def plot(params, rates):
canvas = Canvas(width=500, height=500)
draw_axes(params)
draw_rates(params, rates)
HGCAL_label(text='HGCAL Simulation', pad=canvas)
canvas.SetLogy()
canvas.RedrawAxis()
canvas.Print('%s.png' % params.name)
canvas.Print('%s.pdf' % params.name)
canvas.Print('%s.C' % params.name)
def plot(params, efficiency):
canvas = Canvas(width=500, height=500)
draw_axes(params)
draw_efficiency(params, efficiency)
draw_lines(params)
HGCAL_label(text='HGCAL Simulation', pad=canvas)
draw_legends(params)
canvas.RedrawAxis()
canvas.Print('%s.png' % params.name)
canvas.Print('%s.pdf' % params.name)
canvas.Print('%s.C' % params.name)
return canvas
def main(args):
fileName = '/data/nawoods/lepsForSIP/lepsForSIP_M2500.root'
checker = MuonCandMatchChecker('Check PF cand matching', [fileName])
checker.processSample()
h = checker.hists['pfCandPt']
h.Sumw2()
h.drawstyle = 'HIST'
h.legendstyle = 'L'
h.linewidth = 2
h.color = 'red'
c = Canvas(1000, 1000)
(xax, yax), lims = draw(
h,
c,
ytitle='PF Cands',
xtitle='PF Cand p_T',
)
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
c.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/matchedCandPt.png')
h2 = checker.hists['pfCandPtVsGenPt']
c2 = Canvas(1000, 1000)
h2.xaxis.SetTitleOffset(1.8)
h2.yaxis.SetTitleOffset(1.7)
h2.xaxis.SetTitleSize(0.5 * h2.xaxis.GetTitleSize())
h2.yaxis.SetTitleSize(0.5 * h2.yaxis.GetTitleSize())
h2.xaxis.SetLabelSize(0.6 * h2.xaxis.GetLabelSize())
h2.yaxis.SetLabelSize(0.6 * h2.yaxis.GetLabelSize())
h2.xaxis.SetTitle("Gen #mu p_{T}")
h2.yaxis.SetTitle("PF cand p_{T}")
h2.Draw("COLZ")
c2.Print(
'/afs/cern.ch/user/n/nawoods/www/ptStudy/matchedCandPtVsGenPt.png')
print "Out of {} good muons, {} failed PF ID ({}%).".format(
checker.nTot, checker.nFailPF,
100. * float(checker.nFailPF) / checker.nTot)
print(
"Out of those failing PF, {} shared a track with a PF charged hadron "
"({}%, {}% of the total)").format(
checker.nPFMatched, 100. * checker.nPFMatched / checker.nFailPF,
100. * checker.nPFMatched / checker.nTot)
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 plot_comparisons(input_file, comparison_plots):
with root_open(input_file) as file:
for name,histo_config in comparison_plots.items():
canvas = Canvas(1200,700)
canvas.SetLeftMargin(0.08)
canvas.SetRightMargin(0.12)
histogram = file.Get(histo_config.name)
if len(histo_config.range)==2:
histogram.SetAxisRange(histo_config.range[0], histo_config.range[1], 'X')
histogram.color = histo_config.color
histogram.markerstyle = histo_config.marker
histogram.Draw()
canvas.Print('./plots/{}.png'.format(name))
def plot_event(name, canvas):
primitives = canvas.GetListOfPrimitives()
histo = None
point = None
for primitive in primitives:
if primitive.InheritsFrom(TH2Poly.Class()):
histo = primitive
if primitive.InheritsFrom(TMarker.Class()):
point = primitive
histo.SetXTitle('x [cm]')
histo.SetYTitle('y [cm]')
canvas_new = Canvas(700, 700)
histo.SetAxisRange(77,100,'X')
histo.Draw('colz')
point.Draw()
canvas_new.SetLogz()
canvas_new.Print('./plots/event_{}.png'.format(name))
def plot_distributions(input_file, distribution_plots):
with root_open(input_file) as file:
for name,histo_configs in distribution_plots.items():
canvas = Canvas(700,700)
legend = Legend(len(histo_configs), leftmargin=0.4, margin=0.3)
histograms = []
for histo_config in histo_configs:
histogram = file.Get(histo_config.name)
if len(histo_config.range)==2:
histogram.SetAxisRange(histo_config.range[0], histo_config.range[1], 'X')
histogram.color = histo_config.color
histogram.markerstyle = histo_config.marker
legend.AddEntry(histogram, label=histo_config.legend, style='ep')
histograms.append(histogram)
xmin, xmax, ymin, ymax = get_limits(histograms, logy=False)
option = ''
for histogram in histograms:
histogram.SetAxisRange(ymin,ymax,'Y')
histogram.Draw(option)
option += ' same'
legend.Draw()
canvas.Print('plots/{}.png'.format(name))
histname.yaxis.SetTitle(title)
histname.xaxis.set_label_size(20)
histname.yaxis.set_label_size(20)
histname.zaxis.set_title_size(20)
histname.zaxis.set_label_size(10)
histname.set_title_size(20)
label = ROOT.TLatex(.17, .97,
signalprocess + signaljets)
label.SetTextFont(43)
label.SetTextSize(18)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
canvas.Print(outputfolder + "/" + xvariable + "_" +
variable + "_" + background_name +
"_2D_2L.png")
for (rootfile, data_weight, data_legend,
data_bool) in zip(data_list, data_weight_list, data_legend_list,
data_bool_list):
if data_bool == "Plot":
data = root_open(data_location + "/" + rootfile)
compressed = data.CompressedAnalysis
for (variable, min, max, nbins, title, ytitle,
yplot) in zip(variable_list, min_list, max_list, nbins_list,
title_list, ytitle_list, yplot_list):
if yplot == "Plot":
print variable + " " + min + " " + max + " " + nbins + " " + title + " " + ytitle
for (xvariable, xmin, xmax, xnbins, xtitle, xytitle,
xplot) in zip(xvariable_list, xmin_list, xmax_list,
#pad2.Draw()
#pad2.cd()
#pad2.SetGrid()
# dataobject.SetMinimum(0.0001)
# dataobject.SetMaximum(2)
# dataobject.xaxis.SetTextSize(10)
# dataobject.xaxis.SetTitle(xtitle)
# dataobject.yaxis.SetTitle("Data/MC")
# dataobject.yaxis.divisions = 5
# dataobject.Divide(sum(stack))
## dataobject.xaxis.getTitle().Center()
# dataobject.xaxis.set_label_size(25)
# dataobject.yaxis.set_label_size(25)
# object.SetLineColor("black")
# dataobject.SetLineWidth(0)
# dataobject.drawstyle = "hist"
# dataobject.SetLineStyle(linestyle)
# dataobject.SetFillColor("goldenrod")
#dataobject.set
# dataobject.Draw()
#pad2.Modified()
#pad2.Update()
canvas.Print(outputfolder + "/" + variable + "_3L.png")
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))
histpad.cd()
drawXandYlabels(stack,histRatio,xlabel,ylabel,defaultlabelsize,ratiopad,histpad,doRatio,labelmargin)
drawPlotInformation(DataPeriods,atlasStatus,RegionLabel,histpad,defaultlabelsize,labelmargin,donminus1)
drawTopLeftInformation(Region,CRlist,VVCRlist,TOPCRlist,ABCDlist,donminus1,backgroundstacks,datastack,defaultlabelsize,labelmargin)
drawDataLegend(datastack,stack,plotData,BLINDEDLIST,defaultlabelsize,Region)
drawBackgroundLegend(backgroundstacks,stack,LegendExtraInformation,defaultlabelsize,Region)
dataoutputfolder = os.path.join(outputfolder,"Data" + DataPeriods )
os.system("mkdir -p " + dataoutputfolder )
os.system("mkdir -p " + os.path.join(dataoutputfolder,Region) )
os.system("mkdir -p " + os.path.join(dataoutputfolder,Region,"N-1") )
filenamevariable = variable.replace("/","_").replace("_1000","")
if donminus1:
canvas.Print(dataoutputfolder + "/" + Region + "/N-1/log_"+ filenamevariable+ "_" + Region + "_"+systematics+".png")
else:
canvas.Print(dataoutputfolder + "/" + Region + "/log_"+ filenamevariable+ "_" + Region + "_"+systematics+".png")
histpad.SetLogy(0)
stack.SetMinimum(0.0)
stack.SetMaximum(3*stack.sum.GetMaximum())
canvas.Print(dataoutputfolder + "/" + Region + "/lin_"+ filenamevariable+ "_" + Region + "_"+systematics+".png")
print "[CANVAS][PLOTTED]" + str(round(time.time() - t0,0)) + "seconds"
#pad2.Draw()
#pad2.cd()
#pad2.SetGrid()
# dataobject.SetMinimum(0.0001)
# dataobject.SetMaximum(2)
# dataobject.xaxis.SetTextSize(10)
# dataobject.xaxis.SetTitle(xtitle)
# dataobject.yaxis.SetTitle("Data/MC")
# dataobject.yaxis.divisions = 5
# dataobject.Divide(sum(stack))
## dataobject.xaxis.getTitle().Center()
# dataobject.xaxis.set_label_size(25)
# dataobject.yaxis.set_label_size(25)
# object.SetLineColor("black")
# dataobject.SetLineWidth(0)
# dataobject.drawstyle = "hist"
# dataobject.SetLineStyle(linestyle)
# dataobject.SetFillColor("goldenrod")
#dataobject.set
# dataobject.Draw()
#pad2.Modified()
#pad2.Update()
canvas.Print(outputfolder + "/" + variable + "_2LNJ_SRZ.png")
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))
histogram2D.yaxis.SetTitle(xtitle2)
histogram2D.yaxis.SetTitleOffset(1.5)
histogram2D.yaxis.set_label_size(
int(canvasheight * labelscale * 1.5))
#canvas.cd()
canvas.Update()
canvas.Modified()
#os.system("mkdir -p " + dataoutputfolder )
dataoutputfolder = os.path.join(outputfolder,
"Data" + DataPeriods,
Region)
os.system("mkdir -p " + dataoutputfolder)
canvas.Print(dataoutputfolder + "/log_" +
variable.replace("/", "_") + "_" +
variable2.replace("/", "_") + "_" + Region +
"_" + systematics + ".png")
#backgroundstacks[LegendEntry].Add(temphist.Clone())
#plotzjets(hist)
# skimmed = 1
# stack.yaxis.SetTitle(ytitle)
# stack.yaxis.SetTitleOffset(2)
# stack.yaxis.set_label_size(int(canvasheight*labelscale*1.5))
# canvas.cd()
#dataoutputfolder = os.path.join(outputfolder,"Data" + DataPeriods )
xytitle) in zip(xvariable_list, xmin_list, xmax_list,
xnbins_list, xtitle_list, xytitle_list):
# Canvas Variables and declaration
print xvariable + " " + xmin + " " + xmax + " " + xnbins + " " + xtitle + " " + xytitle
canvas = Canvas(width=700, height=500)
# pad.SetTopMargin(0.2)
canvas.Draw()
#canvas.SetLogy()
canvas.SetGrid()
histname = "hist" + variable + "_" + xvariable + background_name
print histname
histogram = histname + "(" + xnbins + "," + xmin + "," + xmax + "," + nbins + "," + min + "," + max + ")"
print histogram
histname = compressed.Draw(xvariable + ":" + variable + ">>" +
histogram,
selection=total & weight,
drawstyle='COLZ')
histname.SetTitle(background_name)
#histname.zaxis.SetTitle(
histname.xaxis.SetTitle(xtitle)
histname.yaxis.SetTitle(title)
histname.xaxis.set_label_size(15)
histname.yaxis.set_label_size(20)
canvas.Update()
canvas.Print(outputfolder + "/" + xvariable + "_" + variable +
background_name + "2L.png")
def main(args):
fileName = '/data/nawoods/lepsForSIP/lepsForSIP_M2500.root'
checker = MuonPtMatchChecker('Check Pt', [fileName])
checker.processSample()
hs = checker.hists.copy()
for var, h in hs.iteritems():
h.Sumw2()
h.drawstyle = 'HIST'
h.legendstyle = 'L'
h.linewidth = 2
h.color = 'red'
cRes = Canvas(1000,1000)
hs['pfPtRes'].color = 'black'
hs['pfPtRes'].title = 'PF'
hs['tunePPtResUncor'].title = 'Tune P'
(xax,yax), lims = draw([hs['pfPtRes'], hs['tunePPtResUncor']], cRes,
xtitle='#frac{reco p_T - gen p_T}{gen p_T}',
ytitle='Muons')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
legRes = Legend([hs['pfPtRes'], hs['tunePPtResUncor']], cRes)
legRes.Draw("same")
cRes.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptRes.png')
cResComp = Canvas(1000,1000)
(xax,yax), lims = draw(hs['resCompare'], cResComp, ytitle='Muons',
xtitle='#frac{|PF p_T - gen p_T|}{gen p_T} - #frac{|tuneP p_T - gen p_T|}{gen p_T}')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
cResComp.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResComp.png')
cResCor = Canvas(1000,1000)
hs['pfPtRes'].color = 'black'
hs['pfPtRes'].title = 'PF'
hs['tunePPtRes'].title = 'Tune P'
(xax,yax), lims = draw([hs['pfPtRes'], hs['tunePPtRes']], cResCor,
xtitle='#frac{reco p_T - gen p_T}{gen p_T}',
ytitle='Muons')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
legResCor = Legend([hs['pfPtRes'], hs['tunePPtRes']], cResCor)
legResCor.Draw("same")
cResCor.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResCor.png')
cResCompCor = Canvas(1000,1000)
(xax,yax), lims = draw(hs['resCompareBothCor'], cResCompCor, ytitle='Muons',
xtitle='#frac{|PF p_T - gen p_T|}{gen p_T} - #frac{|tuneP p_T - gen p_T|}{gen p_T}')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
cResCompCor.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResCompCor.png')
cResUncor = Canvas(1000,1000)
hs['pfPtResUncor'].color = 'black'
hs['pfPtResUncor'].title = 'PF'
hs['tunePPtResUncor'].title = 'Tune P'
(xax,yax), lims = draw([hs['pfPtResUncor'], hs['tunePPtResUncor']], cResUncor,
xtitle='#frac{reco p_T - gen p_T}{gen p_T}',
ytitle='Muons')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
legResUncor = Legend([hs['pfPtResUncor'], hs['tunePPtResUncor']], cResUncor)
legResUncor.Draw("same")
cResUncor.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResUncor.png')
cResCompUncor = Canvas(1000,1000)
(xax,yax), lims = draw(hs['resCompareUncor'], cResCompUncor, ytitle='Muons',
xtitle='#frac{|PF p_T - gen p_T|}{gen p_T} - #frac{|tuneP p_T - gen p_T|}{gen p_T}')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
cResCompUncor.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResCompUncor.png')
hs2 = checker.hists2D.copy()
hs2['pfPtResVsPt'].yaxis.title = '#frac{PF p_T - gen p_T}{gen p_T}'
hs2['tunePPtResVsPt'].yaxis.title = '#frac{tuneP p_T - gen p_T}{gen p_T}'
hs2['pfPtResVsPt'].yaxis.title = '#frac{|PF p_T - gen p_T|}{gen p_T} - #frac{|tuneP p_T - gen p_T|}{gen p_T}'
for var, h in hs2.iteritems():
c = Canvas(1000,1000)
h.xaxis.title = 'gen p_T'
h.drawstyle = 'COLZ'
h.xaxis.SetTitleSize(0.5 * h.xaxis.GetTitleSize())
h.xaxis.SetLabelSize(0.7 * h.xaxis.GetLabelSize())
h.xaxis.SetTitleOffset(1.4)
h.yaxis.SetTitleSize(0.5 * h.yaxis.GetTitleSize())
h.yaxis.SetLabelSize(0.7 * h.yaxis.GetLabelSize())
h.yaxis.SetTitleOffset(1.4)
h.draw()
c.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/{}.png'.format(var))
# randomly choose starting bin
start = randint(1, h.nbins(axis) - 1)
end = randint(start + 1, min(start + 5 * h.GetDimension(), h.nbins(axis)))
return h.merge_bins([(start, end)], axis=axis)
# create an animation of a 1D histogram
c1 = Canvas()
if not BATCH and os.path.isfile('binmerge1d.gif'):
os.unlink('binmerge1d.gif')
a = Hist(100, -5, 5)
a.fill_array(np.random.randn(10000))
while a.nbins(0) > 1:
a = random_bin_merge(a)
a.Draw('hist')
if not BATCH:
c1.Print('binmerge1d.gif+20')
time.sleep(.1)
if not BATCH:
c1.Print('binmerge1d.gif++')
# create an animation of a 2D histogram
c2 = Canvas()
if not BATCH and os.path.isfile('binmerge2d.gif'):
os.unlink('binmerge2d.gif')
c2.SetRightMargin(0.1)
b = Hist2D(100, -5, 5, 100, -5, 5)
b.fill_array(np.random.randn(10000, 2))
while b.nbins(0) > 1 or b.nbins(1) > 1:
b = random_bin_merge(b)
b.Draw('LEGO20')
if not BATCH:
def main(args):
fileList = glob('/data/nawoods/lepsForSIP/*.root')
files = {int(f.split('_M')[1].split('.')[0]) : f for f in fileList}
checkers = {m : MuonSIPChecker('m={}'.format(m), [f]) for m,f in files.iteritems()}
sipRMS = Graph(len(fileList), type='errors')
sipHists = []
dxyRMS = Graph(len(fileList), type='errors')
dxyHists = []
dzRMS = Graph(len(fileList), type='errors')
dzHists = []
ipHists = []
ipErrHists = []
fracFailing = Graph(len(fileList), type='errors')
for i, m in enumerate(sorted(files.keys())):
checkers[m].processSample()
checkers[m].hists['sip'].Sumw2()
sipRMS.SetPoint(i, float(m), checkers[m].hists['sip'].GetRMS())
sipRMS.SetPointError(i, 0., checkers[m].hists['sip'].GetRMSError())
sipHists.append(checkers[m].hists['sip'])
sipHists[-1].color = getColor(m)
sipHists[-1].title = "m_{{H}} = {}".format(m)
sipHists[-1].drawstyle = 'hist'
sipHists[-1].legendstyle = 'L'
sipHists[-1].linewidth = 2
sipHists[-1].scale(1./sipHists[-1].integral())
ipHists.append(checkers[m].hists['ip'])
ipHists[-1].color = getColor(m)
ipHists[-1].title = "m_{{H}} = {}".format(m)
ipHists[-1].drawstyle = 'hist'
ipHists[-1].legendstyle = 'L'
ipHists[-1].linewidth = 2
ipHists[-1].scale(1./ipHists[-1].integral())
ipErrHists.append(checkers[m].hists['ipErr'])
ipErrHists[-1].color = getColor(m)
ipErrHists[-1].title = "m_{{H}} = {}".format(m)
ipErrHists[-1].drawstyle = 'hist'
ipErrHists[-1].legendstyle = 'L'
ipErrHists[-1].linewidth = 2
ipErrHists[-1].scale(1./ipErrHists[-1].integral())
checkers[m].hists['dxy'].Sumw2()
dxyRMS.SetPoint(i, float(m), checkers[m].hists['dxy'].GetRMS())
dxyRMS.SetPointError(i, 0., checkers[m].hists['dxy'].GetRMSError())
dxyHists.append(checkers[m].hists['dxy'])
dxyHists[-1].color = getColor(m)
dxyHists[-1].title = "m_{{H}} = {}".format(m)
dxyHists[-1].drawstyle = 'hist'
dxyHists[-1].legendstyle = 'L'
dxyHists[-1].linewidth = 2
dxyHists[-1].scale(1./dxyHists[-1].integral())
checkers[m].hists['dz'].Sumw2()
dzRMS.SetPoint(i, float(m), checkers[m].hists['dz'].GetRMS())
dzRMS.SetPointError(i, 0., checkers[m].hists['dz'].GetRMSError())
dzHists.append(checkers[m].hists['dz'])
dzHists[-1].color = getColor(m)
dzHists[-1].title = "m_{{H}} = {}".format(m)
dzHists[-1].drawstyle = 'hist'
dzHists[-1].legendstyle = 'L'
dzHists[-1].linewidth = 2
dzHists[-1].scale(1./dzHists[-1].integral())
fracFailing.SetPoint(i, float(m),
1. - float(checkers[m].nPassSIP) / checkers[m].nTot)
cSIP = Canvas(1000,1000)
draw(sipHists, cSIP, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.')
legSIP = Legend(sipHists, cSIP, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legSIP.Draw("same")
cSIP.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sips.png')
cSIPLog = Canvas(1000,1000)
draw(sipHists, cSIPLog, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.', logy=True)
legSIPLog = Legend(sipHists, cSIPLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legSIPLog.Draw("same")
cSIPLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sipsLog.png')
cSIPRMS = Canvas(1000, 1000)
sipRMS.color = 'b'
sipRMS.drawstyle = 'PE'
sipRMS.legendstyle = 'PE'
draw(sipRMS, cSIPRMS, xtitle="m_{H}", ytitle="RMS(SIP_{3D})", xlimits=(0.,2600.))
cSIPRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sipRMS.png')
cIP = Canvas(1000,1000)
draw(ipHists, cIP, xtitle='IP_{3D}',
ytitle='arb.')
legIP = Legend(ipHists, cIP, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legIP.Draw("same")
cIP.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ips.png')
cIPLog = Canvas(1000,1000)
draw(ipHists, cIPLog, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.', logy=True)
legIPLog = Legend(ipHists, cIPLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legIPLog.Draw("same")
cIPLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipsLog.png')
cIPErr = Canvas(1000,1000)
draw(ipErrHists, cIPErr, xtitle='#sigma_{IP_{3D}}',
ytitle='arb.')
legIPErr = Legend(ipErrHists, cIPErr, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legIPErr.Draw("same")
cIPErr.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipErrs.png')
cIPErrLog = Canvas(1000,1000)
draw(ipErrHists, cIPErrLog, xtitle='#sigma_{IP_{3D}}',
ytitle='arb.', logy=True)
legIPErrLog = Legend(ipErrHists, cIPErrLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legIPErrLog.Draw("same")
cIPErrLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipErrsLog.png')
cFail = Canvas(1000, 1000)
fracFailing.color = 'b'
fracFailing.drawstyle = 'PE'
fracFailing.legendstyle = 'PE'
draw(fracFailing, cSIPRMS, xtitle="m_{H}", ytitle="Fraction failing SIP", xlimits=(0.,2600.))
cSIPRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/fracFailing.png')
cDXY = Canvas(1000,1000)
draw(dxyHists, cDXY, xtitle='#Delta_{xy}',
ytitle='arb.')
legDXY = Legend(dxyHists, cDXY, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legDXY.Draw("same")
cDXY.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dxys.png')
cDXYRMS = Canvas(1000, 1000)
dxyRMS.color = 'b'
dxyRMS.drawstyle = 'PE'
dxyRMS.legendstyle = 'PE'
draw(dxyRMS, cDXYRMS, xtitle="m_{H}", ytitle="RMS(#Delta_{xy})", xlimits=(0.,2600.))
cDXYRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dxyRMS.png')
cDZ = Canvas(1000,1000)
draw(dzHists, cDZ, xtitle='#Delta_{z}',
ytitle='arb.')
legDZ = Legend(dzHists, cDZ, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legDZ.Draw("same")
cDZ.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dzs.png')
cDZRMS = Canvas(1000, 1000)
dzRMS.color = 'b'
dzRMS.drawstyle = 'PE'
dzRMS.legendstyle = 'PE'
draw(dzRMS, cDZRMS, xtitle="m_{H}", ytitle="RMS(#Delta_{z})", xlimits=(0.,2600.))
cDZRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dzRMS.png')
sys.stdout.flush()
# resample with replacement
# http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.random.choice.html
sample_idx = np.random.choice(len(array), size=len(array), replace=True)
array_bootstrapped = array[sample_idx]
# convert back to a TTree and write it out
tree_bootstrapped = array2tree(
array_bootstrapped,
name='bootstrap_{0}'.format(bootstrap_idx))
tree_bootstrapped.Write()
tree_bootstrapped.Delete()
# fill the ROOT histogram with the numpy array
hist.Reset()
fill_hist(hist, rec2array(array_bootstrapped))
hist.Draw()
hist.xaxis.title = 'x'
hist.yaxis.title = 'y'
hist.zaxis.title = 'Events'
hist.xaxis.limits = (-2.5, 2.5)
hist.yaxis.limits = (-2.5, 2.5)
hist.zaxis.range_user = (0, 60)
hist.xaxis.divisions = 5
hist.yaxis.divisions = 5
hist.zaxis.divisions = 5
canvas.Print('bootstrap.gif+50')
# loop the gif
canvas.Print('bootstrap.gif++')
output.Close()
print
stackError.fillcolor = 'black'
stackError.drawstyle = '2'
# stack.Draw('HIST')
# stackError.Draw('2 [] same')
objects.append(stackError)
import ROOT
from FireROOT.Analysis.Utils import *
ROOT.gPad.SetLogy(True)
draw(objects,
xtitle='Some Variable [Units]',
ytitle='Events',
ypadding=0.05,
logy=True,
ylimits=(1e-3, 1e3))
print(ROOT.gStyle.GetPadTopMargin())
label = ROOT.TLatex(ROOT.gStyle.GetPadLeftMargin(),
0.9 + ROOT.gStyle.GetPadTopMargin(), 'This is a title')
label.SetTextFont(43)
label.SetTextAlign(11)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
# canvas.Modified()
# canvas.Update()
canvas.Print('test2.pdf')
