fix: remove unused parameters in co_occurence

src/squidpy/_utils.py

@@ -278,6 +278,37 @@ def verbosity(level: int) -> Generator[None, None, None]:
         sc.settings.verbosity = verbosity
 
 
+def deprecated_params(
+    params: dict[str, str],
+) -> Callable[..., Any]:
+    """Decorator that warns when deprecated keyword arguments are passed.
+
+    Parameters
+    ----------
+    params
+        Mapping of deprecated parameter names to the version in which
+        they will be removed, e.g. ``{"n_jobs": "1.10.0"}``.
+    """
+
+    def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
+        @functools.wraps(func)
+        def wrapper(*args: Any, **kwargs: Any) -> Any:
+            for k in list(kwargs):
+                if k in params:
+                    warnings.warn(
+                        f"Parameter `{k}` of `{func.__name__}()` is deprecated "
+                        f"and has no effect. It will be removed in squidpy v{params[k]}.",
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+                    kwargs.pop(k)
+            return func(*args, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
 string_types = (bytes, str)
 
 

src/squidpy/gr/_ppatterns.py

@@ -23,7 +23,7 @@
 from squidpy._constants._constants import SpatialAutocorr
 from squidpy._constants._pkg_constants import Key
 from squidpy._docs import d, inject_docs
-from squidpy._utils import NDArrayA, Signal, SigQueue, _get_n_cores, parallelize
+from squidpy._utils import NDArrayA, Signal, SigQueue, _get_n_cores, deprecated_params, parallelize
 from squidpy.gr._utils import (
     _assert_categorical_obs,
     _assert_connectivity_key,
@@ -342,16 +342,13 @@ def _co_occurrence_helper(v_x: NDArrayA, v_y: NDArrayA, v_radium: NDArrayA, labs
 
 
 @d.dedent
+@deprecated_params({"n_splits": "1.10.0", "n_jobs": "1.10.0", "backend": "1.10.0", "show_progress_bar": "1.10.0"})
 def co_occurrence(
     adata: AnnData | SpatialData,
     cluster_key: str,
     spatial_key: str = Key.obsm.spatial,
     interval: int | NDArrayA = 50,
     copy: bool = False,
-    n_splits: int | None = None,
-    n_jobs: int | None = None,
-    backend: str = "loky",
-    show_progress_bar: bool = True,
 ) -> tuple[NDArrayA, NDArrayA] | None:
     """
     Compute co-occurrence probability of clusters.
@@ -365,10 +362,6 @@ def co_occurrence(
         Distances interval at which co-occurrence is computed. If :class:`int`, uniformly spaced interval
         of the given size will be used.
     %(copy)s
-    n_splits
-        Number of splits in which to divide the spatial coordinates in
-        :attr:`anndata.AnnData.obsm` ``['{spatial_key}']``.
-    %(parallelize)s
 
     Returns
     -------
@@ -406,9 +399,7 @@ def co_occurrence(
 
     # Compute co-occurrence probabilities using the fast numba routine.
     out = _co_occurrence_helper(spatial_x, spatial_y, interval, labs)
-    start = logg.info(
-        f"Calculating co-occurrence probabilities for `{len(interval)}` intervals using `{n_jobs}` core(s) and `{n_splits}` splits"
-    )
+    start = logg.info(f"Calculating co-occurrence probabilities for `{len(interval)}` intervals")
 
     if copy:
         logg.info("Finish", time=start)

tests/graph/test_ppatterns.py

@@ -137,12 +137,10 @@ def test_co_occurrence(adata: AnnData):
     assert arr.shape[1] == arr.shape[0] == adata.obs["leiden"].unique().shape[0]
 
 
-# @pytest.mark.parametrize(("ys", "xs"), [(10, 10), (None, None), (10, 20)])
-@pytest.mark.parametrize(("n_jobs", "n_splits"), [(1, 2), (2, 2)])
-def test_co_occurrence_reproducibility(adata: AnnData, n_jobs: int, n_splits: int):
+def test_co_occurrence_reproducibility(adata: AnnData):
     """Check co_occurrence reproducibility results."""
-    arr_1, interval_1 = co_occurrence(adata, cluster_key="leiden", copy=True, n_jobs=n_jobs, n_splits=n_splits)
-    arr_2, interval_2 = co_occurrence(adata, cluster_key="leiden", copy=True, n_jobs=n_jobs, n_splits=n_splits)
+    arr_1, interval_1 = co_occurrence(adata, cluster_key="leiden", copy=True)
+    arr_2, interval_2 = co_occurrence(adata, cluster_key="leiden", copy=True)
 
     np.testing.assert_array_equal(sorted(interval_1), sorted(interval_2))
     np.testing.assert_allclose(arr_1, arr_2)