Function has_no_error

pythonwhat/checks/has_funcs.py:748–806

Check whether the submission did not generate a runtime error. If all SCTs for an exercise pass, before marking the submission as correct pythonwhat will automatically check whether the student submission generated an error. This means it is not needed to use ``has_no_error()`` explicitly.

(
    state,
    incorrect_msg="Have a look at the console: your code contains an error. Fix it and try again!",
)

def has_no_error(
    state,
    incorrect_msg="Have a look at the console: your code contains an error. Fix it and try again!",
):
    """Check whether the submission did not generate a runtime error.

    If all SCTs for an exercise pass, before marking the submission as correct pythonwhat will automatically check whether
    the student submission generated an error. This means it is not needed to use ``has_no_error()`` explicitly.

    However, in some cases, using ``has_no_error()`` explicitly somewhere throughout your SCT execution can be helpful:

    - If you want to make sure people didn't write typos when writing a long function name.
    - If you want to first verify whether a function actually runs, before checking whether the arguments were specified correctly.
    - More generally, if, because of the content, it's instrumental that the script runs without
      errors before doing any other verifications.

    Args:
        incorrect_msg: if specified, this overrides the default message if the student code generated an error.

    :Example:

        Suppose you're verifying an exercise about model training and validation: ::

            # pre exercise code
            import numpy as np
            from sklearn.model_selection import train_test_split
            from sklearn import datasets
            from sklearn import svm

            iris = datasets.load_iris()
            iris.data.shape, iris.target.shape

            # solution
            X_train, X_test, y_train, y_test = train_test_split(
                iris.data, iris.target, test_size=0.4, random_state=0)

        If you want to make sure that ``train_test_split()`` ran without errors,
        which would check if the student typed the function without typos and used
        sensical arguments, you could use the following SCT: ::

            Ex().has_no_error()
            Ex().check_function('sklearn.model_selection.train_test_split').multi(
                check_args(['arrays', 0]).has_equal_value(),
                check_args(['arrays', 0]).has_equal_value(),
                check_args(['options', 'test_size']).has_equal_value(),
                check_args(['options', 'random_state']).has_equal_value()
            )

        If, on the other hand, you want to fall back onto pythonwhat's built in behavior,
        that checks for an error before marking the exercise as correct, you can simply
        leave of the ``has_no_error()`` step.

    """
    state.assert_execution_root("has_no_error")

    if state.reporter.errors:
        state.report(incorrect_msg, {"error": str(state.reporter.errors[0])})