def make_val_plots(self, sample_sets, bin_var, prefix, outdirpath):
val_histo_summary = []
var = Var(bin_var, self.settings.channel)
for sample_set in sample_sets:
val_histo = self.get_histo_for_val(sample_set, var)
val_histo_summary.append(val_histo)
descriptions = {
"plottype": "ProjectWork",
"xaxis": var.tex,
"channel": self.settings.channel,
"CoM": "13",
"lumi": "35.87",
"title": "ANTIISO1"
}
outfilepath = "{0}/{1}_{2}_val_{3}_{4}.png".format(
outdirpath, self.settings.channel, prefix, sample_set.name,
bin_var)
self.create_plot(val_histo, descriptions, outfilepath)
descriptions = {
"plottype": "ProjectWork",
"xaxis": var.tex,
"channel": self.settings.channel,
"CoM": "13",
"lumi": "35.87",
"title": "ANTIISO1"
}
outfileprefix = "{0}/{1}_{2}_val_{3}_{4}".format(
outdirpath, self.settings.channel, prefix, "inclusive", bin_var)
def make_classification_plots(self,
sample_sets,
bin_var,
prefix,
outdirpath,
inclusive=False):
fraction_histo_summary = {}
var = Var(bin_var, self.settings.channel)
for sample_set in sample_sets:
histos = self.get_histos_for_classification(sample_set, var)
fraction_histo_summary[sample_set.name] = histos
descriptions = {
"plottype": "Project Work",
"xaxis": var.tex,
"channel": self.settings.channel,
"CoM": "13",
"lumi": "41.5",
"title": "",
"yaxis": "Background Fractions (Classification)"
}
newoutpath = os.path.join(outdirpath, "classification")
try:
if not os.path.exists(newoutpath):
os.makedirs(newoutpath)
except OSError as e:
if e.errno != errno.EEXIST:
raise
outfile = "{0}/{1}_{2}_frac_{3}_{4}".format(
newoutpath, self.settings.channel, prefix, sample_set.name,
bin_var)
self.create_plot(histos, descriptions, "{0}.png".format(outfile))
self.create_normalized_plot(histos, descriptions,
"{0}_norm.png".format(outfile))
descriptions = {
"plottype": "Project Work",
"xaxis": var.tex,
"channel": self.settings.channel,
"CoM": "13",
"lumi": "41.5",
"title": "",
"yaxis": "Background Fractions (Classification)"
}
outfileprefix = "{0}/{1}_{2}_frac_{3}_{4}".format(
newoutpath, self.settings.channel, prefix, "inclusive", bin_var)
if inclusive:
inclusive_histos = self.get_inclusive(var, fraction_histo_summary)
self.create_normalized_plot(inclusive_histos, descriptions,
"{0}_norm.png".format(outfileprefix))
self.create_plot(inclusive_histos, descriptions,
"{0}.png".format(outfileprefix))
def make_distribution_plots(self,
sample_sets,
bin_var,
prefix,
outdirpath,
inclusive=False,
ylabel="NN Distribution"):
fraction_histo_summary = {}
var = Var(bin_var, self.settings.channel)
for sample_set in sample_sets:
histo = self.get_histo_for_distribution(sample_set, var)
fraction_histo_summary[sample_set.name] = histo
descriptions = {
"plottype": "Project Work",
"xaxis": var.tex,
"channel": self.settings.channel,
"CoM": "13",
"lumi": "41.5",
"title": "",
"yaxis": "Background Fractions (Classification)"
}
newoutpath = os.path.join(outdirpath, "distribution")
try:
if not os.path.exists(newoutpath):
os.makedirs(newoutpath)
except OSError as e:
if e.errno != errno.EEXIST:
raise
#outfile = "{0}/{1}_{2}_frac_{3}_{4}".format(newoutpath, self.settings.channel, prefix, sample_set.name, bin_var)
#self.create_plot(histos, descriptions, "{0}.png".format(outfile))
#self.create_normalized_plot(histos, descriptions, "{0}_norm.png".format(outfile))
descriptions = {
"plottype": "Project Work",
"xaxis": var.tex,
"channel": self.settings.channel,
"CoM": "13",
"lumi": "41.5",
"title": "",
"yaxis": "Normalized Event Count"
}
outfileprefix = "{0}/{1}_{2}_frac_{3}_{4}".format(
newoutpath, self.settings.channel, prefix, "inclusive", bin_var)
new_histos = {}
for histname in fraction_histo_summary:
new_histos[get_pretty_name(
histname)] = fraction_histo_summary[histname]
if inclusive:
#inclusive_histos = self.get_inclusive(var, fraction_histo_summary)
self.create_normalized_plot2(new_histos,
descriptions,
"{0}_norm.png".format(outfileprefix),
applyFracColors=True,
constrainLegend=False,
legendWidthMultiplier=2)
descriptions["yaxis"] = "Event Count"
self.create_plot2(new_histos,
descriptions,
"{0}.png".format(outfileprefix),
legend="outer",
applyFracColors=True,
constrainLegend=False,
legendWidthMultiplier=2)
merged_histos = self.merge_histos(new_histos)
self.create_plot2(merged_histos,
descriptions,
"{0}_merge.png".format(outfileprefix),
legend="inner",
applyFracColors=True,
constrainLegend=True,
legendWidthMultiplier=3)
descriptions["yaxis"] = "Normalized Event Count"
norm_yield_histos = self.normalize_yields(merged_histos)
self.create_plot2(norm_yield_histos,
descriptions,
"{0}_merge_norm.png".format(outfileprefix),
legend="inner",
applyFracColors=True,
constrainLegend=True,
legendWidthMultiplier=3)
self.create_transparent_plot(
norm_yield_histos,
descriptions,
"{0}_merge_norm_tr.png".format(outfileprefix),
legend="inner",
applyFracColors=True,
constrainLegend=True,
legendWidthMultiplier=3)
def makePlot(channel, variable, indir, era, dir, args, outdir="", syst=False):
var = Var(variable)
overlay = True
plottype = args.type
if plottype == "postfit":
plottype = "postfit_sb"
if "2016" in era: lumi = "35.9"
if "2017" in era: lumi = "41.5"
print "indir: {0}".format(indir)
print "outdir: {0}".format(outdir)
if indir and syst:
root_datacard = "/".join(
[indir, "2017_datacard_shapes_{0}.root".format(plottype)])
elif indir:
root_datacard = "/".join([
indir,
"htt_{0}.inputs-sm-Run{1}-{2}.root".format(channel, era, var.name)
])
else:
root_datacard = "{0}/htt_{1}.inputs-sm-Run{2}-{3}.root".format(
"_".join([era, "datacards"]), channel, era, var.name)
file = R.TFile(root_datacard)
for category in file.GetListOfKeys():
cat = category.GetName()
interesting_ones = {}
for h in [
"W", "VVT", "VVL", "VVJ", "TTT", "TTL", "TTJ", "ZTT", "ZL",
"ZJ", "QCD", "jetFakes", "jetFakes_W", "jetFakes_TT",
"jetFakes_QCD", "EMB", "data_obs", "ggH", "qqH"
]:
if h == "data_obs":
interesting_ones["data"] = copy.deepcopy(
file.Get("{0}/{1}".format(cat, h)))
else:
interesting_ones[h] = copy.deepcopy(
file.Get("{0}/{1}".format(cat, h)))
plots = [
("_def", [
"W", "VVT", "VVL", "VVJ", "TTT", "TTL", "TTJ", "ZTT", "ZL",
"ZJ", "QCD", "data"
]),
("_def_EMB", [
"W", "VVL", "VVJ", "TTL", "TTJ", "EMB", "ZL", "ZJ", "QCD",
"data"
]),
("_ff",
["VVT", "VVL", "TTT", "TTL", "ZTT", "ZL", "jetFakes", "data"]),
("_ff_split", [
"VVT", "VVL", "TTT", "TTL", "ZTT", "ZL", "jetFakes_W",
"jetFakes_TT", "jetFakes_QCD", "data"
]),
("_ff_EMB_split", [
"VVL", "TTL", "ZL", "EMB", "jetFakes_W", "jetFakes_TT",
"jetFakes_QCD", "data"
]), ("_ff_EMB", ["VVL", "TTL", "EMB", "ZL", "jetFakes", "data"])
]
print "indir: {0}".format(indir)
print "outdir: {0}".format(outdir)
for p in plots:
histos = {}
plot = True
for h in p[1]:
if type(interesting_ones[h]) == R.TObject: plot = False
histos[h] = interesting_ones[h]
if not outdir:
outdir = "_".join([era, "plots/"])
if plot and overlay:
textlines = collect(dir, args, plottype)
pl.plot(histos,
canvas="linear",
signal=[],
descriptions={
"plottype": "Project Work",
"xaxis": var.tex,
"channel": channel,
"CoM": "13",
"lumi": lumi
},
outfile=outdir + "/" + cat + "_" + var.name + p[0] +
".png",
era=era,
overlay=textlines)
pl.plot(histos,
canvas="log",
signal=[],
descriptions={
"plottype": "Project Work",
"xaxis": var.tex,
"channel": channel,
"CoM": "13",
"lumi": lumi
},
outfile=outdir + "/" + cat + "_" + var.name + p[0] +
".png",
era=era,
overlay=textlines)
pl.plot(histos,
canvas="semi",
signal=[],
descriptions={
"plottype": "Project Work",
"xaxis": var.tex,
"channel": channel,
"CoM": "13",
"lumi": lumi
},
outfile=outdir + "/" + cat + "_" + var.name + p[0] +
".png",
era=era,
overlay=textlines)
elif plot:
pl.plot(histos,
canvas="linear",
signal=[],
descriptions={
"plottype": "Project Work",
"xaxis": var.tex,
"channel": channel,
"CoM": "13",
"lumi": lumi
},
outfile=outdir + "/" + cat + "_" + var.name + p[0] +
".png",
era=era)
pl.plot(histos,
canvas="log",
signal=[],
descriptions={
"plottype": "Project Work",
"xaxis": var.tex,
"channel": channel,
"CoM": "13",
"lumi": lumi
},
outfile=outdir + "/" + cat + "_" + var.name + p[0] +
".png",
era=era)
pl.plot(histos,
canvas="semi",
signal=[],
descriptions={
"plottype": "Project Work",
"xaxis": var.tex,
"channel": channel,
"CoM": "13",
"lumi": lumi
},
outfile=outdir + "/" + cat + "_" + var.name + p[0] +
".png",
era=era)
# if plot and overlay:
# textlines = collect(dir, args, plottype)
# pl.plot(histos, canvas="linear", signal = [], descriptions = {"plottype": "Project Work", "xaxis":var.tex, "channel":channel,"CoM": "13", "lumi":lumi }, outfile = outdir +"/"+ cat+"_"+var.name + p[0] +".png")
# pl.plot(histos, canvas="log", signal = [], descriptions = {"plottype": "ProjectWork", "xaxis":var.tex, "channel":channel,"CoM": "13", "lumi":lumi }, outfile = outdir +"/"+ cat+"_"+var.name + p[0] +".png")
# pl.plot(histos, canvas="semi", signal = [], descriptions = {"plottype": "ProjectWork", "xaxis":var.tex, "channel":channel,"CoM": "13", "lumi":lumi }, outfile = outdir +"/"+ cat+"_"+var.name + p[0] +".png")
# elif plot:
# pl.plot(histos, canvas="linear", signal = [], descriptions = {"plottype": "Project Work", "xaxis":var.tex, "channel":channel,"CoM": "13", "lumi":lumi }, outfile = outdir +"/"+ cat+"_"+var.name + p[0] +".png")
# pl.plot(histos, canvas="log", signal = [], descriptions = {"plottype": "ProjectWork", "xaxis":var.tex, "channel":channel,"CoM": "13", "lumi":lumi }, outfile = outdir +"/"+ cat+"_"+var.name + p[0] +".png")
# pl.plot(histos, canvas="semi", signal = [], descriptions = {"plottype": "ProjectWork", "xaxis":var.tex, "channel":channel,"CoM": "13", "lumi":lumi }, outfile = outdir +"/"+ cat+"_"+var.name + p[0] +".png")
file.Close()