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Module « numpy.testing »

Fonction assert_array_equal - module numpy.testing

Signature de la fonction assert_array_equal

def assert_array_equal(actual, desired, err_msg='', verbose=True, *, strict=False) 

Description

help(numpy.testing.assert_array_equal)

Raises an AssertionError 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 (but see the Notes for the special
handling of a scalar). 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.

.. note:: When either `actual` or `desired` is already an instance of
    `numpy.ndarray` and `desired` is not a ``dict``, the behavior of
    ``assert_equal(actual, desired)`` is identical to the behavior of this
    function. Otherwise, this function performs `np.asanyarray` on the
    inputs before comparison, whereas `assert_equal` defines special
    comparison rules for common Python types. For example, only
    `assert_equal` can be used to compare nested Python lists. In new code,
    consider using only `assert_equal`, explicitly converting either
    `actual` or `desired` to arrays if the behavior of `assert_array_equal`
    is desired.

Parameters
----------
actual : array_like
    The actual object to check.
desired : array_like
    The desired, expected object.
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.
strict : bool, optional
    If True, raise an AssertionError when either the shape or the data
    type of the array_like objects does not match. The special
    handling for scalars mentioned in the Notes section is disabled.

    .. versionadded:: 1.24.0

Raises
------
AssertionError
    If actual and desired objects are not equal.

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

Notes
-----
When one of `actual` and `desired` is a scalar and the other is array_like,
the function checks that each element of the array_like object is equal to
the scalar. This behaviour can be disabled with the `strict` parameter.

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 imprecision with floats:

>>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
...                               [1, np.sqrt(np.pi)**2, np.nan])
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not equal
<BLANKLINE>
Mismatched elements: 1 / 3 (33.3%)
Max absolute difference among violations: 4.4408921e-16
Max relative difference among violations: 1.41357986e-16
 ACTUAL: array([1.      , 3.141593,      nan])
 DESIRED: array([1.      , 3.141593,      nan])

Use `assert_allclose` or one of the nulp (number of floating point values)
functions for these cases instead:

>>> np.testing.assert_allclose([1.0,np.pi,np.nan],
...                            [1, np.sqrt(np.pi)**2, np.nan],
...                            rtol=1e-10, atol=0)

As mentioned in the Notes section, `assert_array_equal` has special
handling for scalars. Here the test checks that each value in `x` is 3:

>>> x = np.full((2, 5), fill_value=3)
>>> np.testing.assert_array_equal(x, 3)

Use `strict` to raise an AssertionError when comparing a scalar with an
array:

>>> np.testing.assert_array_equal(x, 3, strict=True)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not equal
<BLANKLINE>
(shapes (2, 5), () mismatch)
 ACTUAL: array([[3, 3, 3, 3, 3],
       [3, 3, 3, 3, 3]])
 DESIRED: array(3)

The `strict` parameter also ensures that the array data types match:

>>> x = np.array([2, 2, 2])
>>> y = np.array([2., 2., 2.], dtype=np.float32)
>>> np.testing.assert_array_equal(x, y, strict=True)
Traceback (most recent call last):
    ...
AssertionError:
Arrays are not equal
<BLANKLINE>
(dtypes int64, float32 mismatch)
 ACTUAL: array([2, 2, 2])
 DESIRED: array([2., 2., 2.], dtype=float32)

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