def printCounters(counters):
countTotalBkg(counters)
blindGroups = [g for g in counters.keys() if g!='data']
unblindGroups = [g for g in counters.keys()]
tableSr = CutflowTable(samples=blindGroups, selections=signalRegions(), countsSampleSel=counters)
tablePre = CutflowTable(samples=blindGroups, selections=controlRegions(), countsSampleSel=counters)
tableBld = CutflowTable(samples=unblindGroups, selections=blindRegions(), countsSampleSel=counters)
for table in [tableSr, tablePre, tableBld] : table.nDecimal = 6
print 4*'-',' sig regions ',4*'-'
print tableSr.csv()
print 4*'-',' pre regions ',4*'-'
print tablePre.csv()
print 4*'-',' blind regions ',4*'-'
print tableBld.csv()
def printSummary(counts, outfilename='') :
samples = counts.keys()
signal = [s for s in samples if isSigSample(s)][0]
counts = renameDictKey(counts, signal, 'signal')
samples = counts.keys()
selections = sorted(first(counts).keys())
def isSignal(s) : return isSigSample(s) or s=='signal'
if 'totbkg' not in counts :
counts['totbkg'] = dict([(sel, sum(countsSample[sel]
for sam, countsSample in counts.iteritems() if not isSignal(sam)))
for sel in first(counts).keys()])
bkgUnc = 0.30
zn = r.RooStats.NumberCountingUtils.BinomialExpZ
counts['Zn'] = dict([(sel, zn(counts['signal'][sel], counts['totbkg'][sel], bkgUnc)) for sel in selections])
firstThreeColumns = ['Zn', 'signal', 'totbkg']
otherSamples = sorted([s for s in samples if s not in firstThreeColumns])
table = CutflowTable(firstThreeColumns + otherSamples, selections, counts)
table.nDecimal = 2
if outfilename :
with open(outfilename, 'w') as f :
f.write('\n'+table.latex()+'\n')
else :
print
print table.latex()
def printCounters(counters):
countTotalBkg(counters)
blindGroups = [g for g in counters.keys() if g != 'data']
unblindGroups = [g for g in counters.keys()]
tableSr = CutflowTable(samples=blindGroups,
selections=signalRegions(),
countsSampleSel=counters)
tablePre = CutflowTable(samples=blindGroups,
selections=controlRegions(),
countsSampleSel=counters)
tableBld = CutflowTable(samples=unblindGroups,
selections=blindRegions(),
countsSampleSel=counters)
for table in [tableSr, tablePre, tableBld]:
table.nDecimal = 6
print 4 * '-', ' sig regions ', 4 * '-'
print tableSr.csv()
print 4 * '-', ' pre regions ', 4 * '-'
print tablePre.csv()
print 4 * '-', ' blind regions ', 4 * '-'
print tableBld.csv()
allSamples += ['totbkg'] if printTotBkg else []
allSelects = sorted(list(set([k.pr for k in histosByType.keys()])))
if verbose : print 'allSamples : ',allSamples
if verbose : print 'allSelects : ',allSelects
# get the counts (adding up what needs to be merged by samplename)
sampleCountsPerSel = dict() # counts[sample][sel]
countsSampleSel = dict([(s, collections.defaultdict(float)) for s in allSamples])
for t, histos in refHistos.iteritems() :
if t.ch != channel : continue
for h in histos :
sample, sel = h.sample, h.type.pr
cnt = h.GetEntries() if rawcnt else h.Integral()
skipIt = not printData and sample=='data'
countIt = not skipIt
countsSampleSel[sample][sel] += cnt if countIt else 0.0
if printTotBkg and isBkgSample(sample) :
countsSampleSel['totbkg'][sel] += cnt
ct = CutflowTable(allSamples, allSelects, countsSampleSel,
isRawCount=rawcnt, selectionRegexp=selRegexp)
csv = ct.csv()
print csv
if csvFile :
with open(csvFile, 'w') as f : f.write(csv)
if texFile :
with open(texFile, 'w') as f : f.write(ct.latex())
if pklFile :
dumpToPickle(pklFile, countsSampleSel)
