def main():
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-i', '--inputfile', dest='inputfile' , help='Name of the input fileectory containing the histograms.' , default=None)
parser.add_option('-b', '--batch' , dest='batch' , help='Use this flag to run root in batch mode.' , action='store_true', default=False)
parser.add_option('-v', '--verbose' , dest='verbose' , help='Verbose level. [default = %default]' , action='store', type='int', default=0)
(opt, args) = parser.parse_args()
if opt.inputfile is None:
parser.error('No input file defined!')
inputfile = opt.inputfile
batch = opt.batch
if batch:
## run ROOT in batch mode
sys.argv.append( '-b' )
ROOT.gROOT.SetBatch()
sfs = {}
i_file = mRF.openTFile(inputfile)
channels = ['e','mu']
print 'channels: ',channels
levels = set([k.split('_',1)[-1] for k in mRF.GetKeyNames(i_file,'data') if 'charge' in k and not 'chargeCheck' in k and not len(k.split('_')) == 2])
print 'levels: ',levels
if opt.verbose==0:
print '-----------------------------------------------------------------------------------------------------------------'
print ' : W_sf | diff data | diff MC | diff wjets| A data (%) | A wjets (%) | %Wjets | data/MC'
print '-----------------------------------------------------------------------------------------------------------------'
for chan in channels:
for level in levels:
sf = getNormalization(i_file,chan,chan+'_'+level, verbose=opt.verbose)
sfs[chan+'_'+level.replace('_charge','')] = sf
if opt.verbose==0: print '-----------------------------------------------------------------------------------------------------------------'
# pprint(sfs)
## write weights to a root file
sf_hist = TH1F('wjetssf','wjetssf',len(sfs),0,len(sfs))
i=0
for l in sfs:
i+=1
sf_hist.GetXaxis().SetBinLabel(i,l)
sf_hist.SetBinContent(i,sfs[l][0])
sf_hist.SetBinError(i,sfs[l][1])
sf_out = mRF.openTFile('data/weights/top_wjetssf.root','RECREATE')
sf_hist.Write()
sf_out.Close()
def main():
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-i', '--inputfile', dest='inputfile' , help='Name of the input file containing the histograms.' , default=None)
parser.add_option('-b', '--batch', dest='batch' , help='Use this flag to run root in batch mode.', action='store_true' , default=False)
parser.add_option('-q', '--qcdtemplates', dest='qcdtemplates', help='Name of the input file containing the QCD templates.' , default=None)
parser.add_option('-o', '--outputdir', dest='outputdir' , help='Name of the local output directory.' , default='QCDNormalization')
(opt, args) = parser.parse_args()
## Handle input parameters:
if opt.inputfile is None:
parser.error('No input file defined!')
inputfile = opt.inputfile
if opt.batch:
sys.argv.append( '-b' )
ROOT.gROOT.SetBatch()
i_file = mRF.openTFile(inputfile)
channels = ['e','mu']
print 'channels: ',channels
# levels = set([k.split('_')[-1].replace('charge','met') for k in mRF.GetKeyNames(i_file,'data') if 'charge' in k])
levels = set([k.split('_',1)[-1].replace('charge','met') for k in mRF.GetKeyNames(i_file,'data') if 'charge' in k and not 'chargeCheck' in k and not len(k.split('_')) == 2])
print 'levels: ',levels
sfs = {}
for chan in channels:
# levels = [k.split('_')[1] for k in mRF.GetKeyNames(i_file,chan) if ('met' in k and not 'flow' in k)]
for level in list(set(levels)):
sf = getNormalization(i_file,chan,level,outputdir=opt.outputdir)
sfs[chan+'_'+level] = sf
pprint(sfs)
## write weights to a root file
sf_hist = TH1F('qcdsf','qcdsf',len(sfs),0,len(sfs))
i=0
for l in sfs:
i+=1
sf_hist.GetXaxis().SetBinLabel(i,l)
sf_hist.SetBinContent(i,sfs[l][0])
sf_hist.SetBinError(i,sfs[l][1])
sf_out = mRF.openTFile('data/weights/top_qcdsf.root','RECREATE')
sf_hist.Write()
sf_out.Close()
def getSummedShapes(path, tag, chan=''):
## FIXME get the following numbers from the file
lxy_max = 5.
lxy_bins = 50
if chan != '':
chan = '_' + chan
## prepare a summed histogram
summed_cat = []
## open the corresponfing file
tfile = mRF.openTFile(path)
# nevents = mRF.getHist(tfile,'map_counter').Integral(0,2)
nevents = 0.
for i in xrange(ybin - 1):
map_cat = mRF.getHist(tfile, 'map' + chan + '_cat' + str(i))
# n = map_cat.Integral(0,map_cat.GetNbinsX()+1)
n = map_cat.Integral()
nevents += n
checksum = 0.
checkweight = 0.
## loop over categories
for i in xrange(ybin - 1):
summed = TH1F(tag + '_summed_lxy' + chan + '_cat' + str(i),
tag + '_summed_lxy' + chan + '_cat' + str(i), lxy_bins,
0., lxy_max).Clone()
summed.Sumw2()
## get the map histo
map_cat = mRF.getHist(tfile, 'map' + chan + '_cat' + str(i))
map_cat.Sumw2()
checksum += map_cat.GetEntries()
## get the gamma binning per category
nbins = map_cat.GetNbinsX()
for j in xrange(nbins):
w = float(map_cat.GetBinContent(j + 1))
checkweight += w
if tag == 'templ':
hist = mRF.getHist(tfile,
tag + '_lxy_cat' + str(i) + '_bin' + str(j))
else:
hist = mRF.getHist(
tfile,
tag + '_lxy' + chan + '_cat' + str(i) + '_bin' + str(j))
hist.Sumw2()
## scale the histogram
integr = float(hist.Integral(0, hist.GetNbinsX() + 1))
if integr > 0 and tag == 'templ':
hist.Scale(w / integr)
## sum the histograms
summed.Add(hist.Clone())
summed_cat.append(summed)
print 'checksum (map): ', checksum
print 'checksum: ', sum([h.GetEntries() for h in summed_cat])
print 'checkweight: ', checkweight
return summed_cat
def getSummedShapes(path, tag, chan=''):
## FIXME get the following numbers from the file
lxy_max = 5.
lxy_bins = 50
if chan != '':
chan = '_'+chan
## prepare a summed histogram
summed_cat = []
## open the corresponfing file
tfile = mRF.openTFile(path)
# nevents = mRF.getHist(tfile,'map_counter').Integral(0,2)
nevents = 0.
for i in xrange(ybin-1):
map_cat = mRF.getHist(tfile,'map'+chan+'_cat'+str(i))
# n = map_cat.Integral(0,map_cat.GetNbinsX()+1)
n = map_cat.Integral()
nevents+=n
checksum = 0.
checkweight = 0.
## loop over categories
for i in xrange(ybin-1):
summed = TH1F(tag+'_summed_lxy'+chan+'_cat'+str(i),tag+'_summed_lxy'+chan+'_cat'+str(i),lxy_bins, 0., lxy_max).Clone()
summed.Sumw2()
## get the map histo
map_cat = mRF.getHist(tfile,'map'+chan+'_cat'+str(i))
map_cat.Sumw2()
checksum+=map_cat.GetEntries()
## get the gamma binning per category
nbins = map_cat.GetNbinsX()
for j in xrange(nbins):
w = float(map_cat.GetBinContent(j+1))
checkweight+=w
if tag=='templ':
hist = mRF.getHist(tfile,tag+'_lxy_cat'+str(i)+'_bin'+str(j))
else:
hist = mRF.getHist(tfile,tag+'_lxy'+chan+'_cat'+str(i)+'_bin'+str(j))
hist.Sumw2()
## scale the histogram
integr = float(hist.Integral(0, hist.GetNbinsX()+1))
if integr > 0 and tag=='templ':
hist.Scale(w/integr)
## sum the histograms
summed.Add(hist.Clone())
summed_cat.append(summed)
print 'checksum (map): ',checksum
print 'checksum: ',sum([h.GetEntries() for h in summed_cat])
print 'checkweight: ',checkweight
return summed_cat
def main():
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-i', '--inputfile' , dest='inputfile' , help='Name of the input file containing the histograms.' , default=None)
parser.add_option('-o', '--outputfile', dest='outputfile' , help='Name of the output file containing the histograms.', default='lxyhist.root')
parser.add_option('-b', '--batch' , dest='batch' , help='Use this flag to run root in batch mode.' , default=False, action='store_true')
(opt, args) = parser.parse_args()
if opt.inputfile is None:
parser.error('No input file defined!')
inputfile = opt.inputfile
outputfile = opt.outputfile
batch = opt.batch
if batch:
sys.argv.append( '-b' )
ROOT.gROOT.SetBatch()
i_file = mRF.openTFile(inputfile)
channels = ['e','mu']
procs = [k for k in mRF.GetKeyNames(i_file,'')]
hists = {}
for proc in procs:
keys = [k for k in mRF.GetKeyNames(i_file,proc)]
for key in keys:
## Select jetlxy or secvtxmass histograms for e and mu channels:
try: chan, var = key.split('_', 1)
except ValueError: continue
if not (chan == 'e' or chan == 'mu'): continue
if not (var == 'jetlxy' or var == 'secvtxmass'): continue
hist = mRF.getHist(i_file, proc + '/' + key)
if var == 'jetlxy':
var = var.replace('jet','max')
if var == 'secvtxmass':
var = var.replace('secvtx','jet')
## Extract generated top mass
if not 'ttbar' in proc_dict[proc]:
mass = '0'
else:
try:
mass = proc.split('}')[-1].replace('.','')
except IndexError:
mass = '1725'
if mass == '' or 'syst' in mass:
mass = '1725'
name = chan+'_'+var+'_'+proc_dict[proc]+'_'+mass
# print name
hists[name] = hist.Clone() ## FIXME (name.replace(' ',''))
o_file = ROOT.TFile.Open(outputfile,'recreate')
for chan in channels:
o_file.mkdir(chan)
for h in hists:
channel = h.split('_')[0]
if channel == 'e': o_file.cd(channel)
elif channel == 'mu': o_file.cd(channel)
else: continue
hists[h].Write()
o_file.Close()
