Merge pull request #148 from jo-mueller/add-feature-histogram

added a Feature Histogram Widget

Author: dstansby

baseline/test_feature_histogram2.png

@@ -1,7 +1,11 @@
 Changelog
 =========
-1.0.3
+1.1.0
 -----
+Additions
+~~~~~~~~~
+- Added a widget to draw a histogram of features.
+
 Changes
 ~~~~~~~
 - The slice widget is now limited to slicing along the x/y dimensions. Support

docs/changelog.rst

@@ -30,6 +30,7 @@ These widgets plot the data stored in the ``.features`` attribute of individual
 Currently available are:
 
 - 2D scatter plots of two features against each other.
+- Histograms of individual features.
 
 To use these:
 

docs/user_guide.rst

@@ -0,0 +1,9 @@
+from napari.layers import Labels, Points, Shapes, Tracks, Vectors
+
+FEATURES_LAYER_TYPES = (
+    Labels,
+    Points,
+    Shapes,
+    Tracks,
+    Vectors,
+)

src/napari_matplotlib/features.py

@@ -1,13 +1,15 @@
-from typing import Optional
+from typing import Any, List, Optional, Tuple
 
 import napari
 import numpy as np
-from qtpy.QtWidgets import QWidget
+import numpy.typing as npt
+from qtpy.QtWidgets import QComboBox, QLabel, QVBoxLayout, QWidget
 
 from .base import SingleAxesWidget
+from .features import FEATURES_LAYER_TYPES
 from .util import Interval
 
-__all__ = ["HistogramWidget"]
+__all__ = ["HistogramWidget", "FeaturesHistogramWidget"]
 
 _COLORS = {"r": "tab:red", "g": "tab:green", "b": "tab:blue"}
 
@@ -61,3 +63,112 @@ def draw(self) -> None:
             self.axes.hist(data.ravel(), bins=bins, label=layer.name)
 
         self.axes.legend()
+
+
+class FeaturesHistogramWidget(SingleAxesWidget):
+    """
+    Display a histogram of selected feature attached to selected layer.
+    """
+
+    n_layers_input = Interval(1, 1)
+    # All layers that have a .features attributes
+    input_layer_types = FEATURES_LAYER_TYPES
+
+    def __init__(
+        self,
+        napari_viewer: napari.viewer.Viewer,
+        parent: Optional[QWidget] = None,
+    ):
+        super().__init__(napari_viewer, parent=parent)
+
+        self.layout().addLayout(QVBoxLayout())
+        self._key_selection_widget = QComboBox()
+        self.layout().addWidget(QLabel("Key:"))
+        self.layout().addWidget(self._key_selection_widget)
+
+        self._key_selection_widget.currentTextChanged.connect(
+            self._set_axis_keys
+        )
+
+        self._update_layers(None)
+
+    @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) -> 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[Optional[npt.NDArray[Any]], 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 None, ""
+
+        feature_table = self.layers[0].features
+
+        if (len(feature_table) == 0) or (self.x_axis_key is None):
+            return None, ""
+
+        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:
+        """
+        Called when the layer selection changes by ``self.update_layers()``.
+        """
+        # reset the axis keys
+        self._x_axis_key = None
+
+        # Clear combobox
+        self._key_selection_widget.clear()
+        self._key_selection_widget.addItems(self._get_valid_axis_keys())
+
+    def draw(self) -> None:
+        """Clear the axes and histogram the currently selected layer/slice."""
+        data, x_axis_name = self._get_data()
+
+        if data is None:
+            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/histogram.py

@@ -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/napari.yaml

@@ -5,6 +5,7 @@
 from qtpy.QtWidgets import QComboBox, QLabel, QVBoxLayout, QWidget
 
 from .base import SingleAxesWidget
+from .features import FEATURES_LAYER_TYPES
 from .util import Interval
 
 __all__ = ["ScatterBaseWidget", "ScatterWidget", "FeaturesScatterWidget"]
@@ -94,13 +95,7 @@ class FeaturesScatterWidget(ScatterBaseWidget):
 
     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,
-    )
+    input_layer_types = FEATURES_LAYER_TYPES
 
     def __init__(
         self,

src/napari_matplotlib/scatter.py

@@ -1,8 +1,13 @@
 from copy import deepcopy
 
+import numpy as np
 import pytest
 
-from napari_matplotlib import HistogramWidget
+from napari_matplotlib import FeaturesHistogramWidget, HistogramWidget
+from napari_matplotlib.tests.helpers import (
+    assert_figures_equal,
+    assert_figures_not_equal,
+)
 
 
 @pytest.mark.mpl_image_compare
@@ -28,3 +33,90 @@ def test_histogram_3D(make_napari_viewer, brain_data):
     # Need to return a copy, as original figure is too eagerley garbage
     # collected by the widget
     return deepcopy(fig)
+
+
+def test_feature_histogram(make_napari_viewer):
+    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},
+        name="points1",
+    )
+    viewer.add_points(
+        random_points,
+        properties={"feature1": feature1, "feature2": feature2},
+        name="points2",
+    )
+
+    widget = FeaturesHistogramWidget(viewer)
+    viewer.window.add_dock_widget(widget)
+
+    # Check whether changing the selected key changes the plot
+    widget._set_axis_keys("feature1")
+    fig1 = deepcopy(widget.figure)
+
+    widget._set_axis_keys("feature2")
+    assert_figures_not_equal(widget.figure, fig1)
+
+    # check whether selecting a different layer produces the same plot
+    viewer.layers.selection.clear()
+    viewer.layers.selection.add(viewer.layers[1])
+    assert_figures_equal(widget.figure, fig1)
+
+
+@pytest.mark.mpl_image_compare
+def test_feature_histogram2(make_napari_viewer):
+    import numpy as np
+
+    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},
+        name="points1",
+    )
+    viewer.add_points(
+        random_points,
+        properties={"feature1": feature1, "feature2": feature2},
+        name="points2",
+    )
+
+    widget = FeaturesHistogramWidget(viewer)
+    viewer.window.add_dock_widget(widget)
+    widget._set_axis_keys("feature1")
+
+    fig = FeaturesHistogramWidget(viewer).figure
+    return deepcopy(fig)
+
+
+def test_change_layer(make_napari_viewer, brain_data, astronaut_data):
+    viewer = make_napari_viewer()
+    widget = HistogramWidget(viewer)
+
+    viewer.add_image(brain_data[0], **brain_data[1])
+    viewer.add_image(astronaut_data[0], **astronaut_data[1])
+
+    # Select first layer
+    viewer.layers.selection.clear()
+    viewer.layers.selection.add(viewer.layers[0])
+    fig1 = deepcopy(widget.figure)
+
+    # Re-selecting first layer should produce identical plot
+    viewer.layers.selection.clear()
+    viewer.layers.selection.add(viewer.layers[0])
+    assert_figures_equal(widget.figure, fig1)
+
+    # Plotting the second layer should produce a different plot
+    viewer.layers.selection.clear()
+    viewer.layers.selection.add(viewer.layers[1])
+    assert_figures_not_equal(widget.figure, fig1)