def histVariables2D(self, vX, vY, plot_name, sample, cat):
# get number of bins and binrange from config file
binsX = binning.getNbins(vX)
binsY = binning.getNbins(vY)
rangeX = binning.getBinrange(vX)
rangeY = binning.getBinrange(vY)
# check if bin_range was found
if not rangeX:
maxValue = max(self.samples[sample].cut_data[cat][vX].values)
minValue = min(self.samples[sample].cut_data[cat][vX].values)
config_string = "variables[\""+vX+"\"]\t\t\t= Variable(bin_range = [{},{}])\n".format(minValue, maxValue)
with open("new_variable_configs.txt", "a") as f:
f.write(config_string)
rangeX = [minValue, maxValue]
if not rangeY:
maxValue = max(self.samples[sample].cut_data[cat][vY].values)
minValue = min(self.samples[sample].cut_data[cat][vY].values)
config_string = "variables[\""+vY+"\"]\t\t\t= Variable(bin_range = [{},{}])\n".format(minValue, maxValue)
with open("new_variable_configs.txt", "a") as f:
f.write(config_string)
rangeY = [minValue, maxValue]
# fill hist
weights = self.samples[sample].cut_data[cat]["weight"].values
valuesX = self.samples[sample].cut_data[cat][vX].values
valuesY = self.samples[sample].cut_data[cat][vY].values
hist = setup.setupHistogram2D(
valuesX = valuesX,
valuesY = valuesY,
weights = weights,
binsX = binsX,
binsY = binsY,
rangeX = rangeX,
rangeY = rangeY,
titleX = vX,
titleY = vY)
canvas = setup.drawHistOnCanvas2D(
hist = hist,
canvasName = vX+"_vs_"+vY,
catLabel = JTcut.getJTlabel(cat),
sampleName = sample)
# add lumi and category to plot
setup.printLumi(canvas, lumi = self.options["lumiScale"], twoDim = True)
# save canvas
setup.saveCanvas(canvas, plot_name)
def histVariable(self, variable, plot_name, cat):
# get number of bins and binrange from config filea
bins = binning.getNbins(variable)
bin_range = binning.getBinrange(variable)
# check if bin_range was found
if not bin_range:
maxValue = -999
minValue = 999
for key in self.samples:
maxValue = max(
maxValue,
max(self.samples[key].cut_data[cat][variable].values))
minValue = min(
minValue,
min(self.samples[key].cut_data[cat][variable].values))
config_string = "variables[\"" + variable + "\"]\t\t\t= Variable(bin_range = [{},{}])\n".format(
minValue, maxValue)
with open("new_variable_configs.txt", "a") as f:
f.write(config_string)
bin_range = [minValue, maxValue]
bkgHists = []
bkgLabels = []
weightIntegral = 0
# loop over bachgrounds and fill hists
for sampleName in self.ordered_stack:
sample = self.samples[sampleName]
# get weights
weights = sample.cut_data[cat]["weight"].values
weightIntegral += sum(weights)
# setup histogram
hist = setup.setupHistogram(
values=sample.cut_data[cat][variable].values,
weights=weights,
nbins=bins,
bin_range=bin_range,
color=sample.plotColor,
xtitle=cat + "_" + sample.sampleName + "_" + variable,
ytitle=setup.GetyTitle(),
filled=True)
bkgHists.append(hist)
bkgLabels.append(sample.sampleName)
sigHists = []
sigLabels = []
sigScales = []
# if not background was added, the weight integral is equal to 0
if weightIntegral == 0:
self.options["scaleSignal"] = 1
# loop over signals and fill hists
for key in self.samples:
sample = self.samples[key]
if not sample.isSignal: continue
# get weights
weights = sample.cut_data[cat]["weight"].values
# determine scale factor
if self.options["scaleSignal"] == -1:
scaleFactor = weightIntegral / (sum(weights) + 1e-9)
else:
scaleFactor = float(self.options["scaleSignal"])
# setup histogram
hist = setup.setupHistogram(
values=sample.cut_data[cat][variable].values,
weights=weights,
nbins=bins,
bin_range=bin_range,
color=sample.plotColor,
xtitle=cat + "_" + sample.sampleName + "_" + variable,
ytitle=setup.GetyTitle(),
filled=False)
hist.Scale(scaleFactor)
sigHists.append(hist)
sigLabels.append(sample.sampleName)
sigScales.append(scaleFactor)
# init canvas
canvas = setup.drawHistsOnCanvas(sigHists,
bkgHists,
self.options,
canvasName=cat + "_" + variable)
# setup legend
legend = setup.getLegend()
# add signal entries
for iSig in range(len(sigHists)):
legend.AddEntry(
sigHists[iSig],
sigLabels[iSig] + " x {:4.0f}".format(sigScales[iSig]), "L")
# add background entries
for iBkg in range(len(bkgHists)):
legend.AddEntry(bkgHists[iBkg], bkgLabels[iBkg], "F")
# draw loegend
legend.Draw("same")
# add lumi and category to plot
setup.printLumi(canvas,
lumi=self.options["lumiScale"],
ratio=self.options["ratio"])
setup.printCategoryLabel(canvas,
JTcut.getJTlabel(cat),
ratio=self.options["ratio"])
# save canvas
setup.saveCanvas(canvas, plot_name)
def histVariable1D(self, sample, name, vX, vY, plot_name, cat):
# get number of bins and binrange from config file
binsX = binning.getNbins(vX)
binsY = binning.getNbins(vY)
rangeX = binning.getBinrange(vX)
rangeY = binning.getBinrange(vY)
# check if bin_range was found
if not rangeX:
maxValue = max(sample.cut_data[cat][vX].values)
minValue = min(sample.cut_data[cat][vX].values)
config_string = "variables[\""+vX+"\"]\t\t\t= Variable(bin_range = [{},{}])\n".format(minValue, maxValue)
with open("new_variable_configs.txt", "a") as f:
f.write(config_string)
rangeX = [minValue, maxValue]
if not rangeY:
maxValue = max(sample.cut_data[cat][vY].values)
minValue = min(sample.cut_data[cat][vY].values)
config_string = "variables[\""+vY+"\"]\t\t\t= Variable(bin_range = [{},{}])\n".format(minValue, maxValue)
with open("new_variable_configs.txt", "a") as f:
f.write(config_string)
rangeY = [minValue, maxValue]
# get averages
bins = int((binsX + binsY)/2.)
bin_range = [min(rangeX[0],rangeY[0]), max(rangeX[1],rangeY[1])]
weights = sample.cut_data[cat]["weight"].values
valuesX = sample.cut_data[cat][vX].values
valuesY = sample.cut_data[cat][vY].values
hX = setup.setupHistogram(
values = valuesX,
weights = weights,
nbins = bins,
bin_range = bin_range,
color = ROOT.kBlack,
xtitle = cat+"_"+sample.sampleName+"_"+vX,
ytitle = setup.GetyTitle(self.options["lumiScale"]),
filled = False)
hY = setup.setupHistogram(
values = valuesY,
weights = weights,
nbins = bins,
bin_range = bin_range,
color = ROOT.kRed,
xtitle = cat+"_"+sample.sampleName+"_"+vY,
ytitle = setup.GetyTitle(self.options["lumiScale"]),
filled = False)
# init canvas
canvas = setup.drawHistsOnCanvas(
hX, hY, self.options,
canvasName = "[{}] {}".format(sample.sampleName, name))
# setup legend
legend = setup.getLegend()
legend.AddEntry( hX, self.options["xName"], "L")
labelY = self.options["yName"]
# add KS score to label if activated
if self.options["KSscore"]:
KSscore = setup.calculateKSscore(hX, hY)
labelY="#splitline{"+labelY+"}{KSscore = %.3f}"%(KSscore)
legend.AddEntry( hY, labelY, "L")
# draw loegend
legend.Draw("same")
# add lumi and category to plot
setup.printLumi(canvas, lumi = self.options["lumiScale"], ratio = self.options["ratio"])
setup.printCategoryLabel(canvas, JTcut.getJTlabel(cat), ratio = self.options["ratio"])
if self.options["privateWork"]:
setup.printPrivateWork(canvas, ratio = self.options["ratio"])
# save canvas
setup.saveCanvas(canvas, plot_name)
def histVariable2D(self, name, vX, vY, plot_name, cat):
# get number of bins and binrange from config file
binsX = binning.getNbins(vX)
binsY = binning.getNbins(vY)
rangeX = binning.getBinrange(vX)
rangeY = binning.getBinrange(vY)
# check if bin_range was found
if not rangeX:
maxValue = max([max(self.samples[sample].cut_data[cat][vX].values) for sample in self.samples])
minValue = min([min(self.samples[sample].cut_data[cat][vX].values) for sample in self.samples])
config_string = "variables[\""+vX+"\"]\t\t\t= Variable(bin_range = [{},{}])\n".format(minValue, maxValue)
with open("new_variable_configs.txt", "a") as f:
f.write(config_string)
rangeX = [minValue, maxValue]
if not rangeY:
maxValue = max([max(self.samples[sample].cut_data[cat][vY].values) for sample in self.samples])
minValue = min([min(self.samples[sample].cut_data[cat][vY].values) for sample in self.samples])
config_string = "variables[\""+vY+"\"]\t\t\t= Variable(bin_range = [{},{}])\n".format(minValue, maxValue)
with open("new_variable_configs.txt", "a") as f:
f.write(config_string)
rangeY = [minValue, maxValue]
# init value lists
weights = np.array([])
valuesX = np.array([])
valuesY = np.array([])
for sample in self.samples:
weights = np.append(weights, self.samples[sample].cut_data[cat]["weight"].values)
valuesX = np.append(valuesX, self.samples[sample].cut_data[cat][vX].values)
valuesY = np.append(valuesY, self.samples[sample].cut_data[cat][vY].values)
hist = setup.setupHistogram2D(
valuesX = valuesX,
valuesY = valuesY,
weights = weights,
binsX = binsX,
binsY = binsY,
rangeX = rangeX,
rangeY = rangeY,
titleX = vX,
titleY = vY)
canvas = setup.drawHistOnCanvas2D(
hist = hist,
canvasName = name,
catLabel = JTcut.getJTlabel(cat),
sampleName = name)
# add lumi
setup.printLumi(canvas, lumi = self.options["lumiScale"], twoDim = True)
if self.options["privateWork"]:
setup.printPrivateWork(canvas, ratio = self.options["ratio"], twoDim = True)
if self.options["getCorr"]:
correlation = hist.GetCorrelationFactor()
setup.printCorrelation(canvas, correlation)
# save canvas
setup.saveCanvas(canvas, plot_name)
# plot distributions in 1D if activated
if self.options["plot1D"]:
# get averages
bins = int((binsX + binsY)/2.)
bin_range = [min(rangeX[0],rangeY[0]), max(rangeX[1],rangeY[1])]
hX = setup.setupHistogram(
values = valuesX,
weights = weights,
nbins = bins,
bin_range = bin_range,
color = ROOT.kBlack,
xtitle = vX+"1D",
ytitle = setup.GetyTitle(self.options["lumiScale"]),
filled = False)
if self.options["lumiScale"] == 0.:
hXInt = hX.Integral()
hX.Scale(1./hXInt)
hY = setup.setupHistogram(
values = valuesY,
weights = weights,
nbins = bins,
bin_range = bin_range,
color = ROOT.kRed,
xtitle = vY+"1D",
ytitle = setup.GetyTitle(self.options["lumiScale"]),
filled = False)
if self.options["lumiScale"] == 0.:
hYInt = hY.Integral()
hY.Scale(1./hYInt)
# init canvas
canvas = setup.drawHistsOnCanvas(
hX, hY, self.options,
canvasName = name)
# setup legend
legend = setup.getLegend()
legend.AddEntry( hX, self.options["xName"], "L")
labelY = self.options["yName"]
# add KS score to label if activated
if self.options["KSscore"]:
KSscore = setup.calculateKSscore(hX, hY)
labelY="#splitline{"+labelY+"}{KSscore = %.3f}"%(KSscore)
legend.AddEntry( hY, labelY, "L")
# draw loegend
legend.Draw("same")
# add lumi and category to plot
setup.printLumi(canvas, lumi = self.options["lumiScale"], ratio = self.options["ratio"])
setup.printCategoryLabel(canvas, JTcut.getJTlabel(cat), ratio = self.options["ratio"])
if self.options["privateWork"]:
setup.printPrivateWork(canvas, ratio = self.options["ratio"])
# save canvas
setup.saveCanvas(canvas, plot_name.replace(".pdf","_1D.pdf"))
def histVariable(self, variable, plot_name, cat):
histInfo = {}
# get number of bins and binrange from config file
bins = binning.getNbins(variable)
bin_range = binning.getBinrange(variable)
# check if bin_range was found
if not bin_range:
maxValue = max([max(self.samples[sample].cut_data[cat][variable].values) for sample in self.samples])
minValue = min([min(self.samples[sample].cut_data[cat][variable].values) for sample in self.samples])
config_string = "variables[\""+variable+"\"]\t\t\t= Variable(bin_range = [{},{}])\n".format(minValue, maxValue)
with open("new_variable_configs.txt", "a") as f:
f.write(config_string)
bin_range = [minValue, maxValue]
histInfo["nbins"] = bins
histInfo["range"] = bin_range
bkgHists = []
bkgLabels = []
weightIntegral = 0
# loop over backgrounds and fill hists
for sampleName in self.ordered_stack:
sample = self.samples[sampleName]
# get weights
weights = sample.cut_data[cat]["weight"].values
# get values
values = sample.cut_data[cat][variable].values
#weights = [weights[i] for i in range(len(weights)) if not np.isnan(values[i])]
#values = [values[i] for i in range(len(values)) if not np.isnan(values[i])]
weightIntegral += sum(weights)
# setup histogram
hist = setup.setupHistogram(
values = values,
weights = weights,
nbins = bins,
bin_range = bin_range,
color = sample.plotColor,
xtitle = cat+"_"+sample.sampleName+"_"+variable,
ytitle = setup.GetyTitle(self.options["lumiScale"]),
filled = sample.filled)
bkgHists.append(hist)
bkgLabels.append(sample.sampleName)
sigHists = []
sigLabels = []
sigScales = []
# if not background was added, the weight integral is equal to 0
if weightIntegral == 0:
self.options["scaleSignal"] = 0
histInfo["bkgYield"] = weightIntegral
# scale stack to one if lumiScale is set to zero
if self.options["lumiScale"] == 0:
for hist in bkgHists:
hist.Scale(1./weightIntegral)
weightIntegral = 1.
# loop over signals and fill hists
for key in self.samples:
sample = self.samples[key]
if not sample.isSignal: continue
# get weights
weights = sample.cut_data[cat]["weight"].values
# determine scale factor
if self.options["scaleSignal"] == -1:
scaleFactor = weightIntegral/(sum(weights)+1e-9)
elif self.options["scaleSignal"] == 0:
scaleFactor = (1./(sum(weights)+1e-9))
else:
scaleFactor = float(self.options["scaleSignal"])
# setup histogram
hist = setup.setupHistogram(
values = sample.cut_data[cat][variable].values,
weights = weights,
nbins = bins,
bin_range = bin_range,
color = sample.plotColor,
xtitle = cat+"_"+sample.sampleName+"_"+variable,
ytitle = setup.GetyTitle(),
filled = sample.filled)
hist.Scale(scaleFactor)
sigHists.append(hist)
sigLabels.append(sample.sampleName)
sigScales.append(scaleFactor)
# init canvas
canvas = setup.drawHistsOnCanvas(
sigHists, bkgHists, self.options,
canvasName = variable)
# setup legend
legend = setup.getLegend()
# add signal entriesa
for iSig in range(len(sigHists)):
labelstring = sigLabels[iSig]
if not self.options["lumiScale"] == 0.:
labelstring = sigLabels[iSig]+" x {:4.0f}".format(sigScales[iSig])
# add KS score to label if activated
if self.options["KSscore"]:
KSscore = setup.calculateKSscore(bkgHists[0],sigHists[iSig])
labelstring="#splitline{"+labelstring+"}{KSscore = %.3f}"%(KSscore)
histInfo["KSScore"] = KSscore
legend.AddEntry(sigHists[iSig], labelstring, "L")
# add background entries
for iBkg in range(len(bkgHists)):
legend.AddEntry(bkgHists[iBkg], bkgLabels[iBkg], "F")
# draw loegend
legend.Draw("same")
# add lumi and category to plot
setup.printLumi(canvas, lumi = self.options["lumiScale"], ratio = self.options["ratio"])
setup.printCategoryLabel(canvas, JTcut.getJTlabel(cat), ratio = self.options["ratio"])
if self.options["privateWork"]:
setup.printPrivateWork(canvas, ratio = self.options["ratio"])
# save canvas
setup.saveCanvas(canvas, plot_name)
return histInfo