def __init__(self, data, alpha=0.05, linear=None, freundlich=None, langmuir=None):
# validate input
try:
val.validateData(data)
val.validateAlpha(alpha)
except val.InputError as ie:
print(ie)
return None
# intialize fitting with Linear isotherm
self.Linear = isotherm.Linear(data, userParams=linear, validateInput=False)
# test for linear model validity
if not self.Linear.modelValidity:
self.LinearFailCode()
return None
# test for statistical significance (Asymptotic Confidence Interval)
Kd = [self.Linear.isoModelResult.params.valuesdict()["Kd"]]
covar = self.Linear.isoModelResult.covar
confIntrvl = AAstats.RegConfAsym(data[0], Kd, covar, alpha)
if confIntrvl["lower"][0] <= 0:
self.LinearFailCode
return None
# fit the other isotherms
self.Freundlich = isotherm.Freundlich(data, userParams=freundlich, validateInput=False)
self.Langmuir = isotherm.Langmuir(data, userParams=langmuir, validateInput=False)
def __init__(
self, data, userParams=None,
fitMethods=default_fitMethods,
validateInput=True):
# validate input
if validateInput:
try:
val.validateData(data)
if userParams:
val.validate_userParams(userParams, self.IsothermFunc)
except val.InputError as ie:
print(ie)
return None
except Exception as inst:
print(type(inst))
print(inst)
return None
# initiate lmfit's wrapper around the isotherm function
isoModel = lmfit.Model(self.IsothermFunc)
# replace inititial params with userParams
if userParams:
for key in userParams:
isoModel.__dict__['def_vals'][key] = userParams[key]
# fit models using given fitMethods
isoModelResult = None
for fit_method in fitMethods:
if isoModelResult:
isoModelResult = isoModel.fit(
data=data[1], x=data[0],
params=isoModelResult.params, # this is what's different
method=fit_method,
fit_kws=fitMethods[fit_method])
else:
isoModelResult = isoModel.fit(
data=data[1], x=data[0],
method=fit_method,
fit_kws=fitMethods[fit_method])
self.isoModelResult = isoModelResult
# validate fit model against isotherm theory
self.modelValidity = self.ValidateFit()