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Classe « ufunc »

Méthode numpy.ufunc.resolve_dtypes

Signature de la méthode resolve_dtypes

Description

help(ufunc.resolve_dtypes)

resolve_dtypes(dtypes, *, signature=None, casting=None, reduction=False)

    Find the dtypes NumPy will use for the operation.  Both input and
    output dtypes are returned and may differ from those provided.

    .. note::

        This function always applies NEP 50 rules since it is not provided
        any actual values.  The Python types ``int``, ``float``, and
        ``complex`` thus behave weak and should be passed for "untyped"
        Python input.

    Parameters
    ----------
    dtypes : tuple of dtypes, None, or literal int, float, complex
        The input dtypes for each operand.  Output operands can be
        None, indicating that the dtype must be found.
    signature : tuple of DTypes or None, optional
        If given, enforces exact DType (classes) of the specific operand.
        The ufunc ``dtype`` argument is equivalent to passing a tuple with
        only output dtypes set.
    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
        The casting mode when casting is necessary.  This is identical to
        the ufunc call casting modes.
    reduction : boolean
        If given, the resolution assumes a reduce operation is happening
        which slightly changes the promotion and type resolution rules.
        `dtypes` is usually something like ``(None, np.dtype("i2"), None)``
        for reductions (first input is also the output).

        .. note::

            The default casting mode is "same_kind", however, as of
            NumPy 1.24, NumPy uses "unsafe" for reductions.

    Returns
    -------
    dtypes : tuple of dtypes
        The dtypes which NumPy would use for the calculation.  Note that
        dtypes may not match the passed in ones (casting is necessary).


    Examples
    --------
    This API requires passing dtypes, define them for convenience:

    >>> import numpy as np
    >>> int32 = np.dtype("int32")
    >>> float32 = np.dtype("float32")

    The typical ufunc call does not pass an output dtype.  `numpy.add` has two
    inputs and one output, so leave the output as ``None`` (not provided):

    >>> np.add.resolve_dtypes((int32, float32, None))
    (dtype('float64'), dtype('float64'), dtype('float64'))

    The loop found uses "float64" for all operands (including the output), the
    first input would be cast.

    ``resolve_dtypes`` supports "weak" handling for Python scalars by passing
    ``int``, ``float``, or ``complex``:

    >>> np.add.resolve_dtypes((float32, float, None))
    (dtype('float32'), dtype('float32'), dtype('float32'))

    Where the Python ``float`` behaves similar to a Python value ``0.0``
    in a ufunc call.  (See :ref:`NEP 50 <NEP50>` for details.)

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