pythonwhat/checks/has_funcs.py
def has_equal_ast(state, incorrect_msg=None, code=None, exact=True, append=None):
    """Test whether abstract syntax trees match between the student and solution code.

    ``has_equal_ast()`` can be used in two ways:

    * As a robust version of ``has_code()``. By setting ``code``, you can look for the AST representation of ``code`` in the student's submission.
      But be aware that ``a`` and ``a = 1`` won't match, as reading and assigning are not the same in an AST.
      Use ``ast.dump(ast.parse(code))`` to see an AST representation of ``code``.
    * As an expression-based check when using more advanced SCT chain, e.g. to compare the equality of expressions to set function arguments.

    Args:
        incorrect_msg: message displayed when ASTs mismatch. When you specify ``code`` yourself, you have to specify this.
        code: optional code to use instead of the solution AST.
        exact: whether the representations must match exactly. If false, the solution AST
               only needs to be contained within the student AST (similar to using test student typed).
               Defaults to ``True``, unless the ``code`` argument has been specified.

    :Example:

        Student and Solution Code::

            dict(a = 'value').keys()

        SCT::

            # all pass
            Ex().has_equal_ast()
            Ex().has_equal_ast(code = "dict(a = 'value').keys()")
            Ex().has_equal_ast(code = "dict(a = 'value')", exact = False)

        Student and Solution Code::

            import numpy as np
            arr = np.array([1, 2, 3, 4, 5])
            np.mean(arr)

        SCT::

            # Check underlying value of arugment a of np.mean:
            Ex().check_function('numpy.mean').check_args('a').has_equal_ast()

            # Only check AST equality of expression used to specify argument a:
            Ex().check_function('numpy.mean').check_args('a').has_equal_ast()

    """
    if utils.v2_only():
        state.assert_is_not(["object_assignments"], "has_equal_ast", ["check_object"])
        state.assert_is_not(["function_calls"], "has_equal_ast", ["check_function"])

    if code and incorrect_msg is None:
        raise InstructorError(
            "If you manually specify the code to match inside has_equal_ast(), "
            "you have to explicitly set the `incorrect_msg` argument."
        )

    if (
        append is None
    ):  # if not specified, set to False if incorrect_msg was manually specified
        append = incorrect_msg is None
    if incorrect_msg is None:
        incorrect_msg = "Expected `{{sol_str}}`, but got `{{stu_str}}`."

    def parse_tree(tree):
        # get contents of module.body if only 1 element
        crnt = (
            tree.body[0]
            if isinstance(tree, ast.Module) and len(tree.body) == 1
            else tree
        )

        # remove Expr if it exists
        return ast.dump(crnt.value if isinstance(crnt, ast.Expr) else crnt)

    stu_rep = parse_tree(state.student_ast)
    sol_rep = parse_tree(state.solution_ast if not code else ast.parse(code))

    fmt_kwargs = {
        "sol_str": state.solution_code if not code else code,
        "stu_str": state.student_code,
    }

    _msg = state.build_message(incorrect_msg, fmt_kwargs, append=append)

    if exact and not code:
        state.do_test(EqualTest(stu_rep, sol_rep, Feedback(_msg, state)))
    elif not sol_rep in stu_rep:
        state.report(Feedback(_msg, state))

    return state
def has_equal_ast(state, incorrect_msg=None, code=None, exact=True, append=None):
    """Test whether abstract syntax trees match between the student and solution code.

    ``has_equal_ast()`` can be used in two ways:

    * As a robust version of ``has_code()``. By setting ``code``, you can look for the AST representation of ``code`` in the student's submission.
      But be aware that ``a`` and ``a = 1`` won't match, as reading and assigning are not the same in an AST.
      Use ``ast.dump(ast.parse(code))`` to see an AST representation of ``code``.
    * As an expression-based check when using more advanced SCT chain, e.g. to compare the equality of expressions to set function arguments.

    Args:
        incorrect_msg: message displayed when ASTs mismatch. When you specify ``code`` yourself, you have to specify this.
        code: optional code to use instead of the solution AST.
        exact: whether the representations must match exactly. If false, the solution AST
               only needs to be contained within the student AST (similar to using test student typed).
               Defaults to ``True``, unless the ``code`` argument has been specified.

    :Example:

        Student and Solution Code::

            dict(a = 'value').keys()

        SCT::

            # all pass
            Ex().has_equal_ast()
            Ex().has_equal_ast(code = "dict(a = 'value').keys()")
            Ex().has_equal_ast(code = "dict(a = 'value')", exact = False)

        Student and Solution Code::

            import numpy as np
            arr = np.array([1, 2, 3, 4, 5])
            np.mean(arr)

        SCT::

            # Check underlying value of arugment a of np.mean:
            Ex().check_function('numpy.mean').check_args('a').has_equal_ast()

            # Only check AST equality of expression used to specify argument a:
            Ex().check_function('numpy.mean').check_args('a').has_equal_ast()

    """
    if utils.v2_only():
        state.assert_is_not(["object_assignments"], "has_equal_ast", ["check_object"])
        state.assert_is_not(["function_calls"], "has_equal_ast", ["check_function"])

    if code and incorrect_msg is None:
        raise InstructorError(
            "If you manually specify the code to match inside has_equal_ast(), "
            "you have to explicitly set the `incorrect_msg` argument."
        )

    if (
        append is None
    ):  # if not specified, set to False if incorrect_msg was manually specified
        append = incorrect_msg is None
    if incorrect_msg is None:
        incorrect_msg = "Expected `{{sol_str}}`, but got `{{stu_str}}`."

    def parse_tree(tree):
        # get contents of module.body if only 1 element
        crnt = (
            tree.body[0]
            if isinstance(tree, ast.Module) and len(tree.body) == 1
            else tree
        )

        # remove Expr if it exists
        return ast.dump(crnt.value if isinstance(crnt, ast.Expr) else crnt)

    stu_rep = parse_tree(state.student_ast)
    sol_rep = parse_tree(state.solution_ast if not code else ast.parse(code))

    fmt_kwargs = {
        "sol_str": state.solution_code if not code else code,
        "stu_str": state.student_code,
    }

    _msg = state.build_message(incorrect_msg, fmt_kwargs, append=append)

    if exact and not code:
        state.do_test(EqualTest(stu_rep, sol_rep, Feedback(_msg, state)))
    elif not sol_rep in stu_rep:
        state.report(Feedback(_msg, state))

    return state
Test whether abstract syntax trees match between the student and solution code.

    ``has_equal_ast()`` can be used in two ways:

    * As a robust version of ``has_code()``. By setting ``code``, you can look for the AST representation of ``code`` in the student's submission.
      But be aware that ``a`` and ``a = 1`` won't match, as reading and assigning are not the same in an AST.
      Use ``ast.dump(ast.parse(code))`` to see an AST representation of ``code``.
    * As an expression-based check when using more advanced SCT chain, e.g. to compare the equality of expressions to set function arguments.

    Args:
        incorrect_msg: message displayed when ASTs mismatch. When you specify ``code`` yourself, you have to specify this.
        code: optional code to use instead of the solution AST.
        exact: whether the representations must match exactly. If false, the solution AST
               only needs to be contained within the student AST (similar to using test student typed).
               Defaults to ``True``, unless the ``code`` argument has been specified.

    :Example:

        Student and Solution Code::

            dict(a = 'value').keys()

        SCT::

            # all pass
            Ex().has_equal_ast()
            Ex().has_equal_ast(code = "dict(a = 'value').keys()")
            Ex().has_equal_ast(code = "dict(a = 'value')", exact = False)

        Student and Solution Code::

            import numpy as np
            arr = np.array([1, 2, 3, 4, 5])
            np.mean(arr)

        SCT::

            # Check underlying value of arugment a of np.mean:
            Ex().check_function('numpy.mean').check_args('a').has_equal_ast()

            # Only check AST equality of expression used to specify argument a:
            Ex().check_function('numpy.mean').check_args('a').has_equal_ast()