def make_systematics_histos(var, cuts, cuts_antiiso, systematics, outdir="/".join([os.environ["STPOL_DIR"], "lqetafit", "histos"]), indir="/".join([os.environ["STPOL_DIR"], "step3_latest"]), channel="mu", coupling="powheg", binning=None, plot_range=None, asymmetry=None, mtmetcut=None):
#logging.basicConfig(level="INFO")
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
logging.debug('This message should appear on the console')
#print "outdir", outdir
#system.exit(1)
try:
shutil.rmtree(outdir)
except OSError:
logging.warning("Couldn't remove directory %s" % outdir)
mkdir_p(outdir)
for main_syst, sub_systs in systematics.items():
systname = main_syst
if systname == "partial":
for sub_syst, updown in sub_systs.items():
for (k, v) in updown.items():
ss = {}
ss[k] = v
make_histos_for_syst(var, sub_syst, ss, cuts, cuts_antiiso, outdir, indir, channel, coupling=coupling, binning=binning, plot_range=plot_range, asymmetry=asymmetry, mtmetcut=mtmetcut)
elif systname != "nominal":
for sub_syst, path in sub_systs.items():
ss = {}
ss[sub_syst] = path
make_histos_for_syst(var, systname, ss, cuts, cuts_antiiso, outdir, indir, channel, coupling=coupling, binning=binning, plot_range=plot_range, asymmetry=asymmetry, mtmetcut=mtmetcut)
else:
make_histos_for_syst(var, systname, sub_systs, cuts, cuts_antiiso, outdir, indir, channel, coupling=coupling, binning=binning, plot_range=plot_range, asymmetry=asymmetry, mtmetcut=mtmetcut)
add_histos(outdir, var, channel, "mva" in cuts, mtmetcut)
def make_histos(cut_name, cut, samples, out_dir):
samp_out_dir = "/".join((out_dir, cut_name))
mkdir_p(samp_out_dir)
for s in samples:
fi = ROOT.TFile(samp_out_dir + "/WJets_flavour_fracs__%s" % s, "RECREATE")
counts = get_hf_frac(s, cut)
count_list = counts.keys()
fi.cd()
hi = Hist(len(count_list), 0, len(count_list) - 1, type="f", name="flavour_counts")
i = 1
for count in count_list:
hi.SetBinContent(i, counts[count])
hi.SetBinError(i, math.sqrt(counts[count]))
hi.GetXaxis().SetBinLabel(i, count)
i += 1
hi.Sumw2()
# Normalize to according to all events
hi.Scale(10000.0 / hi.GetBinContent(i - 1))
fi.cd()
hi.SetDirectory(fi)
hi.Write()
logging.info("Wrote file %s" % fi.GetPath())
fi.Close()
return
def plot_sherpa_vs_madgraph(var, cut_name, cut, samples, out_dir, recreate=False, **kwargs):
hname = var["varname"]
out_dir = out_dir + "/" + cut_name
if recreate and os.path.exists(out_dir):
logger.info("Output directory %s exists, removing" % out_dir)
shutil.rmtree(out_dir)
mkdir_p(out_dir)
logger.info("Using output directory %s" % out_dir)
logger.info("Using output directory %s" % out_dir)
coll = data_mc(var["var"], cut_name, cut, Weights.total()*Weights.mu, samples, out_dir, recreate, LUMI_TOTAL, reweight_madgraph=True, flavour_split=True, plot_range=var["range"], **kwargs)
logging.debug(str(coll.hists))
for hn, hist in coll.hists.items():
sample_name = coll.metadata[hn].sample_name
process_name = coll.metadata[hn].process_name
match = re.match(".*/cut__flavour__(W_[Hl][Hl])/.*", hn)
if match:
flavour_scenario = match.group(1)
else:
flavour_scenario = None
try:
if sample_types.is_mc(sample_name):
Styling.mc_style(hist, process_name)
else:
Styling.data_style(hist)
except KeyError as e:
logger.warning("Couldn't style histogram %s" % hn)
if flavour_scenario:
logger.debug("Matched flavour split histogram %s, %s" % (hn, flavour_scenario))
#Styling.mc_style(hist, process_name)
if re.match("W_H[lH]", flavour_scenario):
logger.debug("Changing colour of %s" % (hn))
hist.SetFillColor(hist.GetFillColor()+1)
hist.SetLineColor(hist.GetLineColor()+1)
logger.debug("pre merge: %s" % str([ (hn, coll.hists[hn].GetLineColor()) for hn in coll.hists.keys() if "sherpa" in hn]))
merges = dict()
merge_cmds = get_merge_cmds()
merge_cmds.pop("WJets")
merges["madgraph/unweighted"] = merge_cmds.copy()
merges["madgraph/weighted"] = merge_cmds.copy()
merges["sherpa/unweighted"] = merge_cmds.copy()
merges["sherpa/weighted"] = merge_cmds.copy()
merges["sherpa/unweighted"]["WJets_hf"] = ["weight__nominal/cut__flavour__W_heavy/WJets_sherpa_nominal"]
merges["sherpa/unweighted"]["WJets_lf"] = ["weight__nominal/cut__flavour__W_light/WJets_sherpa_nominal"]
merges["sherpa/weighted"]["WJets_hf"] = ["weight__sherpa_flavour/cut__flavour__W_heavy/WJets_sherpa_nominal"]
merges["sherpa/weighted"]["WJets_lf"] = ["weight__sherpa_flavour/cut__flavour__W_light/WJets_sherpa_nominal"]
merges["madgraph/unweighted"]["WJets_hf"] = ["weight__nominal/cut__flavour__W_heavy/W[1-4]Jets_exclusive"]
merges["madgraph/unweighted"]["WJets_lf"] = ["weight__nominal/cut__flavour__W_light/W[1-4]Jets_exclusive"]
merges["madgraph/weighted"]["WJets_hf"] = ["weight__reweight_madgraph/cut__flavour__W_heavy/W[1-4]Jets_exclusive"]
merges["madgraph/weighted"]["WJets_lf"] = ["weight__reweight_madgraph/cut__flavour__W_light/W[1-4]Jets_exclusive"]
hmerged = dict()
for k in merges.keys():
hmerged[k] = merge_hists(copy.deepcopy(coll.hists), merges[k])
logger.debug("post merge: %s" % str([ (hn, hmerged["sherpa/weighted"][hn].GetLineColor()) for hn in hmerged["sherpa/weighted"].keys()]))
#w_mg_sh = 1.0416259307303726 #sherpa to madgraph ratio
w_mg_sh = 1.0821535639376414
hmerged["sherpa/weighted"]["WJets_hf"].Scale(w_mg_sh)
hmerged["sherpa/weighted"]["WJets_lf"].Scale(w_mg_sh)
logger.info("Drawing madgraph unweighted plot")
canv = ROOT.TCanvas("c2", "c2")
suffix = "__%s__%s" % (var["var"], cut_name)
suffix = escape(suffix)
plot(canv, "madgraph_unw"+suffix, hmerged["madgraph/unweighted"], out_dir, **kwargs)
kwargs = dict({"x_label": var["varname"]}, **kwargs)
for k, v in hmerged.items():
logger.debug("Group %s" % k)
for hn, h in v.items():
logger.debug("Sample %s = %.2f" % (hn, h.Integral()))
logger.info("%s data=%.2f" % (k, v["data"].Integral()))
logger.info("%s MC=%.2f" % (k, sum([h.Integral() for k, h in v.items() if k!="data"])))
hists_flavours_merged = dict()
hists_flavours_merged["madgraph/weighted"] = merge_hists(hmerged["madgraph/weighted"], {"WJets": ["WJets_hf", "WJets_lf"]})
hists_flavours_merged["madgraph/unweighted"] = merge_hists(hmerged["madgraph/unweighted"], {"WJets": ["WJets_hf", "WJets_lf"]})
hists_flavours_merged["sherpa/unweighted"] = merge_hists(hmerged["sherpa/unweighted"], {"WJets": ["WJets_hf", "WJets_lf"]})
hists_flavours_merged["sherpa/weighted"] = merge_hists(hmerged["sherpa/weighted"], {"WJets": ["WJets_hf", "WJets_lf"]})
logger.info("Drawing sherpa weighted plot")
canv = ROOT.TCanvas("c1", "c1")
plot(canv, "sherpa_rew"+suffix, hmerged["sherpa/weighted"], out_dir, **kwargs)
logger.info("Drawing sherpa unweighted plot")
canv = ROOT.TCanvas("c1", "c1")
plot(canv, "sherpa_unw"+suffix, hmerged["sherpa/unweighted"], out_dir, **kwargs)
logger.info("Drawing madgraph plot")
canv = ROOT.TCanvas("c2", "c2")
plot(canv, "madgraph_rew"+suffix, hmerged["madgraph/weighted"], out_dir, **kwargs)
total_madgraph = copy.deepcopy(hmerged["madgraph/unweighted"])
merged_colls = dict()
for k, v in hmerged.items():
merged_colls[k] = HistCollection(copy.deepcopy(v), name=k)
logger.info("Drawing sherpa vs. madgraph shape comparison plots")
hists = [
("sherpa unw hf", hmerged["sherpa/unweighted"]["WJets_hf"]),
("sherpa rew hf", hmerged["sherpa/weighted"]["WJets_hf"]),
("madgraph unw hf", hmerged["madgraph/unweighted"]["WJets_hf"]),
("madgraph rew hf", hmerged["madgraph/weighted"]["WJets_hf"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
canv.SaveAs(out_dir + "/weighted_flavour_hf_%s.png" % hname)
canv.Close()
hists = [
("data", hmerged["madgraph/unweighted"]["data"]),
("sherpa", hists_flavours_merged["sherpa/unweighted"]["WJets"]),
("madgraph", hists_flavours_merged["madgraph/unweighted"]["WJets"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
canv.SaveAs(out_dir + "/unweighted_sherpa_mg_%s.png" % hname)
canv.Close()
hists = [
("data", hmerged["madgraph/unweighted"]["data"]),
("sherpa", hists_flavours_merged["sherpa/weighted"]["WJets"]),
("madgraph", hists_flavours_merged["madgraph/weighted"]["WJets"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
canv.SaveAs(out_dir + "/weighted_sherpa_mg_%s.png" % hname)
canv.Close()
hists = [
("sherpa unw lf", hmerged["sherpa/unweighted"]["WJets_lf"]),
("sherpa rew lf", hmerged["sherpa/weighted"]["WJets_lf"]),
("madgraph unw lf", hmerged["madgraph/unweighted"]["WJets_lf"]),
("madgraph rew lf", hmerged["madgraph/weighted"]["WJets_lf"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
canv.SaveAs(out_dir + "/weighted_flavour_lf_%s.png" % hname)
canv.Close()
hists = [
("data", hmerged["madgraph/unweighted"]["data"]),
("madgraph unw", hists_flavours_merged["madgraph/unweighted"]["WJets"]),
("madgraph rew", hists_flavours_merged["madgraph/weighted"]["WJets"]),
("sherpa unw", hists_flavours_merged["sherpa/unweighted"]["WJets"]),
("sherpa rew", hists_flavours_merged["sherpa/weighted"]["WJets"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
hists[0].SetTitle("")
canv.Update()
canv.SaveAs(out_dir + "/shapes_%s.png" % hname)
canv.Close()
# hists = [
# ("sherpa hf", hmerged["sherpa"]["WJets_hf"]),
# ("madgraph unw hf", hmerged["madgraph/unweighted"]["WJets_hf"]),
# ("madgraph rew hf", hmerged["madgraph/weighted"]["WJets_hf"]),
# ]
# hists = copy.deepcopy(hists)
# for hn, h in hists:
# h.SetTitle(hn + " %.2f" % h.Integral())
# h.Scale(1.0/h.Integral())
# hists = [h[1] for h in hists]
# ColorStyleGen.style_hists(hists)
# canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
# leg = legend(hists, styles=["f", "f"], **kwargs)
# hists[0].SetTitle("madgraph sherpa rew hf")
# canv.SaveAs(out_dir + "/shapes_hf_%s.png" % hname)
# canv.Close()
return coll, merged_colls
def plot_ratios(cut_name, cut, samples, out_dir, recreate, flavour_scenario=flavour_scenarios[0]):
out_dir += "/" + cut_name
mkdir_p(out_dir)
colls = dict()
samples_WJets = filter(lambda x: sample_types.is_wjets(x.name), samples)
for sc in flavour_scenario:
logger.info("Drawing ratio with cut %s" % sc)
cut_ = cut*getattr(Cuts, sc)
colls[sc] = data_mc(costheta["var"], cut_name + "__" + sc, cut_, Weights.total()*Weights.mu, samples_WJets, out_dir, recreate, LUMI_TOTAL, plot_range=costheta["range"])
logger.debug(colls[flavour_scenario[0]].hists["weight__nominal/cut__all/WJets_sherpa_nominal"].Integral())
logger.debug(colls[flavour_scenario[1]].hists["weight__nominal/cut__all/WJets_sherpa_nominal"].Integral())
coll = dict()
for k, c in colls.items():
for hn, h in c.hists.items():
coll[hn + "/" + k] = h
for k, h in coll.items():
logger.debug("%s = %s" % (k, str([y for y in h.y()])))
logger.debug(coll)
#coll = HistCollection(coll, name=cut_name)
merges = {}
for sc in flavour_scenario:
merges["madgraph/%s" % sc] = ["weight__nominal/cut__all/W[1-4]Jets_exclusive/%s" % sc]
merges["sherpa/unweighted/%s" % sc] = ["weight__nominal/cut__all/WJets_sherpa_nominal/%s" % sc]
merges["sherpa/weighted/%s" % sc] = ["weight__sherpa_flavour/cut__all/WJets_sherpa_nominal/%s" % sc]
merged = merge_hists(coll, merges)
for k, h in merged.items():
logger.debug("%s = %s" % (k, str([y for y in h.y()])))
hists_flavour = dict()
hists_flavour["madgraph"] = ROOT.TH1F("madgraph", "madgraph", len(flavour_scenario), 0, len(flavour_scenario)-1)
hists_flavour["sherpa/unweighted"] = ROOT.TH1F("sherpa_unw", "sherpa unweighted", len(flavour_scenario), 0, len(flavour_scenario)-1)
hists_flavour["sherpa/weighted"] = ROOT.TH1F("sherpa_rew", "sherpa weighted", len(flavour_scenario), 0, len(flavour_scenario)-1)
for i, sc in zip(range(1,len(flavour_scenario)+1), flavour_scenario):
sh1_int, sh1_err = calc_int_err(merged["sherpa/unweighted/%s" % sc])
sh2_int, sh2_err = calc_int_err(merged["sherpa/weighted/%s" % sc])
mg_int, mg_err = calc_int_err(merged["madgraph/%s" % sc])
logger.debug("%.2f %.2f" % (sh1_int, sh1_err))
logger.debug("%.2f %.2f" % (sh2_int, sh2_err))
logger.debug("%.2f %.2f" % (mg_int, mg_err))
hists_flavour["madgraph"].SetBinContent(i, mg_int)
hists_flavour["madgraph"].SetBinError(i, mg_err)
hists_flavour["sherpa/unweighted"].SetBinContent(i, sh1_int)
hists_flavour["sherpa/unweighted"].SetBinError(i, sh1_err)
hists_flavour["sherpa/weighted"].SetBinContent(i, sh2_int)
hists_flavour["sherpa/weighted"].SetBinError(i, sh2_err)
hists_flavour["madgraph"].GetXaxis().SetBinLabel(i, sc)
hists_flavour["sherpa/unweighted"].GetXaxis().SetBinLabel(i, sc)
hists_flavour["sherpa/weighted"].GetXaxis().SetBinLabel(i, sc)
hists_flavour["sherpa/weighted"].Sumw2()
hists_flavour["sherpa/unweighted"].Sumw2()
hists_flavour["madgraph"].Sumw2()
hists_flavour["ratio/unweighted"] = hists_flavour["madgraph"].Clone("ratio_unw")
hists_flavour["ratio/unweighted"].Divide(hists_flavour["sherpa/unweighted"])
hists_flavour["ratio/weighted"] = hists_flavour["madgraph"].Clone("ratio_rew")
hists_flavour["ratio/weighted"].Divide(hists_flavour["sherpa/weighted"])
for i, sc in zip(range(1,len(flavour_scenario)+1), flavour_scenario):
logger.info("weights[%s] = %.6f; //error=%.6f [%d]" % (sc, hists_flavour["ratio/unweighted"].GetBinContent(i), hists_flavour["ratio/unweighted"].GetBinError(i), i))
flavour_ratio_coll = HistCollection(hists_flavour, name="hists__flavour_ratios")
flavour_ratio_coll.save(out_dir)
for sc in flavour_scenario:
hists = [merged["madgraph/%s" % sc], merged["sherpa/unweighted/%s" % sc], merged["sherpa/weighted/%s" % sc]]
for hist in hists:
norm(hist)
#hist.SetName(sc)
#hist.SetTitle(sc)
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=costheta["varname"])
leg = legend(hists, styles=["f", "f"], nudge_x=-0.2)
chi2 = hists[0].Chi2Test(hists[1], "WW CHI2/NDF")
hists[0].SetTitle("madgraph to sherpa comparison #chi^{2}/ndf=%.2f" % chi2)
canv.Update()
canv.SaveAs(out_dir + "/flavours__%s.png" % (sc))
md_merged = dict()
for sc in flavour_scenario:
logger.info("Calculating ratio for %s" % sc)
hi = merged["sherpa/unweighted/%s" % sc].Clone("ratio__%s" % sc)
hi.Divide(merged["madgraph/%s" % sc])
merged[hi.GetName()] = hi
hc_merged = HistCollection(merged, md_merged, "hists__costheta_flavours_merged")
hc_merged.save(out_dir)
logger.info("Saved merged histogram collection")
parser.add_argument('--tag', type=str, default="test")
args = parser.parse_args()
if args.recreate:
samples = load_samples(os.environ["STPOL_DIR"])
for s in samples.values():
if sample_types.is_wjets(s.name):
s.tree.AddFriend("trees/WJets_weights", s.tfile)
else:
samples = {}
out_dir = os.environ["STPOL_DIR"] + "/out/plots/wjets"
if args.tag:
out_dir += "/" + args.tag
mkdir_p(out_dir)
# plot_ratios("2J0T", Cuts.final(2,0), samples, out_dir, args.recreate)
# plot_ratios("2J1T", Cuts.final(2,1), samples, out_dir, args.recreate)
colls_in, colls_out = plot_sherpa_vs_madgraph(
costheta, "2J",
Cuts.mu*Cuts.final_jet(2),
samples.values(), out_dir, recreate=args.recreate, legend_pos="top-left", nudge_x=-0.03, nudge_y=0, systematic="nominal"
)
coll_in, coll_out = plot_sherpa_vs_madgraph(
costheta, "2J0T",
Cuts.mu*Cuts.final(2,0),
samples.values(), out_dir, recreate=args.recreate, legend_pos="top-left", nudge_x=-0.03, nudge_y=0
)
if args.var == "C" or args.var.startswith("mva"):
cut_str = str(Cuts.mva_iso(args.channel, mva_var=args.var, mtcut=args.mtmetcut))
cut_str_antiiso = str(Cuts.mva_antiiso(args.channel, mva_var=args.var, mtcut=args.mtmetcut))
if args.var.startswith("mva"):
plot_range = [20, -1, 1]
else:
plot_range = [20, 0, 1]
else:
cut_str = str(Cuts.eta_fit(args.channel, mtcut=args.mtmetcut))
cut_str_antiiso = str(Cuts.eta_fit_antiiso(args.channel, mtcut=args.mtmetcut))
var = "eta_lj"
plot_range = [15, 0, 4.5]
indir = args.path
outdir = os.path.join(os.environ["STPOL_DIR"], "final_fit", "histos", "input", generate_out_dir(args.channel, args.var, "-1", args.coupling, args.asymmetry, args.mtmetcut, extra=args.extra))
outdir_final = os.path.join(os.environ["STPOL_DIR"], "final_fit", "histos")
#generate the systematics to use
systematics = generate_systematics(args.channel, args.coupling)
#make histograms with all the systematic variations
make_systematics_histos(args.var, cut_str, cut_str_antiiso, systematics, outdir, indir, args.channel, args.coupling, plot_range=plot_range, asymmetry=args.asymmetry, mtmetcut=args.mtmetcut)
mkdir_p(outdir_final)
#move results file from temporary location
shutil.move(
'/'.join([outdir, "lqeta.root"]),
'/'.join([
outdir_final,
generate_out_dir(args.channel, args.var, "-1", args.coupling, args.asymmetry, args.mtmetcut, extra=args.extra)+ ".root"]
)
)
print "finished"
