Use layer names for indexing, not uuids

src/napari_clusters_plotter/_algorithm_widget.py

@@ -39,7 +39,7 @@ def _get_features(self):
             _features = layer.features[self.common_columns].copy()
 
             # Add layer name as a categorical column
-            _features["layer"] = layer.unique_id
+            _features["layer"] = layer.name
             _features["layer"] = _features["layer"].astype("category")
             features = pd.concat([features, _features], axis=0)
 

src/napari_clusters_plotter/_dim_reduction_and_clustering.py

@@ -62,7 +62,7 @@ def _process_result(self, result):
 
             # add the columns to the features
             layer_feature_subset = features_clustered[
-                features_clustered["layer"] == layer.unique_id
+                features_clustered["layer"] == layer.name
             ]
             current_features[column_name] = layer_feature_subset[
                 column_name
@@ -114,7 +114,7 @@ def _process_result(self, result: pd.DataFrame):
             current_features = layer.features
             for column in column_names:
                 layer_feature_subset = result[
-                    result["layer"] == layer.unique_id
+                    result["layer"] == layer.name
                 ]
 
                 # add the columns to the features

src/napari_clusters_plotter/_new_plotter_widget.py

@@ -145,7 +145,7 @@ def _on_export_clusters(self):
         # get the layer to export from
         for layer in self.layers:
             features_subset = features[
-                features["layer"] == layer.unique_id
+                features["layer"] == layer.name
             ].reset_index()
             indices = features_subset[hue_column].values == selected_cluster
             if not np.any(indices):
@@ -243,7 +243,7 @@ def _on_finish_draw(self, color_indices: np.ndarray):
         features = self._get_features()
         for layer in self.layers:
             layer_indices = features[
-                features["layer"] == layer.unique_id
+                features["layer"] == layer.name
             ].index
 
             # store latest cluster indeces in the features table
@@ -727,7 +727,7 @@ def _update_layer_colors(self, use_color_indices: bool = False) -> None:
                     active_artist.color_indices
                 )
                 layer_indices = features[
-                    features["layer"] == selected_layer.unique_id
+                    features["layer"] == selected_layer.name
                 ].index
                 self._set_layer_color(
                     selected_layer, rgba_colors[layer_indices]
@@ -810,15 +810,12 @@ def _on_highlighted_changed(self, boolean_object_selected: bool):
         if not np.any(boolean_object_selected):
             return
         if np.count_nonzero(boolean_object_selected) > 1:
-            print(
-                "Focus only works for single object selection, not focusing."
+            napari.utils.notifications.show_info(
+                "Multiple objects selected - only single objects can be highlighted."
             )
             return
         features = self._get_features()
-        features_sub = features.iloc[
-            np.argwhere(boolean_object_selected).flatten()
-        ]
-        layer = features_sub["layer"].values[0]
+        layer = features[boolean_object_selected]["layer"].values[0]
         boolean_object_selected_in_layer = boolean_object_selected[
             features["layer"] == layer
         ]
@@ -882,7 +879,7 @@ def _focus_object(layer, boolean_object_selected):
         transformed_center = _apply_affine_transform(
             center, n_dims, affine_net
         )
-        _set_viewer_camera(viewer, transformed_center)
+
         # Set the selected data in the layer (only displays if single layer is selected)
         layer.selected_data = set(
             np.argwhere(boolean_object_selected).flatten()
@@ -895,7 +892,6 @@ def _focus_object(layer, boolean_object_selected):
         transformed_center = _apply_affine_transform(
             center, n_dims, affine_net
         )
-        _set_viewer_camera(viewer, transformed_center)
         # Set the selected data in the layer (only displays if single layer is selected)
         layer.selected_label = selected_label
     elif isinstance(layer, napari.layers.Surface):
@@ -904,7 +900,6 @@ def _focus_object(layer, boolean_object_selected):
         transformed_center = _apply_affine_transform(
             center, n_dims, affine_net
         )
-        _set_viewer_camera(viewer, transformed_center)
     elif isinstance(layer, napari.layers.Shapes):
         selected_shape = layer.data[
             np.nonzero(boolean_object_selected)[0][0]
@@ -914,7 +909,6 @@ def _focus_object(layer, boolean_object_selected):
         transformed_center = _apply_affine_transform(
             center, n_dims, affine_net
         )
-        _set_viewer_camera(viewer, transformed_center)
         layer.selected_data = set(
             np.argwhere(boolean_object_selected).flatten()
         )
@@ -925,7 +919,8 @@ def _focus_object(layer, boolean_object_selected):
         transformed_center = _apply_affine_transform(
             center, n_dims, affine_net
         )
-        _set_viewer_camera(viewer, transformed_center)
+
+    _set_viewer_camera(viewer, transformed_center)
 
 
 # TODO: Optionally uncomment this and call it in _set_viewer_camera if we want to zoom-in on highlighted objects

src/napari_clusters_plotter/_tests/test_dimensionality_reduction.py

@@ -128,7 +128,7 @@ def test_layer_update(make_napari_viewer, widget_config):
         for layer in possible_selection:
             for feature in widget.common_columns:
                 assert feature in collected_features.columns
-                assert layer.unique_id in collected_features["layer"].values
+                assert layer.name in collected_features["layer"].values
 
 
 def test_feature_update(make_napari_viewer, widget_config):

src/napari_clusters_plotter/_tests/test_plotter.py

@@ -801,16 +801,27 @@ def test_focus_object_on_highlighted_multi_selected_points_layers(
     boolean_object_selected = np.zeros(
         len(layer.data) + len(layer2.data), dtype=bool
     )
-    boolean_object_selected[len(layer.data) + 3] = True
+    boolean_object_selected[np.random.randint(0, len(boolean_object_selected))] = True
+    layer_name = widget._get_features()[boolean_object_selected]['layer'].values[0]
+    layer = viewer.layers[layer_name]
 
     # Set highlighted property
     widget.plotting_widget.active_artist.highlighted = boolean_object_selected
 
     # Check that the viewer camera is centered on the selected point (considering layer translation)
-    np.testing.assert_allclose(
-        viewer.camera.center, layer2.data[3][-3:] + translate, rtol=1e-5
+    index_in_data = np.where(
+        boolean_object_selected[widget._get_features()['layer'] == layer_name]
+    )[0][0]
+    assert np.allclose(
+        np.asarray(viewer.camera.center), layer.data[index_in_data][-3:] + layer.translate[-3:], rtol=1e-5
     )
     # Check that the viewer's current step is set to the selected point
-    np.testing.assert_allclose(
-        viewer.dims.current_step[0], layer2.data[3][0], rtol=1e-5
+    assert np.allclose(
+        viewer.dims.current_step[0], layer.data[index_in_data][0], rtol=1e-5
     )
+
+
+if __name__ == '__main__':
+    import napari
+
+    test_focus_object_on_highlighted_multi_selected_points_layers(napari.Viewer)