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1""" 

2============================ 

3Typing (:mod:`numpy.typing`) 

4============================ 

5 

6.. versionadded:: 1.20 

7 

8Large parts of the NumPy API have :pep:`484`-style type annotations. In 

9addition a number of type aliases are available to users, most prominently 

10the two below: 

11 

12- `ArrayLike`: objects that can be converted to arrays 

13- `DTypeLike`: objects that can be converted to dtypes 

14 

15.. _typing-extensions: https://pypi.org/project/typing-extensions/ 

16 

17Mypy plugin 

18----------- 

19 

20.. versionadded:: 1.21 

21 

22.. automodule:: numpy.typing.mypy_plugin 

23 

24.. currentmodule:: numpy.typing 

25 

26Differences from the runtime NumPy API 

27-------------------------------------- 

28 

29NumPy is very flexible. Trying to describe the full range of 

30possibilities statically would result in types that are not very 

31helpful. For that reason, the typed NumPy API is often stricter than 

32the runtime NumPy API. This section describes some notable 

33differences. 

34 

35ArrayLike 

36~~~~~~~~~ 

37 

38The `ArrayLike` type tries to avoid creating object arrays. For 

39example, 

40 

41.. code-block:: python 

42 

43 >>> np.array(x**2 for x in range(10)) 

44 array(<generator object <genexpr> at ...>, dtype=object) 

45 

46is valid NumPy code which will create a 0-dimensional object 

47array. Type checkers will complain about the above example when using 

48the NumPy types however. If you really intended to do the above, then 

49you can either use a ``# type: ignore`` comment: 

50 

51.. code-block:: python 

52 

53 >>> np.array(x**2 for x in range(10)) # type: ignore 

54 

55or explicitly type the array like object as `~typing.Any`: 

56 

57.. code-block:: python 

58 

59 >>> from typing import Any 

60 >>> array_like: Any = (x**2 for x in range(10)) 

61 >>> np.array(array_like) 

62 array(<generator object <genexpr> at ...>, dtype=object) 

63 

64ndarray 

65~~~~~~~ 

66 

67It's possible to mutate the dtype of an array at runtime. For example, 

68the following code is valid: 

69 

70.. code-block:: python 

71 

72 >>> x = np.array([1, 2]) 

73 >>> x.dtype = np.bool_ 

74 

75This sort of mutation is not allowed by the types. Users who want to 

76write statically typed code should instead use the `numpy.ndarray.view` 

77method to create a view of the array with a different dtype. 

78 

79DTypeLike 

80~~~~~~~~~ 

81 

82The `DTypeLike` type tries to avoid creation of dtype objects using 

83dictionary of fields like below: 

84 

85.. code-block:: python 

86 

87 >>> x = np.dtype({"field1": (float, 1), "field2": (int, 3)}) 

88 

89Although this is valid NumPy code, the type checker will complain about it, 

90since its usage is discouraged. 

91Please see : :ref:`Data type objects <arrays.dtypes>` 

92 

93Number precision 

94~~~~~~~~~~~~~~~~ 

95 

96The precision of `numpy.number` subclasses is treated as a covariant generic 

97parameter (see :class:`~NBitBase`), simplifying the annotating of processes 

98involving precision-based casting. 

99 

100.. code-block:: python 

101 

102 >>> from typing import TypeVar 

103 >>> import numpy as np 

104 >>> import numpy.typing as npt 

105 

106 >>> T = TypeVar("T", bound=npt.NBitBase) 

107 >>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]": 

108 ... ... 

109 

110Consequently, the likes of `~numpy.float16`, `~numpy.float32` and 

111`~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to 

112runtime, they're not necessarily considered as sub-classes. 

113 

114Timedelta64 

115~~~~~~~~~~~ 

116 

117The `~numpy.timedelta64` class is not considered a subclass of 

118`~numpy.signedinteger`, the former only inheriting from `~numpy.generic` 

119while static type checking. 

120 

1210D arrays 

122~~~~~~~~~ 

123 

124During runtime numpy aggressively casts any passed 0D arrays into their 

125corresponding `~numpy.generic` instance. Until the introduction of shape 

126typing (see :pep:`646`) it is unfortunately not possible to make the 

127necessary distinction between 0D and >0D arrays. While thus not strictly 

128correct, all operations are that can potentially perform a 0D-array -> scalar 

129cast are currently annotated as exclusively returning an `ndarray`. 

130 

131If it is known in advance that an operation _will_ perform a 

1320D-array -> scalar cast, then one can consider manually remedying the 

133situation with either `typing.cast` or a ``# type: ignore`` comment. 

134 

135Record array dtypes 

136~~~~~~~~~~~~~~~~~~~ 

137 

138The dtype of `numpy.recarray`, and the `numpy.rec` functions in general, 

139can be specified in one of two ways: 

140 

141* Directly via the ``dtype`` argument. 

142* With up to five helper arguments that operate via `numpy.format_parser`: 

143 ``formats``, ``names``, ``titles``, ``aligned`` and ``byteorder``. 

144 

145These two approaches are currently typed as being mutually exclusive, 

146*i.e.* if ``dtype`` is specified than one may not specify ``formats``. 

147While this mutual exclusivity is not (strictly) enforced during runtime, 

148combining both dtype specifiers can lead to unexpected or even downright 

149buggy behavior. 

150 

151API 

152--- 

153 

154""" 

155# NOTE: The API section will be appended with additional entries 

156# further down in this file 

157 

158from numpy._typing import ( 

159 ArrayLike, 

160 DTypeLike, 

161 NBitBase, 

162 NDArray, 

163) 

164 

165__all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"] 

166 

167if __doc__ is not None: 

168 from numpy._typing._add_docstring import _docstrings 

169 __doc__ += _docstrings 

170 __doc__ += '\n.. autoclass:: numpy.typing.NBitBase\n' 

171 del _docstrings 

172 

173from numpy._pytesttester import PytestTester 

174test = PytestTester(__name__) 

175del PytestTester