mvp for render_points

src/spatialdata_plot/pl/_categorical_utils.py

@@ -450,3 +450,41 @@ def _add_categorical_legend(
                 fontsize=legend_fontsize,
                 path_effects=legend_fontoutline,
             )
+
+
+def _get_colors_for_categorical_obs(categories: Sequence[Union[str, int]]) -> list[str]:
+    """
+    Return a list of colors for a categorical observation.
+
+    Parameters
+    ----------
+    adata
+        AnnData object
+    value_to_plot
+        Name of a valid categorical observation
+    categories
+        categories of the categorical observation.
+
+    Returns
+    -------
+    None
+    """
+    length = len(categories)
+
+    # check if default matplotlib palette has enough colors
+    if len(rcParams["axes.prop_cycle"].by_key()["color"]) >= length:
+        cc = rcParams["axes.prop_cycle"]()
+        palette = [next(cc)["color"] for _ in range(length)]
+
+    else:
+        if length <= 20:
+            palette = default_20
+        elif length <= 28:
+            palette = default_28
+        elif length <= len(default_102):  # 103 colors
+            palette = default_102
+        else:
+            palette = ["grey" for _ in range(length)]
+            logging.info("input has more than 103 categories. Uniform " "'grey' color will be used for all categories.")
+
+    return palette[:length]

src/spatialdata_plot/pl/basic.py

@@ -21,7 +21,13 @@
 
 from ..accessor import register_spatial_data_accessor
 from ..pp.utils import _get_instance_key, _get_region_key, _verify_plotting_tree_exists
-from .render import _render_channels, _render_images, _render_labels, _render_shapes
+from .render import (
+    _render_channels,
+    _render_images,
+    _render_labels,
+    _render_points,
+    _render_shapes,
+)
 from .utils import (
     _get_color_key_dtype,
     _get_color_key_values,
@@ -197,6 +203,55 @@ def render_shapes(
 
         return sdata
 
+    def render_points(
+        self,
+        palette: Optional[Union[str, list[str], None]] = None,
+        color_key: Optional[str] = None,
+        **scatter_kwargs: Optional[str],
+    ) -> sd.SpatialData:
+        """Render the points contained in the given sd.SpatialData object
+
+        Parameters
+        ----------
+        self : sd.SpatialData
+            The sd.SpatialData object.
+        palette : list[str], optional (default: None)
+            A list of colors to use for rendering the images. If `None`, the
+            default colors will be used.
+        instance_key : str
+            The name of the column in the table that identifies individual shapes
+        color_key : str or None, optional (default: None)
+            The name of the column in the table to use for coloring shapes.
+
+        Returns
+        -------
+        sd.SpatialData
+            The input sd.SpatialData with a command added to the plotting tree
+
+        """
+        if palette is not None:
+            if isinstance(palette, str):
+                palette = [palette]
+
+            if isinstance(palette, list):
+                if not all(isinstance(p, str) for p in palette):
+                    raise TypeError("The palette argument must be a list of strings or a single string.")
+            else:
+                raise TypeError("The palette argument must be a list of strings or a single string.")
+
+        if color_key is not None and not isinstance(color_key, str):
+            raise TypeError("When giving a 'color_key', it must be of type 'str'.")
+
+        sdata = self._copy()
+        sdata = _verify_plotting_tree_exists(sdata)
+        n_steps = len(sdata.plotting_tree.keys())
+        sdata.plotting_tree[f"{n_steps+1}_render_points"] = {
+            "palette": palette,
+            "color_key": color_key,
+        }
+
+        return sdata
+
     def render_images(
         self,
         palette: Optional[Union[str, list[str]]] = None,
@@ -511,6 +566,7 @@ def show(
             "render_images",
             "render_shapes",
             "render_labels",
+            "render_points",
         ]
 
         if len(plotting_tree.keys()) > 0:
@@ -646,6 +702,17 @@ def show(
                         key = list(sdata.shapes.keys())[idx]
                         _render_shapes(sdata=sdata, params=params, key=key, ax=ax, extent=extent)
 
+                elif cmd == "render_points":
+                    for idx, ax in enumerate(axs):
+                        key = list(sdata.points.keys())[idx]
+                        if params["color_key"] is not None:
+                            if params["color_key"] not in sdata.points[key].columns:
+                                raise ValueError(
+                                    f"The column '{params['color_key']}' is not present in the 'metadata' of the points."
+                                )
+
+                        _render_points(sdata=sdata, params=params, key=key, ax=ax, extent=extent)
+
                 elif cmd == "render_labels":
                     if (
                         sdata.table is not None

src/spatialdata_plot/pl/render.py

@@ -8,9 +8,11 @@
 import spatialdata as sd
 import xarray as xr
 from matplotlib.colors import ListedColormap, to_rgb
+from pandas.api.types import is_categorical_dtype
 from skimage.segmentation import find_boundaries
 from sklearn.decomposition import PCA
 
+from ..pl._categorical_utils import _get_colors_for_categorical_obs
 from ..pl.utils import _normalize
 from ..pp.utils import _get_linear_colormap, _get_region_key
 
@@ -58,13 +60,11 @@ def _render_shapes(
 ) -> None:
     if sdata.table is not None and isinstance(params["instance_key"], str) and isinstance(params["color_key"], str):
         colors = [to_rgb(c) for c in sdata.table.uns[f"{params['color_key']}_colors"]]
-
     elif isinstance(params["palette"], str):
         colors = [params["palette"]]
     elif isinstance(params["palette"], Iterable):
         colors = [to_rgb(c) for c in list(params["palette"])]
     else:
-        # assert isinstance(params["palette"], Iterable)
         colors = [params["palette"]]
 
     ax.set_xlim(extent["x"][0], extent["x"][1])
@@ -82,6 +82,45 @@ def _render_shapes(
     ax.set_title(key)
 
 
+def _render_points(
+    sdata: sd.SpatialData,
+    params: dict[str, Union[str, int, float, Iterable[str]]],
+    key: str,
+    ax: matplotlib.axes.SubplotBase,
+    extent: dict[str, list[int]],
+) -> None:
+    ax.set_xlim(extent["x"][0], extent["x"][1])
+    ax.set_ylim(extent["y"][0], extent["y"][1])
+
+    if isinstance(params["color_key"], str):
+        colors = sdata.points[key][params["color_key"]].compute()
+
+        if is_categorical_dtype(colors):
+            category_colors = _get_colors_for_categorical_obs(colors.cat.categories)
+
+            for i, cat in enumerate(colors.cat.categories):
+                ax.scatter(
+                    x=sdata.points[key]["x"].compute()[colors == cat],
+                    y=sdata.points[key]["y"].compute()[colors == cat],
+                    color=category_colors[i],
+                    label=cat,
+                )
+
+        else:
+            ax.scatter(
+                x=sdata.points[key]["x"].compute(),
+                y=sdata.points[key]["y"].compute(),
+                c=colors,
+            )
+    else:
+        ax.scatter(
+            x=sdata.points[key]["x"].compute(),
+            y=sdata.points[key]["y"].compute(),
+        )
+
+    ax.set_title(key)
+
+
 def _render_images(
     sdata: sd.SpatialData,
     params: dict[str, Union[str, int, float]],