def test_numeric_override(self):
with self.settings(
COUNTRIES_OVERRIDE={
"AU": None,
"NZ": {"numeric": None},
"US": {"numeric": 900},
}
):
self.assertEqual(countries.numeric("AU"), None)
self.assertEqual(countries.numeric("NZ"), None)
self.assertEqual(countries.numeric("US"), 900)
def test_override_complex(self):
with self.settings(
COUNTRIES_OVERRIDE={
"XX": {"names": ["New", "Newer"], "alpha3": "XXX", "numeric": 900},
"YY": {"name": "y", "numeric": 950},
}
):
self.assertEqual(countries.name("XX"), "New")
self.assertEqual(countries.alpha3("XX"), "XXX")
self.assertEqual(countries.numeric("XX"), 900)
self.assertEqual(countries.name("YY"), "y")
self.assertEqual(countries.alpha3("YY"), None)
self.assertEqual(countries.numeric("YY"), 950)
def _substitute_geochart(self, *, html):
users = self.challenge.get_participants().select_related(
"user_profile", "verification")
country_data = (users.exclude(
user_profile__country="").values("user_profile__country").annotate(
country_count=Count("user_profile__country")).order_by(
"-country_count").values_list("user_profile__country",
"country_count"))
content = render_to_string(
"grandchallenge/partials/geochart.html",
{
"user_count":
users.count(),
"country_data": [{
"id": countries.numeric(c[0], padded=True),
"participants": c[1],
} for c in country_data],
},
)
s = Substitution(
tag_name="project_statistics",
replacement=format_html("<h1>Statistics</h1>{}", content),
)
return s.sub(html)
def numeric_padded(self):
return countries.numeric(self.code, padded=True)
def numeric(self):
return countries.numeric(self.code)
def numeric_padded(self):
return countries.numeric(self.code, padded=True)
def numeric(self):
return countries.numeric(self.code)
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
days = 30
max_num_results = 10
User = get_user_model() # noqa: N806
time_period = timezone.now() - timedelta(days=days)
public_challenges = Challenge.objects.filter(hidden=False)
country_data = (User.objects.exclude(
user_profile__country="").values("user_profile__country").annotate(
country_count=Count("user_profile__country")).order_by(
"-country_count").values_list("user_profile__country",
"country_count"))
extra = {
"days":
days,
"max_num_results":
max_num_results,
"number_of_users":
User.objects.filter(is_active=True).count(),
"country_data": [{
"id": countries.numeric(c[0], padded=True),
"participants": c[1],
} for c in country_data],
"new_users_period":
(User.objects.filter(date_joined__gt=time_period).count()),
"logged_in_period":
(User.objects.filter(last_login__gt=time_period).count()),
"public_challenges":
public_challenges.count(),
"hidden_challenges":
Challenge.objects.filter(hidden=True).count(),
"submissions":
Submission.objects.count(),
"submissions_period":
(Submission.objects.filter(created__gt=time_period).count()),
"latest_public_challenge":
(public_challenges.order_by("-created").first()),
"mp_group": (Group.objects.filter(
participants_of_challenge__in=public_challenges).annotate(
num_users=Count("user")).order_by("-num_users").first()),
"challenge_registrations_period":
(self._challenge_qs_to_list_with_url(
public_challenges.filter(
registrationrequest__created__gt=time_period).annotate(
num_registrations_period=Count("registrationrequest")).
order_by("-num_registrations_period").values(
"short_name",
"num_registrations_period")[:max_num_results])),
"mp_challenge_submissions":
(public_challenges.annotate(num_submissions=Count(
"phase__submission")).order_by("-num_submissions").first()),
"challenge_submissions_period":
(self._challenge_qs_to_list_with_url(
public_challenges.filter(
phase__submission__created__gt=time_period).annotate(
num_submissions_period=Count("phase__submission")).
order_by("-num_submissions_period").values(
"short_name",
"num_submissions_period")[:max_num_results])),
"latest_result": (EvaluationJob.objects.filter(
published=True,
submission__phase__challenge__hidden=False,
rank__gt=0,
status=EvaluationJob.SUCCESS,
).select_related("submission__phase__challenge").order_by(
"-created").first()),
"using_auto_eval":
(Challenge.objects.filter(use_evaluation=True).count()),
"public_algorithms":
(Algorithm.objects.filter(public=True).count()),
"private_algorithms":
(Algorithm.objects.filter(public=False).count()),
"algorithm_jobs":
AlgorithmJob.objects.count(),
"algorithm_jobs_period":
AlgorithmJob.objects.filter(created__gt=time_period).count(),
"public_reader_studies":
ReaderStudy.objects.filter(public=True).count(),
"private_reader_studies":
ReaderStudy.objects.filter(public=False).count(),
"questions":
Question.objects.count(),
"answers":
Answer.objects.count(),
"public_workstations":
Workstation.objects.filter(public=True).count(),
"private_workstations":
Workstation.objects.filter(public=False).count(),
"workstation_sessions":
Session.objects.count(),
"total_session_duration": (Session.objects.aggregate(
Sum("maximum_duration"))["maximum_duration__sum"]),
"public_archives":
Archive.objects.filter(public=True).count(),
"private_archives":
Archive.objects.filter(public=False).count(),
"images":
Image.objects.count(),
}
context.update(extra)
return context
