clip

Clip (limit) the values in the array.

Return an array whose values are limited to [min, max]. One of min or max must be given.

Parameters:

Name	Type	Description	Default
a			required
a_min	scalar or `SparseArray` or `None`	Minimum value. If None, clipping is not performed on lower interval edge.	None
a_max	scalar or `SparseArray` or `None`	Maximum value. If None, clipping is not performed on upper interval edge.	None
out	SparseArray	If provided, the results will be placed in this array. It may be the input array for in-place clipping. out must be of the right shape to hold the output. Its type is preserved.	None

Returns:

Name	Type	Description
clipped_array	SparseArray	An array with the elements of self, but where values < min are replaced with min, and those > max with max.

Examples:

>>> import sparse
>>> x = sparse.COO.from_numpy([0, 0, 0, 1, 2, 3])
>>> sparse.clip(x, a_min=1).todense()
array([1, 1, 1, 1, 2, 3])
>>> sparse.clip(x, a_max=1).todense()
array([0, 0, 0, 1, 1, 1])
>>> sparse.clip(x, a_min=1, a_max=2).todense()
array([1, 1, 1, 1, 2, 2])

See Also

numpy.clip : Equivalent NumPy function

Source code in sparse/numba_backend/_coo/common.py

def clip(a, a_min=None, a_max=None, out=None):
    """
    Clip (limit) the values in the array.

    Return an array whose values are limited to ``[min, max]``. One of min
    or max must be given.

    Parameters
    ----------
    a
    a_min : scalar or `SparseArray` or `None`
        Minimum value. If `None`, clipping is not performed on lower
        interval edge.
    a_max : scalar or `SparseArray` or `None`
        Maximum value. If `None`, clipping is not performed on upper
        interval edge.
    out : SparseArray, optional
        If provided, the results will be placed in this array. It may be
        the input array for in-place clipping. `out` must be of the right
        shape to hold the output. Its type is preserved.

    Returns
    -------
    clipped_array : SparseArray
        An array with the elements of `self`, but where values < `min` are
        replaced with `min`, and those > `max` with `max`.

    Examples
    --------
    >>> import sparse
    >>> x = sparse.COO.from_numpy([0, 0, 0, 1, 2, 3])
    >>> sparse.clip(x, a_min=1).todense()  # doctest: +NORMALIZE_WHITESPACE
    array([1, 1, 1, 1, 2, 3])
    >>> sparse.clip(x, a_max=1).todense()  # doctest: +NORMALIZE_WHITESPACE
    array([0, 0, 0, 1, 1, 1])
    >>> sparse.clip(x, a_min=1, a_max=2).todense()  # doctest: +NORMALIZE_WHITESPACE
    array([1, 1, 1, 1, 2, 2])

    See Also
    --------
    numpy.clip : Equivalent NumPy function
    """
    a = asCOO(a, name="clip")
    return a.clip(a_min, a_max)