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ParametersRaises
assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True)
note

:None:None:`assert_array_almost_equal_nulp` or :None:None:`assert_array_max_ulp` instead of this function for more consistent floating point comparisons.

The test verifies that the elements of :None:None:`actual` and :None:None:`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.

Raises an AssertionError if two items are not equal up to desired 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

This example is valid syntax, but we were not able to check execution
>>> 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
This example is valid syntax, but we were not able to check execution
>>> 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
<BLANKLINE>
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])
See :

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GitHub : /numpy/testing/_private/utils.py#476
type: <class 'function'>
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