plot continous column render labels (#444)

Author: LucaMarconato

src/spatialdata_plot/pl/render.py

@@ -1023,6 +1023,20 @@ def _render_labels(
         table_layer=table_layer,
     )
 
+    # rasterize could have removed labels from label
+    # only problematic if color is specified
+    if rasterize and color is not None:
+        labels_in_rasterized_image = np.unique(label.values)
+        mask = np.isin(instance_id, labels_in_rasterized_image)
+        instance_id = instance_id[mask]
+        color_vector = color_vector[mask]
+        if isinstance(color_vector.dtype, pd.CategoricalDtype):
+            color_vector = color_vector.remove_unused_categories()
+            assert color_source_vector is not None
+            color_source_vector = color_source_vector[mask]
+        else:
+            assert color_source_vector is None
+
     def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float) -> matplotlib.image.AxesImage:
         labels = _map_color_seg(
             seg=label.values,

tests/_images/Labels_can_handle_dropping_small_labels_after_rasterize_categorical.png

@@ -9,7 +9,7 @@
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData, deepcopy, get_element_instances
-from spatialdata.models import TableModel
+from spatialdata.models import Labels2DModel, TableModel
 
 import spatialdata_plot  # noqa: F401
 from tests.conftest import DPI, PlotTester, PlotTesterMeta, _viridis_with_under_over
@@ -76,7 +76,13 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
                 fill_alpha=1,
                 outline_alpha=0,
             )
-            .pl.render_labels(element="blobs_labels", na_color="blue", fill_alpha=0, outline_alpha=1, contour_px=15)
+            .pl.render_labels(
+                element="blobs_labels",
+                na_color="blue",
+                fill_alpha=0,
+                outline_alpha=1,
+                contour_px=15,
+            )
             .pl.show()
         )
 
@@ -146,7 +152,11 @@ def test_plot_two_calls_with_coloring_result_in_two_colorbars(self, sdata_blobs:
 
     def test_plot_can_control_label_outline(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_labels(
-            "blobs_labels", color="channel_0_sum", outline_alpha=0.4, fill_alpha=0.0, contour_px=15
+            "blobs_labels",
+            color="channel_0_sum",
+            outline_alpha=0.4,
+            fill_alpha=0.0,
+            contour_px=15,
         ).pl.show()
 
     def test_plot_can_control_label_infill(self, sdata_blobs: SpatialData):
@@ -162,7 +172,11 @@ def test_plot_label_colorbar_uses_alpha_of_less_transparent_infill(
         sdata_blobs: SpatialData,
     ):
         sdata_blobs.pl.render_labels(
-            "blobs_labels", color="channel_0_sum", fill_alpha=0.1, outline_alpha=0.7, contour_px=15
+            "blobs_labels",
+            color="channel_0_sum",
+            fill_alpha=0.1,
+            outline_alpha=0.7,
+            contour_px=15,
         ).pl.show()
 
     def test_plot_label_colorbar_uses_alpha_of_less_transparent_outline(
@@ -233,7 +247,10 @@ def _make_tablemodel_with_categorical_labels(self, sdata_blobs, labels_name: str
 
     def test_plot_can_color_with_norm_and_clipping(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_labels(
-            "blobs_labels", color="channel_0_sum", norm=Normalize(400, 1000, clip=True), cmap=_viridis_with_under_over()
+            "blobs_labels",
+            color="channel_0_sum",
+            norm=Normalize(400, 1000, clip=True),
+            cmap=_viridis_with_under_over(),
         ).pl.show()
 
     def test_plot_can_color_with_norm_no_clipping(self, sdata_blobs: SpatialData):
@@ -247,3 +264,38 @@ def test_plot_can_color_with_norm_no_clipping(self, sdata_blobs: SpatialData):
     def test_plot_can_annotate_labels_with_table_layer(self, sdata_blobs: SpatialData):
         sdata_blobs["table"].layers["normalized"] = RNG.random(sdata_blobs["table"].X.shape)
         sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", table_layer="normalized").pl.show()
+
+    def _prepare_small_labels(self, sdata_blobs: SpatialData) -> SpatialData:
+        # add a categorical column
+        adata = sdata_blobs["table"]
+        sdata_blobs["table"].obs["category"] = ["a"] * 10 + ["b"] * 10 + ["c"] * 6
+
+        sdata_blobs["table"].obs["category"] = sdata_blobs["table"].obs["category"].astype("category")
+
+        labels = sdata_blobs["blobs_labels"].data.compute()
+
+        # make label 1 small
+        mask = labels == 1
+        labels[mask] = 0
+        labels[200, 200] = 1
+
+        sdata_blobs["blobs_labels"] = Labels2DModel.parse(labels)
+
+        # tile the labels object
+        arr = da.tile(sdata_blobs["blobs_labels"], (4, 4))
+        sdata_blobs["blobs_labels_large"] = Labels2DModel.parse(arr)
+
+        adata.obs["region"] = "blobs_labels_large"
+        sdata_blobs.set_table_annotates_spatialelement("table", region="blobs_labels_large")
+        return sdata_blobs
+
+    def test_plot_can_handle_dropping_small_labels_after_rasterize_continuous(self, sdata_blobs: SpatialData):
+        # reported here https://github.com/scverse/spatialdata-plot/issues/443
+        sdata_blobs = self._prepare_small_labels(sdata_blobs)
+
+        sdata_blobs.pl.render_labels("blobs_labels_large", color="channel_0_sum", table_name="table").pl.show()
+
+    def test_plot_can_handle_dropping_small_labels_after_rasterize_categorical(self, sdata_blobs: SpatialData):
+        sdata_blobs = self._prepare_small_labels(sdata_blobs)
+
+        sdata_blobs.pl.render_labels("blobs_labels_large", color="category", table_name="table").pl.show()