1229 lines
52 KiB
Python
1229 lines
52 KiB
Python
#
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# File: capa/capa_problem.py
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#
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# Nomenclature:
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#
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# A capa Problem is a collection of text and capa Response questions.
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# Each Response may have one or more Input entry fields.
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# The capa problem may include a solution.
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#
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"""
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Main module which shows problems (of "capa" type).
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This is used by capa_block.
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"""
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import logging
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import os.path
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import re
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from collections import OrderedDict
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from copy import deepcopy
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from datetime import datetime
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from xml.sax.saxutils import unescape
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from django.conf import settings
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from lxml import etree
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from pytz import UTC
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import xmodule.capa.customrender as customrender
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import xmodule.capa.inputtypes as inputtypes
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import xmodule.capa.responsetypes as responsetypes
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import xmodule.capa.xqueue_interface as xqueue_interface
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from xmodule.capa.correctmap import CorrectMap
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from xmodule.capa.safe_exec import safe_exec
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from xmodule.capa.util import contextualize_text, convert_files_to_filenames, get_course_id_from_capa_block
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from openedx.core.djangolib.markup import HTML, Text
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from openedx.core.lib.safe_lxml.xmlparser import XML
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from xmodule.stringify import stringify_children
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# extra things displayed after "show answers" is pressed
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solution_tags = ['solution']
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# fully accessible capa input types
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ACCESSIBLE_CAPA_INPUT_TYPES = [
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'checkboxgroup',
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'radiogroup',
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'choicegroup',
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'optioninput',
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'textline',
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'formulaequationinput',
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'textbox',
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]
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# these get captured as student responses
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response_properties = ["codeparam", "responseparam", "answer", "openendedparam"]
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# special problem tags which should be turned into innocuous HTML
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html_transforms = {
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'problem': {'tag': 'div'},
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'text': {'tag': 'span'},
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'math': {'tag': 'span'},
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}
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# These should be removed from HTML output, including all subelements
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html_problem_semantics = [
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"additional_answer",
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"codeparam",
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"responseparam",
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"answer",
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"script",
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"hintgroup",
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"openendedparam",
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"openendedrubric",
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]
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log = logging.getLogger(__name__)
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#-----------------------------------------------------------------------------
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# main class for this module
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class LoncapaSystem(object):
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"""
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An encapsulation of resources needed from the outside.
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These interfaces are collected here so that a caller of LoncapaProblem
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can provide these resources however make sense for their environment, and
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this code can remain independent.
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Attributes:
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i18n: an object implementing the `gettext.Translations` interface so
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that we can use `.ugettext` to localize strings.
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See :class:`DescriptorSystem` for documentation of other attributes.
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"""
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def __init__(
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self,
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ajax_url,
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anonymous_student_id,
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cache,
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can_execute_unsafe_code,
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get_python_lib_zip,
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DEBUG,
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i18n,
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render_template,
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resources_fs,
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seed, # Why do we do this if we have self.seed?
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xqueue,
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matlab_api_key=None
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):
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self.ajax_url = ajax_url
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self.anonymous_student_id = anonymous_student_id
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self.cache = cache
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self.can_execute_unsafe_code = can_execute_unsafe_code
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self.get_python_lib_zip = get_python_lib_zip
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self.DEBUG = DEBUG # pylint: disable=invalid-name
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self.i18n = i18n
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self.render_template = render_template
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self.resources_fs = resources_fs
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self.seed = seed # Why do we do this if we have self.seed?
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self.STATIC_URL = settings.STATIC_URL # pylint: disable=invalid-name
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self.xqueue = xqueue
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self.matlab_api_key = matlab_api_key
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class LoncapaProblem(object):
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"""
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Main class for capa Problems.
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"""
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def __init__(self, problem_text, id, capa_system, capa_block, # pylint: disable=redefined-builtin
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state=None, seed=None, minimal_init=False, extract_tree=True):
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"""
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Initializes capa Problem.
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Arguments:
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problem_text (string): xml defining the problem.
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id (string): identifier for this problem, often a filename (no spaces).
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capa_system (LoncapaSystem): LoncapaSystem instance which provides OS,
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rendering, user context, and other resources.
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capa_block: instance needed to access runtime/logging
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state (dict): containing the following keys:
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- `seed` (int) random number generator seed
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- `student_answers` (dict) maps input id to the stored answer for that input
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- 'has_saved_answers' (Boolean) True if the answer has been saved since last submit.
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- `correct_map` (CorrectMap) a map of each input to their 'correctness'
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- `done` (bool) indicates whether or not this problem is considered done
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- `input_state` (dict) maps input_id to a dictionary that holds the state for that input
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seed (int): random number generator seed.
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minimal_init (bool): whether to skip pre-processing student answers
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extract_tree (bool): whether to parse the problem XML and store the HTML
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"""
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## Initialize class variables from state
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self.do_reset()
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self.problem_id = id
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self.capa_system = capa_system
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self.capa_block = capa_block
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state = state or {}
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# Set seed according to the following priority:
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# 1. Contained in problem's state
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# 2. Passed into capa_problem via constructor
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self.seed = state.get('seed', seed)
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assert self.seed is not None, "Seed must be provided for LoncapaProblem."
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self.student_answers = state.get('student_answers', {})
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self.has_saved_answers = state.get('has_saved_answers', False)
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if 'correct_map' in state:
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self.correct_map.set_dict(state['correct_map'])
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self.done = state.get('done', False)
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self.input_state = state.get('input_state', {})
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# Convert startouttext and endouttext to proper <text></text>
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problem_text = re.sub(r"startouttext\s*/", "text", problem_text)
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problem_text = re.sub(r"endouttext\s*/", "/text", problem_text)
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self.problem_text = problem_text
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# parse problem XML file into an element tree
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if isinstance(problem_text, str):
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# etree chokes on Unicode XML with an encoding declaration
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problem_text = problem_text.encode('utf-8')
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self.tree = XML(problem_text)
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try:
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self.make_xml_compatible(self.tree)
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except Exception:
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capa_block = self.capa_block
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log.exception(
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"CAPAProblemError: %s, id:%s, data: %s",
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capa_block.display_name,
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self.problem_id,
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capa_block.data
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)
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raise
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# handle any <include file="foo"> tags
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self._process_includes()
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# construct script processor context (eg for customresponse problems)
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if minimal_init:
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self.context = {}
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else:
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self.context = self._extract_context(self.tree)
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# Pre-parse the XML tree: modifies it to add ID's and perform some in-place
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# transformations. This also creates the dict (self.responders) of Response
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# instances for each question in the problem. The dict has keys = xml subtree of
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# Response, values = Response instance
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self.problem_data = self._preprocess_problem(self.tree, minimal_init)
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if not minimal_init:
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if not self.student_answers: # True when student_answers is an empty dict
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self.set_initial_display()
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# dictionary of InputType objects associated with this problem
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# input_id string -> InputType object
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self.inputs = {}
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# Run response late_transforms last (see MultipleChoiceResponse)
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# Sort the responses to be in *_1 *_2 ... order.
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responses = list(self.responders.values())
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responses = sorted(responses, key=lambda resp: int(resp.id[resp.id.rindex('_') + 1:]))
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for response in responses:
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if hasattr(response, 'late_transforms'):
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response.late_transforms(self)
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if extract_tree:
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self.extracted_tree = self._extract_html(self.tree)
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def make_xml_compatible(self, tree):
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"""
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Adjust tree xml in-place for compatibility before creating
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a problem from it.
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The idea here is to provide a central point for XML translation,
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for example, supporting an old XML format. At present, there just two translations.
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1. <additional_answer> compatibility translation:
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old: <additional_answer>ANSWER</additional_answer>
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convert to
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new: <additional_answer answer="ANSWER">OPTIONAL-HINT</addional_answer>
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2. <optioninput> compatibility translation:
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optioninput works like this internally:
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<optioninput options="('yellow','blue','green')" correct="blue" />
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With extended hints there is a new <option> tag, like this
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<option correct="True">blue <optionhint>sky color</optionhint> </option>
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This translation takes in the new format and synthesizes the old option= attribute
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so all downstream logic works unchanged with the new <option> tag format.
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"""
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def is_optioninput_valid(optioninput):
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"""
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Verifies if a given optioninput xml is valid or not.
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A given optioninput(Dropdown) problem is invalid if it has more than one correct answer.
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Argument:
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optioninput: dropdown specification tree
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Returns:
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boolean: signifying if the optioninput is valid or not.
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"""
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correct_options = [
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option.get('correct').upper() == 'TRUE' for option in optioninput.findall('./option')
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]
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return correct_options.count(True) in (0, 1)
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additionals = tree.xpath('//stringresponse/additional_answer')
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for additional in additionals:
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answer = additional.get('answer')
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text = additional.text
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if not answer and text: # trigger of old->new conversion
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additional.set('answer', text)
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additional.text = ''
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for optioninput in tree.xpath('//optioninput'):
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if not is_optioninput_valid(optioninput):
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raise responsetypes.LoncapaProblemError("Dropdown questions can only have one correct answer.")
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correct_option = None
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child_options = []
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for option_element in optioninput.findall('./option'):
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text = option_element.text
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text = text or ''
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option_name = text.strip()
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if option_element.get('correct').upper() == 'TRUE':
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correct_option = option_name
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child_options.append("'" + option_name + "'")
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if len(child_options) > 0:
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options_string = '(' + ','.join(child_options) + ')'
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optioninput.attrib.update({'options': options_string})
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if correct_option:
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optioninput.attrib.update({'correct': correct_option})
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def do_reset(self):
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"""
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Reset internal state to unfinished, with no answers
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"""
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self.student_answers = {}
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self.has_saved_answers = False
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self.correct_map = CorrectMap()
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self.done = False
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def set_initial_display(self):
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"""
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Set the student's answers to the responders' initial displays, if specified.
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"""
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initial_answers = {}
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for responder in self.responders.values():
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if hasattr(responder, 'get_initial_display'):
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initial_answers.update(responder.get_initial_display())
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self.student_answers = initial_answers
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def __str__(self):
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return "LoncapaProblem ({0})".format(self.problem_id)
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def get_state(self):
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"""
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Stored per-user session data neeeded to:
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1) Recreate the problem
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2) Populate any student answers.
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"""
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return {'seed': self.seed,
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'student_answers': self.student_answers,
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'has_saved_answers': self.has_saved_answers,
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'correct_map': self.correct_map.get_dict(),
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'input_state': self.input_state,
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'done': self.done}
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def get_max_score(self):
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"""
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Return the maximum score for this problem.
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"""
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maxscore = 0
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for responder in self.responders.values():
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maxscore += responder.get_max_score()
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return maxscore
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def calculate_score(self, correct_map=None):
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"""
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Compute score for this problem. The score is the number of points awarded.
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Returns a dictionary {'score': integer, from 0 to get_max_score(),
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'total': get_max_score()}.
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Takes an optional correctness map for use in the rescore workflow.
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"""
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if correct_map is None:
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correct_map = self.correct_map
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correct = 0
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for key in correct_map:
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try:
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correct += correct_map.get_npoints(key)
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except Exception:
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log.error('key=%s, correct_map = %s', key, correct_map)
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raise
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return {'score': correct, 'total': self.get_max_score()}
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def update_score(self, score_msg, queuekey):
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"""
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Deliver grading response (e.g. from async code checking) to
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the specific ResponseType that requested grading
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Returns an updated CorrectMap
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"""
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cmap = CorrectMap()
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cmap.update(self.correct_map)
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for responder in self.responders.values():
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if hasattr(responder, 'update_score'):
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# Each LoncapaResponse will update its specific entries in cmap
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# cmap is passed by reference
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responder.update_score(score_msg, cmap, queuekey)
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self.correct_map.set_dict(cmap.get_dict())
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return cmap
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def ungraded_response(self, xqueue_msg, queuekey):
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"""
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Handle any responses from the xqueue that do not contain grades
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Will try to pass the queue message to all inputtypes that can handle ungraded responses
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Does not return any value
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"""
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# check against each inputtype
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for the_input in self.inputs.values():
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# if the input type has an ungraded function, pass in the values
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if hasattr(the_input, 'ungraded_response'):
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the_input.ungraded_response(xqueue_msg, queuekey)
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def is_queued(self):
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"""
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Returns True if any part of the problem has been submitted to an external queue
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(e.g. for grading.)
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"""
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return any(self.correct_map.is_queued(answer_id) for answer_id in self.correct_map)
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def get_recentmost_queuetime(self):
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"""
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Returns a DateTime object that represents the timestamp of the most recent
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queueing request, or None if not queued
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"""
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if not self.is_queued():
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return None
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# Get a list of timestamps of all queueing requests, then convert it to a DateTime object
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queuetime_strs = [
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self.correct_map.get_queuetime_str(answer_id)
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for answer_id in self.correct_map
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if self.correct_map.is_queued(answer_id)
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]
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queuetimes = [
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datetime.strptime(qt_str, xqueue_interface.dateformat).replace(tzinfo=UTC)
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for qt_str in queuetime_strs
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]
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return max(queuetimes)
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def grade_answers(self, answers):
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"""
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Grade student responses. Called by capa_block.submit_problem.
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`answers` is a dict of all the entries from request.POST, but with the first part
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of each key removed (the string before the first "_").
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Thus, for example, input_ID123 -> ID123, and input_fromjs_ID123 -> fromjs_ID123
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Calls the Response for each question in this problem, to do the actual grading.
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"""
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# if answers include File objects, convert them to filenames.
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self.student_answers = convert_files_to_filenames(answers)
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new_cmap = self.get_grade_from_current_answers(answers)
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self.correct_map = new_cmap # lint-amnesty, pylint: disable=attribute-defined-outside-init
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return self.correct_map
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def supports_rescoring(self):
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"""
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Checks that the current problem definition permits rescoring.
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More precisely, it checks that there are no response types in
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the current problem that are not fully supported (yet) for rescoring.
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This includes responsetypes for which the student's answer
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is not properly stored in state, i.e. file submissions. At present,
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we have no way to know if an existing response was actually a real
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answer or merely the filename of a file submitted as an answer.
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It turns out that because rescoring is a background task, limiting
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it to responsetypes that don't support file submissions also means
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that the responsetypes are synchronous. This is convenient as it
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permits rescoring to be complete when the rescoring call returns.
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"""
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return all('filesubmission' not in responder.allowed_inputfields for responder in self.responders.values())
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def get_grade_from_current_answers(self, student_answers):
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"""
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Gets the grade for the currently-saved problem state, but does not save it
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to the block.
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For new student_answers being graded, `student_answers` is a dict of all the
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entries from request.POST, but with the first part of each key removed
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(the string before the first "_"). Thus, for example,
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input_ID123 -> ID123, and input_fromjs_ID123 -> fromjs_ID123.
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For rescoring, `student_answers` is None.
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Calls the Response for each question in this problem, to do the actual grading.
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"""
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# old CorrectMap
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oldcmap = self.correct_map
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# start new with empty CorrectMap
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newcmap = CorrectMap()
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# Call each responsetype instance to do actual grading
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for responder in self.responders.values():
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# File objects are passed only if responsetype explicitly allows
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# for file submissions. But we have no way of knowing if
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# student_answers contains a proper answer or the filename of
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# an earlier submission, so for now skip these entirely.
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# TODO: figure out where to get file submissions when rescoring.
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if 'filesubmission' in responder.allowed_inputfields and student_answers is None:
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_ = self.capa_system.i18n.gettext
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raise Exception(_("Cannot rescore problems with possible file submissions"))
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# use 'student_answers' only if it is provided, and if it might contain a file
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# submission that would not exist in the persisted "student_answers".
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if 'filesubmission' in responder.allowed_inputfields and student_answers is not None:
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results = responder.evaluate_answers(student_answers, oldcmap)
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else:
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results = responder.evaluate_answers(self.student_answers, oldcmap)
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newcmap.update(results)
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return newcmap
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def get_question_answers(self):
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"""
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|
Returns a dict of answer_ids to answer values. If we cannot generate
|
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an answer (this sometimes happens in customresponses), that answer_id is
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not included. Called by "show answers" button JSON request
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(see capa_block)
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"""
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# dict of (id, correct_answer)
|
|
answer_map = {}
|
|
for response in self.responders.keys(): # lint-amnesty, pylint: disable=consider-iterating-dictionary
|
|
results = self.responder_answers[response]
|
|
answer_map.update(results)
|
|
|
|
# include solutions from <solution>...</solution> 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.keys(): # lint-amnesty, pylint: disable=consider-iterating-dictionary
|
|
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
|
|
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 <p> or <label> 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:
|
|
#
|
|
# <p />
|
|
# <optionresponse id="a0effb954cca4759994f1ac9e9434bf4_2">
|
|
# <optioninput id="a0effb954cca4759994f1ac9e9434bf4_3_1" />
|
|
# <optionresponse>
|
|
#
|
|
# 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 <p> or <label>, but skip <description> because it doesn't
|
|
# contain the question text.
|
|
#
|
|
# E.g if we have this:
|
|
# <p /> <description /> <optionresponse /> <optionresponse />
|
|
#
|
|
# then from the first optionresponse we'll end with the <p>.
|
|
# 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('//*[@id="{answer_id}"]//*[@name="{choice_number}"]'.format(
|
|
answer_id=answer_id,
|
|
choice_number=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):
|
|
"""
|
|
Implements targeted-feedback in-place on <multiplechoiceresponse> --
|
|
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 <multiplechoiceresponse> tag)
|
|
choicegroup = mult_choice_response.xpath('./choicegroup[@type="MultipleChoice"]')[0]
|
|
choices_list = list(choicegroup.iter('choice'))
|
|
|
|
# Find the student answer key that matches our <choicegroup> 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 <solution> or <solutionset>
|
|
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)
|
|
else:
|
|
log.warning("Could not find matching input for id: %s", input_id)
|
|
return {}
|
|
|
|
# ======= Private Methods Below ========
|
|
|
|
def _process_includes(self):
|
|
"""
|
|
Handle any <include file="foo"> 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: # lint-amnesty, pylint: disable=broad-except
|
|
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: # lint-amnesty, pylint: disable=no-else-raise
|
|
raise
|
|
else:
|
|
continue
|
|
try:
|
|
# read in and convert to XML
|
|
incxml = etree.XML(ifp.read())
|
|
except Exception as err: # lint-amnesty, pylint: disable=broad-except
|
|
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: # lint-amnesty, pylint: disable=no-else-raise
|
|
raise
|
|
else:
|
|
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: # lint-amnesty, 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 <script>...</script> 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: " + all_code) # lint-amnesty, pylint: disable=logging-not-lazy
|
|
msg = Text("Error while executing script code: %s" % str(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(self, problemtree): # private
|
|
"""
|
|
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
|
|
|
|
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
|
|
|
|
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'] = "%s_%i_%i" % (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 in self.responders.keys(): # lint-amnesty, pylint: disable=consider-iterating-dictionary
|
|
try:
|
|
self.responder_answers[response] = self.responders[response].get_answers()
|
|
except:
|
|
log.debug('responder %s failed to properly return get_answers()',
|
|
self.responders[response]) # FIXME
|
|
raise
|
|
|
|
# <solution>...</solution> 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'] = "%s_solution_%i" % (self.problem_id, solution_id)
|
|
solution_id += 1
|
|
|
|
return problem_data
|
|
|
|
def response_a11y_data(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 = 'multiinput-group-label-{}'.format(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 = 'multiinput-group-description-{}-{}'.format(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 <label> 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 <label> 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 <p> tag before responsetype, this <p> 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[
|
|
"description_%s_%i" % (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
|
|
}
|