tag format.
"""
def is_optioninput_valid(optioninput):
"""
Verifies if a given optioninput xml is valid or not.
A given optioninput(Dropdown) problem is invalid if it has more than one correct answer.
Argument:
optioninput: dropdown specification tree
Returns:
boolean: signifying if the optioninput is valid or not.
"""
correct_options = [option.get("correct").upper() == "TRUE" for option in optioninput.findall("./option")]
return correct_options.count(True) in (0, 1)
additionals = tree.xpath("//stringresponse/additional_answer")
for additional in additionals:
answer = additional.get("answer")
text = additional.text
if not answer and text: # trigger of old->new conversion
additional.set("answer", text)
additional.text = ""
for optioninput in tree.xpath("//optioninput"):
if not is_optioninput_valid(optioninput):
raise responsetypes.LoncapaProblemError("Dropdown questions can only have one correct answer.")
correct_option = None
child_options = []
for option_element in optioninput.findall("./option"):
text = option_element.text
text = text or ""
option_name = text.strip()
if option_element.get("correct").upper() == "TRUE":
correct_option = option_name
child_options.append("'" + option_name + "'")
if len(child_options) > 0:
options_string = "(" + ",".join(child_options) + ")"
optioninput.attrib.update({"options": options_string})
if correct_option:
optioninput.attrib.update({"correct": correct_option})
def do_reset(self):
"""
Reset internal state to unfinished, with no answers
"""
self.student_answers = {}
self.student_answers_history = []
self.has_saved_answers = False
self.correct_map = CorrectMap()
self.correct_map_history = []
self.done = False
def set_initial_display(self):
"""
Set the student's answers to the responders' initial displays, if specified.
"""
initial_answers = {}
for responder in self.responders.values():
if hasattr(responder, "get_initial_display"):
initial_answers.update(responder.get_initial_display())
self.student_answers = initial_answers
def __str__(self):
return f"LoncapaProblem ({self.problem_id})"
def get_state(self):
"""
Stored per-user session data neeeded to:
1) Recreate the problem
2) Populate any student answers.
"""
return {
"seed": self.seed,
"student_answers": self.student_answers,
"has_saved_answers": self.has_saved_answers,
"correct_map": self.correct_map.get_dict(),
"correct_map_history": [cmap.get_dict() for cmap in self.correct_map_history],
"input_state": self.input_state,
"done": self.done,
}
def get_max_score(self):
"""
Return the maximum score for this problem.
"""
maxscore = 0
for responder in self.responders.values():
maxscore += responder.get_max_score()
return maxscore
def calculate_score(self, correct_map=None):
"""
Compute score for this problem. The score is the number of points awarded.
Returns a dictionary {'score': integer, from 0 to get_max_score(),
'total': get_max_score()}.
Takes an optional correctness map for use in the rescore workflow.
"""
if correct_map is None:
correct_map = self.correct_map
correct = 0
for key in correct_map:
try:
correct += correct_map.get_npoints(key)
except Exception:
log.error("key=%s, correct_map = %s", key, correct_map)
raise
return {"score": correct, "total": self.get_max_score()}
def update_score(self, score_msg, queuekey):
"""
Deliver grading response (e.g. from async code checking) to
the specific ResponseType that requested grading
Returns an updated CorrectMap
"""
cmap = CorrectMap()
cmap.update(self.correct_map)
for responder in self.responders.values():
if hasattr(responder, "update_score"):
# Each LoncapaResponse will update its specific entries in cmap
# cmap is passed by reference
responder.update_score(score_msg, cmap, queuekey)
self.correct_map.set_dict(cmap.get_dict())
return cmap
def ungraded_response(self, xqueue_msg, queuekey):
"""
Handle any responses from the xqueue that do not contain grades
Will try to pass the queue message to all inputtypes that can handle ungraded responses
Does not return any value
"""
# check against each inputtype
for the_input in self.inputs.values():
# if the input type has an ungraded function, pass in the values
if hasattr(the_input, "ungraded_response"):
the_input.ungraded_response(xqueue_msg, queuekey)
def is_queued(self):
"""
Returns True if any part of the problem has been submitted to an external queue
(e.g. for grading.)
"""
return any(self.correct_map.is_queued(answer_id) for answer_id in self.correct_map)
def get_recentmost_queuetime(self):
"""
Returns a DateTime object that represents the timestamp of the most recent
queueing request, or None if not queued
"""
if not self.is_queued():
return None
# Get a list of timestamps of all queueing requests, then convert it to a DateTime object
queuetime_strs = [
self.correct_map.get_queuetime_str(answer_id)
for answer_id in self.correct_map
if self.correct_map.is_queued(answer_id)
]
queuetimes = [
datetime.strptime(qt_str, xqueue_interface.DATEFORMAT).replace(
tzinfo=ZoneInfo("UTC")
)
for qt_str in queuetime_strs
]
return max(queuetimes)
def grade_answers(self, answers):
"""
Grade student responses. Called by capa_block.submit_problem.
`answers` is a dict of all the entries from request.POST, but with the first part
of each key removed (the string before the first "_").
Thus, for example, input_ID123 -> ID123, and input_fromjs_ID123 -> fromjs_ID123
Calls the Response for each question in this problem, to do the actual grading.
"""
# if answers include File objects, convert them to filenames.
self.student_answers = convert_files_to_filenames(answers)
new_cmap = self.get_grade_from_current_answers(answers)
self.correct_map = new_cmap # pylint: disable=attribute-defined-outside-init
self.correct_map_history.append(deepcopy(new_cmap))
return self.correct_map
def supports_rescoring(self):
"""
Checks that the current problem definition permits rescoring.
More precisely, it checks that there are no response types in
the current problem that are not fully supported (yet) for rescoring.
This includes responsetypes for which the student's answer
is not properly stored in state, i.e. file submissions. At present,
we have no way to know if an existing response was actually a real
answer or merely the filename of a file submitted as an answer.
It turns out that because rescoring is a background task, limiting
it to responsetypes that don't support file submissions also means
that the responsetypes are synchronous. This is convenient as it
permits rescoring to be complete when the rescoring call returns.
"""
return all("filesubmission" not in responder.allowed_inputfields for responder in self.responders.values())
def get_grade_from_current_answers(self, student_answers, correct_map: Optional[CorrectMap] = None):
"""
Gets the grade for the currently-saved problem state, but does not save it
to the block.
For new student_answers being graded, `student_answers` is a dict of all the
entries from request.POST, but with the first part of each key removed
(the string before the first "_"). Thus, for example,
input_ID123 -> ID123, and input_fromjs_ID123 -> fromjs_ID123.
For rescoring, `student_answers` is None.
Calls the Response for each question in this problem, to do the actual grading.
When the grading method is enabled, this method is used for rescore. In this case,
the `correct_map` and the `student_answers` passed as arguments will be used,
corresponding to each pair in the fields that store the history (correct_map_history
and student_answers_history). The correct map will always be updated, depending on
the student answers. The student answers will always remain the same over time.
"""
oldcmap = correct_map if self.is_grading_method_enabled else self.correct_map
# start new with empty CorrectMap
newcmap = CorrectMap()
# Call each responsetype instance to do actual grading
for responder in self.responders.values():
# File objects are passed only if responsetype explicitly allows
# for file submissions. But we have no way of knowing if
# student_answers contains a proper answer or the filename of
# an earlier submission, so for now skip these entirely.
# TODO: figure out where to get file submissions when rescoring.
if "filesubmission" in responder.allowed_inputfields and student_answers is None:
_ = self.capa_system.i18n.gettext
raise Exception( # pylint: disable=broad-exception-raised
_("Cannot rescore problems with possible file submissions")
)
# use 'student_answers' only if it is provided, and if it might contain a file
# submission that would not exist in the persisted "student_answers".
# If grading method is enabled, we need to pass each student answers and the
# correct map in the history fields.
if (
"filesubmission" in responder.allowed_inputfields and student_answers is not None
) or self.is_grading_method_enabled:
results = responder.evaluate_answers(student_answers, oldcmap)
else:
results = responder.evaluate_answers(self.student_answers, oldcmap)
newcmap.update(results)
return newcmap
def get_question_answers(self):
"""
Returns a dict of answer_ids to answer values. If we cannot generate
an answer (this sometimes happens in customresponses), that answer_id is
not included. Called by "show answers" button JSON request
(see capa_block)
"""
# dict of (id, correct_answer)
answer_map = {}
for response in self.responders:
results = self.responder_answers[response]
answer_map.update(results)
# include solutions from ... stanzas
for entry in self.tree.xpath("//" + "|//".join(solution_tags)):
answer = etree.tostring(entry).decode("utf-8")
if answer:
answer_map[entry.get("id")] = contextualize_text(answer, self.context)
log.debug("answer_map = %s", answer_map)
return answer_map
def get_answer_ids(self):
"""
Return the IDs of all the responses -- these are the keys used for
the dicts returned by grade_answers and get_question_answers. (Though
get_question_answers may only return a subset of these.
"""
answer_ids = []
for response in self.responders:
results = self.responder_answers[response]
answer_ids.append(list(results.keys()))
return answer_ids
def find_correct_answer_text(self, answer_id):
"""
Returns the correct answer(s) for the provided answer_id as a single string.
Arguments::
answer_id (str): a string like "98e6a8e915904d5389821a94e48babcf_13_1"
Returns:
str: A string containing the answer or multiple answers separated by commas.
"""
xml_elements = self.tree.xpath('//*[@id="' + answer_id + '"]')
if not xml_elements:
return None
xml_element = xml_elements[0]
answer_text = xml_element.xpath("@answer")
if answer_text:
return answer_id[0]
if xml_element.tag == "optioninput":
return xml_element.xpath("@correct")[0]
return ", ".join(xml_element.xpath('*[@correct="true"]/text()'))
def find_question_label(self, answer_id):
"""
Obtain the most relevant question text for a particular answer.
E.g. in a problem like "How much is 2+2?" "Two"/"Three"/"More than three",
this function returns the "How much is 2+2?" text.
It uses, in order:
- the question prompt, if the question has one
- the or element which precedes the choices (skipping descriptive elements)
- a text like "Question 5" if no other name could be found
Arguments::
answer_id: a string like "98e6a8e915904d5389821a94e48babcf_13_1"
Returns:
a string with the question text
"""
def generate_default_question_label():
"""
To create question string like "Question 2" by adding "Question" and its position number.
For instance 'd2e35c1d294b4ba0b3b1048615605d2a_2_1' contains 2,
which is used in question number 1 (see example XML in comment above)
There's no question 0 (question IDs start at 1, answer IDs at 2)
"""
question_nr = int(answer_id.split("_")[-2]) - 1
return _("Question {}").format(question_nr)
_ = self.capa_system.i18n.gettext
# Some questions define a prompt with this format: >>This is a prompt<<
try:
prompt = self.problem_data[answer_id].get("label")
except KeyError:
prompt = None
if prompt:
question_text = prompt.striptags()
else:
# If no prompt, then we must look for something resembling a question ourselves
#
# We have a structure like:
#
#
#
#
#
# Starting from answer (the optioninput in this example) we go up and backwards
xml_elems = self.tree.xpath('//*[@id="' + answer_id + '"]')
if len(xml_elems) != 1:
return generate_default_question_label()
xml_elem = xml_elems[0].getparent()
# Get the element that probably contains the question text
questiontext_elem = xml_elem.getprevious()
# Go backwards looking for a or , but skip because it doesn't
# contain the question text.
#
# E.g if we have this:
#
.
# If we start in the second optionresponse, we'll find another response in the way,
# stop early, and instead of a question we'll report "Question 2".
skip_elems = ["description"]
label_elems = ["p", "label"]
while questiontext_elem is not None and questiontext_elem.tag in skip_elems:
questiontext_elem = questiontext_elem.getprevious()
if questiontext_elem is not None and questiontext_elem.tag in label_elems:
question_text = questiontext_elem.text
else:
question_text = generate_default_question_label()
return question_text
def find_answer_text(self, answer_id, current_answer):
"""
Process a raw answer text to make it more meaningful.
E.g. in a choice problem like "How much is 2+2?" "Two"/"Three"/"More than three",
this function will transform "choice_1" (which is the internal response given by
many capa methods) to the human version, e.g. "More than three".
If the answers are multiple (e.g. because they're from a multiple choice problem),
this will join them with a comma.
If passed a normal string which is already the answer, it doesn't change it.
TODO merge with response_a11y_data?
Arguments:
answer_id: a string like "98e6a8e915904d5389821a94e48babcf_13_1"
current_answer: a data structure as found in `LoncapaProblem.student_answers`
which represents the best response we have until now
Returns:
a string with the human version of the response
"""
if isinstance(current_answer, list):
# Multiple answers. This case happens e.g. in multiple choice problems
answer_text = ", ".join(self.find_answer_text(answer_id, answer) for answer in current_answer)
elif isinstance(current_answer, str) and current_answer.startswith("choice_"):
# Many problem (e.g. checkbox) report "choice_0" "choice_1" etc.
# Here we transform it
elems = self.tree.xpath(f'//*[@id="{answer_id}"]//*[@name="{current_answer}"]')
if len(elems) == 0:
log.warning("Answer Text Missing for answer id: %s and choice number: %s", answer_id, current_answer)
answer_text = "Answer Text Missing"
elif len(elems) == 1:
choicegroup = elems[0].getparent()
input_cls = inputtypes.registry.get_class_for_tag(choicegroup.tag)
choices_map = dict(input_cls.extract_choices(choicegroup, self.capa_system.i18n, text_only=True))
answer_text = choices_map.get(current_answer, "Answer Text Missing")
else:
log.warning("Multiple answers found for answer id: %s and choice number: %s", answer_id, current_answer)
answer_text = "Multiple answers found"
elif isinstance(current_answer, str):
# Already a string with the answer
answer_text = current_answer
else:
raise NotImplementedError()
return answer_text or "Answer Text Missing"
def do_targeted_feedback(self, tree): # pylint: disable=too-many-locals,too-many-branches
"""
Implements targeted-feedback in-place on --
choice-level explanations shown to a student after submission.
Does nothing if there is no targeted-feedback attribute.
"""
_ = self.capa_system.i18n.gettext
# Note that the modifications has been done, avoiding problems if called twice.
if hasattr(self, "has_targeted"):
return
self.has_targeted = True # pylint: disable=attribute-defined-outside-init
for mult_choice_response in tree.xpath("//multiplechoiceresponse[@targeted-feedback]"):
show_explanation = mult_choice_response.get("targeted-feedback") == "alwaysShowCorrectChoiceExplanation"
# Grab the first choicegroup (there should only be one within each tag)
choicegroup = mult_choice_response.xpath('./choicegroup[@type="MultipleChoice"]')[0]
choices_list = list(choicegroup.iter("choice"))
# Find the student answer key that matches our id
student_answer = self.student_answers.get(choicegroup.get("id"))
expl_id_for_student_answer = None
# Keep track of the explanation-id that corresponds to the student's answer
# Also, keep track of the solution-id
solution_id = None
choice_correctness_for_student_answer = _("Incorrect")
for choice in choices_list:
if choice.get("name") == student_answer:
expl_id_for_student_answer = choice.get("explanation-id")
if choice.get("correct") == "true":
choice_correctness_for_student_answer = _("Correct")
if choice.get("correct") == "true":
solution_id = choice.get("explanation-id")
# Filter out targetedfeedback that doesn't correspond to the answer the student selected
# Note: following-sibling will grab all following siblings, so we just want the first in the list
targetedfeedbackset = mult_choice_response.xpath("./following-sibling::targetedfeedbackset")
if len(targetedfeedbackset) != 0:
targetedfeedbackset = targetedfeedbackset[0]
targetedfeedbacks = targetedfeedbackset.xpath("./targetedfeedback")
# find the legend by id in choicegroup.html for aria-describedby
problem_legend_id = str(choicegroup.get("id")) + "-legend"
for targetedfeedback in targetedfeedbacks:
screenreadertext = etree.Element("span")
targetedfeedback.insert(0, screenreadertext)
screenreadertext.set("class", "sr")
screenreadertext.text = choice_correctness_for_student_answer
targetedfeedback.set("role", "group")
targetedfeedback.set("aria-describedby", problem_legend_id)
# Don't show targeted feedback if the student hasn't answer the problem
# or if the target feedback doesn't match the student's (incorrect) answer
if not self.done or targetedfeedback.get("explanation-id") != expl_id_for_student_answer:
targetedfeedbackset.remove(targetedfeedback)
# Do not displace the solution under these circumstances
if not show_explanation or not self.done:
continue
# The next element should either be or
next_element = targetedfeedbackset.getnext()
parent_element = tree
solution_element = None
if next_element is not None and next_element.tag == "solution":
solution_element = next_element
elif next_element is not None and next_element.tag == "solutionset":
solutions = next_element.xpath("./solution")
for solution in solutions:
if solution.get("explanation-id") == solution_id:
parent_element = next_element
solution_element = solution
# If could not find the solution element, then skip the remaining steps below
if solution_element is None:
continue
# Change our correct-choice explanation from a "solution explanation" to within
# the set of targeted feedback, which means the explanation will render on the page
# without the student clicking "Show Answer" or seeing a checkmark next to the correct choice
parent_element.remove(solution_element)
# Add our solution instead to the targetedfeedbackset and change its tag name
solution_element.tag = "targetedfeedback"
targetedfeedbackset.append(solution_element)
def get_html(self):
"""
Main method called externally to get the HTML to be rendered for this capa Problem.
"""
self.do_targeted_feedback(self.tree)
html = contextualize_text(etree.tostring(self._extract_html(self.tree)).decode("utf-8"), self.context)
return html
def handle_input_ajax(self, data):
"""
InputTypes can support specialized AJAX calls. Find the correct input and pass along the correct data
Also, parse out the dispatch from the get so that it can be passed onto the input type nicely
"""
# pull out the id
input_id = data["input_id"]
if self.inputs[input_id]:
dispatch = data["dispatch"]
return self.inputs[input_id].handle_ajax(dispatch, data)
log.warning("Could not find matching input for id: %s", input_id)
return {}
# ======= Private Methods Below ========
def _process_includes(self):
"""
Handle any tags by reading in the specified file and inserting it
into our XML tree. Fail gracefully if debugging.
"""
includes = self.tree.findall(".//include")
for inc in includes:
filename = inc.get("file")
if filename is not None:
try:
# open using LoncapaSystem OSFS filesystem
ifp = self.capa_system.resources_fs.open(filename)
except Exception as err: # pylint: disable=broad-exception-caught
log.warning("Error %s in problem xml include: %s", err, etree.tostring(inc, pretty_print=True))
log.warning("Cannot find file %s in %s", filename, self.capa_system.resources_fs)
# if debugging, don't fail - just log error
# TODO (vshnayder): need real error handling, display to users
if not self.capa_system.DEBUG:
raise
continue
try:
# read in and convert to XML
incxml = etree.XML(ifp.read())
except Exception as err: # pylint: disable=broad-exception-caught
log.warning("Error %s in problem xml include: %s", err, etree.tostring(inc, pretty_print=True))
log.warning("Cannot parse XML in %s", (filename))
# if debugging, don't fail - just log error
# TODO (vshnayder): same as above
if not self.capa_system.DEBUG:
raise
continue
# insert new XML into tree in place of include
parent = inc.getparent()
parent.insert(parent.index(inc), incxml)
parent.remove(inc)
log.debug("Included %s into %s", filename, self.problem_id)
def _extract_system_path(self, script):
"""
Extracts and normalizes additional paths for code execution.
For now, there's a default path of data/course/code; this may be removed
at some point.
script : ?? (TODO)
"""
default_path = ["code"]
# Separate paths by :, like the system path.
raw_path = script.get("system_path", "").split(":") + default_path
# find additional comma-separated modules search path
path = []
for dir in raw_path: # pylint: disable=redefined-builtin
if not dir:
continue
# path is an absolute path or a path relative to the data dir
dir = os.path.join(self.capa_system.resources_fs.root_path, dir)
# Check that we are within the resources_fs tree.
reldir = os.path.relpath(dir, self.capa_system.resources_fs.root_path)
if ".." in reldir:
log.warning("Ignoring Python directory outside of course: %r", dir)
continue
abs_dir = os.path.normpath(dir)
path.append(abs_dir)
return path
def _extract_context(self, tree):
"""
Extract content of from the problem.xml file, and exec it in the
context of this problem. Provides ability to randomize problems, and also set
variables for problem answer checking.
Problem XML goes to Python execution context. Runs everything in script tags.
"""
context = {}
context["seed"] = self.seed
context["anonymous_student_id"] = self.capa_system.anonymous_student_id
all_code = ""
python_path = []
for script in tree.findall(".//script"):
stype = script.get("type")
if stype:
if "javascript" in stype:
continue # skip javascript
if "perl" in stype:
continue # skip perl
# TODO: evaluate only python
for d in self._extract_system_path(script):
if d not in python_path and os.path.exists(d):
python_path.append(d)
xmlesc = {"'": "'", """: '"'}
code = unescape(script.text, xmlesc)
all_code += code
extra_files = []
if all_code:
# An asset named python_lib.zip can be imported by Python code.
zip_lib = self.capa_system.get_python_lib_zip()
if zip_lib is not None:
extra_files.append(("python_lib.zip", zip_lib))
python_path.append("python_lib.zip")
try:
safe_exec(
all_code,
context,
random_seed=self.seed,
python_path=python_path,
extra_files=extra_files,
cache=self.capa_system.cache,
limit_overrides_context=get_course_id_from_capa_block(self.capa_block),
slug=self.problem_id,
unsafely=self.capa_system.can_execute_unsafe_code(),
)
except Exception as err:
log.exception("Error while execing script code: %s", all_code)
msg = Text(f"Error while executing script code: {err}")
raise responsetypes.LoncapaProblemError(msg)
# Store code source in context, along with the Python path needed to run it correctly.
context["script_code"] = all_code
context["python_path"] = python_path
context["extra_files"] = extra_files or None
return context
def _extract_html( # private
self, problemtree
): # pylint: disable=too-many-statements,too-many-locals,too-many-branches,too-many-return-statements
"""
Main (private) function which converts Problem XML tree to HTML.
Calls itself recursively.
Returns Element tree of XHTML representation of problemtree.
Calls render_html of Response instances to render responses into XHTML.
Used by get_html.
"""
if not isinstance(problemtree.tag, str):
# Comment and ProcessingInstruction nodes are not Elements,
# and we're ok leaving those behind.
# BTW: etree gives us no good way to distinguish these things
# other than to examine .tag to see if it's a string. :(
return None
if problemtree.tag == "script" and problemtree.get("type") and "javascript" in problemtree.get("type"):
# leave javascript intact.
return deepcopy(problemtree)
if problemtree.tag in html_problem_semantics:
return None
problemid = problemtree.get("id") # my ID
if problemtree.tag in inputtypes.registry.registered_tags():
# If this is an inputtype subtree, let it render itself.
response_data = self.problem_data[problemid]
status = "unsubmitted"
msg = ""
hint = ""
hintmode = None
input_id = problemtree.get("id")
answervariable = None
if problemid in self.correct_map:
pid = input_id
# If we're withholding correctness, don't show adaptive hints either.
# Note that regular, "demand" hints will be shown, if the course author has added them to the problem.
if not self.capa_block.correctness_available():
status = "submitted"
else:
# If the the problem has not been saved since the last submit set the status to the
# current correctness value and set the message as expected. Otherwise we do not want to
# display correctness because the answer may have changed since the problem was graded.
if not self.has_saved_answers:
status = self.correct_map.get_correctness(pid)
msg = self.correct_map.get_msg(pid)
hint = self.correct_map.get_hint(pid)
hintmode = self.correct_map.get_hintmode(pid)
answervariable = self.correct_map.get_property(pid, "answervariable")
value = ""
if self.student_answers and problemid in self.student_answers:
value = self.student_answers[problemid]
if input_id not in self.input_state:
self.input_state[input_id] = {}
# do the rendering
state = {
"value": value,
"status": status,
"id": input_id,
"input_state": self.input_state[input_id],
"answervariable": answervariable,
"response_data": response_data,
"has_saved_answers": self.has_saved_answers,
"feedback": {
"message": msg,
"hint": hint,
"hintmode": hintmode,
},
}
input_type_cls = inputtypes.registry.get_class_for_tag(problemtree.tag)
# save the input type so that we can make ajax calls on it if we need to
self.inputs[input_id] = input_type_cls(self.capa_system, problemtree, state)
return self.inputs[input_id].get_html()
# let each Response render itself
if problemtree in self.responders:
overall_msg = self.correct_map.get_overall_message()
return self.responders[problemtree].render_html(self._extract_html, response_msg=overall_msg)
# let each custom renderer render itself:
if problemtree.tag in customrender.registry.registered_tags():
renderer_class = customrender.registry.get_class_for_tag(problemtree.tag)
renderer = renderer_class(self.capa_system, problemtree)
return renderer.get_html()
# otherwise, render children recursively, and copy over attributes
tree = etree.Element(problemtree.tag)
for item in problemtree:
item_xhtml = self._extract_html(item)
if item_xhtml is not None:
tree.append(item_xhtml)
if tree.tag in html_transforms:
tree.tag = html_transforms[problemtree.tag]["tag"]
else:
# copy attributes over if not innocufying
for key, value in problemtree.items():
tree.set(key, value)
tree.text = problemtree.text
tree.tail = problemtree.tail
return tree
def _preprocess_problem(self, tree, minimal_init): # private
"""
Assign IDs to all the responses
Assign sub-IDs to all entries (textline, schematic, etc.)
Annoted correctness and value
In-place transformation
Also create capa Response instances for each responsetype and save as self.responders
Obtain all responder answers and save as self.responder_answers dict (key = response)
"""
response_id = 1
problem_data = {}
self.responders = {}
for response in tree.xpath("//" + "|//".join(responsetypes.registry.registered_tags())):
responsetype_id = self.problem_id + "_" + str(response_id)
# create and save ID for this response
response.set("id", responsetype_id)
response_id += 1
answer_id = 1
input_tags = inputtypes.registry.registered_tags()
inputfields = tree.xpath(
"|".join(["//" + response.tag + "[@id=$id]//" + x for x in input_tags]), id=responsetype_id
)
# assign one answer_id for each input type
for entry in inputfields:
entry.attrib["response_id"] = str(response_id)
entry.attrib["answer_id"] = str(answer_id)
entry.attrib["id"] = f"{self.problem_id}_{response_id}_{answer_id}"
answer_id = answer_id + 1
self.response_a11y_data(response, inputfields, responsetype_id, problem_data)
# instantiate capa Response
responsetype_cls = responsetypes.registry.get_class_for_tag(response.tag)
responder = responsetype_cls(
response, inputfields, self.context, self.capa_system, self.capa_block, minimal_init
)
# save in list in self
self.responders[response] = responder
if not minimal_init:
# get responder answers (do this only once, since there may be a performance cost,
# eg with externalresponse)
self.responder_answers = {}
for response, responder in self.responders.items():
try:
self.responder_answers[response] = responder.get_answers()
except Exception:
log.debug("responder %s failed to properly return get_answers()", responder) # FIXME
raise
# ... may not be associated with any specific response; give
# IDs for those separately
# TODO: We should make the namespaces consistent and unique (e.g. %s_problem_%i).
solution_id = 1
for solution in tree.findall(".//solution"):
solution.attrib["id"] = f"{self.problem_id}_solution_{solution_id}"
solution_id += 1
return problem_data
def response_a11y_data( # pylint: disable=too-many-locals,too-many-branches
self, response, inputfields, responsetype_id, problem_data
):
"""
Construct data to be used for a11y.
Arguments:
response (object): xml response object
inputfields (list): list of inputfields in a responsetype
responsetype_id (str): responsetype id
problem_data (dict): dict to be filled with response data
"""
# if there are no inputtypes then don't do anything
if not inputfields:
return
element_to_be_deleted = None
label = ""
if len(inputfields) > 1:
response.set("multiple_inputtypes", "true")
group_label_tag = response.find("label")
group_description_tags = response.findall("description")
group_label_tag_id = f"multiinput-group-label-{responsetype_id}"
group_label_tag_text = ""
if group_label_tag is not None:
group_label_tag.tag = "p"
group_label_tag.set("id", group_label_tag_id)
group_label_tag.set("class", "multi-inputs-group-label")
group_label_tag_text = stringify_children(group_label_tag)
response.set("multiinput-group-label-id", group_label_tag_id)
group_description_ids = []
for index, group_description_tag in enumerate(group_description_tags):
group_description_tag_id = f"multiinput-group-description-{responsetype_id}-{index}"
group_description_tag.tag = "p"
group_description_tag.set("id", group_description_tag_id)
group_description_tag.set("class", "multi-inputs-group-description question-description")
group_description_ids.append(group_description_tag_id)
if group_description_ids:
response.set("multiinput-group_description_ids", " ".join(group_description_ids))
for inputfield in inputfields:
problem_data[inputfield.get("id")] = {
"group_label": group_label_tag_text,
"label": HTML(inputfield.attrib.get("label", "")),
"descriptions": {},
}
else:
# Extract label value from tag or label attribute from inside the responsetype
responsetype_label_tag = response.find("label")
if responsetype_label_tag is not None:
label = stringify_children(responsetype_label_tag)
# store tag containing question text to delete
# it later otherwise question will be rendered twice
element_to_be_deleted = responsetype_label_tag
elif "label" in inputfields[0].attrib:
# in this case we have old problems with label attribute and p tag having question in it
# we will pick the first sibling of responsetype if its a p tag and match the text with
# the label attribute text. if they are equal then we will use this text as question.
# Get first tag before responsetype, this
may contains the question text.
p_tag = response.xpath("preceding-sibling::*[1][self::p]")
if p_tag and p_tag[0].text == inputfields[0].attrib["label"]:
label = stringify_children(p_tag[0])
element_to_be_deleted = p_tag[0]
else:
# In this case the problems don't have tag or label attribute inside the responsetype
# so we will get the first preceding label tag w.r.t to this responsetype.
# This will take care of those multi-question problems that are not using --- in their markdown.
label_tag = response.xpath("preceding-sibling::*[1][self::label]")
if label_tag:
label = stringify_children(label_tag[0])
element_to_be_deleted = label_tag[0]
# delete label or p element only if inputtype is fully accessible
if inputfields[0].tag in ACCESSIBLE_CAPA_INPUT_TYPES and element_to_be_deleted is not None:
element_to_be_deleted.getparent().remove(element_to_be_deleted)
# Extract descriptions and set unique id on each description tag
description_tags = response.findall("description")
description_id = 1
descriptions = OrderedDict()
for description in description_tags:
descriptions[f"description_{responsetype_id}_{description_id}"] = HTML(stringify_children(description))
response.remove(description)
description_id += 1
problem_data[inputfields[0].get("id")] = {
"label": HTML(label.strip()) if label else "",
"descriptions": descriptions,
}