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 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 slice_and_save(sample,histo,outfile):
outfile.cd()
histo_stack=THStack(histo,'x','Stack_'+histo.GetName(),'')
histo_1d=histo_stack.GetHists()
histo_stack.Write(histo_stack.GetName()+'_'+sample)
nextinlist=TIter(histo_1d)
obj=nextinlist()
while obj:
obj.Write(obj.GetName()+'_'+sample)
obj=nextinlist()
histo.Write(histo.GetName()+'_'+sample)
def Stack(can,reverse=False) :
if can.GetPrimitive('pad_top') :
return Stack(can.GetPrimitive('pad_top'),reverse=reverse)
from ROOT import TH1,THStack
stack = THStack('stack','stack')
xaxislabel,yaxislabel = '',''
binlabels = []
if reverse :
the_primitives = reversed(can.GetListOfPrimitives())
else :
the_primitives = can.GetListOfPrimitives()
for i in the_primitives :
if issubclass(type(i),TH1) :
stack.Add(i)
if not xaxislabel : xaxislabel = i.GetXaxis().GetTitle()
if not yaxislabel : yaxislabel = i.GetYaxis().GetTitle()
if not binlabels and i.GetXaxis().GetBinLabel(1) :
for j in range(i.GetNbinsX()) :
binlabels.append(i.GetXaxis().GetBinLabel(j+1))
# The original objects are cleared from the histogram:
can.Clear()
tobject_collector.append(stack)
# Set draw option of underlying histograms for automatically nice legends
for hist in stack.GetHists() :
hist.SetOption('f')
can.cd()
stack.Draw('hist')
stack.GetXaxis().SetTitle(xaxislabel)
stack.GetYaxis().SetTitle(yaxislabel)
if binlabels :
for i in range(stack.GetXaxis().GetNbins()) :
stack.GetXaxis().SetBinLabel(i+1,binlabels[i])
can.RedrawAxis()
can.Modified()
can.Update()
return
def doPlotPhiPtOfFailingMatches():
file = TFile.Open('L1MuonHistogram.root')
#Prepare canvas
canvas = TCanvas("canvasPtPhiHoMatchFail", "PtPhiHoMatchFail", 1200, 1200)
canvas.cd().Draw()
#prepare histogram
hist = file.Get("hoMuonAnalyzer/etaPhi/3D/NoTrgTdmiNotInGA_EtaPhiPt")
stack = THStack(hist, "zy", "2dStack", "", -1, -1, -1, -1, "zy", "")
#Create new histogram and add the histograms from the stack
histNew = TH2D(
"histPtEtaHoMatchFail",
"p_{T} vs. #phi distribution for events not in HO acceptance;#phi;p_{T} / 5 GeV;#",
80, -3.2, 3.2, 40, 0, 200)
histNew.GetYaxis().SetTitleOffset(1.2)
histNew.Sumw2()
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
PlotStyle.setupAxes(histNew)
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/NoL1NotInHoAcceptancePhiPt.pdf')
canvas.SaveAs('plots/NoL1NotInHoAcceptancePhiPt.png')
return canvas, hist, stack, histNew, label, palette, file
def read_file(arr, meas_type, mapsa_fitter_inst, path):
if not os.path.isfile(str(path + arr[0])):
print "Root file not found at", str(path + arr[0])
#sys.exit(1)
return
f = TFile(str(path + arr[0]), 'READ')
if (f.IsZombie()):
print "Error opening file"
return
else:
print "Reading File ", arr[0]
#f.ls()
tree = f.Get('tree')
f_GlobalData_Map = ROOT.TMap()
f_GlobalData_Map.Add(ROOT.TObjString("tree"), tree)
#tree.Print()
outfile = TString(arr[0])
outfile.ReplaceAll(".root", "")
outfile.ReplaceAll(" ", "")
# print outfile
if (meas_type == 0):
outfile = "pedestal"
if (not g.FindKey(str(outfile))):
g.mkdir(str(outfile))
g.cd(str(outfile))
else:
return
channels = 288
if (arr[2] == 'inv'):
channels = 96
mapsa_mat = [[1, 0, 0], [1, 0, 0]]
elif (arr[2] == 'norm'):
channels = 288
mapsa_mat = [[1, 1, 1], [1, 0, 1]]
#print "channels", channels
#print "mapsa_mat", mapsa_mat
c1 = TCanvas('c1', 'Pixel Monitor ', 700, 900)
c2 = TCanvas('c2', 'Pixel Monitor ', 500, 500)
c3 = TCanvas('c3', 'Pixel Monitor ', 1280, 720)
c4 = TCanvas('c4', 'Pixel Monitor ', 500, 500)
c5 = TCanvas('c5', 'Pixel Monitor ', 500, 500)
f_GlobalData_Map.Add(ROOT.TObjString("c1"), c1)
f_GlobalData_Map.Add(ROOT.TObjString("c2"), c2)
f_GlobalData_Map.Add(ROOT.TObjString("c3"), c3)
f_GlobalData_Map.Add(ROOT.TObjString("c4"), c4)
f_GlobalData_Map.Add(ROOT.TObjString("c5"), c5)
# c2.Divide(2,1)
#c2.cd(0)
c1.Divide(3, 2)
for i in range(1, 7):
c1.cd(i)
ROOT.gPad.SetGridx()
ROOT.gPad.SetGridy()
# channelcounts = TH2I('HitMap','Counts; Channel; DAC Value (1.456 mV)', 288, .5,288.5,256, .5, 256.5)
channelcounts = TH2I('HitMap', 'Counts; Channel; DAC Value (a.u.)', 288,
.5, 288.5, 256, .5, 256.5)
channelcounts_norm = TH2F('HitMap_norm',
'Occupancy ; Channel; DAC Value (a.u.)', 288, .5,
288.5, 256, .5, 256.5)
f_GlobalData_Map.Add(ROOT.TObjString("HitMap"), channelcounts)
f_GlobalData_Map.Add(ROOT.TObjString("HitMap"), channelcounts_norm)
norm_2d = TH2F('Norm2D', 'Normalization; Column; Row', 48, .5, 48.5, 6, .5,
6.5)
mean_2d = TH2F('Mean2D', 'Mean; Column; Row', 48, .5, 48.5, 6, .5, 6.5)
sigma_2d = TH2F('Sigma2D', 'Sigma; Column; Row', 48, .5, 48.5, 6, .5, 6.5)
chisquare = TH2F('Chisquare2D', 'Chisquare; Column; Row', 48, .5, 48.5, 6,
.5, 6.5)
f_GlobalData_Map.Add(ROOT.TObjString("Norm2D"), norm_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Mean2D"), mean_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Sigma2D"), sigma_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Chisquare2D"), chisquare)
objarr2d = []
objarr2d.append(norm_2d)
objarr2d.append(mean_2d)
objarr2d.append(sigma_2d)
objarr2d.append(chisquare)
normgraph = TGraphErrors()
meangraph = TGraphErrors()
sigmagraph = TGraphErrors()
chisquaregraph = TGraphErrors()
mean_corrgraph = TGraphErrors()
f_GlobalData_Map.Add(ROOT.TObjString("normgraph "), normgraph)
f_GlobalData_Map.Add(ROOT.TObjString("meangraph "), meangraph)
f_GlobalData_Map.Add(ROOT.TObjString("sigmagraph "), sigmagraph)
f_GlobalData_Map.Add(ROOT.TObjString("chisquaregraph"), chisquaregraph)
f_GlobalData_Map.Add(ROOT.TObjString("mean_corrgraph"), mean_corrgraph)
meanhist = TH1F('meanhist', 'Mean DAC; DAC Value (a.u.); counts', 2560, 0,
255)
sigmahist = TH1F('sigmahist', 'Sigma DAC; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_std = TH1F('meanhist_std',
'Mean DAC Standard; DAC Value (a.u.); counts', 2560,
0, 255)
sigmahist_std = TH1F('sigmahist_std',
'Sigma DAC Standard; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_double = TH1F('meanhist_double',
'Mean DAC Double; DAC Value (a.u.); counts', 2560,
0, 255)
sigmahist_double = TH1F('sigmahist_double',
'Sigma DAC Double; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_double_neighbour = TH1F(
'meanhist_double_neighbour',
'Mean DAC Double Neighbour; DAC Value (a.u.); counts', 2560, 0, 255)
sigmahist_double_neighbour = TH1F(
'sigmahist_double_neighbour',
'Sigma DAC Double Neighbour; DAC Value (a.u.); counts', 100, 0, 10)
objarr = []
objarr.append(normgraph)
objarr.append(meangraph)
objarr.append(sigmagraph)
objarr.append(chisquaregraph)
objarr.append(mean_corrgraph)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist'), meanhist)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist'), sigmahist)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_std'), meanhist_std)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_std'), sigmahist_std)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_double'), meanhist_double)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_double'), sigmahist_double)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_double_neighbour'),
meanhist_double_neighbour)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_double_neighbour'),
sigmahist_double_neighbour)
objarr.append(meanhist)
objarr.append(sigmahist)
objarr.append(meanhist_std)
objarr.append(sigmahist_std)
objarr.append(meanhist_double)
objarr.append(sigmahist_double)
objarr.append(meanhist_double_neighbour)
objarr.append(sigmahist_double_neighbour)
for objs in objarr:
objs.SetMarkerColor(2)
objs.SetMarkerStyle(20)
objs.SetMarkerSize(1)
normgraph.SetName('normgraph')
meangraph.SetName('meangraph')
sigmagraph.SetName('sigmagraph')
chisquaregraph.SetName('chisquare')
mean_corrgraph.SetName('mean_corr')
normgraph.SetTitle('Normalization; Channel; Normalization')
meangraph.SetTitle('Mean; Channel; DAC Value (a.u.)')
sigmagraph.SetTitle('Sigma; Channel; DAC Value (a.u.)')
chisquaregraph.SetTitle('Chisquared/NDF_gr; Channel; Chisquared/NDF ')
ROOT.gStyle.SetOptFit(1111)
stack = THStack('a', ';DAC Value (a.u.); Occupancy')
f_GlobalData_Map.Add(ROOT.TObjString("stack"), stack)
fitfuncs = []
fitparams = []
gr1 = []
for pixel in range(0, channels):
gr1.append(
TH1D(
str(pixel).zfill(3),
str(pixel + 1).zfill(3) + ';DAC Value (a.u.); Occupancy ', 256,
0.5, 256.5))
f_GlobalData_Map.Add(ROOT.TObjString(str(pixel).zfill(3)), gr1[pixel])
#gr2.append(TH1F('th1f_'+str(pixel).zfill(3),str(pixel+1).zfill(3)+';DAC Value (a.u.); Occupancy',256,0.5,256.5))
color = pixel % 8 + 1
formating_th1(gr1[pixel], color)
if (meas_type == 0):
fitfuncs.append(
TF1('gauss' + str(pixel + 1).zfill(3), 'gaus(0)', 0, 256))
fitfuncs[pixel].SetNpx(256)
fitfuncs[pixel].SetLineColor(color)
f_GlobalData_Map.Add(
ROOT.TObjString('gauss' + str(pixel).zfill(3)),
fitfuncs[pixel])
#Here we read the data and fill the histogram
for event in tree:
eventstr = []
for counter, vals in enumerate(tree.AR_MPA):
#eventstr.append(vals)
channelcounts.Fill(counter, tree.THRESHOLD, vals)
if (counter < channels):
gr1[counter].Fill(tree.THRESHOLD, vals)
#if tree.THRESHOLD%20==0 and tree.REPETITION==0:
#print eventstr
#print ("Threshold %d Repetion %d" % (tree.THRESHOLD,tree.REPETITION))
#print tree.AR_MPA
#now we make a small analysis of the curves fitting different functions to it:
print "Finished Reading the Tree\n Normalization of Histograms\n"
for pixel in range(0, channels):
#gr1[pixel].ResetStats()
for j in range(0, gr1[pixel].GetXaxis().GetNbins() + 1):
gr1[pixel].SetBinError(
gr1[pixel].GetBin(j),
TMath.Sqrt(gr1[pixel].GetBinContent(gr1[pixel].GetBin(j))))
#if(pixel==0):
#gr1[pixel].Print("all")
#gr1[pixel].Sumw2(ROOT.kTRUE)
gr1[pixel].Scale(1 / arr[1])
#if(pixel==0):
#gr1[pixel].Print("all")
stack.Add(gr1[pixel])
#first create a THStack with histograms:
iterator = stack.GetHists()
if (meas_type == 0):
for idx, it in enumerate(iterator):
fitparams.append([])
if (it.Integral() > 0):
if (idx < channels):
#fitfuncs.append(TF1('combined'+str(idx),combined, 0,256,5))
#fitfuncs.append(TF1('combined_same_mean'+str(idx),combined_mean, 0,256,4))
#fitfuncs.append(TF1('double_gauss'+str(idx),'gaus(0)+gaus(3)',0,256))
#fitfuncs.append(TF1('gauss'+str(idx),'gaus(0)',0,256))
#fitfuncs.append(TF1('double_gauss_same_mean'+str(idx),double_gauss, 0,256,5))
#print it.GetName(), idx
#fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean()+1,it.GetRMS(),it.GetMean()-1,it.GetRMS());
#fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean(),it.GetRMS()*0.1,it.GetRMS()*0.1);
#print ("Channels %f\t%f\t%f\n" % (it.GetMaximum(),it.GetMean(),it.GetRMS()))
fitfuncs[idx].SetParameters(it.GetMaximum(), it.GetMean(),
it.GetRMS())
#fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),it.GetMean(),10*it.GetRMS());
#fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),10*it.GetRMS());
#it.Fit(fitfuncs[idx],'lr0 rob=0.95','same',0,256)
#it.Fit(fitfuncs[idx],'lr0q ','',0,256)
it.Fit(fitfuncs[idx], 'r0q ', '', 0, 256)
fitparams[idx].append(fitfuncs[idx].GetParameter(0))
fitparams[idx].append(fitfuncs[idx].GetParameter(1))
fitparams[idx].append(fitfuncs[idx].GetParameter(2))
fitparams[idx].append(fitfuncs[idx].GetParError(0))
fitparams[idx].append(fitfuncs[idx].GetParError(1))
fitparams[idx].append(fitfuncs[idx].GetParError(2))
if (fitfuncs[idx].GetNDF() > 0):
fitparams[idx].append(fitfuncs[idx].GetChisquare() /
fitfuncs[idx].GetNDF())
else:
for kk in range(0, 7):
fitparams[idx].append(0)
#print "fitparamarray"
fitarray = np.array(fitparams)
## print fitarray
for pointno, it in enumerate(fitarray):
if (fitarray[pointno][0] > 0):
normgraph.SetPoint(pointno, pointno + 1, fitarray[pointno][0])
normgraph.SetPointError(pointno, 0, fitarray[pointno][3])
meangraph.SetPoint(pointno, pointno + 1, fitarray[pointno][1])
meangraph.SetPointError(pointno, 0, fitarray[pointno][4])
meanhist.Fill(fitarray[pointno][1])
sigmagraph.SetPoint(pointno, pointno + 1, fitarray[pointno][2])
sigmagraph.SetPointError(pointno, 0, fitarray[pointno][5])
sigmahist.Fill(fitarray[pointno][2])
chisquaregraph.SetPoint(pointno, pointno + 1,
fitarray[pointno][6])
chisquaregraph.SetPointError(pointno, 0, 0)
## iterator.ls()
# Map the data to the pixel layout:
tmp_objarr = []
tmp_objarr.extend(
[meanhist_std, meanhist_double, meanhist_double_neighbour])
tmp_objarr.extend(
[sigmahist_std, sigmahist_double, sigmahist_double_neighbour])
for i in tmp_objarr:
print str(i.GetName())
fill2d(fitarray[:, 0], mapsa_mat, objarr2d[0])
fill2d(fitarray[:, 1], mapsa_mat, objarr2d[1])
fill2d(fitarray[:, 2], mapsa_mat, objarr2d[2])
fill2d(fitarray[:, 6], mapsa_mat, objarr2d[3])
fill1d_edges(objarr2d[1], tmp_objarr[0:3])
fill1d_edges(objarr2d[2], tmp_objarr[3:])
g.cd(str(outfile))
mapsa_fitter_inst.Make_dirs()
mapsa_fitter_inst.Set_run_no(outfile)
if (meas_type == 1):
for idx, it in enumerate(iterator):
if (it.Integral() > 0):
if (idx < channels):
mapsa_fitter_inst.Find_signal(it, idx, 0.0025, 3)
g.cd()
#g.mkdir(str(outfile)+"/Channels")
#g.cd(str(outfile)+"/Channels")
#iterator.Write()
g.cd(str(outfile))
g.mkdir(str(outfile) + "/Overview")
## iterator.First().Print("all")
Maximum = TMath.Power(10, (round(TMath.Log10(stack.GetMaximum())) - 1))
#Minimum = TMath.Power(10,(round(TMath.Log10(stack.GetMinimum()))+1))
Minimum = .1
ROOT.gStyle.SetLabelSize(0.06, "xyz")
ROOT.gStyle.SetTitleSize(0.06, "xyz")
ROOT.gStyle.SetTitleOffset(1.2, "y")
ROOT.gStyle.SetTitleOffset(.825, "x")
ROOT.gStyle.SetPadGridX(1)
ROOT.gStyle.SetPadGridY(1)
ROOT.gStyle.SetOptStat(0)
# ROOT.gStyle.SetPadLeftMargin(.2);
# ROOT.gStyle.SetPadRightMargin(.1);
c1.cd(1)
stack.Draw("nostack hist e1 x0")
stack.GetXaxis().SetRangeUser(0, 256)
stack.SetMinimum(Minimum)
stack.SetMaximum(Maximum)
ROOT.gPad.SetLogy()
c2.cd(0)
stack.Draw("nostack hist e1 x0")
#if(outfile.Contains("SR_90_on_top")):
#stack.GetXaxis().SetRangeUser(0,256)
#else:
#stack.GetXaxis().SetRangeUser(0,100)
stack.SetMinimum(Minimum)
stack.SetMaximum(Maximum)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetRightMargin(.05)
ROOT.gPad.SetLogy()
ROOT.gPad.Update()
#for idx, it in enumerate(fitfuncs):
## if idx>0 and idx<7:
#c1.cd(1)
#fitfuncs[idx].Draw("same")
#c2.cd(0)
#fitfuncs[idx].DrawCopy("psame")
## it.SetLineColor(idx%9+1)
## it.Draw("same")
#g.cd(str(outfile)+"/Channels")
#it.Write("HitMap_py_"+str(idx+1)+"_fit")
g.cd(str(outfile) + "/Overview")
c1.cd(2)
chisquaregraph.Draw("ap")
c1.cd(3)
normgraph.Draw("ap")
c1.cd(4)
sigmagraph.Draw("ap")
sigmagraph.GetYaxis().SetRangeUser(0, 5)
sigmagraph.GetXaxis().SetRangeUser(0, channels + 1)
c2.cd(2)
sigmagraph.Draw("ap")
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetRightMargin(.05)
c1.cd(5)
meangraph.Draw("ap")
c1.cd(6)
channelcounts.Draw("colz")
channelcounts.GetXaxis().SetRangeUser(0, channels + 1)
# c2.cd(3)
c3.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
copy = channelcounts.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy"),copy)
#if(outfile.Contains("SR_90_on_top")):
#copy.SetMaximum(100)
#copy.SetMinimum(1)
copy.GetYaxis().SetTitle("DAC Value (a.u.)")
c4.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
copy1 = sigma_2d.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy1"),copy1)
copy1.GetZaxis().SetTitle("Sigma (a.u.)")
copy1.GetZaxis().SetTitleOffset(1.2)
ROOT.gPad.SetRightMargin(.2)
if (arr[2] == 'inv'):
copy1.GetXaxis().SetRangeUser(.5, 16.5)
copy1.SetMaximum(5)
copy1.SetMinimum(0)
c5.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
#copy1 = chisquare.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy2"),copy1)
copy1 = sigma_2d.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy2"),copy1)
copy1.GetZaxis().SetTitle("sigma (a.u.)")
if (arr[2] == 'inv'):
copy1.GetXaxis().SetRangeUser(.5, 16.5)
copy1.SetMaximum(5)
copy1.SetMinimum(0)
c1.Update()
c1.Modified()
c2.Update()
c2.Modified()
c3.Update()
c3.Modified()
c4.Update()
c4.Modified()
c5.Update()
c5.Modified()
## c1.SaveAs("double_gauss_same_mean.pdf")
## time.sleep(2)
g.cd(str(outfile) + "/Overview")
#for objs in objarr:
#objs.Write(objs.GetName())
#norm_2d.GetZaxis().SetRangeUser(1E5,2E6)
#mean_2d.GetZaxis().SetRangeUser(54,64)
## norm_2d.GetZaxis().SetRangeUser(TMath.Power(10,(round(TMath.Log10(norm_2d.GetStdDev(3))-2)), TMath.Power(10,(round(TMath.Log10(norm_2d.GetStdDev(3)))-1)))
## mean_2d.GetZaxis().SetRangeUser(TMath.Power(10,(round(TMath.Log10(mean_2d.mean_2d.GetStdDev(3)))-1))-5,TMath.Power(10,(round(TMath.Log10(mean_2d.GetStdDev(3)))-1))+5)
#sigma_2d.GetZaxis().SetRangeUser(0,5)
#chisquare.GetZaxis().SetRangeUser(0,10000 )
#for objs in objarr2d:
#objs.Write(objs.GetName())
#c1.Write("c1")
#outfile1=outfile+TString(".pdf")
#c2.SaveAs(str(outfile1))
#c2.Write("c2")
#c3.SaveAs("c3"+str(outfile1))
#c3.Write("c3")
#c4.SaveAs("c4"+str(outfile1))
#c4.Write("c4")
## while (TObject(iterator.Next())):
## print iterator.Next().Title()
#stack.Write("stack")
#g.cd(str(outfile))
#channelcounts.Write(str(outfile))
#f.Close()
c1.Close()
c2.Close()
c3.Close()
c4.Close()
c5.Close()
f_GlobalData_Map.DeleteAll()
f.Close()
# eventstr.append(vals)
channelcounts.Fill(counter + 1, tree.THRESHOLD, vals)
if (counter < channels):
gr1[counter].Fill(tree.THRESHOLD, vals)
gr1[counter].SetBinError(
gr1[counter].GetBin(tree.THRESHOLD),
TMath.Sqrt(gr1[counter].GetBinContent(gr1[counter].GetBin(
tree.THRESHOLD))))
# print eventstr
if tree.THRESHOLD % 10 == 0 and tree.REPETITION == 0:
print tree.THRESHOLD
# print tree.AR_MPA
#now we make a small analysis of the curves fitting different functions to it:
#first create a THStack with histograms:
iterator = stack.GetHists()
for idx, it in enumerate(iterator):
fitparams.append([])
# if(it.Integral() >1):
if (idx < channels):
# fitfuncs.append(TF1('combined'+str(idx),combined, 0,256,5))
# fitfuncs.append(TF1('combined_same_mean'+str(idx),combined_mean, 0,256,4))
# fitfuncs.append(TF1('double_gauss'+str(idx),'gaus(0)+gaus(3)',0,256))
# fitfuncs.append(TF1('gauss'+str(idx),'gaus(0)',0,256))
# fitfuncs.append(TF1('double_gauss_same_mean'+str(idx),double_gauss, 0,256,5))
# print it.GetName(), idx
# fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean()+1,it.GetRMS(),it.GetMean()-1,it.GetRMS());
# fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean(),it.GetRMS()*0.1,it.GetRMS()*0.1);
fitfuncs[idx].SetParameters(it.GetMaximum(), it.GetMean(),
it.GetRMS())
# fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),it.GetMean(),10*it.GetRMS());
