adds test and release note

Author: Intron7

docs/release-notes/0.13.5.md

@@ -2,6 +2,8 @@
 
 ```{rubric} Features
 ```
+* Adds support for multi-GPU clustering via Dask to `tl.leiden` and `tl.louvain` {pr}`502` {smaller}`S Dicks`
+* Adds support for specifying multiple resolutions as a list in `tl.leiden` and `tl.louvain` {pr}`502` {smaller}`S Dicks`
 
 ```{rubric} Performance
 ```

tests/dask/test_dask_clustering.py

@@ -24,11 +24,9 @@ def test_dask_clustering(client, clustering_function):
 @pytest.mark.parametrize("resolution", [0.1, [0.5, 1.0]])
 def test_dask_clustering_resolution(client, clustering_function, resolution):
     adata = pbmc3k_processed()
-    print(resolution)
     clustering_function(
         adata, use_dask=True, key_added="test_dask", resolution=resolution
     )
-    print(adata.obs.columns)
     if isinstance(resolution, list):
         for r in resolution:
             assert f"test_dask_{r}" in adata.obs.columns

tests/test_clustering.py

@@ -48,7 +48,6 @@ def test_clustering_subset(adata_neighbors, key):
     clustering(adata_neighbors, key_added=key)
 
     for c in adata_neighbors.obs[key].unique():
-        print("Analyzing cluster ", c)
         cells_in_c = adata_neighbors.obs[key] == c
         ncells_in_c = adata_neighbors.obs[key].value_counts().loc[c]
         key_sub = str(key) + "_sub"
@@ -71,6 +70,18 @@ def test_clustering_subset(adata_neighbors, key):
         assert len(common_cat) == 0
 
 
+@pytest.mark.parametrize("clustering_function", [rsc.tl.leiden, rsc.tl.louvain])
+@pytest.mark.parametrize("resolution", [0.1, [0.5, 1.0]])
+def test_clustering_resolution(adata_neighbors, clustering_function, resolution):
+    adata = adata_neighbors.copy()
+    clustering_function(adata, key_added="test_clustering", resolution=resolution)
+    if isinstance(resolution, list):
+        for r in resolution:
+            assert f"test_clustering_{r}" in adata.obs.columns
+    else:
+        assert "test_clustering" in adata.obs.columns
+
+
 def test_kmeans_basic(adata_neighbors):
     rsc.tl.kmeans(adata_neighbors)
     assert adata_neighbors.obs["kmeans"].nunique() == 8