Method apply_function

mne/source_estimate.py:643–691

Apply a function to a subset of vertices. %(applyfun_summary_stc)s Parameters ---------- %(fun_applyfun_stc)s %(picks_all)s %(dtype_applyfun)s %(n_jobs)s Ignored if ``vertice_wise=False`` as the workload is split across vertices.

(
        self, fun, picks=None, dtype=None, n_jobs=None, verbose=None, **kwargs
    )

    @verbose
    def apply_function(
        self, fun, picks=None, dtype=None, n_jobs=None, verbose=None, **kwargs
    ):
        """Apply a function to a subset of vertices.

        %(applyfun_summary_stc)s

        Parameters
        ----------
        %(fun_applyfun_stc)s
        %(picks_all)s
        %(dtype_applyfun)s
        %(n_jobs)s Ignored if ``vertice_wise=False`` as the workload
            is split across vertices.
        %(verbose)s
        %(kwargs_fun)s

        Returns
        -------
        self : instance of SourceEstimate
            The SourceEstimate object with transformed data.
        """
        _check_preload(self, "source_estimate.apply_function")
        picks = _picks_to_idx(len(self._data), picks, exclude=(), with_ref_meg=False)

        if not callable(fun):
            raise ValueError("fun needs to be a function")

        data_in = self._data
        if dtype is not None and dtype != self._data.dtype:
            self._data = self._data.astype(dtype)

        # check the dimension of the source estimate data
        _check_option("source_estimate.ndim", self._data.ndim, [2, 3])

        parallel, p_fun, n_jobs = parallel_func(_check_fun, n_jobs)
        if n_jobs == 1:
            # modify data inplace to save memory
            for idx in picks:
                self._data[idx, :] = _check_fun(fun, data_in[idx, :], **kwargs)
        else:
            # use parallel function
            data_picks_new = parallel(
                p_fun(fun, data_in[p, :], **kwargs) for p in picks
            )
            for pp, p in enumerate(picks):
                self._data[p, :] = data_picks_new[pp]

        return self

    @verbose
    def apply_baseline(self, baseline=(None, 0), *, verbose=None):