def rf501_simultaneouspdf(): # C r e a t e m o d e l f o r p h y s i c s s a m p l e # ------------------------------------------------------------- # Create observables x = RooRealVar( "x", "x", -8, 8 ) # Construct signal pdf mean = RooRealVar( "mean", "mean", 0, -8, 8 ) sigma = RooRealVar( "sigma", "sigma", 0.3, 0.1, 10 ) gx = RooGaussian( "gx", "gx", x, mean, sigma ) # Construct background pdf a0 = RooRealVar( "a0", "a0", -0.1, -1, 1 ) a1 = RooRealVar( "a1", "a1", 0.004, -1, 1 ) px = RooChebychev( "px", "px", x, RooArgList( a0, a1 ) ) # Construct composite pdf f = RooRealVar( "f", "f", 0.2, 0., 1. ) model = RooAddPdf( "model", "model", RooArgList( gx, px ), RooArgList( f ) ) # C r e a t e m o d e l f o r c o n t r o l s a m p l e # -------------------------------------------------------------- # Construct signal pdf. # NOTE that sigma is shared with the signal sample model mean_ctl = RooRealVar( "mean_ctl", "mean_ctl", -3, -8, 8 ) gx_ctl = RooGaussian( "gx_ctl", "gx_ctl", x, mean_ctl, sigma ) # Construct the background pdf a0_ctl = RooRealVar( "a0_ctl", "a0_ctl", -0.1, -1, 1 ) a1_ctl = RooRealVar( "a1_ctl", "a1_ctl", 0.5, -0.1, 1 ) px_ctl = RooChebychev( "px_ctl", "px_ctl", x, RooArgList( a0_ctl, a1_ctl ) ) # Construct the composite model f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 1. ) model_ctl = RooAddPdf( "model_ctl", "model_ctl", RooArgList( gx_ctl, px_ctl ), RooArgList( f_ctl ) ) # G e n e r a t e e v e n t s f o r b o t h s a m p l e s # --------------------------------------------------------------- # Generate 1000 events in x and y from model data = model.generate( RooArgSet( x ), 100 ) data_ctl = model_ctl.generate( RooArgSet( x ), 2000 ) # C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s # --------------------------------------------------------------------------- # Define category to distinguish physics and control samples events sample = RooCategory( "sample", "sample" ) sample.defineType( "physics" ) sample.defineType( "control" ) # Construct combined dataset in (x,sample) combData = RooDataSet( "combData", "combined data", RooArgSet(x), RooFit.Index( sample ), RooFit.Import( "physics", data ), RooFit.Import( "control", data_ctl ) ) # C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e ) # ----------------------------------------------------------------------------------- # Construct a simultaneous pdf using category sample as index simPdf = RooSimultaneous( "simPdf", "simultaneous pdf", sample ) # Associate model with the physics state and model_ctl with the control state simPdf.addPdf( model, "physics" ) simPdf.addPdf( model_ctl, "control" ) # P e r f o r m a s i m u l t a n e o u s f i t # --------------------------------------------------- # Perform simultaneous fit of model to data and model_ctl to data_ctl simPdf.fitTo( combData ) # P l o t m o d e l s l i c e s o n d a t a s l i c e s # ---------------------------------------------------------------- # Make a frame for the physics sample frame1 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Physics sample" ) ) # Plot all data tagged as physics sample combData.plotOn( frame1, RooFit.Cut( "sample==sample::physics" ) ) # Plot "physics" slice of simultaneous pdf. # NBL You _must_ project the sample index category with data using ProjWData # as a RooSimultaneous makes no prediction on the shape in the index category # and can thus not be integrated simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ), RooFit.ProjWData( RooArgSet(sample), combData ) ) simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ), RooFit.Components( "px" ), RooFit.ProjWData( RooArgSet(sample), combData ), RooFit.LineStyle( kDashed ) ) # The same plot for the control sample slice frame2 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Control sample" ) ) combData.plotOn( frame2, RooFit.Cut( "sample==sample::control" ) ) simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ), RooFit.ProjWData( RooArgSet(sample), combData ) ) simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ), RooFit.Components( "px_ctl" ), RooFit.ProjWData( RooArgSet(sample), combData ), RooFit.LineStyle( kDashed ) ) c = TCanvas( "rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400 ) c.Divide( 2 ) c.cd( 1 ) gPad.SetLeftMargin( 0.15 ) frame1.GetYaxis().SetTitleOffset( 1.4 ) frame1.Draw() c.cd( 2 ) gPad.SetLeftMargin( 0.15 ) frame2.GetYaxis().SetTitleOffset( 1.4 ) frame2.Draw() raw_input()
fitregion_1S = x.setRange('fitregion_1S',10.35,10.8) fitregion_2S = x.setRange('fitregion_2S',10.35,10.8) fitregion_3S = x.setRange('fitregion_3S',10.35,10.8) #simPdf.fitTo(combData,RooFit.Save(), RooFit.Range('fitregion'),RooFit.SplitRange()) simPdf.fitTo(combData,RooFit.Save()) #plotting frame1 = x.frame() combData.plotOn(frame1, RooFit.Cut("sample==sample::b1"), RooFit.MarkerSize(0.7)) simPdf.plotOn(frame1, RooFit.Slice(sample,'b1'), RooFit.ProjWData(RooArgSet(sample),combData), RooFit.LineWidth(2)) c1 = TCanvas('c1','c1') frame1.Draw() frame2 = x.frame() combData.plotOn(frame2, RooFit.Cut("sample==sample::b2"), RooFit.MarkerSize(0.7)) simPdf.plotOn(frame2, RooFit.Slice(sample,'b2'), RooFit.ProjWData(RooArgSet(sample),combData), RooFit.LineWidth(2)) dataset2S.plotOn(frame2)
def mbc_gau_che(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa, fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2, title1, title2, epsfile, txtfile, ymin=0.5, cuts=None, err_type='SYMM', test=False): from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet, RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous, RooArgusBG, RooFormulaVar, RooChebychev, RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta, TPaveText) set_root_style(stat=1, grid=0) mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV') ebeam = RooRealVar('ebeam','Ebeam', 1.8815, 1.892, 'GeV') dflav = RooCategory('dflav','D0 flavor') dflav.defineType('dflav',1) dflav.defineType('dbarflav',-1) if cuts != None: if 'kkmass' in cuts: kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV') ras = RooArgSet(mbc, ebeam, kkmass, dflav) dataset = RooDataSet.read(evtfile, ras) elif 'kpimass' in cuts: kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4, 'GeV') ras = RooArgSet(mbc, ebeam, kpimass, dflav) dataset = RooDataSet.read(evtfile, ras) else: raise NameError(cuts) sys.stdout.write('Using cuts: %s...' %cuts) dataset = dataset.reduce(cuts) sys.stdout.write(' selected %s events.\n' % dataset.numEntries()) else: ras = RooArgSet(mbc, ebeam, dflav) dataset = RooDataSet.read(evtfile, ras) #sigma = RooRealVar('sigma', 'D width', 0.0001, 0.005, 'GeV') sigma = RooRealVar('sigma', 'D width', 0.00468, 'GeV') mbc_dp = RooRealVar('mbc_dp', 'D+ Mass', 1.86962, 'GeV') sigpdf = RooGaussian('gauss_dp', 'D+ gaussian', mbc, mbc_dp, sigma) #arg_cutoff = RooRealVar('arg_cutoff', 'Argus cutoff', 1.88, 1.89, 'GeV') #arg_slope = RooRealVar('arg_slope', 'Argus slope', -10, -100, -1) #bkgpdf = RooArgusBG('argus', 'Argus BG', mbc, arg_cutoff, arg_slope) con0 = RooRealVar('c0', 'constant', -1, 1) con1 = RooRealVar('c1', 'linear', -10, 10) con2 = RooRealVar('c2', 'quadratic', 1) bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2)) yld = RooRealVar('yld', 'D yield', 100, 0, 2000) bkg = RooRealVar('bkg', 'Background', 100, 0, 1000) sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf), RooArgList(yld, bkg)) yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000) bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000) sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigpdf, bkgpdf), RooArgList(yldbar, bkgbar)) totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav) totalpdf.addPdf(sumpdf, 'dflav') totalpdf.addPdf(sumpdfbar, 'dbarflav') MINUIT = 'ermh4' if err_type == 'ASYM': MINUIT = 'erh4' if test: sys.stdout.write('Will save epsfile as: %s \n' %epsfile) sys.stdout.write('Will save txtfile as: %s \n' %txtfile) return if dataset.numEntries() == 0: yld.setVal(0) yldbar.setVal(0) else: # Start Fitting fitres = totalpdf.fitTo(dataset, MINUIT) fitres.Print('v') # Save plots canvas = TCanvas('canvas','mbc', 400, 400); xframe=mbc.frame(50) ProjWData = RooFit.ProjWData(dataset) RooAbsData.plotOn(dataset, xframe) totalpdf.plotOn(xframe, ProjWData) totalpdf.paramOn(xframe) xframe.Draw() canvas.Print(epsfile) # Save fitting parameters pars = [bkg, bkgbar, con1, yld, yldbar] save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_dline_che(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa, fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2, title1, title2, epsfile, txtfile, ymin=0.5, cuts=None, err_type='SYMM', test=False): from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet, RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous, RooArgusBG, RooFormulaVar, RooChebychev, RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta, TPaveText, RooDLineShape) set_root_style(stat=1, grid=0) mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV') ebeam = RooRealVar('ebeam','Ebeam', 1.8815, 1.892, 'GeV') dflav = RooCategory('dflav','D0 flavor') dflav.defineType('dflav',1) dflav.defineType('dbarflav',-1) if cuts != None: if 'kkmass' in cuts: kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV') ras = RooArgSet(mbc, ebeam, kkmass, dflav) dataset = RooDataSet.read(evtfile, ras) else: raise NameError(cuts) sys.stdout.write('Using cuts: %s...' %cuts) dataset = dataset.reduce(cuts) sys.stdout.write(' selected %s events.\n' % dataset.numEntries()) else: ras = RooArgSet(mbc, ebeam, dflav) dataset = RooDataSet.read(evtfile, ras) res = RooRealVar("datares", "datares", mc) mres = RooRealVar("mres","mres", setMres) gamma = RooRealVar('gamma', 'gamma', setGamma) r = RooRealVar('r', 'r', setR) sigmaE = RooRealVar("sigmaE","sigmaE", 0.0021) sigmap1 = RooRealVar("sigmap1","sigmap1", sp1, 0.002, 0.040) scalep2 = RooRealVar("scalep2","scalep2",2.00,1.500,5.500) scalep3 = RooRealVar("scalep3","scalep3",5.00,3.00,10.000) scalep2.setVal(sp2) scalep2.setConstant(1) scalep3.setVal(sp3) scalep3.setConstant(1) as12 = RooArgList(sigmap1,scalep2) sigmap2 = RooFormulaVar("sigmap2","sigma2","sigmap1*scalep2", as12) as123 = RooArgList(sigmap1,scalep2,scalep3) sigmap3 = RooFormulaVar("sigmap3","sigma3","sigmap1*scalep2*scalep3", as123) md = RooRealVar("md","md", setmd,1.863,1.875) f2 = RooRealVar("f2","f2", fa) f3 = RooRealVar("f3","f3", fb) al23 = RooArgList(f2,f3) f1 = RooFormulaVar("f1","f1","1.0-f2-f3", al23) # Construct signal shape fcn1_1 = RooDLineShape("DLineshape1_1","DLineShape1_1",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap1,md,res) fcn1_2 = RooDLineShape("DLineshape1_2","DLineShape1_2",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap2,md,res) fcn1_3 = RooDLineShape("DLineshape1_3","DLineShape1_3",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap3,md,res) fcn2_1 = RooDLineShape("DLineshape2_1","DLineShape2_1",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap1,md,res) fcn2_2 = RooDLineShape("DLineshape2_2","DLineShape2_2",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap2,md,res) fcn2_3 = RooDLineShape("DLineshape2_3","DLineShape2_3",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap3,md,res) alf1_123 = RooArgList(fcn1_1,fcn1_2,fcn1_3) af12 = RooArgList(f1,f2) sigpdf = RooAddPdf("signal1_3","signal1_3", alf1_123, af12) alf2_123 = RooArgList(fcn2_1,fcn2_2,fcn2_3) sigbarpdf = RooAddPdf("signal2_3","signal2_3", alf2_123, af12) con0 = RooRealVar('c0', 'constant', -1, 1) con1 = RooRealVar('c1', 'linear', -10, 10) con2 = RooRealVar('c2', 'quadratic', 1) bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2)) bkgbarpdf = RooChebychev('bkgbarpdf', 'Background', mbc, RooArgList(con1, con2)) yld = RooRealVar('yld', 'D yield', 100, 0, 2000) bkg = RooRealVar('bkg', 'Background', 100, 0, 1000) sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf), RooArgList(yld, bkg)) yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000) bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000) sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigbarpdf, bkgbarpdf), RooArgList(yldbar, bkgbar)) totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav) totalpdf.addPdf(sumpdf, 'dflav') totalpdf.addPdf(sumpdfbar, 'dbarflav') MINUIT = 'ermh4' if err_type == 'ASYM': MINUIT = 'erh4' if test: sys.stdout.write('Will save epsfile as: %s \n' %epsfile) sys.stdout.write('Will save txtfile as: %s \n' %txtfile) return if dataset.numEntries() == 0: yld.setVal(0) yldbar.setVal(0) else: # Start Fitting fitres = totalpdf.fitTo(dataset, MINUIT) fitres.Print('v') # Save plots canvas = TCanvas('canvas','mbc', 400, 400); xframe=mbc.frame(50) ProjWData = RooFit.ProjWData(dataset) RooAbsData.plotOn(dataset, xframe) totalpdf.plotOn(xframe, ProjWData) totalpdf.paramOn(xframe) xframe.Draw() canvas.Print(epsfile) # Save fitting parameters pars = [bkg, bkgbar, con1, md, sigmap1, yld, yldbar] save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_dline_che(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa, fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2, title1, title2, epsfile, txtfile, ymin=0.5, cuts=None, err_type='SYMM', test=False): from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet, RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous, RooArgusBG, RooFormulaVar, RooChebychev, RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta, TPaveText, RooDLineShape) set_root_style(stat=1, grid=0) mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV') ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV') dflav = RooCategory('dflav', 'D0 flavor') dflav.defineType('dflav', 1) dflav.defineType('dbarflav', -1) if cuts != None: if 'kkmass' in cuts: kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV') ras = RooArgSet(mbc, ebeam, kkmass, dflav) dataset = RooDataSet.read(evtfile, ras) else: raise NameError(cuts) sys.stdout.write('Using cuts: %s...' % cuts) dataset = dataset.reduce(cuts) sys.stdout.write(' selected %s events.\n' % dataset.numEntries()) else: ras = RooArgSet(mbc, ebeam, dflav) dataset = RooDataSet.read(evtfile, ras) res = RooRealVar("datares", "datares", mc) mres = RooRealVar("mres", "mres", setMres) gamma = RooRealVar('gamma', 'gamma', setGamma) r = RooRealVar('r', 'r', setR) sigmaE = RooRealVar("sigmaE", "sigmaE", 0.0021) sigmap1 = RooRealVar("sigmap1", "sigmap1", sp1, 0.002, 0.040) scalep2 = RooRealVar("scalep2", "scalep2", 2.00, 1.500, 5.500) scalep3 = RooRealVar("scalep3", "scalep3", 5.00, 3.00, 10.000) scalep2.setVal(sp2) scalep2.setConstant(1) scalep3.setVal(sp3) scalep3.setConstant(1) as12 = RooArgList(sigmap1, scalep2) sigmap2 = RooFormulaVar("sigmap2", "sigma2", "sigmap1*scalep2", as12) as123 = RooArgList(sigmap1, scalep2, scalep3) sigmap3 = RooFormulaVar("sigmap3", "sigma3", "sigmap1*scalep2*scalep3", as123) md = RooRealVar("md", "md", setmd, 1.863, 1.875) f2 = RooRealVar("f2", "f2", fa) f3 = RooRealVar("f3", "f3", fb) al23 = RooArgList(f2, f3) f1 = RooFormulaVar("f1", "f1", "1.0-f2-f3", al23) # Construct signal shape fcn1_1 = RooDLineShape("DLineshape1_1", "DLineShape1_1", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap1, md, res) fcn1_2 = RooDLineShape("DLineshape1_2", "DLineShape1_2", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap2, md, res) fcn1_3 = RooDLineShape("DLineshape1_3", "DLineShape1_3", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap3, md, res) fcn2_1 = RooDLineShape("DLineshape2_1", "DLineShape2_1", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap1, md, res) fcn2_2 = RooDLineShape("DLineshape2_2", "DLineShape2_2", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap2, md, res) fcn2_3 = RooDLineShape("DLineshape2_3", "DLineShape2_3", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap3, md, res) alf1_123 = RooArgList(fcn1_1, fcn1_2, fcn1_3) af12 = RooArgList(f1, f2) sigpdf = RooAddPdf("signal1_3", "signal1_3", alf1_123, af12) alf2_123 = RooArgList(fcn2_1, fcn2_2, fcn2_3) sigbarpdf = RooAddPdf("signal2_3", "signal2_3", alf2_123, af12) con0 = RooRealVar('c0', 'constant', -1, 1) con1 = RooRealVar('c1', 'linear', -10, 10) con2 = RooRealVar('c2', 'quadratic', 1) bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2)) bkgbarpdf = RooChebychev('bkgbarpdf', 'Background', mbc, RooArgList(con1, con2)) yld = RooRealVar('yld', 'D yield', 100, 0, 2000) bkg = RooRealVar('bkg', 'Background', 100, 0, 1000) sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf), RooArgList(yld, bkg)) yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000) bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000) sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigbarpdf, bkgbarpdf), RooArgList(yldbar, bkgbar)) totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav) totalpdf.addPdf(sumpdf, 'dflav') totalpdf.addPdf(sumpdfbar, 'dbarflav') MINUIT = 'ermh4' if err_type == 'ASYM': MINUIT = 'erh4' if test: sys.stdout.write('Will save epsfile as: %s \n' % epsfile) sys.stdout.write('Will save txtfile as: %s \n' % txtfile) return if dataset.numEntries() == 0: yld.setVal(0) yldbar.setVal(0) else: # Start Fitting fitres = totalpdf.fitTo(dataset, MINUIT) fitres.Print('v') # Save plots canvas = TCanvas('canvas', 'mbc', 400, 400) xframe = mbc.frame(50) ProjWData = RooFit.ProjWData(dataset) RooAbsData.plotOn(dataset, xframe) totalpdf.plotOn(xframe, ProjWData) totalpdf.paramOn(xframe) xframe.Draw() canvas.Print(epsfile) # Save fitting parameters pars = [bkg, bkgbar, con1, md, sigmap1, yld, yldbar] save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_single_3s(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa, fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2, title1, title2, epsfile, txtfile, ymin=0.5, cuts=None, err_type='SYMM', test=False): from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet, RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous, RooArgusBG, RooFormulaVar, RooDLineShape, RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta, TPaveText) set_root_style(stat=1, grid=0) # // sp1 = sigma of signal # // sp2 = ratio of sigmas betwwen sigma2 sigma 1 # // sp3 = ratio of sigmas betwwen sigma3 sigma 2 # // fa, fb, - fractions # // xi_side - slope of argus # // p_side - power of argus # mc = 1 Monte Carlo Model: EvtGenModels/Class/EvtVPHOtoVISR.cc # mc = 3 Data Model: with BES 2007 paper (BES2006 lineshape hepex/0612056) mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV') ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV') dflav = RooCategory('dflav','D flavor') dflav.defineType('dflav', 1) dflav.defineType('dbarflav', -1) if cuts != None: if 'kkmass' in cuts: kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV') ras = RooArgSet(mbc, ebeam, kkmass, dflav) dataset = RooDataSet.read(evtfile, ras) elif 'kpimass' in cuts: kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4, 'GeV') ras = RooArgSet(mbc, ebeam, kpimass, dflav) dataset = RooDataSet.read(evtfile, ras) else: raise NameError(cuts) sys.stdout.write('Using cuts: %s...' %cuts) dataset = dataset.reduce(cuts) sys.stdout.write(' selected %s events.\n' % dataset.numEntries()) else: ras = RooArgSet(mbc, ebeam, dflav) dataset = RooDataSet.read(evtfile, ras) res = RooRealVar("datares", "datares", mc) mres = RooRealVar("mres","mres", setMres) gamma = RooRealVar('gamma', 'gamma', setGamma) r = RooRealVar('r', 'r', setR) sigmaE = RooRealVar("sigmaE","sigmaE", 0.0021) sigmap1 = RooRealVar("sigmap1","sigmap1", sp1, 0.002, 0.040) scalep2 = RooRealVar("scalep2","scalep2",2.00,1.500,5.500) scalep3 = RooRealVar("scalep3","scalep3",5.00,3.00,10.000) scalep2.setVal(sp2) scalep2.setConstant(1) scalep3.setVal(sp3) scalep3.setConstant(1) as12 = RooArgList(sigmap1,scalep2) sigmap2 = RooFormulaVar("sigmap2","sigma2","sigmap1*scalep2", as12) as123 = RooArgList(sigmap1,scalep2,scalep3) sigmap3 = RooFormulaVar("sigmap3","sigma3","sigmap1*scalep2*scalep3", as123) md = RooRealVar("md","md", setmd,1.863,1.875) f2 = RooRealVar("f2","f2", fa) f3 = RooRealVar("f3","f3", fb) al23 = RooArgList(f2,f3) f1 = RooFormulaVar("f1","f1","1.0-f2-f3", al23) # Construct signal shape fcn1_1 = RooDLineShape("DLineshape1_1","DLineShape1_1",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap1,md,res) fcn1_2 = RooDLineShape("DLineshape1_2","DLineShape1_2",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap2,md,res) fcn1_3 = RooDLineShape("DLineshape1_3","DLineShape1_3",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap3,md,res) fcn2_1 = RooDLineShape("DLineshape2_1","DLineShape2_1",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap1,md,res) fcn2_2 = RooDLineShape("DLineshape2_2","DLineShape2_2",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap2,md,res) fcn2_3 = RooDLineShape("DLineshape2_3","DLineShape2_3",4,mbc,ebeam, mres,gamma,r,sigmaE,sigmap3,md,res) alf1_123 = RooArgList(fcn1_1,fcn1_2,fcn1_3) af12 = RooArgList(f1,f2) signal1_3 = RooAddPdf("signal1_3","signal1_3", alf1_123, af12) alf2_123 = RooArgList(fcn2_1,fcn2_2,fcn2_3) signal2_3 = RooAddPdf("signal2_3","signal2_3", alf2_123, af12) p = RooRealVar("p","p", setp, 0.1, 1.5) xi= RooRealVar("xi","xi",setxi,-100.0,-0.1) Bkgd1 = RooArgusBG("argus1","argus1",mbc,ebeam,xi,p) Bkgd2 = RooArgusBG("argus2","argus2",mbc,ebeam,xi,p) shapes1 = RooArgList(signal1_3) shapes1.add(signal1_3) shapes1.add(Bkgd1) shapes2 = RooArgList(signal2_3) shapes2.add(signal2_3) shapes2.add(Bkgd2) N1 = RooRealVar("N1","N1",setN1,0.0,200000000.0) N2 = RooRealVar("N2","N2",setN2,0.0,200000000.0) Nbkgd1 = RooRealVar("Nbkgd1","Nbkgd1",setNbkgd1, 0.0, 200000000.0) Nbkgd2 = RooRealVar("Nbkgd2","Nbkgd2",setNbkgd2, 0.0, 200000000.0) yields1 = RooArgList(N1) yields1.add(N1) yields1.add(Nbkgd1) yields2 = RooArgList(N2) yields2.add(N2) yields2.add(Nbkgd2) totalPdf1 = RooAddPdf("totalPdf1","totalPdf1", shapes1,yields1) totalPdf2 = RooAddPdf("totalPdf2","totalPdf2", shapes2,yields2) totalPdf = RooSimultaneous("totalPdf","totalPdf",dflav) totalPdf.addPdf(totalPdf1,"dflav") totalPdf.addPdf(totalPdf2,"dbarflav") # Check fitTo options at: # http://root.cern.ch/root/html512/RooAbsPdf.html#RooAbsPdf:fitTo # # Available fit options: # "m" = MIGRAD only, i.e. no MINOS # "s" = estimate step size with HESSE before starting MIGRAD # "h" = run HESSE after MIGRAD # "e" = Perform extended MLL fit # "0" = Run MIGRAD with strategy MINUIT 0 # (no correlation matrix calculation at end) # Does not apply to HESSE or MINOS, if run afterwards. # "q" = Switch off verbose mode # "l" = Save log file with parameter values at each MINUIT step # "v" = Show changed parameters at each MINUIT step # "t" = Time fit # "r" = Save fit output in RooFitResult object # Available optimizer options # "c" = Cache and precalculate components of PDF that exclusively # depend on constant parameters # "2" = Do NLL calculation in multi-processor mode on 2 processors # "3" = Do NLL calculation in multi-processor mode on 3 processors # "4" = Do NLL calculation in multi-processor mode on 4 processors MINUIT = 'ermh4' if err_type == 'ASYM': MINUIT = 'erh4' if test: sys.stdout.write('Will save epsfile as: %s \n' %epsfile) sys.stdout.write('Will save txtfile as: %s \n' %txtfile) return if dataset.numEntries() == 0: N1.setVal(0) N2.setVal(0) else: # Start Fitting fitres = totalPdf.fitTo(dataset, MINUIT) fitres.Print('v') # Save plots canvas = TCanvas('canvas','mbc', 1200, 400); canvas.Divide(3,1) canvas_1 = canvas.GetListOfPrimitives().FindObject('canvas_1') canvas_2 = canvas.GetListOfPrimitives().FindObject('canvas_2') canvas_1.SetLogy(1) canvas_2.SetLogy(1) LineColorRed = RooFit.LineColor(kRed) LineColorBlue = RooFit.LineColor(kBlue) LineWidth = RooFit.LineWidth(1) #0.6) # Plot the D canvas.cd(1) mbcFrame=mbc.frame() mbcFrame=mbc.frame(60) dflav.setLabel('dflav') ebas = RooArgSet(ebeam, dflav) ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset) dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dflav")) mbcFrame.getAttMarker().SetMarkerSize(0.6) mbcFrame.Draw() Slice = RooFit.Slice(dflav) ProjWData = RooFit.ProjWData(ebas, ebeamdata) totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData) chisq1 = mbcFrame.chiSquare()*mbcFrame.GetNbinsX() mbcFrame.Draw() as_bkg1 = RooArgSet(Bkgd1) cp_bkg1 = RooFit.Components(as_bkg1) totalPdf.plotOn(mbcFrame, cp_bkg1, Slice, LineColorBlue, LineWidth, ProjWData) mbcFrame.SetTitle(title1) mbcFrame.SetMinimum(ymin) mbcFrame.Draw() # Plot the D bar canvas.cd(2) mbcFrame=mbc.frame() mbcFrame=mbc.frame(60) dflav.setLabel('dbarflav') ebas = RooArgSet(ebeam, dflav) ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset) dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dbarflav")) mbcFrame.getAttMarker().SetMarkerSize(0.6) mbcFrame.Draw() Slice = RooFit.Slice(dflav) ProjWData = RooFit.ProjWData(ebas, ebeamdata) totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData) chisq2 = mbcFrame.chiSquare()*mbcFrame.GetNbinsX() mbcFrame.Draw() as_bkg2 = RooArgSet(Bkgd2) cp_bkg2 = RooFit.Components(as_bkg2) totalPdf.plotOn(mbcFrame, cp_bkg2, Slice, LineColorBlue, LineWidth, ProjWData) mbcFrame.SetTitle(title2) mbcFrame.SetMinimum(ymin) mbcFrame.Draw() # Plot Statistics Box canvas.cd(3) mbcFrame = mbc.frame() paramWin1 = totalPdf.paramOn(mbcFrame,dataset, "",2,"NELU",0.1,0.9,0.9) mbcFrame.GetXaxis().SetLabelSize(0) mbcFrame.GetXaxis().SetTickLength(0) mbcFrame.GetXaxis().SetLabelSize(0) mbcFrame.GetXaxis().SetTitle("") mbcFrame.GetXaxis().CenterTitle() mbcFrame.GetYaxis().SetLabelSize(0) mbcFrame.GetYaxis().SetTitleSize(0.03) mbcFrame.GetYaxis().SetTickLength(0) paramWin1.getAttText().SetTextSize(0.06) mbcFrame.Draw() mbcFrame.SetTitle("Fit Parameters") ATextBox = TPaveText(.1, .1, .8, .2,"BRNDC") tempString = "#chi^{2}_{1} = %.1f, #chi^{2}_{2} = %.1f" % (chisq1,chisq2) ATextBox.AddText(tempString) ATextBox.SetFillColor(0) ATextBox.SetBorderSize(1) mbcFrame.addObject(ATextBox) mbcFrame.Draw() canvas.Print(epsfile) rootfile = epsfile.replace('.eps', '.root') canvas.Print(rootfile) # Save fitting parameters pars = [N1, N2, Nbkgd1, Nbkgd2, md, p, sigmap1, xi] save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_single_3s(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa, fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2, title1, title2, epsfile, txtfile, ymin=0.5, cuts=None, err_type='SYMM', test=False): from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet, RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous, RooArgusBG, RooFormulaVar, RooDLineShape, RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta, TPaveText) set_root_style(stat=1, grid=0) # // sp1 = sigma of signal # // sp2 = ratio of sigmas betwwen sigma2 sigma 1 # // sp3 = ratio of sigmas betwwen sigma3 sigma 2 # // fa, fb, - fractions # // xi_side - slope of argus # // p_side - power of argus # mc = 1 Monte Carlo Model: EvtGenModels/Class/EvtVPHOtoVISR.cc # mc = 3 Data Model: with BES 2007 paper (BES2006 lineshape hepex/0612056) mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV') ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV') dflav = RooCategory('dflav', 'D flavor') dflav.defineType('dflav', 1) dflav.defineType('dbarflav', -1) if cuts != None: if 'kkmass' in cuts: kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV') ras = RooArgSet(mbc, ebeam, kkmass, dflav) dataset = RooDataSet.read(evtfile, ras) elif 'kpimass' in cuts: kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4, 'GeV') ras = RooArgSet(mbc, ebeam, kpimass, dflav) dataset = RooDataSet.read(evtfile, ras) else: raise NameError(cuts) sys.stdout.write('Using cuts: %s...' % cuts) dataset = dataset.reduce(cuts) sys.stdout.write(' selected %s events.\n' % dataset.numEntries()) else: ras = RooArgSet(mbc, ebeam, dflav) dataset = RooDataSet.read(evtfile, ras) res = RooRealVar("datares", "datares", mc) mres = RooRealVar("mres", "mres", setMres) gamma = RooRealVar('gamma', 'gamma', setGamma) r = RooRealVar('r', 'r', setR) sigmaE = RooRealVar("sigmaE", "sigmaE", 0.0021) sigmap1 = RooRealVar("sigmap1", "sigmap1", sp1, 0.002, 0.040) scalep2 = RooRealVar("scalep2", "scalep2", 2.00, 1.500, 5.500) scalep3 = RooRealVar("scalep3", "scalep3", 5.00, 3.00, 10.000) scalep2.setVal(sp2) scalep2.setConstant(1) scalep3.setVal(sp3) scalep3.setConstant(1) as12 = RooArgList(sigmap1, scalep2) sigmap2 = RooFormulaVar("sigmap2", "sigma2", "sigmap1*scalep2", as12) as123 = RooArgList(sigmap1, scalep2, scalep3) sigmap3 = RooFormulaVar("sigmap3", "sigma3", "sigmap1*scalep2*scalep3", as123) md = RooRealVar("md", "md", setmd, 1.863, 1.875) f2 = RooRealVar("f2", "f2", fa) f3 = RooRealVar("f3", "f3", fb) al23 = RooArgList(f2, f3) f1 = RooFormulaVar("f1", "f1", "1.0-f2-f3", al23) # Construct signal shape fcn1_1 = RooDLineShape("DLineshape1_1", "DLineShape1_1", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap1, md, res) fcn1_2 = RooDLineShape("DLineshape1_2", "DLineShape1_2", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap2, md, res) fcn1_3 = RooDLineShape("DLineshape1_3", "DLineShape1_3", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap3, md, res) fcn2_1 = RooDLineShape("DLineshape2_1", "DLineShape2_1", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap1, md, res) fcn2_2 = RooDLineShape("DLineshape2_2", "DLineShape2_2", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap2, md, res) fcn2_3 = RooDLineShape("DLineshape2_3", "DLineShape2_3", 4, mbc, ebeam, mres, gamma, r, sigmaE, sigmap3, md, res) alf1_123 = RooArgList(fcn1_1, fcn1_2, fcn1_3) af12 = RooArgList(f1, f2) signal1_3 = RooAddPdf("signal1_3", "signal1_3", alf1_123, af12) alf2_123 = RooArgList(fcn2_1, fcn2_2, fcn2_3) signal2_3 = RooAddPdf("signal2_3", "signal2_3", alf2_123, af12) p = RooRealVar("p", "p", setp, 0.1, 1.5) xi = RooRealVar("xi", "xi", setxi, -100.0, -0.1) Bkgd1 = RooArgusBG("argus1", "argus1", mbc, ebeam, xi, p) Bkgd2 = RooArgusBG("argus2", "argus2", mbc, ebeam, xi, p) shapes1 = RooArgList(signal1_3) shapes1.add(signal1_3) shapes1.add(Bkgd1) shapes2 = RooArgList(signal2_3) shapes2.add(signal2_3) shapes2.add(Bkgd2) N1 = RooRealVar("N1", "N1", setN1, 0.0, 200000000.0) N2 = RooRealVar("N2", "N2", setN2, 0.0, 200000000.0) Nbkgd1 = RooRealVar("Nbkgd1", "Nbkgd1", setNbkgd1, 0.0, 200000000.0) Nbkgd2 = RooRealVar("Nbkgd2", "Nbkgd2", setNbkgd2, 0.0, 200000000.0) yields1 = RooArgList(N1) yields1.add(N1) yields1.add(Nbkgd1) yields2 = RooArgList(N2) yields2.add(N2) yields2.add(Nbkgd2) totalPdf1 = RooAddPdf("totalPdf1", "totalPdf1", shapes1, yields1) totalPdf2 = RooAddPdf("totalPdf2", "totalPdf2", shapes2, yields2) totalPdf = RooSimultaneous("totalPdf", "totalPdf", dflav) totalPdf.addPdf(totalPdf1, "dflav") totalPdf.addPdf(totalPdf2, "dbarflav") # Check fitTo options at: # http://root.cern.ch/root/html512/RooAbsPdf.html#RooAbsPdf:fitTo # # Available fit options: # "m" = MIGRAD only, i.e. no MINOS # "s" = estimate step size with HESSE before starting MIGRAD # "h" = run HESSE after MIGRAD # "e" = Perform extended MLL fit # "0" = Run MIGRAD with strategy MINUIT 0 # (no correlation matrix calculation at end) # Does not apply to HESSE or MINOS, if run afterwards. # "q" = Switch off verbose mode # "l" = Save log file with parameter values at each MINUIT step # "v" = Show changed parameters at each MINUIT step # "t" = Time fit # "r" = Save fit output in RooFitResult object # Available optimizer options # "c" = Cache and precalculate components of PDF that exclusively # depend on constant parameters # "2" = Do NLL calculation in multi-processor mode on 2 processors # "3" = Do NLL calculation in multi-processor mode on 3 processors # "4" = Do NLL calculation in multi-processor mode on 4 processors MINUIT = 'ermh4' if err_type == 'ASYM': MINUIT = 'erh4' if test: sys.stdout.write('Will save epsfile as: %s \n' % epsfile) sys.stdout.write('Will save txtfile as: %s \n' % txtfile) return if dataset.numEntries() == 0: N1.setVal(0) N2.setVal(0) else: # Start Fitting fitres = totalPdf.fitTo(dataset, MINUIT) fitres.Print('v') # Save plots canvas = TCanvas('canvas', 'mbc', 1200, 400) canvas.Divide(3, 1) canvas_1 = canvas.GetListOfPrimitives().FindObject('canvas_1') canvas_2 = canvas.GetListOfPrimitives().FindObject('canvas_2') canvas_1.SetLogy(1) canvas_2.SetLogy(1) LineColorRed = RooFit.LineColor(kRed) LineColorBlue = RooFit.LineColor(kBlue) LineWidth = RooFit.LineWidth(1) #0.6) # Plot the D canvas.cd(1) mbcFrame = mbc.frame() mbcFrame = mbc.frame(60) dflav.setLabel('dflav') ebas = RooArgSet(ebeam, dflav) ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset) dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dflav")) mbcFrame.getAttMarker().SetMarkerSize(0.6) mbcFrame.Draw() Slice = RooFit.Slice(dflav) ProjWData = RooFit.ProjWData(ebas, ebeamdata) totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData) chisq1 = mbcFrame.chiSquare() * mbcFrame.GetNbinsX() mbcFrame.Draw() as_bkg1 = RooArgSet(Bkgd1) cp_bkg1 = RooFit.Components(as_bkg1) totalPdf.plotOn(mbcFrame, cp_bkg1, Slice, LineColorBlue, LineWidth, ProjWData) mbcFrame.SetTitle(title1) mbcFrame.SetMinimum(ymin) mbcFrame.Draw() # Plot the D bar canvas.cd(2) mbcFrame = mbc.frame() mbcFrame = mbc.frame(60) dflav.setLabel('dbarflav') ebas = RooArgSet(ebeam, dflav) ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset) dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dbarflav")) mbcFrame.getAttMarker().SetMarkerSize(0.6) mbcFrame.Draw() Slice = RooFit.Slice(dflav) ProjWData = RooFit.ProjWData(ebas, ebeamdata) totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData) chisq2 = mbcFrame.chiSquare() * mbcFrame.GetNbinsX() mbcFrame.Draw() as_bkg2 = RooArgSet(Bkgd2) cp_bkg2 = RooFit.Components(as_bkg2) totalPdf.plotOn(mbcFrame, cp_bkg2, Slice, LineColorBlue, LineWidth, ProjWData) mbcFrame.SetTitle(title2) mbcFrame.SetMinimum(ymin) mbcFrame.Draw() # Plot Statistics Box canvas.cd(3) mbcFrame = mbc.frame() paramWin1 = totalPdf.paramOn(mbcFrame, dataset, "", 2, "NELU", 0.1, 0.9, 0.9) mbcFrame.GetXaxis().SetLabelSize(0) mbcFrame.GetXaxis().SetTickLength(0) mbcFrame.GetXaxis().SetLabelSize(0) mbcFrame.GetXaxis().SetTitle("") mbcFrame.GetXaxis().CenterTitle() mbcFrame.GetYaxis().SetLabelSize(0) mbcFrame.GetYaxis().SetTitleSize(0.03) mbcFrame.GetYaxis().SetTickLength(0) paramWin1.getAttText().SetTextSize(0.06) mbcFrame.Draw() mbcFrame.SetTitle("Fit Parameters") ATextBox = TPaveText(.1, .1, .8, .2, "BRNDC") tempString = "#chi^{2}_{1} = %.1f, #chi^{2}_{2} = %.1f" % (chisq1, chisq2) ATextBox.AddText(tempString) ATextBox.SetFillColor(0) ATextBox.SetBorderSize(1) mbcFrame.addObject(ATextBox) mbcFrame.Draw() canvas.Print(epsfile) rootfile = epsfile.replace('.eps', '.root') canvas.Print(rootfile) # Save fitting parameters pars = [N1, N2, Nbkgd1, Nbkgd2, md, p, sigmap1, xi] save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_gau_che(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa, fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2, title1, title2, epsfile, txtfile, ymin=0.5, cuts=None, err_type='SYMM', test=False): from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet, RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous, RooArgusBG, RooFormulaVar, RooChebychev, RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta, TPaveText) set_root_style(stat=1, grid=0) mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV') ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV') dflav = RooCategory('dflav', 'D0 flavor') dflav.defineType('dflav', 1) dflav.defineType('dbarflav', -1) if cuts != None: if 'kkmass' in cuts: kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV') ras = RooArgSet(mbc, ebeam, kkmass, dflav) dataset = RooDataSet.read(evtfile, ras) elif 'kpimass' in cuts: kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4, 'GeV') ras = RooArgSet(mbc, ebeam, kpimass, dflav) dataset = RooDataSet.read(evtfile, ras) else: raise NameError(cuts) sys.stdout.write('Using cuts: %s...' % cuts) dataset = dataset.reduce(cuts) sys.stdout.write(' selected %s events.\n' % dataset.numEntries()) else: ras = RooArgSet(mbc, ebeam, dflav) dataset = RooDataSet.read(evtfile, ras) #sigma = RooRealVar('sigma', 'D width', 0.0001, 0.005, 'GeV') sigma = RooRealVar('sigma', 'D width', 0.00468, 'GeV') mbc_dp = RooRealVar('mbc_dp', 'D+ Mass', 1.86962, 'GeV') sigpdf = RooGaussian('gauss_dp', 'D+ gaussian', mbc, mbc_dp, sigma) #arg_cutoff = RooRealVar('arg_cutoff', 'Argus cutoff', 1.88, 1.89, 'GeV') #arg_slope = RooRealVar('arg_slope', 'Argus slope', -10, -100, -1) #bkgpdf = RooArgusBG('argus', 'Argus BG', mbc, arg_cutoff, arg_slope) con0 = RooRealVar('c0', 'constant', -1, 1) con1 = RooRealVar('c1', 'linear', -10, 10) con2 = RooRealVar('c2', 'quadratic', 1) bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2)) yld = RooRealVar('yld', 'D yield', 100, 0, 2000) bkg = RooRealVar('bkg', 'Background', 100, 0, 1000) sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf), RooArgList(yld, bkg)) yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000) bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000) sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigpdf, bkgpdf), RooArgList(yldbar, bkgbar)) totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav) totalpdf.addPdf(sumpdf, 'dflav') totalpdf.addPdf(sumpdfbar, 'dbarflav') MINUIT = 'ermh4' if err_type == 'ASYM': MINUIT = 'erh4' if test: sys.stdout.write('Will save epsfile as: %s \n' % epsfile) sys.stdout.write('Will save txtfile as: %s \n' % txtfile) return if dataset.numEntries() == 0: yld.setVal(0) yldbar.setVal(0) else: # Start Fitting fitres = totalpdf.fitTo(dataset, MINUIT) fitres.Print('v') # Save plots canvas = TCanvas('canvas', 'mbc', 400, 400) xframe = mbc.frame(50) ProjWData = RooFit.ProjWData(dataset) RooAbsData.plotOn(dataset, xframe) totalpdf.plotOn(xframe, ProjWData) totalpdf.paramOn(xframe) xframe.Draw() canvas.Print(epsfile) # Save fitting parameters pars = [bkg, bkgbar, con1, yld, yldbar] save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def rf501_simultaneouspdf(): signal_1, bkg_1, signal_2, bkg_2 = get_templates() # C r e a t e m o d e l f o r p h y s i c s s a m p l e # ------------------------------------------------------------- # Create observables x = RooRealVar( "x", "x", 0, 200 ) x.setBins(n_bins) nsig = RooRealVar( "nsig", "#signal events", N_signal_obs, 0., 2*N_data ) nbkg = RooRealVar( "nbkg", "#background events", N_bkg1_obs, 0., 2*N_data ) # Construct signal pdf # mean = RooRealVar( "mean", "mean", mu4, 40, 200 ) # sigma = RooRealVar( "sigma", "sigma", sigma4, 0.1, 20 ) # gx = RooGaussian( "gx", "gx", x, mean, sigma ) roofit_signal_1 = RooDataHist( 'signal_1', 'signal_1', RooArgList(x), signal_1 ) signal_1_pdf = RooHistPdf ( 'signal_1_pdf' , 'signal_1_pdf', RooArgSet(x), roofit_signal_1) # Construct background pdf # mean_bkg = RooRealVar( "mean_bkg", "mean_bkg", mu3, 40, 200 ) # sigma_bkg = RooRealVar( "sigma_bkg", "sigma_bkg", sigma3, 0.1, 20 ) # px = RooGaussian( "px", "px", x, mean_bkg, sigma_bkg ) roofit_bkg_1 = RooDataHist( 'bkg_1', 'bkg_1', RooArgList(x), bkg_1 ) bkg_1_pdf = RooHistPdf ( 'bkg_1_pdf' , 'bkg_1_pdf', RooArgSet(x), roofit_bkg_1) # Construct composite pdf model = RooAddPdf( "model", "model", RooArgList( signal_1_pdf, bkg_1_pdf ), RooArgList( nsig, nbkg ) ) # C r e a t e m o d e l f o r c o n t r o l s a m p l e # -------------------------------------------------------------- # Construct signal pdf. # NOTE that sigma is shared with the signal sample model y = RooRealVar( "y", "y", 0, 200 ) y.setBins(n_bins) mean_ctl = RooRealVar( "mean_ctl", "mean_ctl", mu2, 0, 200 ) sigma_ctl = RooRealVar( "sigma", "sigma", sigma2, 0.1, 10 ) gx_ctl = RooGaussian( "gx_ctl", "gx_ctl", y, mean_ctl, sigma_ctl ) # Construct the background pdf mean_bkg_ctl = RooRealVar( "mean_bkg_ctl", "mean_bkg_ctl", mu1, 0, 200 ) sigma_bkg_ctl = RooRealVar( "sigma_bkg_ctl", "sigma_bkg_ctl", sigma1, 0.1, 20 ) px_ctl = RooGaussian( "px_ctl", "px_ctl", y, mean_bkg_ctl, sigma_bkg_ctl ) # Construct the composite model # f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 20. ) model_ctl = RooAddPdf( "model_ctl", "model_ctl", RooArgList( gx_ctl, px_ctl ), RooArgList( nsig, nbkg ) ) # G e t e v e n t s f o r b o t h s a m p l e s # --------------------------------------------------------------- real_data, real_data_ctl = get_data() real_data_hist = RooDataHist( 'real_data_hist', 'real_data_hist', RooArgList( x ), real_data ) real_data_ctl_hist = RooDataHist( 'real_data_ctl_hist', 'real_data_ctl_hist', RooArgList( y ), real_data_ctl ) input_hists = MapStrRootPtr() input_hists.insert( StrHist( "physics", real_data ) ) input_hists.insert( StrHist( "control", real_data_ctl ) ) # C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s # --------------------------------------------------------------------------- # Define category to distinguish physics and control samples events sample = RooCategory( "sample", "sample" ) sample.defineType( "physics" ) sample.defineType( "control" ) # Construct combined dataset in (x,sample) combData = RooDataHist( "combData", "combined data", RooArgList( x), sample , input_hists ) # C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e ) # ----------------------------------------------------------------------------------- # Construct a simultaneous pdf using category sample as index simPdf = RooSimultaneous( "simPdf", "simultaneous pdf", sample ) # Associate model with the physics state and model_ctl with the control state simPdf.addPdf( model, "physics" ) simPdf.addPdf( model_ctl, "control" ) #60093.048127 173.205689173 44.7112503776 # P e r f o r m a s i m u l t a n e o u s f i t # --------------------------------------------------- model.fitTo( real_data_hist, RooFit.Minimizer( "Minuit2", "Migrad" ), RooFit.NumCPU( 1 ), # RooFit.Extended(), # RooFit.Save(), ) summary = 'fit in signal region\n' summary += 'nsig: ' + str( nsig.getValV() ) + ' +- ' + str( nsig.getError() ) + '\n' summary += 'nbkg: ' + str( nbkg.getValV() ) + ' +- ' + str( nbkg.getError() ) + '\n' # # model_ctl.fitTo( real_data_ctl_hist ) # summary += 'fit in control region\n' # summary += 'nsig: ' + str( nsig.getValV() ) + ' +- ' + str( nsig.getError() ) + '\n' # summary += 'nbkg: ' + str( nbkg.getValV() ) + ' +- ' + str( nbkg.getError() ) + '\n' # # # Perform simultaneous fit of model to data and model_ctl to data_ctl # simPdf.fitTo( combData ) # summary += 'Combined fit\n' # summary += 'nsig: ' + str( nsig.getValV() ) + ' +- ' + str( nsig.getError() ) + '\n' # summary += 'nbkg: ' + str( nbkg.getValV() ) + ' +- ' + str( nbkg.getError() ) + '\n' # P l o t m o d e l s l i c e s o n d a t a s l i c e s # ---------------------------------------------------------------- # Make a frame for the physics sample frame1 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Physics sample" ) ) # Plot all data tagged as physics sample combData.plotOn( frame1, RooFit.Cut( "sample==sample::physics" ) ) # Plot "physics" slice of simultaneous pdf. # NBL You _must_ project the sample index category with data using ProjWData # as a RooSimultaneous makes no prediction on the shape in the index category # and can thus not be integrated simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ), RooFit.ProjWData( RooArgSet( sample ), combData ), ) simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ), RooFit.Components( "signal_1_pdf" ), RooFit.ProjWData( RooArgSet( sample ), combData ), RooFit.LineStyle( kDashed ), ) simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ), RooFit.Components( "bkg_1_pdf" ), RooFit.ProjWData( RooArgSet( sample ), combData ), RooFit.LineStyle( kDashed ), RooFit.LineColor( kRed ) ) # The same plot for the control sample slice frame2 = y.frame( RooFit.Bins( 30 ), RooFit.Title( "Control sample" ) ) combData.plotOn( frame2, RooFit.Cut( "sample==sample::control" ) ) simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ), RooFit.ProjWData( RooArgSet( sample ), combData ) ) simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ), RooFit.Components( "px_ctl" ), RooFit.ProjWData( RooArgSet( sample ), combData ), RooFit.LineStyle( kDashed ) ) c = TCanvas( "rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400 ) c.Divide( 2 ) c.cd( 1 ) gPad.SetLeftMargin( 0.15 ) frame1.GetYaxis().SetTitleOffset( 1.4 ) frame1.Draw() c.cd( 2 ) gPad.SetLeftMargin( 0.15 ) frame2.GetYaxis().SetTitleOffset( 1.4 ) frame2.Draw() print summary print real_data.Integral() raw_input()
total_pdf.fitTo(datahist) #Plotting fits canvs = [] fitPads = [] pullPads = [] pulls = [] for i in range(n_regions) : region = regions[i] canvs.append(TCanvas(region+"_canv", region+" D0 Mass Region Fit")) frame = deltam.frame() datahist.plotOn(frame, Cut("regionIndex==regionIndex::"+region)) regionIndexSet = RooArgSet(regionIndex) total_pdf.plotOn(frame, Slice(regionIndex, region), ProjWData(regionIndexSet, datahist)) pulls.append(frame.pullHist()) total_pdf.plotOn(frame, Slice(regionIndex, region), ProjWData(regionIndexSet, datahist), Components("bg_pdf"), LineColor(kGreen+2), LineStyle(2)) total_pdf.plotOn(frame, Slice(regionIndex, region), ProjWData(regionIndexSet, datahist), Components("signal_pdf_"+region), LineColor(kRed+2), LineStyle(3)) #Creating pad to plot fit to data fitPads.append(TPad("fitPad_{}".format(i), "fitPad_{}".format(i), 0., 0.3, 1., 1.)) fitPads[i].SetMargin(0.08, 0.05, 0.05, 0.1) fitPads[i].Draw() fitPads[i].cd() frame.Draw() #Creating pad to plot pull ( (actual - fit) / uncertainty ) canvs[i].cd() pullPads.append(TPad("pullPad_{}".format(i), "pullPad_{}".format(i), 0., 0., 1., 0.3)) pullPads[i].SetMargin(0.08, 0.05, 0.1, 0.05)
def rf501_simultaneouspdf(): # C r e a t e m o d e l f o r p h y s i c s s a m p l e # ------------------------------------------------------------- # Create observables x = RooRealVar("x", "x", 40, 200) nsig = RooRealVar("nsig", "#signal events", 200, 0., 10000) nbkg = RooRealVar("nbkg", "#background events", 800, 0., 200000) # Construct signal pdf mean = RooRealVar("mean", "mean", mu4, 40, 200) sigma = RooRealVar("sigma", "sigma", sigma4, 0.1, 20) gx = RooGaussian("gx", "gx", x, mean, sigma) # Construct background pdf mean_bkg = RooRealVar("mean_bkg", "mean_bkg", mu3, 40, 200) sigma_bkg = RooRealVar("sigma_bkg", "sigma_bkg", sigma3, 0.1, 20) px = RooGaussian("px", "px", x, mean_bkg, sigma_bkg) # Construct composite pdf model = RooAddPdf("model", "model", RooArgList(gx, px), RooArgList(nsig, nbkg)) # C r e a t e m o d e l f o r c o n t r o l s a m p l e # -------------------------------------------------------------- # Construct signal pdf. # NOTE that sigma is shared with the signal sample model y = RooRealVar("y", "y", 40, 200) mean_ctl = RooRealVar("mean_ctl", "mean_ctl", mu2, 40, 200) sigma_ctl = RooRealVar("sigma", "sigma", sigma2, 0.1, 10) gx_ctl = RooGaussian("gx_ctl", "gx_ctl", y, mean_ctl, sigma_ctl) # Construct the background pdf mean_bkg_ctl = RooRealVar("mean_bkg_ctl", "mean_bkg_ctl", mu1, 40, 200) sigma_bkg_ctl = RooRealVar("sigma_bkg_ctl", "sigma_bkg_ctl", sigma1, 0.1, 20) px_ctl = RooGaussian("px_ctl", "px_ctl", y, mean_bkg_ctl, sigma_bkg_ctl) # Construct the composite model # f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 20. ) model_ctl = RooAddPdf("model_ctl", "model_ctl", RooArgList(gx_ctl, px_ctl), RooArgList(nsig, nbkg)) # G e t e v e n t s f o r b o t h s a m p l e s # --------------------------------------------------------------- real_data, real_data_ctl = get_data() real_data_hist = RooDataHist('real_data_hist', 'real_data_hist', RooArgList(x), real_data) real_data_ctl_hist = RooDataHist('real_data_ctl_hist', 'real_data_ctl_hist', RooArgList(y), real_data_ctl) input_hists = MapStrRootPtr() input_hists.insert(StrHist("physics", real_data)) input_hists.insert(StrHist("control", real_data_ctl)) # C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s # --------------------------------------------------------------------------- # Define category to distinguish physics and control samples events sample = RooCategory("sample", "sample") sample.defineType("physics") sample.defineType("control") # Construct combined dataset in (x,sample) combData = RooDataHist("combData", "combined data", RooArgList(x), sample, input_hists) # C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e ) # ----------------------------------------------------------------------------------- # Construct a simultaneous pdf using category sample as index simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample) # Associate model with the physics state and model_ctl with the control state simPdf.addPdf(model, "physics") simPdf.addPdf(model_ctl, "control") # P e r f o r m a s i m u l t a n e o u s f i t # --------------------------------------------------- model.fitTo(real_data_hist) summary = 'fit in signal region\n' summary += 'nsig: ' + str(nsig.getValV()) + ' +- ' + str( nsig.getError()) + '\n' summary += 'nbkg: ' + str(nbkg.getValV()) + ' +- ' + str( nbkg.getError()) + '\n' model_ctl.fitTo(real_data_ctl_hist) summary += 'fit in control region\n' summary += 'nsig: ' + str(nsig.getValV()) + ' +- ' + str( nsig.getError()) + '\n' summary += 'nbkg: ' + str(nbkg.getValV()) + ' +- ' + str( nbkg.getError()) + '\n' # Perform simultaneous fit of model to data and model_ctl to data_ctl simPdf.fitTo(combData) summary += 'Combined fit\n' summary += 'nsig: ' + str(nsig.getValV()) + ' +- ' + str( nsig.getError()) + '\n' summary += 'nbkg: ' + str(nbkg.getValV()) + ' +- ' + str( nbkg.getError()) + '\n' # P l o t m o d e l s l i c e s o n d a t a s l i c e s # ---------------------------------------------------------------- # Make a frame for the physics sample frame1 = x.frame(RooFit.Bins(30), RooFit.Title("Physics sample")) # Plot all data tagged as physics sample combData.plotOn(frame1, RooFit.Cut("sample==sample::physics")) # Plot "physics" slice of simultaneous pdf. # NBL You _must_ project the sample index category with data using ProjWData # as a RooSimultaneous makes no prediction on the shape in the index category # and can thus not be integrated simPdf.plotOn(frame1, RooFit.Slice(sample, "physics"), RooFit.ProjWData(RooArgSet(sample), combData)) simPdf.plotOn(frame1, RooFit.Slice(sample, "physics"), RooFit.Components("px"), RooFit.ProjWData(RooArgSet(sample), combData), RooFit.LineStyle(kDashed)) # The same plot for the control sample slice frame2 = y.frame(RooFit.Bins(30), RooFit.Title("Control sample")) combData.plotOn(frame2, RooFit.Cut("sample==sample::control")) simPdf.plotOn(frame2, RooFit.Slice(sample, "control"), RooFit.ProjWData(RooArgSet(sample), combData)) simPdf.plotOn(frame2, RooFit.Slice(sample, "control"), RooFit.Components("px_ctl"), RooFit.ProjWData(RooArgSet(sample), combData), RooFit.LineStyle(kDashed)) simPdf.plotOn(frame2, RooFit.Slice(sample, "control"), RooFit.Components("gx_ctl"), RooFit.ProjWData(RooArgSet(sample), combData), RooFit.LineStyle(kDashed)) c = TCanvas("rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400) c.Divide(2) c.cd(1) gPad.SetLeftMargin(0.15) frame1.GetYaxis().SetTitleOffset(1.4) frame1.Draw() c.cd(2) gPad.SetLeftMargin(0.15) frame2.GetYaxis().SetTitleOffset(1.4) frame2.Draw() print summary raw_input()
def rf501_simultaneouspdf(): # C r e a t e m o d e l f o r p h y s i c s s a m p l e # ------------------------------------------------------------- # Create observables x = RooRealVar("x", "x", 40, 200) nsig = RooRealVar("nsig", "#signal events", 200, 0.0, 10000) nbkg = RooRealVar("nbkg", "#background events", 800, 0.0, 200000) # Construct signal pdf mean = RooRealVar("mean", "mean", mu4, 40, 200) sigma = RooRealVar("sigma", "sigma", sigma4, 0.1, 20) gx = RooGaussian("gx", "gx", x, mean, sigma) # Construct background pdf mean_bkg = RooRealVar("mean_bkg", "mean_bkg", mu3, 40, 200) sigma_bkg = RooRealVar("sigma_bkg", "sigma_bkg", sigma3, 0.1, 20) px = RooGaussian("px", "px", x, mean_bkg, sigma_bkg) # Construct composite pdf model = RooAddPdf("model", "model", RooArgList(gx, px), RooArgList(nsig, nbkg)) # C r e a t e m o d e l f o r c o n t r o l s a m p l e # -------------------------------------------------------------- # Construct signal pdf. # NOTE that sigma is shared with the signal sample model y = RooRealVar("y", "y", 40, 200) mean_ctl = RooRealVar("mean_ctl", "mean_ctl", mu2, 40, 200) sigma_ctl = RooRealVar("sigma", "sigma", sigma2, 0.1, 10) gx_ctl = RooGaussian("gx_ctl", "gx_ctl", y, mean_ctl, sigma_ctl) # Construct the background pdf mean_bkg_ctl = RooRealVar("mean_bkg_ctl", "mean_bkg_ctl", mu1, 40, 200) sigma_bkg_ctl = RooRealVar("sigma_bkg_ctl", "sigma_bkg_ctl", sigma1, 0.1, 20) px_ctl = RooGaussian("px_ctl", "px_ctl", y, mean_bkg_ctl, sigma_bkg_ctl) # Construct the composite model # f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 20. ) model_ctl = RooAddPdf("model_ctl", "model_ctl", RooArgList(gx_ctl, px_ctl), RooArgList(nsig, nbkg)) # G e t e v e n t s f o r b o t h s a m p l e s # --------------------------------------------------------------- real_data, real_data_ctl = get_data() real_data_hist = RooDataHist("real_data_hist", "real_data_hist", RooArgList(x), real_data) real_data_ctl_hist = RooDataHist("real_data_ctl_hist", "real_data_ctl_hist", RooArgList(y), real_data_ctl) input_hists = MapStrRootPtr() input_hists.insert(StrHist("physics", real_data)) input_hists.insert(StrHist("control", real_data_ctl)) # C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s # --------------------------------------------------------------------------- # Define category to distinguish physics and control samples events sample = RooCategory("sample", "sample") sample.defineType("physics") sample.defineType("control") # Construct combined dataset in (x,sample) combData = RooDataHist("combData", "combined data", RooArgList(x), sample, input_hists) # C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e ) # ----------------------------------------------------------------------------------- # Construct a simultaneous pdf using category sample as index simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample) # Associate model with the physics state and model_ctl with the control state simPdf.addPdf(model, "physics") simPdf.addPdf(model_ctl, "control") # P e r f o r m a s i m u l t a n e o u s f i t # --------------------------------------------------- model.fitTo(real_data_hist) summary = "fit in signal region\n" summary += "nsig: " + str(nsig.getValV()) + " +- " + str(nsig.getError()) + "\n" summary += "nbkg: " + str(nbkg.getValV()) + " +- " + str(nbkg.getError()) + "\n" model_ctl.fitTo(real_data_ctl_hist) summary += "fit in control region\n" summary += "nsig: " + str(nsig.getValV()) + " +- " + str(nsig.getError()) + "\n" summary += "nbkg: " + str(nbkg.getValV()) + " +- " + str(nbkg.getError()) + "\n" # Perform simultaneous fit of model to data and model_ctl to data_ctl simPdf.fitTo(combData) summary += "Combined fit\n" summary += "nsig: " + str(nsig.getValV()) + " +- " + str(nsig.getError()) + "\n" summary += "nbkg: " + str(nbkg.getValV()) + " +- " + str(nbkg.getError()) + "\n" # P l o t m o d e l s l i c e s o n d a t a s l i c e s # ---------------------------------------------------------------- # Make a frame for the physics sample frame1 = x.frame(RooFit.Bins(30), RooFit.Title("Physics sample")) # Plot all data tagged as physics sample combData.plotOn(frame1, RooFit.Cut("sample==sample::physics")) # Plot "physics" slice of simultaneous pdf. # NBL You _must_ project the sample index category with data using ProjWData # as a RooSimultaneous makes no prediction on the shape in the index category # and can thus not be integrated simPdf.plotOn(frame1, RooFit.Slice(sample, "physics"), RooFit.ProjWData(RooArgSet(sample), combData)) simPdf.plotOn( frame1, RooFit.Slice(sample, "physics"), RooFit.Components("px"), RooFit.ProjWData(RooArgSet(sample), combData), RooFit.LineStyle(kDashed), ) # The same plot for the control sample slice frame2 = y.frame(RooFit.Bins(30), RooFit.Title("Control sample")) combData.plotOn(frame2, RooFit.Cut("sample==sample::control")) simPdf.plotOn(frame2, RooFit.Slice(sample, "control"), RooFit.ProjWData(RooArgSet(sample), combData)) simPdf.plotOn( frame2, RooFit.Slice(sample, "control"), RooFit.Components("px_ctl"), RooFit.ProjWData(RooArgSet(sample), combData), RooFit.LineStyle(kDashed), ) simPdf.plotOn( frame2, RooFit.Slice(sample, "control"), RooFit.Components("gx_ctl"), RooFit.ProjWData(RooArgSet(sample), combData), RooFit.LineStyle(kDashed), ) c = TCanvas("rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400) c.Divide(2) c.cd(1) gPad.SetLeftMargin(0.15) frame1.GetYaxis().SetTitleOffset(1.4) frame1.Draw() c.cd(2) gPad.SetLeftMargin(0.15) frame2.GetYaxis().SetTitleOffset(1.4) frame2.Draw() print summary raw_input()
def rooFit501(): print ">>> setup model for physics sample..." x = RooRealVar("x", "x", -8, 8) mean = RooRealVar("mean", "mean", 0, -8, 8) sigma = RooRealVar("sigma", "sigma", 0.3, 0.1, 10) gauss = RooGaussian("gx", "gx", x, mean, sigma) a0 = RooRealVar("a0", "a0", -0.1, -1, 1) a1 = RooRealVar("a1", "a1", 0.004, -1, 1) px = RooChebychev("px", "px", x, RooArgList(a0, a1)) f = RooRealVar("f", "f", 0.2, 0., 1.) model = RooAddPdf("model", "model", RooArgList(gauss, px), RooArgList(f)) print ">>> setup model for control sample..." # NOTE: sigma is shared with the signal sample model mean_ctrl = RooRealVar("mean_ctrl", "mean_ctrl", -3, -8, 8) gauss_ctrl = RooGaussian("gauss_ctrl", "gauss_ctrl", x, mean_ctrl, sigma) a0_ctrl = RooRealVar("a0_ctrl", "a0_ctrl", -0.1, -1, 1) a1_ctrl = RooRealVar("a1_ctrl", "a1_ctrl", 0.5, -0.1, 1) px_ctrl = RooChebychev("px_ctrl", "px_ctrl", x, RooArgList(a0_ctrl, a1_ctrl)) f_ctrl = RooRealVar("f_ctrl", "f_ctrl", 0.5, 0., 1.) model_ctrl = RooAddPdf("model_ctrl", "model_ctrl", RooArgList(gauss_ctrl, px_ctrl), RooArgList(f_ctrl)) print ">>> generate events for both samples..." data = model.generate(RooArgSet(x), 100) # RooDataSet data_ctrl = model_ctrl.generate(RooArgSet(x), 2000) # RooDataSet print ">>> create index category and join samples..." # Define category to distinguish physics and control samples events sample = RooCategory("sample", "sample") sample.defineType("physics") sample.defineType("control") print ">>> construct combined dataset in (x,sample)..." combData = RooDataSet("combData", "combined data", RooArgSet(x), Index(sample), Import("physics", data), Import("control", data_ctrl)) print ">>> construct a simultaneous pdf in (x,sample)..." # Construct a simultaneous pdf using category sample as index simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample) # Associate model with the physics state and model_ctrl with the control state simPdf.addPdf(model, "physics") simPdf.addPdf(model_ctrl, "control") print ">>> perform a simultaneous fit..." # Perform simultaneous fit of model to data and model_ctrl to data_ctrl simPdf.fitTo(combData) print "\n>>> plot model slices on data slices..." frame1 = x.frame(Bins(30), Title("Physics sample")) # RooPlot combData.plotOn(frame1, Cut("sample==sample::physics")) # Plot "physics" slice of simultaneous pdf. # NBL You _must_ project the sample index category with data using ProjWData # as a RooSimultaneous makes no prediction on the shape in the index category # and can thus not be integrated simPdf.plotOn(frame1, Slice(sample, "physics"), ProjWData(RooArgSet(sample), combData)) simPdf.plotOn(frame1, Slice(sample, "physics"), Components("px"), ProjWData(RooArgSet(sample), combData), LineStyle(kDashed)) print "\n>>> plot control sample slices..." frame2 = x.frame(Bins(30), Title("Control sample")) # RooPlot combData.plotOn(frame2, Cut("sample==sample::control")) simPdf.plotOn(frame2, Slice(sample, "control"), ProjWData(RooArgSet(sample), combData)) simPdf.plotOn(frame2, Slice(sample, "control"), Components("px_ctrl"), ProjWData(RooArgSet(sample), combData), LineStyle(kDashed)) print "\n>>> draw on canvas..." canvas = TCanvas("canvas", "canvas", 100, 100, 1400, 600) canvas.Divide(2) canvas.cd(1) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame1.GetYaxis().SetLabelOffset(0.008) frame1.GetYaxis().SetTitleOffset(1.6) frame1.GetYaxis().SetTitleSize(0.045) frame1.GetXaxis().SetTitleSize(0.045) frame1.Draw() canvas.cd(2) gPad.SetLeftMargin(0.15) gPad.SetRightMargin(0.02) frame2.GetYaxis().SetLabelOffset(0.008) frame2.GetYaxis().SetTitleOffset(1.6) frame2.GetYaxis().SetTitleSize(0.045) frame2.GetXaxis().SetTitleSize(0.045) frame2.Draw() canvas.SaveAs("rooFit501.png")
data_fail = gauss1.generate(RooArgSet(Mkk), 4000) combData = RooDataSet("combData", "combined data", RooArgSet(Mkk), RooFit.Index(myFitCat), RooFit.Import("pass", data_pass), RooFit.Import("fail", data_fail)) #FIT totalPdf.fitTo(combData) #PLOT sampleSet = RooArgSet(myFitCat) frame1 = Mkk.frame(RooFit.Bins(50), RooFit.Title("Passed sample")) # Plot all data tagged as passed sample combData.plotOn(frame1, RooFit.Cut("myFitCat==myFitCat::pass")) totalPdf.plotOn(frame1, RooFit.Slice(myFitCat, "pass"), RooFit.ProjWData(sampleSet, combData), RooFit.LineStyle(kDashed)) totalPdf.paramOn(frame1, RooFit.Layout(0.55, 0.9, 0.9), RooFit.Format("NEU", RooFit.AutoPrecision(1))) # The same plot for the failled sample slice frame2 = Mkk.frame(RooFit.Bins(50), RooFit.Title("Failed sample")) combData.plotOn(frame2, RooFit.Cut("myFitCat==myFitCat::fail")) totalPdf.plotOn(frame2, RooFit.Slice(myFitCat, "fail"), RooFit.ProjWData(sampleSet, combData)) #simPdf.plotOn(frame2, RooFit.Slice(myFitCat, "fail"), RooFit.ProjWData(sampleSet, combData), RooFit.LineStyle(kDashed)) c = TCanvas("simultaneouspdf", "simultaneouspdf", 800, 400) c.Divide(2) c.cd(1) frame1.Draw()
# 2. Fit model to data simPdf.fitTo(combined) # 3. Create frame frame1 = x.frame(RooFit.Bins(30), RooFit.Title("Passed sample")) frame2 = x.frame(RooFit.Bins(30), RooFit.Title("Failed sample")) # 4. Put data on frame combined.plotOn(frame1, RooFit.Cut("myCat==myCat::passed")) combined.plotOn(frame2, RooFit.Cut("myCat==myCat::failed")) sampleSet = RooArgSet(myCat) # 5. Put model on frame # put one component simPdf.plotOn(frame1, RooFit.Slice(myCat, "passed"), RooFit.Components("px_ctl"), RooFit.ProjWData(sampleSet, combined), RooFit.LineStyle(kDashed), RooFit.LineColor(kRed)) # put one component simPdf.plotOn(frame1, RooFit.Slice(myCat, "passed"), RooFit.Components("gx_ctl"), RooFit.ProjWData(sampleSet, combined), RooFit.LineColor(kBlack), RooFit.LineStyle(kDashed)) # put whole model simPdf.plotOn(frame2, RooFit.Slice(myCat, "failed"), RooFit.ProjWData(sampleSet, combined), RooFit.LineStyle(kDashed), RooFit.LineColor(kRed)) simPdf.paramOn(frame2, RooFit.Layout(0.55, 0.9, 0.9), RooFit.Format("NEU", RooFit.AutoPrecision(1))) c = TCanvas("c", "c", 1000, 600) c.Divide(2)