def get_or_create_classroom_users(**options):
classroom = options['classroom']
n_users = options['n_users']
user_data = options['user_data']
facility = options['facility']
# The headers in the user_data.csv file that we use to generate user Full Names
# Note, we randomly pick from these to give deliberately varied (and sometimes idiosyncratic)
# Full names - because we should never assume that users have names like us
user_data_name_fields = ["GivenName", "MiddleInitial", "Surname"]
n_in_classroom = HierarchyRelationsFilter(
FacilityUser.objects.all()).filter_by_hierarchy(
ancestor_collection=classroom,
target_user=F("id"),
).count()
# Only generate new users if there are fewer users than requested.
n_to_create = n_users - n_in_classroom
if n_to_create > 0:
logger.info(
'Generating {n} user object(s) for class: {classroom} in facility: {facility}'
.format(
n=n_to_create,
classroom=classroom,
facility=facility,
))
for i in range(0, n_to_create):
# Get the first base data that does not have a matching user already
base_data = user_data[n_in_classroom + i]
# Randomly create the name from 1 to 3 of the three user name fields
name = " ".join([
base_data[key] for key in random.sample(
user_data_name_fields, random.randint(1, 3))
if base_data[key]
])
user = FacilityUser.objects.create(facility=facility,
full_name=name,
username=base_data['Username'])
# Set a dummy password so that if we want to login as this learner later, we can.
user.set_password('password')
user.save()
# Add the user to the current classroom
classroom.add_member(user)
return HierarchyRelationsFilter(
FacilityUser.objects.all()).filter_by_hierarchy(
target_user=F("id"),
ancestor_collection=classroom,
)[0:n_users]
def readable_by_user_filter(self, user, queryset):
if isinstance(user, AnonymousUser):
return queryset.none()
return HierarchyRelationsFilter(queryset).filter_by_hierarchy(
target_user=user,
ancestor_collection=F("collection"),
).filter(Q(exam__active=True) | Q(exam__examlogs__user=user))
def get_queryset(self):
assignments = HierarchyRelationsFilter(LessonAssignment.objects.all()) \
.filter_by_hierarchy(
target_user=self.request.user,
ancestor_collection=F('collection')
)
return Lesson.objects.filter(lesson_assignments__in=assignments,
is_active=True)
def retrieve(self, request, pk=None, **kwargs):
exam = get_object_or_404(models.Exam.objects.all(), id=pk)
assignment = HierarchyRelationsFilter(exam.assignments.get_queryset()).filter_by_hierarchy(
target_user=request.user,
ancestor_collection=F('collection'),
).first()
serializer = serializers.UserExamSerializer(assignment, context={'request': request})
return Response(serializer.data)
def readable_by_user_filter(self, user, queryset):
if isinstance(user, AnonymousUser):
return queryset.none()
from kolibri.core.exams.models import ExamAssignment
assignments = HierarchyRelationsFilter(
ExamAssignment.objects.all()).filter_by_hierarchy(
target_user=user,
ancestor_collection=F("collection"),
)
return queryset.filter(assignments__in=assignments).filter(
Q(active=True) | Q(examlogs__user=user))
def user_can_read_object(self, user, obj):
if isinstance(user, AnonymousUser):
return False
# Import here to avoid circular import.
from kolibri.logger.models import ExamLog
# If they are not a member of the assignment's collection, don't bother with any other checks
return HierarchyRelationsFilter(
obj.assignments.all()).filter_by_hierarchy(
target_user=user,
ancestor_collection=F("collection")).exists() and (
obj.active
or ExamLog.objects.filter(exam=obj, user=user).exists())
def filter_learner_group(self, queryset, name, value):
return HierarchyRelationsFilter(queryset).filter_by_hierarchy(
ancestor_collection=value,
target_user=F("user"),
)
def filter_collection(self, queryset, name, collection):
return HierarchyRelationsFilter(queryset).filter_by_hierarchy(
target_user=F('user'),
ancestor_collection=collection,
)
def filter_classroom(self, queryset, value):
return HierarchyRelationsFilter(queryset).filter_by_hierarchy(
ancestor_collection=value,
target_user=F("user"),
)
def handle(self, *args, **options): # noqa: max-complexity=16
# The headers in the user_data.csv file that we use to generate user Full Names
# Note, we randomly pick from these to give deliberately varied (and sometimes idiosyncratic)
# Full names - because we should never assume that users have names like us
user_data_name_fields = [
"GivenName",
"MiddleInitial",
"Surname",
]
# Load in the user data from the csv file to give a predictable source of user data
with open(os.path.join(os.path.dirname(__file__), 'user_data.csv')) as f:
user_data = [data for data in csv.DictReader(f)]
# Set the random seed so that all operations will be randomized predictably
random.seed(options['seed'])
num_facilities = Facility.objects.all().count()
# Only generate new facilities if there are fewer facilities than requested.
if num_facilities < options['facilities']:
num_to_create = options['facilities'] - num_facilities
self.stdout.write('Generating {i} facility object(s)'.format(i=num_to_create))
facilities = [Facility.objects.create(name='Test Facility {i}'.format(i=i+1)) for i in range(0, num_to_create)]
facilities = Facility.objects.all()[0:options['facilities']]
# Generate data up to the current time
now = timezone.now()
for facility in facilities:
num_classes = Classroom.objects.filter(parent=facility).count()
# Only generate new classes if there are fewer classes than requested.
if num_classes < options['classes']:
num_to_create = options['classes'] - num_classes
self.stdout.write('Generating {i} classroom object(s) for facility: {facility}'.format(
i=num_to_create,
facility=facility,
))
for i in range(0, num_to_create):
Classroom.objects.create(
parent=facility,
name='Classroom {i}{a}'.format(i=i+1, a=random.choice(string.ascii_uppercase))
)
classes = Classroom.objects.filter(parent=facility)[0:options['classes']]
# Get all the user data at once so that it is distinct across classes
facility_user_data = random.sample(user_data, options['classes'] * options['users'])
for i, classroom in enumerate(classes):
classroom_user_data = facility_user_data[i*options['users']: (i+1)*options['users']]
num_users = HierarchyRelationsFilter(FacilityUser.objects.all()).filter_by_hierarchy(
target_user=F("id"),
ancestor_collection=classroom,
).count()
# Only generate new users if there are fewer users than requested.
if num_users < options['users']:
num_to_create = options['users'] - num_users
self.stdout.write('Generating {i} user object(s) for class: {classroom} in facility: {facility}'.format(
i=num_to_create,
classroom=classroom,
facility=facility,
))
for i in range(0, num_to_create):
# Get the first base data that does not have a matching user already
base_data = classroom_user_data[num_users + i]
# Randomly create the name from 1 to 3 of the three user name fields
name = " ".join([base_data[key] for key in random.sample(user_data_name_fields, random.randint(1, 3))])
user = FacilityUser.objects.create(facility=facility, full_name=name, username=base_data['Username'])
# Set a dummy password so that if we want to login as this learner later, we can.
user.set_password('password')
# Add the user to the current classroom
classroom.add_member(user)
users = HierarchyRelationsFilter(FacilityUser.objects.all()).filter_by_hierarchy(
target_user=F("id"),
ancestor_collection=classroom,
)[0:options['users']]
# Iterate through the slice of the facility_user_data specific to this classroom
for user, base_data in zip(users, classroom_user_data):
# The user data we are fetching from has 'Age' as a characteristic, use this as the "age" of the user
# in terms of their content interaction history - older, more content items interacted with!
number_of_content_items = int(base_data['Age'])
# Loop over all local channels to generate data for each channel
for channel in ChannelMetadataCache.objects.all():
channel_id = channel.id
set_active_content_database(channel_id)
default_channel_content = ContentNode.objects.exclude(kind=content_kinds.TOPIC)
self.stdout.write('Generating {i} user interaction(s) for user: {user} for channel: {channel}'.format(
i=number_of_content_items,
user=user,
channel=channel.name
))
# Generate a content interaction history for this many content items
for i in range(0, number_of_content_items):
# Use this to randomly select a content node to generate the interaction for
index = random.randint(0, default_channel_content.count() - 1)
random_node = default_channel_content[index]
# We will generate between 1 and 5 content session logs for this content item
session_logs = []
for j in range(0, random.randint(1, 5)):
# How many minutes did they spend in this session? Up to 15
duration = random.random()*15
# Assume they spent some of this session time not doing anything - the lazy...
idle_time = random.random() * duration
session_logs.append(ContentSessionLog(
user=user,
channel_id=channel_id,
content_id=random_node.content_id,
start_timestamp=now - datetime.timedelta(i + j, 0, duration),
end_timestamp=now - datetime.timedelta(i + j),
# How many seconds did they actually spend doing something?
time_spent=(duration - idle_time)*60,
progress=random.random(),
kind=random_node.kind,
))
# Assume they have not completed
completion_timestamp = None
cumulative_progress = 0
# Go through all the session logs and add up the progress in each
for session_log in session_logs:
cumulative_progress = min(cumulative_progress + session_log.progress, 1.0)
# If the progress is 1 or more, they have completed! Set the completion timestamp
# For the end of this session, for the sake of argument.
if cumulative_progress >= 1.0:
completion_timestamp = session_log.end_timestamp
session_log.save()
# Now that we have created all the Session Logs, infer the summary log from them
summary_log, created = ContentSummaryLog.objects.get_or_create(
user=user,
kind=random_node.kind,
content_id=random_node.content_id,
# Use defaults here so that we don't try to create a new Summary Log with the same
# kind/content_id/user combo, as this would violate uniqueness constraints
defaults={
'channel_id': channel_id,
# Start timestamp is the earliest start timestamp of the session logs
'start_timestamp': min(session_logs, key=lambda x: x.start_timestamp).start_timestamp,
# End timestamp is the latest of all the end timestamps
'end_timestamp': max(session_logs, key=lambda x: x.end_timestamp).end_timestamp,
'completion_timestamp': completion_timestamp,
'time_spent': sum(session_log.time_spent for session_log in session_logs),
'progress': min(sum(session_log.progress for session_log in session_logs), 1.0),
}
)
if not created:
# We didn't create the summary log this time, so it probably means it has other session logs
# Aggregate the information from there to update the relevant fields on the summary log
updates = ContentSessionLog.objects.filter(
user=user,
kind=random_node.kind,
content_id=random_node.content_id
).aggregate(
start_timestamp=Min('start_timestamp'),
end_timestamp=Max('end_timestamp'),
progress=Sum('progress')
)
summary_log.start_timestamp = updates['start_timestamp']
summary_log.end_timestamp = updates['end_timestamp']
if summary_log.progress < 1.0 and updates['progress'] >= 1.0:
# If it was not previously completed, and is now, set the completion timestamp to the
# final end timestamp of the session logs.
summary_log.completion_timestamp = updates['end_timestamp']
summary_log.progress = min(1.0, updates['progress'])
summary_log.save()
# If we are dealing with anything but an assessment (currently only exercises)
# we are done - if not, create additional data here
if random_node.kind == content_kinds.EXERCISE:
# Generate a mastery log if needed
mastery_log, created = MasteryLog.objects.get_or_create(
user=user,
mastery_level=1,
summarylog=summary_log,
defaults={
'start_timestamp': summary_log.start_timestamp,
'end_timestamp': summary_log.end_timestamp,
'complete': summary_log.progress >= 1.0,
'completion_timestamp': completion_timestamp,
'mastery_criterion': {
'm': 5,
'n': 5,
'type': 'm_of_n',
},
}
)
if not created:
# Not created, so update relevant fields on it based on new interactions
if not mastery_log.complete and summary_log.progress >= 1.0:
mastery_log.complete = True
mastery_log.completion_timestamp = summary_log.completion_timestamp
mastery_log.end_timestamp = summary_log.end_timestamp
# Get the list of assessment item ids from the assessment meta data
assessment_item_ids = random_node.assessmentmetadata.first().assessment_item_ids
for i, session_log in enumerate(reversed(session_logs)):
# Always make students get 5 attempts correct in the most recent session
# if the exercise is complete
complete = (i == 0 and mastery_log.complete)
if complete:
n = 5
else:
# Otherwise, let them have answered between 1 and 5 questions per session
n = random.randint(1, 5)
# How long did they spend on these n questions?
timespan = session_log.end_timestamp - session_log.start_timestamp
# Index through each individual question
for j in range(0, n):
if complete:
# If this is the session where they completed the exercise, always
# make them get it right
correct = True
else:
# Otherwise only let students get odd indexed questions right,
# ensuring they will always have a mastery breaking sequence
# as zero based indexing means their first attempt will always be wrong!
correct = j % 2 == 1
start_timestamp = session_log.end_timestamp - (timespan/n)*(j+1)
end_timestamp = session_log.end_timestamp - (timespan/n)*j
# If incorrect, must have made at least two attempts at the question
question_attempts = 1 if correct else random.randint(2, 5)
question_interval = (end_timestamp - start_timestamp)/question_attempts
# If they got it wrong, give 20/80 chance that they took a hint to do so
hinted = random.choice((False, False, False, False, not correct))
if hinted:
first_interaction = {
'correct': False,
'type': 'hint',
}
else:
first_interaction = {
'correct': correct,
'type': 'answer',
}
first_interaction.update({
'answer': {},
'timestamp': start_timestamp + question_interval
})
interaction_history = [first_interaction]
# If it is correct, this can be our only response, otherwise, add more.
if not correct:
for att in range(1, question_attempts - 1):
# Add on additional attempts for intervening incorrect responses
interaction_history += [{
'correct': False,
'type': 'answer',
'answer': {},
'timestamp': start_timestamp + question_interval*(att + 1),
}]
# Finally, add a correct response that allows the user to move onto the next question
interaction_history += [{
'correct': True,
'type': 'answer',
'answer': {},
'timestamp': end_timestamp,
}]
AttemptLog.objects.create(
# Choose a random assessment item id from the exercise
item=random.choice(assessment_item_ids),
# Just let each attempt be a fixed proportion of the total time spent on the exercise
start_timestamp=start_timestamp,
end_timestamp=end_timestamp,
time_spent=timespan.total_seconds(),
# Mark all attempts as complete, as assume that student gave correct answer eventually
complete=True,
# Mark as correct or incorrect
correct=correct,
hinted=hinted,
# We can't meaningfully generate fake answer data for Perseus exercises
# (which are currently our only exercise type) - so don't bother.
answer={},
simple_answer='',
interaction_history=interaction_history,
user=user,
masterylog=mastery_log,
sessionlog=session_log,
)