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Fonction right_shift - module numpy

Signature de la fonction right_shift

Description

right_shift.__doc__

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

Shift the bits of an integer to the right.

Bits are shifted to the right `x2`.  Because the internal
representation of numbers is in binary format, this operation is
equivalent to dividing `x1` by ``2**x2``.

Parameters
----------
x1 : array_like, int
    Input values.
x2 : array_like, int
    Number of bits to remove at the right of `x1`.
    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, int
    Return `x1` with bits shifted `x2` times to the right.
    This is a scalar if both `x1` and `x2` are scalars.

See Also
--------
left_shift : Shift the bits of an integer to the left.
binary_repr : Return the binary representation of the input number
    as a string.

Examples
--------
>>> np.binary_repr(10)
'1010'
>>> np.right_shift(10, 1)
5
>>> np.binary_repr(5)
'101'

>>> np.right_shift(10, [1,2,3])
array([5, 2, 1])

The ``>>`` operator can be used as a shorthand for ``np.right_shift`` on
ndarrays.

>>> x1 = 10
>>> x2 = np.array([1,2,3])
>>> x1 >> x2
array([5, 2, 1])