def test_row_has_must_have_metrics_non_existant_metric(self):
csvlines = FittingUtilTestCase.csv_content.splitlines()
dictreader = csv.DictReader(csvlines)
metrics = ['xxx']
for d in dictreader:
with self.assertRaises(KeyError):
f_util.row_has_must_have_metrics(d, metrics)
def test_row_has_must_have_metrics_bad_data(self):
csvlines = FittingUtilTestCase.csv_content.splitlines()
dictreader = csv.DictReader(csvlines)
metrics = ['tss']
row_tests = []
for d in dictreader:
row_tests.append(f_util.row_has_must_have_metrics(d, metrics))
self.assertFalse(all(row_tests))
def test_row_has_must_have_metrics2(self):
csvlines = FittingUtilTestCase.csv_content3.splitlines()
dictreader = csv.DictReader(csvlines)
metrics = ['60m_critical_power', 'tss']
row_tests = []
for d in dictreader:
row_tests.append(f_util.row_has_must_have_metrics(d, metrics))
self.assertTrue(row_tests.count(True) == 6)
self.assertTrue(row_tests.count(False) == 2)
def test_row_has_must_have_metrics(self):
csvlines = FittingUtilTestCase.csv_content.splitlines()
metrics = ['60m_critical_power', 'skiba_bike_score']
dictreader = csv.DictReader(csvlines)
for d in dictreader:
self.assertTrue(f_util.row_has_must_have_metrics(d, metrics))
