added a Feature Histogram Widget

Co-Authored-By: Marcelo Zoccoler <[email protected]>

src/napari_matplotlib/napari.yaml

@@ -14,6 +14,10 @@ contributions:
       python_name: napari_matplotlib:FeaturesScatterWidget
       title: Make a scatter plot of layer features
 
+    - id: napari-matplotlib.features_histogram
+      python_name: napari_matplotlib:FeaturesHistogramWidget
+      title: Plot feature histograms
+
     - id: napari-matplotlib.slice
       python_name: napari_matplotlib:SliceWidget
       title: Plot a 1D slice
@@ -28,5 +32,8 @@ contributions:
     - command: napari-matplotlib.features_scatter
       display_name: FeaturesScatter
 
+    - command: napari-matplotlib.features_histogram
+      display_name: FeaturesHistogram
+
     - command: napari-matplotlib.slice
       display_name: 1D slice

src/napari_matplotlib/scatter.py

@@ -9,7 +9,7 @@
 from .base import NapariMPLWidget
 from .util import Interval
 
-__all__ = ["ScatterWidget", "FeaturesScatterWidget"]
+__all__ = ["ScatterWidget", "FeaturesScatterWidget", "FeaturesHistogramWidget"]
 
 
 class ScatterBaseWidget(NapariMPLWidget):
@@ -222,3 +222,124 @@ def _on_update_layers(self) -> None:
         # reset the axis keys
         self._x_axis_key = None
         self._y_axis_key = None
+
+
+class FeaturesHistogramWidget(NapariMPLWidget):
+    n_layers_input = Interval(1, 1)
+    # All layers that have a .features attributes
+    input_layer_types = (
+        napari.layers.Labels,
+        napari.layers.Points,
+        napari.layers.Shapes,
+        napari.layers.Tracks,
+        napari.layers.Vectors,
+    )
+
+    def __init__(self, napari_viewer: napari.viewer.Viewer):
+        super().__init__(napari_viewer)
+        self.axes = self.canvas.figure.subplots()
+
+        self._key_selection_widget = magicgui(
+            self._set_axis_keys,
+            x_axis_key={"choices": self._get_valid_axis_keys},
+            call_button="plot",
+        )
+        self.layout().addWidget(self._key_selection_widget.native)
+
+        self.update_layers(None)
+
+    def clear(self) -> None:
+        """
+        Clear the axes.
+        """
+        self.axes.clear()
+
+        self.layout().addWidget(self._key_selection_widget.native)
+
+    @property
+    def x_axis_key(self) -> Optional[str]:
+        """Key to access x axis data from the FeaturesTable"""
+        return self._x_axis_key
+
+    @x_axis_key.setter
+    def x_axis_key(self, key: Optional[str]) -> None:
+        self._x_axis_key = key
+        self._draw()
+
+    def _set_axis_keys(self, x_axis_key: str) -> None:
+        """Set both axis keys and then redraw the plot"""
+        self._x_axis_key = x_axis_key
+        self._draw()
+
+    def _get_valid_axis_keys(
+        self, combo_widget: Optional[ComboBox] = None
+    ) -> List[str]:
+        """
+        Get the valid axis keys from the layer FeatureTable.
+
+        Returns
+        -------
+        axis_keys : List[str]
+            The valid axis keys in the FeatureTable. If the table is empty
+            or there isn't a table, returns an empty list.
+        """
+        if len(self.layers) == 0 or not (hasattr(self.layers[0], "features")):
+            return []
+        else:
+            return self.layers[0].features.keys()
+
+    def _get_data(self) -> Tuple[List[np.ndarray], str, str]:
+        """Get the plot data.
+
+        Returns
+        -------
+        data : List[np.ndarray]
+            List contains X and Y columns from the FeatureTable. Returns
+            an empty array if nothing to plot.
+        x_axis_name : str
+            The title to display on the x axis. Returns
+            an empty string if nothing to plot.
+        """
+        if not hasattr(self.layers[0], "features"):
+            # if the selected layer doesn't have a featuretable,
+            # skip draw
+            return [], ""
+
+        feature_table = self.layers[0].features
+
+        if (
+            (len(feature_table) == 0)
+            or (self.x_axis_key is None)
+        ):
+            return [], ""
+
+        data = feature_table[self.x_axis_key]
+        x_axis_name = self.x_axis_key.replace("_", " ")
+
+        return data, x_axis_name
+
+    def _on_update_layers(self) -> None:
+        """
+        This is called when the layer selection changes by
+        ``self.update_layers()``.
+        """
+        if hasattr(self, "_key_selection_widget"):
+            self._key_selection_widget.reset_choices()
+
+        # reset the axis keys
+        self._x_axis_key = None
+
+    def draw(self) -> None:
+        """Clear the axes and histogram the currently selected layer/slice."""
+
+        data, x_axis_name = self._get_data()
+
+        if len(data) == 0:
+            return
+
+        _, _, _ = self.axes.hist(data, bins=50, edgecolor='white',
+                                 linewidth=0.3)
+
+        # # set ax labels
+        self.axes.set_xlabel(x_axis_name)
+        self.axes.set_ylabel('Counts [#]')

src/napari_matplotlib/tests/test_histogram.py

@@ -1,8 +1,27 @@
-from napari_matplotlib import HistogramWidget
+from napari_matplotlib import HistogramWidget, FeaturesHistogramWidget
 
 
 def test_example_q_widget(make_napari_viewer, image_data):
     # Smoke test adding a histogram widget
     viewer = make_napari_viewer()
     viewer.add_image(image_data[0], **image_data[1])
     HistogramWidget(viewer)
+
+def test_feature_histogram(make_napari_viewer):
+
+    import numpy as np
+
+    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}, face_color='feature1', size=1)
+
+    widget = FeaturesHistogramWidget(viewer)
+    viewer.window.add_dock_widget(widget)
+    widget._set_axis_keys('feature1')
+    widget._key_selection_widget()
+    widget._set_axis_keys('feature2')
+    widget._key_selection_widget()