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Commit dc1f3bb

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dstansbydstansby
authored
Merge pull request #148 from jo-mueller/add-feature-histogram
added a Feature Histogram Widget
2 parents 3a8261a + e1ccfb1 commit dc1f3bb
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+231
-12
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‎baseline/test_feature_histogram2.png

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‎docs/changelog.rst

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

‎docs/user_guide.rst

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

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from napari.layers import Labels, Points, Shapes, Tracks, Vectors
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FEATURES_LAYER_TYPES = (
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Labels,
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Points,
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Shapes,
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Tracks,
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Vectors,
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)

‎src/napari_matplotlib/histogram.py

Copy file name to clipboardExpand all lines: src/napari_matplotlib/histogram.py
+114-3Lines changed: 114 additions & 3 deletions
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@@ -1,13 +1,15 @@
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from typing import Optional
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from typing import Any, List, Optional, Tuple
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import napari
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import numpy as np
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from qtpy.QtWidgets import QWidget
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import numpy.typing as npt
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from qtpy.QtWidgets import QComboBox, QLabel, QVBoxLayout, QWidget
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from .base import SingleAxesWidget
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from .features import FEATURES_LAYER_TYPES
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from .util import Interval
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__all__ = ["HistogramWidget"]
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__all__ = ["HistogramWidget", "FeaturesHistogramWidget"]
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_COLORS = {"r": "tab:red", "g": "tab:green", "b": "tab:blue"}
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@@ -61,3 +63,112 @@ def draw(self) -> None:
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self.axes.hist(data.ravel(), bins=bins, label=layer.name)
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self.axes.legend()
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class FeaturesHistogramWidget(SingleAxesWidget):
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"""
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Display a histogram of selected feature attached to selected layer.
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"""
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n_layers_input = Interval(1, 1)
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# All layers that have a .features attributes
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input_layer_types = FEATURES_LAYER_TYPES
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def __init__(
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self,
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napari_viewer: napari.viewer.Viewer,
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parent: Optional[QWidget] = None,
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):
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super().__init__(napari_viewer, parent=parent)
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self.layout().addLayout(QVBoxLayout())
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self._key_selection_widget = QComboBox()
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self.layout().addWidget(QLabel("Key:"))
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self.layout().addWidget(self._key_selection_widget)
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self._key_selection_widget.currentTextChanged.connect(
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self._set_axis_keys
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)
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self._update_layers(None)
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@property
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def x_axis_key(self) -> Optional[str]:
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"""Key to access x axis data from the FeaturesTable"""
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return self._x_axis_key
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@x_axis_key.setter
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def x_axis_key(self, key: Optional[str]) -> None:
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self._x_axis_key = key
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self._draw()
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def _set_axis_keys(self, x_axis_key: str) -> None:
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"""Set both axis keys and then redraw the plot"""
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self._x_axis_key = x_axis_key
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self._draw()
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def _get_valid_axis_keys(self) -> List[str]:
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"""
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Get the valid axis keys from the layer FeatureTable.
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Returns
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-------
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axis_keys : List[str]
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The valid axis keys in the FeatureTable. If the table is empty
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or there isn't a table, returns an empty list.
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"""
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if len(self.layers) == 0 or not (hasattr(self.layers[0], "features")):
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return []
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else:
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return self.layers[0].features.keys()
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def _get_data(self) -> Tuple[Optional[npt.NDArray[Any]], str]:
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"""Get the plot data.
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Returns
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-------
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data : List[np.ndarray]
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List contains X and Y columns from the FeatureTable. Returns
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an empty array if nothing to plot.
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x_axis_name : str
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The title to display on the x axis. Returns
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an empty string if nothing to plot.
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"""
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if not hasattr(self.layers[0], "features"):
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# if the selected layer doesn't have a featuretable,
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# skip draw
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return None, ""
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feature_table = self.layers[0].features
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if (len(feature_table) == 0) or (self.x_axis_key is None):
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return None, ""
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data = feature_table[self.x_axis_key]
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x_axis_name = self.x_axis_key.replace("_", " ")
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return data, x_axis_name
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def on_update_layers(self) -> None:
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"""
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Called when the layer selection changes by ``self.update_layers()``.
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"""
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# reset the axis keys
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self._x_axis_key = None
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# Clear combobox
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self._key_selection_widget.clear()
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self._key_selection_widget.addItems(self._get_valid_axis_keys())
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def draw(self) -> None:
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"""Clear the axes and histogram the currently selected layer/slice."""
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data, x_axis_name = self._get_data()
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if data is None:
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return
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self.axes.hist(data, bins=50, edgecolor="white", linewidth=0.3)
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# set ax labels
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self.axes.set_xlabel(x_axis_name)
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self.axes.set_ylabel("Counts [#]")

‎src/napari_matplotlib/napari.yaml

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

‎src/napari_matplotlib/scatter.py

Copy file name to clipboardExpand all lines: src/napari_matplotlib/scatter.py
+2-7Lines changed: 2 additions & 7 deletions
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@@ -5,6 +5,7 @@
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from qtpy.QtWidgets import QComboBox, QLabel, QVBoxLayout, QWidget
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from .base import SingleAxesWidget
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from .features import FEATURES_LAYER_TYPES
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from .util import Interval
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__all__ = ["ScatterBaseWidget", "ScatterWidget", "FeaturesScatterWidget"]
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n_layers_input = Interval(1, 1)
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# All layers that have a .features attributes
97-
input_layer_types = (
98-
napari.layers.Labels,
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napari.layers.Points,
100-
napari.layers.Shapes,
101-
napari.layers.Tracks,
102-
napari.layers.Vectors,
103-
)
98+
input_layer_types = FEATURES_LAYER_TYPES
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105100
def __init__(
106101
self,

‎src/napari_matplotlib/tests/baseline/test_feature_histogram.png

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9.45 KB
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‎src/napari_matplotlib/tests/baseline/test_feature_histogram2.png

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‎src/napari_matplotlib/tests/test_histogram.py

Copy file name to clipboardExpand all lines: src/napari_matplotlib/tests/test_histogram.py
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from copy import deepcopy
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import numpy as np
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import pytest
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5-
from napari_matplotlib import HistogramWidget
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from napari_matplotlib import FeaturesHistogramWidget, HistogramWidget
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from napari_matplotlib.tests.helpers import (
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assert_figures_equal,
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assert_figures_not_equal,
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)
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@pytest.mark.mpl_image_compare
@@ -28,3 +33,90 @@ def test_histogram_3D(make_napari_viewer, brain_data):
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# Need to return a copy, as original figure is too eagerley garbage
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# collected by the widget
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return deepcopy(fig)
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def test_feature_histogram(make_napari_viewer):
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n_points = 1000
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random_points = np.random.random((n_points, 3)) * 10
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feature1 = np.random.random(n_points)
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feature2 = np.random.normal(size=n_points)
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viewer = make_napari_viewer()
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viewer.add_points(
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random_points,
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properties={"feature1": feature1, "feature2": feature2},
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name="points1",
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)
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viewer.add_points(
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random_points,
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properties={"feature1": feature1, "feature2": feature2},
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name="points2",
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)
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widget = FeaturesHistogramWidget(viewer)
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viewer.window.add_dock_widget(widget)
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# Check whether changing the selected key changes the plot
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widget._set_axis_keys("feature1")
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fig1 = deepcopy(widget.figure)
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widget._set_axis_keys("feature2")
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assert_figures_not_equal(widget.figure, fig1)
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# check whether selecting a different layer produces the same plot
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viewer.layers.selection.clear()
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viewer.layers.selection.add(viewer.layers[1])
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assert_figures_equal(widget.figure, fig1)
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@pytest.mark.mpl_image_compare
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def test_feature_histogram2(make_napari_viewer):
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import numpy as np
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np.random.seed(0)
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n_points = 1000
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random_points = np.random.random((n_points, 3)) * 10
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feature1 = np.random.random(n_points)
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feature2 = np.random.normal(size=n_points)
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viewer = make_napari_viewer()
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viewer.add_points(
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random_points,
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properties={"feature1": feature1, "feature2": feature2},
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name="points1",
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)
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viewer.add_points(
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random_points,
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properties={"feature1": feature1, "feature2": feature2},
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name="points2",
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)
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widget = FeaturesHistogramWidget(viewer)
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viewer.window.add_dock_widget(widget)
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widget._set_axis_keys("feature1")
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fig = FeaturesHistogramWidget(viewer).figure
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return deepcopy(fig)
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def test_change_layer(make_napari_viewer, brain_data, astronaut_data):
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viewer = make_napari_viewer()
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widget = HistogramWidget(viewer)
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viewer.add_image(brain_data[0], **brain_data[1])
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viewer.add_image(astronaut_data[0], **astronaut_data[1])
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# Select first layer
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viewer.layers.selection.clear()
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viewer.layers.selection.add(viewer.layers[0])
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fig1 = deepcopy(widget.figure)
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# Re-selecting first layer should produce identical plot
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viewer.layers.selection.clear()
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viewer.layers.selection.add(viewer.layers[0])
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assert_figures_equal(widget.figure, fig1)
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# Plotting the second layer should produce a different plot
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viewer.layers.selection.clear()
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viewer.layers.selection.add(viewer.layers[1])
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assert_figures_not_equal(widget.figure, fig1)

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