def checkOnMC(unfolding, method):
global bins, nbins
RooUnfold.SVD_n_toy = 1000
pulls = []
for sub in range(1,9):
inputFile2 = File('../data/unfolding_merged_sub%d.root' % sub, 'read')
h_data = asrootpy(inputFile2.unfoldingAnalyserElectronChannel.measured.Rebin(nbins, 'measured', bins))
nEvents = inputFile2.EventFilter.EventCounter.GetBinContent(1)
lumiweight = 164.5 * 5050 / nEvents
# print sub, nEvents
h_data.Scale(lumiweight)
doUnfoldingSequence(unfolding, h_data, method, '_sub%d' %sub)
pull = unfolding.pull_inputErrorOnly()
# unfolding.printTable()
pulls.append(pull)
unfolding.Reset()
allpulls = []
for pull in pulls:
allpulls.extend(pull)
h_allpulls = Hist(100,-30,30)
filling = h_allpulls.Fill
for entry in allpulls:
filling(entry)
fit = h_allpulls.Fit('gaus', 'WWS')
h_fit = asrootpy(h_allpulls.GetFunction("gaus").GetHistogram())
canvas = Canvas(width=1600, height=1000)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.10)
canvas.SetRightMargin(0.05)
h_allpulls.Draw()
fit.Draw('same')
canvas.SaveAs('plots/Pull_allBins_withFit.png')
plt.figure(figsize=(16, 10), dpi=100)
rplt.errorbar(h_allpulls, label=r'Pull distribution for all bins', emptybins=False)
rplt.hist(h_fit, label=r'fit')
plt.xlabel('(unfolded-true)/error', CMS.x_axis_title)
plt.ylabel('entries', CMS.y_axis_title)
plt.title('Pull distribution for all bins', CMS.title)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.legend(numpoints=1)
plt.savefig('plots/Pull_allBins.png')
#individual bins
for bin_i in range(nbins):
h_pull = Hist(100,-30,30)
for pull in pulls:
h_pull.Fill(pull[bin_i])
plt.figure(figsize=(16, 10), dpi=100)
rplt.errorbar(h_pull, label=r'Pull distribution for bin %d' % (bin_i + 1), emptybins=False)
plt.xlabel('(unfolded-true)/error', CMS.x_axis_title)
plt.ylabel('entries', CMS.y_axis_title)
plt.title('Pull distribution for bin %d' % (bin_i + 1), CMS.title)
plt.savefig('Pull_bin_%d.png' % (bin_i + 1))
def plot_comparisons(input_file, comparison_plots):
with root_open(input_file) as file:
for name,histo_config in comparison_plots.items():
canvas = Canvas(1200,700)
canvas.SetLeftMargin(0.08)
canvas.SetRightMargin(0.12)
histogram = file.Get(histo_config.name)
if len(histo_config.range)==2:
histogram.SetAxisRange(histo_config.range[0], histo_config.range[1], 'X')
histogram.color = histo_config.color
histogram.markerstyle = histo_config.marker
histogram.Draw()
canvas.Print('./plots/{}.png'.format(name))
def CreatCanvas(self):
H_ref = 600
W_ref = 800
W = W_ref
H = H_ref
T = 0.08 * H_ref
B = 0.12 * H_ref
L = 0.12 * W_ref
R = 0.04 * W_ref
# Set the tdr style
canvas = Canvas(width=W, height=H)
canvas.SetFillColor(0)
canvas.SetBorderMode(0)
canvas.SetFrameFillStyle(0)
canvas.SetFrameBorderMode(0)
canvas.SetLeftMargin(L / W)
canvas.SetRightMargin(R / W)
canvas.SetTopMargin(T / H)
canvas.SetBottomMargin(B / H)
canvas.SetTickx(0)
canvas.SetTicky(0)
return canvas
this_n = np.array(this_n)
this_r0 = np.array(this_r0)
this_R = np.array(this_R)
B += magnetic_field(r, this_n, this_r0, this_R)
Bx = B[:, 0]
By = B[:, 1]
Bz = B[:, 2]
Bx.shape = X.shape
By.shape = Y.shape
Bz.shape = Z.shape
Bnorm = np.sqrt(Bx**2 + By**2 + Bz**2)
c2 = Canvas()
c2.SetRightMargin(0.1)
h2_B_XY = Hist2D(101, -5, 5, 101, -5, 5)
print "lenght X=%d , Y=%d, Z=%d, B=%d " % (len(X), len(Y), len(Z), len(Bnorm))
for i in range(0, len(X)):
for j in range(0, len(Y)):
h2_B_XY.Fill(X[i][j][121], Y[i][j][121], Bnorm[i][j][121])
h2_B_XY.Draw('COLZ')
#c2.Print("Helmoltz_XY.jpg")
c3 = Canvas()
c3.SetRightMargin(0.1)
h2_B_ZY = Hist2D(241, -12, 12, 101, -5, 5)
for i in range(0, 241):
for j in range(0, len(Y)):
h2_B_ZY.Fill(Z[51][j][i], Y[51][j][i], Bnorm[51][j][i])
# easily set visual attributes
h_simple.linecolor = 'blue'
h_simple.fillcolor = 'green'
h_simple.fillstyle = '/'
# attributes may be accessed in the same way
print(h_simple.name)
print(h_simple.title)
print(h_simple.markersize)
# plot
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.10)
canvas.SetRightMargin(0.05)
h_simple.Draw()
# create the legend
legend = Legend([h_simple],
pad=canvas,
header='Header',
leftmargin=0.05,
rightmargin=0.5)
legend.Draw()
# 2D and 3D histograms are handled in the same way
# the constructor arguments are repetitions of #bins, left bound, right bound.
h2d = Hist2D(10, 0, 1, 50, -40, 10, name='2d hist')
h3d = Hist3D(3, -1, 4, 10, -1000, -200, 2, 0, 1, name='3d hist')
if yplot == "Plot":
print variable + " " + min + " " + max + " " + nbins + " " + title + " " + ytitle
for (xvariable, xmin, xmax, xnbins, xtitle, xytitle,
xplot) in zip(xvariable_list, xmin_list, xmax_list,
xnbins_list, xtitle_list, xytitle_list,
xplot_list):
if xplot == "Plot":
if xvariable == variable:
continue
# Canvas Variables and declaration
print xvariable + " " + xmin + " " + xmax + " " + xnbins + " " + xtitle + " " + xytitle
canvas = Canvas(width=700, height=500)
#canvas.SetLeftMargin(0.1)
canvas.SetRightMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.Draw()
#canvas.SetLogy()
canvas.SetGrid()
histname = "hist" + xvariable + "_" + variable + background_name
print histname
histogram = histname + "(" + xnbins + "," + xmin + "," + xmax + "," + nbins + "," + min + "," + max + ")"
print histogram
histname = compressed.Draw(xvariable + ":" + variable +
">>" + histogram,
selection=weight * total,
drawstyle='COLZ')
histname.set_title(signalprocess + " " +
while True:
# non-empty dict, so break
if plots_paths.get(topLevelPath, {}):
break
topLevelPath = os.path.dirname(topLevelPath)
# top level path empty so break
if not topLevelPath:
break
plots_path = plots_paths.get(topLevelPath, {})
# create new canvas
canvasConfigs = copy.copy(plots_config.get('canvas', {}))
canvasConfigs.update(plots_path.get('canvas', {}))
canvas = Canvas(canvasConfigs.get('width', 500), canvasConfigs.get('height', 500))
canvas.SetRightMargin(canvasConfigs.get('rightmargin', 0.1))
canvas.SetBottomMargin(canvasConfigs.get('bottommargin', 0.2))
canvas.SetLeftMargin(canvasConfigs.get('leftmargin', 0.2))
if canvasConfigs.get('logy', False) == True:
canvas.set_logy()
# create a legend (an entry for each group)
legendConfigs = copy.copy(plots_config.get('legend', {}))
legendConfigs.update(plots_path.get('legend', {}))
legend_numColumns = legendConfigs.get('numcolumns', 1)
if "numcolumns" in legendConfigs: del legendConfigs['numcolumns']
legend = Legend(len(h), **legendConfigs)
legend.SetNColumns(legend_numColumns)
# scale the histograms before doing anything else
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.)
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'))
def significance_scan_2d(
x,
bins=(16, 2000., 6000., 10, 100., 300.),
xtitle='#font[12]{H}_{#font[132]{T}} [GeV]',
ytitle='#font[12]{E}_{#font[132]{T}}^{#font[132]{miss}} [GeV]',
selection='',
weight=''):
"""
The signficance is calculated using Cowan's analytic formula for discovery
significance of a signal with background and uncertainty on the background.
See: https://www.pp.rhul.ac.uk/~cowan/stat/notes/medsigNote.pdf
"""
global data
global bkgs
global sigs
global treename
global datasearchpath
global datadrivensearchpath
global bkgsearchpath
global sigsearchpath
global lumi
assert x
assert len(bins) == 6
assert bkgs
assert sigs
assert lumi
assert bkgsearchpath
assert sigsearchpath
save = ['pdf', 'png']
## only use first sig
assert len(sigs) == 1
sig = sigs[0]
## save stuff to bookkeep and return
stuff = dict()
stuff['x'] = x
## get background histograms
h_bkgs = list()
n_bkgs = list()
if bkgs:
for bkg in bkgs:
if isinstance(bkg, list):
h_subtotal = None
for dsid in bkg:
assert isinstance(dsid, str)
h_bkg = None
if dsid.isdigit():
## mc backgrounds
sp = bkgsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx,
bins=bins,
xtitle=xtitle,
ytitle=ytitle,
selection=selection,
weight=weight)
else:
## data-driven backgrounds
assert dsid == 'fakes' or dsid == 'efakes'
sp = datadrivensearchpath
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx,
bins=bins,
xtitle=xtitle,
ytitle=ytitle,
selection=selection,
weight=weight)
if h_bkg:
if h_subtotal:
h_subtotal.Add(h_bkg)
else:
h_subtotal = h_bkg.Clone()
if h_subtotal:
h_bkgs.append(h_subtotal)
dsid = bkg[
0] ## lists of combined backgrounds use the first dsid
n_bkgs.append(dsid)
else:
dsid = bkg
assert isinstance(dsid, str)
h_bkg = None
if dsid.isdigit():
## mc backgrounds
sp = bkgsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx,
bins=bins,
xtitle=xtitle,
ytitle=ytitle,
selection=selection,
weight=weight)
else:
## data-driven backgrounds
assert dsid == 'fakes' or dsid == 'efakes'
sp = datadrivensearchpath
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx,
bins=bins,
xtitle=xtitle,
ytitle=ytitle,
selection=selection,
weight=weight)
if h_bkg:
h_bkgs.append(h_bkg)
n_bkgs.append(dsid)
assert h_bkgs
## get signal histograms
h_sig = None
if sig:
dsid = sig
sp = sigsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_sig = ipyhep.tree.project(newx,
bins=bins,
xtitle=xtitle,
ytitle=ytitle,
selection=selection,
weight=weight)
assert h_sig
## scale background histograms
if h_bkgs:
assert len(h_bkgs) == len(n_bkgs), '%s\n%s' % (h_bkgs, n_bkgs)
for h, dsid in zip(h_bkgs, n_bkgs):
sf = ipyhep.sampleops.get_sf(dsid)
if dsid.isdigit():
sf *= lumi / __ntuple_lumi
h.Scale(sf)
## scale signal histogram
if h_sig:
sf = ipyhep.sampleops.get_sf(sig)
sf *= lumi / __ntuple_lumi
h_sig.Scale(sf)
## set systematic errors on backgrounds
if h_bkgs:
assert len(h_bkgs) == len(n_bkgs), '%s\n%s' % (h_bkgs, n_bkgs)
# HACK: systematics updated 2017-06-08 for the SUSY diphoton analysis
syst_fracs = dict()
syst_fracs['407013'] = 0.50 # #gamma#gamma
syst_fracs['361039'] = 0.50 # #gammaj+jj
syst_fracs['fakes'] = 0.50 # fakes
syst_fracs['efakes'] = 0.20 # efakes
syst_fracs['301890'] = 0.20 # W#gamma
syst_fracs['301899'] = 0.20 # Z#gamma
syst_fracs['407022'] = 0.27 # W#gamma#gamma
syst_fracs['407025'] = 0.45 # Z#gamma#gamma
syst_fracs['407028'] = 0.45 # Z#gamma#gamma
for h, dsid in zip(h_bkgs, n_bkgs):
nbinsx = h.GetNbinsX()
nbinsy = h.GetNbinsY()
for i_x in xrange(nbinsx):
for i_y in xrange(nbinsy):
c = h.GetBinContent(i_x, i_y)
e = h.GetBinError(i_x, i_y)
syst = syst_fracs.get(dsid, 0.0)
assert syst
h.SetBinError(i_x, i_y,
math.sqrt(e * e + c * c * syst * syst))
## total background
h_bkg_total = None
if h_bkgs:
for h_bkg in h_bkgs:
if h_bkg_total:
h_bkg_total.Add(h_bkg)
else:
h_bkg_total = h_bkg.Clone()
assert h_bkg_total
## significance scan
h_signif = Hist2D(*bins)
h_signif.SetTitle(';%s;%s' % (xtitle, ytitle))
nbinsx = h_signif.GetNbinsX()
nbinsy = h_signif.GetNbinsY()
for i_x in xrange(1, nbinsx + 1):
for i_y in xrange(1, nbinsy + 1):
sigma = ROOT.Double(0)
s = h_sig.Integral(i_x, nbinsx + 1, i_y, nbinsy + 1)
b = h_bkg_total.IntegralAndError(i_x, nbinsx + 1, i_y, nbinsy + 1,
sigma)
sigma = float(sigma)
# if b < 0.10: ## HACK: force background to be at least 0.10 events with 100% uncert.
# b = 0.10
# sigma = 0.10
if b < 0.20 and sigma < 0.20:
sigma = 0.20
za = _calculate_significance(s, b, sigma)
xval = h_signif.GetXaxis().GetBinLowEdge(i_x)
yval = h_signif.GetYaxis().GetBinLowEdge(i_y)
h_signif.Fill(xval, yval, za)
## make canvas
canvas = Canvas(800, 600)
canvas.SetRightMargin(0.17)
canvas.cd()
stuff['canvas'] = canvas
## draw
ROOT.gStyle.SetPaintTextFormat("3.1f")
h_signif.Draw('COLZ')
h_signif2 = h_signif.Clone()
h_signif2.SetMarkerSize(1.2)
h_signif2.SetMarkerColor(ipyhep.style.black)
h_signif2.Draw('TEXT SAME')
stuff['h_signif'] = h_signif
stuff['h_signif2'] = h_signif2
canvas.Update()
## save figures
if save:
ipyhep.file.save_figures(canvas, x, save)
return stuff