testing.
assert_almost_equal
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.
assert_allclose
assert_array_almost_equal_nulp
assert_array_max_ulp
The test verifies that the elements of actual and desired satisfy.
actual
desired
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
assert_array_almost_equal
The object to check.
The expected object.
Desired precision, default is 7.
The error message to be printed in case of failure.
If True, the conflicting values are appended to the error message.
If actual and desired are not equal up to specified precision.
See also
Compare two array_like objects for equality with desired relative and/or absolute precision.
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])
numpy.testing