numpy.testing.assert_array_almost_equal — NumPy v1.11 Manual (original) (raw)
numpy.testing.assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True)[source]¶
Raises an AssertionError if two objects are not equal up to desired precision.
The test verifies identical shapes and verifies values withabs(desired-actual) < 0.5 * 10**(-decimal).
Given two array_like objects, check that the shape is equal and all elements of these objects are almost equal. An exception is raised at shape mismatch or conflicting values. In contrast to the standard usage in numpy, NaNs are compared like numbers, no assertion is raised if both objects have NaNs in the same positions.
| Parameters: | x : array_like The actual object to check. y : array_like The desired, expected object. decimal : int, optional Desired precision, default is 6. 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
the first assert does not raise an exception
np.testing.assert_array_almost_equal([1.0,2.333,np.nan], [1.0,2.333,np.nan])
np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], ... [1.0,2.33339,np.nan], decimal=5) ... <type 'exceptions.AssertionError'>: AssertionError: Arrays are not almost equal
(mismatch 50.0%) x: array([ 1. , 2.33333, NaN]) y: array([ 1. , 2.33339, NaN])
np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], ... [1.0,2.33333, 5], decimal=5) <type 'exceptions.ValueError'>: ValueError: Arrays are not almost equal x: array([ 1. , 2.33333, NaN]) y: array([ 1. , 2.33333, 5. ])