How to use the phylib.utils._types._as_array function in phylib

def get_clusters_data(self, load_all=None):
        """Return a list of Bunch instances, with attributes pos and spike_ids."""
        if not len(self.cluster_ids):
            return
        cluster_ids = list(self.cluster_ids)
        # Don't need the background when splitting.
        if not load_all:
            # Add None cluster which means background spikes.
            cluster_ids = [None] + cluster_ids
        bunchs = self.amplitudes[self.amplitudes_type](cluster_ids, load_all=load_all) or ()
        # Add a pos attribute in bunchs in addition to x and y.
        for i, (cluster_id, bunch) in enumerate(zip(cluster_ids, bunchs)):
            spike_ids = _as_array(bunch.spike_ids)
            spike_times = _as_array(bunch.spike_times)
            amplitudes = _as_array(bunch.amplitudes)
            assert spike_ids.shape == spike_times.shape == amplitudes.shape
            # Ensure that bunch.pos exists, as it used by the LassoMixin.
            bunch.pos = np.c_[spike_times, amplitudes]
            assert bunch.pos.ndim == 2
            bunch.cluster_id = cluster_id
            bunch.color = (
                selected_cluster_color(i - 1, self.marker_alpha)
                # Background amplitude color.
                if cluster_id is not None else (.5, .5, .5, .5))
        return bunchs

def _concatenate_spike_clusters(*pairs):
    """Concatenate a list of pairs (spike_ids, spike_clusters)."""
    pairs = [(_as_array(x), _as_array(y)) for (x, y) in pairs]
    concat = np.vstack([np.hstack((x[:, None], y[:, None])) for x, y in pairs])
    reorder = np.argsort(concat[:, 0])
    concat = concat[reorder, :]
    return concat[:, 0].astype(np.int64), concat[:, 1].astype(np.int64)

def _zoom_aspect(self, zoom=None):
        zoom = zoom if zoom is not None else self._zoom
        zoom = _as_array(zoom)
        aspect = (self._canvas_aspect * self._aspect if self._aspect is not None else 1.)
        return zoom * aspect

def _do_assign(self, spike_ids, new_spike_clusters):
        """Make spike-cluster assignments after the spike selection has
        been extended to full clusters."""

        # Ensure spike_clusters has the right shape.
        spike_ids = _as_array(spike_ids)
        if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
            new_spike_clusters = np.ones(len(spike_ids), dtype=np.int64) * new_spike_clusters[0]
        old_spike_clusters = self._spike_clusters[spike_ids]

        assert len(spike_ids) == len(old_spike_clusters)
        assert len(new_spike_clusters) == len(spike_ids)

        # Update the spikes per cluster structure.
        old_clusters = _unique(old_spike_clusters)

        # NOTE: shortcut to a merge if this assignment is effectively a merge
        # i.e. if all spikes are assigned to a single cluster.
        # The fact that spike selection has been previously extended to
        # whole clusters is critical here.
        new_clusters = _unique(new_spike_clusters)
        if len(new_clusters) == 1:

def get_clusters_data(self, load_all=None):
        """Return a list of Bunch instances, with attributes pos and spike_ids."""
        if not len(self.cluster_ids):
            return
        cluster_ids = list(self.cluster_ids)
        # Don't need the background when splitting.
        if not load_all:
            # Add None cluster which means background spikes.
            cluster_ids = [None] + cluster_ids
        bunchs = self.amplitudes[self.amplitudes_type](cluster_ids, load_all=load_all) or ()
        # Add a pos attribute in bunchs in addition to x and y.
        for i, (cluster_id, bunch) in enumerate(zip(cluster_ids, bunchs)):
            spike_ids = _as_array(bunch.spike_ids)
            spike_times = _as_array(bunch.spike_times)
            amplitudes = _as_array(bunch.amplitudes)
            assert spike_ids.shape == spike_times.shape == amplitudes.shape
            # Ensure that bunch.pos exists, as it used by the LassoMixin.
            bunch.pos = np.c_[spike_times, amplitudes]
            assert bunch.pos.ndim == 2
            bunch.cluster_id = cluster_id
            bunch.color = (
                selected_cluster_color(i - 1, self.marker_alpha)
                # Background amplitude color.
                if cluster_id is not None else (.5, .5, .5, .5))
        return bunchs

def _extend_assignment(spike_ids, old_spike_clusters, spike_clusters_rel, new_cluster_id):
    # 1. Add spikes that belong to modified clusters.
    # 2. Find new cluster ids for all changed clusters.

    old_spike_clusters = _as_array(old_spike_clusters)
    spike_ids = _as_array(spike_ids)

    assert isinstance(spike_clusters_rel, (list, np.ndarray))
    spike_clusters_rel = _as_array(spike_clusters_rel)
    assert spike_clusters_rel.min() >= 0

    # We renumber the new cluster indices.
    new_spike_clusters = (spike_clusters_rel + (new_cluster_id - spike_clusters_rel.min()))

    # We find the spikes belonging to modified clusters.
    extended_spike_ids = _extend_spikes(spike_ids, old_spike_clusters)
    if len(extended_spike_ids) == 0:
        return spike_ids, new_spike_clusters

    # We take their clusters.
    extended_spike_clusters = old_spike_clusters[extended_spike_ids]

def _extend_assignment(spike_ids, old_spike_clusters, spike_clusters_rel, new_cluster_id):
    # 1. Add spikes that belong to modified clusters.
    # 2. Find new cluster ids for all changed clusters.

    old_spike_clusters = _as_array(old_spike_clusters)
    spike_ids = _as_array(spike_ids)

    assert isinstance(spike_clusters_rel, (list, np.ndarray))
    spike_clusters_rel = _as_array(spike_clusters_rel)
    assert spike_clusters_rel.min() >= 0

    # We renumber the new cluster indices.
    new_spike_clusters = (spike_clusters_rel + (new_cluster_id - spike_clusters_rel.min()))

    # We find the spikes belonging to modified clusters.
    extended_spike_ids = _extend_spikes(spike_ids, old_spike_clusters)
    if len(extended_spike_ids) == 0:
        return spike_ids, new_spike_clusters

    # We take their clusters.
    extended_spike_clusters = old_spike_clusters[extended_spike_ids]
    # Use relative numbers in extended_spike_clusters.

def get_clusters_data(self, load_all=None):
        """Return a list of Bunch instances, with attributes pos and spike_ids."""
        if not len(self.cluster_ids):
            return
        cluster_ids = list(self.cluster_ids)
        # Don't need the background when splitting.
        if not load_all:
            # Add None cluster which means background spikes.
            cluster_ids = [None] + cluster_ids
        bunchs = self.amplitudes[self.amplitudes_type](cluster_ids, load_all=load_all) or ()
        # Add a pos attribute in bunchs in addition to x and y.
        for i, (cluster_id, bunch) in enumerate(zip(cluster_ids, bunchs)):
            spike_ids = _as_array(bunch.spike_ids)
            spike_times = _as_array(bunch.spike_times)
            amplitudes = _as_array(bunch.amplitudes)
            assert spike_ids.shape == spike_times.shape == amplitudes.shape
            # Ensure that bunch.pos exists, as it used by the LassoMixin.
            bunch.pos = np.c_[spike_times, amplitudes]
            assert bunch.pos.ndim == 2
            bunch.cluster_id = cluster_id
            bunch.color = (
                selected_cluster_color(i - 1, self.marker_alpha)
                # Background amplitude color.
                if cluster_id is not None else (.5, .5, .5, .5))
        return bunchs