nonzero

返回数组中非零元素的索引。

如果 x 具有复浮点数据类型，则非零元素是指至少有一个分量（实部或虚部）非零的元素。

如果 x 具有布尔数据类型，则非零元素是指等于 True 的元素。

参数

名称	类型	描述	默认值
x		输入具有正维度的数组。如果 x 是零维的，则该函数会引发异常。	必需

返回值

名称	类型	描述
out	Tuple[array, ...]	一个由 k 个数组组成的元组，其中每个数组对应 x 的一个维度，每个数组的大小为 n（其中 n 是非零元素的总数），包含该维度中非零元素的索引。索引以行主序、C 风格的顺序返回。

示例

>>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
>>> o = sparse.nonzero(a)
>>> o
(array([0, 1]), array([1, 0]))

源代码位于 sparse/numba_backend/_common.py

def nonzero(x, /):
    """
    Returns the indices of the array elements which are non-zero.

    If ``x`` has a complex floating-point data type, non-zero elements are those elements having at least
    one component (real or imaginary) which is non-zero.

    If ``x`` has a boolean data type, non-zero elements are those elements which are equal to ``True``.

    Parameters
    ----------
    x: array
        input array having a positive rank.
        If ``x`` is zero-dimensional, the function raises an exception.

    Returns
    -------
    out: Tuple[array, ...]
        a tuple of ``k`` arrays, one for each dimension of ``x`` and each of size ``n`` (where ``n`` is
        the total number of non-zero elements), containing the indices of the non-zero elements in that
        dimension. The indices must are returned in row-major, C-style order.

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
    >>> o = sparse.nonzero(a)
    >>> o
    (array([0, 1]), array([1, 0]))
    """
    return x.nonzero()