def efficiency_graph(pass_function,
function_inputs,
xs,
bins=None,
error=0.005):
pass_results = pass_function(function_inputs)
if bins is None: # Automatic binning
# Compute the number of bins such that the error on the efficiency is equal to 'error' in each bin
# The calculation is based on binomial errors and assumes that the efficiency is flat (that the distributions of all and selected events are the same)
k = float(np.count_nonzero(pass_results))
n = float(len(pass_results))
percentiles = [0., 100.]
if k > 0:
nbins = (error * n)**2 / k / (1 - k / n)
# Compute the bin bounaries with the same number of events in all bins
percentiles = np.arange(0., 100., 100. / nbins)
percentiles[-1] = 100.
bins = np.unique(np.percentile(xs, percentiles))
# Fill histograms of selected and all events and compute efficiency
histo_pass = Hist(bins)
histo_total = Hist(bins)
fill_hist(histo_pass, xs, pass_results)
fill_hist(histo_total, xs)
efficiency = Graph()
efficiency.Divide(histo_pass, histo_total)
return efficiency
def rate(et, thresholds, total_events):
pass_thresholds = pass_threshold(et, thresholds)
# fill pass and total numbers of events
bins = (pass_thresholds[:-1, 0] + pass_thresholds[1:, 0]) / 2.
bins = np.append([-bins[0] + 2. * pass_thresholds[0, 0]], bins)
bins = np.append(bins, [2. * pass_thresholds[-1, 0] - bins[-1]])
histo_pass = Hist(bins)
histo_total = Hist(bins)
for b, n in zip(histo_pass[1:-1], pass_thresholds[:, 1]):
b.value = n
b.error = np.sqrt(n)
for b in histo_total[1:-1]:
b.value = total_events
b.error = np.sqrt(total_events)
rates = Graph()
rates.Divide(histo_pass, histo_total)
return rates
tmplabels.append(b)
# use them in the legend
axes.legend(tmphandles, tmplabels, loc='best', numpoints=1)
axislabel = ""
for chunk in histogramName.split("_"):
if "SR" in chunk or "minus" in chunk:
continue
axislabel += chunk
# axislabel = " ".join(histogramName.split("_")[2:])
axislabel = axislabel.split("<")[0].split(">")[0]
if 'DataMain' in hists:
if hists["DataMain"].Integral() and plotWithMPL:
ratioplot = Graph()
ratioplot.Divide(hists['DataMain'], stack.sum, 'pois')
# ratioplot.color = "green"
tmpyerror, tmpyerror2 = zip(*list(ratioplot.yerr()))
tmpx = list(ratioplot.x())
tmpy = list(ratioplot.y())
tmpxy = zip(tmpx, tmpy, tmpyerror)
# print tmpxy
tmpxy = [tmp for tmp in tmpxy if tmp[1] != 0]
# print tmpxy
tmpx, tmpy, tmpyerror = zip(*tmpxy)
# print tmpyerror
# axes_ratio.errorbar(tmpx,tmpy, yerr = tmpyerror,xerr=False, emptybins=False, marker='o', lw=1, color="black")
axes_ratio.errorbar(
tmpx,
tmpy,
# list(ratioplot.y()),
