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))