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fixed automated assignment

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This commit is contained in:
Khalim Conn-Kowlessar 2024-06-04 13:41:59 +01:00
parent 548672cc10
commit b3f8a9dccc
1 changed files with 33 additions and 10 deletions
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43
backend/app/plan/router.py
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@ -830,8 +830,14 @@ async def build_mds(body: MdsRequest):
results = []
for p in input_properties:
sap_before = int(p.data["current-energy-efficiency"])
epc_before = p.data["current-energy-rating"]
heat_demand_before = p.data["energy-consumption-current"]
carbon_before = p.data["co2-emissions-current"]
package_comparison = []
for _id in recommendations[p.id].keys():
sap_prediction = all_predictions["sap_change_predictions"][
(all_predictions["sap_change_predictions"]["property_id"] == str(p.id)) &
(all_predictions["sap_change_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
@ -854,8 +860,10 @@ async def build_mds(body: MdsRequest):
sap_target = epc_to_sap_lower_bound(epc_target)
# Define the measures
sap_threshold_barrier = sap_prediction[sap_prediction["predictions"] >= sap_target]
meets_threshold = True
if sap_threshold_barrier.empty:
raise NotImplementedError("FIX ME")
sap_threshold_barrier = sap_prediction.tail(1)
meets_threshold = False
sap_threshold_barrier = sap_threshold_barrier.head(1)
sap_prediction = sap_prediction[
@ -881,16 +889,9 @@ async def build_mds(body: MdsRequest):
]
costs = sum(costs)
sap_before = int(p.data["current-energy-efficiency"])
sap_after = sap_prediction["predictions"].values[-1]
epc_before = p.data["current-energy-rating"]
epc_after = sap_to_epc(sap_after)
heat_demand_before = p.data["energy-consumption-current"]
heat_demand_after = heat_demand_prediction["predictions"].values[-1]
carbon_before = p.data["co2-emissions-current"]
carbon_after = carbon_prediction["predictions"].values[-1]
current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
@ -924,13 +925,35 @@ async def build_mds(body: MdsRequest):
"carbon_after": carbon_after,
"bill_savings": bill_savings,
"energy_savings": energy_savings,
"meets_threshold": meets_threshold
}
)
package_comparison = pd.DataFrame(package_comparison)
# Find the smallest cost package
package_comparison = package_comparison.sort_values("cost")
package_comparison = package_comparison.head(1).to_dict("records")[0]
if not package_comparison.empty:
# We check if any of the packages meet the threshold
if package_comparison["meets_threshold"].any():
package_comparison = package_comparison[package_comparison["meets_threshold"]]
package_comparison = package_comparison.sort_values("cost")
package_comparison = package_comparison.head(1).to_dict("records")[0]
else:
package_comparison = {
"measures": [],
"sap_before": sap_before,
"sap_after": sap_before,
"epc_before": epc_before,
"epc_after": epc_before,
"heat_demand_before": heat_demand_before,
"heat_demand_after": heat_demand_before,
"carbon_before": carbon_before,
"carbon_after": carbon_before,
"bill_savings": 0,
"energy_savings": 0,
"meets_threshold": False
}
config = [c for c in plan_input if c["uprn"] == str(p.uprn)]
if not config: