numpy.testing.assert_array_almost_equal()

Raises an AssertionError if two objects are not equal up to desired precision.

The test verifies identical shapes and verifies values with abs(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.

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.     ])