Class errstate

numpy/core/_ufunc_config.py:368–438

errstate(**kwargs) Context manager for floating-point error handling. Using an instance of `errstate` as a context manager allows statements in that context to execute with a known error handling behavior. Upon entering the context the error handling is set with `seterr` and `

@set_module('numpy')
class errstate(contextlib.ContextDecorator):
    """
    errstate(**kwargs)

    Context manager for floating-point error handling.

    Using an instance of `errstate` as a context manager allows statements in
    that context to execute with a known error handling behavior. Upon entering
    the context the error handling is set with `seterr` and `seterrcall`, and
    upon exiting it is reset to what it was before.

    ..  versionchanged:: 1.17.0
        `errstate` is also usable as a function decorator, saving
        a level of indentation if an entire function is wrapped.
        See :py:class:`contextlib.ContextDecorator` for more information.

    Parameters
    ----------
    kwargs : {divide, over, under, invalid}
        Keyword arguments. The valid keywords are the possible floating-point
        exceptions. Each keyword should have a string value that defines the
        treatment for the particular error. Possible values are
        {'ignore', 'warn', 'raise', 'call', 'print', 'log'}.

    See Also
    --------
    seterr, geterr, seterrcall, geterrcall

    Notes
    -----
    For complete documentation of the types of floating-point exceptions and
    treatment options, see `seterr`.

    Examples
    --------
    >>> olderr = np.seterr(all='ignore')  # Set error handling to known state.

    >>> np.arange(3) / 0.
    array([nan, inf, inf])
    >>> with np.errstate(divide='warn'):
    ...     np.arange(3) / 0.
    array([nan, inf, inf])

    >>> np.sqrt(-1)
    nan
    >>> with np.errstate(invalid='raise'):
    ...     np.sqrt(-1)
    Traceback (most recent call last):
      File "<stdin>", line 2, in <module>
    FloatingPointError: invalid value encountered in sqrt

    Outside the context the error handling behavior has not changed:

    >>> np.geterr()
    {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}

    """