def read(scan, param, files, chop, remove_near_min, rezero,
remove_delta=None, improve=False, remove_dups=True):
# print files
goodfiles = [f for f in files if plot.TFileIsGood(f)]
limit = plot.MakeTChain(goodfiles, 'limit')
graph = plot.TGraphFromTree(
limit, param, '2*%s' % DELTANLL, 'quantileExpected > -1.5')
# print 'INPUT'
# graph.Print()
graph.SetName(scan)
graph.Sort()
if remove_dups:
plot.RemoveGraphXDuplicates(graph)
if remove_delta is not None:
plot.RemoveSmallDelta(graph, remove_delta)
plot.RemoveGraphYAbove(graph, chop)
plot.ReZeroTGraph(graph, rezero)
if remove_near_min is not None:
plot.RemoveNearMin(graph, remove_near_min)
if improve:
global NAMECOUNTER
spline = ROOT.TSpline3("spline3", graph)
func = ROOT.TF1('splinefn' + str(NAMECOUNTER), partial(Eval, spline),
graph.GetX()[0], graph.GetX()[graph.GetN() - 1], 1)
func.SetNpx(NPX)
NAMECOUNTER += 1
plot.ImproveMinimum(graph, func, True)
# graph.Print()
if FILTER is not None:
plot.FilterGraph(graph, FILTER)
if REMOVE_X_RANGES is not None:
for remove_x in REMOVE_X_RANGES:
plot.RemoveInXRange(graph, remove_x[0], remove_x[1])
return graph
def read(scan,
param,
files,
chop,
remove_near_min,
rezero,
remove_delta=None,
improve=False):
# print files
goodfiles = [f for f in files if plot.TFileIsGood(f)]
limit = plot.MakeTChain(goodfiles, 'limit')
# require quantileExpected > -0.5 to avoid the final point which is always committed twice
# (even if the fit fails)
graph = plot.TGraphFromTree(limit, param, '2*deltaNLL',
'quantileExpected > -0.5')
graph.SetName(scan)
graph.Sort()
plot.RemoveGraphXDuplicates(graph)
if remove_delta is not None: plot.RemoveSmallDelta(graph, remove_delta)
plot.RemoveGraphYAbove(graph, chop)
plot.ReZeroTGraph(graph, rezero)
if remove_near_min is not None: plot.RemoveNearMin(graph, remove_near_min)
if improve:
global NAMECOUNTER
spline = ROOT.TSpline3("spline3", graph)
func = ROOT.TF1('splinefn' + str(NAMECOUNTER), partial(Eval, spline),
graph.GetX()[0],
graph.GetX()[graph.GetN() - 1], 1)
NAMECOUNTER += 1
plot.ImproveMinimum(graph, func, True)
# graph.Print()
return graph
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, files, ycut):
goodfiles = [f for f in files if plot.TFileIsGood(f)]
limit = plot.MakeTChain(goodfiles, 'limit')
graph = plot.TGraphFromTree(limit, param, '2*deltaNLL', 'quantileExpected > -1.5')
graph.SetName(scan)
graph.Sort()
plot.RemoveGraphXDuplicates(graph)
plot.RemoveGraphYAbove(graph, ycut)
# graph.Print()
return graph
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)
def read(scan, param, other_param, files, remove_dups=True):
# print files
goodfiles = [f for f in files if plot.TFileIsGood(f)]
limit = plot.MakeTChain(goodfiles, 'limit')
graph = plot.TGraphFromTree(limit, param, other_param,
'quantileExpected > -0.5')
# print 'INPUT'
# graph.Print()
graph.SetName(scan)
graph.Sort()
if remove_dups:
plot.RemoveGraphXDuplicates(graph)
# graph.Print()
return graph
def ReadScanFromTFiles(self,
filenames,
param_name,
tree_selection='quantileExpected > -1.5'):
# TODO: should report bad files here
goodfiles = [f for f in filenames if plotting.TFileIsGood(f)]
if len(goodfiles) == 0:
raise RuntimeError('[ReadScanFromTFiles] no valid TFiles')
limit = plotting.MakeTChain(goodfiles, 'limit')
graph = plotting.TGraphFromTree(limit, param_name, '2*deltaNLL',
tree_selection)
# graph.SetName(label)
graph.Sort()
if self.verbosity >= 2:
print '[ReadScanFromTFiles] Produced TGraph:'
graph.Print()
return graph
####limit2 = plot.MakeTChain(['new/higgsCombineCvCf.MultiDimFit.mH125.root',], 'limit')
###limit2 = plot.MakeTChain([args.files[1],], 'limit')
###graph2 = plot.TGraph2DFromTree(
### 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:
LineColor=ROOT.kBlack,
LineWidth=2,
FillStyle=1001)
gr.Draw(fillstyle)
gr.Draw("LSAME")
# We just want the top pad to look like a box, so set all the text and tick
# sizes to zero
# Draw the legend in the top TPad
legend = plot.PositionedLegend(0.3, 0.2, 3, 0.015)
legend.AddEntry(contours68['obs'][0], "68% CL", "F")
legend.AddEntry(contours95['obs'][0], "95% CL", "F")
#Add best fit value (From MultiDimFit)
if args.best_fit:
limit_best = plot.MakeTChain(args.best_fit, 'limit')
best = plot.TGraphFromTree(limit_best, "r_ggH", "r_bbH", 'deltaNLL == 0')
best.SetMarkerStyle(34)
best.SetMarkerSize(3)
best.Draw("P SAME")
legend.AddEntry(best, "Best fit", "P")
if args.mass:
legend.SetHeader("m_{#phi} = " + args.mass + " GeV")
legend.Draw("SAME")
# Draw logos and titles
plot.DrawCMSLogo(pads[0], 'CMS', args.cms_sub, 11, 0.045, 0.035, 1.2, '', 1.0)
plot.DrawTitle(pads[0], args.title_right, 3)
plot.DrawTitle(pads[0], args.title_left, 1)
plot.FixOverlay()
