class _EfficiencyCurve(object):
def __init__(self, name, bins, threshold):
from rootpy.plotting import Hist
self._pass = Hist(bins, name=name + '_pass')
self._total = Hist(bins, name=name + '_total')
self._dist = Hist(bins, name=name + '_dist')
self._threshold = threshold
self._efficiency = None
def fill(self, recoValue, l1Value, weight=1.):
""" Fills the histograms used for efficiency calculation
:param recoValue: the reconstructed quanity
:type recoValue: float
:param l1Value: the L1 Trigger quantity
:type l1Value: float
:param weight: weight to fill the histograms with, default=1.0
:type weight: float
"""
self._total.fill(recoValue, weight)
self._dist.fill(l1Value, weight)
if l1Value > self._threshold:
self._pass.fill(recoValue, weight)
def calculate_efficiency(self):
from rootpy import asrootpy
from ROOT import TEfficiency
self._efficiency = asrootpy(TEfficiency(self._pass, self._total))
self._efficiency.SetName(self._total.GetName() + '_eff')
def get_efficiency(self):
if not self._efficiency:
self.calculate_efficiency()
return self._efficiency
def _project(tree,
var,
selection='',
weight=1.0,
bins=None,
includeover=False):
h = None
if var.count(':') == 0:
## Hist (1D)
if bins:
if isinstance(bins, tuple):
assert len(bins) == 3
h = Hist(*bins)
elif isinstance(bins, list):
h = Hist(bins)
else:
assert False
else:
assert False
elif var.count(':') == 1:
## Hist2D
## like rootpy, we use a convention where var=x:y, unlike ROOT
varx, vary = var.split(':')
var = ':'.join([vary, varx])
if bins:
if isinstance(bins, tuple):
assert len(bins) == 6
h = Hist2D(*bins)
elif isinstance(bins, list):
## TODO: support variable bins for Hist2D
h = Hist2D(*bins)
#assert False
else:
assert False
else:
assert False
else:
assert False
assert h
# kwargs['hist'] = h
## add the weight to the selection via a TCut
weighted_selection = str(selection)
if weight and weight != 1.0:
weighted_selection = Cut(weighted_selection)
weighted_selection = weighted_selection * weight
weighted_selection = str(weighted_selection)
tree.Draw('%s>>%s' % (var, h.GetName()), weighted_selection)
## print tree.GetSelectedRows() ## debuging
# for event in t:
# x = getattr(event, var)
# h.fill(x)
if h:
h.SetDirectory(0)
# elist = ROOT.gDirectory.Get('elist')
# elist.Print('all')
if var.count(':') == 0 and includeover:
## include overflow for Hist (1D)
nbins = h.GetNbinsX()
c1 = h.GetBinContent(nbins)
c2 = h.GetBinContent(nbins + 1)
e1 = h.GetBinError(nbins)
e2 = h.GetBinError(nbins + 1)
h.SetBinContent(nbins, c1 + c2)
h.SetBinError(
nbins,
math.sqrt((c1 * e1 * e1 + c2 * e2 * e2) /
(c1 + c2)) if c1 + c2 != 0.0 else 0.0)
h.SetBinContent(nbins + 1, 0.0)
h.SetBinError(nbins + 1, 0.0)
return h
h = Hist(toDraw['edges'], name=histName)
elif histDimension == 2:
h = Hist2D(toDraw['edges'][0],
toDraw['edges'][1],
name=histName)
elif histDimension == 3:
h = Hist2D(toDraw['edges'][0],
toDraw['edges'][1],
toDraw['edges'][2],
name=histName)
else:
raise ValueError(
'I dunno how to handle {0}'.format(toDraw))
else:
print "ERROR: problem configuring the binning of the histograms..."
exit()
# things look ok, so we draw to the histogram
print "\t\tdrawing {0}\n\t\twith cut ({1})*({2})".format(
toDraw['draw'], args.eventWeightBranch, cut['name'])
tree.Draw(toDraw['draw'],
'({0:s})*({1:s})'.format(args.eventWeightBranch,
cut['cut']),
hist=h)
samename = h.GetName()
hnew = h.merge_bins([(-2, -1)]) # merge upper overflow bin
hnew.SetName(samename)
# write to file
print "\t\twriting to file"
hnew.write()
out_file.close()