numpy.testing.assert_almost_equal¶
-
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.
Note
It is recommended to use one of
assert_allclose
,assert_array_almost_equal_nulp
orassert_array_max_ulp
instead of this function for more consistent floating point comparisons.The test verifies 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 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_equalParameters: 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.
See also
assert_allclose
- Compare two array_like objects for equality with desired relative and/or absolute precision.
assert_array_almost_equal_nulp
,assert_array_max_ulp
,assert_equal
Examples
>>> import numpy.testing as npt >>> npt.assert_almost_equal(2.3333333333333, 2.33333334) >>> npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) ... <type 'exceptions.AssertionError'>: Items are not equal: ACTUAL: 2.3333333333333002 DESIRED: 2.3333333399999998
>>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]), ... np.array([1.0,2.33333334]), decimal=9) ... <type 'exceptions.AssertionError'>: Arrays are not almost equal (mismatch 50.0%) x: array([ 1. , 2.33333333]) y: array([ 1. , 2.33333334])