def makeGraph(self, name='', xtitle='', ytitle=''):
"""This function returns an instance of ROOTs TGraphErrors, made with the points in from this class.
Some of the graph's default settings are changed:
- black points
- every point has the symbol of 'x'
- x- and y-axis are centered
Arguments:
name -- ROOT internal name of graph (default = '')
xtitle -- title of x-axis (default = '')
ytitle -- title of y-axis (default = '')
"""
if self.points:
x = self.getX()
y = self.getY()
ex = self.getEX()
ey = self.getEY()
graph = TGraphErrors(self.getLength(), array.array('f', x),
array.array('f', y), array.array('f', ex),
array.array('f', ey))
graph.SetName(name)
graph.SetMarkerColor(1)
graph.SetMarkerStyle(5)
graph.SetTitle("")
graph.GetXaxis().SetTitle(xtitle)
graph.GetXaxis().CenterTitle()
graph.GetYaxis().SetTitle(ytitle)
graph.GetYaxis().CenterTitle()
return graph
else:
return None
def Ratio(histo1,histo2, recorded, title):
#gSystem.Exec("mkdir -p ZPlots")
can1 = makeCMSCanvas(str(random.random()),"mult vs lumi ",900,700)
lumi = []
lumi_err = []
ratio = []
ratio_err = []
sumLumi = 0.
for i in range(0,len(recorded)):
sumLumi += float(recorded[i])
lumi.append(sumLumi - float(recorded[i])/2)
lumi_err.append(float(recorded[i])/2)
ratio.append(histo1[i].GetEntries()/histo2[i].GetEntries())
ratio_err.append(0)
graph1 = TGraphErrors(len(recorded),array('d',lumi),array('d',ratio),array('d',lumi_err),array('d',ratio_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle("e^{+}e^{-}/#mu^{+}#mu^{-}")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs("ZPlots/Z_ratio_"+title+".pdf")
can1.SaveAs("ZPlots/Z_ratio_"+title+".png")
return;
def create_resolutiongraph(n, energies, sigmasmeans, energieserrors, sigmasmeanserrors, graphname):
"""Function to perform ROOT graphs of resolutions"""
#How many points
n = int(n)
TGraphresolution = TGraphErrors(n, energies, sigmasmeans, energieserrors, sigmasmeanserrors)
#Draw + DrawOptions, Fit + parameter estimation
Style = gStyle
Style.SetOptFit()
XAxis = TGraphresolution.GetXaxis() #TGraphresolution
TGraphresolution.SetMarkerColor(4)
TGraphresolution.SetMarkerStyle(20)
TGraphresolution.SetMarkerSize(2)
XAxis.SetTitle("Energy (GeV)")
YAxis = TGraphresolution.GetYaxis()
YAxis.SetTitle("Sigma/Mean")
resolutionfit = TF1("resolutionfit", '([0]/((x)**0.5))+[1]', 0, max(energies)) #somma non quadratura
TGraphresolution.Fit("resolutionfit")
a = resolutionfit.GetParameter(0)
b = resolutionfit.GetParameter(1)
TGraphresolution.Draw("AP")
gPad.SaveAs(graphname)
gPad.Close()
return a, b
def CompareToys(MwValuemT, MwValuemTStat):
print(len(MwValuemT), len(MwValuemTStat))
n = len(MwValuemT)
x, y = array('d'), array('d')
ex, ey = array('d'), array('d')
for i in range(0, n):
x.append(i + 1)
ex.append(0)
y.append(MwValuemT[i])
ey.append(MwValuemTStat[i])
gr = TGraphErrors(n, x, y, ex, ey)
gr.Draw("P")
gr.SetLineWidth(0)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(1)
xax = gr.GetXaxis()
for i in range(0, n):
binIndex = xax.FindBin(i)
xax.SetBinLabel(binIndex, "toys")
Output = ROOT.TFile.Open("Matrix.root", "RECREATE")
gr.Write("gr")
def make1DSummaryPlot(binned_mw, bins, channel, variable, treeSuffix):
nBins = len(bins)
xValues, yValues = array('d'), array('d')
xErrors, yErrors = array('d'), array('d')
for bin in bins:
mW = binned_mw[bin]
lowBinEdge = bins[bin][0]
highBinEdge = bins[bin][1]
binWidth = (bins[bin][1] - bins[bin][0]) / 2
binCentre = bins[bin][1] - binWidth
if bin.split('_')[-1] == 'inf':
binCentre = lowBinEdge * 1.1
binWidth = lowBinEdge * 0.1
# print binCentre
# print bin,bins[bin],mW.getVal(),mW.getError()
xValues.append(binCentre)
yValues.append(mW.getVal())
xErrors.append(binWidth)
yErrors.append(mW.getError())
c = TCanvas('c1', 'A Simple Graph Example', 200, 10, 700, 500)
gr = TGraphErrors(nBins, xValues, yValues, xErrors, yErrors)
gr.SetMarkerColor(4)
gr.SetMarkerStyle(3)
gr.GetXaxis().SetTitle('X title')
gr.GetYaxis().SetTitle('Y title')
gr.SetMinimum(75)
gr.SetMaximum(85)
gr.Draw('AP')
c.Update()
outputDir = 'plots/WStudies/%s%s/%s' % (channel, treeSuffix, variable)
c.Print('%s/Summary.pdf' % outputDir)
def ZMultVsLumi(histo, recorded, outputDir, title):
#gSystem.Exec("mkdir -p ZPlots")
can1 = makeCMSCanvas(str(random.random()),"mult vs lumi ",900,700)
lumi = []
lumi_err = []
mult = []
mult_err = []
sumLumi = 0.
for i in range(0,len(recorded)):
sumLumi += float(recorded[i])
lumi.append(sumLumi - float(recorded[i])/2)
lumi_err.append(float(recorded[i])/2)
mult.append(histo[i].GetEntries()/float(recorded[i]))
mult_err.append(math.sqrt(histo[i].GetEntries()/float(recorded[i])))
graph1 = TGraphErrors(len(lumi),array('d',lumi),array('d',mult),array('d',lumi_err),array('d',mult_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle("#Z / fb^{-1}")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs(str(outputDir) + "/Z_multiplicity_"+title+".pdf")
can1.SaveAs(str(outputDir) + "/Z_multiplicity_"+title+".png")
return graph1;
def DrawScat(all_, wrong_, DCAs_, title, fname):
c = TCanvas("c2", "", 800, 600)
# Normal arrays don't work for whatever reason, must be a ROOT thing
x, y, ex, ey = array('d'), array('d'), array('d'), array('d')
n = len(all_)
# if(n != len(wrongHist)):
# print("*** Error, hist arrays different length ***")
# return
for i in range(0, n):
frac = wrong_[i].GetEntries() / all_[i].GetEntries()
x.append(DCAs_[i])
ex.append(0)
y.append(frac)
ey.append(0)
print(
str(DCAs_[i]) + " * " + str(frac) + " * " +
str(wrong_[i].GetEntries()) + " * " + str(all_[i].GetEntries()))
scat = TGraphErrors(n, x, y, ex, ey)
scat.SetTitle(title)
scat.GetXaxis().SetTitleSize(.04)
scat.GetYaxis().SetTitleSize(.04)
scat.GetXaxis().SetTitleOffset(1.1)
scat.GetYaxis().SetTitleOffset(1.25)
scat.GetXaxis().CenterTitle(1)
scat.GetYaxis().CenterTitle(1)
# scat.GetYaxis().SetRangeUser(0.086,0.106)
scat.GetXaxis().SetRangeUser(-5, 505)
scat.GetYaxis().SetMaxDigits(4)
#scat.SetMarkerSize(3)
#scat.SetLineWidth(3)
scat.SetMarkerStyle(20) # Full circle
#scat.SetMarkerColor(4)
#scat.SetLineColor(4)
scat.Draw("AP")
c.SaveAs(fname)
return
def plotGainSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgEffGain = TGraphErrors(len(self.GAIN_AVG_POINTS))
gDet_AvgEffGain.SetName("g_{0}_EffGainAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxEffGain = TGraphErrors(len(self.GAIN_MAX_POINTS))
gDet_MaxEffGain.SetName("g_{0}_EffGainMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinEffGain = TGraphErrors(len(self.GAIN_MIN_POINTS))
gDet_MinEffGain.SetName("g_{0}_EffGainMin".format(strDetName))
#Set and print the points
#print "===============Printing Gain Data==============="
#print "[BEGIN_DATA]"
#print "\tVAR_INDEP,VAR_DEP,VAR_DEP_ERR"
for i in range(0, len(self.GAIN_AVG_POINTS)):
#Average
gDet_AvgEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_AVG_POINTS[i])
gDet_AvgEffGain.SetPointError(i, 0, self.GAIN_STDDEV_POINTS[i])
#print "\t%f,%f,%f"%(self.DET_IMON_POINTS[i],self.GAIN_AVG_POINTS[i],self.GAIN_STDDEV_POINTS[i])
#Max
gDet_MaxEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_MAX_POINTS[i])
#Min
gDet_MinEffGain.SetPoint(i, self.DET_IMON_POINTS[i],
self.GAIN_MIN_POINTS[i])
pass
#print "[END_DATA]"
#print ""
#Draw
canv_AvgEffGain = TCanvas(
"canv_{0}_EffGainAvg".format(strDetName),
"{0} Average Effective Gain".format(strDetName), 600, 600)
canv_AvgEffGain.cd()
canv_AvgEffGain.cd().SetLogy()
gDet_AvgEffGain.GetXaxis().SetTitle("HV")
gDet_AvgEffGain.GetYaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgEffGain.GetYaxis().SetRangeUser(1e2, 1e6)
gDet_AvgEffGain.SetMarkerStyle(21)
gDet_AvgEffGain.Draw("AP")
gDet_MaxEffGain.Draw("sameL")
gDet_MinEffGain.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.mkdir("Summary")
dir_Summary.cd()
canv_AvgEffGain.Write()
gDet_AvgEffGain.Write()
gDet_MaxEffGain.Write()
gDet_MinEffGain.Write()
return
def GraphVsLumi(result, outputDir, title):
#gSystem.Exec("mkdir -p ZPlots")
can1 = makeCMSCanvas(str(random.random()), "mean vs lumi ", 900, 700)
can2 = makeCMSCanvas(str(random.random()), "width vs lumi ", 900, 700)
lumi = []
lumi_err = []
mean = []
mean_err = []
width = []
width_err = []
sumLumi = 0.
for i in range(0, len(result)):
sumLumi += float(result[i].lumi)
lumi.append(sumLumi - float(result[i].lumi) / 2)
lumi_err.append(float(result[i].lumi) / 2)
mean.append(result[i].mean)
mean_err.append(result[i].mean_err)
width.append(result[i].width)
width_err.append(result[i].width_err)
graph1 = TGraphErrors(len(result), array('d', lumi), array('d', mean),
array('d', lumi_err), array('d', mean_err))
graph2 = TGraphErrors(len(result), array('d', lumi), array('d', width),
array('d', lumi_err), array('d', width_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle("Mass [GeV]")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs(str(outputDir) + "/" + title + "_mean.pdf")
can1.SaveAs(str(outputDir) + "/" + title + "_mean.png")
can2.cd()
graph2.SetTitle("")
graph2.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph2.GetYaxis().SetTitle("Width [GeV]")
graph2.SetMarkerStyle(20)
graph2.SetMarkerSize(1)
graph2.Draw("AP")
printLumiPrelOut(can2)
can2.SaveAs(str(outputDir) + "/" + title + "_width.pdf")
can2.SaveAs(str(outputDir) + "/" + title + "_width.png")
return graph1, graph2
def MeanRMSVsLumi(histo, recorded, outputDir, title):
can1 = makeCMSCanvas(str(random.random()), "mean vs lumi ", 900, 700)
can2 = makeCMSCanvas(str(random.random()), "RMS vs lumi ", 900, 700)
lumi = []
lumi_err = []
mean = []
mean_err = []
RMS = []
RMS_err = []
sumLumi = 0.
for i in range(0, len(recorded)):
sumLumi += float(recorded[i])
lumi.append(sumLumi - float(recorded[i]) / 2)
lumi_err.append(float(recorded[i]) / 2)
mean.append(histo[i].GetMean())
mean_err.append(0.) #dont put error on ISO and SIP histo[i].GetRMS()
RMS.append(histo[i].GetRMS())
RMS_err.append(0.)
graph1 = TGraphErrors(len(recorded), array('d', lumi), array('d', mean),
array('d', lumi_err), array('d', mean_err))
graph2 = TGraphErrors(len(recorded), array('d', lumi), array('d', RMS),
array('d', lumi_err), array('d', RMS_err))
can1.cd()
graph1.SetTitle("")
graph1.GetXaxis().SetTitle("Lumi [fb^{-1}]")
graph1.GetYaxis().SetTitle(" ")
graph1.SetMarkerStyle(20)
graph1.SetMarkerSize(1)
graph1.Draw("AP")
printLumiPrelOut(can1)
can1.SaveAs(str(outputDir) + "/" + title + "_mean.pdf")
can1.SaveAs(str(outputDir) + "/" + title + "_mean.png")
can2.cd()
graph2.SetTitle("")
graph2.GetXaxis().SetTitle(" ")
graph2.GetYaxis().SetTitle("Width [GeV]")
graph2.SetMarkerStyle(20)
graph2.SetMarkerSize(1)
graph2.Draw("AP")
printLumiPrelOut(can2)
can2.SaveAs(str(outputDir) + "/" + title + "_width.pdf")
can2.SaveAs(str(outputDir) + "/" + title + "_width.png")
return graph1, graph2
def plotPDSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgPD = TGraphErrors(len(self.PD_AVG_POINTS))
gDet_AvgPD.SetName("g_{0}_PDAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxPD = TGraphErrors(len(self.PD_MAX_POINTS))
gDet_MaxPD.SetName("g_{0}_PDMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinPD = TGraphErrors(len(self.PD_MIN_POINTS))
gDet_MinPD.SetName("g_" + strDetName + "_PDMin")
gDet_MinPD.SetName("g_{0}_PDMin".format(strDetName))
#Set the points
for i in range(0, len(self.PD_AVG_POINTS)):
#Average
gDet_AvgPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_AVG_POINTS[i])
gDet_AvgPD.SetPointError(i, self.GAIN_STDDEV_POINTS[i],
self.PD_STDDEV_POINTS[i])
#Max
gDet_MaxPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_MAX_POINTS[i])
#Min
gDet_MinPD.SetPoint(i, self.GAIN_AVG_POINTS[i],
self.PD_MIN_POINTS[i])
#Draw
canv_AvgPD = TCanvas("canv_{0}_PDAvg".format(strDetName),
"{0} Discharge Probability".format(strDetName),
600, 600)
canv_AvgPD.cd()
canv_AvgPD.cd().SetLogx()
canv_AvgPD.cd().SetLogy()
gDet_AvgPD.GetXaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgPD.GetYaxis().SetTitle("Discharge Probability P_{D}")
gDet_AvgPD.GetYaxis().SetRangeUser(1e-11, 1e-6)
gDet_AvgPD.SetMarkerStyle(21)
gDet_AvgPD.Draw("AP")
gDet_MaxPD.Draw("sameL")
gDet_MinPD.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.GetDirectory("Summary")
dir_Summary.cd()
canv_AvgPD.Write()
gDet_AvgPD.Write()
gDet_MaxPD.Write()
gDet_MinPD.Write()
return
def DrawScat(hists, DCAs, flag, title, fname):
c = TCanvas("c2", "", 800, 600)
# Normal arrays don't work for whatever reason, must be a ROOT thing
x, y, ex, ey = array('d'), array('d'), array('d'), array('d')
n = len(hists)
for i in range(0, n):
if (flag == 0):
print(str(DCAs[i]) + " * " + str(hists[i].GetMean()))
x.append(DCAs[i])
ex.append(0)
y.append(hists[i].GetMean())
ey.append(hists[i].GetMeanError())
else:
print(str(DCAs[i]) + " * " + str(hists[i].GetEntries()))
x.append(DCAs[i])
ex.append(0)
y.append(hists[i].GetEntries())
ey.append(0)
scat = TGraphErrors(n, x, y, ex, ey)
scat.SetTitle(title)
scat.GetXaxis().SetTitleSize(.04)
scat.GetYaxis().SetTitleSize(.04)
scat.GetXaxis().SetTitleOffset(1.1)
scat.GetYaxis().SetTitleOffset(1.25)
scat.GetXaxis().CenterTitle(1)
scat.GetYaxis().CenterTitle(1)
# scat.GetYaxis().SetRangeUser(0.086,0.106)
scat.GetXaxis().SetRangeUser(-5, 505)
scat.GetYaxis().SetMaxDigits(4)
#scat.SetMarkerSize(3)
#scat.SetLineWidth(3)
scat.SetMarkerStyle(20) # Full circle
#scat.SetMarkerColor(4)
#scat.SetLineColor(4)
scat.Draw("AP")
c.SaveAs(fname)
def plotGainSummary(self, strDetName):
#Create the Plot - Average
gDet_AvgEffGain = TGraphErrors( len(self.GAIN_AVG_POINTS) )
#gDet_AvgEffGain.SetName("g_" + strDetName + "_EffGainAvg")
gDet_AvgEffGain.SetName("g_{0}_EffGainAvg".format(strDetName))
#Create the Plot - Max Gain
gDet_MaxEffGain = TGraphErrors( len(self.GAIN_MAX_POINTS) )
#gDet_MaxEffGain.SetName("g_" + strDetName + "_EffGainMax")
gDet_MaxEffGain.SetName("g_{0}_EffGainMax".format(strDetName))
#Create the Plot - Min Gain
gDet_MinEffGain = TGraphErrors( len(self.GAIN_MIN_POINTS) )
#gDet_MinEffGain.SetName("g_" + strDetName + "_EffGainMin")
gDet_MinEffGain.SetName("g_{0}_EffGainMin".format(strDetName))
#Set the points
for i in range(0, len(self.GAIN_AVG_POINTS) ):
#Average
gDet_AvgEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_AVG_POINTS[i])
gDet_AvgEffGain.SetPointError(i,0,self.GAIN_STDDEV_POINTS[i])
#Max
gDet_MaxEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_MAX_POINTS[i])
#Min
gDet_MinEffGain.SetPoint(i,self.DET_IMON_POINTS[i],self.GAIN_MIN_POINTS[i])
#Draw
#canv_AvgEffGain = TCanvas("canv_" + strDetName + "_EffGainAvg",strDetName + " Average Effective Gain",600,600)
canv_AvgEffGain = TCanvas("canv_{0}_EffGainAvg".format(strDetName),"{0} Average Effective Gain".format(strDetName),600,600)
canv_AvgEffGain.cd()
canv_AvgEffGain.cd().SetLogy()
gDet_AvgEffGain.GetXaxis().SetTitle("HV")
gDet_AvgEffGain.GetYaxis().SetTitle("#LT Effective Gain #GT")
gDet_AvgEffGain.GetYaxis().SetRangeUser(1e2,1e6)
gDet_AvgEffGain.SetMarkerStyle(21)
gDet_AvgEffGain.Draw("AP")
gDet_MaxEffGain.Draw("sameL")
gDet_MinEffGain.Draw("sameL")
#Write
dir_Summary = self.FILE_OUT.mkdir("Summary")
dir_Summary.cd()
canv_AvgEffGain.Write()
gDet_AvgEffGain.Write()
gDet_MaxEffGain.Write()
gDet_MinEffGain.Write()
return
def plot( self, plotoptions, opt="?" ):
vx= array( "d", self.aostand.getPointsCenter() )
values= self.values
sterrs= self.sterrs
if "m" in opt:
print "AnalysisObservable::plot: use errors from error matrix"
sterrs= array( "d", self.aostand.getErrors( "m" ) )
syerrs= self.syerrs
npoints= len(vx)
if "xshift" in plotoptions:
for i in range(npoints):
vx[i]+= plotoptions["xshift"]
vex= array( "d", npoints*[0.0] )
tgest= TGraphErrors( npoints, vx, values, vex, sterrs )
toterrs= np.sqrt( np.add( np.square( sterrs ), np.square( syerrs ) ) )
tgesy= TGraphErrors( npoints, vx, values, vex, toterrs )
tgesy.SetMarkerStyle( plotoptions["markerStyle"] )
tgesy.SetMarkerSize( plotoptions["markerSize"] )
drawas= plotoptions["drawas"] if "drawas" in plotoptions else "p"
tgesy.SetName( self.obs )
if "fillcolor" in plotoptions:
tgesy.SetFillColor(plotoptions["fillcolor"])
tgest.SetFillColor(plotoptions["fillcolor"])
if "s" in opt:
tgesy.Draw( "psame" )
else:
if "title" in plotoptions:
tgesy.SetTitle( plotoptions["title"] )
else:
tgesy.SetTitle( self.obs )
tgesy.SetMinimum( plotoptions["ymin"] )
tgesy.SetMaximum( plotoptions["ymax"] )
xaxis= tgesy.GetXaxis()
xaxis.SetLimits( plotoptions["xmin"], plotoptions["xmax"] )
if "xlabel" in plotoptions:
xaxis.SetTitle( plotoptions["xlabel"] )
if "ylabel" in plotoptions:
tgesy.GetYaxis().SetTitle( plotoptions["ylabel"] )
tgesy.Draw( "a"+drawas )
optlogx= plotoptions["logx"] if "logx" in plotoptions else 0
gPad.SetLogx( optlogx )
optlogy= plotoptions["logy"] if "logy" in plotoptions else 0
gPad.SetLogy( optlogy )
tgest.Draw( "same"+drawas )
return tgest, tgesy
def graphTruth():
fname = "PionMinusG4.txt"
kineticEnergy = []
crossSec = []
crossSec_el = []
crossSec_inel = []
zero = []
title = ""
with open(fname) as f:
for fLine in f.readlines():
w = fLine.split()
if is_number(w[0]):
runIn = int(w[0])
ke = float(w[1])
xstot = float(w[4])
kineticEnergy.append(ke)
crossSec.append(xstot)
zero.append(0.)
else:
if "for" not in fLine:
continue
title = fLine[9:]
#define some data points . . .
x = array('f', kineticEnergy)
y = array('f', crossSec)
y_el = array('f', crossSec_el)
y_inel = array('f', crossSec_inel)
exl = array('f', zero)
exr = array('f', zero)
nPoints = len(x)
# . . . and hand over to TGraphErros object
gr = TGraphErrors(nPoints, x, y, exl, exr)
gr.SetTitle(title + "; Kinetic Energy [MeV]; Cross Section [barn]")
gr.GetXaxis().SetRangeUser(0, 1000)
gr.GetYaxis().SetRangeUser(0, 2.)
gr.SetLineWidth(2)
gr.SetLineColor(kGreen - 2)
gr.SetFillColor(0)
return gr
def create_linearitygraph(n, energies, energieserrors, means, sigmameans, graphname):
"""Function to perform ROOT graphs of resolutions"""
#How many points
n = int(n)
TGraphlinearity = TGraphErrors(n, energies, means, energieserrors, sigmameans)
#Draw + DrawOptions, Fit + parameter estimation
Style = gStyle
Style.SetOptFit()
XAxis = TGraphlinearity.GetXaxis() #TGraphresolution
TGraphlinearity.SetMarkerColor(4)
TGraphlinearity.SetMarkerStyle(20)
TGraphlinearity.SetMarkerSize(2)
XAxis.SetTitle("Energy (GeV)")
YAxis = TGraphlinearity.GetYaxis()
YAxis.SetTitle("Mean/TrueEnergy")
TGraphlinearity.Draw("AP")
gPad.SaveAs(graphname)
gPad.Close()
def killXErr(graph):
if not graph:
return
ArrX = array('d')
ArrY = array('d')
ArrXErr = array('d')
ArrYErr = array('d')
for point in range(graph.GetN()):
ArrX.append(graph.GetX()[point])
ArrY.append(graph.GetY()[point])
ArrXErr.append(0)
ArrYErr.append(graph.GetEY()[point])
tmpG = TGraphErrors(len(ArrX), ArrX, ArrY, ArrXErr, ArrYErr)
tmpG.SetName(graph.GetName() + "_noErr")
tmpG.SetTitle(graph.GetTitle())
tmpG.GetXaxis().SetTitle(graph.GetXaxis().GetTitle())
tmpG.GetYaxis().SetTitle(graph.GetYaxis().GetTitle())
#tmpG.Write()
return tmpG
class Thickness:
def __init__(self, name):
self.name = name
self.canvas = TCanvas(name, name)
self.canvas.SetFillColor(0)
self.name_lin = name + "_lin"
self.linearize()
self.make_graph()
def linearize(self):
with open(self.name) as file:
with open(self.name_lin, "w") as out_file:
for line in file:
x, i = [float(_) for _ in line.split()]
i_err = 1 / sqrt(i)
i = log(i)
output_line = [x, i, 0, i_err]
output_line = " ".join([str(x) for x in output_line])
output_line += "\n"
out_file.write(output_line)
def make_graph(self):
self.graph = TGraphErrors(self.name_lin)
self.graph.SetTitle(self.name)
self.graph.GetYaxis().SetTitle("log(n_events)")
self.graph.GetXaxis().SetTitle("thickness #[]{#mu m}")
self.graph.SetMarkerStyle(8)
self.graph.Fit("pol1")
def draw(self):
self.canvas.cd()
self.graph.Draw("ap")
def save(self):
self.canvas.SaveAs(self.name + ".eps")
class waveLength:
""" ***classe waveLength***
legge un file formattato con tre colonne:
ordine nonio A nonio B
5 213.54 33.58
4 212.52 32.48
... ... ...
(significa 213°54', 33°58' per il massimo al quinto ordine etc.)
opera automaticamente la conversione e la media,
per poi disporre in grafico (self.graph).
self.fitGraph() esegue l'interpolazione lineare
self.showGraph() mostra il grafico
self.saveToEps() salve il grafico in formato .eps
la lunghezza d'onda con si calcola con self.getWaveLen(),
che restituisce una valore ed errore in una lista
"""
# a = (12.65e-6, 0.05e-6) #passo del reticolo, con errore
# measerr = 5.*2/300 #errore (in gradi) stimato sulla misura col nonio
def __init__(self, fileName):
self.wavelen = [0, 0]
self.name = fileName
self.nData = 0
self.deg = array('d')
self.degerr = array('d')
self.ord = array('d')
self.pars = array('d', [0, 0]) #fit parameters
self.parerrs = array('d', [0, 0]) #fit parameter errors
self.fillDeg() #reads data from file
self.convertToSine() #centers mean maximum, calculates sines
self.initGraph() #initialize graph, set style
def fillDeg(self):
file = open(self.name)
for line in file:
o = float(line.split()[0])
n1 = float(line.split()[1]) - 180
n2 = float(line.split()[2])
self.ord.append(o)
int1 = floor(n1)
int2 = floor(n2)
rem1 = 5. * (n1 - int1) / 3
rem2 = 5. * (n2 - int2) / 3
int1 += rem1
int2 += rem2
self.deg.append((int1 + int2) / 2)
self.degerr.append(measerr / sqrt(2))
self.nData += 1
file.close()
def convertToSine(self):
j = self.ord.index(0) #finds central maximum
center = self.deg[j]
for i in xrange(self.nData):
angle = self.deg[i]
angle -= center
self.deg[i] = angle
self.deg[i] = sin(pi * angle / 180)
self.degerr[i] = cos(angle) * argerr
def initGraph(self):
self.graph = TGraphErrors(self.nData, self.ord, self.deg,
array('d', [0] * self.nData), self.degerr)
self.setGraphStyle()
pass
def fitGraph(self):
line = TF1('line', 'pol1', -5, 5) #funzione lineare per fit
self.graph.Fit('line', 'QR')
line.GetParameters(self.pars)
self.parerrs[0] = line.GetParError(0)
self.parerrs[1] = line.GetParError(1)
return zip(self.pars, self.parerrs)
def getWaveLen(self):
self.wavelen[0] = fabs(self.pars[1] * a[0]) * 1e9
self.wavelen[1] = self.wavelen[0] * sqrt(
(self.parerrs[1] / self.pars[1])**2 + (a[1] / a[0])**2)
print 'lambda %s = %.1f \pm %.1f nm' % (self.name, self.wavelen[0],
self.wavelen[1])
return self.wavelen
def showGraph(self):
c = TCanvas(self.name, self.name)
self.graph.Draw('AEP')
raw_input('Press ENTER to continue...')
def saveToEps(self):
if self.pars[1]:
c = TCanvas(self.name, self.name)
self.graph.Draw('AEP')
c.SaveAs(self.name + '.fit.eps')
else:
pass
def printData(self):
"""test per verificare la corretta lettura dei dati"""
for o, d, e in zip(self.ord, self.deg, self.degerr):
print '%i \t %.3f \t %.3f' % (o, d, e)
def setGraphStyle(self):
self.graph.SetMarkerStyle(8)
self.graph.GetXaxis().SetTitle("order")
self.graph.GetYaxis().SetTitle("sine")
self.graph.GetYaxis().SetTitleOffset(1.2)
self.graph.GetXaxis().SetTitleSize(0.03)
self.graph.GetYaxis().SetTitleSize(0.03)
self.graph.GetXaxis().SetLabelSize(0.03)
self.graph.GetYaxis().SetLabelSize(0.03)
self.graph.GetXaxis().SetDecimals()
self.graph.GetYaxis().SetDecimals()
# self.graph.SetStats( kFALSE );
self.graph.SetTitle(self.name)
canvas.SetLogy(0)
text.SetX1NDC(0.12)
text.SetX2NDC(0.52)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events = TGraphErrors(14)
for x in xrange(1, 15):
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetPoint(x - 1, jetToMETTriggerRate.GetY()[x], metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetY()[x])
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetPointError(x - 1, jetToMETTriggerRate.GetEY()[x], metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetEY()[x])
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetTitle("ff_H_WW_enuenu_1000events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetName("efficiencyPlotWithRate_ff_H_WW_enuenu_1000events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetYaxis().SetTitle("% accepted events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetYaxis().SetTitleOffset(1.2)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetYaxis().SetRangeUser(0, 1.1)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetMarkerColor(4)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetLineColor(1)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetMarkerStyle(21)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetXaxis().SetTitle("Rate [Hz]")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetXaxis().SetTitleOffset(1.2)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.Draw("AP")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.Write()
line_200kHz.Draw()
label_200kHz.Draw()
text.Draw()
canvas.Update()
canvas.Print("metEfficiencyPlotWithRate_ff_H_WW_enuenu_1000events.svg", "svg")
canvas.Print("metEfficiencyPlotWithRate_ff_H_WW_enuenu_1000events.png", "png")
canvas.Print("metEfficiencyPlotWithRate_ff_H_WW_enuenu_1000events.pdf", "pdf")
canvas.SetLogx(0)
canvas.SetLogy(0)
text.SetX1NDC(0.5)
text.SetX2NDC(0.9)
labelSize=10
makerSize = 0.3
gStyle.SetEndErrorSize(0)
gr = TGraphErrors( n, energyVec, recoEnergyVec, energyErrorVec, recoEnergyErrorVec )
gr.SetName('gr')
gr.SetLineColor( 1 )
gr.SetLineWidth( 1 )
gr.SetLineStyle( 2 )
gr.SetMarkerColor( 2 )
gr.SetMarkerStyle( 20 )
gr.SetMarkerSize( makerSize )
textsize = labelSize/(gPad.GetWh()*gPad.GetAbsHNDC())
gr.SetTitle( '' )
gr.GetXaxis().SetTitle( 'E_{particle} (GeV)' )
gr.GetYaxis().SetTitle( 'E_{reco} (GeV)' )
gr.GetYaxis().SetTitleSize(textsize)
gr.GetYaxis().SetLabelSize(textsize)
gr.GetYaxis().SetTitleOffset(1.4)
gr.GetYaxis().SetRangeUser(2, 100)
gPad.SetLeftMargin(0.15)
gr.Draw( 'AP' )
func1 = TF1('fun1', 'x', 0., 100.)
func1.SetLineColor(4)
func1.SetLineStyle(2)
func1.Draw('same')
####
g_reco_eff = TGraphErrors(len(eff_list), array(tolerance_list), reco_eff,
no_err, array(eff_err_list))
g_eff = TGraphErrors(len(eff_list), array(tolerance_list), array(eff_list),
no_err, array(eff_err_list))
g_purity = TGraphErrors(len(purity_list), array(tolerance_list),
array(purity_list), no_err, array(purity_err_list))
g_acceptance = TGraphErrors(len(acceptance), array(tolerance_list),
array(acceptance), no_err, array(purity_err_list))
g_reco_eff_data = TGraphErrors(len(reco_eff_list), array(tolerance_list),
array(reco_eff_list), no_err,
array(eff_err_list))
g_data_eff = TGraphErrors(len(reco_eff_list), array(tolerance_list),
array(data_eff_list), no_err, array(eff_err_list))
g_eff.Draw("APL")
g_eff.GetXaxis().SetTitle("#it{d}_{max} [cm]")
g_eff.GetYaxis().SetTitle("[%]")
g_eff.GetYaxis().SetRangeUser(90, 100)
g_eff.SetMarkerStyle(20)
g_eff.SetLineColor(kRed + 1)
g_eff.SetLineWidth(2)
g_purity.Draw("PL")
g_purity.SetMarkerStyle(21)
g_purity.SetLineColor(kBlue + 1)
g_purity.SetLineWidth(2)
g_acceptance.Draw("PL")
g_acceptance.SetMarkerStyle(23)
g_acceptance.SetLineColor(kGreen + 2)
g_acceptance.SetLineWidth(2)
g_reco_eff.Draw("PL")
g_reco_eff.SetLineColor(kGray + 2)
# calibration fausse d'un facteur 3 (7.2/2.4)
thrc = "%.0f_{HR}/%.0f_{LR} fC" % (r["hrq"] * 3, r["lrq"] * 3)
print(' i %i %f %f ' % (i, x[n], y5[n]))
if (y4[n] < leff):
leff = y4[n]
if (y5[n] < leff):
leff = y5[n]
n = n + 1
gr4 = TGraphErrors(n, x, y4, dx, dy4)
gr4.SetMarkerColor(1)
gr4.SetMarkerStyle(20)
gr4.SetTitle('HV scan Threshold=%s' % thrc)
#gr4.GetXaxis().SetTitle( 'Threshold (fC)' )
gr4.GetXaxis().SetTitle('HV_{eff} (V)')
gr4.GetYaxis().SetTitle('Efficiency (%)')
gr4.GetYaxis().SetRangeUser(leff - 10, 103.)
gr4.Draw('AP')
gr5 = TGraphErrors(n, x, y5, dx, dy5)
gr5.SetMarkerColor(2)
gr5.SetMarkerStyle(22)
gr5.Draw('PSAME')
gra4 = TGraphErrors(n, x, ya4, dx, dya4)
gra4.SetMarkerColor(3)
gra4.SetMarkerStyle(20)
gra4.Draw('PSAME')
gra5 = TGraphErrors(n, x, ya5, dx, dya5)
gra5.SetMarkerColor(4)
def signal(channel, stype):
if 'VBF' in channel:
stype = 'XZHVBF'
else:
stype = 'XZH'
# HVT model
if stype.startswith('X'):
signalType = 'HVT'
genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
massPoints = [x for x in range(800, 5000+1, 100)]
interPar = True
else:
print "Signal type", stype, "not recognized"
return
n = len(genPoints)
category = channel
cColor = color[category] if category in color else 1
nElec = channel.count('e')
nMuon = channel.count('m')
nLept = nElec + nMuon
nBtag = channel.count('b')
if '0b' in channel:
nBtag = 0
X_name = "VH_mass"
if not os.path.exists(PLOTDIR+stype+category): os.makedirs(PLOTDIR+stype+category)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar( "X_mass", "m_{ZH}", XBINMIN, XBINMAX, "GeV")
J_mass = RooRealVar( "H_mass", "jet mass", LOWMIN, HIGMAX, "GeV")
V_mass = RooRealVar( "V_mass", "V jet mass", -9., 1.e6, "GeV")
CSV1 = RooRealVar( "H_csv1", "", -999., 2. )
CSV2 = RooRealVar( "H_csv2", "", -999., 2. )
DeepCSV1= RooRealVar( "H_deepcsv1", "", -999., 2. )
DeepCSV2= RooRealVar( "H_deepcsv2", "", -999., 2. )
H_ntag = RooRealVar( "H_ntag", "", -9., 9. )
H_dbt = RooRealVar( "H_dbt", "", -2., 2. )
H_tau21 = RooRealVar( "H_tau21", "", -9., 2. )
H_eta = RooRealVar( "H_eta", "", -9., 9. )
H_tau21_ddt = RooRealVar( "H_ddt", "", -9., 2. )
MaxBTag = RooRealVar( "MaxBTag", "", -10., 2. )
H_chf = RooRealVar( "H_chf", "", -1., 2. )
MinDPhi = RooRealVar( "MinDPhi", "", -1., 99. )
DPhi = RooRealVar( "DPhi", "", -1., 99. )
DEta = RooRealVar( "DEta", "", -1., 99. )
Mu1_relIso = RooRealVar( "Mu1_relIso", "", -1., 99. )
Mu2_relIso = RooRealVar( "Mu2_relIso", "", -1., 99. )
nTaus = RooRealVar( "nTaus", "", -1., 99. )
Vpt = RooRealVar( "V.Pt()", "", -1., 1.e6 )
V_pt = RooRealVar( "V_pt", "", -1., 1.e6 )
H_pt = RooRealVar( "H_pt", "", -1., 1.e6 )
VH_deltaR=RooRealVar( "VH_deltaR", "", -1., 99. )
isZtoNN = RooRealVar( "isZtoNN", "", 0., 2. )
isZtoEE = RooRealVar( "isZtoEE", "", 0., 2. )
isZtoMM = RooRealVar( "isZtoMM", "", 0., 2. )
isHtobb = RooRealVar( "isHtobb", "", 0., 2. )
isVBF = RooRealVar( "isVBF", "", 0., 2. )
isMaxBTag_loose = RooRealVar( "isMaxBTag_loose", "", 0., 2. )
weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1.e9 )
Xmin = XBINMIN
Xmax = XBINMAX
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass, J_mass, V_mass, CSV1, CSV2, H_ntag, H_dbt, H_tau21)
variables.add(RooArgSet(DEta, DPhi, MaxBTag, MinDPhi, nTaus, Vpt))
variables.add(RooArgSet(DeepCSV1, DeepCSV2,VH_deltaR, H_tau21_ddt))
variables.add(RooArgSet(isZtoNN, isZtoEE, isZtoMM, isHtobb, isMaxBTag_loose, weight))
variables.add(RooArgSet(isVBF, Mu1_relIso, Mu2_relIso, H_chf, H_pt, V_pt,H_eta))
#X_mass.setRange("X_extended_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", Xmin, Xmax)
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/100))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
SRcut = selection[category]+selection['SR']
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
a1 = {}
a2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_fit = RooRealVar("sig_p1_fit", "Variation of the resonance position with the fit uncertainty", 0.005, -1., 1.)
smean_fit = RooRealVar("CMSRunII_sig_p1_fit", "Change of the resonance position with the fit uncertainty", 0., -10, 10)
xmean_jes = RooRealVar("sig_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.010, -1., 1.) #0.001
smean_jes = RooRealVar("CMSRunII_sig_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10)
xmean_e = RooRealVar("sig_p1_scale_e", "Variation of the resonance position with the electron energy scale", 0.001, -1., 1.)
smean_e = RooRealVar("CMSRunII_sig_p1_scale_e", "Change of the resonance position with the electron energy scale", 0., -10, 10)
xmean_m = RooRealVar("sig_p1_scale_m", "Variation of the resonance position with the muon energy scale", 0.001, -1., 1.)
smean_m = RooRealVar("CMSRunII_sig_p1_scale_m", "Change of the resonance position with the muon energy scale", 0., -10, 10)
xsigma_fit = RooRealVar("sig_p2_fit", "Variation of the resonance width with the fit uncertainty", 0.02, -1., 1.)
ssigma_fit = RooRealVar("CMSRunII_sig_p2_fit", "Change of the resonance width with the fit uncertainty", 0., -10, 10)
xsigma_jes = RooRealVar("sig_p2_scale_jes", "Variation of the resonance width with the jet energy scale", 0.010, -1., 1.) #0.001
ssigma_jes = RooRealVar("CMSRunII_sig_p2_jes", "Change of the resonance width with the jet energy scale", 0., -10, 10)
xsigma_jer = RooRealVar("sig_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.020, -1., 1.)
ssigma_jer = RooRealVar("CMSRunII_sig_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10)
xsigma_e = RooRealVar("sig_p2_scale_e", "Variation of the resonance width with the electron energy scale", 0.001, -1., 1.)
ssigma_e = RooRealVar("CMSRunII_sig_p2_scale_e", "Change of the resonance width with the electron energy scale", 0., -10, 10)
xsigma_m = RooRealVar("sig_p2_scale_m", "Variation of the resonance width with the muon energy scale", 0.040, -1., 1.)
ssigma_m = RooRealVar("CMSRunII_sig_p2_scale_m", "Change of the resonance width with the muon energy scale", 0., -10, 10)
xalpha1_fit = RooRealVar("sig_p3_fit", "Variation of the resonance alpha with the fit uncertainty", 0.03, -1., 1.)
salpha1_fit = RooRealVar("CMSRunII_sig_p3_fit", "Change of the resonance alpha with the fit uncertainty", 0., -10, 10)
xslope1_fit = RooRealVar("sig_p4_fit", "Variation of the resonance slope with the fit uncertainty", 0.10, -1., 1.)
sslope1_fit = RooRealVar("CMSRunII_sig_p4_fit", "Change of the resonance slope with the fit uncertainty", 0., -10, 10)
xmean_fit.setConstant(True)
smean_fit.setConstant(True)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xmean_e.setConstant(True)
smean_e.setConstant(True)
xmean_m.setConstant(True)
smean_m.setConstant(True)
xsigma_fit.setConstant(True)
ssigma_fit.setConstant(True)
xsigma_jes.setConstant(True)
ssigma_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
xsigma_e.setConstant(True)
ssigma_e.setConstant(True)
xsigma_m.setConstant(True)
ssigma_m.setConstant(True)
xalpha1_fit.setConstant(True)
salpha1_fit.setConstant(True)
xslope1_fit.setConstant(True)
sslope1_fit.setConstant(True)
# the alpha method is now done.
for m in massPoints:
signalString = "M%d" % m
signalMass = "%s_M%d" % (stype, m)
signalName = "%s%s_M%d" % (stype, category, m)
signalColor = sample[signalMass]['linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m*0.5, m*1.25)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)", RooArgList(vmean[m], xmean_e, smean_e, xmean_m, smean_m, xmean_jes, smean_jes, xmean_fit, smean_fit))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m*0.035, m*0.01, m*0.4)
sigmaList = RooArgList(vsigma[m], xsigma_e, ssigma_e, xsigma_m, ssigma_m, xsigma_jes, ssigma_jes, xsigma_jer, ssigma_jer)
sigmaList.add(RooArgList(xsigma_fit, ssigma_fit))
ssigma[m] = RooFormulaVar(signalName + "_sigma", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)*(1+@9*@10)", sigmaList)
valpha1[m] = RooRealVar(signalName + "_valpha1", "Crystal Ball alpha", 1., 0., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0*(1+@1*@2)", RooArgList(valpha1[m], xalpha1_fit, salpha1_fit))
vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope", 10., 1., 60.) # slope of the power tail #10 1 60
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0*(1+@1*@2)", RooArgList(vslope1[m], xslope1_fit, sslope1_fit))
salpha2[m] = RooRealVar(signalName + "_alpha2", "Crystal Ball alpha", 2, 1., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
sslope2[m] = RooRealVar(signalName + "_slope2", "Crystal Ball slope", 10, 1.e-1, 115.) # slope of the power tail
#define polynomial
#a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", m, 0.5*m, 2*m)
a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", 0.001*m, 0.0005*m, 0.01*m)
a2[m] = RooRealVar(signalName + "_a2", "par 2 for polynomial", 0.05, -1.,1.)
#if channel=='nnbbVBF' or channel=='nn0bVBF':
# signal[m] = RooPolynomial(signalName,"m_{%s'} = %d GeV" % (stype[1], m) , X_mass, RooArgList(a1[m],a2[m]))
#else:
# signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName+"_yield", "signalYield", 100, 0., 1.e6)
signalNorm[m] = RooRealVar(signalName+"_norm", "signalNorm", 1., 0., 1.e6)
signalXS[m] = RooRealVar(signalName+"_xs", "signalXS", 1., 0., 1.e6)
signalExt[m] = RooExtendPdf(signalName+"_ext", "extended p.d.f", signal[m], signalYield[m])
vslope1[m].setMax(50.)
vslope1[m].setVal(20.)
#valpha1[m].setVal(1.0)
#valpha1[m].setConstant(True)
if 'bb' in channel and 'VBF' not in channel:
if 'nn' in channel:
valpha1[m].setVal(0.5)
elif '0b' in channel and 'VBF' not in channel:
if 'nn' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.04)
elif 'ee' in channel:
valpha1[m].setVal(0.8)
if m==800:
#valpha1[m].setVal(1.2)
valpha1[m].setVal(2.5)
vslope1[m].setVal(50.)
elif 'mm' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.03)
else:
vmean[m].setVal(m*0.9)
vsigma[m].setVal(m*0.08)
elif 'bb' in channel and 'VBF' in channel:
if 'nn' in channel:
if m!=1800:
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
valpha1[m].setVal(0.7)
elif 'mm' in channel:
if m==800:
vslope1[m].setVal(50.)
valpha1[m].setVal(0.7)
elif '0b' in channel and 'VBF' in channel:
if 'nn' in channel:
valpha1[m].setVal(3.)
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
if m<2500:
valpha1[m].setVal(2.)
if m==800:
vsigma[m].setVal(m*0.05)
elif m==1000:
vsigma[m].setVal(m*0.03)
elif m>1000 and m<1800:
vsigma[m].setVal(m*0.04)
elif 'mm' in channel:
if m<2000:
valpha1[m].setVal(2.)
if m==1000 or m==1800:
vsigma[m].setVal(m*0.03)
elif m==1200 or m==1600:
vsigma[m].setVal(m*0.04)
#if m < 1000: vsigma[m].setVal(m*0.06)
# If it's not the proper channel, make it a gaussian
#if nLept==0 and 'VBF' in channel:
# valpha1[m].setVal(5)
# valpha1[m].setConstant(True)
# vslope1[m].setConstant(True)
# salpha2[m].setConstant(True)
# sslope2[m].setConstant(True)
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
for j, ss in enumerate(sample[signalMass]['files']):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
if treeSign[m].GetEntries() <= 0.:
if VERBOSE: print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeSign[m]))
if VERBOSE: print " - Dataset with", setSignal[m].sumEntries(), "events loaded"
# FIT
signalYield[m].setVal(setSignal[m].sumEntries())
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1), RooFit.Extended(True), RooFit.SumW2Error(True), RooFit.PrintLevel(-1))
if VERBOSE: print "********** Fit result [", m, "] **", category, "*"*40, "\n", frSignal[m].Print(), "\n", "*"*80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass, stype+channel, X_mass, signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in channel", channel, "has 0 entries or does not exist"
# Remove HVT cross section (which is the same for Zlep and Zinv)
if stype == "XZHVBF":
sample_name = 'Zprime_VBF_Zh_Zlephinc_narrow_M-%d' % m
else:
sample_name = 'ZprimeToZHToZlepHinc_narrow_M%d' % m
xs = xsection[sample_name]['xsec']
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[m].getVal(), "/", boundaryFactor, "events"
if channel=='nnbb' and m==5000:
signalNorm[m].setVal(2.5)
elif channel=='nn0b' and m==5000:
signalNorm[m].setVal(6.7)
else:
signalNorm[m].setVal( boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal()) # here normalize to sigma(X) x Br(X->VH) = 1 [fb]
a1[m].setConstant(True)
a2[m].setConstant(True)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
# ====== CONTROL PLOT ======
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for m in genPoints[:-2]:
if m in signalExt.keys():
signal[m].plotOn(frame_signal, RooFit.LineColor(sample["%s_M%d" % (stype, m)]['linecolor']), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 6500)
frame_signal.Draw()
drawCMS(-1, YEAR, "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.pdf")
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 800, 5000) #"pol5" if not channel=="XZHnnbb" else "pol6" #pol5*TMath::Floor(x-1800) + ([5]*x + [6]*x*x)*(1-TMath::Floor(x-1800))
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 5000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 5000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol0", 0, 5000)
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol0", 0, 5000)
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol0", 0, 5000)
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol0", 0, 5000)
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
signalString = "M%d" % m
signalName = "%s_M%d" % (stype, m)
if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0, min(vmean[m].getError(), vmean[m].getVal()*0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(n, 0, min(vsigma[m].getError(), vsigma[m].getVal()*0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(n, 0, min(valpha1[m].getError(), valpha1[m].getVal()*0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(n, 0, min(vslope1[m].getError(), vslope1[m].getVal()*0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(n, 0, min(salpha2[m].getError(), salpha2[m].getVal()*0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(n, 0, min(sslope2[m].getError(), sslope2[m].getVal()*0.10))
n = n + 1
print "fit on gmean:"
gmean.Fit(fmean, "Q0", "SAME")
print "fit on gsigma:"
gsigma.Fit(fsigma, "Q0", "SAME")
print "fit on galpha:"
galpha1.Fit(falpha1, "Q0", "SAME")
print "fit on gslope:"
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
gnorm.Fit(fnorm, "Q", "SAME", 700, 5000)
for m in massPoints:
signalName = "%s_M%d" % (stype, m)
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1.-spline/sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
jmean = gmean.Eval(m)
jsigma = gsigma.Eval(m)
jalpha1 = galpha1.Eval(m)
jslope1 = gslope1.Eval(m)
else:
jmean = fmean.GetParameter(0) + fmean.GetParameter(1)*m + fmean.GetParameter(2)*m*m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(1)*m + fsigma.GetParameter(2)*m*m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(1)*m + falpha1.GetParameter(2)*m*m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(1)*m + fslope1.GetParameter(2)*m*m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(syield)
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1==0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
c1 = TCanvas("c1", "Crystal Ball", 1200, 800)
c1.Divide(2, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(channel)
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(channel)
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(channel)
galpha1.GetYaxis().SetRangeUser(0., 5.)
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(channel)
gslope1.GetYaxis().SetRangeUser(0., 125.)
if False:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(channel)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(channel)
gslope2.GetYaxis().SetRangeUser(0., 10.)
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.pdf")
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0]-100, genPoints[-1]+100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum()*1.25)
drawCMS(-1,YEAR , "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.pdf")
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("ZH_RunII", "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile("%s%s.root" % (WORKDIR, stype+channel), True)
print "Workspace", "%s%s.root" % (WORKDIR, stype+channel), "saved successfully"
sys.exit()
err6s = array( 'f', [0.102, 0.070, 0.073, 0.088, 0.078, 0.076, 0.080, 0.081, 0.100, 0.155, 0.189, 0.180, 0.107, 0.101, 0.086, 0.083, 0.076, 0.080, 0.075, 0.080, 0.110])
err6 = array( 'f', [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ,0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
n4s = 21
nxs = 33
n2s = 21
c1 = TCanvas( 'c1', 'Y', 200, 10, 1400, 1000 )
g1s = TGraphErrors(nxs, Arr1, det1s, err1, err1s)
g1s.SetTitle( 'Y=0 Detected PE' )
g1s.SetMarkerColor( 4 )
g1s.SetLineColor( 4 )
g1s.SetMarkerStyle( 21 )
g1s.Draw('APC')
g1s.GetYaxis().SetRangeUser(0,45)
g1s.GetYaxis().SetTitle("Hits [PE]")
g1s.GetXaxis().SetTitle("X [cm]")
g2xs = TGraphErrors(nxs, Arr2x, det2xs, err2x, err2xs)
g2xs.SetTitle( 'Y=0 Detected PE' )
g2xs.SetMarkerColor( 9 )
g2xs.SetLineColor( 9 )
g2xs.SetMarkerStyle( 5 )
g2xs.Draw('PC SAME')
#c1.Print("xs.png")
#c2 = TCanvas( 'c1', 'Y', 200, 10, 700, 500 )
'''
g2s = TGraphErrors(nxs, Arr2, det2s, err2, err2s)
g2s.SetTitle( 'Y=0 Detected PE' )
g2s.SetMarkerColor( 800 )
g2s.SetLineColor( 800 )
g2s.SetMarkerSize( 2 )
g2s.SetMarkerStyle( 22 )
lengtharray.append(fit.Get().Parameter(4))
sigmaErr.append(fit.Get().ParError(2))
breakzErr.append(fit.Get().ParError(3))
lengthErr.append(fit.Get().ParError(4))
fit_params.Write("fit {0} GeV".format(mass))
c.Print(remainder[0] + ".pdf", "Title:mass_{0}".format(mass))
c.Clear()
outfile.cd()
gPad.SetLogy(0)
graph = TGraphErrors(len(massarray), massarray, sigmaarray, zeroArr, sigmaErr)
graph.Draw("A*")
graph.SetTitle("Vertex Resolution")
graph.GetXaxis().SetTitle("mass [GeV]")
graph.GetYaxis().SetTitle("sigma [mm]")
graph.Write("sigmaz")
c.Print(remainder[0] + ".pdf", "Title:sigmaz")
graph = TGraphErrors(len(massarray), massarray, breakzarray, zeroArr,
breakzErr)
graph.Draw("A*")
graph.SetTitle("Tail Z")
graph.GetXaxis().SetTitle("mass [GeV]")
graph.GetYaxis().SetTitle("tail Z [mm]")
graph.Write("breakz")
c.Print(remainder[0] + ".pdf", "Title:tailz")
graph = TGraphErrors(len(massarray), massarray, lengtharray, zeroArr,
lengthErr)
for i in range(len(timestamps)):
timestamps_array[i] = timestamps[i]
peak3_array[i] = peak3[i]
error3_array[i] = error3[i]
scan_nums = ''
for i in range(len(scan)):
scan_nums += '_%s' % (scan[i])
time_pdf = '%sscan%s_peak3_time.pdf' % (OutputPath, scan_nums)
# Draw graphs
c1 = TCanvas("c1", "Peak VS Time", 200, 10, 700, 500)
graph1 = TGraphErrors(len(timestamps), timestamps_array, peak3_array, time_error, error3_array)
graph1.SetTitle("peak 3 over time")
graph1.GetXaxis().SetTitle("Datetime")
graph1.GetYaxis().SetTitle("Peaks Amp (mV)")
print("Bin Number: " + str(graph1.GetXaxis().GetNbins()))
for i in range(len(timestamps_array)):
binx = graph1.GetXaxis().FindBin(timestamps_array[i])
graph1.GetXaxis().SetBinLabel(binx, time.ctime(timestamps_array[i]))
graph1.Draw()
c1.Update()
c1.Print(time_pdf)
c1.Clear()
temp_pdf = '%sscan%s_peak3_temperature.pdf' % (OutputPath, scan_nums)
graph2 = TGraphErrors(len(timestamps), timestamps_array, peak3_array, time_error, error3_array)
graph2.SetTitle("Peak3 Temperature")
graph2.GetXaxis().SetTitle("Temperature (degree Celsius)")
def MeasurePSF_in_Sections(data, fitted_line, nsecs = 3, tgraph_filename = '', DEBUG = False, DEBUG_Filenum = 0):
#get new coefficients as distance to line uses straight line of form ax + by + c = 0
a = -1. * fitted_line.a
b = 1
c = -1. * fitted_line.b
histmin = -1 * max(data.shape[0], data.shape[1])
histmax = max(data.shape[0], data.shape[1])
nbins = (histmax - histmin) * 2
hists = []
for i in range(nsecs):
hists.append(TH1F('Track section ' + str(i), 'Track section ' + str(i), nbins, histmin, histmax))
for xcoord in range(data.shape[0]):
for ycoord in range(data.shape[1]):
x = xcoord + 0.5 #adjust for bin centers - NB This is important!
y = ycoord + 0.5 #adjust for bin centers - NB This is important!
secnum = GetSecNum(data, x, y, nsecs, None) # TODO!
value = float(data[xcoord,ycoord])
# if value < 800:
non_abs_distance = (a*x + b*y + c) / (a**2 + b**2)**0.5
hists[secnum].Fill(non_abs_distance, value)
sigmas, sigma_errors = [], []
for i, hist in enumerate(hists):
fitmin, fitmax = GetLastBinAboveX(hist, 0.1)
# viewmin, viewmax = GetLastBinAboveX(hist, 0.1)
viewmin, viewmax = -2,2
hist.GetXaxis().SetRangeUser(viewmin,viewmax)
fit_func = TF1("gaus", "gaus", fitmin, fitmax)
fit_func.SetNpx(1000)
hist.Fit(fit_func, "MEQ", "", fitmin, fitmax)
legend_text = []
sigma = fit_func.GetParameter(2)
sigma_error = fit_func.GetParError(2)
sigmas.append(abs(15*sigma)) #15 for the 15um per pixel
sigma_errors.append(abs(15*sigma_error)) #15 for the 15um per pixel
mean = fit_func.GetParameter(15*1) #15 for the 15um per pixel
mean_error = fit_func.GetParError(15*1) #10 for the 15um per pixel
chisq = fit_func.GetChisquare()
NDF = fit_func.GetNDF()
try:
chisqr_over_NDF = chisq/NDF
except:
chisqr_over_NDF = -1
# if chisqr_over_NDF > 500 or chisqr_over_NDF <= 1:
# return [], [], []
legend_text.append('mean = ' + str(mean) + ' #pm ' + str(mean_error) + " #mum")
legend_text.append('#sigma = ' + str(round(sigma,4)) + ' #pm ' + str(round(sigma_error,4)) + " #mum")
if DEBUG: #For showing each of n PSF *Histograms* per track
c1 = TCanvas( 'canvas', 'canvas', CANVAS_WIDTH,CANVAS_HEIGHT)
hist.Draw("")
if legend_text != '':
from ROOT import TPaveText
textbox = TPaveText(0.0,1.0,0.2,0.8,"NDC")
for line in legend_text:
textbox.AddText(line)
textbox.SetFillColor(0)
textbox.Draw("same")
c1.SaveAs(OUTPUT_PATH + str(DEBUG_Filenum) + "psf_section_" + str(i) + FILE_TYPE)
del c1
from ROOT import TGraphErrors
c2 = TCanvas( 'canvas', 'canvas', CANVAS_WIDTH,CANVAS_HEIGHT)
assert nsecs == len(sigmas) == len(sigma_errors)
xpoints = GenXPoints(nsecs, 250.)
gr = TGraphErrors(nsecs, np.asarray(xpoints,dtype = float), np.asarray(sigmas,dtype = float), np.asarray([0 for i in range(nsecs)],dtype = float), np.asarray(sigma_errors,dtype = float)) #populate graph with data points
gr.SetLineColor(2)
gr.SetMarkerColor(2)
gr.Draw("AP")
fit_func = TF1("line","[1]*x + [0]", -1, nsecs+1)
fit_func.SetNpx(1000)
gr.Fit(fit_func, "MEQ", "")
a = fit_func.GetParameter(1)
a_error = fit_func.GetParError(1)
if DEBUG:
if tgraph_filename == '': tgraph_filename = OUTPUT_PATH + 'psf_graph' + '.png'
gr.SetTitle("")
gr.GetYaxis().SetTitle('PSF #sigma (#mum)')
gr.GetXaxis().SetTitle('Av. Si Depth (#mum)')
c2.SaveAs(tgraph_filename)
# if a_error >= a:
# print "Inconclusive muon directionality - skipped track %s"%tgraph_filename
# return [],[],[]
del c2, hists, gr
import gc
gc.collect()
for j in range(nsecs):
if sigma_errors[j] > sigmas[j]:
print "bad fit skipped"
return [],[],[]
if a < 0:
sigmas.reverse()
sigma_errors.reverse()
return xpoints, sigmas, sigma_errors
else:
return xpoints, sigmas, sigma_errors
gr1 = TGraphErrors(17, xarray, yarray1, xarrayerror, yarrayerror)
gr.SetLineStyle(1)
gr.SetMarkerColor(2)
gr.SetMarkerStyle(8)
gr.SetLineWidth(2)
gr1.SetLineStyle(1)
gr1.SetMarkerColor(3)
gr1.SetMarkerStyle(8)
gr1.SetLineWidth(2)
gr1.SetTitle(";Concentration(1/cm^{3}) ;# of impurities ")
gr1.GetHistogram().SetMaximum(30)
gr1.GetHistogram().SetMinimum(0)
gr1.GetXaxis().SetLimits(math.pow(10, 10 + 0 * 0.5),
math.pow(10, 10 + 16 * 0.5))
leg1 = TLegend(0.2, 0.7, 0.5, 0.9)
leg1.SetFillColor(0)
leg1.SetFillStyle(0)
leg1.SetTextSize(0.05)
leg1.SetBorderSize(0)
leg1.SetTextFont(22)
leg1.AddEntry("", "GeIA group", "")
leg1.AddEntry("", "Avalanche region:", "")
leg1.AddEntry(gr, str(Avalanche_region) + "cm(Square)", "lp")
leg1.AddEntry(gr1, str(Avalanche_region1) + "cm(Circle)", "lp")
line = TLine(
math.pow(10, 15), 0, math.pow(10, 15),
Number_of_Atom(Avalanche_region, math.pow(math.pow(10, 15), 0.33)))
def plot_2(var,cuts):
for s in attr:
c1 = TCanvas("c1", "Signals", 1200, 800)
if log_y == 1:
c1.SetLogy()
c1.SetTopMargin(0.08)#0.12
c1.SetBottomMargin(0.11)#0.12
c1.SetLeftMargin(0.14)
c1.SetRightMargin(0.14)#0.24
c1.cd()
#c1.SetGrid()
gStyle.SetTitleFontSize(0.04)
if ct_dep == 0:
if s in ('elf','muf','cm','nm','chm'):
c1.SetLogx()
for cc in channel:
#hist[cc][s].SetMaximum(0.44)
if 'combind' in cc:
fc = 30
#hist[cc][s].SetFillStyle()#3005)
elif 'VBF' in cc:
fc = 38
hist[cc][s].SetFillStyle(3444)
elif 'HT50' in cc:
fc = 7
hist[cc][s].SetFillStyle(3001)
elif 'HT100' in cc:
fc = 4
hist[cc][s].SetFillStyle(3002)
elif 'HT200' in cc:
fc = 6
hist[cc][s].SetFillStyle(3003)
elif 'HT300' in cc:
fc = 9
hist[cc][s].SetFillStyle(3004)
if histFillColOn == 1:
pass
#hist[cc][s].SetFillColor(fc)
hist[cc][s].Draw(histStyl)
#legend = TLegend(0.76, 0.56, 0.99, 0.88)
legend = TLegend(0.60, 0.9-0.04*2, 0.85, 0.9) #x_left y_bottom x_right y_top
legend.SetBorderSize(0)
legend.SetFillStyle(0)#1001
legend.SetFillColor(0)
#legend.SetHeader( entry['entries'] )
for cc in channel:
legend.AddEntry(hist[cc][s],cc)
legend.Draw()
for ct in cut_text:
cut_text[ct].Draw()
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Line for critical value
if s == 'chm':
l = TLine(11.4,0.0,11.4,0.084)
elif s == 'cHadEFrac':
l = TLine(0.38,0.0,0.38,0.027)
elif s == 'FracCal':
l = TLine(30,0.0,30,0.44)
#l.SetLineColor(2)
#l.SetLineWidth(3)
if critical_line == 1:
l.SetLineColor(46)#4,2
l.SetLineWidth(3)
l.Draw('same')
c1.Print(path1 + s + var + cuts.replace('(','_').replace(')','_').replace('&&','_').replace('>','LG').replace('<','LS').replace('=','EQ').replace('.','P').replace('-','N').replace('Jet','J').replace('GenBquark','GBQ') + ".pdf")
elif ct_dep == 1:
eac0 = str( entries_after_cut['ct0'][s] )
c1.SetLogx()
#gr = TGraph( len_of_lt , x , yy['sgn'][s] )
gr = TGraphErrors( len_of_lt , x , yy['sgn'][s] , ex , ey['sgn'][s] )
gr.SetMarkerSize(1.5)
gr.SetMarkerStyle(1)
gr.GetYaxis().SetTitleOffset(1.6)
gr.SetLineColor(4)
gr.SetLineWidth(4)
gr.SetTitle('mean ' + s )
gr.GetXaxis().SetTitle('decaying length (mm)')
gr.GetYaxis().SetTitle('mean normalized number of events')
gr.GetXaxis().SetTitleOffset(1.4)
gr.SetMaximum( plotrange[s] * 1.12 )
gr.SetName('sgn')
gr.Draw('ACP') # '' sets up the scattering style
gr1 = TGraphErrors( len_of_lt , x , yy['QCD'][s] , ex , ey['QCD'][s] )
gr1.SetMarkerSize(1.0)
gr1.SetMarkerStyle(1)
gr.GetYaxis().SetTitleOffset(1.6)
gr1.SetLineColor(2)
gr1.SetLineWidth(2)
gr1.SetName('QCD')
#gr1.SetTitle('averaged ' + s)
#gr1.GetXaxis().SetTitle('decaying length (mm)')
#gr1.GetYaxis().SetTitle('mean frequency')
gr1.Draw('CP') # '' sets up the scattering style
legend = TLegend(0.76, 0.56, 0.99, 0.88)
legend.SetHeader( 'Entries: ' + eac0 )
legend.AddEntry('QCD', legendb, 'l')
legend.AddEntry('sgn', legends, 'l')
legend.Draw()
for ct in cut_text:
cut_text[ct].Draw()
c1.Print(path1 + 'mean_' + s + var + cuts.replace('(','_').replace(')','_').replace('&&','_').replace('>','LG').replace('<','LS').replace('=','EQ').replace('.','P').replace('-','N').replace('Jet','J').replace('GenBquark','GBQ') + ".pdf")
c1.Update()
c1.Close()
print('|||||||||||||||||||||||||||||||||||||||||||||||||||')