def main(): ''' Main entry point of execution. ''' ## Mitigate numerical noise paying lower performance. if debuglevel == 0: ROOT.RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-9) ROOT.RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-9) ## Assemble the job name name = '_'.join([em, src, cat]) f = ModeAndEffSigmaFitter(name, debuglevel) f.run() ## Store RooFit objects in a rootfile w = ROOT.RooWorkspace('w') for item in [f.data, f.data_half_odd, f.data_half_even, f.model]: w.Import(item) w.Import(f.fit_data, f.name + '_fit_data') w.Import(f.train_data, f.name + '_train_data') w.Import(f.fit_result, f.name + '_fit_result') w.writeToFile(output_filename) ## Store canvases in a rootfile outfile = ROOT.TFile.Open(output_filename, 'UPDATE') outfile.mkdir('Canvases').cd() for c in canvases.canvases: if c: c.Write(c.GetName()) ## Make the plots canvases.make_plots('eps png C'.split())
def main(): ''' Main entry point of execution. ''' ## Mitigate numerical noise paying lower performance. if debuglevel == 0: ROOT.RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-9) ROOT.RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-9) ## Assemble the job name name = '_'.join([em, src, cat]) f = ModeAndEffSigmaFitter(name, debuglevel) f.run() ## Store RooFit objects in a rootfile w = ROOT.RooWorkspace('w') for item in [f.data, f.data_half_odd, f.data_half_even, f.model]: w.Import(item) w.Import(f.fit_data, f.name + '_fit_data') w.Import(f.train_data, f.name + '_train_data') w.Import(f.fit_result, f.name + '_fit_result') w.writeToFile(output_filename) ## Store canvases in a rootfile outfile = ROOT.TFile.Open(output_filename, 'UPDATE') outfile.mkdir('Canvases').cd() for c in canvases.canvases: if c: c.Write(c.GetName()) ## Make the plots canvases.make_plots('eps png C'.split())
debuglevel = 0 ## Mitigate numerical noise paying lower performance. if debuglevel == 0: ROOT.RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-9) ROOT.RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-9) fitters = [] for em in 'pho ele'.split(): for src in 'data mc'.split(): for icat in range(4): cat = 'cat%d' % icat print '+++ ', em, src, cat fitter = ModeAndEffSigmaFitter(name = '_'.join([em, src, cat]), debuglevel = debuglevel) fitter.run() fitters.append(fitter) for icalcat in range(8): cat = 'calcat%d' % icalcat print '+++ ', em, src, cat fitter = ModeAndEffSigmaFitter(name = '_'.join([em, src, cat]), debuglevel = debuglevel) fitter.run() fitters.append(fitter) ## Store RooFit objects in a rootfile w = ROOT.RooWorkspace('w') for f in fitters: for item in [f.data, f.data_half_odd, f.data_half_even, f.model]: w.Import(item) w.Import(f.fit_data, f.name + '_fit_data')
debuglevel = 0 ## Mitigate numerical noise paying lower performance. if debuglevel == 0: ROOT.RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-9) ROOT.RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-9) fitters = [] for em in 'pho ele'.split(): for src in 'data mc'.split(): for icat in range(4): cat = 'cat%d' % icat print '+++ ', em, src, cat fitter = ModeAndEffSigmaFitter(name='_'.join([em, src, cat]), debuglevel=debuglevel) fitter.run() fitters.append(fitter) for icalcat in range(8): cat = 'calcat%d' % icalcat print '+++ ', em, src, cat fitter = ModeAndEffSigmaFitter(name='_'.join([em, src, cat]), debuglevel=debuglevel) fitter.run() fitters.append(fitter) ## Store RooFit objects in a rootfile w = ROOT.RooWorkspace('w') for f in fitters: for item in [f.data, f.data_half_odd, f.data_half_even, f.model]: w.Import(item) w.Import(f.fit_data, f.name + '_fit_data')