Simplify scatter code

CHANGELOG.rst

@@ -1,12 +1,12 @@
-0.0.2
+0.4.0
 =====
 
-New features
-------------
-- `HistogramWidget` now shows individual histograms for RGB channels when
-  present.
-
-
-Bug fixes
----------
-- `HistogramWidget` now works properly with 2D images.
+Changes
+-------
+- The scatter widgets no longer use a LogNorm() for 2D histogram scaling.
+  This is to move the widget in line with the philosophy of using Matplotlib default
+  settings throughout ``napari-matplotlib``. This still leaves open the option of
+  adding the option to change the normalization in the future. If this is something
+  you would be interested in please open an issue at https://github.com/matplotlib/napari-matplotlib.
+- Labels plotting with the features scatter widget no longer have underscores
+  replaced with spaces.

src/napari_matplotlib/scatter.py

@@ -1,6 +1,5 @@
 from typing import Any, List, Optional, Tuple
 
-import matplotlib.colors as mcolor
 import napari
 import numpy.typing as npt
 from magicgui import magicgui
@@ -17,15 +16,8 @@ class ScatterBaseWidget(NapariMPLWidget):
     Base class for widgets that scatter two datasets against each other.
     """
 
-    # opacity value for the markers
-    _marker_alpha = 0.5
-
-    # flag set to True if histogram should be used
-    # for plotting large points
-    _histogram_for_large_data = True
-
     # if the number of points is greater than this value,
-    # the scatter is plotted as a 2dhist
+    # the scatter is plotted as a 2D histogram
     _threshold_to_switch_to_histogram = 500
 
     def __init__(self, napari_viewer: napari.viewer.Viewer):
@@ -44,40 +36,32 @@ def draw(self) -> None:
         """
         Scatter the currently selected layers.
         """
-        data, x_axis_name, y_axis_name = self._get_data()
-
-        if len(data) == 0:
-            # don't plot if there isn't data
-            return
+        x, y, x_axis_name, y_axis_name = self._get_data()
 
-        if self._histogram_for_large_data and (
-            data[0].size > self._threshold_to_switch_to_histogram
-        ):
+        if x.size > self._threshold_to_switch_to_histogram:
             self.axes.hist2d(
-                data[0].ravel(),
-                data[1].ravel(),
+                x.ravel(),
+                y.ravel(),
                 bins=100,
-                norm=mcolor.LogNorm(),
             )
         else:
-            self.axes.scatter(data[0], data[1], alpha=self._marker_alpha)
+            self.axes.scatter(x, y, alpha=0.5)
 
         self.axes.set_xlabel(x_axis_name)
         self.axes.set_ylabel(y_axis_name)
 
-    def _get_data(self) -> Tuple[List[npt.NDArray[Any]], str, str]:
-        """Get the plot data.
+    def _get_data(self) -> Tuple[npt.NDArray[Any], npt.NDArray[Any], str, str]:
+        """
+        Get the plot data.
 
         This must be implemented on the subclass.
 
         Returns
         -------
-        data : np.ndarray
-            The list containing the scatter plot data.
-        x_axis_name : str
-            The label to display on the x axis
-        y_axis_name: str
-            The label to display on the y axis
+        x, y : np.ndarray
+            x and y values of plot data.
+        x_axis_name, y_axis_name : str
+            Label to display on the x/y axis
         """
         raise NotImplementedError
 
@@ -93,7 +77,7 @@ class ScatterWidget(ScatterBaseWidget):
     n_layers_input = Interval(2, 2)
     input_layer_types = (napari.layers.Image,)
 
-    def _get_data(self) -> Tuple[List[npt.NDArray[Any]], str, str]:
+    def _get_data(self) -> Tuple[npt.NDArray[Any], npt.NDArray[Any], str, str]:
         """
         Get the plot data.
 
@@ -106,11 +90,12 @@ def _get_data(self) -> Tuple[List[npt.NDArray[Any]], str, str]:
         y_axis_name: str
             The title to display on the y axis
         """
-        data = [layer.data[self.current_z] for layer in self.layers]
+        x = self.layers[0].data[self.current_z]
+        y = self.layers[1].data[self.current_z]
         x_axis_name = self.layers[0].name
         y_axis_name = self.layers[1].name
 
-        return data, x_axis_name, y_axis_name
+        return x, y, x_axis_name, y_axis_name
 
 
 class FeaturesScatterWidget(ScatterBaseWidget):
@@ -191,9 +176,33 @@ def _get_valid_axis_keys(
         else:
             return self.layers[0].features.keys()
 
-    def _get_data(self) -> Tuple[List[npt.NDArray[Any]], str, str]:
+    def _ready_to_scatter(self) -> bool:
         """
-        Get the plot data.
+        Return True if selected layer has a feature table we can scatter with,
+        and the two columns to be scatterd have been selected.
+        """
+        if not hasattr(self.layers[0], "features"):
+            return False
+
+        feature_table = self.layers[0].features
+        return (
+            feature_table is not None
+            and len(feature_table) > 0
+            and self.x_axis_key is not None
+            and self.y_axis_key is not None
+        )
+
+    def draw(self) -> None:
+        """
+        Scatter two features from the currently selected layer.
+        """
+        if self._ready_to_scatter():
+            super().draw()
+
+    def _get_data(self) -> Tuple[npt.NDArray[Any], npt.NDArray[Any], str, str]:
+        """
+        Get the plot data from the ``features`` attribute of the first
+        selected layer.
 
         Returns
         -------
@@ -207,28 +216,15 @@ def _get_data(self) -> Tuple[List[npt.NDArray[Any]], str, str]:
             The title to display on the y 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)
-            or (self.y_axis_key is None)
-        ):
-            return [], "", ""
-
-        data_x = feature_table[self.x_axis_key]
-        data_y = feature_table[self.y_axis_key]
-        data = [data_x, data_y]
+        x = feature_table[self.x_axis_key]
+        y = feature_table[self.y_axis_key]
 
-        x_axis_name = self.x_axis_key.replace("_", " ")
-        y_axis_name = self.y_axis_key.replace("_", " ")
+        x_axis_name = str(self.x_axis_key)
+        y_axis_name = str(self.y_axis_key)
 
-        return data, x_axis_name, y_axis_name
+        return x, y, x_axis_name, y_axis_name
 
     def _on_update_layers(self) -> None:
         """

src/napari_matplotlib/tests/test_scatter.py

@@ -39,7 +39,9 @@ def make_labels_layer_with_features() -> (
 
 
 def test_features_scatter_get_data(make_napari_viewer):
-    """Test the get data method"""
+    """
+    Test the get data method.
+    """
     # make the label image
     label_image, feature_table = make_labels_layer_with_features()
 
@@ -55,17 +57,16 @@ def test_features_scatter_get_data(make_napari_viewer):
     y_column = "feature_2"
     scatter_widget.y_axis_key = y_column
 
-    data, x_axis_name, y_axis_name = scatter_widget._get_data()
-    np.testing.assert_allclose(
-        data, np.stack((feature_table[x_column], feature_table[y_column]))
-    )
-    assert x_axis_name == x_column.replace("_", " ")
-    assert y_axis_name == y_column.replace("_", " ")
+    x, y, x_axis_name, y_axis_name = scatter_widget._get_data()
+    np.testing.assert_allclose(x, feature_table[x_column])
+    np.testing.assert_allclose(y, np.stack(feature_table[y_column]))
+    assert x_axis_name == x_column
+    assert y_axis_name == y_column
 
 
 def test_get_valid_axis_keys(make_napari_viewer):
-    """Test the values returned from
-    FeaturesScatterWidget._get_valid_keys() when there
+    """
+    Test the values returned from _get_valid_keys() when there
     are valid keys.
     """
     # make the label image