def __init__( self, nbins, arg, **kwargs ):
''' To initialize the class one has to provide the number of bins that will be used
in the transformed distribution < nbins > and an argument, that can be a set of
values or a histogram. If it is a set of values, the option to use the adaptive
binning technique is set by the < adaptbin > parameter. If no such technique is used,
one can control the number of bin for the input sample with the input parameter
< ntrbins >. '''
verbose = kwargs.get( 'verbose', True )
self.AdaptBin = kwargs.get( 'adaptbin', False )
if isinstance( arg, TH1 ):
if verbose:
print ( 'INFO: After the transformation, all the values greater than one will be ' +
'attached to the last bin' )
if self.AdaptBin:
print 'WARNING: Adaptive binned method not available with a TH1 object as input'
self.AdaptBin = False
self.MainHist = arg
self.Nbins = nbins
else:
if self.AdaptBin:
if verbose:
print ( 'INFO: The output histogram comes from an adaptive binned transformation. ' +
'Is constructed as: x in bin if x > min and x <= max.' )
length = len( arg )
self.MainHist = MakeAdaptiveBinnedHist( '', length/nbins, arg )
self.Nbins = nbins
for val in arg:
self.MainHist.Fill( val )
else:
ntrbins = kwargs.get( 'ntrbins', 100 )
self.__init__( nbins, MakeHistogram( arg, nbins = ntrbins ) )
self.CumulativeHist = MakeCumulative( self.MainHist )
class IntegralTransformer:
def __init__( self, nbins, arg, **kwargs ):
''' To initialize the class one has to provide the number of bins that will be used
in the transformed distribution < nbins > and an argument, that can be a set of
values or a histogram. If it is a set of values, the option to use the adaptive
binning technique is set by the < adaptbin > parameter. If no such technique is used,
one can control the number of bin for the input sample with the input parameter
< ntrbins >. '''
verbose = kwargs.get( 'verbose', True )
self.AdaptBin = kwargs.get( 'adaptbin', False )
if isinstance( arg, TH1 ):
if verbose:
print ( 'INFO: After the transformation, all the values greater than one will be ' +
'attached to the last bin' )
if self.AdaptBin:
print 'WARNING: Adaptive binned method not available with a TH1 object as input'
self.AdaptBin = False
self.MainHist = arg
self.Nbins = nbins
else:
if self.AdaptBin:
if verbose:
print ( 'INFO: The output histogram comes from an adaptive binned transformation. ' +
'Is constructed as: x in bin if x > min and x <= max.' )
length = len( arg )
self.MainHist = MakeAdaptiveBinnedHist( '', length/nbins, arg )
self.Nbins = nbins
for val in arg:
self.MainHist.Fill( val )
else:
ntrbins = kwargs.get( 'ntrbins', 100 )
self.__init__( nbins, MakeHistogram( arg, nbins = ntrbins ) )
self.CumulativeHist = MakeCumulative( self.MainHist )
def Transform( self, name, arg, **kwargs ):
''' Transforms the distribution from the given set of values using the class
distribution. One must provide the name of the output histogram and an argument.
This argument can be a histogram or an iterable. The title and the type of histogram
are set using the < title > and < htype > parameters. '''
title = kwargs.get( 'title', name )
histcall = HistFromType( kwargs.get( 'htype', 'double' ) )
transf = histcall( name, title, self.Nbins, 0, 1 )
'''
If the argument is an histogram, it creates the list with the values and
associated weights.
'''
if isinstance( arg, TH1 ):
values = [ ( arg.GetBinCenter( ib ), arg.GetBinContent( ib ) )
for ib in xrange( 1, arg.GetNbinsX() + 1 ) ]
else:
values = zip( arg, len( arg )*[ 1. ] )
if self.AdaptBin:
nbins = self.MainHist.GetNbinsX()
blist = [ self.MainHist.GetBinLowEdge( ib ) for ib in xrange( 1, nbins + 2 ) ]
bins = array( 'd', blist )
abhist = self.MainHist.__class__( '', '', nbins, bins )
for val, wgt in values:
abhist.Fill( val, wgt )
sw = abhist.GetSumOfWeights()
for ib in xrange( 1, self.MainHist.GetNbinsX() + 1 ):
cont = abhist.GetBinContent( ib )
transf.SetBinContent( ib, cont/sw )
transf.SetBinError( ib, sqrt( cont/sw**2 + sw*cont**2/sw**4 ) )
else:
addifone = 1 - transf.GetBinWidth( 1 )/2.
for val, wgt in values:
fval = self.CumulativeHist.Interpolate( val )
if fval != 1:
transf.Fill( fval, wgt )
else:
transf.Fill( addifone, wgt )
return transf
def DeTransfValue( self, value, retallinfo = False ):
'''
Returns the de-transformated value associated to that given. If the option
< retallinfo > is set to True, it will return the bin center and the bin width
associated with that value.
'''
hist = self.CumulativeHist
binvals = [ hist.GetBinContent( ib ) for ib in xrange( 1, hist.GetNbinsX() + 1 ) ]
pos = bisect( binvals, value ) - 1
if retallinfo:
return hist.GetBinCenter( pos ), hist.GetBinWidth( pos )
else:
return hist.GetBinCenter( pos )