fitResult = pdf.fitTo( fitData, SumW2Error = True if corrSFitErr == 'matrix' else False, Minos = RooMinPars, Save = True
                          , Range = fitRange, **fitOpts )

    # print parameter values
    from P2VV.Imports import parNames, parValues2011 as parValues
    print 'plotProjectionsSixKKbins: parameters:'
    fitResult.PrintSpecial( text = True, LaTeX = True, normal = True, ParNames = parNames, ParValues = parValues )
    fitResult.covarianceMatrix().Print()
    fitResult.correlationMatrix().Print()

    print 120 * '=' + '\n'

if parFileOut :
    # write parameters to file
    pdfConfig.getParametersFromPdf( pdf, fitData )
    pdfConfig.writeParametersToFile( filePath = parFileOut )


###########################################################################################################################################
## make plots ##
################

# import plotting tools
from P2VV.Load import LHCbStyle
from P2VV.Utilities.Plotting import plot, CPcomponentsPlotingToolkit
from ROOT import TCanvas, kRed, kGreen, kMagenta, kBlue, kSolid

#Initialaze the CP components ploting toolkit
CpPlotsKit = CPcomponentsPlotingToolkit(pdf,sigData)

#Get dictionary with all the pdfs in the KKmass bins
Example #2
0
for constr in pdf.ExternalConstraints() : constr.Print()

if doFit :
    # fit data
    print 120 * '='
    print 'Bs2JpsiKK2011Fit: fitting %d events (%s)' % ( fitData.numEntries(), 'weighted' if fitData.isWeighted() else 'not weighted' )

    RooMinPars = [ ]
    if MinosPars :
        print 'Bs2JpsiKK2011Fit: running Minos for parameters',
        for parName in MinosPars :
            RooMinPars.append( pdfPars.find(parName) )
            print '"%s"' % RooMinPars[-1],
        print

    fitResult = pdf.fitTo( fitData, SumW2Error = False, Minos = RooMinPars, Save = True, Range = fitRange, **fitOpts )

    # print parameter values
    from P2VV.Imports import parNames, parValues2011 as parValues
    print 'Bs2JpsiKK2011Fit: parameters:'
    fitResult.PrintSpecial( text = True, LaTeX = True, normal = True, ParNames = parNames, ParValues = parValues )
    fitResult.covarianceMatrix().Print()
    fitResult.correlationMatrix().Print()

    print 120 * '=' + '\n'

    if parFileOut :
        # write parameters to file
        pdfConfig.getParametersFromPdf( pdf, fitData )
        pdfConfig.writeParametersToFile( filePath = parFileOut, FitStatus = ( fitResult.status(), fitResult.minNll(), fitResult.edm() ) )