assert_equal(actual, desired, err_msg='', verbose=True)
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.
When one of :None:None:`actual` and :None:None:`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 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.
The object to check.
The expected object.
The error message to be printed in case of failure.
If True, the conflicting values are appended to the error message.
If actual and desired are not equal.
Raises an AssertionError if two objects are not equal.
>>> 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.
This example is valid syntax, but we were not able to check execution>>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.testing._private.utils.assert_approx_equal
numpy.testing._private.utils.assert_array_almost_equal
numpy.testing._private.utils.assert_almost_equal
numpy.testing._private.utils.assert_array_equal
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