numpy.allclose¶

numpy.
allclose
(a, b, rtol=1e05, atol=1e08, equal_nan=False)[source]¶ Returns True if two arrays are elementwise 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.
If either array contains one or more NaNs, False is returned. 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.
New in version 1.10.0.
Returns:  allclose : bool
Returns True if the two arrays are equal within the given tolerance; False otherwise.
Notes
If the following equation is elementwise 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 fromallclose(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 forequal
but notarray_equal
.Examples
>>> np.allclose([1e10,1e7], [1.00001e10,1e8]) False >>> np.allclose([1e10,1e8], [1.00001e10,1e9]) True >>> np.allclose([1e10,1e8], [1.0001e10,1e9]) 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