Method transform

mne/source_estimate.py:1299–1400

Apply linear transform. The transform is applied to each source time course independently. Parameters ---------- func : callable The transform to be applied, including parameters (see, e.g., :func:`functools.partial`). The first parameter of

(self, func, idx=None, tmin=None, tmax=None, copy=False)

        return data_t

    def transform(self, func, idx=None, tmin=None, tmax=None, copy=False):
        """Apply linear transform.

        The transform is applied to each source time course independently.

        Parameters
        ----------
        func : callable
            The transform to be applied, including parameters (see, e.g.,
            :func:`functools.partial`). The first parameter of the function is
            the input data. The first two dimensions of the transformed data
            should be (i) vertices and (ii) time.  See Notes for details.
        idx : array | None
            Indices of source time courses for which to compute transform.
            If None, all time courses are used.
        tmin : float | int | None
            First time point to include (ms). If None, self.tmin is used.
        tmax : float | int | None
            Last time point to include (ms). If None, self.tmax is used.
        copy : bool
            If True, return a new instance of SourceEstimate instead of
            modifying the input inplace.

        Returns
        -------
        stcs : SourceEstimate | VectorSourceEstimate | list
            The transformed stc or, in the case of transforms which yield
            N-dimensional output (where N > 2), a list of stcs. For a list,
            copy must be True.

        Notes
        -----
        Transforms which yield 3D
        output (e.g. time-frequency transforms) are valid, so long as the
        first two dimensions are vertices and time.  In this case, the
        copy parameter must be True and a list of
        SourceEstimates, rather than a single instance of SourceEstimate,
        will be returned, one for each index of the 3rd dimension of the
        transformed data.  In the case of transforms yielding 2D output
        (e.g. filtering), the user has the option of modifying the input
        inplace (copy = False) or returning a new instance of
        SourceEstimate (copy = True) with the transformed data.

        Applying transforms can be significantly faster if the
        SourceEstimate object was created using "(kernel, sens_data)", for
        the "data" parameter as the transform is applied in sensor space.
        Inverse methods, e.g., "apply_inverse_epochs", or "apply_lcmv_epochs"
        do this automatically (if possible).
        """
        # min and max data indices to include
        times = 1000.0 * self.times
        t_idx = np.where(_time_mask(times, tmin, tmax, sfreq=self.sfreq))[0]
        if tmin is None:
            tmin_idx = None
        else:
            tmin_idx = t_idx[0]

        if tmax is None: