description(num_name)
output_dict_mc = {}
for name, opts in options.items():
nbins = len(opts['bins']) - 1
eff_output = np.zeros(nbins,np.dtype('<U99,float,float,float,float'))
# TODO Remove Excl/Incl naming for MC in DiElectronEfficiencies.py script (lxplus)
if False:
if 'Excl' in num_path:
num_path = num_path.replace('Excl','Incl')
denom_path = denom_path.replace('Excl','Incl')
elif 'Incl' in num_path:
num_path = num_path.replace('Incl','Excl')
denom_path = denom_path.replace('Incl','Excl')
num_h = f.Get(''.join([num_path,opts['suffix']]))
denom_h = f.Get(''.join([denom_path,opts['suffix']]))
for i in range(1, nbins + 1):
try: num_bin = num_h.ProjectionX('num_bin',i,i+1)
except AttributeError('Cannot Find Numerator Histogram'): continue
try: denom_bin = denom_h.ProjectionX('denom_bin',i,i+1)
except AttributeError('Cannot Find Denominator Histogram'): continue
num_counts, num_err, chisqr = fit_jpsi_simple(num_bin,
signal_model='dcb',
signal_params=paramset[num_name][name]['num'][i-1] if paramset else None,
bkg_model=None,
get_params=False,
get_chisqr=True,
savename=f'plots/fits/mc/{name}/{num_name}_{name}binned_bin{i}_mc_num.png' if savefits else False)
# if num_bin.GetEntries() < 3: num_counts, num_err = 0, 0
if rejectFit(num_counts, num_err, chisqr, isMC=True):
num_counts, num_err = 0, 0
badfits_mc.append({'trigger' : num_name, 'key': name, 'bin' : i, 'frac' : 'num', 'chi square' : chisqr})
denom_counts, denom_err, chisqr = fit_jpsi_simple(denom_bin,
signal_model='dcb',
bkg_model=None,
signal_params=paramset[num_name][name]['denom'][i-1] if paramset else None,
get_params=False,
get_chisqr=True,
savename=f'plots/fits/mc/{name}/{num_name}_{name}binned_bin{i}_mc_denom.png' if savefits else False)
# if denom_bin.GetEntries() < 3: num_counts, num_err = 0, 0
if rejectFit(denom_counts, denom_err, chisqr, isMC=True):
denom_counts, denom_err = 0, 0
badfits_mc.append({'trigger' : num_name, 'key': name, 'bin' : i, 'frac' : 'den', 'chi square' : chisqr})
eff_output[i-1] = (num_name,num_counts,num_err,denom_counts,denom_err)
output_dict_mc[name] = eff_output
outputs_mc.append(output_dict_mc)
return outputs_mc, badfits_mc
# In[6]:
def processDataEffs(filenames, trigger_dict, verbose=False, paramset=None, savefits=False):
print('Calculating Efficiencies for Data...')
ROOT.RooMsgService.instance().setGlobalKillBelow(ROOT.RooFit.MsgLevel.ERROR)
files = filenames
outputs_data = []
badfits_data = []
for j, (num_name, (num_path, denom_path)) in enumerate(pbar := tqdm(trigger_dict.items(), ncols=100, unit=' Triggers')):
pbar.set_description(num_name)
output_dict_data = {}
for name, opts in options.items():
nbins = len(opts['bins']) - 1
eff_output = np.zeros(nbins, np.dtype('<U99,float,float,float,float'))
for filename in files:
if num_name not in filename: continue
f = ROOT.TFile(filename)
num_h = f.Get(''.join([num_path,opts['suffix']]))
denom_h = f.Get(''.join([denom_path,opts['suffix']]))
for i in range(1, nbins + 1):
try: num_bin = num_h.ProjectionX('num_bin',i,i+1)
except AttributeError('Cannot Find Numerator Histogram'): continue
try: denom_bin = denom_h.ProjectionX('denom_bin',i,i+1)
except AttributeError('Cannot Find Denominator Histogram'): continue
bkg_shape = 'poly' if 'dr' in name else 'exp'
num_counts, num_err, _, chisqr = fit_jpsi_simple(num_bin,
signal_model='dcb',
signal_params=paramset[num_name][name]['num'][i-1] if paramset else None,
bkg_model=bkg_shape,
get_params=True,
get_chisqr=True,
savename=f'plots/fits/data/{name}/{num_name}_{name}binned_bin{i}_data_num.png' if savefits else False)
if num_bin.GetEntries() < 10: num_counts, num_err = 0, 0
if rejectFit(num_counts, num_err, chisqr):
num_counts, num_err = 0, 0
badfits_data.append({'trigger' : num_name, 'key': name, 'bin' : i, 'frac' : 'num', 'chi square' : chisqr})
denom_counts, denom_err, _, chisqr = fit_jpsi_simple(denom_bin,
signal_model='dcb',
signal_params=paramset[num_name][name]['denom'][i-1] if paramset else None,
bkg_model=bkg_shape,
get_params=True,
get_chisqr=True,
savename=f'plots/fits/data/{name}/{num_name}_{name}binned_bin{i}_data_denom.png' if savefits else False)
if denom_bin.GetEntries() < 10: num_counts, num_err = 0, 0
if rejectFit(denom_counts, denom_err, chisqr):
denom_counts, denom_err = 0, 0
badfits_data.append({'trigger' : num_name, 'key': name, 'bin' : i, 'frac' : 'den', 'chi square' : chisqr})
eff_output[i-1] = (num_name,num_counts,num_err,denom_counts,denom_err)
output_dict_data[name] = eff_output
# if np.any(np.array([tuple(eff_output[i-1])[1:] for i in range(1, nbins + 1)])): output_dict_data[name] = eff_output
outputs_data.append(output_dict_data)
# if output_dict_data: outputs_data.append(output_dict_data)
return outputs_data, badfits_data
ROOT.RooMsgService.instance().setGlobalKillBelow(ROOT.RooFit.MsgLevel.WARNING)
options = {
'pt' : {'suffix' : '_ptbinned', 'bins' : np.array([5, 7, 9, 10, 11, 12, 13, 20], dtype=np.double), 'xlabel' : r'Sublead \ Electron \ p_{T} \ [GeV]'},
# 'dr' : {'suffix' : '_drbinned', 'bins' : np.array([0, 0.12, 0.2, 0.28, 0.44, 1.], dtype=np.double), 'xlabel' : r'\Delta R(e_{1},e_{2})'},
# 'npv' : {'suffix' : '_npvbinned', 'bins' : np.array([0, 22, 27,31, 36, 100], dtype=np.double), 'xlabel' : r'N_{PV}'},
# 'eta' : {'suffix' : '_etabinned', 'bins' : np.array([-1.22, -0.7, -.2, 0.2, .7, 1.22], dtype=np.double), 'xlabel' : r'Sublead \ Electron \ \eta'},
# 'diept' : {'suffix' : '_dieptbinned', 'bins' : np.array([5, 10, 11, 12, 15, 20, 40, 70, 100], dtype=np.double), 'xlabel' : r' Di-Electron p_{T} \ [GeV]'},
}
trigger_dict = {
'trigger_OR' : ('plots/diel_m_trigger_OR_num', 'plots/diel_m_trigger_OR_denom'),
'L1_11p0_HLT_6p5_Incl' : ('plots/diel_m_L1_11p0_HLT_6p5_Incl_Final_num', 'plots/diel_m_L1_11p0_HLT_6p5_Incl_Final_denom'),
'L1_10p5_HLT_6p5_Incl' : ('plots/diel_m_L1_10p5_HLT_6p5_Incl_Final_num', 'plots/diel_m_L1_10p5_HLT_6p5_Incl_Final_denom'),
'L1_10p5_HLT_5p0_Incl' : ('plots/diel_m_L1_10p5_HLT_5p0_Incl_Final_num', 'plots/diel_m_L1_10p5_HLT_5p0_Incl_Final_denom'),
'L1_9p0_HLT_6p0_Incl' : ('plots/diel_m_L1_9p0_HLT_6p0_Incl_Final_num', 'plots/diel_m_L1_9p0_HLT_6p0_Incl_Final_denom'),
'L1_8p5_HLT_5p5_Incl' : ('plots/diel_m_L1_8p5_HLT_5p5_Incl_Final_num', 'plots/diel_m_L1_8p5_HLT_5p5_Incl_Final_denom'),
'L1_8p5_HLT_5p0_Incl' : ('plots/diel_m_L1_8p5_HLT_5p0_Incl_Final_num', 'plots/diel_m_L1_8p5_HLT_5p0_Incl_Final_denom'),
'L1_8p0_HLT_5p0_Incl' : ('plots/diel_m_L1_8p0_HLT_5p0_Incl_Final_num', 'plots/diel_m_L1_8p0_HLT_5p0_Incl_Final_denom'),
'L1_7p5_HLT_5p0_Incl' : ('plots/diel_m_L1_7p5_HLT_5p0_Incl_Final_num', 'plots/diel_m_L1_7p5_HLT_5p0_Incl_Final_denom'),
'L1_7p0_HLT_5p0_Incl' : ('plots/diel_m_L1_7p0_HLT_5p0_Incl_Final_num', 'plots/diel_m_L1_7p0_HLT_5p0_Incl_Final_denom'),
'L1_6p5_HLT_4p5_Incl' : ('plots/diel_m_L1_6p5_HLT_4p5_Incl_Final_num', 'plots/diel_m_L1_6p5_HLT_4p5_Incl_Final_denom'),
'L1_6p0_HLT_4p0_Incl' : ('plots/diel_m_L1_6p0_HLT_4p0_Incl_Final_num', 'plots/diel_m_L1_6p0_HLT_4p0_Incl_Final_denom'),
'L1_5p5_HLT_6p0_Incl' : ('plots/diel_m_L1_5p5_HLT_6p0_Incl_Final_num', 'plots/diel_m_L1_5p5_HLT_6p0_Incl_Final_denom'),
'L1_5p5_HLT_4p0_Incl' : ('plots/diel_m_L1_5p5_HLT_4p0_Incl_Final_num', 'plots/diel_m_L1_5p5_HLT_4p0_Incl_Final_denom'),
'L1_5p0_HLT_4p0_Incl' : ('plots/diel_m_L1_5p0_HLT_4p0_Incl_Final_num', 'plots/diel_m_L1_5p0_HLT_4p0_Incl_Final_denom'),
'L1_4p5_HLT_4p0_Incl' : ('plots/diel_m_L1_4p5_HLT_4p0_Incl_Final_num', 'plots/diel_m_L1_4p5_HLT_4p0_Incl_Final_denom'),
'L1_11p0_HLT_6p5_Excl' : ('plots/diel_m_L1_11p0_HLT_6p5_Excl_Final_num', 'plots/diel_m_L1_11p0_HLT_6p5_Excl_Final_denom'),
'L1_10p5_HLT_6p5_Excl' : ('plots/diel_m_L1_10p5_HLT_6p5_Excl_Final_num', 'plots/diel_m_L1_10p5_HLT_6p5_Excl_Final_denom'),
'L1_10p5_HLT_5p0_Excl' : ('plots/diel_m_L1_10p5_HLT_5p0_Excl_Final_num', 'plots/diel_m_L1_10p5_HLT_5p0_Excl_Final_denom'),
'L1_9p0_HLT_6p0_Excl' : ('plots/diel_m_L1_9p0_HLT_6p0_Excl_Final_num', 'plots/diel_m_L1_9p0_HLT_6p0_Excl_Final_denom'),
'L1_8p5_HLT_5p5_Excl' : ('plots/diel_m_L1_8p5_HLT_5p5_Excl_Final_num', 'plots/diel_m_L1_8p5_HLT_5p5_Excl_Final_denom'),
'L1_8p5_HLT_5p0_Excl' : ('plots/diel_m_L1_8p5_HLT_5p0_Excl_Final_num', 'plots/diel_m_L1_8p5_HLT_5p0_Excl_Final_denom'),
'L1_8p0_HLT_5p0_Excl' : ('plots/diel_m_L1_8p0_HLT_5p0_Excl_Final_num', 'plots/diel_m_L1_8p0_HLT_5p0_Excl_Final_denom'),
'L1_7p5_HLT_5p0_Excl' : ('plots/diel_m_L1_7p5_HLT_5p0_Excl_Final_num', 'plots/diel_m_L1_7p5_HLT_5p0_Excl_Final_denom'),
'L1_7p0_HLT_5p0_Excl' : ('plots/diel_m_L1_7p0_HLT_5p0_Excl_Final_num', 'plots/diel_m_L1_7p0_HLT_5p0_Excl_Final_denom'),
'L1_6p5_HLT_4p5_Excl' : ('plots/diel_m_L1_6p5_HLT_4p5_Excl_Final_num', 'plots/diel_m_L1_6p5_HLT_4p5_Excl_Final_denom'),
'L1_6p0_HLT_4p0_Excl' : ('plots/diel_m_L1_6p0_HLT_4p0_Excl_Final_num', 'plots/diel_m_L1_6p0_HLT_4p0_Excl_Final_denom'),
'L1_5p5_HLT_6p0_Excl' : ('plots/diel_m_L1_5p5_HLT_6p0_Excl_Final_num', 'plots/diel_m_L1_5p5_HLT_6p0_Excl_Final_denom'),
'L1_5p5_HLT_4p0_Excl' : ('plots/diel_m_L1_5p5_HLT_4p0_Excl_Final_num', 'plots/diel_m_L1_5p5_HLT_4p0_Excl_Final_denom'),
'L1_5p0_HLT_4p0_Excl' : ('plots/diel_m_L1_5p0_HLT_4p0_Excl_Final_num', 'plots/diel_m_L1_5p0_HLT_4p0_Excl_Final_denom'),
'L1_4p5_HLT_4p0_Excl' : ('plots/diel_m_L1_4p5_HLT_4p0_Excl_Final_num', 'plots/diel_m_L1_4p5_HLT_4p0_Excl_Final_denom'),
}
data_pathname = 'root_files/data/baseline'
# mc_filename = 'root_files/mc/Efficiencies_BuToKJpsi_Toee_Loose.root'
# mc_filename = 'root_files/mc/Efficiencies_BuToKJpsi_Toee_baseline_Tight.root'
mc_filename = 'root_files/mc/Efficiencies_BuToKJpsi_Toee_Reweighted.root'
mc_params = processMCFitParams(mc_filename, trigger_dict)
outputs_mc, badfits_mc = processMCEffs(mc_filename, trigger_dict, verbose=True, paramset=mc_params, savefits=True)
outputs_data, badfits_data = processDataEffs(glob(data_pathname+'/*.root'), trigger_dict, verbose=False, paramset=mc_params, savefits=True)
print('Data Fits:', badfits_data)
print('MC Fits:', badfits_mc)
# ## Plotting
# ### Single Hist Plotter
# In[9]:
# import matplotlib.pyplot as plt
# # ROOT.TH1.SetDefaultSumw2()
# ROOT.gROOT.SetBatch(True)
# options = {
# 'pt' : {'suffix' : '_ptbin', 'nbins' : 7, 'bins' : np.array([5, 5.4, 6.2, 7.4, 9.6, 10.5, 12., 50.], dtype=np.double) , 'xlabel' : r'Sublead Electron p_{T} [GeV]'},
# 'eta' : {'suffix' : '_etabin', 'nbins' : 5, 'bins' : np.array([-1.22, -0.7, -.2, 0.2, .7, 1.22], dtype=np.double), 'xlabel' : r'Sublead Electron \eta'},
# 'dr' : {'suffix' : '_drbin', 'nbins' : 4, 'bins' : np.array([0, 0.1, 0.3, 0.6, 0.8], dtype=np.double), 'xlabel' : r'\Delta R(e_{1},e_{2})'},
# 'npv' : {'suffix' : '_npvbin', 'nbins' : 2, 'bins' : np.array([0, 35, 100], dtype=np.double), 'xlabel' : r'N_{PV}'},
# 'diept' : {'suffix' : '_dieptbin', 'nbins' : 7, 'bins' : np.array([5, 5.4, 6.2, 7.4, 9.6, 10.5, 12., 50.], dtype=np.double), 'xlabel' : r'Di-Electron p_{T} [GeV]'},
# }
# usedata = True
# outputs = outputs_data if usedata else outputs_mc
# key = 'pt'
# overlap = False
# if overlap:
# plt.figure(figsize=(10,8))
# plt.title('Di-Electron Trigger Turn-On Curves')
# plt.ylabel('Efficiency',loc='top')
# plt.xlabel(xlabel,loc='right')
# plt.ylim(0,1.)
# plt.xlim(options[key]['bins'][0],options[key]['bins'][-1])
# for output in outputs:
# eff_output = output[key]
# bins = options[key]['bins']
# xlabel = options[key]['xlabel']
# for i in range(1):
# xs = (bins[1:] + bins[:-1]) / 2
# x_err=np.diff(bins)/2
# title, nums, num_errs, denoms, denom_errs = np.array([[*i] for i in eff_output]).T
# title = np.asarray(title)[np.nonzero(np.asarray(title))][0] if len(np.asarray(title)[np.nonzero(np.asarray(title))]) else ''
# hpass = ROOT.TH1F('hpass', 'hpass', len(bins)-1, bins)
# hall = ROOT.TH1F('hall', 'hall', len(bins)-1, bins)
# nums = nums.astype(np.double)
# num_errs = num_errs.astype(np.double)
# denoms = denoms.astype(np.double)
# denom_errs = denom_errs.astype(np.double)
# for ibin, (num, num_err, denom, denom_err) in enumerate(zip(nums, num_errs, denoms, denom_errs)):
# if num > denom: continue
# hpass.SetBinContent(ibin+1,num)
# hpass.SetBinError(ibin+1,num_err)
# hall.SetBinContent(ibin+1,denom)
# hall.SetBinError(ibin+1,denom_err)
# eff = ROOT.TEfficiency(hpass, hall)
# del hpass
# del hall
# ys = []
# y_uerr = []
# y_derr = []
# for ibin in range(len(bins)-1):
# ys.append(eff.GetEfficiency(ibin+1))
# y_uerr.append(eff.GetEfficiencyErrorUp(ibin+1))
# y_derr.append(eff.GetEfficiencyErrorLow(ibin+1))
# y_err = [y_uerr,y_derr]
# if not overlap:
# eff.SetTitle(f'{title}; {xlabel}; Efficiency')
# c = ROOT.TCanvas('c', 'c', 800,800)
# eff.Draw()
# ROOT.gPad.Update()
# g = eff.GetPaintedGraph()
# g.GetXaxis().SetLimits(options[key]['bins'][0],options[key]['bins'][-1])
# g.GetHistogram().SetMinimum(0)
# g.GetHistogram().SetMaximum(1)
# g.Draw('E')
# ROOT.gPad.Update()
# c.SaveAs(f'plots/eff_{key}_{title}.pdf') if usedata else c.SaveAs(f'plots/eff_{key}_{title}_mc.pdf')
# del c
# plt.figure(figsize=(6,6))
# plt.title(title)
# plt.ylabel('Efficiency',loc='top')
# plt.xlabel(xlabel,loc='right')
# plt.ylim(0,1.)
# plt.xlim(options[key]['bins'][0],options[key]['bins'][-1])
# plt.errorbar(xs,ys,xerr=x_err,yerr=y_err,capsize=2,linestyle='none',label=title)
# if overlap: plt.legend(loc=1)
# ### Comparison Plotter
import matplotlib.pyplot as plt
from root_plotting.EfficiencyPlot import EfficiencyPlot
# import root_plotting
ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = kError;')
def saveEffsFromArrays(name, data, key, bins, effs, errup, errdown):
filename = '_'.join(['Trigger_Effs/TrigEffs', 'Data' if data else 'MC', 'Incl', key+'binned.json'])
if os.path.isfile(filename) and os.access(filename, os.R_OK):
with open(filename, 'r') as f:
out_dict = json.load(f)
else: out_dict = {}
if name not in out_dict.keys(): out_dict[name] = {}
out_dict[name][key] = bins[:-1].tolist()
out_dict[name].update({'effs' : [(round(eff,3),round(err,3)) for eff,err in zip(effs,np.maximum(errup, errdown).tolist())]})
with open(filename, 'w+') as f:
json.dump(out_dict, f, indent=4)
def get_subplots_grid(i):
if i==1: return 1,1
for j in range(1,i):
if i%j==0 and i/j<9 and i/j>1:
return int(i/j), j
return get_subplots_grid(i+1)
def plotEffComparison(dict_1, dict_2, options, key='pt', justIncl=False, show=True, labels=None, save=False):
RooEff = EfficiencyPlot()
label1, label2 = ('Data', 'MC') if labels is None else labels
bins = options[key]['bins']
xlabel = f'${options[key]["xlabel"]}$'
nplots = 1 if justIncl or (len(dict_1)==1 and len(dict_2)==1) else len(dict_1)
if show:
if nplots==1:
fig, axs = plt.subplots(figsize=(8,8))
else:
if len(dict_1) != len(dict_2): raise ValueError('Make sure Data and MC effs Match')
nplots = len(dict_1)
cols, rows = get_subplots_grid(nplots)
fig, axs = plt.subplots(rows, cols, figsize=(5 * cols, 4 * rows))
fig.text(.5, 0, xlabel, ha='center', va='center', fontsize=18)
fig.text(.09, .6, 'Efficiency', va='top', rotation='vertical', fontsize=18)
if cols*rows%nplots!=0: axs.ravel()[-1].axis("off")
for i, (output_1, output_2) in enumerate(zip(dict_1, dict_2)):
eff_output_1 = output_1[key]
eff_output_2 = output_2[key]
xs = (bins[1:] + bins[:-1]) / 2
x_err=np.diff(bins)/2
title, nums_1, num_errs_1, denoms_1, denom_errs_1 = np.array([[*i] for i in eff_output_1]).T
title, nums_2, num_errs_2, denoms_2, denom_errs_2 = np.array([[*i] for i in eff_output_2]).T
title = np.asarray(title)[np.nonzero(np.asarray(title))][0] if len(np.asarray(title)[np.nonzero(np.asarray(title))]) else ''
if justIncl and 'trigger_OR' not in title: continue
# # FIXME
# title = title.replace('Incl', 'Excl')
hpass_1 = ROOT.TH1F('hpass_1', 'hpass_1', len(bins)-1, bins)
hpass_2 = ROOT.TH1F('hpass_2', 'hpass_2', len(bins)-1, bins)
hall_1 = ROOT.TH1F('hall_1', 'hall_1', len(bins)-1, bins)
hall_2 = ROOT.TH1F('hall_2', 'hall_2', len(bins)-1, bins)
nums_1 = nums_1.astype(np.double)
num_errs_1 = num_errs_1.astype(np.double)
denoms_1 = denoms_1.astype(np.double)
denom_errs_1 = denom_errs_1.astype(np.double)
nums_2 = nums_2.astype(np.double)
num_errs_2 = num_errs_2.astype(np.double)
denoms_2 = denoms_2.astype(np.double)
denom_errs_2 = denom_errs_2.astype(np.double)
for ibin, (num, num_err, denom, denom_err) in enumerate(zip(nums_1, num_errs_1, denoms_1, denom_errs_1)):
if num > denom: continue
hpass_1.SetBinContent(ibin+1,num)
hall_1.SetBinContent(ibin+1,denom)
hpass_1.SetBinError(ibin+1,num_err)
hall_1.SetBinError(ibin+1,denom_err)
for ibin, (num, num_err, denom, denom_err) in enumerate(zip(nums_2, num_errs_2, denoms_2, denom_errs_2)):
if num > denom: continue
hpass_2.SetBinContent(ibin+1,num)
hall_2.SetBinContent(ibin+1,denom)
hpass_2.SetBinError(ibin+1,num_err)
hall_2.SetBinError(ibin+1,denom_err)
eff_1 = ROOT.TEfficiency(hpass_1, hall_1)
eff_1.SetStatisticOption(ROOT.TEfficiency.kBBayesian)
eff_2 = ROOT.TEfficiency(hpass_2, hall_2)
eff_2.SetStatisticOption(ROOT.TEfficiency.kBBayesian)
# ROOT Plot
RooEff.set_params({'title_string' : f'{title};{options[key]["xlabel"]};Efficiency',
# 'canvas_size' : (800,800),
'xrange' : (bins[0],bins[-1]),
'yrange' : (0.,1.1),
# 'rrange' : (.7,1.3) if justIncl else ((0,2) if 'dr' in key else (.2,3)),
})
a = RooEff.plotEfficiencies(eff_1, eff_2, ratio=True, h1_title='Data', h2_title='MC', save=f'plots/eff_{key}_{title}_comparison.png', addIntegral=False)
del hpass_1, hall_1, hpass_2, hall_2
# Show matplotlib panel
if show:
ys_1 = [eff_1.GetEfficiency(ibin) for ibin in range(1,len(bins))]
ys_2 = [eff_2.GetEfficiency(ibin) for ibin in range(1,len(bins))]
y_err_1 = np.array([[eff_1.GetEfficiencyErrorUp(ibin), eff_1.GetEfficiencyErrorLow(ibin)] for ibin in range(1,len(bins))]).T
y_err_2 = np.array([[eff_2.GetEfficiencyErrorUp(ibin), eff_2.GetEfficiencyErrorLow(ibin)] for ibin in range(1,len(bins))]).T
ax = axs if nplots==1 else axs.ravel()[i]
if nplots==1:
ax.set_ylabel('Efficiency',loc='top')
ax.set_xlabel(xlabel,loc='right')
ax.set_title(title)
ax.set_ylim(0,1.)
ax.set_xlim(options[key]['bins'][0],options[key]['bins'][-1])
ax.errorbar(xs,ys_1,xerr=x_err, yerr=y_err_1, capsize=2,linestyle='none',label=label1)
ax.errorbar(xs,ys_2,xerr=x_err, yerr=y_err_2, capsize=2,linestyle='none',label=label2)
ax.legend()
# Save JSON
if save:
saveEffsFromArrays(title, True, key, bins, ys_1, y_err_1[0], y_err_1[1])
saveEffsFromArrays(title, False, key, bins, ys_2, y_err_2[0], y_err_2[1])
# In[16]:
# Use cached fits
# with open('effs.pkl','rb') as f: mc_params, outputs_mc, outputs_data = pickle.load(f)
plotEffComparison(outputs_data, outputs_mc, options, key='pt', justIncl=False, show=True, save=True)
# plotEffComparison(outputs_data, outputs_mc, options, key='dr', justIncl=False, show=True, save=True)
# plotEffComparison(outputs_data, outputs_mc, options, key='npv', justIncl=False, show=True, save=True)
# plotEffComparison(outputs_data, outputs_mc, options, key='eta', justIncl=False, show=True, save=True)
# plotEffComparison(outputs_data, outputs_mc, options, key='diept', justIncl=False, show=True)
# ## Write SF JSON
import pandas as pd
from IPython.display import display
def saveEffScaleFactors(key='pt', save=False):
output_dict = {}
out_data_effs = []
out_mc_effs = []
data_acc = []
mc_acc = []
for output_data, output_mc in zip(outputs_data,outputs_mc):
eff_output_data = output_data[key]
eff_output_mc = output_mc[key]
bins = options[key]['bins']
xlabel = options[key]['xlabel']
xs = (bins[1:] + bins[:-1]) / 2
# x_err=np.diff(bins)/2
title, nums_data, num_errs_data, denoms_data, denom_errs_data = np.array([[*i] for i in eff_output_data]).T
title, nums_mc, num_errs_mc, denoms_mc, denom_errs_mc = np.array([[*i] for i in eff_output_mc]).T
title = np.asarray(title)[np.nonzero(np.asarray(title))][0] if len(np.asarray(title)[np.nonzero(np.asarray(title))]) else ''
# title = title.replace('Incl', 'Excl')
nums_data = nums_data.astype(np.double)
num_errs_data = num_errs_data.astype(np.double)
denoms_data = denoms_data.astype(np.double)
denom_errs_data = denom_errs_data.astype(np.double)
nums_mc = nums_mc.astype(np.double)
num_errs_mc = num_errs_mc.astype(np.double)
denoms_mc = denoms_mc.astype(np.double)
denom_errs_mc = denom_errs_mc.astype(np.double)
data_acc.append(nums_data)
mc_acc.append(nums_mc)
out_data_effs.append((100*np.divide(nums_data, denoms_data, out=np.zeros_like(nums_data), where=denoms_data!=0)).round(1))
out_mc_effs.append((100*np.divide(nums_mc, denoms_mc, out=np.zeros_like(nums_mc), where=denoms_mc!=0)).round(1))
sfs = []
for num_data, err_num_data, denom_data, err_denom_data, num_mc, err_num_mc, denom_mc, err_denom_mc in zip(nums_data, num_errs_data, denoms_data, denom_errs_data, nums_mc, num_errs_mc, denoms_mc, denom_errs_mc):
eff_data = num_data / denom_data if denom_data != 0 else np.NaN
eff_mc = num_mc / denom_mc if denom_mc != 0 else np.NaN
# print(title, key, eff_data, eff_mc)
if not np.isnan(eff_data) and not np.isnan(eff_mc):
sf = eff_data / eff_mc if eff_mc else np.NaN
# err_sf = sf * np.sqrt(((err_num_data / num_data)**2 * (err_denom_data / denom_data)**2 * (err_num_mc / num_mc)**2 * (err_denom_mc / denom_mc)**2))
sfs.append((round(sf, 3), round(abs(1 - sf), 3)))
else: sfs.append((np.NaN, np.NaN))
output_dict[title] = {key : (bins[:-1]).tolist(), 'effs' : sfs}
if save:
with open('dietrig_scalefactors.json', 'w') as outfile:
json.dump(output_dict, outfile, indent=4)
out_data_df = pd.DataFrame( out_data_effs,
columns=[f'{ibin} - {jbin}' for ibin, jbin in zip(bins[:-1],bins[1:])],
index=[list(output.values())[0][0][0] for output in outputs_data])
out_mc_df = pd.DataFrame( out_mc_effs,
columns=[f'{ibin} - {jbin}' for ibin, jbin in zip(bins[:-1],bins[1:])],
index=[list(output.values())[0][0][0] for output in outputs_data])
return output_dict, out_data_df, out_mc_df, np.array(data_acc), np.array(mc_acc)
def add_wgt_row(df, wgts, name='Average'):
avgs = np.array([lst for lst in [df[col].values[1:] for col in df]])
int_avgs = [round(np.sum(i),1) for i in wgts * avgs]
df = df.append(pd.DataFrame({key:avg for key,avg in zip(df.columns,int_avgs)}, index=[name], columns=df.columns))
return df
# ## Write Eff JSONs
import pandas as pd
from IPython.display import display
def saveEffs(key='pt', save=False):
output_dict = {}
out_data_effs = []
data_acc = []
for output_data in outputs_data:
eff_output_data = output_data[key]
bins = options[key]['bins']
xlabel = options[key]['xlabel']
xs = (bins[1:] + bins[:-1]) / 2
title, nums_data, num_errs_data, denoms_data, denom_errs_data = np.array([[*i] for i in eff_output_data]).T
title = np.asarray(title)[np.nonzero(np.asarray(title))][0] if len(np.asarray(title)[np.nonzero(np.asarray(title))]) else ''
# title = title.replace('Incl', 'Excl')
nums_data = nums_data.astype(np.double)
num_errs_data = num_errs_data.astype(np.double)
denoms_data = denoms_data.astype(np.double)
denom_errs_data = denom_errs_data.astype(np.double)
data_acc.append(nums_data)
out_data_effs.append((100*np.divide(nums_data, denoms_data, out=np.zeros_like(nums_data), where=denoms_data!=0)).round(1))
effs = []
for num_data, err_num_data, denom_data, err_denom_data in zip(nums_data, num_errs_data, denoms_data, denom_errs_data):
eff_data = num_data / denom_data if denom_data != 0 else np.NaN
err_data = eff_data * np.sqrt((err_num_data / num_data)**2 * (err_denom_data / denom_data)**2) if (denom_data != 0 and num_data != 0) else np.NaN
if not np.isnan(eff_data):
effs.append((round(eff_data,3),round(err_data,3)))
else: effs.append(np.NaN)
output_dict[title] = {key : (bins[:-1]).tolist(), 'effs' : effs}
if save:
with open(save, 'w') as outfile:
json.dump(output_dict, outfile, indent=4)
out_data_df = pd.DataFrame( out_data_effs,
columns=[f'{ibin} - {jbin}' for ibin, jbin in zip(bins[:-1],bins[1:])],
index=[list(output.values())[0][0][0] for output in outputs_data])
return output_dict, out_data_df, np.array(data_acc)
def saveEffsMC(key='pt', save=False):
output_dict = {}
out_mc_effs = []
mc_acc = []
for output_mc in outputs_mc:
eff_output_mc = output_mc[key]
bins = options[key]['bins']
xlabel = options[key]['xlabel']
xs = (bins[1:] + bins[:-1]) / 2
title, nums_mc, num_errs_mc, denoms_mc, denom_errs_mc = np.array([[*i] for i in eff_output_mc]).T
title = np.asarray(title)[np.nonzero(np.asarray(title))][0] if len(np.asarray(title)[np.nonzero(np.asarray(title))]) else ''
# title = title.replace('Incl', 'Excl')
nums_mc = nums_mc.astype(np.double)
num_errs_mc = num_errs_mc.astype(np.double)
denoms_mc = denoms_mc.astype(np.double)
denom_errs_mc = denom_errs_mc.astype(np.double)
mc_acc.append(nums_mc)
out_mc_effs.append((100*np.divide(nums_mc, denoms_mc, out=np.zeros_like(nums_mc), where=denoms_mc!=0)).round(1))
effs = []
for num_mc, err_num_mc, denom_mc, err_denom_mc in zip(nums_mc, num_errs_mc, denoms_mc, denom_errs_mc):
eff_mc = num_mc / denom_mc if denom_mc != 0 else np.NaN
err_mc = eff_mc * np.sqrt((err_num_mc / num_mc)**2 * (err_denom_mc / denom_mc)**2) if (denom_mc != 0 and num_mc != 0) else np.NaN
if not np.isnan(eff_mc):
effs.append((round(eff_mc,3),round(err_mc,3)))
else: effs.append(np.NaN)
output_dict[title] = {key : (bins[:-1]).tolist(), 'effs' : effs}
if save:
with open(save, 'w') as outfile:
json.dump(output_dict, outfile, indent=4)
out_mc_df = pd.DataFrame(out_mc_effs,
columns=[f'{ibin} - {jbin}' for ibin, jbin in zip(bins[:-1],bins[1:])],
index=[list(output.values())[0][0][0] for output in outputs_mc])
return output_dict, out_mc_df, np.array(mc_acc)
def add_wgt_row(df, wgts, name='Average'):
avgs = np.array([lst for lst in [df[col].values[1:] for col in df]])
int_avgs = [round(np.sum(i),1) for i in wgts * avgs]
df = df.append(pd.DataFrame({key:avg for key,avg in zip(df.columns,int_avgs)}, index=[name], columns=df.columns))
return df
# In[34]:
sf_dict, sf_data_df, sf_mc_df, sf_data_acc, sf_mc_acc = saveEffScaleFactors(key='eta',save=True)
data_eff_dict, data_df, data_acc = saveEffs(key='eta',save='Trigger_Effs/TrigEffs_Incl_5_11_23_etabinned.json')
mc_eff_dict, mc_df, mc_acc = saveEffsMC(key='eta',save='Trigger_Effs/TrigEffs_MC_Incl_5_11_23_etabinned.json')
# data_acc_wgts = np.array([a / np.sum(a) for a in data_acc[1:].T])
# incl_lumi_wgts = np.array([ 1.5349e-01, 1.4637e-01, 1.4121e-01, 1.4075e-01, 1.0062e-01,
# 8.5480e-02, 8.2440e-02, 5.1190e-02, 4.3760e-02, 3.1700e-02,
# 1.5330e-02, 3.9500e-03, 3.2600e-03, 3.2000e-04, 1.4000e-04 ])
# # df_data = add_wgt_row(df_data, incl_lumi_wgts, 'Lumi Weighted Avg')
# df_data = add_wgt_row(df_data, data_acc_wgts, 'Acc Weighted Avg')
# mc_acc_wgts = np.array([a / np.sum(a) for a in mc_acc[1:].T])
# df_mc = add_wgt_row(df_mc, mc_acc_wgts, 'Acc Weighted Avg')
# In[ ]:
# histOut_binned_L1_11_HLT_6p5_Skim.root
# histOut_binned_L1_10p5_HLT_6p5_Skim.root
# histOut_binned_L1_10p5_HLT_5_Skim.root
# histOut_binned_L1_8p5_HLT_5_Skim.root
# histOut_binned_L1_8_HLT_5_Skim.root
# histOut_binned_L1_7_HLT_5_Skim.root
# histOut_binned_L1_6p5_HLT_4p5_Skim.root
# histOut_binned_L1_6_HLT_4_Skim.root
# histOut_binned_L1_5p5_HLT_6_Skim.root
# histOut_binned_L1_5p5_HLT_4_Skim.root
# histOut_binned_Incl_Skim.root
f1 = ROOT.TFile('histOut_binned_Incl_Skim_pt7p4to10p5.root')
f2 = ROOT.TFile('histOut_mc_pt7p4to10p5.root')
plots = ['sublead_el_pt', 'sublead_el_eta', 'sublead_el_phi', 'diel_m']
ROOT.gStyle.SetOptStat(0)
h_1 = f1.plots.Get('lead_el_pt')
# h_2 = f1.plots.Get('sublead_el_pt')
h_3 = f2.plots.Get('lead_el_pt')
# h_4 = f2.plots.Get('sublead_el_pt')
h_1.Rebin(4)
# h_2.Rebin(4)
h_3.Rebin(4)
# h_4.Rebin(4)
h_3.GetXaxis().SetRangeUser(0,50)
# h_4.SetMaximum(.25)
h_1.Scale(1/f1.plots.lead_el_pt.GetEntries())
# h_2.Scale(1/f1.plots.sublead_el_pt.GetEntries())
h_3.Scale(1/f2.plots.lead_el_pt.GetEntries())
# h_4.Scale(1/f2.plots.sublead_el_pt.GetEntries())
# print(h_1.GetSumOfWeights())
# print(h_2.GetSumOfWeights())
# print(h_3.GetSumOfWeights())
# print(h_4.GetSumOfWeights())
# h_4.SetTitle('')
c = ROOT.TCanvas('c', 'c', 600,600)
# leg = ROOT.TLegend(.4,.12,.6,.3)
leg = ROOT.TLegend(.65,.65,.88,.88)
# ROOT.gPad.SetLogy()
h_1.SetLineColor(600)
h_1.SetLineWidth(2)
# h_2.SetLineColor(600)
# h_2.SetLineWidth(2)
# h_2.SetLineStyle(2)
h_3.SetLineColor(632)
h_3.SetLineWidth(2)
# h_4.SetLineColor(632)
# h_4.SetLineWidth(2)
# h_4.SetLineStyle(2)
leg.AddEntry(h_1, '[Data]', 'le')
# leg.AddEntry(h_2, 'Sublead p_{T} [Data]', 'le')
leg.AddEntry(h_3, '[MC]', 'le')
# leg.AddEntry(h_4, 'Sublead p_{T} [MC]', 'le')
# h_4.Draw('HIST E ')
# h_2.Draw('HIST E SAME')
h_3.Draw('HIST E ')
h_1.Draw('HIST E SAME')
leg.Draw()
ROOT.gPad.Update()
c.Draw()
# In[ ]:
print('MC Bad Fits:')
print_badfits(badfits_mc)
print('Data Bad Fits:')
print_badfits(badfits_data)
# In[208]:
with open('JSON/Eras_CDEFG_Simplified/L1_6p5_HLT_4p5_Excl_Final.json','r') as f:
path1_excl = json.load(f)
with open('JSON/Eras_CDEFG_Simplified/L1_8p0_HLT_5p0_Excl_Final.json','r') as f:
path2_excl = json.load(f)
with open('JSON/Eras_CDEFG_Simplified/L1_9p0_HLT_6p0_Excl_Final.json','r') as f:
path3_excl = json.load(f)
with open('JSON/Eras_CDEFG_Simplified/L1_6p5_HLT_4p5_Incl_Final.json','r') as f:
path1_incl = json.load(f)
with open('JSON/Eras_CDEFG_Simplified/L1_8p0_HLT_5p0_Incl_Final.json','r') as f:
path2_incl = json.load(f)
with open('JSON/Eras_CDEFG_Simplified/L1_9p0_HLT_6p0_Incl_Final.json','r') as f:
path3_incl = json.load(f)
def mergeIntervals(arr1, arr2):
arr = arr1
arr.extend(arr2)
arr.sort(key=lambda x: x[0])
index = 0
for i in range(1, len(arr)):
if (arr[index][1] >= arr[i][0]):
arr[index][1] = max(arr[index][1], arr[i][1])
else:
index = index + 1
arr[index] = arr[i]
out = []
for i in range(index+1):
out.append(arr[i])
return out
def merge(A, B, f):
# Start with symmetric difference; keys either in A or B, but not both
merged = {k: A.get(k, B.get(k)) for k in A.keys() ^ B.keys()}
# Update with `f()` applied to the intersection
merged.update({k: f(A[k], B[k]) for k in A.keys() & B.keys()})
return merged
excl_12 = merge(path1_excl, path2_excl, mergeIntervals)
excl_final = merge(excl_12, path3_excl, mergeIntervals)
with open('JSON/Eras_CDEFG_Simplified/trigger_OR_Excl.json', 'w') as write_file:
json.dump(excl_final, write_file)
incl_12 = merge(path1_incl, path2_incl, mergeIntervals)
incl_final = merge(incl_12, path3_incl, mergeIntervals)