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 )