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Module « numpy.testing »

Fonction assert_equal - module numpy.testing

Signature de la fonction assert_equal

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

Description

help(numpy.testing.assert_equal)

Raises an AssertionError if two objects are not equal.

Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
check that all elements of these objects are equal. An exception is raised
at the first conflicting values.

This function handles NaN comparisons as if NaN was a "normal" number.
That is, AssertionError is not raised if both objects have NaNs in the same
positions.  This is in contrast to the IEEE standard on NaNs, which says
that NaN compared to anything must return False.

Parameters
----------
actual : array_like
    The object to check.
desired : array_like
    The 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 and either of the `actual` and `desired` arguments is an array,
    raise an ``AssertionError`` when either the shape or the data type of
    the arguments does not match. If neither argument is an array, this
    parameter has no effect.

    .. versionadded:: 2.0.0

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

See Also
--------
assert_allclose
assert_array_almost_equal_nulp,
assert_array_max_ulp,

Notes
-----
By default, when one of `actual` and `desired` is a scalar and the other is
an array, the function checks that each element of the array is equal to
the scalar. This behaviour can be disabled by setting ``strict==True``.

Examples
--------
>>> np.testing.assert_equal([4, 5], [4, 6])
Traceback (most recent call last):
    ...
AssertionError:
Items are not equal:
item=1
 ACTUAL: 5
 DESIRED: 6

The following comparison does not raise an exception.  There are NaNs
in the inputs, but they are in the same positions.

>>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])

As mentioned in the Notes section, `assert_equal` has special
handling for scalars when one of the arguments is an array.
Here, the test checks that each value in `x` is 3:

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

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

>>> np.testing.assert_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_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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