v0.7.4

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.7.3
+current_version = 0.7.4
 commit = False
 tag = False
 

genetools/__init__.py

@@ -2,7 +2,7 @@
 
 __author__ = """Maxim Zaslavsky"""
 __email__ = "maxim@maximz.com"
-__version__ = "0.7.3"
+__version__ = "0.7.4"
 
 # We want genetools.[submodule].[function] to be accessible simply by importing genetools, without having to import genetools.[submodule]
 from . import helpers, plots, stats, arrays

genetools/plots.py

@@ -879,7 +879,8 @@ def size_norm(x: Union[np.ndarray, pd.Series, List[float]]) -> np.ndarray:
             )
         else:
             plot_vmin, plot_vmax = color_vmin, color_vmax
-            color_norm = None
+            # Create a norm even when vcenter is None
+            color_norm = matplotlib.colors.Normalize(vmin=plot_vmin, vmax=plot_vmax)
 
         scatter = ax.scatter(
             data[x_axis_key].values,
@@ -987,11 +988,11 @@ def size_norm(x: Union[np.ndarray, pd.Series, List[float]]) -> np.ndarray:
                 representative_colors = np.clip(
                     representative_colors, color_vmin, color_vmax
                 )
-            if color_norm is not None:
-                # Apply a color norm
-                representative_colors = [
-                    color_norm(value) for value in representative_colors
-                ]
+
+            # Apply color norm
+            # Always normalize colors using the same norm as the scatter plot
+            representative_colors = color_norm(representative_colors)
+
             # Apply color cmap
             # First, cast string cmaps to a callable function
             color_cmap_func = matplotlib.cm.get_cmap(color_cmap)

setup.py

@@ -49,7 +49,7 @@
     test_suite="tests",
     tests_require=test_requirements,
     url="https://github.com/maximz/genetools",
-    version="0.7.3",
+    version="0.7.4",
     zip_safe=False,
     extras_require={
         # This will also install anndata and UMAP packages

tests/test_plots.py

@@ -725,6 +725,34 @@ def test_dotplot_single_key(dotplot_data):
             return fig
 
 
+@snapshot_image
+def test_dotplot_confirm_legend_colors_match_dot_colors():
+    # Regression test to confirm that the colors in the legend match the colors of the dots, for data where the max value is a small positive float.
+    df = pd.DataFrame(
+        [
+            {"feature": "feature1", "value": 0.12},
+            {"feature": "feature2", "value": 0.02},
+            {"feature": "feature3", "value": 0.03},
+            {"feature": "feature4", "value": 0.0},
+            {"feature": "feature5", "value": 0.08},
+            {"feature": "feature6", "value": 0.03},
+            {"feature": "feature7", "value": 0.06},
+            {"feature": "feature8", "value": 0.0},
+        ]
+    ).assign(classname="class1")
+    with sns.plotting_context("paper"), sns.axes_style("white"):
+        fig, _ = genetools.plots.plot_color_and_size_dotplot(
+            data=df,
+            x_axis_key="classname",
+            y_axis_key="feature",
+            value_key="value",
+            color_cmap=sns.color_palette("magma_r", as_cmap=True),
+            figsize=(5, 6),
+            grid=False,
+        )
+        return fig
+
+
 ####