jsonFile.close() if data['type'] == 'erfexp': fitter.erfexp('modelJ', 'mjj') fitter.w.var("c_0").setVal(data['c_0']) fitter.w.var("c_0").setConstant(1) fitter.w.var("c_1").setVal(data['c_1']) fitter.w.var("c_1").setConstant(1) fitter.w.var("c_2").setVal(data['c_2']) fitter.w.var("c_2").setConstant(1) if data['type'] == 'expo': fitter.expo('modelJ', 'mjj') fitter.w.var("c_0").setVal(data['c_0']) fitter.w.var("c_0").setConstant(1) #now create the variables of the erfpow formulas = {} for p, val in orderInfo.iteritems(): STR = '0' DEPS = ['mjj'] for i in range(0, val + 1): if p == 'p0': mini = -8.0 maxi = -3.0 mean = -5.0
"--function", dest="function", help="name", default="bernstein") (options, args) = parser.parse_args() parameterization = {} f = ROOT.TFile(args[0]) histo = f.Get(options.histo) fitter = Fitter(['x']) fitter.importBinnedData(histo, ['x'], 'data') if options.function == 'expo': fitter.expo('model', 'x') parameterization['type'] = 'expo' if options.function == 'erfpow': fitter.erfpow('model', 'x') parameterization['type'] = 'erfpow' if options.function == 'erfexp': fitter.erfexp('model', 'x') parameterization['type'] = 'erfexp' if options.function == 'erfexpCB': fitter.erfexpCB('model', 'x') parameterization['type'] = 'erfexpCB' if options.function == 'erfexpTimesCB':