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':
