numpy.allclose¶
- numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)[source]¶
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.
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 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.
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