def alter_list_data_to_serialize(self, request, to_be_serialized):
"""
Defines a hook to process list view data before being serialized.
We pluck out the "user" and replace with the fields we're interested in (username, facility name, is_teacher),
and pluck out the content_id and replace with the content_title (if found).
This is to make the csv output more human friendly.
:param request: HTTP request object
:param to_be_serialized: the unprocessed list of objects that will be serialized
:return: the _processed_ list of objects to serialize
"""
from kalite.topic_tools import get_content_data, get_exercise_data
filtered_bundles = [
bundle for bundle in to_be_serialized["objects"]
if (bundle.data["difficulty"], bundle.data["quality"]) != (0, 0)
]
serializable_objects = []
for bundle in filtered_bundles:
user_id = bundle.data["user"].data["id"]
user = self._facility_users.get(user_id)
bundle.data["username"] = user.username
bundle.data["facility_name"] = user.facility.name
bundle.data["is_teacher"] = user.is_teacher
bundle.data.pop("user")
content_id = bundle.data.pop("content_id", None)
content = get_content_data(
request, content_id) or get_exercise_data(request, content_id)
bundle.data["content_title"] = content.get(
"title", "Missing title") if content else "Unknown content"
serializable_objects.append(bundle)
to_be_serialized["objects"] = serializable_objects
return to_be_serialized
def obj_get(self, bundle, **kwargs):
id = kwargs.get("id", None)
exercise = get_exercise_data(bundle.request, id)
if exercise:
return Exercise(**exercise)
else:
raise NotFound('Exercise with id %s not found' % id)
def obj_get(self, bundle, **kwargs):
id = kwargs.get("id", None)
exercise = get_exercise_data(bundle.request, id)
if exercise:
return Exercise(**exercise)
else:
raise NotFound('Exercise with id %s not found' % id)
def alter_list_data_to_serialize(self, request, to_be_serialized):
"""
Defines a hook to process list view data before being serialized.
We pluck out the "user" and replace with the fields we're interested in (username, facility name, is_teacher),
and pluck out the content_id and replace with the content_title (if found).
This is to make the csv output more human friendly.
:param request: HTTP request object
:param to_be_serialized: the unprocessed list of objects that will be serialized
:return: the _processed_ list of objects to serialize
"""
from kalite.topic_tools import get_content_data, get_exercise_data
filtered_bundles = [bundle for bundle in to_be_serialized["objects"] if
(bundle.data["difficulty"], bundle.data["quality"]) != (0, 0)]
serializable_objects = []
for bundle in filtered_bundles:
user_id = bundle.data["user"].data["id"]
user = self._facility_users.get(user_id)
bundle.data["username"] = user.username
bundle.data["facility_name"] = user.facility.name
bundle.data["is_teacher"] = user.is_teacher
bundle.data.pop("user")
content_id = bundle.data.pop("content_id", None)
content = get_content_data(request, content_id) or get_exercise_data(request, content_id)
bundle.data["content_title"] = content.get("title", "Missing title") if content else "Unknown content"
serializable_objects.append(bundle)
to_be_serialized["objects"] = serializable_objects
return to_be_serialized
def get_next_recommendations(user, request):
"""Get the recommendations for the Next section, and return them as a list.
Logic:
Next recommendations are currently comprised of 3 main subgroups: group recommendations,
struggling exercises, and topic tree based data. Group recommendations consist of
finding the most common item tackled immediately after the most recent item, struggling
is determined by the "struggling" model attribute, and topic tree data is based off
the graphical distance between neighboring exercise/topic nodes.
Args:
user -- The current user as a facility user model object.
"""
exercise_parents_table = get_exercise_parents_lookup_table()
most_recent = get_most_recent_exercises(user)
complete_exercises = set(get_completed_exercises(user))
def filter_complete(ex):
return ex not in complete_exercises
if len(most_recent) > 0 and most_recent[0] in exercise_parents_table:
current_subtopic = exercise_parents_table[most_recent[0]]['subtopic_id']
else:
current_subtopic = None
#logic for recommendations based off of the topic tree structure
if current_subtopic:
topic_tree_based_data = generate_recommendation_data()[current_subtopic]['related_subtopics'][:settings.TOPIC_RECOMMENDATION_DEPTH]
topic_tree_based_data = get_exercises_from_topics(topic_tree_based_data)
else:
topic_tree_based_data = []
#for checking that only exercises that have not been accessed are returned
topic_tree_based_data = [ex for ex in topic_tree_based_data if ex not in most_recent or filter_complete(ex)]
#logic to generate recommendations based on exercises student is struggling with
struggling = filter(filter_complete, get_exercise_prereqs(get_struggling_exercises(user)))
#logic to get recommendations based on group patterns, if applicable
group = filter(filter_complete, get_group_recommendations(user))
#now append titles and other metadata to each exercise id
final = [] # final data to return
for exercise_id in (group[:2] + struggling[:2] + topic_tree_based_data[:1]): #notice the concatenation
if exercise_id in exercise_parents_table:
subtopic_id = exercise_parents_table[exercise_id]['subtopic_id']
exercise = get_exercise_data(request, exercise_id)
exercise["topic"] = get_topic_data(request, subtopic_id)
final.append(exercise)
#final recommendations are a combination of struggling, group filtering, and topic_tree filtering
return final
def get_next_recommendations(user, request):
"""Get the recommendations for the Next section, and return them as a list.
Logic:
Next recommendations are currently comprised of 3 main subgroups: group recommendations,
struggling exercises, and topic tree based data. Group recommendations consist of
finding the most common item tackled immediately after the most recent item, struggling
is determined by the "struggling" model attribute, and topic tree data is based off
the graphical distance between neighboring exercise/topic nodes.
Args:
user -- The current user as a facility user model object.
"""
exercise_parents_table = get_exercise_parents_lookup_table()
most_recent = get_most_recent_exercises(user)
complete_exercises = set(get_completed_exercises(user))
def filter_complete(ex):
return ex not in complete_exercises
if len(most_recent) > 0 and most_recent[0] in exercise_parents_table:
current_subtopic = exercise_parents_table[most_recent[0]]['subtopic_id']
else:
current_subtopic = None
#logic for recommendations based off of the topic tree structure
if current_subtopic:
topic_tree_based_data = generate_recommendation_data()[current_subtopic]['related_subtopics'][:settings.TOPIC_RECOMMENDATION_DEPTH]
topic_tree_based_data = get_exercises_from_topics(topic_tree_based_data)
else:
topic_tree_based_data = []
#for checking that only exercises that have not been accessed are returned
topic_tree_based_data = [ex for ex in topic_tree_based_data if ex not in most_recent or filter_complete(ex)]
#logic to generate recommendations based on exercises student is struggling with
struggling = filter(filter_complete, get_exercise_prereqs(get_struggling_exercises(user)))
#logic to get recommendations based on group patterns, if applicable
group = filter(filter_complete, get_group_recommendations(user))
#now append titles and other metadata to each exercise id
final = [] # final data to return
for exercise_id in (group[:2] + struggling[:2] + topic_tree_based_data[:1]): #notice the concatenation
if exercise_id in exercise_parents_table:
subtopic_id = exercise_parents_table[exercise_id]['subtopic_id']
exercise = get_exercise_data(request, exercise_id)
exercise["topic"] = get_topic_data(request, subtopic_id)
final.append(exercise)
#final recommendations are a combination of struggling, group filtering, and topic_tree filtering
return final
def get_resume_recommendations(user, request):
"""Get the recommendation for the Resume section.
Logic:
Find the most recent incomplete item (video or exercise) and
return that as the recommendation.
Args:
user -- The current user as a facility user model object.
"""
final = get_most_recent_incomplete_item(user)
if final:
if final.get("kind") == "Content":
return [get_content_data(request, final.get("id"))]
if final.get("kind") == "Exercise":
return [get_exercise_data(request, final.get("id"))]
else:
return []
def get_resume_recommendations(user, request):
"""Get the recommendation for the Resume section.
Logic:
Find the most recent incomplete item (video or exercise) and
return that as the recommendation.
Args:
user -- The current user as a facility user model object.
"""
final = get_most_recent_incomplete_item(user)
if final:
if final.get("kind") == "Content":
return [get_content_data(request, final.get("id"))]
if final.get("kind") == "Exercise":
return [get_exercise_data(request, final.get("id"))]
else:
return []