def mergeFunc(self, products):
dd = collections.defaultdict
epochs = dd(lambda: dd(set))
prescales = {}
for ps in products['prescales']:
prescales.update(ps)
if not prescales: return
for (run, lumi), val in prescales.iteritems():
epochs[val][run].add(lumi)
lumiDir = self.outputFileName
if not os.path.exists(lumiDir): utils.mkdir(lumiDir)
lumis = utils.luminosity.recordedInvMicrobarnsShotgun(
[utils.jsonFromRunDict(rundict) for rundict in epochs.values()],
cores=4,
cacheDir=lumiDir)
lumiA, lumiB, lumiBPF = [
sum(lumis)
for lumis in zip(*[(lumi / A if A else 0, lumi / B if B else 0,
lumi / BPF if BPF else 0)
for (A, B,
BPF), lumi in zip(epochs.keys(), lumis)])
]
lumiHist = r.TH1D('lumis', 'luminosity by path;;1/pb', 3, 0, 3)
lumiHist.SetBinContent(1, lumiA * 1e-6)
lumiHist.GetXaxis().SetBinLabel(1, '2011A')
lumiHist.SetBinContent(2, lumiB * 1e-6)
lumiHist.GetXaxis().SetBinLabel(2, '2011B')
lumiHist.SetBinContent(3, lumiBPF * 1e-6)
lumiHist.GetXaxis().SetBinLabel(3, '2011BPF')
lumiHist.Write()
return
def mergeFunc(self, products):
self.setupDicts()
for eLumis in products["staticEpochLumis"]:
for epoch in eLumis:
for run in eLumis[epoch]:
self.epochLumis[epoch][run] |= eLumis[epoch][run]
if not self.epochLumis: return
lumiDir = self.outputFileName
if not os.path.exists(lumiDir): utils.mkdir(lumiDir)
lumis = utils.luminosity.recordedInvMicrobarnsShotgun([
utils.jsonFromRunDict(self.epochLumis[epoch])
for epoch in self.epochLumis
],
cores=4,
cacheDir=lumiDir)
probs = [[1. / prescale if prescale else 0 for prescale in epoch]
for epoch in self.epochLumis]
inclu = [[
utils.unionProbability(prob[:i + 1]) for i in range(len(prob))
] for prob in probs]
thresholds = sorted(set(self.thresholds))
inclusives = [[
max([0] + [p for p, t in zip(inc, self.thresholds) if t is thresh])
for thresh in thresholds
] for inc in inclu]
weights = np.array(lumis).dot(inclusives) / sum(lumis)
# write total lumi and weight by threshold
lumiHist = r.TH1D('susyTreeLumi', 'luminosity from susyTree;;1/pb', 1,
0, 1)
lumiHist.SetBinContent(1, sum(lumis))
lumiHist.Write()
weightHist = r.TH1D(
self.name, ";%s;%s" % (self.var, self.name), len(thresholds),
np.array([0.] + thresholds + [max(thresholds) + 10]))
for i, w in enumerate(weights):
weightHist.SetBinContent(i + 2, w)
weightHist.SetBinContent(len(thresholds) + 3, 1)
weightHist.Write()
