def get_bash_history_info():
try:
old_history_length = int(
Path(os.environ[BASH_HISTORY_INFO_PATH_ENV]).read_text())
except FileNotFoundError:
raise InstructorError.from_message(
"`update_bash_history_info` wasn't called")
return old_history_length
def run_call(args, node, process, get_func, **kwargs):
# Get function expression
if isinstance(node, ast.FunctionDef): # function name
func_expr = ast.Name(id=node.name, ctx=ast.Load())
elif isinstance(node, ast.Lambda): # lambda body expr
func_expr = node
else:
raise InstructorError.from_message(
"Only function definition or lambda may be called")
ast.fix_missing_locations(func_expr)
return get_func(process=process, tree=func_expr, call=args, **kwargs)
def has_chosen(state, correct, msgs):
"""Test multiple choice exercise.
Test for a MultipleChoiceExercise. The correct answer (as an integer) and feedback messages
are passed to this function.
Args:
correct (int): the index of the correct answer (should be an instruction). Starts at 1.
msgs (list(str)): a list containing all feedback messages belonging to each choice of the
student. The list should have the same length as the number of options.
"""
if not issubclass(type(correct), int):
raise InstructorError.from_message(
"Inside `has_chosen()`, the argument `correct` should be an integer."
)
student_process = state.student_process
if not isDefinedInProcess(MC_VAR_NAME, student_process):
raise InstructorError.from_message(
"Option not available in the student process")
else:
selected_option = getOptionFromProcess(student_process, MC_VAR_NAME)
if not issubclass(type(selected_option), int):
raise InstructorError.from_message(
"selected_option should be an integer")
if selected_option < 1 or correct < 1:
raise InstructorError.from_message(
"selected_option and correct should be greater than zero")
if selected_option > len(msgs) or correct > len(msgs):
raise InstructorError.from_message(
"there are not enough feedback messages defined")
feedback_msg = msgs[selected_option - 1]
state.reporter.success_msg = msgs[correct - 1]
state.do_test(EqualTest(selected_option, correct, feedback_msg))
def is_instance(state, inst, not_instance_msg=None):
"""Check whether an object is an instance of a certain class.
``is_instance()`` can currently only be used when chained from ``check_object()``, the function that is
used to 'zoom in' on the object of interest.
Args:
inst (class): The class that the object should have.
not_instance_msg (str): When specified, this overrides the automatically generated message in case
the object does not have the expected class.
state (State): The state that is passed in through the SCT chain (don't specify this).
:Example:
Student code and solution code::
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
SCT::
# Verify the class of arr
import numpy
Ex().check_object('arr').is_instance(numpy.ndarray)
"""
state.assert_is(["object_assignments"], "is_instance", ["check_object"])
sol_name = state.solution_parts.get("name")
stu_name = state.student_parts.get("name")
if not_instance_msg is None:
not_instance_msg = "Is it a {{inst.__name__}}?"
if not isInstanceInProcess(sol_name, inst, state.solution_process):
raise InstructorError.from_message(
"`is_instance()` noticed that `%s` is not a `%s` in the solution process."
% (sol_name, inst.__name__))
feedback = FeedbackComponent(not_instance_msg, {"inst": inst})
state.do_test(
InstanceProcessTest(stu_name, inst, state.student_process, feedback))
return state
def get_signature(name, mapped_name, signature, manual_sigs, env):
if isinstance(signature, str):
if signature in manual_sigs:
signature = inspect.Signature(manual_sigs[signature])
else:
raise InstructorError.from_message("signature error - specified signature not found")
if signature is None:
# establish function
try:
fun = eval(mapped_name, env)
except:
raise InstructorError.from_message("%s() was not found." % mapped_name)
# first go through manual sigs
# try to get signature
try:
if name in manual_sigs:
signature = inspect.Signature(manual_sigs[name])
else:
# it might be a method, and we have to find the general method name
if "." in mapped_name:
els = name.split(".")
try:
els[0] = type(eval(els[0], env)).__name__
generic_name = ".".join(els[:])
except:
raise InstructorError.from_message("signature error - cannot convert call")
if generic_name in manual_sigs:
signature = inspect.Signature(manual_sigs[generic_name])
else:
raise InstructorError.from_message(
"signature error - %s not in builtins" % generic_name
)
else:
raise InstructorError.from_message("manual signature not found")
except Exception as e:
try:
signature = inspect.signature(fun)
except:
raise InstructorError.from_message(e.args[0] + " and cannot determine signature")
return signature
def has_printout(state,
index,
not_printed_msg=None,
pre_code=None,
name=None,
copy=False):
"""Check if the right printouts happened.
``has_printout()`` will look for the printout in the solution code that you specified with ``index`` (0 in this case), rerun the ``print()`` call in
the solution process, capture its output, and verify whether the output is present in the output of the student.
This is more robust as ``Ex().check_function('print')`` initiated chains as students can use as many
printouts as they want, as long as they do the correct one somewhere.
Args:
index (int): index of the ``print()`` call in the solution whose output you want to search for in the student output.
not_printed_msg (str): if specified, this overrides the default message that is generated when the output
is not found in the student output.
pre_code (str): Python code as a string that is executed before running the targeted student call.
This is the ideal place to set a random seed, for example.
copy (bool): whether to try to deep copy objects in the environment, such as lists, that could
accidentally be mutated. Disabled by default, which speeds up SCTs.
state (State): state as passed by the SCT chain. Don't specify this explicitly.
:Example:
Suppose you want somebody to print out 4: ::
print(1, 2, 3, 4)
The following SCT would check that: ::
Ex().has_printout(0)
All of the following SCTs would pass: ::
print(1, 2, 3, 4)
print('1 2 3 4')
print(1, 2, '3 4')
print("random"); print(1, 2, 3, 4)
:Example:
Watch out: ``has_printout()`` will effectively **rerun** the ``print()`` call in the solution process after the entire solution script was executed.
If your solution script updates the value of `x` after executing it, ``has_printout()`` will not work.
Suppose you have the following solution: ::
x = 4
print(x)
x = 6
The following SCT will not work: ::
Ex().has_printout(0)
Why? When the ``print(x)`` call is executed, the value of ``x`` will be 6, and pythonwhat will look for the output `'6`' in the output the student generated.
In cases like these, ``has_printout()`` cannot be used.
:Example:
Inside a for loop ``has_printout()``
Suppose you have the following solution: ::
for i in range(5):
print(i)
The following SCT will not work: ::
Ex().check_for_loop().check_body().has_printout(0)
The reason is that ``has_printout()`` can only be called from the root state. ``Ex()``.
If you want to check printouts done in e.g. a for loop, you have to use a `check_function('print')` chain instead: ::
Ex().check_for_loop().check_body().\\
set_context(0).check_function('print').\\
check_args(0).has_equal_value()
"""
extra_msg = "If you want to check printouts done in e.g. a for loop, you have to use a `check_function('print')` chain instead."
state.assert_execution_root("has_printout", extra_msg=extra_msg)
if not_printed_msg is None:
not_printed_msg = (
"Have you used `{{sol_call}}` to do the appropriate printouts?")
try:
sol_call_ast = state.ast_dispatcher.find(
"function_calls", state.solution_ast)["print"][index]["node"]
except (KeyError, IndexError):
raise InstructorError.from_message(
"`has_printout({})` couldn't find the {} print call in your solution."
.format(index, get_ord(index + 1)))
out_sol, str_sol = getOutputInProcess(
tree=sol_call_ast,
process=state.solution_process,
context=state.solution_context,
env=state.solution_env,
pre_code=pre_code,
copy=copy,
)
sol_call_str = state.solution_ast_tokens.get_text(sol_call_ast)
if isinstance(str_sol, Exception):
with debugger(state):
state.report(
"Evaluating the solution expression {} raised error in solution process."
"Error: {} - {}".format(sol_call_str, type(out_sol), str_sol))
has_output(
state,
out_sol.strip(),
pattern=False,
no_output_msg=FeedbackComponent(not_printed_msg,
{"sol_call": sol_call_str}),
)
return state
def has_import(
state,
name,
same_as=False,
not_imported_msg="Did you import `{{pkg}}`?",
incorrect_as_msg="Did you import `{{pkg}}` as `{{alias}}`?",
):
"""Checks whether student imported a package or function correctly.
Python features many ways to import packages.
All of these different methods revolve around the ``import``, ``from`` and ``as`` keywords.
``has_import()`` provides a robust way to check whether a student correctly imported a certain package.
By default, ``has_import()`` allows for different ways of aliasing the imported package or function.
If you want to make sure the correct alias was used to refer to the package or function that was imported,
set ``same_as=True``.
Args:
name (str): the name of the package that has to be checked.
same_as (bool): if True, the alias of the package or function has to be the same. Defaults to False.
not_imported_msg (str): feedback message when the package is not imported.
incorrect_as_msg (str): feedback message if the alias is wrong.
:Example:
Example 1, where aliases don't matter (defaut): ::
# solution
import matplotlib.pyplot as plt
# sct
Ex().has_import("matplotlib.pyplot")
# passing submissions
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
import matplotlib.pyplot as pltttt
# failing submissions
import matplotlib as mpl
Example 2, where the SCT is coded so aliases do matter: ::
# solution
import matplotlib.pyplot as plt
# sct
Ex().has_import("matplotlib.pyplot", same_as=True)
# passing submissions
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
# failing submissions
import matplotlib.pyplot as pltttt
"""
student_imports = state.ast_dispatcher.find("imports", state.student_ast)
solution_imports = state.ast_dispatcher.find("imports", state.solution_ast)
if name not in solution_imports:
raise InstructorError.from_message(
"`has_import()` couldn't find an import of the package %s in your solution code."
% name)
fmt_kwargs = {"pkg": name, "alias": solution_imports[name]}
state.do_test(
DefinedCollTest(name, student_imports,
FeedbackComponent(not_imported_msg, fmt_kwargs)))
if same_as:
state.do_test(
EqualTest(
solution_imports[name],
student_imports[name],
FeedbackComponent(incorrect_as_msg, fmt_kwargs),
))
return state
def has_expr(
state,
incorrect_msg=None,
error_msg=None,
undefined_msg=None,
append=None,
extra_env=None,
context_vals=None,
pre_code=None,
expr_code=None,
name=None,
copy=True,
func=None,
override=None,
test=None, # todo: default or arg before state
):
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:
if name:
incorrect_msg = DEFAULT_INCORRECT_NAME_MSG
elif expr_code:
incorrect_msg = DEFAULT_INCORRECT_EXPR_CODE_MSG
else:
incorrect_msg = DEFAULT_INCORRECT_MSG
if undefined_msg is None:
undefined_msg = DEFAULT_UNDEFINED_NAME_MSG
if error_msg is None:
if test == "error":
error_msg = DEFAULT_ERROR_MSG_INV
else:
error_msg = DEFAULT_ERROR_MSG
if state.solution_code is not None and isinstance(expr_code, str):
expr_code = expr_code.replace("__focus__", state.solution_code)
get_func = partial(
evalCalls[test],
extra_env=extra_env,
context_vals=context_vals,
pre_code=pre_code,
expr_code=expr_code,
name=name,
copy=copy,
)
if override is not None:
# don't bother with running expression and fetching output/value
# eval_sol, str_sol = eval
eval_sol, str_sol = override, str(override)
else:
eval_sol, str_sol = get_func(
tree=state.solution_ast,
process=state.solution_process,
context=state.solution_context,
env=state.solution_env,
)
if (test == "error") ^ isinstance(eval_sol, Exception):
raise InstructorError.from_message(
"Evaluating expression raised error in solution process (or didn't raise if testing for one). "
"Error: {} - {}".format(type(eval_sol), str_sol))
if isinstance(eval_sol, ReprFail):
raise InstructorError.from_message(
"Couldn't extract the value for the highlighted expression from the solution process: "
+ eval_sol.info)
eval_stu, str_stu = get_func(
tree=state.student_ast,
process=state.student_process,
context=state.student_context,
env=state.student_env,
)
# kwargs ---
fmt_kwargs = {
"stu_part": state.student_parts,
"sol_part": state.solution_parts,
"name": name,
"test": test,
"test_desc": "" if test == "value" else "the %s " % test,
"expr_code": expr_code,
}
fmt_kwargs["stu_eval"] = utils.shorten_str(str(eval_stu))
fmt_kwargs["sol_eval"] = utils.shorten_str(str(eval_sol))
if incorrect_msg == DEFAULT_INCORRECT_MSG and (
fmt_kwargs["stu_eval"] is None or fmt_kwargs["sol_eval"] is None
or fmt_kwargs["stu_eval"] == fmt_kwargs["sol_eval"]):
incorrect_msg = "Expected something different."
# tests ---
# error in process
if (test == "error") ^ isinstance(eval_stu, Exception):
fmt_kwargs["stu_str"] = str_stu
state.report(error_msg, fmt_kwargs, append=append)
# name is undefined after running expression
if isinstance(eval_stu, UndefinedValue):
state.report(undefined_msg, fmt_kwargs, append=append)
# test equality of results
state.do_test(
EqualTest(
eval_stu,
eval_sol,
FeedbackComponent(incorrect_msg, fmt_kwargs, append=append),
func,
))
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.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 test_function_v2(state,
name,
index=1,
params=[],
signature=True,
eq_condition="equal",
do_eval=True,
not_called_msg=None,
params_not_matched_msg=None,
params_not_specified_msg=None,
incorrect_msg=None,
add_more=False,
**kwargs):
index = index - 1
if not isinstance(params, list):
raise InstructorError.from_message(
"Inside test_function_v2, make sure to specify a LIST of params.")
if isinstance(do_eval, bool) or do_eval is None:
do_eval = [do_eval] * len(params)
if len(params) != len(do_eval):
raise InstructorError.from_message(
"Inside test_function_v2, make sure that do_eval has the same length as params."
)
# if params_not_specified_msg is a str or None, convert into list
if isinstance(params_not_specified_msg,
str) or params_not_specified_msg is None:
params_not_specified_msg = [params_not_specified_msg] * len(params)
if len(params) != len(params_not_specified_msg):
raise InstructorError.from_message(
"Inside test_function_v2, make sure that params_not_specified_msg has the same length as params."
)
# if incorrect_msg is a str or None, convert into list
if isinstance(incorrect_msg, str) or incorrect_msg is None:
incorrect_msg = [incorrect_msg] * len(params)
if len(params) != len(incorrect_msg):
raise InstructorError.from_message(
"Inside test_function_v2, make sure that incorrect_msg has the same length as params."
)
# if root-level (not in compound statement) calls that can be evaluated: use has_printout
eligible = do_eval[0] if isinstance(do_eval,
list) and len(do_eval) > 0 else do_eval
if name == "print" and state.parent_state is None and eligible:
try:
return has_printout(state,
index=index,
not_printed_msg=incorrect_msg[0])
except TestFail:
# The test didn't pass; just continue with the more strict check_function test.
pass
if len(params) == 0:
signature = False
fun_state = check_function(
state,
name=name,
index=index,
missing_msg=not_called_msg,
params_not_matched_msg=params_not_matched_msg,
signature=signature,
)
for i in range(len(params)):
arg_test(
fun_state,
name=params[i],
do_eval=do_eval[i],
missing_msg=params_not_specified_msg[i],
incorrect_msg=incorrect_msg[i],
)
return state
def set_context(state, *args, **kwargs):
"""Update context values for student and solution environments.
When ``has_equal_x()`` is used after this, the context values (in ``for`` loops and function definitions, for example)
will have the values specified through his function. It is the function equivalent of the ``context_vals`` argument of
the ``has_equal_x()`` functions.
- Note 1: excess args and unmatched kwargs will be unused in the student environment.
- Note 2: When you try to set context values that don't match any target variables in the solution code,
``set_context()`` raises an exception that lists the ones available.
- Note 3: positional arguments are more robust to the student using different names for context values.
- Note 4: You have to specify arguments either by position, either by name. A combination is not possible.
:Example:
Solution code::
total = 0
for i in range(10):
print(i ** 2)
Student submission that will pass (different iterator, different calculation)::
total = 0
for j in range(10):
print(j * j)
SCT::
# set_context is robust against different names of context values.
Ex().check_for_loop().check_body().multi(
set_context(1).has_equal_output(),
set_context(2).has_equal_output(),
set_context(3).has_equal_output()
)
# equivalent SCT, by setting context_vals in has_equal_output()
Ex().check_for_loop().check_body().\\
multi([s.has_equal_output(context_vals=[i]) for i in range(1, 4)])
"""
stu_crnt = state.student_context.context
sol_crnt = state.solution_context.context
# for now, you can't specify both
if len(args) > 0 and len(kwargs) > 0:
raise InstructorError.from_message(
"In `set_context()`, specify arguments either by position, either by name."
)
# set args specified by pos -----------------------------------------------
if args:
# stop if too many pos args for solution
if len(args) > len(sol_crnt):
raise InstructorError.from_message(
"Too many positional args. There are {} context vals, but tried to set {}"
.format(len(sol_crnt), len(args)))
# set pos args
upd_sol = sol_crnt.update(dict(zip(sol_crnt.keys(), args)))
upd_stu = stu_crnt.update(dict(zip(stu_crnt.keys(), args)))
else:
upd_sol = sol_crnt
upd_stu = stu_crnt
# set args specified by keyword -------------------------------------------
if kwargs:
# stop if keywords don't match with solution
if set(kwargs) - set(upd_sol):
raise InstructorError.from_message(
"`set_context()` failed: context val names are {}, but you tried to set {}."
.format(upd_sol or "missing", sorted(list(kwargs.keys()))))
out_sol = upd_sol.update(kwargs)
# need to match keys in kwargs with corresponding keys in stu context
# in case they used, e.g., different loop variable names
match_keys = dict(zip(sol_crnt.keys(), stu_crnt.keys()))
out_stu = upd_stu.update(
{match_keys[k]: v
for k, v in kwargs.items() if k in match_keys})
else:
out_sol = upd_sol
out_stu = upd_stu
return state.to_child(student_context=out_stu,
solution_context=out_sol,
highlight=state.highlight)
def check_keys(state, key, missing_msg=None, expand_msg=None):
"""Check whether an object (dict, DataFrame, etc) has a key.
``check_keys()`` can currently only be used when chained from ``check_object()``, the function that is
used to 'zoom in' on the object of interest.
Args:
key (str): Name of the key that the object should have.
missing_msg (str): When specified, this overrides the automatically generated
message in case the key does not exist.
expand_msg (str): If specified, this overrides any messages that are prepended by previous SCT chains.
state (State): The state that is passed in through the SCT chain (don't specify this).
:Example:
Student code and solution code::
x = {'a': 2}
SCT::
# Verify that x contains a key a
Ex().check_object('x').check_keys('a')
# Verify that x contains a key a and a is correct.
Ex().check_object('x').check_keys('a').has_equal_value()
"""
state.assert_is(["object_assignments"], "is_instance",
["check_object", "check_df"])
if missing_msg is None:
missing_msg = "There is no {{ 'column' if 'DataFrame' in parent.typestr else 'key' }} `'{{key}}'`."
if expand_msg is None:
expand_msg = "Did you correctly set the {{ 'column' if 'DataFrame' in parent.typestr else 'key' }} `'{{key}}'`? "
sol_name = state.solution_parts.get("name")
stu_name = state.student_parts.get("name")
if not isDefinedCollInProcess(sol_name, key, state.solution_process):
raise InstructorError.from_message(
"`check_keys()` couldn't find key `%s` in object `%s` in the solution process."
% (key, sol_name))
# check if key available
state.do_test(
DefinedCollProcessTest(stu_name, key, state.student_process,
FeedbackComponent(missing_msg, {"key": key})))
def get_part(name, key, highlight):
if isinstance(key, str):
slice_val = ast.Str(s=key)
else:
slice_val = ast.parse(str(key)).body[0].value
expr = ast.Subscript(
value=ast.Name(id=name, ctx=ast.Load()),
slice=ast.Index(value=slice_val),
ctx=ast.Load(),
)
ast.fix_missing_locations(expr)
return {"node": expr, "highlight": highlight}
stu_part = get_part(stu_name, key, state.student_parts.get("highlight"))
sol_part = get_part(sol_name, key, state.solution_parts.get("highlight"))
append_message = FeedbackComponent(expand_msg, {"key": key})
child = part_to_child(stu_part, sol_part, append_message, state)
return child
def check_object(state,
index,
missing_msg=None,
expand_msg=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_execution_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}}`? "
if (not isDefinedInProcess(index, state.solution_process)
and state.has_different_processes()):
raise InstructorError.from_message(
"`check_object()` couldn't find object `%s` in the solution process."
% index)
append_message = FeedbackComponent(expand_msg, {
"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.ast_dispatcher.find("object_assignments",
state.student_ast).get(
index, fallback())
sol_part = state.ast_dispatcher.find("object_assignments",
state.solution_ast).get(
index, fallback())
# test object exists
state.do_test(
DefinedProcessTest(
index,
state.student_process,
FeedbackComponent(missing_msg, append_message.kwargs),
))
child = part_to_child(stu_part,
sol_part,
append_message,
state,
node_name="object_assignments")
return child
def diagnose(_):
raise InstructorError.from_message("problem")