def test_dataset_filter(self):
# Add a 3rd metric we expect not to see.
factories.MetricFactory(type='OtherHistogram')
response = self.client.get(self.url, data={'ds': self.dataset.id})
metrics = [m['id'] for m in response.json()['metrics']]
self.assertItemsEqual(metrics,
[self.count_metric.id, self.flag_metric.id])
def setUpTestData(cls):
# This will create 3 datasets (1 of which being hidden) with 2
# populations of 1 metric each.
def create_collections(dataset, population, flag_metric, count_metric):
kwargs = {'dataset': dataset}
if population:
kwargs['population'] = population
count_collection = factories.CollectionFactory(metric=count_metric,
**kwargs)
factories.PointFactory.create_batch(3, collection=count_collection)
flag_collection = factories.CollectionFactory(metric=flag_metric,
**kwargs)
factories.PointFactory.create_batch(3, collection=flag_collection)
count_metric = factories.MetricFactory(type='CountHistogram')
flag_metric = factories.MetricFactory(type='FlagHistogram')
# Create 2 viewable datasets.
dataset_older = factories.DataSetFactory()
create_collections(dataset_older, 'control', flag_metric, count_metric)
create_collections(dataset_older, None, flag_metric, count_metric)
dataset = factories.DataSetFactory()
create_collections(dataset, 'control', flag_metric, count_metric)
create_collections(dataset, None, flag_metric, count_metric)
# Create 1 non-viewable dataset.
dataset_hidden = factories.DataSetFactory(display=False)
create_collections(dataset_hidden, 'control', flag_metric,
count_metric)
create_collections(dataset_hidden, None, flag_metric, count_metric)
# Save these for use in tests.
cls.dataset = dataset
cls.dataset_older = dataset_older
cls.dataset_hidden = dataset_hidden
cls.flag_metric = flag_metric
cls.count_metric = count_metric
def test_metric_not_in_collection(self):
metric = factories.MetricFactory()
url = reverse('metric', args=[metric.id])
response = self.client.get(url)
self.assertEqual(response.status_code, 404)
def setUpTestData(cls):
# This will create 3 datasets (1 of which being hidden) with 2
# populations of 1 metric each.
def collections_and_points(metric, **kwargs):
coll = factories.CollectionFactory(metric=metric, **kwargs)
factories.PointFactory.create_batch(3, collection=coll)
return coll
def create_collections(dataset,
population,
flag_metric,
count_metric,
create_subgroups=True):
kwargs = {'dataset': dataset}
if population:
kwargs['population'] = population
coll = collections_and_points(count_metric, **kwargs)
if create_subgroups:
kwargs.pop('population', None)
for sg in SUBGROUPS:
collections_and_points(count_metric,
population=coll.population,
subgroup=sg,
**kwargs)
if population:
kwargs['population'] = population
coll = collections_and_points(flag_metric, **kwargs)
if create_subgroups:
kwargs.pop('population', None)
for sg in SUBGROUPS:
collections_and_points(flag_metric,
population=coll.population,
subgroup=sg,
**kwargs)
count_metric = factories.MetricFactory(type='CountHistogram')
flag_metric = factories.MetricFactory(type='FlagHistogram')
# Create 2 viewable datasets.
dataset_older = factories.DataSetFactory()
create_collections(dataset_older, 'control', flag_metric, count_metric)
create_collections(dataset_older, 'chaos', flag_metric, count_metric)
dataset = factories.DataSetFactory()
create_collections(dataset, 'control', flag_metric, count_metric)
create_collections(dataset, 'chaos', flag_metric, count_metric)
# Create 1 non-viewable dataset.
dataset_hidden = factories.DataSetFactory(display=False)
create_collections(dataset_hidden,
'control',
flag_metric,
count_metric,
create_subgroups=False)
create_collections(dataset_hidden,
'chaos',
flag_metric,
count_metric,
create_subgroups=False)
# Create some stats tied to datasets.
for pop in ('control', 'chaos'):
factories.StatsFactory(dataset=dataset,
metric=None,
population=pop)
factories.StatsFactory(dataset=dataset,
metric=None,
population=pop)
# Save these for use in tests.
cls.dataset = dataset
cls.dataset_older = dataset_older
cls.dataset_hidden = dataset_hidden
cls.flag_metric = flag_metric
cls.count_metric = count_metric
def setUpTestData(cls):
scalar_metric = factories.MetricFactory(source_name='scalar_metric',
type='UintScalar',
description='scalar metric')
dataset = factories.DataSetFactory()
coll = factories.CollectionFactory(metric=scalar_metric,
dataset=dataset,
population='control')
total = 3053.0
data = {
1: 100,
2: 500,
3: 300,
4: 200,
5: 190,
6: 180,
7: 170,
8: 160,
9: 150,
10: 140,
11: 130,
12: 120,
13: 110,
14: 100,
15: 90,
16: 80,
17: 70,
18: 60,
19: 50,
20: 40,
21: 30,
22: 20,
23: 10,
24: 9,
25: 8,
26: 7,
27: 6,
28: 5,
29: 4,
30: 3,
31: 2,
32: 1,
33: 1,
34: 1,
35: 1,
36: 1,
37: 1,
38: 1,
39: 1,
40: 1
}
for rank, bucket in enumerate(sorted(data.keys()), 1):
count = data[bucket]
factories.PointFactory(collection=coll,
bucket=bucket,
proportion=count / total,
count=count,
rank=rank)
cls.dataset = dataset
cls.metric = scalar_metric