numpy.testing.assert_approx_equal#

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:

actualscalar: The object to check.
desiredscalar: The expected object.
significantint, optional: Desired precision, default is 7.
err_msgstr, optional: The error message to be printed in case of failure.
verbosebool, 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

>>> 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)
Traceback (most recent call last):
    ...
AssertionError:
Items are not equal to 8 significant digits:
 ACTUAL: 1.234567e-21
 DESIRED: 1.2345672e-21

the evaluated condition that raises the exception is

>>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
True