datashader speedup and bugfixes (#309)

Co-authored-by: Sonja Stockhaus <[email protected]>
Co-authored-by: Luca Marconato <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Tim Treis <[email protected]>

Author: 5 people

CHANGELOG.md

@@ -8,18 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
-## [0.2.7] - 2024-09-04
+## [0.2.7] - tbd
 
 ### Added
 
--
+-   The user can now specify `datashader_reduction` to control the rendering behaviour (#309)
+-   Rendering outlines of shapes with datashader works now (#309)
 
 ### Changed
 
 -
 
 ### Fixed
 
+-   datashader now uses canvas size = image size which speeds up the rendering (#309)
+-   datashader now uses the `linear` as interpolation method for colormaps instead of the default `eq_hist` to make it equivalent to matplotlib (#309)
+-   point sizes of datashader now agree with matplotlib also when dpi != 100 (#309)
 -   Giving a custom colormap when rendering a multiscale image now works (#586)
 
 ## [0.2.6] - 2024-09-04

src/spatialdata_plot/pl/basic.py

@@ -227,8 +227,15 @@ def render_shapes(
             Name of the table containing the color(s) columns. If one name is given than the table is used for each
             spatial element to be plotted if the table annotates it. If you want to use different tables for particular
             elements, as specified under element.
+
         **kwargs : Any
-            Additional arguments to be passed to cmap and norm.
+            Additional arguments for customization. This can include:
+
+            datashader_reduction : Literal[
+                "sum", "mean", "any", "count", "std", "var", "max", "min"
+            ], default: "sum"
+                Reduction method for datashader when coloring by continuous values. Defaults to 'sum'.
+
 
         Notes
         -----
@@ -264,13 +271,13 @@ def render_shapes(
             scale=scale,
             table_name=table_name,
             method=method,
+            ds_reduction=kwargs.get("datashader_reduction", None),
         )
 
         sdata = self._copy()
         sdata = _verify_plotting_tree(sdata)
         n_steps = len(sdata.plotting_tree.keys())
         outline_params = _set_outline(outline_alpha > 0, outline_width, outline_color)
-
         for element, param_values in params_dict.items():
             cmap_params = _prepare_cmap_norm(
                 cmap=cmap,
@@ -291,7 +298,8 @@ def render_shapes(
                 transfunc=kwargs.get("transfunc", None),
                 table_name=param_values["table_name"],
                 zorder=n_steps,
-                method=method,
+                method=param_values["method"],
+                ds_reduction=param_values["ds_reduction"],
             )
             n_steps += 1
 
@@ -361,8 +369,14 @@ def render_points(
             Name of the table containing the color(s) columns. If one name is given than the table is used for each
             spatial element to be plotted if the table annotates it. If you want to use different tables for particular
             elements, as specified under element.
-        kwargs
-            Additional arguments to be passed to cmap and norm.
+
+        **kwargs : Any
+            Additional arguments for customization. This can include:
+
+            datashader_reduction : Literal[
+                "sum", "mean", "any", "count", "std", "var", "max", "min"
+            ], default: "sum"
+                Reduction method for datashader when coloring by continuous values. Defaults to 'sum'.
 
         Returns
         -------
@@ -388,6 +402,7 @@ def render_points(
             norm=norm,
             size=size,
             table_name=table_name,
+            ds_reduction=kwargs.get("datashader_reduction", None),
         )
 
         if method is not None:
@@ -419,6 +434,7 @@ def render_points(
                 table_name=param_values["table_name"],
                 zorder=n_steps,
                 method=method,
+                ds_reduction=param_values["ds_reduction"],
             )
             n_steps += 1