def addEnergyAxisToPatches(subsystem, hist, processingOptions):
""" Adds an additional axis showing the conversion from ADC counts to Energy.
These conditions are set for "{EMCal,DCal}(Max)PatchAmp".
It creates a new TGaxis that shows the ADC to Energy conversion. It then draws it on selected
histogram.
Warning:
This function assumes that there is already a canvas created.
Note:
TGaxis removes ownership from Python to ensure that it continues to exist outside of the
function scope.
Args:
hist (TH1): The histogram to be processed.
Returns:
None
"""
kEMCL1ADCtoGeV = 0.07874 # Conversion from EMCAL Level1 ADC to energy
adcMin = hist.hist.GetXaxis().GetXmin()
adcMax = hist.hist.GetXaxis().GetXmax()
EMax = adcMax * kEMCL1ADCtoGeV
EMin = adcMin * kEMCL1ADCtoGeV
#yMax = gPad.GetUymax() # this function does not work here (log problem)
yMax = 2 * hist.hist.GetMaximum()
energyAxis = TGaxis(adcMin, yMax, adcMax, yMax, EMin, EMax, 510, "-")
SetOwnership(energyAxis, False)
energyAxis.SetTitle("Energy (GeV)")
energyAxis.Draw()
def testIthr():
lines = get_lines('DAC_scan_ithr_0x40to0xf0.dat')
gr1 = TGraphErrors()
gr2 = TGraphErrors()
fUnit = 1000. / 0.7
yUnit = 'e^{-}'
for line in lines:
if len(line) == 0: continue
if line[0] in ['#', '\n']: continue
fs = line.rstrip().split()
ix = int(fs[0])
gr1.SetPoint(ix, float(fs[1]), float(fs[2]) * fUnit)
gr1.SetPointError(ix, 0, float(fs[3]) * fUnit)
gr2.SetPoint(ix, float(fs[1]), float(fs[4]) * fUnit)
gr2.SetPointError(ix, 0, float(fs[5]) * fUnit)
useAtlasStyle()
gStyle.SetMarkerStyle(20)
gr1.SetMarkerStyle(20)
gr1.Draw('AP')
h1 = gr1.GetHistogram()
h1.GetYaxis().SetTitle("Threshold [" + yUnit + "]")
h1.GetXaxis().SetTitle("I_{Thre} code")
# h1.GetYaxis().SetRangeUser(0,0.2)
gPad.SetTicks(1, 0)
gPad.SetRightMargin(0.16)
y1b = 0
y2b = 15
x1 = h1.GetXaxis().GetXmax()
y1 = h1.GetYaxis().GetXmin()
y2 = h1.GetYaxis().GetXmax()
raxis = TGaxis(x1, y1, x1, y2, y1b, y2b, 506, "+L")
raxis.SetLineColor(2)
raxis.SetLabelColor(2)
raxis.SetTitleColor(2)
raxis.SetTitle("ENC [" + yUnit + "]")
raxis.Draw()
nP = gr2.GetN()
Ys = gr2.GetY()
EYs = gr2.GetEY()
Y = array(
'd', [y1 + (y2 - y1) / (y2b - y1b) * (Ys[i] - y1b) for i in range(nP)])
EY = array('d', [(y2 - y1) / (y2b - y1b) * EYs[i] for i in range(nP)])
gr2x = TGraphErrors(nP, gr2.GetX(), Y, gr2.GetEX(), EY)
gr2x.SetMarkerStyle(24)
gr2x.SetLineColor(2)
gr2x.SetMarkerColor(2)
gr2x.Draw('Psame')
waitRootCmdX()
def addVerticalAxis(self, title, ymin, ymax):
self.yaxis2 = TGaxis(self.xmax, self.ymin, self.xmax, self.ymax, ymin,
ymax, 510, "+L")
self.yaxis2.SetName("axis")
self.yaxis2.SetLabelOffset(0.01)
self.yaxis2.SetTitle(title)
self.yaxis2.Draw()
self.ymin2 = ymin
self.ymax2 = ymax
return self.yaxis2
def get_rank_section(directory):
# do Rank histo png
imgname = "RankSummary.png"
gStyle.SetPadTickY(0)
c = TCanvas("ranks", "ranks", 500, 400)
#gStyle.SetOptStat(0)
c.cd()
h = directory.rank_histo
rank_histof = TH1F(h.GetName(), "", h.GetNbinsX(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
rank_histof.SetLineWidth(2)
for i in xrange(0, h.GetNbinsX() + 1):
rank_histof.SetBinContent(i, h.GetBinContent(i))
h.SetTitle("Ranks Summary;Rank;Frequency")
h.Draw("Hist")
c.Update()
rank_histof.ComputeIntegral()
integral = rank_histof.GetIntegral()
rank_histof.SetContent(integral)
rightmax = 1.1 * rank_histof.GetMaximum()
scale = gPad.GetUymax() / rightmax
rank_histof.SetLineColor(kRed)
rank_histof.Scale(scale)
rank_histof.Draw("same")
#draw an axis on the right side
axis = TGaxis(gPad.GetUxmax(), gPad.GetUymin(), gPad.GetUxmax(),
gPad.GetUymax(), 0, rightmax, 510, "+L")
axis.SetTitle("Cumulative")
axis.SetTitleColor(kRed)
axis.SetLineColor(kRed)
axis.SetLabelColor(kRed)
axis.Draw()
rank_histof.Draw("Same")
c.Print(imgname)
page_html = '<div class="span-20"><h2 class="alt"><a name="rank_summary">Ranks Summary</a></h2>'
page_html += '<div class="span-19"><img src="%s"></div>' % imgname
page_html += '</div> <a href="#top">Top...</a><hr>'
return page_html
def ratioplot():
# create required parts
h1 = createH1()
h2 = createH2()
h3 = createRatio(h1, h2)
c, pad1, pad2 = createCanvasPads()
# draw everything
pad1.cd()
h1.Draw()
h2.Draw("same")
# to avoid clipping the bottom zero, redraw a small axis
h1.GetYaxis().SetLabelSize(0.0)
axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, "")
axis.SetLabelFont(43)
axis.SetLabelSize(15)
axis.Draw()
pad2.cd()
h3.Draw("ep")
def make_comparison_plot(outdir, var, groupname1, groupname2, category, weight = "PU_weight*MC_weight*bTagSF_weight*leptonSF_weight*triggerSF_weight*FR_weight", dataset = "ttH"):
var_name = var[0]
n_bins = var[3]
range_low = var[4]
range_high = var[5]
histos = []
plot_name = "p" #segfault when using different names for each
#"%s_%s_%s_%s_%s_vs_%s" % (outdir, var_name, dataset, category, groupname1, groupname2)
name1 = "plot_%s_%s" % (groupname1, plot_name)
plot1 = single_plot("%s/%s" % (groups[groupname1]["inputPath"], groups[groupname1]["inputFiles"][dataset]),
name1,
"%s_%s" % (groups[groupname1]["treePrefix"], category),
var_name,
weight,
n_bins, range_low, range_high)
name2 = "plot_%s_%s" % (groupname2, plot_name)
plot2 = single_plot("%s/%s" % (groups[groupname2]["inputPath"], groups[groupname2]["inputFiles"][dataset]),
name2,
"%s_%s" % (groups[groupname2]["treePrefix"], category),
var_name,
weight,
n_bins, range_low, range_high)
histos.append(plot1)
histos.append(plot2)
leg_entry = []
leg_entry.append("%s:\t N=%d, mean=%.2f, RMS=%.2f" % (groupname1, plot1.GetEntries(), plot1.GetMean(), plot1.GetRMS()))
leg_entry.append("%s:\t N=%d, mean=%.2f, RMS=%.2f" % (groupname2, plot2.GetEntries(), plot2.GetMean(), plot2.GetRMS()))
leg=TLegend(0.45,0.74,0.85,0.89)
leg.SetHeader(get_header(category));
leg.SetBorderSize(0)
leg.SetTextSize(0.04)
leg.SetFillColor(0)
for i in range(len(histos)):
normalize_histogram(histos[i])
leg.AddEntry(histos[i], leg_entry[i])
histos[0].SetLineColor(ROOT.kOrange-2)
histos[0].SetFillColor(ROOT.kOrange-2)
histos[0].SetStats(0)
histos[0].SetTitle("")
histos[1].SetLineColor(1)
histos[1].SetLineWidth(2)
c=TCanvas("c_%s" % plot_name,"c_%s" % plot_name, 800, 600)
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetLeftMargin(0.15)
pad1.SetBottomMargin(0) # Upper and lower plot are joined
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
max_val = 0.1
min_val = 0
for i in range(len(histos)):
max_val = max(histos[i].GetMaximum(), max_val)
min_val = min(histos[i].GetMinimum(), min_val)
for i in range(len(histos)):
histos[i].SetMaximum(1.4*max_val)
histos[i].SetMinimum(1.4*min_val)
histos[i].GetXaxis().SetTitle(var_name)
histos[i].GetYaxis().SetTitle("a.u.")
histos[i].GetYaxis().SetTitleOffset(1.7)
#histos[i].GetYaxis().SetTickLength(0)
#histos[i].GetYaxis().SetLabelOffset(999)
texl = TLatex(histos[0].GetBinLowEdge(1), 1.01*1.4*max_val, "CMS Preliminary, Simulation ttH #sqrt{s}=13 TeV")
texl.SetTextSize(0.04)
texl.Draw("same")
histos[0].Draw("hist")
histos[1].Draw("same")
leg.Draw("same")
texl.Draw("same")
histos[0].GetYaxis().SetLabelSize(0.)
axis = TGaxis(range_low, min_val, range_low, max_val, min_val, max_val, 510, "")
axis.SetLabelFont(43) #Absolute font size in pixel (precision 3)
axis.SetLabelSize(15)
axis.Draw()
# lower plot will be in pad
c.cd() # Go back to the main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetLeftMargin(0.15)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.35)
pad2.Draw()
pad2.cd()
h_ratio = make_ratio_histogram("h_ratio", histos[1], histos[0])
"""
h_ratio = h[1].Clone("h_ratio")
"""
h_ratio.SetTitle("")
h_ratio.Draw() #Draw the ratio plot
# Y axis histo_emul plot settings
histos[0].GetYaxis().SetTitleSize(20)
histos[0].GetYaxis().SetTitleFont(43)
histos[0].GetYaxis().SetTitleOffset(1.5)
# Ratio plot (h_ratio) settings
#h_ratio.SetTitle("") # Remove the ratio title
h_ratio.GetYaxis().SetLabelSize(0.)
axis2 = TGaxis( range_low, 0.01, range_low, 1.99, 0.01, 1.99, 505,"")
axis2.SetLabelFont(43) # Absolute font size in pixel (precision 3)
axis2.SetLabelSize(15)
axis2.Draw()
# Y axis ratio plot settings
h_ratio.GetYaxis().SetTitle("%s/%s" % (groupname2, groupname1))
h_ratio.GetYaxis().SetNdivisions(505)
h_ratio.GetYaxis().SetTitleSize(20)
h_ratio.GetYaxis().SetTitleFont(43)
h_ratio.GetYaxis().SetTitleOffset(1.5)
#h_ratio.GetYaxis().SetLabelFont(43) # Absolute font size in pixel (precision 3)
#h_ratio.GetYaxis().SetLabelSize(15)
# X axis ratio plot settings
h_ratio.GetXaxis().SetTitleSize(20)
h_ratio.GetXaxis().SetTitleFont(43)
h_ratio.GetXaxis().SetTitleOffset(4.)
h_ratio.GetXaxis().SetLabelFont(43) # Absolute font size in pixel (precision 3)
h_ratio.GetXaxis().SetLabelSize(15)
line = TLine(range_low, 1., range_high,1.)
line.Draw("same")
plot_file_name="%s" % (var_name)
dirname = ("%s_vs_%s/%s/%s" % (groupname1, groupname2, category, outdir)).replace(" ", "_")
for file_format in ["png", "pdf"]:
mkdir_p("plots_test/%s/%s" % (dirname, file_format))
c.SaveAs("plots_test/%s/%s/%s.%s" % (dirname, file_format, plot_file_name, file_format))
texts[-2].SetTextColor(level['color'])
lines[-1].SetLineColor(level['color'])
if 'life' in level:
texts.append(
TText(
xcenter, level['energy'] + 2 * param['TextFloat'] +
(plot_range['ymax'] - plot_range['ymin']) *
param['TextSize'], str(level['life'])))
texts[-1].SetTextAlign(21)
texts[-1].SetTextColor(texts[-2].GetTextColor())
if 'Axis' in param:
paxis = param['Axis']
axis_margin = 0.015 * (plot_range['ymax'] - plot_range['ymin'])
axis = TGaxis(-param['space'], paxis['range_min'], -param['space'],
paxis['range_max'], paxis['range_min'], paxis['range_max'],
paxis['ticks'], "")
axis.SetTitle(paxis['title'])
axis.SetLabelSize(paxis['label_size'])
axis.SetTitleSize(paxis['title_size'])
axis.SetTitleOffset(paxis['title_offset'])
axis.Draw()
for line in lines:
line.SetLineWidth(param['LineWidth'])
line.Draw()
for text in texts:
text.SetTextSize(param['TextSize'])
text.Draw()
c_allmases.cd(3)
ymax = 1.
graph_pratio_allmases.Draw("AP")
graph_pratio_allmases.GetXaxis().SetTitle("Nr of tracks")
graph_pratio_allmases.GetYaxis().SetTitle("<ratio>")
graph_pratio_allmases.GetYaxis().SetRangeUser(0., ymax)
graph_pratio_allmases.Draw("AP")
graph_ntr_allmases.Draw("*")
leg_all = TLegend(0.65, 0.35, 0.9, 0.55)
leg_all.AddEntry(graph_pratio_allmases, "pratio", "l")
leg_all.AddEntry(graph_ntr_allmases, "number of tracks", "p")
leg_all.SetTextSize(0.015)
leg_all.Draw()
axis = TGaxis(10.8, 0., 10.8, ymax, 0, ymax * rightmax / leftmax, 510, "+L")
axis.SetLineColor(2)
axis.SetLabelColor(2)
axis.Draw()
c_allmases.cd(4)
zmax = 0.07
graph_deltar_allmases.Draw("AP")
graph_deltar_allmases.GetXaxis().SetTitle("Nr of tracks")
graph_deltar_allmases.GetYaxis().SetTitle("deltaR")
graph_deltar_allmases.GetYaxis().SetRangeUser(0., zmax)
graph_deltar_allmases.Draw("AP")
graph_ntr_allmases2.Draw("*")
axis2 = TGaxis(10.8, 0., 10.8, zmax, 0, zmax * rightmax2 / leftmax2, 510, "+L")
axis2.SetLineColor(2)
def doTH1Plots(datasets, TH1List, signifCutDirection=None):
# Loop over all hName and expressions in the histogram list. Create & Draw plot
counter = 0
for h in TH1List:
print "*** Processing histo", h.name
hName = h.name
units = h.units
xLabel = h.xlabel
bLogX = h.bLogX
binWidthX = h.binWidthX
yLabel = h.ylabel
bLogY = h.bLogY
bRatio = h.bRatio
bNormalizeToOne = h.bNormalizeToOne
xLegMin = h.xLegMin
xLegMax = h.xLegMax
yLegMin = h.yLegMin
yLegMax = h.yLegMax
# Create customised legend
histograms.createLegend.setDefaults(x1=xLegMin,
x2=xLegMax,
y1=yLegMin,
y2=yLegMax)
# Create the plot
p = createTH1Plot(datasets, hName, normalizeToOne=bNormalizeToOne)
xAxis = TGaxis()
if "Discriminant" in h.name:
xAxis.SetMaxDigits(3)
else:
xAxis.SetMaxDigits(6)
# Customise the plot
drawPlot = getCustomisedHisto(datasets, h, False)
# Determine name of file that histos will be saved to according to the user-options
saveName = getFullSaveName(datasets,
histoName="TH1_%s" % (hName),
dataEra=myDataEra,
bNormalized=bNormalizeToOne,
bStackedHistos=bStackHistos)
# Draw the plot
drawPlot(p,
saveName,
xlabel=xLabel,
ylabel=yLabel,
rebinToWidthX=binWidthX,
cutLine=getHistoCutLines(h))
if len(signifCutDirection) != 0:
doBinSignifPlots(p, drawPlot, datasets, h, hName, xLabel, yLabel,
saveName)
for cutDir in signifCutDirection:
if (cutDir == ">" or cutDir == "<"):
doSignifPlots(p, drawPlot, datasets, h, hName, xLabel,
yLabel, saveName, cutDir)
return
simple_nue.SetStats(0)
rebin_nue.SetLineColor(38)
simple_nue.SetLineColor(46)
rebin_nue.SetLineWidth(6)
simple_nue.SetLineWidth(6)
simple_nue.GetXaxis().SetRangeUser(0, 4)
rebin_nue.GetXaxis().SetRangeUser(0, 4)
simple_nue.Draw("hist")
rebin_nue.Draw("hist same")
# Do not draw the Y axis label on the upper plot and redraw a small
# axis instead, in order to avoid the first label (0) to be clipped.
rebin_nue.GetYaxis().SetLabelSize(0.)
axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, "")
axis.SetLabelFont(43) # Absolute font size in pixel (precision 3)
axis.SetLabelSize(15)
axis.Draw()
leg_nue = TLegend(0.75, 0.95, 0.75, 0.95)
leg_nue.AddEntry(simple_nue, "nue gSimple Flux", "l")
leg_nue.AddEntry(rebin_nue, "nue dk2nu Flux", "l")
leg_nue.Draw()
c6.cd()
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0.1)
pad2.SetBottomMargin(0.1)
pad2.SetGridx() # vertical grid
pad2.Draw()
hRawYieldDistr.append(
inFileMT.Get(f'hRawYield_pT_{ptMin*10:.0f}-{ptMax*10:.0f}'))
rms = hRawYieldDistr[-1].GetRMS() / hRawYieldDistr[-1].GetMean() * 100
shift = TMath.Abs(
hRawYields.GetBinContent(iPt) -
hRawYieldDistr[-1].GetMean()) / hRawYieldDistr[-1].GetMean() * 100
syst = TMath.Sqrt(rms**2 + shift**2)
hRMS.SetBinContent(iPt, rms)
hMeanShift.SetBinContent(iPt, shift)
hSyst.SetBinContent(iPt, syst)
cRMS = TCanvas('cRMS', '', 800, 800)
cRMS.DrawFrame(hRMS.GetBinLowEdge(1), 0.1, ptMax, 20.,
';#it{p}_{T} (GeV/#it{c});RMS (%)')
hRMS.DrawCopy('same')
axisSoverB = TGaxis(gPad.GetUxmax(), gPad.GetUymin(), gPad.GetUxmax(),
gPad.GetUymax(), 0.01, 20., 510, "+LG")
axisSoverB.SetLineColor(kRed + 1)
axisSoverB.SetLabelColor(kRed + 1)
axisSoverB.SetLabelFont(42)
axisSoverB.SetLabelSize(0.045)
axisSoverB.SetTitle('S/B (3#sigma)')
axisSoverB.SetTitleOffset(1.2)
axisSoverB.SetLabelOffset(0.012)
axisSoverB.SetTitleColor(kRed + 1)
axisSoverB.SetTitleFont(42)
axisSoverB.SetTitleSize(0.05)
axisSoverB.SetMaxDigits(3)
axisSoverB.Draw()
hSoverB.DrawCopy('same')
cRMS.Update()
gr_plus.SetMarkerSize(3)
gr_plus.SetMarkerStyle(20)
gr_plus.SetMarkerColor(2)
gr_minus.SetLineColor(4)
gr_minus.SetLineWidth(4)
gr_minus.SetMarkerSize(3)
gr_minus.SetMarkerStyle(21)
gr_minus.SetMarkerColor(4)
gr_plus.Draw("ep same")
gr_minus.Draw("ep same")
canvas.Update()
axis = TGaxis(ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymin(),
ROOT.gPad.GetUxmax(), ROOT.gPad.GetUymax(), 0, 2,
510, "+L")
axis.SetTitle("Temperature (before - after) [degC]")
axis.SetTitleOffset(1.5)
axis.SetTitleFont(42)
axis.SetLineColor(6)
axis.SetTextColor(6)
axis.SetLabelFont(42)
axis.Draw()
gr_temp.SetLineColor(6)
gr_temp.SetLineWidth(4)
gr_temp.SetMarkerSize(3)
gr_temp.SetMarkerStyle(22)
gr_temp.SetMarkerColor(6)
def makeTwoScalesGraph(file):
print(file)
ch1 = OPData.fromPath('../data/part3/%s' % file, 1)
ch2 = OPData.fromPath('../data/part3/%s' % file, 2)
print('make canvas + pad')
c = TCanvas('c-%s' % file, '', 1280, 720)
pad = TPad('pad-%s' % file, '', 0, 0, 1, 1)
pad.Draw()
pad.cd()
print('frame')
frame = pad.DrawFrame(0,
min(ch1.getY()) * 1.1, 0.051,
max(ch1.getY()) * 1.1)
frame.SetXTitle('Zeit t / s')
frame.GetXaxis().CenterTitle()
frame.SetYTitle('Spannung Spule 2: U_{2} / V')
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetLabelColor(4)
frame.GetYaxis().SetTitleColor(4)
print('g1')
g1 = ch1.makeGraph('g1-%s' % file)
prepareGraph(g1)
g1.Draw('PX')
print('overlay')
c.cd()
overlay = TPad('overlay-%s' % file, '', 0, 0, 1, 1)
overlay.SetFillStyle(4000) # transparent
overlay.SetFillColor(0) # white
overlay.SetFrameFillStyle(4000) # transparent
overlay.Draw()
overlay.cd()
print('g2')
g2 = ch2.makeGraph('g2-%s' % file)
prepareGraph(g2, 2)
g2ymin = min(ch2.getY())
xmin = pad.GetUxmin()
xmax = pad.GetUxmax()
ymin = 1.1 * g2ymin
ymax = abs(ymin)
if file == '07.tab': # same scale like 06.tab
ymin, ymax = -0.07128, 0.07128
oframe = overlay.DrawFrame(xmin, ymin, xmax, ymax)
oframe.GetXaxis().SetLabelOffset(99)
oframe.GetYaxis().SetLabelOffset(99)
oframe.GetYaxis().SetTickLength(0)
g2.Draw('PX')
print('axis')
axis = TGaxis(xmax, ymin, xmax, ymax, ymin, ymax, 510, "+L")
axis.SetTitle('Spannung Photodiode: U_{ph} / V')
axis.CenterTitle()
axis.SetTitleOffset(1.2)
axis.Draw()
print('print')
c.Update()
c.Print('../img/part3/%s.pdf' % file[:-4], 'pdf')
def makeBeamspotCutPlots(name, points):
nPoints = len(points)
x = TGraphErrors(nPoints)
x.SetName(name)
x.SetTitle(name + " x; lb; x [mm]")
y = TGraphErrors(nPoints)
y.SetName(name)
y.SetTitle(name + " y; lb; y [mm]")
z = TGraphErrors(nPoints)
z.SetName(name)
z.SetTitle(name + " z; lb; z [mm]")
sx = TGraphErrors(nPoints)
sx.SetName(name)
sx.SetTitle(name + " sx; lb; #sigma(x) [mm]")
sy = TGraphErrors(nPoints)
sy.SetName(name)
sy.SetTitle(name + " sy; lb; #sigma(y) [mm]")
sz = TGraphErrors(nPoints)
sz.SetName(name)
sz.SetTitle(name + " sz; lb; #sigma(z) [mm]")
xrms = TGraphErrors(nPoints)
xrms.SetName(name + "_rms")
xrms.SetTitle(name + "_rms" + " x")
yrms = TGraphErrors(nPoints)
yrms.SetName(name + "_rms")
yrms.SetTitle(name + "_rms" + " y")
zrms = TGraphErrors(nPoints)
zrms.SetName(name + "_rms")
zrms.SetTitle(name + "_rms" + " z")
sxrms = TGraphErrors(nPoints)
sxrms.SetName(name + "_rms")
sxrms.SetTitle(name + "_rms" + " sx")
syrms = TGraphErrors(nPoints)
syrms.SetName(name + "_rms")
syrms.SetTitle(name + "_rms" + " sy")
szrms = TGraphErrors(nPoints)
szrms.SetName(name + "_rms")
szrms.SetTitle(name + "_rms" + " sz")
graphs.extend([x, y, z, sx, sy, sz, xrms, yrms, zrms, sxrms, syrms, szrms])
print len(points)
for i in range(len(points)):
xval = points[i][0]
yvals = points[i][1]
xerr = 0
x.SetPoint(i, xval, yvals[0][0])
y.SetPoint(i, xval, yvals[1][0])
z.SetPoint(i, xval, yvals[2][0])
sx.SetPoint(i, xval, yvals[3][0])
sy.SetPoint(i, xval, yvals[4][0])
sz.SetPoint(i, xval, yvals[5][0])
x.SetPointError(i, xerr, yvals[0][1])
y.SetPointError(i, xerr, yvals[1][1])
z.SetPointError(i, xerr, yvals[2][1])
sx.SetPointError(i, xerr, yvals[3][1])
sy.SetPointError(i, xerr, yvals[4][1])
sz.SetPointError(i, xerr, yvals[5][1])
xrms.SetPoint(i, xval, yvals[0][2])
yrms.SetPoint(i, xval, yvals[1][2])
zrms.SetPoint(i, xval, yvals[2][2])
sxrms.SetPoint(i, xval, yvals[3][2])
syrms.SetPoint(i, xval, yvals[4][2])
szrms.SetPoint(i, xval, yvals[5][2])
xrms.SetPointError(i, xerr, yvals[0][3])
yrms.SetPointError(i, xerr, yvals[1][3])
zrms.SetPointError(i, xerr, yvals[2][3])
sxrms.SetPointError(i, xerr, yvals[3][3])
syrms.SetPointError(i, xerr, yvals[4][3])
szrms.SetPointError(i, xerr, yvals[5][3])
c = TCanvas("cx")
c.Divide(3, 1)
c.cd(1)
x.Draw("ap")
c.cd(2)
y.Draw("ap+Y+")
c.cd(3)
yaxis_xrms = TGaxis(-1, 0.2, 1, 0.2, -1, 2, 510, "+R")
yaxis_xrms.ImportAxisAttributes(y.GetHistogram().GetYaxis())
#g = TMultiGraph()
#g.Add(x,"aXp")
#g.Add(y,"aY*")
#g.Draw("a")
# graphs.append(g)
# aa = y.GetHistogram()
#x.Draw("ap")
yaxis_xrms.Draw()
#axis.PaintAxis(0,0.5,0.1,0.6,0.4,1.4,510,"+R")
#y.Draw("pY+sames")
#b.Draw("sames")
c.Modified()
c.Update()
#c.Print()
canvases.append(c)
def PlotOnCanvas(self,pdf_name):
tdrstyle.setTDRStyle()
canvas = TCanvas("c", "c", 600, 600)
pad1 = TPad("pad1", "pad1", 0, 0.0, 1, 1.0)
pad1.SetTopMargin(0.15)
pad1.SetBottomMargin(0.32)
pad1.SetRightMargin(0.05)
pad1.SetLeftMargin(0.15)
if self.title == '':
pad1.SetTopMargin(0.05)
pad1.SetGridx()
pad1.Draw()
pad1.cd()
self.histo1.SetStats(0)
self.histo1.Draw(self.option)
self.histo2.Draw(self.option+" same")
if self.logx:
pad1.SetLogx()
if self.logy:
pad1.SetLogy()
# Redraw axis to avoid clipping 0
self.histo1.GetXaxis().SetLabelSize(0.)
self.histo1.GetXaxis().SetTitle('')
axis = TGaxis(-9,-2.8,-9,2.8,0,10000,50510,"")
axis.SetLabelFont(43)
axis.SetLabelSize(15)
axis.Draw("same")
pad2 = TPad("pad2", "pad2", 0, 0.0, 1, 0.3)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.4)
pad2.SetRightMargin(0.05)
pad2.SetLeftMargin(0.15)
pad2.SetGridx()
pad2.Draw()
pad2.cd()
if self.logx:
pad2.SetLogx()
self.ratio.SetLineColor(ROOT.kBlack)
self.ratio.SetMinimum(0.5)
self.ratio.SetMaximum(1.5)
self.ratio.SetStats(0)
self.ratio.SetMarkerStyle(21)
self.ratio.Draw("ep")
self.histo1.SetLineColor(ROOT.kBlue+1)
self.histo1.SetLineWidth(2)
max_y = max(self.histo1.GetMaximum(),self.histo2.GetMaximum())
self.histo1.SetMaximum(max_y*1.1)
self.histo1.GetXaxis().SetTitleOffset(1.5)
self.histo1.GetXaxis().SetLabelSize(0.)
self.histo1.GetXaxis().SetTitleSize(0.)
self.histo1.GetYaxis().SetTitleOffset(1.5)
self.histo1.GetYaxis().SetLabelSize(0.03)
self.histo1.GetYaxis().SetTitleSize(0.05)
self.histo1.GetYaxis().SetNdivisions(505)
self.histo2.SetLineColor(ROOT.kRed)
self.histo2.SetLineWidth(2)
self.ratio.SetTitle("")
self.ratio.GetXaxis().SetNdivisions(510)
self.ratio.GetXaxis().SetTitleOffset(1.1)
self.ratio.GetXaxis().SetLabelSize(0.10)
self.ratio.GetXaxis().SetTitleSize(0.15)
if len(self.xlabel) > 30:
self.ratio.GetXaxis().SetTitleSize(0.13)
if len(self.xlabel) > 60:
self.ratio.GetXaxis().SetTitleSize(0.11)
self.ratio.GetYaxis().SetTitle("Ratio")
self.ratio.GetYaxis().SetNdivisions(505)
self.ratio.GetYaxis().SetTitleOffset(0.5)
self.ratio.GetYaxis().SetLabelSize(0.10)
self.ratio.GetYaxis().SetTitleSize(0.15)
if len(self.ylabel) > 30:
self.ratio.GetYaxis().SetTitleSize(0.13)
if len(self.ylabel) > 60:
self.ratio.GetYaxis().SetTitleSize(0.11)
# Legend #
canvas.cd()
if self.title == '':
self.legend = TLegend(0.60,0.80,0.93,0.93)
else:
self.legend = TLegend(0.60,0.70,0.93,0.83)
self.legend.SetTextSize(0.05)
self.legend.SetBorderSize(0)
self.legend.SetFillColor(0)
self.legend.AddEntry(self.histo1,self.legend1,"l")
self.legend.AddEntry(self.histo2,self.legend2,"l")
self.legend.Draw()
ROOT.SetOwnership(canvas, False)
ROOT.SetOwnership(pad1, False)
ROOT.SetOwnership(pad2, False)
return canvas,self.filename
ctau_cms_8TeV_1g)
graph_exclusion_cms_8TeV_1g.SetFillColorAlpha(kAzure + 10, 0.65)
graph_exclusion_cms_8TeV_1g.SetLineColorAlpha(kAzure + 10, 0.65)
######CMS 8TeV, two photons
mass_cms_8TeV_2g = np.array([198., 227., 256., 256., 227., 198.])
ctau_cms_8TeV_2g = 0.01 * np.array([0.4, 2, 9, 9, 25., 50.])
graph_exclusion_cms_8TeV_2g = TGraph(6, mass_cms_8TeV_2g, ctau_cms_8TeV_2g)
graph_exclusion_cms_8TeV_2g.SetFillColorAlpha(kGray, 0.6)
graph_exclusion_cms_8TeV_2g.SetLineColorAlpha(kGray, 0.6)
####legend
#Lambda axis
f1_lambda = TF1("f1", "(x+6.00)/1.454", 72.902, 416.78)
A1_lambda = TGaxis(100.0, 0.0125, 600.0, 0.0125, "f1", 1010, "NI")
A1_lambda.SetLabelFont(42)
A1_lambda.SetLabelSize(0.035)
A1_lambda.SetTextFont(42)
A1_lambda.SetTextSize(1.3)
A1_lambda.SetTitle("#Lambda (TeV)")
A1_lambda.SetTitleSize(0.04)
A1_lambda.SetTitleOffset(0.9)
### only
graph_exclusion_exp_ = TGraph(
len(lambda_point_boundary_exp_),
np.array(1.454 * lambda_point_boundary_exp_ - 6.0),
0.01 * np.array(ctau_points))
graph_exclusion_exp_p1sig_ = TGraph(
len(lambda_point_boundary_exp_p1sig_),
def plot_ratio(ih_,max_val_,xbins__,comb_ID_):
# // Define two gaussian histograms. Note the X and Y title are defined
# // at booking time using the convention "Hist_title ; X_title ; Y_title"
# TH1F *h1 = new TH1F("h1", "Two gaussian plots and their ratio;x title; h1 and h2 gaussian histograms", 100, -5, 5);
# TH1F *h2 = new TH1F("h2", "h2", 100, -5, 5);
# h1.FillRandom("gaus");
# h2.FillRandom("gaus");
# // Define the Canvas
#TCanvas *c = new TCanvas("c", "canvas", 800, 800);
cc = TCanvas("cc", "canvas", 800, 800)
h1 = ih_[0][0]
h2 = ih_[1][0]
#print 'h1 = ',h1
#print 'h2 = ',h2
#print'h1.GetMaximum() = ' ,h1.GetMaximum()
#// Upper plot will be in pad1
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0) # Upper and lower plot are joined
#pad1.SetGridx() #Vertical grid, dashed lines
pad1.Draw() #Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
h1.SetStats(0) # No statistics on upper plot
h1.GetXaxis().SetNdivisions(xbins__)
h1.Draw() # Draw h1
# pad1.Update()
# lline = TLine(pad1.GetUxmin(),20,pad1.GetUxmax(),20)
# #lline.SetNDC(1)
# lline.SetLineStyle(3)
# lline.Draw('same')
h2.Draw("same") # Draw h2 on top of h1
# // Do not draw the Y axis label on the upper plot and redraw a small
# // axis instead, in order to avoid the first label (0) to be clipped.
h1.GetYaxis().SetLabelSize(0.)
#TGaxis *axis = new TGaxis( -5, 20, -5, 220, 20,220,510,"");
#axis = TGaxis( -5, 20, -5, 220, 20,220,510,"") #xmin ymin xmax ymax
axis = TGaxis( 0, 0, 0, max_val_, 0.001,max_val_,510,"")
axis.SetLabelFont(43) #Absolute font size in pixel (precision 3)
axis.SetLabelSize(15)
axis.Draw()
#lower plot will be in pad
cc.cd() # Go back to the main canvas before defining pad2
#TPad *pad2 = new TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0) # can change to separate top and bottom
#pad2.SetBottomMargin(0.2)
#pad2.SetBottomMargin(0)
#pad2.SetGridx() # vertical grid, dashed lines
pad2.Draw()
pad2.cd() # pad2 becomes the current pad
# Define the ratio plot
#TH1F *h3 = (TH1F*)h1.Clone("h3");
h3 = h2.Clone("h3")
h3.SetLineColor(1)
h3.SetMinimum(0.5) # Define Y ..
h3.SetMaximum(1.5) # .. range
h3.Sumw2()
h3.SetStats(0) # No statistics on lower plot
h3.Divide(h1)
h3.SetMarkerStyle(21)
#gPad.Modified()
#gPad.Update()
h3.Draw("ep") # Draw the ratio plot
pad2.Update()
lline = TLine(pad2.GetUxmin(),1,pad2.GetUxmax(),1)
#lline.SetNDC(1)
lline.SetLineStyle(1)
lline.Draw('same')
#// h1 settings
h1.SetLineColor(600+1)
h1.SetLineWidth(2)
#// Y axis h1 plot settings
h1.GetYaxis().SetTitleSize(20)
h1.GetYaxis().SetTitleFont(43)
h1.GetYaxis().SetTitleOffset(1.55)
#print 'xbins__ = ',xbins__
h1.GetXaxis().SetNdivisions(xbins__)
#h1.GetXaxis().SetNdivisions(0)
#// h2 settings
h2.SetLineColor(632)
h2.SetLineWidth(2)
# // Ratio plot (h3) settings
h3.SetTitle("") # Remove the ratio title
#// Y axis ratio plot settings
h3.GetYaxis().SetTitle("Reco/Gen")
h3.GetYaxis().SetNdivisions(505)
h3.GetYaxis().SetTitleSize(20)
h3.GetYaxis().SetTitleFont(43)
h3.GetYaxis().SetTitleOffset(1.55)
h3.GetYaxis().SetLabelFont(43) #Absolute font size in pixel (precision 3)
h3.GetYaxis().SetLabelSize(15)
# // X axis ratio plot settings
#h3.GetXaxis().SetNdivisions(xbins__)
h3.GetXaxis().SetNdivisions(xbins__)
#h3.GetXaxis().SetNdivisions(0)
h3.GetXaxis().SetTitleSize(20)
h3.GetXaxis().SetTitleFont(43)
h3.GetXaxis().SetTitleOffset(4.)
h3.GetXaxis().SetLabelFont(43) # Absolute font size in pixel (precision 3)
h3.GetXaxis().SetLabelSize(15)
pad1.cd()
leg_ = TLegend(0.6, 0.75, 0.89, 0.89)
#for hi_,h_ in enumerate(hists_):
a = ih_[:]
#print'a = ',a
#for i,hist_info_ in enumerate(a):
for hist_info_ in a:
this_h = hist_info_[0]
this_label = hist_info_[1]
leg_.AddEntry(this_h,this_label,'lf') # histo object, ID
#leg.SetTextSize(0.02)
leg_.Draw('same')
#cc.SaveAs(output_Loc + "Gen_Reco_" + comb_ID_ + ".png")
cc.SaveAs(output_Loc + "Gen_Reco_" + comb_ID_ + ".pdf")
cc.SaveAs(output_Loc + "Gen_Reco_" + comb_ID_ + ".png")
return 0
def plot_dir(dlist,
binfo,
outfile,
prefix,
infix,
color=[kBlack],
legend=[],
norm=False):
c = TCanvas()
c.SetGrid(0)
bnarr = binfo.keys()
bnarr.sort()
bncode = reduce(lambda x, y: x + y, bnarr)
namex = bnarr[-2]
namey = bnarr[-1]
xarr = binfo[namex]
yarr = binfo[namey]
c.GetPad(0).SetMargin(0.2, 0.15, 0.2, 0.15)
c.Divide(len(xarr) - 1, len(yarr) - 1, 0.0, 0.0)
#CanvasPartition(c,len(xarr)-1,len(yarr)-1)
Ntot = 1
for k in range(len(bnarr) - 2):
Ntot *= len(binfo[bnarr[k]]) - 1
for k in range(Ntot):
indarr = []
fact = 1
for n in range(len(bnarr) - 2):
edg = binfo[bnarr[n]]
indarr.append((k / fact) % (len(edg) - 1))
fact *= len(binfo[bnarr[n]]) - 1
for j in range(len(yarr) - 1, 0, -1):
YMAX = -100000000.
YMIN = 100000000.
row = j
for i in range(len(xarr) - 1):
col = i + 1
bcode = reduce(lambda x, y: str(x) + str(y), indarr)
bcode = str(bcode) + str(col - 1) + str(row - 1)
cnt = 0
for d in dlist:
h = d.Get(prefix[cnt] + bncode + "_" + bcode)
maxb = h.GetMaximumBin()
minb = h.GetMinimumBin()
ymaxtest = h.GetBinContent(maxb)
ymintest = h.GetBinContent(minb)
# if True:
# print "before::" + d.GetPath() + "/" + h.GetName() + "::row::" + str(row) + ":: ymax / integral / bin / content(bin): " + str(ymaxtest) + " / " + str(h.Integral()) + " / " + str( maxb) + " / " + str( h.GetBinContent(maxb))
if norm and h.Integral() != 0:
ymaxtest = ymaxtest / h.Integral()
ymintest = ymintest / h.Integral()
if norm and h.Integral() == 0:
ymaxtest = 0
ymintest = 0
if ymaxtest > YMAX:
YMAX = ymaxtest
if ymintest < YMIN:
YMIN = ymintest
cnt += 1
# if norm:
# print bncode + "::row::" + str(row) + "::ymax: " + str(YMAX)
for i in range(len(xarr) - 1):
col = i + 1
pad = c.cd(col + (len(yarr) - 1 - row) * (len(xarr) - 1))
pad.SetGridy(0)
#pad.SetGridx(0)
pad.SetTicks(0, 0)
bcode = reduce(lambda x, y: str(x) + str(y), indarr)
bcode = str(bcode) + str(col - 1) + str(row - 1)
cnt = 0
leg = TLegend(0.77, 0.64, 0.95, 0.89)
leg.SetTextSize(0.15)
for d in dlist:
h = d.Get(prefix[cnt] + bncode + "_" + bcode)
h.GetYaxis().SetRangeUser(YMIN, YMAX * 1.1)
if "ac" in prefix[cnt] or "Acceptance" in d.GetPath():
h.GetYaxis().SetRangeUser(0, 1)
h.GetYaxis().SetLabelSize(0.15)
h.GetXaxis().SetLabelSize(0.1)
h.GetYaxis().SetLabelSize(0.15)
xtitle = str(
binfo[namex][col - 1]) + '<' + namex + '<' + str(
binfo[namex][col]) + " " + prefix[cnt].split(
'_')[0][1:]
h.GetXaxis().SetTitle(xtitle)
h.GetXaxis().CenterTitle()
h.GetXaxis().SetTitleSize(0.12)
ytitle = str(
binfo[namey][row - 1]) + '<' + namey + '<' + str(
binfo[namey][row])
h.GetXaxis().SetNdivisions(505)
h.GetYaxis().SetNdivisions(505)
h.SetLineWidth(3)
h.SetLineColor(color[cnt])
h.SetMarkerColor(color[cnt])
if (legend != []):
le = leg.AddEntry(h, legend[cnt], 'lp')
le.SetTextColor(color[cnt])
if cnt == 0:
if not norm: h.DrawCopy()
elif (h.Integral() != 0):
hnorm = h.DrawNormalized("e")
hnorm.GetYaxis().SetRangeUser(YMIN, YMAX * 1.1)
else:
h.DrawCopy()
if 'hM_' in h.GetName():
rhopeak = TLine(0.77, 0, 0.77, YMAX)
rhopeak.SetLineStyle(kDashed)
rhopeak.SetLineWidth(1)
rhopeak.SetLineColor(kRed)
rhopeak.DrawLine(0.77, 0, 0.77, YMAX)
pad.Update()
pt = pad.FindObject("title")
pt.SetX1NDC(0.15)
pt.SetTextSizePixels(12)
if col == len(xarr) - 1:
yaxis = TGaxis(pad.GetUxmax(), pad.GetUymin(),
pad.GetUxmax(), pad.GetUymax(), 0,
1, 1, "U+-")
yaxis.SetTitle(ytitle + " ")
yaxis.SetTitleSize(0.12)
yaxis.SetTitleOffset(-0.25)
yaxis.SetName(h.GetName() + "_ax_y")
yaxis.Draw("")
pad.GetListOfPrimitives().Add(yaxis)
pad.Update()
else:
if not norm: h.DrawCopy("same")
elif (h.Integral() != 0):
hnorm = h.DrawNormalized("samee")
hnorm.GetYaxis().SetRangeUser(YMIN, YMAX * 1.1)
else:
h.DrawCopy("samee")
cnt += 1
if (legend != []):
leg.Draw()
pad.GetListOfPrimitives().Add(leg)
outfile.cd()
bcode = reduce(lambda x, y: str(x) + str(y), indarr)
cncode = reduce(lambda x, y: x + y, bnarr[-3])
c.Write("can_" + infix + "_" + str(cncode) + "_" + str(bcode),
TObject.kOverwrite)
def train_and_apply():
np.random.seed(1)
ROOT.gROOT.SetBatch()
#Extract data from root file
tree = uproot.open("out_all.root")["outA/Tevts"]
branch_mc = [
"MC_B_P", "MC_B_eta", "MC_B_phi", "MC_B_pt", "MC_D0_P", "MC_D0_eta",
"MC_D0_phi", "MC_D0_pt", "MC_Dst_P", "MC_Dst_eta", "MC_Dst_phi",
"MC_Dst_pt", "MC_Est_mu", "MC_M2_miss", "MC_mu_P", "MC_mu_eta",
"MC_mu_phi", "MC_mu_pt", "MC_pis_P", "MC_pis_eta", "MC_pis_phi",
"MC_pis_pt", "MC_q2"
]
branch_rec = [
"B_P", "B_eta", "B_phi", "B_pt", "D0_P", "D0_eta", "D0_phi", "D0_pt",
"Dst_P", "Dst_eta", "Dst_phi", "Dst_pt", "Est_mu", "M2_miss", "mu_P",
"mu_eta", "mu_phi", "mu_pt", "pis_P", "pis_eta", "pis_phi", "pis_pt",
"q2"
]
nvariable = len(branch_mc)
x_train = tree.array(branch_mc[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_train = np.vstack(
(x_train, tree.array(branch_mc[i], entrystop=options.maxevents)))
x_test = tree.array(branch_rec[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_test = np.vstack(
(x_test, tree.array(branch_rec[i], entrystop=options.maxevents)))
x_train = x_train.T
x_test = x_test.T
x_test = array2D_float(x_test)
#Different type of reconstruction variables
#BN normalization
gamma = 0
beta = 0.2
ar = np.array(x_train)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_train = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_train = np.vstack((x_train, a))
x_train = x_train.T
ar = np.array(x_test)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_test = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_test = np.vstack((x_test, a))
x_test = x_test.T
#Add noise, remain to be improved
noise = np.random.normal(loc=0.0, scale=0.01, size=x_train.shape)
x_train_noisy = x_train + noise
noise = np.random.normal(loc=0.0, scale=0.01, size=x_test.shape)
x_test_noisy = x_test + noise
x_train = np.clip(x_train, -1., 1.)
x_test = np.clip(x_test, -1., 1.)
x_train_noisy = np.clip(x_train_noisy, -1., 1.)
x_test_noisy = np.clip(x_test_noisy, -1., 1.)
# Network parameters
input_shape = (x_train.shape[1], )
batch_size = 128
latent_dim = 2
# Build the Autoencoder Model
# First build the Encoder Model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# Shape info needed to build Decoder Model
shape = K.int_shape(x)
# Generate the latent vector
latent = Dense(latent_dim, name='latent_vector')(x)
# Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()
# Build the Decoder Model
latent_inputs = Input(shape=(latent_dim, ), name='decoder_input')
x = Dense(shape[1])(latent_inputs)
x = Reshape((shape[1], ))(x)
outputs = Activation('tanh', name='decoder_output')(x)
# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()
autoencoder.compile(loss='mse', optimizer='adam')
# Train the autoencoder
autoencoder.fit(x_train_noisy,
x_train,
validation_data=(x_test_noisy, x_test),
epochs=options.epochs,
batch_size=batch_size)
# Predict the Autoencoder output from corrupted test imformation
x_decoded = autoencoder.predict(x_test_noisy)
# Draw Comparision Plots
c = TCanvas("c", "c", 700, 700)
fPads1 = TPad("pad1", "Run2", 0.0, 0.29, 1.00, 1.00)
fPads2 = TPad("pad2", "", 0.00, 0.00, 1.00, 0.29)
fPads1.SetBottomMargin(0.007)
fPads1.SetLeftMargin(0.10)
fPads1.SetRightMargin(0.03)
fPads2.SetLeftMargin(0.10)
fPads2.SetRightMargin(0.03)
fPads2.SetBottomMargin(0.25)
fPads1.Draw()
fPads2.Draw()
fPads1.cd()
nbin = 50
min = -1.
max = 1.
variable = "P^{B}"
lbin = (max - min) / nbin
lbin = str(float((max - min) / nbin))
xtitle = branch_rec[options.branch - 1]
ytitle = "Events/" + lbin + "GeV"
h_rec = TH1D("h_rec", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_rec.Sumw2()
h_pre = TH1D("h_pre", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_pre.Sumw2()
for i in range(x_test_noisy.shape[0]):
h_rec.Fill(x_test_noisy[i][options.branch - 1])
h_pre.Fill(x_decoded[i][options.branch - 1])
h_rec = UnderOverFlow1D(h_rec)
h_pre = UnderOverFlow1D(h_pre)
maxY = TMath.Max(h_rec.GetMaximum(), h_pre.GetMaximum())
h_rec.SetLineColor(2)
h_rec.SetFillStyle(0)
h_rec.SetLineWidth(2)
h_rec.SetLineStyle(1)
h_pre.SetLineColor(3)
h_pre.SetFillStyle(0)
h_pre.SetLineWidth(2)
h_pre.SetLineStyle(1)
h_rec.SetStats(0)
h_pre.SetStats(0)
h_rec.GetYaxis().SetRangeUser(0, maxY * 1.1)
h_rec.Draw("HIST")
h_pre.Draw("same HIST")
h_rec.GetYaxis().SetTitleSize(0.06)
h_rec.GetYaxis().SetTitleOffset(0.78)
theLeg = TLegend(0.5, 0.45, 0.95, 0.82, "", "NDC")
theLeg.SetName("theLegend")
theLeg.SetBorderSize(0)
theLeg.SetLineColor(0)
theLeg.SetFillColor(0)
theLeg.SetFillStyle(0)
theLeg.SetLineWidth(0)
theLeg.SetLineStyle(0)
theLeg.SetTextFont(42)
theLeg.SetTextSize(.05)
theLeg.AddEntry(h_rec, "Reconstruction", "L")
theLeg.AddEntry(h_pre, "Prediction", "L")
theLeg.SetY1NDC(0.9 - 0.05 * 6 - 0.005)
theLeg.SetY1(theLeg.GetY1NDC())
fPads1.cd()
theLeg.Draw()
title = TLatex(
0.91, 0.93, "AE prediction compare with reconstruction, epochs=" +
str(options.epochs))
title.SetNDC()
title.SetTextSize(0.05)
title.SetTextFont(42)
title.SetTextAlign(31)
title.SetLineWidth(2)
title.Draw()
fPads2.cd()
h_Ratio = h_pre.Clone("h_Ratio")
h_Ratio.Divide(h_rec)
h_Ratio.SetLineColor(1)
h_Ratio.SetLineWidth(2)
h_Ratio.SetMarkerStyle(8)
h_Ratio.SetMarkerSize(0.7)
h_Ratio.GetYaxis().SetRangeUser(0, 2)
h_Ratio.GetYaxis().SetNdivisions(504, 0)
h_Ratio.GetYaxis().SetTitle("Pre/Rec")
h_Ratio.GetYaxis().SetTitleOffset(0.35)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetLabelSize(0.11)
h_Ratio.GetXaxis().SetLabelSize(0.1)
h_Ratio.GetXaxis().SetTitleOffset(0.8)
h_Ratio.GetXaxis().SetTitleSize(0.14)
h_Ratio.SetStats(0)
axis1 = TGaxis(min, 1, max, 1, 0, 0, 0, "L")
axis1.SetLineColor(1)
axis1.SetLineWidth(1)
for i in range(1, h_Ratio.GetNbinsX() + 1, 1):
D = h_rec.GetBinContent(i)
eD = h_rec.GetBinError(i)
if D == 0: eD = 0.92
B = h_pre.GetBinContent(i)
eB = h_pre.GetBinError(i)
if B < 0.1 and eB >= B:
eB = 0.92
Err = 0.
if B != 0.:
Err = TMath.Sqrt((eD * eD) / (B * B) + (D * D * eB * eB) /
(B * B * B * B))
h_Ratio.SetBinContent(i, D / B)
h_Ratio.SetBinError(i, Err)
if B == 0.:
Err = TMath.Sqrt((eD * eD) / (eB * eB) + (D * D * eB * eB) /
(eB * eB * eB * eB))
h_Ratio.SetBinContent(i, D / 0.92)
h_Ratio.SetBinError(i, Err)
if D == 0 and B == 0:
h_Ratio.SetBinContent(i, -1)
h_Ratio.SetBinError(i, 0)
h_Ratio.Draw("e0")
axis1.Draw()
c.SaveAs(branch_rec[options.branch - 1] + "_comparision.png")
mg.GetYaxis().SetMoreLogLabels()
mg.GetYaxis().SetTitleOffset(1.9)
legend.SetBorderSize(0)
legend.SetTextSize(0.03)
legend.Draw("same")
c.SetGrid()
gStyle.SetGridStyle(2)
gStyle.SetGridColor(ROOT.kGray)
latex = Text(gPad, 0.88,0.65, "#scale[1.2]{#bf{CMS}} #font[50]{Preliminary}", align = 33)
latex = Text(gPad, 0.88,0.60,"Run2 Data (13 TeV)", align = 33,size = 0.03)
if args.glob: latex = Text(gPad, 0.88,0.55,"Z#rightarrow ee events, EB", align = 33,size = 0.03)
if args.loc: latex = Text(gPad, 0.88,0.55,"Same cluster, EB", align = 33,size = 0.03)
latex = Text(gPad, 0.88,0.48,"#sigma = #frac{N}{A_{eff}} #font[62]{#oplus} #sqrt{2}C", align = 33,size = 0.04)
top_axis = TGaxis(xmin ,mg.GetHistogram().GetMaximum(),xmax,mg.GetHistogram().GetMaximum(),ADC2GEV_B*xmin,ADC2GEV_B*xmax,510,"G-");
top_axis.SetTitle("E [GeV]")
top_axis.SetTitleSize(0.035)
top_axis.SetLabelSize(0.035)
top_axis.SetTitleFont(42)
top_axis.SetLabelFont(42)
top_axis.Draw()
Frame(gPad)
c.Update()
c.Modified()
input()
c.SaveAs("plots/"+args.tag+"/allYears/sigma_"+name+".png")
c.SaveAs("plots/"+args.tag+"/allYears/sigma_"+name+".pdf")
legend.AddEntry(bgFreeFunc, "Background free", "L")
legend.AddEntry(lowBGFunc, "0.1 counts/ROI/t/y", "L")
legend.AddEntry(tonScaleFunc, "1.0 count/ROI/t/y", "L")
legend.AddEntry(hiBGFunc, str(highBG) + " counts/ROI/t/y", "L")
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw()
if drawMbb:
tHalfLow_y = SensClass.GetTHalf(mbbMax_meV * 1.e-3 * CLHEP.eV, isotope,
nmeModel, ga) / CLHEP.year
tHalfHigh_y = SensClass.GetTHalf(mbbMin_meV * 1.e-3 * CLHEP.eV, isotope,
nmeModel, ga) / CLHEP.year
nLabels = 4
gAxis = TGaxis(expMax_ty * 1.01, mbbMax_meV,
expMax_ty - (expMax_ty - expMin_ty) / 10000., mbbMin_meV,
tHalfLow_y, tHalfHigh_y, nLabels, "-G")
if (options.xenon136):
gAxis.SetTitle("^{136}Xe T^{0#nu}_{1/2} sensitivity (90% CL) [years]")
else:
gAxis.SetTitle("^{76}Ge T^{0#nu}_{1/2} sensitivity (90% CL) [years]")
gAxis.SetTitleOffset(1.4)
gAxis.SetBit(TAxis.kRotateTitle)
gAxis.SetBit(TAxis.kCenterTitle)
gAxis.Draw()
else:
mbbHi_meV = SensClass.GetMbb(tHalfMin_y * CLHEP.year, isotope, nmeModel,
ga) / CLHEP.meV
mbbLow_meV = SensClass.GetMbb(tHalfMax_y * CLHEP.year, isotope, nmeModel,
ga) / CLHEP.meV
nLabels = 4
leg_2d_exclusion_v1_v2.SetFillStyle(1001)
leg_2d_exclusion_v1_v2.AddEntry(graph_exclusion_exp_v1, v1_label + " Exp", "L")
leg_2d_exclusion_v1_v2.AddEntry(graph_exclusion_exp_v2, v2_label + " Exp", "L")
if drawObs:
leg_2d_exclusion_v1_v2.AddEntry(graph_exclusion_obs_v1, v1_label + " Obs",
"L")
leg_2d_exclusion_v1_v2.AddEntry(graph_exclusion_obs_v2, v2_label + " Obs",
"L")
leg_2d_exclusion_v1_v2.Draw()
drawCMS2(myC2D, 13, lumi)
f1_lambda = TF1("f1", "(x+6.00)/1.454", 72.902, 416.78)
A1_lambda = TGaxis(100.0, 0.02, 600.0, 0.02, "f1", 1010, "NI")
A1_lambda.SetLabelFont(42)
A1_lambda.SetLabelSize(0.035)
A1_lambda.SetTextFont(42)
A1_lambda.SetTextSize(1.2)
A1_lambda.SetTitle("#Lambda [TeV]")
A1_lambda.SetTitleSize(0.04)
A1_lambda.SetTitleOffset(0.9)
A1_lambda.Draw()
myC2D.SaveAs(outputDir + "/limits" + "/limit_exclusion_region_2D_" +
year_to_plot + "_" + v1_tag + "_vs_" + v2_tag + ".pdf")
myC2D.SaveAs(outputDir + "/limits" + "/limit_exclusion_region_2D_" +
year_to_plot + "_" + v1_tag + "_vs_" + v2_tag + ".png")
myC2D.SaveAs(outputDir + "/limits" + "/limit_exclusion_region_2D_" +
year_to_plot + "_" + v1_tag + "_vs_" + v2_tag + ".C")
def pdf_logPt2_incoh():
#PDF fit to log_10(pT^2)
#tree_in = tree_incoh
tree_in = tree
#ptbin = 0.04
ptbin = 0.12
ptmin = -5.
ptmax = 1.
mmin = 2.8
mmax = 3.2
#fitran = [-5., 1.]
fitran = [-0.9, 0.1]
binned = False
#gamma-gamma 131 evt for pT<0.18
#output log file
out = open("out.txt", "w")
ut.log_results(
out, "in " + infile + " in_coh " + infile_coh + " in_gg " + infile_gg)
loglist = [(x, eval(x)) for x in
["ptbin", "ptmin", "ptmax", "mmin", "mmax", "fitran", "binned"]]
strlog = ut.make_log_string(loglist)
ut.log_results(out, strlog + "\n")
#input data
pT = RooRealVar("jRecPt", "pT", 0, 10)
m = RooRealVar("jRecM", "mass", 0, 10)
dataIN = RooDataSet("data", "data", tree_in, RooArgSet(pT, m))
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
data = dataIN.reduce(strsel)
#x is RooRealVar for log(Pt2)
draw = "TMath::Log10(jRecPt*jRecPt)"
draw_func = RooFormulaVar("x", "log_{10}( #it{p}_{T}^{2} ) (GeV^{2})",
draw, RooArgList(pT))
x = data.addColumn(draw_func)
x.setRange("fitran", fitran[0], fitran[1])
#binned data
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPt = TH1D("hPt", "hPt", nbins, ptmin, ptmax)
tree_in.Draw(draw + " >> hPt", strsel)
dataH = RooDataHist("dataH", "dataH", RooArgList(x), hPt)
#range for plot
x.setMin(ptmin)
x.setMax(ptmax)
x.setRange("plotran", ptmin, ptmax)
#create the pdf
b = RooRealVar("b", "b", 5., 0., 10.)
pdf_func = "log(10.)*pow(10.,x)*exp(-b*pow(10.,x))"
pdf_logPt2 = RooGenericPdf("pdf_logPt2", pdf_func, RooArgList(x, b))
#make the fit
if binned == True:
r1 = pdf_logPt2.fitTo(dataH, rf.Range("fitran"), rf.Save())
else:
r1 = pdf_logPt2.fitTo(data, rf.Range("fitran"), rf.Save())
ut.log_results(out, ut.log_fit_result(r1))
#calculate norm to number of events
xset = RooArgSet(x)
ipdf = pdf_logPt2.createIntegral(xset, rf.NormSet(xset),
rf.Range("fitran"))
print "PDF integral:", ipdf.getVal()
if binned == True:
nevt = tree_incoh.Draw(
"", strsel + " && " + draw + ">{0:.3f}".format(fitran[0]) +
" && " + draw + "<{1:.3f}".format(fitran[0], fitran[1]))
else:
nevt = data.sumEntries("x", "fitran")
print "nevt:", nevt
pdf_logPt2.setNormRange("fitran")
print "PDF norm:", pdf_logPt2.getNorm(RooArgSet(x))
#a = nevt/ipdf.getVal()
a = nevt / pdf_logPt2.getNorm(RooArgSet(x))
ut.log_results(out, "log_10(pT^2) parametrization:")
ut.log_results(out, "A = {0:.2f}".format(a))
ut.log_results(out, ut.log_fit_parameters(r1, 0, 2))
print "a =", a
#Coherent contribution
hPtCoh = ut.prepare_TH1D("hPtCoh", ptbin, ptmin, ptmax)
hPtCoh.Sumw2()
#tree_coh.Draw(draw + " >> hPtCoh", strsel)
tree_coh.Draw("TMath::Log10(jGenPt*jGenPt) >> hPtCoh", strsel)
ut.norm_to_data(hPtCoh, hPt, rt.kBlue, -5., -2.2) # norm for coh
#ut.norm_to_data(hPtCoh, hPt, rt.kBlue, -5, -2.1)
#ut.norm_to_num(hPtCoh, 405, rt.kBlue)
print "Coherent integral:", hPtCoh.Integral()
#TMath::Log10(jRecPt*jRecPt)
#Sartre generated coherent shape
sartre = TFile.Open(
"/home/jaroslav/sim/sartre_tx/sartre_AuAu_200GeV_Jpsi_coh_2p7Mevt.root"
)
sartre_tree = sartre.Get("sartre_tree")
hSartre = ut.prepare_TH1D("hSartre", ptbin, ptmin, ptmax)
sartre_tree.Draw("TMath::Log10(pT*pT) >> hSartre",
"rapidity>-1 && rapidity<1")
ut.norm_to_data(hSartre, hPt, rt.kViolet, -5, -2) # norm for Sartre
#gamma-gamma contribution
hPtGG = ut.prepare_TH1D("hPtGG", ptbin, ptmin, ptmax)
tree_gg.Draw(draw + " >> hPtGG", strsel)
#ut.norm_to_data(hPtGG, hPt, rt.kGreen, -5., -2.9)
ut.norm_to_num(hPtGG, 131., rt.kGreen)
print "Int GG:", hPtGG.Integral()
#psi' contribution
psiP = TFile.Open(basedir_mc + "/ana_slight14e4x1_s6_sel5z.root")
psiP_tree = psiP.Get("jRecTree")
hPtPsiP = ut.prepare_TH1D("hPtPsiP", ptbin, ptmin, ptmax)
psiP_tree.Draw(draw + " >> hPtPsiP", strsel)
ut.norm_to_num(hPtPsiP, 12, rt.kViolet)
#sum of all contributions
hSum = ut.prepare_TH1D("hSum", ptbin, ptmin, ptmax)
hSum.SetLineWidth(3)
#add ggel to the sum
hSum.Add(hPtGG)
#add incoherent contribution
func_logPt2 = TF1("pdf_logPt2",
"[0]*log(10.)*pow(10.,x)*exp(-[1]*pow(10.,x))", -10.,
10.)
func_logPt2.SetParameters(a, b.getVal())
hInc = ut.prepare_TH1D("hInc", ptbin, ptmin, ptmax)
ut.fill_h1_tf(hInc, func_logPt2)
hSum.Add(hInc)
#add coherent contribution
hSum.Add(hPtCoh)
#add psi(2S) contribution
#hSum.Add(hPtPsiP)
#set to draw as a lines
ut.line_h1(hSum, rt.kBlack)
#create canvas frame
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.01)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
frame.SetTitle("")
frame.SetMaximum(75)
frame.SetYTitle("Events / ({0:.3f}".format(ptbin) + " GeV^{2})")
print "Int data:", hPt.Integral()
#plot the data
if binned == True:
dataH.plotOn(frame, rf.Name("data"))
else:
data.plotOn(frame, rf.Name("data"))
pdf_logPt2.plotOn(frame, rf.Range("fitran"), rf.LineColor(rt.kRed),
rf.Name("pdf_logPt2"))
pdf_logPt2.plotOn(frame, rf.Range("plotran"), rf.LineColor(rt.kRed),
rf.Name("pdf_logPt2_full"), rf.LineStyle(rt.kDashed))
frame.Draw()
amin = TMath.Power(10, ptmin)
amax = TMath.Power(10, ptmax) - 1
print amin, amax
pt2func = TF1("f1", "TMath::Power(10, x)", amin,
amax) #TMath::Power(x, 10)
aPt2 = TGaxis(-5, 75, 1, 75, "f1", 510, "-")
ut.set_axis(aPt2)
aPt2.SetTitle("pt2")
#aPt2.Draw();
leg = ut.prepare_leg(0.57, 0.78, 0.14, 0.19, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
hx = ut.prepare_TH1D("hx", 1, 0, 1)
hx.Draw("same")
ln = ut.col_lin(rt.kRed)
leg.AddEntry(hx, "Data")
leg.AddEntry(hPtCoh, "Sartre MC", "l")
leg.AddEntry(hPtGG, "#gamma#gamma#rightarrow e^{+}e^{-} MC", "l")
#leg.AddEntry(ln, "ln(10)*#it{A}*10^{log_{10}#it{p}_{T}^{2}}exp(-#it{b}10^{log_{10}#it{p}_{T}^{2}})", "l")
#leg.AddEntry(ln, "Incoherent fit", "l")
leg.Draw("same")
l0 = ut.cut_line(fitran[0], 0.9, frame)
l1 = ut.cut_line(fitran[1], 0.9, frame)
#l0.Draw()
#l1.Draw()
desc = pdesc(frame, 0.14, 0.8, 0.054)
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", frame.chiSquare("pdf_logPt2", "data", 2), -1,
rt.kRed)
desc.itemD("#it{A}", a, -1, rt.kRed)
desc.itemR("#it{b}", b, rt.kRed)
desc.draw()
#put the sum
#hSum.Draw("same")
#gPad.SetLogy()
frame.Draw("same")
#put gamma-gamma
hPtGG.Draw("same")
#put coherent J/psi
hPtCoh.Draw("same")
#put Sartre generated coherent shape
#hSartre.Draw("same")
#put psi(2S) contribution
#hPtPsiP.Draw("same")
leg2 = ut.prepare_leg(0.14, 0.9, 0.14, 0.08, 0.03)
leg2.AddEntry(
ln,
"ln(10)*#it{A}*10^{log_{10}#it{p}_{T}^{2}}exp(-#it{b}10^{log_{10}#it{p}_{T}^{2}})",
"l")
#leg2.AddEntry(hPtCoh, "Sartre MC reconstructed", "l")
#leg2.AddEntry(hSartre, "Sartre MC generated", "l")
leg2.Draw("same")
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def fit():
#fit to log_10(pT^2) with components and plot of plain pT^2
#range in log_10(pT^2)
ptbin = 0.12
ptmin = -5.
ptmax = 0.99 # 1.01
#range in pT^2
ptsq_bin = 0.03
ptsq_min = 1e-5
ptsq_max = 1
mmin = 2.8
mmax = 3.2
#range for incoherent fit
fitran = [-0.9, 0.1]
#number of gamma-gamma events
ngg = 131
#number of psi' events
npsiP = 20
#input data
pT = RooRealVar("jRecPt", "pT", 0, 10)
m = RooRealVar("jRecM", "mass", 0, 10)
data_all = RooDataSet("data", "data", tree, RooArgSet(pT, m))
#select for mass range
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
data = data_all.reduce(strsel)
#create log(pT^2) from pT
ptsq_draw = "jRecPt*jRecPt" # will be used for pT^2
logPtSq_draw = "TMath::Log10(" + ptsq_draw + ")"
logPtSq_form = RooFormulaVar("logPtSq", "logPtSq", logPtSq_draw,
RooArgList(pT))
logPtSq = data.addColumn(logPtSq_form)
logPtSq.setRange("fitran", fitran[0], fitran[1])
#bins and range for the plot
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
logPtSq.setMin(ptmin)
logPtSq.setMax(ptmax)
logPtSq.setRange("plotran", ptmin, ptmax)
#range for pT^2
ptsq_nbins, ptsq_max = ut.get_nbins(ptsq_bin, ptsq_min, ptsq_max)
#incoherent parametrization
bval = RooRealVar("bval", "bval", 3.3, 0, 10)
inc_form = "log(10.)*pow(10.,logPtSq)*exp(-bval*pow(10.,logPtSq))"
incpdf = RooGenericPdf("incpdf", inc_form, RooArgList(logPtSq, bval))
#make the incoherent fit
res = incpdf.fitTo(data, rf.Range("fitran"), rf.Save())
#get incoherent norm to the number of events
lset = RooArgSet(logPtSq)
iinc = incpdf.createIntegral(lset, rf.NormSet(lset), rf.Range("fitran"))
inc_nevt = data.sumEntries("logPtSq", "fitran")
incpdf.setNormRange("fitran")
aval = RooRealVar("aval", "aval", inc_nevt / incpdf.getNorm(lset))
#print "A =", aval.getVal()
#print "b =", bval.getVal()
#incoherent distribution from log_10(pT^2) function for the sum with gamma-gamma
hIncPdf = ut.prepare_TH1D_n("hGG", nbins, ptmin, ptmax)
func_incoh_logPt2 = TF1("func_incoh_logPt2",
"[0]*log(10.)*pow(10.,x)*exp(-[1]*pow(10.,x))",
-10., 10.)
func_incoh_logPt2.SetNpx(1000)
func_incoh_logPt2.SetLineColor(rt.kMagenta)
func_incoh_logPt2.SetParameters(
aval.getVal(),
bval.getVal()) # 4.9 from incoherent mc, 3.3 from data fit
ut.fill_h1_tf(hIncPdf, func_incoh_logPt2, rt.kMagenta)
#gamma-gamma contribution
hGG = ut.prepare_TH1D_n("hGG", nbins, ptmin, ptmax)
tree_gg.Draw(logPtSq_draw + " >> hGG", strsel)
ut.norm_to_num(hGG, ngg, rt.kGreen + 1)
#sum of incoherent distribution and gamma-gamma
hSumIncGG = ut.prepare_TH1D_n("hSumIncGG", nbins, ptmin, ptmax)
hSumIncGG.Add(hIncPdf)
hSumIncGG.Add(hGG)
ut.line_h1(hSumIncGG, rt.kMagenta)
#gamma-gamma in pT^2
hGG_ptsq = ut.prepare_TH1D_n("hGG_ptsq", ptsq_nbins, ptsq_min, ptsq_max)
tree_gg.Draw(ptsq_draw + " >> hGG_ptsq", strsel)
ut.norm_to_num(hGG_ptsq, ngg, rt.kGreen + 1)
#psi' contribution
psiP_file = TFile.Open(basedir_mc + "/ana_slight14e4x1_s6_sel5z.root")
psiP_tree = psiP_file.Get("jRecTree")
hPsiP = ut.prepare_TH1D_n("hPsiP", nbins, ptmin, ptmax)
psiP_tree.Draw(logPtSq_draw + " >> hPsiP", strsel)
ut.norm_to_num(hPsiP, npsiP, rt.kViolet)
#psi' in pT^2
hPsiP_ptsq = ut.prepare_TH1D_n("hPsiP_ptsq", ptsq_nbins, ptsq_min,
ptsq_max)
psiP_tree.Draw(ptsq_draw + " >> hPsiP_ptsq", strsel)
ut.norm_to_num(hPsiP_ptsq, npsiP, rt.kViolet)
#create canvas frame
gStyle.SetPadTickY(1)
can = ut.box_canvas(1086, 543) # square area is still 768^2
can.SetMargin(0, 0, 0, 0)
can.Divide(2, 1, 0, 0)
gStyle.SetLineWidth(1)
can.cd(1)
ut.set_margin_lbtr(gPad, 0.11, 0.1, 0.01, 0)
frame = logPtSq.frame(rf.Bins(nbins))
frame.SetTitle("")
frame.SetMaximum(80)
frame.SetYTitle("Events / ({0:.3f}".format(ptbin) + " GeV^{2})")
frame.SetXTitle("log_{10}( #it{p}_{T}^{2} ) (GeV^{2})")
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().SetTitleOffset(1.6)
#plot the data
data.plotOn(frame, rf.Name("data"), rf.LineWidth(2))
#incoherent parametrization
incpdf.plotOn(frame, rf.Range("fitran"), rf.LineColor(rt.kRed),
rf.Name("incpdf"), rf.LineWidth(2))
incpdf.plotOn(frame, rf.Range("plotran"), rf.LineColor(rt.kRed),
rf.Name("incpdf_full"), rf.LineStyle(rt.kDashed),
rf.LineWidth(2))
frame.Draw()
#add gamma-gamma contribution
hGG.Draw("same")
#sum of incoherent distribution and gamma-gamma
#hSumIncGG.Draw("same")
#add psi'
#hPsiP.Draw("same")
#legend for log_10(pT^2)
leg = ut.prepare_leg(0.15, 0.77, 0.28, 0.19, 0.035)
hxl = ut.prepare_TH1D("hxl", 1, 0, 1)
hxl.Draw("same")
ilin = ut.col_lin(rt.kRed, 2)
ilin2 = ut.col_lin(rt.kRed, 2)
ilin2.SetLineStyle(rt.kDashed)
leg.AddEntry(ilin, "Incoherent parametrization, fit region", "l")
leg.AddEntry(ilin2, "Incoherent parametrization, extrapolation region",
"l")
leg.AddEntry(hGG, "#gamma#gamma#rightarrow e^{+}e^{-}", "l")
#leg.AddEntry(hxl, "Data", "lp")
leg.AddEntry(hxl, "Data, log_{10}( #it{p}_{T}^{2} )", "lp")
leg.Draw("same")
#----- plot pT^2 on the right -----
#pT^2 variable from pT
ptsq_form = RooFormulaVar("ptsq", "ptsq", ptsq_draw, RooArgList(pT))
ptsq = data.addColumn(ptsq_form)
#range for pT^2 plot
ptsq.setMin(ptsq_min)
ptsq.setMax(ptsq_max)
#make the pT^2 plot
can.cd(2)
gPad.SetLogy()
#gPad.SetLineWidth(3)
#gPad.SetFrameLineWidth(1)
ut.set_margin_lbtr(gPad, 0, 0.1, 0.01, 0.15)
ptsq_frame = ptsq.frame(rf.Bins(ptsq_nbins), rf.Title(""))
#print type(ptsq_frame), type(ptsq)
ptsq_frame.SetTitle("")
ptsq_frame.SetXTitle("#it{p}_{T}^{2} (GeV^{2})")
ptsq_frame.GetXaxis().SetTitleOffset(1.2)
data.plotOn(ptsq_frame, rf.Name("data"), rf.LineWidth(2))
ptsq_frame.SetMaximum(9e2)
ptsq_frame.SetMinimum(0.8) # 0.101
ptsq_frame.Draw()
#incoherent parametrization in pT^2 over the fit region, scaled to the plot
inc_ptsq = TF1("inc_ptsq", "[0]*exp(-[1]*x)", 10**fitran[0], 10**fitran[1])
inc_ptsq.SetParameters(aval.getVal() * ptsq_bin, bval.getVal())
#incoherent parametrization in the extrapolation region, below and above the fit region
inc_ptsq_ext1 = TF1("inc_ptsq_ext1", "[0]*exp(-[1]*x)", 0., 10**fitran[0])
inc_ptsq_ext2 = TF1("inc_ptsq_ext2", "[0]*exp(-[1]*x)", 10**fitran[1], 10)
inc_ptsq_ext1.SetParameters(aval.getVal() * ptsq_bin, bval.getVal())
inc_ptsq_ext1.SetLineStyle(rt.kDashed)
inc_ptsq_ext2.SetParameters(aval.getVal() * ptsq_bin, bval.getVal())
inc_ptsq_ext2.SetLineStyle(rt.kDashed)
inc_ptsq.Draw("same")
inc_ptsq_ext1.Draw("same")
inc_ptsq_ext2.Draw("same")
#add gamma-gamma in pT^2
hGG_ptsq.Draw("same")
#add psi' in pT^2
#hPsiP_ptsq.Draw("same")
#redraw the frame
#ptsq_frame.Draw("same")
ptsq_frame.GetXaxis().SetLimits(-9e-3, ptsq_frame.GetXaxis().GetXmax())
#vertical axis for pT^2 plot
xpos = ptsq_frame.GetXaxis().GetXmax()
ypos = ptsq_frame.GetMaximum()
ymin = ptsq_frame.GetMinimum()
ptsq_axis = TGaxis(xpos, 0, xpos, ypos, ymin, ypos, 510, "+GL")
ut.set_axis(ptsq_axis)
ptsq_axis.SetMoreLogLabels()
ptsq_axis.SetTitle("Events / ({0:.3f}".format(ptsq_bin) + " GeV^{2})")
ptsq_axis.SetTitleOffset(2.2)
ptsq_axis.Draw()
#legend for input data
#dleg = ut.prepare_leg(0.4, 0.77, 0.14, 0.18, 0.035)
dleg = ut.prepare_leg(0.4, 0.71, 0.16, 0.24, 0.035)
dleg.AddEntry(None, "#bf{|#kern[0.3]{#it{y}}| < 1}", "")
ut.add_leg_mass(dleg, mmin, mmax)
dleg.AddEntry(None, "AuAu@200 GeV", "")
dleg.AddEntry(None, "UPC sample", "")
dleg.AddEntry(hxl, "Data, #it{p}_{T}^{2}", "lp")
dleg.Draw("same")
#ut.invert_col_can(can)
can.SaveAs("01fig.pdf")
def plot_hist(histlist,
labellist,
norm,
log,
overflow,
filename,
options,
scaling=[]):
canv = TCanvas("c1", "c1", 800, 600)
canv.SetTopMargin(10)
canv.SetRightMargin(100)
if log: gPad.SetLogy()
histstack = THStack("stack", histlist[0].GetTitle())
legend = TLegend(0.76, 0.88 - 0.08 * len(histlist), 0.91, 0.88, '', 'NDC')
colorlist = [4, 8, 2, 6, 1]
if len(scaling) > 1 and scaling[1] == 0: scaling = []
if options: options += " NOSTACK"
else: options = "NOSTACK"
maximum = 0
for i in range(len(histlist)):
entries = histlist[i].GetEntries()
mean = histlist[i].GetMean()
histlist[i].SetLineColorAlpha(colorlist[i], 0.65)
histlist[i].SetLineWidth(3)
nbins = histlist[i].GetNbinsX()
legend.AddEntry(
histlist[i], "#splitline{" + labellist[i] +
"}{#splitline{%d entries}{mean=%.2f}}" % (entries, mean), "l")
if overflow:
histlist[i].SetBinContent(
nbins, histlist[i].GetBinContent(nbins) +
histlist[i].GetBinContent(nbins + 1)) #overflow
histlist[i].SetBinContent(1, histlist[i].GetBinContent(0) +
histlist[i].GetBinContent(1)) #underflow
if entries and norm: histlist[i].Scale(1. / entries)
if histlist[i].GetMaximum() > maximum:
maximum = histlist[i].GetMaximum()
histstack.Add(histlist[i])
histstack.SetMaximum(maximum * 1.4)
histstack.Draw(options)
histstack.GetXaxis().SetTitle(histlist[0].GetXaxis().GetTitle())
histstack.GetYaxis().SetTitle(histlist[0].GetYaxis().GetTitle())
xlimit = [
histlist[0].GetBinLowEdge(1),
histlist[0].GetBinLowEdge(histlist[0].GetNbinsX()) +
histlist[0].GetBinWidth(histlist[0].GetNbinsX())
]
if len(scaling) != 0:
top_axis = TGaxis(xlimit[0], maximum * 1.4, xlimit[1], maximum * 1.4,
(xlimit[0] - scaling[0]) / scaling[1],
(xlimit[1] - scaling[0]) / scaling[1], 510, "-")
top_axis.SetTitle("normalized scale")
top_axis.SetTickSize(0)
top_axis.Draw("SAME")
legend.SetTextSize(0.02)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw("SAME")
canv.SaveAs(filename)
if log: gPad.Clear()
canv.Clear()
del canv
def __reweightCanvas(self, can, addLabels, addTopXNumbers):
import numpy as np, ROOT
from ROOT import TGaxis, TLatex, kBlack
if can.GetPrimitive('pad_top'):
self.__reweightCanvas(can.GetPrimitive('pad_top'), False,
addTopXNumbers)
if can.GetPrimitive('pad_bot'):
self.__reweightCanvas(can.GetPrimitive('pad_bot'), self.addLabels,
False)
if can.GetPrimitive('pad_bot') or can.GetPrimitive('pad_bot'): return
can.cd()
# First we "remove" previous axis
xaxis = GetXaxis(can)
# Now create new axis
#xaxis.SetName("__xaxis_ignored")
# Now draw new canvas
#xmin, xmax = GetXaxisRanges(can)
#import os
#c = ROOT.TCanvas(self.func.GetName(),self.func.GetName())
#self.func.Draw()
#c.SaveAs( os.path.join( 'e26_RunByRun_noAdjustment', c.GetName() +'.pdf' ) )
#can.cd()
#chopt = "+C" + ("U" if not addLabels else "")
chopt = "+CU"
axis = TGaxis(can.GetUxmin(), can.GetUymin(), can.GetUxmax(),
can.GetUymin(), self.func.GetName(),
10000 * self.N3 + 100 * self.N2 + 1 * self.N1, chopt)
axis.SetNoExponent(False)
#axis.SetExponentOffset()
if xaxis:
axis.ImportAxisAttributes(xaxis)
xaxis.SetTickLength(0)
xaxis.SetLabelOffset(999.)
if self.labelSize: axis.SetLabelSize(self.labelSize)
#1 + 100*N2 + *N3N
axis.Draw()
from .PlotFunctions import tobject_collector
if addTopXNumbers:
_, ymax = GetYaxisRanges(can,
check_all=True,
ignorezeros=True,
ignoreErrors=False)
for xc in self.xcenters:
text = TLatex(self.func.Eval(xc), ymax, str(xc))
text.SetTextFont(43)
text.SetTextSize(2)
text.SetTextAlign(12)
text.SetTextAngle(90)
text.SetTextColor(ROOT.kBlack)
text.Draw()
tobject_collector.append(text)
if addLabels:
binLow, binHigh, nBins = self.__getXEdges(chopt)
#labelPos = map( lambda x: (x,self.func.GetX(x),(self.func.GetX(x)-self.xedges[0])/(self.xedges[-1]-self.xedges[0]))
# , np.linspace(binLow,binHigh, nBins) )
labelPos = map(
lambda x:
(x, self.func.Eval(x), (self.func.Eval(x) - self.xedges[0]) /
(self.xedges[-1] - self.xedges[0])),
np.linspace(binLow, binHigh, nBins))
ypos = can.GetUymin() - 0.04 * (can.GetUymax() - can.GetUymin())
#labels = []
for x, mx, pmx in labelPos:
text = TLatex(mx, ypos, str(x))
text.SetTextFont(43)
#text.SetTextSizePixels(textPerc)
text.SetTextAlign(23)
text.SetTextColor(ROOT.kBlack)
text.SetTextSize(14)
text.Draw()
tobject_collector.append(text)
#labels.append(text)
#gxc = (self.xedges[-1]-self.xedges[0] )/2
#lLabels = len(labels)
#for i, text in enumerate(labels):
# xc = text.GetBBoxCenter()
# if xc < gxc:
# if i+1 < lLabels:
# text2 = labels[i+1]
# while conflict(text.GetBBox(),text2.GetBBox()):
# sizes[i] = possible_sizes[sizes[i]+1]
# text.SetTextSize( getNewSize
##AdjustBinSize (Double_t A1, Double_t A2, Int_t nold, Double_t &BinLow, Double_t &BinHigh, Int_t &nbins, Double_t &BinWidth)
#lLabels = [labels.At(i) for i in range(labels.GetEntries())]
tobject_collector.append(axis)
#can.Modified()
#can.Update()
return axis
def main(): # pylint: disable=too-many-locals, too-many-statements, too-many-branches
"""
Main plotting function
"""
gROOT.SetBatch(True)
# pylint: disable=unused-variable
parser = argparse.ArgumentParser()
parser.add_argument("--database-analysis",
"-d",
dest="database_analysis",
help="analysis database to be used",
required=True)
parser.add_argument("--analysis",
"-a",
dest="type_ana",
help="choose type of analysis",
required=True)
parser.add_argument("--input",
"-i",
dest="input_file",
help="results input file",
required=True)
args = parser.parse_args()
typean = args.type_ana
shape = typean[len("jet_"):]
print("Shape:", shape)
file_in = args.input_file
with open(args.database_analysis, "r") as file_db:
data_param = yaml.safe_load(file_db)
case = list(data_param.keys())[0]
datap = data_param[case]
logger = get_logger()
i_cut = file_in.rfind("/")
rootpath = file_in[:i_cut]
# plotting
# LaTeX string
p_latexnhadron = datap["analysis"][typean]["latexnamehadron"]
p_latexbin2var = datap["analysis"][typean]["latexbin2var"]
v_varshape_latex = datap["analysis"][typean]["var_shape_latex"]
# first variable (hadron pt)
lpt_finbinmin = datap["analysis"][typean]["sel_an_binmin"]
lpt_finbinmax = datap["analysis"][typean]["sel_an_binmax"]
var1ranges = lpt_finbinmin.copy()
var1ranges.append(lpt_finbinmax[-1])
# second variable (jet pt)
v_var2_binning = datap["analysis"][typean]["var_binning2"] # name
lvar2_binmin_reco = datap["analysis"][typean].get("sel_binmin2_reco", None)
lvar2_binmax_reco = datap["analysis"][typean].get("sel_binmax2_reco", None)
p_nbin2_reco = len(lvar2_binmin_reco) # number of reco bins
lvar2_binmin_gen = datap["analysis"][typean].get("sel_binmin2_gen", None)
lvar2_binmax_gen = datap["analysis"][typean].get("sel_binmax2_gen", None)
p_nbin2_gen = len(lvar2_binmin_gen) # number of gen bins
var2ranges_reco = lvar2_binmin_reco.copy()
var2ranges_reco.append(lvar2_binmax_reco[-1])
var2binarray_reco = array(
"d",
var2ranges_reco) # array of bin edges to use in histogram constructors
var2ranges_gen = lvar2_binmin_gen.copy()
var2ranges_gen.append(lvar2_binmax_gen[-1])
var2binarray_gen = array(
"d",
var2ranges_gen) # array of bin edges to use in histogram constructors
# observable (z, shape,...)
v_varshape_binning = datap["analysis"][typean][
"var_binningshape"] # name (reco)
v_varshape_binning_gen = datap["analysis"][typean][
"var_binningshape_gen"] # name (gen)
lvarshape_binmin_reco = \
datap["analysis"][typean].get("sel_binminshape_reco", None)
lvarshape_binmax_reco = \
datap["analysis"][typean].get("sel_binmaxshape_reco", None)
p_nbinshape_reco = len(lvarshape_binmin_reco) # number of reco bins
lvarshape_binmin_gen = \
datap["analysis"][typean].get("sel_binminshape_gen", None)
lvarshape_binmax_gen = \
datap["analysis"][typean].get("sel_binmaxshape_gen", None)
p_nbinshape_gen = len(lvarshape_binmin_gen) # number of gen bins
varshaperanges_reco = lvarshape_binmin_reco.copy()
varshaperanges_reco.append(lvarshape_binmax_reco[-1])
varshapebinarray_reco = array(
"d", varshaperanges_reco
) # array of bin edges to use in histogram constructors
varshaperanges_gen = lvarshape_binmin_gen.copy()
varshaperanges_gen.append(lvarshape_binmax_gen[-1])
varshapebinarray_gen = array(
"d", varshaperanges_gen
) # array of bin edges to use in histogram constructors
file_results = TFile.Open(file_in)
if not file_results:
logger.fatal(make_message_notfound(file_in))
ibin2 = 1
suffix = "%s_%g_%g" % (v_var2_binning, lvar2_binmin_gen[ibin2],
lvar2_binmax_gen[ibin2])
# HF data
nameobj = "%s_hf_data_%d_stat" % (shape, ibin2)
hf_data_stat = file_results.Get(nameobj)
if not hf_data_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_hf_data_%d_syst" % (shape, ibin2)
hf_data_syst = file_results.Get(nameobj)
if not hf_data_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# HF PYTHIA
nameobj = "%s_hf_pythia_%d_stat" % (shape, ibin2)
hf_pythia_stat = file_results.Get(nameobj)
if not hf_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
# HF ratio
nameobj = "%s_hf_ratio_%d_stat" % (shape, ibin2)
hf_ratio_stat = file_results.Get(nameobj)
if not hf_ratio_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_hf_ratio_%d_syst" % (shape, ibin2)
hf_ratio_syst = file_results.Get(nameobj)
if not hf_ratio_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive data
nameobj = "%s_incl_data_%d_stat" % (shape, ibin2)
incl_data_stat = file_results.Get(nameobj)
if not incl_data_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_data_%d_syst" % (shape, ibin2)
incl_data_syst = file_results.Get(nameobj)
if not incl_data_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive PYTHIA
nameobj = "%s_incl_pythia_%d_stat" % (shape, ibin2)
incl_pythia_stat = file_results.Get(nameobj)
if not incl_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_pythia_%d_syst" % (shape, ibin2)
incl_pythia_syst = file_results.Get(nameobj)
if not incl_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive ratio
nameobj = "%s_incl_ratio_%d_stat" % (shape, ibin2)
incl_ratio_stat = file_results.Get(nameobj)
if not incl_ratio_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_ratio_%d_syst" % (shape, ibin2)
incl_ratio_syst = file_results.Get(nameobj)
if not incl_ratio_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# quark PYTHIA
nameobj = "%s_quark_pythia_%d_stat" % (shape, ibin2)
quark_pythia_stat = file_results.Get(nameobj)
if not quark_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_quark_pythia_%d_syst" % (shape, ibin2)
quark_pythia_syst = file_results.Get(nameobj)
if not quark_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# gluon PYTHIA
nameobj = "%s_gluon_pythia_%d_stat" % (shape, ibin2)
gluon_pythia_stat = file_results.Get(nameobj)
if not gluon_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_gluon_pythia_%d_syst" % (shape, ibin2)
gluon_pythia_syst = file_results.Get(nameobj)
if not gluon_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# plot the results with systematic uncertainties and models
size_can = [800, 800]
offsets_axes = [0.8, 1.1]
margins_can = [0.1, 0.13, 0.1, 0.03]
size_thg = 0.05
offset_thg = 0.85
gStyle.SetErrorX(0) # do not plot horizontal error bars of histograms
fontsize = 0.035
opt_leg_g = "FP"
opt_plot_g = "2"
list_new = [] # list to avoid loosing objects created in loops
# labels
x_latex = 0.16
y_latex_top = 0.83
y_step = 0.055
title_x = v_varshape_latex
title_y = "(1/#it{N}_{jet}) d#it{N}/d%s" % v_varshape_latex
title_full = ";%s;%s" % (title_x, title_y)
title_full_ratio = ";%s;data/MC: ratio of %s" % (title_x, title_y)
text_alice = "#bf{ALICE} Preliminary, pp, #sqrt{#it{s}} = 13 TeV"
text_alice_sim = "#bf{ALICE} Simulation, pp, #sqrt{#it{s}} = 13 TeV"
text_pythia = "PYTHIA 8 (Monash)"
text_pythia_split = "#splitline{PYTHIA 8}{(Monash)}"
text_jets = "charged jets, anti-#it{k}_{T}, #it{R} = 0.4"
text_ptjet = "%g #leq %s < %g GeV/#it{c}, #left|#it{#eta}_{jet}#right| #leq 0.5" % (
lvar2_binmin_reco[ibin2], p_latexbin2var, lvar2_binmax_reco[ibin2])
text_pth = "%g #leq #it{p}_{T}^{%s} < %g GeV/#it{c}, #left|#it{y}_{%s}#right| #leq 0.8" % (
lpt_finbinmin[0], p_latexnhadron,
min(lpt_finbinmax[-1], lvar2_binmax_reco[ibin2]), p_latexnhadron)
text_ptcut = "#it{p}_{T, incl. ch. jet}^{leading track} #geq 5.33 GeV/#it{c}"
text_ptcut_sim = "#it{p}_{T, incl. ch. jet}^{leading h^{#pm}} #geq 5.33 GeV/#it{c} (varied)"
text_sd = "Soft Drop (#it{z}_{cut} = 0.1, #it{#beta} = 0)"
title_thetag = "#it{#theta}_{g} = #it{R}_{g}/#it{R}"
radius_jet = 0.4
# colour and marker indeces
c_hf_data = 0
c_incl_data = 1
c_hf_mc = 2
c_incl_mc = 6
c_quark_mc = 5
c_gluon_mc = 0
# markers
m_hf_data = get_marker(0)
m_incl_data = get_marker(1)
m_hf_mc = get_marker(0, 2)
m_incl_mc = get_marker(1, 2)
m_quark_mc = get_marker(2)
m_gluon_mc = get_marker(3)
# make the horizontal error bars smaller
if shape == "nsd":
for gr in [
hf_data_syst, incl_data_syst, hf_ratio_syst, incl_ratio_syst,
incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst
]:
for i in range(gr.GetN()):
gr.SetPointEXlow(i, 0.1)
gr.SetPointEXhigh(i, 0.1)
# data, HF and inclusive
hf_data_syst_cl = hf_data_syst.Clone()
leg_pos = [.72, .75, .85, .85]
list_obj = [hf_data_syst, incl_data_syst, hf_data_stat, incl_data_stat]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive", "", ""]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_data, list_obj_data_new = make_plot("cshape_data_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip((hf_data_syst, incl_data_syst), (c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
list_obj_data_new[0].SetTextSize(fontsize)
if shape == "nsd":
hf_data_syst.GetXaxis().SetNdivisions(5)
# Draw a line through the points.
if shape == "nsd":
for h in (hf_data_stat, incl_data_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_data.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_data_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data.Update()
cshape_data.SaveAs("%s/%s_data_%s.pdf" % (rootpath, shape, suffix))
# data and PYTHIA, HF
leg_pos = [.72, .65, .85, .85]
list_obj = [hf_data_syst_cl, hf_data_stat, hf_pythia_stat]
labels_obj = ["data", "", text_pythia_split]
colours = [
get_colour(i, j)
for i, j in zip((c_hf_data, c_hf_data, c_hf_mc), (2, 1, 1))
]
markers = [m_hf_data, m_hf_data, m_hf_mc]
y_margin_up = 0.4
y_margin_down = 0.05
cshape_data_mc_hf, list_obj_data_mc_hf_new = make_plot("cshape_data_mc_hf_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip([hf_data_syst_cl], [c_hf_data]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_hf = list_obj_data_mc_hf_new[0]
leg_data_mc_hf.SetHeader("%s-tagged" % p_latexnhadron)
leg_data_mc_hf.SetTextSize(fontsize)
if shape == "nsd":
hf_data_syst_cl.GetXaxis().SetNdivisions(5)
#axis_nsd = hf_data_syst_cl.GetHistogram().GetXaxis()
#x1 = axis_nsd.GetBinLowEdge(1)
#x2 = axis_nsd.GetBinUpEdge(axis_nsd.GetNbins())
#axis_nsd.Set(5, x1, x2)
#for ibin in range(axis_nsd.GetNbins()):
# axis_nsd.SetBinLabel(ibin + 1, "%d" % ibin)
#axis_nsd.SetNdivisions(5)
cshape_data_mc_hf.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_data_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data_mc_hf.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data_mc_hf.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data_mc_hf = []
for text_latex in [text_alice, text_jets, text_ptjet, text_pth, text_sd]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data_mc_hf.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data_mc_hf.Update()
cshape_data_mc_hf.SaveAs("%s/%s_data_mc_hf_%s.pdf" %
(rootpath, shape, suffix))
# data and PYTHIA, inclusive
#leg_pos = [.68, .65, .85, .85]
list_obj = [
incl_data_syst, incl_pythia_syst, incl_data_stat, incl_pythia_stat
]
labels_obj = ["data", text_pythia_split]
colours = [
get_colour(i, j) for i, j in zip((c_incl_data, c_incl_mc, c_incl_data,
c_incl_mc), (2, 2, 1, 1))
]
markers = [m_incl_data, m_incl_mc, m_incl_data, m_incl_mc]
y_margin_up = 0.4
y_margin_down = 0.05
cshape_data_mc_incl, list_obj_data_mc_incl_new = make_plot("cshape_data_mc_incl_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip([incl_data_syst, incl_pythia_syst],
[c_incl_data, c_incl_mc]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_incl = list_obj_data_mc_incl_new[0]
leg_data_mc_incl.SetHeader("inclusive")
leg_data_mc_incl.SetTextSize(fontsize)
if shape == "nsd":
incl_data_syst.GetXaxis().SetNdivisions(5)
cshape_data_mc_incl.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = incl_data_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data_mc_incl.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data_mc_incl.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data_mc_incl = []
for text_latex in [text_alice, text_jets, text_ptjet, text_ptcut, text_sd]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data_mc_incl.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data_mc_incl.Update()
cshape_data_mc_incl.SaveAs("%s/%s_data_mc_incl_%s.pdf" %
(rootpath, shape, suffix))
# Ratios data/MC, HF and inclusive
line_1 = TLine(lvarshape_binmin_reco[0], 1, lvarshape_binmax_reco[-1], 1)
line_1.SetLineStyle(9)
line_1.SetLineColor(1)
line_1.SetLineWidth(3)
#leg_pos = [.72, .7, .85, .85] # with header
leg_pos = [.72, .75, .85, .85] # without header
list_obj = [
hf_ratio_syst, line_1, incl_ratio_syst, hf_ratio_stat, incl_ratio_stat
]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive"]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.52
y_margin_down = 0.05
if shape == "nsd":
y_margin_up = 0.22
cshape_ratio, list_obj_ratio_new = make_plot("cshape_ratio_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full_ratio)
cshape_ratio.Update()
for gr, c in zip((hf_ratio_syst, incl_ratio_syst),
(c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
leg_ratio = list_obj_ratio_new[0]
leg_ratio.SetTextSize(fontsize)
#leg_ratio.SetHeader("data/MC")
if shape == "nsd":
hf_ratio_syst.GetXaxis().SetNdivisions(5)
cshape_ratio.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_ratio_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_ratio.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_ratio.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_ratio = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd,
text_pythia
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_ratio.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_ratio.Update()
cshape_ratio.SaveAs("%s/%s_ratio_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, inclusive, quark, gluon
incl_pythia_syst_cl = incl_pythia_syst.Clone()
y_min_h, y_max_h = get_y_window_his([
hf_pythia_stat, incl_pythia_stat, quark_pythia_stat, gluon_pythia_stat
])
y_min_g, y_max_g = get_y_window_gr(
[incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst])
y_min = min(y_min_h, y_min_g)
y_max = max(y_max_h, y_max_g)
y_margin_up = 0.46
y_margin_down = 0.05
y_min_plot, y_max_plot = get_plot_range(y_min, y_max, y_margin_down,
y_margin_up)
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .55, .85, .85]
list_obj = [
incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst, hf_pythia_stat,
incl_pythia_stat, quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["inclusive", "quark", "gluon", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_mc, c_quark_mc, c_gluon_mc, c_hf_mc, c_incl_mc,
c_quark_mc, c_gluon_mc), (2, 2, 2, 1, 1, 1, 1))
]
markers = [
m_incl_mc, m_quark_mc, m_gluon_mc, m_hf_mc, m_incl_mc, m_quark_mc,
m_gluon_mc
]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
cshape_mc.Update()
for gr, c in zip((incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst),
(c_incl_mc, c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
incl_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, quark, gluon
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .61, .85, .85]
list_obj = [
quark_pythia_syst, gluon_pythia_syst, hf_pythia_stat,
quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["quark", "gluon", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_quark_mc, c_gluon_mc, c_hf_mc, c_quark_mc,
c_gluon_mc), (2, 2, 1, 1, 1))
]
markers = [m_quark_mc, m_gluon_mc, m_hf_mc, m_quark_mc, m_gluon_mc]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_qgd_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
cshape_mc.Update()
for gr, c in zip((quark_pythia_syst, gluon_pythia_syst),
(c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
quark_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_qgd_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, inclusive
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .67, .85, .85]
list_obj = [incl_pythia_syst_cl, incl_pythia_stat, hf_pythia_stat]
labels_obj = ["inclusive", "", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_mc, c_incl_mc, c_hf_mc), (2, 1, 1))
]
markers = [m_incl_mc, m_incl_mc, m_hf_mc]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_id_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
# Draw a line through the points.
if shape == "nsd":
for h in (incl_pythia_stat, hf_pythia_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_mc.Update()
incl_pythia_syst_cl.SetMarkerColor(get_colour(c_incl_mc))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
incl_pythia_syst_cl.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_id_%s.pdf" % (rootpath, shape, suffix))
# data inclusive vs PYTHIA, quark, gluon
#leg_pos = [.6, .65, .75, .85]
#leg_pos = [.72, .55, .85, .85]
leg_pos = [.6, .7, .85, .85]
list_obj = [
incl_data_syst, quark_pythia_syst, gluon_pythia_syst, incl_data_stat,
quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["inclusive (data)", "quark (PYTHIA 8)", "gluon (PYTHIA 8)"]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_data, c_quark_mc, c_gluon_mc, c_incl_data,
c_quark_mc, c_gluon_mc), (2, 2, 2, 1, 1, 1))
]
markers = [
m_incl_data, m_quark_mc, m_gluon_mc, m_incl_data, m_quark_mc,
m_gluon_mc
]
y_margin_up = 0.3
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_data_iqg" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip((incl_data_syst, quark_pythia_syst, gluon_pythia_syst),
(c_incl_data, c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_data_i_mc_qg_%s.pdf" % (rootpath, shape, suffix))
all_graphs[0].SetMinimum(calo_init.args.axisMin)
canv.Update()
lines = []
for iLine, line in enumerate(avgSF):
lines.append(TLine(0, avgSF[iLine], Nslices * sliceWidth, avgSF[iLine]))
lines[iLine].SetLineColor(iLine + 1)
lines[iLine].Draw('same')
# add second axis
canv.SetRightMargin(0.1)
all_graphs[0].GetXaxis().SetRangeUser(0, 68)
all_graphs[0].GetXaxis().SetLabelOffset(0.02)
gPad.RangeAxis(0, gPad.GetUymin(), 68, gPad.GetUxmax())
axis = TGaxis(gPad.GetUxmin(), gPad.GetUymin(), gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmin() * calo_init.args.X0density,
gPad.GetUxmax() * calo_init.args.X0density, 506, "-")
axis.SetLabelSize(0.05)
axis.SetTitleSize(0.07)
axis.SetTitleOffset(0.9)
axis.SetLabelOffset(0.02)
axis.Draw()
axis.SetLabelFont(42)
unit1 = draw_text(['(cm)'], [0.91, 0.09, 1, 0.14], 1, 0)
unit1.SetTextSize(0.05)
unit1.SetTextFont(42)
unit2 = draw_text(['(X_{0})'], [0.91, 0.16, 1, 0.21], 1, 0)
unit2.SetTextSize(0.05)
unit2.SetTextFont(42)
canv.Update()
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")
def PlotOnCanvas(self, pdf_name):
#gROOT.SetBatch(False)
tdrstyle.setTDRStyle()
canvas = TCanvas("c1", "c1", 600, 600)
pad1 = TPad("pad1", "pad1", 0, 0.0, 1, 1.0)
pad1.SetBottomMargin(0.32)
pad1.SetGridx()
pad1.Draw()
pad1.cd()
self.histo1.SetStats(0)
self.histo1.Draw(self.option)
self.histo2.Draw(self.option + " same")
if self.logx:
pad1.SetLogx()
if self.logy:
pad1.SetLogy()
legend = TLegend(0.65, 0.7, 0.85, 0.83)
legend.SetHeader("Legend", "C")
legend.AddEntry(self.histo1, self.legend1, "l")
legend.AddEntry(self.histo2, self.legend2, "l")
legend.Draw()
# Redraw axis to avoid clipping 0
self.histo1.GetXaxis().SetLabelSize(0.)
self.histo1.GetXaxis().SetTitle('')
axis = TGaxis(-9, -2.8, -9, 2.8, 0, 10000, 50510, "")
axis.SetLabelFont(43)
axis.SetLabelSize(15)
axis.Draw("same")
pad2 = TPad("pad2", "pad2", 0, 0.0, 1, 0.3)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.4)
pad2.SetGridx()
pad2.Draw()
pad2.cd()
if self.logx:
pad2.SetLogx()
self.ratio.SetLineColor(ROOT.kBlack)
self.ratio.SetMinimum(0.5)
self.ratio.SetMaximum(1.5)
self.ratio.SetStats(0)
self.ratio.SetMarkerStyle(21)
self.ratio.Draw("ep")
self.histo1.SetLineColor(ROOT.kBlue + 1)
self.histo1.SetLineWidth(2)
max_y = max(self.histo1.GetMaximum(), self.histo2.GetMaximum())
self.histo1.SetMaximum(max_y * 1.1)
self.histo1.GetYaxis().SetNdivisions(505)
self.histo1.GetYaxis().SetTitleSize(20)
self.histo1.GetYaxis().SetTitleFont(43)
self.histo1.GetYaxis().SetTitleOffset(1.8)
self.histo2.SetLineColor(ROOT.kRed)
self.histo2.SetLineWidth(2)
self.ratio.SetTitle("")
self.ratio.GetYaxis().SetTitle("Ratio")
self.ratio.GetYaxis().SetNdivisions(505)
self.ratio.GetYaxis().SetTitleSize(20)
self.ratio.GetYaxis().SetTitleFont(43)
self.ratio.GetYaxis().SetTitleOffset(1.8)
self.ratio.GetYaxis().SetLabelFont(43)
self.ratio.GetYaxis().SetLabelSize(15)
self.ratio.GetXaxis().SetNdivisions(510)
self.ratio.GetXaxis().SetTitleSize(20)
self.ratio.GetXaxis().SetTitleFont(43)
self.ratio.GetXaxis().SetTitleOffset(4.)
self.ratio.GetXaxis().SetLabelFont(43)
self.ratio.GetXaxis().SetLabelSize(15)
ROOT.SetOwnership(canvas, False)
ROOT.SetOwnership(pad1, False)
ROOT.SetOwnership(pad2, False)
canvas.Print(pdf_name, 'Title:' + self.title)
canvas.Close()