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.from_message(
"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,
}
if exact and not code:
state.do_test(
EqualTest(
stu_rep,
sol_rep,
FeedbackComponent(incorrect_msg, fmt_kwargs, append=append),
))
elif sol_rep not in stu_rep:
state.report(incorrect_msg, fmt_kwargs, append=append)
return state
def check_object(index,
missing_msg=None,
expand_msg=None,
state=None,
typestr="variable"):
"""Check object existence (and equality)
Check whether an object is defined in the student's process, and zoom in on its value in both
student and solution process to inspect quality (with has_equal_value().
In ``pythonbackend``, both the student's submission as well as the solution code are executed, in separate processes.
``check_object()`` looks at these processes and checks if the referenced object is available in the student process.
Next, you can use ``has_equal_value()`` to check whether the objects in the student and solution process correspond.
Args:
index (str): the name of the object which value has to be checked.
missing_msg (str): feedback message when the object is not defined in the student process.
expand_msg (str): If specified, this overrides any messages that are prepended by previous SCT chains.
:Example:
Suppose you want the student to create a variable ``x``, equal to 15: ::
x = 15
The following SCT will verify this: ::
Ex().check_object("x").has_equal_value()
- ``check_object()`` will check if the variable ``x`` is defined in the student process.
- ``has_equal_value()`` will check whether the value of ``x`` in the solution process is the same as in the student process.
Note that ``has_equal_value()`` only looks at **end result** of a variable in the student process.
In the example, how the object ``x`` came about in the student's submission, does not matter.
This means that all of the following submission will also pass the above SCT: ::
x = 15
x = 12 + 3
x = 3; x += 12
:Example:
As the previous example mentioned, ``has_equal_value()`` only looks at the **end result**. If your exercise is
first initializing and object and further down the script is updating the object, you can only look at the final value!
Suppose you want the student to initialize and populate a list `my_list` as follows: ::
my_list = []
for i in range(20):
if i % 3 == 0:
my_list.append(i)
There is no robust way to verify whether `my_list = [0]` was coded correctly in a separate way.
The best SCT would look something like this: ::
msg = "Have you correctly initialized `my_list`?"
Ex().check_correct(
check_object('my_list').has_equal_value(),
multi(
# check initialization: [] or list()
check_or(
has_equal_ast(code = "[]", incorrect_msg = msg),
check_function('list')
),
check_for_loop().multi(
check_iter().has_equal_value(),
check_body().check_if_else().multi(
check_test().multi(
set_context(2).has_equal_value(),
set_context(3).has_equal_value()
),
check_body().set_context(3).\\
set_env(my_list = [0]).\\
has_equal_value(name = 'my_list')
)
)
)
)
- ``check_correct()`` is used to robustly check whether ``my_list`` was built correctly.
- If ``my_list`` is not correct, **both** the initialization and the population code are checked.
:Example:
Because checking object correctness incorrectly is such a common misconception, we're adding another example: ::
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
df['c'] = [7, 8, 9]
The following SCT would be **wrong**, as it does not factor in the possibility that the 'add column ``c``' step could've been wrong: ::
Ex().check_correct(
check_object('df').has_equal_value(),
check_function('pandas.DataFrame').check_args(0).has_equal_value()
)
The following SCT would be better, as it is specific to the steps: ::
# verify the df = pd.DataFrame(...) step
Ex().check_correct(
check_df('df').multi(
check_keys('a').has_equal_value(),
check_keys('b').has_equal_value()
),
check_function('pandas.DataFrame').check_args(0).has_equal_value()
)
# verify the df['c'] = [...] step
Ex().check_df('df').check_keys('c').has_equal_value()
:Example:
pythonwhat compares the objects in the student and solution process with the ``==`` operator.
For basic objects, this ``==`` is operator is properly implemented, so that the objects can be effectively compared.
For more complex objects that are produced by third-party packages, however, it's possible that this equality operator is not implemented in a way you'd expect.
Often, for these object types the ``==`` will compare the actual object instances: ::
# pre exercise code
class Number():
def __init__(self, n):
self.n = n
# solution
x = Number(1)
# sct that won't work
Ex().check_object().has_equal_value()
# sct
Ex().check_object().has_equal_value(expr_code = 'x.n')
# submissions that will pass this sct
x = Number(1)
x = Number(2 - 1)
The basic SCT like in the previous example will notwork here.
Notice how we used the ``expr_code`` argument to _override_ which value `has_equal_value()` is checking.
Instead of checking whether `x` corresponds between student and solution process, it's now executing the expression ``x.n``
and seeing if the result of running this expression in both student and solution process match.
"""
# Only do the assertion if PYTHONWHAT_V2_ONLY is set to '1'
if v2_only():
extra_msg = "If you want to check the value of an object in e.g. a for loop, use `has_equal_value(name = 'my_obj')` instead."
state.assert_root('check_object', extra_msg=extra_msg)
if missing_msg is None:
missing_msg = "Did you define the {{typestr}} `{{index}}` without errors?"
if expand_msg is None:
expand_msg = "Did you correctly define the {{typestr}} `{{index}}`? "
rep = Reporter.active_reporter
if not isDefinedInProcess(
index, state.solution_process) and state.has_different_processes():
raise InstructorError(
"`check_object()` couldn't find object `%s` in the solution process."
% index)
append_message = {
'msg': expand_msg,
'kwargs': {
'index': index,
'typestr': typestr
}
}
# create child state, using either parser output, or create part from name
fallback = lambda: ObjectAssignmentParser.get_part(index)
stu_part = state.student_object_assignments.get(index, fallback())
sol_part = state.solution_object_assignments.get(index, fallback())
# test object exists
_msg = state.build_message(missing_msg, append_message['kwargs'])
rep.do_test(
DefinedProcessTest(index, state.student_process, Feedback(_msg)))
child = part_to_child(stu_part,
sol_part,
append_message,
state,
node_name='object_assignments')
return child
