numpy.testing.assert_array_almost_equal — NumPy v1.17 Manual (original) (raw)
numpy.testing. assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True)[source]¶
Raises an AssertionError if two objects are not equal up to desired precision.
The test verifies identical shapes and that the elements of actual anddesired satisfy.
abs(desired-actual) < 1.5 * 10**(-decimal)
That is a looser test than originally documented, but agrees with what the actual implementation did up to rounding vagaries. An exception is raised at shape mismatch or conflicting values. In contrast to the standard usage in numpy, NaNs are compared like numbers, no assertion is raised if both objects have NaNs in the same positions.
| Parameters: | x : array_like The actual object to check. y : array_like The desired, expected object. decimal : int, optional Desired precision, default is 6. err_msg : str, optional The error message to be printed in case of failure. verbose : bool, optional If True, the conflicting values are appended to the error message. |
|---|---|
| Raises: | AssertionError If actual and desired are not equal up to specified precision. |
Examples
the first assert does not raise an exception
np.testing.assert_array_almost_equal([1.0,2.333,np.nan], ... [1.0,2.333,np.nan])
np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], ... [1.0,2.33339,np.nan], decimal=5) Traceback (most recent call last): ... AssertionError: Arrays are not almost equal to 5 decimals Mismatch: 33.3% Max absolute difference: 6.e-05 Max relative difference: 2.57136612e-05 x: array([1. , 2.33333, nan]) y: array([1. , 2.33339, nan])
np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], ... [1.0,2.33333, 5], decimal=5) Traceback (most recent call last): ... AssertionError: Arrays are not almost equal to 5 decimals x and y nan location mismatch: x: array([1. , 2.33333, nan]) y: array([1. , 2.33333, 5. ])