numpy.isclose — NumPy v1.13 Manual (original) (raw)
numpy. isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)[source]¶
Returns a boolean array where 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.
| 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. |
|---|---|
| Returns: | y : array_like Returns a boolean array of where a and b are equal within the given tolerance. If both a and b are scalars, returns a single boolean value. |
Notes
New in version 1.7.0.
For finite values, isclose uses the following equation to test whether two floating point values are equivalent.
absolute(a - b) <= (atol + rtol * absolute(b))
The above equation is not symmetric in a and b, so that_isclose(a, b)_ might be different from isclose(b, a) in some rare cases.
Examples
np.isclose([1e10,1e-7], [1.00001e10,1e-8]) array([True, False]) np.isclose([1e10,1e-8], [1.00001e10,1e-9]) array([True, True]) np.isclose([1e10,1e-8], [1.0001e10,1e-9]) array([False, True]) np.isclose([1.0, np.nan], [1.0, np.nan]) array([True, False]) np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) array([True, True])