[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -19,7 +19,7 @@ repos:
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: v0.14.5
+  rev: v0.16.4
   hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix]

setup.py

@@ -12,7 +12,7 @@
 if __name__ == "__main__":
     try:
         setup(use_scm_version={"version_scheme": "no-guess-dev"})
-    except:  # noqa
+    except:
         print(
             "\n\nAn error occurred while building the project, "
             "please ensure you have the most updated version of setuptools, "

src/cellarr_array/core/base.py

@@ -6,7 +6,7 @@
 except ImportError:
     # TODO: This is required for Python <3.10. Remove once Python 3.9 reaches EOL in October 2025
     EllipsisType = type(...)
-from typing import Any, List, Literal, Optional, Tuple, Union
+from typing import Any, Literal
 
 import numpy as np
 import tiledb
@@ -25,11 +25,11 @@ class CellArray(ABC):
 
     def __init__(
         self,
-        uri: Optional[str] = None,
-        tiledb_array_obj: Optional[tiledb.Array] = None,
+        uri: str | None = None,
+        tiledb_array_obj: tiledb.Array | None = None,
         attr: str = "data",
-        mode: Optional[Literal["r", "w", "d", "m"]] = None,
-        config_or_context: Optional[Union[tiledb.Config, tiledb.Ctx]] = None,
+        mode: Literal["r", "w", "d", "m"] | None = None,
+        config_or_context: tiledb.Config | tiledb.Ctx | None = None,
         validate: bool = True,
     ):
         """Initialize the object.
@@ -141,14 +141,14 @@ def _validate(self, attr):
                 )
 
     @property
-    def mode(self) -> Optional[str]:
+    def mode(self) -> str | None:
         """Get current array mode. If an external array is used, this is its open mode."""
         if self._array_passed_in and self._opened_array_external is not None:
             return self._opened_array_external.mode
         return self._mode
 
     @mode.setter
-    def mode(self, value: Optional[str]):
+    def mode(self, value: str | None):
         """Set array mode for subsequent operations if not using an external array.
 
         This action does not affect an already passed-in external array's mode.
@@ -167,7 +167,7 @@ def mode(self, value: Optional[str]):
         self._mode = value
 
     @property
-    def dim_names(self) -> List[str]:
+    def dim_names(self) -> list[str]:
         """Get dimension names of the array."""
         if self._dim_names is None:
             with self.open_array(mode="r") as A:
@@ -176,7 +176,7 @@ def dim_names(self) -> List[str]:
         return self._dim_names
 
     @property
-    def attr_names(self) -> List[str]:
+    def attr_names(self) -> list[str]:
         """Get attribute names of the array."""
         if self._attr_names is None:
             with self.open_array(mode="r") as A:
@@ -185,7 +185,7 @@ def attr_names(self) -> List[str]:
         return self._attr_names
 
     @property
-    def shape(self) -> Tuple[int, ...]:
+    def shape(self) -> tuple[int, ...]:
         if self._shape is None:
             with self.open_array(mode="r") as A:
                 shape_list = []
@@ -202,7 +202,7 @@ def shape(self) -> Tuple[int, ...]:
         return self._shape
 
     @property
-    def nonempty_domain(self) -> Optional[Tuple[Any, ...]]:
+    def nonempty_domain(self) -> tuple[Any, ...] | None:
         if self._nonempty_domain is None:
             with self.open_array(mode="r") as A:
                 # nonempty_domain() can return None if the array is empty.
@@ -225,7 +225,7 @@ def ndim(self) -> int:
         return self._ndim
 
     @property
-    def dim_dtypes(self) -> List[np.dtype]:
+    def dim_dtypes(self) -> list[np.dtype]:
         """Get dimension dtypes of the array."""
         if self._dim_dtypes is None:
             with self.open_array(mode="r") as A:
@@ -234,7 +234,7 @@ def dim_dtypes(self) -> List[np.dtype]:
         return self._dim_dtypes
 
     @contextmanager
-    def open_array(self, mode: Optional[str] = None):
+    def open_array(self, mode: str | None = None):
         """Context manager for array operations.
 
         Uses the externally provided array if available, otherwise opens from URI.
@@ -283,7 +283,7 @@ def open_array(self, mode: Optional[str] = None):
             finally:
                 array.close()
 
-    def __getitem__(self, key: Union[slice, EllipsisType, Tuple[Union[slice, List[int]], ...], EllipsisType, str]):
+    def __getitem__(self, key: slice | EllipsisType | tuple[slice | list[int], ...] | str):
         """Get item implementation that routes to either direct slicing, multi_index,
         or query based on the type of indices provided.
 
@@ -334,20 +334,18 @@ def __getitem__(self, key: Union[slice, EllipsisType, Tuple[Union[slice, List[in
             return self._multi_index(normalized_key)
 
     @abstractmethod
-    def _direct_slice(self, key: Tuple[Union[slice, EllipsisType], ...]) -> np.ndarray:
+    def _direct_slice(self, key: tuple[slice | EllipsisType, ...]) -> np.ndarray:
         """Implementation for direct slicing."""
-        pass
 
     @abstractmethod
-    def _multi_index(self, key: Tuple[Union[slice, List[int]], ...]) -> np.ndarray:
+    def _multi_index(self, key: tuple[slice | list[int], ...]) -> np.ndarray:
         """Implementation for multi-index access."""
-        pass
 
     def vacuum(self) -> None:
         """Remove deleted fragments from the array."""
         tiledb.vacuum(self.uri)
 
-    def consolidate(self, config: Optional[ConsolidationConfig] = None) -> None:
+    def consolidate(self, config: ConsolidationConfig | None = None) -> None:
         """Consolidate array fragments.
 
         Args:
@@ -371,7 +369,7 @@ def consolidate(self, config: Optional[ConsolidationConfig] = None) -> None:
             self.vacuum()
 
     @abstractmethod
-    def write_batch(self, data: Union[np.ndarray, sparse.spmatrix], start_row: int, **kwargs) -> None:
+    def write_batch(self, data: np.ndarray | sparse.spmatrix, start_row: int, **kwargs) -> None:
         """Write a batch of data to the array starting at the specified row.
 
         Args:
@@ -384,9 +382,8 @@ def write_batch(self, data: Union[np.ndarray, sparse.spmatrix], start_row: int,
             **kwargs:
                 Additional arguments for write operation.
         """
-        pass
 
-    def get_unique_dim_values(self, dim_name: Optional[str] = None) -> np.ndarray:
+    def get_unique_dim_values(self, dim_name: str | None = None) -> np.ndarray:
         """Get unique values for a dimension.
 
         Args:

src/cellarr_array/core/dense.py

@@ -3,7 +3,6 @@
 except ImportError:
     # TODO: This is required for Python <3.10. Remove once Python 3.9 reaches EOL in October 2025
     EllipsisType = type(...)
-from typing import List, Tuple, Union
 
 import numpy as np
 from scipy import sparse as sp
@@ -19,7 +18,7 @@
 class DenseCellArray(CellArray):
     """Implementation for dense TileDB arrays."""
 
-    def _direct_slice(self, key: Tuple[Union[slice, EllipsisType], ...]) -> np.ndarray:
+    def _direct_slice(self, key: tuple[slice | EllipsisType, ...]) -> np.ndarray:
         """Implementation for direct slicing of dense arrays.
 
         Args:
@@ -33,7 +32,7 @@ def _direct_slice(self, key: Tuple[Union[slice, EllipsisType], ...]) -> np.ndarr
             res = array[key]
             return res[self._attr] if self._attr is not None else res
 
-    def _multi_index(self, key: Tuple[Union[slice, List[int]], ...]) -> np.ndarray:
+    def _multi_index(self, key: tuple[slice | list[int], ...]) -> np.ndarray:
         """Implementation for multi-index access of dense arrays.
 
         Args:
@@ -70,7 +69,7 @@ def _multi_index(self, key: Tuple[Union[slice, List[int]], ...]) -> np.ndarray:
             res = array.multi_index[tuple(tiledb_key)]
             return res[self._attr] if self._attr is not None else res
 
-    def write_batch(self, data: Union[np.ndarray, sp.spmatrix], start_row: int, **kwargs) -> None:
+    def write_batch(self, data: np.ndarray | sp.spmatrix, start_row: int, **kwargs) -> None:
         """Write a batch of data to the dense array.
 
         This method supports both dense (numpy.ndarray) and sparse

src/cellarr_array/core/helpers.py

@@ -3,7 +3,6 @@
 except ImportError:
     # TODO: This is required for Python <3.10. Remove once Python 3.9 reaches EOL in October 2025
     EllipsisType = type(...)
-from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import tiledb
@@ -17,13 +16,13 @@
 
 def create_cellarray(
     uri: str,
-    shape: Optional[Tuple[Optional[int], ...]] = None,
-    attr_dtype: Optional[Union[str, np.dtype]] = None,
+    shape: tuple[int | None, ...] | None = None,
+    attr_dtype: str | np.dtype | None = None,
     sparse: bool = False,
     mode: str = None,
-    config: Optional[CellArrConfig] = None,
-    dim_names: Optional[List[str]] = None,
-    dim_dtypes: Optional[List[Union[str, np.dtype]]] = None,
+    config: CellArrConfig | None = None,
+    dim_names: list[str] | None = None,
+    dim_dtypes: list[str | np.dtype] | None = None,
     attr_name: str = "data",
     **kwargs,
 ):
@@ -165,7 +164,7 @@ class SliceHelper:
     """Helper class for handling array slicing operations."""
 
     @staticmethod
-    def is_contiguous_indices(indices: List[int]) -> Optional[slice]:
+    def is_contiguous_indices(indices: list[int]) -> slice | None:
         """Checks if a list of indices is contiguous and can be converted to a slice.
 
         Returns None if the list is not contiguous or contains non-integers.
@@ -191,10 +190,10 @@ def is_contiguous_indices(indices: List[int]) -> Optional[slice]:
 
     @staticmethod
     def normalize_index(
-        idx: Union[int, range, slice, List, str, EllipsisType],
+        idx: int | range | slice | list | str | EllipsisType,
         dim_size: int,
         dim_dtype: np.dtype,
-    ) -> Union[slice, List, EllipsisType]:
+    ) -> slice | list | EllipsisType:
         """Normalize index to handle negative indices and ensure consistency."""
 
         is_string_dim = np.issubdtype(dim_dtype, np.str_) or np.issubdtype(dim_dtype, np.bytes_)

src/cellarr_array/core/sparse.py

@@ -3,7 +3,7 @@
 except ImportError:
     # TODO: This is required for Python <3.10. Remove once Python 3.9 reaches EOL in October 2025
     EllipsisType = type(...)
-from typing import Dict, List, Literal, Optional, Tuple, Union
+from typing import Literal
 
 import numpy as np
 import tiledb
@@ -22,13 +22,13 @@ class SparseCellArray(CellArray):
 
     def __init__(
         self,
-        uri: Optional[str] = None,
-        tiledb_array_obj: Optional[tiledb.Array] = None,
+        uri: str | None = None,
+        tiledb_array_obj: tiledb.Array | None = None,
         attr: str = "data",
-        mode: Optional[Literal["r", "w", "d", "m"]] = None,
-        config_or_context: Optional[Union[tiledb.Config, tiledb.Ctx]] = None,
+        mode: Literal["r", "w", "d", "m"] | None = None,
+        config_or_context: tiledb.Config | tiledb.Ctx | None = None,
         return_sparse: bool = True,
-        sparse_format: Union[sparse.csr_matrix, sparse.csc_matrix] = sparse.csr_matrix,
+        sparse_format: sparse.csr_matrix | sparse.csc_matrix = sparse.csr_matrix,
         validate: bool = True,
         **kwargs,
     ):
@@ -89,7 +89,7 @@ def __init__(
         self.sparse_format = sparse.csr_matrix if sparse_format is None else sparse_format
         self._list_remaps = {}
 
-    def _validate_matrix_dims(self, data: sparse.spmatrix) -> Tuple[sparse.coo_matrix, bool]:
+    def _validate_matrix_dims(self, data: sparse.spmatrix) -> tuple[sparse.coo_matrix, bool]:
         """Validate and adjust matrix dimensions if needed.
 
         Args:
@@ -116,7 +116,7 @@ def _validate_matrix_dims(self, data: sparse.spmatrix) -> Tuple[sparse.coo_matri
 
         return coo_data, is_1d
 
-    def _get_slice_details(self, key: Tuple[Union[slice, List], ...]) -> ...:
+    def _get_slice_details(self, key: tuple[slice | list, ...]) -> ...:
         """Calculates the shape, remapping info, and if a remap is needed for a slice."""
         shape = []
         origins_or_maps = []
@@ -154,8 +154,8 @@ def _get_slice_details(self, key: Tuple[Union[slice, List], ...]) -> ...:
         return tuple(shape), origins_or_maps, is_list_remap
 
     def _to_sparse_format(
-        self, result: Dict[str, np.ndarray], key: Tuple[Union[slice, List[int]], ...]
-    ) -> Union[np.ndarray, sparse.spmatrix]:
+        self, result: dict[str, np.ndarray], key: tuple[slice | list[int], ...]
+    ) -> np.ndarray | sparse.spmatrix:
         """Convert TileDB result to CSR format or dense array."""
         data = result[self._attr]
 
@@ -197,7 +197,7 @@ def _to_sparse_format(
 
         return matrix
 
-    def _direct_slice(self, key: Tuple[Union[slice, EllipsisType], ...]) -> Union[np.ndarray, sparse.coo_matrix]:
+    def _direct_slice(self, key: tuple[slice | EllipsisType, ...]) -> np.ndarray | sparse.coo_matrix:
         """Implementation for direct slicing of sparse arrays."""
         self._list_remaps.clear()
 
@@ -209,7 +209,7 @@ def _direct_slice(self, key: Tuple[Union[slice, EllipsisType], ...]) -> Union[np
 
             return self._to_sparse_format(result, key)
 
-    def _multi_index(self, key: Tuple[Union[slice, List[int]], ...]) -> Union[np.ndarray, sparse.coo_matrix]:
+    def _multi_index(self, key: tuple[slice | list[int], ...]) -> np.ndarray | sparse.coo_matrix:
         """Implementation for multi-index access of sparse arrays."""
         self._list_remaps.clear()
 
@@ -241,7 +241,7 @@ def _multi_index(self, key: Tuple[Union[slice, List[int]], ...]) -> Union[np.nda
             return self._to_sparse_format(result, key)
 
     def write_batch(
-        self, data: Union[sparse.spmatrix, sparse.csc_matrix, sparse.coo_matrix], start_row: int, **kwargs
+        self, data: sparse.spmatrix | sparse.csc_matrix | sparse.coo_matrix, start_row: int, **kwargs
     ) -> None:
         """Write a batch of sparse data to the array.
 

src/cellarr_array/dataloaders/denseloader.py

@@ -1,4 +1,3 @@
-from typing import Optional
 from warnings import warn
 
 import numpy as np
@@ -18,9 +17,9 @@ def __init__(
         self,
         array_uri: str,
         attribute_name: str = "data",
-        num_rows: Optional[int] = None,
-        num_columns: Optional[int] = None,
-        cellarr_ctx_config: Optional[dict] = None,
+        num_rows: int | None = None,
+        num_columns: int | None = None,
+        cellarr_ctx_config: dict | None = None,
         transform=None,
     ):
         """PyTorch Dataset for dense TileDB arrays accessed via DenseCellArray.
@@ -142,8 +141,8 @@ def __getitem__(self, idx):
 def construct_dense_array_dataloader(
     array_uri: str,
     attribute_name: str = "data",
-    num_rows: Optional[int] = None,
-    num_columns: Optional[int] = None,
+    num_rows: int | None = None,
+    num_columns: int | None = None,
     batch_size: int = 1000,
     num_workers_dl: int = 2,
 ) -> DataLoader: