Support empty features

phy/apps/base.py

@@ -329,7 +329,16 @@ def _get_feature_view_spike_ids(self, cluster_id=None, load_all=False):
         """Return some or all spikes belonging to a given cluster."""
         if cluster_id is None:
             spike_ids = self.get_background_spike_ids(self.n_spikes_features_background)
-        else:
+        # Compute features on the fly from spike waveforms.
+        elif self.model.features is None and self.model.spike_waveforms is not None:
+            spike_ids = self.get_spike_ids(cluster_id)
+            assert len(spike_ids)
+            spike_ids = np.intersect1d(spike_ids, self.model.spike_waveforms.spike_ids)
+            if len(spike_ids) == 0:
+                logger.debug("empty spikes for cluster %s", str(cluster_id))
+            return spike_ids
+        # Retrieve features from the self.model.features array.
+        elif self.model.features is not None:
             # Load all spikes from the cluster if load_all is True.
             n = self.n_spikes_features if not load_all else None
             spike_ids = self.get_spike_ids(cluster_id, n=n)
@@ -341,13 +350,16 @@ def _get_feature_view_spike_ids(self, cluster_id=None, load_all=False):
     def _get_feature_view_spike_times(self, cluster_id=None, load_all=False):
         """Return the times of some or all spikes belonging to a given cluster."""
         spike_ids = self._get_feature_view_spike_ids(cluster_id, load_all=load_all)
+        if len(spike_ids) == 0:
+            return
         spike_times = self._get_spike_times_reordered(spike_ids)
         return Bunch(
             data=spike_times,
             spike_ids=spike_ids,
             lim=(0., self.model.duration))
 
     def _get_spike_features(self, spike_ids, channel_ids):
+        assert len(spike_ids)
         data = self.model.get_features(spike_ids, channel_ids)
         assert data.shape[:2] == (len(spike_ids), len(channel_ids))
         # Replace NaN values by zeros.
@@ -361,6 +373,8 @@ def _get_spike_features(self, spike_ids, channel_ids):
     def _get_features(self, cluster_id=None, channel_ids=None, load_all=False):
         """Return the features of a given cluster on specified channels."""
         spike_ids = self._get_feature_view_spike_ids(cluster_id, load_all=load_all)
+        if len(spike_ids) == 0:
+            return Bunch()
         # Use the best channels only if a cluster is specified and
         # channels are not specified.
         if cluster_id is not None and channel_ids is None:

phy/cluster/views/feature.py

@@ -218,6 +218,8 @@ def _get_axis_bounds(self, dim, bunch):
         return (-self._lim, +self._lim)
 
     def _plot_points(self, bunch, clu_idx=None):
+        if not bunch:
+            return
         cluster_id = self.cluster_ids[clu_idx] if clu_idx is not None else None
         for i, j, dim_x, dim_y in self._iter_subplots():
             px = self._get_axis_data(bunch, dim_x, cluster_id=cluster_id)
@@ -278,6 +280,8 @@ def _plot_axes(self):
         self.canvas.update_visual(self.line_visual)
 
     def _get_lim(self, bunchs):
+        if not bunchs:  # pragma: no cover
+            return 1
         m, M = min(bunch.data.min() for bunch in bunchs), max(bunch.data.max() for bunch in bunchs)
         M = max(abs(m), abs(M))
         return M
@@ -303,15 +307,18 @@ def get_clusters_data(self, fixed_channels=None, load_all=None):
         # choose the first cluster's best channels.
         c = self.channel_ids if fixed_channels else None
         bunchs = [self.features(cluster_id, channel_ids=c) for cluster_id in self.cluster_ids]
+        bunchs = [b for b in bunchs if b]
+        if not bunchs:  # pragma: no cover
+            return []
         for cluster_id, bunch in zip(self.cluster_ids, bunchs):
             bunch.cluster_id = cluster_id
 
         # Choose the channels based on the first selected cluster.
-        channel_ids = list(bunchs[0].channel_ids) if bunchs else []
+        channel_ids = list(bunchs[0].get('channel_ids', [])) if bunchs else []
         common_channels = list(channel_ids)
         # Intersection (with order kept) of channels belonging to all clusters.
         for bunch in bunchs:
-            common_channels = [c for c in bunch.channel_ids if c in common_channels]
+            common_channels = [c for c in bunch.get('channel_ids', []) if c in common_channels]
         # The selected channels will be (1) the channels common to all clusters, followed
         # by (2) remaining channels from the first cluster (excluding those already selected
         # in (1)).
@@ -328,9 +335,9 @@ def get_clusters_data(self, fixed_channels=None, load_all=None):
         # Channel labels.
         self.channel_labels = {}
         for d in bunchs:
-            chl = d.get('channel_labels', ['%d' % ch for ch in d.channel_ids])
+            chl = d.get('channel_labels', ['%d' % ch for ch in d.get('channel_ids', [])])
             self.channel_labels.update({
-                channel_id: chl[i] for i, channel_id in enumerate(d.channel_ids)})
+                channel_id: chl[i] for i, channel_id in enumerate(d.get('channel_ids', []))})
 
         return bunchs
 
@@ -346,6 +353,9 @@ def plot(self, **kwargs):
 
         # Get the clusters data.
         bunchs = self.get_clusters_data(fixed_channels=fixed_channels)
+        bunchs = [b for b in bunchs if b]
+        if not bunchs:
+            return
         self._lim = self._get_lim(bunchs)
 
         # Get the background data.
@@ -473,6 +483,8 @@ def on_request_split(self, sender=None):
         for cluster_id in self.cluster_ids:
             # Load all spikes.
             bunch = self.features(cluster_id, channel_ids=self.channel_ids, load_all=True)
+            if not bunch:
+                continue
             px = self._get_axis_data(bunch, dim_x, cluster_id=cluster_id, load_all=True)
             py = self._get_axis_data(bunch, dim_y, cluster_id=cluster_id, load_all=True)
             points = np.c_[px.data, py.data]