def test_timeseries_resample(self):
t_start = dt.datetime(2017, 1, 1)
t_end = dt.datetime(2018, 1, 1)
index = pd.date_range(t_start, t_end, freq='T', closed='left')
data = range(len(index))
quality = [i % 2 for i in range(len(index))]
ts0 = Timeseries(index=index, data=data, quality=quality)
ts0.set_update_timestamp(index)
ts1 = deepcopy(ts0)
ts1.resample('month', 'first')
assert len(ts1.dataframe) == 12
assert isclose(ts1.dataframe.data[0], 0)
assert isclose(ts1.dataframe.data[1], 44640)
assert isclose(ts1.dataframe.data[-1], 480960)
assert np.all(np.isclose(ts1.dataframe.quality, 0.5))
ts2 = deepcopy(ts0)
ts2.resample('day', 'max')
assert len(ts2.dataframe) == 365
assert isclose(ts2.dataframe.data[0], 1439)
assert isclose(ts2.dataframe.data[1], 2879)
assert isclose(ts2.dataframe.data[-1], 525599)
ts3 = deepcopy(ts0)
ts3.resample('30min', 'sum')
assert len(ts3.dataframe) == len(index) / 30
for i in range(len(ts3.dataframe)):
assert isclose(
ts3.dataframe.data[i], sum(range(30 * i, 30 * (i + 1))))
def test_timeseries_tsdump(self):
timestamp_l = [
dt.datetime(2017, 1, 1) + dt.timedelta(n) for n in range(5)
]
value_l = [0, 1, 2, 3, 4]
quality_l = [1, 1, 0.5, None, 0.69]
update_ts_l = [dt.datetime.now()] * 5
ts = Timeseries(index=timestamp_l, data=value_l, quality=quality_l)
ts.set_update_timestamp(update_ts_l)
assert np.isnan(ts.dataframe['quality'][3])
ts_list = tsdump(ts)
assert len(ts_list) == 5
assert all(len(v) == 4 for v in ts_list)
assert [v['timestamp'] for v in ts_list] == timestamp_l
assert [v['value'] for v in ts_list] == value_l
assert [v['update_ts'] for v in ts_list] == update_ts_l
assert [v['quality'] for v in ts_list] == quality_l
def test_timeseries_init_dataframe(self):
t_start = dt.datetime(2017, 1, 1)
t_end = dt.datetime(2017, 1, 6)
index = pd.date_range(t_start, t_end, freq='D', closed='left')
# index column must be a pandas.DatetimeIndex
with pytest.raises(TimeseriesInvalidIndexTypeError):
Timeseries(index=range(5), data=range(5))
# quality can not be set without data
with pytest.raises(TimeseriesMissingColumnError):
Timeseries(index=index, quality=np.random.rand(len(index)))
# data can not be set without index
with pytest.raises(TimeseriesMissingColumnError):
Timeseries(data=range(len(index)))
# validate dataframe
ts = Timeseries(index=index, data=range(len(index)))
# no custom column allowed
ts.dataframe['dummy'] = range(len(index))
with pytest.raises(TimeseriesInvalidColumnsError):
ts.validate()
# droping a column unvalidates timeseries
ts.dataframe.drop(columns=['dummy', 'data'], inplace=True)
with pytest.raises(TimeseriesMissingColumnError):
ts.validate()
# repr timeseries
ts_data = range(len(index))
ts = Timeseries(index=index, data=ts_data)
assert repr(ts) == (
'<Timeseries>(count={}, start={}, end={}, min={}, max={})'.format(
len(index), min(index), max(index),
float(min(ts_data)), float(max(ts_data))))
# quality defaults to 1
ts = Timeseries(index=index, data=range(len(index)))
assert (ts.dataframe['quality'] == 1).all()
# update_ts can only be set using set_update_timestamp method
ts = Timeseries(
index=index, data=range(len(index)),
quality=np.random.rand(len(index)))
assert pd.isnull(ts.dataframe['update_ts']).all()
# update with a pandas.Series
ts.set_update_timestamp(index)
assert ts.dataframe.at[index[0], 'update_ts'] == index.values[0]
# update with a datetime
t_update = dt.datetime.now()
ts.set_update_timestamp(t_update)
assert ts.dataframe.at[index[0], 'update_ts'] == t_update
ts.validate()
assert ts.dataframe.at[index[0], 'update_ts'] == t_update
# update with a list of datetimes
t_update = [dt.datetime.now()] * len(index)
ts.set_update_timestamp(t_update)
assert ts.dataframe.at[index[0], 'update_ts'] == t_update[0]
ts.validate()
assert ts.dataframe.at[index[0], 'update_ts'] == t_update[0]
# data length must be equal to index length
with pytest.raises(ValueError):
Timeseries(index=index, data=range(len(index)+1))