code: def predict(self, dt=None, fx_args=()):
          r
  
          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()
docs: 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.