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
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():
norm(h, setName=False)
if hn!="unw":
h.Divide(hists_normed["unw"])
hists_normed["unw"].Divide(hists_normed["unw"])
