# no entries, just write empty histograms to file
if tree is None or tree.get_entries() == 0:
print("\tNo entries in ntuple, writing empty histograms and finishing.")
for histName, st3 in boundaries.iteritems():
h = Hist(st3[2], st3[0], st3[1], name=histName)
h.write()
else:
differences = []
#c = ROOT.TCanvas("canvas", "canvas", 500, 500)
for subercuts in combinations(supercuts, len(supercuts)-1):
# hold the differences and create a text file with them later for reference
# use integers to denote them
differences.append([x for x in supercuts if x not in subercuts][0])
# get the selection we apply to draw it
selection = optimize.cuts_to_selection(subercuts)
# get the branch we need to draw
selection_string = differences[-1]['selections']
print("\tLooking at selection: {0}".format(selection_string))
branchesSpecified = set(optimize.selection_to_branches(selection_string, tree))
# get actual list of branches in the file
availableBranches = optimize.tree_get_branches(tree, args.eventWeightBranch)
# remove anything that doesn't exist
branchesToUse = [branch for branch in branchesSpecified if branch in availableBranches]
# more than one branch, we skip and move to the next
if len(branchesToUse) == 0:
print("\t\tWarning: selection has no branches.")
del differences[-1]
continue
branchesSpecified = list(set(itertools.chain.from_iterable(selection_to_branches(supercut['selections'], tree) for supercut in supercuts)))
eventWeightBranchesSpecified = list(set(selection_to_branches(eventWeightBranch, tree)))
# get actual list of branches in the file
availableBranches = tree_get_branches(tree, eventWeightBranchesSpecified)
# remove anything that doesn't exist
branchesToUse = [branch for branch in branchesSpecified if branch in availableBranches]
branchesSkipped = list(set(branchesSpecified) - set(branchesToUse))
if branchesSkipped:
logger.info("The following branches have been skipped...")
for branch in branchesSkipped:
logger.info("\t{0:s}".format(branch))
tree = rnp.tree2array(tree, branches=eventWeightBranchesSpecified+branchesToUse)
entireSelection = '{0:s}*{1:s}'.format(eventWeightBranch, cuts_to_selection(supercuts))
result = ne.evaluate(entireSelection, local_dict = tree)
for event_number in result[np.where(result!=0)]:
eventNumbers[event_number].append(region)
# print "\t", event_number
overlapsByColumn = [0]*len(regions)
atLeastOneOverlap = 0
print "{0:s}\t\t{1:s}\t| {2:s}".format("Event #", "\t".join(map(lambda x: os.path.basename(x).split('.')[0], regions)), "# Overlaps")
print "-"*80
for event_number, in_regions in eventNumbers.iteritems():
overlaps = [bool(region in in_regions) for region in regions]
# remove anything that doesn't exist
branchesToUse = [
branch for branch in branchesSpecified if branch in availableBranches
]
branchesSkipped = list(set(branchesSpecified) - set(branchesToUse))
if branchesSkipped:
logger.info("The following branches have been skipped...")
for branch in branchesSkipped:
logger.info("\t{0:s}".format(branch))
tree = rnp.tree2array(tree,
branches=eventWeightBranchesSpecified +
branchesToUse)
entireSelection = '{0:s}*{1:s}'.format(eventWeightBranch,
cuts_to_selection(supercuts))
result = ne.evaluate(entireSelection, local_dict=tree)
for event_number in result[np.where(result != 0)]:
eventNumbers[event_number].append(region)
# print "\t", event_number
overlapsByColumn = [0] * len(regions)
atLeastOneOverlap = 0
print("{0:s}\t\t{1:s}\t| {2:s}".format(
"Event #", "\t".join([os.path.basename(x).split('.')[0] for x in regions]),
"# Overlaps"))
print("-" * 80)
