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Module « numpy.testing »

Fonction assert_array_almost_equal - module numpy.testing

Signature de la fonction assert_array_almost_equal

def assert_array_almost_equal(actual, desired, decimal=6, err_msg='', verbose=True) 

Description

help(numpy.testing.assert_array_almost_equal)

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
----------
actual : array_like
    The actual object to check.
desired : 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)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not almost equal to 5 decimals
<BLANKLINE>
Mismatched elements: 1 / 3 (33.3%)
Max absolute difference among violations: 6.e-05
Max relative difference among violations: 2.57136612e-05
 ACTUAL: array([1.     , 2.33333,     nan])
 DESIRED: array([1.     , 2.33339,     nan])

>>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
...                                      [1.0,2.33333, 5], decimal=5)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not almost equal to 5 decimals
<BLANKLINE>
nan location mismatch:
 ACTUAL: array([1.     , 2.33333,     nan])
 DESIRED: array([1.     , 2.33333, 5.     ])

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