Colorbar no longer autoscales to [0, 1] (#155)

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

Author: timtreis

CHANGELOG.md

@@ -12,14 +12,15 @@ and this project adheres to [Semantic Versioning][].
 
 ### Added
 
--   Multipolygons are now handled correctly (#93)
 -   Can now scale shapes (#152)
 -   Can now plot columns from GeoDataFrame (#149)
 
 ### Fixed
 
+-   Multipolygons are now handled correctly (#93)
 -   Legend order is now deterministic (#143)
 -   Images no longer normalised by default (#150)
+-   Colorbar no longer autoscales to [0, 1] (#155)
 
 ## [0.0.4] - 2023-08-11
 

src/spatialdata_plot/pl/basic.py

@@ -152,7 +152,7 @@ def render_shapes(
         layer: str | None = None,
         palette: ListedColormap | str | None = None,
         cmap: Colormap | str | None = None,
-        norm: None | Normalize = None,
+        norm: bool | Normalize = False,
         na_color: str | tuple[float, ...] | None = "lightgrey",
         outline_alpha: float = 1.0,
         fill_alpha: float = 1.0,

src/spatialdata_plot/pl/render.py

@@ -90,8 +90,10 @@ def _render_shapes(
             alpha=render_params.fill_alpha,
         )
 
+        values_are_categorical = color_source_vector is not None
+
         # color_source_vector is None when the values aren't categorical
-        if color_source_vector is None and render_params.transfunc is not None:
+        if values_are_categorical and render_params.transfunc is not None:
             color_vector = render_params.transfunc(color_vector)
 
         norm = copy(render_params.cmap_params.norm)
@@ -113,15 +115,17 @@ def _render_shapes(
             # **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)
 
         # 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
         )
 
-        # print(len(set(color_vector)) == 1)
-        # print(set(color_source_vector[0]) == to_hex(render_params.cmap_params.na_color))
         if not (
             len(set(color_vector)) == 1 and list(set(color_vector))[0] == to_hex(render_params.cmap_params.na_color)
         ):
@@ -190,7 +194,7 @@ def _render_points(
                     key=render_params.color,
                     palette=render_params.palette,
                 )
-        # print(p)
+
         color_source_vector, color_vector, _ = _set_color_source_vec(
             sdata=sdata_filt,
             element=points,

src/spatialdata_plot/pl/utils.py

@@ -564,7 +564,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
     cmap: Colormap | str | None = None,
-    norm: Normalize | Sequence[Normalize] | None = None,
+    norm: Normalize | bool = False,
     na_color: str | tuple[float, ...] = (0.0, 0.0, 0.0, 0.0),
     vmin: float | None = None,
     vmax: float | None = None,
@@ -576,6 +576,8 @@ def _prepare_cmap_norm(
 
     if isinstance(norm, Normalize):
         pass  # TODO
+    elif not norm:
+        pass
     elif vcenter is None:
         norm = Normalize(vmin=vmin, vmax=vmax)
     else:

tests/_images/Shapes_can_color_from_geodataframe.png

@@ -100,3 +100,11 @@ def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
 
     def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_shapes(elements="blobs_circles", scale=0.5).pl.show()
+
+    def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
+        sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
+        sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
+
+    def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
+        sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
+        sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()