def run(self): ## P e r f o r m c h i 2 f i t t o X + / - d x a n d Y + / - d Y v a l u e s ## --------------------------------------------------------------------------------------- if self.noxerrors: ## Fit chi^2 using Y errors only self.fresult = self.f.chi2FitTo(self.dxy_noxerrors, roo.YVar(self.y), roo.Save(), roo.Minos()) else: ## Fit chi^2 using X and Y errors self.fresult = self.f.chi2FitTo(self.dxy, roo.YVar(self.y), roo.Save(), roo.Minos()) self.frame = self.make_plot() self.draw_plot(self.frame) self.decorate_plot() canvases.update()
def make_plot(self): frame = self.x.frame(roo.Title(self.title)) ## Visualize 2- and 1-sigma errors using linear error propagation. self.f.plotOn(frame, roo.VisualizeError(self.fresult, 2), roo.FillColor(ROOT.kGreen)) self.f.plotOn(frame, roo.VisualizeError(self.fresult, 1), roo.FillColor(ROOT.kYellow)) ## Visualize 1-sigma errors using a curve sampling method. #self.f.plotOn(frame, roo.VisualizeError(self.fresult, 1, False), #roo.DrawOption("L"), roo.LineWidth(2), #roo.LineColor(ROOT.kRed)) ## Plot fitted function self.f.plotOn(frame) ## Overlay dataset in X-Y interpretation self.dxy.plotOnXY(frame, roo.YVar(self.y)) return frame
def plot_sanity_checks(data): pdfname = '_'.join(['bananaPdf', data.GetName()]) pdf = ROOT.RooNDKeysPdf(pdfname, pdfname, ROOT.RooArgList(mmMass, mmgMass), data, "a", 1.5) canvases.next(pdf.GetName() + '_mmgMassProj') plot = mmgMass.frame(roo.Range(60, 120)) data.plotOn(plot) pdf.plotOn(plot) plot.Draw() canvases.next(pdf.GetName() + '_mmMassProj') plot = mmMass.frame(roo.Range(10, 120)) data.plotOn(plot) pdf.plotOn(plot) plot.Draw() canvases.next(pdf.GetName()).SetGrid() h_pdf = pdf.createHistogram('h_' + pdf.GetName(), mmMass, roo.Binning(40, 40, 80), roo.YVar(mmgMass, roo.Binning(40, 70, 110))) h_pdf.Draw("cont1") canvases.next(data.GetName()).SetGrid() h_data = data.createHistogram(mmMass, mmgMass, 130, 100, '', 'h_' + data.GetName()) h_data.GetXaxis().SetRangeUser(40, 80) h_data.GetYaxis().SetRangeUser(70, 110) h_data.Draw("cont1") canvases.next('_'.join([pdf.GetName(), 'mmgMassSlices'])) plot = mmgMass.frame(roo.Range(60, 120)) for mmmassval, color in zip([55, 60, 65, 70], 'Red Orange Green Blue'.split()): mmMass.setVal(mmmassval) color = getattr(ROOT, 'k' + color) pdf.plotOn(plot, roo.LineColor(color)) plot.Draw() canvases.update()
c1 = canvases.next('xt1') c1.SetWindowSize(800, 400) c1.Divide(2,1) c1.cd(1).SetGrid() t.setRange(*t1range) t.SetTitle('log(m_{#mu#mu#gamma,E_{gen}^{#gamma}}^{2} - m_{#mu#mu}^{2})') hxt1d = xt1data.createHistogram(mmMass, t, 100, 100, '', 'hxt1') hxt1d.SetTitle('Data') hxt1d.GetXaxis().SetTitle(mmMass.GetTitle() + ' (GeV)') hxt1d.GetYaxis().SetTitle(t.GetTitle()) hxt1d.GetXaxis().SetRangeUser(*mmMass_range) hxt1d.GetYaxis().SetRangeUser(*t1range) hxt1d.Draw('cont0') c1.cd(2).SetGrid() hxt1f = xt1pdf.createHistogram('hxt1f', mmMass, roo.YVar(t)) hxt1f.SetTitle('PDF') hxt1f.Draw("cont0") ## Plot fXT1(x, t1) projected on x axis canvases.next('xt1_proj_x') plot = mmMass.frame() xt1data.plotOn(plot) xt1pdf.plotOn(plot) plot.Draw() ## Plot fXT1(x, t1) projected on t1 axis canvases.next('xt1_proj_t1') t.setRange(*t1range) t.SetTitle('log(m_{#mu#mu#gamma,E_{gen}^{#gamma}}^{2} - m_{#mu#mu}^{2})') plot = t.frame()
def __init__(self, name, title, mass, phos, phor, data, workspace, phostarget, phortargets, rho=1.5, mirror=ROOT.RooKeysPdf.NoMirror, mrangetrain=(40, 140), mrangenorm=(50, 130), mrangefit=(60, 120)): '''PhosphorModel4(str name, str title, RooRealVar mass, RooRealVar phos, RooRealVar phor, RooDataSet data, float phostarget, [float] phortargets, float rho=1.5, int mirror=ROOT.RooKeysPdf.NoMirror) name - PDF name title - PDF title mass - mumugamma invariant mass (GeV), observable phos - photon energy scale (%), parameter phor - photon energy resolution (%), paramter data - (mmMass, mmgMass, phoERes = 100*(phoEreco/phoEgen - 1), dataset on which the shapes of reference PDFs are trained. phostarget - target reference value of the photon energy scale at which the model is being trained phortargetss - a list of target photon energy resolution values for the moment morphing rho - passed to trained RooKeysPdfs mirror - passed to trained RooKeysPdfs ''' ## Attach args. self._name = name self._title = title self._mass = mass self._phos = phos self._phor = phor self._data = data self._phostarget = phostarget self._phortargets = phortargets[:] self._rho = rho self._mirror = mirror ## Attach other attributes. self._massrange = (mass.getMin(), mass.getMax()) self._sdata_list = [] self._phostrue_list = [] self._phortrue_list = [] self._dm_dphos_list = [] self._msubs_list = [] self._keys_pdfs = [] self._keys_modes = [] self._keys_effsigmas = [] self._keys_fitresults = [] self._pdfs = [] self._custs = [] # self._pdfrefs = [] self._mrefs = [] self._phormorphs = [] self._phorhists = [] self._workspace = workspace ## self._workspace = ROOT.RooWorkspace(name + '_workspace', ## title + ' workspace') w = self._workspace self.w = w ## Import important args in workspace. # w.Import(ROOT.RooArgSet(mass, phos, phor)) w.Import(mass) w.Import(phos) w.Import(phor) w.Import(self._data) w.factory('ConstVar::{name}_phostarget({value})'.format( name=name, value=self._phostarget)) ## Define the morphing parameter. This an identity with the ## photon resolution for now. mpar = self._mpar = w.factory( 'expr::{name}_mpar("{phor}", {{{phor}}})'.format( ## 'expr::{name}_mpar("2 + sqrt(0.5^2 + 0.05 * {phor}^2)", {{{phor}}})'.format( ## 'expr::{name}_mpar("2.325+sqrt(0.4571 + (0.1608*{phor})^2)", {{{phor}}})'.format( name=name, phor=phor.GetName())) ## Define the formula for dlog(m)/dphos. self._dm_dphos_func = w.factory(''' expr::dm_dphos_func("0.5 * (1 - mmMass^2 / mmgMass^2) * mmgMass", {mmMass, mmgMass}) ''') ## Get the calibrator. self._calibrator = MonteCarloCalibrator(self._data) ## Loop over target reference photon energy resolutions in phortargets. for index, phortarget in enumerate(self._phortargets): ## Store the target photon resolution value. w.factory('ConstVar::{name}_phortarget_{index}({value})'.format( name=name, index=index, value=phortarget)) ## Get the corresponding smeared RooDataSet sdata named ## {name}_sdata_{index} and attach it to self._sdata sdata = self._calibrator.get_smeared_data( self._phostarget, phortarget, name + '_sdata_%d' % index, title + ' sdata %d' % index, ## Get the true scale and resolution with errors. (This can be ## added to the calibrator and snapshots of ## self._calibrator.s and self._calibrator.r stored as ## {name}_sdata_{index}_sr in self._calibrator.w) dofit=True) self._sdata_list.append(sdata) w.Import(sdata) phostrue = ROOT.RooRealVar(self._calibrator.s, name + '_phostrue_%d' % index) phortrue = ROOT.RooRealVar(self._calibrator.r, name + '_phortrue_%d' % index) phostrue.setConstant(True) phortrue.setConstant(True) self._phostrue_list.append(phostrue) self._phortrue_list.append(phortrue) w.Import(phostrue) w.Import(phortrue) ## Calculate the dlogm/dphos {name}_dm_dphos_{index} ## for the smeared dataset. sdata.addColumn(self._dm_dphos_func) dm_dphos = w.factory(''' {name}_dm_dphos_{index}[{mean}, 0, 1] '''.format(name=name, index=index, mean=sdata.mean(sdata.get()['dm_dphos_func']))) dm_dphos.setConstant(True) self._dm_dphos_list.append(dm_dphos) ## Define the mass scaling {name}_msubs{i} introducing ## the dependence on phos. This needs self._calibrator.s and ## dm_dphos. ## msubs = w.factory( ## ''' ## LinearVar::{msubs}( ## {mass}, 1, ## expr::{offset}( ## "- 0.01 * {dm_dphos} * ({phos} - {phostrue})", ## {{ {dm_dphos}, {phos}, {phostrue} }} ## ) ## ) ## '''.format(msubs = name + '_msubs_%d' % index, ## offset = name + '_msubs_offset_%d' % index, ## mass = self._mass.GetName(), ## dm_dphos = dm_dphos.GetName(), ## phos = self._phos.GetName(), ## phostrue = phostrue.GetName()) ## ) ## LinearVar cannot be persisted. msubs = w.factory(''' expr::{msubs}( "{mass} - 0.01 * {dm_dphos} * ({phos} - {phostrue})", {{ {mass}, {dm_dphos}, {phos}, {phostrue} }} ) '''.format(msubs=name + '_msubs_%d' % index, mass=self._mass.GetName(), dm_dphos=dm_dphos.GetName(), phos=self._phos.GetName(), phostrue=phostrue.GetName())) self._msubs_list.append(msubs) ## Build the corresponding parametrized KEYS PDF {name}_kyes_{index} ## with {name}_keys_mode_{index} and ## {name}_keys_effsigma_{index}. keys_mode = w.factory( '{name}_keys_mode_{index}[91.2, 60, 120]'.format(name=name, index=index)) keys_effsigma = w.factory( '{name}_keys_effsigma_{index}[3, 0.1, 60]'.format(name=name, index=index)) mass.setRange(*mrangetrain) keys_pdf = ParametrizedKeysPdf(name + '_keys_pdf_%d' % index, name + '_keys_pdf_%d' % index, mass, keys_mode, keys_effsigma, sdata, rho=self._rho) self._keys_modes.append(keys_mode) self._keys_effsigmas.append(keys_effsigma) self._keys_pdfs.append(keys_pdf) ## Fit the KEYS PDF to the training data and save the result ## {name}_keys_fitresult_{index} and parameter snapshots ## {name}_keys_mctrue_{index}. mass.setRange(*mrangenorm) keys_fitresult = keys_pdf.fitTo(sdata, roo.Range(*mrangefit), roo.Strategy(2), roo.NumCPU(8), roo.Save(True)) self._keys_fitresults.append(keys_fitresult) w.Import(keys_fitresult, name + '_keys_fitresult_%d' % index) w.saveSnapshot( name + '_mctrue_%d' % index, ','.join([ phostrue.GetName(), phortrue.GetName(), keys_mode.GetName(), keys_effsigma.GetName() ])) ## Sample the fitted KEYS PDF to a histogram {name}_hist_{index}. mass.setRange(*mrangetrain) hist = keys_pdf.createHistogram(name + '_hist_%d' % index, mass, roo.Binning('cache')) ## Build a RooDataHist {name}_dhist{index} of the sampled histogram. dhist = ROOT.RooDataHist(name + '_dhist_%d' % index, name + '_dhist_%d' % index, ROOT.RooArgList(mass), hist) w.Import(dhist) ## Build a RooHistPdf {name}_pdf_{index} using the dhist and msubs. ## pdf = w.factory( pdf = w.factory('HistPdf::{name}({{{mass}}}, {dhist}, 1)'.format( name=name + '_pdf_%d' % index, msubs=msubs.GetName(), mass=mass.GetName(), dhist=dhist.GetName())) self._pdfs.append(pdf) mass.setRange(*self._massrange) ## Calculate morphing parameter reference values float mref[index]. phorval = phor.getVal() phor.setVal(phortrue.getVal()) self._mrefs.append(mpar.getVal()) phor.setVal(phorval) ## End of loop over target phortargets ## Make the reference and cache binnings in phor self._check_phor_ranges() partitions = self._partition_binning(self._phor, 'cache', 'reference') ## Loop over phor reference bins and define pairwise RooMomentMorphs. for ilo in range(len(self._mrefs) - 1): ihi = ilo + 1 mlo = self._mrefs[ilo] mhi = self._mrefs[ihi] pdflo = self._pdfs[ilo] pdfhi = self._pdfs[ihi] phormorph = w.factory(''' MomentMorph::{name}_phormorph_{ilo}to{ihi}( {mpar}, {{{mass}}}, {{{pdfs}}}, {{{mrefs}}} ) '''.format(name=name, ilo=ilo, ihi=ihi, mpar=mpar.GetName(), mass=mass.GetName(), pdfs='%s, %s' % (pdflo.GetName(), pdfhi.GetName()), mrefs='%f, %f' % (mlo, mhi))) self._phormorphs.append(phormorph) ## Sample the morph in a 2D histogram in mass and phor. phorhist = phormorph.createHistogram( name + '_phorhist_%dto%d' % (ilo, ihi), mass, roo.Binning('cache'), roo.YVar(phor, roo.Binning(partitions[ilo]))) self._phorhists.append(phorhist) ## End of loop over phor reference bins. self._stitch_phorhists() self._phor_dhist = phor_dhist = ROOT.RooDataHist( name + '_phor_dhist', name + '_phor_dhist', ROOT.RooArgList(mass, phor), self._phorhist) average_index = (len(self._msubs_list) + 1) / 2 msubs = self._msubs_list[average_index] ## self._model = model = ROOT.RooHistPdf( ## name + '_phor_histpdf', name + '_phor_histpdf', ## ROOT.RooArgList(msubs, phor), ROOT.RooArgList(mass, phor), phor_dhist, 2 ## ) ## self._model = model = ROOT.RooPhosphorPdf( ## name + '_phor_histpdf', name + '_phor_histpdf', mass, msubs, phor, phor_dhist, 2 ## ) ## ## Quick hack to make things work. ## self._customizer = customizer = ROOT.RooCustomizer(model, 'msub') ## customizer.replaceArg(mass, msubs) ## model = customizer.build() # ROOT.RooHistPdf.__init__(self, model) ROOT.RooPhosphorPdf.__init__(self, name, title, mass, msubs, phos, phor, phor_dhist, 2) self.SetName(name) self.SetTitle(title)
def test_substituting_for_mmgMassPhoGenE(): pdfname = '_'.join(['pdf_mmMass_mmgMassPhoGenE', data.GetName()]) pdf_mmMass_mmgMassPhoGenE = ROOT.RooNDKeysPdf( pdfname, pdfname, ROOT.RooArgList(mmMass, mmgMassPhoGenE), data, "a", 1.5) ## Test substituting for mmgMassPhoGenE ## This formula is approximate for s = phoERes << 1 ## 1/(1+s) - 1 ~ -s ## chachebins = ROOT.RooUniformBinning(-10, 10, 2, 'cache') ## phoERes.setBinning(chachebins) phoERes.setBins(3, 'cache') mmgMassFunc = w.factory('''expr::mmgMassFunc( "sqrt(mmgMass^2 - 0.01 * phoERes * (mmgMass^2 - mmMass^2))", {mmMass, mmgMass, phoERes} )''') ## mmgMassFunc = w.factory('''cexpr::mmgMassFunc( ## "sqrt(mmgMass*mmgMass - 0.01 * phoERes * (mmgMass*mmgMass - mmMass*mmMass))", ## {mmMass, mmgMass, phoERes} ## )''' ## ) cust = ROOT.RooCustomizer(pdf_mmMass_mmgMassPhoGenE, 'subs') cust.replaceArg(mmgMassPhoGenE, mmgMassFunc) pdf_mmMass_mmgMass = cust.build() pdf_mmMass_mmgMass.addOwnedComponents(ROOT.RooArgSet(mmgMassFunc)) pdf_mmMass_mmgMass.SetName('pdf_mmMass_mmgMass') ## WARNING: The caching related lines below cause segmentation violation! ## pdf_mmMass_mmgMass.setNormValueCaching(2) ## print '-- Before chache --' ## w.Print() ## print '-- Calculating cache ... --' ## ## Trigger the cache calculation ## pdf_mmMass_mmgMass.getVal(ROOT.RooArgSet(mmMass, mmgMass)) ## print '-- After chache --' ## w.Print() pdf = pdf_mmMass_mmgMassPhoGenE canvases.next(pdf.GetName()).SetGrid() h_pdf = pdf.createHistogram( 'h_' + pdf.GetName(), mmMass, roo.Binning(40, 40, 80), roo.YVar(mmgMassPhoGenE, roo.Binning(40, 70, 110))) h_pdf.Draw("cont1") pdf = pdf_mmMass_mmgMass phoERes.setVal(0) canvases.next(pdf.GetName() + '_s0').SetGrid() h_pdf = pdf.createHistogram('h_' + pdf.GetName() + '_s0', mmMass, roo.Binning(40, 40, 80), roo.YVar(mmgMass, roo.Binning(40, 70, 110))) h_pdf.Draw("cont1") phoERes.setVal(10) canvases.next(pdf.GetName() + '_s10').SetGrid() h_pdf = pdf.createHistogram('h_' + pdf.GetName() + '_s10', mmMass, roo.Binning(40, 40, 80), roo.YVar(mmgMass, roo.Binning(40, 70, 110))) h_pdf.Draw("cont1") phoERes.setVal(-10) canvases.next(pdf.GetName() + '_sm10').SetGrid() h_pdf = pdf.createHistogram('h_' + pdf.GetName() + '_sm10', mmMass, roo.Binning(40, 40, 80), roo.YVar(mmgMass, roo.Binning(40, 70, 110))) h_pdf.Draw("cont1")