Merge branch 'main' into pre-commit-ci-update-config

CHANGELOG.md

@@ -12,167 +12,169 @@ and this project adheres to [Semantic Versioning][].
 
 ### Fixed
 
-- Transformations of Points and Shapes are now applied before rendering with datashader (#378)
-- Fix bug due to `sc.get.obs_df()` returning a different type (#393)
+-   Transformations of Points and Shapes are now applied before rendering with datashader (#378)
+-   Fix bug due to `sc.get.obs_df()` returning a different type (#393)
+-   Allowing instance mismatch between shapes and tables (#396)
+-   Fix bug when plotting categorical points with datashader (#395)
 
 ## [0.2.8] - 2024-11-26
 
 ### Changed
 
-- Support for `xarray.DataTree` (which moved from `datatree.DataTree`) (#380)
+-   Support for `xarray.DataTree` (which moved from `datatree.DataTree`) (#380)
 
 ## [0.2.7] - 2024-10-24
 
 ### Added
 
-- The user can now specify `datashader_reduction` to control the rendering behaviour (#309)
-- Rendering outlines of shapes with datashader works now (#309)
+-   The user can now specify `datashader_reduction` to control the rendering behavior (#309)
+-   Rendering outlines of shapes with datashader works now (#309)
 
 ### Fixed
 
-- datashader now uses canvas size = image size which speeds up the rendering (#309)
-- datashader now uses the `linear` as interpolation method for colormaps instead of the default `eq_hist` to make it equivalent to matplotlib (#309)
-- point sizes of datashader now agree with matplotlib also when dpi != 100 (#309)
-- Giving a custom colormap when rendering a multiscale image now works (#586)
+-   datashader now uses canvas size = image size which speeds up the rendering (#309)
+-   datashader now uses the `linear` as interpolation method for colormaps instead of the default `eq_hist` to make it equivalent to matplotlib (#309)
+-   point sizes of datashader now agree with matplotlib also when dpi != 100 (#309)
+-   Giving a custom colormap when rendering a multiscale image now works (#586)
 
 ## [0.2.6] - 2024-09-04
 
 ### Changed
 
-- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
-- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
-- When rendering a single image channel, a colorbar is now shown (#346)
-- Removed `percentiles_for_norm` parameter (#346)
-- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
+-   Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+-   When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+-   When rendering a single image channel, a colorbar is now shown (#346)
+-   Removed `percentiles_for_norm` parameter (#346)
+-   Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
-- Filtering with `groups` now preserves original cmap (#344)
-- Non-selected `groups` are now not shown in `na_color` (#344)
-- Several issues associated with `norm` and `colorbar` (#346)
+-   Filtering with `groups` now preserves original cmap (#344)
+-   Non-selected `groups` are now not shown in `na_color` (#344)
+-   Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 
 ### Changed
 
-- Replaced `outline` parameter in `render_labels` with alpha-based logic (#323)
-- Lowered RMSE-threshold for plot-based tests from 60 to 45 (#323)
-- Removed `preprocessing` (.pp) accessor (#329)
+-   Replaced `outline` parameter in `render_labels` with alpha-based logic (#323)
+-   Lowered RMSE-threshold for plot-based tests from 60 to 45 (#323)
+-   Removed `preprocessing` (.pp) accessor (#329)
 
 ### Fixed
 
-- Minor fixes for several tests as a result of the threshold change (#323)
+-   Minor fixes for several tests as a result of the threshold change (#323)
 
 ## [0.2.4] - 2024-08-07
 
 ### Added
 
-- Added utils function for 0-transparent cmaps (#302)
+-   Added utils function for 0-transparent cmaps (#302)
 
 ### Fixed
 
-- Took RNG out of categorical label test (#306)
-- Performance bug when plotting shapes (#298)
-- scale parameter was ignored for single-scale images (#301)
-- Changes to support for dask-expr (#283)
-- Added error handling for non-existent elements (#305)
-- Specifying vmin and vmax properly clips image data (#307)
-- import bug `get_cmap()` (8fd969c)
+-   Took RNG out of categorical label test (#306)
+-   Performance bug when plotting shapes (#298)
+-   scale parameter was ignored for single-scale images (#301)
+-   Changes to support for dask-expr (#283)
+-   Added error handling for non-existent elements (#305)
+-   Specifying vmin and vmax properly clips image data (#307)
+-   import bug `get_cmap()` (8fd969c)
 
 ## [0.2.3] - 2024-07-03
 
 ### Added
 
-- Datashader support for points and shapes (#244)
+-   Datashader support for points and shapes (#244)
 
 ### Changed
 
-- All parameters are now provided for a single element (#272)
+-   All parameters are now provided for a single element (#272)
 
 ### Fixed
 
-- Fix color assignment for NaN values (#257)
-- Zorder of rendering now strictly follows the order of the render_x calls (#244)
+-   Fix color assignment for NaN values (#257)
+-   Zorder of rendering now strictly follows the order of the render_x calls (#244)
 
 ## [0.2.2] - 2024-05-02
 
 ### Fixed
 
-- Fixed `fill_alpha` ignoring `alpha` channel from custom cmap (#236)
-- Fix channel str support (#221)
+-   Fixed `fill_alpha` ignoring `alpha` channel from custom cmap (#236)
+-   Fix channel str support (#221)
 
 ## [0.2.1] - 2024-03-26
 
 ### Minor
 
-- Adjusted GitHub worklows
+-   Adjusted GitHub worklows
 
 ## [0.2.0] - 2024-03-24
 
 ### Added
 
-- Support for plotting multiple tables @melonora
+-   Support for plotting multiple tables @melonora
 
 ### Fixed
 
-- Several bugfixes, especially for colors and palettes @melonora
+-   Several bugfixes, especially for colors and palettes @melonora
 
 ## [0.1.0] - 2024-01-17
 
 ### Added
 
-- Multiscale image handling: user can specify a scale, else the best scale is selected automatically given the figure size and dpi (#164)
-- Large images are automatically rasterized to speed up performance (#164)
-- Added better error message for mismatch in cs and ax number (#185)
-- Beter test coverage for correct plotting of elements after transformation (#198)
-- Can now stack render commands (#190, #192)
-- The `color` argument in render_shapes/points now accepts actual colors as well (#199)
-- Input arguments are now evaulated for their types in basic.py (#199)
+-   Multiscale image handling: user can specify a scale, else the best scale is selected automatically given the figure size and dpi (#164)
+-   Large images are automatically rasterized to speed up performance (#164)
+-   Added better error message for mismatch in cs and ax number (#185)
+-   Beter test coverage for correct plotting of elements after transformation (#198)
+-   Can now stack render commands (#190, #192)
+-   The `color` argument in render_shapes/points now accepts actual colors as well (#199)
+-   Input arguments are now evaulated for their types in basic.py (#199)
 
 ### Fixed
 
-- Now dropping index when plotting shapes after spatial query (#177)
-- Points are now being correctly rotated (#198)
-- User can now pass Colormap objects to the cmap argument in render_images. When only one cmap is given for 3 channels, it is now applied to each channel (#188, #194)
+-   Now dropping index when plotting shapes after spatial query (#177)
+-   Points are now being correctly rotated (#198)
+-   User can now pass Colormap objects to the cmap argument in render_images. When only one cmap is given for 3 channels, it is now applied to each channel (#188, #194)
 
 ## [0.0.6] - 2023-11-06
 
 ### Added
 
-- Pushed `get_extent` functionality upstream to `spatialdata` (#162)
+-   Pushed `get_extent` functionality upstream to `spatialdata` (#162)
 
 ## [0.0.5] - 2023-10-02
 
 ### Added
 
-- Can now scale shapes (#152)
-- Can now plot columns from GeoDataFrame (#149)
+-   Can now scale shapes (#152)
+-   Can now plot columns from GeoDataFrame (#149)
 
 ### Fixed
 
-- Multipolygons are now handled correctly (#93)
-- Legend order is now deterministic (#143)
-- Images no longer normalised by default (#150)
-- Filtering of shapes and points using the `groups` argument is now possible, coloring by palette and cmap arguments works for shapes and points (#153)
-- Colorbar no longer autoscales to [0, 1] (#155)
-- Plotting shapes after a spatial query is now possible (#163)
+-   Multipolygons are now handled correctly (#93)
+-   Legend order is now deterministic (#143)
+-   Images no longer normalised by default (#150)
+-   Filtering of shapes and points using the `groups` argument is now possible, coloring by palette and cmap arguments works for shapes and points (#153)
+-   Colorbar no longer autoscales to [0, 1] (#155)
+-   Plotting shapes after a spatial query is now possible (#163)
 
 ## [0.0.4] - 2023-08-11
 
 ### Fixed
 
-- Multi-scale images/labels are now correctly substituted and the action is logged (#131).
-- Empty geometries among the shapes can be handeled (#133).
-- `outline_width` parameter in render_shapes is now a float that actually determines the line width (#139).
+-   Multi-scale images/labels are now correctly substituted and the action is logged (#131).
+-   Empty geometries among the shapes can be handeled (#133).
+-   `outline_width` parameter in render_shapes is now a float that actually determines the line width (#139).
 
 ## [0.0.2] - 2023-06-25
 
 ### Fixed
 
-- Multiple bugfixes of which I didn't keep track of.
+-   Multiple bugfixes of which I didn't keep track of.
 
 ## [0.0.1] - 2023-04-04
 
 ### Added
 
-- Initial release of `spatialdata-plot` with support for `images`, `labels`, `points` and `shapes`.
+-   Initial release of `spatialdata-plot` with support for `images`, `labels`, `points` and `shapes`.

src/spatialdata_plot/pl/render.py

@@ -17,7 +17,7 @@
 from matplotlib.cm import ScalarMappable
 from matplotlib.colors import ListedColormap, Normalize
 from scanpy._settings import settings as sc_settings
-from spatialdata import get_extent
+from spatialdata import get_extent, join_spatialelement_table
 from spatialdata.models import PointsModel, ShapesModel, get_table_keys
 from spatialdata.transformations import get_transformation, set_transformation
 from spatialdata.transformations.transformations import Identity
@@ -76,13 +76,18 @@ def _render_shapes(
         filter_tables=bool(render_params.table_name),
     )
 
-    shapes = sdata[element]
-
     if (table_name := render_params.table_name) is None:
         table = None
+        shapes = sdata_filt[element]
     else:
-        _, region_key, _ = get_table_keys(sdata[table_name])
-        table = sdata[table_name][sdata[table_name].obs[region_key].isin([element])]
+        element_dict, joined_table = join_spatialelement_table(
+            sdata, spatial_element_names=element, table_name=table_name, how="inner"
+        )
+        sdata_filt[element] = shapes = element_dict[element]
+        joined_table.uns["spatialdata_attrs"]["region"] = (
+            joined_table.obs[joined_table.uns["spatialdata_attrs"]["region_key"]].unique().tolist()
+        )
+        sdata_filt[table_name] = table = joined_table
 
     if (
         col_for_color is not None
@@ -520,7 +525,12 @@ def _render_points(
         # use datashader for the visualization of points
         cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_ext, y_range=y_ext)
 
-        color_by_categorical = col_for_color is not None and points[col_for_color].values.dtype == object
+        color_by_categorical = col_for_color is not None and transformed_element[col_for_color].values.dtype in (
+            object,
+            "categorical",
+        )
+        if color_by_categorical and transformed_element[col_for_color].values.dtype == object:
+            transformed_element[col_for_color] = transformed_element[col_for_color].astype("category")
         aggregate_with_reduction = None
         if col_for_color is not None and (render_params.groups is None or len(render_params.groups) > 1):
             if color_by_categorical:

tests/pl/test_render_points.py

@@ -111,6 +111,18 @@ def test_plot_datashader_continuous_color(self, sdata_blobs: SpatialData):
             element="blobs_points", size=40, color="instance_id", alpha=0.6, method="datashader"
         ).pl.show()
 
+    def test_plot_points_categorical_color_column_matplotlib(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_points("blobs_points", color="genes", method="matplotlib").pl.show()
+
+    def test_plot_points_categorical_color_column_datashader(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_points("blobs_points", color="genes", method="datashader").pl.show()
+
+    def test_plot_points_continuous_color_column_matplotlib(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_points("blobs_points", color="instance_id", method="matplotlib").pl.show()
+
+    def test_plot_points_continuous_color_column_datashader(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_points("blobs_points", color="instance_id", method="datashader").pl.show()
+
     def test_plot_datashader_matplotlib_stack(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_points(
             element="blobs_points", size=40, color="red", method="datashader"

tests/pl/test_render_shapes.py

@@ -169,7 +169,6 @@ def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData
         cropped_blob.pl.render_shapes().pl.show()
 
     def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
-        new_table = sdata_blobs["table"].copy()
         sdata_blobs["table"].obs["region"] = "blobs_circles"
         new_table = sdata_blobs["table"][:5]
         new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
@@ -447,3 +446,12 @@ def test_plot_datashader_can_transform_circles(self, sdata_blobs: SpatialData):
         _set_transformations(sdata_blobs["blobs_circles"], {"global": seq})
 
         sdata_blobs.pl.render_shapes("blobs_circles", method="datashader", outline_alpha=1.0).pl.show()
+
+    def test_plot_can_do_non_matching_table(self, sdata_blobs: SpatialData):
+        table_shapes = sdata_blobs["table"][:3].copy()
+        table_shapes.obs.instance_id = list(range(3))
+        table_shapes.obs["region"] = "blobs_circles"
+        table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
+        sdata_blobs["new_table"] = table_shapes
+
+        sdata_blobs.pl.render_shapes("blobs_circles", color="instance_id").pl.show()