pre-commit linting

Author: lgray

.gitignore

@@ -34,6 +34,9 @@ MANIFEST
 *.manifest
 *.spec
 
+# scikit-build-core dynamic version
+src/cubed/version.py
+
 # Installer logs
 pip-log.txt
 pip-delete-this-directory.txt

src/cubed/vendor/dask/array/core.py

@@ -4,7 +4,7 @@
 from numbers import Number
 
 import numpy as np
-from tlz import first, frequencies
+from tlz import frequencies
 
 from ..utils import is_integer, parse_bytes
 
@@ -312,7 +312,7 @@ def auto_chunks(chunks, shape, limit, dtype, previous_chunks=None):
         return tuple(chunks)
 
     if limit is None:
-        limit = "128MiB" # config.get("array.chunk-size")
+        limit = "128MiB"  # config.get("array.chunk-size")
     if isinstance(limit, str):
         limit = parse_bytes(limit)
 

src/cubed/vendor/dask/array/core.pyi

@@ -3,18 +3,12 @@ from typing import List, Optional, Tuple
 from cubed.types import T_Chunks, T_DType, T_RectangularChunks, T_Shape
 
 def _check_regular_chunks(chunkset: T_RectangularChunks) -> bool: ...
-
-def broadcast_chunks(
-    *chunkss
-) -> Tuple[Tuple[int, ...]]: ...
-
+def broadcast_chunks(*chunkss) -> Tuple[Tuple[int, ...]]: ...
 def common_blockdim(blockdims: List[Tuple[int, ...]]) -> Tuple[int, ...]: ...
-
 def normalize_chunks(
     chunks: T_Chunks,
     shape: Optional[T_Shape] = None,
     limit: Optional[int] = None,
     dtype: Optional[T_DType] = None,
     previous_chunks: Optional[T_RectangularChunks] = None,
-) -> T_RectangularChunks:
-    ...
+) -> T_RectangularChunks: ...

src/cubed/vendor/dask/blockwise.py

@@ -358,11 +358,6 @@ def lol_product(head, values):
         return lol_product(head + (values[0],), values[1:])
 
 
-
-
-
-
-
 def broadcast_dimensions(argpairs, numblocks, sentinels=(1, (1,)), consolidate=None):
     """Find block dimensions from arguments
 

src/cubed/vendor/rechunker/algorithm.py

@@ -1,4 +1,5 @@
 """Core rechunking algorithm stuff."""
+
 import logging
 import warnings
 from math import ceil, floor, prod
@@ -270,7 +271,6 @@ def multistage_rechunking_plan(
 
     # increase the number of stages until min_mem is exceeded
     for stage_count in range(1, MAX_STAGES):
-
         stage_chunks = calculate_stage_chunks(read_chunks, write_chunks, stage_count)
         pre_chunks = [read_chunks] + stage_chunks
         post_chunks = stage_chunks + [write_chunks]
@@ -297,6 +297,7 @@ def multistage_rechunking_plan(
                 f"Consider decreasing min_mem ({min_mem}) or increasing "
                 f"({max_mem}) to find a more efficient plan.",
                 category=ExcessiveIOWarning,
+                stacklevel=2,
             )
             assert prev_plan is not None
             return prev_plan

src/cubed/vendor/rechunker/types.py

@@ -1,4 +1,5 @@
 """Types definitions used by executors."""
+
 from dataclasses import dataclass
 from typing import (
     Any,
@@ -61,12 +62,8 @@ class CopySpec(NamedTuple):
 
 
 Config = Any  # TODO: better typing for config
-SingleArgumentStageFunction = Callable[
-    [Any, NamedArg(type=Any, name="config")], None
-]  # noqa: F821
-NoArgumentStageFunction = Callable[
-    [NamedArg(type=Any, name="config")], None
-]  # noqa: F821
+SingleArgumentStageFunction = Callable[[Any, NamedArg(type=Any, name="config")], None]  # noqa: F821
+NoArgumentStageFunction = Callable[[NamedArg(type=Any, name="config")], None]  # noqa: F821
 StageFunction = Union[NoArgumentStageFunction, SingleArgumentStageFunction]