method=args.bin_method,
force_x_width=args.force_x_width,
force_y_width=args.force_y_width)
h_axis = h_proto
h_axis = plot.TH2FromTGraph2D(graphs[types[0]])
# Create the debug output file if requested
if args.debug_output is not None:
debug = ROOT.TFile(args.debug_output, 'RECREATE')
else:
debug = None
# Fill TH2s by interpolating the TGraph2Ds, then extract contours
for c in types:
print 'Filling histo for %s' % c
hists[c] = h_proto.Clone(c)
plot.fillTH2(hists[c], graphs[c])
contours[c] = plot.contourFromTH2(hists[c], CL, 5, frameValue=1)
if debug is not None:
debug.WriteTObject(hists[c], 'hist_%s' % c)
for i, cont in enumerate(contours[c]):
debug.WriteTObject(cont, 'cont_%s_%i' % (c, i))
# Setup the canvas: we'll use a two pad split, with a small top pad to contain
# the CMS logo and the legend
canv = ROOT.TCanvas(args.output, args.output)
pads = plot.TwoPadSplit(0.8, 0, 0)
pads[1].cd()
h_axis.GetXaxis().SetTitle(args.x_title)
h_axis.GetYaxis().SetTitle(args.y_title)
h_axis.Draw()
hists = {}
conts68 = {}
conts95 = {}
outfile = ROOT.TFile(args.output + ".root", "RECREATE")
for scan in order:
if scan not in infiles:
continue
graphs[scan], bestfits[scan] = read(scan, SETTINGS[scan]["xvar"], SETTINGS[scan]["yvar"], infiles[scan])
outfile.WriteTObject(graphs[scan], scan + "_graph")
outfile.WriteTObject(bestfits[scan])
# hists[scan] = makeHist(
# scan + '_hist', 40 * SETTINGS[scan]['multi'] * args.multi, graph_test)
hists[scan] = plot.TH2FromTGraph2D(graphs[scan], method="BinCenterAligned")
plot.fillTH2(hists[scan], graphs[scan])
# fillTH2(hists[scan], graphs[scan])
outfile.WriteTObject(hists[scan], hists[scan].GetName() + "_input")
fixZeros(hists[scan])
outfile.WriteTObject(hists[scan], hists[scan].GetName() + "_processed")
conts68[scan] = plot.contourFromTH2(hists[scan], ROOT.Math.chisquared_quantile_c(1 - 0.68, 2), frameValue=10)
if scan in ["cms", "atlas", "comb"]:
conts95[scan] = plot.contourFromTH2(hists[scan], ROOT.Math.chisquared_quantile_c(1 - 0.95, 2))
for i, c in enumerate(conts68[scan]):
c.SetName("graph68_%s_%i" % (scan, i))
if args.thin > 1:
newgr = ROOT.TGraph(c.GetN() / args.thin)
needLast = True
for a, p in enumerate(range(0, c.GetN(), args.thin)):
if p == c.GetN() - 1:
needLast = False
legend = ROOT.TLegend(0.14, 0.53, 0.35, 0.74, '', 'NBNDC')
graphs = {}
bestfits = {}
hists = {}
conts68 = {}
conts95 = {}
outfile = ROOT.TFile(args.output + '.root', 'RECREATE')
order = ['default']
for scan in order:
graphs[scan], bestfits[scan] = read(scan, args.x_axis, args.y_axis, infile)
outfile.WriteTObject(graphs[scan], scan + '_graph')
outfile.WriteTObject(bestfits[scan])
hists[scan] = axis.Clone(scan + '_hist')
plot.fillTH2(hists[scan], graphs[scan])
outfile.WriteTObject(hists[scan], hists[scan].GetName() + '_input')
fixZeros(hists[scan])
hists[scan].GetZaxis().SetTitle('-2 #Delta ln #Lambda(%s,%s)' %
(x_axis, y_axis))
# hists[scan].GetZaxis().SetTitleOffset(0)
hists[scan].SetContour(255)
hists[scan].Draw('COLZSAME')
hists[scan].SetMinimum(0)
hists[scan].SetMaximum(20)
outfile.WriteTObject(hists[scan], hists[scan].GetName() + '_processed')
conts68[scan] = plot.contourFromTH2(
hists[scan], ROOT.Math.chisquared_quantile_c(1 - 0.68, 2))
conts95[scan] = plot.contourFromTH2(
hists[scan], ROOT.Math.chisquared_quantile_c(1 - 0.95, 2))
for i, c in enumerate(conts68[scan]):
#h_obs = plot.makeVarBinHist2D("h_obs", mA_list, tanb_list)
#h_minus1sigma = plot.makeVarBinHist2D("h_minus1sigma", mA_list, tanb_list)
#h_plus1sigma = plot.makeVarBinHist2D("h_plus1sigma", mA_list, tanb_list)
#h_minus2sigma = plot.makeVarBinHist2D("h_minus2sigma", mA_list, tanb_list)
#h_plus2sigma = plot.makeVarBinHist2D("h_plus2sigma", mA_list, tanb_list)
h_exp = plot.makeHist2D("h_exp", mA_bins, tanb_bins, graph_exp)
h_obs = plot.makeHist2D("h_obs", mA_bins, tanb_bins, graph_obs)
h_minus1sigma = plot.makeHist2D("h_minus1sigma", mA_bins, tanb_bins,
graph_minus1sigma)
h_plus1sigma = plot.makeHist2D("h_plus1sigma", mA_bins, tanb_bins,
graph_plus1sigma)
h_minus2sigma = plot.makeHist2D("h_minus2sigma", mA_bins, tanb_bins,
graph_minus2sigma)
h_plus2sigma = plot.makeHist2D("h_plus2sigma", mA_bins, tanb_bins,
graph_plus2sigma)
plot.fillTH2(h_exp, graph_exp)
plot.fillTH2(h_obs, graph_obs)
plot.fillTH2(h_minus1sigma, graph_minus1sigma)
plot.fillTH2(h_plus1sigma, graph_plus1sigma)
plot.fillTH2(h_minus2sigma, graph_minus2sigma)
plot.fillTH2(h_plus2sigma, graph_plus2sigma)
axis.Draw()
#Possibility to draw CLs heat map, would be a useful option, using e.g.
#h_exp.Draw("colzsame")
#Extract exclusion contours from the TH2Ds, use threshold 1.0 for limit and 0.05 for CLs
threshold = 0.05
#threshold=1
cont_exp = plot.contourFromTH2(h_exp, threshold, 20)
cont_obs = plot.contourFromTH2(h_obs, threshold, 5)
cont_minus1sigma = plot.contourFromTH2(h_minus1sigma, threshold, 20)
h_proto = plot.TH2FromTGraph2D(
graphs[types[0]], method=args.bin_method, force_x_width=args.force_x_width, force_y_width=args.force_y_width
)
h_axis = h_proto
h_axis = plot.TH2FromTGraph2D(graphs[types[0]])
# Create the debug output file if requested
if args.debug_output is not None:
debug = ROOT.TFile(args.debug_output, "RECREATE")
else:
debug = None
# Fill TH2s by interpolating the TGraph2Ds, then extract contours
for c in types:
print "Filling histo for %s" % c
hists[c] = h_proto.Clone(c)
plot.fillTH2(hists[c], graphs[c])
contours[c] = plot.contourFromTH2(hists[c], CL, 5, frameValue=1)
if debug is not None:
debug.WriteTObject(hists[c], "hist_%s" % c)
for i, cont in enumerate(contours[c]):
debug.WriteTObject(cont, "cont_%s_%i" % (c, i))
# Setup the canvas: we'll use a two pad split, with a small top pad to contain
# the CMS logo and the legend
canv = ROOT.TCanvas(args.output, args.output)
pads = plot.TwoPadSplit(0.8, 0, 0)
pads[1].cd()
h_axis.GetXaxis().SetTitle(args.x_title)
h_axis.GetYaxis().SetTitle(args.y_title)
h_axis.Draw()
#Note the binning of the TH2D for the interpolation should ~ match the initial input grid
#Could in future implement variable binning here
#h_exp = plot.makeVarBinHist2D("h_exp", mA_list, tanb_list)
#h_obs = plot.makeVarBinHist2D("h_obs", mA_list, tanb_list)
#h_minus1sigma = plot.makeVarBinHist2D("h_minus1sigma", mA_list, tanb_list)
#h_plus1sigma = plot.makeVarBinHist2D("h_plus1sigma", mA_list, tanb_list)
#h_minus2sigma = plot.makeVarBinHist2D("h_minus2sigma", mA_list, tanb_list)
#h_plus2sigma = plot.makeVarBinHist2D("h_plus2sigma", mA_list, tanb_list)
h_exp = plot.makeHist2D("h_exp", mA_bins, tanb_bins, graph_exp)
h_obs = plot.makeHist2D("h_obs", mA_bins, tanb_bins, graph_obs)
h_minus1sigma = plot.makeHist2D("h_minus1sigma", mA_bins, tanb_bins, graph_minus1sigma)
h_plus1sigma = plot.makeHist2D("h_plus1sigma", mA_bins, tanb_bins, graph_plus1sigma)
h_minus2sigma = plot.makeHist2D("h_minus2sigma", mA_bins, tanb_bins, graph_minus2sigma)
h_plus2sigma = plot.makeHist2D("h_plus2sigma", mA_bins, tanb_bins, graph_plus2sigma)
plot.fillTH2(h_exp, graph_exp)
plot.fillTH2(h_obs, graph_obs)
plot.fillTH2(h_minus1sigma, graph_minus1sigma)
plot.fillTH2(h_plus1sigma, graph_plus1sigma)
plot.fillTH2(h_minus2sigma, graph_minus2sigma)
plot.fillTH2(h_plus2sigma, graph_plus2sigma)
axis.Draw()
#Possibility to draw CLs heat map, would be a useful option, using e.g.
#h_exp.Draw("colzsame")
#Extract exclusion contours from the TH2Ds, use threshold 1.0 for limit and 0.05 for CLs
threshold=0.05
#threshold=1
cont_exp = plot.contourFromTH2(h_exp, threshold, 20)
cont_obs = plot.contourFromTH2(h_obs, threshold, 5)
cont_minus1sigma = plot.contourFromTH2(h_minus1sigma, threshold, 20)
