function (k). Returns the square root of noise matrix of dynamic model
                      on iteration k.
            k (iteration number). starts at 0

        This function is implemented to use SVD prediction step.

def Q_srk(self, k):
        """
        function (k). Returns the square root of noise matrix of dynamic model
                      on iteration k.
            k (iteration number). starts at 0

        This function is implemented to use SVD prediction step.
        """
        ind = self.index[self.Q_time_var_index, k]
        Q = self.Q[:, :, ind]

        if (Q.shape[0] == 1):  # 1-D case handle simplier. No storage
            # of the result, just compute it each time.
            square_root = np.sqrt(Q)
        else:
            if self.svd_each_time:

                (U, S, Vh) = sp.linalg.svd(Q, full_matrices=False,
                                           compute_uv=True,
                                           overwrite_a=False,
                                           check_finite=True)

                square_root = U * np.sqrt(S)
            else:

                if ind in self.Q_square_root:
                    square_root = self.Q_square_root[ind]
                else:
                    (U, S, Vh) = sp.linalg.svd(Q, full_matrices=False,
                                               compute_uv=True,
                                               overwrite_a=False,
                                               check_finite=True)

                    square_root = U * np.sqrt(S)

                    self.Q_square_root[ind] = square_root

        return square_root