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 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) < 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.

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])