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__init__.py	6.84 KB	-rw-r--r--
_add_docstring.py	3.83 KB	-rw-r--r--
_array_like.py	4.14 KB	-rw-r--r--
_callable.pyi	10.86 KB	-rw-r--r--
_char_codes.py	5.78 KB	-rw-r--r--
_dtype_like.py	5.53 KB	-rw-r--r--
_extended_precision.py	777 B	-rw-r--r--
_nbit.py	345 B	-rw-r--r--
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setup.py	337 B	-rw-r--r--

Code Editor : _dtype_like.py

from collections.abc import Sequence
from typing import (
    Any,
    Sequence,
    Union,
    TypeVar,
    Protocol,
    TypedDict,
    runtime_checkable,
)

import numpy as np

from ._shape import _ShapeLike

from ._char_codes import (
    _BoolCodes,
    _UInt8Codes,
    _UInt16Codes,
    _UInt32Codes,
    _UInt64Codes,
    _Int8Codes,
    _Int16Codes,
    _Int32Codes,
    _Int64Codes,
    _Float16Codes,
    _Float32Codes,
    _Float64Codes,
    _Complex64Codes,
    _Complex128Codes,
    _ByteCodes,
    _ShortCodes,
    _IntCCodes,
    _IntPCodes,
    _IntCodes,
    _LongLongCodes,
    _UByteCodes,
    _UShortCodes,
    _UIntCCodes,
    _UIntPCodes,
    _UIntCodes,
    _ULongLongCodes,
    _HalfCodes,
    _SingleCodes,
    _DoubleCodes,
    _LongDoubleCodes,
    _CSingleCodes,
    _CDoubleCodes,
    _CLongDoubleCodes,
    _DT64Codes,
    _TD64Codes,
    _StrCodes,
    _BytesCodes,
    _VoidCodes,
    _ObjectCodes,
)

_SCT = TypeVar("_SCT", bound=np.generic)
_DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype[Any])

_DTypeLikeNested = Any  # TODO: wait for support for recursive types

# Mandatory keys
class _DTypeDictBase(TypedDict):
    names: Sequence[str]
    formats: Sequence[_DTypeLikeNested]

# Mandatory + optional keys
class _DTypeDict(_DTypeDictBase, total=False):
    # Only `str` elements are usable as indexing aliases,
    # but `titles` can in principle accept any object
    offsets: Sequence[int]
    titles: Sequence[Any]
    itemsize: int
    aligned: bool

# A protocol for anything with the dtype attribute
@runtime_checkable
class _SupportsDType(Protocol[_DType_co]):
    @property
    def dtype(self) -> _DType_co: ...

# A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic`
_DTypeLike = Union[
    np.dtype[_SCT],
    type[_SCT],
    _SupportsDType[np.dtype[_SCT]],
]

# Would create a dtype[np.void]
_VoidDTypeLike = Union[
    # (flexible_dtype, itemsize)
    tuple[_DTypeLikeNested, int],
    # (fixed_dtype, shape)
    tuple[_DTypeLikeNested, _ShapeLike],
    # [(field_name, field_dtype, field_shape), ...]
    #
    # The type here is quite broad because NumPy accepts quite a wide
    # range of inputs inside the list; see the tests for some
    # examples.
    list[Any],
    # {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ...,
    #  'itemsize': ...}
    _DTypeDict,
    # (base_dtype, new_dtype)
    tuple[_DTypeLikeNested, _DTypeLikeNested],
]

# Anything that can be coerced into numpy.dtype.
# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
DTypeLike = Union[
    np.dtype[Any],
    # default data type (float64)
    None,
    # array-scalar types and generic types
    type[Any],  # NOTE: We're stuck with `type[Any]` due to object dtypes
    # anything with a dtype attribute
    _SupportsDType[np.dtype[Any]],
    # character codes, type strings or comma-separated fields, e.g., 'float64'
    str,
    _VoidDTypeLike,
]

# NOTE: while it is possible to provide the dtype as a dict of
# dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`),
# this syntax is officially discourged and
# therefore not included in the Union defining `DTypeLike`.
#
# See https://github.com/numpy/numpy/issues/16891 for more details.

# Aliases for commonly used dtype-like objects.
# Note that the precision of `np.number` subclasses is ignored herein.
_DTypeLikeBool = Union[
    type[bool],
    type[np.bool_],
    np.dtype[np.bool_],
    _SupportsDType[np.dtype[np.bool_]],
    _BoolCodes,
]
_DTypeLikeUInt = Union[
    type[np.unsignedinteger],
    np.dtype[np.unsignedinteger],
    _SupportsDType[np.dtype[np.unsignedinteger]],
    _UInt8Codes,
    _UInt16Codes,
    _UInt32Codes,
    _UInt64Codes,
    _UByteCodes,
    _UShortCodes,
    _UIntCCodes,
    _UIntPCodes,
    _UIntCodes,
    _ULongLongCodes,
]
_DTypeLikeInt = Union[
    type[int],
    type[np.signedinteger],
    np.dtype[np.signedinteger],
    _SupportsDType[np.dtype[np.signedinteger]],
    _Int8Codes,
    _Int16Codes,
    _Int32Codes,
    _Int64Codes,
    _ByteCodes,
    _ShortCodes,
    _IntCCodes,
    _IntPCodes,
    _IntCodes,
    _LongLongCodes,
]
_DTypeLikeFloat = Union[
    type[float],
    type[np.floating],
    np.dtype[np.floating],
    _SupportsDType[np.dtype[np.floating]],
    _Float16Codes,
    _Float32Codes,
    _Float64Codes,
    _HalfCodes,
    _SingleCodes,
    _DoubleCodes,
    _LongDoubleCodes,
]
_DTypeLikeComplex = Union[
    type[complex],
    type[np.complexfloating],
    np.dtype[np.complexfloating],
    _SupportsDType[np.dtype[np.complexfloating]],
    _Complex64Codes,
    _Complex128Codes,
    _CSingleCodes,
    _CDoubleCodes,
    _CLongDoubleCodes,
]
_DTypeLikeDT64 = Union[
    type[np.timedelta64],
    np.dtype[np.timedelta64],
    _SupportsDType[np.dtype[np.timedelta64]],
    _TD64Codes,
]
_DTypeLikeTD64 = Union[
    type[np.datetime64],
    np.dtype[np.datetime64],
    _SupportsDType[np.dtype[np.datetime64]],
    _DT64Codes,
]
_DTypeLikeStr = Union[
    type[str],
    type[np.str_],
    np.dtype[np.str_],
    _SupportsDType[np.dtype[np.str_]],
    _StrCodes,
]
_DTypeLikeBytes = Union[
    type[bytes],
    type[np.bytes_],
    np.dtype[np.bytes_],
    _SupportsDType[np.dtype[np.bytes_]],
    _BytesCodes,
]
_DTypeLikeVoid = Union[
    type[np.void],
    np.dtype[np.void],
    _SupportsDType[np.dtype[np.void]],
    _VoidCodes,
    _VoidDTypeLike,
]
_DTypeLikeObject = Union[
    type,
    np.dtype[np.object_],
    _SupportsDType[np.dtype[np.object_]],
    _ObjectCodes,
]

_DTypeLikeComplex_co = Union[
    _DTypeLikeBool,
    _DTypeLikeUInt,
    _DTypeLikeInt,
    _DTypeLikeFloat,
    _DTypeLikeComplex,
]

${this.title}

Code Editor : _dtype_like.py