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Fonction bitwise_or - module numpy

Signature de la fonction bitwise_or

Description

bitwise_or.__doc__

bitwise_or(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Compute the bit-wise OR of two arrays element-wise.

Computes the bit-wise OR of the underlying binary representation of
the integers in the input arrays. This ufunc implements the C/Python
operator ``|``.

Parameters
----------
x1, x2 : array_like
    Only integer and boolean types are handled.
    If ``x1.shape != x2.shape``, they must be broadcastable to a common
    shape (which becomes the shape of the output).
out : ndarray, None, or tuple of ndarray and None, optional
    A location into which the result is stored. If provided, it must have
    a shape that the inputs broadcast to. If not provided or None,
    a freshly-allocated array is returned. A tuple (possible only as a
    keyword argument) must have length equal to the number of outputs.
where : array_like, optional
    This condition is broadcast over the input. At locations where the
    condition is True, the `out` array will be set to the ufunc result.
    Elsewhere, the `out` array will retain its original value.
    Note that if an uninitialized `out` array is created via the default
    ``out=None``, locations within it where the condition is False will
    remain uninitialized.
**kwargs
    For other keyword-only arguments, see the
    :ref:`ufunc docs <ufuncs.kwargs>`.

Returns
-------
out : ndarray or scalar
    Result.
    This is a scalar if both `x1` and `x2` are scalars.

See Also
--------
logical_or
bitwise_and
bitwise_xor
binary_repr :
    Return the binary representation of the input number as a string.

Examples
--------
The number 13 has the binaray representation ``00001101``. Likewise,
16 is represented by ``00010000``.  The bit-wise OR of 13 and 16 is
then ``000111011``, or 29:

>>> np.bitwise_or(13, 16)
29
>>> np.binary_repr(29)
'11101'

>>> np.bitwise_or(32, 2)
34
>>> np.bitwise_or([33, 4], 1)
array([33,  5])
>>> np.bitwise_or([33, 4], [1, 2])
array([33,  6])

>>> np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))
array([  6,   5, 255])
>>> np.array([2, 5, 255]) | np.array([4, 4, 4])
array([  6,   5, 255])
>>> np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32),
...               np.array([4, 4, 4, 2147483647], dtype=np.int32))
array([         6,          5,        255, 2147483647])
>>> np.bitwise_or([True, True], [False, True])
array([ True,  True])

The ``|`` operator can be used as a shorthand for ``np.bitwise_or`` on
ndarrays.

>>> x1 = np.array([2, 5, 255])
>>> x2 = np.array([4, 4, 4])
>>> x1 | x2
array([  6,   5, 255])