projektAI/venv/Lib/site-packages/numpy/typing/__init__.py

"""
============================
Typing (:mod:`numpy.typing`)
============================

.. warning::

  Some of the types in this module rely on features only present in
  the standard library in Python 3.8 and greater. If you want to use
  these types in earlier versions of Python, you should install the
  typing-extensions_ package.

Large parts of the NumPy API have PEP-484-style type annotations. In
addition a number of type aliases are available to users, most prominently
the two below:

- `ArrayLike`: objects that can be converted to arrays
- `DTypeLike`: objects that can be converted to dtypes

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

Differences from the runtime NumPy API
--------------------------------------

NumPy is very flexible. Trying to describe the full range of
possibilities statically would result in types that are not very
helpful. For that reason, the typed NumPy API is often stricter than
the runtime NumPy API. This section describes some notable
differences.

ArrayLike
~~~~~~~~~

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

.. code-block:: python

    >>> np.array(x**2 for x in range(10))
    array(<generator object <genexpr> at ...>, dtype=object)

is valid NumPy code which will create a 0-dimensional object
array. Type checkers will complain about the above example when using
the NumPy types however. If you really intended to do the above, then
you can either use a ``# type: ignore`` comment:

.. code-block:: python

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

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

.. code-block:: python

    >>> from typing import Any
    >>> array_like: Any = (x**2 for x in range(10))
    >>> np.array(array_like)
    array(<generator object <genexpr> at ...>, dtype=object)

ndarray
~~~~~~~

It's possible to mutate the dtype of an array at runtime. For example,
the following code is valid:

.. code-block:: python

    >>> x = np.array([1, 2])
    >>> x.dtype = np.bool_

This sort of mutation is not allowed by the types. Users who want to
write statically typed code should insted use the `numpy.ndarray.view`
method to create a view of the array with a different dtype.

DTypeLike
~~~~~~~~~

The `DTypeLike` type tries to avoid creation of dtype objects using
dictionary of fields like below:

.. code-block:: python

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

Although this is valid Numpy code, the type checker will complain about it,
since its usage is discouraged.
Please see : :ref:`Data type objects <arrays.dtypes>`

Number Precision
~~~~~~~~~~~~~~~~

The precision of `numpy.number` subclasses is treated as a covariant generic
parameter (see :class:`~NBitBase`), simplifying the annoting of proccesses
involving precision-based casting.

.. code-block:: python

    >>> from typing import TypeVar
    >>> import numpy as np
    >>> import numpy.typing as npt

    >>> T = TypeVar("T", bound=npt.NBitBase)
    >>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]":
    ...     ...

Consequently, the likes of `~numpy.float16`, `~numpy.float32` and
`~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to
runtime, they're not necessarily considered as sub-classes.

Timedelta64
~~~~~~~~~~~

The `~numpy.timedelta64` class is not considered a subclass of `~numpy.signedinteger`,
the former only inheriting from `~numpy.generic` while static type checking.

API
---

"""
# NOTE: The API section will be appended with additional entries
# further down in this file

from typing import TYPE_CHECKING, List

if TYPE_CHECKING:
    import sys
    if sys.version_info >= (3, 8):
        from typing import final
    else:
        from typing_extensions import final
else:
    def final(f): return f

if not TYPE_CHECKING:
    __all__ = ["ArrayLike", "DTypeLike", "NBitBase"]
else:
    # Ensure that all objects within this module are accessible while
    # static type checking. This includes private ones, as we need them
    # for internal use.
    #
    # Declare to mypy that `__all__` is a list of strings without assigning
    # an explicit value
    __all__: List[str]


@final  # Dissallow the creation of arbitrary `NBitBase` subclasses
class NBitBase:
    """
    An object representing `numpy.number` precision during static type checking.

    Used exclusively for the purpose static type checking, `NBitBase`
    represents the base of a hierachieral set of subclasses.
    Each subsequent subclass is herein used for representing a lower level
    of precision, *e.g.* ``64Bit > 32Bit > 16Bit``.

    Examples
    --------
    Below is a typical usage example: `NBitBase` is herein used for annotating a
    function that takes a float and integer of arbitrary precision as arguments
    and returns a new float of whichever precision is largest
    (*e.g.* ``np.float16 + np.int64 -> np.float64``).

    .. code-block:: python

        >>> from typing import TypeVar, TYPE_CHECKING
        >>> import numpy as np
        >>> import numpy.typing as npt

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

        >>> def add(a: "np.floating[T]", b: "np.integer[T]") -> "np.floating[T]":
        ...     return a + b

        >>> a = np.float16()
        >>> b = np.int64()
        >>> out = add(a, b)

        >>> if TYPE_CHECKING:
        ...     reveal_locals()
        ...     # note: Revealed local types are:
        ...     # note:     a: numpy.floating[numpy.typing._16Bit*]
        ...     # note:     b: numpy.signedinteger[numpy.typing._64Bit*]
        ...     # note:     out: numpy.floating[numpy.typing._64Bit*]

    """

    def __init_subclass__(cls) -> None:
        allowed_names = {
            "NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit",
            "_64Bit", "_32Bit", "_16Bit", "_8Bit",
        }
        if cls.__name__ not in allowed_names:
            raise TypeError('cannot inherit from final class "NBitBase"')
        super().__init_subclass__()


# Silence errors about subclassing a `@final`-decorated class
class _256Bit(NBitBase): ...  # type: ignore[misc]
class _128Bit(_256Bit): ...  # type: ignore[misc]
class _96Bit(_128Bit): ...  # type: ignore[misc]
class _80Bit(_96Bit): ...  # type: ignore[misc]
class _64Bit(_80Bit): ...  # type: ignore[misc]
class _32Bit(_64Bit): ...  # type: ignore[misc]
class _16Bit(_32Bit): ...  # type: ignore[misc]
class _8Bit(_16Bit): ...  # type: ignore[misc]

# Clean up the namespace
del TYPE_CHECKING, final, List

from ._scalars import (
    _CharLike,
    _BoolLike,
    _IntLike,
    _FloatLike,
    _ComplexLike,
    _NumberLike,
    _ScalarLike,
    _VoidLike,
)
from ._array_like import _SupportsArray, ArrayLike as ArrayLike
from ._shape import _Shape, _ShapeLike
from ._dtype_like import _SupportsDType, _VoidDTypeLike, DTypeLike as DTypeLike

if __doc__ is not None:
    from ._add_docstring import _docstrings
    __doc__ += _docstrings
    __doc__ += '\n.. autoclass:: numpy.typing.NBitBase\n'
    del _docstrings

from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester