Python plotSummary示例

示例#1

文件： evaluateClosure.py 项目： jbsauvan/H2Taus-Studies

    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))
    #histoEst.SetName('{FAKETYPE}_Histo_Est_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoDiff.SetName('{FAKETYPE}_Histo_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothDiff.SetName('{FAKETYPE}_Histo_Smooth_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoRatio.SetName('{FAKETYPE}_Histo_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothRatio.SetName('{FAKETYPE}_Histo_Smooth_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothRatioError.SetName('{FAKETYPE}_Histo_SmoothError_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    ##cdfTrue.Write()

示例#2

文件： evaluateMTCorrection.py 项目： jbsauvan/H2Taus-Studies

    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()
    ##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))
    #histoEst.SetName('{FAKETYPE}_Histo_Est_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoDiff.SetName('{FAKETYPE}_Histo_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothDiff.SetName('{FAKETYPE}_Histo_Smooth_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoRatio.SetName('{FAKETYPE}_Histo_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))

示例#3

文件： evaluateClosureSS.py 项目： jbsauvan/H2Taus-Studies

    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))
    #histoEst.SetName('{FAKETYPE}_Histo_Est_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoDiff.SetName('{FAKETYPE}_Histo_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothDiff.SetName('{FAKETYPE}_Histo_Smooth_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoRatio.SetName('{FAKETYPE}_Histo_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothRatio.SetName('{FAKETYPE}_Histo_Smooth_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothRatioError.SetName('{FAKETYPE}_Histo_SmoothError_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    ##cdfTrue.Write()

示例#4

文件： evaluateMTCorrection.py 项目： jbsauvan/H2Taus-Studies

    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()
    ##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))
    #histoEst.SetName('{FAKETYPE}_Histo_Est_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoDiff.SetName('{FAKETYPE}_Histo_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothDiff.SetName('{FAKETYPE}_Histo_Smooth_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoRatio.SetName('{FAKETYPE}_Histo_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothRatio.SetName('{FAKETYPE}_Histo_Smooth_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))

示例#5

                     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))
    #histoEst.SetName('{FAKETYPE}_Histo_Est_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoDiff.SetName('{FAKETYPE}_Histo_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothDiff.SetName('{FAKETYPE}_Histo_Smooth_Diff_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoRatio.SetName('{FAKETYPE}_Histo_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothRatio.SetName('{FAKETYPE}_Histo_Smooth_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))
    #histoSmoothRatioError.SetName('{FAKETYPE}_Histo_SmoothError_Ratio_{H}'.format(FAKETYPE=fakeType,H=binid))