def intervalFromValues( self, values, weights ):
## Calculates the selected interval from weighted values
# Symmetrically removes entries on both side until the desired fraction
# of weights remains. Uncertainties are estimated by interpreting the fraction
# of entries below and above the limits as an efficiency measurements.
# Limits are then given from the 1-sigma confidence interval.
# @param values list of values
# @param weights list of weights (should match length of values)
# @result (lower, upper) boundary
self._reset()
from ROOT import TEfficiency
import numpy
values = numpy.array( values )
# simply calculate the interval that contains the desired fraction of weights
self.low, self.up = truncatedInterval( values, self.fraction, weights )
nTotal = len( values )
nLow = len( values[values <= self.low] )
nUp = len( values[values < self.up] )
# interpret entries below and above as counting experiment
# for 1-sigma confidence intervals the corresponding percentiles are calculated
# as an estimate of the uncertainty on the percentile values above
self.lowLow, self.lowUp, self.upLow, self.upUp = percentiles( values,
[ TEfficiency.ClopperPearson( nTotal, nLow, 0.68, False ),
TEfficiency.ClopperPearson( nTotal, nLow, 0.68, True ),
TEfficiency.ClopperPearson( nTotal, nUp, 0.68, False ),
TEfficiency.ClopperPearson( nTotal, nUp, 0.68, True ) ],
weights )
return self.low, self.up
def histsToRoc(hsig, hbg, w_error=False):
'''Produce ROC curve from 2 input histograms.
Partly adapted from Giovanni's ttH code.
'''
nbins = hsig.GetNbinsX() + 2 - 1 # include under/overflow; remove events not passing selection
si = [hsig.GetBinContent(i) for i in xrange(nbins+1)]
bi = [hbg.GetBinContent(i) for i in xrange(nbins+1)]
del si[1]
del bi[1]
if hsig.GetMean() > hbg.GetMean():
si.reverse()
bi.reverse()
sums, sumb = sum(si), sum(bi)
if sums == 0 or sumb == 0:
print 'WARNING: Either signal or background histogram empty', sums, sumb
return None
for i in xrange(1, nbins):
si[i] += si[i - 1]
bi[i] += bi[i - 1]
fullsi, fullbi = si[:], bi[:]
si, bi = [], []
for i in xrange(1, nbins):
# skip negative weights
if si and (fullsi[i] < si[-1] or fullbi[i] < bi[-1]):
continue
# skip repetitions
if fullsi[i] != fullsi[i - 1] or fullbi[i] != fullbi[i - 1]:
si.append(fullsi[i])
bi.append(fullbi[i])
if len(si) == 2:
si = [si[0]]
bi = [bi[0]]
bins = len(si)
if not w_error:
roc = ROOT.TGraph(bins)
for i in xrange(bins):
roc.SetPoint(i, si[i] / sums, bi[i] / sumb)
return roc
roc = ROOT.TGraphAsymmErrors(bins)
for i in xrange(bins):
interval = 0.683
e_s_low = si[i] / sums - TEfficiency.ClopperPearson(sums, si[i], interval, False)
e_s_up = TEfficiency.ClopperPearson(sums, si[i], interval, True) - si[i] / sums
e_b_low = bi[i] / sumb - TEfficiency.ClopperPearson(sumb, bi[i], interval, False)
e_b_up = TEfficiency.ClopperPearson(sumb, bi[i], interval, True) - bi[i] / sumb
roc.SetPoint(i, si[i] / sums, bi[i] / sumb)
roc.SetPointError(i, e_s_low, e_s_up, e_b_low, e_b_up)
return roc
def acceptance(cutlist, labellist):
basecut = cutlist[0]
dim = len(cutlist)
#cutlist.remove(cutlist[:1][0])
if basecut == 'SSemu':
basecutstr = '((Lepton_pdgId[0]*Lepton_pdgId[1]==11*13)||(Lepton_pdgId[0]*Lepton_pdgId[1]==-11*-13))'
elif basecut == 'SSmue':
basecutstr = '((Lepton_pdgId[0]*Lepton_pdgId[1]==13*11)||(Lepton_pdgId[0]*Lepton_pdgId[1]==-13*-11))'
elif basecut == 'SSee':
basecutstr = '((Lepton_pdgId[0]*Lepton_pdgId[1]==11*11)||(Lepton_pdgId[0]*Lepton_pdgId[1]==-11*-11))'
elif basecut == 'SSmumu':
basecutstr = '((Lepton_pdgId[0]*Lepton_pdgId[1]==13*13)||(Lepton_pdgId[0]*Lepton_pdgId[1]==-13*-13))'
cutlist = [basecutstr if x == basecut else x for x in cutlist]
file = {}
tree = {}
effs = {}
hist = {}
GrAsym = {}
yErrorUp = {}
yErrorDown = {}
totEve = 0
#Compute Eff on different triggers
for i, s in enumerate(signals):
file[s] = TFile(NTUPLESIG + samples[s]['files'][0] + ".root",
"READ") # Read TFile
tree[s] = file[s].Get("Events") # Read TTree
effs[s] = [0] * (dim + 1)
yErrorUp[s] = [0] * (dim + 1)
yErrorDown[s] = [0] * (dim + 1)
totEve = tree[s].GetEntries(basecutstr)
GrAsym[s] = TGraphAsymmErrors()
for j, c in enumerate(cutlist):
n = tree[s].GetEntries(basecutstr + " && " + cutlist[j])
effs[s][j] = float(n) / (totEve)
yErrorUp[s][j] = float(
TEfficiency.ClopperPearson(totEve, n, 0.68, True) - effs[s][j])
yErrorDown[s][j] = float(
effs[s][j] -
TEfficiency.ClopperPearson(totEve, n, 0.68, False))
GrAsym[s].SetPoint(j, j + 0.5, effs[s][j])
# i , exl, exh, eyl, eyh
GrAsym[s].SetPointError(j, 0, 0, yErrorUp[s][j], yErrorDown[s][j])
GrAsym[s].SetLineColor(colors[i])
GrAsym[s].SetLineWidth(3)
GrAsym[s].SetMarkerStyle(8)
GrAsym[s].SetMarkerColor(colors[i])
for k, cs in enumerate(labellist):
GrAsym[s].GetHistogram().GetXaxis().Set(dim, 0, dim)
GrAsym[s].GetHistogram().GetXaxis().SetBinLabel(
k + 1, "%s" % labellist[k])
leg = TLegend(0.7, 0.9 - 0.035 * len(signals), 0.9, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(1001)
leg.SetFillColor(0)
for i, s in enumerate(signals):
leg.AddEntry(GrAsym[s], s, "l")
#leg.AddEntry(hist[s], samples[s]['label'][0], "l")
c1 = TCanvas("c1", "Signals Acceptance", 800, 600)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
gStyle.SetOptStat(0)
GrAsym[signals[0]].SetMaximum(1.3)
GrAsym[signals[0]].SetMinimum(0.)
for i, s in enumerate(signals):
if i == 0:
GrAsym[s].GetHistogram().GetXaxis().SetTitle("Trigg")
GrAsym[s].GetHistogram().GetYaxis().SetTitle("Signal Acceptance")
#hist[s].GetYaxis().SetRangeUser(0., 1.)
GrAsym[s].Draw("pa" if i == 0 else "SAME p")
leg.Draw()
drawCMS(LUMI, "Work In Progress")
drawRegion(basecut)
if not os.path.exists('plots/Signal/Acceptance/'):
os.system('mkdir -p plots/Signal/Acceptance/')
c1.Print("plots/Signal/Acceptance/Acc_" + basecut + ".png")
c1.Print("plots/Signal/Acceptance/Acc_" + basecut + ".pdf")
#if not options.runBash: raw_input("Press Enter to continue...")
pass
def significanceSB(cutlist, labellist):
basecut = labellist[0]
dim = len(cutlist)
significance = [0] * (dim + 1)
file = {}
tree = {}
effs = {}
hist = {}
GrAsym = {}
yErrorUp = {}
yErrorDown = {}
totEve = 0
GrAsym = TGraphAsymmErrors()
cuts = ""
for j, c in enumerate(cutlist):
s = 0.
b = 0.
cuts += cutlist[0] if j == 0 else " && " + cutlist[j]
print "cuts = ", cuts
for num1, v in enumerate(signals):
#print "Signal = ", v
for num2, filename in enumerate(samples[v]['files']):
#print "Signal rootfile read = ", filename
file[filename] = TFile(NTUPLESIG + filename + ".root",
"READ") # Read TFile
tree[filename] = file[filename].Get("Events") # Read TTree
nevents = float(sample[filename]['nevents'])
xs = float(sample[filename]['xsec']) * float(
sample[filename]['kfactor'])
LumiMC = nevents / xs
Weight = float(LUMI) / float(LumiMC)
sig_entries = tree[filename].GetEntries(cuts)
#print "s = ", float(sig_entries) * float(Weight)
s += float(sig_entries) * float(Weight)
print "TOT SIG = ", s
for num1, k in enumerate(back):
#print "backgrounds = ", k
for num2, filename in enumerate(samples[k]['files']):
#print "backgrounds rootfile read = ", filename
file[filename] = TFile(NTUPLEDIR + filename + ".root",
"READ") # Read TFile
tree[filename] = file[filename].Get("Events") # Read TTree
nevents = float(sample[filename]['nevents'])
xs = float(sample[filename]['xsec']) * float(
sample[filename]['kfactor'])
LumiMC = nevents / xs
Weight = float(LUMI) / float(LumiMC)
bkg_entries = tree[filename].GetEntries(cuts)
#print "b = ", float(bkg_entries) * float(Weight)
b += float(bkg_entries) * float(Weight)
print "TOT BKG = ", b
##End of cutlist
#COMPUTE
#print "s = ", s
#print "b = ", b
#print "sqrt(b) = ", math.sqrt(b)
#print "significance = ", float(s/math.sqrt(b))
significance[j] = float(s / math.sqrt(b))
yErrorUp[j] = float(
TEfficiency.ClopperPearson(math.sqrt(b), s, 0.68, True) -
significance[j])
yErrorDown[j] = float(
significance[j] -
TEfficiency.ClopperPearson(math.sqrt(b), s, 0.68, False))
GrAsym.SetPoint(j, j + 0.5, significance[j])
GrAsym.SetPointError(j, 0, 0, yErrorUp[j], yErrorDown[j])
for k, cs in enumerate(labellist):
GrAsym.GetHistogram().GetXaxis().Set(dim, 0, dim)
GrAsym.GetHistogram().GetXaxis().SetBinLabel(k + 1,
"%s" % labellist[k])
GrAsym.SetLineColor(2)
GrAsym.SetLineWidth(3)
GrAsym.SetMarkerStyle(8)
GrAsym.SetMarkerColor(2)
c1 = TCanvas("c1", "Signals Acceptance", 800, 600)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
gStyle.SetOptStat(0)
#GrAsym.SetMaximum(1.3)
#GrAsym.SetMinimum(0.)
GrAsym.GetHistogram().GetXaxis().SetTitle("")
GrAsym.GetHistogram().GetYaxis().SetTitle("Significance (S/#sqrt{B})")
GrAsym.Draw("pa")
drawCMS(LUMI, "Work In Progress")
drawRegion(basecut)
if not os.path.exists('plots/Signal/Significance/'):
os.system('mkdir -p plots/Signal/Significance/')
c1.Print("plots/Signal/Significance/Sigf_SB_" + basecut + ".png")
c1.Print("plots/Signal/Significance/Sigf_SB_" + basecut + ".pdf")
#if not options.runBash: raw_input("Press Enter to continue...")
pass
def Plot1DEfficiency(num, den, plotname, topTitle, xAxisTitle, xAxisRangeLow,
xAxisRangeHigh):
nbins = num.GetXaxis().GetNbins()
x = list()
y = list()
xErrLow = list()
xErrHigh = list()
yErrLow = list()
yErrHigh = list()
for b in range(1, nbins):
xtemp = num.GetXaxis().GetBinCenter(b + 1)
xerrlow = num.GetXaxis().GetBinCenter(
b + 1) - num.GetXaxis().GetBinLowEdge(b + 1)
xerrhigh = num.GetXaxis().GetBinUpEdge(
b + 1) - num.GetXaxis().GetBinCenter(b + 1)
ratio = 0
errLow = 0
errHigh = 0
n1 = int(num.GetBinContent(b + 1))
n2 = int(den.GetBinContent(b + 1))
print "numerator: " + str(n1) + " and denominator: " + str(n2)
if (n1 > n2):
n1 = n2
if (n2 > 0):
ratio = float(n1) / float(n2)
if (ratio > 1):
ratio = 1
errLow = ratio - TEfficiency.ClopperPearson(
n2, n1, 0.68269, False)
errHigh = TEfficiency.ClopperPearson(n2, n1, 0.68269, True) - ratio
print " done bin " + str(
b) + " " + str(xtemp) + " : " + str(n1) + "(" + str(
num.GetBinContent(b + 1)) + ")" + " / " + str(n2) + "(" + str(
den.GetBinContent(b + 1)) + ")" + " = " + str(
ratio) + " " + str(errLow) + " " + str(errHigh)
ytemp = ratio
yerrlowtemp = errLow
yerrhightemp = errHigh
print "x: " + str(xtemp) + " and y: " + str(ytemp)
x.append(xtemp)
y.append(ytemp)
xErrLow.append(xerrlow)
xErrHigh.append(xerrhigh)
yErrLow.append(yerrlowtemp)
yErrHigh.append(yerrhightemp)
c = TCanvas("cv", "cv", 800, 800)
c.SetLeftMargin(0.12)
#must convert list into array for TGraphAsymmErrors to work
xArr = array.array('f', x)
yArr = array.array('f', y)
xErrLowArr = array.array('f', xErrLow)
xErrHighArr = array.array('f', xErrHigh)
yErrLowArr = array.array('f', yErrLow)
yErrHighArr = array.array('f', yErrHigh)
effGraph = TGraphAsymmErrors(nbins, xArr, yArr, xErrLowArr, xErrHighArr,
yErrLowArr, yErrHighArr)
effGraph.Draw("APE")
effGraph.SetTitle("")
effGraph.GetXaxis().SetTitle(xAxisTitle)
effGraph.GetXaxis().SetTitleSize(0.05)
effGraph.GetXaxis().SetTitleOffset(0.90)
effGraph.GetXaxis().SetLabelSize(0.03)
effGraph.GetXaxis().SetRangeUser(xAxisRangeLow, xAxisRangeHigh)
effGraph.GetYaxis().SetTitle("Efficiency")
effGraph.GetYaxis().SetTitleSize(0.05)
effGraph.GetYaxis().SetTitleOffset(1.05)
effGraph.GetYaxis().SetLabelSize(0.03)
title = TLatex()
title.SetTextSize(0.05)
title.DrawLatexNDC(.2, .93, topTitle)
c.Update()
c.SaveAs(plotname + ".gif")
def Plot1DEfficiencyWithBkgSubtraction(tree, num, den, axis, pixel, plotname,
topTitle, xAxisTitle, xAxisRangeLow,
xAxisRangeHigh):
#make amp histogram
c = TCanvas("c", "c", 800, 800)
#Each pixel has slightly different time distribution and efficiency of time window cut differs a bit
#We derive corresponding efficiency corrections for each pixel
timeWindowCutEfficiency = 1.0
if (pixel == "5_3"):
timeWindowCutEfficiency = 0.989
if (pixel == "5_4"):
timeWindowCutEfficiency = 0.9478
if (pixel == "5_10"):
timeWindowCutEfficiency = 0.9643
#ampHist = TH1F("ampHist",";Amplitude [mV]; Number of Events", 25,0,50)
#tree.Draw("amp[3]>>ampHist"," x_dut[2] > 19.6 && x_dut[2] < 19.7 && y_dut[2] > 23.5 && y_dut[2] < 24.0 && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)")
nbins = num.GetXaxis().GetNbins()
x = list()
y = list()
xErrLow = list()
xErrHigh = list()
yErrLow = list()
yErrHigh = list()
for b in range(1, nbins):
xtemp = num.GetXaxis().GetBinCenter(b + 1)
xerrlow = num.GetXaxis().GetBinCenter(
b + 1) - num.GetXaxis().GetBinLowEdge(b + 1)
xerrhigh = num.GetXaxis().GetBinUpEdge(
b + 1) - num.GetXaxis().GetBinCenter(b + 1)
#Noise templates:
#Pixel 5,3 : amp <= 6mV gives 0.4417 of the total and has 0 signal contamination
#Pixel 5,4 : amp <= 10mV gives 0.0404 of the total and has 0 signal contamination
#Pixel 5,10 : amp < gives 0. of the total and has 0 signal contamination
#We will assume that bins 0+1+2 (0-6mV) do not contain ANY signal.
#We count the number of events in those bins and divide by 0.4417 to get
#the total number of noise events. We subtract those from the numerator.
#Noise template is made from this data (outside of sensor region AND outside of time window):
#/eos/uscms/store/user/cmstestbeam/2019_04_April_CMSTiming/KeySightScope/RecoData/TimingDAQRECO/RecoWithTracks/v6_CACTUSSkim/Completed/Data_CACTUSAnalog_Pixel5_3_16216-16263.root
#pulse->Draw("amp[3]>>ampHist(25,0,50)","!(x_dut[2] > 19.4 && x_dut[2] < 20.6 && y_dut[2] > 23.4 && y_dut[2] < 24.1) && !((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)")
noiseSelection = ""
noiseSelectionCRFraction = 1
xPositionSelection = ""
yPositionSelection = ""
if (pixel == "5_3"):
noiseSelection = " && amp[3] <= 6"
noiseSelectionCRFraction = 0.4417
xPositionSelection = " && x_dut[2] > 19.5 && x_dut[2] < 20.5 "
yPositionSelection = " && y_dut[2] > 23.5 && y_dut[2] < 24.0 "
if (pixel == "5_4"):
noiseSelection = " && amp[3] <= 14"
noiseSelectionCRFraction = 0.4053
xPositionSelection = " && x_dut[2] > 18.5 && x_dut[2] < 19.5 "
yPositionSelection = " && y_dut[2] > 23.5 && y_dut[2] < 24.0 "
if (pixel == "5_10"):
noiseSelection = " && amp[3] <= 13"
noiseSelectionCRFraction = 0.3802
xPositionSelection = " && x_dut[2] > 19.5 && x_dut[2] < 20.5 "
yPositionSelection = " && y_dut[2] > 23.0 && y_dut[2] < 23.5 "
print "noise selection = " + noiseSelection + " " + str(
noiseSelectionCRFraction)
positionSelectionString = ""
if (axis == "x"):
positionSelectionString = " && x_dut[2] > " + str(
num.GetXaxis().GetBinLowEdge(b + 1)) + " && x_dut[2] < " + str(
num.GetXaxis().GetBinUpEdge(b + 1)) + yPositionSelection
if (axis == "y"):
positionSelectionString = xPositionSelection + " && y_dut[2] > " + str(
num.GetXaxis().GetBinLowEdge(b + 1)) + " && y_dut[2] < " + str(
num.GetXaxis().GetBinUpEdge(b + 1)) + " "
print "numerator: " + "ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " + positionSelectionString + " && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)"
ampHist = TH1F("ampHist" + "_" + str(b),
";Amplitude [mV]; Number of Events", 25, 0, 50)
denominatorCount = tree.GetEntries(
"ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " +
positionSelectionString + " ")
tmpNumeratorTotalCount = tree.GetEntries(
"ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " +
positionSelectionString +
" && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)"
)
tmpNumeratorNoiseControlRegionCount = tree.GetEntries(
"ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " +
positionSelectionString +
" && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16) "
+ noiseSelection)
tmpNumeratorSignalCount = tmpNumeratorTotalCount - tmpNumeratorNoiseControlRegionCount / noiseSelectionCRFraction
print "ntracks==1 && y_dut[0] > 0 && npix>0 && nback>0 " + positionSelectionString + " && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16) " + noiseSelection
ratio = 0
errLow = 0
errHigh = 0
n1 = int(tmpNumeratorSignalCount)
n2 = int(denominatorCount)
print "numerator: " + str(n1) + " and denominator: " + str(n2)
if (n1 > n2):
n1 = n2
if (n2 > 0):
ratio = float(n1) / float(n2)
if (ratio > 1):
ratio = 1
errLow = ratio - TEfficiency.ClopperPearson(
n2, n1, 0.68269, False)
errHigh = TEfficiency.ClopperPearson(n2, n1, 0.68269, True) - ratio
print " done bin " + str(b) + " : " + str(
num.GetXaxis().GetBinLowEdge(b + 1)) + " - " + str(
num.GetXaxis().GetBinUpEdge(b + 1))
print " num = " + str(n1) + " = " + str(
tmpNumeratorTotalCount) + " - " + str(
tmpNumeratorNoiseControlRegionCount) + " / " + str(
noiseSelectionCRFraction) + " | den = " + str(n2)
print "ratio = " + str(ratio) + " " + str(errLow) + " " + str(errHigh)
ytemp = ratio / timeWindowCutEfficiency #here we correct for the time window cut inefficiency
yerrlowtemp = errLow
yerrhightemp = errHigh
print "x: " + str(xtemp) + " and y: " + str(ytemp)
x.append(xtemp)
y.append(ytemp)
xErrLow.append(xerrlow)
xErrHigh.append(xerrhigh)
yErrLow.append(yerrlowtemp)
yErrHigh.append(yerrhightemp)
c = TCanvas("cv", "cv", 800, 800)
c.SetLeftMargin(0.12)
#must convert list into array for TGraphAsymmErrors to work
xArr = array.array('f', x)
yArr = array.array('f', y)
xErrLowArr = array.array('f', xErrLow)
xErrHighArr = array.array('f', xErrHigh)
yErrLowArr = array.array('f', yErrLow)
yErrHighArr = array.array('f', yErrHigh)
effGraph = TGraphAsymmErrors(nbins, xArr, yArr, xErrLowArr, xErrHighArr,
yErrLowArr, yErrHighArr)
effGraph.Draw("APE")
effGraph.SetTitle("")
effGraph.GetXaxis().SetTitle(xAxisTitle)
effGraph.GetXaxis().SetTitleSize(0.05)
effGraph.GetXaxis().SetTitleOffset(0.90)
effGraph.GetXaxis().SetLabelSize(0.03)
effGraph.GetXaxis().SetRangeUser(xAxisRangeLow, xAxisRangeHigh)
effGraph.GetYaxis().SetTitle("Efficiency")
effGraph.GetYaxis().SetTitleSize(0.05)
effGraph.GetYaxis().SetTitleOffset(1.05)
effGraph.GetYaxis().SetLabelSize(0.03)
title = TLatex()
title.SetTextSize(0.05)
title.DrawLatexNDC(.2, .93, topTitle)
c.Update()
c.SaveAs(plotname + ".gif")
def Plot1DEfficiencyWithFit(tree, plotname, topTitle, xAxisTitle,
xAxisRangeLow, xAxisRangeHigh):
#make amp histogram
c = TCanvas("c", "c", 800, 800)
ampHist = TH1F("ampHist", ";Amplitude [mV]; Number of Events", 25, 0, 50)
tree.Draw(
"amp[3]>>ampHist",
" x_dut[2] > 19.6 && x_dut[2] < 19.7 && y_dut[2] > 23.5 && y_dut[2] < 24.0 && ((t_peak[3] - t_peak[0])*1e9 > 6 && (t_peak[3] - t_peak[0])*1e9 < 16)"
)
# create function for fitting
fitFunction = TF1("NoisePlusLandauGaus", NoisePlusLandauGaus, 0, 50, 5)
fitFunction.SetParameters(10, 0.95, 20.0, 2.5, 3.0)
fitFunction.SetParNames("a", "f", "mpv", "sigmaLandau", "sigmaGaus")
#fitFunction.SetParLimits(0, -1, -4)
#fitFunction.SetParLimits(1, 0.01, 0.2)
#fitFunction.SetParLimits(2, 0, 2)
#fitFunction.SetParLimits(3, 0, 1000)
ampHist.Fit("NoisePlusLandauGaus")
c.SaveAs("fit.gif")
return
nbins = num.GetXaxis().GetNbins()
x = list()
y = list()
xErrLow = list()
xErrHigh = list()
yErrLow = list()
yErrHigh = list()
for b in range(1, nbins):
xtemp = num.GetXaxis().GetBinCenter(b + 1)
xerrlow = num.GetXaxis().GetBinCenter(
b + 1) - num.GetXaxis().GetBinLowEdge(b + 1)
xerrhigh = num.GetXaxis().GetBinUpEdge(
b + 1) - num.GetXaxis().GetBinCenter(b + 1)
ratio = 0
errLow = 0
errHigh = 0
n1 = int(num.GetBinContent(b + 1))
n2 = int(den.GetBinContent(b + 1))
print "numerator: " + str(n1) + " and denominator: " + str(n2)
if (n1 > n2):
n1 = n2
if (n2 > 0):
ratio = float(n1) / float(n2)
if (ratio > 1):
ratio = 1
errLow = ratio - TEfficiency.ClopperPearson(
n2, n1, 0.68269, False)
errHigh = TEfficiency.ClopperPearson(n2, n1, 0.68269, True) - ratio
print " done bin " + str(
b) + " " + str(xtemp) + " : " + str(n1) + "(" + str(
num.GetBinContent(b + 1)) + ")" + " / " + str(n2) + "(" + str(
den.GetBinContent(b + 1)) + ")" + " = " + str(
ratio) + " " + str(errLow) + " " + str(errHigh)
ytemp = ratio
yerrlowtemp = errLow
yerrhightemp = errHigh
print "x: " + str(xtemp) + " and y: " + str(ytemp)
x.append(xtemp)
y.append(ytemp)
xErrLow.append(xerrlow)
xErrHigh.append(xerrhigh)
yErrLow.append(yerrlowtemp)
yErrHigh.append(yerrhightemp)
c = TCanvas("cv", "cv", 800, 800)
c.SetLeftMargin(0.12)
#must convert list into array for TGraphAsymmErrors to work
xArr = array.array('f', x)
yArr = array.array('f', y)
xErrLowArr = array.array('f', xErrLow)
xErrHighArr = array.array('f', xErrHigh)
yErrLowArr = array.array('f', yErrLow)
yErrHighArr = array.array('f', yErrHigh)
effGraph = TGraphAsymmErrors(nbins, xArr, yArr, xErrLowArr, xErrHighArr,
yErrLowArr, yErrHighArr)
effGraph.Draw("APE")
effGraph.SetTitle("")
effGraph.GetXaxis().SetTitle(xAxisTitle)
effGraph.GetXaxis().SetTitleSize(0.05)
effGraph.GetXaxis().SetTitleOffset(0.90)
effGraph.GetXaxis().SetLabelSize(0.03)
effGraph.GetXaxis().SetRangeUser(xAxisRangeLow, xAxisRangeHigh)
effGraph.GetYaxis().SetTitle("Efficiency")
effGraph.GetYaxis().SetTitleSize(0.05)
effGraph.GetYaxis().SetTitleOffset(1.05)
effGraph.GetYaxis().SetLabelSize(0.03)
title = TLatex()
title.SetTextSize(0.05)
title.DrawLatexNDC(.2, .93, topTitle)
c.Update()
c.SaveAs(plotname + ".gif")
def getEffandError(num, den):
eff = 0 if den == 0 else num / den
hi = TEfficiency.ClopperPearson(int(den), int(num), sigma, True)
lo = TEfficiency.ClopperPearson(int(den), int(num), sigma, False)
return (eff, (eff - lo) / eff, (hi - eff) / eff)
wvl = 4.135667516e-15 * 299792458.0 / e * 1e3
wvl_min = min(wvl)
wvl_max = max(wvl)
nbins = int((wvl_max - wvl_min) / args.bin_width)
eff = TEfficiency("%d-pass" % i, "%d-eff" % i, nbins, wvl_min, wvl_max)
eff.SetUseWeightedEvents()
eff.SetStatisticOption(TEfficiency.kFNormal)
for wvli, ni in zip(wvl, n):
eff.FillWeighted(True, ni, wvli)
eff.FillWeighted(False, n_particles - ni, wvli)
x = np.array(
[eff.GetTotalHistogram().GetBinCenter(i) for i in xrange(nbins)])
y = np.array([eff.GetEfficiency(i) for i in xrange(nbins)]) * 100.0
yerr_low = np.array([
eff.ClopperPearson(int(n_particles),
int(eff.GetEfficiency(i) * n_particles), 0.68,
False) for i in xrange(nbins)
]) * 100.0
yerr_up = np.array([
eff.ClopperPearson(int(n_particles),
int(eff.GetEfficiency(i) * n_particles), 0.68, True)
for i in xrange(nbins)
]) * 100.0
# Plot bin statistics.
label = ("%s +%.1f V" % (name, v_ov * 1e6))
# Plot PDE
ax.errorbar(x, y, yerr=(y - yerr_low, yerr_up - y), fmt=".", label=label)
# Set legend titles.
ax.set_xlabel("wavelength / nm")
ax.set_ylabel("photon detection efficiency / %")
def makeCombinedEtaPlot(self, tight=False):
hist = self.makeL1TimeVsEtaPlot(('tight_' if tight else '') +
'dtOnly_bxidVsEta')[2]
countsInL1 = []
for x in np.arange(-.95, 1.05, 0.1):
totalCounter = 0
zeroCount = 0
for y in range(-2, 3):
totalCounter += hist.GetBinContent(hist.FindBin(x, y))
if y == 0:
zeroCount = hist.GetBinContent(hist.FindBin(x, y))
countsInL1.append({
'total': totalCounter,
'zero': zeroCount,
'eta': x
})
#Graph for results
graph1 = TEfficiency(hist.GetName(), "", 8, -9.195, -.5)
graph2 = TEfficiency(hist.GetName(), "", 8, .5, 9.195)
for item in countsInL1:
if item['total'] == 0:
continue
print item['total'], item['zero'], item['eta']
if item['eta'] < 0:
graph1.SetTotalEvents(
graph1.FindFixBin(-0.5 + item['eta'] / 0.087),
int(item['total']))
graph1.SetPassedEvents(
graph1.FindFixBin(-0.5 + item['eta'] / 0.087),
int(item['zero']))
else:
graph2.SetTotalEvents(
graph2.FindFixBin(0.5 + item['eta'] / 0.087),
int(item['total']))
graph2.SetPassedEvents(
graph2.FindFixBin(0.5 + item['eta'] / 0.087),
int(item['zero']))
histHo = None
if tight:
histHo = self.plotTightHoTimeVsEta()[3][1]
else:
histHo = self.plotHoTimeVsEta()[3][1]
histHo.SetTitle(('Tight ' if tight else '') + 'Unmatched DT + HO')
canvas = TCanvas(
'combinedPlot' + ('Tight ' if tight else '') + hist.GetName(),
'combinedPlot')
canvas.cd().SetTopMargin(.15)
histHo.Draw('ap')
canvas.Update()
canvas.cd().SetTicks(0, 0)
histHo.SetMarkerStyle(2)
histHo.SetLineColor(colorRwthDarkBlue)
histHo.SetMarkerColor(colorRwthDarkBlue)
histHo.GetPaintedGraph().GetXaxis().SetRangeUser(-12, 12)
histHo.GetPaintedGraph().GetXaxis().SetLabelColor(colorRwthDarkBlue)
histHo.GetPaintedGraph().GetXaxis().SetTitleColor(colorRwthDarkBlue)
histHo.GetPaintedGraph().GetXaxis().SetAxisColor(colorRwthDarkBlue)
yMax = gPad.GetFrame().GetY2()
yMin = gPad.GetFrame().GetY1()
#Print average Fraction excluding iEta +/-2
x = Double(0)
y = Double(0)
mean = 0
var = 0
for i in range(0, histHo.GetPaintedGraph().GetN()):
histHo.GetPaintedGraph().GetPoint(i, x, y)
if abs(x) == 2:
continue
mean += y
var += histHo.GetPaintedGraph().GetErrorY(
i) * histHo.GetPaintedGraph().GetErrorY(i)
mean /= histHo.GetPaintedGraph().GetN() - 2
sigma = sqrt(var / (histHo.GetPaintedGraph().GetN() - 2))
self.debug(
"Average fraction excluding iEta +/- 2 %s: %5.2f%% +/- %5.2f%%" %
('[Tight]' if tight else '', mean * 100, sigma * 100))
nTotal = 0
nPassed = 0
for item in countsInL1:
if fabs(item['eta']) == 2 or fabs(item['eta'] == 0):
continue
nTotal += item['total']
nPassed += item['zero']
#Print again with ClopperPearson uncertainty, the counts are for L1!!!
mean = nPassed / nTotal * 100
sigmaPlus = TEfficiency.ClopperPearson(int(nTotal), int(nPassed), .68,
1) * 100 - mean
sigmaMinus = mean - TEfficiency.ClopperPearson(
int(nTotal), int(nPassed), .68, 0) * 100
#self.debug("Average fraction excluding iEta +/- 2 %s with Clop.Pear.: %5.2f%% +%5.2f%% -%5.2f%%"
# % ('[Tight]' if tight else '',mean,sigmaPlus,sigmaMinus))
#Left axis part
f1 = TF1("f1", "x", -0.87, 0)
A1 = TGaxis(-10, yMax, -0.5, yMax, "f1", 010, "-")
A1.SetLineColor(colorRwthRot)
A1.SetLabelColor(colorRwthRot)
A1.Draw()
#Right axis part
f2 = TF1("f2", "x", 0, 0.87)
A2 = TGaxis(0.5, yMax, 10, yMax, "f2", 010, "-")
A2.SetLineColor(colorRwthRot)
A2.SetLabelColor(colorRwthRot)
A2.Draw()
#Box for shading out 0
box = TBox(-.5, yMin, 0.5, yMax)
box.SetLineColor(colorRwthDarkGray)
box.SetFillColor(colorRwthDarkGray)
box.SetFillStyle(3013)
box.Draw('same')
#Left L1 eta
graph1.SetMarkerColor(colorRwthRot)
graph1.SetLineColor(colorRwthRot)
graph1.SetMarkerStyle(20)
graph1.Draw('same,p')
#Right L1Eta
graph2.SetMarkerColor(colorRwthRot)
graph2.SetLineColor(colorRwthRot)
graph2.SetMarkerStyle(20)
graph2.Draw('same,p')
#Label for extra axis
axisLabel = TPaveText(0.83, 0.85, 0.89, 0.9, "NDC")
axisLabel.AddText('#eta_{L1}')
axisLabel.SetBorderSize(0)
axisLabel.SetFillStyle(0)
axisLabel.SetTextColor(colorRwthRot)
axisLabel.SetTextSize(0.05)
axisLabel.Draw()
#Legend
legend = getLegend(x1=0.1, y1=0.1, x2=0.4, y2=.35)
legend.AddEntry(histHo, 'HO #in #pm12.5 ns', 'pe')
legend.AddEntry(graph1, ('Tight ' if tight else '') + 'L1 BXID = 0',
'pe')
legend.Draw()
canvas.Update()
self.storeCanvas(canvas,
"combinedFractionL1AndHo" +
('Tight' if tight else ''),
drawMark=False)
return histHo, graph1, canvas, A1, f1, A2, f2, box, graph2, axisLabel, legend
