isinf

Tests each element x_i of the input array x to determine if equal to positive or negative infinity.

Parameters:

Name	Type	Description	Default
x		input array of a numeric data type.	required

Returns:

Name	Type	Description
out	array	an array containing test results. The returned array has a data type of bool.

Special Cases

For real-valued floating-point operands,

• If x_i is either +infinity or -infinity, the result is True.
• In the remaining cases, the result is False.

For complex floating-point operands, let a = real(x_i), b = imag(x_i), and

• If a is either +infinity or -infinity and b is any value (including NaN), the result is True.
• If a is either a finite number or NaN and b is either +infinity or -infinity, the result is True.
• In the remaining cases, the result is False.

Examples:

>>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, np.inf]]))
>>> o = sparse.isinf(a)
>>> o.todense()
array([[False, False],
       [False,  True]])

Source code in sparse/numba_backend/_common.py

@_support_numpy
def isinf(x, /):
    """
    Tests each element ``x_i`` of the input array ``x`` to determine if equal to positive or negative infinity.

    Parameters
    ----------
    x: array
        input array of a numeric data type.

    Returns
    -------
    out: array
        an array containing test results. The returned array has a data type of ``bool``.

    Special Cases
    -------------

    For real-valued floating-point operands,

    - If ``x_i`` is either ``+infinity`` or ``-infinity``, the result is ``True``.
    - In the remaining cases, the result is ``False``.

    For complex floating-point operands, let ``a = real(x_i)``, ``b = imag(x_i)``, and

    - If ``a`` is either ``+infinity`` or ``-infinity`` and ``b`` is any value (including ``NaN``), the result
        is ``True``.
    - If ``a`` is either a finite number or ``NaN`` and ``b`` is either ``+infinity`` or ``-infinity``, the result
        is ``True``.
    - In the remaining cases, the result is ``False``.

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, np.inf]]))
    >>> o = sparse.isinf(a)  # doctest: +SKIP
    >>> o.todense()  # doctest: +SKIP
    array([[False, False],
           [False,  True]])
    """
    return x.isinf()