for name2 in funcNames :
covs[name1][name2] += ( momCoefs[name1][0] - funcMeans[name1] ) * ( momCoefs[name2][0] - funcMeans[name2] )
covsW[name1][name2] += ( momCoefsW[name1][0] - funcMeans[name1] ) * ( momCoefsW[name2][0] - funcMeans[name2] )
# divide covariance sums by number of entries to get covariances
for name1 in funcNames :
for name2 in funcNames :
covs[name1][name2] /= float(nIters)
covsW[name1][name2] /= float(nIters)
# print data sets from last iteration
noWeightData.Print()
weightData.Print()
for evIt in range(10) :
print 'not weighted: %+.2f (%.2f) weighted: %+.2f (%.2f)'\
% ( noWeightData.get(evIt).getRealValue('x'), noWeightData.weight(), weightData.get(evIt).getRealValue('x'), weightData.weight() )
print
# print moments from last iteration and computed covariances
maxLenName = max( [ len(name) for name in funcNames ] )
from math import sqrt
def printCovs(covMat) :
print ' errors:'
for name in funcNames :
print ( ' {0:>%ds}: {1:>7.5f}' % maxLenName ).format( name, sqrt( covMat[name][name] ) )
print
print ' correlations:'
print ' ' * ( maxLenName + 5 ) + ' '.join( '{0:>6s}'.format(name) for name in funcNames )
for name1 in funcNames :
corrs = [ ( covMat[name1][name2] / sqrt( covMat[name1][name1] * covMat[name2][name2] ) )\
weightVar = dataSetAsym.addColumn(weightVar)
obsSet.add(weightVar)
dataSetAsymW = RooDataSet( 'asymDataW', 'asymDataW', obsSet, Import = dataSetAsym, WeightVar = ( 'weightVar', True ) )
del dataSetAsym
ws.put(dataSetAsymW)
del dataSetAsymW
dataSetAsymW = ws['asymDataW']
obsSet = RooArgSet( dataSetAsymW.get() )
dataSetAsymW.Print()
# get sums of weights
sumW = dict( plus = 0., minus = 0. )
for evSet in dataSetAsymW :
if evSet.getCatIndex('asymCat') == 1 :
sumW['plus'] += dataSetAsymW.weight()
else :
sumW['minus'] += dataSetAsymW.weight()
assert abs( dataSetAsymW.sumEntries() - sumW['plus'] - sumW['minus'] ) < 1.e-5
if applyAngWeights :
ASumW = 2. * sumW['plus'] - ( sumW['plus'] + sumW['minus'] )
else :
ASumW = 2. * sumW['plus'] / ( sumW['plus'] + sumW['minus'] ) - 1.
# create arrays of time bins
from array import array
timeArr = array( 'd', [ ( periodShift + ( float(it) + binOffset + 0.5 ) / float(numTimeBins) ) * oscPeriod\
for it in range( numTimeBins + 1 ) ] )
timeErrArr = array( 'd', [ 0.5 / float(numTimeBins) * oscPeriod ] * ( numTimeBins + 1 ) )
timeArrPdf = array( 'd', [ ( periodShift + ( float(it) + binOffset + float(frac) ) / float(numTimeBins) ) * oscPeriod\
for it in range( numTimeBins + 1 ) for frac in timeFracs ]\
