Method fit

mne/transforms.py:1105–1202

Fit the warp from source points to destination points. Parameters ---------- source : array, shape (n_src, 3) The source points. destination : array, shape (n_dest, 3) The destination points. order : int Order of the spheri

(
        self,
        source,
        destination,
        order=4,
        reg=1e-5,
        center=True,
        match="oct5",
        verbose=None,
    )

    @verbose
    def fit(
        self,
        source,
        destination,
        order=4,
        reg=1e-5,
        center=True,
        match="oct5",
        verbose=None,
    ):
        """Fit the warp from source points to destination points.

        Parameters
        ----------
        source : array, shape (n_src, 3)
            The source points.
        destination : array, shape (n_dest, 3)
            The destination points.
        order : int
            Order of the spherical harmonic fit.
        reg : float
            Regularization of the TPS warp.
        center : bool
            If True, center the points by fitting a sphere to points
            that are in a reasonable region for head digitization.
        match : str
            The uniformly-spaced points to match on the two surfaces.
            Can be "ico#" or "oct#" where "#" is an integer.
            The default is "oct5".
        %(verbose)s

        Returns
        -------
        inst : instance of SphericalSurfaceWarp
            The warping object (for chaining).
        """
        from .bem import _fit_sphere
        from .source_space._source_space import _check_spacing

        match_rr = _check_spacing(match, verbose=False)[2]["rr"]
        logger.info("Computing TPS warp")
        src_center = dest_center = np.zeros(3)
        if center:
            logger.info("    Centering data")
            hsp = np.array([p for p in source if not (p[2] < -1e-6 and p[1] > 1e-6)])
            src_center = _fit_sphere(hsp)[1]
            source = source - src_center
            hsp = np.array([p for p in destination if not (p[2] < 0 and p[1] > 0)])
            dest_center = _fit_sphere(hsp)[1]
            destination = destination - dest_center
            logger.info(
                "    Using centers {np.array_str(src_center, None, 3)} -> "
                "{np.array_str(dest_center, None, 3)}"
            )
        self._fit_params = dict(
            n_src=len(source),
            n_dest=len(destination),
            match=match,