Unify properties

src/biaplotter/_tests/test_artists.py

@@ -50,7 +50,7 @@ def on_color_indices_changed(color_indices):
     assert scatter.color_indices.shape == (size,)
 
     # Test scatter colors
-    colors = scatter._scatter.get_facecolors()
+    colors = scatter._mpl_artists['scatter'].get_facecolors()
     assert np.all(colors[0] == scatter.overlay_colormap(0))
     assert np.all(colors[50] == scatter.overlay_colormap(2))
 
@@ -69,27 +69,27 @@ def on_color_indices_changed(color_indices):
     # Test size property
     scatter.size = 5.0
     assert scatter.size == 5.0
-    sizes = scatter._scatter.get_sizes()
+    sizes = scatter._mpl_artists['scatter'].get_sizes()
     assert np.all(sizes == 5.0)
 
     scatter.size = np.linspace(1, 10, size)
     assert np.all(scatter.size == np.linspace(1, 10, size))
-    sizes = scatter._scatter.get_sizes()
+    sizes = scatter._mpl_artists['scatter'].get_sizes()
     assert np.all(sizes == np.linspace(1, 10, size))
 
     # Test size reset when new data is set
     scatter.data = np.random.rand(size // 2, 2)
     assert np.all(scatter.size == 50.0)  # that's the default
-    sizes = scatter._scatter.get_sizes()
+    sizes = scatter._mpl_artists['scatter'].get_sizes()
     assert np.all(sizes == 50.0)
 
     # test alpha
     scatter.alpha = 0.5
-    assert np.all(scatter._scatter.get_alpha() == 0.5)
+    assert np.all(scatter._mpl_artists['scatter'].get_alpha() == 0.5)
 
     # test alpha reset when new data is set
     scatter.data = np.random.rand(size, 2)
-    assert np.all(scatter._scatter.get_alpha() == 1.0)
+    assert np.all(scatter._mpl_artists['scatter'].get_alpha() == 1.0)
 
     # Test changing overlay_colormap
     assert scatter.overlay_colormap.name == "cat10_modified"
@@ -98,7 +98,7 @@ def on_color_indices_changed(color_indices):
 
     # Test scatter color indices after continuous overlay_colormap
     scatter.color_indices = np.linspace(0, 1, size)
-    colors = scatter._scatter.get_facecolors()
+    colors = scatter._mpl_artists['scatter'].get_facecolors()
     assert np.all(colors[0] == plt.cm.viridis(0))
 
     # Test scatter color_normalization_method
@@ -181,7 +181,7 @@ def on_color_indices_changed(color_indices):
     assert histogram.cmin == 0
 
     # Test overlay colors
-    overlay_array = histogram._overlay_histogram_image.get_array()
+    overlay_array = histogram._mpl_artists['overlay_histogram_image'].get_array()
     assert overlay_array.shape == (bins, bins, 4)
     # indices where overlay_array is not zero
     indices = np.where(overlay_array[..., -1] != 0)
@@ -229,7 +229,7 @@ def on_color_indices_changed(color_indices):
 
     # Don't draw overlay histogram if color_indices are nan
     histogram.color_indices = np.nan
-    assert histogram._overlay_histogram_image is None
+    assert 'overlay_histogram_image' not in histogram._mpl_artists.keys()
 
 
 # Test calculate_statistic_histogram_method for different statistics
@@ -245,43 +245,3 @@ def on_color_indices_changed(color_indices):
         [[0.0, np.nan, np.nan], [np.nan, 3.0, np.nan], [np.nan, np.nan, 7.5]]
     ),
 ]
-
-
-@pytest.mark.parametrize(
-    "statistic,expected_array",
-    zip(statistics, expected_results),
-    ids=statistics,
-)
-def test_calculate_statistic_histogram_method(statistic, expected_array):
-    input_xy_data = np.array(
-        [
-            [1, 2],
-            [3, 4],
-            [3, 5],
-            [4, 5],
-            [5, 6],
-            [6, 7],
-        ]
-    )
-    bins = 3
-    input_features = np.array([0, 1, 2, 6, 7, 8])
-
-    expected_histogram_array = np.array(
-        [[1.0, 0.0, 0.0], [0.0, 3.0, 0.0], [0.0, 0.0, 2.0]]
-    )
-
-    histogram = Histogram2D(data=input_xy_data, bins=bins)
-    histogram_array, x_edges, y_edges = histogram.histogram
-    assert np.all(histogram_array == expected_histogram_array)
-    # Get the bin index for each x value ( -1 to start from index 0 and clip to handle edge cases)
-    x_bin_indices = (
-        np.digitize(input_xy_data[:, 0], x_edges, right=False) - 1
-    ).clip(0, len(x_edges) - 2)
-    # Get the bin index for each y value ( -1 to start from index 0 and clip to handle edge cases)
-    y_bin_indices = (
-        np.digitize(input_xy_data[:, 1], y_edges, right=False) - 1
-    ).clip(0, len(y_edges) - 2)
-    result = histogram._calculate_statistic_histogram(
-        x_bin_indices, y_bin_indices, input_features, statistic=statistic
-    )
-    assert np.array_equal(result, expected_array, equal_nan=True)