def plotSummary(name, closure, xmin=0, xmax=1, xTitle='m_{vis} [GeV]'):
histoSmoothRatios = []
for hname, graph in closure.data['True/Est'].items():
if 'Histo' in hname and 'Smooth' in hname and not 'Error' in hname:
histoSmoothRatios.append(graph)
################ Plot ratios
histoDummyRatio = Hist(1, xmin, xmax, type='F')
values = []
for ratio in histoSmoothRatios:
for p in xrange(ratio.GetN()):
values.append(ratio.GetY()[p])
maxi = max(values)
mini = min(values)
maxi = maxi * 1.1 if maxi > 0 else maxi * 0.9
mini = mini * 1.1 if mini < 0 else mini * 0.9
histoDummyRatio.SetAxisRange(mini, maxi, 'Y')
##
for ratio in histoSmoothRatios:
ratio.SetLineColor(ROOT.kGray + 3)
ratio.SetLineWidth(1)
##
canvasRatio = Canvas(800, 800)
canvasRatio.SetName('Ratio_Summary_Canvas')
histoDummyRatio.SetXTitle(xTitle)
histoDummyRatio.SetYTitle('Ratio')
histoDummyRatio.Draw()
for ratio in histoSmoothRatios:
ratio.Draw('l same')
canvasRatio.Print(
'results/{NAME}_NonClosure_Ratio_Summary.png'.format(NAME=name))
def plotDataMC(name, closureData, closureMC, bins, xTitle='m_{vis} [GeV]'):
binid = hash(str(bins))
#
histoRatioData = closureData.data['True/Est']['Histo_{H}'.format(H=binid)]
histoSmoothRatioData = closureData.data['True/Est'][
'Histo_{H}_Smooth'.format(H=binid)]
histoSmoothRatioErrorData = closureData.data['True/Est'][
'Histo_{H}_SmoothError'.format(H=binid)]
histoRatioMC = closureMC.data['True/Est']['Histo_{H}'.format(H=binid)]
histoSmoothRatioMC = closureMC.data['True/Est']['Histo_{H}_Smooth'.format(
H=binid)]
histoSmoothRatioErrorMC = closureMC.data['True/Est'][
'Histo_{H}_SmoothError'.format(H=binid)]
#
################ Plot ratios
histoDummyRatio = Hist(1, bins[0], bins[-1], type='F')
values = []
values.append(histoRatioData.GetMaximum())
values.append(histoRatioData.GetMinimum())
values.append(histoRatioMC.GetMaximum())
values.append(histoRatioMC.GetMinimum())
maxi = max(values)
mini = min(values)
maxi = maxi * 1.1 if maxi > 0 else maxi * 0.9
mini = mini * 1.1 if mini < 0 else mini * 0.9
histoDummyRatio.SetAxisRange(mini, maxi, 'Y')
##
histoRatioData.SetMarkerColor(ROOT.kBlack)
histoSmoothRatioData.SetLineColor(ROOT.kGray + 3)
histoSmoothRatioData.SetLineWidth(2)
histoSmoothRatioErrorData.SetLineColor(ROOT.kGray + 1)
histoSmoothRatioErrorData.SetFillColor(ROOT.kGray + 1)
##
histoRatioMC.SetMarkerColor(ROOT.kRed)
histoRatioMC.SetMarkerStyle(21)
histoSmoothRatioMC.SetLineColor(ROOT.kRed)
histoSmoothRatioMC.SetLineWidth(2)
histoSmoothRatioErrorMC.SetLineColor(ROOT.kRed - 7)
#histoSmoothRatioErrorMC.SetFillColorAlpha(ROOT.kRed-7, 0.35)
histoSmoothRatioErrorMC.SetFillStyle(3344)
histoSmoothRatioErrorMC.SetFillColor(ROOT.kRed - 7)
##
canvasRatio = Canvas(800, 800)
canvasRatio.SetName('{NAME}_Ratio_Canvas'.format(NAME=name))
histoDummyRatio.SetXTitle(xTitle)
histoDummyRatio.SetYTitle('Ratio')
histoDummyRatio.Draw()
histoSmoothRatioErrorData.Draw('fl same')
histoSmoothRatioData.Draw('pl same')
histoSmoothRatioErrorMC.Draw('fl same')
histoSmoothRatioMC.Draw('pl same')
histoRatioData.Draw('E1 same')
histoRatioMC.Draw('E1 same')
#
legend = ROOT.TLegend(0.15, 0.75, 0.45, 0.9)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.AddEntry(histoRatioMC, 'MC', 'lp')
legend.AddEntry(histoRatioData, 'Data', 'lp')
legend.Draw()
canvasRatio.Print('results/{NAME}_NonClosure_Ratio.png'.format(NAME=name))
def main(parameters):
input_files = parameters.input_files
tree_name = parameters.input_tree
means = Hist(4*len(input_files), 0, 4*len(input_files), name='mean values')
rmss = Hist(4*len(input_files), 0, 4*len(input_files), name='rms values')
cov_means = Hist(2*len(input_files), 0, 2*len(input_files), name='covariance mean values')
cov_rmss = Hist(2*len(input_files), 0, 2*len(input_files), name='covariance rms values')
means.SetYTitle('Variance mean [cm^{2}]')
rmss.SetYTitle('Variance RMS [cm^{2}]')
cov_means.SetYTitle('Covariance mean [cm^{2}]')
cov_rmss.SetYTitle('Covariance RMS [cm^{2}]')
with root_open(parameters.output_file, 'recreate') as output_file:
for i, (name, input_file_name) in enumerate(input_files):
means.GetXaxis().SetBinLabel(i*4+1, name+' x')
means.GetXaxis().SetBinLabel(i*4+2, name+' y')
means.GetXaxis().SetBinLabel(i*4+3, name+' u')
means.GetXaxis().SetBinLabel(i*4+4, name+' v')
rmss.GetXaxis().SetBinLabel(i*4+1, name+' x')
rmss.GetXaxis().SetBinLabel(i*4+2, name+' y')
rmss.GetXaxis().SetBinLabel(i*4+3, name+' u')
rmss.GetXaxis().SetBinLabel(i*4+4, name+' v')
cov_means.GetXaxis().SetBinLabel(i*2+1, name+' x/y')
cov_rmss.GetXaxis().SetBinLabel(i*2+1, name+' x/y')
cov_means.GetXaxis().SetBinLabel(i*2+2, name+' u/v')
cov_rmss.GetXaxis().SetBinLabel(i*2+2, name+' u/v')
with root_open(input_file_name) as input_file:
tree = input_file.Get(tree_name)
moment_2nd_x, moment_2nd_y, moment_2nd_xy, moment_2nd_u, moment_2nd_v, moment_2nd_uv = shape_distribution(tree, parameters.log_weights)
print name
print 'x variance Mu =', moment_2nd_x.GetMean(), '+/-', moment_2nd_x.GetMeanError(), 'RMS =', moment_2nd_x.GetRMS(), '+/-', moment_2nd_x.GetRMSError()
print 'y variance Mu =', moment_2nd_y.GetMean(), '+/-', moment_2nd_y.GetMeanError(), 'RMS =', moment_2nd_y.GetRMS(), '+/-', moment_2nd_y.GetRMSError()
print 'u variance Mu =', moment_2nd_u.GetMean(), '+/-', moment_2nd_u.GetMeanError(), 'RMS =', moment_2nd_u.GetRMS(), '+/-', moment_2nd_u.GetRMSError()
print 'v variance Mu =', moment_2nd_v.GetMean(), '+/-', moment_2nd_v.GetMeanError(), 'RMS =', moment_2nd_v.GetRMS(), '+/-', moment_2nd_v.GetRMSError()
print 'x-y covariance Mu =', moment_2nd_xy.GetMean(), '+/-', moment_2nd_xy.GetMeanError(), 'RMS =', moment_2nd_xy.GetRMS(), '+/-', moment_2nd_xy.GetRMSError()
print 'u-v covariance Mu =', moment_2nd_uv.GetMean(), '+/-', moment_2nd_uv.GetMeanError(), 'RMS =', moment_2nd_uv.GetRMS(), '+/-', moment_2nd_uv.GetRMSError()
# fill mean values of variances
means.SetBinContent(i*4+1, moment_2nd_x.GetMean())
means.SetBinError(i*4+1, moment_2nd_x.GetMeanError())
means.SetBinContent(i*4+2, moment_2nd_y.GetMean())
means.SetBinError(i*4+2, moment_2nd_y.GetMeanError())
means.SetBinContent(i*4+3, moment_2nd_u.GetMean())
means.SetBinError(i*4+3, moment_2nd_u.GetMeanError())
means.SetBinContent(i*4+4, moment_2nd_v.GetMean())
means.SetBinError(i*4+4, moment_2nd_v.GetMeanError())
# fill rms values of variances
rmss.SetBinContent(i*4+1, moment_2nd_x.GetRMS())
rmss.SetBinError(i*4+1, moment_2nd_x.GetRMSError())
rmss.SetBinContent(i*4+2, moment_2nd_y.GetRMS())
rmss.SetBinError(i*4+2, moment_2nd_y.GetRMSError())
rmss.SetBinContent(i*4+3, moment_2nd_u.GetRMS())
rmss.SetBinError(i*4+3, moment_2nd_u.GetRMSError())
rmss.SetBinContent(i*4+4, moment_2nd_v.GetRMS())
rmss.SetBinError(i*4+4, moment_2nd_v.GetRMSError())
# fill mean values of covariances
cov_means.SetBinContent(i*2+1, moment_2nd_xy.GetMean())
cov_means.SetBinError(i*2+1, moment_2nd_xy.GetMeanError())
cov_means.SetBinContent(i*2+2, moment_2nd_uv.GetMean())
cov_means.SetBinError(i*2+2, moment_2nd_uv.GetMeanError())
# fill rms values of covariances
cov_rmss.SetBinContent(i*2+1, moment_2nd_xy.GetRMS())
cov_rmss.SetBinError(i*2+1, moment_2nd_xy.GetRMSError())
cov_rmss.SetBinContent(i*2+2, moment_2nd_uv.GetRMS())
cov_rmss.SetBinError(i*2+2, moment_2nd_uv.GetRMSError())
# write variance distributions
moment_2nd_x.SetName(moment_2nd_x.GetName()+'_'+name)
moment_2nd_y.SetName(moment_2nd_y.GetName()+'_'+name)
moment_2nd_xy.SetName(moment_2nd_xy.GetName()+'_'+name)
moment_2nd_u.SetName(moment_2nd_u.GetName()+'_'+name)
moment_2nd_v.SetName(moment_2nd_v.GetName()+'_'+name)
output_file.cd()
moment_2nd_x.Write()
moment_2nd_y.Write()
moment_2nd_xy.Write()
moment_2nd_u.Write()
moment_2nd_v.Write()
means.Write()
rmss.Write()
cov_means.Write()
cov_rmss.Write()
def plotClosure(name,
closure,
bins,
smoothWidth=0.1,
kernelDistance='Adapt',
doErrors=False,
xTitle='m_{vis} [GeV]'):
binid = hash(str(bins))
closure.computeHisto('True', bins)
closure.computeHisto('Est', bins)
closure.computeRatio('Est', 'True', 'Histo_{H}'.format(H=binid),
smoothWidth, kernelDistance, doErrors)
#
histoTrue = closure.data['True']['Histo_{H}'.format(H=binid)]
histoEst = closure.data['Est']['Histo_{H}'.format(H=binid)]
histoRatio = closure.data['True/Est']['Histo_{H}'.format(H=binid)]
histoSmoothRatio = closure.data['True/Est']['Histo_{H}_Smooth'.format(
H=binid)]
histoSmoothRatioError = closure.data['True/Est'][
'Histo_{H}_SmoothError'.format(H=binid)]
#
############ plot raw distributions
histoDummy = Hist(1, bins[0], bins[-1], type='F')
values = []
values.append(histoTrue.GetMaximum())
values.append(histoTrue.GetMinimum())
values.append(histoEst.GetMaximum())
values.append(histoEst.GetMinimum())
maxi = max(values) * 1.1
mini = min(values)
histoDummy.SetAxisRange(mini, maxi, 'Y')
##
histoTrue.SetMarkerColor(ROOT.kBlack)
histoEst.SetMarkerStyle(24)
histoEst.SetMarkerColor(ROOT.kGray + 3)
##
canvas = Canvas(800, 800)
canvas.SetName('{NAME}_Canvas'.format(NAME=name))
histoDummy.SetXTitle(xTitle)
histoDummy.SetYTitle('Events')
histoDummy.Draw()
histoTrue.Draw('same')
histoEst.Draw('same')
legend = ROOT.TLegend(0.4, 0.7, 0.9, 0.9)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.AddEntry(histoTrue, 'True background', 'lp')
legend.AddEntry(histoEst, 'Estimated background', 'lp')
legend.Draw()
canvas.Print('results/{NAME}_NonClosure.png'.format(NAME=name))
################ Plot ratios
histoDummyRatio = Hist(1, bins[0], bins[-1], type='F')
values = []
values.append(histoRatio.GetMaximum())
values.append(histoRatio.GetMinimum())
maxi = max(values)
mini = min(values)
maxi = maxi * 1.1 if maxi > 0 else maxi * 0.9
mini = mini * 1.1 if mini < 0 else mini * 0.9
histoDummyRatio.SetAxisRange(mini, maxi, 'Y')
##
histoRatio.SetMarkerColor(ROOT.kBlack)
histoSmoothRatio.SetLineColor(ROOT.kGray + 3)
histoSmoothRatio.SetLineWidth(2)
histoSmoothRatioError.SetLineColor(ROOT.kGray + 1)
histoSmoothRatioError.SetFillColor(ROOT.kGray + 1)
##
canvasRatio = Canvas(800, 800)
canvasRatio.SetName('{NAME}_Ratio_Canvas'.format(NAME=name))
histoDummyRatio.SetXTitle(xTitle)
histoDummyRatio.SetYTitle('Ratio')
histoDummyRatio.Draw()
histoSmoothRatioError.Draw('fl same')
histoSmoothRatio.Draw('pl same')
histoRatio.Draw('same')
canvasRatio.Print('results/{NAME}_NonClosure_Ratio.png'.format(NAME=name))
