Merge pull request #216 from jo-mueller/color-histogram-bars

Color histogram bars

Author: dstansby

src/napari_matplotlib/histogram.py

@@ -1,8 +1,9 @@
-from typing import Any, Optional
+from typing import Any, Optional, cast
 
 import napari
 import numpy as np
 import numpy.typing as npt
+from matplotlib.container import BarContainer
 from qtpy.QtWidgets import QComboBox, QLabel, QVBoxLayout, QWidget
 
 from .base import SingleAxesWidget
@@ -162,12 +163,39 @@ def on_update_layers(self) -> None:
 
     def draw(self) -> None:
         """Clear the axes and histogram the currently selected layer/slice."""
+        # get the colormap from the layer depending on its type
+        if isinstance(self.layers[0], napari.layers.Points):
+            colormap = self.layers[0].face_colormap
+            self.layers[0].face_color = self.x_axis_key
+        elif isinstance(self.layers[0], napari.layers.Vectors):
+            colormap = self.layers[0].edge_colormap
+            self.layers[0].edge_color = self.x_axis_key
+        else:
+            colormap = None
+
+        # apply new colors to the layer
+        self.viewer.layers[self.layers[0].name].refresh_colors(True)
+        self.viewer.layers[self.layers[0].name].refresh()
+
+        # Draw the histogram
         data, x_axis_name = self._get_data()
 
         if data is None:
             return
 
-        self.axes.hist(data, bins=50, edgecolor="white", linewidth=0.3)
+        _, bins, patches = self.axes.hist(
+            data, bins=50, edgecolor="white", linewidth=0.3
+        )
+        patches = cast(BarContainer, patches)
+
+        # recolor the histogram plot
+        if colormap is not None:
+            self.bins_norm = (bins - bins.min()) / (bins.max() - bins.min())
+            colors = colormap.map(self.bins_norm)
+
+            # Set histogram style:
+            for idx, patch in enumerate(patches):
+                patch.set_facecolor(colors[idx])
 
         # set ax labels
         self.axes.set_xlabel(x_axis_name)

src/napari_matplotlib/tests/baseline/test_feature_histogram2.png

@@ -38,6 +38,8 @@ def test_histogram_3D(make_napari_viewer, brain_data):
 def test_feature_histogram(make_napari_viewer):
     n_points = 1000
     random_points = np.random.random((n_points, 3)) * 10
+    random_directions = np.random.random((n_points, 3)) * 10
+    random_vectors = np.stack([random_points, random_directions], axis=1)
     feature1 = np.random.random(n_points)
     feature2 = np.random.normal(size=n_points)
 
@@ -47,10 +49,10 @@ def test_feature_histogram(make_napari_viewer):
         properties={"feature1": feature1, "feature2": feature2},
         name="points1",
     )
-    viewer.add_points(
-        random_points,
+    viewer.add_vectors(
+        random_vectors,
         properties={"feature1": feature1, "feature2": feature2},
-        name="points2",
+        name="vectors1",
     )
 
     widget = FeaturesHistogramWidget(viewer)
@@ -70,26 +72,42 @@ def test_feature_histogram(make_napari_viewer):
 
 
 @pytest.mark.mpl_image_compare
-def test_feature_histogram2(make_napari_viewer):
-    import numpy as np
+def test_feature_histogram_vectors(make_napari_viewer):
+    n_points = 1000
+    np.random.seed(42)
+    random_points = np.random.random((n_points, 3)) * 10
+    random_directions = np.random.random((n_points, 3)) * 10
+    random_vectors = np.stack([random_points, random_directions], axis=1)
+    feature1 = np.random.random(n_points)
+
+    viewer = make_napari_viewer()
+    viewer.add_vectors(
+        random_vectors,
+        properties={"feature1": feature1},
+        name="vectors1",
+    )
+
+    widget = FeaturesHistogramWidget(viewer)
+    viewer.window.add_dock_widget(widget)
+    widget._set_axis_keys("feature1")
 
+    fig = FeaturesHistogramWidget(viewer).figure
+    return deepcopy(fig)
+
+
+@pytest.mark.mpl_image_compare
+def test_feature_histogram_points(make_napari_viewer):
     np.random.seed(0)
     n_points = 1000
     random_points = np.random.random((n_points, 3)) * 10
     feature1 = np.random.random(n_points)
-    feature2 = np.random.normal(size=n_points)
 
     viewer = make_napari_viewer()
     viewer.add_points(
         random_points,
-        properties={"feature1": feature1, "feature2": feature2},
+        properties={"feature1": feature1},
         name="points1",
     )
-    viewer.add_points(
-        random_points,
-        properties={"feature1": feature1, "feature2": feature2},
-        name="points2",
-    )
 
     widget = FeaturesHistogramWidget(viewer)
     viewer.window.add_dock_widget(widget)