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ParametersRaises
assert_approx_equal(actual, desired, significant=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.

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.

Raises an AssertionError if two items are not equal up to significant digits.

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
>>> 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

This example is valid syntax, but we were not able to check execution
>>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
True
See :

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GitHub : /numpy/testing/_private/utils.py#602
type: <class 'function'>
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