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plot_postfit.py
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plot_postfit.py
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#! /usr/bin/env python
# Author: Izaak Neutelings (August 2020)
# Description: Simple plotting script for pico analysis tuples
# Instructions:
# ./plot_v10.py -y 2018 -c mutau
# ./plot_v10.py -y 2018 -c config/setup_mutau.yml
# ./plot_v10.py -y 2018 -c mutau -S baseline -V m_vis
#>>>>IMPORTANT!!
#>>>>Run with --serial option if using py3:
# ./plot_v10.py -y 2018 -c mutau --serial
from config.samples_v12 import *
from TauFW.Plotter.plot.string import filtervars
from TauFW.Plotter.plot.utils import LOG as PLOG
from TauFW.Plotter.plot.Plot import Plot, deletehist
import TauFW.Plotter.sample.SampleStyle as STYLE
import yaml
def plot(sampleset,setup,region,parallel=True,tag="",extratext="",outdir="plots",era="",
varfilter=None,selfilter=None,fraction=False,pdf=False):
"""Test plotting of SampleSet class for data/MC comparison."""
LOG.header("plot")
# Define the channel : mutau only supported for now
channel = setup["channel"]
selections = [ ]
# Check region and use the right TES value from
print("Region = %s" %(region))
if region in setup['regions']: # add extra regions on top of baseline
print(region)
if region == 'DM0' :
#channel = "mutau_TES0p914"
#channel = "mutau_TES0p930"
channel = "mutau_TES0p932"
#channel = "mutau_DM0_mt65"
# elif region == 'DM0_pt1' :
# #channel = "mutau_TES0p914"
# #channel = "mutau_TES0p930"
# channel = "mutau_TES0p892"
# elif region == 'DM0_pt2' :
# #channel = "mutau_TES0p914"
# #channel = "mutau_TES0p930"
# channel = "mutau_TES0p952"
elif region == 'DM1':
#channel = "mutau_TES0p980"
channel = "mutau_TES0p984"
#channel = "mutau_TES0p901"
#channel = "mutau_DM1_mt65"
elif region == 'DM10':
#channel = "mutau_TES0p998"
#channel = "mutau_TES0p972"
#channel = "mutau_TES1p012"
channel = "mutau_TES1p002"
#channel = "mutau_DM10_mt65"
elif region == 'DM11':
#channel = "mutau_TES1p012"
#channel = "mutau_TES1p008"
#channel = "mutau_TES1p018"
channel = "mutau_TES1p006"
#channel = "mutau_DM11_mt65"
else :
channel = setup["channel"]
print("Channel = %s" %(channel))
skwargs = setup['regions'][region].copy() # extra key-word options
assert 'definition' in skwargs
selstr = setup['baselineCuts']+" && "+skwargs.pop('definition')
selections.append(Sel(region,selstr,**skwargs))
# Define selection
selections = filtervars(selections,selfilter) # filter variable list with -S/--sel flag
# VARIABLES
variables = [
Var('pt_1', "Muon pt", 40, 0, 120, ctitle={'etau':"Electron pt",'tautau':"Leading tau_h pt",'mumu':"Leading muon pt",'emu':"Electron pt"},cbins={"nbtag\w*>":(40,0,200)}),
Var('pt_2', "tau_h pt", 40, 0, 120, ctitle={'tautau':"Subleading tau_h pt",'mumu':"Subleading muon pt",'emu':"Muon pt"},cbins={"nbtag\w*>":(40,0,200)}),
Var('eta_1', "Muon eta", 30, -3, 3, ctitle={'etau':"Electron eta",'tautau':"Leading tau_h eta",'mumu':"Leading muon eta",'emu':"Electron eta"},ymargin=1.7,pos='T',ncols=2),
Var('eta_2', "tau_h eta", 30, -3, 3, ctitle={'etau':"Electron eta",'tautau':"Subleading tau_h eta",'mumu':"Subleading muon eta",'emu':"Muon eta"},ymargin=1.7,pos='T',ncols=2),
Var('mt_1', "mt(mu,MET)", 40, 0, 200, ctitle={'etau':"mt(mu,MET)",'tautau':"mt(tau,MET)",'emu':"mt(e,MET)"},cbins={"nbtag\w*>":(50,0,250)}),
Var("jpt_1", 29, 10, 300, veto=[r"njets\w*==0"]),
Var("jpt_2", 29, 10, 300, veto=[r"njets\w*==0"]),
Var("jeta_1", 53, -5.4, 5.2, ymargin=1.6,pos='T',ncols=2,veto=[r"njets\w*==0"]),
Var("jeta_2", 53, -5.4, 5.2, ymargin=1.6,pos='T',ncols=2,veto=[r"njets\w*==0"]),
Var('npv', 40, 0, 80),
Var('njets', 8, 0, 8),
Var('nbtag', "Number of b jets (Medium, pt > 30 GeV)", 8, 0, 8),
Var('met', 50, 0, 150,cbins={"nbtag\w*>":(50,0,250)}),
#Var('genmet', 50, 0, 150, fname="$VAR_log", logyrange=4, data=False, logy=True, ncols=2, pos='TT'),
Var('pt_ll', "p_{T}(mutau_h)", 25, 0, 200, ctitle={'etau':"p_{T}(etau_h)",'tautau':"p_{T}(tau_htau_h)",'emu':"p_{T}(emu)"}),
Var('dR_ll', "DR(mutau_h)", 30, 0, 6.0, ctitle={'etau':"DR(etau_h)",'tautau':"DR(tau_htau_h)",'emu':"DR(emu)"}),
Var('deta_ll', "deta(mutau_h)", 20, 0, 6.0, ctitle={'etau':"deta(etau_h)",'tautau':"deta(tautau)",'emu':"deta(emu)"},logy=True,pos='TRR',cbins={"abs(deta_ll)<":(10,0,3)}), #, ymargin=8, logyrange=2.6
Var('dzeta', 56, -180, 100, pos='L;y=0.87',units='GeV',cbins={"nbtag\w*>":(35,-220,130)}),
]
if 'tau' in channel: # mutau, etau, tautau
loadmacro("python/macros/mapDecayModes.C") # for mapRecoDM
dmlabels = ["h^{#pm}","h^{#pm}h^{0}","h^{#pm}h^{#mp}h^{#pm}","h^{#pm}h^{#mp}h^{#pm}h^{0}","Other"]
variables += [
Var('m_vis', 40, 0, 200, fname="mvis",ctitle={'mumu':"m_mumu",'emu':"m_emu"},logy=False, cbins={"pt_\d>":(50,0,250),"nbtag\w*>":(60,0,300)},cpos={"pt_\d>[1678]0":'LL;y=0.88'}),
#Var('m_vis', 20, 0, 200, fname="mvis_coarse",ctitle={'mumu':"m_mumu",'emu':"m_emu"},logy=False, cbins={"pt_\d>":(25,0,250),"nbtag\w*>":(30,0,300)},cpos={"pt_\d>[1678]0":'LL;y=0.88'}),
# Var("m_2", 30, 0, 3, title="m_tau",veto=["njet","nbtag","dm_2==0"]),
# Var("dm_2", 14, 0, 14, fname="dm_2",title="Reconstructed tau_h decay mode",veto="dm_2==",position="TMC",ymargin=1.2),
#Var("mapRecoDM(dm_2)", 5, 0, 5, fname="dm_2_label",title="Reconstructed tau_h decay mode",veto="dm_2==",position="TT",labels=dmlabels,ymargin=1.2),
#Var("pzetavis", 50, 0, 200 ),
#Var('rawDeepTau2017v2p1VSjet_2', "rawDeepTau2017v2p1VSjet", 50, 0.00, 1, ymin = 1e3, ncols=2,pos='L;y=0.85',logy=True,ymargin=1.5,cbins={"VSjet_2>":(60,0.4,1)}),
# Var('rawDeepTau2017v2p1VSjet_2', "rawDeepTau2017v2p1VSjet", 60, 0.85, 1, ymin = 1e2, fname="$VAR_zoom",ncols=2,pos='L;y=0.85'),
# Var('rawDeepTau2017v2p1VSjet_2', "rawDeepTau2017v2p1VSjet", 35, 0.88, 1, ymin = 1e2, fname="$VAR_zoom1",ncols=2,pos='L;y=0.85'),
# Var('rawDeepTau2017v2p1VSjet_2', "rawDeepTau2017v2p1VSjet", 63, 0.88, 1, ymin = 1e2, fname="$VAR_zoom2",ncols=2,pos='L;y=0.85'),
# Var('rawDeepTau2017v2p1VSjet_2', "rawDeepTau2017v2p1VSjet", 125, 0.88, 1, ymin = 1e2, fname="$VAR_zoom3",ncols=2,pos='L;y=0.85'),
# Var('rawDeepTau2017v2p1VSe_2', "rawDeepTau2017v2p1VSe", 90, 0.10, 1, ymin = 1e2, fname="$VAR_zoom",ncols=2,logy=True,logyrange=4,pos='L;y=0.85'),
# Var('rawDeepTau2017v2p1VSmu_2', "rawDeepTau2017v2p1VSmu", 50, 0.80, 1, ymin = 1e1, fname="$VAR_zoom",ncols=2,logy=True,logyrange=5,pos='L;y=0.85'),
# # #Var('rawDeepTau2018v2p5VSjet_2', "rawDeepTau2018v2p5VSjet", 50, 0.00, 1, ymin = 1e3, ncols=2,pos='L;y=0.85',logy=True,ymargin=1.5,cbins={"VSjet_2>":(60,0.4,1)}),
# Var('rawDeepTau2018v2p5VSjet_2', "rawDeepTau2018v2p5VSjet", 20, 0.95, 1, ymin = 1e2, fname="$VAR_zoom",ncols=2,pos='L;y=0.85'),
# # Var('rawDeepTau2018v2p5VSjet_2', "rawDeepTau2018v2p5VSjet", 21, 0.96, 1, ymin = 1e2, fname="$VAR_zoom0",ncols=2,pos='L;y=0.85'),
# # Var('rawDeepTau2018v2p5VSjet_2', "rawDeepTau2018v2p5VSjet", 42, 0.96, 1, ymin = 1e2, fname="$VAR_zoom1",ncols=2,pos='L;y=0.85'),
# # Var('rawDeepTau2018v2p5VSjet_2', "rawDeepTau2018v2p5VSjet", 84, 0.96, 1, ymin = 1e2, fname="$VAR_zoom2",ncols=2,pos='L;y=0.85'),
# # Var('rawDeepTau2018v2p5VSjet_2', "rawDeepTau2018v2p5VSjet", 250, 0.96, 1, ymin = 1e2, fname="$VAR_zoom3",ncols=2,pos='L;y=0.85'),
# Var('rawDeepTau2018v2p5VSe_2', "rawDeepTau2018v2p5VSe", 80, 0.20, 1, ymin = 1e2, fname="$VAR_zoom",ncols=2,logy=True,logyrange=4,pos='L;y=0.85'),
# Var('rawDeepTau2018v2p5VSmu_2', "rawDeepTau2018v2p5VSmu", 25, 0.90, 1, ymin = 1e1, fname="$VAR_zoom",ncols=2,logy=True,logyrange=5,pos='L;y=0.85'),
# Var('rawDeepTau2018v2p5VSjet_2', "rawDeepTau2018v2p5VSjet", 50, 0.00, 1, ymin = 1e1, fname="$VAR_allRange", ncols=2,pos='L;y=0.85',logy=True,ymargin=1.5),
#Var('rawDeepTau2018v2p5VSe_2', "rawDeepTau2018v2p5VSe", 50, 0.00, 1, ymin = 1e3, fname="$VAR_allRange", ncols=2,pos='L;y=0.85',logy=True,ymargin=1.5),
#Var('rawDeepTau2018v2p5VSmu_2', "rawDeepTau2018v2p5VSmu", 50, 0.00, 1, ymin = 1e3, fname="$VAR_allRange", ncols=2,pos='L;y=0.85',logy=True,ymargin=1.5),
]
variables = filtervars(variables,varfilter) # filter variable list with -V/--var flag
# PLOT
outdir = ensuredir(repkey(outdir,CHANNEL=channel,ERA=era))
exts = ['png','pdf'] if pdf else ['png'] # extensions
for selection in selections:
print(">>> Selection %r: %r"%(selection.title,selection.selection))
# Mapping for region replacement
region_mapping = {'DM0': 'dm_2==0', 'DM1': 'dm_2==1', 'DM10': 'dm_2==10', 'DM11': 'dm_2==11'}
region_cut = region_mapping.get(region, region)
# Extract relevant parameters for modifying the sample
sampleAppend = ""
if region == "DM0":
dy_shape_val = 0.355279177427
elif region == "DM1":
dy_shape_val = -0.714294493198
elif region == "DM10":
dy_shape_val = 0.264388531446
elif region == "DM11":
dy_shape_val = -0.808251798153
else:
dy_shape_val = 1
dy_weight_ZTT = "(genmatch_2==5 ? 1+((zptweight+0.1*(zptweight-1))*%s ): 1)" %(dy_shape_val)
dy_weight_ZL = "(genmatch_2>0 && genmatch_2<5 ? 1+((zptweight+0.1*(zptweight-1))*%s): 1)" %(dy_shape_val)
dy_weight_ZJ = "(genmatch_2==0 ? 1+((zptweight+0.1*(zptweight-1))*%s ): 1)" %(dy_shape_val)
# Create a new sample set with systematic variations
# sampleset.gethists(obsset,selection,method=method,split=True,
# parallel=parallel,filter=filters,veto=vetoes,replaceweight=weightReplaced)
stacks = sampleset.getstack(variables,selection,method='QCD_OSSS',scale=1, parallel=parallel)
# sampleset.get("ZTT", unique=True,split=True,method='QCD_OSSS').setextraweight(dy_weight_ZTT)
# sampleset.get("ZL", unique=True,split=True,method='QCD_OSSS').setextraweight(dy_weight_ZL)
# sampleset.get("ZJ", unique=True,split=True,method='QCD_OSSS').setextraweight(dy_weight_ZJ)
print("sampleset = %s" %(sampleset))
# Applying SFs on specific processes -- do after splitting and renaming!
if "scaleFactors" in setup:
#print("scaleFactors")
for SF in setup["scaleFactors"]:
#print("Scale Factor =" , SF)
SFset = setup["scaleFactors"][SF]
print("Reweighting with SF -- %s -- for the following processes: %s"%(SF, SFset["processes"]))
for proc in SFset["processes"]:
#print("proc : %s" %(proc))
for cond in SFset["values"]:
#print("cond = ", cond)
#print("region_cut = ", region_cut)
if cond == region_cut :
weight = SFset["values"][cond]
print("Applying weight: %s to process %s" %(weight,proc))
for stack, variable in stacks.items():
for h in stack.hists:
if proc in h.GetName().split('_')[1]:
#print("hist name =" , h.GetName())
#print("hist name split('_')[1] =" , h.GetName().split('_')[1])
h.Scale(weight)
print("stacks = %s" %(stacks))
fname = "%s/$VAR_%s-%s-%s$TAG"%(outdir,channel.replace('mu','m').replace('tau','t'),selection.filename,era)
text = "%s: %s"%(channel.replace('mu',"#mu").replace('tau',"#tau_{h}"),selection.title)
if extratext:
text += ("" if '\n' in extratext[:3] else ", ") + extratext
#for stack, variable in stacks.iteritems():
for stack, variable in stacks.items(): # python 3
position = "" #variable.position or 'topright'
stack.draw(fraction=fraction)
stack.drawlegend() #position)
stack.drawtext(text)
stack.saveas(fname,ext=exts,tag=tag)
stack.close()
def main(args):
configs = args.configs
eras = args.eras
parallel = args.parallel
varfilter = args.varfilter
selfilter = args.selfilter
notauidsf = args.notauidsf
extratext = args.text
fraction = args.fraction
pdf = args.pdf
outdir = "plots/$ERA/$CHANNEL"
fname = "$PICODIR/$SAMPLE_$CHANNEL$TAG.root"
#fname = "/nfs/user/pmastra/DeepTau2p5/analysis/$ERA/$CHANNEL/$GROUP/$SAMPLE_$CHANNEL$TAG.root"
# LOOP over configs / channels
for config in configs:
if not config.endswith(".yml"): # config = channel name
config = "config/setup_%s.yml"%(config) # assume this file name pattern
print(">>> Using configuration file: %s"%config)
with open(config, 'r') as file:
setup = yaml.safe_load(file)
tag = setup.get('tag',"")+args.tag
for era in eras:
setera(era) # set era for plot style and lumi-xsec normalization
#addsfs = setup["samples"].get("addSFs",[]) #"getTauIDSF(dm_2,genmatch_2)"]
addsfs = [ ] #"getTauIDSF(dm_2,genmatch_2)"]
rmsfs = [ ] if (setup['channel']=='mumu' or not notauidsf) else ['idweight_2','ltfweight_2'] # remove tau ID SFs
split = ['DY','ST','TT']
sampleset = getsampleset(setup['channel'],era,fname=fname,rmsf=rmsfs,addsf=addsfs,split=split)
print("split = ", split)
print(">>>>>>>sampleset")
split_list = [["ZTT","genmatch_2==5"], ["ZL","genmatch_2>0 && genmatch_2<5"], ["ZJ","genmatch_2==0"],
["TTT","genmatch_2==5"], ["TTL","genmatch_2>0 && genmatch_2<5"], ["TTJ","genmatch_2==0"],
["ST","genmatch_2==5 && genmatch_2<5"],["STJ","genmatch_2<5"]]
sampleset.split(split_list)
for region in setup["regions"] :
plot(sampleset,setup,region,parallel=parallel,tag=tag,extratext=extratext,outdir=outdir,era=era,
varfilter=varfilter,selfilter=selfilter,fraction=fraction,pdf=pdf)
sampleset.close()
if __name__ == "__main__":
from argparse import ArgumentParser, RawTextHelpFormatter
eras = ['2016','2017','2018','UL2016_preVFP','UL2016_postVFP','UL2017','UL2018','2022_preEE','2022_postEE', '2023C']
description = """Simple plotting script for pico analysis tuples"""
parser = ArgumentParser(prog="plot",description=description,epilog="Good luck!")
parser.add_argument('-y', '--era', dest='eras', nargs='*', choices=eras, default=['2017'],
help="set era" )
parser.add_argument('-c', '--config', '--channel',
dest='configs', type=str, nargs='+', default=['config/setup_mutau.yml'], action='store',
help="config file(s) containing channel setup for samples and selections, default=%(default)r" )
parser.add_argument('-V', '--var', dest='varfilter', nargs='+',
help="only plot the variables passing this filter (glob patterns allowed)" )
parser.add_argument('-S', '--sel', dest='selfilter', nargs='+',
help="only plot the selection passing this filter (glob patterns allowed)" )
parser.add_argument('-s', '--serial', dest='parallel', action='store_false',
help="run Tree::MultiDraw serial instead of in parallel" )
parser.add_argument('-F', '--fraction',dest='fraction', action='store_true',
help="include fraction stack in ratio plot" )
parser.add_argument('-p', '--pdf', dest='pdf', action='store_true',
help="create pdf version of each plot" )
parser.add_argument('-r', '--nosf', dest='notauidsf', action='store_true',
help="remove DeepTau ID SF" )
parser.add_argument('-t', '--tag', default="", help="extra tag for output" )
parser.add_argument('-T', '--text', default="", help="extra text on plot" )
parser.add_argument('-v', '--verbose', dest='verbosity', type=int, nargs='?', const=1, default=0, action='store',
help="set verbosity" )
args = parser.parse_args()
LOG.verbosity = args.verbosity
PLOG.verbosity = args.verbosity
main(args)
print("\n>>> Done.")