def setUp(self):
""" Set up a user for testing """
self.user, self.info = Factory.create_user()
self.user.is_staff = True
self.user.save()
Factory.create_set_of_submissions(self.user)
# Login
self.client.login(username=self.user.username, password=Factory.default_password)
def setUp(self):
""" Set up submission data in an organization for testing """
# Admin account without leaduserinfo
self.admin = Factory.create_admin()
# Create an organization with three sessions
self.org = Factory.create_organization(self.admin)
self.session1 = Factory.create_session(self.org, self.admin)
self.session2 = Factory.create_session(self.org, self.admin)
self.session3 = Factory.create_session(self.org, self.admin)
self.sessions = [self.session1, self.session2, self.session3]
# Attach an explicit, non staff user, to the organization
self.user, self.info = Factory.create_user(self.session1)
# Generate data (3 sets of submissions) for each session
for session in self.sessions:
for i in range(0, 3):
user, info = Factory.create_user(session)
Factory.create_set_of_submissions(user)
def test_enough_data(self):
""" Metrics are returned correctly if enough data exists
Independent of whether or not user is staff.
"""
# Generate the 10th set of data
Factory.create_set_of_submissions(self.user)
# Generate the data for a non staff
data = generate_data_from_sessions(self.sessions, self.user)
# Verify non staff do not have access to analysis
self.assertIn('submission_counts', data)
self.assertNotIn('metrics_analysis', data)
self.assertIn('users', data)
# Set staff to check for analysis
self.user.is_staff = True
self.user.save()
# Load the data
data = generate_data_from_sessions(self.sessions, self.user)
# Verify staff have all data
self.assertIn('submission_counts', data)
self.assertIn('metrics_analysis', data)
self.assertIn('users', data)
# Verify each submission has a submission count
self.assertEqual(6, len(data['submission_counts']))
# Verify the submission count is correct
for submission in data['submission_counts']:
correct_count = Submission.objects.filter(inventory_id=submission['inventory_id']).count()
self.assertEqual(submission['count'], correct_count)
# Verify that there is a metric analysis for each metric
# That does not belong to Via.
correct_count = Metric.objects.exclude(
submission__inventory_id=Via.inventory_id
).distinct('key', 'submission__inventory_id').count()
self.assertEqual(len(data['metrics_analysis']), correct_count)
# Verify that the metric analysis for each inventory is correct
for analysis in data['metrics_analysis']:
correct_analysis = Metric.objects.filter(
key=analysis['key'],
submission__inventory_id=analysis['submission__inventory_id']
).values(
'key', 'submission__inventory_id'
).annotate(
min=Min('value'),
max=Max('value'),
mean=Avg('value'),
standard_deviation=StdDev('value')
)
self.assertDictEqual(analysis, correct_analysis[0])
# Verify that the user data is correct
for user in data['users']:
# The correct number of submissions has been attached
num_submissions = Submission.objects.filter(user=user).count()
self.assertEqual(len(user.submissions), num_submissions)
# For each user submission
for submission in user.submissions:
# The submission belongs to the user
self.assertEqual(user, submission.user)
# The correct metrics exist in the submission
num_metrics = Metric.objects.filter(submission=submission).count()
self.assertEqual(len(submission.metrics), num_metrics)
# For each submission metric
for metric in submission.metrics:
# The metric belongs to the submission
self.assertEqual(submission, metric.submission)
def add_user(self, session, inventory_cls_list):
user, info = Factory.create_user(session)
Factory.create_set_of_submissions(user, inventory_cls_list)
print('.', end='')
sys.stdout.flush()
