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Fonction allclose - module numpy

Signature de la fonction allclose

def allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False) 

Description

allclose.__doc__

    Returns True if two arrays are element-wise equal within a tolerance.

    The tolerance values are positive, typically very small numbers.  The
    relative difference (`rtol` * abs(`b`)) and the absolute difference
    `atol` are added together to compare against the absolute difference
    between `a` and `b`.

    NaNs are treated as equal if they are in the same place and if
    ``equal_nan=True``.  Infs are treated as equal if they are in the same
    place and of the same sign in both arrays.

    Parameters
    ----------
    a, b : array_like
        Input arrays to compare.
    rtol : float
        The relative tolerance parameter (see Notes).
    atol : float
        The absolute tolerance parameter (see Notes).
    equal_nan : bool
        Whether to compare NaN's as equal.  If True, NaN's in `a` will be
        considered equal to NaN's in `b` in the output array.

        .. versionadded:: 1.10.0

    Returns
    -------
    allclose : bool
        Returns True if the two arrays are equal within the given
        tolerance; False otherwise.

    See Also
    --------
    isclose, all, any, equal

    Notes
    -----
    If the following equation is element-wise True, then allclose returns
    True.

     absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))

    The above equation is not symmetric in `a` and `b`, so that
    ``allclose(a, b)`` might be different from ``allclose(b, a)`` in
    some rare cases.

    The comparison of `a` and `b` uses standard broadcasting, which
    means that `a` and `b` need not have the same shape in order for
    ``allclose(a, b)`` to evaluate to True.  The same is true for
    `equal` but not `array_equal`.

    `allclose` is not defined for non-numeric data types.

    Examples
    --------
    >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])
    False
    >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])
    True
    >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])
    False
    >>> np.allclose([1.0, np.nan], [1.0, np.nan])
    False
    >>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
    True