numpy.testing.assert_almost_equal — NumPy v1.17 Manual (original) (raw)
numpy.testing. assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True)[source]¶
Raises an AssertionError if two items are not equal up to desired precision.
The test verifies that the elements of actual and desired satisfy.
abs(desired-actual) < 1.5 * 10**(-decimal)
That is a looser test than originally documented, but agrees with what the actual implementation in assert_array_almost_equal did up to rounding vagaries. An exception is raised at conflicting values. For ndarrays this delegates to assert_array_almost_equal
| Parameters: | actual : array_like The object to check. desired : array_like The expected object. decimal : int, optional Desired precision, default is 7. 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
import numpy.testing as npt npt.assert_almost_equal(2.3333333333333, 2.33333334) npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) Traceback (most recent call last): ... AssertionError: Arrays are not almost equal to 10 decimals ACTUAL: 2.3333333333333 DESIRED: 2.33333334
npt.assert_almost_equal(np.array([1.0,2.3333333333333]), ... np.array([1.0,2.33333334]), decimal=9) Traceback (most recent call last): ... AssertionError: Arrays are not almost equal to 9 decimals Mismatch: 50% Max absolute difference: 6.66669964e-09 Max relative difference: 2.85715698e-09 x: array([1. , 2.333333333]) y: array([1. , 2.33333334])