numpy.testing.assert_array_equal#
- testing.assert_array_equal(actual, desired, err_msg='', verbose=True, *, strict=False)[source]#
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 adict
, the behavior ofassert_equal(actual, desired)
is identical to the behavior of this function. Otherwise, this function performs np.asanyarray on the inputs before comparison, whereasassert_equal
defines special comparison rules for common Python types. For example, onlyassert_equal
can be used to compare nested Python lists. In new code, consider using onlyassert_equal
, explicitly converting either actual or desired to arrays if the behavior ofassert_array_equal
is desired.- Parameters:
- actualarray_like
The actual object to check.
- desiredarray_like
The desired, expected object.
- err_msgstr, optional
The error message to be printed in case of failure.
- verbosebool, optional
If True, the conflicting values are appended to the error message.
- strictbool, 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.
New in version 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 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 (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 (dtypes int64, float32 mismatch) ACTUAL: array([2, 2, 2]) DESIRED: array([2., 2., 2.], dtype=float32)