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)
if doFit :
# fit data
print 120 * '='
print 'JvLFit: fitting %d events (%s)' % ( dataSet.numEntries(), 'weighted' if dataSet.isWeighted() else 'not weighted' )
RooMinPars = [ ]
if MinosPars :
print 'JvLFit: running Minos for parameters',
for parName in MinosPars :
RooMinPars.append( pdfPars.find(parName) )
print '"%s"' % RooMinPars[-1],
print
fitResult = pdf.fitTo( dataSet, SumW2Error = False
, Minos = RooMinPars, Save = True, Range = fitRange
, **fitOpts
)
from P2VV.Imports import parNames, parValues
print 'JvLFit: 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, dataSet )
pdfConfig.writeParametersToFile( filePath = parFileOut )
