def data_mc(var, cut_name, cut, weight, samples, out_dir, recreate, lumi, **kwargs):
plot_name = "%s__%s" % (var, cut_name)
plot_name = escape(plot_name)
systematic = kwargs.get("systematic", "nominal")
if recreate:
hists = {}
metadata = {}
pbar = ProgressBar(
widgets=["Plotting %s:" % plot_name] + widgets, maxval=sum([s.getEventCount() for s in samples])
).start()
nSamp = 1
for sample in samples:
hname = sample.name
if sample.isMC:
hname_ = sample.name
hist = sample.drawHistogram(var, str(cut), weight=str(weight), **kwargs)
hist.Scale(sample.lumiScaleFactor(lumi))
hists[hname_] = hist
metadata[hname_] = HistMetaData(sample_name=sample.name, process_name=sample.process_name)
else:
hist = sample.drawHistogram(var, str(cut), **kwargs)
hists[hname] = hist
metadata[hname] = HistMetaData(sample_name=sample.name, process_name=sample.process_name)
pbar.update(nSamp)
nSamp += sample.getEventCount()
hist_coll = HistCollection(hists, metadata, plot_name)
hist_coll.save(out_dir)
logger.debug("saved hist collection %s" % (out_dir))
pbar.finish()
hist_coll = HistCollection.load(out_dir + "/%s.root" % plot_name)
logger.debug("loaded hist collection %s" % (out_dir + "/%s.root" % plot_name))
return hist_coll
def shape_variation(var, plot_range, varname, recreate, out_name):
tot_hists = OrderedDict()
tdrstyle()
for wn, w in [
("unw", Weight("1.0")),
("nominal", Weights.wjets_madgraph_shape_weight("nominal")),
("shape_up", Weights.wjets_madgraph_shape_weight("wjets_up")),
("shape_down", Weights.wjets_madgraph_shape_weight("wjets_down"))
]:
p = data_mc(
var, "2J0T_%s" % (wn),
cut, Weights.total("mu", "nominal")*w,
samples, ".", recreate, lumi_iso["mu"], plot_range=plot_range,
)
for hn, h in p.hists.items():
logger.debug("%s %.2f" % (hn, h.Integral()))
#sumw = [(sample.name, numpy.mean(root_numpy.root2array(sample.tfile.GetPath()[:-2], "trees/WJets_weights", branches=[str(w)])[str(w)])) for sample in samples]
s = sum(p.hists.values()).Clone()
tot_hists[wn] = s
hc = HistCollection(tot_hists, name=out_name)
for hn, h in hc.hists.items():
print hn, h.Integral()
canv = plot_hists_dict(hc.hists, do_chi2=False, do_ks=True, x_label=varname, legend_pos="top-left")
hc.hists.values()[0].SetTitle("shape variation")
canv.SaveAs(out_name + ".png")
hc.save(out_name)
return hc, canv
recreate = True
if recreate:
for sn in sampnames:
sample = Sample.fromFile(path + "/" + sn + ".root")
for wn, w in weights:
hi = sample.drawHistogram(var, str(cut), weight=w, plot_range=plot_range)
hi.Scale(sample.lumiScaleFactor(lumi))
hists[wn][sample.name] = hi
merged = dict()
for wn, w in weights:
merged[wn] = merge_hists(hists[wn], merge_cmds)
hc = HistCollection(merged[wn], name="bweight_%s" % wn)
hc.save(".")
colls = dict()
for wn, w in weights:
hc = HistCollection.load("./bweight_%s.root" % wn)
colls[wn] = hc
for proc in merge_cmds.keys():
hists = OrderedDict([(wn, colls[wn].hists[proc]) for wn, w in weights])
for hn, h in hists.items():
h.SetTitle(pretty_names_weights[hn])
hists_normed = copy.deepcopy(hists)
for hn, h in hists_normed.items():
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")
