closure = Closure()
closure.fillData('True', input_files, treeTrue, 'mt', 'Weight',
global_weights)
closure.fillData('Est', input_files, treeEst, 'mt', 'Weight',
global_weights)
closure.computeCDF('True')
closure.computeCDF('Est')
forceLowMTLinearDistance(closure, 'True')
forceLowMTLinearDistance(closure, 'Est')
for name, bins in mt_bins.items():
plotClosure(fakeType + '_' + name,
closure,
bins,
smoothWidth=0.1,
kernelDistance='Adapt',
doErrors=True,
xTitle='m_{T} [GeV]')
#plotSummary(fakeType, closure, 0, 250, xTitle='m_{T} [GeV]')
output_file.cd()
writeCorrectionAnderrors(closure, fakeType, output_file)
closure.clearData()
closures[fakeType] = closure
#closure.data['True']['CDF'].Write()
#closure.data['Est']['CDF'].Write()
plotDataMC('DataMC',
closures['Data'],
global_weights = inputs['Weights']
treeEst = inputs['TreeEst']
treeTrue = inputs['TreeTrue']
closure = Closure()
closure.fillData('True', input_files, treeTrue, 'mt', 'Weight', global_weights)
closure.fillData('Est', input_files, treeEst, 'mt', 'Weight', global_weights)
closure.computeCDF('True')
closure.computeCDF('Est')
forceLowMTLinearDistance(closure, 'True')
forceLowMTLinearDistance(closure, 'Est')
for name,bins in mt_bins.items():
plotClosure(fakeType+'_'+name, closure, bins, smoothWidth=0.1, kernelDistance='Adapt', doErrors=True, xTitle='m_{T} [GeV]')
#for nbins in [100,50,25]:
#cdf = closure.data['True']['CDFInvert']
#bins = [cdf.Eval(i/float(nbins)) for i in xrange(nbins+1)]
#plotClosure(fakeType+'_NBins'+str(nbins), closure, bins)
plotSummary(fakeType, closure, 0, 250, xTitle='m_{T} [GeV]')
output_file.cd()
writeCorrectionAnderrors(closure, output_file)
#closure.data['True']['CDF'].Write()
#closure.data['Est']['CDF'].Write()
#canvas.Write()
setPlotStyle()
for fakeType,inputs in closure_inputs.items():
input_files = inputs['Files']
global_weights = inputs['Weights']
treeEst = inputs['TreeEst']
treeTrue = inputs['TreeTrue']
closure = Closure()
closure.fillData('True', input_files, treeTrue, 'mvis', 'Weight', global_weights)
closure.fillData('Est', input_files, treeEst, 'mvis', 'Weight', global_weights)
closure.computeCDF('True')
closure.computeCDF('Est')
for name,bins in mvis_bins.items():
plotClosure(fakeType+'_'+name, closure, bins, doErrors=True)
#for nbins in [100,50,25]:
#cdf = closure.data['True']['CDFInvert']
#bins = [cdf.Eval(i/float(nbins)) for i in xrange(nbins+1)]
#plotClosure(fakeType+'_NBins'+str(nbins), closure, bins)
plotSummary(fakeType, closure, 0, 600)
#
writeNonClosure(closure, output_file, '{FAKETYPE}_Histo_Smooth_Ratio'.format(FAKETYPE=fakeType))
#canvas.Write()
##cdfTrue.SetName('{FAKETYPE}_CDF_True'.format(FAKETYPE=fakeType))
##cdfEst.SetName('{FAKETYPE}_CDF_Est'.format(FAKETYPE=fakeType))
#histoTrue.SetName('{FAKETYPE}_Histo_True_{H}'.format(FAKETYPE=fakeType,H=binid))
setPlotStyle()
for fakeType,inputs in closure_inputs.items():
input_files = inputs['Files']
global_weights = inputs['Weights']
treeEst = inputs['TreeEst']
treeTrue = inputs['TreeTrue']
closure = Closure()
closure.fillData('True', input_files, treeTrue, 'mvis', 'Weight', global_weights)
closure.fillData('Est', input_files, treeEst, 'mvis', 'Weight', global_weights)
closure.computeCDF('True')
closure.computeCDF('Est')
for name,bins in mvis_bins.items():
plotClosure(fakeType+'_'+name, closure, bins, doErrors=True)
#for nbins in [100,50,25]:
#cdf = closure.data['True']['CDFInvert']
#bins = [cdf.Eval(i/float(nbins)) for i in xrange(nbins+1)]
#plotClosure(fakeType+'_NBins'+str(nbins), closure, bins)
plotSummary(fakeType, closure, 0, 350)
#
writeNonClosure(closure, output_file, '{FAKETYPE}_Histo_Smooth_Ratio'.format(FAKETYPE=fakeType))
#canvas.Write()
##cdfTrue.SetName('{FAKETYPE}_CDF_True'.format(FAKETYPE=fakeType))
##cdfEst.SetName('{FAKETYPE}_CDF_Est'.format(FAKETYPE=fakeType))
#histoTrue.SetName('{FAKETYPE}_Histo_True_{H}'.format(FAKETYPE=fakeType,H=binid))
