for subdir, dirs, files in os.walk(path):
for file in files:
if file.find("tree.root") >= 0:
the_file = os.path.join(subdir, file)
if useChunk == False and the_file.find("Chunk") >= 0: continue
Trees.append(the_file)
####################
# check the trees
####################
out_file = open("diagnostic.txt", "w")
out_file.write("The tree list with event issue found :\n")
for tree in Trees:
print "check tree " + str(Trees.index(tree) + 1) + "/" + str(len(Trees))
rf = TFile(tree)
t = rf.Get("events")
numberOfEntries = t.GetEntries()
bad_status = False
show_every = 5
count = 1
for entry in xrange(numberOfEntries):
ratio = 100. * float(entry) / float(numberOfEntries)
if ratio > show_every * count:
print "Done " + str(int(ratio)) + "% (" + str(entry) + "/" + str(
numberOfEntries) + ")"
count += 1
bad_event_status = t.GetEntry(entry)
if bad_event_status == -1:
bad_status = True
break
if bad_status == True: out_file.write(tree + "\n")
reader.AddVariable("m_Wj1Wj2", m_Wj1Wj2)
reader.AddVariable("nllKinFit", nllKinFit)
reader.AddVariable("pT_Wj2", pT_Wj2)
reader.AddVariable("pT_bWj1Wj2", pT_bWj1Wj2)
reader.AddVariable("qg_Wj2", qg_Wj2)
#reader.BookMVA("testmva", "Results/TMVABDT_hadTopTagger_maxDepth3_9Var_ttH.xml")
reader.BookMVA(
"testmva",
"Results_10Oct2017/TMVABDT_hadTopTagger_maxDepth3_9Var_ps75.xml")
if process == "signal":
file = TFile(
"/scratch/arun/mvaTraining/22Sep2017/histograms_harvested_stage1_hadTopTagger_ttHToNonbb_fastsim.root"
)
tree = file.Get("analyze_hadTopTagger/evtntuple/signal/evtTree")
elif process == "TT":
file = TFile(
"/scratch/arun/mvaTraining/22Sep2017/histograms_harvested_stage1_hadTopTagger_TTToSemilepton_fastsim.root"
)
tree = file.Get("analyze_hadTopTagger/evtntuple/TT/evtTree")
elif process == "TTV":
file1 = TFile(
"/hdfs/local/veelken/ttHAnalysis/2016/2017Aug31/histograms/hadTopTagger/histograms_harvested_stage1_hadTopTagger_TTWJetsToLNu_fastsim.root"
)
tree_ttw = file1.Get("analyze_hadTopTagger/evtntuple/TTW/evtTree")
file2 = TFile(
"/hdfs/local/veelken/ttHAnalysis/2016/2017Aug31/histograms/hadTopTagger/histograms_harvested_stage1_hadTopTagger_TTZToLLNuNu_fastsim.root"
)
tree_ttz = file2.Get("analyze_hadTopTagger/evtntuple/TTZ/evtTree")
list = TList()
pileupCut = 0.1
# script arguments
if (len(sys.argv) > 1): fileNum = int(sys.argv[1])
if (len(sys.argv) > 2):
dataDir = str(sys.argv[2])
if (len(sys.argv) > 3):
fileList = str(sys.argv[3])
inarray = np.loadtxt(fileList, dtype=float)
fileNum = int(inarray[fileNum])
layers = int(sys.argv[4])
fname = "hist_" + str(fileNum) + ".root"
print "File " + dataDir + fname
fin = TFile(dataDir + fname, 'read')
fakeTree = fin.Get('fakeTree')
realTree = fin.Get('realTree')
pileupTree = fin.Get('pileupTree')
print("Fake Tree Events: " + str(fakeTree.GetEntries()))
print("Real Tree Events: " + str(realTree.GetEntries()))
print("Pileup Tree Events: " + str(pileupTree.GetEntries()))
def pileupMatching(track):
min_dz = 10e6
for vertex in event.pileupZPosition:
dZ = abs(track.vz - vertex)
if dZ < min_dz: min_dz = dZ
print("DZ Matched: ", min_dz)
return min_dz
def test_PyROOT():
f = TFile(load('single1.root'))
tree = f.Get('tree')
rnp.tree2array(tree)
from ROOT import TFile
import sys
import array as arr
import configparser
####################################### CONFIGURATION START #######################################
config = configparser.ConfigParser()
config.read(sys.argv[1])
input_file = TFile(config['Head']['InputFile'])
object_list = input_file.Get(config['Head']['ListName'])
particle_name = config['Head']['Particle']
trig_pt_low = float(config['Pt']['TrigPtLow'])
trig_pt_high = float(config['Pt']['TrigPtHigh'])
assoc_pt_low = float(config['Pt']['AssocPtLow'])
assoc_pt_high = float(config['Pt']['AssocPtHigh'])
cent_low = float(config['Cent']['CentLow'])
cent_high = float(config['Cent']['CentHigh'])
peak_low = float(config['MassRegions']['PeakLow'])
peak_high = float(config['MassRegions']['PeakHigh'])
rsb_low = float(config['MassRegions']['RightSidebandLow'])
rsb_high = float(config['MassRegions']['RightSidebandHigh'])
lsb_low = float(config['MassRegions']['LeftSidebandLow'])
lsb_high = float(config['MassRegions']['LeftSidebandHigh'])
####################################### CONFIGURATION END #######################################
def reset_ranges(hist, dim):
#can we just talk about how stupid it is that TH1,2,3D's don't have a GetAxis attr
if dim == 1:
hist.GetXaxis().SetRangeUser(0, 0)
color=kAzure + 4,
markerstyle=kOpenSquare,
markersize=1.5,
linewidh=2,
linestyle=7)
if args.ptweights:
infileWeigts = TFile.Open(args.ptweights[0])
hPtWeights = infileWeigts.Get(args.ptweights[1])
hRecoPrompt, hRecoFD, hGenPrompt, hGenFD = ([] for iHisto in range(4))
infile = TFile(args.inFileName)
for iPt, (ptMin, ptMax) in enumerate(zip(ptMins, ptMaxs)):
hRecoPrompt.append(
infile.Get('hPromptPt_%0.f_%0.f' % (ptMin * 10, ptMax * 10)))
hRecoFD.append(infile.Get('hFDPt_%0.f_%0.f' % (ptMin * 10, ptMax * 10)))
hGenPrompt.append(
infile.Get('hPromptGenPt_%0.f_%0.f' % (ptMin * 10, ptMax * 10)))
hGenFD.append(infile.Get('hFDGenPt_%0.f_%0.f' % (ptMin * 10, ptMax * 10)))
# get unweighted yields (for uncertainty)
nRecoPrompt = hRecoPrompt[iPt].Integral()
nGenPrompt = hGenPrompt[iPt].Integral()
nRecoFD = hRecoFD[iPt].Integral()
nGenFD = hGenFD[iPt].Integral()
# get weighted yields
nRecoPromptWeighted, nGenPromptWeighted, nRecoFDWeighted, nGenFDWeighted = (
0 for iVal in range(4))
for iBin in range(hRecoPrompt[iPt].GetNbinsX()):
def main():
parser = ArgumentParser()
parser.add_argument('--indir',
default="FILLMEPLEASE",
help='directory name of input files')
parser.add_argument('--infiles', default="*", help='name of input files')
parser.add_argument('--tag',
default=time.strftime("%a%d%b%Y_%Hh%Mm%Ss"),
help='name of output directory')
parser.add_argument(
'--nevents',
type=int,
default=-1,
help='maximum number of events for each dataset to process')
parser.add_argument(
'--nmaxevtsperjob',
type=int,
default=200000,
help='maximum number of events per job (otherwise split)')
parser.add_argument('--ncpu',
type=int,
default=-1,
help='number of CPU to use in parallel')
parser.add_argument('--topmatchingdir',
default="FILLME",
help='name of training directory')
parser.add_argument('--tthfselectordir',
default="FILLME",
help='name of training directory')
parser.add_argument('--reweightingdir',
default="FILLME",
help='name of training directory')
parser.add_argument('--cTagSFFile',
default="FILLME",
help='PATH to file containing c-tagger SFs')
args = parser.parse_args()
workingdir = os.getcwd()
if not os.path.isdir(workingdir + "/SELECTED_" + args.tag):
os.mkdir(workingdir + "/SELECTED_" + args.tag)
# Search for the input directory
indir = os.path.abspath(args.indir) + "/"
if not os.path.isdir(indir):
print "Error: could not find directory '%s'" % indir
sys.exit(1)
# Take only files defined by args.infiles
if not args.infiles == "*":
filelist = []
tags = args.infiles.split("*")
for f in [f for f in os.listdir(indir)]:
for t in tags:
if t == "": continue
if t in f: filelist.append(f)
else:
filelist = [
f for f in os.listdir(indir) if not "ttH" in f and not "TTZ" in f
] # and not "31Mar" in f]
# Count number of events in each file
nevts_dict = {}
print "Counting number of events in each file"
for f in filelist:
tfile = TFile(indir + f,
workingdir + "/SELECTED_" + args.tag + "/" + f)
tree_ = tfile.Get("tree")
nevts = tree_.GetEntries()
if nevts > args.nevents and args.nevents > 0: nevts = args.nevents
nevts_dict[f] = nevts
for _f, _n in nevts_dict.iteritems():
print "**** %s: %i events ****" % (_f, _n)
#sys.exit(1)
# Create batch temp
tmpdirname = "BATCH_TMP_" + args.tag
if not os.path.isdir(workingdir + "/" + tmpdirname):
os.mkdir(workingdir + "/" + tmpdirname)
ff_ = open(workingdir + "/" + tmpdirname + "/bigsub.txt", 'w')
split_jobs_dict = {}
for f in filelist:
split_jobs_dict[f] = False
# See if jobs need to be split
if args.nmaxevtsperjob > 0 and nevts_dict[f] > args.nmaxevtsperjob:
split_jobs_dict[f] = True
eventsList = []
startEvent = 0
while (startEvent < nevts_dict[f]):
eventsList.append(startEvent)
startEvent += args.nmaxevtsperjob
eventsList.append(nevts_dict[f])
print "Dataset %s was splitted in %i jobs" % (f,
len(eventsList) - 1)
for i in range(len(eventsList) - 1):
if not os.path.isdir(workingdir + "/" + tmpdirname + "/" +
f.split(".root")[0] + "_events_" +
str(eventsList[i]) + "_" +
str(eventsList[i + 1] - 1)):
os.mkdir(workingdir + "/" + tmpdirname + "/" +
f.split(".root")[0] + "_events_" +
str(eventsList[i]) + "_" +
str(eventsList[i + 1] - 1))
flaunch_ = open(
workingdir + "/" + tmpdirname + "/" + f.split(".root")[0] +
"_events_" + str(eventsList[i]) + "_" +
str(eventsList[i + 1] - 1) + "/launch.sh", 'w')
flaunch_.write("#!/bin/bash \n")
flaunch_.write("source /user/smoortga/sklearn_setenv.sh \n")
flaunch_.write(
"cdir=/user/smoortga/Analysis/2017/ttcc_Analysis/CMSSW_8_0_25/src/ttcc/ \n"
)
flaunch_.write(
"export LD_LIBRARY_PATH=${cdir}:${cdir}/selection:$LD_LIBRARY_PATH \n"
)
flaunch_.write(
"cd /user/smoortga/Analysis/2017/ttcc_Analysis/CMSSW_8_0_25/src/ttcc/setup \n"
)
flaunch_.write("root -l setup.C \n")
flaunch_.write(
"cd /user/smoortga/Analysis/2017/ttcc_Analysis/CMSSW_8_0_25/src/ttcc/analyse \n"
)
flaunch_.write(
"python Analyze.py --infile=%s --outfile=%s --topmatchingdir=%s --tthfselectordir=%s --reweightingdir=%s --cTagSFFile=%s --firstEvt=%i --lastEvt=%i --splitted=1 \n"
% (indir + f, workingdir + "/SELECTED_" + args.tag + "/" +
f, args.topmatchingdir, args.tthfselectordir,
args.reweightingdir, args.cTagSFFile, eventsList[i],
eventsList[i + 1]))
flaunch_.close()
ff_.write(
"qsub -q localgrid -o %s/script.stdout -e %s/script.stderr -l walltime=04:59:59 %s/launch.sh \n"
% (workingdir + "/" + tmpdirname + "/" +
f.split(".root")[0] + "_events_" + str(eventsList[i]) +
"_" + str(eventsList[i + 1] - 1), workingdir + "/" +
tmpdirname + "/" + f.split(".root")[0] + "_events_" +
str(eventsList[i]) + "_" + str(eventsList[i + 1] - 1),
workingdir + "/" + tmpdirname + "/" +
f.split(".root")[0] + "_events_" + str(eventsList[i]) +
"_" + str(eventsList[i + 1] - 1)))
#res = p.apply_async(Analyze, args = (indir+f,workingdir+"/SELECTED_"+args.tag+"/"+f, args.topmatchingdir,eventsList[i], eventsList[i+1],True,))
else:
if not os.path.isdir(workingdir + "/" + tmpdirname + "/" +
f.split(".root")[0]):
os.mkdir(workingdir + "/" + tmpdirname + "/" +
f.split(".root")[0])
flaunch_ = open(
workingdir + "/" + tmpdirname + "/" + f.split(".root")[0] +
"/launch.sh", 'w')
flaunch_.write("#!/bin/bash \n")
flaunch_.write("source /user/smoortga/sklearn_setenv.sh \n")
flaunch_.write(
"cdir=/user/smoortga/Analysis/2017/ttcc_Analysis/CMSSW_8_0_25/src/ttcc/ \n"
)
flaunch_.write(
"export LD_LIBRARY_PATH=${cdir}:${cdir}/selection:$LD_LIBRARY_PATH \n"
)
flaunch_.write(
"cd /user/smoortga/Analysis/2017/ttcc_Analysis/CMSSW_8_0_25/src/ttcc/setup \n"
)
flaunch_.write("root -l setup.C \n")
flaunch_.write(
"cd /user/smoortga/Analysis/2017/ttcc_Analysis/CMSSW_8_0_25/src/ttcc/analyse \n"
)
flaunch_.write(
"python Analyze.py --infile=%s --outfile=%s --topmatchingdir=%s --tthfselectordir=%s --reweightingdir=%s --cTagSFFile=%s --firstEvt=0 --lastEvt=%i --splitted=0 \n"
% (indir + f, workingdir + "/SELECTED_" + args.tag + "/" + f,
args.topmatchingdir, args.tthfselectordir,
args.reweightingdir, args.cTagSFFile, nevts_dict[f]))
flaunch_.close()
ff_.write(
"qsub -q localgrid -o %s/script.stdout -e %s/script.stderr -l walltime=04:59:59 %s/launch.sh \n"
% (workingdir + "/" + tmpdirname + "/" + f.split(".root")[0],
workingdir + "/" + tmpdirname + "/" + f.split(".root")[0],
workingdir + "/" + tmpdirname + "/" + f.split(".root")[0]))
#res = p.apply_async(Analyze, args = (indir+f,workingdir+"/SELECTED_"+args.tag+"/"+f, args.topmatchingdir,0,nevts_dict[f],False,))
ff_.close()
h_TopMatch = TH1F("h_TopMatch", "", nbins, edges)
h_Wmatch = TH1F("h_Wmatch", "", nbins, edges)
h_unmatch = TH1F("h_unmatch", "", nbins, edges)
h_ttFailed = TH1F("h_ttFailed", "", nbins, edges)
h_ttPassed = TH1F("h_ttPassed", "", nbins, edges)
h_ttPassed_Match = TH1F("h_ttPassed_Match", "", nbins, edges)
h_ttPassed_Wmatch = TH1F("h_ttPassed_Wmatch", "", nbins, edges)
h_ttPassed_unmatch = TH1F("h_ttPassed_unmatch", "", nbins, edges)
h_pfMC = TH1F("h_pfMC", "", 2, 0, 2)
h_pfMCpassf = TH1F("h_pfMCpassf", "", 2, 0, 2)
h_pfMCpassp = TH1F("h_pfMCpassp", "", 2, 0, 2)
h_pfMCfailf = TH1F("h_pfMCfailf", "", 2, 0, 2)
h_pfMCfailp = TH1F("h_pfMCfailp", "", 2, 0, 2)
openTop = TFile(Top_path, "read")
h_total_mcweight_Top = openTop.Get("h_total_mcweight")
totalEventsTop = h_total_mcweight_Top.Integral()
treeTop = openTop.Get("monoHbb_Tope_boosted")
EventsTop = treeTop.GetEntries()
for i in range(EventsTop):
treeTop.GetEntry(i)
st_TopMatching = getattr(treeTop, 'st_TopMatching')
CSV_Top = getattr(treeTop, 'FJetCSV')
SD_Top = getattr(treeTop, 'FJetMass')
dPhi_Top = getattr(treeTop, 'min_dPhi')
if (st_TopMatching == 2) and (SD_Top > 100.0) and (SD_Top < 150.0) and (
dPhi_Top > 0.4):
h_TopMatch.Fill(CSV_Top)
if (st_TopMatching == 3) and (SD_Top > 100.0) and (SD_Top < 150.0) and (
dPhi_Top > 0.4):
import ROOT
from ROOT import TFile, TTree, TCanvas, TGraph, TMultiGraph, TGraphErrors, TLegend
file10p = TFile("10p/upper_limits.root")
file50p = TFile("50p/upper_limits.root")
file100p = TFile("100p/upper_limits.root")
file300p = TFile("300p/upper_limits.root")
g10p = file10p.Get("Graph")
g50p = file50p.Get("Graph")
g100p = file100p.Get("Graph")
g300p = file300p.Get("Graph")
g50p.SetLineColor(2)
g100p.SetLineColor(3)
g300p.SetLineColor(4)
W = 800
H = 600
T = 0.08*H
B = 0.12*H
L = 0.12*W
R = 0.04*W
c = TCanvas("c","c",100,100,W,H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
c.SetLeftMargin( L/W )
c.SetRightMargin( R/W )
#---------------------------------#
p = argparse.ArgumentParser(description='Specify whether the MVA was performed on the muon or the electron sample')
p.add_argument('isMuon_option', help='Type <<muon>> or <<electron>>')
args = p.parse_args()
# Switch from muon to electron channel
if args.isMuon_option == "muon":
isMuon = True
if args.isMuon_option == "electron":
isMuon = False
#---------------------------------#
# Get input trees (MC)
if isMuon:
fIn_bkg = TFile("/eos/user/j/jdioquin/www/MVA_studies/TreeFiles_31718/Tree_MC_Background_mu.root")
tbkg_in = fIn_bkg.Get("minitree_background_mu")
fIn_sig = TFile("/eos/user/j/jdioquin/www/MVA_studies/TreeFiles_31718/Tree_MC_Signal_mu.root")
tsig_in = fIn_sig.Get("minitree_signal_mu")
fOut = TFile("/eos/user/j/jdioquin/www/MVA_studies/BDTeval2818/BDTeval_mu.root","RECREATE")
else:
fIn_bkg = TFile("/eos/user/j/jdioquin/www/MVA_studies/TreeFiles_31718/Tree_MC_Background_ele.root")
tbkg_in = fIn_bkg.Get("minitree_background_ele")
fIn_sig = TFile("/eos/user/j/jdioquin/www/MVA_studies/TreeFiles_31718/Tree_MC_Signal_ele.root")
tsig_in = fIn_sig.Get("minitree_signal_ele")
fOut = TFile("/eos/user/j/jdioquin/www/MVA_studies/BDTeval2818/BDTeval_ele.root","RECREATE")
# Create arrays for the reader
from ROOT import TFile, TH2F, TCanvas, TH3F
from HistEfficiency2D import HistEfficiency2D
from style import papas_style, cms_style
papas_file = TFile('./rootfiles/papassampletuned.root')
cms_file = TFile('./rootfiles/cmssamplejetted.root')
papas_tree = papas_file.Get('events')
cms_tree = cms_file.Get('events')
#histo = TH2F('2D_eff1', 'PAPAS efficiency : P_{T} : eta', 50, 0, 10, 50, -4, 4)
#papas_tree.Project('2D_eff1', 'jet1_eta:jet1_pt', 'jet1_pt>0')
#can1 = TCanvas()
#histo.Draw('LEGO')
#histo2 = TH2F('2D_eff2', 'PAPAS efficiency : P_{T} : eta', 50, 0, 10, 50, -4, 4)
#papas_tree.Project('2D_eff2', 'jet1_eta:jet1_pt',
# 'jet1_pt>0 && jet1_rec_211_num==1')
#can2 = TCanvas()
#histo2.Draw('LEGO')
#div_histo = histo2.Clone('2D_eff')
#div_histo.Divide(histo)
#can3 = TCanvas()
#div_histo.Draw('LEGO')
from ROOT import TFile, TH2D, TDirectory, TCanvas, TList, gPad, gStyle, TLegend, TVector2, TMath
import math
Data = TFile('user.luadamek.20219352._000196.hist-output.root')
Monte = TFile('user.luadamek.20219380._000083.hist-output.root')
histTreeData = Data.Get(
'LA_EoverP_InDetTrackParticlesSortedLooseIsolatedVertexAssociated_tree')
histTreeMonte = Monte.Get(
'LA_EoverP_InDetTrackParticlesSortedLooseIsolatedVertexAssociated_tree')
entriesdata = histTreeData.GetEntriesFast()
for jentry in xrange(entriesdata):
nb = histTreeData.GetEntry(jentry)
if nb <= 0:
continue
etacal = 0
ntrt = histTreeData.trk_nTRT
E = histTreeData.trk_nclusters_EM_200 + histTreeData.trk_nclusters_HAD_200
if (ntrt > 20 and E > 0):
print("Fill histogram here")
entriesmonte = histTreeMonte.GetEntriesFast()
for jentry in xrange(entriesmonte):
nb = histTreeMonte.GetEntry(jentry)
if nb <= 0:
continue
InSyncRuns = list()
OutOfSyncRuns = list()
for Run in range(StartRun,StopRun+1,1):
IsInSync = False
inputfile = "/eos/uscms/store/user/cmstestbeam/2019_04_April_CMSTiming/KeySightScope/RecoData/TimingDAQRECO/RecoWithTracks/v6/run_scope"+str(Run)+"_converted.root"
if not os.path.exists(inputfile):
print "input file "+inputfile+" does not exist"
continue
file = TFile(inputfile)
tree = file.Get("pulse")
c = TCanvas("cv","cv",800,800)
num = TH2F("num",";x;y",25,0,50,25,0,50)
den = TH2F("den",";x;y",25,0,50,25,0,50)
tree.Draw("y_dut[0]:x_dut[0]>>den","ntracks==1 && y_dut[0] > 0 ","colz")
tree.Draw("y_dut[0]:x_dut[0]>>num","ntracks==1 && y_dut[0] > 0 && amp[1] > 200 ","colz")
num.Divide(den)
effInside = num.GetBinContent(num.GetXaxis().FindFixBin(18.0),num.GetYaxis().FindFixBin(25.0))
effOutside = num.GetBinContent(num.GetXaxis().FindFixBin(18.0),num.GetYaxis().FindFixBin(18.0))
print num.GetXaxis().FindFixBin(18.0), " ", num.GetYaxis().FindFixBin(25.0), " ", effInside
print num.GetXaxis().FindFixBin(18.0), " ", num.GetYaxis().FindFixBin(18.0), " ", effOutside
inFile3 = TFile(filename3)
inFile4 = TFile(filename4)
inFile5 = TFile(filename5)
inFile6 = TFile(filename6)
zFile1 = TFile(
"/home/mrsolt/hps/Vertexing/L0/1pt05/ap/upperlimit/golden_massbin.root")
zFile2 = TFile("/home/mrsolt/hps/Vertexing/L0/1pt05/ap/L1L2/golden.root")
zFile3 = TFile("/home/mrsolt/hps/Vertexing/L0/1pt05/ap/L2L2/golden.root")
zFile4 = TFile("/home/mrsolt/hps/Vertexing/L0/1pt05/ap/L2L3/golden.root")
zFile5 = TFile("/home/mrsolt/hps/Vertexing/L0/1pt05/ap/L3L3/golden.root")
zFile6 = ""
c = TCanvas("c", "c", 800, 600)
XaxisTitle = inFile1.Get(histoTitle).GetXaxis().GetTitle()
YaxisTitle = inFile1.Get(histoTitle).GetYaxis().GetTitle()
Histo1 = inFile1.Get(histoTitle)
Histo2 = inFile2.Get("eff_all_unscaled")
Histo3 = inFile3.Get(histoTitle)
Histo4 = inFile4.Get(histoTitle)
Histo5 = inFile5.Get(histoTitle)
Histo6 = inFile6.Get("eff_all_unscaled")
normHisto = inFile1.Get("eff_all_unscaled").GetFunction("exppol4").Eval(-5)
print normHisto
Histo2.Scale(1. / normHisto)
Histo6.Scale(1. / normHisto)
func1 = inFile1.Get(histoTitle).GetFunction("exppol4")
print(
"Plotting of the enrgy distribution can be performed only for one energy value"
)
exit()
energy = calo_init.energy(0)
canv = prepare_single_canvas('energyDistribution_' + str(energy) + 'GeV',
'Energy distribution for ' + str(energy) + 'GeV')
canv.cd()
energy = calo_init.energy(0)
resolution_list = []
mean_list = []
# files represent different types for the same particle energy:
for ifile, filename in enumerate(calo_init.filenamesIn):
f = TFile(filename, "READ")
hEn = f.Get(histogramNames[ifile])
hEn.Sumw2()
if hEn.GetEntries() > 0:
hEn.Scale(1. / hEn.GetEntries())
hEn.Rebin(4)
hEn.SetTitle('')
if calo_init.args.axisMax:
hEn.GetYaxis().SetRangeUser(0, calo_init.args.axisMax)
fitPre = TF1("fitPre", "gaus",
hEn.GetMean() - 1. * hEn.GetRMS(),
hEn.GetMean() + 1. * hEn.GetRMS())
resultPre = hEn.Fit(fitPre, "SQRN")
fit = TF1("fit", "gaus",
resultPre.Get().Parameter(1) - 2. * resultPre.Get().Parameter(2),
resultPre.Get().Parameter(1) + 2. * resultPre.Get().Parameter(2))
fit.SetLineColor(coloursFit[ifile])
from ROOT import TFile, TH2F, TCanvas, TF1
from papas_analysis_gael.tools.style import papas_style, cms_style, cms_style2, papas_style2
from papas_analysis_gael.tools.HistComparator import HistComparator
from cpyroot.tools.fitter2d import Fitter2D
fil = TFile('./rootfiles/photon_test_tree.root')
tree = fil.Get('events')
eres = TF1('eres', 'sqrt(([0]/sqrt(x))**2+([1]/x)**2+[2]**2)')
comp = HistComparator(tree, style1 = cms_style, style2 = papas_style,
nbin = 200, xmin = 0.5, xmax = 1.5, xvar = 'E_{rec}/E_{gen}',
var1 = 'cmsjet_e/gen_jet_e',
cut = 'gen_jet_pt>1 && gen_jet_pt<10 && abs(gen_jet_eta)<1.479 && simtrack_len==3',
var2 = 'papasjet_e/gen_jet_e')
comp2 = HistComparator(tree, style1 = cms_style, style2 = papas_style,
nbin = 200, xmin = 0.4, xmax = 1.6, xvar = 'E_{rec}/E_{gen}',
var1 = 'cmsjet_e/gen_jet_e',
cut = 'gen_jet_pt>1 && gen_jet_pt<10 && abs(gen_jet_eta)>1.479 && abs(gen_jet_eta)<3 && simtrack_len==3',
var2 = 'papasjet_e/gen_jet_e')
fitter = Fitter2D('histo2d','E_{rec}/E_{gen}vsE_{gen} endcap', 198, 1, 100, 200, 0, 2)
tree.Project('histo2d','cmsjet_e/gen_jet_e:gen_jet_e','gen_jet_pt>1 && abs(gen_jet_eta)>1.479 && abs(gen_jet_eta)<3 && simtrack_len==3 && cmsjet_e>0')
fitter.fit_slices(selection=3)
can3 = TCanvas()
fitter.h2d.Draw()
can = TCanvas()
fitter.hmean.SetTitle('Fitted value of mean endcap')
fitter.hmean.Draw()
fitter.hsigma.SetTitle('Fitted value of Sigma endcap')
from ROOT import TFile, TH1F, TCanvas, TF1, gStyle, gROOT, gPad, TLegend, TH2F
from numpy import arctan
gROOT.SetBatch()
gStyle.SetOptStat(0)
fontsize = 0.045
gStyle.SetPadTickX(1)
gStyle.SetPadTickY(1)
gStyle.SetTitleOffset(1.5)
f = TFile("../data.root")
tree = f.Get("drift_4000V")
th = 0.02
cut = "Chn1_Height>{0}&&Chn3_Height>{0}&&Chn7_Height>{0}&&Chn8_Height>{0}".format(
th)
theta_max = arctan(100 / 290.)
print("Maximum angle [rad]: {}".format(theta_max))
print("Number of total events: {}".format(tree.GetEntries()))
print("Number of events after applying threshold: {}".format(
tree.GetEntries(cut)))
print("Number of events after removing bad fits: {}".format(
tree.GetEntries(cut + "&&X0Err<6&&ThetaErr<0.04")))
print("Number of events after removing bad fits (x0 only): {}".format(
isBlinded = True
ROOT.TH1.SetDefaultSumw2(True)
##########################
# get data tree
##########################
treeName = "TauCheck"
if channel == "mt":
fdata = TFile(indirData + "/" + "SingleMuon_Run2016BCD.root", "read")
if channel == "et":
fdata = TFile(indirData + "/" + "SingleElectron_Run2016BCD.root", "read")
tdata = fdata.Get(treeName)
#redefine tree name for MC
treeName = "TauCheck" + systName
##########################
# define file lists
##########################
lvv = {
"ST_t-channel_top_4f_leptonDecays": {
"xsec": 136.95 * 3 * 0.108,
"isBinned": False,
"binVar": "",
"files": ["ST_t-channel_top_4f_leptonDecays"]
},
# filename='/nfs/dust/cms/user/albrechs/CMSSW_8_1_0/src/DijetCombineLimitCode/plots/fitDiagnosticsZZ_T7_m21p00'+postfix+'0_invMass_combined.root'
filename = '/nfs/dust/cms/user/albrechs/CMSSW_8_1_0/src/DijetCombineLimitCode/plots/fitDiagnosticsZZ_T7_m15p40' + postfix + '0_invMass_combined.root'
# filename='/nfs/dust/cms/user/albrechs/CMSSW_8_1_0/src/DijetCombineLimitCode/plots/fitDiagnosticsZZ_T0_m1p080_invMass_combined.root'
# filename='/nfs/dust/cms/user/albrechs/CMSSW_8_1_0/src/DijetCombineLimitCode/plots/fitDiagnosticsZZ_M0_105p000_invMass_combined_1GeV.root'
# filename='/nfs/dust/cms/user/albrechs/CMSSW_8_1_0/src/DijetCombineLimitCode/plots/fitDiagnosticsZZ_M0_105p000_invMass_combined_1GeV.root'
File = TFile(filename, 'READ')
pointname = filename.split('/')[-1].split('_')
canvTitle = pointname[1] + ' ' + pointname[2] + ' ' + pointname[3][:-1]
pointname = pointname[1] + '_' + pointname[2] + '_' + pointname[3][:-1]
for channel in ['ch1', 'ch2']:
if channel == 'ch1':
name = 'VV'
if channel == 'ch2':
name = 'VBF'
signal = File.Get('shapes_fit_s/' + channel + '/total_signal')
background = File.Get('shapes_fit_s/' + channel + '/total_background')
data = File.Get('shapes_fit_s/' + channel + '/data')
# signal.Scale(10)
# TH1 h; // the histogram (you should set the number of bins, the title etc)
# auto nPoints = graph.GetN(); // number of points in your TGraph
# for(int i=0; i < nPoints; ++i) {
# double x,y;
# graph.GetPoint(i, x, y);
# h->Fill(x,y); // ?
# }
N = 0
datahist = TH1F('data', 'data', 3451, 1050, 4500)
fin_ZB = TFile('../../toHist/data/ptH_%s/mc_ptH_%s_ptV%d.hist_ZB.root'%(ptH,ptH,i))
fin_JP0 = TFile('../../toHist/data/ptH_%s/mc_ptH_%s_ptV%d.hist_JP0.root'%(ptH,ptH,i))
fin_JP1 = TFile('../../toHist/data/ptH_%s/mc_ptH_%s_ptV%d.hist_JP1.root'%(ptH,ptH,i))
fin_JP2 = TFile('../../toHist/data/ptH_%s/mc_ptH_%s_ptV%d.hist_JP2.root'%(ptH,ptH,i))
fin_AJP = TFile('../../toHist/data/ptH_%s/mc_ptH_%s_ptV%d.hist_AJP.root'%(ptH,ptH,i))
for j in reckind_List:
print j
for m in xrange(0,2):
print m
for k in particle_List:
tc=TCanvas('c','c',700,500)
print k
h_ZB=fin_ZB.Get('h_%s_pid_parent_%s_J%d_PT%d'%(j,k,m,i-1))
h_JP0=fin_JP0.Get('h_%s_pid_parent_%s_J%d_PT%d'%(j,k,m,i-1))
h_JP1=fin_JP1.Get('h_%s_pid_parent_%s_J%d_PT%d'%(j,k,m,i-1))
h_JP2=fin_JP2.Get('h_%s_pid_parent_%s_J%d_PT%d'%(j,k,m,i-1))
h_AJP=fin_AJP.Get('h_%s_pid_parent_%s_J%d_PT%d'%(j,k,m,i-1))
h_ZB.Scale(1.0000/(h_ZB.Integral()))
h_JP0.Scale(1.0000/(h_JP0.Integral()))
h_JP1.Scale(1.0000/(h_JP1.Integral()))
h_JP2.Scale(1.0000/(h_JP2.Integral()))
h_AJP.Scale(1.0000/(h_AJP.Integral()))
'''
h_ZB.GetXaxis().SetDecimals(1)
h_JP0.GetXaxis().SetDecimals(1)
h_JP1.GetXaxis().SetDecimals(1)
if not isxgbs:
for vkey, ivar in vardir.items():
if vkey.find('xgbs') != -1:
vardir.pop(vkey)
print '-' * 80
for vkey, ivar in vardir.items():
print '->', vkey.ljust(20), ivar
print '-' * 80
for type, ivar in ddir.items():
file = TFile(ivar['file'])
tree = file.Get('tree')
print ivar['file'], 'is read ...'
var_tuples, hists = returnTuples(type, vardir)
cut = '(' + ivar['acut'] + ')*' + ivar['weight']
print(type, cut)
tree.MultiDraw(var_tuples, cut)
multihists.update(copy.deepcopy(hists))
writes = {}
for vkey, ivar in vardir.items():
def main(argv):
parser = argparse.ArgumentParser(
description='Makes training data set for various vertex/decay ID')
parser.add_argument('-c',
'--config',
help="JSON with script configuration",
default='config.json')
parser.add_argument('-t', '--type', help="Input file format")
parser.add_argument('-i', '--input', help="Input directory")
parser.add_argument('-o', '--output', help="Output directory")
parser.add_argument('-v', '--view', help="view")
args = parser.parse_args()
config = read_config(args.config)
print '#' * 50, '\nPrepare data for CNN'
INPUT_TYPE = config['prepare_data_vtx_id']['input_type']
INPUT_DIR = config['prepare_data_vtx_id']['input_dir']
OUTPUT_DIR = config['prepare_data_vtx_id']['output_dir']
PATCH_SIZE_W = config['prepare_data_vtx_id']['patch_size_w']
PATCH_SIZE_D = config['prepare_data_vtx_id']['patch_size_d']
print 'Using %s as input dir, and %s as output dir' % (INPUT_DIR,
OUTPUT_DIR)
print '#' * 50
rootModule = config['prepare_data_vtx_id'][
'module_name'] # larsoft module name used for data dumps in ROOT format
selected_view_idx = config['prepare_data_vtx_id'][
'selected_view_idx'] # set the view id
nearby_empty = config['prepare_data_vtx_id'][
'nearby_empty'] # number of patches near each vtx, but with empty area in the central pixel
nearby_on_track = config['prepare_data_vtx_id'][
'nearby_on_track'] # number of patches on tracks or showers, somewhere close to each vtx
crop_event = config['prepare_data_vtx_id'][
'crop_event'] # use true only if no crop on LArSoft level and not a noise dump
print 'Using', nearby_empty, 'empty and', nearby_on_track, 'on track patches per each verex in view', selected_view_idx
max_capacity = 300000
db = np.zeros((max_capacity, PATCH_SIZE_W, PATCH_SIZE_D), dtype=np.float32)
db_y = np.zeros((max_capacity, 5), dtype=np.int32)
kHadr = 0x1 # hadronic inelastic scattering
kPi0 = 0x2 # pi0 produced in this vertex
kDecay = 0x4 # point of particle decay (except pi0 decays)
kConv = 0x8 # gamma conversion
cnt_ind = 0
cnt_vtx = 0
cnt_decay = 0
cnt_gamma = 0
cnt_nu = 0
cnt_trk = 0
cnt_void = 0
fcount = 0
event_list = []
if INPUT_TYPE == "root":
fnames = [f for f in os.listdir(INPUT_DIR) if '.root' in f]
for n in fnames:
rootFile = TFile(INPUT_DIR + '/' + n)
keys = [
rootModule + '/' + k.GetName()[:-4]
for k in rootFile.Get(rootModule).GetListOfKeys()
if '_raw' in k.GetName()
]
event_list.append((rootFile, keys))
else:
keys = [f[:-4] for f in os.listdir(INPUT_DIR) if '.raw' in f
] # only main part of file name, without extension
event_list.append(
(INPUT_DIR, keys)) # single entry in the list of txt files
for entry in event_list:
folder = entry[0]
event_names = entry[1]
for evname in event_names:
finfo = evname.split('_')
evt_no = finfo[2]
tpc_idx = int(finfo[8])
view_idx = int(finfo[10])
if view_idx != selected_view_idx: continue
fcount += 1
print 'Process event', fcount, evname, 'NO.', evt_no
# get clipped data, margin depends on patch size in drift direction
raw, deposit, pdg, tracks, showers = get_data(
folder, evname, PATCH_SIZE_D / 2 + 2, crop_event)
if raw is None:
print 'Skip empty event...'
continue
vtx = get_vertices(pdg)
nuvtx = get_nu_vertices(pdg)
print 'Found', vtx.shape[
0], 'hadronic vertices/decay', nuvtx.shape[
0], 'neutrino vertices'
for v in range(vtx.shape[0]):
flags = 0
if vtx.shape[0] > 0:
flags = vtx[v, 2]
nuflags = 0
if nuvtx.shape[0] > 0:
nuflags = nuvtx[v, 2]
if (flags & kHadr) > 0 or (flags & kDecay) > 0 or (
(flags & kPi0) > 0 and (flags & kConv) > 0):
wire = vtx[v, 0]
drif = vtx[v, 1]
x_start = np.max([0, wire - PATCH_SIZE_W / 2])
x_stop = np.min([raw.shape[0], x_start + PATCH_SIZE_W])
y_start = np.max([0, drif - PATCH_SIZE_D / 2])
y_stop = np.min([raw.shape[1], y_start + PATCH_SIZE_D])
if x_stop - x_start != PATCH_SIZE_W or y_stop - y_start != PATCH_SIZE_D:
continue
target = np.zeros(
5, dtype=np.int32
) # [decay, hadronic_vtx, g_conversion, nu_primary, not_vtx]
if nuflags > 0:
target[3] = 1
cnt_nu += 1
elif (flags & kDecay) > 0:
target[0] = 1
cnt_decay += 1
elif (flags & kHadr) > 0:
target[1] = 1
cnt_vtx += 1
elif (flags & kConv) > 0:
target[2] = 1
cnt_gamma += 1
patch = get_patch(raw, wire, drif, PATCH_SIZE_W,
PATCH_SIZE_D)
if cnt_ind < max_capacity:
db[cnt_ind] = patch
db_y[cnt_ind] = target
cnt_ind += 1
else:
break
n_empty = 0
n_trials = 0
while n_empty < nearby_empty and n_trials < 500:
wi = np.random.randint(x_start + 1, x_stop - 1)
di = np.random.randint(y_start + 1, y_stop - 1)
if (wi < wire - 1
or wi > wire + 1) and (di < drif - 2
or di > drif + 2):
if tracks[wi, di] == 0 and showers[wi, di] == 0:
if cnt_ind < max_capacity:
patch = get_patch(raw, wi, di,
PATCH_SIZE_W,
PATCH_SIZE_D)
target = np.zeros(5, dtype=np.int32)
target[4] = 1
db[cnt_ind] = patch
db_y[cnt_ind] = target
cnt_void += 1
cnt_ind += 1
n_empty += 1
else:
break
n_trials += 1
n_track = 0
n_trials = 0
while n_track < nearby_on_track and n_trials < 500:
wi = np.random.randint(x_start + 1, x_stop - 1)
di = np.random.randint(y_start + 1, y_stop - 1)
if (wi < wire - 1
or wi > wire + 1) and (di < drif - 2
or di > drif + 2):
if tracks[wi, di] == 1 or showers[wi, di] == 1:
if cnt_ind < max_capacity:
patch = get_patch(raw, wi, di,
PATCH_SIZE_W,
PATCH_SIZE_D)
target = np.zeros(5, dtype=np.int32)
target[4] = 1
db[cnt_ind] = patch
db_y[cnt_ind] = target
cnt_trk += 1
cnt_ind += 1
n_track += 1
else:
break
n_trials += 1
print 'Total size', cnt_ind, ':: hadronic:', cnt_vtx, 'decays:', cnt_decay, 'nu-primary:', cnt_nu, 'g-conv:', cnt_gamma, 'empty:', cnt_void, 'on-track:', cnt_trk
np.save(OUTPUT_DIR + '/db_view_' + str(selected_view_idx) + '_x',
db[:cnt_ind])
np.save(OUTPUT_DIR + '/db_view_' + str(selected_view_idx) + '_y',
db_y[:cnt_ind])
def setPUHistogram(l):
# https://raw.githubusercontent.com/cms-sw/cmssw/CMSSW_7_4_X/SimGeneral/MixingModule/python/mix_2015_25ns_Startup_PoissonOOTPU_cfi.py
probValue = [
4.8551E-07, 1.74806E-06, 3.30868E-06, 1.62972E-05, 4.95667E-05,
0.000606966, 0.003307249, 0.010340741, 0.022852296, 0.041948781,
0.058609363, 0.067475755, 0.072817826, 0.075931405, 0.076782504,
0.076202319, 0.074502547, 0.072355135, 0.069642102, 0.064920999,
0.05725576, 0.047289348, 0.036528446, 0.026376131, 0.017806872,
0.011249422, 0.006643385, 0.003662904, 0.001899681, 0.00095614,
0.00050028, 0.000297353, 0.000208717, 0.000165856, 0.000139974,
0.000120481, 0.000103826, 8.88868E-05, 7.53323E-05, 6.30863E-05,
5.21356E-05, 4.24754E-05, 3.40876E-05, 2.69282E-05, 2.09267E-05,
1.5989E-05, 4.8551E-06, 2.42755E-06, 4.8551E-07, 2.42755E-07,
1.21378E-07, 4.8551E-08
]
mc = TH1F("mc", "nPV distribution", 50, 0, 50)
mc.Sumw2()
for i in range(50):
mc.SetBinContent(i + 1, probValue[i])
mc.SetLineWidth(3)
mc.SetLineColor(1)
mc.SetLineStyle(2)
mc.Scale(1. / mc.Integral())
puFile = TFile("./PU/PU_%s_69000.root" % l, "READ")
data = puFile.Get("pileup")
data.SetLineWidth(3)
data.SetLineColor(1)
data.Scale(1. / data.Integral())
puUpFile = TFile("./PU/PU_%s_72450.root" % l, "READ")
dataUp = puUpFile.Get("pileup")
dataUp.SetLineWidth(3)
dataUp.SetLineColor(634)
dataUp.Scale(1. / dataUp.Integral())
puDownFile = TFile("./PU/PU_%s_65550.root" % l, "READ")
dataDown = puDownFile.Get("pileup")
dataDown.SetLineWidth(3)
dataDown.SetLineColor(598)
dataDown.Scale(1. / dataDown.Integral())
ratio = data.Clone("ratio")
ratioUp = dataUp.Clone("ratioUp")
ratioDown = dataDown.Clone("ratioDown")
ratio.Divide(mc)
ratioUp.Divide(mc)
ratioDown.Divide(mc)
outFile = TFile("./PU/PU_%s.root" % l, "RECREATE")
outFile.cd()
mc.Write()
data.Write()
dataUp.Write()
dataDown.Write()
ratio.Write()
ratioUp.Write()
ratioDown.Write()
outFile.Close()
print "Histograms written to ./PU/PU_%s.root file" % l
leg = TLegend(0.65, 0.7, 0.98, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("pile-up reweighting")
leg.AddEntry(dataUp, "Up", "pl")
leg.AddEntry(data, "Central", "pl")
leg.AddEntry(dataDown, "Down", "pl")
leg.AddEntry(mc, "MC 25ns", "pl")
c1 = TCanvas("c1", "PileUp reweighting", 800, 800)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
dataDown.SetTitle(";number of true interactions")
dataDown.GetXaxis().SetRangeUser(0., 30)
dataDown.Draw("HIST")
dataUp.Draw("SAME, HIST")
data.Draw("SAME, HIST")
mc.Draw("SAME, L")
leg.Draw()
c1.Print("PU/PU_%s.pdf" % l)
c1.Print("PU/PU_%s.png" % l)
from ROOT import TFile, TGraph, TMarker, TLine, TLegend, TCanvas
f_hmssm = TFile('data/hMSSM_13TeV.root')
h_width_A = f_hmssm.Get('width_A')
h_br_A_tt = f_hmssm.Get('br_A_tt') # to calculate partial width
mass = 600.
bin_mass = h_width_A.FindBin(mass)
h_width = h_width_A.ProjectionY('h_width', bin_mass, bin_mass)
h_br = h_br_A_tt.ProjectionY('h_br', bin_mass, bin_mass)
h_coupling = h_width.Clone('h_coupling')
for i in xrange(1, h_coupling.GetNbinsX() + 1):
h_coupling.SetBinContent(i, 1. / h_coupling.GetXaxis().GetBinCenter(i)**2)
n_points = h_width.GetNbinsX()
g_width = TGraph(n_points)
g_partial_width = TGraph(n_points)
for i in xrange(n_points):
g_width.SetPoint(i, h_coupling.GetBinContent(i + 1),
h_width.GetBinContent(i + 1))
g_partial_width.SetPoint(
i, h_coupling.GetBinContent(i + 1),
h_width.GetBinContent(i + 1) * h_br.GetBinContent(i + 1))
# Points/lines for simulated samples
file_100TeV = TFile(
"minBias_100TeV_particleDistributions/MinBiasDistribution_100TeV.root")
file_13TeV = TFile(
"minBias_13TeV_particleDistributions/MinBiasDistribution_13TeV.root")
file_13TeV_DelphesCMS = TFile(
"minBias_13TeV_DelphesCMS_particleDistributions/MinBiasDistribution_13TeV_DelphesCMS.root"
)
file_13TeV_DelphesFCC_CMSJets = TFile(
"minBias_13TeV_DelphesFCC_CMSJets_particleDistributions/MinBiasDistribution_13TeV_DelphesFCC_CMSJets.root"
)
file_100TeV_DelphesFCC_CMSJets = TFile(
"minBias_100TeV_DelphesFCC_CMSJets_particleDistributions/MinBiasDistribution_100TeV_DelphesFCC_CMSJets.root"
)
# Retrieving plots
jetRecoPtDistribution_TH1I_100TeV = file_100TeV.Get("jetRecoPtDistribution")
jetRecoPtDistribution_TH1I_13TeV = file_13TeV.Get("jetRecoPtDistribution")
jetRecoPtDistribution_TH1I_13TeV_DelphesCMS = file_13TeV_DelphesCMS.Get(
"jetRecoPtDistribution")
jetRecoPtDistribution_TH1I_13TeV_DelphesFCC_CMSJets = file_13TeV_DelphesFCC_CMSJets.Get(
"jetRecoPtDistribution")
jetRecoPtDistribution_TH1I_100TeV_DelphesFCC_CMSJets = file_100TeV_DelphesFCC_CMSJets.Get(
"jetRecoPtDistribution")
# Converting to float histogram
jetRecoPtDistribution_TH1F_100TeV = TH1F()
jetRecoPtDistribution_TH1F_13TeV = TH1F()
jetRecoPtDistribution_TH1F_13TeV_DelphesCMS = TH1F()
jetRecoPtDistribution_TH1F_13TeV_DelphesFCC_CMSJets = TH1F()
jetRecoPtDistribution_TH1F_100TeV_DelphesFCC_CMSJets = TH1F()
jetRecoPtDistribution_TH1F_100TeV.Add(jetRecoPtDistribution_TH1I_100TeV)
parser.add_argument("input_file",
nargs=1,
metavar='FILE',
type=lambda x: is_valid_file(parser, x))
parser.add_argument("-t",
"--tree-name",
dest='tree_name',
default="MatchChi2Algo/match_chi2")
arguments = parser.parse_args()
from ROOT import TFile, TCanvas
from ROOT import TTree, gDirectory
input_file = TFile(arguments.input_file[0])
tree = input_file.Get(arguments.tree_name)
if not tree:
print 'Could not find tree %s in %s' % (arguments.tree_name,
arguments.input_file[0])
sys.exit(-1)
chi2_bkg = []
chi2_sig = []
for entry in tree:
if entry.true_muon == 1 and entry.muon_overlap > 0.9:
chi2_sig.append(entry.chi2DoF)
else:
chi2_bkg.append(entry.chi2DoF)
chi2_bkg = sorted(chi2_bkg)
gROOT.SetBatch()
input_files = args.input_files
out_histo = None
for filename in input_files:
if not isfile(filename):
logging.info("missing: " + filename)
continue
logging.debug(filename)
tfile = TFile(filename)
thisto = tfile.Get(args.histo_name)
if not out_histo:
out_histo = thisto.Clone()
out_histo.Clone()
out_histo.SetDirectory(0)
else:
out_histo.Add(thisto)
tfile.Close()
fout = TFile(args.output, "RECREATE")
fout.Write()
out_histo.Write()
line = "Integral of " + hist + " in " + inputFile.GetName(
) + " from " + str(xlo) + " to " + str(xhi) + ": " + str(
integral) + " +- " + str(intError)
# print line
return integral
outputFile = os.environ[
'CMSSW_BASE'] + "/src/DisappTrks/StandardAnalysis/data/systematic_values__" + systematic_name + ".txt"
if not makeRewtdPlot:
fout = open(outputFile, "w")
# get rewt histogram
fileWithWtHist = TFile(str(ratioHistFile))
hwts = fileWithWtHist.Get(ratioHistName)
print "Debug: using hwts hist=", ratioHistName, ", ratioHistFile=", ratioHistFile
for sample in datasets:
numEvents = getIntegral(sample, central_condor_dir, channel, "numEvents",
0, 10, 0)
central_yield = getIntegral(sample, central_condor_dir, channel,
histRewtName, histMin, histMax, 0)
plus_yield = getIntegral(sample, central_condor_dir, channel, histRewtName,
histMin, histMax, hwts)
central_gen_yield = getIntegral(sample, central_gen_condor_dir,
channelNoCuts, histRewtName, histMin,
histMax, 0)
plus_gen_yield = getIntegral(sample, central_gen_condor_dir, channelNoCuts,
histRewtName, histMin, histMax, hwts)
def skim_c(name, newFileName):
#--------------------------------
Jet_old_dict = {}
for j in range(num_of_jets):
#if 'ctauS' in name:
Jet_old_dict[j + 1] = JetType()
#if 'ctauS' in name:
if NJT == 1:
Jet_old_dict[j + 1] = JetTypeSgn()
#--------------------------------
print 'filename:', name
oldFile = TFile(name, "READ")
oldTree = oldFile.Get("reconstruction/tree")
#locate and register the Jet branches of the old ttree
#oldTree.SetBranchAddress("Jet1", AddressOf(Jet1o, 'pt') );
for j in range(num_of_jets):
if 'QCD' in name:
oldTree.SetBranchAddress('Jet' + str(j + 1),
AddressOf(Jet_old_dict[j + 1], 'pt'))
elif 'ctauS' in name:
oldTree.SetBranchAddress('MatchedJet' + str(j + 1),
AddressOf(Jet_old_dict[j + 1], 'pt'))
print 'skimming file', oldFile.GetName(), '\tevents =', oldTree.GetEntries(
), '\tweight =', oldTree.GetWeight()
newFile = TFile('Skim/' + newFileName, "RECREATE")
newFile.cd()
newTree = TTree("tree44", "tree44")
newTree.Branch(
'Jet1s', Jets1,
'pt/F:eta/F:chf/F:nhf/F:phf/F:elf/F:muf/F:chm/I:cm/I:nm/I:dR_q1/F:dR_q2/F:dR_q3/F:dR_q4/F'
)
#newTree.Branch( 'Jet2s', Jets2, 'pt/F:eta/F:chf/F:nhf/F:phf/F:elf/F:muf/F:chm/I:cm/I:nm/I:dR_q1/F:dR_q2/F:dR_q3/F:dR_q4/F' )
# this attribute list must exactly match (the order of) the features in the header file!!!!
ti = 80000
#theweight = oldTree.GetWeight()
for i in range(0, oldTree.GetEntries()): # why -1?
if i == 0:
start = timer()
elif i % ti == 2:
start = timer()
oldTree.GetEntry(i)
# selections
# Trigger
"""
if Jet1o.pt>15 \
and \
(Jet1o.eta>-2.4 or Jet1o.eta<2.4) \
and \
( \
(Jet1o.dR_q1<0.4 and Jet1o.dR_q2>0.4 and Jet1o.dR_q3>0.4 and Jet1o.dR_q4>0.4) \
or \
(Jet1o.dR_q2<0.4 and Jet1o.dR_q1>0.4 and Jet1o.dR_q3>0.4 and Jet1o.dR_q4>0.4) \
or \
(Jet1o.dR_q3<0.4 and Jet1o.dR_q2>0.4 and Jet1o.dR_q1>0.4 and Jet1o.dR_q4>0.4) \
or \
(Jet1o.dR_q4<0.4 and Jet1o.dR_q2>0.4 and Jet1o.dR_q3>0.4 and Jet1o.dR_q1>0.4) \
) \
:
# set new leaf values to old ones
# this attribute list must exactly match (the order of) the features in the header file!!!!
Jets1.pt = Jet1o.pt
Jets1.eta = Jet1o.eta
Jets1.phi = Jet1o.phi
Jets1.chf = Jet1o.chf
Jets1.nhf = Jet1o.nhf
Jets1.phf = Jet1o.phf
Jets1.elf = Jet1o.elf
Jets1.muf = Jet1o.muf
Jets1.chm = Jet1o.chm
Jets1.cm = Jet1o.cm
Jets1.nm = Jet1o.nm
Jets1.dR_q1 = Jet1o.dR_q1
Jets1.dR_q2 = Jet1o.dR_q2
Jets1.dR_q3 = Jet1o.dR_q3
Jets1.dR_q4 = Jet1o.dR_q4
newTree.Fill()
"""
for j in range(num_of_jets):
if cut_on == 0:
condition_str_dict[j + 1] = '1'
if eval(condition_str_dict[j + 1]):
Jets1.pt = Jet_old_dict[j + 1].pt
Jets1.eta = Jet_old_dict[j + 1].eta
Jets1.phi = Jet_old_dict[j + 1].phi
Jets1.chf = Jet_old_dict[j + 1].chf
Jets1.nhf = Jet_old_dict[j + 1].nhf
Jets1.phf = Jet_old_dict[j + 1].phf
Jets1.elf = Jet_old_dict[j + 1].elf
Jets1.muf = Jet_old_dict[j + 1].muf
Jets1.chm = Jet_old_dict[j + 1].chm
Jets1.cm = Jet_old_dict[j + 1].cm
Jets1.nm = Jet_old_dict[j + 1].nm
Jets1.dR_q1 = Jet_old_dict[j + 1].dR_q1
Jets1.dR_q2 = Jet_old_dict[j + 1].dR_q2
Jets1.dR_q3 = Jet_old_dict[j + 1].dR_q3
Jets1.dR_q4 = Jet_old_dict[j + 1].dR_q4
newTree.Fill()
#########################################################
if i % 2 == 0:
ss = '.'
elif i % 2 == 1:
ss = 'o'
if i % ti == 1 and i > ti:
end = timer()
dt = end - start
tl = int(((oldTree.GetEntries() - i) / ti) * dt)
if tl > 60:
sys.stdout.write("\r" + 'time left: ' + str(tl / 60) + 'min' +
ss)
sys.stdout.flush()
else:
sys.stdout.write("\r" + 'time left: ' + str(tl / 60) + 's')
sys.stdout.flush()
#########################################################
print 'produced skimmed file', newFile.GetName(
), '\tevents =', newTree.GetEntries(), '\tweight =', newTree.GetWeight()
newFile.cd()
newFile.Write()
newFile.Close()
oldFile.Close()
def plot(path, pt_title, ecms, arrow_left, arrow_bottom, arrow_right,
arrow_top, D_sample):
try:
f_incMC = TFile(path[0])
f_sigMC = TFile(path[1])
t_incMC = f_incMC.Get('save')
t_sigMC = f_sigMC.Get('save')
entries_incMC = t_incMC.GetEntries()
entries_sigMC = t_sigMC.GetEntries()
logging.info('inclusive MC entries :' + str(entries_incMC))
logging.info('D2(2460) MC entries :' + str(entries_sigMC))
except:
logging.error('Files are invalid!')
sys.exit()
mbc = TCanvas('mbc', 'mbc', 800, 600)
set_canvas_style(mbc)
xbins = 100
M_D = 0.
step = 0.
if D_sample == 'Dplus':
M_D = 1.86965
step = (1.89555 - M_D) / xbins
if D_sample == 'D0':
M_D = 1.86483
step = (1.95 - M_D) / xbins
h_FOM, ientry, arrow_top = cal_significance(t_incMC, t_sigMC, xbins, step,
ecms, M_D, D_sample)
h_FOM.Draw()
arrow_bottom = 0.
arrow = TArrow(arrow_left, arrow_bottom, arrow_right, arrow_top, 0.01, '>')
set_arrow(arrow)
arrow.Draw()
pt = TPaveText(0.6, 0.8, 0.85, 0.85, "BRNDC")
set_pavetext(pt)
pt.Draw()
pt.AddText(pt_title)
mass_low = 0.
mass_up = 0.
window = 0.
if D_sample == 'Dplus' or D_sample == 'D0':
mass_low = str(M_D - arrow_left)
mass_up = str(M_D + arrow_left)
window = str(2 * arrow_left)
range = 'Mass window of rawm_D : [' + mass_low + ', ' + mass_up + '] GeV/c2' + ' with mass window width: ' + window + ' GeV/c2'
print range
if not os.path.exists('./txts/'):
os.makedirs('./txts/')
path_out = './txts/window_rawm_D_' + str(ecms) + '_' + D_sample + '.txt'
f_out = open(path_out, 'w')
f_out.write(range)
f_out.close()
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.Update()
mbc.SaveAs('./figs/opt_rawm_D_' + str(ecms) + '_' + D_sample + '.pdf')
raw_input('Enter anything to end...')
