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: |
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Raises: |
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See also
assert_allclose
assert_array_almost_equal_nulp
, assert_array_max_ulp
, assert_equal
>>> 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 Mismatch: 50% Max absolute difference: 6.66669964e-09 Max relative difference: 2.85715698e-09 x: array([1. , 2.333333333]) y: array([1. , 2.33333334])
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https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.testing.assert_almost_equal.html