numpy.testing.assert_array_almost_equal

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.

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.

The test verifies identical shapes and that the elements of actual and desired satisfy.

abs(desired-actual) < 1.5 * 10**(-decimal)

That is a looser test than originally documented, but agrees with what the actual implementation did up to rounding vagaries. 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.

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

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.     ])