diff --git a/baseline/test_feature_histogram2.png b/baseline/test_feature_histogram2.png
deleted file mode 100644
index b7bb19e0..00000000
Binary files a/baseline/test_feature_histogram2.png and /dev/null differ
diff --git a/src/napari_matplotlib/histogram.py b/src/napari_matplotlib/histogram.py
index c853988a..f78a8503 100644
--- a/src/napari_matplotlib/histogram.py
+++ b/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)
diff --git a/src/napari_matplotlib/tests/baseline/test_feature_histogram2.png b/src/napari_matplotlib/tests/baseline/test_feature_histogram2.png
deleted file mode 100644
index c53a890c..00000000
Binary files a/src/napari_matplotlib/tests/baseline/test_feature_histogram2.png and /dev/null differ
diff --git a/src/napari_matplotlib/tests/baseline/test_feature_histogram_points.png b/src/napari_matplotlib/tests/baseline/test_feature_histogram_points.png
new file mode 100644
index 00000000..b98a0170
Binary files /dev/null and b/src/napari_matplotlib/tests/baseline/test_feature_histogram_points.png differ
diff --git a/src/napari_matplotlib/tests/baseline/test_feature_histogram_vectors.png b/src/napari_matplotlib/tests/baseline/test_feature_histogram_vectors.png
new file mode 100644
index 00000000..3b90586e
Binary files /dev/null and b/src/napari_matplotlib/tests/baseline/test_feature_histogram_vectors.png differ
diff --git a/src/napari_matplotlib/tests/test_histogram.py b/src/napari_matplotlib/tests/test_histogram.py
index 006c042f..1ceca519 100644
--- a/src/napari_matplotlib/tests/test_histogram.py
+++ b/src/napari_matplotlib/tests/test_histogram.py
@@ -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)
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