def __init__(self):
Load('TMVABranchAdder')
self.ba = root.TMVABranchAdder()
self.ba.defaultValue = -1.2
self.ba.presel = 'fj1ECFN_2_4_20>0'
for v in tagcfg.variables:
self.ba.AddVariable(v[0], v[2])
for v in tagcfg.formulae:
self.ba.AddFormula(v[0], v[2])
for s in tagcfg.spectators:
self.ba.AddSpectator(s[0])
self.ba.BookMVA('top_ecf_bdt',
data_dir + '/trainings/top_ecfbdt_v8_BDT.weights.xml')
def add_bdt():
# now run the BDT
Load('TMVABranchAdder')
ba = root.TMVABranchAdder()
ba.treename = 'events'
ba.defaultValue = -1.2
ba.presel = 'fj1ECFN_2_4_20>0'
for v in tagcfg.variables:
ba.AddVariable(v[0],v[2])
for v in tagcfg.formulae:
ba.AddFormula(v[0],v[2])
for s in tagcfg.spectators:
ba.AddSpectator(s[0])
ba.BookMVA('top_ecf_bdt',data_dir+'/trainings/top_ecfbdt_v8_BDT.weights.xml')
ba.RunFile('output.root')
from time import clock, time
import json
which = int(argv[1])
submit_id = int(argv[2])
sname = argv[0]
argv = []
import ROOT as root
from PandaCore.Tools.Misc import *
from PandaCore.Tools.Load import *
import PandaCore.Tools.job_config as cb
import PandaAnalysis.LPC_T3.job_utilities as utils
from PandaAnalysis.Flat.analysis import monojet
Load('PandaAnalyzer')
data_dir = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
def fn(input_name, isData, full_path):
PInfo(sname + '.fn', 'Starting to process ' + input_name)
# now we instantiate and configure the analyzer
skimmer = root.PandaAnalyzer()
analysis = monojet(True)
analysis.processType = utils.classify_sample(full_path, isData)
analysis.genOnly = False
skimmer.SetAnalysis(analysis)
skimmer.isData = isData
skimmer.SetPreselectionBit(root.PandaAnalyzer.kDiMu)
def fn(shortName, longName, isData, xsec):
# first we do some I/O stuff
outdir = 'XXXX'
outfilename = shortName + '.root'
PInfo(sname, 'Output: %s/%s' % (outdir, outfilename))
if path.isfile('%s/%s' % (outdir, outfilename)):
PWarning(sname, "Found %s, skipping!" % outfilename)
return
start = clock()
eosPath = 'root://eoscms.cern.ch//store/user/snarayan'
eosEXOPath = 'root://eoscms.cern.ch//store/group/phys_exotica'
cernboxPath = 'root://eosuser//eos/user/s/snarayan'
cernboxBPath = 'root://eosuser//eos/user/b/bmaier'
fullPath = sub(
r'\${CERNBOXB}', cernboxBPath,
sub(
r'\${CERNBOX}', cernboxPath,
sub(r'\${EOS}', eosPath, sub(r'\${EOS2}', eosEXOPath,
longName))))
PInfo(sname, fullPath)
# xrdcp if remote, copy if local
if 'root://' in fullPath:
system('xrdcp %s input.root' % fullPath)
else:
system('cp %s input.root' % fullPath)
# now we instantiate and configure the analyzer
skimmer = root.PandaAnalyzer()
skimmer.isData = isData
skimmer.SetFlag('firstGen', False)
# skimmer.SetFlag('puppi',False)
skimmer.SetPreselectionBit(root.PandaAnalyzer.kRecoil)
#skimmer.SetPreselectionBit(root.PandaAnalyzer.kMonotop)
processType = root.PandaAnalyzer.kNone
if not isData:
if 'ST_' in fullPath:
processType = root.PandaAnalyzer.kTop
elif 'ZJets' in fullPath or 'DY' in fullPath:
processType = root.PandaAnalyzer.kZ
elif 'WJets' in fullPath:
processType = root.PandaAnalyzer.kW
elif 'GJets' in fullPath:
processType = root.PandaAnalyzer.kA
elif 'TTJets' in fullPath or 'TT_' in fullPath:
processType = root.PandaAnalyzer.kTT
skimmer.processType = processType
# read the inputs
try:
fin = root.TFile.Open('input.root')
tree = fin.FindObjectAny("events")
infotree = fin.FindObjectAny("all")
except:
exit(2) # file open error => xrootd?
skimmer.SetDataDir(getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/')
skimmer.SetOutputFile('output.root')
skimmer.Init(tree, infotree)
# run and save output
skimmer.Run()
skimmer.Terminate()
# now run the BDT
Load('Learning', 'TMVABranchAdder')
ba = root.TMVABranchAdder()
ba.treename = 'events'
ba.defaultValue = -1.2
ba.presel = 'fj1ECFN_2_4_20>0'
for v in tagcfg.variables:
ba.AddVariable(v[0], v[2])
for v in tagcfg.formulae:
ba.AddFormula(v[0], v[2])
for s in tagcfg.spectators:
ba.AddSpectator(s[0])
ba.BookMVA(
'top_ecf_bdt',
getenv('CMSSW_BASE') +
'/src/PandaAnalysis/data/trainings/top_ecfbdt_v8_BDT.weights.xml')
ba.RunFile('output.root')
# stageout
mvargs = 'mv $PWD/output.root %s/%s' % (outdir, outfilename)
PInfo(sname, mvargs)
system(mvargs)
system('rm input.root')
PInfo(sname, 'finished in %f' % (clock() - start))
start = clock()
parser.add_argument('--cut', metavar='cut', type=str, default='1==1')
parser.add_argument('--region', metavar='region', type=str, default=None)
args = parser.parse_args()
lumi = 36560
blind = True
linear = False
region = args.region
sname = argv[0]
argv = []
import ROOT as root
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
import PandaCore.Tools.Functions
import PandaAnalysis.VBF.MonojetSelection as sel
Load('Drawers', 'PlotUtility')
### DEFINE REGIONS ###
cut = tAND(sel.cuts[args.region], args.cut)
PInfo(sname, 'using cut: ' + cut)
### LOAD PLOTTING UTILITY ###
plot = root.PlotUtility()
plot.Stack(True)
plot.Logy(not (linear))
if 'signal' in region and blind:
plot.SetLumi(lumi / 5000.)
else:
plot.SetLumi(lumi / 1000)
plot.SetSignalScale(10)
#!/usr/bin/env python
import ROOT as root
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
from os import getenv
from sys import argv
#import cfgSmall as cfg
import makeInvariantRatios as cfg
Load('Learning','TMVABranchAdder')
workdir = getenv('SCRAMJETFLAT')
ba = root.TMVABranchAdder()
ba.treename='puppiCA15'
for v in cfg.variables:
ba.AddVariable(v[0],v[0])
for v in cfg.formulae:
ba.AddFormula(v[0],v[0])
for s in cfg.spectators:
ba.AddSpectator(s[0])
ba.BookMVA('top_ecf43_bdt',workdir+'/training/top_ecfbdt_43_BDT.weights.xml')
ba.RunFile(workdir+'/'+argv[1]+'.root')
import json
debug_level = 2
torun = argv[1]
output = 'snapshot.root'
if len(argv)>2:
debug_level = int(argv[2])
if len(argv)>3:
output = argv[3]
argv = []
import ROOT as root
from PandaCore.Tools.Load import *
Load('Clusterer')
camera = root.redpanda.Camera(debug_level)
camera.firstEvent=1
camera.lastEvent=2
fin = root.TFile.Open(torun)
tree = fin.FindObjectAny("events")
camera.SetOutputFile(output)
camera.Init(tree)
camera.Run()
camera.Terminate()
#!/usr/bin/env python
from sys import exit,argv
import argparse
parser = argparse.ArgumentParser(description='skim a file')
parser.add_argument('--input',type=str)
parser.add_argument('--output',type=str,default=None)
parser.add_argument('--cut',type=str)
args = parser.parse_args()
argv=[]
from PandaCore.Tools.Load import *
import ROOT as root
Load('Tools','Cutter')
c = root.Cutter()
c.treeName = 'puppiCA15'
if not args.output:
args.output = args.input.replace('.root','_slim.root')
c.Cut(args.input,args.output,args.cut)
ncontours = 999;
stops = [ 0.10, 0.2000, 0.40, 0.600, 0.800, 1.0000]
red = [255./255., 169./255., 91./255., 36./255., 8./255., 6./255.]
green = [255./255., 194./255., 149./255., 99./255., 46./255., 0./255.]
blue = [255./255., 243./255., 240./255.,240./255., 240./255., 233./255.]
stopsArray = array('d',stops)
redArray = array('d',red)
greenArray = array('d',green)
blueArray = array('d',blue)
root.TColor.CreateGradientColorTable(len(stops), stopsArray, redArray, greenArray, blueArray, ncontours);
'''
root.gStyle.SetNumberContours(999)
root.gStyle.SetPalette(root.kBird)
root.gStyle.SetPaintTextFormat(".3g")
Load('CanvasDrawer')
plot = root.CanvasDrawer()
plot.SetTDRStyle()
root.gStyle.SetPadRightMargin(0.2)
c = root.TCanvas()
cut1s = set([])
cut2s = set([])
if var3:
listoffiles = glob(basedir + '/higgsCombine%s*%s*%s*.root' %
(var1, var2, var3))
else:
listoffiles = glob(basedir + '/higgsCombine%s*%s*.root' % (var1, var2))
for f in listoffiles:
#!/usr/bin/env python
from os import getenv
from sys import argv
#import cfgModeled as cfg
import cfgAll as cfg
sample = argv[1]
argv = []
import ROOT as root
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
Load('TMVABranchAdder')
workdir = getenv('PANDA_FLATDIR')
trainingdir = '/home/snarayan/home000/store/scramjet/v7/training/'
ba = root.TMVABranchAdder()
ba.treename = 'events'
ba.defaultValue = -1.2
ba.presel = 'fj1ECFN_2_4_20>0'
for v in cfg.variables:
ba.AddVariable(v[0], v[2])
for v in cfg.formulae:
ba.AddFormula(v[0], v[2])
from re import sub
from sys import argv, exit
from os import system, getenv, path
from json import load as loadJson
from time import clock
from random import shuffle
from PandaCore.Tools.Misc import *
from PandaCore.Tools.MultiThreading import GenericRunner
from PandaCore.Tools.Load import *
nPerJob = int(argv[2])
sname = argv[0]
if __name__ == "__main__":
Load('SCRAMJetAnalyzer', 'Analyzer')
def fn(shortName, longName, counter, xsec, isData, outPath):
outfileName = '%s_%i' % (shortName, counter)
if path.isfile(outPath + '/' + outfileName + '.lock'):
return # another job is processing this file
if path.isfile(outPath + '/' + outfileName + '.root'):
return # another job has processed this file
system('touch %s/%s.lock' % (outPath, outfileName))
start = clock()
eosPath = 'root://eoscms//eos/cms/store/user/%s' % (getenv('USER'))
cernboxPath = 'root://eosuser//eos/user/%s/%s' % (getenv('USER')[0],
getenv('USER'))
cernboxBPath = 'root://eosuser//eos/user/b/bmaier'
from os import system, getenv, path
from time import clock, time
import json
which = int(argv[1])
submit_id = int(argv[2])
sname = argv[0]
argv = []
import ROOT as root
from PandaCore.Tools.Misc import *
from PandaCore.Tools.Load import *
import PandaCore.Tools.job_management as cb
import PandaAnalysis.Tagging.cfg_v8 as tagcfg
Load('PandaLeptonicAnalyzer')
data_dir = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
stopwatch = clock()
def print_time(label):
global stopwatch
now_ = clock()
PDebug(
sname + '.print_time:' + str(time()),
'%.3f s elapsed performing "%s"' % ((now_ - stopwatch) / 1000., label))
stopwatch = now_
def copy_local(long_name):
#!/usr/bin/env python
import ROOT as root
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
from os import getenv
import cfgInvar as cfg
#import makeInvariantRatios as cfg
Load('Learning', 'TMVATrainer')
workdir = getenv('SCRAMJETFLAT')
trainer = root.TMVATrainer('top_ecfbdt_50', workdir + 'training/')
trainer.treename = 'puppiCA15'
trainer.sigweight = 'ptweight*normalizedWeight'
trainer.bgweight = 'ptweight_analytic*normalizedWeight'
sanitycut = 'tau32>0 && pt<1000 && mSD>110 && mSD<210'
#trainer.bgcut = tAND(sanitycut,'eventNumber%15==0')
trainer.bgcut = sanitycut
trainer.sigcut = tAND(sanitycut, 'matched==1 && gensize<1.2')
for v in cfg.variables:
trainer.AddVariable(v[0], v[1])
for v in cfg.formulae:
trainer.AddVariable(v[0], v[1])
for s in cfg.spectators:
trainer.AddSpectator(s[0], s[1])
#!/usr/bin/env python
from sys import argv
f1 = argv[1]
f2 = argv[2]
f3 = argv[3]
argv = []
import PandaCore.Tools.Load as Load
import ROOT as root
Load.Load('Tools','DuplicateRemover')
dr = root.DuplicateRemover()
dr.Merge(f1,f2,f3)
if not args.cut:
print 'please choose a cut'
exit(1)
elif args.cut=='mass':
cut = 'massCut_'
plotlabel = '110 < m_{SD} < 210 GeV'
elif args.cut=='btag':
cut = 'btagCut_'
plotlabel = '#splitline{110 < m_{SD} < 210 GeV}{max SJ CSV>0.46}'
elif args.cut=='masstau':
cut = 'masstauCut_'
plotlabel = '#splitline{110 < m_{SD} < 210 GeV}{#tau_{32}<0.6}'
Load('Drawers','ROCTool')
roc = root.ROCTool()
fin = root.TFile(figsdir+'/'+cut+'hists.root')
roc.Logy()
roc.SetPlotRange(0.005,1)
roc.InitCanvas(.65,.15,.94,.6)
roc.SetFile(fin)
roc.c.AddPlotLabel(plotlabel,.18,.77,False,42,.04)
variables = [
('top_ecfv7_bdt','ECF+#tau_{32}^{SD} BDT',1,1),
('top_ecfv6_bdt','ECF BDT',2,1),
('tau32SD','Groomed #tau_{32}',3,2),
('tau32','#tau_{32}',4,2),
#!/usr/bin/env python
from sys import exit,argv
import argparse
parser = argparse.ArgumentParser(description='skim a file')
parser.add_argument('--input',type=str)
parser.add_argument('--output',type=str,default=None)
parser.add_argument('--cut',type=str)
args = parser.parse_args()
argv=[]
from PandaCore.Tools.Load import *
import ROOT as root
Load('Cutter')
c = root.Cutter()
c.treeName = 'events'
if not args.output:
args.output = args.input.replace('.root','_slim.root')
c.Cut(args.input,args.output,args.cut)
def fitMC(nprongs, isPass, model='GAUSSEXPERF', smear=None, paramset=None):
fmc = root.TFile(basedir + '/tag__prong%i.root' % nprongs)
tmc = fmc.Get('prong%i' % nprongs)
if isPass:
cut = 'top_ecfv8_bdt>0.66'
else:
cut = 'top_ecfv8_bdt<0.66'
ds = RooFitUtils.treeToDS(tmc, [mass, tag.rvar, weight.rvar],
cut=cut,
weight='weight')
hmass = root.TH1D('hmass', 'hmass', nbins, 50, 350)
tmc.Draw('mSD>>hmass', 'weight*(%s)' % cut, '')
integral = hmass.Integral(
) # get the integral so we can do an extended likelihood by default
smear_params = {
'mu': (0, -20, 20),
'sigma': (10, .1, 100),
'n_cb': (1, 0.01, 5),
'alpha': (-1, -100, 0),
}
dparams = {}
dparams['fail'] = {
'a': (140, -1000, 1000),
'b': (58.1, 0.01, 1000),
'c': (-0.0155, -10, 0),
'alpha': (-1.4, -10, 10),
'mu': (165, 145, 185),
'sigma': (20, 10, 100),
'n_cb': (1.7, 0, 5),
'norm_gauss': (integral, 1, integral * 2),
'norm_bw': (integral, 1, integral * 2),
'norm_cb': (integral, 1, integral * 2),
'norm_experf': (integral * 0.3, 1, integral * 2),
'w_mu': (80, 78, 82),
'w_sigma': (8, 5, 20),
}
dparams['pass'] = {
'a': (200, -1000, 1000),
'b': (50, 0.01, 1000),
'c': (-0.0155, -10, 0),
'alpha': (-1.4, -10, 10),
'mu': (165, 145, 185),
'sigma': (20, 10, 100),
'n_cb': (1.7, 0, 5),
'norm_gauss': (integral, 1, integral * 2),
'norm_bw': (integral, 1, integral * 2),
'norm_cb': (integral, 1, integral * 2),
'norm_experf': (integral * 0.2, 1, integral * 2),
'w_mu': (80, 78, 82),
'w_sigma': (8, 5, 20),
}
dparams['fail2'] = {
'a': (200, -1000, 1000),
'b': (5.6, 0.01, 1000),
'c': (-0.0155, -10, 0),
'alpha': (-1.4, -10, 10),
'mu': (80, 70, 90),
'sigma': (20, 10, 100),
'n_cb': (1.7, 0, 5),
'norm_gauss': (integral, 1, integral * 2),
'norm_bw': (integral, 1, integral * 2),
'norm_cb': (integral, 1, integral * 2),
'norm_experf': (integral * 0.2, 1, integral * 2),
'w_mu': (80, 78, 82),
'w_sigma': (8, 5, 20),
}
if not paramset:
params = dparams['pass'] if isPass else dparams['fail']
else:
params = dparams[paramset]
suffix = '_pass' if isPass else '_fail'
model = cat.buildModel('prong%i' % nprongs + suffix, mass, model, params)
if smear:
smeared_model = cat.smearModel('prong%i_smeared' % nprongs, mass,
model, smear, smear_params)
smeared_model.fitTo(ds, root.RooFit.SumW2Error(False))
else:
model.fitTo(ds, root.RooFit.SumW2Error(False))
Load('Drawers', 'CanvasDrawer')
plot = root.CanvasDrawer()
plot.Ratio(True)
plot.SetTDRStyle()
c = root.TCanvas()
plot.SetCanvas(c)
plot.SplitCanvas()
plot.AddCMSLabel()
pad1 = plot.GetPad1()
pad1.cd()
mass.setBins(nbins)
frame = mass.frame(root.RooFit.Bins(nbins))
ds.plotOn(frame, root.RooFit.DrawOption('EP'), root.RooFit.MarkerSize(0.8),
root.RooFit.Name('MC'))
model.plotOn(frame, root.RooFit.Name('Fit'))
frame.GetYaxis().SetTitle('Events/10 GeV')
frame.SetMaximum(frame.GetMaximum() * 1.5)
frame.Draw()
pad2 = plot.GetPad2()
pad2.cd()
frame2 = mass.frame(root.RooFit.Bins(nbins))
frame2.addObject(frame.pullHist('MC', 'Fit'), 'EP')
frame2.SetMinimum(-5)
frame2.SetMaximum(5)
frame2.GetXaxis().SetTitle('m_{SD} [GeV]')
frame2.GetYaxis().SetTitle("#frac{Data-Fit}{#sigma_{Data}}")
frame2.GetYaxis().SetNdivisions(5)
frame2.GetYaxis().SetTitleSize(15)
frame2.GetYaxis().SetTitleFont(43)
frame2.GetYaxis().SetTitleOffset(1.55)
frame2.GetYaxis().SetLabelFont(43)
frame2.GetYaxis().SetLabelSize(15)
frame2.GetXaxis().SetTitleSize(20)
frame2.GetXaxis().SetTitleFont(43)
frame2.GetXaxis().SetTitleOffset(4.)
frame2.GetXaxis().SetLabelFont(43)
frame2.GetXaxis().SetLabelSize(15)
frame2.Draw()
hZero = root.TH1D('zero', 'zero', nbins, 50, 450)
hZero.SetLineColor(1)
hZero.Draw('hist same')
pad1.cd()
plot.Draw(
'~/public_html/figs/toptagging/datavalidation/v8/templates/parametric/',
'prong%i' % nprongs + suffix)
from os import path, getenv
import argparse
parser = argparse.ArgumentParser(description='make forest')
parser.add_argument('--region', metavar='region', type=str, default=None)
toProcess = parser.parse_args().region
argv = []
import ROOT as root
from PandaCore.Tools.Misc import *
from PandaCore.Tools.Load import *
import PandaCore.Tools.Functions
import PandaAnalysis.MonoH.Selection as sel
#import PandaAnalysis.MonoH.Selection_mw as sel
#import PandaAnalysis.MonoH.Selection_t21 as sel
Load('PandaAnalysisFlat', 'LimitTreeBuilder')
subfolder = ''
if toProcess == 'signal':
subfolder = 'preselection/'
elif toProcess == 'wmn':
subfolder = 'cr_w_mu/'
elif toProcess == 'wen':
subfolder = 'cr_w_el/'
elif toProcess == 'tm':
subfolder = 'cr_ttbar_mu/'
elif toProcess == 'te':
subfolder = 'cr_ttbar_el/'
elif toProcess == 'zmm':
subfolder = 'cr_dimuon/'
elif toProcess == 'zee':
args = parser.parse_args()
args.outdir += '/'
lumi = 36560.
blind = True
linear = False
sname = argv[0]
argv = []
import ROOT as root
root.gROOT.SetBatch()
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
import PandaCore.Tools.Functions
Load('Drawers', 'HistogramDrawer')
plot = root.HistogramDrawer()
plot.Logy(True)
plot.SetTDRStyle()
plot.InitLegend()
plot.AddCMSLabel()
plot.SetLumi(lumi / 1000)
plot.AddLumiLabel(True)
plot.SetNormFactor(True)
plotr = root.HistogramDrawer()
plotr.SetRatioStyle()
plotr.InitLegend()
plotr.AddCMSLabel()
plotr.SetLumi(lumi / 1000)
parser = argparse.ArgumentParser(description='plot stuff')
parser.add_argument('--indir',metavar='indir',type=str,default=basedir)
parser.add_argument('--outdir',metavar='outdir',type=str,default=outdir)
parser.add_argument('--sel',metavar='sel',type=str,default='tag')
parser.add_argument('--cat',metavar='cat',type=str)
args = parser.parse_args()
outdir = args.outdir
basedir = args.indir
argv=[]
import ROOT as root
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
tcut = root.TCut
Load('PandaAnalysisFlat','SFTreeBuilder')
### SET GLOBAL VARIABLES ###
lumi = 12918.
logy=False
nlo = 'sf_ewkV*sf_qcdV'
if args.sel=='mistag':
cut = 'nFatjet==1 && fj1Pt>250 && fj1MaxCSV<0.46 && nLooseLep==1 && nTightMuon==1 && nLooseElectron==0 && nLoosePhoton==0 && nTau==0 && UWmag>250 && isojetNBtags==0'
weight = '%f*normalizedWeight*sf_pu*sf_lep*%s*sf_sjbtag0*sf_btag0*sf_tt*sf_metTrig'%(lumi,nlo)
outfileprefix = 'mistag_'
elif args.sel=='photon':
cut = 'nFatjet==1 && fj1Pt>250 && nLooseLep==0 && nLoosePhoton==1 && loosePho1IsTight==1 && nTau==0 && UAmag>250'
weight = '%f*normalizedWeight*sf_pu*sf_lep*%s*sf_tt*sf_phoTrig*0.93'%(lumi,nlo)
outfileprefix = 'photon_'
else:
cut = 'nFatjet==1 && fj1Pt>250 && fj1MaxCSV>0.46 && nLooseLep==1 && nTightMuon==1 && nLooseElectron==0 && nLoosePhoton==0 && nTau==0 && UWmag>250 && isojetNBtags==1'
#!/usr/bin/env python
from sys import argv
f1 = argv[1]
f2 = argv[2]
f3 = argv[3]
argv = []
import PandaCore.Tools.Load as Load
import ROOT as root
Load.Load('DuplicateRemover')
dr = root.DuplicateRemover()
dr.Merge(f1, f2, f3)
#!/usr/bin/env python
from sys import argv
from os import getenv
import argparse
parser = argparse.ArgumentParser(description='reweight CSV shape for fakes')
parser.add_argument('--reweight', metavar='reweight', type=str)
parser.add_argument('--infile', metavar='infile', type=str)
args = parser.parse_args()
argv = []
import ROOT as root
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
import PandaCore.Tools.Functions
Load('Tools', 'BranchAdder')
freweight = root.TFile.Open(args.reweight)
hreweight = freweight.Get('hratio')
ba = root.BranchAdder()
ba.formula = 'isojet1CSV'
ba.newBranchName = 'fakeCSVWeight'
ba.cut = 'isojet1CSV>0 && isojet1Flav==0'
fin = root.TFile(args.infile, 'UPDATE')
tin = fin.Get('events')
ba.AddBranchFromHistogram(tin, hreweight)
fin.WriteTObject(tin, 'events', 'Overwrite')
fin.Close()
#!/usr/bin/env python
from sys import argv
from os import getenv
which = argv[1]
trigger = argv[2]
argv=[]
import ROOT as root
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
import PandaCore.Tools.Functions
Load('BranchAdder')
ba = root.BranchAdder()
fk = root.TFile(trigger)
h_ratio = fk.Get('hden_monojet_recoil_clone_passed')
ba.formula = 'pfUZmag'
ba.newBranchName = 'sf_metTrigZmm'
fin = root.TFile(getenv('PANDA_FLATDIR')+which+'.root','UPDATE')
tin = fin.Get('events')
ba.AddBranchFromHistogram(tin,h_ratio)
fin.WriteTObject(tin,'events','Overwrite')
fin.Close()
#!/usr/bin/env python
from ROOT import gSystem, gROOT
import ROOT as root
from array import array
from glob import glob
from re import sub
from sys import argv
from os import environ, system, path
from PandaCore.Tools.process import *
from PandaCore.Tools.Load import *
Load('Tools', 'Normalizer')
pds = {}
for k, v in processes.iteritems():
if v[1] == 'MC':
pds[v[0]] = (k, v[2])
else:
pds[v[0]] = (k, -1)
VERBOSE = False
user = environ['USER']
system('mkdir -p /tmp/%s/split' % user) # tmp dir
system('mkdir -p /tmp/%s/merged' % user) # tmp dir
base = environ['SCRAMJETFLAT']
inbase = base + '/batch/'
which = int(argv[1])
sname = argv[0]
if len(argv) > 2:
nper = int(argv[2])
else:
nper = 1
argv = []
import ROOT as root
from PandaCore.Tools.Misc import *
from PandaCore.Tools.Load import *
import PandaAnalysis.Tagging.cfg_v8 as tagcfg
if __name__ == "__main__":
Load('PandaAnalysisFlat', 'PandaAnalyzer')
def fn(shortName, longName, isData, xsec):
# first we do some I/O stuff
outdir = 'XXXX'
outfilename = shortName + '.root'
PInfo(sname, 'Output: %s/%s' % (outdir, outfilename))
if path.isfile('%s/%s' % (outdir, outfilename)):
PWarning(sname, "Found %s, skipping!" % outfilename)
return
start = clock()
eosPath = 'root://eoscms.cern.ch//store/user/snarayan'
eosEXOPath = 'root://eoscms.cern.ch//store/group/phys_exotica'
cernboxPath = 'root://eosuser//eos/user/s/snarayan'
#!/usr/bin/env python
import ROOT as root
from PandaCore.Tools.Misc import *
from PandaCore.Tools.Load import *
import re
Load('Statistics', 'RooExpErf')
def treeToDS(tree, rvars, cut=None, weight=None):
args = ['data', 'data', root.RooArgSet(*rvars), root.RooFit.Import(tree)]
if cut:
args.append(root.RooFit.Cut(cut))
if weight:
args.append(root.RooFit.WeightVar(weight))
ds = root.RooDataSet(*args)
return ds
def treeToKDE(tree, rvars, cut=None, weight=None, bw=1):
ds = treeToDS(tree, rvars, cut, weight)
kde = root.RooKeysPdf('kde', 'kde', rvars[0], ds,
root.RooKeysPdf.MirrorBoth, bw)
return kde
def factoryExtractName(s):
return s.split('::')[1].split('(')[0]
basedir = getenv('PANDA_FLATDIR')
parser = argparse.ArgumentParser(description='plot stuff')
parser.add_argument('--proc')
parser.add_argument('--plot', type=str, default=None)
args = parser.parse_args()
argv = []
import ROOT as root
from PandaCore.Tools.Load import *
from PandaCore.Tools.Misc import *
from PandaCore.Tools.root_interface import Selector
if args.plot:
Load('HistogramDrawer')
plot = root.HistogramDrawer()
plot.SetTDRStyle()
plot.AddSqrtSLabel()
plot.InitLegend()
root.gStyle.SetOptStat(0)
binning = (40, 450, 1200)
n_partons = {
'Top_lo': 3,
'Top': 3,
'QCD': 1,
'Higgs': 2,
'W': 2,
from array import array
from glob import glob
from re import sub
from sys import argv
from os import environ, system, path
sname = argv[0]
arguments = [x for x in argv[1:]]
argv = []
import ROOT as root
from PandaCore.Tools.process import *
from PandaCore.Tools.Misc import *
from PandaCore.Tools.Load import *
Load('Normalizer')
pds = {}
for k, v in processes.iteritems():
if v[1] == 'MC':
pds[v[0]] = (k, v[2])
else:
pds[v[0]] = (k, -1)
VERBOSE = False
user = environ['USER']
system('mkdir -p /tmp/%s/split' % user) # tmp dir
system('mkdir -p /tmp/%s/merged' % user) # tmp dir
inbase = environ['PANDA_ZEYNEPDIR']
# blue = [ 6./255., 8./255., 36./255., 91./255., 169./255., 255./255., 243./255., 240./255.,240./255., 240./255., 233./255.]
stops = [0.0000, 0.50, 1.0000]
red = [239. / 255., 255. / 255., 92. / 255.]
green = [87. / 255., 255. / 255., 123. / 255.]
blue = [76. / 255., 255. / 255., 237. / 255.]
stopsArray = array('d', stops)
redArray = array('d', red)
greenArray = array('d', green)
blueArray = array('d', blue)
root.TColor.CreateGradientColorTable(len(stops), stopsArray, redArray,
greenArray, blueArray, ncontours)
root.gStyle.SetNumberContours(999)
# root.gStyle.SetPalette(root.kLightTemperature)
root.gStyle.SetPaintTextFormat(".1g")
Load('Drawers', 'CanvasDrawer')
plot = root.CanvasDrawer()
plot.SetTDRStyle()
root.gStyle.SetPadRightMargin(0.15)
root.gStyle.SetPadLeftMargin(0.2)
root.gStyle.SetPadBottomMargin(0.2)
c = root.TCanvas()
varlist = [
('tau32', '#tau_{32}'),
('tau32SD', '#tau_{32}^{SD}'),
('maxcsv', 'max CSV', (50, 0, 1)),
('(ecfN_1_2_20/pow(ecfN_1_2_10,2.00))', 'e(1,2,2)/e(1,2,1)^{2}', (50, 2,
10)),
parser.add_argument('--outdir',metavar='outdir',type=str,default='.')
parser.add_argument('--cut',metavar='cut',type=str,default=None)
parser.add_argument('--pt',metavar='pt',type=str,default='inc')
args = parser.parse_args()
figsdir = args.outdir
sname = argv[0]
argv = []
import ROOT as root
from PandaCore.Tools.Load import *
cut = ''
plotlabel = '110 < m_{SD} < 210 GeV'
xcut = 'mSD>110 && mSD<210 && pt<1000'
Load('PandaCoreDrawers')
roc = root.ROCTool()
fin = root.TFile(figsdir+'/'+'hists.root')
roc.Logy()
roc.SetPlotRange(0.005,1)
roc.InitCanvas(.7,.2,.85,.45, False)
roc.SetFile(fin)
roc.c.AddPlotLabel(plotlabel,.2,.82,False,42,.04)
variables = [
# ('top_ecfv14_bdt','ECF+#tau_{32}^{SD}+f_{rec} BDT v2',1,3),
# ('top_ecfv12_bdt','ECF BDT v2',2,3),
# ('top_ecfv8_bdt','ECF+#tau_{32}^{SD}+f_{rec} BDT',1,1),
('top_ecfv8_bdt','BDT',1,1),
