Merge branch 'main' into dask_mg_support

conda/rsc_rapids_24.06.yml → conda/rsc_rapids_24.10.yml

@@ -5,9 +5,9 @@ channels:
  - conda-forge
  - bioconda
 dependencies:
- - rapids=24.06
+ - rapids=24.10
  - python=3.11
- - cuda-version=11.8
+ - cuda-version=12.5
  - cudnn
  - cutensor
  - cusparselt

docker/docker-push.sh

@@ -2,7 +2,7 @@
 set -euxo pipefail
 
 docker_account=scverse
-rapids_version=24.08
+rapids_version=24.10
 grep -v -- '- rapids-singlecell' conda/rsc_rapids_${rapids_version}.yml > rsc_rapids.yml
 docker build -t rapids-singlecell-deps:latest -f docker/Dockerfile.deps .
 rm rsc_rapids.yml

docs/release-notes/0.10.11.md

@@ -0,0 +1,18 @@
+### 0.10.11 {small}`2024-10-08`
+
+```{rubric} Features
+```
+
+
+```{rubric} Performance
+```
+
+```{rubric} Bug fixes
+```
+
+```{rubric} Misc
+```
+* Adds support for `Numpy-2.0` {pr}`277` {smaller}`S Dicks`
+* Adds support for `rapids-24.10` {pr}`277` {smaller}`S Dicks`
+* Updates testing to use `rapids-24.10` {pr}`277` {smaller}`S Dicks`
+* Updates `docker` image to use `rapids-24.10` {pr}`277` {smaller}`S Dicks`

docs/release-notes/index.md

@@ -3,6 +3,8 @@
 # Release notes
 
 ## Version 0.10.0
+```{include} /release-notes/0.10.11.md
+```
 ```{include} /release-notes/0.10.10.md
 ```
 ```{include} /release-notes/0.10.9.md

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 [project]
 name = "rapids_singlecell"
 description = "running single cell analysis on Nvidia GPUs"
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 license = {file = "LICENSE"}
 authors = [{name = "Severin Dicks"}]
 readme = {file = "README.md", content-type="text/markdown"}
@@ -26,8 +26,8 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-rapids11 = ["cupy-cuda11x","cudf-cu11==24.8.*", "cuml-cu11==24.8.*", "cugraph-cu11==24.8.*"]
-rapids12 = ["cupy-cuda12x","cudf-cu12==24.8.*", "cuml-cu12==24.8.*", "cugraph-cu12==24.8.*"]
+rapids11 = ["cupy-cuda11x","cudf-cu11==24.10.*", "cuml-cu11==24.10.*", "cugraph-cu11==24.10.*"]
+rapids12 = ["cupy-cuda12x","cudf-cu12==24.10.*", "cuml-cu12==24.10.*", "cugraph-cu12==24.10.*"]
 doc = [
     "sphinx>=4.5.0",
     "sphinx-copybutton",
@@ -41,7 +41,7 @@ doc = [
 test = [
     "pytest",
     "profimp",
-    "scanpy[test]>=1.10.0",
+    "scanpy>=1.10.0",
     "bbknn",
 ]
 

src/rapids_singlecell/_utils/__init__.py

@@ -4,4 +4,4 @@
 
 import numpy as np
 
-AnyRandom = Union[int, np.random.RandomState, None]
+AnyRandom = Union[int, np.random.RandomState, None]  # noqa: UP007

src/rapids_singlecell/get/_aggregated.py

@@ -2,8 +2,6 @@
 
 from typing import (
     TYPE_CHECKING,
-    Collection,
-    Iterable,
     Literal,
     Union,
     get_args,
@@ -20,10 +18,12 @@
 from rapids_singlecell.preprocessing._utils import _check_gpu_X
 
 if TYPE_CHECKING:
+    from collections.abc import Collection, Iterable
+
     import pandas as pd
     from numpy.typing import NDArray
 
-Array = Union[cp.ndarray, cp_sparse.csc_matrix, cp_sparse.csr_matrix]
+Array = Union[cp.ndarray, cp_sparse.csc_matrix, cp_sparse.csr_matrix]  # noqa: UP007
 AggType = Literal["count_nonzero", "mean", "sum", "var"]
 
 

src/rapids_singlecell/get/_anndata.py

@@ -16,8 +16,8 @@
 if TYPE_CHECKING:
     from anndata import AnnData
 
-GPU_ARRAY_TYPE = Union[cp.ndarray, csr_matrix_gpu, csc_matrix_gpu]
-CPU_ARRAY_TYPE = Union[np.ndarray, csr_matrix_cpu, csc_matrix_cpu]
+GPU_ARRAY_TYPE = Union[cp.ndarray, csr_matrix_gpu, csc_matrix_gpu]  # noqa: UP007
+CPU_ARRAY_TYPE = Union[np.ndarray, csr_matrix_cpu, csc_matrix_cpu]  # noqa: UP007
 
 
 def anndata_to_GPU(

src/rapids_singlecell/preprocessing/_neighbors.py

@@ -2,7 +2,7 @@
 
 import math
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Any, Literal, Mapping, Union
+from typing import TYPE_CHECKING, Any, Literal
 
 import cupy as cp
 import numpy as np
@@ -14,9 +14,11 @@
 from rapids_singlecell.tools._utils import _choose_representation
 
 if TYPE_CHECKING:
+    from collections.abc import Mapping
+
     from anndata import AnnData
 
-AnyRandom = Union[None, int, np.random.RandomState]
+AnyRandom = None | int | np.random.RandomState
 _Alogithms = Literal["brute", "ivfflat", "ivfpq", "cagra"]
 _MetricsDense = Literal[
     "l2",
@@ -54,7 +56,7 @@
     "minkowski",
     "taxicab",
 ]
-_Metrics = Union[_MetricsDense, _MetricsSparse]
+_Metrics = _MetricsDense | _MetricsSparse
 
 
 def _brute_knn(

src/rapids_singlecell/squidpy_gpu/_autocorr.py

@@ -2,8 +2,7 @@
 
 from typing import (
     TYPE_CHECKING,
-    Literal,  # < 3.8
-    Sequence,
+    Literal,
 )
 
 import cupy as cp
@@ -18,6 +17,8 @@
 from ._utils import _p_value_calc
 
 if TYPE_CHECKING:
+    from collections.abc import Sequence
+
     from anndata import AnnData
 
 

src/rapids_singlecell/squidpy_gpu/_ligrec.py

@@ -1,12 +1,10 @@
 from __future__ import annotations
 
 import math
+from collections.abc import Iterable, Mapping, Sequence
 from itertools import product
 from typing import (
-    Iterable,
     Literal,
-    Mapping,
-    Sequence,
 )
 
 import cupy as cp

src/rapids_singlecell/tools/_clustering.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import TYPE_CHECKING, Sequence
+from typing import TYPE_CHECKING
 
 import cudf
 import numpy as np
@@ -11,6 +11,8 @@
 from scanpy.tools._utils_clustering import rename_groups, restrict_adjacency
 
 if TYPE_CHECKING:
+    from collections.abc import Sequence
+
     from anndata import AnnData
     from scipy import sparse
 

src/rapids_singlecell/tools/_embedding_density.py

@@ -1,12 +1,14 @@
 from __future__ import annotations
 
 import math
-from typing import TYPE_CHECKING, Sequence
+from typing import TYPE_CHECKING
 
 import cupy as cp
 import numpy as np
 
 if TYPE_CHECKING:
+    from collections.abc import Sequence
+
     from anndata import AnnData
 
 

src/rapids_singlecell/tools/_rank_gene_groups.py

@@ -1,12 +1,14 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Iterable, Literal
+from typing import TYPE_CHECKING, Literal
 
 import cupy as cp
 import numpy as np
 import pandas as pd
 
 if TYPE_CHECKING:
+    from collections.abc import Iterable
+
     from anndata import AnnData
 
 
@@ -103,7 +105,7 @@ def rank_genes_groups_logreg(
     # for clarity, rename variable
     if groups == "all" or groups is None:
         groups_order = "all"
-    elif isinstance(groups, (str, int)):
+    elif isinstance(groups, str | int):
         raise ValueError("Specify a sequence of groups")
     else:
         groups_order = list(groups)

src/rapids_singlecell/tools/_score_genes.py

@@ -13,8 +13,7 @@
 from ._utils import _nan_mean
 
 if TYPE_CHECKING:
-    from collections.abc import Generator
-    from typing import Sequence
+    from collections.abc import Generator, Sequence
 
     from anndata import AnnData
 

tests/test_get_anndata.py

@@ -21,10 +21,10 @@ def test_utils(mtype):
     rsc.get.anndata_to_GPU(adata)
     rsc.preprocessing._utils._check_gpu_X(adata.X)
     rsc.get.anndata_to_CPU(adata)
-    assert isinstance(adata.X, (np.ndarray, csr_matrix, csc_matrix))
+    assert isinstance(adata.X, np.ndarray | csr_matrix | csc_matrix)
     # check layers
     adata.layers["test"] = adata.X.copy()
     rsc.get.anndata_to_GPU(adata, convert_all=True)
     _check_gpu_X(adata.layers["test"])
     rsc.get.anndata_to_CPU(adata, convert_all=True)
-    assert isinstance(adata.layers["test"], (np.ndarray, csr_matrix, csc_matrix))
+    assert isinstance(adata.layers["test"], np.ndarray | csr_matrix | csc_matrix)

tests/test_hvg.py

@@ -264,8 +264,8 @@ def _check_pearson_hvg_columns(output_df, n_top_genes):
     assert np.nanmax(output_df["highly_variable_rank"].values) <= n_top_genes - 1
 
 
-@pytest.mark.parametrize("clip", [None, np.Inf, 30])
-@pytest.mark.parametrize("theta", [100, np.Inf])
+@pytest.mark.parametrize("clip", [None, np.inf, 30])
+@pytest.mark.parametrize("theta", [100, np.inf])
 @pytest.mark.parametrize("n_top_genes", [100, 200])
 @pytest.mark.parametrize("dtype", ["float32", "float64"])
 @pytest.mark.parametrize("sparse", [True, False])

tests/test_ligrec.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
 import pickle
+from collections.abc import Sequence
 from itertools import product
 from pathlib import Path
-from typing import TYPE_CHECKING, Sequence, Tuple
+from typing import TYPE_CHECKING
 
 import numpy as np
 import pandas as pd
@@ -16,8 +17,8 @@
 HERE = Path(__file__).parent
 
 _CK = "leiden"
-Interactions_t = Tuple[Sequence[str], Sequence[str]]
-Complexes_t = Sequence[Tuple[str, str]]
+Interactions_t = tuple[Sequence[str], Sequence[str]]
+Complexes_t = Sequence[tuple[str, str]]
 
 
 @pytest.fixture()

tests/test_normalization.py

@@ -41,8 +41,8 @@ def test_normalize_total_layers(dtype):
     "sparsity_func", [cp.array, csr_matrix], ids=lambda x: x.__name__
 )
 @pytest.mark.parametrize("dtype", [np.float32, np.float64])
-@pytest.mark.parametrize("theta", [0.01, 1.0, 100, np.Inf])
-@pytest.mark.parametrize("clip", [None, 1.0, np.Inf])
+@pytest.mark.parametrize("theta", [0.01, 1.0, 100, np.inf])
+@pytest.mark.parametrize("clip", [None, 1.0, np.inf])
 def test_normalize_pearson_residuals_values(sparsity_func, dtype, theta, clip):
     # toy data
     X = cp.array([[3, 6], [2, 4], [1, 0]], dtype=dtype)