def _group_continuous_week_post(session, observation, observation_values):
# observation_values expected to be sorted
# observation values already have uneven frequency checked
gid = quality_mapping.check_if_series_flagged(
observation_values['quality_flag'], 'UNEVEN FREQUENCY').cumsum()
# make series of week + year integers to further
# split data to post at most one week at a time
# ~10,000 pts of 1min data
week_int = (gid.index.week + gid.index.year).values
# combine the continuous groups with groups of weeks
# gid is unique for each group since week_int and cumsum
# increase monotonically and are positive
gid += week_int
observation_values['gid'] = gid
for _, group in observation_values.groupby('gid'):
session.post_observation_values(observation.observation_id,
group[['value', 'quality_flag']],
params='donotvalidate')
def test_check_if_series_flagged_key_fail():
with pytest.raises(KeyError):
quality_mapping.check_if_series_flagged(pd.Series([2, 3, 35]),
['NOK'])
def test_check_if_series_flagged_validated_fail():
with pytest.raises(ValueError):
quality_mapping.check_if_series_flagged(pd.Series([0, 1, 0]), 'OK')
def test_check_if_series_flagged_type_fail():
with pytest.raises(TypeError):
quality_mapping.check_if_series_flagged(pd.Series([2, 3, 35]),
['OK', b'CLOUDY', []])
def test_check_if_series_flagged(expected, desc):
flags = pd.Series([2, 3, 2 | 1 << 9, 2 | 1 << 5 | 1 << 12 | 1])
out = quality_mapping.check_if_series_flagged(flags, desc)
assert_series_equal(out, expected)