148a63df4c40c063e1aaf36b09a2057efd38daf9,hyperspy/models/model1d.py,Model1D,_jacobian,#Model1D#Any#Any#Any#,543

Before Change

                                mode="valid")
                            if parameter._twins:
                                for par in parameter._twins:
                                    if not callable(par.grad):
                                        raise ValueError(
                                            f"Analytical gradient not available for {par}. "
                                            "Consider using a numerical approximation for the gradient "
                                            "instead by calling `m.fit(grad="2-point")`."
                                        )
                                    np.add(par_grad, np.convolve(
                                        par.grad(
                                            self.convolution_axis),
                                        self.low_loss(self.axes_manager),

After Change

                                missing_gradients.append(par)

        if len(missing_gradients) > 0:
            pars = ", ".join(str(x) for x in missing_gradients)

            raise ValueError(
                f"Analytical gradient not available for {pars}. "

Instances