def RatioCanvas(canvas_name,
canvas_title,
canw=500,
canh=600,
ratio_size_as_fraction=0.35):
from ROOT import TCanvas, TPad
c = TCanvas(canvas_name, canvas_title, canw, canh)
c.cd()
top = TPad("pad_top", "This is the top pad", 0.0, ratio_size_as_fraction,
1.0, 1.0)
top.SetBottomMargin(0.02 / float(top.GetHNDC()))
top.SetTopMargin(0.04 / float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw()
tobject_collector.append(top)
c.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0, 1.0,
ratio_size_as_fraction)
bot.SetBottomMargin(0.11 / float(bot.GetHNDC()))
bot.SetTopMargin(0.02 / float(bot.GetHNDC()))
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw()
tobject_collector.append(bot)
return c
def __plot_quadrant_template2(self, h_oo, h_xo, h_ox, h_xx, alias, xlabel,
plotname, doLogY):
from TrigEgammaDevelopments.plots import AutoFixAxes
from TrigEgammaDevelopments.helper.util import setBoxes
from ROOT import TCanvas, gStyle, TLegend, kRed, kBlue, kGreen, kGray, kBlack, TLine, TPad, TLatex
from TrigEgammaDevelopments.plots.AtlasStyle import AtlasStyle, atlas_template
ratio_size_as_fraction = 0.35
# Internal method
def sortHistograms(hists):
nevents = [o.GetEntries() for o in hists]
from operator import itemgetter
hist_sorted_index = sorted(enumerate(nevents), key=itemgetter(1))
hist_sorted = list()
for index in hist_sorted_index:
hist_sorted.append(hists[index[0]])
hist_sorted.reverse()
return hist_sorted
h_agree = h_xx + h_oo
h_disagree = h_ox + h_xo
hsum = h_agree + h_disagree
gStyle.SetOptStat(111111)
canvas = TCanvas('canvas', 'canvas', 2500, 1600)
drawopt = 'pE1'
canvas.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
canvas.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0, 1.0,
ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
#bot.SetTopMargin (0.02/float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
top.cd()
h_agree.SetLineWidth(1)
h_agree.SetLineColor(kBlack)
h_agree.SetMarkerColor(kBlack)
h_disagree.SetLineWidth(1)
h_disagree.SetLineColor(kRed)
h_disagree.SetMarkerColor(kRed)
h_agree.SetName('Agreement')
h_disagree.SetName('Disagreement')
h_agree.SetStats(1)
h_disagree.SetStats(1)
# top
h1 = sortHistograms([h_agree, h_disagree])
# bot
h2 = sortHistograms([h_agree.Clone(), h_disagree.Clone()])
h1[0].GetXaxis().SetTitle('')
h1[0].GetYaxis().SetTitle('Counts')
#h1[0].GetYaxis().SetTitleOffset(0.5)
#h1[0].GetYaxis().SetLabelSize(0.10)
#h1[0].GetYaxis().SetTitleSize(0.10)
#h1[0].GetXaxis().SetLabelSize(0.10)
h1[0].Draw()
for i in range(1, len(h1)):
h1[i].Draw('sames')
top.Update()
setBoxes(top, h1)
if doLogY:
top.SetLogy()
AutoFixAxes(top, False, False, 9000)
else:
AutoFixAxes(top, False, False, 2)
bot.cd()
if doLogY:
bot.SetLogy()
hsum.Sumw2()
for h in h2:
h.SetStats(0)
h.Sumw2()
h.Divide(h, hsum, 1., 1.)
h2[0].GetYaxis().SetTitle('ratio')
h2[0].GetXaxis().SetTitle(xlabel)
h2[0].GetYaxis().SetTitleOffset(0.5)
h2[0].GetYaxis().SetLabelSize(0.10)
h2[0].GetYaxis().SetTitleSize(0.10)
h2[0].GetXaxis().SetLabelSize(0.10)
h2[0].GetXaxis().SetTitleSize(0.10)
if not doLogY:
h2[0].GetYaxis().SetRangeUser(0, 1.05)
h2[0].Draw('ep1')
for i in range(1, len(h2)):
h2[i].Draw('sames')
if doLogY:
AutoFixAxes(bot, False, False, 1.1)
# Loop over histograms
canvas.SaveAs(plotname)
def plot(self, **kw):
from ROOT import kRed
dirname = retrieve_kw(kw, 'dirname', 'Distribution')
basecolor = retrieve_kw(kw, 'basecolor', kRed - 7)
pdftitle = retrieve_kw(kw, 'pdftitle', 'Distributions')
pdfoutput = retrieve_kw(kw, 'pdfoutput', 'distributions')
import os
# Organize outputs (.py and .pdf)
prefix = self._basepath.split('/')[-1]
localpath = os.getcwd() + '/' + dirname + '/' + prefix
try:
if not os.path.exists(localpath):
os.makedirs(localpath)
except:
self._logger.warning('The director %s exist.', localpath)
hist_names = [
'et', 'eta', 'mu', 'nvtx', 'reta', 'eratio', 'weta2', 'rhad',
'rphi', 'f1', 'f3'
]
hist_labels = [
'E_{T}', "#eta", "<#mu>", 'N_{vtx}', 'R_{eta}', 'E_{ratio}',
'W_{eta2}', 'R_{had}', 'R_{phi}', 'f_{1}', 'f_{3}'
]
from ROOT import TCanvas, TH1F, gStyle, TLegend, TPad
from ROOT import kGreen, kRed, kBlue, kBlack, kGray, gPad, kAzure
from TrigEgammaDevelopments.plots.AtlasStyle import AtlasStyle, atlas_template, setLegend1
canvas1 = TCanvas('canvas1', 'canvas1', 2500, 1600)
canvas1.Divide(4, 3)
# Concatenate distributions for all regions
def sumAllRegions(histname):
h = None
for etBinIdx in range(len(self._etBins) - 1):
for etaBinIdx in range(len(self._etaBins) - 1):
binningname = ('et%d_eta%d') % (etBinIdx, etaBinIdx)
path = self._basepath + '/' + self.currentDir(
) + '/' + binningname
if h:
h += self.storeSvc().histogram(path + '/' + histname)
else:
h = self.storeSvc().histogram(path + '/' +
histname).Clone()
return h
collector = []
figures = {
'rings': [],
'rnnOutput': [],
'ringer_profile': str(),
'shower_shapes': str()
}
"""
Plot all shower shapes distributins
"""
for idx, histname in enumerate(hist_names):
self.setDir('Data')
h_data = sumAllRegions(histname)
self.setDir('MonteCarlo')
h_mc = sumAllRegions(histname)
#h_mc, h_data = self.__scale_histograms(h_mc, h_data, 100, 0.01, 0.01)
pad = canvas1.cd(idx + 1)
gStyle.SetOptStat(110011)
collector.append(pad)
h_mc.SetFillColor(basecolor)
h_mc.SetLineColor(basecolor)
h_data.SetLineColor(kBlack)
h_mc.Scale(1. / h_mc.GetMaximum())
h_data.Scale(1. / h_data.GetMaximum())
h_mc.Draw()
h_data.Draw('same')
leg1 = TLegend(0.2, 0.75, 0.5, 0.95)
setLegend1(leg1)
leg1.AddEntry(h_mc, 'MC')
leg1.AddEntry(h_data, 'Data')
leg1.Draw()
collector[-1].Update()
collector.append(h_mc)
collector.append(h_data)
collector.append(leg1)
canvas1.SaveAs(localpath + '/shower_shapes_distributions.pdf')
figures[
'shower_shapes'] = localpath + '/shower_shapes_distributions.pdf'
"""
Plot all shower ringer shapes for each ring
"""
ratio_size_as_fraction = 0.35
from RingerCore import progressbar
rings_localpath = []
for r in progressbar(range(100),
100,
step=1,
logger=self._logger,
prefix="Looping over rings (Plotting...) "):
canvas2 = TCanvas('canvas2', 'canvas2', 2500, 1600)
drawopt = 'pE1'
canvas2.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
canvas2.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0, 1.0,
ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
#bot.SetTopMargin (0.02/float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
self.setDir('MonteCarlo')
h_mc = sumAllRegions('rings/ring_' + str(r))
self.setDir('Data')
h_data = sumAllRegions('rings/ring_' + str(r))
gStyle.SetOptStat(000000)
h_mc, h_data = self.__scale_histograms(h_mc, h_data, 100, 0.0001,
0.025)
h_mc.Scale(1. / h_mc.GetMaximum())
h_data.Scale(1. / h_data.GetMaximum())
from ROOT import TH1, kGray
divide = ""
drawopt = 'pE1'
bot.cd()
ref = h_mc.Clone()
h = h_data.Clone()
ref.Sumw2()
h.Sumw2()
ratioplot = h.Clone()
ratioplot.Sumw2()
ratioplot.SetName(h.GetName() + '_ratio')
ratioplot.Divide(h, ref, 1., 1., '')
ratioplot.SetFillColor(0)
ratioplot.SetFillStyle(0)
ratioplot.SetMarkerColor(1)
ratioplot.SetLineColor(kGray)
ratioplot.SetMarkerStyle(24)
ratioplot.SetMarkerSize(1.2)
ratioplot.GetYaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetRangeUser(-1.6, 3.7)
ratioplot.GetYaxis().SetTitleOffset(0.7)
ratioplot.GetYaxis().SetTitle('Data/MC')
ratioplot.GetXaxis().SetTitle('Ring #' + str(r + 1) + ' [MeV]')
ratioplot.Draw(drawopt)
from ROOT import TLine
nbins = h_data.GetNbinsX()
xmin = h_data.GetXaxis().GetBinLowEdge(1)
xmax = h_data.GetXaxis().GetBinLowEdge(nbins + 1)
l1 = TLine(xmin, 1, xmax, 1)
l1.SetLineColor(kRed)
l1.SetLineStyle(2)
l1.Draw()
bot.Update()
top.cd()
h_mc.SetFillColor(basecolor)
h_mc.SetLineWidth(1)
h_mc.SetLineColor(basecolor)
h_data.SetLineColor(kBlack)
h_data.SetLineWidth(1)
h_mc.GetYaxis().SetTitle('Count')
h_mc.Draw()
h_data.Draw('same')
leg1 = TLegend(0.8, 0.70, 0.95, 0.95)
setLegend1(leg1)
leg1.AddEntry(h_mc, 'MC')
leg1.AddEntry(h_data, 'Data')
leg1.Draw()
atlas_template(top)
top.Update()
canvas2.SaveAs(localpath + '/distribution_ring_' + str(r + 1) +
'.pdf')
figures['rings'].append(localpath + '/distribution_ring_' +
str(r + 1) + '.pdf')
"""
Plot ringer mean shapes
"""
h_mean_data = TH1F('h_mean_data', '', 100, 0, 100)
h_mean_mc = TH1F('h_mean_mc', '', 100, 0, 100)
for bin in range(100):
self.setDir('MonteCarlo')
h_mc = sumAllRegions('rings/ring_' + str(bin))
self.setDir('Data')
h_data = sumAllRegions('rings/ring_' + str(bin))
h_mean_data.SetBinContent(bin + 1, h_data.GetMean())
h_mean_mc.SetBinContent(bin + 1, h_mc.GetMean())
canvas3 = TCanvas('canvas3', 'canvas3', 2500, 1600)
drawopt = 'pE1'
canvas3.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
canvas3.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0, 1.0,
ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
#bot.SetTopMargin (0.02/float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
gStyle.SetOptStat(000000)
from ROOT import TH1, kGray
divide = ""
drawopt = 'pE1'
bot.cd()
ref = h_mean_mc.Clone()
h = h_mean_data.Clone()
ref.Sumw2()
h.Sumw2()
ratioplot = h.Clone()
ratioplot.Sumw2()
ratioplot.SetName(h.GetName() + '_ratio')
ratioplot.Divide(h, ref, 1., 1., '')
ratioplot.SetFillColor(0)
ratioplot.SetFillStyle(0)
ratioplot.SetMarkerColor(1)
ratioplot.SetLineColor(kGray)
ratioplot.SetMarkerStyle(24)
ratioplot.SetMarkerSize(1.2)
ratioplot.GetYaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetRangeUser(-1.6, 3.7)
ratioplot.GetYaxis().SetTitleOffset(0.7)
ratioplot.GetYaxis().SetTitle('Data/MC')
ratioplot.GetXaxis().SetTitle('Rings')
ratioplot.Draw(drawopt)
from ROOT import TLine
nbins = h_mean_data.GetNbinsX()
xmin = h_mean_data.GetXaxis().GetBinLowEdge(1)
xmax = h_mean_data.GetXaxis().GetBinLowEdge(nbins + 1)
l1 = TLine(xmin, 1, xmax, 1)
l1.SetLineColor(kRed)
l1.SetLineStyle(2)
l1.Draw()
bot.Update()
top.cd()
h_mean_mc.SetFillColor(basecolor)
h_mean_mc.SetLineWidth(1)
h_mean_mc.SetLineColor(basecolor)
h_mean_data.SetLineColor(kBlack)
h_mean_data.SetLineWidth(1)
#h_mean_mc.Scale( 1./h_mean_mc.GetEntries() )
#h_mean_data.Scale( 1./h_mean_data.GetEntries() )
if h_mean_mc.GetMaximum() > h_mean_data.GetMaximum():
ymin = h_mean_mc.GetMinimum()
ymax = h_mean_mc.GetMaximum()
h_mean_mc.Draw()
h_mean_mc.GetYaxis().SetTitle('E[Ring] MeV')
h_mean_data.Draw('same')
else:
ymin = h_mean_data.GetMinimum()
ymax = h_mean_data.GetMaximum()
h_mean_data.GetYaxis().SetTitle('E[Ring] MeV')
h_mean_data.Draw()
h_mean_mc.Draw('same')
h_mean_data.Draw('same')
# prepare ringer lines
def gen_line_90(x, ymin, ymax, text):
from ROOT import TLine, TLatex
ymax = 1.05 * ymax
l = TLine(x, ymin, x, ymax)
l.SetLineStyle(2)
l.Draw()
txt = TLatex()
txt.SetTextFont(12)
txt.SetTextAngle(90)
txt.SetTextSize(0.04)
txt.DrawLatex(x - 1, (ymax - ymin) / 2., text)
return l, txt
l_ps, t_ps = gen_line_90(8, ymin, ymax, 'presampler')
l_em1, t_em1 = gen_line_90(72, ymin, ymax, 'EM.1')
l_em2, t_em2 = gen_line_90(80, ymin, ymax, 'EM.2')
l_em3, t_em3 = gen_line_90(88, ymin, ymax, 'EM.3')
l_had1, t_had1 = gen_line_90(92, ymin, ymax, 'Had.1')
l_had2, t_had2 = gen_line_90(96, ymin, ymax, 'Had.2')
l_had3, t_had3 = gen_line_90(100, ymin, ymax, 'Had.3')
leg1 = TLegend(0.8, 0.70, 0.95, 0.95)
setLegend1(leg1)
leg1.AddEntry(h_mean_mc, 'MC')
leg1.AddEntry(h_mean_data, 'Data')
leg1.Draw()
atlas_template(top)
top.Update()
canvas3.SaveAs(localpath + '/ringer_profile.pdf')
figures['ringer_profile'] = localpath + '/ringer_profile.pdf'
"""
Plot all NN distributions for each calo region
"""
for algname in self._discrList:
for etBinIdx in range(len(self._etBins) - 1):
for etaBinIdx in range(len(self._etaBins) - 1):
binningname = ('et%d_eta%d') % (etBinIdx, etaBinIdx)
path = self._basepath + '/MonteCarlo/' + binningname
h_mc = self.storeSvc().histogram(
path + '/' + algname +
'/discriminantVsMu').ProjectionX().Clone()
path = self._basepath + '/Data/' + binningname
h_data = self.storeSvc().histogram(
path + '/' + algname +
'/discriminantVsMu').ProjectionX().Clone()
h_mc.Rebin(10)
h_data.Rebin(10)
h_mc.Scale(1. / h_mc.GetMaximum())
h_data.Scale(1. / h_data.GetMaximum())
canvas4 = TCanvas('canvas4', 'canvas4', 2500, 1600)
drawopt = 'pE1'
canvas4.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
canvas4.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0,
1.0, ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
gStyle.SetOptStat(000000)
from ROOT import TH1, kGray
divide = ""
drawopt = 'pE1'
bot.cd()
ref = h_mc.Clone()
h = h_data.Clone()
ref.Sumw2()
h.Sumw2()
ratioplot = h.Clone()
ratioplot.Sumw2()
ratioplot.SetName(h.GetName() + '_ratio')
ratioplot.Divide(h, ref, 1., 1., '')
ratioplot.SetFillColor(0)
ratioplot.SetFillStyle(0)
ratioplot.SetMarkerColor(1)
ratioplot.SetLineColor(kGray)
ratioplot.SetMarkerStyle(24)
ratioplot.SetMarkerSize(1.2)
ratioplot.GetYaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetRangeUser(-1.6, 3.7)
ratioplot.GetYaxis().SetTitleOffset(0.7)
ratioplot.GetYaxis().SetTitle('Data/MC')
ratioplot.GetXaxis().SetTitle(
'Neural Network (Discriminant)')
ratioplot.Draw(drawopt)
from ROOT import TLine
nbins = h_data.GetNbinsX()
xmin = h_data.GetXaxis().GetBinLowEdge(1)
xmax = h_data.GetXaxis().GetBinLowEdge(nbins + 1)
l1 = TLine(xmin, 1, xmax, 1)
l1.SetLineColor(kRed)
l1.SetLineStyle(2)
l1.Draw()
bot.Update()
top.cd()
h_mc.SetFillColor(basecolor)
h_mc.SetLineWidth(1)
h_mc.SetLineColor(basecolor)
h_data.SetLineColor(kBlack)
h_data.SetLineWidth(1)
h_mc.Scale(1. / h_mc.GetMaximum())
h_data.Scale(1. / h_data.GetMaximum())
h_mc.GetYaxis().SetTitle(
('Counts (%s)') % (binningname.replace('_', ',')))
h_mc.Draw()
h_data.Draw('same')
leg1 = TLegend(0.8, 0.70, 0.95, 0.95)
setLegend1(leg1)
leg1.AddEntry(h_mc, 'MC')
leg1.AddEntry(h_data, 'Data')
leg1.Draw()
atlas_template(top)
top.Update()
canvas4.SaveAs(localpath + '/' + algname + '_rnnOutput_' +
binningname + '.pdf')
figures['rnnOutput'].append(localpath + '/' + algname +
'_rnnOutput_' + binningname +
'.pdf')
#from RingerCore.tex.TexAPI import *
from RingerCore.tex.BeamerAPI import BeamerTexReportTemplate1, BeamerSection, BeamerMultiFigureSlide, BeamerFigureSlide
with BeamerTexReportTemplate1(theme='Berlin',
_toPDF=True,
title=pdftitle,
outputFile=pdfoutput,
font='structurebold'):
with BeamerSection(name='Shower Shapes'):
BeamerMultiFigureSlide(
title='Shower Shapes (MC and Data)',
paths=[figures['shower_shapes']],
nDivWidth=1 # x
,
nDivHeight=1 # y
,
texts=None,
fortran=False,
usedHeight=0.8,
usedWidth=1.1)
with BeamerSection(name='Ringer Shapes Profile'):
BeamerMultiFigureSlide(
title='Ringer Profile (MC and Data)',
paths=[figures['ringer_profile']],
nDivWidth=1 # x
,
nDivHeight=1 # y
,
texts=None,
fortran=False,
usedHeight=0.8,
usedWidth=1.1)
with BeamerSection(name='Ringer Shapes'):
for s in range(4):
paths1 = [
path for path in figures['rings'][s * 25:s * 25 + 25]
]
BeamerMultiFigureSlide(
title='Ringer Shapes (MC and Data)',
paths=paths1,
nDivWidth=5 # x
,
nDivHeight=5 # y
,
texts=None,
fortran=False,
usedHeight=0.8,
usedWidth=1.1)
for algname in self._discrList:
with BeamerSection(name=('%s Neural Network Output') %
(algname.replace('_', '\_'))):
paths2 = []
for etBinIdx in range(len(self._etBins) - 1):
for etaBinIdx in range(len(self._etaBins) - 1):
binningname = ('et%d_eta%d') % (etBinIdx,
etaBinIdx)
paths2.append(localpath + '/' + algname +
'_rnnOutput_' + binningname + '.pdf')
BeamerMultiFigureSlide(
title=algname.replace('_', '\_'),
paths=paths2,
nDivWidth=len(self._etaBins) # x
,
nDivHeight=len(self._etBins) # y
,
texts=None,
fortran=False,
usedHeight=1.0,
usedWidth=1.1)
return StatusCode.SUCCESS
def __plot_profiles_in_same_canvas(self, hist_objs, hist_names, legends,
y_limits, **kwargs):
legend_position = retrieve_kw(kwargs, 'legend_position',
(0.36, 0.20, 0.66, 0.36))
legend_prefix = retrieve_kw(kwargs, 'legend_prefix',
'Z#rightarrowee, ')
legend_header = retrieve_kw(kwargs, 'legend_header', 'Trigger step')
ylabel = retrieve_kw(kwargs, 'ylabel', 'Trigger Efficiency')
title = retrieve_kw(kwargs, 'title', 'Trigger Efficiency')
oname = retrieve_kw(kwargs, 'oname', 'plot_efficiencys')
column = retrieve_kw(kwargs, 'column', 2)
doRatio = retrieve_kw(kwargs, 'doRatio', False)
canvas_size = retrieve_kw(kwargs, 'canvas_size', (1800, 1500))
# FIXME: This must be disable for now, there is some problem into the xaxis scale
# The top and bot axis must be match!
tobject_collector = []
ratio_size_as_fraction = 0.35
from ROOT import TCanvas, TLegend, TProfile, TPad
rows = int(round(len(hist_objs) / float(column)))
canvas = TCanvas('canvas', 'canvas', canvas_size[0], canvas_size[1])
canvas.Divide(rows, column)
leg_holder = []
for index, hist_str in enumerate(hist_names):
hists = hist_objs[hist_str]
pad = canvas.cd(index + 1)
# Force disable if != of 2
if len(hists) != 2:
doRatio = False
if doRatio:
drawopt = 'pE1'
pad.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
tobject_collector.append(top)
pad.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0, 1.0,
ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
#bot.SetTopMargin (0.02/float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
tobject_collector.append(bot)
if type(legend_prefix) is not list:
legend_prefix = [legend_prefix] * len(legends)
if doRatio:
top.cd()
leg = TLegend(legend_position[0], legend_position[1],
legend_position[2], legend_position[3])
from AtlasStyle import setLegend1, atlas_template
setLegend1(leg)
leg.SetHeader(legend_header)
for i, eff in enumerate(hists):
eff.SetLineColor(self._curve_color[i])
eff.SetMarkerColor(self._curve_color[i])
eff.SetMarkerStyle(self._marker_style[i])
if type(eff) is TProfile: eff.SetStats(0)
leg.AddEntry(eff, legend_prefix[i] + legends[i], 'p')
if i is 0: eff.Draw()
else: eff.Draw('same')
leg.SetTextSize(0.03)
leg.SetBorderSize(0)
leg.Draw()
if doRatio:
top.Modified()
top.Update
pad.Modified()
pad.Update()
from ROOT import TProfile, TEfficiency, kFALSE
xmin = 0
xmax = 999
if type(hists[0]) is TProfile:
hists[0].GetYaxis().SetRangeUser(y_limits[index][0],
y_limits[index][1])
elif type(hists[0]) is TEfficiency:
hists[0].GetPaintedGraph().GetYaxis().SetRangeUser(
y_limits[index][0], y_limits[index][1])
hists[0].GetPaintedGraph().GetYaxis().SetTitle('A')
# Fix the X axis
nbins = hists[0].GetTotalHistogram().GetNbinsX()
xmin = hists[0].GetTotalHistogram().GetXaxis().GetBinLowEdge(1)
xmax = hists[0].GetTotalHistogram().GetXaxis().GetBinLowEdge(
nbins + 1)
hists[0].GetPaintedGraph().GetXaxis().SetLimits(xmin, xmax)
else:
hists[0].GetYaxis().SetRangeUser(y_limits[index][0],
y_limits[index][1])
pad.Modified()
pad.Update()
tobject_collector.append(leg)
if doRatio:
atlas_template(top, **kwargs)
top.Update()
from ROOT import TH1
divide = ""
drawopt = 'pE1'
bot.cd()
ref = hists[0].GetPassedHistogram().Clone()
h = hists[1].GetPassedHistogram().Clone()
#ref = hists[0].Clone()
#h = hists[1].Clone()
ref.Sumw2()
h.Sumw2()
ref.Divide(hists[0].GetTotalHistogram().Clone())
h.Divide(hists[1].GetTotalHistogram().Clone())
#ratioplot = TEfficiency(h,ref)
ratioplot = h.Clone()
ratioplot.Sumw2()
ratioplot.SetName(h.GetName() + '_ratio')
ratioplot.Divide(h, ref, 1., 1., '')
#ratioplot.Divide(ref)
ratioplot.SetFillColor(0)
ratioplot.SetFillStyle(0)
ratioplot.SetMarkerColor(1)
ratioplot.SetLineColor(kBlack)
ratioplot.SetMarkerStyle(24)
ratioplot.SetMarkerSize(1.2)
ratioplot.GetYaxis().SetTitle('trigger / ref')
ratioplot.GetYaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetRangeUser(0.9, 1.07)
ratioplot.Draw(drawopt)
tobject_collector.append(ratioplot)
#atlas_template(bot, **kwargs)
from ROOT import TLine
l1 = TLine(xmin, 1, xmax, 1)
l1.SetLineColor(kRed)
l1.SetLineStyle(2)
l1.Draw()
tobject_collector.append(l1)
bot.Update()
else:
atlas_template(pad, **kwargs)
pad.Update()
canvas.SaveAs(oname + '.pdf')