Fix clims when plotting shapes element annotations with matplotlib rendering (#368)

Author: melonora

src/spatialdata_plot/pl/basic.py

@@ -241,6 +241,13 @@ def render_shapes(
         sd.SpatialData
             The modified SpatialData object with the rendered shapes.
         """
+        # TODO add Normalize object in tutorial notebook and point to that notebook here
+        if "vmin" in kwargs or "vmax" in kwargs:
+            warnings.warn(
+                "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         params_dict = _validate_shape_render_params(
             self._sdata,
             element=element,
@@ -269,7 +276,6 @@ def render_shapes(
                 cmap=cmap,
                 norm=norm,
                 na_color=params_dict[element]["na_color"],  # type: ignore[arg-type]
-                **kwargs,
             )
             sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
                 element=element,
@@ -363,6 +369,13 @@ def render_points(
         sd.SpatialData
             The modified SpatialData object with the rendered shapes.
         """
+        # TODO add Normalize object in tutorial notebook and point to that notebook here
+        if "vmin" in kwargs or "vmax" in kwargs:
+            warnings.warn(
+                "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         params_dict = _validate_points_render_params(
             self._sdata,
             element=element,
@@ -392,7 +405,6 @@ def render_points(
                 cmap=cmap,
                 norm=norm,
                 na_color=param_values["na_color"],  # type: ignore[arg-type]
-                **kwargs,
             )
             sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
                 element=element,
@@ -473,6 +485,13 @@ def render_images(
         sd.SpatialData
             The SpatialData object with the rendered images.
         """
+        # TODO add Normalize object in tutorial notebook and point to that notebook here
+        if "vmin" in kwargs or "vmax" in kwargs:
+            warnings.warn(
+                "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         params_dict = _validate_image_render_params(
             self._sdata,
             element=element,
@@ -498,7 +517,6 @@ def render_images(
                         cmap=c,
                         norm=norm,
                         na_color=param_values["na_color"],
-                        **kwargs,
                     )
                     for c in cmap
                 ]
@@ -598,6 +616,13 @@ def render_labels(
         -------
         None
         """
+        # TODO add Normalize object in tutorial notebook and point to that notebook here
+        if "vmin" in kwargs or "vmax" in kwargs:
+            warnings.warn(
+                "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         params_dict = _validate_label_render_params(
             self._sdata,
             element=element,
@@ -623,7 +648,6 @@ def render_labels(
                 cmap=cmap,
                 norm=norm,
                 na_color=param_values["na_color"],  # type: ignore[arg-type]
-                **kwargs,
             )
             sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
                 element=element,

src/spatialdata_plot/pl/utils.py

@@ -32,7 +32,6 @@
     LinearSegmentedColormap,
     ListedColormap,
     Normalize,
-    TwoSlopeNorm,
     to_rgba,
 )
 from matplotlib.figure import Figure
@@ -339,7 +338,7 @@ def _get_collection_shape(
                 c = cmap(c)
             else:
                 try:
-                    norm = colors.Normalize(vmin=min(c), vmax=max(c))
+                    norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
                 except ValueError as e:
                     raise ValueError(
                         "Could not convert values in the `color` column to float, if `color` column represents"
@@ -353,7 +352,7 @@ def _get_collection_shape(
             c = cmap(c)
         else:
             try:
-                norm = colors.Normalize(vmin=min(c), vmax=max(c))
+                norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
             except ValueError as e:
                 raise ValueError(
                     "Could not convert values in the `color` column to float, if `color` column represents"
@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
     cmap: Colormap | str | None = None,
     norm: Normalize | None = None,
     na_color: ColorLike | None = None,
-    vmin: float | None = None,
-    vmax: float | None = None,
-    vcenter: float | None = None,
-    **kwargs: Any,
 ) -> CmapParams:
+    # TODO: check refactoring norm out here as it gets overwritten later
     cmap_is_default = cmap is None
     if cmap is None:
         cmap = rcParams["image.cmap"]
@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
     cmap = copy(cmap)
 
     if norm is None:
-        norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
-    elif isinstance(norm, Normalize) or not norm:
-        pass  # TODO
-    elif vcenter is None:
-        norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
-    else:
-        norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
+        norm = Normalize(vmin=None, vmax=None, clip=False)
 
     na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
     cmap.set_bad(na_color)

tests/_images/Labels_can_render_given_scale_of_multiscale_labels.png

@@ -97,7 +97,7 @@ def _make_multi():
 
     def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
         blob = deepcopy(sdata_blobs)
-        blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
+        blob["table"].obs["region"] = "blobs_polygons"
         blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
         blob.pl.render_shapes(
@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
     def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
         _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
-        sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = "blobs_polygons"
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
         sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
         )
 
     def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = "blobs_polygons"
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
         sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
         ).pl.show()
 
     def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = "blobs_polygons"
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
-        sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
+        sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()
 
     def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = "blobs_polygons"
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
         norm = Normalize(vmin=0, vmax=5, clip=True)
@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob
 
     def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
         sdata_blobs["table"].obs["region"] = "blobs_circles"
-        new_table = sdata_blobs["table"][:5]
+        new_table = sdata_blobs["table"][:5].copy()
         new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
         new_table.obs["instance_id"] = np.array(range(5))
 
@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd
 
     def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
         sdata_blobs["table"].obs["region"] = "blobs_circles"
-        new_table = sdata_blobs["table"][:10]
+        new_table = sdata_blobs["table"][:10].copy()
         new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
         new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))
 
@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa
 
     def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
         sdata_blobs["table"].obs["region"] = "blobs_circles"
-        new_table = sdata_blobs["table"][:10]
+        new_table = sdata_blobs["table"][:10].copy()
         new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
         new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))
 
@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()
 
     def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
-        sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
+        sdata_blobs["table"].obs["region"] = "blobs_polygons"
         sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
         sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
         sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()
+
+    def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
+        table_shapes = sdata_blobs["table"][:5].copy()
+        table_shapes.obs.instance_id = list(range(5))
+        table_shapes.obs["region"] = "blobs_circles"
+        table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
+        table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
+        sdata_blobs["new_table"] = table_shapes
+
+        norm = Normalize(vmin=20, vmax=40, clip=True)
+        sdata_blobs.pl.render_shapes(
+            "blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
+        ).pl.show()