basic shapes datashader rendering

src/spatialdata_plot/pl/basic.py

@@ -160,6 +160,7 @@ def render_shapes(
  cmap: Colormap | str | None = None,
  norm: bool | Normalize = False,
  scale: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -204,6 +205,9 @@ def render_shapes(
  Colormap normalization for continuous annotations.
  scale : float | int, default 1.0
  Value to scale circles, if present.
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  **kwargs : Any
  Additional arguments to be passed to cmap and norm.
 
@@ -317,6 +321,12 @@ def render_shapes(
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -343,6 +353,7 @@ def render_shapes(
  fill_alpha=fill_alpha,
  transfunc=kwargs.get("transfunc", None),
  zorder=n_steps,
+ method=method,
  )
 
  return sdata
@@ -358,6 +369,7 @@ def render_points(
  cmap: Colormap | str | None = None,
  norm: None | Normalize = None,
  size: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -392,6 +404,9 @@ def render_points(
  Colormap normalization for continuous annotations.
  size : float | int, default 1.0
  Size of the points
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  kwargs
  Additional arguments to be passed to cmap and norm.
 
@@ -479,6 +494,12 @@ def render_points(
  if size < 0:
  raise ValueError("Parameter 'size' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -501,6 +522,7 @@ def render_points(
  transfunc=kwargs.get("transfunc", None),
  size=size,
  zorder=n_steps,
+ method=method,
  )
 
  return sdata

src/spatialdata_plot/pl/render.py

@@ -122,35 +122,6 @@ def _render_shapes(
  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,
- c=color_vector,
- render_params=render_params,
- rasterized=sc_settings._vector_friendly,
- cmap=render_params.cmap_params.cmap,
- norm=norm,
- fill_alpha=render_params.fill_alpha,
- outline_alpha=render_params.outline_alpha,
- zorder=render_params.zorder,
- # **kwargs,
- )
-
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
-
- cax = ax.add_collection(_cax)
-
- # Apply the transformation to the PatchCollection's paths
- trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
- affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
- trans = mtransforms.Affine2D(matrix=affine_trans)
-
- for path in _cax.get_paths():
- path.vertices = trans.transform(path.vertices)
 
  # Using dict.fromkeys here since set returns in arbitrary order
  # remove the color of NaN values, else it might be assigned to a category
@@ -160,6 +131,98 @@ def _render_shapes(
  else:
  palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+ # Apply the transformation to the PatchCollection's paths
+ trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
+ affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
+ trans = mtransforms.Affine2D(matrix=affine_trans)
+
+ shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
+ # Determine which method to use for rendering
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(shapes) > 100 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "matplotlib":
+ logger.info(f"Using {method}")
+ _cax = _get_collection_shape(
+ shapes=shapes,
+ s=render_params.scale,
+ c=color_vector,
+ render_params=render_params,
+ rasterized=sc_settings._vector_friendly,
+ cmap=render_params.cmap_params.cmap,
+ norm=norm,
+ fill_alpha=render_params.fill_alpha,
+ outline_alpha=render_params.outline_alpha,
+ zorder=render_params.zorder,
+ # **kwargs,
+ )
+ cax = ax.add_collection(_cax)
+
+ # Transform the paths in PatchCollection
+ for path in _cax.get_paths():
+ path.vertices = trans.transform(path.vertices)
+ cax = ax.add_collection(_cax)
+ elif method == "datashader":
+ logger.info(f"Using {method}")
+
+ # Where to put this
+ trans = mtransforms.Affine2D(matrix=affine_trans) + ax.transData
+
+ extent = get_extent(sdata.shapes[e])
+ x_ext = extent["x"][1]
+ y_ext = extent["y"][1]
+ # previous_xlim = fig_params.ax.get_xlim()
+ # previous_ylim = fig_params.ax.get_ylim()
+ x_range = [0, x_ext]
+ y_range = [0, y_ext]
+ # round because we need integers
+ plot_width = int(np.round(x_range[1] - x_range[0]))
+ plot_height = int(np.round(y_range[1] - y_range[0]))
+
+ cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_range, y_range=y_range)
+
+ _geometry = shapes["geometry"]
+ is_point = _geometry.type == "Point"
+
+ # Handle circles encoded as points with radius
+ if is_point.any(): # TODO
+ scale = shapes[is_point]["radius"] * render_params.scale
+ shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale)
+
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ # Render shapes with datashader
+ if render_params.col_for_color is not None and (
+ render_params.groups is None or len(render_params.groups) > 1
+ ):
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.by(render_params.col_for_color, ds.count()))
+ else:
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ color_key = (
+ [x[:-2] for x in color_vector.categories.values]
+ if (type(color_vector) == pd.core.arrays.categorical.Categorical)
+ and (len(color_vector.categories.values) > 1)
+ else None
+ )
+ ds_result = ds.tf.shade(
+ agg, cmap=color_vector[0][:-2], alpha=render_params.fill_alpha * 255, color_key=color_key, min_alpha=200
+ )
+
+ # Render image
+ rgba_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
+ _cax = ax.imshow(rgba_image, cmap=palette, zorder=render_params.zorder)
+ _cax.set_transform(trans)
+ cax = ax.add_image(_cax)
+
+ # Sets the limits of the colorbar to the values instead of [0, 1]
+ if not norm and not values_are_categorical:
+ _cax.set_clim(min(color_vector), max(color_vector))
+
  if not (
  len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
  ):
@@ -278,9 +341,13 @@ def _render_points(
 
  norm = copy(render_params.cmap_params.norm)
 
- # optionally render points using datashader
- # TODO: maybe move this, add heuristic
- if len(points) > 50:
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(points.shape[0]) > 10000 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "datashader":
  extent = get_extent(sdata_filt.points[e], coordinate_system=coordinate_system)
  x_ext = extent["x"][1]
  y_ext = extent["y"][1]
@@ -334,7 +401,7 @@ def _render_points(
  rbga_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
  ax.imshow(rbga_image, zorder=render_params.zorder)
  cax = None
-else:
+elif method == "matplotlib":
  # original way of plotting points
  _cax = ax.scatter(
  adata[:, 0].X.flatten(),

src/spatialdata_plot/pl/render_params.py

@@ -81,6 +81,7 @@ class ShapesRenderParams:
  fill_alpha: float = 0.3
  scale: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 
@@ -97,6 +98,7 @@ class PointsRenderParams:
  alpha: float = 1.0
  size: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 

src/spatialdata_plot/pl/basic.py

@@ -160,6 +160,7 @@ def render_shapes(
  cmap: Colormap | str | None = None,
  norm: bool | Normalize = False,
  scale: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -204,6 +205,9 @@ def render_shapes(
  Colormap normalization for continuous annotations.
  scale : float | int, default 1.0
  Value to scale circles, if present.
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  **kwargs : Any
  Additional arguments to be passed to cmap and norm.
 
@@ -317,6 +321,12 @@ def render_shapes(
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -343,6 +353,7 @@ def render_shapes(
  fill_alpha=fill_alpha,
  transfunc=kwargs.get("transfunc", None),
  zorder=n_steps,
+ method=method,
  )
 
  return sdata
@@ -358,6 +369,7 @@ def render_points(
  cmap: Colormap | str | None = None,
  norm: None | Normalize = None,
  size: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -392,6 +404,9 @@ def render_points(
  Colormap normalization for continuous annotations.
  size : float | int, default 1.0
  Size of the points
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  kwargs
  Additional arguments to be passed to cmap and norm.
 
@@ -479,6 +494,12 @@ def render_points(
  if size < 0:
  raise ValueError("Parameter 'size' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -501,6 +522,7 @@ def render_points(
  transfunc=kwargs.get("transfunc", None),
  size=size,
  zorder=n_steps,
+ method=method,
  )
 
  return sdata

src/spatialdata_plot/pl/render.py

@@ -122,35 +122,6 @@ def _render_shapes(
  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,
- c=color_vector,
- render_params=render_params,
- rasterized=sc_settings._vector_friendly,
- cmap=render_params.cmap_params.cmap,
- norm=norm,
- fill_alpha=render_params.fill_alpha,
- outline_alpha=render_params.outline_alpha,
- zorder=render_params.zorder,
- # **kwargs,
- )
-
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
-
- cax = ax.add_collection(_cax)
-
- # Apply the transformation to the PatchCollection's paths
- trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
- affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
- trans = mtransforms.Affine2D(matrix=affine_trans)
-
- for path in _cax.get_paths():
- path.vertices = trans.transform(path.vertices)
 
  # Using dict.fromkeys here since set returns in arbitrary order
  # remove the color of NaN values, else it might be assigned to a category
@@ -160,6 +131,98 @@ def _render_shapes(
  else:
  palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+ # Apply the transformation to the PatchCollection's paths
+ trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
+ affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
+ trans = mtransforms.Affine2D(matrix=affine_trans)
+
+ shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
+ # Determine which method to use for rendering
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(shapes) > 100 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "matplotlib":
+ logger.info(f"Using {method}")
+ _cax = _get_collection_shape(
+ shapes=shapes,
+ s=render_params.scale,
+ c=color_vector,
+ render_params=render_params,
+ rasterized=sc_settings._vector_friendly,
+ cmap=render_params.cmap_params.cmap,
+ norm=norm,
+ fill_alpha=render_params.fill_alpha,
+ outline_alpha=render_params.outline_alpha,
+ zorder=render_params.zorder,
+ # **kwargs,
+ )
+ cax = ax.add_collection(_cax)
+
+ # Transform the paths in PatchCollection
+ for path in _cax.get_paths():
+ path.vertices = trans.transform(path.vertices)
+ cax = ax.add_collection(_cax)
+ elif method == "datashader":
+ logger.info(f"Using {method}")
+
+ # Where to put this
+ trans = mtransforms.Affine2D(matrix=affine_trans) + ax.transData
+
+ extent = get_extent(sdata.shapes[e])
+ x_ext = extent["x"][1]
+ y_ext = extent["y"][1]
+ # previous_xlim = fig_params.ax.get_xlim()
+ # previous_ylim = fig_params.ax.get_ylim()
+ x_range = [0, x_ext]
+ y_range = [0, y_ext]
+ # round because we need integers
+ plot_width = int(np.round(x_range[1] - x_range[0]))
+ plot_height = int(np.round(y_range[1] - y_range[0]))
+
+ cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_range, y_range=y_range)
+
+ _geometry = shapes["geometry"]
+ is_point = _geometry.type == "Point"
+
+ # Handle circles encoded as points with radius
+ if is_point.any(): # TODO
+ scale = shapes[is_point]["radius"] * render_params.scale
+ shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale)
+
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ # Render shapes with datashader
+ if render_params.col_for_color is not None and (
+ render_params.groups is None or len(render_params.groups) > 1
+ ):
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.by(render_params.col_for_color, ds.count()))
+ else:
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ color_key = (
+ [x[:-2] for x in color_vector.categories.values]
+ if (type(color_vector) == pd.core.arrays.categorical.Categorical)
+ and (len(color_vector.categories.values) > 1)
+ else None
+ )
+ ds_result = ds.tf.shade(
+ agg, cmap=color_vector[0][:-2], alpha=render_params.fill_alpha * 255, color_key=color_key, min_alpha=200
+ )
+
+ # Render image
+ rgba_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
+ _cax = ax.imshow(rgba_image, cmap=palette, zorder=render_params.zorder)
+ _cax.set_transform(trans)
+ cax = ax.add_image(_cax)
+
+ # Sets the limits of the colorbar to the values instead of [0, 1]
+ if not norm and not values_are_categorical:
+ _cax.set_clim(min(color_vector), max(color_vector))
+
  if not (
  len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
  ):
@@ -278,9 +341,13 @@ def _render_points(
 
  norm = copy(render_params.cmap_params.norm)
 
- # optionally render points using datashader
- # TODO: maybe move this, add heuristic
- if len(points) > 50:
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(points.shape[0]) > 10000 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "datashader":
  extent = get_extent(sdata_filt.points[e], coordinate_system=coordinate_system)
  x_ext = extent["x"][1]
  y_ext = extent["y"][1]
@@ -334,7 +401,7 @@ def _render_points(
  rbga_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
  ax.imshow(rbga_image, zorder=render_params.zorder)
  cax = None
-else:
+elif method == "matplotlib":
  # original way of plotting points
  _cax = ax.scatter(
  adata[:, 0].X.flatten(),

src/spatialdata_plot/pl/render_params.py

@@ -81,6 +81,7 @@ class ShapesRenderParams:
  fill_alpha: float = 0.3
  scale: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 
@@ -97,6 +98,7 @@ class PointsRenderParams:
  alpha: float = 1.0
  size: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 

src/spatialdata_plot/pl/basic.py

@@ -160,6 +160,7 @@ def render_shapes(
  cmap: Colormap | str | None = None,
  norm: bool | Normalize = False,
  scale: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -204,6 +205,9 @@ def render_shapes(
  Colormap normalization for continuous annotations.
  scale : float | int, default 1.0
  Value to scale circles, if present.
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  **kwargs : Any
  Additional arguments to be passed to cmap and norm.
 
@@ -317,6 +321,12 @@ def render_shapes(
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -343,6 +353,7 @@ def render_shapes(
  fill_alpha=fill_alpha,
  transfunc=kwargs.get("transfunc", None),
  zorder=n_steps,
+ method=method,
  )
 
  return sdata
@@ -358,6 +369,7 @@ def render_points(
  cmap: Colormap | str | None = None,
  norm: None | Normalize = None,
  size: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -392,6 +404,9 @@ def render_points(
  Colormap normalization for continuous annotations.
  size : float | int, default 1.0
  Size of the points
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  kwargs
  Additional arguments to be passed to cmap and norm.
 
@@ -479,6 +494,12 @@ def render_points(
  if size < 0:
  raise ValueError("Parameter 'size' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -501,6 +522,7 @@ def render_points(
  transfunc=kwargs.get("transfunc", None),
  size=size,
  zorder=n_steps,
+ method=method,
  )
 
  return sdata

src/spatialdata_plot/pl/render.py

@@ -122,35 +122,6 @@ def _render_shapes(
  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,
- c=color_vector,
- render_params=render_params,
- rasterized=sc_settings._vector_friendly,
- cmap=render_params.cmap_params.cmap,
- norm=norm,
- fill_alpha=render_params.fill_alpha,
- outline_alpha=render_params.outline_alpha,
- zorder=render_params.zorder,
- # **kwargs,
- )
-
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
-
- cax = ax.add_collection(_cax)
-
- # Apply the transformation to the PatchCollection's paths
- trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
- affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
- trans = mtransforms.Affine2D(matrix=affine_trans)
-
- for path in _cax.get_paths():
- path.vertices = trans.transform(path.vertices)
 
  # Using dict.fromkeys here since set returns in arbitrary order
  # remove the color of NaN values, else it might be assigned to a category
@@ -160,6 +131,98 @@ def _render_shapes(
  else:
  palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+ # Apply the transformation to the PatchCollection's paths
+ trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
+ affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
+ trans = mtransforms.Affine2D(matrix=affine_trans)
+
+ shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
+ # Determine which method to use for rendering
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(shapes) > 100 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "matplotlib":
+ logger.info(f"Using {method}")
+ _cax = _get_collection_shape(
+ shapes=shapes,
+ s=render_params.scale,
+ c=color_vector,
+ render_params=render_params,
+ rasterized=sc_settings._vector_friendly,
+ cmap=render_params.cmap_params.cmap,
+ norm=norm,
+ fill_alpha=render_params.fill_alpha,
+ outline_alpha=render_params.outline_alpha,
+ zorder=render_params.zorder,
+ # **kwargs,
+ )
+ cax = ax.add_collection(_cax)
+
+ # Transform the paths in PatchCollection
+ for path in _cax.get_paths():
+ path.vertices = trans.transform(path.vertices)
+ cax = ax.add_collection(_cax)
+ elif method == "datashader":
+ logger.info(f"Using {method}")
+
+ # Where to put this
+ trans = mtransforms.Affine2D(matrix=affine_trans) + ax.transData
+
+ extent = get_extent(sdata.shapes[e])
+ x_ext = extent["x"][1]
+ y_ext = extent["y"][1]
+ # previous_xlim = fig_params.ax.get_xlim()
+ # previous_ylim = fig_params.ax.get_ylim()
+ x_range = [0, x_ext]
+ y_range = [0, y_ext]
+ # round because we need integers
+ plot_width = int(np.round(x_range[1] - x_range[0]))
+ plot_height = int(np.round(y_range[1] - y_range[0]))
+
+ cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_range, y_range=y_range)
+
+ _geometry = shapes["geometry"]
+ is_point = _geometry.type == "Point"
+
+ # Handle circles encoded as points with radius
+ if is_point.any(): # TODO
+ scale = shapes[is_point]["radius"] * render_params.scale
+ shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale)
+
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ # Render shapes with datashader
+ if render_params.col_for_color is not None and (
+ render_params.groups is None or len(render_params.groups) > 1
+ ):
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.by(render_params.col_for_color, ds.count()))
+ else:
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ color_key = (
+ [x[:-2] for x in color_vector.categories.values]
+ if (type(color_vector) == pd.core.arrays.categorical.Categorical)
+ and (len(color_vector.categories.values) > 1)
+ else None
+ )
+ ds_result = ds.tf.shade(
+ agg, cmap=color_vector[0][:-2], alpha=render_params.fill_alpha * 255, color_key=color_key, min_alpha=200
+ )
+
+ # Render image
+ rgba_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
+ _cax = ax.imshow(rgba_image, cmap=palette, zorder=render_params.zorder)
+ _cax.set_transform(trans)
+ cax = ax.add_image(_cax)
+
+ # Sets the limits of the colorbar to the values instead of [0, 1]
+ if not norm and not values_are_categorical:
+ _cax.set_clim(min(color_vector), max(color_vector))
+
  if not (
  len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
  ):
@@ -278,9 +341,13 @@ def _render_points(
 
  norm = copy(render_params.cmap_params.norm)
 
- # optionally render points using datashader
- # TODO: maybe move this, add heuristic
- if len(points) > 50:
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(points.shape[0]) > 10000 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "datashader":
  extent = get_extent(sdata_filt.points[e], coordinate_system=coordinate_system)
  x_ext = extent["x"][1]
  y_ext = extent["y"][1]
@@ -334,7 +401,7 @@ def _render_points(
  rbga_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
  ax.imshow(rbga_image, zorder=render_params.zorder)
  cax = None
-else:
+elif method == "matplotlib":
  # original way of plotting points
  _cax = ax.scatter(
  adata[:, 0].X.flatten(),

src/spatialdata_plot/pl/render_params.py

@@ -81,6 +81,7 @@ class ShapesRenderParams:
  fill_alpha: float = 0.3
  scale: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 
@@ -97,6 +98,7 @@ class PointsRenderParams:
  alpha: float = 1.0
  size: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 

src/spatialdata_plot/pl/basic.py

@@ -160,6 +160,7 @@ def render_shapes(
  cmap: Colormap | str | None = None,
  norm: bool | Normalize = False,
  scale: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -204,6 +205,9 @@ def render_shapes(
  Colormap normalization for continuous annotations.
  scale : float | int, default 1.0
  Value to scale circles, if present.
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  **kwargs : Any
  Additional arguments to be passed to cmap and norm.
 
@@ -317,6 +321,12 @@ def render_shapes(
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -343,6 +353,7 @@ def render_shapes(
  fill_alpha=fill_alpha,
  transfunc=kwargs.get("transfunc", None),
  zorder=n_steps,
+ method=method,
  )
 
  return sdata
@@ -358,6 +369,7 @@ def render_points(
  cmap: Colormap | str | None = None,
  norm: None | Normalize = None,
  size: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -392,6 +404,9 @@ def render_points(
  Colormap normalization for continuous annotations.
  size : float | int, default 1.0
  Size of the points
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  kwargs
  Additional arguments to be passed to cmap and norm.
 
@@ -479,6 +494,12 @@ def render_points(
  if size < 0:
  raise ValueError("Parameter 'size' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -501,6 +522,7 @@ def render_points(
  transfunc=kwargs.get("transfunc", None),
  size=size,
  zorder=n_steps,
+ method=method,
  )
 
  return sdata

src/spatialdata_plot/pl/render.py

@@ -122,35 +122,6 @@ def _render_shapes(
  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,
- c=color_vector,
- render_params=render_params,
- rasterized=sc_settings._vector_friendly,
- cmap=render_params.cmap_params.cmap,
- norm=norm,
- fill_alpha=render_params.fill_alpha,
- outline_alpha=render_params.outline_alpha,
- zorder=render_params.zorder,
- # **kwargs,
- )
-
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
-
- cax = ax.add_collection(_cax)
-
- # Apply the transformation to the PatchCollection's paths
- trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
- affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
- trans = mtransforms.Affine2D(matrix=affine_trans)
-
- for path in _cax.get_paths():
- path.vertices = trans.transform(path.vertices)
 
  # Using dict.fromkeys here since set returns in arbitrary order
  # remove the color of NaN values, else it might be assigned to a category
@@ -160,6 +131,98 @@ def _render_shapes(
  else:
  palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+ # Apply the transformation to the PatchCollection's paths
+ trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
+ affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
+ trans = mtransforms.Affine2D(matrix=affine_trans)
+
+ shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
+ # Determine which method to use for rendering
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(shapes) > 100 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "matplotlib":
+ logger.info(f"Using {method}")
+ _cax = _get_collection_shape(
+ shapes=shapes,
+ s=render_params.scale,
+ c=color_vector,
+ render_params=render_params,
+ rasterized=sc_settings._vector_friendly,
+ cmap=render_params.cmap_params.cmap,
+ norm=norm,
+ fill_alpha=render_params.fill_alpha,
+ outline_alpha=render_params.outline_alpha,
+ zorder=render_params.zorder,
+ # **kwargs,
+ )
+ cax = ax.add_collection(_cax)
+
+ # Transform the paths in PatchCollection
+ for path in _cax.get_paths():
+ path.vertices = trans.transform(path.vertices)
+ cax = ax.add_collection(_cax)
+ elif method == "datashader":
+ logger.info(f"Using {method}")
+
+ # Where to put this
+ trans = mtransforms.Affine2D(matrix=affine_trans) + ax.transData
+
+ extent = get_extent(sdata.shapes[e])
+ x_ext = extent["x"][1]
+ y_ext = extent["y"][1]
+ # previous_xlim = fig_params.ax.get_xlim()
+ # previous_ylim = fig_params.ax.get_ylim()
+ x_range = [0, x_ext]
+ y_range = [0, y_ext]
+ # round because we need integers
+ plot_width = int(np.round(x_range[1] - x_range[0]))
+ plot_height = int(np.round(y_range[1] - y_range[0]))
+
+ cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_range, y_range=y_range)
+
+ _geometry = shapes["geometry"]
+ is_point = _geometry.type == "Point"
+
+ # Handle circles encoded as points with radius
+ if is_point.any(): # TODO
+ scale = shapes[is_point]["radius"] * render_params.scale
+ shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale)
+
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ # Render shapes with datashader
+ if render_params.col_for_color is not None and (
+ render_params.groups is None or len(render_params.groups) > 1
+ ):
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.by(render_params.col_for_color, ds.count()))
+ else:
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ color_key = (
+ [x[:-2] for x in color_vector.categories.values]
+ if (type(color_vector) == pd.core.arrays.categorical.Categorical)
+ and (len(color_vector.categories.values) > 1)
+ else None
+ )
+ ds_result = ds.tf.shade(
+ agg, cmap=color_vector[0][:-2], alpha=render_params.fill_alpha * 255, color_key=color_key, min_alpha=200
+ )
+
+ # Render image
+ rgba_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
+ _cax = ax.imshow(rgba_image, cmap=palette, zorder=render_params.zorder)
+ _cax.set_transform(trans)
+ cax = ax.add_image(_cax)
+
+ # Sets the limits of the colorbar to the values instead of [0, 1]
+ if not norm and not values_are_categorical:
+ _cax.set_clim(min(color_vector), max(color_vector))
+
  if not (
  len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
  ):
@@ -278,9 +341,13 @@ def _render_points(
 
  norm = copy(render_params.cmap_params.norm)
 
- # optionally render points using datashader
- # TODO: maybe move this, add heuristic
- if len(points) > 50:
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(points.shape[0]) > 10000 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "datashader":
  extent = get_extent(sdata_filt.points[e], coordinate_system=coordinate_system)
  x_ext = extent["x"][1]
  y_ext = extent["y"][1]
@@ -334,7 +401,7 @@ def _render_points(
  rbga_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
  ax.imshow(rbga_image, zorder=render_params.zorder)
  cax = None
-else:
+elif method == "matplotlib":
  # original way of plotting points
  _cax = ax.scatter(
  adata[:, 0].X.flatten(),

src/spatialdata_plot/pl/render_params.py

@@ -81,6 +81,7 @@ class ShapesRenderParams:
  fill_alpha: float = 0.3
  scale: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 
@@ -97,6 +98,7 @@ class PointsRenderParams:
  alpha: float = 1.0
  size: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 

src/spatialdata_plot/pl/basic.py

@@ -160,6 +160,7 @@ def render_shapes(
  cmap: Colormap | str | None = None,
  norm: bool | Normalize = False,
  scale: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -204,6 +205,9 @@ def render_shapes(
  Colormap normalization for continuous annotations.
  scale : float | int, default 1.0
  Value to scale circles, if present.
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  **kwargs : Any
  Additional arguments to be passed to cmap and norm.
 
@@ -317,6 +321,12 @@ def render_shapes(
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -343,6 +353,7 @@ def render_shapes(
  fill_alpha=fill_alpha,
  transfunc=kwargs.get("transfunc", None),
  zorder=n_steps,
+ method=method,
  )
 
  return sdata
@@ -358,6 +369,7 @@ def render_points(
  cmap: Colormap | str | None = None,
  norm: None | Normalize = None,
  size: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -392,6 +404,9 @@ def render_points(
  Colormap normalization for continuous annotations.
  size : float | int, default 1.0
  Size of the points
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  kwargs
  Additional arguments to be passed to cmap and norm.
 
@@ -479,6 +494,12 @@ def render_points(
  if size < 0:
  raise ValueError("Parameter 'size' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -501,6 +522,7 @@ def render_points(
  transfunc=kwargs.get("transfunc", None),
  size=size,
  zorder=n_steps,
+ method=method,
  )
 
  return sdata

src/spatialdata_plot/pl/render.py

@@ -122,35 +122,6 @@ def _render_shapes(
  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,
- c=color_vector,
- render_params=render_params,
- rasterized=sc_settings._vector_friendly,
- cmap=render_params.cmap_params.cmap,
- norm=norm,
- fill_alpha=render_params.fill_alpha,
- outline_alpha=render_params.outline_alpha,
- zorder=render_params.zorder,
- # **kwargs,
- )
-
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
-
- cax = ax.add_collection(_cax)
-
- # Apply the transformation to the PatchCollection's paths
- trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
- affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
- trans = mtransforms.Affine2D(matrix=affine_trans)
-
- for path in _cax.get_paths():
- path.vertices = trans.transform(path.vertices)
 
  # Using dict.fromkeys here since set returns in arbitrary order
  # remove the color of NaN values, else it might be assigned to a category
@@ -160,6 +131,98 @@ def _render_shapes(
  else:
  palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+ # Apply the transformation to the PatchCollection's paths
+ trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
+ affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
+ trans = mtransforms.Affine2D(matrix=affine_trans)
+
+ shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
+ # Determine which method to use for rendering
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(shapes) > 100 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "matplotlib":
+ logger.info(f"Using {method}")
+ _cax = _get_collection_shape(
+ shapes=shapes,
+ s=render_params.scale,
+ c=color_vector,
+ render_params=render_params,
+ rasterized=sc_settings._vector_friendly,
+ cmap=render_params.cmap_params.cmap,
+ norm=norm,
+ fill_alpha=render_params.fill_alpha,
+ outline_alpha=render_params.outline_alpha,
+ zorder=render_params.zorder,
+ # **kwargs,
+ )
+ cax = ax.add_collection(_cax)
+
+ # Transform the paths in PatchCollection
+ for path in _cax.get_paths():
+ path.vertices = trans.transform(path.vertices)
+ cax = ax.add_collection(_cax)
+ elif method == "datashader":
+ logger.info(f"Using {method}")
+
+ # Where to put this
+ trans = mtransforms.Affine2D(matrix=affine_trans) + ax.transData
+
+ extent = get_extent(sdata.shapes[e])
+ x_ext = extent["x"][1]
+ y_ext = extent["y"][1]
+ # previous_xlim = fig_params.ax.get_xlim()
+ # previous_ylim = fig_params.ax.get_ylim()
+ x_range = [0, x_ext]
+ y_range = [0, y_ext]
+ # round because we need integers
+ plot_width = int(np.round(x_range[1] - x_range[0]))
+ plot_height = int(np.round(y_range[1] - y_range[0]))
+
+ cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_range, y_range=y_range)
+
+ _geometry = shapes["geometry"]
+ is_point = _geometry.type == "Point"
+
+ # Handle circles encoded as points with radius
+ if is_point.any(): # TODO
+ scale = shapes[is_point]["radius"] * render_params.scale
+ shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale)
+
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ # Render shapes with datashader
+ if render_params.col_for_color is not None and (
+ render_params.groups is None or len(render_params.groups) > 1
+ ):
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.by(render_params.col_for_color, ds.count()))
+ else:
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ color_key = (
+ [x[:-2] for x in color_vector.categories.values]
+ if (type(color_vector) == pd.core.arrays.categorical.Categorical)
+ and (len(color_vector.categories.values) > 1)
+ else None
+ )
+ ds_result = ds.tf.shade(
+ agg, cmap=color_vector[0][:-2], alpha=render_params.fill_alpha * 255, color_key=color_key, min_alpha=200
+ )
+
+ # Render image
+ rgba_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
+ _cax = ax.imshow(rgba_image, cmap=palette, zorder=render_params.zorder)
+ _cax.set_transform(trans)
+ cax = ax.add_image(_cax)
+
+ # Sets the limits of the colorbar to the values instead of [0, 1]
+ if not norm and not values_are_categorical:
+ _cax.set_clim(min(color_vector), max(color_vector))
+
  if not (
  len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
  ):
@@ -278,9 +341,13 @@ def _render_points(
 
  norm = copy(render_params.cmap_params.norm)
 
- # optionally render points using datashader
- # TODO: maybe move this, add heuristic
- if len(points) > 50:
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(points.shape[0]) > 10000 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "datashader":
  extent = get_extent(sdata_filt.points[e], coordinate_system=coordinate_system)
  x_ext = extent["x"][1]
  y_ext = extent["y"][1]
@@ -334,7 +401,7 @@ def _render_points(
  rbga_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
  ax.imshow(rbga_image, zorder=render_params.zorder)
  cax = None
-else:
+elif method == "matplotlib":
  # original way of plotting points
  _cax = ax.scatter(
  adata[:, 0].X.flatten(),

src/spatialdata_plot/pl/render_params.py

@@ -81,6 +81,7 @@ class ShapesRenderParams:
  fill_alpha: float = 0.3
  scale: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 
@@ -97,6 +98,7 @@ class PointsRenderParams:
  alpha: float = 1.0
  size: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 

src/spatialdata_plot/pl/basic.py

@@ -160,6 +160,7 @@ def render_shapes(
  cmap: Colormap | str | None = None,
  norm: bool | Normalize = False,
  scale: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -204,6 +205,9 @@ def render_shapes(
  Colormap normalization for continuous annotations.
  scale : float | int, default 1.0
  Value to scale circles, if present.
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  **kwargs : Any
  Additional arguments to be passed to cmap and norm.
 
@@ -317,6 +321,12 @@ def render_shapes(
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -343,6 +353,7 @@ def render_shapes(
  fill_alpha=fill_alpha,
  transfunc=kwargs.get("transfunc", None),
  zorder=n_steps,
+ method=method,
  )
 
  return sdata
@@ -358,6 +369,7 @@ def render_points(
  cmap: Colormap | str | None = None,
  norm: None | Normalize = None,
  size: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -392,6 +404,9 @@ def render_points(
  Colormap normalization for continuous annotations.
  size : float | int, default 1.0
  Size of the points
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  kwargs
  Additional arguments to be passed to cmap and norm.
 
@@ -479,6 +494,12 @@ def render_points(
  if size < 0:
  raise ValueError("Parameter 'size' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -501,6 +522,7 @@ def render_points(
  transfunc=kwargs.get("transfunc", None),
  size=size,
  zorder=n_steps,
+ method=method,
  )
 
  return sdata

src/spatialdata_plot/pl/render.py

@@ -122,35 +122,6 @@ def _render_shapes(
  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,
- c=color_vector,
- render_params=render_params,
- rasterized=sc_settings._vector_friendly,
- cmap=render_params.cmap_params.cmap,
- norm=norm,
- fill_alpha=render_params.fill_alpha,
- outline_alpha=render_params.outline_alpha,
- zorder=render_params.zorder,
- # **kwargs,
- )
-
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
-
- cax = ax.add_collection(_cax)
-
- # Apply the transformation to the PatchCollection's paths
- trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
- affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
- trans = mtransforms.Affine2D(matrix=affine_trans)
-
- for path in _cax.get_paths():
- path.vertices = trans.transform(path.vertices)
 
  # Using dict.fromkeys here since set returns in arbitrary order
  # remove the color of NaN values, else it might be assigned to a category
@@ -160,6 +131,98 @@ def _render_shapes(
  else:
  palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+ # Apply the transformation to the PatchCollection's paths
+ trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
+ affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
+ trans = mtransforms.Affine2D(matrix=affine_trans)
+
+ shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
+ # Determine which method to use for rendering
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(shapes) > 100 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "matplotlib":
+ logger.info(f"Using {method}")
+ _cax = _get_collection_shape(
+ shapes=shapes,
+ s=render_params.scale,
+ c=color_vector,
+ render_params=render_params,
+ rasterized=sc_settings._vector_friendly,
+ cmap=render_params.cmap_params.cmap,
+ norm=norm,
+ fill_alpha=render_params.fill_alpha,
+ outline_alpha=render_params.outline_alpha,
+ zorder=render_params.zorder,
+ # **kwargs,
+ )
+ cax = ax.add_collection(_cax)
+
+ # Transform the paths in PatchCollection
+ for path in _cax.get_paths():
+ path.vertices = trans.transform(path.vertices)
+ cax = ax.add_collection(_cax)
+ elif method == "datashader":
+ logger.info(f"Using {method}")
+
+ # Where to put this
+ trans = mtransforms.Affine2D(matrix=affine_trans) + ax.transData
+
+ extent = get_extent(sdata.shapes[e])
+ x_ext = extent["x"][1]
+ y_ext = extent["y"][1]
+ # previous_xlim = fig_params.ax.get_xlim()
+ # previous_ylim = fig_params.ax.get_ylim()
+ x_range = [0, x_ext]
+ y_range = [0, y_ext]
+ # round because we need integers
+ plot_width = int(np.round(x_range[1] - x_range[0]))
+ plot_height = int(np.round(y_range[1] - y_range[0]))
+
+ cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_range, y_range=y_range)
+
+ _geometry = shapes["geometry"]
+ is_point = _geometry.type == "Point"
+
+ # Handle circles encoded as points with radius
+ if is_point.any(): # TODO
+ scale = shapes[is_point]["radius"] * render_params.scale
+ shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale)
+
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ # Render shapes with datashader
+ if render_params.col_for_color is not None and (
+ render_params.groups is None or len(render_params.groups) > 1
+ ):
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.by(render_params.col_for_color, ds.count()))
+ else:
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ color_key = (
+ [x[:-2] for x in color_vector.categories.values]
+ if (type(color_vector) == pd.core.arrays.categorical.Categorical)
+ and (len(color_vector.categories.values) > 1)
+ else None
+ )
+ ds_result = ds.tf.shade(
+ agg, cmap=color_vector[0][:-2], alpha=render_params.fill_alpha * 255, color_key=color_key, min_alpha=200
+ )
+
+ # Render image
+ rgba_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
+ _cax = ax.imshow(rgba_image, cmap=palette, zorder=render_params.zorder)
+ _cax.set_transform(trans)
+ cax = ax.add_image(_cax)
+
+ # Sets the limits of the colorbar to the values instead of [0, 1]
+ if not norm and not values_are_categorical:
+ _cax.set_clim(min(color_vector), max(color_vector))
+
  if not (
  len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
  ):
@@ -278,9 +341,13 @@ def _render_points(
 
  norm = copy(render_params.cmap_params.norm)
 
- # optionally render points using datashader
- # TODO: maybe move this, add heuristic
- if len(points) > 50:
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(points.shape[0]) > 10000 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "datashader":
  extent = get_extent(sdata_filt.points[e], coordinate_system=coordinate_system)
  x_ext = extent["x"][1]
  y_ext = extent["y"][1]
@@ -334,7 +401,7 @@ def _render_points(
  rbga_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
  ax.imshow(rbga_image, zorder=render_params.zorder)
  cax = None
-else:
+elif method == "matplotlib":
  # original way of plotting points
  _cax = ax.scatter(
  adata[:, 0].X.flatten(),

src/spatialdata_plot/pl/render_params.py

@@ -81,6 +81,7 @@ class ShapesRenderParams:
  fill_alpha: float = 0.3
  scale: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 
@@ -97,6 +98,7 @@ class PointsRenderParams:
  alpha: float = 1.0
  size: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 

src/spatialdata_plot/pl/basic.py

@@ -160,6 +160,7 @@ def render_shapes(
  cmap: Colormap | str | None = None,
  norm: bool | Normalize = False,
  scale: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -204,6 +205,9 @@ def render_shapes(
  Colormap normalization for continuous annotations.
  scale : float | int, default 1.0
  Value to scale circles, if present.
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  **kwargs : Any
  Additional arguments to be passed to cmap and norm.
 
@@ -317,6 +321,12 @@ def render_shapes(
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -343,6 +353,7 @@ def render_shapes(
  fill_alpha=fill_alpha,
  transfunc=kwargs.get("transfunc", None),
  zorder=n_steps,
+ method=method,
  )
 
  return sdata
@@ -358,6 +369,7 @@ def render_points(
  cmap: Colormap | str | None = None,
  norm: None | Normalize = None,
  size: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -392,6 +404,9 @@ def render_points(
  Colormap normalization for continuous annotations.
  size : float | int, default 1.0
  Size of the points
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  kwargs
  Additional arguments to be passed to cmap and norm.
 
@@ -479,6 +494,12 @@ def render_points(
  if size < 0:
  raise ValueError("Parameter 'size' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -501,6 +522,7 @@ def render_points(
  transfunc=kwargs.get("transfunc", None),
  size=size,
  zorder=n_steps,
+ method=method,
  )
 
  return sdata

src/spatialdata_plot/pl/render.py

@@ -122,35 +122,6 @@ def _render_shapes(
  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,
- c=color_vector,
- render_params=render_params,
- rasterized=sc_settings._vector_friendly,
- cmap=render_params.cmap_params.cmap,
- norm=norm,
- fill_alpha=render_params.fill_alpha,
- outline_alpha=render_params.outline_alpha,
- zorder=render_params.zorder,
- # **kwargs,
- )
-
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
-
- cax = ax.add_collection(_cax)
-
- # Apply the transformation to the PatchCollection's paths
- trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
- affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
- trans = mtransforms.Affine2D(matrix=affine_trans)
-
- for path in _cax.get_paths():
- path.vertices = trans.transform(path.vertices)
 
  # Using dict.fromkeys here since set returns in arbitrary order
  # remove the color of NaN values, else it might be assigned to a category
@@ -160,6 +131,98 @@ def _render_shapes(
  else:
  palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+ # Apply the transformation to the PatchCollection's paths
+ trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
+ affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
+ trans = mtransforms.Affine2D(matrix=affine_trans)
+
+ shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
+ # Determine which method to use for rendering
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(shapes) > 100 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "matplotlib":
+ logger.info(f"Using {method}")
+ _cax = _get_collection_shape(
+ shapes=shapes,
+ s=render_params.scale,
+ c=color_vector,
+ render_params=render_params,
+ rasterized=sc_settings._vector_friendly,
+ cmap=render_params.cmap_params.cmap,
+ norm=norm,
+ fill_alpha=render_params.fill_alpha,
+ outline_alpha=render_params.outline_alpha,
+ zorder=render_params.zorder,
+ # **kwargs,
+ )
+ cax = ax.add_collection(_cax)
+
+ # Transform the paths in PatchCollection
+ for path in _cax.get_paths():
+ path.vertices = trans.transform(path.vertices)
+ cax = ax.add_collection(_cax)
+ elif method == "datashader":
+ logger.info(f"Using {method}")
+
+ # Where to put this
+ trans = mtransforms.Affine2D(matrix=affine_trans) + ax.transData
+
+ extent = get_extent(sdata.shapes[e])
+ x_ext = extent["x"][1]
+ y_ext = extent["y"][1]
+ # previous_xlim = fig_params.ax.get_xlim()
+ # previous_ylim = fig_params.ax.get_ylim()
+ x_range = [0, x_ext]
+ y_range = [0, y_ext]
+ # round because we need integers
+ plot_width = int(np.round(x_range[1] - x_range[0]))
+ plot_height = int(np.round(y_range[1] - y_range[0]))
+
+ cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_range, y_range=y_range)
+
+ _geometry = shapes["geometry"]
+ is_point = _geometry.type == "Point"
+
+ # Handle circles encoded as points with radius
+ if is_point.any(): # TODO
+ scale = shapes[is_point]["radius"] * render_params.scale
+ shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale)
+
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ # Render shapes with datashader
+ if render_params.col_for_color is not None and (
+ render_params.groups is None or len(render_params.groups) > 1
+ ):
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.by(render_params.col_for_color, ds.count()))
+ else:
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ color_key = (
+ [x[:-2] for x in color_vector.categories.values]
+ if (type(color_vector) == pd.core.arrays.categorical.Categorical)
+ and (len(color_vector.categories.values) > 1)
+ else None
+ )
+ ds_result = ds.tf.shade(
+ agg, cmap=color_vector[0][:-2], alpha=render_params.fill_alpha * 255, color_key=color_key, min_alpha=200
+ )
+
+ # Render image
+ rgba_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
+ _cax = ax.imshow(rgba_image, cmap=palette, zorder=render_params.zorder)
+ _cax.set_transform(trans)
+ cax = ax.add_image(_cax)
+
+ # Sets the limits of the colorbar to the values instead of [0, 1]
+ if not norm and not values_are_categorical:
+ _cax.set_clim(min(color_vector), max(color_vector))
+
  if not (
  len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
  ):
@@ -278,9 +341,13 @@ def _render_points(
 
  norm = copy(render_params.cmap_params.norm)
 
- # optionally render points using datashader
- # TODO: maybe move this, add heuristic
- if len(points) > 50:
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(points.shape[0]) > 10000 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "datashader":
  extent = get_extent(sdata_filt.points[e], coordinate_system=coordinate_system)
  x_ext = extent["x"][1]
  y_ext = extent["y"][1]
@@ -334,7 +401,7 @@ def _render_points(
  rbga_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
  ax.imshow(rbga_image, zorder=render_params.zorder)
  cax = None
-else:
+elif method == "matplotlib":
  # original way of plotting points
  _cax = ax.scatter(
  adata[:, 0].X.flatten(),

src/spatialdata_plot/pl/render_params.py

@@ -81,6 +81,7 @@ class ShapesRenderParams:
  fill_alpha: float = 0.3
  scale: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 
@@ -97,6 +98,7 @@ class PointsRenderParams:
  alpha: float = 1.0
  size: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 

src/spatialdata_plot/pl/basic.py

@@ -160,6 +160,7 @@ def render_shapes(
  cmap: Colormap | str | None = None,
  norm: bool | Normalize = False,
  scale: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -204,6 +205,9 @@ def render_shapes(
  Colormap normalization for continuous annotations.
  scale : float | int, default 1.0
  Value to scale circles, if present.
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  **kwargs : Any
  Additional arguments to be passed to cmap and norm.
 
@@ -317,6 +321,12 @@ def render_shapes(
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -343,6 +353,7 @@ def render_shapes(
  fill_alpha=fill_alpha,
  transfunc=kwargs.get("transfunc", None),
  zorder=n_steps,
+ method=method,
  )
 
  return sdata
@@ -358,6 +369,7 @@ def render_points(
  cmap: Colormap | str | None = None,
  norm: None | Normalize = None,
  size: float | int = 1.0,
+ method: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
  """
@@ -392,6 +404,9 @@ def render_points(
  Colormap normalization for continuous annotations.
  size : float | int, default 1.0
  Size of the points
+ method : str | None, optional
+ Whether to use 'matplotlib' and 'datashader'. When None, the method is
+ chosen based on the size of the data.
  kwargs
  Additional arguments to be passed to cmap and norm.
 
@@ -479,6 +494,12 @@ def render_points(
  if size < 0:
  raise ValueError("Parameter 'size' must be a positive number.")
 
+ if method is not None:
+ if not isinstance(method, str):
+ raise TypeError("Parameter 'method' must be a string.")
+ if method not in ["matplotlib", "datashader"]:
+ raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
+
  sdata = self._copy()
  sdata = _verify_plotting_tree(sdata)
  n_steps = len(sdata.plotting_tree.keys())
@@ -501,6 +522,7 @@ def render_points(
  transfunc=kwargs.get("transfunc", None),
  size=size,
  zorder=n_steps,
+ method=method,
  )
 
  return sdata

src/spatialdata_plot/pl/render.py

@@ -122,35 +122,6 @@ def _render_shapes(
  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,
- c=color_vector,
- render_params=render_params,
- rasterized=sc_settings._vector_friendly,
- cmap=render_params.cmap_params.cmap,
- norm=norm,
- fill_alpha=render_params.fill_alpha,
- outline_alpha=render_params.outline_alpha,
- zorder=render_params.zorder,
- # **kwargs,
- )
-
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
-
- cax = ax.add_collection(_cax)
-
- # Apply the transformation to the PatchCollection's paths
- trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
- affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
- trans = mtransforms.Affine2D(matrix=affine_trans)
-
- for path in _cax.get_paths():
- path.vertices = trans.transform(path.vertices)
 
  # Using dict.fromkeys here since set returns in arbitrary order
  # remove the color of NaN values, else it might be assigned to a category
@@ -160,6 +131,98 @@ def _render_shapes(
  else:
  palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+ # Apply the transformation to the PatchCollection's paths
+ trans = get_transformation(sdata_filt.shapes[e], get_all=True)[coordinate_system]
+ affine_trans = trans.to_affine_matrix(input_axes=("x", "y"), output_axes=("x", "y"))
+ trans = mtransforms.Affine2D(matrix=affine_trans)
+
+ shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
+
+ # Determine which method to use for rendering
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(shapes) > 100 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "matplotlib":
+ logger.info(f"Using {method}")
+ _cax = _get_collection_shape(
+ shapes=shapes,
+ s=render_params.scale,
+ c=color_vector,
+ render_params=render_params,
+ rasterized=sc_settings._vector_friendly,
+ cmap=render_params.cmap_params.cmap,
+ norm=norm,
+ fill_alpha=render_params.fill_alpha,
+ outline_alpha=render_params.outline_alpha,
+ zorder=render_params.zorder,
+ # **kwargs,
+ )
+ cax = ax.add_collection(_cax)
+
+ # Transform the paths in PatchCollection
+ for path in _cax.get_paths():
+ path.vertices = trans.transform(path.vertices)
+ cax = ax.add_collection(_cax)
+ elif method == "datashader":
+ logger.info(f"Using {method}")
+
+ # Where to put this
+ trans = mtransforms.Affine2D(matrix=affine_trans) + ax.transData
+
+ extent = get_extent(sdata.shapes[e])
+ x_ext = extent["x"][1]
+ y_ext = extent["y"][1]
+ # previous_xlim = fig_params.ax.get_xlim()
+ # previous_ylim = fig_params.ax.get_ylim()
+ x_range = [0, x_ext]
+ y_range = [0, y_ext]
+ # round because we need integers
+ plot_width = int(np.round(x_range[1] - x_range[0]))
+ plot_height = int(np.round(y_range[1] - y_range[0]))
+
+ cvs = ds.Canvas(plot_width=plot_width, plot_height=plot_height, x_range=x_range, y_range=y_range)
+
+ _geometry = shapes["geometry"]
+ is_point = _geometry.type == "Point"
+
+ # Handle circles encoded as points with radius
+ if is_point.any(): # TODO
+ scale = shapes[is_point]["radius"] * render_params.scale
+ shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale)
+
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ # Render shapes with datashader
+ if render_params.col_for_color is not None and (
+ render_params.groups is None or len(render_params.groups) > 1
+ ):
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.by(render_params.col_for_color, ds.count()))
+ else:
+ agg = cvs.polygons(shapes, geometry="geometry", agg=ds.count())
+
+ color_key = (
+ [x[:-2] for x in color_vector.categories.values]
+ if (type(color_vector) == pd.core.arrays.categorical.Categorical)
+ and (len(color_vector.categories.values) > 1)
+ else None
+ )
+ ds_result = ds.tf.shade(
+ agg, cmap=color_vector[0][:-2], alpha=render_params.fill_alpha * 255, color_key=color_key, min_alpha=200
+ )
+
+ # Render image
+ rgba_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
+ _cax = ax.imshow(rgba_image, cmap=palette, zorder=render_params.zorder)
+ _cax.set_transform(trans)
+ cax = ax.add_image(_cax)
+
+ # Sets the limits of the colorbar to the values instead of [0, 1]
+ if not norm and not values_are_categorical:
+ _cax.set_clim(min(color_vector), max(color_vector))
+
  if not (
  len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
  ):
@@ -278,9 +341,13 @@ def _render_points(
 
  norm = copy(render_params.cmap_params.norm)
 
- # optionally render points using datashader
- # TODO: maybe move this, add heuristic
- if len(points) > 50:
+ method = render_params.method
+ if method is None:
+ method = "datashader" if len(points.shape[0]) > 10000 else "matplotlib"
+ elif method not in ["matplotlib", "datashader"]:
+ raise ValueError("Method must be either 'matplotlib' or 'datashader'.")
+
+ if method == "datashader":
  extent = get_extent(sdata_filt.points[e], coordinate_system=coordinate_system)
  x_ext = extent["x"][1]
  y_ext = extent["y"][1]
@@ -334,7 +401,7 @@ def _render_points(
  rbga_image = np.transpose(ds_result.to_numpy().base, (0, 1, 2))
  ax.imshow(rbga_image, zorder=render_params.zorder)
  cax = None
-else:
+elif method == "matplotlib":
  # original way of plotting points
  _cax = ax.scatter(
  adata[:, 0].X.flatten(),

src/spatialdata_plot/pl/render_params.py

@@ -81,6 +81,7 @@ class ShapesRenderParams:
  fill_alpha: float = 0.3
  scale: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None
 
 
@@ -97,6 +98,7 @@ class PointsRenderParams:
  alpha: float = 1.0
  size: float = 1.0
  transfunc: Callable[[float], float] | None = None
+ method: str | None = None
  zorder: int | None = None