def test_adjust_timeseries_for_interval_label_no_tz():
test_data = TEST_DATA.tz_localize(None)
label = None
start = pd.Timestamp('2019-01-24T00:00Z')
end = pd.Timestamp('2019-01-24T00:04Z')
with pytest.raises(ValueError):
utils.adjust_timeseries_for_interval_label(test_data, label, start,
end)
def test_adjust_timeseries_for_interval_label_no_tz_empty():
test_data = pd.DataFrame()
label = None
start = pd.Timestamp('2019-01-24T00:00Z')
end = pd.Timestamp('2019-01-24T00:04Z')
out = utils.adjust_timeseries_for_interval_label(test_data, label, start,
end)
pdt.assert_frame_equal(test_data, out)
def get_observation_values(self,
observation_id,
start,
end,
interval_label=None):
"""
Get observation values from start to end for observation_id from the
API
Parameters
----------
observation_id : string
UUID of the observation object.
start : timelike object
Start time in interval to retrieve values for
end : timelike object
End time of the interval
interval_label : str or None
If beginning, ending, adjust the data to return only data that is
valid between start and end. If None or instant, return any data
between start and end inclusive of the endpoints.
Returns
-------
pandas.DataFrame
With a datetime index and (value, quality_flag) columns
Raises
------
ValueError
If start or end cannot be converted into a Pandas Timestamp
"""
req = self.get(f'/observations/{observation_id}/values',
params={
'start': start,
'end': end
})
out = json_payload_to_observation_df(req.json())
return adjust_timeseries_for_interval_label(out, interval_label, start,
end)
def get_forecast_values(self,
forecast_id,
start,
end,
interval_label=None):
"""
Get forecast values from start to end for forecast_id
Parameters
----------
forecast_id : string
UUID of the forecast object
start : timelike object
Start of the interval to retrieve values for
end : timelike object
End of the interval
interval_label : str or None
If beginning, ending, adjust the data to return only data that is
valid between start and end. If None or instant, return any data
between start and end inclusive of the endpoints.
Returns
-------
pandas.Series
With the forecast values and a datetime index
Raises
------
ValueError
If start or end cannot be converted into a Pandas Timestamp
"""
req = self.get(f'/forecasts/single/{forecast_id}/values',
params={
'start': start,
'end': end
})
out = json_payload_to_forecast_series(req.json())
return adjust_timeseries_for_interval_label(out, interval_label, start,
end)
def test_adjust_timeseries_for_interval_label_series(label, exp):
start = pd.Timestamp('2019-01-24T00:00Z')
end = pd.Timestamp('2019-01-24T00:04Z')
out = utils.adjust_timeseries_for_interval_label(TEST_DATA['value'], label,
start, end)
pdt.assert_series_equal(exp, out)
def test_adjust_timeseries_for_interval_label(label, exp, start, end):
start = start or pd.Timestamp('2019-01-24T00:00Z')
end = end or pd.Timestamp('2019-01-24T00:04Z')
out = utils.adjust_timeseries_for_interval_label(TEST_DATA, label, start,
end)
pdt.assert_frame_equal(exp, out)
def load_data(observation, data_start, data_end):
data = obs_data.loc[data_start:data_end]
return adjust_timeseries_for_interval_label(data,
observation.interval_label,
data_start, data_end)
