Fix channel str support

.gitignore

@@ -19,6 +19,7 @@ __pycache__/
 /.pytest_cache/
 /.cache/
 /data/
+*failed-diff.png
 
 # docs
 /docs/generated/

CHANGELOG.md

@@ -8,11 +8,19 @@ 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
 
+## [0.2.3] - tbd
+
+### Fixed
+
+-   Fix color assignment for NaN values (#257)
+-   Fix channel str support #221
+
 ## [0.2.2] - 2024-05-02
 
 ### Fixed
 
 -   Fixed `fill_alpha` ignoring `alpha` channel from custom cmap
+-   Fix channel str support (#221)
 
 ## [0.2.1] - 2024-03-26
 

src/spatialdata_plot/pl/render.py

@@ -442,7 +442,7 @@ def _render_images(
 
         # 1) Image has only 1 channel
         if n_channels == 1 and not isinstance(render_params.cmap_params, list):
-            layer = img.sel(c=channels).squeeze()
+            layer = img.sel(c=channels[0]).squeeze()
 
             if render_params.quantiles_for_norm != (None, None):
                 layer = _normalize(

src/spatialdata_plot/pl/utils.py

@@ -1307,7 +1307,7 @@ def _multiscale_to_spatial_image(
     data_var_keys = list(multiscale_image[optimal_scale].data_vars)
     image = multiscale_image[optimal_scale][data_var_keys[0]]
 
-    return Labels2DModel.parse(image) if is_label else Image2DModel.parse(image)
+    return Labels2DModel.parse(image) if is_label else Image2DModel.parse(image, c_coords=image.coords["c"].values)
 
 
 def _get_elements_to_be_rendered(

tests/conftest.py

@@ -55,6 +55,11 @@ def sdata_blobs() -> SpatialData:
     return blobs()
 
 
+@pytest.fixture()
+def sdata_blobs_str() -> SpatialData:
+    return blobs(n_channels=5, c_coords=["c1", "c2", "c3", "c4", "c5"])
+
+
 @pytest.fixture()
 def sdata_raccoon() -> SpatialData:
     return raccoon()

tests/pl/test_render_images.py

@@ -44,9 +44,30 @@ def test_plot_can_pass_cmap_list(self, sdata_blobs: SpatialData):
     def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_images(elements="blobs_image", channel=0).pl.show()
 
+    def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images(elements="blobs_multiscale_image", channel=0).pl.show()
+
+    def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images(channel=0).pl.show()
+
+    def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
+        sdata_blobs_str.pl.render_images(elements="blobs_image", channel="c1").pl.show()
+
+    def test_plot_can_render_a_single_channel_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
+        sdata_blobs_str.pl.render_images(elements="blobs_multiscale_image", channel="c1").pl.show()
+
     def test_plot_can_render_two_channels_from_image(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_images(elements="blobs_image", channel=[0, 1]).pl.show()
 
+    def test_plot_can_render_two_channels_from_multiscale_image(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images(elements="blobs_multiscale_image", channel=[0, 1]).pl.show()
+
+    def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: SpatialData):
+        sdata_blobs_str.pl.render_images(elements="blobs_image", channel=["c1", "c2"]).pl.show()
+
+    def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
+        sdata_blobs_str.pl.render_images(elements="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
+
     def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_images(elements="blobs_image", channel=1, palette="red").pl.show()
 

tests/pl/test_render_labels.py

@@ -2,6 +2,7 @@
 import matplotlib
 import numpy as np
 import pandas as pd
+import pytest
 import scanpy as sc
 import spatialdata_plot  # noqa: F401
 from anndata import AnnData
@@ -100,39 +101,33 @@ def test_can_plot_with_one_element_color_table(self, sdata_blobs: SpatialData):
             color=["channel_0_sum", "channel_1_sum"], table_name=["table", "multi_table"]
         ).pl.show()
 
-    def test_plot_label_categorical_color(self, sdata_blobs: SpatialData):
-        n_obs = max(_get_unique_label_values_as_index(sdata_blobs["blobs_labels"]))
+    @pytest.mark.parametrize(
+        "label",
+        [
+            "blobs_labels",
+            "blobs_multiscale_labels",
+        ],
+    )
+    def test_plot_label_categorical_color(self, sdata_blobs: SpatialData, label: str):
+        self._make_tablemodel_with_categorical_labels(sdata_blobs, label)
+
+    def _make_tablemodel_with_categorical_labels(self, sdata_blobs, label):
+        n_obs = max(_get_unique_label_values_as_index(sdata_blobs[label]))
         adata = AnnData(
-            RNG.normal(size=(n_obs, 10)), obs=pd.DataFrame(RNG.normal(size=(n_obs, 3)), columns=["a", "b", "c"])
+            RNG.normal(size=(n_obs, 10)),
+            obs=pd.DataFrame(RNG.normal(size=(n_obs, 3)), columns=["a", "b", "c"]),
         )
         adata.obs["instance_id"] = np.arange(adata.n_obs)
         adata.obs["category"] = RNG.choice(["a", "b", "c"], size=adata.n_obs)
+        adata.obs["category"][:3] = ["a", "b", "c"]
         adata.obs["instance_id"] = list(range(adata.n_obs))
-        adata.obs["region"] = "blobs_labels"
-        table = TableModel.parse(adata=adata, region_key="region", instance_key="instance_id", region="blobs_labels")
-        sdata_blobs["other_table"] = table
-
-        # with pytest.raises(ValueError, match="could not convert string"):
-        #     sdata_blobs.pl.render_labels('blobs_labels', color='category').pl.show()
-        sdata_blobs["other_table"].obs["category"] = sdata_blobs["other_table"].obs["category"].astype("category")
-        sdata_blobs.pl.render_labels("blobs_labels", color="category").pl.show()
-
-    def test_plot_multiscale_label_categorical_color(self, sdata_blobs: SpatialData):
-        # recreate RNG to get same plot acorss 3.9 and 3.10 workers
-        RNG = np.random.default_rng(seed=42)
-
-        n_obs = max(_get_unique_label_values_as_index(sdata_blobs["blobs_multiscale_labels"]))
-        adata = AnnData(
-            RNG.normal(size=(n_obs, 10)), obs=pd.DataFrame(RNG.normal(size=(n_obs, 3)), columns=["a", "b", "c"])
-        )
-        adata.obs["instance_id"] = np.arange(adata.n_obs)
-        adata.obs["category"] = RNG.choice(["a", "b", "c"], size=adata.n_obs)
-        adata.obs["instance_id"] = list(range(adata.n_obs))
-        adata.obs["region"] = "blobs_multiscale_labels"
+        adata.obs["region"] = label
         table = TableModel.parse(
-            adata=adata, region_key="region", instance_key="instance_id", region="blobs_multiscale_labels"
+            adata=adata,
+            region_key="region",
+            instance_key="instance_id",
+            region=label,
         )
         sdata_blobs["other_table"] = table
-
         sdata_blobs["other_table"].obs["category"] = sdata_blobs["other_table"].obs["category"].astype("category")
-        sdata_blobs.pl.render_labels("blobs_multiscale_labels", color="category").pl.show()
+        sdata_blobs.pl.render_labels(label, color="category").pl.show()