Apply ruff/flake8-type-checking rule TC006

TC006 Add quotes to type expression in `typing.cast()`

Author: DimitriPapadopoulos

src/zarr/api/asynchronous.py

@@ -321,7 +321,7 @@ async def open(
         try:
             metadata_dict = await get_array_metadata(store_path, zarr_format=zarr_format)
             # TODO: remove this cast when we fix typing for array metadata dicts
-            _metadata_dict = cast(ArrayMetadataDict, metadata_dict)
+            _metadata_dict = cast("ArrayMetadataDict", metadata_dict)
             # for v2, the above would already have raised an exception if not an array
             zarr_format = _metadata_dict["zarr_format"]
             is_v3_array = zarr_format == 3 and _metadata_dict.get("node_type") == "array"

src/zarr/codecs/crc32c_.py

@@ -40,7 +40,9 @@ async def _decode_single(
         inner_bytes = data[:-4]
 
         # Need to do a manual cast until https://github.com/numpy/numpy/issues/26783 is resolved
-        computed_checksum = np.uint32(crc32c(cast(typing_extensions.Buffer, inner_bytes))).tobytes()
+        computed_checksum = np.uint32(
+            crc32c(cast("typing_extensions.Buffer", inner_bytes))
+        ).tobytes()
         stored_checksum = bytes(crc32_bytes)
         if computed_checksum != stored_checksum:
             raise ValueError(
@@ -55,7 +57,7 @@ async def _encode_single(
     ) -> Buffer | None:
         data = chunk_bytes.as_numpy_array()
         # Calculate the checksum and "cast" it to a numpy array
-        checksum = np.array([crc32c(cast(typing_extensions.Buffer, data))], dtype=np.uint32)
+        checksum = np.array([crc32c(cast("typing_extensions.Buffer", data))], dtype=np.uint32)
         # Append the checksum (as bytes) to the data
         return chunk_spec.prototype.buffer.from_array_like(np.append(data, checksum.view("B")))
 

src/zarr/codecs/sharding.py

@@ -115,7 +115,7 @@ class _ShardIndex(NamedTuple):
     def chunks_per_shard(self) -> ChunkCoords:
         result = tuple(self.offsets_and_lengths.shape[0:-1])
         # The cast is required until https://github.com/numpy/numpy/pull/27211 is merged
-        return cast(ChunkCoords, result)
+        return cast("ChunkCoords", result)
 
     def _localize_chunk(self, chunk_coords: ChunkCoords) -> ChunkCoords:
         return tuple(

src/zarr/codecs/transpose.py

@@ -23,7 +23,7 @@ def parse_transpose_order(data: JSON | Iterable[int]) -> tuple[int, ...]:
         raise TypeError(f"Expected an iterable. Got {data} instead.")
     if not all(isinstance(a, int) for a in data):
         raise TypeError(f"Expected an iterable of integers. Got {data} instead.")
-    return tuple(cast(Iterable[int], data))
+    return tuple(cast("Iterable[int]", data))
 
 
 @dataclass(frozen=True)

src/zarr/core/array.py

@@ -268,7 +268,7 @@ def __init__(
         if isinstance(metadata, dict):
             zarr_format = metadata["zarr_format"]
             # TODO: remove this when we extensively type the dict representation of metadata
-            _metadata = cast(dict[str, JSON], metadata)
+            _metadata = cast("dict[str, JSON]", metadata)
             if zarr_format == 2:
                 metadata = ArrayV2Metadata.from_dict(_metadata)
             elif zarr_format == 3:
@@ -898,7 +898,7 @@ async def open(
         store_path = await make_store_path(store)
         metadata_dict = await get_array_metadata(store_path, zarr_format=zarr_format)
         # TODO: remove this cast when we have better type hints
-        _metadata_dict = cast(ArrayV3MetadataDict, metadata_dict)
+        _metadata_dict = cast("ArrayV3MetadataDict", metadata_dict)
         return cls(store_path=store_path, metadata=_metadata_dict)
 
     @property
@@ -1394,7 +1394,7 @@ async def _set_selection(
             if isinstance(array_like, np._typing._SupportsArrayFunc):
                 # TODO: need to handle array types that don't support __array_function__
                 # like PyTorch and JAX
-                array_like_ = cast(np._typing._SupportsArrayFunc, array_like)
+                array_like_ = cast("np._typing._SupportsArrayFunc", array_like)
             value = np.asanyarray(value, dtype=self.metadata.dtype, like=array_like_)
         else:
             if not hasattr(value, "shape"):
@@ -1408,7 +1408,7 @@ async def _set_selection(
                     value = value.astype(dtype=self.metadata.dtype, order="A")
                 else:
                     value = np.array(value, dtype=self.metadata.dtype, order="A")
-        value = cast(NDArrayLike, value)
+        value = cast("NDArrayLike", value)
         # We accept any ndarray like object from the user and convert it
         # to a NDBuffer (or subclass). From this point onwards, we only pass
         # Buffer and NDBuffer between components.
@@ -2431,11 +2431,11 @@ def __getitem__(self, selection: Selection) -> NDArrayLikeOrScalar:
         """
         fields, pure_selection = pop_fields(selection)
         if is_pure_fancy_indexing(pure_selection, self.ndim):
-            return self.vindex[cast(CoordinateSelection | MaskSelection, selection)]
+            return self.vindex[cast("CoordinateSelection | MaskSelection", selection)]
         elif is_pure_orthogonal_indexing(pure_selection, self.ndim):
             return self.get_orthogonal_selection(pure_selection, fields=fields)
         else:
-            return self.get_basic_selection(cast(BasicSelection, pure_selection), fields=fields)
+            return self.get_basic_selection(cast("BasicSelection", pure_selection), fields=fields)
 
     def __setitem__(self, selection: Selection, value: npt.ArrayLike) -> None:
         """Modify data for an item or region of the array.
@@ -2530,11 +2530,11 @@ def __setitem__(self, selection: Selection, value: npt.ArrayLike) -> None:
         """
         fields, pure_selection = pop_fields(selection)
         if is_pure_fancy_indexing(pure_selection, self.ndim):
-            self.vindex[cast(CoordinateSelection | MaskSelection, selection)] = value
+            self.vindex[cast("CoordinateSelection | MaskSelection", selection)] = value
         elif is_pure_orthogonal_indexing(pure_selection, self.ndim):
             self.set_orthogonal_selection(pure_selection, value, fields=fields)
         else:
-            self.set_basic_selection(cast(BasicSelection, pure_selection), value, fields=fields)
+            self.set_basic_selection(cast("BasicSelection", pure_selection), value, fields=fields)
 
     @_deprecate_positional_args
     def get_basic_selection(
@@ -3652,7 +3652,7 @@ def update_attributes(self, new_attributes: dict[str, JSON]) -> Array:
         # TODO: remove this cast when type inference improves
         new_array = sync(self._async_array.update_attributes(new_attributes))
         # TODO: remove this cast when type inference improves
-        _new_array = cast(AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata], new_array)
+        _new_array = cast("AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata]", new_array)
         return type(self)(_new_array)
 
     def __repr__(self) -> str:
@@ -4238,7 +4238,7 @@ async def init_array(
             serializer=serializer,
             dtype=dtype_parsed,
         )
-        sub_codecs = cast(tuple[Codec, ...], (*array_array, array_bytes, *bytes_bytes))
+        sub_codecs = cast("tuple[Codec, ...]", (*array_array, array_bytes, *bytes_bytes))
         codecs_out: tuple[Codec, ...]
         if shard_shape_parsed is not None:
             index_location = None
@@ -4509,7 +4509,7 @@ def _parse_keep_array_attr(
                 compressors = "auto"
         if serializer == "keep":
             if zarr_format == 3 and data.metadata.zarr_format == 3:
-                serializer = cast(SerializerLike, data.serializer)
+                serializer = cast("SerializerLike", data.serializer)
             else:
                 serializer = "auto"
         if fill_value is None:
@@ -4687,7 +4687,7 @@ def _parse_chunk_encoding_v3(
         if isinstance(filters, dict | Codec):
             maybe_array_array = (filters,)
         else:
-            maybe_array_array = cast(Iterable[Codec | dict[str, JSON]], filters)
+            maybe_array_array = cast("Iterable[Codec | dict[str, JSON]]", filters)
         out_array_array = tuple(_parse_array_array_codec(c) for c in maybe_array_array)
 
     if serializer == "auto":
@@ -4704,7 +4704,7 @@ def _parse_chunk_encoding_v3(
         if isinstance(compressors, dict | Codec):
             maybe_bytes_bytes = (compressors,)
         else:
-            maybe_bytes_bytes = cast(Iterable[Codec | dict[str, JSON]], compressors)
+            maybe_bytes_bytes = cast("Iterable[Codec | dict[str, JSON]]", compressors)
 
         out_bytes_bytes = tuple(_parse_bytes_bytes_codec(c) for c in maybe_bytes_bytes)
 

src/zarr/core/array_spec.py

@@ -64,7 +64,7 @@ def from_dict(cls, data: ArrayConfigParams) -> Self:
         """
         kwargs_out: ArrayConfigParams = {}
         for f in fields(ArrayConfig):
-            field_name = cast(Literal["order", "write_empty_chunks"], f.name)
+            field_name = cast("Literal['order', 'write_empty_chunks']", f.name)
             if field_name not in data:
                 kwargs_out[field_name] = zarr_config.get(f"array.{field_name}")
             else:

src/zarr/core/buffer/core.py

@@ -159,7 +159,7 @@ def create_zero_length(cls) -> Self:
         if cls is Buffer:
             raise NotImplementedError("Cannot call abstract method on the abstract class 'Buffer'")
         return cls(
-            cast(ArrayLike, None)
+            cast("ArrayLike", None)
         )  # This line will never be reached, but it satisfies the type checker
 
     @classmethod
@@ -207,7 +207,7 @@ def from_buffer(cls, buffer: Buffer) -> Self:
         if cls is Buffer:
             raise NotImplementedError("Cannot call abstract method on the abstract class 'Buffer'")
         return cls(
-            cast(ArrayLike, None)
+            cast("ArrayLike", None)
         )  # This line will never be reached, but it satisfies the type checker
 
     @classmethod
@@ -227,7 +227,7 @@ def from_bytes(cls, bytes_like: BytesLike) -> Self:
         if cls is Buffer:
             raise NotImplementedError("Cannot call abstract method on the abstract class 'Buffer'")
         return cls(
-            cast(ArrayLike, None)
+            cast("ArrayLike", None)
         )  # This line will never be reached, but it satisfies the type checker
 
     def as_array_like(self) -> ArrayLike:
@@ -358,7 +358,7 @@ def create(
                 "Cannot call abstract method on the abstract class 'NDBuffer'"
             )
         return cls(
-            cast(NDArrayLike, None)
+            cast("NDArrayLike", None)
         )  # This line will never be reached, but it satisfies the type checker
 
     @classmethod
@@ -395,7 +395,7 @@ def from_numpy_array(cls, array_like: npt.ArrayLike) -> Self:
                 "Cannot call abstract method on the abstract class 'NDBuffer'"
             )
         return cls(
-            cast(NDArrayLike, None)
+            cast("NDArrayLike", None)
         )  # This line will never be reached, but it satisfies the type checker
 
     def as_ndarray_like(self) -> NDArrayLike:
@@ -427,7 +427,7 @@ def as_scalar(self) -> ScalarType:
         """Returns the buffer as a scalar value"""
         if self._data.size != 1:
             raise ValueError("Buffer does not contain a single scalar value")
-        return cast(ScalarType, self.as_numpy_array()[()])
+        return cast("ScalarType", self.as_numpy_array()[()])
 
     @property
     def dtype(self) -> np.dtype[Any]:

src/zarr/core/buffer/gpu.py

@@ -103,7 +103,7 @@ def from_bytes(cls, bytes_like: BytesLike) -> Self:
         return cls.from_array_like(cp.frombuffer(bytes_like, dtype="B"))
 
     def as_numpy_array(self) -> npt.NDArray[Any]:
-        return cast(npt.NDArray[Any], cp.asnumpy(self._data))
+        return cast("npt.NDArray[Any]", cp.asnumpy(self._data))
 
     def __add__(self, other: core.Buffer) -> Self:
         other_array = other.as_array_like()
@@ -204,7 +204,7 @@ def as_numpy_array(self) -> npt.NDArray[Any]:
         -------
             NumPy array of this buffer (might be a data copy)
         """
-        return cast(npt.NDArray[Any], cp.asnumpy(self._data))
+        return cast("npt.NDArray[Any]", cp.asnumpy(self._data))
 
     def __getitem__(self, key: Any) -> Self:
         return self.__class__(self._data.__getitem__(key))

src/zarr/core/chunk_key_encodings.py

@@ -20,7 +20,7 @@
 def parse_separator(data: JSON) -> SeparatorLiteral:
     if data not in (".", "/"):
         raise ValueError(f"Expected an '.' or '/' separator. Got {data} instead.")
-    return cast(SeparatorLiteral, data)
+    return cast("SeparatorLiteral", data)
 
 
 class ChunkKeyEncodingParams(TypedDict):
@@ -48,7 +48,7 @@ def from_dict(cls, data: dict[str, JSON] | ChunkKeyEncodingLike) -> ChunkKeyEnco
             data = {"name": data["name"], "configuration": {"separator": data["separator"]}}
 
         # TODO: remove this cast when we are statically typing the JSON metadata completely.
-        data = cast(dict[str, JSON], data)
+        data = cast("dict[str, JSON]", data)
 
         # configuration is optional for chunk key encodings
         name_parsed, config_parsed = parse_named_configuration(data, require_configuration=False)

src/zarr/core/common.py

@@ -157,7 +157,7 @@ def parse_fill_value(data: Any) -> Any:
 
 def parse_order(data: Any) -> Literal["C", "F"]:
     if data in ("C", "F"):
-        return cast(Literal["C", "F"], data)
+        return cast("Literal['C', 'F']", data)
     raise ValueError(f"Expected one of ('C', 'F'), got {data} instead.")
 
 
@@ -201,4 +201,4 @@ def _warn_order_kwarg() -> None:
 
 def _default_zarr_format() -> ZarrFormat:
     """Return the default zarr_version"""
-    return cast(ZarrFormat, int(zarr_config.get("default_zarr_format", 3)))
+    return cast("ZarrFormat", int(zarr_config.get("default_zarr_format", 3)))