From 5a58dd9a0e45ed10febd50a836fb9e84333b8ca1 Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Fri, 6 May 2022 16:22:04 +0200
Subject: [PATCH 01/10] initial feature scatter

---
 src/napari_matplotlib/base.py    |  10 +-
 src/napari_matplotlib/scatter.py | 179 ++++++++++++++++++++++++++++---
 2 files changed, 173 insertions(+), 16 deletions(-)

diff --git a/src/napari_matplotlib/base.py b/src/napari_matplotlib/base.py
index 6bfbd093..f828b8dc 100644
--- a/src/napari_matplotlib/base.py
+++ b/src/napari_matplotlib/base.py
@@ -79,13 +79,14 @@ def setup_callbacks(self) -> None:
         # z-step changed in viewer
         self.viewer.dims.events.current_step.connect(self._draw)
         # Layer selection changed in viewer
-        self.viewer.layers.selection.events.active.connect(self.update_layers)
+        self.viewer.layers.selection.events.changed.connect(self.update_layers)
 
     def update_layers(self, event: napari.utils.events.Event) -> None:
         """
         Update the currently selected layers and re-draw.
         """
         self.layers = list(self.viewer.layers.selection)
+        self._on_update_layers()
         self._draw()
 
     def _draw(self) -> None:
@@ -95,6 +96,7 @@ def _draw(self) -> None:
         """
         self.clear()
         if self.n_selected_layers != self.n_layers_input:
+            self.canvas.draw()
             return
         self.draw()
         self.canvas.draw()
@@ -112,3 +114,9 @@ def draw(self) -> None:
 
         This is a no-op, and is intended for derived classes to override.
         """
+
+    def _on_update_layers(self) -> None:
+        """This function is called when self.layers is updated via self.update_layer()
+
+        This is a no-op, and is intended for derived classes to override.
+        """
diff --git a/src/napari_matplotlib/scatter.py b/src/napari_matplotlib/scatter.py
index c3b12742..75f8bff6 100644
--- a/src/napari_matplotlib/scatter.py
+++ b/src/napari_matplotlib/scatter.py
@@ -1,39 +1,188 @@
+from typing import List, Tuple, Union
+
 import matplotlib.colors as mcolor
 import napari
+import numpy as np
+from magicgui import magicgui
 
 from .base import NapariMPLWidget
 
 __all__ = ["ScatterWidget"]
 
 
-class ScatterWidget(NapariMPLWidget):
-    """
-    Widget to display scatter plot of two similarly shaped layers.
+class ScatterBaseWidget(NapariMPLWidget):
+    def __init__(
+        self,
+        napari_viewer: napari.viewer.Viewer,
+        marker_alpha: float = 0.5,
+        histogram_for_large_data: bool = True,
+    ):
+        super().__init__(napari_viewer)
 
-    If there are more than 500 data points, a 2D histogram is displayed instead
-    of a scatter plot, to avoid too many scatter points.
-    """
+        # flag set to True if histogram should be used
+        # for plotting large points
+        self.histogram_for_large_data = histogram_for_large_data
 
-    n_layers_input = 2
+        # set plotting visualization attributes
+        self._marker_alpha = 0.5
 
-    def __init__(self, napari_viewer: napari.viewer.Viewer):
-        super().__init__(napari_viewer)
         self.axes = self.canvas.figure.subplots()
         self.update_layers(None)
 
+    @property
+    def marker_alpha(self) -> float:
+        """Alpha (opacity) for the scatter markers"""
+        return self._marker_alpha
+
+    @marker_alpha.setter
+    def marker_alpha(self, alpha: float):
+        self._marker_alpha = alpha
+        self._draw()
+
+    def clear(self) -> None:
+        self.axes.clear()
+
     def draw(self) -> None:
         """
         Clear the axes and scatter the currently selected layers.
         """
-        data = [layer.data[self.current_z] for layer in self.layers]
-        if data[0].size < 500:
-            self.axes.scatter(data[0], data[1], alpha=0.5)
-        else:
+        data, x_axis_name, y_axis_name = self._get_data()
+
+        if len(data) == 0:
+            # don't plot if there isn't data
+            return
+
+        if self.histogram_for_large_data and (data[0].size > 500):
             self.axes.hist2d(
                 data[0].ravel(),
                 data[1].ravel(),
                 bins=100,
                 norm=mcolor.LogNorm(),
             )
-        self.axes.set_xlabel(self.layers[0].name)
-        self.axes.set_ylabel(self.layers[1].name)
+        else:
+            self.axes.scatter(data[0], data[1], alpha=self.marker_alpha)
+
+        self.axes.set_xlabel(x_axis_name)
+        self.axes.set_ylabel(y_axis_name)
+
+    def _get_data(self) -> Tuple[np.ndarray, str, str]:
+        raise NotImplementedError
+
+
+class ScatterWidget(ScatterBaseWidget):
+    """
+    Widget to display scatter plot of two similarly shaped layers.
+
+    If there are more than 500 data points, a 2D histogram is displayed instead
+    of a scatter plot, to avoid too many scatter points.
+    """
+
+    n_layers_input = 2
+
+    def __init__(
+        self,
+        napari_viewer: napari.viewer.Viewer,
+        marker_alpha: float = 0.5,
+        histogram_for_large_data: bool = True,
+    ):
+        super().__init__(
+            napari_viewer,
+            marker_alpha=marker_alpha,
+            histogram_for_large_data=histogram_for_large_data,
+        )
+
+    def _get_data(self) -> Tuple[np.ndarray, str, str]:
+        data = [layer.data[self.current_z] for layer in self.layers]
+        x_axis_name = self.layers[0].name
+        y_axis_name = self.layers[1].name
+
+        return data, x_axis_name, y_axis_name
+
+
+class FeaturesScatterWidget(ScatterBaseWidget):
+    n_layers_input = 1
+
+    def __init__(
+        self,
+        napari_viewer: napari.viewer.Viewer,
+        marker_alpha: float = 0.5,
+        histogram_for_large_data: bool = True,
+        key_selection_gui: bool = True,
+    ):
+        self._key_selection_widget = None
+        super().__init__(
+            napari_viewer,
+            marker_alpha=marker_alpha,
+            histogram_for_large_data=histogram_for_large_data,
+        )
+
+        if key_selection_gui is True:
+            self._key_selection_widget = magicgui(
+                self._set_axis_keys,
+                x_axis_key={"choices": self._get_valid_axis_keys},
+                y_axis_key={"choices": self._get_valid_axis_keys},
+                call_button="plot",
+            )
+            self.layout().addWidget(self._key_selection_widget.native)
+
+    @property
+    def x_axis_key(self) -> Union[None, 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: Union[None, str]):
+        self._x_axis_key = key
+        self._draw()
+
+    @property
+    def y_axis_key(self) -> Union[None, str]:
+        """Key to access y axis data from the FeaturesTable"""
+        return self._y_axis_key
+
+    @y_axis_key.setter
+    def y_axis_key(self, key: Union[None, str]):
+        self._y_axis_key = key
+        self._draw()
+
+    def _set_axis_keys(self, x_axis_key: str, y_axis_key: str):
+        """Set both axis keys and then redraw the plot"""
+        self._x_axis_key = x_axis_key
+        self._y_axis_key = y_axis_key
+        self._draw()
+
+    def _get_valid_axis_keys(self, combo_widget=None) -> List[str]:
+        if len(self.layers) == 0:
+            return []
+        else:
+            return self.layers[0].features.keys()
+
+    def _get_data(self) -> Tuple[np.ndarray, str, str]:
+        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 np.array([]), "", ""
+
+        data_x = feature_table[self.x_axis_key]
+        data_y = feature_table[self.y_axis_key]
+        data = np.stack((data_x, data_y))
+
+        x_axis_name = self.x_axis_key.replace("_", " ")
+        y_axis_name = self.y_axis_key.replace("_", " ")
+
+        return data, x_axis_name, y_axis_name
+
+    def _on_update_layers(self) -> None:
+        """This is called when the layer selection changes
+        by self.update_layers().
+        """
+        if self._key_selection_widget is not None:
+            self._key_selection_widget.reset_choices()
+
+        # reset the axis keys
+        self._x_axis_key = None
+        self._y_axis_key = None

From ef1029b1c15273b6decb26ff44b437e5ae0ff32e Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Fri, 6 May 2022 16:51:24 +0200
Subject: [PATCH 02/10] update docstrings

---
 src/napari_matplotlib/scatter.py | 61 +++++++++++++++++++++++++++++---
 1 file changed, 56 insertions(+), 5 deletions(-)

diff --git a/src/napari_matplotlib/scatter.py b/src/napari_matplotlib/scatter.py
index 75f8bff6..7b774960 100644
--- a/src/napari_matplotlib/scatter.py
+++ b/src/napari_matplotlib/scatter.py
@@ -65,7 +65,20 @@ def draw(self) -> None:
         self.axes.set_xlabel(x_axis_name)
         self.axes.set_ylabel(y_axis_name)
 
-    def _get_data(self) -> Tuple[np.ndarray, str, str]:
+    def _get_data(self) -> Tuple[List[np.ndarray], 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 title to display on the x axis
+        y_axis_name: str
+            The title to display on the y axis
+        """
         raise NotImplementedError
 
 
@@ -91,7 +104,18 @@ def __init__(
             histogram_for_large_data=histogram_for_large_data,
         )
 
-    def _get_data(self) -> Tuple[np.ndarray, str, str]:
+    def _get_data(self) -> Tuple[List[np.ndarray], str, str]:
+        """Get the plot data.
+
+        Returns
+        -------
+        data : List[np.ndarray]
+            List contains the in view slice of X and Y axis images.
+        x_axis_name : str
+            The title to display on the x axis
+        y_axis_name: str
+            The title to display on the y axis
+        """
         data = [layer.data[self.current_z] for layer in self.layers]
         x_axis_name = self.layers[0].name
         y_axis_name = self.layers[1].name
@@ -152,12 +176,39 @@ def _set_axis_keys(self, x_axis_key: str, y_axis_key: str):
         self._draw()
 
     def _get_valid_axis_keys(self, combo_widget=None) -> List[str]:
-        if len(self.layers) == 0:
+        """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[np.ndarray, str, str]:
+    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.
+        y_axis_name: 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 np.array([]), "", ""
+
         feature_table = self.layers[0].features
 
         if (
@@ -169,7 +220,7 @@ def _get_data(self) -> Tuple[np.ndarray, str, str]:
 
         data_x = feature_table[self.x_axis_key]
         data_y = feature_table[self.y_axis_key]
-        data = np.stack((data_x, data_y))
+        data = [data_x, data_y]
 
         x_axis_name = self.x_axis_key.replace("_", " ")
         y_axis_name = self.y_axis_key.replace("_", " ")

From 91928e10ed4f535c34123e61f9ab19a2e2b38224 Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Fri, 6 May 2022 16:51:47 +0200
Subject: [PATCH 03/10] expose scatter plots

---
 src/napari_matplotlib/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/napari_matplotlib/__init__.py b/src/napari_matplotlib/__init__.py
index 7e8ccf69..5b112cda 100644
--- a/src/napari_matplotlib/__init__.py
+++ b/src/napari_matplotlib/__init__.py
@@ -5,4 +5,4 @@
 
 
 from .histogram import *  # NoQA
-from .scatter import *  # NoQA
+from .scatter import FeaturesScatterWidget, ScatterWidget  # NoQA

From 22fd3ee6308894bf8d42212e031a0ae3ca7a222d Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Fri, 6 May 2022 17:28:00 +0200
Subject: [PATCH 04/10] add tests

---
 src/napari_matplotlib/tests/test_scatter.py | 92 ++++++++++++++++++++-
 1 file changed, 90 insertions(+), 2 deletions(-)

diff --git a/src/napari_matplotlib/tests/test_scatter.py b/src/napari_matplotlib/tests/test_scatter.py
index 75a6fda6..8103968e 100644
--- a/src/napari_matplotlib/tests/test_scatter.py
+++ b/src/napari_matplotlib/tests/test_scatter.py
@@ -1,11 +1,99 @@
 import numpy as np
 
-from napari_matplotlib import ScatterWidget
+from napari_matplotlib import FeaturesScatterWidget, ScatterWidget
 
 
 def test_scatter(make_napari_viewer):
-    # Smoke test adding a histogram widget
+    # Smoke test adding a scatter widget
     viewer = make_napari_viewer()
     viewer.add_image(np.random.random((100, 100)))
     viewer.add_image(np.random.random((100, 100)))
     ScatterWidget(viewer)
+
+
+def test_features_scatter_widget(make_napari_viewer):
+    # Smoke test adding a features scatter widget
+    viewer = make_napari_viewer()
+    viewer.add_image(np.random.random((100, 100)))
+    viewer.add_labels(np.random.randint(0, 5, (100, 100)))
+    FeaturesScatterWidget(viewer)
+
+
+def make_labels_layer_with_features():
+    label_image = np.zeros((100, 100), dtype=np.uint16)
+    for label_value, start_index in enumerate([10, 30, 50], start=1):
+        end_index = start_index + 10
+        label_image[start_index:end_index, start_index:end_index] = label_value
+    feature_table = {
+        "index": [1, 2, 3],
+        "feature_0": np.random.random((3,)),
+        "feature_1": np.random.random((3,)),
+        "feature_2": np.random.random((3,)),
+    }
+    return label_image, feature_table
+
+
+def test_features_scatter_get_data(make_napari_viewer):
+    """test the get data method"""
+    # make the label image
+    label_image, feature_table = make_labels_layer_with_features()
+
+    viewer = make_napari_viewer()
+    labels_layer = viewer.add_labels(label_image, features=feature_table)
+    scatter_widget = FeaturesScatterWidget(viewer)
+
+    # select the labels layer
+    viewer.layers.selection = [labels_layer]
+
+    x_column = "feature_0"
+    scatter_widget.x_axis_key = x_column
+    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("_", " ")
+
+
+def test_get_valid_axis_keys(make_napari_viewer):
+    """test the values returned from
+    FeaturesScatterWidget._get_valid_keys() when there
+    are valid keys.
+    """
+    # make the label image
+    label_image, feature_table = make_labels_layer_with_features()
+
+    viewer = make_napari_viewer()
+    labels_layer = viewer.add_labels(label_image, features=feature_table)
+    scatter_widget = FeaturesScatterWidget(viewer)
+
+    viewer.layers.selection = [labels_layer]
+    valid_keys = scatter_widget._get_valid_axis_keys()
+    assert set(valid_keys) == set(feature_table.keys())
+
+
+def test_get_valid_axis_keys_no_valid_keys(make_napari_viewer):
+    """test the values returned from
+    FeaturesScatterWidget._get_valid_keys() when there
+    are not valid keys.
+    """
+    # make the label image
+    label_image, _ = make_labels_layer_with_features()
+
+    viewer = make_napari_viewer()
+    labels_layer = viewer.add_labels(label_image)
+    image_layer = viewer.add_image(np.random.random((100, 100)))
+    scatter_widget = FeaturesScatterWidget(viewer)
+
+    # no features in a label image
+    viewer.layers.selection = [labels_layer]
+    valid_keys = scatter_widget._get_valid_axis_keys()
+    assert set(valid_keys) == set()
+
+    # image layer doesn't have features
+    viewer.layers.selection = [image_layer]
+    valid_keys = scatter_widget._get_valid_axis_keys()
+    assert set(valid_keys) == set()

From e79eeafa3fae055bfe726b703d26bf761f72608a Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Fri, 6 May 2022 17:35:15 +0200
Subject: [PATCH 05/10] add example

---
 examples/features_scatter.py | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 examples/features_scatter.py

diff --git a/examples/features_scatter.py b/examples/features_scatter.py
new file mode 100644
index 00000000..0894d0a4
--- /dev/null
+++ b/examples/features_scatter.py
@@ -0,0 +1,35 @@
+import napari
+import numpy as np
+from skimage.measure import regionprops_table
+
+from napari_matplotlib.scatter import FeaturesScatterWidget
+
+# make a test label image
+label_image = np.zeros((100, 100), dtype=np.uint16)
+
+label_image[10:20, 10:20] = 1
+label_image[50:70, 50:70] = 2
+
+feature_table_1 = regionprops_table(label_image, properties=("label",))
+feature_table_1["index"] = feature_table_1["label"]
+
+# make the points data
+n_points = 100
+points_data = 100 * np.random.random((100, 2))
+points_features = {
+    "feature_0": np.random.random((n_points,)),
+    "feature_1": np.random.random((n_points,)),
+    "feature_2": np.random.random((n_points,)),
+}
+
+# create the viewer
+viewer = napari.Viewer()
+viewer.add_labels(label_image, features=feature_table_1)
+viewer.add_points(points_data, features=points_features)
+
+# make the widget
+features_widget = FeaturesScatterWidget(viewer, histogram_for_large_data=False)
+viewer.window.add_dock_widget(features_widget)
+
+if __name__ == "__main__":
+    napari.run()

From 9d3a0420c0d3673b84cf925b6453d51672871dd1 Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Sat, 7 May 2022 05:00:34 -0700
Subject: [PATCH 06/10] fix imports

---
 src/napari_matplotlib/__init__.py | 2 +-
 src/napari_matplotlib/scatter.py  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/napari_matplotlib/__init__.py b/src/napari_matplotlib/__init__.py
index 5b112cda..7e8ccf69 100644
--- a/src/napari_matplotlib/__init__.py
+++ b/src/napari_matplotlib/__init__.py
@@ -5,4 +5,4 @@
 
 
 from .histogram import *  # NoQA
-from .scatter import FeaturesScatterWidget, ScatterWidget  # NoQA
+from .scatter import *  # NoQA
diff --git a/src/napari_matplotlib/scatter.py b/src/napari_matplotlib/scatter.py
index 7b774960..5a504218 100644
--- a/src/napari_matplotlib/scatter.py
+++ b/src/napari_matplotlib/scatter.py
@@ -7,7 +7,7 @@
 
 from .base import NapariMPLWidget
 
-__all__ = ["ScatterWidget"]
+__all__ = ["ScatterWidget", "FeaturesScatterWidget"]
 
 
 class ScatterBaseWidget(NapariMPLWidget):

From fc03d7834d09dfe114f3af9facb580dc58076e8e Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Sat, 7 May 2022 05:40:43 -0700
Subject: [PATCH 07/10] update example

---
 examples/features_scatter.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/examples/features_scatter.py b/examples/features_scatter.py
index 0894d0a4..28c8d006 100644
--- a/examples/features_scatter.py
+++ b/examples/features_scatter.py
@@ -10,7 +10,9 @@
 label_image[10:20, 10:20] = 1
 label_image[50:70, 50:70] = 2
 
-feature_table_1 = regionprops_table(label_image, properties=("label",))
+feature_table_1 = regionprops_table(
+    label_image, properties=("label", "area", "perimeter")
+)
 feature_table_1["index"] = feature_table_1["label"]
 
 # make the points data

From 0340c54d41d1f3cb8307e32af15d1308714d5a48 Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Sun, 8 May 2022 05:42:33 -0700
Subject: [PATCH 08/10] Apply suggestions from code review

Co-authored-by: David Stansby <dstansby@gmail.com>
---
 src/napari_matplotlib/base.py    | 2 +-
 src/napari_matplotlib/scatter.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/napari_matplotlib/base.py b/src/napari_matplotlib/base.py
index f828b8dc..b49e368c 100644
--- a/src/napari_matplotlib/base.py
+++ b/src/napari_matplotlib/base.py
@@ -116,7 +116,7 @@ def draw(self) -> None:
         """
 
     def _on_update_layers(self) -> None:
-        """This function is called when self.layers is updated via self.update_layer()
+        """This function is called when self.layers is updated via self.update_layers()
 
         This is a no-op, and is intended for derived classes to override.
         """
diff --git a/src/napari_matplotlib/scatter.py b/src/napari_matplotlib/scatter.py
index 5a504218..7035326a 100644
--- a/src/napari_matplotlib/scatter.py
+++ b/src/napari_matplotlib/scatter.py
@@ -75,16 +75,16 @@ def _get_data(self) -> Tuple[List[np.ndarray], str, str]:
         data : np.ndarray
             The list containing the scatter plot data.
         x_axis_name : str
-            The title to display on the x axis
+            The label to display on the x axis
         y_axis_name: str
-            The title to display on the y axis
+            The label to display on the y axis
         """
         raise NotImplementedError
 
 
 class ScatterWidget(ScatterBaseWidget):
     """
-    Widget to display scatter plot of two similarly shaped layers.
+    Widget to display scatter plot of two similarly shaped image layers.
 
     If there are more than 500 data points, a 2D histogram is displayed instead
     of a scatter plot, to avoid too many scatter points.

From 9f4cbb95dba366c766a75df262b49cd98bd9e16e Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Sun, 8 May 2022 05:55:35 -0700
Subject: [PATCH 09/10] expose FeaturesScatterWidget as plugin

---
 examples/features_scatter.py      |  7 +++---
 src/napari_matplotlib/napari.yaml |  9 ++++++-
 src/napari_matplotlib/scatter.py  | 42 ++++++++++---------------------
 3 files changed, 24 insertions(+), 34 deletions(-)

diff --git a/examples/features_scatter.py b/examples/features_scatter.py
index 28c8d006..ac8580d7 100644
--- a/examples/features_scatter.py
+++ b/examples/features_scatter.py
@@ -2,8 +2,6 @@
 import numpy as np
 from skimage.measure import regionprops_table
 
-from napari_matplotlib.scatter import FeaturesScatterWidget
-
 # make a test label image
 label_image = np.zeros((100, 100), dtype=np.uint16)
 
@@ -30,8 +28,9 @@
 viewer.add_points(points_data, features=points_features)
 
 # make the widget
-features_widget = FeaturesScatterWidget(viewer, histogram_for_large_data=False)
-viewer.window.add_dock_widget(features_widget)
+viewer.window.add_plugin_dock_widget(
+    plugin_name="napari-matplotlib", widget_name="FeaturesScatter"
+)
 
 if __name__ == "__main__":
     napari.run()
diff --git a/src/napari_matplotlib/napari.yaml b/src/napari_matplotlib/napari.yaml
index 3ff66090..3beba8a7 100644
--- a/src/napari_matplotlib/napari.yaml
+++ b/src/napari_matplotlib/napari.yaml
@@ -8,7 +8,11 @@ contributions:
 
     - id: napari-matplotlib.scatter
       python_name: napari_matplotlib:ScatterWidget
-      title: Make a scatter plot
+      title: Make a scatter plot of image intensities
+
+    - id: napari-matplotlib.features_scatter
+      python_name: napari_matplotlib:FeaturesScatterWidget
+      title: Make a scatter plot of layer features
 
   widgets:
     - command: napari-matplotlib.histogram
@@ -16,3 +20,6 @@ contributions:
 
     - command: napari-matplotlib.scatter
       display_name: Scatter
+
+    - command: napari-matplotlib.features_scatter
+      display_name: FeaturesScatter
diff --git a/src/napari_matplotlib/scatter.py b/src/napari_matplotlib/scatter.py
index 7035326a..3de2e88e 100644
--- a/src/napari_matplotlib/scatter.py
+++ b/src/napari_matplotlib/scatter.py
@@ -11,34 +11,26 @@
 
 
 class ScatterBaseWidget(NapariMPLWidget):
+    # 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
+    _threshold_to_switch_to_histogram = 500
+
     def __init__(
         self,
         napari_viewer: napari.viewer.Viewer,
-        marker_alpha: float = 0.5,
-        histogram_for_large_data: bool = True,
     ):
         super().__init__(napari_viewer)
 
-        # flag set to True if histogram should be used
-        # for plotting large points
-        self.histogram_for_large_data = histogram_for_large_data
-
-        # set plotting visualization attributes
-        self._marker_alpha = 0.5
-
         self.axes = self.canvas.figure.subplots()
         self.update_layers(None)
 
-    @property
-    def marker_alpha(self) -> float:
-        """Alpha (opacity) for the scatter markers"""
-        return self._marker_alpha
-
-    @marker_alpha.setter
-    def marker_alpha(self, alpha: float):
-        self._marker_alpha = alpha
-        self._draw()
-
     def clear(self) -> None:
         self.axes.clear()
 
@@ -52,7 +44,7 @@ def draw(self) -> None:
             # don't plot if there isn't data
             return
 
-        if self.histogram_for_large_data and (data[0].size > 500):
+        if self._histogram_for_large_data and (data[0].size > 500):
             self.axes.hist2d(
                 data[0].ravel(),
                 data[1].ravel(),
@@ -60,7 +52,7 @@ def draw(self) -> None:
                 norm=mcolor.LogNorm(),
             )
         else:
-            self.axes.scatter(data[0], data[1], alpha=self.marker_alpha)
+            self.axes.scatter(data[0], data[1], alpha=self._marker_alpha)
 
         self.axes.set_xlabel(x_axis_name)
         self.axes.set_ylabel(y_axis_name)
@@ -95,13 +87,9 @@ class ScatterWidget(ScatterBaseWidget):
     def __init__(
         self,
         napari_viewer: napari.viewer.Viewer,
-        marker_alpha: float = 0.5,
-        histogram_for_large_data: bool = True,
     ):
         super().__init__(
             napari_viewer,
-            marker_alpha=marker_alpha,
-            histogram_for_large_data=histogram_for_large_data,
         )
 
     def _get_data(self) -> Tuple[List[np.ndarray], str, str]:
@@ -129,15 +117,11 @@ class FeaturesScatterWidget(ScatterBaseWidget):
     def __init__(
         self,
         napari_viewer: napari.viewer.Viewer,
-        marker_alpha: float = 0.5,
-        histogram_for_large_data: bool = True,
         key_selection_gui: bool = True,
     ):
         self._key_selection_widget = None
         super().__init__(
             napari_viewer,
-            marker_alpha=marker_alpha,
-            histogram_for_large_data=histogram_for_large_data,
         )
 
         if key_selection_gui is True:

From e1941b0a35c4dc8cedf476d98d3a225371ca8865 Mon Sep 17 00:00:00 2001
From: Kevin Yamauchi <kevin.yamauchi@gmail.com>
Date: Sun, 8 May 2022 06:07:40 -0700
Subject: [PATCH 10/10] add threshold for histogram switch

---
 src/napari_matplotlib/scatter.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/napari_matplotlib/scatter.py b/src/napari_matplotlib/scatter.py
index 3de2e88e..324e9126 100644
--- a/src/napari_matplotlib/scatter.py
+++ b/src/napari_matplotlib/scatter.py
@@ -44,7 +44,9 @@ def draw(self) -> None:
             # don't plot if there isn't data
             return
 
-        if self._histogram_for_large_data and (data[0].size > 500):
+        if self._histogram_for_large_data and (
+            data[0].size > self._threshold_to_switch_to_histogram
+        ):
             self.axes.hist2d(
                 data[0].ravel(),
                 data[1].ravel(),