Deprecate louvain

docs/api/deprecated.md

@@ -12,4 +12,5 @@
    pp.filter_genes_dispersion
    pp.normalize_per_cell
    pp.subsample
+   tl.louvain
 ```

docs/api/plotting.md

@@ -118,7 +118,7 @@ Compute densities on embeddings.
 
 #### Branching trajectories and pseudotime, clustering
 
-Visualize clusters using one of the embedding methods passing `color='louvain'`.
+Visualize clusters using one of the embedding methods passing e.g. `color='leiden'`.
 
 ```{eval-rst}
 .. autosummary::

docs/api/tools.md

@@ -42,7 +42,6 @@ Compute densities on embeddings.
    :toctree: ../generated/
 
    tl.leiden
-   tl.louvain
    tl.dendrogram
    tl.dpt
    tl.paga

docs/dev/documentation.md

@@ -45,10 +45,11 @@ Some key points:
 - When docs exist in the same file as code, line length restrictions still apply. In files which are just docs, go with a sentence per line (for easier `git diff`s).
 - Check that the docs look like what you expect them too! It's easy to forget to add a reference to function, be sure it got added and looks right.
 
-Look at [sc.tl.louvain](https://github.com/scverse/scanpy/blob/a811fee0ef44fcaecbde0cad6336336bce649484/scanpy/tools/_louvain.py#L22-L90) as an example for everything mentioned here.
+Look at [`sc.tl.leiden`’s docstring][] as an example for everything mentioned here.
 
 [napolean guide to numpy style docstrings]: https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html#example-numpy
 [sphinx rst primer]: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html
+[`sc.tl.leiden`’s docstring]: https://github.com/scverse/scanpy/blob/350c3424d2f96c4a3a7bb3b7d0428d38d842ebe8/src/scanpy/tools/_leiden.py#L49-L120
 
 ### Plots in docstrings
 

docs/release-notes/3658.misc.md

@@ -0,0 +1 @@
+Deprecate {func}`scanpy.tl.louvain`. {smaller}`P Angerer`

pyproject.toml

@@ -101,7 +101,6 @@ test = [
     "zarr<3",
     # additional tested algorithms
     "scanpy[scrublet]",
-    "scanpy[louvain]",
     "scanpy[leiden]",
     "scanpy[skmisc]",
     "scanpy[dask-ml]",
@@ -133,14 +132,14 @@ dev = [
 ]
 # Algorithms
 paga = [ "igraph" ]
-louvain = [ "igraph", "louvain>=0.8.2" ]          # Louvain community detection
-leiden = [ "igraph>=0.10.8", "leidenalg>=0.9.0" ] # Leiden community detection
-bbknn = [ "bbknn" ]                               # Batch balanced KNN (batch correction)
-magic = [ "magic-impute>=2.0.4" ]                 # MAGIC imputation method
-skmisc = [ "scikit-misc>=0.5.1" ]                 # highly_variable_genes method 'seurat_v3'
-harmony = [ "harmonypy" ]                         # Harmony dataset integration
-scanorama = [ "scanorama" ]                       # Scanorama dataset integration
-scrublet = [ "scikit-image>=0.20.0" ]             # Doublet detection with automatic thresholds
+louvain = [ "igraph", "louvain>=0.8.2", "setuptools" ] # Louvain community detection
+leiden = [ "igraph>=0.10.8", "leidenalg>=0.9.0" ]      # Leiden community detection
+bbknn = [ "bbknn" ]                                    # Batch balanced KNN (batch correction)
+magic = [ "magic-impute>=2.0.4" ]                      # MAGIC imputation method
+skmisc = [ "scikit-misc>=0.5.1" ]                      # highly_variable_genes method 'seurat_v3'
+harmony = [ "harmonypy" ]                              # Harmony dataset integration
+scanorama = [ "scanorama" ]                            # Scanorama dataset integration
+scrublet = [ "scikit-image>=0.20.0" ]                  # Doublet detection with automatic thresholds
 # Acceleration
 rapids = [ "cudf>=0.9", "cuml>=0.9", "cugraph>=0.9" ] # GPU accelerated calculation of neighbors
 dask = [ "dask[array]>=2023.5.1" ]                    # Use the Dask parallelization engine
@@ -211,6 +210,7 @@ exclude_also = [
     "if TYPE_CHECKING:",
     # https://github.com/numba/numba/issues/4268
     '@(numba\.|nb\.)?njit.*',
+    "@deprecated.*",
 ]
 
 [tool.ruff]

src/scanpy/tools/_louvain.py

@@ -11,7 +11,7 @@
 
 from .. import _utils
 from .. import logging as logg
-from .._compat import old_positionals
+from .._compat import deprecated, old_positionals
 from .._utils import _choose_graph, dematrix
 from ._utils_clustering import rename_groups, restrict_adjacency
 
@@ -48,6 +48,7 @@ class MutableVertexPartition:
     "obsp",
     "copy",
 )
+@deprecated("Use `scanpy.tl.leiden` instead")
 def louvain(  # noqa: PLR0912, PLR0913, PLR0915
     adata: AnnData,
     resolution: float | None = None,
@@ -67,6 +68,9 @@ def louvain(  # noqa: PLR0912, PLR0913, PLR0915
 ) -> AnnData | None:
     """Cluster cells into subgroups :cite:p:`Blondel2008,Levine2015,Traag2017`.
 
+    .. deprecated:: 1.12.0
+       Use :func:`scanpy.tl.leiden` instead.
+
     Cluster cells using the Louvain algorithm :cite:p:`Blondel2008` in the implementation
     of :cite:t:`Traag2017`. The Louvain algorithm was proposed for single-cell
     analysis by :cite:t:`Levine2015`.

tests/test_clustering.py

@@ -149,7 +149,6 @@ def test_leiden_objective_function(adata_neighbors):
 @pytest.mark.parametrize(
     ("clustering", "key"),
     [
-        pytest.param(sc.tl.louvain, "louvain", marks=needs.louvain),
         pytest.param(sc.tl.leiden, "leiden", marks=needs.leidenalg),
     ],
 )
@@ -180,45 +179,10 @@ def test_clustering_subset(adata_neighbors, clustering, key):
         assert len(common_cat) == 0
 
 
-@needs.louvain
-@needs.igraph
-def test_louvain_basic(adata_neighbors):
-    sc.tl.louvain(adata_neighbors)
-    sc.tl.louvain(adata_neighbors, use_weights=True)
-    sc.tl.louvain(adata_neighbors, use_weights=True, flavor="igraph")
-    sc.tl.louvain(adata_neighbors, flavor="igraph")
-
-
-@needs.louvain
-@pytest.mark.parametrize("random_state", [10, 999])
-@pytest.mark.parametrize("resolution", [0.9, 1.1])
-def test_louvain_custom_key(adata_neighbors, resolution, random_state):
-    sc.tl.louvain(
-        adata_neighbors,
-        key_added="louvain_custom",
-        random_state=random_state,
-        resolution=resolution,
-    )
-    assert (
-        adata_neighbors.uns["louvain_custom"]["params"]["random_state"] == random_state
-    )
-    assert adata_neighbors.uns["louvain_custom"]["params"]["resolution"] == resolution
-
-
-@needs.louvain
-@needs.igraph
-def test_partition_type(adata_neighbors):
-    import louvain
-
-    sc.tl.louvain(adata_neighbors, partition_type=louvain.RBERVertexPartition)
-    sc.tl.louvain(adata_neighbors, partition_type=louvain.SurpriseVertexPartition)
-
-
 @pytest.mark.parametrize(
     ("clustering", "default_key", "default_res", "custom_resolutions"),
     [
         pytest.param(sc.tl.leiden, "leiden", 0.8, [0.9, 1.1], marks=needs.leidenalg),
-        pytest.param(sc.tl.louvain, "louvain", 0.8, [0.9, 1.1], marks=needs.louvain),
     ],
 )
 def test_clustering_custom_key(

tests/test_neighbors_key_added.py

@@ -89,23 +89,3 @@ def test_neighbors_key_obsp(adata, field):
             adata.uns["paga"]["connectivities_tree"].toarray(),
             adata1.uns["paga"]["connectivities_tree"].toarray(),
         )
-
-
-@needs.louvain
-@pytest.mark.parametrize("field", ["neighbors_key", "obsp"])
-def test_neighbors_key_obsp_louvain(adata, field):
-    adata1 = adata.copy()
-
-    sc.pp.neighbors(adata, n_neighbors=n_neighbors, random_state=0)
-    sc.pp.neighbors(adata1, n_neighbors=n_neighbors, random_state=0, key_added=key)
-
-    if field == "neighbors_key":
-        arg = {field: key}
-    else:
-        arg = {field: adata1.uns[key]["connectivities_key"]}
-
-    sc.tl.louvain(adata, random_state=0)
-    sc.tl.louvain(adata1, random_state=0, **arg)
-
-    assert adata.uns["louvain"]["params"] == adata1.uns["louvain"]["params"]
-    assert np.all(adata.obs["louvain"] == adata1.obs["louvain"])