Coverage for /usr/lib/python3/dist-packages/numpy/_typing/_dtype_like.py: 100%
35 statements
« prev ^ index » next coverage.py v7.4.4, created at 2025-06-14 15:25 +0200
1from collections.abc import Sequence
2from typing import (
3 Any,
4 Sequence,
5 Union,
6 TypeVar,
7 Protocol,
8 TypedDict,
9 runtime_checkable,
10)
12import numpy as np
14from ._shape import _ShapeLike
16from ._char_codes import (
17 _BoolCodes,
18 _UInt8Codes,
19 _UInt16Codes,
20 _UInt32Codes,
21 _UInt64Codes,
22 _Int8Codes,
23 _Int16Codes,
24 _Int32Codes,
25 _Int64Codes,
26 _Float16Codes,
27 _Float32Codes,
28 _Float64Codes,
29 _Complex64Codes,
30 _Complex128Codes,
31 _ByteCodes,
32 _ShortCodes,
33 _IntCCodes,
34 _IntPCodes,
35 _IntCodes,
36 _LongLongCodes,
37 _UByteCodes,
38 _UShortCodes,
39 _UIntCCodes,
40 _UIntPCodes,
41 _UIntCodes,
42 _ULongLongCodes,
43 _HalfCodes,
44 _SingleCodes,
45 _DoubleCodes,
46 _LongDoubleCodes,
47 _CSingleCodes,
48 _CDoubleCodes,
49 _CLongDoubleCodes,
50 _DT64Codes,
51 _TD64Codes,
52 _StrCodes,
53 _BytesCodes,
54 _VoidCodes,
55 _ObjectCodes,
56)
58_SCT = TypeVar("_SCT", bound=np.generic)
59_DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype[Any])
61_DTypeLikeNested = Any # TODO: wait for support for recursive types
64# Mandatory keys
65class _DTypeDictBase(TypedDict):
66 names: Sequence[str]
67 formats: Sequence[_DTypeLikeNested]
70# Mandatory + optional keys
71class _DTypeDict(_DTypeDictBase, total=False):
72 # Only `str` elements are usable as indexing aliases,
73 # but `titles` can in principle accept any object
74 offsets: Sequence[int]
75 titles: Sequence[Any]
76 itemsize: int
77 aligned: bool
80# A protocol for anything with the dtype attribute
81@runtime_checkable
82class _SupportsDType(Protocol[_DType_co]):
83 @property
84 def dtype(self) -> _DType_co: ...
87# A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic`
88_DTypeLike = Union[
89 np.dtype[_SCT],
90 type[_SCT],
91 _SupportsDType[np.dtype[_SCT]],
92]
95# Would create a dtype[np.void]
96_VoidDTypeLike = Union[
97 # (flexible_dtype, itemsize)
98 tuple[_DTypeLikeNested, int],
99 # (fixed_dtype, shape)
100 tuple[_DTypeLikeNested, _ShapeLike],
101 # [(field_name, field_dtype, field_shape), ...]
102 #
103 # The type here is quite broad because NumPy accepts quite a wide
104 # range of inputs inside the list; see the tests for some
105 # examples.
106 list[Any],
107 # {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ...,
108 # 'itemsize': ...}
109 _DTypeDict,
110 # (base_dtype, new_dtype)
111 tuple[_DTypeLikeNested, _DTypeLikeNested],
112]
114# Anything that can be coerced into numpy.dtype.
115# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
116DTypeLike = Union[
117 np.dtype[Any],
118 # default data type (float64)
119 None,
120 # array-scalar types and generic types
121 type[Any], # NOTE: We're stuck with `type[Any]` due to object dtypes
122 # anything with a dtype attribute
123 _SupportsDType[np.dtype[Any]],
124 # character codes, type strings or comma-separated fields, e.g., 'float64'
125 str,
126 _VoidDTypeLike,
127]
129# NOTE: while it is possible to provide the dtype as a dict of
130# dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`),
131# this syntax is officially discourged and
132# therefore not included in the Union defining `DTypeLike`.
133#
134# See https://github.com/numpy/numpy/issues/16891 for more details.
136# Aliases for commonly used dtype-like objects.
137# Note that the precision of `np.number` subclasses is ignored herein.
138_DTypeLikeBool = Union[
139 type[bool],
140 type[np.bool_],
141 np.dtype[np.bool_],
142 _SupportsDType[np.dtype[np.bool_]],
143 _BoolCodes,
144]
145_DTypeLikeUInt = Union[
146 type[np.unsignedinteger],
147 np.dtype[np.unsignedinteger],
148 _SupportsDType[np.dtype[np.unsignedinteger]],
149 _UInt8Codes,
150 _UInt16Codes,
151 _UInt32Codes,
152 _UInt64Codes,
153 _UByteCodes,
154 _UShortCodes,
155 _UIntCCodes,
156 _UIntPCodes,
157 _UIntCodes,
158 _ULongLongCodes,
159]
160_DTypeLikeInt = Union[
161 type[int],
162 type[np.signedinteger],
163 np.dtype[np.signedinteger],
164 _SupportsDType[np.dtype[np.signedinteger]],
165 _Int8Codes,
166 _Int16Codes,
167 _Int32Codes,
168 _Int64Codes,
169 _ByteCodes,
170 _ShortCodes,
171 _IntCCodes,
172 _IntPCodes,
173 _IntCodes,
174 _LongLongCodes,
175]
176_DTypeLikeFloat = Union[
177 type[float],
178 type[np.floating],
179 np.dtype[np.floating],
180 _SupportsDType[np.dtype[np.floating]],
181 _Float16Codes,
182 _Float32Codes,
183 _Float64Codes,
184 _HalfCodes,
185 _SingleCodes,
186 _DoubleCodes,
187 _LongDoubleCodes,
188]
189_DTypeLikeComplex = Union[
190 type[complex],
191 type[np.complexfloating],
192 np.dtype[np.complexfloating],
193 _SupportsDType[np.dtype[np.complexfloating]],
194 _Complex64Codes,
195 _Complex128Codes,
196 _CSingleCodes,
197 _CDoubleCodes,
198 _CLongDoubleCodes,
199]
200_DTypeLikeDT64 = Union[
201 type[np.timedelta64],
202 np.dtype[np.timedelta64],
203 _SupportsDType[np.dtype[np.timedelta64]],
204 _TD64Codes,
205]
206_DTypeLikeTD64 = Union[
207 type[np.datetime64],
208 np.dtype[np.datetime64],
209 _SupportsDType[np.dtype[np.datetime64]],
210 _DT64Codes,
211]
212_DTypeLikeStr = Union[
213 type[str],
214 type[np.str_],
215 np.dtype[np.str_],
216 _SupportsDType[np.dtype[np.str_]],
217 _StrCodes,
218]
219_DTypeLikeBytes = Union[
220 type[bytes],
221 type[np.bytes_],
222 np.dtype[np.bytes_],
223 _SupportsDType[np.dtype[np.bytes_]],
224 _BytesCodes,
225]
226_DTypeLikeVoid = Union[
227 type[np.void],
228 np.dtype[np.void],
229 _SupportsDType[np.dtype[np.void]],
230 _VoidCodes,
231 _VoidDTypeLike,
232]
233_DTypeLikeObject = Union[
234 type,
235 np.dtype[np.object_],
236 _SupportsDType[np.dtype[np.object_]],
237 _ObjectCodes,
238]
240_DTypeLikeComplex_co = Union[
241 _DTypeLikeBool,
242 _DTypeLikeUInt,
243 _DTypeLikeInt,
244 _DTypeLikeFloat,
245 _DTypeLikeComplex,
246]