def main(args):
# Get tree with scan values from all input files
limit = plot.MakeTChain(args.files, 'limit')
# Write Tree entries in TGraph to get structure for likelihood database
graph = plot.TGraph2DFromTree(
limit, args.x_var, args.y_var, 'deltaNLL',
'quantileExpected > -0.5 && deltaNLL < 1000')
# 'quantileExpected > -0.5 && deltaNLL > 0 && deltaNLL < 1000')
# rezero the TGraph to have sensible values in the output file
min_delta_nll = rezero_tgraph2d(graph)
df = convert_graph_to_dataframe(graph)
df.to_csv(args.output,
sep=" ",
float_format="%.6f",
header=False,
index=False,
na_rep="NaN",
columns=[args.x_var, args.y_var, "deltaNLL"])
best = plot.TGraphFromTree(limit, args.x_var, args.y_var, 'deltaNLL == 0')
plot.RemoveGraphXDuplicates(best)
# Write back a root TTree with the modified contents.
outfile = ROOT.TFile(args.output.replace(".txt", ".root"), "recreate")
tree = convert_dataframe_to_tree(df, best, offset=min_delta_nll)
# Add the best fit values from the limit trees
outfile.Write()
return
def read(scan, param_x, param_y, file):
# print files
goodfiles = [f for f in [file] if plot.TFileIsGood(f)]
limit = plot.MakeTChain(goodfiles, 'limit')
graph = plot.TGraph2DFromTree(limit, param_x, param_y, '2*deltaNLL', 'quantileExpected > -0.5 && deltaNLL > 0')
best = plot.TGraphFromTree(limit, param_x, param_y, 'quantileExpected > -0.5 && deltaNLL == 0')
plot.RemoveGraphXDuplicates(best)
assert(best.GetN() == 1)
graph.SetName(scan)
best.SetName(scan+'_best')
# graph.Print()
return (graph, best)
### limit2, "CV", "CF", '2*deltaNLL', 'quantileExpected > -0.5 && deltaNLL > 0 && deltaNLL < 1000')
###best2 = plot.TGraphFromTree(
### limit2, "CV", "CF", 'deltaNLL == 0')
###plot.RemoveGraphXDuplicates(best2)
####hists2 = plot.TH2FromTGraph2D(graph2, method='BinCenterAligned')
###hists.append( plot.TH2FromTGraph2D(graph2, method='BinCenterAligned') )
####plot.fastFillTH2(hists2, graph,interpolateMissing=True)
###if args.bg_exp:
### limit_bg = plot.MakeTChain(args.bg_exp, 'limit')
### best_bg = plot.TGraphFromTree(
### limit_bg, "CV", "CF", 'deltaNLL == 0')
### plot.RemoveGraphXDuplicates(best_bg)
limit = plot.MakeTChain(args.files, 'limit')
print limit
graph = plot.TGraph2DFromTree(
limit, "CV", "CF", '2*deltaNLL',
'quantileExpected > -0.5 && deltaNLL > 0 && deltaNLL < 1000')
best = plot.TGraphFromTree(limit, "CV", "CF", 'deltaNLL == 0')
plot.RemoveGraphXDuplicates(best)
hists = plot.TH2FromTGraph2D(graph, method='BinCenterAligned')
plot.fastFillTH2(hists, graph, interpolateMissing=True)
if args.bg_exp:
limit_bg = plot.MakeTChain(args.bg_exp, 'limit')
best_bg = plot.TGraphFromTree(limit_bg, "CV", "CF", 'deltaNLL == 0')
plot.RemoveGraphXDuplicates(best_bg)
# If included just plot SM exp at 1,1
if args.sm_exp:
limit_sm = plot.MakeTChain(args.sm_exp, 'limit')
best_sm = ROOT.TGraph(1, array('d', [
1,
sigma_SM_HZZ = 290.58626
sigma_3_HZZ = 44.670158
sigma_13_int = 1937.15
sigma_13_intOnly = (sigma_13_int - 2. * sigma_SM_HTT) * math.sqrt(
sigma_3_HTT / sigma_SM_HTT)
print " sigma_intOnly= ", sigma_13_intOnly
# muV->muV*(sigma_SM_HTT)/( (1.-abs(CMS_zz4l_fai1))*sigma_SM_HTT + abs(CMS_zz4l_fai1)*sigma_SM_HZZ*sigma_3_HTT/sigma_3_HZZ + sign(CMS_zz4l_fai1)*math.sqrt((1-abs(CMS_zz4l_fai1))*abs(CMS_zz4l_fai1)*sigma_SM_HTT/sigma_3_HTT)*sigma_13_intOnly )
# "muV"->"muV*(%s)/( (1.-abs(CMS_zz4l_fai1))*%s + abs(CMS_zz4l_fai1)*%s*%s/%s + sign(CMS_zz4l_fai1)*math.sqrt((1-abs(CMS_zz4l_fai1))*abs(CMS_zz4l_fai1)*%s/%s)*%s )" % (sigma_SM_HTT,sigma_SM_HTT,sigma_SM_HZZ,sigma_3_HTT,sigma_3_HZZ,sigma_SM_HTT,sigma_3_HTT,sigma_13_intOnly)
limit = plot.MakeTChain(args.files, 'limit')
graph = plot.TGraph2DFromTree(
# limit, "muV", "CMS_zz4l_fai1", '2*deltaNLL', 'quantileExpected > -0.5 && deltaNLL > 0 && deltaNLL < 10000000000000000')
# limit, "muV", "CMS_zz4l_fai1", '2*deltaNLL', 'quantileExpected > -0.5')
limit,
"muV*( (1.-abs(CMS_zz4l_fai1))*%s + abs(CMS_zz4l_fai1)*%s*%s/%s + sign(CMS_zz4l_fai1)*sqrt((1-abs(CMS_zz4l_fai1))*abs(CMS_zz4l_fai1)*%s/%s)*%s )/(%s)"
% (sigma_SM_HTT, sigma_SM_HZZ, sigma_3_HTT, sigma_3_HZZ, sigma_SM_HTT,
sigma_3_HTT, sigma_13_intOnly, sigma_SM_HTT),
"CMS_zz4l_fai1",
'2*deltaNLL',
'quantileExpected > -0.5')
best = plot.TGraphFromTree(
# limit, "muV", "CMS_zz4l_fai1", 'deltaNLL == 0')
limit,
"muV*( (1.-abs(CMS_zz4l_fai1))*%s + abs(CMS_zz4l_fai1)*%s*%s/%s + sign(CMS_zz4l_fai1)*sqrt((1-abs(CMS_zz4l_fai1))*abs(CMS_zz4l_fai1)*%s/%s)*%s )/(%s)"
% (sigma_SM_HTT, sigma_SM_HZZ, sigma_3_HTT, sigma_3_HZZ, sigma_SM_HTT,
sigma_3_HTT, sigma_13_intOnly, sigma_SM_HTT),
"CMS_zz4l_fai1",
'deltaNLL == 0')
plot.RemoveGraphXDuplicates(best)
hists = plot.TH2FromTGraph2D(graph, method='BinCenterAligned')
plot.fastFillTH2(hists, graph, interpolateMissing=True)
#Create canvas and TH2D for each component
plot.ModTDRStyle(width=600, l=0.12)
ROOT.gStyle.SetNdivisions(510, 'XYZ')
plot.SetBirdPalette()
canv = ROOT.TCanvas(args.output, args.output)
pads = plot.OnePad()
if args.debug_output is not None:
debug = ROOT.TFile(args.debug_output, 'RECREATE')
else:
debug = None
limit = plot.MakeTChain(args.files, 'limit')
graph = plot.TGraph2DFromTree(
limit, "muV", "CMS_zz4l_fai1", '2*deltaNLL',
'quantileExpected > -0.5 && deltaNLL > 0 && deltaNLL < 1000')
best = plot.TGraphFromTree(limit, "muV", "CMS_zz4l_fai1", 'deltaNLL == 0')
plot.RemoveGraphXDuplicates(best)
hists = plot.TH2FromTGraph2D(graph, method='BinCenterAligned')
plot.fastFillTH2(hists, graph, interpolateMissing=True)
if args.bg_exp:
limit_bg = plot.MakeTChain(args.bg_exp, 'limit')
best_bg = plot.TGraphFromTree(limit_bg, "muV", "CMS_zz4l_fai1",
'deltaNLL == 0')
plot.RemoveGraphXDuplicates(best_bg)
# If included just plot SM exp at 1,1
if args.sm_exp:
limit_sm = plot.MakeTChain(args.sm_exp, 'limit')
best_sm = ROOT.TGraph(1, array('d', [
#Create canvas and TH2D for each component
plot.ModTDRStyle(width=600, l=0.12)
ROOT.gStyle.SetNdivisions(510, 'XYZ')
plot.SetBirdPalette()
canv = ROOT.TCanvas(args.output, args.output)
pads = plot.OnePad()
if args.debug_output is not None:
debug = ROOT.TFile(args.debug_output, 'RECREATE')
else:
debug = None
limit = plot.MakeTChain(args.files, 'limit')
if args.CPodd:
graph = plot.TGraph2DFromTree(
limit, "Yt_A", "Yb_A", '2*deltaNLL',
'quantileExpected > -0.5 && deltaNLL > 0 && deltaNLL < 1000')
best = plot.TGraphFromTree(limit, "Yt_A", "Yb_A", 'deltaNLL == 0')
else:
graph = plot.TGraph2DFromTree(
limit, "Yt_H", "Yb_H", '2*deltaNLL',
'quantileExpected > -0.5 && deltaNLL > 0 && deltaNLL < 1000')
best = plot.TGraphFromTree(limit, "Yt_H", "Yb_H", 'deltaNLL == 0')
plot.RemoveGraphXDuplicates(best)
hists = plot.TH2FromTGraph2D(graph, method='BinCenterAligned')
plot.fastFillTH2(hists, graph, interpolateMissing=True)
if args.bg_exp:
limit_bg = plot.MakeTChain(args.bg_exp, 'limit')
best_bg = plot.TGraphFromTree(limit_bg, "alpha", "muF", 'deltaNLL == 0')
plot.RemoveGraphXDuplicates(best_bg)
