/ usr / lib / python3 / dist-packages / numpy / typing / [ HOME SHELL ]

Name	Size	Permission	Action
__pycache__	[ DIR ]	drwxr-xr-x
tests	[ DIR ]	drwxr-xr-x
__init__.py	11.02 KB	-rw-r--r--
_add_docstring.py	3.73 KB	-rw-r--r--
_array_like.py	3.39 KB	-rw-r--r--
_callable.py	12.44 KB	-rw-r--r--
_char_codes.py	7.33 KB	-rw-r--r--
_dtype_like.py	5.72 KB	-rw-r--r--
_extended_precision.py	1.09 KB	-rw-r--r--
_generic_alias.py	6.23 KB	-rw-r--r--
_nbit.py	345 B	-rw-r--r--
_scalars.py	957 B	-rw-r--r--
_shape.py	380 B	-rw-r--r--
_ufunc.pyi	11.23 KB	-rw-r--r--
mypy_plugin.py	4.3 KB	-rw-r--r--
setup.py	409 B	-rw-r--r--

Code Editor : _array_like.py

from __future__ import annotations

import sys
from typing import (
    Any,
    overload,
    Sequence,
    TYPE_CHECKING,
    Union,
    TypeVar,
    Generic,
)

from numpy import (
    ndarray,
    dtype,
    generic,
    bool_,
    unsignedinteger,
    integer,
    floating,
    complexfloating,
    number,
    timedelta64,
    datetime64,
    object_,
    void,
    str_,
    bytes_,
)
from ._dtype_like import DTypeLike

if sys.version_info >= (3, 8):
    from typing import Protocol
    HAVE_PROTOCOL = True
else:
    try:
        from typing_extensions import Protocol
    except ImportError:
        HAVE_PROTOCOL = False
    else:
        HAVE_PROTOCOL = True

_T = TypeVar("_T")
_ScalarType = TypeVar("_ScalarType", bound=generic)
_DType = TypeVar("_DType", bound="dtype[Any]")
_DType_co = TypeVar("_DType_co", covariant=True, bound="dtype[Any]")

if TYPE_CHECKING or HAVE_PROTOCOL:
    # The `_SupportsArray` protocol only cares about the default dtype
    # (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned
    # array.
    # Concrete implementations of the protocol are responsible for adding
    # any and all remaining overloads
    class _SupportsArray(Protocol[_DType_co]):
        def __array__(self) -> ndarray[Any, _DType_co]: ...
else:
    class _SupportsArray(Generic[_DType_co]):
        pass

# TODO: Wait for support for recursive types
_NestedSequence = Union[
    _T,
    Sequence[_T],
    Sequence[Sequence[_T]],
    Sequence[Sequence[Sequence[_T]]],
    Sequence[Sequence[Sequence[Sequence[_T]]]],
]
_RecursiveSequence = Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]]

# A union representing array-like objects; consists of two typevars:
# One representing types that can be parametrized w.r.t. `np.dtype`
# and another one for the rest
_ArrayLike = Union[
    _NestedSequence[_SupportsArray[_DType]],
    _NestedSequence[_T],
]

# TODO: support buffer protocols once
#
# https://bugs.python.org/issue27501
#
# is resolved. See also the mypy issue:
#
# https://github.com/python/typing/issues/593
ArrayLike = Union[
    _RecursiveSequence,
    _ArrayLike[
        dtype,
        Union[bool, int, float, complex, str, bytes]
    ],
]

# `ArrayLike<X>_co`: array-like objects that can be coerced into `X`
# given the casting rules `same_kind`
_ArrayLikeBool_co = _ArrayLike[
    "dtype[bool_]",
    bool,
]
_ArrayLikeUInt_co = _ArrayLike[
    "dtype[Union[bool_, unsignedinteger[Any]]]",
    bool,
]
_ArrayLikeInt_co = _ArrayLike[
    "dtype[Union[bool_, integer[Any]]]",
    Union[bool, int],
]
_ArrayLikeFloat_co = _ArrayLike[
    "dtype[Union[bool_, integer[Any], floating[Any]]]",
    Union[bool, int, float],
]
_ArrayLikeComplex_co = _ArrayLike[
    "dtype[Union[bool_, integer[Any], floating[Any], complexfloating[Any, Any]]]",
    Union[bool, int, float, complex],
]
_ArrayLikeNumber_co = _ArrayLike[
    "dtype[Union[bool_, number[Any]]]",
    Union[bool, int, float, complex],
]
_ArrayLikeTD64_co = _ArrayLike[
    "dtype[Union[bool_, integer[Any], timedelta64]]",
    Union[bool, int],
]
_ArrayLikeDT64_co = _NestedSequence[_SupportsArray["dtype[datetime64]"]]
_ArrayLikeObject_co = _NestedSequence[_SupportsArray["dtype[object_]"]]

_ArrayLikeVoid_co = _NestedSequence[_SupportsArray["dtype[void]"]]
_ArrayLikeStr_co = _ArrayLike[
    "dtype[str_]",
    str,
]
_ArrayLikeBytes_co = _ArrayLike[
    "dtype[bytes_]",
    bytes,
]

_ArrayLikeInt = _ArrayLike[
    "dtype[integer[Any]]",
    int,
]

Urban Research Lab

Urban Research Lab

The Urban Research Lab (URL) at IIIT-Delhi was established in 2020, during a time when the pandemic brought the complexities of urban life more visible than ever. What started as an initiative by faculty and students has grown into a collaborative space for critically exploring and reimagining the urban questions through research, dialogue, and interdisciplinary engagement.