def plotRatio(var,name,hist1,hist2):
"""Plot minimal ratio of two histograms."""
canvas = TCanvas('canvas','canvas',100,100,800,800)
canvas.Divide(2)
canvas.cd(1)
gPad.SetPad('pad1','pad1',0,0.42,1,1,0,-1,0)
gPad.SetTopMargin( 0.10 ); gPad.SetBottomMargin( 0.01 )
hist1.Draw()
hist2.Draw('SAME')
hist1.SetLineColor(kBlue)
hist2.SetLineColor(kRed)
hist1.SetLineWidth(2)
hist2.SetLineWidth(2)
hist1.SetLineStyle(1)
hist2.SetLineStyle(2)
hist1.Draw('HIST')
hist2.Draw('HIST SAMES')
gPad.Update()
stats1 = hist1.GetListOfFunctions().FindObject('stats')
stats2 = hist2.GetListOfFunctions().FindObject('stats')
stats1.SetY1NDC(.74); stats1.SetY2NDC(.94)
stats2.SetY1NDC(.50); stats2.SetY2NDC(.70)
stats1.Draw()
stats2.Draw()
canvas.cd(2)
gPad.SetPad('pad2','pad2',0,0,1,0.41,0,-1,0)
gPad.SetTopMargin( 0.05 ); gPad.SetBottomMargin( 0.24 )
ratio = hist1.Clone('ratio')
ratio.Divide(hist2)
for i, (y1, y2, r) in enumerate(zip(hist1,hist2,ratio),0):
if hist1.GetBinContent(i)==0 and hist2.GetBinContent(i)==0:
ratio.SetBinContent(i,1)
ratio.GetXaxis().SetTitle(var)
ratio.GetXaxis().SetLabelSize(0.045)
ratio.GetYaxis().SetLabelSize(0.045)
ratio.GetXaxis().SetTitleSize(0.060)
ratio.SetMinimum(0.2)
ratio.SetMaximum(1.8)
ratio.Draw()
statsr = ratio.GetListOfFunctions().FindObject('stats')
statsr.SetY1NDC(.65); statsr.SetY2NDC(.98)
canvas.SaveAs(name)
canvas.Close()
gDirectory.Delete(hist1.GetName())
gDirectory.Delete(hist2.GetName())
gDirectory.Delete(ratio.GetName())
def phot_xy():
#photon detector first point in xy
xbin = 0.1
xmin = -12
xmax = 12
can = ut.box_canvas()
hX = ut.prepare_TH2D("hX", xbin, xmin, xmax, xbin, xmin, xmax)
#tree.Draw("phot_y/10:phot_x/10 >> hX")#, "phot_en<1000")
tree.Draw("phot_hy/10:phot_hx/10 >> hX") #, "phot_en<1000")
hX.SetXTitle("Horizontal #it{x} (cm)")
hX.SetYTitle("Vertical #it{y} (cm)")
hX.GetXaxis().CenterTitle()
hX.GetYaxis().CenterTitle()
hX.SetTitleOffset(1.2, "Y")
hX.SetTitleOffset(1.2, "X")
gPad.SetTopMargin(0.01)
gPad.SetRightMargin(0.11)
gPad.SetBottomMargin(0.09)
gPad.SetLeftMargin(0.09)
hX.Draw()
gPad.SetLogz()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_en():
#energy distribution of generated photons
ebin = 0.1
emin = 4
emax = 28
can = ut.box_canvas()
hE = ut.prepare_TH1D("hE", ebin, emin, emax)
tree.Draw("phot_en >> hE")
hE.SetYTitle("Events / ({0:.3f}".format(ebin) + " GeV)")
hE.SetXTitle("#it{E}_{#gamma} (GeV)")
hE.SetTitleOffset(1.9, "Y")
hE.SetTitleOffset(1.3, "X")
gPad.SetTopMargin(0.02)
gPad.SetRightMargin(0.01)
gPad.SetBottomMargin(0.1)
gPad.SetLeftMargin(0.14)
hE.GetYaxis().SetMoreLogLabels()
hE.Draw()
gPad.SetLogy()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_theta():
#polar angle of generated photons
tbin = 0.01
tmax = 3
can = ut.box_canvas()
ht = ut.prepare_TH1D("ht", tbin, 0, tmax)
tree.Draw("(TMath::Pi()-phot_theta)*1000 >> ht")
ht.SetYTitle("Events / ({0:.3f}".format(tbin) + " mrad)")
ht.SetXTitle("#vartheta (mrad)")
ht.SetTitleOffset(1.5, "Y")
ht.SetTitleOffset(1.3, "X")
gPad.SetTopMargin(0.02)
gPad.SetRightMargin(0.025)
gPad.SetBottomMargin(0.1)
gPad.SetLeftMargin(0.11)
ht.Draw()
leg = ut.prepare_leg(0.2, 0.87, 0.18, 0.08, 0.035)
leg.AddEntry(None, "Angular distribution of Bethe-Heitler photons", "")
#leg.Draw("same")
gPad.SetLogy()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def gPadSet():
gPad.SetTickx(1)
gPad.SetTicky(1)
gPad.SetLeftMargin(0.12)
gPad.SetRightMargin(1.4)
gPad.SetBottomMargin(0.10)
gPad.SetFrameLineWidth(2)
gPad.RedrawAxis()
def setPadStyle():
gPad.SetLeftMargin(0.05)
gPad.SetRightMargin(0.08)
gPad.SetTopMargin(0.10)
gPad.SetBottomMargin(0.10)
gPad.SetLogz(plots[plot].zLog)
gPad.SetFillColor(kWhite)
gPad.SetBorderMode(0)
def draw(self):
self.canvas = TCanvas('can' + self.name, self.name, 1000, 800)
if self.histo == None:
self.bookCorrelationHisto()
self.histo.Draw('boxtext')
gPad.SetBottomMargin(0.3)
gPad.SetLeftMargin(0.3)
gPad.SetRightMargin(0.2)
gPad.SaveAs('cutCorr_' + self.name + '_' +
varStringToFileName(self.addtlCut) + '.png')
def phot_theta():
#polar angle of generated photons
tbin = 0.01
tmax = 4
gdir = "/home/jaroslav/sim/GETaLM/cards/"
inp = "bg.root"
infile = TFile.Open(gdir + inp)
tree = infile.Get("ltree")
can = ut.box_canvas()
ht1 = ut.prepare_TH1D("ht1", tbin, 0, tmax)
ht2 = ut.prepare_TH1D("ht2", tbin, 0, tmax)
ht2.SetMarkerColor(rt.kRed)
ht2.SetLineColor(rt.kRed)
#tree.Draw("(TMath::Pi()-true_phot_theta)*1000 >> ht")
tree.Draw("(TMath::Pi()-phot_theta)*1000 >> ht1",
"vtx_z>5000 && vtx_z<12000")
tree.Draw("(TMath::Pi()-phot_theta)*1000 >> ht2",
"vtx_z>-5000 && vtx_z<5000")
#ht.SetYTitle("Events / ({0:.3f}".format(tbin)+" mrad)")
#ht.SetYTitle("Counts")
#ht.SetXTitle("Photon #it{#theta} (mrad)")
#ht.SetTitleOffset(1.5, "Y")
#ht.SetTitleOffset(1.3, "X")
gPad.SetTopMargin(0.02)
gPad.SetRightMargin(0.025)
gPad.SetBottomMargin(0.1)
gPad.SetLeftMargin(0.11)
gPad.SetGrid()
ht1.Draw()
ht2.Draw("e1same")
#leg = ut.prepare_leg(0.2, 0.87, 0.18, 0.08, 0.035)
#leg.AddEntry(None, "Angular distribution of Bethe-Heitler photons", "")
#leg.Draw("same")
gPad.SetLogy()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_zdc_vtx():
#ZDC vertex from selected events and from all triggered events
vbin = 0.1
vmin = -5.
vmax = 7.
mmin = 1.5
mmax = 5.
can = ut.box_canvas()
#selection string
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
#make the histograms
hZdcVtx = ut.prepare_TH1D("hZdcVtx", vbin, vmin, vmax)
hZdcVtxAll = ut.prepare_TH1D("hZdcVtxAll", vbin / 10., vmin, vmax)
#convert to meters for plot
tree.Draw("jZDCVtxZ/100. >> hZdcVtx", strsel)
treeAll = inp.Get("jAllTree")
treeAll.Draw("jZDCVtxZ/100. >> hZdcVtxAll")
ut.norm_to_data(hZdcVtxAll, hZdcVtx, rt.kRed)
hZdcVtx.SetYTitle("Events / {0:.0f} cm".format(vbin * 100.))
hZdcVtx.SetXTitle("ZDC vertex along #it{z} (meters)")
hZdcVtx.SetTitleOffset(1.5, "Y")
hZdcVtx.SetTitleOffset(1.1, "X")
gPad.SetTopMargin(0.01)
gPad.SetRightMargin(0.04)
gPad.SetBottomMargin(0.08)
gPad.SetLeftMargin(0.1)
leg = ut.prepare_leg(0.67, 0.8, 0.28, 0.14, 0.025)
leg.SetMargin(0.17)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hZdcVtx, "Selected events")
leg.AddEntry(hZdcVtxAll, "All UPC-JpsiB triggers", "l")
#gPad.SetLogy()
hZdcVtx.Draw()
hZdcVtxAll.Draw("same")
leg.Draw("same")
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_vtx_z():
#primary vertex position along z from data and MC
vbin = 4.
vmax = 120.
mmin = 1.5
mmax = 5
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hVtx = ut.prepare_TH1D("hVtx", vbin, -vmax, vmax)
hVtxMC = ut.prepare_TH1D("hVtxMC", vbin/2., -vmax, vmax)
tree.Draw("jVtxZ >> hVtx", strsel)
mctree.Draw("jVtxZ >> hVtxMC", strsel)
ut.norm_to_data(hVtxMC, hVtx, rt.kBlue, -40, 40)
hVtx.SetYTitle("Counts / {0:.0f} cm".format(vbin));
hVtx.SetXTitle("#it{z} of primary vertex (cm)");
hVtx.SetTitleOffset(1.5, "Y")
hVtx.SetTitleOffset(1.3, "X")
gPad.SetTopMargin(0.02)
gPad.SetRightMargin(0.02)
gPad.SetBottomMargin(0.1)
gPad.SetLeftMargin(0.11)
cut_lo = ut.cut_line(-30, 0.8, hVtx)
cut_hi = ut.cut_line(30, 0.8, hVtx)
leg = ut.prepare_leg(0.16, 0.82, 0.26, 0.12, 0.025)
#leg.SetMargin(0.15)
#leg.SetBorderSize(1)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hVtx, "Data")
#leg.AddEntry(hVtxMC, "MC, coherent J/#it{#psi}", "l")
leg.AddEntry(hVtxMC, "MC, #it{#gamma}#it{#gamma} #rightarrow e^{+}e^{-}", "l")
leg.AddEntry(cut_lo, "Cut at #pm30 cm", "l")
hVtx.Draw()
hVtxMC.Draw("same")
leg.Draw("same")
cut_lo.Draw("same")
cut_hi.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def setpad(left, right, top, bottom):
gPad.SetFillColor(10)
gPad.SetBorderMode(0)
gPad.SetBorderSize(0)
gPad.SetFrameFillColor(10)
gPad.SetFrameBorderMode(0)
gPad.SetFrameBorderSize(0)
gPad.SetLeftMargin(left)
gPad.SetRightMargin(right)
gPad.SetTopMargin(top)
gPad.SetBottomMargin(bottom)
gPad.SetGridx(0)
gPad.SetGridy(0)
gStyle.SetOptStat(0)
def plot_zdc():
#ZDC ADC counts east or west 1D
ew = 0
zbin = 10.
zmin = 0.
zmax = 1300.
znam = ["jZDCUnAttEast", "jZDCUnAttWest"]
xtit = ["ZDC East ADC", "ZDC West ADC"]
lhead = ["East ZDC", "West ZDC"]
#global gPad
can = ut.box_canvas()
hZdc = ut.prepare_TH1D("hZdc", zbin, zmin, zmax)
hZdcAll = ut.prepare_TH1D("hZdcAll", zbin, zmin, zmax)
tree.Draw(znam[ew] + " >> hZdc")
treeAll = inp.Get("jAllTree")
treeAll.Draw(znam[ew] + " >> hZdcAll")
ut.norm_to_data(hZdcAll, hZdc, rt.kRed)
hZdc.SetYTitle("Events / {0:.1f}".format(zbin))
hZdc.SetXTitle(xtit[ew])
hZdc.SetTitleOffset(1.5, "Y")
hZdc.SetTitleOffset(1.1, "X")
gPad.SetTopMargin(0.01)
gPad.SetRightMargin(0.08)
gPad.SetBottomMargin(0.08)
gPad.SetLeftMargin(0.1)
leg = ut.prepare_leg(0.5, 0.76, 0.39, 0.16, 0.035)
leg.SetMargin(0.17)
leg.AddEntry(None, lhead[ew], "")
leg.AddEntry(hZdc, "Selected events")
leg.AddEntry(hZdcAll, "All UPC-JpsiB triggers", "l")
hZdc.Draw()
hZdcAll.Draw("same")
leg.Draw("same")
#ut.print_pad(rt.gPad)
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_zdc_vtx_alltrg():
#ZDC vertex from all triggered events
vbin = 0.01
#vmin = -20.
#vmax = 20.
#vmin = -26
#vmax = -12
vmin = -800
vmax = 800
can = ut.box_canvas()
#make the histogram
hZdcVtx = ut.prepare_TH1D("hZdcVtxAll", vbin, vmin, vmax)
hZdcVtx.SetOption("L")
#convert to meters for plot
treeAll = inp.Get("jAllTree")
print("Number of all trg events:", treeAll.GetEntries())
treeAll.Draw("jZDCVtxZ/100. >> hZdcVtxAll")
hZdcVtx.SetYTitle("Events / {0:.0f} cm".format(vbin * 100.))
hZdcVtx.SetXTitle("ZDC vertex along #it{z} (meters)")
hZdcVtx.SetTitleOffset(1.5, "Y")
hZdcVtx.SetTitleOffset(1.1, "X")
gPad.SetTopMargin(0.04)
gPad.SetRightMargin(0.04)
gPad.SetBottomMargin(0.08)
gPad.SetLeftMargin(0.1)
leg = ut.prepare_leg(0.67, 0.8, 0.28, 0.14, 0.025)
leg.SetMargin(0.17)
#leg.AddEntry(None, "UPC-JpsiB 2014", "")
#leg.AddEntry(None, "UPC-main 2010+2011", "")
#leg.AddEntry(None, "UPC-main 2014", "")
leg.AddEntry(None, "Zero bias 2014", "")
#leg.AddEntry(None, "VPD-ZDC-novtx-mon", "")
#gPad.SetLogy()
hZdcVtx.Draw()
leg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_projection(frame, ew):
xtit = ["ZDC East ADC", "ZDC West ADC"]
frame.SetXTitle(xtit[ew])
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().SetTitleOffset(1.4)
gPad.SetTopMargin(0.01)
gPad.SetRightMargin(0.01)
gPad.SetBottomMargin(0.1)
gPad.SetLeftMargin(0.1)
frame.Draw()
def plot_2d(plot_pdf):
plot_pdf.pdf.GetXaxis().SetTitleOffset(2.3)
plot_pdf.pdf.GetYaxis().SetTitleOffset(1.9)
plot_pdf.pdf.GetZaxis().SetTitleOffset(1.7)
gPad.SetTopMargin(0.01)
gPad.SetRightMargin(0.05)
gPad.SetBottomMargin(0.11)
gPad.SetLeftMargin(0.12)
plot_pdf.plot()
gPad.SetPhi(-125.)
gPad.Update()
def up_down_en():
#up and down spectrometers energy sum
ebin = 0.1
emin = 0
emax = 20
edet = 1
can = ut.box_canvas()
hE = ut.prepare_TH1D("hE", ebin, emin, emax)
sel = "up_en>" + str(edet * 1e3) + " && down_en>" + str(edet * 1e3)
#tree.Draw("(up_en+down_en)/1000. >> hE", sel)
#tree.Draw("up_en/1000. >> hE")#, sel)
#tree.Draw("down_en/1000. >> hE")
#tree.Draw("up_en >> hE")#, sel)
tree.Draw("down_en >> hE")
print "Entries:", hE.GetEntries()
hE.SetYTitle("Events / ({0:.3f}".format(ebin) + " GeV)")
hE.SetXTitle("#it{E}_{#gamma} = #it{E}_{up} + #it{E}_{down} (GeV)")
hE.SetTitleOffset(1.4, "Y")
hE.SetTitleOffset(1.3, "X")
gPad.SetTopMargin(0.01)
gPad.SetRightMargin(0.02)
gPad.SetBottomMargin(0.1)
gPad.SetLeftMargin(0.1)
#hE.GetYaxis().SetMoreLogLabels()
hE.Draw()
gPad.SetLogy()
leg = ut.prepare_leg(0.58, 0.8, 0.2, 0.15, 0.035) # x, y, dx, dy, tsiz
leg.AddEntry(hE, "#it{E}_{up} + #it{E}_{down}", "lp")
leg.AddEntry(None, "#it{E}_{up}>1 #bf{and} #it{E}_{down}>1 GeV", "")
leg.Draw("same")
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def draw_canvas_2d(name):
f_mc = TFile("plots/mc/%s_mcc7.root" % name)
h_mc = gDirectory.Get("h_%s_reco" % name)
f = TFile("plots/data/e_%s_pandoraCosmic.root" % name)
h = gDirectory.Get("h_%s" % name)
h.Divide(h_mc)
h.GetZaxis().SetRangeUser(0.5, 1.5)
#h.GetZaxis().SetTitle("Data/Monte Carlo")
#h.GetZaxis().RotateTitle()
h.SetMarkerSize(2)
x_minbin = h.FindFirstBinAbove(0, 1)
low_x = h.GetXaxis().GetBinLowEdge(x_minbin)
x_maxbin = h.FindLastBinAbove(0, 1)
high_x = h.GetXaxis().GetBinUpEdge(x_maxbin)
y_minbin = h.FindFirstBinAbove(0, 2)
low_y = h.GetYaxis().GetBinLowEdge(y_minbin)
y_maxbin = h.FindLastBinAbove(0, 2)
high_y = h.GetYaxis().GetBinUpEdge(y_maxbin)
h.GetXaxis().SetRangeUser(low_x, high_x)
h.GetYaxis().SetRangeUser(low_y, high_y)
c = TCanvas("c_%s" % name)
h.SetMarkerSize(2.5)
gPad.SetBottomMargin(0.17)
gPad.SetLeftMargin(0.13)
gPad.SetTopMargin(0.15)
gPad.SetRightMargin(0.15)
h.GetYaxis().SetTitleSize(0.07)
h.GetXaxis().SetTitleSize(0.07)
h.GetYaxis().SetTitleOffset(0.8)
pt = TPaveText(0.13, 0.855, 0.42, 0.98, "ndc")
pt.AddText("MicroBooNE")
pt.SetFillColor(0)
pt.SetBorderSize(0)
pt.SetShadowColor(0)
h.Draw("colz texte")
pt.Draw()
c.Update()
c.SaveAs("plots/%s.pdf" % name)
c.SaveAs("plots/%s.p" % name)
return c
def plot_zdc_tpc_vtx():
#2D ZDC and TPC vertices
zbin = 2.
#zmin = -50.
#zmax = 100.
zmin = -100
zmax = 150
tbin = 2.
#tmin = -55.
#tmax = 55.
tmin = -110
tmax = 110
mmin = 1.5
mmax = 5.
can = ut.box_canvas()
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
hVtx = ut.prepare_TH2D("hVtx", tbin, tmin, tmax, zbin, zmin, zmax)
tree.Draw("jZDCVtxZ:jVtxZ >> hVtx", strsel)
hVtx.SetXTitle("TPC vertex along #it{z} / " + "{0:.0f} cm".format(zbin))
hVtx.SetYTitle("ZDC vertex along #it{z} / " + "{0:.0f} cm".format(zbin))
hVtx.SetTitleOffset(1.5, "Y")
hVtx.SetTitleOffset(1.1, "X")
gPad.SetTopMargin(0.02)
gPad.SetRightMargin(0.09)
gPad.SetBottomMargin(0.08)
gPad.SetLeftMargin(0.11)
leg = ut.prepare_leg(0.16, 0.82, 0.23, 0.05, 0.025)
leg.SetMargin(0.02)
leg.SetBorderSize(1)
ut.add_leg_mass(leg, mmin, mmax)
hVtx.Draw()
leg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def up_xy():
#up spectrometer first point in xy
xbin = 0.1
xmin = -12
xmax = 12
ymin = 3
ymax = 25
emin = 0.7
can = ut.box_canvas()
hX = ut.prepare_TH2D("hX", xbin, xmin, xmax, xbin, ymin, ymax)
sel = "up_en>" + str(emin * 1e3)
#tree.Draw("up_y/10:up_x/10 >> hX", sel)
tree.Draw("up_hy/10:up_hx/10 >> hX")
hX.SetXTitle("Horizontal #it{x} (cm)")
hX.SetYTitle("Vertical #it{y} (cm)")
hX.GetXaxis().CenterTitle()
hX.GetYaxis().CenterTitle()
hX.SetTitleOffset(1.2, "Y")
hX.SetTitleOffset(1.2, "X")
gPad.SetTopMargin(0.02)
gPad.SetRightMargin(0.11)
gPad.SetBottomMargin(0.09)
gPad.SetLeftMargin(0.09)
hX.Draw()
gPad.SetLogz()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def draw(self, elements):
gROOT.SetBatch()
gStyle.SetPadTickY(1)
gStyle.SetPadTickX(1)
c1 = TCanvas("c1", "c1", 1000, 700)
frame = gPad.DrawFrame(self.zmin, self.xmin, self.zmax,
self.xmax) # xmin, ymin, xmax, ymax in ROOT
frame.SetTitle(";Length #it{z} (m);Horizontal #it{x} (cm)")
siz = 0.035
frame.SetTitleSize(siz)
frame.SetLabelSize(siz)
frame.SetTitleSize(siz, "Y")
frame.SetLabelSize(siz, "Y")
frame.GetYaxis().SetTitleOffset(1)
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().CenterTitle()
frame.GetXaxis().CenterTitle()
gPad.SetLeftMargin(0.07)
gPad.SetRightMargin(0.01)
gPad.SetTopMargin(0.02)
gPad.SetBottomMargin(0.09)
#loop over elements
for el in elements.itervalues():
if not hasattr(el, "draw_2d"): continue
el.draw_2d()
#redraw these elements on the top
#draw_top = ["Q0EF", "beam_electron", "beam_hadron"]
draw_top = ["ew"]
for i in draw_top:
el = elements.get(i)
if el is not None: el.draw_2d()
gPad.SetGrid()
#self.invert_col(gPad)
c1.SaveAs("01fig.pdf")
#
deltaE_f.append( (reso[0] - energyNominal)/energyNominal )
# resolution (relative error)
relEnergyErrorVec.append( reso[1]/reso[0] )
#
relEnergyErrorErrorVec.append( reso[3]/reso[0] )
small=1.e-5
c1.Divide(1,2,small,small)
c1.cd(1)
gPad.SetPad(small, 0.3, 1.-small, 1.-small)
gPad.SetBottomMargin(small)
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())
def plot_proj_both(frame2, frame_east, frame_west, adc_bin, adc_min, adc_max,
ptmax, mmin, mmax):
can = ut.box_canvas(800, 700) #900, 700
xp = Double(0)
yp = Double(0)
#east data on the left
hEast = frame_east.getHist("data")
plot_east = frame2.Clone("plot_east")
plot_east.SetMarkerStyle(22)
plot_east.SetMarkerSize(1.4)
ut.set_H1D_col(plot_east, rt.kBlue)
for ibin in xrange(hEast.GetN()):
hEast.GetPoint(ibin, xp, yp)
plot_east.SetBinContent(ibin + 1, yp)
plot_east.SetBinError(ibin + 1, hEast.GetErrorY(ibin))
frame2.SetMaximum(1.2 * plot_east.GetMaximum()) # 1.15
#west data on the right
hWest = frame_west.getHist("data")
plot_west = frame2.Clone("plot_west")
plot_west.SetMarkerStyle(21)
ut.set_H1D_col(plot_west, rt.kRed)
for i in xrange(hWest.GetN()):
hWest.GetPoint(i, xp, yp)
ibin = frame2.GetNbinsX() - i
plot_west.SetBinContent(ibin, yp)
plot_west.SetBinError(ibin, hWest.GetErrorY(i))
#east fit
cEast = frame_east.getCurve("model")
gEast = TGraph(cEast.GetN() - 1)
for i in xrange(cEast.GetN() - 1):
cEast.GetPoint(i, xp, yp)
gEast.SetPoint(i, xp, yp)
gEast.SetLineColor(rt.kBlue)
gEast.SetLineWidth(3)
gEast.SetLineStyle(rt.kDashed)
#gEast.SetLineStyle(rt.kDashDotted)
#west fit on the left
cWest = frame_west.getCurve("model")
gWest = TGraph(cWest.GetN() - 1)
xmax = frame2.GetBinCenter(
frame2.GetNbinsX()) + frame2.GetBinWidth(frame2.GetNbinsX()) / 2.
for i in xrange(cWest.GetN() - 1):
cWest.GetPoint(i, xp, yp)
xplot = xmax - xp
gWest.SetPoint(i, xplot, yp)
gWest.SetLineColor(rt.kRed)
gWest.SetLineWidth(3)
#gWest.SetLineStyle(rt.kDashDotted)
#gWest.SetLineStyle(rt.kDashed)
#horizontal axis
frame2.SetMinimum(0)
#frame2.SetMinimum(0.98)
frame2.GetXaxis().SetNdivisions(0, rt.kFALSE)
#east axis
ypos = frame2.GetYaxis().GetXmin()
axisE = TGaxis(adc_min, ypos, adc_max, ypos, adc_min, adc_max)
ut.set_axis(axisE)
axisE.SetWmin(axisE.GetWmin() * 0.01)
axisE.SetWmax(axisE.GetWmax() * 0.01)
axisE.SetTitle("ZDC East #times100")
axisE.SetTitleOffset(1.1)
#west axis
xpos = frame2.GetXaxis().GetXmax()
axisW = TGaxis(xpos, ypos, xpos - adc_max, ypos, adc_min, adc_max, 510,
"-")
ut.set_axis(axisW)
axisW.SetWmin(axisW.GetWmin() * 0.01)
axisW.SetWmax(axisW.GetWmax() * 0.01)
axisW.SetLabelOffset(-0.024)
axisW.SetTitle("ZDC West #times100")
axisW.SetTitleOffset(1.1)
#vertical axis
yvpos = 1. * frame2.GetMaximum()
axisV = TGaxis(xpos, 0, xpos, yvpos, 0, yvpos, 510, "+L")
#axisV = TGaxis(xpos, 0, xpos, yvpos, 0.98, yvpos, 510, "+G")
ut.set_axis(axisV)
frame2.SetYTitle("ZDC East / ({0:.0f} ADC units)".format(adc_bin))
axisV.SetTitle("ZDC West / ({0:.0f} ADC units)".format(adc_bin))
frame2.GetYaxis().SetTitleOffset(1.5)
axisV.SetTitleOffset(1.5)
gPad.SetTopMargin(0.05) # 0.01
gPad.SetRightMargin(0.1)
gPad.SetBottomMargin(0.08)
gPad.SetLeftMargin(0.1)
frame2.Draw()
plot_east.Draw("e1same")
plot_west.Draw("e1same")
gEast.Draw("lsame")
gWest.Draw("lsame")
axisE.Draw()
axisW.Draw()
axisV.Draw()
#kinematics legend
#kleg = ut.prepare_leg(0.16, 0.78, 0.32, 0.2, 0.035)
kleg = ut.prepare_leg(0.16, 0.73, 0.32, 0.2, 0.035)
kleg.AddEntry(None, "AuAu@200 GeV", "")
kleg.AddEntry(None, "UPC sample", "")
ut.add_leg_pt_mass(kleg, ptmax, mmin, mmax)
kleg.Draw("same")
#data legend
dleg = ut.prepare_leg(0.6, 0.8, 0.15, 0.08, 0.03)
dleg.AddEntry(plot_east, "ZDC East", "p")
dleg.AddEntry(plot_west, "ZDC West", "p")
#dleg.Draw("same")
#projections legend
#pleg = ut.prepare_leg(0.24, 0.56, 0.25, 0.2, 0.035)
pleg = ut.prepare_leg(0.24, 0.51, 0.25, 0.2, 0.035)
pleg.AddEntry(plot_east, "ZDC East", "p")
pleg.AddEntry(plot_west, "ZDC West", "p")
pleg.AddEntry(gEast, "Fit projection to east", "l")
pleg.AddEntry(gWest, "Fit projection to west", "l")
pleg.Draw("same")
#gPad.SetLogy()
#gPad.SetGrid()
#ut.invert_col(gPad)
can.SaveAs("01fig.pdf")
######################################################
gROOT.SetStyle("Plain")
gStyle.SetLabelSize(0.035, "xyz")
######################################################
gROOT.SetStyle("Plain")
gStyle.SetLabelSize(0.035, "xyz")
c1 = TCanvas("c_massjj", "BPRE", 10, 10, 600, 500)
c1.Divide(1, 1, 0.008, 0.007)
ps1 = TPostScript(epsfig, 113)
c1.SetGrid()
c1.cd(1)
gPad.SetLogy(0)
gPad.SetTopMargin(0.05)
gPad.SetBottomMargin(0.14)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.05)
print "Read:\ndata/test.data\ndata/neuralnet.data"
data1_in, data1_out = getData("data/test.data")
data2_in, data2_out = getData("data/neuralnet.data")
xmin = -4000 - 1
xmax = 4000 - 1
bins = 100
print "Calculate histogram ranges"
data = []
for i in xrange(len(data1_in)):
inputs1 = data1_in[i]
output1 = data1_out[i]
def plot_data_histogram(fits, name):
return
test_canvas = TCanvas("TestCanvas", "Ds Fit", 0, 0, 800, 575)
gStyle.SetPadBorderMode(0)
gStyle.SetFrameBorderMode(0)
test_canvas.Divide(1, 2, 0, 0)
upper_pad = test_canvas.GetPad(1)
lower_pad = test_canvas.GetPad(2)
low, high = 0.05, 0.95
upper_pad.SetPad(low, 0.4, high, high)
lower_pad.SetPad(low, low, high, 0.4)
test_canvas.cd(1)
ROOT.IABstyles.canvas_style(test_canvas, 0.25, 0.05, 0.02, 0.15, 0, 0)
h_Mjj = TH1D("h_Mjj", "Mass Spectrum", 100, 0.2, 12)
h_Mjj.GetYaxis().SetTitle("num. events")
h_Mjj.GetXaxis().SetTitle("M [Tev/c^{-2}]")
ROOT.IABstyles.h1_style(h_Mjj, ROOT.IABstyles.lWidth // 2,
ROOT.IABstyles.Scolor, 1, 0, 0, -1111.0, -1111.0,
508, 508, 8, ROOT.IABstyles.Scolor, 0.1, 0)
h_Mjj.GetYaxis().SetRangeUser(0.1, 1e6)
h_Mjj.GetXaxis().SetRangeUser(1, 10)
h_Mjj.GetXaxis().SetTitleOffset(1)
h_Mjj.GetYaxis().SetTitleOffset(1.1)
upper_pad.SetLogy(1)
upper_pad.SetLogx(1)
lower_pad.SetLogx(1)
gr = TGraphErrors(fits.num_bins, array("d", fits.xmiddle),
array("d", fits.data), array("d", fits.xwidth),
array("d", fits.errors))
ROOT.IABstyles.h1_style(gr, ROOT.IABstyles.lWidth // 2,
ROOT.IABstyles.Scolor, 1, 0, 0, -1111, -1111, 505,
505, 8, ROOT.IABstyles.Scolor, 0.1, 0)
grFit = TGraph(fits.num_bins, array("d", fits.xmiddle),
array("d", fits.data_fits))
ROOT.IABstyles.h1_style(grFit, ROOT.IABstyles.lWidth // 2, 632, 1, 0, 0,
-1111, -1111, 505, 505, 8, 632, 0.1, 0)
h_Mjj.Draw("axis")
gr.Draw("P")
grFit.Draw("c")
test_canvas.Update()
gPad.SetBottomMargin(1e-5)
test_canvas.cd(2)
gPad.SetTopMargin(1e-5)
h2 = TH1D("h2", "", 100, 0.2, 12)
h2.GetXaxis().SetRangeUser(1, 10)
h2.GetYaxis().SetRangeUser(-10, 10)
h2.SetStats(False) # don't show stats box
h2.Draw("axis")
sig_values = [(data - theory) / theory if
(data != 0 and theory != 0) else -100
for data, theory in zip(fits.data, fits.data_fits)]
sig = TGraph(fits.num_bins, array("d", fits.xmiddle),
array("d", sig_values))
#ROOT.IABstyles.h1_style(sig, ROOT.IABstyles.lWidth/2, 632, 1, 0, 0, -1111, -1111, 505, 505, 8, 632, 0.1, 0)
ROOT.IABstyles.h1_style(gr, ROOT.IABstyles.lWidth // 2,
ROOT.IABstyles.Scolor, 1, 0, 0, -1111, -1111, 505,
505, 8, ROOT.IABstyles.Scolor, 0.1, 0)
sig.SetMarkerStyle(22) # triangle
sig.SetMarkerColor(2) # red
sig.SetMarkerSize(0.8)
sig.Draw("P")
#lower_pad.Draw()
label = ROOT.TText()
label.SetNDC()
label.SetTextSize(0.03)
label.DrawText(0.5, 0.7, "{0}GeV q*".format(MASS_FILE))
# test_canvas.SaveAs("output_qstar.pdf")
test_canvas.SaveAs("plots/{0}/{2}/plot-{1}-{2}-hist.png".format(
CLUSTER_ID, name, MASS_FILE))
ut.log_results(out, "Alpha and n in 3-digits:")
ut.log_results(
out, "{0:6} {1:.3f} +/- {2:.3f}".format("alpha:", alpha.getVal(),
alpha.getError()))
ut.log_results(
out, "{0:6} {1:.3f} +/- {2:.3f}".format("n:", n.getVal(),
n.getError()))
#create the plot
gStyle.SetPadTickX(1)
gStyle.SetFrameLineWidth(2)
can = ut.box_canvas()
gPad.SetRightMargin(0.02)
gPad.SetTopMargin(0.04)
gPad.SetBottomMargin(0.09)
gPad.SetLeftMargin(0.11)
frame = m.frame(rf.Bins(nbins), rf.Title(""))
frame.SetTitle("")
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().SetTitleOffset(1.6)
if binned == True:
dataH.plotOn(frame, rf.Name("data"))
else:
data.plotOn(frame, rf.Name("data"))
cb.plotOn(frame, rf.Precision(1e-6), rf.Name("CrystalBall"),
rf.LineColor(ccb))
frame.Draw()
eff = reco / geant
error = math.sqrt((eff * (1 - eff)) / geant)
h_theta_phi_reco.SetBinContent(i, j, eff)
h_theta_phi_reco.SetBinError(i, j, error)
pt = TPaveText(0.10, 0.905, 0.40, 0.98, "ndc")
#pt.AddText("MicroBooNE in progress")
pt.SetFillColor(0)
pt.SetBorderSize(0)
pt.SetShadowColor(0)
h_theta_phi_reco.GetXaxis().SetRangeUser(60, 120)
h_theta_phi_reco.GetYaxis().SetRangeUser(-90, -45)
h_theta_phi_reco.GetZaxis().SetRangeUser(0, 1)
h_theta_phi_reco.SetMarkerSize(2.5)
gPad.SetBottomMargin(0.17)
gPad.SetLeftMargin(0.13)
gPad.SetTopMargin(0.15)
gPad.SetRightMargin(0.15)
h_theta_phi_reco.GetYaxis().SetTitleSize(0.07)
h_theta_phi_reco.GetXaxis().SetTitleSize(0.07)
h_theta_phi_reco.GetYaxis().SetTitleOffset(0.8)
pt = TPaveText(0.13, 0.855, 0.42, 0.98, "ndc")
pt.AddText("MicroBooNE")
pt.SetFillColor(0)
pt.SetBorderSize(0)
pt.SetShadowColor(0)
h_theta_phi_reco.Draw("colz texte")
for i in range(0, N):
graph_MEM.SetPoint(i, mA[i], mH[i], AUC_MEM[i])
graph_DNN.SetPoint(i, mA[i], mH[i], AUC_DNN[i])
graph_MEM.SetNpx(500)
graph_MEM.SetNpy(500)
graph_DNN.SetNpx(500)
graph_DNN.SetNpy(500)
canvas = TCanvas('c1', 'c1', 1800, 700)
canvas.Divide(2, 1, 0.005)
canvas.cd(1)
gPad.SetRightMargin(0.2)
gPad.SetLeftMargin(0.15)
gPad.SetBottomMargin(0.15)
base_hist = TH2F('', '', 500, 50, 850, 500, 200, 1000)
graph_MEM.SetHistogram(base_hist)
graph_MEM.SetMinimum(np.amin(AUC_MEM))
graph_MEM.SetMaximum(np.amax(AUC_MEM))
graph_MEM.GetHistogram().GetXaxis().SetLabelSize(0.04)
graph_MEM.GetHistogram().GetYaxis().SetLabelSize(0.04)
graph_MEM.GetHistogram().GetZaxis().SetLabelSize(0.04)
graph_MEM.GetHistogram().GetXaxis().SetTitleSize(0.06)
graph_MEM.GetHistogram().GetYaxis().SetTitleSize(0.06)
graph_MEM.GetHistogram().GetZaxis().SetTitleSize(0.06)
graph_MEM.GetHistogram().GetXaxis().SetTitleOffset(1.1)
graph_MEM.GetHistogram().GetYaxis().SetTitleOffset(1.2)
graph_MEM.GetHistogram().GetZaxis().SetTitleOffset(1.1)
graph_MEM.Draw("colz")
graph_MEM.SetTitle(
def rooFit109():
print ">>> setup model..."
x = RooRealVar("x", "x", -10, 10)
sigma = RooRealVar("sigma", "sigma", 3, 0.1, 10)
mean = RooRealVar("mean", "mean", 0, -10, 10)
gauss = RooGaussian("gauss", "gauss", x, RooConst(0),
sigma) # RooConst(0) gives segfaults
data = gauss.generate(RooArgSet(x), 100000) # RooDataSet
#sigma = 3.15 # overwrites RooRealVar with a float
sigma.setVal(3.15)
print ">>> plot data and slightly distorted model..."
frame1 = x.frame(Title("Data with distorted Gaussian pdf"),
Bins(40)) # RooPlot
data.plotOn(frame1, DataError(RooAbsData.SumW2))
gauss.plotOn(frame1)
print ">>> calculate chi^2..."
# Show the chi^2 of the curve w.r.t. the histogram
# If multiple curves or datasets live in the frame you can specify
# the name of the relevant curve and/or dataset in chiSquare()
print ">>> chi^2 = %.2f" % frame1.chiSquare()
print ">>> construct histograms with the residuals and pull of the data wrt the curve"
hresid = frame1.residHist() # RooHist
hpull = frame1.pullHist() # RooHist
frame2 = x.frame(Title("Residual Distribution")) # RooPlot
frame2.addPlotable(hresid, "P")
frame3 = x.frame(Title("Pull Distribution")) # RooPlot
frame3.addPlotable(hpull, "P")
print ">>> draw functions and toy data on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 2000, 400)
canvas.Divide(3)
for i, frame in enumerate([frame1, frame2, frame3], 1):
canvas.cd(i)
gPad.SetLeftMargin(0.14)
gPad.SetRightMargin(0.04)
frame.GetYaxis().SetTitleOffset(1.5)
frame.GetYaxis().SetLabelOffset(0.010)
frame.GetYaxis().SetTitleSize(0.045)
frame.GetYaxis().SetLabelSize(0.042)
frame.GetXaxis().SetTitleSize(0.045)
frame.GetXaxis().SetLabelSize(0.042)
frame.Draw()
canvas.SaveAs("rooFit109.png")
canvas.Close()
# ratio/pull/residual plot
print ">>> draw with pull plot..."
canvas = TCanvas("canvas", "canvas", 100, 100, 1000, 1000)
canvas.Divide(2)
canvas.cd(1)
gPad.SetPad("pad1", "pad1", 0, 0.33, 1, 1, 0, -1, 0)
gPad.SetTopMargin(0.10)
gPad.SetBottomMargin(0.03)
gPad.SetLeftMargin(0.14)
gPad.SetRightMargin(0.04)
gPad.SetBorderMode(0)
gStyle.SetTitleFontSize(0.062)
frame1.GetYaxis().SetTitle("Events / %.3g GeV" % frame1.getFitRangeBinW())
frame1.GetYaxis().SetTitleSize(0.059)
frame1.GetYaxis().SetTitleOffset(1.21)
#frame1.GetYaxis().SetLabelOffset(0.010)
frame1.GetXaxis().SetLabelSize(0)
frame1.GetYaxis().SetLabelSize(0.045)
frame1.Draw()
canvas.cd(2)
gPad.SetPad("pad2", "pad2", 0, 0, 1, 0.33, 0, -1, 0)
gPad.SetTopMargin(0.01)
gPad.SetBottomMargin(0.30)
gPad.SetLeftMargin(0.14)
gPad.SetRightMargin(0.04)
gPad.SetBorderMode(0)
gPad.SetGridy(kTRUE)
line1 = TLine(frame3.GetXaxis().GetXmin(), 0,
frame3.GetXaxis().GetXmax(), 0)
line2 = TLine(frame3.GetXaxis().GetXmin(), 0,
frame3.GetXaxis().GetXmax(), 0)
line1.SetLineColor(0) # white to clear dotted grid lines
line2.SetLineColor(12) # dark grey
line2.SetLineStyle(2)
frame3.SetTitle("")
frame3.GetYaxis().SetTitle("pull")
frame3.GetXaxis().SetTitle("#Deltam^{2}_{ll} [GeV]")
frame3.GetXaxis().SetTitleSize(0.13)
frame3.GetYaxis().SetTitleSize(0.12)
frame3.GetXaxis().SetTitleOffset(1.0)
frame3.GetYaxis().SetTitleOffset(0.58)
frame3.GetXaxis().SetLabelSize(0.10)
frame3.GetYaxis().SetLabelSize(0.10)
frame3.GetXaxis().SetLabelOffset(0.02)
frame3.GetYaxis().SetLabelOffset(0.01)
frame3.GetYaxis().SetRangeUser(-5, 5)
frame3.GetYaxis().CenterTitle(True)
frame3.GetYaxis().SetNdivisions(505)
frame3.Draw("")
line1.Draw("SAME")
line2.Draw("SAME")
frame3.Draw("SAME")
canvas.SaveAs("rooFit109_ratiolike.png")
canvas.Close()
def main(argv):
P0DBANFFStyle = INTERFACE.GetThisStyle()
INTERFACE.SetStyle(P0DBANFFStyle.GetName())
OUTPUTFILENAME = "BANFFReactionCodeStacksOfficial_Joint2018_Numode_Prefit_P0DOnly_Momentum.pdf"
inputFile = TFile(sys.argv[0])
dataFile = TFile(sys.argv[1])
dummyPage = TCanvas()
dummyPage.Print(OUTPUTFILENAME + "[")
sampleNames = [
"P0D_Air_NuMu_CC_CC1Track", "P0D_Air_NuMu_CC_CCNTracks",
"P0D_Water_NuMu_CC1Track", "P0D_Water_NuMu_CCNTracks"
]
fileNameRoots = [
"P0D_Air_NuMu_CC1Track", "P0D_Air_NuMu_CCNTracks",
"P0D_Water_NuMu_CC1Track", "P0D_Water_NuMu_CCNTracks"
]
stackHistos = [
"#nu CCQE", "#nu CC 2p-2h", "#nu CC Res 1#pi", "#nu CC Coh 1#pi",
"#nu CC Other", "#nu NC modes", "#bar{#nu} modes"
]
P0DBANFFStyle.SetTitleBorderSize(0)
P0DBANFFStyle.SetTitleX(0.02)
P0DBANFFStyle.SetTitleY(0.95)
P0DBANFFStyle.SetTitleW(0.3)
P0DBANFFStyle.SetTitleH(0.07)
# Align left, centered in y)
P0DBANFFStyle.SetTitleAlign(11)
stackColours = [1300, 1302, 1303, 1304, 1305, 1308, 1310]
stackFillStyle = [1001, 1001, 1001, 1001, 1001, 1001, 1001]
"""
The graphs will be of the form <sampleName>_rxnPredMC_<rxnCode+100>
So for each sample, we'll loop from 0-200, and if the THnD is not NULL when we
try and get it, add it to a list for stacking.
for sampleName in sampleNames:
"""
for ii in xrange(0, len(sampleNames)):
sampleName = sampleNames[ii]
# Total the number of events for this sample going into the histogram.
# (Remember: Sand is excluded here.)
mcSampleTotal = 0.0
# Now make the set of histograms to show, which combine several interaction
# types.
# Load in the data histogram and use it to assemble the TH2Ds for the MC, as
# well as making it one of the histograms on the stack.
dataHist = dataFile.Get(sampleName + "_data").Projection(1, 0)
dataMomProj = dataFile.Get(sampleName + "_data").Projection(
1, 0).ProjectionX("dataratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
dataMomProj.SetDirectory(0)
dataMomProj.SetTitle("")
prefitMomProj = inputFile.Get(sampleName + "_prefit").Projection(
1, 0).ProjectionX("prefitratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
prefitMomProj.SetDirectory(0)
postfitMomProj = inputFile.Get(sampleName + "_postfit_0_0").Projection(
1, 0).ProjectionX("postfitratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
postfitMomProj.SetDirectory(0)
for ibx in xrange(1, dataMomProj.GetNbinsX() + 1):
dataMomProj.SetBinContent(
ibx,
dataMomProj.GetBinContent(ibx) / prefitMomProj.GetBinContent(ibx))
dataMomProj.SetBinError(
ibx,
ROOT.Math.sqrt(dataMomProj.GetBinContent(ibx)) /
prefitMomProj.GetBinContent(ibx))
print "Data = " + str(
dataMomProj.GetBinContent(ibx)) + " MC = " + str(
prefitMomProj.GetBinContent(ibx)) + " Ratio = " + str(
dataMomProj.GetBinContent(ibx) /
prefitMomProj.GetBinContent(ibx))
dataMomProj.GetYaxis().SetRangeUser(0.75, 1.25)
dataMomProj.GetXaxis().SetRangeUser(MINMOMENTUM, MAXMOMENTUM)
dataMomProj.GetXaxis().SetTitle("Reconstructed muon momentum (MeV/c)")
dataMomProj.GetYaxis().SetTitle("Data / Sim.")
# There is only one data plot for each stack, so:
# Momentum projection
# Assemble them here.
dataOneDimPlots = []
dataOneDimPlots.append(
dataHist.ProjectionX("data" + sampleName + "_px", 1,
dataHist.GetNbinsY()))
dataNXBins = dataHist.GetXaxis().GetNbins()
dataxarray = dataHist.GetXaxis().GetXbins().GetArray()
# Now, loop through all the theta bins and add the slices in.
for iby in xrange(1, dataHist.GetNbinsY() + 1):
thetaLow = dataHist.GetYaxis().GetBinLowEdge(iby)
thetaHigh = dataHist.GetYaxis().GetBinUpEdge(iby)
thetaString = " < cos# theta < "
thetaString = str(thetaLow) + thetaString + str(thetaHigh)
dataOneDimPlots.append(
TH1D("dataslice_" + sampleName + str(iby), thetaString, dataNXBins,
dataxarray))
# Now, go through the x bins in this slice and fill the plot we
# just created.
for ibx in xrange(1, dataHist.GetNbinsX() + 1):
# Get the bin dimensions for dividing purposes to area
# normalize.
dataOneDimPlots[len(dataOneDimPlots) - 1].SetBinContent(
ibx, dataHist.GetBinContent(ibx, iby))
# Now that the dataOneDimPlots array is filled, loop through a area
# normalize the bins.
for dataOneDimHist in dataOneDimPlots:
for ibx in xrange(1, dataOneDimHist.GetNbinsX() + 1):
mpdim = (dataOneDimHist.GetXaxis().GetBinUpEdge(ibx) -
dataOneDimHist.GetXaxis().GetBinLowEdge(ibx)) / 100.0
dataOneDimHist.SetBinError(
ibx,
math.sqrt(dataOneDimHist.GetBinContent(ibx)) / mpdim)
dataOneDimHist.SetBinContent(
ibx,
dataOneDimHist.GetBinContent(ibx) / mpdim)
histosToStack = []
for histo in stackHistos:
histosToStack.append(
TH2D(sampleName + "_" + histo, sampleName + "_" + histo,
dataHist.GetXaxis().GetNbins(),
dataHist.GetXaxis().GetXbins().GetArray(),
dataHist.GetYaxis().GetNbins(),
dataHist.GetYaxis().GetXbins().GetArray()))
# Now pull the histograms from the file, and add their content to the
# relevant combined histogram.
for i in xrange(0, 200):
if inputFile.Get(sampleName + "_rxnPredMC_" + str(i)):
histosToStack[CorrespondingIndex(i)].Add(
inputFile.Get(sampleName + "_rxnPredMC_" + str(i)).Projection(
1, 0))
"""
# OK, so now we have an array of 2D histograms for this sample that we'd
# like to stack. Need to divide it up into individual ones. Do Momentum
# Projection, then momentum in theta slices. Normalize by bin area.
# For storing all the one dimensional plots for this sample.
"""
oneDimPlots = []
# Add nothing for the momentum projection, will append the thetaStrings as
# needed.
stackTitles = []
for i in xrange(0, len(stackHistos)):
# For storing all the one dimensional plots for this reaction code.
oneDimPlotsThisReac = []
nameBase = histosToStack[i].GetTitle()
oneDimPlotsThisReac.append(histosToStack[i].ProjectionX(
nameBase + "_px", 1, histosToStack[i].GetNbinsY()))
stackTitles.append("Momentum projection")
# Area normalize this now.
NXBins = histosToStack[i].GetXaxis().GetNbins()
for ibx in xrange(1, NXBins + 1):
mpdim = (histosToStack[i].GetXaxis().GetBinUpEdge(ibx) -
histosToStack[i].GetXaxis().GetBinLowEdge(ibx)) / 100.0
oneDimPlotsThisReac[0].SetBinContent(
ibx, oneDimPlotsThisReac[0].GetBinContent(ibx) / mpdim)
mcSampleTotal += oneDimPlotsThisReac[0].GetSumOfWeights()
# Now that the x bins are filled, loop on to the next histogram.
# With all the y-slices done, oneDimPlotsThisReac is now full. Append
# it to oneDimPlots.
oneDimPlots.append(oneDimPlotsThisReac)
""""
At this point in time, oneDimPlots has a momentum projection and theta
slices for each reaction code grouping in this sample. So now we just need
to make TStacks for them and print them out to PDF.
The stack contains the plot from the same index of each entry of
oneDimPlots.
They should all have the same length, so use the first one to loop
through (number of slices plus 1 full momenutm projection.)
"""
for odi in xrange(0, len(oneDimPlots[0])):
# For each one create a TStack
sampleStack = THStack(
"mpStack_" + sampleName + str(odi),
";Reconstructed muon momentum (MeV/c);Events/(100 MeV/c)")
leg = TLegend(0.7, 0.15, 0.94, 0.93)
leg.SetFillColor(0)
# Make data the first entry.
leg.AddEntry(dataOneDimPlots[odi], "Data", "LEP")
dataOneDimPlots[odi].SetMarkerStyle(20)
dataOneDimPlots[odi].SetMarkerColor(kBlack)
dataOneDimPlots[odi].SetLineColor(kBlack)
for odj in xrange(0, len(oneDimPlots)):
# Apply the appropriate plot options from the beginning.
oneDimPlots[odj][odi].SetFillColor(stackColours[odj])
oneDimPlots[odj][odi].SetFillStyle(stackFillStyle[odj])
oneDimPlots[odj][odi].SetMarkerStyle(1) # Don't want P0DBANFFStyle to interfere.
sampleStack.Add(oneDimPlots[odj][odi])
print oneDimPlots[odj][odi].GetTitle() + " " + str(
oneDimPlots[odj][odi].GetSumOfWeights())
leg.AddEntry(oneDimPlots[odj][odi], stackHistos[odj], "F")
c1 = TCanvas()
upperPad = TPad("upperPad", "upperPad", .05, .35, .96, .95)
lowerPad = TPad("lowerPad", "lowerPad", .05, .1, .96, .35)
upperPad.Draw()
lowerPad.Draw()
upperPad.cd()
gPad.SetBottomMargin(1e-5)
c1.SetTicks(1, 1)
sampleStack.Draw("")
dataOneDimPlots[odi].Draw("PEsame")
sampleStack.SetMaximum(
max(sampleStack.GetMaximum(),
FindPlotMax(dataOneDimPlots[odi], dataOneDimPlots[odi])))
leg.Draw()
sampleStack.GetXaxis().SetRangeUser(MINMOMENTUM, MAXMOMENTUM)
sampleStack.GetXaxis().SetTitle("")
sampleStack.GetXaxis().SetTickLength(0)
sampleStack.GetXaxis().SetLabelSize(0)
sampleStack.SetTitle("# nu-mode")
lowerPad.cd()
ROOT.gPad.SetTopMargin(1e-5)
ROOT.gPad.SetBottomMargin(0.35)
ROOT.gPad.SetTickx()
dataMomProj.SetLineColor(2)
dataMomProj.SetMarkerColor(2)
dataMomProj.GetYaxis().SetNdivisions(8, 1)
dataMomProj.GetYaxis().SetLabelSize(
dataMomProj.GetYaxis().GetLabelSize() + 0.07)
dataMomProj.GetYaxis().SetTitleSize(
dataMomProj.GetYaxis().GetLabelSize() + 0.05)
dataMomProj.GetYaxis().SetTitleOffset(0.335)
dataMomProj.GetXaxis().SetLabelSize(
dataMomProj.GetXaxis().GetLabelSize() + 0.08)
dataMomProj.GetXaxis().SetTitleSize(
dataMomProj.GetXaxis().GetLabelSize() + 0.04)
dataMomProj.GetXaxis().SetTitleOffset(0.9)
dataMomProj.GetXaxis().SetTickLength(0.04)
dataMomProj.Draw("PE")
line = TLine(0.0, 1.0, 5000.0, 1.0)
line.SetLineWidth(2)
line.SetLineColor(1)
line.Draw("SAME")
c1.Modified()
c1.Update()
c1.Print(OUTPUTFILENAME)
outFileName = fileNameRoots[sampleNames.index(
sampleName)] + "_mumom_rxn_postfit"
c1.Print(outFileName + ".pdf")
c1.Print(outFileName + ".png")
c1.Print(outFileName + ".eps")
dummyPage.Print(OUTPUTFILENAME + ']')
def main():
zmin = -40000.
zmax = -10100.
xmin = -1300.
xmax = 800.
c1 = TCanvas("c1", "c1", 1000, 700)
frame = gPad.DrawFrame(zmin, xmin, zmax,
xmax) # xmin, ymin, xmax, ymax in ROOT
frame.SetTitle(";Length #it{z} (mm);Horizontal #it{x} (mm)")
siz = 0.035
frame.SetTitleSize(siz)
frame.SetLabelSize(siz)
frame.SetTitleSize(siz, "Y")
frame.SetLabelSize(siz, "Y")
frame.GetYaxis().SetTitleOffset(1.3)
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().CenterTitle()
frame.GetXaxis().CenterTitle()
gPad.SetLeftMargin(0.09)
gPad.SetRightMargin(0.01)
gPad.SetTopMargin(0.02)
gPad.SetBottomMargin(0.09)
#geometry
geo = rt.GeoParser("../../config/pro1/geom_all.in")
#spectrometer magnet
mag = magnet("lumi_dipole", geo)
mag.label = "Spectr. dipole"
mag.draw()
#B2BeR magnet
b2b = magnet("B2BeR", geo)
b2b.label = "B2BeR"
b2b.draw()
#Q3eR magnet
q3 = magnet("Q3eR", geo)
q3.label = "Q3eR"
q3.draw()
#beam vacuum
bvac = vacuum(geo)
bvac.add_point("vac_b2b_drift", "zQB", "xQB")
bvac.add_point("vac_b2b_drift", "zQT", "xQT")
bvac.add_point("vac_b2b_drift", "zW", "xW")
bvac.add_point("vac_b2b_window", "win_z", "win_xmax")
bvac.add_point_2("vac_b2b_window.b2b_end_z", "B2BeR.r2")
bvac.add_point("vac_tag1_win", "zB", "xB")
bvac.draw()
#vacuum in front of Tagger 1
vac_t1 = vacuum(geo)
vac_t1.add_point("vac_tag1_win", "zTB", "xTB")
vac_t1.add_point("vac_tag1_win", "zT", "xT")
vac_t1.add_point("vac_tag1_win", "zB", "xB")
vac_t1.draw()
#vacuum in front of Tagger 2
vac_t2 = vacuum(geo)
vac_t2.add_point("vac_tag2_win", "zTB", "xTB")
vac_t2.add_point("vac_tag2_win", "zT", "xT")
vac_t2.add_point("vac_tag2_win", "zB", "xB")
vac_t2.draw()
#vacuum from exit window to spectrometer magnet
vac_win = vacuum(geo)
vac_win.add_point("vac_lumi_win_mag", "z1", "dX1", -1.)
vac_win.add_point("vac_lumi_win_mag", "z1", "dX1")
vac_win.add_point("vac_lumi_win_mag", "z0", "dX0")
vac_win.add_point("vac_lumi_win_mag", "z0", "dX0", -1.)
#vac_win.add_point("", "", "")
#vac_win.draw()
#vacuum from spectrometer magnet to spectrometer detectors
vac_mag = vacuum(geo)
vac_mag.add_point("vac_lumi_mag_spec", "z1", "dX1", -1.)
vac_mag.add_point("vac_lumi_mag_spec", "z1", "dX1")
vac_mag.add_point("vac_lumi_mag_spec", "z0", "dX0")
vac_mag.add_point("vac_lumi_mag_spec", "z0", "dX0", -1.)
#vac_mag.draw()
#vacuum section from spectrometers to direct photon detector
vac_phot = vacuum(geo)
vac_phot.add_point("vac_lumi_spec_phot", "z1", "dX1", -1.)
vac_phot.add_point("vac_lumi_spec_phot", "z1", "dX1")
vac_phot.add_point("vac_lumi_spec_phot", "z0", "dX0")
vac_phot.add_point("vac_lumi_spec_phot", "z0", "dX0", -1.)
#vac_phot.draw()
#Tagger1
tag1 = segment("Tagger1box", geo)
tag1.label = "Tagger 1"
tag1.draw()
#Tagger2
tag2 = segment("Tagger2box", geo)
tag2.label = "Tagger 2"
tag2.draw()
#Luminosity exit window
ew = segment("ExitWinBox", geo)
ew.label = "Exit window"
ew.fill_col = rt.kGreen + 1
ew.draw()
#up spectrometer for both spectrometers
up = segment("LumiSUbox", geo)
up.theta = 0
up.label = "Spectrometers"
up.draw()
#Luminosity direct photon detector
phot = segment("LumiDbox", geo)
phot.label = "Photon detector"
phot.draw()
leg = ut.prepare_leg(0.8, 0.21, 0.25, 0.15,
0.03) #, 0.027) # x, y, dx, dy, tsiz
leg.AddEntry(tag1.gbox, "Detector", "f")
leg.AddEntry(b2b.gbox, "Magnet", "f")
leg.AddEntry(bvac.gbox, "Vacuum", "f")
leg.Draw("same")
gPad.SetGrid()
ut.invert_col(gPad)
c1.SaveAs("01fig.pdf")
