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cutvar_yield_eff.py
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import argparse
import os
import yaml
import ROOT
import uproot
import numpy as np
import pandas as pd
import sys
from particle import Particle
import copy
from utils.analysis_utils import loadAO2D, applySelections, applySelections2, perform_roofit_fit, get_chi2_significance_sb, set_param
def compute_efficiency(nRec, nGen):
eff = nRec / nGen
effErr = eff * np.sqrt((1 - eff) / nRec)
return eff, effErr
# main
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('configfile',
metavar='text',
help='config file name with inputs and cut configurations')
args = parser.parse_args()
with open(args.configfile, 'r') as ymlCfgFile:
cfg = yaml.load(ymlCfgFile, yaml.FullLoader)
# Load MC dataframes from files, and apply preliminary selections
# We can have multiple files corresponding to different MC samples
mcFileNames = cfg['fileNameMC']
# initialize output files
outFolder = cfg['outputFolder']
if not os.path.exists(outFolder):
os.makedirs(outFolder)
outfile_prefix = cfg['outputFilePrefix']
# Load and skim MC dataframes
if not isinstance(mcFileNames, list):
mcFileNames = [mcFileNames]
inTreeNameRec = cfg['treeNameRec']
inTreeNameGen = cfg['treeNameGen']
weights = cfg['mcWeights']
dfRecList = []
dfGenList = []
for fileName in mcFileNames:
dfRec = loadAO2D(fileName, inTreeNameRec)
dfRecFiltered = applySelections(dfRec, cfg, isMC=True)
dfGen = loadAO2D(fileName, inTreeNameGen)
dfRecList.append(dfRecFiltered)
dfGenList.append(dfGen)
# Load data dataframe from file, and apply preliminary selections
dataFileName = cfg['fileNameData']
inTreeNameData = cfg['treeNameData']
dfData = loadAO2D(dataFileName, inTreeNameData)
dfDataFiltered = applySelections(dfData, cfg, isMC=False)
# apply central cut selections except on BdtScore
centralCuts = cfg['centralCuts']
flagsToKeep = cfg['acceptFlags']
dfDataCentralCut = applySelections2(dfDataFiltered, centralCuts)
dfDataCentralCut = dfDataCentralCut[['fM','fPt','fPtBach0', 'fMlScoreBkgBach0', 'fMlScoreNonPromptBach0']]
dfRecList = [applySelections2(dfRec, centralCuts, isMC=True, selectedFlags=flagsToKeep) for irec, dfRec in enumerate(dfRecList)]
# Load configurations to test
configurations = cfg['nonPromptCutvar']['configurations']
fit_conf = cfg['rawYield']['default_fit_config']
fit_mass_min = fit_conf['mass_min']
fit_mass_max = fit_conf['mass_max']
fit_signal_pdf = fit_conf['signal_pdf']
fit_bkg_pdf = fit_conf['bkg_pdf']
fit_params_def = fit_conf['parameters']
pt_bins_min = cfg['cutVars']['pt']['min']
pt_bins_max = cfg['cutVars']['pt']['max']
pt_limits = pt_bins_min.copy()
pt_limits.append(pt_bins_max[-1])
pt_limits = np.array(pt_limits, np.float64)
pt_bins_dau_min = cfg['cutVars']['ptBach0']['min']
pt_bins_dau_max = cfg['cutVars']['ptBach0']['max']
# Define 2 ROOT.TH2 histograms
hist_rawyield_cutvar = []
hist_eff_prompt_cutvar = []
hist_eff_non_prompt_cutvar = []
for icut, _ in enumerate(configurations):
hist_rawyield_cutvar.append(
ROOT.TH1F("hist_rawyield", ";#it{p}_{T} (GeV/#it{c}); raw yield", len(pt_bins_min), pt_limits))
hist_eff_prompt_cutvar.append(
ROOT.TH1F("hist_eff_prompt", ";#it{p}_{T} (GeV/#it{c}); raw yield", len(pt_bins_min), pt_limits))
hist_eff_non_prompt_cutvar.append(
ROOT.TH1F("hist_eff_non_prompt", ";#it{p}_{T} (GeV/#it{c}); raw yield", len(pt_bins_min), pt_limits))
hist_rawyield_cutvar[icut].SetDirectory(0)
hist_eff_prompt_cutvar[icut].SetDirectory(0)
hist_eff_non_prompt_cutvar[icut].SetDirectory(0)
centralMeans = [0]*len(pt_bins_min)
centralSigmas = [0]*len(pt_bins_min)
for i_conf, conf in enumerate(configurations):
# 0) retrieve configurations
bdt_cuts = conf['cuts']
if len(bdt_cuts) != len(pt_bins_dau_max):
print(f"ERROR: Number of BDT cuts is not equal to the number of D bacehelor pt bins, {len(bdt_cuts)}, {len(pt_bins_dau_max)}")
sys.exit(1)
fit_params_list = [copy.deepcopy(fit_params_def)]*len(pt_bins_min)
conf_name = conf['name']
outfile = ROOT.TFile(f"{outFolder}/{outfile_prefix}_{conf_name}.root", "RECREATE")
for ipt, (pt_min, pt_max) in enumerate(zip(pt_bins_min, pt_bins_max)):
# split dataframes in pt bins
dfDataPt = dfDataCentralCut[(dfDataCentralCut['fPt'] >= pt_min) & (dfDataCentralCut['fPt'] < pt_max)]
mcRecPtList = [dfRec[(dfRec['fPt'] >= pt_min) & (dfRec['fPt'] < pt_max)] for dfRec in dfRecList]
mcGenPtList = [dfGen[(dfGen['fPt'] >= pt_min) & (dfGen['fPt'] < pt_max)] for dfGen in dfGenList]
# 1) apply BDT cuts, differential in bachelor pT
dataSel = pd.DataFrame()
mcRecSel = [pd.DataFrame(), pd.DataFrame()]
for i, (pt_min_d, pt_max_d, cut) in enumerate(zip(pt_bins_dau_min, pt_bins_dau_max, bdt_cuts)):
# apply selections
dataSel = pd.concat([dataSel, dfDataPt[(dfDataPt['fPtBach0'] >= pt_min_d) & (dfDataPt['fPtBach0'] < pt_max_d) & (dfDataPt['fMlScoreNonPromptBach0'] > cut)]])
mcRecSel = [pd.concat([mcRecSel[irec], dfRec[(dfRec['fPtBach0'] >= pt_min_d) & (dfRec['fPtBach0'] < pt_max_d) & (dfRec['fMlScoreNonPromptBach0'] > cut)]]) for irec, dfRec in enumerate(mcRecPtList)]
# 2) compute efficiency
nRecPrompt, nGenPrompt = 0, 0
nRecNonPrompt, nGenNonPrompt = 0, 0
for i, (dfRec, dfGen, w) in enumerate(zip(mcRecSel, mcGenPtList, cfg['mcWeights'])):
nGenPrompt += w * len(dfGen[dfGen['fOrigin'] == 1])
nRecPrompt += w * len(dfRec[(dfRec['fOrigin'] == 1)])
nGenNonPrompt += w * len(dfGen[dfGen['fOrigin'] == 2])
nRecNonPrompt += w * len(dfRec[(dfRec['fOrigin'] == 2)])
nRecPrompt = nRecPrompt / np.sum(cfg['mcWeights'])
nGenPrompt = nGenPrompt / np.sum(cfg['mcWeights'])
effPrompt, effErrPrompt = compute_efficiency(nRecPrompt, nGenPrompt)
nRecNonPrompt = nRecNonPrompt / np.sum(cfg['mcWeights'])
nGenNonPrompt = nGenNonPrompt / np.sum(cfg['mcWeights'])
effNonPrompt, effErrNonPrompt = compute_efficiency(nRecNonPrompt, nGenNonPrompt)
hist_eff_prompt_cutvar[i_conf].SetBinContent(ipt + 1, effPrompt)
hist_eff_prompt_cutvar[i_conf].SetBinError(ipt + 1, effErrPrompt)
hist_eff_non_prompt_cutvar[i_conf].SetBinContent(ipt + 1, effNonPrompt)
hist_eff_non_prompt_cutvar[i_conf].SetBinError(ipt + 1, effErrNonPrompt)
# 3) compute raw yield
if i_conf > 0:
set_param(fit_params_list[ipt], "sigma", centralSigmas[ipt], centralSigmas[ipt], centralSigmas[ipt])
set_param(fit_params_list[ipt], "mean", centralMeans[ipt], centralMeans[ipt], centralMeans[ipt])
ws = perform_roofit_fit(dataSel, fit_signal_pdf, fit_bkg_pdf, fit_params_list[ipt], fit_mass_min, fit_mass_max)
chi2, significance, sb = get_chi2_significance_sb(ws, 100, outfile = outfile, name = f"c_{pt_min}_{pt_max}_cut_{conf['name']}")
S = ws.var("nSig").getVal()
S_err = ws.var("nSig").getError()
sigma = ws.var("sigma").getVal()
sigma_err = ws.var("sigma").getError()
mean = ws.var("mean").getVal()
mean_err = ws.var("mean").getError()
if i_conf == 0:
centralMeans[ipt] = mean
centralSigmas[ipt] = sigma
hist_rawyield_cutvar[i_conf].SetBinContent(ipt + 1, S)
hist_rawyield_cutvar[i_conf].SetBinError(ipt + 1, S_err)
hist_rawyield_cutvar[i_conf].Write()
hist_eff_prompt_cutvar[i_conf].Write()
hist_eff_non_prompt_cutvar[i_conf].Write()
outfile.Close()