def power_flows_week(
time_series: TimeSeries,
network_region_code: str,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
query = interconnector_power_flow(time_series=time_series,
network_region=network_region_code)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
if len(row) < 1:
raise Exception("No results from query: {}".format(query))
imports = [
DataQueryResult(interval=i[0],
result=i[2],
group_by="imports" if len(i) > 1 else None)
for i in row
]
exports = [
DataQueryResult(interval=i[0],
result=i[3],
group_by="exports" if len(i) > 1 else None)
for i in row
]
result = stats_factory(
imports,
# code=network_region_code or network.code,
network=time_series.network,
period=human_to_period("7d"),
interval=human_to_interval("5m"),
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
)
if not result:
raise Exception("No results")
result_exports = stats_factory(
exports,
# code=network_region_code or network.code,
network=time_series.network,
period=human_to_period("7d"),
interval=human_to_interval("5m"),
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
)
result.append_set(result_exports)
return result
def gov_stats_cpi() -> Optional[OpennemDataSet]:
engine = get_database_engine()
query = country_stats_query(StatTypes.CPI)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats = [
DataQueryResult(interval=i[0], result=i[1], group_by=i[2] if len(i) > 1 else None)
for i in row
]
if len(stats) < 1:
logger.error("No results for gov_stats_cpi returing blank set")
return None
result = stats_factory(
stats,
code="au.cpi",
network=NetworkNEM,
interval=human_to_interval("1Q"),
period=human_to_period("all"),
units=get_unit("cpi"),
group_field="gov",
)
return result
def demand_week(
time_series: TimeSeries,
network_region_code: Optional[str],
networks_query: Optional[List[NetworkSchema]] = None,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
query = network_demand_query(
time_series=time_series,
network_region=network_region_code,
networks_query=networks_query,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
if len(row) < 1:
logger.error(
"No results from network_demand_query with {}".format(time_series))
return None
demand = [
DataQueryResult(interval=i[0],
result=i[2],
group_by="demand" if len(i) > 1 else None) for i in row
]
result = stats_factory(
demand,
# code=network_region_code or network.code,
network=time_series.network,
period=human_to_period("7d"),
interval=human_to_interval("5m"),
units=get_unit("demand"),
region=network_region_code,
)
if not result:
logger.error(
"No results from network_demand_query with {}".format(time_series))
return None
return result
def emissions_for_network_interval(
time_series: TimeSeries,
network_region_code: str = None,
) -> OpennemDataSet | None:
engine = get_database_engine()
if network_region_code and not re.match(_valid_region,
network_region_code):
raise OpenNEMInvalidNetworkRegion()
query = emission_network_fueltech_query(
time_series=time_series,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
if len(stats) < 1:
logger.error(
"No results from emissions_for_network_interval query with {}".
format(time_series))
return None
result = stats_factory(
stats,
# code=network_region_code or network.code,
network=time_series.network,
interval=time_series.interval,
period=time_series.period,
units=get_unit("emissions"),
region=network_region_code,
fueltech_group=True,
)
return result
def get_power_example() -> OpennemDataSet:
network = network_from_network_code("NEM")
interval = human_to_interval("5m")
units = get_unit("power")
period = human_to_period("7d")
network_region_code = "NSW1"
test_rows = []
dt = datetime.fromisoformat("2021-01-15 10:00:00")
for ft in ["coal_black", "coal_brown"]:
for v in range(0, 3):
test_rows.append([dt, ft, v])
dt = dt + timedelta(minutes=5)
stats = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None)
for i in test_rows
]
assert len(stats) == 6, "Should have 6 stats"
result = stats_factory(
stats,
code=network_region_code or network.code,
network=network,
interval=interval,
period=period,
units=units,
region=network_region_code,
fueltech_group=True,
)
if not result:
raise Exception("Bad unit test data")
with open("power-nsw1.json", "w") as fh:
fh.write(result.json(indent=4))
return result
def power_flows_network_week(
time_series: TimeSeries,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
query = interconnector_flow_network_regions_query(time_series=time_series)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
if len(row) < 1:
logger.error(
"No results from interconnector_flow_network_regions_query with {}".format(time_series)
)
return None
imports = [
DataQueryResult(interval=i[0], result=i[4], group_by=i[1] if len(i) > 1 else None)
for i in row
]
result = stats_factory(
imports,
# code=network_region_code or network.code,
network=time_series.network,
period=time_series.period,
interval=time_series.interval,
units=get_unit("regional_trade"),
# fueltech_group=True,
group_field="power",
include_group_code=True,
include_code=True,
)
if not result:
logger.error(
"No results from interconnector_flow_network_regions_query with {}".format(time_series)
)
return None
return result
def energy_interconnector_emissions_region_daily(
time_series: TimeSeries,
network_region_code: str,
networks_query: Optional[List[NetworkSchema]] = None,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
period: TimePeriod = human_to_period("1Y")
units = get_unit("emissions")
query = energy_network_interconnector_emissions_query(
time_series=time_series,
network_region=network_region_code,
networks_query=networks_query,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
if len(row) < 1:
return None
stats = [
RegionFlowEmissionsResult(
interval=i[0],
flow_from=i[1],
flow_to=i[2],
energy=i[3],
flow_from_emissions=i[4],
flow_to_emissions=i[5],
)
for i in row
]
stats_grouped = net_flows_emissions(network_region_code, stats, time_series.interval)
imports = stats_grouped["imports"]
exports = stats_grouped["exports"]
# imports = [DataQueryResult(interval=i[0], group_by="imports", result=i[5]) for i in row]
# exports = [DataQueryResult(interval=i[0], group_by="exports", result=i[4]) for i in row]
result = stats_factory(
imports,
network=time_series.network,
period=period,
interval=time_series.interval,
units=units,
region=network_region_code,
fueltech_group=True,
localize=False,
)
# Bail early on no interconnector
# don't error
if not result:
return result
result_exports = stats_factory(
exports,
network=time_series.network,
period=period,
interval=time_series.interval,
units=units,
region=network_region_code,
fueltech_group=True,
localize=False,
)
result.append_set(result_exports)
return result
def energy_interconnector_region_daily(
time_series: TimeSeries,
network_region_code: str,
networks_query: Optional[List[NetworkSchema]] = None,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
period: TimePeriod = human_to_period("1Y")
units = get_unit("energy_giga")
query = energy_network_flow_query(
time_series=time_series,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
if len(row) < 1:
return None
imports = [DataQueryResult(interval=i[0], group_by="imports", result=i[1]) for i in row]
exports = [DataQueryResult(interval=i[0], group_by="exports", result=i[2]) for i in row]
imports_mv = [DataQueryResult(interval=i[0], group_by="imports", result=i[3]) for i in row]
exports_mv = [DataQueryResult(interval=i[0], group_by="exports", result=i[4]) for i in row]
result = stats_factory(
imports,
network=time_series.network,
period=period,
interval=time_series.interval,
units=units,
region=network_region_code,
fueltech_group=True,
# localize=False,
)
# Bail early on no interconnector
# don't error
if not result:
logger.warn("No interconnector energy result")
return result
result_exports = stats_factory(
exports,
network=time_series.network,
period=period,
interval=time_series.interval,
units=units,
region=network_region_code,
fueltech_group=True,
)
result.append_set(result_exports)
result_imports_mv = stats_factory(
imports_mv,
units=get_unit("market_value"),
network=time_series.network,
fueltech_group=True,
interval=time_series.interval,
region=network_region_code,
period=time_series.period,
code=time_series.network.code.lower(),
localize=False,
)
result.append_set(result_imports_mv)
result_export_mv = stats_factory(
exports_mv,
units=get_unit("market_value"),
network=time_series.network,
fueltech_group=True,
interval=time_series.interval,
region=network_region_code,
period=time_series.period,
code=time_series.network.code.lower(),
localize=False,
)
result.append_set(result_export_mv)
return result
def energy_fueltech_daily(
time_series: TimeSeries,
network_region_code: Optional[str] = None,
networks_query: Optional[List[NetworkSchema]] = None,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
units = get_unit("energy_giga")
query = energy_network_fueltech_query(
time_series=time_series,
network_region=network_region_code,
networks_query=networks_query,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
results_energy = [
DataQueryResult(interval=i[0], group_by=i[1], result=i[2] if len(i) > 1 else None)
for i in row
]
results_market_value = [
DataQueryResult(interval=i[0], group_by=i[1], result=i[3] if len(i) > 1 else None)
for i in row
]
results_emissions = [
DataQueryResult(interval=i[0], group_by=i[1], result=i[4] if len(i) > 1 else None)
for i in row
]
if len(results_energy) < 1:
logger.error("No results from query: {}".format(query))
return None
stats = stats_factory(
stats=results_energy,
units=units,
network=time_series.network,
fueltech_group=True,
interval=time_series.interval,
region=network_region_code,
period=time_series.period,
localize=True,
# code=network.code.lower(),
)
if not stats:
return None
stats_market_value = stats_factory(
stats=results_market_value,
units=get_unit("market_value"),
network=time_series.network,
fueltech_group=True,
interval=time_series.interval,
region=network_region_code,
period=time_series.period,
code=time_series.network.code.lower(),
localize=True,
)
stats.append_set(stats_market_value)
stats_emissions = stats_factory(
stats=results_emissions,
units=get_unit("emissions"),
network=time_series.network,
fueltech_group=True,
interval=time_series.interval,
region=network_region_code,
period=time_series.period,
code=time_series.network.code.lower(),
localize=True,
)
stats.append_set(stats_emissions)
return stats
def weather_daily(
time_series: TimeSeries,
station_code: str,
unit_name: str = "temperature_mean",
include_min_max: bool = True,
network_region: Optional[str] = None,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
units = get_unit(unit_name)
query = weather_observation_query(
time_series=time_series,
station_codes=[station_code],
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
temp_avg = [
DataQueryResult(interval=i[0], group_by=i[1], result=i[2] if len(i) > 1 else None)
for i in row
]
temp_min = [
DataQueryResult(interval=i[0], group_by=i[1], result=i[3] if len(i) > 1 else None)
for i in row
]
temp_max = [
DataQueryResult(interval=i[0], group_by=i[1], result=i[4] if len(i) > 1 else None)
for i in row
]
if len(temp_avg) < 1:
logger.error("No results from weather_observation_query with {}".format(time_series))
return None
stats = stats_factory(
stats=temp_avg,
units=units,
network=time_series.network,
interval=time_series.interval,
region=network_region,
code="bom",
group_field="temperature",
localize=False,
)
if not stats:
logger.error(
"No results from weather_observation_query stats factory with {}".format(time_series)
)
return None
if include_min_max:
stats_min = stats_factory(
stats=temp_min,
units=get_unit("temperature_min"),
network=time_series.network,
interval=time_series.interval,
region=network_region,
code="bom",
group_field="temperature",
localize=False,
)
stats_max = stats_factory(
stats=temp_max,
units=get_unit("temperature_max"),
network=time_series.network,
interval=time_series.interval,
region=network_region,
code="bom",
group_field="temperature",
localize=False,
)
stats.append_set(stats_min)
stats.append_set(stats_max)
return stats
def power_week(
time_series: TimeSeries,
network_region_code: str = None,
networks_query: Optional[List[NetworkSchema]] = None,
include_capacities: bool = False,
include_code: Optional[bool] = True,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
query = power_network_fueltech_query(
time_series=time_series,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats = [
DataQueryResult(interval=i[0], result=i[2], group_by=i[1] if len(i) > 1 else None)
for i in row
]
if len(stats) < 1:
logger.error("No results from power week query with {}".format(time_series))
return None
result = stats_factory(
stats,
# code=network_region_code or network.code,
network=time_series.network,
interval=time_series.interval,
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
include_code=include_code,
)
if not result:
logger.error("No results from power week status factory with {}".format(time_series))
return None
if include_capacities and network_region_code:
region_fueltech_capacities = get_facility_capacities(
time_series.network, network_region_code
)
for ft in result.data:
if ft.fuel_tech in region_fueltech_capacities:
ft.x_capacity_at_present = region_fueltech_capacities[ft.fuel_tech]
# price
time_series_price = time_series.copy()
time_series_price.interval = human_to_interval("30m")
query = price_network_query(
time_series=time_series_price,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats_price = [
DataQueryResult(interval=i[0], result=i[2], group_by=i[1] if len(i) > 1 else None)
for i in row
]
stats_market_value = stats_factory(
stats=stats_price,
code=network_region_code or time_series.network.code.lower(),
units=get_unit("price_energy_mega"),
network=time_series.network,
interval=human_to_interval("30m"),
region=network_region_code,
period=time_series.period,
include_code=include_code,
)
result.append_set(stats_market_value)
# rooftop solar
time_series_rooftop = time_series.copy()
time_series_rooftop.interval = human_to_interval("30m")
query = power_network_rooftop_query(
time_series=time_series_rooftop,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
rooftop_power = [
DataQueryResult(interval=i[0], result=i[2], group_by=i[1] if len(i) > 1 else None)
for i in row
]
rooftop = stats_factory(
rooftop_power,
# code=network_region_code or network.code,
network=time_series.network,
interval=human_to_interval("30m"),
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
include_code=include_code,
cast_nulls=False,
)
# rooftop forecast
rooftop_forecast = None
if rooftop and rooftop.data and len(rooftop.data) > 0:
time_series_rooftop_forecast = time_series_rooftop.copy()
time_series_rooftop_forecast.start = rooftop.data[0].history.last
time_series_rooftop_forecast.forecast = True
query = power_network_rooftop_query(
time_series=time_series_rooftop_forecast,
networks_query=networks_query,
network_region=network_region_code,
forecast=True,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
rooftop_forecast_power = [
DataQueryResult(interval=i[0], result=i[2], group_by=i[1] if len(i) > 1 else None)
for i in row
]
rooftop_forecast = stats_factory(
rooftop_forecast_power,
# code=network_region_code or network.code,
network=time_series.network,
interval=human_to_interval("30m"),
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
include_code=include_code,
cast_nulls=False,
)
if rooftop and rooftop_forecast:
if (
hasattr(rooftop, "data")
and len(rooftop.data) > 0
and rooftop_forecast.data
and len(rooftop_forecast.data) > 0
):
rooftop.data[0].forecast = rooftop_forecast.data[0].history
result.append_set(rooftop)
return result
def station_observations_api(
station_code: str = Query(None, description="Station code"),
interval_human: str = Query("15m", description="Interval"),
period_human: str = Query("7d", description="Period"),
station_codes: List[str] = [],
timezone: str = None,
offset: str = None,
year: int = None,
engine=Depends(get_database_engine),
) -> OpennemDataSet:
units = get_unit("temperature")
if not interval_human:
interval_human = "15m"
if not period_human:
period_human = "7d"
if station_code:
station_codes = [station_code]
interval = human_to_interval(interval_human)
period = human_to_period(period_human)
if timezone:
timezone = ZoneInfo(timezone)
if offset:
timezone = get_fixed_timezone(offset)
query = observation_query(
station_codes=station_codes,
interval=interval,
period=period,
year=year,
)
with engine.connect() as c:
results = list(c.execute(query))
stats = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in results
]
if len(stats) < 1:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="Station stats not found",
)
result = stats_factory(
stats=stats,
units=units,
interval=interval,
period=period,
code="bom",
group_field="temperature",
)
return result
def energy_interconnector_flows_and_emissions(
time_series: TimeSeries,
network_region_code: str,
networks_query: Optional[List[NetworkSchema]] = None,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
period: TimePeriod = human_to_period("1Y")
unit_energy = get_unit("energy_giga")
unit_emissions = get_unit("emissions")
query = energy_network_interconnector_emissions_query(
time_series=time_series,
network_region=network_region_code,
networks_query=networks_query,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
if len(row) < 1:
return None
imports = [
DataQueryResult(interval=i[0], group_by="imports", result=i[1])
for i in row
]
exports = [
DataQueryResult(interval=i[0], group_by="exports", result=i[2])
for i in row
]
import_emissions = [
DataQueryResult(interval=i[0], group_by="imports", result=i[3])
for i in row
]
export_emissions = [
DataQueryResult(interval=i[0], group_by="exports", result=i[4])
for i in row
]
import_mv = [
DataQueryResult(interval=i[0], group_by="imports", result=i[5])
for i in row
]
export_mv = [
DataQueryResult(interval=i[0], group_by="exports", result=i[6])
for i in row
]
result = stats_factory(
imports,
network=time_series.network,
period=period,
interval=time_series.interval,
units=unit_energy,
region=network_region_code,
fueltech_group=True,
)
if not result:
raise Exception("No results from flow controller")
result_exports = stats_factory(
exports,
network=time_series.network,
period=period,
interval=time_series.interval,
units=unit_energy,
region=network_region_code,
fueltech_group=True,
)
result.append_set(result_exports)
result_import_emissions = stats_factory(
import_emissions,
network=time_series.network,
period=period,
interval=time_series.interval,
units=unit_emissions,
region=network_region_code,
fueltech_group=True,
localize=False,
)
result.append_set(result_import_emissions)
result_export_emissions = stats_factory(
export_emissions,
network=time_series.network,
period=period,
interval=time_series.interval,
units=unit_emissions,
region=network_region_code,
fueltech_group=True,
localize=False,
)
result.append_set(result_export_emissions)
# market value for flows
result_import_mv = stats_factory(
import_mv,
network=time_series.network,
period=period,
interval=time_series.interval,
units=get_unit("market_value"),
region=network_region_code,
fueltech_group=True,
localize=False,
)
result.append_set(result_import_mv)
result_export_mv = stats_factory(
export_mv,
network=time_series.network,
period=period,
interval=time_series.interval,
units=get_unit("market_value"),
region=network_region_code,
fueltech_group=True,
localize=False,
)
result.append_set(result_export_mv)
return result
def demand_network_region_daily(
time_series: TimeSeries,
network_region_code: str | None = None,
networks: list[NetworkSchema] = [],
) -> OpennemDataSet | None:
"""Gets demand market_value and energy for a network -> network_region"""
engine = get_database_engine()
query = demand_network_region_query(time_series=time_series,
network_region=network_region_code,
networks=networks)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
results_energy = [
DataQueryResult(interval=i[0],
group_by=i[2],
result=i[3] if len(i) > 1 else None) for i in row
]
results_market_value = [
DataQueryResult(interval=i[0],
group_by=i[2],
result=i[4] if len(i) > 1 else None) for i in row
]
if len(results_energy) < 1:
logger.error("No results from query: {}".format(query))
return None
# demand based values for VWP
stats = stats_factory(
stats=results_energy,
units=get_unit("demand.energy_giga"),
network=time_series.network,
fueltech_group=False,
interval=time_series.interval,
period=time_series.period,
localize=True,
)
if not stats:
raise Exception(
f"Not stats for demand_network_region_daily: {network_region_code}"
)
stats_market_value = stats_factory(
stats=results_market_value,
units=get_unit("demand.market_value"),
network=time_series.network,
fueltech_group=False,
interval=time_series.interval,
period=time_series.period,
code=time_series.network.code.lower(),
localize=True,
)
if stats_market_value:
stats.append_set(stats_market_value)
return stats
def power_week(
time_series: TimeSeries,
network_region_code: str = None,
networks_query: Optional[List[NetworkSchema]] = None,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
query = power_network_fueltech_query(
time_series=time_series,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
if len(stats) < 1:
raise Exception("No results from query: {}".format(query))
result = stats_factory(
stats,
# code=network_region_code or network.code,
network=time_series.network,
interval=time_series.interval,
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
)
if not result:
raise Exception("No results")
# price
time_series_price = time_series.copy()
time_series_price.interval = human_to_interval("30m")
query = price_network_query(
time_series=time_series_price,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats_price = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
stats_market_value = stats_factory(
stats=stats_price,
code=network_region_code or time_series.network.code.lower(),
units=get_unit("price_energy_mega"),
network=time_series.network,
interval=human_to_interval("30m"),
region=network_region_code,
period=time_series.period,
)
result.append_set(stats_market_value)
# rooftop solar
time_series_rooftop = time_series.copy()
time_series_rooftop.interval = human_to_interval("30m")
query = power_network_rooftop_query(
time_series=time_series_rooftop,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
rooftop_power = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
rooftop = stats_factory(
rooftop_power,
# code=network_region_code or network.code,
network=time_series.network,
interval=human_to_interval("30m"),
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
)
# rooftop forecast
time_series_rooftop_forecast = time_series.copy()
time_series_rooftop_forecast.interval = human_to_interval("30m")
time_series_rooftop_forecast.forecast = True
query = power_network_rooftop_query(
time_series=time_series_rooftop,
networks_query=networks_query,
network_region=network_region_code,
forecast=True,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
rooftop_forecast_power = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
rooftop_forecast = stats_factory(
rooftop_forecast_power,
# code=network_region_code or network.code,
network=time_series.network,
interval=human_to_interval("30m"),
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
)
if rooftop and rooftop_forecast:
if (hasattr(rooftop, "data") and len(rooftop.data) > 0
and rooftop_forecast.data and len(rooftop_forecast.data) > 0):
rooftop.data[0].forecast = rooftop_forecast.data[0].history
result.append_set(rooftop)
return result
