def predict(self, dt=None, fx_args=()):
        r""" Performs the predict step of the CKF. On return, self.x and
        self.P contain the predicted state (x) and covariance (P).

        Important: this MUST be called before update() is called for the first
        time.

        Parameters
        ----------

        dt : double, optional
            If specified, the time step to be used for this prediction.
            self._dt is used if this is not provided.

        fx_args : tuple, optional, default (,)
            optional arguments to be passed into fx() after the required state
            variable.
        """

        if dt is None:
            dt = self._dt

        if not isinstance(fx_args, tuple):
            fx_args = (fx_args,)

        sigmas = spherical_radial_sigmas(self.x, self.P)

        # evaluate cubature points
        for k in range(self._num_sigmas):
            self.sigmas_f[k] = self.fx(sigmas[k], dt, *fx_args)

        self.x, self.P = ckf_transform(self.sigmas_f, self.Q)

        # save prior
        self.x_prior = self.x.copy()
        self.P_prior = self.P.copy()
r""" Performs the predict step of the CKF. On return, self.x and
        self.P contain the predicted state (x) and covariance (P).

        Important: this MUST be called before update() is called for the first
        time.

        Parameters
        ----------

        dt : double, optional
            If specified, the time step to be used for this prediction.
            self._dt is used if this is not provided.

        fx_args : tuple, optional, default (,)
            optional arguments to be passed into fx() after the required state
            variable.