Escape empty selection

src/napari_clusters_plotter/_algorithm_widget.py

@@ -1,3 +1,5 @@
+import warnings
+
 import pandas as pd
 from magicgui import magicgui
 from napari.layers import (
@@ -50,6 +52,13 @@
             ].astype("category")
         return features.reset_index(drop=True)
 
+    def _clean_up(self):
+        """Determines what happens in case of no layer selected"""
+
+        raise NotImplementedError(
+            "This function should be implemented in the subclass."
+        )
+
     @property
     def common_columns(self):
         if len(self.layers) == 0:
@@ -137,6 +146,13 @@
         return features
 
     def _wait_for_finish(self, worker):
+        # escape empty input data
+        if self.selected_algorithm_widget.data.value.empty:
+            warnings.warn(
+                "No features selected. Please select features before running the algorithm.",
+                stacklevel=1,
+            )
+            return
         self.worker = worker
         self.worker.start()
         self.worker.returned.connect(self._process_result)
@@ -164,6 +180,7 @@
 
         # don't do anything if no layer is selected
         if self.n_selected_layers == 0:
+            self._clean_up()
             return
 
         # check if the selected layers are of the correct type
@@ -189,6 +206,16 @@
         self.feature_selection_widget.addItems(sorted(features_to_add.columns))
         self._update_features()
 
+    def _clean_up(self):
+        """
+        Clean up the widget when it is closed.
+        """
+
+        # block signals for feature selection
+        self.feature_selection_widget.blockSignals(True)
+        self.feature_selection_widget.clear()
+        self.feature_selection_widget.blockSignals(False)
+
     @property
     def selected_algorithm(self):
         return self.algorithm_selection.currentText()

src/napari_clusters_plotter/_new_plotter_widget.py

@@ -149,6 +149,9 @@
         # if the hue axis is not set to MANUAL_CLUSTER_ID, set it to that
         # otherwise replot the data
 
+        if self.n_selected_layers == 0:
+            return
+
         features = self._get_features()
         for layer in self.viewer.layers.selection:
             layer_indices = features[features["layer"] == layer.name].index
@@ -335,6 +338,7 @@
         """
         # don't do anything if no layer is selected
         if self.n_selected_layers == 0:
+            self._clean_up()
             return
 
         # check if the selected layers are of the correct type
@@ -362,6 +366,23 @@
         for layer in self.layers:
             layer.events.features.connect(self._update_feature_selection)
 
+    def _clean_up(self):
+        """In case of empty layer selection"""
+
+        # disconnect the events from the layers
+        for layer in self.viewer.layers.selection:
+            layer.events.features.disconnect(self._update_feature_selection)
+
+        # reset the selected layers
+        self.layers = []
+
+        # reset the selectors
+        for dim in ["x", "y", "hue"]:
+            selector = self._selectors[dim]
+            selector.blockSignals(True)
+            selector.clear()
+            selector.blockSignals(False)
+
     def _update_feature_selection(
         self, event: napari.utils.events.Event
     ) -> None:
@@ -450,6 +471,10 @@
         """
         Reset the selection in the current plotting widget.
         """
+
+        if self.n_selected_layers == 0:
+            return
+
         self.plotting_widget.active_artist.color_indices = np.zeros(
             len(self._get_features())
         )

src/napari_clusters_plotter/_tests/test_dimensionality_reduction.py

@@ -77,6 +77,11 @@ def test_initialization(make_napari_viewer, widget_config):
     # check that all features are in reduce_wdiget.feature_selection_widget
     assert len(feature_selection_items) == len(layer.features.columns)
 
+    # clear layers to make sure cleanup works
+    viewer.layers.clear()
+
+    assert widget.feature_selection_widget.count() == 0
+
 
 def test_layer_update(make_napari_viewer, widget_config):
     viewer = make_napari_viewer()

src/napari_clusters_plotter/_tests/test_plotter.py

@@ -344,6 +344,45 @@ def test_categorical_handling(make_napari_viewer, create_sample_layers):
     assert categorical_columns[1] == "layer"
 
 
+def test_empty_layer_clean_up(make_napari_viewer, n_samples: int = 100):
+    """
+    This test checks what happenns when you add some layers,
+    do a manual clustering , then delete the layers and add some others
+    """
+    from napari_clusters_plotter import PlotterWidget
+
+    viewer = make_napari_viewer()
+
+    points1, points2 = create_multi_point_layer(n_samples=n_samples)
+    vectors1, _ = create_multi_vectors_layer(n_samples=n_samples)
+
+    # add points to viewer
+    viewer.add_layer(points1)
+    viewer.add_layer(points2)
+
+    widget = PlotterWidget(viewer)
+    viewer.window.add_dock_widget(widget, area="right")
+    viewer.layers.selection.active = points1
+
+    # do a random drawing
+    assert "MANUAL_CLUSTER_ID" in points1.features.columns
+    random_cluster_indeces = np.random.randint(0, 2, len(points1.data))
+    widget._on_finish_draw(random_cluster_indeces)
+
+    # delete the layers
+    viewer.layers.clear()
+
+    # check that all widget._selectros ('x', 'y', 'hue') are empty
+    assert widget._selectors["x"].currentText() == ""
+    assert widget._selectors["y"].currentText() == ""
+    assert widget._selectors["hue"].currentText() == ""
+
+    widget._reset()
+
+    # add vectors to viewer
+    viewer.add_layer(vectors1)
+
+
 @pytest.mark.parametrize(
     "create_sample_layers",
     [