def tsa_group_by(self, datestring, tsa, subkeys, group_func):
"""
TODO: make this method static, inteval should be in tsa
group given tsa by subkeys, and use group_func to aggregate data
first all Timeseries will be aligned in time, to get proper points in timeline
parameters:
tsa <TimeseriesArrayLazy>
subkey <tuple> could also be empty, to aggregate everything
group_func <func> like lambda a,b : (a+b)/2 to get averages
slotlength <int> interval in seconds to correct every timeseries to
returns:
<TimeseriesArrayLazy>
"""
# intermediated tsa
tsa2 = TimeseriesArrayLazy(index_keys=subkeys, value_keys=tsa.value_keys, ts_key=tsa.ts_key, datatypes=tsa.datatypes)
start_ts, _ = DataLogger.get_ts_for_datestring(datestring)
ts_keyname = tsa.ts_key
for data in tsa.export():
# align timestamp
nearest_slot = round((data[ts_keyname] - start_ts) / self.__interval)
data[ts_keyname] = int(start_ts + nearest_slot * self.__interval)
#data[ts_keyname] = align_timestamp(data[ts_keyname])
tsa2.group_add(data, group_func)
return tsa2
