def create_context_review(request):
data = {}
if "id" in request.GET:
user = RequestContext(request)["user"]
challenge = Challenge.objects.get(pk=request.GET.get("id"))
if not challenge.is_enabled_for_user(user):
raise Http404
if challenge.has_enough_user_reviews(user):
raise Http404
if not challenge.submitted_by_user(user):
raise Http404
review = Review.get_open_review(challenge, user)
if not review:
# number of hours needed to pass until elaboration is applicable as candidate
offset = randint(ReviewConfig.get_candidate_offset_min(), ReviewConfig.get_candidate_offset_max())
review_candidate = Elaboration.get_review_candidate(challenge, user, offset)
if review_candidate:
review = Review(elaboration=review_candidate, reviewer=user)
review.save()
else:
return data
data["review"] = review
data["stack_id"] = challenge.get_stack().id
review_questions = ReviewQuestion.objects.filter(challenge=challenge).order_by("order")
data["questions"] = review_questions
return data
def create_context_review(request):
data = {}
if 'id' in request.GET:
user = RequestContext(request)['user']
challenge = Challenge.objects.get(pk=request.GET.get('id'))
if not challenge.is_enabled_for_user(user):
raise Http404
if challenge.has_enough_user_reviews(user):
raise Http404
if not challenge.submitted_by_user(user):
raise Http404
review = Review.get_open_review(challenge, user)
if not review:
# number of hours needed to pass until elaboration is applicable as candidate
offset = randint(ReviewConfig.get_candidate_offset_min(),
ReviewConfig.get_candidate_offset_max())
review_candidate = Elaboration.get_review_candidate(
challenge, user, offset)
if review_candidate:
review = Review(elaboration=review_candidate, reviewer=user)
review.save()
else:
return data
data['review'] = review
data['stack_id'] = challenge.get_stack().id
review_questions = ReviewQuestion.objects.filter(
challenge=challenge).order_by("order")
data['questions'] = review_questions
return data
def get_random_review_candidate(challenge, user):
# Wait x hours before making elaborations available for review
offset = randint(ReviewConfig.get_candidate_offset_min(),
ReviewConfig.get_candidate_offset_max())
threshold = datetime.now() - timedelta(hours=offset)
# Exclude elaborations the user has already submitted a review for
already_submitted_reviews_ids = (Review.objects.filter(
reviewer=user,
elaboration__challenge=challenge).values_list('elaboration__id',
flat=True))
# Get all possible candidates, ignore treshold for now because we need
# to fall back to a random elaboration if no elaboration is old enough
candidates = (Elaboration.objects.filter(
challenge=challenge,
submission_time__isnull=False,
user__is_staff=False).annotate(
num_reviews=Count('review')).exclude(user=user).exclude(
id__in=already_submitted_reviews_ids)
).order_by('num_reviews')
# Separate candidates
old_enough_candidates, newer_candidates = [], []
for candidate in candidates:
old_enough_candidates.append(
candidate
) if candidate.submission_time < threshold else newer_candidates.append(
candidate)
if len(old_enough_candidates) > 0:
chosen_candidate = old_enough_candidates[0]
elif len(newer_candidates) > 0:
chosen_candidate = newer_candidates[0]
else:
# Fall back to dummy elaborations
candidates = (Elaboration.objects.filter(
challenge=challenge, submission_time__isnull=False).annotate(
num_reviews=Count('review')).exclude(user=user).exclude(
id__in=already_submitted_reviews_ids)
).order_by('num_reviews')
chosen_candidate = candidates[0]
return {'chosen_by': 'random', 'candidate': chosen_candidate}
def get_random_review_candidate(challenge, user):
# Wait x hours before making elaborations available for review
offset = randint(ReviewConfig.get_candidate_offset_min(), ReviewConfig.get_candidate_offset_max())
threshold = datetime.now() - timedelta(hours=offset)
# Exclude elaborations the user has already submitted a review for
already_submitted_reviews_ids = (
Review.objects
.filter(reviewer=user, elaboration__challenge=challenge)
.values_list('elaboration__id', flat=True)
)
# Get all possible candidates, ignore treshold for now because we need
# to fall back to a random elaboration if no elaboration is old enough
candidates = (
Elaboration.objects
.filter(challenge=challenge, submission_time__isnull=False, user__is_staff=False)
.annotate(num_reviews=Count('review'))
.exclude(user=user)
.exclude(id__in=already_submitted_reviews_ids)
).order_by('num_reviews')
# Separate candidates
old_enough_candidates, newer_candidates = [], []
for candidate in candidates:
old_enough_candidates.append(candidate) if candidate.submission_time < threshold else newer_candidates.append(candidate)
if len(old_enough_candidates) > 0:
chosen_candidate = old_enough_candidates[0]
elif len(newer_candidates) > 0:
chosen_candidate = newer_candidates[0]
else:
# Fall back to dummy elaborations
candidates = (
Elaboration.objects
.filter(challenge=challenge, submission_time__isnull=False)
.annotate(num_reviews=Count('review'))
.exclude(user=user)
.exclude(id__in=already_submitted_reviews_ids)
).order_by('num_reviews')
chosen_candidate = candidates[0]
return { 'chosen_by': 'random', 'candidate': chosen_candidate }
def test_review_config_offset(self):
assert ReviewConfig.get_candidate_offset_min() == 0
assert ReviewConfig.get_candidate_offset_max() == 0
ReviewConfig(candidate_offset_min=1, candidate_offset_max=2).save()
assert ReviewConfig.get_candidate_offset_min() == 1
assert ReviewConfig.get_candidate_offset_max() == 2
def test_review_config_offset(self):
assert ReviewConfig.get_candidate_offset_min() == 0
assert ReviewConfig.get_candidate_offset_max() == 0
ReviewConfig(candidate_offset_min=1, candidate_offset_max=2).save()
assert ReviewConfig.get_candidate_offset_min() == 1
assert ReviewConfig.get_candidate_offset_max() == 2