L-BFGS-B_grad_error.py

>>> m.fit(fitter="L-BFGS-B", bounded=True, grad=True)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-118-e2c35f346c6e> in <module>()
     50 
     51 #m.fit(fitter="leastsq", bounded=True, grad=True)
---> 52 m.fit(fitter="L-BFGS-B", bounded=True, grad=True)
     53 
     54 

/Users/thomas/Dropbox/0_Git/hyperspy/hyperspy/models/eelsmodel.py in fit(self, fitter, method, grad, bounded, ext_bounding, update_plot, kind, **kwargs)
    347                         ext_bounding=ext_bounding,
    348                         update_plot=update_plot,
--> 349                         **kwargs)
    350         else:
    351             raise ValueError('kind must be either \'std\' or \'smart\'.'

/Users/thomas/Dropbox/0_Git/hyperspy/hyperspy/model.py in fit(self, fitter, method, grad, bounded, ext_bounding, update_plot, **kwargs)
   1154                     self.p0 = minimize(tominimize, self.p0, jac=fprime,
   1155                                        args=args, method=fitter,
-> 1156                                        bounds=self.free_parameters_boundaries, **kwargs).x
   1157 
   1158                 # Global optimizers

/Users/thomas/miniconda3/envs/hs/lib/python3.5/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
    448     elif meth == 'l-bfgs-b':
    449         return _minimize_lbfgsb(fun, x0, args, jac, bounds,
--> 450                                 callback=callback, **options)
    451     elif meth == 'tnc':
    452         return _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,

/Users/thomas/miniconda3/envs/hs/lib/python3.5/site-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)
    326             # until the completion of the current minimization iteration.
    327             # Overwrite f and g:
--> 328             f, g = func_and_grad(x)
    329         elif task_str.startswith(b'NEW_X'):
    330             # new iteration

/Users/thomas/miniconda3/envs/hs/lib/python3.5/site-packages/scipy/optimize/lbfgsb.py in func_and_grad(x)
    277         def func_and_grad(x):
    278             f = fun(x, *args)
--> 279             g = jac(x, *args)
    280             return f, g
    281 

/Users/thomas/Dropbox/0_Git/hyperspy/hyperspy/models/model1d.py in _gradient_ls(self, param, y, weights)
    513     def _gradient_ls(self, param, y, weights=None):
    514         gls = (2 * self._errfunc(param, y, weights) *
--> 515                self._jacobian(param, y)).sum(1)
    516         return gls
    517 

/Users/thomas/Dropbox/0_Git/hyperspy/hyperspy/models/model1d.py in _jacobian(self, param, y, weights)
    455                                     np.add(par_grad, np.convolve(
    456                                         par.grad(
--> 457                                             self.convolution_axis),
    458                                         self.low_loss(self.axes_manager),
    459                                         mode="valid"), par_grad)

TypeError: 'NoneType' object is not callable