numpy.testing.assert_array_equal

numpy.testing.assert_array_equal(x, y, err_msg='', verbose=True)

Raise an assertion if two array_like objects are not equal.

Given two array_like objects, check that the shape is equal and all elements of these objects are 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.

The usual caution for verifying equality with floating point numbers is advised.

Parameters:

x : array_like

The actual object to check.

y : array_like

The desired, expected object.

err_msg : string

The error message to be printed in case of failure.

verbose : bool

If True, the conflicting values are appended to the error message.

Raises:

AssertionError :

If actual and desired objects are not equal.

See also

assert_array_almost_equal

test objects for equality up to precision

assert_equal

tests objects for equality

Examples

the first assert does not raise an exception

>>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
                    [np.exp(0),2.33333, np.nan])

assert fails with numerical inprecision with floats

>>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
                    [1, np.sqrt(np.pi)**2, np.nan])
...
<type 'exceptions.ValueError'>:
AssertionError:
Arrays are not equal
<BLANKLINE>
(mismatch 50.0%)
 x: array([ 1.        ,  3.14159265,         NaN])
 y: array([ 1.        ,  3.14159265,         NaN])

use assert_array_almost_equal for these cases instead

>>> np.testing.assert_array_almost_equal([1.0,np.pi,np.nan],
                    [1, np.sqrt(np.pi)**2, np.nan], decimal=15)