WIP: Add more tests

src/napari_clusters_plotter/_tests/test_plotter.py

@@ -1,4 +1,170 @@
 import numpy as np
+import pandas as pd
+import pytest
+
+np.random.seed(0)
+
+
+def create_random_features(n_samples=100, n_features=5):
+    features = pd.DataFrame(
+        {f"feature{i}": np.random.random(n_samples) for i in range(n_features)}
+    )
+
+    return features
+
+
+def create_random_points_data(n_samples=100):
+    from napari.layers import Points
+
+    points = np.random.random((n_samples, 3)) * 1000
+    features = create_random_features(n_samples)
+    points_layer = Points(points, features=features)
+
+    return points_layer
+
+
+def create_random_shapes_data(n_samples=20):
+    from napari.layers import Shapes
+
+    box_centers = np.random.random((n_samples, 2)) * 1000
+    box_widths = np.random.random(n_samples) * 100
+    box_heights = np.random.random(n_samples) * 100
+    features = create_random_features(n_samples)
+
+    boxes = []
+    for i in range(n_samples):
+        corners = np.array(
+            [
+                [
+                    box_centers[i, 0] - box_widths[i] / 2,
+                    box_centers[i, 1] - box_heights[i] / 2,
+                ],
+                [
+                    box_centers[i, 0] + box_widths[i] / 2,
+                    box_centers[i, 1] - box_heights[i] / 2,
+                ],
+                [
+                    box_centers[i, 0] + box_widths[i] / 2,
+                    box_centers[i, 1] + box_heights[i] / 2,
+                ],
+                [
+                    box_centers[i, 0] - box_widths[i] / 2,
+                    box_centers[i, 1] + box_heights[i] / 2,
+                ],
+            ]
+        )
+        boxes.append(corners)
+
+    shapes_layer = Shapes(boxes, shape_type="polygon", features=features)
+
+    return shapes_layer
+
+
+def create_random_labels_data():
+    from napari.layers import Labels
+    from skimage import data, measure
+
+    blobs_3d = data.binary_blobs(length=128, n_dim=3, rng=42)
+    labels = measure.label(blobs_3d)
+    features = create_random_features(labels.max() + 1)
+    labels_layer = Labels(labels, features=features)
+
+    return labels_layer
+
+
+def create_random_vectors_data(n_samples=10):
+    from napari.layers import Vectors
+
+    random_points = np.random.random((n_samples, 3)) * 1000
+    random_directions = np.random.uniform(-1, 1, (n_samples, 3))
+
+    random_vectors = np.stack(
+        [random_points, random_points + random_directions], axis=1
+    )
+    features = create_random_features(n_samples)
+    vectors_layer = Vectors(random_vectors, features=features)
+
+    return vectors_layer
+
+
+@pytest.fixture(
+    params=[
+        create_random_points_data,
+        create_random_shapes_data,
+        create_random_labels_data,
+        create_random_vectors_data,
+    ]
+)
+def random_layer(request):
+    return request.param()
+
+
+def test_adding_layers(make_napari_viewer, random_layer):
+    from napari_clusters_plotter import PlotterWidget
+
+    viewer = make_napari_viewer()
+    viewer.add_layer(random_layer)
+    assert len(viewer.layers) == 1
+
+    widget = PlotterWidget(viewer)
+    viewer.window.add_dock_widget(widget)
+
+    # check that the features are correctly retrieved through the plotter widget
+    features = widget._get_features()
+
+    # Make sure that Plotter inserted 'layer' column that indicates which layer the
+    # feature was retrieved from
+    assert "layer" in features.columns
+
+    # make sure that the features are the same as the ones in the layer
+    for column in random_layer.features.columns:
+        assert np.array_equal(features[column], random_layer.features[column])
+
+
+def test_cluster_selection(make_napari_viewer, random_layer):
+    # test that objects are colored correctly when clusters are selected
+    from napari.layers import Labels, Points, Shapes, Vectors
+
+    from napari_clusters_plotter import PlotterWidget
+
+    viewer = make_napari_viewer()
+    viewer.add_layer(random_layer)
+    assert len(viewer.layers) == 1
+
+    widget = PlotterWidget(viewer)
+    viewer.window.add_dock_widget(widget)
+
+    widget._selectors["x"].setCurrentIndex(0)
+    widget._selectors["y"].setCurrentIndex(1)
+    n_samples = len(widget.plotting_widget.active_artist.color_indices)
+    clusters = np.random.randint(0, 3, size=n_samples)
+    widget.plotting_widget.active_artist.color_indices = clusters
+
+    colormap = widget.plotting_widget.active_artist.categorical_colormap
+    colors = colormap(clusters)
+
+    if isinstance(random_layer, Points) or isinstance(random_layer, Shapes):
+        assert np.allclose(random_layer.face_color, colors)
+
+    elif isinstance(random_layer, Labels):
+        pass
+        # TODO implement test for Labels
+
+    elif isinstance(random_layer, Vectors):
+        assert np.allclose(random_layer.edge_color, colors)
+
+
+def test_multi_layer_selection(make_napari_viewer):
+    # This test adds multiple layer of the same type and checks that the features
+    # are correctly retrieved when selecting multiple layers
+    pass
+
+
+def test_cached_cluster_selection(make_napari_viewer):
+    # This test adds multiple layers, draw a selection on the first layer,
+    # selects a different layer and then re-selects the first layer. The
+    # previous selection should still be present.
+    pass
 
 
 def test_mixed_layers(make_napari_viewer):
@@ -23,5 +189,3 @@ def test_mixed_layers(make_napari_viewer):
 
     viewer.add_image(random_image)
     viewer.add_labels(sample_labels)
-
-    #