def cutflow(cuts, processes, relative=False, weighed=False, f=sys.stdout):
expanded_proc = []
procs = []
for proc in processes:
subs = [p for p in Process.expand(proc) if str(p) in cuts[0].processes()]
if len(subs) > 0:
expanded_proc.append(subs)
procs.append(proc)
cutdata = [[sum(float(cut[p]) for p in ps) for ps in expanded_proc] for cut in cuts]
if weighed:
for n, c in enumerate(reversed(cuts)):
if not isinstance(c, StaticCut):
break
ratios = [a / (b if b != 0 else 1) for a, b in zip(cutdata[-1], cutdata[-(n + 1)])]
cutdata = cutdata[:-n]
for i in xrange(3, len(cutdata)):
cutdata[i] = [a * b for a, b in zip(cutdata[i], ratios)]
if relative:
for i in xrange(1, len(cutdata)):
cutdata[-i] = [(float(b) / a if a != 0 else 0)
for a, b in zip(cutdata[-(i + 1)], cutdata[-i])]
print_cuts(cuts, procs, cutdata, expanded_proc, "Cut", f, 5 if relative else 2)
def get_event_count(cls, f, proc, category, fmt, unweighed):
p = cls.__plots['Events']
p.clear()
p.read(f, category, Process.expand(proc), fmt=fmt)
if unweighed:
return p._get_histogram(proc).GetEntries()
return p._get_histogram(proc).GetBinContent(1)
def cutflow(cuts, procs, relative=False, f=sys.stdout):
expanded_proc = [Process.expand(proc) for proc in procs]
cutdata = [[sum(float(cut[p]) for p in ps) for ps in expanded_proc] for cut in cuts]
if relative:
for i in xrange(1, len(cutdata)):
cutdata[-i] = [float(b) / a for a, b in zip(cutdata[-(i + 1)], cutdata[-i])]
namelength = max(len(unicode(cut)) for cut in cuts)
fieldlengths = []
for proc, subprocs in zip(procs, expanded_proc):
val = sum(cuts[0][p] for p in subprocs)
length = max(len(proc), len("{:.2f}".format(float(val))))
fieldlengths.append(length)
header = u"{{:{0}}}".format(namelength) \
+ u"".join(u" {{:{0}}}".format(fl) for fl in fieldlengths) \
+ u"\n"
format = u"{{:{0}}}".format(namelength) \
+ "".join(" {{:{0}.2f}}".format(fl) for fl in fieldlengths) \
+ "\n"
f.write(header.format("Cut", *procs))
f.write("-" * namelength + "".join(" " + "-" * fl for fl in fieldlengths) + "\n")
for cut, data in zip(cuts, cutdata):
f.write(format.format(cut, *data))
def cutflow(cuts, procs, relative=False, f=sys.stdout):
expanded_proc = [Process.expand(proc) for proc in procs]
cutdata = [[sum(float(cut[p]) for p in ps) for ps in expanded_proc]
for cut in cuts]
if relative:
for i in xrange(1, len(cutdata)):
cutdata[-i] = [
float(b) / a for a, b in zip(cutdata[-(i + 1)], cutdata[-i])
]
namelength = max(len(unicode(cut)) for cut in cuts)
fieldlengths = []
for proc, subprocs in zip(procs, expanded_proc):
val = sum(cuts[0][p] for p in subprocs)
length = max(len(proc), len("{:.2f}".format(float(val))))
fieldlengths.append(length)
header = u"{{:{0}}}".format(namelength) \
+ u"".join(u" {{:{0}}}".format(fl) for fl in fieldlengths) \
+ u"\n"
format = u"{{:{0}}}".format(namelength) \
+ "".join(" {{:{0}.2f}}".format(fl) for fl in fieldlengths) \
+ "\n"
f.write(header.format("Cut", *procs))
f.write("-" * namelength + "".join(" " + "-" * fl
for fl in fieldlengths) + "\n")
for cut, data in zip(cuts, cutdata):
f.write(format.format(cut, *data))
def read_inputs(config, setup):
from ttH.TauRoast.processing import Process
fn = os.path.join(config.get("indir", config["outdir"]), "ntuple.root")
signal = None
signal_weights = None
for proc, weight in sum([cfg.items() for cfg in setup['signals']], []):
for p in sum([Process.expand(proc)], []):
logging.debug('reading {}'.format(p))
d = rec2array(root2array(fn, str(p), setup['variables']))
if isinstance(weight, float) or isinstance(weight, int):
w = np.array([weight] * len(d))
else:
w = rec2array(root2array(fn, str(p), [weight])).ravel()
w *= p.cross_section / p.events
if signal is not None:
signal = np.concatenate((signal, d))
signal_weights = np.concatenate((signal_weights, w))
else:
signal = d
signal_weights = w
background = None
background_weights = None
for proc, weight in sum([cfg.items() for cfg in setup['backgrounds']], []):
for p in sum([Process.expand(proc)], []):
logging.debug('reading {}'.format(p))
d = rec2array(root2array(fn, str(p), setup['variables']))
if isinstance(weight, float) or isinstance(weight, int):
w = np.array([weight] * len(d))
else:
w = rec2array(root2array(fn, str(p), [weight])).ravel()
w *= p.cross_section / p.events
if background is not None:
background = np.concatenate((background, d))
background_weights = np.concatenate((background_weights, w))
else:
background = d
background_weights = w
factor = np.sum(signal_weights) / np.sum(background_weights)
logging.info("renormalizing background events by factor {}".format(factor))
background_weights *= factor
return signal, signal_weights, background, background_weights
def add_mva(args, config):
fn = os.path.join(config["outdir"], "ntuple.root")
for proc in set(sum((Process.expand(p) for p in config['plot'] + config['limits']), [])):
systematics = ['NA']
if args.systematics:
weights = config.get(proc.cutflow + ' weights')
systematics = config.get(proc.cutflow + ' systematics', [])
systematics = set([s for s, w in expand_systematics(systematics, weights)])
for unc in systematics:
logging.info("using systematics: " + unc)
proc.add_mva(config, fn, unc)
def analyze(args, config):
fn = os.path.join(config["outdir"], "ntuple.root")
if args.reuse:
cutflows = split_cuts(load_cutflows(config))
else:
if os.path.exists(fn):
os.unlink(fn)
cutflows = setup_cuts(config)
for proc in set(sum((Process.expand(p) for p in config['plot'] + config['limits']), [])):
uncertainties = ['NA']
if args.systematics:
weights = config.get(proc.cutflow + ' weights')
systematics = config.get(proc.cutflow + ' systematics', [])
uncertainties = [s for s, w in expand_systematics(systematics, weights)]
for unc in uncertainties:
suffix = '' if unc == 'NA' else '_' + unc
counts, cuts, weights = cutflows[proc.cutflow + suffix]
if len(counts) > 0 and str(proc) in counts[0].processes():
continue
logging.info("using systematics: " + unc)
local_cuts = list(cuts)
for cfg in proc.additional_cuts:
local_cuts.insert(0, Cut(*cfg))
proc.analyze(config, fn, counts, local_cuts, weights, unc, args.debug_cuts)
concatenated_cutflows = Cutflows()
for name, (counts, cuts, weights) in cutflows.items():
cuts = counts + cuts + weights
normalize(cuts, config["lumi"], config.get("event limit"))
concatenated_cutflows[name] = cuts
concatenated_cutflows.save(config)
def _get_histogram(self, process):
procs = Process.expand(process)
h = self.__hists[procs[0]].Clone()
for proc in procs[1:]:
h.Add(self.__hists[proc])
return h
def _get_histogram(self, process):
procs = Process.expand(process)
h = self.__hists[procs[0]].Clone()
for proc in procs[1:]:
h.Add(self.__hists[proc])
return h
