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
orassert_array_max_ulp
instead of this function for more consistent floating point comparisons.The test verifies identical shapes and that the elements of
actual
anddesired
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. ])