How to use the pyemma.coordinates.data._base.datasource.IteratorState.is_uniform_stride function in pyEMMA

itraj : int
            trajectory index
        stride : int
            return value is the number of frames in the trajectory when
            running through it with a step size of `stride`.
        skip: int or None
            skip n frames.

        Returns
        -------
        int : length of trajectory
        """
        if itraj >= self.ntraj:
            raise IndexError("given index (%s) exceeds number of data sets (%s)."
                             " Zero based indexing!" % (itraj, self.ntraj))
        if not IteratorState.is_uniform_stride(stride):
            selection = stride[stride[:, 0] == itraj][:, 0]
            return 0 if itraj not in selection else len(selection)
        else:
            res = max((self._lengths[itraj] - skip - 1) // int(stride) + 1, 0)
            return res

def __init_stride(self, stride):
        self.state.stride = stride
        if isinstance(stride, np.ndarray):
            keys = stride[:, 0]
            if keys.max() >= self.number_of_trajectories():
                raise ValueError("provided too large trajectory index in stride argument (given max index: %s, "
                                 "allowed: %s)" % (keys.max(), self.number_of_trajectories() - 1))
            self.state.traj_keys, self.state.trajectory_lengths = np.unique(keys, return_counts=True)
            self.state.ra_indices_for_traj_dict = {}
            for traj in self.state.traj_keys:
                self.state.ra_indices_for_traj_dict[traj] = self.state.stride[self.state.stride[:, 0] == traj][:, 1]
        else:
            self.state.traj_keys = None
        self.state.uniform_stride = IteratorState.is_uniform_stride(stride)
        if not IteratorState.is_uniform_stride(stride):
            if not self.state.is_stride_sorted():
                raise ValueError("Only sorted arrays allowed for iterator pseudo random access")
            # skip trajs which are not included in stride
            while self.state.itraj not in self.state.traj_keys and self.state.itraj < self._data_source.ntraj:
                self.state.itraj += 1

def __init_stride(self, stride):
        self.state.stride = stride
        if isinstance(stride, np.ndarray):
            keys = stride[:, 0]
            if keys.max() >= self.number_of_trajectories():
                raise ValueError("provided too large trajectory index in stride argument (given max index: %s, "
                                 "allowed: %s)" % (keys.max(), self.number_of_trajectories() - 1))
            self.state.traj_keys, self.state.trajectory_lengths = np.unique(keys, return_counts=True)
            self.state.ra_indices_for_traj_dict = {}
            for traj in self.state.traj_keys:
                self.state.ra_indices_for_traj_dict[traj] = self.state.stride[self.state.stride[:, 0] == traj][:, 1]
        else:
            self.state.traj_keys = None
        self.state.uniform_stride = IteratorState.is_uniform_stride(stride)
        if not IteratorState.is_uniform_stride(stride):
            if not self.state.is_stride_sorted():
                raise ValueError("Only sorted arrays allowed for iterator pseudo random access")
            # skip trajs which are not included in stride
            while self.state.itraj not in self.state.traj_keys and self.state.itraj < self._data_source.ntraj:
                self.state.itraj += 1

self.state.stride = stride
        if isinstance(stride, np.ndarray):
            # shift frame indices by skip
            self.state.stride[:, 1] += self.state.skip
            keys = stride[:, 0]
            if keys.max() >= self.number_of_trajectories():
                raise ValueError("provided too large trajectory index in stride argument (given max index: %s, "
                                 "allowed: %s)" % (keys.max(), self.number_of_trajectories() - 1))
            self.state.traj_keys, self.state.trajectory_lengths = np.unique(keys, return_counts=True)
            self.state.ra_indices_for_traj_dict = {}
            for traj in self.state.traj_keys:
                self.state.ra_indices_for_traj_dict[traj] = self.state.stride[self.state.stride[:, 0] == traj][:, 1]
        else:
            self.state.traj_keys = None
        self.state.uniform_stride = IteratorState.is_uniform_stride(stride)
        if not IteratorState.is_uniform_stride(stride):
            if not self.state.is_stride_sorted():
                raise ValueError("Only sorted arrays allowed for iterator pseudo random access")
            # skip trajs which are not included in stride
            while self.state.itraj not in self.state.traj_keys and self.state.itraj < self._data_source.ntraj:
                self.state.itraj += 1

def number_of_trajectories(self, stride=None):
        r""" Returns the number of trajectories.

        Parameters
        ----------
        stride: None (default) or np.ndarray

        Returns
        -------
            int : number of trajectories
        """
        if not IteratorState.is_uniform_stride(stride):
            n = len(np.unique(stride[:, 0]))
        else:
            n = self.ntraj
        return n

def is_uniform_stride(stride):
        return IteratorState.is_uniform_stride(stride)