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_equal- Parameters
- actualarray_like
The object to check.
- desiredarray_like
The expected object.
- decimalint, optional
Desired precision, default is 7.
- err_msgstr, optional
The error message to be printed in case of failure.
- verbosebool, 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) 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 Mismatched elements: 1 / 2 (50%) Max absolute difference: 6.66669964e-09 Max relative difference: 2.85715698e-09 x: array([1. , 2.333333333]) y: array([1. , 2.33333334])