numpy.testing.assert_approx_equal(actual, desired, significant=7, err_msg='', verbose=True)
[source]
Raises an AssertionError if two items are not equal up to significant digits.
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.
Given two numbers, check that they are approximately equal. Approximately equal is defined as the number of significant digits that agree.
Parameters: |
actual : scalar The object to check. desired : scalar The expected object. significant : 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
assert_array_almost_equal_nulp
, assert_array_max_ulp
, assert_equal
>>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, significant=8) >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, significant=8) ... <type 'exceptions.AssertionError'>: Items are not equal to 8 significant digits: ACTUAL: 1.234567e-021 DESIRED: 1.2345672000000001e-021
the evaluated condition that raises the exception is
>>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) True
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https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.testing.assert_approx_equal.html