def test_create_and_update(self):
delete_all_datasets_by_name(self.api, self.dataset_name)
filters = {"rp_entity_id": {"$in": ['AAAAAA']}}
dataset = Dataset(
name=self.dataset_name,
filters=filters, # a dataset with a filter
)
dataset = self.api.create_dataset(dataset)
assert dataset.id is not None
dataset_id = dataset.id
# change the dataset
new_filters = {"rp_entity_id": {"$in": ['BBBBBB']}}
dataset.filters = new_filters
dataset.save()
# get the dataset again
dataset = self.api.get_dataset(dataset_id)
assert dataset.filters == new_filters
new_filters = {"rp_entity_id": {"$in": ['CCCCCC']}}
dataset.filters = new_filters
dataset.save()
dataset.delete()
assert delete_all_datasets_by_name(self.api, self.dataset_name) == 0
def test_granular_dataset(self):
self.api.log_curl_commands = True
granular_dataset = Dataset(
name='Test-granular-dataset',
filters={"$and": [{"rp_entity_id": {"$in": ["D8442A"]}}, {"relevance": 90}]},
)
granular_dataset = self.api.create_dataset(granular_dataset)
try:
granular_dataset.json('2018-01-01 00:00', '2018-01-02 00:00')
finally:
granular_dataset.delete()
def test_dataset_copy_updated(self):
source_dataset = Dataset(api=self.api, id='us30')
new_dataset = Dataset(
api=self.api,
name="copy of the us30 dataset",
filters=source_dataset.filters,
fields=['timestamp_utc', 'rp_entity_id', 'avg_sentiment'],
custom_fields=[{
"avg_sentiment": {
"avg": {
"field": "EVENT_SENTIMENT_SCORE",
}
}
}],
frequency='daily',
tags=['copy', 'test'])
new_dataset.save()
new_dataset.delete()
def test_indicator_dataset(self):
indicator_dataset = Dataset(
name='Test-indicator-dataset',
filters={"$and": [{
"rp_entity_id": {
"$in": ["D8442A"]
}
}]},
fields=[{
"average": {
"avg": {
"field": "EVENT_SENTIMENT_SCORE"
}
}
}],
frequency='daily',
)
indicator_dataset = self.api.create_dataset(indicator_dataset)
try:
# ask the indicator dataset for its data
response = indicator_dataset.json('2018-01-01 00:00',
'2018-01-02 00:00')
assert len(response) == 2 # we should get 2 rows
assert {r['rp_entity_id']
for r in response} == {'D8442A', 'ROLLUP'}
# do a request overriding fields and frequency to see the underlying data
response = indicator_dataset.json(
'2018-01-01 00:00',
'2018-01-02 00:00',
fields=['rp_story_id', 'rp_entity_id'],
frequency='granular')
assert len(
response) > 200, "We should have many granular analytics rows"
assert {r['rp_entity_id']
for r in response} == {'D8442A'
}, "All rows should be D8442A"
finally:
indicator_dataset.delete()
