numpy.testing.assert_almost_equal — NumPy v1.11 Manual (original) (raw)
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.
The test is equivalent to abs(desired-actual) < 0.5 * 10**(-decimal).
Given two objects (numbers or ndarrays), check that all elements of these objects are almost equal. An exception is raised at conflicting values. For ndarrays this delegates to assert_array_almost_equal
| Parameters: | actual : array_like The object to check. desired : array_like The expected object. decimal : 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. |
Examples
import numpy.testing as npt npt.assert_almost_equal(2.3333333333333, 2.33333334) npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) ... <type 'exceptions.AssertionError'>: Items are not equal: ACTUAL: 2.3333333333333002 DESIRED: 2.3333333399999998
npt.assert_almost_equal(np.array([1.0,2.3333333333333]), ... np.array([1.0,2.33333334]), decimal=9) ... <type 'exceptions.AssertionError'>: Arrays are not almost equal
(mismatch 50.0%) x: array([ 1. , 2.33333333]) y: array([ 1. , 2.33333334])