Buckets:
| from collections.abc import Callable | |
| # Using a private class is by no means ideal, but it is simply a consequence | |
| # of a `contextlib.context` returning an instance of aforementioned class | |
| from contextlib import _GeneratorContextManager | |
| from typing import ( | |
| Any, | |
| Final, | |
| Literal, | |
| SupportsIndex, | |
| TypeAlias, | |
| TypedDict, | |
| type_check_only, | |
| ) | |
| import numpy as np | |
| from numpy._typing import NDArray, _CharLike_co, _FloatLike_co | |
| __all__ = [ | |
| "array2string", | |
| "array_repr", | |
| "array_str", | |
| "format_float_positional", | |
| "format_float_scientific", | |
| "get_printoptions", | |
| "printoptions", | |
| "set_printoptions", | |
| ] | |
| ### | |
| _FloatMode: TypeAlias = Literal["fixed", "unique", "maxprec", "maxprec_equal"] | |
| _LegacyNoStyle: TypeAlias = Literal["1.21", "1.25", "2.1", False] | |
| _Legacy: TypeAlias = Literal["1.13", _LegacyNoStyle] | |
| _Sign: TypeAlias = Literal["-", "+", " "] | |
| _Trim: TypeAlias = Literal["k", ".", "0", "-"] | |
| _ReprFunc: TypeAlias = Callable[[NDArray[Any]], str] | |
| class _FormatDict(TypedDict, total=False): | |
| bool: Callable[[np.bool], str] | |
| int: Callable[[np.integer], str] | |
| timedelta: Callable[[np.timedelta64], str] | |
| datetime: Callable[[np.datetime64], str] | |
| float: Callable[[np.floating], str] | |
| longfloat: Callable[[np.longdouble], str] | |
| complexfloat: Callable[[np.complexfloating], str] | |
| longcomplexfloat: Callable[[np.clongdouble], str] | |
| void: Callable[[np.void], str] | |
| numpystr: Callable[[_CharLike_co], str] | |
| object: Callable[[object], str] | |
| all: Callable[[object], str] | |
| int_kind: Callable[[np.integer], str] | |
| float_kind: Callable[[np.floating], str] | |
| complex_kind: Callable[[np.complexfloating], str] | |
| str_kind: Callable[[_CharLike_co], str] | |
| class _FormatOptions(TypedDict): | |
| precision: int | |
| threshold: int | |
| edgeitems: int | |
| linewidth: int | |
| suppress: bool | |
| nanstr: str | |
| infstr: str | |
| formatter: _FormatDict | None | |
| sign: _Sign | |
| floatmode: _FloatMode | |
| legacy: _Legacy | |
| ### | |
| __docformat__: Final = "restructuredtext" # undocumented | |
| def set_printoptions( | |
| precision: SupportsIndex | None = None, | |
| threshold: int | None = None, | |
| edgeitems: int | None = None, | |
| linewidth: int | None = None, | |
| suppress: bool | None = None, | |
| nanstr: str | None = None, | |
| infstr: str | None = None, | |
| formatter: _FormatDict | None = None, | |
| sign: _Sign | None = None, | |
| floatmode: _FloatMode | None = None, | |
| *, | |
| legacy: _Legacy | None = None, | |
| override_repr: _ReprFunc | None = None, | |
| ) -> None: ... | |
| def get_printoptions() -> _FormatOptions: ... | |
| # public numpy export | |
| def array2string( | |
| a: NDArray[Any], | |
| max_line_width: int | None = None, | |
| precision: SupportsIndex | None = None, | |
| suppress_small: bool | None = None, | |
| separator: str = " ", | |
| prefix: str = "", | |
| *, | |
| formatter: _FormatDict | None = None, | |
| threshold: int | None = None, | |
| edgeitems: int | None = None, | |
| sign: _Sign | None = None, | |
| floatmode: _FloatMode | None = None, | |
| suffix: str = "", | |
| legacy: _Legacy | None = None, | |
| ) -> str: ... | |
| def format_float_scientific( | |
| x: _FloatLike_co, | |
| precision: int | None = None, | |
| unique: bool = True, | |
| trim: _Trim = "k", | |
| sign: bool = False, | |
| pad_left: int | None = None, | |
| exp_digits: int | None = None, | |
| min_digits: int | None = None, | |
| ) -> str: ... | |
| def format_float_positional( | |
| x: _FloatLike_co, | |
| precision: int | None = None, | |
| unique: bool = True, | |
| fractional: bool = True, | |
| trim: _Trim = "k", | |
| sign: bool = False, | |
| pad_left: int | None = None, | |
| pad_right: int | None = None, | |
| min_digits: int | None = None, | |
| ) -> str: ... | |
| def array_repr( | |
| arr: NDArray[Any], | |
| max_line_width: int | None = None, | |
| precision: SupportsIndex | None = None, | |
| suppress_small: bool | None = None, | |
| ) -> str: ... | |
| def array_str( | |
| a: NDArray[Any], | |
| max_line_width: int | None = None, | |
| precision: SupportsIndex | None = None, | |
| suppress_small: bool | None = None, | |
| ) -> str: ... | |
| def printoptions( | |
| precision: SupportsIndex | None = ..., | |
| threshold: int | None = ..., | |
| edgeitems: int | None = ..., | |
| linewidth: int | None = ..., | |
| suppress: bool | None = ..., | |
| nanstr: str | None = ..., | |
| infstr: str | None = ..., | |
| formatter: _FormatDict | None = ..., | |
| sign: _Sign | None = None, | |
| floatmode: _FloatMode | None = None, | |
| *, | |
| legacy: _Legacy | None = None, | |
| override_repr: _ReprFunc | None = None, | |
| ) -> _GeneratorContextManager[_FormatOptions]: ... | |
Xet Storage Details
- Size:
- 4.53 kB
- Xet hash:
- 2dfe1e750dbf7606d4d7e74ca74c59552e89395ab7f87621c24e16897ac6baa6
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.