def store(self, timeseries_dict):
""" Input the list of Enki result timeseries_dict,
where the keys are the wanted SmG ts-path names
and the values are Enki result api.shyft_timeseries_double, time-series.
If the named time-series does not exist, create it.
Then store time-series data to the named entities.
"""
# 0. First, get the list of ts identities that Tss uses
list_of_names = timeseries_dict.keys()
ListOf_TsIdentities = self._namelist_to_ListOf_TsIdentities(
list_of_names)
ok = False
with repository(self.env) as tss:
# 1. We check if any of the tsnames are missing..
exists_kv_pairs = tss.repo.Exists(ListOf_TsIdentities)
missing_list = List[MetaInfo]([])
# 2. We create those missing..
for e in exists_kv_pairs:
if e.Value == False:
tsid = e.Key
mi = MetaInfo()
mi.Identity = tsid
mi.Description = 'Automatically created by shyft '
mi.Type = 9000 # just a general time-series
# Here we might fill in some properties to the created timeseries
# e.g. unit, if we could figure out that
missing_list.Add(mi)
if missing_list.Count > 0: # Yes, something was missing, create them
created_list = tss.repo.Create(missing_list, True)
#TODO verify we got them created
# fetch tsids from the names
ts_id_list = tss.repo.GetIdentities(
tss.repo.FindMetaInfo(ListOf_TsIdentities))
name_to_ts_id = {x.Name: x for x in ts_id_list}
# 3. We store the datapoints (identity period, then time,value)
ssa_timeseries_list = List[TimeSeriesPointSegments](
[]) # This is what Tss Xts eats
for name, shyft_ts in iter(timeseries_dict.items()):
ssa_ts = self._make_ssa_tsps_from_shyft_ts(
name_to_ts_id[name], shyft_ts)
ssa_timeseries_list.Add(ssa_ts)
error_list = tss.repo.Write(ssa_timeseries_list,
False) # Write into SmG!
if error_list is None: ok = True
return ok
def read_forecast(self,list_of_fc_id,period):
if not period.valid():
raise SmgDataError("period should be valid() of type api.UtcPeriod")
result = {}
ListOf_fc_identities=self._namelist_to_ListOf_TsIdentities(list_of_fc_id)
ts_id_list=[]
with repository(self.env) as tss:
ts_id_list= tss.repo.GetIdentities (tss.repo.FindMetaInfo(ListOf_fc_identities))
ssa_period=self._make_ssa_Period_from_shyft_period(period)
fcr= ForecastRepositorySmg(self.fc_env)
read_forecasts = fcr.repo.ReadForecast(ts_id_list,ssa_period)
if ts_id_list.Count != read_forecasts.Count:
print( "WARNING: Could only find {} out of {} requested timeseries".format(read_forecasts.Count, ts_id_list.Count))
for fc_ts in read_forecasts:
key=fc_ts.Name
result[key]=self._make_shyft_ts_from_ssa_ts(fc_ts)
return result
def store(self, timeseries_dict):
""" Input the list of Enki result timeseries_dict,
where the keys are the wanted SmG ts-path names
and the values are Enki result api.shyft_timeseries_double, time-series.
If the named time-series does not exist, create it.
Then store time-series data to the named entities.
"""
# 0. First, get the list of ts identities that Tss uses
list_of_names = timeseries_dict.keys()
ListOf_TsIdentities = self._namelist_to_ListOf_TsIdentities(list_of_names)
ok = False
with repository(self.env) as tss:
# 1. We check if any of the tsnames are missing..
exists_kv_pairs = tss.repo.Exists(ListOf_TsIdentities)
missing_list = List[MetaInfo]([])
# 2. We create those missing..
for e in exists_kv_pairs:
if e.Value == False:
tsid = e.Key
mi = MetaInfo()
mi.Identity = tsid
mi.Description = "Automatically created by shyft "
mi.Type = 9000 # just a general time-series
# Here we might fill in some properties to the created timeseries
# e.g. unit, if we could figure out that
missing_list.Add(mi)
if missing_list.Count > 0: # Yes, something was missing, create them
created_list = tss.repo.Create(missing_list, True)
# TODO verify we got them created
# fetch tsids from the names
ts_id_list = tss.repo.GetIdentities(tss.repo.FindMetaInfo(ListOf_TsIdentities))
name_to_ts_id = {x.Name: x for x in ts_id_list}
# 3. We store the datapoints (identity period, then time,value)
ssa_timeseries_list = List[TimeSeriesPointSegments]([]) # This is what Tss Xts eats
for name, shyft_ts in iter(timeseries_dict.items()):
ssa_ts = self._make_ssa_tsps_from_shyft_ts(name_to_ts_id[name], shyft_ts)
ssa_timeseries_list.Add(ssa_ts)
error_list = tss.repo.Write(ssa_timeseries_list, False) # Write into SmG!
if error_list is None:
ok = True
return ok
def read(self,list_of_ts_id,period):
"""Open a connection to the SMG database and fetch all the time series given in list_of_ts_id.
ts_id is currently the full unique name of the smg-ts. We could/should also support using
unique number/keys instead. -more efficient, and more robust to namechanges.
Return the result as a dictionary of shyft_ts."""
if not period.valid():
raise SmgDataError("period should be valid() of type api.UtcPeriod")
result = {}
raw_data = []
ListOf_TsIdentities=self._namelist_to_ListOf_TsIdentities(list_of_ts_id)
ssa_period=self._make_ssa_Period_from_shyft_period(period)
with repository(self.env) as tsr:
raw_data = tsr.repo.ReadRawPoints(ListOf_TsIdentities,ssa_period)
if len(list_of_ts_id) != raw_data.Count:
print( "WARNING: Could only find {} out of {} requested timeseries".format(raw_data.Count, len(list_of_ts_id)))
for d in raw_data:
key = d.Name #todo : if self.keys_are_names else d.Info.Id
result[key] = self._make_shyft_ts_from_ssa_ts(d)
return result
def read_forecast(self, list_of_fc_id, period):
if not period.valid():
raise SmgDataError(
"period should be valid() of type api.UtcPeriod")
result = {}
ListOf_fc_identities = self._namelist_to_ListOf_TsIdentities(
list_of_fc_id)
ts_id_list = []
with repository(self.env) as tss:
ts_id_list = tss.repo.GetIdentities(
tss.repo.FindMetaInfo(ListOf_fc_identities))
ssa_period = self._make_ssa_Period_from_shyft_period(period)
fcr = ForecastRepositorySmg(self.fc_env)
read_forecasts = fcr.repo.ReadForecast(ts_id_list, ssa_period)
if ts_id_list.Count != read_forecasts.Count:
print("WARNING: Could only find {} out of {} requested timeseries".
format(read_forecasts.Count, ts_id_list.Count))
for fc_ts in read_forecasts:
key = fc_ts.Name
result[key] = self._make_shyft_ts_from_ssa_ts(fc_ts)
return result
def read(self, list_of_ts_id, period):
"""Open a connection to the SMG database and fetch all the time series given in list_of_ts_id.
ts_id is currently the full unique name of the smg-ts. We could/should also support using
unique number/keys instead. -more efficient, and more robust to namechanges.
Return the result as a dictionary of shyft_ts."""
if not period.valid():
raise SmgDataError(
"period should be valid() of type api.UtcPeriod")
result = {}
raw_data = []
ListOf_TsIdentities = self._namelist_to_ListOf_TsIdentities(
list_of_ts_id)
ssa_period = self._make_ssa_Period_from_shyft_period(period)
with repository(self.env) as tsr:
raw_data = tsr.repo.ReadRawPoints(ListOf_TsIdentities, ssa_period)
if len(list_of_ts_id) != raw_data.Count:
print("WARNING: Could only find {} out of {} requested timeseries".
format(raw_data.Count, len(list_of_ts_id)))
for d in raw_data:
key = d.Name #todo : if self.keys_are_names else d.Info.Id
result[key] = self._make_shyft_ts_from_ssa_ts(d)
return result