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Description
Hi there, I'm trying to show a leiden clustering in space for an sdata object with many coordinate systems. I'm calling
(
sdata
.pl.render_shapes(fill_alpha=1, method="matplotlib", color="leiden")
.pl.show(colorbar=False)
)which should show segmentation masks, colored by the leiden clustering. This works for individual samples (when using .filter_by_coordinate_system(), and it also work when all clusters are present in every coordinate system. It fails when the number of categories per coordinate systems differs, with:
File [/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/basic.py:937](https://jupyter.euler.hpc.ethz.ch/user/mlange/lab/workspaces/auto-o/tree/cluster/home/mlange/mlange/github/gli3_merscope_analysis/analysis/preprocessing/post_segmentation/v4/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/basic.py#line=936), in PlotAccessor.show(self, coordinate_systems, legend_fontsize, legend_fontweight, legend_loc, legend_fontoutline, na_in_legend, colorbar, wspace, hspace, ncols, frameon, figsize, dpi, fig, title, share_extent, pad_extent, ax, return_ax, save)
932 wanted_elements, wanted_shapes_on_this_cs, wants_shapes = _get_wanted_render_elements(
933 sdata, wanted_elements, params_copy, cs, "shapes"
934 )
936 if wanted_shapes_on_this_cs:
--> 937 _render_shapes(
938 sdata=sdata,
939 render_params=params_copy,
940 coordinate_system=cs,
941 ax=ax,
942 fig_params=fig_params,
943 scalebar_params=scalebar_params,
944 legend_params=legend_params,
945 )
947 elif cmd == "render_points" and has_points:
948 wanted_elements, wanted_points_on_this_cs, wants_points = _get_wanted_render_elements(
949 sdata, wanted_elements, params_copy, cs, "points"
950 )
File [/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/render.py:109](https://jupyter.euler.hpc.ethz.ch/user/mlange/lab/workspaces/auto-o/tree/cluster/home/mlange/mlange/github/gli3_merscope_analysis/analysis/preprocessing/post_segmentation/v4/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/render.py#line=108), in _render_shapes(sdata, render_params, coordinate_system, ax, fig_params, scalebar_params, legend_params)
106 sdata_filt[table_name].obs[col_for_color] = sdata_filt[table_name].obs[col_for_color].astype("category")
108 # get color vector (categorical or continuous)
--> 109 color_source_vector, color_vector, _ = _set_color_source_vec(
110 sdata=sdata_filt,
111 element=sdata_filt[element],
112 element_name=element,
113 value_to_plot=col_for_color,
114 groups=groups,
115 palette=render_params.palette,
116 na_color=render_params.color or render_params.cmap_params.na_color,
117 cmap_params=render_params.cmap_params,
118 table_name=table_name,
119 table_layer=table_layer,
120 )
122 values_are_categorical = color_source_vector is not None
124 # color_source_vector is None when the values aren't categorical
File [/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/utils.py:757](https://jupyter.euler.hpc.ethz.ch/user/mlange/lab/workspaces/auto-o/tree/cluster/home/mlange/mlange/github/gli3_merscope_analysis/analysis/preprocessing/post_segmentation/v4/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/utils.py#line=756), in _set_color_source_vec(sdata, element, value_to_plot, na_color, element_name, groups, palette, cmap_params, table_name, table_layer)
753 return None, color_source_vector, False
755 color_source_vector = pd.Categorical(color_source_vector) # convert, e.g., `pd.Series`
--> 757 color_mapping = _get_categorical_color_mapping(
758 adata=sdata.table,
759 cluster_key=value_to_plot,
760 color_source_vector=color_source_vector,
761 groups=groups,
762 palette=palette,
763 na_color=na_color,
764 )
766 color_source_vector = color_source_vector.set_categories(color_mapping.keys())
767 if color_mapping is None:
File [/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/utils.py:934](https://jupyter.euler.hpc.ethz.ch/user/mlange/lab/workspaces/auto-o/tree/cluster/home/mlange/mlange/github/gli3_merscope_analysis/analysis/preprocessing/post_segmentation/v4/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/utils.py#line=933), in _get_categorical_color_mapping(adata, na_color, cluster_key, color_source_vector, groups, palette)
932 base_mapping = _get_default_categorial_color_mapping(color_source_vector)
933 else:
--> 934 base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
936 return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
File [/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/utils.py:867](https://jupyter.euler.hpc.ethz.ch/user/mlange/lab/workspaces/auto-o/tree/cluster/home/mlange/mlange/github/gli3_merscope_analysis/analysis/preprocessing/post_segmentation/v4/cluster/project/treutlein/USERS/mlange/github/spatialdata-plot/src/spatialdata_plot/pl/utils.py#line=866), in _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
864 na_color = to_hex(to_rgba(na_color)[:3])
866 if na_color and len(categories) > len(colors):
--> 867 return dict(zip(categories, colors + [na_color], strict=True))
869 return dict(zip(categories, colors, strict=True))
871 return _get_default_categorial_color_mapping(color_source_vector)
ValueError: zip() argument 2 is shorter than argument 1
What seems to be happening is that colors = adata.uns[f"{cluster_key}_colors"] is correctly subsetted, but categories = color_source_vector.categories.tolist() + ["NaN"] is not. Eventually, these two differ in length. It's easy to fix this, by removing unused categories from the color_source_vector, i.e. changing that one line to:
categories = color_source_vector.remove_unused_categories().categories.tolist() + ["NaN"]