render_labels and render_shapes now scan for and respect xyz_colors in adata.uns

.gitignore

@@ -44,3 +44,6 @@ tests/figures/
 # other
 _version.py
 /temp/
+
+# pixi
+pixi.lock

src/spatialdata_plot/pl/basic.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import sys
-import warnings
 from collections import OrderedDict
 from copy import deepcopy
 from pathlib import Path
@@ -23,6 +22,7 @@
 from xarray import DataArray, DataTree
 
 from spatialdata_plot._accessor import register_spatial_data_accessor
+from spatialdata_plot._logging import logger
 from spatialdata_plot.pl.render import (
     _render_images,
     _render_labels,
@@ -272,11 +272,7 @@ def render_shapes(
         """
         # TODO add Normalize object in tutorial notebook and point to that notebook here
         if "vmin" in kwargs or "vmax" in kwargs:
-            warnings.warn(
-                "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
+            logger.warning("`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.")
         params_dict = _validate_shape_render_params(
             self._sdata,
             element=element,
@@ -423,11 +419,7 @@ def render_points(
         """
         # TODO add Normalize object in tutorial notebook and point to that notebook here
         if "vmin" in kwargs or "vmax" in kwargs:
-            warnings.warn(
-                "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
+            logger.warning("`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.")
         params_dict = _validate_points_render_params(
             self._sdata,
             element=element,
@@ -544,11 +536,7 @@ def render_images(
         """
         # TODO add Normalize object in tutorial notebook and point to that notebook here
         if "vmin" in kwargs or "vmax" in kwargs:
-            warnings.warn(
-                "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
+            logger.warning("`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.")
         params_dict = _validate_image_render_params(
             self._sdata,
             element=element,
@@ -679,11 +667,7 @@ def render_labels(
         """
         # TODO add Normalize object in tutorial notebook and point to that notebook here
         if "vmin" in kwargs or "vmax" in kwargs:
-            warnings.warn(
-                "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
+            logger.warning("`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.")
         params_dict = _validate_label_render_params(
             self._sdata,
             element=element,
@@ -918,9 +902,7 @@ def show(
         # go through tree
 
         for i, cs in enumerate(coordinate_systems):
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", category=UserWarning)
-                sdata = self._copy()
+            sdata = self._copy()
             _, has_images, has_labels, has_points, has_shapes = (
                 cs_contents.query(f"cs == '{cs}'").iloc[0, :].values.tolist()
             )

src/spatialdata_plot/pl/render.py

@@ -147,9 +147,9 @@ def _render_shapes(
         color_vector = np.asarray(color_vector, dtype=float)
         if np.isnan(color_vector).any():
             nan_count = int(np.isnan(color_vector).sum())
-            logger.warning(
-                f"Found {nan_count} NaN values in color data. These observations will be colored with the 'na_color'."
-            )
+            msg = f"Found {nan_count} NaN values in color data. These observations will be colored with the 'na_color'."
+            warnings.warn(msg, UserWarning, stacklevel=2)
+            logger.warning(msg)
 
     # Using dict.fromkeys here since set returns in arbitrary order
     # remove the color of NaN values, else it might be assigned to a category
@@ -656,12 +656,14 @@ def _render_points(
         cols = sc.get.obs_df(adata, [col_for_color])
         # maybe set color based on type
         if isinstance(cols[col_for_color].dtype, pd.CategoricalDtype):
-            _maybe_set_colors(
-                source=adata,
-                target=adata,
-                key=col_for_color,
-                palette=palette,
-            )
+            uns_color_key = f"{col_for_color}_colors"
+            if uns_color_key in adata.uns:
+                _maybe_set_colors(
+                    source=adata,
+                    target=adata,
+                    key=col_for_color,
+                    palette=palette,
+                )
 
     # when user specified a single color, we emulate the form of `na_color` and use it
     default_color = (
@@ -778,7 +780,7 @@ def _render_points(
                     agg = agg.where((agg <= norm.vmin) | (np.isnan(agg)), other=2)
                     agg = agg.where((agg != norm.vmin) | (np.isnan(agg)), other=0.5)
 
-        color_key = (
+        color_key: list[str] | None = (
             list(color_vector.categories.values)
             if (type(color_vector) is pd.core.arrays.categorical.Categorical)
             and (len(color_vector.categories.values) > 1)