def ProjectDraw(var, cut, Lumi, samplelist, pd):
plt.cfg.register(samplelist)
groupList = plt.cfg.getGroupPlot()
histList = OrderedDict()
VAR = var
# remove extra space
CUT = ' '.join(filter(None, selection[cut].split(" ")))
# group tag
for igroup in groupList:
CUT_ = CUT
if igroup == 'BkgSum': continue
#if igroup in [ 'Fake', 'DATA' ] : CUT_ = CUT.replace("isbVeto && ","")
print col.CYAN + "drawLambda::GroupTag : ", igroup + col.ENDC
histList[igroup] = {}
##sample tag
for isample in groupList[igroup]['samples']:
print("isample : ", isample)
histList[igroup][isample] = []
if 'weights' in samples[isample].keys():
for jsample in samples[isample]['weights'].keys():
WEIGHTS = '(%s)*(%s)' % (
expressAliases(samples[isample]['weight']),
samples[isample]['weights'][jsample])
#WEIGHTS = '(%s)*(%s)' %( samples[isample]['weight'] , samples[isample]['weights'][jsample] )
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(
float(Lumi) / 1000.), WEIGHTS)
filelist = [
x for x in samples[isample]['name']
if os.path.basename(x).split('_', 1)[-1].replace(
'.root', '').split('__part')[0] == jsample
]
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
histList[igroup][isample].append(
makeHisto(df, VAR, CUT_, WEIGHTS, jsample))
else:
WEIGHTS = expressAliases(samples[isample]['weight'])
#WEIGHTS = samples[isample]['weight']
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(float(Lumi) / 1000.), WEIGHTS)
filelist = samples[isample]['name']
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
histList[igroup][isample].append(
makeHisto(df, VAR, CUT_, WEIGHTS, isample))
pass
# apply action
return applyAction(histList, groupList)
def test1DirectWarningCall( self ):
"""Test Cling warning translation"""
import warnings, ROOT
warnings.filterwarnings( 'ignore', 'c1test' )
# namespaced call, as 'Warning' is a builtin exception
ROOT.Warning( 'c1test', 'c1test' )
from modules.DataMCPlot import DataMCPlot
# from modules.DDE import DDE
from modules.binning import binning_dimuonmass
from modules.path_to_NeuralNet import path_to_NeuralNet
# from modules.nn import run_nn
# import modules.fr_net as fr_net
# from CMGTools.RootTools.DataMC.Histogram import Histogram
from modules.SignalReweighter import reweightSignals
from pdb import set_trace
from ROOT import ROOT, RDataFrame, TH1F, TFile, TTree, TTreeFormula, gInterpreter, gROOT, gSystem
# Enable ROOT's implicit multi-threading for all objects that provide an internal parallelisation mechanism
# ROOT.EnableImplicitMT()
# ROOT.EnableImplicitMT(68)
ROOT.EnableImplicitMT(20)
def initHist(hist, vcfg):
hist.Sumw2()
xtitle = vcfg.xtitle
if vcfg.unit:
xtitle += ' ({})'.format(vcfg.unit)
hist.GetXaxis().SetTitle(xtitle)
hist.SetStats(False)
class CreateHists(object):
def __init__(self,
hist_cfg,
analysis_dir='/home/dehuazhu/SESSD/4_production/',
def plot_signal( var, cut, norm=False ):
colors = [ 616+4 , 800 , 416+1 , 800+7 , 860+10 , 600 , 616 , 921 , 922 ]
if cut in selection: plotdir = cut
pathname = PLOTDIR+"/signals/"+plotdir
if not os.path.exists(pathname): os.system('mkdir -p %s'%pathname)
histlist={}
plt.cfg.register(sign) #sign is grouptag
samples = plt.cfg.getModule('samples')
CUT = ' '.join(filter( None , selection[cut].split(" ") ))
signlist = plt.cfg.getGroupPlot()['Higgs']['samples']
for i,isample in enumerate(signlist):
WEIGHTS = "%s*(%s)" %( str(float(LUMI)/1000.) , expressAliases(samples[isample]['weight']) )
filelist = [ x for x in samples[isample]['name'] ]
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files); gROOT.cd()
histlist[isample] = makeHisto( df , var , CUT , WEIGHTS , isample )
histlist[isample] = histlist[isample].GetPtr()
histlist[isample].Sumw2()
histlist[isample].SetLineWidth(2)
histlist[isample].SetLineColor(colors[i])
#######################################################################################################
# add total histograms
histlist['Higgs']=None
for i, ihist in enumerate(histlist):
if i==0:
histlist['Higgs'] = histlist[ihist].Clone('totsignal')
histlist['Higgs'].Sumw2()
histlist['Higgs'].Add(histlist[ihist])
leg = TLegend( 0.7 if not options.Statebox else 0.4 , 0.9-0.035*len(signlist), 0.9, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(1001) #1001
leg.SetFillColor(0)
for s in signlist : leg.AddEntry(histlist[s], '%s [%.1f]' %( s , histlist[s].Integral() ) , "l")
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd().SetLogy() if options.logy else c1.cd()
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
hmax = 0.
for i, s in enumerate(signlist):
if histlist[s].GetMaximum() > hmax: hmax = histlist[s].GetMaximum()
if not options.logy:
histlist[signlist[0]].SetMaximum(hmax*1.2)
histlist[signlist[0]].SetMinimum(0.)
else:
histlist[signlist[0]].SetMaximum(hmax*4)
c1.cd()
for i, s in enumerate(signlist):
histlist[s].Draw("HIST" if i==0 else "SAMES, HIST")
if options.Statebox:
c1.GetPad(0).Update()
if i==1:
lof = histlist[s].GetListOfFunctions()
statbox = lof.FindObject('stats')
statbox.SetX1NDC(0.779026); statbox.SetX2NDC(0.979401)
statbox.SetY1NDC(0.593168); statbox.SetY2NDC(0.754658)
leg.Draw()
drawCMS(LUMI, "Simulation")
printTable_signal( histlist , pathname , '%s_%s.txt' %(plotdir,var) , signlist , histplease=True )
### fitting mass shape?
########################
if gROOT.IsBatch():
c1.Print(pathname+"/"+var.replace('.', '_')+".png")
#c1.Print(pathname+"/"+var.replace('.', '_')+".pdf")
#print col.WARNING+"PURGE OBJECTS IN MEMORY"+col.ENDC
#for process in histlist:
# histlist[process].Delete()
pass
import os, multiprocessing
import copy
import math
from array import array
from ROOT import ROOT, gROOT, gStyle, gRandom, TSystemDirectory
from ROOT import TFile, TChain, TTree, TCut, TH1, TH1F, TH2F, THStack, TGraph, TGraphAsymmErrors
from ROOT import TStyle, TCanvas, TPad
from ROOT import TLegend, TLatex, TText, TLine, TBox
from PhysicsTools.NanoAODTools.LambPlot.Utils.configs import Config
import PhysicsTools.NanoAODTools.LambPlot.Utils.color as col
from PhysicsTools.NanoAODTools.LambPlot.Utils.drawLambda import *
import importlib
from collections import OrderedDict
ROOT.EnableImplicitMT(12)
########## SETTINGS ##########
import optparse
usage = "usage: %prog [options]"
parser = optparse.OptionParser(usage)
parser.add_option("-v",
"--variable",
action="store",
type="string",
dest="variable",
default="")
parser.add_option("-c",
"--cut",
action="store",
samples = plt.cfg.getModule('samples')
variables = plt.cfg.getModule('variables')
selection = plt.cfg.getModule('selection')
aliases = plt.cfg.getModule('aliases')
########################################
from collections import OrderedDict
import pandas as pds
import numpy as np
gROOT.SetBatch(True)
#gROOT.Macro('functions.C')
gStyle.SetOptStat(0)
#GO PARALLELISM!!!
ROOT.EnableImplicitMT(12)
def expressAliases(cut_):
newcut = []
for icut in cut_.split('*'):
# level 1 expressing
if icut in aliases.keys():
for jcut in aliases[icut]['expr'].split('*'):
# level 2 expressing
if jcut in aliases.keys():
for kcut in aliases[jcut]['expr'].split('*'):
# level 3 expressing
if kcut in aliases.keys():
for lcut in aliases[kcut]['expr'].split('*'):
def ProjectDraw(var, cut, Lumi, samplelist, pd):
plt.cfg.register(samplelist)
groupList = plt.cfg.getGroupPlot()
histList = {}
VAR = var
# remove extra space
CUT = ' '.join(filter(None, selection[cut].split(" ")))
# group tag
for igroup in groupList:
CUT_ = CUT
if igroup == 'BkgSum': continue
#if igroup in [ 'Fake', 'DATA' ] : CUT_ = CUT.replace("isbVeto && ","")
print col.OKGREEN + "drawLambda::GroupTag : ", igroup + col.ENDC
#histList[igroup]={} ;
hists = {}
##sample tag
for isample in groupList[igroup]['samples']:
print("isample : ", isample)
hists[isample] = {}
isptr = False
## check if weights exist
if 'weights' in samples[isample].keys():
## sub-samples tag
subhists = {}
for jsample in samples[isample]['weights'].keys():
subhists[jsample] = {}
WEIGHTS = '(%s)*(%s)' % (
expressAliases(samples[isample]['weight']),
samples[isample]['weights'][jsample])
#WEIGHTS = '(%s)*(%s)' %( samples[isample]['weight'] , samples[isample]['weights'][jsample] )
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(
float(Lumi) / 1000.), WEIGHTS)
filelist = [
x for x in samples[isample]['name']
if os.path.basename(x).split('_', 1)[-1].replace(
'.root', '').split('__part')[0] == jsample
]
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
subhists[jsample] = makeHisto(df, VAR, CUT_, WEIGHTS,
jsample)
# Wg = (Wg*w1,Wg*w2)
hists[isample] = sumHistoPtr(subhists, isample, True)
else:
## all sub-samples share common weights
###print col.OKGREEN+ "drawLambda::Sub-Samples : " , samples[isample]['weights'].keys() + col.ENDC
isptr = True
WEIGHTS = expressAliases(samples[isample]['weight'])
#WEIGHTS = samples[isample]['weight']
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(float(Lumi) / 1000.), WEIGHTS)
filelist = samples[isample]['name']
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
hists[isample] = makeHisto(df, VAR, CUT_, WEIGHTS, isample)
# Vg = sum(Wg,Zq)
histList[igroup] = sumHistoPtr(hists, igroup, isptr)
print col.YELLOW + "group tag processed : ", histList.keys(), col.ENDC
histList[igroup].Sumw2()
histList[igroup].SetFillColor(groupList[igroup]['fillcolor'])
histList[igroup].SetFillStyle(groupList[igroup]['fillstyle'])
histList[igroup].SetLineColor(groupList[igroup]['linecolor'])
histList[igroup].SetLineStyle(groupList[igroup]['linestyle'])
return histList