Batch processes a sequences of measurements.

        Parameters
        ----------
        Zs : list-like
            list of measurements at each time step `self.dt` Missing
            measurements must be represented by 'None'.

        update_first : bool, default=False, optional,
            controls whether the order of operations is update followed by
            predict, or predict followed by update.

        saver : filterpy.common.Saver, optional
            filterpy.common.Saver object. If provided, saver.save() will be
            called after every epoch

        Returns
        -------
        means: ndarray ((n, dim_x, 1))
            array of the state for each time step. Each entry is an np.array.
            In other words `means[k,:]` is the state at step `k`.

        covariance: ndarray((n, dim_x, dim_x))
            array of the covariances for each time step. In other words
            `covariance[k, :, :]` is the covariance at step `k`.

def batch_filter(self, Zs,update_first=False, saver=False):
        """ Batch processes a sequences of measurements.

        Parameters
        ----------
        Zs : list-like
            list of measurements at each time step `self.dt` Missing
            measurements must be represented by 'None'.

        update_first : bool, default=False, optional,
            controls whether the order of operations is update followed by
            predict, or predict followed by update.

        saver : filterpy.common.Saver, optional
            filterpy.common.Saver object. If provided, saver.save() will be
            called after every epoch

        Returns
        -------
        means: ndarray ((n, dim_x, 1))
            array of the state for each time step. Each entry is an np.array.
            In other words `means[k,:]` is the state at step `k`.

        covariance: ndarray((n, dim_x, dim_x))
            array of the covariances for each time step. In other words
            `covariance[k, :, :]` is the covariance at step `k`.
        """

        n = np.size(Zs, 0)

        # mean estimates from H-Infinity Filter
        means = zeros((n, self.dim_x, 1))

        # state covariances from H-Infinity Filter
        covariances = zeros((n, self.dim_x, self.dim_x))

        if update_first:
            for i, z in enumerate(Zs):
                self.update(z)
                means[i, :] = self.x
                covariances[i, :, :] = self.P
                self.predict()

                if saver is not None:
                    saver.save()
        else:
            for i, z in enumerate(Zs):
                self.predict()
                self.update(z)

                means[i, :] = self.x
                covariances[i, :, :] = self.P

                if saver is not None:
                    saver.save()

        return (means, covariances)