testing.assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True)
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)<float64(1.5*10**(-decimal))That is a looser test than originally documented, but agrees with what the actual implementation in
did up to rounding vagaries. An exception is raised at conflicting values. For ndarrays this delegates to assert_array_almost_equal
Parameters:actualarray_likeThe object to check.
desiredarray_likeThe expected object.
decimalint, optionalDesired precision, default is 7.
err_msgstr, optionalThe error message to be printed in case of failure.
verbosebool, optionalIf True, the conflicting values are appended to the error message.
Raises:AssertionErrorIf actual and desired are not equal up to specified precision.
Examples
>>> fromnumpy.testingimportassert_almost_equal>>> assert_almost_equal(2.3333333333333,2.33333334)>>> 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>>> 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 decimalsMismatched elements: 1 / 2 (50%)Mismatch at index: [1]: 2.3333333333333 (ACTUAL), 2.33333334 (DESIRED)Max absolute difference among violations: 6.66669964e-09Max relative difference among violations: 2.85715698e-09 ACTUAL: array([1. , 2.333333333]) DESIRED: array([1. , 2.33333334])