numpy.isclose¶
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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.
See also
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])