176 lines
5.1 KiB
Python
176 lines
5.1 KiB
Python
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"""
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============================
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Typing (:mod:`numpy.typing`)
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============================
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.. versionadded:: 1.20
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Large parts of the NumPy API have PEP-484-style type annotations. In
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addition a number of type aliases are available to users, most prominently
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the two below:
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- `ArrayLike`: objects that can be converted to arrays
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- `DTypeLike`: objects that can be converted to dtypes
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.. _typing-extensions: https://pypi.org/project/typing-extensions/
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Mypy plugin
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-----------
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.. versionadded:: 1.21
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.. automodule:: numpy.typing.mypy_plugin
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.. currentmodule:: numpy.typing
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Differences from the runtime NumPy API
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--------------------------------------
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NumPy is very flexible. Trying to describe the full range of
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possibilities statically would result in types that are not very
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helpful. For that reason, the typed NumPy API is often stricter than
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the runtime NumPy API. This section describes some notable
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differences.
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ArrayLike
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~~~~~~~~~
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The `ArrayLike` type tries to avoid creating object arrays. For
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example,
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.. code-block:: python
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>>> np.array(x**2 for x in range(10))
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array(<generator object <genexpr> at ...>, dtype=object)
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is valid NumPy code which will create a 0-dimensional object
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array. Type checkers will complain about the above example when using
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the NumPy types however. If you really intended to do the above, then
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you can either use a ``# type: ignore`` comment:
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.. code-block:: python
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>>> np.array(x**2 for x in range(10)) # type: ignore
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or explicitly type the array like object as `~typing.Any`:
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.. code-block:: python
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>>> from typing import Any
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>>> array_like: Any = (x**2 for x in range(10))
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>>> np.array(array_like)
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array(<generator object <genexpr> at ...>, dtype=object)
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ndarray
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~~~~~~~
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It's possible to mutate the dtype of an array at runtime. For example,
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the following code is valid:
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.. code-block:: python
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>>> x = np.array([1, 2])
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>>> x.dtype = np.bool_
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This sort of mutation is not allowed by the types. Users who want to
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write statically typed code should instead use the `numpy.ndarray.view`
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method to create a view of the array with a different dtype.
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DTypeLike
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~~~~~~~~~
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The `DTypeLike` type tries to avoid creation of dtype objects using
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dictionary of fields like below:
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.. code-block:: python
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>>> x = np.dtype({"field1": (float, 1), "field2": (int, 3)})
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Although this is valid NumPy code, the type checker will complain about it,
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since its usage is discouraged.
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Please see : :ref:`Data type objects <arrays.dtypes>`
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Number precision
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~~~~~~~~~~~~~~~~
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The precision of `numpy.number` subclasses is treated as a covariant generic
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parameter (see :class:`~NBitBase`), simplifying the annotating of processes
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involving precision-based casting.
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.. code-block:: python
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>>> from typing import TypeVar
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>>> import numpy as np
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>>> import numpy.typing as npt
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>>> T = TypeVar("T", bound=npt.NBitBase)
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>>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]":
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... ...
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Consequently, the likes of `~numpy.float16`, `~numpy.float32` and
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`~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to
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runtime, they're not necessarily considered as sub-classes.
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Timedelta64
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~~~~~~~~~~~
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The `~numpy.timedelta64` class is not considered a subclass of
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`~numpy.signedinteger`, the former only inheriting from `~numpy.generic`
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while static type checking.
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0D arrays
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~~~~~~~~~
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During runtime numpy aggressively casts any passed 0D arrays into their
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corresponding `~numpy.generic` instance. Until the introduction of shape
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typing (see :pep:`646`) it is unfortunately not possible to make the
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necessary distinction between 0D and >0D arrays. While thus not strictly
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correct, all operations are that can potentially perform a 0D-array -> scalar
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cast are currently annotated as exclusively returning an `ndarray`.
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If it is known in advance that an operation _will_ perform a
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0D-array -> scalar cast, then one can consider manually remedying the
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situation with either `typing.cast` or a ``# type: ignore`` comment.
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Record array dtypes
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~~~~~~~~~~~~~~~~~~~
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The dtype of `numpy.recarray`, and the `numpy.rec` functions in general,
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can be specified in one of two ways:
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* Directly via the ``dtype`` argument.
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* With up to five helper arguments that operate via `numpy.format_parser`:
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``formats``, ``names``, ``titles``, ``aligned`` and ``byteorder``.
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These two approaches are currently typed as being mutually exclusive,
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*i.e.* if ``dtype`` is specified than one may not specify ``formats``.
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While this mutual exclusivity is not (strictly) enforced during runtime,
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combining both dtype specifiers can lead to unexpected or even downright
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buggy behavior.
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API
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---
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"""
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# NOTE: The API section will be appended with additional entries
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# further down in this file
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from numpy._typing import (
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ArrayLike,
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DTypeLike,
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NBitBase,
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NDArray,
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)
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__all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"]
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if __doc__ is not None:
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from numpy._typing._add_docstring import _docstrings
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__doc__ += _docstrings
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__doc__ += '\n.. autoclass:: numpy.typing.NBitBase\n'
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del _docstrings
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from numpy._pytesttester import PytestTester
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test = PytestTester(__name__)
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del PytestTester
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