coloring shapes by categorical variable

CHANGELOG.md

@@ -22,6 +22,7 @@ and this project adheres to [Semantic Versioning][].
 -   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)
 
 ## [0.0.4] - 2023-08-11

src/spatialdata_plot/pl/basic.py

@@ -14,7 +14,7 @@
 from dask.dataframe.core import DataFrame as DaskDataFrame
 from geopandas import GeoDataFrame
 from matplotlib.axes import Axes
-from matplotlib.colors import Colormap, ListedColormap, Normalize
+from matplotlib.colors import Colormap, Normalize
 from matplotlib.figure import Figure
 from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
 from pandas.api.types import is_categorical_dtype
@@ -150,7 +150,7 @@ def render_shapes(
         outline_width: float = 1.5,
         outline_color: str | list[float] = "#000000ff",
         layer: str | None = None,
-        palette: ListedColormap | str | None = None,
+        palette: str | list[str] | None = None,
         cmap: Colormap | str | None = None,
         norm: bool | Normalize = False,
         na_color: str | tuple[float, ...] | None = "lightgrey",
@@ -182,9 +182,13 @@ def render_shapes(
         layer
             Key in :attr:`anndata.AnnData.layers` or `None` for :attr:`anndata.AnnData.X`.
         palette
-            Palette for discrete annotations, see :class:`matplotlib.colors.Colormap`.
+            Palette for discrete annotations. List of valid color names that should be used
+            for the categories (all or as specified by `groups`). For a single category,
+            a valid color name can be given as string.
         cmap
             Colormap for continuous annotations, see :class:`matplotlib.colors.Colormap`.
+            If no palette is given and `color` refers to a categorical, the colors are
+            sampled from this colormap.
         norm
             Colormap normalization for continuous annotations, see :class:`matplotlib.colors.Normalize`.
         na_color
@@ -235,7 +239,7 @@ def render_points(
         color: str | None = None,
         groups: str | Sequence[str] | None = None,
         size: float = 1.0,
-        palette: ListedColormap | str | None = None,
+        palette: str | list[str] | None = None,
         cmap: Colormap | str | None = None,
         norm: None | Normalize = None,
         na_color: str | tuple[float, ...] | None = (0.0, 0.0, 0.0, 0.0),
@@ -258,9 +262,13 @@ def render_points(
         size
             Value to scale points.
         palette
-            Palette for discrete annotations, see :class:`matplotlib.colors.Colormap`.
+            Palette for discrete annotations. List of valid color names that should be used
+            for the categories (all or as specified by `groups`). For a single category,
+            a valid color name can be given as string.
         cmap
             Colormap for continuous annotations, see :class:`matplotlib.colors.Colormap`.
+            If no palette is given and `color` refers to a categorical, the colors are
+            sampled from this colormap.
         norm
             Colormap normalization for continuous annotations, see :class:`matplotlib.colors.Normalize`.
         na_color
@@ -303,7 +311,7 @@ def render_images(
         cmap: list[Colormap] | list[str] | Colormap | str | None = None,
         norm: None | Normalize = None,
         na_color: str | tuple[float, ...] | None = (0.0, 0.0, 0.0, 0.0),
-        palette: ListedColormap | str | None = None,
+        palette: str | list[str] | None = None,
         alpha: float = 1.0,
         quantiles_for_norm: tuple[float | None, float | None] = (None, None),
         **kwargs: Any,
@@ -381,7 +389,7 @@ def render_labels(
         contour_px: int = 3,
         outline: bool = False,
         layer: str | None = None,
-        palette: ListedColormap | str | None = None,
+        palette: str | list[str] | None = None,
         cmap: Colormap | str | None = None,
         norm: None | Normalize = None,
         na_color: str | tuple[float, ...] | None = (0.0, 0.0, 0.0, 0.0),

src/spatialdata_plot/pl/render.py

@@ -4,6 +4,7 @@
 from copy import copy
 from typing import Union
 
+import dask
 import geopandas as gpd
 import matplotlib
 import numpy as np
@@ -18,6 +19,7 @@
 from spatialdata.models import (
     Image2DModel,
     Labels2DModel,
+    PointsModel,
 )
 
 from spatialdata_plot._logging import logger
@@ -57,6 +59,12 @@ def _render_shapes(
 ) -> None:
     elements = render_params.elements
 
+    if render_params.groups is not None:
+        if isinstance(render_params.groups, str):
+            render_params.groups = [render_params.groups]
+        if not all(isinstance(g, str) for g in render_params.groups):
+            raise TypeError("All groups must be strings.")
+
     sdata_filt = sdata.filter_by_coordinate_system(
         coordinate_system=coordinate_system,
         filter_table=sdata.table is not None,
@@ -68,7 +76,6 @@ def _render_shapes(
         elements = list(sdata_filt.shapes.keys())
 
     for e in elements:
-        # shapes = [sdata.shapes[e] for e in elements]
         shapes = sdata.shapes[e]
         n_shapes = sum([len(s) for s in shapes])
 
@@ -88,6 +95,7 @@ def _render_shapes(
             palette=render_params.palette,
             na_color=render_params.cmap_params.na_color,
             alpha=render_params.fill_alpha,
+            cmap_params=render_params.cmap_params,
         )
 
         values_are_categorical = color_source_vector is not None
@@ -101,7 +109,15 @@ def _render_shapes(
         if len(color_vector) == 0:
             color_vector = [render_params.cmap_params.na_color]
 
+        # filter by `groups`
+        if render_params.groups is not None and color_source_vector is not None:
+            mask = color_source_vector.isin(render_params.groups)
+            shapes = shapes[mask]
+            shapes = shapes.reset_index()
+            color_source_vector = color_source_vector[mask]
+            color_vector = color_vector[mask]
         shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
         _cax = _get_collection_shape(
             shapes=shapes,
             s=render_params.scale,
@@ -122,9 +138,12 @@ def _render_shapes(
         cax = ax.add_collection(_cax)
 
         # Using dict.fromkeys here since set returns in arbitrary order
-        palette = (
-            ListedColormap(dict.fromkeys(color_vector)) if render_params.palette is None else render_params.palette
-        )
+        # remove the color of NaN values, else it might be assigned to a category
+        # order of color in the palette should agree to order of occurence
+        if color_source_vector is None:
+            palette = ListedColormap(dict.fromkeys(color_vector))
+        else:
+            palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
         if not (
             len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
@@ -159,6 +178,12 @@ def _render_points(
     scalebar_params: ScalebarParams,
     legend_params: LegendParams,
 ) -> None:
+    if render_params.groups is not None:
+        if isinstance(render_params.groups, str):
+            render_params.groups = [render_params.groups]
+        if not all(isinstance(g, str) for g in render_params.groups):
+            raise TypeError("All groups must be strings.")
+
     elements = render_params.elements
 
     sdata_filt = sdata.filter_by_coordinate_system(
@@ -178,6 +203,14 @@ def _render_points(
             color = [render_params.color] if isinstance(render_params.color, str) else render_params.color
             coords.extend(color)
 
+        points = points[coords].compute()
+        # points[color[0]].cat.set_categories(render_params.groups, inplace=True)
+        if render_params.groups is not None:
+            points = points[points[color].isin(render_params.groups).values]
+            points[color[0]] = points[color[0]].cat.set_categories(render_params.groups)
+        points = dask.dataframe.from_pandas(points, npartitions=1)
+        sdata_filt.points[e] = PointsModel.parse(points, coordinates={"x": "x", "y": "y"})
+
         point_df = points[coords].compute()
 
         # we construct an anndata to hack the plotting functions
@@ -204,6 +237,7 @@ def _render_points(
             palette=render_params.palette,
             na_color=render_params.cmap_params.na_color,
             alpha=render_params.alpha,
+            cmap_params=render_params.cmap_params,
         )
 
         # color_source_vector is None when the values aren't categorical
@@ -226,14 +260,19 @@ def _render_points(
         if not (
             len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
         ):
+            if color_source_vector is None:
+                palette = ListedColormap(dict.fromkeys(color_vector))
+            else:
+                palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
+
             _ = _decorate_axs(
                 ax=ax,
                 cax=cax,
                 fig_params=fig_params,
                 adata=adata,
                 value_to_plot=render_params.color,
                 color_source_vector=color_source_vector,
-                palette=render_params.palette,
+                palette=palette,
                 alpha=render_params.alpha,
                 na_color=render_params.cmap_params.na_color,
                 legend_fontsize=legend_params.legend_fontsize,
@@ -415,6 +454,12 @@ def _render_labels(
 ) -> None:
     elements = render_params.elements
 
+    if render_params.groups is not None:
+        if isinstance(render_params.groups, str):
+            render_params.groups = [render_params.groups]
+        if not all(isinstance(g, str) for g in render_params.groups):
+            raise TypeError("All groups must be strings.")
+
     sdata_filt = sdata.filter_by_coordinate_system(
         coordinate_system=coordinate_system,
         filter_table=sdata.table is not None,
@@ -441,7 +486,7 @@ def _render_labels(
 
             table = sdata.table[sdata.table.obs[region_key].isin([label_key])]
 
-            # get isntance id based on subsetted table
+            # get instance id based on subsetted table
             instance_id = table.obs[instance_key].values
 
         # get color vector (categorical or continuous)
@@ -455,6 +500,7 @@ def _render_labels(
             palette=render_params.palette,
             na_color=render_params.cmap_params.na_color,
             alpha=render_params.fill_alpha,
+            cmap_params=render_params.cmap_params,
         )
 
         if (render_params.fill_alpha != render_params.outline_alpha) and render_params.contour_px is not None:

src/spatialdata_plot/pl/render_params.py

@@ -19,6 +19,7 @@ class CmapParams:
     cmap: Colormap
     norm: Normalize
     na_color: str | tuple[float, ...] = (0.0, 0.0, 0.0, 0.0)
+    is_default: bool = True
 
 
 @dataclass