def write_hist(hist_nano: ROOT.TH1D,
category_dict: dict,
name: str,
isMC: bool = True):
"""
Make a new histogram for categorised events passing the cuts and write it to file
Args:
hist_nano : nanoAOD histogram
category_dict : dictionary of category cuts
name : category name
isMC : flag for MC/data
"""
index_new = 0
hist_limits = ROOT.TH1D("hist", "hist", len(category_dict), 0,
len(category_dict))
print(f"Writing hist {name}")
for index, category in category_dict.items():
index_new += 1
hist_content = hist_nano.GetBinContent(hist_nano.FindBin(index))
hist_error = hist_nano.GetBinError(hist_nano.FindBin(index))
print(index, index_new, category, hist_content)
hist_limits.SetBinContent(index_new, hist_content)
hist_limits.SetBinError(index_new, hist_error)
hist_limits.GetXaxis().SetBinLabel(index_new, category)
if isMC:
remove_neg_entries(hist_limits)
hist_limits.SetTitle(name)
hist_limits.SetName(name)
hist_limits.SetDirectory(root_file)
hist_limits.Write()
def remove_neg_entries(hist: ROOT.TH1D):
""" Removes negative and/or small entries from a histogram """
alpha = 1. - 0.6827
upErr = ROOT.Math.gamma_quantile_c(alpha / 2, 1, 1)
avgWeight = hist.Integral() / hist.GetEntries() if hist.GetEntries(
) > 0 else -1
#print "weight",avgWeight
for ibin in range(hist.GetNbinsX()):
c = hist.GetBinContent(ibin + 1)
if c < 10**-4:
hist.SetBinContent(ibin + 1, 10**-3)
#note: in case of 0 entries the uncertainty is also small
#(this is not the case with negative events)
if hist.GetBinError(ibin + 1) < 10**-4 and avgWeight > 0:
#set uncertainties for empy bins
#https://twiki.cern.ch/twiki/bin/viewauth/CMS/PoissonErrorBars
hist.SetBinError(ibin + 1, upErr * avgWeight)
else:
hist.SetBinError(ibin + 1, 10**-4)
