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 print_xsections_with_uncertainties(xsections, channel, toFile=True):
global savePath, variable, k_value, met_type, b_tag_bin
printout = '\n'
printout += '=' * 60
printout = '\n'
printout += 'Results for %s variable, %s channel, k-value %s, met type %s, %s b-tag region\n' % (
variable, channel, k_value, met_type, b_tag_bin)
printout += '=' * 60
printout += '\n'
# rows = {}
printout += '%s bin & $\sigma_{meas}$ \\\\ \n' % variable
printout += '\hline\n'
uncertainties = {}
header = 'Uncertainty'
bins = variable_bins_ROOT[variable]
assert (len(bins) == len(xsections['central']))
for bin_i, variable_bin in enumerate(bins):
header += '& %s bin %s' % (variable, variable_bin)
centralresult = xsections['central'][bin_i]
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)
scale = 100
central_measurement = centralresult[0]
fit_error = centralresult[1]
formatting = (variable_bins_latex[variable_bin],
central_measurement * scale, fit_error * scale,
uncertainty_total_plus * scale,
uncertainty_total_minus * scale)
text = '%s & $%.2f \pm %.2f (fit)^{+%.2f}_{-%.2f} (sys) \cdot 10^{-2}$\\\\ \n' % formatting
if doSymmetricErrors:
relativeError = getRelativeError(
central_measurement, fit_error + uncertainty_total_plus)
formatting = (variable_bins_latex[variable_bin],
central_measurement * scale, fit_error * scale,
uncertainty_total_plus * scale)
text = '%s & $\\left(%.2f \\pm %.2f \\text{ (fit)} \pm %.2f \\text{ (syst.)}\\right)' % formatting + '(%.2f' % (
relativeError *
100) + '\%) \\times 10^{-2}\, \\GeV^{-1}$\\\\ \n'
printout += text
for source in uncertainty.keys():
unc_result = uncertainty[source]
if not uncertainties.has_key(source):
if source in metsystematics_sources:
uncertainties[
source] = metsystematics_sources_latex[source] + ' & '
else:
uncertainties[source] = source + ' & '
relativeError = getRelativeError(centralresult[0], unc_result[0])
# text = ' $(%.2f \pm %.2f) \cdot 10^{-2} $ ' % (unc_result[0]*scale,unc_result[1]*scale) + '(%.2f' % (relativeError * 100) + '\%) &'
text = '%.2f' % (relativeError * 100) + '\% &'
# text = ' $%.2f \pm %.2f $ ' % (unc_result[0]*scale,unc_result[1]*scale) + '(%.2f' % (relativeError * 100) + '\%) &'
uncertainties[source] += text
printout += '\\\\ \n'
for source in sorted(uncertainties.keys()):
value = uncertainties[source]
value = value.rstrip('&')
value += '\\\\ \n'
printout += value
make_folder_if_not_exists(savePath + '/' + variable)
if toFile:
output_file = open(
savePath + '/' + variable + '/normalised_xsection_main_result_' +
channel + '_' + met_type + '_kv' + str(k_value) + '.tex', 'w')
output_file.write(printout)
output_file.close()
else:
print printout
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 print_xsections_with_uncertainties(xsections, channel, toFile = True):
global savePath, variable, k_value, met_type, b_tag_bin
printout = '\n'
printout += '=' * 60
printout = '\n'
printout += 'Results for %s variable, %s channel, k-value %s, met type %s, %s b-tag region\n' % (variable, channel, k_value, met_type, b_tag_bin)
printout += '=' * 60
printout += '\n'
# rows = {}
printout += '%s bin & $\sigma_{meas}$ \\\\ \n' % variable
printout += '\hline\n'
uncertainties = {}
header = 'Uncertainty'
bins = variable_bins_ROOT[variable]
assert(len(bins) == len(xsections['central']))
for bin_i, variable_bin in enumerate(bins):
header += '& %s bin %s' % (variable, variable_bin)
centralresult = xsections['central'][bin_i]
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)
scale = 100
central_measurement = centralresult[0]
fit_error = centralresult[1]
formatting = (variable_bins_latex[variable_bin], central_measurement * scale,
fit_error * scale, uncertainty_total_plus * scale,
uncertainty_total_minus * scale)
text = '%s & $%.2f \pm %.2f (fit)^{+%.2f}_{-%.2f} (sys) \cdot 10^{-2}$\\\\ \n' % formatting
if doSymmetricErrors:
relativeError = getRelativeError(central_measurement, fit_error+uncertainty_total_plus)
formatting = (variable_bins_latex[variable_bin], central_measurement * scale,
fit_error * scale, uncertainty_total_plus * scale)
text = '%s & $\\left(%.2f \\pm %.2f \\text{ (fit)} \pm %.2f \\text{ (syst.)}\\right)' % formatting + '(%.2f' % (relativeError * 100) + '\%) \\times 10^{-2}\, \\GeV^{-1}$\\\\ \n'
printout += text
for source in uncertainty.keys():
unc_result = uncertainty[source]
if not uncertainties.has_key(source):
if source in metsystematics_sources:
uncertainties[source] = metsystematics_sources_latex[source] + ' & '
else:
uncertainties[source] = source + ' & '
relativeError = getRelativeError(centralresult[0], unc_result[0])
# text = ' $(%.2f \pm %.2f) \cdot 10^{-2} $ ' % (unc_result[0]*scale,unc_result[1]*scale) + '(%.2f' % (relativeError * 100) + '\%) &'
text = '%.2f' % (relativeError * 100) + '\% &'
# text = ' $%.2f \pm %.2f $ ' % (unc_result[0]*scale,unc_result[1]*scale) + '(%.2f' % (relativeError * 100) + '\%) &'
uncertainties[source] += text
printout += '\\\\ \n'
for source in sorted(uncertainties.keys()):
value = uncertainties[source]
value = value.rstrip('&')
value += '\\\\ \n'
printout += value
make_folder_if_not_exists(savePath + '/' + variable)
if toFile:
output_file = open(savePath + '/' + variable + '/normalised_xsection_main_result_' + channel + '_' + met_type + '_kv' + str(k_value) + '.tex', 'w')
output_file.write(printout)
output_file.close()
else:
print printout
