def test_truncate_fact_table_cli(self):
data_marts_api.create_taxon_dimension('taxon_dim')
data_marts_api.create_fact_table('fact_table', ['taxon_dim'], ['n'])
class TestCLIFactTablePublisher(BaseFactTablePublisher):
@classmethod
def get_key(cls):
return 'test_cli_fact_table_publisher'
def _process(self, *args, **kwargs):
dim = data_marts_api.get_dimension('taxon_dim')
df = dim.get_values()
df['n'] = df.index
df['taxon_dim_id'] = df.index
return df[['taxon_dim_id', 'n']]
data_marts_api.populate_fact_table('fact_table',
'test_cli_fact_table_publisher')
runner = CliRunner()
result = runner.invoke(
data_marts.truncate_fact_table_cli,
["fact_table"],
)
self.assertEqual(result.exit_code, 0)
def test_get_registered_fact_tables(self):
fact_tables = data_marts_api.get_registered_fact_tables()
self.assertEqual(len(fact_tables), 0)
data_marts_api.create_vector_dimension("ncl_adm1")
data_marts_api.create_fact_table(
'fact_table',
['ncl_adm1', ],
['measure_1', ]
)
fact_tables = data_marts_api.get_registered_fact_tables()
self.assertEqual(len(fact_tables), 1)
def test_create_fact_table(self):
data_marts_api.create_vector_dimension("ncl_adm1")
data_marts_api.create_fact_table(
'fact_table',
['ncl_adm1', ],
['measure_1', ],
)
data_marts_api.get_fact_table('fact_table')
self.assertRaises(
DimensionNotRegisteredError,
data_marts_api.create_fact_table,
'fact_table_2',
['ncl', ],
['measure_2', ],
)
def test_truncate_fact_table(self):
dim_1 = TestDimension("dim_1")
dim_2 = TestDimension("dim_2")
dim_1.create_dimension()
dim_2.create_dimension()
dim_1.populate_from_publisher()
dim_2.populate_from_publisher()
data_marts_api.create_fact_table(
"test_fact",
dimension_names=['dim_1', 'dim_2'],
measure_names=['measure_1'],
)
data_marts_api.populate_fact_table(
'test_fact',
TestFactTablePublisher.get_key()
)
data_marts_api.truncate_fact_table('test_fact')
def create_fact_table_cli(name, dimension, measure):
"""
Create and register a fact table from existing dimensions.
Use -d <dimension_name> for each dimension, and -m <measure_name> for each
measure.
"""
from niamoto.api import data_marts_api
click.echo(
"Creating the '{}' fact table...".format(name)
)
data_marts_api.create_fact_table(
name,
dimension_names=dimension,
measure_names=measure
)
click.echo(
"The '{}' fact table had been successfully created!".format(name)
)
def test_get_dimensional_model(self):
dim_1 = TestDimension("dim_1")
dim_2 = TestDimension("dim_2")
dim_1.create_dimension()
dim_2.create_dimension()
data_marts_api.create_fact_table(
"test_fact",
dimension_names=['dim_1', 'dim_2'],
measure_names=['measure_1'],
)
dm = data_marts_api.get_dimensional_model(
'test_fact',
[
{
"name": "measure_1_sum",
"function": "sum",
"measure": "measure_1",
},
]
)
self.assertIsInstance(dm, DimensionalModel)
wk = dm.get_cubes_workspace()