Buckets:
| import os | |
| from _typeshed import SupportsRead, SupportsWrite | |
| from typing import Any, BinaryIO, Final, TypeAlias, TypeGuard | |
| import numpy as np | |
| import numpy.typing as npt | |
| from numpy.lib._utils_impl import drop_metadata as drop_metadata | |
| __all__: list[str] = [] | |
| _DTypeDescr: TypeAlias = list[tuple[str, str]] | list[tuple[str, str, tuple[int, ...]]] | |
| ### | |
| EXPECTED_KEYS: Final[set[str]] = ... | |
| MAGIC_PREFIX: Final = b"\x93NUMPY" | |
| MAGIC_LEN: Final = 8 | |
| ARRAY_ALIGN: Final = 64 | |
| BUFFER_SIZE: Final = 262_144 # 1 << 18 | |
| GROWTH_AXIS_MAX_DIGITS: Final = 21 | |
| _MAX_HEADER_SIZE: Final = 10_000 | |
| def magic(major: int, minor: int) -> bytes: ... | |
| def read_magic(fp: SupportsRead[bytes]) -> tuple[int, int]: ... | |
| def dtype_to_descr(dtype: np.dtype) -> _DTypeDescr: ... | |
| def descr_to_dtype(descr: _DTypeDescr) -> np.dtype: ... | |
| def header_data_from_array_1_0(array: np.ndarray) -> dict[str, Any]: ... | |
| def write_array_header_1_0(fp: SupportsWrite[bytes], d: dict[str, Any]) -> None: ... | |
| def write_array_header_2_0(fp: SupportsWrite[bytes], d: dict[str, Any]) -> None: ... | |
| def read_array_header_1_0(fp: SupportsRead[bytes], max_header_size: int = 10_000) -> tuple[tuple[int, ...], bool, np.dtype]: ... | |
| def read_array_header_2_0(fp: SupportsRead[bytes], max_header_size: int = 10_000) -> tuple[tuple[int, ...], bool, np.dtype]: ... | |
| def write_array( | |
| fp: SupportsWrite[bytes], | |
| array: np.ndarray, | |
| version: tuple[int, int] | None = None, | |
| allow_pickle: bool = True, | |
| pickle_kwargs: dict[str, Any] | None = None, | |
| ) -> None: ... | |
| def read_array( | |
| fp: SupportsRead[bytes], | |
| allow_pickle: bool = False, | |
| pickle_kwargs: dict[str, Any] | None = None, | |
| *, | |
| max_header_size: int = 10_000, | |
| ) -> np.ndarray: ... | |
| def open_memmap( | |
| filename: str | os.PathLike[Any], | |
| mode: str = "r+", | |
| dtype: npt.DTypeLike | None = None, | |
| shape: tuple[int, ...] | None = None, | |
| fortran_order: bool = False, | |
| version: tuple[int, int] | None = None, | |
| *, | |
| max_header_size: int = 10_000, | |
| ) -> np.memmap: ... | |
| def isfileobj(f: object) -> TypeGuard[BinaryIO]: ... # don't use `typing.TypeIs` | |
Xet Storage Details
- Size:
- 2.08 kB
- Xet hash:
- b8987db219d1bbb67cbc736fae319b536cb7ae71c7edada25c33b4f64cf5a807
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.