I'm trying to build a model using lmfit (link to docs) and I can't seems to find out why I keep getting a ValueError: The input contains nan values when I try to fit the model.
from lmfit import minimize, Minimizer, Parameters, Parameter, report_fit, Model
import numpy as np
def cde(t, Qi, at, vw, R, rhob_cb, al, d, r):
# t (time), is the independent variable
return Qi / (8 * np.pi * ((at * vw)/R) * t * rhob_cb * (np.sqrt(np.pi * ((al * vw)/R * t)))) * \
np.exp(- (R * (d - (t * vw)/ R)**2) / (4 * (al * vw) * t) - (R * r**2)/ (4 * (at * vw) * t))
model_cde = Model(cde)
# Allowed to vary
model_cde.set_param_hint('vw', value =10**-4, min=0.000001)
model_cde.set_param_hint('d', value = -0.038, min = 0.0001)
model_cde.set_param_hint('r', value = 5.637e-10)
model_cde.set_param_hint('at', value =0.1)
model_cde.set_param_hint('al', value =0.15)
# Fixed
model_cde.set_param_hint('Qi', value = 1000, vary = False)
model_cde.set_param_hint('R', value =1.7, vary = False)
model_cde.set_param_hint('rhob_cb', value =3000, vary = False)
# test data
data = [ 1.37, 1.51, 1.65, 1.79, 1.91, 2.02, 2.12, 2.2 ,
2.27, 2.32, 2.36, 2.38, 2.4 , 2.41, 2.42, 2.41, 2.4 ,
2.39, 2.37, 2.35, 2.33, 2.31, 2.29, 2.26, 2.23, 2.2 ,
2.17, 2.14, 2.11, 2.08, 2.06, 2.02, 1.99, 1.97, 1.94,
1.91, 1.88, 1.85, 1.83, 1.8 , 1.78, 1.75, 1.72, 1.7 ,
1.68, 1.65, 1.63, 1.61, 1.58]
time = list(range(5,250,5))
model_cde.fit(data, t= time)
Produces the following error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-785fcc6a994b> in <module>()
----> 1 model_cde.fit(data, t= time)
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/model.py in fit(self, data, params, weights, method, iter_cb, scale_covar, verbose, fit_kws, **kwargs)
539 scale_covar=scale_covar, fcn_kws=kwargs,
540 **fit_kws)
--> 541 output.fit(data=data, weights=weights)
542 output.components = self.components
543 return output
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/model.py in fit(self, data, params, weights, method, **kwargs)
745 self.init_fit = self.model.eval(params=self.params, **self.userkws)
746
--> 747 _ret = self.minimize(method=self.method)
748
749 for attr in dir(_ret):
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/minimizer.py in minimize(self, method, params, **kws)
1240 val.lower().startswith(user_method)):
1241 kwargs['method'] = val
-> 1242 return function(**kwargs)
1243
1244
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/minimizer.py in leastsq(self, params, **kws)
1070 np.seterr(all='ignore')
1071
-> 1072 lsout = scipy_leastsq(self.__residual, vars, **lskws)
1073 _best, _cov, infodict, errmsg, ier = lsout
1074 result.aborted = self._abort
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/scipy/optimize/minpack.py in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)
385 maxfev = 200*(n + 1)
386 retval = _minpack._lmdif(func, x0, args, full_output, ftol, xtol,
--> 387 gtol, maxfev, epsfcn, factor, diag)
388 else:
389 if col_deriv:
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/minimizer.py in __residual(self, fvars, apply_bounds_transformation)
369
370 out = self.userfcn(params, *self.userargs, **self.userkws)
--> 371 out = _nan_policy(out, nan_policy=self.nan_policy)
372
373 if callable(self.iter_cb):
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/minimizer.py in _nan_policy(a, nan_policy, handle_inf)
1430
1431 if contains_nan:
-> 1432 raise ValueError("The input contains nan values")
1433 return a
1434
ValueError: The input contains nan values
However the results of the following checks for NaNs confirms that there were no NaN values in my data:
print(np.any(np.isnan(data)), np.any(np.isnan(time)))
False False
So far I've tried converting 1 and/or both of data and time from lists to numpy ndarrays, removing the 0th time step (in case there was a dividing by 0 error), explicitly specifying the t as being independent and allowing all variables to vary. However these all throw the same error.
Does anyone have ideas what is causing this error to be thrown? Thanks.
time = np.arange(.5,25.,.5)it seems to work. There must be some sort of illegal operation in the model with the time values you picked. – Realitytimevariable. Did you try starting in a fresh session? Maybe some variables are being shadowed. – Realitynp.arange(5., 25., 5.)incase it was something to do with ints/floats but it threw the same error as previously. I'll think I'll keep trying to find a solution...failing that perhaps I'll need to change the units throughout my data.. – Delousenp.sqrtwhich evaluates them tonanby default. – Delouse