def calc_svd(self, lapack_driver='gesdd'):
        """ Return the SVD decomposition of data

        The input data np.ndarray shall be of dimension 2,
            with time as the first dimension, and the channels in the second
            Hence data should be of shape (nt, nch)

        Uses scipy.linalg.svd(), with:
            full_matrices = True
            compute_uv = True
            overwrite_a = False
            check_finite = True

        See scipy online doc for details

        Return
        ------
        chronos:    np.ndarray
            First arg (u) returned by scipy.linalg.svd()
            Contains the so-called 'chronos', of shape (nt, nt)
                i.e.: the time-dependent part of the decoposition
        s:          np.ndarray
            Second arg (s) returned by scipy.linalg.svd()
            Contains the singular values, of shape (nch,)
                i.e.: the channel-dependent part of the decoposition
        topos:      np.ndarray
            Third arg (v) returned by scipy.linalg.svd()
            Contains the so-called 'topos', of shape (nch, nch)
                i.e.: the channel-dependent part of the decoposition

        """
        if self._isSpectral():
            msg = "svd not implemented yet for spectral data class"
            raise Exception(msg)
        chronos, s, topos = _comp.calc_svd(self.data, lapack_driver=lapack_driver)
        return u, s, v
Return the SVD decomposition of data

        The input data np.ndarray shall be of dimension 2,
            with time as the first dimension, and the channels in the second
            Hence data should be of shape (nt, nch)

        Uses scipy.linalg.svd(), with:
            full_matrices = True
            compute_uv = True
            overwrite_a = False
            check_finite = True

        See scipy online doc for details

        Return
        ------
        chronos:    np.ndarray
            First arg (u) returned by scipy.linalg.svd()
            Contains the so-called 'chronos', of shape (nt, nt)
                i.e.: the time-dependent part of the decoposition
        s:          np.ndarray
            Second arg (s) returned by scipy.linalg.svd()
            Contains the singular values, of shape (nch,)
                i.e.: the channel-dependent part of the decoposition
        topos:      np.ndarray
            Third arg (v) returned by scipy.linalg.svd()
            Contains the so-called 'topos', of shape (nch, nch)
                i.e.: the channel-dependent part of the decoposition