def rewtOneHist(dataset, hwts):
fileName = condor_dir + "/" + dataset + ".root"
if not os.path.exists(fileName):
print "WARNING: didn't find ", fileName
return
print "About to reweight histogram in " + fileName
inFile = TFile(fileName, "UPDATE")
if inFile.IsZombie() or not inFile.GetNkeys():
return
inFile.cd()
h = inFile.Get(str(arguments.histToBeReWeighted)).Clone()
if not h:
print " Could not find hist named " + arguments.histToBeReWeighted + " in " + inFile.GetName(
)
return
h.SetDirectory(0)
newName = h.GetName() + str(arguments.suffixRename)
h.SetName(newName)
dir = arguments.histToBeReWeighted
dir = dir[:dir.rfind("/")]
print "Will write hist to directory " + dir
inFile.cd(dir)
tdir = inFile.GetDirectory(dir)
tdir.Delete(newName + ";*")
for i in range(1, h.GetNbinsX() + 1):
val = h.GetBinContent(i)
err = h.GetBinError(i)
binCtr = h.GetBinCenter(i)
wt = hwts.GetBinContent(hwts.FindBin(binCtr))
h.SetBinContent(i, val * wt)
h.SetBinError(i, err * wt)
h.Write()
inFile.Close()
from ROOT import TFile, gROOT, gDirectory, TH1, TH2, TH3, TIter, TKey
gROOT.SetBatch()
outputFile = TFile(condor_dir + "/" + outputFileName, "RECREATE")
channels = []
processed_datasets = []
#### check which input datasets have valid output files
for sample in datasets:
fileName = qcd_dir + "/" + sample + ".root"
if not os.path.exists(fileName):
print fileName, "does not exist"
continue
testFile = TFile(fileName)
if testFile.IsZombie() or not testFile.GetNkeys():
continue
processed_datasets.append(sample)
if len(processed_datasets) is 0:
sys.exit("No datasets have been processed")
#### open first input file and re-make its directory structure in the output file
testFile = TFile(qcd_dir + "/" + processed_datasets[0] + ".root")
testFile.cd()
for key in testFile.GetListOfKeys():
if (key.GetClassName() != "TDirectoryFile"):
continue
outputFile.cd()
outputFile.mkdir(key.GetName())
rootDirectory = key.GetName()
def GetFittedQCDYieldAndError(pathToDir, distribution):
BackgroundHistograms = []
fileName = condor_dir + "/" + data_dataset + ".root"
if not os.path.exists(fileName):
return 0
inputFile = TFile(fileName)
if inputFile.IsZombie() or not inputFile.GetNkeys():
return 0
TargetHistogram = inputFile.Get("OSUAnalysis/" + region_names['A'] + "/" +
distribution['name']).Clone()
TargetHistogram.SetDirectory(0)
QCDHistogram = inputFile.Get("OSUAnalysis/" + region_names['C'] + "/" +
distribution['name']).Clone()
QCDHistogram.SetDirectory(0)
BackgroundHistograms.append(QCDHistogram)
CutFlowHistogram = inputFile.Get("OSUAnalysis/" + region_names['C'] +
"CutFlow")
QCDInputYield = CutFlowHistogram.GetBinContent(
CutFlowHistogram.GetNbinsX())
inputFile.Close()
for sample in fitting_backgrounds: # loop over different samples that get held constant in the fit
dataset_file = "%s/%s.root" % (condor_dir, sample)
inputFile = TFile(dataset_file)
HistogramObj = inputFile.Get(pathToDir + "/" + region_names['A'] +
"/" + distribution['name'])
if not HistogramObj:
print "WARNING: Could not find histogram " + pathToDir + "/" + distribution[
'name'] + " in file " + dataset_file + ". Will skip it and continue."
continue
Histogram = HistogramObj.Clone()
Histogram.SetDirectory(0)
inputFile.Close()
BackgroundHistograms.append(Histogram)
nBackgrounds = len(BackgroundHistograms)
def fitf(x, par):
xBin = BackgroundHistograms[0].FindBin(x[0])
value = 0.0
# create the fit function to be used, with one parameter for each yield and one parameter for the error (to be set to -1,0,1 for varying by +-1 sigma)
for i in range(0, len(BackgroundHistograms)):
value += par[i] * BackgroundHistograms[i].GetBinContent(
xBin) + par[i +
len(BackgroundHistograms
)] * BackgroundHistograms[i].GetBinError(xBin)
return value
if distribution.has_key('lowerLimit'):
lowerLimit = distribution['lowerLimit']
else:
lowerLimit = TargetHistogram.GetBinLowEdge(1)
if distribution.has_key('upperLimit'):
upperLimit = distribution['upperLimit']
else:
upperLimit = TargetHistogram.GetBinLowEdge(
TargetHistogram.GetNbinsX()) + TargetHistogram.GetBinWidth(
TargetHistogram.GetNbinsX())
func = TF1("fit", fitf, lowerLimit, upperLimit, 2 * (nBackgrounds))
# initialize QCD scale factor parameter
func.SetParameter(0, 1.0)
func.SetParName(0, 'QCD_ScaleFactor')
# initialize the other backgrounds that are held constant
for i in range(1, nBackgrounds):
func.FixParameter(i, 1.0)
nameString = "background_" + str(i)
func.SetParName(i, nameString)
# shift each constant background component up and down by 1 sigma, refit and save new yields
parErrorRanges = []
for i in range(1, len(BackgroundHistograms)):
for j in [-1, 1]:
for k in range(len(BackgroundHistograms),
2 * len(BackgroundHistograms)):
func.FixParameter(k, 0)
func.FixParameter(i + len(BackgroundHistograms), j)
for k in range(0, 9):
if j == -1:
print "Scale down " + func.GetParName(
i) + " iteration " + str(k + 1) + "..."
if j == 1:
print "Scale up " + func.GetParName(
i) + " iteration " + str(k + 1) + "..."
TargetHistogram.Fit("fit", "QEMR0")
TargetHistogram.Fit("fit", "QEMR0")
# add the new QCD yields to the list of errors
parErrorRanges.append(func.GetParameter(0))
# get the QCD yield for the central values of the background histograms
for i in range(nBackgrounds, 2 * (nBackgrounds)):
func.FixParameter(i, 0)
for i in range(0, 9):
print "Iteration " + str(i + 1) + "..."
TargetHistogram.Fit("fit", "QEMR0")
TargetHistogram.Fit("fit", "QEMR0")
# take average of the deviations from the central value
scaleDown = parErrorRanges[0]
scaleUp = parErrorRanges[1]
parError = (abs(scaleUp - scaleDown)) / 2
yieldAndError = []
yieldAndError.append(func.GetParameter(0) * QCDInputYield)
yieldAndError.append(parError)
return yieldAndError
def MakeOneDHist(pathToDir, distribution):
numFittingSamples = 0
HeaderLabel = TPaveLabel(header_x_left, header_y_bottom, header_x_right,
header_y_top, HeaderText, "NDC")
HeaderLabel.SetTextAlign(32)
HeaderLabel.SetBorderSize(0)
HeaderLabel.SetFillColor(0)
HeaderLabel.SetFillStyle(0)
LumiLabel = TPaveLabel(topLeft_x_left, topLeft_y_bottom, topLeft_x_right,
topLeft_y_top, LumiText, "NDC")
LumiLabel.SetBorderSize(0)
LumiLabel.SetFillColor(0)
LumiLabel.SetFillStyle(0)
NormLabel = TPaveLabel()
NormLabel.SetDrawOption("NDC")
NormLabel.SetX1NDC(topLeft_x_left)
NormLabel.SetX2NDC(topLeft_x_right)
NormLabel.SetBorderSize(0)
NormLabel.SetFillColor(0)
NormLabel.SetFillStyle(0)
NormText = ""
if arguments.normalizeToUnitArea:
NormText = "Scaled to unit area"
elif arguments.normalizeToData:
NormText = "MC scaled to data"
NormLabel.SetLabel(NormText)
YieldsLabel = TPaveText(0.39, 0.7, 0.59, 0.9, "NDC")
YieldsLabel.SetBorderSize(0)
YieldsLabel.SetFillColor(0)
YieldsLabel.SetFillStyle(0)
YieldsLabel.SetTextAlign(12)
RatiosLabel = TPaveText()
RatiosLabel.SetDrawOption("NDC")
RatiosLabel.SetBorderSize(0)
RatiosLabel.SetFillColor(0)
RatiosLabel.SetFillStyle(0)
RatiosLabel.SetTextAlign(32)
Legend = TLegend()
Legend.SetBorderSize(0)
Legend.SetFillColor(0)
Legend.SetFillStyle(0)
fittingIntegral = 0
scaleFactor = 1
HistogramsToFit = []
TargetDataset = distribution['target_dataset']
FittingLegendEntries = []
DataLegendEntries = []
FittingHistogramDatasets = []
Stack_list = []
Stack_list.append(THStack("stack_before", distribution['name']))
Stack_list.append(THStack("stack_after", distribution['name']))
fileName = condor_dir + "/" + distribution['target_dataset'] + ".root"
if not os.path.exists(fileName):
return
inputFile = TFile(fileName)
if inputFile.IsZombie() or not inputFile.GetNkeys():
return
Target = inputFile.Get("OSUAnalysis/" + distribution['channel'] + "/" +
distribution['name']).Clone()
Target.SetDirectory(0)
inputFile.Close()
Target.SetMarkerStyle(20)
Target.SetMarkerSize(0.8)
Target.SetFillStyle(0)
Target.SetLineColor(colors[TargetDataset])
Target.SetLineStyle(1)
Target.SetLineWidth(2)
targetIntegral = Target.Integral()
if (arguments.normalizeToUnitArea and Target.Integral() > 0):
Target.Scale(1. / Target.Integral())
if arguments.rebinFactor:
RebinFactor = int(arguments.rebinFactor)
#don't rebin histograms which will have less than 5 bins or any gen-matching histograms
if Target.GetNbinsX() >= RebinFactor * 5 and Target.GetName().find(
"GenMatch") is -1:
Target.Rebin(RebinFactor)
### formatting target histogram and adding to legend
legendIndex = 0
Legend.AddEntry(Target, labels[TargetDataset], "LEP")
legendIndex = legendIndex + 1
if not outputFile.Get("OSUAnalysis"):
outputFile.mkdir("OSUAnalysis")
if not outputFile.Get("OSUAnalysis/" + distribution['channel']):
outputFile.Get("OSUAnalysis").mkdir(distribution['channel'])
for sample in distribution[
'datasets']: # loop over different samples requested to be fit
dataset_file = "%s/%s.root" % (condor_dir, sample)
inputFile = TFile(dataset_file)
HistogramObj = inputFile.Get(pathToDir + "/" +
distribution['channel'] + "/" +
distribution['name'])
if not HistogramObj:
print "WARNING: Could not find histogram " + pathToDir + "/" + distribution[
'channel'] + "/" + distribution[
'name'] + " in file " + dataset_file + ". Will skip it and continue."
continue
Histogram = HistogramObj.Clone()
Histogram.SetDirectory(0)
inputFile.Close()
if arguments.rebinFactor:
RebinFactor = int(arguments.rebinFactor)
#don't rebin histograms which will have less than 5 bins or any gen-matching histograms
if Histogram.GetNbinsX() >= RebinFactor * 5 and Histogram.GetName(
).find("GenMatch") is -1:
Histogram.Rebin(RebinFactor)
xAxisLabel = Histogram.GetXaxis().GetTitle()
unitBeginIndex = xAxisLabel.find("[")
unitEndIndex = xAxisLabel.find("]")
if unitBeginIndex is not -1 and unitEndIndex is not -1: #x axis has a unit
yAxisLabel = "Entries / " + str(Histogram.GetXaxis().GetBinWidth(
1)) + " " + xAxisLabel[unitBeginIndex + 1:unitEndIndex]
else:
yAxisLabel = "Entries per bin (" + str(
Histogram.GetXaxis().GetBinWidth(1)) + " width)"
if not arguments.makeFancy:
histoTitle = Histogram.GetTitle()
else:
histoTitle = ""
legLabel = labels[sample]
if (arguments.printYields):
yieldHist = Histogram.Integral()
legLabel = legLabel + " (%.1f)" % yieldHist
FittingLegendEntries.append(legLabel)
if (types[sample] == "bgMC"):
numFittingSamples += 1
fittingIntegral += Histogram.Integral()
Histogram.SetLineStyle(1)
if (arguments.noStack):
Histogram.SetFillStyle(0)
Histogram.SetLineColor(colors[sample])
Histogram.SetLineWidth(2)
else:
Histogram.SetFillStyle(1001)
Histogram.SetFillColor(colors[sample])
Histogram.SetLineColor(1)
Histogram.SetLineWidth(1)
elif (types[sample] == "signalMC"):
numFittingSamples += 1
Histogram.SetFillStyle(0)
Histogram.SetLineColor(colors[sample])
Histogram.SetLineStyle(1)
Histogram.SetLineWidth(2)
if (arguments.normalizeToUnitArea and Histogram.Integral() > 0):
Histogram.Scale(1. / Histogram.Integral())
HistogramsToFit.append(Histogram)
FittingHistogramDatasets.append(sample)
#scaling histograms as per user's specifications
if targetIntegral > 0 and fittingIntegral > 0:
scaleFactor = targetIntegral / fittingIntegral
for fittingHist in HistogramsToFit:
if arguments.normalizeToData:
fittingHist.Scale(scaleFactor)
if arguments.normalizeToUnitArea and not arguments.noStack and fittingIntegral > 0:
fittingHist.Scale(1. / fittingIntegral)
elif arguments.normalizeToUnitArea and arguments.noStack and fittingHist.Integral(
) > 0:
fittingHist.Scale(1. / fittingHist.Integral())
def fitf(x, par):
xBin = HistogramsToFit[0].FindBin(x[0])
value = 0.0
for i in range(0, len(HistogramsToFit)):
value += par[i] * HistogramsToFit[i].GetBinContent(xBin) + par[
i +
len(HistogramsToFit)] * HistogramsToFit[i].GetBinError(xBin)
return value
lowerLimit = Target.GetBinLowEdge(1)
upperLimit = Target.GetBinLowEdge(Target.GetNbinsX()) + Target.GetBinWidth(
Target.GetNbinsX())
if 'lowerLimit' in distribution:
lowerLimit = distribution['lowerLimit']
if 'upperLimit' in distribution:
upperLimit = distribution['upperLimit']
func = TF1("fit", fitf, lowerLimit, upperLimit, 2 * len(HistogramsToFit))
for i in range(0, len(HistogramsToFit)):
if 'fixed_datasets' in distribution and distribution['datasets'][
i] in distribution['fixed_datasets']:
func.FixParameter(i, 1.0)
else:
func.SetParameter(i, 1.0)
# func.SetParLimits (i, 0.0, 1.0e2) # comment this out so we don't have to pre-normalize the QCD input sample
func.SetParName(i, labels[FittingHistogramDatasets[i]])
shiftedScaleFactors = []
if arguments.parametricErrors:
# loop over all input histograms and shift them +- 1 sigma
for i in range(0, len(HistogramsToFit)):
sfs = []
# -1 => -1 sigma, +1 => +1 sigma
for j in [-1, 1]:
# loop over the parameters holding the errors for each dataset, fixing all to 0
for k in range(len(HistogramsToFit), 2 * len(HistogramsToFit)):
func.FixParameter(k, 0)
# fix the error of the dataset of interest to +-1
func.FixParameter(i + len(HistogramsToFit), j)
# perform new fit
for k in range(0, distribution['iterations'] - 1):
if j == -1:
print "Scale down " + labels[FittingHistogramDatasets[
i]] + " iteration " + str(k + 1) + "..."
if j == 1:
print "Scale up " + labels[FittingHistogramDatasets[
i]] + " iteration " + str(k + 1) + "..."
Target.Fit("fit", "QEMR0")
Target.Fit("fit", "VEMR0")
# save the new scale factors for each dataset
for k in range(0, len(HistogramsToFit)):
sfs.append(func.GetParameter(k))
shiftedScaleFactors.append(sfs)
# reset the parameters with the errors of each dataset to 0
for i in range(len(HistogramsToFit), 2 * len(HistogramsToFit)):
func.FixParameter(i, 0)
# do the fit to get the central values
for i in range(0, distribution['iterations'] - 1):
print "Iteration " + str(i + 1) + "..."
Target.Fit("fit", "QEMR0")
Target.Fit("fit", "VEMR0")
if arguments.parametricErrors:
# make a list of the largest errors on each contribution by shifting any other contribution
parErrors = []
# loop over all the datasets
for i in range(0, len(HistogramsToFit)):
centralValue = func.GetParameter(i)
maxError = 0
# find the maximum deviation from the central value and save that
for shiftedScaleFactor in shiftedScaleFactors[i]:
currentError = abs(shiftedScaleFactor - centralValue)
if currentError > maxError:
maxError = currentError
parErrors.append(maxError)
finalMax = 0
if not arguments.noStack:
for fittingHist in HistogramsToFit:
finalMax += fittingHist.GetMaximum()
else:
for fittingHist in HistogramsToFit:
if (fittingHist.GetMaximum() > finalMax):
finalMax = fittingHist.GetMaximum()
if (Target.GetMaximum() > finalMax):
finalMax = Target.GetMaximum()
Target.SetMaximum(1.1 * finalMax)
Target.SetMinimum(0.0001)
Canvas = TCanvas(distribution['name'] + "_FitFunction")
Canvas.cd(1)
Target.Draw()
func.Draw("same")
outputFile.cd("OSUAnalysis/" + distribution['channel'])
Canvas.Write()
if arguments.savePDFs:
if histogram == input_histograms[0]:
Canvas.Print(pdfFileName + "(", "pdf")
else:
Canvas.Print(pdfFileName, "pdf")
Target.SetStats(0)
### formatting bgMC histograms and adding to legend
legendIndex = numFittingSamples - 1
for Histogram in reversed(HistogramsToFit):
if (arguments.noStack):
Legend.AddEntry(Histogram, FittingLegendEntries[legendIndex], "L")
else:
Legend.AddEntry(Histogram, FittingLegendEntries[legendIndex], "F")
legendIndex = legendIndex - 1
### Drawing histograms to canvas
makeRatioPlots = arguments.makeRatioPlots
makeDiffPlots = arguments.makeDiffPlots
yAxisMin = 0.0001
if arguments.setYMin:
yAxisMin = float(arguments.setYMin)
### Draw everything to the canvases !!!!
for i in range(0, 2): # 0 => before, 1 => after
integrals = []
ratios = []
errors = []
if i == 1:
# loop over each dataset, saving it's yield and the errors on it
for j in range(0, len(HistogramsToFit)):
integrals.append(HistogramsToFit[j].Integral())
HistogramsToFit[j].Scale(func.GetParameter(j))
ratios.append(func.GetParameter(j))
errors.append(func.GetParError(j))
for fittingHist in HistogramsToFit:
if not arguments.noStack:
Stack_list[i].Add(fittingHist)
#creating the histogram to represent the statistical errors on the stack
if not arguments.noStack:
ErrorHisto = HistogramsToFit[0].Clone("errors")
ErrorHisto.SetFillStyle(3001)
ErrorHisto.SetFillColor(13)
ErrorHisto.SetLineWidth(0)
if i == 1:
Legend.AddEntry(ErrorHisto, "Stat. Errors", "F")
for Histogram in HistogramsToFit:
if Histogram is not HistogramsToFit[0]:
ErrorHisto.Add(Histogram)
if i == 0:
Canvas = TCanvas(distribution['name'] + "_Before")
if i == 1:
Canvas = TCanvas(distribution['name'] + "_After")
if makeRatioPlots or makeDiffPlots:
Canvas.SetFillStyle(0)
Canvas.Divide(1, 2)
Canvas.cd(1)
gPad.SetPad(0, 0.25, 1, 1)
gPad.SetMargin(0.15, 0.05, 0.01, 0.07)
gPad.SetFillStyle(0)
gPad.Update()
gPad.Draw()
if arguments.setLogY:
gPad.SetLogy()
Canvas.cd(2)
gPad.SetPad(0, 0, 1, 0.25)
# format: gPad.SetMargin(l,r,b,t)
gPad.SetMargin(0.15, 0.05, 0.4, 0.01)
gPad.SetFillStyle(0)
gPad.SetGridy(1)
gPad.Update()
gPad.Draw()
Canvas.cd(1)
### finding the maximum value of anything going on the canvas, so we know how to set the y-axis
finalMax = 0
if numFittingSamples is not 0 and not arguments.noStack:
finalMax = ErrorHisto.GetMaximum() + ErrorHisto.GetBinError(
ErrorHisto.GetMaximumBin())
else:
for bgMCHist in HistogramsToFit:
if (bgMCHist.GetMaximum() > finalMax):
finalMax = bgMCHist.GetMaximum()
if (Target.GetMaximum() > finalMax):
finalMax = Target.GetMaximum() + Target.GetBinError(
Target.GetMaximumBin())
finalMax = 1.15 * finalMax
if arguments.setYMax:
finalMax = float(arguments.setYMax)
if not arguments.noStack: # draw stacked background samples
Stack_list[i].SetTitle(histoTitle)
Stack_list[i].Draw("HIST")
Stack_list[i].GetXaxis().SetTitle(xAxisLabel)
Stack_list[i].GetYaxis().SetTitle(yAxisLabel)
Stack_list[i].SetMaximum(finalMax)
Stack_list[i].SetMinimum(yAxisMin)
if makeRatioPlots or makeDiffPlots:
Stack_list[i].GetHistogram().GetXaxis().SetLabelSize(0)
#draw shaded error bands
ErrorHisto.Draw("A E2 SAME")
else: #draw the unstacked backgrounds
HistogramsToFit[0].SetTitle(histoTitle)
HistogramsToFit[0].Draw("HIST")
HistogramsToFit[0].GetXaxis().SetTitle(xAxisLabel)
HistogramsToFit[0].GetYaxis().SetTitle(yAxisLabel)
HistogramsToFit[0].SetMaximum(finalMax)
HistogramsToFit[0].SetMinimum(yAxisMin)
for bgMCHist in HistogramsToFit:
bgMCHist.Draw("A HIST SAME")
Target.Draw("A E X0 SAME")
#legend coordinates, empirically determined :-)
x_left = 0.6761745
x_right = 0.9328859
x_width = x_right - x_left
y_max = 0.9
entry_height = 0.05
if (numFittingSamples is not 0): #then draw the data & bgMC legend
numExtraEntries = 2 # count the target and (lack of) title
Legend.SetX1NDC(x_left)
numExtraEntries = numExtraEntries + 1 # count the stat. errors entry
Legend.SetY1NDC(y_max - entry_height *
(numExtraEntries + numFittingSamples))
Legend.SetX2NDC(x_right)
Legend.SetY2NDC(y_max)
Legend.Draw()
RatiosLabel.SetX1NDC(x_left - 0.1)
RatiosLabel.SetX2NDC(x_right)
RatiosLabel.SetY2NDC(Legend.GetY1NDC() - 0.1)
RatiosLabel.SetY1NDC(RatiosLabel.GetY2NDC() - entry_height *
(numFittingSamples))
# Deciding which text labels to draw and drawing them
drawLumiLabel = False
drawNormLabel = False
offsetNormLabel = False
drawHeaderLabel = False
if not arguments.normalizeToUnitArea: #don't draw the lumi label if there's no data and it's scaled to unit area
drawLumiLabel = True
# move the normalization label down before drawing if we drew the lumi. label
offsetNormLabel = True
if arguments.normalizeToUnitArea or arguments.normalizeToData:
drawNormLabel = True
if arguments.makeFancy:
drawHeaderLabel = True
drawLumiLabel = False
# now that flags are set, draw the appropriate labels
if drawLumiLabel:
LumiLabel.Draw()
if drawNormLabel:
if offsetNormLabel:
NormLabel.SetY1NDC(topLeft_y_bottom - topLeft_y_offset)
NormLabel.SetY2NDC(topLeft_y_top - topLeft_y_offset)
else:
NormLabel.SetY1NDC(topLeft_y_bottom)
NormLabel.SetY2NDC(topLeft_y_top)
NormLabel.Draw()
if drawHeaderLabel:
HeaderLabel.Draw()
YieldsLabel.Clear()
mcYield = Stack_list[i].GetStack().Last().Integral()
dataYield = Target.Integral()
if i == 0:
YieldsLabel.AddText("Before Fit to Data")
if i == 1:
YieldsLabel.AddText("After Fit to Data")
YieldsLabel.AddText("data yield: " + '%.1f' % dataYield)
YieldsLabel.AddText("bkgd yield: " + '%.1f' % mcYield)
YieldsLabel.AddText("data/bkgd: " + '%.2f' % (dataYield / mcYield))
if i == 1:
for j in range(0, len(FittingLegendEntries)):
if abs(ratios[j] - 1) < 0.001 and abs(
errors[j]
) < 0.001: #then it probably was held fixed
continue
if arguments.showFittedYields:
yield_ = ratios[j] * integrals[j]
yielderror_ = errors[j] * yield_
text = FittingLegendEntries[
j] + " yield: " + '%.0f' % yield_ + ' #pm %.0f' % yielderror_
else:
text = FittingLegendEntries[
j] + " ratio: " + '%.2f' % ratios[
j] + ' #pm %.2f' % errors[j]
text = text + " (fit)"
if arguments.parametricErrors:
yield_ = ratios[j] * integrals[j]
yieldParError_ = parErrors[j] * yield_
if arguments.showFittedYields:
text += ' #pm %.2f' % yieldParError_
else:
text += ' #pm %.2f' % parErrors[j]
text = text + " (sys)"
RatiosLabel.AddText(text)
YieldsLabel.Draw()
RatiosLabel.Draw()
# drawing the ratio or difference plot if requested
if (makeRatioPlots or makeDiffPlots):
Canvas.cd(2)
BgSum = Stack_list[i].GetStack().Last()
if makeRatioPlots:
if arguments.ratioRelErrMax:
Comparison = ratioHistogram(Target, BgSum,
arguments.ratioRelErrMax)
else:
Comparison = ratioHistogram(Target, BgSum)
elif makeDiffPlots:
Comparison = Target.Clone("diff")
Comparison.Add(BgSum, -1)
Comparison.SetTitle("")
Comparison.GetYaxis().SetTitle("Data-Bkgd")
Comparison.GetXaxis().SetTitle(xAxisLabel)
Comparison.GetYaxis().CenterTitle()
Comparison.GetYaxis().SetTitleSize(0.1)
Comparison.GetYaxis().SetTitleOffset(0.5)
Comparison.GetXaxis().SetTitleSize(0.15)
Comparison.GetYaxis().SetLabelSize(0.1)
Comparison.GetXaxis().SetLabelSize(0.15)
if makeRatioPlots:
RatioYRange = 1.15
if arguments.ratioYRange:
RatioYRange = float(arguments.ratioYRange)
Comparison.GetYaxis().SetRangeUser(-1 * RatioYRange,
RatioYRange)
elif makeDiffPlots:
YMax = Comparison.GetMaximum()
YMin = Comparison.GetMinimum()
if YMax <= 0 and YMin <= 0:
Comparison.GetYaxis().SetRangeUser(-1.2 * YMin, 0)
elif YMax >= 0 and YMin >= 0:
Comparison.GetYaxis().SetRangeUser(0, 1.2 * YMax)
else: #axis crosses y=0
if abs(YMax) > abs(YMin):
Comparison.GetYaxis().SetRangeUser(
-1.2 * YMax, 1.2 * YMax)
else:
Comparison.GetYaxis().SetRangeUser(
-1.2 * YMin, 1.2 * YMin)
Comparison.GetYaxis().SetNdivisions(205)
Comparison.Draw("E0")
if i == 0:
Canvas.Write(distribution['name'] + "_Before")
if arguments.savePDFs:
pathToDirString = plainTextString(pathToDir)
Canvas.SaveAs(condor_dir + "/fitting_histogram_pdfs/" +
pathToDirString + "/" + distribution['name'] +
"_Before.pdf")
if i == 1:
Canvas.Write(distribution['name'] + "_After")
if arguments.savePDFs:
pathToDirString = plainTextString(pathToDir)
Canvas.SaveAs(condor_dir + "/fitting_histogram_pdfs/" +
pathToDirString + "/" + distribution['name'] +
"_After.pdf")
from ROOT import TFile, TGraph, TCanvas, TLegend, TPad
from array import array
import sys
# plot all on canvas
graphs = {}
print(len(sys.argv[1:]))
for filename in sys.argv[1:]:
f = TFile(filename + '.root', 'READ')
f.GetList().ls()
for i in range(0, f.GetNkeys()):
g = f.Get('igprof_cumulative' + str(i))
graphs.setdefault(g.GetTitle(), [[], []])[0].append(g)
graphs[g.GetTitle()][1].append(filename)
for title, graphsList in graphs.items():
# check Y axis range
minval = []
maxval = []
for g in graphsList[0]:
minval.append(g.GetYaxis().GetXmin())
maxval.append(g.GetYaxis().GetXmax())
canv = TCanvas("igprof", "igprof runtime", 0, 0, 1600, 1000)
canv.Divide(2, 1)
pad = canv.cd(1)
leg = TLegend(0.1, 0.1, 0.9, 0.9)
for i, g in enumerate(graphsList[0]):
g.SetMarkerColor(i + 1)
g.SetLineColor(i + 1)
g.SetMarkerStyle(20 + i)
Legend = TLegend(0.70, 0.65, 0.9, 0.9)
Legend.SetBorderSize(0)
Legend.SetFillColor(0)
Legend.SetFillStyle(0)
finalMax = 0
Histograms = []
for histogram in input_histograms:
fileName = "condor/" + histogram['condor_dir'] + "/" + histogram[
'dataset'] + ".root"
if not os.path.exists(fileName):
continue
inputFile = TFile(fileName)
if inputFile.IsZombie() or not inputFile.GetNkeys():
continue
Numerator = inputFile.Get("OSUAnalysis/" + histogram['channel_numerator'] +
"/" + histogram['name']).Clone()
Denominator = inputFile.Get("OSUAnalysis/" +
histogram['channel_denominator'] + "/" +
histogram['name']).Clone()
Histogram = ROOT.TGraphAsymmErrors(Numerator, Denominator)
inputFile.Close()
fullTitle = Histogram.GetTitle()
splitTitle = fullTitle.split(":")
Histogram.SetTitle(splitTitle[1].lstrip(" "))
[[], []])[0].append(float(parameter))
result[method_name][1].append(float(total.replace("'", "")))
#TODO: add total column as well?
#print(result)
paramname = "energy (GeV)"
accuracy = 1.e-1
file = TFile(sys.argv[1] + '.root', 'RECREATE')
i = 0
for name, x in result.items(): #iteritems():
if len(x[0]) != len(x[1]):
print("Lengths of x0 and x1 differ! Terminating.")
exit()
# check if sth is const
mean = sum(x[1]) / len(x[1])
if not (all(abs(item - mean) < accuracy for item in x[1])):
x_arr = array('f', x[0])
y_arr = array('f', x[1])
graph = TGraph(len(x[0]), x_arr, y_arr)
graph.SetName("igprof_cumulative" + str(i))
graph.SetTitle(name)
i += 1
graph.GetXaxis().SetTitle(paramname)
graph.GetYaxis().SetTitle("%")
graph.SetMarkerColor(1)
graph.SetMarkerStyle(20)
graph.SetMarkerSize(1.5)
graph.Write()
print("Created " + str(file.GetNkeys()) + " graphs")
file.Close()
def GetFittedQCDYieldAndError(pathToDir):
fileName = condor_dir + "/" + data_dataset + ".root"
if not os.path.exists(fileName):
return 0
inputFile = TFile(fileName)
if inputFile.IsZombie() or not inputFile.GetNkeys():
return 0
DataHistogram = inputFile.Get("OSUAnalysis/" + region_names['A'] +
"CutFlow").Clone()
DataHistogram.SetDirectory(0)
nBins = DataHistogram.GetNbinsX()
content = DataHistogram.GetBinContent(nBins)
error = DataHistogram.GetBinError(nBins)
relError = error / content if content > 0 else 0
print 'Data : ' + str(content) + ' +- ' + str(error) + ' ( +-' + str(
relError * 100.0) + '% )'
inputFile.Close()
for sample in impurities: # loop over different samples that get subtracted from the total and save the cutflow table
dataset_file = "%s/%s.root" % (condor_dir, sample)
inputFile = TFile(dataset_file)
HistogramObj = inputFile.Get(pathToDir + "/" + region_names['A'] +
"CutFlow")
if not HistogramObj:
print "WARNING: Could not find histogram " + pathToDir + "CutFlow" + " in file " + dataset_file + ". Will skip it and continue."
continue
MCHistogram = HistogramObj.Clone()
MCHistogram.SetDirectory(0)
inputFile.Close()
nBins = MCHistogram.GetNbinsX()
content = MCHistogram.GetBinContent(nBins)
error = MCHistogram.GetBinError(nBins)
statRelError = error / content if content > 0 else 0
sysRelError = getSystematicError(sample)
# add the stat. and sys. errors and apply them to the last bin of the cutflow table
totalRelError = sqrt(statRelError * statRelError +
sysRelError * sysRelError)
MCHistogram.SetBinError(nBins, totalRelError * content)
# subtract the MC histogram from the data
DataHistogram.Add(MCHistogram, -1)
print str(sample) + ' : ' + str(content) + ' +- ' + str(
MCHistogram.GetBinError(nBins)) + ' ( +-' + str(
totalRelError * 100.0) + '% )'
nBins = DataHistogram.GetNbinsX()
content = DataHistogram.GetBinContent(nBins)
error = DataHistogram.GetBinError(nBins)
yieldAndError = []
yieldAndError.append(content)
yieldAndError.append(error)
return yieldAndError
