numpy.testing.assert_almost_equal#

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 or assert_array_max_ulp instead of this function for more consistent floating point comparisons.

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 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

>>> from numpy.testing import assert_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 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])