I want to do a parameter fit with the minimize function from lmfit

result = minimize(residuals_all_exp, p, args=(t_grid_online, y0_dict, c_dict, datasets_dict), method='leastsq', nan_policy= "omit") 

p

is a parameter structure like this

p.add("viability", value=0.9, min=0.80, max=1.20 , vary = True) 

t_grid_online

is

[29.06150676 29.06290012 29.06429324 29.06568692 29.06707991 29.06847032 29.06986228 29.07125381 29.07264628 29.07403913 29.07543133 29.07682025] 

y0_dict

is

{'current_fermentation': [1, 0.9141716, 0.0001, 0.5]} 

c_dict

is

{'current_fermentation': {'feed_on': 0, 'feed_rate': 0.0069, 'csf': 200, 'M_base': 5, 'gas_flow': 30, 'T': 32, 'wf_on': 1, 'wf_CO2': 1, 'pressure': 1058.0}} 

and

datasets_dict

is

{'current_fermentation': [          base_rate t                   7.181545 0.081033,  Empty DataFrame Columns: [cX, cS, cE] Index: [],  t         CO2    7.181545 5.0 

I saw other posts which suggested to change the dtype to float, but I don´t know which one.

Full Traceback:

TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) in 1 start = time.time() 2 ----> 3 result = minimize(residuals_all_exp, p, args=(t_grid_online, y0_dict, c_dict, datasets_dict), method='leastsq', nan_policy= "omit") 4 5 end = time.time()
~\anaconda3\lib\site-packages\lmfit\minimizer.py in minimize(fcn, params, method, args, kws, iter_cb, scale_covar, nan_policy, reduce_fcn, calc_covar, max_nfev, **fit_kws) 2592
nan_policy=nan_policy, reduce_fcn=reduce_fcn, 2593
calc_covar=calc_covar, max_nfev=max_nfev, **fit_kws) -> 2594 return fitter.minimize(method=method)
~\anaconda3\lib\site-packages\lmfit\minimizer.py in minimize(self, method, params, **kws) 2361
val.lower().startswith(user_method)): 2362
kwargs['method'] = val -> 2363 return function(**kwargs) 2364 2365
~\anaconda3\lib\site-packages\lmfit\minimizer.py in leastsq(self, params, max_nfev, **kws) 1698 result.call_kws = lskws
1699 try: -> 1700 lsout = scipy_leastsq(self.__residual, variables, **lskws) 1701 except AbortFitException: 1702 pass
~\anaconda3\lib\site-packages\scipy\optimize\minpack.py in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag) 408 if not isinstance(args, tuple): 409 args = (args,) --> 410 shape, dtype = _check_func('leastsq', 'func', func, x0, args, n) 411 m = shape[0] 412
~\anaconda3\lib\site-packages\scipy\optimize\minpack.py in _check_func(checker, argname, thefunc, x0, args, numinputs, output_shape) 22 def _check_func(checker, argname, thefunc, x0, args, numinputs, 23 output_shape=None): ---> 24 res = atleast_1d(thefunc(*((x0[:numinputs],) + args))) 25 if (output_shape is not None) and (shape(res) != output_shape): 26 if (output_shape[0] != 1):
~\anaconda3\lib\site-packages\lmfit\minimizer.py in __residual(self, fvars, apply_bounds_transformation) 602 raise AbortFitException("fit aborted by user.") 603 else: --> 604 return _nan_policy(np.asarray(out).ravel(), 605 nan_policy=self.nan_policy) 606
~\anaconda3\lib\site-packages\lmfit\minimizer.py in _nan_policy(arr, nan_policy, handle_inf) 2422 if nan_policy == 'omit': 2423
mask locates any values to remove
-> 2424 mask = ~handler_func(arr) 2425 if not np.all(mask): # there are some NaNs/infs/missing values 2426
return arr[mask]
~\anaconda3\lib\site-packages\lmfit\minimizer.py in (x)
2416 2417 if handle_inf: -> 2418 handler_func = lambda x: ~np.isfinite(x) 2419 else: 2420 handler_func = isnull
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'