def findAntiprotonsInFile(filename, name):
event = 0
results = []
histogram = TH2D("", "", 256, 0, 256, 256, 0, 256)
pixels = 0
timeDelay = 0
numberOfLines = 0
timeAntiproton = 0
for line in open(filename):
numberOfLines += 1
columns = line.split()
if columns[0] == "new":
isAntiproton = False
if pixels > 70:
print "test one antiproton"
isAntiproton = evaluateHistogram(histogram, name, event)
event += 1
if isAntiproton:
timeDelay = timeAntiproton * 1.0 / pixels - float(columns[2])
results.append(timeDelay)
histogram = TH2D("", "", 256, 0, 256, 256, 0, 256)
timeAntiproton = 0
pixels = 0
else:
pixels += 1
timeAntiproton += float(columns[3])
histogram.SetBinContent(int(columns[0]), int(columns[1]),
float(columns[2]))
if numberOfLines < 800:
return "noFile"
else:
return results
def rmsvstime(sector, aoh, ch):
gStyle.SetOptStat(0)
logfile = open('rmsvstime.log', 'w')
c2 = TCanvas("c2", "rmsvstime", 200, 10, 800, 600)
t0 = time.time()
tmax = 5.
h = TH2D('hd', "", 2, 0, tmax, 2, 0, 1024)
h.Draw()
gm = TGraph(100)
gr = TGraph(100)
gm.SetMarkerSize(0.4)
gm.SetMarkerColor(1)
gr.SetMarkerSize(0.4)
gr.SetMarkerColor(2)
i = 0
while c2:
gm.Draw('P')
gr.Draw('P')
c2.Update()
t = time.time() - t0
response = sector.aoh[aoh].fed.query("rms %d" %
sector.aoh[aoh].fedchannels[ch])
#response="24 %f %f dummy"%(300+100*math.sin(t*0.1),5.+0.1*math.cos(t*0.2)); time.sleep(random.uniform(0.1,0.5))
channel, mean, rms, name = response.split()
logfile.write("%8.2f %s %s\n" % (t, mean, rms))
gm.SetPoint(i, t, float(mean))
gr.SetPoint(i, t, float(rms) * 100.)
i += 1
if t > tmax:
tmax = tmax * 2
h = TH2D('hd', "", 2, 0, tmax, 2, 0, 1024)
h.Draw()
def findAntiprotonsInFile(filename, name):
results = []
histogram = TH2D("", "", 256, 0, 256, 256, 0, 256)
pixels = 0
timeDelay = 0
numberOfLines = 0
timeAntiproton = 0
print "processing file", filename
for line in open(filename):
numberOfLines += 1
columns = line.split()
if columns[0] == "new":
isAntiproton = False
if pixels > 10:
isAntiproton = evaluateHistogram(histogram)
if isAntiproton:
timeDelay = timeAntiproton * 1.0 / pixels - float(columns[2])
results.append(timeDelay)
histogram = TH2D("", "", 256, 0, 256, 256, 0, 256)
timeAntiproton = 0
pixels = 0
else:
pixels += 1
timeAntiproton += float(columns[3])
histogram.SetBinContent(int(columns[0]), int(columns[1]),
float(columns[2]))
print "number of lines", numberOfLines
if numberOfLines < 800:
return "noFile"
else:
return results
def bookHist(h,
key=None,
title='',
nbinsx=100,
xmin=0,
xmax=0,
nbinsy=0,
ymin=0,
ymax=0,
nbinsz=0,
zmin=0,
zmax=0):
if key == None:
print 'missing key'
return
rkey = str(
key) # in case somebody wants to use integers, or floats as keys
if h.has_key(key): h[key].Reset()
elif hasattr(nbinsx, 'itemsize'):
if xmin == 0:
h[key] = TH1D(rkey, title, len(nbinsx) - 1, nbinsx)
else:
h[key] = TH2D(rkey, title,
len(nbinsx) - 1, nbinsx, xmin, xmax, nbinsy)
elif nbinsz > 0:
h[key] = TH3D(rkey, title, nbinsx, xmin, xmax, nbinsy, ymin, ymax,
nbinsz, zmin, zmax)
elif nbinsy > 0:
h[key] = TH2D(rkey, title, nbinsx, xmin, xmax, nbinsy, ymin, ymax)
else:
h[key] = TH1D(rkey, title, nbinsx, xmin, xmax)
h[key].SetDirectory(gROOT)
def plotEtaPhiForAllL1(self):
canvas = TCanvas("cEtaPhi", "Eta Phi", 1200, 1200)
canvas.Divide(2, 1)
graphAll = self.fileHandler.getGraph('graphs/L1MuonPresent')
graphWithHo = self.fileHandler.getGraph('graphs/L1Muon3x3')
halfPhiBinwidth = L1_PHI_BIN / 2.
halfEtaBinwidth = L1_ETA_BIN / 2.
histAll = TH2D('hEtaPhiAll', "#eta#phi for all L1", 30,
-15 * L1_ETA_BIN, 15 * L1_ETA_BIN, 289,
-math.pi - halfPhiBinwidth, math.pi + halfPhiBinwidth)
histWithHo = TH2D('hEtaPhiWithHO', "#eta#phi L1 + HO (3x3)", 30,
-15 * L1_ETA_BIN, 15 * L1_ETA_BIN, 289,
-math.pi - halfPhiBinwidth,
math.pi + halfPhiBinwidth)
x = Double(0)
y = Double(0)
for i in range(0, graphAll.GetN()):
graphAll.GetPoint(i, x, y)
histAll.Fill(x, y)
for i in range(0, graphWithHo.GetN()):
graphWithHo.GetPoint(i, x, y)
histWithHo.Fill(x, y)
canvas.cd(1)
histAll.SetStats(0)
histAll.GetXaxis().SetRangeUser(-1, 1)
histAll.SetTitle(histAll.GetTitle() + ';#eta;#phi;Entries')
setupAxes(histAll)
histAll.Draw('colz')
label1 = self.drawLabel()
canvas.Update()
setupPalette(histAll)
canvas.cd(2)
histWithHo.SetStats(0)
histWithHo.GetXaxis().SetRangeUser(-1, 1)
histWithHo.SetTitle(histWithHo.GetTitle() + ';#eta;#phi;Entries')
setupAxes(histWithHo)
histWithHo.Draw('colz')
label2 = self.drawLabel()
canvas.Update()
setupPalette(histWithHo)
canvas.Update()
canvas.SaveAs('plots/etaPhiForAllL1.pdf')
return canvas, histAll, histWithHo, label1, label2
def plot_2D(xvar, yvar, title, xtitle, ytitle, xnbin, xlow, xhigh, ynbin, ylow,
yhigh, region, cutter):
if cutter == "":
weight = "event_weight"
else:
weight = "(" + cutter + ")*event_weight"
can = TCanvas("can", "can", 1000, 900)
can.cd()
# gPad.SetLogy(1)
# gPad.SetLogz(1)
gStyle.SetOptStat(0)
backs = TH2D('a', "DiPhoton;" + xtitle + ";" + ytitle, xnbin, xlow, xhigh,
ynbin, ylow, yhigh)
diphob = TH1F('b', "DiPhoton;" + xtitle + ";", xnbin, xlow, xhigh)
Variables = {}
for tre in ListBacks:
histName = xvar + yvar + tre
Variables[tre] = TH2F(histName, ";" + xtitle + ";" + ytitle, xnbin,
xlow, xhigh, ynbin, ylow, yhigh)
treepj[tre].Draw(yvar + ":" + xvar + ">>" + histName, weight)
Variables[tre].Sumw2()
Variables[tre].Scale(lumi * crossx[tre] * kFact[tre] / Nevents[tre])
if tre.startswith("GJets"):
backs.Add(Variables[tre])
if tre.startswith("QCD"):
backs.Add(Variables[tre])
if tre.startswith("DiPhoton"):
backs.Add(Variables[tre])
backs.Draw("COLZ")
can.SaveAs("plots/Background" + region + "-" + xvar + "-" + yvar + ".pdf")
Variables['Data'] = TH2D('Data',
"Data/Background;" + xtitle + ";" + ytitle, xnbin,
xlow, xhigh, ynbin, ylow, yhigh)
treepj['Data'].Draw(yvar + ":" + xvar + ">>Data", cutter)
Pull = TH2D('Pull', title + ";" + xtitle + ";" + ytitle, xnbin, xlow,
xhigh, ynbin, ylow, yhigh)
Pull = Variables['data'].Clone()
Pull.Divide(backs)
Pull.SetMaximum(2.0)
Pull.SetMinimum(0.0)
for i in range(xnbin):
i += 1
for j in range(ynbin):
j += 1
if Variables['data'].GetBinContent(i, j) != 0:
Pull.SetBinContent(i, j, Pull.GetBinContent(i, j))
# print "x: "+str(i)+" y: "+str(j)+" Data: "+str(Variables['data'].GetBinContent(i,j))+" Backgrounds: "+str(backs.GetBinContent(i,j))+" Data/Backgrounds: "+str(Pull.GetBinContent(i,j))
else:
Pull.SetBinContent(i, j, 0)
can4 = TCanvas("can4", "can", 1000, 900)
Pull.Draw("COLZ")
can4.SaveAs("plots/Pull2D-" + region + "-" + xvar + "-" + yvar + ".pdf")
def plotClusters(filepath):
print "filepath", filepath
histogram = TH2D("clusterNumber1", "clusterNumber1", 256, 0, 256, 256, 0,
256)
histogramTotal = TH2D("total", "total", 256, 0, 256, 256, 0, 256)
histogramTime = TH1D("default", "default", 1, 0, 1)
outputRootFile = filepath.replace("clustering",
"histograms").replace(".dat", ".root")
myFile = TFile(outputRootFile, "RECREATE")
clusterNumber = 0
hasStarted = False
average = 0
modeTime = 0
totalTime = 0
numberOfPixels = 0
fileS.write("new\n")
print "working with file ", filepath
for line in open(filepath):
print line
columns = line.split()
if columns[0] == "new":
if hasStarted == False:
modeTime = float(columns[2])
histogramTime = TH1D("ToA", "ToA", 200, -1000, 1000)
#if hasStarted and histogram.GetEntries()>10 and histogram.GetEntries()<1000:
if hasStarted and histogram.GetEntries(
) > 0 and histogram.GetEntries() < 1000:
average = totalTime / (1.0 * numberOfPixels)
delay = average - modeTime
fileS.write(str(histogram.GetEntries()) + "\n")
if delay > 1000:
histogram.SetTitle("delayed " + str(delay))
histogram.Write()
print " er vi her"
clusterNumber = clusterNumber + 1
totalTime = 0
numberOfPixels = 0
#modeTime=float(columns[2])
histogram = TH2D("clusterNumber " + str(clusterNumber),
"clusterNumber ", 256, 0, 256, 256, 0, 256)
else:
print "er vi her da"
numberOfPixels = numberOfPixels + 1
totalTime = float(columns[3]) + totalTime
histogram.Fill(int(columns[0]), int(columns[1]), float(columns[2]))
histogramTotal.Fill(int(columns[0]), int(columns[1]),
float(columns[2]))
histogramTime.Fill(float(columns[3]) - modeTime)
hasStarted = True
histogramTotal.Write()
histogramTime.Write()
del histogramTime
myFile.Write()
def plotEtaPhiForTightL1(self):
canvas = TCanvas("cEtaPhi","Eta Phi",1200,1200)
canvas.Divide(2,1)
graphAll = self.fileHandler.getGraph('graphs/patTightToL1Muons')
graphWithHo = self.fileHandler.getGraph('graphs/patTightToL1Muons3x3')
halfPhiBinwidth = L1_PHI_BIN/2.
l1BinOffset = L1_PHI_BIN*3/4.
histAll = TH2D('hEtaPhiAll',"#eta#phi for tight L1",30,-15*L1_ETA_BIN ,15*L1_ETA_BIN,
144, -math.pi,math.pi)
histWithHo = TH2D('hEtaPhiWithHO',"#eta#phi tight L1 + HO (3x3)",30,-15*L1_ETA_BIN,15*L1_ETA_BIN,
144, -math.pi,math.pi)
x = Double(0)
y = Double(0)
for i in range(0,graphAll.GetN()):
graphAll.GetPoint(i,x,y)
histAll.Fill(x,y)
for i in range(0,graphWithHo.GetN()):
graphWithHo.GetPoint(i,x,y)
histWithHo.Fill(x,y)
canvas.cd(1)
histAll.SetStats(0)
histAll.GetXaxis().SetRangeUser(-1,1)
histAll.SetTitle(histAll.GetTitle() + ';#eta_{L1};#phi_{L1};Entries')
histAll.Draw('colz')
canvas.Update()
setupAxes(histAll)
setupPalette(histAll)
label1 = self.drawLabel()
canvas.Update()
canvas.cd(2)
histWithHo.SetStats(0)
histWithHo.GetXaxis().SetRangeUser(-1,1)
histWithHo.SetTitle(histWithHo.GetTitle() + ';#eta_{L1};#phi_{L1};Entries')
histWithHo.Draw('colz')
label2 = self.drawLabel()
canvas.Update()
setupAxes(histWithHo)
setupPalette(histWithHo)
canvas.Update()
self.storeCanvas(canvas, 'etaPhiForTightL1')
return canvas,histAll,histWithHo,label1,label2
def two_dimensional_weight(mcfile,
fixdata,
var2weight1,
var2weight2,
nbins,
xmin,
xmax,
ymin,
ymax,
weightfile,
weightplot,
denominator_weight=None,
numerator_weight="sig_sw"):
#global settings
lhcbStyle.lhcbStyle.SetPadTopMargin(0.08)
lhcbStyle.lhcbStyle.SetPadRightMargin(0.15)
weightvar = var2weight1 + ":" + var2weight2
cut = "1"
#obtain mc histo
f1 = TFile(mcfile)
t1 = f1.Get("DecayTree")
h1 = TH2D("h1", "h1", nbins, xmin, xmax, nbins, ymin, ymax)
h1.Sumw2()
print(denominator_weight)
if denominator_weight is None:
t1.Project("h1", weightvar, cut)
else:
t1.Project("h1", weightvar, "({0})*{1}".format(cut,
denominator_weight))
h1.Scale(1. / h1.Integral())
#obtain data histo
f2 = TFile(fixdata)
t2 = f2.Get("DecayTree")
h2 = TH2D("h2", "h2", nbins, xmin, xmax, nbins, ymin, ymax)
h2.Sumw2()
t2.Project("h2", weightvar, "({0})*{1}".format(cut, numerator_weight))
h2.Scale(1. / h2.Integral())
#calculate weights
fw = TFile(weightfile, "RECREATE")
hw = TH2D("hw", "hw", nbins, xmin, xmax, nbins, ymin, ymax)
hw.Divide(h2, h1, 1, 1, "B")
#weight plot save
c1 = TCanvas("c1", "c1")
hw.Draw("COL Z TEXT")
hw.SetXTitle(var2weight1)
hw.SetYTitle(var2weight2)
c1.SaveAs(weightplot)
#save weight to root file
hw.Write()
def plotTightL1EtaPhiRatio(self):
gL1Tight = self.fileHandler.getGraph('graphs/patTightToL1Muons')
gL1Tight3x3 = self.fileHandler.getGraph('graphs/patTightToL1Muons3x3')
halfPhiBinwidth = L1_PHI_BIN / 2.
hL1Tight = TH2D('hL1Tight', 'L1Tight', 30, -15 * L1_ETA_BIN,
15 * L1_ETA_BIN, 144, -math.pi, math.pi)
hL1Tight3x3 = TH2D('hL1Tight3x3', 'L1Tight3x3', 30, -15 * L1_ETA_BIN,
15 * L1_ETA_BIN, 144, -math.pi, math.pi)
hL1Tight = fillGraphIn2DHist(gL1Tight, hL1Tight)
hL1Tight3x3 = fillGraphIn2DHist(gL1Tight3x3, hL1Tight3x3)
c1 = TCanvas("available tight L1 for matching")
hClone = hL1Tight.Clone('hClone')
hClone.Draw('colz')
hRatio = hL1Tight3x3.Clone('asdfasdf')
hRatio.Divide(hL1Tight)
c = TCanvas('2dMap')
hRatio.SetTitle(
'Local Efficiency per tight L1 coordinate (3x3 Matching);#eta_{L1};#phi_{L1};#epsilon'
)
hRatio.GetXaxis().SetRangeUser(-0.8, .8)
hRatio.Draw('colz')
hRatio.SetStats(0)
c.Update()
setupAxes(hRatio)
setupPalette(hRatio)
label = self.drawLabel()
c.Update()
self.storeCanvas(c, 'localTightL1Efficiency')
c2 = TCanvas('projections')
c2.Divide(2, 1)
c2.cd(1)
hEta = hRatio.ProjectionX()
hEta.Scale(1 / float(144)) #72 phi bins
hEta.Draw()
c2.cd(2)
hPhi = hRatio.ProjectionY()
hPhi.Scale(1 / float(16)) #16 eta bins, cutoff due to |eta| < 0.8
hPhi.Draw()
return c, hRatio, label, c2, hEta, hPhi, c1, hClone
def __plot_2d(self):
global unique_cnt
uid = next(unique_cnt)
header = self.__parser.get_header_info()
hdata = self.__parser.get_histogram_data()['DATA']
name = header['H_NAME'] + ' ' + str(uid)
name = name[1:]
nxbins = header['XBINS']
nybins = header['YBINS']
nzbins = header['ZBINS']
xl = header['XRAN'][0]
xu = header['XRAN'][1]
yl = header['YRAN'][0]
yu = header['YRAN'][1]
zl = header['ZRAN'][0]
zu = header['ZRAN'][1]
n_of_histo = nzbins
self.__histo = TH2D(name, name, int(nzbins), float(zl), float(zu),
int(nybins), float(yl), float(yu))
self.__histo.SetXTitle("Z [cm]")
self.__histo.GetXaxis().CenterTitle(kTRUE)
self.__histo.GetXaxis().SetTitleOffset(1.1)
self.__histo.GetXaxis().SetTitleSize(0.04)
self.__histo.GetXaxis().SetLabelSize(0.03)
self.__histo.GetXaxis().SetTickLength(0.02)
self.__histo.GetXaxis().SetNdivisions(20510)
self.__histo.SetYTitle("Y [cm]")
self.__histo.GetYaxis().CenterTitle(kTRUE)
self.__histo.GetYaxis().SetTitleOffset(1.2)
self.__histo.GetYaxis().SetTitleSize(0.04)
self.__histo.GetYaxis().SetLabelSize(0.03)
self.__histo.GetYaxis().SetTickLength(0.02)
self.__histo.GetYaxis().SetNdivisions(20510)
self.__histo.SetLineColor(kRed)
self.__histo.SetMinimum(1e-9)
self.__histo.GetZaxis().SetTitle("a.u.")
nxbins = header['XBINS']
ResX = (header['XRAN'][1] - header['XRAN'][0]) / nxbins
ResY = (yu - yl) / nybins
ResZ = (zu - zl) / nzbins
FirstX = header['XRAN'][0] + ResX / 2.
FirstY = yl + ResY / 2.
FirstZ = zl + ResZ / 2.
N = nxbins * nybins * nzbins
zPos = [None] * N
xPos = [None] * N
yPos = [None] * N
# -> fill the histo now!
pos_cnt = 0
for zentry in range(nzbins):
zPos[zentry] = FirstZ + float(zentry) * ResZ
for yentry in range(nybins):
for xentry in range(nxbins):
data_point = hdata[pos_cnt]
xPos[xentry] = FirstX + float(xentry) * ResX
yPos[yentry] = FirstY + float(yentry) * ResY
self.__histo.Fill(zPos[zentry], yPos[yentry], data_point)
pos_cnt += 1
def plotEtaPhiForDeltaPhiOne(self):
canvas = TCanvas("cEtaPhiDeltaPhiOne", "Eta Phi For DPhi 1", 1200,
1200)
graph = self.fileHandler.getGraph('graphs/averageEnergyDeltaPhi1')
halfbinwidth = L1_PHI_BIN / 2.
hist = TH2D('hEtaPhiDeltaPhi1', "#eta#phi of #Delta#phi=1 evts.", 30,
-15 * L1_ETA_BIN, 15 * L1_ETA_BIN, 289,
-math.pi - halfbinwidth, math.pi + halfbinwidth)
x = Double(0)
y = Double(0)
for i in range(0, graph.GetN()):
graph.GetPoint(i, x, y)
hist.Fill(x, y)
hist.SetStats(0)
hist.GetXaxis().SetRangeUser(-1, 1)
hist.SetTitle(hist.GetTitle() + ';#eta;#phi;Entries')
setupAxes(hist)
hist.Draw('colz')
canvas.Update()
setupPalette(hist)
label = self.drawLabel()
canvas.Update()
self.storeCanvas(canvas, 'etaPhiForDeltaPhiOne')
canvas.SaveAs('plots/etaPhiForDeltaPhiOne.pdf')
return canvas, hist, label
def butterPlots2D(dsList):
basicCut = "trapENFCal > 0.8 && gain==0 && mH==1 && isGood && !muVeto && !wfDCBits && !isLNFill1 && !isLNFill2 && trapETailMin < 0 && channel!=596 && channel!=676 && channel!=676 && channel!=612 && channel!=1104 && channel!=1200 && channel!=1334 && channel!=1336"
bcrCut = " && tOffset < 10 && waveS5/TMath::Power(trapENFCal,1/4) < 1200 && (waveS3-waveS2)/trapENFCal > 100 && (waveS3-waveS2)/trapENFCal < 300"
# bcrCut += " && (waveS3-waveS4)/trapENFCal < 100 && (waveS3-waveS4)/trapENFCal > 0" # this doesn't do much
ds3noisyRunCut = " && !(channel==692 && (run==16974 || run==16975 || run==16976 || run==16977 || run==16979))"
bn, lo, hi = 100, 0, 20
c = TCanvas("", "", 800, 600)
for dsNum in dsList:
print "Plotting DS-%d ..." % dsNum
ch = TChain("skimTree")
ch.Add(str("~/project/wavelet-skim/waveletSkimDS%d*" % dsNum))
h1 = TH2D("h1", "h1", 100, 0, 20, 100, 0, 22000)
ch.Project("h1", "butterTime:trapENFCal",
basicCut + bcrCut + ds3noisyRunCut)
h1.GetXaxis().SetTitle("Energy (keV)")
h1.GetYaxis().SetTitle("Deriv.MaxTime")
h1.GetYaxis().SetTitleOffset(1.5)
h1.Draw()
c.SetLeftMargin(0.15)
c.Print("./plots/butterTime_DS%d.pdf" % dsNum)
def h2_generated(ptbins, ctbins, name='Generated'):
th2 = TH2D(name, ';#it{p}_{T} (GeV/#it{c});c#it{t} (cm); Generated',
len(ptbins) - 1, np.array(ptbins, 'double'),
len(ctbins) - 1, np.array(ctbins, 'double'))
th2.SetDirectory(0)
return th2
def plot_ratio_data(df1, df2, data, c=None):
from ROOT import TH2D
if c is None:
from ROOT import TCanvas
c = TCanvas()
plot = PlotData()
plot.canvas = c
nktbins = 5
ncentbins = 2
h = TH2D('hist', 'Hist; kT (GeV); Centrality',
nktbins, -0.5, nktbins+0.5,
ncentbins, -0.5, ncentbins+0.5)
h.SetStats(0)
cents = []
kts = []
ratios = []
for cent, cdf in df1.groupby('cent'):
cents.append(cent)
for kt, ktdf in cdf.groupby('kt'):
pass
# h.FillRandom()
# h2 = h.Clone('h2')
# h2.Fill
# h.Divide(h2)
np.frombuffer(h.GetArray(), count=h.GetNcells())[:] = data.flatten()
h.SetBinContent(0,0)
h.Draw('COLZ')
plot.h = h
return plot
def hist2D(self, var1, nBins1, a1, b1, var2, nBins2, a2, b2, **kwargs):
name = kwargs.get(
'name',
makeHistName(self.label + "_merged", "%s_vs_%s" % (var1, var2)))
title = kwargs.get('title', self.label)
blind = kwargs.get('blind', self.blind)
kwargs['scale'] = self.scale * kwargs.get('scale',
1.0) # pass scale down
verbosity = kwargs.get('verbosity', 0)
printVerbose(
">>>\n>>> Samples - %s, %s vs. %s: %s" %
(color(name, color="grey"), var1, var2, self.filenameshort),
verbosity)
printVerbose(">>> scale: %.4f" % (kwargs['scale']), verbosity)
hist2D = TH2D(name, title, nBins2, a2, b2, nBins1, a1, b1)
for sample in self.samples:
if 'name' in kwargs: # prevent memory leaks
kwargs['name'] = makeHistName(
sample.label, name.replace(self.label + '_', ''))
hist2D.Add(
sample.hist2D(var1, nBins1, a1, b1, var2, nBins2, a2, b2,
**kwargs))
return hist2D
def makeCoordinatePlot(self, source):
c = TCanvas(source, source, 1200, 1200)
graphDt = self.fileHandler.getGraph('graphs/L1MuonPresent_' + source)
histAll = TH2D('hEtaPhi' + source, ";#eta_{L1};#phi_{L1};#", 30,
-15 * L1_ETA_BIN, 15 * L1_ETA_BIN, 144, -math.pi,
math.pi)
fillGraphIn2DHist(graphDt, histAll)
###
'''
Temporary stuff to check the eta coordinates in the graphs
'''
x = Double(0)
y = Double(0)
listeDt = []
for i in range(0, graphDt.GetN()):
graphDt.GetPoint(i, x, y)
listeDt.append(float(x))
histAll.SetStats(0)
histAll.Draw('colz')
c.Update()
setupAxes(histAll)
label = self.drawLabel()
c.Update()
return c, histAll, label
def doPlotEtaPtOfSuccessfulMatches():
file = TFile.Open('L1MuonHistogram.root')
#Prepare canvas
canvas = TCanvas("canvasPtEtaHoMatch", "PtEtaHoMatch", 1200, 1200)
canvas.cd().Draw()
#prepare histogram
hist = file.Get("hoMuonAnalyzer/etaPhi/3D/NoTrgTdmiAboveThr_EtaPhiPt")
stack = THStack(hist, "zx", "2dStack", "", -1, -1, -1, -1, "zx", "")
#Create new histogram and add the histograms from the stack
histNew = TH2D("histPtEtaHoMatch",
"p_{T} vs. #eta distribution;#eta;p_{T} / 5 GeV;#", 40,
-1.6, 1.6, 40, 0, 200)
histNew.GetYaxis().SetTitleOffset(1.2)
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
histNew.Draw('COLZ')
canvas.Update()
palette = histNew.FindObject("palette")
palette.SetX1NDC(0.9)
palette.SetX2NDC(0.92)
#add label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
canvas.SaveAs('plots/NoL1HoMatchPtEta.pdf')
canvas.SaveAs('plots/NoL1HoMatchPtEta.png')
return canvas, hist, stack, histNew, label, palette, file
def genPartPlots(tree, name, cut):
plots = []
# Kinetic energy
plots.append(
TH1D(name + 'Ekin', 'Kinetic Energy of ' + name + ' (MeV)', 200, 0,
50))
tree.Draw('PartEk >> ' + name + 'Ekin', cut)
# Time offset
plots.append(
TH1D(name + 'Time', 'Time Offset of ' + name + ' (ns)', 200, 0, 50))
tree.Draw('PartDt >> ' + name + 'Time', cut)
# Origin
plots.append(
TH1D(name + 'Orig', 'Particle Origin of ' + name + ' (mm)', 200,
-200000, 0))
tree.Draw('PartZ >> ' + name + 'Orig', cut)
# Radial distribution
plots.append(
TH1D(name + 'Rad', 'Radial Distribution of ' + name + ' (mm)', 200, 0,
4000))
tree.Draw('sqrt(PartX*PartX+PartY*PartY) >> ' + name + 'Rad', cut)
# xy distribution
plots.append(
TH2D(name + 'XY', 'XY Distribution of ' + name + ' (mm)', 200, -4000,
4000, 200, -4000, 4000))
tree.Draw('PartX:PartY >> ' + name + 'XY', cut)
return plots
def model_hist(xvar, yvar, modfuncs, nbins=95, crange=(-10.0, 10.0)):
"""Construct histogram of model functions, based on bin integrals.
xvar, yvar: Coordinate variables.
modfuncs: Model functions used in fit.
nbins: Number of bins in histograms.
crange: Range of coordinates.
"""
hists = [
TH2D('hmodel{0}{1}'.format(c, i), 'hmodel{0}{1}'.format(c, i), nbins,
crange[0], crange[1], nbins, crange[0], crange[1])
for (i, c) in ic
]
for xbin in range(nbins):
xlo = hists[0].GetXaxis().GetBinLowEdge(xbin + 1)
xup = hists[0].GetXaxis().GetBinUpEdge(xbin + 1)
for ybin in range(nbins):
ylo = hists[0].GetXaxis().GetBinLowEdge(ybin + 1)
yup = hists[0].GetXaxis().GetBinUpEdge(ybin + 1)
name = 'bin_{0}_{1}'.format(xbin, ybin)
xvar.setRange(name, xlo, xup)
yvar.setRange(name, ylo, yup)
for hist, modfunc in zip(hists, modfuncs):
integral = modfunc.createIntegral(
RooArgSet(xvar,
yvar), RooFit.NormSet(RooArgSet(xvar, yvar)),
RooFit.Range(name)).getVal()
hist.SetBinContent(xbin + 1, ybin + 1, integral)
return hists
def merge_sf(filename, histname_barrel, histname_endcap):
file = ensureTFile(filename)
hist_barrel = file.Get(histname_barrel)
hist_endcap = file.Get(histname_endcap)
nxbin = hist_barrel.GetXaxis().GetNbins()
nybin = hist_barrel.GetYaxis().GetNbins()
new_histname = "%s" % histname_barrel[:-7]
if '2017' in filename:
ptmax = 1000
else:
ptmax = 2000
new_hist = TH2D(new_histname,"SF for barrel and endcap",nxbin,-2.5,2.5,nybin,0,ptmax)
for pt in np.arange(0,ptmax,10):
for eta in np.arange(-2.5,2.5,0.1):
if abs(eta)>1.5:
etabin = hist_endcap.GetXaxis().FindBin(eta)
ptbin = hist_endcap.GetYaxis().FindBin(pt)
value = hist_endcap.GetBinContent(etabin,ptbin)
else:
etabin = hist_barrel.GetXaxis().FindBin(eta)
ptbin = hist_barrel.GetYaxis().FindBin(pt)
value = hist_barrel.GetBinContent(etabin,ptbin)
etabin_new = new_hist.GetXaxis().FindBin(eta)
ptbin_new = new_hist.GetYaxis().FindBin(pt)
new_hist.SetBinContent(etabin_new,ptbin_new,value)
new_hist.Write()
file.Close
def __plot_3d(self):
#definite_integral=0.
header = self.__parser.get_header_info()
hdata = self.__parser.get_histogram_data()
nrbins = header['RBINS']
nzbins = header['ZBINS']
rl = header['RRAN'][0]
ru = header['RRAN'][1]
pl = header['PRAN'][0]
pu = header['PRAN'][1]
zl = header['ZRAN'][0]
zu = header['ZRAN'][1]
n_of_histo = len(hdata['DATA']) / nrbins
## ------ TH2D::TH2D(const char* name, const char* title, int nbinsx, double xlow, double xup, int nbinsy, double ylow, double yup)
self.__histo = TH2D(header['H_NAME'], header['H_NAME'],
len(hdata['DATA']), float(zl), float(zu),
len(hdata['DATA']), float(pl), float(pu))
self.__histo.SetXTitle("R [cm]")
self.__histo.GetXaxis().CenterTitle(kTRUE)
self.__histo.GetXaxis().SetTitleOffset(1.1)
self.__histo.GetXaxis().SetTitleSize(0.04)
self.__histo.GetXaxis().SetLabelSize(0.03)
self.__histo.GetXaxis().SetTickLength(0.02)
self.__histo.GetXaxis().SetNdivisions(20510)
self.__histo.SetYTitle("N")
self.__histo.GetYaxis().CenterTitle(kTRUE)
self.__histo.GetYaxis().SetTitleOffset(1.2)
self.__histo.GetYaxis().SetTitleSize(0.04)
self.__histo.GetYaxis().SetLabelSize(0.03)
self.__histo.GetYaxis().SetTickLength(0.02)
self.__histo.GetYaxis().SetNdivisions(20510)
self.__histo.SetLineColor(kRed)
#self.__histo.SetMinimum(1e-9);
self.__histo.GetZaxis().SetTitle("Z [cm]")
# -> fill the histo now!
self.__histo = [None] * n_of_histo
for it in xrange(len(self.__histo)):
self.__histo[it] = TH1F(header['H_NAME'], header['H_NAME'],
int(nrbins), float(rl), float(ru))
for indx, data_point in enumerate(hdata['DATA']):
self.__histo[int(indx / nrbins)].SetBinContent(
indx % nrbins + 1, data_point)
for indx, error_point in enumerate(hdata['ERRORS']):
error_point *= self.__histo[int(
indx / nrbins)].GetBinContent(indx % nrbins + 1) * 0.01 / 2
self.__histo[int(indx / nrbins)].SetBinError(
indx % nrbins + 1, error_point)
min_val = min(hdata['DATA'])
max_val = max(hdata['DATA'])
for l in xrange(n_of_histo):
self.__histo[l].SetMarkerStyle(20)
self.__histo[l].SetMarkerSize(0.6)
def MakeDataFrameFlat(channel, data, var_name1, var_name2, label, Target):
data_copy = data.copy(
deep=True
) ## Making a deep copy of data ( => separate data and index from data)
data_copy_small = data.loc[(
data_copy[target] == Target)] ## choose b/w signal and background
FileName = "{}/{}_{}_{}_{}.root".format(channel, "Histo2D", var_name1,
var_name2, label)
file = TFile.Open(FileName)
canvas = file.Get("c1") ## Extracting the canvas stored in the root file
histo2D = TH2D()
histo2D = canvas.GetPrimitive("histo2D")
#print("No. of entries ", histo2D.GetEntries())
list_sf_values = [] ## Creating empty lists
for index, row in data_copy_small.iterrows():
#print("(X,Y) Values: ", row[var_name1], row[var_name2])
SF = getSF_from_TH2(histo2D, row[var_name1], row[var_name2])
Value = 0.
if (SF == 0.):
Value = 1.0 ## Such events will have unit weights
else:
Value = 1.0 / SF
#print("Scale Factor: ", Value)
list_sf_values.append(Value)
nparray_sf_values = np.array(
list_sf_values) ## converting list to numpy array
data_copy_small[
"Kin_weight"] = nparray_sf_values ## Adding numpy array as new column to dataframe
#print data_copy_small
return data_copy_small
def produce2DPlots(processes, selections, variables2D, colors, intLumi, pdir, lt, rt, logZ, hfile):
print ''
print 'Preparing 2D plots ...'
gROOT.SetBatch(True)
intLumiab = intLumi/1e+06
ff = "eps"
logstr = ''
if logZ:
logstr = 'log'
else:
logstr = 'lin'
nsel = 0
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v in variables2D.keys() :
i = 0
for p in processes:
filename = '{}_{}_{}_{}'.format(p, v, selstr, logstr)
filename = formatted(filename)
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH2D.Clone(h)
hh.Scale(intLumi)
draw2D(filename, lt, rt, ff, pdir, logZ, hh)
print 'DONE.'
def make_plot(self, events):
f_det = TFile(
'{}/text_gen.{}{}.root'.format(
self.data_dir, self.file_basename,
'.exclude_noise' if self.exclude_noise else ''), 'RECREATE')
multiple_particle_event_count = 0
h_count = TH1D('h_count', 'h_count', 100, -0.5, 99.5)
h_timing = TH1D('h_timing', 'h_timing', 5000, 0., 50.e3) # ns
h_xy = TH2D('h_xy', 'h_xy', 600, -150, 150, 600, -150, 150) # cm
h_z = TH1D('h_z', 'h_z', 500, -1, 1) # cm
for i, event in enumerate(events):
h_count.Fill(len(event))
if len(event) > 1:
multiple_particle_event_count += 1
for particle in event:
h_timing.Fill(particle[-1])
is_noise = particle[0]
if is_noise:
continue
x = particle[-4]
y = particle[-3]
z = particle[-2]
h_xy.Fill(x, y)
h_z.Fill(z)
print('len(events) = {}'.format(len(events)))
print('multiple_particle_event_count = {}'.format(
multiple_particle_event_count))
h_count.Write('h_particle_count_per_event')
h_timing.Write('h_timing')
h_xy.Write('h_xy')
h_z.Write('h_z')
f_det.Close()
def createPlots(plotopts, rootopts):
from ROOT import TH1D, TProfile, TH2D, TProfile2D
plots = {}
#creating histos
for plotopt in plotopts:
plots[plotopt] = {}
for rootopt in rootopts:
if (plotopt.profile):
if (plotopt.i2d):
plot = TProfile2D(
str(hash(plotopt)) + str(hash(rootopt)),
plotopt.display_name, plotopt.nbins[0], 0, 0,
plotopt.nbins[1], 0, 0)
else:
plot = TProfile(
str(hash(plotopt)) + str(hash(rootopt)),
plotopt.display_name, plotopt.nbins, 0, 0)
else:
if (plotopt.i2d):
plot = TH2D(
str(hash(plotopt)) + str(hash(rootopt)),
plotopt.display_name, plotopt.nbins[0], 0, 0,
plotopt.nbins[1], 0, 0)
else:
plot = TH1D(
str(hash(plotopt)) + str(hash(rootopt)),
plotopt.display_name, plotopt.nbins, 0, 0)
plot.SetBuffer(1000000)
plots[plotopt][rootopt] = plot
return plots
def makeCorrPlots(self, xs, sigcut='', bgcut=''):
# format of xs is [ [var,title,(optional nbins, binlo, binhi)] ]
nX = len(xs)
h2 = TH2D('hcorr', 'hcorr', nX, 0, nX, nX, 0, nX)
h2.GetZaxis().SetTitle('Correlation')
# label X and Y in opposite order
for iB in xrange(1, nX + 1):
h2.GetXaxis().SetBinLabel(iB, xs[iB - 1][1])
h2.GetYaxis().SetBinLabel(nX - iB + 1, xs[iB - 1][1])
h2bg = h2.Clone('hcorrbg')
for iX in xrange(nX):
for iY in xrange(iX, nX):
x = xs[iX]
y = xs[iY]
if not (x == y):
xbins = None if len(x) < 3 else x[2]
ybins = None if len(y) < 3 else y[2]
PInfo("SquarePlotter.makeCorrPlots",
"%s vs %s" % (x[0], y[0]))
corrsig = getPearson(self.tsig, x[0], y[0], sigcut, xbins,
ybins)
corrbg = getPearson(self.tbg, x[0], y[0], bgcut, xbins,
ybins)
else:
corrsig = 1
corrbg = 1
h2.SetBinContent(iX + 1, nX - iY, corrsig)
h2bg.SetBinContent(iX + 1, nX - iY, corrbg)
return h2, h2bg
def DoPatternRecognition(self, i, tolerance, scale=1):
trackDistX = []
clusterDistX = []
trackDistY = []
clusterDistY = []
tmp_track_X = []
tmp_track_Y = []
for ind in range(i, i + scaler):
for track in self.AllTracks[ind]:
tmp_track_X.append(track.trackX[3])
tmp_track_Y.append(track.trackY[3])
allTrack_tmp = []
allCluster_tmp = []
for tracks in self.AllTracks[i:i + scale]:
for track in tracks:
allTrack_tmp.append(track)
allCluster_tmp = self.AllClusters[i]
pattern = TH2D("", "", 14000, -npix_Y * pitchY / 2,
npix_Y * pitchY / 2,
len(allCluster_tmp) + len(allTrack_tmp), 0,
len(allCluster_tmp) + len(allTrack_tmp))
count = 0
for index, cluster in enumerate(allCluster_tmp):
pass
def plotPtAndPhiOfWrongBxId(self):
#Prepare canvas
canvas = TCanvas("canvasPtPhiBxWrong","PtPhiBxWrong",1200,1200)
canvas.cd().Draw()
#prepare histogram
hist = self.fileHandler.getHistogram("etaPhi/3D/BxWrongGen_EtaPhiPt")
stack = THStack(hist,"zy","2dStack","",-1,-1,-1,-1,"zy","")
#Create new histogram and add the histograms from the stack
histNew = TH2D("histPtPhiBxWrong","p_{T} vs. #phi distribution for wrong BX ID;#phi;p_{T} / 5 GeV;#",80,-3.2,3.2,40,0,200)
histNew.GetYaxis().SetTitleOffset(1.2)
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
setupAxes(histNew)
histNew.Draw('COLZ')
canvas.Update()
palette = histNew.FindObject("palette")
palette.SetX1NDC(0.9)
palette.SetX2NDC(0.92)
#add label
label = getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
self.storeCanvas(canvas,"bxWrongPtPhi")
return canvas,hist,stack,histNew,label
def bookHist(h,
key=None,
title='',
nbinsx=100,
xmin=0,
xmax=1,
nbinsy=0,
ymin=0,
ymax=1,
nbinsz=0,
zmin=0,
zmax=1):
if key == None:
print('missing key')
return
rkey = str(
key) # in case somebody wants to use integers, or floats as keys
if key in h: h[key].Reset()
elif nbinsz > 0:
h[key] = TH3D(rkey, title, nbinsx, xmin, xmax, nbinsy, ymin, ymax,
nbinsz, zmin, zmax)
elif nbinsy > 0:
h[key] = TH2D(rkey, title, nbinsx, xmin, xmax, nbinsy, ymin, ymax)
else:
h[key] = TH1D(rkey, title, nbinsx, xmin, xmax)
h[key].SetDirectory(gROOT)
