make ready for new spatialdata (#482)

Author: melonora

src/spatialdata_plot/pl/utils.py

@@ -780,8 +780,9 @@ def _set_color_source_vec(
 
         color_source_vector = pd.Categorical(color_source_vector)  # convert, e.g., `pd.Series`
 
+        # TODO check why table_name is not passed here.
         color_mapping = _get_categorical_color_mapping(
-            adata=sdata.table,
+            adata=sdata["table"],
             cluster_key=value_to_plot,
             color_source_vector=color_source_vector,
             cmap_params=cmap_params,

tests/conftest.py

@@ -154,7 +154,7 @@ def test_sdata_multiple_images_diverging_dims():
 def sdata_blobs_shapes_annotated() -> SpatialData:
     """Get blobs sdata with continuous annotation of polygons."""
     blob = blobs()
-    blob["table"].obs["region"] = "blobs_polygons"
+    blob["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * blob["table"].n_obs)
     blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
     blob.shapes["blobs_polygons"]["value"] = [1, 2, 3, 4, 5]
     return blob

tests/pl/test_render_labels.py

@@ -33,12 +33,12 @@ def test_plot_can_render_labels(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_labels(element="blobs_labels").pl.show()
 
     def test_plot_can_render_multiscale_labels(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_multiscale_labels"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_multiscale_labels"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_multiscale_labels"
         sdata_blobs.pl.render_labels("blobs_multiscale_labels").pl.show()
 
     def test_plot_can_render_given_scale_of_multiscale_labels(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_multiscale_labels"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_multiscale_labels"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_multiscale_labels"
         sdata_blobs.pl.render_labels("blobs_multiscale_labels", scale="scale1").pl.show()
 
@@ -50,7 +50,7 @@ def test_plot_can_do_rasterization(self, sdata_blobs: SpatialData):
         img.attrs["transform"] = sdata_blobs["blobs_labels"].transform
         sdata_blobs["blobs_giant_labels"] = img
 
-        sdata_blobs["table"].obs["region"] = "blobs_giant_labels"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_giant_labels"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_giant_labels"
 
         sdata_blobs.pl.render_labels("blobs_giant_labels").pl.show()
@@ -63,7 +63,7 @@ def test_plot_can_stop_rasterization_with_scale_full(self, sdata_blobs: SpatialD
         img.attrs["transform"] = sdata_blobs["blobs_labels"].transform
         sdata_blobs["blobs_giant_labels"] = img
 
-        sdata_blobs["table"].obs["region"] = "blobs_giant_labels"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_giant_labels"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_giant_labels"
 
         sdata_blobs.pl.render_labels("blobs_giant_labels", scale="full").pl.show()
@@ -110,7 +110,7 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
             max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
             max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
             adata.obs["which_max"] = max_col
-            adata.obs["region"] = label
+            adata.obs["region"] = pd.Categorical([label] * adata.n_obs)
             del adata.uns["spatialdata_attrs"]
             table = TableModel.parse(
                 adata=adata,
@@ -142,7 +142,7 @@ def test_plot_two_calls_with_coloring_result_in_two_colorbars(self, sdata_blobs:
         sdata_blobs_local = deepcopy(sdata_blobs)
 
         table = sdata_blobs_local["table"].copy()
-        table.obs["region"] = "blobs_multiscale_labels"
+        table.obs["region"] = pd.Categorical(["blobs_multiscale_labels"] * table.n_obs)
         table.uns["spatialdata_attrs"]["region"] = "blobs_multiscale_labels"
         table = table[:, ~table.var_names.isin(["channel_0_sum"])]
         sdata_blobs_local["multi_table"] = table
@@ -187,7 +187,7 @@ def test_plot_label_colorbar_uses_alpha_of_less_transparent_outline(
 
     def test_can_plot_with_one_element_color_table(self, sdata_blobs: SpatialData):
         table = sdata_blobs["table"].copy()
-        table.obs["region"] = "blobs_multiscale_labels"
+        table.obs["region"] = pd.Categorical(["blobs_multiscale_labels"] * table.n_obs)
         table.uns["spatialdata_attrs"]["region"] = "blobs_multiscale_labels"
         table = table[:, ~table.var_names.isin(["channel_0_sum"])]
         sdata_blobs["multi_table"] = table
@@ -196,9 +196,9 @@ def test_can_plot_with_one_element_color_table(self, sdata_blobs: SpatialData):
         ).pl.show()
 
     def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: SpatialData):
-        max_col = sdata_blobs.table.to_df().idxmax(axis=1)
-        max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
-        sdata_blobs.table.obs["which_max"] = max_col
+        max_col = sdata_blobs["table"].to_df().idxmax(axis=1)
+        max_col = pd.Categorical(max_col, categories=sdata_blobs["table"].to_df().columns, ordered=True)
+        sdata_blobs["table"].obs["which_max"] = max_col
 
         _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
@@ -210,9 +210,9 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
         ).pl.show(ax=axs[1])
 
     def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(self, sdata_blobs: SpatialData):
-        max_col = sdata_blobs.table.to_df().idxmax(axis=1)
-        max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
-        sdata_blobs.table.obs["which_max"] = max_col
+        max_col = sdata_blobs["table"].to_df().idxmax(axis=1)
+        max_col = pd.Categorical(max_col, categories=sdata_blobs["table"].to_df().columns, ordered=True)
+        sdata_blobs["table"].obs["which_max"] = max_col
 
         _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 

tests/pl/test_render_points.py

@@ -37,7 +37,7 @@ def test_plot_can_render_points(self, sdata_blobs: SpatialData):
     def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
         _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
-        sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_points"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
 
         sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
@@ -46,7 +46,7 @@ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialD
     def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
         _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
-        sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_points"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
 
         sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
@@ -55,19 +55,19 @@ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialDa
         )
 
     def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_points"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
         sdata_blobs.pl.render_points(
             color="genes", groups=["gene_a", "gene_b"], palette=["lightgreen", "darkblue"]
         ).pl.show()
 
     def test_plot_coloring_with_cmap(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_points"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
         sdata_blobs.pl.render_points(color="genes", cmap="rainbow").pl.show()
 
     def test_plot_can_stack_render_points(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_points"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
         (
             sdata_blobs.pl.render_points(element="blobs_points", na_color="red", size=30)
@@ -86,7 +86,7 @@ def test_plot_points_coercable_categorical_color(self, sdata_blobs: SpatialData)
         adata.obs["instance_id"] = np.arange(adata.n_obs)
         adata.obs["category"] = RNG.choice(["a", "b", "c"], size=adata.n_obs)
         adata.obs["instance_id"] = list(range(adata.n_obs))
-        adata.obs["region"] = "blobs_points"
+        adata.obs["region"] = pd.Categorical(["blobs_points"] * adata.n_obs)
         table = TableModel.parse(adata=adata, region_key="region", instance_key="instance_id", region="blobs_points")
         sdata_blobs["other_table"] = table
 
@@ -100,7 +100,7 @@ def test_plot_points_categorical_color(self, sdata_blobs: SpatialData):
         adata.obs["instance_id"] = np.arange(adata.n_obs)
         adata.obs["category"] = RNG.choice(["a", "b", "c"], size=adata.n_obs)
         adata.obs["instance_id"] = list(range(adata.n_obs))
-        adata.obs["region"] = "blobs_points"
+        adata.obs["region"] = pd.Categorical(["blobs_points"] * adata.n_obs)
         table = TableModel.parse(adata=adata, region_key="region", instance_key="instance_id", region="blobs_points")
         sdata_blobs["other_table"] = table
 

tests/pl/test_render_shapes.py

@@ -101,7 +101,7 @@ def _make_multi():
 
     def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
         blob = deepcopy(sdata_blobs)
-        blob["table"].obs["region"] = "blobs_polygons"
+        blob["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * blob["table"].n_obs)
         blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
         blob.pl.render_shapes(
@@ -115,7 +115,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
     def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
         _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
-        sdata_blobs["table"].obs["region"] = "blobs_polygons"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
         sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
@@ -129,7 +129,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
         )
 
     def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_polygons"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
         sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
@@ -142,21 +142,21 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
         ).pl.show()
 
     def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_polygons"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
         sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()
 
     def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_polygons"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
         norm = Normalize(vmin=0, vmax=5, clip=True)
         sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
     def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
         # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included
-        blob = SpatialData.from_elements_dict(
+        blob = SpatialData.init_from_elements(
             {
                 "blobs_circles": sdata_blobs.shapes["blobs_circles"],
                 "blobs_multipolygons": sdata_blobs.shapes["blobs_multipolygons"],
@@ -169,7 +169,7 @@ 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):
-        sdata_blobs["table"].obs["region"] = "blobs_circles"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_circles"] * sdata_blobs["table"].n_obs)
         new_table = sdata_blobs["table"][:5]
         new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
         new_table.obs["instance_id"] = np.array(range(5))
@@ -188,7 +188,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob
         sdata_blobs.pl.render_shapes("blobs_circles", color="annotation").pl.show()
 
     def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_circles"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_circles"] * sdata_blobs["table"].n_obs)
         new_table = sdata_blobs["table"][:5].copy()
         new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
         new_table.obs["instance_id"] = np.array(range(5))
@@ -216,11 +216,12 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd
         sdata_cropped.pl.render_shapes("blobs_circles", color="annotation").pl.show()
 
     def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_circles"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_circles"] * sdata_blobs["table"].n_obs)
         new_table = sdata_blobs["table"][:10].copy()
         new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
         new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))
 
+        new_table.obs["region"] = new_table.obs["region"].cat.add_categories(["blobs_polygons"])
         new_table.obs.loc[5 * [False] + 5 * [True], "region"] = "blobs_polygons"
         new_table.obs["annotation"] = ["a", "b", "c", "d", "e", "v", "w", "x", "y", "z"]
         new_table.obs["annotation"] = new_table.obs["annotation"].astype("category")
@@ -232,11 +233,12 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa
         ).pl.show()
 
     def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_circles"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_circles"] * sdata_blobs["table"].n_obs)
         new_table = sdata_blobs["table"][:10].copy()
         new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
         new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))
 
+        new_table.obs["region"] = new_table.obs["region"].cat.add_categories(["blobs_polygons"])
         new_table.obs.loc[5 * [False] + 5 * [True], "region"] = "blobs_polygons"
         new_table.obs["annotation"] = ["a", "b", "c", "d", "e", "v", "w", "x", "y", "z"]
         new_table.obs["annotation"] = new_table.obs["annotation"].astype("category")
@@ -364,7 +366,7 @@ def test_plot_can_color_by_category_with_cmap(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", cmap="cool").pl.show()
 
     def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = "blobs_polygons"
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
         sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()
@@ -374,13 +376,13 @@ def test_plot_datashader_can_color_by_identical_value(self, sdata_blobs: Spatial
         We test this, because datashader internally scales the values, so when all shapes have the same value,
         the scaling would lead to all of them being assigned an alpha of 0, so we wouldn't see anything
         """
-        sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 1, 1, 1, 1]
         sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()
 
     def test_plot_datashader_shades_with_linear_cmap(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = pd.Categorical(["blobs_polygons"] * sdata_blobs["table"].n_obs)
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 2, 1, 20, 1]
         sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()
@@ -414,7 +416,7 @@ def test_plot_datashader_can_render_with_rgba_colored_outline(self, sdata_blobs:
     def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
         table_shapes = sdata_blobs["table"][:5].copy()
         table_shapes.obs.instance_id = list(range(5))
-        table_shapes.obs["region"] = "blobs_circles"
+        table_shapes.obs["region"] = pd.Categorical(["blobs_circles"] * table_shapes.n_obs)
         table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
         table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
         sdata_blobs["new_table"] = table_shapes
@@ -493,7 +495,7 @@ def test_plot_datashader_can_transform_circles(self, sdata_blobs: SpatialData):
     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.obs["region"] = pd.Categorical(["blobs_circles"] * table_shapes.n_obs)
         table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
         sdata_blobs["new_table"] = table_shapes