def plot_central_and_systematics(channel, systematics, exclude=[], suffix='altogether'):
global variable, variables_latex, k_value, b_tag_bin, maximum, ttbar_generator_systematics
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.6, y1=0.5)
hist_data_central = read_xsection_measurement_results('central', channel)[0]['unfolded']
hist_data_central.GetXaxis().SetTitle(variables_latex[variable] + ' [GeV]')
hist_data_central.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' + variables_latex[variable] + '} [GeV^{-1}]')
hist_data_central.GetXaxis().SetTitleSize(0.05)
hist_data_central.GetYaxis().SetTitleSize(0.05)
hist_data_central.SetMinimum(0)
hist_data_central.SetMaximum(maximum[variable])
hist_data_central.SetMarkerSize(1)
hist_data_central.SetMarkerStyle(20)
gStyle.SetEndErrorSize(20)
hist_data_central.Draw('P')
legend.AddEntry(hist_data_central, 'measured (unfolded)', 'P')
for systematic in systematics:
if systematic in exclude or systematic == 'central':
continue
hist_data_systematic = read_xsection_measurement_results(systematic, channel)[0]['unfolded']
hist_data_systematic.SetMarkerSize(0.5)
hist_data_systematic.SetMarkerStyle(20)
colour_number = systematics.index(systematic)+1
if colour_number == 10:
colour_number = 42
hist_data_systematic.SetMarkerColor(colour_number)
hist_data_systematic.Draw('same P')
legend.AddEntry(hist_data_systematic, systematic, 'P')
legend.Draw()
cms_label, channel_label = get_cms_labels(channel)
cms_label.Draw()
channel_label.Draw()
canvas.Modified()
canvas.Update()
path = output_folder + str(measurement_config.centre_of_mass) + 'TeV/' + variable
make_folder_if_not_exists(path)
for output_format in output_formats:
canvas.SaveAs(path + '/normalised_xsection_' + channel + '_' + suffix + '_kv' + str(k_value) + '.' + output_format)
def make_template_plots(histograms, category, channel):
global variable, output_folder
for variable_bin in variable_bins_ROOT[variable]:
path = output_folder + str(measurement_config.centre_of_mass) + 'TeV/' + variable + '/' + category + '/fit_templates/'
make_folder_if_not_exists(path)
plotname = path + channel + '_templates_bin_' + variable_bin
#check if template plots exist already
for output_format in output_formats:
if os.path.isfile(plotname + '.' + output_format):
continue
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.7, y1=0.8)
h_signal = histograms[variable_bin]['signal']
h_VJets = histograms[variable_bin]['V+Jets']
h_QCD = histograms[variable_bin]['QCD']
h_signal.GetXaxis().SetTitle('Lepton #eta')
h_signal.GetYaxis().SetTitle('Normalised Events')
h_signal.GetXaxis().SetTitleSize(0.05)
h_signal.GetYaxis().SetTitleSize(0.05)
h_signal.SetMinimum(0)
h_signal.SetMaximum(0.2)
h_signal.SetLineWidth(2)
h_VJets.SetLineWidth(2)
h_QCD.SetLineWidth(2)
h_signal.SetLineColor(kRed + 1)
h_VJets.SetLineColor(kBlue)
h_QCD.SetLineColor(kYellow)
h_signal.Draw('hist')
h_VJets.Draw('hist same')
h_QCD.Draw('hist same')
legend.AddEntry(h_signal, 'signal', 'l')
legend.AddEntry(h_VJets, 'V+Jets', 'l')
legend.AddEntry(h_QCD, 'QCD', 'l')
legend.Draw()
cms_label, channel_label = get_cms_labels(channel)
cms_label.Draw()
channel_label.Draw()
canvas.Modified()
canvas.Update()
for output_format in output_formats:
canvas.SaveAs(plotname + '.' + output_format)
def make_plots(histograms, category, output_folder, histname):
global variable, variables_latex, measurements_latex, k_value, b_tag_bin, maximum
channel = 'electron'
if 'electron' in histname:
channel = 'electron'
elif 'muon' in histname:
channel = 'muon'
else:
channel = 'combined'
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.6, y1=0.5)
hist_data = histograms['unfolded']
hist_data.GetXaxis().SetTitle(variables_latex[variable] + ' [GeV]')
hist_data.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' + variables_latex[variable] + '} [GeV^{-1}]')
hist_data.GetXaxis().SetTitleSize(0.05)
hist_data.GetYaxis().SetTitleSize(0.05)
hist_data.SetMinimum(0)
hist_data.SetMaximum(maximum[variable])
hist_data.SetMarkerSize(1)
hist_data.SetMarkerStyle(8)
plotAsym = TGraphAsymmErrors(hist_data)
plotStatErr = TGraphAsymmErrors(hist_data)
xsections = read_unfolded_xsections(channel)
bins = variable_bins_ROOT[variable]
assert(len(bins) == len(xsections['central']))
# for bin_i in range(len(bins)):
# scale = 1# / width
# centralresult = xsections['central'][bin_i]
# fit_error = centralresult[1]
# uncertainty = calculateTotalUncertainty(xsections, bin_i)
# uncertainty_total_plus = uncertainty['Total+'][0]
# uncertainty_total_minus = uncertainty['Total-'][0]
# uncertainty_total_plus, uncertainty_total_minus = symmetriseErrors(uncertainty_total_plus, uncertainty_total_minus)
# error_up = sqrt(fit_error ** 2 + uncertainty_total_plus ** 2) * scale
# error_down = sqrt(fit_error ** 2 + uncertainty_total_minus ** 2) * scale
# plotStatErr.SetPointEYhigh(bin_i, fit_error * scale)
# plotStatErr.SetPointEYlow(bin_i, fit_error * scale)
# plotAsym.SetPointEYhigh(bin_i, error_up)
# plotAsym.SetPointEYlow(bin_i, error_down)
gStyle.SetEndErrorSize(20)
plotAsym.SetLineWidth(2)
plotStatErr.SetLineWidth(2)
hist_data.Draw('P')
plotStatErr.Draw('same P')
plotAsym.Draw('same P Z')
legend.AddEntry(hist_data, 'unfolded', 'P')
hist_measured = histograms['measured']
hist_measured.SetMarkerSize(1)
hist_measured.SetMarkerStyle(20)
hist_measured.SetMarkerColor(2)
hist_measured.Draw('same P')
legend.AddEntry(hist_measured, 'measured', 'P')
for key, hist in histograms.iteritems():
if not 'unfolded' in key and not 'measured' in key:
hist.SetLineStyle(7)
hist.SetLineWidth(2)
#setting colours
if 'POWHEG' in key or 'matchingdown' in key:
hist.SetLineColor(kBlue)
elif 'MADGRAPH' in key or 'matchingup' in key:
hist.SetLineColor(kRed + 1)
elif 'MCATNLO' in key or 'scaleup' in key:
hist.SetLineColor(kMagenta + 3)
elif 'scaledown' in key:
hist.SetLineColor(kGreen)
hist.Draw('hist same')
legend.AddEntry(hist, measurements_latex[key], 'l')
legend.Draw()
cms_label, channel_label = get_cms_labels(channel)
cms_label.Draw()
channel_label.Draw()
canvas.Modified()
canvas.Update()
path = output_folder + str(measurement_config.centre_of_mass) + 'TeV/' + variable + '/' + category
make_folder_if_not_exists(path)
for output_format in output_formats:
canvas.SaveAs(path + '/' + histname + '_kv' + str(k_value) + '.' + output_format)
def plot_fit_results(histograms, category, channel):
global variable, translate_options, b_tag_bin, output_folder
#ROOT.TH1.SetDefaultSumw2(False)
for variable_bin in variable_bins_ROOT[variable]:
path = output_folder + str(measurement_config.centre_of_mass) + 'TeV/' + variable + '/' + category + '/fit_results/'
make_folder_if_not_exists(path)
plotname = path + channel + '_bin_' + variable_bin
# check if template plots exist already
for output_format in output_formats:
if os.path.isfile(plotname + '.' + output_format):
continue
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.7, y1=0.8)
h_data = histograms[variable_bin]['data']
h_signal = histograms[variable_bin]['signal']
h_background = histograms[variable_bin]['background']
h_data.GetXaxis().SetTitle('Lepton #eta')
h_data.GetYaxis().SetTitle('Number of Events')
h_data.GetXaxis().SetTitleSize(0.05)
h_data.GetYaxis().SetTitleSize(0.05)
h_data.SetMinimum(0)
h_data.SetMarkerSize(1)
h_data.SetMarkerStyle(20)
gStyle.SetEndErrorSize(20)
h_data.Draw('P')
h_signal.SetFillColor(kRed + 1)
h_background.SetFillColor(kGreen-3)
h_signal.SetLineWidth(2)
h_background.SetLineWidth(2)
h_signal.SetFillStyle(1001)
h_background.SetFillStyle(1001)
mcStack = THStack("MC", "MC")
mcStack.Add(h_background)
mcStack.Add(h_signal)
mcStack.Draw('hist same')
h_data.Draw('error P same')
legend.AddEntry(h_data, 'data', 'P')
legend.AddEntry(h_signal, 'signal', 'F')
legend.AddEntry(h_background, 'background', 'F')
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if channel == 'electron':
channelLabel.AddText("e, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
elif channel == 'muon':
channelLabel.AddText("#mu, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
else:
channelLabel.AddText("combined, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8));
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
channelLabel.Draw()
canvas.Modified()
canvas.Update()
for output_format in output_formats:
canvas.SaveAs(plotname + '.' + output_format)
def make_plots_ROOT(histograms, category, save_path, histname, channel):
global variable, translateOptions, k_value, b_tag_bin, maximum
ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
plotting.setStyle()
gStyle.SetTitleYOffset(2.)
ROOT.gROOT.ForceStyle()
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.18)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.6, y1=0.5)
hist_data = histograms['unfolded']
hist_data.GetXaxis().SetTitle(translate_options[variable] + ' [GeV]')
hist_data.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' +
translate_options[variable] + '} [GeV^{-1}]')
hist_data.GetXaxis().SetTitleSize(0.05)
hist_data.GetYaxis().SetTitleSize(0.05)
hist_data.SetMinimum(0)
hist_data.SetMaximum(maximum[variable])
hist_data.SetMarkerSize(1)
hist_data.SetMarkerStyle(8)
plotAsym = TGraphAsymmErrors(hist_data)
plotStatErr = TGraphAsymmErrors(hist_data)
xsections = read_unfolded_xsections(channel)
bins = variable_bins_ROOT[variable]
assert (len(bins) == len(xsections['central']))
for bin_i in range(len(bins)):
scale = 1 # / width
centralresult = xsections['central'][bin_i]
fit_error = centralresult[1]
uncertainty = calculateTotalUncertainty(xsections, bin_i)
uncertainty_total_plus = uncertainty['Total+'][0]
uncertainty_total_minus = uncertainty['Total-'][0]
uncertainty_total_plus, uncertainty_total_minus = symmetriseErrors(
uncertainty_total_plus, uncertainty_total_minus)
error_up = sqrt(fit_error**2 + uncertainty_total_plus**2) * scale
error_down = sqrt(fit_error**2 + uncertainty_total_minus**2) * scale
plotStatErr.SetPointEYhigh(bin_i, fit_error * scale)
plotStatErr.SetPointEYlow(bin_i, fit_error * scale)
plotAsym.SetPointEYhigh(bin_i, error_up)
plotAsym.SetPointEYlow(bin_i, error_down)
gStyle.SetEndErrorSize(20)
plotAsym.SetLineWidth(2)
plotStatErr.SetLineWidth(2)
hist_data.Draw('P')
plotStatErr.Draw('same P')
plotAsym.Draw('same P Z')
legend.AddEntry(hist_data, 'unfolded', 'P')
hist_measured = histograms['measured']
hist_measured.SetMarkerSize(1)
hist_measured.SetMarkerStyle(20)
hist_measured.SetMarkerColor(2)
#hist_measured.Draw('same P')
#legend.AddEntry(hist_measured, 'measured', 'P')
for key, hist in sorted(histograms.iteritems()):
if not 'unfolded' in key and not 'measured' in key:
hist.SetLineStyle(7)
hist.SetLineWidth(2)
# setting colours
if 'POWHEG' in key or 'matchingdown' in key:
hist.SetLineColor(kBlue)
elif 'MADGRAPH' in key or 'matchingup' in key:
hist.SetLineColor(kRed + 1)
elif 'MCATNLO' in key or 'scaleup' in key:
hist.SetLineColor(kGreen - 3)
elif 'scaledown' in key:
hist.SetLineColor(kMagenta + 3)
hist.Draw('hist same')
legend.AddEntry(hist, translate_options[key], 'l')
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if 'electron' in histname:
channelLabel.AddText(
"e, %s, %s, k = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
elif 'muon' in histname:
channelLabel.AddText(
"#mu, %s, %s, k = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
else:
channelLabel.AddText(
"combined, %s, %s, k = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" %
(5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
channelLabel.Draw()
canvas.Modified()
canvas.Update()
path = save_path + '/' + variable + '/' + category
make_folder_if_not_exists(path)
canvas.SaveAs(path + '/' + histname + '_kv' + str(k_value) + '.png')
canvas.SaveAs(path + '/' + histname + '_kv' + str(k_value) + '.pdf')
def plot_central_and_systematics(channel):
global variable, translate_options, k_value, b_tag_bin, maximum, categories
ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
plotting.setStyle()
gStyle.SetTitleYOffset(1.4)
ROOT.gROOT.ForceStyle()
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.6, y1=0.5)
hist_data_central = read_xsection_measurement_results(
'central', channel)[0]['unfolded']
hist_data_central.GetXaxis().SetTitle(translate_options[variable] +
' [GeV]')
hist_data_central.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' +
translate_options[variable] +
'} [GeV^{-1}]')
hist_data_central.GetXaxis().SetTitleSize(0.05)
hist_data_central.GetYaxis().SetTitleSize(0.05)
hist_data_central.SetMinimum(0)
hist_data_central.SetMaximum(maximum[variable])
hist_data_central.SetMarkerSize(1)
hist_data_central.SetMarkerStyle(20)
# plotAsym = TGraphAsymmErrors(hist_data)
# plotStatErr = TGraphAsymmErrors(hist_data)
gStyle.SetEndErrorSize(20)
hist_data_central.Draw('P')
# plotStatErr.Draw('same P')
# plotAsym.Draw('same P Z')
legend.AddEntry(hist_data_central, 'measured (unfolded)', 'P')
for systematic in categories:
if systematic != 'central':
hist_data_systematic = read_xsection_measurement_results(
systematic, channel)[0]['unfolded']
hist_data_systematic.SetMarkerSize(0.5)
hist_data_systematic.SetMarkerStyle(20)
colour_number = categories.index(systematic) + 1
if colour_number == 10:
colour_number = 42
hist_data_systematic.SetMarkerColor(colour_number)
hist_data_systematic.Draw('same P')
legend.AddEntry(hist_data_systematic, systematic, 'P')
# for central_generator in ['MADGRAPH', 'POWHEG', 'MCATNLO']:
# hist_MC = read_xsection_measurement_results('central', channel)[0][central_generator]
# hist_MC.SetLineStyle(7)
# hist_MC.SetLineWidth(2)
# #setting colours
# if central_generator == 'POWHEG':
# hist_MC.SetLineColor(kBlue)
# elif central_generator == 'MADGRAPH':
# hist_MC.SetLineColor(kRed + 1)
# elif central_generator == 'MCATNLO':
# hist_MC.SetLineColor(kMagenta + 3)
# hist_MC.Draw('hist same')
#legend.AddEntry(hist_MC, translate_options[central_generator], 'l')
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if channel == 'electron':
channelLabel.AddText(
"e, %s, %s, k_v = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
elif channel == 'muon':
channelLabel.AddText(
"#mu, %s, %s, k_v = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
else:
channelLabel.AddText(
"combined, %s, %s, k_v = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" %
(5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
if not channel == 'combination':
channelLabel.Draw()
canvas.Modified()
canvas.Update()
path = save_path + '/' + variable
make_folder_if_not_exists(path)
canvas.SaveAs(path + '/normalised_xsection_' + channel + '_altogether_kv' +
str(k_value) + '.png')
canvas.SaveAs(path + '/normalised_xsection_' + channel + '_altogether_kv' +
str(k_value) + '.pdf')
def make_template_plots(histograms, category, channel):
global variable, translate_options, b_tag_bin, save_path
ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
plotting.setStyle()
gStyle.SetTitleYOffset(1.4)
ROOT.gROOT.ForceStyle()
for variable_bin in variable_bins_ROOT[variable]:
path = save_path + '/' + variable + '/' + category + '/fit_templates/'
make_folder_if_not_exists(path)
plotname = path + channel + '_templates_bin_' + variable_bin + '.png'
# check if template plots exist already
if os.path.isfile(plotname):
continue
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.7, y1=0.8)
h_signal = histograms[variable_bin]['signal']
h_VJets = histograms[variable_bin]['V+Jets']
h_QCD = histograms[variable_bin]['QCD']
h_signal.GetXaxis().SetTitle('Lepton #eta')
h_signal.GetYaxis().SetTitle('Normalised Events')
h_signal.GetXaxis().SetTitleSize(0.05)
h_signal.GetYaxis().SetTitleSize(0.05)
h_signal.SetMinimum(0)
h_signal.SetMaximum(0.2)
h_signal.SetLineWidth(2)
h_VJets.SetLineWidth(2)
h_QCD.SetLineWidth(2)
h_signal.SetLineColor(kRed + 1)
h_VJets.SetLineColor(kBlue)
h_QCD.SetLineColor(kYellow)
h_signal.Draw('hist')
h_VJets.Draw('hist same')
h_QCD.Draw('hist same')
legend.AddEntry(h_signal, 'signal', 'l')
legend.AddEntry(h_VJets, 'V+Jets', 'l')
legend.AddEntry(h_QCD, 'QCD', 'l')
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if channel == 'electron':
channelLabel.AddText("e, %s, %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
elif channel == 'muon':
channelLabel.AddText("#mu, %s, %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
else:
channelLabel.AddText("combined, %s, %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
mytext.AddText(
"CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
channelLabel.Draw()
canvas.Modified()
canvas.Update()
canvas.SaveAs(plotname)
canvas.SaveAs(plotname.replace('png', 'pdf'))
def plot_fit_results(histograms, category, channel):
global variable, translate_options, b_tag_bin, save_path
#ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
plotting.setStyle()
gStyle.SetTitleYOffset(1.4)
ROOT.gROOT.ForceStyle()
for variable_bin in variable_bins_ROOT[variable]:
path = save_path + '/' + variable + '/' + category + '/fit_results/'
make_folder_if_not_exists(path)
plotname = path + channel + '_bin_' + variable_bin + '.png'
# check if template plots exist already
if os.path.isfile(plotname):
continue
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.7, y1=0.8)
h_data = histograms[variable_bin]['data']
h_signal = histograms[variable_bin]['signal']
h_background = histograms[variable_bin]['background']
h_data.GetXaxis().SetTitle('Lepton #eta')
h_data.GetYaxis().SetTitle('Number of Events')
h_data.GetXaxis().SetTitleSize(0.05)
h_data.GetYaxis().SetTitleSize(0.05)
h_data.SetMinimum(0)
h_data.SetMarkerSize(1)
h_data.SetMarkerStyle(20)
gStyle.SetEndErrorSize(20)
h_data.Draw('P')
h_signal.SetFillColor(kRed + 1)
h_background.SetFillColor(kGreen - 3)
h_signal.SetLineWidth(2)
h_background.SetLineWidth(2)
h_signal.SetFillStyle(1001)
h_background.SetFillStyle(1001)
mcStack = THStack("MC", "MC")
mcStack.Add(h_background)
mcStack.Add(h_signal)
mcStack.Draw('hist same')
h_data.Draw('error P same')
legend.AddEntry(h_data, 'data', 'P')
legend.AddEntry(h_signal, 'signal', 'F')
legend.AddEntry(h_background, 'background', 'F')
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if channel == 'electron':
channelLabel.AddText("e, %s, %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
elif channel == 'muon':
channelLabel.AddText("#mu, %s, %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
else:
channelLabel.AddText("combined, %s, %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
mytext.AddText(
"CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
channelLabel.Draw()
canvas.Modified()
canvas.Update()
canvas.SaveAs(plotname)
canvas.SaveAs(plotname.replace('png', 'pdf'))
def make_plots(histograms, category, output_folder, histname, show_before_unfolding = False):
global variable, variables_latex, measurements_latex, k_value, b_tag_bin, maximum
channel = 'electron'
if 'electron' in histname:
channel = 'electron'
elif 'muon' in histname:
channel = 'muon'
else:
channel = 'combined'
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.6, y1=0.5)
hist_data = histograms['unfolded']
hist_data.GetXaxis().SetTitle(variables_latex[variable] + ' [GeV]')
hist_data.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' + variables_latex[variable] + '} [GeV^{-1}]')
hist_data.GetXaxis().SetTitleSize(0.05)
hist_data.GetYaxis().SetTitleSize(0.05)
hist_data.SetMinimum(0)
hist_data.SetMaximum(maximum[variable])
hist_data.SetMarkerSize(1)
hist_data.SetMarkerStyle(8)
hist_data_with_systematics = histograms['unfolded_with_systematics']
hist_data_with_systematics.GetXaxis().SetTitle(variables_latex[variable] + ' [GeV]')
hist_data_with_systematics.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' + variables_latex[variable] + '} [GeV^{-1}]')
hist_data_with_systematics.GetXaxis().SetTitleSize(0.05)
hist_data_with_systematics.GetYaxis().SetTitleSize(0.05)
hist_data_with_systematics.SetMinimum(0)
hist_data_with_systematics.SetMaximum(maximum[variable])
hist_data_with_systematics.SetMarkerSize(1)
hist_data_with_systematics.SetMarkerStyle(8)
plotAsym = TGraphAsymmErrors(hist_data_with_systematics)
plotStatErr = TGraphAsymmErrors(hist_data)
xsections = read_unfolded_xsections(channel)
bins = variable_bins_ROOT[variable]
assert(len(bins) == len(xsections['central']))
gStyle.SetEndErrorSize(20)
plotAsym.SetLineWidth(2)
plotStatErr.SetLineWidth(2)
hist_data.Draw('P')
plotStatErr.Draw('same P')
plotAsym.Draw('same P Z')
legend.AddEntry(hist_data, 'data', 'P')
if show_before_unfolding:
hist_measured = histograms['measured']
hist_measured.SetMarkerSize(1)
hist_measured.SetMarkerStyle(20)
hist_measured.SetMarkerColor(2)
hist_measured.Draw('same P')
legend.AddEntry(hist_measured, 'data (before unfolding)', 'P')
for key, hist in histograms.iteritems():
if not 'unfolded' in key and not 'measured' in key:
hist.SetLineStyle(7)
hist.SetLineWidth(2)
#setting colours
if 'POWHEG' in key or 'matchingdown' in key:
hist.SetLineColor(kBlue)
elif 'MADGRAPH' in key or 'matchingup' in key:
hist.SetLineColor(kRed + 1)
elif 'MCATNLO' in key or 'scaleup' in key:
hist.SetLineColor(kMagenta + 3)
elif 'scaledown' in key:
hist.SetLineColor(kGreen)
hist.Draw('hist same')
legend.AddEntry(hist, measurements_latex[key], 'l')
legend.Draw()
cms_label, channel_label = get_cms_labels(channel)
cms_label.Draw()
channel_label.Draw()
canvas.Modified()
canvas.Update()
path = output_folder + str(measurement_config.centre_of_mass) + 'TeV/' + variable + '/' + category
make_folder_if_not_exists(path)
for output_format in output_formats:
canvas.SaveAs(path + '/' + histname + '_kv' + str(k_value) + '.' + output_format)
def plot_central_and_systematics(channel):
global variable, translate_options, k_value, b_tag_bin, maximum, categories
ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine("gErrorIgnoreLevel = 1001;")
plotting.setStyle()
gStyle.SetTitleYOffset(1.4)
ROOT.gROOT.ForceStyle()
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.6, y1=0.5)
hist_data_central = read_xsection_measurement_results("central", channel)[0]["unfolded"]
hist_data_central.GetXaxis().SetTitle(translate_options[variable] + " [GeV]")
hist_data_central.GetYaxis().SetTitle(
"#frac{1}{#sigma} #frac{d#sigma}{d" + translate_options[variable] + "} [GeV^{-1}]"
)
hist_data_central.GetXaxis().SetTitleSize(0.05)
hist_data_central.GetYaxis().SetTitleSize(0.05)
hist_data_central.SetMinimum(0)
hist_data_central.SetMaximum(maximum[variable])
hist_data_central.SetMarkerSize(1)
hist_data_central.SetMarkerStyle(20)
# plotAsym = TGraphAsymmErrors(hist_data)
# plotStatErr = TGraphAsymmErrors(hist_data)
gStyle.SetEndErrorSize(20)
hist_data_central.Draw("P")
# plotStatErr.Draw('same P')
# plotAsym.Draw('same P Z')
legend.AddEntry(hist_data_central, "measured (unfolded)", "P")
for systematic in categories:
if systematic != "central":
hist_data_systematic = read_xsection_measurement_results(systematic, channel)[0]["unfolded"]
hist_data_systematic.SetMarkerSize(0.5)
hist_data_systematic.SetMarkerStyle(20)
colour_number = categories.index(systematic) + 1
if colour_number == 10:
colour_number = 42
hist_data_systematic.SetMarkerColor(colour_number)
hist_data_systematic.Draw("same P")
legend.AddEntry(hist_data_systematic, systematic, "P")
# for central_generator in ['MADGRAPH', 'POWHEG', 'MCATNLO']:
# hist_MC = read_xsection_measurement_results('central', channel)[0][central_generator]
# hist_MC.SetLineStyle(7)
# hist_MC.SetLineWidth(2)
# #setting colours
# if central_generator == 'POWHEG':
# hist_MC.SetLineColor(kBlue)
# elif central_generator == 'MADGRAPH':
# hist_MC.SetLineColor(kRed + 1)
# elif central_generator == 'MCATNLO':
# hist_MC.SetLineColor(kMagenta + 3)
# hist_MC.Draw('hist same')
# legend.AddEntry(hist_MC, translate_options[central_generator], 'l')
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if channel == "electron":
channelLabel.AddText("e, %s, %s, k_v = %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
elif channel == "muon":
channelLabel.AddText("#mu, %s, %s, k_v = %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
else:
channelLabel.AddText("combined, %s, %s, k_v = %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
if not channel == "combination":
channelLabel.Draw()
canvas.Modified()
canvas.Update()
path = save_path + "/" + variable
make_folder_if_not_exists(path)
canvas.SaveAs(path + "/normalised_xsection_" + channel + "_altogether_kv" + str(k_value) + ".png")
canvas.SaveAs(path + "/normalised_xsection_" + channel + "_altogether_kv" + str(k_value) + ".pdf")
def make_plots_ROOT(histograms, category, save_path, histname, channel):
global variable, translateOptions, k_value, b_tag_bin, maximum
ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine("gErrorIgnoreLevel = 1001;")
plotting.setStyle()
gStyle.SetTitleYOffset(2.0)
ROOT.gROOT.ForceStyle()
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.18)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.6, y1=0.5)
hist_data = histograms["unfolded"]
hist_data.GetXaxis().SetTitle(translate_options[variable] + " [GeV]")
hist_data.GetYaxis().SetTitle("#frac{1}{#sigma} #frac{d#sigma}{d" + translate_options[variable] + "} [GeV^{-1}]")
hist_data.GetXaxis().SetTitleSize(0.05)
hist_data.GetYaxis().SetTitleSize(0.05)
hist_data.SetMinimum(0)
hist_data.SetMaximum(maximum[variable])
hist_data.SetMarkerSize(1)
hist_data.SetMarkerStyle(8)
plotAsym = TGraphAsymmErrors(hist_data)
plotStatErr = TGraphAsymmErrors(hist_data)
xsections = read_unfolded_xsections(channel)
bins = variable_bins_ROOT[variable]
assert len(bins) == len(xsections["central"])
for bin_i in range(len(bins)):
scale = 1 # / width
centralresult = xsections["central"][bin_i]
fit_error = centralresult[1]
uncertainty = calculateTotalUncertainty(xsections, bin_i)
uncertainty_total_plus = uncertainty["Total+"][0]
uncertainty_total_minus = uncertainty["Total-"][0]
uncertainty_total_plus, uncertainty_total_minus = symmetriseErrors(
uncertainty_total_plus, uncertainty_total_minus
)
error_up = sqrt(fit_error ** 2 + uncertainty_total_plus ** 2) * scale
error_down = sqrt(fit_error ** 2 + uncertainty_total_minus ** 2) * scale
plotStatErr.SetPointEYhigh(bin_i, fit_error * scale)
plotStatErr.SetPointEYlow(bin_i, fit_error * scale)
plotAsym.SetPointEYhigh(bin_i, error_up)
plotAsym.SetPointEYlow(bin_i, error_down)
gStyle.SetEndErrorSize(20)
plotAsym.SetLineWidth(2)
plotStatErr.SetLineWidth(2)
hist_data.Draw("P")
plotStatErr.Draw("same P")
plotAsym.Draw("same P Z")
legend.AddEntry(hist_data, "unfolded", "P")
hist_measured = histograms["measured"]
hist_measured.SetMarkerSize(1)
hist_measured.SetMarkerStyle(20)
hist_measured.SetMarkerColor(2)
# hist_measured.Draw('same P')
# legend.AddEntry(hist_measured, 'measured', 'P')
for key, hist in sorted(histograms.iteritems()):
if not "unfolded" in key and not "measured" in key:
hist.SetLineStyle(7)
hist.SetLineWidth(2)
# setting colours
if "POWHEG" in key or "matchingdown" in key:
hist.SetLineColor(kBlue)
elif "MADGRAPH" in key or "matchingup" in key:
hist.SetLineColor(kRed + 1)
elif "MCATNLO" in key or "scaleup" in key:
hist.SetLineColor(kGreen - 3)
elif "scaledown" in key:
hist.SetLineColor(kMagenta + 3)
hist.Draw("hist same")
legend.AddEntry(hist, translate_options[key], "l")
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if "electron" in histname:
channelLabel.AddText("e, %s, %s, k = %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
elif "muon" in histname:
channelLabel.AddText("#mu, %s, %s, k = %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
else:
channelLabel.AddText("combined, %s, %s, k = %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
channelLabel.Draw()
canvas.Modified()
canvas.Update()
path = save_path + "/" + variable + "/" + category
make_folder_if_not_exists(path)
canvas.SaveAs(path + "/" + histname + "_kv" + str(k_value) + ".png")
canvas.SaveAs(path + "/" + histname + "_kv" + str(k_value) + ".pdf")
def plot_fit_results(histograms, category, channel):
global variable, translate_options, b_tag_bin, save_path
# ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine("gErrorIgnoreLevel = 1001;")
plotting.setStyle()
gStyle.SetTitleYOffset(1.4)
ROOT.gROOT.ForceStyle()
for variable_bin in variable_bins_ROOT[variable]:
path = save_path + "/" + variable + "/" + category + "/fit_results/"
make_folder_if_not_exists(path)
plotname = path + channel + "_bin_" + variable_bin + ".png"
# check if template plots exist already
if os.path.isfile(plotname):
continue
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.7, y1=0.8)
h_data = histograms[variable_bin]["data"]
h_signal = histograms[variable_bin]["signal"]
h_background = histograms[variable_bin]["background"]
h_data.GetXaxis().SetTitle("Lepton #eta")
h_data.GetYaxis().SetTitle("Number of Events")
h_data.GetXaxis().SetTitleSize(0.05)
h_data.GetYaxis().SetTitleSize(0.05)
h_data.SetMinimum(0)
h_data.SetMarkerSize(1)
h_data.SetMarkerStyle(20)
gStyle.SetEndErrorSize(20)
h_data.Draw("P")
h_signal.SetFillColor(kRed + 1)
h_background.SetFillColor(kGreen - 3)
h_signal.SetLineWidth(2)
h_background.SetLineWidth(2)
h_signal.SetFillStyle(1001)
h_background.SetFillStyle(1001)
mcStack = THStack("MC", "MC")
mcStack.Add(h_background)
mcStack.Add(h_signal)
mcStack.Draw("hist same")
h_data.Draw("error P same")
legend.AddEntry(h_data, "data", "P")
legend.AddEntry(h_signal, "signal", "F")
legend.AddEntry(h_background, "background", "F")
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if channel == "electron":
channelLabel.AddText("e, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
elif channel == "muon":
channelLabel.AddText("#mu, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
else:
channelLabel.AddText("combined, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
channelLabel.Draw()
canvas.Modified()
canvas.Update()
canvas.SaveAs(plotname)
canvas.SaveAs(plotname.replace("png", "pdf"))
def make_template_plots(histograms, category, channel):
global variable, translate_options, b_tag_bin, save_path
ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine("gErrorIgnoreLevel = 1001;")
plotting.setStyle()
gStyle.SetTitleYOffset(1.4)
ROOT.gROOT.ForceStyle()
for variable_bin in variable_bins_ROOT[variable]:
path = save_path + "/" + variable + "/" + category + "/fit_templates/"
make_folder_if_not_exists(path)
plotname = path + channel + "_templates_bin_" + variable_bin + ".png"
# check if template plots exist already
if os.path.isfile(plotname):
continue
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.7, y1=0.8)
h_signal = histograms[variable_bin]["signal"]
h_VJets = histograms[variable_bin]["V+Jets"]
h_QCD = histograms[variable_bin]["QCD"]
h_signal.GetXaxis().SetTitle("Lepton #eta")
h_signal.GetYaxis().SetTitle("Normalised Events")
h_signal.GetXaxis().SetTitleSize(0.05)
h_signal.GetYaxis().SetTitleSize(0.05)
h_signal.SetMinimum(0)
h_signal.SetMaximum(0.2)
h_signal.SetLineWidth(2)
h_VJets.SetLineWidth(2)
h_QCD.SetLineWidth(2)
h_signal.SetLineColor(kRed + 1)
h_VJets.SetLineColor(kBlue)
h_QCD.SetLineColor(kYellow)
h_signal.Draw("hist")
h_VJets.Draw("hist same")
h_QCD.Draw("hist same")
legend.AddEntry(h_signal, "signal", "l")
legend.AddEntry(h_VJets, "V+Jets", "l")
legend.AddEntry(h_QCD, "QCD", "l")
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if channel == "electron":
channelLabel.AddText("e, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
elif channel == "muon":
channelLabel.AddText("#mu, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
else:
channelLabel.AddText("combined, %s, %s" % ("#geq 4 jets", b_tag_bins_latex[b_tag_bin]))
mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" % (5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
channelLabel.Draw()
canvas.Modified()
canvas.Update()
canvas.SaveAs(plotname)
canvas.SaveAs(plotname.replace("png", "pdf"))